fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

79016d568d1d2d7d5bfde9336a443d48a6be49e7

1,683

py

Python

configs/positional_encoding_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k.py

plutoyuxie/mmgeneration

0a7f5d16c970de1766ebf049d7a0264fe506504b

[ "Apache-2.0" ]

718

2021-04-15T11:26:20.000Z

2022-03-31T03:11:56.000Z

configs/positional_encoding_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k.py

plutoyuxie/mmgeneration

0a7f5d16c970de1766ebf049d7a0264fe506504b

[ "Apache-2.0" ]

191

2021-04-15T12:13:34.000Z

2022-03-31T16:04:36.000Z

configs/positional_encoding_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k.py

plutoyuxie/mmgeneration

0a7f5d16c970de1766ebf049d7a0264fe506504b

[ "Apache-2.0" ]

107

2021-04-15T12:38:41.000Z

2022-03-27T02:47:16.000Z

_base_ = [ '../_base_/datasets/ffhq_flip.py', '../_base_/models/stylegan/stylegan2_base.py', '../_base_/default_runtime.py' ] model = dict( type='MSPIEStyleGAN2', generator=dict( type='MSStyleGANv2Generator', head_pos_encoding=dict(type='CSG'), deconv2conv=True, up_after_conv=True, head_pos_size=(4, 4), up_config=dict(scale_factor=2, mode='bilinear', align_corners=True), out_size=256), discriminator=dict( type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True)) train_cfg = dict( num_upblocks=6, multi_input_scales=[0, 2, 4], multi_scale_probability=[0.5, 0.25, 0.25]) data = dict( samples_per_gpu=3, train=dict(dataset=dict(imgs_root='./data/ffhq/ffhq_imgs/ffhq_512'))) ema_half_life = 10. custom_hooks = [ dict( type='VisualizeUnconditionalSamples', output_dir='training_samples', interval=5000), dict( type='ExponentialMovingAverageHook', module_keys=('generator_ema', ), interval=1, interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))), priority='VERY_HIGH') ] checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=40) lr_config = None log_config = dict( interval=100, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook'), ]) cudnn_benchmark = False total_iters = 1100002 metrics = dict( fid50k=dict( type='FID', num_images=50000, inception_pkl='work_dirs/inception_pkl/ffhq-256-50k-rgb.pkl', bgr2rgb=True), pr10k3=dict(type='PR', num_images=10000, k=3))

26.714286

79

0.649436

_base_ = [ '../_base_/datasets/ffhq_flip.py', '../_base_/models/stylegan/stylegan2_base.py', '../_base_/default_runtime.py' ] model = dict( type='MSPIEStyleGAN2', generator=dict( type='MSStyleGANv2Generator', head_pos_encoding=dict(type='CSG'), deconv2conv=True, up_after_conv=True, head_pos_size=(4, 4), up_config=dict(scale_factor=2, mode='bilinear', align_corners=True), out_size=256), discriminator=dict( type='MSStyleGAN2Discriminator', in_size=256, with_adaptive_pool=True)) train_cfg = dict( num_upblocks=6, multi_input_scales=[0, 2, 4], multi_scale_probability=[0.5, 0.25, 0.25]) data = dict( samples_per_gpu=3, train=dict(dataset=dict(imgs_root='./data/ffhq/ffhq_imgs/ffhq_512'))) ema_half_life = 10. custom_hooks = [ dict( type='VisualizeUnconditionalSamples', output_dir='training_samples', interval=5000), dict( type='ExponentialMovingAverageHook', module_keys=('generator_ema', ), interval=1, interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))), priority='VERY_HIGH') ] checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=40) lr_config = None log_config = dict( interval=100, hooks=[ dict(type='TextLoggerHook'), ]) cudnn_benchmark = False total_iters = 1100002 metrics = dict( fid50k=dict( type='FID', num_images=50000, inception_pkl='work_dirs/inception_pkl/ffhq-256-50k-rgb.pkl', bgr2rgb=True), pr10k3=dict(type='PR', num_images=10000, k=3))

true

79016e655840dcf25f5b90b8bffc280f87a56c79

4,446

py

Python

pandas_ta/overlap/hilo.py

MyBourse/pandas-ta

5998e92e39b71cd79a6e75d7c599492181af5f65

[ "MIT" ]

2

2021-03-30T01:23:14.000Z

2021-04-02T18:04:51.000Z

pandas_ta/overlap/hilo.py

lukaszbinden/pandas-ta

98478f8bf049a4c8748d6f3c795f4f335ced05ca

[ "MIT" ]

null

pandas_ta/overlap/hilo.py

lukaszbinden/pandas-ta

98478f8bf049a4c8748d6f3c795f4f335ced05ca

[ "MIT" ]

null

# -*- coding: utf-8 -*- from numpy import NaN as npNaN from pandas import DataFrame, Series # from pandas_ta.overlap.ma import ma from .ma import ma from pandas_ta.utils import get_offset, verify_series def hilo(high, low, close, high_length=None, low_length=None, mamode=None, offset=None, **kwargs): """Indicator: Gann HiLo (HiLo)""" # Validate Arguments high = verify_series(high) low = verify_series(low) close = verify_series(close) high_length = int(high_length) if high_length and high_length > 0 else 13 low_length = int(low_length) if low_length and low_length > 0 else 21 mamode = mamode.lower() if isinstance(mamode, str) else "sma" offset = get_offset(offset) # Calculate Result m = close.size hilo = Series(npNaN, index=close.index) long = Series(npNaN, index=close.index) short = Series(npNaN, index=close.index) high_ma = ma(mamode, high, length=high_length) low_ma = ma(mamode, low, length=low_length) for i in range(1, m): if close.iloc[i] > high_ma.iloc[i - 1]: hilo.iloc[i] = long.iloc[i] = low_ma.iloc[i] elif close.iloc[i] < low_ma.iloc[i - 1]: hilo.iloc[i] = short.iloc[i] = high_ma.iloc[i] else: hilo.iloc[i] = hilo.iloc[i - 1] long.iloc[i] = short.iloc[i] = hilo.iloc[i - 1] # Offset if offset != 0: hilo = hilo.shift(offset) long = long.shift(offset) short = short.shift(offset) # Handle fills if "fillna" in kwargs: hilo.fillna(kwargs["fillna"], inplace=True) long.fillna(kwargs["fillna"], inplace=True) short.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: hilo.fillna(method=kwargs["fill_method"], inplace=True) long.fillna(method=kwargs["fill_method"], inplace=True) short.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category _props = f"_{high_length}_{low_length}" data = {f"HILO{_props}": hilo, f"HILOl{_props}": long, f"HILOs{_props}": short} df = DataFrame(data, index=close.index) df.name = f"HILO{_props}" df.category = "overlap" return df hilo.__doc__ = \ """Gann HiLo Activator(HiLo) The Gann High Low Activator Indicator was created by Robert Krausz in a 1998 issue of Stocks & Commodities Magazine. It is a moving average based trend indicator consisting of two different simple moving averages. The indicator tracks both curves (of the highs and the lows). The close of the bar defines which of the two gets plotted. Increasing high_length and decreasing low_length better for short trades, vice versa for long positions. Sources: https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=447&Name=Gann_HiLo_Activator https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/ https://www.tradingview.com/script/XNQSLIYb-Gann-High-Low/ Calculation: Default Inputs: high_length=13, low_length=21, mamode="sma" EMA = Exponential Moving Average HMA = Hull Moving Average SMA = Simple Moving Average # Default if "ema": high_ma = EMA(high, high_length) low_ma = EMA(low, low_length) elif "hma": high_ma = HMA(high, high_length) low_ma = HMA(low, low_length) else: # "sma" high_ma = SMA(high, high_length) low_ma = SMA(low, low_length) # Similar to Supertrend MA selection hilo = Series(npNaN, index=close.index) for i in range(1, m): if close.iloc[i] > high_ma.iloc[i - 1]: hilo.iloc[i] = low_ma.iloc[i] elif close.iloc[i] < low_ma.iloc[i - 1]: hilo.iloc[i] = high_ma.iloc[i] else: hilo.iloc[i] = hilo.iloc[i - 1] Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's high_length (int): It's period. Default: 13 low_length (int): It's period. Default: 21 mamode (str): Options: 'sma' or 'ema'. Default: 'sma' offset (int): How many periods to offset the result. Default: 0 Kwargs: adjust (bool): Default: True presma (bool, optional): If True, uses SMA for initial value. fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.DataFrame: HILO (line), HILOl (long), HILOs (short) columns. """

34.734375

111

0.65857

from numpy import NaN as npNaN from pandas import DataFrame, Series from .ma import ma from pandas_ta.utils import get_offset, verify_series def hilo(high, low, close, high_length=None, low_length=None, mamode=None, offset=None, **kwargs): high = verify_series(high) low = verify_series(low) close = verify_series(close) high_length = int(high_length) if high_length and high_length > 0 else 13 low_length = int(low_length) if low_length and low_length > 0 else 21 mamode = mamode.lower() if isinstance(mamode, str) else "sma" offset = get_offset(offset) m = close.size hilo = Series(npNaN, index=close.index) long = Series(npNaN, index=close.index) short = Series(npNaN, index=close.index) high_ma = ma(mamode, high, length=high_length) low_ma = ma(mamode, low, length=low_length) for i in range(1, m): if close.iloc[i] > high_ma.iloc[i - 1]: hilo.iloc[i] = long.iloc[i] = low_ma.iloc[i] elif close.iloc[i] < low_ma.iloc[i - 1]: hilo.iloc[i] = short.iloc[i] = high_ma.iloc[i] else: hilo.iloc[i] = hilo.iloc[i - 1] long.iloc[i] = short.iloc[i] = hilo.iloc[i - 1] if offset != 0: hilo = hilo.shift(offset) long = long.shift(offset) short = short.shift(offset) if "fillna" in kwargs: hilo.fillna(kwargs["fillna"], inplace=True) long.fillna(kwargs["fillna"], inplace=True) short.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: hilo.fillna(method=kwargs["fill_method"], inplace=True) long.fillna(method=kwargs["fill_method"], inplace=True) short.fillna(method=kwargs["fill_method"], inplace=True) _props = f"_{high_length}_{low_length}" data = {f"HILO{_props}": hilo, f"HILOl{_props}": long, f"HILOs{_props}": short} df = DataFrame(data, index=close.index) df.name = f"HILO{_props}" df.category = "overlap" return df hilo.__doc__ = \ """Gann HiLo Activator(HiLo) The Gann High Low Activator Indicator was created by Robert Krausz in a 1998 issue of Stocks & Commodities Magazine. It is a moving average based trend indicator consisting of two different simple moving averages. The indicator tracks both curves (of the highs and the lows). The close of the bar defines which of the two gets plotted. Increasing high_length and decreasing low_length better for short trades, vice versa for long positions. Sources: https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=447&Name=Gann_HiLo_Activator https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/ https://www.tradingview.com/script/XNQSLIYb-Gann-High-Low/ Calculation: Default Inputs: high_length=13, low_length=21, mamode="sma" EMA = Exponential Moving Average HMA = Hull Moving Average SMA = Simple Moving Average # Default if "ema": high_ma = EMA(high, high_length) low_ma = EMA(low, low_length) elif "hma": high_ma = HMA(high, high_length) low_ma = HMA(low, low_length) else: # "sma" high_ma = SMA(high, high_length) low_ma = SMA(low, low_length) # Similar to Supertrend MA selection hilo = Series(npNaN, index=close.index) for i in range(1, m): if close.iloc[i] > high_ma.iloc[i - 1]: hilo.iloc[i] = low_ma.iloc[i] elif close.iloc[i] < low_ma.iloc[i - 1]: hilo.iloc[i] = high_ma.iloc[i] else: hilo.iloc[i] = hilo.iloc[i - 1] Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's high_length (int): It's period. Default: 13 low_length (int): It's period. Default: 21 mamode (str): Options: 'sma' or 'ema'. Default: 'sma' offset (int): How many periods to offset the result. Default: 0 Kwargs: adjust (bool): Default: True presma (bool, optional): If True, uses SMA for initial value. fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.DataFrame: HILO (line), HILOl (long), HILOs (short) columns. """

true

79016f66e59c16e4a1773919bca295e44f37e80c

4,367

py

Python

rasa/nlu/tokenizers/tokenizer.py

Performek/rasa

d4a88c3b97ca4cf81d011834bfbb63abbf39d697

[ "Apache-2.0" ]

1

2020-02-18T03:48:44.000Z

rasa/nlu/tokenizers/tokenizer.py

Doometnick/rasa

969dc83a83f989a7774b2ff3ba186272b18bc73a

[ "Apache-2.0" ]

4

2020-09-25T18:31:22.000Z

2022-02-09T23:27:20.000Z

rasa/nlu/tokenizers/tokenizer.py

Doometnick/rasa

969dc83a83f989a7774b2ff3ba186272b18bc73a

[ "Apache-2.0" ]

null

import logging from typing import Text, List, Optional, Dict, Any from rasa.nlu.config import RasaNLUModelConfig from rasa.nlu.training_data import TrainingData, Message from rasa.nlu.components import Component from rasa.nlu.constants import ( RESPONSE_ATTRIBUTE, TEXT_ATTRIBUTE, CLS_TOKEN, TOKENS_NAMES, MESSAGE_ATTRIBUTES, INTENT_ATTRIBUTE, ) logger = logging.getLogger(__name__) class Token(object): def __init__( self, text: Text, start: int, data: Optional[Dict[Text, Any]] = None, lemma: Optional[Text] = None, end: Optional[int] = None, ) -> None: self.start = start self.text = text self.end = start + len(text) self.data = data if data else {} self.lemma = lemma or text self.end = end if end else start + len(text) def set(self, prop: Text, info: Any) -> None: self.data[prop] = info def get(self, prop: Text, default: Optional[Any] = None) -> Any: return self.data.get(prop, default) def __eq__(self, other): if not isinstance(other, Token): return NotImplemented return (self.start, self.end, self.text, self.lemma) == ( other.start, other.end, other.text, other.lemma, ) def __lt__(self, other): if not isinstance(other, Token): return NotImplemented return (self.start, self.end, self.text, self.lemma) < ( other.start, other.end, other.text, other.lemma, ) class Tokenizer(Component): def __init__(self, component_config: Dict[Text, Any] = None) -> None: """Construct a new tokenizer using the WhitespaceTokenizer framework.""" super().__init__(component_config) # flag to check whether to split intents self.intent_tokenization_flag = self.component_config.get( "intent_tokenization_flag", False ) # split symbol for intents self.intent_split_symbol = self.component_config.get("intent_split_symbol", "_") def tokenize(self, message: Message, attribute: Text) -> List[Token]: """Tokenizes the text of the provided attribute of the incoming message.""" raise NotImplementedError def train( self, training_data: TrainingData, config: Optional[RasaNLUModelConfig] = None, **kwargs: Any, ) -> None: """Tokenize all training data.""" for example in training_data.training_examples: for attribute in MESSAGE_ATTRIBUTES: if example.get(attribute) is not None: if attribute == INTENT_ATTRIBUTE: tokens = self._split_intent(example) else: tokens = self.tokenize(example, attribute) tokens = self.add_cls_token(tokens, attribute) example.set(TOKENS_NAMES[attribute], tokens) def process(self, message: Message, **kwargs: Any) -> None: """Tokenize the incoming message.""" tokens = self.tokenize(message, TEXT_ATTRIBUTE) tokens = self.add_cls_token(tokens, TEXT_ATTRIBUTE) message.set(TOKENS_NAMES[TEXT_ATTRIBUTE], tokens) def _split_intent(self, message: Message): text = message.get(INTENT_ATTRIBUTE) words = ( text.split(self.intent_split_symbol) if self.intent_tokenization_flag else [text] ) return self._convert_words_to_tokens(words, text) @staticmethod def _convert_words_to_tokens(words: List[Text], text: Text) -> List[Token]: running_offset = 0 tokens = [] for word in words: word_offset = text.index(word, running_offset) word_len = len(word) running_offset = word_offset + word_len tokens.append(Token(word, word_offset)) return tokens @staticmethod def add_cls_token(tokens: List[Token], attribute: Text) -> List[Token]: if attribute in [RESPONSE_ATTRIBUTE, TEXT_ATTRIBUTE] and tokens: # +1 to have a space between the last token and the __cls__ token idx = tokens[-1].end + 1 tokens.append(Token(CLS_TOKEN, idx)) return tokens

31.644928

88

0.608198

import logging from typing import Text, List, Optional, Dict, Any from rasa.nlu.config import RasaNLUModelConfig from rasa.nlu.training_data import TrainingData, Message from rasa.nlu.components import Component from rasa.nlu.constants import ( RESPONSE_ATTRIBUTE, TEXT_ATTRIBUTE, CLS_TOKEN, TOKENS_NAMES, MESSAGE_ATTRIBUTES, INTENT_ATTRIBUTE, ) logger = logging.getLogger(__name__) class Token(object): def __init__( self, text: Text, start: int, data: Optional[Dict[Text, Any]] = None, lemma: Optional[Text] = None, end: Optional[int] = None, ) -> None: self.start = start self.text = text self.end = start + len(text) self.data = data if data else {} self.lemma = lemma or text self.end = end if end else start + len(text) def set(self, prop: Text, info: Any) -> None: self.data[prop] = info def get(self, prop: Text, default: Optional[Any] = None) -> Any: return self.data.get(prop, default) def __eq__(self, other): if not isinstance(other, Token): return NotImplemented return (self.start, self.end, self.text, self.lemma) == ( other.start, other.end, other.text, other.lemma, ) def __lt__(self, other): if not isinstance(other, Token): return NotImplemented return (self.start, self.end, self.text, self.lemma) < ( other.start, other.end, other.text, other.lemma, ) class Tokenizer(Component): def __init__(self, component_config: Dict[Text, Any] = None) -> None: super().__init__(component_config) self.intent_tokenization_flag = self.component_config.get( "intent_tokenization_flag", False ) self.intent_split_symbol = self.component_config.get("intent_split_symbol", "_") def tokenize(self, message: Message, attribute: Text) -> List[Token]: raise NotImplementedError def train( self, training_data: TrainingData, config: Optional[RasaNLUModelConfig] = None, **kwargs: Any, ) -> None: for example in training_data.training_examples: for attribute in MESSAGE_ATTRIBUTES: if example.get(attribute) is not None: if attribute == INTENT_ATTRIBUTE: tokens = self._split_intent(example) else: tokens = self.tokenize(example, attribute) tokens = self.add_cls_token(tokens, attribute) example.set(TOKENS_NAMES[attribute], tokens) def process(self, message: Message, **kwargs: Any) -> None: tokens = self.tokenize(message, TEXT_ATTRIBUTE) tokens = self.add_cls_token(tokens, TEXT_ATTRIBUTE) message.set(TOKENS_NAMES[TEXT_ATTRIBUTE], tokens) def _split_intent(self, message: Message): text = message.get(INTENT_ATTRIBUTE) words = ( text.split(self.intent_split_symbol) if self.intent_tokenization_flag else [text] ) return self._convert_words_to_tokens(words, text) @staticmethod def _convert_words_to_tokens(words: List[Text], text: Text) -> List[Token]: running_offset = 0 tokens = [] for word in words: word_offset = text.index(word, running_offset) word_len = len(word) running_offset = word_offset + word_len tokens.append(Token(word, word_offset)) return tokens @staticmethod def add_cls_token(tokens: List[Token], attribute: Text) -> List[Token]: if attribute in [RESPONSE_ATTRIBUTE, TEXT_ATTRIBUTE] and tokens: idx = tokens[-1].end + 1 tokens.append(Token(CLS_TOKEN, idx)) return tokens

true

79016fa6c6014fafe5eec382b6be4da591309ba9

32

py

Python

edureka.py

jatin06/learning-git

4e531193a95f6dc8daeb5c4c7a15a2fe786583ae

[ "MIT" ]

null

edureka.py

jatin06/learning-git

4e531193a95f6dc8daeb5c4c7a15a2fe786583ae

[ "MIT" ]

null

edureka.py

jatin06/learning-git

4e531193a95f6dc8daeb5c4c7a15a2fe786583ae

[ "MIT" ]

null

print ("welcome to edureka!! ")

16

31

0.65625

print ("welcome to edureka!! ")

true

790171254393847e77c757edc13e944620e1e566

6,472

py

Python

dask/array/wrap.py

BlueOwlDev/dask

a1187b13321d69565b9c21359d739c239bd04c65

[ "BSD-3-Clause" ]

null

dask/array/wrap.py

BlueOwlDev/dask

a1187b13321d69565b9c21359d739c239bd04c65

[ "BSD-3-Clause" ]

null

dask/array/wrap.py

BlueOwlDev/dask

a1187b13321d69565b9c21359d739c239bd04c65

[ "BSD-3-Clause" ]

null

from functools import partial from itertools import product import numpy as np from tlz import curry from ..base import tokenize from ..utils import funcname from .blockwise import BlockwiseCreateArray from .core import Array, normalize_chunks from .utils import ( meta_from_array, empty_like_safe, full_like_safe, ones_like_safe, zeros_like_safe, ) def _parse_wrap_args(func, args, kwargs, shape): if isinstance(shape, np.ndarray): shape = shape.tolist() if not isinstance(shape, (tuple, list)): shape = (shape,) name = kwargs.pop("name", None) chunks = kwargs.pop("chunks", "auto") dtype = kwargs.pop("dtype", None) if dtype is None: dtype = func(shape, *args, **kwargs).dtype dtype = np.dtype(dtype) chunks = normalize_chunks(chunks, shape, dtype=dtype) name = name or funcname(func) + "-" + tokenize( func, shape, chunks, dtype, args, kwargs ) return { "shape": shape, "dtype": dtype, "kwargs": kwargs, "chunks": chunks, "name": name, } def wrap_func_shape_as_first_arg(func, *args, **kwargs): """ Transform np creation function into blocked version """ if "shape" not in kwargs: shape, args = args[0], args[1:] else: shape = kwargs.pop("shape") if isinstance(shape, Array): raise TypeError( "Dask array input not supported. " "Please use tuple, list, or a 1D numpy array instead." ) parsed = _parse_wrap_args(func, args, kwargs, shape) shape = parsed["shape"] dtype = parsed["dtype"] chunks = parsed["chunks"] name = parsed["name"] kwargs = parsed["kwargs"] func = partial(func, dtype=dtype, **kwargs) graph = BlockwiseCreateArray( name, func, shape, chunks, ) return Array(graph, name, chunks, dtype=dtype, meta=kwargs.get("meta", None)) def wrap_func_like(func, *args, **kwargs): """ Transform np creation function into blocked version """ x = args[0] meta = meta_from_array(x) shape = kwargs.get("shape", x.shape) parsed = _parse_wrap_args(func, args, kwargs, shape) shape = parsed["shape"] dtype = parsed["dtype"] chunks = parsed["chunks"] name = parsed["name"] kwargs = parsed["kwargs"] keys = product([name], *[range(len(bd)) for bd in chunks]) shapes = product(*chunks) shapes = list(shapes) kw = [kwargs for _ in shapes] for i, s in enumerate(list(shapes)): kw[i]["shape"] = s vals = ((partial(func, dtype=dtype, **k),) + args for (k, s) in zip(kw, shapes)) dsk = dict(zip(keys, vals)) return Array(dsk, name, chunks, meta=meta.astype(dtype)) def wrap_func_like_safe(func, func_like, *args, **kwargs): """ Safe implementation for wrap_func_like(), attempts to use func_like(), if the shape keyword argument, falls back to func(). """ try: return func_like(*args, **kwargs) except TypeError: return func(*args, **kwargs) @curry def wrap(wrap_func, func, **kwargs): func_like = kwargs.pop("func_like", None) if func_like is None: f = partial(wrap_func, func, **kwargs) else: f = partial(wrap_func, func_like, **kwargs) template = """ Blocked variant of %(name)s Follows the signature of %(name)s exactly except that it also features optional keyword arguments ``chunks: int, tuple, or dict`` and ``name: str``. Original signature follows below. """ if func.__doc__ is not None: f.__doc__ = template % {"name": func.__name__} + func.__doc__ f.__name__ = "blocked_" + func.__name__ return f w = wrap(wrap_func_shape_as_first_arg) @curry def _broadcast_trick_inner(func, shape, meta=(), *args, **kwargs): if shape == (): return np.broadcast_to(func(meta, shape=(), *args, **kwargs), shape) else: return np.broadcast_to(func(meta, shape=1, *args, **kwargs), shape) def broadcast_trick(func): """ Provide a decorator to wrap common numpy function with a broadcast trick. Dask arrays are currently immutable; thus when we know an array is uniform, we can replace the actual data by a single value and have all elements point to it, thus reducing the size. >>> x = np.broadcast_to(1, (100,100,100)) >>> x.base.nbytes 8 Those array are not only more efficient locally, but dask serialisation is aware of the _real_ size of those array and thus can send them around efficiently and schedule accordingly. Note that those array are read-only and numpy will refuse to assign to them, so should be safe. """ inner = _broadcast_trick_inner(func) if func.__doc__ is not None: inner.__doc__ = func.__doc__ inner.__name__ = func.__name__ if inner.__name__.endswith("_like_safe"): inner.__name__ = inner.__name__[:-10] return inner ones = w(broadcast_trick(ones_like_safe), dtype="f8") zeros = w(broadcast_trick(zeros_like_safe), dtype="f8") empty = w(broadcast_trick(empty_like_safe), dtype="f8") w_like = wrap(wrap_func_like_safe) empty_like = w_like(np.empty, func_like=np.empty_like) # full and full_like require special casing due to argument check on fill_value # Generate wrapped functions only once _full = w(broadcast_trick(full_like_safe)) _full_like = w_like(np.full, func_like=np.full_like) # workaround for numpy doctest failure: https://github.com/numpy/numpy/pull/17472 _full.__doc__ = _full.__doc__.replace( "array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])", "array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])", ) def full(shape, fill_value, *args, **kwargs): # np.isscalar has somewhat strange behavior: # https://docs.scipy.org/doc/numpy/reference/generated/numpy.isscalar.html if np.ndim(fill_value) != 0: raise ValueError( f"fill_value must be scalar. Received {type(fill_value).__name__} instead." ) return _full(shape=shape, fill_value=fill_value, *args, **kwargs) def full_like(a, fill_value, *args, **kwargs): if np.ndim(fill_value) != 0: raise ValueError( f"fill_value must be scalar. Received {type(fill_value).__name__} instead." ) return _full_like( a=a, fill_value=fill_value, *args, **kwargs, ) full.__doc__ = _full.__doc__ full_like.__doc__ = _full_like.__doc__

27.896552

87

0.645705

from functools import partial from itertools import product import numpy as np from tlz import curry from ..base import tokenize from ..utils import funcname from .blockwise import BlockwiseCreateArray from .core import Array, normalize_chunks from .utils import ( meta_from_array, empty_like_safe, full_like_safe, ones_like_safe, zeros_like_safe, ) def _parse_wrap_args(func, args, kwargs, shape): if isinstance(shape, np.ndarray): shape = shape.tolist() if not isinstance(shape, (tuple, list)): shape = (shape,) name = kwargs.pop("name", None) chunks = kwargs.pop("chunks", "auto") dtype = kwargs.pop("dtype", None) if dtype is None: dtype = func(shape, *args, **kwargs).dtype dtype = np.dtype(dtype) chunks = normalize_chunks(chunks, shape, dtype=dtype) name = name or funcname(func) + "-" + tokenize( func, shape, chunks, dtype, args, kwargs ) return { "shape": shape, "dtype": dtype, "kwargs": kwargs, "chunks": chunks, "name": name, } def wrap_func_shape_as_first_arg(func, *args, **kwargs): if "shape" not in kwargs: shape, args = args[0], args[1:] else: shape = kwargs.pop("shape") if isinstance(shape, Array): raise TypeError( "Dask array input not supported. " "Please use tuple, list, or a 1D numpy array instead." ) parsed = _parse_wrap_args(func, args, kwargs, shape) shape = parsed["shape"] dtype = parsed["dtype"] chunks = parsed["chunks"] name = parsed["name"] kwargs = parsed["kwargs"] func = partial(func, dtype=dtype, **kwargs) graph = BlockwiseCreateArray( name, func, shape, chunks, ) return Array(graph, name, chunks, dtype=dtype, meta=kwargs.get("meta", None)) def wrap_func_like(func, *args, **kwargs): x = args[0] meta = meta_from_array(x) shape = kwargs.get("shape", x.shape) parsed = _parse_wrap_args(func, args, kwargs, shape) shape = parsed["shape"] dtype = parsed["dtype"] chunks = parsed["chunks"] name = parsed["name"] kwargs = parsed["kwargs"] keys = product([name], *[range(len(bd)) for bd in chunks]) shapes = product(*chunks) shapes = list(shapes) kw = [kwargs for _ in shapes] for i, s in enumerate(list(shapes)): kw[i]["shape"] = s vals = ((partial(func, dtype=dtype, **k),) + args for (k, s) in zip(kw, shapes)) dsk = dict(zip(keys, vals)) return Array(dsk, name, chunks, meta=meta.astype(dtype)) def wrap_func_like_safe(func, func_like, *args, **kwargs): try: return func_like(*args, **kwargs) except TypeError: return func(*args, **kwargs) @curry def wrap(wrap_func, func, **kwargs): func_like = kwargs.pop("func_like", None) if func_like is None: f = partial(wrap_func, func, **kwargs) else: f = partial(wrap_func, func_like, **kwargs) template = """ Blocked variant of %(name)s Follows the signature of %(name)s exactly except that it also features optional keyword arguments ``chunks: int, tuple, or dict`` and ``name: str``. Original signature follows below. """ if func.__doc__ is not None: f.__doc__ = template % {"name": func.__name__} + func.__doc__ f.__name__ = "blocked_" + func.__name__ return f w = wrap(wrap_func_shape_as_first_arg) @curry def _broadcast_trick_inner(func, shape, meta=(), *args, **kwargs): if shape == (): return np.broadcast_to(func(meta, shape=(), *args, **kwargs), shape) else: return np.broadcast_to(func(meta, shape=1, *args, **kwargs), shape) def broadcast_trick(func): inner = _broadcast_trick_inner(func) if func.__doc__ is not None: inner.__doc__ = func.__doc__ inner.__name__ = func.__name__ if inner.__name__.endswith("_like_safe"): inner.__name__ = inner.__name__[:-10] return inner ones = w(broadcast_trick(ones_like_safe), dtype="f8") zeros = w(broadcast_trick(zeros_like_safe), dtype="f8") empty = w(broadcast_trick(empty_like_safe), dtype="f8") w_like = wrap(wrap_func_like_safe) empty_like = w_like(np.empty, func_like=np.empty_like) _full = w(broadcast_trick(full_like_safe)) _full_like = w_like(np.full, func_like=np.full_like) _full.__doc__ = _full.__doc__.replace( "array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])", "array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])", ) def full(shape, fill_value, *args, **kwargs): if np.ndim(fill_value) != 0: raise ValueError( f"fill_value must be scalar. Received {type(fill_value).__name__} instead." ) return _full(shape=shape, fill_value=fill_value, *args, **kwargs) def full_like(a, fill_value, *args, **kwargs): if np.ndim(fill_value) != 0: raise ValueError( f"fill_value must be scalar. Received {type(fill_value).__name__} instead." ) return _full_like( a=a, fill_value=fill_value, *args, **kwargs, ) full.__doc__ = _full.__doc__ full_like.__doc__ = _full_like.__doc__

true

79017191ffb25ad0bc2d39bcd4d208278628a040

924

py

Python

Scripts/GridSearch/ModelBuilderRLF.py

bio-hpc/sibila

337ea84692d6ea4f4d3e4de9da51f5ee53cff6d7

[ "Apache-2.0" ]

1

2022-03-07T11:05:31.000Z

Scripts/GridSearch/ModelBuilderRLF.py

bio-hpc/sibila

337ea84692d6ea4f4d3e4de9da51f5ee53cff6d7

[ "Apache-2.0" ]

null

Scripts/GridSearch/ModelBuilderRLF.py

bio-hpc/sibila

337ea84692d6ea4f4d3e4de9da51f5ee53cff6d7

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ModelBuilderRLF.py: """ __author__ = "Antonio Jesús Banegas-Luna" __version__ = "1.0" __maintainer__ = "Antonio" __email__ = "ajbanegas@ucam.edu" __status__ = "Development" from BaseModelBuilder import BaseModelBuilder class ModelBuilderRLF(BaseModelBuilder): def get_default_model(self): p = {} p['model'] = self.model_name p['train_grid'] = 'NONE' p['type_ml'] = 'classification' p['n_jobs'] = 8 p['params'] = {} p['params']['tree_size'] = 4 p['params']['sample_fract'] = 'default' p['params']['max_rules'] = 2000 p['params']['memory_par'] = 0.01 p['params']['rfmode'] = 'classify' p['params']['lin_trim_quantile'] = 0.025 p['params']['lin_standardise'] = True p['params']['exp_rand_tree_size'] = True p['params_grid'] = {} return p

24.972973

48

0.580087

__author__ = "Antonio Jesús Banegas-Luna" __version__ = "1.0" __maintainer__ = "Antonio" __email__ = "ajbanegas@ucam.edu" __status__ = "Development" from BaseModelBuilder import BaseModelBuilder class ModelBuilderRLF(BaseModelBuilder): def get_default_model(self): p = {} p['model'] = self.model_name p['train_grid'] = 'NONE' p['type_ml'] = 'classification' p['n_jobs'] = 8 p['params'] = {} p['params']['tree_size'] = 4 p['params']['sample_fract'] = 'default' p['params']['max_rules'] = 2000 p['params']['memory_par'] = 0.01 p['params']['rfmode'] = 'classify' p['params']['lin_trim_quantile'] = 0.025 p['params']['lin_standardise'] = True p['params']['exp_rand_tree_size'] = True p['params_grid'] = {} return p

true

79017262dd9a268afd502f5cc32e22c15e723371

154

py

Python

analysis/tools/count_histories.py

beykyle/omp-uq

7d9b720d874b634f3a56878ce34f29553441194e

[ "MIT" ]

null

analysis/tools/count_histories.py

beykyle/omp-uq

7d9b720d874b634f3a56878ce34f29553441194e

[ "MIT" ]

null

analysis/tools/count_histories.py

beykyle/omp-uq

7d9b720d874b634f3a56878ce34f29553441194e

[ "MIT" ]

null

import sys from CGMFtk import histories as fh if __name__ == "__main__": hist = fh.Histories(sys.argv[1]) print(len(hist.getFissionHistories()))

22

42

0.714286

import sys from CGMFtk import histories as fh if __name__ == "__main__": hist = fh.Histories(sys.argv[1]) print(len(hist.getFissionHistories()))

true

790173969efb945f4bb3a24471e09eea76be1304

299

py

Python

nsls2_catalogs/tes/__init__.py

NSLS-II/nsls2-catalogs

8dab7db65335ccd20eacf7f2e999d64ec7325620

[ "BSD-3-Clause" ]

null

nsls2_catalogs/tes/__init__.py

NSLS-II/nsls2-catalogs

8dab7db65335ccd20eacf7f2e999d64ec7325620

[ "BSD-3-Clause" ]

null

nsls2_catalogs/tes/__init__.py

NSLS-II/nsls2-catalogs

8dab7db65335ccd20eacf7f2e999d64ec7325620

[ "BSD-3-Clause" ]

null

from databroker.v1 import from_config from databroker.v0 import Broker from .. import load_config name = 'tes' v0_catalog = Broker.from_config(load_config(f'{name}/{name}.yml')) v1_catalog = from_config(load_config(f'{name}/{name}.yml')) catalog = from_config(load_config(f'{name}/{name}.yml')).v2

33.222222

66

0.755853

from databroker.v1 import from_config from databroker.v0 import Broker from .. import load_config name = 'tes' v0_catalog = Broker.from_config(load_config(f'{name}/{name}.yml')) v1_catalog = from_config(load_config(f'{name}/{name}.yml')) catalog = from_config(load_config(f'{name}/{name}.yml')).v2

true

790173bd5e7c35d6cbb74a0b6289f9a3f37db36b

3,356

py

Python

airsenal/framework/player_model.py

JPKFin/AIrsenal

7824ae4c07c9f21336f2986c0439549e9c346433

[ "MIT" ]

null

airsenal/framework/player_model.py

JPKFin/AIrsenal

7824ae4c07c9f21336f2986c0439549e9c346433

[ "MIT" ]

null

airsenal/framework/player_model.py

JPKFin/AIrsenal

7824ae4c07c9f21336f2986c0439549e9c346433

[ "MIT" ]

null

import jax.numpy as jnp import jax.random as random import numpyro import numpyro.distributions as dist from numpyro.infer import MCMC, NUTS from typing import Any, Dict, Optional class PlayerModel(object): """ numpyro implementation of the AIrsenal player model. """ def __init__(self): self.player_ids = None self.samples = None @staticmethod def _model( nplayer: int, nmatch: int, minutes: jnp.array, y: jnp.array, alpha: jnp.array ): theta = dist.Dirichlet(concentration=alpha) # one sample from the prior per player with numpyro.plate("nplayer", nplayer): dprobs = numpyro.sample("probs", theta) # now it's all about how to broadcast in the right dimensions..... prob_score = numpyro.deterministic( "prob_score", dprobs[:, 0, None] * (minutes / 90.0) ) prob_assist = numpyro.deterministic( "prob_assist", dprobs[:, 1, None] * (minutes / 90.0) ) prob_neither = numpyro.deterministic( "prob_neither", dprobs[:, 2, None] * (minutes / 90.0) + (90.0 - minutes) ) theta_mins = dist.Multinomial( probs=jnp.moveaxis(jnp.array([prob_score, prob_assist, prob_neither]), 0, 2) ) return numpyro.sample("obs", theta_mins, obs=y) def fit( self, data, random_state: int = 42, num_warmup: int = 500, num_samples: int = 2000, mcmc_kwargs: Optional[Dict[str, Any]] = None, run_kwargs: Optional[Dict[str, Any]] = None, ): self.player_ids = data["player_ids"] kernel = NUTS(self._model) mcmc = MCMC( kernel, num_warmup=num_warmup, num_samples=num_samples, num_chains=1, progress_bar=True, **(mcmc_kwargs or {}), ) rng_key, rng_key_predict = random.split(random.PRNGKey(44)) mcmc.run( rng_key, data["nplayer"], data["nmatch"], data["minutes"], data["y"], data["alpha"], **(run_kwargs or {}), ) self.samples = mcmc.get_samples() return self def get_probs(self): prob_dict = { "player_id": [], "prob_score": [], "prob_assist": [], "prob_neither": [], } for i, pid in enumerate(self.player_ids): prob_dict["player_id"].append(pid) prob_dict["prob_score"].append(float(self.samples["probs"][:, i, 0].mean())) prob_dict["prob_assist"].append( float(self.samples["probs"][:, i, 1].mean()) ) prob_dict["prob_neither"].append( float(self.samples["probs"][:, i, 2].mean()) ) return prob_dict def get_probs_for_player(self, player_id): try: index = list(self.player_ids).index(player_id) except (ValueError): raise RuntimeError(f"Unknown player_id {player_id}") prob_score = float(self.samples["probs"][:, index, 0].mean()) prob_assist = float(self.samples["probs"][:, index, 1].mean()) prob_neither = float(self.samples["probs"][:, index, 2].mean()) return (prob_score, prob_assist, prob_neither)

33.227723

88

0.554827

import jax.numpy as jnp import jax.random as random import numpyro import numpyro.distributions as dist from numpyro.infer import MCMC, NUTS from typing import Any, Dict, Optional class PlayerModel(object): def __init__(self): self.player_ids = None self.samples = None @staticmethod def _model( nplayer: int, nmatch: int, minutes: jnp.array, y: jnp.array, alpha: jnp.array ): theta = dist.Dirichlet(concentration=alpha) with numpyro.plate("nplayer", nplayer): dprobs = numpyro.sample("probs", theta) prob_score = numpyro.deterministic( "prob_score", dprobs[:, 0, None] * (minutes / 90.0) ) prob_assist = numpyro.deterministic( "prob_assist", dprobs[:, 1, None] * (minutes / 90.0) ) prob_neither = numpyro.deterministic( "prob_neither", dprobs[:, 2, None] * (minutes / 90.0) + (90.0 - minutes) ) theta_mins = dist.Multinomial( probs=jnp.moveaxis(jnp.array([prob_score, prob_assist, prob_neither]), 0, 2) ) return numpyro.sample("obs", theta_mins, obs=y) def fit( self, data, random_state: int = 42, num_warmup: int = 500, num_samples: int = 2000, mcmc_kwargs: Optional[Dict[str, Any]] = None, run_kwargs: Optional[Dict[str, Any]] = None, ): self.player_ids = data["player_ids"] kernel = NUTS(self._model) mcmc = MCMC( kernel, num_warmup=num_warmup, num_samples=num_samples, num_chains=1, progress_bar=True, **(mcmc_kwargs or {}), ) rng_key, rng_key_predict = random.split(random.PRNGKey(44)) mcmc.run( rng_key, data["nplayer"], data["nmatch"], data["minutes"], data["y"], data["alpha"], **(run_kwargs or {}), ) self.samples = mcmc.get_samples() return self def get_probs(self): prob_dict = { "player_id": [], "prob_score": [], "prob_assist": [], "prob_neither": [], } for i, pid in enumerate(self.player_ids): prob_dict["player_id"].append(pid) prob_dict["prob_score"].append(float(self.samples["probs"][:, i, 0].mean())) prob_dict["prob_assist"].append( float(self.samples["probs"][:, i, 1].mean()) ) prob_dict["prob_neither"].append( float(self.samples["probs"][:, i, 2].mean()) ) return prob_dict def get_probs_for_player(self, player_id): try: index = list(self.player_ids).index(player_id) except (ValueError): raise RuntimeError(f"Unknown player_id {player_id}") prob_score = float(self.samples["probs"][:, index, 0].mean()) prob_assist = float(self.samples["probs"][:, index, 1].mean()) prob_neither = float(self.samples["probs"][:, index, 2].mean()) return (prob_score, prob_assist, prob_neither)

true

7901746461fddaa02bec11dd3bbf9afc1a3b1382

49,985

py

Python

ty_lib/test_pattern_generator2.py

colour-science/sample_code

8bda35b674d770da5a0e6c210634a77691527fce

[ "BSD-3-Clause" ]

1

2021-01-23T03:06:53.000Z

ty_lib/test_pattern_generator2.py

colour-science/sample_code

8bda35b674d770da5a0e6c210634a77691527fce

[ "BSD-3-Clause" ]

null

ty_lib/test_pattern_generator2.py

colour-science/sample_code

8bda35b674d770da5a0e6c210634a77691527fce

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 評価用のテストパターン作成ツール集 """ import os import cv2 import matplotlib.pyplot as plt import numpy as np from colour.colorimetry import CMFS, ILLUMINANTS from colour.models import XYZ_to_xy, xy_to_XYZ, XYZ_to_RGB, RGB_to_XYZ from colour.models import xy_to_xyY, xyY_to_XYZ, Lab_to_XYZ from colour.models import BT709_COLOURSPACE from colour.utilities import normalise_maximum from colour import models from colour import RGB_COLOURSPACES, COLOURCHECKERS from scipy.spatial import Delaunay from scipy.ndimage.filters import convolve import math import transfer_functions as tf CMFS_NAME = 'CIE 1931 2 Degree Standard Observer' D65_WHITE = ILLUMINANTS[CMFS_NAME]['D65'] YCBCR_CHECK_MARKER = [0, 0, 0] UNIVERSAL_COLOR_LIST = ["#F6AA00", "#FFF100", "#03AF7A", "#005AFF", "#4DC4FF", "#804000"] def preview_image(img, order='rgb', over_disp=False): if order == 'rgb': cv2.imshow('preview', img[:, :, ::-1]) elif order == 'bgr': cv2.imshow('preview', img) elif order == 'mono': cv2.imshow('preview', img) else: raise ValueError("order parameter is invalid") if over_disp: cv2.resizeWindow('preview', ) cv2.waitKey(0) cv2.destroyAllWindows() def equal_devision(length, div_num): """ # 概要 length を div_num で分割する。端数が出た場合は誤差拡散法を使って上手い具合に分散させる。 """ base = length / div_num ret_array = [base for x in range(div_num)] # 誤差拡散法を使った辻褄合わせを適用 # ------------------------------------------- diff = 0 for idx in range(div_num): diff += math.modf(ret_array[idx])[0] if diff >= 1.0: diff -= 1.0 ret_array[idx] = int(math.floor(ret_array[idx]) + 1) else: ret_array[idx] = int(math.floor(ret_array[idx])) # 計算誤差により最終点が +1 されない場合への対処 # ------------------------------------------- diff = length - sum(ret_array) if diff != 0: ret_array[-1] += diff # 最終確認 # ------------------------------------------- if length != sum(ret_array): raise ValueError("the output of equal_division() is abnormal.") return ret_array def do_matrix(img, mtx): """ img に対して mtx を適用する。 """ base_shape = img.shape r, g, b = img[..., 0], img[..., 1], img[..., 2] ro = r * mtx[0][0] + g * mtx[0][1] + b * mtx[0][2] go = r * mtx[1][0] + g * mtx[1][1] + b * mtx[1][2] bo = r * mtx[2][0] + g * mtx[2][1] + b * mtx[2][2] out_img = np.dstack((ro, go, bo)).reshape(base_shape) return out_img def _get_cmfs_xy(): """ xy色度図のプロットのための馬蹄形の外枠のxy値を求める。 Returns ------- array_like xy coordinate for chromaticity diagram """ # 基本パラメータ設定 # ------------------ cmf = CMFS.get(CMFS_NAME) d65_white = D65_WHITE # 馬蹄形のxy値を算出 # -------------------------- cmf_xy = XYZ_to_xy(cmf.values, d65_white) return cmf_xy def get_primaries(name='ITU-R BT.2020'): """ prmary color の座標を求める Parameters ---------- name : str a name of the color space. Returns ------- array_like prmaries. [[rx, ry], [gx, gy], [bx, by], [rx, ry]] """ primaries = RGB_COLOURSPACES[name].primaries primaries = np.append(primaries, [primaries[0, :]], axis=0) rgb = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) return primaries, rgb def xy_to_rgb(xy, name='ITU-R BT.2020', normalize='maximum', specific=None): """ xy値からRGB値を算出する。いい感じに正規化もしておく。 Parameters ---------- xy : array_like xy value. name : string color space name. normalize : string normalize method. You can select 'maximum', 'specific' or None. Returns ------- array_like rgb value. the value is normalized. """ illuminant_XYZ = D65_WHITE illuminant_RGB = D65_WHITE chromatic_adaptation_transform = 'CAT02' large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name) if normalize == 'specific': xyY = xy_to_xyY(xy) xyY[..., 2] = specific large_xyz = xyY_to_XYZ(xyY) else: large_xyz = xy_to_XYZ(xy) rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform) """ そのままだとビデオレベルが低かったりするので、各ドット毎にRGB値を正規化＆最大化する。必要であれば。 """ if normalize == 'maximum': rgb = normalise_maximum(rgb, axis=-1) else: if(np.sum(rgb > 1.0) > 0): print("warning: over flow has occured at xy_to_rgb") if(np.sum(rgb < 0.0) > 0): print("warning: under flow has occured at xy_to_rgb") rgb[rgb < 0] = 0 rgb[rgb > 1.0] = 1.0 return rgb def get_white_point(name): """ white point を求める。CIE1931ベース。 """ if name != "DCI-P3": illuminant = RGB_COLOURSPACES[name].illuminant white_point = ILLUMINANTS[CMFS_NAME][illuminant] else: white_point = ILLUMINANTS[CMFS_NAME]["D65"] return white_point def get_secondaries(name='ITU-R BT.2020'): """ secondary color の座標を求める Parameters ---------- name : str a name of the color space. Returns ------- array_like secondaries. the order is magenta, yellow, cyan. """ secondary_rgb = np.array([[1.0, 0.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0, 1.0]]) illuminant_XYZ = D65_WHITE illuminant_RGB = D65_WHITE chromatic_adaptation_transform = 'CAT02' rgb_to_xyz_matrix = get_rgb_to_xyz_matrix(name) large_xyz = RGB_to_XYZ(secondary_rgb, illuminant_RGB, illuminant_XYZ, rgb_to_xyz_matrix, chromatic_adaptation_transform) xy = XYZ_to_xy(large_xyz, illuminant_XYZ) return xy, secondary_rgb.reshape((3, 3)) # def plot_chromaticity_diagram( # rate=480/755.0*2, xmin=0.0, xmax=0.8, ymin=0.0, ymax=0.9, **kwargs): # # キーワード引数の初期値設定 # # ------------------------------------ # monitor_primaries = kwargs.get('monitor_primaries', None) # secondaries = kwargs.get('secondaries', None) # test_scatter = kwargs.get('test_scatter', None) # intersection = kwargs.get('intersection', None) # # プロット用データ準備 # # --------------------------------- # xy_image = get_chromaticity_image( # xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax) # cmf_xy = _get_cmfs_xy() # bt709_gamut, _ = get_primaries(name=cs.BT709) # bt2020_gamut, _ = get_primaries(name=cs.BT2020) # dci_p3_gamut, _ = get_primaries(name=cs.P3_D65) # ap0_gamut, _ = get_primaries(name=cs.ACES_AP0) # ap1_gamut, _ = get_primaries(name=cs.ACES_AP1) # xlim = (min(0, xmin), max(0.8, xmax)) # ylim = (min(0, ymin), max(0.9, ymax)) # ax1 = pu.plot_1_graph(fontsize=20 * rate, # figsize=((xmax - xmin) * 10 * rate, # (ymax - ymin) * 10 * rate), # graph_title="CIE1931 Chromaticity Diagram", # graph_title_size=None, # xlabel=None, ylabel=None, # axis_label_size=None, # legend_size=18 * rate, # xlim=xlim, ylim=ylim, # xtick=[x * 0.1 + xmin for x in # range(int((xlim[1] - xlim[0])/0.1) + 1)], # ytick=[x * 0.1 + ymin for x in # range(int((ylim[1] - ylim[0])/0.1) + 1)], # xtick_size=17 * rate, # ytick_size=17 * rate, # linewidth=4 * rate, # minor_xtick_num=2, # minor_ytick_num=2) # ax1.plot(cmf_xy[..., 0], cmf_xy[..., 1], '-k', lw=3.5*rate, label=None) # ax1.plot((cmf_xy[-1, 0], cmf_xy[0, 0]), (cmf_xy[-1, 1], cmf_xy[0, 1]), # '-k', lw=3.5*rate, label=None) # ax1.plot(bt709_gamut[:, 0], bt709_gamut[:, 1], # c=UNIVERSAL_COLOR_LIST[0], label="BT.709", lw=2.75*rate) # ax1.plot(bt2020_gamut[:, 0], bt2020_gamut[:, 1], # c=UNIVERSAL_COLOR_LIST[1], label="BT.2020", lw=2.75*rate) # ax1.plot(dci_p3_gamut[:, 0], dci_p3_gamut[:, 1], # c=UNIVERSAL_COLOR_LIST[2], label="DCI-P3", lw=2.75*rate) # ax1.plot(ap1_gamut[:, 0], ap1_gamut[:, 1], # c=UNIVERSAL_COLOR_LIST[3], label="ACES AP1", lw=2.75*rate) # ax1.plot(ap0_gamut[:, 0], ap0_gamut[:, 1], # c=UNIVERSAL_COLOR_LIST[4], label="ACES AP0", lw=2.75*rate) # if monitor_primaries is not None: # ax1.plot(monitor_primaries[:, 0], monitor_primaries[:, 1], # c="#202020", label="???", lw=3*rate) # if secondaries is not None: # xy, rgb = secondaries # ax1.scatter(xy[..., 0], xy[..., 1], s=700*rate, marker='s', c=rgb, # edgecolors='#404000', linewidth=2*rate) # if test_scatter is not None: # xy, rgb = test_scatter # ax1.scatter(xy[..., 0], xy[..., 1], s=300*rate, marker='s', c=rgb, # edgecolors='#404040', linewidth=2*rate) # if intersection is not None: # ax1.scatter(intersection[..., 0], intersection[..., 1], # s=300*rate, marker='s', c='#CCCCCC', # edgecolors='#404040', linewidth=2*rate) # ax1.imshow(xy_image, extent=(xmin, xmax, ymin, ymax)) # plt.legend(loc='upper right') # plt.savefig('temp_fig.png', bbox_inches='tight') # plt.show() def get_chromaticity_image(samples=1024, antialiasing=True, bg_color=0.9, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0): """ xy色度図の馬蹄形の画像を生成する Returns ------- ndarray rgb image. """ """ 色域設定。sRGBだと狭くて少し変だったのでBT.2020に設定。若干色が薄くなるのが難点。暇があれば改良したい。 """ # color_space = models.BT2020_COLOURSPACE # color_space = models.S_GAMUT3_COLOURSPACE color_space = models.ACES_CG_COLOURSPACE # 馬蹄形のxy値を算出 # -------------------------- cmf_xy = _get_cmfs_xy() """ 馬蹄の内外の判別をするために三角形で領域分割する(ドロネー図を作成)。ドロネー図を作れば後は外積計算で領域の内外を判別できる（たぶん）。なお、作成したドロネー図は以下のコードでプロット可能。 1点補足しておくと、```plt.triplot``` の第三引数は、第一、第二引数から三角形を作成するための **インデックス** のリストになっている。[[0, 1, 2], [2, 4, 3], ...]的な。 ```python plt.figure() plt.triplot(xy[:, 0], xy[:, 1], triangulation.simplices.copy(), '-o') plt.title('triplot of Delaunay triangulation') plt.show() ``` """ triangulation = Delaunay(cmf_xy) """ ```triangulation.find_simplex()``` で xy がどのインデックスの領域か調べることができる。戻り値が ```-1``` の場合は領域に含まれないため、 0以下のリストで領域判定の mask を作ることができる。 """ xx, yy\ = np.meshgrid(np.linspace(xmin, xmax, samples), np.linspace(ymax, ymin, samples)) xy = np.dstack((xx, yy)) mask = (triangulation.find_simplex(xy) < 0).astype(np.float) # アンチエイリアシングしてアルファチャンネルを滑らかに # ------------------------------------------------ if antialiasing: kernel = np.array([ [0, 1, 0], [1, 2, 1], [0, 1, 0], ]).astype(np.float) kernel /= np.sum(kernel) mask = convolve(mask, kernel) # ネガポジ反転 # -------------------------------- mask = 1 - mask[:, :, np.newaxis] # xy のメッシュから色を復元 # ------------------------ illuminant_XYZ = D65_WHITE illuminant_RGB = color_space.whitepoint chromatic_adaptation_transform = 'XYZ Scaling' large_xyz_to_rgb_matrix = color_space.XYZ_to_RGB_matrix xy[xy == 0.0] = 1.0 # ゼロ割対策 large_xyz = xy_to_XYZ(xy) rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform) """ そのままだとビデオレベルが低かったりするので、各ドット毎にRGB値を正規化＆最大化する。 """ rgb[rgb == 0] = 1.0 # ゼロ割対策 rgb = normalise_maximum(rgb, axis=-1) # mask 適用 # ------------------------------------- mask_rgb = np.dstack((mask, mask, mask)) rgb *= mask_rgb # 背景色をグレーに変更 # ------------------------------------- bg_rgb = np.ones_like(rgb) bg_rgb *= (1 - mask_rgb) * bg_color rgb += bg_rgb rgb = rgb ** (1/2.2) return rgb def get_csf_color_image(width=640, height=480, lv1=np.uint16(np.array([1.0, 1.0, 1.0]) * 1023 * 0x40), lv2=np.uint16(np.array([1.0, 1.0, 1.0]) * 512 * 0x40), stripe_num=18): """ 長方形を複数個ズラして重ねることでCSFパターンっぽいのを作る。入力信号レベルは16bitに限定する。 Parameters ---------- width : numeric. width of the pattern image. height : numeric. height of the pattern image. lv1 : numeric video level 1. this value must be 10bit. lv2 : numeric video level 2. this value must be 10bit. stripe_num : numeric number of the stripe. Returns ------- array_like a cms pattern image. """ width_list = equal_devision(width, stripe_num) height_list = equal_devision(height, stripe_num) h_pos_list = equal_devision(width // 2, stripe_num) v_pos_list = equal_devision(height // 2, stripe_num) lv1_16bit = lv1 lv2_16bit = lv2 img = np.zeros((height, width, 3), dtype=np.uint16) width_temp = width height_temp = height h_pos_temp = 0 v_pos_temp = 0 for idx in range(stripe_num): lv = lv1_16bit if (idx % 2) == 0 else lv2_16bit temp_img = np.ones((height_temp, width_temp, 3), dtype=np.uint16) # temp_img *= lv temp_img[:, :] = lv ed_pos_h = h_pos_temp + width_temp ed_pos_v = v_pos_temp + height_temp img[v_pos_temp:ed_pos_v, h_pos_temp:ed_pos_h] = temp_img width_temp -= width_list[stripe_num - 1 - idx] height_temp -= height_list[stripe_num - 1 - idx] h_pos_temp += h_pos_list[idx] v_pos_temp += v_pos_list[idx] return img def plot_xyY_color_space(name='ITU-R BT.2020', samples=1024, antialiasing=True): """ SONY の HDR説明資料にあるような xyY の図を作る。 Parameters ---------- name : str name of the target color space. Returns ------- None """ # 馬蹄の領域判別用データ作成 # -------------------------- primary_xy, _ = get_primaries(name=name) triangulation = Delaunay(primary_xy) xx, yy\ = np.meshgrid(np.linspace(0, 1, samples), np.linspace(1, 0, samples)) xy = np.dstack((xx, yy)) mask = (triangulation.find_simplex(xy) < 0).astype(np.float) # アンチエイリアシングしてアルファチャンネルを滑らかに # ------------------------------------------------ if antialiasing: kernel = np.array([ [0, 1, 0], [1, 2, 1], [0, 1, 0], ]).astype(np.float) kernel /= np.sum(kernel) mask = convolve(mask, kernel) # ネガポジ反転 # -------------------------------- mask = 1 - mask[:, :, np.newaxis] # xy のメッシュから色を復元 # ------------------------ illuminant_XYZ = D65_WHITE illuminant_RGB = RGB_COLOURSPACES[name].whitepoint chromatic_adaptation_transform = 'CAT02' large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name) rgb_to_large_xyz_matrix = get_rgb_to_xyz_matrix(name) large_xyz = xy_to_XYZ(xy) rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform) """ そのままだとビデオレベルが低かったりするので、各ドット毎にRGB値を正規化＆最大化する。 """ rgb_org = normalise_maximum(rgb, axis=-1) # mask 適用 # ------------------------------------- mask_rgb = np.dstack((mask, mask, mask)) rgb = rgb_org * mask_rgb rgba = np.dstack((rgb, mask)) # こっからもういちど XYZ に変換。Yを求めるために。 # --------------------------------------------- large_xyz2 = RGB_to_XYZ(rgb, illuminant_RGB, illuminant_XYZ, rgb_to_large_xyz_matrix, chromatic_adaptation_transform) # ログスケールに変換する準備 # -------------------------- large_y = large_xyz2[..., 1] * 1000 large_y[large_y < 1] = 1.0 fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # ax.plot_wireframe(xy[..., 0], xy[..., 1], np.log10(large_y), # rcount=100, ccount=100) ax.plot_surface(xy[..., 0], xy[..., 1], np.log10(large_y), rcount=64, ccount=64, facecolors=rgb_org) ax.set_xlabel("x") ax.set_ylabel("y") ax.set_zlabel("Y") ax.set_zticks([0, 1, 2, 3]) ax.set_zticklabels([1, 10, 100, 1000]) # chromatcity_image の取得。z=0 の位置に貼り付ける # ---------------------------------------------- cie1931_rgb = get_chromaticity_image(samples=samples, bg_color=0.0) alpha = np.zeros_like(cie1931_rgb[..., 0]) rgb_sum = np.sum(cie1931_rgb, axis=-1) alpha[rgb_sum > 0.00001] = 1 cie1931_rgb = np.dstack((cie1931_rgb[..., 0], cie1931_rgb[..., 1], cie1931_rgb[..., 2], alpha)) zz = np.zeros_like(xy[..., 0]) ax.plot_surface(xy[..., 0], xy[..., 1], zz, facecolors=cie1931_rgb) plt.show() def log_tick_formatter(val, pos=None): return "{:.0e}".format(10**val) def get_3d_grid_cube_format(grid_num=4): """ # 概要 (0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 0, 1), ... みたいな配列を返す。 CUBE形式の3DLUTを作成する時に便利。 """ base = np.linspace(0, 1, grid_num) ones_x = np.ones((grid_num, grid_num, 1)) ones_y = np.ones((grid_num, 1, grid_num)) ones_z = np.ones((1, grid_num, grid_num)) r_3d = base[np.newaxis, np.newaxis, :] * ones_x g_3d = base[np.newaxis, :, np.newaxis] * ones_y b_3d = base[:, np.newaxis, np.newaxis] * ones_z r_3d = r_3d.flatten() g_3d = g_3d.flatten() b_3d = b_3d.flatten() return np.dstack((r_3d, g_3d, b_3d)) def quadratic_bezier_curve(t, p0, p1, p2, samples=1024): # x = ((1 - t) ** 2) * p0[0] + 2 * (1 - t) * t * p1[0]\ # + (t ** 2) * p2[0] # y = ((1 - t) ** 2) * p0[1] + 2 * (1 - t) * t * p1[1]\ # + (t ** 2) * p2[1] x = ((1 - t) ** 2) * p0[0] + 2 * (1 - t) * t * p1[0]\ + (t ** 2) * p2[0] y = ((1 - t) ** 2) * p0[1] + 2 * (1 - t) * t * p1[1]\ + (t ** 2) * p2[1] # ax1 = pu.plot_1_graph(fontsize=20, # figsize=(10, 8), # graph_title="Title", # graph_title_size=None, # xlabel="X Axis Label", ylabel="Y Axis Label", # axis_label_size=None, # legend_size=17, # xlim=None, # ylim=None, # xtick=None, # ytick=None, # xtick_size=None, ytick_size=None, # linewidth=3, # minor_xtick_num=None, # minor_ytick_num=None) # ax1.plot(x, y, label='aaa') # plt.legend(loc='upper left') # plt.show() def gen_step_gradation(width=1024, height=128, step_num=17, bit_depth=10, color=(1.0, 1.0, 1.0), direction='h', debug=False): """ # 概要階段状に変化するグラデーションパターンを作る。なお、引数の調整により正確に1階調ずつ変化するパターンも作成可能。 # 注意事項正確に1階調ずつ変化するグラデーションを作る場合は ```step_num = (2 ** bit_depth) + 1``` となるようにパラメータを指定すること。具体例は以下のExample参照。 # Example ``` grad_8 = gen_step_gradation(width=grad_width, height=grad_height, step_num=257, bit_depth=8, color=(1.0, 1.0, 1.0), direction='h') grad_10 = gen_step_gradation(width=grad_width, height=grad_height, step_num=1025, bit_depth=10, color=(1.0, 1.0, 1.0), direction='h') ``` """ max = 2 ** bit_depth # グラデーション方向設定 # ---------------------- if direction == 'h': pass else: temp = height height = width width = temp if (max + 1 != step_num): """ 1階調ずつの増加では無いパターン。末尾のデータが 256 や 1024 になるため -1 する。 """ val_list = np.linspace(0, max, step_num) val_list[-1] -= 1 else: """ 正確に1階調ずつ変化するパターン。末尾のデータが 256 や 1024 になるため除外する。 """ val_list = np.linspace(0, max, step_num)[0:-1] step_num -= 1 # step_num は引数で余計に +1 されてるので引く # 念のため1階調ずつの変化か確認 # --------------------------- diff = val_list[1:] - val_list[0:-1] if (diff == 1).all(): pass else: raise ValueError("calculated value is invalid.") # まずは水平1LINEのグラデーションを作る # ----------------------------------- step_length_list = equal_devision(width, step_num) step_bar_list = [] for step_idx, length in enumerate(step_length_list): step = [np.ones((length)) * color[c_idx] * val_list[step_idx] for c_idx in range(3)] if direction == 'h': step = np.dstack(step) step_bar_list.append(step) step_bar = np.hstack(step_bar_list) else: step = np.dstack(step).reshape((length, 1, 3)) step_bar_list.append(step) step_bar = np.vstack(step_bar_list) # ブロードキャストを利用して2次元に拡張する # ------------------------------------------ if direction == 'h': img = step_bar * np.ones((height, 1, 3)) else: img = step_bar * np.ones((1, height, 3)) # np.uint16 にコンバート # ------------------------------ # img = np.uint16(np.round(img * (2 ** (16 - bit_depth)))) if debug: preview_image(img, 'rgb') return img def merge(img_a, img_b, pos=(0, 0)): """ img_a に img_b をマージする。 img_a にデータを上書きする。 pos = (horizontal_st, vertical_st) """ b_width = img_b.shape[1] b_height = img_b.shape[0] img_a[pos[1]:b_height+pos[1], pos[0]:b_width+pos[0]] = img_b def merge_with_alpha(bg_img, fg_img, tf_str=tf.SRGB, pos=(0, 0)): """ 合成する。 Parameters ---------- bg_img : array_like(float, 3-channel) image data. fg_img : array_like(float, 4-channel) image data tf : strings transfer function pos : list(int) (pos_h, pos_v) """ f_width = fg_img.shape[1] f_height = fg_img.shape[0] bg_merge_area = bg_img[pos[1]:f_height+pos[1], pos[0]:f_width+pos[0]] bg_linear = tf.eotf_to_luminance(bg_merge_area, tf_str) fg_linear = tf.eotf_to_luminance(fg_img, tf_str) alpha = fg_linear[:, :, 3:] / tf.PEAK_LUMINANCE[tf_str] out_linear = (1 - alpha) * bg_linear + fg_linear[:, :, :-1] out_merge_area = tf.oetf_from_luminance(out_linear, tf_str) bg_img[pos[1]:f_height+pos[1], pos[0]:f_width+pos[0]] = out_merge_area return bg_img def dot_pattern(dot_size=4, repeat=4, color=np.array([1.0, 1.0, 1.0])): """ dot pattern 作る。 Parameters ---------- dot_size : integer dot size. repeat : integer The number of high-low pairs. color : array_like color value. Returns ------- array_like dot pattern image. """ # 水平・垂直のピクセル数 pixel_num = dot_size * 2 * repeat # High-Log の論理配列を生成 even_logic = [(np.arange(pixel_num) % (dot_size * 2)) - dot_size < 0] even_logic = np.dstack((even_logic, even_logic, even_logic)) odd_logic = np.logical_not(even_logic) # 着色 color = color.reshape((1, 1, 3)) even_line = (np.ones((1, pixel_num, 3)) * even_logic) * color odd_line = (np.ones((1, pixel_num, 3)) * odd_logic) * color # V方向にコピー＆Even-Oddの結合 even_block = np.repeat(even_line, dot_size, axis=0) odd_block = np.repeat(odd_line, dot_size, axis=0) pair_block = np.vstack((even_block, odd_block)) img = np.vstack([pair_block for x in range(repeat)]) return img def complex_dot_pattern(kind_num=3, whole_repeat=2, fg_color=np.array([1.0, 1.0, 1.0]), bg_color=np.array([0.15, 0.15, 0.15])): """ dot pattern 作る。 Parameters ---------- kind_num : integer 作成するドットサイズの種類。例えば、kind_num=3 ならば、1dot, 2dot, 4dot のパターンを作成。 whole_repeat : integer 異なる複数種類のドットパターンの組数。例えば、kind_num=3, whole_repeat=2 ならば、 1dot, 2dot, 4dot のパターンを水平・垂直に2組作る。 fg_color : array_like foreground color value. bg_color : array_like background color value. reduce : bool HDRテストパターンの3840x2160専用。縦横を半分にする。 Returns ------- array_like dot pattern image. """ max_dot_width = 2 ** kind_num img_list = [] for size_idx in range(kind_num)[::-1]: dot_size = 2 ** size_idx repeat = max_dot_width // dot_size dot_img = dot_pattern(dot_size, repeat, fg_color) img_list.append(dot_img) img_list.append(np.ones_like(dot_img) * bg_color) # preview_image(dot_img) line_upper_img = np.hstack(img_list) line_upper_img = np.hstack([line_upper_img for x in range(whole_repeat)]) line_lower_img = line_upper_img.copy()[:, ::-1, :] h_unit_img = np.vstack((line_upper_img, line_lower_img)) img = np.vstack([h_unit_img for x in range(kind_num * whole_repeat)]) # preview_image(img) # cv2.imwrite("hoge.tiff", np.uint8(img * 0xFF)[..., ::-1]) return img def make_csf_color_image(width=640, height=640, lv1=np.array([940, 940, 940], dtype=np.uint16), lv2=np.array([1023, 1023, 1023], dtype=np.uint16), stripe_num=6): """ 長方形を複数個ズラして重ねることでCSFパターンっぽいのを作る。入力信号レベルは10bitに限定する。 Parameters ---------- width : numeric. width of the pattern image. height : numeric. height of the pattern image. lv1 : array_like video level 1. this value must be 10bit. lv2 : array_like video level 2. this value must be 10bit. stripe_num : numeric number of the stripe. Returns ------- array_like a cms pattern image. """ width_list = equal_devision(width, stripe_num) height_list = equal_devision(height, stripe_num) h_pos_list = equal_devision(width // 2, stripe_num) v_pos_list = equal_devision(height // 2, stripe_num) img = np.zeros((height, width, 3), dtype=np.uint16) width_temp = width height_temp = height h_pos_temp = 0 v_pos_temp = 0 for idx in range(stripe_num): lv = lv1 if (idx % 2) == 0 else lv2 temp_img = np.ones((height_temp, width_temp, 3), dtype=np.uint16) temp_img = temp_img * lv.reshape((1, 1, 3)) ed_pos_h = h_pos_temp + width_temp ed_pos_v = v_pos_temp + height_temp img[v_pos_temp:ed_pos_v, h_pos_temp:ed_pos_h] = temp_img width_temp -= width_list[stripe_num - 1 - idx] height_temp -= height_list[stripe_num - 1 - idx] h_pos_temp += h_pos_list[idx] v_pos_temp += v_pos_list[idx] # preview_image(img / 1023) return img def make_tile_pattern(width=480, height=960, h_tile_num=4, v_tile_num=4, low_level=(940, 940, 940), high_level=(1023, 1023, 1023)): """ タイル状の縞々パターンを作る """ width_array = equal_devision(width, h_tile_num) height_array = equal_devision(height, v_tile_num) high_level = np.array(high_level, dtype=np.uint16) low_level = np.array(low_level, dtype=np.uint16) v_buf = [] for v_idx, height in enumerate(height_array): h_buf = [] for h_idx, width in enumerate(width_array): tile_judge = (h_idx + v_idx) % 2 == 0 h_temp = np.zeros((height, width, 3), dtype=np.uint16) h_temp[:, :] = high_level if tile_judge else low_level h_buf.append(h_temp) v_buf.append(np.hstack(h_buf)) img = np.vstack(v_buf) # preview_image(img/1024.0) return img def get_marker_idx(img, marker_value): return np.all(img == marker_value, axis=-1) def make_ycbcr_checker(height=480, v_tile_num=4): """ YCbCr係数誤りを確認するテストパターンを作る。正直かなり汚い組み方です。雑に作ったパターンを悪魔合体させています。 Parameters ---------- height : numeric. height of the pattern image. v_tile_num : numeric number of the tile in the vertical direction. Note ---- 横長のパターンになる。以下の式が成立する。 ``` h_tile_num = v_tile_num * 2 width = height * 2 ``` Returns ------- array_like ycbcr checker image """ cyan_img = make_tile_pattern(width=height, height=height, h_tile_num=v_tile_num, v_tile_num=v_tile_num, low_level=[0, 990, 990], high_level=[0, 1023, 1023]) magenta_img = make_tile_pattern(width=height, height=height, h_tile_num=v_tile_num, v_tile_num=v_tile_num, low_level=[990, 0, 312], high_level=[1023, 0, 312]) out_img = np.hstack([cyan_img, magenta_img]) # preview_image(out_img/1023.0) return out_img def plot_color_checker_image(rgb, rgb2=None, size=(1920, 1080), block_size=1/4.5, padding=0.01): """ ColorCheckerをプロットする Parameters ---------- rgb : array_like RGB value of the ColorChecker. RGB's shape must be (24, 3). rgb2 : array_like It's a optional parameter. If You want to draw two different ColorCheckers, set the RGB value to this variable. size : tuple canvas size. block_size : float A each block's size. This value is ratio to height of the canvas. padding : float A padding to the block. Returns ------- array_like A ColorChecker image. """ IMG_HEIGHT = size[1] IMG_WIDTH = size[0] COLOR_CHECKER_SIZE = block_size COLOR_CHECKER_H_NUM = 6 COLOR_CHECKER_V_NUM = 4 COLOR_CHECKER_PADDING = 0.01 # 基本パラメータ算出 # -------------------------------------- COLOR_CHECKER_H_NUM = 6 COLOR_CHECKER_V_NUM = 4 img_height = IMG_HEIGHT img_width = IMG_WIDTH patch_st_h = int(IMG_WIDTH / 2.0 - (IMG_HEIGHT * COLOR_CHECKER_SIZE * COLOR_CHECKER_H_NUM / 2.0 + (IMG_HEIGHT * COLOR_CHECKER_PADDING * (COLOR_CHECKER_H_NUM / 2.0 - 0.5)) / 2.0)) patch_st_v = int(IMG_HEIGHT / 2.0 - (IMG_HEIGHT * COLOR_CHECKER_SIZE * COLOR_CHECKER_V_NUM / 2.0 + (IMG_HEIGHT * COLOR_CHECKER_PADDING * (COLOR_CHECKER_V_NUM / 2.0 - 0.5)) / 2.0)) patch_width = int(img_height * COLOR_CHECKER_SIZE) patch_height = patch_width patch_space = int(img_height * COLOR_CHECKER_PADDING) # 24ループで1枚の画像に24パッチを描画 # ------------------------------------------------- img_all_patch = np.zeros((img_height, img_width, 3), dtype=np.uint8) for idx in range(COLOR_CHECKER_H_NUM * COLOR_CHECKER_V_NUM): v_idx = idx // COLOR_CHECKER_H_NUM h_idx = (idx % COLOR_CHECKER_H_NUM) patch = np.ones((patch_height, patch_width, 3)) patch[:, :] = rgb[idx] st_h = patch_st_h + (patch_width + patch_space) * h_idx st_v = patch_st_v + (patch_height + patch_space) * v_idx img_all_patch[st_v:st_v+patch_height, st_h:st_h+patch_width] = patch # pt1 = (st_h, st_v) # upper left pt2 = (st_h + patch_width, st_v) # upper right pt3 = (st_h, st_v + patch_height) # lower left pt4 = (st_h + patch_width, st_v + patch_height) # lower right pts = np.array((pt2, pt3, pt4)) sub_color = rgb[idx].tolist() if rgb2 is None else rgb2[idx].tolist() cv2.fillPoly(img_all_patch, [pts], sub_color) preview_image(img_all_patch) return img_all_patch def get_log10_x_scale( sample_num=8, ref_val=1.0, min_exposure=-1, max_exposure=6): """ Log10スケールのx軸データを作る。 Examples -------- >>> get_log2_x_scale( ... sample_num=8, ref_val=1.0, min_exposure=-1, max_exposure=6) array([ 1.0000e-01 1.0000e+00 1.0000e+01 1.0000e+02 1.0000e+03 1.0000e+04 1.0000e+05 1.0000e+06]) """ x_min = np.log10(ref_val * (10 ** min_exposure)) x_max = np.log10(ref_val * (10 ** max_exposure)) x = np.linspace(x_min, x_max, sample_num) return 10.0 ** x def get_log2_x_scale( sample_num=32, ref_val=1.0, min_exposure=-6.5, max_exposure=6.5): """ Log2スケールのx軸データを作る。 Examples -------- >>> get_log2_x_scale(sample_num=10, min_exposure=-4.0, max_exposure=4.0) array([[ 0.0625 0.11573434 0.214311 0.39685026 0.73486725 1.36079 2.5198421 4.66611616 8.64047791 16. ]]) """ x_min = np.log2(ref_val * (2 ** min_exposure)) x_max = np.log2(ref_val * (2 ** max_exposure)) x = np.linspace(x_min, x_max, sample_num) return 2.0 ** x def shaper_func_linear_to_log2( x, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5): """ ACESutil.Lin_to_Log2_param.ctl を参考に作成。 https://github.com/ampas/aces-dev/blob/master/transforms/ctl/utilities/ACESutil.Lin_to_Log2_param.ctl Parameters ---------- x : array_like linear data. mid_gray : float 18% gray value on linear scale. min_exposure : float minimum value on log scale. max_exposure : float maximum value on log scale. Returns ------- array_like log2 value that is transformed from linear x value. Examples -------- >>> shaper_func_linear_to_log2( ... x=0.18, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5) 0.5 >>> shaper_func_linear_to_log2( ... x=np.array([0.00198873782209, 16.2917402385]) ... mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5) array([ 1.58232402e-13 1.00000000e+00]) """ # log2空間への変換。mid_gray が 0.0 となるように補正 y = np.log2(x / mid_gray) # min, max の範囲で正規化。 y_normalized = (y - min_exposure) / (max_exposure - min_exposure) y_normalized[y_normalized < 0] = 0 return y_normalized def shaper_func_log2_to_linear( x, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5): """ ACESutil.Log2_to_Lin_param.ctl を参考に作成。 https://github.com/ampas/aces-dev/blob/master/transforms/ctl/utilities/ACESutil.Log2_to_Lin_param.ctl Log2空間の補足は shaper_func_linear_to_log2() の説明を参照 Examples -------- >>> x = np.array([0.0, 1.0]) >>> shaper_func_log2_to_linear( ... x, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5) array([0.00198873782209, 16.2917402385]) """ x_re_scale = x * (max_exposure - min_exposure) + min_exposure y = (2.0 ** x_re_scale) * mid_gray # plt.plot(x, y) # plt.show() return y def draw_straight_line(img, pt1, pt2, color, thickness): """ 直線を引く。OpenCV だと 8bit しか対応してないっぽいので自作。 Parameters ---------- img : array_like image data. pt1 : list(pos_h, pos_v) start point. pt2 : list(pos_h, pos_v) end point. color : array_like color thickness : int thickness. Returns ------- array_like image data with line. Notes ----- thickness のパラメータは pt1 の点から右下方向に効きます。 pt1 を中心として太さではない事に注意。 Examples -------- >>> pt1 = (0, 0) >>> pt2 = (1920, 0) >>> color = (940, 940, 940) >>> thickness = 4 >>> draw_straight_line(img, pt1, pt2, color, thickness) """ # parameter check if (pt1[0] != pt2[0]) and (pt1[1] != pt2[1]): raise ValueError("invalid pt1, pt2 parameters") # check direction if pt1[0] == pt2[0]: thickness_direction = 'h' else: thickness_direction = 'v' if thickness_direction == 'h': for h_idx in range(thickness): img[pt1[1]:pt2[1], pt1[0] + h_idx, :] = color elif thickness_direction == 'v': for v_idx in range(thickness): img[pt1[1] + v_idx, pt1[0]:pt2[0], :] = color def draw_outline(img, fg_color, outline_width): """ img に対して外枠線を引く Parameters ---------- img : array_like image data. fg_color : array_like color outline_width : int thickness. Returns ------- array_like image data with line. Examples -------- >>> img = np.zeros((1080, 1920, 3)) >>> color = (940, 940, 940) >>> thickness = 2 >>> draw_outline(img, color, thickness) """ width = img.shape[1] height = img.shape[0] # upper left pt1 = (0, 0) pt2 = (width, 0) draw_straight_line( img, pt1, pt2, fg_color, outline_width) pt1 = (0, 0) pt2 = (0, height) draw_straight_line( img, pt1, pt2, fg_color, outline_width) # lower right pt1 = (width - outline_width, 0) pt2 = (width - outline_width, height) draw_straight_line( img, pt1, pt2, fg_color, outline_width) pt1 = (0, height - outline_width) pt2 = (width, height - outline_width) draw_straight_line( img, pt1, pt2, fg_color, outline_width) def convert_luminance_to_color_value(luminance, transfer_function): """ 輝度[cd/m2] から code value の RGB値に変換する。 luminance の単位は [cd/m2]。無彩色である。 Examples -------- >>> convert_luminance_to_color_value(100, tf.GAMMA24) >>> [ 1.0 1.0 1.0 ] >>> convert_luminance_to_color_value(100, tf.ST2084) >>> [ 0.50807842 0.50807842 0.50807842 ] """ code_value = convert_luminance_to_code_value( luminance, transfer_function) return np.array([code_value, code_value, code_value]) def convert_luminance_to_code_value(luminance, transfer_function): """ 輝度[cd/m2] から code value に変換する。 luminance の単位は [cd/m2] """ return tf.oetf_from_luminance(luminance, transfer_function) def calc_rad_patch_idx2(outmost_num=5, current_num=3): """ 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの RGB値のリストを得る。 https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png 得られたデータは並べ替えが済んでいないため、calc_rad_patch_idx2() で得られる変換テーブルを使った変換が必要。本関数はまさにその変換を行う。 """ base = np.arange(outmost_num ** 2).reshape((outmost_num, outmost_num)) # print(base) t_idx = (outmost_num - current_num) // 2 trimmed = base[t_idx:t_idx+current_num, t_idx:t_idx+current_num] # print(trimmed) # print(np.arange(current_num**2).reshape((current_num, current_num))) half_num = current_num // 2 conv_idx = [] for idx in range(half_num): val = (current_num ** 2) // 2 + half_num - current_num * idx conv_idx.append(val) for idx in range(current_num)[::-1]: conv_idx.append(idx) for idx in range(1, current_num - 1): conv_idx.append(idx * current_num) for idx in range(current_num): val = (current_num ** 2) - current_num + idx conv_idx.append(val) for idx in range(1, half_num): val = (current_num ** 2) - 1 - idx * current_num conv_idx.append(val) conv_idx = trimmed.flatten()[conv_idx] return conv_idx def _calc_rgb_from_same_lstar_radial_data( lstar, temp_chroma, current_num, color_space): """ 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの RGB値のリストを得る。 https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png 得られたデータは並べ替えが済んでいないため、calc_rad_patch_idx2() で得られる変換テーブルを使った変換が必要。 """ current_patch_num = (current_num - 1) * 4 if current_num > 1 else 1 rad = np.linspace(0, 2 * np.pi, current_patch_num, endpoint=False) ll = np.ones((current_patch_num)) * lstar aa = np.cos(rad) * temp_chroma bb = np.sin(rad) * temp_chroma lab = np.dstack((ll, aa, bb)) large_xyz = Lab_to_XYZ(lab) rgb = XYZ_to_RGB(large_xyz, D65_WHITE, D65_WHITE, color_space.XYZ_to_RGB_matrix) return np.clip(rgb, 0.0, 1.0) def calc_same_lstar_radial_color_patch_data( lstar=58, chroma=32.5, outmost_num=9, color_space=BT709_COLOURSPACE, transfer_function=tf.GAMMA24): """ 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの RGB値のリストを得る。 https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png 得られた RGB値のリストは最初のデータが画像左上の緑データ、最後のデータが画像右下の紫データとなるよう既に**並べ替え**が行われている。よってパッチをプロットする場合はRGB値リストの先頭から順にデータを取り出し、右下に向かって並べていけば良い。 """ patch_num = outmost_num ** 2 transfer_function = tf.GAMMA24 rgb_list = np.ones((patch_num, 3)) current_num_list = range(1, outmost_num + 1, 2) chroma_list = np.linspace(0, chroma, len(current_num_list)) for temp_chroma, current_num in zip(chroma_list, current_num_list): current_patch_num = (current_num - 1) * 4 if current_num > 1 else 1 rgb = _calc_rgb_from_same_lstar_radial_data( lstar, temp_chroma, current_num, color_space) rgb = np.reshape(rgb, (current_patch_num, 3)) rgb = tf.oetf(rgb, transfer_function) conv_idx = calc_rad_patch_idx2( outmost_num=outmost_num, current_num=current_num) for idx in range(current_patch_num): rgb_list[conv_idx[idx]] = rgb[idx] return rgb_list def _plot_same_lstar_radial_color_patch_data( lstar=58, chroma=32.5, outmost_num=9, color_space=BT709_COLOURSPACE, transfer_function=tf.GAMMA24): patch_size = 1080 // outmost_num img = np.ones((1080, 1080, 3)) * 0.0 rgb = calc_same_lstar_radial_color_patch_data( lstar=lstar, chroma=chroma, outmost_num=outmost_num, color_space=color_space, transfer_function=transfer_function) for idx in range(outmost_num ** 2): h_idx = idx % outmost_num v_idx = idx // outmost_num st_pos = (h_idx * patch_size, v_idx * patch_size) temp_img = np.ones((patch_size, patch_size, 3))\ * rgb[idx][np.newaxis, np.newaxis, :] merge(img, temp_img, st_pos) cv2.imwrite("hoge2.tiff", np.uint16(np.round(img[:, :, ::-1] * 0xFFFF))) def get_accelerated_x_1x(sample_num=64): """ 単調増加ではなく、加速度が 0→1→0 となるような x を作る Parameters ---------- sample_num : int the number of the sample. Returns ------- array_like accelerated value list Examples -------- >>> x0 = np.linspace(0, 1, 8) >>> x1 = get_accelerated_x_1x(8) >>> print(x0) >>> [ 0. 0.142 0.285 0.428 0.571 0.714 0.857 1. ] >>> print(x1) >>> [ 0. 0.049 0.188 0.388 0.611 0.811 0.950 1. ] """ rad = np.linspace(-0.5 * np.pi, 0.5 * np.pi, sample_num) x = (np.sin(rad) + 1) / 2 return x def get_accelerated_x_2x(sample_num=64): """ 単調増加ではなく、加速度が 0→1→0 となるような x を作る。加速度が `get_accelerated_x_1x` の2倍！！ Parameters ---------- sample_num : int the number of the sample. Returns ------- array_like accelerated value list Examples -------- >>> x0 = np.linspace(0, 1, 8) >>> x2 = get_accelerated_x_2x(8) >>> print(x0) >>> [ 0. 0.142 0.285 0.428 0.571 0.714 0.857 1. ] >>> print(x2) >>> [ 0. 0.006 0.084 0.328 0.671 0.915 0.993 1. ] """ rad = np.linspace(-0.5 * np.pi, 0.5 * np.pi, sample_num) rad = np.sin(rad) * 0.5 * np.pi x = (np.sin(rad) + 1) / 2 return x def get_accelerated_x_4x(sample_num=64): """ 単調増加ではなく、加速度が 0→1→0 となるような x を作る。加速度が `get_accelerated_x_1x` の4倍！！ Parameters ---------- sample_num : int the number of the sample. Returns ------- array_like accelerated value list """ rad = np.linspace(-0.5 * np.pi, 0.5 * np.pi, sample_num) rad = np.sin(rad) * 0.5 * np.pi rad = np.sin(rad) * 0.5 * np.pi x = (np.sin(rad) + 1) / 2 return x def get_accelerated_x_8x(sample_num=64): """ 単調増加ではなく、加速度が 0→1→0 となるような x を作る。加速度が `get_accelerated_x_1x` の4倍！！ Parameters ---------- sample_num : int the number of the sample. Returns ------- array_like accelerated value list """ rad = np.linspace(-0.5 * np.pi, 0.5 * np.pi, sample_num) rad = np.sin(rad) * 0.5 * np.pi rad = np.sin(rad) * 0.5 * np.pi rad = np.sin(rad) * 0.5 * np.pi x = (np.sin(rad) + 1) / 2 return x def generate_color_checker_rgb_value( color_space=BT709_COLOURSPACE, target_white=D65_WHITE): """ Generate the 24 RGB values of the color checker. Parameters ---------- color_space : color space color space object in `colour` module. target_white : array_like the xy values of the white point of target color space. Returns ------- array_like 24 RGB values. This is linear. OETF is not applied. Examples -------- >>> generate_color_checker_rgb_value( ... color_space=colour.models.BT709_COLOURSPACE, ... target_white=[0.3127, 0.3290]) >>> [[ 0.17289286 0.08205728 0.05714562] >>> [ 0.5680292 0.29250401 0.21951748] >>> [ 0.10435534 0.19656108 0.32958666] >>> [ 0.1008804 0.14839018 0.05327639] >>> [ 0.22303549 0.2169701 0.43166537] >>> [ 0.10715338 0.513512 0.41415978] >>> [ 0.74639182 0.20020473 0.03081343] >>> [ 0.05947812 0.10659045 0.39897686] >>> [ 0.5673215 0.08485376 0.11945382] >>> [ 0.11177253 0.04285397 0.14166202] >>> [ 0.34250836 0.5062777 0.0557734 ] >>> [ 0.79262553 0.35803886 0.025485 ] >>> [ 0.01864598 0.05139665 0.28886469] >>> [ 0.054392 0.29876719 0.07187681] >>> [ 0.45628547 0.03075684 0.04092033] >>> [ 0.85379178 0.56503558 0.01475575] >>> [ 0.53533883 0.09006355 0.3047824 ] >>> [-0.03662977 0.24753781 0.39824679] >>> [ 0.91177068 0.91497623 0.89427332] >>> [ 0.57973934 0.59203191 0.59370647] >>> [ 0.35495537 0.36538027 0.36772001] >>> [ 0.19009594 0.19180133 0.19316719] >>> [ 0.08524707 0.08890587 0.09255774] >>> [ 0.03038879 0.03118623 0.03279615]] """ colour_checker_param = COLOURCHECKERS.get('ColorChecker 2005') # 今回の処理では必要ないデータもあるので xyY と whitepoint だけ抽出 # ------------------------------------------------------------- _name, data, whitepoint = colour_checker_param temp_xyY = [] for key in data.keys(): temp_xyY.append(data[key]) temp_xyY = np.array(temp_xyY) large_xyz = xyY_to_XYZ(temp_xyY) rgb_white_point = D65_WHITE illuminant_XYZ = whitepoint # ColorCheckerのオリジナルデータの白色点 illuminant_RGB = rgb_white_point # XYZ to RGB 変換後の白色点を設定 chromatic_adaptation_transform = 'CAT02' large_xyz_to_rgb_matrix = color_space.XYZ_to_RGB_matrix rgb = XYZ_to_RGB( large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform) return rgb def make_color_checker_image(rgb, width=1920, padding_rate=0.01): """ 6x4 のカラーチェッカーの画像を作る。 Height は Width から自動計算される。padding_rate で少し値が変わる。 """ h_patch_num = 6 v_patch_num = 4 # 各種パラメータ計算 each_padding = int(width * padding_rate + 0.5) h_padding_total = each_padding * (h_patch_num + 1) h_patch_width_total = width - h_padding_total patch_height = h_patch_width_total // h_patch_num height = patch_height * v_patch_num + each_padding * (v_patch_num + 1) patch_width_list = equal_devision(h_patch_width_total, h_patch_num) # パッチを並べる img = np.zeros((height, width, 3)) for v_idx in range(v_patch_num): h_pos_st = each_padding v_pos_st = each_padding + v_idx * (patch_height + each_padding) for h_idx in range(h_patch_num): rgb_idx = v_idx * h_patch_num + h_idx pos = (h_pos_st, v_pos_st) patch_img = np.ones((patch_height, patch_width_list[h_idx], 3))\ * rgb[rgb_idx] merge(img, patch_img, pos) h_pos_st += (patch_width_list[h_idx] + each_padding) return img def calc_st_pos_for_centering(bg_size, fg_size): """ Calculate start postion for centering. Parameters ---------- bg_size : touple(int) (width, height) of the background image. fg_size : touple(int) (width, height) of the foreground image. Returns ------- touple (int) (st_pos_h, st_pos_v) Examples -------- >>> calc_st_pos_for_centering(bg_size=(1920, 1080), fg_size=(640, 480)) >>> (640, 300) """ bg_width = bg_size[0] bg_height = bg_size[1] fg_width = fg_size[0] fg_height = fg_size[1] st_pos_h = bg_width // 2 - fg_width // 2 st_pos_v = bg_height // 2 - fg_height // 2 return (st_pos_h, st_pos_v) def get_size_from_image(img): """ `calc_st_pos_for_centering()` の引数計算が面倒だったので関数化。 """ return (img.shape[1], img.shape[0]) if __name__ == '__main__': os.chdir(os.path.dirname(os.path.abspath(__file__))) # print(calc_rad_patch_idx(outmost_num=9, current_num=1)) # _plot_same_lstar_radial_color_patch_data( # lstar=58, chroma=32.5, outmost_num=7, # color_space=BT709_COLOURSPACE, # transfer_function=tf.GAMMA24) # calc_rad_patch_idx2(outmost_num=9, current_num=7) # print(convert_luminance_to_color_value(100, tf.ST2084)) # print(generate_color_checker_rgb_value(target_white=[0.3127, 0.3290])) print(calc_st_pos_for_centering(bg_size=(1920, 1080), fg_size=(640, 480)))

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import os import cv2 import matplotlib.pyplot as plt import numpy as np from colour.colorimetry import CMFS, ILLUMINANTS from colour.models import XYZ_to_xy, xy_to_XYZ, XYZ_to_RGB, RGB_to_XYZ from colour.models import xy_to_xyY, xyY_to_XYZ, Lab_to_XYZ from colour.models import BT709_COLOURSPACE from colour.utilities import normalise_maximum from colour import models from colour import RGB_COLOURSPACES, COLOURCHECKERS from scipy.spatial import Delaunay from scipy.ndimage.filters import convolve import math import transfer_functions as tf CMFS_NAME = 'CIE 1931 2 Degree Standard Observer' D65_WHITE = ILLUMINANTS[CMFS_NAME]['D65'] YCBCR_CHECK_MARKER = [0, 0, 0] UNIVERSAL_COLOR_LIST = ["#F6AA00", "#FFF100", "#03AF7A", "#005AFF", "#4DC4FF", "#804000"] def preview_image(img, order='rgb', over_disp=False): if order == 'rgb': cv2.imshow('preview', img[:, :, ::-1]) elif order == 'bgr': cv2.imshow('preview', img) elif order == 'mono': cv2.imshow('preview', img) else: raise ValueError("order parameter is invalid") if over_disp: cv2.resizeWindow('preview', ) cv2.waitKey(0) cv2.destroyAllWindows() def equal_devision(length, div_num): base = length / div_num ret_array = [base for x in range(div_num)] diff = 0 for idx in range(div_num): diff += math.modf(ret_array[idx])[0] if diff >= 1.0: diff -= 1.0 ret_array[idx] = int(math.floor(ret_array[idx]) + 1) else: ret_array[idx] = int(math.floor(ret_array[idx])) diff = length - sum(ret_array) if diff != 0: ret_array[-1] += diff if length != sum(ret_array): raise ValueError("the output of equal_division() is abnormal.") return ret_array def do_matrix(img, mtx): base_shape = img.shape r, g, b = img[..., 0], img[..., 1], img[..., 2] ro = r * mtx[0][0] + g * mtx[0][1] + b * mtx[0][2] go = r * mtx[1][0] + g * mtx[1][1] + b * mtx[1][2] bo = r * mtx[2][0] + g * mtx[2][1] + b * mtx[2][2] out_img = np.dstack((ro, go, bo)).reshape(base_shape) return out_img def _get_cmfs_xy(): cmf = CMFS.get(CMFS_NAME) d65_white = D65_WHITE cmf_xy = XYZ_to_xy(cmf.values, d65_white) return cmf_xy def get_primaries(name='ITU-R BT.2020'): primaries = RGB_COLOURSPACES[name].primaries primaries = np.append(primaries, [primaries[0, :]], axis=0) rgb = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) return primaries, rgb def xy_to_rgb(xy, name='ITU-R BT.2020', normalize='maximum', specific=None): illuminant_XYZ = D65_WHITE illuminant_RGB = D65_WHITE chromatic_adaptation_transform = 'CAT02' large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name) if normalize == 'specific': xyY = xy_to_xyY(xy) xyY[..., 2] = specific large_xyz = xyY_to_XYZ(xyY) else: large_xyz = xy_to_XYZ(xy) rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform) if normalize == 'maximum': rgb = normalise_maximum(rgb, axis=-1) else: if(np.sum(rgb > 1.0) > 0): print("warning: over flow has occured at xy_to_rgb") if(np.sum(rgb < 0.0) > 0): print("warning: under flow has occured at xy_to_rgb") rgb[rgb < 0] = 0 rgb[rgb > 1.0] = 1.0 return rgb def get_white_point(name): if name != "DCI-P3": illuminant = RGB_COLOURSPACES[name].illuminant white_point = ILLUMINANTS[CMFS_NAME][illuminant] else: white_point = ILLUMINANTS[CMFS_NAME]["D65"] return white_point def get_secondaries(name='ITU-R BT.2020'): secondary_rgb = np.array([[1.0, 0.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0, 1.0]]) illuminant_XYZ = D65_WHITE illuminant_RGB = D65_WHITE chromatic_adaptation_transform = 'CAT02' rgb_to_xyz_matrix = get_rgb_to_xyz_matrix(name) large_xyz = RGB_to_XYZ(secondary_rgb, illuminant_RGB, illuminant_XYZ, rgb_to_xyz_matrix, chromatic_adaptation_transform) xy = XYZ_to_xy(large_xyz, illuminant_XYZ) return xy, secondary_rgb.reshape((3, 3)) (samples=1024, antialiasing=True, bg_color=0.9, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0): color_space = models.ACES_CG_COLOURSPACE cmf_xy = _get_cmfs_xy() triangulation = Delaunay(cmf_xy) xx, yy\ = np.meshgrid(np.linspace(xmin, xmax, samples), np.linspace(ymax, ymin, samples)) xy = np.dstack((xx, yy)) mask = (triangulation.find_simplex(xy) < 0).astype(np.float) if antialiasing: kernel = np.array([ [0, 1, 0], [1, 2, 1], [0, 1, 0], ]).astype(np.float) kernel /= np.sum(kernel) mask = convolve(mask, kernel) mask = 1 - mask[:, :, np.newaxis] illuminant_XYZ = D65_WHITE illuminant_RGB = color_space.whitepoint chromatic_adaptation_transform = 'XYZ Scaling' large_xyz_to_rgb_matrix = color_space.XYZ_to_RGB_matrix xy[xy == 0.0] = 1.0 large_xyz = xy_to_XYZ(xy) rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform) rgb[rgb == 0] = 1.0 rgb = normalise_maximum(rgb, axis=-1) mask_rgb = np.dstack((mask, mask, mask)) rgb *= mask_rgb bg_rgb = np.ones_like(rgb) bg_rgb *= (1 - mask_rgb) * bg_color rgb += bg_rgb rgb = rgb ** (1/2.2) return rgb def get_csf_color_image(width=640, height=480, lv1=np.uint16(np.array([1.0, 1.0, 1.0]) * 1023 * 0x40), lv2=np.uint16(np.array([1.0, 1.0, 1.0]) * 512 * 0x40), stripe_num=18): width_list = equal_devision(width, stripe_num) height_list = equal_devision(height, stripe_num) h_pos_list = equal_devision(width // 2, stripe_num) v_pos_list = equal_devision(height // 2, stripe_num) lv1_16bit = lv1 lv2_16bit = lv2 img = np.zeros((height, width, 3), dtype=np.uint16) width_temp = width height_temp = height h_pos_temp = 0 v_pos_temp = 0 for idx in range(stripe_num): lv = lv1_16bit if (idx % 2) == 0 else lv2_16bit temp_img = np.ones((height_temp, width_temp, 3), dtype=np.uint16) temp_img[:, :] = lv ed_pos_h = h_pos_temp + width_temp ed_pos_v = v_pos_temp + height_temp img[v_pos_temp:ed_pos_v, h_pos_temp:ed_pos_h] = temp_img width_temp -= width_list[stripe_num - 1 - idx] height_temp -= height_list[stripe_num - 1 - idx] h_pos_temp += h_pos_list[idx] v_pos_temp += v_pos_list[idx] return img def plot_xyY_color_space(name='ITU-R BT.2020', samples=1024, antialiasing=True): primary_xy, _ = get_primaries(name=name) triangulation = Delaunay(primary_xy) xx, yy\ = np.meshgrid(np.linspace(0, 1, samples), np.linspace(1, 0, samples)) xy = np.dstack((xx, yy)) mask = (triangulation.find_simplex(xy) < 0).astype(np.float) if antialiasing: kernel = np.array([ [0, 1, 0], [1, 2, 1], [0, 1, 0], ]).astype(np.float) kernel /= np.sum(kernel) mask = convolve(mask, kernel) mask = 1 - mask[:, :, np.newaxis] illuminant_XYZ = D65_WHITE illuminant_RGB = RGB_COLOURSPACES[name].whitepoint chromatic_adaptation_transform = 'CAT02' large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name) rgb_to_large_xyz_matrix = get_rgb_to_xyz_matrix(name) large_xyz = xy_to_XYZ(xy) rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform) rgb_org = normalise_maximum(rgb, axis=-1) mask_rgb = np.dstack((mask, mask, mask)) rgb = rgb_org * mask_rgb rgba = np.dstack((rgb, mask)) large_xyz2 = RGB_to_XYZ(rgb, illuminant_RGB, illuminant_XYZ, rgb_to_large_xyz_matrix, chromatic_adaptation_transform) large_y = large_xyz2[..., 1] * 1000 large_y[large_y < 1] = 1.0 fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.plot_surface(xy[..., 0], xy[..., 1], np.log10(large_y), rcount=64, ccount=64, facecolors=rgb_org) ax.set_xlabel("x") ax.set_ylabel("y") ax.set_zlabel("Y") ax.set_zticks([0, 1, 2, 3]) ax.set_zticklabels([1, 10, 100, 1000]) cie1931_rgb = get_chromaticity_image(samples=samples, bg_color=0.0) alpha = np.zeros_like(cie1931_rgb[..., 0]) rgb_sum = np.sum(cie1931_rgb, axis=-1) alpha[rgb_sum > 0.00001] = 1 cie1931_rgb = np.dstack((cie1931_rgb[..., 0], cie1931_rgb[..., 1], cie1931_rgb[..., 2], alpha)) zz = np.zeros_like(xy[..., 0]) ax.plot_surface(xy[..., 0], xy[..., 1], zz, facecolors=cie1931_rgb) plt.show() def log_tick_formatter(val, pos=None): return "{:.0e}".format(10**val) def get_3d_grid_cube_format(grid_num=4): base = np.linspace(0, 1, grid_num) ones_x = np.ones((grid_num, grid_num, 1)) ones_y = np.ones((grid_num, 1, grid_num)) ones_z = np.ones((1, grid_num, grid_num)) r_3d = base[np.newaxis, np.newaxis, :] * ones_x g_3d = base[np.newaxis, :, np.newaxis] * ones_y b_3d = base[:, np.newaxis, np.newaxis] * ones_z r_3d = r_3d.flatten() g_3d = g_3d.flatten() b_3d = b_3d.flatten() return np.dstack((r_3d, g_3d, b_3d)) def quadratic_bezier_curve(t, p0, p1, p2, samples=1024): x = ((1 - t) ** 2) * p0[0] + 2 * (1 - t) * t * p1[0]\ + (t ** 2) * p2[0] y = ((1 - t) ** 2) * p0[1] + 2 * (1 - t) * t * p1[1]\ + (t ** 2) * p2[1] def gen_step_gradation(width=1024, height=128, step_num=17, bit_depth=10, color=(1.0, 1.0, 1.0), direction='h', debug=False): max = 2 ** bit_depth if direction == 'h': pass else: temp = height height = width width = temp if (max + 1 != step_num): val_list = np.linspace(0, max, step_num) val_list[-1] -= 1 else: """ 正確に1階調ずつ変化するパターン。末尾のデータが 256 や 1024 になるため除外する。 """ val_list = np.linspace(0, max, step_num)[0:-1] step_num -= 1 diff = val_list[1:] - val_list[0:-1] if (diff == 1).all(): pass else: raise ValueError("calculated value is invalid.") step_length_list = equal_devision(width, step_num) step_bar_list = [] for step_idx, length in enumerate(step_length_list): step = [np.ones((length)) * color[c_idx] * val_list[step_idx] for c_idx in range(3)] if direction == 'h': step = np.dstack(step) step_bar_list.append(step) step_bar = np.hstack(step_bar_list) else: step = np.dstack(step).reshape((length, 1, 3)) step_bar_list.append(step) step_bar = np.vstack(step_bar_list) if direction == 'h': img = step_bar * np.ones((height, 1, 3)) else: img = step_bar * np.ones((1, height, 3)) if debug: preview_image(img, 'rgb') return img def merge(img_a, img_b, pos=(0, 0)): b_width = img_b.shape[1] b_height = img_b.shape[0] img_a[pos[1]:b_height+pos[1], pos[0]:b_width+pos[0]] = img_b def merge_with_alpha(bg_img, fg_img, tf_str=tf.SRGB, pos=(0, 0)): f_width = fg_img.shape[1] f_height = fg_img.shape[0] bg_merge_area = bg_img[pos[1]:f_height+pos[1], pos[0]:f_width+pos[0]] bg_linear = tf.eotf_to_luminance(bg_merge_area, tf_str) fg_linear = tf.eotf_to_luminance(fg_img, tf_str) alpha = fg_linear[:, :, 3:] / tf.PEAK_LUMINANCE[tf_str] out_linear = (1 - alpha) * bg_linear + fg_linear[:, :, :-1] out_merge_area = tf.oetf_from_luminance(out_linear, tf_str) bg_img[pos[1]:f_height+pos[1], pos[0]:f_width+pos[0]] = out_merge_area return bg_img def dot_pattern(dot_size=4, repeat=4, color=np.array([1.0, 1.0, 1.0])): pixel_num = dot_size * 2 * repeat even_logic = [(np.arange(pixel_num) % (dot_size * 2)) - dot_size < 0] even_logic = np.dstack((even_logic, even_logic, even_logic)) odd_logic = np.logical_not(even_logic) color = color.reshape((1, 1, 3)) even_line = (np.ones((1, pixel_num, 3)) * even_logic) * color odd_line = (np.ones((1, pixel_num, 3)) * odd_logic) * color even_block = np.repeat(even_line, dot_size, axis=0) odd_block = np.repeat(odd_line, dot_size, axis=0) pair_block = np.vstack((even_block, odd_block)) img = np.vstack([pair_block for x in range(repeat)]) return img def complex_dot_pattern(kind_num=3, whole_repeat=2, fg_color=np.array([1.0, 1.0, 1.0]), bg_color=np.array([0.15, 0.15, 0.15])): max_dot_width = 2 ** kind_num img_list = [] for size_idx in range(kind_num)[::-1]: dot_size = 2 ** size_idx repeat = max_dot_width // dot_size dot_img = dot_pattern(dot_size, repeat, fg_color) img_list.append(dot_img) img_list.append(np.ones_like(dot_img) * bg_color) line_upper_img = np.hstack(img_list) line_upper_img = np.hstack([line_upper_img for x in range(whole_repeat)]) line_lower_img = line_upper_img.copy()[:, ::-1, :] h_unit_img = np.vstack((line_upper_img, line_lower_img)) img = np.vstack([h_unit_img for x in range(kind_num * whole_repeat)]) return img def make_csf_color_image(width=640, height=640, lv1=np.array([940, 940, 940], dtype=np.uint16), lv2=np.array([1023, 1023, 1023], dtype=np.uint16), stripe_num=6): width_list = equal_devision(width, stripe_num) height_list = equal_devision(height, stripe_num) h_pos_list = equal_devision(width // 2, stripe_num) v_pos_list = equal_devision(height // 2, stripe_num) img = np.zeros((height, width, 3), dtype=np.uint16) width_temp = width height_temp = height h_pos_temp = 0 v_pos_temp = 0 for idx in range(stripe_num): lv = lv1 if (idx % 2) == 0 else lv2 temp_img = np.ones((height_temp, width_temp, 3), dtype=np.uint16) temp_img = temp_img * lv.reshape((1, 1, 3)) ed_pos_h = h_pos_temp + width_temp ed_pos_v = v_pos_temp + height_temp img[v_pos_temp:ed_pos_v, h_pos_temp:ed_pos_h] = temp_img width_temp -= width_list[stripe_num - 1 - idx] height_temp -= height_list[stripe_num - 1 - idx] h_pos_temp += h_pos_list[idx] v_pos_temp += v_pos_list[idx] return img def make_tile_pattern(width=480, height=960, h_tile_num=4, v_tile_num=4, low_level=(940, 940, 940), high_level=(1023, 1023, 1023)): width_array = equal_devision(width, h_tile_num) height_array = equal_devision(height, v_tile_num) high_level = np.array(high_level, dtype=np.uint16) low_level = np.array(low_level, dtype=np.uint16) v_buf = [] for v_idx, height in enumerate(height_array): h_buf = [] for h_idx, width in enumerate(width_array): tile_judge = (h_idx + v_idx) % 2 == 0 h_temp = np.zeros((height, width, 3), dtype=np.uint16) h_temp[:, :] = high_level if tile_judge else low_level h_buf.append(h_temp) v_buf.append(np.hstack(h_buf)) img = np.vstack(v_buf) return img def get_marker_idx(img, marker_value): return np.all(img == marker_value, axis=-1) def make_ycbcr_checker(height=480, v_tile_num=4): cyan_img = make_tile_pattern(width=height, height=height, h_tile_num=v_tile_num, v_tile_num=v_tile_num, low_level=[0, 990, 990], high_level=[0, 1023, 1023]) magenta_img = make_tile_pattern(width=height, height=height, h_tile_num=v_tile_num, v_tile_num=v_tile_num, low_level=[990, 0, 312], high_level=[1023, 0, 312]) out_img = np.hstack([cyan_img, magenta_img]) return out_img def plot_color_checker_image(rgb, rgb2=None, size=(1920, 1080), block_size=1/4.5, padding=0.01): IMG_HEIGHT = size[1] IMG_WIDTH = size[0] COLOR_CHECKER_SIZE = block_size COLOR_CHECKER_H_NUM = 6 COLOR_CHECKER_V_NUM = 4 COLOR_CHECKER_PADDING = 0.01 COLOR_CHECKER_H_NUM = 6 COLOR_CHECKER_V_NUM = 4 img_height = IMG_HEIGHT img_width = IMG_WIDTH patch_st_h = int(IMG_WIDTH / 2.0 - (IMG_HEIGHT * COLOR_CHECKER_SIZE * COLOR_CHECKER_H_NUM / 2.0 + (IMG_HEIGHT * COLOR_CHECKER_PADDING * (COLOR_CHECKER_H_NUM / 2.0 - 0.5)) / 2.0)) patch_st_v = int(IMG_HEIGHT / 2.0 - (IMG_HEIGHT * COLOR_CHECKER_SIZE * COLOR_CHECKER_V_NUM / 2.0 + (IMG_HEIGHT * COLOR_CHECKER_PADDING * (COLOR_CHECKER_V_NUM / 2.0 - 0.5)) / 2.0)) patch_width = int(img_height * COLOR_CHECKER_SIZE) patch_height = patch_width patch_space = int(img_height * COLOR_CHECKER_PADDING) img_all_patch = np.zeros((img_height, img_width, 3), dtype=np.uint8) for idx in range(COLOR_CHECKER_H_NUM * COLOR_CHECKER_V_NUM): v_idx = idx // COLOR_CHECKER_H_NUM h_idx = (idx % COLOR_CHECKER_H_NUM) patch = np.ones((patch_height, patch_width, 3)) patch[:, :] = rgb[idx] st_h = patch_st_h + (patch_width + patch_space) * h_idx st_v = patch_st_v + (patch_height + patch_space) * v_idx img_all_patch[st_v:st_v+patch_height, st_h:st_h+patch_width] = patch = (st_h + patch_width, st_v) pt3 = (st_h, st_v + patch_height) pt4 = (st_h + patch_width, st_v + patch_height) pts = np.array((pt2, pt3, pt4)) sub_color = rgb[idx].tolist() if rgb2 is None else rgb2[idx].tolist() cv2.fillPoly(img_all_patch, [pts], sub_color) preview_image(img_all_patch) return img_all_patch def get_log10_x_scale( sample_num=8, ref_val=1.0, min_exposure=-1, max_exposure=6): x_min = np.log10(ref_val * (10 ** min_exposure)) x_max = np.log10(ref_val * (10 ** max_exposure)) x = np.linspace(x_min, x_max, sample_num) return 10.0 ** x def get_log2_x_scale( sample_num=32, ref_val=1.0, min_exposure=-6.5, max_exposure=6.5): x_min = np.log2(ref_val * (2 ** min_exposure)) x_max = np.log2(ref_val * (2 ** max_exposure)) x = np.linspace(x_min, x_max, sample_num) return 2.0 ** x def shaper_func_linear_to_log2( x, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5): y = np.log2(x / mid_gray) y_normalized = (y - min_exposure) / (max_exposure - min_exposure) y_normalized[y_normalized < 0] = 0 return y_normalized def shaper_func_log2_to_linear( x, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5): x_re_scale = x * (max_exposure - min_exposure) + min_exposure y = (2.0 ** x_re_scale) * mid_gray return y def draw_straight_line(img, pt1, pt2, color, thickness): if (pt1[0] != pt2[0]) and (pt1[1] != pt2[1]): raise ValueError("invalid pt1, pt2 parameters") if pt1[0] == pt2[0]: thickness_direction = 'h' else: thickness_direction = 'v' if thickness_direction == 'h': for h_idx in range(thickness): img[pt1[1]:pt2[1], pt1[0] + h_idx, :] = color elif thickness_direction == 'v': for v_idx in range(thickness): img[pt1[1] + v_idx, pt1[0]:pt2[0], :] = color def draw_outline(img, fg_color, outline_width): width = img.shape[1] height = img.shape[0] pt1 = (0, 0) pt2 = (width, 0) draw_straight_line( img, pt1, pt2, fg_color, outline_width) pt1 = (0, 0) pt2 = (0, height) draw_straight_line( img, pt1, pt2, fg_color, outline_width) pt1 = (width - outline_width, 0) pt2 = (width - outline_width, height) draw_straight_line( img, pt1, pt2, fg_color, outline_width) pt1 = (0, height - outline_width) pt2 = (width, height - outline_width) draw_straight_line( img, pt1, pt2, fg_color, outline_width) def convert_luminance_to_color_value(luminance, transfer_function): code_value = convert_luminance_to_code_value( luminance, transfer_function) return np.array([code_value, code_value, code_value]) def convert_luminance_to_code_value(luminance, transfer_function): return tf.oetf_from_luminance(luminance, transfer_function) def calc_rad_patch_idx2(outmost_num=5, current_num=3): base = np.arange(outmost_num ** 2).reshape((outmost_num, outmost_num)) t_idx = (outmost_num - current_num) // 2 trimmed = base[t_idx:t_idx+current_num, t_idx:t_idx+current_num] half_num = current_num // 2 conv_idx = [] for idx in range(half_num): val = (current_num ** 2) // 2 + half_num - current_num * idx conv_idx.append(val) for idx in range(current_num)[::-1]: conv_idx.append(idx) for idx in range(1, current_num - 1): conv_idx.append(idx * current_num) for idx in range(current_num): val = (current_num ** 2) - current_num + idx conv_idx.append(val) for idx in range(1, half_num): val = (current_num ** 2) - 1 - idx * current_num conv_idx.append(val) conv_idx = trimmed.flatten()[conv_idx] return conv_idx def _calc_rgb_from_same_lstar_radial_data( lstar, temp_chroma, current_num, color_space): current_patch_num = (current_num - 1) * 4 if current_num > 1 else 1 rad = np.linspace(0, 2 * np.pi, current_patch_num, endpoint=False) ll = np.ones((current_patch_num)) * lstar aa = np.cos(rad) * temp_chroma bb = np.sin(rad) * temp_chroma lab = np.dstack((ll, aa, bb)) large_xyz = Lab_to_XYZ(lab) rgb = XYZ_to_RGB(large_xyz, D65_WHITE, D65_WHITE, color_space.XYZ_to_RGB_matrix) return np.clip(rgb, 0.0, 1.0) def calc_same_lstar_radial_color_patch_data( lstar=58, chroma=32.5, outmost_num=9, color_space=BT709_COLOURSPACE, transfer_function=tf.GAMMA24): patch_num = outmost_num ** 2 transfer_function = tf.GAMMA24 rgb_list = np.ones((patch_num, 3)) current_num_list = range(1, outmost_num + 1, 2) chroma_list = np.linspace(0, chroma, len(current_num_list)) for temp_chroma, current_num in zip(chroma_list, current_num_list): current_patch_num = (current_num - 1) * 4 if current_num > 1 else 1 rgb = _calc_rgb_from_same_lstar_radial_data( lstar, temp_chroma, current_num, color_space) rgb = np.reshape(rgb, (current_patch_num, 3)) rgb = tf.oetf(rgb, transfer_function) conv_idx = calc_rad_patch_idx2( outmost_num=outmost_num, current_num=current_num) for idx in range(current_patch_num): rgb_list[conv_idx[idx]] = rgb[idx] return rgb_list def _plot_same_lstar_radial_color_patch_data( lstar=58, chroma=32.5, outmost_num=9, color_space=BT709_COLOURSPACE, transfer_function=tf.GAMMA24): patch_size = 1080 // outmost_num img = np.ones((1080, 1080, 3)) * 0.0 rgb = calc_same_lstar_radial_color_patch_data( lstar=lstar, chroma=chroma, outmost_num=outmost_num, color_space=color_space, transfer_function=transfer_function) for idx in range(outmost_num ** 2): h_idx = idx % outmost_num v_idx = idx // outmost_num st_pos = (h_idx * patch_size, v_idx * patch_size) temp_img = np.ones((patch_size, patch_size, 3))\ * rgb[idx][np.newaxis, np.newaxis, :] merge(img, temp_img, st_pos) cv2.imwrite("hoge2.tiff", np.uint16(np.round(img[:, :, ::-1] * 0xFFFF))) def get_accelerated_x_1x(sample_num=64): rad = np.linspace(-0.5 * np.pi, 0.5 * np.pi, sample_num) x = (np.sin(rad) + 1) / 2 return x def get_accelerated_x_2x(sample_num=64): rad = np.linspace(-0.5 * np.pi, 0.5 * np.pi, sample_num) rad = np.sin(rad) * 0.5 * np.pi x = (np.sin(rad) + 1) / 2 return x def get_accelerated_x_4x(sample_num=64): rad = np.linspace(-0.5 * np.pi, 0.5 * np.pi, sample_num) rad = np.sin(rad) * 0.5 * np.pi rad = np.sin(rad) * 0.5 * np.pi x = (np.sin(rad) + 1) / 2 return x def get_accelerated_x_8x(sample_num=64): rad = np.linspace(-0.5 * np.pi, 0.5 * np.pi, sample_num) rad = np.sin(rad) * 0.5 * np.pi rad = np.sin(rad) * 0.5 * np.pi rad = np.sin(rad) * 0.5 * np.pi x = (np.sin(rad) + 1) / 2 return x def generate_color_checker_rgb_value( color_space=BT709_COLOURSPACE, target_white=D65_WHITE): colour_checker_param = COLOURCHECKERS.get('ColorChecker 2005') _name, data, whitepoint = colour_checker_param temp_xyY = [] for key in data.keys(): temp_xyY.append(data[key]) temp_xyY = np.array(temp_xyY) large_xyz = xyY_to_XYZ(temp_xyY) rgb_white_point = D65_WHITE illuminant_XYZ = whitepoint illuminant_RGB = rgb_white_point chromatic_adaptation_transform = 'CAT02' large_xyz_to_rgb_matrix = color_space.XYZ_to_RGB_matrix rgb = XYZ_to_RGB( large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform) return rgb def make_color_checker_image(rgb, width=1920, padding_rate=0.01): h_patch_num = 6 v_patch_num = 4 each_padding = int(width * padding_rate + 0.5) h_padding_total = each_padding * (h_patch_num + 1) h_patch_width_total = width - h_padding_total patch_height = h_patch_width_total // h_patch_num height = patch_height * v_patch_num + each_padding * (v_patch_num + 1) patch_width_list = equal_devision(h_patch_width_total, h_patch_num) img = np.zeros((height, width, 3)) for v_idx in range(v_patch_num): h_pos_st = each_padding v_pos_st = each_padding + v_idx * (patch_height + each_padding) for h_idx in range(h_patch_num): rgb_idx = v_idx * h_patch_num + h_idx pos = (h_pos_st, v_pos_st) patch_img = np.ones((patch_height, patch_width_list[h_idx], 3))\ * rgb[rgb_idx] merge(img, patch_img, pos) h_pos_st += (patch_width_list[h_idx] + each_padding) return img def calc_st_pos_for_centering(bg_size, fg_size): bg_width = bg_size[0] bg_height = bg_size[1] fg_width = fg_size[0] fg_height = fg_size[1] st_pos_h = bg_width // 2 - fg_width // 2 st_pos_v = bg_height // 2 - fg_height // 2 return (st_pos_h, st_pos_v) def get_size_from_image(img): return (img.shape[1], img.shape[0]) if __name__ == '__main__': os.chdir(os.path.dirname(os.path.abspath(__file__))) print(calc_st_pos_for_centering(bg_size=(1920, 1080), fg_size=(640, 480)))

true

790175423e938eeaeb79260c59952e39d2b5a8cf

1,637

py

Python

recognition/base.py

ReanGD/smart-home

0d3ebe3213ad275f64490218ca3dbc0128c12339

[ "Apache-2.0" ]

1

2018-07-31T21:17:37.000Z

recognition/base.py

ReanGD/smart-home

0d3ebe3213ad275f64490218ca3dbc0128c12339

[ "Apache-2.0" ]

null

recognition/base.py

ReanGD/smart-home

0d3ebe3213ad275f64490218ca3dbc0128c12339

[ "Apache-2.0" ]

null

from audio import Stream, AudioSettings class PhraseRecognizer(object): def __init__(self, config, audio_settings: AudioSettings): self._config = config self._audio_settings = audio_settings def get_config(self): return self._config def get_audio_settings(self) -> AudioSettings: return self._audio_settings async def recognize(self, stream: Stream, recv_callback): raise Exception('Not implemented "recognize"') class HotwordRecognizer(object): def __init__(self, config): self._config = config def get_audio_settings(self) -> AudioSettings: raise Exception('Not implemented "get_audio_settings"') def start(self): pass def is_hotword(self, raw_frames) -> bool: raise Exception('Not implemented "is_hotword"') class VADRecognizer(object): def __init__(self, config): self._config = config def get_audio_settings(self) -> AudioSettings: raise Exception('Not implemented "get_audio_settings"') def is_speech(self, raw_frames) -> bool: raise Exception('Not implemented "is_speech"') class PhraseRecognizerConfig(object): def create_phrase_recognizer(self) -> PhraseRecognizer: raise Exception('Not implemented "create_phrase_recognizer"') class HotwordRecognizerConfig(object): def create_hotword_recognizer(self) -> HotwordRecognizer: raise Exception('Not implemented "create_hotword_recognizer"') class VADRecognizerConfig(object): def create_vad_recognizer(self) -> VADRecognizer: raise Exception('Not implemented "create_vad_recognizer"')

28.719298

70

0.717776

from audio import Stream, AudioSettings class PhraseRecognizer(object): def __init__(self, config, audio_settings: AudioSettings): self._config = config self._audio_settings = audio_settings def get_config(self): return self._config def get_audio_settings(self) -> AudioSettings: return self._audio_settings async def recognize(self, stream: Stream, recv_callback): raise Exception('Not implemented "recognize"') class HotwordRecognizer(object): def __init__(self, config): self._config = config def get_audio_settings(self) -> AudioSettings: raise Exception('Not implemented "get_audio_settings"') def start(self): pass def is_hotword(self, raw_frames) -> bool: raise Exception('Not implemented "is_hotword"') class VADRecognizer(object): def __init__(self, config): self._config = config def get_audio_settings(self) -> AudioSettings: raise Exception('Not implemented "get_audio_settings"') def is_speech(self, raw_frames) -> bool: raise Exception('Not implemented "is_speech"') class PhraseRecognizerConfig(object): def create_phrase_recognizer(self) -> PhraseRecognizer: raise Exception('Not implemented "create_phrase_recognizer"') class HotwordRecognizerConfig(object): def create_hotword_recognizer(self) -> HotwordRecognizer: raise Exception('Not implemented "create_hotword_recognizer"') class VADRecognizerConfig(object): def create_vad_recognizer(self) -> VADRecognizer: raise Exception('Not implemented "create_vad_recognizer"')

true

79017579a201dcf5f57280058af9232110541daf

6,839

py

Python

sdk/python/pulumi_azure_nextgen/network/get_express_route_gateway.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

31

2020-09-21T09:41:01.000Z

2021-02-26T13:21:59.000Z

sdk/python/pulumi_azure_nextgen/network/get_express_route_gateway.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

231

2020-09-21T09:38:45.000Z

2021-03-01T11:16:03.000Z

sdk/python/pulumi_azure_nextgen/network/get_express_route_gateway.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

4

2020-09-29T14:14:59.000Z

2021-02-10T20:38:16.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** 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 from .. import _utilities, _tables from . import outputs __all__ = [ 'GetExpressRouteGatewayResult', 'AwaitableGetExpressRouteGatewayResult', 'get_express_route_gateway', ] @pulumi.output_type class GetExpressRouteGatewayResult: """ ExpressRoute gateway resource. """ def __init__(__self__, auto_scale_configuration=None, etag=None, express_route_connections=None, id=None, location=None, name=None, provisioning_state=None, tags=None, type=None, virtual_hub=None): if auto_scale_configuration and not isinstance(auto_scale_configuration, dict): raise TypeError("Expected argument 'auto_scale_configuration' to be a dict") pulumi.set(__self__, "auto_scale_configuration", auto_scale_configuration) if etag and not isinstance(etag, str): raise TypeError("Expected argument 'etag' to be a str") pulumi.set(__self__, "etag", etag) if express_route_connections and not isinstance(express_route_connections, list): raise TypeError("Expected argument 'express_route_connections' to be a list") pulumi.set(__self__, "express_route_connections", express_route_connections) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if location and not isinstance(location, str): raise TypeError("Expected argument 'location' to be a str") pulumi.set(__self__, "location", location) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if tags and not isinstance(tags, dict): raise TypeError("Expected argument 'tags' to be a dict") pulumi.set(__self__, "tags", tags) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) if virtual_hub and not isinstance(virtual_hub, dict): raise TypeError("Expected argument 'virtual_hub' to be a dict") pulumi.set(__self__, "virtual_hub", virtual_hub) @property @pulumi.getter(name="autoScaleConfiguration") def auto_scale_configuration(self) -> Optional['outputs.ExpressRouteGatewayPropertiesResponseAutoScaleConfiguration']: """ Configuration for auto scaling. """ return pulumi.get(self, "auto_scale_configuration") @property @pulumi.getter def etag(self) -> str: """ A unique read-only string that changes whenever the resource is updated. """ return pulumi.get(self, "etag") @property @pulumi.getter(name="expressRouteConnections") def express_route_connections(self) -> Sequence['outputs.ExpressRouteConnectionResponse']: """ List of ExpressRoute connections to the ExpressRoute gateway. """ return pulumi.get(self, "express_route_connections") @property @pulumi.getter def id(self) -> Optional[str]: """ Resource ID. """ return pulumi.get(self, "id") @property @pulumi.getter def location(self) -> Optional[str]: """ Resource location. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> str: """ Resource name. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: """ The provisioning state of the express route gateway resource. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: """ Resource tags. """ return pulumi.get(self, "tags") @property @pulumi.getter def type(self) -> str: """ Resource type. """ return pulumi.get(self, "type") @property @pulumi.getter(name="virtualHub") def virtual_hub(self) -> 'outputs.VirtualHubIdResponse': """ The Virtual Hub where the ExpressRoute gateway is or will be deployed. """ return pulumi.get(self, "virtual_hub") class AwaitableGetExpressRouteGatewayResult(GetExpressRouteGatewayResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetExpressRouteGatewayResult( auto_scale_configuration=self.auto_scale_configuration, etag=self.etag, express_route_connections=self.express_route_connections, id=self.id, location=self.location, name=self.name, provisioning_state=self.provisioning_state, tags=self.tags, type=self.type, virtual_hub=self.virtual_hub) def get_express_route_gateway(express_route_gateway_name: Optional[str] = None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetExpressRouteGatewayResult: """ ExpressRoute gateway resource. API Version: 2020-08-01. :param str express_route_gateway_name: The name of the ExpressRoute gateway. :param str resource_group_name: The name of the resource group. """ __args__ = dict() __args__['expressRouteGatewayName'] = express_route_gateway_name __args__['resourceGroupName'] = resource_group_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:network:getExpressRouteGateway', __args__, opts=opts, typ=GetExpressRouteGatewayResult).value return AwaitableGetExpressRouteGatewayResult( auto_scale_configuration=__ret__.auto_scale_configuration, etag=__ret__.etag, express_route_connections=__ret__.express_route_connections, id=__ret__.id, location=__ret__.location, name=__ret__.name, provisioning_state=__ret__.provisioning_state, tags=__ret__.tags, type=__ret__.type, virtual_hub=__ret__.virtual_hub)

36.967568

201

0.663109

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from .. import _utilities, _tables from . import outputs __all__ = [ 'GetExpressRouteGatewayResult', 'AwaitableGetExpressRouteGatewayResult', 'get_express_route_gateway', ] @pulumi.output_type class GetExpressRouteGatewayResult: def __init__(__self__, auto_scale_configuration=None, etag=None, express_route_connections=None, id=None, location=None, name=None, provisioning_state=None, tags=None, type=None, virtual_hub=None): if auto_scale_configuration and not isinstance(auto_scale_configuration, dict): raise TypeError("Expected argument 'auto_scale_configuration' to be a dict") pulumi.set(__self__, "auto_scale_configuration", auto_scale_configuration) if etag and not isinstance(etag, str): raise TypeError("Expected argument 'etag' to be a str") pulumi.set(__self__, "etag", etag) if express_route_connections and not isinstance(express_route_connections, list): raise TypeError("Expected argument 'express_route_connections' to be a list") pulumi.set(__self__, "express_route_connections", express_route_connections) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if location and not isinstance(location, str): raise TypeError("Expected argument 'location' to be a str") pulumi.set(__self__, "location", location) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if tags and not isinstance(tags, dict): raise TypeError("Expected argument 'tags' to be a dict") pulumi.set(__self__, "tags", tags) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) if virtual_hub and not isinstance(virtual_hub, dict): raise TypeError("Expected argument 'virtual_hub' to be a dict") pulumi.set(__self__, "virtual_hub", virtual_hub) @property @pulumi.getter(name="autoScaleConfiguration") def auto_scale_configuration(self) -> Optional['outputs.ExpressRouteGatewayPropertiesResponseAutoScaleConfiguration']: return pulumi.get(self, "auto_scale_configuration") @property @pulumi.getter def etag(self) -> str: return pulumi.get(self, "etag") @property @pulumi.getter(name="expressRouteConnections") def express_route_connections(self) -> Sequence['outputs.ExpressRouteConnectionResponse']: return pulumi.get(self, "express_route_connections") @property @pulumi.getter def id(self) -> Optional[str]: return pulumi.get(self, "id") @property @pulumi.getter def location(self) -> Optional[str]: return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: return pulumi.get(self, "provisioning_state") @property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: return pulumi.get(self, "tags") @property @pulumi.getter def type(self) -> str: return pulumi.get(self, "type") @property @pulumi.getter(name="virtualHub") def virtual_hub(self) -> 'outputs.VirtualHubIdResponse': return pulumi.get(self, "virtual_hub") class AwaitableGetExpressRouteGatewayResult(GetExpressRouteGatewayResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetExpressRouteGatewayResult( auto_scale_configuration=self.auto_scale_configuration, etag=self.etag, express_route_connections=self.express_route_connections, id=self.id, location=self.location, name=self.name, provisioning_state=self.provisioning_state, tags=self.tags, type=self.type, virtual_hub=self.virtual_hub) def get_express_route_gateway(express_route_gateway_name: Optional[str] = None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetExpressRouteGatewayResult: __args__ = dict() __args__['expressRouteGatewayName'] = express_route_gateway_name __args__['resourceGroupName'] = resource_group_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:network:getExpressRouteGateway', __args__, opts=opts, typ=GetExpressRouteGatewayResult).value return AwaitableGetExpressRouteGatewayResult( auto_scale_configuration=__ret__.auto_scale_configuration, etag=__ret__.etag, express_route_connections=__ret__.express_route_connections, id=__ret__.id, location=__ret__.location, name=__ret__.name, provisioning_state=__ret__.provisioning_state, tags=__ret__.tags, type=__ret__.type, virtual_hub=__ret__.virtual_hub)

true

790175df71984046c36d0e478a37d48f8b80d535

236

py

Python

tests/sample_sdk_https.py

jframos/sdklib

0cc1126e94b823fad6cc47e6a00549cad6d2f771

[ "BSD-2-Clause" ]

3

2016-12-15T15:54:37.000Z

2021-08-10T03:16:18.000Z

tests/sample_sdk_https.py

jframos/sdklib

0cc1126e94b823fad6cc47e6a00549cad6d2f771

[ "BSD-2-Clause" ]

44

2016-04-13T08:19:45.000Z

2022-01-14T12:58:44.000Z

tests/sample_sdk_https.py

jframos/sdklib

0cc1126e94b823fad6cc47e6a00549cad6d2f771

[ "BSD-2-Clause" ]

5

2016-11-22T11:23:28.000Z

2020-01-28T12:26:10.000Z

from sdklib.http import HttpSdk class SampleHttpsHttpSdk(HttpSdk): DEFAULT_HOST = "https://www.google.com" API_IVANPRJCTS_PATH = "/ivanprjcts" def get_ivanprjcts(self): return self.get(self.API_IVANPRJCTS_PATH)

19.666667

49

0.728814

from sdklib.http import HttpSdk class SampleHttpsHttpSdk(HttpSdk): DEFAULT_HOST = "https://www.google.com" API_IVANPRJCTS_PATH = "/ivanprjcts" def get_ivanprjcts(self): return self.get(self.API_IVANPRJCTS_PATH)

true

79017678f78ba6aa7a00b756d1da6a0797025124

3,270

py

Python

src/unittest/python/plugins/python/test_plugin_helper_tests.py

igordertigor/pybuilder

772cf66a6fea86c59bd76f22388b0ce964b2fc1a

[ "Apache-2.0" ]

1

2019-01-17T03:35:32.000Z

src/unittest/python/plugins/python/test_plugin_helper_tests.py

igordertigor/pybuilder

772cf66a6fea86c59bd76f22388b0ce964b2fc1a

[ "Apache-2.0" ]

1

2022-03-10T13:19:18.000Z

src/unittest/python/plugins/python/test_plugin_helper_tests.py

igordertigor/pybuilder

772cf66a6fea86c59bd76f22388b0ce964b2fc1a

[ "Apache-2.0" ]

1

2020-11-02T10:06:11.000Z

# -*- coding: utf-8 -*- # # This file is part of PyBuilder # # Copyright 2011-2015 PyBuilder 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 unittest from pybuilder.errors import BuildFailedException from pybuilder.plugins.python.test_plugin_helper import ReportsProcessor from test_utils import Mock, patch class ReportsProcessorTests(unittest.TestCase): def setUp(self): self.reports_processor = ReportsProcessor(Mock(), Mock()) total_time = Mock() total_time.get_millis.return_value = 42 self.reports_processor.process_reports([], total_time) def test_should_raise_exception_when_not_all_tests_pass(self): self.reports_processor.tests_failed = 1 self.assertRaises(BuildFailedException, self.reports_processor.write_report_and_ensure_all_tests_passed) def test_should_not_raise_exception_when_all_tests_pass(self): self.reports_processor.tests_failed = 0 self.reports_processor.write_report_and_ensure_all_tests_passed() @patch("pybuilder.plugins.python.test_plugin_helper.render_report", return_value='rendered-report') def test_should_write_report(self, render_report): self.reports_processor.write_report_and_ensure_all_tests_passed() self.reports_processor.project.write_report.assert_called_with("integrationtest.json", 'rendered-report') def test_should_parse_reports(self): reports = [ {'test': 'name1', 'test_file': 'file1', 'success': False, 'time': 1}, {'test': 'name2', 'test_file': 'file2', 'success': False, 'time': 2}, {'test': 'name3', 'test_file': 'file3', 'success': True, 'time': 3}, {'test': 'name4', 'test_file': 'file4', 'success': True, 'time': 4} ] self.reports_processor.process_reports(reports, Mock()) self.assertEqual(self.reports_processor.tests_failed, 2) self.assertEqual(self.reports_processor.tests_executed, 4) def test_should_create_test_report_with_attributes(self): mock_time = Mock() mock_time.get_millis.return_value = 42 self.reports_processor.process_reports([], mock_time) self.reports_processor.tests_failed = 4 self.reports_processor.tests_executed = 42 self.reports_processor.reports = ['a', 'b', 'c'] self.assertEqual(self.reports_processor.test_report, { 'num_of_tests': 42, 'success': False, 'tests': ['a', 'b', 'c'], 'tests_failed': 4, 'time': 42 } )

39.878049

113

0.654434

import unittest from pybuilder.errors import BuildFailedException from pybuilder.plugins.python.test_plugin_helper import ReportsProcessor from test_utils import Mock, patch class ReportsProcessorTests(unittest.TestCase): def setUp(self): self.reports_processor = ReportsProcessor(Mock(), Mock()) total_time = Mock() total_time.get_millis.return_value = 42 self.reports_processor.process_reports([], total_time) def test_should_raise_exception_when_not_all_tests_pass(self): self.reports_processor.tests_failed = 1 self.assertRaises(BuildFailedException, self.reports_processor.write_report_and_ensure_all_tests_passed) def test_should_not_raise_exception_when_all_tests_pass(self): self.reports_processor.tests_failed = 0 self.reports_processor.write_report_and_ensure_all_tests_passed() @patch("pybuilder.plugins.python.test_plugin_helper.render_report", return_value='rendered-report') def test_should_write_report(self, render_report): self.reports_processor.write_report_and_ensure_all_tests_passed() self.reports_processor.project.write_report.assert_called_with("integrationtest.json", 'rendered-report') def test_should_parse_reports(self): reports = [ {'test': 'name1', 'test_file': 'file1', 'success': False, 'time': 1}, {'test': 'name2', 'test_file': 'file2', 'success': False, 'time': 2}, {'test': 'name3', 'test_file': 'file3', 'success': True, 'time': 3}, {'test': 'name4', 'test_file': 'file4', 'success': True, 'time': 4} ] self.reports_processor.process_reports(reports, Mock()) self.assertEqual(self.reports_processor.tests_failed, 2) self.assertEqual(self.reports_processor.tests_executed, 4) def test_should_create_test_report_with_attributes(self): mock_time = Mock() mock_time.get_millis.return_value = 42 self.reports_processor.process_reports([], mock_time) self.reports_processor.tests_failed = 4 self.reports_processor.tests_executed = 42 self.reports_processor.reports = ['a', 'b', 'c'] self.assertEqual(self.reports_processor.test_report, { 'num_of_tests': 42, 'success': False, 'tests': ['a', 'b', 'c'], 'tests_failed': 4, 'time': 42 } )

true

790177febcf816c121cc0c602b65cc695b8cd1ec

692

py

Python

nltk_utils.py

Serkanbezek/Chatbot-NLP-PyTorch

680dfa788fa3e3162470a79e7bbd4aa02088a24d

[ "MIT" ]

1

2022-03-03T18:27:23.000Z

nltk_utils.py

Serkanbezek/Chatbot-NLP-PyTorch

680dfa788fa3e3162470a79e7bbd4aa02088a24d

[ "MIT" ]

null

nltk_utils.py

Serkanbezek/Chatbot-NLP-PyTorch

680dfa788fa3e3162470a79e7bbd4aa02088a24d

[ "MIT" ]

null

import nltk import numpy as np #nltk.download('punkt') #downloading a package with a pretrained tokenizer from nltk.stem.porter import PorterStemmer stemmer = PorterStemmer() def tokenize(sentence): #splitting a string into meaningful units return nltk.word_tokenize(sentence) def stem(word): #Generating the root form of the words return stemmer.stem(word.lower()) def bag_of_words(tokenized_sentence, all_words): tokenized_sentence = [stem(w) for w in tokenized_sentence] bag = np.zeros(len(all_words), dtype = np.float32) for idx, w in enumerate(all_words): if w in tokenized_sentence: bag[idx] = 1.0 return bag

26.615385

80

0.703757

import nltk import numpy as np r = PorterStemmer() def tokenize(sentence): return nltk.word_tokenize(sentence) def stem(word): return stemmer.stem(word.lower()) def bag_of_words(tokenized_sentence, all_words): tokenized_sentence = [stem(w) for w in tokenized_sentence] bag = np.zeros(len(all_words), dtype = np.float32) for idx, w in enumerate(all_words): if w in tokenized_sentence: bag[idx] = 1.0 return bag

true

790178b67adb1c4d32e83058746158b4273e9c8f

2,349

py

Python

simiki/config.py

timgates42/simiki

22e544254577477c3f624c9d201f644580f36231

[ "MIT" ]

1,034

2015-01-04T05:50:05.000Z

2022-03-23T03:08:25.000Z

simiki/config.py

timgates42/simiki

22e544254577477c3f624c9d201f644580f36231

[ "MIT" ]

102

2015-01-12T01:20:10.000Z

2020-12-31T01:47:25.000Z

simiki/config.py

timgates42/simiki

22e544254577477c3f624c9d201f644580f36231

[ "MIT" ]

215

2015-01-25T13:49:49.000Z

2022-03-22T09:14:35.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import absolute_import, unicode_literals import os import os.path import sys import io import logging import datetime from pprint import pprint import yaml import tzlocal class ConfigFileNotFound(Exception): pass def _set_default_config(): config = { "url": "", "title": "", "keywords": "", "description": "", "author": "", "root": "/", "source": "content", "destination": "output", "attach": "attach", "themes_dir": "themes", "theme": "simple2", "default_ext": "md", "pygments": True, "debug": False, "time": datetime.datetime.now(tzlocal.get_localzone()), } return config def _post_process(config): for k, v in config.items(): if v is None: config[k] = "" if config["url"].endswith("/"): config["url"] = config["url"][:-1] return config def get_default_config(): return _post_process(_set_default_config()) def parse_config(config_file): if not os.path.exists(config_file): raise ConfigFileNotFound("{0} not exists".format(config_file)) default_config = _set_default_config() with io.open(config_file, "rt", encoding="utf-8") as fd: config = yaml.load(fd, Loader=yaml.FullLoader) default_config.update(config) config = _post_process(default_config) return config if __name__ == "__main__": # pylint: disable=pointless-string-statement """ Usage: python -m simiki.config : to test config template python -m simiki.config _config.yml : to test _config.yml file in \ curren dir """ if len(sys.argv) == 1: base_dir = os.path.dirname(__file__) _config_file = os.path.join(base_dir, "conf_templates", "_config.yml.in") elif len(sys.argv) == 2: base_dir = os.getcwd() _config_file = os.path.join(base_dir, sys.argv[1]) else: logging.error("Use the template config file by default, " "you can specify the config file to parse. \n" "Usage: `python -m simiki.config [_config.yml]'") sys.exit(1) pprint(parse_config(_config_file))

24.989362

75

0.585355

from __future__ import absolute_import, unicode_literals import os import os.path import sys import io import logging import datetime from pprint import pprint import yaml import tzlocal class ConfigFileNotFound(Exception): pass def _set_default_config(): config = { "url": "", "title": "", "keywords": "", "description": "", "author": "", "root": "/", "source": "content", "destination": "output", "attach": "attach", "themes_dir": "themes", "theme": "simple2", "default_ext": "md", "pygments": True, "debug": False, "time": datetime.datetime.now(tzlocal.get_localzone()), } return config def _post_process(config): for k, v in config.items(): if v is None: config[k] = "" if config["url"].endswith("/"): config["url"] = config["url"][:-1] return config def get_default_config(): return _post_process(_set_default_config()) def parse_config(config_file): if not os.path.exists(config_file): raise ConfigFileNotFound("{0} not exists".format(config_file)) default_config = _set_default_config() with io.open(config_file, "rt", encoding="utf-8") as fd: config = yaml.load(fd, Loader=yaml.FullLoader) default_config.update(config) config = _post_process(default_config) return config if __name__ == "__main__": if len(sys.argv) == 1: base_dir = os.path.dirname(__file__) _config_file = os.path.join(base_dir, "conf_templates", "_config.yml.in") elif len(sys.argv) == 2: base_dir = os.getcwd() _config_file = os.path.join(base_dir, sys.argv[1]) else: logging.error("Use the template config file by default, " "you can specify the config file to parse. \n" "Usage: `python -m simiki.config [_config.yml]'") sys.exit(1) pprint(parse_config(_config_file))

true

790178e3ed0bcfa7aa502d7406e2a2ec79590747

3,678

py

Python

ckanext/stats/stats.py

mabah-mst/ckan

105f613272c2e31daa0081ead24c678bf1b55c22

[ "Apache-2.0" ]

6

2015-11-09T00:44:51.000Z

2019-11-21T14:56:01.000Z

ckanext/stats/stats.py

syats/ckan

599ff35f9c289bab674f544367d5acdb1d2c9423

[ "Apache-2.0" ]

39

2015-02-18T17:32:23.000Z

2022-03-11T18:03:36.000Z

ckanext/stats/stats.py

cascaoSDC/ckan

75a08caa7c688ce70229dfea7070cc667a15c5e8

[ "BSD-3-Clause" ]

17

2015-03-13T18:05:05.000Z

2020-11-06T13:55:32.000Z

# encoding: utf-8 import datetime import logging from ckan.common import config from six import text_type from sqlalchemy import Table, select, join, func, and_ import ckan.plugins as p import ckan.model as model log = logging.getLogger(__name__) cache_enabled = p.toolkit.asbool( config.get('ckanext.stats.cache_enabled', False) ) if cache_enabled: log.warn( 'ckanext.stats does not support caching in current implementations' ) DATE_FORMAT = '%Y-%m-%d' def table(name): return Table(name, model.meta.metadata, autoload=True) def datetime2date(datetime_): return datetime.date(datetime_.year, datetime_.month, datetime_.day) class Stats(object): @classmethod def largest_groups(cls, limit=10): member = table('member') package = table('package') j = join(member, package, member.c.table_id == package.c.id) s = select( [member.c.group_id, func.count(member.c.table_id)] ).select_from(j).group_by(member.c.group_id).where( and_( member.c.group_id != None, member.c.table_name == 'package', package.c.private == False, package.c.state == 'active' ) ).order_by(func.count(member.c.table_id).desc()).limit(limit) res_ids = model.Session.execute(s).fetchall() res_groups = [ (model.Session.query(model.Group).get(text_type(group_id)), val) for group_id, val in res_ids ] return res_groups @classmethod def top_tags(cls, limit=10, returned_tag_info='object'): # by package assert returned_tag_info in ('name', 'id', 'object') tag = table('tag') package_tag = table('package_tag') package = table('package') if returned_tag_info == 'name': from_obj = [package_tag.join(tag)] tag_column = tag.c.name else: from_obj = None tag_column = package_tag.c.tag_id j = join( package_tag, package, package_tag.c.package_id == package.c.id ) s = select([tag_column, func.count(package_tag.c.package_id)], from_obj=from_obj).select_from(j).where( and_( package_tag.c.state == 'active', package.c.private == False, package.c.state == 'active' ) ) s = s.group_by(tag_column).order_by( func.count(package_tag.c.package_id).desc() ).limit(limit) res_col = model.Session.execute(s).fetchall() if returned_tag_info in ('id', 'name'): return res_col elif returned_tag_info == 'object': res_tags = [ (model.Session.query(model.Tag).get(text_type(tag_id)), val) for tag_id, val in res_col ] return res_tags @classmethod def top_package_creators(cls, limit=10): userid_count = model.Session.query( model.Package.creator_user_id, func.count(model.Package.creator_user_id) ).filter(model.Package.state == 'active' ).filter(model.Package.private == False).group_by( model.Package.creator_user_id ).order_by(func.count(model.Package.creator_user_id).desc() ).limit(limit).all() user_count = [ (model.Session.query(model.User).get(text_type(user_id)), count) for user_id, count in userid_count if user_id ] return user_count

32.839286

76

0.576672

import datetime import logging from ckan.common import config from six import text_type from sqlalchemy import Table, select, join, func, and_ import ckan.plugins as p import ckan.model as model log = logging.getLogger(__name__) cache_enabled = p.toolkit.asbool( config.get('ckanext.stats.cache_enabled', False) ) if cache_enabled: log.warn( 'ckanext.stats does not support caching in current implementations' ) DATE_FORMAT = '%Y-%m-%d' def table(name): return Table(name, model.meta.metadata, autoload=True) def datetime2date(datetime_): return datetime.date(datetime_.year, datetime_.month, datetime_.day) class Stats(object): @classmethod def largest_groups(cls, limit=10): member = table('member') package = table('package') j = join(member, package, member.c.table_id == package.c.id) s = select( [member.c.group_id, func.count(member.c.table_id)] ).select_from(j).group_by(member.c.group_id).where( and_( member.c.group_id != None, member.c.table_name == 'package', package.c.private == False, package.c.state == 'active' ) ).order_by(func.count(member.c.table_id).desc()).limit(limit) res_ids = model.Session.execute(s).fetchall() res_groups = [ (model.Session.query(model.Group).get(text_type(group_id)), val) for group_id, val in res_ids ] return res_groups @classmethod def top_tags(cls, limit=10, returned_tag_info='object'): assert returned_tag_info in ('name', 'id', 'object') tag = table('tag') package_tag = table('package_tag') package = table('package') if returned_tag_info == 'name': from_obj = [package_tag.join(tag)] tag_column = tag.c.name else: from_obj = None tag_column = package_tag.c.tag_id j = join( package_tag, package, package_tag.c.package_id == package.c.id ) s = select([tag_column, func.count(package_tag.c.package_id)], from_obj=from_obj).select_from(j).where( and_( package_tag.c.state == 'active', package.c.private == False, package.c.state == 'active' ) ) s = s.group_by(tag_column).order_by( func.count(package_tag.c.package_id).desc() ).limit(limit) res_col = model.Session.execute(s).fetchall() if returned_tag_info in ('id', 'name'): return res_col elif returned_tag_info == 'object': res_tags = [ (model.Session.query(model.Tag).get(text_type(tag_id)), val) for tag_id, val in res_col ] return res_tags @classmethod def top_package_creators(cls, limit=10): userid_count = model.Session.query( model.Package.creator_user_id, func.count(model.Package.creator_user_id) ).filter(model.Package.state == 'active' ).filter(model.Package.private == False).group_by( model.Package.creator_user_id ).order_by(func.count(model.Package.creator_user_id).desc() ).limit(limit).all() user_count = [ (model.Session.query(model.User).get(text_type(user_id)), count) for user_id, count in userid_count if user_id ] return user_count

true

7901792d7e756f8286ce38810accc3742ee607a0

1,299

py

Python

blockchain/Transaction.py

kaifkhan1040/voting

272f7eaed7793d86e35a6c10001ee852432cf6ee

[ "MIT" ]

12

2019-05-11T11:28:37.000Z

2021-02-25T19:40:58.000Z

blockchain/Transaction.py

kaifkhan1040/voting

272f7eaed7793d86e35a6c10001ee852432cf6ee

[ "MIT" ]

8

2019-05-07T18:50:45.000Z

2020-10-21T11:23:16.000Z

blockchain/Transaction.py

kaifkhan1040/voting

272f7eaed7793d86e35a6c10001ee852432cf6ee

[ "MIT" ]

7

2019-05-11T08:16:03.000Z

2020-11-30T08:34:15.000Z

import hashlib from fastecdsa import keys, curve, ecdsa from hashlib import sha256 from uuid import uuid4 class Transaction: def __init__(self, from_address, to_address, amount): self.from_address = from_address self.to_address = to_address self.amount = amount self.id = str(uuid4()).replace('-', '') self.signature = None def calculate_hash(self): return sha256((str(self.from_address) + str(self.to_address) + str(self.amount) + self.id).encode()).hexdigest() def sign_tx(self, priv_key): hash_tx = self.calculate_hash() self.signature = ecdsa.sign(hash_tx, priv_key, hashfunc=sha256) def is_valid(self): if self.signature is None: return True if len(self.signature) == 0 and self.to_address is None: return False hash_tx = self.calculate_hash() pubkey = keys.get_public_keys_from_sig(self.signature, hash_tx, curve=curve.P256, hashfunc=sha256) valid = ecdsa.verify(self.signature, hash_tx, pubkey[0], hashfunc=sha256) return valid def serialize(self): return { 'id': self.id, 'from_address': self.from_address, 'to_address': self.to_address, 'amount': self.amount }

30.928571

120

0.635874

import hashlib from fastecdsa import keys, curve, ecdsa from hashlib import sha256 from uuid import uuid4 class Transaction: def __init__(self, from_address, to_address, amount): self.from_address = from_address self.to_address = to_address self.amount = amount self.id = str(uuid4()).replace('-', '') self.signature = None def calculate_hash(self): return sha256((str(self.from_address) + str(self.to_address) + str(self.amount) + self.id).encode()).hexdigest() def sign_tx(self, priv_key): hash_tx = self.calculate_hash() self.signature = ecdsa.sign(hash_tx, priv_key, hashfunc=sha256) def is_valid(self): if self.signature is None: return True if len(self.signature) == 0 and self.to_address is None: return False hash_tx = self.calculate_hash() pubkey = keys.get_public_keys_from_sig(self.signature, hash_tx, curve=curve.P256, hashfunc=sha256) valid = ecdsa.verify(self.signature, hash_tx, pubkey[0], hashfunc=sha256) return valid def serialize(self): return { 'id': self.id, 'from_address': self.from_address, 'to_address': self.to_address, 'amount': self.amount }

true

790179b1396fdd73fefa5d3753233d7ab0acfc5c

1,185

py

Python

api/src/opentrons/hardware_control/g_code_parsing/g_code_functionality_defs/tempdeck/get_temp_g_code_functionality_def.py

knownmed/opentrons

d02eb3c6cbf9f1c8c05c5e9e1dac30a92a8c5e6c

[ "Apache-2.0" ]

null

api/src/opentrons/hardware_control/g_code_parsing/g_code_functionality_defs/tempdeck/get_temp_g_code_functionality_def.py

knownmed/opentrons

d02eb3c6cbf9f1c8c05c5e9e1dac30a92a8c5e6c

[ "Apache-2.0" ]

null

api/src/opentrons/hardware_control/g_code_parsing/g_code_functionality_defs/tempdeck/get_temp_g_code_functionality_def.py

knownmed/opentrons

d02eb3c6cbf9f1c8c05c5e9e1dac30a92a8c5e6c

[ "Apache-2.0" ]

null

import re from typing import Dict from opentrons.hardware_control.g_code_parsing.g_code_functionality_defs.g_code_functionality_def_base import ( # noqa: E501 GCodeFunctionalityDefBase, ) class GetTempGCodeFunctionalityDef(GCodeFunctionalityDefBase): RESPONSE_RE = re.compile(r"T:(?P<set_temp>.*?)C:(?P<current_temp>\d+.\d+)") @classmethod def _generate_command_explanation(cls, g_code_args: Dict[str, str]) -> str: return "Getting temperature" @classmethod def _generate_response_explanation(cls, response: str) -> str: match = cls.RESPONSE_RE.match(response) message = "" if match is not None: current_temp = match.groupdict()["current_temp"].strip() set_temp = match.groupdict()["set_temp"].strip() if set_temp == "none": message = ( f"Temp deck is disengaged. " f"Current temperature is {current_temp}C" ) else: message = ( f"Set temperature is {set_temp}C. " f"Current temperature is {current_temp}C" ) return message

34.852941

125

0.605063

import re from typing import Dict from opentrons.hardware_control.g_code_parsing.g_code_functionality_defs.g_code_functionality_def_base import ( GCodeFunctionalityDefBase, ) class GetTempGCodeFunctionalityDef(GCodeFunctionalityDefBase): RESPONSE_RE = re.compile(r"T:(?P<set_temp>.*?)C:(?P<current_temp>\d+.\d+)") @classmethod def _generate_command_explanation(cls, g_code_args: Dict[str, str]) -> str: return "Getting temperature" @classmethod def _generate_response_explanation(cls, response: str) -> str: match = cls.RESPONSE_RE.match(response) message = "" if match is not None: current_temp = match.groupdict()["current_temp"].strip() set_temp = match.groupdict()["set_temp"].strip() if set_temp == "none": message = ( f"Temp deck is disengaged. " f"Current temperature is {current_temp}C" ) else: message = ( f"Set temperature is {set_temp}C. " f"Current temperature is {current_temp}C" ) return message

true

79017a40dd5a88091830cc6c4a40fd29b48a50dc

1,105

py

Python

Python3/Tornado/apps/pg/PG_Wallet/src/lib/sql.py

youngqqcn/QBlockChainNotes

85122049024dc5555705bf016312491a51966621

[ "MIT" ]

24

2018-11-01T03:36:43.000Z

2022-03-28T08:20:30.000Z

Python3/Tornado/apps/pg/PG_Wallet/src/lib/sql.py

songning4/QBlockChainNotes

d65ede073f5a20f728f41cc6850409693820cdb1

[ "MIT" ]

57

2019-12-04T08:26:47.000Z

2022-03-08T07:35:15.000Z

Python3/Tornado/apps/pg/PG_Wallet/src/lib/sql.py

youngqqcn/QBlockChainNotes

85122049024dc5555705bf016312491a51966621

[ "MIT" ]

11

2019-01-04T08:41:57.000Z

2022-03-16T03:51:36.000Z

#!coding:utf8 #author:yqq #date:2020/4/30 0030 17:11 #description: import os import pymysql SQL_PASSWD = os.environ.get('SQL_PWD') def open(host : str,usr : str, passwd : str,db_name : str): conn = pymysql.connect(host=host, user=usr, password=passwd, db=db_name, charset='utf8', cursorclass=pymysql.cursors.DictCursor) return conn def close(conn): conn.close() def execute(conn,cmd): cur = conn.cursor() cur.execute(cmd) conn.commit() #fixed bug by yqq 2019-05-01 return cur.fetchall() def run(cmd): conn = open() result = execute(conn,cmd) close(conn) return result def get_column_values(conn,table_name,column_name): cmd = "SELECT {0} FROM {1}".format(column_name,table_name) return execute(conn,cmd) def main(): host = '192.168.10.29' usr = 'root' passwd = 'eWFuZ3FpbmdxaW5n' dbname = 'test_1' conn = open(host=host, usr=usr, passwd=passwd, db_name=dbname ) print(get_column_values(conn,'t_test_student','name')) close(conn) if __name__ == "__main__": main()

21.666667

71

0.643439

import os import pymysql SQL_PASSWD = os.environ.get('SQL_PWD') def open(host : str,usr : str, passwd : str,db_name : str): conn = pymysql.connect(host=host, user=usr, password=passwd, db=db_name, charset='utf8', cursorclass=pymysql.cursors.DictCursor) return conn def close(conn): conn.close() def execute(conn,cmd): cur = conn.cursor() cur.execute(cmd) conn.commit() return cur.fetchall() def run(cmd): conn = open() result = execute(conn,cmd) close(conn) return result def get_column_values(conn,table_name,column_name): cmd = "SELECT {0} FROM {1}".format(column_name,table_name) return execute(conn,cmd) def main(): host = '192.168.10.29' usr = 'root' passwd = 'eWFuZ3FpbmdxaW5n' dbname = 'test_1' conn = open(host=host, usr=usr, passwd=passwd, db_name=dbname ) print(get_column_values(conn,'t_test_student','name')) close(conn) if __name__ == "__main__": main()

true

79017c0607032cf5f29a9166dd2136111da20517

3,223

py

Python

demo.py

ijinmao/CAM-Localization

dfa214be984f77d577dba1065e2c63e0c1b0b82b

[ "MIT" ]

4

2017-09-07T05:55:58.000Z

2019-09-05T04:02:41.000Z

demo.py

ijinmao/CAM-Localization

dfa214be984f77d577dba1065e2c63e0c1b0b82b

[ "MIT" ]

null

demo.py

ijinmao/CAM-Localization

dfa214be984f77d577dba1065e2c63e0c1b0b82b

[ "MIT" ]

1

2019-04-02T05:03:25.000Z

import numpy as np import cv2 import matplotlib.pylab as plt from keras.preprocessing.image import load_img from keras.models import model_from_json from models import ( create_cam_model, preprocess_image, get_cam_img ) # Define CAM conv layer name CAM_CONV_LAYER = 'cam_conv_layer' def read_model(model_path, weigths_path): """Load your pretrained model """ model = model_from_json(open(model_path).read()) model.load_weights(weigths_path) return model def train_cam_model(X_train, Y_train, X_test, Y_test, batch_size, nb_epoch): """Train CAM model based on your pretrained model # Arguments model: your pretrained model, CAM model is trained based on this model. """ # Use your allready trained model pretrained_model_path = '' pretrained_weights_path = '' # Your pretrained model name pretrained_model_name = 'VGG16' # Label class num num_classes = 10 # CAM input spacial size gap_spacial_size = 14 # The layer before CAM(GAP) layers. # CAM paper suggests to use the last convnet(VGG) or mergenet(Inception, or other architectures) # Change this name based on your model. if pretrained_model_name == 'VGG16': in_layer_name = 'block5_conv3' elif pretrained_model_name == 'InceptionV3': in_layer_name = 'batchnormalization_921' elif pretrained_model_name == 'ResNet50': in_layer_name = 'merge_13' else: in_layer_name = '' # Load your allready trained model, transfer it to CAM model pretrained_model = read_model(pretrained_model_path, pretrained_weights_path) # Create CAM model based on trained model model = create_cam_model(pretrained_model, gap_spacial_size, num_classes, in_layer_name, CAM_CONV_LAYER) # Train your CAM model model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, shuffle=True, verbose=1, validation_data=(X_test, Y_test)) # Save model model.save_weights('') return model def cam_model(): """ Return your trained CAM model """ return def plot_cam_map(img_path, img_size, batch_size, label_plot): """Plot class activation map. """ # CAM input spacial size gap_spacial_size = 14 # Use your trained CAM model model = cam_model() # Load and format data im_ori = np.asarray(load_img(img_path, target_size=(img_size, img_size))) test_data = preprocess_image(img_path, img_size, expand_dims=True) # Get class map image im_cam = get_cam_img(model, test_data, label_plot, CAM_CONV_LAYER, ratio=img_size / gap_spacial_size) # Resize if the shape of class map is not equal to original image if im_cam.shape != im_ori[:, :, 0].shape: im_cam = cv2.resize(im_cam, (img_size, img_size), cv2.INTER_LINEAR) # Show the predictions. You can analyze the class map with the predictions. prediction_labels = model.predict(test_data.astype('float32'), batch_size=batch_size, verbose=1) print('Info: Predictions:\n{}'.format(prediction_labels)) # Plot original image and the class map plt.imshow(im_ori) plt.imshow(im_cam, cmap='jet', alpha=0.5, interpolation='bilinear') plt.show()

25.784

97

0.730996

import numpy as np import cv2 import matplotlib.pylab as plt from keras.preprocessing.image import load_img from keras.models import model_from_json from models import ( create_cam_model, preprocess_image, get_cam_img ) CAM_CONV_LAYER = 'cam_conv_layer' def read_model(model_path, weigths_path): model = model_from_json(open(model_path).read()) model.load_weights(weigths_path) return model def train_cam_model(X_train, Y_train, X_test, Y_test, batch_size, nb_epoch): pretrained_model_path = '' pretrained_weights_path = '' pretrained_model_name = 'VGG16' num_classes = 10 gap_spacial_size = 14 if pretrained_model_name == 'VGG16': in_layer_name = 'block5_conv3' elif pretrained_model_name == 'InceptionV3': in_layer_name = 'batchnormalization_921' elif pretrained_model_name == 'ResNet50': in_layer_name = 'merge_13' else: in_layer_name = '' pretrained_model = read_model(pretrained_model_path, pretrained_weights_path) model = create_cam_model(pretrained_model, gap_spacial_size, num_classes, in_layer_name, CAM_CONV_LAYER) model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, shuffle=True, verbose=1, validation_data=(X_test, Y_test)) model.save_weights('') return model def cam_model(): return def plot_cam_map(img_path, img_size, batch_size, label_plot): gap_spacial_size = 14 model = cam_model() im_ori = np.asarray(load_img(img_path, target_size=(img_size, img_size))) test_data = preprocess_image(img_path, img_size, expand_dims=True) im_cam = get_cam_img(model, test_data, label_plot, CAM_CONV_LAYER, ratio=img_size / gap_spacial_size) if im_cam.shape != im_ori[:, :, 0].shape: im_cam = cv2.resize(im_cam, (img_size, img_size), cv2.INTER_LINEAR) prediction_labels = model.predict(test_data.astype('float32'), batch_size=batch_size, verbose=1) print('Info: Predictions:\n{}'.format(prediction_labels)) plt.imshow(im_ori) plt.imshow(im_cam, cmap='jet', alpha=0.5, interpolation='bilinear') plt.show()

true

79017c62423711590bccd2042772a130938484bb

25,242

py

Python

python/pyspark/sql/session.py

YoufuLi/radlog

669c2b28f7ef4f9f6d94d353a3b180c1ad3b99e5

[ "BSD-3-Clause-Open-MPI", "PSF-2.0", "Apache-2.0", "BSD-2-Clause", "MIT", "MIT-0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause-Clear", "PostgreSQL", "BSD-3-Clause" ]

2

2019-04-03T06:39:06.000Z

2019-06-23T16:43:49.000Z

python/pyspark/sql/session.py

YoufuLi/radlog

669c2b28f7ef4f9f6d94d353a3b180c1ad3b99e5

[ "BSD-3-Clause-Open-MPI", "PSF-2.0", "Apache-2.0", "BSD-2-Clause", "MIT", "MIT-0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause-Clear", "PostgreSQL", "BSD-3-Clause" ]

1

2021-08-31T03:40:28.000Z

2021-08-31T06:38:38.000Z

python/pyspark/sql/session.py

YoufuLi/radlog

669c2b28f7ef4f9f6d94d353a3b180c1ad3b99e5

[ "BSD-3-Clause-Open-MPI", "PSF-2.0", "Apache-2.0", "BSD-2-Clause", "MIT", "MIT-0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause-Clear", "PostgreSQL", "BSD-3-Clause" ]

1

2019-04-03T05:00:51.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import print_function import sys import warnings from functools import reduce from threading import RLock if sys.version >= '3': basestring = unicode = str else: from itertools import imap as map from pyspark import since from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.sql.catalog import Catalog from pyspark.sql.conf import RuntimeConfig from pyspark.sql.dataframe import DataFrame from pyspark.sql.readwriter import DataFrameReader from pyspark.sql.streaming import DataStreamReader from pyspark.sql.types import Row, DataType, StringType, StructType, _verify_type, \ _infer_schema, _has_nulltype, _merge_type, _create_converter, _parse_datatype_string from pyspark.sql.utils import install_exception_handler __all__ = ["SparkSession"] def _monkey_patch_RDD(sparkSession): def toDF(self, schema=None, sampleRatio=None): """ Converts current :class:`RDD` into a :class:`DataFrame` This is a shorthand for ``spark.createDataFrame(rdd, schema, sampleRatio)`` :param schema: a :class:`pyspark.sql.types.StructType` or list of names of columns :param samplingRatio: the sample ratio of rows used for inferring :return: a DataFrame >>> rdd.toDF().collect() [Row(name=u'Alice', age=1)] """ return sparkSession.createDataFrame(self, schema, sampleRatio) RDD.toDF = toDF class SparkSession(object): """The entry point to programming Spark with the Dataset and DataFrame API. A SparkSession can be used create :class:`DataFrame`, register :class:`DataFrame` as tables, execute SQL over tables, cache tables, and read parquet files. To create a SparkSession, use the following builder pattern: >>> spark = SparkSession.builder \\ ... .master("local") \\ ... .appName("Word Count") \\ ... .config("spark.some.config.option", "some-value") \\ ... .getOrCreate() """ class Builder(object): """Builder for :class:`SparkSession`. """ _lock = RLock() _options = {} @since(2.0) def config(self, key=None, value=None, conf=None): """Sets a config option. Options set using this method are automatically propagated to both :class:`SparkConf` and :class:`SparkSession`'s own configuration. For an existing SparkConf, use `conf` parameter. >>> from pyspark.conf import SparkConf >>> SparkSession.builder.config(conf=SparkConf()) <pyspark.sql.session... For a (key, value) pair, you can omit parameter names. >>> SparkSession.builder.config("spark.some.config.option", "some-value") <pyspark.sql.session... :param key: a key name string for configuration property :param value: a value for configuration property :param conf: an instance of :class:`SparkConf` """ with self._lock: if conf is None: self._options[key] = str(value) else: for (k, v) in conf.getAll(): self._options[k] = v return self @since(2.0) def master(self, master): """Sets the Spark master URL to connect to, such as "local" to run locally, "local[4]" to run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster. :param master: a url for spark master """ return self.config("spark.master", master) @since(2.0) def appName(self, name): """Sets a name for the application, which will be shown in the Spark web UI. If no application name is set, a randomly generated name will be used. :param name: an application name """ return self.config("spark.app.name", name) @since(2.0) def enableHiveSupport(self): """Enables Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions. """ return self.config("spark.sql.catalogImplementation", "hive") @since(2.0) def getOrCreate(self): """Gets an existing :class:`SparkSession` or, if there is no existing one, creates a new one based on the options set in this builder. This method first checks whether there is a valid global default SparkSession, and if yes, return that one. If no valid global default SparkSession exists, the method creates a new SparkSession and assigns the newly created SparkSession as the global default. >>> s1 = SparkSession.builder.config("k1", "v1").getOrCreate() >>> s1.conf.get("k1") == s1.sparkContext.getConf().get("k1") == "v1" True In case an existing SparkSession is returned, the config options specified in this builder will be applied to the existing SparkSession. >>> s2 = SparkSession.builder.config("k2", "v2").getOrCreate() >>> s1.conf.get("k1") == s2.conf.get("k1") True >>> s1.conf.get("k2") == s2.conf.get("k2") True """ with self._lock: from pyspark.context import SparkContext from pyspark.conf import SparkConf session = SparkSession._instantiatedSession if session is None or session._sc._jsc is None: sparkConf = SparkConf() for key, value in self._options.items(): sparkConf.set(key, value) sc = SparkContext.getOrCreate(sparkConf) # This SparkContext may be an existing one. for key, value in self._options.items(): # we need to propagate the confs # before we create the SparkSession. Otherwise, confs like # warehouse path and metastore url will not be set correctly ( # these confs cannot be changed once the SparkSession is created). sc._conf.set(key, value) session = SparkSession(sc) for key, value in self._options.items(): session.conf.set(key, value) for key, value in self._options.items(): session.sparkContext._conf.set(key, value) return session builder = Builder() _instantiatedSession = None @ignore_unicode_prefix def __init__(self, sparkContext, jsparkSession=None): """Creates a new SparkSession. >>> from datetime import datetime >>> spark = SparkSession(sc) >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1, ... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1), ... time=datetime(2014, 8, 1, 14, 1, 5))]) >>> df = allTypes.toDF() >>> df.createOrReplaceTempView("allTypes") >>> spark.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' ... 'from allTypes where b and i > 0').collect() [Row((i + CAST(1 AS BIGINT))=2, (d + CAST(1 AS DOUBLE))=2.0, (NOT b)=False, list[1]=2, \ dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)] >>> df.rdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect() [(1, u'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] """ from pyspark.sql.context import SQLContext self._sc = sparkContext self._jsc = self._sc._jsc self._jvm = self._sc._jvm if jsparkSession is None: jsparkSession = self._jvm.SparkSession(self._jsc.sc()) self._jsparkSession = jsparkSession self._jwrapped = self._jsparkSession.sqlContext() self._wrapped = SQLContext(self._sc, self, self._jwrapped) _monkey_patch_RDD(self) install_exception_handler() # If we had an instantiated SparkSession attached with a SparkContext # which is stopped now, we need to renew the instantiated SparkSession. # Otherwise, we will use invalid SparkSession when we call Builder.getOrCreate. if SparkSession._instantiatedSession is None \ or SparkSession._instantiatedSession._sc._jsc is None: SparkSession._instantiatedSession = self @since(2.0) def newSession(self): """ Returns a new SparkSession as new session, that has separate SQLConf, registered temporary views and UDFs, but shared SparkContext and table cache. """ return self.__class__(self._sc, self._jsparkSession.newSession()) @property @since(2.0) def sparkContext(self): """Returns the underlying :class:`SparkContext`.""" return self._sc @property @since(2.0) def version(self): """The version of Spark on which this application is running.""" return self._jsparkSession.version() @property @since(2.0) def conf(self): """Runtime configuration interface for Spark. This is the interface through which the user can get and set all Spark and Hadoop configurations that are relevant to Spark SQL. When getting the value of a config, this defaults to the value set in the underlying :class:`SparkContext`, if any. """ if not hasattr(self, "_conf"): self._conf = RuntimeConfig(self._jsparkSession.conf()) return self._conf @property @since(2.0) def catalog(self): """Interface through which the user may create, drop, alter or query underlying databases, tables, functions etc. """ if not hasattr(self, "_catalog"): self._catalog = Catalog(self) return self._catalog @property @since(2.0) def udf(self): """Returns a :class:`UDFRegistration` for UDF registration. :return: :class:`UDFRegistration` """ from pyspark.sql.context import UDFRegistration return UDFRegistration(self._wrapped) @since(2.0) def range(self, start, end=None, step=1, numPartitions=None): """ Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named ``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with step value ``step``. :param start: the start value :param end: the end value (exclusive) :param step: the incremental step (default: 1) :param numPartitions: the number of partitions of the DataFrame :return: :class:`DataFrame` >>> spark.range(1, 7, 2).collect() [Row(id=1), Row(id=3), Row(id=5)] If only one argument is specified, it will be used as the end value. >>> spark.range(3).collect() [Row(id=0), Row(id=1), Row(id=2)] """ if numPartitions is None: numPartitions = self._sc.defaultParallelism if end is None: jdf = self._jsparkSession.range(0, int(start), int(step), int(numPartitions)) else: jdf = self._jsparkSession.range(int(start), int(end), int(step), int(numPartitions)) return DataFrame(jdf, self._wrapped) def _inferSchemaFromList(self, data): """ Infer schema from list of Row or tuple. :param data: list of Row or tuple :return: :class:`pyspark.sql.types.StructType` """ if not data: raise ValueError("can not infer schema from empty dataset") first = data[0] if type(first) is dict: warnings.warn("inferring schema from dict is deprecated," "please use pyspark.sql.Row instead") schema = reduce(_merge_type, map(_infer_schema, data)) if _has_nulltype(schema): raise ValueError("Some of types cannot be determined after inferring") return schema def _inferSchema(self, rdd, samplingRatio=None): """ Infer schema from an RDD of Row or tuple. :param rdd: an RDD of Row or tuple :param samplingRatio: sampling ratio, or no sampling (default) :return: :class:`pyspark.sql.types.StructType` """ first = rdd.first() if not first: raise ValueError("The first row in RDD is empty, " "can not infer schema") if type(first) is dict: warnings.warn("Using RDD of dict to inferSchema is deprecated. " "Use pyspark.sql.Row instead") if samplingRatio is None: schema = _infer_schema(first) if _has_nulltype(schema): for row in rdd.take(100)[1:]: schema = _merge_type(schema, _infer_schema(row)) if not _has_nulltype(schema): break else: raise ValueError("Some of types cannot be determined by the " "first 100 rows, please try again with sampling") else: if samplingRatio < 0.99: rdd = rdd.sample(False, float(samplingRatio)) schema = rdd.map(_infer_schema).reduce(_merge_type) return schema def _createFromRDD(self, rdd, schema, samplingRatio): """ Create an RDD for DataFrame from an existing RDD, returns the RDD and schema. """ if schema is None or isinstance(schema, (list, tuple)): struct = self._inferSchema(rdd, samplingRatio) converter = _create_converter(struct) rdd = rdd.map(converter) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif not isinstance(schema, StructType): raise TypeError("schema should be StructType or list or None, but got: %s" % schema) # convert python objects to sql data rdd = rdd.map(schema.toInternal) return rdd, schema def _createFromLocal(self, data, schema): """ Create an RDD for DataFrame from a list or pandas.DataFrame, returns the RDD and schema. """ # make sure data could consumed multiple times if not isinstance(data, list): data = list(data) if schema is None or isinstance(schema, (list, tuple)): struct = self._inferSchemaFromList(data) converter = _create_converter(struct) data = map(converter, data) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif not isinstance(schema, StructType): raise TypeError("schema should be StructType or list or None, but got: %s" % schema) # convert python objects to sql data data = [schema.toInternal(row) for row in data] return self._sc.parallelize(data), schema @since(2.0) @ignore_unicode_prefix def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True): """ Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`. When ``schema`` is a list of column names, the type of each column will be inferred from ``data``. When ``schema`` is ``None``, it will try to infer the schema (column names and types) from ``data``, which should be an RDD of :class:`Row`, or :class:`namedtuple`, or :class:`dict`. When ``schema`` is :class:`pyspark.sql.types.DataType` or :class:`pyspark.sql.types.StringType`, it must match the real data, or an exception will be thrown at runtime. If the given schema is not :class:`pyspark.sql.types.StructType`, it will be wrapped into a :class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value", each record will also be wrapped into a tuple, which can be converted to row later. If schema inference is needed, ``samplingRatio`` is used to determined the ratio of rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``. :param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean, etc.), or :class:`list`, or :class:`pandas.DataFrame`. :param schema: a :class:`pyspark.sql.types.DataType` or a :class:`pyspark.sql.types.StringType` or a list of column names, default is ``None``. The data type string format equals to :class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use ``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`. We can also use ``int`` as a short name for ``IntegerType``. :param samplingRatio: the sample ratio of rows used for inferring :param verifySchema: verify data types of every row against schema. :return: :class:`DataFrame` .. versionchanged:: 2.0.1 Added verifySchema. >>> l = [('Alice', 1)] >>> spark.createDataFrame(l).collect() [Row(_1=u'Alice', _2=1)] >>> spark.createDataFrame(l, ['name', 'age']).collect() [Row(name=u'Alice', age=1)] >>> d = [{'name': 'Alice', 'age': 1}] >>> spark.createDataFrame(d).collect() [Row(age=1, name=u'Alice')] >>> rdd = sc.parallelize(l) >>> spark.createDataFrame(rdd).collect() [Row(_1=u'Alice', _2=1)] >>> df = spark.createDataFrame(rdd, ['name', 'age']) >>> df.collect() [Row(name=u'Alice', age=1)] >>> from pyspark.sql import Row >>> Person = Row('name', 'age') >>> person = rdd.map(lambda r: Person(*r)) >>> df2 = spark.createDataFrame(person) >>> df2.collect() [Row(name=u'Alice', age=1)] >>> from pyspark.sql.types import * >>> schema = StructType([ ... StructField("name", StringType(), True), ... StructField("age", IntegerType(), True)]) >>> df3 = spark.createDataFrame(rdd, schema) >>> df3.collect() [Row(name=u'Alice', age=1)] >>> spark.createDataFrame(df.toPandas()).collect() # doctest: +SKIP [Row(name=u'Alice', age=1)] >>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP [Row(0=1, 1=2)] >>> spark.createDataFrame(rdd, "a: string, b: int").collect() [Row(a=u'Alice', b=1)] >>> rdd = rdd.map(lambda row: row[1]) >>> spark.createDataFrame(rdd, "int").collect() [Row(value=1)] >>> spark.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... Py4JJavaError: ... """ if isinstance(data, DataFrame): raise TypeError("data is already a DataFrame") if isinstance(schema, basestring): schema = _parse_datatype_string(schema) try: import pandas has_pandas = True except Exception: has_pandas = False if has_pandas and isinstance(data, pandas.DataFrame): if schema is None: schema = [str(x) for x in data.columns] data = [r.tolist() for r in data.to_records(index=False)] verify_func = _verify_type if verifySchema else lambda _, t: True if isinstance(schema, StructType): def prepare(obj): verify_func(obj, schema) return obj elif isinstance(schema, DataType): dataType = schema schema = StructType().add("value", schema) def prepare(obj): verify_func(obj, dataType) return obj, else: if isinstance(schema, list): schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema] prepare = lambda obj: obj if isinstance(data, RDD): rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio) else: rdd, schema = self._createFromLocal(map(prepare, data), schema) jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd()) jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) df = DataFrame(jdf, self._wrapped) df._schema = schema return df @ignore_unicode_prefix @since(2.0) def sql(self, sqlQuery): """Returns a :class:`DataFrame` representing the result of the given query. :return: :class:`DataFrame` >>> df.createOrReplaceTempView("table1") >>> df2 = spark.sql("SELECT field1 AS f1, field2 as f2 from table1") >>> df2.collect() [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')] """ return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped) @since(2.0) def table(self, tableName): """Returns the specified table as a :class:`DataFrame`. :return: :class:`DataFrame` >>> df.createOrReplaceTempView("table1") >>> df2 = spark.table("table1") >>> sorted(df.collect()) == sorted(df2.collect()) True """ return DataFrame(self._jsparkSession.table(tableName), self._wrapped) @property @since(2.0) def read(self): """ Returns a :class:`DataFrameReader` that can be used to read data in as a :class:`DataFrame`. :return: :class:`DataFrameReader` """ return DataFrameReader(self._wrapped) @property @since(2.0) def readStream(self): """ Returns a :class:`DataStreamReader` that can be used to read data streams as a streaming :class:`DataFrame`. .. note:: Experimental. :return: :class:`DataStreamReader` """ return DataStreamReader(self._wrapped) @property @since(2.0) def streams(self): """Returns a :class:`StreamingQueryManager` that allows managing all the :class:`StreamingQuery` StreamingQueries active on `this` context. .. note:: Experimental. :return: :class:`StreamingQueryManager` """ from pyspark.sql.streaming import StreamingQueryManager return StreamingQueryManager(self._jsparkSession.streams()) @since(2.0) def stop(self): """Stop the underlying :class:`SparkContext`. """ self._sc.stop() SparkSession._instantiatedSession = None @since(2.0) def __enter__(self): """ Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax. """ return self @since(2.0) def __exit__(self, exc_type, exc_val, exc_tb): """ Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax. Specifically stop the SparkSession on exit of the with block. """ self.stop() def _test(): import os import doctest from pyspark.context import SparkContext from pyspark.sql import Row import pyspark.sql.session os.chdir(os.environ["SPARK_HOME"]) globs = pyspark.sql.session.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc globs['spark'] = SparkSession(sc) globs['rdd'] = rdd = sc.parallelize( [Row(field1=1, field2="row1"), Row(field1=2, field2="row2"), Row(field1=3, field2="row3")]) globs['df'] = rdd.toDF() (failure_count, test_count) = doctest.testmod( pyspark.sql.session, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()

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100

0.598605

from __future__ import print_function import sys import warnings from functools import reduce from threading import RLock if sys.version >= '3': basestring = unicode = str else: from itertools import imap as map from pyspark import since from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.sql.catalog import Catalog from pyspark.sql.conf import RuntimeConfig from pyspark.sql.dataframe import DataFrame from pyspark.sql.readwriter import DataFrameReader from pyspark.sql.streaming import DataStreamReader from pyspark.sql.types import Row, DataType, StringType, StructType, _verify_type, \ _infer_schema, _has_nulltype, _merge_type, _create_converter, _parse_datatype_string from pyspark.sql.utils import install_exception_handler __all__ = ["SparkSession"] def _monkey_patch_RDD(sparkSession): def toDF(self, schema=None, sampleRatio=None): return sparkSession.createDataFrame(self, schema, sampleRatio) RDD.toDF = toDF class SparkSession(object): class Builder(object): _lock = RLock() _options = {} @since(2.0) def config(self, key=None, value=None, conf=None): with self._lock: if conf is None: self._options[key] = str(value) else: for (k, v) in conf.getAll(): self._options[k] = v return self @since(2.0) def master(self, master): return self.config("spark.master", master) @since(2.0) def appName(self, name): return self.config("spark.app.name", name) @since(2.0) def enableHiveSupport(self): return self.config("spark.sql.catalogImplementation", "hive") @since(2.0) def getOrCreate(self): with self._lock: from pyspark.context import SparkContext from pyspark.conf import SparkConf session = SparkSession._instantiatedSession if session is None or session._sc._jsc is None: sparkConf = SparkConf() for key, value in self._options.items(): sparkConf.set(key, value) sc = SparkContext.getOrCreate(sparkConf) for key, value in self._options.items(): sc._conf.set(key, value) session = SparkSession(sc) for key, value in self._options.items(): session.conf.set(key, value) for key, value in self._options.items(): session.sparkContext._conf.set(key, value) return session builder = Builder() _instantiatedSession = None @ignore_unicode_prefix def __init__(self, sparkContext, jsparkSession=None): from pyspark.sql.context import SQLContext self._sc = sparkContext self._jsc = self._sc._jsc self._jvm = self._sc._jvm if jsparkSession is None: jsparkSession = self._jvm.SparkSession(self._jsc.sc()) self._jsparkSession = jsparkSession self._jwrapped = self._jsparkSession.sqlContext() self._wrapped = SQLContext(self._sc, self, self._jwrapped) _monkey_patch_RDD(self) install_exception_handler() if SparkSession._instantiatedSession is None \ or SparkSession._instantiatedSession._sc._jsc is None: SparkSession._instantiatedSession = self @since(2.0) def newSession(self): return self.__class__(self._sc, self._jsparkSession.newSession()) @property @since(2.0) def sparkContext(self): return self._sc @property @since(2.0) def version(self): return self._jsparkSession.version() @property @since(2.0) def conf(self): if not hasattr(self, "_conf"): self._conf = RuntimeConfig(self._jsparkSession.conf()) return self._conf @property @since(2.0) def catalog(self): if not hasattr(self, "_catalog"): self._catalog = Catalog(self) return self._catalog @property @since(2.0) def udf(self): from pyspark.sql.context import UDFRegistration return UDFRegistration(self._wrapped) @since(2.0) def range(self, start, end=None, step=1, numPartitions=None): if numPartitions is None: numPartitions = self._sc.defaultParallelism if end is None: jdf = self._jsparkSession.range(0, int(start), int(step), int(numPartitions)) else: jdf = self._jsparkSession.range(int(start), int(end), int(step), int(numPartitions)) return DataFrame(jdf, self._wrapped) def _inferSchemaFromList(self, data): if not data: raise ValueError("can not infer schema from empty dataset") first = data[0] if type(first) is dict: warnings.warn("inferring schema from dict is deprecated," "please use pyspark.sql.Row instead") schema = reduce(_merge_type, map(_infer_schema, data)) if _has_nulltype(schema): raise ValueError("Some of types cannot be determined after inferring") return schema def _inferSchema(self, rdd, samplingRatio=None): first = rdd.first() if not first: raise ValueError("The first row in RDD is empty, " "can not infer schema") if type(first) is dict: warnings.warn("Using RDD of dict to inferSchema is deprecated. " "Use pyspark.sql.Row instead") if samplingRatio is None: schema = _infer_schema(first) if _has_nulltype(schema): for row in rdd.take(100)[1:]: schema = _merge_type(schema, _infer_schema(row)) if not _has_nulltype(schema): break else: raise ValueError("Some of types cannot be determined by the " "first 100 rows, please try again with sampling") else: if samplingRatio < 0.99: rdd = rdd.sample(False, float(samplingRatio)) schema = rdd.map(_infer_schema).reduce(_merge_type) return schema def _createFromRDD(self, rdd, schema, samplingRatio): if schema is None or isinstance(schema, (list, tuple)): struct = self._inferSchema(rdd, samplingRatio) converter = _create_converter(struct) rdd = rdd.map(converter) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif not isinstance(schema, StructType): raise TypeError("schema should be StructType or list or None, but got: %s" % schema) rdd = rdd.map(schema.toInternal) return rdd, schema def _createFromLocal(self, data, schema): if not isinstance(data, list): data = list(data) if schema is None or isinstance(schema, (list, tuple)): struct = self._inferSchemaFromList(data) converter = _create_converter(struct) data = map(converter, data) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif not isinstance(schema, StructType): raise TypeError("schema should be StructType or list or None, but got: %s" % schema) data = [schema.toInternal(row) for row in data] return self._sc.parallelize(data), schema @since(2.0) @ignore_unicode_prefix def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True): if isinstance(data, DataFrame): raise TypeError("data is already a DataFrame") if isinstance(schema, basestring): schema = _parse_datatype_string(schema) try: import pandas has_pandas = True except Exception: has_pandas = False if has_pandas and isinstance(data, pandas.DataFrame): if schema is None: schema = [str(x) for x in data.columns] data = [r.tolist() for r in data.to_records(index=False)] verify_func = _verify_type if verifySchema else lambda _, t: True if isinstance(schema, StructType): def prepare(obj): verify_func(obj, schema) return obj elif isinstance(schema, DataType): dataType = schema schema = StructType().add("value", schema) def prepare(obj): verify_func(obj, dataType) return obj, else: if isinstance(schema, list): schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema] prepare = lambda obj: obj if isinstance(data, RDD): rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio) else: rdd, schema = self._createFromLocal(map(prepare, data), schema) jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd()) jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) df = DataFrame(jdf, self._wrapped) df._schema = schema return df @ignore_unicode_prefix @since(2.0) def sql(self, sqlQuery): return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped) @since(2.0) def table(self, tableName): return DataFrame(self._jsparkSession.table(tableName), self._wrapped) @property @since(2.0) def read(self): return DataFrameReader(self._wrapped) @property @since(2.0) def readStream(self): return DataStreamReader(self._wrapped) @property @since(2.0) def streams(self): from pyspark.sql.streaming import StreamingQueryManager return StreamingQueryManager(self._jsparkSession.streams()) @since(2.0) def stop(self): self._sc.stop() SparkSession._instantiatedSession = None @since(2.0) def __enter__(self): return self @since(2.0) def __exit__(self, exc_type, exc_val, exc_tb): self.stop() def _test(): import os import doctest from pyspark.context import SparkContext from pyspark.sql import Row import pyspark.sql.session os.chdir(os.environ["SPARK_HOME"]) globs = pyspark.sql.session.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc globs['spark'] = SparkSession(sc) globs['rdd'] = rdd = sc.parallelize( [Row(field1=1, field2="row1"), Row(field1=2, field2="row2"), Row(field1=3, field2="row3")]) globs['df'] = rdd.toDF() (failure_count, test_count) = doctest.testmod( pyspark.sql.session, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()

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py

Python

core/tests/test_helpers.py

uktrade/directory-ui-supplier

b91bb07dbcb1d69a032a0536b8eff6e0e96196ef

[ "MIT" ]

2

2017-06-02T09:09:06.000Z

2017-07-19T22:51:16.000Z

core/tests/test_helpers.py

uktrade/directory-ui-supplier

b91bb07dbcb1d69a032a0536b8eff6e0e96196ef

[ "MIT" ]

409

2016-12-28T12:14:27.000Z

2019-08-01T11:11:48.000Z

core/tests/test_helpers.py

uktrade/directory-ui-supplier

b91bb07dbcb1d69a032a0536b8eff6e0e96196ef

[ "MIT" ]

5

2017-08-30T08:11:29.000Z

2019-06-04T20:40:34.000Z

import pytest import requests from directory_constants import expertise, sectors from django.shortcuts import Http404 from django.urls import reverse from core import helpers import core.tests.helpers @pytest.mark.parametrize('status_code,exception', ( (400, requests.exceptions.HTTPError), (404, Http404), (500, requests.exceptions.HTTPError), )) def test_handle_cms_response_error(status_code, exception): response = core.tests.helpers.create_response(status_code=status_code) with pytest.raises(exception): helpers.handle_cms_response(response) def test_handle_cms_response_ok(): response = core.tests.helpers.create_response( status_code=200, json_payload={'field': 'value'} ) assert helpers.handle_cms_response(response) == {'field': 'value'} @pytest.mark.parametrize('path,expect_code', ( ('/', None), ('?language=pt', 'pt'), ('/?language=ar', 'ar'), ('/industries?language=es', 'es'), ('/industries/?language=zh-hans', 'zh-hans'), ('/industries/aerospace?language=de', 'de'), ('/industries/automotive/?language=fr', 'fr'), ('?lang=fr', 'fr'), ('?language=de&lang=de', 'de'), ('?lang=pt&language=es', 'es') )) def test_get_language_from_querystring(path, expect_code, rf): url = reverse('index') request = rf.get(url + path) language_code = helpers.get_language_from_querystring(request) assert language_code == expect_code def test_company_parser_serialize_for_template(retrieve_profile_data): company = helpers.CompanyParser(retrieve_profile_data) assert company.serialize_for_template() == { 'address': '123 Fake Street, Fakeville, London, E14 6XK', 'address_line_1': '123 Fake Street', 'address_line_2': 'Fakeville', 'country': 'GB', 'date_of_creation': '02 March 2015', 'description': 'Ecommerce website', 'email_address': 'test@example.com', 'email_full_name': 'Jeremy', 'employees': '501-1,000', 'expertise_countries': '', 'expertise_industries': '', 'expertise_languages': '', 'expertise_products_services': {}, 'expertise_regions': '', 'facebook_url': 'http://www.facebook.com', 'has_expertise': False, 'keywords': 'word1, word2', 'linkedin_url': 'http://www.linkedin.com', 'locality': 'London', 'logo': 'nice.jpg', 'mobile_number': '07506043448', 'modified': '2016-11-23T11:21:10.977518Z', 'name': 'Great company', 'number': '01234567', 'po_box': 'abc', 'postal_code': 'E14 6XK', 'postal_full_name': 'Jeremy', 'sectors': 'Security', 'slug': 'great-company', 'summary': 'this is a short summary', 'supplier_case_studies': [], 'twitter_url': 'http://www.twitter.com', 'verified_with_code': True, 'website': 'http://example.com', 'company_type': 'COMPANIES_HOUSE', 'is_published_investment_support_directory': True, 'is_published_find_a_supplier': True, 'is_in_companies_house': True } def test_company_parser_serialize_for_template_empty(): company = helpers.CompanyParser({}) assert company.serialize_for_template() == {} def test_get_results_from_search_response_xss(retrieve_profile_data): response = core.tests.helpers.create_response(json_payload={ 'hits': { 'total': 1, 'hits': [ { '_source': retrieve_profile_data, 'highlight': { 'description': [ '<a onmouseover=javascript:func()>stuff</a>', 'to the max wolf.' ] } } ] } }) formatted = helpers.get_results_from_search_response(response) assert formatted['results'][0]['highlight'] == ( '<a onmouseover=javascript:func()>stuff</a>...to the max ' 'wolf.' ) def test_get_filters_labels(): filters = { 'expertise_languages': ['aa'], 'q': 'foo', 'page': 5, 'expertise_regions': ['NORTH_EAST'], 'expertise_products_services_financial': [expertise.FINANCIAL[1]], 'industries': [sectors.AEROSPACE, sectors.ADVANCED_MANUFACTURING], 'expertise_products_services_human_resources': [ 'Employment and talent research' ], } expected = [ 'Afar', 'North East', 'Insurance', 'Aerospace', 'Advanced manufacturing', 'Employment and talent research', ] assert helpers.get_filters_labels(filters) == expected

31.388158

78

0.607001

import pytest import requests from directory_constants import expertise, sectors from django.shortcuts import Http404 from django.urls import reverse from core import helpers import core.tests.helpers @pytest.mark.parametrize('status_code,exception', ( (400, requests.exceptions.HTTPError), (404, Http404), (500, requests.exceptions.HTTPError), )) def test_handle_cms_response_error(status_code, exception): response = core.tests.helpers.create_response(status_code=status_code) with pytest.raises(exception): helpers.handle_cms_response(response) def test_handle_cms_response_ok(): response = core.tests.helpers.create_response( status_code=200, json_payload={'field': 'value'} ) assert helpers.handle_cms_response(response) == {'field': 'value'} @pytest.mark.parametrize('path,expect_code', ( ('/', None), ('?language=pt', 'pt'), ('/?language=ar', 'ar'), ('/industries?language=es', 'es'), ('/industries/?language=zh-hans', 'zh-hans'), ('/industries/aerospace?language=de', 'de'), ('/industries/automotive/?language=fr', 'fr'), ('?lang=fr', 'fr'), ('?language=de&lang=de', 'de'), ('?lang=pt&language=es', 'es') )) def test_get_language_from_querystring(path, expect_code, rf): url = reverse('index') request = rf.get(url + path) language_code = helpers.get_language_from_querystring(request) assert language_code == expect_code def test_company_parser_serialize_for_template(retrieve_profile_data): company = helpers.CompanyParser(retrieve_profile_data) assert company.serialize_for_template() == { 'address': '123 Fake Street, Fakeville, London, E14 6XK', 'address_line_1': '123 Fake Street', 'address_line_2': 'Fakeville', 'country': 'GB', 'date_of_creation': '02 March 2015', 'description': 'Ecommerce website', 'email_address': 'test@example.com', 'email_full_name': 'Jeremy', 'employees': '501-1,000', 'expertise_countries': '', 'expertise_industries': '', 'expertise_languages': '', 'expertise_products_services': {}, 'expertise_regions': '', 'facebook_url': 'http://www.facebook.com', 'has_expertise': False, 'keywords': 'word1, word2', 'linkedin_url': 'http://www.linkedin.com', 'locality': 'London', 'logo': 'nice.jpg', 'mobile_number': '07506043448', 'modified': '2016-11-23T11:21:10.977518Z', 'name': 'Great company', 'number': '01234567', 'po_box': 'abc', 'postal_code': 'E14 6XK', 'postal_full_name': 'Jeremy', 'sectors': 'Security', 'slug': 'great-company', 'summary': 'this is a short summary', 'supplier_case_studies': [], 'twitter_url': 'http://www.twitter.com', 'verified_with_code': True, 'website': 'http://example.com', 'company_type': 'COMPANIES_HOUSE', 'is_published_investment_support_directory': True, 'is_published_find_a_supplier': True, 'is_in_companies_house': True } def test_company_parser_serialize_for_template_empty(): company = helpers.CompanyParser({}) assert company.serialize_for_template() == {} def test_get_results_from_search_response_xss(retrieve_profile_data): response = core.tests.helpers.create_response(json_payload={ 'hits': { 'total': 1, 'hits': [ { '_source': retrieve_profile_data, 'highlight': { 'description': [ '<a onmouseover=javascript:func()>stuff</a>', 'to the max wolf.' ] } } ] } }) formatted = helpers.get_results_from_search_response(response) assert formatted['results'][0]['highlight'] == ( '<a onmouseover=javascript:func()>stuff</a>...to the max ' 'wolf.' ) def test_get_filters_labels(): filters = { 'expertise_languages': ['aa'], 'q': 'foo', 'page': 5, 'expertise_regions': ['NORTH_EAST'], 'expertise_products_services_financial': [expertise.FINANCIAL[1]], 'industries': [sectors.AEROSPACE, sectors.ADVANCED_MANUFACTURING], 'expertise_products_services_human_resources': [ 'Employment and talent research' ], } expected = [ 'Afar', 'North East', 'Insurance', 'Aerospace', 'Advanced manufacturing', 'Employment and talent research', ] assert helpers.get_filters_labels(filters) == expected

true

79017dde051b610290823593c239642b85ff636f

6,965

py

Python

auction/models/bases.py

JohnRomanski/django-auction

bc6982c8f34a9a6914badb203424eca7f3219685

[ "MIT" ]

1

2021-02-04T21:48:53.000Z

auction/models/bases.py

JohnRomanski/django-auction

bc6982c8f34a9a6914badb203424eca7f3219685

[ "MIT" ]

null

auction/models/bases.py

JohnRomanski/django-auction

bc6982c8f34a9a6914badb203424eca7f3219685

[ "MIT" ]

null

from decimal import Decimal from django.db import models from polymorphic.models import PolymorphicModel from django.utils.translation import ugettext_lazy as _ from django.contrib.auth.models import User from django.contrib.contenttypes.models import ContentType from django.contrib.contenttypes.fields import GenericForeignKey from auction.utils.loader import get_model_string from django.conf import settings class CurrencyField(models.DecimalField): def to_python(self, value): try: return super(CurrencyField, self).to_python(value=value).quantize(Decimal("0.01")) except AttributeError: return None class BaseAuction(PolymorphicModel): name = models.CharField(max_length=255, verbose_name=_('Auction name')) slug = models.SlugField(unique=True, verbose_name=_('Slug')) start_date = models.DateTimeField(verbose_name=_('Start date')) end_date = models.DateTimeField(verbose_name=_('End date')) active = models.BooleanField(default=False, verbose_name=_('Active')) total_bids = models.IntegerField(default=0, verbose_name=_('Total bids')) date_added = models.DateTimeField(auto_now_add=True, verbose_name=_('Date added')) last_modified = models.DateTimeField(auto_now=True, verbose_name=_('Last modified')) class Meta: abstract = True app_label = 'auction' verbose_name = _('Auction') verbose_name_plural = _('Auctions') def __unicode__(self): return self.name class BaseBidBasket(models.Model): """ This models functions similarly to a shopping cart, except it expects a logged in user. """ user = models.OneToOneField(User, on_delete=models.CASCADE, related_name="%(app_label)s_%(class)s_related", verbose_name=_('User')) date_added = models.DateTimeField(auto_now_add=True, verbose_name=_('Date added')) last_modified = models.DateTimeField(auto_now=True, verbose_name=_('Last modified')) class Meta: abstract = True app_label = 'auction' verbose_name = _('Bid basket') verbose_name_plural = _('Bid baskets') def add_bid(self, lot, amount): from auction.models import BidItem self.save() if not lot.is_biddable: return False try: amount = Decimal(amount) except Exception as e: amount = Decimal('0') from auction.models.lot import Lot item,created = BidItem.objects.get_or_create(bid_basket=self, content_type=ContentType.objects.get_for_model(Lot), lot_id=lot.pk) if item: item.amount=amount item.save() return item def update_bid(self, bid_basket_item_id, amount): """ Update amount of bid. Delete bid if amount is 0. """ try: amount = Decimal(amount) except Exception as e: amount = Decimal('0') bid_basket_item = self.bids.get(pk=bid_basket_item_id) if not bid_basket_item.is_locked(): if amount == 0: bid_basket_item.delete() else: bid_basket_item.amount = amount bid_basket_item.save() self.save() return bid_basket_item def delete_bid(self, bid_basket_item_id): """ Delete a single item from bid basket. """ bid_basket_item = self.bids.get(pk=bid_basket_item_id) if not bid_basket_item.is_locked(): bid_basket_item.delete() return bid_basket_item def empty(self): """ Remove all bids from bid basket. """ if self.pk: bids = self.bids.all() for bid in bids: if not bid.is_locked(): bid.delete() @property def bids(self): """ Used as accessor for abstract related (BaseBidItem.bid_items). If you override BaseBidItem and use a label other than "auction" you will also need to set AUCTION_BIDBASKET_BIDS_RELATED_NAME. Example: foo_biditem_related (where your label is "foo" and your model is "BidItem") """ bids = getattr(settings, 'AUCTION_BIDBASKET_BIDS_RELATED_NAME', 'auction_biditem_related') return getattr(self, bids) @property def total_bids(self): """ Returns total bids in basket. """ return len(self.bids.all()) class BaseAuctionLot(PolymorphicModel): name = models.CharField(max_length=255, verbose_name=_('Lot name')) slug = models.SlugField(auto_created=True, verbose_name=_('Slug')) active = models.BooleanField(default=False, verbose_name=_('Active')) is_biddable = models.BooleanField(default=False, verbose_name=_('Is biddable?')) content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE, related_name="%(app_label)s_%(class)s_lots", verbose_name=_('Content type')) object_id = models.PositiveIntegerField(verbose_name=_('Object ID')) content_object = GenericForeignKey('content_type', 'object_id') date_added = models.DateTimeField(auto_now_add=True, verbose_name=_('Date added')) last_modified = models.DateTimeField(auto_now=True, verbose_name=_('Last modified')) class Meta: abstract = True app_label = 'auction' verbose_name = _('Auction lot') verbose_name_plural = _('Auction lots') def __unicode__(self): return self.name @property def is_locked(self): """ This property is meant to be overwritten with your own logic. Bid baskets check this method to find out if a bid can be manipulated. """ import auction.utils.generic now = auction.utils.generic.get_current_time() return self.content_object.end_date <= now class BaseBidItem(models.Model): """ This is a holder for total number of bids and a pointer to item being bid on. """ bid_basket = models.ForeignKey(get_model_string("BidBasket"), on_delete=models.CASCADE, related_name="%(app_label)s_%(class)s_related", verbose_name=_('Bid basket')) content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE, related_name="%(app_label)s_%(class)s_related", verbose_name=_('Content type')) lot_id = models.PositiveIntegerField(verbose_name=_('Lot ID')) lot_object = GenericForeignKey('content_type', 'lot_id') amount = CurrencyField(max_digits=10, decimal_places=2, null=True, blank=True, verbose_name=_('Amount')) class Meta: abstract = True app_label = 'auction' verbose_name = _('Bid item') verbose_name_plural = _('Bid items') def is_locked(self): return self.lot.is_locked @property def lot(self): return self.lot_object

36.657895

169

0.649103

from decimal import Decimal from django.db import models from polymorphic.models import PolymorphicModel from django.utils.translation import ugettext_lazy as _ from django.contrib.auth.models import User from django.contrib.contenttypes.models import ContentType from django.contrib.contenttypes.fields import GenericForeignKey from auction.utils.loader import get_model_string from django.conf import settings class CurrencyField(models.DecimalField): def to_python(self, value): try: return super(CurrencyField, self).to_python(value=value).quantize(Decimal("0.01")) except AttributeError: return None class BaseAuction(PolymorphicModel): name = models.CharField(max_length=255, verbose_name=_('Auction name')) slug = models.SlugField(unique=True, verbose_name=_('Slug')) start_date = models.DateTimeField(verbose_name=_('Start date')) end_date = models.DateTimeField(verbose_name=_('End date')) active = models.BooleanField(default=False, verbose_name=_('Active')) total_bids = models.IntegerField(default=0, verbose_name=_('Total bids')) date_added = models.DateTimeField(auto_now_add=True, verbose_name=_('Date added')) last_modified = models.DateTimeField(auto_now=True, verbose_name=_('Last modified')) class Meta: abstract = True app_label = 'auction' verbose_name = _('Auction') verbose_name_plural = _('Auctions') def __unicode__(self): return self.name class BaseBidBasket(models.Model): user = models.OneToOneField(User, on_delete=models.CASCADE, related_name="%(app_label)s_%(class)s_related", verbose_name=_('User')) date_added = models.DateTimeField(auto_now_add=True, verbose_name=_('Date added')) last_modified = models.DateTimeField(auto_now=True, verbose_name=_('Last modified')) class Meta: abstract = True app_label = 'auction' verbose_name = _('Bid basket') verbose_name_plural = _('Bid baskets') def add_bid(self, lot, amount): from auction.models import BidItem self.save() if not lot.is_biddable: return False try: amount = Decimal(amount) except Exception as e: amount = Decimal('0') from auction.models.lot import Lot item,created = BidItem.objects.get_or_create(bid_basket=self, content_type=ContentType.objects.get_for_model(Lot), lot_id=lot.pk) if item: item.amount=amount item.save() return item def update_bid(self, bid_basket_item_id, amount): try: amount = Decimal(amount) except Exception as e: amount = Decimal('0') bid_basket_item = self.bids.get(pk=bid_basket_item_id) if not bid_basket_item.is_locked(): if amount == 0: bid_basket_item.delete() else: bid_basket_item.amount = amount bid_basket_item.save() self.save() return bid_basket_item def delete_bid(self, bid_basket_item_id): bid_basket_item = self.bids.get(pk=bid_basket_item_id) if not bid_basket_item.is_locked(): bid_basket_item.delete() return bid_basket_item def empty(self): if self.pk: bids = self.bids.all() for bid in bids: if not bid.is_locked(): bid.delete() @property def bids(self): bids = getattr(settings, 'AUCTION_BIDBASKET_BIDS_RELATED_NAME', 'auction_biditem_related') return getattr(self, bids) @property def total_bids(self): return len(self.bids.all()) class BaseAuctionLot(PolymorphicModel): name = models.CharField(max_length=255, verbose_name=_('Lot name')) slug = models.SlugField(auto_created=True, verbose_name=_('Slug')) active = models.BooleanField(default=False, verbose_name=_('Active')) is_biddable = models.BooleanField(default=False, verbose_name=_('Is biddable?')) content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE, related_name="%(app_label)s_%(class)s_lots", verbose_name=_('Content type')) object_id = models.PositiveIntegerField(verbose_name=_('Object ID')) content_object = GenericForeignKey('content_type', 'object_id') date_added = models.DateTimeField(auto_now_add=True, verbose_name=_('Date added')) last_modified = models.DateTimeField(auto_now=True, verbose_name=_('Last modified')) class Meta: abstract = True app_label = 'auction' verbose_name = _('Auction lot') verbose_name_plural = _('Auction lots') def __unicode__(self): return self.name @property def is_locked(self): import auction.utils.generic now = auction.utils.generic.get_current_time() return self.content_object.end_date <= now class BaseBidItem(models.Model): bid_basket = models.ForeignKey(get_model_string("BidBasket"), on_delete=models.CASCADE, related_name="%(app_label)s_%(class)s_related", verbose_name=_('Bid basket')) content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE, related_name="%(app_label)s_%(class)s_related", verbose_name=_('Content type')) lot_id = models.PositiveIntegerField(verbose_name=_('Lot ID')) lot_object = GenericForeignKey('content_type', 'lot_id') amount = CurrencyField(max_digits=10, decimal_places=2, null=True, blank=True, verbose_name=_('Amount')) class Meta: abstract = True app_label = 'auction' verbose_name = _('Bid item') verbose_name_plural = _('Bid items') def is_locked(self): return self.lot.is_locked @property def lot(self): return self.lot_object

true

79017ec0b14fe9afce886b1930ce41463eeb6e58

2,871

py

Python

cvpods/engine/predictor.py

reinforcementdriving/cvpods

32d98b74745020be035a0e20337ad934201615c4

[ "Apache-2.0" ]

null

cvpods/engine/predictor.py

reinforcementdriving/cvpods

32d98b74745020be035a0e20337ad934201615c4

[ "Apache-2.0" ]

null

cvpods/engine/predictor.py

reinforcementdriving/cvpods

32d98b74745020be035a0e20337ad934201615c4

[ "Apache-2.0" ]

null

#!/usr/bin/python3 # -*- coding:utf-8 -*- from copy import deepcopy import torch from cvpods.checkpoint import DefaultCheckpointer from cvpods.data import build_transform_gens __all__ = ["DefaultPredictor"] class DefaultPredictor: """ Create a simple end-to-end predictor with the given config that runs on single device for a single input image. Compared to using the model directly, this class does the following additions: 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. 4. Take one input image and produce a single output, instead of a batch. If you'd like to do anything more fancy, please refer to its source code as examples to build and use the model manually. Attributes: metadata (Metadata): the metadata of the underlying dataset, obtained from cfg.DATASETS.TEST. Examples: .. code-block:: python pred = DefaultPredictor(cfg) inputs = cv2.imread("input.jpg") outputs = pred(inputs) """ def __init__(self, cfg, meta): self.cfg = deepcopy(cfg) if self.cfg.MODEL.DEVICE.startswith("cuda:"): torch.cuda.set_device(self.cfg.MODEL.DEVICE) self.cfg.MODEL.DEVICE = "cuda" self.model = cfg.build_model(self.cfg) self.model.eval() self.metadata = meta checkpointer = DefaultCheckpointer(self.model) checkpointer.load(cfg.MODEL.WEIGHTS) self.transform_gen = build_transform_gens(cfg.INPUT.AUG.TEST_PIPELINES) self.input_format = cfg.INPUT.FORMAT assert self.input_format in ["RGB", "BGR"], self.input_format def __call__(self, original_image): """ Args: original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). Returns: predictions (dict): the output of the model for one image only. See :doc:`/tutorials/models` for details about the format. """ with torch.no_grad( ): # https://github.com/sphinx-doc/sphinx/issues/4258 # Apply pre-processing to image. if self.input_format == "RGB": # whether the model expects BGR inputs or RGB original_image = original_image[:, :, ::-1] height, width = original_image.shape[:2] image = original_image for tfm_gen in self.transform_gen: image = tfm_gen.get_transform(image).apply_image(image) image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) inputs = {"image": image, "height": height, "width": width} predictions = self.model([inputs])[0] return predictions

35.012195

93

0.634622

from copy import deepcopy import torch from cvpods.checkpoint import DefaultCheckpointer from cvpods.data import build_transform_gens __all__ = ["DefaultPredictor"] class DefaultPredictor: def __init__(self, cfg, meta): self.cfg = deepcopy(cfg) if self.cfg.MODEL.DEVICE.startswith("cuda:"): torch.cuda.set_device(self.cfg.MODEL.DEVICE) self.cfg.MODEL.DEVICE = "cuda" self.model = cfg.build_model(self.cfg) self.model.eval() self.metadata = meta checkpointer = DefaultCheckpointer(self.model) checkpointer.load(cfg.MODEL.WEIGHTS) self.transform_gen = build_transform_gens(cfg.INPUT.AUG.TEST_PIPELINES) self.input_format = cfg.INPUT.FORMAT assert self.input_format in ["RGB", "BGR"], self.input_format def __call__(self, original_image): with torch.no_grad( ): if self.input_format == "RGB": original_image = original_image[:, :, ::-1] height, width = original_image.shape[:2] image = original_image for tfm_gen in self.transform_gen: image = tfm_gen.get_transform(image).apply_image(image) image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) inputs = {"image": image, "height": height, "width": width} predictions = self.model([inputs])[0] return predictions

true

7901805fc8cdf86e45d1ce43b59c72b49765ded2

16,435

py

Python

mezzanine/core/managers.py

abendig/mezzanine

3219ac9ba2d6d94ce63e8b2a747c3b264b13beec

[ "BSD-2-Clause" ]

null

mezzanine/core/managers.py

abendig/mezzanine

3219ac9ba2d6d94ce63e8b2a747c3b264b13beec

[ "BSD-2-Clause" ]

null

mezzanine/core/managers.py

abendig/mezzanine

3219ac9ba2d6d94ce63e8b2a747c3b264b13beec

[ "BSD-2-Clause" ]

null

from __future__ import unicode_literals from future.builtins import int, zip from functools import reduce from operator import ior, iand from string import punctuation from django.core.exceptions import ImproperlyConfigured from django.db.models import Manager, Q, CharField, TextField from django.db.models.loading import get_models from django.db.models.manager import ManagerDescriptor from django.db.models.query import QuerySet from django.contrib.sites.managers import CurrentSiteManager as DjangoCSM from django.utils.timezone import now from django.utils.translation import ugettext_lazy as _ from mezzanine.conf import settings from mezzanine.utils.models import get_model from mezzanine.utils.sites import current_site_id from mezzanine.utils.urls import home_slug class PublishedManager(Manager): """ Provides filter for restricting items returned by status and publish date when the given user is not a staff member. """ def published(self, for_user=None): """ For non-staff users, return items with a published status and whose publish and expiry dates fall before and after the current date when specified. """ from mezzanine.core.models import CONTENT_STATUS_PUBLISHED if for_user is not None and for_user.is_staff: return self.all() return self.filter( Q(publish_date__lte=now()) | Q(publish_date__isnull=True), Q(expiry_date__gte=now()) | Q(expiry_date__isnull=True), Q(status=CONTENT_STATUS_PUBLISHED)) def get_by_natural_key(self, slug): return self.get(slug=slug) def search_fields_to_dict(fields): """ In ``SearchableQuerySet`` and ``SearchableManager``, search fields can either be a sequence, or a dict of fields mapped to weights. This function converts sequences to a dict mapped to even weights, so that we're consistently dealing with a dict of fields mapped to weights, eg: ("title", "content") -> {"title": 1, "content": 1} """ if not fields: return {} try: int(list(dict(fields).values())[0]) except (TypeError, ValueError): fields = dict(zip(fields, [1] * len(fields))) return fields class SearchableQuerySet(QuerySet): """ QuerySet providing main search functionality for ``SearchableManager``. """ def __init__(self, *args, **kwargs): self._search_ordered = False self._search_terms = set() self._search_fields = kwargs.pop("search_fields", {}) super(SearchableQuerySet, self).__init__(*args, **kwargs) def search(self, query, search_fields=None): """ Build a queryset matching words in the given search query, treating quoted terms as exact phrases and taking into account + and - symbols as modifiers controlling which terms to require and exclude. """ # ### DETERMINE FIELDS TO SEARCH ### # Use search_fields arg if given, otherwise use search_fields # initially configured by the manager class. if search_fields: self._search_fields = search_fields_to_dict(search_fields) if not self._search_fields: return self.none() # ### BUILD LIST OF TERMS TO SEARCH FOR ### # Remove extra spaces, put modifiers inside quoted terms. terms = " ".join(query.split()).replace("+ ", "+") \ .replace('+"', '"+') \ .replace("- ", "-") \ .replace('-"', '"-') \ .split('"') # Strip punctuation other than modifiers from terms and create # terms list, first from quoted terms and then remaining words. terms = [("" if t[0:1] not in "+-" else t[0:1]) + t.strip(punctuation) for t in terms[1::2] + "".join(terms[::2]).split()] # Remove stop words from terms that aren't quoted or use # modifiers, since words with these are an explicit part of # the search query. If doing so ends up with an empty term # list, then keep the stop words. terms_no_stopwords = [t for t in terms if t.lower() not in settings.STOP_WORDS] get_positive_terms = lambda terms: [t.lower().strip(punctuation) for t in terms if t[0:1] != "-"] positive_terms = get_positive_terms(terms_no_stopwords) if positive_terms: terms = terms_no_stopwords else: positive_terms = get_positive_terms(terms) # Append positive terms (those without the negative modifier) # to the internal list for sorting when results are iterated. if not positive_terms: return self.none() else: self._search_terms.update(positive_terms) # ### BUILD QUERYSET FILTER ### # Create the queryset combining each set of terms. excluded = [reduce(iand, [~Q(**{"%s__icontains" % f: t[1:]}) for f in self._search_fields.keys()]) for t in terms if t[0:1] == "-"] required = [reduce(ior, [Q(**{"%s__icontains" % f: t[1:]}) for f in self._search_fields.keys()]) for t in terms if t[0:1] == "+"] optional = [reduce(ior, [Q(**{"%s__icontains" % f: t}) for f in self._search_fields.keys()]) for t in terms if t[0:1] not in "+-"] queryset = self if excluded: queryset = queryset.filter(reduce(iand, excluded)) if required: queryset = queryset.filter(reduce(iand, required)) # Optional terms aren't relevant to the filter if there are # terms that are explicitly required. elif optional: queryset = queryset.filter(reduce(ior, optional)) return queryset.distinct() def _clone(self, *args, **kwargs): """ Ensure attributes are copied to subsequent queries. """ for attr in ("_search_terms", "_search_fields", "_search_ordered"): kwargs[attr] = getattr(self, attr) return super(SearchableQuerySet, self)._clone(*args, **kwargs) def order_by(self, *field_names): """ Mark the filter as being ordered if search has occurred. """ if not self._search_ordered: self._search_ordered = len(self._search_terms) > 0 return super(SearchableQuerySet, self).order_by(*field_names) def iterator(self): """ If search has occurred and no ordering has occurred, decorate each result with the number of search terms so that it can be sorted by the number of occurrence of terms. In the case of search fields that span model relationships, we cannot accurately match occurrences without some very complicated traversal code, which we won't attempt. So in this case, namely when there are no matches for a result (count=0), and search fields contain relationships (double underscores), we assume one match for one of the fields, and use the average weight of all search fields with relationships. """ results = super(SearchableQuerySet, self).iterator() if self._search_terms and not self._search_ordered: results = list(results) for i, result in enumerate(results): count = 0 related_weights = [] for (field, weight) in self._search_fields.items(): if "__" in field: related_weights.append(weight) for term in self._search_terms: field_value = getattr(result, field, None) if field_value: count += field_value.lower().count(term) * weight if not count and related_weights: count = int(sum(related_weights) / len(related_weights)) results[i].result_count = count return iter(results) return results class SearchableManager(Manager): """ Manager providing a chainable queryset. Adapted from http://www.djangosnippets.org/snippets/562/ search method supports spanning across models that subclass the model being used to search. """ def __init__(self, *args, **kwargs): self._search_fields = kwargs.pop("search_fields", {}) super(SearchableManager, self).__init__(*args, **kwargs) def get_search_fields(self): """ Returns the search field names mapped to weights as a dict. Used in ``get_queryset`` below to tell ``SearchableQuerySet`` which search fields to use. Also used by ``DisplayableAdmin`` to populate Django admin's ``search_fields`` attribute. Search fields can be populated via ``SearchableManager.__init__``, which then get stored in ``SearchableManager._search_fields``, which serves as an approach for defining an explicit set of fields to be used. Alternatively and more commonly, ``search_fields`` can be defined on models themselves. In this case, we look at the model and all its base classes, and build up the search fields from all of those, so the search fields are implicitly built up from the inheritence chain. Finally if no search fields have been defined at all, we fall back to any fields that are ``CharField`` or ``TextField`` instances. """ search_fields = self._search_fields.copy() if not search_fields: for cls in reversed(self.model.__mro__): super_fields = getattr(cls, "search_fields", {}) search_fields.update(search_fields_to_dict(super_fields)) if not search_fields: search_fields = [] for f in self.model._meta.fields: if isinstance(f, (CharField, TextField)): search_fields.append(f.name) search_fields = search_fields_to_dict(search_fields) return search_fields def get_queryset(self): search_fields = self.get_search_fields() return SearchableQuerySet(self.model, search_fields=search_fields) def contribute_to_class(self, model, name): """ Django 1.5 explicitly prevents managers being accessed from abstract classes, which is behaviour the search API has relied on for years. Here we reinstate it. """ super(SearchableManager, self).contribute_to_class(model, name) setattr(model, name, ManagerDescriptor(self)) def search(self, *args, **kwargs): """ Proxy to queryset's search method for the manager's model and any models that subclass from this manager's model if the model is abstract. """ if not settings.SEARCH_MODEL_CHOICES: # No choices defined - build a list of leaf models (those # without subclasses) that inherit from Displayable. models = [m for m in get_models() if issubclass(m, self.model)] parents = reduce(ior, [m._meta.get_parent_list() for m in models]) models = [m for m in models if m not in parents] elif getattr(self.model._meta, "abstract", False): # When we're combining model subclasses for an abstract # model (eg Displayable), we only want to use models that # are represented by the ``SEARCH_MODEL_CHOICES`` setting. # Now this setting won't contain an exact list of models # we should use, since it can define superclass models such # as ``Page``, so we check the parent class list of each # model when determining whether a model falls within the # ``SEARCH_MODEL_CHOICES`` setting. search_choices = set() models = set() parents = set() errors = [] for name in settings.SEARCH_MODEL_CHOICES: try: model = get_model(*name.split(".", 1)) except LookupError: errors.append(name) else: search_choices.add(model) if errors: raise ImproperlyConfigured("Could not load the model(s) " "%s defined in the 'SEARCH_MODEL_CHOICES' setting." % ", ".join(errors)) for model in get_models(): # Model is actually a subclasses of what we're # searching (eg Displayabale) is_subclass = issubclass(model, self.model) # Model satisfies the search choices list - either # there are no search choices, model is directly in # search choices, or its parent is. this_parents = set(model._meta.get_parent_list()) in_choices = not search_choices or model in search_choices in_choices = in_choices or this_parents & search_choices if is_subclass and (in_choices or not search_choices): # Add to models we'll seach. Also maintain a parent # set, used below for further refinement of models # list to search. models.add(model) parents.update(this_parents) # Strip out any models that are superclasses of models, # specifically the Page model which will generally be the # superclass for all custom content types, since if we # query the Page model as well, we will get duplicate # results. models -= parents else: models = [self.model] all_results = [] user = kwargs.pop("for_user", None) for model in models: try: queryset = model.objects.published(for_user=user) except AttributeError: queryset = model.objects.get_queryset() all_results.extend(queryset.search(*args, **kwargs)) return sorted(all_results, key=lambda r: r.result_count, reverse=True) class CurrentSiteManager(DjangoCSM): """ Extends Django's site manager to first look up site by ID stored in the request, the session, then domain for the current request (accessible via threadlocals in ``mezzanine.core.request``), the environment variable ``MEZZANINE_SITE_ID`` (which can be used by management commands with the ``--site`` arg, finally falling back to ``settings.SITE_ID`` if none of those match a site. """ def __init__(self, field_name=None, *args, **kwargs): super(DjangoCSM, self).__init__(*args, **kwargs) self.__field_name = field_name self.__is_validated = False def get_queryset(self): if not self.__is_validated: try: # Django <= 1.6 self._validate_field_name() except AttributeError: # Django >= 1.7: will populate "self.__field_name". self._get_field_name() lookup = {self.__field_name + "__id__exact": current_site_id()} return super(DjangoCSM, self).get_queryset().filter(**lookup) class DisplayableManager(CurrentSiteManager, PublishedManager, SearchableManager): """ Manually combines ``CurrentSiteManager``, ``PublishedManager`` and ``SearchableManager`` for the ``Displayable`` model. """ def url_map(self, for_user=None, **kwargs): """ Returns a dictionary of urls mapped to Displayable subclass instances, including a fake homepage instance if none exists. Used in ``mezzanine.core.sitemaps``. """ home = self.model(title=_("Home")) setattr(home, "get_absolute_url", home_slug) items = {home.get_absolute_url(): home} for model in get_models(): if issubclass(model, self.model): for item in (model.objects.published(for_user=for_user) .filter(**kwargs) .exclude(slug__startswith="http://") .exclude(slug__startswith="https://")): items[item.get_absolute_url()] = item return items

43.364116

78

0.61369

from __future__ import unicode_literals from future.builtins import int, zip from functools import reduce from operator import ior, iand from string import punctuation from django.core.exceptions import ImproperlyConfigured from django.db.models import Manager, Q, CharField, TextField from django.db.models.loading import get_models from django.db.models.manager import ManagerDescriptor from django.db.models.query import QuerySet from django.contrib.sites.managers import CurrentSiteManager as DjangoCSM from django.utils.timezone import now from django.utils.translation import ugettext_lazy as _ from mezzanine.conf import settings from mezzanine.utils.models import get_model from mezzanine.utils.sites import current_site_id from mezzanine.utils.urls import home_slug class PublishedManager(Manager): def published(self, for_user=None): from mezzanine.core.models import CONTENT_STATUS_PUBLISHED if for_user is not None and for_user.is_staff: return self.all() return self.filter( Q(publish_date__lte=now()) | Q(publish_date__isnull=True), Q(expiry_date__gte=now()) | Q(expiry_date__isnull=True), Q(status=CONTENT_STATUS_PUBLISHED)) def get_by_natural_key(self, slug): return self.get(slug=slug) def search_fields_to_dict(fields): if not fields: return {} try: int(list(dict(fields).values())[0]) except (TypeError, ValueError): fields = dict(zip(fields, [1] * len(fields))) return fields class SearchableQuerySet(QuerySet): def __init__(self, *args, **kwargs): self._search_ordered = False self._search_terms = set() self._search_fields = kwargs.pop("search_fields", {}) super(SearchableQuerySet, self).__init__(*args, **kwargs) def search(self, query, search_fields=None): rch_fields) if not self._search_fields: return self.none() "', '"+') \ .replace("- ", "-") \ .replace('-"', '"-') \ .split('"') # Strip punctuation other than modifiers from terms and create # terms list, first from quoted terms and then remaining words. terms = [("" if t[0:1] not in "+-" else t[0:1]) + t.strip(punctuation) for t in terms[1::2] + "".join(terms[::2]).split()] # Remove stop words from terms that aren't quoted or use # modifiers, since words with these are an explicit part of # the search query. If doing so ends up with an empty term # list, then keep the stop words. terms_no_stopwords = [t for t in terms if t.lower() not in settings.STOP_WORDS] get_positive_terms = lambda terms: [t.lower().strip(punctuation) for t in terms if t[0:1] != "-"] positive_terms = get_positive_terms(terms_no_stopwords) if positive_terms: terms = terms_no_stopwords else: positive_terms = get_positive_terms(terms) # Append positive terms (those without the negative modifier) # to the internal list for sorting when results are iterated. if not positive_terms: return self.none() else: self._search_terms.update(positive_terms) # ### BUILD QUERYSET FILTER ### # Create the queryset combining each set of terms. excluded = [reduce(iand, [~Q(**{"%s__icontains" % f: t[1:]}) for f in self._search_fields.keys()]) for t in terms if t[0:1] == "-"] required = [reduce(ior, [Q(**{"%s__icontains" % f: t[1:]}) for f in self._search_fields.keys()]) for t in terms if t[0:1] == "+"] optional = [reduce(ior, [Q(**{"%s__icontains" % f: t}) for f in self._search_fields.keys()]) for t in terms if t[0:1] not in "+-"] queryset = self if excluded: queryset = queryset.filter(reduce(iand, excluded)) if required: queryset = queryset.filter(reduce(iand, required)) # Optional terms aren't relevant to the filter if there are # terms that are explicitly required. elif optional: queryset = queryset.filter(reduce(ior, optional)) return queryset.distinct() def _clone(self, *args, **kwargs): for attr in ("_search_terms", "_search_fields", "_search_ordered"): kwargs[attr] = getattr(self, attr) return super(SearchableQuerySet, self)._clone(*args, **kwargs) def order_by(self, *field_names): if not self._search_ordered: self._search_ordered = len(self._search_terms) > 0 return super(SearchableQuerySet, self).order_by(*field_names) def iterator(self): results = super(SearchableQuerySet, self).iterator() if self._search_terms and not self._search_ordered: results = list(results) for i, result in enumerate(results): count = 0 related_weights = [] for (field, weight) in self._search_fields.items(): if "__" in field: related_weights.append(weight) for term in self._search_terms: field_value = getattr(result, field, None) if field_value: count += field_value.lower().count(term) * weight if not count and related_weights: count = int(sum(related_weights) / len(related_weights)) results[i].result_count = count return iter(results) return results class SearchableManager(Manager): def __init__(self, *args, **kwargs): self._search_fields = kwargs.pop("search_fields", {}) super(SearchableManager, self).__init__(*args, **kwargs) def get_search_fields(self): search_fields = self._search_fields.copy() if not search_fields: for cls in reversed(self.model.__mro__): super_fields = getattr(cls, "search_fields", {}) search_fields.update(search_fields_to_dict(super_fields)) if not search_fields: search_fields = [] for f in self.model._meta.fields: if isinstance(f, (CharField, TextField)): search_fields.append(f.name) search_fields = search_fields_to_dict(search_fields) return search_fields def get_queryset(self): search_fields = self.get_search_fields() return SearchableQuerySet(self.model, search_fields=search_fields) def contribute_to_class(self, model, name): super(SearchableManager, self).contribute_to_class(model, name) setattr(model, name, ManagerDescriptor(self)) def search(self, *args, **kwargs): if not settings.SEARCH_MODEL_CHOICES: # No choices defined - build a list of leaf models (those # without subclasses) that inherit from Displayable. models = [m for m in get_models() if issubclass(m, self.model)] parents = reduce(ior, [m._meta.get_parent_list() for m in models]) models = [m for m in models if m not in parents] elif getattr(self.model._meta, "abstract", False): # When we're combining model subclasses for an abstract # model (eg Displayable), we only want to use models that # are represented by the ``SEARCH_MODEL_CHOICES`` setting. # Now this setting won't contain an exact list of models # we should use, since it can define superclass models such # as ``Page``, so we check the parent class list of each # model when determining whether a model falls within the # ``SEARCH_MODEL_CHOICES`` setting. search_choices = set() models = set() parents = set() errors = [] for name in settings.SEARCH_MODEL_CHOICES: try: model = get_model(*name.split(".", 1)) except LookupError: errors.append(name) else: search_choices.add(model) if errors: raise ImproperlyConfigured("Could not load the model(s) " "%s defined in the 'SEARCH_MODEL_CHOICES' setting." % ", ".join(errors)) for model in get_models(): # Model is actually a subclasses of what we're # searching (eg Displayabale) is_subclass = issubclass(model, self.model) # Model satisfies the search choices list - either # there are no search choices, model is directly in # search choices, or its parent is. this_parents = set(model._meta.get_parent_list()) in_choices = not search_choices or model in search_choices in_choices = in_choices or this_parents & search_choices if is_subclass and (in_choices or not search_choices): # Add to models we'll seach. Also maintain a parent # set, used below for further refinement of models # list to search. models.add(model) parents.update(this_parents) # Strip out any models that are superclasses of models, # specifically the Page model which will generally be the # superclass for all custom content types, since if we # query the Page model as well, we will get duplicate # results. models -= parents else: models = [self.model] all_results = [] user = kwargs.pop("for_user", None) for model in models: try: queryset = model.objects.published(for_user=user) except AttributeError: queryset = model.objects.get_queryset() all_results.extend(queryset.search(*args, **kwargs)) return sorted(all_results, key=lambda r: r.result_count, reverse=True) class CurrentSiteManager(DjangoCSM): def __init__(self, field_name=None, *args, **kwargs): super(DjangoCSM, self).__init__(*args, **kwargs) self.__field_name = field_name self.__is_validated = False def get_queryset(self): if not self.__is_validated: try: # Django <= 1.6 self._validate_field_name() except AttributeError: # Django >= 1.7: will populate "self.__field_name". self._get_field_name() lookup = {self.__field_name + "__id__exact": current_site_id()} return super(DjangoCSM, self).get_queryset().filter(**lookup) class DisplayableManager(CurrentSiteManager, PublishedManager, SearchableManager): def url_map(self, for_user=None, **kwargs): home = self.model(title=_("Home")) setattr(home, "get_absolute_url", home_slug) items = {home.get_absolute_url(): home} for model in get_models(): if issubclass(model, self.model): for item in (model.objects.published(for_user=for_user) .filter(**kwargs) .exclude(slug__startswith="http://") .exclude(slug__startswith="https://")): items[item.get_absolute_url()] = item return items

true

790180b6fa6ecd539778f977414d0a204e12f6bf

2,465

py

Python

tests/dashboard/widgets/test_reg_error_normality_widget.py

Tapot/evidently

ab9b91425d622566b663565508dd1c43e741f515

[ "Apache-2.0" ]

null

tests/dashboard/widgets/test_reg_error_normality_widget.py

Tapot/evidently

ab9b91425d622566b663565508dd1c43e741f515

[ "Apache-2.0" ]

null

tests/dashboard/widgets/test_reg_error_normality_widget.py

Tapot/evidently

ab9b91425d622566b663565508dd1c43e741f515

[ "Apache-2.0" ]

null

from typing import Optional import pandas as pd import pytest from evidently.analyzers.regression_performance_analyzer import RegressionPerformanceAnalyzer from evidently.model.widget import BaseWidgetInfo from evidently.options import OptionsProvider from evidently.pipeline.column_mapping import ColumnMapping from evidently.dashboard.widgets.reg_error_normality_widget import RegErrorNormalityWidget @pytest.fixture def widget() -> RegErrorNormalityWidget: options_provider = OptionsProvider() widget = RegErrorNormalityWidget("test_widget") widget.options_provider = options_provider return widget def test_reg_error_normality_widget_analyzer_list(widget: RegErrorNormalityWidget) -> None: assert widget.analyzers() == [RegressionPerformanceAnalyzer] @pytest.mark.parametrize( "reference_data, current_data, data_mapping, dataset, expected_result", ( ( pd.DataFrame({"target": [1, 2, 3, 4], "prediction": [1, 2, 3, 4]}), None, ColumnMapping(), None, BaseWidgetInfo(type="big_graph", title="test_widget", size=1), ), ( pd.DataFrame({"target": [1, 2, 3, 4], "prediction": [1, 2, 3, 4]}), pd.DataFrame({"target": [1, 2, 3, 4], "prediction": [1, 2, 3, 4]}), ColumnMapping(), "reference", BaseWidgetInfo(type="big_graph", title="test_widget", size=1), ), ), ) def test_reg_error_normality_widget_simple_case( widget: RegErrorNormalityWidget, reference_data: pd.DataFrame, current_data: pd.DataFrame, data_mapping: ColumnMapping, dataset: Optional[str], expected_result: BaseWidgetInfo, ) -> None: if dataset is not None: widget.dataset = dataset analyzer = RegressionPerformanceAnalyzer() analyzer.options_provider = widget.options_provider analyzer_results = analyzer.calculate(reference_data, current_data, data_mapping) result = widget.calculate( reference_data, current_data, data_mapping, {RegressionPerformanceAnalyzer: analyzer_results} ) if expected_result is not None: # we have some widget for visualization assert result.type == expected_result.type assert result.title == expected_result.title assert result.size == expected_result.size assert result.params is not None else: # no widget data, show nothing assert result is None

33.310811

101

0.696552

from typing import Optional import pandas as pd import pytest from evidently.analyzers.regression_performance_analyzer import RegressionPerformanceAnalyzer from evidently.model.widget import BaseWidgetInfo from evidently.options import OptionsProvider from evidently.pipeline.column_mapping import ColumnMapping from evidently.dashboard.widgets.reg_error_normality_widget import RegErrorNormalityWidget @pytest.fixture def widget() -> RegErrorNormalityWidget: options_provider = OptionsProvider() widget = RegErrorNormalityWidget("test_widget") widget.options_provider = options_provider return widget def test_reg_error_normality_widget_analyzer_list(widget: RegErrorNormalityWidget) -> None: assert widget.analyzers() == [RegressionPerformanceAnalyzer] @pytest.mark.parametrize( "reference_data, current_data, data_mapping, dataset, expected_result", ( ( pd.DataFrame({"target": [1, 2, 3, 4], "prediction": [1, 2, 3, 4]}), None, ColumnMapping(), None, BaseWidgetInfo(type="big_graph", title="test_widget", size=1), ), ( pd.DataFrame({"target": [1, 2, 3, 4], "prediction": [1, 2, 3, 4]}), pd.DataFrame({"target": [1, 2, 3, 4], "prediction": [1, 2, 3, 4]}), ColumnMapping(), "reference", BaseWidgetInfo(type="big_graph", title="test_widget", size=1), ), ), ) def test_reg_error_normality_widget_simple_case( widget: RegErrorNormalityWidget, reference_data: pd.DataFrame, current_data: pd.DataFrame, data_mapping: ColumnMapping, dataset: Optional[str], expected_result: BaseWidgetInfo, ) -> None: if dataset is not None: widget.dataset = dataset analyzer = RegressionPerformanceAnalyzer() analyzer.options_provider = widget.options_provider analyzer_results = analyzer.calculate(reference_data, current_data, data_mapping) result = widget.calculate( reference_data, current_data, data_mapping, {RegressionPerformanceAnalyzer: analyzer_results} ) if expected_result is not None: assert result.type == expected_result.type assert result.title == expected_result.title assert result.size == expected_result.size assert result.params is not None else: assert result is None

true

7901825d081d4734618c345f5991cc10b05e2e24

1,370

py

Python

Chapter13/listing13_7.py

hohsieh/osgeopy-code

932157c748c8fedb67d862b266a983fdd29ead56

[ "MIT" ]

160

2015-01-11T06:45:11.000Z

2022-03-07T15:09:57.000Z

Chapter13/listing13_7.py

sthagen/osgeopy-code

bc85f4ec7a630b53502ee491e400057b67cdab22

[ "MIT" ]

3

2018-09-29T11:34:13.000Z

2020-07-20T16:45:23.000Z

Chapter13/listing13_7.py

sthagen/osgeopy-code

bc85f4ec7a630b53502ee491e400057b67cdab22

[ "MIT" ]

108

2015-05-28T11:29:01.000Z

2022-02-12T12:01:46.000Z

# Script that uses meshgrid to get map coordinates and then plots # the DEM in 3d. import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from osgeo import gdal ds = gdal.Open(r'D:\osgeopy-data\Washington\dem\sthelens_utm.tif') band = ds.GetRasterBand(1) ov_band = band.GetOverview(band.GetOverviewCount() - 3) data = ov_band.ReadAsArray() # Calculate bounding coordinates. geotransform = ds.GetGeoTransform() minx = geotransform[0] maxy = geotransform[3] maxx = minx + ov_band.XSize * geotransform[1] miny = maxy + ov_band.YSize * geotransform[5] # Get the x and y arrays. x = np.arange(minx, maxx, geotransform[1]) y = np.arange(maxy, miny, geotransform[5]) x, y = np.meshgrid(x[:ov_band.XSize], y[:ov_band.YSize]) # Make the 3D plot. fig = plt.figure() ax = fig.gca(projection='3d') ax.plot_surface(x, y, data, cmap='gist_earth', lw=0) plt.axis('equal') # # Change the viewpoint and turn the ticks off. # ax.view_init(elev=55, azim=60) # plt.axis('off') # # Create an animation. # import matplotlib.animation as animation # def animate(i): # ax.view_init(elev=65, azim=i) # anim = animation.FuncAnimation( # fig, animate, frames=range(0, 360, 10), interval=100) # plt.axis('off') # # If you have FFmpeg and it's in your path, you can save the # # animation. # anim.save('d:/temp/helens.mp4', 'ffmpeg') plt.show()

27.4

66

0.713869

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from osgeo import gdal ds = gdal.Open(r'D:\osgeopy-data\Washington\dem\sthelens_utm.tif') band = ds.GetRasterBand(1) ov_band = band.GetOverview(band.GetOverviewCount() - 3) data = ov_band.ReadAsArray() geotransform = ds.GetGeoTransform() minx = geotransform[0] maxy = geotransform[3] maxx = minx + ov_band.XSize * geotransform[1] miny = maxy + ov_band.YSize * geotransform[5] x = np.arange(minx, maxx, geotransform[1]) y = np.arange(maxy, miny, geotransform[5]) x, y = np.meshgrid(x[:ov_band.XSize], y[:ov_band.YSize]) fig = plt.figure() ax = fig.gca(projection='3d') ax.plot_surface(x, y, data, cmap='gist_earth', lw=0) plt.axis('equal')

true

790182ffa231c5da4b7f2cb69babcff3bf2c1dc2

4,946

py

Python

ebl/fragmentarium/application/annotations_service.py

ElectronicBabylonianLiterature/dictionary

5977a57314cf57f94f75cd12520f178b1d6a6555

[ "MIT" ]

null

ebl/fragmentarium/application/annotations_service.py

ElectronicBabylonianLiterature/dictionary

5977a57314cf57f94f75cd12520f178b1d6a6555

[ "MIT" ]

null

ebl/fragmentarium/application/annotations_service.py

ElectronicBabylonianLiterature/dictionary

5977a57314cf57f94f75cd12520f178b1d6a6555

[ "MIT" ]

null

from io import BytesIO from typing import Tuple, Sequence import attr from PIL import Image from ebl.changelog import Changelog from ebl.ebl_ai_client import EblAiClient from ebl.files.application.file_repository import FileRepository from ebl.fragmentarium.application.annotations_repository import AnnotationsRepository from ebl.fragmentarium.application.annotations_schema import AnnotationsSchema from ebl.fragmentarium.application.cropped_sign_image import CroppedSign from ebl.fragmentarium.application.cropped_sign_images_repository import ( CroppedSignImage, CroppedSignImagesRepository, ) from ebl.fragmentarium.application.fragment_repository import FragmentRepository from ebl.fragmentarium.domain.annotation import ( Annotations, AnnotationValueType, ) from ebl.transliteration.domain.line_label import LineLabel from ebl.transliteration.domain.museum_number import MuseumNumber from ebl.users.domain.user import User @attr.attrs(auto_attribs=True, frozen=True) class AnnotationsService: _ebl_ai_client: EblAiClient _annotations_repository: AnnotationsRepository _photo_repository: FileRepository _changelog: Changelog _fragments_repository: FragmentRepository _photos_repository: FileRepository _cropped_sign_images_repository: CroppedSignImagesRepository def generate_annotations( self, number: MuseumNumber, threshold: float = 0.3 ) -> Annotations: fragment_image = self._photo_repository.query_by_file_name(f"{number}.jpg") return self._ebl_ai_client.generate_annotations( number, fragment_image, threshold ) def find(self, number: MuseumNumber) -> Annotations: return self._annotations_repository.query_by_museum_number(number) def _label_by_line_number( self, line_number_to_match: int, labels: Sequence[LineLabel] ) -> str: matching_label = None for label in labels: label_line_number = label.line_number if label_line_number and label_line_number.is_matching_number( line_number_to_match ): matching_label = label return matching_label.formatted_label if matching_label else "" def _cropped_image_from_annotations_helper( self, annotations: Annotations, image: Image.Image, script: str, labels: Sequence[LineLabel], ) -> Tuple[Annotations, Sequence[CroppedSignImage]]: cropped_sign_images = [] updated_cropped_annotations = [] for annotation in annotations.annotations: label = ( self._label_by_line_number(annotation.data.path[0], labels) if annotation.data.type != AnnotationValueType.BLANK else "" ) cropped_image = annotation.crop_image(image) cropped_sign_image = CroppedSignImage.create(cropped_image) cropped_sign_images.append(cropped_sign_image) updated_cropped_annotation = attr.evolve( annotation, cropped_sign=CroppedSign( cropped_sign_image.image_id, script, label, ), ) updated_cropped_annotations.append(updated_cropped_annotation) return ( attr.evolve(annotations, annotations=updated_cropped_annotations), cropped_sign_images, ) def _cropped_image_from_annotations( self, annotations: Annotations ) -> Tuple[Annotations, Sequence[CroppedSignImage]]: fragment = self._fragments_repository.query_by_museum_number( annotations.fragment_number ) fragment_image = self._photos_repository.query_by_file_name( f"{annotations.fragment_number}.jpg" ) image_bytes = fragment_image.read() image = Image.open(BytesIO(image_bytes), mode="r") return self._cropped_image_from_annotations_helper( annotations, image, fragment.script, fragment.text.labels ) def update(self, annotations: Annotations, user: User) -> Annotations: old_annotations = self._annotations_repository.query_by_museum_number( annotations.fragment_number ) _id = str(annotations.fragment_number) schema = AnnotationsSchema() ( annotations_with_image_ids, cropped_sign_images, ) = self._cropped_image_from_annotations(annotations) self._annotations_repository.create_or_update(annotations_with_image_ids) self._cropped_sign_images_repository.create_many(cropped_sign_images) self._changelog.create( "annotations", user.profile, {"_id": _id, **schema.dump(old_annotations)}, {"_id": _id, **schema.dump(annotations_with_image_ids)}, ) return annotations_with_image_ids

37.755725

86

0.697736

from io import BytesIO from typing import Tuple, Sequence import attr from PIL import Image from ebl.changelog import Changelog from ebl.ebl_ai_client import EblAiClient from ebl.files.application.file_repository import FileRepository from ebl.fragmentarium.application.annotations_repository import AnnotationsRepository from ebl.fragmentarium.application.annotations_schema import AnnotationsSchema from ebl.fragmentarium.application.cropped_sign_image import CroppedSign from ebl.fragmentarium.application.cropped_sign_images_repository import ( CroppedSignImage, CroppedSignImagesRepository, ) from ebl.fragmentarium.application.fragment_repository import FragmentRepository from ebl.fragmentarium.domain.annotation import ( Annotations, AnnotationValueType, ) from ebl.transliteration.domain.line_label import LineLabel from ebl.transliteration.domain.museum_number import MuseumNumber from ebl.users.domain.user import User @attr.attrs(auto_attribs=True, frozen=True) class AnnotationsService: _ebl_ai_client: EblAiClient _annotations_repository: AnnotationsRepository _photo_repository: FileRepository _changelog: Changelog _fragments_repository: FragmentRepository _photos_repository: FileRepository _cropped_sign_images_repository: CroppedSignImagesRepository def generate_annotations( self, number: MuseumNumber, threshold: float = 0.3 ) -> Annotations: fragment_image = self._photo_repository.query_by_file_name(f"{number}.jpg") return self._ebl_ai_client.generate_annotations( number, fragment_image, threshold ) def find(self, number: MuseumNumber) -> Annotations: return self._annotations_repository.query_by_museum_number(number) def _label_by_line_number( self, line_number_to_match: int, labels: Sequence[LineLabel] ) -> str: matching_label = None for label in labels: label_line_number = label.line_number if label_line_number and label_line_number.is_matching_number( line_number_to_match ): matching_label = label return matching_label.formatted_label if matching_label else "" def _cropped_image_from_annotations_helper( self, annotations: Annotations, image: Image.Image, script: str, labels: Sequence[LineLabel], ) -> Tuple[Annotations, Sequence[CroppedSignImage]]: cropped_sign_images = [] updated_cropped_annotations = [] for annotation in annotations.annotations: label = ( self._label_by_line_number(annotation.data.path[0], labels) if annotation.data.type != AnnotationValueType.BLANK else "" ) cropped_image = annotation.crop_image(image) cropped_sign_image = CroppedSignImage.create(cropped_image) cropped_sign_images.append(cropped_sign_image) updated_cropped_annotation = attr.evolve( annotation, cropped_sign=CroppedSign( cropped_sign_image.image_id, script, label, ), ) updated_cropped_annotations.append(updated_cropped_annotation) return ( attr.evolve(annotations, annotations=updated_cropped_annotations), cropped_sign_images, ) def _cropped_image_from_annotations( self, annotations: Annotations ) -> Tuple[Annotations, Sequence[CroppedSignImage]]: fragment = self._fragments_repository.query_by_museum_number( annotations.fragment_number ) fragment_image = self._photos_repository.query_by_file_name( f"{annotations.fragment_number}.jpg" ) image_bytes = fragment_image.read() image = Image.open(BytesIO(image_bytes), mode="r") return self._cropped_image_from_annotations_helper( annotations, image, fragment.script, fragment.text.labels ) def update(self, annotations: Annotations, user: User) -> Annotations: old_annotations = self._annotations_repository.query_by_museum_number( annotations.fragment_number ) _id = str(annotations.fragment_number) schema = AnnotationsSchema() ( annotations_with_image_ids, cropped_sign_images, ) = self._cropped_image_from_annotations(annotations) self._annotations_repository.create_or_update(annotations_with_image_ids) self._cropped_sign_images_repository.create_many(cropped_sign_images) self._changelog.create( "annotations", user.profile, {"_id": _id, **schema.dump(old_annotations)}, {"_id": _id, **schema.dump(annotations_with_image_ids)}, ) return annotations_with_image_ids

true

79018386380f0f5a1f9ccfd59456ae05b5b003cf

1,562

py

Python

procedures/points_B_ICG_Lozaano_Equation.py

k-cybulski/sigman-project

1f51e04dddb375eb58182664296b7b3f1db71756

[ "MIT" ]

1

2017-11-10T10:42:07.000Z

procedures/points_B_ICG_Lozaano_Equation.py

k-cybulski/sigman-project

1f51e04dddb375eb58182664296b7b3f1db71756

[ "MIT" ]

21

2017-12-28T13:39:55.000Z

2018-07-16T14:34:29.000Z

procedures/points_B_ICG_Lozaano_Equation.py

k-cybulski/sigman-project

1f51e04dddb375eb58182664296b7b3f1db71756

[ "MIT" ]

1

2018-02-25T13:57:50.000Z

import numpy as np from sigman.analyzer import InvalidArgumentError procedure_type = 'points' description = ( """Procedure calculate time of B point from equation: RB = 1.233RZ-0.0032RZ^2-31.59 where RZ - time between R and dz/dt max [ms] RB - time between R and B Equation was proposed by D.L. Lozano in paper "Where to B in dZ/dt" (2007) """) author = 'mzylinski' arguments = { } default_arguments = { } output_type = 'B' required_waves = ['Signal'] required_points = [ 'R','dzdtmax'] def procedure(waves, points, begin_time, end_time, settings): wave = waves['Signal'] R = points['R'] dzdtmax = points['dzdtmax'] r_x = [] r_y = [] for i in range(0,len(R)-1): data = wave.data_slice(R.data_x[i], R.data_x[i+1]) RZ = (dzdtmax.data_x[i] - R.data_x[i])/wave.sample_length RB = 1.233*RZ -0.0032*(RZ*RZ)-31.59 t = int(round(RB)) if (t<0): t = 0 r_y.append(data[t]) r_x.append(R.data_x[i] + t*wave.sample_length) return r_x, r_y def interpret_arguments(waves, points, arguments): output_arguments = {} for key, item in arguments.items(): try: output_arguments[key] = float(item) except: raise InvalidArgumentError("{} is invalid.".format(arguments[key])) return output_arguments def execute(waves, points, begin_time, end_time, arguments): arguments = interpret_arguments(waves, points, arguments) return procedure(waves, points, begin_time, end_time, arguments)

26.931034

79

0.630602

import numpy as np from sigman.analyzer import InvalidArgumentError procedure_type = 'points' description = ( """Procedure calculate time of B point from equation: RB = 1.233RZ-0.0032RZ^2-31.59 where RZ - time between R and dz/dt max [ms] RB - time between R and B Equation was proposed by D.L. Lozano in paper "Where to B in dZ/dt" (2007) """) author = 'mzylinski' arguments = { } default_arguments = { } output_type = 'B' required_waves = ['Signal'] required_points = [ 'R','dzdtmax'] def procedure(waves, points, begin_time, end_time, settings): wave = waves['Signal'] R = points['R'] dzdtmax = points['dzdtmax'] r_x = [] r_y = [] for i in range(0,len(R)-1): data = wave.data_slice(R.data_x[i], R.data_x[i+1]) RZ = (dzdtmax.data_x[i] - R.data_x[i])/wave.sample_length RB = 1.233*RZ -0.0032*(RZ*RZ)-31.59 t = int(round(RB)) if (t<0): t = 0 r_y.append(data[t]) r_x.append(R.data_x[i] + t*wave.sample_length) return r_x, r_y def interpret_arguments(waves, points, arguments): output_arguments = {} for key, item in arguments.items(): try: output_arguments[key] = float(item) except: raise InvalidArgumentError("{} is invalid.".format(arguments[key])) return output_arguments def execute(waves, points, begin_time, end_time, arguments): arguments = interpret_arguments(waves, points, arguments) return procedure(waves, points, begin_time, end_time, arguments)

true

790183bb62764197eb48efd6c0be97946020eba2

396

py

Python

kongoauth/wsgi.py

toast38coza/KongOAuth

827d6f0cb47c67903f0a0236f56cd20c18bb84bb

[ "MIT" ]

null

kongoauth/wsgi.py

toast38coza/KongOAuth

827d6f0cb47c67903f0a0236f56cd20c18bb84bb

[ "MIT" ]

3

2020-02-11T23:09:10.000Z

2021-06-10T18:21:30.000Z

kongoauth/wsgi.py

toast38coza/KongOAuth

827d6f0cb47c67903f0a0236f56cd20c18bb84bb

[ "MIT" ]

null

""" WSGI config for kongoauth project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.11/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "kongoauth.settings") application = get_wsgi_application()

23.294118

78

0.787879

import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "kongoauth.settings") application = get_wsgi_application()

true

7901840e1caccf4c39615aa05782447db4ea89d4

13,614

py

Python

tabular/src/autogluon/tabular/models/knn/knn_model.py

taesup-aws/autogluon

51b20c4a18de148b4f06b384e56b102c86727153

[ "Apache-2.0" ]

null

tabular/src/autogluon/tabular/models/knn/knn_model.py

taesup-aws/autogluon

51b20c4a18de148b4f06b384e56b102c86727153

[ "Apache-2.0" ]

null

tabular/src/autogluon/tabular/models/knn/knn_model.py

taesup-aws/autogluon

51b20c4a18de148b4f06b384e56b102c86727153

[ "Apache-2.0" ]

null

import logging import numpy as np import math import psutil import time from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_OBJECT, S_BOOL, S_TEXT_NGRAM, S_TEXT_SPECIAL, S_DATETIME_AS_INT from autogluon.core.constants import REGRESSION from autogluon.core.utils.exceptions import NotEnoughMemoryError from autogluon.core.models.abstract.model_trial import skip_hpo from autogluon.core.models import AbstractModel from autogluon.core.utils.utils import normalize_pred_probas logger = logging.getLogger(__name__) # TODO: Normalize data! class KNNModel(AbstractModel): """ KNearestNeighbors model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html """ def __init__(self, **kwargs): super().__init__(**kwargs) self._X_unused_index = None # Keeps track of unused training data indices, necessary for LOO OOF generation def _get_model_type(self): if self.params_aux.get('use_daal', True): try: # TODO: Add more granular switch, currently this affects all future KNN models even if they had `use_daal=False` from sklearnex import patch_sklearn patch_sklearn("knn_classifier") patch_sklearn("knn_regressor") # daal backend for KNN seems to be 20-40x+ faster than native sklearn with no downsides. logger.log(15, '\tUsing daal4py KNN backend...') except: pass try: from ._knn_loo_variants import KNeighborsClassifier, KNeighborsRegressor except: from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor logger.warning('WARNING: Leave-one-out variants of KNN failed to import. Falling back to standard KNN implementations.') if self.problem_type == REGRESSION: return KNeighborsRegressor else: return KNeighborsClassifier def _preprocess(self, X, **kwargs): X = super()._preprocess(X, **kwargs) X = X.fillna(0).to_numpy(dtype=np.float32) return X def _set_default_params(self): default_params = { 'weights': 'uniform', 'n_jobs': -1, } for param, val in default_params.items(): self._set_default_param_value(param, val) def _get_default_auxiliary_params(self) -> dict: default_auxiliary_params = super()._get_default_auxiliary_params() extra_auxiliary_params = dict( ignored_type_group_raw=[R_BOOL, R_CATEGORY, R_OBJECT], # TODO: Eventually use category features ignored_type_group_special=[S_BOOL, S_TEXT_NGRAM, S_TEXT_SPECIAL, S_DATETIME_AS_INT], ) default_auxiliary_params.update(extra_auxiliary_params) return default_auxiliary_params @classmethod def _get_default_ag_args(cls) -> dict: default_ag_args = super()._get_default_ag_args() extra_ag_args = {'valid_stacker': False} default_ag_args.update(extra_ag_args) return default_ag_args @classmethod def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: default_ag_args_ensemble = super()._get_default_ag_args_ensemble(**kwargs) extra_ag_args_ensemble = {'use_child_oof': True} default_ag_args_ensemble.update(extra_ag_args_ensemble) return default_ag_args_ensemble # TODO: Enable HPO for KNN def _get_default_searchspace(self): spaces = {} return spaces def _fit(self, X, y, time_limit=None, sample_weight=None, **kwargs): time_start = time.time() X = self.preprocess(X) self._validate_fit_memory_usage(X=X) # TODO: Can incorporate this into samples, can fit on portion of data to satisfy memory instead of raising exception immediately if sample_weight is not None: # TODO: support logger.log(15, "sample_weight not yet supported for KNNModel, this model will ignore them in training.") num_rows_max = len(X) # FIXME: v0.1 Must store final num rows for refit_full or else will use everything! Worst case refit_full could train far longer than the original model. if time_limit is None or num_rows_max <= 10000: self.model = self._get_model_type()(**self._get_model_params()).fit(X, y) else: self.model = self._fit_with_samples(X=X, y=y, time_limit=time_limit - (time.time() - time_start)) def _validate_fit_memory_usage(self, X): max_memory_usage_ratio = self.params_aux['max_memory_usage_ratio'] model_size_bytes = 4 * X.shape[0] * X.shape[1] # Assuming float32 types expected_final_model_size_bytes = model_size_bytes * 3.6 # Roughly what can be expected of the final KNN model in memory size if expected_final_model_size_bytes > 10000000: # Only worth checking if expected model size is >10MB available_mem = psutil.virtual_memory().available model_memory_ratio = expected_final_model_size_bytes / available_mem if model_memory_ratio > (0.15 * max_memory_usage_ratio): logger.warning(f'\tWarning: Model is expected to require {round(model_memory_ratio * 100, 2)}% of available memory...') if model_memory_ratio > (0.20 * max_memory_usage_ratio): raise NotEnoughMemoryError # don't train full model to avoid OOM error # TODO: Won't work for RAPIDS without modification # TODO: Technically isn't OOF, but can be used inplace of OOF. Perhaps rename to something more accurate? def get_oof_pred_proba(self, X, normalize=None, **kwargs): """X should be the same X passed to `.fit`""" y_oof_pred_proba = self._get_oof_pred_proba(X=X, **kwargs) if normalize is None: normalize = self.normalize_pred_probas if normalize: y_oof_pred_proba = normalize_pred_probas(y_oof_pred_proba, self.problem_type) y_oof_pred_proba = y_oof_pred_proba.astype(np.float32) return y_oof_pred_proba def _get_oof_pred_proba(self, X, **kwargs): if callable(getattr(self.model, "predict_proba_loo", None)): y_oof_pred_proba = self.model.predict_proba_loo() elif callable(getattr(self.model, "predict_loo", None)): y_oof_pred_proba = self.model.predict_loo() else: raise AssertionError(f'Model class {type(self.model)} does not support out-of-fold prediction generation.') y_oof_pred_proba = self._convert_proba_to_unified_form(y_oof_pred_proba) if X is not None and self._X_unused_index: X_unused = X.iloc[self._X_unused_index] y_pred_proba_new = self.predict_proba(X_unused) X_unused_index = set(self._X_unused_index) num_rows = len(X) X_used_index = [i for i in range(num_rows) if i not in X_unused_index] oof_pred_shape = y_oof_pred_proba.shape if len(oof_pred_shape) == 1: y_oof_tmp = np.zeros(num_rows, dtype=np.float32) y_oof_tmp[X_used_index] = y_oof_pred_proba y_oof_tmp[self._X_unused_index] = y_pred_proba_new else: y_oof_tmp = np.zeros((num_rows, oof_pred_shape[1]), dtype=np.float32) y_oof_tmp[X_used_index, :] = y_oof_pred_proba y_oof_tmp[self._X_unused_index, :] = y_pred_proba_new y_oof_pred_proba = y_oof_tmp return y_oof_pred_proba # TODO: Consider making this fully generic and available to all models def _fit_with_samples(self, X, y, time_limit, start_samples=10000, max_samples=None, sample_growth_factor=2, sample_time_growth_factor=8): """ Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used. X and y must already be preprocessed. Parameters ---------- X : np.ndarray The training data features (preprocessed). y : Series The training data ground truth labels. time_limit : float, default = None Time limit in seconds to adhere to when fitting model. start_samples : int, default = 10000 Number of samples to start with. This will be multiplied by sample_growth_factor after each model fit to determine the next number of samples. For example, if start_samples=10000, sample_growth_factor=2, then the number of samples per model fit would be [10000, 20000, 40000, 80000, ...] max_samples : int, default = None The maximum number of samples to use. If None or greater than the number of rows in X, then it is set equal to the number of rows in X. sample_growth_factor : float, default = 2 The rate of growth in sample size between each model fit. If 2, then the sample size doubles after each fit. sample_time_growth_factor : float, default = 8 The multiplier to the expected fit time of the next model. If `sample_time_growth_factor=8` and a model took 10 seconds to train, the next model fit will be expected to take 80 seconds. If an expected time is greater than the remaining time in `time_limit`, the model will not be trained and the method will return early. """ time_start = time.time() num_rows_samples = [] if max_samples is None: num_rows_max = len(X) else: num_rows_max = min(len(X), max_samples) num_rows_cur = start_samples while True: num_rows_cur = min(num_rows_cur, num_rows_max) num_rows_samples.append(num_rows_cur) if num_rows_cur == num_rows_max: break num_rows_cur *= sample_growth_factor num_rows_cur = math.ceil(num_rows_cur) if num_rows_cur * 1.5 >= num_rows_max: num_rows_cur = num_rows_max def sample_func(chunk, frac): # Guarantee at least 1 sample (otherwise log_loss would crash or model would return different column counts in pred_proba) n = max(math.ceil(len(chunk) * frac), 1) return chunk.sample(n=n, replace=False, random_state=0) if self.problem_type != REGRESSION: y_df = y.to_frame(name='label').reset_index(drop=True) else: y_df = None time_start_sample_loop = time.time() time_limit_left = time_limit - (time_start_sample_loop - time_start) model_type = self._get_model_type() idx = None for i, samples in enumerate(num_rows_samples): if samples != num_rows_max: if self.problem_type == REGRESSION: idx = np.random.choice(num_rows_max, size=samples, replace=False) else: idx = y_df.groupby('label', group_keys=False).apply(sample_func, frac=samples/num_rows_max).index X_samp = X[idx, :] y_samp = y.iloc[idx] else: X_samp = X y_samp = y idx = None self.model = model_type(**self._get_model_params()).fit(X_samp, y_samp) time_limit_left_prior = time_limit_left time_fit_end_sample = time.time() time_limit_left = time_limit - (time_fit_end_sample - time_start) time_fit_sample = time_limit_left_prior - time_limit_left time_required_for_next = time_fit_sample * sample_time_growth_factor logger.log(15, f'\t{round(time_fit_sample, 2)}s \t= Train Time (Using {samples}/{num_rows_max} rows) ({round(time_limit_left, 2)}s remaining time)') if time_required_for_next > time_limit_left and i != len(num_rows_samples) - 1: logger.log(20, f'\tNot enough time to train KNN model on all training rows. Fit {samples}/{num_rows_max} rows. (Training KNN model on {num_rows_samples[i+1]} rows is expected to take {round(time_required_for_next, 2)}s)') break if idx is not None: idx = set(idx) self._X_unused_index = [i for i in range(num_rows_max) if i not in idx] return self.model # TODO: Add HPO def _hyperparameter_tune(self, **kwargs): return skip_hpo(self, **kwargs) def _more_tags(self): return {'valid_oof': True} class FAISSModel(KNNModel): def _get_model_type(self): from .knn_utils import FAISSNeighborsClassifier, FAISSNeighborsRegressor if self.problem_type == REGRESSION: return FAISSNeighborsRegressor else: return FAISSNeighborsClassifier def _set_default_params(self): default_params = { 'index_factory_string': 'Flat', } for param, val in default_params.items(): self._set_default_param_value(param, val) super()._set_default_params() @classmethod def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: default_ag_args_ensemble = super()._get_default_ag_args_ensemble(**kwargs) extra_ag_args_ensemble = {'use_child_oof': False} default_ag_args_ensemble.update(extra_ag_args_ensemble) return default_ag_args_ensemble def _more_tags(self): return {'valid_oof': False}

48.106007

237

0.653518

import logging import numpy as np import math import psutil import time from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_OBJECT, S_BOOL, S_TEXT_NGRAM, S_TEXT_SPECIAL, S_DATETIME_AS_INT from autogluon.core.constants import REGRESSION from autogluon.core.utils.exceptions import NotEnoughMemoryError from autogluon.core.models.abstract.model_trial import skip_hpo from autogluon.core.models import AbstractModel from autogluon.core.utils.utils import normalize_pred_probas logger = logging.getLogger(__name__) class KNNModel(AbstractModel): def __init__(self, **kwargs): super().__init__(**kwargs) self._X_unused_index = None def _get_model_type(self): if self.params_aux.get('use_daal', True): try: from sklearnex import patch_sklearn patch_sklearn("knn_classifier") patch_sklearn("knn_regressor") logger.log(15, '\tUsing daal4py KNN backend...') except: pass try: from ._knn_loo_variants import KNeighborsClassifier, KNeighborsRegressor except: from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor logger.warning('WARNING: Leave-one-out variants of KNN failed to import. Falling back to standard KNN implementations.') if self.problem_type == REGRESSION: return KNeighborsRegressor else: return KNeighborsClassifier def _preprocess(self, X, **kwargs): X = super()._preprocess(X, **kwargs) X = X.fillna(0).to_numpy(dtype=np.float32) return X def _set_default_params(self): default_params = { 'weights': 'uniform', 'n_jobs': -1, } for param, val in default_params.items(): self._set_default_param_value(param, val) def _get_default_auxiliary_params(self) -> dict: default_auxiliary_params = super()._get_default_auxiliary_params() extra_auxiliary_params = dict( ignored_type_group_raw=[R_BOOL, R_CATEGORY, R_OBJECT], ignored_type_group_special=[S_BOOL, S_TEXT_NGRAM, S_TEXT_SPECIAL, S_DATETIME_AS_INT], ) default_auxiliary_params.update(extra_auxiliary_params) return default_auxiliary_params @classmethod def _get_default_ag_args(cls) -> dict: default_ag_args = super()._get_default_ag_args() extra_ag_args = {'valid_stacker': False} default_ag_args.update(extra_ag_args) return default_ag_args @classmethod def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: default_ag_args_ensemble = super()._get_default_ag_args_ensemble(**kwargs) extra_ag_args_ensemble = {'use_child_oof': True} default_ag_args_ensemble.update(extra_ag_args_ensemble) return default_ag_args_ensemble def _get_default_searchspace(self): spaces = {} return spaces def _fit(self, X, y, time_limit=None, sample_weight=None, **kwargs): time_start = time.time() X = self.preprocess(X) self._validate_fit_memory_usage(X=X) if sample_weight is not None: logger.log(15, "sample_weight not yet supported for KNNModel, this model will ignore them in training.") num_rows_max = len(X) if time_limit is None or num_rows_max <= 10000: self.model = self._get_model_type()(**self._get_model_params()).fit(X, y) else: self.model = self._fit_with_samples(X=X, y=y, time_limit=time_limit - (time.time() - time_start)) def _validate_fit_memory_usage(self, X): max_memory_usage_ratio = self.params_aux['max_memory_usage_ratio'] model_size_bytes = 4 * X.shape[0] * X.shape[1] expected_final_model_size_bytes = model_size_bytes * 3.6 if expected_final_model_size_bytes > 10000000: available_mem = psutil.virtual_memory().available model_memory_ratio = expected_final_model_size_bytes / available_mem if model_memory_ratio > (0.15 * max_memory_usage_ratio): logger.warning(f'\tWarning: Model is expected to require {round(model_memory_ratio * 100, 2)}% of available memory...') if model_memory_ratio > (0.20 * max_memory_usage_ratio): raise NotEnoughMemoryError # TODO: Won't work for RAPIDS without modification def get_oof_pred_proba(self, X, normalize=None, **kwargs): y_oof_pred_proba = self._get_oof_pred_proba(X=X, **kwargs) if normalize is None: normalize = self.normalize_pred_probas if normalize: y_oof_pred_proba = normalize_pred_probas(y_oof_pred_proba, self.problem_type) y_oof_pred_proba = y_oof_pred_proba.astype(np.float32) return y_oof_pred_proba def _get_oof_pred_proba(self, X, **kwargs): if callable(getattr(self.model, "predict_proba_loo", None)): y_oof_pred_proba = self.model.predict_proba_loo() elif callable(getattr(self.model, "predict_loo", None)): y_oof_pred_proba = self.model.predict_loo() else: raise AssertionError(f'Model class {type(self.model)} does not support out-of-fold prediction generation.') y_oof_pred_proba = self._convert_proba_to_unified_form(y_oof_pred_proba) if X is not None and self._X_unused_index: X_unused = X.iloc[self._X_unused_index] y_pred_proba_new = self.predict_proba(X_unused) X_unused_index = set(self._X_unused_index) num_rows = len(X) X_used_index = [i for i in range(num_rows) if i not in X_unused_index] oof_pred_shape = y_oof_pred_proba.shape if len(oof_pred_shape) == 1: y_oof_tmp = np.zeros(num_rows, dtype=np.float32) y_oof_tmp[X_used_index] = y_oof_pred_proba y_oof_tmp[self._X_unused_index] = y_pred_proba_new else: y_oof_tmp = np.zeros((num_rows, oof_pred_shape[1]), dtype=np.float32) y_oof_tmp[X_used_index, :] = y_oof_pred_proba y_oof_tmp[self._X_unused_index, :] = y_pred_proba_new y_oof_pred_proba = y_oof_tmp return y_oof_pred_proba # TODO: Consider making this fully generic and available to all models def _fit_with_samples(self, X, y, time_limit, start_samples=10000, max_samples=None, sample_growth_factor=2, sample_time_growth_factor=8): time_start = time.time() num_rows_samples = [] if max_samples is None: num_rows_max = len(X) else: num_rows_max = min(len(X), max_samples) num_rows_cur = start_samples while True: num_rows_cur = min(num_rows_cur, num_rows_max) num_rows_samples.append(num_rows_cur) if num_rows_cur == num_rows_max: break num_rows_cur *= sample_growth_factor num_rows_cur = math.ceil(num_rows_cur) if num_rows_cur * 1.5 >= num_rows_max: num_rows_cur = num_rows_max def sample_func(chunk, frac): # Guarantee at least 1 sample (otherwise log_loss would crash or model would return different column counts in pred_proba) n = max(math.ceil(len(chunk) * frac), 1) return chunk.sample(n=n, replace=False, random_state=0) if self.problem_type != REGRESSION: y_df = y.to_frame(name='label').reset_index(drop=True) else: y_df = None time_start_sample_loop = time.time() time_limit_left = time_limit - (time_start_sample_loop - time_start) model_type = self._get_model_type() idx = None for i, samples in enumerate(num_rows_samples): if samples != num_rows_max: if self.problem_type == REGRESSION: idx = np.random.choice(num_rows_max, size=samples, replace=False) else: idx = y_df.groupby('label', group_keys=False).apply(sample_func, frac=samples/num_rows_max).index X_samp = X[idx, :] y_samp = y.iloc[idx] else: X_samp = X y_samp = y idx = None self.model = model_type(**self._get_model_params()).fit(X_samp, y_samp) time_limit_left_prior = time_limit_left time_fit_end_sample = time.time() time_limit_left = time_limit - (time_fit_end_sample - time_start) time_fit_sample = time_limit_left_prior - time_limit_left time_required_for_next = time_fit_sample * sample_time_growth_factor logger.log(15, f'\t{round(time_fit_sample, 2)}s \t= Train Time (Using {samples}/{num_rows_max} rows) ({round(time_limit_left, 2)}s remaining time)') if time_required_for_next > time_limit_left and i != len(num_rows_samples) - 1: logger.log(20, f'\tNot enough time to train KNN model on all training rows. Fit {samples}/{num_rows_max} rows. (Training KNN model on {num_rows_samples[i+1]} rows is expected to take {round(time_required_for_next, 2)}s)') break if idx is not None: idx = set(idx) self._X_unused_index = [i for i in range(num_rows_max) if i not in idx] return self.model # TODO: Add HPO def _hyperparameter_tune(self, **kwargs): return skip_hpo(self, **kwargs) def _more_tags(self): return {'valid_oof': True} class FAISSModel(KNNModel): def _get_model_type(self): from .knn_utils import FAISSNeighborsClassifier, FAISSNeighborsRegressor if self.problem_type == REGRESSION: return FAISSNeighborsRegressor else: return FAISSNeighborsClassifier def _set_default_params(self): default_params = { 'index_factory_string': 'Flat', } for param, val in default_params.items(): self._set_default_param_value(param, val) super()._set_default_params() @classmethod def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: default_ag_args_ensemble = super()._get_default_ag_args_ensemble(**kwargs) extra_ag_args_ensemble = {'use_child_oof': False} default_ag_args_ensemble.update(extra_ag_args_ensemble) return default_ag_args_ensemble def _more_tags(self): return {'valid_oof': False}

true

7901846bb4048b9e0097cb7fd952f59be6683ece

1,750

py

Python

tests/test_cache.py

gvigneron/webapp2_caffeine

1c920e77b48555886dff5206cc5e83179f23c8f1

[ "Apache-2.0" ]

null

tests/test_cache.py

gvigneron/webapp2_caffeine

1c920e77b48555886dff5206cc5e83179f23c8f1

[ "Apache-2.0" ]

null

tests/test_cache.py

gvigneron/webapp2_caffeine

1c920e77b48555886dff5206cc5e83179f23c8f1

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from datetime import datetime import time import unittest from webapp2_caffeine.cache import CacheContainer from webapp2_caffeine.cache import flush class DummyCache(CacheContainer): key = 'dummy_cache' @property def fresh_value(self): return datetime.now() class CacheContainerTest(unittest.TestCase): def setUp(self): flush() def tearDown(self): flush() def test_fresh_value(self): container = CacheContainer() with self.assertRaises(NotImplementedError): container.fresh_value def test_set(self): container = CacheContainer() with self.assertRaises(ValueError): container.set('my value') container = DummyCache() value, expiration = container.set('my value') self.assertEqual(value, 'my value') self.assertTrue(21000 < expiration - time.time() < 21600) self.assertEqual(container.get(), 'my value') def test_get(self): container = DummyCache() self.assertEqual(container.get(), None) container.set('my value', 1000) self.assertEqual(container.get(), None) container.set('my value') self.assertEqual(container.get(), 'my value') def test_delete(self): container = DummyCache() container.set('my value') container.delete() self.assertEqual(container.get(), None) def test_update(self): container = DummyCache() container.update() self.assertTrue(container.get()) def test_value(self): container = DummyCache() old_value = container.value self.assertTrue(old_value) self.assertTrue(container.value, old_value)

26.119403

65

0.641143

from datetime import datetime import time import unittest from webapp2_caffeine.cache import CacheContainer from webapp2_caffeine.cache import flush class DummyCache(CacheContainer): key = 'dummy_cache' @property def fresh_value(self): return datetime.now() class CacheContainerTest(unittest.TestCase): def setUp(self): flush() def tearDown(self): flush() def test_fresh_value(self): container = CacheContainer() with self.assertRaises(NotImplementedError): container.fresh_value def test_set(self): container = CacheContainer() with self.assertRaises(ValueError): container.set('my value') container = DummyCache() value, expiration = container.set('my value') self.assertEqual(value, 'my value') self.assertTrue(21000 < expiration - time.time() < 21600) self.assertEqual(container.get(), 'my value') def test_get(self): container = DummyCache() self.assertEqual(container.get(), None) container.set('my value', 1000) self.assertEqual(container.get(), None) container.set('my value') self.assertEqual(container.get(), 'my value') def test_delete(self): container = DummyCache() container.set('my value') container.delete() self.assertEqual(container.get(), None) def test_update(self): container = DummyCache() container.update() self.assertTrue(container.get()) def test_value(self): container = DummyCache() old_value = container.value self.assertTrue(old_value) self.assertTrue(container.value, old_value)

true

790184f2ee82e1be0871186debe15bfcb841f23a

3,103

py

Python

app/service/socketservice.py

mohansd/cyx-xElec-server

bef67274ba85d6172ac1ef4dd3df8c8ce86c6c61

[ "Apache-2.0" ]

null

app/service/socketservice.py

mohansd/cyx-xElec-server

bef67274ba85d6172ac1ef4dd3df8c8ce86c6c61

[ "Apache-2.0" ]

null

app/service/socketservice.py

mohansd/cyx-xElec-server

bef67274ba85d6172ac1ef4dd3df8c8ce86c6c61

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from app.libs.utils import data_decode import socket, socketserver, threading import traceback class ThreadedTCPRequestHandler(socketserver.BaseRequestHandler): ip = "" port = 0 timeOut = 100 def __init__(self, request, client_address, server): from app.service.device import Device self.socket = None self.addr = None self.cloud_id = None self.device = Device() self.sign = None self.device_id = None self.timestamp = None super().__init__(request, client_address, server) def setup(self): self.ip = self.client_address[0].strip() self.port = self.client_address[1] self.request.settimeout(self.timeOut) self.addr = self.ip + str(self.port) self.socket = self.request print(self.ip) def handle(self): try: while True: try: # time.sleep(1) data = self.request.recv(1024) except socket.timeout: print(self.ip + ":" + str(self.port) + "接收超时") break if data: data = data_decode(data) self.device.parse_data(data, self) else: break except Exception as e: with open("err_log.log", "a+") as f: f.write(traceback.format_exc()+'\r\r') print(self.client_address, "连接断开") finally: self.request.close() def finish(self): if self.cloud_id is None: print(self.ip + ":" + str(self.port) + "断开连接！") else: get_instance().remove_client(self.cloud_id) print(self.ip + ":" + str(self.port) + self.cloud_id + "断开连接！") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): pass class TCPServer: instance = None @staticmethod def get_instance(): print("start") if TCPServer.instance is None: TCPServer.instance = TCPServer() return TCPServer.instance def __init__(self): self.clients = {} self.server = None try: self.server = ThreadedTCPServer(("0.0.0.0", 5002), ThreadedTCPRequestHandler) server_thread = threading.Thread(target=self.server.serve_forever) server_thread.daemon = True server_thread.start() # server_thread.join() except (KeyboardInterrupt, SystemExit, Exception) as e: print(e) print("end") self.server.shutdown() self.server.close() def add_client(self, cloud, sock): self.clients[cloud] = sock print("this is clients", self.clients) def remove_client(self, cloud): if cloud in self.clients: print("删除设备" + cloud) from app.service.device import Device Device.offline_alarm(self.clients[cloud]) self.clients.pop(cloud) def get_instance(): return TCPServer.get_instance()

30.126214

89

0.565259

from app.libs.utils import data_decode import socket, socketserver, threading import traceback class ThreadedTCPRequestHandler(socketserver.BaseRequestHandler): ip = "" port = 0 timeOut = 100 def __init__(self, request, client_address, server): from app.service.device import Device self.socket = None self.addr = None self.cloud_id = None self.device = Device() self.sign = None self.device_id = None self.timestamp = None super().__init__(request, client_address, server) def setup(self): self.ip = self.client_address[0].strip() self.port = self.client_address[1] self.request.settimeout(self.timeOut) self.addr = self.ip + str(self.port) self.socket = self.request print(self.ip) def handle(self): try: while True: try: data = self.request.recv(1024) except socket.timeout: print(self.ip + ":" + str(self.port) + "接收超时") break if data: data = data_decode(data) self.device.parse_data(data, self) else: break except Exception as e: with open("err_log.log", "a+") as f: f.write(traceback.format_exc()+'\r\r') print(self.client_address, "连接断开") finally: self.request.close() def finish(self): if self.cloud_id is None: print(self.ip + ":" + str(self.port) + "断开连接！") else: get_instance().remove_client(self.cloud_id) print(self.ip + ":" + str(self.port) + self.cloud_id + "断开连接！") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): pass class TCPServer: instance = None @staticmethod def get_instance(): print("start") if TCPServer.instance is None: TCPServer.instance = TCPServer() return TCPServer.instance def __init__(self): self.clients = {} self.server = None try: self.server = ThreadedTCPServer(("0.0.0.0", 5002), ThreadedTCPRequestHandler) server_thread = threading.Thread(target=self.server.serve_forever) server_thread.daemon = True server_thread.start() except (KeyboardInterrupt, SystemExit, Exception) as e: print(e) print("end") self.server.shutdown() self.server.close() def add_client(self, cloud, sock): self.clients[cloud] = sock print("this is clients", self.clients) def remove_client(self, cloud): if cloud in self.clients: print("删除设备" + cloud) from app.service.device import Device Device.offline_alarm(self.clients[cloud]) self.clients.pop(cloud) def get_instance(): return TCPServer.get_instance()

true

79018509c992165fc5ca9150cd98660c06939562

651

py

Python

app/Meetup/Filter.py

benjifs/site_bot

9d342d39e927e4f0b175ccb186c3f8c997bd8d35

[ "MIT" ]

1

2019-10-27T13:13:12.000Z

app/Meetup/Filter.py

benjifs/site_bot

9d342d39e927e4f0b175ccb186c3f8c997bd8d35

[ "MIT" ]

10

2019-10-02T12:34:03.000Z

2020-10-28T00:19:20.000Z

app/Meetup/Filter.py

OpenTwinCities/site_bot

a6e5d056462bed1559eed8232e4d1c0e6323e3c4

[ "MIT" ]

2

2019-10-06T23:12:49.000Z

2019-10-22T23:22:09.000Z

# -*- coding: utf8 -*- def filter_event(event, happening_before): """Check if the following keys are present. These keys only show up when using the API. If fetching from the iCal, JSON, or RSS feeds it will just compare the dates """ status = True visibility = True actions = True if 'status' in event: status = event['status'] == 'upcoming' if 'visibility' in event: visibility = event['visibility'] == 'public' if 'self' in event: actions = 'announce' not in event['self']['actions'] return (status and visibility and actions and event['time'] < happening_before)

28.304348

60

0.6298

def filter_event(event, happening_before): status = True visibility = True actions = True if 'status' in event: status = event['status'] == 'upcoming' if 'visibility' in event: visibility = event['visibility'] == 'public' if 'self' in event: actions = 'announce' not in event['self']['actions'] return (status and visibility and actions and event['time'] < happening_before)

true

79018541666340e37da7601f2619ebf06e3d9707

26,877

py

Python

keras/initializers/initializers_v2.py

winnerineast/keras

1e94c43d7ba0d7b6b629b2300e40470f495bdbe0

[ "Apache-2.0" ]

null

keras/initializers/initializers_v2.py

winnerineast/keras

1e94c43d7ba0d7b6b629b2300e40470f495bdbe0

[ "Apache-2.0" ]

null

keras/initializers/initializers_v2.py

winnerineast/keras

1e94c43d7ba0d7b6b629b2300e40470f495bdbe0

[ "Apache-2.0" ]

null

# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Keras initializers for TF 2. """ # pylint: disable=g-classes-have-attributes from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from keras import backend from tensorflow.python.ops import init_ops_v2 from tensorflow.python.util.tf_export import keras_export @keras_export('keras.initializers.Initializer') class Initializer(object): """Initializer base class: all Keras initializers inherit from this class. Initializers should implement a `__call__` method with the following signature: ```python def __call__(self, shape, dtype=None, **kwargs): # returns a tensor of shape `shape` and dtype `dtype` # containing values drawn from a distribution of your choice. ``` Optionally, you an also implement the method `get_config` and the class method `from_config` in order to support serialization -- just like with any Keras object. Here's a simple example: a random normal initializer. ```python import tensorflow as tf class ExampleRandomNormal(tf.keras.initializers.Initializer): def __init__(self, mean, stddev): self.mean = mean self.stddev = stddev def __call__(self, shape, dtype=None, **kwargs): return tf.random.normal( shape, mean=self.mean, stddev=self.stddev, dtype=dtype) def get_config(self): # To support serialization return {"mean": self.mean, "stddev": self.stddev} ``` Note that we don't have to implement `from_config` in the example above since the constructor arguments of the class the keys in the config returned by `get_config` are the same. In this case, the default `from_config` works fine. """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized as specified by the initializer. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. **kwargs: Additional keyword arguments. """ raise NotImplementedError def get_config(self): """Returns the configuration of the initializer as a JSON-serializable dict. Returns: A JSON-serializable Python dict. """ return {} @classmethod def from_config(cls, config): """Instantiates an initializer from a configuration dictionary. Example: ```python initializer = RandomUniform(-1, 1) config = initializer.get_config() initializer = RandomUniform.from_config(config) ``` Args: config: A Python dictionary, the output of `get_config`. Returns: A `tf.keras.initializers.Initializer` instance. """ config.pop('dtype', None) return cls(**config) @keras_export('keras.initializers.Zeros', 'keras.initializers.zeros', v1=[]) class Zeros(tf.zeros_initializer, Initializer): """Initializer that generates tensors initialized to 0. Also available via the shortcut function `tf.keras.initializers.zeros`. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.Zeros() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.Zeros() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized as specified by the initializer. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. Only numeric or boolean dtypes are supported. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`). **kwargs: Additional keyword arguments. """ return super(Zeros, self).__call__(shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.Ones', 'keras.initializers.ones', v1=[]) class Ones(tf.ones_initializer, Initializer): """Initializer that generates tensors initialized to 1. Also available via the shortcut function `tf.keras.initializers.ones`. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.Ones() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.Ones() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized as specified by the initializer. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. Only numeric or boolean dtypes are supported. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`). **kwargs: Additional keyword arguments. """ return super(Ones, self).__call__(shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.Constant', 'keras.initializers.constant', v1=[]) class Constant(Initializer): """Initializer that generates tensors with constant values. Also available via the shortcut function `tf.keras.initializers.constant`. Only scalar values are allowed. The constant value provided must be convertible to the dtype requested when calling the initializer. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.Constant(3.) >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.Constant(3.) >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: value: A Python scalar. """ def __init__(self, value=0): self.value = value def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized to `self.value`. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`). **kwargs: Additional keyword arguments. """ del kwargs return tf.constant( self.value, dtype=_get_dtype(dtype), shape=shape) def get_config(self): return {'value': self.value} @keras_export('keras.initializers.RandomUniform', 'keras.initializers.random_uniform', v1=[]) class RandomUniform(tf.random_uniform_initializer, Initializer): """Initializer that generates tensors with a uniform distribution. Also available via the shortcut function `tf.keras.initializers.random_uniform`. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.RandomUniform(minval=0., maxval=1.) >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.RandomUniform(minval=0., maxval=1.) >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: minval: A python scalar or a scalar tensor. Lower bound of the range of random values to generate (inclusive). maxval: A python scalar or a scalar tensor. Upper bound of the range of random values to generate (exclusive). seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized as specified by the initializer. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. Only floating point and integer types are supported. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`). **kwargs: Additional keyword arguments. """ return super(RandomUniform, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.RandomNormal', 'keras.initializers.random_normal', v1=[]) class RandomNormal(tf.random_normal_initializer, Initializer): """Initializer that generates tensors with a normal distribution. Also available via the shortcut function `tf.keras.initializers.random_normal`. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.) >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.) >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: mean: a python scalar or a scalar tensor. Mean of the random values to generate. stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate. seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized to random normal values. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`) **kwargs: Additional keyword arguments. """ return super(RandomNormal, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.TruncatedNormal', 'keras.initializers.truncated_normal', v1=[]) class TruncatedNormal(init_ops_v2.TruncatedNormal, Initializer): """Initializer that generates a truncated normal distribution. Also available via the shortcut function `tf.keras.initializers.truncated_normal`. The values generated are similar to values from a `tf.keras.initializers.RandomNormal` initializer except that values more than two standard deviations from the mean are discarded and re-drawn. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.) >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.) >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: mean: a python scalar or a scalar tensor. Mean of the random values to generate. stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate. seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized to random normal values (truncated). Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`) **kwargs: Additional keyword arguments. """ return super(TruncatedNormal, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.VarianceScaling', 'keras.initializers.variance_scaling', v1=[]) class VarianceScaling(init_ops_v2.VarianceScaling, Initializer): """Initializer capable of adapting its scale to the shape of weights tensors. Also available via the shortcut function `tf.keras.initializers.variance_scaling`. With `distribution="truncated_normal" or "untruncated_normal"`, samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) `stddev = sqrt(scale / n)`, where `n` is: - number of input units in the weight tensor, if `mode="fan_in"` - number of output units, if `mode="fan_out"` - average of the numbers of input and output units, if `mode="fan_avg"` With `distribution="uniform"`, samples are drawn from a uniform distribution within `[-limit, limit]`, where `limit = sqrt(3 * scale / n)`. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.VarianceScaling( ... scale=0.1, mode='fan_in', distribution='uniform') >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.VarianceScaling( ... scale=0.1, mode='fan_in', distribution='uniform') >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: scale: Scaling factor (positive float). mode: One of "fan_in", "fan_out", "fan_avg". distribution: Random distribution to use. One of "truncated_normal", "untruncated_normal" and "uniform". seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized as specified by the initializer. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`) **kwargs: Additional keyword arguments. """ return super(VarianceScaling, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.Orthogonal', 'keras.initializers.orthogonal', v1=[]) class Orthogonal(init_ops_v2.Orthogonal, Initializer): """Initializer that generates an orthogonal matrix. Also available via the shortcut function `tf.keras.initializers.orthogonal`. If the shape of the tensor to initialize is two-dimensional, it is initialized with an orthogonal matrix obtained from the QR decomposition of a matrix of random numbers drawn from a normal distribution. If the matrix has fewer rows than columns then the output will have orthogonal rows. Otherwise, the output will have orthogonal columns. If the shape of the tensor to initialize is more than two-dimensional, a matrix of shape `(shape[0] * ... * shape[n - 2], shape[n - 1])` is initialized, where `n` is the length of the shape vector. The matrix is subsequently reshaped to give a tensor of the desired shape. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.Orthogonal() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.Orthogonal() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: gain: multiplicative factor to apply to the orthogonal matrix seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. References: [Saxe et al., 2014](https://openreview.net/forum?id=_wzZwKpTDF_9C) ([pdf](https://arxiv.org/pdf/1312.6120.pdf)) """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized to an orthogonal matrix. Args: shape: Shape of the tensor. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`) **kwargs: Additional keyword arguments. """ return super(Orthogonal, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.Identity', 'keras.initializers.identity', v1=[]) class Identity(init_ops_v2.Identity, Initializer): """Initializer that generates the identity matrix. Also available via the shortcut function `tf.keras.initializers.identity`. Only usable for generating 2D matrices. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.Identity() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.Identity() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: gain: Multiplicative factor to apply to the identity matrix. """ def __call__(self, shape, dtype=None, **kwargs): """Returns a tensor object initialized to a 2D identity matrix. Args: shape: Shape of the tensor. It should have exactly rank 2. dtype: Optional dtype of the tensor. Only floating point types are supported. If not specified, `tf.keras.backend.floatx()` is used, which default to `float32` unless you configured it otherwise (via `tf.keras.backend.set_floatx(float_dtype)`) **kwargs: Additional keyword arguments. """ return super(Identity, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.GlorotUniform', 'keras.initializers.glorot_uniform', v1=[]) class GlorotUniform(VarianceScaling): """The Glorot uniform initializer, also called Xavier uniform initializer. Also available via the shortcut function `tf.keras.initializers.glorot_uniform`. Draws samples from a uniform distribution within `[-limit, limit]`, where `limit = sqrt(6 / (fan_in + fan_out))` (`fan_in` is the number of input units in the weight tensor and `fan_out` is the number of output units). Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.GlorotUniform() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.GlorotUniform() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. References: [Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html) ([pdf](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)) """ def __init__(self, seed=None): super(GlorotUniform, self).__init__( scale=1.0, mode='fan_avg', distribution='uniform', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.GlorotNormal', 'keras.initializers.glorot_normal', v1=[]) class GlorotNormal(VarianceScaling): """The Glorot normal initializer, also called Xavier normal initializer. Also available via the shortcut function `tf.keras.initializers.glorot_normal`. Draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(2 / (fan_in + fan_out))` where `fan_in` is the number of input units in the weight tensor and `fan_out` is the number of output units in the weight tensor. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.GlorotNormal() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.GlorotNormal() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Args: seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. References: [Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html) ([pdf](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)) """ def __init__(self, seed=None): super(GlorotNormal, self).__init__( scale=1.0, mode='fan_avg', distribution='truncated_normal', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.LecunNormal', 'keras.initializers.lecun_normal', v1=[]) class LecunNormal(VarianceScaling): """Lecun normal initializer. Also available via the shortcut function `tf.keras.initializers.lecun_normal`. Initializers allow you to pre-specify an initialization strategy, encoded in the Initializer object, without knowing the shape and dtype of the variable being initialized. Draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(1 / fan_in)` where `fan_in` is the number of input units in the weight tensor. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.LecunNormal() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.LecunNormal() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Arguments: seed: A Python integer. Used to seed the random generator. References: - Self-Normalizing Neural Networks, [Klambauer et al., 2017] (https://papers.nips.cc/paper/6698-self-normalizing-neural-networks) ([pdf] (https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf)) - Efficient Backprop, [Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) """ def __init__(self, seed=None): super(LecunNormal, self).__init__( scale=1., mode='fan_in', distribution='truncated_normal', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.LecunUniform', 'keras.initializers.lecun_uniform', v1=[]) class LecunUniform(VarianceScaling): """Lecun uniform initializer. Also available via the shortcut function `tf.keras.initializers.lecun_uniform`. Draws samples from a uniform distribution within `[-limit, limit]`, where `limit = sqrt(3 / fan_in)` (`fan_in` is the number of input units in the weight tensor). Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.LecunUniform() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.LecunUniform() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Arguments: seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. References: - Self-Normalizing Neural Networks, [Klambauer et al., 2017](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks) # pylint: disable=line-too-long ([pdf](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf)) - Efficient Backprop, [Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) """ def __init__(self, seed=None): super(LecunUniform, self).__init__( scale=1., mode='fan_in', distribution='uniform', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.HeNormal', 'keras.initializers.he_normal', v1=[]) class HeNormal(VarianceScaling): """He normal initializer. Also available via the shortcut function `tf.keras.initializers.he_normal`. It draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(2 / fan_in)` where `fan_in` is the number of input units in the weight tensor. Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.HeNormal() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.HeNormal() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Arguments: seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. References: [He et al., 2015](https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html) # pylint: disable=line-too-long ([pdf](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)) """ def __init__(self, seed=None): super(HeNormal, self).__init__( scale=2., mode='fan_in', distribution='truncated_normal', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.HeUniform', 'keras.initializers.he_uniform', v1=[]) class HeUniform(VarianceScaling): """He uniform variance scaling initializer. Also available via the shortcut function `tf.keras.initializers.he_uniform`. Draws samples from a uniform distribution within `[-limit, limit]`, where `limit = sqrt(6 / fan_in)` (`fan_in` is the number of input units in the weight tensor). Examples: >>> # Standalone usage: >>> initializer = tf.keras.initializers.HeUniform() >>> values = initializer(shape=(2, 2)) >>> # Usage in a Keras layer: >>> initializer = tf.keras.initializers.HeUniform() >>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer) Arguments: seed: A Python integer. An initializer created with a given seed will always produce the same random tensor for a given shape and dtype. References: [He et al., 2015](https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html) # pylint: disable=line-too-long ([pdf](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)) """ def __init__(self, seed=None): super(HeUniform, self).__init__( scale=2., mode='fan_in', distribution='uniform', seed=seed) def get_config(self): return {'seed': self.seed} def _get_dtype(dtype): if dtype is None: dtype = backend.floatx() return tf.as_dtype(dtype)

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from keras import backend from tensorflow.python.ops import init_ops_v2 from tensorflow.python.util.tf_export import keras_export @keras_export('keras.initializers.Initializer') class Initializer(object): def __call__(self, shape, dtype=None, **kwargs): raise NotImplementedError def get_config(self): return {} @classmethod def from_config(cls, config): config.pop('dtype', None) return cls(**config) @keras_export('keras.initializers.Zeros', 'keras.initializers.zeros', v1=[]) class Zeros(tf.zeros_initializer, Initializer): def __call__(self, shape, dtype=None, **kwargs): return super(Zeros, self).__call__(shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.Ones', 'keras.initializers.ones', v1=[]) class Ones(tf.ones_initializer, Initializer): def __call__(self, shape, dtype=None, **kwargs): return super(Ones, self).__call__(shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.Constant', 'keras.initializers.constant', v1=[]) class Constant(Initializer): def __init__(self, value=0): self.value = value def __call__(self, shape, dtype=None, **kwargs): del kwargs return tf.constant( self.value, dtype=_get_dtype(dtype), shape=shape) def get_config(self): return {'value': self.value} @keras_export('keras.initializers.RandomUniform', 'keras.initializers.random_uniform', v1=[]) class RandomUniform(tf.random_uniform_initializer, Initializer): def __call__(self, shape, dtype=None, **kwargs): return super(RandomUniform, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.RandomNormal', 'keras.initializers.random_normal', v1=[]) class RandomNormal(tf.random_normal_initializer, Initializer): def __call__(self, shape, dtype=None, **kwargs): return super(RandomNormal, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.TruncatedNormal', 'keras.initializers.truncated_normal', v1=[]) class TruncatedNormal(init_ops_v2.TruncatedNormal, Initializer): def __call__(self, shape, dtype=None, **kwargs): return super(TruncatedNormal, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.VarianceScaling', 'keras.initializers.variance_scaling', v1=[]) class VarianceScaling(init_ops_v2.VarianceScaling, Initializer): def __call__(self, shape, dtype=None, **kwargs): return super(VarianceScaling, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.Orthogonal', 'keras.initializers.orthogonal', v1=[]) class Orthogonal(init_ops_v2.Orthogonal, Initializer): def __call__(self, shape, dtype=None, **kwargs): return super(Orthogonal, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.Identity', 'keras.initializers.identity', v1=[]) class Identity(init_ops_v2.Identity, Initializer): def __call__(self, shape, dtype=None, **kwargs): return super(Identity, self).__call__( shape, dtype=_get_dtype(dtype), **kwargs) @keras_export('keras.initializers.GlorotUniform', 'keras.initializers.glorot_uniform', v1=[]) class GlorotUniform(VarianceScaling): def __init__(self, seed=None): super(GlorotUniform, self).__init__( scale=1.0, mode='fan_avg', distribution='uniform', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.GlorotNormal', 'keras.initializers.glorot_normal', v1=[]) class GlorotNormal(VarianceScaling): def __init__(self, seed=None): super(GlorotNormal, self).__init__( scale=1.0, mode='fan_avg', distribution='truncated_normal', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.LecunNormal', 'keras.initializers.lecun_normal', v1=[]) class LecunNormal(VarianceScaling): def __init__(self, seed=None): super(LecunNormal, self).__init__( scale=1., mode='fan_in', distribution='truncated_normal', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.LecunUniform', 'keras.initializers.lecun_uniform', v1=[]) class LecunUniform(VarianceScaling): def __init__(self, seed=None): super(LecunUniform, self).__init__( scale=1., mode='fan_in', distribution='uniform', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.HeNormal', 'keras.initializers.he_normal', v1=[]) class HeNormal(VarianceScaling): def __init__(self, seed=None): super(HeNormal, self).__init__( scale=2., mode='fan_in', distribution='truncated_normal', seed=seed) def get_config(self): return {'seed': self.seed} @keras_export('keras.initializers.HeUniform', 'keras.initializers.he_uniform', v1=[]) class HeUniform(VarianceScaling): def __init__(self, seed=None): super(HeUniform, self).__init__( scale=2., mode='fan_in', distribution='uniform', seed=seed) def get_config(self): return {'seed': self.seed} def _get_dtype(dtype): if dtype is None: dtype = backend.floatx() return tf.as_dtype(dtype)

true

790187d3fe7399dfc406b03a12c16e7f62d46c93

9,607

py

Python

tools/c7n_mailer/c7n_mailer/cli.py

blade2005/cloud-custodian

21ecdd60ae8a78887cf9d135367b283ce88b0fd9

[ "Apache-2.0" ]

null

tools/c7n_mailer/c7n_mailer/cli.py

blade2005/cloud-custodian

21ecdd60ae8a78887cf9d135367b283ce88b0fd9

[ "Apache-2.0" ]

79

2019-03-20T12:27:06.000Z

2019-08-14T14:07:04.000Z

tools/c7n_mailer/c7n_mailer/cli.py

blade2005/cloud-custodian

21ecdd60ae8a78887cf9d135367b283ce88b0fd9

[ "Apache-2.0" ]

2

2019-04-22T15:20:23.000Z

2019-08-27T12:37:51.000Z

from __future__ import absolute_import, division, print_function, unicode_literals import argparse import functools import logging from os import path import boto3 import jsonschema from c7n_mailer import deploy, utils from c7n_mailer.azure_mailer.azure_queue_processor import MailerAzureQueueProcessor from c7n_mailer.azure_mailer import deploy as azure_deploy from c7n_mailer.sqs_queue_processor import MailerSqsQueueProcessor from c7n_mailer.utils import get_provider, Providers from ruamel import yaml AZURE_KV_SECRET_SCHEMA = { 'type': 'object', 'properties': { 'type': {'enum': ['azure.keyvault']}, 'secret': {'type': 'string'} }, 'required': ['type', 'secret'], 'additionalProperties': False } SECURED_STRING_SCHEMA = { 'oneOf': [ {'type': 'string'}, AZURE_KV_SECRET_SCHEMA ] } CONFIG_SCHEMA = { 'type': 'object', 'additionalProperties': False, 'required': ['queue_url'], 'properties': { 'queue_url': {'type': 'string'}, 'from_address': {'type': 'string'}, 'contact_tags': {'type': 'array', 'items': {'type': 'string'}}, 'org_domain': {'type': 'string'}, # Standard Lambda Function Config 'region': {'type': 'string'}, 'role': {'type': 'string'}, 'runtime': {'type': 'string'}, 'memory': {'type': 'integer'}, 'timeout': {'type': 'integer'}, 'subnets': {'type': 'array', 'items': {'type': 'string'}}, 'security_groups': {'type': 'array', 'items': {'type': 'string'}}, 'dead_letter_config': {'type': 'object'}, 'lambda_name': {'type': 'string'}, 'lambda_description': {'type': 'string'}, 'lambda_tags': {'type': 'object'}, 'lambda_schedule': {'type': 'string'}, # Azure Function Config 'function_properties': { 'type': 'object', 'appInsights': { 'type': 'object', 'oneOf': [ {'type': 'string'}, {'type': 'object', 'properties': { 'name': 'string', 'location': 'string', 'resourceGroupName': 'string'} } ] }, 'storageAccount': { 'type': 'object', 'oneOf': [ {'type': 'string'}, {'type': 'object', 'properties': { 'name': 'string', 'location': 'string', 'resourceGroupName': 'string'} } ] }, 'servicePlan': { 'type': 'object', 'oneOf': [ {'type': 'string'}, {'type': 'object', 'properties': { 'name': 'string', 'location': 'string', 'resourceGroupName': 'string', 'skuTier': 'string', 'skuName': 'string'} } ] }, }, 'function_schedule': {'type': 'string'}, 'function_skuCode': {'type': 'string'}, 'function_sku': {'type': 'string'}, # Mailer Infrastructure Config 'cache_engine': {'type': 'string'}, 'smtp_server': {'type': 'string'}, 'smtp_port': {'type': 'integer'}, 'smtp_ssl': {'type': 'boolean'}, 'smtp_username': {'type': 'string'}, 'smtp_password': SECURED_STRING_SCHEMA, 'ldap_email_key': {'type': 'string'}, 'ldap_uid_tags': {'type': 'array', 'items': {'type': 'string'}}, 'debug': {'type': 'boolean'}, 'ldap_uid_regex': {'type': 'string'}, 'ldap_uri': {'type': 'string'}, 'ldap_bind_dn': {'type': 'string'}, 'ldap_bind_user': {'type': 'string'}, 'ldap_uid_attribute': {'type': 'string'}, 'ldap_manager_attribute': {'type': 'string'}, 'ldap_email_attribute': {'type': 'string'}, 'ldap_bind_password_in_kms': {'type': 'boolean'}, 'ldap_bind_password': {'type': 'string'}, 'cross_accounts': {'type': 'object'}, 'ses_region': {'type': 'string'}, 'redis_host': {'type': 'string'}, 'redis_port': {'type': 'integer'}, 'datadog_api_key': {'type': 'string'}, # TODO: encrypt with KMS? 'datadog_application_key': {'type': 'string'}, # TODO: encrypt with KMS? 'slack_token': {'type': 'string'}, 'slack_webhook': {'type': 'string'}, 'sendgrid_api_key': SECURED_STRING_SCHEMA, 'splunk_hec_url': {'type': 'string'}, 'splunk_hec_token': {'type': 'string'}, 'splunk_remove_paths': { 'type': 'array', 'items': {'type': 'string'} }, 'splunk_actions_list': {'type': 'boolean'}, 'splunk_max_attempts': {'type': 'integer'}, 'splunk_hec_max_length': {'type': 'integer'}, # SDK Config 'profile': {'type': 'string'}, 'http_proxy': {'type': 'string'}, 'https_proxy': {'type': 'string'}, # Mapping account / emails 'account_emails': {'type': 'object'} } } def session_factory(mailer_config): return boto3.Session( region_name=mailer_config['region'], profile_name=mailer_config.get('profile', None)) def get_logger(debug=False): log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(level=logging.INFO, format=log_format) logging.getLogger('botocore').setLevel(logging.WARNING) if debug: logging.getLogger('botocore').setLevel(logging.DEBUG) debug_logger = logging.getLogger('custodian-mailer') debug_logger.setLevel(logging.DEBUG) return debug_logger else: return logging.getLogger('custodian-mailer') def get_and_validate_mailer_config(args): with open(args.config) as fh: config = yaml.load(fh.read(), Loader=yaml.SafeLoader) jsonschema.validate(config, CONFIG_SCHEMA) utils.setup_defaults(config) return config def get_c7n_mailer_parser(): parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', required=True, help='mailer.yml config file') debug_help_msg = 'sets c7n_mailer logger to debug, for maximum output (the default is INFO)' parser.add_argument('--debug', action='store_true', help=debug_help_msg) max_num_processes_help_msg = 'will run the mailer in parallel, integer of max processes allowed' parser.add_argument('--max-num-processes', type=int, help=max_num_processes_help_msg) templates_folder_help_msg = 'message templates folder location' parser.add_argument('-t', '--templates', help=templates_folder_help_msg) group = parser.add_mutually_exclusive_group(required=True) update_lambda_help_msg = 'packages your c7n_mailer, uploads the zip to aws lambda as a function' group.add_argument('--update-lambda', action='store_true', help=update_lambda_help_msg) run_help_msg = 'run c7n-mailer locally, process sqs messages and send emails or sns messages' group.add_argument('--run', action='store_true', help=run_help_msg) return parser def run_mailer_in_parallel(processor, max_num_processes): max_num_processes = int(max_num_processes) if max_num_processes < 1: raise Exception processor.max_num_processes = max_num_processes processor.run(parallel=True) def main(): parser = get_c7n_mailer_parser() args = parser.parse_args() mailer_config = get_and_validate_mailer_config(args) args_dict = vars(args) logger = get_logger(debug=args_dict.get('debug', False)) module_dir = path.dirname(path.abspath(__file__)) default_templates = [path.abspath(path.join(module_dir, 'msg-templates')), path.abspath(path.join(module_dir, '..', 'msg-templates')), path.abspath('.')] templates = args_dict.get('templates', None) if templates: default_templates.append(path.abspath(path.expanduser(path.expandvars(templates)))) mailer_config['templates_folders'] = default_templates provider = get_provider(mailer_config) if args_dict.get('update_lambda'): if args_dict.get('debug'): print('\n** --debug is only supported with --run, not --update-lambda **\n') return if args_dict.get('max_num_processes'): print('\n** --max-num-processes is only supported ' 'with --run, not --update-lambda **\n') return if provider == Providers.Azure: azure_deploy.provision(mailer_config) elif provider == Providers.AWS: deploy.provision(mailer_config, functools.partial(session_factory, mailer_config)) if args_dict.get('run'): max_num_processes = args_dict.get('max_num_processes') # Select correct processor if provider == Providers.Azure: processor = MailerAzureQueueProcessor(mailer_config, logger) elif provider == Providers.AWS: aws_session = session_factory(mailer_config) processor = MailerSqsQueueProcessor(mailer_config, aws_session, logger) # Execute if max_num_processes: run_mailer_in_parallel(processor, max_num_processes) else: processor.run() if __name__ == '__main__': main()

37.527344

100

0.575518

from __future__ import absolute_import, division, print_function, unicode_literals import argparse import functools import logging from os import path import boto3 import jsonschema from c7n_mailer import deploy, utils from c7n_mailer.azure_mailer.azure_queue_processor import MailerAzureQueueProcessor from c7n_mailer.azure_mailer import deploy as azure_deploy from c7n_mailer.sqs_queue_processor import MailerSqsQueueProcessor from c7n_mailer.utils import get_provider, Providers from ruamel import yaml AZURE_KV_SECRET_SCHEMA = { 'type': 'object', 'properties': { 'type': {'enum': ['azure.keyvault']}, 'secret': {'type': 'string'} }, 'required': ['type', 'secret'], 'additionalProperties': False } SECURED_STRING_SCHEMA = { 'oneOf': [ {'type': 'string'}, AZURE_KV_SECRET_SCHEMA ] } CONFIG_SCHEMA = { 'type': 'object', 'additionalProperties': False, 'required': ['queue_url'], 'properties': { 'queue_url': {'type': 'string'}, 'from_address': {'type': 'string'}, 'contact_tags': {'type': 'array', 'items': {'type': 'string'}}, 'org_domain': {'type': 'string'}, 'region': {'type': 'string'}, 'role': {'type': 'string'}, 'runtime': {'type': 'string'}, 'memory': {'type': 'integer'}, 'timeout': {'type': 'integer'}, 'subnets': {'type': 'array', 'items': {'type': 'string'}}, 'security_groups': {'type': 'array', 'items': {'type': 'string'}}, 'dead_letter_config': {'type': 'object'}, 'lambda_name': {'type': 'string'}, 'lambda_description': {'type': 'string'}, 'lambda_tags': {'type': 'object'}, 'lambda_schedule': {'type': 'string'}, 'function_properties': { 'type': 'object', 'appInsights': { 'type': 'object', 'oneOf': [ {'type': 'string'}, {'type': 'object', 'properties': { 'name': 'string', 'location': 'string', 'resourceGroupName': 'string'} } ] }, 'storageAccount': { 'type': 'object', 'oneOf': [ {'type': 'string'}, {'type': 'object', 'properties': { 'name': 'string', 'location': 'string', 'resourceGroupName': 'string'} } ] }, 'servicePlan': { 'type': 'object', 'oneOf': [ {'type': 'string'}, {'type': 'object', 'properties': { 'name': 'string', 'location': 'string', 'resourceGroupName': 'string', 'skuTier': 'string', 'skuName': 'string'} } ] }, }, 'function_schedule': {'type': 'string'}, 'function_skuCode': {'type': 'string'}, 'function_sku': {'type': 'string'}, 'cache_engine': {'type': 'string'}, 'smtp_server': {'type': 'string'}, 'smtp_port': {'type': 'integer'}, 'smtp_ssl': {'type': 'boolean'}, 'smtp_username': {'type': 'string'}, 'smtp_password': SECURED_STRING_SCHEMA, 'ldap_email_key': {'type': 'string'}, 'ldap_uid_tags': {'type': 'array', 'items': {'type': 'string'}}, 'debug': {'type': 'boolean'}, 'ldap_uid_regex': {'type': 'string'}, 'ldap_uri': {'type': 'string'}, 'ldap_bind_dn': {'type': 'string'}, 'ldap_bind_user': {'type': 'string'}, 'ldap_uid_attribute': {'type': 'string'}, 'ldap_manager_attribute': {'type': 'string'}, 'ldap_email_attribute': {'type': 'string'}, 'ldap_bind_password_in_kms': {'type': 'boolean'}, 'ldap_bind_password': {'type': 'string'}, 'cross_accounts': {'type': 'object'}, 'ses_region': {'type': 'string'}, 'redis_host': {'type': 'string'}, 'redis_port': {'type': 'integer'}, 'datadog_api_key': {'type': 'string'}, 'datadog_application_key': {'type': 'string'}, 'slack_token': {'type': 'string'}, 'slack_webhook': {'type': 'string'}, 'sendgrid_api_key': SECURED_STRING_SCHEMA, 'splunk_hec_url': {'type': 'string'}, 'splunk_hec_token': {'type': 'string'}, 'splunk_remove_paths': { 'type': 'array', 'items': {'type': 'string'} }, 'splunk_actions_list': {'type': 'boolean'}, 'splunk_max_attempts': {'type': 'integer'}, 'splunk_hec_max_length': {'type': 'integer'}, 'profile': {'type': 'string'}, 'http_proxy': {'type': 'string'}, 'https_proxy': {'type': 'string'}, 'account_emails': {'type': 'object'} } } def session_factory(mailer_config): return boto3.Session( region_name=mailer_config['region'], profile_name=mailer_config.get('profile', None)) def get_logger(debug=False): log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(level=logging.INFO, format=log_format) logging.getLogger('botocore').setLevel(logging.WARNING) if debug: logging.getLogger('botocore').setLevel(logging.DEBUG) debug_logger = logging.getLogger('custodian-mailer') debug_logger.setLevel(logging.DEBUG) return debug_logger else: return logging.getLogger('custodian-mailer') def get_and_validate_mailer_config(args): with open(args.config) as fh: config = yaml.load(fh.read(), Loader=yaml.SafeLoader) jsonschema.validate(config, CONFIG_SCHEMA) utils.setup_defaults(config) return config def get_c7n_mailer_parser(): parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', required=True, help='mailer.yml config file') debug_help_msg = 'sets c7n_mailer logger to debug, for maximum output (the default is INFO)' parser.add_argument('--debug', action='store_true', help=debug_help_msg) max_num_processes_help_msg = 'will run the mailer in parallel, integer of max processes allowed' parser.add_argument('--max-num-processes', type=int, help=max_num_processes_help_msg) templates_folder_help_msg = 'message templates folder location' parser.add_argument('-t', '--templates', help=templates_folder_help_msg) group = parser.add_mutually_exclusive_group(required=True) update_lambda_help_msg = 'packages your c7n_mailer, uploads the zip to aws lambda as a function' group.add_argument('--update-lambda', action='store_true', help=update_lambda_help_msg) run_help_msg = 'run c7n-mailer locally, process sqs messages and send emails or sns messages' group.add_argument('--run', action='store_true', help=run_help_msg) return parser def run_mailer_in_parallel(processor, max_num_processes): max_num_processes = int(max_num_processes) if max_num_processes < 1: raise Exception processor.max_num_processes = max_num_processes processor.run(parallel=True) def main(): parser = get_c7n_mailer_parser() args = parser.parse_args() mailer_config = get_and_validate_mailer_config(args) args_dict = vars(args) logger = get_logger(debug=args_dict.get('debug', False)) module_dir = path.dirname(path.abspath(__file__)) default_templates = [path.abspath(path.join(module_dir, 'msg-templates')), path.abspath(path.join(module_dir, '..', 'msg-templates')), path.abspath('.')] templates = args_dict.get('templates', None) if templates: default_templates.append(path.abspath(path.expanduser(path.expandvars(templates)))) mailer_config['templates_folders'] = default_templates provider = get_provider(mailer_config) if args_dict.get('update_lambda'): if args_dict.get('debug'): print('\n** --debug is only supported with --run, not --update-lambda **\n') return if args_dict.get('max_num_processes'): print('\n** --max-num-processes is only supported ' 'with --run, not --update-lambda **\n') return if provider == Providers.Azure: azure_deploy.provision(mailer_config) elif provider == Providers.AWS: deploy.provision(mailer_config, functools.partial(session_factory, mailer_config)) if args_dict.get('run'): max_num_processes = args_dict.get('max_num_processes') if provider == Providers.Azure: processor = MailerAzureQueueProcessor(mailer_config, logger) elif provider == Providers.AWS: aws_session = session_factory(mailer_config) processor = MailerSqsQueueProcessor(mailer_config, aws_session, logger) if max_num_processes: run_mailer_in_parallel(processor, max_num_processes) else: processor.run() if __name__ == '__main__': main()

true

790188ae02814f0707a255110003d194b42b7e35

17,512

py

Python

sdk/python/pulumi_azure_native/recoveryservices/v20181220/outputs.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/recoveryservices/v20181220/outputs.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/recoveryservices/v20181220/outputs.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** 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__ = [ 'WorkloadCrrAccessTokenResponse', ] @pulumi.output_type class WorkloadCrrAccessTokenResponse(dict): def __init__(__self__, *, object_type: str, access_token_string: Optional[str] = None, b_ms_active_region: Optional[str] = None, backup_management_type: Optional[str] = None, container_id: Optional[str] = None, container_name: Optional[str] = None, container_type: Optional[str] = None, coordinator_service_stamp_id: Optional[str] = None, coordinator_service_stamp_uri: Optional[str] = None, datasource_container_name: Optional[str] = None, datasource_id: Optional[str] = None, datasource_name: Optional[str] = None, datasource_type: Optional[str] = None, policy_id: Optional[str] = None, policy_name: Optional[str] = None, protectable_object_container_host_os_name: Optional[str] = None, protectable_object_friendly_name: Optional[str] = None, protectable_object_parent_logical_container_name: Optional[str] = None, protectable_object_protection_state: Optional[str] = None, protectable_object_unique_name: Optional[str] = None, protectable_object_workload_type: Optional[str] = None, protection_container_id: Optional[float] = None, protection_service_stamp_id: Optional[str] = None, protection_service_stamp_uri: Optional[str] = None, recovery_point_id: Optional[str] = None, recovery_point_time: Optional[str] = None, resource_group_name: Optional[str] = None, resource_id: Optional[str] = None, resource_name: Optional[str] = None, rp_is_managed_virtual_machine: Optional[bool] = None, rp_original_sa_option: Optional[bool] = None, rp_tier_information: Optional[Mapping[str, str]] = None, rp_vm_size_description: Optional[str] = None, subscription_id: Optional[str] = None, token_extended_information: Optional[str] = None): """ :param str object_type: Type of the specific object - used for deserializing Expected value is 'WorkloadCrrAccessToken'. :param str access_token_string: Access token used for authentication :param str b_ms_active_region: Active region name of BMS Stamp :param str backup_management_type: Backup Management Type :param str container_id: Container Id :param str container_name: Container Unique name :param str container_type: Container Type :param str coordinator_service_stamp_id: CoordinatorServiceStampId to be used by BCM in restore call :param str coordinator_service_stamp_uri: CoordinatorServiceStampUri to be used by BCM in restore call :param str datasource_container_name: Datasource Container Unique Name :param str datasource_id: Datasource Id :param str datasource_name: Datasource Friendly Name :param str datasource_type: Datasource Type :param str policy_id: Policy Id :param str policy_name: Policy Name :param float protection_container_id: Protected item container id :param str protection_service_stamp_id: ProtectionServiceStampId to be used by BCM in restore call :param str protection_service_stamp_uri: ProtectionServiceStampUri to be used by BCM in restore call :param str recovery_point_id: Recovery Point Id :param str recovery_point_time: Recovery Point Time :param str resource_group_name: Resource Group name of the source vault :param str resource_id: Resource Id of the source vault :param str resource_name: Resource Name of the source vault :param bool rp_is_managed_virtual_machine: Recovery point information: Managed virtual machine :param bool rp_original_sa_option: Recovery point information: Original SA option :param Mapping[str, str] rp_tier_information: Recovery point Tier Information :param str rp_vm_size_description: Recovery point information: VM size description :param str subscription_id: Subscription Id of the source vault :param str token_extended_information: Extended Information about the token like FileSpec etc. """ pulumi.set(__self__, "object_type", 'WorkloadCrrAccessToken') if access_token_string is not None: pulumi.set(__self__, "access_token_string", access_token_string) if b_ms_active_region is not None: pulumi.set(__self__, "b_ms_active_region", b_ms_active_region) if backup_management_type is not None: pulumi.set(__self__, "backup_management_type", backup_management_type) if container_id is not None: pulumi.set(__self__, "container_id", container_id) if container_name is not None: pulumi.set(__self__, "container_name", container_name) if container_type is not None: pulumi.set(__self__, "container_type", container_type) if coordinator_service_stamp_id is not None: pulumi.set(__self__, "coordinator_service_stamp_id", coordinator_service_stamp_id) if coordinator_service_stamp_uri is not None: pulumi.set(__self__, "coordinator_service_stamp_uri", coordinator_service_stamp_uri) if datasource_container_name is not None: pulumi.set(__self__, "datasource_container_name", datasource_container_name) if datasource_id is not None: pulumi.set(__self__, "datasource_id", datasource_id) if datasource_name is not None: pulumi.set(__self__, "datasource_name", datasource_name) if datasource_type is not None: pulumi.set(__self__, "datasource_type", datasource_type) if policy_id is not None: pulumi.set(__self__, "policy_id", policy_id) if policy_name is not None: pulumi.set(__self__, "policy_name", policy_name) if protectable_object_container_host_os_name is not None: pulumi.set(__self__, "protectable_object_container_host_os_name", protectable_object_container_host_os_name) if protectable_object_friendly_name is not None: pulumi.set(__self__, "protectable_object_friendly_name", protectable_object_friendly_name) if protectable_object_parent_logical_container_name is not None: pulumi.set(__self__, "protectable_object_parent_logical_container_name", protectable_object_parent_logical_container_name) if protectable_object_protection_state is not None: pulumi.set(__self__, "protectable_object_protection_state", protectable_object_protection_state) if protectable_object_unique_name is not None: pulumi.set(__self__, "protectable_object_unique_name", protectable_object_unique_name) if protectable_object_workload_type is not None: pulumi.set(__self__, "protectable_object_workload_type", protectable_object_workload_type) if protection_container_id is not None: pulumi.set(__self__, "protection_container_id", protection_container_id) if protection_service_stamp_id is not None: pulumi.set(__self__, "protection_service_stamp_id", protection_service_stamp_id) if protection_service_stamp_uri is not None: pulumi.set(__self__, "protection_service_stamp_uri", protection_service_stamp_uri) if recovery_point_id is not None: pulumi.set(__self__, "recovery_point_id", recovery_point_id) if recovery_point_time is not None: pulumi.set(__self__, "recovery_point_time", recovery_point_time) if resource_group_name is not None: pulumi.set(__self__, "resource_group_name", resource_group_name) if resource_id is not None: pulumi.set(__self__, "resource_id", resource_id) if resource_name is not None: pulumi.set(__self__, "resource_name", resource_name) if rp_is_managed_virtual_machine is not None: pulumi.set(__self__, "rp_is_managed_virtual_machine", rp_is_managed_virtual_machine) if rp_original_sa_option is not None: pulumi.set(__self__, "rp_original_sa_option", rp_original_sa_option) if rp_tier_information is not None: pulumi.set(__self__, "rp_tier_information", rp_tier_information) if rp_vm_size_description is not None: pulumi.set(__self__, "rp_vm_size_description", rp_vm_size_description) if subscription_id is not None: pulumi.set(__self__, "subscription_id", subscription_id) if token_extended_information is not None: pulumi.set(__self__, "token_extended_information", token_extended_information) @property @pulumi.getter(name="objectType") def object_type(self) -> str: """ Type of the specific object - used for deserializing Expected value is 'WorkloadCrrAccessToken'. """ return pulumi.get(self, "object_type") @property @pulumi.getter(name="accessTokenString") def access_token_string(self) -> Optional[str]: """ Access token used for authentication """ return pulumi.get(self, "access_token_string") @property @pulumi.getter(name="bMSActiveRegion") def b_ms_active_region(self) -> Optional[str]: """ Active region name of BMS Stamp """ return pulumi.get(self, "b_ms_active_region") @property @pulumi.getter(name="backupManagementType") def backup_management_type(self) -> Optional[str]: """ Backup Management Type """ return pulumi.get(self, "backup_management_type") @property @pulumi.getter(name="containerId") def container_id(self) -> Optional[str]: """ Container Id """ return pulumi.get(self, "container_id") @property @pulumi.getter(name="containerName") def container_name(self) -> Optional[str]: """ Container Unique name """ return pulumi.get(self, "container_name") @property @pulumi.getter(name="containerType") def container_type(self) -> Optional[str]: """ Container Type """ return pulumi.get(self, "container_type") @property @pulumi.getter(name="coordinatorServiceStampId") def coordinator_service_stamp_id(self) -> Optional[str]: """ CoordinatorServiceStampId to be used by BCM in restore call """ return pulumi.get(self, "coordinator_service_stamp_id") @property @pulumi.getter(name="coordinatorServiceStampUri") def coordinator_service_stamp_uri(self) -> Optional[str]: """ CoordinatorServiceStampUri to be used by BCM in restore call """ return pulumi.get(self, "coordinator_service_stamp_uri") @property @pulumi.getter(name="datasourceContainerName") def datasource_container_name(self) -> Optional[str]: """ Datasource Container Unique Name """ return pulumi.get(self, "datasource_container_name") @property @pulumi.getter(name="datasourceId") def datasource_id(self) -> Optional[str]: """ Datasource Id """ return pulumi.get(self, "datasource_id") @property @pulumi.getter(name="datasourceName") def datasource_name(self) -> Optional[str]: """ Datasource Friendly Name """ return pulumi.get(self, "datasource_name") @property @pulumi.getter(name="datasourceType") def datasource_type(self) -> Optional[str]: """ Datasource Type """ return pulumi.get(self, "datasource_type") @property @pulumi.getter(name="policyId") def policy_id(self) -> Optional[str]: """ Policy Id """ return pulumi.get(self, "policy_id") @property @pulumi.getter(name="policyName") def policy_name(self) -> Optional[str]: """ Policy Name """ return pulumi.get(self, "policy_name") @property @pulumi.getter(name="protectableObjectContainerHostOsName") def protectable_object_container_host_os_name(self) -> Optional[str]: return pulumi.get(self, "protectable_object_container_host_os_name") @property @pulumi.getter(name="protectableObjectFriendlyName") def protectable_object_friendly_name(self) -> Optional[str]: return pulumi.get(self, "protectable_object_friendly_name") @property @pulumi.getter(name="protectableObjectParentLogicalContainerName") def protectable_object_parent_logical_container_name(self) -> Optional[str]: return pulumi.get(self, "protectable_object_parent_logical_container_name") @property @pulumi.getter(name="protectableObjectProtectionState") def protectable_object_protection_state(self) -> Optional[str]: return pulumi.get(self, "protectable_object_protection_state") @property @pulumi.getter(name="protectableObjectUniqueName") def protectable_object_unique_name(self) -> Optional[str]: return pulumi.get(self, "protectable_object_unique_name") @property @pulumi.getter(name="protectableObjectWorkloadType") def protectable_object_workload_type(self) -> Optional[str]: return pulumi.get(self, "protectable_object_workload_type") @property @pulumi.getter(name="protectionContainerId") def protection_container_id(self) -> Optional[float]: """ Protected item container id """ return pulumi.get(self, "protection_container_id") @property @pulumi.getter(name="protectionServiceStampId") def protection_service_stamp_id(self) -> Optional[str]: """ ProtectionServiceStampId to be used by BCM in restore call """ return pulumi.get(self, "protection_service_stamp_id") @property @pulumi.getter(name="protectionServiceStampUri") def protection_service_stamp_uri(self) -> Optional[str]: """ ProtectionServiceStampUri to be used by BCM in restore call """ return pulumi.get(self, "protection_service_stamp_uri") @property @pulumi.getter(name="recoveryPointId") def recovery_point_id(self) -> Optional[str]: """ Recovery Point Id """ return pulumi.get(self, "recovery_point_id") @property @pulumi.getter(name="recoveryPointTime") def recovery_point_time(self) -> Optional[str]: """ Recovery Point Time """ return pulumi.get(self, "recovery_point_time") @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> Optional[str]: """ Resource Group name of the source vault """ return pulumi.get(self, "resource_group_name") @property @pulumi.getter(name="resourceId") def resource_id(self) -> Optional[str]: """ Resource Id of the source vault """ return pulumi.get(self, "resource_id") @property @pulumi.getter(name="resourceName") def resource_name(self) -> Optional[str]: """ Resource Name of the source vault """ return pulumi.get(self, "resource_name") @property @pulumi.getter(name="rpIsManagedVirtualMachine") def rp_is_managed_virtual_machine(self) -> Optional[bool]: """ Recovery point information: Managed virtual machine """ return pulumi.get(self, "rp_is_managed_virtual_machine") @property @pulumi.getter(name="rpOriginalSAOption") def rp_original_sa_option(self) -> Optional[bool]: """ Recovery point information: Original SA option """ return pulumi.get(self, "rp_original_sa_option") @property @pulumi.getter(name="rpTierInformation") def rp_tier_information(self) -> Optional[Mapping[str, str]]: """ Recovery point Tier Information """ return pulumi.get(self, "rp_tier_information") @property @pulumi.getter(name="rpVMSizeDescription") def rp_vm_size_description(self) -> Optional[str]: """ Recovery point information: VM size description """ return pulumi.get(self, "rp_vm_size_description") @property @pulumi.getter(name="subscriptionId") def subscription_id(self) -> Optional[str]: """ Subscription Id of the source vault """ return pulumi.get(self, "subscription_id") @property @pulumi.getter(name="tokenExtendedInformation") def token_extended_information(self) -> Optional[str]: """ Extended Information about the token like FileSpec etc. """ return pulumi.get(self, "token_extended_information")

41.794749

134

0.672624

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities __all__ = [ 'WorkloadCrrAccessTokenResponse', ] @pulumi.output_type class WorkloadCrrAccessTokenResponse(dict): def __init__(__self__, *, object_type: str, access_token_string: Optional[str] = None, b_ms_active_region: Optional[str] = None, backup_management_type: Optional[str] = None, container_id: Optional[str] = None, container_name: Optional[str] = None, container_type: Optional[str] = None, coordinator_service_stamp_id: Optional[str] = None, coordinator_service_stamp_uri: Optional[str] = None, datasource_container_name: Optional[str] = None, datasource_id: Optional[str] = None, datasource_name: Optional[str] = None, datasource_type: Optional[str] = None, policy_id: Optional[str] = None, policy_name: Optional[str] = None, protectable_object_container_host_os_name: Optional[str] = None, protectable_object_friendly_name: Optional[str] = None, protectable_object_parent_logical_container_name: Optional[str] = None, protectable_object_protection_state: Optional[str] = None, protectable_object_unique_name: Optional[str] = None, protectable_object_workload_type: Optional[str] = None, protection_container_id: Optional[float] = None, protection_service_stamp_id: Optional[str] = None, protection_service_stamp_uri: Optional[str] = None, recovery_point_id: Optional[str] = None, recovery_point_time: Optional[str] = None, resource_group_name: Optional[str] = None, resource_id: Optional[str] = None, resource_name: Optional[str] = None, rp_is_managed_virtual_machine: Optional[bool] = None, rp_original_sa_option: Optional[bool] = None, rp_tier_information: Optional[Mapping[str, str]] = None, rp_vm_size_description: Optional[str] = None, subscription_id: Optional[str] = None, token_extended_information: Optional[str] = None): pulumi.set(__self__, "object_type", 'WorkloadCrrAccessToken') if access_token_string is not None: pulumi.set(__self__, "access_token_string", access_token_string) if b_ms_active_region is not None: pulumi.set(__self__, "b_ms_active_region", b_ms_active_region) if backup_management_type is not None: pulumi.set(__self__, "backup_management_type", backup_management_type) if container_id is not None: pulumi.set(__self__, "container_id", container_id) if container_name is not None: pulumi.set(__self__, "container_name", container_name) if container_type is not None: pulumi.set(__self__, "container_type", container_type) if coordinator_service_stamp_id is not None: pulumi.set(__self__, "coordinator_service_stamp_id", coordinator_service_stamp_id) if coordinator_service_stamp_uri is not None: pulumi.set(__self__, "coordinator_service_stamp_uri", coordinator_service_stamp_uri) if datasource_container_name is not None: pulumi.set(__self__, "datasource_container_name", datasource_container_name) if datasource_id is not None: pulumi.set(__self__, "datasource_id", datasource_id) if datasource_name is not None: pulumi.set(__self__, "datasource_name", datasource_name) if datasource_type is not None: pulumi.set(__self__, "datasource_type", datasource_type) if policy_id is not None: pulumi.set(__self__, "policy_id", policy_id) if policy_name is not None: pulumi.set(__self__, "policy_name", policy_name) if protectable_object_container_host_os_name is not None: pulumi.set(__self__, "protectable_object_container_host_os_name", protectable_object_container_host_os_name) if protectable_object_friendly_name is not None: pulumi.set(__self__, "protectable_object_friendly_name", protectable_object_friendly_name) if protectable_object_parent_logical_container_name is not None: pulumi.set(__self__, "protectable_object_parent_logical_container_name", protectable_object_parent_logical_container_name) if protectable_object_protection_state is not None: pulumi.set(__self__, "protectable_object_protection_state", protectable_object_protection_state) if protectable_object_unique_name is not None: pulumi.set(__self__, "protectable_object_unique_name", protectable_object_unique_name) if protectable_object_workload_type is not None: pulumi.set(__self__, "protectable_object_workload_type", protectable_object_workload_type) if protection_container_id is not None: pulumi.set(__self__, "protection_container_id", protection_container_id) if protection_service_stamp_id is not None: pulumi.set(__self__, "protection_service_stamp_id", protection_service_stamp_id) if protection_service_stamp_uri is not None: pulumi.set(__self__, "protection_service_stamp_uri", protection_service_stamp_uri) if recovery_point_id is not None: pulumi.set(__self__, "recovery_point_id", recovery_point_id) if recovery_point_time is not None: pulumi.set(__self__, "recovery_point_time", recovery_point_time) if resource_group_name is not None: pulumi.set(__self__, "resource_group_name", resource_group_name) if resource_id is not None: pulumi.set(__self__, "resource_id", resource_id) if resource_name is not None: pulumi.set(__self__, "resource_name", resource_name) if rp_is_managed_virtual_machine is not None: pulumi.set(__self__, "rp_is_managed_virtual_machine", rp_is_managed_virtual_machine) if rp_original_sa_option is not None: pulumi.set(__self__, "rp_original_sa_option", rp_original_sa_option) if rp_tier_information is not None: pulumi.set(__self__, "rp_tier_information", rp_tier_information) if rp_vm_size_description is not None: pulumi.set(__self__, "rp_vm_size_description", rp_vm_size_description) if subscription_id is not None: pulumi.set(__self__, "subscription_id", subscription_id) if token_extended_information is not None: pulumi.set(__self__, "token_extended_information", token_extended_information) @property @pulumi.getter(name="objectType") def object_type(self) -> str: return pulumi.get(self, "object_type") @property @pulumi.getter(name="accessTokenString") def access_token_string(self) -> Optional[str]: return pulumi.get(self, "access_token_string") @property @pulumi.getter(name="bMSActiveRegion") def b_ms_active_region(self) -> Optional[str]: return pulumi.get(self, "b_ms_active_region") @property @pulumi.getter(name="backupManagementType") def backup_management_type(self) -> Optional[str]: return pulumi.get(self, "backup_management_type") @property @pulumi.getter(name="containerId") def container_id(self) -> Optional[str]: return pulumi.get(self, "container_id") @property @pulumi.getter(name="containerName") def container_name(self) -> Optional[str]: return pulumi.get(self, "container_name") @property @pulumi.getter(name="containerType") def container_type(self) -> Optional[str]: return pulumi.get(self, "container_type") @property @pulumi.getter(name="coordinatorServiceStampId") def coordinator_service_stamp_id(self) -> Optional[str]: return pulumi.get(self, "coordinator_service_stamp_id") @property @pulumi.getter(name="coordinatorServiceStampUri") def coordinator_service_stamp_uri(self) -> Optional[str]: return pulumi.get(self, "coordinator_service_stamp_uri") @property @pulumi.getter(name="datasourceContainerName") def datasource_container_name(self) -> Optional[str]: return pulumi.get(self, "datasource_container_name") @property @pulumi.getter(name="datasourceId") def datasource_id(self) -> Optional[str]: return pulumi.get(self, "datasource_id") @property @pulumi.getter(name="datasourceName") def datasource_name(self) -> Optional[str]: return pulumi.get(self, "datasource_name") @property @pulumi.getter(name="datasourceType") def datasource_type(self) -> Optional[str]: return pulumi.get(self, "datasource_type") @property @pulumi.getter(name="policyId") def policy_id(self) -> Optional[str]: return pulumi.get(self, "policy_id") @property @pulumi.getter(name="policyName") def policy_name(self) -> Optional[str]: return pulumi.get(self, "policy_name") @property @pulumi.getter(name="protectableObjectContainerHostOsName") def protectable_object_container_host_os_name(self) -> Optional[str]: return pulumi.get(self, "protectable_object_container_host_os_name") @property @pulumi.getter(name="protectableObjectFriendlyName") def protectable_object_friendly_name(self) -> Optional[str]: return pulumi.get(self, "protectable_object_friendly_name") @property @pulumi.getter(name="protectableObjectParentLogicalContainerName") def protectable_object_parent_logical_container_name(self) -> Optional[str]: return pulumi.get(self, "protectable_object_parent_logical_container_name") @property @pulumi.getter(name="protectableObjectProtectionState") def protectable_object_protection_state(self) -> Optional[str]: return pulumi.get(self, "protectable_object_protection_state") @property @pulumi.getter(name="protectableObjectUniqueName") def protectable_object_unique_name(self) -> Optional[str]: return pulumi.get(self, "protectable_object_unique_name") @property @pulumi.getter(name="protectableObjectWorkloadType") def protectable_object_workload_type(self) -> Optional[str]: return pulumi.get(self, "protectable_object_workload_type") @property @pulumi.getter(name="protectionContainerId") def protection_container_id(self) -> Optional[float]: return pulumi.get(self, "protection_container_id") @property @pulumi.getter(name="protectionServiceStampId") def protection_service_stamp_id(self) -> Optional[str]: return pulumi.get(self, "protection_service_stamp_id") @property @pulumi.getter(name="protectionServiceStampUri") def protection_service_stamp_uri(self) -> Optional[str]: return pulumi.get(self, "protection_service_stamp_uri") @property @pulumi.getter(name="recoveryPointId") def recovery_point_id(self) -> Optional[str]: return pulumi.get(self, "recovery_point_id") @property @pulumi.getter(name="recoveryPointTime") def recovery_point_time(self) -> Optional[str]: return pulumi.get(self, "recovery_point_time") @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> Optional[str]: return pulumi.get(self, "resource_group_name") @property @pulumi.getter(name="resourceId") def resource_id(self) -> Optional[str]: return pulumi.get(self, "resource_id") @property @pulumi.getter(name="resourceName") def resource_name(self) -> Optional[str]: return pulumi.get(self, "resource_name") @property @pulumi.getter(name="rpIsManagedVirtualMachine") def rp_is_managed_virtual_machine(self) -> Optional[bool]: return pulumi.get(self, "rp_is_managed_virtual_machine") @property @pulumi.getter(name="rpOriginalSAOption") def rp_original_sa_option(self) -> Optional[bool]: return pulumi.get(self, "rp_original_sa_option") @property @pulumi.getter(name="rpTierInformation") def rp_tier_information(self) -> Optional[Mapping[str, str]]: return pulumi.get(self, "rp_tier_information") @property @pulumi.getter(name="rpVMSizeDescription") def rp_vm_size_description(self) -> Optional[str]: return pulumi.get(self, "rp_vm_size_description") @property @pulumi.getter(name="subscriptionId") def subscription_id(self) -> Optional[str]: return pulumi.get(self, "subscription_id") @property @pulumi.getter(name="tokenExtendedInformation") def token_extended_information(self) -> Optional[str]: return pulumi.get(self, "token_extended_information")

true

7901891fab450fcfb03bc8eb0dc2bfbb1fba0b44

1,181

py

Python

djangocms_tacc_section/cms_plugins.py

tacc-wbomar/Core-CMS-Plugin-Section

9b5f652c6e01e46df5d5caa09cfa9ea6663823d0

[ "BSD-2-Clause" ]

null

djangocms_tacc_section/cms_plugins.py

tacc-wbomar/Core-CMS-Plugin-Section

9b5f652c6e01e46df5d5caa09cfa9ea6663823d0

[ "BSD-2-Clause" ]

null

djangocms_tacc_section/cms_plugins.py

tacc-wbomar/Core-CMS-Plugin-Section

9b5f652c6e01e46df5d5caa09cfa9ea6663823d0

[ "BSD-2-Clause" ]

null

from djangocms_style.cms_plugins import StylePlugin from cms.plugin_pool import plugin_pool from django.utils.translation import gettext_lazy as _ from .models import TaccsiteSection # Plugins @plugin_pool.register_plugin class TaccsiteSectionPlugin(StylePlugin): """ Patterns > "Section" Plugin https://confluence.tacc.utexas.edu/x/c5TtDg """ module = 'TACC Site' model = TaccsiteSection name = _('Section') # Copied from djangocms_style sans 'Inline style settings' # FAQ: If user wants to override spacing, they may: # - use Style plugin (if they have permission) # - request Design & Dev standardize use case # https://github.com/django-cms/djangocms-style/blob/3.0.0/djangocms_style/cms_plugins.py#L15-L40 fieldsets = ( (None, { 'fields': ( 'label', ('class_name', 'tag_type'), ) }), (_('Advanced settings'), { 'classes': ('collapse',), 'fields': ( 'additional_classes', 'id_name', 'template', 'attributes', ), }), )

28.119048

101

0.58171

from djangocms_style.cms_plugins import StylePlugin from cms.plugin_pool import plugin_pool from django.utils.translation import gettext_lazy as _ from .models import TaccsiteSection @plugin_pool.register_plugin class TaccsiteSectionPlugin(StylePlugin): module = 'TACC Site' model = TaccsiteSection name = _('Section') ldsets = ( (None, { 'fields': ( 'label', ('class_name', 'tag_type'), ) }), (_('Advanced settings'), { 'classes': ('collapse',), 'fields': ( 'additional_classes', 'id_name', 'template', 'attributes', ), }), )

true

79018926c625feed767a46b117b2755cd86f2c6a

9,602

py

Python

tests/test_transition.py

kishorehariram/django-logic

955f18211443b30ce39a845495e136d7590183a6

[ "MIT" ]

null

tests/test_transition.py

kishorehariram/django-logic

955f18211443b30ce39a845495e136d7590183a6

[ "MIT" ]

null

tests/test_transition.py

kishorehariram/django-logic

955f18211443b30ce39a845495e136d7590183a6

[ "MIT" ]

null

from unittest.mock import patch from django.test import TestCase from django_logic.state import State from django_logic.transition import Transition from tests.models import Invoice def disable_invoice(invoice: Invoice, *args, **kwargs): invoice.is_available = False invoice.save() def update_invoice(invoice, is_available, customer_received, *args, **kwargs): invoice.is_available = is_available invoice.customer_received = customer_received invoice.save() def enable_invoice(invoice: Invoice, *args, **kwargs): invoice.is_available = True invoice.save() def fail_invoice(invoice: Invoice, *args, **kwargs): raise Exception def receive_invoice(invoice: Invoice, *args, **kwargs): invoice.customer_received = True invoice.save() def debug_action(*args, **kwargs): pass class TransitionSideEffectsTestCase(TestCase): def setUp(self) -> None: self.invoice = Invoice.objects.create(status='draft') def test_one_side_effect(self): transition = Transition('test', sources=[], target='cancelled', side_effects=[disable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, transition.target) self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_many_side_effects(self): transition = Transition('test', sources=[], target='cancelled', side_effects=[disable_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, transition.target) self.assertTrue(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_failure_during_side_effect(self): transition = Transition('test', sources=[], target='cancelled', side_effects=[disable_invoice, fail_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'draft') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_failure_during_side_effect_with_failed_state(self): transition = Transition('test', sources=[], target='cancelled', failed_state='failed', side_effects=[disable_invoice, fail_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_side_effect_with_parameters(self): update_invoice(self.invoice, is_available=True, customer_received=True) transition = Transition('test', sources=[], target='cancelled', failed_state='failed', side_effects=[update_invoice]) self.invoice.refresh_from_db() self.assertTrue(self.invoice.is_available) self.assertTrue(self.invoice.customer_received) state = State(self.invoice, 'status') transition.change_state(state, is_available=False, customer_received=False) self.invoice.refresh_from_db() self.assertFalse(self.invoice.is_available) self.assertFalse(self.invoice.customer_received) self.assertFalse(state.is_locked()) class TransitionCallbacksTestCase(TestCase): def setUp(self) -> None: self.invoice = Invoice.objects.create(status='draft') def test_one_callback(self): transition = Transition('test', sources=[], target='cancelled', callbacks=[disable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, transition.target) self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_many_callbacks(self): transition = Transition('test', sources=[], target='cancelled', callbacks=[disable_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, transition.target) self.assertTrue(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_failure_during_callbacks(self): transition = Transition('test', sources=[], target='cancelled', callbacks=[disable_invoice, fail_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'cancelled') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_failure_during_callbacks_with_failed_state(self): transition = Transition('test', sources=[], target='cancelled', failed_state='failed', side_effects=[disable_invoice, fail_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_callbacks_with_parameters(self): update_invoice(self.invoice, is_available=True, customer_received=True) transition = Transition('test', sources=[], target='cancelled', failed_state='failed', callbacks=[update_invoice]) self.invoice.refresh_from_db() self.assertTrue(self.invoice.is_available) self.assertTrue(self.invoice.customer_received) state = State(self.invoice, 'status') transition.change_state(state, is_available=False, customer_received=False) self.invoice.refresh_from_db() self.assertFalse(self.invoice.is_available) self.assertFalse(self.invoice.customer_received) self.assertFalse(state.is_locked()) class TransitionFailureCallbacksTestCase(TestCase): def setUp(self) -> None: self.invoice = Invoice.objects.create(status='draft') def test_one_callback(self): transition = Transition('test', sources=[], target='success', side_effects=[fail_invoice], failure_callbacks=[disable_invoice], failed_state='failed') self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_many_callback(self): transition = Transition('test', sources=[], target='success', side_effects=[fail_invoice], failure_callbacks=[disable_invoice, receive_invoice], failed_state='failed') self.assertTrue(self.invoice.is_available) self.assertFalse(self.invoice.customer_received) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertTrue(self.invoice.customer_received) self.assertFalse(state.is_locked()) def test_callbacks_with_parameters(self): update_invoice(self.invoice, is_available=True, customer_received=True) transition = Transition('test', sources=[], target='success', failed_state='failed', side_effects=[fail_invoice], failure_callbacks=[update_invoice]) self.invoice.refresh_from_db() self.assertTrue(self.invoice.is_available) self.assertTrue(self.invoice.customer_received) state = State(self.invoice, 'status') transition.change_state(state, is_available=False, customer_received=False) self.invoice.refresh_from_db() self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertFalse(self.invoice.customer_received) self.assertFalse(state.is_locked()) @patch('tests.test_transition.debug_action') def test_failure_callback_exception_passed(self, debug_mock): update_invoice(self.invoice, is_available=True, customer_received=True) transition = Transition('test', sources=[], target='success', failed_state='failed', side_effects=[fail_invoice], failure_callbacks=[debug_action]) self.invoice.refresh_from_db() state = State(self.invoice, 'status') transition.change_state(state, foo="bar") self.assertTrue(debug_mock.called) self.assertEqual(debug_mock.call_count, 1) call_args = debug_mock.call_args[0] call_kwargs = debug_mock.call_args[1] self.assertEqual(call_args, (self.invoice,)) self.assertEqual(len(call_kwargs), 2) self.assertTrue(isinstance(call_kwargs['exception'], Exception)) self.assertEqual(call_kwargs['foo'], 'bar')

45.507109

108

0.687669

from unittest.mock import patch from django.test import TestCase from django_logic.state import State from django_logic.transition import Transition from tests.models import Invoice def disable_invoice(invoice: Invoice, *args, **kwargs): invoice.is_available = False invoice.save() def update_invoice(invoice, is_available, customer_received, *args, **kwargs): invoice.is_available = is_available invoice.customer_received = customer_received invoice.save() def enable_invoice(invoice: Invoice, *args, **kwargs): invoice.is_available = True invoice.save() def fail_invoice(invoice: Invoice, *args, **kwargs): raise Exception def receive_invoice(invoice: Invoice, *args, **kwargs): invoice.customer_received = True invoice.save() def debug_action(*args, **kwargs): pass class TransitionSideEffectsTestCase(TestCase): def setUp(self) -> None: self.invoice = Invoice.objects.create(status='draft') def test_one_side_effect(self): transition = Transition('test', sources=[], target='cancelled', side_effects=[disable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, transition.target) self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_many_side_effects(self): transition = Transition('test', sources=[], target='cancelled', side_effects=[disable_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, transition.target) self.assertTrue(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_failure_during_side_effect(self): transition = Transition('test', sources=[], target='cancelled', side_effects=[disable_invoice, fail_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'draft') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_failure_during_side_effect_with_failed_state(self): transition = Transition('test', sources=[], target='cancelled', failed_state='failed', side_effects=[disable_invoice, fail_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_side_effect_with_parameters(self): update_invoice(self.invoice, is_available=True, customer_received=True) transition = Transition('test', sources=[], target='cancelled', failed_state='failed', side_effects=[update_invoice]) self.invoice.refresh_from_db() self.assertTrue(self.invoice.is_available) self.assertTrue(self.invoice.customer_received) state = State(self.invoice, 'status') transition.change_state(state, is_available=False, customer_received=False) self.invoice.refresh_from_db() self.assertFalse(self.invoice.is_available) self.assertFalse(self.invoice.customer_received) self.assertFalse(state.is_locked()) class TransitionCallbacksTestCase(TestCase): def setUp(self) -> None: self.invoice = Invoice.objects.create(status='draft') def test_one_callback(self): transition = Transition('test', sources=[], target='cancelled', callbacks=[disable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, transition.target) self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_many_callbacks(self): transition = Transition('test', sources=[], target='cancelled', callbacks=[disable_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, transition.target) self.assertTrue(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_failure_during_callbacks(self): transition = Transition('test', sources=[], target='cancelled', callbacks=[disable_invoice, fail_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'cancelled') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_failure_during_callbacks_with_failed_state(self): transition = Transition('test', sources=[], target='cancelled', failed_state='failed', side_effects=[disable_invoice, fail_invoice, enable_invoice]) self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_callbacks_with_parameters(self): update_invoice(self.invoice, is_available=True, customer_received=True) transition = Transition('test', sources=[], target='cancelled', failed_state='failed', callbacks=[update_invoice]) self.invoice.refresh_from_db() self.assertTrue(self.invoice.is_available) self.assertTrue(self.invoice.customer_received) state = State(self.invoice, 'status') transition.change_state(state, is_available=False, customer_received=False) self.invoice.refresh_from_db() self.assertFalse(self.invoice.is_available) self.assertFalse(self.invoice.customer_received) self.assertFalse(state.is_locked()) class TransitionFailureCallbacksTestCase(TestCase): def setUp(self) -> None: self.invoice = Invoice.objects.create(status='draft') def test_one_callback(self): transition = Transition('test', sources=[], target='success', side_effects=[fail_invoice], failure_callbacks=[disable_invoice], failed_state='failed') self.assertTrue(self.invoice.is_available) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertFalse(state.is_locked()) def test_many_callback(self): transition = Transition('test', sources=[], target='success', side_effects=[fail_invoice], failure_callbacks=[disable_invoice, receive_invoice], failed_state='failed') self.assertTrue(self.invoice.is_available) self.assertFalse(self.invoice.customer_received) state = State(self.invoice, 'status') transition.change_state(state) self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertTrue(self.invoice.customer_received) self.assertFalse(state.is_locked()) def test_callbacks_with_parameters(self): update_invoice(self.invoice, is_available=True, customer_received=True) transition = Transition('test', sources=[], target='success', failed_state='failed', side_effects=[fail_invoice], failure_callbacks=[update_invoice]) self.invoice.refresh_from_db() self.assertTrue(self.invoice.is_available) self.assertTrue(self.invoice.customer_received) state = State(self.invoice, 'status') transition.change_state(state, is_available=False, customer_received=False) self.invoice.refresh_from_db() self.assertEqual(self.invoice.status, 'failed') self.assertFalse(self.invoice.is_available) self.assertFalse(self.invoice.customer_received) self.assertFalse(state.is_locked()) @patch('tests.test_transition.debug_action') def test_failure_callback_exception_passed(self, debug_mock): update_invoice(self.invoice, is_available=True, customer_received=True) transition = Transition('test', sources=[], target='success', failed_state='failed', side_effects=[fail_invoice], failure_callbacks=[debug_action]) self.invoice.refresh_from_db() state = State(self.invoice, 'status') transition.change_state(state, foo="bar") self.assertTrue(debug_mock.called) self.assertEqual(debug_mock.call_count, 1) call_args = debug_mock.call_args[0] call_kwargs = debug_mock.call_args[1] self.assertEqual(call_args, (self.invoice,)) self.assertEqual(len(call_kwargs), 2) self.assertTrue(isinstance(call_kwargs['exception'], Exception)) self.assertEqual(call_kwargs['foo'], 'bar')

true

79018a4edf7335e2c6b90bf59e8f83a975bfb1c9

1,625

py

Python

Head/typer.py

D3crypT0r/D3crypt

1e8b0a61e604442590d72c7df05921384584968e

[ "MIT" ]

null

Head/typer.py

D3crypT0r/D3crypt

1e8b0a61e604442590d72c7df05921384584968e

[ "MIT" ]

null

Head/typer.py

D3crypT0r/D3crypt

1e8b0a61e604442590d72c7df05921384584968e

[ "MIT" ]

null

#!/usr/bin/env python3 # # MIT License # # Copyright (c) 2020 EntySec # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # import sys import tty import termios from Head.d3crypt import ghost class typer: def __init__(self): self.d3crypt = d3crypt() def get_char(self): fd = sys.stdin.fileno() old = termios.tcgetattr(fd) try: tty.setraw(fd) return sys.stdin.read(1) finally: termios.tcsetattr(fd, termios.TCSADRAIN, old) def send_char(self, char): self.ghost.send_command("shell", "input text " + char, False, False)

33.854167

80

0.720615

import sys import tty import termios from Head.d3crypt import ghost class typer: def __init__(self): self.d3crypt = d3crypt() def get_char(self): fd = sys.stdin.fileno() old = termios.tcgetattr(fd) try: tty.setraw(fd) return sys.stdin.read(1) finally: termios.tcsetattr(fd, termios.TCSADRAIN, old) def send_char(self, char): self.ghost.send_command("shell", "input text " + char, False, False)

true

79018a7b4549f9cc20a13df03126349d8fd41ee5

5,524

py

Python

api/dailymed/tests/test_api.py

coderxio/dailymed-api

90fe5f8b40a854ff7543ec9b8c4fc0232fdd0dfc

[ "MIT" ]

10

2020-09-23T14:13:35.000Z

2022-03-01T17:39:23.000Z

api/dailymed/tests/test_api.py

coderxio/dailymed-api

90fe5f8b40a854ff7543ec9b8c4fc0232fdd0dfc

[ "MIT" ]

30

2020-09-04T14:43:35.000Z

2021-01-24T01:17:08.000Z

api/dailymed/tests/test_api.py

coderxio/dailymed-api

90fe5f8b40a854ff7543ec9b8c4fc0232fdd0dfc

[ "MIT" ]

3

2020-12-22T01:49:32.000Z

2022-02-10T01:56:05.000Z

from django.test import TestCase from django.urls import reverse from rest_framework import status from rest_framework.test import APIClient from dailymed.models import Set, Spl, InactiveIngredient from dailymed.serializers import SplSerializer import json from pathlib import Path SPL_URL = reverse('spl-list') PRODUCT_URL = reverse('product-list') PACKAGE_URL = reverse('package-list') class PublicApiTest(TestCase): """Test public daily med API""" def setUp(self): self.client = APIClient() """Creates sample data for database""" cwd = Path(__file__).parent.absolute() with open(f'{cwd}/test.json', 'r') as f: default = json.load(f) for data in default['results']: set_id = data.pop('set_id') products_data = data.pop('products') set_obj = Set.objects.create(id=set_id) spl_obj = set_obj.spls.create(**data) for product_data in products_data: product_data.pop('name') packages_data = product_data.pop('packages') if 'inactive_ingredients' in product_data: inactive_ingredients_data = product_data\ .pop('inactive_ingredients') inactive_ingredients_list = [] for inactive_ingredient_data in inactive_ingredients_data: try: ingredient = InactiveIngredient.objects.get( **inactive_ingredient_data ) inactive_ingredients_list.append(ingredient) except Exception: ingredient = InactiveIngredient.objects.create( **inactive_ingredient_data ) inactive_ingredients_list.append(ingredient) product_obj = spl_obj.products.create(**product_data) product_obj.inactive_ingredients\ .add(*inactive_ingredients_list) for package_data in packages_data: product_obj.packages.create(**package_data) def test_retrieve_spls(self): """Test retrieving spls""" res = self.client.get( SPL_URL, format='json' ) serializer = SplSerializer(Spl.objects.filter(), many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_set(self): """Test retrieving a spl by set filter""" set_id = Set.objects.first() res = self.client.get( SPL_URL, {'set_id': set_id.id}, format='json') serializer = SplSerializer( Spl.objects.filter(set__id=set_id.id), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_inactive_ing(self): """Test retrieving a spl by inactive ingredient filter""" inactive_ing = 'alcohol' res = self.client.get( SPL_URL, {'inactive_ingredient_name': inactive_ing}, format='json') serializer = SplSerializer( Spl.objects.filter( products__inactive_ingredients__name__icontains=inactive_ing) .distinct(), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_schedule(self): """Test retrieving spls by schedule filter""" schedule = 'CIV' res = self.client.get( SPL_URL, {'schedule': schedule}, format='json') serializer = SplSerializer(Spl.objects.filter( products__schedule=schedule).distinct(), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_drug_name(self): """Test retrieving spls by drug name filter""" name = 'Ciprofloxacin' res = self.client.get( SPL_URL, {'product_name': name}, format='json') serializer = SplSerializer(Spl.objects.filter( products__name=name).distinct(), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_complex(self): """Test retrieving spls filtered by set & inactive ingredient""" set_id = 'b88efb93-f1d1-4606-a669-6896f432a27f' inactive_ing = 'alcohol' res = self.client.get( SPL_URL, {'set_id': set_id, 'inactive_ingredient_name': inactive_ing}, format='json' ) serializer = SplSerializer( Spl.objects.filter( products__inactive_ingredients__name__icontains=inactive_ing, set__id=set_id) .distinct(), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data['results']), 1) self.assertEqual(serializer.data, res.data['results'])

33.478788

78

0.589971

from django.test import TestCase from django.urls import reverse from rest_framework import status from rest_framework.test import APIClient from dailymed.models import Set, Spl, InactiveIngredient from dailymed.serializers import SplSerializer import json from pathlib import Path SPL_URL = reverse('spl-list') PRODUCT_URL = reverse('product-list') PACKAGE_URL = reverse('package-list') class PublicApiTest(TestCase): def setUp(self): self.client = APIClient() cwd = Path(__file__).parent.absolute() with open(f'{cwd}/test.json', 'r') as f: default = json.load(f) for data in default['results']: set_id = data.pop('set_id') products_data = data.pop('products') set_obj = Set.objects.create(id=set_id) spl_obj = set_obj.spls.create(**data) for product_data in products_data: product_data.pop('name') packages_data = product_data.pop('packages') if 'inactive_ingredients' in product_data: inactive_ingredients_data = product_data\ .pop('inactive_ingredients') inactive_ingredients_list = [] for inactive_ingredient_data in inactive_ingredients_data: try: ingredient = InactiveIngredient.objects.get( **inactive_ingredient_data ) inactive_ingredients_list.append(ingredient) except Exception: ingredient = InactiveIngredient.objects.create( **inactive_ingredient_data ) inactive_ingredients_list.append(ingredient) product_obj = spl_obj.products.create(**product_data) product_obj.inactive_ingredients\ .add(*inactive_ingredients_list) for package_data in packages_data: product_obj.packages.create(**package_data) def test_retrieve_spls(self): res = self.client.get( SPL_URL, format='json' ) serializer = SplSerializer(Spl.objects.filter(), many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_set(self): set_id = Set.objects.first() res = self.client.get( SPL_URL, {'set_id': set_id.id}, format='json') serializer = SplSerializer( Spl.objects.filter(set__id=set_id.id), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_inactive_ing(self): inactive_ing = 'alcohol' res = self.client.get( SPL_URL, {'inactive_ingredient_name': inactive_ing}, format='json') serializer = SplSerializer( Spl.objects.filter( products__inactive_ingredients__name__icontains=inactive_ing) .distinct(), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_schedule(self): schedule = 'CIV' res = self.client.get( SPL_URL, {'schedule': schedule}, format='json') serializer = SplSerializer(Spl.objects.filter( products__schedule=schedule).distinct(), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_drug_name(self): name = 'Ciprofloxacin' res = self.client.get( SPL_URL, {'product_name': name}, format='json') serializer = SplSerializer(Spl.objects.filter( products__name=name).distinct(), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(serializer.data, res.data['results']) def test_retrieve_spls_filter_by_complex(self): set_id = 'b88efb93-f1d1-4606-a669-6896f432a27f' inactive_ing = 'alcohol' res = self.client.get( SPL_URL, {'set_id': set_id, 'inactive_ingredient_name': inactive_ing}, format='json' ) serializer = SplSerializer( Spl.objects.filter( products__inactive_ingredients__name__icontains=inactive_ing, set__id=set_id) .distinct(), many=True ) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data['results']), 1) self.assertEqual(serializer.data, res.data['results'])

true

79018a9ee37928d5ce7164c02c528c9f2c624d84

16,071

py

Python

botorch/acquisition/monte_carlo.py

BradyBromley/botorch

ea7f8fa2cead9c581309437a1f2f59ed070cb59e

[ "MIT" ]

1

2020-07-21T21:25:16.000Z

botorch/acquisition/monte_carlo.py

zpao/botorch

270599207f5b9bf8c66e1197ad2632bb69c3d3b9

[ "MIT" ]

null

botorch/acquisition/monte_carlo.py

zpao/botorch

270599207f5b9bf8c66e1197ad2632bb69c3d3b9

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved r""" Batch acquisition functions using the reparameterization trick in combination with (quasi) Monte-Carlo sampling. See [Rezende2014reparam]_ and [Wilson2017reparam]_ .. [Rezende2014reparam] D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. ICML 2014. .. [Wilson2017reparam] J. T. Wilson, R. Moriconi, F. Hutter, and M. P. Deisenroth. The reparameterization trick for acquisition functions. ArXiv 2017. """ import math from abc import ABC, abstractmethod from typing import Optional, Union import torch from torch import Tensor from ..exceptions.errors import UnsupportedError from ..models.model import Model from ..sampling.samplers import MCSampler, SobolQMCNormalSampler from ..utils.transforms import ( concatenate_pending_points, match_batch_shape, t_batch_mode_transform, ) from .acquisition import AcquisitionFunction from .objective import IdentityMCObjective, MCAcquisitionObjective from .utils import prune_inferior_points class MCAcquisitionFunction(AcquisitionFunction, ABC): r"""Abstract base class for Monte-Carlo based batch acquisition functions.""" def __init__( self, model: Model, sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, ) -> None: r"""Constructor for the MCAcquisitionFunction base class. Args: model: A fitted model. sampler: The sampler used to draw base samples. Defaults to `SobolQMCNormalSampler(num_samples=512, collapse_batch_dims=True)`. objective: The MCAcquisitionObjective under which the samples are evaluated. Defaults to `IdentityMCObjective()`. X_pending: A `m x d`-dim Tensor of `m` design points that have points that have been submitted for function evaluation but have not yet been evaluated. """ super().__init__(model=model) if sampler is None: sampler = SobolQMCNormalSampler(num_samples=512, collapse_batch_dims=True) self.add_module("sampler", sampler) if objective is None: objective = IdentityMCObjective() elif not isinstance(objective, MCAcquisitionObjective): raise UnsupportedError( "Only objectives of type MCAcquisitionObjective are supported for " "MC acquisition functions." ) self.add_module("objective", objective) self.set_X_pending(X_pending) @abstractmethod def forward(self, X: Tensor) -> Tensor: r"""Takes in a `(b) x q x d` X Tensor of `(b)` t-batches with `q` `d`-dim design points each, and returns a one-dimensional Tensor with `(b)` elements. Should utilize the result of set_X_pending as needed to account for pending function evaluations. """ pass # pragma: no cover class qExpectedImprovement(MCAcquisitionFunction): r"""MC-based batch Expected Improvement. This computes qEI by (1) sampling the joint posterior over q points (2) evaluating the improvement over the current best for each sample (3) maximizing over q (4) averaging over the samples `qEI(X) = E(max(max Y - best_f, 0)), Y ~ f(X), where X = (x_1,...,x_q)` Example: >>> model = SingleTaskGP(train_X, train_Y) >>> best_f = train_Y.max()[0] >>> sampler = SobolQMCNormalSampler(1000) >>> qEI = qExpectedImprovement(model, best_f, sampler) >>> qei = qEI(test_X) """ def __init__( self, model: Model, best_f: Union[float, Tensor], sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, ) -> None: r"""q-Expected Improvement. Args: model: A fitted model. best_f: The best objective value observed so far (assumed noiseless). sampler: The sampler used to draw base samples. Defaults to `SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)` objective: The MCAcquisitionObjective under which the samples are evaluated. Defaults to `IdentityMCObjective()`. X_pending: A `m x d`-dim Tensor of `m` design points that have points that have been submitted for function evaluation but have not yet been evaluated. Concatenated into X upon forward call. Copied and set to have no gradient. """ super().__init__( model=model, sampler=sampler, objective=objective, X_pending=X_pending ) if not torch.is_tensor(best_f): best_f = torch.tensor(float(best_f)) self.register_buffer("best_f", best_f) @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: r"""Evaluate qExpectedImprovement on the candidate set `X`. Args: X: A `(b) x q x d`-dim Tensor of `(b)` t-batches with `q` `d`-dim design points each. Returns: A `(b)`-dim Tensor of Expected Improvement values at the given design points `X`. """ posterior = self.model.posterior(X) samples = self.sampler(posterior) obj = self.objective(samples) obj = (obj - self.best_f).clamp_min(0) q_ei = obj.max(dim=-1)[0].mean(dim=0) return q_ei class qNoisyExpectedImprovement(MCAcquisitionFunction): r"""MC-based batch Noisy Expected Improvement. This function does not assume a `best_f` is known (which would require noiseless observations). Instead, it uses samples from the joint posterior over the `q` test points and previously observed points. The improvement over previously observed points is computed for each sample and averaged. `qNEI(X) = E(max(max Y - max Y_baseline, 0))`, where `(Y, Y_baseline) ~ f((X, X_baseline)), X = (x_1,...,x_q)` Example: >>> model = SingleTaskGP(train_X, train_Y) >>> sampler = SobolQMCNormalSampler(1000) >>> qNEI = qNoisyExpectedImprovement(model, train_X, sampler) >>> qnei = qNEI(test_X) """ def __init__( self, model: Model, X_baseline: Tensor, sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, prune_baseline: bool = False, ) -> None: r"""q-Noisy Expected Improvement. Args: model: A fitted model. X_baseline: A `r x d`-dim Tensor of `r` design points that have already been observed. These points are considered as the potential best design point. sampler: The sampler used to draw base samples. Defaults to `SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)`. objective: The MCAcquisitionObjective under which the samples are evaluated. Defaults to `IdentityMCObjective()`. X_pending: A `m x d`-dim Tensor of `m` design points that have points that have been submitted for function evaluation but have not yet been evaluated. Concatenated into X upon forward call. Copied and set to have no gradient. prune_baseline: If True, remove points in `X_baseline` that are highly unlikely to be the best point. This can significantly improve performance and is generally recommended. In order to customize pruning parameters, instead manually call `botorch.acquisition.utils.prune_inferior_points` on `X_baseline` before instantiating the acquisition function. """ super().__init__( model=model, sampler=sampler, objective=objective, X_pending=X_pending ) if prune_baseline: X_baseline = prune_inferior_points( model=model, X=X_baseline, objective=objective ) self.register_buffer("X_baseline", X_baseline) @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: r"""Evaluate qNoisyExpectedImprovement on the candidate set `X`. Args: X: A `(b) x q x d`-dim Tensor of `(b)` t-batches with `q` `d`-dim design points each. Returns: A `(b)`-dim Tensor of Noisy Expected Improvement values at the given design points `X`. """ q = X.shape[-2] X_full = torch.cat([X, match_batch_shape(self.X_baseline, X)], dim=-2) # TODO (T41248036): Implement more efficient way to compute posterior # over both training and test points in GPyTorch posterior = self.model.posterior(X_full) samples = self.sampler(posterior) obj = self.objective(samples) diffs = obj[:, :, :q].max(dim=-1)[0] - obj[:, :, q:].max(dim=-1)[0] return diffs.clamp_min(0).mean(dim=0) class qProbabilityOfImprovement(MCAcquisitionFunction): r"""MC-based batch Probability of Improvement. Estimates the probability of improvement over the current best observed value by sampling from the joint posterior distribution of the q-batch. MC-based estimates of a probability involves taking expectation of an indicator function; to support auto-differntiation, the indicator is replaced with a sigmoid function with temperature parameter `tau`. `qPI(X) = P(max Y >= best_f), Y ~ f(X), X = (x_1,...,x_q)` Example: >>> model = SingleTaskGP(train_X, train_Y) >>> best_f = train_Y.max()[0] >>> sampler = SobolQMCNormalSampler(1000) >>> qPI = qProbabilityOfImprovement(model, best_f, sampler) >>> qpi = qPI(test_X) """ def __init__( self, model: Model, best_f: Union[float, Tensor], sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, tau: float = 1e-3, ) -> None: r"""q-Probability of Improvement. Args: model: A fitted model. best_f: The best objective value observed so far (assumed noiseless). sampler: The sampler used to draw base samples. Defaults to `SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)` objective: The MCAcquisitionObjective under which the samples are evaluated. Defaults to `IdentityMCObjective()`. X_pending: A `m x d`-dim Tensor of `m` design points that have points that have been submitted for function evaluation but have not yet been evaluated. Concatenated into X upon forward call. Copied and set to have no gradient. tau: The temperature parameter used in the sigmoid approximation of the step function. Smaller values yield more accurate approximations of the function, but result in gradients estimates with higher variance. """ super().__init__( model=model, sampler=sampler, objective=objective, X_pending=X_pending ) if not torch.is_tensor(best_f): best_f = torch.tensor(float(best_f)) self.register_buffer("best_f", best_f) if not torch.is_tensor(tau): tau = torch.tensor(float(tau)) self.register_buffer("tau", tau) @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: r"""Evaluate qProbabilityOfImprovement on the candidate set `X`. Args: X: A `(b) x q x d`-dim Tensor of `(b)` t-batches with `q` `d`-dim design points each. Returns: A `(b)`-dim Tensor of Probability of Improvement values at the given design points `X`. """ posterior = self.model.posterior(X) samples = self.sampler(posterior) obj = self.objective(samples) max_obj = obj.max(dim=-1)[0] val = torch.sigmoid((max_obj - self.best_f) / self.tau).mean(dim=0) return val class qSimpleRegret(MCAcquisitionFunction): r"""MC-based batch Simple Regret. Samples from the joint posterior over the q-batch and computes the simple regret. `qSR(X) = E(max Y), Y ~ f(X), X = (x_1,...,x_q)` Example: >>> model = SingleTaskGP(train_X, train_Y) >>> sampler = SobolQMCNormalSampler(1000) >>> qSR = qSimpleRegret(model, sampler) >>> qsr = qSR(test_X) """ @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: r"""Evaluate qSimpleRegret on the candidate set `X`. Args: X: A `(b) x q x d`-dim Tensor of `(b)` t-batches with `q` `d`-dim design points each. Returns: A `(b)`-dim Tensor of Simple Regret values at the given design points `X`. """ posterior = self.model.posterior(X) samples = self.sampler(posterior) obj = self.objective(samples) val = obj.max(dim=-1)[0].mean(dim=0) return val class qUpperConfidenceBound(MCAcquisitionFunction): r"""MC-based batch Upper Confidence Bound. Uses a reparameterization to extend UCB to qUCB for q > 1 (See Appendix A of [Wilson2017reparam].) `qUCB = E(max(mu + |Y_tilde - mu|))`, where `Y_tilde ~ N(mu, beta pi/2 Sigma)` and `f(X)` has distribution `N(mu, Sigma)`. Example: >>> model = SingleTaskGP(train_X, train_Y) >>> sampler = SobolQMCNormalSampler(1000) >>> qUCB = qUpperConfidenceBound(model, 0.1, sampler) >>> qucb = qUCB(test_X) """ def __init__( self, model: Model, beta: float, sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, ) -> None: r"""q-Upper Confidence Bound. Args: model: A fitted model. beta: Controls tradeoff between mean and standard deviation in UCB. sampler: The sampler used to draw base samples. Defaults to `SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)` objective: The MCAcquisitionObjective under which the samples are evaluated. Defaults to `IdentityMCObjective()`. X_pending: A `m x d`-dim Tensor of `m` design points that have points that have been submitted for function evaluation but have not yet been evaluated. Concatenated into X upon forward call. Copied and set to have no gradient. """ super().__init__( model=model, sampler=sampler, objective=objective, X_pending=X_pending ) self.beta_prime = math.sqrt(beta * math.pi / 2) @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: r"""Evaluate qUpperConfidenceBound on the candidate set `X`. Args: X: A `(b) x q x d`-dim Tensor of `(b)` t-batches with `q` `d`-dim design points each. Returns: A `(b)`-dim Tensor of Upper Confidence Bound values at the given design points `X`. """ posterior = self.model.posterior(X) samples = self.sampler(posterior) obj = self.objective(samples) mean = obj.mean(dim=0) ucb_samples = mean + self.beta_prime * (obj - mean).abs() return ucb_samples.max(dim=-1)[0].mean(dim=0)

39.197561

86

0.62759

import math from abc import ABC, abstractmethod from typing import Optional, Union import torch from torch import Tensor from ..exceptions.errors import UnsupportedError from ..models.model import Model from ..sampling.samplers import MCSampler, SobolQMCNormalSampler from ..utils.transforms import ( concatenate_pending_points, match_batch_shape, t_batch_mode_transform, ) from .acquisition import AcquisitionFunction from .objective import IdentityMCObjective, MCAcquisitionObjective from .utils import prune_inferior_points class MCAcquisitionFunction(AcquisitionFunction, ABC): def __init__( self, model: Model, sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, ) -> None: super().__init__(model=model) if sampler is None: sampler = SobolQMCNormalSampler(num_samples=512, collapse_batch_dims=True) self.add_module("sampler", sampler) if objective is None: objective = IdentityMCObjective() elif not isinstance(objective, MCAcquisitionObjective): raise UnsupportedError( "Only objectives of type MCAcquisitionObjective are supported for " "MC acquisition functions." ) self.add_module("objective", objective) self.set_X_pending(X_pending) @abstractmethod def forward(self, X: Tensor) -> Tensor: pass class qExpectedImprovement(MCAcquisitionFunction): def __init__( self, model: Model, best_f: Union[float, Tensor], sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, ) -> None: super().__init__( model=model, sampler=sampler, objective=objective, X_pending=X_pending ) if not torch.is_tensor(best_f): best_f = torch.tensor(float(best_f)) self.register_buffer("best_f", best_f) @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: posterior = self.model.posterior(X) samples = self.sampler(posterior) obj = self.objective(samples) obj = (obj - self.best_f).clamp_min(0) q_ei = obj.max(dim=-1)[0].mean(dim=0) return q_ei class qNoisyExpectedImprovement(MCAcquisitionFunction): def __init__( self, model: Model, X_baseline: Tensor, sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, prune_baseline: bool = False, ) -> None: super().__init__( model=model, sampler=sampler, objective=objective, X_pending=X_pending ) if prune_baseline: X_baseline = prune_inferior_points( model=model, X=X_baseline, objective=objective ) self.register_buffer("X_baseline", X_baseline) @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: q = X.shape[-2] X_full = torch.cat([X, match_batch_shape(self.X_baseline, X)], dim=-2) posterior = self.model.posterior(X_full) samples = self.sampler(posterior) obj = self.objective(samples) diffs = obj[:, :, :q].max(dim=-1)[0] - obj[:, :, q:].max(dim=-1)[0] return diffs.clamp_min(0).mean(dim=0) class qProbabilityOfImprovement(MCAcquisitionFunction): def __init__( self, model: Model, best_f: Union[float, Tensor], sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, tau: float = 1e-3, ) -> None: super().__init__( model=model, sampler=sampler, objective=objective, X_pending=X_pending ) if not torch.is_tensor(best_f): best_f = torch.tensor(float(best_f)) self.register_buffer("best_f", best_f) if not torch.is_tensor(tau): tau = torch.tensor(float(tau)) self.register_buffer("tau", tau) @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: posterior = self.model.posterior(X) samples = self.sampler(posterior) obj = self.objective(samples) max_obj = obj.max(dim=-1)[0] val = torch.sigmoid((max_obj - self.best_f) / self.tau).mean(dim=0) return val class qSimpleRegret(MCAcquisitionFunction): @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: posterior = self.model.posterior(X) samples = self.sampler(posterior) obj = self.objective(samples) val = obj.max(dim=-1)[0].mean(dim=0) return val class qUpperConfidenceBound(MCAcquisitionFunction): def __init__( self, model: Model, beta: float, sampler: Optional[MCSampler] = None, objective: Optional[MCAcquisitionObjective] = None, X_pending: Optional[Tensor] = None, ) -> None: super().__init__( model=model, sampler=sampler, objective=objective, X_pending=X_pending ) self.beta_prime = math.sqrt(beta * math.pi / 2) @concatenate_pending_points @t_batch_mode_transform() def forward(self, X: Tensor) -> Tensor: posterior = self.model.posterior(X) samples = self.sampler(posterior) obj = self.objective(samples) mean = obj.mean(dim=0) ucb_samples = mean + self.beta_prime * (obj - mean).abs() return ucb_samples.max(dim=-1)[0].mean(dim=0)

true

79018b8a2227af6bfbfbee3c81e94b7f005b0dbb

923

py

Python

ml/rl/test/models/test_sequence_model.py

joshrose/Horizon

a2eb407b31a16560ae78aa6751eb83672a122a7e

[ "BSD-3-Clause" ]

2

2021-01-11T18:16:32.000Z

2021-11-30T09:34:58.000Z

ml/rl/test/models/test_sequence_model.py

joshrose/Horizon

a2eb407b31a16560ae78aa6751eb83672a122a7e

[ "BSD-3-Clause" ]

null

ml/rl/test/models/test_sequence_model.py

joshrose/Horizon

a2eb407b31a16560ae78aa6751eb83672a122a7e

[ "BSD-3-Clause" ]

2

2021-01-06T01:06:50.000Z

2021-06-24T01:12:52.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import logging import unittest from ml.rl.models.example_sequence_model import ExampleSequenceModel from ml.rl.test.models.test_utils import check_save_load logger = logging.getLogger(__name__) class TestExampleSequenceModel(unittest.TestCase): def test_basic(self): state_dim = 8 model = ExampleSequenceModel(state_dim) input = model.input_prototype() output = model(input) self.assertEqual((1, 1), output.value.shape) def test_save_load(self): state_dim = 8 model = ExampleSequenceModel(state_dim) # ONNX sure exports a lot of parameters... expected_num_params, expected_num_inputs, expected_num_outputs = 133, 3, 1 check_save_load( self, model, expected_num_params, expected_num_inputs, expected_num_outputs )

30.766667

87

0.713976

import logging import unittest from ml.rl.models.example_sequence_model import ExampleSequenceModel from ml.rl.test.models.test_utils import check_save_load logger = logging.getLogger(__name__) class TestExampleSequenceModel(unittest.TestCase): def test_basic(self): state_dim = 8 model = ExampleSequenceModel(state_dim) input = model.input_prototype() output = model(input) self.assertEqual((1, 1), output.value.shape) def test_save_load(self): state_dim = 8 model = ExampleSequenceModel(state_dim) expected_num_params, expected_num_inputs, expected_num_outputs = 133, 3, 1 check_save_load( self, model, expected_num_params, expected_num_inputs, expected_num_outputs )

true

79018bc4d2b547771235d267e6304eb5b3a7f9bc

5,093

py

Python

RA_project/code_python/image_score_posi.py

erialc-cal/NLP-FOMC

2a8ad113a87e79f5d7beefa6cfd4653f445c92d5

[ "MIT" ]

null

RA_project/code_python/image_score_posi.py

erialc-cal/NLP-FOMC

2a8ad113a87e79f5d7beefa6cfd4653f445c92d5

[ "MIT" ]

null

RA_project/code_python/image_score_posi.py

erialc-cal/NLP-FOMC

2a8ad113a87e79f5d7beefa6cfd4653f445c92d5

[ "MIT" ]

null

import pandas as pd import datetime import matplotlib.pyplot as plt import ast from gensim.parsing.preprocessing import STOPWORDS from nltk.corpus import stopwords from collections import defaultdict from nltk.stem import WordNetLemmatizer import datetime stop_words = stopwords.words('english') lemmatizer = WordNetLemmatizer() """ Dates and dico """ df_sentiment = pd.read_excel('/Users/etiennelenaour/Desktop/Stage/vocab_sentiment.xlsx') project_directory = '/Users/etiennelenaour/Desktop/Stage/' l_month = ['January','February','March','April','May','June','July','August','September','October','November','December'] l_dates = list() with open ('/Users/etiennelenaour/Desktop/Stage/csv_files/dates_fomc.csv', 'r') as doc : head = doc.readline() dates = doc.readlines() dates_to_chg = [] for line in dates : if line.split(',')[1] == ' Y' : dates_to_chg += [line.split(';')[0]] date = 0 m = 1 for month in l_month : if month[:3] == line.split(';')[0].split('/')[0] : date += 100 * m m += 1 date += int(line.split(',')[0].split('/')[2])*10000 date += int(line.split(',')[0].split('/')[1]) l_dates.append(date) l_dates_final = l_dates[101:] date_to_append = [20120125, 20120425, 20120620, 20120801, 20120913, 20121024, 20121212, 20130130, 20130130, 20130320, 20130501, 20130619, 20130918, 20131030, 20131218, 20140129, 20140129, 20140430, 20140618, 20140917, 20141029, 20141217] for date in date_to_append: l_dates_final.append(date) """ cleaning functions """ def clean_dico_new_line(dico): new_dico = defaultdict(lambda: list()) for keys, list_dico in dico.items(): new_liste = [string.rstrip("\\n").lower() for string in list_dico] new_dico[keys] = new_liste return new_dico def remove_stop_word(dico): new_dico = defaultdict(lambda: list()) for keys, list_dico in dico.items(): final_list = list() for ele in list_dico: if (ele not in STOPWORDS) and (ele not in stop_words): final_list.append(ele) new_dico[keys] = final_list return new_dico def remove_nan_from_list(liste): new_liste = list() for ele in liste: if type(ele) == str: new_liste.append(ele) else: pass return new_liste """ Score functions """ negative_word_list = [ele.lower() for ele in df_sentiment.Negative.tolist()] positive_word_list = [ele.lower() for ele in remove_nan_from_list(df_sentiment.Positive.tolist())] def compute_positivity(dico): """ This computes the positivity score of each statement. Takes a dictionary with each statement as liste item and the corresponding interlocutor's name in names item """ dico_score = defaultdict(lambda: list()) for name, liste in dico.items(): neg_score = 0 pos_score = 0 for ele in liste: if ele in negative_word_list: neg_score += 1 elif ele in positive_word_list: pos_score += 1 else: pass if neg_score < 30 or pos_score < 30: pass else: score = (pos_score - neg_score) / (pos_score + neg_score) dico_score[name] = score return dico_score def compute_mean_positivity(dico): neg_score = 0 pos_score = 0 for liste in dico.values(): for ele in liste: if ele in negative_word_list: neg_score += 1 elif ele in positive_word_list: pos_score += 1 else: pass score = (pos_score - neg_score) / (pos_score + neg_score) return score """ Date function """ def from_int_dates(integ): string = str(integ) new_string = string[0]+ string[1] + string[2] + string[3] + "/" + string[4] + string[5] + "/" + string[6] + string[7] return datetime.datetime.strptime(new_string, "%Y/%m/%d") """ plot positivity """ def plot_positivity_persons(date, dico_score, score_moyen): list_score = list() list_names = list() for name, score in dico_score.items(): list_score.append(score) list_names.append(name) plt.bar(list_names, list_score, color='r') plt.grid() plt.xticks(rotation=90) plt.text(-1, 0, date, horizontalalignment='left', verticalalignment='top', fontweight='bold') plt.hlines(y=score_moyen, xmin = -1, xmax = len(list_names)) plt.ylabel("Score de positivité") plt.title("Score de positivité des principaux speakers") plt.tight_layout() #plt.show() plt.savefig(project_directory + 'image_score_posi/' + 'score_posi_' + str(date) + '.png') plt.close() return None """ Main """ for date in l_dates_final[-50:]: with open (project_directory+'sentences_by_names/'+str(date)+'meeting.txt', 'r') as doc: content = doc.readlines()[0] dictionary = ast.literal_eval(content) #Cleaning dico_clean = remove_stop_word(clean_dico_new_line(dictionary)) plot_positivity_persons(date, compute_positivity(dico_clean), compute_mean_positivity(dico_clean))

20.703252

121

0.650697

import pandas as pd import datetime import matplotlib.pyplot as plt import ast from gensim.parsing.preprocessing import STOPWORDS from nltk.corpus import stopwords from collections import defaultdict from nltk.stem import WordNetLemmatizer import datetime stop_words = stopwords.words('english') lemmatizer = WordNetLemmatizer() df_sentiment = pd.read_excel('/Users/etiennelenaour/Desktop/Stage/vocab_sentiment.xlsx') project_directory = '/Users/etiennelenaour/Desktop/Stage/' l_month = ['January','February','March','April','May','June','July','August','September','October','November','December'] l_dates = list() with open ('/Users/etiennelenaour/Desktop/Stage/csv_files/dates_fomc.csv', 'r') as doc : head = doc.readline() dates = doc.readlines() dates_to_chg = [] for line in dates : if line.split(',')[1] == ' Y' : dates_to_chg += [line.split(';')[0]] date = 0 m = 1 for month in l_month : if month[:3] == line.split(';')[0].split('/')[0] : date += 100 * m m += 1 date += int(line.split(',')[0].split('/')[2])*10000 date += int(line.split(',')[0].split('/')[1]) l_dates.append(date) l_dates_final = l_dates[101:] date_to_append = [20120125, 20120425, 20120620, 20120801, 20120913, 20121024, 20121212, 20130130, 20130130, 20130320, 20130501, 20130619, 20130918, 20131030, 20131218, 20140129, 20140129, 20140430, 20140618, 20140917, 20141029, 20141217] for date in date_to_append: l_dates_final.append(date) def clean_dico_new_line(dico): new_dico = defaultdict(lambda: list()) for keys, list_dico in dico.items(): new_liste = [string.rstrip("\\n").lower() for string in list_dico] new_dico[keys] = new_liste return new_dico def remove_stop_word(dico): new_dico = defaultdict(lambda: list()) for keys, list_dico in dico.items(): final_list = list() for ele in list_dico: if (ele not in STOPWORDS) and (ele not in stop_words): final_list.append(ele) new_dico[keys] = final_list return new_dico def remove_nan_from_list(liste): new_liste = list() for ele in liste: if type(ele) == str: new_liste.append(ele) else: pass return new_liste negative_word_list = [ele.lower() for ele in df_sentiment.Negative.tolist()] positive_word_list = [ele.lower() for ele in remove_nan_from_list(df_sentiment.Positive.tolist())] def compute_positivity(dico): dico_score = defaultdict(lambda: list()) for name, liste in dico.items(): neg_score = 0 pos_score = 0 for ele in liste: if ele in negative_word_list: neg_score += 1 elif ele in positive_word_list: pos_score += 1 else: pass if neg_score < 30 or pos_score < 30: pass else: score = (pos_score - neg_score) / (pos_score + neg_score) dico_score[name] = score return dico_score def compute_mean_positivity(dico): neg_score = 0 pos_score = 0 for liste in dico.values(): for ele in liste: if ele in negative_word_list: neg_score += 1 elif ele in positive_word_list: pos_score += 1 else: pass score = (pos_score - neg_score) / (pos_score + neg_score) return score def from_int_dates(integ): string = str(integ) new_string = string[0]+ string[1] + string[2] + string[3] + "/" + string[4] + string[5] + "/" + string[6] + string[7] return datetime.datetime.strptime(new_string, "%Y/%m/%d") def plot_positivity_persons(date, dico_score, score_moyen): list_score = list() list_names = list() for name, score in dico_score.items(): list_score.append(score) list_names.append(name) plt.bar(list_names, list_score, color='r') plt.grid() plt.xticks(rotation=90) plt.text(-1, 0, date, horizontalalignment='left', verticalalignment='top', fontweight='bold') plt.hlines(y=score_moyen, xmin = -1, xmax = len(list_names)) plt.ylabel("Score de positivité") plt.title("Score de positivité des principaux speakers") plt.tight_layout() plt.savefig(project_directory + 'image_score_posi/' + 'score_posi_' + str(date) + '.png') plt.close() return None for date in l_dates_final[-50:]: with open (project_directory+'sentences_by_names/'+str(date)+'meeting.txt', 'r') as doc: content = doc.readlines()[0] dictionary = ast.literal_eval(content) dico_clean = remove_stop_word(clean_dico_new_line(dictionary)) plot_positivity_persons(date, compute_positivity(dico_clean), compute_mean_positivity(dico_clean))

true

79018e109b0d2d3d27d91efe0ab7e0e7574f3780

8,692

py

Python

docs/conf.py

SherazKhan/cortex

f0430d11cc81a64c78edda1a62513f6d739ab8e1

[ "MIT" ]

null

docs/conf.py

SherazKhan/cortex

f0430d11cc81a64c78edda1a62513f6d739ab8e1

[ "MIT" ]

null

docs/conf.py

SherazKhan/cortex

f0430d11cc81a64c78edda1a62513f6d739ab8e1

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('.')) # -- Hack for ReadTheDocs ------------------------------------------------------ # This hack is necessary since RTD does not issue `sphinx-apidoc` before running # `sphinx-build -b html . _build/html`. See Issue: # https://github.com/rtfd/readthedocs.org/issues/1139 # DON'T FORGET: Check the box "Install your project inside a virtualenv using # setup.py install" in the RTD Advanced Settings. import os on_rtd = os.environ.get('READTHEDOCS', None) == 'True' if on_rtd: import inspect from sphinx import apidoc __location__ = os.path.join(os.getcwd(), os.path.dirname( inspect.getfile(inspect.currentframe()))) output_dir = os.path.join(__location__, "../docs/api") module_dir = os.path.join(__location__, "../cortex") cmd_line_template = "sphinx-apidoc -f -o {outputdir} {moduledir}" cmd_line = cmd_line_template.format(outputdir=output_dir, moduledir=module_dir) apidoc.main(cmd_line.split(" ")) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.autosummary', 'sphinx.ext.viewcode', 'sphinx.ext.coverage', 'sphinx.ext.doctest', 'sphinx.ext.ifconfig', 'sphinx.ext.pngmath', 'sphinx.ext.napoleon'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'cortex' copyright = u'2017, sherazkhan' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '' # Is set by calling `setup.py docs` # The full version, including alpha/beta/rc tags. release = '' # Is set by calling `setup.py docs` # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'alabaster' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". try: from cortex import __version__ as version except ImportError: pass else: release = version # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = "" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'cortex-doc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # 'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'user_guide.tex', u'cortex Documentation', u'sherazkhan', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = "" # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. # latex_show_pagerefs = False # If true, show URL addresses after external links. # latex_show_urls = False # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_domain_indices = True # -- External mapping ------------------------------------------------------------ python_version = '.'.join(map(str, sys.version_info[0:2])) intersphinx_mapping = { 'sphinx': ('http://sphinx.pocoo.org', None), 'python': ('http://docs.python.org/' + python_version, None), 'matplotlib': ('http://matplotlib.sourceforge.net', None), 'numpy': ('http://docs.scipy.org/doc/numpy', None), 'sklearn': ('http://scikit-learn.org/stable', None), 'pandas': ('http://pandas.pydata.org/pandas-docs/stable', None), 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), }

34.768

85

0.70168

import sys # setup.py install" in the RTD Advanced Settings. import os on_rtd = os.environ.get('READTHEDOCS', None) == 'True' if on_rtd: import inspect from sphinx import apidoc __location__ = os.path.join(os.getcwd(), os.path.dirname( inspect.getfile(inspect.currentframe()))) output_dir = os.path.join(__location__, "../docs/api") module_dir = os.path.join(__location__, "../cortex") cmd_line_template = "sphinx-apidoc -f -o {outputdir} {moduledir}" cmd_line = cmd_line_template.format(outputdir=output_dir, moduledir=module_dir) apidoc.main(cmd_line.split(" ")) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.autosummary', 'sphinx.ext.viewcode', 'sphinx.ext.coverage', 'sphinx.ext.doctest', 'sphinx.ext.ifconfig', 'sphinx.ext.pngmath', 'sphinx.ext.napoleon'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'cortex' copyright = u'2017, sherazkhan' # The version info for the project you're documenting, acts as replacement for version = '' release = '' exclude_patterns = ['_build'] pygments_style = 'sphinx' html_theme = 'alabaster' try: from cortex import __version__ as version except ImportError: pass else: release = version html_static_path = ['_static'] htmlhelp_basename = 'cortex-doc' latex_elements = { } latex_documents = [ ('index', 'user_guide.tex', u'cortex Documentation', u'sherazkhan', 'manual'), ] python_version = '.'.join(map(str, sys.version_info[0:2])) intersphinx_mapping = { 'sphinx': ('http://sphinx.pocoo.org', None), 'python': ('http://docs.python.org/' + python_version, None), 'matplotlib': ('http://matplotlib.sourceforge.net', None), 'numpy': ('http://docs.scipy.org/doc/numpy', None), 'sklearn': ('http://scikit-learn.org/stable', None), 'pandas': ('http://pandas.pydata.org/pandas-docs/stable', None), 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), }

true

7901910f895ef5344f1b27738ca27ce8ce0d37e9

6,947

py

Python

magenta/models/image_stylization/image_stylization_finetune.py

dubreuia/magenta

2679a1a096001808957ad99a1859181f3926cfdf

[ "Apache-2.0" ]

1

2019-11-29T15:18:32.000Z

magenta/models/image_stylization/image_stylization_finetune.py

hdanak/magenta

acd6dedc315ea159c6f15750dd09aabdadc47515

[ "Apache-2.0" ]

null

magenta/models/image_stylization/image_stylization_finetune.py

hdanak/magenta

acd6dedc315ea159c6f15750dd09aabdadc47515

[ "Apache-2.0" ]

1

2021-09-22T18:37:38.000Z

# Copyright 2019 The Magenta Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Trains an N-styles style transfer model on the cheap. Training is done by finetuning the instance norm parameters of a pre-trained N-styles style transfer model. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import ast import os from magenta.models.image_stylization import image_utils from magenta.models.image_stylization import learning from magenta.models.image_stylization import model from magenta.models.image_stylization import vgg import tensorflow as tf from tensorflow.contrib import slim as contrib_slim slim = contrib_slim DEFAULT_CONTENT_WEIGHTS = '{"vgg_16/conv3": 1.0}' DEFAULT_STYLE_WEIGHTS = ('{"vgg_16/conv1": 1e-4, "vgg_16/conv2": 1e-4,' ' "vgg_16/conv3": 1e-4, "vgg_16/conv4": 1e-4}') flags = tf.app.flags flags.DEFINE_float('clip_gradient_norm', 0, 'Clip gradients to this norm') flags.DEFINE_float('learning_rate', 1e-3, 'Learning rate') flags.DEFINE_integer('batch_size', 16, 'Batch size.') flags.DEFINE_integer('image_size', 256, 'Image size.') flags.DEFINE_integer('num_styles', None, 'Number of styles.') flags.DEFINE_float('alpha', 1.0, 'Width multiplier') flags.DEFINE_integer('ps_tasks', 0, 'Number of parameter servers. If 0, parameters ' 'are handled locally by the worker.') flags.DEFINE_integer('save_summaries_secs', 15, 'Frequency at which summaries are saved, in seconds.') flags.DEFINE_integer('save_interval_secs', 15, 'Frequency at which the model is saved, in seconds.') flags.DEFINE_integer('task', 0, 'Task ID. Used when training with multiple ' 'workers to identify each worker.') flags.DEFINE_integer('train_steps', 40000, 'Number of training steps.') flags.DEFINE_string('checkpoint', None, 'Checkpoint file for the pretrained model.') flags.DEFINE_string('content_weights', DEFAULT_CONTENT_WEIGHTS, 'Content weights') flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.') flags.DEFINE_string('style_coefficients', None, 'Scales the style weights conditioned on the style image.') flags.DEFINE_string('style_dataset_file', None, 'Style dataset file.') flags.DEFINE_string('style_weights', DEFAULT_STYLE_WEIGHTS, 'Style weights') flags.DEFINE_string('train_dir', None, 'Directory for checkpoints and summaries.') FLAGS = flags.FLAGS def main(unused_argv=None): with tf.Graph().as_default(): # Force all input processing onto CPU in order to reserve the GPU for the # forward inference and back-propagation. device = '/cpu:0' if not FLAGS.ps_tasks else '/job:worker/cpu:0' with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks, worker_device=device)): inputs, _ = image_utils.imagenet_inputs(FLAGS.batch_size, FLAGS.image_size) # Load style images and select one at random (for each graph execution, a # new random selection occurs) _, style_labels, style_gram_matrices = image_utils.style_image_inputs( os.path.expanduser(FLAGS.style_dataset_file), batch_size=FLAGS.batch_size, image_size=FLAGS.image_size, square_crop=True, shuffle=True) with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)): # Process style and weight flags num_styles = FLAGS.num_styles if FLAGS.style_coefficients is None: style_coefficients = [1.0 for _ in range(num_styles)] else: style_coefficients = ast.literal_eval(FLAGS.style_coefficients) if len(style_coefficients) != num_styles: raise ValueError( 'number of style coefficients differs from number of styles') content_weights = ast.literal_eval(FLAGS.content_weights) style_weights = ast.literal_eval(FLAGS.style_weights) # Rescale style weights dynamically based on the current style image style_coefficient = tf.gather( tf.constant(style_coefficients), style_labels) style_weights = dict((key, style_coefficient * value) for key, value in style_weights.items()) # Define the model stylized_inputs = model.transform( inputs, alpha=FLAGS.alpha, normalizer_params={ 'labels': style_labels, 'num_categories': num_styles, 'center': True, 'scale': True }) # Compute losses. total_loss, loss_dict = learning.total_loss( inputs, stylized_inputs, style_gram_matrices, content_weights, style_weights) for key, value in loss_dict.items(): tf.summary.scalar(key, value) instance_norm_vars = [var for var in slim.get_variables('transformer') if 'InstanceNorm' in var.name] other_vars = [var for var in slim.get_variables('transformer') if 'InstanceNorm' not in var.name] # Function to restore VGG16 parameters. init_fn_vgg = slim.assign_from_checkpoint_fn(vgg.checkpoint_file(), slim.get_variables('vgg_16')) # Function to restore N-styles parameters. init_fn_n_styles = slim.assign_from_checkpoint_fn( os.path.expanduser(FLAGS.checkpoint), other_vars) def init_fn(session): init_fn_vgg(session) init_fn_n_styles(session) # Set up training. optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate) train_op = slim.learning.create_train_op( total_loss, optimizer, clip_gradient_norm=FLAGS.clip_gradient_norm, variables_to_train=instance_norm_vars, summarize_gradients=False) # Run training. slim.learning.train( train_op=train_op, logdir=os.path.expanduser(FLAGS.train_dir), master=FLAGS.master, is_chief=FLAGS.task == 0, number_of_steps=FLAGS.train_steps, init_fn=init_fn, save_summaries_secs=FLAGS.save_summaries_secs, save_interval_secs=FLAGS.save_interval_secs) def console_entry_point(): tf.app.run(main) if __name__ == '__main__': console_entry_point()

41.35119

80

0.678854

from __future__ import absolute_import from __future__ import division from __future__ import print_function import ast import os from magenta.models.image_stylization import image_utils from magenta.models.image_stylization import learning from magenta.models.image_stylization import model from magenta.models.image_stylization import vgg import tensorflow as tf from tensorflow.contrib import slim as contrib_slim slim = contrib_slim DEFAULT_CONTENT_WEIGHTS = '{"vgg_16/conv3": 1.0}' DEFAULT_STYLE_WEIGHTS = ('{"vgg_16/conv1": 1e-4, "vgg_16/conv2": 1e-4,' ' "vgg_16/conv3": 1e-4, "vgg_16/conv4": 1e-4}') flags = tf.app.flags flags.DEFINE_float('clip_gradient_norm', 0, 'Clip gradients to this norm') flags.DEFINE_float('learning_rate', 1e-3, 'Learning rate') flags.DEFINE_integer('batch_size', 16, 'Batch size.') flags.DEFINE_integer('image_size', 256, 'Image size.') flags.DEFINE_integer('num_styles', None, 'Number of styles.') flags.DEFINE_float('alpha', 1.0, 'Width multiplier') flags.DEFINE_integer('ps_tasks', 0, 'Number of parameter servers. If 0, parameters ' 'are handled locally by the worker.') flags.DEFINE_integer('save_summaries_secs', 15, 'Frequency at which summaries are saved, in seconds.') flags.DEFINE_integer('save_interval_secs', 15, 'Frequency at which the model is saved, in seconds.') flags.DEFINE_integer('task', 0, 'Task ID. Used when training with multiple ' 'workers to identify each worker.') flags.DEFINE_integer('train_steps', 40000, 'Number of training steps.') flags.DEFINE_string('checkpoint', None, 'Checkpoint file for the pretrained model.') flags.DEFINE_string('content_weights', DEFAULT_CONTENT_WEIGHTS, 'Content weights') flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.') flags.DEFINE_string('style_coefficients', None, 'Scales the style weights conditioned on the style image.') flags.DEFINE_string('style_dataset_file', None, 'Style dataset file.') flags.DEFINE_string('style_weights', DEFAULT_STYLE_WEIGHTS, 'Style weights') flags.DEFINE_string('train_dir', None, 'Directory for checkpoints and summaries.') FLAGS = flags.FLAGS def main(unused_argv=None): with tf.Graph().as_default(): device = '/cpu:0' if not FLAGS.ps_tasks else '/job:worker/cpu:0' with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks, worker_device=device)): inputs, _ = image_utils.imagenet_inputs(FLAGS.batch_size, FLAGS.image_size) _, style_labels, style_gram_matrices = image_utils.style_image_inputs( os.path.expanduser(FLAGS.style_dataset_file), batch_size=FLAGS.batch_size, image_size=FLAGS.image_size, square_crop=True, shuffle=True) with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)): num_styles = FLAGS.num_styles if FLAGS.style_coefficients is None: style_coefficients = [1.0 for _ in range(num_styles)] else: style_coefficients = ast.literal_eval(FLAGS.style_coefficients) if len(style_coefficients) != num_styles: raise ValueError( 'number of style coefficients differs from number of styles') content_weights = ast.literal_eval(FLAGS.content_weights) style_weights = ast.literal_eval(FLAGS.style_weights) style_coefficient = tf.gather( tf.constant(style_coefficients), style_labels) style_weights = dict((key, style_coefficient * value) for key, value in style_weights.items()) stylized_inputs = model.transform( inputs, alpha=FLAGS.alpha, normalizer_params={ 'labels': style_labels, 'num_categories': num_styles, 'center': True, 'scale': True }) total_loss, loss_dict = learning.total_loss( inputs, stylized_inputs, style_gram_matrices, content_weights, style_weights) for key, value in loss_dict.items(): tf.summary.scalar(key, value) instance_norm_vars = [var for var in slim.get_variables('transformer') if 'InstanceNorm' in var.name] other_vars = [var for var in slim.get_variables('transformer') if 'InstanceNorm' not in var.name] init_fn_vgg = slim.assign_from_checkpoint_fn(vgg.checkpoint_file(), slim.get_variables('vgg_16')) init_fn_n_styles = slim.assign_from_checkpoint_fn( os.path.expanduser(FLAGS.checkpoint), other_vars) def init_fn(session): init_fn_vgg(session) init_fn_n_styles(session) optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate) train_op = slim.learning.create_train_op( total_loss, optimizer, clip_gradient_norm=FLAGS.clip_gradient_norm, variables_to_train=instance_norm_vars, summarize_gradients=False) slim.learning.train( train_op=train_op, logdir=os.path.expanduser(FLAGS.train_dir), master=FLAGS.master, is_chief=FLAGS.task == 0, number_of_steps=FLAGS.train_steps, init_fn=init_fn, save_summaries_secs=FLAGS.save_summaries_secs, save_interval_secs=FLAGS.save_interval_secs) def console_entry_point(): tf.app.run(main) if __name__ == '__main__': console_entry_point()

true

79019284060513350aab929d8bb76a390a968486

2,942

py

Python

hmc5883lStream/code/quick2wire/test_gpio.py

roblee357/JRone

f6c3080f260858f12da0be9f353cc9b62fd47c06

[ "MIT" ]

null

hmc5883lStream/code/quick2wire/test_gpio.py

roblee357/JRone

f6c3080f260858f12da0be9f353cc9b62fd47c06

[ "MIT" ]

null

hmc5883lStream/code/quick2wire/test_gpio.py

roblee357/JRone

f6c3080f260858f12da0be9f353cc9b62fd47c06

[ "MIT" ]

null

import os from quick2wire.gpio import pins, In, Out, PullDown, gpio_admin import pytest @pytest.mark.gpio @pytest.mark.loopback class TestGPIO: def test_pin_must_be_opened_before_use_and_is_unusable_after_being_closed(self): pin = pins.pin(0) with pytest.raises(IOError): pin.value pin.open() try: pin.value finally: pin.close() with pytest.raises(IOError): pin.value def test_opens_and_closes_itself_when_used_as_a_context_manager(self): pin = pins.pin(0) with pin: pin.value with pytest.raises(IOError): pin.value def test_exports_gpio_device_to_userspace_when_opened_and_unexports_when_closed(self): with pins.pin(0) as pin: assert os.path.exists('/sys/class/gpio/gpio17/value') assert not os.path.exists('/sys/class/gpio/gpio17/value') def test_can_set_and_query_direction_of_pin_when_open(self): with pins.pin(0) as pin: pin.direction = Out assert pin.direction == Out assert content_of("/sys/class/gpio/gpio17/direction") == "out\n" pin.direction = In assert pin.direction == In assert content_of("/sys/class/gpio/gpio17/direction") == "in\n" def test_can_set_direction_on_construction(self): pin = pins.pin(0, Out) assert pin.direction == Out assert not os.path.exists("/sys/class/gpio/gpio17/direction") with pin: assert content_of("/sys/class/gpio/gpio17/direction") == "out\n" assert pin.direction == Out def test_setting_value_of_output_pin_writes_to_device_file(self): with pins.pin(0) as pin: pin.direction = Out pin.value = 1 assert pin.value == 1 assert content_of('/sys/class/gpio/gpio17/value') == '1\n' pin.value = 0 assert pin.value == 0 assert content_of('/sys/class/gpio/gpio17/value') == '0\n' def test_direction_and_value_of_pin_is_reset_when_closed(self): with pins.pin(0, Out) as pin: pin.value = 1 gpio_admin("export", 17, PullDown) try: assert content_of('/sys/class/gpio/gpio17/value') == '0\n' assert content_of('/sys/class/gpio/gpio17/direction') == 'in\n' finally: gpio_admin("unexport", 17) def test_cannot_get_a_pin_with_an_invalid_index(self): with pytest.raises(IndexError): pins.pin(-1) with pytest.raises(IndexError): pins.pin(len(pins)) def content_of(filename): with open(filename, 'r') as f: return f.read()

28.563107

90

0.569001

import os from quick2wire.gpio import pins, In, Out, PullDown, gpio_admin import pytest @pytest.mark.gpio @pytest.mark.loopback class TestGPIO: def test_pin_must_be_opened_before_use_and_is_unusable_after_being_closed(self): pin = pins.pin(0) with pytest.raises(IOError): pin.value pin.open() try: pin.value finally: pin.close() with pytest.raises(IOError): pin.value def test_opens_and_closes_itself_when_used_as_a_context_manager(self): pin = pins.pin(0) with pin: pin.value with pytest.raises(IOError): pin.value def test_exports_gpio_device_to_userspace_when_opened_and_unexports_when_closed(self): with pins.pin(0) as pin: assert os.path.exists('/sys/class/gpio/gpio17/value') assert not os.path.exists('/sys/class/gpio/gpio17/value') def test_can_set_and_query_direction_of_pin_when_open(self): with pins.pin(0) as pin: pin.direction = Out assert pin.direction == Out assert content_of("/sys/class/gpio/gpio17/direction") == "out\n" pin.direction = In assert pin.direction == In assert content_of("/sys/class/gpio/gpio17/direction") == "in\n" def test_can_set_direction_on_construction(self): pin = pins.pin(0, Out) assert pin.direction == Out assert not os.path.exists("/sys/class/gpio/gpio17/direction") with pin: assert content_of("/sys/class/gpio/gpio17/direction") == "out\n" assert pin.direction == Out def test_setting_value_of_output_pin_writes_to_device_file(self): with pins.pin(0) as pin: pin.direction = Out pin.value = 1 assert pin.value == 1 assert content_of('/sys/class/gpio/gpio17/value') == '1\n' pin.value = 0 assert pin.value == 0 assert content_of('/sys/class/gpio/gpio17/value') == '0\n' def test_direction_and_value_of_pin_is_reset_when_closed(self): with pins.pin(0, Out) as pin: pin.value = 1 gpio_admin("export", 17, PullDown) try: assert content_of('/sys/class/gpio/gpio17/value') == '0\n' assert content_of('/sys/class/gpio/gpio17/direction') == 'in\n' finally: gpio_admin("unexport", 17) def test_cannot_get_a_pin_with_an_invalid_index(self): with pytest.raises(IndexError): pins.pin(-1) with pytest.raises(IndexError): pins.pin(len(pins)) def content_of(filename): with open(filename, 'r') as f: return f.read()

true

790192adbfddfd2c9c18cf72bae9e8e8538e918a

10,194

py

Python

assignment3/a3_mongo_queries_abw.py

ekselan/DS-Unit-3-Sprint-2-SQL-and-Databases

cde39e22d82362f4ebb771677ab838946c89bb52

[ "MIT" ]

null

assignment3/a3_mongo_queries_abw.py

ekselan/DS-Unit-3-Sprint-2-SQL-and-Databases

cde39e22d82362f4ebb771677ab838946c89bb52

[ "MIT" ]

null

assignment3/a3_mongo_queries_abw.py

ekselan/DS-Unit-3-Sprint-2-SQL-and-Databases

cde39e22d82362f4ebb771677ab838946c89bb52

[ "MIT" ]

null

# inclass/mongo_queries.py import pymongo import os from dotenv import load_dotenv import sqlite3 load_dotenv() DB_USER = os.getenv("MONGO_USER", default="OOPS") DB_PASSWORD = os.getenv("MONGO_PASSWORD", default="OOPS") CLUSTER_NAME = os.getenv("MONGO_CLUSTER_NAME", default="OOPS") connection_uri = f"mongodb+srv://{DB_USER}:{DB_PASSWORD}@{CLUSTER_NAME}.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE" print("----------------") print("URI:", connection_uri) client = pymongo.MongoClient(connection_uri) print("----------------") print("CLIENT:", type(client), client) # print(dir(client)) # print("DB NAMES:", client.list_database_names()) #> ['admin', 'local'] db = client.ds14_db # "ds14_db" or whatever you want to call it # print("----------------") # print("DB:", type(db), db) # collection = db.ds14_pokemon_collection # "ds14_collection" or whatever you want to call it # print("----------------") # print("COLLECTION:", type(collection), collection) # print("----------------") # # print("COLLECTIONS:") # # print(db.list_collection_names()) # print("--------------------------------------") ################## ASSIGNMENT III ############################# # INSERT RPG DATA INTO MONGODB INSTANCE # Create RPG database db = client.rpg_data_db # Establish sqlite3 connection to access rpg data sl_conn = sqlite3.connect("data/rpg_db_original.sqlite3") sl_curs = sl_conn.cursor() ################# CHARACTERS ########################### # ## Create new collection for RPG data # col_characters = db.character_collection # ## Establish SQL syntax for query # rpg_characters = 'SELECT * FROM charactercreator_character' # # Function to loop through characters and return list of dictionaries # def all_chars(): # query = rpg_characters # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "character_id": row[0], # "name": row[1], # "level": row[2], # "exp": row[3], # "hp": row[4], # "strength": row[5], # "intelligence": row[6], # "dexterity": row[7], # "wisdom": row[8] # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # # print(character_dict_list) # col_characters.insert_many(character_dict_list) # print("DOCS(Num Characters):", col_characters.count_documents({})) # # SELECT count(distinct id) from characters ################# MAGES ########################### # col_mage = db.mage_collection # mages = 'SELECT * FROM charactercreator_mage' # def all_chars(): # query = mages # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "character_ptr_id": row[0], # "has_pet": row[1], # "mana": row[2], # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # col_mage.insert_many(character_dict_list) # print("DOCS:", col_mage.count_documents({})) ################# THIEVES ########################### # col_thief = db.thief_collection # thieves = 'SELECT * FROM charactercreator_thief' # def all_chars(): # query = thieves # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "character_ptr_id": row[0], # "is_sneaking": row[1], # "energy": row[2], # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # col_thief.insert_many(character_dict_list) # print("DOCS:", col_thief.count_documents({})) ################# CLERICS ########################### # col_cleric = db.cleric_collection # clerics = 'SELECT * FROM charactercreator_cleric' # def all_chars(): # query = clerics # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "character_ptr_id": row[0], # "using_shield": row[1], # "mana": row[2], # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # col_cleric.insert_many(character_dict_list) # print("DOCS:", col_cleric.count_documents({})) ################# FIGHTERS ########################### # col_fighter = db.fighter_collection # fighters = 'SELECT * FROM charactercreator_fighter' # def all_chars(): # query = fighters # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "character_ptr_id": row[0], # "using_shield": row[1], # "rage": row[2], # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # col_fighter.insert_many(character_dict_list) # print("DOCS:", col_fighter.count_documents({})) ################# NECROMANCERS ########################### # col_mancer = db.mancer_collection # mancers = 'SELECT * FROM charactercreator_necromancer' # def all_chars(): # query = mancers # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "mage_ptr_id": row[0], # "talisman_charged": row[1], # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # col_mancer.insert_many(character_dict_list) # print("DOCS:", col_mancer.count_documents({})) ################# ITEMS ########################### # col_items = db.items_collection # items = 'SELECT * FROM armory_item' # def all_chars(): # query = items # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "item_id": row[0], # "name": row[1], # "value": row[2], # "weight": row[3] # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # col_items.insert_many(character_dict_list) # print("DOCS:", col_items.count_documents({})) ################# WEAPONS ########################### # col_weapons = db.weapons_collection # weapons = 'SELECT * FROM armory_weapon' # def all_chars(): # query = weapons # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "item_ptr_id": row[0], # "power": row[1] # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # col_weapons.insert_many(character_dict_list) # print("DOCS:", col_weapons.count_documents({})) ################# INVENTORY ########################### # col_inventory = db.inventory_collection # records = 'SELECT * FROM charactercreator_character_inventory' # def all_chars(): # query = records # chars = sl_curs.execute(query) # char_data = [] # for row in chars: # character = { # "id": row[0], # "character_id": row[1], # "item_id": row[2] # } # char_data.append(character) # result = char_data # return result # character_dict_list = all_chars() # col_inventory.insert_many(character_dict_list) # print("DOCS:", col_inventory.count_documents({})) # print("COLLECTIONS:") # print(db.list_collection_names()) #################### IN-CLASS POKEMON INSERTS ############################# # collection.insert_one({ # "name": "Pikachu", # "level": 30, # "exp": 76000000000, # "hp": 400, # "fav_icecream_flavors":["vanila_bean", "choc"], # "stats":{"a":1,"b":2,"c":[1,2,3]} # }) # print("DOCS:", collection.count_documents({})) # SELECT count(distinct id) from pokemon # print(collection.count_documents({"name": "Pikachu"})) # SELECT # count(distinct id) from pokemon WHERE name = "Pikachu" # mewtwo = { # "name": "Mewtwo", # "level": 100, # "exp": 76000000000, # "hp": 450, # "strength": 550, # "intelligence": 450, # "dexterity": 300, # "wisdom": 575 # } # blastoise = { # "name": "Blastoise", # "lvl": 70, # OOPS we made a mistake with the structure of this dict # } # charmander = { # "nameeeeeee": "Charmander", # "level": 70, # "random_stat": {"a":2} # } # skarmory = { # "name": "Skarmory", # "level": 22, # "exp": 42000, # "hp": 85, # "strength": 750, # "intelligence": 8, # "dexterity": 57 # } # cubone = { # "name": "Cubone", # "level": 20, # "exp": 35000, # "hp": 80, # "strength": 600, # "intelligence": 60, # "dexterity": 200, # "wisdom": 200 # } # scyther = { # "name": "Scyther", # "level": 99, # "exp": 7000, # "hp": 40, # "strength": 50, # "intelligence": 40, # "dexterity": 30, # "wisdom": 57 # } # slowpoke = { # "name": "Slowpoke", # "level": 1, # "exp": 100, # "hp": 80, # "strength": 100, # "intelligence": 10, # "dexterity": 50, # "wisdom": 200 # } # pokemon_team = [mewtwo, blastoise, skarmory, cubone, scyther, slowpoke, charmander] # collection.insert_many(pokemon_team) # print("DOCS:", collection.count_documents({})) # SELECT count(distinct id) from pokemon # #collection.insert_one({"_id": "OURVAL", "name":"TEST"}) # # can overwrite the _id but not insert duplicate _id values # #breakpoint() # pikas = list(collection.find({"name": "Pikachu"})) # SELECT * FROM pokemon WHERE name = "Pikachu" # # print(len(pikas), "PIKAS") # # print(pikas[0]["_id"]) #> ObjectId('5ebc31c79c171e43bb5ed469') # # print(pikas[0]["name"]) # # strong = list(collection.find({"level": {"$gte": 60}} $or {"lvl": {"$gte": 60}})) # # strong = list(collection.find({"level": {"$gte": 60}, "$or" "lvl": {"$gte": 60}})) # strong = list(collection.find({"$or": [{"level": {"$gte": 60}}, {"lvl": {"$gte": 60}}]})) # # TODO: also try to account for our mistakes "lvl" vs "level" # breakpoint() # print(strong)

26.685864

150

0.566314

import pymongo import os from dotenv import load_dotenv import sqlite3 load_dotenv() DB_USER = os.getenv("MONGO_USER", default="OOPS") DB_PASSWORD = os.getenv("MONGO_PASSWORD", default="OOPS") CLUSTER_NAME = os.getenv("MONGO_CLUSTER_NAME", default="OOPS") connection_uri = f"mongodb+srv://{DB_USER}:{DB_PASSWORD}@{CLUSTER_NAME}.mongodb.net/test?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE" print("----------------") print("URI:", connection_uri) client = pymongo.MongoClient(connection_uri) print("----------------") print("CLIENT:", type(client), client)

true

790193b2894b656e53418e9157030a6e99a9f686

2,909

py

Python

django/contrib/contenttypes/tests.py

jamespacileo/django

9d3f86c72f5d22113b8cb5cd006abb9297f2fd4e

[ "BSD-3-Clause" ]

2

2016-01-21T14:59:43.000Z

2017-09-21T09:50:36.000Z

django/contrib/contenttypes/tests.py

coderanger/django

4d358bd07c81badb95bf672d60fc131fe1c28789

[ "BSD-3-Clause" ]

1

2022-02-11T15:34:08.000Z

django/contrib/contenttypes/tests.py

jamespacileo/django

9d3f86c72f5d22113b8cb5cd006abb9297f2fd4e

[ "BSD-3-Clause" ]

null

from django import db from django.conf import settings from django.contrib.contenttypes.models import ContentType from django.contrib.sites.models import Site from django.contrib.contenttypes.views import shortcut from django.core.exceptions import ObjectDoesNotExist from django.http import HttpRequest from django.test import TestCase class ContentTypesTests(TestCase): def setUp(self): # First, let's make sure we're dealing with a blank slate (and that # DEBUG is on so that queries get logged) self.old_DEBUG = settings.DEBUG self.old_Site_meta_installed = Site._meta.installed settings.DEBUG = True ContentType.objects.clear_cache() db.reset_queries() def tearDown(self): settings.DEBUG = self.old_DEBUG Site._meta.installed = self.old_Site_meta_installed def test_lookup_cache(self): """ Make sure that the content type cache (see ContentTypeManager) works correctly. Lookups for a particular content type -- by model or by ID -- should hit the database only on the first lookup. """ # At this point, a lookup for a ContentType should hit the DB ContentType.objects.get_for_model(ContentType) self.assertEqual(1, len(db.connection.queries)) # A second hit, though, won't hit the DB, nor will a lookup by ID ct = ContentType.objects.get_for_model(ContentType) self.assertEqual(1, len(db.connection.queries)) ContentType.objects.get_for_id(ct.id) self.assertEqual(1, len(db.connection.queries)) # Once we clear the cache, another lookup will again hit the DB ContentType.objects.clear_cache() ContentType.objects.get_for_model(ContentType) len(db.connection.queries) self.assertEqual(2, len(db.connection.queries)) def test_shortcut_view(self): """ Check that the shortcut view (used for the admin "view on site" functionality) returns a complete URL regardless of whether the sites framework is installed """ request = HttpRequest() request.META = { "SERVER_NAME": "Example.com", "SERVER_PORT": "80", } from django.contrib.auth.models import User user_ct = ContentType.objects.get_for_model(User) obj = User.objects.create(username="john") if Site._meta.installed: current_site = Site.objects.get_current() response = shortcut(request, user_ct.id, obj.id) self.assertEqual("http://%s/users/john/" % current_site.domain, response._headers.get("location")[1]) Site._meta.installed = False response = shortcut(request, user_ct.id, obj.id) self.assertEqual("http://Example.com/users/john/", response._headers.get("location")[1])

38.786667

77

0.663802

from django import db from django.conf import settings from django.contrib.contenttypes.models import ContentType from django.contrib.sites.models import Site from django.contrib.contenttypes.views import shortcut from django.core.exceptions import ObjectDoesNotExist from django.http import HttpRequest from django.test import TestCase class ContentTypesTests(TestCase): def setUp(self): self.old_DEBUG = settings.DEBUG self.old_Site_meta_installed = Site._meta.installed settings.DEBUG = True ContentType.objects.clear_cache() db.reset_queries() def tearDown(self): settings.DEBUG = self.old_DEBUG Site._meta.installed = self.old_Site_meta_installed def test_lookup_cache(self): ContentType.objects.get_for_model(ContentType) self.assertEqual(1, len(db.connection.queries)) ct = ContentType.objects.get_for_model(ContentType) self.assertEqual(1, len(db.connection.queries)) ContentType.objects.get_for_id(ct.id) self.assertEqual(1, len(db.connection.queries)) # Once we clear the cache, another lookup will again hit the DB ContentType.objects.clear_cache() ContentType.objects.get_for_model(ContentType) len(db.connection.queries) self.assertEqual(2, len(db.connection.queries)) def test_shortcut_view(self): request = HttpRequest() request.META = { "SERVER_NAME": "Example.com", "SERVER_PORT": "80", } from django.contrib.auth.models import User user_ct = ContentType.objects.get_for_model(User) obj = User.objects.create(username="john") if Site._meta.installed: current_site = Site.objects.get_current() response = shortcut(request, user_ct.id, obj.id) self.assertEqual("http://%s/users/john/" % current_site.domain, response._headers.get("location")[1]) Site._meta.installed = False response = shortcut(request, user_ct.id, obj.id) self.assertEqual("http://Example.com/users/john/", response._headers.get("location")[1])

true

790193e67c0db381911710bf7ce3a6907825f58b

1,737

py

Python

flash_examples/object_detection.py

tszumowski/lightning-flash

d094fee4065d3d8d1337eed451041ee17fdf50aa

[ "Apache-2.0" ]

null

flash_examples/object_detection.py

tszumowski/lightning-flash

d094fee4065d3d8d1337eed451041ee17fdf50aa

[ "Apache-2.0" ]

null

flash_examples/object_detection.py

tszumowski/lightning-flash

d094fee4065d3d8d1337eed451041ee17fdf50aa

[ "Apache-2.0" ]

null

# Copyright The PyTorch Lightning 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 torch import flash from flash.core.data.utils import download_data from flash.image import ObjectDetectionData, ObjectDetector # 1. Create the DataModule # Dataset Credit: https://www.kaggle.com/ultralytics/coco128 download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/") datamodule = ObjectDetectionData.from_coco( train_folder="data/coco128/images/train2017/", train_ann_file="data/coco128/annotations/instances_train2017.json", val_split=0.1, batch_size=2, ) # 2. Build the task model = ObjectDetector(model="retinanet", num_classes=datamodule.num_classes) # 3. Create the trainer and finetune the model trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count()) trainer.finetune(model, datamodule=datamodule) # 4. Detect objects in a few images! predictions = model.predict( [ "data/coco128/images/train2017/000000000625.jpg", "data/coco128/images/train2017/000000000626.jpg", "data/coco128/images/train2017/000000000629.jpg", ] ) print(predictions) # 5. Save the model! trainer.save_checkpoint("object_detection_model.pt")

34.74

106

0.763961

import torch import flash from flash.core.data.utils import download_data from flash.image import ObjectDetectionData, ObjectDetector download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/") datamodule = ObjectDetectionData.from_coco( train_folder="data/coco128/images/train2017/", train_ann_file="data/coco128/annotations/instances_train2017.json", val_split=0.1, batch_size=2, ) model = ObjectDetector(model="retinanet", num_classes=datamodule.num_classes) trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count()) trainer.finetune(model, datamodule=datamodule) predictions = model.predict( [ "data/coco128/images/train2017/000000000625.jpg", "data/coco128/images/train2017/000000000626.jpg", "data/coco128/images/train2017/000000000629.jpg", ] ) print(predictions) trainer.save_checkpoint("object_detection_model.pt")

true

7901947e2a69429f5625edd884761977b10d5f6b

1,815

py

Python

tests/test_electricity.py

tngTUDOR/premise

f3ab48b590afaefe6ef431846561e934cac35de9

[ "BSD-3-Clause" ]

null

tests/test_electricity.py

tngTUDOR/premise

f3ab48b590afaefe6ef431846561e934cac35de9

[ "BSD-3-Clause" ]

null

tests/test_electricity.py

tngTUDOR/premise

f3ab48b590afaefe6ef431846561e934cac35de9

[ "BSD-3-Clause" ]

null

# content of test_electricity.py from premise import DATA_DIR from premise.electricity import Electricity from premise.data_collection import IAMDataCollection REGION_MAPPING_FILEPATH = (DATA_DIR / "regionmappingH12.csv") PRODUCTION_PER_TECH = (DATA_DIR / "electricity" / "electricity_production_volumes_per_tech.csv") LOSS_PER_COUNTRY = (DATA_DIR / "electricity" / "losses_per_country.csv") LHV_FUELS = (DATA_DIR / "fuels_lower_heating_value.txt") def get_db(): dummy_db = [{ 'name': 'fake activity', 'reference product': 'fake product', 'location': 'IAI Area, Africa', 'unit': 'kilogram', 'exchanges': [ {'name': 'fake activity', 'product': 'fake product', 'amount': 1, 'type': 'production', 'unit': 'kilogram', 'input': ('dummy_db', '6543541'), }, {'name': '1,4-Butanediol', 'categories': ('air', 'urban air close to ground'), 'amount': 1, 'type': 'biosphere', 'unit': 'kilogram', 'input': ('dummy_bio', '123'), }, ] }] version = 3.5 return dummy_db, version rdc = IAMDataCollection(model="remind", pathway='SSP2-Base', year=2012, filepath_iam_files=DATA_DIR / "iam_output_files") db, _ = get_db() el = Electricity(db=db, iam_data=rdc, model="remind", pathway='SSP2-Base', year=2012) def test_losses(): assert len(el.losses) == 174 assert el.losses['AL']['Production volume'] == 7630 def test_fuels_lhv(): assert float(el.fuels_lhv['hard coal']) == 20.1 def test_powerplant_map(): s = el.powerplant_map['Biomass IGCC CCS'] assert isinstance(s, set) def test_emissions_map(): s = el.emissions_map['Sulfur dioxide'] assert isinstance(s, str)

30.25

121

0.616529

from premise import DATA_DIR from premise.electricity import Electricity from premise.data_collection import IAMDataCollection REGION_MAPPING_FILEPATH = (DATA_DIR / "regionmappingH12.csv") PRODUCTION_PER_TECH = (DATA_DIR / "electricity" / "electricity_production_volumes_per_tech.csv") LOSS_PER_COUNTRY = (DATA_DIR / "electricity" / "losses_per_country.csv") LHV_FUELS = (DATA_DIR / "fuels_lower_heating_value.txt") def get_db(): dummy_db = [{ 'name': 'fake activity', 'reference product': 'fake product', 'location': 'IAI Area, Africa', 'unit': 'kilogram', 'exchanges': [ {'name': 'fake activity', 'product': 'fake product', 'amount': 1, 'type': 'production', 'unit': 'kilogram', 'input': ('dummy_db', '6543541'), }, {'name': '1,4-Butanediol', 'categories': ('air', 'urban air close to ground'), 'amount': 1, 'type': 'biosphere', 'unit': 'kilogram', 'input': ('dummy_bio', '123'), }, ] }] version = 3.5 return dummy_db, version rdc = IAMDataCollection(model="remind", pathway='SSP2-Base', year=2012, filepath_iam_files=DATA_DIR / "iam_output_files") db, _ = get_db() el = Electricity(db=db, iam_data=rdc, model="remind", pathway='SSP2-Base', year=2012) def test_losses(): assert len(el.losses) == 174 assert el.losses['AL']['Production volume'] == 7630 def test_fuels_lhv(): assert float(el.fuels_lhv['hard coal']) == 20.1 def test_powerplant_map(): s = el.powerplant_map['Biomass IGCC CCS'] assert isinstance(s, set) def test_emissions_map(): s = el.emissions_map['Sulfur dioxide'] assert isinstance(s, str)

true

7901954b2a84511020bb138f9a4bd5abd9e54aa6

11,497

py

Python

optuna/structs.py

VladSkripniuk/optuna

81d5b67a81ae14d606e6d6120ce50d02e90b0942

[ "MIT" ]

null

optuna/structs.py

VladSkripniuk/optuna

81d5b67a81ae14d606e6d6120ce50d02e90b0942

[ "MIT" ]

null

optuna/structs.py

VladSkripniuk/optuna

81d5b67a81ae14d606e6d6120ce50d02e90b0942

[ "MIT" ]

null

import enum import warnings from optuna import exceptions from optuna import logging from optuna import type_checking if type_checking.TYPE_CHECKING: from datetime import datetime # NOQA from typing import Any # NOQA from typing import Dict # NOQA from typing import Optional # NOQA from optuna.distributions import BaseDistribution # NOQA class TrialState(enum.Enum): """State of a :class:`~optuna.trial.Trial`. Attributes: RUNNING: The :class:`~optuna.trial.Trial` is running. COMPLETE: The :class:`~optuna.trial.Trial` has been finished without any error. PRUNED: The :class:`~optuna.trial.Trial` has been pruned with :class:`~optuna.exceptions.TrialPruned`. FAIL: The :class:`~optuna.trial.Trial` has failed due to an uncaught error. """ RUNNING = 0 COMPLETE = 1 PRUNED = 2 FAIL = 3 WAITING = 4 def __repr__(self): # type: () -> str return str(self) def is_finished(self): # type: () -> bool return self != TrialState.RUNNING and self != TrialState.WAITING class StudyDirection(enum.Enum): """Direction of a :class:`~optuna.study.Study`. Attributes: NOT_SET: Direction has not been set. MINIMIZE: :class:`~optuna.study.Study` minimizes the objective function. MAXIMIZE: :class:`~optuna.study.Study` maximizes the objective function. """ NOT_SET = 0 MINIMIZE = 1 MAXIMIZE = 2 class FrozenTrial(object): """Status and results of a :class:`~optuna.trial.Trial`. Attributes: number: Unique and consecutive number of :class:`~optuna.trial.Trial` for each :class:`~optuna.study.Study`. Note that this field uses zero-based numbering. state: :class:`TrialState` of the :class:`~optuna.trial.Trial`. value: Objective value of the :class:`~optuna.trial.Trial`. datetime_start: Datetime where the :class:`~optuna.trial.Trial` started. datetime_complete: Datetime where the :class:`~optuna.trial.Trial` finished. params: Dictionary that contains suggested parameters. distributions: Dictionary that contains the distributions of :attr:`params`. user_attrs: Dictionary that contains the attributes of the :class:`~optuna.trial.Trial` set with :func:`optuna.trial.Trial.set_user_attr`. intermediate_values: Intermediate objective values set with :func:`optuna.trial.Trial.report`. """ def __init__( self, number, # type: int state, # type: TrialState value, # type: Optional[float] datetime_start, # type: Optional[datetime] datetime_complete, # type: Optional[datetime] params, # type: Dict[str, Any] distributions, # type: Dict[str, BaseDistribution] user_attrs, # type: Dict[str, Any] system_attrs, # type: Dict[str, Any] intermediate_values, # type: Dict[int, float] trial_id, # type: int ): # type: (...) -> None self.number = number self.state = state self.value = value self.datetime_start = datetime_start self.datetime_complete = datetime_complete self.params = params self.user_attrs = user_attrs self.system_attrs = system_attrs self.intermediate_values = intermediate_values self._distributions = distributions self._trial_id = trial_id # Ordered list of fields required for `__repr__`, `__hash__` and dataframe creation. # TODO(hvy): Remove this list in Python 3.6 as the order of `self.__dict__` is preserved. _ordered_fields = [ 'number', 'value', 'datetime_start', 'datetime_complete', 'params', '_distributions', 'user_attrs', 'system_attrs', 'intermediate_values', '_trial_id', 'state', ] def __eq__(self, other): # type: (Any) -> bool if not isinstance(other, FrozenTrial): return NotImplemented return other.__dict__ == self.__dict__ def __lt__(self, other): # type: (Any) -> bool if not isinstance(other, FrozenTrial): return NotImplemented return self.number < other.number def __le__(self, other): # type: (Any) -> bool if not isinstance(other, FrozenTrial): return NotImplemented return self.number <= other.number def __hash__(self): # type: () -> int return hash(tuple(getattr(self, field) for field in self._ordered_fields)) def __repr__(self): # type: () -> str return ('{cls}({kwargs})'.format( cls=self.__class__.__name__, kwargs=', '.join('{field}={value}'.format( field=field if not field.startswith('_') else field[1:], value=repr(getattr(self, field))) for field in self._ordered_fields))) def _validate(self): # type: () -> None if self.datetime_start is None: raise ValueError('`datetime_start` is supposed to be set.') if self.state.is_finished(): if self.datetime_complete is None: raise ValueError('`datetime_complete` is supposed to be set for a finished trial.') else: if self.datetime_complete is not None: raise ValueError( '`datetime_complete` is supposed to not be set for a finished trial.') if self.state == TrialState.COMPLETE and self.value is None: raise ValueError('`value` is supposed to be set for a complete trial.') if set(self.params.keys()) != set(self.distributions.keys()): raise ValueError('Inconsistent parameters {} and distributions {}.'.format( set(self.params.keys()), set(self.distributions.keys()))) for param_name, param_value in self.params.items(): distribution = self.distributions[param_name] param_value_in_internal_repr = distribution.to_internal_repr(param_value) if not distribution._contains(param_value_in_internal_repr): raise ValueError( "The value {} of parameter '{}' isn't contained in the distribution {}.". format(param_value, param_name, distribution)) @property def distributions(self): # type: () -> Dict[str, BaseDistribution] """Return the distributions for this trial. Returns: The distributions. """ return self._distributions @distributions.setter def distributions(self, value): # type: (Dict[str, BaseDistribution]) -> None """Set the distributions for this trial. Args: value: The distributions. """ self._distributions = value @property def trial_id(self): # type: () -> int """Return the trial ID. .. deprecated:: 0.19.0 The direct use of this attribute is deprecated and it is recommended that you use :attr:`~optuna.trial.FrozenTrial.number` instead. Returns: The trial ID. """ warnings.warn( 'The use of `FrozenTrial.trial_id` is deprecated. ' 'Please use `FrozenTrial.number` instead.', DeprecationWarning) logger = logging.get_logger(__name__) logger.warning( 'The use of `FrozenTrial.trial_id` is deprecated. ' 'Please use `FrozenTrial.number` instead.') return self._trial_id @property def last_step(self): # type: () -> Optional[int] if len(self.intermediate_values) == 0: return None else: return max(self.intermediate_values.keys()) class StudySummary(object): """Basic attributes and aggregated results of a :class:`~optuna.study.Study`. See also :func:`optuna.study.get_all_study_summaries`. Attributes: study_name: Name of the :class:`~optuna.study.Study`. direction: :class:`StudyDirection` of the :class:`~optuna.study.Study`. best_trial: :class:`FrozenTrial` with best objective value in the :class:`~optuna.study.Study`. user_attrs: Dictionary that contains the attributes of the :class:`~optuna.study.Study` set with :func:`optuna.study.Study.set_user_attr`. system_attrs: Dictionary that contains the attributes of the :class:`~optuna.study.Study` internally set by Optuna. n_trials: The number of trials ran in the :class:`~optuna.study.Study`. datetime_start: Datetime where the :class:`~optuna.study.Study` started. """ def __init__( self, study_name, # type: str direction, # type: StudyDirection best_trial, # type: Optional[FrozenTrial] user_attrs, # type: Dict[str, Any] system_attrs, # type: Dict[str, Any] n_trials, # type: int datetime_start, # type: Optional[datetime] study_id, # type: int ): # type: (...) -> None self.study_name = study_name self.direction = direction self.best_trial = best_trial self.user_attrs = user_attrs self.system_attrs = system_attrs self.n_trials = n_trials self.datetime_start = datetime_start self._study_id = study_id def __eq__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return other.__dict__ == self.__dict__ def __lt__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return self._study_id < other._study_id def __le__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return self._study_id <= other._study_id @property def study_id(self): # type: () -> int """Return the study ID. .. deprecated:: 0.20.0 The direct use of this attribute is deprecated and it is recommended that you use :attr:`~optuna.structs.StudySummary.study_name` instead. Returns: The study ID. """ message = 'The use of `StudySummary.study_id` is deprecated. ' \ 'Please use `StudySummary.study_name` instead.' warnings.warn(message, DeprecationWarning) logger = logging.get_logger(__name__) logger.warning(message) return self._study_id class TrialPruned(exceptions.TrialPruned): """Exception for pruned trials. .. deprecated:: 0.19.0 This class was moved to :mod:`~optuna.exceptions`. Please use :class:`~optuna.exceptions.TrialPruned` instead. """ def __init__(self, *args, **kwargs): # type: (Any, Any) -> None message = 'The use of `optuna.structs.TrialPruned` is deprecated. ' \ 'Please use `optuna.exceptions.TrialPruned` instead.' warnings.warn(message, DeprecationWarning) logger = logging.get_logger(__name__) logger.warning(message)

32.02507

99

0.604593

import enum import warnings from optuna import exceptions from optuna import logging from optuna import type_checking if type_checking.TYPE_CHECKING: from datetime import datetime from typing import Any from typing import Dict from typing import Optional from optuna.distributions import BaseDistribution class TrialState(enum.Enum): RUNNING = 0 COMPLETE = 1 PRUNED = 2 FAIL = 3 WAITING = 4 def __repr__(self): return str(self) def is_finished(self): return self != TrialState.RUNNING and self != TrialState.WAITING class StudyDirection(enum.Enum): NOT_SET = 0 MINIMIZE = 1 MAXIMIZE = 2 class FrozenTrial(object): def __init__( self, number, state, value, datetime_start, datetime_complete, params, distributions, user_attrs, system_attrs, intermediate_values, trial_id, ): self.number = number self.state = state self.value = value self.datetime_start = datetime_start self.datetime_complete = datetime_complete self.params = params self.user_attrs = user_attrs self.system_attrs = system_attrs self.intermediate_values = intermediate_values self._distributions = distributions self._trial_id = trial_id _ordered_fields = [ 'number', 'value', 'datetime_start', 'datetime_complete', 'params', '_distributions', 'user_attrs', 'system_attrs', 'intermediate_values', '_trial_id', 'state', ] def __eq__(self, other): if not isinstance(other, FrozenTrial): return NotImplemented return other.__dict__ == self.__dict__ def __lt__(self, other): if not isinstance(other, FrozenTrial): return NotImplemented return self.number < other.number def __le__(self, other): if not isinstance(other, FrozenTrial): return NotImplemented return self.number <= other.number def __hash__(self): return hash(tuple(getattr(self, field) for field in self._ordered_fields)) def __repr__(self): return ('{cls}({kwargs})'.format( cls=self.__class__.__name__, kwargs=', '.join('{field}={value}'.format( field=field if not field.startswith('_') else field[1:], value=repr(getattr(self, field))) for field in self._ordered_fields))) def _validate(self): if self.datetime_start is None: raise ValueError('`datetime_start` is supposed to be set.') if self.state.is_finished(): if self.datetime_complete is None: raise ValueError('`datetime_complete` is supposed to be set for a finished trial.') else: if self.datetime_complete is not None: raise ValueError( '`datetime_complete` is supposed to not be set for a finished trial.') if self.state == TrialState.COMPLETE and self.value is None: raise ValueError('`value` is supposed to be set for a complete trial.') if set(self.params.keys()) != set(self.distributions.keys()): raise ValueError('Inconsistent parameters {} and distributions {}.'.format( set(self.params.keys()), set(self.distributions.keys()))) for param_name, param_value in self.params.items(): distribution = self.distributions[param_name] param_value_in_internal_repr = distribution.to_internal_repr(param_value) if not distribution._contains(param_value_in_internal_repr): raise ValueError( "The value {} of parameter '{}' isn't contained in the distribution {}.". format(param_value, param_name, distribution)) @property def distributions(self): # type: () -> Dict[str, BaseDistribution] return self._distributions @distributions.setter def distributions(self, value): # type: (Dict[str, BaseDistribution]) -> None self._distributions = value @property def trial_id(self): # type: () -> int warnings.warn( 'The use of `FrozenTrial.trial_id` is deprecated. ' 'Please use `FrozenTrial.number` instead.', DeprecationWarning) logger = logging.get_logger(__name__) logger.warning( 'The use of `FrozenTrial.trial_id` is deprecated. ' 'Please use `FrozenTrial.number` instead.') return self._trial_id @property def last_step(self): # type: () -> Optional[int] if len(self.intermediate_values) == 0: return None else: return max(self.intermediate_values.keys()) class StudySummary(object): def __init__( self, study_name, # type: str direction, # type: StudyDirection best_trial, # type: Optional[FrozenTrial] user_attrs, # type: Dict[str, Any] system_attrs, # type: Dict[str, Any] n_trials, # type: int datetime_start, # type: Optional[datetime] study_id, # type: int ): # type: (...) -> None self.study_name = study_name self.direction = direction self.best_trial = best_trial self.user_attrs = user_attrs self.system_attrs = system_attrs self.n_trials = n_trials self.datetime_start = datetime_start self._study_id = study_id def __eq__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return other.__dict__ == self.__dict__ def __lt__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return self._study_id < other._study_id def __le__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return self._study_id <= other._study_id @property def study_id(self): # type: () -> int message = 'The use of `StudySummary.study_id` is deprecated. ' \ 'Please use `StudySummary.study_name` instead.' warnings.warn(message, DeprecationWarning) logger = logging.get_logger(__name__) logger.warning(message) return self._study_id class TrialPruned(exceptions.TrialPruned): def __init__(self, *args, **kwargs): # type: (Any, Any) -> None message = 'The use of `optuna.structs.TrialPruned` is deprecated. ' \ 'Please use `optuna.exceptions.TrialPruned` instead.' warnings.warn(message, DeprecationWarning) logger = logging.get_logger(__name__) logger.warning(message)

true

79019570f0ce5422b5ebfd62f0b79654a0cff3e6

4,541

py

Python

airbyte-integrations/connectors/source-tiktok-marketing/source_tiktok_marketing/spec.py

Daemonxiao/airbyte

34146564ba17423da8000e983722094f2426367e

[ "MIT" ]

null

airbyte-integrations/connectors/source-tiktok-marketing/source_tiktok_marketing/spec.py

Daemonxiao/airbyte

34146564ba17423da8000e983722094f2426367e

[ "MIT" ]

null

airbyte-integrations/connectors/source-tiktok-marketing/source_tiktok_marketing/spec.py

Daemonxiao/airbyte

34146564ba17423da8000e983722094f2426367e

[ "MIT" ]

null

# # Copyright (c) 2021 Airbyte, Inc., all rights reserved. # import json import re from typing import Union from jsonschema import RefResolver from pydantic import BaseModel, Field from .streams import DEFAULT_START_DATE, ReportGranularity class OauthCredSpec(BaseModel): class Config: title = "OAuth2.0" auth_type: str = Field(default="oauth2.0", const=True, order=0) app_id: str = Field(title="App ID", description="The App ID applied by the developer.", airbyte_secret=True) secret: str = Field(title="Secret", description="The private key of the developer's application.", airbyte_secret=True) access_token: str = Field(title="Access Token", description="Long-term Authorized Access Token.", airbyte_secret=True) class SandboxEnvSpec(BaseModel): class Config: title = "Sandbox Access Token" auth_type: str = Field(default="sandbox_access_token", const=True, order=0) # it is string because UI has the bug https://github.com/airbytehq/airbyte/issues/6875 advertiser_id: str = Field( title="Advertiser ID", description="The Advertiser ID which generated for the developer's Sandbox application." ) access_token: str = Field(title="Access Token", description="The Long-term Authorized Access Token.", airbyte_secret=True) class ProductionEnvSpec(BaseModel): class Config: title = "Production Access Token" auth_type: str = Field(default="prod_access_token", const=True, order=0) # it is float because UI has the bug https://github.com/airbytehq/airbyte/issues/6875 app_id: str = Field(description="The App ID applied by the developer.", title="App ID") secret: str = Field(title="Secret", description="The private key of the developer application.", airbyte_secret=True) access_token: str = Field(title="Access Token", description="The Long-term Authorized Access Token.", airbyte_secret=True) class SourceTiktokMarketingSpec(BaseModel): class Config: title = "TikTok Marketing Source Spec" start_date: str = Field( title="Start Date", default=DEFAULT_START_DATE, pattern="^[0-9]{4}-[0-9]{2}-[0-9]{2}$", description="The Start Date in format: YYYY-MM-DD. Any data before this date will not be replicated. " "If this parameter is not set, all data will be replicated.", order=0, ) report_granularity: str = Field( title="Report Granularity", description="Which time granularity should be grouped by; for LIFETIME there will be no grouping. " "This option is used for reports' streams only.", default=ReportGranularity.default().value, enum=[g.value for g in ReportGranularity], order=1, ) credentials: Union[OauthCredSpec, ProductionEnvSpec, SandboxEnvSpec] = Field( title="Authorization Method", order=3, default={}, type="object" ) @classmethod def change_format_to_oneOf(cls, schema: dict) -> dict: new_schema = {} for key, value in schema.items(): if isinstance(value, dict): value = cls.change_format_to_oneOf(value) if key == "anyOf": new_schema["oneOf"] = value else: new_schema[key] = value return new_schema @staticmethod def resolve_refs(schema: dict) -> dict: json_schema_ref_resolver = RefResolver.from_schema(schema) str_schema = json.dumps(schema) for ref_block in re.findall(r'{"\$ref": "#\/definitions\/.+?(?="})"}', str_schema): ref = json.loads(ref_block)["$ref"] str_schema = str_schema.replace(ref_block, json.dumps(json_schema_ref_resolver.resolve(ref)[1])) pyschema = json.loads(str_schema) del pyschema["definitions"] return pyschema @classmethod def schema(cls) -> dict: """we're overriding the schema classmethod to enable some post-processing""" schema = super().schema() schema = cls.change_format_to_oneOf(schema) return cls.resolve_refs(schema) class CompleteOauthOutputSpecification(BaseModel): access_token: str = Field(path_in_connector_config=["credentials", "access_token"]) class CompleteOauthServerInputSpecification(BaseModel): app_id: str = Field() secret: str = Field() class CompleteOauthServerOutputSpecification(BaseModel): app_id: str = Field(path_in_connector_config=["credentials", "app_id"]) secret: str = Field(path_in_connector_config=["credentials", "secret"])

36.328

126

0.68355

import json import re from typing import Union from jsonschema import RefResolver from pydantic import BaseModel, Field from .streams import DEFAULT_START_DATE, ReportGranularity class OauthCredSpec(BaseModel): class Config: title = "OAuth2.0" auth_type: str = Field(default="oauth2.0", const=True, order=0) app_id: str = Field(title="App ID", description="The App ID applied by the developer.", airbyte_secret=True) secret: str = Field(title="Secret", description="The private key of the developer's application.", airbyte_secret=True) access_token: str = Field(title="Access Token", description="Long-term Authorized Access Token.", airbyte_secret=True) class SandboxEnvSpec(BaseModel): class Config: title = "Sandbox Access Token" auth_type: str = Field(default="sandbox_access_token", const=True, order=0) # it is string because UI has the bug https://github.com/airbytehq/airbyte/issues/6875 advertiser_id: str = Field( title="Advertiser ID", description="The Advertiser ID which generated for the developer's Sandbox application." ) access_token: str = Field(title="Access Token", description="The Long-term Authorized Access Token.", airbyte_secret=True) class ProductionEnvSpec(BaseModel): class Config: title = "Production Access Token" auth_type: str = Field(default="prod_access_token", const=True, order=0) app_id: str = Field(description="The App ID applied by the developer.", title="App ID") secret: str = Field(title="Secret", description="The private key of the developer application.", airbyte_secret=True) access_token: str = Field(title="Access Token", description="The Long-term Authorized Access Token.", airbyte_secret=True) class SourceTiktokMarketingSpec(BaseModel): class Config: title = "TikTok Marketing Source Spec" start_date: str = Field( title="Start Date", default=DEFAULT_START_DATE, pattern="^[0-9]{4}-[0-9]{2}-[0-9]{2}$", description="The Start Date in format: YYYY-MM-DD. Any data before this date will not be replicated. " "If this parameter is not set, all data will be replicated.", order=0, ) report_granularity: str = Field( title="Report Granularity", description="Which time granularity should be grouped by; for LIFETIME there will be no grouping. " "This option is used for reports' streams only.", default=ReportGranularity.default().value, enum=[g.value for g in ReportGranularity], order=1, ) credentials: Union[OauthCredSpec, ProductionEnvSpec, SandboxEnvSpec] = Field( title="Authorization Method", order=3, default={}, type="object" ) @classmethod def change_format_to_oneOf(cls, schema: dict) -> dict: new_schema = {} for key, value in schema.items(): if isinstance(value, dict): value = cls.change_format_to_oneOf(value) if key == "anyOf": new_schema["oneOf"] = value else: new_schema[key] = value return new_schema @staticmethod def resolve_refs(schema: dict) -> dict: json_schema_ref_resolver = RefResolver.from_schema(schema) str_schema = json.dumps(schema) for ref_block in re.findall(r'{"\$ref": "#\/definitions\/.+?(?="})"}', str_schema): ref = json.loads(ref_block)["$ref"] str_schema = str_schema.replace(ref_block, json.dumps(json_schema_ref_resolver.resolve(ref)[1])) pyschema = json.loads(str_schema) del pyschema["definitions"] return pyschema @classmethod def schema(cls) -> dict: schema = super().schema() schema = cls.change_format_to_oneOf(schema) return cls.resolve_refs(schema) class CompleteOauthOutputSpecification(BaseModel): access_token: str = Field(path_in_connector_config=["credentials", "access_token"]) class CompleteOauthServerInputSpecification(BaseModel): app_id: str = Field() secret: str = Field() class CompleteOauthServerOutputSpecification(BaseModel): app_id: str = Field(path_in_connector_config=["credentials", "app_id"]) secret: str = Field(path_in_connector_config=["credentials", "secret"])

true

7901972788c7e85bdf00e42e2955aa26e1516f7f

839

py

Python

examples/Basic_Disease_Models/Example_1/generate_events.py

healthbadge/episimmer

fcb3f7df812be045e2a6d031cac42080ad850d60

[ "BSD-3-Clause" ]

16

2021-04-26T14:52:32.000Z

2022-01-22T07:13:06.000Z

examples/Basic_Disease_Models/Example_1/generate_events.py

healthbadge/episimmer

fcb3f7df812be045e2a6d031cac42080ad850d60

[ "BSD-3-Clause" ]

34

2021-05-21T12:53:24.000Z

2022-02-09T16:30:40.000Z

examples/Basic_Disease_Models/Example_1/generate_events.py

healthbadge/episimmer

fcb3f7df812be045e2a6d031cac42080ad850d60

[ "BSD-3-Clause" ]

4

2021-04-08T07:52:06.000Z

2021-05-29T05:58:15.000Z

import random import numpy as np def write_to_file(filename,no_locations,no_agents): info_dict={} #ID enumerates from 0 to n-1 header='Location Index:Agents:Time Interval' n=random.randint(10,20) f=open(filename,'w') f.write(str(n)+'\n') f.write(header+'\n') for i in range(n): line=str(random.randint(0,no_locations-1))+':' for i in range(random.randint(0,20)): line+=str(random.randint(0,no_agents-1))+',' line+=str(random.randint(0,no_agents-1)) line+=':'+str(random.choice([10,30,45,60]))+'\n' f.write(line) write_to_file('monday_events.txt',10,100) write_to_file('tuesday_events.txt',10,100) write_to_file('wednesday_events.txt',10,100) write_to_file('thursday_events.txt',10,100) write_to_file('friday_events.txt',10,100) write_to_file('saturday_events.txt',5,100) write_to_file('sunday_events.txt',2,100)

27.064516

51

0.72944

import random import numpy as np def write_to_file(filename,no_locations,no_agents): info_dict={} header='Location Index:Agents:Time Interval' n=random.randint(10,20) f=open(filename,'w') f.write(str(n)+'\n') f.write(header+'\n') for i in range(n): line=str(random.randint(0,no_locations-1))+':' for i in range(random.randint(0,20)): line+=str(random.randint(0,no_agents-1))+',' line+=str(random.randint(0,no_agents-1)) line+=':'+str(random.choice([10,30,45,60]))+'\n' f.write(line) write_to_file('monday_events.txt',10,100) write_to_file('tuesday_events.txt',10,100) write_to_file('wednesday_events.txt',10,100) write_to_file('thursday_events.txt',10,100) write_to_file('friday_events.txt',10,100) write_to_file('saturday_events.txt',5,100) write_to_file('sunday_events.txt',2,100)

true

790198c2c1cf564a270c200b70a30d603910f570

7,354

py

Python

python_modules/dagster/dagster/core/storage/event_log/polling_event_watcher.py

dbatten5/dagster

d76e50295054ffe5a72f9b292ef57febae499528

[ "Apache-2.0" ]

4,606

2018-06-21T17:45:20.000Z

2022-03-31T23:39:42.000Z

python_modules/dagster/dagster/core/storage/event_log/polling_event_watcher.py

dbatten5/dagster

d76e50295054ffe5a72f9b292ef57febae499528

[ "Apache-2.0" ]

6,221

2018-06-12T04:36:01.000Z

2022-03-31T21:43:05.000Z

python_modules/dagster/dagster/core/storage/event_log/polling_event_watcher.py

dbatten5/dagster

d76e50295054ffe5a72f9b292ef57febae499528

[ "Apache-2.0" ]

619

2018-08-22T22:43:09.000Z

2022-03-31T22:48:06.000Z

import threading from typing import Callable, List, MutableMapping, NamedTuple from dagster import check from dagster.core.events.log import EventLogEntry from .sql_event_log import SqlEventLogStorage POLLING_CADENCE = 0.1 # 100 ms class CallbackAfterCursor(NamedTuple): """Callback passed from Observer class in event polling start_cursor (int): Only process EventLogEntrys with an id >= start_cursor (earlier ones have presumably already been processed) callback (Callable[[EventLogEntry], None]): callback passed from Observer to call on new EventLogEntrys """ start_cursor: int callback: Callable[[EventLogEntry], None] class SqlPollingEventWatcher: """Event Log Watcher that uses a multithreaded polling approach to retrieving new events for run_ids This class' job is to manage a collection of threads that each poll the event log for a given run_id Uses one thread (SqlPollingRunIdEventWatcherThread) per watched run_id LOCKING INFO: ORDER: _dict_lock -> run_id_thread.callback_fn_list_lock INVARIANTS: _dict_lock protects _run_id_to_watcher_dict """ def __init__(self, event_log_storage: SqlEventLogStorage): self._event_log_storage = check.inst_param( event_log_storage, "event_log_storage", SqlEventLogStorage ) # INVARIANT: dict_lock protects _run_id_to_watcher_dict self._dict_lock: threading.Lock = threading.Lock() self._run_id_to_watcher_dict: MutableMapping[str, SqlPollingRunIdEventWatcherThread] = {} self._disposed = False def has_run_id(self, run_id: str) -> bool: run_id = check.str_param(run_id, "run_id") with self._dict_lock: _has_run_id = run_id in self._run_id_to_watcher_dict return _has_run_id def watch_run(self, run_id: str, start_cursor: int, callback: Callable[[EventLogEntry], None]): run_id = check.str_param(run_id, "run_id") start_cursor = check.int_param(start_cursor, "start_cursor") callback = check.callable_param(callback, "callback") with self._dict_lock: if run_id not in self._run_id_to_watcher_dict: self._run_id_to_watcher_dict[run_id] = SqlPollingRunIdEventWatcherThread( self._event_log_storage, run_id ) self._run_id_to_watcher_dict[run_id].daemon = True self._run_id_to_watcher_dict[run_id].start() self._run_id_to_watcher_dict[run_id].add_callback(start_cursor, callback) def unwatch_run(self, run_id: str, handler: Callable[[EventLogEntry], None]): run_id = check.str_param(run_id, "run_id") handler = check.callable_param(handler, "handler") with self._dict_lock: if run_id in self._run_id_to_watcher_dict: self._run_id_to_watcher_dict[run_id].remove_callback(handler) if self._run_id_to_watcher_dict[run_id].should_thread_exit.is_set(): del self._run_id_to_watcher_dict[run_id] def __del__(self): self.close() def close(self): if not self._disposed: self._disposed = True with self._dict_lock: for watcher_thread in self._run_id_to_watcher_dict.values(): if not watcher_thread.should_thread_exit.is_set(): watcher_thread.should_thread_exit.set() for run_id in self._run_id_to_watcher_dict: self._run_id_to_watcher_dict[run_id].join() del self._run_id_to_watcher_dict class SqlPollingRunIdEventWatcherThread(threading.Thread): """subclass of Thread that watches a given run_id for new Events by polling every POLLING_CADENCE Holds a list of callbacks (_callback_fn_list) each passed in by an `Observer`. Note that the callbacks have a cursor associated; this means that the callbacks should be only executed on EventLogEntrys with an associated id >= callback.start_cursor Exits when `self.should_thread_exit` is set. LOCKING INFO: INVARIANTS: _callback_fn_list_lock protects _callback_fn_list """ def __init__(self, event_log_storage: SqlEventLogStorage, run_id: str): super(SqlPollingRunIdEventWatcherThread, self).__init__() self._event_log_storage = check.inst_param( event_log_storage, "event_log_storage", SqlEventLogStorage ) self._run_id = check.str_param(run_id, "run_id") self._callback_fn_list_lock: threading.Lock = threading.Lock() self._callback_fn_list: List[CallbackAfterCursor] = [] self._should_thread_exit = threading.Event() self.name = f"mysql-event-watch-run-id-{self._run_id}" @property def should_thread_exit(self) -> threading.Event: return self._should_thread_exit def add_callback(self, start_cursor: int, callback: Callable[[EventLogEntry], None]): """Observer has started watching this run. Add a callback to execute on new EventLogEntrys st. id >= start_cursor Args: start_cursor (int): minimum event_id for the callback to execute callback (Callable[[EventLogEntry], None]): callback to update the Dagster UI """ start_cursor = check.int_param(start_cursor, "start_cursor") callback = check.callable_param(callback, "callback") with self._callback_fn_list_lock: self._callback_fn_list.append(CallbackAfterCursor(start_cursor, callback)) def remove_callback(self, callback: Callable[[EventLogEntry], None]): """Observer has stopped watching this run; Remove a callback from the list of callbacks to execute on new EventLogEntrys Also kill thread if no callbacks remaining (i.e. no Observers are watching this run_id) Args: callback (Callable[[EventLogEntry], None]): callback to remove from list of callbacks """ callback = check.callable_param(callback, "callback") with self._callback_fn_list_lock: self._callback_fn_list = [ callback_with_cursor for callback_with_cursor in self._callback_fn_list if callback_with_cursor.callback != callback ] if not self._callback_fn_list: self._should_thread_exit.set() def run(self): """Polling function to update Observers with EventLogEntrys from Event Log DB. Wakes every POLLING_CADENCE & 1. executes a SELECT query to get new EventLogEntrys 2. fires each callback (taking into account the callback.cursor) on the new EventLogEntrys Uses max_index_so_far as a cursor in the DB to make sure that only new records are retrieved """ cursor = -1 while not self._should_thread_exit.wait(POLLING_CADENCE): events = self._event_log_storage.get_logs_for_run(self._run_id, cursor=cursor) for event_record in events: cursor += 1 with self._callback_fn_list_lock: for callback_with_cursor in self._callback_fn_list: if callback_with_cursor.start_cursor < cursor: callback_with_cursor.callback(event_record)

44.841463

104

0.68371

import threading from typing import Callable, List, MutableMapping, NamedTuple from dagster import check from dagster.core.events.log import EventLogEntry from .sql_event_log import SqlEventLogStorage POLLING_CADENCE = 0.1 class CallbackAfterCursor(NamedTuple): start_cursor: int callback: Callable[[EventLogEntry], None] class SqlPollingEventWatcher: def __init__(self, event_log_storage: SqlEventLogStorage): self._event_log_storage = check.inst_param( event_log_storage, "event_log_storage", SqlEventLogStorage ) self._dict_lock: threading.Lock = threading.Lock() self._run_id_to_watcher_dict: MutableMapping[str, SqlPollingRunIdEventWatcherThread] = {} self._disposed = False def has_run_id(self, run_id: str) -> bool: run_id = check.str_param(run_id, "run_id") with self._dict_lock: _has_run_id = run_id in self._run_id_to_watcher_dict return _has_run_id def watch_run(self, run_id: str, start_cursor: int, callback: Callable[[EventLogEntry], None]): run_id = check.str_param(run_id, "run_id") start_cursor = check.int_param(start_cursor, "start_cursor") callback = check.callable_param(callback, "callback") with self._dict_lock: if run_id not in self._run_id_to_watcher_dict: self._run_id_to_watcher_dict[run_id] = SqlPollingRunIdEventWatcherThread( self._event_log_storage, run_id ) self._run_id_to_watcher_dict[run_id].daemon = True self._run_id_to_watcher_dict[run_id].start() self._run_id_to_watcher_dict[run_id].add_callback(start_cursor, callback) def unwatch_run(self, run_id: str, handler: Callable[[EventLogEntry], None]): run_id = check.str_param(run_id, "run_id") handler = check.callable_param(handler, "handler") with self._dict_lock: if run_id in self._run_id_to_watcher_dict: self._run_id_to_watcher_dict[run_id].remove_callback(handler) if self._run_id_to_watcher_dict[run_id].should_thread_exit.is_set(): del self._run_id_to_watcher_dict[run_id] def __del__(self): self.close() def close(self): if not self._disposed: self._disposed = True with self._dict_lock: for watcher_thread in self._run_id_to_watcher_dict.values(): if not watcher_thread.should_thread_exit.is_set(): watcher_thread.should_thread_exit.set() for run_id in self._run_id_to_watcher_dict: self._run_id_to_watcher_dict[run_id].join() del self._run_id_to_watcher_dict class SqlPollingRunIdEventWatcherThread(threading.Thread): def __init__(self, event_log_storage: SqlEventLogStorage, run_id: str): super(SqlPollingRunIdEventWatcherThread, self).__init__() self._event_log_storage = check.inst_param( event_log_storage, "event_log_storage", SqlEventLogStorage ) self._run_id = check.str_param(run_id, "run_id") self._callback_fn_list_lock: threading.Lock = threading.Lock() self._callback_fn_list: List[CallbackAfterCursor] = [] self._should_thread_exit = threading.Event() self.name = f"mysql-event-watch-run-id-{self._run_id}" @property def should_thread_exit(self) -> threading.Event: return self._should_thread_exit def add_callback(self, start_cursor: int, callback: Callable[[EventLogEntry], None]): start_cursor = check.int_param(start_cursor, "start_cursor") callback = check.callable_param(callback, "callback") with self._callback_fn_list_lock: self._callback_fn_list.append(CallbackAfterCursor(start_cursor, callback)) def remove_callback(self, callback: Callable[[EventLogEntry], None]): callback = check.callable_param(callback, "callback") with self._callback_fn_list_lock: self._callback_fn_list = [ callback_with_cursor for callback_with_cursor in self._callback_fn_list if callback_with_cursor.callback != callback ] if not self._callback_fn_list: self._should_thread_exit.set() def run(self): cursor = -1 while not self._should_thread_exit.wait(POLLING_CADENCE): events = self._event_log_storage.get_logs_for_run(self._run_id, cursor=cursor) for event_record in events: cursor += 1 with self._callback_fn_list_lock: for callback_with_cursor in self._callback_fn_list: if callback_with_cursor.start_cursor < cursor: callback_with_cursor.callback(event_record)

true

790199eeccb4e794f0e44752ad662d398aed9d94

1,332

py

Python

djangocms_newsletter/cmsplugin_newsletter/cms_plugins.py

nephila/djangocms-newsletter

5ebd8d3e1e2c85b2791d0261a954469f2548c840

[ "BSD-3-Clause" ]

null

djangocms_newsletter/cmsplugin_newsletter/cms_plugins.py

nephila/djangocms-newsletter

5ebd8d3e1e2c85b2791d0261a954469f2548c840

[ "BSD-3-Clause" ]

null

djangocms_newsletter/cmsplugin_newsletter/cms_plugins.py

nephila/djangocms-newsletter

5ebd8d3e1e2c85b2791d0261a954469f2548c840

[ "BSD-3-Clause" ]

2

2021-03-15T13:33:53.000Z

2021-05-18T20:34:47.000Z

"""Plugins for CMS""" from django.utils.translation import ugettext_lazy as _ from cms.plugin_base import CMSPluginBase from cms.plugin_pool import plugin_pool from emencia.django.newsletter.cmsplugin_newsletter import settings from emencia.django.newsletter.cmsplugin_newsletter.models import SubscriptionFormPlugin from emencia.django.newsletter.forms import MailingListSubscriptionForm class CMSSubscriptionFormPlugin(CMSPluginBase): module = _('newsletter') model = SubscriptionFormPlugin name = _('Subscription Form') render_template = 'newsletter/cms/subscription_form.html' text_enabled = False admin_preview = False def render(self, context, instance, placeholder): request = context['request'] if request.method == "POST" and (settings.FORM_NAME in request.POST.keys()): form = MailingListSubscriptionForm(data=request.POST) if form.is_valid(): form.save(instance.mailing_list) form.saved = True else: form = MailingListSubscriptionForm() context.update({ 'object': instance, 'form': form, 'form_name': settings.FORM_NAME, 'placeholder': placeholder, }) return context plugin_pool.register_plugin(CMSSubscriptionFormPlugin)

34.153846

88

0.698949

from django.utils.translation import ugettext_lazy as _ from cms.plugin_base import CMSPluginBase from cms.plugin_pool import plugin_pool from emencia.django.newsletter.cmsplugin_newsletter import settings from emencia.django.newsletter.cmsplugin_newsletter.models import SubscriptionFormPlugin from emencia.django.newsletter.forms import MailingListSubscriptionForm class CMSSubscriptionFormPlugin(CMSPluginBase): module = _('newsletter') model = SubscriptionFormPlugin name = _('Subscription Form') render_template = 'newsletter/cms/subscription_form.html' text_enabled = False admin_preview = False def render(self, context, instance, placeholder): request = context['request'] if request.method == "POST" and (settings.FORM_NAME in request.POST.keys()): form = MailingListSubscriptionForm(data=request.POST) if form.is_valid(): form.save(instance.mailing_list) form.saved = True else: form = MailingListSubscriptionForm() context.update({ 'object': instance, 'form': form, 'form_name': settings.FORM_NAME, 'placeholder': placeholder, }) return context plugin_pool.register_plugin(CMSSubscriptionFormPlugin)

true

79019cd943ae685404eb86da4eca1e080ec0167d

3,361

py

Python

lab01/lab01/settings.py

car1os1/TECSUP-DAE-2021-2-B

263be9e52814ec96708650d4e417ab393075d74e

[ "MIT" ]

null

lab01/lab01/settings.py

car1os1/TECSUP-DAE-2021-2-B

263be9e52814ec96708650d4e417ab393075d74e

[ "MIT" ]

null

lab01/lab01/settings.py

car1os1/TECSUP-DAE-2021-2-B

263be9e52814ec96708650d4e417ab393075d74e

[ "MIT" ]

null

""" Django settings for lab01 project. Generated by 'django-admin startproject' using Django 3.2.6. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-7-8hv&pc-$$1)7eiiy2m#m^o6cx%oqqv9@z071ec0%218iwt0!' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'lab01.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'lab01.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

26.674603

92

0.675097

from pathlib import Path BASE_DIR = Path(__file__).resolve().parent.parent SECRET_KEY = 'django-insecure-7-8hv&pc-$$1)7eiiy2m#m^o6cx%oqqv9@z071ec0%218iwt0!' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'lab01.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'lab01.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

true

79019d36bbcb74e56567c705cb5879346cda1bc4

11,711

py

Python

test/test_client.py

roidnn/google-maps-services-python

ca439ee9b5aaca21ffba54134c91e991dcccb4b4

[ "Apache-2.0" ]

1

2021-09-01T16:52:26.000Z

test/test_client.py

lamantin/google-maps-services-python

396e03ce3ffc7d1d98634c9932408272cfc20c18

[ "Apache-2.0" ]

null

test/test_client.py

lamantin/google-maps-services-python

396e03ce3ffc7d1d98634c9932408272cfc20c18

[ "Apache-2.0" ]

null

# # Copyright 2014 Google Inc. All rights reserved. # # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. # """Tests for client module.""" import responses import time import googlemaps from googlemaps import client as _client import test as _test import requests class ClientTest(_test.TestCase): def test_no_api_key(self): with self.assertRaises(Exception): client = googlemaps.Client() client.directions("Sydney", "Melbourne") def test_invalid_api_key(self): with self.assertRaises(Exception): client = googlemaps.Client(key="Invalid key.") client.directions("Sydney", "Melbourne") def test_urlencode(self): # See GH #72. encoded_params = _client.urlencode_params([("address", "=Sydney ~")]) self.assertEqual("address=%3DSydney+~", encoded_params) @responses.activate def test_queries_per_second(self): # This test assumes that the time to run a mocked query is # relatively small, eg a few milliseconds. We define a rate of # 3 queries per second, and run double that, which should take at # least 1 second but no more than 2. queries_per_second = 3 query_range = range(queries_per_second * 2) for _ in query_range: responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf", queries_per_second=queries_per_second) start = time.time() for _ in query_range: client.geocode("Sesame St.") end = time.time() self.assertTrue(start + 1 < end < start + 2) @responses.activate def test_key_sent(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.") self.assertEqual(1, len(responses.calls)) self.assertURLEqual("https://maps.googleapis.com/maps/api/geocode/json?" "key=AIzaasdf&address=Sesame+St.", responses.calls[0].request.url) @responses.activate def test_extra_params(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.", extra_params={"foo": "bar"}) self.assertEqual(1, len(responses.calls)) self.assertURLEqual("https://maps.googleapis.com/maps/api/geocode/json?" "key=AIzaasdf&address=Sesame+St.&foo=bar", responses.calls[0].request.url) def test_hmac(self): """ From http://en.wikipedia.org/wiki/Hash-based_message_authentication_code HMAC_SHA1("key", "The quick brown fox jumps over the lazy dog") = 0xde7c9b85b8b78aa6bc8a7a36f70a90701c9db4d9 """ message = "The quick brown fox jumps over the lazy dog" key = "a2V5" # "key" -> base64 signature = "3nybhbi3iqa8ino29wqQcBydtNk=" self.assertEqual(signature, _client.sign_hmac(key, message)) @responses.activate def test_url_signed(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(client_id="foo", client_secret="a2V5") client.geocode("Sesame St.") self.assertEqual(1, len(responses.calls)) # Check ordering of parameters. self.assertIn("address=Sesame+St.&client=foo&signature", responses.calls[0].request.url) self.assertURLEqual("https://maps.googleapis.com/maps/api/geocode/json?" "address=Sesame+St.&client=foo&" "signature=fxbWUIcNPZSekVOhp2ul9LW5TpY=", responses.calls[0].request.url) @responses.activate def test_ua_sent(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.") self.assertEqual(1, len(responses.calls)) user_agent = responses.calls[0].request.headers["User-Agent"] self.assertTrue(user_agent.startswith("GoogleGeoApiClientPython")) @responses.activate def test_retry(self): class request_callback: def __init__(self): self.first_req = True def __call__(self, req): if self.first_req: self.first_req = False return (200, {}, '{"status":"OVER_QUERY_LIMIT"}') return (200, {}, '{"status":"OK","results":[]}') responses.add_callback(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", content_type='application/json', callback=request_callback()) client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.") self.assertEqual(2, len(responses.calls)) self.assertEqual(responses.calls[0].request.url, responses.calls[1].request.url) @responses.activate def test_transport_error(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", status=404, content_type='application/json') client = googlemaps.Client(key="AIzaasdf") with self.assertRaises(googlemaps.exceptions.HTTPError) as e: client.geocode("Foo") self.assertEqual(e.exception.status_code, 404) @responses.activate def test_host_override(self): responses.add(responses.GET, "https://foo.com/bar", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") client._get("/bar", {}, base_url="https://foo.com") self.assertEqual(1, len(responses.calls)) @responses.activate def test_custom_extract(self): def custom_extract(resp): return resp.json() responses.add(responses.GET, "https://maps.googleapis.com/bar", body='{"error":"errormessage"}', status=403, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") b = client._get("/bar", {}, extract_body=custom_extract) self.assertEqual(1, len(responses.calls)) self.assertEqual("errormessage", b["error"]) @responses.activate def test_retry_intermittent(self): class request_callback: def __init__(self): self.first_req = True def __call__(self, req): if self.first_req: self.first_req = False return (500, {}, 'Internal Server Error.') return (200, {}, '{"status":"OK","results":[]}') responses.add_callback(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", content_type="application/json", callback=request_callback()) client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.") self.assertEqual(2, len(responses.calls)) def test_channel_without_client_id(self): with self.assertRaises(ValueError): client = googlemaps.Client(key="AIzaasdf", channel="mychannel") def test_invalid_channel(self): # Cf. limitations here: # https://developers.google.com/maps/premium/reports # /usage-reports#channels with self.assertRaises(ValueError): client = googlemaps.Client(client_id="foo", client_secret="a2V5", channel="auieauie$? ") def test_auth_url_with_channel(self): client = googlemaps.Client(key="AIzaasdf", client_id="foo", client_secret="a2V5", channel="MyChannel_1") # Check ordering of parameters + signature. auth_url = client._generate_auth_url("/test", {"param": "param"}, accepts_clientid=True) self.assertEqual(auth_url, "/test?param=param" "&channel=MyChannel_1" "&client=foo" "&signature=OH18GuQto_mEpxj99UimKskvo4k=") # Check if added to requests to API with accepts_clientid=False auth_url = client._generate_auth_url("/test", {"param": "param"}, accepts_clientid=False) self.assertEqual(auth_url, "/test?param=param&key=AIzaasdf") def test_requests_version(self): client_args_timeout = { "key": "AIzaasdf", "client_id": "foo", "client_secret": "a2V5", "channel": "MyChannel_1", "connect_timeout": 5, "read_timeout": 5 } client_args = client_args_timeout.copy() del client_args["connect_timeout"] del client_args["read_timeout"] requests.__version__ = '2.3.0' with self.assertRaises(NotImplementedError): googlemaps.Client(**client_args_timeout) googlemaps.Client(**client_args) requests.__version__ = '2.4.0' googlemaps.Client(**client_args_timeout) googlemaps.Client(**client_args) @responses.activate def test_no_retry_over_query_limit(self): responses.add(responses.GET, "https://maps.googleapis.com/foo", body='{"status":"OVER_QUERY_LIMIT"}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf", retry_over_query_limit=False) with self.assertRaises(googlemaps.exceptions.ApiError): client._request("/foo", {}) self.assertEqual(1, len(responses.calls))

38.14658

88

0.571343

import responses import time import googlemaps from googlemaps import client as _client import test as _test import requests class ClientTest(_test.TestCase): def test_no_api_key(self): with self.assertRaises(Exception): client = googlemaps.Client() client.directions("Sydney", "Melbourne") def test_invalid_api_key(self): with self.assertRaises(Exception): client = googlemaps.Client(key="Invalid key.") client.directions("Sydney", "Melbourne") def test_urlencode(self): encoded_params = _client.urlencode_params([("address", "=Sydney ~")]) self.assertEqual("address=%3DSydney+~", encoded_params) @responses.activate def test_queries_per_second(self): queries_per_second = 3 query_range = range(queries_per_second * 2) for _ in query_range: responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf", queries_per_second=queries_per_second) start = time.time() for _ in query_range: client.geocode("Sesame St.") end = time.time() self.assertTrue(start + 1 < end < start + 2) @responses.activate def test_key_sent(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.") self.assertEqual(1, len(responses.calls)) self.assertURLEqual("https://maps.googleapis.com/maps/api/geocode/json?" "key=AIzaasdf&address=Sesame+St.", responses.calls[0].request.url) @responses.activate def test_extra_params(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.", extra_params={"foo": "bar"}) self.assertEqual(1, len(responses.calls)) self.assertURLEqual("https://maps.googleapis.com/maps/api/geocode/json?" "key=AIzaasdf&address=Sesame+St.&foo=bar", responses.calls[0].request.url) def test_hmac(self): message = "The quick brown fox jumps over the lazy dog" key = "a2V5" signature = "3nybhbi3iqa8ino29wqQcBydtNk=" self.assertEqual(signature, _client.sign_hmac(key, message)) @responses.activate def test_url_signed(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(client_id="foo", client_secret="a2V5") client.geocode("Sesame St.") self.assertEqual(1, len(responses.calls)) self.assertIn("address=Sesame+St.&client=foo&signature", responses.calls[0].request.url) self.assertURLEqual("https://maps.googleapis.com/maps/api/geocode/json?" "address=Sesame+St.&client=foo&" "signature=fxbWUIcNPZSekVOhp2ul9LW5TpY=", responses.calls[0].request.url) @responses.activate def test_ua_sent(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.") self.assertEqual(1, len(responses.calls)) user_agent = responses.calls[0].request.headers["User-Agent"] self.assertTrue(user_agent.startswith("GoogleGeoApiClientPython")) @responses.activate def test_retry(self): class request_callback: def __init__(self): self.first_req = True def __call__(self, req): if self.first_req: self.first_req = False return (200, {}, '{"status":"OVER_QUERY_LIMIT"}') return (200, {}, '{"status":"OK","results":[]}') responses.add_callback(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", content_type='application/json', callback=request_callback()) client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.") self.assertEqual(2, len(responses.calls)) self.assertEqual(responses.calls[0].request.url, responses.calls[1].request.url) @responses.activate def test_transport_error(self): responses.add(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", status=404, content_type='application/json') client = googlemaps.Client(key="AIzaasdf") with self.assertRaises(googlemaps.exceptions.HTTPError) as e: client.geocode("Foo") self.assertEqual(e.exception.status_code, 404) @responses.activate def test_host_override(self): responses.add(responses.GET, "https://foo.com/bar", body='{"status":"OK","results":[]}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") client._get("/bar", {}, base_url="https://foo.com") self.assertEqual(1, len(responses.calls)) @responses.activate def test_custom_extract(self): def custom_extract(resp): return resp.json() responses.add(responses.GET, "https://maps.googleapis.com/bar", body='{"error":"errormessage"}', status=403, content_type="application/json") client = googlemaps.Client(key="AIzaasdf") b = client._get("/bar", {}, extract_body=custom_extract) self.assertEqual(1, len(responses.calls)) self.assertEqual("errormessage", b["error"]) @responses.activate def test_retry_intermittent(self): class request_callback: def __init__(self): self.first_req = True def __call__(self, req): if self.first_req: self.first_req = False return (500, {}, 'Internal Server Error.') return (200, {}, '{"status":"OK","results":[]}') responses.add_callback(responses.GET, "https://maps.googleapis.com/maps/api/geocode/json", content_type="application/json", callback=request_callback()) client = googlemaps.Client(key="AIzaasdf") client.geocode("Sesame St.") self.assertEqual(2, len(responses.calls)) def test_channel_without_client_id(self): with self.assertRaises(ValueError): client = googlemaps.Client(key="AIzaasdf", channel="mychannel") def test_invalid_channel(self): with self.assertRaises(ValueError): client = googlemaps.Client(client_id="foo", client_secret="a2V5", channel="auieauie$? ") def test_auth_url_with_channel(self): client = googlemaps.Client(key="AIzaasdf", client_id="foo", client_secret="a2V5", channel="MyChannel_1") auth_url = client._generate_auth_url("/test", {"param": "param"}, accepts_clientid=True) self.assertEqual(auth_url, "/test?param=param" "&channel=MyChannel_1" "&client=foo" "&signature=OH18GuQto_mEpxj99UimKskvo4k=") auth_url = client._generate_auth_url("/test", {"param": "param"}, accepts_clientid=False) self.assertEqual(auth_url, "/test?param=param&key=AIzaasdf") def test_requests_version(self): client_args_timeout = { "key": "AIzaasdf", "client_id": "foo", "client_secret": "a2V5", "channel": "MyChannel_1", "connect_timeout": 5, "read_timeout": 5 } client_args = client_args_timeout.copy() del client_args["connect_timeout"] del client_args["read_timeout"] requests.__version__ = '2.3.0' with self.assertRaises(NotImplementedError): googlemaps.Client(**client_args_timeout) googlemaps.Client(**client_args) requests.__version__ = '2.4.0' googlemaps.Client(**client_args_timeout) googlemaps.Client(**client_args) @responses.activate def test_no_retry_over_query_limit(self): responses.add(responses.GET, "https://maps.googleapis.com/foo", body='{"status":"OVER_QUERY_LIMIT"}', status=200, content_type="application/json") client = googlemaps.Client(key="AIzaasdf", retry_over_query_limit=False) with self.assertRaises(googlemaps.exceptions.ApiError): client._request("/foo", {}) self.assertEqual(1, len(responses.calls))

true

79019d60a3b662abbc61d3a75776f4170b15dd80

1,294

py

Python

autoshort.py

lawja/AutoSHRTNR

7c9242df3b40913449a7a714fdd02abf4c608f26

[ "MIT" ]

1

2017-12-09T21:23:53.000Z

autoshort.py

lawja/AutoSHRTNR

7c9242df3b40913449a7a714fdd02abf4c608f26

[ "MIT" ]

null

autoshort.py

lawja/AutoSHRTNR

7c9242df3b40913449a7a714fdd02abf4c608f26

[ "MIT" ]

null

import inspect import os import pyperclip import requests import time from urllib.parse import quote # a list of the request error classes request_errors = [obj for name, obj in inspect.getmembers(requests.exceptions) if inspect.isclass(obj) and issubclass(obj, Exception)] # main daemon loop while True: # get clipboard value clipboard = pyperclip.paste() try: # percent encode the clipboard value safe_cb = quote(clipboard,safe='') # bitly API access token token = os.environ.get('BITLY_TOKEN') # URL that will make the API call bitly_url = 'https://api-ssl.bitly.com/v3/shorten?' + \ 'access_token=' + token + '&longUrl=' + safe_cb # get the json return from the API call short_url = requests.get(bitly_url).json() # if everything went as planned if(short_url['status_txt'] == 'OK'): pyperclip.copy(short_url['data']['url']) except Exception as e: # if something went wrong with the request, i.e. not a link if(any(issubclass(e.__class__, lv) for lv in request_errors)): pass else: raise(e) # wait until the clipboard changes while(pyperclip.paste() == clipboard): time.sleep(.1)

34.972973

78

0.629057

import inspect import os import pyperclip import requests import time from urllib.parse import quote request_errors = [obj for name, obj in inspect.getmembers(requests.exceptions) if inspect.isclass(obj) and issubclass(obj, Exception)] while True: clipboard = pyperclip.paste() try: safe_cb = quote(clipboard,safe='') token = os.environ.get('BITLY_TOKEN') bitly_url = 'https://api-ssl.bitly.com/v3/shorten?' + \ 'access_token=' + token + '&longUrl=' + safe_cb short_url = requests.get(bitly_url).json() if(short_url['status_txt'] == 'OK'): pyperclip.copy(short_url['data']['url']) except Exception as e: if(any(issubclass(e.__class__, lv) for lv in request_errors)): pass else: raise(e) while(pyperclip.paste() == clipboard): time.sleep(.1)

true

79019e6692b7080cea95100ec92b84990cdcb1bc

761

py

Python

setup.py

adalekin/ngenix-test

8d0b001e614cc6d18002ccd224cb8c3568128774

[ "MIT" ]

null

setup.py

adalekin/ngenix-test

8d0b001e614cc6d18002ccd224cb8c3568128774

[ "MIT" ]

null

setup.py

adalekin/ngenix-test

8d0b001e614cc6d18002ccd224cb8c3568128774

[ "MIT" ]

null

import versioneer commands = versioneer.get_cmdclass().copy() try: from setuptools import setup, find_packages except ImportError: from distutils.core import setup, find_packages setup( name='ngenix-test', version=versioneer.get_version(), packages=find_packages(), url='https://github.com/adalekin/ngenix-test', license='MIT', author='Aleksey Dalekin', author_email='adalekin@gmail.com', description='A te.', long_description=open('README.md', 'rt').read(), package_dir={'ngenix_test': 'ngenix_test'}, include_package_data=True, install_requires=[ ], cmdclass=commands, entry_points=''' [console_scripts] nginx-test=nginx_test.run:main ''' )

26.241379

53

0.65703

import versioneer commands = versioneer.get_cmdclass().copy() try: from setuptools import setup, find_packages except ImportError: from distutils.core import setup, find_packages setup( name='ngenix-test', version=versioneer.get_version(), packages=find_packages(), url='https://github.com/adalekin/ngenix-test', license='MIT', author='Aleksey Dalekin', author_email='adalekin@gmail.com', description='A te.', long_description=open('README.md', 'rt').read(), package_dir={'ngenix_test': 'ngenix_test'}, include_package_data=True, install_requires=[ ], cmdclass=commands, entry_points=''' [console_scripts] nginx-test=nginx_test.run:main ''' )

true

79019ffe232bf2e663c2267cee507d701388e4be

8,841

py

Python

wandb/fastai/__init__.py

MPGek/client

541d760c5cb8776b1ad5fcf1362d7382811cbc61

[ "Apache-2.0" ]

1

2020-08-20T14:02:47.000Z

wandb/fastai/__init__.py

MPGek/client

541d760c5cb8776b1ad5fcf1362d7382811cbc61

[ "Apache-2.0" ]

null

wandb/fastai/__init__.py

MPGek/client

541d760c5cb8776b1ad5fcf1362d7382811cbc61

[ "Apache-2.0" ]

null

''' This module hooks fast.ai Learners to Weights & Biases through a callback. Requested logged data can be configured through the callback constructor. Examples: WandbCallback can be used when initializing the Learner:: ``` from wandb.fastai import WandbCallback [...] learn = Learner(data, ..., callback_fns=WandbCallback) learn.fit(epochs) ``` Custom parameters can be given using functools.partial:: ``` from wandb.fastai import WandbCallback from functools import partial [...] learn = Learner(data, ..., callback_fns=partial(WandbCallback, ...)) learn.fit(epochs) ``` Finally, it is possible to use WandbCallback only when starting training. In this case it must be instantiated:: ``` learn.fit(..., callbacks=WandbCallback(learn)) ``` or, with custom parameters:: ``` learn.fit(..., callbacks=WandbCallback(learn, ...)) ``` ''' import wandb import fastai from fastai.callbacks import TrackerCallback from pathlib import Path import random try: import matplotlib matplotlib.use('Agg') # non-interactive backend (avoid tkinter issues) import matplotlib.pyplot as plt except: print('Warning: matplotlib required if logging sample image predictions') class WandbCallback(TrackerCallback): """ Automatically saves model topology, losses & metrics. Optionally logs weights, gradients, sample predictions and best trained model. Args: learn (fastai.basic_train.Learner): the fast.ai learner to hook. log (str): "gradients", "parameters", "all", or None. Losses & metrics are always logged. save_model (bool): save model at the end of each epoch. It will also load best model at the end of training. monitor (str): metric to monitor for saving best model. None uses default TrackerCallback monitor value. mode (str): "auto", "min" or "max" to compare "monitor" values and define best model. input_type (str): "images" or None. Used to display sample predictions. validation_data (list): data used for sample predictions if input_type is set. predictions (int): number of predictions to make if input_type is set and validation_data is None. seed (int): initialize random generator for sample predictions if input_type is set and validation_data is None. """ # Record if watch has been called previously (even in another instance) _watch_called = False def __init__(self, learn, log="gradients", save_model=True, monitor=None, mode='auto', input_type=None, validation_data=None, predictions=36, seed=12345): # Check if wandb.init has been called if wandb.run is None: raise ValueError( 'You must call wandb.init() before WandbCallback()') # Adapted from fast.ai "SaveModelCallback" if monitor is None: # use default TrackerCallback monitor value super().__init__(learn, mode=mode) else: super().__init__(learn, monitor=monitor, mode=mode) self.save_model = save_model self.model_path = Path(wandb.run.dir) / 'bestmodel.pth' self.log = log self.input_type = input_type self.best = None # Select items for sample predictions to see evolution along training self.validation_data = validation_data if input_type and not self.validation_data: wandbRandom = random.Random(seed) # For repeatability predictions = min(predictions, len(learn.data.valid_ds)) indices = wandbRandom.sample(range(len(learn.data.valid_ds)), predictions) self.validation_data = [learn.data.valid_ds[i] for i in indices] def on_train_begin(self, **kwargs): "Call watch method to log model topology, gradients & weights" # Set self.best, method inherited from "TrackerCallback" by "SaveModelCallback" super().on_train_begin() # Ensure we don't call "watch" multiple times if not WandbCallback._watch_called: WandbCallback._watch_called = True # Logs model topology and optionally gradients and weights wandb.watch(self.learn.model, log=self.log) def on_epoch_end(self, epoch, smooth_loss, last_metrics, **kwargs): "Logs training loss, validation loss and custom metrics & log prediction samples & save model" if self.save_model: # Adapted from fast.ai "SaveModelCallback" current = self.get_monitor_value() if current is not None and self.operator(current, self.best): print( 'Better model found at epoch {} with {} value: {}.'.format( epoch, self.monitor, current)) self.best = current # Save within wandb folder with self.model_path.open('wb') as model_file: self.learn.save(model_file) # Log sample predictions if learn.predict is available if self.validation_data: try: self._wandb_log_predictions() except FastaiError as e: wandb.termwarn(e.message) self.validation_data = None # prevent from trying again on next loop except Exception as e: wandb.termwarn("Unable to log prediction samples.\n{}".format(e)) self.validation_data=None # prevent from trying again on next loop # Log losses & metrics # Adapted from fast.ai "CSVLogger" logs = { name: stat for name, stat in list( zip(self.learn.recorder.names, [epoch, smooth_loss] + last_metrics)) } wandb.log(logs) def on_train_end(self, **kwargs): "Load the best model." if self.save_model: # Adapted from fast.ai "SaveModelCallback" if self.model_path.is_file(): with self.model_path.open('rb') as model_file: self.learn.load(model_file, purge=False) print('Loaded best saved model from {}'.format( self.model_path)) def _wandb_log_predictions(self): "Log prediction samples" pred_log = [] for x, y in self.validation_data: try: pred=self.learn.predict(x) except: raise FastaiError('Unable to run "predict" method from Learner to log prediction samples.') # scalar -> likely to be a category if not pred[1].shape: pred_log.append( wandb.Image( x.data, caption='Ground Truth: {}\nPrediction: {}'.format( y, pred[0]))) # most vision datasets have a "show" function we can use elif hasattr(x, "show"): # log input data pred_log.append( wandb.Image(x.data, caption='Input data', grouping=3)) # log label and prediction for im, capt in ((pred[0], "Prediction"), (y, "Ground Truth")): # Resize plot to image resolution # from https://stackoverflow.com/a/13714915 my_dpi = 100 fig = plt.figure(frameon=False, dpi=my_dpi) h, w = x.size fig.set_size_inches(w / my_dpi, h / my_dpi) ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.set_axis_off() fig.add_axes(ax) # Superpose label or prediction to input image x.show(ax=ax, y=im) pred_log.append(wandb.Image(fig, caption=capt)) plt.close(fig) # likely to be an image elif hasattr(y, "shape") and ( (len(y.shape) == 2) or (len(y.shape) == 3 and y.shape[0] in [1, 3, 4])): pred_log.extend([ wandb.Image(x.data, caption='Input data', grouping=3), wandb.Image(pred[0].data, caption='Prediction'), wandb.Image(y.data, caption='Ground Truth') ]) # we just log input data else: pred_log.append(wandb.Image(x.data, caption='Input data')) wandb.log({"Prediction Samples": pred_log}, commit=False) class FastaiError(wandb.Error): pass

37.944206

120

0.575274

import wandb import fastai from fastai.callbacks import TrackerCallback from pathlib import Path import random try: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt except: print('Warning: matplotlib required if logging sample image predictions') class WandbCallback(TrackerCallback): _watch_called = False def __init__(self, learn, log="gradients", save_model=True, monitor=None, mode='auto', input_type=None, validation_data=None, predictions=36, seed=12345): if wandb.run is None: raise ValueError( 'You must call wandb.init() before WandbCallback()') if monitor is None: super().__init__(learn, mode=mode) else: super().__init__(learn, monitor=monitor, mode=mode) self.save_model = save_model self.model_path = Path(wandb.run.dir) / 'bestmodel.pth' self.log = log self.input_type = input_type self.best = None self.validation_data = validation_data if input_type and not self.validation_data: wandbRandom = random.Random(seed) predictions = min(predictions, len(learn.data.valid_ds)) indices = wandbRandom.sample(range(len(learn.data.valid_ds)), predictions) self.validation_data = [learn.data.valid_ds[i] for i in indices] def on_train_begin(self, **kwargs): super().on_train_begin() if not WandbCallback._watch_called: WandbCallback._watch_called = True # Logs model topology and optionally gradients and weights wandb.watch(self.learn.model, log=self.log) def on_epoch_end(self, epoch, smooth_loss, last_metrics, **kwargs): if self.save_model: # Adapted from fast.ai "SaveModelCallback" current = self.get_monitor_value() if current is not None and self.operator(current, self.best): print( 'Better model found at epoch {} with {} value: {}.'.format( epoch, self.monitor, current)) self.best = current # Save within wandb folder with self.model_path.open('wb') as model_file: self.learn.save(model_file) # Log sample predictions if learn.predict is available if self.validation_data: try: self._wandb_log_predictions() except FastaiError as e: wandb.termwarn(e.message) self.validation_data = None # prevent from trying again on next loop except Exception as e: wandb.termwarn("Unable to log prediction samples.\n{}".format(e)) self.validation_data=None # prevent from trying again on next loop # Log losses & metrics # Adapted from fast.ai "CSVLogger" logs = { name: stat for name, stat in list( zip(self.learn.recorder.names, [epoch, smooth_loss] + last_metrics)) } wandb.log(logs) def on_train_end(self, **kwargs): if self.save_model: # Adapted from fast.ai "SaveModelCallback" if self.model_path.is_file(): with self.model_path.open('rb') as model_file: self.learn.load(model_file, purge=False) print('Loaded best saved model from {}'.format( self.model_path)) def _wandb_log_predictions(self): pred_log = [] for x, y in self.validation_data: try: pred=self.learn.predict(x) except: raise FastaiError('Unable to run "predict" method from Learner to log prediction samples.') # scalar -> likely to be a category if not pred[1].shape: pred_log.append( wandb.Image( x.data, caption='Ground Truth: {}\nPrediction: {}'.format( y, pred[0]))) # most vision datasets have a "show" function we can use elif hasattr(x, "show"): # log input data pred_log.append( wandb.Image(x.data, caption='Input data', grouping=3)) # log label and prediction for im, capt in ((pred[0], "Prediction"), (y, "Ground Truth")): # Resize plot to image resolution # from https://stackoverflow.com/a/13714915 my_dpi = 100 fig = plt.figure(frameon=False, dpi=my_dpi) h, w = x.size fig.set_size_inches(w / my_dpi, h / my_dpi) ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.set_axis_off() fig.add_axes(ax) # Superpose label or prediction to input image x.show(ax=ax, y=im) pred_log.append(wandb.Image(fig, caption=capt)) plt.close(fig) # likely to be an image elif hasattr(y, "shape") and ( (len(y.shape) == 2) or (len(y.shape) == 3 and y.shape[0] in [1, 3, 4])): pred_log.extend([ wandb.Image(x.data, caption='Input data', grouping=3), wandb.Image(pred[0].data, caption='Prediction'), wandb.Image(y.data, caption='Ground Truth') ]) # we just log input data else: pred_log.append(wandb.Image(x.data, caption='Input data')) wandb.log({"Prediction Samples": pred_log}, commit=False) class FastaiError(wandb.Error): pass

true

7901a00a8ef641d86ee6c4066844159e728c9071

1,193

py

Python

app/controllers/auth/register.py

TheSynt4x/flask-blog

11176c15e390f5652ad286b5395f5a27af1c9989

[ "MIT" ]

null

app/controllers/auth/register.py

TheSynt4x/flask-blog

11176c15e390f5652ad286b5395f5a27af1c9989

[ "MIT" ]

null

app/controllers/auth/register.py

TheSynt4x/flask-blog

11176c15e390f5652ad286b5395f5a27af1c9989

[ "MIT" ]

null

from flask import render_template, flash, redirect, url_for, request from flask.views import MethodView from app.middleware import auth from app.models.user import User from app.validators.register_form import RegisterForm from app.services import avatar_service class RegisterController(MethodView): @auth.optional def get(self): """ Show register form Returns: Register template with form """ return render_template('auth/register.html', form=RegisterForm()) @auth.optional def post(self): """ Handle the POST request and sign up the user if form validation passes Returns: A redirect or a template with the validation errors """ form = RegisterForm() if form.validate_on_submit(): form.validate_username(form.username) avatar = 'no-image.png' if 'avatar' in request.files and request.files['avatar']: avatar = avatar_service.save(form.avatar.data) User.create(form.username.data, form.password.data, avatar) flash('Your account has been created. You may now login.', 'info') return redirect(url_for('login')) return render_template('auth/register.html', form=form)

25.934783

74

0.709975

from flask import render_template, flash, redirect, url_for, request from flask.views import MethodView from app.middleware import auth from app.models.user import User from app.validators.register_form import RegisterForm from app.services import avatar_service class RegisterController(MethodView): @auth.optional def get(self): return render_template('auth/register.html', form=RegisterForm()) @auth.optional def post(self): form = RegisterForm() if form.validate_on_submit(): form.validate_username(form.username) avatar = 'no-image.png' if 'avatar' in request.files and request.files['avatar']: avatar = avatar_service.save(form.avatar.data) User.create(form.username.data, form.password.data, avatar) flash('Your account has been created. You may now login.', 'info') return redirect(url_for('login')) return render_template('auth/register.html', form=form)

true

7901a0eb62057284280314ec3af6eb03662f1df9

1,602

py

Python

hue/hue_api.py

BenDoan/playground

2d9dea78eccb22c7118414b163fb434c52eec078

[ "MIT" ]

1

2015-05-24T08:36:04.000Z

hue/hue_api.py

BenDoan/playground

2d9dea78eccb22c7118414b163fb434c52eec078

[ "MIT" ]

9

2021-02-08T20:47:00.000Z

2022-02-18T03:22:11.000Z

hue/hue_api.py

BenDoan/playground

2d9dea78eccb22c7118414b163fb434c52eec078

[ "MIT" ]

null

import json import requests HUE_NUPNP_URL = "https://www.meethue.com/api/nupnp" class APIException(Exception): pass class HueAPI(object): def __init__(self, username): self.username = username self.ip = self.discover_hub_ip() @property def base_url(self): return "http://{}/api/{}".format(self.ip, self.username) def get_groups(self): url = "{}/groups".format(self.base_url) try: r = requests.get(url) except: raise APIException("Failed to send group get GET") try: return list(r.json().keys()) except: raise APIException("Failed to decode group get json response") def set_group(self, group_id, state): url = "{}/groups/{}/action".format(self.base_url, group_id) try: r = requests.put(url, data=json.dumps({"on": state})) except: raise APIException("Failed to send group set PUT") def set_groups(self, state): for group in self.get_groups(): self.set_group(group, state) def discover_hub_ip(self): try: r = requests.get(HUE_NUPNP_URL) except: raise APIException("Failed to send hub ip GET") try: json_resp = r.json() except: raise APIException("Failed to decode hub ip json response") if len(json_resp) > 0: return [0]['internalipaddress'] else: raise APIException("Failed to find hub ip") def _main(): pass if __name__ == '__main__': _main()

23.910448

74

0.576779

import json import requests HUE_NUPNP_URL = "https://www.meethue.com/api/nupnp" class APIException(Exception): pass class HueAPI(object): def __init__(self, username): self.username = username self.ip = self.discover_hub_ip() @property def base_url(self): return "http://{}/api/{}".format(self.ip, self.username) def get_groups(self): url = "{}/groups".format(self.base_url) try: r = requests.get(url) except: raise APIException("Failed to send group get GET") try: return list(r.json().keys()) except: raise APIException("Failed to decode group get json response") def set_group(self, group_id, state): url = "{}/groups/{}/action".format(self.base_url, group_id) try: r = requests.put(url, data=json.dumps({"on": state})) except: raise APIException("Failed to send group set PUT") def set_groups(self, state): for group in self.get_groups(): self.set_group(group, state) def discover_hub_ip(self): try: r = requests.get(HUE_NUPNP_URL) except: raise APIException("Failed to send hub ip GET") try: json_resp = r.json() except: raise APIException("Failed to decode hub ip json response") if len(json_resp) > 0: return [0]['internalipaddress'] else: raise APIException("Failed to find hub ip") def _main(): pass if __name__ == '__main__': _main()

true

7901a103c51919e85415fd5af4bf3af003105056

1,330

py

Python

fastapi_example/util/auth_util.py

pkyosx/fastapi-example

234b2da6b3d60989f9e75483671bc0c2710592bd

[ "MIT" ]

null

fastapi_example/util/auth_util.py

pkyosx/fastapi-example

234b2da6b3d60989f9e75483671bc0c2710592bd

[ "MIT" ]

null

fastapi_example/util/auth_util.py

pkyosx/fastapi-example

234b2da6b3d60989f9e75483671bc0c2710592bd

[ "MIT" ]

null

import time from dataclasses import dataclass import jwt from util.enum_util import EnumBase class Role(EnumBase): USER = "USER" ADMIN = "ADMIN" class Perm(EnumBase): NONE = "NONE" READ_MSG = "READ_MSG" WRITE_MSG = "WRITE_MSG" @dataclass class Identity: user: str role: Role.to_enum() perm_mapping = { Role.USER: [Perm.READ_MSG], Role.ADMIN: [Perm.READ_MSG, Perm.WRITE_MSG], } def has_permission(self, perm: Perm) -> bool: return perm in self.perm_mapping[self.role] class JWTAuthenticator(object): ACCESS_JWT_ALGORITHM = "HS256" @classmethod def dump_access_token(cls, key: str, identity: Identity, exp: int) -> str: current_ts = int(time.time()) return jwt.encode( payload=dict( user=identity.user, role=identity.role, nbf=current_ts - 300, # not before exp=current_ts + exp, ), key=key, algorithm=cls.ACCESS_JWT_ALGORITHM, ) @classmethod def load_access_token(cls, key: str, access_token: str) -> Identity: payload = jwt.decode( jwt=access_token, key=key, algorithms=[cls.ACCESS_JWT_ALGORITHM] ) return Identity(user=payload["user"], role=payload["role"])

23.333333

78

0.605263

import time from dataclasses import dataclass import jwt from util.enum_util import EnumBase class Role(EnumBase): USER = "USER" ADMIN = "ADMIN" class Perm(EnumBase): NONE = "NONE" READ_MSG = "READ_MSG" WRITE_MSG = "WRITE_MSG" @dataclass class Identity: user: str role: Role.to_enum() perm_mapping = { Role.USER: [Perm.READ_MSG], Role.ADMIN: [Perm.READ_MSG, Perm.WRITE_MSG], } def has_permission(self, perm: Perm) -> bool: return perm in self.perm_mapping[self.role] class JWTAuthenticator(object): ACCESS_JWT_ALGORITHM = "HS256" @classmethod def dump_access_token(cls, key: str, identity: Identity, exp: int) -> str: current_ts = int(time.time()) return jwt.encode( payload=dict( user=identity.user, role=identity.role, nbf=current_ts - 300, exp=current_ts + exp, ), key=key, algorithm=cls.ACCESS_JWT_ALGORITHM, ) @classmethod def load_access_token(cls, key: str, access_token: str) -> Identity: payload = jwt.decode( jwt=access_token, key=key, algorithms=[cls.ACCESS_JWT_ALGORITHM] ) return Identity(user=payload["user"], role=payload["role"])

true

7901a15e89880b5c6796a693c8ee4e1f1b87d075

42,243

py

Python

devito/passes/clusters/aliases.py

garg-aayush/devito

b1e8fffdee7d6b556ff19a372d69ed1aebee675a

[ "MIT" ]

1

2021-05-31T04:56:33.000Z

devito/passes/clusters/aliases.py

garg-aayush/devito

b1e8fffdee7d6b556ff19a372d69ed1aebee675a

[ "MIT" ]

null

devito/passes/clusters/aliases.py

garg-aayush/devito

b1e8fffdee7d6b556ff19a372d69ed1aebee675a

[ "MIT" ]

null

from collections import OrderedDict, defaultdict, namedtuple from functools import partial from itertools import groupby from cached_property import cached_property import numpy as np from devito.ir import (SEQUENTIAL, PARALLEL, PARALLEL_IF_PVT, ROUNDABLE, DataSpace, Forward, IterationInstance, IterationSpace, Interval, IntervalGroup, LabeledVector, Context, detect_accesses, build_intervals, normalize_properties) from devito.passes.clusters.utils import timed_pass from devito.symbolics import (Uxmapper, compare_ops, estimate_cost, q_constant, q_leaf, retrieve_indexed, search, uxreplace) from devito.tools import as_tuple, flatten, split from devito.types import (Array, TempFunction, Eq, Symbol, ModuloDimension, CustomDimension, IncrDimension) __all__ = ['cire'] @timed_pass(name='cire') def cire(clusters, mode, sregistry, options, platform): """ Cross-iteration redundancies elimination. Parameters ---------- cluster : Cluster Input Cluster, subject of the optimization pass. mode : str The transformation mode. Accepted: ['invariants', 'sops']. * 'invariants' is for sub-expressions that are invariant w.r.t. one or more Dimensions. * 'sops' stands for sums-of-products, that is redundancies are searched across all expressions in sum-of-product form. sregistry : SymbolRegistry The symbol registry, to create unique temporary names. options : dict The optimization options. Accepted: ['min-storage', 'cire-maxpar', 'cire-rotate', 'cire-maxalias']. * 'min-storage': if True, the pass will try to minimize the amount of storage introduced for the tensor temporaries. This might also reduce the operation count. On the other hand, this might affect fusion and therefore data locality. Defaults to False (legacy). * 'cire-maxpar': if True, privilege parallelism over working set size, that is the pass will try to create as many parallel loops as possible, even though this will require more space (Dimensions) for the temporaries. Defaults to False. * 'cire-rotate': if True, the pass will use modulo indexing for the outermost Dimension iterated over by the temporaries. This will sacrifice a parallel loop for a reduced working set size. Defaults to False (legacy). * 'cire-maxalias': if True, capture the largest redundancies. This will minimize the flop count while maximizing the number of tensor temporaries, thus increasing the working set size. platform : Platform The underlying platform. Used to optimize the shape of the introduced tensor symbols. Examples -------- 1) 'invariants'. Here's an expensive expression invariant w.r.t. `t` t0 = (cos(a[x,y,z])*sin(b[x,y,z]))*c[t,x,y,z] which after CIRE becomes t1[x,y,z] = cos(a[x,y,z])*sin(b[x,y,z]) t0 = t1[x,y,z]*c[t,x,y,z] 2) 'sops'. Below we see two expressions in sum-of-product form (in this case, the sum degenerates to a single product). t0 = 2.0*a[x,y,z]*b[x,y,z] t1 = 3.0*a[x,y,z+1]*b[x,y,z+1] CIRE detects that these two expressions are actually redundant and rewrites them as: t2[x,y,z] = a[x,y,z]*b[x,y,z] t0 = 2.0*t2[x,y,z] t1 = 3.0*t2[x,y,z+1] """ if mode == 'invariants': space = ('inv-basic', 'inv-compound') elif mode in ('sops',): space = (mode,) else: assert False, "Unknown CIRE mode `%s`" % mode processed = [] for c in clusters: # We don't care about sparse Clusters. Their computational cost is # negligible and processing all of them would only increase compilation # time and potentially make the generated code more chaotic if not c.is_dense: processed.append(c) continue # Some of the CIRE transformers need to look inside all scopes # surrounding `c` to perform data dependencies analysis context = Context(c).process(clusters) # Applying CIRE may change `c` as well as creating one or more new Clusters transformed = _cire(c, context, space, sregistry, options, platform) processed.extend(transformed) return processed def _cire(cluster, context, space, sregistry, options, platform): # Construct the space of variants variants = [modes[mode](sregistry, options).make_schedule(cluster, context) for mode in space] if not any(i.schedule for i in variants): return [cluster] # Pick the variant with the highest score, that is the variant with the best # trade-off between operation count reduction and working set size increase schedule, exprs = pick_best(variants) # Schedule -> [Clusters] schedule = optimize_schedule(cluster, schedule, platform, sregistry, options) clusters, subs = lower_schedule(cluster, schedule, sregistry, options) clusters.append(rebuild(cluster, exprs, subs, schedule)) return clusters class Cire(object): """ Base class for CIRE transformers. """ optname = None mode = None def __init__(self, sregistry, options): self.sregistry = sregistry self._opt_minstorage = options['min-storage'] self._opt_mincost = options['cire-mincost'][self.optname] self._opt_maxpar = options['cire-maxpar'] self._opt_maxalias = options['cire-maxalias'] def make_schedule(self, cluster, context): # Capture aliases within `exprs` aliases = AliasMapper() score = 0 exprs = cluster.exprs ispace = cluster.ispace for n in range(self._nrepeats(cluster)): # Extract potentially aliasing expressions mapper = self._extract(exprs, context, n) # Search aliasing expressions found = collect(mapper.extracted, ispace, self._opt_minstorage) # Choose the aliasing expressions with a good flops/memory trade-off exprs, chosen, pscore = choose(found, exprs, mapper, self._selector) aliases.update(chosen) score += pscore # AliasMapper -> Schedule schedule = lower_aliases(cluster, aliases, self._in_writeto, self._opt_maxpar) # The actual score is a 2-tuple <flop-reduction-score, workin-set-score> score = (score, len(aliases)) return SpacePoint(schedule, exprs, score) def _make_symbol(self): return Symbol(name=self.sregistry.make_name('dummy')) def _nrepeats(self, cluster): raise NotImplementedError def _extract(self, exprs, context, n): raise NotImplementedError def _in_writeto(self, dim, cluster): raise NotImplementedError def _selector(self, e, naliases): raise NotImplementedError class CireInvariants(Cire): optname = 'invariants' def _nrepeats(self, cluster): return 1 def _rule(self, e): return (e.is_Function or (e.is_Pow and e.exp.is_Number and e.exp < 1)) def _extract(self, exprs, context, n): mapper = Uxmapper() for prefix, clusters in context.items(): if not prefix: continue exclude = set().union(*[c.scope.writes for c in clusters]) exclude.add(prefix[-1].dim) for e in exprs: for i in search(e, self._rule, 'all', 'bfs_first_hit'): if {a.function for a in i.free_symbols} & exclude: continue mapper.add(i, self._make_symbol) return mapper def _in_writeto(self, dim, cluster): return PARALLEL in cluster.properties[dim] def _selector(self, e, naliases): if all(i.function.is_Symbol for i in e.free_symbols): # E.g., `dt**(-2)` mincost = self._opt_mincost['scalar'] else: mincost = self._opt_mincost['tensor'] return estimate_cost(e, True)*naliases // mincost class CireInvariantsBasic(CireInvariants): mode = 'inv-basic' class CireInvariantsCompound(CireInvariants): mode = 'inv-compound' def _extract(self, exprs, context, n): extracted = super()._extract(exprs, context, n).extracted rule = lambda e: any(a in extracted for a in e.args) mapper = Uxmapper() for e in exprs: for i in search(e, rule, 'all', 'dfs'): if not i.is_commutative: continue key = lambda a: a in extracted terms, others = split(i.args, key) mapper.add(i, self._make_symbol, terms) return mapper class CireSOPS(Cire): optname = 'sops' mode = 'sops' def _nrepeats(self, cluster): # The `nrepeats` is calculated such that we analyze all potential derivatives # in `cluster` return potential_max_deriv_order(cluster.exprs) def _extract(self, exprs, context, n): # Forbid CIRE involving Dimension-independent dependencies, e.g.: # r0 = ... # u[x, y] = ... r0*a[x, y] ... # NOTE: if one uses the DSL in a conventional way and sticks to the default # compilation pipelines where CSE always happens after CIRE, then `exclude` # will always be empty exclude = {i.source.indexed for i in context[None].scope.d_flow.independent()} mapper = Uxmapper() for e in exprs: for i in search_potential_deriv(e, n): if i.free_symbols & exclude: continue key = lambda a: a.is_Add terms, others = split(i.args, key) if self._opt_maxalias: # Treat `e` as an FD expression and pull out the derivative # coefficient from `i` # Note: typically derivative coefficients are numbers, but # sometimes they could be provided in symbolic form through an # arbitrary Function. In the latter case, we rely on the # heuristic that such Function's basically never span the whole # grid, but rather a single Grid dimension (e.g., `c[z, n]` for a # stencil of diameter `n` along `z`) if e.grid is not None and terms: key = partial(maybe_coeff_key, e.grid) others, more_terms = split(others, key) terms += more_terms mapper.add(i, self._make_symbol, terms) return mapper def _in_writeto(self, dim, cluster): return self._opt_maxpar and PARALLEL in cluster.properties[dim] def _selector(self, e, naliases): if naliases <= 1: return 0 else: return estimate_cost(e, True)*naliases // self._opt_mincost modes = { CireInvariantsBasic.mode: CireInvariantsBasic, CireInvariantsCompound.mode: CireInvariantsCompound, CireSOPS.mode: CireSOPS } def collect(extracted, ispace, min_storage): """ Find groups of aliasing expressions. We shall introduce the following (loose) terminology: * A ``terminal`` is the leaf of a mathematical operation. Terminals can be numbers (n), literals (l), or Indexeds (I). * ``R`` is the relaxation operator := ``R(n) = n``, ``R(l) = l``, ``R(I) = J``, where ``J`` has the same base as ``I`` but with all offsets stripped away. For example, ``R(a[i+2,j-1]) = a[i,j]``. * A ``relaxed expression`` is an expression in which all of the terminals are relaxed. Now we define the concept of aliasing. We say that an expression A aliases an expression B if: * ``R(A) == R(B)`` * all pairwise Indexeds in A and B access memory locations at a fixed constant distance along each Dimension. For example, consider the following expressions: * a[i+1] + b[i+1] * a[i+1] + b[j+1] * a[i] + c[i] * a[i+2] - b[i+2] * a[i+2] + b[i] * a[i-1] + b[i-1] Out of the expressions above, the following alias to `a[i] + b[i]`: * a[i+1] + b[i+1] : same operands and operations, distance along i: 1 * a[i-1] + b[i-1] : same operands and operations, distance along i: -1 Whereas the following do not: * a[i+1] + b[j+1] : because at least one index differs * a[i] + c[i] : because at least one of the operands differs * a[i+2] - b[i+2] : because at least one operation differs * a[i+2] + b[i] : because the distances along ``i`` differ (+2 and +0) """ # Find the potential aliases found = [] for expr in extracted: assert not expr.is_Equality indexeds = retrieve_indexed(expr) bases = [] offsets = [] for i in indexeds: ii = IterationInstance(i) if ii.is_irregular: break base = [] offset = [] for e, ai in zip(ii, ii.aindices): if q_constant(e): base.append(e) else: base.append(ai) offset.append((ai, e - ai)) bases.append(tuple(base)) offsets.append(LabeledVector(offset)) if not indexeds or len(bases) == len(indexeds): found.append(Candidate(expr, ispace, indexeds, bases, offsets)) # Create groups of aliasing expressions mapper = OrderedDict() unseen = list(found) while unseen: c = unseen.pop(0) group = [c] for u in list(unseen): # Is the arithmetic structure of `c` and `u` equivalent ? if not compare_ops(c.expr, u.expr): continue # Is `c` translated w.r.t. `u` ? if not c.translated(u): continue group.append(u) unseen.remove(u) group = Group(group) if min_storage: k = group.dimensions_translated else: k = group.dimensions mapper.setdefault(k, []).append(group) aliases = AliasMapper() queue = list(mapper.values()) while queue: groups = queue.pop(0) while groups: # For each Dimension, determine the Minimum Intervals (MI) spanning # all of the Groups diameters # Example: x's largest_diameter=2 => [x[-2,0], x[-1,1], x[0,2]] # Note: Groups that cannot evaluate their diameter are dropped mapper = defaultdict(int) for g in list(groups): try: mapper.update({d: max(mapper[d], v) for d, v in g.diameter.items()}) except ValueError: groups.remove(g) intervalss = {d: make_rotations_table(d, v) for d, v in mapper.items()} # For each Group, find a rotation that is compatible with a given MI mapper = {} for d, intervals in intervalss.items(): # Not all groups may access all dimensions # Example: `d=t` and groups=[Group(...[t, x]...), Group(...[time, x]...)] impacted = [g for g in groups if d in g.dimensions] for interval in list(intervals): found = {g: g.find_rotation_distance(d, interval) for g in impacted} if all(distance is not None for distance in found.values()): # `interval` is OK ! mapper[interval] = found break if len(mapper) == len(intervalss): break # Try again with fewer groups # Heuristic: first try retaining the larger ones smallest = len(min(groups, key=len)) fallback = groups groups, remainder = split(groups, lambda g: len(g) > smallest) if groups: queue.append(remainder) elif len(remainder) > 1: # No luck with the heuristic, e.g. there are two groups # and both have same `len` queue.append(fallback[1:]) groups = [fallback.pop(0)] else: break for g in groups: c = g.pivot distances = defaultdict(int, [(i.dim, v.get(g)) for i, v in mapper.items()]) # Create the basis alias offsets = [LabeledVector([(l, v[l] + distances[l]) for l in v.labels]) for v in c.offsets] subs = {i: i.function[[l + v.fromlabel(l, 0) for l in b]] for i, b, v in zip(c.indexeds, c.bases, offsets)} alias = uxreplace(c.expr, subs) # All aliased expressions aliaseds = [extracted[i.expr] for i in g] # Distance of each aliased expression from the basis alias distances = [] for i in g: distance = [o.distance(v) for o, v in zip(i.offsets, offsets)] distance = [(d, set(v)) for d, v in LabeledVector.transpose(*distance)] distances.append(LabeledVector([(d, v.pop()) for d, v in distance])) aliases.add(alias, list(mapper), aliaseds, distances) return aliases def choose(aliases, exprs, mapper, selector): """ Analyze the detected aliases and, after applying a cost model to rule out the aliases with a bad flops/memory trade-off, inject them into the original expressions. """ tot = 0 retained = AliasMapper() # Pass 1: a set of aliasing expressions is retained only if its cost # exceeds the mode's threshold candidates = OrderedDict() aliaseds = [] others = [] for e, v in aliases.items(): score = selector(e, len(v.aliaseds)) if score > 0: candidates[e] = score aliaseds.extend(v.aliaseds) else: others.append(e) # Do not waste time if unneccesary if not candidates: return exprs, retained, tot # Project the candidate aliases into exprs to determine what the new # working set would be mapper = {k: v for k, v in mapper.items() if v.free_symbols & set(aliaseds)} templated = [uxreplace(e, mapper) for e in exprs] # Pass 2: a set of aliasing expressions is retained only if the tradeoff # between operation count reduction and working set increase is favorable owset = wset(others + templated) for e, v in aliases.items(): try: score = candidates[e] except KeyError: score = 0 if score > 1 or \ score == 1 and max(len(wset(e)), 1) > len(wset(e) & owset): retained[e] = v tot += score # Do not waste time if unneccesary if not retained: return exprs, retained, tot # Substitute the chosen aliasing sub-expressions mapper = {k: v for k, v in mapper.items() if v.free_symbols & set(retained.aliaseds)} exprs = [uxreplace(e, mapper) for e in exprs] return exprs, retained, tot def lower_aliases(cluster, aliases, in_writeto, maxpar): """ Create a Schedule from an AliasMapper. """ dmapper = {} processed = [] for alias, v in aliases.items(): imapper = {**{i.dim: i for i in v.intervals}, **{i.dim.parent: i for i in v.intervals if i.dim.is_NonlinearDerived}} intervals = [] writeto = [] sub_iterators = {} indicess = [[] for _ in v.distances] for i in cluster.ispace.intervals: try: interval = imapper[i.dim] except KeyError: # E.g., `x0_blk0` or (`a[y_m+1]` => `y not in imapper`) intervals.append(i) continue assert i.stamp >= interval.stamp if not (writeto or interval != interval.zero() or in_writeto(i.dim, cluster)): # The alias doesn't require a temporary Dimension along i.dim intervals.append(i) continue assert not i.dim.is_NonlinearDerived # `i.dim` is necessarily part of the write-to region, so # we have to adjust the Interval's stamp. For example, consider # `i=x[0,0]<1>` and `interval=x[-4,4]<0>`; here we need to # use `<1>` as stamp, which is what appears in `cluster` interval = interval.lift(i.stamp) # We further bump the interval stamp if we were requested to trade # fusion for more collapse-parallelism interval = interval.lift(interval.stamp + int(maxpar)) writeto.append(interval) intervals.append(interval) if i.dim.is_Incr: # Suitable IncrDimensions must be used to avoid OOB accesses. # E.g., r[xs][ys][z] => both `xs` and `ys` must be initialized such # that all accesses are within bounds. This requires traversing the # hierarchy of IncrDimensions to set `xs` (`ys`) in a way that # consecutive blocks access consecutive regions in `r` (e.g., # `xs=x0_blk1-x0_blk0` with `blocklevels=2`; `xs=0` with # `blocklevels=1`, that is it degenerates in this case) try: d = dmapper[i.dim] except KeyError: dd = i.dim.parent assert dd.is_Incr if dd.parent.is_Incr: # An IncrDimension in between IncrDimensions m = i.dim.symbolic_min - i.dim.parent.symbolic_min else: m = 0 d = dmapper[i.dim] = IncrDimension("%ss" % i.dim.name, i.dim, m, dd.symbolic_size, 1, dd.step) sub_iterators[i.dim] = d else: d = i.dim # Given the iteration `interval`, lower distances to indices for distance, indices in zip(v.distances, indicess): indices.append(d - interval.lower + distance[interval.dim]) # The alias write-to space writeto = IterationSpace(IntervalGroup(writeto), sub_iterators) # The alias iteration space intervals = IntervalGroup(intervals, cluster.ispace.relations) ispace = IterationSpace(intervals, cluster.sub_iterators, cluster.directions) ispace = ispace.augment(sub_iterators) processed.append(ScheduledAlias(alias, writeto, ispace, v.aliaseds, indicess)) # The [ScheduledAliases] must be ordered so as to reuse as many of the # `cluster`'s IterationIntervals as possible in order to honor the # write-to region. Another fundamental reason for ordering is to ensure # deterministic code generation processed = sorted(processed, key=lambda i: cit(cluster.ispace, i.ispace)) return Schedule(*processed, dmapper=dmapper) def optimize_schedule(cluster, schedule, platform, sregistry, options): """ Rewrite the schedule for performance optimization. """ if options['cire-rotate']: schedule = _optimize_schedule_rotations(schedule, sregistry) schedule = _optimize_schedule_padding(cluster, schedule, platform) return schedule def _optimize_schedule_rotations(schedule, sregistry): """ Transform the schedule such that the tensor temporaries "rotate" along the outermost Dimension. This trades a parallel Dimension for a smaller working set size. """ # The rotations Dimension is the outermost ridx = 0 rmapper = defaultdict(list) processed = [] for k, group in groupby(schedule, key=lambda i: i.writeto): g = list(group) candidate = k[ridx] d = candidate.dim try: ds = schedule.dmapper[d] except KeyError: # Can't do anything if `d` isn't an IncrDimension over a block processed.extend(g) continue n = candidate.min_size assert n > 0 iis = candidate.lower iib = candidate.upper ii = ModuloDimension('%sii' % d, ds, iis, incr=iib) cd = CustomDimension(name='%s%s' % (d, d), symbolic_min=ii, symbolic_max=iib, symbolic_size=n) dsi = ModuloDimension('%si' % ds, cd, cd + ds - iis, n) mapper = OrderedDict() for i in g: # Update `indicess` to use `xs0`, `xs1`, ... mds = [] for indices in i.indicess: v = indices[ridx] try: md = mapper[v] except KeyError: name = sregistry.make_name(prefix='%sr' % d.name) md = mapper.setdefault(v, ModuloDimension(name, ds, v, n)) mds.append(md) indicess = [indices[:ridx] + [md] + indices[ridx + 1:] for md, indices in zip(mds, i.indicess)] # Update `writeto` by switching `d` to `dsi` intervals = k.intervals.switch(d, dsi).zero(dsi) sub_iterators = dict(k.sub_iterators) sub_iterators[d] = dsi writeto = IterationSpace(intervals, sub_iterators) # Transform `alias` by adding `i` alias = i.alias.xreplace({d: d + cd}) # Extend `ispace` to iterate over rotations d1 = writeto[ridx+1].dim # Note: we're by construction in-bounds here intervals = IntervalGroup(Interval(cd, 0, 0), relations={(d, cd, d1)}) rispace = IterationSpace(intervals, {cd: dsi}, {cd: Forward}) aispace = i.ispace.zero(d) aispace = aispace.augment({d: mds + [ii]}) ispace = IterationSpace.union(rispace, aispace) processed.append(ScheduledAlias(alias, writeto, ispace, i.aliaseds, indicess)) # Update the rotations mapper rmapper[d].extend(list(mapper.values())) return Schedule(*processed, dmapper=schedule.dmapper, rmapper=rmapper) def _optimize_schedule_padding(cluster, schedule, platform): """ Round up the innermost IterationInterval of the tensor temporaries IterationSpace to a multiple of the SIMD vector length. This is not always possible though (it depends on how much halo is safely accessible in all read Functions). """ processed = [] for i in schedule: try: it = i.ispace.itintervals[-1] if ROUNDABLE in cluster.properties[it.dim]: vl = platform.simd_items_per_reg(cluster.dtype) ispace = i.ispace.add(Interval(it.dim, 0, it.interval.size % vl)) else: ispace = i.ispace processed.append(ScheduledAlias(i.alias, i.writeto, ispace, i.aliaseds, i.indicess)) except (TypeError, KeyError): processed.append(i) return Schedule(*processed, dmapper=schedule.dmapper, rmapper=schedule.rmapper) def lower_schedule(cluster, schedule, sregistry, options): """ Turn a Schedule into a sequence of Clusters. """ ftemps = options['cire-ftemps'] if ftemps: make = TempFunction else: # Typical case -- the user does *not* "see" the CIRE-created temporaries make = Array clusters = [] subs = {} for alias, writeto, ispace, aliaseds, indicess in schedule: # Basic info to create the temporary that will hold the alias name = sregistry.make_name() dtype = cluster.dtype if writeto: # The Dimensions defining the shape of Array # Note: with SubDimensions, we may have the following situation: # # for zi = z_m + zi_ltkn; zi <= z_M - zi_rtkn; ... # r[zi] = ... # # Instead of `r[zi - z_m - zi_ltkn]` we have just `r[zi]`, so we'll need # as much room as in `zi`'s parent to avoid going OOB # Aside from ugly generated code, the reason we do not rather shift the # indices is that it prevents future passes to transform the loop bounds # (e.g., MPI's comp/comm overlap does that) dimensions = [d.parent if d.is_Sub else d for d in writeto.itdimensions] # The halo must be set according to the size of writeto space halo = [(abs(i.lower), abs(i.upper)) for i in writeto] # The indices used to write into the Array indices = [] for i in writeto: try: # E.g., `xs` sub_iterators = writeto.sub_iterators[i.dim] assert len(sub_iterators) == 1 indices.append(sub_iterators[0]) except KeyError: # E.g., `z` -- a non-shifted Dimension indices.append(i.dim - i.lower) obj = make(name=name, dimensions=dimensions, halo=halo, dtype=dtype) expression = Eq(obj[indices], alias) callback = lambda idx: obj[idx] else: # Degenerate case: scalar expression assert writeto.size == 0 obj = Symbol(name=name, dtype=dtype) expression = Eq(obj, alias) callback = lambda idx: obj # Create the substitution rules for the aliasing expressions subs.update({aliased: callback(indices) for aliased, indices in zip(aliaseds, indicess)}) # Construct the `alias` DataSpace accesses = detect_accesses(expression) parts = {k: IntervalGroup(build_intervals(v)).add(ispace.intervals).relaxed for k, v in accesses.items() if k} dspace = DataSpace(cluster.dspace.intervals, parts) # Drop or weaken parallelism if necessary properties = dict(cluster.properties) for d, v in cluster.properties.items(): if any(i.is_Modulo for i in ispace.sub_iterators[d]): properties[d] = normalize_properties(v, {SEQUENTIAL}) elif d not in writeto.dimensions: properties[d] = normalize_properties(v, {PARALLEL_IF_PVT}) # Finally, build the `alias` Cluster clusters.append(cluster.rebuild(exprs=expression, ispace=ispace, dspace=dspace, properties=properties)) return clusters, subs def pick_best(variants): """ Use the variant score and heuristics to return the variant with the best trade-off between operation count reduction and working set increase. """ best = variants.pop(0) for i in variants: best_flop_score, best_ws_score = best.score if best_flop_score == 0: best = i continue i_flop_score, i_ws_score = i.score # The current heustic is fairly basic: the one with smaller working # set size increase wins, unless there's a massive reduction in operation # count in the other one delta = i_ws_score - best_ws_score if (delta > 0 and i_flop_score / best_flop_score > 100) or \ (delta == 0 and i_flop_score > best_flop_score) or \ (delta < 0 and best_flop_score / i_flop_score <= 100): best = i schedule, exprs, _ = best return schedule, exprs def rebuild(cluster, exprs, subs, schedule): """ Plug the optimized aliases into the input Cluster. This leads to creating a new Cluster with suitable IterationSpace and DataSpace. """ exprs = [uxreplace(e, subs) for e in exprs] ispace = cluster.ispace.augment(schedule.dmapper) ispace = ispace.augment(schedule.rmapper) accesses = detect_accesses(exprs) parts = {k: IntervalGroup(build_intervals(v)).relaxed for k, v in accesses.items() if k} dspace = DataSpace(cluster.dspace.intervals, parts) return cluster.rebuild(exprs=exprs, ispace=ispace, dspace=dspace) # Utilities class Candidate(object): def __init__(self, expr, ispace, indexeds, bases, offsets): self.expr = expr self.shifts = ispace.intervals self.indexeds = indexeds self.bases = bases self.offsets = offsets def __repr__(self): return "Candidate(expr=%s)" % self.expr def translated(self, other): """ True if ``self`` is translated w.r.t. ``other``, False otherwise. Examples -------- Two candidates are translated if their bases are the same and their offsets are pairwise translated. c := A[i,j] op A[i,j+1] -> Toffsets = {i: [0,0], j: [0,1]} u := A[i+1,j] op A[i+1,j+1] -> Toffsets = {i: [1,1], j: [0,1]} Then `c` is translated w.r.t. `u` with distance `{i: 1, j: 0}` """ if len(self.Toffsets) != len(other.Toffsets): return False if len(self.bases) != len(other.bases): return False # Check the bases if any(b0 != b1 for b0, b1 in zip(self.bases, other.bases)): return False # Check the offsets for (d0, o0), (d1, o1) in zip(self.Toffsets, other.Toffsets): if d0 is not d1: return False distance = set(o0 - o1) if len(distance) != 1: return False return True @cached_property def Toffsets(self): return LabeledVector.transpose(*self.offsets) @cached_property def dimensions(self): return frozenset(i for i, _ in self.Toffsets) class Group(tuple): """ A collection of aliasing expressions. """ def __repr__(self): return "Group(%s)" % ", ".join([str(i) for i in self]) def find_rotation_distance(self, d, interval): """ The distance from the Group pivot of a rotation along Dimension ``d`` that can safely iterate over the ``interval``. """ assert d is interval.dim for rotation, distance in self._pivot_legal_rotations[d]: # Does `rotation` cover the `interval` ? if rotation.union(interval) != rotation: continue # Infer the `rotation`'s min_intervals from the pivot's min_interval = self._pivot_min_intervals[d].translate(-distance) # Does the `interval` actually cover the `rotation`'s `min_interval`? if interval.union(min_interval) == interval: return distance return None @cached_property def Toffsets(self): return [LabeledVector.transpose(*i) for i in zip(*[i.offsets for i in self])] @cached_property def diameter(self): """ The size of the iteration space required to evaluate all aliasing expressions in this Group, along each Dimension. """ ret = defaultdict(int) for i in self.Toffsets: for d, v in i: try: distance = int(max(v) - min(v)) except TypeError: # An entry in `v` has symbolic components, e.g. `x_m + 2` if len(set(v)) == 1: continue else: raise ValueError ret[d] = max(ret[d], distance) return ret @property def pivot(self): """ A deterministically chosen Candidate for this Group. """ return self[0] @property def dimensions(self): return self.pivot.dimensions @property def dimensions_translated(self): return frozenset(d for d, v in self.diameter.items() if v > 0) @cached_property def _pivot_legal_rotations(self): """ All legal rotations along each Dimension for the Group pivot. """ ret = {} for d, (maxd, mini) in self._pivot_legal_shifts.items(): # Rotation size = mini (min-increment) - maxd (max-decrement) v = mini - maxd # Build the table of all possible rotations m = make_rotations_table(d, v) distances = [] for rotation in m: # Distance of the rotation `i` from `c` distance = maxd - rotation.lower assert distance == mini - rotation.upper distances.append(distance) ret[d] = list(zip(m, distances)) return ret @cached_property def _pivot_min_intervals(self): """ The minimum Interval along each Dimension such that by evaluating the pivot, all Candidates are evaluated too. """ c = self.pivot ret = defaultdict(lambda: [np.inf, -np.inf]) for i in self: distance = [o.distance(v) for o, v in zip(i.offsets, c.offsets)] distance = [(d, set(v)) for d, v in LabeledVector.transpose(*distance)] for d, v in distance: value = v.pop() ret[d][0] = min(ret[d][0], value) ret[d][1] = max(ret[d][1], value) ret = {d: Interval(d, m, M) for d, (m, M) in ret.items()} return ret @cached_property def _pivot_legal_shifts(self): """ The max decrement and min increment along each Dimension such that the Group pivot does not go OOB. """ c = self.pivot ret = defaultdict(lambda: (-np.inf, np.inf)) for i, ofs in zip(c.indexeds, c.offsets): f = i.function for l in ofs.labels: # `f`'s cumulative halo size along `l` hsize = sum(f._size_halo[l]) # Any `ofs`'s shift due to non-[0,0] iteration space lower, upper = c.shifts[l].offsets try: # Assume `ofs[d]` is a number (typical case) maxd = min(0, max(ret[l][0], -ofs[l] - lower)) mini = max(0, min(ret[l][1], hsize - ofs[l] - upper)) ret[l] = (maxd, mini) except TypeError: # E.g., `ofs[d] = x_m - x + 5` ret[l] = (0, 0) return ret AliasedGroup = namedtuple('AliasedGroup', 'intervals aliaseds distances') ScheduledAlias = namedtuple('ScheduledAlias', 'alias writeto ispace aliaseds indicess') ScheduledAlias.__new__.__defaults__ = (None,) * len(ScheduledAlias._fields) SpacePoint = namedtuple('SpacePoint', 'schedule exprs score') class Schedule(tuple): def __new__(cls, *items, dmapper=None, rmapper=None): obj = super(Schedule, cls).__new__(cls, items) obj.dmapper = dmapper or {} obj.rmapper = rmapper or {} return obj class AliasMapper(OrderedDict): def add(self, alias, intervals, aliaseds, distances): assert len(aliaseds) == len(distances) self[alias] = AliasedGroup(intervals, aliaseds, distances) def update(self, aliases): for k, v in aliases.items(): try: v0 = self[k] if v0.intervals != v.intervals: raise ValueError v0.aliaseds.extend(v.aliaseds) v0.distances.extend(v.distances) except KeyError: self[k] = v @property def aliaseds(self): return flatten(i.aliaseds for i in self.values()) def make_rotations_table(d, v): """ All possible rotations of `range(v+1)`. """ m = np.array([[j-i if j > i else 0 for j in range(v+1)] for i in range(v+1)]) m = (m - m.T)[::-1, :] # Shift the table so that the middle rotation is at the top m = np.roll(m, int(-np.floor(v/2)), axis=0) # Turn into a more compact representation as a list of Intervals m = [Interval(d, min(i), max(i)) for i in m] return m def cit(ispace0, ispace1): """ The Common IterationIntervals of two IterationSpaces. """ found = [] for it0, it1 in zip(ispace0.itintervals, ispace1.itintervals): if it0 == it1: found.append(it0) else: break return tuple(found) def maybe_coeff_key(grid, expr): """ True if `expr` could be the coefficient of an FD derivative, False otherwise. """ if expr.is_Number: return True indexeds = [i for i in expr.free_symbols if i.is_Indexed] return any(not set(grid.dimensions) <= set(i.function.dimensions) for i in indexeds) def wset(exprs): """ Extract the working set out of a set of equations. """ return {i.function for i in flatten([e.free_symbols for e in as_tuple(exprs)]) if i.function.is_AbstractFunction} def potential_max_deriv_order(exprs): """ The maximum FD derivative order in a list of expressions. """ # NOTE: e might propagate the Derivative(...) information down from the # symbolic language, but users may do crazy things and write their own custom # expansions "by hand" (i.e., not resorting to Derivative(...)), hence instead # of looking for Derivative(...) we use the following heuristic: # add(mul, mul, ...) -> stems from first order derivative # add(mul(add(mul, mul, ...), ...), ...) -> stems from second order derivative # ... nadds = lambda e: (int(e.is_Add) + max([nadds(a) for a in e.args], default=0) if not q_leaf(e) else 0) return max([nadds(e) for e in exprs], default=0) def search_potential_deriv(expr, n, c=0): """ Retrieve the expressions at depth `n` that potentially stem from FD derivatives. """ assert n >= c >= 0 if q_leaf(expr) or expr.is_Pow: return [] elif expr.is_Mul: if c == n: return [expr] else: return flatten([search_potential_deriv(a, n, c+1) for a in expr.args]) else: return flatten([search_potential_deriv(a, n, c) for a in expr.args])

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from collections import OrderedDict, defaultdict, namedtuple from functools import partial from itertools import groupby from cached_property import cached_property import numpy as np from devito.ir import (SEQUENTIAL, PARALLEL, PARALLEL_IF_PVT, ROUNDABLE, DataSpace, Forward, IterationInstance, IterationSpace, Interval, IntervalGroup, LabeledVector, Context, detect_accesses, build_intervals, normalize_properties) from devito.passes.clusters.utils import timed_pass from devito.symbolics import (Uxmapper, compare_ops, estimate_cost, q_constant, q_leaf, retrieve_indexed, search, uxreplace) from devito.tools import as_tuple, flatten, split from devito.types import (Array, TempFunction, Eq, Symbol, ModuloDimension, CustomDimension, IncrDimension) __all__ = ['cire'] @timed_pass(name='cire') def cire(clusters, mode, sregistry, options, platform): if mode == 'invariants': space = ('inv-basic', 'inv-compound') elif mode in ('sops',): space = (mode,) else: assert False, "Unknown CIRE mode `%s`" % mode processed = [] for c in clusters: # negligible and processing all of them would only increase compilation # time and potentially make the generated code more chaotic if not c.is_dense: processed.append(c) continue # Some of the CIRE transformers need to look inside all scopes # surrounding `c` to perform data dependencies analysis context = Context(c).process(clusters) # Applying CIRE may change `c` as well as creating one or more new Clusters transformed = _cire(c, context, space, sregistry, options, platform) processed.extend(transformed) return processed def _cire(cluster, context, space, sregistry, options, platform): # Construct the space of variants variants = [modes[mode](sregistry, options).make_schedule(cluster, context) for mode in space] if not any(i.schedule for i in variants): return [cluster] # Pick the variant with the highest score, that is the variant with the best # trade-off between operation count reduction and working set size increase schedule, exprs = pick_best(variants) # Schedule -> [Clusters] schedule = optimize_schedule(cluster, schedule, platform, sregistry, options) clusters, subs = lower_schedule(cluster, schedule, sregistry, options) clusters.append(rebuild(cluster, exprs, subs, schedule)) return clusters class Cire(object): optname = None mode = None def __init__(self, sregistry, options): self.sregistry = sregistry self._opt_minstorage = options['min-storage'] self._opt_mincost = options['cire-mincost'][self.optname] self._opt_maxpar = options['cire-maxpar'] self._opt_maxalias = options['cire-maxalias'] def make_schedule(self, cluster, context): # Capture aliases within `exprs` aliases = AliasMapper() score = 0 exprs = cluster.exprs ispace = cluster.ispace for n in range(self._nrepeats(cluster)): # Extract potentially aliasing expressions mapper = self._extract(exprs, context, n) # Search aliasing expressions found = collect(mapper.extracted, ispace, self._opt_minstorage) # Choose the aliasing expressions with a good flops/memory trade-off exprs, chosen, pscore = choose(found, exprs, mapper, self._selector) aliases.update(chosen) score += pscore # AliasMapper -> Schedule schedule = lower_aliases(cluster, aliases, self._in_writeto, self._opt_maxpar) # The actual score is a 2-tuple <flop-reduction-score, workin-set-score> score = (score, len(aliases)) return SpacePoint(schedule, exprs, score) def _make_symbol(self): return Symbol(name=self.sregistry.make_name('dummy')) def _nrepeats(self, cluster): raise NotImplementedError def _extract(self, exprs, context, n): raise NotImplementedError def _in_writeto(self, dim, cluster): raise NotImplementedError def _selector(self, e, naliases): raise NotImplementedError class CireInvariants(Cire): optname = 'invariants' def _nrepeats(self, cluster): return 1 def _rule(self, e): return (e.is_Function or (e.is_Pow and e.exp.is_Number and e.exp < 1)) def _extract(self, exprs, context, n): mapper = Uxmapper() for prefix, clusters in context.items(): if not prefix: continue exclude = set().union(*[c.scope.writes for c in clusters]) exclude.add(prefix[-1].dim) for e in exprs: for i in search(e, self._rule, 'all', 'bfs_first_hit'): if {a.function for a in i.free_symbols} & exclude: continue mapper.add(i, self._make_symbol) return mapper def _in_writeto(self, dim, cluster): return PARALLEL in cluster.properties[dim] def _selector(self, e, naliases): if all(i.function.is_Symbol for i in e.free_symbols): # E.g., `dt**(-2)` mincost = self._opt_mincost['scalar'] else: mincost = self._opt_mincost['tensor'] return estimate_cost(e, True)*naliases // mincost class CireInvariantsBasic(CireInvariants): mode = 'inv-basic' class CireInvariantsCompound(CireInvariants): mode = 'inv-compound' def _extract(self, exprs, context, n): extracted = super()._extract(exprs, context, n).extracted rule = lambda e: any(a in extracted for a in e.args) mapper = Uxmapper() for e in exprs: for i in search(e, rule, 'all', 'dfs'): if not i.is_commutative: continue key = lambda a: a in extracted terms, others = split(i.args, key) mapper.add(i, self._make_symbol, terms) return mapper class CireSOPS(Cire): optname = 'sops' mode = 'sops' def _nrepeats(self, cluster): # The `nrepeats` is calculated such that we analyze all potential derivatives # in `cluster` return potential_max_deriv_order(cluster.exprs) def _extract(self, exprs, context, n): # Forbid CIRE involving Dimension-independent dependencies, e.g.: # r0 = ... # u[x, y] = ... r0*a[x, y] ... # NOTE: if one uses the DSL in a conventional way and sticks to the default # compilation pipelines where CSE always happens after CIRE, then `exclude` # will always be empty exclude = {i.source.indexed for i in context[None].scope.d_flow.independent()} mapper = Uxmapper() for e in exprs: for i in search_potential_deriv(e, n): if i.free_symbols & exclude: continue key = lambda a: a.is_Add terms, others = split(i.args, key) if self._opt_maxalias: # Treat `e` as an FD expression and pull out the derivative # coefficient from `i` # Note: typically derivative coefficients are numbers, but # sometimes they could be provided in symbolic form through an # arbitrary Function. In the latter case, we rely on the # heuristic that such Function's basically never span the whole if e.grid is not None and terms: key = partial(maybe_coeff_key, e.grid) others, more_terms = split(others, key) terms += more_terms mapper.add(i, self._make_symbol, terms) return mapper def _in_writeto(self, dim, cluster): return self._opt_maxpar and PARALLEL in cluster.properties[dim] def _selector(self, e, naliases): if naliases <= 1: return 0 else: return estimate_cost(e, True)*naliases // self._opt_mincost modes = { CireInvariantsBasic.mode: CireInvariantsBasic, CireInvariantsCompound.mode: CireInvariantsCompound, CireSOPS.mode: CireSOPS } def collect(extracted, ispace, min_storage): found = [] for expr in extracted: assert not expr.is_Equality indexeds = retrieve_indexed(expr) bases = [] offsets = [] for i in indexeds: ii = IterationInstance(i) if ii.is_irregular: break base = [] offset = [] for e, ai in zip(ii, ii.aindices): if q_constant(e): base.append(e) else: base.append(ai) offset.append((ai, e - ai)) bases.append(tuple(base)) offsets.append(LabeledVector(offset)) if not indexeds or len(bases) == len(indexeds): found.append(Candidate(expr, ispace, indexeds, bases, offsets)) mapper = OrderedDict() unseen = list(found) while unseen: c = unseen.pop(0) group = [c] for u in list(unseen): if not compare_ops(c.expr, u.expr): continue if not c.translated(u): continue group.append(u) unseen.remove(u) group = Group(group) if min_storage: k = group.dimensions_translated else: k = group.dimensions mapper.setdefault(k, []).append(group) aliases = AliasMapper() queue = list(mapper.values()) while queue: groups = queue.pop(0) while groups: # Note: Groups that cannot evaluate their diameter are dropped mapper = defaultdict(int) for g in list(groups): try: mapper.update({d: max(mapper[d], v) for d, v in g.diameter.items()}) except ValueError: groups.remove(g) intervalss = {d: make_rotations_table(d, v) for d, v in mapper.items()} # For each Group, find a rotation that is compatible with a given MI mapper = {} for d, intervals in intervalss.items(): # Not all groups may access all dimensions # Example: `d=t` and groups=[Group(...[t, x]...), Group(...[time, x]...)] impacted = [g for g in groups if d in g.dimensions] for interval in list(intervals): found = {g: g.find_rotation_distance(d, interval) for g in impacted} if all(distance is not None for distance in found.values()): # `interval` is OK ! mapper[interval] = found break if len(mapper) == len(intervalss): break # Try again with fewer groups # Heuristic: first try retaining the larger ones smallest = len(min(groups, key=len)) fallback = groups groups, remainder = split(groups, lambda g: len(g) > smallest) if groups: queue.append(remainder) elif len(remainder) > 1: # No luck with the heuristic, e.g. there are two groups # and both have same `len` queue.append(fallback[1:]) groups = [fallback.pop(0)] else: break for g in groups: c = g.pivot distances = defaultdict(int, [(i.dim, v.get(g)) for i, v in mapper.items()]) # Create the basis alias offsets = [LabeledVector([(l, v[l] + distances[l]) for l in v.labels]) for v in c.offsets] subs = {i: i.function[[l + v.fromlabel(l, 0) for l in b]] for i, b, v in zip(c.indexeds, c.bases, offsets)} alias = uxreplace(c.expr, subs) # All aliased expressions aliaseds = [extracted[i.expr] for i in g] # Distance of each aliased expression from the basis alias distances = [] for i in g: distance = [o.distance(v) for o, v in zip(i.offsets, offsets)] distance = [(d, set(v)) for d, v in LabeledVector.transpose(*distance)] distances.append(LabeledVector([(d, v.pop()) for d, v in distance])) aliases.add(alias, list(mapper), aliaseds, distances) return aliases def choose(aliases, exprs, mapper, selector): tot = 0 retained = AliasMapper() # Pass 1: a set of aliasing expressions is retained only if its cost # exceeds the mode's threshold candidates = OrderedDict() aliaseds = [] others = [] for e, v in aliases.items(): score = selector(e, len(v.aliaseds)) if score > 0: candidates[e] = score aliaseds.extend(v.aliaseds) else: others.append(e) if not candidates: return exprs, retained, tot mapper = {k: v for k, v in mapper.items() if v.free_symbols & set(aliaseds)} templated = [uxreplace(e, mapper) for e in exprs] owset = wset(others + templated) for e, v in aliases.items(): try: score = candidates[e] except KeyError: score = 0 if score > 1 or \ score == 1 and max(len(wset(e)), 1) > len(wset(e) & owset): retained[e] = v tot += score if not retained: return exprs, retained, tot mapper = {k: v for k, v in mapper.items() if v.free_symbols & set(retained.aliaseds)} exprs = [uxreplace(e, mapper) for e in exprs] return exprs, retained, tot def lower_aliases(cluster, aliases, in_writeto, maxpar): dmapper = {} processed = [] for alias, v in aliases.items(): imapper = {**{i.dim: i for i in v.intervals}, **{i.dim.parent: i for i in v.intervals if i.dim.is_NonlinearDerived}} intervals = [] writeto = [] sub_iterators = {} indicess = [[] for _ in v.distances] for i in cluster.ispace.intervals: try: interval = imapper[i.dim] except KeyError: intervals.append(i) continue assert i.stamp >= interval.stamp if not (writeto or interval != interval.zero() or in_writeto(i.dim, cluster)): intervals.append(i) continue assert not i.dim.is_NonlinearDerived # `i.dim` is necessarily part of the write-to region, so # we have to adjust the Interval's stamp. For example, consider interval = interval.lift(i.stamp) interval = interval.lift(interval.stamp + int(maxpar)) writeto.append(interval) intervals.append(interval) if i.dim.is_Incr: try: d = dmapper[i.dim] except KeyError: dd = i.dim.parent assert dd.is_Incr if dd.parent.is_Incr: m = i.dim.symbolic_min - i.dim.parent.symbolic_min else: m = 0 d = dmapper[i.dim] = IncrDimension("%ss" % i.dim.name, i.dim, m, dd.symbolic_size, 1, dd.step) sub_iterators[i.dim] = d else: d = i.dim for distance, indices in zip(v.distances, indicess): indices.append(d - interval.lower + distance[interval.dim]) writeto = IterationSpace(IntervalGroup(writeto), sub_iterators) intervals = IntervalGroup(intervals, cluster.ispace.relations) ispace = IterationSpace(intervals, cluster.sub_iterators, cluster.directions) ispace = ispace.augment(sub_iterators) processed.append(ScheduledAlias(alias, writeto, ispace, v.aliaseds, indicess)) # write-to region. Another fundamental reason for ordering is to ensure # deterministic code generation processed = sorted(processed, key=lambda i: cit(cluster.ispace, i.ispace)) return Schedule(*processed, dmapper=dmapper) def optimize_schedule(cluster, schedule, platform, sregistry, options): if options['cire-rotate']: schedule = _optimize_schedule_rotations(schedule, sregistry) schedule = _optimize_schedule_padding(cluster, schedule, platform) return schedule def _optimize_schedule_rotations(schedule, sregistry): # The rotations Dimension is the outermost ridx = 0 rmapper = defaultdict(list) processed = [] for k, group in groupby(schedule, key=lambda i: i.writeto): g = list(group) candidate = k[ridx] d = candidate.dim try: ds = schedule.dmapper[d] except KeyError: # Can't do anything if `d` isn't an IncrDimension over a block processed.extend(g) continue n = candidate.min_size assert n > 0 iis = candidate.lower iib = candidate.upper ii = ModuloDimension('%sii' % d, ds, iis, incr=iib) cd = CustomDimension(name='%s%s' % (d, d), symbolic_min=ii, symbolic_max=iib, symbolic_size=n) dsi = ModuloDimension('%si' % ds, cd, cd + ds - iis, n) mapper = OrderedDict() for i in g: # Update `indicess` to use `xs0`, `xs1`, ... mds = [] for indices in i.indicess: v = indices[ridx] try: md = mapper[v] except KeyError: name = sregistry.make_name(prefix='%sr' % d.name) md = mapper.setdefault(v, ModuloDimension(name, ds, v, n)) mds.append(md) indicess = [indices[:ridx] + [md] + indices[ridx + 1:] for md, indices in zip(mds, i.indicess)] # Update `writeto` by switching `d` to `dsi` intervals = k.intervals.switch(d, dsi).zero(dsi) sub_iterators = dict(k.sub_iterators) sub_iterators[d] = dsi writeto = IterationSpace(intervals, sub_iterators) # Transform `alias` by adding `i` alias = i.alias.xreplace({d: d + cd}) # Extend `ispace` to iterate over rotations d1 = writeto[ridx+1].dim # Note: we're by construction in-bounds here intervals = IntervalGroup(Interval(cd, 0, 0), relations={(d, cd, d1)}) rispace = IterationSpace(intervals, {cd: dsi}, {cd: Forward}) aispace = i.ispace.zero(d) aispace = aispace.augment({d: mds + [ii]}) ispace = IterationSpace.union(rispace, aispace) processed.append(ScheduledAlias(alias, writeto, ispace, i.aliaseds, indicess)) rmapper[d].extend(list(mapper.values())) return Schedule(*processed, dmapper=schedule.dmapper, rmapper=rmapper) def _optimize_schedule_padding(cluster, schedule, platform): processed = [] for i in schedule: try: it = i.ispace.itintervals[-1] if ROUNDABLE in cluster.properties[it.dim]: vl = platform.simd_items_per_reg(cluster.dtype) ispace = i.ispace.add(Interval(it.dim, 0, it.interval.size % vl)) else: ispace = i.ispace processed.append(ScheduledAlias(i.alias, i.writeto, ispace, i.aliaseds, i.indicess)) except (TypeError, KeyError): processed.append(i) return Schedule(*processed, dmapper=schedule.dmapper, rmapper=schedule.rmapper) def lower_schedule(cluster, schedule, sregistry, options): ftemps = options['cire-ftemps'] if ftemps: make = TempFunction else: make = Array clusters = [] subs = {} for alias, writeto, ispace, aliaseds, indicess in schedule: name = sregistry.make_name() dtype = cluster.dtype if writeto: # as much room as in `zi`'s parent to avoid going OOB dimensions = [d.parent if d.is_Sub else d for d in writeto.itdimensions] # The halo must be set according to the size of writeto space halo = [(abs(i.lower), abs(i.upper)) for i in writeto] # The indices used to write into the Array indices = [] for i in writeto: try: # E.g., `xs` sub_iterators = writeto.sub_iterators[i.dim] assert len(sub_iterators) == 1 indices.append(sub_iterators[0]) except KeyError: # E.g., `z` -- a non-shifted Dimension indices.append(i.dim - i.lower) obj = make(name=name, dimensions=dimensions, halo=halo, dtype=dtype) expression = Eq(obj[indices], alias) callback = lambda idx: obj[idx] else: # Degenerate case: scalar expression assert writeto.size == 0 obj = Symbol(name=name, dtype=dtype) expression = Eq(obj, alias) callback = lambda idx: obj # Create the substitution rules for the aliasing expressions subs.update({aliased: callback(indices) for aliased, indices in zip(aliaseds, indicess)}) # Construct the `alias` DataSpace accesses = detect_accesses(expression) parts = {k: IntervalGroup(build_intervals(v)).add(ispace.intervals).relaxed for k, v in accesses.items() if k} dspace = DataSpace(cluster.dspace.intervals, parts) # Drop or weaken parallelism if necessary properties = dict(cluster.properties) for d, v in cluster.properties.items(): if any(i.is_Modulo for i in ispace.sub_iterators[d]): properties[d] = normalize_properties(v, {SEQUENTIAL}) elif d not in writeto.dimensions: properties[d] = normalize_properties(v, {PARALLEL_IF_PVT}) # Finally, build the `alias` Cluster clusters.append(cluster.rebuild(exprs=expression, ispace=ispace, dspace=dspace, properties=properties)) return clusters, subs def pick_best(variants): best = variants.pop(0) for i in variants: best_flop_score, best_ws_score = best.score if best_flop_score == 0: best = i continue i_flop_score, i_ws_score = i.score # The current heustic is fairly basic: the one with smaller working # set size increase wins, unless there's a massive reduction in operation delta = i_ws_score - best_ws_score if (delta > 0 and i_flop_score / best_flop_score > 100) or \ (delta == 0 and i_flop_score > best_flop_score) or \ (delta < 0 and best_flop_score / i_flop_score <= 100): best = i schedule, exprs, _ = best return schedule, exprs def rebuild(cluster, exprs, subs, schedule): exprs = [uxreplace(e, subs) for e in exprs] ispace = cluster.ispace.augment(schedule.dmapper) ispace = ispace.augment(schedule.rmapper) accesses = detect_accesses(exprs) parts = {k: IntervalGroup(build_intervals(v)).relaxed for k, v in accesses.items() if k} dspace = DataSpace(cluster.dspace.intervals, parts) return cluster.rebuild(exprs=exprs, ispace=ispace, dspace=dspace) class Candidate(object): def __init__(self, expr, ispace, indexeds, bases, offsets): self.expr = expr self.shifts = ispace.intervals self.indexeds = indexeds self.bases = bases self.offsets = offsets def __repr__(self): return "Candidate(expr=%s)" % self.expr def translated(self, other): if len(self.Toffsets) != len(other.Toffsets): return False if len(self.bases) != len(other.bases): return False if any(b0 != b1 for b0, b1 in zip(self.bases, other.bases)): return False for (d0, o0), (d1, o1) in zip(self.Toffsets, other.Toffsets): if d0 is not d1: return False distance = set(o0 - o1) if len(distance) != 1: return False return True @cached_property def Toffsets(self): return LabeledVector.transpose(*self.offsets) @cached_property def dimensions(self): return frozenset(i for i, _ in self.Toffsets) class Group(tuple): def __repr__(self): return "Group(%s)" % ", ".join([str(i) for i in self]) def find_rotation_distance(self, d, interval): assert d is interval.dim for rotation, distance in self._pivot_legal_rotations[d]: if rotation.union(interval) != rotation: continue min_interval = self._pivot_min_intervals[d].translate(-distance) if interval.union(min_interval) == interval: return distance return None @cached_property def Toffsets(self): return [LabeledVector.transpose(*i) for i in zip(*[i.offsets for i in self])] @cached_property def diameter(self): ret = defaultdict(int) for i in self.Toffsets: for d, v in i: try: distance = int(max(v) - min(v)) except TypeError: # An entry in `v` has symbolic components, e.g. `x_m + 2` if len(set(v)) == 1: continue else: raise ValueError ret[d] = max(ret[d], distance) return ret @property def pivot(self): return self[0] @property def dimensions(self): return self.pivot.dimensions @property def dimensions_translated(self): return frozenset(d for d, v in self.diameter.items() if v > 0) @cached_property def _pivot_legal_rotations(self): ret = {} for d, (maxd, mini) in self._pivot_legal_shifts.items(): # Rotation size = mini (min-increment) - maxd (max-decrement) v = mini - maxd # Build the table of all possible rotations m = make_rotations_table(d, v) distances = [] for rotation in m: # Distance of the rotation `i` from `c` distance = maxd - rotation.lower assert distance == mini - rotation.upper distances.append(distance) ret[d] = list(zip(m, distances)) return ret @cached_property def _pivot_min_intervals(self): c = self.pivot ret = defaultdict(lambda: [np.inf, -np.inf]) for i in self: distance = [o.distance(v) for o, v in zip(i.offsets, c.offsets)] distance = [(d, set(v)) for d, v in LabeledVector.transpose(*distance)] for d, v in distance: value = v.pop() ret[d][0] = min(ret[d][0], value) ret[d][1] = max(ret[d][1], value) ret = {d: Interval(d, m, M) for d, (m, M) in ret.items()} return ret @cached_property def _pivot_legal_shifts(self): c = self.pivot ret = defaultdict(lambda: (-np.inf, np.inf)) for i, ofs in zip(c.indexeds, c.offsets): f = i.function for l in ofs.labels: # `f`'s cumulative halo size along `l` hsize = sum(f._size_halo[l]) lower, upper = c.shifts[l].offsets try: # Assume `ofs[d]` is a number (typical case) maxd = min(0, max(ret[l][0], -ofs[l] - lower)) mini = max(0, min(ret[l][1], hsize - ofs[l] - upper)) ret[l] = (maxd, mini) except TypeError: # E.g., `ofs[d] = x_m - x + 5` ret[l] = (0, 0) return ret AliasedGroup = namedtuple('AliasedGroup', 'intervals aliaseds distances') ScheduledAlias = namedtuple('ScheduledAlias', 'alias writeto ispace aliaseds indicess') ScheduledAlias.__new__.__defaults__ = (None,) * len(ScheduledAlias._fields) SpacePoint = namedtuple('SpacePoint', 'schedule exprs score') class Schedule(tuple): def __new__(cls, *items, dmapper=None, rmapper=None): obj = super(Schedule, cls).__new__(cls, items) obj.dmapper = dmapper or {} obj.rmapper = rmapper or {} return obj class AliasMapper(OrderedDict): def add(self, alias, intervals, aliaseds, distances): assert len(aliaseds) == len(distances) self[alias] = AliasedGroup(intervals, aliaseds, distances) def update(self, aliases): for k, v in aliases.items(): try: v0 = self[k] if v0.intervals != v.intervals: raise ValueError v0.aliaseds.extend(v.aliaseds) v0.distances.extend(v.distances) except KeyError: self[k] = v @property def aliaseds(self): return flatten(i.aliaseds for i in self.values()) def make_rotations_table(d, v): m = np.array([[j-i if j > i else 0 for j in range(v+1)] for i in range(v+1)]) m = (m - m.T)[::-1, :] # Shift the table so that the middle rotation is at the top m = np.roll(m, int(-np.floor(v/2)), axis=0) # Turn into a more compact representation as a list of Intervals m = [Interval(d, min(i), max(i)) for i in m] return m def cit(ispace0, ispace1): found = [] for it0, it1 in zip(ispace0.itintervals, ispace1.itintervals): if it0 == it1: found.append(it0) else: break return tuple(found) def maybe_coeff_key(grid, expr): if expr.is_Number: return True indexeds = [i for i in expr.free_symbols if i.is_Indexed] return any(not set(grid.dimensions) <= set(i.function.dimensions) for i in indexeds) def wset(exprs): return {i.function for i in flatten([e.free_symbols for e in as_tuple(exprs)]) if i.function.is_AbstractFunction} def potential_max_deriv_order(exprs): # NOTE: e might propagate the Derivative(...) information down from the # symbolic language, but users may do crazy things and write their own custom # expansions "by hand" (i.e., not resorting to Derivative(...)), hence instead # of looking for Derivative(...) we use the following heuristic: # add(mul, mul, ...) -> stems from first order derivative # add(mul(add(mul, mul, ...), ...), ...) -> stems from second order derivative # ... nadds = lambda e: (int(e.is_Add) + max([nadds(a) for a in e.args], default=0) if not q_leaf(e) else 0) return max([nadds(e) for e in exprs], default=0) def search_potential_deriv(expr, n, c=0): assert n >= c >= 0 if q_leaf(expr) or expr.is_Pow: return [] elif expr.is_Mul: if c == n: return [expr] else: return flatten([search_potential_deriv(a, n, c+1) for a in expr.args]) else: return flatten([search_potential_deriv(a, n, c) for a in expr.args])

true

7901a174287c6ae84ab3d0881bd2c6713655d9cf

1,879

py

Python

tests/incident/test_get.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

tests/incident/test_get.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

tests/incident/test_get.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

# -*- coding: utf-8 -*- import unittest.mock import pytest import pycamunda.incident from tests.mock import raise_requests_exception_mock, not_ok_response_mock def test_get_params(engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') assert get_incident.url == engine_url + '/incident/anId' assert get_incident.query_parameters() == {} assert get_incident.body_parameters() == {} @unittest.mock.patch('pycamunda.incident.Incident.load', unittest.mock.MagicMock()) @unittest.mock.patch('requests.Session.request') def test_get_calls_requests(mock, engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') get_incident() assert mock.called assert mock.call_args[1]['method'].upper() == 'GET' @unittest.mock.patch('requests.Session.request', raise_requests_exception_mock) def test_get_raises_pycamunda_exception(engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') with pytest.raises(pycamunda.PyCamundaException): get_incident() @unittest.mock.patch('requests.Session.request', not_ok_response_mock) @unittest.mock.patch('pycamunda.incident.Incident', unittest.mock.MagicMock()) @unittest.mock.patch('pycamunda.base._raise_for_status') def test_get_raises_for_status(mock, engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') get_incident() assert mock.called @unittest.mock.patch('requests.Session.request', unittest.mock.MagicMock()) @unittest.mock.patch('pycamunda.base.from_isoformat', unittest.mock.MagicMock()) @unittest.mock.patch('pycamunda.incident.IncidentType', unittest.mock.MagicMock()) def test_get_returns_incident(engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') incident = get_incident() assert isinstance(incident, pycamunda.incident.Incident)

34.796296

83

0.77009

import unittest.mock import pytest import pycamunda.incident from tests.mock import raise_requests_exception_mock, not_ok_response_mock def test_get_params(engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') assert get_incident.url == engine_url + '/incident/anId' assert get_incident.query_parameters() == {} assert get_incident.body_parameters() == {} @unittest.mock.patch('pycamunda.incident.Incident.load', unittest.mock.MagicMock()) @unittest.mock.patch('requests.Session.request') def test_get_calls_requests(mock, engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') get_incident() assert mock.called assert mock.call_args[1]['method'].upper() == 'GET' @unittest.mock.patch('requests.Session.request', raise_requests_exception_mock) def test_get_raises_pycamunda_exception(engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') with pytest.raises(pycamunda.PyCamundaException): get_incident() @unittest.mock.patch('requests.Session.request', not_ok_response_mock) @unittest.mock.patch('pycamunda.incident.Incident', unittest.mock.MagicMock()) @unittest.mock.patch('pycamunda.base._raise_for_status') def test_get_raises_for_status(mock, engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') get_incident() assert mock.called @unittest.mock.patch('requests.Session.request', unittest.mock.MagicMock()) @unittest.mock.patch('pycamunda.base.from_isoformat', unittest.mock.MagicMock()) @unittest.mock.patch('pycamunda.incident.IncidentType', unittest.mock.MagicMock()) def test_get_returns_incident(engine_url): get_incident = pycamunda.incident.Get(url=engine_url, id_='anId') incident = get_incident() assert isinstance(incident, pycamunda.incident.Incident)

true

7901a26f2959e2b1afa84e883181b3ce059e4fa4

25,608

py

Python

lib/googlecloudsdk/api_lib/compute/containers_utils.py

kustodian/google-cloud-sdk

b6bae4137d4b58030adb3dcb1271216dfb19f96d

[ "Apache-2.0" ]

null

lib/googlecloudsdk/api_lib/compute/containers_utils.py

kustodian/google-cloud-sdk

b6bae4137d4b58030adb3dcb1271216dfb19f96d

[ "Apache-2.0" ]

11

2020-02-29T02:51:12.000Z

2022-03-30T23:20:08.000Z

lib/googlecloudsdk/api_lib/compute/containers_utils.py

kustodian/google-cloud-sdk

b6bae4137d4b58030adb3dcb1271216dfb19f96d

[ "Apache-2.0" ]

1

2020-07-24T18:47:35.000Z

# -*- coding: utf-8 -*- # # Copyright 2016 Google LLC. 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. """Functions for creating GCE container (Docker) deployments.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import itertools import re import enum from googlecloudsdk.api_lib.compute import exceptions from googlecloudsdk.api_lib.compute import metadata_utils from googlecloudsdk.api_lib.compute.operations import poller from googlecloudsdk.api_lib.util import waiter from googlecloudsdk.calliope import exceptions as calliope_exceptions from googlecloudsdk.core import yaml from googlecloudsdk.core.util import files from googlecloudsdk.core.util import times import six USER_INIT_TEMPLATE = """#cloud-config runcmd: - ['/usr/bin/kubelet', '--allow-privileged=%s', '--manifest-url=http://metadata.google.internal/computeMetadata/v1/instance/attributes/google-container-manifest', '--manifest-url-header=Metadata-Flavor:Google', '--config=/etc/kubernetes/manifests'] """ MANIFEST_DISCLAIMER = """# DISCLAIMER: # This container declaration format is not a public API and may change without # notice. Please use gcloud command-line tool or Google Cloud Console to run # Containers on Google Compute Engine. """ USER_DATA_KEY = 'user-data' CONTAINER_MANIFEST_KEY = 'google-container-manifest' GCE_CONTAINER_DECLARATION = 'gce-container-declaration' STACKDRIVER_LOGGING_AGENT_CONFIGURATION = 'google-logging-enabled' GKE_DOCKER = 'gci-ensure-gke-docker' ALLOWED_PROTOCOLS = ['TCP', 'UDP'] # Prefix of all COS image major release names COS_MAJOR_RELEASE_PREFIX = 'cos-stable-' # Pin this version of gcloud to COS image major release version COS_MAJOR_RELEASE = COS_MAJOR_RELEASE_PREFIX + '55' COS_PROJECT = 'cos-cloud' _MIN_PREFERRED_COS_VERSION = 63 # Translation from CLI to API wording RESTART_POLICY_API = { 'never': 'Never', 'on-failure': 'OnFailure', 'always': 'Always' } class MountVolumeMode(enum.Enum): READ_ONLY = 1, READ_WRITE = 2, def isReadOnly(self): return self == MountVolumeMode.READ_ONLY _DEFAULT_MODE = MountVolumeMode.READ_WRITE def _GetUserInit(allow_privileged): """Gets user-init metadata value for COS image.""" allow_privileged_val = 'true' if allow_privileged else 'false' return USER_INIT_TEMPLATE % (allow_privileged_val) class Error(exceptions.Error): """Base exception for containers.""" class InvalidMetadataKeyException(Error): """InvalidMetadataKeyException is for not allowed metadata keys.""" def __init__(self, metadata_key): super(InvalidMetadataKeyException, self).__init__( 'Metadata key "{0}" is not allowed when running containerized VM.' .format(metadata_key)) class NoGceContainerDeclarationMetadataKey(Error): """Raised on attempt to update-container on instance without containers.""" def __init__(self): super(NoGceContainerDeclarationMetadataKey, self).__init__( "Instance doesn't have {} metadata key - it is not a container.".format( GCE_CONTAINER_DECLARATION)) def ValidateUserMetadata(metadata): """Validates if user-specified metadata. Checks if it contains values which may conflict with container deployment. Args: metadata: user-specified VM metadata. Raises: InvalidMetadataKeyException: if there is conflict with user-provided metadata """ for entry in metadata.items: if entry.key in [USER_DATA_KEY, CONTAINER_MANIFEST_KEY, GKE_DOCKER]: raise InvalidMetadataKeyException(entry.key) def CreateTagsMessage(messages, tags): """Create tags message with parameters for container VM or VM templates.""" if tags: return messages.Tags(items=tags) def GetLabelsMessageWithCosVersion( labels, image_uri, resources, resource_class): """Returns message with labels for instance / instance template. Args: labels: dict, labels to assign to the resource. image_uri: URI of image used as a base for the resource. The function extracts COS version from the URI and uses it as a value of `container-vm` label. resources: object that can parse image_uri. resource_class: class of the resource to which labels will be assigned. Must contain LabelsValue class and resource_class.LabelsValue must contain AdditionalProperty class. """ cos_version = resources.Parse( image_uri, collection='compute.images').Name().replace('/', '-') if labels is None: labels = {} labels['container-vm'] = cos_version additional_properties = [ resource_class.LabelsValue.AdditionalProperty(key=k, value=v) for k, v in sorted(six.iteritems(labels))] return resource_class.LabelsValue(additionalProperties=additional_properties) class NoCosImageException(Error): """Raised when COS image could not be found.""" def __init__(self): super(NoCosImageException, self).__init__( 'Could not find COS (Cloud OS) for release family \'{0}\'' .format(COS_MAJOR_RELEASE)) def ExpandCosImageFlag(compute_client): """Select a COS image to run Docker.""" compute = compute_client.apitools_client images = compute_client.MakeRequests([( compute.images, 'List', compute_client.messages.ComputeImagesListRequest(project=COS_PROJECT) )]) return _SelectNewestCosImage(images) def _SelectNewestCosImage(images): """Selects newest COS image from the list.""" cos_images = sorted([image for image in images if image.name.startswith(COS_MAJOR_RELEASE)], key=lambda x: times.ParseDateTime(x.creationTimestamp)) if not cos_images: raise NoCosImageException() return cos_images[-1].selfLink def _ValidateAndParsePortMapping(port_mappings): """Parses and validates port mapping.""" ports_config = [] for port_mapping in port_mappings: mapping_match = re.match(r'^(\d+):(\d+):(\S+)$', port_mapping) if not mapping_match: raise calliope_exceptions.InvalidArgumentException( '--port-mappings', 'Port mappings should follow PORT:TARGET_PORT:PROTOCOL format.') port, target_port, protocol = mapping_match.groups() if protocol not in ALLOWED_PROTOCOLS: raise calliope_exceptions.InvalidArgumentException( '--port-mappings', 'Protocol should be one of [{0}]'.format( ', '.join(ALLOWED_PROTOCOLS))) ports_config.append({ 'containerPort': int(target_port), 'hostPort': int(port), 'protocol': protocol}) return ports_config def ExpandKonletCosImageFlag(compute_client): """Select a COS image to run Konlet. This function scans three families in order: - stable - beta - dev looking for the first image with version at least _MIN_PREFERRED_COS_VERSION. Args: compute_client: ClientAdapter, The Compute API client adapter Returns: COS image at version _MIN_PREFERRED_COS_VERSION or later. Raises: NoCosImageException: No COS image at version at least _MIN_PREFERRED_COS_VERSION was found. This should not happen if backend is healthy. """ compute = compute_client.apitools_client images = compute_client.MakeRequests( [(compute.images, 'List', compute_client.messages.ComputeImagesListRequest(project=COS_PROJECT))]) name_re_template = r'cos-{}-(\d+)-.*' image_families = ['stable', 'beta', 'dev'] for family in image_families: name_re = name_re_template.format(family) def MakeCreateComparisonKey(name_re): def CreateComparisonKey(image): version = int(re.match(name_re, image.name).group(1)) timestamp = times.ParseDateTime(image.creationTimestamp) return version, timestamp return CreateComparisonKey cos_images = sorted( [image for image in images if re.match(name_re, image.name)], key=MakeCreateComparisonKey(name_re)) if (cos_images and MakeCreateComparisonKey(name_re)(cos_images[-1])[0] >= _MIN_PREFERRED_COS_VERSION): return cos_images[-1].selfLink raise NoCosImageException() def _ReadDictionary(filename): # pylint:disable=line-too-long r"""Read environment variable from file. File format: It is intended (but not guaranteed) to follow standard docker format [](https://docs.docker.com/engine/reference/commandline/run/#set-environment-variables--e---env---env-file) but without capturing environment variables from host machine. Lines starting by "#" character are comments. Empty lines are ignored. Below grammar production follow in EBNF format. file = (whitespace* statement '\n')* statement = comment | definition whitespace = ' ' | '\t' comment = '#' [^\n]* definition = [^#=\n] [^= \t\n]* '=' [^\n]* Args: filename: str, name of the file to read Returns: A dictionary mapping environment variable names to their values. """ env_vars = {} if not filename: return env_vars with files.FileReader(filename) as f: for i, line in enumerate(f): # Strip whitespace at the beginning and end of line line = line.strip() # Ignore comments and empty lines if len(line) <= 1 or line[0] == '#': continue # Find first left '=' character assignment_op_loc = line.find('=') if assignment_op_loc == -1: raise calliope_exceptions.BadFileException( 'Syntax error in {}:{}: Expected VAR=VAL, got {}'.format( filename, i, line)) env = line[:assignment_op_loc] val = line[assignment_op_loc+1:] if ' ' in env or '\t' in env: raise calliope_exceptions.BadFileException( 'Syntax error in {}:{} Variable name cannot contain whitespaces,' ' got "{}"'.format(filename, i, env)) env_vars[env] = val return env_vars def _GetHostPathDiskName(idx): return 'host-path-{}'.format(idx) def _GetTmpfsDiskName(idx): return 'tmpfs-{}'.format(idx) def _GetPersistentDiskName(idx): return 'pd-{}'.format(idx) def _AddMountedDisksToManifest(container_mount_disk, volumes, volume_mounts, used_names=None, disks=None): """Add volume specs from --container-mount-disk.""" used_names = used_names or [] disks = disks or [] idx = 0 for mount_disk in container_mount_disk: while _GetPersistentDiskName(idx) in used_names: idx += 1 device_name = mount_disk.get('name') partition = mount_disk.get('partition') def _GetMatchingVolume(device_name, partition): for volume_spec in volumes: pd = volume_spec.get('gcePersistentDisk', {}) if (pd.get('pdName') == device_name and pd.get('partition') == partition): return volume_spec repeated = _GetMatchingVolume(device_name, partition) if repeated: name = repeated['name'] else: name = _GetPersistentDiskName(idx) used_names.append(name) if not device_name: # This should not be needed - any command that accepts container mount # disks should validate that there is only one disk before calling this # function. if len(disks) != 1: raise calliope_exceptions.InvalidArgumentException( '--container-mount-disk', 'Must specify the name of the disk to be mounted unless exactly ' 'one disk is attached to the instance.') device_name = disks[0].get('name') if disks[0].get('device-name', device_name) != device_name: raise exceptions.InvalidArgumentException( '--container-mount-disk', 'Must not have a device-name that is different from disk name if ' 'disk is being attached to the instance and mounted to a container:' ' [{}]'.format(disks[0].get('device-name'))) volume_mounts.append({ 'name': name, 'mountPath': mount_disk['mount-path'], 'readOnly': mount_disk.get('mode', _DEFAULT_MODE).isReadOnly()}) if repeated: continue volume_spec = { 'name': name, 'gcePersistentDisk': { 'pdName': device_name, 'fsType': 'ext4'}} if partition: volume_spec['gcePersistentDisk'].update({'partition': partition}) volumes.append(volume_spec) idx += 1 def _CreateContainerManifest(args, instance_name, container_mount_disk_enabled=False, container_mount_disk=None): """Create container manifest from argument namespace and instance name.""" container = {'image': args.container_image, 'name': instance_name} if args.container_command is not None: container['command'] = [args.container_command] if args.container_arg is not None: container['args'] = args.container_arg container['stdin'] = args.container_stdin container['tty'] = args.container_tty container['securityContext'] = {'privileged': args.container_privileged} env_vars = _ReadDictionary(args.container_env_file) for env_var_dict in args.container_env or []: for env, val in six.iteritems(env_var_dict): env_vars[env] = val if env_vars: container['env'] = [{ 'name': env, 'value': val } for env, val in six.iteritems(env_vars)] volumes = [] volume_mounts = [] for idx, volume in enumerate(args.container_mount_host_path or []): volumes.append({ 'name': _GetHostPathDiskName(idx), 'hostPath': { 'path': volume['host-path'] }, }) volume_mounts.append({ 'name': _GetHostPathDiskName(idx), 'mountPath': volume['mount-path'], 'readOnly': volume.get('mode', _DEFAULT_MODE).isReadOnly() }) for idx, tmpfs in enumerate(args.container_mount_tmpfs or []): volumes.append( {'name': _GetTmpfsDiskName(idx), 'emptyDir': {'medium': 'Memory'}}) volume_mounts.append( {'name': _GetTmpfsDiskName(idx), 'mountPath': tmpfs['mount-path']}) if container_mount_disk_enabled: container_mount_disk = container_mount_disk or [] disks = (args.disk or []) + (args.create_disk or []) _AddMountedDisksToManifest(container_mount_disk, volumes, volume_mounts, disks=disks) container['volumeMounts'] = volume_mounts manifest = { 'spec': { 'containers': [container], 'volumes': volumes, 'restartPolicy': RESTART_POLICY_API[args.container_restart_policy] } } return manifest def DumpYaml(data): """Dumps data dict to YAML in format expected by Konlet.""" return MANIFEST_DISCLAIMER + yaml.dump(data) def _CreateYamlContainerManifest(args, instance_name, container_mount_disk_enabled=False, container_mount_disk=None): """Helper to create the container manifest.""" return DumpYaml(_CreateContainerManifest( args, instance_name, container_mount_disk_enabled=container_mount_disk_enabled, container_mount_disk=container_mount_disk)) def CreateKonletMetadataMessage(messages, args, instance_name, user_metadata, container_mount_disk_enabled=False, container_mount_disk=None): """Helper to create the metadata for konlet.""" konlet_metadata = { GCE_CONTAINER_DECLARATION: _CreateYamlContainerManifest( args, instance_name, container_mount_disk_enabled=container_mount_disk_enabled, container_mount_disk=container_mount_disk), # Since COS 69, having logs for Container-VMs written requires enabling # Stackdriver Logging agent. STACKDRIVER_LOGGING_AGENT_CONFIGURATION: 'true', } return metadata_utils.ConstructMetadataMessage( messages, metadata=konlet_metadata, existing_metadata=user_metadata) def UpdateInstance(holder, client, instance_ref, instance, args, container_mount_disk_enabled=False, container_mount_disk=None): """Update an instance and its container metadata.""" # find gce-container-declaration metadata entry for metadata in instance.metadata.items: if metadata.key == GCE_CONTAINER_DECLARATION: UpdateMetadata( holder, metadata, args, instance, container_mount_disk_enabled=container_mount_disk_enabled, container_mount_disk=container_mount_disk) # update Google Compute Engine resource operation = client.apitools_client.instances.SetMetadata( client.messages.ComputeInstancesSetMetadataRequest( metadata=instance.metadata, **instance_ref.AsDict())) operation_ref = holder.resources.Parse( operation.selfLink, collection='compute.zoneOperations') operation_poller = poller.Poller(client.apitools_client.instances) set_metadata_waiter = waiter.WaitFor( operation_poller, operation_ref, 'Updating specification of container [{0}]'.format( instance_ref.Name())) if (instance.status == client.messages.Instance.StatusValueValuesEnum.TERMINATED): return set_metadata_waiter elif (instance.status == client.messages.Instance.StatusValueValuesEnum.SUSPENDED): return _StopVm(holder, client, instance_ref) else: _StopVm(holder, client, instance_ref) return _StartVm(holder, client, instance_ref) raise NoGceContainerDeclarationMetadataKey() def _StopVm(holder, client, instance_ref): """Stop the Virtual Machine.""" operation = client.apitools_client.instances.Stop( client.messages.ComputeInstancesStopRequest( **instance_ref.AsDict())) operation_ref = holder.resources.Parse( operation.selfLink, collection='compute.zoneOperations') operation_poller = poller.Poller(client.apitools_client.instances) return waiter.WaitFor( operation_poller, operation_ref, 'Stopping instance [{0}]'.format(instance_ref.Name())) def _StartVm(holder, client, instance_ref): """Start the Virtual Machine.""" operation = client.apitools_client.instances.Start( client.messages.ComputeInstancesStartRequest( **instance_ref.AsDict())) operation_ref = holder.resources.Parse( operation.selfLink, collection='compute.zoneOperations') operation_poller = poller.Poller(client.apitools_client.instances) return waiter.WaitFor( operation_poller, operation_ref, 'Starting instance [{0}]'.format(instance_ref.Name())) def UpdateMetadata(holder, metadata, args, instance, container_mount_disk_enabled=False, container_mount_disk=None): """Update konlet metadata entry using user-supplied data.""" # precondition: metadata.key == GCE_CONTAINER_DECLARATION manifest = yaml.load(metadata.value) if args.IsSpecified('container_image'): manifest['spec']['containers'][0]['image'] = args.container_image if args.IsSpecified('container_command'): manifest['spec']['containers'][0]['command'] = [args.container_command] if args.IsSpecified('clear_container_command'): manifest['spec']['containers'][0].pop('command', None) if args.IsSpecified('container_arg'): manifest['spec']['containers'][0]['args'] = args.container_arg if args.IsSpecified('clear_container_args'): manifest['spec']['containers'][0].pop('args', None) if args.container_privileged is True: manifest['spec']['containers'][0]['securityContext']['privileged'] = True if args.container_privileged is False: manifest['spec']['containers'][0]['securityContext']['privileged'] = False if container_mount_disk_enabled: container_mount_disk = container_mount_disk or [] disks = instance.disks else: container_mount_disk = [] # Only need disks for updating the container mount disk. disks = [] _UpdateMounts(holder, manifest, args.remove_container_mounts or [], args.container_mount_host_path or [], args.container_mount_tmpfs or [], container_mount_disk, disks) _UpdateEnv(manifest, itertools.chain.from_iterable(args.remove_container_env or []), args.container_env_file, args.container_env or []) if args.container_stdin is True: manifest['spec']['containers'][0]['stdin'] = True if args.container_stdin is False: manifest['spec']['containers'][0]['stdin'] = False if args.container_tty is True: manifest['spec']['containers'][0]['tty'] = True if args.container_tty is False: manifest['spec']['containers'][0]['tty'] = False if args.IsSpecified('container_restart_policy'): manifest['spec']['restartPolicy'] = RESTART_POLICY_API[ args.container_restart_policy] metadata.value = DumpYaml(manifest) def _UpdateMounts(holder, manifest, remove_container_mounts, container_mount_host_path, container_mount_tmpfs, container_mount_disk, disks): """Updates mounts in container manifest.""" _CleanupMounts(manifest, remove_container_mounts, container_mount_host_path, container_mount_tmpfs, container_mount_disk=container_mount_disk) used_names = [volume['name'] for volume in manifest['spec']['volumes']] volumes = [] volume_mounts = [] next_volume_index = 0 for volume in container_mount_host_path: while _GetHostPathDiskName(next_volume_index) in used_names: next_volume_index += 1 name = _GetHostPathDiskName(next_volume_index) next_volume_index += 1 volumes.append({ 'name': name, 'hostPath': { 'path': volume['host-path'] }, }) volume_mounts.append({ 'name': name, 'mountPath': volume['mount-path'], 'readOnly': volume.get('mode', _DEFAULT_MODE).isReadOnly() }) for tmpfs in container_mount_tmpfs: while _GetTmpfsDiskName(next_volume_index) in used_names: next_volume_index += 1 name = _GetTmpfsDiskName(next_volume_index) next_volume_index += 1 volumes.append({'name': name, 'emptyDir': {'medium': 'Memory'}}) volume_mounts.append({'name': name, 'mountPath': tmpfs['mount-path']}) if container_mount_disk: # Convert to dict to match helper input needs. # The disk must already have a device name that matches its # name. For disks that were attached to the instance already. disks = [{'device-name': disk.deviceName, 'name': holder.resources.Parse(disk.source).Name()} for disk in disks] _AddMountedDisksToManifest(container_mount_disk, volumes, volume_mounts, used_names=used_names, disks=disks) manifest['spec']['containers'][0]['volumeMounts'].extend(volume_mounts) manifest['spec']['volumes'].extend(volumes) def _CleanupMounts(manifest, remove_container_mounts, container_mount_host_path, container_mount_tmpfs, container_mount_disk=None): """Remove all specified mounts from container manifest.""" container_mount_disk = container_mount_disk or [] # volumeMounts stored in this list should be removed mount_paths_to_remove = remove_container_mounts[:] for host_path in container_mount_host_path: mount_paths_to_remove.append(host_path['mount-path']) for tmpfs in container_mount_tmpfs: mount_paths_to_remove.append(tmpfs['mount-path']) for disk in container_mount_disk: mount_paths_to_remove.append(disk['mount-path']) # volumeMounts stored in this list are used used_mounts = [] used_mounts_names = [] removed_mount_names = [] for mount in manifest['spec']['containers'][0].get('volumeMounts', []): if mount['mountPath'] not in mount_paths_to_remove: used_mounts.append(mount) used_mounts_names.append(mount['name']) else: removed_mount_names.append(mount['name']) # override volumeMounts manifest['spec']['containers'][0]['volumeMounts'] = used_mounts # garbage collect volumes which become orphaned, skip volumes orphaned before # start of the procedure used_volumes = [] for volume in manifest['spec'].get('volumes', []): if (volume['name'] in used_mounts_names or volume['name'] not in removed_mount_names): used_volumes.append(volume) # override volumes manifest['spec']['volumes'] = used_volumes def _UpdateEnv(manifest, remove_container_env, container_env_file, container_env): """Update environment variables in container manifest.""" current_env = {} for env_val in manifest['spec']['containers'][0].get('env', []): current_env[env_val['name']] = env_val['value'] for env in remove_container_env: current_env.pop(env, None) current_env.update(_ReadDictionary(container_env_file)) for env_var_dict in container_env: for env, val in six.iteritems(env_var_dict): current_env[env] = val if current_env: manifest['spec']['containers'][0]['env'] = [{ 'name': env, 'value': val } for env, val in six.iteritems(current_env)]

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from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import itertools import re import enum from googlecloudsdk.api_lib.compute import exceptions from googlecloudsdk.api_lib.compute import metadata_utils from googlecloudsdk.api_lib.compute.operations import poller from googlecloudsdk.api_lib.util import waiter from googlecloudsdk.calliope import exceptions as calliope_exceptions from googlecloudsdk.core import yaml from googlecloudsdk.core.util import files from googlecloudsdk.core.util import times import six USER_INIT_TEMPLATE = """#cloud-config runcmd: - ['/usr/bin/kubelet', '--allow-privileged=%s', '--manifest-url=http://metadata.google.internal/computeMetadata/v1/instance/attributes/google-container-manifest', '--manifest-url-header=Metadata-Flavor:Google', '--config=/etc/kubernetes/manifests'] """ MANIFEST_DISCLAIMER = """# DISCLAIMER: # This container declaration format is not a public API and may change without # notice. Please use gcloud command-line tool or Google Cloud Console to run # Containers on Google Compute Engine. """ USER_DATA_KEY = 'user-data' CONTAINER_MANIFEST_KEY = 'google-container-manifest' GCE_CONTAINER_DECLARATION = 'gce-container-declaration' STACKDRIVER_LOGGING_AGENT_CONFIGURATION = 'google-logging-enabled' GKE_DOCKER = 'gci-ensure-gke-docker' ALLOWED_PROTOCOLS = ['TCP', 'UDP'] COS_MAJOR_RELEASE_PREFIX = 'cos-stable-' COS_MAJOR_RELEASE = COS_MAJOR_RELEASE_PREFIX + '55' COS_PROJECT = 'cos-cloud' _MIN_PREFERRED_COS_VERSION = 63 RESTART_POLICY_API = { 'never': 'Never', 'on-failure': 'OnFailure', 'always': 'Always' } class MountVolumeMode(enum.Enum): READ_ONLY = 1, READ_WRITE = 2, def isReadOnly(self): return self == MountVolumeMode.READ_ONLY _DEFAULT_MODE = MountVolumeMode.READ_WRITE def _GetUserInit(allow_privileged): allow_privileged_val = 'true' if allow_privileged else 'false' return USER_INIT_TEMPLATE % (allow_privileged_val) class Error(exceptions.Error): class InvalidMetadataKeyException(Error): def __init__(self, metadata_key): super(InvalidMetadataKeyException, self).__init__( 'Metadata key "{0}" is not allowed when running containerized VM.' .format(metadata_key)) class NoGceContainerDeclarationMetadataKey(Error): def __init__(self): super(NoGceContainerDeclarationMetadataKey, self).__init__( "Instance doesn't have {} metadata key - it is not a container.".format( GCE_CONTAINER_DECLARATION)) def ValidateUserMetadata(metadata): for entry in metadata.items: if entry.key in [USER_DATA_KEY, CONTAINER_MANIFEST_KEY, GKE_DOCKER]: raise InvalidMetadataKeyException(entry.key) def CreateTagsMessage(messages, tags): if tags: return messages.Tags(items=tags) def GetLabelsMessageWithCosVersion( labels, image_uri, resources, resource_class): cos_version = resources.Parse( image_uri, collection='compute.images').Name().replace('/', '-') if labels is None: labels = {} labels['container-vm'] = cos_version additional_properties = [ resource_class.LabelsValue.AdditionalProperty(key=k, value=v) for k, v in sorted(six.iteritems(labels))] return resource_class.LabelsValue(additionalProperties=additional_properties) class NoCosImageException(Error): def __init__(self): super(NoCosImageException, self).__init__( 'Could not find COS (Cloud OS) for release family \'{0}\'' .format(COS_MAJOR_RELEASE)) def ExpandCosImageFlag(compute_client): compute = compute_client.apitools_client images = compute_client.MakeRequests([( compute.images, 'List', compute_client.messages.ComputeImagesListRequest(project=COS_PROJECT) )]) return _SelectNewestCosImage(images) def _SelectNewestCosImage(images): cos_images = sorted([image for image in images if image.name.startswith(COS_MAJOR_RELEASE)], key=lambda x: times.ParseDateTime(x.creationTimestamp)) if not cos_images: raise NoCosImageException() return cos_images[-1].selfLink def _ValidateAndParsePortMapping(port_mappings): ports_config = [] for port_mapping in port_mappings: mapping_match = re.match(r'^(\d+):(\d+):(\S+)$', port_mapping) if not mapping_match: raise calliope_exceptions.InvalidArgumentException( '--port-mappings', 'Port mappings should follow PORT:TARGET_PORT:PROTOCOL format.') port, target_port, protocol = mapping_match.groups() if protocol not in ALLOWED_PROTOCOLS: raise calliope_exceptions.InvalidArgumentException( '--port-mappings', 'Protocol should be one of [{0}]'.format( ', '.join(ALLOWED_PROTOCOLS))) ports_config.append({ 'containerPort': int(target_port), 'hostPort': int(port), 'protocol': protocol}) return ports_config def ExpandKonletCosImageFlag(compute_client): compute = compute_client.apitools_client images = compute_client.MakeRequests( [(compute.images, 'List', compute_client.messages.ComputeImagesListRequest(project=COS_PROJECT))]) name_re_template = r'cos-{}-(\d+)-.*' image_families = ['stable', 'beta', 'dev'] for family in image_families: name_re = name_re_template.format(family) def MakeCreateComparisonKey(name_re): def CreateComparisonKey(image): version = int(re.match(name_re, image.name).group(1)) timestamp = times.ParseDateTime(image.creationTimestamp) return version, timestamp return CreateComparisonKey cos_images = sorted( [image for image in images if re.match(name_re, image.name)], key=MakeCreateComparisonKey(name_re)) if (cos_images and MakeCreateComparisonKey(name_re)(cos_images[-1])[0] >= _MIN_PREFERRED_COS_VERSION): return cos_images[-1].selfLink raise NoCosImageException() def _ReadDictionary(filename): # pylint:disable=line-too-long env_vars = {} if not filename: return env_vars with files.FileReader(filename) as f: for i, line in enumerate(f): # Strip whitespace at the beginning and end of line line = line.strip() # Ignore comments and empty lines if len(line) <= 1 or line[0] == ' continue # Find first left '=' character assignment_op_loc = line.find('=') if assignment_op_loc == -1: raise calliope_exceptions.BadFileException( 'Syntax error in {}:{}: Expected VAR=VAL, got {}'.format( filename, i, line)) env = line[:assignment_op_loc] val = line[assignment_op_loc+1:] if ' ' in env or '\t' in env: raise calliope_exceptions.BadFileException( 'Syntax error in {}:{} Variable name cannot contain whitespaces,' ' got "{}"'.format(filename, i, env)) env_vars[env] = val return env_vars def _GetHostPathDiskName(idx): return 'host-path-{}'.format(idx) def _GetTmpfsDiskName(idx): return 'tmpfs-{}'.format(idx) def _GetPersistentDiskName(idx): return 'pd-{}'.format(idx) def _AddMountedDisksToManifest(container_mount_disk, volumes, volume_mounts, used_names=None, disks=None): used_names = used_names or [] disks = disks or [] idx = 0 for mount_disk in container_mount_disk: while _GetPersistentDiskName(idx) in used_names: idx += 1 device_name = mount_disk.get('name') partition = mount_disk.get('partition') def _GetMatchingVolume(device_name, partition): for volume_spec in volumes: pd = volume_spec.get('gcePersistentDisk', {}) if (pd.get('pdName') == device_name and pd.get('partition') == partition): return volume_spec repeated = _GetMatchingVolume(device_name, partition) if repeated: name = repeated['name'] else: name = _GetPersistentDiskName(idx) used_names.append(name) if not device_name: # This should not be needed - any command that accepts container mount # disks should validate that there is only one disk before calling this # function. if len(disks) != 1: raise calliope_exceptions.InvalidArgumentException( '--container-mount-disk', 'Must specify the name of the disk to be mounted unless exactly ' 'one disk is attached to the instance.') device_name = disks[0].get('name') if disks[0].get('device-name', device_name) != device_name: raise exceptions.InvalidArgumentException( '--container-mount-disk', 'Must not have a device-name that is different from disk name if ' 'disk is being attached to the instance and mounted to a container:' ' [{}]'.format(disks[0].get('device-name'))) volume_mounts.append({ 'name': name, 'mountPath': mount_disk['mount-path'], 'readOnly': mount_disk.get('mode', _DEFAULT_MODE).isReadOnly()}) if repeated: continue volume_spec = { 'name': name, 'gcePersistentDisk': { 'pdName': device_name, 'fsType': 'ext4'}} if partition: volume_spec['gcePersistentDisk'].update({'partition': partition}) volumes.append(volume_spec) idx += 1 def _CreateContainerManifest(args, instance_name, container_mount_disk_enabled=False, container_mount_disk=None): container = {'image': args.container_image, 'name': instance_name} if args.container_command is not None: container['command'] = [args.container_command] if args.container_arg is not None: container['args'] = args.container_arg container['stdin'] = args.container_stdin container['tty'] = args.container_tty container['securityContext'] = {'privileged': args.container_privileged} env_vars = _ReadDictionary(args.container_env_file) for env_var_dict in args.container_env or []: for env, val in six.iteritems(env_var_dict): env_vars[env] = val if env_vars: container['env'] = [{ 'name': env, 'value': val } for env, val in six.iteritems(env_vars)] volumes = [] volume_mounts = [] for idx, volume in enumerate(args.container_mount_host_path or []): volumes.append({ 'name': _GetHostPathDiskName(idx), 'hostPath': { 'path': volume['host-path'] }, }) volume_mounts.append({ 'name': _GetHostPathDiskName(idx), 'mountPath': volume['mount-path'], 'readOnly': volume.get('mode', _DEFAULT_MODE).isReadOnly() }) for idx, tmpfs in enumerate(args.container_mount_tmpfs or []): volumes.append( {'name': _GetTmpfsDiskName(idx), 'emptyDir': {'medium': 'Memory'}}) volume_mounts.append( {'name': _GetTmpfsDiskName(idx), 'mountPath': tmpfs['mount-path']}) if container_mount_disk_enabled: container_mount_disk = container_mount_disk or [] disks = (args.disk or []) + (args.create_disk or []) _AddMountedDisksToManifest(container_mount_disk, volumes, volume_mounts, disks=disks) container['volumeMounts'] = volume_mounts manifest = { 'spec': { 'containers': [container], 'volumes': volumes, 'restartPolicy': RESTART_POLICY_API[args.container_restart_policy] } } return manifest def DumpYaml(data): return MANIFEST_DISCLAIMER + yaml.dump(data) def _CreateYamlContainerManifest(args, instance_name, container_mount_disk_enabled=False, container_mount_disk=None): return DumpYaml(_CreateContainerManifest( args, instance_name, container_mount_disk_enabled=container_mount_disk_enabled, container_mount_disk=container_mount_disk)) def CreateKonletMetadataMessage(messages, args, instance_name, user_metadata, container_mount_disk_enabled=False, container_mount_disk=None): konlet_metadata = { GCE_CONTAINER_DECLARATION: _CreateYamlContainerManifest( args, instance_name, container_mount_disk_enabled=container_mount_disk_enabled, container_mount_disk=container_mount_disk), # Since COS 69, having logs for Container-VMs written requires enabling # Stackdriver Logging agent. STACKDRIVER_LOGGING_AGENT_CONFIGURATION: 'true', } return metadata_utils.ConstructMetadataMessage( messages, metadata=konlet_metadata, existing_metadata=user_metadata) def UpdateInstance(holder, client, instance_ref, instance, args, container_mount_disk_enabled=False, container_mount_disk=None): # find gce-container-declaration metadata entry for metadata in instance.metadata.items: if metadata.key == GCE_CONTAINER_DECLARATION: UpdateMetadata( holder, metadata, args, instance, container_mount_disk_enabled=container_mount_disk_enabled, container_mount_disk=container_mount_disk) # update Google Compute Engine resource operation = client.apitools_client.instances.SetMetadata( client.messages.ComputeInstancesSetMetadataRequest( metadata=instance.metadata, **instance_ref.AsDict())) operation_ref = holder.resources.Parse( operation.selfLink, collection='compute.zoneOperations') operation_poller = poller.Poller(client.apitools_client.instances) set_metadata_waiter = waiter.WaitFor( operation_poller, operation_ref, 'Updating specification of container [{0}]'.format( instance_ref.Name())) if (instance.status == client.messages.Instance.StatusValueValuesEnum.TERMINATED): return set_metadata_waiter elif (instance.status == client.messages.Instance.StatusValueValuesEnum.SUSPENDED): return _StopVm(holder, client, instance_ref) else: _StopVm(holder, client, instance_ref) return _StartVm(holder, client, instance_ref) raise NoGceContainerDeclarationMetadataKey() def _StopVm(holder, client, instance_ref): operation = client.apitools_client.instances.Stop( client.messages.ComputeInstancesStopRequest( **instance_ref.AsDict())) operation_ref = holder.resources.Parse( operation.selfLink, collection='compute.zoneOperations') operation_poller = poller.Poller(client.apitools_client.instances) return waiter.WaitFor( operation_poller, operation_ref, 'Stopping instance [{0}]'.format(instance_ref.Name())) def _StartVm(holder, client, instance_ref): operation = client.apitools_client.instances.Start( client.messages.ComputeInstancesStartRequest( **instance_ref.AsDict())) operation_ref = holder.resources.Parse( operation.selfLink, collection='compute.zoneOperations') operation_poller = poller.Poller(client.apitools_client.instances) return waiter.WaitFor( operation_poller, operation_ref, 'Starting instance [{0}]'.format(instance_ref.Name())) def UpdateMetadata(holder, metadata, args, instance, container_mount_disk_enabled=False, container_mount_disk=None): # precondition: metadata.key == GCE_CONTAINER_DECLARATION manifest = yaml.load(metadata.value) if args.IsSpecified('container_image'): manifest['spec']['containers'][0]['image'] = args.container_image if args.IsSpecified('container_command'): manifest['spec']['containers'][0]['command'] = [args.container_command] if args.IsSpecified('clear_container_command'): manifest['spec']['containers'][0].pop('command', None) if args.IsSpecified('container_arg'): manifest['spec']['containers'][0]['args'] = args.container_arg if args.IsSpecified('clear_container_args'): manifest['spec']['containers'][0].pop('args', None) if args.container_privileged is True: manifest['spec']['containers'][0]['securityContext']['privileged'] = True if args.container_privileged is False: manifest['spec']['containers'][0]['securityContext']['privileged'] = False if container_mount_disk_enabled: container_mount_disk = container_mount_disk or [] disks = instance.disks else: container_mount_disk = [] # Only need disks for updating the container mount disk. disks = [] _UpdateMounts(holder, manifest, args.remove_container_mounts or [], args.container_mount_host_path or [], args.container_mount_tmpfs or [], container_mount_disk, disks) _UpdateEnv(manifest, itertools.chain.from_iterable(args.remove_container_env or []), args.container_env_file, args.container_env or []) if args.container_stdin is True: manifest['spec']['containers'][0]['stdin'] = True if args.container_stdin is False: manifest['spec']['containers'][0]['stdin'] = False if args.container_tty is True: manifest['spec']['containers'][0]['tty'] = True if args.container_tty is False: manifest['spec']['containers'][0]['tty'] = False if args.IsSpecified('container_restart_policy'): manifest['spec']['restartPolicy'] = RESTART_POLICY_API[ args.container_restart_policy] metadata.value = DumpYaml(manifest) def _UpdateMounts(holder, manifest, remove_container_mounts, container_mount_host_path, container_mount_tmpfs, container_mount_disk, disks): _CleanupMounts(manifest, remove_container_mounts, container_mount_host_path, container_mount_tmpfs, container_mount_disk=container_mount_disk) used_names = [volume['name'] for volume in manifest['spec']['volumes']] volumes = [] volume_mounts = [] next_volume_index = 0 for volume in container_mount_host_path: while _GetHostPathDiskName(next_volume_index) in used_names: next_volume_index += 1 name = _GetHostPathDiskName(next_volume_index) next_volume_index += 1 volumes.append({ 'name': name, 'hostPath': { 'path': volume['host-path'] }, }) volume_mounts.append({ 'name': name, 'mountPath': volume['mount-path'], 'readOnly': volume.get('mode', _DEFAULT_MODE).isReadOnly() }) for tmpfs in container_mount_tmpfs: while _GetTmpfsDiskName(next_volume_index) in used_names: next_volume_index += 1 name = _GetTmpfsDiskName(next_volume_index) next_volume_index += 1 volumes.append({'name': name, 'emptyDir': {'medium': 'Memory'}}) volume_mounts.append({'name': name, 'mountPath': tmpfs['mount-path']}) if container_mount_disk: # Convert to dict to match helper input needs. # The disk must already have a device name that matches its # name. For disks that were attached to the instance already. disks = [{'device-name': disk.deviceName, 'name': holder.resources.Parse(disk.source).Name()} for disk in disks] _AddMountedDisksToManifest(container_mount_disk, volumes, volume_mounts, used_names=used_names, disks=disks) manifest['spec']['containers'][0]['volumeMounts'].extend(volume_mounts) manifest['spec']['volumes'].extend(volumes) def _CleanupMounts(manifest, remove_container_mounts, container_mount_host_path, container_mount_tmpfs, container_mount_disk=None): container_mount_disk = container_mount_disk or [] # volumeMounts stored in this list should be removed mount_paths_to_remove = remove_container_mounts[:] for host_path in container_mount_host_path: mount_paths_to_remove.append(host_path['mount-path']) for tmpfs in container_mount_tmpfs: mount_paths_to_remove.append(tmpfs['mount-path']) for disk in container_mount_disk: mount_paths_to_remove.append(disk['mount-path']) # volumeMounts stored in this list are used used_mounts = [] used_mounts_names = [] removed_mount_names = [] for mount in manifest['spec']['containers'][0].get('volumeMounts', []): if mount['mountPath'] not in mount_paths_to_remove: used_mounts.append(mount) used_mounts_names.append(mount['name']) else: removed_mount_names.append(mount['name']) # override volumeMounts manifest['spec']['containers'][0]['volumeMounts'] = used_mounts # garbage collect volumes which become orphaned, skip volumes orphaned before # start of the procedure used_volumes = [] for volume in manifest['spec'].get('volumes', []): if (volume['name'] in used_mounts_names or volume['name'] not in removed_mount_names): used_volumes.append(volume) # override volumes manifest['spec']['volumes'] = used_volumes def _UpdateEnv(manifest, remove_container_env, container_env_file, container_env): current_env = {} for env_val in manifest['spec']['containers'][0].get('env', []): current_env[env_val['name']] = env_val['value'] for env in remove_container_env: current_env.pop(env, None) current_env.update(_ReadDictionary(container_env_file)) for env_var_dict in container_env: for env, val in six.iteritems(env_var_dict): current_env[env] = val if current_env: manifest['spec']['containers'][0]['env'] = [{ 'name': env, 'value': val } for env, val in six.iteritems(current_env)]

true

7901a3e8f02d8f8d5c8f255e43848232d4a5ec4e

41

py

Python

tcodtest.py

Rosuav/libtcodpy

7d76cac7cd3e6930f09558c6735ef44859ebb4e3

[ "Unlicense" ]

3

2018-03-14T23:48:00.000Z

2019-02-15T17:50:21.000Z

tcodtest.py

Rosuav/libtcodpy

7d76cac7cd3e6930f09558c6735ef44859ebb4e3

[ "Unlicense" ]

null

tcodtest.py

Rosuav/libtcodpy

7d76cac7cd3e6930f09558c6735ef44859ebb4e3

[ "Unlicense" ]

null

import libtcodpy libtcodpy.say_hello()

8.2

21

0.804878

import libtcodpy libtcodpy.say_hello()

true

7901a405bba454615704364a1d7b2850bd853fea

151

py

Python

suggestions/urls.py

MindMantraSIH/paathshaala

28fcee05f49e7b5dec734d6b9c46a5630e687c5d

[ "MIT" ]

null

suggestions/urls.py

MindMantraSIH/paathshaala

28fcee05f49e7b5dec734d6b9c46a5630e687c5d

[ "MIT" ]

null

suggestions/urls.py

MindMantraSIH/paathshaala

28fcee05f49e7b5dec734d6b9c46a5630e687c5d

[ "MIT" ]

null

from django.urls import path from . import views from django.urls import path, include urlpatterns = [ path('',views.savedata,name="savedata"), ]

18.875

44

0.728477

from django.urls import path from . import views from django.urls import path, include urlpatterns = [ path('',views.savedata,name="savedata"), ]

true

7901a54bf20b6019a3838d28b9a1ebd9102164c7

196

py

Python

CodeChef/problems/PROBCAT/main.py

object-oriented-human/competitive

9e761020e887d8980a39a64eeaeaa39af0ecd777

[ "MIT" ]

1

2022-02-21T15:43:01.000Z

CodeChef/problems/PROBCAT/main.py

foooop/competitive

9e761020e887d8980a39a64eeaeaa39af0ecd777

[ "MIT" ]

null

CodeChef/problems/PROBCAT/main.py

foooop/competitive

9e761020e887d8980a39a64eeaeaa39af0ecd777

[ "MIT" ]

null

tc = int(input()) while tc: tc -= 1 x = int(input()) if 1 <= x and x < 100: print("Easy") elif 100 <= x and x < 200: print("Medium") else: print("Hard")

19.6

30

0.44898

tc = int(input()) while tc: tc -= 1 x = int(input()) if 1 <= x and x < 100: print("Easy") elif 100 <= x and x < 200: print("Medium") else: print("Hard")

true

7901a5c2e696a85bd807869a99aea8105a03f612

30,278

py

Python

experiment code/GPU Experiments Code/task_submit_save.py

qore-dl/qore-dl-code

dc60df8fd072df5c641005992630f43892b7f78e

[ "Apache-2.0" ]

null

experiment code/GPU Experiments Code/task_submit_save.py

qore-dl/qore-dl-code

dc60df8fd072df5c641005992630f43892b7f78e

[ "Apache-2.0" ]

null

experiment code/GPU Experiments Code/task_submit_save.py

qore-dl/qore-dl-code

dc60df8fd072df5c641005992630f43892b7f78e

[ "Apache-2.0" ]

null

#https://blog.csdn.net/orangefly0214/article/details/81387077 import MultiTemplate from MultiTemplate import TaskTemplate # https://blog.csdn.net/u013812710/article/details/72886491 # https://blog.csdn.net/ismr_m/article/details/53100896 #https://blog.csdn.net/bcfdsagbfcisbg/article/details/78134172 import kubernetes import os import influxdb import time import yaml def check_path(name): train_dir = os.path.join('/tfdata/k8snfs/', name) print(train_dir) if not os.path.exists(train_dir): os.makedirs(train_dir) return train_dir def check_ns(name): kubernetes.config.load_kube_config() v1 = kubernetes.client.CoreV1Api() # v1.create_namespace() exist_ns = v1.list_namespace() exist_ns_name = [] for i in exist_ns.items: exist_ns_name.append(i.metadata.name) if name in exist_ns_name: return True else: return False class SubTask(): def __init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag): self.template_id = template_id self.ps_replicas = ps_replicas self.worker_replicas = worker_replicas self.training_step = training_step self.interval = interval self.batch_size = batch_size self.task_id = task_id self.tag = tag self.rtimes = rtimes self.influx_client = influxdb.InfluxDBClient(host='192.168.128.10',port=8086,username='admin',password='admin',database="NODEMESSAGE") self.node_list = ['k8s-master','k8s-worker0','k8s-worker2','k8sworker1','k8s-worker3','k8s-worker4','k8s-worker5'] #self.node_list = ['k8s-master','k8s-worker0','k8s-worker2','k8sworker1'] self.node_cpu = {} self.node_cpu['k8s-master'] = 32000 self.node_cpu['k8s-worker0'] = 24000 self.node_cpu['k8s-worker2'] = 24000 self.node_cpu['k8sworker1'] = 16000 self.node_cpu['k8s-worker3'] = 24000 self.node_cpu['k8s-worker4'] = 16000 self.node_cpu['k8s-worker5'] = 24000 self.node_memory = {} self.node_memory['k8s-master'] = float(251*1024) self.node_memory['k8s-worker0'] = float(94*1024) self.node_memory['k8s-worker2'] = float(94*1024) self.node_memory['k8sworker1'] = float(125*1024) self.node_memory['k8s-worker3'] = float(94 * 1024) self.node_memory['k8s-worker4'] = float(125 * 1024) self.node_memory['k8s-worker5'] = float(94 * 1024) self.args = ['--training_step='+str(self.training_step),'--batch_size='+str(self.batch_size),'--interval='+str(self.interval),'--task_id='+str(self.task_id),'--rtimes='+str(self.rtimes),"--tag="+self.tag] class VGGTask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,channel1,channel2,channel3,channel4,channel5,num_layer1,num_layer2,num_layer3,num_layer4,num_layer5): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.channel1 = channel1 self.channel2 = channel2 self.channel3 = channel3 self.channel4 = channel4 self.channel5 = channel5 self.num_layer1 = num_layer1 self.num_layer2 = num_layer2 self.num_layer3 = num_layer3 self.num_layer4 = num_layer4 self.num_layer5 = num_layer5 self.num_layers = num_layer1+num_layer2+num_layer3+num_layer4+num_layer5+3 self.template = TaskTemplate.VGG self.v1 = v1 self.name = 'vgg-'+str(self.task_id)+'-'+str(self.rtimes) def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--channel1='+str(self.channel1)) self.args.append('--channel2='+str(self.channel2)) self.args.append('--channel3='+str(self.channel3)) self.args.append('--channel4='+str(self.channel4)) self.args.append('--channel5='+str(self.channel5)) self.args.append('--num_layer1='+str(self.num_layer1)) self.args.append('--num_layer2='+str(self.num_layer2)) self.args.append('--num_layer3='+str(self.num_layer3)) self.args.append('--num_layer4='+str(self.num_layer4)) self.args.append('--num_layer5='+str(self.num_layer5)) self.args.append('--num_layers='+str(self.num_layers)) def create_tf(self): name = 'vgg-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from "+"NODEMESSAGE"+" group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0]))/self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]]*0.6+memory_base[nodes[i]]*0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes '+key+' woksch-' os.system(command) command2 = 'kubectl label nodes '+key+' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes '+key+priori os.system(command3) if cpu_base[key] <= 0.57 and memory_base[key] <= 0.6: command = 'kubectl label nodes '+key+' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' # f = open(log_dir+str(name)+'.yaml', "w") f = open(log_dir + str(name) + '.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 'vgg-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) class RESTask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,bottle,layer1,layer2,layer3,layer4,channel1,channel2,channel3,channel4): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.channel1 = channel1 self.channel2 = channel2 self.channel3 = channel3 self.channel4 = channel4 self.bottle = bottle self.layer1 = layer1 self.layer2 = layer2 self.layer3 = layer3 self.layer4 = layer4 self.name = 'res-'+str(self.task_id)+'-'+str(self.rtimes) if self.bottle == 1: self.num_layers = 3*(layer1+layer4+layer3+layer2)+2 else: self.num_layers = 2 * (layer1 + layer4 + layer3 + layer2) + 2 self.template = TaskTemplate.RES self.v1 = v1 def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--bottle=' + str(self.bottle)) self.args.append('--channel1='+str(self.channel1)) self.args.append('--channel2='+str(self.channel2)) self.args.append('--channel3='+str(self.channel3)) self.args.append('--channel4='+str(self.channel4)) self.args.append('--layer1='+str(self.layer1)) self.args.append('--layer2='+str(self.layer2)) self.args.append('--layer3='+str(self.layer3)) self.args.append('--layer4='+str(self.layer4)) def create_tf(self): name = 'res-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from " + "NODEMESSAGE" + " group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0])) / self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]] * 0.6 + memory_base[nodes[i]] * 0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes ' + key + ' woksch-' os.system(command) command2 = 'kubectl label nodes ' + key + ' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes ' + key + priori os.system(command3) if cpu_base[key] <= 0.6 and memory_base[key] <= 0.6: command = 'kubectl label nodes ' + key + ' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir+str(name)+'.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 'res-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) class RETask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,stack,channel1,channel2,channel3,channel4): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.channel1 = channel1 self.channel2 = channel2 self.channel3 = channel3 self.channel4 = channel4 self.stack = stack self.num_layers = 6*self.stack+2 self.template = TaskTemplate.RE self.name = 're-'+str(self.task_id)+'-'+str(self.rtimes) self.v1 = v1 def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--stack='+str(self.stack)) self.args.append('--channel1='+str(self.channel1)) self.args.append('--channel2='+str(self.channel2)) self.args.append('--channel3='+str(self.channel3)) self.args.append('--channel4='+str(self.channel4)) def create_tf(self): name = 're-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from " + "NODEMESSAGE" + " group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0])) / self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]] * 0.6 + memory_base[nodes[i]] * 0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes ' + key + ' woksch-' os.system(command) command2 = 'kubectl label nodes ' + key + ' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes ' + key + priori os.system(command3) if cpu_base[key] <= 0.6 and memory_base[key] <= 0.6: command = 'kubectl label nodes ' + key + ' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir+str(name)+'.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 're-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) class XCETask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,repeat,channel1,channel2,channel3,channel4,channel5,channel6,channel7,channel8): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.channel1 = channel1 self.channel2 = channel2 self.channel3 = channel3 self.channel4 = channel4 self.channel5 = channel5 self.channel6 = channel6 self.channel7 = channel7 self.channel8 = channel8 self.repeat = repeat self.template = TaskTemplate.XCEPTION self.v1 = v1 self.name = 'xception-'+str(self.task_id)+'-'+str(self.rtimes) def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--repeat='+str(self.repeat)) self.args.append('--channel1='+str(self.channel1)) self.args.append('--channel2='+str(self.channel2)) self.args.append('--channel3='+str(self.channel3)) self.args.append('--channel4='+str(self.channel4)) self.args.append('--channel5=' + str(self.channel5)) self.args.append('--channel6=' + str(self.channel6)) self.args.append('--channel7=' + str(self.channel7)) self.args.append('--channel8=' + str(self.channel8)) def create_tf(self): name = 'xception-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from " + "NODEMESSAGE" + " group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0])) / self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]] * 0.6 + memory_base[nodes[i]] * 0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes ' + key + ' woksch-' os.system(command) command2 = 'kubectl label nodes ' + key + ' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes ' + key + priori os.system(command3) if cpu_base[key] <= 0.6 and memory_base[key] <= 0.6: command = 'kubectl label nodes ' + key + ' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir+str(name)+'.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 'xception-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) class DENTask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,L,k,BC): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.L = L self.k = k self.BC = BC self.template = TaskTemplate.DEN self.v1 = v1 self.name = 'den-'+str(self.task_id)+'-'+str(self.rtimes) def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--L='+str(self.L)) self.args.append('--k='+str(self.k)) self.args.append('--BC='+str(self.BC)) def create_tf(self): name = 'den-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from " + "NODEMESSAGE" + " group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0])) / self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]] * 0.6 + memory_base[nodes[i]] * 0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes ' + key + ' woksch-' os.system(command) command2 = 'kubectl label nodes ' + key + ' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes ' + key + priori os.system(command3) if cpu_base[key] <= 0.6 and memory_base[key] <= 0.6: command = 'kubectl label nodes ' + key + ' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir+str(name)+'.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 'den-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) if __name__ == '__main__': kubernetes.config.load_kube_config() v1 = kubernetes.client.CoreV1Api() # v1.create_namespace() v1.list_namespace() check_path('ceshi') # vgg = VGGTask(1,2,4,80,1.0,2,1,"ms",32,64,128,256,512,2,3,3,4,4) # vgg.create_tf()

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import MultiTemplate from MultiTemplate import TaskTemplate import kubernetes import os import influxdb import time import yaml def check_path(name): train_dir = os.path.join('/tfdata/k8snfs/', name) print(train_dir) if not os.path.exists(train_dir): os.makedirs(train_dir) return train_dir def check_ns(name): kubernetes.config.load_kube_config() v1 = kubernetes.client.CoreV1Api() exist_ns = v1.list_namespace() exist_ns_name = [] for i in exist_ns.items: exist_ns_name.append(i.metadata.name) if name in exist_ns_name: return True else: return False class SubTask(): def __init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag): self.template_id = template_id self.ps_replicas = ps_replicas self.worker_replicas = worker_replicas self.training_step = training_step self.interval = interval self.batch_size = batch_size self.task_id = task_id self.tag = tag self.rtimes = rtimes self.influx_client = influxdb.InfluxDBClient(host='192.168.128.10',port=8086,username='admin',password='admin',database="NODEMESSAGE") self.node_list = ['k8s-master','k8s-worker0','k8s-worker2','k8sworker1','k8s-worker3','k8s-worker4','k8s-worker5'] self.node_cpu = {} self.node_cpu['k8s-master'] = 32000 self.node_cpu['k8s-worker0'] = 24000 self.node_cpu['k8s-worker2'] = 24000 self.node_cpu['k8sworker1'] = 16000 self.node_cpu['k8s-worker3'] = 24000 self.node_cpu['k8s-worker4'] = 16000 self.node_cpu['k8s-worker5'] = 24000 self.node_memory = {} self.node_memory['k8s-master'] = float(251*1024) self.node_memory['k8s-worker0'] = float(94*1024) self.node_memory['k8s-worker2'] = float(94*1024) self.node_memory['k8sworker1'] = float(125*1024) self.node_memory['k8s-worker3'] = float(94 * 1024) self.node_memory['k8s-worker4'] = float(125 * 1024) self.node_memory['k8s-worker5'] = float(94 * 1024) self.args = ['--training_step='+str(self.training_step),'--batch_size='+str(self.batch_size),'--interval='+str(self.interval),'--task_id='+str(self.task_id),'--rtimes='+str(self.rtimes),"--tag="+self.tag] class VGGTask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,channel1,channel2,channel3,channel4,channel5,num_layer1,num_layer2,num_layer3,num_layer4,num_layer5): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.channel1 = channel1 self.channel2 = channel2 self.channel3 = channel3 self.channel4 = channel4 self.channel5 = channel5 self.num_layer1 = num_layer1 self.num_layer2 = num_layer2 self.num_layer3 = num_layer3 self.num_layer4 = num_layer4 self.num_layer5 = num_layer5 self.num_layers = num_layer1+num_layer2+num_layer3+num_layer4+num_layer5+3 self.template = TaskTemplate.VGG self.v1 = v1 self.name = 'vgg-'+str(self.task_id)+'-'+str(self.rtimes) def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--channel1='+str(self.channel1)) self.args.append('--channel2='+str(self.channel2)) self.args.append('--channel3='+str(self.channel3)) self.args.append('--channel4='+str(self.channel4)) self.args.append('--channel5='+str(self.channel5)) self.args.append('--num_layer1='+str(self.num_layer1)) self.args.append('--num_layer2='+str(self.num_layer2)) self.args.append('--num_layer3='+str(self.num_layer3)) self.args.append('--num_layer4='+str(self.num_layer4)) self.args.append('--num_layer5='+str(self.num_layer5)) self.args.append('--num_layers='+str(self.num_layers)) def create_tf(self): name = 'vgg-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from "+"NODEMESSAGE"+" group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0]))/self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]]*0.6+memory_base[nodes[i]]*0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes '+key+' woksch-' os.system(command) command2 = 'kubectl label nodes '+key+' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes '+key+priori os.system(command3) if cpu_base[key] <= 0.57 and memory_base[key] <= 0.6: command = 'kubectl label nodes '+key+' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir + str(name) + '.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 'vgg-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) class RESTask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,bottle,layer1,layer2,layer3,layer4,channel1,channel2,channel3,channel4): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.channel1 = channel1 self.channel2 = channel2 self.channel3 = channel3 self.channel4 = channel4 self.bottle = bottle self.layer1 = layer1 self.layer2 = layer2 self.layer3 = layer3 self.layer4 = layer4 self.name = 'res-'+str(self.task_id)+'-'+str(self.rtimes) if self.bottle == 1: self.num_layers = 3*(layer1+layer4+layer3+layer2)+2 else: self.num_layers = 2 * (layer1 + layer4 + layer3 + layer2) + 2 self.template = TaskTemplate.RES self.v1 = v1 def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--bottle=' + str(self.bottle)) self.args.append('--channel1='+str(self.channel1)) self.args.append('--channel2='+str(self.channel2)) self.args.append('--channel3='+str(self.channel3)) self.args.append('--channel4='+str(self.channel4)) self.args.append('--layer1='+str(self.layer1)) self.args.append('--layer2='+str(self.layer2)) self.args.append('--layer3='+str(self.layer3)) self.args.append('--layer4='+str(self.layer4)) def create_tf(self): name = 'res-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from " + "NODEMESSAGE" + " group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0])) / self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]] * 0.6 + memory_base[nodes[i]] * 0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes ' + key + ' woksch-' os.system(command) command2 = 'kubectl label nodes ' + key + ' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes ' + key + priori os.system(command3) if cpu_base[key] <= 0.6 and memory_base[key] <= 0.6: command = 'kubectl label nodes ' + key + ' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir+str(name)+'.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 'res-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) class RETask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,stack,channel1,channel2,channel3,channel4): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.channel1 = channel1 self.channel2 = channel2 self.channel3 = channel3 self.channel4 = channel4 self.stack = stack self.num_layers = 6*self.stack+2 self.template = TaskTemplate.RE self.name = 're-'+str(self.task_id)+'-'+str(self.rtimes) self.v1 = v1 def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--stack='+str(self.stack)) self.args.append('--channel1='+str(self.channel1)) self.args.append('--channel2='+str(self.channel2)) self.args.append('--channel3='+str(self.channel3)) self.args.append('--channel4='+str(self.channel4)) def create_tf(self): name = 're-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from " + "NODEMESSAGE" + " group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0])) / self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]] * 0.6 + memory_base[nodes[i]] * 0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes ' + key + ' woksch-' os.system(command) command2 = 'kubectl label nodes ' + key + ' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes ' + key + priori os.system(command3) if cpu_base[key] <= 0.6 and memory_base[key] <= 0.6: command = 'kubectl label nodes ' + key + ' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir+str(name)+'.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 're-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) class XCETask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,repeat,channel1,channel2,channel3,channel4,channel5,channel6,channel7,channel8): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.channel1 = channel1 self.channel2 = channel2 self.channel3 = channel3 self.channel4 = channel4 self.channel5 = channel5 self.channel6 = channel6 self.channel7 = channel7 self.channel8 = channel8 self.repeat = repeat self.template = TaskTemplate.XCEPTION self.v1 = v1 self.name = 'xception-'+str(self.task_id)+'-'+str(self.rtimes) def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--repeat='+str(self.repeat)) self.args.append('--channel1='+str(self.channel1)) self.args.append('--channel2='+str(self.channel2)) self.args.append('--channel3='+str(self.channel3)) self.args.append('--channel4='+str(self.channel4)) self.args.append('--channel5=' + str(self.channel5)) self.args.append('--channel6=' + str(self.channel6)) self.args.append('--channel7=' + str(self.channel7)) self.args.append('--channel8=' + str(self.channel8)) def create_tf(self): name = 'xception-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from " + "NODEMESSAGE" + " group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0])) / self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]] * 0.6 + memory_base[nodes[i]] * 0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes ' + key + ' woksch-' os.system(command) command2 = 'kubectl label nodes ' + key + ' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes ' + key + priori os.system(command3) if cpu_base[key] <= 0.6 and memory_base[key] <= 0.6: command = 'kubectl label nodes ' + key + ' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir+str(name)+'.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 'xception-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) class DENTask(SubTask): def __init__(self,v1,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag,L,k,BC): SubTask.__init__(self,template_id,ps_replicas,worker_replicas,training_step,batch_size,interval,task_id,rtimes,tag) self.L = L self.k = k self.BC = BC self.template = TaskTemplate.DEN self.v1 = v1 self.name = 'den-'+str(self.task_id)+'-'+str(self.rtimes) def get_node_list(self): node_list = [i.metadata.name for i in self.v1.list_node().items] return node_list def make_args(self): self.args.append('--L='+str(self.L)) self.args.append('--k='+str(self.k)) self.args.append('--BC='+str(self.BC)) def create_tf(self): name = 'den-'+str(self.task_id)+'-'+str(self.rtimes) ns_body = TaskTemplate.NS ns_body['metadata']['name'] = name if not check_ns(name): self.v1.create_namespace(ns_body) train_dir = check_path(name) time.sleep(12) result = self.influx_client.query("select * from " + "NODEMESSAGE" + " group by nodes order by desc limit 3") node_list = self.get_node_list() result_keys = result.keys() nodes = [i[-1]['nodes'] for i in result_keys] node_mg = [list(result[i]) for i in result_keys] cpu_base = {} memory_base = {} point_base = {} point_base_list = [] for i in range(len(node_mg)): cpu_base[nodes[i]] = 0 memory_base[nodes[i]] = 0 point_base[nodes[i]] = 0.0 for j in range(len(node_mg[0])): cpu_base[nodes[i]] += node_mg[i][j]['cpu'] memory_base[nodes[i]] += node_mg[i][j]['memory'] cpu_base[nodes[i]] = (cpu_base[nodes[i]] / len(node_mg[0])) / self.node_cpu[nodes[i]] memory_base[nodes[i]] = (memory_base[nodes[i]] / len(node_mg[0])) / self.node_memory[nodes[i]] tmp = cpu_base[nodes[i]] * 0.6 + memory_base[nodes[i]] * 0.4 point_base[nodes[i]] = tmp point_base_list.append(tmp) list.sort(point_base_list) for key in nodes: command = 'kubectl label nodes ' + key + ' woksch-' os.system(command) command2 = 'kubectl label nodes ' + key + ' wokpro-' os.system(command2) nod_prori = point_base_list.index(point_base[key]) priori = ' wokpro=%d' % nod_prori command3 = 'kubectl label nodes ' + key + priori os.system(command3) if cpu_base[key] <= 0.6 and memory_base[key] <= 0.6: command = 'kubectl label nodes ' + key + ' woksch=true' os.system(command) else: command = 'kubectl label nodes ' + key + ' woksch=false' os.system(command) self.template['metadata']['name'] = name self.template['metadata']['namespace'] = name self.template['spec']['tfReplicaSpecs']['PS']['replicas'] = self.ps_replicas self.template['spec']['tfReplicaSpecs']['Worker']['replicas'] = self.worker_replicas self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['volumes'][0]['hostPath']['path'] = train_dir self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['volumeMounts'][0]['name'] = name self.make_args() self.template['spec']['tfReplicaSpecs']['PS']['template']['spec']['containers'][0]['args'] = self.args[:] self.template['spec']['tfReplicaSpecs']['Worker']['template']['spec']['containers'][0]['args'] = self.args[:] log_dir = '/tfdata/tfcnn/expjob/' f = open(log_dir+str(name)+'.yaml', "w") yaml.dump(self.template, f) f.close() response = os.system('kubectl create -f '+log_dir+str(name)+'.yaml') if response == 0: print('create task sucess') else: print("Error code:"+str(response)) def delete_tf(self): name = 'den-'+str(self.task_id)+'-'+str(self.rtimes) log_dir = '/tfdata/tfcnn/expjob/' response = os.system('kubectl delete -f ' + log_dir + str(name) + '.yaml') if response == 0: print('delete task sucess') else: print("Error code:" + str(response)) self.v1.delete_namespace(name=name) if __name__ == '__main__': kubernetes.config.load_kube_config() v1 = kubernetes.client.CoreV1Api() v1.list_namespace() check_path('ceshi')

true

7901a7ae2350f0fe3414e33aee4cd0df685ec183

15,431

py

Python

python/mxnet/gluon/nn/basic_layers.py

IIMarch/mxnet

64c35f2d41f5bad3f9cbf4d4fda9cf3bf3dadb4b

[ "Apache-2.0" ]

null

python/mxnet/gluon/nn/basic_layers.py

IIMarch/mxnet

64c35f2d41f5bad3f9cbf4d4fda9cf3bf3dadb4b

[ "Apache-2.0" ]

null

python/mxnet/gluon/nn/basic_layers.py

IIMarch/mxnet

64c35f2d41f5bad3f9cbf4d4fda9cf3bf3dadb4b

[ "Apache-2.0" ]

1

2018-11-30T21:34:24.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # coding: utf-8 # pylint: disable= arguments-differ """Basic neural network layers.""" from ..block import Block, HybridBlock from ..utils import _indent class Sequential(Block): """Stacks `Block`s sequentially. Example:: net = nn.Sequential() # use net's name_scope to give child Blocks appropriate names. with net.name_scope(): net.add(nn.Dense(10, activation='relu')) net.add(nn.Dense(20)) """ def __init__(self, prefix=None, params=None): super(Sequential, self).__init__(prefix=prefix, params=params) def add(self, block): """Adds block on top of the stack.""" self.register_child(block) def forward(self, x): for block in self._children: x = block(x) return x def __repr__(self): s = '{name}(\n{modstr}\n)' modstr = '\n'.join([' ({key}): {block}'.format(key=key, block=_indent(block.__repr__(), 2)) for key, block in enumerate(self._children) if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr) def __getitem__(self, i): return self._children[i] def __len__(self): return len(self._children) class HybridSequential(HybridBlock): """Stacks `HybridBlock`s sequentially. Example:: net = nn.Sequential() # use net's name_scope to give child Blocks appropriate names. with net.name_scope(): net.add(nn.Dense(10, activation='relu')) net.add(nn.Dense(20)) """ def __init__(self, prefix=None, params=None): super(HybridSequential, self).__init__(prefix=prefix, params=params) def add(self, block): """Adds block on top of the stack.""" self.register_child(block) def hybrid_forward(self, F, x): for block in self._children: x = block(x) return x def __repr__(self): s = '{name}(\n{modstr}\n)' modstr = '\n'.join([' ({key}): {block}'.format(key=key, block=_indent(block.__repr__(), 2)) for key, block in enumerate(self._children) if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr) def __getitem__(self, i): return self._children[i] def __len__(self): return len(self._children) class Dense(HybridBlock): """Just your regular densely-connected NN layer. `Dense` implements the operation: `output = activation(dot(input, weight) + bias)` where `activation` is the element-wise activation function passed as the `activation` argument, `weight` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only applicable if `use_bias` is `True`). Note: the input must be a tensor with rank 2. Use `flatten` to convert it to rank 2 manually if necessary. Parameters ---------- units : int Dimensionality of the output space. activation : str Activation function to use. See help on `Activation` layer. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias : bool Whether the layer uses a bias vector. weight_initializer : str or `Initializer` Initializer for the `kernel` weights matrix. bias_initializer: str or `Initializer` Initializer for the bias vector. in_units : int, optional Size of the input data. If not specified, initialization will be deferred to the first time `forward` is called and `in_units` will be inferred from the shape of input data. prefix : str or None See document of `Block`. params : ParameterDict or None See document of `Block`. Input shape: A 2D input with shape `(batch_size, in_units)`. Output shape: The output would have shape `(batch_size, units)`. """ def __init__(self, units, activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_units=0, **kwargs): super(Dense, self).__init__(**kwargs) with self.name_scope(): self._units = units self._in_units = in_units self.weight = self.params.get('weight', shape=(units, in_units), init=weight_initializer, allow_deferred_init=True) if use_bias: self.bias = self.params.get('bias', shape=(units,), init=bias_initializer, allow_deferred_init=True) else: self.bias = None if activation is not None: self.act = Activation(activation, prefix=activation+'_') else: self.act = None def hybrid_forward(self, F, x, weight, bias=None): if bias is None: act = F.FullyConnected(x, weight, no_bias=True, num_hidden=self._units, name='fwd') else: act = F.FullyConnected(x, weight, bias, num_hidden=self._units, name='fwd') if self.act is not None: act = self.act(act) return act def __repr__(self): s = '{name}({layout}, {act})' return s.format(name=self.__class__.__name__, act=self.act if self.act else 'linear', layout='{0} -> {1}'.format(self._in_units, self._units) if self._in_units else self._units) class Activation(HybridBlock): """Applies an activation function to input. Parameters ---------- activation : str Name of activation function to use. See :func:`~mxnet.ndarray.Activation` for available choices. Input shape: Arbitrary. Output shape: Same shape as input. """ def __init__(self, activation, **kwargs): self._act_type = activation super(Activation, self).__init__(**kwargs) def _alias(self): return self._act_type def hybrid_forward(self, F, x): return F.Activation(x, act_type=self._act_type, name='fwd') def __repr__(self): s = '{name}({_act_type})' return s.format(name=self.__class__.__name__, **self.__dict__) class Dropout(HybridBlock): """Applies Dropout to the input. Dropout consists in randomly setting a fraction `rate` of input units to 0 at each update during training time, which helps prevent overfitting. Parameters ---------- rate : float Fraction of the input units to drop. Must be a number between 0 and 1. Input shape: Arbitrary. Output shape: Same shape as input. References ---------- `Dropout: A Simple Way to Prevent Neural Networks from Overfitting <http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf>`_ """ def __init__(self, rate, **kwargs): super(Dropout, self).__init__(**kwargs) self._rate = rate def hybrid_forward(self, F, x): return F.Dropout(x, p=self._rate, name='fwd') def __repr__(self): s = '{name}(p = {_rate})' return s.format(name=self.__class__.__name__, **self.__dict__) class BatchNorm(HybridBlock): """Batch normalization layer (Ioffe and Szegedy, 2014). Normalizes the input at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1. Parameters ---------- axis : int, default 1 The axis that should be normalized. This is typically the channels (C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`, set `axis=1` in `BatchNorm`. If `layout='NHWC'`, then set `axis=3`. momentum: float, default 0.9 Momentum for the moving average. epsilon: float, default 1e-5 Small float added to variance to avoid dividing by zero. center: bool, default True If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: bool, default True If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling will be done by the next layer. beta_initializer: str or `Initializer`, default 'zeros' Initializer for the beta weight. gamma_initializer: str or `Initializer`, default 'ones' Initializer for the gamma weight. moving_mean_initializer: str or `Initializer`, default 'zeros' Initializer for the moving mean. moving_variance_initializer: str or `Initializer`, default 'ones' Initializer for the moving variance. in_channels : int, default 0 Number of channels (feature maps) in input data. If not specified, initialization will be deferred to the first time `forward` is called and `in_channels` will be inferred from the shape of input data. Input shape: Arbitrary. Output shape: Same shape as input. """ def __init__(self, axis=1, momentum=0.9, epsilon=1e-5, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', running_mean_initializer='zeros', running_variance_initializer='ones', in_channels=0, **kwargs): super(BatchNorm, self).__init__(**kwargs) self._kwargs = {'axis': axis, 'eps': epsilon, 'momentum': momentum, 'fix_gamma': not scale} if in_channels != 0: self.in_channels = in_channels self.gamma = self.params.get('gamma', grad_req='write' if scale else 'null', shape=(in_channels,), init=gamma_initializer, allow_deferred_init=True, differentiable=scale) self.beta = self.params.get('beta', grad_req='write' if center else 'null', shape=(in_channels,), init=beta_initializer, allow_deferred_init=True, differentiable=center) self.running_mean = self.params.get('running_mean', grad_req='null', shape=(in_channels,), init=running_mean_initializer, allow_deferred_init=True, differentiable=False) self.running_var = self.params.get('running_var', grad_req='null', shape=(in_channels,), init=running_variance_initializer, allow_deferred_init=True, differentiable=False) def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var): return F.BatchNorm(x, gamma, beta, running_mean, running_var, name='fwd', **self._kwargs) def __repr__(self): s = '{name}({content}' if hasattr(self, 'in_channels'): s += ', in_channels={0}'.format(self.in_channels) s += ')' return s.format(name=self.__class__.__name__, content=', '.join(['='.join([k, v.__repr__()]) for k, v in self._kwargs.items()])) class LeakyReLU(HybridBlock): """Leaky version of a Rectified Linear Unit. It allows a small gradient when the unit is not active:: `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`. Parameters ---------- alpha : float slope coefficient for the negative half axis. Must be >= 0. Input shape: Arbitrary. Output shape: Same shape as input. """ def __init__(self, alpha, **kwargs): super(LeakyReLU, self).__init__(**kwargs) self._alpha = alpha def hybrid_forward(self, F, x): return F.LeakyReLU(x, act_type='leaky', slope=self._alpha, name='fwd') def __repr__(self): s = '{name}({alpha})' return s.format(name=self.__class__.__name__, alpha=self._alpha) class Embedding(HybridBlock): """Turns non-negative integers (indexes/tokens) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]] Parameters ---------- input_dim : int Size of the vocabulary, i.e. maximum integer index + 1. output_dim : int Dimension of the dense embedding. dtype : str or np.dtype, default 'float32' Data type of output embeddings. weight_initializer : Initializer Initializer for the `embeddings` matrix. Input shape: 2D tensor with shape: `(N, M)`. Output shape: 3D tensor with shape: `(N, M, output_dim)`. """ def __init__(self, input_dim, output_dim, dtype='float32', weight_initializer=None, **kwargs): super(Embedding, self).__init__(**kwargs) self._kwargs = {'input_dim': input_dim, 'output_dim': output_dim, 'dtype': dtype} self.weight = self.params.get('weight', shape=(input_dim, output_dim), init=weight_initializer, allow_deferred_init=True) def hybrid_forward(self, F, x, weight): return F.Embedding(x, weight, name='fwd', **self._kwargs) def __repr__(self): s = '{block_name}({input_dim} -> {output_dim}, {dtype})' return s.format(block_name=self.__class__.__name__, **self._kwargs) class Flatten(HybridBlock): """Flattens the input to two dimensional. Input shape: Arbitrary shape `(N, a, b, c, ...)` Output shape: 2D tensor with shape: `(N, a*b*c...)` """ def __init__(self, **kwargs): super(Flatten, self).__init__(**kwargs) def hybrid_forward(self, F, x): return x.reshape((0, -1)) def __repr__(self): return self.__class__.__name__

35.637413

97

0.580325

from ..block import Block, HybridBlock from ..utils import _indent class Sequential(Block): def __init__(self, prefix=None, params=None): super(Sequential, self).__init__(prefix=prefix, params=params) def add(self, block): self.register_child(block) def forward(self, x): for block in self._children: x = block(x) return x def __repr__(self): s = '{name}(\n{modstr}\n)' modstr = '\n'.join([' ({key}): {block}'.format(key=key, block=_indent(block.__repr__(), 2)) for key, block in enumerate(self._children) if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr) def __getitem__(self, i): return self._children[i] def __len__(self): return len(self._children) class HybridSequential(HybridBlock): def __init__(self, prefix=None, params=None): super(HybridSequential, self).__init__(prefix=prefix, params=params) def add(self, block): self.register_child(block) def hybrid_forward(self, F, x): for block in self._children: x = block(x) return x def __repr__(self): s = '{name}(\n{modstr}\n)' modstr = '\n'.join([' ({key}): {block}'.format(key=key, block=_indent(block.__repr__(), 2)) for key, block in enumerate(self._children) if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr) def __getitem__(self, i): return self._children[i] def __len__(self): return len(self._children) class Dense(HybridBlock): def __init__(self, units, activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_units=0, **kwargs): super(Dense, self).__init__(**kwargs) with self.name_scope(): self._units = units self._in_units = in_units self.weight = self.params.get('weight', shape=(units, in_units), init=weight_initializer, allow_deferred_init=True) if use_bias: self.bias = self.params.get('bias', shape=(units,), init=bias_initializer, allow_deferred_init=True) else: self.bias = None if activation is not None: self.act = Activation(activation, prefix=activation+'_') else: self.act = None def hybrid_forward(self, F, x, weight, bias=None): if bias is None: act = F.FullyConnected(x, weight, no_bias=True, num_hidden=self._units, name='fwd') else: act = F.FullyConnected(x, weight, bias, num_hidden=self._units, name='fwd') if self.act is not None: act = self.act(act) return act def __repr__(self): s = '{name}({layout}, {act})' return s.format(name=self.__class__.__name__, act=self.act if self.act else 'linear', layout='{0} -> {1}'.format(self._in_units, self._units) if self._in_units else self._units) class Activation(HybridBlock): def __init__(self, activation, **kwargs): self._act_type = activation super(Activation, self).__init__(**kwargs) def _alias(self): return self._act_type def hybrid_forward(self, F, x): return F.Activation(x, act_type=self._act_type, name='fwd') def __repr__(self): s = '{name}({_act_type})' return s.format(name=self.__class__.__name__, **self.__dict__) class Dropout(HybridBlock): def __init__(self, rate, **kwargs): super(Dropout, self).__init__(**kwargs) self._rate = rate def hybrid_forward(self, F, x): return F.Dropout(x, p=self._rate, name='fwd') def __repr__(self): s = '{name}(p = {_rate})' return s.format(name=self.__class__.__name__, **self.__dict__) class BatchNorm(HybridBlock): def __init__(self, axis=1, momentum=0.9, epsilon=1e-5, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', running_mean_initializer='zeros', running_variance_initializer='ones', in_channels=0, **kwargs): super(BatchNorm, self).__init__(**kwargs) self._kwargs = {'axis': axis, 'eps': epsilon, 'momentum': momentum, 'fix_gamma': not scale} if in_channels != 0: self.in_channels = in_channels self.gamma = self.params.get('gamma', grad_req='write' if scale else 'null', shape=(in_channels,), init=gamma_initializer, allow_deferred_init=True, differentiable=scale) self.beta = self.params.get('beta', grad_req='write' if center else 'null', shape=(in_channels,), init=beta_initializer, allow_deferred_init=True, differentiable=center) self.running_mean = self.params.get('running_mean', grad_req='null', shape=(in_channels,), init=running_mean_initializer, allow_deferred_init=True, differentiable=False) self.running_var = self.params.get('running_var', grad_req='null', shape=(in_channels,), init=running_variance_initializer, allow_deferred_init=True, differentiable=False) def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var): return F.BatchNorm(x, gamma, beta, running_mean, running_var, name='fwd', **self._kwargs) def __repr__(self): s = '{name}({content}' if hasattr(self, 'in_channels'): s += ', in_channels={0}'.format(self.in_channels) s += ')' return s.format(name=self.__class__.__name__, content=', '.join(['='.join([k, v.__repr__()]) for k, v in self._kwargs.items()])) class LeakyReLU(HybridBlock): def __init__(self, alpha, **kwargs): super(LeakyReLU, self).__init__(**kwargs) self._alpha = alpha def hybrid_forward(self, F, x): return F.LeakyReLU(x, act_type='leaky', slope=self._alpha, name='fwd') def __repr__(self): s = '{name}({alpha})' return s.format(name=self.__class__.__name__, alpha=self._alpha) class Embedding(HybridBlock): def __init__(self, input_dim, output_dim, dtype='float32', weight_initializer=None, **kwargs): super(Embedding, self).__init__(**kwargs) self._kwargs = {'input_dim': input_dim, 'output_dim': output_dim, 'dtype': dtype} self.weight = self.params.get('weight', shape=(input_dim, output_dim), init=weight_initializer, allow_deferred_init=True) def hybrid_forward(self, F, x, weight): return F.Embedding(x, weight, name='fwd', **self._kwargs) def __repr__(self): s = '{block_name}({input_dim} -> {output_dim}, {dtype})' return s.format(block_name=self.__class__.__name__, **self._kwargs) class Flatten(HybridBlock): def __init__(self, **kwargs): super(Flatten, self).__init__(**kwargs) def hybrid_forward(self, F, x): return x.reshape((0, -1)) def __repr__(self): return self.__class__.__name__

true

7901a80de89eb080152807d040a6b0cd565910ee

3,346

py

Python

Clases/Palabras.py

JorgeSchelotto/TrabajoFinalSeminarioPython

aae8cc914ff55cc09b7538722f27e1ec22954e57

[ "MIT" ]

null

Clases/Palabras.py

JorgeSchelotto/TrabajoFinalSeminarioPython

aae8cc914ff55cc09b7538722f27e1ec22954e57

[ "MIT" ]

null

Clases/Palabras.py

JorgeSchelotto/TrabajoFinalSeminarioPython

aae8cc914ff55cc09b7538722f27e1ec22954e57

[ "MIT" ]

null

__author__ = 'Burgos, Agustin - Schelotto, Jorge' # -*- coding: utf-8 -*- # Copyright 2018 autors: Burgos Agustin, Schelotto Jorge # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated # documentation files (the "Software"), to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, # and to permit persons to whom the Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED # TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. import pygame class Palabras(pygame.sprite.Sprite): def __init__(self, ruta, nombre, x, y): super().__init__() self.__palabra = nombre self.__click = False self.image = pygame.image.load(ruta).convert_alpha() self.rect = self.image.get_rect() self.collide = False self.posX = x self.posY = y def getPosX(self): return self.posX def getPosY(self): return self.posY def getPalabra(self): return self.__palabra def getPalabraImagen(self): return self.image def setClick(self, bool): self.__click = bool def getClick(self): return self.__click def getRect(self): return self.rect def colli(self, x, y): if x > 20: # Achica la imagen center = self.rect.center x = x - 1 y = y - 1 self.image = pygame.transform.scale(self.image, (x, y)) self.rect = self.image.get_rect() self.rect.center = center self.image = pygame.transform.rotozoom(self.image, -90, 0.8) elif x <= 20: # Para que no de x < 0 center = self.rect.center self.image = pygame.transform.scale(self.image, (0, 0)) self.rect = self.image.get_rect() self.rect.center = center self.image = pygame.transform.rotozoom(self.image, -90, 0.5) def update(self,surface): """Controla los eventos y coliciones de los sprites Palabras""" if not self.getClick() and not self.collide: self.rect.center = (self.posX, self.posY) if self.getClick(): #Si se hace click en la imagen self.rect.center = pygame.mouse.get_pos() if self.collide: # Si hay colision x = self.image.get_rect().size[0] y = self.image.get_rect().size[1] self.colli(x,y) # Saca la imagen de la zona de colición. if self.image.get_rect().size[0] <= 20: self.rect.center = (0,0) surface.blit(self.getPalabraImagen(), self.getRect())

35.978495

128

0.633293

__author__ = 'Burgos, Agustin - Schelotto, Jorge' import pygame class Palabras(pygame.sprite.Sprite): def __init__(self, ruta, nombre, x, y): super().__init__() self.__palabra = nombre self.__click = False self.image = pygame.image.load(ruta).convert_alpha() self.rect = self.image.get_rect() self.collide = False self.posX = x self.posY = y def getPosX(self): return self.posX def getPosY(self): return self.posY def getPalabra(self): return self.__palabra def getPalabraImagen(self): return self.image def setClick(self, bool): self.__click = bool def getClick(self): return self.__click def getRect(self): return self.rect def colli(self, x, y): if x > 20: center = self.rect.center x = x - 1 y = y - 1 self.image = pygame.transform.scale(self.image, (x, y)) self.rect = self.image.get_rect() self.rect.center = center self.image = pygame.transform.rotozoom(self.image, -90, 0.8) elif x <= 20: center = self.rect.center self.image = pygame.transform.scale(self.image, (0, 0)) self.rect = self.image.get_rect() self.rect.center = center self.image = pygame.transform.rotozoom(self.image, -90, 0.5) def update(self,surface): if not self.getClick() and not self.collide: self.rect.center = (self.posX, self.posY) if self.getClick(): self.rect.center = pygame.mouse.get_pos() if self.collide: x = self.image.get_rect().size[0] y = self.image.get_rect().size[1] self.colli(x,y) if self.image.get_rect().size[0] <= 20: self.rect.center = (0,0) surface.blit(self.getPalabraImagen(), self.getRect())

true

7901a83dc9858dc4b704285fb8cf552df259a434

7,107

py

Python

examples/pytorch/ogb/line/reading_data.py

harshgrovr/Graphs_Thesis

9ffd0d23c8f8b4bd53db9fd5b9bf5776666814e0

[ "Apache-2.0" ]

2

2020-08-05T07:21:51.000Z

2021-02-20T10:22:23.000Z

examples/pytorch/ogb/line/reading_data.py

xyanAI/dgl

36daf66f6216bad4d30651311bcb87aa45dd33d5

[ "Apache-2.0" ]

1

2019-02-06T02:02:41.000Z

2019-02-06T20:22:32.000Z

examples/pytorch/ogb/line/reading_data.py

xyanAI/dgl

36daf66f6216bad4d30651311bcb87aa45dd33d5

[ "Apache-2.0" ]

3

2019-03-04T12:46:05.000Z

2019-08-14T18:53:19.000Z

import os import numpy as np import scipy.sparse as sp import pickle import torch from torch.utils.data import DataLoader from dgl.data.utils import download, _get_dgl_url, get_download_dir, extract_archive import random import time import dgl def ReadTxtNet(file_path="", undirected=True): """ Read the txt network file. Notations: The network is unweighted. Parameters ---------- file_path str : path of network file undirected bool : whether the edges are undirected Return ------ net dict : a dict recording the connections in the graph node2id dict : a dict mapping the nodes to their embedding indices id2node dict : a dict mapping nodes embedding indices to the nodes """ if file_path == 'youtube' or file_path == 'blog': name = file_path dir = get_download_dir() zip_file_path='{}/{}.zip'.format(dir, name) download(_get_dgl_url(os.path.join('dataset/DeepWalk/', '{}.zip'.format(file_path))), path=zip_file_path) extract_archive(zip_file_path, '{}/{}'.format(dir, name)) file_path = "{}/{}/{}-net.txt".format(dir, name, name) node2id = {} id2node = {} cid = 0 src = [] dst = [] weight = [] net = {} with open(file_path, "r") as f: for line in f.readlines(): tup = list(map(int, line.strip().split(" "))) assert len(tup) in [2, 3], "The format of network file is unrecognizable." if len(tup) == 3: n1, n2, w = tup elif len(tup) == 2: n1, n2 = tup w = 1 if n1 not in node2id: node2id[n1] = cid id2node[cid] = n1 cid += 1 if n2 not in node2id: node2id[n2] = cid id2node[cid] = n2 cid += 1 n1 = node2id[n1] n2 = node2id[n2] if n1 not in net: net[n1] = {n2: w} src.append(n1) dst.append(n2) weight.append(w) elif n2 not in net[n1]: net[n1][n2] = w src.append(n1) dst.append(n2) weight.append(w) if undirected: if n2 not in net: net[n2] = {n1: w} src.append(n2) dst.append(n1) weight.append(w) elif n1 not in net[n2]: net[n2][n1] = w src.append(n2) dst.append(n1) weight.append(w) print("node num: %d" % len(net)) print("edge num: %d" % len(src)) assert max(net.keys()) == len(net) - 1, "error reading net, quit" sm = sp.coo_matrix( (np.array(weight), (src, dst)), dtype=np.float32) return net, node2id, id2node, sm def net2graph(net_sm): """ Transform the network to DGL graph Return ------ G DGLGraph : graph by DGL """ start = time.time() G = dgl.DGLGraph(net_sm) end = time.time() t = end - start print("Building DGLGraph in %.2fs" % t) return G def make_undirected(G): #G.readonly(False) G.add_edges(G.edges()[1], G.edges()[0]) return G def find_connected_nodes(G): nodes = torch.nonzero(G.out_degrees()).squeeze(-1) return nodes class LineDataset: def __init__(self, net_file, batch_size, num_samples, negative=5, gpus=[0], fast_neg=True, ogbl_name="", load_from_ogbl=False, ogbn_name="", load_from_ogbn=False, ): """ This class has the following functions: 1. Transform the txt network file into DGL graph; 2. Generate random walk sequences for the trainer; 3. Provide the negative table if the user hopes to sample negative nodes according to nodes' degrees; Parameter --------- net_file str : path of the dgl network file walk_length int : number of nodes in a sequence window_size int : context window size num_walks int : number of walks for each node batch_size int : number of node sequences in each batch negative int : negative samples for each positve node pair fast_neg bool : whether do negative sampling inside a batch """ self.batch_size = batch_size self.negative = negative self.num_samples = num_samples self.num_procs = len(gpus) self.fast_neg = fast_neg if load_from_ogbl: assert len(gpus) == 1, "ogb.linkproppred is not compatible with multi-gpu training." from load_dataset import load_from_ogbl_with_name self.G = load_from_ogbl_with_name(ogbl_name) elif load_from_ogbn: assert len(gpus) == 1, "ogb.linkproppred is not compatible with multi-gpu training." from load_dataset import load_from_ogbn_with_name self.G = load_from_ogbn_with_name(ogbn_name) else: self.G = dgl.load_graphs(net_file)[0][0] self.G = make_undirected(self.G) print("Finish reading graph") self.num_nodes = self.G.number_of_nodes() start = time.time() seeds = np.random.choice(np.arange(self.G.number_of_edges()), self.num_samples, replace=True) # edge index self.seeds = torch.split(torch.LongTensor(seeds), int(np.ceil(self.num_samples / self.num_procs)), 0) end = time.time() t = end - start print("generate %d samples in %.2fs" % (len(seeds), t)) # negative table for true negative sampling self.valid_nodes = find_connected_nodes(self.G) if not fast_neg: node_degree = self.G.out_degrees(self.valid_nodes).numpy() node_degree = np.power(node_degree, 0.75) node_degree /= np.sum(node_degree) node_degree = np.array(node_degree * 1e8, dtype=np.int) self.neg_table = [] for idx, node in enumerate(self.valid_nodes): self.neg_table += [node] * node_degree[idx] self.neg_table_size = len(self.neg_table) self.neg_table = np.array(self.neg_table, dtype=np.long) del node_degree def create_sampler(self, i): """ create random walk sampler """ return EdgeSampler(self.G, self.seeds[i]) def save_mapping(self, map_file): with open(map_file, "wb") as f: pickle.dump(self.node2id, f) class EdgeSampler(object): def __init__(self, G, seeds): self.G = G self.seeds = seeds self.edges = torch.cat((self.G.edges()[0].unsqueeze(0), self.G.edges()[1].unsqueeze(0)), 0).t() def sample(self, seeds): """ seeds torch.LongTensor : a batch of indices of edges """ return self.edges[torch.LongTensor(seeds)]

33.523585

113

0.555649

import os import numpy as np import scipy.sparse as sp import pickle import torch from torch.utils.data import DataLoader from dgl.data.utils import download, _get_dgl_url, get_download_dir, extract_archive import random import time import dgl def ReadTxtNet(file_path="", undirected=True): if file_path == 'youtube' or file_path == 'blog': name = file_path dir = get_download_dir() zip_file_path='{}/{}.zip'.format(dir, name) download(_get_dgl_url(os.path.join('dataset/DeepWalk/', '{}.zip'.format(file_path))), path=zip_file_path) extract_archive(zip_file_path, '{}/{}'.format(dir, name)) file_path = "{}/{}/{}-net.txt".format(dir, name, name) node2id = {} id2node = {} cid = 0 src = [] dst = [] weight = [] net = {} with open(file_path, "r") as f: for line in f.readlines(): tup = list(map(int, line.strip().split(" "))) assert len(tup) in [2, 3], "The format of network file is unrecognizable." if len(tup) == 3: n1, n2, w = tup elif len(tup) == 2: n1, n2 = tup w = 1 if n1 not in node2id: node2id[n1] = cid id2node[cid] = n1 cid += 1 if n2 not in node2id: node2id[n2] = cid id2node[cid] = n2 cid += 1 n1 = node2id[n1] n2 = node2id[n2] if n1 not in net: net[n1] = {n2: w} src.append(n1) dst.append(n2) weight.append(w) elif n2 not in net[n1]: net[n1][n2] = w src.append(n1) dst.append(n2) weight.append(w) if undirected: if n2 not in net: net[n2] = {n1: w} src.append(n2) dst.append(n1) weight.append(w) elif n1 not in net[n2]: net[n2][n1] = w src.append(n2) dst.append(n1) weight.append(w) print("node num: %d" % len(net)) print("edge num: %d" % len(src)) assert max(net.keys()) == len(net) - 1, "error reading net, quit" sm = sp.coo_matrix( (np.array(weight), (src, dst)), dtype=np.float32) return net, node2id, id2node, sm def net2graph(net_sm): start = time.time() G = dgl.DGLGraph(net_sm) end = time.time() t = end - start print("Building DGLGraph in %.2fs" % t) return G def make_undirected(G): G.add_edges(G.edges()[1], G.edges()[0]) return G def find_connected_nodes(G): nodes = torch.nonzero(G.out_degrees()).squeeze(-1) return nodes class LineDataset: def __init__(self, net_file, batch_size, num_samples, negative=5, gpus=[0], fast_neg=True, ogbl_name="", load_from_ogbl=False, ogbn_name="", load_from_ogbn=False, ): self.batch_size = batch_size self.negative = negative self.num_samples = num_samples self.num_procs = len(gpus) self.fast_neg = fast_neg if load_from_ogbl: assert len(gpus) == 1, "ogb.linkproppred is not compatible with multi-gpu training." from load_dataset import load_from_ogbl_with_name self.G = load_from_ogbl_with_name(ogbl_name) elif load_from_ogbn: assert len(gpus) == 1, "ogb.linkproppred is not compatible with multi-gpu training." from load_dataset import load_from_ogbn_with_name self.G = load_from_ogbn_with_name(ogbn_name) else: self.G = dgl.load_graphs(net_file)[0][0] self.G = make_undirected(self.G) print("Finish reading graph") self.num_nodes = self.G.number_of_nodes() start = time.time() seeds = np.random.choice(np.arange(self.G.number_of_edges()), self.num_samples, replace=True) self.seeds = torch.split(torch.LongTensor(seeds), int(np.ceil(self.num_samples / self.num_procs)), 0) end = time.time() t = end - start print("generate %d samples in %.2fs" % (len(seeds), t)) self.valid_nodes = find_connected_nodes(self.G) if not fast_neg: node_degree = self.G.out_degrees(self.valid_nodes).numpy() node_degree = np.power(node_degree, 0.75) node_degree /= np.sum(node_degree) node_degree = np.array(node_degree * 1e8, dtype=np.int) self.neg_table = [] for idx, node in enumerate(self.valid_nodes): self.neg_table += [node] * node_degree[idx] self.neg_table_size = len(self.neg_table) self.neg_table = np.array(self.neg_table, dtype=np.long) del node_degree def create_sampler(self, i): return EdgeSampler(self.G, self.seeds[i]) def save_mapping(self, map_file): with open(map_file, "wb") as f: pickle.dump(self.node2id, f) class EdgeSampler(object): def __init__(self, G, seeds): self.G = G self.seeds = seeds self.edges = torch.cat((self.G.edges()[0].unsqueeze(0), self.G.edges()[1].unsqueeze(0)), 0).t() def sample(self, seeds): return self.edges[torch.LongTensor(seeds)]

true

7901a89ee3d3c11e49347b9e169696120963f78e

1,884

py

Python

meraki/api/mg_dhcp_settings.py

NoFliesOnYou/dashboard-api-python

3185d0e8a9a38eba9127ac640dcbb02444e7adf2

[ "MIT" ]

null

meraki/api/mg_dhcp_settings.py

NoFliesOnYou/dashboard-api-python

3185d0e8a9a38eba9127ac640dcbb02444e7adf2

[ "MIT" ]

3

2020-11-08T08:50:59.000Z

2021-12-13T20:47:15.000Z

flask/meraki/api/mg_dhcp_settings.py

cyberdevnet/mer-hacker

a7dddd03c5b02a2f8c84d711b69868d2b94f1f99

[ "MIT" ]

null

class MGDHCPSettings(object): def __init__(self, session): super(MGDHCPSettings, self).__init__() self._session = session def getNetworkCellularGatewaySettingsDhcp(self, networkId: str): """ **List common DHCP settings of MGs** https://developer.cisco.com/meraki/api/#!get-network-cellular-gateway-settings-dhcp - networkId (string) """ metadata = { 'tags': ['MG DHCP settings'], 'operation': 'getNetworkCellularGatewaySettingsDhcp', } resource = f'/networks/{networkId}/cellularGateway/settings/dhcp' return self._session.get(metadata, resource) def updateNetworkCellularGatewaySettingsDhcp(self, networkId: str, **kwargs): """ **Update common DHCP settings of MGs** https://developer.cisco.com/meraki/api/#!update-network-cellular-gateway-settings-dhcp - networkId (string) - dhcpLeaseTime (string): DHCP Lease time for all MG of the network. It can be '30 minutes', '1 hour', '4 hours', '12 hours', '1 day' or '1 week'. - dnsNameservers (string): DNS name servers mode for all MG of the network. It can take 4 different values: 'upstream_dns', 'google_dns', 'opendns', 'custom'. - dnsCustomNameservers (array): list of fixed IP representing the the DNS Name servers when the mode is 'custom' """ kwargs.update(locals()) metadata = { 'tags': ['MG DHCP settings'], 'operation': 'updateNetworkCellularGatewaySettingsDhcp', } resource = f'/networks/{networkId}/cellularGateway/settings/dhcp' body_params = ['dhcpLeaseTime', 'dnsNameservers', 'dnsCustomNameservers'] payload = {k: v for (k, v) in kwargs.items() if k in body_params} return self._session.put(metadata, resource, payload)

40.956522

166

0.636943

class MGDHCPSettings(object): def __init__(self, session): super(MGDHCPSettings, self).__init__() self._session = session def getNetworkCellularGatewaySettingsDhcp(self, networkId: str): metadata = { 'tags': ['MG DHCP settings'], 'operation': 'getNetworkCellularGatewaySettingsDhcp', } resource = f'/networks/{networkId}/cellularGateway/settings/dhcp' return self._session.get(metadata, resource) def updateNetworkCellularGatewaySettingsDhcp(self, networkId: str, **kwargs): kwargs.update(locals()) metadata = { 'tags': ['MG DHCP settings'], 'operation': 'updateNetworkCellularGatewaySettingsDhcp', } resource = f'/networks/{networkId}/cellularGateway/settings/dhcp' body_params = ['dhcpLeaseTime', 'dnsNameservers', 'dnsCustomNameservers'] payload = {k: v for (k, v) in kwargs.items() if k in body_params} return self._session.put(metadata, resource, payload)

true

7901a98d4fe4226ee2b868d6b15ef9b15f874051

8,738

py

Python

sdk/graphrbac/azure-graphrbac/azure/graphrbac/models/application_update_parameters.py

16pierre/azure-sdk-for-python

1505d348c6660c1d5a39630522a059a2e3e38839

[ "MIT" ]

null

sdk/graphrbac/azure-graphrbac/azure/graphrbac/models/application_update_parameters.py

16pierre/azure-sdk-for-python

1505d348c6660c1d5a39630522a059a2e3e38839

[ "MIT" ]

null

sdk/graphrbac/azure-graphrbac/azure/graphrbac/models/application_update_parameters.py

16pierre/azure-sdk-for-python

1505d348c6660c1d5a39630522a059a2e3e38839

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .application_base import ApplicationBase class ApplicationUpdateParameters(ApplicationBase): """Request parameters for updating a new application. :param allow_guests_sign_in: A property on the application to indicate if the application accepts other IDPs or not or partially accepts. :type allow_guests_sign_in: bool :param allow_passthrough_users: Indicates that the application supports pass through users who have no presence in the resource tenant. :type allow_passthrough_users: bool :param app_logo_url: The url for the application logo image stored in a CDN. :type app_logo_url: str :param app_roles: The collection of application roles that an application may declare. These roles can be assigned to users, groups or service principals. :type app_roles: list[~azure.graphrbac.models.AppRole] :param app_permissions: The application permissions. :type app_permissions: list[str] :param available_to_other_tenants: Whether the application is available to other tenants. :type available_to_other_tenants: bool :param error_url: A URL provided by the author of the application to report errors when using the application. :type error_url: str :param group_membership_claims: Configures the groups claim issued in a user or OAuth 2.0 access token that the app expects. Possible values include: 'None', 'SecurityGroup', 'All' :type group_membership_claims: str or ~azure.graphrbac.models.GroupMembershipClaimTypes :param homepage: The home page of the application. :type homepage: str :param informational_urls: URLs with more information about the application. :type informational_urls: ~azure.graphrbac.models.InformationalUrl :param is_device_only_auth_supported: Specifies whether this application supports device authentication without a user. The default is false. :type is_device_only_auth_supported: bool :param key_credentials: A collection of KeyCredential objects. :type key_credentials: list[~azure.graphrbac.models.KeyCredential] :param known_client_applications: Client applications that are tied to this resource application. Consent to any of the known client applications will result in implicit consent to the resource application through a combined consent dialog (showing the OAuth permission scopes required by the client and the resource). :type known_client_applications: list[str] :param logout_url: the url of the logout page :type logout_url: str :param oauth2_allow_implicit_flow: Whether to allow implicit grant flow for OAuth2 :type oauth2_allow_implicit_flow: bool :param oauth2_allow_url_path_matching: Specifies whether during a token Request Azure AD will allow path matching of the redirect URI against the applications collection of replyURLs. The default is false. :type oauth2_allow_url_path_matching: bool :param oauth2_permissions: The collection of OAuth 2.0 permission scopes that the web API (resource) application exposes to client applications. These permission scopes may be granted to client applications during consent. :type oauth2_permissions: list[~azure.graphrbac.models.OAuth2Permission] :param oauth2_require_post_response: Specifies whether, as part of OAuth 2.0 token requests, Azure AD will allow POST requests, as opposed to GET requests. The default is false, which specifies that only GET requests will be allowed. :type oauth2_require_post_response: bool :param org_restrictions: A list of tenants allowed to access application. :type org_restrictions: list[str] :param optional_claims: :type optional_claims: ~azure.graphrbac.models.OptionalClaims :param password_credentials: A collection of PasswordCredential objects :type password_credentials: list[~azure.graphrbac.models.PasswordCredential] :param pre_authorized_applications: list of pre-authorized applications. :type pre_authorized_applications: list[~azure.graphrbac.models.PreAuthorizedApplication] :param public_client: Specifies whether this application is a public client (such as an installed application running on a mobile device). Default is false. :type public_client: bool :param publisher_domain: Reliable domain which can be used to identify an application. :type publisher_domain: str :param reply_urls: A collection of reply URLs for the application. :type reply_urls: list[str] :param required_resource_access: Specifies resources that this application requires access to and the set of OAuth permission scopes and application roles that it needs under each of those resources. This pre-configuration of required resource access drives the consent experience. :type required_resource_access: list[~azure.graphrbac.models.RequiredResourceAccess] :param saml_metadata_url: The URL to the SAML metadata for the application. :type saml_metadata_url: str :param sign_in_audience: Audience for signing in to the application (AzureADMyOrganization, AzureADAllOrganizations, AzureADAndMicrosoftAccounts). :type sign_in_audience: str :param www_homepage: The primary Web page. :type www_homepage: str :param display_name: The display name of the application. :type display_name: str :param identifier_uris: A collection of URIs for the application. :type identifier_uris: list[str] """ _attribute_map = { 'allow_guests_sign_in': {'key': 'allowGuestsSignIn', 'type': 'bool'}, 'allow_passthrough_users': {'key': 'allowPassthroughUsers', 'type': 'bool'}, 'app_logo_url': {'key': 'appLogoUrl', 'type': 'str'}, 'app_roles': {'key': 'appRoles', 'type': '[AppRole]'}, 'app_permissions': {'key': 'appPermissions', 'type': '[str]'}, 'available_to_other_tenants': {'key': 'availableToOtherTenants', 'type': 'bool'}, 'error_url': {'key': 'errorUrl', 'type': 'str'}, 'group_membership_claims': {'key': 'groupMembershipClaims', 'type': 'str'}, 'homepage': {'key': 'homepage', 'type': 'str'}, 'informational_urls': {'key': 'informationalUrls', 'type': 'InformationalUrl'}, 'is_device_only_auth_supported': {'key': 'isDeviceOnlyAuthSupported', 'type': 'bool'}, 'key_credentials': {'key': 'keyCredentials', 'type': '[KeyCredential]'}, 'known_client_applications': {'key': 'knownClientApplications', 'type': '[str]'}, 'logout_url': {'key': 'logoutUrl', 'type': 'str'}, 'oauth2_allow_implicit_flow': {'key': 'oauth2AllowImplicitFlow', 'type': 'bool'}, 'oauth2_allow_url_path_matching': {'key': 'oauth2AllowUrlPathMatching', 'type': 'bool'}, 'oauth2_permissions': {'key': 'oauth2Permissions', 'type': '[OAuth2Permission]'}, 'oauth2_require_post_response': {'key': 'oauth2RequirePostResponse', 'type': 'bool'}, 'org_restrictions': {'key': 'orgRestrictions', 'type': '[str]'}, 'optional_claims': {'key': 'optionalClaims', 'type': 'OptionalClaims'}, 'password_credentials': {'key': 'passwordCredentials', 'type': '[PasswordCredential]'}, 'pre_authorized_applications': {'key': 'preAuthorizedApplications', 'type': '[PreAuthorizedApplication]'}, 'public_client': {'key': 'publicClient', 'type': 'bool'}, 'publisher_domain': {'key': 'publisherDomain', 'type': 'str'}, 'reply_urls': {'key': 'replyUrls', 'type': '[str]'}, 'required_resource_access': {'key': 'requiredResourceAccess', 'type': '[RequiredResourceAccess]'}, 'saml_metadata_url': {'key': 'samlMetadataUrl', 'type': 'str'}, 'sign_in_audience': {'key': 'signInAudience', 'type': 'str'}, 'www_homepage': {'key': 'wwwHomepage', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'identifier_uris': {'key': 'identifierUris', 'type': '[str]'}, } def __init__(self, **kwargs): super(ApplicationUpdateParameters, self).__init__(**kwargs) self.display_name = kwargs.get('display_name', None) self.identifier_uris = kwargs.get('identifier_uris', None)

55.656051

114

0.707027

from .application_base import ApplicationBase class ApplicationUpdateParameters(ApplicationBase): _attribute_map = { 'allow_guests_sign_in': {'key': 'allowGuestsSignIn', 'type': 'bool'}, 'allow_passthrough_users': {'key': 'allowPassthroughUsers', 'type': 'bool'}, 'app_logo_url': {'key': 'appLogoUrl', 'type': 'str'}, 'app_roles': {'key': 'appRoles', 'type': '[AppRole]'}, 'app_permissions': {'key': 'appPermissions', 'type': '[str]'}, 'available_to_other_tenants': {'key': 'availableToOtherTenants', 'type': 'bool'}, 'error_url': {'key': 'errorUrl', 'type': 'str'}, 'group_membership_claims': {'key': 'groupMembershipClaims', 'type': 'str'}, 'homepage': {'key': 'homepage', 'type': 'str'}, 'informational_urls': {'key': 'informationalUrls', 'type': 'InformationalUrl'}, 'is_device_only_auth_supported': {'key': 'isDeviceOnlyAuthSupported', 'type': 'bool'}, 'key_credentials': {'key': 'keyCredentials', 'type': '[KeyCredential]'}, 'known_client_applications': {'key': 'knownClientApplications', 'type': '[str]'}, 'logout_url': {'key': 'logoutUrl', 'type': 'str'}, 'oauth2_allow_implicit_flow': {'key': 'oauth2AllowImplicitFlow', 'type': 'bool'}, 'oauth2_allow_url_path_matching': {'key': 'oauth2AllowUrlPathMatching', 'type': 'bool'}, 'oauth2_permissions': {'key': 'oauth2Permissions', 'type': '[OAuth2Permission]'}, 'oauth2_require_post_response': {'key': 'oauth2RequirePostResponse', 'type': 'bool'}, 'org_restrictions': {'key': 'orgRestrictions', 'type': '[str]'}, 'optional_claims': {'key': 'optionalClaims', 'type': 'OptionalClaims'}, 'password_credentials': {'key': 'passwordCredentials', 'type': '[PasswordCredential]'}, 'pre_authorized_applications': {'key': 'preAuthorizedApplications', 'type': '[PreAuthorizedApplication]'}, 'public_client': {'key': 'publicClient', 'type': 'bool'}, 'publisher_domain': {'key': 'publisherDomain', 'type': 'str'}, 'reply_urls': {'key': 'replyUrls', 'type': '[str]'}, 'required_resource_access': {'key': 'requiredResourceAccess', 'type': '[RequiredResourceAccess]'}, 'saml_metadata_url': {'key': 'samlMetadataUrl', 'type': 'str'}, 'sign_in_audience': {'key': 'signInAudience', 'type': 'str'}, 'www_homepage': {'key': 'wwwHomepage', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'identifier_uris': {'key': 'identifierUris', 'type': '[str]'}, } def __init__(self, **kwargs): super(ApplicationUpdateParameters, self).__init__(**kwargs) self.display_name = kwargs.get('display_name', None) self.identifier_uris = kwargs.get('identifier_uris', None)

true

7901aaade44c85abaf27653db1e6058e1609af80

7,148

py

Python

venv/lib/python3.6/site-packages/ansible_collections/community/general/plugins/modules/scaleway_ip.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

1

2020-01-22T13:11:23.000Z

venv/lib/python3.6/site-packages/ansible_collections/community/general/plugins/modules/scaleway_ip.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

12

2020-02-21T07:24:52.000Z

2020-04-14T09:54:32.000Z

venv/lib/python3.6/site-packages/ansible_collections/community/general/plugins/modules/scaleway_ip.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- # # Scaleway IP management module # # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = ''' --- module: scaleway_ip short_description: Scaleway IP management module author: Remy Leone (@remyleone) description: - This module manages IP on Scaleway account U(https://developer.scaleway.com) extends_documentation_fragment: - community.general.scaleway options: state: type: str description: - Indicate desired state of the IP. default: present choices: - present - absent organization: type: str description: - Scaleway organization identifier required: true region: type: str description: - Scaleway region to use (for example par1). required: true choices: - ams1 - EMEA-NL-EVS - par1 - EMEA-FR-PAR1 - par2 - EMEA-FR-PAR2 - waw1 - EMEA-PL-WAW1 id: type: str description: - id of the Scaleway IP (UUID) server: type: str description: - id of the server you want to attach an IP to. - To unattach an IP don't specify this option reverse: type: str description: - Reverse to assign to the IP ''' EXAMPLES = ''' - name: Create an IP community.general.scaleway_ip: organization: '{{ scw_org }}' state: present region: par1 register: ip_creation_task - name: Make sure IP deleted community.general.scaleway_ip: id: '{{ ip_creation_task.scaleway_ip.id }}' state: absent region: par1 ''' RETURN = ''' data: description: This is only present when C(state=present) returned: when C(state=present) type: dict sample: { "ips": [ { "organization": "951df375-e094-4d26-97c1-ba548eeb9c42", "reverse": null, "id": "dd9e8df6-6775-4863-b517-e0b0ee3d7477", "server": { "id": "3f1568ca-b1a2-4e98-b6f7-31a0588157f1", "name": "ansible_tuto-1" }, "address": "212.47.232.136" } ] } ''' from ansible_collections.community.general.plugins.module_utils.scaleway import SCALEWAY_LOCATION, scaleway_argument_spec, Scaleway from ansible.module_utils.basic import AnsibleModule def ip_attributes_should_be_changed(api, target_ip, wished_ip): patch_payload = {} if target_ip["reverse"] != wished_ip["reverse"]: patch_payload["reverse"] = wished_ip["reverse"] # IP is assigned to a server if target_ip["server"] is None and wished_ip["server"]: patch_payload["server"] = wished_ip["server"] # IP is unassigned to a server try: if target_ip["server"]["id"] and wished_ip["server"] is None: patch_payload["server"] = wished_ip["server"] except (TypeError, KeyError): pass # IP is migrated between 2 different servers try: if target_ip["server"]["id"] != wished_ip["server"]: patch_payload["server"] = wished_ip["server"] except (TypeError, KeyError): pass return patch_payload def payload_from_wished_ip(wished_ip): return dict( (k, v) for k, v in wished_ip.items() if k != 'id' and v is not None ) def present_strategy(api, wished_ip): changed = False response = api.get('ips') if not response.ok: api.module.fail_json(msg='Error getting IPs [{0}: {1}]'.format( response.status_code, response.json['message'])) ips_list = response.json["ips"] ip_lookup = dict((ip["id"], ip) for ip in ips_list) if wished_ip["id"] not in ip_lookup.keys(): changed = True if api.module.check_mode: return changed, {"status": "An IP would be created."} # Create IP creation_response = api.post('/ips', data=payload_from_wished_ip(wished_ip)) if not creation_response.ok: msg = "Error during ip creation: %s: '%s' (%s)" % (creation_response.info['msg'], creation_response.json['message'], creation_response.json) api.module.fail_json(msg=msg) return changed, creation_response.json["ip"] target_ip = ip_lookup[wished_ip["id"]] patch_payload = ip_attributes_should_be_changed(api=api, target_ip=target_ip, wished_ip=wished_ip) if not patch_payload: return changed, target_ip changed = True if api.module.check_mode: return changed, {"status": "IP attributes would be changed."} ip_patch_response = api.patch(path="ips/%s" % target_ip["id"], data=patch_payload) if not ip_patch_response.ok: api.module.fail_json(msg='Error during IP attributes update: [{0}: {1}]'.format( ip_patch_response.status_code, ip_patch_response.json['message'])) return changed, ip_patch_response.json["ip"] def absent_strategy(api, wished_ip): response = api.get('ips') changed = False status_code = response.status_code ips_json = response.json ips_list = ips_json["ips"] if not response.ok: api.module.fail_json(msg='Error getting IPs [{0}: {1}]'.format( status_code, response.json['message'])) ip_lookup = dict((ip["id"], ip) for ip in ips_list) if wished_ip["id"] not in ip_lookup.keys(): return changed, {} changed = True if api.module.check_mode: return changed, {"status": "IP would be destroyed"} response = api.delete('/ips/' + wished_ip["id"]) if not response.ok: api.module.fail_json(msg='Error deleting IP [{0}: {1}]'.format( response.status_code, response.json)) return changed, response.json def core(module): wished_ip = { "organization": module.params['organization'], "reverse": module.params["reverse"], "id": module.params["id"], "server": module.params["server"] } region = module.params["region"] module.params['api_url'] = SCALEWAY_LOCATION[region]["api_endpoint"] api = Scaleway(module=module) if module.params["state"] == "absent": changed, summary = absent_strategy(api=api, wished_ip=wished_ip) else: changed, summary = present_strategy(api=api, wished_ip=wished_ip) module.exit_json(changed=changed, scaleway_ip=summary) def main(): argument_spec = scaleway_argument_spec() argument_spec.update(dict( state=dict(default='present', choices=['absent', 'present']), organization=dict(required=True), server=dict(), reverse=dict(), region=dict(required=True, choices=list(SCALEWAY_LOCATION.keys())), id=dict() )) module = AnsibleModule( argument_spec=argument_spec, supports_check_mode=True, ) core(module) if __name__ == '__main__': main()

27.178707

131

0.615837

from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = ''' --- module: scaleway_ip short_description: Scaleway IP management module author: Remy Leone (@remyleone) description: - This module manages IP on Scaleway account U(https://developer.scaleway.com) extends_documentation_fragment: - community.general.scaleway options: state: type: str description: - Indicate desired state of the IP. default: present choices: - present - absent organization: type: str description: - Scaleway organization identifier required: true region: type: str description: - Scaleway region to use (for example par1). required: true choices: - ams1 - EMEA-NL-EVS - par1 - EMEA-FR-PAR1 - par2 - EMEA-FR-PAR2 - waw1 - EMEA-PL-WAW1 id: type: str description: - id of the Scaleway IP (UUID) server: type: str description: - id of the server you want to attach an IP to. - To unattach an IP don't specify this option reverse: type: str description: - Reverse to assign to the IP ''' EXAMPLES = ''' - name: Create an IP community.general.scaleway_ip: organization: '{{ scw_org }}' state: present region: par1 register: ip_creation_task - name: Make sure IP deleted community.general.scaleway_ip: id: '{{ ip_creation_task.scaleway_ip.id }}' state: absent region: par1 ''' RETURN = ''' data: description: This is only present when C(state=present) returned: when C(state=present) type: dict sample: { "ips": [ { "organization": "951df375-e094-4d26-97c1-ba548eeb9c42", "reverse": null, "id": "dd9e8df6-6775-4863-b517-e0b0ee3d7477", "server": { "id": "3f1568ca-b1a2-4e98-b6f7-31a0588157f1", "name": "ansible_tuto-1" }, "address": "212.47.232.136" } ] } ''' from ansible_collections.community.general.plugins.module_utils.scaleway import SCALEWAY_LOCATION, scaleway_argument_spec, Scaleway from ansible.module_utils.basic import AnsibleModule def ip_attributes_should_be_changed(api, target_ip, wished_ip): patch_payload = {} if target_ip["reverse"] != wished_ip["reverse"]: patch_payload["reverse"] = wished_ip["reverse"] # IP is assigned to a server if target_ip["server"] is None and wished_ip["server"]: patch_payload["server"] = wished_ip["server"] # IP is unassigned to a server try: if target_ip["server"]["id"] and wished_ip["server"] is None: patch_payload["server"] = wished_ip["server"] except (TypeError, KeyError): pass # IP is migrated between 2 different servers try: if target_ip["server"]["id"] != wished_ip["server"]: patch_payload["server"] = wished_ip["server"] except (TypeError, KeyError): pass return patch_payload def payload_from_wished_ip(wished_ip): return dict( (k, v) for k, v in wished_ip.items() if k != 'id' and v is not None ) def present_strategy(api, wished_ip): changed = False response = api.get('ips') if not response.ok: api.module.fail_json(msg='Error getting IPs [{0}: {1}]'.format( response.status_code, response.json['message'])) ips_list = response.json["ips"] ip_lookup = dict((ip["id"], ip) for ip in ips_list) if wished_ip["id"] not in ip_lookup.keys(): changed = True if api.module.check_mode: return changed, {"status": "An IP would be created."} # Create IP creation_response = api.post('/ips', data=payload_from_wished_ip(wished_ip)) if not creation_response.ok: msg = "Error during ip creation: %s: '%s' (%s)" % (creation_response.info['msg'], creation_response.json['message'], creation_response.json) api.module.fail_json(msg=msg) return changed, creation_response.json["ip"] target_ip = ip_lookup[wished_ip["id"]] patch_payload = ip_attributes_should_be_changed(api=api, target_ip=target_ip, wished_ip=wished_ip) if not patch_payload: return changed, target_ip changed = True if api.module.check_mode: return changed, {"status": "IP attributes would be changed."} ip_patch_response = api.patch(path="ips/%s" % target_ip["id"], data=patch_payload) if not ip_patch_response.ok: api.module.fail_json(msg='Error during IP attributes update: [{0}: {1}]'.format( ip_patch_response.status_code, ip_patch_response.json['message'])) return changed, ip_patch_response.json["ip"] def absent_strategy(api, wished_ip): response = api.get('ips') changed = False status_code = response.status_code ips_json = response.json ips_list = ips_json["ips"] if not response.ok: api.module.fail_json(msg='Error getting IPs [{0}: {1}]'.format( status_code, response.json['message'])) ip_lookup = dict((ip["id"], ip) for ip in ips_list) if wished_ip["id"] not in ip_lookup.keys(): return changed, {} changed = True if api.module.check_mode: return changed, {"status": "IP would be destroyed"} response = api.delete('/ips/' + wished_ip["id"]) if not response.ok: api.module.fail_json(msg='Error deleting IP [{0}: {1}]'.format( response.status_code, response.json)) return changed, response.json def core(module): wished_ip = { "organization": module.params['organization'], "reverse": module.params["reverse"], "id": module.params["id"], "server": module.params["server"] } region = module.params["region"] module.params['api_url'] = SCALEWAY_LOCATION[region]["api_endpoint"] api = Scaleway(module=module) if module.params["state"] == "absent": changed, summary = absent_strategy(api=api, wished_ip=wished_ip) else: changed, summary = present_strategy(api=api, wished_ip=wished_ip) module.exit_json(changed=changed, scaleway_ip=summary) def main(): argument_spec = scaleway_argument_spec() argument_spec.update(dict( state=dict(default='present', choices=['absent', 'present']), organization=dict(required=True), server=dict(), reverse=dict(), region=dict(required=True, choices=list(SCALEWAY_LOCATION.keys())), id=dict() )) module = AnsibleModule( argument_spec=argument_spec, supports_check_mode=True, ) core(module) if __name__ == '__main__': main()

true

7901aca03203d142adecf88e42823f69d90c6575

52,847

py

Python

egs/wsj/s5/steps/cleanup/internal/segment_ctm_edits.py

HunterJiang/kaldi

3fe38c82fa0936f7a4ea3347a54e9e00fb2471a8

[ "Apache-2.0" ]

319

2016-10-24T23:08:04.000Z

2022-03-08T02:36:51.000Z

egs/wsj/s5/steps/cleanup/internal/segment_ctm_edits.py

zwcih/kaldi-ctc

2c47b99f5efba22cb3989eed1a7757bf5d9927ce

[ "Apache-2.0" ]

18

2017-01-12T12:08:07.000Z

2020-06-18T07:37:20.000Z

egs/wsj/s5/steps/cleanup/internal/segment_ctm_edits.py

zwcih/kaldi-ctc

2c47b99f5efba22cb3989eed1a7757bf5d9927ce

[ "Apache-2.0" ]

87

2016-10-25T04:39:48.000Z

2021-12-24T07:47:31.000Z

#!/usr/bin/env python # Copyright 2016 Vimal Manohar # 2016 Johns Hopkins University (author: Daniel Povey) # Apache 2.0 from __future__ import print_function import sys, operator, argparse, os from collections import defaultdict # This script reads 'ctm-edits' file format that is produced by get_ctm_edits.py # and modified by modify_ctm_edits.py and taint_ctm_edits.py Its function is to # produce a segmentation and text from the ctm-edits input. # The ctm-edits file format that this script expects is as follows # <file-id> <channel> <start-time> <duration> <conf> <hyp-word> <ref-word> <edit> ['tainted'] # [note: file-id is really utterance-id at this point]. parser = argparse.ArgumentParser( description = "This program produces segmentation and text information " "based on reading ctm-edits input format which is produced by " "steps/cleanup/internal/get_ctm_edits.py, steps/cleanup/internal/modify_ctm_edits.py and " "steps/cleanup/internal/taint_ctm_edits.py.", formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("--min-segment-length", type = float, default = 0.5, help = "Minimum allowed segment length (in seconds) for any " "segment; shorter segments than this will be discarded.") parser.add_argument("--min-new-segment-length", type = float, default = 1.0, help = "Minimum allowed segment length (in seconds) for newly " "created segments (i.e. not identical to the input utterances). " "Expected to be >= --min-segment-length.") parser.add_argument("--frame-length", type = float, default = 0.01, help = "This only affects rounding of the output times; they will " "be constrained to multiples of this value.") parser.add_argument("--max-tainted-length", type = float, default = 0.05, help = "Maximum allowed length of any 'tainted' line. Note: " "'tainted' lines may only appear at the boundary of a " "segment") parser.add_argument("--max-edge-silence-length", type = float, default = 0.5, help = "Maximum allowed length of silence if it appears at the " "edge of a segment (will be truncated). This rule is " "relaxed if such truncation would take a segment below " "the --min-segment-length or --min-new-segment-length.") parser.add_argument("--max-edge-non-scored-length", type = float, default = 0.5, help = "Maximum allowed length of a non-scored word (noise, cough, etc.) " "if it appears at the edge of a segment (will be truncated). " "This rule is relaxed if such truncation would take a " "segment below the --min-segment-length.") parser.add_argument("--max-internal-silence-length", type = float, default = 2.0, help = "Maximum allowed length of silence if it appears inside a segment " "(will cause the segment to be split).") parser.add_argument("--max-internal-non-scored-length", type = float, default = 2.0, help = "Maximum allowed length of a non-scored word (noise, etc.) if " "it appears inside a segment (will cause the segment to be " "split). Note: reference words which are real words but OOV " "are not included in this category.") parser.add_argument("--unk-padding", type = float, default = 0.05, help = "Amount of padding with <unk> that we do if a segment boundary is " "next to errors (ins, del, sub). That is, we add this amount of " "time to the segment and add the <unk> word to cover the acoustics. " "If nonzero, the --oov-symbol-file option must be supplied.") parser.add_argument("--max-junk-proportion", type = float, default = 0.1, help = "Maximum proportion of the time of the segment that may " "consist of potentially bad data, in which we include 'tainted' lines of " "the ctm-edits input and unk-padding.") parser.add_argument("--max-deleted-words-kept-when-merging", type = str, default = 1, help = "When merging segments that are found to be overlapping or " "adjacent after all other processing, keep in the transcript the " "reference words that were deleted between the segments [if any] " "as long as there were no more than this many reference words. " "Setting this to zero will mean that any reference words that " "were deleted between the segments we're about to reattach will " "not appear in the generated transcript (so we'll match the hyp).") parser.add_argument("--oov-symbol-file", type = str, default = None, help = "Filename of file such as data/lang/oov.txt which contains " "the text form of the OOV word, normally '<unk>'. Supplied as " "a file to avoid complications with escaping. Necessary if " "the --unk-padding option has a nonzero value (which it does " "by default.") parser.add_argument("--ctm-edits-out", type = str, help = "Filename to output an extended version of the ctm-edits format " "with segment start and end points noted. This file is intended to be " "read by humans; there are currently no scripts that will read it.") parser.add_argument("--word-stats-out", type = str, help = "Filename for output of word-level stats, of the form " "'<word> <bad-proportion> <total-count-in-ref>', e.g. 'hello 0.12 12408', " "where the <bad-proportion> is the proportion of the time that this " "reference word does not make it into a segment. It can help reveal words " "that have problematic pronunciations or are associated with " "transcription errors.") parser.add_argument("non_scored_words_in", metavar = "<non-scored-words-file>", help="Filename of file containing a list of non-scored words, " "one per line. See steps/cleanup/internal/get_nonscored_words.py.") parser.add_argument("ctm_edits_in", metavar = "<ctm-edits-in>", help = "Filename of input ctm-edits file. " "Use /dev/stdin for standard input.") parser.add_argument("text_out", metavar = "<text-out>", help = "Filename of output text file (same format as data/train/text, i.e. " "<new-utterance-id> <word1> <word2> ... <wordN>") parser.add_argument("segments_out", metavar = "<segments-out>", help = "Filename of output segments. This has the same format as data/train/segments, " "but instead of <recording-id>, the second field is the old utterance-id, i.e " "<new-utterance-id> <old-utterance-id> <start-time> <end-time>") args = parser.parse_args() def IsTainted(split_line_of_utt): return len(split_line_of_utt) > 8 and split_line_of_utt[8] == 'tainted' # This function returns a list of pairs (start-index, end-index) representing # the cores of segments (so if a pair is (s, e), then the core of a segment # would span (s, s+1, ... e-1). # # By the 'core of a segment', we mean a sequence of ctm-edits lines including at # least one 'cor' line and a contiguous sequence of other lines of the type # 'cor', 'fix' and 'sil' that must be not tainted. The segment core excludes # any tainted lines at the edge of a segment, which will be added later. # # We only initiate segments when it contains something correct and not realized # as unk (i.e. ref==hyp); and we extend it with anything that is 'sil' or 'fix' # or 'cor' that is not tainted. Contiguous regions of 'true' in the resulting # boolean array will then become the cores of prototype segments, and we'll add # any adjacent tainted words (or parts of them). def ComputeSegmentCores(split_lines_of_utt): num_lines = len(split_lines_of_utt) line_is_in_segment_core = [ False] * num_lines for i in range(num_lines): if split_lines_of_utt[i][7] == 'cor' and \ split_lines_of_utt[i][4] == split_lines_of_utt[i][6]: line_is_in_segment_core[i] = True # extend each proto-segment forwards as far as we can: for i in range(1, num_lines): if line_is_in_segment_core[i-1] and not line_is_in_segment_core[i]: edit_type = split_lines_of_utt[i][7] if not IsTainted(split_lines_of_utt[i]) and \ (edit_type == 'cor' or edit_type == 'sil' or edit_type == 'fix'): line_is_in_segment_core[i] = True # extend each proto-segment backwards as far as we can: for i in reversed(range(0, num_lines - 1)): if line_is_in_segment_core[i+1] and not line_is_in_segment_core[i]: edit_type = split_lines_of_utt[i][7] if not IsTainted(split_lines_of_utt[i]) and \ (edit_type == 'cor' or edit_type == 'sil' or edit_type == 'fix'): line_is_in_segment_core[i] = True segment_ranges = [] cur_segment_start = None for i in range(0, num_lines): if line_is_in_segment_core[i]: if cur_segment_start == None: cur_segment_start = i else: if cur_segment_start != None: segment_ranges.append( (cur_segment_start, i) ) cur_segment_start = None if cur_segment_start != None: segment_ranges.append( (cur_segment_start, num_lines) ) return segment_ranges class Segment: def __init__(self, split_lines_of_utt, start_index, end_index, debug_str = None): self.split_lines_of_utt = split_lines_of_utt # start_index is the index of the first line that appears in this # segment, and end_index is one past the last line. This does not # include unk-padding. self.start_index = start_index self.end_index = end_index # If the following values are nonzero, then when we create the segment # we will add <unk> at the start and end of the segment [representing # partial words], with this amount of additional audio. self.start_unk_padding = 0.0 self.end_unk_padding = 0.0 # debug_str keeps track of the 'core' of the segment. if debug_str == None: debug_str = 'core-start={0},core-end={1}'.format(start_index,end_index) self.debug_str = debug_str # This gives the proportion of the time of the first line in the segment # that we keep. Usually 1.0 but may be less if we've trimmed away some # proportion of the time. self.start_keep_proportion = 1.0 # This gives the proportion of the time of the last line in the segment # that we keep. Usually 1.0 but may be less if we've trimmed away some # proportion of the time. self.end_keep_proportion = 1.0 # This is stage 1 of segment processing (after creating the boundaries of the # core of the segment, which is done outside of this class).a # # This function may reduce start_index and/or increase end_index by # including a single adjacent 'tainted' line from the ctm-edits file. This # is only done if the lines at the boundaries of the segment are currently # real non-silence words and not non-scored words. The idea is that we # probably don't want to start or end the segment right at the boundary of a # real word, we want to add some kind of padding. def PossiblyAddTaintedLines(self): global non_scored_words split_lines_of_utt = self.split_lines_of_utt # we're iterating over the segment (start, end) for b in [False, True]: if b: boundary_index = self.end_index - 1 adjacent_index = self.end_index else: boundary_index = self.start_index adjacent_index = self.start_index - 1 if adjacent_index >= 0 and adjacent_index < len(split_lines_of_utt): # only consider merging the adjacent word into the segment if we're not # at a segment boundary. adjacent_line_is_tainted = IsTainted(split_lines_of_utt[adjacent_index]) # if the adjacent line wasn't tainted, then there must have been # another stronger reason why we didn't include it in the core # of the segment (probably that it was an ins, del or sub), so # there is no point considering it. if adjacent_line_is_tainted: boundary_edit_type = split_lines_of_utt[boundary_index][7] boundary_hyp_word = split_lines_of_utt[boundary_index][7] # we only add the tainted line to the segment if the word at # the boundary was a non-silence word that was correctly # decoded and not fixed [see modify_ctm_edits.py.] if boundary_edit_type == 'cor' and \ not boundary_hyp_word in non_scored_words: # Add the adjacent tainted line to the segment. if b: self.end_index += 1 else: self.start_index -= 1 # This is stage 2 of segment processing. # This function will split a segment into multiple pieces if any of the # internal [non-boundary] silences or non-scored words are longer # than the allowed values --max-internal-silence-length and # --max-internal-non-scored-length. This function returns a # list of segments. In the normal case (where there is no splitting) # it just returns an array with a single element 'self'. def PossiblySplitSegment(self): global non_scored_words, args # make sure the segment hasn't been processed more than we expect. assert self.start_unk_padding == 0.0 and self.end_unk_padding == 0.0 and \ self.start_keep_proportion == 1.0 and self.end_keep_proportion == 1.0 segments = [] # the answer cur_start_index = self.start_index cur_start_is_split = False # only consider splitting at non-boundary lines. [we'd just truncate # the boundary lines.] for index_to_split_at in range(cur_start_index + 1, self.end_index - 1): this_split_line = self.split_lines_of_utt[index_to_split_at] this_duration = float(this_split_line[3]) this_edit_type = this_split_line[7] this_ref_word = this_split_line[6] if (this_edit_type == 'sil' and this_duration > args.max_internal_silence_length) or \ (this_ref_word in non_scored_words and this_duration > args.max_internal_non_scored_length): # We split this segment at this index, dividing the word in two # [later on, in PossiblyTruncateBoundaries, it may be further # truncated.] # Note: we use 'index_to_split_at + 1' because the Segment constructor # takes an 'end-index' which is interpreted as one past the end. new_segment = Segment(self.split_lines_of_utt, cur_start_index, index_to_split_at + 1, self.debug_str) if cur_start_is_split: new_segment.start_keep_proportion = 0.5 new_segment.end_keep_proportion = 0.5 cur_start_is_split = True cur_start_index = index_to_split_at segments.append(new_segment) if len(segments) == 0: # We did not split. segments.append(self) else: # We did split. Add the very last segment. new_segment = Segment(self.split_lines_of_utt, cur_start_index, self.end_index, self.debug_str) assert cur_start_is_split new_segment.start_keep_proportion = 0.5 segments.append(new_segment) return segments # This is stage 3 of segment processing. It will truncate the silences and # non-scored words at the segment boundaries if they are longer than the # --max-edge-silence-length and --max-edge-non-scored-length respectively # (and to the extent that this wouldn't take us below the # --min-segment-length or --min-new-segment-length). def PossiblyTruncateBoundaries(self): for b in [True, False]: if b: this_index = self.start_index else: this_index = self.end_index - 1 this_split_line = self.split_lines_of_utt[this_index] truncated_duration = None this_duration = float(this_split_line[3]) this_edit = this_split_line[7] this_ref_word = this_split_line[6] if this_edit == 'sil' and \ this_duration > args.max_edge_silence_length: truncated_duration = args.max_edge_silence_length elif this_ref_word in non_scored_words and \ this_duration > args.max_edge_non_scored_length: truncated_duration = args.max_edge_non_scored_length if truncated_duration != None: keep_proportion = truncated_duration / this_duration if b: self.start_keep_proportion = keep_proportion else: self.end_keep_proportion = keep_proportion # This relaxes the segment-boundary truncation of # PossiblyTruncateBoundaries(), if it would take us below # min-new-segment-length or min-segment-length. Note: this does not relax # the boundary truncation for a particular boundary (start or end) if that # boundary corresponds to a 'tainted' line of the ctm (because it's # dangerous to include too much 'tainted' audio). def RelaxBoundaryTruncation(self): # this should be called before adding unk padding. assert self.start_unk_padding == self.end_unk_padding == 0.0 if self.start_keep_proportion == self.end_keep_proportion == 1.0: return # nothing to do there was no truncation. length_cutoff = max(args.min_new_segment_length, args.min_segment_length) length_with_truncation = self.Length() if length_with_truncation >= length_cutoff: return # Nothing to do. orig_start_keep_proportion = self.start_keep_proportion orig_end_keep_proportion = self.end_keep_proportion if not IsTainted(self.split_lines_of_utt[self.start_index]): self.start_keep_proportion = 1.0 if not IsTainted(self.split_lines_of_utt[self.end_index - 1]): self.end_keep_proportion = 1.0 length_with_relaxed_boundaries = self.Length() if length_with_relaxed_boundaries <= length_cutoff: # Completely undo the truncation [to the extent allowed by the # presence of tainted lines at the start/end] if, even without # truncation, we'd be below the length cutoff. This segment may be # removed later on (but it may not, if removing truncation makes us # identical to the input utterance, and the length is between # min_segment_length min_new_segment_length). return # Next, compute an interpolation constant a such that the # {start,end}_keep_proportion values will equal a * # [values-computed-by-PossiblyTruncateBoundaries()] + (1-a) * [completely-relaxed-values]. # we're solving the equation: # length_cutoff = a * length_with_truncation + (1-a) * length_with_relaxed_boundaries # -> length_cutoff - length_with_relaxed_boundaries = # a * (length_with_truncation - length_with_relaxed_boundaries) # -> a = (length_cutoff - length_with_relaxed_boundaries) / (length_with_truncation - length_with_relaxed_boundaries) a = (length_cutoff - length_with_relaxed_boundaries) / \ (length_with_truncation - length_with_relaxed_boundaries) if a < 0.0 or a > 1.0: print("segment_ctm_edits.py: bad 'a' value = {0}".format(a), file = sys.stderr) return self.start_keep_proportion = \ a * orig_start_keep_proportion + (1-a) * self.start_keep_proportion self.end_keep_proportion = \ a * orig_end_keep_proportion + (1-a) * self.end_keep_proportion if not abs(self.Length() - length_cutoff) < 0.01: print("segment_ctm_edits.py: possible problem relaxing boundary " "truncation, length is {0} vs {1}".format(self.Length(), length_cutoff), file = sys.stderr) # This is stage 4 of segment processing. # This function may set start_unk_padding and end_unk_padding to nonzero # values. This is done if the current boundary words are real, scored # words and we're not next to the beginning or end of the utterance. def PossiblyAddUnkPadding(self): for b in [True, False]: if b: this_index = self.start_index else: this_index = self.end_index - 1 this_split_line = self.split_lines_of_utt[this_index] this_start_time = float(this_split_line[2]) this_ref_word = this_split_line[6] this_edit = this_split_line[7] if this_edit == 'cor' and not this_ref_word in non_scored_words: # we can consider adding unk-padding. if b: # start of utterance. unk_padding = args.unk_padding if unk_padding > this_start_time: # close to beginning of file unk_padding = this_start_time # If we could add less than half of the specified # unk-padding, don't add any (because when we add # unk-padding we add the unknown-word symbol '<unk>', and if # there isn't enough space to traverse the HMM we don't want # to do it at all. if unk_padding < 0.5 * args.unk_padding: unk_padding = 0.0 self.start_unk_padding = unk_padding else: # end of utterance. this_end_time = this_start_time + float(this_split_line[3]) last_line = self.split_lines_of_utt[-1] utterance_end_time = float(last_line[2]) + float(last_line[3]) max_allowable_padding = utterance_end_time - this_end_time assert max_allowable_padding > -0.01 unk_padding = args.unk_padding if unk_padding > max_allowable_padding: unk_padding = max_allowable_padding # If we could add less than half of the specified # unk-padding, don't add any (because when we add # unk-padding we add the unknown-word symbol '<unk>', and if # there isn't enough space to traverse the HMM we don't want # to do it at all. if unk_padding < 0.5 * args.unk_padding: unk_padding = 0.0 self.end_unk_padding = unk_padding # This function will merge the segment in 'other' with the segment # in 'self'. It is only to be called when 'self' and 'other' are from # the same utterance, 'other' is after 'self' in time order (based on # the original segment cores), and self.EndTime() >= other.StartTime(). # Note: in this situation there will normally be deleted words # between the two segments. What this program does with the deleted # words depends on '--max-deleted-words-kept-when-merging'. If there # were any inserted words in the transcript (less likely), this # program will keep the reference. def MergeWithSegment(self, other): assert self.EndTime() >= other.StartTime() and \ self.StartTime() < other.EndTime() and \ self.split_lines_of_utt is other.split_lines_of_utt orig_self_end_index = self.end_index self.debug_str = "({0}/merged-with/{1})".format(self.debug_str, other.debug_str) # everything that relates to the end of this segment gets copied # from 'other'. self.end_index = other.end_index self.end_unk_padding = other.end_unk_padding self.end_keep_proportion = other.end_keep_proportion # The next thing we have to do is to go over any lines of the ctm that # appear between 'self' and 'other', or are shared between both (this # would only happen for tainted silence or non-scored-word segments), # and decide what to do with them. We'll keep the reference for any # substitutions or insertions (which anyway are unlikely to appear # in these merged segments). Note: most of this happens in self.Text(), # but at this point we need to decide whether to mark any deletions # as 'discard-this-word'. first_index_of_overlap = min(orig_self_end_index - 1, other.start_index) last_index_of_overlap = max(orig_self_end_index - 1, other.start_index) num_deleted_words = 0 for i in range(first_index_of_overlap, last_index_of_overlap + 1): edit_type = self.split_lines_of_utt[i][7] if edit_type == 'del': num_deleted_words += 1 if num_deleted_words > args.max_deleted_words_kept_when_merging: for i in range(first_index_of_overlap, last_index_of_overlap + 1): if self.split_lines_of_utt[i][7] == 'del': self.split_lines_of_utt[i].append('do-not-include-in-text') # Returns the start time of the utterance (within the enclosing utterance) # This is before any rounding. def StartTime(self): first_line = self.split_lines_of_utt[self.start_index] first_line_start = float(first_line[2]) first_line_duration = float(first_line[3]) first_line_end = first_line_start + first_line_duration return first_line_end - self.start_unk_padding \ - (first_line_duration * self.start_keep_proportion) # Returns some string-valued information about 'this' that is useful for debugging. def DebugInfo(self): return 'start=%d,end=%d,unk-padding=%.2f,%.2f,keep-proportion=%.2f,%.2f,' % \ (self.start_index, self.end_index, self.start_unk_padding, self.end_unk_padding, self.start_keep_proportion, self.end_keep_proportion) + \ self.debug_str # Returns the start time of the utterance (within the enclosing utterance) def EndTime(self): last_line = self.split_lines_of_utt[self.end_index - 1] last_line_start = float(last_line[2]) last_line_duration = float(last_line[3]) return last_line_start + (last_line_duration * self.end_keep_proportion) \ + self.end_unk_padding # Returns the segment length in seconds. def Length(self): return self.EndTime() - self.StartTime() def IsWholeUtterance(self): # returns true if this segment corresponds to the whole utterance that # it's a part of (i.e. its start/end time are zero and the end-time of # the last segment. last_line_of_utt = self.split_lines_of_utt[-1] last_line_end_time = float(last_line_of_utt[2]) + float(last_line_of_utt[3]) return abs(self.StartTime() - 0.0) < 0.001 and \ abs(self.EndTime() - last_line_end_time) < 0.001 # Returns the proportion of the duration of this segment that consists of # unk-padding and tainted lines of input (will be between 0.0 and 1.0). def JunkProportion(self): # Note: only the first and last lines could possibly be tainted as # that's how we create the segments; and if either or both are tainted # the utterance must contain other lines, so double-counting is not a # problem. junk_duration = self.start_unk_padding + self.end_unk_padding first_split_line = self.split_lines_of_utt[self.start_index] if IsTainted(first_split_line): first_duration = float(first_split_line[3]) junk_duration += first_duration * self.start_keep_proportion last_split_line = self.split_lines_of_utt[self.end_index - 1] if IsTainted(last_split_line): last_duration = float(last_split_line[3]) junk_duration += last_duration * self.end_keep_proportion return junk_duration / self.Length() # This function will remove something from the beginning of the # segment if it's possible to cleanly lop off a bit that contains # more junk, as a proportion of its length, than 'args.junk_proportion'. # Junk is defined as unk-padding and/or tainted segments. # It considers as a potential split point, the first silence # segment or non-tainted non-scored-word segment in the # utterance. See also TruncateEndForJunkProportion def PossiblyTruncateStartForJunkProportion(self): begin_junk_duration = self.start_unk_padding first_split_line = self.split_lines_of_utt[self.start_index] if IsTainted(first_split_line): first_duration = float(first_split_line[3]) begin_junk_duration += first_duration * self.start_keep_proportion if begin_junk_duration == 0.0: # nothing to do. return candidate_start_index = None # the following iterates over all lines internal to the utterance. for i in range(self.start_index + 1, self.end_index - 1): this_split_line = self.split_lines_of_utt[i] this_edit_type = this_split_line[7] this_ref_word = this_split_line[6] # We'll consider splitting on silence and on non-scored words. # (i.e. making the silence or non-scored word the left boundary of # the new utterance and discarding the piece to the left of that). if this_edit_type == 'sil' or \ (this_edit_type == 'cor' and this_ref_word in non_scored_words): candidate_start_index = i candidate_start_time = float(this_split_line[2]) break # Consider only the first potential truncation. if candidate_start_index == None: return # Nothing to do as there is no place to split. candidate_removed_piece_duration = candidate_start_time - self.StartTime() if begin_junk_duration / candidate_removed_piece_duration < args.max_junk_proportion: return # Nothing to do as the candidate piece to remove has too # little junk. # OK, remove the piece. self.start_index = candidate_start_index self.start_unk_padding = 0.0 self.start_keep_proportion = 1.0 self.debug_str += ',truncated-start-for-junk' # This is like PossiblyTruncateStartForJunkProportion(), but # acts on the end of the segment; see comments there. def PossiblyTruncateEndForJunkProportion(self): end_junk_duration = self.end_unk_padding last_split_line = self.split_lines_of_utt[self.end_index - 1] if IsTainted(last_split_line): last_duration = float(last_split_line[3]) end_junk_duration += last_duration * self.end_keep_proportion if end_junk_duration == 0.0: # nothing to do. return candidate_end_index = None # the following iterates over all lines internal to the utterance # (starting from the end). for i in reversed(range(self.start_index + 1, self.end_index - 1)): this_split_line = self.split_lines_of_utt[i] this_edit_type = this_split_line[7] this_ref_word = this_split_line[6] # We'll consider splitting on silence and on non-scored words. # (i.e. making the silence or non-scored word the right boundary of # the new utterance and discarding the piece to the right of that). if this_edit_type == 'sil' or \ (this_edit_type == 'cor' and this_ref_word in non_scored_words): candidate_end_index = i + 1 # note: end-indexes are one past the last. candidate_end_time = float(this_split_line[2]) + float(this_split_line[3]) break # Consider only the latest potential truncation. if candidate_end_index == None: return # Nothing to do as there is no place to split. candidate_removed_piece_duration = self.EndTime() - candidate_end_time if end_junk_duration / candidate_removed_piece_duration < args.max_junk_proportion: return # Nothing to do as the candidate piece to remove has too # little junk. # OK, remove the piece. self.end_index = candidate_end_index self.end_unk_padding = 0.0 self.end_keep_proportion = 1.0 self.debug_str += ',truncated-end-for-junk' # this will return true if there is at least one word in the utterance # that's a scored word (not a non-scored word) and not an OOV word that's # realized as unk. This becomes a filter on keeping segments. def ContainsAtLeastOneScoredNonOovWord(self): global non_scored_words for i in range(self.start_index, self.end_index): this_split_line = self.split_lines_of_utt[i] this_hyp_word = this_split_line[4] this_ref_word = this_split_line[6] this_edit = this_split_line[7] if this_edit == 'cor' and not this_ref_word in non_scored_words \ and this_ref_word == this_hyp_word: return True return False # Returns the text corresponding to this utterance, as a string. def Text(self): global oov_symbol text_array = [] if self.start_unk_padding != 0.0: text_array.append(oov_symbol) for i in range(self.start_index, self.end_index): this_split_line = self.split_lines_of_utt[i] this_edit = this_split_line[7] this_ref_word = this_split_line[6] if this_ref_word != '<eps>' and this_split_line[-1] != 'do-not-include-in-text': text_array.append(this_ref_word) if self.end_unk_padding != 0.0: text_array.append(oov_symbol) return ' '.join(text_array) # Here, 'text' will be something that indicates the stage of processing, # e.g. 'Stage 0: segment cores', 'Stage 1: add tainted lines', #, etc. def AccumulateSegmentStats(segment_list, text): global segment_total_length, num_segments for segment in segment_list: num_segments[text] += 1 segment_total_length[text] += segment.Length() def PrintSegmentStats(): global segment_total_length, num_segments, \ num_utterances, num_utterances_without_segments, \ total_length_of_utterances print('Number of utterances is %d, of which %.2f%% had no segments after ' 'all processing; total length of data in original utterances (in seconds) ' 'was %d' % (num_utterances, num_utterances_without_segments * 100.0 / num_utterances, total_length_of_utterances), file = sys.stderr) keys = sorted(segment_total_length.keys()) for i in range(len(keys)): key = keys[i] if i > 0: delta_percentage = '[%+.2f%%]' % ((segment_total_length[key] - segment_total_length[keys[i-1]]) * 100.0 / total_length_of_utterances) print('At %s, num-segments is %d, total length %.2f%% of original total %s' % ( key, num_segments[key], segment_total_length[key] * 100.0 / total_length_of_utterances, delta_percentage if i > 0 else ''), file = sys.stderr) # This function creates the segments for an utterance as a list # of class Segment. # It returns a 2-tuple (list-of-segments, list-of-deleted-segments) # where the deleted segments are only useful for diagnostic printing. # Note: split_lines_of_utt is a list of lists, one per line, each containing the # sequence of fields. def GetSegmentsForUtterance(split_lines_of_utt): global num_utterances, num_utterances_without_segments, total_length_of_utterances num_utterances += 1 segment_ranges = ComputeSegmentCores(split_lines_of_utt) utterance_end_time = float(split_lines_of_utt[-1][2]) + float(split_lines_of_utt[-1][3]) total_length_of_utterances += utterance_end_time segments = [ Segment(split_lines_of_utt, x[0], x[1]) for x in segment_ranges ] AccumulateSegmentStats(segments, 'stage 0 [segment cores]') for segment in segments: segment.PossiblyAddTaintedLines() AccumulateSegmentStats(segments, 'stage 1 [add tainted lines]') new_segments = [] for s in segments: new_segments += s.PossiblySplitSegment() segments = new_segments AccumulateSegmentStats(segments, 'stage 2 [split segments]') for s in segments: s.PossiblyTruncateBoundaries() AccumulateSegmentStats(segments, 'stage 3 [truncate boundaries]') for s in segments: s.RelaxBoundaryTruncation() AccumulateSegmentStats(segments, 'stage 4 [relax boundary truncation]') for s in segments: s.PossiblyAddUnkPadding() AccumulateSegmentStats(segments, 'stage 5 [unk-padding]') deleted_segments = [] new_segments = [] for s in segments: # the 0.999 allows for roundoff error. if (not s.IsWholeUtterance() and s.Length() < 0.999 * args.min_new_segment_length): s.debug_str += '[deleted-because-of--min-new-segment-length]' deleted_segments.append(s) else: new_segments.append(s) segments = new_segments AccumulateSegmentStats(segments, 'stage 6 [remove new segments under --min-new-segment-length') new_segments = [] for s in segments: # the 0.999 allows for roundoff error. if s.Length() < 0.999 * args.min_segment_length: s.debug_str += '[deleted-because-of--min-segment-length]' deleted_segments.append(s) else: new_segments.append(s) segments = new_segments AccumulateSegmentStats(segments, 'stage 7 [remove segments under --min-segment-length') for s in segments: s.PossiblyTruncateStartForJunkProportion() AccumulateSegmentStats(segments, 'stage 8 [truncate segment-starts for --max-junk-proportion') for s in segments: s.PossiblyTruncateEndForJunkProportion() AccumulateSegmentStats(segments, 'stage 9 [truncate segment-ends for --max-junk-proportion') new_segments = [] for s in segments: if s.ContainsAtLeastOneScoredNonOovWord(): new_segments.append(s) else: s.debug_str += '[deleted-because-no-scored-non-oov-words]' deleted_segments.append(s) segments = new_segments AccumulateSegmentStats(segments, 'stage 10 [remove segments without scored,non-OOV words]') new_segments = [] for s in segments: j = s.JunkProportion() if j <= args.max_junk_proportion: new_segments.append(s) else: s.debug_str += '[deleted-because-junk-proportion={0}]'.format(j) deleted_segments.append(s) segments = new_segments AccumulateSegmentStats(segments, 'stage 11 [remove segments with junk exceeding --max-junk-proportion]') new_segments = [] if len(segments) > 0: new_segments.append(segments[0]) for i in range(1, len(segments)): if new_segments[-1].EndTime() >= segments[i].StartTime(): new_segments[-1].MergeWithSegment(segments[i]) else: new_segments.append(segments[i]) segments = new_segments AccumulateSegmentStats(segments, 'stage 12 [merge overlapping or touching segments]') for i in range(len(segments) - 1): if segments[i].EndTime() > segments[i+1].StartTime(): # this just adds something to --ctm-edits-out output segments[i+1].debug_str += ",overlaps-previous-segment" if len(segments) == 0: num_utterances_without_segments += 1 return (segments, deleted_segments) # this prints a number with a certain number of digits after # the point, while removing trailing zeros. def FloatToString(f): num_digits = 6 # we want to print 6 digits after the zero g = f while abs(g) > 1.0: g *= 0.1 num_digits += 1 format_str = '%.{0}g'.format(num_digits) return format_str % f # Gives time in string form as an exact multiple of the frame-length, e.g. 0.01 # (after rounding). def TimeToString(time, frame_length): n = round(time / frame_length) assert n >= 0 # The next function call will remove trailing zeros while printing it, so # that e.g. 0.01 will be printed as 0.01 and not 0.0099999999999999. It # seems that doing this in a simple way is not really possible (at least, # not without assuming that frame_length is of the form 10^-n, which we # don't really want to do). return FloatToString(n * frame_length) def WriteSegmentsForUtterance(text_output_handle, segments_output_handle, old_utterance_name, segments): for n in range(len(segments)): segment = segments[n] # split utterances will be named foo-bar-1 foo-bar-2, etc. new_utterance_name = old_utterance_name + "-" + str(n + 1) # print a line to the text output of the form like # <new-utterance-id> <text> # like: # foo-bar-1 hello this is dan print(new_utterance_name, segment.Text(), file = text_output_handle) # print a line to the segments output of the form # <new-utterance-id> <old-utterance-id> <start-time> <end-time> # like: # foo-bar-1 foo-bar 5.1 7.2 print(new_utterance_name, old_utterance_name, TimeToString(segment.StartTime(), args.frame_length), TimeToString(segment.EndTime(), args.frame_length), file = segments_output_handle) # Note, this is destrutive of 'segments_for_utterance', but it won't matter. def PrintDebugInfoForUtterance(ctm_edits_out_handle, split_lines_of_cur_utterance, segments_for_utterance, deleted_segments_for_utterance): # info_to_print will be list of 2-tuples (time, 'start-segment-n'|'end-segment-n') # representing the start or end times of segments. info_to_print = [] for n in range(len(segments_for_utterance)): segment = segments_for_utterance[n] start_string = 'start-segment-' + str(n+1) + '[' + segment.DebugInfo() + ']' info_to_print.append( (segment.StartTime(), start_string) ) end_string = 'end-segment-' + str(n+1) info_to_print.append( (segment.EndTime(), end_string) ) # for segments that were deleted we print info like start-deleted-segment-1, and # otherwise similar info to segments that were retained. for n in range(len(deleted_segments_for_utterance)): segment = deleted_segments_for_utterance[n] start_string = 'start-deleted-segment-' + str(n+1) + '[' + segment.DebugInfo() + ']' info_to_print.append( (segment.StartTime(), start_string) ) end_string = 'end-deleted-segment-' + str(n+1) info_to_print.append( (segment.EndTime(), end_string) ) info_to_print = sorted(info_to_print) for i in range(len(split_lines_of_cur_utterance)): split_line=split_lines_of_cur_utterance[i] split_line[0] += '[' + str(i) + ']' # add an index like [0], [1], to # the utterance-id so we can easily # look up segment indexes. start_time = float(split_line[2]) end_time = start_time + float(split_line[3]) split_line_copy = list(split_line) while len(info_to_print) > 0 and info_to_print[0][0] <= end_time: (segment_start, string) = info_to_print[0] # shift the first element off of info_to_print. info_to_print = info_to_print[1:] # add a field like 'start-segment1[...]=3.21' to what we're about to print. split_line_copy.append(string + "=" + TimeToString(segment_start, args.frame_length)) print(' '.join(split_line_copy), file = ctm_edits_out_handle) # This accumulates word-level stats about, for each reference word, with what # probability it will end up in the core of a segment. Words with low # probabilities of being in segments will generally be associated with some kind # of error (there is a higher probability of having a wrong lexicon entry). def AccWordStatsForUtterance(split_lines_of_utt, segments_for_utterance): # word_count_pair is a map from a string (the word) to # a list [total-count, count-not-within-segments] global word_count_pair line_is_in_segment = [ False ] * len(split_lines_of_utt) for segment in segments_for_utterance: for i in range(segment.start_index, segment.end_index): line_is_in_segment[i] = True for i in range(len(split_lines_of_utt)): this_ref_word = split_lines_of_utt[i][6] if this_ref_word != '<eps>': word_count_pair[this_ref_word][0] += 1 if not line_is_in_segment[i]: word_count_pair[this_ref_word][1] += 1 def PrintWordStats(word_stats_out): try: f = open(word_stats_out, 'w') except: sys.exit("segment_ctm_edits.py: error opening word-stats file --word-stats-out={0} " "for writing".format(word_stats_out)) global word_count_pair # Sort from most to least problematic. We want to give more prominence to # words that are most frequently not in segments, but also to high-count # words. Define badness = pair[1] / pair[0], and total_count = pair[0], # where 'pair' is a value of word_count_pair. We'll reverse sort on # badness^3 * total_count = pair[1]^3 / pair[0]^2. for key, pair in sorted(word_count_pair.items(), key = lambda item: (item[1][1] ** 3) * 1.0 / (item[1][0] ** 2), reverse = True): badness = pair[1] * 1.0 / pair[0] total_count = pair[0] print(key, badness, total_count, file = f) try: f.close() except: sys.exit("segment_ctm_edits.py: error closing file --word-stats-out={0} " "(full disk?)".format(word_stats_out)) print("segment_ctm_edits.py: please see the file {0} for word-level statistics " "saying how frequently each word was excluded for a segment; format is " "<word> <proportion-of-time-excluded> <total-count>. Particularly " "problematic words appear near the top of the file.".format(word_stats_out), file = sys.stderr) def ProcessData(): try: f_in = open(args.ctm_edits_in) except: sys.exit("modify_ctm_edits.py: error opening ctm-edits input " "file {0}".format(args.ctm_edits_in)) try: text_output_handle = open(args.text_out, 'w') except: sys.exit("modify_ctm_edits.py: error opening text output " "file {0}".format(args.text_out)) try: segments_output_handle = open(args.segments_out, 'w') except: sys.exit("modify_ctm_edits.py: error opening segments output " "file {0}".format(args.text_out)) if args.ctm_edits_out != None: try: ctm_edits_output_handle = open(args.ctm_edits_out, 'w') except: sys.exit("modify_ctm_edits.py: error opening ctm-edits output " "file {0}".format(args.ctm_edits_out)) # Most of what we're doing in the lines below is splitting the input lines # and grouping them per utterance, before giving them to ProcessUtterance() # and then printing the modified lines. first_line = f_in.readline() if first_line == '': sys.exit("modify_ctm_edits.py: empty input") split_pending_line = first_line.split() if len(split_pending_line) == 0: sys.exit("modify_ctm_edits.py: bad input line " + first_line) cur_utterance = split_pending_line[0] split_lines_of_cur_utterance = [] while True: if len(split_pending_line) == 0 or split_pending_line[0] != cur_utterance: (segments_for_utterance, deleted_segments_for_utterance) = GetSegmentsForUtterance(split_lines_of_cur_utterance) AccWordStatsForUtterance(split_lines_of_cur_utterance, segments_for_utterance) WriteSegmentsForUtterance(text_output_handle, segments_output_handle, cur_utterance, segments_for_utterance) if args.ctm_edits_out != None: PrintDebugInfoForUtterance(ctm_edits_output_handle, split_lines_of_cur_utterance, segments_for_utterance, deleted_segments_for_utterance) split_lines_of_cur_utterance = [] if len(split_pending_line) == 0: break else: cur_utterance = split_pending_line[0] split_lines_of_cur_utterance.append(split_pending_line) next_line = f_in.readline() split_pending_line = next_line.split() if len(split_pending_line) == 0: if next_line != '': sys.exit("modify_ctm_edits.py: got an empty or whitespace input line") try: text_output_handle.close() segments_output_handle.close() if args.ctm_edits_out != None: ctm_edits_output_handle.close() except: sys.exit("modify_ctm_edits.py: error closing one or more outputs " "(broken pipe or full disk?)") def ReadNonScoredWords(non_scored_words_file): global non_scored_words try: f = open(non_scored_words_file) except: sys.exit("modify_ctm_edits.py: error opening file: " "--non-scored-words=" + non_scored_words_file) for line in f.readlines(): a = line.split() if not len(line.split()) == 1: sys.exit("modify_ctm_edits.py: bad line in non-scored-words " "file {0}: {1}".format(non_scored_words_file, line)) non_scored_words.add(a[0]) f.close() non_scored_words = set() ReadNonScoredWords(args.non_scored_words_in) oov_symbol = None if args.oov_symbol_file != None: try: with open(args.oov_symbol_file) as f: line = f.readline() assert len(line.split()) == 1 oov_symbol = line.split()[0] assert f.readline() == '' except Exception as e: sys.exit("segment_ctm_edits.py: error reading file --oov-symbol-file=" + args.oov_symbol_file + ", error is: " + str(e)) elif args.unk_padding != 0.0: sys.exit("segment_ctm_edits.py: if the --unk-padding option is nonzero (which " "it is by default, the --oov-symbol-file option must be supplied.") # segment_total_length and num_segments are maps from # 'stage' strings; see AccumulateSegmentStats for details. segment_total_length = defaultdict(int) num_segments = defaultdict(int) # the lambda expression below is an anonymous function that takes no arguments # and returns the new list [0, 0]. word_count_pair = defaultdict(lambda: [0, 0]) num_utterances = 0 num_utterances_without_segments = 0 total_length_of_utterances = 0 ProcessData() PrintSegmentStats() if args.word_stats_out != None: PrintWordStats(args.word_stats_out) if args.ctm_edits_out != None: print("segment_ctm_edits.py: detailed utterance-level debug information " "is in " + args.ctm_edits_out, file = sys.stderr)

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from __future__ import print_function import sys, operator, argparse, os from collections import defaultdict parser = argparse.ArgumentParser( description = "This program produces segmentation and text information " "based on reading ctm-edits input format which is produced by " "steps/cleanup/internal/get_ctm_edits.py, steps/cleanup/internal/modify_ctm_edits.py and " "steps/cleanup/internal/taint_ctm_edits.py.", formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("--min-segment-length", type = float, default = 0.5, help = "Minimum allowed segment length (in seconds) for any " "segment; shorter segments than this will be discarded.") parser.add_argument("--min-new-segment-length", type = float, default = 1.0, help = "Minimum allowed segment length (in seconds) for newly " "created segments (i.e. not identical to the input utterances). " "Expected to be >= --min-segment-length.") parser.add_argument("--frame-length", type = float, default = 0.01, help = "This only affects rounding of the output times; they will " "be constrained to multiples of this value.") parser.add_argument("--max-tainted-length", type = float, default = 0.05, help = "Maximum allowed length of any 'tainted' line. Note: " "'tainted' lines may only appear at the boundary of a " "segment") parser.add_argument("--max-edge-silence-length", type = float, default = 0.5, help = "Maximum allowed length of silence if it appears at the " "edge of a segment (will be truncated). This rule is " "relaxed if such truncation would take a segment below " "the --min-segment-length or --min-new-segment-length.") parser.add_argument("--max-edge-non-scored-length", type = float, default = 0.5, help = "Maximum allowed length of a non-scored word (noise, cough, etc.) " "if it appears at the edge of a segment (will be truncated). " "This rule is relaxed if such truncation would take a " "segment below the --min-segment-length.") parser.add_argument("--max-internal-silence-length", type = float, default = 2.0, help = "Maximum allowed length of silence if it appears inside a segment " "(will cause the segment to be split).") parser.add_argument("--max-internal-non-scored-length", type = float, default = 2.0, help = "Maximum allowed length of a non-scored word (noise, etc.) if " "it appears inside a segment (will cause the segment to be " "split). Note: reference words which are real words but OOV " "are not included in this category.") parser.add_argument("--unk-padding", type = float, default = 0.05, help = "Amount of padding with <unk> that we do if a segment boundary is " "next to errors (ins, del, sub). That is, we add this amount of " "time to the segment and add the <unk> word to cover the acoustics. " "If nonzero, the --oov-symbol-file option must be supplied.") parser.add_argument("--max-junk-proportion", type = float, default = 0.1, help = "Maximum proportion of the time of the segment that may " "consist of potentially bad data, in which we include 'tainted' lines of " "the ctm-edits input and unk-padding.") parser.add_argument("--max-deleted-words-kept-when-merging", type = str, default = 1, help = "When merging segments that are found to be overlapping or " "adjacent after all other processing, keep in the transcript the " "reference words that were deleted between the segments [if any] " "as long as there were no more than this many reference words. " "Setting this to zero will mean that any reference words that " "were deleted between the segments we're about to reattach will " "not appear in the generated transcript (so we'll match the hyp).") parser.add_argument("--oov-symbol-file", type = str, default = None, help = "Filename of file such as data/lang/oov.txt which contains " "the text form of the OOV word, normally '<unk>'. Supplied as " "a file to avoid complications with escaping. Necessary if " "the --unk-padding option has a nonzero value (which it does " "by default.") parser.add_argument("--ctm-edits-out", type = str, help = "Filename to output an extended version of the ctm-edits format " "with segment start and end points noted. This file is intended to be " "read by humans; there are currently no scripts that will read it.") parser.add_argument("--word-stats-out", type = str, help = "Filename for output of word-level stats, of the form " "'<word> <bad-proportion> <total-count-in-ref>', e.g. 'hello 0.12 12408', " "where the <bad-proportion> is the proportion of the time that this " "reference word does not make it into a segment. It can help reveal words " "that have problematic pronunciations or are associated with " "transcription errors.") parser.add_argument("non_scored_words_in", metavar = "<non-scored-words-file>", help="Filename of file containing a list of non-scored words, " "one per line. See steps/cleanup/internal/get_nonscored_words.py.") parser.add_argument("ctm_edits_in", metavar = "<ctm-edits-in>", help = "Filename of input ctm-edits file. " "Use /dev/stdin for standard input.") parser.add_argument("text_out", metavar = "<text-out>", help = "Filename of output text file (same format as data/train/text, i.e. " "<new-utterance-id> <word1> <word2> ... <wordN>") parser.add_argument("segments_out", metavar = "<segments-out>", help = "Filename of output segments. This has the same format as data/train/segments, " "but instead of <recording-id>, the second field is the old utterance-id, i.e " "<new-utterance-id> <old-utterance-id> <start-time> <end-time>") args = parser.parse_args() def IsTainted(split_line_of_utt): return len(split_line_of_utt) > 8 and split_line_of_utt[8] == 'tainted' # any adjacent tainted words (or parts of them). def ComputeSegmentCores(split_lines_of_utt): num_lines = len(split_lines_of_utt) line_is_in_segment_core = [ False] * num_lines for i in range(num_lines): if split_lines_of_utt[i][7] == 'cor' and \ split_lines_of_utt[i][4] == split_lines_of_utt[i][6]: line_is_in_segment_core[i] = True # extend each proto-segment forwards as far as we can: for i in range(1, num_lines): if line_is_in_segment_core[i-1] and not line_is_in_segment_core[i]: edit_type = split_lines_of_utt[i][7] if not IsTainted(split_lines_of_utt[i]) and \ (edit_type == 'cor' or edit_type == 'sil' or edit_type == 'fix'): line_is_in_segment_core[i] = True # extend each proto-segment backwards as far as we can: for i in reversed(range(0, num_lines - 1)): if line_is_in_segment_core[i+1] and not line_is_in_segment_core[i]: edit_type = split_lines_of_utt[i][7] if not IsTainted(split_lines_of_utt[i]) and \ (edit_type == 'cor' or edit_type == 'sil' or edit_type == 'fix'): line_is_in_segment_core[i] = True segment_ranges = [] cur_segment_start = None for i in range(0, num_lines): if line_is_in_segment_core[i]: if cur_segment_start == None: cur_segment_start = i else: if cur_segment_start != None: segment_ranges.append( (cur_segment_start, i) ) cur_segment_start = None if cur_segment_start != None: segment_ranges.append( (cur_segment_start, num_lines) ) return segment_ranges class Segment: def __init__(self, split_lines_of_utt, start_index, end_index, debug_str = None): self.split_lines_of_utt = split_lines_of_utt # start_index is the index of the first line that appears in this # segment, and end_index is one past the last line. This does not # include unk-padding. self.start_index = start_index self.end_index = end_index # If the following values are nonzero, then when we create the segment # we will add <unk> at the start and end of the segment [representing # partial words], with this amount of additional audio. self.start_unk_padding = 0.0 self.end_unk_padding = 0.0 # debug_str keeps track of the 'core' of the segment. if debug_str == None: debug_str = 'core-start={0},core-end={1}'.format(start_index,end_index) self.debug_str = debug_str # This gives the proportion of the time of the first line in the segment # that we keep. Usually 1.0 but may be less if we've trimmed away some self.start_keep_proportion = 1.0 # proportion of the time. self.end_keep_proportion = 1.0 # This is stage 1 of segment processing (after creating the boundaries of the # core of the segment, which is done outside of this class).a # # This function may reduce start_index and/or increase end_index by # including a single adjacent 'tainted' line from the ctm-edits file. This # is only done if the lines at the boundaries of the segment are currently # real non-silence words and not non-scored words. The idea is that we # probably don't want to start or end the segment right at the boundary of a def PossiblyAddTaintedLines(self): global non_scored_words split_lines_of_utt = self.split_lines_of_utt for b in [False, True]: if b: boundary_index = self.end_index - 1 adjacent_index = self.end_index else: boundary_index = self.start_index adjacent_index = self.start_index - 1 if adjacent_index >= 0 and adjacent_index < len(split_lines_of_utt): # only consider merging the adjacent word into the segment if we're not adjacent_line_is_tainted = IsTainted(split_lines_of_utt[adjacent_index]) # another stronger reason why we didn't include it in the core if adjacent_line_is_tainted: boundary_edit_type = split_lines_of_utt[boundary_index][7] boundary_hyp_word = split_lines_of_utt[boundary_index][7] if boundary_edit_type == 'cor' and \ not boundary_hyp_word in non_scored_words: if b: self.end_index += 1 else: self.start_index -= 1 def PossiblySplitSegment(self): global non_scored_words, args assert self.start_unk_padding == 0.0 and self.end_unk_padding == 0.0 and \ self.start_keep_proportion == 1.0 and self.end_keep_proportion == 1.0 segments = [] # the answer cur_start_index = self.start_index cur_start_is_split = False # only consider splitting at non-boundary lines. [we'd just truncate for index_to_split_at in range(cur_start_index + 1, self.end_index - 1): this_split_line = self.split_lines_of_utt[index_to_split_at] this_duration = float(this_split_line[3]) this_edit_type = this_split_line[7] this_ref_word = this_split_line[6] if (this_edit_type == 'sil' and this_duration > args.max_internal_silence_length) or \ (this_ref_word in non_scored_words and this_duration > args.max_internal_non_scored_length): new_segment = Segment(self.split_lines_of_utt, cur_start_index, index_to_split_at + 1, self.debug_str) if cur_start_is_split: new_segment.start_keep_proportion = 0.5 new_segment.end_keep_proportion = 0.5 cur_start_is_split = True cur_start_index = index_to_split_at segments.append(new_segment) if len(segments) == 0: segments.append(self) else: new_segment = Segment(self.split_lines_of_utt, cur_start_index, self.end_index, self.debug_str) assert cur_start_is_split new_segment.start_keep_proportion = 0.5 segments.append(new_segment) return segments # --min-segment-length or --min-new-segment-length). def PossiblyTruncateBoundaries(self): for b in [True, False]: if b: this_index = self.start_index else: this_index = self.end_index - 1 this_split_line = self.split_lines_of_utt[this_index] truncated_duration = None this_duration = float(this_split_line[3]) this_edit = this_split_line[7] this_ref_word = this_split_line[6] if this_edit == 'sil' and \ this_duration > args.max_edge_silence_length: truncated_duration = args.max_edge_silence_length elif this_ref_word in non_scored_words and \ this_duration > args.max_edge_non_scored_length: truncated_duration = args.max_edge_non_scored_length if truncated_duration != None: keep_proportion = truncated_duration / this_duration if b: self.start_keep_proportion = keep_proportion else: self.end_keep_proportion = keep_proportion # This relaxes the segment-boundary truncation of # PossiblyTruncateBoundaries(), if it would take us below # min-new-segment-length or min-segment-length. Note: this does not relax # the boundary truncation for a particular boundary (start or end) if that # boundary corresponds to a 'tainted' line of the ctm (because it's def RelaxBoundaryTruncation(self): assert self.start_unk_padding == self.end_unk_padding == 0.0 if self.start_keep_proportion == self.end_keep_proportion == 1.0: return length_cutoff = max(args.min_new_segment_length, args.min_segment_length) length_with_truncation = self.Length() if length_with_truncation >= length_cutoff: return orig_start_keep_proportion = self.start_keep_proportion orig_end_keep_proportion = self.end_keep_proportion if not IsTainted(self.split_lines_of_utt[self.start_index]): self.start_keep_proportion = 1.0 if not IsTainted(self.split_lines_of_utt[self.end_index - 1]): self.end_keep_proportion = 1.0 length_with_relaxed_boundaries = self.Length() if length_with_relaxed_boundaries <= length_cutoff: # removed later on (but it may not, if removing truncation makes us # identical to the input utterance, and the length is between # min_segment_length min_new_segment_length). return # Next, compute an interpolation constant a such that the # {start,end}_keep_proportion values will equal a * # [values-computed-by-PossiblyTruncateBoundaries()] + (1-a) * [completely-relaxed-values]. # we're solving the equation: a = (length_cutoff - length_with_relaxed_boundaries) / \ (length_with_truncation - length_with_relaxed_boundaries) if a < 0.0 or a > 1.0: print("segment_ctm_edits.py: bad 'a' value = {0}".format(a), file = sys.stderr) return self.start_keep_proportion = \ a * orig_start_keep_proportion + (1-a) * self.start_keep_proportion self.end_keep_proportion = \ a * orig_end_keep_proportion + (1-a) * self.end_keep_proportion if not abs(self.Length() - length_cutoff) < 0.01: print("segment_ctm_edits.py: possible problem relaxing boundary " "truncation, length is {0} vs {1}".format(self.Length(), length_cutoff), file = sys.stderr) def PossiblyAddUnkPadding(self): for b in [True, False]: if b: this_index = self.start_index else: this_index = self.end_index - 1 this_split_line = self.split_lines_of_utt[this_index] this_start_time = float(this_split_line[2]) this_ref_word = this_split_line[6] this_edit = this_split_line[7] if this_edit == 'cor' and not this_ref_word in non_scored_words: # we can consider adding unk-padding. if b: # start of utterance. unk_padding = args.unk_padding if unk_padding > this_start_time: # close to beginning of file unk_padding = this_start_time # If we could add less than half of the specified # unk-padding, don't add any (because when we add if unk_padding < 0.5 * args.unk_padding: unk_padding = 0.0 self.start_unk_padding = unk_padding else: this_end_time = this_start_time + float(this_split_line[3]) last_line = self.split_lines_of_utt[-1] utterance_end_time = float(last_line[2]) + float(last_line[3]) max_allowable_padding = utterance_end_time - this_end_time assert max_allowable_padding > -0.01 unk_padding = args.unk_padding if unk_padding > max_allowable_padding: unk_padding = max_allowable_padding # unk-padding we add the unknown-word symbol '<unk>', and if # there isn't enough space to traverse the HMM we don't want # to do it at all. if unk_padding < 0.5 * args.unk_padding: unk_padding = 0.0 self.end_unk_padding = unk_padding # This function will merge the segment in 'other' with the segment # in 'self'. It is only to be called when 'self' and 'other' are from # the same utterance, 'other' is after 'self' in time order (based on # the original segment cores), and self.EndTime() >= other.StartTime(). # Note: in this situation there will normally be deleted words # between the two segments. What this program does with the deleted # words depends on '--max-deleted-words-kept-when-merging'. If there # were any inserted words in the transcript (less likely), this # program will keep the reference. def MergeWithSegment(self, other): assert self.EndTime() >= other.StartTime() and \ self.StartTime() < other.EndTime() and \ self.split_lines_of_utt is other.split_lines_of_utt orig_self_end_index = self.end_index self.debug_str = "({0}/merged-with/{1})".format(self.debug_str, other.debug_str) # everything that relates to the end of this segment gets copied # from 'other'. self.end_index = other.end_index self.end_unk_padding = other.end_unk_padding self.end_keep_proportion = other.end_keep_proportion # The next thing we have to do is to go over any lines of the ctm that # appear between 'self' and 'other', or are shared between both (this # would only happen for tainted silence or non-scored-word segments), # and decide what to do with them. We'll keep the reference for any first_index_of_overlap = min(orig_self_end_index - 1, other.start_index) last_index_of_overlap = max(orig_self_end_index - 1, other.start_index) num_deleted_words = 0 for i in range(first_index_of_overlap, last_index_of_overlap + 1): edit_type = self.split_lines_of_utt[i][7] if edit_type == 'del': num_deleted_words += 1 if num_deleted_words > args.max_deleted_words_kept_when_merging: for i in range(first_index_of_overlap, last_index_of_overlap + 1): if self.split_lines_of_utt[i][7] == 'del': self.split_lines_of_utt[i].append('do-not-include-in-text') def StartTime(self): first_line = self.split_lines_of_utt[self.start_index] first_line_start = float(first_line[2]) first_line_duration = float(first_line[3]) first_line_end = first_line_start + first_line_duration return first_line_end - self.start_unk_padding \ - (first_line_duration * self.start_keep_proportion) def DebugInfo(self): return 'start=%d,end=%d,unk-padding=%.2f,%.2f,keep-proportion=%.2f,%.2f,' % \ (self.start_index, self.end_index, self.start_unk_padding, self.end_unk_padding, self.start_keep_proportion, self.end_keep_proportion) + \ self.debug_str def EndTime(self): last_line = self.split_lines_of_utt[self.end_index - 1] last_line_start = float(last_line[2]) last_line_duration = float(last_line[3]) return last_line_start + (last_line_duration * self.end_keep_proportion) \ + self.end_unk_padding def Length(self): return self.EndTime() - self.StartTime() def IsWholeUtterance(self): # the last segment. last_line_of_utt = self.split_lines_of_utt[-1] last_line_end_time = float(last_line_of_utt[2]) + float(last_line_of_utt[3]) return abs(self.StartTime() - 0.0) < 0.001 and \ abs(self.EndTime() - last_line_end_time) < 0.001 # Returns the proportion of the duration of this segment that consists of # unk-padding and tainted lines of input (will be between 0.0 and 1.0). def JunkProportion(self): # Note: only the first and last lines could possibly be tainted as # that's how we create the segments; and if either or both are tainted junk_duration = self.start_unk_padding + self.end_unk_padding first_split_line = self.split_lines_of_utt[self.start_index] if IsTainted(first_split_line): first_duration = float(first_split_line[3]) junk_duration += first_duration * self.start_keep_proportion last_split_line = self.split_lines_of_utt[self.end_index - 1] if IsTainted(last_split_line): last_duration = float(last_split_line[3]) junk_duration += last_duration * self.end_keep_proportion return junk_duration / self.Length() # more junk, as a proportion of its length, than 'args.junk_proportion'. # Junk is defined as unk-padding and/or tainted segments. # It considers as a potential split point, the first silence # segment or non-tainted non-scored-word segment in the # utterance. See also TruncateEndForJunkProportion def PossiblyTruncateStartForJunkProportion(self): begin_junk_duration = self.start_unk_padding first_split_line = self.split_lines_of_utt[self.start_index] if IsTainted(first_split_line): first_duration = float(first_split_line[3]) begin_junk_duration += first_duration * self.start_keep_proportion if begin_junk_duration == 0.0: # nothing to do. return candidate_start_index = None # the following iterates over all lines internal to the utterance. for i in range(self.start_index + 1, self.end_index - 1): this_split_line = self.split_lines_of_utt[i] this_edit_type = this_split_line[7] this_ref_word = this_split_line[6] # We'll consider splitting on silence and on non-scored words. if this_edit_type == 'sil' or \ (this_edit_type == 'cor' and this_ref_word in non_scored_words): candidate_start_index = i candidate_start_time = float(this_split_line[2]) break if candidate_start_index == None: return candidate_removed_piece_duration = candidate_start_time - self.StartTime() if begin_junk_duration / candidate_removed_piece_duration < args.max_junk_proportion: return self.start_index = candidate_start_index self.start_unk_padding = 0.0 self.start_keep_proportion = 1.0 self.debug_str += ',truncated-start-for-junk' def PossiblyTruncateEndForJunkProportion(self): end_junk_duration = self.end_unk_padding last_split_line = self.split_lines_of_utt[self.end_index - 1] if IsTainted(last_split_line): last_duration = float(last_split_line[3]) end_junk_duration += last_duration * self.end_keep_proportion if end_junk_duration == 0.0: return candidate_end_index = None for i in reversed(range(self.start_index + 1, self.end_index - 1)): this_split_line = self.split_lines_of_utt[i] this_edit_type = this_split_line[7] this_ref_word = this_split_line[6] # (i.e. making the silence or non-scored word the right boundary of # the new utterance and discarding the piece to the right of that). if this_edit_type == 'sil' or \ (this_edit_type == 'cor' and this_ref_word in non_scored_words): candidate_end_index = i + 1 # note: end-indexes are one past the last. candidate_end_time = float(this_split_line[2]) + float(this_split_line[3]) break # Consider only the latest potential truncation. if candidate_end_index == None: return # Nothing to do as there is no place to split. candidate_removed_piece_duration = self.EndTime() - candidate_end_time if end_junk_duration / candidate_removed_piece_duration < args.max_junk_proportion: return # Nothing to do as the candidate piece to remove has too # little junk. # OK, remove the piece. self.end_index = candidate_end_index self.end_unk_padding = 0.0 self.end_keep_proportion = 1.0 self.debug_str += ',truncated-end-for-junk' # this will return true if there is at least one word in the utterance # that's a scored word (not a non-scored word) and not an OOV word that's # realized as unk. This becomes a filter on keeping segments. def ContainsAtLeastOneScoredNonOovWord(self): global non_scored_words for i in range(self.start_index, self.end_index): this_split_line = self.split_lines_of_utt[i] this_hyp_word = this_split_line[4] this_ref_word = this_split_line[6] this_edit = this_split_line[7] if this_edit == 'cor' and not this_ref_word in non_scored_words \ and this_ref_word == this_hyp_word: return True return False # Returns the text corresponding to this utterance, as a string. def Text(self): global oov_symbol text_array = [] if self.start_unk_padding != 0.0: text_array.append(oov_symbol) for i in range(self.start_index, self.end_index): this_split_line = self.split_lines_of_utt[i] this_edit = this_split_line[7] this_ref_word = this_split_line[6] if this_ref_word != '<eps>' and this_split_line[-1] != 'do-not-include-in-text': text_array.append(this_ref_word) if self.end_unk_padding != 0.0: text_array.append(oov_symbol) return ' '.join(text_array) # Here, 'text' will be something that indicates the stage of processing, # e.g. 'Stage 0: segment cores', 'Stage 1: add tainted lines', #, etc. def AccumulateSegmentStats(segment_list, text): global segment_total_length, num_segments for segment in segment_list: num_segments[text] += 1 segment_total_length[text] += segment.Length() def PrintSegmentStats(): global segment_total_length, num_segments, \ num_utterances, num_utterances_without_segments, \ total_length_of_utterances print('Number of utterances is %d, of which %.2f%% had no segments after ' 'all processing; total length of data in original utterances (in seconds) ' 'was %d' % (num_utterances, num_utterances_without_segments * 100.0 / num_utterances, total_length_of_utterances), file = sys.stderr) keys = sorted(segment_total_length.keys()) for i in range(len(keys)): key = keys[i] if i > 0: delta_percentage = '[%+.2f%%]' % ((segment_total_length[key] - segment_total_length[keys[i-1]]) * 100.0 / total_length_of_utterances) print('At %s, num-segments is %d, total length %.2f%% of original total %s' % ( key, num_segments[key], segment_total_length[key] * 100.0 / total_length_of_utterances, delta_percentage if i > 0 else ''), file = sys.stderr) # This function creates the segments for an utterance as a list # of class Segment. # It returns a 2-tuple (list-of-segments, list-of-deleted-segments) # where the deleted segments are only useful for diagnostic printing. # Note: split_lines_of_utt is a list of lists, one per line, each containing the # sequence of fields. def GetSegmentsForUtterance(split_lines_of_utt): global num_utterances, num_utterances_without_segments, total_length_of_utterances num_utterances += 1 segment_ranges = ComputeSegmentCores(split_lines_of_utt) utterance_end_time = float(split_lines_of_utt[-1][2]) + float(split_lines_of_utt[-1][3]) total_length_of_utterances += utterance_end_time segments = [ Segment(split_lines_of_utt, x[0], x[1]) for x in segment_ranges ] AccumulateSegmentStats(segments, 'stage 0 [segment cores]') for segment in segments: segment.PossiblyAddTaintedLines() AccumulateSegmentStats(segments, 'stage 1 [add tainted lines]') new_segments = [] for s in segments: new_segments += s.PossiblySplitSegment() segments = new_segments AccumulateSegmentStats(segments, 'stage 2 [split segments]') for s in segments: s.PossiblyTruncateBoundaries() AccumulateSegmentStats(segments, 'stage 3 [truncate boundaries]') for s in segments: s.RelaxBoundaryTruncation() AccumulateSegmentStats(segments, 'stage 4 [relax boundary truncation]') for s in segments: s.PossiblyAddUnkPadding() AccumulateSegmentStats(segments, 'stage 5 [unk-padding]') deleted_segments = [] new_segments = [] for s in segments: # the 0.999 allows for roundoff error. if (not s.IsWholeUtterance() and s.Length() < 0.999 * args.min_new_segment_length): s.debug_str += '[deleted-because-of--min-new-segment-length]' deleted_segments.append(s) else: new_segments.append(s) segments = new_segments AccumulateSegmentStats(segments, 'stage 6 [remove new segments under --min-new-segment-length') new_segments = [] for s in segments: # the 0.999 allows for roundoff error. if s.Length() < 0.999 * args.min_segment_length: s.debug_str += '[deleted-because-of--min-segment-length]' deleted_segments.append(s) else: new_segments.append(s) segments = new_segments AccumulateSegmentStats(segments, 'stage 7 [remove segments under --min-segment-length') for s in segments: s.PossiblyTruncateStartForJunkProportion() AccumulateSegmentStats(segments, 'stage 8 [truncate segment-starts for --max-junk-proportion') for s in segments: s.PossiblyTruncateEndForJunkProportion() AccumulateSegmentStats(segments, 'stage 9 [truncate segment-ends for --max-junk-proportion') new_segments = [] for s in segments: if s.ContainsAtLeastOneScoredNonOovWord(): new_segments.append(s) else: s.debug_str += '[deleted-because-no-scored-non-oov-words]' deleted_segments.append(s) segments = new_segments AccumulateSegmentStats(segments, 'stage 10 [remove segments without scored,non-OOV words]') new_segments = [] for s in segments: j = s.JunkProportion() if j <= args.max_junk_proportion: new_segments.append(s) else: s.debug_str += '[deleted-because-junk-proportion={0}]'.format(j) deleted_segments.append(s) segments = new_segments AccumulateSegmentStats(segments, 'stage 11 [remove segments with junk exceeding --max-junk-proportion]') new_segments = [] if len(segments) > 0: new_segments.append(segments[0]) for i in range(1, len(segments)): if new_segments[-1].EndTime() >= segments[i].StartTime(): new_segments[-1].MergeWithSegment(segments[i]) else: new_segments.append(segments[i]) segments = new_segments AccumulateSegmentStats(segments, 'stage 12 [merge overlapping or touching segments]') for i in range(len(segments) - 1): if segments[i].EndTime() > segments[i+1].StartTime(): # this just adds something to --ctm-edits-out output segments[i+1].debug_str += ",overlaps-previous-segment" if len(segments) == 0: num_utterances_without_segments += 1 return (segments, deleted_segments) # this prints a number with a certain number of digits after # the point, while removing trailing zeros. def FloatToString(f): num_digits = 6 # we want to print 6 digits after the zero g = f while abs(g) > 1.0: g *= 0.1 num_digits += 1 format_str = '%.{0}g'.format(num_digits) return format_str % f # Gives time in string form as an exact multiple of the frame-length, e.g. 0.01 # (after rounding). def TimeToString(time, frame_length): n = round(time / frame_length) assert n >= 0 # The next function call will remove trailing zeros while printing it, so # that e.g. 0.01 will be printed as 0.01 and not 0.0099999999999999. It # seems that doing this in a simple way is not really possible (at least, # not without assuming that frame_length is of the form 10^-n, which we # don't really want to do). return FloatToString(n * frame_length) def WriteSegmentsForUtterance(text_output_handle, segments_output_handle, old_utterance_name, segments): for n in range(len(segments)): segment = segments[n] new_utterance_name = old_utterance_name + "-" + str(n + 1) print(new_utterance_name, segment.Text(), file = text_output_handle) print(new_utterance_name, old_utterance_name, TimeToString(segment.StartTime(), args.frame_length), TimeToString(segment.EndTime(), args.frame_length), file = segments_output_handle) def PrintDebugInfoForUtterance(ctm_edits_out_handle, split_lines_of_cur_utterance, segments_for_utterance, deleted_segments_for_utterance): # info_to_print will be list of 2-tuples (time, 'start-segment-n'|'end-segment-n') # representing the start or end times of segments. info_to_print = [] for n in range(len(segments_for_utterance)): segment = segments_for_utterance[n] start_string = 'start-segment-' + str(n+1) + '[' + segment.DebugInfo() + ']' info_to_print.append( (segment.StartTime(), start_string) ) end_string = 'end-segment-' + str(n+1) info_to_print.append( (segment.EndTime(), end_string) ) # for segments that were deleted we print info like start-deleted-segment-1, and # otherwise similar info to segments that were retained. for n in range(len(deleted_segments_for_utterance)): segment = deleted_segments_for_utterance[n] start_string = 'start-deleted-segment-' + str(n+1) + '[' + segment.DebugInfo() + ']' info_to_print.append( (segment.StartTime(), start_string) ) end_string = 'end-deleted-segment-' + str(n+1) info_to_print.append( (segment.EndTime(), end_string) ) info_to_print = sorted(info_to_print) for i in range(len(split_lines_of_cur_utterance)): split_line=split_lines_of_cur_utterance[i] split_line[0] += '[' + str(i) + ']' # add an index like [0], [1], to # the utterance-id so we can easily # look up segment indexes. start_time = float(split_line[2]) end_time = start_time + float(split_line[3]) split_line_copy = list(split_line) while len(info_to_print) > 0 and info_to_print[0][0] <= end_time: (segment_start, string) = info_to_print[0] # shift the first element off of info_to_print. info_to_print = info_to_print[1:] # add a field like 'start-segment1[...]=3.21' to what we're about to print. split_line_copy.append(string + "=" + TimeToString(segment_start, args.frame_length)) print(' '.join(split_line_copy), file = ctm_edits_out_handle) def AccWordStatsForUtterance(split_lines_of_utt, segments_for_utterance): global word_count_pair line_is_in_segment = [ False ] * len(split_lines_of_utt) for segment in segments_for_utterance: for i in range(segment.start_index, segment.end_index): line_is_in_segment[i] = True for i in range(len(split_lines_of_utt)): this_ref_word = split_lines_of_utt[i][6] if this_ref_word != '<eps>': word_count_pair[this_ref_word][0] += 1 if not line_is_in_segment[i]: word_count_pair[this_ref_word][1] += 1 def PrintWordStats(word_stats_out): try: f = open(word_stats_out, 'w') except: sys.exit("segment_ctm_edits.py: error opening word-stats file --word-stats-out={0} " "for writing".format(word_stats_out)) global word_count_pair # badness^3 * total_count = pair[1]^3 / pair[0]^2. for key, pair in sorted(word_count_pair.items(), key = lambda item: (item[1][1] ** 3) * 1.0 / (item[1][0] ** 2), reverse = True): badness = pair[1] * 1.0 / pair[0] total_count = pair[0] print(key, badness, total_count, file = f) try: f.close() except: sys.exit("segment_ctm_edits.py: error closing file --word-stats-out={0} " "(full disk?)".format(word_stats_out)) print("segment_ctm_edits.py: please see the file {0} for word-level statistics " "saying how frequently each word was excluded for a segment; format is " "<word> <proportion-of-time-excluded> <total-count>. Particularly " "problematic words appear near the top of the file.".format(word_stats_out), file = sys.stderr) def ProcessData(): try: f_in = open(args.ctm_edits_in) except: sys.exit("modify_ctm_edits.py: error opening ctm-edits input " "file {0}".format(args.ctm_edits_in)) try: text_output_handle = open(args.text_out, 'w') except: sys.exit("modify_ctm_edits.py: error opening text output " "file {0}".format(args.text_out)) try: segments_output_handle = open(args.segments_out, 'w') except: sys.exit("modify_ctm_edits.py: error opening segments output " "file {0}".format(args.text_out)) if args.ctm_edits_out != None: try: ctm_edits_output_handle = open(args.ctm_edits_out, 'w') except: sys.exit("modify_ctm_edits.py: error opening ctm-edits output " "file {0}".format(args.ctm_edits_out)) # Most of what we're doing in the lines below is splitting the input lines first_line = f_in.readline() if first_line == '': sys.exit("modify_ctm_edits.py: empty input") split_pending_line = first_line.split() if len(split_pending_line) == 0: sys.exit("modify_ctm_edits.py: bad input line " + first_line) cur_utterance = split_pending_line[0] split_lines_of_cur_utterance = [] while True: if len(split_pending_line) == 0 or split_pending_line[0] != cur_utterance: (segments_for_utterance, deleted_segments_for_utterance) = GetSegmentsForUtterance(split_lines_of_cur_utterance) AccWordStatsForUtterance(split_lines_of_cur_utterance, segments_for_utterance) WriteSegmentsForUtterance(text_output_handle, segments_output_handle, cur_utterance, segments_for_utterance) if args.ctm_edits_out != None: PrintDebugInfoForUtterance(ctm_edits_output_handle, split_lines_of_cur_utterance, segments_for_utterance, deleted_segments_for_utterance) split_lines_of_cur_utterance = [] if len(split_pending_line) == 0: break else: cur_utterance = split_pending_line[0] split_lines_of_cur_utterance.append(split_pending_line) next_line = f_in.readline() split_pending_line = next_line.split() if len(split_pending_line) == 0: if next_line != '': sys.exit("modify_ctm_edits.py: got an empty or whitespace input line") try: text_output_handle.close() segments_output_handle.close() if args.ctm_edits_out != None: ctm_edits_output_handle.close() except: sys.exit("modify_ctm_edits.py: error closing one or more outputs " "(broken pipe or full disk?)") def ReadNonScoredWords(non_scored_words_file): global non_scored_words try: f = open(non_scored_words_file) except: sys.exit("modify_ctm_edits.py: error opening file: " "--non-scored-words=" + non_scored_words_file) for line in f.readlines(): a = line.split() if not len(line.split()) == 1: sys.exit("modify_ctm_edits.py: bad line in non-scored-words " "file {0}: {1}".format(non_scored_words_file, line)) non_scored_words.add(a[0]) f.close() non_scored_words = set() ReadNonScoredWords(args.non_scored_words_in) oov_symbol = None if args.oov_symbol_file != None: try: with open(args.oov_symbol_file) as f: line = f.readline() assert len(line.split()) == 1 oov_symbol = line.split()[0] assert f.readline() == '' except Exception as e: sys.exit("segment_ctm_edits.py: error reading file --oov-symbol-file=" + args.oov_symbol_file + ", error is: " + str(e)) elif args.unk_padding != 0.0: sys.exit("segment_ctm_edits.py: if the --unk-padding option is nonzero (which " "it is by default, the --oov-symbol-file option must be supplied.") segment_total_length = defaultdict(int) num_segments = defaultdict(int) word_count_pair = defaultdict(lambda: [0, 0]) num_utterances = 0 num_utterances_without_segments = 0 total_length_of_utterances = 0 ProcessData() PrintSegmentStats() if args.word_stats_out != None: PrintWordStats(args.word_stats_out) if args.ctm_edits_out != None: print("segment_ctm_edits.py: detailed utterance-level debug information " "is in " + args.ctm_edits_out, file = sys.stderr)

true

7901acf7eec972aa6135b6cc59d029e7919989aa

12,518

py

Python

train_semisup.py

danielvarga/vat_tf

0b40b256922b7996558504a5d2c3556b5f9fff15

[ "MIT" ]

null

train_semisup.py

danielvarga/vat_tf

0b40b256922b7996558504a5d2c3556b5f9fff15

[ "MIT" ]

null

train_semisup.py

danielvarga/vat_tf

0b40b256922b7996558504a5d2c3556b5f9fff15

[ "MIT" ]

null

import time import numpy as np import tensorflow as tf import layers as L import vat FLAGS = tf.app.flags.FLAGS tf.app.flags.DEFINE_string('device', '/gpu:0', "device") tf.app.flags.DEFINE_string('dataset', 'cifar10', "{cifar10, svhn}") tf.app.flags.DEFINE_string('log_dir', "", "log_dir") tf.app.flags.DEFINE_integer('seed', 1, "initial random seed") tf.app.flags.DEFINE_bool('validation', False, "") tf.app.flags.DEFINE_integer('batch_size', 32, "the number of examples in a batch") tf.app.flags.DEFINE_integer('ul_batch_size', 128, "the number of unlabeled examples in a batch") tf.app.flags.DEFINE_integer('eval_batch_size', 100, "the number of eval examples in a batch") tf.app.flags.DEFINE_integer('eval_freq', 5, "") tf.app.flags.DEFINE_integer('num_epochs', 120, "the number of epochs for training") tf.app.flags.DEFINE_integer('epoch_decay_start', 80, "epoch of starting learning rate decay") tf.app.flags.DEFINE_integer('num_iter_per_epoch', 400, "the number of updates per epoch") tf.app.flags.DEFINE_float('learning_rate', 0.001, "initial leanring rate") tf.app.flags.DEFINE_float('mom1', 0.9, "initial momentum rate") tf.app.flags.DEFINE_float('mom2', 0.5, "momentum rate after epoch_decay_start") tf.app.flags.DEFINE_string('method', 'vat', "{vat, vatent, baseline}") if FLAGS.dataset == 'cifar10': from cifar10 import inputs, unlabeled_inputs elif FLAGS.dataset == 'svhn': from svhn import inputs, unlabeled_inputs else: raise NotImplementedError NUM_EVAL_EXAMPLES = 5000 def build_training_graph(x, y, ul_x, ul_u, lr, mom): global_step = tf.get_variable( name="global_step", shape=[], dtype=tf.float32, initializer=tf.constant_initializer(0.0), trainable=False, ) logit = vat.forward(x) nll_loss = L.ce_loss(logit, y) with tf.variable_scope(tf.get_variable_scope(), reuse=True): if FLAGS.method == 'vat': ul_logit = vat.forward(ul_x, is_training=True, update_batch_stats=False) vat_loss, ul_u_updated = vat.virtual_adversarial_loss(ul_x, ul_u, ul_logit) additional_loss = vat_loss elif FLAGS.method == 'vatent': ul_logit = vat.forward(ul_x, is_training=True, update_batch_stats=False) vat_loss, ul_u_updated = vat.virtual_adversarial_loss(ul_x, ul_u, ul_logit) ent_loss = L.entropy_y_x(ul_logit) additional_loss = vat_loss + ent_loss elif FLAGS.method == 'baseline': additional_loss = 0 else: raise NotImplementedError loss = nll_loss + additional_loss opt = tf.train.AdamOptimizer(learning_rate=lr, beta1=mom) tvars = tf.trainable_variables() grads_and_vars = opt.compute_gradients(loss, tvars) train_op = opt.apply_gradients(grads_and_vars, global_step=global_step) return loss, train_op, global_step, ul_u_updated def build_eval_graph(x, y, ul_x, ul_u): losses = {} logit = vat.forward(x, is_training=False, update_batch_stats=False) nll_loss = L.ce_loss(logit, y) losses['NLL'] = nll_loss acc = L.accuracy(logit, y) losses['Acc'] = acc scope = tf.get_variable_scope() scope.reuse_variables() # at_loss = vat.adversarial_loss(x, y, nll_loss, is_training=False) # losses['AT_loss'] = at_loss ul_logit = vat.forward(ul_x, is_training=False, update_batch_stats=False) vat_loss = vat.virtual_adversarial_loss(ul_x, ul_u, ul_logit, is_training=False) losses['VAT_loss'] = vat_loss return losses def main(_): print(FLAGS.epsilon, FLAGS.top_bn) np.random.seed(seed=FLAGS.seed) tf.set_random_seed(np.random.randint(1234)) with tf.Graph().as_default() as g: with tf.device("/cpu:0"): images, labels = inputs(batch_size=FLAGS.batch_size, train=True, validation=FLAGS.validation, shuffle=True) ul_images = tf.placeholder(shape=images.shape, dtype=tf.float32) '''unlabeled_inputs(batch_size=FLAGS.ul_batch_size, validation=FLAGS.validation, shuffle=True)''' images_eval_train, labels_eval_train = inputs(batch_size=FLAGS.eval_batch_size, train=True, validation=FLAGS.validation, shuffle=True) ul_images_eval_train = unlabeled_inputs(batch_size=FLAGS.eval_batch_size, validation=FLAGS.validation, shuffle=True) images_eval_test, labels_eval_test = inputs(batch_size=FLAGS.eval_batch_size, train=False, validation=FLAGS.validation, shuffle=True) def placeholder_like(x, name=None): return tf.placeholder(shape=x.shape, dtype=tf.float32, name=name) def random_sphere(shape): n = tf.random_normal(shape=shape, dtype=tf.float32) n = tf.reshape(n, shape=(int(shape[0]), -1)) n = tf.nn.l2_normalize(n, dim=1) n = tf.reshape(n, shape) return n def random_sphere_numpy(shape): n = np.random.normal(size=shape) proj_shape = tuple([n.shape[0]] + [1 for _ in range(len(shape) - 1)]) return n / np.linalg.norm(n.reshape((n.shape[0], -1)), axis=1).reshape(proj_shape) print(ul_images.shape) # ul_u = random_sphere(ul_images.shape) # ul_u_eval_train = random_sphere(ul_images_eval_train.shape) # ul_u_eval_test = random_sphere(images_eval_test.shape) ul_u = placeholder_like(ul_images, "ul_u") ul_u_eval_train = placeholder_like(ul_images_eval_train, "ul_u_eval_train") ul_u_eval_test = placeholder_like(images_eval_test, "ul_u_eval_test") with tf.device(FLAGS.device): lr = tf.placeholder(tf.float32, shape=[], name="learning_rate") mom = tf.placeholder(tf.float32, shape=[], name="momentum") with tf.variable_scope("CNN") as scope: # Build training graph loss, train_op, global_step, ul_u_updated = build_training_graph( images, labels, ul_images, ul_u, lr, mom) scope.reuse_variables() # Build eval graph losses_eval_train = build_eval_graph(images_eval_train, labels_eval_train, ul_images_eval_train, ul_u_eval_train) losses_eval_test = build_eval_graph(images_eval_test, labels_eval_test, images_eval_test, ul_u_eval_test) init_op = tf.global_variables_initializer() if not FLAGS.log_dir: logdir = None writer_train = None writer_test = None else: logdir = FLAGS.log_dir writer_train = tf.summary.FileWriter(FLAGS.log_dir + "/train", g) writer_test = tf.summary.FileWriter(FLAGS.log_dir + "/test", g) saver = tf.train.Saver(tf.global_variables()) sv = tf.train.Supervisor( is_chief=True, logdir=logdir, init_op=init_op, init_feed_dict={lr: FLAGS.learning_rate, mom: FLAGS.mom1}, saver=saver, global_step=global_step, summary_op=None, summary_writer=None, save_model_secs=150, recovery_wait_secs=0) ul_images_np = np.load("train_images.npy").reshape((-1, 32, 32, 3)) print("TRUNCATING UL DATA") ul_images_np = ul_images_np[:FLAGS.batch_size] ul_u_np = random_sphere_numpy(ul_images_np.shape) print(ul_images_np.shape, ul_u_np.shape) print("Training...") with sv.managed_session() as sess: for ep in range(FLAGS.num_epochs): if sv.should_stop(): break if ep < FLAGS.epoch_decay_start: feed_dict = {lr: FLAGS.learning_rate, mom: FLAGS.mom1} else: decayed_lr = ((FLAGS.num_epochs - ep) / float( FLAGS.num_epochs - FLAGS.epoch_decay_start)) * FLAGS.learning_rate feed_dict = {lr: decayed_lr, mom: FLAGS.mom2} sum_loss = 0 start = time.time() for i in range(FLAGS.num_iter_per_epoch): picked = range(FLAGS.batch_size) # np.random.choice(len(ul_images_np), size=FLAGS.batch_size, replace=False) feed_dict[ul_images] = ul_images_np[picked] feed_dict[ul_u] = ul_u_np[picked] ul_u_updated_np, _, batch_loss, _ = sess.run([ul_u_updated, train_op, loss, global_step], feed_dict=feed_dict) delta = ul_u_updated_np - ul_u_np[picked] # print("pos", ul_u_updated_np.reshape((FLAGS.batch_size, -1))[0, :4]) # print("delta", np.linalg.norm(delta.reshape((FLAGS.batch_size, -1)), axis=1)[:4]) print(np.linalg.norm(ul_u_updated_np - ul_u_np[picked]), ul_u_updated_np.reshape((FLAGS.batch_size, -1))[0, :3]) ul_u_np[picked] = ul_u_updated_np sum_loss += batch_loss end = time.time() print("Epoch:", ep, "CE_loss_train:", sum_loss / FLAGS.num_iter_per_epoch, "elapsed_time:", end - start) if (ep + 1) % FLAGS.eval_freq == 0 or ep + 1 == FLAGS.num_epochs: # Eval on training data act_values_dict = {} feed_dict = {ul_u_eval_train: random_sphere_numpy(ul_u_eval_train.shape)} for key, _ in losses_eval_train.iteritems(): act_values_dict[key] = 0 n_iter_per_epoch = NUM_EVAL_EXAMPLES / FLAGS.eval_batch_size for i in range(n_iter_per_epoch): values = losses_eval_train.values() act_values = sess.run(values, feed_dict=feed_dict) for key, value in zip(act_values_dict.keys(), act_values): act_values_dict[key] += value summary = tf.Summary() current_global_step = sess.run(global_step) for key, value in act_values_dict.iteritems(): print("train-" + key, value / n_iter_per_epoch) summary.value.add(tag=key, simple_value=value / n_iter_per_epoch) if writer_train is not None: writer_train.add_summary(summary, current_global_step) # Eval on test data act_values_dict = {} print("HOW COME THIS DOES NOT DEPEND ON ul_images_eval_train? SOMETHING'S WRONG HERE.") feed_dict = {ul_u_eval_test: random_sphere_numpy(ul_u_eval_test.shape)} for key, _ in losses_eval_test.iteritems(): act_values_dict[key] = 0 n_iter_per_epoch = NUM_EVAL_EXAMPLES / FLAGS.eval_batch_size for i in range(n_iter_per_epoch): values = losses_eval_test.values() act_values = sess.run(values, feed_dict=feed_dict) for key, value in zip(act_values_dict.keys(), act_values): act_values_dict[key] += value summary = tf.Summary() current_global_step = sess.run(global_step) for key, value in act_values_dict.iteritems(): print("test-" + key, value / n_iter_per_epoch) summary.value.add(tag=key, simple_value=value / n_iter_per_epoch) if writer_test is not None: writer_test.add_summary(summary, current_global_step) saver.save(sess, sv.save_path, global_step=global_step) sv.stop() if __name__ == "__main__": tf.app.run()

47.778626

132

0.582202

import time import numpy as np import tensorflow as tf import layers as L import vat FLAGS = tf.app.flags.FLAGS tf.app.flags.DEFINE_string('device', '/gpu:0', "device") tf.app.flags.DEFINE_string('dataset', 'cifar10', "{cifar10, svhn}") tf.app.flags.DEFINE_string('log_dir', "", "log_dir") tf.app.flags.DEFINE_integer('seed', 1, "initial random seed") tf.app.flags.DEFINE_bool('validation', False, "") tf.app.flags.DEFINE_integer('batch_size', 32, "the number of examples in a batch") tf.app.flags.DEFINE_integer('ul_batch_size', 128, "the number of unlabeled examples in a batch") tf.app.flags.DEFINE_integer('eval_batch_size', 100, "the number of eval examples in a batch") tf.app.flags.DEFINE_integer('eval_freq', 5, "") tf.app.flags.DEFINE_integer('num_epochs', 120, "the number of epochs for training") tf.app.flags.DEFINE_integer('epoch_decay_start', 80, "epoch of starting learning rate decay") tf.app.flags.DEFINE_integer('num_iter_per_epoch', 400, "the number of updates per epoch") tf.app.flags.DEFINE_float('learning_rate', 0.001, "initial leanring rate") tf.app.flags.DEFINE_float('mom1', 0.9, "initial momentum rate") tf.app.flags.DEFINE_float('mom2', 0.5, "momentum rate after epoch_decay_start") tf.app.flags.DEFINE_string('method', 'vat', "{vat, vatent, baseline}") if FLAGS.dataset == 'cifar10': from cifar10 import inputs, unlabeled_inputs elif FLAGS.dataset == 'svhn': from svhn import inputs, unlabeled_inputs else: raise NotImplementedError NUM_EVAL_EXAMPLES = 5000 def build_training_graph(x, y, ul_x, ul_u, lr, mom): global_step = tf.get_variable( name="global_step", shape=[], dtype=tf.float32, initializer=tf.constant_initializer(0.0), trainable=False, ) logit = vat.forward(x) nll_loss = L.ce_loss(logit, y) with tf.variable_scope(tf.get_variable_scope(), reuse=True): if FLAGS.method == 'vat': ul_logit = vat.forward(ul_x, is_training=True, update_batch_stats=False) vat_loss, ul_u_updated = vat.virtual_adversarial_loss(ul_x, ul_u, ul_logit) additional_loss = vat_loss elif FLAGS.method == 'vatent': ul_logit = vat.forward(ul_x, is_training=True, update_batch_stats=False) vat_loss, ul_u_updated = vat.virtual_adversarial_loss(ul_x, ul_u, ul_logit) ent_loss = L.entropy_y_x(ul_logit) additional_loss = vat_loss + ent_loss elif FLAGS.method == 'baseline': additional_loss = 0 else: raise NotImplementedError loss = nll_loss + additional_loss opt = tf.train.AdamOptimizer(learning_rate=lr, beta1=mom) tvars = tf.trainable_variables() grads_and_vars = opt.compute_gradients(loss, tvars) train_op = opt.apply_gradients(grads_and_vars, global_step=global_step) return loss, train_op, global_step, ul_u_updated def build_eval_graph(x, y, ul_x, ul_u): losses = {} logit = vat.forward(x, is_training=False, update_batch_stats=False) nll_loss = L.ce_loss(logit, y) losses['NLL'] = nll_loss acc = L.accuracy(logit, y) losses['Acc'] = acc scope = tf.get_variable_scope() scope.reuse_variables() ul_logit = vat.forward(ul_x, is_training=False, update_batch_stats=False) vat_loss = vat.virtual_adversarial_loss(ul_x, ul_u, ul_logit, is_training=False) losses['VAT_loss'] = vat_loss return losses def main(_): print(FLAGS.epsilon, FLAGS.top_bn) np.random.seed(seed=FLAGS.seed) tf.set_random_seed(np.random.randint(1234)) with tf.Graph().as_default() as g: with tf.device("/cpu:0"): images, labels = inputs(batch_size=FLAGS.batch_size, train=True, validation=FLAGS.validation, shuffle=True) ul_images = tf.placeholder(shape=images.shape, dtype=tf.float32) images_eval_train, labels_eval_train = inputs(batch_size=FLAGS.eval_batch_size, train=True, validation=FLAGS.validation, shuffle=True) ul_images_eval_train = unlabeled_inputs(batch_size=FLAGS.eval_batch_size, validation=FLAGS.validation, shuffle=True) images_eval_test, labels_eval_test = inputs(batch_size=FLAGS.eval_batch_size, train=False, validation=FLAGS.validation, shuffle=True) def placeholder_like(x, name=None): return tf.placeholder(shape=x.shape, dtype=tf.float32, name=name) def random_sphere(shape): n = tf.random_normal(shape=shape, dtype=tf.float32) n = tf.reshape(n, shape=(int(shape[0]), -1)) n = tf.nn.l2_normalize(n, dim=1) n = tf.reshape(n, shape) return n def random_sphere_numpy(shape): n = np.random.normal(size=shape) proj_shape = tuple([n.shape[0]] + [1 for _ in range(len(shape) - 1)]) return n / np.linalg.norm(n.reshape((n.shape[0], -1)), axis=1).reshape(proj_shape) print(ul_images.shape) ul_u = placeholder_like(ul_images, "ul_u") ul_u_eval_train = placeholder_like(ul_images_eval_train, "ul_u_eval_train") ul_u_eval_test = placeholder_like(images_eval_test, "ul_u_eval_test") with tf.device(FLAGS.device): lr = tf.placeholder(tf.float32, shape=[], name="learning_rate") mom = tf.placeholder(tf.float32, shape=[], name="momentum") with tf.variable_scope("CNN") as scope: loss, train_op, global_step, ul_u_updated = build_training_graph( images, labels, ul_images, ul_u, lr, mom) scope.reuse_variables() losses_eval_train = build_eval_graph(images_eval_train, labels_eval_train, ul_images_eval_train, ul_u_eval_train) losses_eval_test = build_eval_graph(images_eval_test, labels_eval_test, images_eval_test, ul_u_eval_test) init_op = tf.global_variables_initializer() if not FLAGS.log_dir: logdir = None writer_train = None writer_test = None else: logdir = FLAGS.log_dir writer_train = tf.summary.FileWriter(FLAGS.log_dir + "/train", g) writer_test = tf.summary.FileWriter(FLAGS.log_dir + "/test", g) saver = tf.train.Saver(tf.global_variables()) sv = tf.train.Supervisor( is_chief=True, logdir=logdir, init_op=init_op, init_feed_dict={lr: FLAGS.learning_rate, mom: FLAGS.mom1}, saver=saver, global_step=global_step, summary_op=None, summary_writer=None, save_model_secs=150, recovery_wait_secs=0) ul_images_np = np.load("train_images.npy").reshape((-1, 32, 32, 3)) print("TRUNCATING UL DATA") ul_images_np = ul_images_np[:FLAGS.batch_size] ul_u_np = random_sphere_numpy(ul_images_np.shape) print(ul_images_np.shape, ul_u_np.shape) print("Training...") with sv.managed_session() as sess: for ep in range(FLAGS.num_epochs): if sv.should_stop(): break if ep < FLAGS.epoch_decay_start: feed_dict = {lr: FLAGS.learning_rate, mom: FLAGS.mom1} else: decayed_lr = ((FLAGS.num_epochs - ep) / float( FLAGS.num_epochs - FLAGS.epoch_decay_start)) * FLAGS.learning_rate feed_dict = {lr: decayed_lr, mom: FLAGS.mom2} sum_loss = 0 start = time.time() for i in range(FLAGS.num_iter_per_epoch): picked = range(FLAGS.batch_size) feed_dict[ul_images] = ul_images_np[picked] feed_dict[ul_u] = ul_u_np[picked] ul_u_updated_np, _, batch_loss, _ = sess.run([ul_u_updated, train_op, loss, global_step], feed_dict=feed_dict) delta = ul_u_updated_np - ul_u_np[picked] print(np.linalg.norm(ul_u_updated_np - ul_u_np[picked]), ul_u_updated_np.reshape((FLAGS.batch_size, -1))[0, :3]) ul_u_np[picked] = ul_u_updated_np sum_loss += batch_loss end = time.time() print("Epoch:", ep, "CE_loss_train:", sum_loss / FLAGS.num_iter_per_epoch, "elapsed_time:", end - start) if (ep + 1) % FLAGS.eval_freq == 0 or ep + 1 == FLAGS.num_epochs: act_values_dict = {} feed_dict = {ul_u_eval_train: random_sphere_numpy(ul_u_eval_train.shape)} for key, _ in losses_eval_train.iteritems(): act_values_dict[key] = 0 n_iter_per_epoch = NUM_EVAL_EXAMPLES / FLAGS.eval_batch_size for i in range(n_iter_per_epoch): values = losses_eval_train.values() act_values = sess.run(values, feed_dict=feed_dict) for key, value in zip(act_values_dict.keys(), act_values): act_values_dict[key] += value summary = tf.Summary() current_global_step = sess.run(global_step) for key, value in act_values_dict.iteritems(): print("train-" + key, value / n_iter_per_epoch) summary.value.add(tag=key, simple_value=value / n_iter_per_epoch) if writer_train is not None: writer_train.add_summary(summary, current_global_step) act_values_dict = {} print("HOW COME THIS DOES NOT DEPEND ON ul_images_eval_train? SOMETHING'S WRONG HERE.") feed_dict = {ul_u_eval_test: random_sphere_numpy(ul_u_eval_test.shape)} for key, _ in losses_eval_test.iteritems(): act_values_dict[key] = 0 n_iter_per_epoch = NUM_EVAL_EXAMPLES / FLAGS.eval_batch_size for i in range(n_iter_per_epoch): values = losses_eval_test.values() act_values = sess.run(values, feed_dict=feed_dict) for key, value in zip(act_values_dict.keys(), act_values): act_values_dict[key] += value summary = tf.Summary() current_global_step = sess.run(global_step) for key, value in act_values_dict.iteritems(): print("test-" + key, value / n_iter_per_epoch) summary.value.add(tag=key, simple_value=value / n_iter_per_epoch) if writer_test is not None: writer_test.add_summary(summary, current_global_step) saver.save(sess, sv.save_path, global_step=global_step) sv.stop() if __name__ == "__main__": tf.app.run()

true

7901adc9c188a39b0cf7d9c63de2e761bf3b34c6

503

py

Python

ongabot/handler/helpcommand.py

walkerjens/telegram.ongabot

3c4edd8ba9815c087ed18b07f3f4bc9c90701d60

[ "MIT" ]

null

ongabot/handler/helpcommand.py

walkerjens/telegram.ongabot

3c4edd8ba9815c087ed18b07f3f4bc9c90701d60

[ "MIT" ]

null

ongabot/handler/helpcommand.py

walkerjens/telegram.ongabot

3c4edd8ba9815c087ed18b07f3f4bc9c90701d60

[ "MIT" ]

null

"""This module contains the HelpCommandHandler class.""" from telegram import Update from telegram.ext import CommandHandler, CallbackContext import utils.helper as helper class HelpCommandHandler(CommandHandler): """Handler for /help command""" def __init__(self): CommandHandler.__init__(self, "help", callback) def callback(update: Update, _: CallbackContext): """Print the help text for a /start or /help command""" update.message.reply_text(helper.create_help_text())

27.944444

59

0.747515

from telegram import Update from telegram.ext import CommandHandler, CallbackContext import utils.helper as helper class HelpCommandHandler(CommandHandler): def __init__(self): CommandHandler.__init__(self, "help", callback) def callback(update: Update, _: CallbackContext): update.message.reply_text(helper.create_help_text())

true

7901adeebc4eddb9811775a1dd8834093c7ac65d

2,057

py

Python

examples/pylab_examples/mri_with_eeg.py

yuvallanger/matplotlib

e0020d318a9a9685594c6bff4631f74599321459

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

8

2017-04-11T08:55:30.000Z

2022-03-25T04:31:26.000Z

examples/pylab_examples/mri_with_eeg.py

epgauss/matplotlib

c9898ea9a30c67c579ab27cd61b68e2abae0fb0e

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

null

examples/pylab_examples/mri_with_eeg.py

epgauss/matplotlib

c9898ea9a30c67c579ab27cd61b68e2abae0fb0e

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

14

2015-10-05T04:15:46.000Z

2020-06-11T18:06:02.000Z

#!/usr/bin/env python """ This now uses the imshow command instead of pcolor which *is much faster* """ from __future__ import division, print_function import numpy as np from matplotlib.pyplot import * from matplotlib.collections import LineCollection import matplotlib.cbook as cbook # I use if 1 to break up the different regions of code visually if 1: # load the data # data are 256x256 16 bit integers dfile = cbook.get_sample_data('s1045.ima.gz') im = np.fromstring(dfile.read(), np.uint16).astype(float) im.shape = 256, 256 if 1: # plot the MRI in pcolor subplot(221) imshow(im, cmap=cm.gray) axis('off') if 1: # plot the histogram of MRI intensity subplot(222) im = np.ravel(im) im = im[np.nonzero(im)] # ignore the background im = im/(2.0**15) # normalize hist(im, 100) xticks([-1, -.5, 0, .5, 1]) yticks([]) xlabel('intensity') ylabel('MRI density') if 1: # plot the EEG # load the data numSamples, numRows = 800,4 eegfile = cbook.get_sample_data('eeg.dat', asfileobj=False) print('loading eeg %s' % eegfile) data = np.fromstring(open(eegfile, 'rb').read(), float) data.shape = numSamples, numRows t = 10.0 * np.arange(numSamples, dtype=float)/numSamples ticklocs = [] ax = subplot(212) xlim(0,10) xticks(np.arange(10)) dmin = data.min() dmax = data.max() dr = (dmax - dmin)*0.7 # Crowd them a bit. y0 = dmin y1 = (numRows-1) * dr + dmax ylim(y0, y1) segs = [] for i in range(numRows): segs.append(np.hstack((t[:,np.newaxis], data[:,i,np.newaxis]))) ticklocs.append(i*dr) offsets = np.zeros((numRows,2), dtype=float) offsets[:,1] = ticklocs lines = LineCollection(segs, offsets=offsets, transOffset=None, ) ax.add_collection(lines) # set the yticks to use axes coords on the y axis ax.set_yticks(ticklocs) ax.set_yticklabels(['PG3', 'PG5', 'PG7', 'PG9']) xlabel('time (s)') show()

26.037975

71

0.618376

from __future__ import division, print_function import numpy as np from matplotlib.pyplot import * from matplotlib.collections import LineCollection import matplotlib.cbook as cbook if 1: dfile = cbook.get_sample_data('s1045.ima.gz') im = np.fromstring(dfile.read(), np.uint16).astype(float) im.shape = 256, 256 if 1: subplot(221) imshow(im, cmap=cm.gray) axis('off') if 1: subplot(222) im = np.ravel(im) im = im[np.nonzero(im)] im = im/(2.0**15) hist(im, 100) xticks([-1, -.5, 0, .5, 1]) yticks([]) xlabel('intensity') ylabel('MRI density') if 1: numSamples, numRows = 800,4 eegfile = cbook.get_sample_data('eeg.dat', asfileobj=False) print('loading eeg %s' % eegfile) data = np.fromstring(open(eegfile, 'rb').read(), float) data.shape = numSamples, numRows t = 10.0 * np.arange(numSamples, dtype=float)/numSamples ticklocs = [] ax = subplot(212) xlim(0,10) xticks(np.arange(10)) dmin = data.min() dmax = data.max() dr = (dmax - dmin)*0.7 y0 = dmin y1 = (numRows-1) * dr + dmax ylim(y0, y1) segs = [] for i in range(numRows): segs.append(np.hstack((t[:,np.newaxis], data[:,i,np.newaxis]))) ticklocs.append(i*dr) offsets = np.zeros((numRows,2), dtype=float) offsets[:,1] = ticklocs lines = LineCollection(segs, offsets=offsets, transOffset=None, ) ax.add_collection(lines) ax.set_yticks(ticklocs) ax.set_yticklabels(['PG3', 'PG5', 'PG7', 'PG9']) xlabel('time (s)') show()

true

7901b00068d35d764431ee575b195d337b0598bd

840

py

Python

pyNastran/gui/matplotlib_backend.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

293

2015-03-22T20:22:01.000Z

2022-03-14T20:28:24.000Z

pyNastran/gui/matplotlib_backend.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

512

2015-03-14T18:39:27.000Z

2022-03-31T16:15:43.000Z

pyNastran/gui/matplotlib_backend.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

136

2015-03-19T03:26:06.000Z

2022-03-25T22:14:54.000Z

""" Selects a matplotlib backend so you can run without a GUI/tkinter. Supports: - PyQt5 - PySide2 - WX - Tkinter """ from pyNastran.gui import IS_DEV if IS_DEV: # there is no interactive backend when testing on TravisCI matplotlib_backend = 'Agg' else: # fails if using the terminal and PyQt/PySide & qtpy are installed # how do I check if there is a terminal vs just running in command line? # try: from pyNastran.gui.qt_version import qt_int matplotlib_backend = 'Qt%iAgg' % qt_int except ImportError: try: # hasn't been tested on a machine without a backend... # default matplotlib backend import tkinter matplotlib_backend = 'tkAgg' except ImportError: # no-gui backend matplotlib_backend = 'Agg'

28.965517

77

0.642857

from pyNastran.gui import IS_DEV if IS_DEV: matplotlib_backend = 'Agg' else: try: from pyNastran.gui.qt_version import qt_int matplotlib_backend = 'Qt%iAgg' % qt_int except ImportError: try: # default matplotlib backend import tkinter matplotlib_backend = 'tkAgg' except ImportError: # no-gui backend matplotlib_backend = 'Agg'

true

7901b022a8a252e56a5eb0648b1a4bcfdfff373f

110

py

Python

ocra/download.py

mzntaka0/ocra

037afc508ac319efbcec99a72b9b3793cecf3fc9

[ "Apache-2.0" ]

4

2018-12-27T01:43:51.000Z

2019-08-15T03:01:15.000Z

audy/mix/speaker.py

mzntaka0/audy

e347aac79ceb783df23a51e842672aaa7b1f7514

[ "Apache-2.0" ]

1

2019-09-09T08:46:18.000Z

audy/mix/speaker.py

mzntaka0/audy

e347aac79ceb783df23a51e842672aaa7b1f7514

[ "Apache-2.0" ]

1

2019-11-08T13:48:51.000Z

# -*- coding: utf-8 -*- """ """ import argparse import os import sys if __name__ == '__main__': pass

7.857143

26

0.572727

import argparse import os import sys if __name__ == '__main__': pass

true

7901b0441f6d52e9d452fb15661e518e8db12f07

2,615

py

Python

brainrender_gui/widgets/add_regions.py

brainglobe/bg-brainrender-gui

4048f789fbdc1a5d4c5c652a4f37222446c8aa2f

[ "BSD-3-Clause" ]

7

2020-07-09T10:27:38.000Z

2020-10-13T13:16:20.000Z

brainrender_gui/widgets/add_regions.py

brainglobe/bg-brainrender-gui

4048f789fbdc1a5d4c5c652a4f37222446c8aa2f

[ "BSD-3-Clause" ]

12

2020-07-31T15:03:49.000Z

2020-12-11T08:00:20.000Z

brainrender_gui/widgets/add_regions.py

brainglobe/bg-brainrender-gui

4048f789fbdc1a5d4c5c652a4f37222446c8aa2f

[ "BSD-3-Clause" ]

null

from qtpy.QtWidgets import QDialog, QLineEdit, QPushButton, QLabel, QVBoxLayout from brainrender_gui.style import style, update_css class AddRegionsWindow(QDialog): left = 250 top = 250 width = 400 height = 300 label_msg = ( "Write the acronyms of brainregions " + "you wish to add.\n[as 'space' separated strings (e.g.: STN TH)]" ) def __init__(self, main_window, palette): """ Creates a new window for user to input which regions to add to scene. Arguments: ---------- main_window: reference to the App's main window palette: main_window's palette, used to style widgets """ super().__init__() self.setWindowTitle("Add brain regions") self.ui() self.main_window = main_window self.setStyleSheet(update_css(style, palette)) def ui(self): """ Define UI's elements """ self.setGeometry(self.left, self.top, self.width, self.height) layout = QVBoxLayout() # Regions label = QLabel(self) label.setObjectName("PopupLabel") label.setText(self.label_msg) self.textbox = QLineEdit(self) # Alpha alpha_label = QLabel(self) alpha_label.setObjectName("PopupLabel") alpha_label.setText("Alpha") self.alpha_textbox = QLineEdit(self) self.alpha_textbox.setText(str(1.0)) # Color color_label = QLabel(self) color_label.setObjectName("PopupLabel") color_label.setText("Color") self.color_textbox = QLineEdit(self) self.color_textbox.setText("atlas") # Create a button in the window self.button = QPushButton("Add regions", self) self.button.clicked.connect(self.on_click) self.button.setObjectName("RegionsButton") layout.addWidget(label) layout.addWidget(self.textbox) layout.addWidget(alpha_label) layout.addWidget(self.alpha_textbox) layout.addWidget(color_label) layout.addWidget(self.color_textbox) layout.addWidget(self.button) self.setLayout(layout) self.show() def on_click(self): """ On click or 'Enter' get the regions from the input and call the add_regions method of the main window """ regions = self.textbox.text().split(" ") self.main_window.add_regions( regions, self.alpha_textbox.text(), self.color_textbox.text() ) self.close()

27.526316

79

0.602677

from qtpy.QtWidgets import QDialog, QLineEdit, QPushButton, QLabel, QVBoxLayout from brainrender_gui.style import style, update_css class AddRegionsWindow(QDialog): left = 250 top = 250 width = 400 height = 300 label_msg = ( "Write the acronyms of brainregions " + "you wish to add.\n[as 'space' separated strings (e.g.: STN TH)]" ) def __init__(self, main_window, palette): super().__init__() self.setWindowTitle("Add brain regions") self.ui() self.main_window = main_window self.setStyleSheet(update_css(style, palette)) def ui(self): self.setGeometry(self.left, self.top, self.width, self.height) layout = QVBoxLayout() label = QLabel(self) label.setObjectName("PopupLabel") label.setText(self.label_msg) self.textbox = QLineEdit(self) alpha_label = QLabel(self) alpha_label.setObjectName("PopupLabel") alpha_label.setText("Alpha") self.alpha_textbox = QLineEdit(self) self.alpha_textbox.setText(str(1.0)) color_label = QLabel(self) color_label.setObjectName("PopupLabel") color_label.setText("Color") self.color_textbox = QLineEdit(self) self.color_textbox.setText("atlas") self.button = QPushButton("Add regions", self) self.button.clicked.connect(self.on_click) self.button.setObjectName("RegionsButton") layout.addWidget(label) layout.addWidget(self.textbox) layout.addWidget(alpha_label) layout.addWidget(self.alpha_textbox) layout.addWidget(color_label) layout.addWidget(self.color_textbox) layout.addWidget(self.button) self.setLayout(layout) self.show() def on_click(self): regions = self.textbox.text().split(" ") self.main_window.add_regions( regions, self.alpha_textbox.text(), self.color_textbox.text() ) self.close()

true

7901b06d39b2aefc93c03f56e1b5273f667d41c6

1,652

py

Python

federation/protocols/activitypub/signing.py

weex/federation

01357aacb04b076442ce5f803a0fc65df5a74d09

[ "BSD-3-Clause" ]

93

2016-11-26T10:52:13.000Z

2022-01-15T20:07:35.000Z

federation/protocols/activitypub/signing.py

weex/federation

01357aacb04b076442ce5f803a0fc65df5a74d09

[ "BSD-3-Clause" ]

75

2016-10-18T10:15:44.000Z

2019-10-05T22:16:32.000Z

federation/protocols/activitypub/signing.py

weex/federation

01357aacb04b076442ce5f803a0fc65df5a74d09

[ "BSD-3-Clause" ]

9

2017-04-08T08:03:45.000Z

2021-09-13T22:00:48.000Z

""" Thank you Funkwhale for inspiration on the HTTP signatures parts <3 https://funkwhale.audio/ """ import datetime import logging from typing import Union import pytz from Crypto.PublicKey.RSA import RsaKey from requests_http_signature import HTTPSignatureHeaderAuth from federation.types import RequestType from federation.utils.network import parse_http_date from federation.utils.text import encode_if_text logger = logging.getLogger("federation") def get_http_authentication(private_key: RsaKey, private_key_id: str) -> HTTPSignatureHeaderAuth: """ Get HTTP signature authentication for a request. """ key = private_key.exportKey() return HTTPSignatureHeaderAuth( headers=["(request-target)", "user-agent", "host", "date"], algorithm="rsa-sha256", key=key, key_id=private_key_id, ) def verify_request_signature(request: RequestType, public_key: Union[str, bytes]): """ Verify HTTP signature in request against a public key. """ key = encode_if_text(public_key) date_header = request.headers.get("Date") if not date_header: raise ValueError("Rquest Date header is missing") ts = parse_http_date(date_header) dt = datetime.datetime.utcfromtimestamp(ts).replace(tzinfo=pytz.utc) past_delta = datetime.timedelta(hours=24) future_delta = datetime.timedelta(seconds=30) now = datetime.datetime.utcnow().replace(tzinfo=pytz.utc) if dt < now - past_delta or dt > now + future_delta: raise ValueError("Request Date is too far in future or past") HTTPSignatureHeaderAuth.verify(request, key_resolver=lambda **kwargs: key)

31.769231

97

0.734867

import datetime import logging from typing import Union import pytz from Crypto.PublicKey.RSA import RsaKey from requests_http_signature import HTTPSignatureHeaderAuth from federation.types import RequestType from federation.utils.network import parse_http_date from federation.utils.text import encode_if_text logger = logging.getLogger("federation") def get_http_authentication(private_key: RsaKey, private_key_id: str) -> HTTPSignatureHeaderAuth: key = private_key.exportKey() return HTTPSignatureHeaderAuth( headers=["(request-target)", "user-agent", "host", "date"], algorithm="rsa-sha256", key=key, key_id=private_key_id, ) def verify_request_signature(request: RequestType, public_key: Union[str, bytes]): key = encode_if_text(public_key) date_header = request.headers.get("Date") if not date_header: raise ValueError("Rquest Date header is missing") ts = parse_http_date(date_header) dt = datetime.datetime.utcfromtimestamp(ts).replace(tzinfo=pytz.utc) past_delta = datetime.timedelta(hours=24) future_delta = datetime.timedelta(seconds=30) now = datetime.datetime.utcnow().replace(tzinfo=pytz.utc) if dt < now - past_delta or dt > now + future_delta: raise ValueError("Request Date is too far in future or past") HTTPSignatureHeaderAuth.verify(request, key_resolver=lambda **kwargs: key)

true

7901b4a1191898ebebf5d5a2dddf1341b901ae6e

4,005

py

Python

dhis2_core/src/dhis2/code_list/svcm.py

dhis2/dhis2-python-cli

d5ec976a5c04e6897756e3be14924ec74a4456fd

[ "BSD-3-Clause" ]

7

2020-10-15T08:54:50.000Z

2021-12-19T14:37:49.000Z

dhis2_core/src/dhis2/code_list/svcm.py

dhis2/dhis2-python-cli

d5ec976a5c04e6897756e3be14924ec74a4456fd

[ "BSD-3-Clause" ]

3

2016-08-12T14:11:14.000Z

2021-03-08T17:06:29.000Z

dhis2_core/src/dhis2/code_list/svcm.py

dhis2/dhis2-python-cli

d5ec976a5c04e6897756e3be14924ec74a4456fd

[ "BSD-3-Clause" ]

4

2016-02-10T23:03:08.000Z

2020-12-28T13:18:49.000Z

import json import logging import sys from typing import Any, Callable, Dict, List from dhis2.core.http import BaseHttpRequest from dhis2.core.inventory import HostResolved, Inventory, resolve_one from fhir.resources.bundle import Bundle from .models.svcm import CodeList, SVCMConfig from .svcm_resources import build_bundle log = logging.getLogger(__name__) def get_source(config: SVCMConfig, inventory: Inventory) -> Callable[[Any], Any]: host = resolve_one(config.source.id, inventory) if "dhis2" not in host.type: log.error("Only 'dhis2' source type is currently supported") sys.exit(-1) log.info(f"Creating source from '{host.key}' with base url '{host.baseUrl}'") def fn(): filters = [] # https://docs.dhis2.org/2.35/en/developer/html/webapi_metadata_object_filter.html if config.source.lastUpdated: filters.append(f"lastUpdated:ge:{config.source.lastUpdated}") option_sets_filter = list(map(lambda x: f"id:eq:{x}", config.source.filters.optionSets)) option_sets_filter.extend(filters) option_sets = BaseHttpRequest(host).get( "api/optionSets", params={ "fields": "id,code,name,version,translations,options[id,code,name,translations]", "rootJunction": "OR", "filter": option_sets_filter, "paging": False, }, ) categories_filter = list(map(lambda x: f"id:eq:{x}", config.source.filters.categories)) categories_filter.extend(filters) categories = BaseHttpRequest(host).get( "api/categories", params={ "fields": "id,code,name,translations,categoryOptions::rename(options)[id,code,name,translations]", "rootJunction": "OR", "filter": categories_filter, "paging": False, }, ) data = { "optionSets": option_sets.get("optionSets", []), "categories": categories.get("categories", []), } return ( host, data, ) return fn def get_target(config: SVCMConfig, inventory: Inventory) -> Callable[[Any], Any]: id = config.target.id if "log://" == id: log.info("Creating 'log://' target") def target_log(data: Any): log.info("Writing result to stdout") print(json.dumps(data[1].as_json(), indent=2)) return target_log elif "null://" == id: log.info("Creating 'null://' target") def target_null(data: Any): log.info("Doing nothing with result") return target_null host = resolve_one(id, inventory) if "dhis2" in host.type: log.error("'dhis2' target type is not currently supported") sys.exit(-1) log.info(f"Creating target from '{host.key}' with base url '{host.baseUrl}'") def target_push(data: Any): payload: Bundle = data[1] return BaseHttpRequest(host).post("", data=payload.as_json()) return target_push def transform(config: SVCMConfig, data: Any): host: HostResolved = data[0] payload: Dict[str, Any] = data[1] code_lists: List[CodeList] = [] option_sets = payload.get("optionSets", []) categories = payload.get("categories", []) for option_set in option_sets: code_lists.append(CodeList(**option_set)) for category in categories: code_lists.append(CodeList(**category, type="categories")) return ( host, build_bundle(code_lists, host.baseUrl), ) def run(config: SVCMConfig, inventory: Inventory): log.info(f"SVCM job '{config.id}'' starting") source = get_source(config, inventory) target = get_target(config, inventory) data = source() data = transform(config, data) data = target(data) if data: log.info(f"Got response from target system {data}") log.info(f"SVCM job '{config.id}' finished")

28.204225

114

0.615481

import json import logging import sys from typing import Any, Callable, Dict, List from dhis2.core.http import BaseHttpRequest from dhis2.core.inventory import HostResolved, Inventory, resolve_one from fhir.resources.bundle import Bundle from .models.svcm import CodeList, SVCMConfig from .svcm_resources import build_bundle log = logging.getLogger(__name__) def get_source(config: SVCMConfig, inventory: Inventory) -> Callable[[Any], Any]: host = resolve_one(config.source.id, inventory) if "dhis2" not in host.type: log.error("Only 'dhis2' source type is currently supported") sys.exit(-1) log.info(f"Creating source from '{host.key}' with base url '{host.baseUrl}'") def fn(): filters = [] if config.source.lastUpdated: filters.append(f"lastUpdated:ge:{config.source.lastUpdated}") option_sets_filter = list(map(lambda x: f"id:eq:{x}", config.source.filters.optionSets)) option_sets_filter.extend(filters) option_sets = BaseHttpRequest(host).get( "api/optionSets", params={ "fields": "id,code,name,version,translations,options[id,code,name,translations]", "rootJunction": "OR", "filter": option_sets_filter, "paging": False, }, ) categories_filter = list(map(lambda x: f"id:eq:{x}", config.source.filters.categories)) categories_filter.extend(filters) categories = BaseHttpRequest(host).get( "api/categories", params={ "fields": "id,code,name,translations,categoryOptions::rename(options)[id,code,name,translations]", "rootJunction": "OR", "filter": categories_filter, "paging": False, }, ) data = { "optionSets": option_sets.get("optionSets", []), "categories": categories.get("categories", []), } return ( host, data, ) return fn def get_target(config: SVCMConfig, inventory: Inventory) -> Callable[[Any], Any]: id = config.target.id if "log://" == id: log.info("Creating 'log://' target") def target_log(data: Any): log.info("Writing result to stdout") print(json.dumps(data[1].as_json(), indent=2)) return target_log elif "null://" == id: log.info("Creating 'null://' target") def target_null(data: Any): log.info("Doing nothing with result") return target_null host = resolve_one(id, inventory) if "dhis2" in host.type: log.error("'dhis2' target type is not currently supported") sys.exit(-1) log.info(f"Creating target from '{host.key}' with base url '{host.baseUrl}'") def target_push(data: Any): payload: Bundle = data[1] return BaseHttpRequest(host).post("", data=payload.as_json()) return target_push def transform(config: SVCMConfig, data: Any): host: HostResolved = data[0] payload: Dict[str, Any] = data[1] code_lists: List[CodeList] = [] option_sets = payload.get("optionSets", []) categories = payload.get("categories", []) for option_set in option_sets: code_lists.append(CodeList(**option_set)) for category in categories: code_lists.append(CodeList(**category, type="categories")) return ( host, build_bundle(code_lists, host.baseUrl), ) def run(config: SVCMConfig, inventory: Inventory): log.info(f"SVCM job '{config.id}'' starting") source = get_source(config, inventory) target = get_target(config, inventory) data = source() data = transform(config, data) data = target(data) if data: log.info(f"Got response from target system {data}") log.info(f"SVCM job '{config.id}' finished")

true

7901b729a1fcda59aaac394589b9db9c6d3c000a

93

py

Python

src/UOJ_1933 - (3425561) Accepted.py

miguelarauj1o/UOJ

eb195754829c42c3dcf1a68616e63da1386cb5a9

[ "MIT" ]

80

2015-01-07T01:18:40.000Z

2021-05-04T15:23:18.000Z

src/UOJ_1933 - (3425561) Accepted.py

miguelarauj1o/OJ

eb195754829c42c3dcf1a68616e63da1386cb5a9

[ "MIT" ]

1

2019-01-07T01:13:32.000Z

src/UOJ_1933 - (3425561) Accepted.py

miguelarauj1o/OJ

eb195754829c42c3dcf1a68616e63da1386cb5a9

[ "MIT" ]

28

2015-03-05T11:53:23.000Z

2020-07-05T15:50:42.000Z

a, b = raw_input().split() a = int(a) b = int(b) if b > a: print(b) else: print(a)1

11.625

27

0.505376

a, b = raw_input().split() a = int(a) b = int(b) if b > a: print(b) else: print(a)1

false

true

7901b7d9d3e6f43d5bf1e3baba09c66f4e443df2

1,943

py

Python

Twitter_scraping/graph_builder.py

TristanThomson/Year-3-Final-Project

07a588ff3312040c6ff41fd170c1909357991c66

[ "OML" ]

2

2020-01-01T16:04:04.000Z

2020-01-27T13:14:22.000Z

Twitter_scraping/graph_builder.py

TristanThomson/Year-3-Final-Project

07a588ff3312040c6ff41fd170c1909357991c66

[ "OML" ]

null

Twitter_scraping/graph_builder.py

TristanThomson/Year-3-Final-Project

07a588ff3312040c6ff41fd170c1909357991c66

[ "OML" ]

null

import os import networkx as nx import pandas as pd from pathlib import Path from Twitter_scraping.scraper_helper import RandomPicker G = nx.DiGraph() # initialises empty NetworkX graph min_list = RandomPicker().min_df["Twitter"].dropna() # Pandas series from the "Twitter" col of the SYI dataset mep_list = RandomPicker().all_df["Twitter"].dropna() rootdir = os.getcwd() # path to parent folder of current file def check_minorities(): for path in Path(rootdir).rglob('*.csv'): curparent = str(path.parent.name) if curparent in map(lambda x: x.lower(),min_list["Twitter"].dropna()) and not path.parent.parent.name == "minority": print(curparent) original = str(rootdir) + "/" + str(path.parent.parent.parent.name) + "/majority/" + str( curparent) + "/" + str(path.name) new = str(rootdir) + "/" + str(path.parent.parent.parent.name) + "/minority/" + str(curparent) + "/" + str( path.name) os.rename(original, new) for path in Path(rootdir).rglob('*.csv'): curparent = str(path.parent.name) curfile = pd.read_csv(path, encoding='utf-8-sig') if curparent.lower() in map(lambda x: x.lower(), min_list): G.add_node(curparent, is_mep=1) if str(path.name) == "following.csv": print(path.name) for i in curfile["username"]: if i in map(lambda x: x.lower(), mep_list): G.add_node(str(i), is_mep=1) else: G.add_node(str(i), is_mep=0) G.add_edge(curparent, i) else: print(path.name) for i in curfile["username"]: if i in map(lambda x: x.lower(), mep_list): G.add_node(str(i), is_mep=1) else: G.add_node(str(i), is_mep=0) G.add_edge(str(i), curparent) nx.write_gexf(G, "minority.gexf")

41.340426

124

0.58106

import os import networkx as nx import pandas as pd from pathlib import Path from Twitter_scraping.scraper_helper import RandomPicker G = nx.DiGraph() min_list = RandomPicker().min_df["Twitter"].dropna() mep_list = RandomPicker().all_df["Twitter"].dropna() rootdir = os.getcwd() def check_minorities(): for path in Path(rootdir).rglob('*.csv'): curparent = str(path.parent.name) if curparent in map(lambda x: x.lower(),min_list["Twitter"].dropna()) and not path.parent.parent.name == "minority": print(curparent) original = str(rootdir) + "/" + str(path.parent.parent.parent.name) + "/majority/" + str( curparent) + "/" + str(path.name) new = str(rootdir) + "/" + str(path.parent.parent.parent.name) + "/minority/" + str(curparent) + "/" + str( path.name) os.rename(original, new) for path in Path(rootdir).rglob('*.csv'): curparent = str(path.parent.name) curfile = pd.read_csv(path, encoding='utf-8-sig') if curparent.lower() in map(lambda x: x.lower(), min_list): G.add_node(curparent, is_mep=1) if str(path.name) == "following.csv": print(path.name) for i in curfile["username"]: if i in map(lambda x: x.lower(), mep_list): G.add_node(str(i), is_mep=1) else: G.add_node(str(i), is_mep=0) G.add_edge(curparent, i) else: print(path.name) for i in curfile["username"]: if i in map(lambda x: x.lower(), mep_list): G.add_node(str(i), is_mep=1) else: G.add_node(str(i), is_mep=0) G.add_edge(str(i), curparent) nx.write_gexf(G, "minority.gexf")

true

7901b7f2fa29741d72328bdbdbf92fc4d5c5f847

12,675

py

Python

mmdet/models/backbones/res2net.py

evgps/mmdetection_trashcan

aaf4237c2c0d473425cdc7b741d3009177b79751

[ "Apache-2.0" ]

12,377

2017-12-04T02:46:57.000Z

2022-03-31T16:48:31.000Z

mmdet/models/backbones/res2net.py

evgps/mmdetection_trashcan

aaf4237c2c0d473425cdc7b741d3009177b79751

[ "Apache-2.0" ]

1,851

2017-12-05T05:41:23.000Z

2022-03-30T13:06:22.000Z

mmdet/models/backbones/res2net.py

evgps/mmdetection_trashcan

aaf4237c2c0d473425cdc7b741d3009177b79751

[ "Apache-2.0" ]

4,198

2017-12-05T02:57:19.000Z

2022-03-30T10:29:37.000Z

import math import torch import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init, kaiming_init) from mmcv.runner import load_checkpoint from torch.nn.modules.batchnorm import _BatchNorm from mmdet.utils import get_root_logger from ..builder import BACKBONES from .resnet import Bottleneck as _Bottleneck from .resnet import ResNet class Bottle2neck(_Bottleneck): expansion = 4 def __init__(self, inplanes, planes, scales=4, base_width=26, base_channels=64, stage_type='normal', **kwargs): """Bottle2neck block for Res2Net. If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is "caffe", the stride-two layer is the first 1x1 conv layer. """ super(Bottle2neck, self).__init__(inplanes, planes, **kwargs) assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.' width = int(math.floor(self.planes * (base_width / base_channels))) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, width * scales, postfix=1) self.norm3_name, norm3 = build_norm_layer( self.norm_cfg, self.planes * self.expansion, postfix=3) self.conv1 = build_conv_layer( self.conv_cfg, self.inplanes, width * scales, kernel_size=1, stride=self.conv1_stride, bias=False) self.add_module(self.norm1_name, norm1) if stage_type == 'stage' and self.conv2_stride != 1: self.pool = nn.AvgPool2d( kernel_size=3, stride=self.conv2_stride, padding=1) convs = [] bns = [] fallback_on_stride = False if self.with_dcn: fallback_on_stride = self.dcn.pop('fallback_on_stride', False) if not self.with_dcn or fallback_on_stride: for i in range(scales - 1): convs.append( build_conv_layer( self.conv_cfg, width, width, kernel_size=3, stride=self.conv2_stride, padding=self.dilation, dilation=self.dilation, bias=False)) bns.append( build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1]) self.convs = nn.ModuleList(convs) self.bns = nn.ModuleList(bns) else: assert self.conv_cfg is None, 'conv_cfg must be None for DCN' for i in range(scales - 1): convs.append( build_conv_layer( self.dcn, width, width, kernel_size=3, stride=self.conv2_stride, padding=self.dilation, dilation=self.dilation, bias=False)) bns.append( build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1]) self.convs = nn.ModuleList(convs) self.bns = nn.ModuleList(bns) self.conv3 = build_conv_layer( self.conv_cfg, width * scales, self.planes * self.expansion, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) self.stage_type = stage_type self.scales = scales self.width = width delattr(self, 'conv2') delattr(self, self.norm2_name) def forward(self, x): """Forward function.""" def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) if self.with_plugins: out = self.forward_plugin(out, self.after_conv1_plugin_names) spx = torch.split(out, self.width, 1) sp = self.convs[0](spx[0].contiguous()) sp = self.relu(self.bns[0](sp)) out = sp for i in range(1, self.scales - 1): if self.stage_type == 'stage': sp = spx[i] else: sp = sp + spx[i] sp = self.convs[i](sp.contiguous()) sp = self.relu(self.bns[i](sp)) out = torch.cat((out, sp), 1) if self.stage_type == 'normal' or self.conv2_stride == 1: out = torch.cat((out, spx[self.scales - 1]), 1) elif self.stage_type == 'stage': out = torch.cat((out, self.pool(spx[self.scales - 1])), 1) if self.with_plugins: out = self.forward_plugin(out, self.after_conv2_plugin_names) out = self.conv3(out) out = self.norm3(out) if self.with_plugins: out = self.forward_plugin(out, self.after_conv3_plugin_names) if self.downsample is not None: identity = self.downsample(x) out += identity return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out class Res2Layer(nn.Sequential): """Res2Layer to build Res2Net style backbone. Args: block (nn.Module): block used to build ResLayer. inplanes (int): inplanes of block. planes (int): planes of block. num_blocks (int): number of blocks. stride (int): stride of the first block. Default: 1 avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottle2neck. Default: False conv_cfg (dict): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') scales (int): Scales used in Res2Net. Default: 4 base_width (int): Basic width of each scale. Default: 26 """ def __init__(self, block, inplanes, planes, num_blocks, stride=1, avg_down=True, conv_cfg=None, norm_cfg=dict(type='BN'), scales=4, base_width=26, **kwargs): self.block = block downsample = None if stride != 1 or inplanes != planes * block.expansion: downsample = nn.Sequential( nn.AvgPool2d( kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False), build_conv_layer( conv_cfg, inplanes, planes * block.expansion, kernel_size=1, stride=1, bias=False), build_norm_layer(norm_cfg, planes * block.expansion)[1], ) layers = [] layers.append( block( inplanes=inplanes, planes=planes, stride=stride, downsample=downsample, conv_cfg=conv_cfg, norm_cfg=norm_cfg, scales=scales, base_width=base_width, stage_type='stage', **kwargs)) inplanes = planes * block.expansion for i in range(1, num_blocks): layers.append( block( inplanes=inplanes, planes=planes, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, scales=scales, base_width=base_width, **kwargs)) super(Res2Layer, self).__init__(*layers) @BACKBONES.register_module() class Res2Net(ResNet): """Res2Net backbone. Args: scales (int): Scales used in Res2Net. Default: 4 base_width (int): Basic width of each scale. Default: 26 depth (int): Depth of res2net, from {50, 101, 152}. in_channels (int): Number of input image channels. Default: 3. num_stages (int): Res2net stages. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. dilations (Sequence[int]): Dilation of each stage. out_indices (Sequence[int]): Output from which stages. style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottle2neck. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. norm_cfg (dict): Dictionary to construct and config norm layer. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. plugins (list[dict]): List of plugins for stages, each dict contains: - cfg (dict, required): Cfg dict to build plugin. - position (str, required): Position inside block to insert plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'. - stages (tuple[bool], optional): Stages to apply plugin, length should be same as 'num_stages'. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Example: >>> from mmdet.models import Res2Net >>> import torch >>> self = Res2Net(depth=50, scales=4, base_width=26) >>> self.eval() >>> inputs = torch.rand(1, 3, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 256, 8, 8) (1, 512, 4, 4) (1, 1024, 2, 2) (1, 2048, 1, 1) """ arch_settings = { 50: (Bottle2neck, (3, 4, 6, 3)), 101: (Bottle2neck, (3, 4, 23, 3)), 152: (Bottle2neck, (3, 8, 36, 3)) } def __init__(self, scales=4, base_width=26, style='pytorch', deep_stem=True, avg_down=True, **kwargs): self.scales = scales self.base_width = base_width super(Res2Net, self).__init__( style='pytorch', deep_stem=True, avg_down=True, **kwargs) def make_res_layer(self, **kwargs): return Res2Layer( scales=self.scales, base_width=self.base_width, base_channels=self.base_channels, **kwargs) def init_weights(self, pretrained=None): """Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to None. """ if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=False, logger=logger) elif pretrained is None: for m in self.modules(): if isinstance(m, nn.Conv2d): kaiming_init(m) elif isinstance(m, (_BatchNorm, nn.GroupNorm)): constant_init(m, 1) if self.dcn is not None: for m in self.modules(): if isinstance(m, Bottle2neck): # dcn in Res2Net bottle2neck is in ModuleList for n in m.convs: if hasattr(n, 'conv_offset'): constant_init(n.conv_offset, 0) if self.zero_init_residual: for m in self.modules(): if isinstance(m, Bottle2neck): constant_init(m.norm3, 0) else: raise TypeError('pretrained must be a str or None')

36.008523

79

0.527732

import math import torch import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init, kaiming_init) from mmcv.runner import load_checkpoint from torch.nn.modules.batchnorm import _BatchNorm from mmdet.utils import get_root_logger from ..builder import BACKBONES from .resnet import Bottleneck as _Bottleneck from .resnet import ResNet class Bottle2neck(_Bottleneck): expansion = 4 def __init__(self, inplanes, planes, scales=4, base_width=26, base_channels=64, stage_type='normal', **kwargs): super(Bottle2neck, self).__init__(inplanes, planes, **kwargs) assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.' width = int(math.floor(self.planes * (base_width / base_channels))) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, width * scales, postfix=1) self.norm3_name, norm3 = build_norm_layer( self.norm_cfg, self.planes * self.expansion, postfix=3) self.conv1 = build_conv_layer( self.conv_cfg, self.inplanes, width * scales, kernel_size=1, stride=self.conv1_stride, bias=False) self.add_module(self.norm1_name, norm1) if stage_type == 'stage' and self.conv2_stride != 1: self.pool = nn.AvgPool2d( kernel_size=3, stride=self.conv2_stride, padding=1) convs = [] bns = [] fallback_on_stride = False if self.with_dcn: fallback_on_stride = self.dcn.pop('fallback_on_stride', False) if not self.with_dcn or fallback_on_stride: for i in range(scales - 1): convs.append( build_conv_layer( self.conv_cfg, width, width, kernel_size=3, stride=self.conv2_stride, padding=self.dilation, dilation=self.dilation, bias=False)) bns.append( build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1]) self.convs = nn.ModuleList(convs) self.bns = nn.ModuleList(bns) else: assert self.conv_cfg is None, 'conv_cfg must be None for DCN' for i in range(scales - 1): convs.append( build_conv_layer( self.dcn, width, width, kernel_size=3, stride=self.conv2_stride, padding=self.dilation, dilation=self.dilation, bias=False)) bns.append( build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1]) self.convs = nn.ModuleList(convs) self.bns = nn.ModuleList(bns) self.conv3 = build_conv_layer( self.conv_cfg, width * scales, self.planes * self.expansion, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) self.stage_type = stage_type self.scales = scales self.width = width delattr(self, 'conv2') delattr(self, self.norm2_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) if self.with_plugins: out = self.forward_plugin(out, self.after_conv1_plugin_names) spx = torch.split(out, self.width, 1) sp = self.convs[0](spx[0].contiguous()) sp = self.relu(self.bns[0](sp)) out = sp for i in range(1, self.scales - 1): if self.stage_type == 'stage': sp = spx[i] else: sp = sp + spx[i] sp = self.convs[i](sp.contiguous()) sp = self.relu(self.bns[i](sp)) out = torch.cat((out, sp), 1) if self.stage_type == 'normal' or self.conv2_stride == 1: out = torch.cat((out, spx[self.scales - 1]), 1) elif self.stage_type == 'stage': out = torch.cat((out, self.pool(spx[self.scales - 1])), 1) if self.with_plugins: out = self.forward_plugin(out, self.after_conv2_plugin_names) out = self.conv3(out) out = self.norm3(out) if self.with_plugins: out = self.forward_plugin(out, self.after_conv3_plugin_names) if self.downsample is not None: identity = self.downsample(x) out += identity return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out class Res2Layer(nn.Sequential): def __init__(self, block, inplanes, planes, num_blocks, stride=1, avg_down=True, conv_cfg=None, norm_cfg=dict(type='BN'), scales=4, base_width=26, **kwargs): self.block = block downsample = None if stride != 1 or inplanes != planes * block.expansion: downsample = nn.Sequential( nn.AvgPool2d( kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False), build_conv_layer( conv_cfg, inplanes, planes * block.expansion, kernel_size=1, stride=1, bias=False), build_norm_layer(norm_cfg, planes * block.expansion)[1], ) layers = [] layers.append( block( inplanes=inplanes, planes=planes, stride=stride, downsample=downsample, conv_cfg=conv_cfg, norm_cfg=norm_cfg, scales=scales, base_width=base_width, stage_type='stage', **kwargs)) inplanes = planes * block.expansion for i in range(1, num_blocks): layers.append( block( inplanes=inplanes, planes=planes, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, scales=scales, base_width=base_width, **kwargs)) super(Res2Layer, self).__init__(*layers) @BACKBONES.register_module() class Res2Net(ResNet): arch_settings = { 50: (Bottle2neck, (3, 4, 6, 3)), 101: (Bottle2neck, (3, 4, 23, 3)), 152: (Bottle2neck, (3, 8, 36, 3)) } def __init__(self, scales=4, base_width=26, style='pytorch', deep_stem=True, avg_down=True, **kwargs): self.scales = scales self.base_width = base_width super(Res2Net, self).__init__( style='pytorch', deep_stem=True, avg_down=True, **kwargs) def make_res_layer(self, **kwargs): return Res2Layer( scales=self.scales, base_width=self.base_width, base_channels=self.base_channels, **kwargs) def init_weights(self, pretrained=None): if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=False, logger=logger) elif pretrained is None: for m in self.modules(): if isinstance(m, nn.Conv2d): kaiming_init(m) elif isinstance(m, (_BatchNorm, nn.GroupNorm)): constant_init(m, 1) if self.dcn is not None: for m in self.modules(): if isinstance(m, Bottle2neck): for n in m.convs: if hasattr(n, 'conv_offset'): constant_init(n.conv_offset, 0) if self.zero_init_residual: for m in self.modules(): if isinstance(m, Bottle2neck): constant_init(m.norm3, 0) else: raise TypeError('pretrained must be a str or None')

true

7901b864d323897254d20ffd6ca52e6cb5e50268

27,129

py

Python

legal-api/tests/unit/services/test_authorization.py

leksmall/lear

cc7d75be830d12bfcc33b89bb2c4f34795bcd518

[ "Apache-2.0" ]

null

legal-api/tests/unit/services/test_authorization.py

leksmall/lear

cc7d75be830d12bfcc33b89bb2c4f34795bcd518

[ "Apache-2.0" ]

null

legal-api/tests/unit/services/test_authorization.py

leksmall/lear

cc7d75be830d12bfcc33b89bb2c4f34795bcd518

[ "Apache-2.0" ]

null

# Copyright © 2019 Province of British Columbia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests to assure the Authorization Services. Test-Suite to ensure that the Authorization Service is working as expected. """ from http import HTTPStatus import pytest from flask import jsonify from legal_api.models.business import Business from legal_api.services.authz import BASIC_USER, COLIN_SVC_ROLE, STAFF_ROLE, authorized, get_allowed, is_allowed from tests import integration_authorization, not_github_ci from .utils import helper_create_jwt def test_jwt_manager_initialized(jwt): """Assert that the jwt_manager is created as part of the fixtures.""" assert jwt @not_github_ci def test_jwt_manager_correct_test_config(app_request, jwt): """Assert that the test configuration for the JWT is working as expected.""" message = 'This is a protected end-point' protected_route = '/fake_jwt_route' @app_request.route(protected_route) @jwt.has_one_of_roles([STAFF_ROLE]) def get(): return jsonify(message=message) # assert that JWT is setup correctly for a known role token = helper_create_jwt(jwt, [STAFF_ROLE]) headers = {'Authorization': 'Bearer ' + token} rv = app_request.test_client().get(protected_route, headers=headers) assert rv.status_code == HTTPStatus.OK # assert the JWT fails for an unknown role token = helper_create_jwt(jwt, ['SHOULD-FAIL']) headers = {'Authorization': 'Bearer ' + token} rv = app_request.test_client().get(protected_route, headers=headers) assert rv.status_code == HTTPStatus.UNAUTHORIZED TEST_AUTHZ_DATA = [ ('staff_role', # test name 'CP1234567', # business identifier 'happy-staff', # username [STAFF_ROLE], # roles ['view', 'edit'], # allowed actions ['edit'], # requested action HTTPStatus.OK), # expected response ('colin svc role', 'CP1234567', 'CP1234567', [COLIN_SVC_ROLE], ['view', 'edit'], ['edit'], HTTPStatus.OK), ('authorized_user', 'CP0001237', 'CP1234567', [BASIC_USER], ['view', 'edit'], ['edit'], HTTPStatus.OK), ('unauthorized_user', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, ['edit'], HTTPStatus.METHOD_NOT_ALLOWED), ('missing_action', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, None, HTTPStatus.METHOD_NOT_ALLOWED), ('invalid_action', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, ['scrum'], HTTPStatus.METHOD_NOT_ALLOWED), ('add_comment_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['add_comment'], HTTPStatus.METHOD_NOT_ALLOWED), ('court_order_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['court_order'], HTTPStatus.METHOD_NOT_ALLOWED), ('registrars_notation_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['registrars_notation'], HTTPStatus.METHOD_NOT_ALLOWED), ('registrars_order_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['registrars_order'], HTTPStatus.METHOD_NOT_ALLOWED), ] @not_github_ci @pytest.mark.parametrize('test_name,identifier,username,roles,allowed_actions,requested_actions,expected', TEST_AUTHZ_DATA) def test_authorized_user(monkeypatch, app_request, jwt, test_name, identifier, username, roles, allowed_actions, requested_actions, expected): """Assert that the type of user authorization is correct, based on the expected outcome.""" from requests import Response print(test_name) # mocks, the get and json calls for requests.Response def mock_get(*args, **kwargs): # pylint: disable=unused-argument; mocks of library methods resp = Response() resp.status_code = 200 return resp def mock_json(self, **kwargs): # pylint: disable=unused-argument; mocks of library methods return {'roles': allowed_actions} monkeypatch.setattr('requests.sessions.Session.get', mock_get) monkeypatch.setattr('requests.Response.json', mock_json) # setup @app_request.route('/fake_jwt_route/<string:identifier>') @jwt.requires_auth def get_fake(identifier: str): if not authorized(identifier, jwt, ['view']): return jsonify(message='failed'), HTTPStatus.METHOD_NOT_ALLOWED return jsonify(message='success'), HTTPStatus.OK token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} # test it rv = app_request.test_client().get(f'/fake_jwt_route/{identifier}', headers=headers) # check it assert rv.status_code == expected TEST_INTEG_AUTHZ_DATA = [ ('staff_role', # test name 'CP1234567', # business identifier 'happy-staff', # username [STAFF_ROLE], # roles ['view', 'edit'], # allowed actions ['edit'], # requested action HTTPStatus.OK), # expected response ('colin svc role', 'CP1234567', 'CP1234567', [COLIN_SVC_ROLE], ['view', 'edit'], ['edit'], HTTPStatus.OK), ('unauthorized_user', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, ['edit'], HTTPStatus.METHOD_NOT_ALLOWED), ('missing_action', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, None, HTTPStatus.METHOD_NOT_ALLOWED), ('invalid_action', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, ['scrum'], HTTPStatus.METHOD_NOT_ALLOWED), ('add_comment_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['add_comment'], HTTPStatus.METHOD_NOT_ALLOWED), ('court_order_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['court_order'], HTTPStatus.METHOD_NOT_ALLOWED), ('registrars_notation_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['registrars_notation'], HTTPStatus.METHOD_NOT_ALLOWED), ('registrars_order_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['registrars_order'], HTTPStatus.METHOD_NOT_ALLOWED), ] @integration_authorization @pytest.mark.parametrize('test_name,identifier,username,roles,allowed_actions,requested_actions,expected', TEST_INTEG_AUTHZ_DATA) def test_authorized_user_integ(monkeypatch, app, jwt, test_name, identifier, username, roles, allowed_actions, requested_actions, expected): """Assert that the type of user authorization is correct, based on the expected outcome.""" import flask # noqa: F401; import actually used in mock # setup token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): # pylint: disable=unused-argument; mocks of library methods return headers['Authorization'] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) rv = authorized(identifier, jwt, ['view']) # check it if expected == HTTPStatus.OK: assert rv else: assert not rv def test_authorized_missing_args(): """Assert that the missing args return False.""" identifier = 'a corp' jwt = 'fake' action = 'fake' rv = authorized(identifier, jwt, None) assert not rv rv = authorized(identifier, None, action) assert not rv rv = authorized(None, jwt, action) assert not rv def test_authorized_bad_url(monkeypatch, app, jwt): """Assert that an invalid auth service URL returns False.""" import flask # noqa: F401; import actually used in mock # setup identifier = 'CP1234567' username = 'username' roles = [BASIC_USER] token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): # pylint: disable=unused-argument; mocks of library methods return headers['Authorization'] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) auth_svc_url = app.config['AUTH_SVC_URL'] app.config['AUTH_SVC_URL'] = 'http://no.way.this.works/dribble' rv = authorized(identifier, jwt, ['view']) app.config['AUTH_SVC_URL'] = auth_svc_url assert not rv def test_authorized_invalid_roles(monkeypatch, app, jwt): """Assert that an invalid role returns False.""" import flask # noqa: F401 ; import actually used in mock # setup noqa: I003 identifier = 'CP1234567' username = 'username' roles = ['NONE'] token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): # pylint: disable=unused-argument; mocks of library methods return headers['Authorization'] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) rv = authorized(identifier, jwt, ['view']) assert not rv @pytest.mark.parametrize( 'test_name,state,legal_type,username,roles,expected', [ # active business ('staff_active_cp', Business.State.ACTIVE, 'CP', 'staff', [STAFF_ROLE], ['annualReport', 'changeOfAddress', 'changeOfDirectors', 'correction', 'courtOrder', 'dissolution', 'incorporationApplication', 'specialResolution', 'registrarsNotation', 'registrarsOrder']), ('staff_active_bc', Business.State.ACTIVE, 'BC', 'staff', [STAFF_ROLE], ['alteration', 'courtOrder', 'dissolution', 'incorporationApplication', 'transition', 'registrarsNotation', 'registrarsOrder']), ('staff_active_ben', Business.State.ACTIVE, 'BEN', 'staff', [STAFF_ROLE], ['alteration', 'annualReport', 'changeOfAddress', 'changeOfDirectors', 'conversion', 'correction', 'courtOrder', 'dissolution', 'incorporationApplication', 'transition', 'registrarsNotation', 'registrarsOrder']), ('staff_active_cc', Business.State.ACTIVE, 'CC', 'staff', [STAFF_ROLE], ['courtOrder', 'dissolution', 'registrarsNotation', 'registrarsOrder']), ('staff_active_ulc', Business.State.ACTIVE, 'ULC', 'staff', [STAFF_ROLE], ['alteration', 'courtOrder', 'dissolution', 'registrarsNotation', 'registrarsOrder']), ('staff_active_llc', Business.State.ACTIVE, 'LLC', 'staff', [STAFF_ROLE], ['courtOrder', 'dissolution', 'registrarsNotation', 'registrarsOrder']), ('staff_active_sp', Business.State.ACTIVE, 'SP', 'staff', [STAFF_ROLE], ['changeOfRegistration', 'conversion', 'dissolution', 'registration']), ('staff_active_gp', Business.State.ACTIVE, 'GP', 'staff', [STAFF_ROLE], ['changeOfRegistration', 'conversion', 'dissolution', 'registration']), ('user_active_cp', Business.State.ACTIVE, 'CP', 'user', [BASIC_USER], ['annualReport', 'changeOfAddress', 'changeOfDirectors', 'dissolution', 'incorporationApplication', 'specialResolution']), ('user_active_bc', Business.State.ACTIVE, 'BC', 'user', [BASIC_USER], ['alteration', 'dissolution', 'incorporationApplication', 'transition']), ('user_active_ben', Business.State.ACTIVE, 'BEN', 'user', [BASIC_USER], ['alteration', 'annualReport', 'changeOfAddress', 'changeOfDirectors', 'dissolution', 'incorporationApplication', 'transition']), ('user_active_cc', Business.State.ACTIVE, 'CC', 'user', [BASIC_USER], ['dissolution']), ('user_active_ulc', Business.State.ACTIVE, 'ULC', 'user', [BASIC_USER], ['alteration', 'dissolution']), ('user_active_llc', Business.State.ACTIVE, 'LLC', 'user', [BASIC_USER], ['dissolution']), ('user_active_sp', Business.State.ACTIVE, 'SP', 'user', [BASIC_USER], ['changeOfRegistration', 'dissolution', 'registration']), ('user_active_gp', Business.State.ACTIVE, 'GP', 'user', [BASIC_USER], ['changeOfRegistration', 'dissolution', 'registration']), # historical business ('staff_historical_cp', Business.State.HISTORICAL, 'CP', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration']}]), ('staff_historical_bc', Business.State.HISTORICAL, 'BC', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('staff_historical_ben', Business.State.HISTORICAL, 'BEN', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('staff_historical_cc', Business.State.HISTORICAL, 'CC', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('staff_historical_ulc', Business.State.HISTORICAL, 'ULC', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('staff_historical_llc', Business.State.HISTORICAL, 'LLC', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('user_historical_llc', Business.State.HISTORICAL, 'LLC', 'user', [BASIC_USER], []), ] ) def test_get_allowed(monkeypatch, app, jwt, test_name, state, legal_type, username, roles, expected): """Assert that get allowed returns valid filings.""" token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): # pylint: disable=unused-argument; mocks of library methods return headers[one] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) filing_types = get_allowed(state, legal_type, jwt) assert filing_types == expected @pytest.mark.parametrize( 'test_name,state,filing_type,sub_filing_type,legal_types,username,roles,expected', [ # active business ('staff_active_allowed', Business.State.ACTIVE, 'alteration', None, ['BC', 'BEN', 'ULC'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'alteration', None, ['CP', 'CC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'annualReport', None, ['CP', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'annualReport', None, ['BC', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'changeOfAddress', None, ['CP', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'changeOfAddress', None, ['BC', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'changeOfDirectors', None, ['CP', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'changeOfDirectors', None, ['BC', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'correction', None, ['CP', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'correction', None, ['BC', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'courtOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'dissolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC', 'SP', 'GP'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'incorporationApplication', None, ['CP', 'BC', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'restoration', 'fullRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active', Business.State.ACTIVE, 'restoration', 'limitedRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'specialResolution', None, ['CP'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'specialResolution', None, ['BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'transition', None, ['BC', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'transition', None, ['CP', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'registrarsNotation', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'registrarsOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'registration', None, ['SP', 'GP'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'changeOfRegistration', None, ['SP', 'GP'], 'staff', [STAFF_ROLE], True), ('user_active_allowed', Business.State.ACTIVE, 'alteration', None, ['BC', 'BEN', 'ULC'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'alteration', None, ['CP', 'CC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'annualReport', None, ['CP', 'BEN'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'annualReport', None, ['BC', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'changeOfAddress', None, ['CP', 'BEN'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'changeOfAddress', None, ['BC', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'changeOfDirectors', None, ['CP', 'BEN'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'changeOfDirectors', None, ['BC', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'correction', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'courtOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'dissolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC', 'SP', 'GP'], 'user', [BASIC_USER], True), ('user_active_allowed', Business.State.ACTIVE, 'incorporationApplication', None, ['CP', 'BC', 'BEN'], 'user', [BASIC_USER], True), ('user_active_allowed', Business.State.ACTIVE, 'registration', None, ['SP', 'GP'], 'user', [BASIC_USER], True), ('user_active_allowed', Business.State.ACTIVE, 'changeOfRegistration', None, ['SP', 'GP'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'restoration', 'fullRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'restoration', 'limitedRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'specialResolution', None, ['CP'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'specialResolution', None, ['BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'transition', None, ['BC', 'BEN'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'transition', None, ['CP', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'registrarsNotation', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'registrarsOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), # historical business ('staff_historical', Business.State.HISTORICAL, 'alteration', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'annualReport', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'changeOfAddress', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'changeOfDirectors', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'correction', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical_allowed', Business.State.HISTORICAL, 'courtOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical', Business.State.HISTORICAL, 'dissolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC', 'SP', 'GP'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'incorporationApplication', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical_allowed', Business.State.HISTORICAL, 'restoration', 'fullRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical_allowed', Business.State.HISTORICAL, 'restoration', 'limitedRestoration', ['BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical', Business.State.HISTORICAL, 'restoration', 'limitedRestoration', ['CP'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'specialResolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'transition', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical_allowed', Business.State.HISTORICAL, 'registrarsNotation', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical_allowed', Business.State.HISTORICAL, 'registrarsOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical', Business.State.HISTORICAL, 'registration', None, ['SP', 'GP'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'changeOfRegistration', None, ['SP', 'GP'], 'staff', [STAFF_ROLE], False), ('user_historical', Business.State.HISTORICAL, 'alteration', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'annualReport', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'changeOfAddress', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'changeOfDirectors', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'correction', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'courtOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'dissolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC', 'SP', 'GP', 'SP', 'GP'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'incorporationApplication', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'restoration', 'fullRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'restoration', 'limitedRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'specialResolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'transition', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'registrarsNotation', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'registrarsOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'registration', None, ['SP', 'GP'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'changeOfRegistration', None, ['SP', 'GP'], 'user', [BASIC_USER], False), ] ) def test_is_allowed(monkeypatch, app, jwt, test_name, state, filing_type, sub_filing_type, legal_types, username, roles, expected): """Assert that get allowed returns valid filings.""" token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): # pylint: disable=unused-argument; mocks of library methods return headers[one] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) for legal_type in legal_types: filing_types = is_allowed(state, filing_type, legal_type, jwt, sub_filing_type) assert filing_types == expected

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from http import HTTPStatus import pytest from flask import jsonify from legal_api.models.business import Business from legal_api.services.authz import BASIC_USER, COLIN_SVC_ROLE, STAFF_ROLE, authorized, get_allowed, is_allowed from tests import integration_authorization, not_github_ci from .utils import helper_create_jwt def test_jwt_manager_initialized(jwt): assert jwt @not_github_ci def test_jwt_manager_correct_test_config(app_request, jwt): message = 'This is a protected end-point' protected_route = '/fake_jwt_route' @app_request.route(protected_route) @jwt.has_one_of_roles([STAFF_ROLE]) def get(): return jsonify(message=message) token = helper_create_jwt(jwt, [STAFF_ROLE]) headers = {'Authorization': 'Bearer ' + token} rv = app_request.test_client().get(protected_route, headers=headers) assert rv.status_code == HTTPStatus.OK token = helper_create_jwt(jwt, ['SHOULD-FAIL']) headers = {'Authorization': 'Bearer ' + token} rv = app_request.test_client().get(protected_route, headers=headers) assert rv.status_code == HTTPStatus.UNAUTHORIZED TEST_AUTHZ_DATA = [ ('staff_role', 'CP1234567', 'happy-staff', [STAFF_ROLE], ['view', 'edit'], ['edit'], HTTPStatus.OK), ('colin svc role', 'CP1234567', 'CP1234567', [COLIN_SVC_ROLE], ['view', 'edit'], ['edit'], HTTPStatus.OK), ('authorized_user', 'CP0001237', 'CP1234567', [BASIC_USER], ['view', 'edit'], ['edit'], HTTPStatus.OK), ('unauthorized_user', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, ['edit'], HTTPStatus.METHOD_NOT_ALLOWED), ('missing_action', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, None, HTTPStatus.METHOD_NOT_ALLOWED), ('invalid_action', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, ['scrum'], HTTPStatus.METHOD_NOT_ALLOWED), ('add_comment_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['add_comment'], HTTPStatus.METHOD_NOT_ALLOWED), ('court_order_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['court_order'], HTTPStatus.METHOD_NOT_ALLOWED), ('registrars_notation_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['registrars_notation'], HTTPStatus.METHOD_NOT_ALLOWED), ('registrars_order_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['registrars_order'], HTTPStatus.METHOD_NOT_ALLOWED), ] @not_github_ci @pytest.mark.parametrize('test_name,identifier,username,roles,allowed_actions,requested_actions,expected', TEST_AUTHZ_DATA) def test_authorized_user(monkeypatch, app_request, jwt, test_name, identifier, username, roles, allowed_actions, requested_actions, expected): from requests import Response print(test_name) def mock_get(*args, **kwargs): resp = Response() resp.status_code = 200 return resp def mock_json(self, **kwargs): return {'roles': allowed_actions} monkeypatch.setattr('requests.sessions.Session.get', mock_get) monkeypatch.setattr('requests.Response.json', mock_json) @app_request.route('/fake_jwt_route/<string:identifier>') @jwt.requires_auth def get_fake(identifier: str): if not authorized(identifier, jwt, ['view']): return jsonify(message='failed'), HTTPStatus.METHOD_NOT_ALLOWED return jsonify(message='success'), HTTPStatus.OK token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} rv = app_request.test_client().get(f'/fake_jwt_route/{identifier}', headers=headers) assert rv.status_code == expected TEST_INTEG_AUTHZ_DATA = [ ('staff_role', 'CP1234567', 'happy-staff', [STAFF_ROLE], ['view', 'edit'], ['edit'], HTTPStatus.OK), ('colin svc role', 'CP1234567', 'CP1234567', [COLIN_SVC_ROLE], ['view', 'edit'], ['edit'], HTTPStatus.OK), ('unauthorized_user', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, ['edit'], HTTPStatus.METHOD_NOT_ALLOWED), ('missing_action', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, None, HTTPStatus.METHOD_NOT_ALLOWED), ('invalid_action', 'CP1234567', 'Not-Match-Identifier', [BASIC_USER], None, ['scrum'], HTTPStatus.METHOD_NOT_ALLOWED), ('add_comment_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['add_comment'], HTTPStatus.METHOD_NOT_ALLOWED), ('court_order_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['court_order'], HTTPStatus.METHOD_NOT_ALLOWED), ('registrars_notation_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['registrars_notation'], HTTPStatus.METHOD_NOT_ALLOWED), ('registrars_order_not_allowed', 'CP0001237', 'CP1234567', [BASIC_USER], None, ['registrars_order'], HTTPStatus.METHOD_NOT_ALLOWED), ] @integration_authorization @pytest.mark.parametrize('test_name,identifier,username,roles,allowed_actions,requested_actions,expected', TEST_INTEG_AUTHZ_DATA) def test_authorized_user_integ(monkeypatch, app, jwt, test_name, identifier, username, roles, allowed_actions, requested_actions, expected): import flask token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): return headers['Authorization'] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) rv = authorized(identifier, jwt, ['view']) if expected == HTTPStatus.OK: assert rv else: assert not rv def test_authorized_missing_args(): identifier = 'a corp' jwt = 'fake' action = 'fake' rv = authorized(identifier, jwt, None) assert not rv rv = authorized(identifier, None, action) assert not rv rv = authorized(None, jwt, action) assert not rv def test_authorized_bad_url(monkeypatch, app, jwt): import flask identifier = 'CP1234567' username = 'username' roles = [BASIC_USER] token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): return headers['Authorization'] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) auth_svc_url = app.config['AUTH_SVC_URL'] app.config['AUTH_SVC_URL'] = 'http://no.way.this.works/dribble' rv = authorized(identifier, jwt, ['view']) app.config['AUTH_SVC_URL'] = auth_svc_url assert not rv def test_authorized_invalid_roles(monkeypatch, app, jwt): import flask identifier = 'CP1234567' username = 'username' roles = ['NONE'] token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): return headers['Authorization'] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) rv = authorized(identifier, jwt, ['view']) assert not rv @pytest.mark.parametrize( 'test_name,state,legal_type,username,roles,expected', [ ('staff_active_cp', Business.State.ACTIVE, 'CP', 'staff', [STAFF_ROLE], ['annualReport', 'changeOfAddress', 'changeOfDirectors', 'correction', 'courtOrder', 'dissolution', 'incorporationApplication', 'specialResolution', 'registrarsNotation', 'registrarsOrder']), ('staff_active_bc', Business.State.ACTIVE, 'BC', 'staff', [STAFF_ROLE], ['alteration', 'courtOrder', 'dissolution', 'incorporationApplication', 'transition', 'registrarsNotation', 'registrarsOrder']), ('staff_active_ben', Business.State.ACTIVE, 'BEN', 'staff', [STAFF_ROLE], ['alteration', 'annualReport', 'changeOfAddress', 'changeOfDirectors', 'conversion', 'correction', 'courtOrder', 'dissolution', 'incorporationApplication', 'transition', 'registrarsNotation', 'registrarsOrder']), ('staff_active_cc', Business.State.ACTIVE, 'CC', 'staff', [STAFF_ROLE], ['courtOrder', 'dissolution', 'registrarsNotation', 'registrarsOrder']), ('staff_active_ulc', Business.State.ACTIVE, 'ULC', 'staff', [STAFF_ROLE], ['alteration', 'courtOrder', 'dissolution', 'registrarsNotation', 'registrarsOrder']), ('staff_active_llc', Business.State.ACTIVE, 'LLC', 'staff', [STAFF_ROLE], ['courtOrder', 'dissolution', 'registrarsNotation', 'registrarsOrder']), ('staff_active_sp', Business.State.ACTIVE, 'SP', 'staff', [STAFF_ROLE], ['changeOfRegistration', 'conversion', 'dissolution', 'registration']), ('staff_active_gp', Business.State.ACTIVE, 'GP', 'staff', [STAFF_ROLE], ['changeOfRegistration', 'conversion', 'dissolution', 'registration']), ('user_active_cp', Business.State.ACTIVE, 'CP', 'user', [BASIC_USER], ['annualReport', 'changeOfAddress', 'changeOfDirectors', 'dissolution', 'incorporationApplication', 'specialResolution']), ('user_active_bc', Business.State.ACTIVE, 'BC', 'user', [BASIC_USER], ['alteration', 'dissolution', 'incorporationApplication', 'transition']), ('user_active_ben', Business.State.ACTIVE, 'BEN', 'user', [BASIC_USER], ['alteration', 'annualReport', 'changeOfAddress', 'changeOfDirectors', 'dissolution', 'incorporationApplication', 'transition']), ('user_active_cc', Business.State.ACTIVE, 'CC', 'user', [BASIC_USER], ['dissolution']), ('user_active_ulc', Business.State.ACTIVE, 'ULC', 'user', [BASIC_USER], ['alteration', 'dissolution']), ('user_active_llc', Business.State.ACTIVE, 'LLC', 'user', [BASIC_USER], ['dissolution']), ('user_active_sp', Business.State.ACTIVE, 'SP', 'user', [BASIC_USER], ['changeOfRegistration', 'dissolution', 'registration']), ('user_active_gp', Business.State.ACTIVE, 'GP', 'user', [BASIC_USER], ['changeOfRegistration', 'dissolution', 'registration']), ('staff_historical_cp', Business.State.HISTORICAL, 'CP', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration']}]), ('staff_historical_bc', Business.State.HISTORICAL, 'BC', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('staff_historical_ben', Business.State.HISTORICAL, 'BEN', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('staff_historical_cc', Business.State.HISTORICAL, 'CC', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('staff_historical_ulc', Business.State.HISTORICAL, 'ULC', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('staff_historical_llc', Business.State.HISTORICAL, 'LLC', 'staff', [STAFF_ROLE], ['courtOrder', 'registrarsNotation', 'registrarsOrder', {'restoration': ['fullRestoration', 'limitedRestoration']}]), ('user_historical_llc', Business.State.HISTORICAL, 'LLC', 'user', [BASIC_USER], []), ] ) def test_get_allowed(monkeypatch, app, jwt, test_name, state, legal_type, username, roles, expected): token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): return headers[one] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) filing_types = get_allowed(state, legal_type, jwt) assert filing_types == expected @pytest.mark.parametrize( 'test_name,state,filing_type,sub_filing_type,legal_types,username,roles,expected', [ ('staff_active_allowed', Business.State.ACTIVE, 'alteration', None, ['BC', 'BEN', 'ULC'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'alteration', None, ['CP', 'CC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'annualReport', None, ['CP', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'annualReport', None, ['BC', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'changeOfAddress', None, ['CP', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'changeOfAddress', None, ['BC', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'changeOfDirectors', None, ['CP', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'changeOfDirectors', None, ['BC', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'correction', None, ['CP', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'correction', None, ['BC', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'courtOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'dissolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC', 'SP', 'GP'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'incorporationApplication', None, ['CP', 'BC', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'restoration', 'fullRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active', Business.State.ACTIVE, 'restoration', 'limitedRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'specialResolution', None, ['CP'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'specialResolution', None, ['BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'transition', None, ['BC', 'BEN'], 'staff', [STAFF_ROLE], True), ('staff_active', Business.State.ACTIVE, 'transition', None, ['CP', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_active_allowed', Business.State.ACTIVE, 'registrarsNotation', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'registrarsOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'registration', None, ['SP', 'GP'], 'staff', [STAFF_ROLE], True), ('staff_active_allowed', Business.State.ACTIVE, 'changeOfRegistration', None, ['SP', 'GP'], 'staff', [STAFF_ROLE], True), ('user_active_allowed', Business.State.ACTIVE, 'alteration', None, ['BC', 'BEN', 'ULC'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'alteration', None, ['CP', 'CC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'annualReport', None, ['CP', 'BEN'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'annualReport', None, ['BC', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'changeOfAddress', None, ['CP', 'BEN'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'changeOfAddress', None, ['BC', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'changeOfDirectors', None, ['CP', 'BEN'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'changeOfDirectors', None, ['BC', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'correction', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'courtOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'dissolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC', 'SP', 'GP'], 'user', [BASIC_USER], True), ('user_active_allowed', Business.State.ACTIVE, 'incorporationApplication', None, ['CP', 'BC', 'BEN'], 'user', [BASIC_USER], True), ('user_active_allowed', Business.State.ACTIVE, 'registration', None, ['SP', 'GP'], 'user', [BASIC_USER], True), ('user_active_allowed', Business.State.ACTIVE, 'changeOfRegistration', None, ['SP', 'GP'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'restoration', 'fullRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'restoration', 'limitedRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'specialResolution', None, ['CP'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'specialResolution', None, ['BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active_allowed', Business.State.ACTIVE, 'transition', None, ['BC', 'BEN'], 'user', [BASIC_USER], True), ('user_active', Business.State.ACTIVE, 'transition', None, ['CP', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'registrarsNotation', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_active', Business.State.ACTIVE, 'registrarsOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('staff_historical', Business.State.HISTORICAL, 'alteration', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'annualReport', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'changeOfAddress', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'changeOfDirectors', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'correction', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical_allowed', Business.State.HISTORICAL, 'courtOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical', Business.State.HISTORICAL, 'dissolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC', 'SP', 'GP'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'incorporationApplication', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical_allowed', Business.State.HISTORICAL, 'restoration', 'fullRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical_allowed', Business.State.HISTORICAL, 'restoration', 'limitedRestoration', ['BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical', Business.State.HISTORICAL, 'restoration', 'limitedRestoration', ['CP'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'specialResolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'transition', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], False), ('staff_historical_allowed', Business.State.HISTORICAL, 'registrarsNotation', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical_allowed', Business.State.HISTORICAL, 'registrarsOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'staff', [STAFF_ROLE], True), ('staff_historical', Business.State.HISTORICAL, 'registration', None, ['SP', 'GP'], 'staff', [STAFF_ROLE], False), ('staff_historical', Business.State.HISTORICAL, 'changeOfRegistration', None, ['SP', 'GP'], 'staff', [STAFF_ROLE], False), ('user_historical', Business.State.HISTORICAL, 'alteration', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'annualReport', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'changeOfAddress', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'changeOfDirectors', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'correction', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'courtOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'dissolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC', 'SP', 'GP', 'SP', 'GP'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'incorporationApplication', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'restoration', 'fullRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'restoration', 'limitedRestoration', ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'specialResolution', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'transition', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'registrarsNotation', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'registrarsOrder', None, ['CP', 'BC', 'BEN', 'CC', 'ULC', 'LLC'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'registration', None, ['SP', 'GP'], 'user', [BASIC_USER], False), ('user_historical', Business.State.HISTORICAL, 'changeOfRegistration', None, ['SP', 'GP'], 'user', [BASIC_USER], False), ] ) def test_is_allowed(monkeypatch, app, jwt, test_name, state, filing_type, sub_filing_type, legal_types, username, roles, expected): token = helper_create_jwt(jwt, roles=roles, username=username) headers = {'Authorization': 'Bearer ' + token} def mock_auth(one, two): return headers[one] with app.test_request_context(): monkeypatch.setattr('flask.request.headers.get', mock_auth) for legal_type in legal_types: filing_types = is_allowed(state, filing_type, legal_type, jwt, sub_filing_type) assert filing_types == expected

true

7901b8fe19a02fe5084433c0ffdfd6efc080c6c9

3,118

py

Python

nilearn/plotting/__init__.py

OliverWarrington/nilearn

d42d3b10eb543619ed4189f05b74ef2e75a92068

[ "BSD-2-Clause" ]

827

2015-01-30T23:11:42.000Z

2022-03-29T21:21:05.000Z

nilearn/plotting/__init__.py

OliverWarrington/nilearn

d42d3b10eb543619ed4189f05b74ef2e75a92068

[ "BSD-2-Clause" ]

2,845

2015-01-04T22:14:41.000Z

2022-03-31T20:28:09.000Z

nilearn/plotting/__init__.py

OliverWarrington/nilearn

d42d3b10eb543619ed4189f05b74ef2e75a92068

[ "BSD-2-Clause" ]

484

2015-02-03T10:58:19.000Z

2022-03-29T21:57:16.000Z

""" Plotting code for nilearn """ # Original Authors: Chris Filo Gorgolewski, Gael Varoquaux import os import sys import importlib ############################################################################### # Make sure that we don't get DISPLAY problems when running without X on # unices def _set_mpl_backend(): # We are doing local imports here to avoid polluting our namespace try: import matplotlib except ImportError: if importlib.util.find_spec("pytest") is not None: from .._utils.testing import skip_if_running_tests # No need to fail when running tests skip_if_running_tests('matplotlib not installed') raise else: from ..version import (_import_module_with_version_check, OPTIONAL_MATPLOTLIB_MIN_VERSION) # When matplotlib was successfully imported we need to check # that the version is greater that the minimum required one _import_module_with_version_check('matplotlib', OPTIONAL_MATPLOTLIB_MIN_VERSION) current_backend = matplotlib.get_backend().lower() if 'inline' in current_backend or 'nbagg' in current_backend: return # Set the backend to a non-interactive one for unices without X # (see gh-2560) if (sys.platform not in ('darwin', 'win32') and 'DISPLAY' not in os.environ): matplotlib.use('Agg') _set_mpl_backend() ############################################################################### from . import cm from .img_plotting import ( plot_img, plot_anat, plot_epi, plot_roi, plot_stat_map, plot_glass_brain, plot_connectome, plot_connectome_strength, plot_markers, plot_prob_atlas, plot_carpet, plot_img_comparison, show) from .find_cuts import find_xyz_cut_coords, find_cut_slices, \ find_parcellation_cut_coords, find_probabilistic_atlas_cut_coords from .matrix_plotting import (plot_matrix, plot_contrast_matrix, plot_design_matrix, plot_event) from .html_surface import view_surf, view_img_on_surf from .html_stat_map import view_img from .html_connectome import view_connectome, view_markers from .surf_plotting import (plot_surf, plot_surf_stat_map, plot_surf_roi, plot_img_on_surf, plot_surf_contours) __all__ = ['cm', 'plot_img', 'plot_anat', 'plot_epi', 'plot_roi', 'plot_stat_map', 'plot_glass_brain', 'plot_markers', 'plot_connectome', 'plot_prob_atlas', 'find_xyz_cut_coords', 'find_cut_slices', 'plot_img_comparison', 'show', 'plot_matrix', 'plot_design_matrix', 'plot_contrast_matrix', 'plot_event', 'view_surf', 'view_img_on_surf', 'view_img', 'view_connectome', 'view_markers', 'find_parcellation_cut_coords', 'find_probabilistic_atlas_cut_coords', 'plot_surf', 'plot_surf_stat_map', 'plot_surf_roi', 'plot_img_on_surf', 'plot_connectome_strength', 'plot_carpet', 'plot_surf_contours']

42.712329

79

0.645927

import os import sys import importlib

true

7901ba540a18823e6758a809e3c64cd14f3b70b8

662

py

Python

crawler_news/items.py

SecondDim/crawler-base

21ba30a3f6a62f2eaee336331abeca04d2a4ed24

[ "MIT" ]

11

2019-12-21T14:57:17.000Z

2021-07-15T17:32:10.000Z

crawler_news/items.py

SecondDim/crawler-base

21ba30a3f6a62f2eaee336331abeca04d2a4ed24

[ "MIT" ]

6

2020-01-24T13:26:01.000Z

2022-02-01T23:05:28.000Z

crawler_news/items.py

SecondDim/crawler-base

21ba30a3f6a62f2eaee336331abeca04d2a4ed24

[ "MIT" ]

3

2020-02-28T06:07:20.000Z

2021-01-07T09:58:47.000Z

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://docs.scrapy.org/en/latest/topics/items.html import scrapy class CrawlerNewsItem(scrapy.Item): url = scrapy.Field() # str article_from = scrapy.Field() # str article_type = scrapy.Field() # str title = scrapy.Field() # str publish_date = scrapy.Field() # str authors = scrapy.Field() # list json tags = scrapy.Field() # list json text = scrapy.Field() # list json text_html = scrapy.Field() # str images = scrapy.Field() # list json video = scrapy.Field() # list json links = scrapy.Field() # list json

27.583333

53

0.651057

import scrapy class CrawlerNewsItem(scrapy.Item): url = scrapy.Field() article_from = scrapy.Field() article_type = scrapy.Field() title = scrapy.Field() publish_date = scrapy.Field() authors = scrapy.Field() tags = scrapy.Field() text = scrapy.Field() text_html = scrapy.Field() images = scrapy.Field() video = scrapy.Field() links = scrapy.Field()

true

7901bc9f1d31af5d50513e0c15bc57162093e24e

26,892

py

Python

falcon/inspect.py

hzdwang/falcon-1

1df2c0b7f21de773b3de70ea44af26f225c1887c

[ "Apache-2.0" ]

2

2020-12-09T04:13:18.000Z

2020-12-09T04:13:22.000Z

falcon/inspect.py

hzdwang/falcon-1

1df2c0b7f21de773b3de70ea44af26f225c1887c

[ "Apache-2.0" ]

null

falcon/inspect.py

hzdwang/falcon-1

1df2c0b7f21de773b3de70ea44af26f225c1887c

[ "Apache-2.0" ]

null

# Copyright 2020 by Federico Caselli # # 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. """Inspect utilities for falcon applications.""" from functools import partial import inspect from typing import Callable, Dict, List, Optional, Type from falcon import App, app_helpers from falcon.routing import CompiledRouter def inspect_app(app: App) -> 'AppInfo': """Inspects an application. Args: app (falcon.App): The application to inspect. Works with both :class:`falcon.App` and :class:`falcon.asgi.App`. Returns: AppInfo: The information regarding the application. Call :meth:`~.AppInfo.to_string` on the result to obtain a human-friendly representation. """ routes = inspect_routes(app) static = inspect_static_routes(app) sinks = inspect_sinks(app) error_handlers = inspect_error_handlers(app) middleware = inspect_middlewares(app) return AppInfo(routes, middleware, static, sinks, error_handlers, app._ASGI) def inspect_routes(app: App) -> 'List[RouteInfo]': """Inspects the routes of an application. Args: app (falcon.App): The application to inspect. Works with both :class:`falcon.App` and :class:`falcon.asgi.App`. Returns: List[RouteInfo]: A list of route descriptions for the application. """ router = app._router inspect_function = _supported_routers.get(type(router)) if inspect_function is None: raise TypeError( 'Unsupported router class {}. Use "register_router" ' 'to register a function that can inspect the router ' 'used by the provided application'.format(type(router)) ) return inspect_function(router) def register_router(router_class): """Register a function to inspect a particular router. This decorator registers a new function for a custom router class, so that it can be inspected with the function :func:`.inspect_routes`. An inspection function takes the router instance used by the application and returns a list of :class:`.RouteInfo`. Eg:: @register_router(MyRouterClass) def inspect_my_router(router): return [RouteInfo('foo', 'bar', '/path/to/foo.py:42', [])] Args: router_class (Type): The router class to register. If already registered an error will be raised. """ def wraps(fn): if router_class in _supported_routers: raise ValueError( 'Another function is already registered' ' for the router {}'.format(router_class) ) _supported_routers[router_class] = fn return fn return wraps # router inspection registry _supported_routers = {} # type: Dict[Type, Callable] def inspect_static_routes(app: App) -> 'List[StaticRouteInfo]': """Inspects the static routes of an application. Args: app (falcon.App): The application to inspect. Works with both :class:`falcon.App` and :class:`falcon.asgi.App`. Returns: List[StaticRouteInfo]: A list of static routes that have been added to the application. """ routes = [] for sr, _, _ in app._static_routes: info = StaticRouteInfo(sr._prefix, sr._directory, sr._fallback_filename) routes.append(info) return routes def inspect_sinks(app: App) -> 'List[SinkInfo]': """Inspects the sinks of an application. Args: app (falcon.App): The application to inspect. Works with both :class:`falcon.App` and :class:`falcon.asgi.App`. Returns: List[SinkInfo]: A list of sinks used by the application. """ sinks = [] for prefix, sink, _ in app._sinks: source_info, name = _get_source_info_and_name(sink) info = SinkInfo(prefix.pattern, name, source_info) sinks.append(info) return sinks def inspect_error_handlers(app: App) -> 'List[ErrorHandlerInfo]': """Inspects the error handlers of an application. Args: app (falcon.App): The application to inspect. Works with both :class:`falcon.App` and :class:`falcon.asgi.App`. Returns: List[ErrorHandlerInfo]: A list of error handlers used by the application. """ errors = [] for exc, fn in app._error_handlers.items(): source_info, name = _get_source_info_and_name(fn) info = ErrorHandlerInfo(exc.__name__, name, source_info, _is_internal(fn)) errors.append(info) return errors def inspect_middlewares(app: App) -> 'MiddlewareInfo': """Inspects the middleware components of an application. Args: app (falcon.App): The application to inspect. Works with both :class:`falcon.App` and :class:`falcon.asgi.App`. Returns: MiddlewareInfo: Information about the app's middleware components. """ types_ = app_helpers.prepare_middleware(app._unprepared_middleware, True, app._ASGI) type_infos = [] for stack in types_: current = [] for method in stack: _, name = _get_source_info_and_name(method) cls = type(method.__self__) _, cls_name = _get_source_info_and_name(cls) current.append(MiddlewareTreeItemInfo(name, cls_name)) type_infos.append(current) middlewareTree = MiddlewareTreeInfo(*type_infos) middlewareClasses = [] names = 'Process request', 'Process resource', 'Process response' for m in app._unprepared_middleware: fns = app_helpers.prepare_middleware([m], True, app._ASGI) class_source_info, cls_name = _get_source_info_and_name(type(m)) methods = [] for method, name in zip(fns, names): if method: real_func = method[0] source_info = _get_source_info(real_func) methods.append(MiddlewareMethodInfo(real_func.__name__, source_info)) m_info = MiddlewareClassInfo(cls_name, class_source_info, methods) middlewareClasses.append(m_info) return MiddlewareInfo( middlewareTree, middlewareClasses, app._independent_middleware ) @register_router(CompiledRouter) def inspect_compiled_router(router: CompiledRouter) -> 'List[RouteInfo]': """Walk an instance of :class:`~.CompiledRouter` to return a list of defined routes. Default route inspector for CompiledRouter. Args: router (CompiledRouter): The router to inspect. Returns: List[RouteInfo]: A list of :class:`~.RouteInfo`. """ def _traverse(roots, parent): for root in roots: path = parent + '/' + root.raw_segment if root.resource is not None: methods = [] if root.method_map: for method, func in root.method_map.items(): if isinstance(func, partial): real_func = func.func else: real_func = func source_info = _get_source_info(real_func) internal = _is_internal(real_func) method_info = RouteMethodInfo( method, source_info, real_func.__name__, internal ) methods.append(method_info) source_info, class_name = _get_source_info_and_name(root.resource) route_info = RouteInfo(path, class_name, source_info, methods) routes.append(route_info) if root.children: _traverse(root.children, path) routes = [] # type: List[RouteInfo] _traverse(router._roots, '') return routes # ------------------------------------------------------------------------ # Inspection classes # ------------------------------------------------------------------------ class _Traversable: __visit_name__ = 'N/A' def to_string(self, verbose=False, internal=False) -> str: """Return a string representation of this class. Args: verbose (bool, optional): Adds more information. Defaults to False. internal (bool, optional): Also include internal route methods and error handlers added by the framework. Defaults to ``False``. Returns: str: string representation of this class. """ return StringVisitor(verbose, internal).process(self) def __repr__(self): return self.to_string() class RouteMethodInfo(_Traversable): """Describes a responder method. Args: method (str): The HTTP method of this responder. source_info (str): The source path of this function. function_name (str): Name of the function. internal (bool): Whether or not this was a default responder added by the framework. Attributes: suffix (str): The suffix of this route function. This is set to an empty string when the function has no suffix. """ __visit_name__ = 'route_method' def __init__( self, method: str, source_info: str, function_name: str, internal: bool ): self.method = method self.source_info = source_info self.function_name = function_name self.internal = internal # NOTE(CaselIT): internal falcon names do not start with on and do not have suffix if function_name.startswith('on'): self.suffix = '_'.join(function_name.split('_')[2:]) else: self.suffix = '' class RouteInfo(_Traversable): """Describes a route. Args: path (str): The path of this route. class_name (str): The class name of the responder of this route. source_info (str): The source path where this responder was defined. methods (List[RouteMethodInfo]): List of methods defined in the route. """ __visit_name__ = 'route' def __init__( self, path: str, class_name: str, source_info: str, methods: List[RouteMethodInfo], ): self.path = path self.class_name = class_name self.source_info = source_info self.methods = methods class StaticRouteInfo(_Traversable): """Describes a static route. Args: path (str): The prefix of the static route. directory (str): The directory for the static route. fallback_filename (str or None): Fallback filename to serve. """ __visit_name__ = 'static_route' def __init__(self, prefix: str, directory: str, fallback_filename: Optional[str]): self.prefix = prefix self.directory = directory self.fallback_filename = fallback_filename class SinkInfo(_Traversable): """Describes a sink. Args: prefix (str): The prefix of the sink. name (str): The name of the sink function or class. source_info (str): The source path where this sink was defined. """ __visit_name__ = 'sink' def __init__(self, prefix: str, name: str, source_info: str): self.prefix = prefix self.name = name self.source_info = source_info class ErrorHandlerInfo(_Traversable): """Desribes an error handler. Args: error (name): The name of the error type. name (str): The name of the handler. source_info (str): The source path where this error handler was defined. internal (bool): Whether or not this is a default error handler added by the framework. """ __visit_name__ = 'error_handler' def __init__(self, error: str, name: str, source_info: str, internal: bool): self.error = error self.name = name self.source_info = source_info self.internal = internal class MiddlewareMethodInfo(_Traversable): """Describes a middleware method. Args: function_name (str): Name of the method. source_info (str): The source path of the method. """ __visit_name__ = 'middleware_method' def __init__(self, function_name: str, source_info: str): self.function_name = function_name self.source_info = source_info self.internal = False # added for compatibility with RouteMethodInfo class MiddlewareClassInfo(_Traversable): """Describes a middleware class. Args: name (str): The name of the middleware class. source_info (str): The source path where the middleware was defined. methods (List[MiddlewareMethodInfo]): List of method defined by the middleware class. """ __visit_name__ = 'middleware_class' def __init__( self, name: str, source_info: str, methods: List[MiddlewareMethodInfo] ): self.name = name self.source_info = source_info self.methods = methods class MiddlewareTreeItemInfo(_Traversable): """Describes a middleware tree entry. Args: name (str): The name of the method. class_name (str): The class name of the method. """ __visit_name__ = 'middleware_tree_item' _symbols = { 'process_request': '→', 'process_resource': '↣', 'process_response': '↢', } def __init__(self, name: str, class_name: str): self.name = name self.class_name = class_name class MiddlewareTreeInfo(_Traversable): """Describes the middleware methods used by the app. Args: request (List[MiddlewareTreeItemInfo]): The `process_request` methods. resource (List[MiddlewareTreeItemInfo]): The `process_resource` methods. response (List[MiddlewareTreeItemInfo]): The `process_response` methods. """ __visit_name__ = 'middleware_tree' def __init__( self, request: List[MiddlewareTreeItemInfo], resource: List[MiddlewareTreeItemInfo], response: List[MiddlewareTreeItemInfo], ): self.request = request self.resource = resource self.response = response class MiddlewareInfo(_Traversable): """Describes the middleware of the app. Args: middlewareTree (MiddlewareTreeInfo): The middleware tree of the app. middlewareClasses (List[MiddlewareClassInfo]): The middleware classes of the app. independent (bool): Whether or not the middleware components are executed independently. Attributes: independent_text (str): Text created from the `independent` arg. """ __visit_name__ = 'middleware' def __init__( self, middleware_tree: MiddlewareTreeInfo, middleware_classes: List[MiddlewareClassInfo], independent: bool, ): self.middleware_tree = middleware_tree self.middleware_classes = middleware_classes self.independent = independent if independent: self.independent_text = 'Middleware are independent' else: self.independent_text = 'Middleware are dependent' class AppInfo(_Traversable): """Describes an application. Args: routes (List[RouteInfo]): The routes of the application. middleware (MiddlewareInfo): The middleware information in the application. static_routes (List[StaticRouteInfo]): The static routes of this application. sinks (List[SinkInfo]): The sinks of this application. error_handlers (List[ErrorHandlerInfo]): The error handlers of this application. asgi (bool): Whether or not this is an ASGI application. """ __visit_name__ = 'app' def __init__( self, routes: List[RouteInfo], middleware: MiddlewareInfo, static_routes: List[StaticRouteInfo], sinks: List[SinkInfo], error_handlers: List[ErrorHandlerInfo], asgi: bool, ): self.routes = routes self.middleware = middleware self.static_routes = static_routes self.sinks = sinks self.error_handlers = error_handlers self.asgi = asgi def to_string(self, verbose=False, internal=False, name='') -> str: """Return a string representation of this class. Args: verbose (bool, optional): Adds more information. Defaults to False. internal (bool, optional): Also include internal falcon route methods and error handlers. Defaults to ``False``. name (str, optional): The name of the application, to be output at the beginning of the text. Defaults to ``'Falcon App'``. Returns: str: A string representation of the application. """ return StringVisitor(verbose, internal, name).process(self) # ------------------------------------------------------------------------ # Visitor classes # ------------------------------------------------------------------------ class InspectVisitor: """Base visitor class that implements the `process` method. Subclasses must implement ``visit_<name>`` methods for each supported class. """ def process(self, instance: _Traversable): """Process the instance, by calling the appropriate visit method. Uses the `__visit_name__` attribute of the `instance` to obtain the method to use. Args: instance (_Traversable): The instance to process. """ try: return getattr(self, 'visit_{}'.format(instance.__visit_name__))(instance) except AttributeError as e: raise RuntimeError( 'This visitor does not support {}'.format(type(instance)) ) from e class StringVisitor(InspectVisitor): """Visitor that returns a string representation of the info class. This is used automatically by calling ``to_string()`` on the info class. It can also be used directly by calling ``StringVisitor.process(info_instance)``. Args: verbose (bool, optional): Adds more information. Defaults to ``False``. internal (bool, optional): Also include internal route methods and error handlers added by the framework. Defaults to ``False``. name (str, optional): The name of the application, to be output at the beginning of the text. Defaults to ``'Falcon App'``. """ def __init__(self, verbose=False, internal=False, name=''): self.verbose = verbose self.internal = internal self.name = name self.indent = 0 @property def tab(self): """Get the current tabulation.""" return ' ' * self.indent def visit_route_method(self, route_method: RouteMethodInfo) -> str: """Visit a RouteMethodInfo instance. Usually called by `process`.""" text = '{0.method} - {0.function_name}'.format(route_method) if self.verbose: text += ' ({0.source_info})'.format(route_method) return text def _methods_to_string(self, methods: List): """Return a string from the list of methods.""" tab = self.tab + ' ' * 3 methods = _filter_internal(methods, self.internal) if not methods: return '' text_list = [self.process(m) for m in methods] method_text = ['{}├── {}'.format(tab, m) for m in text_list[:-1]] method_text += ['{}└── {}'.format(tab, m) for m in text_list[-1:]] return '\n'.join(method_text) def visit_route(self, route: RouteInfo) -> str: """Visit a RouteInfo instance. Usually called by `process`.""" text = '{0}⇒ {1.path} - {1.class_name}'.format(self.tab, route) if self.verbose: text += ' ({0.source_info})'.format(route) method_text = self._methods_to_string(route.methods) if not method_text: return text return '{}:\n{}'.format(text, method_text) def visit_static_route(self, static_route: StaticRouteInfo) -> str: """Visit a StaticRouteInfo instance. Usually called by `process`.""" text = '{0}↦ {1.prefix} {1.directory}'.format(self.tab, static_route) if static_route.fallback_filename: text += ' [{0.fallback_filename}]'.format(static_route) return text def visit_sink(self, sink: SinkInfo) -> str: """Visit a SinkInfo instance. Usually called by `process`.""" text = '{0}⇥ {1.prefix} {1.name}'.format(self.tab, sink) if self.verbose: text += ' ({0.source_info})'.format(sink) return text def visit_error_handler(self, error_handler: ErrorHandlerInfo) -> str: """Visit a ErrorHandlerInfo instance. Usually called by `process`.""" text = '{0}⇜ {1.error} {1.name}'.format(self.tab, error_handler) if self.verbose: text += ' ({0.source_info})'.format(error_handler) return text def visit_middleware_method(self, middleware_method: MiddlewareMethodInfo) -> str: """Visit a MiddlewareMethodInfo instance. Usually called by `process`.""" text = '{0.function_name}'.format(middleware_method) if self.verbose: text += ' ({0.source_info})'.format(middleware_method) return text def visit_middleware_class(self, middleware_class: MiddlewareClassInfo) -> str: """Visit a ErrorHandlerInfo instance. Usually called by `process`.""" text = '{0}↣ {1.name}'.format(self.tab, middleware_class) if self.verbose: text += ' ({0.source_info})'.format(middleware_class) method_text = self._methods_to_string(middleware_class.methods) if not method_text: return text return '{}:\n{}'.format(text, method_text) def visit_middleware_tree_item(self, mti: MiddlewareTreeItemInfo) -> str: """Visit a MiddlewareTreeItemInfo instance. Usually called by `process`.""" symbol = mti._symbols.get(mti.name, '→') return '{0}{1} {2.class_name}.{2.name}'.format(self.tab, symbol, mti) def visit_middleware_tree(self, m_tree: MiddlewareTreeInfo) -> str: """Visit a MiddlewareTreeInfo instance. Usually called by `process`.""" before = len(m_tree.request) + len(m_tree.resource) after = len(m_tree.response) if before + after == 0: return '' each = 2 initial = self.indent if after > before: self.indent += each * (after - before) text = [] for r in m_tree.request: text.append(self.process(r)) self.indent += each if text: text.append('') for r in m_tree.resource: text.append(self.process(r)) self.indent += each if m_tree.resource or not text: text.append('') self.indent += each text.append('{}├── Process route responder'.format(self.tab)) self.indent -= each if m_tree.response: text.append('') for r in m_tree.response: self.indent -= each text.append(self.process(r)) self.indent = initial return '\n'.join(text) def visit_middleware(self, middleware: MiddlewareInfo) -> str: """Visit a MiddlewareInfo instance. Usually called by `process`.""" text = self.process(middleware.middleware_tree) if self.verbose: self.indent += 4 m_text = '\n'.join(self.process(m) for m in middleware.middleware_classes) self.indent -= 4 if m_text: text += '\n{}- Middlewares classes:\n{}'.format(self.tab, m_text) return text def visit_app(self, app: AppInfo) -> str: """Visit a AppInfo instance. Usually called by `process`.""" type_ = 'ASGI' if app.asgi else 'WSGI' self.indent = 4 text = '{} ({})'.format(self.name or 'Falcon App', type_) if app.routes: routes = '\n'.join(self.process(r) for r in app.routes) text += '\n• Routes:\n{}'.format(routes) middleware_text = self.process(app.middleware) if middleware_text: text += '\n• Middleware ({}):\n{}'.format( app.middleware.independent_text, middleware_text ) if app.static_routes: static_routes = '\n'.join(self.process(sr) for sr in app.static_routes) text += '\n• Static routes:\n{}'.format(static_routes) if app.sinks: sinks = '\n'.join(self.process(s) for s in app.sinks) text += '\n• Sinks:\n{}'.format(sinks) errors = _filter_internal(app.error_handlers, self.internal) if errors: errs = '\n'.join(self.process(e) for e in errors) text += '\n• Error handlers:\n{}'.format(errs) return text # ------------------------------------------------------------------------ # Helpers functions # ------------------------------------------------------------------------ def _get_source_info(obj, default='[unknown file]'): """Try to get the definition file and line of obj. Return default on error. """ try: source_file = inspect.getsourcefile(obj) source_lines = inspect.findsource(obj) source_info = '{}:{}'.format(source_file, source_lines[1]) except Exception: # NOTE(vytas): If Falcon is cythonized, all default # responders coming from cythonized modules will # appear as built-in functions, and raise a # TypeError when trying to locate the source file. source_info = default return source_info def _get_source_info_and_name(obj): """Attempt to get the definition file and line of obj and its name.""" source_info = _get_source_info(obj, None) if source_info is None: # NOTE(caselit): a class instances return None. Try the type source_info = _get_source_info(type(obj)) name = getattr(obj, '__name__', None) if name is None: name = getattr(type(obj), '__name__', '[unknown]') return source_info, name def _is_internal(obj): """Check if the module of the object is a falcon module.""" module = inspect.getmodule(obj) if module: return module.__name__.startswith('falcon.') return False def _filter_internal(iterable, return_internal): """Filter the internal elements of an iterable.""" if return_internal: return iterable return [el for el in iterable if not el.internal]

34.040506

93

0.622564

from functools import partial import inspect from typing import Callable, Dict, List, Optional, Type from falcon import App, app_helpers from falcon.routing import CompiledRouter def inspect_app(app: App) -> 'AppInfo': routes = inspect_routes(app) static = inspect_static_routes(app) sinks = inspect_sinks(app) error_handlers = inspect_error_handlers(app) middleware = inspect_middlewares(app) return AppInfo(routes, middleware, static, sinks, error_handlers, app._ASGI) def inspect_routes(app: App) -> 'List[RouteInfo]': router = app._router inspect_function = _supported_routers.get(type(router)) if inspect_function is None: raise TypeError( 'Unsupported router class {}. Use "register_router" ' 'to register a function that can inspect the router ' 'used by the provided application'.format(type(router)) ) return inspect_function(router) def register_router(router_class): def wraps(fn): if router_class in _supported_routers: raise ValueError( 'Another function is already registered' ' for the router {}'.format(router_class) ) _supported_routers[router_class] = fn return fn return wraps _supported_routers = {} def inspect_static_routes(app: App) -> 'List[StaticRouteInfo]': routes = [] for sr, _, _ in app._static_routes: info = StaticRouteInfo(sr._prefix, sr._directory, sr._fallback_filename) routes.append(info) return routes def inspect_sinks(app: App) -> 'List[SinkInfo]': sinks = [] for prefix, sink, _ in app._sinks: source_info, name = _get_source_info_and_name(sink) info = SinkInfo(prefix.pattern, name, source_info) sinks.append(info) return sinks def inspect_error_handlers(app: App) -> 'List[ErrorHandlerInfo]': errors = [] for exc, fn in app._error_handlers.items(): source_info, name = _get_source_info_and_name(fn) info = ErrorHandlerInfo(exc.__name__, name, source_info, _is_internal(fn)) errors.append(info) return errors def inspect_middlewares(app: App) -> 'MiddlewareInfo': types_ = app_helpers.prepare_middleware(app._unprepared_middleware, True, app._ASGI) type_infos = [] for stack in types_: current = [] for method in stack: _, name = _get_source_info_and_name(method) cls = type(method.__self__) _, cls_name = _get_source_info_and_name(cls) current.append(MiddlewareTreeItemInfo(name, cls_name)) type_infos.append(current) middlewareTree = MiddlewareTreeInfo(*type_infos) middlewareClasses = [] names = 'Process request', 'Process resource', 'Process response' for m in app._unprepared_middleware: fns = app_helpers.prepare_middleware([m], True, app._ASGI) class_source_info, cls_name = _get_source_info_and_name(type(m)) methods = [] for method, name in zip(fns, names): if method: real_func = method[0] source_info = _get_source_info(real_func) methods.append(MiddlewareMethodInfo(real_func.__name__, source_info)) m_info = MiddlewareClassInfo(cls_name, class_source_info, methods) middlewareClasses.append(m_info) return MiddlewareInfo( middlewareTree, middlewareClasses, app._independent_middleware ) @register_router(CompiledRouter) def inspect_compiled_router(router: CompiledRouter) -> 'List[RouteInfo]': def _traverse(roots, parent): for root in roots: path = parent + '/' + root.raw_segment if root.resource is not None: methods = [] if root.method_map: for method, func in root.method_map.items(): if isinstance(func, partial): real_func = func.func else: real_func = func source_info = _get_source_info(real_func) internal = _is_internal(real_func) method_info = RouteMethodInfo( method, source_info, real_func.__name__, internal ) methods.append(method_info) source_info, class_name = _get_source_info_and_name(root.resource) route_info = RouteInfo(path, class_name, source_info, methods) routes.append(route_info) if root.children: _traverse(root.children, path) routes = [] _traverse(router._roots, '') return routes class _Traversable: __visit_name__ = 'N/A' def to_string(self, verbose=False, internal=False) -> str: return StringVisitor(verbose, internal).process(self) def __repr__(self): return self.to_string() class RouteMethodInfo(_Traversable): __visit_name__ = 'route_method' def __init__( self, method: str, source_info: str, function_name: str, internal: bool ): self.method = method self.source_info = source_info self.function_name = function_name self.internal = internal if function_name.startswith('on'): self.suffix = '_'.join(function_name.split('_')[2:]) else: self.suffix = '' class RouteInfo(_Traversable): __visit_name__ = 'route' def __init__( self, path: str, class_name: str, source_info: str, methods: List[RouteMethodInfo], ): self.path = path self.class_name = class_name self.source_info = source_info self.methods = methods class StaticRouteInfo(_Traversable): __visit_name__ = 'static_route' def __init__(self, prefix: str, directory: str, fallback_filename: Optional[str]): self.prefix = prefix self.directory = directory self.fallback_filename = fallback_filename class SinkInfo(_Traversable): __visit_name__ = 'sink' def __init__(self, prefix: str, name: str, source_info: str): self.prefix = prefix self.name = name self.source_info = source_info class ErrorHandlerInfo(_Traversable): __visit_name__ = 'error_handler' def __init__(self, error: str, name: str, source_info: str, internal: bool): self.error = error self.name = name self.source_info = source_info self.internal = internal class MiddlewareMethodInfo(_Traversable): __visit_name__ = 'middleware_method' def __init__(self, function_name: str, source_info: str): self.function_name = function_name self.source_info = source_info self.internal = False class MiddlewareClassInfo(_Traversable): __visit_name__ = 'middleware_class' def __init__( self, name: str, source_info: str, methods: List[MiddlewareMethodInfo] ): self.name = name self.source_info = source_info self.methods = methods class MiddlewareTreeItemInfo(_Traversable): __visit_name__ = 'middleware_tree_item' _symbols = { 'process_request': '→', 'process_resource': '↣', 'process_response': '↢', } def __init__(self, name: str, class_name: str): self.name = name self.class_name = class_name class MiddlewareTreeInfo(_Traversable): __visit_name__ = 'middleware_tree' def __init__( self, request: List[MiddlewareTreeItemInfo], resource: List[MiddlewareTreeItemInfo], response: List[MiddlewareTreeItemInfo], ): self.request = request self.resource = resource self.response = response class MiddlewareInfo(_Traversable): __visit_name__ = 'middleware' def __init__( self, middleware_tree: MiddlewareTreeInfo, middleware_classes: List[MiddlewareClassInfo], independent: bool, ): self.middleware_tree = middleware_tree self.middleware_classes = middleware_classes self.independent = independent if independent: self.independent_text = 'Middleware are independent' else: self.independent_text = 'Middleware are dependent' class AppInfo(_Traversable): __visit_name__ = 'app' def __init__( self, routes: List[RouteInfo], middleware: MiddlewareInfo, static_routes: List[StaticRouteInfo], sinks: List[SinkInfo], error_handlers: List[ErrorHandlerInfo], asgi: bool, ): self.routes = routes self.middleware = middleware self.static_routes = static_routes self.sinks = sinks self.error_handlers = error_handlers self.asgi = asgi def to_string(self, verbose=False, internal=False, name='') -> str: return StringVisitor(verbose, internal, name).process(self) class InspectVisitor: def process(self, instance: _Traversable): try: return getattr(self, 'visit_{}'.format(instance.__visit_name__))(instance) except AttributeError as e: raise RuntimeError( 'This visitor does not support {}'.format(type(instance)) ) from e class StringVisitor(InspectVisitor): def __init__(self, verbose=False, internal=False, name=''): self.verbose = verbose self.internal = internal self.name = name self.indent = 0 @property def tab(self): return ' ' * self.indent def visit_route_method(self, route_method: RouteMethodInfo) -> str: text = '{0.method} - {0.function_name}'.format(route_method) if self.verbose: text += ' ({0.source_info})'.format(route_method) return text def _methods_to_string(self, methods: List): tab = self.tab + ' ' * 3 methods = _filter_internal(methods, self.internal) if not methods: return '' text_list = [self.process(m) for m in methods] method_text = ['{}├── {}'.format(tab, m) for m in text_list[:-1]] method_text += ['{}└── {}'.format(tab, m) for m in text_list[-1:]] return '\n'.join(method_text) def visit_route(self, route: RouteInfo) -> str: text = '{0}⇒ {1.path} - {1.class_name}'.format(self.tab, route) if self.verbose: text += ' ({0.source_info})'.format(route) method_text = self._methods_to_string(route.methods) if not method_text: return text return '{}:\n{}'.format(text, method_text) def visit_static_route(self, static_route: StaticRouteInfo) -> str: text = '{0}↦ {1.prefix} {1.directory}'.format(self.tab, static_route) if static_route.fallback_filename: text += ' [{0.fallback_filename}]'.format(static_route) return text def visit_sink(self, sink: SinkInfo) -> str: text = '{0}⇥ {1.prefix} {1.name}'.format(self.tab, sink) if self.verbose: text += ' ({0.source_info})'.format(sink) return text def visit_error_handler(self, error_handler: ErrorHandlerInfo) -> str: text = '{0}⇜ {1.error} {1.name}'.format(self.tab, error_handler) if self.verbose: text += ' ({0.source_info})'.format(error_handler) return text def visit_middleware_method(self, middleware_method: MiddlewareMethodInfo) -> str: text = '{0.function_name}'.format(middleware_method) if self.verbose: text += ' ({0.source_info})'.format(middleware_method) return text def visit_middleware_class(self, middleware_class: MiddlewareClassInfo) -> str: text = '{0}↣ {1.name}'.format(self.tab, middleware_class) if self.verbose: text += ' ({0.source_info})'.format(middleware_class) method_text = self._methods_to_string(middleware_class.methods) if not method_text: return text return '{}:\n{}'.format(text, method_text) def visit_middleware_tree_item(self, mti: MiddlewareTreeItemInfo) -> str: symbol = mti._symbols.get(mti.name, '→') return '{0}{1} {2.class_name}.{2.name}'.format(self.tab, symbol, mti) def visit_middleware_tree(self, m_tree: MiddlewareTreeInfo) -> str: before = len(m_tree.request) + len(m_tree.resource) after = len(m_tree.response) if before + after == 0: return '' each = 2 initial = self.indent if after > before: self.indent += each * (after - before) text = [] for r in m_tree.request: text.append(self.process(r)) self.indent += each if text: text.append('') for r in m_tree.resource: text.append(self.process(r)) self.indent += each if m_tree.resource or not text: text.append('') self.indent += each text.append('{}├── Process route responder'.format(self.tab)) self.indent -= each if m_tree.response: text.append('') for r in m_tree.response: self.indent -= each text.append(self.process(r)) self.indent = initial return '\n'.join(text) def visit_middleware(self, middleware: MiddlewareInfo) -> str: text = self.process(middleware.middleware_tree) if self.verbose: self.indent += 4 m_text = '\n'.join(self.process(m) for m in middleware.middleware_classes) self.indent -= 4 if m_text: text += '\n{}- Middlewares classes:\n{}'.format(self.tab, m_text) return text def visit_app(self, app: AppInfo) -> str: type_ = 'ASGI' if app.asgi else 'WSGI' self.indent = 4 text = '{} ({})'.format(self.name or 'Falcon App', type_) if app.routes: routes = '\n'.join(self.process(r) for r in app.routes) text += '\n• Routes:\n{}'.format(routes) middleware_text = self.process(app.middleware) if middleware_text: text += '\n• Middleware ({}):\n{}'.format( app.middleware.independent_text, middleware_text ) if app.static_routes: static_routes = '\n'.join(self.process(sr) for sr in app.static_routes) text += '\n• Static routes:\n{}'.format(static_routes) if app.sinks: sinks = '\n'.join(self.process(s) for s in app.sinks) text += '\n• Sinks:\n{}'.format(sinks) errors = _filter_internal(app.error_handlers, self.internal) if errors: errs = '\n'.join(self.process(e) for e in errors) text += '\n• Error handlers:\n{}'.format(errs) return text def _get_source_info(obj, default='[unknown file]'): try: source_file = inspect.getsourcefile(obj) source_lines = inspect.findsource(obj) source_info = '{}:{}'.format(source_file, source_lines[1]) except Exception: source_info = default return source_info def _get_source_info_and_name(obj): source_info = _get_source_info(obj, None) if source_info is None: source_info = _get_source_info(type(obj)) name = getattr(obj, '__name__', None) if name is None: name = getattr(type(obj), '__name__', '[unknown]') return source_info, name def _is_internal(obj): module = inspect.getmodule(obj) if module: return module.__name__.startswith('falcon.') return False def _filter_internal(iterable, return_internal): if return_internal: return iterable return [el for el in iterable if not el.internal]

true

7901bd5b4b172d6f3e1c4b47ac8b79bb97033ac2

6,202

py

Python

tpdatasrc/tpgamefiles/rules/char_class/class016_sorcerer.py

edoipi/TemplePlus

f0e552289822fea908f16daa379fa568b1bd286d

[ "MIT" ]

null

tpdatasrc/tpgamefiles/rules/char_class/class016_sorcerer.py

edoipi/TemplePlus

f0e552289822fea908f16daa379fa568b1bd286d

[ "MIT" ]

null

tpdatasrc/tpgamefiles/rules/char_class/class016_sorcerer.py

edoipi/TemplePlus

f0e552289822fea908f16daa379fa568b1bd286d

[ "MIT" ]

null

from toee import * import char_class_utils import char_editor ################################################### def GetConditionName(): # used by API return "Sorcerer" # def GetSpellCasterConditionName(): # return "Sorcerer Spellcasting" def GetCategory(): return "Core 3.5 Ed Classes" def GetClassDefinitionFlags(): return CDF_BaseClass | CDF_CoreClass def GetClassHelpTopic(): return "TAG_SORCERERS" classEnum = stat_level_sorcerer ################################################### class_feats = { 1: (feat_simple_weapon_proficiency, feat_call_familiar) } class_skills = (skill_alchemy, skill_bluff, skill_concentration, skill_craft, skill_knowledge_arcana, skill_profession, skill_spellcraft) spells_per_day = { 1: (5, 3), 2: (6, 4), 3: (6, 5), 4: (6, 6, 3), 5: (6, 6, 4), 6: (6, 6, 5, 3), 7: (6, 6, 6, 4), 8: (6, 6, 6, 5, 3), 9: (6, 6, 6, 6, 4), 10: (6, 6, 6, 6, 5, 3), 11: (6, 6, 6, 6, 6, 4), 12: (6, 6, 6, 6, 6, 5, 3), 13: (6, 6, 6, 6, 6, 6, 4), 14: (6, 6, 6, 6, 6, 6, 5, 3), 15: (6, 6, 6, 6, 6, 6, 6, 4), 16: (6, 6, 6, 6, 6, 6, 6, 5, 3), 17: (6, 6, 6, 6, 6, 6, 6, 6, 4), 18: (6, 6, 6, 6, 6, 6, 6, 6, 5, 3), 19: (6, 6, 6, 6, 6, 6, 6, 6, 6, 4), 20: (6, 6, 6, 6, 6, 6, 6, 6, 6, 6) #lvl 0 1 2 3 4 5 6 7 8 9 } spells_known = { 1: (4, 2), 2: (5, 2), 3: (5, 3), 4: (6, 3, 1), 5: (6, 4, 2), 6: (7, 4, 2, 1), 7: (7, 5, 3, 2), 8: (8, 5, 3, 2, 1), 9: (8, 5, 4, 3, 2), 10: (9, 5, 4, 3, 2, 1), 11: (9, 5, 5, 4, 3, 2), 12: (9, 5, 5, 4, 3, 2, 1), 13: (9, 5, 5, 4, 4, 3, 2), 14: (9, 5, 5, 4, 4, 3, 2, 1), 15: (9, 5, 5, 4, 4, 4, 3, 2), 16: (9, 5, 5, 4, 4, 4, 3, 2, 1), 17: (9, 5, 5, 4, 4, 4, 3, 3, 2), 18: (9, 5, 5, 4, 4, 4, 3, 3, 2, 1), 19: (9, 5, 5, 4, 4, 4, 3, 3, 3, 2), 20: (9, 5, 5, 4, 4, 4, 3, 3, 3, 3) #lvl 0 1 2 3 4 5 6 7 8 9 } def GetHitDieType(): return 4 def GetSkillPtsPerLevel(): return 2 def GetBabProgression(): return base_attack_bonus_type_non_martial def IsFortSaveFavored(): return 0 def IsRefSaveFavored(): return 0 def IsWillSaveFavored(): return 1 # Spell casting def GetSpellListType(): return spell_list_type_arcane def GetSpellSourceType(): return spell_source_type_arcane def GetSpellReadyingType(): return spell_readying_innate def GetSpellsPerDay(): return spells_per_day caster_levels = range(1, 21) def GetCasterLevels(): return caster_levels def GetSpellDeterminingStat(): return stat_charisma def IsClassSkill(skillEnum): return char_class_utils.IsClassSkill(class_skills, skillEnum) def IsClassFeat(featEnum): return char_class_utils.IsClassFeat(class_feats, featEnum) def GetClassFeats(): return class_feats def IsAlignmentCompatible( alignment): return 1 def ObjMeetsPrereqs( obj ): abScore = obj.stat_base_get(stat_charisma) if abScore > 10: return 1 return 0 ## Levelup callbacks def IsSelectingSpellsOnLevelup( obj ): return 1 def InitSpellSelection( obj, classLvlNew = -1, classLvlIncrement = 1): classLvl = obj.stat_level_get(classEnum) if classLvlNew <= 0: classLvlNew = classLvl + 1 maxSpellLvl = char_editor.get_max_spell_level( obj, classEnum, classLvlNew ) # this regards spell list extension by stuff like Mystic Theurge # Available Spells spAvail = char_editor.get_learnable_spells(obj, classEnum, maxSpellLvl) # add spell level labels for p in range(0,maxSpellLvl+1): spAvail.append(char_editor.KnownSpellInfo(spell_label_level_0 + p, 0, classEnum)) spAvail.sort() char_editor.append_available_spells(spAvail) # newly taken class if classLvlNew == 1: spEnums = [] spEnums.append(char_editor.KnownSpellInfo(spell_label_level_0, 0, classEnum)) # add "Level 0" label for p in range(0,4): # 4 cantrips spEnums.append(char_editor.KnownSpellInfo(spell_new_slot_lvl_0, 3, classEnum)) spEnums.append(char_editor.KnownSpellInfo(spell_label_level_1, 0, classEnum)) # add "Level 1" label for p in range(0,2): # 2 level 1 spells spEnums.append(char_editor.KnownSpellInfo(spell_new_slot_lvl_1, 3, classEnum)) char_editor.append_spell_enums(spEnums) return 0 # Incrementing class level spellListLvl = obj.stat_level_get(stat_spell_list_level, classEnum) + classLvlIncrement # the effective level for getting the number of spells known spEnums = char_editor.get_known_class_spells(obj, classEnum) # get all spells known for this class for spellLvl in range(0, maxSpellLvl+1): spEnums.append(char_editor.KnownSpellInfo(spell_label_level_0 + spellLvl, 0, classEnum)) # add label # add spells newSpellsKnownCount = char_class_utils.GetSpellsKnownAddedCount( spells_known , spellListLvl, spellLvl) print "new num spells for spell level " + str(spellLvl) + ": " + str(newSpellsKnownCount) for q in range(0, newSpellsKnownCount): spEnums.append(char_editor.KnownSpellInfo(spell_new_slot_lvl_0 + spellLvl, 3, classEnum)) isReplacing = 0 if spellListLvl >= 4 and (spellListLvl % 2) == 0: # spell replacement isReplacing = 1 if char_editor.get_class_code() != classEnum: #grant this benefit only for strict levelup (also to prevent some headache...) isReplacing = 0 if isReplacing == 0: spEnums.sort() char_editor.append_spell_enums(spEnums) return 0 # mark as replaceable for p in range(0,len(spEnums)): spEnum = spEnums[p].spell_enum if spell_vacant <= spEnum <= spell_label_level_9: continue if spell_new_slot_lvl_0 <= spEnum <= spell_new_slot_lvl_9: continue if char_editor.get_spell_level(spEnum, classEnum) <= maxSpellLvl-2: spEnums[p].spell_status = 1 # marked as replaceable spEnums.sort() char_editor.append_spell_enums(spEnums) return 0 def LevelupCheckSpells( obj ): classLvl = obj.stat_level_get(classEnum) classLvlNew = classLvl + 1 maxSpellLvl = char_editor.get_max_spell_level( obj, classEnum, classLvlNew ) spell_enums = char_editor.get_spell_enums() for spInfo in spell_enums: if spInfo.spell_enum == spell_vacant: if maxSpellLvl >= 4 and spInfo.spell_level == 0: # in case the cantrips are causing problems continue return 0 return 1 def LevelupSpellsFinalize( obj, classLvlNew = -1 ): spEnums = char_editor.get_spell_enums() char_editor.spell_known_add(spEnums) # internally takes care of duplicates and the labels/vacant slots return

28.319635

149

0.688165

from toee import * import char_class_utils import char_editor lIncrement = 1): classLvl = obj.stat_level_get(classEnum) if classLvlNew <= 0: classLvlNew = classLvl + 1 maxSpellLvl = char_editor.get_max_spell_level( obj, classEnum, classLvlNew ) spAvail = char_editor.get_learnable_spells(obj, classEnum, maxSpellLvl) for p in range(0,maxSpellLvl+1): spAvail.append(char_editor.KnownSpellInfo(spell_label_level_0 + p, 0, classEnum)) spAvail.sort() char_editor.append_available_spells(spAvail) if classLvlNew == 1: spEnums = [] spEnums.append(char_editor.KnownSpellInfo(spell_label_level_0, 0, classEnum)) for p in range(0,4): spEnums.append(char_editor.KnownSpellInfo(spell_new_slot_lvl_0, 3, classEnum)) spEnums.append(char_editor.KnownSpellInfo(spell_label_level_1, 0, classEnum)) for p in range(0,2): spEnums.append(char_editor.KnownSpellInfo(spell_new_slot_lvl_1, 3, classEnum)) char_editor.append_spell_enums(spEnums) return 0 spellListLvl = obj.stat_level_get(stat_spell_list_level, classEnum) + classLvlIncrement spEnums = char_editor.get_known_class_spells(obj, classEnum) for spellLvl in range(0, maxSpellLvl+1): spEnums.append(char_editor.KnownSpellInfo(spell_label_level_0 + spellLvl, 0, classEnum)) newSpellsKnownCount = char_class_utils.GetSpellsKnownAddedCount( spells_known , spellListLvl, spellLvl) print "new num spells for spell level " + str(spellLvl) + ": " + str(newSpellsKnownCount) for q in range(0, newSpellsKnownCount): spEnums.append(char_editor.KnownSpellInfo(spell_new_slot_lvl_0 + spellLvl, 3, classEnum)) isReplacing = 0 if spellListLvl >= 4 and (spellListLvl % 2) == 0: isReplacing = 1 if char_editor.get_class_code() != classEnum: isReplacing = 0 if isReplacing == 0: spEnums.sort() char_editor.append_spell_enums(spEnums) return 0 for p in range(0,len(spEnums)): spEnum = spEnums[p].spell_enum if spell_vacant <= spEnum <= spell_label_level_9: continue if spell_new_slot_lvl_0 <= spEnum <= spell_new_slot_lvl_9: continue if char_editor.get_spell_level(spEnum, classEnum) <= maxSpellLvl-2: spEnums[p].spell_status = 1 spEnums.sort() char_editor.append_spell_enums(spEnums) return 0 def LevelupCheckSpells( obj ): classLvl = obj.stat_level_get(classEnum) classLvlNew = classLvl + 1 maxSpellLvl = char_editor.get_max_spell_level( obj, classEnum, classLvlNew ) spell_enums = char_editor.get_spell_enums() for spInfo in spell_enums: if spInfo.spell_enum == spell_vacant: if maxSpellLvl >= 4 and spInfo.spell_level == 0: continue return 0 return 1 def LevelupSpellsFinalize( obj, classLvlNew = -1 ): spEnums = char_editor.get_spell_enums() char_editor.spell_known_add(spEnums) return

false

true

7901bf183b68ae45dd4aeedf3308e89b5a443829

2,790

py

Python

query_CNFUN.py

CNBP/RCAPI

5d7bb0e3bad0928529e84f404830de90c6c03143

[ "MIT" ]

null

query_CNFUN.py

CNBP/RCAPI

5d7bb0e3bad0928529e84f404830de90c6c03143

[ "MIT" ]

null

query_CNFUN.py

CNBP/RCAPI

5d7bb0e3bad0928529e84f404830de90c6c03143

[ "MIT" ]

null

import sys from query_common import filter_records, ProjectMixins from redcap import Project # note this is from PyCap.redcap from typing import List """ This class of functions are responsible of retrieving relevant data structures from the CNFUN tables """ class CNFUN_project(ProjectMixins): """ One baby can have many admissions CaseIDs. One hospital record can have many CaseIDs. One baby has only one hospital record number. """ def __init__( self, Token, URL, get_all_field=False, ): """ Create a project using PyCap :param Token: :param URL: :return: """ # Several key properties we'll use throughout self.project = Project(URL, Token) # These are very important ID fields from the fields_keyid = ["patientID", "cf_p_cnnpatientui"] # For now, make sure to onyl get the data related to these key ids to reduce load time self.data = self.get_fields(fields_keyid) # if specified, get all the records. if get_all_field: self.data = self.project.export_records() def filter_with_CNNPatientUI(self, CNNPatientUI: str or List[str]): """ Check the list, only retain the relevant records with matching PatientID are retained. :param dataset: CNBPIDs & record ID correspondence list. :param CNNPatientUI: :return: """ list_filtered = None filtered_field = "cf_p_cnnpatientui" # Handling when babyIDs is string instead of list (allowing batch function). if type(CNNPatientUI) is str: CNNPatientUI = [CNNPatientUI] list_filtered = filter_records(self.data, filtered_field, CNNPatientUI) return list_filtered def get_PatientID_with_CNNPatientUI(self, CNNPatientUI: str or List[str]): """ PatientID has 1:1 correspondence with CNNPatientUI which is the same as PatientUI from CNN Baby table. :return: """ # Listify the CNNPatientUI if type(CNNPatientUI) is str: CNNPatientUI = [CNNPatientUI] # Filter with the information list_filtered_dict = self.filter_with_CNNPatientUI(CNNPatientUI) # Aggregate the list_PatientID list_PatientID = [] for case in list_filtered_dict: list_PatientID.append(case["patientid"]) return list_PatientID def get_records_CNFUN(self, PatientID: str or List[str]): """ Retrieve the cases based on their INDEX which is the :param cases: :return: """ if type(PatientID) is str: PatientID = [PatientID] cases_data = self.project.export_records(records=PatientID) return cases_data

32.44186

110

0.650538

import sys from query_common import filter_records, ProjectMixins from redcap import Project from typing import List class CNFUN_project(ProjectMixins): def __init__( self, Token, URL, get_all_field=False, ): self.project = Project(URL, Token) # These are very important ID fields from the fields_keyid = ["patientID", "cf_p_cnnpatientui"] # For now, make sure to onyl get the data related to these key ids to reduce load time self.data = self.get_fields(fields_keyid) # if specified, get all the records. if get_all_field: self.data = self.project.export_records() def filter_with_CNNPatientUI(self, CNNPatientUI: str or List[str]): list_filtered = None filtered_field = "cf_p_cnnpatientui" # Handling when babyIDs is string instead of list (allowing batch function). if type(CNNPatientUI) is str: CNNPatientUI = [CNNPatientUI] list_filtered = filter_records(self.data, filtered_field, CNNPatientUI) return list_filtered def get_PatientID_with_CNNPatientUI(self, CNNPatientUI: str or List[str]): # Listify the CNNPatientUI if type(CNNPatientUI) is str: CNNPatientUI = [CNNPatientUI] # Filter with the information list_filtered_dict = self.filter_with_CNNPatientUI(CNNPatientUI) # Aggregate the list_PatientID list_PatientID = [] for case in list_filtered_dict: list_PatientID.append(case["patientid"]) return list_PatientID def get_records_CNFUN(self, PatientID: str or List[str]): if type(PatientID) is str: PatientID = [PatientID] cases_data = self.project.export_records(records=PatientID) return cases_data

true

7901bf4e043fcf1e2f4bfc4e4937b25cd22a1088

758

py

Python

src/engine/main.py

libercapital/dados_publicos_cnpj_receita_federal

a02f98ebb1e5aa64539cc371d94ba78a49647214

[ "MIT" ]

7

2022-02-04T22:02:01.000Z

2022-03-08T22:55:29.000Z

src/engine/main.py

libercapital/dados_publicos_cnpj_receita_federal

a02f98ebb1e5aa64539cc371d94ba78a49647214

[ "MIT" ]

3

2022-02-04T22:48:01.000Z

2022-02-10T01:53:00.000Z

src/engine/main.py

libercapital/dados_publicos_cnpj_receita_federal

a02f98ebb1e5aa64539cc371d94ba78a49647214

[ "MIT" ]

1

2022-03-18T17:07:18.000Z

from src.engine.company_root import CompanyRoot from src.engine.company_root_simples import CompanyRootSimples from src.engine.partners import Partners from src.engine.company import Company from src.engine.company_tax_regime import CompanyTaxRegime from src.engine.ref_date import main as engine_ref_date from src.io.get_last_ref_date import main as get_last_ref_date def main(ref_date=None): ref_date = ref_date or get_last_ref_date() CompanyRoot(ref_date=ref_date).execute() Partners(ref_date=ref_date).execute() CompanyRootSimples(ref_date=ref_date).execute() CompanyTaxRegime(ref_date=ref_date).execute() Company(ref_date=ref_date).execute() engine_ref_date() if __name__ == '__main__': main()

32.956522

63

0.777045

from src.engine.company_root import CompanyRoot from src.engine.company_root_simples import CompanyRootSimples from src.engine.partners import Partners from src.engine.company import Company from src.engine.company_tax_regime import CompanyTaxRegime from src.engine.ref_date import main as engine_ref_date from src.io.get_last_ref_date import main as get_last_ref_date def main(ref_date=None): ref_date = ref_date or get_last_ref_date() CompanyRoot(ref_date=ref_date).execute() Partners(ref_date=ref_date).execute() CompanyRootSimples(ref_date=ref_date).execute() CompanyTaxRegime(ref_date=ref_date).execute() Company(ref_date=ref_date).execute() engine_ref_date() if __name__ == '__main__': main()

true

7901bfee3778dd08118d7ec1c5e0e0d9e7c93415

1,004

py

Python

alipay/aop/api/response/AlipayBossFncSettleSettlementbillCreateResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

213

2018-08-27T16:49:32.000Z

2021-12-29T04:34:12.000Z

alipay/aop/api/response/AlipayBossFncSettleSettlementbillCreateResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

29

2018-09-29T06:43:00.000Z

2021-09-02T03:27:32.000Z

alipay/aop/api/response/AlipayBossFncSettleSettlementbillCreateResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

59

2018-08-27T16:59:26.000Z

2022-03-25T10:08:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.response.AlipayResponse import AlipayResponse from alipay.aop.api.domain.SettlementbillOpenApiDTO import SettlementbillOpenApiDTO class AlipayBossFncSettleSettlementbillCreateResponse(AlipayResponse): def __init__(self): super(AlipayBossFncSettleSettlementbillCreateResponse, self).__init__() self._result_set = None @property def result_set(self): return self._result_set @result_set.setter def result_set(self, value): if isinstance(value, SettlementbillOpenApiDTO): self._result_set = value else: self._result_set = SettlementbillOpenApiDTO.from_alipay_dict(value) def parse_response_content(self, response_content): response = super(AlipayBossFncSettleSettlementbillCreateResponse, self).parse_response_content(response_content) if 'result_set' in response: self.result_set = response['result_set']

33.466667

120

0.74004

import json from alipay.aop.api.response.AlipayResponse import AlipayResponse from alipay.aop.api.domain.SettlementbillOpenApiDTO import SettlementbillOpenApiDTO class AlipayBossFncSettleSettlementbillCreateResponse(AlipayResponse): def __init__(self): super(AlipayBossFncSettleSettlementbillCreateResponse, self).__init__() self._result_set = None @property def result_set(self): return self._result_set @result_set.setter def result_set(self, value): if isinstance(value, SettlementbillOpenApiDTO): self._result_set = value else: self._result_set = SettlementbillOpenApiDTO.from_alipay_dict(value) def parse_response_content(self, response_content): response = super(AlipayBossFncSettleSettlementbillCreateResponse, self).parse_response_content(response_content) if 'result_set' in response: self.result_set = response['result_set']

true

7901c0b5cba908fba613856a95ce492e8e7595c5

910

py

Python

medium/Q105_ConstructBinaryTreeFromPreorderAndInorderTraversal.py

Kaciras/leetcode

d203aecd1afe1af13a0384a9c657c8424aab322d

[ "MIT" ]

null

medium/Q105_ConstructBinaryTreeFromPreorderAndInorderTraversal.py

Kaciras/leetcode

d203aecd1afe1af13a0384a9c657c8424aab322d

[ "MIT" ]

null

medium/Q105_ConstructBinaryTreeFromPreorderAndInorderTraversal.py

Kaciras/leetcode

d203aecd1afe1af13a0384a9c657c8424aab322d

[ "MIT" ]

null

from utils import TreeNode, binary_tree class Solution: def __init__(self): self.index = 0 # 利用[中序遍历左边元素数量 = 左子树节点总数]可以省掉这个计数的字段 def buildTree(self, preorder, inorder): """ :type preorder: List[int] :type inorder: List[int] :rtype: TreeNode """ if not preorder: return None def build_node(lo, hi): node = TreeNode(preorder[self.index]) self.index += 1 j = inorder.index(node.val, lo, hi) # 有些解法生成字典加快这步，但这会增大空间复杂度 if self.index < len(preorder) and preorder[self.index] in inorder[lo:j]: node.left = build_node(lo, j) if self.index < len(preorder) and preorder[self.index] in inorder[j + 1:hi]: node.right = build_node(j + 1, hi) return node return build_node(0, len(preorder)) if __name__ == '__main__': x = Solution().buildTree([1, 2, 4, 6, 5, 7, 8, 3, 9], [4, 6, 2, 7, 5, 8, 1, 9, 3]) x = Solution().buildTree([3, 9, 20, 15, 7], [9, 3, 15, 20, 7])

26

83

0.642857

from utils import TreeNode, binary_tree class Solution: def __init__(self): self.index = 0 def buildTree(self, preorder, inorder): if not preorder: return None def build_node(lo, hi): node = TreeNode(preorder[self.index]) self.index += 1 j = inorder.index(node.val, lo, hi) if self.index < len(preorder) and preorder[self.index] in inorder[lo:j]: node.left = build_node(lo, j) if self.index < len(preorder) and preorder[self.index] in inorder[j + 1:hi]: node.right = build_node(j + 1, hi) return node return build_node(0, len(preorder)) if __name__ == '__main__': x = Solution().buildTree([1, 2, 4, 6, 5, 7, 8, 3, 9], [4, 6, 2, 7, 5, 8, 1, 9, 3]) x = Solution().buildTree([3, 9, 20, 15, 7], [9, 3, 15, 20, 7])

true

7901c1a4e85e018c419102609aa51b2d41092f36

1,187

py

Python

scripts/batchAnnotator.py

PRIDE-Cluster/cluster-result-importer

354150e2ea527bcc1d3398f75ebbeb346a4c3dc7

[ "Apache-2.0" ]

null

scripts/batchAnnotator.py

PRIDE-Cluster/cluster-result-importer

354150e2ea527bcc1d3398f75ebbeb346a4c3dc7

[ "Apache-2.0" ]

1

2015-02-09T16:35:54.000Z

2015-02-09T16:37:51.000Z

scripts/batchAnnotator.py

PRIDE-Cluster/cluster-result-importer

354150e2ea527bcc1d3398f75ebbeb346a4c3dc7

[ "Apache-2.0" ]

null

import sys from subprocess import Popen import cx_Oracle root_directory = sys.argv[1] def main(directory): public_projects = get_public_project_accessions() for project_accession in public_projects: Popen(['./runAnnotator.sh', directory, str(project_accession)]) # get all the project references from pride archive def get_public_project_accessions(): accessions = list() archive_cursor = connect_archive() archive_cursor.execute( "select accession from project where (submission_type='PRIDE' or submission_type='COMPLETE') and is_public = 1") projects = archive_cursor.fetchall() for project in projects: accessions.append(project[0]) archive_cursor.close() return accessions # connect to pride archive database def connect_archive(): # connect to archive database archive_db = cx_Oracle.connect( "${pride.repo.db.user}/${pride.repo.db.password}@(DESCRIPTION=(ADDRESS=(PROTOCOL=tcp)(HOST=ora-vm-032.ebi.ac.uk)(PORT=1531))(CONNECT_DATA=(SERVICE_NAME=PRIDEPRO)))") # Create an cursor object for archive database return archive_db.cursor() if __name__ == '__main__': main(root_directory)

28.261905

173

0.730413

import sys from subprocess import Popen import cx_Oracle root_directory = sys.argv[1] def main(directory): public_projects = get_public_project_accessions() for project_accession in public_projects: Popen(['./runAnnotator.sh', directory, str(project_accession)]) def get_public_project_accessions(): accessions = list() archive_cursor = connect_archive() archive_cursor.execute( "select accession from project where (submission_type='PRIDE' or submission_type='COMPLETE') and is_public = 1") projects = archive_cursor.fetchall() for project in projects: accessions.append(project[0]) archive_cursor.close() return accessions def connect_archive(): archive_db = cx_Oracle.connect( "${pride.repo.db.user}/${pride.repo.db.password}@(DESCRIPTION=(ADDRESS=(PROTOCOL=tcp)(HOST=ora-vm-032.ebi.ac.uk)(PORT=1531))(CONNECT_DATA=(SERVICE_NAME=PRIDEPRO)))") return archive_db.cursor() if __name__ == '__main__': main(root_directory)

true