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paulo037
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starcoder

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apps/server/tpr_server.py
SDRAST/Instruments_Radipower
0
111397
""" Dare! Total Power Radiometer server Instead of superceding the inherited loggers it may be possible simply to rename them. """ import logging import signal import sys import time from os.path import dirname from support import NamedClass, check_permission, sync_second from support.logs import get_loglevel, initiate_option_parser, init_logging from support.logs import set_module_loggers from support.pyro import PyroServerLauncher, PyroServer from Electronics.Instruments.radiometer import Radiometer from Electronics.Instruments.Radipower import find_radipowers module_logger = logging.getLogger(__name__) class RadiometerServer(PyroServer, Radiometer): """ Pyro server for the Radipower radiometer Public Attributes:: datafile - file object to which the data are written logger - logging.Logger object run - True when server is running Inherited from Radiometer:: integration - 2*update_interval for Nyquist sampling last_reading - results of the last power meter reading logger - logging.Logger object but superceded pm_reader - DeviceReadThread object reader_done - threading.Event object, set when reading has been taken reader_started - threading.Event object, set when a reading is started take_data - threading.Event object to signal readers to take reading update_interval - inverse of reading rate Inherited from Pyroserver:: logger - logging.Logger object but superceded run - True if server is running """ help_text = """ change_rate(rate) - change sampling rate to 'rate' samples per second get_readings() - return the most recent set of readings stop - stop the radiometer server """ def __init__(self, name, logpath="/var/tmp/", rate=1./60): """ Initialize a Radipower radiometer server At present with a Raspberry Pi controller the maximum rate is about 1/s. @param name : name for the Pyro nameserver @type name : str @param logpath : directory for the radiometer datafiles @type logpath : str @param rate : number of readings per second @type rate : float """ super(RadiometerServer,self).__init__() # Replaces inherited logger self.logger = logging.getLogger(module_logger.name+".RadiometerServer") self.logger.debug(" superclass initialized") if check_permission('ops') == False: raise RuntimeError("Insufficient permission to access USB") pm = find_radipowers() self.logger.debug(" initializing") Radiometer.__init__(self, pm, rate=rate) # Open power logging file: self.open_datafile(logpath) self.run = True self.start() def stop(self): """ Stops the radiometer and closes the datafile """ self.close() # for the Radiometer self.datafile.close() self.logger.info("close: finished.") def open_datafile(self, logpath): """ Opens the datafile The filename is RMYYY-DDD-HHMM.csv @param logpath : directory for the radiometer datafiles @type logpath : str """ filename = time.strftime("RM%Y-%j-%H%M.csv", time.gmtime(time.time())) self.datafile = open(logpath+filename,"w") def change_rate(self, rate): """ Change the reading rate Closes the datafile, sets the new rate and open a new datafile. """ # ? suspend the read threads # pause the timer signal.setitimer(signal.ITIMER_REAL, 0) # close the data file logpath = dirname(self.datafile.name) self.datafile.close() # set the new rate self.set_rate(rate) # open a new data file self.open_datafile(logpath+"/") # ? resume the read threads # resume the timer sync_second() signal.setitimer(signal.ITIMER_REAL, self.update_interval, self.update_interval) def help(self): return RadiometerServer.help_text if __name__ == "__main__": def main(name): """ Starts a Pyro server launcher which launches the radiometer server Provides command line options for logging and starts the top-level logger. """ p = initiate_option_parser("Dare! Total Power Radiometer server", """Generic Pyro server which servers as a template for actual servers.""") # Add other options here opts, args = p.parse_args(sys.argv[1:]) # This cannot be delegated to another module or class mylogger = init_logging(logging.getLogger(), loglevel = get_loglevel(opts.file_loglevel), consolevel = get_loglevel(opts.stderr_loglevel), logname = opts.logpath+name+".log") mylogger.debug(" Handlers: %s", mylogger.handlers) loggers = set_module_loggers(eval(opts.modloglevels)) # specify the name by which the Pyro task is known psl = PyroServerLauncher(name+"Server", nameserver_host="crux") m = RadiometerServer(name, logpath=opts.logpath+name+"/") # identifier for the hardware psl.start(m) # clean up after the server stops psl.finish() main("Radiometer")
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python/orca/example/torchmodel/train/resnet_finetune/resnet_finetune.py
DirkFi/BigDL
3
145196
# # Copyright 2016 The BigDL 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. # from optparse import OptionParser import torchvision import torch.nn as nn import torch.nn.functional as F from pyspark.sql.types import StringType, DoubleType from pyspark.sql.functions import col, udf from bigdl.dllib.optim.optimizer import * from bigdl.dllib.nncontext import * from bigdl.dllib.feature.image import * from bigdl.orca import init_orca_context, stop_orca_context from bigdl.orca.torch import TorchModel, TorchLoss from bigdl.dllib.nnframes import * from bigdl.dllib.keras.metrics import Accuracy from bigdl.dllib.utils.utils import detect_conda_env_name # Define model with Pytorch class CatDogModel(nn.Module): def __init__(self): super(CatDogModel, self).__init__() self.features = torchvision.models.resnet18(pretrained=True) # freeze weight update for parameter in self.features.parameters(): parameter.requires_grad_(False) self.dense1 = nn.Linear(1000, 2) def forward(self, x): # freeze BatchNorm self.features.eval() x = self.features(x) x = F.log_softmax(self.dense1(x), dim=1) return x if __name__ == '__main__': parser = OptionParser() parser.add_option("--executor-cores", type=int, dest="cores", default=4, help="number of executor cores") parser.add_option("--num-executors", type=int, dest="executors", default=16, help="number of executors") parser.add_option("--executor-memory", type=str, dest="executorMemory", default="30g", help="executor memory") parser.add_option("--driver-memory", type=str, dest="driverMemory", default="30g", help="driver memory") parser.add_option("--deploy-mode", type=str, dest="deployMode", default="local", help="deploy mode, local, yarn-client or yarn-cluster") (options, args) = parser.parse_args(sys.argv) hadoop_conf = os.environ.get('HADOOP_CONF_DIR') sc = init_orca_context(cluster_mode=options.deployMode, hadoop_conf=hadoop_conf) model = CatDogModel() zoo_model = TorchModel.from_pytorch(model) def lossFunc(input, target): return nn.NLLLoss().forward(input, target.flatten().long()) zoo_loss = TorchLoss.from_pytorch(lossFunc) # prepare training data as Spark DataFrame image_path = sys.argv[1] imageDF = NNImageReader.readImages(image_path, sc, resizeH=256, resizeW=256, image_codec=1) getName = udf(lambda row: os.path.basename(row[0]), StringType()) getLabel = udf(lambda name: 1.0 if name.startswith('cat') else 0.0, DoubleType()) labelDF = imageDF.withColumn("name", getName(col("image"))) \ .withColumn("label", getLabel(col('name'))).cache() (trainingDF, validationDF) = labelDF.randomSplit([0.9, 0.1]) # run training and evaluation featureTransformer = ChainedPreprocessing( [RowToImageFeature(), ImageCenterCrop(224, 224), ImageChannelNormalize(123.0, 117.0, 104.0, 255.0, 255.0, 255.0), ImageMatToTensor(), ImageFeatureToTensor()]) classifier = NNClassifier(zoo_model, zoo_loss, featureTransformer) \ .setLearningRate(0.001) \ .setBatchSize(16) \ .setMaxEpoch(1) \ .setFeaturesCol("image") \ .setCachingSample(False) \ .setValidation(EveryEpoch(), validationDF, [Accuracy()], 16) catdogModel = classifier.fit(trainingDF) shift = udf(lambda p: p - 1, DoubleType()) predictionDF = catdogModel.transform(validationDF) \ .withColumn("prediction", shift(col('prediction'))).cache() correct = predictionDF.filter("label=prediction").count() overall = predictionDF.count() accuracy = correct * 1.0 / overall predictionDF.sample(False, 0.1).show() # expecting: accuracy around 95% print("Validation accuracy = {}, correct {}, total {}".format(accuracy, correct, overall))
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interceptor/migrations/0006_auto_20200514_0637.py
minrock/thaad
0
43626
# Generated by Django 3.0.5 on 2020-05-14 06:37 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('interceptor', '0005_auto_20200514_0617'), ] operations = [ migrations.AddField( model_name='interceptedrequest', name='path', field=models.CharField(default='/', max_length=255), ), migrations.AddField( model_name='interceptedrequest', name='session', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='interceptor.InterceptorSession'), ), ]
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cla_frontend/apps/status/urls.py
ministryofjustice/cla_frontend
3
45460
<gh_stars>1-10 from django.conf.urls import patterns, url from moj_irat.views import HealthcheckView from . import views urlpatterns = patterns( "", url(r"^live/$", views.status, {"probe_type": "live"}, name="live_probe"), url(r"^ready/$", views.status, {"probe_type": "ready"}, name="ready_probe"), url(r"^status.json$", views.smoketests_json), url(r"^ping.json$", views.PingJsonView.as_view(), name="ping_json"), url(r"^healthcheck.json$", HealthcheckView.as_view(), name="healthcheck_json"), )
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amazon/scraper/run_scraper.py
Distributed-Deep-Image-Search-Engine/DIC
2
180585
<gh_stars>1-10 from scraper import ProductData import pandas as pd from collections import OrderedDict df = pd.DataFrame() file_name = 'Mens-fashion-product-links' try: count = 0 for line in open(file_name): url = line.strip() # Making the class object prod = ProductData(url) my_dict = OrderedDict() my_dict['Asin'] = prod.get_asin() my_dict['Title'] = prod.get_title() my_dict['Categories'] = prod.get_category() my_dict['Images_Links'] = prod.get_images() my_dict['MetaData'] = prod.meta_data() df = df.append(my_dict, ignore_index = True) count = count + 1 print(count) except KeyboardInterrupt: df.to_pickle('dataframe_data') dfs = pd.read_pickle('dataframe_data') print(dfs) # #TO load the dataframe from the given file in the exact right format # dfs = pd.read_pickle('dataframe_data') # print(dfs.iloc[0][2])
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yuio/parse.py
taminomara/yuio
1
1615968
<filename>yuio/parse.py # Yuio project, MIT licence. # # https://github.com/taminomara/yuio/ # # You're free to copy this file to your project and edit it for your needs, # just keep this copyright line please :3 """ This module provides several parsers that convert (usually user-provided) strings to python objects, or check user input, and throw :class:`ValueError` in case of any issue. All parsers are descendants of the :class:`Parser` class. On the surface, they are just callables that take a string and return a python object. That is, you can use them in any place that expects such callable, for example in flags in argparse, or in :func:`~yuio.io.ask` method from :mod:`yuio.io`. When parsing fails, we raise :class:`ParsingError`. Base parser ----------- .. autoclass:: Parser :members: .. autoclass:: ParsingError Value parsers ------------- .. autoclass:: Str .. autoclass:: StrLower .. autoclass:: StrUpper .. autoclass:: Int .. autoclass:: Float .. autoclass:: Bool .. autoclass:: Enum File system path parsers ------------------------ .. autoclass:: Path .. autoclass:: NonExistentPath .. autoclass:: ExistingPath .. autoclass:: File .. autoclass:: Dir .. autoclass:: GitRepo Validators ---------- .. autoclass:: Bound .. automethod:: lower_bound .. automethod:: lower_bound_inclusive .. automethod:: upper_bound .. automethod:: upper_bound_inclusive .. autoclass:: OneOf .. autoclass:: Regex """ import abc import argparse import enum import pathlib import re import typing as _t class _Comparable(_t.Protocol): @abc.abstractmethod def __lt__(self, other) -> bool: ... @abc.abstractmethod def __gt__(self, other) -> bool: ... @abc.abstractmethod def __le__(self, other) -> bool: ... @abc.abstractmethod def __ge__(self, other) -> bool: ... @abc.abstractmethod def __eq__(self, other) -> bool: ... T = _t.TypeVar('T') C = _t.TypeVar('C', bound=_Comparable) E = _t.TypeVar('E', bound=enum.Enum) class ParsingError(ValueError, argparse.ArgumentTypeError): """Raised when parsing or validation fails. This exception is derived from both :class:`ValueError` and :class:`argparse.ArgumentTypeError` to ensure that error messages are displayed nicely with argparse, and handled correctly in other places. """ class Parser(_t.Generic[T], abc.ABC): """Base class for parsers. """ def __init__(self): # For some reason PyCharm incorrectly derives some types without # this `__init__`. You can check with the following code: # # ``` # def interact(msg: str, p: Parser[T]) -> T: # return p(msg) # # val = interact('10', Int()) # type for `val` is not derived # ``` pass def __call__(self, value: str) -> T: """Parse and verify user input, raise :class:`ParsingError` on failure. """ parsed = self.parse(value) self.validate(parsed) return parsed @abc.abstractmethod def parse(self, value: str) -> T: """Parse user input, raise :class:`ParsingError` on failure. Don't forget to call :meth:`Parser.validate` after parsing a value. """ @abc.abstractmethod def parse_config(self, value: _t.Any) -> T: """Parse value from a config, raise :class:`ParsingError` on failure. This method accepts python values, i.e. when parsing a json config. Don't forget to call :meth:`Parser.validate` after parsing a value. """ @abc.abstractmethod def validate(self, value: T): """Verify parsed value, raise :class:`ParsingError` on failure. """ def describe(self) -> _t.Optional[str]: """Return a human-readable description of an expected input. """ return None def describe_value(self, value: T) -> _t.Optional[str]: """Return a human-readable description of a given value. """ return None @classmethod @_t.no_type_check def from_type_hint(cls, ty: _t.Any) -> 'Parser[_t.Any]': """Create parser from a type hint. """ origin = _t.get_origin(ty) args = _t.get_args(ty) if origin is _t.Optional: return cls.from_type_hint(args[0]) elif origin is _t.Union and len(args) == 2 and args[1] is type(None): return cls.from_type_hint(args[0]) elif origin is _t.Union and len(args) == 2 and args[0] is type(None): return cls.from_type_hint(args[1]) elif ty is str: return Str() elif ty is int: return Int() elif ty is float: return Float() elif ty is bool: return Bool() elif isinstance(ty, type) and issubclass(ty, enum.Enum): return Enum(ty) else: raise TypeError(f'unsupported type {ty}') class Str(Parser[str]): """Parser for str values. Applies a `modifier` to the value, if one is given. """ def __init__(self, modifier: _t.Optional[_t.Callable[[str], str]] = None): super().__init__() self._modifier = modifier def parse(self, value: str) -> str: if self._modifier is not None: return self._modifier(value) else: return value def parse_config(self, value: _t.Any) -> str: if not isinstance(value, str): raise ParsingError('expected a string') if self._modifier is not None: return self._modifier(value) else: return value def validate(self, value: str): pass class StrLower(Str): """Parser for str values that converts them to lowercase. """ def __init__(self): super().__init__(str.lower) class StrUpper(Str): """Parser for str values that converts them to uppercase. """ def __init__(self): super().__init__(str.upper) class Int(Parser[int]): """Parser for int values. """ def parse(self, value: str) -> int: try: return int(value) except ValueError: raise ParsingError(f'could not parse value {value!r} as an int') def parse_config(self, value: _t.Any) -> int: if isinstance(value, float): if value != int(value): raise ParsingError('expected an int, got a float instead') value = int(value) if not isinstance(value, int): raise ParsingError('expected an int') return value def validate(self, value: int): pass class Float(Parser[float]): """Parser for float values. """ def parse(self, value: str) -> float: try: return float(value) except ValueError: raise ParsingError(f'could not parse value {value!r} as a float') def parse_config(self, value: _t.Any) -> float: if not isinstance(value, (float, int)): raise ParsingError('expected a float') return value def validate(self, value: float): pass class Bool(Parser[bool]): """Parser for bool values, such as `'yes'` or `'no'`. """ def parse(self, value: str) -> bool: value = value.lower() if value in ('y', 'yes', 'true', '1'): return True elif value in ('n', 'no', 'false', '0'): return False else: raise ParsingError(f'could not parse value {value!r},' f' enter either \'yes\' or \'no\'') def parse_config(self, value: _t.Any) -> bool: if not isinstance(value, bool): raise ParsingError('expected a bool') return value def validate(self, value: bool): pass def describe(self) -> _t.Optional[str]: return 'yes|no' def describe_value(self, value: bool) -> _t.Optional[str]: return 'yes' if value else 'no' class Enum(Parser[E]): """Parser for enums, as defined in the standard :mod:`enum` module. """ def __init__(self, enum_type: _t.Type[E]): super().__init__() self._enum_type: _t.Type[E] = enum_type def parse(self, value: str) -> E: try: return self._enum_type[value.upper()] except KeyError: enum_values = ', '.join(e.name for e in self._enum_type) raise ParsingError( f'could not parse value {value!r}' f' as {self._enum_type.__name__},' f' should be one of {enum_values}') def parse_config(self, value: _t.Any) -> E: if not isinstance(value, str): raise ParsingError('expected a string') return self.parse(value) def validate(self, value: E): pass def describe(self) -> _t.Optional[str]: desc = '|'.join(e.name for e in self._enum_type) return desc def describe_value(self, value: E) -> _t.Optional[str]: return value.name class Path(Parser[pathlib.Path]): """Parse a file system path, return a :class:`pathlib.Path`. """ def __init__(self, extensions: _t.Optional[_t.Collection[str]] = None): """Init the class. :param extensions: list of allowed file extensions. """ super().__init__() self._extensions = extensions def parse(self, value: str) -> pathlib.Path: return pathlib.Path(value).expanduser().resolve() def parse_config(self, value: _t.Any) -> pathlib.Path: if not isinstance(value, str): raise ParsingError('expected a string') return self.parse(value) def validate(self, value: pathlib.Path): if self._extensions is not None: if not any(value.name.endswith(ext) for ext in self._extensions): exts = ', '.join(self._extensions) raise ParsingError(f'{value} should have extension {exts}') def describe(self) -> _t.Optional[str]: if self._extensions is not None: return '|'.join('*' + e for e in self._extensions) else: return None class NonExistentPath(Path): """Parse a file system path and verify that it doesn't exist. """ def validate(self, value: pathlib.Path): super().validate(value) if value.exists(): raise ParsingError(f'{value} already exist') class ExistingPath(Path): """Parse a file system path and verify that it exists. """ def validate(self, value: pathlib.Path): super().validate(value) if not value.exists(): raise ParsingError(f'{value} doesn\'t exist') class File(ExistingPath): """Parse path to a file. """ def validate(self, value: pathlib.Path): super().validate(value) if not value.is_file(): raise ParsingError(f'{value} is not a file') class Dir(ExistingPath): """Parse path to a directory. """ def __init__(self): # Disallow passing `extensions`. super().__init__() def validate(self, value: pathlib.Path): super().validate(value) if not value.is_dir(): raise ParsingError(f'{value} is not a directory') class GitRepo(Dir): """Parse path to a git repository. This parser just checks that the given directory has a subdirectory named ``.git``. """ def validate(self, value: pathlib.Path): super().validate(value) if not value.joinpath('.git').is_dir(): raise ParsingError(f'{value} is not a git repository') return value class Bound(Parser[C]): """Check that value is upper- or lower-bound by some constraints. :param inner: inner parser that will be used to actually parse a value before checking its bounds. :param lower: set lower bound for value, so we require that ``value > lower``. Can't be given if `lower_inclusive` is also given. :param lower_inclusive: set lower bound for value, so we require that ``value >= lower``. Can't be given if `lower` is also given. :param upper: set upper bound for value, so we require that ``value < upper``. Can't be given if `upper_inclusive` is also given. :param upper_inclusive: set upper bound for value, so we require that ``value <= upper``. Can't be given if `upper` is also given. """ _Self = _t.TypeVar('_Self', bound='Bound') _lower: _t.Optional[C] = None """Lower bound.""" _lower_inclusive: bool = True """True if lower bound is inclusive.""" _upper: _t.Optional[C] = None """Upper bound.""" _upper_inclusive: bool = True """True if upper bound is inclusive.""" def __init__( self, inner: Parser[C], *, lower: _t.Optional[C] = None, lower_inclusive: _t.Optional[C] = None, upper: _t.Optional[C] = None, upper_inclusive: _t.Optional[C] = None ): super().__init__() self._inner: Parser[C] = inner if lower is not None and lower_inclusive is not None: raise TypeError( 'lower and lower_inclusive cannot be given at the same time') elif lower is not None: self.lower_bound(lower) elif lower_inclusive is not None: self.lower_bound_inclusive(lower_inclusive) if upper is not None and upper_inclusive is not None: raise TypeError( 'upper and upper_inclusive cannot be given at the same time') elif upper is not None: self.upper_bound(upper) elif upper_inclusive is not None: self.upper_bound_inclusive(upper_inclusive) def lower_bound(self: _Self, lower: C) -> _Self: """Set lower bound so we require that `value > lower`. """ self._lower = lower self._lower_inclusive = False return self def lower_bound_inclusive(self: _Self, lower: C) -> _Self: """Set lower bound so we require that `value >= lower`. """ self._lower = lower self._lower_inclusive = True return self def upper_bound(self: _Self, upper: C) -> _Self: """Set upper bound so we require that `value < upper`. """ self._upper = upper self._upper_inclusive = False return self def upper_bound_inclusive(self: _Self, upper: C) -> _Self: """Set upper bound so we require that `value <= upper`. """ self._upper = upper self._upper_inclusive = True return self def parse(self, value: str) -> C: return self._inner.parse(value) def parse_config(self, value: _t.Any) -> C: return self._inner.parse_config(value) def validate(self, value: C): self._inner.validate(value) if self._lower is not None: if self._lower_inclusive and value < self._lower: raise ParsingError( f'value should be greater or equal to {self._lower},' f' got {value} instead') elif not self._lower_inclusive and value <= self._lower: raise ParsingError( f'value should be greater than {self._lower},' f' got {value} instead') if self._upper is not None: if self._upper_inclusive and value > self._upper: raise ParsingError( f'value should be lesser or equal to {self._upper},' f' got {value} instead') elif not self._upper_inclusive and value >= self._upper: raise ParsingError( f'value should be lesser than {self._upper},' f' got {value} instead') class OneOf(Parser[T]): """Check if the parsed value is one of the given set of values. """ def __init__(self, inner: Parser[T], values: _t.Collection[T]): super().__init__() self._inner = inner self._allowed_values = values def parse(self, value: str) -> T: return self._inner.parse(value) def parse_config(self, value: _t.Any) -> T: return self._inner.parse_config(value) def validate(self, value: T): self._inner.validate(value) if value not in self._allowed_values: values = ', '.join(map(str, self._allowed_values)) raise ParsingError( f'could not parse value {value!r},' f' should be one of {values}') def describe(self) -> _t.Optional[str]: desc = '|'.join(str(e) for e in self._allowed_values) if len(desc) < 80: return desc else: return super().describe() class Regex(Parser[str]): """Check if the parsed value is one of the given set of values. """ def __init__(self, inner: Parser[str], regex: _t.Union[str, re.Pattern]): super().__init__() if isinstance(regex, str): regex = re.compile(regex) self._inner = inner self._regex = regex def parse(self, value: str) -> str: return self._inner.parse(value) def parse_config(self, value: _t.Any) -> str: return self._inner.parse_config(value) def validate(self, value: str): self._inner.validate(value) if self._regex.match(value) is None: raise ParsingError( f'value should match regex \'{self._regex.pattern}\'')
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gdrivefs-0.14.9-py3.6.egg/gdrivefs/utility.py
EnjoyLifeFund/macHighSierra-py36-pkgs
0
23943
import logging import json import re import sys import gdrivefs.conf _logger = logging.getLogger(__name__) # TODO(dustin): Make these individual functions. class _DriveUtility(object): """General utility functions loosely related to GD.""" # # Mime-types to translate to, if they appear within the "exportLinks" list. # gd_to_normal_mime_mappings = { # 'application/vnd.google-apps.document': # 'text/plain', # 'application/vnd.google-apps.spreadsheet': # 'application/vnd.ms-excel', # 'application/vnd.google-apps.presentation': #/gd_to_normal_mime_mappings # 'application/vnd.ms-powerpoint', # 'application/vnd.google-apps.drawing': # 'application/pdf', # 'application/vnd.google-apps.audio': # 'audio/mpeg', # 'application/vnd.google-apps.photo': # 'image/png', # 'application/vnd.google-apps.video': # 'video/x-flv' # } # Default extensions for mime-types. # TODO(dustin): !! Move this to the config directory. default_extensions = { 'text/plain': 'txt', 'application/vnd.ms-excel': 'xls', 'application/vnd.ms-powerpoint': 'ppt', 'application/pdf': 'pdf', 'audio/mpeg': 'mp3', 'image/png': 'png', 'video/x-flv': 'flv' } local_character_set = sys.getfilesystemencoding() def __init__(self): self.__load_mappings() def __load_mappings(self): # Allow someone to override our default mappings of the GD types. # TODO(dustin): Isn't actually used, so commenting. # gd_to_normal_mapping_filepath = \ # gdrivefs.conf.Conf.get('gd_to_normal_mapping_filepath') # # try: # with open(gd_to_normal_mapping_filepath, 'r') as f: # self.gd_to_normal_mime_mappings.extend(json.load(f)) # except IOError: # _logger.info("No mime-mapping was found.") # Allow someone to set file-extensions for mime-types, and not rely on # Python's educated guesses. extension_mapping_filepath = \ gdrivefs.conf.Conf.get('extension_mapping_filepath') try: with open(extension_mapping_filepath, 'r') as f: self.default_extensions.extend(json.load(f)) except IOError: _logger.info("No extension-mapping was found.") def get_first_mime_type_by_extension(self, extension): found = [ mime_type for mime_type, temp_extension in self.default_extensions.items() if temp_extension == extension ] if not found: return None return found[0] def translate_filename_charset(self, original_filename): """Convert the given filename to the correct character set.""" # fusepy doesn't support the Python 2.x Unicode type. Expect a native # string (anything but a byte string). return original_filename # # If we're in an older version of Python that still defines the Unicode # # class and the filename isn't unicode, translate it. # # try: # sys.modules['__builtin__'].unicode # except AttributeError: # pass # else: # if issubclass(original_filename.__class__, unicode) is False: # return unicode(original_filename)#original_filename.decode(self.local_character_set) # # # It's already unicode. Don't do anything. # return original_filename def make_safe_for_filename(self, text): """Remove any filename-invalid characters.""" return re.sub('[^a-z0-9\-_\.]+', '', text) utility = _DriveUtility()
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flask_dance/contrib/okta.py
wsheppard/flask-dance
1
133989
<reponame>wsheppard/flask-dance from __future__ import unicode_literals from flask_dance.consumer import OAuth2ConsumerBlueprint from functools import partial from flask.globals import LocalProxy, _lookup_app_object try: from flask import _app_ctx_stack as stack except ImportError: from flask import _request_ctx_stack as stack __maintainer__ = "<NAME> <<EMAIL>>" def make_okta_blueprint( client_id=None, client_secret=None, base_url=None, scope=None, redirect_url=None, token_url=None, redirect_to=None, login_url=None, authorization_url=None, session_class=None, backend=None): """ Make a blueprint for authenticating with Okta using OAuth 2. This requires a client ID and client secret from OKta. You should either pass them to this constructor, or make sure that your Flask application config defines them, using the variables OKTA_OAUTH_CLIENT_ID and OKTA_OAUTH_CLIENT_SECRET. Args: client_id (str): The client ID for your application on Okta. client_secret (str): The client secret for your application on Okta scope (list, optional): list of scopes (str) for the OAuth token redirect_url (str): the URL to redirect to after the authentication dance is complete redirect_to (str): if ``redirect_url`` is not defined, the name of the view to redirect to after the authentication dance is complete. The actual URL will be determined by :func:`flask.url_for` login_url (str, optional): the URL path for the ``login`` view. Defaults to ``/okta`` authorized_url (str, optional): the URL path for the ``authorized`` view. Defaults to ``/okta/authorized``. session_class (class, optional): The class to use for creating a Requests session. Defaults to :class:`~flask_dance.consumer.requests.OAuth2Session`. backend: A storage backend class, or an instance of a storage backend class, to use for this blueprint. Defaults to :class:`~flask_dance.consumer.backend.session.SessionBackend`. :rtype: :class:`~flask_dance.consumer.OAuth2ConsumerBlueprint` :returns: A :ref:`blueprint <flask:blueprints>` to attach to your Flask app. """ scope = scope or ["openid", "email", "profile"] okta_bp = OAuth2ConsumerBlueprint("okta", __name__, client_id=client_id, client_secret=client_secret, scope=scope, base_url=base_url, token_url=token_url, authorization_url=authorization_url, redirect_url=redirect_url, redirect_to=redirect_to, login_url=login_url, session_class=session_class, backend=backend, ) okta_bp.from_config["client_id"] = "OKTA_OAUTH_CLIENT_ID" okta_bp.from_config["client_secret"] = "OKTA_OAUTH_CLIENT_SECRET" @okta_bp.before_app_request def set_applocal_session(): ctx = stack.top ctx.okta_oauth = okta_bp.session return okta_bp okta = LocalProxy(partial(_lookup_app_object, "okta_oauth"))
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tpp_twitter/twitter.py
SuperSonicHub1/twitter_plays_pyboy
0
25676
from dotenv import load_dotenv import tweepy from os import getenv from typing import BinaryIO BASE_BIO = "Inspired by @screenshakes. Powered by PyBoy: http://github.com/Baekalfen/PyBoy\n" load_dotenv() auth = tweepy.OAuthHandler(getenv('TWITTER_KEY'), getenv('TWITTER_SECRET')) auth.set_access_token(getenv('TWITTER_ACCESS'), getenv('TWITTER_ACCESS_TOKEN')) api = tweepy.API(auth) def get_replies_from_latest(): """Gathers replies from latest Tweet in order of popularity.""" latest_status = api.user_timeline(count=1, exclude_replies=True)[0] return tweepy.Cursor(api.search, q="to:TextOnlyGameBoy", since_id=latest_status.id, result_type="recent").items() def update(tweet_image: BinaryIO, profile_image: BinaryIO, text: str = "Image", bio: str = ""): """Send a Tweet with an image and optionally update the bio.""" screenshot = api.media_upload("screenshot.jpg", file=tweet_image) api.update_profile_image("screenshot.jpg", file_=profile_image) api.update_status(text, media_ids=[screenshot.media_id]) if bio: api.update_profile(description=BASE_BIO + bio)
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health.py
adil-zhang/healthy_auto
4
29217
<reponame>adil-zhang/healthy_auto # -*- coding: utf-8 -*- # coding=utf-8 import json import random import requests import argparse import time # 请求内容转json def getval(): values = {"province": "北京", "city": "北京市", "addressType": "整租房", "temperature": "36.7", "dayNum": "", "contactHbPerplr": "无接触", "toWh": "未去过/路过武汉", "familySymptom": "无症状", "remarks": "", "otherDesc": "", "backDate": "2020-02-12", "jtgj": "自驾/步行", "bc": "", "addressOther": "", "hbOther": "", "familyOther": None, "lj": "是", "ljOther": "", "workStatus": "到岗上班", "workOther": "", "returnCountry": "未出国", "returnCountryRemarks": "", "provinceId": "110000", "symptom": "无症状"} values_json = json.dumps(values) return values_json # 请求头 def gethead(): headers = {'content-type': 'application/json'} return headers # 获取GitHub中Securts中的用户名 def getarg(): parser = argparse.ArgumentParser() parser.add_argument("-s", dest="PEOPLE", nargs='*', help="people name of the NAME list") args = parser.parse_args() people = args.PEOPLE return people if __name__ == '__main__': people = getarg() value = getval() head = gethead() # 防止每天时间固定，增加休眠时间(虽然actions的时间本来就不是很准确) sl_s = random.randint(1, 2) time.sleep(sl_s) sl_m = int(sl_s/60) print('本次休眠时间为'+str(sl_m)+'分钟') print('**************耐心等待中*************') if people: for people in people: url = 'https://health.foton.com.cn/health-attendance/health/save/'+people+'@foton' req = requests.post(url, data=value, headers=head) print('今日打卡成功！')
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tests/test_app1/securities/test_sec_register.py
loitd/myflask
0
198819
<filename>tests/test_app1/securities/test_sec_register.py # https://viblo.asia/p/tim-hieu-ve-sql-injection-testing-RQqKLv90l7z import pytest from bs4 import BeautifulSoup as bs from flask import url_for def test_register_sqlinjection(app, client): theurlstr = 'register_blp.reg' with app.test_request_context(): # Test render without errors assert client.get(url_for(theurlstr)).status_code == 200 # Test inject 1 data = dict( inputName='2018 OR ""="', inputEmail='<EMAIL> OR ""="', inputPassword="<PASSWORD>" ) reg = client.post(url_for(theurlstr), data=data, follow_redirects=True) assert reg.status_code == 200 # has feedback assert reg.headers.get("Location") == None assert "Validation failed! Contact <EMAIL> for help" in reg.data.decode('utf-8') assert '<script src="/static/js/login.js"></script>' not in reg.data.decode('utf-8') #not redirected to login data = dict( inputName='2018 OR ""="', inputEmail='<EMAIL>', inputPassword="<PASSWORD>" ) reg = client.post(url_for(theurlstr), data=data, follow_redirects=True) assert reg.status_code == 200 # has feedback assert reg.headers.get("Location") == None assert "Validation failed! Contact <EMAIL> for help" in reg.data.decode('utf-8') assert '<script src="/static/js/login.js"></script>' not in reg.data.decode('utf-8') #not redirected to login data = dict( inputName='A test', inputEmail='<EMAIL> OR ""="', inputPassword="<PASSWORD>" ) reg = client.post(url_for(theurlstr), data=data, follow_redirects=True) assert reg.status_code == 200 # has feedback assert reg.headers.get("Location") == None assert "Validation failed! Contact <EMAIL> for help" in reg.data.decode('utf-8') assert '<script src="/static/js/login.js"></script>' not in reg.data.decode('utf-8') #not redirected to login data = dict( inputName='<NAME>', inputEmail="' OR 1=1", inputPassword="<PASSWORD>" ) reg = client.post(url_for(theurlstr), data=data, follow_redirects=True) assert reg.status_code == 200 # has feedback assert reg.headers.get("Location") == None assert "Validation failed! Contact <EMAIL> for help" in reg.data.decode('utf-8') assert '<script src="/static/js/login.js"></script>' not in reg.data.decode('utf-8') #not redirected to login data = dict( inputName='<NAME>', inputEmail='<EMAIL>; DROP TABLE tbl_users; --', inputPassword="<PASSWORD>" ) reg = client.post(url_for(theurlstr), data=data, follow_redirects=True) assert reg.status_code == 200 # has feedback assert reg.headers.get("Location") == None assert "Validation failed! Contact <EMAIL> for help" in reg.data.decode('utf-8') assert '<script src="/static/js/login.js"></script>' not in reg.data.decode('utf-8') #not redirected to login # Test length of inputName (must be from 6-20) data = dict( inputName='<NAME> i am the one who pass it alllllllllllllllllllllllllllll', inputEmail='<EMAIL>', inputPassword="<PASSWORD>" ) reg = client.post(url_for(theurlstr), data=data, follow_redirects=True) assert reg.status_code == 200 # has feedback assert reg.headers.get("Location") == None assert "Validation failed! Contact <EMAIL> for help" in reg.data.decode('utf-8') assert '<script src="/static/js/login.js"></script>' not in reg.data.decode('utf-8') #not redirected to login data = dict( inputName='A', inputEmail='<EMAIL>', inputPassword="<PASSWORD>" ) reg = client.post(url_for(theurlstr), data=data, follow_redirects=True) assert reg.status_code == 200 # has feedback assert reg.headers.get("Location") == None assert "Validation failed! Contact <EMAIL> for help" in reg.data.decode('utf-8') assert '<script src="/static/js/login.js"></script>' not in reg.data.decode('utf-8') #not redirected to login data = dict( inputName="'; select true; --", inputEmail='<EMAIL>', inputPassword="<PASSWORD>" ) reg = client.post(url_for(theurlstr), data=data, follow_redirects=True) assert reg.status_code == 200 # has feedback assert reg.headers.get("Location") == None assert "Validation failed! Contact <EMAIL> for help" in reg.data.decode('utf-8') assert '<script src="/static/js/login.js"></script>' not in reg.data.decode('utf-8') #not redirected to login
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tensorflow_probability/python/distributions/laplace_test.py
wataruhashimoto52/probability
1
2296
<reponame>wataruhashimoto52/probability # Copyright 2018 The TensorFlow Probability 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. # ============================================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function # Dependency imports import numpy as np from scipy import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1 fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__ == '__main__': tf.test.main()
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model-test.py
shikew/Handwriting-calculator
0
8598
import numpy as np from PIL import Image from keras.models import load_model img_gray = Image.open('1002.png') number = np.array(img_gray) print(number.shape) print('准备的图片的shape：',number.flatten().shape) print('原number:',number) number = number.astype('float32') number = number/255 #归一化 number = number.flatten() print('处理过后的number.shape:',number.shape) model = load_model('mnist-dnn.h5') # model.load_weights('mnist.model.best.hdf5') # def recognize(photo_data): # return clf.predict(photo_data) print(model.predict_classes(np.array([number]))) #print('测试标签为：',test_target[8000])
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src/query_datastream.py
DominiqueMercier/TSViz-Core
0
102401
<filename>src/query_datastream.py import pickle import matplotlib.pyplot as plt import numpy as np from scipy.stats import zscore from tqdm import tqdm def sampleTimeSeriesStream(strides, length, numTimeSteps, numChannels, numAnomalies, minDist, plotData=False): x = smoothSampling(strides * length + numTimeSteps, numChannels) # sc = StandardScaler() # x = sc.fit_transform(x) # print ("X:", x.shape) # introduce anomalies anomaly_list = [] possible_anomalies = np.arange(x.shape[0]) for i in range(numAnomalies): # Anomaly - can be both positive or negative scalingFactor = (np.sign(np.random.normal()) * np.random.normal(loc=3.0, scale=0.75)) anomalyIdx = np.random.permutation(possible_anomalies)[0] # Never introduce anomalies in the first channel anomalyChannel = np.random.randint(1, numChannels) fails = 0 while x[anomalyIdx, anomalyChannel] < 1.0: # Only take significant values pa = set(possible_anomalies) pa.remove(anomalyIdx) possible_anomalies = np.array(list(pa)) anomalyIdx = np.random.permutation(possible_anomalies)[0] fails += 1 if fails > 10: break if fails <= 10: pa = set(possible_anomalies) pa.difference_update(set(np.arange(anomalyIdx - minDist, anomalyIdx + minDist))) possible_anomalies = np.array(list(pa)) x[anomalyIdx, anomalyChannel] *= scalingFactor anomaly_list.append(anomalyIdx) x_data, y_data = [], [] for i in range(length): offset = strides * i x_tmp = x[offset:offset+numTimeSteps] window = np.arange(offset, offset+numTimeSteps) y = 0 # Normal true_a = -1 for a in anomaly_list: if a in window: y = 1 true_a = int(np.argwhere(window==a)) break x_data.append(x_tmp) y_data.append(y) if plotData: plot_sample(x_tmp, y, true_a) return np.array(x_data), np.array(y_data) def plot_sample(x, y, a): fig, ax = plt.subplots() if y == 1: ax.set_title('Label: ' + str(y) + ' | Anomly: ' + str(a)) else: ax.set_title('Label: ' + str(y)) plt.plot(x) plt.show() def smoothSampling(numVals, numChannels, mean=0.0, std=0.05): finalSeries = [] for channel in range(numChannels): stdDev = np.max([np.finfo(float).eps, 0.05 + np.random.normal(scale=std)]) # [0, 1) result = [] y = np.random.normal(loc=mean, scale=stdDev) for _ in range(numVals): result.append(y) y += np.random.normal(loc=mean, scale=stdDev) result = np.array(result) # Normalize result = zscore(result) # @UndefinedVariable finalSeries.append(result) return np.stack(finalSeries, axis=1) def create_dataset(dataPath, strides, length, numTimeSteps, numChannels, numAnomalies, minDist, plotData=False): trainX, trainY = sampleTimeSeriesStream(strides, length, numTimeSteps, numChannels, numAnomalies, minDist, plotData) testX, testY = sampleTimeSeriesStream(strides, length, numTimeSteps, numChannels, numAnomalies, minDist, plotData) # Normalize the data # sc = StandardScaler() # trainX = sc.fit_transform(trainX) # testX = sc.transform(testX) # Dump data to pickle file print("Saving data to file: %s" % (dataPath)) with open(dataPath, "wb") as pickleFile: pickle.dump([trainX, trainY, testX, testY], pickleFile, protocol=pickle.HIGHEST_PROTOCOL) print("Data saved successfully!") if __name__ == "__main__": np.random.seed(1) sampleTimeSeriesStream(strides=1, length=10, numTimeSteps=50, numChannels=3, numAnomalies=3, minDist=10, plotData=True)
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orcinus/workspace/workspace.py
orcinus-lang/orcinus-bootstrap
0
194482
<reponame>orcinus-lang/orcinus-bootstrap # Copyright (C) 2019 <NAME> # # This software may be modified and distributed under the terms # of the MIT license. See the LICENSE file for details. from __future__ import annotations import logging import os import urllib.parse from typing import Sequence, Optional from orcinus.exceptions import OrcinusError from orcinus.signals import Signal from orcinus.workspace.document import Document from orcinus.workspace.package import Package from orcinus.workspace.utils import convert_filename logger = logging.getLogger('orcinus.workspace') class Workspace: """ Active representation of collection of projects """ packages: Sequence[Package] on_document_create: Signal # (document: Document) -> void on_document_remove: Signal # (document: Document) -> void on_document_analyze: Signal # (document: Document) -> void def __init__(self, paths: Sequence[str] = None): paths = list(() or paths) # Standard library path stdlib_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../stdlib')) if stdlib_path not in paths: paths.insert(0, stdlib_path) self.packages = [ Package(self, os.path.abspath(urllib.parse.urlparse(path).path)) for path in paths ] # signals self.on_document_create = Signal() self.on_document_remove = Signal() self.on_document_analyze = Signal() def get_package_for_document(self, doc_uri: str): url = urllib.parse.urlparse(doc_uri) fullname = os.path.abspath(url.path) for package in self.packages: if fullname.startswith(package.path): return package raise OrcinusError(f"Not found file `{url.path}` in packages") def get_or_create_document(self, doc_uri: str) -> Document: """ Return a managed document if-present, else create one pointing at disk. """ package = self.get_package_for_document(doc_uri) return package.get_or_create_document(doc_uri) def get_document(self, doc_uri: str) -> Optional[Document]: """ Returns a managed document if-present, otherwise None """ package = self.get_package_for_document(doc_uri) return package.get_document(doc_uri) def create_document(self, doc_uri, source=None, version=None) -> Document: """ Create new document """ package = self.get_package_for_document(doc_uri) return package.create_document(doc_uri, source, version) def update_document(self, doc_uri: str, source=None, version=None) -> Document: """ Update source of document """ package = self.get_package_for_document(doc_uri) return package.update_document(doc_uri, source, version) def unload_document(self, doc_uri: str): """ Unload document from package """ package = self.get_package_for_document(doc_uri) return package.unload_document(doc_uri) def load_document(self, module_name: str) -> Document: """ Load document for module :param module_name: Module name :return: Document """ for package in self.packages: doc_uri = convert_filename(module_name, package.path) try: return package.get_or_create_document(doc_uri) except IOError: logger.debug(f"Not found module `{module_name}` in file `{doc_uri}`") pass # Continue raise OrcinusError(f'Not found module {module_name}')
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29918, 978, 10114, 297, 934, 23230, 1514, 29918, 5338, 10114, 1159, 13, 18884, 1209, 29871, 396, 2866, 14150, 13, 13, 4706, 12020, 1394, 29883, 13419, 2392, 29898, 29888, 29915, 3664, 1476, 3883, 426, 5453, 29918, 978, 29913, 1495, 13, 2 ]
setup.py
iamsrp/pyfestival
8
26753
<reponame>iamsrp/pyfestival #!/usr/bin/env python from distutils.core import setup, Extension from distutils.util import get_platform import os festival_include = os.environ.get("FESTIVAL_INCLUDE", '/usr/include/festival') speech_tools_include = os.environ.get("SPECCH_INCLUDE", '/usr/include/speech_tools') festival_lib = os.environ.get("FESTIVAL_LIB", '/usr/lib') festival_classifiers = [ "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", "Topic :: Software Development :: Libraries", "Topic :: Utilities", "Topic :: Multimedia :: Sound/Audio :: Sound Synthesis", "Topic :: Multimedia :: Sound/Audio :: Speech" ] long_description = """A Python wrapper around the Festival Speech Synthesis System: http://www.cstr.ed.ac.uk/projects/festival/ pyfestival creates a C module built on top of the festival source code, making it a self-contained Python library (there is no need to run festival-server alongside). pyfestival supports (and is tested on) Python 2.7+ including Python 3 """ libraries = ['Festival', 'estools', 'estbase', 'eststring'] if get_platform().startswith('macosx-'): macos_frameworks = ['CoreAudio', 'AudioToolbox', 'Carbon'] libraries.append('ncurses') else: macos_frameworks = [] setup( name='pyfestival', description='Python Festival module', long_description=long_description, url="https://github.com/techiaith/pyfestival", author="<NAME>", author_email="<EMAIL>", license="BSD", py_modules=['festival'], ext_modules=[ Extension( '_festival', ['_festival.cpp'], include_dirs=[festival_include, speech_tools_include], library_dirs=[festival_lib], libraries=libraries, extra_link_args=[x for name in macos_frameworks for x in ('-framework', name)], ), ], platforms=["*nix"], bugtrack_url="https://github.com/techiaith/pyfestival/issues", version="0.5", )
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MyExtenstion.extension/Gaochao.tab/Gaochao.panel/Annotation_Modify.pulldown/Grid_3DTo2D.pushbutton/script.py
gaochaowyq/MyPyRevitExtentision
0
58875
# -*- coding: utf-8 -*- __doc__="返回选择物体的类型" import rpw from rpw import revit, DB, UI,db,doc #from System.Collections.Generic import List #import json #from scriptutils import this_script #from scriptutils.userinput import CommandSwitchWindow #import subprocess as sp #Change Selected Grid From 3D to 2D #selection = rpw.ui.Selection().elements #Grid=selection[0] #Grid= rpw.db.Element(Grid).parameters.all #print(Grid) #Change Grid buble visibility selection = rpw.ui.Selection() Grid=selection endpoint=DB.DatumEnds.End0 startpoint=DB.DatumEnds.End1 CurrentView=doc.ActiveView DatumExtentType2D=DB.DatumExtentType.ViewSpecific @rpw.db.Transaction.ensure('Hide_Grid_Bubble') def DatumExtentType3DTo2D(_Grid,points,CurrentView): for i in points: _Grid.SetDatumExtentType(i,CurrentView,DatumExtentType2D) for i in Grid: DatumExtentType3DTo2D(i,[endpoint,startpoint],CurrentView) print("Good")
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my_notes/ex127/sieve_module.py
ivalderrama/packt_python_workshop
0
133674
class PrimesBelow: def __init__(self, bound): self.candidate_numbers = list(range(2,bound)) def __iter__(self): return self def __next__(self): if len(self.candidate_numbers) == 0: raise StopIteration next_prime = self.candidate_numbers[0] self.candidate_numbers = [x for x in self.candidate_numbers if x % next_prime != 0] return next_prime
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learning-python-master/learning-python-master/python_gyakorlas3.py
Hanna998/functional_tests
0
176644
<filename>learning-python-master/learning-python-master/python_gyakorlas3.py<gh_stars>0 # -*- coding:Utf-8 -*- ''' Gyakorlatok : 5.11. x Legyenek adottak a következő listák: t1 = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] t2 = ['Január', 'Február', 'Március', 'Április', 'Május', 'Június', '<NAME>', 'Augusztus', 'Szeptember', 'Október', 'November', 'December'] Írjon egy kis programot, ami egy új t3 listáthoz létre. Ennek felváltva kell tartalmazni a két lista minden elemét úgy, hogy minden hónap nevét követnie kell a megfelelő napok számának: ['Január',31,'Február',28,'Március',31, stb...]. 5.12. x Írjon egy programot, ami kiíratja egy lista összes elemét. Ha például a fenti gyakorlat t2 listájára alkalmaznánk, akkor a következőt kellene kapnunk: Január Február Március Április Május Június Július Augusztus Szeptember Október November December 5.13. x Írjon egy programot, ami megkeresi egy adott lista legnagyobb elemét. Például, ha a [32,5, 12, 8, 3, 75, 2, 15], listára alkalmaznánk, akkor a következőt kellene kiírnia : a lista legnagyobb elemének az értéke 75. 5.14. x Írjon egy programot, ami megvizsgálja egy számlista minden elemét (például az előzőpélda listáját) azért, hogy két új listát hozzon létre. Az egyik csak az eredeti lista páros számait tartalmazza, a másik a páratlanokat. Például, ha a kiindulási lista az előző gyakorlatlistája, akkor a programnak egy páros listát kell létrehoznia, ami a [32, 12, 8, 2]-­t tartalmazza és egy páratlan listát ami [5, 3, 75, 15]-­t tartalmazza. Trükk: Gondoljon az előzőekben említett modulo (%) operátor használatára! 5.15. x Írjon egy programot, ami egy szavakból álló lista elemeit egyenként megvizsgálja azért, hogy két új listát hozzon létre. (például: ['Jean', 'Maximilien', 'Brigitte', 'Sonia', 'Jean­Pierre','Sandra'] Az egyikben 6 karakternél rövidebb szavakat legyenek, a másikban 6, vagy annáltöbb karaktert tartalmazzonó szavak legyenek. ''' print ("5.11.") t1 = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] t2 = ['Januar', 'Februar', 'Marcius', 'Aprilis', 'Majus', 'Junius', 'Julius', 'Augusztus', 'Szeptember', 'Oktober', 'November', 'December'] t3=[] for i in range(len(t1)): #ilyet lehet!! t3.append(t1[i]) #hozzáadok egy elemet a listához t3.append(t2[i]) print t3 print '\n' print ("5.12.") for i in range(len(t2)): print t2[i], print '\n' print ("5.13.") max=t1[0] for i in range(len(t1)): if (t1[i]>max): max=t1[i] print max print '\n' print ("5.14.") t1=[1,2,3,4,4,5,6,7,7,8,9] t2=[] t3=[] for i in range(len(t1)): #ilyet lehet!! if (t1[i]%2==0): #ha ps t2.append(t1[i]) #hozzáadom az elemet a ps listához else: t3.append(t1[i]) #hozzáadom az elemet a pt listához print t2 print t3 print '\n' print ("5.15.") t1=['Jean', 'Maximilien', 'Brigitte', 'Sonia', 'Jean­Pierre','Sandra'] #lista t2=[] #6 karakternel rovidebb szavak listaja t3=[] #6 karakternel hoszabb szavak listaja for i in range(len(t1)): #vegigmegyek a lista elemein if (len(t1[i])<6): #ha az i. elem hossza ps t2.append(t1[i]) else: t3.append(t1[i]) print t2 print t3
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lumos/models/__init__.py
numagic/lumos
1
1604281
from lumos.models.composition import ModelMaker from lumos.models.simple_vehicle_on_track import SimpleVehicleOnTrack from lumos.models.tires.pacejka import MF52 from lumos.models.tires.perantoni import PerantoniTire from lumos.models.vehicles.simple_vehicle import SimpleVehicle from lumos.models.kinematics import TrackPosition2D from lumos.models.aero.aero import ConstAero, GPAero, MLPAero ModelMaker.add_to_registry(MF52) ModelMaker.add_to_registry(PerantoniTire) ModelMaker.add_to_registry(SimpleVehicle) ModelMaker.add_to_registry(TrackPosition2D) ModelMaker.add_to_registry(SimpleVehicleOnTrack) ModelMaker.add_to_registry(ConstAero) ModelMaker.add_to_registry(GPAero) ModelMaker.add_to_registry(MLPAero)
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django_comments_xtd/apps.py
dlrsp-dev/django-comments-xtd
0
93025
from django.apps import AppConfig from django.db.models.signals import pre_save class CommentsXtdConfig(AppConfig): default_auto_field = 'django.db.models.AutoField' name = 'django_comments_xtd' verbose_name = 'Comments Xtd' def ready(self): from django_comments_xtd import get_model from django_comments_xtd.models import publish_or_unpublish_on_pre_save model_app_label = get_model()._meta.label pre_save.connect(publish_or_unpublish_on_pre_save, sender=model_app_label)
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FaceLandmarking.LearningProcess/readers/regressor_example_reader.py
TomaszRewak/FaceLandmarking
78
26174
import csv def read_regressor_examples(num_of_features, num_of_decisions, file_path): xs = [] ys = [] with open(file_path, mode='r', encoding='utf-8') as file: reader = csv.reader(file, delimiter=' ') for row in reader: x = [float(value) for value in row[0 : num_of_features]] y = [float(value) for value in row[num_of_features : num_of_features + num_of_decisions]] xs.append(x) ys.append(y) return { 'x': xs, 'y': ys }
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tests/unit_tests/test_tethys_services/test_models/test_SpatialDatasetService.py
ezrajrice/tethys
79
76434
from tethys_sdk.testing import TethysTestCase import tethys_services.models as service_model from unittest import mock class SpatialDatasetServiceTests(TethysTestCase): def set_up(self): pass def tear_down(self): pass def test__str__(self): sds = service_model.SpatialDatasetService( name='test_sds', ) self.assertEqual('test_sds', sds.__str__()) @mock.patch('tethys_services.models.GeoServerSpatialDatasetEngine') def test_get_engine_geo_server(self, mock_sds): sds = service_model.SpatialDatasetService( name='test_sds', engine=service_model.SpatialDatasetService.GEOSERVER, endpoint='http://localhost/geoserver/rest/', public_endpoint='http://publichost/geoserver/rest/', username='foo', password='password' ) sds.save() ret = sds.get_engine() # Check result mock_sds.assert_called_with(endpoint='http://localhost/geoserver/rest/', password='password', username='foo') self.assertEqual('http://publichost/geoserver/rest/', ret.public_endpoint) @mock.patch('tethys_services.models.TDSCatalog') @mock.patch('tethys_services.models.session_manager') def test_get_engine_thredds(self, mock_session_manager, mock_TDSCatalog): sds = service_model.SpatialDatasetService( name='test_sds', engine=service_model.SpatialDatasetService.THREDDS, endpoint='http://localhost/thredds/', public_endpoint='http://publichost/thredds/', username='foo', password='password' ) sds.save() ret = sds.get_engine() mock_session_manager.set_session_options.assert_called_with(auth=('foo', 'password')) mock_TDSCatalog.assert_called_with('http://localhost/thredds/catalog.xml') # Check result self.assertEqual(mock_TDSCatalog(), ret) @mock.patch('tethys_services.models.TDSCatalog') @mock.patch('tethys_services.models.session_manager') def test_get_engine_thredds_no_trailing_slashes(self, mock_session_manager, mock_TDSCatalog): sds = service_model.SpatialDatasetService( name='test_sds', engine=service_model.SpatialDatasetService.THREDDS, endpoint='http://localhost/thredds', public_endpoint='http://publichost/thredds', username='foo', password='password' ) sds.save() ret = sds.get_engine() mock_session_manager.set_session_options.assert_called_with(auth=('foo', 'password')) mock_TDSCatalog.assert_called_with('http://localhost/thredds/catalog.xml') # Check result self.assertEqual(mock_TDSCatalog(), ret) @mock.patch('tethys_services.models.TDSCatalog') @mock.patch('tethys_services.models.session_manager') def test_get_engine_thredds_no_username_password(self, mock_session_manager, mock_TDSCatalog): sds = service_model.SpatialDatasetService( name='test_sds', engine=service_model.SpatialDatasetService.THREDDS, endpoint='http://localhost/thredds', public_endpoint='http://publichost/thredds', ) sds.save() ret = sds.get_engine() mock_session_manager.set_session_options.assert_not_called() mock_TDSCatalog.assert_called_with('http://localhost/thredds/catalog.xml') # Check result self.assertEqual(mock_TDSCatalog(), ret)
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abc191/C.py
Vermee81/practice-coding-contests
0
91183
H, W = map(int, input().split()) field = [[str(i) for i in input()] for _ in range(H)] count = 0 for x in range(W-1): for y in range(H-1): black_num = 0 for x1, y1 in [(x, y), (x+1, y), (x, y+1), (x+1, y+1)]: if field[y1][x1] == '#': black_num += 1 if black_num % 2 == 1: count += 1 print(count)
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auto_test_web/backend/utils/my_logger.py
AI-change-the-world/ai-apps
2
79136
<reponame>AI-change-the-world/ai-apps import logging from logging import INFO, getLogger import os from concurrent_log_handler import ConcurrentRotatingFileHandler current_dir = os.getcwd() log_file = current_dir + os.sep + "logs" + os.sep + 'log.txt' logger = getLogger(__name__) formatter_log = logging.Formatter( '%(asctime)s - %(filename)s [line: %(lineno)d] [%(levelname)s] ----- %(message)s' ) rotateHandler = ConcurrentRotatingFileHandler(log_file, "a", 512 * 1024, 5) rotateHandler.setFormatter(formatter_log) logger.addHandler(rotateHandler) logger.setLevel(INFO)
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services/perso/persotool.py
mkuyper/basicmac
11
79695
#!/usr/bin/env python3 # Copyright (C) 2020-2020 <NAME>. All rights reserved. # # This file is subject to the terms and conditions defined in file 'LICENSE', # which is part of this source code package. from typing import Optional import aioserial import asyncio import click import functools from perso import PTE, PTESerialPort, PersoData, PersoDataV1 from rtlib import Eui class PhysicalPTESerialPort(PTESerialPort): def __init__(self, port:str, baudrate:int) -> None: self.serial = aioserial.AioSerial(port=port, baudrate=baudrate) def send(self, data:bytes) -> None: self.serial.write(data) async def recv(self) -> bytes: return await self.serial.read_until_async(b'\0') class BasedIntParamType(click.ParamType): name = "integer" def convert(self, value, param, ctx): if isinstance(value, int): return value try: return int(value, 0) except ValueError: self.fail(f"{value!r} is not a valid integer", param, ctx) class EuiParamType(click.ParamType): name = "eui" def convert(self, value, param, ctx): try: return Eui(value) except ValueError: self.fail(f"{value!r} is not a valid EUI", param, ctx) class AESKeyType(click.ParamType): name = "aeskey" def convert(self, value, param, ctx): try: key = bytes.fromhex(value) except ValueError: self.fail(f"{value!r} is not a valid AES key", param, ctx) if len(key) != 16: self.fail(f"AES key must have a length of 16 bytes", param, ctx) return key BASED_INT = BasedIntParamType() EUI = EuiParamType() AESKEY = AESKeyType() def coro(f): @functools.wraps(f) def wrapper(*args, **kwargs): return asyncio.run(f(*args, **kwargs)) return wrapper @click.group() @click.option('-p', '--port', default='/dev/ttyACM0', help='serial port') @click.option('-b', '--baud', type=int, default=115200, help='baud rate') @click.pass_context def cli(ctx:click.Context, port:str, baud:int) -> None: ctx.obj['pte'] = PTE(PhysicalPTESerialPort(port, baud)) @cli.command(help='Read personalization data from EEPROM') @click.option('-o', '--offset', type=BASED_INT, default=0x0060, help='Offset of personalization data structure in EEPROM') @click.option('-k', '--show-keys', is_flag=True, help='Show network and application keys') @click.pass_context @coro async def pdread(ctx:click.Context, offset:int, show_keys:bool): pte = ctx.obj['pte'] pd = PersoData.unpack(await pte.ee_read(offset, PersoData.V1_SIZE)) print(f'Hardware ID: 0x{pd.hwid:08x}') print(f'Region ID: 0x{pd.region:08x} ({pd.region})') print(f'Serial No: {pd.serial}') print(f'Device EUI: {pd.deveui}') print(f'Join EUI: {pd.joineui}') if show_keys: print(f'Network key: {pd.nwkkey.hex()}') print(f'App key: {pd.appkey.hex()}') @cli.command(help='Clear personalization data in EEPROM') @click.option('-o', '--offset', type=BASED_INT, default=0x0060, help='Offset of personalization data structure in EEPROM') @click.pass_context @coro async def pdclear(ctx:click.Context, offset:int): pte = ctx.obj['pte'] pd = PersoData.unpack(await pte.ee_read(offset, PersoData.V1_SIZE)) await pte.ee_write(offset, bytes(PersoData.V1_SIZE)) @cli.command(help='Write personalization data to EEPROM') @click.option('-o', '--offset', type=BASED_INT, default=0x0060, help='Offset of personalization data structure in EEPROM') @click.option('--hwid', type=BASED_INT, default=0, help='Hardware ID') @click.option('--region', type=BASED_INT, default=0, help='Region ID') @click.argument('serialno', type=str) @click.argument('deveui', type=EUI) @click.argument('joineui', type=EUI) @click.argument('nwkkey', type=AESKEY) @click.argument('appkey', type=AESKEY, required=False) @click.pass_context @coro async def pdwrite(ctx:click.Context, offset:int, hwid:int, region:int, serialno:str, deveui:Eui, joineui:Eui, nwkkey:bytes, appkey:Optional[bytes]): pte = ctx.obj['pte'] if appkey is None: appkey = nwkkey await pte.ee_write(offset, PersoDataV1(hwid, region, serialno, deveui, joineui, nwkkey, appkey).pack()) if __name__ == '__main__': cli(obj={})
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build/lib/AESEncryptor_heureka_code/Exceptions/TextExceptions.py
heureka-code/AESEncryptor-heureka-code
0
1607950
__date__ = "11.03.2021" __status__ = "Development" __annotations__ = "Enthält die Exceptions für das Verschlüsseln und Entschlüsseln von Texten" __doc__ = """ Text Exceptions: TextError: ist die Klasse, von der die anderen Text Exceptions erben. TextIsEncrypted: wird ausgelöst, wenn der Text nicht verschlüsselt werden kann, weil er das bereits wurde. TextIsNotEncrypted: wird ausgelöst, wenn Text entschlüsselt werden soll, der gar nicht verschlüsselt worden ist. """ class TextError(Exception): CODE = "TextError" def __init__(self, msg): self.__message = "Es gab einen Fehler beim verwendeten Text" super(TextError, self).__init__(self.__message) pass def __str__(self): return self.__message pass class TextIsEncrypted(TextError): CODE = "TextIsEncrypted" def __init__(self): self.__message = "Der verwendete Text kann nicht verschlüsselt werden, da er bereits verschlüsselt wurde." super(TextIsEncrypted, self).__init__(self.__message) pass def __str__(self): return self.__message pass class TextIsNotEncrypted(TextError): CODE = "TextIsNotEncrypted" def __init__(self): self.__message = "Der Text kann nich entschlüsselt werden, da er nicht verschlüsselt wurde." super(TextIsNotEncrypted, self).__init__(self.__message) pass def __str__(self): return self.__message pass
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apps/notifiers/notification_action_processor.py
constructorfleet/AutomationApps
0
176639
<filename>apps/notifiers/notification_action_processor.py from appdaemon import utils from common.base_app import BaseApp from notifiers.notification_action import NotificationAction ACTION = "action" ACTION_NAME = "actionName" ACTION_DATA = "action_data" CATEGORY = "category" ENTITY_ID = "entity_id" ACKNOWLEDGE_ID = "acknowledge_id" class NotificationActionProcessor(BaseApp): async def initialize_app(self): self.acknowledge_listeners = [] self.debug("Starting listener for iOS") await self.listen_event(self.handle_event, event="ios.notification_action_fired") self.debug("Starting listener for mobile app") await self.listen_event(self.handle_event, event="mobile_app_notification_action") self.debug("Starting listener for FCM") await self.listen_event(self.handle_event, event="html5_notification.clicked") def add_acknowledge_listener(self, listener): self.acknowledge_listeners.append(listener) async def handle_event(self, event_name, data, kwargs): self.debug("Event Data {} and kwargs {}".format(str(data), str(kwargs))) self.debug("ACTION NAME {} ".format(data.get(ACTION_NAME, data.get(ACTION, "")).lower())) for name, member in NotificationAction.__members__.items(): self.debug("NAME {} VALUE {}".format(name, member)) found_action = [member for name, member in NotificationAction.__members__.items() if name.lower().replace('_', '') == data.get(ACTION_NAME, data.get(ACTION, "")).lower()] if not found_action: self.warning("No action found for {}".format(str(data))) return action = found_action[0] if not action.service: acknowledge_id = data["data"].get(ACTION_DATA, {}).get(ACKNOWLEDGE_ID, None) \ if event_name == "html5_notification.clicked" \ else data.get(ACTION_DATA, {}).get(ACKNOWLEDGE_ID, None) acknowledge_id = acknowledge_id or action.acknowledge_id if not acknowledge_id: return self.info("Firing event {}".format("notification_action.{}".format(acknowledge_id))) self.fire_event("notification_action.{}".format(acknowledge_id)) for listener in self.acknowledge_listeners: await listener(acknowledge_id, action) return entity_id = data["data"][ACTION_DATA][ENTITY_ID] \ if event_name == "html5_notification.clicked" \ else data[ACTION_DATA][ENTITY_ID] if not action or not entity_id: self.warning("Not enough data") return self.publish_service_call( action.service.split('/')[0], action.service.split('/')[1], { ENTITY_ID: entity_id } )
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tests/api_interface/test_runbooks/test_files/parallel_task.py
tuxtof/calm-dsl
37
53401
""" Calm DSL Sample Runbook for parallel task """ from calm.dsl.runbooks import runbook, parallel, branch from calm.dsl.runbooks import RunbookTask as Task @runbook def ParallelTask(): "Runbook Service example" with parallel() as p: with branch(p): Task.Delay(60, name="Delay1") with branch(p): Task.Delay(60, name="Delay2") with branch(p): Task.Delay(60, name="Delay3")
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2021/Python/6/lanternfish_2_from_Jonathan_Paulson.py
ames0k0/AdventOfCode
0
64780
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ No need to create a list of fishes and add new one to the end of the list """ from collections import defaultdict def main(): # XXX: COPY/PASTED input_file = './input.txt' X = defaultdict(int) with open(input_file, 'r') as ftr: for n in map(int, ftr.read().strip().split(',')): if (n not in X.keys()): X[n] = 0 X[n] += 1 for _ in range(256): Y = defaultdict(int) for x, cnt in X.items(): # add new fish if (x == 0): Y[6] += cnt Y[8] += cnt else: Y[x-1] += cnt X = Y print(sum(X.values())) if __name__ == '__main__': main()
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tsaotun/lib/Addon/loader.py
qazbnm456/tsaotun
47
68398
"""Addon Loader module""" import os import pkgutil import imp from ast import literal_eval from ConfigParser import RawConfigParser from git import RemoteProgress from ..Utils.deepgetattr import deepgetattr from ..Utils.hightlight import hightlight_python from ..Utils.urlutil import is_git_url class Progress(RemoteProgress): """Git Progree Handler""" def line_dropped(self, line): print line def update(self, op_code, cur_count, max_count=None, message=''): print "{}/{}".format(cur_count, max_count) class Loader(object): """Addon Loader""" inspects = {} def __init__(self, argparser=None, tsaotun_global_config="{}/Tsaotun/config.ini".format(os.path.expanduser("~")), addon_path="{}/Tsaotun/addons/".format(os.path.expanduser("~"))): self.argparser = argparser self.__locate(addon_path) try: if not os.path.exists(tsaotun_global_config): open(tsaotun_global_config, 'a').close() self.tsaotun_global_config = tsaotun_global_config except OSError as e: raise RuntimeError( "{}".format(str(e))) def __locate(self, addon_path): """Locate addon path""" try: if not os.path.exists(addon_path): os.makedirs(addon_path) self.addon_path = addon_path except OSError as e: raise RuntimeError( "{}".format(str(e))) def __parse_args(self, configs): """Parsing __argparse__ and assign to argparser""" if self.argparser: for config in configs: if config["position"] == 'Self': parser = self.argparser[ config["namespace"]][config["position"]] for action in config["actions"]: exec("import textwrap; parser.{}".format(action), {'parser': parser}) self.argparser[config["namespace"]][ config["position"]] = parser else: if config["subcommand"]: parser = self.argparser[config["namespace"]][ config["position"]].choices.get(config["subcommand"]) for action in config["actions"]: exec("import textwrap; parser.{}".format(action), {'parser': parser}) self.argparser[config["namespace"]][ config["position"]].choices.update({config["subcommand"]: parser}) else: parser = self.argparser[ config["namespace"]][config["position"]] subparser = None for i, action in enumerate(config["actions"]): if i == 0: exec( "import textwrap; import argparse; global subparser; subparser = parser.{}".format(action)) else: exec("import textwrap; global subparser; subparser.{}".format(action)) self.argparser[config["namespace"]][ config["position"]] = parser else: raise RuntimeError("argparser is not specified.") def __parse_config(self, section, options=None): """Parsing global config file""" cfg = RawConfigParser() with open(self.tsaotun_global_config) as f: cfg.readfp(f, self.tsaotun_global_config) if cfg.has_section(section) is False: cfg.add_section(section) if options: stored = [cfg.get(section, option) if cfg.has_option(section, option) else cfg.set( section, option, 'False') is True for option in options] else: stored = cfg.items(section) with open(self.tsaotun_global_config, 'wb') as f: cfg.write(f) return stored def __active(self, addon_names): """Check if the given addon is active""" return self.__parse_config('addon', addon_names) def load(self): """Load addons""" addon_names = [n for _, n, _ in pkgutil.iter_modules( ["{}".format(self.addon_path)])] addons = {} for addon_name, active in zip(addon_names, self.__active(addon_names)): try: f, filename, description = imp.find_module( addon_name, [self.addon_path]) mod = imp.load_module( addon_name, f, filename, description) self.inspects[addon_name] = ''.join( [mod.__file__.split('.')[0], '.py']) if literal_eval(active): for k, v in mod.__override__.iteritems(): addons["{}|{}".format(k, v)] = deepgetattr( mod, "{}.{}".format(k, v)) self.__parse_args(mod.__argparse__) except ImportError as e: raise RuntimeError(str(e)) return addons, self.argparser def enable(self, addon): """Enable an addon""" try: if self.inspects[addon]: cfg = RawConfigParser() with open(self.tsaotun_global_config) as f: cfg.readfp(f, self.tsaotun_global_config) cfg.set('addon', addon, 'True') with open(self.tsaotun_global_config, 'wb') as f: cfg.write(f) return "Done!" except KeyError as e: raise RuntimeError("No such addon: {}".format(str(e))) def disable(self, addon): """Disable an addon""" try: if self.inspects[addon]: cfg = RawConfigParser() with open(self.tsaotun_global_config) as f: cfg.readfp(f, self.tsaotun_global_config) cfg.set('addon', addon, 'False') with open(self.tsaotun_global_config, 'wb') as f: cfg.write(f) return "Done!" except KeyError as e: raise RuntimeError("No such addon: {}".format(str(e))) def inspect(self, addon): """Inspect an addon""" try: args = {'sourcefile': self.inspects[addon]} return hightlight_python(args) except KeyError as e: raise RuntimeError("No such addon: {}".format(str(e))) def install(self, url, addon): """Install an addon from a given url""" if is_git_url(url): from git import (Repo, GitCommandError) if addon is None: addon = url.split('/')[-1].split('.')[0] dest = os.path.join(self.addon_path, addon) try: Repo.clone_from(url, dest, progress=Progress()) if 'requirements.txt' in os.listdir(dest): os.system("pip install -r {}".format(os.path.join(dest, 'requirements.txt'))) return "'{}' successfully installed!".format(addon) except GitCommandError as e: raise RuntimeError("{}".format(str(e))) except IOError: print "Please run 'pip install -r {}' to install the needed requirements, and then the installation of {} is done.".format(os.path.join(dest, 'requirements.txt'), addon) else: raise RuntimeError("Invalid URL: {}".format(url)) def rm(self, addon): """Remove an addon""" import shutil try: shutil.rmtree(os.path.join(self.addon_path, addon)) return "'{}' successfully removed!".format(addon) except OSError as e: raise RuntimeError("{}".format(str(e))) def addons(self): """List addons""" addon_names = [n for _, n, _ in pkgutil.iter_modules( ["{}".format(self.addon_path)])] return [{'Addon': n, 'Enabled': a} for n, a in zip(addon_names, self.__active(addon_names))]
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pyconcz_2016/sponsors/migrations/0007_auto_20161019_1700.py
pyvec/cz.pycon.org-2016
10
88891
# -*- coding: utf-8 -*- # Generated by Django 1.10.2 on 2016-10-19 15:00 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('sponsors', '0006_auto_20160929_1218'), ] operations = [ migrations.AlterField( model_name='sponsor', name='level', field=models.PositiveSmallIntegerField(choices=[(1, 'Platinum'), (2, 'Gold'), (3, 'Silver'), (4, 'Bronze'), (5, 'Diversity'), (6, 'Media'), (7, 'Coffee')], default=3), ), ]
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python/qitoolchain/test/test_qitoolchain_remove.py
vbarbaresi/qibuild
0
185267
# Copyright (c) 2012-2018 SoftBank Robotics. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the COPYING file. import pytest import qibuild.config from qibuild.test.conftest import TestBuildWorkTree import qitoolchain.toolchain def test_simple(qitoolchain_action): qitoolchain.toolchain.Toolchain("foo") qitoolchain_action("remove", "-f", "foo") # pylint: disable-msg=E1101 with pytest.raises(Exception): qitoolchain.get_toolchain("foo") def test_when_not_exists(qitoolchain_action): # pylint: disable-msg=E1101 with pytest.raises(Exception) as e: qitoolchain_action("remove", "foo") assert "No such toolchain" in str(e.value) def test_when_is_default(qitoolchain_action): qitoolchain_action("create", "foo") qibuild.config.add_build_config("foo", toolchain="foo") test_build_worktre1 = TestBuildWorkTree() test_build_worktre1.set_default_config("foo") qitoolchain_action("remove", "foo", "--force") test_build_worktre2 = TestBuildWorkTree() # pylint:disable-msg=E1101 with pytest.raises(Exception) as e: test_build_worktre2.toolchain # pylint: disable=pointless-statement assert "No such toolchain" in e.value.message
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gen-post.py
younghk/younghk.netlify.com
0
964
<gh_stars>0 import os import errno from datetime import datetime print("Generating A New Post\n") post_name = input('Input Post Name: ') date_time = datetime.now() date_time_dir = date_time.strftime("%Y-%m-%d") date_time_post = date_time.strftime("%Y-%m-%d %H:%M:%S") p_name = post_name.replace(" ","-") p_name = p_name.replace("[","") p_name = p_name.replace("]","") p_name = p_name.lower() f_name = date_time_dir+"---"+p_name dir = "./src/pages/articles/"+f_name+"/" f_dir = dir+f_name+".md" try: if not(os.path.isdir(dir)): os.makedirs(os.path.join(dir)) except OSError as e: if e.errno != errno.EEXIST: print("Failed to create directory!!!!!") raise print("Generating post : ",f_dir) with open(f_dir, 'w') as f: f.write('---') f.write('\n') f.write('draft: true') f.write('\n') f.write('title: \"'+post_name+'\"') f.write('\n') f.write('date: \"'+date_time_post+'\"') f.write('\n') f.write('layout: post') f.write('\n') f.write('path: \"/posts/'+p_name+'/\"') f.write('\n') f.write('category: \"\"') f.write('\n') f.write('tags: ') f.write('\n') f.write('description: ""') f.write('\n') f.write('---') f.write('\n') print("Done :)")
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python/createTestVector.py
marbleton/FPGA_MNIST
7
80072
import numpy as np from numpy.random import rand from numpy import * from pathlib import Path import csv # activation function? if we'd use ReLU with grayscale images the value stays # the same # This script creates a .csv file in the work directory # containing integer numbers in the following form # Input X : x1, x2, x3, ... xN_inputlayer # Weight W1: w11, w12, w13, ... w1N_inputlayer # Weight W1: w21, w22, w23, ... w2N_inputlayer # ... # Weight W1: wN_hiddenlayer1, wN_hiddenlayer2 ... wN_hiddenlayerN_inputlayer # Weight W2: w11, w12, ... w1hiddenlayer; # Weight W2: ... # Weight W2: wN_inputlayer1, ... wN_inputlayerN_hiddenlayer # Output Y : y1, y2, y3, ... yN_outputlayer; ## Parameters N_inputlayer = 28*28; # Nodes Input Layer N_outputlayer = N_inputlayer; # Nodes Output Layer N_hiddenlayer = 200; # Nodes Hidden Layer file_name = "test_vectors" # name of .csv file # Neural Network consits of Input Layer, Hidden Layer, Output Layer # Input Layer has 28 * 28 = 784 nodes # Hidden Layer has a abitrary number of nodes, we choose 200 # Output Layer has 784 nodes ## Input Layer X = floor(rand(1,N_inputlayer)*256); #create 784 random Pixels range 0:255 W1 = floor(rand(N_inputlayer,N_hiddenlayer)*256); #create 784 randoms weights 200 times ## Hidden Layer H1 = X.dot(W1); #multiply Pixels with s_weights H1 = floor(H1 / 255); W2 = floor(rand(N_hiddenlayer,N_inputlayer)*256); # create 200 random weights 784 times ## Output layer Y = floor(H1.dot(W2) / 255); # multiplay hidden layer with weights to #write Data to .csv file #path = str(Path().absolute()) + "\\" + file_name + ".csv"; with open(file_name + ".csv", 'w', newline = '') as csvFile: writer = csv.writer(csvFile) for x in X: writer.writerow(x.astype(integer)) for w1 in W1.transpose(): writer.writerow(w1.astype(integer)) for w2 in W2.transpose(): writer.writerow(w2.astype(integer)) for y in Y: writer.writerow(y.astype(integer))
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custom_components/hacs/frontend/views/static.py
adr29truck/hacs
0
132386
"""Serve static files used by HACS.""" # pylint: disable=broad-except import logging import os from aiohttp import web from aiohttp.web import HTTPNotFound from ...blueprints import HacsViewBase _LOGGER = logging.getLogger('custom_components.hacs.frontend') class HacsStaticView(HacsViewBase): """Serve static files.""" name = "community_static" def __init__(self): """Initilize.""" self.url = self.url_path["static"] + r"/{requested_file:.+}" async def get(self, request, requested_file): # pylint: disable=unused-argument """Serve static files.""" servefile = "{}/custom_components/hacs/frontend/elements/{}".format( self.config_dir, requested_file) if os.path.exists(servefile): return web.FileResponse(servefile) else: servefile += '.gz' if os.path.exists(servefile): return web.FileResponse(servefile) else: raise HTTPNotFound
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wbb/utils/read_lines.py
Imran95942/userbotisl
1
50858
<filename>wbb/utils/read_lines.py from random import choice async def random_line(fname): with open(fname) as f: data = f.read().splitlines() return choice(data)
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Introduction-to-data-visualization-with-matplotlib/2. Plotting time-series/script_2.py
nhutnamhcmus/datacamp-playground
1
189560
import matplotlib.pyplot as plt # Import pandas as pd import pandas as pd # Read the data from file using read_csv climate_change = pd.read_csv('climate_change.csv', parse_dates=['date'], index_col='date') print(climate_change.head()) fig, ax = plt.subplots() # Add the time-series for "relative_temp" to the plot ax.plot(climate_change.index,climate_change['relative_temp']) # Set the x-axis label ax.set_xlabel('Time') # Set the y-axis label ax.set_ylabel('Relative temperature (Celsius)') # Show the figure plt.show()
[ 1, 1053, 22889, 29889, 2272, 5317, 408, 14770, 30004, 13, 29937, 16032, 11701, 408, 10518, 30004, 13, 5215, 11701, 408, 10518, 30004, 13, 30004, 13, 29937, 7523, 278, 848, 515, 934, 773, 1303, 29918, 7638, 30004, 13, 30004, 13, 695, 6490, 29918, 3167, 353, 10518, 29889, 949, 29918, 7638, 877, 695, 6490, 29918, 3167, 29889, 7638, 742, 6088, 29918, 15190, 29922, 1839, 1256, 7464, 2380, 29918, 1054, 2433, 1256, 1495, 30004, 13, 30004, 13, 2158, 29898, 695, 6490, 29918, 3167, 29889, 2813, 3101, 30004, 13, 30004, 13, 1003, 29892, 4853, 353, 14770, 29889, 1491, 26762, 26471, 13, 30004, 13, 29937, 3462, 278, 931, 29899, 13757, 363, 376, 22925, 29918, 7382, 29908, 304, 278, 6492, 30004, 13, 1165, 29889, 5317, 29898, 695, 6490, 29918, 3167, 29889, 2248, 29892, 695, 6490, 29918, 3167, 1839, 22925, 29918, 7382, 2033, 8443, 13, 30004, 13, 29937, 3789, 278, 921, 29899, 8990, 3858, 30004, 13, 1165, 29889, 842, 29918, 29916, 1643, 877, 2481, 1495, 30004, 13, 30004, 13, 29937, 3789, 278, 343, 29899, 8990, 3858, 30004, 13, 1165, 29889, 842, 29918, 29891, 1643, 877, 18278, 10430, 313, 29907, 295, 1039, 375, 29897, 1495, 30004, 13, 30004, 13, 29937, 7704, 278, 4377, 30004, 13, 572, 29873, 29889, 4294, 580, 2 ]
ch01/c118.py
claudiordgz/GoodrichTamassiaGoldwasser
2
123921
__author__ = 'Claudio' """Demonstrate how to use Python’s list comprehension syntax to produce the list [0, 2, 6, 12, 20, 30, 42, 56, 72, 90]. """ def demonstration_list_comprehension(): return [idx*x for idx, x in enumerate(range(1,11))]
[ 1, 4770, 8921, 1649, 353, 525, 20216, 2881, 29915, 13, 13, 13, 15945, 29908, 29928, 9857, 710, 403, 920, 304, 671, 5132, 30010, 29879, 1051, 15171, 2673, 5877, 304, 7738, 13, 1552, 1051, 518, 29900, 29892, 29871, 29906, 29892, 29871, 29953, 29892, 29871, 29896, 29906, 29892, 29871, 29906, 29900, 29892, 29871, 29941, 29900, 29892, 29871, 29946, 29906, 29892, 29871, 29945, 29953, 29892, 29871, 29955, 29906, 29892, 29871, 29929, 29900, 1822, 13, 15945, 29908, 13, 1753, 9004, 362, 29918, 1761, 29918, 510, 1457, 29882, 2673, 7295, 13, 1678, 736, 518, 13140, 29930, 29916, 363, 22645, 29892, 921, 297, 26985, 29898, 3881, 29898, 29896, 29892, 29896, 29896, 28166, 13, 2 ]
x11_hash_py/bmw.py
zebra-lucky/x11-hash-py
0
152381
<gh_stars>0 # -*- coding: utf-8 -*- from struct import pack from . import op from .op import u64 V_INIT = [ u64(0x80818283, 0x84858687), u64(0x88898A8B, 0x8C8D8E8F), u64(0x90919293, 0x94959697), u64(0x98999A9B, 0x9C9D9E9F), u64(0xA0A1A2A3, 0xA4A5A6A7), u64(0xA8A9AAAB, 0xACADAEAF), u64(0xB0B1B2B3, 0xB4B5B6B7), u64(0xB8B9BABB, 0xBCBDBEBF), u64(0xC0C1C2C3, 0xC4C5C6C7), u64(0xC8C9CACB, 0xCCCDCECF), u64(0xD0D1D2D3, 0xD4D5D6D7), u64(0xD8D9DADB, 0xDCDDDEDF), u64(0xE0E1E2E3, 0xE4E5E6E7), u64(0xE8E9EAEB, 0xECEDEEEF), u64(0xF0F1F2F3, 0xF4F5F6F7), u64(0xF8F9FAFB, 0xFCFDFEFF), ] final = [ u64(0xaaaaaaaa, 0xaaaaaaa0), u64(0xaaaaaaaa, 0xaaaaaaa1), u64(0xaaaaaaaa, 0xaaaaaaa2), u64(0xaaaaaaaa, 0xaaaaaaa3), u64(0xaaaaaaaa, 0xaaaaaaa4), u64(0xaaaaaaaa, 0xaaaaaaa5), u64(0xaaaaaaaa, 0xaaaaaaa6), u64(0xaaaaaaaa, 0xaaaaaaa7), u64(0xaaaaaaaa, 0xaaaaaaa8), u64(0xaaaaaaaa, 0xaaaaaaa9), u64(0xaaaaaaaa, 0xaaaaaaaa), u64(0xaaaaaaaa, 0xaaaaaaab), u64(0xaaaaaaaa, 0xaaaaaaac), u64(0xaaaaaaaa, 0xaaaaaaad), u64(0xaaaaaaaa, 0xaaaaaaae), u64(0xaaaaaaaa, 0xaaaaaaaf), ] sb_a = [1, 1, 2, 2, 1, 2] sb_b = [3, 2, 1, 2] sb_c = [4, 13, 19, 28] sb_d = [37, 43, 53, 59] I16 = [None] * 16 I16.append([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) I16.append([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]) I16.append([2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]) I16.append([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]) I16.append([4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]) I16.append([5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]) I16.append([6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]) I16.append([7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]) I16.append([8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) I16.append([9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) I16.append([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]) I16.append([11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]) I16.append([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]) I16.append([13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]) I16.append([14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]) I16.append([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]) M16 = [None] * 16 M16.append([0, 1, 3, 4, 7, 10, 11]) M16.append([1, 2, 4, 5, 8, 11, 12]) M16.append([2, 3, 5, 6, 9, 12, 13]) M16.append([3, 4, 6, 7, 10, 13, 14]) M16.append([4, 5, 7, 8, 11, 14, 15]) M16.append([5, 6, 8, 9, 12, 15, 16]) M16.append([6, 7, 9, 10, 13, 0, 1]) M16.append([7, 8, 10, 11, 14, 1, 2]) M16.append([8, 9, 11, 12, 15, 2, 3]) M16.append([9, 10, 12, 13, 0, 3, 4]) M16.append([10, 11, 13, 14, 1, 4, 5]) M16.append([11, 12, 14, 15, 2, 5, 6]) M16.append([12, 13, 15, 16, 3, 6, 7]) M16.append([13, 14, 0, 1, 4, 7, 8]) M16.append([14, 15, 1, 2, 5, 8, 9]) M16.append([15, 16, 2, 3, 6, 9, 10]) def sb(n, x): if n >= 4: return x.shift_right(sb_a[n]).xor(x) return u64.xor64( x.shift_right(sb_a[n]), x.shift_left(sb_b[n]), x.rotate_left(sb_c[n]), x.rotate_left(sb_d[n]) ) rbn = [0, 5, 11, 27, 32, 37, 43, 53] def rb(n, x): return x.rotate_left(rbn[n]) def makeW(M,H,i, op): a = M[i[0]].xor(H[i[0]]) b = M[i[1]].xor(H[i[1]]) c = M[i[2]].xor(H[i[2]]) d = M[i[3]].xor(H[i[3]]) e = M[i[4]].xor(H[i[4]]) w = op[3](op[2](op[1](op[0](a,b),c),d),e) return w wbn = [ [5, 7, 10, 13, 14], [6, 8, 11, 14, 15], [0, 7, 9, 12, 15], [0, 1, 8, 10, 13], [1, 2, 9, 11, 14], [3, 2, 10, 12, 15], [4, 0, 3, 11, 13], [1, 4, 5, 12, 14], [2, 5, 6, 13, 15], [0, 3, 6, 7, 14], [8, 1, 4, 7, 15], [8, 0, 2, 5, 9], [1, 3, 6, 9, 10], [2, 4, 7, 10, 11], [3, 5, 8, 11, 12], [12, 4, 6, 9, 13], ] def plus(a,b): return a.plus(b) def minus(a,b): return a.minus(b) wboperators = [ [minus, plus, plus, plus], [minus, plus, plus, minus], [plus, plus, minus, plus], [minus, plus, minus, plus], [plus, plus, minus, minus], [minus, plus, minus, plus], [minus, minus, minus, plus], [minus, minus, minus, minus], [minus, minus, plus, minus], [minus, plus, minus, plus], [minus, minus, minus, plus], [minus, minus, minus, plus], [plus, minus, minus, plus], [plus, plus, plus, plus], [minus, plus, minus, minus], [minus, minus, minus, plus], ] def wb(M,H,i): return makeW(M,H,wbn[i],wboperators[i]) def kb(j): fives = u64(0x05555555, 0x55555555) return fives.multiply(u64(0, j)) def addElt(buffer64, state, mVars, i): k = kb(i) elt = (buffer64[mVars[0]].rotate_left(mVars[1]) .add(buffer64[mVars[2]].rotate_left(mVars[3])) .minus(buffer64[mVars[5]].rotate_left(mVars[6])) .add(k) .xor(state[mVars[4]])) return elt def expand2Inner(qt, mf, state, i, iVars, mVars): return (qt[iVars[0]] .plus(rb(1, qt[iVars[1]])) .add(qt[iVars[2]]) .add(rb(2, qt[iVars[3]])) .add(qt[iVars[4]]) .add(rb(3, qt[iVars[5]])) .add(qt[iVars[6]]) .add(rb(4, qt[iVars[7]])) .add(qt[iVars[8]]) .add(rb(5, qt[iVars[9]])) .add(qt[iVars[10]]) .add(rb(6, qt[iVars[11]])) .add(qt[iVars[12]]) .add(rb(7, qt[iVars[13]])) .add(sb(4, qt[iVars[14]])) .add(sb(5, qt[iVars[15]])) .add(addElt(mf, state, mVars, i))) def expand1Inner(qt, mf, state, i, iVars, mVars): return (sb(1, qt[iVars[0]]) .add(sb(2, qt[iVars[1]])) .add(sb(3, qt[iVars[2]])) .add(sb(0, qt[iVars[3]])) .add(sb(1, qt[iVars[4]])) .add(sb(2, qt[iVars[5]])) .add(sb(3, qt[iVars[6]])) .add(sb(0, qt[iVars[7]])) .add(sb(1, qt[iVars[8]])) .add(sb(2, qt[iVars[9]])) .add(sb(3, qt[iVars[10]])) .add(sb(0, qt[iVars[11]])) .add(sb(1, qt[iVars[12]])) .add(sb(2, qt[iVars[13]])) .add(sb(3, qt[iVars[14]])) .add(sb(0, qt[iVars[15]])) .add(addElt(mf, state, mVars, i))) def expand1b(qt, mf, state, i): iVars = I16[i] mVars = M16[i] return expand1Inner(qt, mf, state, i, iVars, mVars) def expand2b(qt, mf, state, i): iVars = I16[i] mVars = M16[i] return expand2Inner(qt, mf, state, i, iVars, mVars) def makeQ(mf, state): qt = [None] * 32 for i in range(16): w = wb(mf,state,i) s = sb(i % 5, w) qt[i] = s.plus(state[(i + 1) % 16]) qt[16] = expand1b(qt, mf, state, 16) qt[17] = expand1b(qt, mf, state, 17) for i in range(18, 32): qt[i] = expand2b(qt, mf, state, i) return qt def fold(int64Buffer, state): out = [None] * 16 qt = makeQ(int64Buffer, state) xl = u64.xor64(qt[16], qt[17], qt[18], qt[19], qt[20], qt[21], qt[22], qt[23]) xh = u64.xor64(xl, qt[24], qt[25], qt[26], qt[27], qt[28], qt[29], qt[30], qt[31]) out[0] = u64.xor64(xh.shift_left(5), qt[16].shift_right(5), int64Buffer[0]).add(u64.xor64(xl, qt[24], qt[0])) out[1] = u64.xor64(xh.shift_right(7), qt[17].shift_left(8), int64Buffer[1]).add(u64.xor64(xl, qt[25], qt[1])) out[2] = u64.xor64(xh.shift_right(5), qt[18].shift_left(5), int64Buffer[2]).add(u64.xor64(xl, qt[26], qt[2])) out[3] = u64.xor64(xh.shift_right(1), qt[19].shift_left(5), int64Buffer[3]).add(u64.xor64(xl, qt[27], qt[3])) out[4] = u64.xor64(xh.shift_right(3), qt[20], int64Buffer[4]).add(u64.xor64(xl, qt[28], qt[4])) out[5] = u64.xor64(xh.shift_left(6), qt[21].shift_right(6), int64Buffer[5]).add(u64.xor64(xl, qt[29], qt[5])) out[6] = u64.xor64(xh.shift_right(4), qt[22].shift_left(6), int64Buffer[6]).add(u64.xor64(xl, qt[30], qt[6])) out[7] = u64.xor64(xh.shift_right(11), qt[23].shift_left(2), int64Buffer[7]).add(u64.xor64(xl, qt[31], qt[7])) out[8] = (out[4].rotate_left(9).add(u64.xor64(xh, qt[24], int64Buffer[8])) .add(u64.xor64(xl.shift_left(8), qt[23], qt[8]))) out[9] = (out[5].rotate_left(10).add(u64.xor64(xh, qt[25], int64Buffer[9])) .add(u64.xor64(xl.shift_right(6), qt[16], qt[9]))) out[10] = (out[6].rotate_left(11).add(u64.xor64(xh, qt[26], int64Buffer[10])) .add(u64.xor64(xl.shift_left(6), qt[17], qt[10]))) out[11] = (out[7].rotate_left(12).add(u64.xor64(xh, qt[27], int64Buffer[11])) .add(u64.xor64(xl.shift_left(4), qt[18], qt[11]))) out[12] = (out[0].rotate_left(13).add(u64.xor64(xh, qt[28], int64Buffer[12])) .add(u64.xor64(xl.shift_right(3), qt[19], qt[12]))) out[13] = (out[1].rotate_left(14).add(u64.xor64(xh, qt[29], int64Buffer[13])) .add(u64.xor64(xl.shift_right(4), qt[20], qt[13]))) out[14] = (out[2].rotate_left(15).add(u64.xor64(xh, qt[30], int64Buffer[14])) .add(u64.xor64(xl.shift_right(7), qt[21], qt[14]))) out[15] = (out[3].rotate_left(16).add(u64.xor64(xh, qt[31], int64Buffer[15])) .add(u64.xor64(xl.shift_right(2), qt[22], qt[15]))) return out def compress(buf, state): buf64 = op.bytes_to_u64_list_le(buf, len(buf)) return fold(buf64, state) def bmw_update(ctx, msg): htmp = [None] * 16 msg_len = len(msg) lenL3 = u64(0, msg_len) lenL3 = lenL3.shift_left(3) ctx['bitCount'].add(lenL3) buf = ctx['buffer'] buf_len = len(buf) ptr = ctx['ptr'] h1 = ctx['state'] h2 = htmp while msg_len > 0: clen = buf_len - ptr if clen > msg_len: clen = msg_len op.buffer_insert(buf, ptr, msg, clen) msg = msg[clen:] msg_len -= clen ptr += clen if ptr is buf_len: h2 = compress(buf, h1) ht = h1 h1 = h2 h2 = ht ptr = 0 ctx['ptr'] = ptr state = ctx['state'] if h1 is not state: state_len = len(state) op.buffer_insert(state, 0, h1, state_len) def bmw_close(ctx): h2 = [None] * 16 buf = ctx['buffer'] ptr = ctx['ptr'] buf_len = len(buf) buf[ptr] = 0x80 ptr += 1 hState = ctx['state'] if ptr > buf_len - 8: set_len = buf_len - ptr buf[ptr:ptr+set_len] = [0] * set_len hState = compress(buf, hState) ptr = 0 set_len = buf_len - 8 - ptr buf[ptr:ptr+set_len] = [0] * set_len enc_pos = buf_len - 8 enc_data = ctx['bitCount'] buf[enc_pos+0:enc_pos+4] = pack('<I', enc_data.lo) buf[enc_pos+4:enc_pos+8] = pack('<I', enc_data.hi) h2 = compress(buf, hState) for u in range(16): enc_pos = 8 * u enc_data = h2[u] buf[enc_pos+0:enc_pos+4] = pack('<I', enc_data.lo) buf[enc_pos+4:enc_pos+8] = pack('<I', enc_data.hi) h1 = compress(buf, final) out = [None] * 16 for u in range(8): v = u + 8 out[2 * u] = op.swap32(h1[v].lo) out[2 * u + 1] = op.swap32(h1[v].hi) return out def bmw(msg, out_array=False, in_array=False): ctx = {} ctx['state'] = V_INIT[:] ctx['ptr'] = 0 ctx['bitCount'] = u64(0, 0) ctx['buffer'] = bytearray(128) if in_array: msg = op.bytes_from_i32_list(msg) bmw_update(ctx, msg) res = bmw_close(ctx) if not out_array: res = op.bytes_from_i32_list(res) return res
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PyMOTW/source/sysconfig/sysconfig_get_path_names.py
axetang/AxePython
1
81430
<reponame>axetang/AxePython #!/usr/bin/env python3 # encoding: utf-8 # # Copyright (c) 2010 <NAME>. All rights reserved. # """The names of the paths in a scheme. """ #end_pymotw_header import sysconfig for name in sysconfig.get_path_names(): print(name)
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diceThing.py
HappyNapalm/dungeon_crawl_python
0
92109
from random import randint while(True): stats=[0,1,2,3,4,5] i=0 while(i<6): a=1 j=0 while(j<4): a = a+randint(1,6) if(a>8&a<18): j=j+1 a = a-3 stats[i] = a i=i+1 print("\nWhat is your race Adventurer?\n") race = input() race_parse = race.lowercase() if (race_parse == "human"): print("Str :",stats[0]) print("Dex :",stats[1]) print("Con :",stats[2]) print("Int :",stats[3]) print("Wis :",stats[4]) print("Cha :",stats[5]) elif (race_parse == "dwarf"): print("Str :",stats[0]) print("Dex :",stats[1]) print("Con :",stats[2]+2) print("Int :",stats[3]) print("Wis :",stats[4]) print("Cha :",stats[5]-2) elif (race_parse == "elf"): print("Str :",stats[0]) print("Dex :",stats[1]+2) print("Con :",stats[2]-2) print("Int :",stats[3]) print("Wis :",stats[4]) print("Cha :",stats[5]) elif (race_parse == "half-elf"): print("Str :",stats[0]) print("Dex :",stats[1]) print("Con :",stats[2]) print("Int :",stats[3]) print("Wis :",stats[4]) print("Cha :",stats[5]) elif (race_parse == "half-orc"): print("Str :",stats[0]+2) print("Dex :",stats[1]) print("Con :",stats[2]) print("Int :",stats[3]-2) print("Wis :",stats[4]) print("Cha :",stats[5]-2) elif (race_parse == "halfling"): print("Str :",stats[0]-2) print("Dex :",stats[1]+2) print("Con :",stats[2]) print("Int :",stats[3]) print("Wis :",stats[4]) print("Cha :",stats[5]) elif (race == "gnome"): print("Str :",stats[0]-2) print("Dex :",stats[1]+2) print("Con :",stats[2]) print("Int :",stats[3]) print("Wis :",stats[4]) print("Cha :",stats[5]) else: print("Speak up Champion!")
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setup.py
finsberg/scholar_bot
0
20466
<reponame>finsberg/scholar_bot # -*- coding: utf-8 -*- import os import sys import platform import glob from setuptools import setup, find_packages, Command if sys.version_info < (3, 6): print("Python 3.6 or higher required, please upgrade.") sys.exit(1) version = "0.1" name = "scholar_bot" description = ("Post updates on Slack about citations " "for the Computational Phyisoligy department at Simula") scripts = glob.glob("bin/*") requirements = ['slackclient', 'scholarly', 'pyyaml'] if platform.system() == "Windows" or "bdist_wininst" in sys.argv: # In the Windows command prompt we can't execute Python scripts # without a .py extension. A solution is to create batch files # that runs the different scripts. batch_files = [] for script in scripts: batch_file = script + ".bat" f = open(batch_file, "w") f.write(r'python "%%~dp0\%s" %%*\n' % os.path.split(script)[1]) f.close() batch_files.append(batch_file) scripts.extend(batch_files) def run_install(): "Run installation" # Call distutils to perform installation setup( name=name, description=description, version=version, author='<NAME>', license="MIT", author_email="<EMAIL>", platforms=["Windows", "Linux", "Solaris", "Mac OS-X", "Unix"], packages=["scholar_bot"], package_dir={"scholar_bot": "scholar_bot"}, # install_requires=requirements, scripts=scripts, zip_safe=False, ) if __name__ == "__main__": run_install()
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Assignment1/old - hpc tested/hw1/Task2-c/src/mapper.py
HyperTars/NYC-Taxi-Analysis
0
194352
#!/usr/bin/env python import sys for line in sys.stdin: # extract data key, val = line.strip().split('\t', 1) # passenger_count try: key = int(val.split(',')[3]) except ValueError: continue # print print '%s\t%s' % (key, 1) ''' cd ~/hw1/Task2-c/ rm -rf NumberPassengersSamp.out hfs -rm -r NumberPassengersSamp.out hjs -D mapreduce.job.reduces=0 \ -file ~/hw1/Task2-c/src/ \ -mapper src/mapper.sh \ -input /user/wl2154/TripFareJoinSamp.txt \ -output /user/wl2154/NumberPassengersSamp.out hfs -get NumberPassengersSamp.out hfs -getmerge NumberPassengersSamp.out NumberPassengersSamp.txt cat NumberPassengersSamp.txt '''
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db/models/example.py
thesnapdragon/python-cli
0
149032
<reponame>thesnapdragon/python-cli from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, Integer, String Base = declarative_base() class Example(Base): __tablename__ = 'examples' id = Column(Integer, primary_key=True) name = Column(String) def __init__(self, id_, name): self.id = id_ self.name = name def __repr__(self): return "<Example(%s, %s)>" % (self.id, self.name)
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deepvariant/runtime_by_region_vis.py
tahashmi/deepvariant
4
6049
<filename>deepvariant/runtime_by_region_vis.py # Copyright 2020 Google LLC. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from this # software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. r"""Create a visual report of make_examples runtime by region. Use this script to visualize the runtime-by-region data generated by running make_examples with --runtime_by_region. """ from typing import Dict, Sequence, List, Tuple, Text, Any, Union from absl import app from absl import flags import altair as alt import pandas as pd import tensorflow as tf from third_party.nucleus.io import sharded_file_utils # Altair uses a lot of method chaining, such as # chart.mark_bar().encode(...).properties(...), so using backslash # continuation to break this into separate lines makes the code more readable. # pylint: disable=g-backslash-continuation VEGA_URL = 'https://storage.googleapis.com/deepvariant/lib/vega' FLAGS = flags.FLAGS flags.DEFINE_string( 'input', None, 'TSV file that was produced when running make_examples ' 'with --runtime_by_region. Can be sharded, e.g. /path/[email protected].') flags.DEFINE_string( 'title', None, 'Title will be shown at the top of the report and will ' 'be used as a prefix for downloaded image files.') flags.DEFINE_string('output', 'runtime_by_region_report.html', 'Path for the output report, which will be an html file.') RUNTIME_COLUMNS = [ 'get reads', 'find candidates', 'make pileup images', 'write outputs' ] COUNT_COLUMNS = ['num reads', 'num candidates', 'num examples'] CSS_STYLES = """ <style> body { font-family: sans-serif; } .chart-container { padding: 30px; } </style> """ def read_sharded_runtime_tsvs(path_string: str) -> pd.DataFrame: """Imports data from a single or sharded path into a pandas dataframe. Args: path_string: The path to the input file, which may be sharded. Returns: A dataframe matching the TSV file(s) but with added Task column. """ if sharded_file_utils.is_sharded_file_spec(path_string): paths = sharded_file_utils.generate_sharded_filenames(path_string) else: paths = [path_string] list_of_dataframes = [] for i, path in enumerate(paths): if path.startswith('gs://'): # Once pandas is updated to 0.24+, pd.read_csv will work for gs:// # without this workaround. with tf.io.gfile.GFile(path) as f: d = pd.read_csv(f, sep='\t') else: d = pd.read_csv(path, sep='\t') d['Task'] = i list_of_dataframes.append(d) return pd.concat(list_of_dataframes, axis=0, ignore_index=True) def format_runtime_string(raw_seconds: float) -> str: """Creates a nice format string from a potentially large number of seconds. Args: raw_seconds: A number of seconds. Returns: The seconds divided into hours, minutes, and remaining seconds, formatted nicely. For example, 2h3m5.012s. """ minutes, seconds = divmod(raw_seconds, 60) hours, minutes = divmod(minutes, 60) seconds = round(seconds, 3) output = '' if hours > 0: output += f'{int(hours)}h' if minutes > 0: output += f'{int(minutes)}m' if seconds > 0 or not output: output += f'{seconds}s' return output def calculate_totals(df: pd.DataFrame) -> pd.DataFrame: """Calculates total runtime, formats it nicely, and sorts by it. Args: df: A dataframe of runtime profiling numbers. Returns: The same dataframe with some additional summary columns. """ # 'total runtime' is a simple sum of the runtime columns. df['total runtime'] = df[RUNTIME_COLUMNS].sum(axis=1) # Create a formatted runtime string for tooltips. df['Runtime'] = df['total runtime'].apply(format_runtime_string) # Sort by descending total region runtime. df.sort_values(by='total runtime', inplace=True, ascending=False) return df def summarize_by_task(df: pd.DataFrame) -> pd.DataFrame: """Groups regions to get the total runtime for each task. Args: df: A dataframe of runtime profiling numbers. Returns: The dataframe grouped by task. """ by_task = df.groupby(by=['Task']).sum() return by_task.reset_index() def stage_histogram(d: pd.DataFrame, title: str = '') -> alt.Chart: """Plots a histogram of runtimes stacked by stage. Args: d: A dataframe of runtimes, either by region or by task. title: A title for the plot. Returns: An altair chart. """ columns_used = RUNTIME_COLUMNS d = d[columns_used] return alt.Chart(d).transform_fold( RUNTIME_COLUMNS, as_=['Stage', 'runtime_by_stage']) \ .mark_bar(opacity=0.3) \ .encode( x=alt.X('runtime_by_stage:Q', bin=alt.Bin(maxbins=100), title='Runtime (seconds)'), y=alt.Y('count()', title='Count of regions', stack=None), color=alt.Color('Stage:N', sort=None) ).properties(title=title) def correlation_scatter_charts(d: pd.DataFrame, title: str = '') -> alt.Chart: """Produces a grid of scatter plots of runtimes of stages versus covariates. Args: d: A pandas dataframe of runtime by regions. title: A title for the plot. Returns: An altair chart """ columns_used = ['region', 'total runtime'] + RUNTIME_COLUMNS + COUNT_COLUMNS d = d[columns_used] return alt.Chart(d).mark_circle(opacity=0.1).encode( x=alt.X(alt.repeat('column'), type='quantitative', axis=alt.Axis(labelExpr="datum.value + 's'")), y=alt.Y(alt.repeat('row'), type='quantitative'), tooltip='region' ).properties(width=100, height=100) \ .repeat( column=['total runtime'] + RUNTIME_COLUMNS, row=COUNT_COLUMNS, ).properties(title=title) def totals_by_stage(d: pd.DataFrame) -> alt.Chart: """Plots total runtimes for each stage. Args: d: A dataframe of runtimes. Returns: An altair chart. """ stage_totals_series = d.sum()[RUNTIME_COLUMNS] stage_totals = pd.DataFrame( stage_totals_series, columns=['Runtime (seconds)']) stage_totals.reset_index(inplace=True) stage_totals = stage_totals.rename(columns={'index': 'Stage'}) stage_totals['Runtime'] = stage_totals['Runtime (seconds)'].apply( format_runtime_string) return alt.Chart(stage_totals).mark_bar().encode( x='Runtime (seconds)', y=alt.Y('Stage', sort=None), tooltip=['Runtime'], fill=alt.Fill('Stage', sort=None)).properties(title='Overall runtime by stage') def pareto_by_task_tooltip(row: pd.Series) -> str: """For one row of a dataframe, computes a tooltip description. Args: row: A Pandas Series, one row of a dataframe containing some specific cumulative sum columns. Returns: A string to show as the tooltip for a pareto curve. """ return (f"{row['task cumsum order'] * 100:.2f}% of regions " f"account for {row['task cumsum fraction'] * 100:.2f}% of " f"the runtime in task {row['Task']}") def calculate_pareto_metrics(df_subset: pd.DataFrame) -> pd.DataFrame: """Calculates cumulative sums for a subset of a dataframe. Args: df_subset: A dataframe subset of one task. Returns: The same dataframe subset with some additional columns. """ # These are the same for all regions in the same task, for the scatter plot: df_subset['task total runtime'] = df_subset['total runtime'].sum() df_subset['Runtime for task'] = df_subset['task total runtime'].apply( format_runtime_string) df_subset['task num examples'] = df_subset['num examples'].sum() # These are cumulative sums for the pareto curves: df_subset['task cumsum fraction'] = df_subset['total runtime'].cumsum( ) / df_subset['total runtime'].sum() n = len(df_subset) df_subset['task cumsum order'] = list(map(lambda x: x / n, range(0, n))) df_subset['tooltip'] = df_subset.apply(pareto_by_task_tooltip, axis=1) return df_subset def pareto_and_runtimes_by_task(df: pd.DataFrame) -> alt.Chart: """Creates an interactive Pareto curve and scatter plot of task runtimes. Tracing each curve shows to what extent a small proportion of long-running regions contribute disproportionately to the overall runtime. That is, "The longest-running X% of regions account for Y% of the total runtime." There is a curve for each task. Args: df: A dataframe of all regions. Returns: An altair chart. """ grouped = df.groupby(df['Task'], sort=False) df = grouped.apply(calculate_pareto_metrics) # Sample along the Pareto curve, ensuring the longest regions are shown. if len(df) > 5000: x = 1000 df = pd.concat([df.nlargest(x, 'total runtime'), df.sample(5000 - x)]) # Limit columns to greatly reduce the size of the html report. columns_used = [ 'task cumsum order', 'task cumsum fraction', 'tooltip', 'Task', 'task total runtime', 'task num examples', 'Runtime for task' ] df = df[columns_used] # Brushing on the task_scatter plot highlights the same tasks in the Pareto # curve. brush = alt.selection_interval() pareto_by_task = alt.Chart(df).mark_line(size=2).encode( x=alt.X( 'task cumsum order', title='The longest-runtime X% of regions', axis=alt.Axis(format='%')), y=alt.Y( 'task cumsum fraction', title='Account for Y% of the total runtime', axis=alt.Axis(format='%')), tooltip='tooltip', color=alt.condition(brush, 'Task:N', alt.value('lightgray'))).properties( title='Pareto curve for each task').interactive() # This chart needs to use the same dataframe as the first chart to enable the # brushing on one to affect the other. Using max(task) for 'text' is a # trick that causes bundling by task to avoid showing multiple overlapping # points which otherwise make the text look funky. task_scatter = alt.Chart(df).mark_point(size=10).encode( x=alt.X('max(task total runtime)', title='Runtime (seconds)'), y=alt.Y('task num examples:Q', title='Number of examples'), color=alt.condition(brush, 'Task:N', alt.value('lightgray')), tooltip=['Task', 'Runtime for task'] ) \ .properties(title='Total runtime for each task (drag to highlight)') \ .add_selection(brush) return pareto_by_task | task_scatter def individual_region_bars(small_df: pd.DataFrame, title: Union[str, Dict[str, str]] = '') -> alt.Chart: """Makes a stacked bar chart with runtime of each stage for individual regions. Args: small_df: A dataframe of regions, each of which will be shown as a bar. title: A title for the plot. If a dict, it should contain 'title' and/or 'subtitle'. Returns: An altair chart. """ columns_used = ['region', 'Runtime'] + RUNTIME_COLUMNS d = small_df[columns_used] return alt.Chart(d).transform_fold( RUNTIME_COLUMNS, as_=['Stage', 'runtime_by_stage']) \ .mark_bar().encode( x=alt.X('region:N', sort=None), y=alt.Y('runtime_by_stage:Q', scale=alt.Scale(type='linear'), title='Runtime (seconds)'), fill=alt.Fill('Stage:N', sort=None), tooltip='Runtime:N' ).properties(title=title) def selected_longest_and_median_regions(df: pd.DataFrame) -> alt.Chart: """Creates a stacked bar charts of the top 20 and median 20 regions. Args: df: A dataframe of all regions. Returns: An altair chart. """ num_rows = len(df) mid = round(num_rows / 2) return individual_region_bars(df.iloc[0:20], 'Top runtime regions') \ | individual_region_bars(df.iloc[mid-10:mid+11], 'Median runtime regions') def top_regions_producing_zero_examples(df: pd.DataFrame) -> alt.Chart: """Creates a chart of the top regions that produced zero examples. Args: df: A dataframe of all regions. Returns: An altair chart. """ regions_with_zero_examples = df[df['num examples'] == 0] runtime_of_zeros = regions_with_zero_examples['total runtime'].sum() / 3600 total_runtime = df['total runtime'].sum() / 3600 subtitle = ( f'Spent {runtime_of_zeros:.2f} hours processing the ' f'{len(regions_with_zero_examples)} regions that produced no examples, ' f'which is {runtime_of_zeros / total_runtime * 100:.2f}% of the total ' f'runtime of {total_runtime:.2f} hours.') return individual_region_bars( regions_with_zero_examples.nlargest(50, 'total runtime'), title={ 'text': 'The longest-running regions that produced no examples', 'subtitle': subtitle }) def write_to_html_report(charts: List[Dict[Text, alt.Chart]], title: str, subtitle: str, html_output: Any) -> None: """Makes the html report with all the charts inserted. Args: charts: A list of altair chart objects. title: The title to show at the top of the report. subtitle: The subtitle to show just below the title on the report. html_output: a writable file object. Returns: None. Writes into the html_output file object. """ # Start the HTML document. html_output.write('<!DOCTYPE html>\n<html>\n<head>') # Add dependencies vega and vega-lite, which render the altair charts. html_output.write('<script type="text/javascript" src="{}/vega@5"></script>' '\n'.format(VEGA_URL)) html_output.write( '<script type="text/javascript" src="{}/[email protected]"></script>' '\n'.format(VEGA_URL)) html_output.write( '<script type="text/javascript" src="{}/vega-embed@6"></script>' '\n'.format(VEGA_URL)) # Add styles (CSS). html_output.write(CSS_STYLES) html_output.write('</head>\n<body>') html_output.write('<h1>{}</h1>\n'.format(title)) html_output.write('<h2>{}</h2>\n'.format(subtitle)) # Make a div containing all the charts. html_output.write('<div>') for chart in charts: html_output.write( '<div class="chart-container" id="vis_{}"></div>\n'.format(chart['id'])) html_output.write('</div>') # Add JSON vega specs and hook them up to the divs with VegaEmbed. html_output.write('<script>\n') for chart in charts: html_output.write('var spec_{} = {};\n'.format(chart['id'], chart['chart'].to_json())) download_filename = '{}_{}'.format(title.replace(' ', '_'), chart['id']) embed_options = {'mode': 'vega-lite', 'downloadFileName': download_filename} html_output.write('vegaEmbed("#vis_{}", spec_{}, {})\n'.format( chart['id'], chart['id'], embed_options)) html_output.write('</script>\n') # Close HTML document. html_output.write('</body></html>') def read_data_and_make_dataframes( input_path: str) -> Tuple[pd.DataFrame, pd.DataFrame]: """Loads data from a file into one dataframe as-is and one by task. Args: input_path: str, path of the input TSV file (may be sharded). Returns: df: A dataframe with one row per region. by_task: A dataframe with one row per task. """ df = read_sharded_runtime_tsvs(input_path) df = calculate_totals(df) by_task = summarize_by_task(df) return df, by_task def make_all_charts( df: pd.DataFrame, by_task: pd.DataFrame) -> List[Dict[Text, Union[str, alt.Chart]]]: """Creates charts and puts them in a list with their ID names. Args: df: A dataframe with one row per region. by_task: A dataframe with one row per task. Returns: list of dicts, each containing a chart and a descriptive ID. """ charts = [{ 'id': 'total_by_stage', 'chart': totals_by_stage(by_task) }, { 'id': 'pareto_and_runtimes_by_task', 'chart': pareto_and_runtimes_by_task(df) }, { 'id': 'histogram_by_task', 'chart': stage_histogram(by_task, title='Stage runtimes for each task') }, { 'id': 'selected_longest_and_median_regions', 'chart': selected_longest_and_median_regions(df) }, { 'id': 'zero_examples', 'chart': top_regions_producing_zero_examples(df) }] # Altair shows a max of 5000 data points. if len(df) <= 5000: # With up to 5000 points, just show them all. charts.extend([{ 'id': 'histogram', 'chart': stage_histogram(df, title='Runtime by stage for all regions') }, { 'id': 'scatter_grid', 'chart': correlation_scatter_charts(df, title='Trends for all regions') }]) else: # With too many points, make different subsets to show trends better. top_100 = df.nlargest(100, 'total runtime') top_5000 = df.nlargest(5000, 'total runtime') # Sample the bottom 99% to avoid outliers that obscure general trends. bottom_99_percent = df.nsmallest(int(len(df) * .99), 'total runtime') if len(bottom_99_percent) > 5000: bottom_99_percent = bottom_99_percent.sample(5000) charts.extend([{ 'id': 'histogram_bottom_99_percent', 'chart': stage_histogram( bottom_99_percent, title='Runtime by stage for regions in the bottom 99%') }, { 'id': 'histogram_top_100', 'chart': stage_histogram( top_100, title='Runtime by stage for regions in the top 100') }, { 'id': 'scatter_grid_top_5000', 'chart': correlation_scatter_charts( top_5000, title='Trends for regions in the top 5000') }, { 'id': 'scatter_grid_bottom_99_percent', 'chart': correlation_scatter_charts( bottom_99_percent, title='Trends for regions in the bottom 99%') }]) return charts def make_report(input_path: str, title: str, html_output: tf.io.gfile.GFile) -> None: """Reads data, creates charts, and composes the charts into an HTML report. Args: input_path: Path of the input TSV file (or sharded files). title: Title to put at the top of the report. html_output: Writable file object where output will be written. """ # Load data into pandas dataframes and add summary columns. df, by_task = read_data_and_make_dataframes(input_path) # Build all the charts. charts = make_all_charts(df, by_task) # Write a subtitle with some top-level stats. subtitle = (f'Runtime profiling for make_examples on {len(df)} regions ' f'across {len(by_task)} task{"(s)" if len(by_task) > 1 else ""}') # Write the HTML report with all the charts. write_to_html_report( charts=charts, title=title, subtitle=subtitle, html_output=html_output) def main(argv: Sequence[str]): if len(argv) > 1: raise app.UsageError( 'Command line parsing failure: this script does not accept ' 'positional arguments, but found these extra arguments: "{}".' ''.format(str(argv[1:]))) # Add html to the output path if that is not already the suffix. if FLAGS.output.endswith('html'): output_filename = FLAGS.output else: output_filename = f'{FLAGS.output}.html' # Start HTML document. Using GFile enables writing to GCS too. html_output = tf.io.gfile.GFile(output_filename, 'w') make_report( input_path=FLAGS.input, title=FLAGS.title, html_output=html_output) html_output.close() # Abstracted out the file open/close to enable testing. print('Output written to:', output_filename) if __name__ == '__main__': flags.mark_flags_as_required(['input', 'title']) app.run(main)
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CEV/ex033.py
EduardoMachadoCostaOliveira/Python
0
76276
# Faça um programa que leia três números e mostre # qual é o Maior e qual é o Menor. a = int(input('\033[34;43mPrimeiro valor:\033[m ')) b = int(input('\033[32;44mSegundo valor:\033[m ')) c = int(input('\033[32;47mTerceiro valor:\033[m ')) # Verificando quem é o menor menor = a if b < a and b < c: menor = b if c < a and c < b: menor = c # Verificando quem é o maior maior = a if b > a and b > c: maior = b if c > a and c > b: maior = c print(f'\033[44mO menor valor digitado foi\033[m\033[4;33;41m {menor} \033[m') print(f'\033[45mO maior valor digitado foi\033[m \033[4;31;43m {maior} \033[m') '''Alternativa01 if a<b and a<c: menor = a if b<a and b<c: menor = b if c<a and c<b: menor = c '''
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gym_tetris/envs/__init__.py
michielcx/Tetris-DQN
13
165860
<reponame>michielcx/Tetris-DQN<gh_stars>10-100 from gym_tetris.envs.tetris_env import TetrisEnv
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convlab2/policy/ppo/multiwoz/__init__.py
Malavikka/ConvLab-2
339
86781
<reponame>Malavikka/ConvLab-2<filename>convlab2/policy/ppo/multiwoz/__init__.py from convlab2.policy.ppo.multiwoz.ppo_policy import PPOPolicy
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src/adventofcode/year_2015/day_24_2015.py
Frazzer951/Advent-Of-Code
0
116424
<gh_stars>0 from functools import reduce from itertools import combinations from operator import mul from typing import List from adventofcode.util.helpers import solution_timer from adventofcode.util.input_helpers import get_input_for_day def splitPresents(weights, num_groups): group_size = sum(weights) // num_groups for i in range(len(weights)): qes = [reduce(mul, c) for c in combinations(weights, i) if sum(c) == group_size] if qes: return min(qes) @solution_timer(2015, 24, 1) def part_one(input_data: List[str]): nums = [int(i) for i in input_data] return splitPresents(nums, 3) @solution_timer(2015, 24, 2) def part_two(input_data: List[str]): nums = [int(i) for i in input_data] return splitPresents(nums, 4) if __name__ == "__main__": data = get_input_for_day(2015, 24) part_one(data) part_two(data)
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Exercise6.6/environment.py
yuishihara/ReinforcementLearningExcersices
0
136575
<filename>Exercise6.6/environment.py class Environment(object): def __init__(self): pass def act(self, state, action): pass def end_state(self, state): return True def reset(self): pass def available_actions(self): return None
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src/robotide/ui/treenodehandlers.py
crylearner/RIDE3X
1
16926
<gh_stars>1-10 # Copyright 2008-2015 Nokia Solutions and Networks # # 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 wx from robotide.controller.commands import ( RenameKeywordOccurrences, RemoveMacro, AddKeyword, AddTestCase, RenameTest, CopyMacroAs, AddVariable, UpdateVariableName, RenameFile, RenameResourceFile, DeleteFile, SortKeywords, Include, Exclude) from robotide.controller.settingcontrollers import VariableController from robotide.controller.macrocontrollers import ( TestCaseController, UserKeywordController) from robotide.controller.filecontrollers import ( TestDataDirectoryController, ResourceFileController, TestCaseFileController, ExcludedDirectoryController, DirtyRobotDataException) from robotide.editor.editordialogs import ( TestCaseNameDialog, UserKeywordNameDialog, ScalarVariableDialog, ListVariableDialog, CopyUserKeywordDialog, DictionaryVariableDialog) from robotide.publish import RideOpenVariableDialog from robotide.ui.progress import LoadProgressObserver from robotide.usages.UsageRunner import Usages, ResourceFileUsages from .filedialogs import ( AddSuiteDialog, AddDirectoryDialog, ChangeFormatDialog, NewResourceDialog, RobotFilePathDialog) from robotide.utils import overrides from robotide.widgets import PopupMenuItems from .progress import RenameProgressObserver from .resourcedialogs import ResourceRenameDialog, ResourceDeleteDialog from robotide.ui.resourcedialogs import FolderDeleteDialog def action_handler_class(controller): return { TestDataDirectoryController: TestDataDirectoryHandler, ResourceFileController: ResourceFileHandler, TestCaseFileController: TestCaseFileHandler, TestCaseController: TestCaseHandler, UserKeywordController: UserKeywordHandler, VariableController: VariableHandler, ExcludedDirectoryController: ExcludedDirectoryHandler }[controller.__class__] class _ActionHandler(wx.Window): is_user_keyword = False is_test_suite = False is_variable = False _label_add_suite = 'New Suite\tCtrl-Shift-F' _label_add_directory = 'New Directory' _label_new_test_case = 'New Test Case\tCtrl-Shift-T' _label_new_user_keyword = 'New User Keyword\tCtrl-Shift-K' _label_sort_keywords = 'Sort Keywords' _label_new_scalar = 'New Scalar\tCtrl-Shift-V' _label_new_list_variable = 'New List Variable\tCtrl-Shift-L' _label_new_dict_variable = 'New Dictionary Variable' _label_change_format = 'Change Format' _label_copy_macro = 'Copy\tCtrl-Shift-C' _label_rename = 'Rename\tF2' _label_add_resource = 'Add Resource' _label_new_resource = 'New Resource' _label_find_usages = 'Find Usages' _label_select_all = 'Select All Tests' _label_deselect_all = 'Deselect All Tests' _label_select_failed_tests = 'Select Only Failed Tests' _label_select_passed_tests = 'Select Only Passed Tests' _label_delete = 'Delete\tCtrl-Shift-D' _label_delete_no_kbsc = 'Delete' _label_exclude = 'Exclude' _label_include = 'Include' _label_expand_all = 'Expand all' _label_collapse_all = 'Collapse all' def __init__(self, controller, tree, node, settings): wx.Window.__init__(self, tree) self.controller = controller self._tree = tree self._node = node self._settings = settings self._rendered = False self.Show(False) self._popup_creator = tree._popup_creator @property def item(self): return self.controller.data @property def node(self): return self._node def show_popup(self): self._popup_creator.show(self, PopupMenuItems(self, self._actions), self.controller) def begin_label_edit(self): return False def double_clicked(self): pass def end_label_edit(self, event): pass def OnDelete(self, event): pass def OnNewSuite(self, event): pass def OnNewDirectory(self, event): pass def OnNewResource(self, event): pass def OnNewUserKeyword(self, event): pass def OnNewTestCase(self, event): pass def OnNewScalar(self, event): pass def OnNewListVariable(self, event): pass def OnNewDictionaryVariable(self, event): pass def OnCopy(self, event): pass def OnFindUsages(self, event): pass def OnSelectAllTests(self, event): self._tree.SelectAllTests(self._node) def OnDeselectAllTests(self, event): self._tree.DeselectAllTests(self._node) def OnSelectOnlyFailedTests(self, event): self._tree.SelectFailedTests(self._node) def OnSelectOnlyPassedTests(self, event): self._tree.SelectPassedTests(self._node) def OnSafeDelete(self, event): pass def OnExclude(self, event): pass def OnInclude(self, event): pass class _CanBeRenamed(object): def OnRename(self, event): self._tree.label_editor.OnLabelEdit() def begin_label_edit(self): def label_edit(): # FIXME: yep.yep.yep.yep.yep node = self._tree._controller.find_node_by_controller( self.controller) if node: self._tree.EditLabel(node) # Must handle pending events before label edit # This is a fix for situations where there is a pending action # that will change this label (Text Editor all changing actions) wx.CallAfter(label_edit) return True def end_label_edit(self, event): if not event.IsEditCancelled(): if self._is_valid_rename(event.GetLabel()): self.rename(event.GetLabel()) else: event.Veto() def _is_valid_rename(self, label): validation = self.controller.validate_name(label) if validation.error_message: self._show_validation_error(validation.error_message) return False return True def _show_validation_error(self, err_msg): wx.MessageBox(err_msg, 'Validation Error', style=wx.ICON_ERROR) class DirectoryHandler(_ActionHandler): is_draggable = False is_test_suite = False can_be_rendered = False _actions = [_ActionHandler._label_new_resource] def OnNewResource(self, event): NewResourceDialog(self.controller, self._settings).execute() class TestDataHandler(_ActionHandler): accepts_drag = lambda self, dragged: \ (isinstance(dragged, UserKeywordHandler) or isinstance(dragged, VariableHandler)) is_draggable = False is_test_suite = True @property def tests(self): return self.controller.tests @property def keywords(self): return self.controller.keywords @property def variables(self): return self.controller.variables def has_been_modified_on_disk(self): return self.item.has_been_modified_on_disk() def do_drop(self, item): self.controller.add_test_or_keyword(item) def rename(self, new_name): return False def OnSortKeywords(self, event): """Sorts the keywords inside the treenode""" self.controller.execute(SortKeywords()) @property def can_be_rendered(self): if not self._has_children(): return False return not self._rendered def _has_children(self): return (self.item.keyword_table or self.item.testcase_table or self.item.variable_table) def set_rendered(self): self._rendered = True def OnChangeFormat(self, event): ChangeFormatDialog(self.controller).execute() def OnNewUserKeyword(self, event): dlg = UserKeywordNameDialog(self.controller) if dlg.ShowModal() == wx.ID_OK: self.controller.execute(AddKeyword(dlg.get_name(), dlg.get_args())) dlg.Destroy() def OnNewScalar(self, event): self._new_var(ScalarVariableDialog) def OnNewListVariable(self, event): class FakePlugin(object): global_settings = self._settings self._new_var(ListVariableDialog, plugin=FakePlugin()) def OnNewDictionaryVariable(self, event): class FakePlugin(object): global_settings = self._settings self._new_var(DictionaryVariableDialog, plugin=FakePlugin()) def _new_var(self, dialog_class, plugin=None): dlg = dialog_class(self._var_controller, plugin=plugin) if dlg.ShowModal() == wx.ID_OK: name, value = dlg.get_value() comment = dlg.get_comment() self.controller.execute(AddVariable(name, value, comment)) @property def _var_controller(self): return self.controller.datafile_controller.variables class TestDataDirectoryHandler(TestDataHandler): def __init__(self, *args): TestDataHandler.__init__(self, *args) self._actions = [ _ActionHandler._label_add_suite, _ActionHandler._label_add_directory, _ActionHandler._label_new_resource, '---', _ActionHandler._label_new_user_keyword, _ActionHandler._label_new_scalar, _ActionHandler._label_new_list_variable, _ActionHandler._label_new_dict_variable, '---', _ActionHandler._label_change_format ] if self.controller.parent: self._actions.extend([_ActionHandler._label_delete_no_kbsc]) self._actions.extend([ '---', _ActionHandler._label_select_all, _ActionHandler._label_deselect_all, _ActionHandler._label_select_failed_tests, _ActionHandler._label_select_passed_tests ]) if self.controller.parent: self._actions.extend(['---', _ActionHandler._label_exclude]) self._actions.extend(['---', _ActionHandler._label_expand_all, _ActionHandler._label_collapse_all]) def OnExpandAll(self, event): self._tree.ExpandAllSubNodes(self._node) def OnCollapseAll(self, event): self._tree.CollapseAllSubNodes(self._node) def OnNewSuite(self, event): AddSuiteDialog(self.controller, self._settings).execute() def OnNewDirectory(self, event): AddDirectoryDialog(self.controller, self._settings).execute() def OnNewResource(self, event): NewResourceDialog(self.controller, self._settings).execute() def OnDelete(self, event): FolderDeleteDialog(self.controller).execute() def OnExclude(self, event): try: self.controller.execute(Exclude()) except DirtyRobotDataException: wx.MessageBox('Directory contains unsaved data!\n' 'You must save data before excluding.') class _FileHandlerThanCanBeRenamed(_CanBeRenamed): @overrides(_CanBeRenamed) def begin_label_edit(self): self._old_label = self._node.GetText() self._set_node_label(self.controller.basename) return _CanBeRenamed.begin_label_edit(self) @overrides(_CanBeRenamed) def end_label_edit(self, event): if not event.IsEditCancelled(): result = self.controller.execute( self._rename_command(event.GetLabel())) if result: self._rename_ok_handler() self._old_label = self.controller.basename else: event.Veto() else: self._set_node_label(self._old_label) def _rename_ok_handler(self): pass def _rename_command(self, label): raise NotImplementedError(self.__class__) def _set_node_label(self, label): self._tree.SetItemText(self._node, label) class ResourceFileHandler(_FileHandlerThanCanBeRenamed, TestDataHandler): is_test_suite = False _actions = [_ActionHandler._label_new_user_keyword, _ActionHandler._label_new_scalar, _ActionHandler._label_new_list_variable, _ActionHandler._label_new_dict_variable, '---', _ActionHandler._label_rename, _ActionHandler._label_change_format, _ActionHandler._label_sort_keywords, _ActionHandler._label_find_usages, _ActionHandler._label_delete] def OnFindUsages(self, event): ResourceFileUsages(self.controller, self._tree.highlight).show() def OnDelete(self, event): ResourceDeleteDialog(self.controller).execute() def OnSafeDelete(self, event): return self.OnDelete(event) @overrides(_FileHandlerThanCanBeRenamed) def _rename_command(self, label): return RenameResourceFile( label, self._check_should_rename_static_imports) def _check_should_rename_static_imports(self): return ResourceRenameDialog(self.controller).execute() class TestCaseFileHandler(_FileHandlerThanCanBeRenamed, TestDataHandler): accepts_drag = lambda *args: True _actions = [_ActionHandler._label_new_test_case, _ActionHandler._label_new_user_keyword, _ActionHandler._label_new_scalar, _ActionHandler._label_new_list_variable, _ActionHandler._label_new_dict_variable, '---', _ActionHandler._label_rename, _ActionHandler._label_change_format, _ActionHandler._label_sort_keywords, _ActionHandler._label_delete, '---', _ActionHandler._label_select_all, _ActionHandler._label_deselect_all, _ActionHandler._label_select_failed_tests, _ActionHandler._label_select_passed_tests ] def OnNewTestCase(self, event): dlg = TestCaseNameDialog(self.controller) if dlg.ShowModal() == wx.ID_OK: self.controller.execute(AddTestCase(dlg.get_name())) dlg.Destroy() def OnDelete(self, event): if wx.MessageBox('Delete test case file', caption='Confirm', style=wx.YES_NO | wx.ICON_QUESTION) == wx.YES: self.controller.execute(DeleteFile()) def OnSafeDelete(self, event): return self.OnDelete(event) @overrides(_FileHandlerThanCanBeRenamed) def _rename_command(self, label): return RenameFile(label) @overrides(_FileHandlerThanCanBeRenamed) def _rename_ok_handler(self): self._tree.DeselectAllTests(self._node) class _TestOrUserKeywordHandler(_CanBeRenamed, _ActionHandler): accepts_drag = lambda *args: False is_draggable = True _actions = [ _ActionHandler._label_copy_macro, 'Move Up\tCtrl-Up', 'Move Down\tCtrl-Down', _ActionHandler._label_rename, '---', 'Delete' ] def remove(self): self.controller.delete() def rename(self, new_name): self.controller.execute(self._create_rename_command(new_name)) def OnCopy(self, event): dlg = self._copy_name_dialog_class(self.controller, self.item) if dlg.ShowModal() == wx.ID_OK: self.controller.execute(CopyMacroAs(dlg.get_name())) dlg.Destroy() def OnMoveUp(self, event): if self.controller.move_up(): self._tree.move_up(self._node) def OnMoveDown(self, event): if self.controller.move_down(): self._tree.move_down(self._node) def OnDelete(self, event): self.controller.execute(RemoveMacro(self.controller)) class TestCaseHandler(_TestOrUserKeywordHandler): _datalist = property(lambda self: self.item.datalist) _copy_name_dialog_class = TestCaseNameDialog def _add_copy_to_tree(self, parent_node, copied): self._tree.add_test(parent_node, copied) def _create_rename_command(self, new_name): return RenameTest(new_name) class UserKeywordHandler(_TestOrUserKeywordHandler): is_user_keyword = True _datalist = property(lambda self: self.item.datalist) _copy_name_dialog_class = CopyUserKeywordDialog _actions = _TestOrUserKeywordHandler._actions + [ _ActionHandler._label_find_usages] def _add_copy_to_tree(self, parent_node, copied): self._tree.add_keyword(parent_node, copied) def _create_rename_command(self, new_name): return RenameKeywordOccurrences( self.controller.name, new_name, RenameProgressObserver(self.GetParent().GetParent()), self.controller.info) def OnFindUsages(self, event): Usages(self.controller, self._tree.highlight).show() class VariableHandler(_CanBeRenamed, _ActionHandler): accepts_drag = lambda *args: False is_draggable = True is_variable = True OnMoveUp = OnMoveDown = lambda *args: None _actions = [_ActionHandler._label_rename, 'Delete'] @overrides(_ActionHandler) def double_clicked(self): RideOpenVariableDialog(controller=self.controller).publish() def OnDelete(self, event): self.remove() def remove(self): self.controller.delete() def rename(self, new_name): self.controller.execute(UpdateVariableName(new_name)) @property def index(self): return self.controller.index class ResourceRootHandler(_ActionHandler): can_be_rendered = is_draggable = is_user_keyword = is_test_suite = False rename = lambda self, new_name: False accepts_drag = lambda self, dragged: False _actions = [_ActionHandler._label_add_resource] @property def item(self): return None def OnAddResource(self, event): path = RobotFilePathDialog( self, self.controller, self._settings).execute() if path: self.controller.load_resource(path, LoadProgressObserver(self)) class ExcludedDirectoryHandler(TestDataDirectoryHandler): is_draggable = False is_test_suite = True def __init__(self, *args): TestDataHandler.__init__(self, *args) self._actions = [_ActionHandler._label_include] def OnInclude(self, event): self.controller.execute(Include())
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api/exceptions.py
SirSkaro/smogon.py
0
78235
class Error(Exception): """Base class for other exceptions""" pass class InvalidSpecies(Error): """Species out of bounds of legitimate species""" pass class InvalidForm(Error): """Form is invalid""" pass class APIError(Error): """Something wrong with the API""" pass
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app.py
sasidharpunna/CRMFBot
0
52352
from flask import Flask, render_template, request from chatterbot import ChatBot import pypyodbc as odbc import re app = Flask(__name__) CRMFBot = ChatBot("Chatterbot", storage_adapter="chatterbot.storage.SQLStorageAdapter") @app.route("/") def home(): return render_template("index.html") @app.route("/get") def get_bot_response(): userText = request.args.get('msg') db_host = 'python1' db_name = 'projectdetails' db_user = 'sa' db_password = '$' mudid='12345' weekno = re.findall('\d+',userText) req=userText.upper().find('TARGET') def get_target_details(required,weekno): connection_string = 'DSN=' + db_host + ';Database=' + db_name + ';UID=' + db_user + ';PWD=' + db_password + ';' db = odbc.connect(connection_string) cur=db.cursor() SQL= "select " + required + " from targetdetails where MUDID = '" + mudid +"' and weekno = '" + weekno[0] +"';" cur.execute(SQL) output=cur.fetchone() print(output[0]) print(type(output)) print(type(output[0])) if output[0] == '': res = 'Most likely, you dont have ' + required + ' for week no ' + str(weekno[0]) print(res) return res else: res = 'Your ' + required + ' for week ' + str(weekno[0]) + ' is ' + str(output[0]) print(res) return res if req != -1 and weekno: required='target' return get_target_details(required,weekno) elif req == -1 and weekno: required='achieved' return get_target_details(required,weekno) else: result = str(CRMFBot.get_response(userText)) return result if __name__ == "__main__": app.run()
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tasks/task 2.py
Mirror-Shard/L2.8
0
116795
<filename>tasks/task 2.py #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ в основной ветке программы вызывается функция cylinder(), которая вычисляет площадь цилиндра. В теле cylinder() определена функция circle(), вычисляющая площадь круга по формуле. В теле cylinder() у пользователя спрашивается, хочет ли он получить только площадь боковой поверхности, которая вычисляется по формуле, или полную площадь цилиндра. В последнем случае к площади боковой поверхности цилиндра должен добавляться удвоенный результат вычислений функции circle(). """ import sys import math # Функция вычисляет площадь цилиндра def cylinder(radius, height, pick): # Вычисляет площадь круга def cylinder_circle(r): circle = math.pi * r ** 2 return circle # Вычисляет боковую сторону цилиндра def cylinder_side(r, h): side = 2 * math.pi * r * h return side # Вычисляет площадь всего цилиндра def full_cylinder(r, h): full = cylinder_side(r, h) + cylinder_circle(r) * 2 return full # Если выбор 1 if pick == 1: print("Площадь всего цилиндра равна:") print(full_cylinder(radius, height)) # Если выбор 2 elif pick == 2: print("Площадь боковой стороны цилиндра равна:") print(cylinder_side(radius, height)) # Недопустимый выбор else: print("Введено недопустимое значение", file=sys.stderr) exit(1) if __name__ == '__main__': # Запрос радиуса и высоты rad = float(input("Введите радиус: ")) hei = float(input("Введите высоту: ")) # Запрос выбора print("Вычислить площадь всего цилиндра или только боковой стороны?") print("1 - всего цилиндра, 2 - только боковой стороны") choice = int(input()) cylinder(rad, hei, choice)
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catalyst/dl/experiment/__init__.py
andrey-avdeev/catalyst
3
18634
<filename>catalyst/dl/experiment/__init__.py # flake8: noqa from .base import BaseExperiment from .config import ConfigExperiment from .supervised import SupervisedExperiment
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examples/scatters/scatter5.py
aengelke/z-plot
22
1605179
<gh_stars>10-100 #! /usr/bin/env python # should be in your PYTHONPATH or installed somewhere from zplot import * ctype = 'eps' if len(sys.argv) < 2 else sys.argv[1] c = canvas(ctype, title='scatter5', dimensions=[300,300]) t = table(file='scatter5.data') d = drawable(canvas=c, xrange=[0,80], yrange=[0,80], coord=[30,30], dimensions=[260,260]) axis(drawable=d, yauto=['','',10], xauto=['','',10]) p = plotter() p.function(drawable=d, function=lambda x: x, xrange=[0,80], step=10, linewidth=0.25, linecolor='gray') # now plot the intervals and points p.points(drawable=d, table=t, xfield='x', yfield='y', style='label', labelfield='state', labelsize=9.0) # now, do a closeup! closeup = drawable(canvas=c, xrange=[51,53], yrange=[0,10], coord=[220,100], dimensions=[100,70]) # this is UGLY -- but get all the data in the scrunhed-up area t2 = table(table=t, where='(x>=50.9) and (x<=51.1)') p.points(drawable=closeup, table=t2, xfield='x', yfield='rownumber', style='label', labelfield='state', labelsize=8.0) c.box(coord=[[205,90],[235,190]], linecolor='red') c.line(coord=[[205,190],[199,202]], linecolor='red', arrow=True, arrowlinecolor='red', arrowfillcolor='red') c.render()
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data/transforms/utils.py
apple/ml-cvnets
209
116019
<reponame>apple/ml-cvnets<gh_stars>100-1000 # # For licensing see accompanying LICENSE file. # Copyright (C) 2022 Apple Inc. All Rights Reserved. # from typing import Any import numpy as np def setup_size(size: Any, error_msg="Need a tuple of length 2"): if isinstance(size, int): return size, size if isinstance(size, (list, tuple)) and len(size) == 1: return size[0], size[0] if len(size) != 2: raise ValueError(error_msg) return size def intersect(box_a, box_b): """Computes the intersection between box_a and box_b""" max_xy = np.minimum(box_a[:, 2:], box_b[2:]) min_xy = np.maximum(box_a[:, :2], box_b[:2]) inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf) return inter[:, 0] * inter[:, 1] def jaccard_numpy(box_a: np.ndarray, box_b: np.ndarray): """ Computes the intersection of two boxes. Args: box_a (np.ndarray): Boxes of shape [Num_boxes_A, 4] box_b (np.ndarray): Box osf shape [Num_boxes_B, 4] Returns: intersection over union scores. Shape is [box_a.shape[0], box_a.shape[1]] """ inter = intersect(box_a, box_b) area_a = (box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1]) # [A,B] area_b = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1]) # [A,B] union = area_a + area_b - inter return inter / union # [A,B]
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package/awesome_panel/application/services/message_service.py
Jhsmit/awesome-panel
179
30712
"""This module implements the MessageService The MessageService enables sending and receiving messages """ import param class MessageService(param.Parameterized): """The MessageService enables sending and receiving messages"""
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pthtemplate/iterator/__init__.py
try-skycn/PytorchTemplate
0
105984
from .epoch import Epoch from .iteration import Iteration
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dacman/plugins/csv/__init__.py
yocheah/dac-man
0
35522
<reponame>yocheah/dac-man from pathlib import Path from collections import defaultdict import numpy as np try: import pandas as pd except ImportError: from dacman.core.utils import dispatch_import_error dispatch_import_error(module_name='pandas', plugin_name='CSV') from dacman.compare import base try: from IPython.display import display # for debugging except ImportError: display = print class ChangeStatus: added = 'ADDED' deleted = 'DELETED' modified = 'MODIFIED' unchanged = 'UNCHANGED' unset = 'UNSET' @classmethod def iter(cls): yield from (cls.added, cls.deleted, cls.modified, cls.unchanged) _S = ChangeStatus class _InternalFields: """ Utility class to access commonly used table/dict fields as constants rather than bare strings. """ LOC_ORIG_ROW = '_loc_orig_row' LOC_ORIG_COL = '_loc_orig_col' ORIG = 'orig' CALC = 'calc' _F = _InternalFields class ChangeMetricsBase: """ Convenience class to access properties from items being compared and calculate comparison metrics from them. """ def __init__(self, key, a=None, b=None): self.key = key self.a = a self.b = b self._comparison_data = { 'status': _S.unset, 'metadata': { 'common': {}, 'changes': {}, }, } def __setitem__(self, key, val): self._comparison_data[key] = val def __getitem__(self, key): return self._comparison_data[key] def keys(self): # TODO maybe enforce some order? return self._comparison_data.keys() def add(self, other_data): self._comparison_data.update(other_data) @property def properties(self): # only interested in common fields, since we have to compare them directly # return self.a.keys() & self.b.keys() # use a dict with dummy values as an ordered set to preserve the order dict_for_ordered_set = {key: None for key in self.a.keys() if key in self.b.keys()} return dict_for_ordered_set.keys() @property def is_missing_a(self): return len(self.a) == 0 @property def is_missing_b(self): return len(self.b) == 0 def change_in(self, prop): if prop not in self.properties: return False try: return self.a[prop] != self.b[prop] except Exception as e: print(e) return False def get_value_common(self, prop): if prop in self.properties and not self.change_in(prop): return self.a[prop] # maybe use a sentinel value other than None here? return None def get_value_single(self, prop): if self.is_missing_a: return self.b[prop] if self.is_missing_b: return self.a[prop] return self.get_value_common(prop) def get_values(self, prop, orient=dict, convert=None, as_type=None, fallback=None): if convert is None and as_type is not None: convert = as_type convert = convert or (lambda x: x) val_a = convert(self.a.get(prop, fallback)) val_b = convert(self.b.get(prop, fallback)) if orient in {tuple, list}: return val_a, val_b if orient in {dict}: return {'a': val_a, 'b': val_b} @property def is_modified(self): # TODO change if we use multiple modified states return self['status'] in {_S.modified} @is_modified.setter def is_modified(self, val): if bool(val) is True: # TODO manage other types as extra "modified" characterizers # e.g. if `val` is a list, append to self['modified_labels'] self['status'] = _S.modified def store_common_value_or_values(self, prop): # TODO there could be some redundant logic in general in this class if self.change_in(prop): self['metadata']['changes'][prop] = self.get_values(prop, orient=dict) self.is_modified = True else: self['metadata']['common'][prop] = self.get_value_single(prop) def calculate(self, exclude=None): exclude = exclude or set() if self.is_missing_a: self['status'] = _S.added elif self.is_missing_b: self['status'] = _S.deleted for prop in self.properties: if prop not in exclude: self.store_common_value_or_values(prop) if self['status'] == _S.unset: self['status'] = _S.unchanged class TableColumnChangeMetrics(ChangeMetricsBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def calculate(self): super().calculate(exclude='values_frame') if self['status'] == _S.unset: self['status'] = _S.unchanged def calculate_from_value_metrics(self, value_metrics_table): col_metrics_from_value_metrics = self.get_column_metrics_from_values(value_metrics_table) if col_metrics_from_value_metrics['frac_changed'] > 0.: self.is_modified = True self['values'] = col_metrics_from_value_metrics def get_column_metrics_from_values(self, metrics_table): m = {} df = metrics_table n_total = len(df) m['n_unchanged'] = (df['status'] == _S.unchanged).sum() m['n_added'] = (df['status'] == _S.added).sum() m['n_deleted'] = (df['status'] == _S.deleted).sum() m['n_modified'] = (df['status'] == _S.modified).sum() m['n_changed'] = (m['n_added'] + m['n_deleted'] + m['n_modified']) m['frac_changed'] = m['n_changed'] / n_total if not (df['delta'].isnull().all()): m['delta_mean'] = df['delta'].mean() m['delta_std'] = df['delta'].std() return m def get_stats(self): stats = {'name': self.key} stats.update(self['values']) stats['status'] = self['status'] return stats class _BaseRecord: @staticmethod def default_key_getter(item): raise NotImplementedError def __init__(self, key_getter=None, **kwargs): super().__init__(**kwargs) self.key_getter = key_getter or type(self).default_key_getter self._mapping = self.get_mapping() def get_mapping(self): return {} def keys(self): return self._mapping.keys() def __getitem__(self, key): return self._mapping[key] def get_empty(self): return dict() def get(self, key): # TODO should we customize the default return type at the level of the class or of the instance? return self._mapping.get(key, self.get_empty()) class TableColumnsRecord(_BaseRecord): # TODO this class should probably also deal with the field renames, # i.e. should receive a mapping with the field renames def __init__(self, loader, **kwargs): self.loader = loader super().__init__(**kwargs) @property def dataframe(self): return self.loader.to_table_dtype() def get_metadata(self, series, name): md = {} md['name'] = name md['series'] = series md['dtype'] = series.dtype md['n_notnull'] = series.notnull().sum() md['n_null'] = series.isna().sum() return md def default_key_getter(self, col_metadata): return col_metadata['name'] def get_mapping(self): df = self.dataframe mapping = {} for colname in df: # TODO in general the metadata collecting can also be done directly in the Record, # since here we don't have several types of items at the same level like in the HDF5 plug-in col_md = self.get_metadata(series=df[colname], name=colname) # we don't need a specific record for table fields, since these are collected as metadata of the columns mapping[self.key_getter(col_md)] = col_md return mapping def get_lines_frame(path, comment_char=None): """Read lines and associated metadata from a file""" with Path(path).open() as f: lines = pd.DataFrame({'content': list(f)}) lines['lineno'] = lines.index + 1 def is_comment(s): if comment_char is None: # get a series where all values are False return s == np.nan return (s .astype(str) .str.startswith(comment_char) ) lines['is_comment'] = is_comment(lines['content']) return lines def infer_dtypes(data, converters=None): # the order here is relevant: strings representing floats can be read (erroneously) as datetimes, # but the reverse is not true, so pd.to_numeric should come first converters = converters or [pd.to_numeric, pd.to_datetime] if data.dtype == 'object': for conv in converters: try: data = conv(data) except (TypeError, ValueError): pass else: break return data class ColumnnProcessor: """ Utility class to perform per-column processing and hold data in various formats as needed to create the column-level metadata. """ def __init__(self, data_orig, name=None): self.data_orig = data_orig self.data_calc = None self.name = name self.header = {} def process_header(self, data, pos_mapper=None, pos='rel', **kwargs): # pos: relative or absolute # relative: count from start of subrange # absolute: use _loc_orig_row indexer = data.iloc if pos.startswith('abs'): indexer = data.loc pos_mapper = pos_mapper or kwargs to_drop = [] for key, pos in pos_mapper.items(): val = indexer[pos] self.header[key] = val to_drop.append(pos) return data.drop(indexer[to_drop].index) def process_rename(self, data, mapper): self.name = self.header.get('name', self.name) self.name = mapper.get(self.name, self.name) if self.name: data.name = self.name return data def process_dtype(self, data, dtype): if dtype is True: data = infer_dtypes(data) else: if dtype is None: dtype = {} data.astype(dtype.get(self.name, data.dtype)) return data def get_values_frame(self, data_orig, data_calc, index=None): df = pd.DataFrame({_F.ORIG: data_orig, _F.CALC: data_calc}, index=data_calc.index) # print(f'{self.name}: dtypes={df.dtypes}') # so here there are three cases for the index: # - a data column # - orig # - none (reset) # - In this case, we don't particularly need to give it a name # we can probably manage to express this by resetting the index in all three cases, # and then setting the index appropriately if isinstance(index, str) and index == 'orig': df = df.reset_index().set_index(df.index.name, drop=False) elif index is not None: df = pd.merge(index, df, how='left', left_index=True, right_index=True).reset_index().set_index(index.name) else: # we need to be sure that we have the loc_orig_row as a column of this table df = df.reset_index() return df class CSVTableColumnsRecord(_BaseRecord): """ Convert a CSV file into a table, and expose a record as Column in the usual way """ def __init__(self, src, comment_char=None, drop_empty_cols=True, table_range=None, header=None, column_renames=None, index=None, dtype=None, ): self.src = src self.comment_char = comment_char self.drop_empty_cols = drop_empty_cols self.table_range = table_range or {} self.header = header or {} self.column_renames = column_renames or {} self.dtype = dtype self.index = index self._mapping = {} @staticmethod def get_lines_frame(path, comment_char=None): """Read lines and associated metadata from a file""" with Path(path).open() as f: lines = pd.DataFrame({'content': list(f)}) lines['lineno'] = lines.index + 1 def is_comment(s): if comment_char is None: # get a series with the same index where all values are False return s == np.nan return (s .astype(str) .str.startswith(comment_char) ) lines['is_comment'] = is_comment(lines['content']) return lines def get_lineno_loadable(self): """ Return a sequence containing the line numbers (1-based) of lines in the source that are loadable by the CSV parser. This is used to associate table index/rows with their position in the source. """ def is_skipped_by_csv_parser(s): # TODO redo this with more robust values # (and maybe invert the logic to "is_included" while we're at it) return s.astype(str) == '\n' # TODO all of this could probably be merged in a regex def is_loadable(df): return ~(df['is_comment'] | is_skipped_by_csv_parser(df['content'])) lines = get_lines_frame(self.src, comment_char=self.comment_char) return (lines [is_loadable] ['lineno'] .to_list() ) def get_table_orig(self, index_col=_F.LOC_ORIG_ROW): load_opts = dict(dtype=object, header=None, comment=self.comment_char) table_range = {'row_start': 1, 'row_end': None} table_range.update(self.table_range) row_idx_start = table_range['row_start'] - 1 row_idx_end = None try: row_idx_end = table_range['row_end'] - 1 except TypeError: pass load_opts['skiprows'] = range(0, row_idx_start) if row_idx_end is not None: # TODO check for off-by-1 errors load_opts['nrows'] = row_idx_end - row_idx_start df = pd.read_csv(self.src, **load_opts) lineno = self.get_lineno_loadable()[row_idx_start:row_idx_end] return (df .assign(**{_F.LOC_ORIG_ROW: lineno}) .set_index(index_col, drop=True) ) def get_metadata(self, col_processor, index=None): md = {} md['colidx'] = col_processor.colidx md['name'] = col_processor.name md['header'] = col_processor.header md['values_frame'] = col_processor.get_values_frame(col_processor.data_orig, col_processor.data_calc, index=index) return md @staticmethod def key_getter(metadata): return metadata['name'] # TODO a more detailed name for this could be "get_column_metadata_mapping" def get_mapping(self): df = self.get_table_orig() if self.drop_empty_cols: df = df.dropna(axis='columns', how='all') processors = [] index = None for col in df: proc = ColumnnProcessor(df[col], name=col) proc.colidx = col proc.data_calc = (proc.data_orig .pipe(proc.process_header, **self.header) .pipe(proc.process_rename, self.column_renames) .pipe(proc.process_dtype, dtype=self.dtype) ) if proc.name: processors.append(proc) proc.is_index = (proc.name == self.index) if proc.is_index: index = proc.data_calc mapping = {} for proc in processors: md = self.get_metadata(proc, index=index) mapping[self.key_getter(md)] = md return mapping def load(self): self._mapping = self.get_mapping() # this is just for display/reference, and it's not used for actual calculations # self.table_calc = pd.DataFrame({name: md['values_frame']['calc'] for name, md in self._mapping.items()}) def get_empty(self): return {'values_frame': pd.DataFrame([], columns=[_F.ORIG, _F.CALC, _F.LOC_ORIG_ROW])} class TableValuesRecord(_BaseRecord): def __init__(self, src: pd.DataFrame, **kwargs): self.src = src super().__init__(**kwargs) def get_mapping(self): df = self.src mapping = {} for colname in df: col = df[colname] for row_idx in col.index: # this is basically the anti-pandas, but this is just here as an example key = (colname, row_idx) val = col[row_idx] mapping[key] = val return mapping class TableColumnValuesChangeMetrics: """ Calculate change metrics on a per-value basis using indexed containers. Instead of calculating value-level changes as a separate step, i.e. creating a Record from each values frame and looping over matching pairs, we use the fact that the value frame's index allow vectorized operations with similar semantics. """ @classmethod def from_column_metrics(cls, col_metrics): values_a, values_b = col_metrics.get_values('values_frame', orient=tuple) return cls(values_a, values_b, col_metrics=col_metrics) def __init__(self, values_a, values_b, col_metrics=None): self.values_a = values_a self.values_b = values_b self.col_metrics = col_metrics self._table = None self._supports_numeric_delta = None @property def supports_numeric_delta(self): return self._supports_numeric_delta def get_table(self): values_frames = {'a': self.values_a, 'b': self.values_b} concat_opts = { 'axis': 'columns', 'join': 'outer', 'sort': False, } df = pd.concat(values_frames, **concat_opts) # TODO factor out this dtype assignment? return (df .astype({('a', _F.LOC_ORIG_ROW): 'Int64', ('b', _F.LOC_ORIG_ROW): 'Int64'}) ) @property def table(self): return self._table def calculate(self): self._table = (self.get_table() .pipe(self.calc_change_metrics_base) .pipe(self.calc_delta) .pipe(self.assign_change_status) ) return self.get_per_status_metrics() def calc_change_metrics_base(self, df): df.loc[:, 'is_equal'] = df[('a', _F.CALC)] == df[('b', _F.CALC)] df.loc[:, 'is_null_a'] = df[('a', _F.CALC)].isna() df.loc[:, 'is_null_b'] = df[('b', _F.CALC)].isna() df.loc[:, 'is_null_both'] = df['is_null_a'] & df['is_null_b'] df.loc[:, 'is_null_any'] = df['is_null_a'] | df['is_null_b'] return df def calc_delta(self, df): # here one could add more specific deltas, e.g. depending on dtype or column name (which is self.key) # check if datatype is numeric, or try with exceptions? # functions to check are: pandas.api.types.is_number etc # TODO since we need to know whether we can do numeric calculations or not in several places, # we could store this information as an attribute/property try: delta = df[('b', _F.CALC)] - df[('a', _F.CALC)] except TypeError: self._supports_numeric_delta = False delta = np.nan else: self._supports_numeric_delta = True df.loc[:, 'delta'] = delta return df def is_value_modified(self, df): epsilon = 1e-6 if self.supports_numeric_delta: return ~df['is_equal'] & (df['delta'].abs() > epsilon) return ~df['is_equal'] def assign_change_status(self, df): # TODO additional logic here would be e.g. implementing per-field or per-dtype thresholds # TODO make this a categorical status = pd.Series(_S.unset, index=df.index) status[df['is_null_a'] & (~df['is_null_b'])] = _S.added status[(~df['is_null_a']) & df['is_null_b']] = _S.deleted status[(~df['is_null_any']) & self.is_value_modified(df)] = _S.modified status[status == _S.unset] = _S.unchanged df.loc[:, 'status'] = status return df def get_per_status_metrics(self): m = {} for status in [_S.unchanged, _S.added, _S.deleted, _S.modified]: m_status = {} values_with_status = self._table[lambda d: d['status'] == status] has_with_status = len(values_with_status) > 0 if not has_with_status: # this "continue" means that change statuses with no values # are not added to the metrics at all (as opposed to having `{count: 0}` or similar) # it could possibly be exposed as an option # depening on what are the requirements for the schema/structure of the output continue f_aggregated = self.map_status_function_aggregated[status] m_status.update(f_aggregated(values_with_status)) f_per_value = self.map_status_function_per_value[status] if f_per_value is not None: per_value_metrics = [] if has_with_status: per_value_metrics = values_with_status.apply(f_per_value, axis=1).to_list() m_status['values'] = per_value_metrics m[status] = m_status return m @property def map_status_function_aggregated(self): return { _S.added: self.get_stats_default, _S.deleted: self.get_stats_default, _S.unchanged: self.get_stats_default, _S.modified: self.get_stats_default, } @property def map_status_function_per_value(self): return { _S.unchanged: None, _S.added: self.get_per_value_added, _S.deleted: self.get_per_value_deleted, _S.modified: self.get_per_value_modified, } def get_stats_default(self, values): return {'count': len(values)} def get_per_value_added(self, value_properties): return { 'value': value_properties[('b', _F.ORIG)], 'loc': {'row': value_properties[('b', _F.LOC_ORIG_ROW)]} } def get_per_value_deleted(self, value_properties): return { 'value': value_properties[('a', _F.ORIG)], 'loc': {'row': value_properties[('a', _F.LOC_ORIG_ROW)]} } def get_per_value_modified(self, value_properties): vp = value_properties m = {} def get_m_for_side(d, side): return { 'original': d[(side, _F.ORIG)], 'calculated_as': d[(side, _F.CALC)], 'loc': { 'row': d[(side, _F.LOC_ORIG_ROW)] } } m['a'] = get_m_for_side(vp, 'a') m['b'] = get_m_for_side(vp, 'b') # the key must be ('delta', '') instead of only 'delta' # otherwise strange and horrible errors (segmentation faults from numpy/pandas) will occur # this could be a pandas bug, so it would be good to investigate this in more detail at some point if self.supports_numeric_delta: m['delta'] = vp[('delta', '')] return m class CSVPlugin(base.Comparator): @staticmethod def supports(): return ['csv'] @classmethod def description(cls): return cls.__doc__ def gen_comparison_pairs(self, a, b): """ Return an iterable of (key, comparison_pair), where comparison_pair is a tuple of (item_a, item_b) with a matching key. """ # union of the keys of the two records # the ordering of the first record takes precedence # an alternative option would be to sort them, lexicographically or with a custom criteria keys_union = {**a, **b}.keys() for key in keys_union: yield key, (a.get(key), b.get(key)) record_opts = {} comment_char = '#' def get_file_metadata(self, src): """ Collect and return file-level metadata. Since it's simple, we can do it here rather than in a dedicated class. """ # TODO have to properly decide if Records should be passed a Processor instance or the raw input path = Path(src) lines = get_lines_frame(path, comment_char=self.comment_char) return { 'path_parent': path.parent, 'filename': path.name, 'n_lines': len(lines), 'n_lines_commented': len(lines[lines['is_comment']]), 'n_lines_uncommented': len(lines[~lines['is_comment']]), 'n_chars': lines['content'].str.len().sum(), } def compare_file(self, src_a, src_b): file_md_a = self.get_file_metadata(src_a) file_md_b = self.get_file_metadata(src_b) metrics = ChangeMetricsBase('file', file_md_a, file_md_b) metrics.calculate() return dict(metrics) def columns_key_getter(self, col_metadata): # here the only choice is which col metadata property to use # "name", "name_header" and "colidx" # TODO consider exposing this as a plug-in-level option (or maybe bool flag?) return col_metadata['name'] def get_record_columns(self, *args, **kwargs): """ Return the Record object used to fetch, process and expose for comparison table columns. """ rec = CSVTableColumnsRecord(*args, comment_char=self.comment_char, **self.record_opts, **kwargs) rec.load() return rec def get_change_metrics_column(self, *args, **kwargs): """ Return the object used to calculate change metrics for table columns. """ return TableColumnChangeMetrics(*args, **kwargs) def get_change_metrics_column_values(self, *args, **kwargs): """ Return the object used to calculate change metrics for table column values. """ return TableColumnValuesChangeMetrics.from_column_metrics(*args, **kwargs) def compare_table_data(self, src_a, src_b, **kwargs): metr_table = { 'columns': {}, 'values_by_column': {}, } rec_a = self.get_record_columns(src_a) rec_b = self.get_record_columns(src_b) # for each comparison pair of columns from the two tables for comp_key, (col_md_a, col_md_b) in self.gen_comparison_pairs(rec_a, rec_b): # first, calculate the change metrics for the column as a whole metr_single_col = self.get_change_metrics_column(comp_key, col_md_a, col_md_b) metr_single_col.calculate() # then, calculate the change metrics for the column values metr_values = self.get_change_metrics_column_values(metr_single_col) metr_values_data = metr_values.calculate() # finally, add info about change in values to the column change metrics metr_single_col.calculate_from_value_metrics(metr_values.table) metr_table['values_by_column'][comp_key] = metr_values_data metr_table['columns'][comp_key] = dict(metr_single_col) return metr_table def compare(self, path_a, path_b, **kwargs): self._percent_change = np.nan all_metrics = {} all_metrics['file'] = self.compare_file(path_a, path_b) # col_metrics also contains the value-level metrics as a DataFrame # these should be collected separately and merged together in a single table all_metrics['table_data'] = self.compare_table_data(path_a, path_b) return self.get_processed_metrics(all_metrics) def get_processed_metrics(self, metrics): return metrics def percent_change(self): return self._percent_change def get_stats_table(self, table_metrics): all_cols_stats = [] for colname, col_metrics in table_metrics['columns'].items(): col_stats = {'name': colname} col_stats.update(col_metrics['values']) col_stats['status'] = col_metrics['status'] all_cols_stats.append(col_stats) df = pd.DataFrame(all_cols_stats).set_index('name') self._percent_change = df['frac_changed'].mean() * 100 return df detail_level = 1 def stats(self, changes, detail_level=None): from .util import to_json detail_level = detail_level or self.detail_level df_table = self.get_stats_table(changes['table_data']) if detail_level >= 0: # single-number change percentage: print(f'percent change: {self.percent_change():04.2f}%') if detail_level >= 1: with pd.option_context('display.max_columns', None): # overview table with per-column statistics for all columns print(df_table) if detail_level >= 2: # complete change data, assumed to be dict-like (with no assumptions on structure) out = to_json(changes) print(out)
[ 1, 529, 276, 1112, 420, 29958, 9029, 1173, 801, 29914, 29881, 562, 29899, 1171, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 16250, 1053, 2322, 8977, 13, 13, 5215, 12655, 408, 7442, 13, 13, 2202, 29901, 13, 1678, 1053, 11701, 408, 10518, 13, 19499, 16032, 2392, 29901, 13, 1678, 515, 270, 562, 1171, 29889, 3221, 29889, 13239, 1053, 13916, 29918, 5215, 29918, 2704, 13, 1678, 13916, 29918, 5215, 29918, 2704, 29898, 5453, 29918, 978, 2433, 15112, 742, 7079, 29918, 978, 2433, 29907, 7597, 1495, 13, 13, 3166, 270, 562, 1171, 29889, 18307, 1053, 2967, 13, 13, 2202, 29901, 13, 1678, 515, 5641, 1656, 29889, 4990, 1053, 2479, 29871, 396, 363, 13490, 13, 19499, 16032, 2392, 29901, 13, 1678, 2479, 353, 1596, 13, 13, 13, 1990, 10726, 5709, 29901, 13, 1678, 2715, 353, 525, 3035, 2287, 29928, 29915, 13, 1678, 11132, 353, 525, 2287, 1307, 29911, 3352, 29915, 13, 1678, 9120, 353, 525, 6720, 4571, 3738, 3352, 29915, 13, 1678, 443, 15033, 353, 525, 3904, 3210, 24336, 29928, 29915, 13, 1678, 443, 842, 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9401, 322, 8147, 10230, 21556, 515, 963, 29889, 13, 1678, 9995, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1820, 29892, 263, 29922, 8516, 29892, 289, 29922, 8516, 1125, 13, 4706, 1583, 29889, 1989, 353, 1820, 13, 4706, 1583, 29889, 29874, 353, 263, 13, 4706, 1583, 29889, 29890, 353, 289, 13, 13, 4706, 1583, 3032, 510, 20941, 29918, 1272, 353, 426, 13, 9651, 525, 4882, 2396, 903, 29903, 29889, 348, 842, 29892, 13, 9651, 525, 19635, 2396, 426, 13, 18884, 525, 9435, 2396, 24335, 13, 18884, 525, 25990, 2396, 24335, 13, 9651, 2981, 13, 4706, 500, 13, 308, 13, 1678, 822, 4770, 842, 667, 12035, 1311, 29892, 1820, 29892, 659, 1125, 13, 4706, 1583, 3032, 510, 20941, 29918, 1272, 29961, 1989, 29962, 353, 659, 13, 308, 13, 1678, 822, 4770, 657, 667, 12035, 1311, 29892, 1820, 1125, 13, 4706, 736, 1583, 3032, 510, 20941, 29918, 1272, 29961, 1989, 29962, 13, 13, 1678, 822, 6611, 29898, 1311, 1125, 13, 4706, 396, 14402, 5505, 427, 10118, 777, 1797, 29973, 13, 4706, 736, 1583, 3032, 510, 20941, 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core/self6dpp/tools/hb_bdp/lm_pbr_so_mlBCE_2_merge_hbBdpAll_InitResultsWithRefinedPosesToJson.py
THU-DA-6D-Pose-Group/self6dpp
33
97943
<filename>core/self6dpp/tools/hb_bdp/lm_pbr_so_mlBCE_2_merge_hbBdpAll_InitResultsWithRefinedPosesToJson.py import os.path as osp import sys import numpy as np import mmcv from tqdm import tqdm from functools import cmp_to_key cur_dir = osp.dirname(osp.abspath(__file__)) PROJ_ROOT = osp.normpath(osp.join(cur_dir, "../../../../")) sys.path.insert(0, PROJ_ROOT) from lib.pysixd import inout, misc from lib.utils.bbox_utils import xyxy_to_xywh from lib.utils.utils import iprint, wprint id2obj = { 2: "benchvise", 7: "driller", 21: "phone", } # bop 2, hb-v1:1 # bop 7, hb-v1:6 # bop 21, hb-v1:20 obj_num = len(id2obj) obj2id = {_name: _id for _id, _name in id2obj.items()} if __name__ == "__main__": new_res_path = osp.join( PROJ_ROOT, "datasets/hb_bench_driller_phone/init_poses/", "resnest50d_a6_AugCosyAAEGray_BG05_mlBCE_lm_pbr_100e_so_GdrnPose_wYolov4PbrBbox_wDeepimPose_hbBdpAll.json", ) if osp.exists(new_res_path): wprint("{} already exists! overriding!".format(new_res_path)) res_root = "output/deepim/lmPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveV2_Flat_lmPbr_SO/" iter_num_test = 4 pkl_paths = [ "benchvise/inference_model_final_wo_optim-956eb2eb/hb_bdp_benchvise_all_lmK/results.pkl", "driller/inference_model_final_wo_optim-45c5dd36/hb_bdp_driller_all_lmK/results.pkl", "phone/inference_model_final_wo_optim-61aad172/hb_bdp_phone_all_lmK/results.pkl", ] obj_names = [obj for obj in obj2id] new_res_dict = {} for obj_name, pred_name in zip(obj_names, pkl_paths): assert obj_name in pred_name, "{} not in {}".format(obj_name, pred_name) pred_path = osp.join(res_root, pred_name) assert osp.exists(pred_path), pred_path iprint(obj_name, pred_path) # pkl scene_im_id key, list of preds preds = mmcv.load(pred_path) for scene_im_id, pred_list in preds.items(): for pred in pred_list: obj_id = pred["obj_id"] score = pred["score"] bbox_crop = pred["bbox_det_xyxy"] # xyxy bbox_crop_xywh = xyxy_to_xywh(bbox_crop) refined_pose = pred["pose_{}".format(iter_num_test)] pose_est = pred["pose_0"] cur_new_res = { "obj_id": obj_id, "score": float(score), # "bbox_crop": bbox_crop_xywh.tolist(), "bbox_est": bbox_crop_xywh.tolist(), "pose_est": pose_est.tolist(), "pose_refine": refined_pose.tolist(), } if scene_im_id not in new_res_dict: new_res_dict[scene_im_id] = [] new_res_dict[scene_im_id].append(cur_new_res) def mycmp(x, y): # compare two scene_im_id x_scene_id = int(x[0].split("/")[0]) y_scene_id = int(y[0].split("/")[0]) if x_scene_id == y_scene_id: x_im_id = int(x[0].split("/")[1]) y_im_id = int(y[0].split("/")[1]) return x_im_id - y_im_id else: return x_scene_id - y_scene_id new_res_dict_sorted = dict(sorted(new_res_dict.items(), key=cmp_to_key(mycmp))) inout.save_json(new_res_path, new_res_dict_sorted) iprint() iprint("new result path: {}".format(new_res_path))
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emitter_observer.py
HerculesJack/grtrans
25
1604550
<reponame>HerculesJack/grtrans import geokerr_interface import numpy as np from scipy.optimize import minimize g = geokerr_interface.geokerr() def cartesian(u,mu,phi): x = 1./u*np.sqrt(1.-mu**2.)*np.cos(phi) y = 1./u*np.sqrt(1.-mu**2.)*np.sin(phi) z = 1./u*mu return x,y,z def calc_orbit(ab,args): a=args[0]; mu0=args[1]; uf=args[2]; tpr=args[3] u,mu,t,lam,phi,tpm,tpr,i1,i2 = g.run_camera(n=[1,1,2],avals=[ab[0],ab[0],ab[1],ab[1]],a=a,mu0=mu0,standard=1,mufill=0,kext=0,next=0,uf=uf,uout=1e-6,tpr=tpr,offset=0) x,y,z = cartesian(u[0,-1],mu[0,-1],-phi[0,-1]) # print 'calc_orbit: ',a,mu0,uf # print 'calc_orbit: ',u,mu,phi return np.array([x,y,z]),np.array([u[0,-1],mu[0,-1],phi[0,-1],tpm[0,-1],tpr[0,-1],g.sm,g.su]) def calc_sep(xobs,xem): return np.sum((xobs[0]-xem[0])**2.+(xobs[1]-xem[1])**2.+(xobs[2]-xem[2])**2.) def iterate(ab,args): xobs,robs = calc_orbit(ab,args[1:]) sep = calc_sep(xobs,args[0]) # print 'iterate: ',ab,xobs,sep return sep # take first guess from my attempted flat space alpha, beta def run(ustar,mustar,phistar,abguess,a=0.99,mu0=np.cos(45./180.*np.pi),tpr=-1): xs,ys,zs = cartesian(ustar,mustar,phistar) if tpr==-1: tpr=-(np.sign(ys)-1)/2 print 'xs: ',xs,ys,zs,tpr args=[np.array([xs,ys,zs]),a,mu0,ustar,tpr] res = minimize(iterate,abguess,args=args) return res
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titration/utils/devices/temperature_probe.py
kieransukachevin/AlkalinityTitrator
0
179211
import adafruit_max31865 import busio import digitalio from titration.utils import constants class Temperature_Probe: def __init__(self, sck, mosi, miso, cs, wires=2): self.spi = busio.SPI(sck, MOSI=mosi, MISO=miso) self.cs = digitalio.DigitalInOut(cs) self.sensor = adafruit_max31865.MAX31865( self.spi, self.cs, wires=wires, rtd_nominal=constants.TEMPERATURE_NOMINAL_RESISTANCE, ref_resistor=constants.TEMPERATURE_REF_RESISTANCE, ) def get_temperature(self): return self.sensor.temperature def get_resistance(self): return self.sensor.resistance
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pkg_dir/src/utils/notion_utils.py
robperch/robase_datalysis
2
4606
## MODULE WITH UTIL FUNCTIONS - NOTION "----------------------------------------------------------------------------------------------------------------------" ####################################################### Imports ######################################################## "----------------------------------------------------------------------------------------------------------------------" ## Standard library imports import requests ## Third party imports import pandas as pd ## Local application imports from pkg_dir.config.config import ( creds_file_path as crds_loc, ) from pkg_dir.src.utils.general_utils import ( read_yaml, ) "----------------------------------------------------------------------------------------------------------------------" ####################################################### Functions ###################################################### "----------------------------------------------------------------------------------------------------------------------" ## Read notion database with api def notion_api_call(db_api_url, db_id, headers): """ Read notion database with api :param db_api_url (string): base url provided by Notion to make api calls :param db_id (string): unique id of the database that will be read :param headers (dictionary): dict with authorization and version info :return req (?): response after calling notions api """ ## Configuring reading URL read_url = db_api_url + db_id + "/query" ## Requesting info via the API req = requests.request( "POST", read_url, headers=headers ) ## Verifying API call status print("API interaction status code: ", req.status_code) return req ## Calling a Notion database as a json via Notion's API def get_notion_db_json(db_id): """ Calling a Notion database as a json via Notion's API :param db_id (string): unique id of the database that will be called :return db_json (json): json with the notion's db contents """ ## Reading credentials from yaml file yaml_file = read_yaml(crds_loc) notion_version = yaml_file["notion_api"]["notion_version"] db_api_url = yaml_file["notion_api"]["db_api_url"] api_key = yaml_file["notion_api"]["api_key"] ## Building headers for the API call headers = { "Authorization": "Bearer " + api_key, "Notion-Version": notion_version } ## Calling notion's api req = notion_api_call(db_api_url, db_id, headers) ## Converting the api response to a json db_json = req.json() return db_json ## Crating a schema of the notion database that was read def create_notion_db_schema(db_json, relevant_properties): """ Crating a schema of the notion database that was read :param db_json (json): json object obtained by calling notion's api :param relevant_properties (list): list of string with the names of the relevant properties :return db_schema (dictionary): schema of the table that includes the properties' data type """ ## Selecting a sample entry to go over all of it's properties sample_entry = db_json["results"][0]["properties"] ## Bulding dictionary (schema) of the relevant properties and their datatypes db_schema = { prop: { "data_type": sample_entry[prop]["type"] } for prop in sample_entry if prop in relevant_properties } # print(db_schema) return db_schema ## Building a the blueprint dictionary for the dataframe (orient=index) def notion_db_blueprint_df(db_json, db_schema, index_prop): """ Building a the blueprint dictionary for the dataframe (orient=index) :param db_json (json): json object obtained by calling notion's api :return db_schema (dictionary): schema of the table that includes the properties' data type :param index_prop (string): name of the property that will serve as the df's index :return df_dict (dict): dictionary that will be used to create a dataframe with the json contents """ ## Empty dictionary that will store all the results df_dict = {} ## Iterating over every row in the dataframe for row in db_json["results"]: ## Defining the table's base attributes #### All properties contained in the notion db row_props = row["properties"] #### Name of the index; key attribute in the notion db row_name = row_props[index_prop]["title"][0]["plain_text"] #### Empty list to store all the row contents row_contents = [] ## Iterating over every relevant property in the table for col in db_schema: ## Identifying the datatype of the property data_type = db_schema[col]["data_type"] ## Set of conditions to determine how the row will be treated #### Skipping the index row if data_type == "title": continue #### Searching for data in specific locations for special data types (1) elif data_type in ["select", "person", "created_by"]: try: row_contents.append(row_props[col][data_type]["name"]) except: row_contents.append("No_data") #### Searching for data in specific locations for special data types (2) elif data_type in ["rich_text"]: try: row_contents.append(row_props[col][data_type][0]["text"]["content"]) except: row_contents.append("No_data") #### Searching for data in specific locations for special data types (2) elif data_type in ["formula"]: try: #### Applying conditions based on the type of formula result if row_props[col][data_type]["type"] == "string": row_contents.append(row_props[col][data_type]["string"]) elif row_props[col][data_type]["type"] == "number": row_contents.append(row_props[col][data_type]["number"]) except: row_contents.append("No_data") #### General procedure to find data else: row_contents.append(row_props[col][db_schema[col]["data_type"]]) ## Saving the row contents gathered df_dict[row_name] = row_contents return df_dict ## Obtaining a dataframe from a notion database def notion_json_to_df(db_json, relevant_properties): """ Obtaining a dataframe from a notion database :param db_json (json): json object obtained by calling notion's api :param relevant_properties (list): list of string with the names of the relevant properties :return df_n (dataframe): resulting dataframe crated based on the blueprint generated """ ## General parameters needed to build the dataframe #### Database schema db_schema = create_notion_db_schema(db_json, relevant_properties) #### Property that will be used as the dataframe's index index_prop = [prop for prop in db_schema if db_schema[prop]["data_type"] == "title"][0] ## Building a the blueprint dictionary for the dataframe (orient=index) df_dict = notion_db_blueprint_df(db_json, db_schema, index_prop) ## Creating dataframe with the resulting blueprint dictionary #### Crating dataframe df_n = pd.DataFrame.from_dict(df_dict, orient="index") #### Inserting the table's index as a column at the end of the df df_n.insert( df_n.shape[1], index_prop, df_n.index ) #### Resetting index df_n.reset_index(inplace=True, drop=True) #### Adjusting column names df_n.columns = [col_n for col_n in db_schema] return df_n ## Obtaining a Notion database as dataframe with the selected columns def notion_db_to_df(db_id, relevant_properties): """ Obtaining a Notion database as dataframe with the selected columns :param db_id (string): unique id to identify the notion database :param relevant_properties (list): list of string with the names of the relevant properties :return df_n (dataframe): resulting dataframe crated based on the blueprint generated """ ## Calling a Notion database as a json via Notion's API db_json = get_notion_db_json(db_id) ## Obtaining a dataframe from a notion database df_n = notion_json_to_df(db_json, relevant_properties) return df_n "----------------------------------------------------------------------------------------------------------------------" "----------------------------------------------------------------------------------------------------------------------" ## END OF FILE ## "----------------------------------------------------------------------------------------------------------------------" "----------------------------------------------------------------------------------------------------------------------"
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pact/test/test_constants.py
dwang7/pact-python
0
15125
from unittest import TestCase from mock import patch from .. import constants class mock_service_exeTestCase(TestCase): def setUp(self): super(mock_service_exeTestCase, self).setUp() self.addCleanup(patch.stopall) self.mock_os = patch.object(constants, 'os', autospec=True).start() def test_other(self): self.mock_os.name = 'posix' self.assertEqual(constants.mock_service_exe(), 'pact-mock-service') def test_windows(self): self.mock_os.name = 'nt' self.assertEqual(constants.mock_service_exe(), 'pact-mock-service.bat') class provider_verifier_exeTestCase(TestCase): def setUp(self): super(provider_verifier_exeTestCase, self).setUp() self.addCleanup(patch.stopall) self.mock_os = patch.object(constants, 'os', autospec=True).start() def test_other(self): self.mock_os.name = 'posix' self.assertEqual( constants.provider_verifier_exe(), 'pact-provider-verifier') def test_windows(self): self.mock_os.name = 'nt' self.assertEqual( constants.provider_verifier_exe(), 'pact-provider-verifier.bat')
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smart_meter/apps.py
GPXenergy/gpx_server_api
0
35750
<reponame>GPXenergy/gpx_server_api<filename>smart_meter/apps.py from django.apps import AppConfig class SmartMeterConfig(AppConfig): name = 'smart_meter'
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cctbx/dmtbx/boost_python/tst_dmtbx.py
dperl-sol/cctbx_project
155
135036
<filename>cctbx/dmtbx/boost_python/tst_dmtbx.py<gh_stars>100-1000 from __future__ import absolute_import, division, print_function from cctbx import dmtbx from cctbx import sgtbx from cctbx.array_family import flex from libtbx.test_utils import approx_equal def exercise_triplet_generator(): sg = sgtbx.space_group_info("P 41").group() i = flex.miller_index(((1,2,3),(2,3,4))) a = flex.double((1,2)) t = dmtbx.ext.triplet_generator(sg, i, None, 0, False, False) assert t.t_den() == sg.t_den() assert t.max_relations_per_reflection() == 0 assert t.sigma_2_only() == False assert t.discard_weights() == False t = dmtbx.ext.triplet_generator(sg, i, a, 3, True, False) assert t.max_relations_per_reflection() == 3 assert t.sigma_2_only() == True assert t.discard_weights() == False t = dmtbx.ext.triplet_generator(sg, i, None, 0, False, True) assert t.sigma_2_only() == False assert t.discard_weights() == True assert tuple(t.n_relations()) == (0,0) assert t.relations_for(0) == () assert approx_equal(tuple(t.sums_of_amplitude_products(a)), (0,0)) s = flex.bool() r = t.raw_apply_tangent_formula(a, a, s, False, False, 1.e-15) assert approx_equal(tuple(r), (1,2)) i = flex.miller_index(((4,6,0),(5,2,5),(6,1,5))) a = flex.double((1,2,3)) t = dmtbx.ext.triplet_generator(sg, i, None, 0, False, False) assert tuple(t.n_relations()) == (4,2,2) assert [r.format(i, 0) for r in t.relations_for(0)] \ == ["(4,6,0) (5,2,5) %s (6,1,5) %s 3 2" % (True, False), "(4,6,0) (5,2,5) %s (6,1,5) %s 9 2" % (False, True)] assert [r.format(i, 1) for r in t.relations_for(1)] \ == ["(5,2,5) (4,6,0) %s (6,1,5) %s 3 2" % (False, False)] assert [r.format(i, 2) for r in t.relations_for(2)] \ == ["(6,1,5) (4,6,0) %s (5,2,5) %s 9 2" % (False, False)] assert approx_equal(tuple(t.sums_of_amplitude_products(a)), (24,6,4)) t = dmtbx.ext.triplet_generator(sg, i, None, 0, False, True) assert tuple(t.n_relations()) == (1,1,1) assert [r.format(i, 0) for r in t.relations_for(0)] \ == ["(4,6,0) (5,2,5) %s (6,1,5) %s 3 1" % (True, False)] assert approx_equal(tuple(t.sums_of_amplitude_products(a)), (6,3,2)) t = dmtbx.ext.triplet_generator(sg, i, None, 0, False, False) r0 = t.relations_for(0) r1 = t.relations_for(1) assert r0[0].is_sigma_2(0) assert r0[0].is_similar_to(r0[1]) assert not r0[0].is_similar_to(r1[0]) i = flex.miller_index(((4,6,0),(5,1,2))) t = dmtbx.ext.triplet_generator(sg, i, None, 0, False, False) assert [r.format(i, 0) for r in t.relations_for(0)] \ == ["(4,6,0) (5,1,2) %s (5,1,2) %s 6 4" % (False, True)] assert [r.format(i, 1) for r in t.relations_for(1)] \ == ["(5,1,2) (4,6,0) %s (5,1,2) %s 6 4" % (False, False)] assert not t.relations_for(0)[0].is_sigma_2(0) t = dmtbx.ext.triplet_generator(sg, i, None, 0, False, True) assert [r.format(i, 0) for r in t.relations_for(0)] \ == ["(4,6,0) (5,1,2) %s (5,1,2) %s 6 1" % (False, True)] assert [r.format(i, 1) for r in t.relations_for(1)] \ == ["(5,1,2) (4,6,0) %s (5,1,2) %s 6 1" % (False, False)] t = dmtbx.ext.triplet_generator(sg, i, None, 0, True, False) assert tuple(t.n_relations()) == (0,0) def run(): exercise_triplet_generator() print("OK") if (__name__ == "__main__"): run()
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shadowsocks/util.py
lyrl/ssmgr-ssrest
0
109580
#!/usr/bin/env python # -*- encoding: utf-8 -*- # Created on 2016-07-26 11:04:34 import socket import urllib2 import json import requests import logging def is_port_using(port): ''' https://docs.python.org/2/library/socket.html#example ''' while True: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: s.bind(('', port)) ## Try to open port except socket.error as e: if e.errno is 98: ## Errorno 98 means address already bound return True else: print e s.close() return False def send_post(url, data): """ HTTP POST Util Args: url (str): resource url eg: http://xxxx.com/api/users data (dict): post body Returns: str: response content """ logging.debug("send data %s to url %s !" % (json.dumps(data), url)) headers = {'content-type': 'application/json'} try: response = requests.post(url, data=json.dumps(data), headers=headers) except requests.exceptions.ConnectionError as e: logging.error(e.message) raise HttpUtilException("Failed to establish a new connection!") logging.debug("return code: %s content: %s" % (response.status_code, response.content)) return response.content class UtilException(Exception): def __init__(self, msg): self.message = msg def __str__(self): return self.message class HttpUtilException(UtilException): def __init__(self, msg): self.message = msg def __str__(self): return self.message if __name__ == '__main__': # port = 1080 # b = is_port_using(port) # print("Is port {0} open {1}".format(port,b)) logging.basicConfig(level=5, format='%(asctime)s [%(module)-16s] %(levelname)-8s %(message)s', datefmt='%Y-%m-%d %H:%M:%S') send_post('http://baidu21231.com', {"test": "test"})
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qqbot_ipc.py
AndrewWayne/bot-flandre
0
154965
# -*- coding: utf-8 -*- """ Created on Wed Feb 26 16:52:29 2020 @author: eliphat """ import win32api import win32con import win32gui import time hwnd = win32gui.FindWindow("TXGuiFoundation", "bot/ctf交流") print(hwnd) def send_msg(s): for c in s: win32api.SendMessage(hwnd, win32con.WM_CHAR, ord(c), 0) time.sleep(0.02) print(s) win32api.SendMessage(hwnd, win32con.WM_KEYDOWN, win32con.VK_RETURN, 0) win32api.SendMessage(hwnd, win32con.WM_KEYUP, win32con.VK_RETURN, 0) print(s) # send_msg("强行中文")
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scripts/opendict_to_json.py
md84419/English-to-IPA
0
1605687
<reponame>md84419/English-to-IPA #!/usr/bin/python # USAGE: # PYTHONPATH=".." python opendict_to_json.py ../eng_to_ipa/resources/Opendict_source_files/en_UK.txt > ../eng_to_ipa/resources/Open_dict.json import getopt, json, io, os, re, sys, subprocess from signal import signal, SIGPIPE, SIG_DFL from eng_to_ipa import tokenize signal(SIGPIPE, SIG_DFL) debug = False debug2 = False SYMBOLS_FILE1 = "opendict.symbols.txt" tokenize.configure( os.path.join(os.path.abspath(os.path.dirname(__file__)), '..','eng_to_ipa','resources','Opendict_source_files',SYMBOLS_FILE1) ) def main(argv): global debug, debug2 input_file = None output_file = None try: opts, args = getopt.getopt(argv, "dDo:") except getopt.GetoptError: print( "{0}: syntax: [-d|-D] [-o output.json] input.txt".format( sys.argv[0]) ) sys.exit(2) for opt, arg in opts: if opt == '-d': debug2 = True if debug2: print( "debug level 1" ) elif opt == '-D': debug = True debug2 = True if debug2: print( "debug level 2" ) elif opt == '-o': output_file = arg if debug2: print( "output_file is {0}".format( output_file ) ) try: input_file = args[0] except: print( "{0}: syntax: [-d|-D] [-o output.json] input.txt".format( sys.argv[0]) ) sys.exit(2) p_out = subprocess.check_output( ['awk', '-f', 'txt_to_json.awk', input_file] ) open_dict = json.loads( p_out ) fix_opendict_words( open_dict ) for key in open_dict: #if debug2: print( open_dict[key][0] ) for idx in range( len( open_dict[key] ) ): open_dict[key][idx] = fix_opendict( open_dict[key][idx] ) open_dict[key][idx] = tokenize.tokenize( open_dict[key][idx], 'ˑ', 'symbols' ) #if debug2: print( "{0} {1}".format( key, open_dict[key][0] ) ) if( output_file != None ): try: with open(output_file, 'wb') as o_fp: json.dump( open_dict, o_fp, check_circular=True, indent=None, sort_keys=True, separators=[',\n', ':'], ensure_ascii=False ) sys.exit(0) except TypeError: pass j = json.dumps( open_dict, check_circular=True, indent=None, sort_keys=True, separators=[',\n', ':'], ensure_ascii=False ) print( j ) sys.exit(0) def fix_opendict(source): destination = source # Map phonetic to phonemic; Anglicise IPA for foreign words (croissant, denouement, frisson, etc) destination = destination.replace("ɐ", "ʌ") destination = destination.replace("ɡ", "g") destination = destination.replace("ɔ̃", "ɔ") destination = destination.replace("ɑ̃", "ɒn") destination = destination.replace("ɹ", "r") destination = destination.replace("ˈɛ", "ˈe") destination = destination.replace("ˌɛ", "ˌe") destination = destination.replace("ɛr", "eə") destination = destination.replace("ˈɛː", "ˈeə") destination = destination.replace("ˌɛː", "ˌeə") destination = destination.replace("ɛː", "eə") destination = destination.replace("ɛ", "e") #mark the diphthongs with the non-breaking space character destination = destination.replace("aɪ", "a‍ɪ") # 39 destination = destination.replace("aʊ", "a‍ʊ") # 45 destination = destination.replace("eə", "e‍ə") # 42 destination = destination.replace("eɪ", "e‍ɪ") # 38 destination = destination.replace("ɔɪ", "ɔ‍ɪ") # 40 destination = destination.replace("əʊ", "ə‍ʊ") # 45 destination = destination.replace("ɛə", "ɛ‍ə") # 42 destination = destination.replace("ɪə", "ɪ‍ə") # 41 destination = destination.replace("ʊə", "ʊ‍ə") # 43 # mark the affricates with the non-breaking space character destination = destination.replace("dʒ", "d‍ʒ") destination = destination.replace("tʃ", "t‍ʃ") # mark US English diphthong /ie/ - we'll undo this again in create_en_GB_dict destination = destination.replace("iə", "i‍ə") # unmark US English /əl/ destination = destination.replace("ə‍l", "əl") # Use the standard (quantitative-qualitative) IPA notation scheme for vowels # See section 5 of https://www.phon.ucl.ac.uk/home/wells/ipa-english-uni.htm destination = re.sub("ɑ(?!‍)", "ɑː", destination) destination = destination.replace("ɒː", "ɒ") destination = re.sub("i(?!‍)", "iː", destination) destination = re.sub("ɔ(?!‍)", "ɔː", destination) destination = re.sub("u(?!‍)", "uː", destination) destination = destination.replace("ːː", "ː") # Replace US diphthongs with UK diphthongs in UK dictionary destination = destination.replace("ɔ‍ɪ‍ə‍ʊ", "ɔ‍ɪə‍ʊ") destination = destination.replace("ɔ‍ɪ‍ə‍ʊ", "ɔ‍ɪə‍ʊ") destination = destination.replace("e‍ɪ‍ə‍ʊ", "e‍ɪə‍ʊ") destination = destination.replace("a‍ɪ‍ə‍ʊ", "a‍ɪə‍ʊ") #Split triphthongs into diphthong followed by schwa or ʊ #'a‍ɪ‍ə', 'a‍ʊ‍ə', 'e‍ɪ‍ə', 'e‍ə‍ʊ', 'ɔ‍ɪ‍ə', 'ɪ‍ə‍ʊ', 'ə‍ʊ‍ə' destination = destination.replace("a‍ɪ‍ə", "a‍ɪə") destination = destination.replace("a‍ʊ‍ə", "a‍ʊə") destination = destination.replace("e‍ɪ‍ə", "e‍ɪə") destination = destination.replace("e‍ə‍ʊ", "e‍əʊ") destination = destination.replace("ɔ‍ɪ‍ə", "ɔ‍ɪə") destination = destination.replace("ɪ‍ə‍ʊ", "ɪ‍əʊ") destination = destination.replace("ə‍ʊ‍ə", "ə‍ʊə") return destination # fix whole words def fix_opendict_words( dct ): dct.update({"ablation": ["ʌblˈe‍ɪʃən","ʌblˈe‍ɪʃn"]}) #dct.update({"abdominal": ["æbdˈɒmənəl"]}) dct.update({"llanos": ["lænˈə‍ʊz"]}) dct.update({"denouement": ["de‍ɪnˈuːmɔː"]}) dct.update({"argyll": ["ˈɑːga‍ɪl"]}) dct.update({"croissant": ["kwæsɒŋ","kwæsɒn"]}) dct.update({"croissants": ["kwæsɒŋs","kwæsɒns"]}) dct.update({"bach": ["bˈɒx"]}) dct.update({"visually": ["vˈɪʒəˈliː"]}) dct.update({"aba":["æbˈæ"]}) dct.update({"abalone":["æbˈælə\u200dʊn"]}) if( __name__ == "__main__"): main(sys.argv[1:])
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tests/helpers/imports.py
neptune-ml/pytorch-lightning
15,666
144950
<reponame>neptune-ml/pytorch-lightning<gh_stars>1000+ import operator from pytorch_lightning.utilities.imports import _compare_version, _TORCHTEXT_LEGACY if _TORCHTEXT_LEGACY: if _compare_version("torchtext", operator.ge, "0.9.0"): from torchtext.legacy.data import Batch, Dataset, Example, Field, Iterator, LabelField else: from torchtext.data import Batch, Dataset, Example, Field, Iterator, LabelField else: Batch = type(None) Dataset = type(None) Example = type(None) Field = type(None) Iterator = type(None) LabelField = type(None)
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facturapi/types/exc.py
cuenca-mx/facturapi-python
1
176059
<filename>facturapi/types/exc.py from dataclasses import dataclass from typing import Any, Dict class FacturapiException(Exception): ... class NoResultFound(FacturapiException): """No results where found""" class MultipleResultsFound(FacturapiException): """One result was expected but multiple were returned""" @dataclass class FacturapiResponseException(FacturapiException): json: Dict[str, Any] status_code: int def __str__(self) -> str: return repr(self)
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db.py
Technik-Tueftler/TeTueTwitchBot
2
168437
#!/usr/bin/env python3 # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Integer, ForeignKey, TIMESTAMP, func, and_, or_ from sqlalchemy.sql import text from sqlalchemy.orm import sessionmaker, relationship, query, backref from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import create_engine import tetueSrc read_successful, cfg = tetueSrc.get_configuration("database") Base = declarative_base() engine = create_engine( f'mariadb+mariadbconnector://{cfg["user"]}:{cfg["password"]}@{cfg["host"]}:{cfg["port"]}/{cfg["database"]}') class Award(Base): __tablename__ = 'awards' id = Column(Integer, primary_key=True) award_user_id = Column(Integer, ForeignKey("users.id")) sponsor_points = Column(Integer, nullable=False, default=0) tinker_points = Column(Integer, nullable=False, default=0) battle_points = Column(Integer, nullable=False, default=0) fitness_points = Column(Integer, nullable=False, default=0) water_points = Column(Integer, nullable=False, default=0) class Quote(Base): __tablename__ = 'quotes' id = Column(Integer, primary_key=True) create_user_id = Column(Integer, ForeignKey("users.id")) author_user_id = Column(Integer, ForeignKey("users.id")) quote_date = Column(TIMESTAMP(timezone=False), nullable=False, server_default=text("NOW()")) quote = Column(String(500), nullable=False) class Raid(Base): __tablename__ = 'raids' id = Column(Integer, primary_key=True) raider_user_id = Column(Integer, ForeignKey("users.id")) raid_date = Column(TIMESTAMP(timezone=False), nullable=False, server_default=text("NOW()")) number_of_raiders = Column(Integer) """class GameStatistic(Base): __tablename__ = 'gamestatistics' id = Column(Integer, primary_key=True) gamer_user_id = Column(Integer, ForeignKey("users.id")) game_date = Column(TIMESTAMP(timezone=False), nullable=False, server_default=text("NOW()")) game_type = Column(String(100), nullable=False) win = Column(Integer, nullable=False, default=0) lost = Column(Integer, nullable=False, default=0)""" """class Phrase(Base): __tablename__ = 'phrases' id = Column(Integer, primary_key=True) explorer_user_id = Column(Integer, ForeignKey("users.id")) phrase_date = Column(TIMESTAMP(timezone=False), nullable=False, server_default=text("NOW()")) phrase = Column(String(500), nullable=False)""" class Category(Base): __tablename__ = 'categorys' id = Column(Integer, primary_key=True) category_name = Column(String(100), nullable=False) win_label = Column(String(100), nullable=False) wins = Column(Integer, nullable=False, default=0) lost_label = Column(String(100), nullable=False) lost = Column(Integer, nullable=False, default=0) bit_type = Column(String(100), nullable=False, default='channel') bits = Column(Integer, nullable=False, default=0) welcome_text = Column(String(200)) class User(Base): __tablename__ = 'users' id = Column(Integer, primary_key=True) twitch_user_id = Column(String(50)) twitch_user_name = Column(String(50)) messages_sent = Column(Integer, nullable=False, default=0) number_of_logins = Column(Integer, nullable=False, default=1) last_visit = Column(TIMESTAMP(timezone=False), nullable=False, server_default=text("NOW()")) loyalty_points = Column(Integer, nullable=False, default=0) eggs = Column(Integer, nullable=False, default=0) egg_lock = Column(TIMESTAMP(timezone=False), nullable=False, server_default=text("NOW()")) warnings = Column(Integer, nullable=False, default=0) lost_counter = Column(Integer, nullable=False, default=0) stream_badges = Column(String(50), nullable=False, default="Tueftlie") hen_name = Column(String(50)) smart_counter = Column(Integer, nullable=False, default=0) honor_counter = Column(Integer, nullable=False, default=0) name_pronunciation = Column(String(50)) shoutout_text = Column(String(500)) # Relationships raids = relationship('Raid', backref='user') # quotes = relationship('Quote', backref='user_quote') session = sessionmaker() session.configure(bind=engine) Base.metadata.create_all(engine) session = session() def add_new_user(twitch_user_id: str, twitch_user_name: str) -> None: new_user = User(twitch_user_id=twitch_user_id, twitch_user_name=twitch_user_name) session.add(new_user) session.commit() def delete_user(user): user_db_for_delete = session.query(User).filter(User.id == user.db_data.id).first() user_db_for_delete.twitch_user_id = 0 user_db_for_delete.twitch_user_name = "gans" user_db_for_delete.messages_sent = 0 user_db_for_delete.number_of_logins = 0 user_db_for_delete.loyalty_points = 0 user_db_for_delete.eggs = 0 user_db_for_delete.warnings = 0 user_db_for_delete.lost_counter = 0 user_db_for_delete.stream_badges = None user_db_for_delete.hen_name = None user_db_for_delete.smart_counter = 0 user_db_for_delete.honor_counter = 0 user_db_for_delete.name_pronunciation = None user_db_for_delete.shoutout_text = None session.commit() def add_new_quote(create_user_id: int, author_user_id: id, quote: str) -> None: session.add(Quote(create_user_id=create_user_id, author_user_id=author_user_id, quote=quote)) session.commit() def check_category_exist_or_create(name: str, win_label: str, lose_label: str, bit_type: str, welcome_text: str ) -> Category: category = session.query(Category).filter(Category.category_name == name).first() if category is not None: return category new_category = Category(category_name=name, win_label=win_label, lost_label=lose_label, bit_type=bit_type, welcome_text=welcome_text) session.add(new_category) session.commit() return new_category def check_award_exist_or_create(user_id: int) -> Award: award = session.query(Award).filter(Award.award_user_id == user_id).first() if award is not None: return award new_award = Award(award_user_id=user_id) session.add(new_award) session.commit() return new_award def check_user_from_name_exist(twitch_user_name: str) -> bool: user_db = session.query(User).filter(User.twitch_user_name == twitch_user_name).first() if user_db is not None: return True return False def first_value_with_id(attribute, search_column, search_value): return session.query(attribute).filter(search_column == search_value).first()[0] def update_user_attribute_with_id(user_id: int, attribute, value) -> None: session.query(User).filter(User.id == user_id).update({attribute: value}) session.commit() def query_auto_vip() -> query.Query: return session.query(User). \ filter(User.stream_badges == 'Tueftlie'). \ order_by(User.warnings.asc(), User.number_of_logins.desc(), User.honor_counter.desc(), User.loyalty_points.desc(), User.eggs.desc()). \ limit(tetueSrc.get_int_element("autovip", "num_max_auto_vips_loyalty")) def query_raid_vip() -> query.Query: return session.query(User.twitch_user_name, User.id). \ join(Raid). \ filter(and_(User.stream_badges == 'Tueftlie', User.warnings == 0)). \ group_by(Raid.raider_user_id). \ order_by(func.count(Raid.raider_user_id).desc(), func.sum(Raid.number_of_raiders).desc()). \ limit(tetueSrc.get_int_element("autovip", "num_max_auto_vips_raid")) def main(): """user = session.query(User).filter(User.id == 79).first() print(user.id) print(user.twitch_user_name) print(user.raids) for element in user.raids: print(element.number_of_raiders) print(element.id) quote = session.query(Quote).filter(Quote.id == 3).first() print(quote.quote)""" """test2 = any([x[0] for x in session.query(Category).filter(Category.category_name == "Science & Technology").all()]) print(test2) if test2 is False: add_new_category("Science & Technology", "Features", "Bugs", "technology", "Viel Spaß beim mittüfteln.") wins = session.query(Category).filter(Category.category_name == "Populous: The Beginning").first() print(wins.win_label)""" # args = ("1", "2", "3") # print(*args[1:]) """user_statistic_list = session.query(User). \ filter(and_(User.twitch_user_name != 'technik_tueftler', User.twitch_user_name != 'gans', User.twitch_user_name != 'streamelements')). \ order_by(User.warnings.asc(), User.number_of_logins.desc(), User.honor_counter.desc(), User.loyalty_points.desc(), User.eggs.desc()).all() print(f'Größe: {len(user_statistic_list)}') print(f'Typ: {type(user_statistic_list)}') user_test = session.query(User).filter(User.twitch_user_name == "kongfurakete").first() try: # search for the item index = user_statistic_list.index(user_test) print('The index of', user_test, 'in the list is:', index) except ValueError: print('item not present')""" test = tetueSrc.get_string_list_only_section("output_text") print(test) list2 = list() for _, value in test.items(): if value['active']: list2 = list2 + value['commands'] print(list2) list3 = list() list3 = [element for key, value in test.items() for element in value['commands'] if value['active']] print(list3) cmd = "wgh" key = [key for key, value in test.items() if cmd in value['commands']] print(key) if len(key) == 0: print("NOPE") #print(test[key[0]]) #print(test[key[0]]["output_string"]) if __name__ == "__main__": main()
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examples/collect_match_result.py
ceb10n/okerr
0
1616307
<reponame>ceb10n/okerr from okerr import Err, Ok, Match class User: def __init__(self, username): self.username = username def __str__(self): return f"I'm {username} and I'm authenticated ;)" def authenticate(username, password): if username == 'ok' and password == '<PASSWORD>': user = User(username) return Ok(user) return Err("You're not allowed here!") def awesome_authenticated_stuff(user: User) -> User: return user def awful_result(msg: str): return msg if __name__ == '__main__': username = input('username: ') password = input('password: ') result = authenticate(username, password) collected_value = Match(result) \ .ok(awesome_authenticated_stuff, result.value) \ .err(awful_result, result.value) \ .collect() print(collected_value)
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labelit/active_learning/scorer/allconv.py
shibing624/labelit
5
135805
<reponame>shibing624/labelit # Copyright 2017 Google Inc. # # 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. """Implements allconv model in keras using tensorflow backend.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import keras import keras.backend as K from keras.layers import Activation from keras.layers import Conv2D from keras.layers import Dropout from keras.layers import GlobalAveragePooling2D from keras.models import Sequential import numpy as np import tensorflow as tf class AllConv(object): """allconv network that matches sklearn api.""" def __init__(self, random_state=1, epochs=50, batch_size=32, solver='rmsprop', learning_rate=0.001, lr_decay=0.): # params self.solver = solver self.epochs = epochs self.batch_size = batch_size self.learning_rate = learning_rate self.lr_decay = lr_decay # data self.encode_map = None self.decode_map = None self.model = None self.random_state = random_state self.n_classes = None def build_model(self, X): # assumes that data axis order is same as the backend input_shape = X.shape[1:] np.random.seed(self.random_state) tf.set_random_seed(self.random_state) model = Sequential() model.add(Conv2D(96, (3, 3), padding='same', input_shape=input_shape, name='conv1')) model.add(Activation('relu')) model.add(Conv2D(96, (3, 3), name='conv2', padding='same')) model.add(Activation('relu')) model.add(Conv2D(96, (3, 3), strides=(2, 2), padding='same', name='conv3')) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Conv2D(192, (3, 3), name='conv4', padding='same')) model.add(Activation('relu')) model.add(Conv2D(192, (3, 3), name='conv5', padding='same')) model.add(Activation('relu')) model.add(Conv2D(192, (3, 3), strides=(2, 2), name='conv6', padding='same')) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Conv2D(192, (3, 3), name='conv7', padding='same')) model.add(Activation('relu')) model.add(Conv2D(192, (1, 1), name='conv8', padding='valid')) model.add(Activation('relu')) model.add(Conv2D(10, (1, 1), name='conv9', padding='valid')) model.add(GlobalAveragePooling2D()) model.add(Activation('softmax', name='activation_top')) model.summary() try: optimizer = getattr(keras.optimizers, self.solver) except: raise NotImplementedError('optimizer not implemented in keras') # All optimizers with the exception of nadam take decay as named arg try: opt = optimizer(lr=self.learning_rate, decay=self.lr_decay) except: opt = optimizer(lr=self.learning_rate, schedule_decay=self.lr_decay) model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) # Save initial weights so that model can be retrained with same # initialization self.initial_weights = copy.deepcopy(model.get_weights()) self.model = model def create_y_mat(self, y): y_encode = self.encode_y(y) y_encode = np.reshape(y_encode, (len(y_encode), 1)) y_mat = keras.utils.to_categorical(y_encode, self.n_classes) return y_mat # Add handling for classes that do not start counting from 0 def encode_y(self, y): if self.encode_map is None: self.classes_ = sorted(list(set(y))) self.n_classes = len(self.classes_) self.encode_map = dict(zip(self.classes_, range(len(self.classes_)))) self.decode_map = dict(zip(range(len(self.classes_)), self.classes_)) mapper = lambda x: self.encode_map[x] transformed_y = np.array(map(mapper, y)) return transformed_y def decode_y(self, y): mapper = lambda x: self.decode_map[x] transformed_y = np.array(map(mapper, y)) return transformed_y def fit(self, X_train, y_train, sample_weight=None): y_mat = self.create_y_mat(y_train) if self.model is None: self.build_model(X_train) # We don't want incremental fit so reset learning rate and weights K.set_value(self.model.optimizer.lr, self.learning_rate) self.model.set_weights(self.initial_weights) self.model.fit( X_train, y_mat, batch_size=self.batch_size, epochs=self.epochs, shuffle=True, sample_weight=sample_weight, verbose=0) def predict(self, X_val): predicted = self.model.predict(X_val) return predicted def score(self, X_val, val_y): y_mat = self.create_y_mat(val_y) val_acc = self.model.evaluate(X_val, y_mat)[1] return val_acc def decision_function(self, X): return self.predict(X) def transform(self, X): model = self.model inp = [model.input] activations = [] # Get activations of the last conv layer. output = [layer.output for layer in model.layers if layer.name == 'conv9'][0] func = K.function(inp + [K.learning_phase()], [output]) for i in range(int(X.shape[0] / self.batch_size) + 1): minibatch = X[i * self.batch_size : min(X.shape[0], (i + 1) * self.batch_size)] list_inputs = [minibatch, 0.] # Learning phase. 0 = Test mode (no dropout or batch normalization) layer_output = func(list_inputs)[0] activations.append(layer_output) output = np.vstack(tuple(activations)) output = np.reshape(output, (output.shape[0], np.product(output.shape[1:]))) return output def get_params(self, deep=False): params = {} params['solver'] = self.solver params['epochs'] = self.epochs params['batch_size'] = self.batch_size params['learning_rate'] = self.learning_rate params['weight_decay'] = self.lr_decay if deep: return copy.deepcopy(params) return copy.copy(params) def set_params(self, **parameters): for parameter, value in parameters.items(): setattr(self, parameter, value) return self
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tests/tools/test_openvino2tf.py
fixstars/arachne
3
108167
import os import subprocess import sys import tarfile import tempfile from dataclasses import asdict import numpy as np import onnxruntime as ort import tensorflow as tf import yaml from tvm.contrib.download import download from arachne.data import ModelSpec, TensorSpec from arachne.tools.openvino2tf import OpenVINO2TF, OpenVINO2TFConfig from arachne.tools.openvino_mo import OpenVINOModelOptConfig, OpenVINOModelOptimizer from arachne.utils.model_utils import init_from_file from arachne.utils.tf_utils import make_tf_gpu_usage_growth def check_openvino2tf_output(onnx_model_path, tf_model_path): tf_loaded = tf.saved_model.load(tf_model_path) resnet18_tf = tf_loaded.signatures["serving_default"] # type: ignore input = np.random.rand(1, 3, 224, 224).astype(np.float32) # type: ignore # onnx runtime sess = ort.InferenceSession(onnx_model_path, providers=["CPUExecutionProvider"]) input_name = sess.get_inputs()[0].name dout = sess.run(output_names=None, input_feed={input_name: input})[0] # tf tf_input = tf.convert_to_tensor(np.transpose(input, (0, 2, 3, 1))) tf_result = resnet18_tf(tf_input) aout = tf_result["tf.identity"].numpy() np.testing.assert_allclose(aout, dout, atol=1e-5, rtol=1e-5) # type: ignore def test_openvino2tf(): with tempfile.TemporaryDirectory() as tmp_dir: os.chdir(tmp_dir) url = ( "https://arachne-public-pkgs.s3.ap-northeast-1.amazonaws.com/models/test/resnet18.onnx" ) onnx_model_path = "resnet18.onnx" download(url, onnx_model_path) input_model = init_from_file(onnx_model_path) m = OpenVINOModelOptimizer.run(input_model, OpenVINOModelOptConfig()) m = OpenVINO2TF.run(m, OpenVINO2TFConfig()) check_openvino2tf_output(onnx_model_path, m.path) def test_cli(): # Due to the test time, we only test one case make_tf_gpu_usage_growth() with tempfile.TemporaryDirectory() as tmp_dir: os.chdir(tmp_dir) url = ( "https://arachne-public-pkgs.s3.ap-northeast-1.amazonaws.com/models/test/resnet18.onnx" ) onnx_model_path = "resnet18.onnx" download(url, onnx_model_path) ret = subprocess.run( [ sys.executable, "-m", "arachne.driver.cli", "+tools=openvino_mo", "model_file=resnet18.onnx", "output_path=output.tar", ] ) assert ret.returncode == 0 model_path = None with tarfile.open("output.tar", "r:gz") as tar: for m in tar.getmembers(): if m.name.endswith("_0"): model_path = m.name tar.extractall(".") assert model_path is not None spec = ModelSpec( inputs=[TensorSpec(name="input0", shape=[1, 3, 224, 224], dtype="float32")], outputs=[TensorSpec(name="output0", shape=[1, 1000], dtype="float32")], ) with open("spec.yaml", "w") as file: yaml.dump(asdict(spec), file) ret2 = subprocess.run( [ sys.executable, "-m", "arachne.driver.cli", "+tools=openvino2tf", f"model_dir={model_path}", "model_spec_file=spec.yaml", "output_path=output2.tar", ] ) assert ret2.returncode == 0 with tarfile.open("output2.tar", "r:gz") as tar: for m in tar.getmembers(): if m.name.endswith("saved_model"): model_path = m.name tar.extractall(".") check_openvino2tf_output(onnx_model_path, model_path)
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lumin/nn/models/blocks/body.py
kiryteo/lumin
0
176036
from typing import Optional, Callable, Any, List, Dict import numpy as np from functools import partial import torch.nn as nn import torch from torch import Tensor from ..layers.activations import lookup_act from ..initialisations import lookup_normal_init from .abs_block import AbsBlock __all__ = ['FullyConnected', 'MultiBlock'] class AbsBody(AbsBlock): def __init__(self, n_in:int, feat_map:Dict[str,List[int]], lookup_init:Callable[[str,Optional[int],Optional[int]],Callable[[Tensor],None]]=lookup_normal_init, lookup_act:Callable[[str],Any]=lookup_act, freeze:bool=False, bn_class:Callable[[int],nn.Module]=nn.BatchNorm1d): super().__init__(lookup_init=lookup_init, freeze=freeze) self.n_in,self.feat_map,self.lookup_act,self.bn_class = n_in,feat_map,lookup_act,bn_class class FullyConnected(AbsBody): r''' Fully connected set of hidden layers. Designed to be passed as a 'body' to :class:`~lumin.nn.models.model_builder.ModelBuilder`. Supports batch normalisation and dropout. Order is dense->activation->BN->DO, except when res is true in which case the BN is applied after the addition. Can optionaly have skip connections between each layer (res=true). Alternatively can concatinate layers (dense=true) growth_rate parameter can be used to adjust the width of layers according to width+(width*(depth-1)*growth_rate) Arguments: n_in: number of inputs to the block feat_map: dictionary mapping input features to the model to outputs of head block depth: number of hidden layers. If res==True and depth is even, depth will be increased by one. width: base width of each hidden layer do: if not None will add dropout layers with dropout rates do bn: whether to use batch normalisation act: string representation of argument to pass to lookup_act res: whether to add an additative skip connection every two dense layers. Mutually exclusive with dense. dense: whether to perform layer-wise concatinations after every layer. Mutually exclusion with res. growth_rate: rate at which width of dense layers should increase with depth beyond the initial layer. Ignored if res=True. Can be negative. lookup_init: function taking choice of activation function, number of inputs, and number of outputs an returning a function to initialise layer weights. lookup_act: function taking choice of activation function and returning an activation function layer freeze: whether to start with module parameters set to untrainable bn_class: class to use for BatchNorm, default is `nn.BatchNorm1d` Examples:: >>> body = FullyConnected(n_in=32, feat_map=head.feat_map, depth=4, ... width=100, act='relu') >>> >>> body = FullyConnected(n_in=32, feat_map=head.feat_map, depth=4, ... width=200, act='relu', growth_rate=-0.3) >>> >>> body = FullyConnected(n_in=32, feat_map=head.feat_map, depth=4, ... width=100, act='swish', do=0.1, res=True) >>> >>> body = FullyConnected(n_in=32, feat_map=head.feat_map, depth=6, ... width=32, act='selu', dense=True, ... growth_rate=0.5) >>> >>> body = FullyConnected(n_in=32, feat_map=head.feat_map, depth=6, ... width=50, act='prelu', bn=True, ... lookup_init=lookup_uniform_init) ''' def __init__(self, n_in:int, feat_map:Dict[str,List[int]], depth:int, width:int, do:float=0, bn:bool=False, act:str='relu', res:bool=False, dense:bool=False, growth_rate:int=0, lookup_init:Callable[[str,Optional[int],Optional[int]],Callable[[Tensor],None]]=lookup_normal_init, lookup_act:Callable[[str],Any]=lookup_act, freeze:bool=False, bn_class:Callable[[int],nn.Module]=nn.BatchNorm1d): super().__init__(n_in=n_in, feat_map=feat_map, lookup_init=lookup_init, lookup_act=lookup_act, freeze=freeze, bn_class=bn_class) self.depth,self.width,self.do,self.bn,self.act,self.res,self.dense,self.growth_rate = depth,width,do,bn,act,res,dense,growth_rate if self.res: self.depth = 1+int(np.floor(self.depth/2)) # One upscale layer + each subsequent block will contain 2 layers self.res_bns = nn.ModuleList([self.bn_class(self.width) for d in range(self.depth-1)]) self.layers = nn.ModuleList([self._get_layer(idx=d, fan_in=self.width, fan_out=self.width) if d > 0 else self._get_layer(idx=d, fan_in=self.n_in, fan_out=self.width) for d in range(self.depth)]) elif self.dense: self.layers = [] for d in range(self.depth): self.layers.append(self._get_layer(idx=d, fan_in=self.n_in if d == 0 else self.n_in+np.sum([l[0].out_features for l in self.layers]), fan_out=max(1,self.width+int(self.width*d*self.growth_rate)))) self.layers = nn.ModuleList(self.layers) else: self.layers = nn.Sequential(*[self._get_layer(idx=d, fan_in=self.width+int(self.width*(d-1)*self.growth_rate), fan_out=self.width+int(self.width*d*self.growth_rate)) if d > 0 else self._get_layer(idx=d, fan_in=self.n_in, fan_out=self.width) for d in range(self.depth)]) if self.freeze: self.freeze_layers() def _get_layer(self, idx:int, fan_in:Optional[int]=None, fan_out:Optional[int]=None) -> nn.Module: fan_in = self.width if fan_in is None else fan_in fan_out = self.width if fan_out is None else fan_out if fan_in < 1: fan_in = 1 if fan_out < 1: fan_out = 1 layers = [] for i in range(2 if self.res and idx > 0 else 1): layers.append(nn.Linear(fan_in, fan_out)) self.lookup_init(self.act, fan_in, fan_out)(layers[-1].weight) nn.init.zeros_(layers[-1].bias) if self.act != 'linear': layers.append(self.lookup_act(self.act)) if self.bn and i == 0: layers.append(self.bn_class(fan_out)) # In case of residual, BN will be added after addition if self.do: if self.act == 'selu': layers.append(nn.AlphaDropout(self.do)) else: layers.append(nn.Dropout(self.do)) return nn.Sequential(*layers) def forward(self, x:Tensor) -> Tensor: if self.dense: for l in self.layers[:-1]: x = torch.cat((l(x), x), -1) x = self.layers[-1](x) elif self.res: for i, l in enumerate(self.layers): if i > 0: x = l(x)+x x = self.res_bns[i-1](x) # Renormalise after addition else: x = l(x) else: x = self.layers(x) return x def get_out_size(self) -> int: r''' Get size width of output layer Returns: Width of output layer ''' return self.layers[-1][0].out_features class MultiBlock(AbsBody): r''' Body block allowing outputs of head block to be split amongst a series of body blocks. Output is the concatination of all sub-body blocks. Optionally, single-neuron 'bottleneck' layers can be used to pass an input to each sub-block based on a learned function of the input features that block would otherwise not receive, i.e. a highly compressed representation of the rest of teh feature space. Arguments: n_in: number of inputs to the block feat_map: dictionary mapping input features to the model to outputs of head block blocks: list of uninstantciated :class:`~lumin.nn.models.blocks.body.AbsBody` blocks to which to pass a subsection of the total inputs. Note that partials should be used to set any relevant parameters at initialisation time feats_per_block: list of lists of names of features to pass to each :class:`~lumin.nn.models.blocks.body.AbsBody`, not that the feat_map provided by :class:`~lumin.nn.models.blocks.head.AbsHead` will map features to their relavant head outputs bottleneck: if true, each block will receive the output of a single neuron which takes as input all the features which each given block does not directly take as inputs bottleneck_act: if set to a string representation of an activation function, the output of each bottleneck neuron will be passed throguh the defined activation function before being passed to their associated blocks lookup_init: function taking choice of activation function, number of inputs, and number of outputs an returning a function to initialise layer weights. lookup_act: function taking choice of activation function and returning an activation function layer freeze: whether to start with module parameters set to untrainable Examples:: >>> body = MultiBlock( ... blocks=[partial(FullyConnected, depth=1, width=50, act='swish'), ... partial(FullyConnected, depth=6, width=55, act='swish', ... dense=True, growth_rate=-0.1)], ... feats_per_block=[[f for f in train_feats if 'DER_' in f], ... [f for f in train_feats if 'PRI_' in f]]) >>> >>> body = MultiBlock( ... blocks=[partial(FullyConnected, depth=1, width=50, act='swish'), ... partial(FullyConnected, depth=6, width=55, act='swish', ... dense=True, growth_rate=-0.1)], ... feats_per_block=[[f for f in train_feats if 'DER_' in f], ... [f for f in train_feats if 'PRI_' in f]], ... bottleneck=True) >>> >>> body = MultiBlock( ... blocks=[partial(FullyConnected, depth=1, width=50, act='swish'), ... partial(FullyConnected, depth=6, width=55, act='swish', ... dense=True, growth_rate=-0.1)], ... feats_per_block=[[f for f in train_feats if 'DER_' in f], ... [f for f in train_feats if 'PRI_' in f]], ... bottleneck=True, bottleneck_act='swish') ''' def __init__(self, n_in:int, feat_map:Dict[str,List[int]], blocks:List[partial], feats_per_block:List[List[str]], bottleneck_sz:int=0, bottleneck_act:Optional[str]=None, lookup_init:Callable[[str,Optional[int],Optional[int]],Callable[[Tensor],None]]=lookup_normal_init, lookup_act:Callable[[str],Any]=lookup_act, freeze:bool=False): super().__init__(n_in=n_in, feat_map=feat_map, lookup_init=lookup_init, lookup_act=lookup_act, freeze=freeze) self.feats_per_block,self.bottleneck_sz,self.bottleneck_act = feats_per_block,bottleneck_sz,bottleneck_act self.blocks,self.n_out,self.masks,self.bottleneck_blocks = [],0,[],None if self.bottleneck_sz > 0: self.bottleneck_blocks,self.bottleneck_masks = [],[] for fpb in self.feats_per_block: tmp_map = {f: self.feat_map[f] for f in self.feat_map if f not in fpb} self.bottleneck_masks.append([i for f in tmp_map for i in tmp_map[f]]) self.bottleneck_blocks.append(self._get_bottleneck(self.bottleneck_masks[-1])) self.bottleneck_blocks = nn.ModuleList(self.bottleneck_blocks) for i, b in enumerate(blocks): tmp_map = {f: self.feat_map[f] for f in self.feat_map if f in self.feats_per_block[i]} self.masks.append([i for f in tmp_map for i in tmp_map[f]]) self.blocks.append(b(n_in=len(self.masks[-1])+self.bottleneck_sz, feat_map=tmp_map, lookup_init=self.lookup_init, lookup_act=self.lookup_act, freeze=self.freeze)) self.n_out += self.blocks[-1].get_out_size() self.blocks = nn.ModuleList(self.blocks) def _get_bottleneck(self, mask:List[int]) -> nn.Module: layers = [nn.Linear(len(mask), self.bottleneck_sz)] if self.bottleneck_act is None: init = self.lookup_init('linear', len(mask), self.bottleneck_sz) else: init = self.lookup_init(self.bottleneck_act, len(mask), self.bottleneck_sz) layers.append(self.lookup_act(self.bottleneck_act)) init(layers[0].weight) nn.init.zeros_(layers[0].bias) return nn.Sequential(*layers) def get_out_size(self) -> int: r''' Get size width of output layer Returns: Total number of outputs across all blocks ''' return self.n_out def forward(self, x:Tensor) -> Tensor: y = None for i, b in enumerate(self.blocks): if self.bottleneck_sz: a = self.bottleneck_blocks[i](x[:,self.bottleneck_masks[i]]) tmp_x = torch.cat((x[:,self.masks[i]], a), -1) else: tmp_x = x[:,self.masks[i]] out = b(tmp_x) if y is None: y = out else: y = torch.cat((y, out), -1) return y
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RSICompute.py
bluefin1986/tinyspark
3
7524
<reponame>bluefin1986/tinyspark # coding: utf-8 # In[1]: import baostock as bs import pandas as pd import numpy as np import talib as ta import matplotlib.pyplot as plt import KlineService import BaoStockUtil import math import datetime from scipy import integrate from RSI import DayRSI,WeekRSI,MonthRSI,SixtyMinRSI from concurrent.futures import ThreadPoolExecutor, as_completed from Stock import Stock import dbutil from IPython.core.debugger import set_trace #算积分用的节点数 INTEGRATE_CALC_RANGE = 4 RSI_OVER_BUY = 80 RSI_OVER_SELL = 20 RSI_OVER_BUY_12 = 75 RSI_OVER_SELL_12 = 25 RSI_OVER_BUY_24 = 70 RSI_OVER_SELL_24 = 30 RSI_MIDDLE = 50 #日线超卖区域积分阈值 RSI_INTE_OVERSELL_THRESHOLD_DAY = 50 # In[3]: def findLatestRSIDate(period): mydb = dbutil.connectDB() collection = mydb[chooseRSICollection(period)] cursor = collection.find().sort("date",-1).limit(1) df = pd.DataFrame(list(cursor)) if df.empty: return "1970-01-01" return df["date"][0] def clearRSI(period): mydb = dbutil.connectDB() collection = mydb[chooseRSICollection(period)] collection.delete_many({}) indexes = collection.index_information() if "code_1_date_1" in indexes.keys(): collection.drop_index( "code_1_date_1" ) def createIndex(period): mydb = dbutil.connectDB() collection = mydb[chooseRSICollection(period)] collection.create_index( [("code", 1), ("date",1)]) def integrateValues(valuesArray): return integrate.trapz(valuesArray, x=None, dx=1.0, axis=-1) ## # 从数据库读指定日期RSI数据 # # def readRSI(period, stockCode, startDate, endDate): mydb = dbutil.connectDB() collection = mydb[chooseRSICollection(period)] if type(startDate) == str: startDate = datetime.datetime.strptime(startDate + "T00:00:00.000Z", "%Y-%m-%dT%H:%M:%S.000Z") endDate = datetime.datetime.strptime(endDate + "T23:59:59.000Z", "%Y-%m-%dT%H:%M:%S.000Z") cursor = collection.find({"code":stockCode,"date":{"$gte":startDate,"$lte":endDate}}) df = pd.DataFrame(list(cursor)) return df ## # 写RSI数据库 # # def writeRSIToDB(period, stockCode, stockName, rsi_df): dataList = [] for index,rsi in rsi_df.iterrows(): rsiDate = rsi['date'] if period == "day": rsiObj = DayRSI(stockCode, stockName) elif period == "week": rsiObj = WeekRSI(stockCode, stockName) elif period == "month": rsiObj = MonthRSI(stockCode, stockName) elif period == "5m": rsiObj = FiveMinRSI(stockCode, stockName) elif period == "15m": rsiObj = FiftyMinRSI(stockCode, stockName) elif period == "30m": rsiObj = ThirtyMinRSI(stockCode, stockName) elif period == "60m": rsiObj = SixtyMinRSI(stockCode, stockName) rsiObj.date = rsiDate rsiObj.rsi_6 = rsi['rsi_6'] rsiObj.rsi_12 = rsi['rsi_12'] rsiObj.rsi_24 = rsi['rsi_24'] rsiObj.overBuy = rsi['overBuyFlag'] rsiObj.overSell = rsi['overSellFlag'] dataList.append(rsiObj.__dict__) mydb = dbutil.connectDB() collection = mydb[chooseRSICollection(period)] if len(dataList) > 0: collection.insert_many(dataList) else: raise RuntimeError("RSI数据为空") def computeStockRSI(period, stockCode, stockName, startDate, endDate): try: # compute1 = datetime.datetime.now().timestamp() df = KlineService.readStockKline(stockCode, period, startDate, endDate) # compute2 = datetime.datetime.now().timestamp() # print("read stockLine:", compute2 - compute1) if df.empty: return False if period == "day": # 剔除日线停盘数据 df = df[df['tradeStatus'] == '1'] rsi_df = computeRSI(df) # compute3 = datetime.datetime.now().timestamp() # print("compute rsi:", compute3 - compute2) writeRSIToDB(period, stockCode, stockName, rsi_df) # compute4 = datetime.datetime.now().timestamp() # print("write to db:", compute4 - compute3) return True except BaseException as e: print ("download " + stockCode + " error:" + str(e)) return False ## # 选择不同的Kline Collection # def chooseRSICollection(period): periodRSICollection = { "day" : "RSI_Day", "week" : "RSI_Week", "month" : "RSI_Month", "5m" : "RSI_5m", "15m" : "RSI_15m", "30m" : "RSI_30m", "60m" : "RSI_60m" } return periodRSICollection.get(period) def computeRSI(klineDataFrame): rsi_12days = ta.RSI(klineDataFrame['closePrice'],timeperiod=12) rsi_6days = ta.RSI(klineDataFrame['closePrice'],timeperiod=6) rsi_24days = ta.RSI(klineDataFrame['closePrice'],timeperiod=24) rsiFrame = pd.DataFrame(klineDataFrame, columns=["date"]) rsiFrame['rsi_6'] = rsi_6days rsiFrame['rsi_12'] = rsi_12days rsiFrame['rsi_24'] = rsi_24days ##添加参考线位置 rsiFrame['overBuy'] = RSI_OVER_BUY rsiFrame['overSell'] = RSI_OVER_SELL rsiFrame['middle'] = RSI_MIDDLE # RSI超卖和超买 rsi_buy_position = rsiFrame['rsi_12'] > RSI_OVER_BUY_12 rsi_sell_position = rsiFrame['rsi_12'] < RSI_OVER_SELL_12 rsiFrame.loc[rsi_buy_position[(rsi_buy_position == True) & (rsi_buy_position.shift() == False)].index, 'overBuyFlag'] = 'Yes' rsiFrame.loc[rsi_sell_position[(rsi_sell_position == True) & (rsi_sell_position.shift() == False)].index, 'overSellFlag'] = 'Yes' return rsiFrame ## # 计算自起始日期起的RSI # # def computeAllRSIDataOfPeriod(period, startDate): # currtime = datetime.datetime.now().timestamp() print("begin clear RSI period:", period) clearRSI(period) print("cleared RSI period:", period) # time1 = datetime.datetime.now().timestamp() # print("clear finished:",time1 - currtime) stockDict = KlineService.allStocks() # time2 = datetime.datetime.now().timestamp() # print("read stocks finished:",time2 - time1) endDate = str(datetime.date.today()) jobStart = datetime.datetime.now().timestamp() processCount = 0 failCount = 0 jobTotal = len(stockDict) ''' #起线程池来跑，单线程太慢了, 事实证明慢个鬼 executor = ThreadPoolExecutor(max_workers=1) funcVars = [] for key,stock in stockDict.items(): #指数没有分钟线，调过指数的RSI分钟线计算 if period.endswith("m") and (key.startswith("sh.000") or key.startswith("sz.399")): continue funcVars.append([period, key, stock["name"], startDate, endDate]) all_task = [executor.submit(computeStockRSI, funcVar[0], funcVar[1], funcVar[2], funcVar[3], funcVar[4]) for funcVar in funcVars] for future in as_completed(all_task): processCount = processCount + 1 if not future.result(): failCount = failCount + 1 if processCount % 100 == 0 and processCount > 0: print ("rsi process:", processCount, " of ", jobTotal ," failed:", failCount) ''' for key,stock in stockDict.items(): processCount = processCount + 1 #指数没有分钟线，调过指数的RSI分钟线计算 if period.endswith("m") and (key.startswith("sh.000") or key.startswith("sz.399")): continue result = computeStockRSI(period, key, stock["name"], startDate, endDate) if not result: failCount = failCount + 1 if processCount % 100 == 0 and processCount > 0: print ("rsi process:", processCount, " of ", jobTotal ," failed:", failCount) jobFinished = datetime.datetime.now().timestamp() createIndex(period) print("write all stock RSI to db finished, cost:", jobFinished - jobStart) return True ## # 计算指定日期的RSI积分 # # def computeAllRSIDataIntegrate(period, specifiedDateStr, includeST): BaoStockUtil.customLogin() specifiedDate = datetime.datetime.strptime(specifiedDateStr, "%Y-%m-%d") today = datetime.date.today() #如果把时间设成未来，自动调成今天 if specifiedDate > datetime.datetime.today(): specifiedDate = datetime.date.today() #避免跨年问题，直接从去年开始取 startDate = specifiedDate - datetime.timedelta(days = 365) #取交易日列表，用作倒推周期使用 rs = bs.query_trade_dates(start_date=datetime.datetime.strftime(startDate, "%Y-%m-%d"), end_date = specifiedDate) BaoStockUtil.customLogout() if rs.error_code != '0': raise RuntimeError("交易日api调用失败了:" + rs.error_code) tradeDates = [] while (rs.error_code == '0') & rs.next(): row = rs.get_row_data() if row[1] == "1": tradeDates.append(row[0]) if len(tradeDates) == 0: raise RuntimeError("取不到最新的交易日") #若期望计算的日期比库里RSI最新日期还晚，数据不全待补齐 rsiLatestDate = findLatestRSIDate(period) rsiLatestDateStr = datetime.datetime.strftime(rsiLatestDate, "%Y-%m-%d") if rsiLatestDate < specifiedDate: raise RuntimeError(specifiedDateStr + " 的 " + period + " RSI的数据不存在，待补齐数据") #找到指定日期以及rsi存量数据最近日期在交易日周期的序号 specifiedDateIndex = tradeDates.index(specifiedDateStr) if specifiedDateIndex == -1: raise RuntimeError(specifiedDateStr + " 可能不是交易日") daysBefore = computeRSIDataStartTradeDateRange(period, specifiedDateStr) startDateIndex = specifiedDateIndex - daysBefore #起始日期index负数，说明rsi数据不够 if startDateIndex < 0: raise RuntimeError(period + " rsi数据不够") startDateStr = tradeDates[startDateIndex] print("compute rsi tradeDates from ", startDateStr, "to", specifiedDateStr) processCount = 0 failCount = 0 startDateIndex = -1 dictStocks = KlineService.allStocks() klineDataFrame = KlineService.readAllStockKline(period, specifiedDateStr, specifiedDateStr) klineDataFrame = klineDataFrame.set_index("code") klineDict = klineDataFrame.to_dict('index') jobTotal = len(dictStocks) rsiValueArrs = [] for i in range(0, 6): rsiValueArrs.append([]) for key,stock in dictStocks.items(): processCount = processCount + 1 #指数没有分钟线，跳过指数的RSI分钟线计算 if period.endswith("m") and stock.stockType != 1: continue #如果不计算ST，跳过 if not includeST and stock["isST"]: continue #退市股就不要算了 if "退" in stock["name"]: continue #科创板不达门槛没法买，不看 if key.startswith("sh.68"): continue try: rsiDF = readRSI(period, key, startDateStr, specifiedDateStr) rsiCount = len(rsiDF) if rsiCount < INTEGRATE_CALC_RANGE: raise RuntimeError("积分计算节点不够") rsiValueArrs[0].append(key) rsiValueArrs[1].append(stock["name"]) rsiValueArrs[2].append(klineDict[key]["closePrice"]) #取最近的数据用于计算积分 rsiValueArrs[3].append(rsiDF["rsi_6"][rsiCount - INTEGRATE_CALC_RANGE : rsiCount]) rsiValueArrs[4].append(rsiDF["rsi_12"][rsiCount - INTEGRATE_CALC_RANGE : rsiCount]) rsiValueArrs[5].append(rsiDF["rsi_24"][rsiCount - INTEGRATE_CALC_RANGE : rsiCount]) except BaseException as e: failCount = failCount + 1 print ("compute rsi integrate " + key + " error:" + str(e)) if processCount % 100 == 0 and processCount > 0: print ("compute rsi integrate process:", processCount, " of ", jobTotal ," failed:", failCount) rsi6Arr = np.array(rsiValueArrs[3]).reshape(-1, INTEGRATE_CALC_RANGE) rsi6InteArr = integrateValues(rsi6Arr) rsi12Arr = np.array(rsiValueArrs[4]).reshape(-1, INTEGRATE_CALC_RANGE) rsi12InteArr = integrateValues(rsi12Arr) rsi24Arr = np.array(rsiValueArrs[5]).reshape(-1, INTEGRATE_CALC_RANGE) rsi24InteArr = integrateValues(rsi24Arr) rsiInteDF = pd.DataFrame() rsiInteDF["code"] = rsiValueArrs[0] rsiInteDF["name"] = rsiValueArrs[1] rsiInteDF["closePrice"] = rsiValueArrs[2] rsiInteDF["rsi_inte_6"] = rsi6InteArr rsiInteDF["rsi_inte_12"] = rsi12InteArr rsiInteDF["rsi_inte_24"] = rsi24InteArr return rsiInteDF #算出计算本周期下指定数据需要的起始交易日 #每个交易日一共4小时，所以取4小时为一天，而不是24小时 #每个计算周期一共至少需要4个节点，分钟线RSI统一除以4*60=240分钟算出所需计算数据天数，最少为一天 #日线不用除分钟 ## TODO 周线没想好怎么算，更别说月线了。 def computeRSIDataStartTradeDateRange(period, specifiedDate): daysBefore = 0 if period.endswith("m"): daysBefore = math.ceil(INTEGRATE_CALC_RANGE * (int(period.replace("m", "")) + 1) / (60 * 4)) elif period == "day": daysBefore = INTEGRATE_CALC_RANGE else: raise RuntimeError("周期有误") return daysBefore
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tests/attr/test_kernel_shap.py
trsvchn/captum
3,140
7398
<filename>tests/attr/test_kernel_shap.py #!/usr/bin/env python3 import io import unittest import unittest.mock from typing import Any, Callable, List, Tuple, Union import torch from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric from captum.attr._core.kernel_shap import KernelShap from tests.helpers.basic import ( BaseTest, assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, ) from tests.helpers.basic_models import ( BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput, ) class Test(BaseTest): def setUp(self) -> None: super().setUp() try: import sklearn # noqa: F401 assert ( sklearn.__version__ >= "0.23.0" ), "Must have sklearn version 0.23.0 or higher" except (ImportError, AssertionError): raise unittest.SkipTest("Skipping KernelShap tests, sklearn not available.") def test_linear_kernel_shap(self) -> None: net = BasicModel_MultiLayer() inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True) baseline = torch.tensor([[10.0, 20.0, 10.0]], requires_grad=True) self._kernel_shap_test_assert( net, inp, [40.0, 120.0, 80.0], n_samples=500, baselines=baseline, expected_coefs=[40.0, 120.0, 80.0], ) def test_simple_kernel_shap(self) -> None: net = BasicModel_MultiLayer() inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True) self._kernel_shap_test_assert( net, inp, [76.66666, 196.66666, 116.66666], perturbations_per_eval=(1, 2, 3), n_samples=500, ) def test_simple_kernel_shap_with_mask(self) -> None: net = BasicModel_MultiLayer() inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True) self._kernel_shap_test_assert( net, inp, [275.0, 275.0, 115.0], feature_mask=torch.tensor([[0, 0, 1]]), perturbations_per_eval=(1, 2, 3), expected_coefs=[275.0, 115.0], ) @unittest.mock.patch("sys.stderr", new_callable=io.StringIO) def test_simple_kernel_shap_with_show_progress(self, mock_stderr) -> None: net = BasicModel_MultiLayer() inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True) # test progress output for each batch size for bsz in (1, 2, 3): self._kernel_shap_test_assert( net, inp, [76.66666, 196.66666, 116.66666], perturbations_per_eval=(bsz,), n_samples=500, show_progress=True, ) output = mock_stderr.getvalue() # to test if progress calculation aligns with the actual iteration # all perturbations_per_eval should reach progress of 100% assert ( "Kernel Shap attribution: 100%" in output ), f"Error progress output: {repr(output)}" mock_stderr.seek(0) mock_stderr.truncate(0) def test_simple_kernel_shap_with_baselines(self) -> None: net = BasicModel_MultiLayer() inp = torch.tensor([[20.0, 50.0, 30.0]]) self._kernel_shap_test_assert( net, inp, [248.0, 248.0, 104.0], feature_mask=torch.tensor([[0, 0, 1]]), baselines=4, perturbations_per_eval=(1, 2, 3), ) def test_simple_batch_kernel_shap(self) -> None: net = BasicModel_MultiLayer() inp = torch.tensor([[2.0, 10.0, 3.0], [20.0, 50.0, 30.0]], requires_grad=True) self._kernel_shap_test_assert( net, inp, [[7.0, 32.5, 10.5], [76.66666, 196.66666, 116.66666]], perturbations_per_eval=(1, 2, 3), n_samples=20000, ) def test_simple_batch_kernel_shap_with_mask(self) -> None: net = BasicModel_MultiLayer() inp = torch.tensor([[2.0, 10.0, 3.0], [20.0, 50.0, 30.0]], requires_grad=True) self._kernel_shap_test_assert( net, inp, [[39.5, 39.5, 10.5], [275.0, 275.0, 115.0]], feature_mask=torch.tensor([[0, 0, 1], [1, 1, 0]]), perturbations_per_eval=(1, 2, 3), n_samples=100, expected_coefs=[[39.5, 10.5], [115.0, 275.0]], ) def test_multi_input_kernel_shap_without_mask(self) -> None: net = BasicModel_MultiLayer_MultiInput() inp1 = torch.tensor([[23.0, 0.0, 0.0]]) inp2 = torch.tensor([[20.0, 0.0, 50.0]]) inp3 = torch.tensor([[0.0, 100.0, 10.0]]) expected = ( [[90, 0, 0]], [[78, 0, 198]], [[0, 398, 38]], ) self._kernel_shap_test_assert( net, (inp1, inp2, inp3), expected, additional_input=(1,), n_samples=2000, ) def test_multi_input_kernel_shap_with_mask(self) -> None: net = BasicModel_MultiLayer_MultiInput() inp1 = torch.tensor([[20.0, 50.0, 30.0]]) inp2 = torch.tensor([[0.0, 100.0, 0.0]]) inp3 = torch.tensor([[2.0, 10.0, 3.0]]) mask1 = torch.tensor([[0, 1, 0]]) mask2 = torch.tensor([[0, 1, 2]]) mask3 = torch.tensor([[0, 0, 0]]) expected = ( [[255.0, 595.0, 255.0]], [[255.0, 595.0, 0.0]], [[255.0, 255.0, 255.0]], ) self._kernel_shap_test_assert( net, (inp1, inp2, inp3), expected, additional_input=(1,), feature_mask=(mask1, mask2, mask3), ) expected_with_baseline = ( [[184, 580.0, 184]], [[184, 580.0, -12.0]], [[184, 184, 184]], ) self._kernel_shap_test_assert( net, (inp1, inp2, inp3), expected_with_baseline, additional_input=(1,), feature_mask=(mask1, mask2, mask3), baselines=(2, 3.0, 4), perturbations_per_eval=(1, 2, 3), ) def test_multi_input_batch_kernel_shap_without_mask(self) -> None: net = BasicModel_MultiLayer_MultiInput() inp1 = torch.tensor([[23.0, 0.0, 0.0], [20.0, 50.0, 30.0]]) inp2 = torch.tensor([[20.0, 0.0, 50.0], [0.0, 100.0, 0.0]]) inp3 = torch.tensor([[0.0, 100.0, 10.0], [0.0, 10.0, 0.0]]) expected = ( [[90, 0, 0], [78.0, 198.0, 118.0]], [[78, 0, 198], [0.0, 398.0, 0.0]], [[0, 398, 38], [0.0, 38.0, 0.0]], ) self._kernel_shap_test_assert( net, (inp1, inp2, inp3), expected, additional_input=(1,), n_samples=2500, expected_coefs=[ [90.0, 0, 0, 78, 0, 198, 0, 398, 38], [78.0, 198.0, 118.0, 0.0, 398.0, 0.0, 0.0, 38.0, 0.0], ], ) def test_multi_input_batch_kernel_shap(self) -> None: net = BasicModel_MultiLayer_MultiInput() inp1 = torch.tensor([[23.0, 100.0, 0.0], [20.0, 50.0, 30.0]]) inp2 = torch.tensor([[20.0, 50.0, 30.0], [0.0, 100.0, 0.0]]) inp3 = torch.tensor([[0.0, 100.0, 10.0], [2.0, 10.0, 3.0]]) mask1 = torch.tensor([[1, 1, 1], [0, 1, 0]]) mask2 = torch.tensor([[0, 1, 2]]) mask3 = torch.tensor([[0, 1, 2], [0, 0, 0]]) expected = ( [[1088.6666, 1088.6666, 1088.6666], [255.0, 595.0, 255.0]], [[76.6666, 1088.6666, 156.6666], [255.0, 595.0, 0.0]], [[76.6666, 1088.6666, 156.6666], [255.0, 255.0, 255.0]], ) self._kernel_shap_test_assert( net, (inp1, inp2, inp3), expected, additional_input=(1,), feature_mask=(mask1, mask2, mask3), n_samples=300, ) expected_with_baseline = ( [[1040, 1040, 1040], [184, 580.0, 184]], [[52, 1040, 132], [184, 580.0, -12.0]], [[52, 1040, 132], [184, 184, 184]], ) self._kernel_shap_test_assert( net, (inp1, inp2, inp3), expected_with_baseline, additional_input=(1,), feature_mask=(mask1, mask2, mask3), baselines=(2, 3.0, 4), perturbations_per_eval=(1, 2, 3), ) # Remaining tests are for cases where forward function returns a scalar # as either a float, integer, 0d tensor or 1d tensor. def test_single_kernel_shap_scalar_float(self) -> None: net = BasicModel_MultiLayer() self._single_input_scalar_kernel_shap_assert( lambda inp: torch.sum(net(inp)).item() ) def test_single_kernel_shap_scalar_tensor_0d(self) -> None: net = BasicModel_MultiLayer() self._single_input_scalar_kernel_shap_assert(lambda inp: torch.sum(net(inp))) def test_single_kernel_shap_scalar_tensor_1d(self) -> None: net = BasicModel_MultiLayer() self._single_input_scalar_kernel_shap_assert( lambda inp: torch.sum(net(inp)).reshape(1) ) def test_single_kernel_shap_scalar_int(self) -> None: net = BasicModel_MultiLayer() self._single_input_scalar_kernel_shap_assert( lambda inp: int(torch.sum(net(inp)).item()) ) def _single_input_scalar_kernel_shap_assert(self, func: Callable) -> None: inp = torch.tensor([[2.0, 10.0, 3.0]], requires_grad=True) mask = torch.tensor([[0, 0, 1]]) self._kernel_shap_test_assert( func, inp, [[79.0, 79.0, 21.0]], feature_mask=mask, perturbations_per_eval=(1,), target=None, ) def test_multi_inp_kernel_shap_scalar_tensor_0d(self) -> None: net = BasicModel_MultiLayer_MultiInput() self._multi_input_scalar_kernel_shap_assert(lambda *inp: torch.sum(net(*inp))) def test_multi_inp_kernel_shap_scalar_tensor_1d(self) -> None: net = BasicModel_MultiLayer_MultiInput() self._multi_input_scalar_kernel_shap_assert( lambda *inp: torch.sum(net(*inp)).reshape(1) ) def test_multi_inp_kernel_shap_scalar_tensor_int(self) -> None: net = BasicModel_MultiLayer_MultiInput() self._multi_input_scalar_kernel_shap_assert( lambda *inp: int(torch.sum(net(*inp)).item()) ) def test_multi_inp_kernel_shap_scalar_float(self) -> None: net = BasicModel_MultiLayer_MultiInput() self._multi_input_scalar_kernel_shap_assert( lambda *inp: torch.sum(net(*inp)).item() ) def _multi_input_scalar_kernel_shap_assert(self, func: Callable) -> None: inp1 = torch.tensor([[23.0, 100.0, 0.0], [20.0, 50.0, 30.0]]) inp2 = torch.tensor([[20.0, 50.0, 30.0], [0.0, 100.0, 0.0]]) inp3 = torch.tensor([[0.0, 100.0, 10.0], [20.0, 10.0, 13.0]]) mask1 = torch.tensor([[1, 1, 1]]) mask2 = torch.tensor([[0, 1, 2]]) mask3 = torch.tensor([[0, 1, 2]]) expected = ( [[3850.6666, 3850.6666, 3850.6666]], [[306.6666, 3850.6666, 410.6666]], [[306.6666, 3850.6666, 410.6666]], ) self._kernel_shap_test_assert( func, (inp1, inp2, inp3), expected, additional_input=(1,), feature_mask=(mask1, mask2, mask3), perturbations_per_eval=(1,), target=None, n_samples=1500, ) def _kernel_shap_test_assert( self, model: Callable, test_input: TensorOrTupleOfTensorsGeneric, expected_attr, feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None, additional_input: Any = None, perturbations_per_eval: Tuple[int, ...] = (1,), baselines: BaselineType = None, target: Union[None, int] = 0, n_samples: int = 100, delta: float = 1.0, expected_coefs: Union[None, List[float], List[List[float]]] = None, show_progress: bool = False, ) -> None: for batch_size in perturbations_per_eval: kernel_shap = KernelShap(model) attributions = kernel_shap.attribute( test_input, target=target, feature_mask=feature_mask, additional_forward_args=additional_input, baselines=baselines, perturbations_per_eval=batch_size, n_samples=n_samples, show_progress=show_progress, ) assertTensorTuplesAlmostEqual( self, attributions, expected_attr, delta=delta, mode="max" ) if expected_coefs is not None: # Test with return_input_shape = False attributions = kernel_shap.attribute( test_input, target=target, feature_mask=feature_mask, additional_forward_args=additional_input, baselines=baselines, perturbations_per_eval=batch_size, n_samples=n_samples, return_input_shape=False, show_progress=show_progress, ) assertTensorAlmostEqual( self, attributions, expected_coefs, delta=delta, mode="max" ) if __name__ == "__main__": unittest.main()
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chainer_/chainercv2/models/common.py
snsnlou/imgclsmob
0
133120
""" Common routines for models in Chainer. """ __all__ = ['round_channels', 'BreakBlock', 'ReLU6', 'HSwish', 'get_activation_layer', 'GlobalAvgPool2D', 'SelectableDense', 'DenseBlock', 'ConvBlock1d', 'conv1x1', 'conv3x3', 'depthwise_conv3x3', 'ConvBlock', 'conv1x1_block', 'conv3x3_block', 'conv5x5_block', 'conv7x7_block', 'dwconv_block', 'dwconv3x3_block', 'dwconv5x5_block', 'dwsconv3x3_block', 'PreConvBlock', 'pre_conv1x1_block', 'pre_conv3x3_block', 'DeconvBlock', 'ChannelShuffle', 'ChannelShuffle2', 'SEBlock', 'SABlock', 'SAConvBlock', 'saconv3x3_block', 'PixelShuffle', 'DucBlock', 'SimpleSequential', 'DualPathSequential', 'Concurrent', 'SequentialConcurrent', 'ParametricSequential', 'ParametricConcurrent', 'Hourglass', 'SesquialteralHourglass', 'MultiOutputSequential', 'ParallelConcurent', 'DualPathParallelConcurent', 'Flatten', 'AdaptiveAvgPool2D', 'NormActivation', 'InterpolationBlock', 'HeatmapMaxDetBlock'] from inspect import isfunction from functools import partial import numpy as np from chainer import Chain import chainer.functions as F import chainer.links as L from chainer import link from chainer.initializers import _get_initializer from chainer.variable import Parameter def round_channels(channels, divisor=8): """ Round weighted channel number (make divisible operation). Parameters: ---------- channels : int or float Original number of channels. divisor : int, default 8 Alignment value. Returns: ------- int Weighted number of channels. """ rounded_channels = max(int(channels + divisor / 2.0) // divisor * divisor, divisor) if float(rounded_channels) < 0.9 * channels: rounded_channels += divisor return rounded_channels class BreakBlock(Chain): """ Break coonnection block for hourglass. """ def __init__(self): super(BreakBlock, self).__init__() def __call__(self, x): return None def __repr__(self): return '{name}()'.format(name=self.__class__.__name__) class ReLU6(Chain): """ ReLU6 activation layer. """ def __call__(self, x): return F.clip(x, 0.0, 6.0) class Swish(Chain): """ Swish activation function from 'Searching for Activation Functions,' https://arxiv.org/abs/1710.05941. """ def __call__(self, x): return x * F.sigmoid(x) class HSigmoid(Chain): """ Approximated sigmoid function, so-called hard-version of sigmoid from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244. """ def __call__(self, x): return F.clip(x + 3.0, 0.0, 6.0) / 6.0 class HSwish(Chain): """ H-Swish activation function from 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244. """ def __call__(self, x): return x * F.clip(x + 3.0, 0.0, 6.0) / 6.0 def get_activation_layer(activation): """ Create activation layer from string/function. Parameters: ---------- activation : function or str Activation function or name of activation function. Returns: ------- function Activation layer. """ assert (activation is not None) if isfunction(activation): return activation() elif isinstance(activation, str): if activation == "relu": return F.relu elif activation == "relu6": return ReLU6() elif activation == "swish": return Swish() # return partial( # F.swish, # beta=[1.0]) elif activation == "hswish": return HSwish() elif activation == "sigmoid": return F.sigmoid elif activation == "hsigmoid": return HSigmoid() else: raise NotImplementedError() else: return activation class GlobalAvgPool2D(Chain): """ Global average pooling operation for spatial data. """ def __call__(self, x): return F.average_pooling_2d(x, ksize=x.shape[2:]) class SelectableDense(link.Link): """ Selectable dense layer. Parameters: ---------- in_channels : int Number of input features. out_channels : int Number of output features. use_bias : bool, default False Whether the layer uses a bias vector. initial_weight : `types.InitializerSpec`, default None Initializer for the `kernel` weights matrix. initial_bias: `types.InitializerSpec`, default 0 Initializer for the bias vector. num_options : int, default 1 Number of selectable options. """ def __init__(self, in_channels, out_channels, use_bias=False, initial_weight=None, initial_bias=0, num_options=1): super(SelectableDense, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.use_bias = use_bias self.num_options = num_options with self.init_scope(): weight_initializer = _get_initializer(initial_weight) self.weight = Parameter( initializer=weight_initializer, shape=(num_options, out_channels, in_channels), name="weight") if use_bias: bias_initializer = _get_initializer(initial_bias) self.bias = Parameter( initializer=bias_initializer, shape=(num_options, out_channels), name="bias") else: self.bias = None def forward(self, x, indices): weight = self.xp.take(self.weight.data, indices=indices, axis=0) x = F.expand_dims(x, axis=-1) x = F.batch_matmul(weight, x) x = F.squeeze(x, axis=-1) if self.use_bias: bias = self.xp.take(self.bias.data, indices=indices, axis=0) x += bias return x @property def printable_specs(self): specs = [ ('in_channels', self.in_channels), ('out_channels', self.out_channels), ('use_bias', self.use_bias), ('num_options', self.num_options), ] for spec in specs: yield spec class DenseBlock(Chain): """ Standard dense block with Batch normalization and activation. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ def __init__(self, in_channels, out_channels, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu)): super(DenseBlock, self).__init__() self.activate = (activation is not None) self.use_bn = use_bn with self.init_scope(): self.fc = L.Linear( in_size=in_channels, out_size=out_channels, nobias=(not use_bias)) if self.use_bn: self.bn = L.BatchNormalization( size=out_channels, eps=bn_eps) if self.activate: self.activ = get_activation_layer(activation) def __call__(self, x): x = self.fc(x) if self.use_bn: x = self.bn(x) if self.activate: x = self.activ(x) return x class ConvBlock1d(Chain): """ Standard 1D convolution block with Batch normalization and activation. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. ksize : int Convolution window size. stride : int Stride of the convolution. pad : int Padding value for convolution layer. dilate : int, default 1 Dilation value for convolution layer. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ def __init__(self, in_channels, out_channels, ksize, stride, pad, dilate=1, groups=1, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu)): super(ConvBlock1d, self).__init__() self.activate = (activation is not None) self.use_bn = use_bn with self.init_scope(): self.conv = L.Convolution1D( in_channels=in_channels, out_channels=out_channels, ksize=ksize, stride=stride, pad=pad, nobias=(not use_bias), dilate=dilate, groups=groups) if self.use_bn: self.bn = L.BatchNormalization( size=out_channels, eps=bn_eps) if self.activate: self.activ = get_activation_layer(activation) def __call__(self, x): x = self.conv(x) if self.use_bn: x = self.bn(x) if self.activate: x = self.activ(x) return x def conv1x1(in_channels, out_channels, stride=1, groups=1, use_bias=False): """ Convolution 1x1 layer. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. """ return L.Convolution2D( in_channels=in_channels, out_channels=out_channels, ksize=1, stride=stride, nobias=(not use_bias), groups=groups) def conv3x3(in_channels, out_channels, stride=1, pad=1, dilate=1, groups=1, use_bias=False): """ Convolution 3x3 layer. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 1 Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. """ return L.Convolution2D( in_channels=in_channels, out_channels=out_channels, ksize=3, stride=stride, pad=pad, nobias=(not use_bias), dilate=dilate, groups=groups) def depthwise_conv3x3(channels, stride=1, pad=1, dilate=1, use_bias=False): """ Depthwise convolution 3x3 layer. Parameters: ---------- channels : int Number of input/output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 1 Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. """ return L.Convolution2D( in_channels=channels, out_channels=channels, ksize=3, stride=stride, pad=pad, nobias=(not use_bias), dilate=dilate, groups=channels) class ConvBlock(Chain): """ Standard convolution block with Batch normalization and activation. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. ksize : int or tuple/list of 2 int Convolution window size. stride : int or tuple/list of 2 int Stride of the convolution. pad : int or tuple/list of 2 int Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ def __init__(self, in_channels, out_channels, ksize, stride, pad, dilate=1, groups=1, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu)): super(ConvBlock, self).__init__() self.activate = (activation is not None) self.use_bn = use_bn with self.init_scope(): self.conv = L.Convolution2D( in_channels=in_channels, out_channels=out_channels, ksize=ksize, stride=stride, pad=pad, nobias=(not use_bias), dilate=dilate, groups=groups) if self.use_bn: self.bn = L.BatchNormalization( size=out_channels, eps=bn_eps) if self.activate: self.activ = get_activation_layer(activation) def __call__(self, x): x = self.conv(x) if self.use_bn: x = self.bn(x) if self.activate: x = self.activ(x) return x def conv1x1_block(in_channels, out_channels, stride=1, groups=1, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu)): """ 1x1 version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ return ConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=1, stride=stride, pad=0, groups=groups, use_bias=use_bias, use_bn=use_bn, bn_eps=bn_eps, activation=activation) def conv3x3_block(in_channels, out_channels, stride=1, pad=1, dilate=1, groups=1, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu)): """ 3x3 version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 1 Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ return ConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=3, stride=stride, pad=pad, dilate=dilate, groups=groups, use_bias=use_bias, use_bn=use_bn, bn_eps=bn_eps, activation=activation) def conv5x5_block(in_channels, out_channels, stride=1, pad=2, dilate=1, groups=1, use_bias=False, bn_eps=1e-5, activation=(lambda: F.relu)): """ 5x5 version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 2 Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ return ConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=5, stride=stride, pad=pad, dilate=dilate, groups=groups, use_bias=use_bias, bn_eps=bn_eps, activation=activation) def conv7x7_block(in_channels, out_channels, stride=1, pad=3, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu)): """ 7x7 version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 3 Padding value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ return ConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=7, stride=stride, pad=pad, use_bias=use_bias, use_bn=use_bn, bn_eps=bn_eps, activation=activation) def dwconv_block(in_channels, out_channels, ksize, stride, pad, dilate=1, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu)): """ Depthwise version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. ksize : int or tuple/list of 2 int Convolution window size. stride : int or tuple/list of 2 int Stride of the convolution. pad : int or tuple/list of 2 int Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ return ConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=ksize, stride=stride, pad=pad, dilate=dilate, groups=out_channels, use_bias=use_bias, use_bn=use_bn, bn_eps=bn_eps, activation=activation) def dwconv3x3_block(in_channels, out_channels, stride=1, pad=1, dilate=1, use_bias=False, bn_eps=1e-5, activation=(lambda: F.relu)): """ 3x3 depthwise version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 1 Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ return dwconv_block( in_channels=in_channels, out_channels=out_channels, ksize=3, stride=stride, pad=pad, dilate=dilate, use_bias=use_bias, bn_eps=bn_eps, activation=activation) def dwconv5x5_block(in_channels, out_channels, stride=1, pad=2, dilate=1, use_bias=False, bn_eps=1e-5, activation=(lambda: F.relu)): """ 5x5 depthwise version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 2 Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ return dwconv_block( in_channels=in_channels, out_channels=out_channels, ksize=5, stride=stride, pad=pad, dilate=dilate, use_bias=use_bias, bn_eps=bn_eps, activation=activation) class DwsConvBlock(Chain): """ Depthwise separable convolution block with BatchNorms and activations at each convolution layers. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. ksize : int or tuple/list of 2 int Convolution window size. stride : int or tuple/list of 2 int Stride of the convolution. pad : int or tuple/list of 2 int Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. dw_use_bn : bool, default True Whether to use BatchNorm layer (depthwise convolution block). pw_use_bn : bool, default True Whether to use BatchNorm layer (pointwise convolution block). bn_eps : float, default 1e-5 Small float added to variance in Batch norm. dw_activation : function or str or None, default F.relu Activation function after the depthwise convolution block. pw_activation : function or str or None, default F.relu Activation function after the pointwise convolution block. """ def __init__(self, in_channels, out_channels, ksize, stride, pad, dilate=1, use_bias=False, dw_use_bn=True, pw_use_bn=True, bn_eps=1e-5, dw_activation=(lambda: F.relu), pw_activation=(lambda: F.relu)): super(DwsConvBlock, self).__init__() with self.init_scope(): self.dw_conv = dwconv_block( in_channels=in_channels, out_channels=in_channels, ksize=ksize, stride=stride, pad=pad, dilate=dilate, use_bias=use_bias, use_bn=dw_use_bn, bn_eps=bn_eps, activation=dw_activation) self.pw_conv = conv1x1_block( in_channels=in_channels, out_channels=out_channels, use_bias=use_bias, use_bn=pw_use_bn, bn_eps=bn_eps, activation=pw_activation) def __call__(self, x): x = self.dw_conv(x) x = self.pw_conv(x) return x def dwsconv3x3_block(in_channels, out_channels, stride=1, pad=1, dilate=1, use_bias=False, bn_eps=1e-5, dw_activation=(lambda: F.relu), pw_activation=(lambda: F.relu), **kwargs): """ 3x3 depthwise separable version of the standard convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 1 Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. dw_activation : function or str or None, default F.relu Activation function after the depthwise convolution block. pw_activation : function or str or None, default F.relu Activation function after the pointwise convolution block. """ return DwsConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=3, stride=stride, pad=pad, dilate=dilate, use_bias=use_bias, bn_eps=bn_eps, dw_activation=dw_activation, pw_activation=pw_activation, **kwargs) class PreConvBlock(Chain): """ Convolution block with Batch normalization and ReLU pre-activation. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. ksize : int or tuple/list of 2 int Convolution window size. stride : int or tuple/list of 2 int Stride of the convolution. pad : int or tuple/list of 2 int Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. return_preact : bool, default False Whether return pre-activation. It's used by PreResNet. activate : bool, default True Whether activate the convolution block. """ def __init__(self, in_channels, out_channels, ksize, stride, pad, dilate=1, use_bias=False, use_bn=True, return_preact=False, activate=True): super(PreConvBlock, self).__init__() self.return_preact = return_preact self.activate = activate self.use_bn = use_bn with self.init_scope(): if self.use_bn: self.bn = L.BatchNormalization( size=in_channels, eps=1e-5) if self.activate: self.activ = F.relu self.conv = L.Convolution2D( in_channels=in_channels, out_channels=out_channels, ksize=ksize, stride=stride, pad=pad, nobias=(not use_bias), dilate=dilate) def __call__(self, x): if self.use_bn: x = self.bn(x) if self.activate: x = self.activ(x) if self.return_preact: x_pre_activ = x x = self.conv(x) if self.return_preact: return x, x_pre_activ else: return x def pre_conv1x1_block(in_channels, out_channels, stride=1, use_bias=False, use_bn=True, return_preact=False, activate=True): """ 1x1 version of the pre-activated convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. return_preact : bool, default False Whether return pre-activation. activate : bool, default True Whether activate the convolution block. """ return PreConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=1, stride=stride, pad=0, use_bias=use_bias, use_bn=use_bn, return_preact=return_preact, activate=activate) def pre_conv3x3_block(in_channels, out_channels, stride=1, pad=1, dilate=1, use_bias=False, use_bn=True, return_preact=False, activate=True): """ 3x3 version of the pre-activated convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 1 Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. return_preact : bool, default False Whether return pre-activation. activate : bool, default True Whether activate the convolution block. """ return PreConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=3, stride=stride, pad=pad, dilate=dilate, use_bias=use_bias, use_bn=use_bn, return_preact=return_preact, activate=activate) class DeconvBlock(Chain): """ Deconvolution block with batch normalization and activation. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. ksize : int or tuple/list of 2 int Convolution window size. stride : int or tuple/list of 2 int Stride of the deconvolution. pad : int or tuple/list of 2 int Padding value for deconvolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for deconvolution layer. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ def __init__(self, in_channels, out_channels, ksize, stride, pad, dilate=1, groups=1, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu), **kwargs): super(DeconvBlock, self).__init__(**kwargs) self.activate = (activation is not None) self.use_bn = use_bn with self.init_scope(): self.conv = L.Deconvolution2D( in_channels=in_channels, out_channels=out_channels, ksize=ksize, stride=stride, pad=pad, nobias=(not use_bias), dilate=dilate, groups=groups) if self.use_bn: self.bn = L.BatchNormalization( size=out_channels, eps=bn_eps) if self.activate: self.activ = get_activation_layer(activation) def __call__(self, x): x = self.conv(x) if self.use_bn: x = self.bn(x) if self.activate: x = self.activ(x) return x def channel_shuffle(x, groups): """ Channel shuffle operation from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- x : chainer.Variable or numpy.ndarray or cupy.ndarray Input variable. groups : int Number of groups. Returns: ------- chainer.Variable or numpy.ndarray or cupy.ndarray Resulted variable. """ batch, channels, height, width = x.shape channels_per_group = channels // groups x = F.reshape(x, shape=(batch, groups, channels_per_group, height, width)) x = F.swapaxes(x, axis1=1, axis2=2) x = F.reshape(x, shape=(batch, channels, height, width)) return x class ChannelShuffle(Chain): """ Channel shuffle layer. This is a wrapper over the same operation. It is designed to save the number of groups. Parameters: ---------- channels : int Number of channels. groups : int Number of groups. """ def __init__(self, channels, groups): super(ChannelShuffle, self).__init__() assert (channels % groups == 0) self.groups = groups def __call__(self, x): return channel_shuffle(x, self.groups) def channel_shuffle2(x, groups): """ Channel shuffle operation from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. The alternative version. Parameters: ---------- x : chainer.Variable or numpy.ndarray or cupy.ndarray Input variable. groups : int Number of groups. Returns: ------- chainer.Variable or numpy.ndarray or cupy.ndarray Resulted variable. """ batch, channels, height, width = x.shape channels_per_group = channels // groups x = F.reshape(x, shape=(batch, channels_per_group, groups, height, width)) x = F.swapaxes(x, axis1=1, axis2=2) x = F.reshape(x, shape=(batch, channels, height, width)) return x class ChannelShuffle2(Chain): """ Channel shuffle layer. This is a wrapper over the same operation. It is designed to save the number of groups. The alternative version. Parameters: ---------- channels : int Number of channels. groups : int Number of groups. """ def __init__(self, channels, groups): super(ChannelShuffle2, self).__init__() assert (channels % groups == 0) self.groups = groups def __call__(self, x): return channel_shuffle2(x, self.groups) class SEBlock(Chain): """ Squeeze-and-Excitation block from 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507. Parameters: ---------- channels : int Number of channels. reduction : int, default 16 Squeeze reduction value. mid_channels : int or None, default None Number of middle channels. round_mid : bool, default False Whether to round middle channel number (make divisible by 8). use_conv : bool, default True Whether to convolutional layers instead of fully-connected ones. activation : function or str, default F.relu Activation function after the first convolution. out_activation : function or str, default F.sigmoid Activation function after the last convolution. """ def __init__(self, channels, reduction=16, mid_channels=None, round_mid=False, use_conv=True, mid_activation=(lambda: F.relu), out_activation=(lambda: F.sigmoid)): super(SEBlock, self).__init__() self.use_conv = use_conv if mid_channels is None: mid_channels = channels // reduction if not round_mid else round_channels(float(channels) / reduction) with self.init_scope(): if use_conv: self.conv1 = conv1x1( in_channels=channels, out_channels=mid_channels, use_bias=True) else: self.fc1 = L.Linear( in_size=channels, out_size=mid_channels) self.activ = get_activation_layer(mid_activation) if use_conv: self.conv2 = conv1x1( in_channels=mid_channels, out_channels=channels, use_bias=True) else: self.fc2 = L.Linear( in_size=mid_channels, out_size=channels) self.sigmoid = get_activation_layer(out_activation) def __call__(self, x): w = F.average_pooling_2d(x, ksize=x.shape[2:]) if not self.use_conv: w = F.reshape(w, shape=(w.shape[0], -1)) w = self.conv1(w) if self.use_conv else self.fc1(w) w = self.activ(w) w = self.conv2(w) if self.use_conv else self.fc2(w) w = self.sigmoid(w) if not self.use_conv: w = F.expand_dims(F.expand_dims(w, axis=2), axis=3) x = x * w return x class SABlock(Chain): """ Split-Attention block from 'ResNeSt: Split-Attention Networks,' https://arxiv.org/abs/2004.08955. Parameters: ---------- out_channels : int Number of output channels. groups : int Number of channel groups (cardinality, without radix). radix : int Number of splits within a cardinal group. reduction : int, default 4 Squeeze reduction value. min_channels : int, default 32 Minimal number of squeezed channels. use_conv : bool, default True Whether to convolutional layers instead of fully-connected ones. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. """ def __init__(self, out_channels, groups, radix, reduction=4, min_channels=32, use_conv=True, bn_eps=1e-5): super(SABlock, self).__init__() self.groups = groups self.radix = radix self.use_conv = use_conv in_channels = out_channels * radix mid_channels = max(in_channels // reduction, min_channels) with self.init_scope(): if use_conv: self.conv1 = conv1x1( in_channels=out_channels, out_channels=mid_channels, use_bias=True) else: self.fc1 = L.Linear( in_size=out_channels, out_size=mid_channels) self.bn = L.BatchNormalization( size=mid_channels, eps=bn_eps) self.activ = F.relu if use_conv: self.conv2 = conv1x1( in_channels=mid_channels, out_channels=in_channels, use_bias=True) else: self.fc2 = L.Linear( in_size=mid_channels, out_size=in_channels) self.softmax = partial( F.softmax, axis=1) def __call__(self, x): batch, channels, height, width = x.shape x = F.reshape(x, shape=(batch, self.radix, channels // self.radix, height, width)) w = F.sum(x, axis=1) w = F.average_pooling_2d(w, ksize=w.shape[2:]) if not self.use_conv: w = F.reshape(w, shape=(w.shape[0], -1)) w = self.conv1(w) if self.use_conv else self.fc1(w) w = self.bn(w) w = self.activ(w) w = self.conv2(w) if self.use_conv else self.fc2(w) w = F.reshape(w, shape=(batch, self.groups, self.radix, -1)) w = F.swapaxes(w, axis1=1, axis2=2) w = self.softmax(w) w = F.reshape(w, shape=(batch, self.radix, -1, 1, 1)) x = x * w x = F.sum(x, axis=1) return x class SAConvBlock(Chain): """ Split-Attention convolution block from 'ResNeSt: Split-Attention Networks,' https://arxiv.org/abs/2004.08955. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. ksize : int or tuple/list of 2 int Convolution window size. stride : int or tuple/list of 2 int Stride of the convolution. pad : int or tuple/list of 2 int Padding value for convolution layer. dilate : int or tuple/list of 2 int, default 1 Dilation value for convolution layer. groups : int, default 1 Number of groups. use_bias : bool, default False Whether the layer uses a bias vector. use_bn : bool, default True Whether to use BatchNorm layer. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. radix : int, default 2 Number of splits within a cardinal group. reduction : int, default 4 Squeeze reduction value. min_channels : int, default 32 Minimal number of squeezed channels. use_conv : bool, default True Whether to convolutional layers instead of fully-connected ones. """ def __init__(self, in_channels, out_channels, ksize, stride, pad, dilate=1, groups=1, use_bias=False, use_bn=True, bn_eps=1e-5, activation=(lambda: F.relu), radix=2, reduction=4, min_channels=32, use_conv=True): super(SAConvBlock, self).__init__() with self.init_scope(): self.conv = ConvBlock( in_channels=in_channels, out_channels=(out_channels * radix), ksize=ksize, stride=stride, pad=pad, dilate=dilate, groups=(groups * radix), use_bias=use_bias, use_bn=use_bn, bn_eps=bn_eps, activation=activation) self.att = SABlock( out_channels=out_channels, groups=groups, radix=radix, reduction=reduction, min_channels=min_channels, use_conv=use_conv, bn_eps=bn_eps) def __call__(self, x): x = self.conv(x) x = self.att(x) return x def saconv3x3_block(in_channels, out_channels, stride=1, pad=1, **kwargs): """ 3x3 version of the Split-Attention convolution block. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. stride : int or tuple/list of 2 int, default 1 Stride of the convolution. pad : int or tuple/list of 2 int, default 1 Padding value for convolution layer. """ return SAConvBlock( in_channels=in_channels, out_channels=out_channels, ksize=3, stride=stride, pad=pad, **kwargs) class PixelShuffle(Chain): """ Pixel-shuffle operation from 'Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network,' https://arxiv.org/abs/1609.05158. Parameters: ---------- scale_factor : int Multiplier for spatial size. """ def __init__(self, scale_factor, **kwargs): super(PixelShuffle, self).__init__(**kwargs) self.scale_factor = scale_factor def __call__(self, x): f1 = self.scale_factor f2 = self.scale_factor batch, channels, height, width = x.shape assert (channels % f1 % f2 == 0) new_channels = channels // f1 // f2 x = F.reshape(x, shape=(batch, new_channels, f1 * f2, height, width)) x = F.reshape(x, shape=(batch, new_channels, f1, f2, height, width)) x = F.transpose(x, axes=(0, 1, 4, 2, 5, 3)) x = F.reshape(x, shape=(batch, new_channels, height * f1, width * f2)) return x class DucBlock(Chain): """ Dense Upsampling Convolution (DUC) block from 'Understanding Convolution for Semantic Segmentation,' https://arxiv.org/abs/1702.08502. Parameters: ---------- in_channels : int Number of input channels. out_channels : int Number of output channels. scale_factor : int Multiplier for spatial size. """ def __init__(self, in_channels, out_channels, scale_factor, **kwargs): super(DucBlock, self).__init__(**kwargs) mid_channels = (scale_factor * scale_factor) * out_channels with self.init_scope(): self.conv = conv3x3_block( in_channels=in_channels, out_channels=mid_channels) self.pix_shuffle = PixelShuffle(scale_factor=scale_factor) def __call__(self, x): x = self.conv(x) x = self.pix_shuffle(x) return x class SimpleSequential(Chain): """ A sequential chain that can be used instead of Sequential. """ def __init__(self): super(SimpleSequential, self).__init__() self.layer_names = [] def __setattr__(self, name, value): super(SimpleSequential, self).__setattr__(name, value) if self.within_init_scope and callable(value): self.layer_names.append(name) def __delattr__(self, name): super(SimpleSequential, self).__delattr__(name) try: self.layer_names.remove(name) except ValueError: pass def __len__(self): return len(self.layer_names) def __call__(self, x): for name in self.layer_names: x = self[name](x) return x def el(self, index): return self[self.layer_names[index]] class DualPathSequential(SimpleSequential): """ A sequential container for blocks with dual inputs/outputs. Blocks will be executed in the order they are added. Parameters: ---------- return_two : bool, default True Whether to return two output after execution. first_ordinals : int, default 0 Number of the first blocks with single input/output. last_ordinals : int, default 0 Number of the final blocks with single input/output. dual_path_scheme : function Scheme of dual path response for a block. dual_path_scheme_ordinal : function Scheme of dual path response for an ordinal block. """ def __init__(self, return_two=True, first_ordinals=0, last_ordinals=0, dual_path_scheme=(lambda block, x1, x2: block(x1, x2)), dual_path_scheme_ordinal=(lambda block, x1, x2: (block(x1), x2))): super(DualPathSequential, self).__init__() self.return_two = return_two self.first_ordinals = first_ordinals self.last_ordinals = last_ordinals self.dual_path_scheme = dual_path_scheme self.dual_path_scheme_ordinal = dual_path_scheme_ordinal def __call__(self, x1, x2=None): length = len(self.layer_names) for i, block_name in enumerate(self.layer_names): block = self[block_name] if (i < self.first_ordinals) or (i >= length - self.last_ordinals): x1, x2 = self.dual_path_scheme_ordinal(block, x1, x2) else: x1, x2 = self.dual_path_scheme(block, x1, x2) if self.return_two: return x1, x2 else: return x1 class Concurrent(SimpleSequential): """ A container for concatenation of modules on the base of the sequential container. Parameters: ---------- axis : int, default 1 The axis on which to concatenate the outputs. stack : bool, default False Whether to concatenate tensors along a new dimension. merge_type : str, default None Type of branch merging. """ def __init__(self, axis=1, stack=False, merge_type=None): super(Concurrent, self).__init__() assert (merge_type is None) or (merge_type in ["cat", "stack", "sum"]) self.axis = axis if merge_type is not None: self.merge_type = merge_type else: self.merge_type = "stack" if stack else "cat" def __call__(self, x): out = [] for name in self.layer_names: out.append(self[name](x)) if self.merge_type == "stack": out = F.stack(tuple(out), axis=self.axis) elif self.merge_type == "cat": out = F.concat(tuple(out), axis=self.axis) elif self.merge_type == "sum": out = F.sum(F.stack(tuple(out), axis=self.axis), self.axis) else: raise NotImplementedError() return out class SequentialConcurrent(SimpleSequential): """ A sequential container with concatenated outputs. Blocks will be executed in the order they are added. Parameters: ---------- axis : int, default 1 The axis on which to concatenate the outputs. stack : bool, default False Whether to concatenate tensors along a new dimension. cat_input : bool, default True Whether to concatenate input tensor. """ def __init__(self, axis=1, stack=False, cat_input=True): super(SequentialConcurrent, self).__init__() self.axis = axis self.stack = stack self.cat_input = cat_input def __call__(self, x): out = [x] if self.cat_input else [] for name in self.layer_names: x = self[name](x) out.append(x) if self.stack: out = F.stack(tuple(out), axis=self.axis) else: out = F.concat(tuple(out), axis=self.axis) return out class ParametricSequential(SimpleSequential): """ A sequential container for modules with parameters. Blocks will be executed in the order they are added. """ def __init__(self): super(ParametricSequential, self).__init__() def __call__(self, x, **kwargs): for name in self.layer_names: x = self[name](x, **kwargs) return x class ParametricConcurrent(SimpleSequential): """ A container for concatenation of modules on the base of the sequential container. Parameters: ---------- axis : int, default 1 The axis on which to concatenate the outputs. """ def __init__(self, axis=1): super(ParametricConcurrent, self).__init__() self.axis = axis def __call__(self, x, **kwargs): out = [] for name in self.layer_names: out.append(self[name](x, **kwargs)) out = F.concat(tuple(out), axis=self.axis) return out class Hourglass(Chain): """ A hourglass block. Parameters: ---------- down_seq : SimpleSequential Down modules as sequential. up_seq : SimpleSequential Up modules as sequential. skip_seq : SimpleSequential Skip connection modules as sequential. merge_type : str, default 'add' Type of concatenation of up and skip outputs. return_first_skip : bool, default False Whether return the first skip connection output. Used in ResAttNet. """ def __init__(self, down_seq, up_seq, skip_seq, merge_type="add", return_first_skip=False): super(Hourglass, self).__init__() self.depth = len(down_seq) assert (merge_type in ["cat", "add"]) assert (len(up_seq) == self.depth) assert (len(skip_seq) in (self.depth, self.depth + 1)) self.merge_type = merge_type self.return_first_skip = return_first_skip self.extra_skip = (len(skip_seq) == self.depth + 1) with self.init_scope(): self.down_seq = down_seq self.up_seq = up_seq self.skip_seq = skip_seq def _merge(self, x, y): if y is not None: if self.merge_type == "cat": x = F.concat((x, y), axis=1) elif self.merge_type == "add": x = x + y return x def __call__(self, x): y = None down_outs = [x] for down_module_name in self.down_seq.layer_names: down_module = self.down_seq[down_module_name] x = down_module(x) down_outs.append(x) for i in range(len(down_outs)): if i != 0: y = down_outs[self.depth - i] skip_module = self.skip_seq.el(self.depth - i) y = skip_module(y) x = self._merge(x, y) if i != len(down_outs) - 1: if (i == 0) and self.extra_skip: skip_module = self.skip_seq.el(self.depth) x = skip_module(x) up_module = self.up_seq.el(self.depth - 1 - i) x = up_module(x) if self.return_first_skip: return x, y else: return x class SesquialteralHourglass(Chain): """ A sesquialteral hourglass block. Parameters: ---------- down1_seq : SimpleSequential The first down modules as sequential. skip1_seq : SimpleSequential The first skip connection modules as sequential. up_seq : SimpleSequential Up modules as sequential. skip2_seq : SimpleSequential The second skip connection modules as sequential. down2_seq : SimpleSequential The second down modules as sequential. merge_type : str, default 'cat' Type of concatenation of up and skip outputs. """ def __init__(self, down1_seq, skip1_seq, up_seq, skip2_seq, down2_seq, merge_type="cat"): super(SesquialteralHourglass, self).__init__() assert (len(down1_seq) == len(up_seq)) assert (len(down1_seq) == len(down2_seq)) assert (len(skip1_seq) == len(skip2_seq)) assert (len(down1_seq) == len(skip1_seq) - 1) assert (merge_type in ["cat", "add"]) self.merge_type = merge_type self.depth = len(down1_seq) with self.init_scope(): self.down1_seq = down1_seq self.skip1_seq = skip1_seq self.up_seq = up_seq self.skip2_seq = skip2_seq self.down2_seq = down2_seq def _merge(self, x, y): if y is not None: if self.merge_type == "cat": x = F.concat((x, y), axis=1) elif self.merge_type == "add": x = x + y return x def __call__(self, x): y = self.skip1_seq[self.skip1_seq.layer_names[0]](x) skip1_outs = [y] for i in range(self.depth): x = self.down1_seq[self.down1_seq.layer_names[i]](x) y = self.skip1_seq[self.skip1_seq.layer_names[i + 1]](x) skip1_outs.append(y) x = skip1_outs[self.depth] y = self.skip2_seq[self.skip2_seq.layer_names[0]](x) skip2_outs = [y] for i in range(self.depth): x = self.up_seq[self.up_seq.layer_names[i]](x) y = skip1_outs[self.depth - 1 - i] x = self._merge(x, y) y = self.skip2_seq[self.skip2_seq.layer_names[i + 1]](x) skip2_outs.append(y) x = self.skip2_seq[self.skip2_seq.layer_names[self.depth]](x) for i in range(self.depth): x = self.down2_seq[self.down2_seq.layer_names[i]](x) y = skip2_outs[self.depth - 1 - i] x = self._merge(x, y) return x class MultiOutputSequential(SimpleSequential): """ A sequential container with multiple outputs. Blocks will be executed in the order they are added. Parameters: ---------- multi_output : bool, default True Whether to return multiple output. dual_output : bool, default False Whether to return dual output. return_last : bool, default True Whether to forcibly return last value. """ def __init__(self, multi_output=True, dual_output=False, return_last=True): super(MultiOutputSequential, self).__init__() self.multi_output = multi_output self.dual_output = dual_output self.return_last = return_last def __call__(self, x): outs = [] for name in self.layer_names: block = self[name] x = block(x) if hasattr(block, "do_output") and block.do_output: outs.append(x) elif hasattr(block, "do_output2") and block.do_output2: assert (type(x) == tuple) outs.extend(x[1]) x = x[0] if self.multi_output: return [x] + outs if self.return_last else outs elif self.dual_output: return x, outs else: return x class ParallelConcurent(SimpleSequential): """ A sequential container with multiple inputs and single/multiple outputs. Modules will be executed in the order they are added. Parameters: ---------- axis : int, default 1 The axis on which to concatenate the outputs. merge_type : str, default 'list' Type of branch merging. """ def __init__(self, axis=1, merge_type="list"): super(ParallelConcurent, self).__init__() assert (merge_type is None) or (merge_type in ["list", "cat", "stack", "sum"]) self.axis = axis self.merge_type = merge_type def __call__(self, x): out = [] for name, xi in zip(self.layer_names, x): out.append(self[name](xi)) if self.merge_type == "list": pass elif self.merge_type == "stack": out = F.stack(tuple(out), axis=self.axis) elif self.merge_type == "cat": out = F.concat(tuple(out), axis=self.axis) elif self.merge_type == "sum": out = F.sum(F.stack(tuple(out), axis=self.axis), self.axis) else: raise NotImplementedError() return out class DualPathParallelConcurent(SimpleSequential): """ A sequential container with multiple dual-path inputs and single/multiple outputs. Modules will be executed in the order they are added. Parameters: ---------- axis : int, default 1 The axis on which to concatenate the outputs. merge_type : str, default 'list' Type of branch merging. """ def __init__(self, axis=1, merge_type="list"): super(DualPathParallelConcurent, self).__init__() assert (merge_type is None) or (merge_type in ["list", "cat", "stack", "sum"]) self.axis = axis self.merge_type = merge_type def __call__(self, x1, x2): x1_out = [] x2_out = [] for name, x1i, x2i in zip(self.layer_names, x1, x2): y1i, y2i = self[name](x1i, x2i) x1_out.append(y1i) x2_out.append(y2i) if self.merge_type == "list": pass elif self.merge_type == "stack": x1_out = F.stack(tuple(x1_out), axis=self.axis) x2_out = F.stack(tuple(x2_out), axis=self.axis) elif self.merge_type == "cat": x1_out = F.concat(tuple(x1_out), axis=self.axis) x2_out = F.concat(tuple(x2_out), axis=self.axis) elif self.merge_type == "sum": x1_out = F.sum(F.stack(tuple(x1_out), axis=self.axis), self.axis) x2_out = F.sum(F.stack(tuple(x2_out), axis=self.axis), self.axis) else: raise NotImplementedError() return x1_out, x2_out class Flatten(Chain): """ Simple flatten block. """ def __call__(self, x): return x.reshape(x.shape[0], -1) class AdaptiveAvgPool2D(Chain): """ Simple adaptive average pooling block. """ def __call__(self, x): return F.average_pooling_2d(x, ksize=x.shape[2:]) class NormActivation(Chain): """ Activation block with preliminary batch normalization. It's used by itself as the final block in PreResNet. Parameters: ---------- in_channels : int Number of input channels. bn_eps : float, default 1e-5 Small float added to variance in Batch norm. activation : function or str or None, default F.relu Activation function or name of activation function. """ def __init__(self, in_channels, bn_eps=1e-5, activation=(lambda: F.relu), **kwargs): super(NormActivation, self).__init__(**kwargs) with self.init_scope(): self.bn = L.BatchNormalization( size=in_channels, eps=bn_eps) self.activ = get_activation_layer(activation) def __call__(self, x): x = self.bn(x) x = self.activ(x) return x class InterpolationBlock(Chain): """ Interpolation block. Parameters: ---------- scale_factor : int Multiplier for spatial size. out_size : tuple of 2 int, default None Spatial size of the output tensor for the bilinear interpolation operation. up : bool, default True Whether to upsample or downsample. mode : str, default 'bilinear' Algorithm used for upsampling. align_corners : bool, default True Whether to align the corner pixels of the input and output tensors. """ def __init__(self, scale_factor, out_size=None, up=True, mode="bilinear", align_corners=True, **kwargs): super(InterpolationBlock, self).__init__(**kwargs) self.scale_factor = scale_factor self.out_size = out_size self.up = up self.mode = mode self.align_corners = align_corners def __call__(self, x, size=None): out_size = self.calc_out_size(x) if size is None else size return F.resize_images(x, output_shape=out_size, mode=self.mode, align_corners=self.align_corners) def calc_out_size(self, x): if self.out_size is not None: return self.out_size if self.up: return tuple(s * self.scale_factor for s in x.shape[2:]) else: return tuple(s // self.scale_factor for s in x.shape[2:]) class HeatmapMaxDetBlock(Chain): """ Heatmap maximum detector block (for human pose estimation task). """ def __init__(self, **kwargs): super(HeatmapMaxDetBlock, self).__init__(**kwargs) def __call__(self, x): heatmap = x vector_dim = 2 batch = heatmap.shape[0] channels = heatmap.shape[1] in_size = x.shape[2:] heatmap_vector = F.reshape(heatmap, shape=(batch, channels, -1)) indices = F.cast(F.expand_dims(F.argmax(heatmap_vector, axis=vector_dim), axis=vector_dim), np.float32) scores = F.max(heatmap_vector, axis=vector_dim, keepdims=True) scores_mask = (scores.array > 0.0).astype(np.float32) pts_x = (indices.array % in_size[1]) * scores_mask pts_y = (indices.array // in_size[1]) * scores_mask pts = F.concat((pts_x, pts_y, scores), axis=vector_dim).array for b in range(batch): for k in range(channels): hm = heatmap[b, k, :, :].array px = int(pts_x[b, k]) py = int(pts_y[b, k]) if (0 < px < in_size[1] - 1) and (0 < py < in_size[0] - 1): pts[b, k, 0] += np.sign(hm[py, px + 1] - hm[py, px - 1]) * 0.25 pts[b, k, 1] += np.sign(hm[py + 1, px] - hm[py - 1, px]) * 0.25 return pts
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dasem/data.py
fronovics/AI_playground
18
63033
<reponame>fronovics/AI_playground """Data. Functions to read datasets from the data subdirectory. """ from os.path import join, split from pandas import read_csv def four_words(): """Read and return four words odd-one-out dataset. Returns ------- >>> df = four_words() >>> df.ix[0, 'word4'] == 'stol' True """ filename = join(split(__file__)[0], 'data', 'four_words.csv') df = read_csv(filename, encoding='utf-8') return df def verbal_analogies(): """Read and return verbal analogies dataset. Returns ------- df : pandas.DataFrame Dataframe with verbal analogies. Examples -------- >>> df = verbal_analogies() >>> df.ix[0, :].tolist() == ['mand', 'kvinde', 'dreng', 'pige'] True """ filename = join(split(__file__)[0], 'data', 'verbal_analogies.csv') df = read_csv(filename, encoding='utf-8') return df def wordsim353(include_problems=False): """Read and return wordsim353 dataset. Parameters ---------- include_problems : bool, optional Indicator for whether rows with problematic translations between Danish and English should be returned [default: False]. Returns ------- df : pandas.DataFrame Dataframe with Danish wordsim353 data. Examples -------- >>> df = wordsim353(include_problems=True) >>> df.shape (353, 6) References ---------- The WordSimilarity-353 Test Collection, http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/ """ filename = join(split(__file__)[0], 'data', 'wordsim353-da', 'combined.csv') df = read_csv(filename, encoding='utf-8') if not include_problems: df = df[df.Problem != 1] return df
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src/graphics/font.py
JacobLondon/pyngine
0
107910
import pygame class Font(object): """@brief Hold a set of fonts and allow for user defined fonts. """ def __init__(self, font, scale): pygame.font.init() # details about the font created self.name = font # scale is the ratio of screen width / number of grids wide the screen is self.scale = scale # make a set of font point sizes for built in components to use self.set = {} # default fonts self.set['small'] = self.named_font(10) self.set['standard'] = self.named_font(20) self.set['large'] = self.named_font(40) def __setitem__(self, key, val): """@brief Add user defined fonts. """ self.set[key] = val def __getitem__(self, key): """@brief Get set fonts. """ return self.set[key] def named_font(self, point): """@brief Create a font from the specified name. """ return Font.create(self.name, point * self.scale) @staticmethod def create(font_name, point): """@brief Simplify creating a font. """ return pygame.font.SysFont(font_name, int(point))
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pinochle/Game.py
Atrus619/DeckOfCards
1
23219
<reponame>Atrus619/DeckOfCards from pinochle.State import State from classes.Deck import Deck from classes.Hand import Hand from pinochle.MeldUtil import MeldUtil from pinochle.Meld import Meld from pinochle.Trick import Trick from pinochle.MeldTuple import MeldTuple from util.Constants import Constants as cs from util.util import print_divider from copy import deepcopy import random import numpy as np import util.state_logger as sl import logging from config import Config as cfg import pinochle.card_util as cu import time import pandas as pd from util.Vectors import Vectors as vs logging.basicConfig(format='%(levelname)s:%(message)s', level=cfg.logging_level) # pinochle rules: https://www.pagat.com/marriage/pin2hand.html class Game: def __init__(self, name, players, run_id="42069", current_cycle=None, human_test=False, config=cfg): # Setting run_id = None results in no records being saved to database self.run_id = run_id self.name = name.upper() self.players = players # This is a list self.number_of_players = len(self.players) self.dealer = players[0] self.trump_card = None self.trump = None self.priority = random.randint(0, 1) self.meld_util = None self.current_cycle = current_cycle # To determine the current value of epsilon self.human_test = human_test self.config = config self.exp_df = pd.DataFrame(columns=['agent_id', 'opponent_id', 'run_id', 'vector', 'action', 'next_vector', 'reward', 'meld_action']) self.last_meld_state = None if self.name == cs.PINOCHLE: self.deck = Deck("pinochle") else: self.deck = Deck() self.hands = {} self.melds = {} self.scores = {} self.meldedCards = {} self.discard_pile = Hand() self.player_inter_trick_history = {} # One entry per player, each entry is a tuple containing (prior_state, row_id entry in initial db update) for player in self.players: self.hands[player] = Hand() self.melds[player] = Meld() self.scores[player] = [0] self.meldedCards[player] = {} def create_state(self, played_card=None): return State(self, played_card) def deal(self): for i in range(12): for player in self.players: self.hands[player].add_cards(self.deck.pull_top_cards(1)) self.trump_card = self.deck.pull_top_cards(1)[0] self.trump = self.trump_card.suit self.meld_util = MeldUtil(self.trump) # Expected card input: VALUE,SUIT. Example: Hindex # H = hand, M = meld def collect_trick_cards(self, player, state): if type(player).__name__ == 'Human': trick_input = player.get_action(state, msg=player.name + " select card for trick:") else: # Bot if self.human_test: logging.debug("Model hand before action:") state.convert_to_human_readable_format(player) trick_index, meld_index = player.get_action(state, self, current_cycle=self.current_cycle, is_trick=True) trick_input, _ = player.convert_model_output(trick_index=trick_index, meld_index=meld_index, game=self, is_trick=True) source = trick_input[0] index = int(trick_input[1:]) if source == "H": card_input = self.hands[player].cards[index] card = self.hands[player].pull_card(card_input) elif source == "M": mt = self.melds[player].pull_melded_card(self.melds[player].melded_cards[index]) card = mt.card print_divider() logging.debug("Player " + player.name + " plays: " + str(card)) # TODO: Fix this later (possible NULL) return card def collect_meld_cards(self, player, state, limit=12): """ Collecting cards for meld scoring from player who won trick :param player: Player we are collecting from :param state: Current state of game :param limit: Maximum number of cards that can be collected :return: list of MeldTuples and whether the interaction was valid (boolean) """ first_hand_card = True valid = True original_hand_cards = deepcopy(self.hands[player]) original_meld_cards = deepcopy(self.melds[player]) collected_hand_cards = [] collected_meld_cards = [] score = 0 meld_class = None combo_name = None if type(player).__name__ == 'Human': while len(collected_hand_cards) + len(collected_meld_cards) < limit: if first_hand_card: print_divider() logging.debug("For meld please select first card from hand.") user_input = player.get_action(state, msg=player.name + " select card, type 'Y' to exit:") if user_input == 'Y': break source = user_input[0] index = int(user_input[1:]) if first_hand_card: if source != "H": print_divider() logging.debug("In case of meld, please select first card from hand.") continue first_hand_card = False if source == "H": card_input = self.hands[player].cards[index] card = self.hands[player].pull_card(card_input) collected_hand_cards.append(card) elif source == "M": mt = self.melds[player].pull_melded_card(self.melds[player].melded_cards[index]) collected_meld_cards.append(mt) # Combine collected hand and meld card lists for score calculation collected_cards = collected_hand_cards + [mt.card for mt in collected_meld_cards] if len(collected_cards) > 0: score, meld_class, combo_name = self.meld_util.calculate_score(collected_cards) if score == 0: valid = False else: for mt in collected_meld_cards: original_meld_class = mt.meld_class if original_meld_class == meld_class: original_meld_score = mt.score if original_meld_score <= score: valid = False break if not valid: self.hands[player] = original_hand_cards self.melds[player] = original_meld_cards else: # Bot valid = True trick_action, meld_action = player.get_action(state, self, current_cycle=self.current_cycle, is_trick=False) if meld_action == vs.MELD_COMBINATIONS_ONE_HOT_VECTOR.__len__(): # model chose to pass melding return [], valid score, meld_class, combo_name, collected_cards = \ player.convert_model_output(trick_index=trick_action, meld_index=meld_action, game=self, is_trick=False) return [MeldTuple(card, combo_name, meld_class, score) for card in collected_cards], valid def play_trick(self): """ priority: 0 or 1 for index in player list :return: index of winner (priority for next trick) """ print_divider() logging.debug(f'Phase 1\tTrick #{12 - len(self.deck)//2}\t{len(self.deck)} card{"s" if len(self.deck) > 1 else ""} remaining in deck') trick_start_state = self.create_state() trick = Trick(self.players, self.trump) # Determine which player goes first based on priority arg """ !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # TRICK PLAYER LIST IS NOT ALWAYS THE SAME AS THE GAME PLAYER LIST # THEY COULD BE IN DIFFERENT ORDER """ player_order = list(self.players) player_1 = player_order.pop(self.priority) player_2 = player_order[0] trick_player_list = [player_1, player_2] # Collect card for trick from each player based on order card_1 = self.collect_trick_cards(player_1, trick_start_state) # Collect card from first player based on priority if self.human_test: time.sleep(cfg.human_test_pause_length) # Recording the first card that was played first_move_state = self.create_state(card_1) if self.human_test and 'get_Qs' in dir(self.players[0].model): print_divider() bot_state = trick_start_state if self.players[0] == player_1 else first_move_state human_state = trick_start_state if self.players[1] == player_1 else first_move_state logging.debug(self.players[0].model.get_Qs(player=self.players[0], player_state=bot_state, opponent=self.players[1], opponent_state=human_state)) if self.players[0] in self.player_inter_trick_history and self.run_id is not None: # Don't update on first trick of game p1_update_dict = {'player': player_1, 'state_1': self.player_inter_trick_history[player_1][0], 'state_2': trick_start_state, 'row_id': self.player_inter_trick_history[player_1][1]} p2_update_dict = {'player': player_2, 'state_1': self.player_inter_trick_history[player_2][0], 'state_2': first_move_state, 'row_id': self.player_inter_trick_history[player_2][1]} self.exp_df = sl.update_state(df=self.exp_df, p1=p1_update_dict, p2=p2_update_dict, win_reward=self.config.win_reward) card_2 = self.collect_trick_cards(player_2, first_move_state) # Collect card from second player based on priority if self.human_test: time.sleep(cfg.human_test_pause_length) print_divider() logging.debug("LETS GET READY TO RUMBLE!!!!!!!!!!!!!!!!!!!!!!!") logging.debug("Card 1: " + str(card_1)) logging.debug("Card 2: " + str(card_2)) if self.human_test: time.sleep(cfg.human_test_pause_length) # Determine winner of trick based on collected cards result = cu.compare_cards(self.trump, card_1, card_2) print_divider() logging.debug("VICTOR : " + str(player_1.name if result == 0 else player_2.name)) if self.human_test: time.sleep(cfg.human_test_pause_length) # Separate winner and loser for scoring, melding, and next hand winner = trick_player_list.pop(result) loser = trick_player_list[0] # Winner draws a card from the stock, followed by the loser drawing a card from the stock # TODO: Come back here and allow winner to choose when down to last 2 cards (optional af) self.hands[winner].add_cards(self.deck.pull_top_cards(1)) if len(self.deck) == 0: self.hands[loser].add_cards(self.trump_card) else: self.hands[loser].add_cards(self.deck.pull_top_cards(1)) # Winner can now meld if they so choose print_divider() logging.debug(winner.name + " select cards for meld:") # Verify that meld is valid. If meld is invalid, force the user to retry. self.last_meld_state = self.create_state() mt_list = [] # no melding in this version while 1: mt_list, valid = self.collect_meld_cards(winner, self.last_meld_state) if valid: break else: print_divider() logging.debug("Invalid combination submitted, please try again.") # Update scores if len(mt_list) == 0: # No cards melded, so score is 0 meld_score = 0 else: meld_score = mt_list[0].score # Score is the same for all MeldTuples in mt_list trick_score = trick.calculate_trick_score(card_1, card_2) total_score = meld_score + trick_score self.discard_pile.add_cards([card_1, card_2]) # log states and actions, player order = TRICK ORDER if self.run_id is not None: p1_dict = {'player': player_1, 'state': trick_start_state, 'card': card_1} p2_dict = {'player': player_2, 'state': first_move_state, 'card': card_2} meld_dict = {'player': winner, 'meld': mt_list} self.exp_df, self.player_inter_trick_history = \ sl.log_state(df=self.exp_df, p1=p1_dict, p2=p2_dict, meld=meld_dict, run_id=self.run_id, history=self.player_inter_trick_history) self.scores[winner].append(self.scores[winner][-1] + total_score) self.scores[loser].append(self.scores[loser][-1]) # Update winner's meld for mt in mt_list: self.melds[winner].add_melded_card(mt) # set new priority self.priority = self.players.index(winner) def play(self): while len(self.deck) > 0: self.play_trick() final_scores = [self.scores[player][-1] for player in self.players] winner_index = np.argmax(final_scores) if self.run_id is not None: # GAME ORDER (because it doesn't matter here) end_game_state = self.create_state() p1_update_dict = {'player': self.players[0], 'state_1': self.player_inter_trick_history[self.players[0]][0], 'state_2': end_game_state, 'row_id': self.player_inter_trick_history[self.players[0]][1]} p2_update_dict = {'player': self.players[1], 'state_1': self.player_inter_trick_history[self.players[1]][0], 'state_2': end_game_state, 'row_id': self.player_inter_trick_history[self.players[1]][1]} self.exp_df = sl.update_state(df=self.exp_df, p1=p1_update_dict, p2=p2_update_dict, winner=self.players[winner_index], win_reward=self.config.win_reward, final_trick_winner=self.players[self.priority]) self.exp_df = sl.log_final_meld(df=self.exp_df, meld_state=self.last_meld_state, history=self.player_inter_trick_history, final_trick_winner=self.players[self.priority], end_game_state=end_game_state, run_id=self.run_id, winner=self.players[winner_index], win_reward=self.config.win_reward) print_divider() logging.debug("Winner: " + str(self.players[winner_index]) + "\tScore: " + str(final_scores[winner_index])) logging.debug( "Loser: " + str(self.players[1 - winner_index]) + "\tScore: " + str(final_scores[1 - winner_index])) return winner_index, None if self.run_id is None else self.exp_df
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fundamentals/15-advance-objects-and-data-structures/5-advance-lists.py
davidokun/Python
0
46298
<filename>fundamentals/15-advance-objects-and-data-structures/5-advance-lists.py # Advance Lists my_list = [1, 2, 3] # Add element print('\n# Add element\n') my_list.append(4) my_list.append(4) print(my_list) # Count element's occurrences print('\n# Count element\'s occurrences\n') print(f'2 = {my_list.count(2)}') print(f'4 = {my_list.count(4)}') print(f'5 = {my_list.count(5)}') # Extend print('\n# Extend\n') x = [1, 2, 3] x.append([4, 5]) print(f'Use append = {x}') x = [1, 2, 3] x.extend([4, 5]) print(f'Use Extend = {x}') # Index print('\n# Index\n') print(f'List = {my_list}') print(f'Index of 2 = {my_list.index(2)}') print(f'Index of 4 = {my_list.index(4)}') # Insert print('\n# Insert\n') print(f'List = {my_list}') my_list.insert(2, 'Inserted') print(f'After insert in pos 2 = {my_list}')
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neon/params/tests/test_val_init.py
zhenglab/neon
0
82262
<reponame>zhenglab/neon #!/usr/bin/env python # ---------------------------------------------------------------------------- # Copyright 2014 Nervana Systems Inc. # 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 math from neon.backends.cpu import CPU from neon.params.val_init import (UniformValGen, AutoUniformValGen, GaussianValGen, NormalValGen, SparseEigenValGen, NodeNormalizedValGen) class TestValInit(object): def __init__(self): # this code gets called prior to each test self.be = CPU() def test_uni_basics(self): uni = UniformValGen(backend=self.be) assert str(uni) == ("UniformValGen utilizing CPU backend\n\t" "low: 0.0, high: 1.0") def test_uni_gen(self): uni = UniformValGen(backend=self.be) res = uni.generate(shape=[1, 1]) assert res.shape == (1, 1) out = self.be.empty((1, 1)) self.be.min(res, axes=None, out=out) assert out.asnumpyarray() >= 0.0 self.be.max(res, axes=None, out=out) assert out.asnumpyarray() < 1.0 def test_uni_params(self): low = -5.5 high = 10.2 uni = UniformValGen(backend=self.be, low=low, high=high) assert str(uni) == ("UniformValGen utilizing CPU backend\n\t" "low: {low}, high: {high}".format(low=low, high=high)) res = uni.generate(shape=[4, 4]) assert res.shape == (4, 4) out = self.be.empty((1, 1)) self.be.min(res, axes=None, out=out) assert out.asnumpyarray() >= low self.be.max(res, axes=None, out=out) assert out.asnumpyarray() < high def test_autouni_gen(self): autouni = AutoUniformValGen(backend=self.be, relu=True) assert autouni.relu is True assert str(autouni) == ("AutoUniformValGen utilizing CPU backend\n\t" "low: nan, high: nan") res = autouni.generate([3, 3]) assert res.shape == (3, 3) out = self.be.empty((1, 1)) self.be.min(res, axes=None, out=out) expected_val = math.sqrt(2) * (1.0 / math.sqrt(3)) assert out.asnumpyarray() >= - expected_val self.be.max(res, axes=None, out=out) assert out.asnumpyarray() < expected_val def test_gaussian_gen(self): loc = 5 scale = 2.0 gauss = GaussianValGen(backend=self.be, loc=loc, scale=scale) assert str(gauss) == ("GaussianValGen utilizing CPU backend\n\t" "loc: {}, scale: {}".format(loc, scale)) res = gauss.generate([5, 10]) assert res.shape == (5, 10) # TODO: test distribution of vals to ensure ~gaussian dist def test_normal_gen(self): loc = -2.5 scale = 3.0 gauss = NormalValGen(backend=self.be, loc=loc, scale=scale) assert str(gauss) == ("GaussianValGen utilizing CPU backend\n\t" "loc: {}, scale: {}".format(loc, scale)) res = gauss.generate([9, 3]) assert res.shape == (9, 3) # TODO: test distribution of vals to ensure ~gaussian dist def test_sparseeig_gen(self): sparseness = 10 eigenvalue = 3.1 eig = SparseEigenValGen(backend=self.be, sparseness=sparseness, eigenvalue=eigenvalue) assert str(eig) == ("SparseEigenValGen utilizing CPU backend\n\t" "sparseness: {}, eigenvalue: " "{}".format(sparseness, eigenvalue)) res = eig.generate([20, 20]) assert res.shape == (20, 20) # TODO: test distribution of vals def test_nodenorm_gen(self): scale = 3.0 nodenorm = NodeNormalizedValGen(backend=self.be, scale=scale) assert str(nodenorm) == ("NodeNormalizedValGen utilizing CPU backend" "\n\tscale: {}".format(scale)) res = nodenorm.generate([8, 9]) assert res.shape == (8, 9) out = self.be.empty((1, 1)) self.be.min(res, axes=None, out=out) expected_val = scale * math.sqrt(6) / math.sqrt(8 + 9.) assert out.asnumpyarray() >= - expected_val self.be.max(res, axes=None, out=out) assert out.asnumpyarray() < expected_val
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Chapter14/c14_11_rainbow_callMaxOn2_viaSimulation.py
John-ye666/Python-for-Finance-Second-Edition
236
30759
""" Name : c14_11_rainbow_callMaxOn2_viaSimulation.py Book : Python for Finance (2nd ed.) Publisher: Packt Publishing Ltd. Author : <NAME> Date : 6/6/2017 email : <EMAIL> <EMAIL> """ import scipy as sp from scipy import zeros, sqrt, shape # sp.random.seed(123) # fix our random numbers s1=100. # stock price 1 s2=95. # stock price 2 k=102.0 # exercise price T=8./12. # maturity in years r=0.08 # risk-free rate rho=0.75 # correlation between 2 sigma1=0.15 # volatility for stock 1 sigma2=0.20 # volatility for stock 1 nSteps=100. # number of steps nSimulation=1000 # number of simulations # # step 1: generate correlated random number dt =T/nSteps call = sp.zeros([nSimulation], dtype=float) x = range(0, int(nSteps), 1) # # step 2: call call prices for j in range(0, nSimulation): x1=sp.random.normal(size=nSimulation) x2=sp.random.normal(size=nSimulation) y1=x1 y2=rho*x1+sp.sqrt(1-rho**2)*x2 sT1=s1 sT2=s2 for i in x[:-1]: e1=y1[i] e2=y2[i] sT1*=sp.exp((r-0.5*sigma1**2)*dt+sigma1*e1*sqrt(dt)) sT2*=sp.exp((r-0.5*sigma2**2)*dt+sigma2*e2*sqrt(dt)) minOf2=min(sT1,sT2) call[j]=max(minOf2-k,0) # # Step 3: summation and discount back call=sp.mean(call)*sp.exp(-r*T) print('Rainbow call on minimum of 2 assets = ', round(call,3))
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python/tests/test_observation.py
lascavana/ecole
0
50001
<gh_stars>0 """Test Ecole observation functions in Python. Most observation functions are written in Ecole C++ library. This is where the logic should be tested. Here, - Some tests automatically run the same assertions on all functions; - Other tests that observation returned form observation functions are bound to the correct types. """ import unittest.mock as mock import numpy as np import ecole.observation as O def test_TupleFunction(model): """Dispach calls and pack the result in a tuple.""" obs_func1, obs_func2 = mock.MagicMock(), mock.MagicMock() tuple_obs_func = O.TupleFunction(obs_func1, obs_func2) tuple_obs_func.reset(model) obs_func1.reset.assert_called_once_with(model) obs_func2.reset.assert_called_once_with(model) obs_func1.obtain_observation.return_value = "something" obs_func2.obtain_observation.return_value = "else" obs = tuple_obs_func.obtain_observation(model) assert obs == ("something", "else") def test_DictFunction(model): """Dispach calls and pack the result in a dict.""" obs_func1, obs_func2 = mock.MagicMock(), mock.MagicMock() dict_obs_func = O.DictFunction(name1=obs_func1, name2=obs_func2) dict_obs_func.reset(model) obs_func1.reset.assert_called_once_with(model) obs_func2.reset.assert_called_once_with(model) obs_func1.obtain_observation.return_value = "something" obs_func2.obtain_observation.return_value = "else" obs = dict_obs_func.obtain_observation(model) assert obs == {"name1": "something", "name2": "else"} def pytest_generate_tests(metafunc): """Parametrize the `observation_function` fixture. Add observation functions here to have them automatically run all the tests that take `observation_function` as input. """ if "observation_function" in metafunc.fixturenames: all_observation_functions = ( O.Nothing(), O.NodeBipartite(), O.StrongBranchingScores(True), O.StrongBranchingScores(False), ) metafunc.parametrize("observation_function", all_observation_functions) def test_default_init(observation_function): """Construct with default arguments.""" type(observation_function)() def test_reset(observation_function, solving_model): """Successive calls to reset.""" observation_function.reset(solving_model) observation_function.reset(solving_model) def test_obtain_observation(observation_function, solving_model): """Obtain observation.""" observation_function.reset(solving_model) observation_function.obtain_observation(solving_model) def make_obs(obs_func, model): obs_func.reset(model) return obs_func.obtain_observation(model) def assert_array(arr, ndim=1, non_empty=True, dtype=np.double): assert isinstance(arr, np.ndarray) assert arr.ndim == ndim assert (not non_empty) or (arr.size > 0) assert arr.dtype == dtype def test_Nothing_observation(model): """Observation of Nothing is None.""" assert make_obs(O.Nothing(), model) is None def test_NodeBipartite_observation(solving_model): """Observation of NodeBipartite is a type with array attributes.""" obs = make_obs(O.NodeBipartite(), solving_model) assert isinstance(obs, O.NodeBipartiteObs) assert_array(obs.column_features, ndim=2) assert_array(obs.row_features, ndim=2) assert_array(obs.edge_features.values) assert_array(obs.edge_features.indices, ndim=2, dtype=np.uint64) def test_StrongBranchingScores_observation(solving_model): """Observation of StrongBranchingScores is a numpy array.""" obs = make_obs(O.StrongBranchingScores(), solving_model) assert_array(obs)
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src/algorithm/__init__.py
ShogoAkiyama/metaworld.pytorch
0
21064
<filename>src/algorithm/__init__.py from .sac import SAC from .eval import EvalAlgorithm
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ozone-framework-python-server/people/urls.py
aamduka/ozone
6
33646
from django.urls import path from rest_framework import routers from .administration.views import AdministrationOfUserAPIView from .views import PersonDetailView, PersonDashboardsWidgetsView, PersonWidgetDefinitionViewSet, PersonStackViewset router = routers.SimpleRouter() router.register(r'admin/users', AdministrationOfUserAPIView) router.register(r'admin/users-widgets', PersonWidgetDefinitionViewSet, base_name='admin_users-widgets') urlpatterns = [ path('me/', PersonDetailView.as_view(), name='user-detail'), path('me/dashboards-widgets/', PersonDashboardsWidgetsView.as_view(), name='user-widgets-dashboards-detail'), path('admin/users-stacks/', PersonStackViewset.as_view(), name='admin_users-stacks') ] urlpatterns += router.urls
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lathermail/default_settings.py
reclosedev/lathermail
13
1601483
<reponame>reclosedev/lathermail import os DB_URI = "sqlite:///" + os.path.expanduser("~/.lathermail.db") DEBUG_MODE = False SMTP_HOST = "127.0.0.1" SMTP_PORT = 2525 API_HOST = "127.0.0.1" API_PORT = 5000 SQLITE_FAST_SAVE = True SQLALCHEMY_TRACK_MODIFICATIONS = False
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acquisitions/providers/fake_agency.py
18F/acqstackdb
2
91553
<filename>acquisitions/providers/fake_agency.py<gh_stars>1-10 import random from faker.providers import BaseProvider class AgencyProvider(BaseProvider): agency_parts = ( ( "Department of", "Office of", "Bureau of", ), ( "the Interior", "Administrating", "Hats", "Management", "Labor", "Finance", "Departments", "Flying" ) ) big_agency_start = ( "Department of", "Office of", "Bureau of", ) big_agency_end = ( "Administration", "Agency", ) medium_agency_end = ( "Division", "Section", ) small_agency_end = ( "Region", "Office", "Room", ) extra_parts = ( "Synergy", "Failure", "High-Profile Success", "First Aid", "Gravy", "Sandwiches", "Wine", "Budget", "Style" ) def agency(self, size="large"): result = [] for part in self.agency_parts: result.append(self.random_element(part)) if random.randint(0, 100) > 70: result.append("and") result.append(self.random_element(self.extra_parts)) return " ".join(result)
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main.py
pepetox/gae-angular-materialize
1
13043
<reponame>pepetox/gae-angular-materialize # Copyright 2013 Google, Inc # # 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 json import modelCourse as model import webapp2 from google.appengine.api import users def AsDict(course): return { 'key': course.key.urlsafe(), 'author': course.author.email(), 'name': course.name, 'description': course.description, 'lang': course.lang, 'date': course.date.strftime("%B %d, %Y") } class RestHandler(webapp2.RequestHandler): def dispatch(self): # time.sleep(1) if (users.get_current_user().email() == '<EMAIL>') | (users.get_current_user().email() == '<EMAIL>'): super(RestHandler, self).dispatch() else: self.abort(402) def SendJson(self, r): self.response.headers['content-type'] = 'text/plain' self.response.write(json.dumps(r)) class QueryHandler(RestHandler): def get(self): courses = model.All() r = [AsDict(course) for course in courses] self.SendJson(r) class UpdateHandler(RestHandler): def post(self): r = json.loads(self.request.body) guest = model.Update(r['key'], r['name'], r['description'], r['lang']) r = AsDict(guest) self.SendJson(r) class InsertHandler(RestHandler): def post(self): r = json.loads(self.request.body) course = model.Insert(r['name'], r['description'], r['lang']) r = AsDict(course) self.SendJson(r) class DeleteHandler(RestHandler): def post(self): r = json.loads(self.request.body) model.Delete(r['key']) class GetUser(RestHandler): def get(self): user = users.get_current_user() if user: email = user.email() url = users.create_logout_url(self.request.uri) url_linktext = 'Logout' else: email = '' url = users.create_login_url(self.request.uri) url_linktext = 'Login' r = {'user': email, 'url': url, 'url_linktext': url_linktext} self.SendJson(r) APP = webapp2.WSGIApplication([ ('/rest/query', QueryHandler), ('/rest/insert', InsertHandler), ('/rest/delete', DeleteHandler), ('/rest/update', UpdateHandler), ('/rest/user', GetUser), ], debug=True)
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apiclient_pydantic/__init__.py
ljnsn/api-client-pydantic
0
1600950
from .serializers import ( ParamsSerializer, ResponseSerializer, params_serializer, response_serializer, serialize, serialize_all_methods, serialize_request, serialize_response, ) __all__ = ( 'params_serializer', 'ParamsSerializer', 'response_serializer', 'ResponseSerializer', 'serialize', 'serialize_all_methods', 'serialize_request', 'serialize_response', )
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src/driver.py
01mu/gamefaqs-scraper
1
38050
# # gamefaqs-scraper # github.com/01mu # from gamefaqs_scraper import GFSBoard from gamefaqs_scraper import GFSThread board = GFSBoard() board.get_site('234547-super-smash-bros-ultimate', 0) threads = board.find() print("Pages: " + str(board.max_page) + "\n") for i in range(len(threads)): print(threads[i].title + "\n" + threads[i].author + "\n" + threads[i].last + "\n" + threads[i].replies + "\n" + threads[i].link + "\n") ''' thread = GFSThread() thread.get_site('234547-super-smash-bros-ultimate/77126753', 0) posts = thread.find() print("Pages: " + str(thread.max_page) + "\n") for i in range(len(posts)): print(posts[i].author + "\n" + posts[i].date + "\n" + posts[i].body + "\n") '''
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LeetCode/0101. Symmetric Tree.py
null-kryptonian/ProblemSolving
1
80328
# Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right class Solution: def isMirror(self, r1: TreeNode, r2: TreeNode) -> bool: if r1 == None and r2 == None: return True if r1 == None or r2 == None: return False return (r1.val == r2.val) and self.isMirror(r1.left, r2.right) and self.isMirror(r1.right, r2.left) def isSymmetric(self, root: TreeNode) -> bool: return self.isMirror(root, root)
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utils/__init__.py
ilvar/lotien
0
56466
<gh_stars>0 from django.conf import settings from django.core.files.storage import get_storage_class thumbnail_storage = get_storage_class(settings.DEFAULT_FILE_STORAGE)()
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mosqito/functions/sharpness/comp_sharpness.py
JosemaAlvarez/MoSQITo
0
137793
<reponame>JosemaAlvarez/MoSQITo # -*- coding: utf-8 -*- """ Created on Mon Nov 16 10:00:50 2020 @author: wantysal """ import sys sys.path.append('../..') # Standard library import import numpy as np # Local imports from mosqito.functions.loudness_zwicker.comp_loudness import comp_loudness from mosqito.functions.sharpness.sharpness_aures import comp_sharpness_aures from mosqito.functions.sharpness.sharpness_din import comp_sharpness_din from mosqito.functions.sharpness.sharpness_bismarck import comp_sharpness_bismarck from mosqito.functions.sharpness.sharpness_fastl import comp_sharpness_fastl def comp_sharpness(is_stationary, signal, fs, method='din', skip=0): """ Acoustic sharpness calculation according to different methods: Aures, <NAME>, DIN 45692, Fastl Parameters: ---------- is_stationary: boolean True if the signal is stationary, false if it is time varying signal: numpy.array time history values fs: integer sampling frequency method: string 'din' by default,'aures', 'bismarck','fastl' skip : float number of second to be cut at the beginning of the analysis Outputs ------ S : float sharpness value """ if method!= 'din' and method!='aures' and method !='fastl' and method != 'bismarck' : raise ValueError("ERROR: method must be 'din', 'aures', 'bismarck', 'fastl'") loudness = comp_loudness(is_stationary, signal, fs) if method == 'din': S = comp_sharpness_din(loudness['values'], loudness['specific values'], is_stationary ) elif method == 'aures': S = comp_sharpness_aures(loudness['values'], loudness['specific values'], is_stationary ) elif method == 'bismarck': S = comp_sharpness_bismarck(loudness['values'], loudness['specific values'], is_stationary ) elif method == 'fastl': S = comp_sharpness_fastl(loudness['values'], loudness['specific values'], is_stationary ) if is_stationary == False: # Cut transient effect time = np.linspace(0, len(signal/fs, len(S))) cut_index = np.argmin(np.abs(time - skip)) S = S[cut_index:] output = { "name" : "sharpness", "method" : method, "values" : S, "skip" : skip } return output
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color/closest_term_colors.py
nixprime/jhome
0
121970
<reponame>nixprime/jhome #!/usr/bin/env python from __future__ import print_function import fileinput import math import re import jcolor CTERM_LAB = {code: jcolor.TERM256_RGB[code].to_xyz().to_lab() for code in xrange(16, 256)} def color_dist(x_lab, y_lab): dl = x_lab.L - y_lab.L da = x_lab.a - y_lab.a db = x_lab.b - y_lab.b return math.sqrt(dl*dl + da*da + db*db) def closest_term_color(srgb): lab = srgb.to_xyz().to_lab() closest_code = 16 closest_dist = color_dist(lab, CTERM_LAB[16]) for code in xrange(17, 256): dist = color_dist(lab, CTERM_LAB[code]) if dist < closest_dist: closest_code = code closest_dist = dist return closest_code if __name__ == "__main__": hex_str_re = re.compile(r"#[0-9a-fA-F]{6}") for line in fileinput.input(): for hex_str in hex_str_re.findall(line): code = closest_term_color(jcolor.sRGBColor.from_str(hex_str)) print("%s => %d (%s)" % ( hex_str, code, str(jcolor.TERM256_RGB[code])))
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.mypy/type_coverage.py
MatthewWilkes/pyramid-realworld-example-app
0
141574
<gh_stars>0 """Verify that minimum type coverage is reached.""" from pathlib import Path import argparse import sys def main(argv=sys.argv) -> None: # pragma: no cover """Run type coverage check.""" parser = argparse.ArgumentParser( usage=("python type_coverage.py coverage=80 file=typecov/linecount.txt \n") ) parser.add_argument( "coverage", type=float, metavar="<coverage>", help="Minimum required type coverage.", ) parser.add_argument( "file", type=str, metavar="<file>", help="File with line count type coverage report.", ) args = parser.parse_args() report = Path(args.file) min_coverage = args.coverage if not report.is_file(): sys.stdout.write(f"ERROR Line count report file not found on: {report}\n") sys.exit(1) with open(report) as f: coverage_summary = f.readline() if not coverage_summary: sys.stdout.write(f"ERROR Line count report file {report} is empty.\n") sys.exit(1) values = coverage_summary.split() coverage = int(values[0]) / int(values[1]) * 100 if coverage >= min_coverage: sys.stdout.write(f"Total coverage: {coverage}%\n") sys.exit(0) else: sys.stdout.write( f"FAIL Required type coverage of {min_coverage}% not reached. Total coverage: {coverage}%\n" ) sys.exit(1) if __name__ == "__main__": main()
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