fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f7295e891b8f82eac3622be486b13ebd9a4de231

582

py

Python

math/increment_num.py

ethyl2/code_challenges

3c9ccca1782f92728e60a515a7ca797f6d470e81

[ "MIT" ]

null

math/increment_num.py

ethyl2/code_challenges

3c9ccca1782f92728e60a515a7ca797f6d470e81

[ "MIT" ]

null

math/increment_num.py

ethyl2/code_challenges

3c9ccca1782f92728e60a515a7ca797f6d470e81

[ "MIT" ]

null

""" https://leetcode.com/problems/plus-one/ Given a non-empty arr of digits representing a non-neg int, increment that int by 1. Most significant digit is at the head of the list. Each el contains a single digit. The int doesn't contain leading zeros, except for the int 0 itself. examples: [1,2,3] -> [1,2,4] [4,3,2,1] -> [4,3,2,2] """ from typing import List class Solution: def plusOne(self, digits: List[int]) -> List[int]: digits[-1] += 1 return int(''.join(map(str, digits))) s = Solution() print(s.plusOne([1, 2, 3])) print(s.plusOne([4, 3, 2, 1]))

23.28

84

0.652921

from typing import List class Solution: def plusOne(self, digits: List[int]) -> List[int]: digits[-1] += 1 return int(''.join(map(str, digits))) s = Solution() print(s.plusOne([1, 2, 3])) print(s.plusOne([4, 3, 2, 1]))

true

f729608a38e760963ece4719b36116f0a7dd4182

511

py

Python

py/signal.py

jzhou/ai

a5efbfb5e93e404129c974491705c24e9bc49c9d

[ "MIT" ]

null

py/signal.py

jzhou/ai

a5efbfb5e93e404129c974491705c24e9bc49c9d

[ "MIT" ]

null

py/signal.py

jzhou/ai

a5efbfb5e93e404129c974491705c24e9bc49c9d

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- import signal, os import time def handler(signum, frame): print ("Signal handler called with signal: " +str(signum)) print ("complete operations needed when alarm received") def main(): signal.signal(signal.SIGALRM, handler) print ("set alarm signal") signal.alarm(3) print ("before alarm") time.sleep(5) signal.alarm(0) # Disable the alarm print ("after alarm, alarm disabled") if __name__=="__main__": main()

22.217391

62

0.643836

import signal, os import time def handler(signum, frame): print ("Signal handler called with signal: " +str(signum)) print ("complete operations needed when alarm received") def main(): signal.signal(signal.SIGALRM, handler) print ("set alarm signal") signal.alarm(3) print ("before alarm") time.sleep(5) signal.alarm(0) print ("after alarm, alarm disabled") if __name__=="__main__": main()

true

f72960a610aeb01762a3fbc0fba450b38edd8a76

489

py

Python

pyhammer/tasks/svn/tortoisesvncommittask.py

webbers/pyhammer

84efafed65ab05c071a55944b91343b9fd1ef58e

[ "MIT" ]

2

2015-07-06T15:57:33.000Z

2016-09-10T11:46:24.000Z

pyhammer/tasks/svn/tortoisesvncommittask.py

webbers/pyhammer

84efafed65ab05c071a55944b91343b9fd1ef58e

[ "MIT" ]

null

pyhammer/tasks/svn/tortoisesvncommittask.py

webbers/pyhammer

84efafed65ab05c071a55944b91343b9fd1ef58e

[ "MIT" ]

null

# -*- coding: utf-8 -*- import subprocess from pyhammer.tasks.taskbase import TaskBase class TortoiseSvnCommitTask( TaskBase ): def __init__( self, path ): super(TortoiseSvnCommitTask, self).__init__() self.__path = path def RunCmd( self, CmdLine, CmdDir = None ): p = subprocess.Popen( CmdLine, cwd = CmdDir ) return p.wait() def build( self ): self.RunCmd( "TortoiseProc /command:commit /path:" + self.__path ) return True

28.764706

74

0.642127

import subprocess from pyhammer.tasks.taskbase import TaskBase class TortoiseSvnCommitTask( TaskBase ): def __init__( self, path ): super(TortoiseSvnCommitTask, self).__init__() self.__path = path def RunCmd( self, CmdLine, CmdDir = None ): p = subprocess.Popen( CmdLine, cwd = CmdDir ) return p.wait() def build( self ): self.RunCmd( "TortoiseProc /command:commit /path:" + self.__path ) return True

true

f72960c7b2824b79159bedb69ae89d8779ae572d

454

py

Python

app/utils/data_imports.py

thiagomurtinho/Iris_Classification

8b04fed7f7162c3a6bd276c0dbfb2c291c02492c

[ "MIT" ]

null

app/utils/data_imports.py

thiagomurtinho/Iris_Classification

8b04fed7f7162c3a6bd276c0dbfb2c291c02492c

[ "MIT" ]

null

app/utils/data_imports.py

thiagomurtinho/Iris_Classification

8b04fed7f7162c3a6bd276c0dbfb2c291c02492c

[ "MIT" ]

null

def extraction_colunms_value(DataFrame, DataCompare, ColumName): data = [] index = DataFrame.Species.str.contains(DataCompare) if(ColumName == 'SepalLengthCm'): data = DataFrame[index].SepalLengthCm if(ColumName == 'SepalWidthCm'): data = DataFrame[index].SepalWidthCm if(ColumName == 'PetalWidthCm'): data = DataFrame[index].PetalWidthCm if(ColumName == 'PetalLengthCm'): data = DataFrame[index].PetalLengthCm return data

32.428571

64

0.72467

def extraction_colunms_value(DataFrame, DataCompare, ColumName): data = [] index = DataFrame.Species.str.contains(DataCompare) if(ColumName == 'SepalLengthCm'): data = DataFrame[index].SepalLengthCm if(ColumName == 'SepalWidthCm'): data = DataFrame[index].SepalWidthCm if(ColumName == 'PetalWidthCm'): data = DataFrame[index].PetalWidthCm if(ColumName == 'PetalLengthCm'): data = DataFrame[index].PetalLengthCm return data

true

f72961a71ee62380a011d47d8b613f6a186272ad

3,766

py

Python

modules/fluid/fluid_variationalform.py

marchirschvogel/ambit

9c21852d2c7c562b7accdd34025fc6b829eb1d3e

[ "BSD-4-Clause" ]

3

2021-03-22T14:17:09.000Z

2021-05-03T15:24:09.000Z

modules/fluid/fluid_variationalform.py

marchirschvogel/ambit

9c21852d2c7c562b7accdd34025fc6b829eb1d3e

[ "BSD-4-Clause" ]

null

modules/fluid/fluid_variationalform.py

marchirschvogel/ambit

9c21852d2c7c562b7accdd34025fc6b829eb1d3e

[ "BSD-4-Clause" ]

2

2021-03-29T10:52:09.000Z

2021-11-26T15:56:38.000Z

#!/usr/bin/env python3 # Copyright (c) 2019-2022, Dr.-Ing. Marc Hirschvogel # All rights reserved. # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. import ufl # fluid mechanics variational forms class # Principle of Virtual Power # TeX: \delta \mathcal{P} = \delta \mathcal{P}_{\mathrm{kin}} + \delta \mathcal{P}_{\mathrm{int}} - \delta \mathcal{P}_{\mathrm{ext}} = 0, \quad \forall \; \delta\boldsymbol{v} class variationalform: def __init__(self, var_v, dv, var_p, dp, n=None): self.var_v = var_v self.var_p = var_p self.dv = dv self.dp = dp self.n = n ### Kinetic virtual power # TeX: \delta \mathcal{P}_{\mathrm{kin}} := \int\limits_{\Omega} \rho \left(\frac{\partial\boldsymbol{v}}{\partial t} + (\boldsymbol{\nabla}\otimes\boldsymbol{v})^{\mathrm{T}}\boldsymbol{v}\right) \cdot \delta\boldsymbol{v} \,\mathrm{d}v def deltaP_kin(self, a, v, rho, ddomain, v_old=None): if v_old is None: return rho*ufl.dot(a + ufl.grad(v) * v, self.var_v)*ddomain else: return rho*ufl.dot(a + ufl.grad(v) * v_old, self.var_v)*ddomain ### Internal virtual power # TeX: \delta \mathcal{P}_{\mathrm{int}} := \int\limits_{\Omega} \boldsymbol{\sigma} : \delta\boldsymbol{\gamma} \,\mathrm{d}v def deltaP_int(self, sig, ddomain): # TeX: \int\limits_{\Omega}\boldsymbol{\sigma} : \delta \boldsymbol{\gamma}\,\mathrm{d}v var_gamma = 0.5*(ufl.grad(self.var_v).T + ufl.grad(self.var_v)) return ufl.inner(sig, var_gamma)*ddomain def deltaP_int_pres(self, v, ddomain): # TeX: \int\limits_{\Omega}\mathrm{div}\boldsymbol{v}\,\delta p\,\mathrm{d}v return ufl.div(v)*self.var_p*ddomain def residual_v_strong(self, a, v, rho, sig): return rho*(a + ufl.grad(v) * v) - ufl.div(sig) def residual_p_strong(self, v): return ufl.div(v) def f_inert(self, a, v, rho): return rho*(a + ufl.grad(v) * v) def f_viscous(self, sig): return ufl.div(dev(sig)) ### External virtual power # Neumann load (Cauchy traction) # TeX: \int\limits_{\Gamma} \hat{\boldsymbol{t}} \cdot \delta\boldsymbol{v} \,\mathrm{d}a def deltaP_ext_neumann(self, func, dboundary): return ufl.dot(func, self.var_v)*dboundary # Neumann load in normal direction (Cauchy traction) # TeX: \int\limits_{\Gamma} p\,\boldsymbol{n}\cdot\delta\boldsymbol{v}\;\mathrm{d}a def deltaP_ext_neumann_normal(self, func, dboundary): return func*ufl.dot(self.n, self.var_v)*dboundary # Robin condition (dashpot) # TeX: \int\limits_{\Gamma} c\,\boldsymbol{v}\cdot\delta\boldsymbol{v}\;\mathrm{d}a def deltaP_ext_robin_dashpot(self, v, c, dboundary): return -c*(ufl.dot(v, self.var_v)*dboundary) # Robin condition (dashpot) in normal direction # TeX: \int\limits_{\Gamma} (\boldsymbol{n}\otimes \boldsymbol{n})\,c\,\boldsymbol{v}\cdot\delta\boldsymbol{v}\;\mathrm{d}a def deltaP_ext_robin_dashpot_normal(self, v, c_n, dboundary): return -c_n*(ufl.dot(v, self.n)*ufl.dot(self.n, self.var_v)*dboundary) ### Flux coupling conditions # flux # TeX: \int\limits_{\Gamma} \boldsymbol{n}\cdot\boldsymbol{v}\;\mathrm{d}a def flux(self, v, dboundary): return ufl.dot(self.n, v)*dboundary # surface - derivative of pressure load w.r.t. pressure # TeX: \int\limits_{\Gamma} \boldsymbol{n}\cdot\delta\boldsymbol{v}\;\mathrm{d}a def surface(self, dboundary): return ufl.dot(self.n, self.var_v)*dboundary

36.211538

241

0.624535

import ufl class variationalform: def __init__(self, var_v, dv, var_p, dp, n=None): self.var_v = var_v self.var_p = var_p self.dv = dv self.dp = dp self.n = n ddomain, v_old=None): if v_old is None: return rho*ufl.dot(a + ufl.grad(v) * v, self.var_v)*ddomain else: return rho*ufl.dot(a + ufl.grad(v) * v_old, self.var_v)*ddomain var_gamma = 0.5*(ufl.grad(self.var_v).T + ufl.grad(self.var_v)) return ufl.inner(sig, var_gamma)*ddomain def deltaP_int_pres(self, v, ddomain): return ufl.div(v)*self.var_p*ddomain def residual_v_strong(self, a, v, rho, sig): return rho*(a + ufl.grad(v) * v) - ufl.div(sig) def residual_p_strong(self, v): return ufl.div(v) def f_inert(self, a, v, rho): return rho*(a + ufl.grad(v) * v) def f_viscous(self, sig): return ufl.div(dev(sig)) func, dboundary): return ufl.dot(func, self.var_v)*dboundary def deltaP_ext_neumann_normal(self, func, dboundary): return func*ufl.dot(self.n, self.var_v)*dboundary def deltaP_ext_robin_dashpot(self, v, c, dboundary): return -c*(ufl.dot(v, self.var_v)*dboundary) def deltaP_ext_robin_dashpot_normal(self, v, c_n, dboundary): return -c_n*(ufl.dot(v, self.n)*ufl.dot(self.n, self.var_v)*dboundary) return ufl.dot(self.n, v)*dboundary def surface(self, dboundary): return ufl.dot(self.n, self.var_v)*dboundary

true

f72961c755fe3f726acfd0ab5f4be711323edce6

51

py

Python

paddlenlp/transformers/nezha/__init__.py

tanhanzhuo/PaddleNLP

d0d20678f2bec820570b4f09ca49cd402d20c3b6

[ "Apache-2.0" ]

7,091

2021-02-05T13:56:25.000Z

2022-03-31T11:42:50.000Z

paddlenlp/transformers/nezha/__init__.py

xihuanafeng/PaddleNLP

14c3209118b2cadcce9a8f66b760c9cddb3a02ad

[ "Apache-2.0" ]

844

2021-02-10T01:09:29.000Z

2022-03-31T12:12:58.000Z

paddlenlp/transformers/nezha/__init__.py

xihuanafeng/PaddleNLP

14c3209118b2cadcce9a8f66b760c9cddb3a02ad

[ "Apache-2.0" ]

1,035

2021-02-05T14:26:48.000Z

2022-03-31T11:42:57.000Z

from .modeling import * from .tokenizer import *

17

25

0.72549

from .modeling import * from .tokenizer import *

true

f72961fc4ed12b1c027b054640810df1e55e9e82

31,380

py

Python

hyperparameter_hunter/reporting.py

mdjabc/hyperparameter_hunter

bfbd1faf63272a62e6f971d7e9a0487d71aea8f6

[ "MIT" ]

1

2019-04-22T02:22:03.000Z

hyperparameter_hunter/reporting.py

mdjabc/hyperparameter_hunter

bfbd1faf63272a62e6f971d7e9a0487d71aea8f6

[ "MIT" ]

null

hyperparameter_hunter/reporting.py

mdjabc/hyperparameter_hunter

bfbd1faf63272a62e6f971d7e9a0487d71aea8f6

[ "MIT" ]

null

################################################## # Import Own Assets ################################################## from hyperparameter_hunter import exceptions from hyperparameter_hunter.settings import G from hyperparameter_hunter.utils.general_utils import now_time, expand_mins_secs ################################################## # Import Miscellaneous Assets ################################################## from contextlib import suppress from datetime import datetime import inspect import logging import os.path import sys class ReportingHandler(object): def __init__( self, heartbeat_path=None, float_format="{:.5f}", console_params=None, heartbeat_params=None, add_frame=False, ): """Class in control of logging methods, log formatting, and initializing Experiment logging Parameters ---------- heartbeat_path: Str path, or None, default=None If string and valid heartbeat path, logging messages will also be saved in this file float_format: String, default='{:.5f}' If not default, must be a valid formatting string for floating point values. If invalid, default will be used console_params: Dict, or None, default=None Parameters passed to :meth:`_configure_console_handler` heartbeat_params: Dict, or None, default=None Parameters passed to :meth:`_configure_heartbeat_handler` add_frame: Boolean, default=False If True, whenever :meth:`log` is called, the source of the call will be prepended to the content being logged""" self.reporting_type = "logging" # TODO: Add `reporting_type` kwarg (logging, advanced) self.heartbeat_path = heartbeat_path self.float_format = float_format self.console_params = console_params or {} self.heartbeat_params = heartbeat_params or {} self.add_frame = add_frame self._validate_parameters() self._configure_reporting_type() def _validate_parameters(self): """Ensure all logging parameters are properly formatted""" #################### reporting_type #################### valid_types = ["logging", "standard", "advanced"] if not isinstance(self.reporting_type, str): raise TypeError(f"reporting_type must be a str. Received {self.reporting_type}") if self.reporting_type not in valid_types: raise ValueError(f"reporting_type must be in {valid_types}, not {self.reporting_type}") #################### heartbeat_path #################### if self.heartbeat_path is not None: if not isinstance(self.heartbeat_path, str): raise TypeError(f"heartbeat_path must be a str. Received {self.heartbeat_path}") head, tail = os.path.split(self.heartbeat_path) if not tail.endswith(".log"): raise ValueError(f"heartbeat_path must end in '.log'. Given {self.heartbeat_path}") if not os.path.exists(head): raise FileNotFoundError( f"heartbeat_path must start with an existing dir. Given {self.heartbeat_path}" ) #################### float_format #################### if not isinstance(self.float_format, str): raise TypeError(f"float_format must be a format str. Received {self.float_format}") if (not self.float_format.startswith("{")) or (not self.float_format.endswith("}")): raise ValueError(f"float_format must be inside '{{' and '}}'. Got {self.float_format}") #################### console_params #################### if not isinstance(self.console_params, dict): raise TypeError(f"console_params must be dict or None. Given {self.console_params}") #################### heartbeat_params #################### if not isinstance(self.heartbeat_params, dict): raise TypeError(f"heartbeat_params must be dict or None. Given {self.heartbeat_params}") def _configure_reporting_type(self): """Set placeholder logging methods to :attr:`reporting_type` specs and initialize logging""" if self.reporting_type == "standard": raise ValueError("Standard logging is not yet implemented. Please choose 'logging'") # setattr(self, 'log', self._standard_log) # setattr(self, 'debug', self._standard_debug) # setattr(self, 'warn', self._standard_warn) elif self.reporting_type == "logging": setattr(self, "log", self._logging_log) setattr(self, "debug", self._logging_debug) setattr(self, "warn", self._logging_warn) self._initialize_logging_logging() elif self.reporting_type == "advanced": raise ValueError("Advanced logging unimplemented. Please use 'logging'") def _initialize_logging_logging(self): """Initialize and configure logging to be handled by the `logging` library""" #################### Clear Logging Configuration #################### root = logging.getLogger() list(map(root.removeHandler, root.handlers[:])) list(map(root.removeFilter, root.filters[:])) #################### Configure Logging #################### exceptions.hook_exception_handler() _logger = logging.getLogger(__name__) _logger.setLevel(logging.DEBUG) handlers = [self._configure_console_handler(**self.console_params)] # Suppress FileExistsError - Raised when self.heartbeat_path is None, meaning heartbeat blacklisted with suppress(FileExistsError): handlers.append(self._configure_heartbeat_handler(**self.heartbeat_params)) logging.basicConfig(handlers=handlers, level=logging.DEBUG) self.debug("Logging Logging has been initialized!") # noinspection PyUnusedLocal @staticmethod def _configure_console_handler(level="INFO", fmt=None, datefmt="%H:%M:%S", style="%", **kwargs): """Configure the console handler in charge of printing log messages Parameters ---------- level: String, or Int, default='DEBUG' Minimum message level for the console. Passed to :meth:`logging.StreamHandler.setlevel` fmt: String, or None, default=None Message formatting string for the console. Passed to :meth:`logging.Formatter.__init__` datefmt: String, or None, default="%H:%M:%S" Date formatting string for the console. Passed to :meth:`logging.Formatter.__init__`. For the `logging` library default, use `datefmt=None` ("%Y-%m-%d %H:%M:%S" + <ms>) style: String, default='%' Type of string formatting used. Passed to :meth:`logging.Formatter.__init__` **kwargs: Dict Extra keyword arguments Returns ------- console_handler: `logging.StreamHandler` instance The instantiated handler for the console""" console_handler = logging.StreamHandler(stream=sys.stdout) console_handler.setLevel(level) fmt = fmt or "<%(asctime)s> %(message)s" formatter = logging.Formatter(fmt=fmt, datefmt=datefmt, style=style) console_handler.setFormatter(formatter) return console_handler # noinspection PyUnusedLocal def _configure_heartbeat_handler( self, level="DEBUG", fmt=None, datefmt=None, style="%", **kwargs ): """Configure the file handler in charge of adding log messages to the heartbeat file Parameters ---------- level: String, or Int, default='DEBUG' Minimum message level for the heartbeat file. Passed to :meth:`logging.FileHandler.setlevel` fmt: String, or None, default=None Message formatting string for the heartbeat file. Passed to :meth:`logging.Formatter.__init__` datefmt: String, or None, default=None Date formatting string for the heartbeat file. Passed to :meth:`logging.Formatter.__init__` style: String, default='%' Type of string formatting used. Passed to :meth:`logging.Formatter.__init__` **kwargs: Dict Extra keyword arguments Returns ------- file_handler: `logging.FileHandler` instance The instantiated handler for the heartbeat file""" if self.heartbeat_path is None: raise FileExistsError file_handler = logging.FileHandler(self.heartbeat_path, mode="w") file_handler.setLevel(level) fmt = fmt or "<%(asctime)s> %(levelname)-8s - %(message)s" formatter = logging.Formatter(fmt=fmt, datefmt=datefmt, style=style) file_handler.setFormatter(formatter) return file_handler ################################################## # Placeholder Methods: ################################################## def log(self, content, **kwargs): """Placeholder method before proper initialization""" def debug(self, content, **kwargs): """Placeholder method before proper initialization""" def warn(self, content, **kwargs): """Placeholder method before proper initialization""" ################################################## # Logging-Logging Methods: ################################################## # noinspection PyUnusedLocal def _logging_log( self, content, verbose_threshold=None, previous_frame=None, add_time=False, **kwargs ): """Log an info message via the `logging` library Parameters ---------- content: String The message to log verbose_threshold: Int, or None, default=None If None, `content` logged normally. If int and `G.Env.verbose` >= `verbose_threshold`, `content` is logged normally. Else if int and `G.Env.verbose` < `verbose_threshold`, then `content` is logged on the `logging.debug` level, instead of `logging.info` previous_frame: Frame, or None, default=None The frame preceding the log call. If not provided, it will be inferred add_time: Boolean, default=False If True, the current time will be added to `content` before logging **kwargs: Dict Extra keyword arguments""" if self.add_frame is True: previous_frame = previous_frame or inspect.currentframe().f_back try: frame_source = format_frame_source(previous_frame) finally: del previous_frame content = f"{frame_source} - {content}" content = add_time_to_content(content, add_time=add_time) if (verbose_threshold is None) or (G.Env.verbose >= verbose_threshold): logging.info(content) else: logging.debug(content) # noinspection PyUnusedLocal def _logging_debug(self, content, previous_frame=None, add_time=False, **kwargs): """Log a debug message via the `logging` library Parameters ---------- content: String The message to log previous_frame: Frame, or None, default=None The frame preceding the debug call. If not provided, it will be inferred add_time: Boolean, default=False If True, the current time will be added to `content` before logging **kwargs: Dict Extra keyword arguments""" if self.add_frame is True: previous_frame = previous_frame or inspect.currentframe().f_back try: frame_source = format_frame_source(previous_frame) finally: del previous_frame content = f"{frame_source} - {content}" content = add_time_to_content(content, add_time=add_time) logging.debug(content) # noinspection PyUnusedLocal def _logging_warn(self, content, **kwargs): """Log a warning message via the `logging` library Parameters ---------- content: String The message to log **kwargs: Dict Extra keyword arguments""" if self.add_frame is True: previous_frame = inspect.currentframe().f_back try: frame_source = format_frame_source(previous_frame) finally: del previous_frame content = f"{frame_source} - {content}" logging.warning(content) class _Color: """Object defining color codes for use with logging""" BLUE = "\033[34m" CYAN = "\033[36m" GREEN = "\033[32m" MAGENTA = "\033[35m" RED = "\033[31m" STOP = "\033[0m" class OptimizationReporter: def __init__(self, parameter_names, verbose=1, show_experiment_id=8, do_maximize=True): """A MixIn class for reporting the results of hyperparameter optimization rounds Parameters ---------- parameter_names: List The names of the hyperparameters being evaluated and optimized verbose: Int in [0, 1, 2], default=1 If 0, all but critical logging is silenced. If 1, normal logging is performed. If 2, detailed logging is performed show_experiment_id: Int, or Boolean, default=8 If True, the experiment_id will be printed in each result row. If False, it will not. If int, the first `show_experiment_id`-many characters of each experiment_id will be printed in each row do_maximize: Boolean, default=True If False, smaller metric values will be considered preferred and will be highlighted to stand out. Else larger metric values will be treated as preferred""" self.original_parameter_names = parameter_names self.verbose = verbose self.show_experiment_id = ( 36 if (show_experiment_id is True or show_experiment_id > 36) else show_experiment_id ) self.do_maximize = do_maximize self.end = " | " self.y_max = None self.x_max = None self.iteration = 0 self.start_time = datetime.now() self.last_round = datetime.now() skip = ("model_init_params", "model_extra_params", "feature_engineer", "feature_selector") self.parameter_names = [_[1:] if _[0] in skip else _ for _ in self.original_parameter_names] self.parameter_names = [_[1:] if _[0] == "params" else _ for _ in self.parameter_names] self.parameter_names = [ _[0] if len(_) == 1 else str(_).replace("'", "").replace('"', "") for _ in self.parameter_names ] self.sizes = [max(len(_), 7) for _ in self.parameter_names] self.sorted_indexes = sorted( range(len(self.parameter_names)), key=self.parameter_names.__getitem__ ) def print_saved_results_header(self): """Print a header signifying that saved Experiment results are being read""" header = f"{_Color.RED}Saved Result Files{_Color.STOP}" self.print_header(header, (_Color.RED + "_" * self._line_len() + _Color.STOP)) def print_random_points_header(self): """Print a header signifying that random point evaluation rounds are starting""" header = f"{_Color.RED}Random Point Evaluation{_Color.STOP}" self.print_header(header, (_Color.RED + "_" * self._line_len() + _Color.STOP)) def print_optimization_header(self): """Print a header signifying that Optimization rounds are starting""" header = f"{_Color.RED}Hyperparameter Optimization{_Color.STOP}" self.print_header(header, (_Color.RED + "_" * self._line_len() + _Color.STOP)) def _line_len(self): """Calculate number of characters a header's underlining should span Returns ------- line_len: Int The number of characters the line should span""" line_len = 29 line_len += sum([_ + 5 for _ in self.sizes]) line_len += self.show_experiment_id + 3 if self.show_experiment_id else 0 return line_len def print_header(self, header, line): """Utility to perform actual printing of headers given formatted inputs Parameters ---------- header: String Specifies the stage of optimization being entered, and the type of results to follow line: String The underlining to follow `header`""" print(header) print(line) self._print_column_name("Step", 5) if self.show_experiment_id: self._print_column_name("ID", self.show_experiment_id) self._print_column_name("Time", 6) self._print_column_name("Value", 10) for index in self.sorted_indexes: self._print_column_name(self.parameter_names[index], self.sizes[index] + 2) print("") def _print_column_name(self, value, size): """Print a column name within a specified `size` constraint Parameters ---------- value: String The name of the column to print size: Int The number of characters that `value` should span""" try: print("{0:>{1}}".format(value, size), end=self.end) except TypeError: # Probably given tuple including param origin (init_params, extra_params, etc.) if len(value) == 1: print("{0:>{1}}".format(value[0], size), end=self.end) else: print("{0:>{1}}".format(str(value), size), end=self.end) def print_result(self, hyperparameters, evaluation, experiment_id=None): """Print a row containing the results of an Experiment just executed Parameters ---------- hyperparameters: List List of hyperparameter values in the same order as :attr:`parameter_names` evaluation: Float An evaluation of the performance of `hyperparameters` experiment_id: Str, or None, default=None If not None, should be a string that is the UUID of the Experiment""" if not self.verbose: return print("{:>5d}".format(self.iteration), end=self.end) #################### Experiment ID #################### if self.show_experiment_id: if experiment_id is not None: print("{}".format(experiment_id[: self.show_experiment_id]), end=self.end) else: print(" " * self.show_experiment_id, end=self.end) #################### Time Elapsed #################### minutes, seconds = divmod((datetime.now() - self.last_round).total_seconds(), 60) print(expand_mins_secs(minutes, seconds), end=self.end) #################### Evaluation Result #################### if ( (self.y_max is None) # First evaluation or (self.do_maximize and self.y_max < evaluation) # Found new max (best) or (not self.do_maximize and self.y_max > evaluation) # Found new min (best) ): self.y_max, self.x_max = evaluation, hyperparameters self._print_target_value(evaluation, pre=_Color.MAGENTA, post=_Color.STOP) self._print_input_values(hyperparameters, pre=_Color.GREEN, post=_Color.STOP) else: self._print_target_value(evaluation) self._print_input_values(hyperparameters) print("") self.last_round = datetime.now() self.iteration += 1 def _print_target_value(self, value, pre="", post=""): """Print the utility of an Experiment Parameters ---------- value: String The utility value to print pre: String, default='' Content to prepend to the formatted `value` string before printing post: String, default='' Content to append to the formatted `value` string before printing""" content = pre + "{: >10.5f}".format(value) + post print(content, end=self.end) def _print_input_values(self, values, pre="", post=""): """Print the value of a hyperparameter used by an Experiment Parameters ---------- value: String The hyperparameter value to print pre: String, default='' Content to prepend to the formatted `value` string before printing post: String, default='' Content to append to the formatted `value` string before printing""" for index in self.sorted_indexes: if isinstance(values[index], float): content = "{0: >{1}.{2}f}".format( values[index], self.sizes[index] + 2, min(self.sizes[index] - 3, 6 - 2) ) else: content = "{0: >{1}}".format(values[index], self.sizes[index] + 2) print(pre + content + post, end=self.end) def reset_timer(self): """Set :attr:`start_time`, and :attr:`last_round` to the current time""" self.start_time = datetime.now() self.last_round = datetime.now() def print_summary(self): """Print a summary of the results of hyperparameter optimization upon completion""" # TODO: Finish this if not self.verbose: return def format_frame_source(previous_frame, **kwargs): """Construct a string describing the location at which a call was made Parameters ---------- previous_frame: Frame A frame depicting the location at which a call was made **kwargs: Dict Any additional kwargs to supply to :func:`reporting.stringify_frame_source` Returns ------- The stringified frame source information of `previous_frame`""" source = inspect.getframeinfo(previous_frame) src_script, src_line_no, src_func, src_class = source[0], source[1], source[2], None with suppress(AttributeError, KeyError): src_class = type(previous_frame.f_locals["self"]).__name__ return stringify_frame_source(src_script, src_line_no, src_func, src_class, **kwargs) def stringify_frame_source( src_file, src_line_no, src_func, src_class, add_line_no=True, max_line_no_size=4, total_max_size=80, ): """Construct a string that neatly displays the location in the code at which a call was made Parameters ---------- src_file: Str A filepath src_line_no: Int The line number in `src_file` at which the call was made src_func: Str The name of the function in `src_file` in which the call was made src_class: Str, or None If not None, the class in `src_file` in which the call was made add_line_no: Boolean, default=False If True, the line number will be included in the `source_content` result max_line_no_size: Int, default=4 Total number (including padding) of characters to be occupied by `src_line_no`. For example, if `src_line_no`=32, and `max_line_no_size`=4, `src_line_no` will be padded to become '32 ' in order to occupy four characters total_max_size: Int, default=80 Total number (including padding) of characters to be occupied by the `source_content` result Returns ------- source_content: Str A formatted string containing the location in the code at which a call was made Examples -------- >>> stringify_frame_source("reporting.py", 570, "stringify_frame_source", None) '570 - reporting.stringify_frame_source() ' >>> stringify_frame_source("reporting.py", 12, "bar", "Foo") '12 - reporting.Foo.bar() ' >>> stringify_frame_source("reporting.py", 12, "bar", "Foo", add_line_no=False) 'reporting.Foo.bar() ' >>> stringify_frame_source("reporting.py", 12, "bar", "Foo", total_max_size=60) '12 - reporting.Foo.bar() '""" source_content = "" if add_line_no is True: # Left-align line_no to size: max_line_no_size source_content += "{0:<{1}}".format(src_line_no, max_line_no_size) source_content += " - " script_name = os.path.splitext(os.path.basename(src_file))[0] if src_class is not None: source_content += "{}.{}.{}()".format(script_name, src_class, src_func) else: source_content += "{}.{}()".format(script_name, src_func) source_content = "{0:<{1}}".format(source_content, total_max_size) return source_content def add_time_to_content(content, add_time=False): """Construct a string containing the original `content`, in addition to the current time Parameters ---------- content: Str The original string, to which the current time will be concatenated add_time: Boolean, default=False If True, the current time will be concatenated onto the end of `content` Returns ------- content: Str Str containing original `content`, along with current time, and additional formatting""" add_content = "" add_time = now_time() if add_time is True else add_time add_content += "Time: {}".format(add_time) if add_time else "" #################### Combine Original and New Content #################### if add_content != "": content += " " if ((content != "") and (not content.endswith(" "))) else "" content += add_content return content def format_fold_run(rep=None, fold=None, run=None, mode="concise"): """Construct a string to display the repetition, fold, and run currently being executed Parameters ---------- rep: Int, or None, default=None The repetition number currently being executed fold: Int, or None, default=None The fold number currently being executed run: Int, or None, default=None The run number currently being executed mode: {"concise", "verbose"}, default="concise" If "concise", the result will contain abbreviations for rep/fold/run Returns ------- content: Str A clean display of the current repetition/fold/run Examples -------- >>> format_fold_run(rep=0, fold=3, run=2, mode="concise") 'R0-f3-r2' >>> format_fold_run(rep=0, fold=3, run=2, mode="verbose") 'Rep-Fold-Run: 0-3-2' >>> format_fold_run(rep=0, fold=3, run="*", mode="concise") 'R0-f3-r*' >>> format_fold_run(rep=0, fold=3, run=2, mode="foo") Traceback (most recent call last): File "reporting.py", line ?, in format_fold_run ValueError: Received invalid mode value: 'foo'""" content = "" if mode == "verbose": content += format("Rep" if rep is not None else "") content += format("-" if rep is not None and fold is not None else "") content += format("Fold" if fold is not None else "") content += format("-" if fold is not None and run is not None else "") content += format("Run" if run is not None else "") content += format(": " if any(_ is not None for _ in [rep, fold, run]) else "") content += format(rep if rep is not None else "") content += format("-" if rep is not None and fold is not None else "") content += format(fold if fold is not None else "") content += format("-" if fold is not None and run is not None else "") content += format(run if run is not None else "") elif mode == "concise": content += format("R" if rep is not None else "") content += format(rep if rep is not None else "") content += format("-" if rep is not None and fold is not None else "") content += format("f" if fold is not None else "") content += format(fold if fold is not None else "") content += format("-" if fold is not None and run is not None else "") content += format("r" if run is not None else "") content += format(run if run is not None else "") else: raise ValueError("Received invalid mode value: '{}'".format(mode)) return content def format_evaluation(results, separator=" | ", float_format="{:.5f}"): """Construct a string to neatly display the results of a model evaluation Parameters ---------- results: Dict The results of a model evaluation, in which keys represent the dataset type evaluated, and values are dicts containing metrics as keys, and metric values as values separator: Str, default=' | ' The string used to join all the metric values into a single string float_format: Str, default='{:.5f}' A python string float formatter, applied to floating metric values Returns ------- content: Str The model's evaluation results""" content = [] for data_type, values in results.items(): if values is None: continue data_type = "OOF" if data_type == "oof" else data_type data_type = "Holdout" if data_type == "holdout" else data_type data_type = "In-Fold" if data_type == "in_fold" else data_type metric_entry = "{}(".format(data_type) metric_entry_vals = [] for metric_id, metric_value in values.items(): try: formatted_value = float_format.format(metric_value) except ValueError: formatted_value = "{}".format(metric_value) metric_entry_vals.append("{}={}".format(metric_id, formatted_value)) metric_entry += ", ".join(metric_entry_vals) + ")" content.append(metric_entry) content = separator.join(content) return content # ADVANCED_FIT_LOGGING_DISPLAY_LAYOUT = [ # { # "column_name": "General", # "sub_columns_names": [ # ["fold", "Fold"], # ["run", "Run"], # ["seed", "Seed"], # ["step", "Step"], # ["start_time", "Start Time"], # ["end_time", "End Time"], # ["time_elapsed", "Time Elapsed"] # ], # "sub_column_min_sizes": [10, 10, 10, 20, 12, 12, 12] # }, # # Will need to alter default "Score" sub-columns according to what metrics are actually being used # { # "column_name": "OOF Scores", # "sub_columns_names": [ # ["oof_f1", "F1"], # ["oof_roc_auc", "ROC_AUC"] # ] # }, # # Check that Holdout dataset is in use before adding "Holdout Scores" column # { # "column_name": "Holdout Scores", # "sub_columns_names": [ # ["holdout_f1", "F1"], # ["holdout_roc_auc", "ROC_AUC"] # ] # }, # { # "column_name": "Losses", # "sub_columns_names": [ # ["train_loss", "Train"], # ["validation_loss", "Validation"] # ] # }, # ] # # # class AdvancedDisplayLayout(object): # def __init__(self): # pass # # # class AdvancedFitLogging(object): # def __init__(self, display_layout=None, ): # self.display_layout = display_layout or ADVANCED_FIT_LOGGING_DISPLAY_LAYOUT # # def _validate_parameters(self): # pass # # def validate_display_layout(self): # pass

40.438144

107

0.605736

true

f7296230975ff63d0a67cb8dd9d9df2ce6ab8ea5

11,474

py

Python

ez_setup.py

mrmin123/snovault

88cbd863ef556c1032c88fdc0fc243e07fb2f922

[ "MIT" ]

null

ez_setup.py

mrmin123/snovault

88cbd863ef556c1032c88fdc0fc243e07fb2f922

[ "MIT" ]

null

ez_setup.py

mrmin123/snovault

88cbd863ef556c1032c88fdc0fc243e07fb2f922

[ "MIT" ]

2

2021-07-07T18:41:25.000Z

2021-07-27T23:45:27.000Z

#!/usr/bin/env python """ Setuptools bootstrapping installer. Run this script to install or upgrade setuptools. """ import os import shutil import sys import tempfile import zipfile import optparse import subprocess import platform import textwrap import contextlib import warnings from distutils import log try: from urllib.request import urlopen except ImportError: from urllib2 import urlopen try: from site import USER_SITE except ImportError: USER_SITE = None DEFAULT_VERSION = "18.5" DEFAULT_URL = "https://pypi.python.org/packages/source/s/setuptools/" DEFAULT_SAVE_DIR = os.curdir def _python_cmd(*args): """ Execute a command. Return True if the command succeeded. """ args = (sys.executable,) + args return subprocess.call(args) == 0 def _install(archive_filename, install_args=()): """Install Setuptools.""" with archive_context(archive_filename): # installing log.warn('Installing Setuptools') if not _python_cmd('setup.py', 'install', *install_args): log.warn('Something went wrong during the installation.') log.warn('See the error message above.') # exitcode will be 2 return 2 def _build_egg(egg, archive_filename, to_dir): """Build Setuptools egg.""" with archive_context(archive_filename): # building an egg log.warn('Building a Setuptools egg in %s', to_dir) _python_cmd('setup.py', '-q', 'bdist_egg', '--dist-dir', to_dir) # returning the result log.warn(egg) if not os.path.exists(egg): raise IOError('Could not build the egg.') class ContextualZipFile(zipfile.ZipFile): """Supplement ZipFile class to support context manager for Python 2.6.""" def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def __new__(cls, *args, **kwargs): """Construct a ZipFile or ContextualZipFile as appropriate.""" if hasattr(zipfile.ZipFile, '__exit__'): return zipfile.ZipFile(*args, **kwargs) return super(ContextualZipFile, cls).__new__(cls) @contextlib.contextmanager def archive_context(filename): """ Unzip filename to a temporary directory, set to the cwd. The unzipped target is cleaned up after. """ tmpdir = tempfile.mkdtemp() log.warn('Extracting in %s', tmpdir) old_wd = os.getcwd() try: os.chdir(tmpdir) with ContextualZipFile(filename) as archive: archive.extractall() # going in the directory subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0]) os.chdir(subdir) log.warn('Now working in %s', subdir) yield finally: os.chdir(old_wd) shutil.rmtree(tmpdir) def _do_download(version, download_base, to_dir, download_delay): """Download Setuptools.""" egg = os.path.join(to_dir, 'setuptools-%s-py%d.%d.egg' % (version, sys.version_info[0], sys.version_info[1])) if not os.path.exists(egg): archive = download_setuptools(version, download_base, to_dir, download_delay) _build_egg(egg, archive, to_dir) sys.path.insert(0, egg) # Remove previously-imported pkg_resources if present (see # https://bitbucket.org/pypa/setuptools/pull-request/7/ for details). if 'pkg_resources' in sys.modules: del sys.modules['pkg_resources'] import setuptools setuptools.bootstrap_install_from = egg def use_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, download_delay=15): """ Ensure that a setuptools version is installed. Return None. Raise SystemExit if the requested version or later cannot be installed. """ to_dir = os.path.abspath(to_dir) # prior to importing, capture the module state for # representative modules. rep_modules = 'pkg_resources', 'setuptools' imported = set(sys.modules).intersection(rep_modules) try: import pkg_resources pkg_resources.require("setuptools>=" + version) # a suitable version is already installed return except ImportError: # pkg_resources not available; setuptools is not installed; download pass except pkg_resources.DistributionNotFound: # no version of setuptools was found; allow download pass except pkg_resources.VersionConflict as VC_err: if imported: _conflict_bail(VC_err, version) # otherwise, unload pkg_resources to allow the downloaded version to # take precedence. del pkg_resources _unload_pkg_resources() return _do_download(version, download_base, to_dir, download_delay) def _conflict_bail(VC_err, version): """ Setuptools was imported prior to invocation, so it is unsafe to unload it. Bail out. """ conflict_tmpl = textwrap.dedent(""" The required version of setuptools (>={version}) is not available, and can't be installed while this script is running. Please install a more recent version first, using 'easy_install -U setuptools'. (Currently using {VC_err.args[0]!r}) """) msg = conflict_tmpl.format(**locals()) sys.stderr.write(msg) sys.exit(2) def _unload_pkg_resources(): del_modules = [ name for name in sys.modules if name.startswith('pkg_resources') ] for mod_name in del_modules: del sys.modules[mod_name] def _clean_check(cmd, target): """ Run the command to download target. If the command fails, clean up before re-raising the error. """ try: subprocess.check_call(cmd) except subprocess.CalledProcessError: if os.access(target, os.F_OK): os.unlink(target) raise def download_file_powershell(url, target): """ Download the file at url to target using Powershell. Powershell will validate trust. Raise an exception if the command cannot complete. """ target = os.path.abspath(target) ps_cmd = ( "[System.Net.WebRequest]::DefaultWebProxy.Credentials = " "[System.Net.CredentialCache]::DefaultCredentials; " "(new-object System.Net.WebClient).DownloadFile(%(url)r, %(target)r)" % vars() ) cmd = [ 'powershell', '-Command', ps_cmd, ] _clean_check(cmd, target) def has_powershell(): """Determine if Powershell is available.""" if platform.system() != 'Windows': return False cmd = ['powershell', '-Command', 'echo test'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_powershell.viable = has_powershell def download_file_curl(url, target): cmd = ['curl', url, '--silent', '--output', target] _clean_check(cmd, target) def has_curl(): cmd = ['curl', '--version'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_curl.viable = has_curl def download_file_wget(url, target): cmd = ['wget', url, '--quiet', '--output-document', target] _clean_check(cmd, target) def has_wget(): cmd = ['wget', '--version'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_wget.viable = has_wget def download_file_insecure(url, target): """Use Python to download the file, without connection authentication.""" src = urlopen(url) try: # Read all the data in one block. data = src.read() finally: src.close() # Write all the data in one block to avoid creating a partial file. with open(target, "wb") as dst: dst.write(data) download_file_insecure.viable = lambda: True def get_best_downloader(): downloaders = ( download_file_powershell, download_file_curl, download_file_wget, download_file_insecure, ) viable_downloaders = (dl for dl in downloaders if dl.viable()) return next(viable_downloaders, None) def download_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, delay=15, downloader_factory=get_best_downloader): """ Download setuptools from a specified location and return its filename. `version` should be a valid setuptools version number that is available as an sdist for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where the egg will be downloaded. `delay` is the number of seconds to pause before an actual download attempt. ``downloader_factory`` should be a function taking no arguments and returning a function for downloading a URL to a target. """ # making sure we use the absolute path to_dir = os.path.abspath(to_dir) zip_name = "setuptools-%s.zip" % version url = download_base + zip_name saveto = os.path.join(to_dir, zip_name) if not os.path.exists(saveto): # Avoid repeated downloads log.warn("Downloading %s", url) downloader = downloader_factory() downloader(url, saveto) return os.path.realpath(saveto) def _build_install_args(options): """ Build the arguments to 'python setup.py install' on the setuptools package. Returns list of command line arguments. """ return ['--user'] if options.user_install else [] def _parse_args(): """Parse the command line for options.""" parser = optparse.OptionParser() parser.add_option( '--user', dest='user_install', action='store_true', default=False, help='install in user site package (requires Python 2.6 or later)') parser.add_option( '--download-base', dest='download_base', metavar="URL", default=DEFAULT_URL, help='alternative URL from where to download the setuptools package') parser.add_option( '--insecure', dest='downloader_factory', action='store_const', const=lambda: download_file_insecure, default=get_best_downloader, help='Use internal, non-validating downloader' ) parser.add_option( '--version', help="Specify which version to download", default=DEFAULT_VERSION, ) parser.add_option( '--to-dir', help="Directory to save (and re-use) package", default=DEFAULT_SAVE_DIR, ) options, args = parser.parse_args() # positional arguments are ignored return options def _download_args(options): """Return args for download_setuptools function from cmdline args.""" return dict( version=options.version, download_base=options.download_base, downloader_factory=options.downloader_factory, to_dir=options.to_dir, ) def main(): """Install or upgrade setuptools and EasyInstall.""" options = _parse_args() archive = download_setuptools(**_download_args(options)) return _install(archive, _build_install_args(options)) if __name__ == '__main__': sys.exit(main())

29.270408

79

0.66228

import os import shutil import sys import tempfile import zipfile import optparse import subprocess import platform import textwrap import contextlib import warnings from distutils import log try: from urllib.request import urlopen except ImportError: from urllib2 import urlopen try: from site import USER_SITE except ImportError: USER_SITE = None DEFAULT_VERSION = "18.5" DEFAULT_URL = "https://pypi.python.org/packages/source/s/setuptools/" DEFAULT_SAVE_DIR = os.curdir def _python_cmd(*args): args = (sys.executable,) + args return subprocess.call(args) == 0 def _install(archive_filename, install_args=()): with archive_context(archive_filename): log.warn('Installing Setuptools') if not _python_cmd('setup.py', 'install', *install_args): log.warn('Something went wrong during the installation.') log.warn('See the error message above.') return 2 def _build_egg(egg, archive_filename, to_dir): with archive_context(archive_filename): log.warn('Building a Setuptools egg in %s', to_dir) _python_cmd('setup.py', '-q', 'bdist_egg', '--dist-dir', to_dir) log.warn(egg) if not os.path.exists(egg): raise IOError('Could not build the egg.') class ContextualZipFile(zipfile.ZipFile): def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def __new__(cls, *args, **kwargs): if hasattr(zipfile.ZipFile, '__exit__'): return zipfile.ZipFile(*args, **kwargs) return super(ContextualZipFile, cls).__new__(cls) @contextlib.contextmanager def archive_context(filename): tmpdir = tempfile.mkdtemp() log.warn('Extracting in %s', tmpdir) old_wd = os.getcwd() try: os.chdir(tmpdir) with ContextualZipFile(filename) as archive: archive.extractall() subdir = os.path.join(tmpdir, os.listdir(tmpdir)[0]) os.chdir(subdir) log.warn('Now working in %s', subdir) yield finally: os.chdir(old_wd) shutil.rmtree(tmpdir) def _do_download(version, download_base, to_dir, download_delay): egg = os.path.join(to_dir, 'setuptools-%s-py%d.%d.egg' % (version, sys.version_info[0], sys.version_info[1])) if not os.path.exists(egg): archive = download_setuptools(version, download_base, to_dir, download_delay) _build_egg(egg, archive, to_dir) sys.path.insert(0, egg) if 'pkg_resources' in sys.modules: del sys.modules['pkg_resources'] import setuptools setuptools.bootstrap_install_from = egg def use_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, download_delay=15): to_dir = os.path.abspath(to_dir) rep_modules = 'pkg_resources', 'setuptools' imported = set(sys.modules).intersection(rep_modules) try: import pkg_resources pkg_resources.require("setuptools>=" + version) return except ImportError: pass except pkg_resources.DistributionNotFound: pass except pkg_resources.VersionConflict as VC_err: if imported: _conflict_bail(VC_err, version) del pkg_resources _unload_pkg_resources() return _do_download(version, download_base, to_dir, download_delay) def _conflict_bail(VC_err, version): conflict_tmpl = textwrap.dedent(""" The required version of setuptools (>={version}) is not available, and can't be installed while this script is running. Please install a more recent version first, using 'easy_install -U setuptools'. (Currently using {VC_err.args[0]!r}) """) msg = conflict_tmpl.format(**locals()) sys.stderr.write(msg) sys.exit(2) def _unload_pkg_resources(): del_modules = [ name for name in sys.modules if name.startswith('pkg_resources') ] for mod_name in del_modules: del sys.modules[mod_name] def _clean_check(cmd, target): try: subprocess.check_call(cmd) except subprocess.CalledProcessError: if os.access(target, os.F_OK): os.unlink(target) raise def download_file_powershell(url, target): target = os.path.abspath(target) ps_cmd = ( "[System.Net.WebRequest]::DefaultWebProxy.Credentials = " "[System.Net.CredentialCache]::DefaultCredentials; " "(new-object System.Net.WebClient).DownloadFile(%(url)r, %(target)r)" % vars() ) cmd = [ 'powershell', '-Command', ps_cmd, ] _clean_check(cmd, target) def has_powershell(): if platform.system() != 'Windows': return False cmd = ['powershell', '-Command', 'echo test'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_powershell.viable = has_powershell def download_file_curl(url, target): cmd = ['curl', url, '--silent', '--output', target] _clean_check(cmd, target) def has_curl(): cmd = ['curl', '--version'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_curl.viable = has_curl def download_file_wget(url, target): cmd = ['wget', url, '--quiet', '--output-document', target] _clean_check(cmd, target) def has_wget(): cmd = ['wget', '--version'] with open(os.path.devnull, 'wb') as devnull: try: subprocess.check_call(cmd, stdout=devnull, stderr=devnull) except Exception: return False return True download_file_wget.viable = has_wget def download_file_insecure(url, target): src = urlopen(url) try: # Read all the data in one block. data = src.read() finally: src.close() # Write all the data in one block to avoid creating a partial file. with open(target, "wb") as dst: dst.write(data) download_file_insecure.viable = lambda: True def get_best_downloader(): downloaders = ( download_file_powershell, download_file_curl, download_file_wget, download_file_insecure, ) viable_downloaders = (dl for dl in downloaders if dl.viable()) return next(viable_downloaders, None) def download_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, delay=15, downloader_factory=get_best_downloader): # making sure we use the absolute path to_dir = os.path.abspath(to_dir) zip_name = "setuptools-%s.zip" % version url = download_base + zip_name saveto = os.path.join(to_dir, zip_name) if not os.path.exists(saveto): # Avoid repeated downloads log.warn("Downloading %s", url) downloader = downloader_factory() downloader(url, saveto) return os.path.realpath(saveto) def _build_install_args(options): return ['--user'] if options.user_install else [] def _parse_args(): parser = optparse.OptionParser() parser.add_option( '--user', dest='user_install', action='store_true', default=False, help='install in user site package (requires Python 2.6 or later)') parser.add_option( '--download-base', dest='download_base', metavar="URL", default=DEFAULT_URL, help='alternative URL from where to download the setuptools package') parser.add_option( '--insecure', dest='downloader_factory', action='store_const', const=lambda: download_file_insecure, default=get_best_downloader, help='Use internal, non-validating downloader' ) parser.add_option( '--version', help="Specify which version to download", default=DEFAULT_VERSION, ) parser.add_option( '--to-dir', help="Directory to save (and re-use) package", default=DEFAULT_SAVE_DIR, ) options, args = parser.parse_args() # positional arguments are ignored return options def _download_args(options): return dict( version=options.version, download_base=options.download_base, downloader_factory=options.downloader_factory, to_dir=options.to_dir, ) def main(): options = _parse_args() archive = download_setuptools(**_download_args(options)) return _install(archive, _build_install_args(options)) if __name__ == '__main__': sys.exit(main())

true

f72962892eadfb62069ec0593f97f3237cf5cc30

4,662

py

Python

rsnet/dataset/raster.py

xyt556/rsnet

5f20f5308f89695e9f26ee4724d5591201d0c52d

[ "MIT" ]

1

2022-03-01T08:47:14.000Z

rsnet/dataset/raster.py

xyt556/rsnet

5f20f5308f89695e9f26ee4724d5591201d0c52d

[ "MIT" ]

null

rsnet/dataset/raster.py

xyt556/rsnet

5f20f5308f89695e9f26ee4724d5591201d0c52d

[ "MIT" ]

1

2022-03-07T06:08:38.000Z

import os import rasterio import numpy as np from ..utils import pair, bytescale from .base import BaseRasterData class RasterSampleDataset(BaseRasterData): """Dataset wrapper for remote sensing data. Args: fname: win_size: step_size: pad_size: band_index: """ def __init__(self, fname, win_size=512, step_size=512, pad_size=0, band_index=None, to_type=None, data_format='channel_last', transform=None): super().__init__(fname=fname) assert data_format in ( 'channel_first', 'channel_last'), "data format must be 'channel_first' or " f"'channel_last', but got type {data_format}" self.data_format = data_format self.win_size = pair(win_size) self.step_size = pair(step_size) self.pad_size = pair(pad_size) total_band_index = [i + 1 for i in range(self.count)] if band_index is None: self.band_index = total_band_index else: assert set(band_index).issubset(set(total_band_index)) self.band_index = band_index self.to_type = to_type self.window_ids = self.get_windows_info() self.transform = transform self.start = 0 self.end = len(self) def get_windows_info(self): left, top = 0, 0 width, height = self.width, self.height left_top_xy = [] # left-top corner coordinates (xmin, ymin) while left < width: if left + self.win_size[0] >= width: left = max(width - self.win_size[0], 0) top = 0 while top < height: if top + self.win_size[1] >= height: top = max(height - self.win_size[1], 0) # right = min(left + self.win_size[0], width - 1) # bottom = min(top + self.win_size[1], height - 1) # save left_top_xy.append((left, top)) if top + self.win_size[1] >= height: break else: top += self.step_size[1] if left + self.win_size[0] >= width: break else: left += self.step_size[0] return left_top_xy def sample(self, x, y): """Get the values of dataset at certain positions. """ xmin, ymin = x, y xsize, ysize = self.win_size xpad, ypad = self.pad_size xmin -= xpad ymin -= ypad left, top = 0, 0 if xmin < 0: xmin = 0 xsize += xpad left = xpad elif xmin + xsize + 2 * xpad > self.width: xsize += xpad else: xsize += 2 * xpad if ymin < 0: ymin = 0 ysize += ypad top = ypad elif ymin + ysize + 2 * ypad > self.height: ysize += ypad else: ysize += 2 * ypad # col_off, row_off, width, height window = rasterio.windows.Window(xmin, ymin, xsize, ysize) # with rasterio.open(self.image_file) as src: # bands = [src.read(k, window=tile_window) for k in self.band_index] # tile_image = np.stack(bands, axis=-1) bands = [self._band.read(k, window=window) for k in self.band_index] if self.to_type and np.dtype(self.to_type) != np.dtype(self.dtype): bmin, bmax = self.minmax msks = [ self._band.read_masks(k, window=window) for k in self.band_index ] bands = [ bytescale(b, msk, bmin[i], bmax[i], dtype=self.to_type) for i, (b, msk) in enumerate(zip(bands, msks)) ] tile_image = np.stack(bands, axis=-1) img = np.zeros( (self.win_size[0] + 2 * xpad, self.win_size[0] + 2 * ypad, len(self.band_index)), dtype=tile_image.dtype) img[top:top + ysize, left:left + xsize] = tile_image if self.data_format == 'channel_first': img = img.transpose(2, 0, 1) return img def __getitem__(self, idx): x, y = self.window_ids[idx] img = self.sample(x, y) if self.transform is not None: img = self.transform(img) return img, x, y def __len__(self): return len(self.window_ids) @property def step(self): return self.step_size @property def pad(self): return self.pad_size

29.506329

80

0.515444

import os import rasterio import numpy as np from ..utils import pair, bytescale from .base import BaseRasterData class RasterSampleDataset(BaseRasterData): def __init__(self, fname, win_size=512, step_size=512, pad_size=0, band_index=None, to_type=None, data_format='channel_last', transform=None): super().__init__(fname=fname) assert data_format in ( 'channel_first', 'channel_last'), "data format must be 'channel_first' or " f"'channel_last', but got type {data_format}" self.data_format = data_format self.win_size = pair(win_size) self.step_size = pair(step_size) self.pad_size = pair(pad_size) total_band_index = [i + 1 for i in range(self.count)] if band_index is None: self.band_index = total_band_index else: assert set(band_index).issubset(set(total_band_index)) self.band_index = band_index self.to_type = to_type self.window_ids = self.get_windows_info() self.transform = transform self.start = 0 self.end = len(self) def get_windows_info(self): left, top = 0, 0 width, height = self.width, self.height left_top_xy = [] while left < width: if left + self.win_size[0] >= width: left = max(width - self.win_size[0], 0) top = 0 while top < height: if top + self.win_size[1] >= height: top = max(height - self.win_size[1], 0) left_top_xy.append((left, top)) if top + self.win_size[1] >= height: break else: top += self.step_size[1] if left + self.win_size[0] >= width: break else: left += self.step_size[0] return left_top_xy def sample(self, x, y): xmin, ymin = x, y xsize, ysize = self.win_size xpad, ypad = self.pad_size xmin -= xpad ymin -= ypad left, top = 0, 0 if xmin < 0: xmin = 0 xsize += xpad left = xpad elif xmin + xsize + 2 * xpad > self.width: xsize += xpad else: xsize += 2 * xpad if ymin < 0: ymin = 0 ysize += ypad top = ypad elif ymin + ysize + 2 * ypad > self.height: ysize += ypad else: ysize += 2 * ypad window = rasterio.windows.Window(xmin, ymin, xsize, ysize) bands = [self._band.read(k, window=window) for k in self.band_index] if self.to_type and np.dtype(self.to_type) != np.dtype(self.dtype): bmin, bmax = self.minmax msks = [ self._band.read_masks(k, window=window) for k in self.band_index ] bands = [ bytescale(b, msk, bmin[i], bmax[i], dtype=self.to_type) for i, (b, msk) in enumerate(zip(bands, msks)) ] tile_image = np.stack(bands, axis=-1) img = np.zeros( (self.win_size[0] + 2 * xpad, self.win_size[0] + 2 * ypad, len(self.band_index)), dtype=tile_image.dtype) img[top:top + ysize, left:left + xsize] = tile_image if self.data_format == 'channel_first': img = img.transpose(2, 0, 1) return img def __getitem__(self, idx): x, y = self.window_ids[idx] img = self.sample(x, y) if self.transform is not None: img = self.transform(img) return img, x, y def __len__(self): return len(self.window_ids) @property def step(self): return self.step_size @property def pad(self): return self.pad_size

true

f729638d4432f934d5191c5b2c629e6a492d192e

12,736

py

Python

sedfitter/sed/sed.py

KainRasleafar/sedfitter

4f0e9e46f7903a853166835bb74857cc15eef219

[ "BSD-2-Clause" ]

15

2015-07-04T02:00:30.000Z

2021-05-13T09:03:10.000Z

sedfitter/sed/sed.py

KainRasleafar/sedfitter

4f0e9e46f7903a853166835bb74857cc15eef219

[ "BSD-2-Clause" ]

45

2015-04-27T20:19:22.000Z

2022-01-28T06:24:31.000Z

sedfitter/sed/sed.py

KainRasleafar/sedfitter

4f0e9e46f7903a853166835bb74857cc15eef219

[ "BSD-2-Clause" ]

19

2015-04-21T15:32:04.000Z

2022-03-02T21:53:46.000Z

from __future__ import print_function, division import os import numpy as np from astropy import log from astropy.io import fits from astropy.table import Table from scipy.interpolate import interp1d from astropy import units as u from ..utils.validator import validate_array from .helpers import parse_unit_safe, assert_allclose_quantity, convert_flux __all__ = ['SED'] class SED(object): def __init__(self): # Metadata self.name = None self.distance = None # Spectral info self.wav = None self.nu = None # Apertures self.apertures = None # Fluxes self.flux = None self.error = None def __eq__(self, other): try: assert self.name == other.name assert_allclose_quantity(self.distance, other.distance) assert_allclose_quantity(self.wav, other.wav) assert_allclose_quantity(self.nu, other.nu) assert_allclose_quantity(self.apertures, other.apertures) assert_allclose_quantity(self.flux, other.flux) assert_allclose_quantity(self.error, other.error) except AssertionError: raise return False else: return True def copy(self): from copy import deepcopy return deepcopy(self) def scale_to_distance(self, distance): """ Returns the SED scaled to distance `distance` Parameters ---------- distance : float The distance in cm Returns ------- sed : SED The SED, scaled to the new distance """ sed = self.copy() sed.distance = distance * u.cm sed.flux = sed.flux * (self.distance.to(u.cm) / sed.distance) ** 2 sed.error = sed.error * (self.distance.to(u.cm) / sed.distance) ** 2 return sed def scale_to_av(self, av, law): sed = self.copy() sed.flux = sed.flux * 10. ** (av * law(sed.wav)) sed.error = sed.error * 10. ** (av * law(sed.wav)) return sed @property def wav(self): """ The wavelengths at which the SED is defined """ if self._wav is None and self._nu is not None: return self._nu.to(u.micron, equivalencies=u.spectral()) else: return self._wav @wav.setter def wav(self, value): if value is None: self._wav = None else: self._wav = validate_array('wav', value, domain='positive', ndim=1, shape=None if self.nu is None else (len(self.nu),), physical_type='length') @property def nu(self): """ The frequencies at which the SED is defined """ if self._nu is None and self._wav is not None: return self._wav.to(u.Hz, equivalencies=u.spectral()) else: return self._nu @nu.setter def nu(self, value): if value is None: self._nu = None else: self._nu = validate_array('nu', value, domain='positive', ndim=1, shape=None if self.wav is None else (len(self.wav),), physical_type='frequency') @property def apertures(self): """ The apertures at which the SED is defined """ return self._apertures @apertures.setter def apertures(self, value): if value is None: self._apertures = None else: self._apertures = validate_array('apertures', value, domain='positive', ndim=1, physical_type='length') @property def flux(self): """ The SED fluxes """ return self._flux @flux.setter def flux(self, value): if value is None: self._flux = value else: self._flux = validate_array('flux', value, ndim=2, shape=(self.n_ap, self.n_wav), physical_type=('power', 'flux', 'spectral flux density')) @property def error(self): """ The convolved flux errors """ return self._error @error.setter def error(self, value): if value is None: self._error = value else: self._error = validate_array('error', value, ndim=2, shape=(self.n_ap, self.n_wav), physical_type=('power', 'flux', 'spectral flux density')) @property def n_ap(self): if self.apertures is None: return 1 else: return len(self.apertures) @property def n_wav(self): if self.wav is None: return None else: return len(self.wav) @classmethod def read(cls, filename, unit_wav=u.micron, unit_freq=u.Hz, unit_flux=u.erg / u.cm ** 2 / u.s, order='nu'): """ Read an SED from a FITS file. Parameters ---------- filename: str The name of the file to read the SED from. unit_wav: `~astropy.units.Unit`, optional The units to convert the wavelengths to. unit_freq: `~astropy.units.Unit`, optional The units to convert the frequency to. unit_flux: `~astropy.units.Unit`, optional The units to convert the flux to. order: str, optional Whether to sort the SED by increasing wavelength (`wav`) or frequency ('nu'). """ # Instantiate SED class sed = cls() # Assume that the filename may be missing the .gz extension if not os.path.exists(filename) and os.path.exists(filename + '.gz'): filename += ".gz" # Open FILE file hdulist = fits.open(filename, memmap=False) # Extract model name sed.name = hdulist[0].header['MODEL'] # Check if distance is specified in header, otherwise assume 1kpc if 'DISTANCE' in hdulist[0].header: sed.distance = hdulist[0].header['DISTANCE'] * u.cm else: log.debug("No distance found in SED file, assuming 1kpc") sed.distance = 1. * u.kpc # Extract SED values wav = hdulist[1].data.field('WAVELENGTH') * parse_unit_safe(hdulist[1].columns[0].unit) nu = hdulist[1].data.field('FREQUENCY') * parse_unit_safe(hdulist[1].columns[1].unit) ap = hdulist[2].data.field('APERTURE') * parse_unit_safe(hdulist[2].columns[0].unit) flux = hdulist[3].data.field('TOTAL_FLUX') * parse_unit_safe(hdulist[3].columns[0].unit) error = hdulist[3].data.field('TOTAL_FLUX_ERR') * parse_unit_safe(hdulist[3].columns[1].unit) # Set SED attributes sed.apertures = ap # Convert wavelength and frequencies to requested units sed.wav = wav.to(unit_wav) sed.nu = nu.to(unit_freq) # Set fluxes sed.flux = convert_flux(nu, flux, unit_flux, distance=sed.distance) sed.error = convert_flux(nu, error, unit_flux, distance=sed.distance) # Sort SED if order not in ('nu', 'wav'): raise ValueError('order should be nu or wav') if (order == 'nu' and sed.nu[0] > sed.nu[-1]) or \ (order == 'wav' and sed.wav[0] > sed.wav[-1]): sed.wav = sed.wav[::-1] sed.nu = sed.nu[::-1] sed.flux = sed.flux[..., ::-1] sed.error = sed.error[..., ::-1] return sed def write(self, filename, overwrite=False): """ Write an SED to a FITS file. Parameters ---------- filename: str The name of the file to write the SED to. """ # Create first HDU with meta-data hdu0 = fits.PrimaryHDU() if self.name is None: raise ValueError("Model name is not set") else: hdu0.header['MODEL'] = self.name if self.distance is None: raise ValueError("Model distance is not set") else: hdu0.header['DISTANCE'] = self.distance.to(u.cm).value hdu0.header['NAP'] = self.n_ap hdu0.header['NWAV'] = self.n_wav # Create wavelength table twav = Table() if self.wav is None: raise ValueError("Wavelengths are not set") else: twav['WAVELENGTH'] = self.wav if self.nu is None: raise ValueError("Frequencies are not set") else: twav['FREQUENCY'] = self.nu twav.sort('FREQUENCY') # TODO: here sorting needs to be applied to fluxes too? hdu1 = fits.BinTableHDU(np.array(twav)) hdu1.columns[0].unit = self.wav.unit.to_string(format='fits') hdu1.columns[1].unit = self.nu.unit.to_string(format='fits') hdu1.header['EXTNAME'] = "WAVELENGTHS" # Create aperture table tap = Table() if self.apertures is None: tap['APERTURE'] = [1.e-30] else: tap['APERTURE'] = self.apertures hdu2 = fits.BinTableHDU(np.array(tap)) if self.apertures is None: hdu2.columns[0].unit = 'cm' else: hdu2.columns[0].unit = self.apertures.unit.to_string(format='fits') hdu2.header['EXTNAME'] = "APERTURES" # Create flux table tflux = Table() tflux['TOTAL_FLUX'] = self.flux if self.flux is None: raise ValueError("Fluxes are not set") else: tflux['TOTAL_FLUX'] = self.flux if self.error is None: raise ValueError("Errors are not set") else: tflux['TOTAL_FLUX_ERR'] = self.error hdu3 = fits.BinTableHDU(np.array(tflux)) hdu3.columns[0].unit = self.flux.unit.to_string(format='fits') hdu3.columns[1].unit = self.error.unit.to_string(format='fits') hdu3.header['EXTNAME'] = "SEDS" hdus = [hdu0, hdu1, hdu2, hdu3] # Create overall FITS file hdulist = fits.HDUList(hdus) hdulist.writeto(filename, clobber=overwrite) def interpolate(self, apertures): """ Interpolate the SED to different apertures """ # If there is only one aperture, we can't interpolate, we can only repeat if self.n_ap == 1: return np.repeat(self.flux[0, :], len(apertures)).reshape(self.n_wav, len(apertures)) # Create interpolating function flux_interp = interp1d(self.apertures, self.flux.swapaxes(0, 1)) # If any apertures are larger than the defined max, reset to max apertures[apertures > self.apertures.max()] = self.apertures.max() # If any apertures are smaller than the defined min, raise Exception if np.any(apertures < self.apertures.min()): raise Exception("Aperture(s) requested too small") return flux_interp(apertures) def interpolate_variable(self, wavelengths, apertures): """ Interpolate the SED to a variable aperture as a function of wavelength. This method should be called with an interpolating function for aperture as a function of wavelength, in log10 space. """ if self.n_ap == 1: return self.flux[0, :] sed_apertures = self.apertures.to(u.au).value sed_wav = self.wav.to(u.micron).value # If any apertures are larger than the defined max, reset to max apertures[apertures > sed_apertures.max()] = sed_apertures.max() * 0.999 # If any apertures are smaller than the defined min, raise Exception if np.any(apertures < sed_apertures.min()): raise Exception("Aperture(s) requested too small") # Find wavelength order order = np.argsort(wavelengths) # Interpolate apertures vs wavelength log10_ap_interp = interp1d(np.log10(wavelengths[order]), np.log10(apertures[order]), bounds_error=False, fill_value=np.nan) # Create interpolating function flux_interp = interp1d(sed_apertures, self.flux.swapaxes(0, 1)) # Interpolate the apertures apertures = 10. ** log10_ap_interp(np.log10(sed_wav)) # Extrapolate on either side apertures[np.log10(sed_wav) < log10_ap_interp.x[0]] = 10. ** log10_ap_interp.y[0] apertures[np.log10(sed_wav) > log10_ap_interp.x[-1]] = 10. ** log10_ap_interp.y[-1] # Interpolate and return only diagonal elements return flux_interp(apertures).diagonal()

31.760599

131

0.568153

from __future__ import print_function, division import os import numpy as np from astropy import log from astropy.io import fits from astropy.table import Table from scipy.interpolate import interp1d from astropy import units as u from ..utils.validator import validate_array from .helpers import parse_unit_safe, assert_allclose_quantity, convert_flux __all__ = ['SED'] class SED(object): def __init__(self): self.name = None self.distance = None self.wav = None self.nu = None self.apertures = None self.flux = None self.error = None def __eq__(self, other): try: assert self.name == other.name assert_allclose_quantity(self.distance, other.distance) assert_allclose_quantity(self.wav, other.wav) assert_allclose_quantity(self.nu, other.nu) assert_allclose_quantity(self.apertures, other.apertures) assert_allclose_quantity(self.flux, other.flux) assert_allclose_quantity(self.error, other.error) except AssertionError: raise return False else: return True def copy(self): from copy import deepcopy return deepcopy(self) def scale_to_distance(self, distance): sed = self.copy() sed.distance = distance * u.cm sed.flux = sed.flux * (self.distance.to(u.cm) / sed.distance) ** 2 sed.error = sed.error * (self.distance.to(u.cm) / sed.distance) ** 2 return sed def scale_to_av(self, av, law): sed = self.copy() sed.flux = sed.flux * 10. ** (av * law(sed.wav)) sed.error = sed.error * 10. ** (av * law(sed.wav)) return sed @property def wav(self): if self._wav is None and self._nu is not None: return self._nu.to(u.micron, equivalencies=u.spectral()) else: return self._wav @wav.setter def wav(self, value): if value is None: self._wav = None else: self._wav = validate_array('wav', value, domain='positive', ndim=1, shape=None if self.nu is None else (len(self.nu),), physical_type='length') @property def nu(self): if self._nu is None and self._wav is not None: return self._wav.to(u.Hz, equivalencies=u.spectral()) else: return self._nu @nu.setter def nu(self, value): if value is None: self._nu = None else: self._nu = validate_array('nu', value, domain='positive', ndim=1, shape=None if self.wav is None else (len(self.wav),), physical_type='frequency') @property def apertures(self): return self._apertures @apertures.setter def apertures(self, value): if value is None: self._apertures = None else: self._apertures = validate_array('apertures', value, domain='positive', ndim=1, physical_type='length') @property def flux(self): return self._flux @flux.setter def flux(self, value): if value is None: self._flux = value else: self._flux = validate_array('flux', value, ndim=2, shape=(self.n_ap, self.n_wav), physical_type=('power', 'flux', 'spectral flux density')) @property def error(self): return self._error @error.setter def error(self, value): if value is None: self._error = value else: self._error = validate_array('error', value, ndim=2, shape=(self.n_ap, self.n_wav), physical_type=('power', 'flux', 'spectral flux density')) @property def n_ap(self): if self.apertures is None: return 1 else: return len(self.apertures) @property def n_wav(self): if self.wav is None: return None else: return len(self.wav) @classmethod def read(cls, filename, unit_wav=u.micron, unit_freq=u.Hz, unit_flux=u.erg / u.cm ** 2 / u.s, order='nu'): sed = cls() if not os.path.exists(filename) and os.path.exists(filename + '.gz'): filename += ".gz" hdulist = fits.open(filename, memmap=False) sed.name = hdulist[0].header['MODEL'] if 'DISTANCE' in hdulist[0].header: sed.distance = hdulist[0].header['DISTANCE'] * u.cm else: log.debug("No distance found in SED file, assuming 1kpc") sed.distance = 1. * u.kpc wav = hdulist[1].data.field('WAVELENGTH') * parse_unit_safe(hdulist[1].columns[0].unit) nu = hdulist[1].data.field('FREQUENCY') * parse_unit_safe(hdulist[1].columns[1].unit) ap = hdulist[2].data.field('APERTURE') * parse_unit_safe(hdulist[2].columns[0].unit) flux = hdulist[3].data.field('TOTAL_FLUX') * parse_unit_safe(hdulist[3].columns[0].unit) error = hdulist[3].data.field('TOTAL_FLUX_ERR') * parse_unit_safe(hdulist[3].columns[1].unit) sed.apertures = ap sed.wav = wav.to(unit_wav) sed.nu = nu.to(unit_freq) sed.flux = convert_flux(nu, flux, unit_flux, distance=sed.distance) sed.error = convert_flux(nu, error, unit_flux, distance=sed.distance) if order not in ('nu', 'wav'): raise ValueError('order should be nu or wav') if (order == 'nu' and sed.nu[0] > sed.nu[-1]) or \ (order == 'wav' and sed.wav[0] > sed.wav[-1]): sed.wav = sed.wav[::-1] sed.nu = sed.nu[::-1] sed.flux = sed.flux[..., ::-1] sed.error = sed.error[..., ::-1] return sed def write(self, filename, overwrite=False): hdu0 = fits.PrimaryHDU() if self.name is None: raise ValueError("Model name is not set") else: hdu0.header['MODEL'] = self.name if self.distance is None: raise ValueError("Model distance is not set") else: hdu0.header['DISTANCE'] = self.distance.to(u.cm).value hdu0.header['NAP'] = self.n_ap hdu0.header['NWAV'] = self.n_wav twav = Table() if self.wav is None: raise ValueError("Wavelengths are not set") else: twav['WAVELENGTH'] = self.wav if self.nu is None: raise ValueError("Frequencies are not set") else: twav['FREQUENCY'] = self.nu twav.sort('FREQUENCY') hdu1 = fits.BinTableHDU(np.array(twav)) hdu1.columns[0].unit = self.wav.unit.to_string(format='fits') hdu1.columns[1].unit = self.nu.unit.to_string(format='fits') hdu1.header['EXTNAME'] = "WAVELENGTHS" tap = Table() if self.apertures is None: tap['APERTURE'] = [1.e-30] else: tap['APERTURE'] = self.apertures hdu2 = fits.BinTableHDU(np.array(tap)) if self.apertures is None: hdu2.columns[0].unit = 'cm' else: hdu2.columns[0].unit = self.apertures.unit.to_string(format='fits') hdu2.header['EXTNAME'] = "APERTURES" tflux = Table() tflux['TOTAL_FLUX'] = self.flux if self.flux is None: raise ValueError("Fluxes are not set") else: tflux['TOTAL_FLUX'] = self.flux if self.error is None: raise ValueError("Errors are not set") else: tflux['TOTAL_FLUX_ERR'] = self.error hdu3 = fits.BinTableHDU(np.array(tflux)) hdu3.columns[0].unit = self.flux.unit.to_string(format='fits') hdu3.columns[1].unit = self.error.unit.to_string(format='fits') hdu3.header['EXTNAME'] = "SEDS" hdus = [hdu0, hdu1, hdu2, hdu3] hdulist = fits.HDUList(hdus) hdulist.writeto(filename, clobber=overwrite) def interpolate(self, apertures): if self.n_ap == 1: return np.repeat(self.flux[0, :], len(apertures)).reshape(self.n_wav, len(apertures)) # Create interpolating function flux_interp = interp1d(self.apertures, self.flux.swapaxes(0, 1)) # If any apertures are larger than the defined max, reset to max apertures[apertures > self.apertures.max()] = self.apertures.max() # If any apertures are smaller than the defined min, raise Exception if np.any(apertures < self.apertures.min()): raise Exception("Aperture(s) requested too small") return flux_interp(apertures) def interpolate_variable(self, wavelengths, apertures): if self.n_ap == 1: return self.flux[0, :] sed_apertures = self.apertures.to(u.au).value sed_wav = self.wav.to(u.micron).value # If any apertures are larger than the defined max, reset to max apertures[apertures > sed_apertures.max()] = sed_apertures.max() * 0.999 # If any apertures are smaller than the defined min, raise Exception if np.any(apertures < sed_apertures.min()): raise Exception("Aperture(s) requested too small") # Find wavelength order order = np.argsort(wavelengths) # Interpolate apertures vs wavelength log10_ap_interp = interp1d(np.log10(wavelengths[order]), np.log10(apertures[order]), bounds_error=False, fill_value=np.nan) # Create interpolating function flux_interp = interp1d(sed_apertures, self.flux.swapaxes(0, 1)) # Interpolate the apertures apertures = 10. ** log10_ap_interp(np.log10(sed_wav)) # Extrapolate on either side apertures[np.log10(sed_wav) < log10_ap_interp.x[0]] = 10. ** log10_ap_interp.y[0] apertures[np.log10(sed_wav) > log10_ap_interp.x[-1]] = 10. ** log10_ap_interp.y[-1] # Interpolate and return only diagonal elements return flux_interp(apertures).diagonal()

true

f729647581b758f0c2709642d4b1e0819a3ee950

1,310

py

Python

shopyo/config.py

ChristianCelora/shopyo

9e602b1f6bc118850875c33f7f2ae5d179767c88

[ "MIT" ]

1

2020-12-23T18:22:21.000Z

shopyo/config.py

ChristianCelora/shopyo

9e602b1f6bc118850875c33f7f2ae5d179767c88

[ "MIT" ]

null

shopyo/config.py

ChristianCelora/shopyo

9e602b1f6bc118850875c33f7f2ae5d179767c88

[ "MIT" ]

null

import os base_path = os.path.dirname(os.path.abspath(__file__)) class Config: """Parent configuration class.""" DEBUG = False SQLALCHEMY_DATABASE_URI = "sqlite:///shopyo.db" SQLALCHEMY_TRACK_MODIFICATIONS = False SECRET_KEY = os.urandom(24) BASE_DIR = base_path STATIC = os.path.join(BASE_DIR, "static") UPLOADED_PATH_IMAGE = os.path.join(STATIC, "uploads", "images") UPLOADED_PATH_THUM = os.path.join(STATIC, "uploads", "thumbs") UPLOADED_PRODUCTPHOTOS_DEST = os.path.join(STATIC, "uploads", "products") UPLOADED_CATEGORYPHOTOS_DEST = os.path.join(STATIC, "uploads", "category") UPLOADED_SUBCATEGORYPHOTOS_DEST = os.path.join(STATIC, "uploads", "subcategory") PASSWORD_SALT = "abcdefghi" class DevelopmentConfig(Config): """Configurations for development""" ENV = "development" DEBUG = True EXPLAIN_TEMPLATE_LOADING = True LOGIN_DISABLED = True class TestingConfig(Config): """Configurations for testsing""" SQLALCHEMY_DATABASE_URI = "sqlite:///testing.db" DEBUG = True LIVESERVER_PORT = 8943 LIVESERVER_TIMEOUT = 10 BCRYPT_LOG_ROUNDS = 4 TESTING = True WTF_CSRF_ENABLED = False app_config = { "development": DevelopmentConfig, "production": Config, "testing": TestingConfig, }

25.686275

84

0.701527

import os base_path = os.path.dirname(os.path.abspath(__file__)) class Config: DEBUG = False SQLALCHEMY_DATABASE_URI = "sqlite:///shopyo.db" SQLALCHEMY_TRACK_MODIFICATIONS = False SECRET_KEY = os.urandom(24) BASE_DIR = base_path STATIC = os.path.join(BASE_DIR, "static") UPLOADED_PATH_IMAGE = os.path.join(STATIC, "uploads", "images") UPLOADED_PATH_THUM = os.path.join(STATIC, "uploads", "thumbs") UPLOADED_PRODUCTPHOTOS_DEST = os.path.join(STATIC, "uploads", "products") UPLOADED_CATEGORYPHOTOS_DEST = os.path.join(STATIC, "uploads", "category") UPLOADED_SUBCATEGORYPHOTOS_DEST = os.path.join(STATIC, "uploads", "subcategory") PASSWORD_SALT = "abcdefghi" class DevelopmentConfig(Config): ENV = "development" DEBUG = True EXPLAIN_TEMPLATE_LOADING = True LOGIN_DISABLED = True class TestingConfig(Config): SQLALCHEMY_DATABASE_URI = "sqlite:///testing.db" DEBUG = True LIVESERVER_PORT = 8943 LIVESERVER_TIMEOUT = 10 BCRYPT_LOG_ROUNDS = 4 TESTING = True WTF_CSRF_ENABLED = False app_config = { "development": DevelopmentConfig, "production": Config, "testing": TestingConfig, }

true

f7296659d3869c20f2288c83afc6f34918f2cc2f

10,867

py

Python

main/MCN.py

Rick0514/VPR_SMCN

7a00dc8e4de0c21438474c05a4a7be18d05367fa

[ "MIT" ]

null

main/MCN.py

Rick0514/VPR_SMCN

7a00dc8e4de0c21438474c05a4a7be18d05367fa

[ "MIT" ]

null

main/MCN.py

Rick0514/VPR_SMCN

7a00dc8e4de0c21438474c05a4a7be18d05367fa

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt import main.utils as utils import time # ---------------------------- 说明 ---------------------------------- # MCN的python复现 # ---------------------------- 说明 ---------------------------------- class MCNParams: """ a struct define the input params MCN class use """ def __init__(self, probAddCon, nCellPerCol, nConPerCol, minColActivity, nColPerPattern, kActiveCol): self.probAddCon = probAddCon self.nCellPerCol = nCellPerCol self.nConPerCol = nConPerCol self.minColActivity = minColActivity self.nColPerPattern = nColPerPattern self.kActiveCol = kActiveCol class MCN: def __init__(self, params): # MCNParams class define the params self.params = params self.nCols = 0 self.winnerCells = [] self.prevWinnerCells = [] self.FF = np.empty((self.params.nConPerCol, self.nCols), dtype=np.int) self.P = np.empty((self.params.nCellPerCol, self.nCols), dtype=np.bool) self.prevP = np.empty_like(self.P, dtype=np.bool) self.burstedCol = np.empty((self.nCols, ), dtype=np.bool) self.predicitionConnections = [] def prepareNewIteration(self): # winnerCells and P need to reset each time self.prevWinnerCells = self.winnerCells self.prevP = self.P self.winnerCells = [] if self.nCols > 0: self.P = np.zeros_like(self.P) self.burstedCol = np.zeros_like(self.burstedCol) def resetPredP(self): self.prevP = np.empty((self.params.nCellPerCol, self.nCols), dtype=np.bool) def createNewColumn(self, inputSDR, nNewColumn): nonZeroIdx = np.where(inputSDR > 0)[0] start_id = self.nCols for i in range(nNewColumn): self.nCols += 1 sampleIdx = np.random.randint(0, len(nonZeroIdx), self.params.nConPerCol) tmp = nonZeroIdx[sampleIdx].reshape((-1, 1)) self.FF = np.concatenate((self.FF, tmp), axis=1) newPcol = np.zeros((self.params.nCellPerCol, 1), dtype=np.bool) self.P = np.concatenate((self.P, newPcol), axis=1) self.prevP = np.concatenate((self.prevP, newPcol), axis=1) self.burstedCol = np.concatenate((self.burstedCol, np.array([0], dtype=bool))) for k in range(nNewColumn * self.params.nCellPerCol): self.predicitionConnections.append([]) return np.arange(start_id, self.nCols) def compute(self, inputSDR, supressLearningFlag): """ compute sequence descriptor :param inputSDR: :param supressLearningFlag: in case of inference, not learning :return: """ self.prepareNewIteration() # compare SDR with minicolumn simScore = np.sum(inputSDR[self.FF], axis=0) / self.params.nConPerCol sort_idx = np.argsort(simScore) topk_sort_idx = sort_idx[-self.params.kActiveCol:] topk_sort_score = simScore[topk_sort_idx] if not supressLearningFlag: # if all activities below threshold, then create a new # activity and make it active # otherwise select the top k most active ones if len(simScore): activeCols = topk_sort_idx[topk_sort_score > self.params.minColActivity] # activeCols = np.array(self.getActiveCols(simScore, supressLearningFlag), dtype=np.int) else: activeCols = np.empty((0, ), dtype=np.int) activeCols = np.concatenate((activeCols, self.createNewColumn(inputSDR, max(0, self.params.nColPerPattern - len(activeCols))))) else: # in non-learning mode, take the k most active columns # activeCols = np.array(self.getActiveCols(simScore, supressLearningFlag), dtype=np.int) activeCols = topk_sort_idx # if len(activeCols) == 0: # sort_idx = np.argsort(simScore) # activeCols = sort_idx[-self.params.nColPerPattern:] for eachActiveCol in activeCols: predictedIdx = np.where(self.prevP[:, eachActiveCol] > 0)[0] if len(predictedIdx): for each_predictedIdx in predictedIdx: self.activatePredictions(eachActiveCol, each_predictedIdx) self.winnerCells.append(eachActiveCol * self.params.nCellPerCol + each_predictedIdx) else: winnerCell = self.burst(eachActiveCol, supressLearningFlag) for each in winnerCell: self.winnerCells.append(eachActiveCol * self.params.nCellPerCol + each) if not supressLearningFlag: self.learnPreditions() # predict newly learned preditions, i think it's useless for colIdx in range(self.nCols): if self.burstedCol[colIdx]: for i in range(self.params.nCellPerCol): self.activatePredictions(colIdx, i) return self.winnerCells def activatePredictions(self, colIdx, cellIdx): predIdx = self.predicitionConnections[colIdx * self.params.nCellPerCol + cellIdx] for each in predIdx: c = each // self.params.nCellPerCol r = each % self.params.nCellPerCol self.P[r, c] = True def burst(self, colIdx, supressLearningFlag): self.burstedCol[colIdx] = True for i in range(self.params.nCellPerCol): self.activatePredictions(colIdx, i) # winnerCell is the cells with fewest connections with other cells st = colIdx * self.params.nCellPerCol nCon = [] for i in range(self.params.nCellPerCol): nCon.append(len(self.predicitionConnections[st + i])) if not supressLearningFlag: # inhibit winning cells from the last iteration for i in self.prevWinnerCells: col = i // self.params.nCellPerCol if col == colIdx: nCon[i % self.params.nCellPerCol] += self.params.nCellPerCol # find the fewest ones candidateIdx = [0] minV = nCon[0] for i in range(1, len(nCon)): if nCon[i] < minV: candidateIdx = [i] minV = nCon[i] elif nCon[i] == minV: candidateIdx.append(i) nCan = len(candidateIdx) if nCan == 1: return [candidateIdx[0]] else: chosenIdx = np.random.randint(0, nCan, 1) return [candidateIdx[chosenIdx[0]]] else: # in case of inference, return all used winner cells winnerIdx = np.where(np.array(nCon) > 0)[0] if len(winnerIdx): return winnerIdx return [np.random.randint(0, self.params.nCellPerCol, 1)[0]] def learnPreditions(self): for prevIdx in self.prevWinnerCells: prevIdxCol = prevIdx // self.params.nCellPerCol for curIdx in self.winnerCells: curIdxCol = curIdx // self.params.nCellPerCol if prevIdxCol == curIdxCol: continue existingPredConFlag = self.checkExistingPredCon(prevIdxCol, curIdx) if not existingPredConFlag or np.random.rand() <= self.params.probAddCon: if curIdx not in self.predicitionConnections[prevIdx]: self.predicitionConnections[prevIdx].append(curIdx) def checkExistingPredCon(self, prevColIdx, curCellIdx): st = prevColIdx * self.params.nCellPerCol for i in range(self.params.nCellPerCol): if curCellIdx in self.predicitionConnections[st + i]: return True return False def visualizeCon(self, displayCol=10): plt.figure() dis = 5 dCol = displayCol plt.title('Prediction Connections') plt.xlim(0, dCol * dis) plt.ylim(0, self.params.nCellPerCol * dis) for k, con in enumerate(self.predicitionConnections): x = k // self.params.nCellPerCol * dis if x >= dCol * dis: break y = k % self.params.nCellPerCol y = (self.params.nCellPerCol - 1 - y) * dis plt.plot(x, y, 'o', color='blue') if len(con): for each in con: cx = each // self.params.nCellPerCol * dis cy = each % self.params.nCellPerCol cy = (self.params.nCellPerCol - 1 - cy) * dis plt.plot([x, cx], [y, cy], '-', color='red') def getSim(w1, w2): """ :param w1: winner cell which should be a list :param w2: :return: simularity score """ w1 = set(w1) w2 = set(w2) return len(w1 & w2) / len(w1 | w2) def runMCN(params, dbFeat, qFeat, gt): # st = time.time() _, old_dims = dbFeat.shape new_dims = 8192 P = np.random.rand(old_dims, new_dims // 2) P /= np.linalg.norm(P, axis=1, keepdims=True) D1_slsbh = utils.getLSBH(dbFeat, P, 0.25) D2_slsbh = utils.getLSBH(qFeat, P, 0.25) mcn = MCN(params) train_winnerCells = [] for i in range(D1_slsbh.shape[0]): train_winnerCells.append(mcn.compute(D1_slsbh[i, :], False)) valid_winnerCells = [] mcn.resetPredP() for i in range(D2_slsbh.shape[0]): valid_winnerCells.append(mcn.compute(D2_slsbh[i, :], True)) # print('Done! cost : %.3f' % (time.time() - st)) # get similarity matrix S_mcn = np.zeros((dbFeat.shape[0], qFeat.shape[0])) for k1, each_v in enumerate(valid_winnerCells): for k2, each_t in enumerate(train_winnerCells): S_mcn[k2, k1] = getSim(each_v, each_t) # time_cost = time.time() - st # P, R = utils.drawPR(S_mcn, gt) # ap = utils.calAvgPred(P, R) del train_winnerCells, valid_winnerCells, mcn return S_mcn def runMCN_SDR(params, dbFeat, qFeat, gt): mcn = MCN(params) train_winnerCells = [] for i in range(dbFeat.shape[0]): train_winnerCells.append(mcn.compute(dbFeat[i, :], False)) valid_winnerCells = [] mcn.resetPredP() for i in range(qFeat.shape[0]): valid_winnerCells.append(mcn.compute(qFeat[i, :], True)) # print('Done! cost : %.3f' % (time.time() - st)) # get similarity matrix S_mcn = np.zeros((dbFeat.shape[0], qFeat.shape[0])) for k1, each_v in enumerate(valid_winnerCells): for k2, each_t in enumerate(train_winnerCells): S_mcn[k2, k1] = getSim(each_v, each_t) # time_cost = time.time() - st # P, R = utils.drawPR(S_mcn, gt) # ap = utils.calAvgPred(P, R) del train_winnerCells, valid_winnerCells, mcn return S_mcn

35.168285

139

0.587743

import numpy as np import matplotlib.pyplot as plt import main.utils as utils import time class MCNParams: def __init__(self, probAddCon, nCellPerCol, nConPerCol, minColActivity, nColPerPattern, kActiveCol): self.probAddCon = probAddCon self.nCellPerCol = nCellPerCol self.nConPerCol = nConPerCol self.minColActivity = minColActivity self.nColPerPattern = nColPerPattern self.kActiveCol = kActiveCol class MCN: def __init__(self, params): self.params = params self.nCols = 0 self.winnerCells = [] self.prevWinnerCells = [] self.FF = np.empty((self.params.nConPerCol, self.nCols), dtype=np.int) self.P = np.empty((self.params.nCellPerCol, self.nCols), dtype=np.bool) self.prevP = np.empty_like(self.P, dtype=np.bool) self.burstedCol = np.empty((self.nCols, ), dtype=np.bool) self.predicitionConnections = [] def prepareNewIteration(self): self.prevWinnerCells = self.winnerCells self.prevP = self.P self.winnerCells = [] if self.nCols > 0: self.P = np.zeros_like(self.P) self.burstedCol = np.zeros_like(self.burstedCol) def resetPredP(self): self.prevP = np.empty((self.params.nCellPerCol, self.nCols), dtype=np.bool) def createNewColumn(self, inputSDR, nNewColumn): nonZeroIdx = np.where(inputSDR > 0)[0] start_id = self.nCols for i in range(nNewColumn): self.nCols += 1 sampleIdx = np.random.randint(0, len(nonZeroIdx), self.params.nConPerCol) tmp = nonZeroIdx[sampleIdx].reshape((-1, 1)) self.FF = np.concatenate((self.FF, tmp), axis=1) newPcol = np.zeros((self.params.nCellPerCol, 1), dtype=np.bool) self.P = np.concatenate((self.P, newPcol), axis=1) self.prevP = np.concatenate((self.prevP, newPcol), axis=1) self.burstedCol = np.concatenate((self.burstedCol, np.array([0], dtype=bool))) for k in range(nNewColumn * self.params.nCellPerCol): self.predicitionConnections.append([]) return np.arange(start_id, self.nCols) def compute(self, inputSDR, supressLearningFlag): self.prepareNewIteration() simScore = np.sum(inputSDR[self.FF], axis=0) / self.params.nConPerCol sort_idx = np.argsort(simScore) topk_sort_idx = sort_idx[-self.params.kActiveCol:] topk_sort_score = simScore[topk_sort_idx] if not supressLearningFlag: if len(simScore): activeCols = topk_sort_idx[topk_sort_score > self.params.minColActivity] else: activeCols = np.empty((0, ), dtype=np.int) activeCols = np.concatenate((activeCols, self.createNewColumn(inputSDR, max(0, self.params.nColPerPattern - len(activeCols))))) else: activeCols = topk_sort_idx for eachActiveCol in activeCols: predictedIdx = np.where(self.prevP[:, eachActiveCol] > 0)[0] if len(predictedIdx): for each_predictedIdx in predictedIdx: self.activatePredictions(eachActiveCol, each_predictedIdx) self.winnerCells.append(eachActiveCol * self.params.nCellPerCol + each_predictedIdx) else: winnerCell = self.burst(eachActiveCol, supressLearningFlag) for each in winnerCell: self.winnerCells.append(eachActiveCol * self.params.nCellPerCol + each) if not supressLearningFlag: self.learnPreditions() for colIdx in range(self.nCols): if self.burstedCol[colIdx]: for i in range(self.params.nCellPerCol): self.activatePredictions(colIdx, i) return self.winnerCells def activatePredictions(self, colIdx, cellIdx): predIdx = self.predicitionConnections[colIdx * self.params.nCellPerCol + cellIdx] for each in predIdx: c = each // self.params.nCellPerCol r = each % self.params.nCellPerCol self.P[r, c] = True def burst(self, colIdx, supressLearningFlag): self.burstedCol[colIdx] = True for i in range(self.params.nCellPerCol): self.activatePredictions(colIdx, i) # winnerCell is the cells with fewest connections with other cells st = colIdx * self.params.nCellPerCol nCon = [] for i in range(self.params.nCellPerCol): nCon.append(len(self.predicitionConnections[st + i])) if not supressLearningFlag: # inhibit winning cells from the last iteration for i in self.prevWinnerCells: col = i // self.params.nCellPerCol if col == colIdx: nCon[i % self.params.nCellPerCol] += self.params.nCellPerCol # find the fewest ones candidateIdx = [0] minV = nCon[0] for i in range(1, len(nCon)): if nCon[i] < minV: candidateIdx = [i] minV = nCon[i] elif nCon[i] == minV: candidateIdx.append(i) nCan = len(candidateIdx) if nCan == 1: return [candidateIdx[0]] else: chosenIdx = np.random.randint(0, nCan, 1) return [candidateIdx[chosenIdx[0]]] else: # in case of inference, return all used winner cells winnerIdx = np.where(np.array(nCon) > 0)[0] if len(winnerIdx): return winnerIdx return [np.random.randint(0, self.params.nCellPerCol, 1)[0]] def learnPreditions(self): for prevIdx in self.prevWinnerCells: prevIdxCol = prevIdx // self.params.nCellPerCol for curIdx in self.winnerCells: curIdxCol = curIdx // self.params.nCellPerCol if prevIdxCol == curIdxCol: continue existingPredConFlag = self.checkExistingPredCon(prevIdxCol, curIdx) if not existingPredConFlag or np.random.rand() <= self.params.probAddCon: if curIdx not in self.predicitionConnections[prevIdx]: self.predicitionConnections[prevIdx].append(curIdx) def checkExistingPredCon(self, prevColIdx, curCellIdx): st = prevColIdx * self.params.nCellPerCol for i in range(self.params.nCellPerCol): if curCellIdx in self.predicitionConnections[st + i]: return True return False def visualizeCon(self, displayCol=10): plt.figure() dis = 5 dCol = displayCol plt.title('Prediction Connections') plt.xlim(0, dCol * dis) plt.ylim(0, self.params.nCellPerCol * dis) for k, con in enumerate(self.predicitionConnections): x = k // self.params.nCellPerCol * dis if x >= dCol * dis: break y = k % self.params.nCellPerCol y = (self.params.nCellPerCol - 1 - y) * dis plt.plot(x, y, 'o', color='blue') if len(con): for each in con: cx = each // self.params.nCellPerCol * dis cy = each % self.params.nCellPerCol cy = (self.params.nCellPerCol - 1 - cy) * dis plt.plot([x, cx], [y, cy], '-', color='red') def getSim(w1, w2): w1 = set(w1) w2 = set(w2) return len(w1 & w2) / len(w1 | w2) def runMCN(params, dbFeat, qFeat, gt): # st = time.time() _, old_dims = dbFeat.shape new_dims = 8192 P = np.random.rand(old_dims, new_dims // 2) P /= np.linalg.norm(P, axis=1, keepdims=True) D1_slsbh = utils.getLSBH(dbFeat, P, 0.25) D2_slsbh = utils.getLSBH(qFeat, P, 0.25) mcn = MCN(params) train_winnerCells = [] for i in range(D1_slsbh.shape[0]): train_winnerCells.append(mcn.compute(D1_slsbh[i, :], False)) valid_winnerCells = [] mcn.resetPredP() for i in range(D2_slsbh.shape[0]): valid_winnerCells.append(mcn.compute(D2_slsbh[i, :], True)) # print('Done! cost : %.3f' % (time.time() - st)) # get similarity matrix S_mcn = np.zeros((dbFeat.shape[0], qFeat.shape[0])) for k1, each_v in enumerate(valid_winnerCells): for k2, each_t in enumerate(train_winnerCells): S_mcn[k2, k1] = getSim(each_v, each_t) # time_cost = time.time() - st # P, R = utils.drawPR(S_mcn, gt) # ap = utils.calAvgPred(P, R) del train_winnerCells, valid_winnerCells, mcn return S_mcn def runMCN_SDR(params, dbFeat, qFeat, gt): mcn = MCN(params) train_winnerCells = [] for i in range(dbFeat.shape[0]): train_winnerCells.append(mcn.compute(dbFeat[i, :], False)) valid_winnerCells = [] mcn.resetPredP() for i in range(qFeat.shape[0]): valid_winnerCells.append(mcn.compute(qFeat[i, :], True)) # print('Done! cost : %.3f' % (time.time() - st)) # get similarity matrix S_mcn = np.zeros((dbFeat.shape[0], qFeat.shape[0])) for k1, each_v in enumerate(valid_winnerCells): for k2, each_t in enumerate(train_winnerCells): S_mcn[k2, k1] = getSim(each_v, each_t) # time_cost = time.time() - st # P, R = utils.drawPR(S_mcn, gt) # ap = utils.calAvgPred(P, R) del train_winnerCells, valid_winnerCells, mcn return S_mcn

true

f7296673f74ba92c24a65b3326f5d137140b8592

25,486

py

Python

pytorch3dunet/augment/transforms.py

FynnBe/pytorch-3dunet

34918e82c3afeff02360b03964de973eac3a4f75

[ "MIT" ]

null

pytorch3dunet/augment/transforms.py

FynnBe/pytorch-3dunet

34918e82c3afeff02360b03964de973eac3a4f75

[ "MIT" ]

null

pytorch3dunet/augment/transforms.py

FynnBe/pytorch-3dunet

34918e82c3afeff02360b03964de973eac3a4f75

[ "MIT" ]

null

import importlib import numpy as np import torch from scipy.ndimage import rotate, map_coordinates, gaussian_filter from scipy.ndimage.filters import convolve from skimage.filters import gaussian from skimage.segmentation import find_boundaries from torchvision.transforms import Compose # WARN: use fixed random state for reproducibility; if you want to randomize on each run seed with `time.time()` e.g. GLOBAL_RANDOM_STATE = np.random.RandomState(47) class RandomFlip: """ Randomly flips the image across the given axes. Image can be either 3D (DxHxW) or 4D (CxDxHxW). When creating make sure that the provided RandomStates are consistent between raw and labeled datasets, otherwise the models won't converge. """ def __init__(self, random_state, axis_prob=0.5, **kwargs): assert random_state is not None, 'RandomState cannot be None' self.random_state = random_state self.axes = (0, 1, 2) self.axis_prob = axis_prob def __call__(self, m): assert m.ndim in [3, 4], 'Supports only 3D (DxHxW) or 4D (CxDxHxW) images' for axis in self.axes: if self.random_state.uniform() > self.axis_prob: if m.ndim == 3: m = np.flip(m, axis) else: channels = [np.flip(m[c], axis) for c in range(m.shape[0])] m = np.stack(channels, axis=0) return m class RandomRotate90: """ Rotate an array by 90 degrees around a randomly chosen plane. Image can be either 3D (DxHxW) or 4D (CxDxHxW). When creating make sure that the provided RandomStates are consistent between raw and labeled datasets, otherwise the models won't converge. IMPORTANT: assumes DHW axis order (that's why rotation is performed across (1,2) axis) """ def __init__(self, random_state, **kwargs): self.random_state = random_state # always rotate around z-axis self.axis = (1, 2) def __call__(self, m): assert m.ndim in [3, 4], 'Supports only 3D (DxHxW) or 4D (CxDxHxW) images' # pick number of rotations at random k = self.random_state.randint(0, 4) # rotate k times around a given plane if m.ndim == 3: m = np.rot90(m, k, self.axis) else: channels = [np.rot90(m[c], k, self.axis) for c in range(m.shape[0])] m = np.stack(channels, axis=0) return m class RandomRotate: """ Rotate an array by a random degrees from taken from (-angle_spectrum, angle_spectrum) interval. Rotation axis is picked at random from the list of provided axes. """ def __init__(self, random_state, angle_spectrum=30, axes=None, mode='reflect', order=0, **kwargs): if axes is None: axes = [(1, 0), (2, 1), (2, 0)] else: assert isinstance(axes, list) and len(axes) > 0 self.random_state = random_state self.angle_spectrum = angle_spectrum self.axes = axes self.mode = mode self.order = order def __call__(self, m): axis = self.axes[self.random_state.randint(len(self.axes))] angle = self.random_state.randint(-self.angle_spectrum, self.angle_spectrum) if m.ndim == 3: m = rotate(m, angle, axes=axis, reshape=False, order=self.order, mode=self.mode, cval=-1) else: channels = [rotate(m[c], angle, axes=axis, reshape=False, order=self.order, mode=self.mode, cval=-1) for c in range(m.shape[0])] m = np.stack(channels, axis=0) return m class RandomContrast: """ Adjust contrast by scaling each voxel to `mean + alpha * (v - mean)`. """ def __init__(self, random_state, alpha=(0.5, 1.5), mean=0.0, execution_probability=0.1, **kwargs): self.random_state = random_state assert len(alpha) == 2 self.alpha = alpha self.mean = mean self.execution_probability = execution_probability def __call__(self, m): if self.random_state.uniform() < self.execution_probability: alpha = self.random_state.uniform(self.alpha[0], self.alpha[1]) result = self.mean + alpha * (m - self.mean) return np.clip(result, -1, 1) return m # it's relatively slow, i.e. ~1s per patch of size 64x200x200, so use multiple workers in the DataLoader # remember to use spline_order=0 when transforming the labels class ElasticDeformation: """ Apply elasitc deformations of 3D patches on a per-voxel mesh. Assumes ZYX axis order (or CZYX if the data is 4D). Based on: https://github.com/fcalvet/image_tools/blob/master/image_augmentation.py#L62 """ def __init__(self, random_state, spline_order, alpha=2000, sigma=50, execution_probability=0.1, apply_3d=True, **kwargs): """ :param spline_order: the order of spline interpolation (use 0 for labeled images) :param alpha: scaling factor for deformations :param sigma: smoothing factor for Gaussian filter :param execution_probability: probability of executing this transform :param apply_3d: if True apply deformations in each axis """ self.random_state = random_state self.spline_order = spline_order self.alpha = alpha self.sigma = sigma self.execution_probability = execution_probability self.apply_3d = apply_3d def __call__(self, m): if self.random_state.uniform() < self.execution_probability: assert m.ndim in [3, 4] if m.ndim == 3: volume_shape = m.shape else: volume_shape = m[0].shape if self.apply_3d: dz = gaussian_filter(self.random_state.randn(*volume_shape), self.sigma, mode="reflect") * self.alpha else: dz = np.zeros_like(m) dy, dx = [ gaussian_filter( self.random_state.randn(*volume_shape), self.sigma, mode="reflect" ) * self.alpha for _ in range(2) ] z_dim, y_dim, x_dim = volume_shape z, y, x = np.meshgrid(np.arange(z_dim), np.arange(y_dim), np.arange(x_dim), indexing='ij') indices = z + dz, y + dy, x + dx if m.ndim == 3: return map_coordinates(m, indices, order=self.spline_order, mode='reflect') else: channels = [map_coordinates(c, indices, order=self.spline_order, mode='reflect') for c in m] return np.stack(channels, axis=0) return m def blur_boundary(boundary, sigma): boundary = gaussian(boundary, sigma=sigma) boundary[boundary >= 0.5] = 1 boundary[boundary < 0.5] = 0 return boundary class CropToFixed: def __init__(self, random_state, size=(256, 256), centered=False, **kwargs): self.random_state = random_state self.crop_y, self.crop_x = size self.centered = centered def __call__(self, m): def _padding(pad_total): half_total = pad_total // 2 return (half_total, pad_total - half_total) def _rand_range_and_pad(crop_size, max_size): """ Returns a tuple: max_value (int) for the corner dimension. The corner dimension is chosen as `self.random_state(max_value)` pad (int): padding in both directions; if crop_size is lt max_size the pad is 0 """ if crop_size < max_size: return max_size - crop_size, (0, 0) else: return 1, _padding(crop_size - max_size) def _start_and_pad(crop_size, max_size): if crop_size < max_size: return (max_size - crop_size) // 2, (0, 0) else: return 0, _padding(crop_size - max_size) _, y, x = m.shape if not self.centered: y_range, y_pad = _rand_range_and_pad(self.crop_y, y) x_range, x_pad = _rand_range_and_pad(self.crop_x, x) y_start = self.random_state.randint(y_range) x_start = self.random_state.randint(x_range) else: y_start, y_pad = _start_and_pad(self.crop_y, y) x_start, x_pad = _start_and_pad(self.crop_x, x) result = m[:, y_start:y_start + self.crop_y, x_start:x_start + self.crop_x] return np.pad(result, pad_width=((0, 0), y_pad, x_pad), mode='reflect') class AbstractLabelToBoundary: AXES_TRANSPOSE = [ (0, 1, 2), # X (0, 2, 1), # Y (2, 0, 1) # Z ] def __init__(self, ignore_index=None, aggregate_affinities=False, append_label=False, **kwargs): """ :param ignore_index: label to be ignored in the output, i.e. after computing the boundary the label ignore_index will be restored where is was in the patch originally :param aggregate_affinities: aggregate affinities with the same offset across Z,Y,X axes :param append_label: if True append the orignal ground truth labels to the last channel :param blur: Gaussian blur the boundaries :param sigma: standard deviation for Gaussian kernel """ self.ignore_index = ignore_index self.aggregate_affinities = aggregate_affinities self.append_label = append_label def __call__(self, m): """ Extract boundaries from a given 3D label tensor. :param m: input 3D tensor :return: binary mask, with 1-label corresponding to the boundary and 0-label corresponding to the background """ assert m.ndim == 3 kernels = self.get_kernels() boundary_arr = [np.where(np.abs(convolve(m, kernel)) > 0, 1, 0) for kernel in kernels] channels = np.stack(boundary_arr) results = [] if self.aggregate_affinities: assert len(kernels) % 3 == 0, "Number of kernels must be divided by 3 (one kernel per offset per Z,Y,X axes" # aggregate affinities with the same offset for i in range(0, len(kernels), 3): # merge across X,Y,Z axes (logical OR) xyz_aggregated_affinities = np.logical_or.reduce(channels[i:i + 3, ...]).astype(np.int) # recover ignore index xyz_aggregated_affinities = _recover_ignore_index(xyz_aggregated_affinities, m, self.ignore_index) results.append(xyz_aggregated_affinities) else: results = [_recover_ignore_index(channels[i], m, self.ignore_index) for i in range(channels.shape[0])] if self.append_label: # append original input data results.append(m) # stack across channel dim return np.stack(results, axis=0) @staticmethod def create_kernel(axis, offset): # create conv kernel k_size = offset + 1 k = np.zeros((1, 1, k_size), dtype=np.int) k[0, 0, 0] = 1 k[0, 0, offset] = -1 return np.transpose(k, axis) def get_kernels(self): raise NotImplementedError class StandardLabelToBoundary: def __init__(self, ignore_index=None, append_label=False, blur=False, sigma=1, mode='thick', blobs=False, **kwargs): self.ignore_index = ignore_index self.append_label = append_label self.blur = blur self.sigma = sigma self.mode = mode self.blobs = blobs def __call__(self, m): assert m.ndim == 3 boundaries = find_boundaries(m, connectivity=2, mode=self.mode) if self.blur: boundaries = blur_boundary(boundaries, self.sigma) results = [] if self.blobs: blobs = (m > 0).astype('uint8') results.append(_recover_ignore_index(blobs, m, self.ignore_index)) results.append(_recover_ignore_index(boundaries, m, self.ignore_index)) if self.append_label: # append original input data results.append(m) return np.stack(results, axis=0) class BlobsWithBoundary: def __init__(self, mode=None, append_label=False, blur=False, sigma=1, **kwargs): if mode is None: mode = ['thick', 'inner', 'outer'] self.mode = mode self.append_label = append_label self.blur = blur self.sigma = sigma def __call__(self, m): assert m.ndim == 3 # get the segmentation mask results = [(m > 0).astype('uint8')] for bm in self.mode: boundary = find_boundaries(m, connectivity=2, mode=bm) if self.blur: boundary = blur_boundary(boundary, self.sigma) results.append(boundary) if self.append_label: results.append(m) return np.stack(results, axis=0) class BlobsToMask: """ Returns binary mask from labeled image, i.e. every label greater than 0 is treated as foreground. """ def __init__(self, append_label=False, boundary=False, cross_entropy=False, **kwargs): self.cross_entropy = cross_entropy self.boundary = boundary self.append_label = append_label def __call__(self, m): assert m.ndim == 3 # get the segmentation mask mask = (m > 0).astype('uint8') results = [mask] if self.boundary: outer = find_boundaries(m, connectivity=2, mode='outer') if self.cross_entropy: # boundary is class 2 mask[outer > 0] = 2 results = [mask] else: results.append(outer) if self.append_label: results.append(m) return np.stack(results, axis=0) class RandomLabelToAffinities(AbstractLabelToBoundary): """ Converts a given volumetric label array to binary mask corresponding to borders between labels. One specify the max_offset (thickness) of the border. Then the offset is picked at random every time you call the transformer (offset is picked form the range 1:max_offset) for each axis and the boundary computed. One may use this scheme in order to make the network more robust against various thickness of borders in the ground truth (think of it as a boundary denoising scheme). """ def __init__(self, random_state, max_offset=10, ignore_index=None, append_label=False, z_offset_scale=2, **kwargs): super().__init__(ignore_index=ignore_index, append_label=append_label, aggregate_affinities=False) self.random_state = random_state self.offsets = tuple(range(1, max_offset + 1)) self.z_offset_scale = z_offset_scale def get_kernels(self): rand_offset = self.random_state.choice(self.offsets) axis_ind = self.random_state.randint(3) # scale down z-affinities due to anisotropy if axis_ind == 2: rand_offset = max(1, rand_offset // self.z_offset_scale) rand_axis = self.AXES_TRANSPOSE[axis_ind] # return a single kernel return [self.create_kernel(rand_axis, rand_offset)] class LabelToAffinities(AbstractLabelToBoundary): """ Converts a given volumetric label array to binary mask corresponding to borders between labels (which can be seen as an affinity graph: https://arxiv.org/pdf/1706.00120.pdf) One specify the offsets (thickness) of the border. The boundary will be computed via the convolution operator. """ def __init__(self, offsets, ignore_index=None, append_label=False, aggregate_affinities=False, z_offsets=None, **kwargs): super().__init__(ignore_index=ignore_index, append_label=append_label, aggregate_affinities=aggregate_affinities) assert isinstance(offsets, list) or isinstance(offsets, tuple), 'offsets must be a list or a tuple' assert all(a > 0 for a in offsets), "'offsets must be positive" assert len(set(offsets)) == len(offsets), "'offsets' must be unique" if z_offsets is not None: assert len(offsets) == len(z_offsets), 'z_offsets length must be the same as the length of offsets' else: # if z_offsets is None just use the offsets for z-affinities z_offsets = list(offsets) self.z_offsets = z_offsets self.kernels = [] # create kernel for every axis-offset pair for xy_offset, z_offset in zip(offsets, z_offsets): for axis_ind, axis in enumerate(self.AXES_TRANSPOSE): final_offset = xy_offset if axis_ind == 2: final_offset = z_offset # create kernels for a given offset in every direction self.kernels.append(self.create_kernel(axis, final_offset)) def get_kernels(self): return self.kernels class LabelToZAffinities(AbstractLabelToBoundary): """ Converts a given volumetric label array to binary mask corresponding to borders between labels (which can be seen as an affinity graph: https://arxiv.org/pdf/1706.00120.pdf) One specify the offsets (thickness) of the border. The boundary will be computed via the convolution operator. """ def __init__(self, offsets, ignore_index=None, append_label=False, **kwargs): super().__init__(ignore_index=ignore_index, append_label=append_label) assert isinstance(offsets, list) or isinstance(offsets, tuple), 'offsets must be a list or a tuple' assert all(a > 0 for a in offsets), "'offsets must be positive" assert len(set(offsets)) == len(offsets), "'offsets' must be unique" self.kernels = [] z_axis = self.AXES_TRANSPOSE[2] # create kernels for z_offset in offsets: self.kernels.append(self.create_kernel(z_axis, z_offset)) def get_kernels(self): return self.kernels class LabelToBoundaryAndAffinities: """ Combines the StandardLabelToBoundary and LabelToAffinities in the hope that that training the network to predict both would improve the main task: boundary prediction. """ def __init__(self, xy_offsets, z_offsets, append_label=False, blur=False, sigma=1, ignore_index=None, mode='thick', blobs=False, **kwargs): # blur only StandardLabelToBoundary results; we don't want to blur the affinities self.l2b = StandardLabelToBoundary(blur=blur, sigma=sigma, ignore_index=ignore_index, mode=mode, blobs=blobs) self.l2a = LabelToAffinities(offsets=xy_offsets, z_offsets=z_offsets, append_label=append_label, ignore_index=ignore_index) def __call__(self, m): boundary = self.l2b(m) affinities = self.l2a(m) return np.concatenate((boundary, affinities), axis=0) class FlyWingBoundary: """ Use if the volume contains a single pixel boundaries between labels. Gives the single pixel boundary in the 1st channel and the 'thick' boundary in the 2nd channel and optional z-affinities """ def __init__(self, append_label=False, thick_boundary=True, ignore_index=None, z_offsets=None, **kwargs): self.append_label = append_label self.thick_boundary = thick_boundary self.ignore_index = ignore_index self.lta = None if z_offsets is not None: self.lta = LabelToZAffinities(z_offsets, ignore_index=ignore_index) def __call__(self, m): boundary = (m == 0).astype('uint8') results = [boundary] if self.thick_boundary: t_boundary = find_boundaries(m, connectivity=1, mode='outer', background=0) results.append(t_boundary) if self.lta is not None: z_affs = self.lta(m) for z_aff in z_affs: results.append(z_aff) if self.ignore_index is not None: for b in results: b[m == self.ignore_index] = self.ignore_index if self.append_label: # append original input data results.append(m) return np.stack(results, axis=0) class LabelToMaskAndAffinities: def __init__(self, xy_offsets, z_offsets, append_label=False, background=0, ignore_index=None, **kwargs): self.background = background self.l2a = LabelToAffinities(offsets=xy_offsets, z_offsets=z_offsets, append_label=append_label, ignore_index=ignore_index) def __call__(self, m): mask = m > self.background mask = np.expand_dims(mask.astype(np.uint8), axis=0) affinities = self.l2a(m) return np.concatenate((mask, affinities), axis=0) class Standardize: """ Apply Z-score normalization to a given input tensor, i.e. re-scaling the values to be 0-mean and 1-std. Mean and std parameter have to be provided explicitly. """ def __init__(self, mean, std, eps=1e-6, **kwargs): self.mean = mean self.std = std self.eps = eps def __call__(self, m): return (m - self.mean) / np.clip(self.std, a_min=self.eps, a_max=None) class Normalize: """ Apply simple min-max scaling to a given input tensor, i.e. shrinks the range of the data in a fixed range of [-1, 1]. """ def __init__(self, min_value, max_value, **kwargs): assert max_value > min_value self.min_value = min_value self.value_range = max_value - min_value def __call__(self, m): norm_0_1 = (m - self.min_value) / self.value_range return np.clip(2 * norm_0_1 - 1, -1, 1) class AdditiveGaussianNoise: def __init__(self, random_state, scale=(0.0, 1.0), execution_probability=0.1, **kwargs): self.execution_probability = execution_probability self.random_state = random_state self.scale = scale def __call__(self, m): if self.random_state.uniform() < self.execution_probability: std = self.random_state.uniform(self.scale[0], self.scale[1]) gaussian_noise = self.random_state.normal(0, std, size=m.shape) return m + gaussian_noise return m class AdditivePoissonNoise: def __init__(self, random_state, lam=(0.0, 1.0), execution_probability=0.1, **kwargs): self.execution_probability = execution_probability self.random_state = random_state self.lam = lam def __call__(self, m): if self.random_state.uniform() < self.execution_probability: lam = self.random_state.uniform(self.lam[0], self.lam[1]) poisson_noise = self.random_state.poisson(lam, size=m.shape) return m + poisson_noise return m class ToTensor: """ Converts a given input numpy.ndarray into torch.Tensor. Adds additional 'channel' axis when the input is 3D and expand_dims=True (use for raw data of the shape (D, H, W)). """ def __init__(self, expand_dims, dtype=np.float32, **kwargs): self.expand_dims = expand_dims self.dtype = dtype def __call__(self, m): assert m.ndim in [3, 4], 'Supports only 3D (DxHxW) or 4D (CxDxHxW) images' # add channel dimension if self.expand_dims and m.ndim == 3: m = np.expand_dims(m, axis=0) return torch.from_numpy(m.astype(dtype=self.dtype)) class Relabel: """ Relabel a numpy array of labels into a consecutive numbers, e.g. [10,10, 0, 6, 6] -> [2, 2, 0, 1, 1]. Useful when one has an instance segmentation volume at hand and would like to create a one-hot-encoding for it. Without a consecutive labeling the task would be harder. """ def __init__(self, **kwargs): pass def __call__(self, m): _, unique_labels = np.unique(m, return_inverse=True) m = unique_labels.reshape(m.shape) return m class Identity: def __init__(self, **kwargs): pass def __call__(self, m): return m def get_transformer(config, min_value, max_value, mean, std): base_config = {'min_value': min_value, 'max_value': max_value, 'mean': mean, 'std': std} return Transformer(config, base_config) class Transformer: def __init__(self, phase_config, base_config): self.phase_config = phase_config self.config_base = base_config self.seed = GLOBAL_RANDOM_STATE.randint(10000000) def raw_transform(self): return self._create_transform('raw') def label_transform(self): return self._create_transform('label') def weight_transform(self): return self._create_transform('weight') @staticmethod def _transformer_class(class_name): m = importlib.import_module('pytorch3dunet.augment.transforms') clazz = getattr(m, class_name) return clazz def _create_transform(self, name): assert name in self.phase_config, f'Could not find {name} transform' return Compose([ self._create_augmentation(c) for c in self.phase_config[name] ]) def _create_augmentation(self, c): config = dict(self.config_base) config.update(c) config['random_state'] = np.random.RandomState(self.seed) aug_class = self._transformer_class(config['name']) return aug_class(**config) def _recover_ignore_index(input, orig, ignore_index): if ignore_index is not None: mask = orig == ignore_index input[mask] = ignore_index return input

36.723343

122

0.635996

import importlib import numpy as np import torch from scipy.ndimage import rotate, map_coordinates, gaussian_filter from scipy.ndimage.filters import convolve from skimage.filters import gaussian from skimage.segmentation import find_boundaries from torchvision.transforms import Compose GLOBAL_RANDOM_STATE = np.random.RandomState(47) class RandomFlip: def __init__(self, random_state, axis_prob=0.5, **kwargs): assert random_state is not None, 'RandomState cannot be None' self.random_state = random_state self.axes = (0, 1, 2) self.axis_prob = axis_prob def __call__(self, m): assert m.ndim in [3, 4], 'Supports only 3D (DxHxW) or 4D (CxDxHxW) images' for axis in self.axes: if self.random_state.uniform() > self.axis_prob: if m.ndim == 3: m = np.flip(m, axis) else: channels = [np.flip(m[c], axis) for c in range(m.shape[0])] m = np.stack(channels, axis=0) return m class RandomRotate90: def __init__(self, random_state, **kwargs): self.random_state = random_state self.axis = (1, 2) def __call__(self, m): assert m.ndim in [3, 4], 'Supports only 3D (DxHxW) or 4D (CxDxHxW) images' k = self.random_state.randint(0, 4) if m.ndim == 3: m = np.rot90(m, k, self.axis) else: channels = [np.rot90(m[c], k, self.axis) for c in range(m.shape[0])] m = np.stack(channels, axis=0) return m class RandomRotate: def __init__(self, random_state, angle_spectrum=30, axes=None, mode='reflect', order=0, **kwargs): if axes is None: axes = [(1, 0), (2, 1), (2, 0)] else: assert isinstance(axes, list) and len(axes) > 0 self.random_state = random_state self.angle_spectrum = angle_spectrum self.axes = axes self.mode = mode self.order = order def __call__(self, m): axis = self.axes[self.random_state.randint(len(self.axes))] angle = self.random_state.randint(-self.angle_spectrum, self.angle_spectrum) if m.ndim == 3: m = rotate(m, angle, axes=axis, reshape=False, order=self.order, mode=self.mode, cval=-1) else: channels = [rotate(m[c], angle, axes=axis, reshape=False, order=self.order, mode=self.mode, cval=-1) for c in range(m.shape[0])] m = np.stack(channels, axis=0) return m class RandomContrast: def __init__(self, random_state, alpha=(0.5, 1.5), mean=0.0, execution_probability=0.1, **kwargs): self.random_state = random_state assert len(alpha) == 2 self.alpha = alpha self.mean = mean self.execution_probability = execution_probability def __call__(self, m): if self.random_state.uniform() < self.execution_probability: alpha = self.random_state.uniform(self.alpha[0], self.alpha[1]) result = self.mean + alpha * (m - self.mean) return np.clip(result, -1, 1) return m # remember to use spline_order=0 when transforming the labels class ElasticDeformation: def __init__(self, random_state, spline_order, alpha=2000, sigma=50, execution_probability=0.1, apply_3d=True, **kwargs): self.random_state = random_state self.spline_order = spline_order self.alpha = alpha self.sigma = sigma self.execution_probability = execution_probability self.apply_3d = apply_3d def __call__(self, m): if self.random_state.uniform() < self.execution_probability: assert m.ndim in [3, 4] if m.ndim == 3: volume_shape = m.shape else: volume_shape = m[0].shape if self.apply_3d: dz = gaussian_filter(self.random_state.randn(*volume_shape), self.sigma, mode="reflect") * self.alpha else: dz = np.zeros_like(m) dy, dx = [ gaussian_filter( self.random_state.randn(*volume_shape), self.sigma, mode="reflect" ) * self.alpha for _ in range(2) ] z_dim, y_dim, x_dim = volume_shape z, y, x = np.meshgrid(np.arange(z_dim), np.arange(y_dim), np.arange(x_dim), indexing='ij') indices = z + dz, y + dy, x + dx if m.ndim == 3: return map_coordinates(m, indices, order=self.spline_order, mode='reflect') else: channels = [map_coordinates(c, indices, order=self.spline_order, mode='reflect') for c in m] return np.stack(channels, axis=0) return m def blur_boundary(boundary, sigma): boundary = gaussian(boundary, sigma=sigma) boundary[boundary >= 0.5] = 1 boundary[boundary < 0.5] = 0 return boundary class CropToFixed: def __init__(self, random_state, size=(256, 256), centered=False, **kwargs): self.random_state = random_state self.crop_y, self.crop_x = size self.centered = centered def __call__(self, m): def _padding(pad_total): half_total = pad_total // 2 return (half_total, pad_total - half_total) def _rand_range_and_pad(crop_size, max_size): if crop_size < max_size: return max_size - crop_size, (0, 0) else: return 1, _padding(crop_size - max_size) def _start_and_pad(crop_size, max_size): if crop_size < max_size: return (max_size - crop_size) // 2, (0, 0) else: return 0, _padding(crop_size - max_size) _, y, x = m.shape if not self.centered: y_range, y_pad = _rand_range_and_pad(self.crop_y, y) x_range, x_pad = _rand_range_and_pad(self.crop_x, x) y_start = self.random_state.randint(y_range) x_start = self.random_state.randint(x_range) else: y_start, y_pad = _start_and_pad(self.crop_y, y) x_start, x_pad = _start_and_pad(self.crop_x, x) result = m[:, y_start:y_start + self.crop_y, x_start:x_start + self.crop_x] return np.pad(result, pad_width=((0, 0), y_pad, x_pad), mode='reflect') class AbstractLabelToBoundary: AXES_TRANSPOSE = [ (0, 1, 2), # X (0, 2, 1), # Y (2, 0, 1) # Z ] def __init__(self, ignore_index=None, aggregate_affinities=False, append_label=False, **kwargs): self.ignore_index = ignore_index self.aggregate_affinities = aggregate_affinities self.append_label = append_label def __call__(self, m): assert m.ndim == 3 kernels = self.get_kernels() boundary_arr = [np.where(np.abs(convolve(m, kernel)) > 0, 1, 0) for kernel in kernels] channels = np.stack(boundary_arr) results = [] if self.aggregate_affinities: assert len(kernels) % 3 == 0, "Number of kernels must be divided by 3 (one kernel per offset per Z,Y,X axes" # aggregate affinities with the same offset for i in range(0, len(kernels), 3): # merge across X,Y,Z axes (logical OR) xyz_aggregated_affinities = np.logical_or.reduce(channels[i:i + 3, ...]).astype(np.int) # recover ignore index xyz_aggregated_affinities = _recover_ignore_index(xyz_aggregated_affinities, m, self.ignore_index) results.append(xyz_aggregated_affinities) else: results = [_recover_ignore_index(channels[i], m, self.ignore_index) for i in range(channels.shape[0])] if self.append_label: # append original input data results.append(m) # stack across channel dim return np.stack(results, axis=0) @staticmethod def create_kernel(axis, offset): # create conv kernel k_size = offset + 1 k = np.zeros((1, 1, k_size), dtype=np.int) k[0, 0, 0] = 1 k[0, 0, offset] = -1 return np.transpose(k, axis) def get_kernels(self): raise NotImplementedError class StandardLabelToBoundary: def __init__(self, ignore_index=None, append_label=False, blur=False, sigma=1, mode='thick', blobs=False, **kwargs): self.ignore_index = ignore_index self.append_label = append_label self.blur = blur self.sigma = sigma self.mode = mode self.blobs = blobs def __call__(self, m): assert m.ndim == 3 boundaries = find_boundaries(m, connectivity=2, mode=self.mode) if self.blur: boundaries = blur_boundary(boundaries, self.sigma) results = [] if self.blobs: blobs = (m > 0).astype('uint8') results.append(_recover_ignore_index(blobs, m, self.ignore_index)) results.append(_recover_ignore_index(boundaries, m, self.ignore_index)) if self.append_label: # append original input data results.append(m) return np.stack(results, axis=0) class BlobsWithBoundary: def __init__(self, mode=None, append_label=False, blur=False, sigma=1, **kwargs): if mode is None: mode = ['thick', 'inner', 'outer'] self.mode = mode self.append_label = append_label self.blur = blur self.sigma = sigma def __call__(self, m): assert m.ndim == 3 # get the segmentation mask results = [(m > 0).astype('uint8')] for bm in self.mode: boundary = find_boundaries(m, connectivity=2, mode=bm) if self.blur: boundary = blur_boundary(boundary, self.sigma) results.append(boundary) if self.append_label: results.append(m) return np.stack(results, axis=0) class BlobsToMask: def __init__(self, append_label=False, boundary=False, cross_entropy=False, **kwargs): self.cross_entropy = cross_entropy self.boundary = boundary self.append_label = append_label def __call__(self, m): assert m.ndim == 3 # get the segmentation mask mask = (m > 0).astype('uint8') results = [mask] if self.boundary: outer = find_boundaries(m, connectivity=2, mode='outer') if self.cross_entropy: # boundary is class 2 mask[outer > 0] = 2 results = [mask] else: results.append(outer) if self.append_label: results.append(m) return np.stack(results, axis=0) class RandomLabelToAffinities(AbstractLabelToBoundary): def __init__(self, random_state, max_offset=10, ignore_index=None, append_label=False, z_offset_scale=2, **kwargs): super().__init__(ignore_index=ignore_index, append_label=append_label, aggregate_affinities=False) self.random_state = random_state self.offsets = tuple(range(1, max_offset + 1)) self.z_offset_scale = z_offset_scale def get_kernels(self): rand_offset = self.random_state.choice(self.offsets) axis_ind = self.random_state.randint(3) # scale down z-affinities due to anisotropy if axis_ind == 2: rand_offset = max(1, rand_offset // self.z_offset_scale) rand_axis = self.AXES_TRANSPOSE[axis_ind] # return a single kernel return [self.create_kernel(rand_axis, rand_offset)] class LabelToAffinities(AbstractLabelToBoundary): def __init__(self, offsets, ignore_index=None, append_label=False, aggregate_affinities=False, z_offsets=None, **kwargs): super().__init__(ignore_index=ignore_index, append_label=append_label, aggregate_affinities=aggregate_affinities) assert isinstance(offsets, list) or isinstance(offsets, tuple), 'offsets must be a list or a tuple' assert all(a > 0 for a in offsets), "'offsets must be positive" assert len(set(offsets)) == len(offsets), "'offsets' must be unique" if z_offsets is not None: assert len(offsets) == len(z_offsets), 'z_offsets length must be the same as the length of offsets' else: z_offsets = list(offsets) self.z_offsets = z_offsets self.kernels = [] for xy_offset, z_offset in zip(offsets, z_offsets): for axis_ind, axis in enumerate(self.AXES_TRANSPOSE): final_offset = xy_offset if axis_ind == 2: final_offset = z_offset self.kernels.append(self.create_kernel(axis, final_offset)) def get_kernels(self): return self.kernels class LabelToZAffinities(AbstractLabelToBoundary): def __init__(self, offsets, ignore_index=None, append_label=False, **kwargs): super().__init__(ignore_index=ignore_index, append_label=append_label) assert isinstance(offsets, list) or isinstance(offsets, tuple), 'offsets must be a list or a tuple' assert all(a > 0 for a in offsets), "'offsets must be positive" assert len(set(offsets)) == len(offsets), "'offsets' must be unique" self.kernels = [] z_axis = self.AXES_TRANSPOSE[2] # create kernels for z_offset in offsets: self.kernels.append(self.create_kernel(z_axis, z_offset)) def get_kernels(self): return self.kernels class LabelToBoundaryAndAffinities: def __init__(self, xy_offsets, z_offsets, append_label=False, blur=False, sigma=1, ignore_index=None, mode='thick', blobs=False, **kwargs): # blur only StandardLabelToBoundary results; we don't want to blur the affinities self.l2b = StandardLabelToBoundary(blur=blur, sigma=sigma, ignore_index=ignore_index, mode=mode, blobs=blobs) self.l2a = LabelToAffinities(offsets=xy_offsets, z_offsets=z_offsets, append_label=append_label, ignore_index=ignore_index) def __call__(self, m): boundary = self.l2b(m) affinities = self.l2a(m) return np.concatenate((boundary, affinities), axis=0) class FlyWingBoundary: def __init__(self, append_label=False, thick_boundary=True, ignore_index=None, z_offsets=None, **kwargs): self.append_label = append_label self.thick_boundary = thick_boundary self.ignore_index = ignore_index self.lta = None if z_offsets is not None: self.lta = LabelToZAffinities(z_offsets, ignore_index=ignore_index) def __call__(self, m): boundary = (m == 0).astype('uint8') results = [boundary] if self.thick_boundary: t_boundary = find_boundaries(m, connectivity=1, mode='outer', background=0) results.append(t_boundary) if self.lta is not None: z_affs = self.lta(m) for z_aff in z_affs: results.append(z_aff) if self.ignore_index is not None: for b in results: b[m == self.ignore_index] = self.ignore_index if self.append_label: results.append(m) return np.stack(results, axis=0) class LabelToMaskAndAffinities: def __init__(self, xy_offsets, z_offsets, append_label=False, background=0, ignore_index=None, **kwargs): self.background = background self.l2a = LabelToAffinities(offsets=xy_offsets, z_offsets=z_offsets, append_label=append_label, ignore_index=ignore_index) def __call__(self, m): mask = m > self.background mask = np.expand_dims(mask.astype(np.uint8), axis=0) affinities = self.l2a(m) return np.concatenate((mask, affinities), axis=0) class Standardize: def __init__(self, mean, std, eps=1e-6, **kwargs): self.mean = mean self.std = std self.eps = eps def __call__(self, m): return (m - self.mean) / np.clip(self.std, a_min=self.eps, a_max=None) class Normalize: def __init__(self, min_value, max_value, **kwargs): assert max_value > min_value self.min_value = min_value self.value_range = max_value - min_value def __call__(self, m): norm_0_1 = (m - self.min_value) / self.value_range return np.clip(2 * norm_0_1 - 1, -1, 1) class AdditiveGaussianNoise: def __init__(self, random_state, scale=(0.0, 1.0), execution_probability=0.1, **kwargs): self.execution_probability = execution_probability self.random_state = random_state self.scale = scale def __call__(self, m): if self.random_state.uniform() < self.execution_probability: std = self.random_state.uniform(self.scale[0], self.scale[1]) gaussian_noise = self.random_state.normal(0, std, size=m.shape) return m + gaussian_noise return m class AdditivePoissonNoise: def __init__(self, random_state, lam=(0.0, 1.0), execution_probability=0.1, **kwargs): self.execution_probability = execution_probability self.random_state = random_state self.lam = lam def __call__(self, m): if self.random_state.uniform() < self.execution_probability: lam = self.random_state.uniform(self.lam[0], self.lam[1]) poisson_noise = self.random_state.poisson(lam, size=m.shape) return m + poisson_noise return m class ToTensor: def __init__(self, expand_dims, dtype=np.float32, **kwargs): self.expand_dims = expand_dims self.dtype = dtype def __call__(self, m): assert m.ndim in [3, 4], 'Supports only 3D (DxHxW) or 4D (CxDxHxW) images' if self.expand_dims and m.ndim == 3: m = np.expand_dims(m, axis=0) return torch.from_numpy(m.astype(dtype=self.dtype)) class Relabel: def __init__(self, **kwargs): pass def __call__(self, m): _, unique_labels = np.unique(m, return_inverse=True) m = unique_labels.reshape(m.shape) return m class Identity: def __init__(self, **kwargs): pass def __call__(self, m): return m def get_transformer(config, min_value, max_value, mean, std): base_config = {'min_value': min_value, 'max_value': max_value, 'mean': mean, 'std': std} return Transformer(config, base_config) class Transformer: def __init__(self, phase_config, base_config): self.phase_config = phase_config self.config_base = base_config self.seed = GLOBAL_RANDOM_STATE.randint(10000000) def raw_transform(self): return self._create_transform('raw') def label_transform(self): return self._create_transform('label') def weight_transform(self): return self._create_transform('weight') @staticmethod def _transformer_class(class_name): m = importlib.import_module('pytorch3dunet.augment.transforms') clazz = getattr(m, class_name) return clazz def _create_transform(self, name): assert name in self.phase_config, f'Could not find {name} transform' return Compose([ self._create_augmentation(c) for c in self.phase_config[name] ]) def _create_augmentation(self, c): config = dict(self.config_base) config.update(c) config['random_state'] = np.random.RandomState(self.seed) aug_class = self._transformer_class(config['name']) return aug_class(**config) def _recover_ignore_index(input, orig, ignore_index): if ignore_index is not None: mask = orig == ignore_index input[mask] = ignore_index return input

true

f7296753d900057b6ee906f435a24ab68c98469f

2,040

py

Python

tests/providers/google/cloud/operators/test_video_intelligence_system.py

emilioego/airflow

3457c7847cd24413ff5b622e65c27d8370f94502

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

79

2021-10-15T07:32:27.000Z

2022-03-28T04:10:19.000Z

tests/providers/google/cloud/operators/test_video_intelligence_system.py

emilioego/airflow

3457c7847cd24413ff5b622e65c27d8370f94502

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

210

2021-07-17T00:25:52.000Z

2021-12-29T00:44:48.000Z

tests/providers/google/cloud/operators/test_video_intelligence_system.py

emilioego/airflow

3457c7847cd24413ff5b622e65c27d8370f94502

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

23

2021-10-15T02:36:37.000Z

2022-03-17T02:59:27.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import os import pytest from airflow.providers.google.cloud.example_dags.example_video_intelligence import GCP_BUCKET_NAME from tests.providers.google.cloud.utils.gcp_authenticator import GCP_AI_KEY, GCP_GCS_KEY from tests.test_utils.gcp_system_helpers import CLOUD_DAG_FOLDER, GoogleSystemTest, provide_gcp_context GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-project") GCP_VIDEO_SOURCE_URL = "https://www.sample-videos.com/video123/mp4/720/big_buck_bunny_720p_1mb.mp4" @pytest.mark.backend("mysql", "postgres") @pytest.mark.credential_file(GCP_AI_KEY) class CloudVideoIntelligenceExampleDagsTest(GoogleSystemTest): @provide_gcp_context(GCP_AI_KEY) def setUp(self): self.create_gcs_bucket(GCP_BUCKET_NAME, location="europe-north1") self.execute_with_ctx( cmd=["bash", "-c", f"curl {GCP_VIDEO_SOURCE_URL} | gsutil cp - gs://{GCP_BUCKET_NAME}/video.mp4"], key=GCP_GCS_KEY, ) super().setUp() @provide_gcp_context(GCP_AI_KEY) def tearDown(self): self.delete_gcs_bucket(GCP_BUCKET_NAME) super().tearDown() @provide_gcp_context(GCP_AI_KEY) def test_example_dag(self): self.run_dag('example_gcp_video_intelligence', CLOUD_DAG_FOLDER)

40.8

110

0.763725

import os import pytest from airflow.providers.google.cloud.example_dags.example_video_intelligence import GCP_BUCKET_NAME from tests.providers.google.cloud.utils.gcp_authenticator import GCP_AI_KEY, GCP_GCS_KEY from tests.test_utils.gcp_system_helpers import CLOUD_DAG_FOLDER, GoogleSystemTest, provide_gcp_context GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-project") GCP_VIDEO_SOURCE_URL = "https://www.sample-videos.com/video123/mp4/720/big_buck_bunny_720p_1mb.mp4" @pytest.mark.backend("mysql", "postgres") @pytest.mark.credential_file(GCP_AI_KEY) class CloudVideoIntelligenceExampleDagsTest(GoogleSystemTest): @provide_gcp_context(GCP_AI_KEY) def setUp(self): self.create_gcs_bucket(GCP_BUCKET_NAME, location="europe-north1") self.execute_with_ctx( cmd=["bash", "-c", f"curl {GCP_VIDEO_SOURCE_URL} | gsutil cp - gs://{GCP_BUCKET_NAME}/video.mp4"], key=GCP_GCS_KEY, ) super().setUp() @provide_gcp_context(GCP_AI_KEY) def tearDown(self): self.delete_gcs_bucket(GCP_BUCKET_NAME) super().tearDown() @provide_gcp_context(GCP_AI_KEY) def test_example_dag(self): self.run_dag('example_gcp_video_intelligence', CLOUD_DAG_FOLDER)

true

f7296786eaf8c32bfc2c367f3dc5235847f3635e

2,002

py

Python

python3.4Smartforest/lib/python3.4/site-packages/setuptools/lib2to3_ex.py

letouriste001/SmartForest_2.0

109b78bf1e8c8404800f377ab969395ccbb617be

[ "MIT" ]

1

2019-06-30T20:04:39.000Z

python3.4Smartforest/lib/python3.4/site-packages/setuptools/lib2to3_ex.py

letouriste001/SmartForest_2.0

109b78bf1e8c8404800f377ab969395ccbb617be

[ "MIT" ]

4

2021-03-18T20:36:02.000Z

2022-01-13T00:47:28.000Z

python3.4Smartforest/lib/python3.4/site-packages/setuptools/lib2to3_ex.py

letouriste001/SmartForest_2.0

109b78bf1e8c8404800f377ab969395ccbb617be

[ "MIT" ]

2

2016-07-23T22:12:34.000Z

2018-06-23T03:40:58.000Z

""" Customized Mixin2to3 support: - adds support for converting doctests This module raises an ImportError on Python 2. """ from distutils.util import Mixin2to3 as _Mixin2to3 from distutils import log from lib2to3.refactor import RefactoringTool, get_fixers_from_package import setuptools class DistutilsRefactoringTool(RefactoringTool): def log_error(self, msg, *args, **kw): log.error(msg, *args) def log_message(self, msg, *args): log.info(msg, *args) def log_debug(self, msg, *args): log.debug(msg, *args) class Mixin2to3(_Mixin2to3): def run_2to3(self, files, doctests = False): # See of the distribution option has been set, otherwise check the # setuptools default. if self.distribution.use_2to3 is not True: return if not files: return log.info("Fixing "+" ".join(files)) self.__build_fixer_names() self.__exclude_fixers() if doctests: if setuptools.run_2to3_on_doctests: r = DistutilsRefactoringTool(self.fixer_names) r.refactor(files, write=True, doctests_only=True) else: _Mixin2to3.run_2to3(self, files) def __build_fixer_names(self): if self.fixer_names: return self.fixer_names = [] for p in setuptools.lib2to3_fixer_packages: self.fixer_names.extend(get_fixers_from_package(p)) if self.distribution.use_2to3_fixers is not None: for p in self.distribution.use_2to3_fixers: self.fixer_names.extend(get_fixers_from_package(p)) def __exclude_fixers(self): excluded_fixers = getattr(self, 'exclude_fixers', []) if self.distribution.use_2to3_exclude_fixers is not None: excluded_fixers.extend(self.distribution.use_2to3_exclude_fixers) for fixer_name in excluded_fixers: if fixer_name in self.fixer_names: self.fixer_names.remove(fixer_name)

31.777778

77

0.668332

from distutils.util import Mixin2to3 as _Mixin2to3 from distutils import log from lib2to3.refactor import RefactoringTool, get_fixers_from_package import setuptools class DistutilsRefactoringTool(RefactoringTool): def log_error(self, msg, *args, **kw): log.error(msg, *args) def log_message(self, msg, *args): log.info(msg, *args) def log_debug(self, msg, *args): log.debug(msg, *args) class Mixin2to3(_Mixin2to3): def run_2to3(self, files, doctests = False): if self.distribution.use_2to3 is not True: return if not files: return log.info("Fixing "+" ".join(files)) self.__build_fixer_names() self.__exclude_fixers() if doctests: if setuptools.run_2to3_on_doctests: r = DistutilsRefactoringTool(self.fixer_names) r.refactor(files, write=True, doctests_only=True) else: _Mixin2to3.run_2to3(self, files) def __build_fixer_names(self): if self.fixer_names: return self.fixer_names = [] for p in setuptools.lib2to3_fixer_packages: self.fixer_names.extend(get_fixers_from_package(p)) if self.distribution.use_2to3_fixers is not None: for p in self.distribution.use_2to3_fixers: self.fixer_names.extend(get_fixers_from_package(p)) def __exclude_fixers(self): excluded_fixers = getattr(self, 'exclude_fixers', []) if self.distribution.use_2to3_exclude_fixers is not None: excluded_fixers.extend(self.distribution.use_2to3_exclude_fixers) for fixer_name in excluded_fixers: if fixer_name in self.fixer_names: self.fixer_names.remove(fixer_name)

true

f729699287f520c8ea12e89ebedc305b0d14814f

512

py

Python

pavement.py

yueranyuan/pyscreenshot

3287b798691de8791bc3b3314f2545f7b0b1cb99

[ "BSD-2-Clause" ]

null

pavement.py

yueranyuan/pyscreenshot

3287b798691de8791bc3b3314f2545f7b0b1cb99

[ "BSD-2-Clause" ]

null

pavement.py

yueranyuan/pyscreenshot

3287b798691de8791bc3b3314f2545f7b0b1cb99

[ "BSD-2-Clause" ]

null

from path import Path from paver.doctools import cog, html from paver.easy import options from paver.options import Bunch from paver.setuputils import setup IMPORTS=[cog, html, setup] options( cog=Bunch( basedir='.', pattern='README.rst', includedir='pyscreenshot', beginspec='#--', endspec='--#', endoutput='#-#', ) ) # get info from setup.py setup_py = ''.join( [x for x in Path('setup.py').lines() if 'setuptools' not in x]) exec(setup_py)

19.692308

67

0.621094

from path import Path from paver.doctools import cog, html from paver.easy import options from paver.options import Bunch from paver.setuputils import setup IMPORTS=[cog, html, setup] options( cog=Bunch( basedir='.', pattern='README.rst', includedir='pyscreenshot', beginspec='#--', endspec='--#', endoutput='#-#', ) ) setup_py = ''.join( [x for x in Path('setup.py').lines() if 'setuptools' not in x]) exec(setup_py)

true

f72969afd180df2df3d7f7508b8f7475f20eba44

213

py

Python

.history/my_classes/FirstClassFunctions/reducing_functions_20210707134010.py

minefarmer/deep-Dive-1

b0675b853180c5b5781888266ea63a3793b8d855

[ "Unlicense" ]

null

.history/my_classes/FirstClassFunctions/reducing_functions_20210707134010.py

minefarmer/deep-Dive-1

b0675b853180c5b5781888266ea63a3793b8d855

[ "Unlicense" ]

null

.history/my_classes/FirstClassFunctions/reducing_functions_20210707134010.py

minefarmer/deep-Dive-1

b0675b853180c5b5781888266ea63a3793b8d855

[ "Unlicense" ]

null

"""Reducing Functions in Python These are functions that recombine an iterable recursively, ending up with a single return value Also called accumulators, aggregators, or folding functions Example """

21.3

96

0.769953

true

f72969ff813909f4ad42f15958aa9f086b6a58bf

2,694

py

Python

qgis3script-importernvdbdata.py

alexdiem/nvdbapi-V3

18265ee6d02aed17d6199e5ed42fe731c9320a08

[ "MIT" ]

null

qgis3script-importernvdbdata.py

alexdiem/nvdbapi-V3

18265ee6d02aed17d6199e5ed42fe731c9320a08

[ "MIT" ]

null

qgis3script-importernvdbdata.py

alexdiem/nvdbapi-V3

18265ee6d02aed17d6199e5ed42fe731c9320a08

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Script for å interaktivt legge til NVDB-vegnett og fagdata via python kommandolinje i QGIS. Se dokumentasjon på bruk av nvdbapi - funskjoner på https://github.com/LtGlahn/nvdbapi-V3 Legg dette scriptet et sted hvor det er lettvint å finne fra QGIS. F.eks. C:/Users/<dittbrukernavn>. EKSEMPLER #Vegnett europaveger Trondheim kommune v = nvdbVegnett() v.addfilter_geo({ 'kommune' : 1601, 'vegreferanse' : 'E' })" ) nvdbsok2qgis( v, lagnavn='Europaveger Trondheim') # Vegnett innenfor kartutsnitt v = nvdbVegnett() nvdb2kart( v, iface) # Bomstasjoner b = nvdbFagdata(45) nvdbsok2qgis( b) # Søk etter fartsgrenser innenfor kartflaten, legg til f = nvdbFagdata(105) nvdb2kart( f, iface) # Søk etter kjent objektID, legg til kartflaten nvdb2kart( 572672190, iface ) """ import sys #########################################################33 ## ## Endre stien til den mappen der du har lastet ned dette ## reposet https://github.com/LtGlahn/nvdbapi-V3 ## ## Merk at hvis du laster ned repos som zip-fil og høyrekikker->Pakk ut alle ## så vil stien være NEDLASTING\\nvdbapi-V3-master\\nvdbapi-V3-master ## # nvdblibrary = 'C:/Data/leveranser/nvdbapi-V3' nvdblibrary = 'C:\\Users\\jajens\Downloads\\nvdbapi-V3-master\\nvdbapi-V3-master' # nvdblibrary = 'C:\Users\<DITT BRUKERNAVN>\Downloads\\nvdbapi-V3-master\nvdbapi-V3-master' # nvdblibrary = '/home/jan/Documents/jobb/nvdbapi-V3' ## Hvis vi ikke klarer å importere nvdbapiv3 så prøver vi å føye ## mappen nvdblibrary til søkestien. try: import nvdbapiv3 except ModuleNotFoundError: print( "Fant ikke nvdbapiv3 i sys.path, legger til mappen", nvdblibrary) sys.path.append( nvdblibrary ) try: import nvdbapiv3 except ModuleNotFoundError as e: print( "\nImport av nvdbapiv3 feiler for", nvdblibrary ) raise ModuleNotFoundError( "==> Variabel nvdblibrary skal peke til mappen https://github.com/LtGlahn/nvdbapi-V3 <==" ) else: print( "SUKSESS - kan importere nvdbapiv3 etter at vi la til", nvdblibrary, "i sys.path" ) else: print( "HURRA - vi kan importere nvdbapiv3 " ) ## Her importerer vi de funksjonene vi trenger from nvdbapiv3 import nvdbFagdata, nvdbVegnett from nvdbapiV3qgis3 import nvdb2kart, nvdbsok2qgis, url2kart, nvdb2kartListe ## Bruk linjene nedenfor for debugging ## Funksjonskallene på python-konsollet i QGIS blir da ## ## >>> sok = nvdbapiv3.nvdbFagdata(86) ## >>> nvdbapiV3qgis3.nvdb2kart( sok, iface ) ## # import importlib # import nvdbapiV3qgis3 # import nvdbapiv3 # importlib.reload(nvdbapiV3qgis3 ) # importlib.reload(nvdbapiv3 )

30.269663

128

0.69562

import sys

true

f7296b28fa92684ef445111a79a67994059f7ec4

1,221

py

Python

uer/layers/layer_norm.py

krevas/ET-BERT

464ce3e7942d4450f55021e267ceb9dd48a36b1f

[ "MIT" ]

null

uer/layers/layer_norm.py

krevas/ET-BERT

464ce3e7942d4450f55021e267ceb9dd48a36b1f

[ "MIT" ]

null

uer/layers/layer_norm.py

krevas/ET-BERT

464ce3e7942d4450f55021e267ceb9dd48a36b1f

[ "MIT" ]

null

import torch import torch.nn as nn class LayerNorm(nn.Module): """ Layer Normalization. https://arxiv.org/abs/1607.06450 """ def __init__(self, hidden_size, eps=1e-6): super(LayerNorm, self).__init__() self.eps = eps self.gamma = nn.Parameter(torch.ones(hidden_size)) self.beta = nn.Parameter(torch.zeros(hidden_size)) def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) hidden_states = self.gamma * (x-mean) / (std + self.eps) return hidden_states + self.beta class T5LayerNorm(nn.Module): """ Construct a layernorm module in the T5 style No bias and no subtraction of mean. """ def __init__(self, hidden_size, eps=1e-6): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): # layer norm should always be calculated in float32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.type_as(self.weight)

30.525

85

0.648649

import torch import torch.nn as nn class LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): super(LayerNorm, self).__init__() self.eps = eps self.gamma = nn.Parameter(torch.ones(hidden_size)) self.beta = nn.Parameter(torch.zeros(hidden_size)) def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) hidden_states = self.gamma * (x-mean) / (std + self.eps) return hidden_states + self.beta class T5LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.type_as(self.weight)

true

f7296ddca1f1526d81de73d1fd2bc229a1a53869

777

py

Python

chapter_3/my_service/scrap/zoo.py

rinjyu/the_red

c099e830ae3ee9063c3e9d29f4ee627241c7eeed

[ "Apache-2.0" ]

13

2021-07-26T06:09:19.000Z

2022-03-22T07:01:22.000Z

chapter_3/my_service/scrap/zoo.py

rinjyu/the_red

c099e830ae3ee9063c3e9d29f4ee627241c7eeed

[ "Apache-2.0" ]

11

2021-07-25T03:35:25.000Z

2021-08-13T23:05:38.000Z

chapter_3/my_service/scrap/zoo.py

rinjyu/the_red

c099e830ae3ee9063c3e9d29f4ee627241c7eeed

[ "Apache-2.0" ]

8

2021-09-02T14:54:17.000Z

2022-03-14T10:28:37.000Z

from kazoo.client import KazooClient from kazoo.exceptions import NoNodeError from kazoo.exceptions import NodeExistsError _callback = None _zk = None def init_kazoo(hosts, data_path, callback, children=True): global _zk global _callback _zk = KazooClient(hosts=hosts) _zk.start() _callback = callback if data_path: if children: @_zk.ChildrenWatch(data_path) def watch_children(children): print("Watch Children") if _callback: _callback(children) else: @_zk.DataWatch(data_path) def watch_node(data, stat): print("Watch Node") if _callback: _callback(data, stat) return _zk

23.545455

58

0.594595

from kazoo.client import KazooClient from kazoo.exceptions import NoNodeError from kazoo.exceptions import NodeExistsError _callback = None _zk = None def init_kazoo(hosts, data_path, callback, children=True): global _zk global _callback _zk = KazooClient(hosts=hosts) _zk.start() _callback = callback if data_path: if children: @_zk.ChildrenWatch(data_path) def watch_children(children): print("Watch Children") if _callback: _callback(children) else: @_zk.DataWatch(data_path) def watch_node(data, stat): print("Watch Node") if _callback: _callback(data, stat) return _zk

true

f7296e2a3e9328088302e2141a4ad4afd4c901b1

1,166

py

Python

docstringer/decorators.py

ttamg/docstringer

f28cdc178e8cb5dd7ca16c885f3837a052807e16

[ "MIT" ]

null

docstringer/decorators.py

ttamg/docstringer

f28cdc178e8cb5dd7ca16c885f3837a052807e16

[ "MIT" ]

null

docstringer/decorators.py

ttamg/docstringer

f28cdc178e8cb5dd7ca16c885f3837a052807e16

[ "MIT" ]

null

import functools from .events import FunctionEvent from .formatters import BaseFormatter, DefaultFormatter def docstringer( _func=None, *, active=True, formatter: BaseFormatter = DefaultFormatter() ): """ A decorator that will output the function docstring, call values and return value when the function is called. Add this decorator to all functions you want to have documented this way. Parameters: - active (bool) default=True - this controls if the docstringer is active - formatter (Formatter instance) - this allows docstringer to output the results in a different format or data structure """ def wrapper(func): @functools.wraps(func) def inner(*args, **kwargs): if not active: return func(*args, **kwargs) event = FunctionEvent(func, *args, **kwargs) formatter.call(event) return_value = func(*args, **kwargs) event.return_value = return_value formatter.end(event) return return_value return inner if _func is None: return wrapper else: return wrapper(_func)

27.116279

124

0.651801

import functools from .events import FunctionEvent from .formatters import BaseFormatter, DefaultFormatter def docstringer( _func=None, *, active=True, formatter: BaseFormatter = DefaultFormatter() ): def wrapper(func): @functools.wraps(func) def inner(*args, **kwargs): if not active: return func(*args, **kwargs) event = FunctionEvent(func, *args, **kwargs) formatter.call(event) return_value = func(*args, **kwargs) event.return_value = return_value formatter.end(event) return return_value return inner if _func is None: return wrapper else: return wrapper(_func)

true

f7296e3c7738ac9586479e919858bf2e64daf411

2,066

py

Python

api/core/management/commands/populate_db.py

mf-tech-solutions/cusgeo

7e15b707bc7f1ae1fd7a091e64c41a6f7c8092c3

[ "MIT" ]

null

api/core/management/commands/populate_db.py

mf-tech-solutions/cusgeo

7e15b707bc7f1ae1fd7a091e64c41a6f7c8092c3

[ "MIT" ]

null

api/core/management/commands/populate_db.py

mf-tech-solutions/cusgeo

7e15b707bc7f1ae1fd7a091e64c41a6f7c8092c3

[ "MIT" ]

null

from django.core.management.base import BaseCommand from django.db.utils import OperationalError from customers.models import Customer from geolocation.models import Location import csv import sys class Command(BaseCommand): """ Command that populates the Customers table """ def __init__(self, *args, **kwargs): super().__init__() self.customers = self._get_customer_from_file() self.cities = [c['city'] for c in self.customers] def handle(self, *args, **options): sys.stdout.write("Populating db...\n") try: for customer in self.customers: Customer.objects.get_or_create( id=customer['id'], email=customer['email'], first_name=customer['first_name'], last_name=customer['last_name'], gender=customer['gender'], company=customer['company'], title=customer['title'] ) i = 1 for city in self.cities: customer = Customer.objects.get(id=i) Location.objects.get_or_create( customer=customer, city=city, latitude=0, longitude=0 ) i += 1 except OperationalError as error: raise error sys.stdout.write("Db populated\n") def _get_customer_from_file(self): with open('./customers.csv') as file: reader = csv.DictReader(file) return [{ 'id': row['id'], 'email': row['email'], 'first_name': row['first_name'], 'last_name': row['last_name'], 'gender': row['gender'], 'company': row['company'], 'title': row['title'], 'city': row['city'] } for row in reader]

30.835821

57

0.484511

from django.core.management.base import BaseCommand from django.db.utils import OperationalError from customers.models import Customer from geolocation.models import Location import csv import sys class Command(BaseCommand): def __init__(self, *args, **kwargs): super().__init__() self.customers = self._get_customer_from_file() self.cities = [c['city'] for c in self.customers] def handle(self, *args, **options): sys.stdout.write("Populating db...\n") try: for customer in self.customers: Customer.objects.get_or_create( id=customer['id'], email=customer['email'], first_name=customer['first_name'], last_name=customer['last_name'], gender=customer['gender'], company=customer['company'], title=customer['title'] ) i = 1 for city in self.cities: customer = Customer.objects.get(id=i) Location.objects.get_or_create( customer=customer, city=city, latitude=0, longitude=0 ) i += 1 except OperationalError as error: raise error sys.stdout.write("Db populated\n") def _get_customer_from_file(self): with open('./customers.csv') as file: reader = csv.DictReader(file) return [{ 'id': row['id'], 'email': row['email'], 'first_name': row['first_name'], 'last_name': row['last_name'], 'gender': row['gender'], 'company': row['company'], 'title': row['title'], 'city': row['city'] } for row in reader]

true

f7296e7682b97a09000c105cac73b1070d2a032f

2,284

py

Python

lib/rtc.py

FlorianPoot/M5StickCWatch

c5e63b5915b1163636084666a14f1d61ae708adf

[ "MIT" ]

21

2019-11-15T15:29:12.000Z

2022-03-20T12:15:48.000Z

lib/rtc.py

FlorianPoot/M5StickCWatch

c5e63b5915b1163636084666a14f1d61ae708adf

[ "MIT" ]

4

2019-10-01T08:48:04.000Z

2020-11-17T12:05:43.000Z

lib/rtc.py

FlorianPoot/M5StickCWatch

c5e63b5915b1163636084666a14f1d61ae708adf

[ "MIT" ]

1

2019-11-15T15:29:17.000Z

import ustruct import i2c_bus class RTC: def __init__(self): self.addr = 0x51 self.i2c = i2c_bus.get(i2c_bus.M_BUS) def get_time(self): buf = self._regchar(0x02, buf=bytearray(3)) seconds = self.bcd2_to_byte(buf[0] & 0x7f) minutes = self.bcd2_to_byte(buf[1] & 0x7f) hours = self.bcd2_to_byte(buf[2] & 0x3f) return hours, minutes, seconds def set_time(self, hours, minutes, seconds): seconds = self.byte_to_bcd2(seconds) minutes = self.byte_to_bcd2(minutes) hours = self.byte_to_bcd2(hours) self._regchar(0x02, (seconds, minutes, hours), buf=bytearray(3)) def get_date(self): buf = self._regchar(0x05, buf=bytearray(4)) date = self.bcd2_to_byte(buf[0] & 0x3f) week_day = self.bcd2_to_byte(buf[1] & 0x07) month = self.bcd2_to_byte(buf[2] & 0x1f) if buf[2] & 0x80: year = 1900 + self.bcd2_to_byte(buf[3] & 0xff) else: year = 2000 + self.bcd2_to_byte(buf[3] & 0xff) return year, month, date, week_day def set_date(self, year, month, date, week_day): date = self.byte_to_bcd2(date) week_day = self.byte_to_bcd2(week_day) if year < 2000: month = self.byte_to_bcd2(month | 0x80) else: month = self.byte_to_bcd2(month | 0x00) year = self.byte_to_bcd2(year % 100) self._regchar(0x05, (date, week_day, month, year), buf=bytearray(4)) def _regchar(self, reg, value=None, buf=bytearray(1)): if value is None: self.i2c.readfrom_mem_into(self.addr, reg, buf) if len(buf) == 1: return buf[0] else: return buf if type(value) is int: ustruct.pack_into('<b', buf, 0, value) else: ustruct.pack_into('<%db' % len(value), buf, 0, *value) return self.i2c.writeto_mem(self.addr, reg, buf) @staticmethod def bcd2_to_byte(value): tmp = ((value & 0xF0) >> 0x4) * 10 return tmp + (value & 0x0F) @staticmethod def byte_to_bcd2(value): bcdhigh = 0 while value >= 10: bcdhigh += 1 value -= 10 return (bcdhigh << 4) | value

27.518072

76

0.569177

import ustruct import i2c_bus class RTC: def __init__(self): self.addr = 0x51 self.i2c = i2c_bus.get(i2c_bus.M_BUS) def get_time(self): buf = self._regchar(0x02, buf=bytearray(3)) seconds = self.bcd2_to_byte(buf[0] & 0x7f) minutes = self.bcd2_to_byte(buf[1] & 0x7f) hours = self.bcd2_to_byte(buf[2] & 0x3f) return hours, minutes, seconds def set_time(self, hours, minutes, seconds): seconds = self.byte_to_bcd2(seconds) minutes = self.byte_to_bcd2(minutes) hours = self.byte_to_bcd2(hours) self._regchar(0x02, (seconds, minutes, hours), buf=bytearray(3)) def get_date(self): buf = self._regchar(0x05, buf=bytearray(4)) date = self.bcd2_to_byte(buf[0] & 0x3f) week_day = self.bcd2_to_byte(buf[1] & 0x07) month = self.bcd2_to_byte(buf[2] & 0x1f) if buf[2] & 0x80: year = 1900 + self.bcd2_to_byte(buf[3] & 0xff) else: year = 2000 + self.bcd2_to_byte(buf[3] & 0xff) return year, month, date, week_day def set_date(self, year, month, date, week_day): date = self.byte_to_bcd2(date) week_day = self.byte_to_bcd2(week_day) if year < 2000: month = self.byte_to_bcd2(month | 0x80) else: month = self.byte_to_bcd2(month | 0x00) year = self.byte_to_bcd2(year % 100) self._regchar(0x05, (date, week_day, month, year), buf=bytearray(4)) def _regchar(self, reg, value=None, buf=bytearray(1)): if value is None: self.i2c.readfrom_mem_into(self.addr, reg, buf) if len(buf) == 1: return buf[0] else: return buf if type(value) is int: ustruct.pack_into('<b', buf, 0, value) else: ustruct.pack_into('<%db' % len(value), buf, 0, *value) return self.i2c.writeto_mem(self.addr, reg, buf) @staticmethod def bcd2_to_byte(value): tmp = ((value & 0xF0) >> 0x4) * 10 return tmp + (value & 0x0F) @staticmethod def byte_to_bcd2(value): bcdhigh = 0 while value >= 10: bcdhigh += 1 value -= 10 return (bcdhigh << 4) | value

true

f7296e825616df59c56e8fca75e277ba8e588d8a

622

py

Python

hw/ip/mono_fm/transform.py

xupsh/pp4fpgas-cn-hls

d14bd0769ce7f9674f206faf93b7622c5bf905bf

[ "Apache-2.0" ]

152

2018-08-06T14:08:59.000Z

2022-03-29T23:15:05.000Z

hw/ip/mono_fm/transform.py

sinjinchang/pp4fpgas-cn-hls

d14bd0769ce7f9674f206faf93b7622c5bf905bf

[ "Apache-2.0" ]

2

2019-04-12T16:30:25.000Z

2019-08-13T19:59:03.000Z

hw/ip/mono_fm/transform.py

sinjinchang/pp4fpgas-cn-hls

d14bd0769ce7f9674f206faf93b7622c5bf905bf

[ "Apache-2.0" ]

63

2018-08-25T10:43:04.000Z

2022-03-26T09:12:35.000Z

import numpy as np detection_file = 'samples.npy' detections = None if detection_file is not None: detections = np.load(detection_file) np.savetxt('samples.txt', detections, fmt='%0.18f') f = open('samples.txt') out = open('complex.txt', "w") lines = f.readlines() for line in lines: for i in line: if i == "+": out.write(" ") elif i == "-": out.write(" -") elif i == "(": i = i elif i == ")": i = i elif i == "j": i = i else: out.write(str(i)) #out.write("\n") #print(line) f.close

20.733333

51

0.485531

import numpy as np detection_file = 'samples.npy' detections = None if detection_file is not None: detections = np.load(detection_file) np.savetxt('samples.txt', detections, fmt='%0.18f') f = open('samples.txt') out = open('complex.txt', "w") lines = f.readlines() for line in lines: for i in line: if i == "+": out.write(" ") elif i == "-": out.write(" -") elif i == "(": i = i elif i == ")": i = i elif i == "j": i = i else: out.write(str(i)) f.close

true

f7296efbb9bd563afe90dc691e822b8ad26cace4

15,124

py

Python

coremltools/converters/mil/mil/ops/defs/recurrent.py

freedomtan/coremltools

5ee9b537b81c44c140a2fa7571e547dfaa24e1ea

[ "BSD-3-Clause" ]

1

2020-12-23T15:42:01.000Z

coremltools/converters/mil/mil/ops/defs/recurrent.py

freedomtan/coremltools

5ee9b537b81c44c140a2fa7571e547dfaa24e1ea

[ "BSD-3-Clause" ]

75

2020-11-24T05:37:45.000Z

2022-02-25T15:14:23.000Z

coremltools/converters/mil/mil/ops/defs/recurrent.py

freedomtan/coremltools

5ee9b537b81c44c140a2fa7571e547dfaa24e1ea

[ "BSD-3-Clause" ]

null

# Copyright (c) 2020, Apple Inc. All rights reserved. # # Use of this source code is governed by a BSD-3-clause license that can be # found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause from coremltools.converters.mil.mil import get_new_symbol from ._op_reqs import * @register_op(doc_str="") class gru(Operation): r""" Gated recurrent unit (GRU). .. math:: r_t = \rm{recurrent\_activation}(W_{ir} x_t + b_{ir} + W_{hr} h_{t-1} + b_{hr}) .. math:: z_t = \rm{recurrent\_activation}(W_{iz} x_t + b_{iz} + W_{hz} h_(t−1) + b_{hz}) .. math:: o_t = activation(W_{io} x_t + b_{io} + r_t * (W_{ho} h_(t−1) + b_{ho})) .. math:: h_t = (1 − z_t) * o_t + z_t * h_{(t−1)} Where: * ``W_{ir}``, ``W_{iz}``, and `` W_{io}`` state input-hidden weight for reset, update and output gate, respectively. * Similar to the above, ``W_{h[r/z/o]}`` states hidden-hidden / recurrent weights. * ``h_t`` is the hidden state at time ``t``. * ``x_t`` is the input at time ``t``. * ``h_(t-1)`` is the hidden state of the layer at time ``t-1`` or the initial hidden state at time ``0``. * ``r_t``, ``z_t``, and ``o_t`` are the reset, update, and new gates, respectively. * ``*`` is elementwise product. Parameters ---------- x: <s, b, I, T> (Required) * ``s`` is the sequence length, ``b`` is the batch size, and ``I`` is the input dimension. initial_h: <b, H, T> (Required) * ``H`` denotes hidden size. weight: const<I+H, 3*H, T> (Required) - Weight matrix * ``weight[:I] = [W_{iz} | W_{ir} | W_{io}]`` where ``[a|b]`` denotes column concatenation and ``[a, b]`` denotes row concatenation. ``W_{iz}``, ``W_{ir}``, and ``W_{io}`` have shape ``(I, H)``. * ``weight[I:] = [W_{hz} | W_{hr} | W_{hn}]``: ``W_{hz}``, ``W_{hr}``, and ``W_{hn}`` have shape ``(H, H)``. bias: const<2, 3*H, T> (Optional) [Default all 0s] * ``bias[0]`` and ``bias[1]`` are input-hidden and hidden-hidden bias, respectively. * ``3*H`` are biases for ``[b_{ir} + b_{hr}, b_{iz} + b_{hz}, b_{io} + b_{ho}]``. direction: const<str> (Optional) [Default=forward] * Either ``forward`` or ``reverse``. output_sequence: const<bool> (Optional) [Default=False] * Outputs every step if ``True``. recurrent_activation: const<str> (Optional) [Default=sigmoid] * Activation applied on update and reset gate. activation: const<str> (Optional) [Default=tanh] * Activation applied on output gate. Returns ------- <s, b, H, T> or <1, b, H, T> * If ``output_sequence == True`` (hidden states from every step): ``<s, b, H, T>``. * Else ``<1, b, H, T>`` (hidden states of the final step). <b, H, T> * Hidden states of the final step. Attributes ---------- T: fp32 """ input_spec = InputSpec( x=TensorInputType(), initial_h=TensorInputType(), weight=TensorInputType(const=True), bias=TensorInputType(const=True, optional=True, default=None), direction=StringInputType(const=True, default="forward"), output_sequence=BoolInputType(const=True, default=False), recurrent_activation=StringInputType(const=True, default="sigmoid"), activation=StringInputType(const=True, default="tanh") ) def __init__(self, **kwargs): super(gru, self).__init__(**kwargs) def type_inference(self): if self.x.rank != 3: raise ValueError( "Invalid input shape. Expecting Rank 3 input, got {}".format( len(self.x.shape) ) ) sequence_length, batch_size, input_size = self.x.shape if self.weight.rank != 2: raise ValueError( "Invalid weight shape. Expecting Rank 2 input, got {}".format( len(self.weight.shape) ) ) input_hidden_size, hidden_dim = self.weight.shape hidden_size = input_hidden_size - input_size direction = self.direction.val valid_directions = {"forward", "reverse"} if direction not in valid_directions: raise ValueError( "Direction {} not supported. Supported directions: {}".format( direction, valid_directions ) ) dim_factor = 3 if hidden_size != (hidden_dim // dim_factor): raise ValueError( "Incorrect weight matrix: hidden dim size mismatch. \ Provided {}. Expecting <b, 3*H>".format( self.weight.shape ) ) out_seq_len = sequence_length if self.output_sequence.val else 1 output_shape = [out_seq_len, batch_size, hidden_size] output_h_shape = [batch_size, hidden_size] return ( types.tensor(self.x.dtype, tuple(output_shape)), types.tensor(self.x.dtype, tuple(output_h_shape)), ) @register_op(doc_str="") class lstm(Operation): r""" Single long short-term memory (LSTM) sequence. .. math:: i_t = \rm{recurrent\_activation}(W_{ii} x_t + B_{ii} + W_{hi} h_(t-1) + B_{hi}) .. math:: f_t = \rm{recurrent\_activation}(W_{if} x_t + B_{if} + W_{hf} h_(t-1) + B_{hf}) .. math:: z_t = cell_activation(W_{iz} x_t + B_{iz} + W_{hz} h_(t-1) + B_{hz}) .. math:: o_t = \rm{recurrent\_activation}(W_{io} x_t + B_{io} + W_{ho} h_(t-1) + B_{ho}) .. math:: c_t = f_t * c_(t-1) + i_t * z_t .. math:: h_t = o_t * activation(c_t) Where: * ``i_t``, ``f_t``, ``o_t``, and ``z_t`` are input, forget, output, and cell gates, respectively, at time ``t``. * ``c_t`` is cell state at time ``t``. * ``h_t`` is the hidden state at time ``t``. * ``W_{ii}``, ``W_{if}``, ``W_{io}``, and ``W_{iz}`` are input weights for input, forget, output and cell gate, respectively. * ``W_{hi}``, ``W_{hf}``, ``W_{ho}``, and ``W_{hz}`` are recurrent weights for input, forget, output and cell gate, respectively. Parameters ---------- x: <s, b, I, T> (Required) * ``s`` is the sequence length, ``b`` is the batch size, and ``I`` is the input dimension. initial_h: <b, DIRECTION*H, T> (Required) * Initial hidden state. ``DIRECTION = 1`` for uni-directional, ``2`` for bi-directional LSTM. * ``H`` denotes hidden size. * ``[b, :H]`` and ``[b, H:]`` represents forward and reverse direction values, respectively. initial_c: <b, DIRECTION*H, T> (Required) * Initial cell state. * Format is same as ``initial_h``. weight: const<I+H, 4*DIRECTION*H, T> (Required) - Weight matrix * Weight tensor should be in order of ``[input_gate, forget_gate, output_gate, cell_gate]``. * ``[I+H, :4*H]`` and ``[I+H, 4*H:]`` represent forward and reverse direction values, respectively. bias: const<2, 4*DIRECTION*H, T> (Optional) [Default all 0s] * ``bias[0]`` and ``bias[1]`` are input-hidden and hidden-hidden bias, respectively. direction: const<str> (Optional) [Default=forward] * One of the following: ``forward``, ``reverse``, or ``bidirectional``. * Must match ``DIRECTIONAL`` in initial states and weight parameters. output_sequence: const<bool> (Optional) [Default=False] * Outputs every step if ``True``. recurrent_activation: const<str> (Optional) [Default=sigmoid] * Activation applied on input, forget, and output gates. cell_activation: const<str> (Optional) [Default=tang] * Activation applied on cell gate. activation: const<str> (Optional) [Default=tanh] * Activation applied on output gate. peephole: const<3*DIRECTION*H, T> (Optional, default to 0) * Weight tensor for peephole. * Order is ``[input_gate, forget_gate, output_gate]``. * Shape of each peephole vector is ``(H,)`` (``H`` is hidden size). clip: const<fp32> (optional) [Default=None] * Cell gate is clipped to ``[-clip, +clip]``. Returns ------- <s, b, DIRECTION*H, T> or <1, b, DIRECTION*H, T> * If ``output_sequence == True`` (hidden states from every step): ``<s, b, DIRECTION*H, T>``. * Else ``<1, b, DIRECTION*H, T>`` (hidden states of the final step). <b, DIRECTION*H, T> * Hidden states of the final step. <b, DIRECTION*H, T> * Memory state of the final step. Attributes ---------- T: fp32 """ input_spec = InputSpec( x=TensorInputType(), initial_h=TensorInputType(), initial_c=TensorInputType(), weight=TensorInputType(const=True), # ifoz layout bias=TensorInputType(const=True, optional=True, default=None), # ifoz layout direction=StringInputType(const=True, default="forward"), output_sequence=BoolInputType(const=True, default=False), recurrent_activation=StringInputType(const=True, default="sigmoid"), cell_activation=StringInputType(const=True, default="tanh"), activation=StringInputType(const=True, default="tanh"), peephole=TensorInputType(const=True, optional=True, default=None), # ifo layout clip=FloatInputType(const=True, optional=True, default=None), ) def __init__(self, **kwargs): super(lstm, self).__init__(**kwargs) def type_inference(self): if self.x.rank != 3: raise ValueError( "Invalid input shape. Expecting Rank 3 input, got {}".format( len(self.x.shape) ) ) sequence_length, batch_size, input_size = self.x.shape if self.weight.rank != 2: raise ValueError( "Invalid weight shape. Expecting Rank 2 input, got {}".format( len(self.weight.shape) ) ) input_hidden_size, hidden_dim = self.weight.shape hidden_size = input_hidden_size - input_size direction = self.direction.val valid_directions = {"forward", "reverse", "bidirectional"} if direction not in valid_directions: raise ValueError( "Direction {} not supported. Supported directions: {}".format( direction, valid_directions ) ) dim_factor = 8 if direction == "bidirectional" else 4 if hidden_size != (hidden_dim // dim_factor): raise ValueError( "Incorrect weight matrix: hidden dim size mismatch. \ Provided {}. Expecting <b, 4*DIRECTION*H>".format( self.weight.shape ) ) out_seq_len = sequence_length if self.output_sequence.val else 1 num_directions = dim_factor // 4 output_shape = [out_seq_len, batch_size, num_directions * hidden_size] output_h_shape = [batch_size, num_directions * hidden_size] output_c_shape = [batch_size, num_directions * hidden_size] return ( types.tensor(self.x.dtype, tuple(output_shape)), types.tensor(self.x.dtype, tuple(output_h_shape)), types.tensor(self.x.dtype, tuple(output_c_shape)), ) @register_op(doc_str="") class rnn(Operation): """ Recurrent neural network (RNN). .. math:: h_t = activation(W_{ih} x_t + b_{ih} + W_{hh} h_(t−1) + b_{hh}) Where: * ``W_{ih}`` is input weight. * ``W_{hh}`` is hidden/recurrent weight. * ``h_t`` is the hidden state at time ``t``. * ``x_t`` is the input at time ``t``. * ``h_(t-1)`` is the hidden state of the layer at time ``t-1`` or the initial hidden state at time ``0``. Parameters ---------- x: <s, b, I, T> (Required) * ``s`` is the sequence length, ``b`` is the batch size, and ``I`` is the input dimension. initial_h: <b, H, T> (Required) * ``H`` denotes hidden size. weight: const<I+H, 3*H, T> (Required) - Weight matrix bias: const<2, H, T> (Optional) [Default all 0s] * ``bias[0]`` and ``bias[1]`` are input-hidden and hidden-hidden bias, respectively. direction: const<str> (Optional) [Default=forward] * Either ``forward`` or ``reverse``. output_sequence: const<bool> (Optional) [Default=False] * Outputs every step if ``True``. activation: const<str> (Optional) [Default=tanh] * Supported activation functions: ``relu``, ``tanh``, ``sigmoid``, ``sigmoid_hard``, ``scaled_tanh``, and ``linear``. Returns ------- <s, b, H, T> or <1, b, H, T> * If ``output_sequence == True`` (hidden states from every step): ``<s, b, H, T>``. * Else ``<1, b, H, T>`` (hidden states of the final step). <b, H, T> * Hidden states of the final step. Attributes ---------- T: fp32 """ input_spec = InputSpec( x=TensorInputType(), initial_h=TensorInputType(), weight=TensorInputType(const=True), bias=TensorInputType(const=True, optional=True, default=None), direction=StringInputType(const=True, default="forward"), output_sequence=BoolInputType(const=True, default=False), activation=StringInputType(const=True, default="tanh"), ) def __init__(self, **kwargs): super(rnn, self).__init__(**kwargs) def type_inference(self): if self.x.rank != 3: raise ValueError( "Invalid input shape. Expecting Rank 3 input, got {}".format( len(self.x.shape) ) ) sequence_length, batch_size, input_size = self.x.shape if self.weight.rank != 2: raise ValueError( "Invalid weight shape. Expecting Rank 2 input, got {}".format( len(self.weight.shape) ) ) _, hidden_size = self.weight.shape direction = self.direction.val valid_directions = {"forward", "reverse"} if direction not in valid_directions: raise ValueError( "Direction {} not supported. Supported directions: {}".format( direction, valid_directions ) ) out_seq_len = sequence_length if self.output_sequence.val else 1 output_shape = [out_seq_len, batch_size, hidden_size] output_h_shape = [batch_size, hidden_size] return ( types.tensor(self.x.dtype, tuple(output_shape)), types.tensor(self.x.dtype, tuple(output_h_shape)), )

36.009524

89

0.560698

from coremltools.converters.mil.mil import get_new_symbol from ._op_reqs import * @register_op(doc_str="") class gru(Operation): input_spec = InputSpec( x=TensorInputType(), initial_h=TensorInputType(), weight=TensorInputType(const=True), bias=TensorInputType(const=True, optional=True, default=None), direction=StringInputType(const=True, default="forward"), output_sequence=BoolInputType(const=True, default=False), recurrent_activation=StringInputType(const=True, default="sigmoid"), activation=StringInputType(const=True, default="tanh") ) def __init__(self, **kwargs): super(gru, self).__init__(**kwargs) def type_inference(self): if self.x.rank != 3: raise ValueError( "Invalid input shape. Expecting Rank 3 input, got {}".format( len(self.x.shape) ) ) sequence_length, batch_size, input_size = self.x.shape if self.weight.rank != 2: raise ValueError( "Invalid weight shape. Expecting Rank 2 input, got {}".format( len(self.weight.shape) ) ) input_hidden_size, hidden_dim = self.weight.shape hidden_size = input_hidden_size - input_size direction = self.direction.val valid_directions = {"forward", "reverse"} if direction not in valid_directions: raise ValueError( "Direction {} not supported. Supported directions: {}".format( direction, valid_directions ) ) dim_factor = 3 if hidden_size != (hidden_dim // dim_factor): raise ValueError( "Incorrect weight matrix: hidden dim size mismatch. \ Provided {}. Expecting <b, 3*H>".format( self.weight.shape ) ) out_seq_len = sequence_length if self.output_sequence.val else 1 output_shape = [out_seq_len, batch_size, hidden_size] output_h_shape = [batch_size, hidden_size] return ( types.tensor(self.x.dtype, tuple(output_shape)), types.tensor(self.x.dtype, tuple(output_h_shape)), ) @register_op(doc_str="") class lstm(Operation): input_spec = InputSpec( x=TensorInputType(), initial_h=TensorInputType(), initial_c=TensorInputType(), weight=TensorInputType(const=True), bias=TensorInputType(const=True, optional=True, default=None), direction=StringInputType(const=True, default="forward"), output_sequence=BoolInputType(const=True, default=False), recurrent_activation=StringInputType(const=True, default="sigmoid"), cell_activation=StringInputType(const=True, default="tanh"), activation=StringInputType(const=True, default="tanh"), peephole=TensorInputType(const=True, optional=True, default=None), clip=FloatInputType(const=True, optional=True, default=None), ) def __init__(self, **kwargs): super(lstm, self).__init__(**kwargs) def type_inference(self): if self.x.rank != 3: raise ValueError( "Invalid input shape. Expecting Rank 3 input, got {}".format( len(self.x.shape) ) ) sequence_length, batch_size, input_size = self.x.shape if self.weight.rank != 2: raise ValueError( "Invalid weight shape. Expecting Rank 2 input, got {}".format( len(self.weight.shape) ) ) input_hidden_size, hidden_dim = self.weight.shape hidden_size = input_hidden_size - input_size direction = self.direction.val valid_directions = {"forward", "reverse", "bidirectional"} if direction not in valid_directions: raise ValueError( "Direction {} not supported. Supported directions: {}".format( direction, valid_directions ) ) dim_factor = 8 if direction == "bidirectional" else 4 if hidden_size != (hidden_dim // dim_factor): raise ValueError( "Incorrect weight matrix: hidden dim size mismatch. \ Provided {}. Expecting <b, 4*DIRECTION*H>".format( self.weight.shape ) ) out_seq_len = sequence_length if self.output_sequence.val else 1 num_directions = dim_factor // 4 output_shape = [out_seq_len, batch_size, num_directions * hidden_size] output_h_shape = [batch_size, num_directions * hidden_size] output_c_shape = [batch_size, num_directions * hidden_size] return ( types.tensor(self.x.dtype, tuple(output_shape)), types.tensor(self.x.dtype, tuple(output_h_shape)), types.tensor(self.x.dtype, tuple(output_c_shape)), ) @register_op(doc_str="") class rnn(Operation): input_spec = InputSpec( x=TensorInputType(), initial_h=TensorInputType(), weight=TensorInputType(const=True), bias=TensorInputType(const=True, optional=True, default=None), direction=StringInputType(const=True, default="forward"), output_sequence=BoolInputType(const=True, default=False), activation=StringInputType(const=True, default="tanh"), ) def __init__(self, **kwargs): super(rnn, self).__init__(**kwargs) def type_inference(self): if self.x.rank != 3: raise ValueError( "Invalid input shape. Expecting Rank 3 input, got {}".format( len(self.x.shape) ) ) sequence_length, batch_size, input_size = self.x.shape if self.weight.rank != 2: raise ValueError( "Invalid weight shape. Expecting Rank 2 input, got {}".format( len(self.weight.shape) ) ) _, hidden_size = self.weight.shape direction = self.direction.val valid_directions = {"forward", "reverse"} if direction not in valid_directions: raise ValueError( "Direction {} not supported. Supported directions: {}".format( direction, valid_directions ) ) out_seq_len = sequence_length if self.output_sequence.val else 1 output_shape = [out_seq_len, batch_size, hidden_size] output_h_shape = [batch_size, hidden_size] return ( types.tensor(self.x.dtype, tuple(output_shape)), types.tensor(self.x.dtype, tuple(output_h_shape)), )

true

f7296f92c8bd8dfa5fbda73084e6170fffd08c76

1,403

py

Python

RPSJacobi2.py.py

KuhlersClassroom/PythonRPS

6393a4d8a2758433bcb5a16190d1699239f8991b

[ "MIT" ]

null

RPSJacobi2.py.py

KuhlersClassroom/PythonRPS

6393a4d8a2758433bcb5a16190d1699239f8991b

[ "MIT" ]

null

RPSJacobi2.py.py

KuhlersClassroom/PythonRPS

6393a4d8a2758433bcb5a16190d1699239f8991b

[ "MIT" ]

null

import random symbols = ['rock', 'paper', 'scissors'] player_wins = 0 computer_wins = 0 while max([player_wins, computer_wins]) < 3: player_symbol = None while player_symbol is None: input_symbol = input("What symbol do you want? ") if input_symbol in symbols: player_symbol = input_symbol else: print('Please pick rock, paper, or scissors.') computer_symbol = random.choice(symbols) print('Player: ', player_symbol) print('Computer: ', computer_symbol) if player_symbol == computer_symbol: print('Tie!') elif player_symbol == 'rock': if computer_symbol == 'paper': print('computer wins!') computer_wins += 1 else: print('player wins!') player_wins += 1 elif player_symbol == 'paper': if computer_symbol == 'scissors': print('computer wins!') computer_wins += 1 else: print('player wins!') player_wins += 1 elif player_symbol == 'scissors': if computer_symbol == 'rock': print('computer wins!') computer_wins += 1 else: print('player wins!') player_wins += 1 print('player wins!: ') print(player_wins) print('computer wins!: ') print(computer_wins)

26.471698

59

0.548824

import random symbols = ['rock', 'paper', 'scissors'] player_wins = 0 computer_wins = 0 while max([player_wins, computer_wins]) < 3: player_symbol = None while player_symbol is None: input_symbol = input("What symbol do you want? ") if input_symbol in symbols: player_symbol = input_symbol else: print('Please pick rock, paper, or scissors.') computer_symbol = random.choice(symbols) print('Player: ', player_symbol) print('Computer: ', computer_symbol) if player_symbol == computer_symbol: print('Tie!') elif player_symbol == 'rock': if computer_symbol == 'paper': print('computer wins!') computer_wins += 1 else: print('player wins!') player_wins += 1 elif player_symbol == 'paper': if computer_symbol == 'scissors': print('computer wins!') computer_wins += 1 else: print('player wins!') player_wins += 1 elif player_symbol == 'scissors': if computer_symbol == 'rock': print('computer wins!') computer_wins += 1 else: print('player wins!') player_wins += 1 print('player wins!: ') print(player_wins) print('computer wins!: ') print(computer_wins)

true

f729700d53fe2914422fc5bfb94c839529955fce

9,972

py

Python

src/openfermion/utils/rdm_mapping_functions_test.py

Emieeel/OpenFermion

c19d9667c5970473893f9bc0183556c4cd354dd7

[ "Apache-2.0" ]

1,291

2017-09-27T22:00:26.000Z

2022-03-25T14:34:50.000Z

src/openfermion/utils/rdm_mapping_functions_test.py

SamarthVadia/OpenFermion

865d8591cad9b0681f6dd25a391a5292ed2de1d4

[ "Apache-2.0" ]

521

2017-09-27T21:36:17.000Z

2022-03-02T12:45:56.000Z

src/openfermion/utils/rdm_mapping_functions_test.py

SamarthVadia/OpenFermion

865d8591cad9b0681f6dd25a391a5292ed2de1d4

[ "Apache-2.0" ]

365

2017-09-27T21:25:38.000Z

2022-03-29T19:28:46.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for rdm_mapping_functions.py""" import os import unittest import numpy import h5py from openfermion.config import DATA_DIRECTORY, THIS_DIRECTORY from openfermion.chem import MolecularData from openfermion.utils.rdm_mapping_functions import ( kronecker_delta, map_two_pdm_to_two_hole_dm, map_two_pdm_to_one_pdm, map_one_pdm_to_one_hole_dm, map_one_hole_dm_to_one_pdm, map_two_pdm_to_particle_hole_dm, map_two_hole_dm_to_two_pdm, map_two_hole_dm_to_one_hole_dm, map_particle_hole_dm_to_one_pdm, map_particle_hole_dm_to_two_pdm) class RDMMappingTest(unittest.TestCase): def setUp(self): # load files and marginals from testing folder tqdm_h2_sto3g = os.path.join(THIS_DIRECTORY, 'testing/tqdm_H2_sto-3g_singlet_1.4.hdf5') with h5py.File(tqdm_h2_sto3g, 'r') as fid: self.tqdm_h2_sto3g = fid['tqdm'][...] phdm_h2_sto3g = os.path.join(THIS_DIRECTORY, 'testing/phdm_H2_sto-3g_singlet_1.4.hdf5') with h5py.File(phdm_h2_sto3g, 'r') as fid: self.phdm_h2_sto3g = fid['phdm'][...] tqdm_h2_6_31g = os.path.join(THIS_DIRECTORY, 'testing/tqdm_H2_6-31g_singlet_0.75.hdf5') with h5py.File(tqdm_h2_6_31g, 'r') as fid: self.tqdm_h2_6_31g = fid['tqdm'][...] phdm_h2_6_31g = os.path.join(THIS_DIRECTORY, 'testing/phdm_H2_6-31g_singlet_0.75.hdf5') with h5py.File(phdm_h2_6_31g, 'r') as fid: self.phdm_h2_6_31g = fid['phdm'][...] tqdm_lih_sto3g = os.path.join( THIS_DIRECTORY, 'testing/tqdm_H1-Li1_sto-3g_singlet_1.45.hdf5') with h5py.File(tqdm_lih_sto3g, 'r') as fid: self.tqdm_lih_sto3g = fid['tqdm'][...] phdm_lih_sto3g = os.path.join( THIS_DIRECTORY, 'testing/phdm_H1-Li1_sto-3g_singlet_1.45.hdf5') with h5py.File(phdm_lih_sto3g, 'r') as fid: self.phdm_lih_sto3g = fid['phdm'][...] def test_kronecker_delta_00(self): assert kronecker_delta(0, 0) == 1 def test_kronecker_delta_01(self): assert kronecker_delta(0, 1) == 0 def test_kronecker_delta_10(self): assert kronecker_delta(1, 0) == 0 def test_kronecker_delta_11(self): assert kronecker_delta(1, 1) == 1 def test_kronecker_delta_nonunit_args(self): assert kronecker_delta(3, 3) == 1 def test_tpdm_to_opdm(self): # for all files in datadirectory check if this map holds for file in filter(lambda x: x.endswith(".hdf5"), os.listdir(DATA_DIRECTORY)): molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, file)) if (molecule.fci_one_rdm is not None and molecule.fci_two_rdm is not None): test_opdm = map_two_pdm_to_one_pdm(molecule.fci_two_rdm, molecule.n_electrons) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) def test_opdm_to_oqdm(self): for file in filter(lambda x: x.endswith(".hdf5"), os.listdir(DATA_DIRECTORY)): molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, file)) if molecule.fci_one_rdm is not None: test_oqdm = map_one_pdm_to_one_hole_dm(molecule.fci_one_rdm) true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm assert numpy.allclose(test_oqdm, true_oqdm) def test_oqdm_to_opdm(self): for file in filter(lambda x: x.endswith(".hdf5"), os.listdir(DATA_DIRECTORY)): molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, file)) if molecule.fci_one_rdm is not None: true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm test_opdm = map_one_hole_dm_to_one_pdm(true_oqdm) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) def test_tqdm_conversions_h2_631g(self): # construct the 2-hole-RDM for LiH the slow way # TODO: speed up this calculation by directly contracting from the wf. filename = "H2_6-31g_singlet_0.75.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_tqdm = self.tqdm_h2_6_31g test_tqdm = map_two_pdm_to_two_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(true_tqdm, test_tqdm) true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm test_oqdm = map_two_hole_dm_to_one_hole_dm( true_tqdm, molecule.n_qubits - molecule.n_electrons) assert numpy.allclose(true_oqdm, test_oqdm) test_tpdm = map_two_hole_dm_to_two_pdm(true_tqdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_tqdm_conversions_h2_sto3g(self): filename = "H2_sto-3g_singlet_1.4.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_tqdm = self.tqdm_h2_sto3g test_tqdm = map_two_pdm_to_two_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(true_tqdm, test_tqdm) true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm test_oqdm = map_two_hole_dm_to_one_hole_dm( true_tqdm, molecule.n_qubits - molecule.n_electrons) assert numpy.allclose(true_oqdm, test_oqdm) test_tpdm = map_two_hole_dm_to_two_pdm(true_tqdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_tqdm_conversions_lih_sto3g(self): filename = "H1-Li1_sto-3g_singlet_1.45.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_tqdm = self.tqdm_lih_sto3g test_tqdm = map_two_pdm_to_two_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(true_tqdm, test_tqdm) true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm test_oqdm = map_two_hole_dm_to_one_hole_dm( true_tqdm, molecule.n_qubits - molecule.n_electrons) assert numpy.allclose(true_oqdm, test_oqdm) test_tpdm = map_two_hole_dm_to_two_pdm(true_tqdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_phdm_conversions_h2_631g(self): filename = "H2_6-31g_singlet_0.75.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_phdm = self.phdm_h2_6_31g test_phdm = map_two_pdm_to_particle_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(test_phdm, true_phdm) test_opdm = map_particle_hole_dm_to_one_pdm(true_phdm, molecule.n_electrons, molecule.n_qubits) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) test_tpdm = map_particle_hole_dm_to_two_pdm(true_phdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_phdm_conversions_h2_sto3g(self): filename = "H2_sto-3g_singlet_1.4.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_phdm = self.phdm_h2_sto3g test_phdm = map_two_pdm_to_particle_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(test_phdm, true_phdm) test_opdm = map_particle_hole_dm_to_one_pdm(true_phdm, molecule.n_electrons, molecule.n_qubits) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) test_tpdm = map_particle_hole_dm_to_two_pdm(true_phdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_phdm_conversions_lih_sto3g(self): filename = "H1-Li1_sto-3g_singlet_1.45.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_phdm = self.phdm_lih_sto3g test_phdm = map_two_pdm_to_particle_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(test_phdm, true_phdm) test_opdm = map_particle_hole_dm_to_one_pdm(true_phdm, molecule.n_electrons, molecule.n_qubits) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) test_tpdm = map_particle_hole_dm_to_two_pdm(true_phdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm)

45.122172

79

0.632872

import os import unittest import numpy import h5py from openfermion.config import DATA_DIRECTORY, THIS_DIRECTORY from openfermion.chem import MolecularData from openfermion.utils.rdm_mapping_functions import ( kronecker_delta, map_two_pdm_to_two_hole_dm, map_two_pdm_to_one_pdm, map_one_pdm_to_one_hole_dm, map_one_hole_dm_to_one_pdm, map_two_pdm_to_particle_hole_dm, map_two_hole_dm_to_two_pdm, map_two_hole_dm_to_one_hole_dm, map_particle_hole_dm_to_one_pdm, map_particle_hole_dm_to_two_pdm) class RDMMappingTest(unittest.TestCase): def setUp(self): tqdm_h2_sto3g = os.path.join(THIS_DIRECTORY, 'testing/tqdm_H2_sto-3g_singlet_1.4.hdf5') with h5py.File(tqdm_h2_sto3g, 'r') as fid: self.tqdm_h2_sto3g = fid['tqdm'][...] phdm_h2_sto3g = os.path.join(THIS_DIRECTORY, 'testing/phdm_H2_sto-3g_singlet_1.4.hdf5') with h5py.File(phdm_h2_sto3g, 'r') as fid: self.phdm_h2_sto3g = fid['phdm'][...] tqdm_h2_6_31g = os.path.join(THIS_DIRECTORY, 'testing/tqdm_H2_6-31g_singlet_0.75.hdf5') with h5py.File(tqdm_h2_6_31g, 'r') as fid: self.tqdm_h2_6_31g = fid['tqdm'][...] phdm_h2_6_31g = os.path.join(THIS_DIRECTORY, 'testing/phdm_H2_6-31g_singlet_0.75.hdf5') with h5py.File(phdm_h2_6_31g, 'r') as fid: self.phdm_h2_6_31g = fid['phdm'][...] tqdm_lih_sto3g = os.path.join( THIS_DIRECTORY, 'testing/tqdm_H1-Li1_sto-3g_singlet_1.45.hdf5') with h5py.File(tqdm_lih_sto3g, 'r') as fid: self.tqdm_lih_sto3g = fid['tqdm'][...] phdm_lih_sto3g = os.path.join( THIS_DIRECTORY, 'testing/phdm_H1-Li1_sto-3g_singlet_1.45.hdf5') with h5py.File(phdm_lih_sto3g, 'r') as fid: self.phdm_lih_sto3g = fid['phdm'][...] def test_kronecker_delta_00(self): assert kronecker_delta(0, 0) == 1 def test_kronecker_delta_01(self): assert kronecker_delta(0, 1) == 0 def test_kronecker_delta_10(self): assert kronecker_delta(1, 0) == 0 def test_kronecker_delta_11(self): assert kronecker_delta(1, 1) == 1 def test_kronecker_delta_nonunit_args(self): assert kronecker_delta(3, 3) == 1 def test_tpdm_to_opdm(self): for file in filter(lambda x: x.endswith(".hdf5"), os.listdir(DATA_DIRECTORY)): molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, file)) if (molecule.fci_one_rdm is not None and molecule.fci_two_rdm is not None): test_opdm = map_two_pdm_to_one_pdm(molecule.fci_two_rdm, molecule.n_electrons) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) def test_opdm_to_oqdm(self): for file in filter(lambda x: x.endswith(".hdf5"), os.listdir(DATA_DIRECTORY)): molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, file)) if molecule.fci_one_rdm is not None: test_oqdm = map_one_pdm_to_one_hole_dm(molecule.fci_one_rdm) true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm assert numpy.allclose(test_oqdm, true_oqdm) def test_oqdm_to_opdm(self): for file in filter(lambda x: x.endswith(".hdf5"), os.listdir(DATA_DIRECTORY)): molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, file)) if molecule.fci_one_rdm is not None: true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm test_opdm = map_one_hole_dm_to_one_pdm(true_oqdm) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) def test_tqdm_conversions_h2_631g(self): filename = "H2_6-31g_singlet_0.75.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_tqdm = self.tqdm_h2_6_31g test_tqdm = map_two_pdm_to_two_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(true_tqdm, test_tqdm) true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm test_oqdm = map_two_hole_dm_to_one_hole_dm( true_tqdm, molecule.n_qubits - molecule.n_electrons) assert numpy.allclose(true_oqdm, test_oqdm) test_tpdm = map_two_hole_dm_to_two_pdm(true_tqdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_tqdm_conversions_h2_sto3g(self): filename = "H2_sto-3g_singlet_1.4.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_tqdm = self.tqdm_h2_sto3g test_tqdm = map_two_pdm_to_two_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(true_tqdm, test_tqdm) true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm test_oqdm = map_two_hole_dm_to_one_hole_dm( true_tqdm, molecule.n_qubits - molecule.n_electrons) assert numpy.allclose(true_oqdm, test_oqdm) test_tpdm = map_two_hole_dm_to_two_pdm(true_tqdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_tqdm_conversions_lih_sto3g(self): filename = "H1-Li1_sto-3g_singlet_1.45.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_tqdm = self.tqdm_lih_sto3g test_tqdm = map_two_pdm_to_two_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(true_tqdm, test_tqdm) true_oqdm = numpy.eye(molecule.n_qubits) - molecule.fci_one_rdm test_oqdm = map_two_hole_dm_to_one_hole_dm( true_tqdm, molecule.n_qubits - molecule.n_electrons) assert numpy.allclose(true_oqdm, test_oqdm) test_tpdm = map_two_hole_dm_to_two_pdm(true_tqdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_phdm_conversions_h2_631g(self): filename = "H2_6-31g_singlet_0.75.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_phdm = self.phdm_h2_6_31g test_phdm = map_two_pdm_to_particle_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(test_phdm, true_phdm) test_opdm = map_particle_hole_dm_to_one_pdm(true_phdm, molecule.n_electrons, molecule.n_qubits) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) test_tpdm = map_particle_hole_dm_to_two_pdm(true_phdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_phdm_conversions_h2_sto3g(self): filename = "H2_sto-3g_singlet_1.4.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_phdm = self.phdm_h2_sto3g test_phdm = map_two_pdm_to_particle_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(test_phdm, true_phdm) test_opdm = map_particle_hole_dm_to_one_pdm(true_phdm, molecule.n_electrons, molecule.n_qubits) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) test_tpdm = map_particle_hole_dm_to_two_pdm(true_phdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm) def test_phdm_conversions_lih_sto3g(self): filename = "H1-Li1_sto-3g_singlet_1.45.hdf5" molecule = MolecularData( filename=os.path.join(DATA_DIRECTORY, filename)) true_phdm = self.phdm_lih_sto3g test_phdm = map_two_pdm_to_particle_hole_dm(molecule.fci_two_rdm, molecule.fci_one_rdm) assert numpy.allclose(test_phdm, true_phdm) test_opdm = map_particle_hole_dm_to_one_pdm(true_phdm, molecule.n_electrons, molecule.n_qubits) assert numpy.allclose(test_opdm, molecule.fci_one_rdm) test_tpdm = map_particle_hole_dm_to_two_pdm(true_phdm, molecule.fci_one_rdm) assert numpy.allclose(test_tpdm, molecule.fci_two_rdm)

true

f72971baca3ca517c2e3eac6e71581887c328f74

20,278

py

Python

botorch/test_functions/multi_objective.py

NTR0314/botorch

f0310c9a415947f3264dac7f3438744784843323

[ "MIT" ]

null

botorch/test_functions/multi_objective.py

NTR0314/botorch

f0310c9a415947f3264dac7f3438744784843323

[ "MIT" ]

1

2021-04-17T11:04:24.000Z

2021-04-17T11:18:12.000Z

botorch/test_functions/multi_objective.py

NTR0314/botorch

f0310c9a415947f3264dac7f3438744784843323

[ "MIT" ]

null

#! /usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. r""" Multi-objective optimization benchmark problems. References .. [Deb2005dtlz] K. Deb, L. Thiele, M. Laumanns, E. Zitzler, A. Abraham, L. Jain, R. Goldberg. "Scalable test problems for evolutionary multi-objective optimization" in Evolutionary Multiobjective Optimization, London, U.K.: Springer-Verlag, pp. 105-145, 2005. .. [GarridoMerchan2020] E. C. Garrido-Merch ́an and D. Hern ́andez-Lobato. Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints. arXiv e-prints, arXiv:2004.00601, Apr. 2020. .. [Gelbart2014] Michael A. Gelbart, Jasper Snoek, and Ryan P. Adams. 2014. Bayesian optimization with unknown constraints. In Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI’14). AUAI Press, Arlington, Virginia, USA, 250–259. .. [Oszycka1995] A. Osyczka, S. Kundu. 1995. A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. In Structural Optimization 10. 94–99. .. [Tanabe2020] Ryoji Tanabe, Hisao Ishibuchi, An easy-to-use real-world multi-objective optimization problem suite, Applied Soft Computing,Volume 89, 2020. .. [Yang2019a] K. Yang, M. Emmerich, A. Deutz, and T. Bäck. 2019. "Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient" in Swarm and evolutionary computation 44, pp. 945--956, 2019. .. [Zitzler2000] E. Zitzler, K. Deb, and L. Thiele, “Comparison of multiobjective evolutionary algorithms: Empirical results,” Evol. Comput., vol. 8, no. 2, pp. 173–195, 2000. """ from __future__ import annotations import math from typing import Optional import torch from botorch.test_functions.base import ( ConstrainedBaseTestProblem, MultiObjectiveTestProblem, ) from botorch.test_functions.synthetic import Branin from botorch.utils.sampling import sample_hypersphere, sample_simplex from botorch.utils.transforms import unnormalize from scipy.special import gamma from torch import Tensor class BraninCurrin(MultiObjectiveTestProblem): r"""Two objective problem composed of the Branin and Currin functions. Branin (rescaled): f(x) = ( 15*x_1 - 5.1 * (15 * x_0 - 5) ** 2 / (4 * pi ** 2) + 5 * (15 * x_0 - 5) / pi - 5 ) ** 2 + (10 - 10 / (8 * pi)) * cos(15 * x_0 - 5)) Currin: f(x) = (1 - exp(-1 / (2 * x_1))) * ( 2300 * x_0 ** 3 + 1900 * x_0 ** 2 + 2092 * x_0 + 60 ) / 100 * x_0 ** 3 + 500 * x_0 ** 2 + 4 * x_0 + 20 """ dim = 2 num_objectives = 2 _bounds = [(0.0, 1.0), (0.0, 1.0)] _ref_point = [18.0, 6.0] _max_hv = 59.36011874867746 # this is approximated using NSGA-II def __init__(self, noise_std: Optional[float] = None, negate: bool = False) -> None: r"""Constructor for Branin-Currin. Args: noise_std: Standard deviation of the observation noise. negate: If True, negate the objectives. """ super().__init__(noise_std=noise_std, negate=negate) self._branin = Branin() def _rescaled_branin(self, X: Tensor) -> Tensor: # return to Branin bounds x_0 = 15 * X[..., 0] - 5 x_1 = 15 * X[..., 1] return self._branin(torch.stack([x_0, x_1], dim=-1)) @staticmethod def _currin(X: Tensor) -> Tensor: x_0 = X[..., 0] x_1 = X[..., 1] factor1 = 1 - torch.exp(-1 / (2 * x_1)) numer = 2300 * x_0.pow(3) + 1900 * x_0.pow(2) + 2092 * x_0 + 60 denom = 100 * x_0.pow(3) + 500 * x_0.pow(2) + 4 * x_0 + 20 return factor1 * numer / denom def evaluate_true(self, X: Tensor) -> Tensor: # branin rescaled with inputsto [0,1]^2 branin = self._rescaled_branin(X=X) currin = self._currin(X=X) return torch.stack([branin, currin], dim=-1) class DTLZ(MultiObjectiveTestProblem): r"""Base class for DTLZ problems. See [Deb2005dtlz]_ for more details on DTLZ. """ def __init__( self, dim: int, num_objectives: int = 2, noise_std: Optional[float] = None, negate: bool = False, ) -> None: if dim <= num_objectives: raise ValueError( f"dim must be > num_objectives, but got {dim} and {num_objectives}" ) self.num_objectives = num_objectives self.dim = dim self.k = self.dim - self.num_objectives + 1 self._bounds = [(0.0, 1.0) for _ in range(self.dim)] self._ref_point = [self._ref_val for _ in range(num_objectives)] super().__init__(noise_std=noise_std, negate=negate) class DTLZ1(DTLZ): r"""DLTZ1 test problem. d-dimensional problem evaluated on `[0, 1]^d`: f_0(x) = 0.5 * x_0 * (1 + g(x)) f_1(x) = 0.5 * (1 - x_0) * (1 + g(x)) g(x) = 100 * \sum_{i=m}^{n-1} ( k + (x_i - 0.5)^2 - cos(20 * pi * (x_i - 0.5)) ) where k = n - m + 1. The pareto front is given by the line (or hyperplane) \sum_i f_i(x) = 0.5. The goal is to minimize both objectives. The reference point comes from [Yang2019]_. """ _ref_val = 400.0 @property def _max_hv(self) -> float: return self._ref_val ** self.num_objectives - 1 / 2 ** self.num_objectives def evaluate_true(self, X: Tensor) -> Tensor: X_m = X[..., -self.k :] X_m_minus_half = X_m - 0.5 sum_term = ( X_m_minus_half.pow(2) - torch.cos(20 * math.pi * X_m_minus_half) ).sum(dim=-1) g_X_m = 100 * (self.k + sum_term) g_X_m_term = 0.5 * (1 + g_X_m) fs = [] for i in range(self.num_objectives): idx = self.num_objectives - 1 - i f_i = g_X_m_term * X[..., :idx].prod(dim=-1) if i > 0: f_i *= 1 - X[..., idx] fs.append(f_i) return torch.stack(fs, dim=-1) def gen_pareto_front(self, n: int) -> Tensor: r"""Generate `n` pareto optimal points. The pareto points randomly sampled from the hyperplane sum_i f(x_i) = 0.5. """ f_X = 0.5 * sample_simplex( n=n, d=self.num_objectives, qmc=True, dtype=self.ref_point.dtype, device=self.ref_point.device, ) if self.negate: f_X *= -1 return f_X class DTLZ2(DTLZ): r"""DLTZ2 test problem. d-dimensional problem evaluated on `[0, 1]^d`: f_0(x) = (1 + g(x)) * cos(x_0 * pi / 2) f_1(x) = (1 + g(x)) * sin(x_0 * pi / 2) g(x) = \sum_{i=m}^{n-1} (x_i - 0.5)^2 The pareto front is given by the unit hypersphere \sum{i} f_i^2 = 1. Note: the pareto front is completely concave. The goal is to minimize both objectives. """ _ref_val = 1.1 @property def _max_hv(self) -> float: # hypercube - volume of hypersphere in R^n such that all coordinates are # positive hypercube_vol = self._ref_val ** self.num_objectives pos_hypersphere_vol = ( math.pi ** (self.num_objectives / 2) / gamma(self.num_objectives / 2 + 1) / 2 ** self.num_objectives ) return hypercube_vol - pos_hypersphere_vol def evaluate_true(self, X: Tensor) -> Tensor: X_m = X[..., -self.k :] g_X = (X_m - 0.5).pow(2).sum(dim=-1) g_X_plus1 = 1 + g_X fs = [] pi_over_2 = math.pi / 2 for i in range(self.num_objectives): idx = self.num_objectives - 1 - i f_i = g_X_plus1.clone() f_i *= torch.cos(X[..., :idx] * pi_over_2).prod(dim=-1) if i > 0: f_i *= torch.sin(X[..., idx] * pi_over_2) fs.append(f_i) return torch.stack(fs, dim=-1) def gen_pareto_front(self, n: int) -> Tensor: r"""Generate `n` pareto optimal points. The pareto points are randomly sampled from the hypersphere's positive section. """ f_X = sample_hypersphere( n=n, d=self.num_objectives, dtype=self.ref_point.dtype, device=self.ref_point.device, qmc=True, ).abs() if self.negate: f_X *= -1 return f_X class VehicleSafety(MultiObjectiveTestProblem): r"""Optimize Vehicle crash-worthiness. See [Tanabe2020]_ for details. The reference point is 1.1 * the nadir point from approximate front provided by [Tanabe2020]_. The maximum hypervolume is computed using the approximate pareto front from [Tanabe2020]_. """ _ref_point = [1864.72022, 11.81993945, 0.2903999384] _max_hv = 246.81607081187002 _bounds = [(1.0, 3.0)] * 5 dim = 5 num_objectives = 3 def evaluate_true(self, X: Tensor) -> Tensor: X1, X2, X3, X4, X5 = torch.split(X, 1, -1) f1 = ( 1640.2823 + 2.3573285 * X1 + 2.3220035 * X2 + 4.5688768 * X3 + 7.7213633 * X4 + 4.4559504 * X5 ) f2 = ( 6.5856 + 1.15 * X1 - 1.0427 * X2 + 0.9738 * X3 + 0.8364 * X4 - 0.3695 * X1 * X4 + 0.0861 * X1 * X5 + 0.3628 * X2 * X4 - 0.1106 * X1.pow(2) - 0.3437 * X3.pow(2) + 0.1764 * X4.pow(2) ) f3 = ( -0.0551 + 0.0181 * X1 + 0.1024 * X2 + 0.0421 * X3 - 0.0073 * X1 * X2 + 0.024 * X2 * X3 - 0.0118 * X2 * X4 - 0.0204 * X3 * X4 - 0.008 * X3 * X5 - 0.0241 * X2.pow(2) + 0.0109 * X4.pow(2) ) f_X = torch.cat([f1, f2, f3], dim=-1) return f_X class ZDT(MultiObjectiveTestProblem): r"""Base class for ZDT problems. See [Zitzler2000]_ for more details on ZDT. """ _ref_point = [11.0, 11.0] def __init__( self, dim: int, num_objectives: int = 2, noise_std: Optional[float] = None, negate: bool = False, ) -> None: if num_objectives != 2: raise NotImplementedError( f"{type(self).__name__} currently only supports 2 objectives." ) if dim < num_objectives: raise ValueError( f"dim must be >= num_objectives, but got {dim} and {num_objectives}" ) self.num_objectives = num_objectives self.dim = dim self._bounds = [(0.0, 1.0) for _ in range(self.dim)] super().__init__(noise_std=noise_std, negate=negate) @staticmethod def _g(X: Tensor) -> Tensor: return 1 + 9 * X[..., 1:].mean(dim=-1) class ZDT1(ZDT): r"""ZDT1 test problem. d-dimensional problem evaluated on `[0, 1]^d`: f_0(x) = x_0 f_1(x) = g(x) * (1 - sqrt(x_0 / g(x)) g(x) = 1 + 9 / (d - 1) * \sum_{i=1}^{d-1} x_i The reference point comes from [Yang2019a]_. The pareto front is convex. """ _max_hv = 120 + 2 / 3 def evaluate_true(self, X: Tensor) -> Tensor: f_0 = X[..., 0] g = self._g(X=X) f_1 = g * (1 - (f_0 / g).sqrt()) return torch.stack([f_0, f_1], dim=-1) def gen_pareto_front(self, n: int) -> Tensor: f_0 = torch.linspace( 0, 1, n, dtype=self.bounds.dtype, device=self.bounds.device ) f_1 = 1 - f_0.sqrt() f_X = torch.stack([f_0, f_1], dim=-1) if self.negate: f_X *= -1 return f_X class ZDT2(ZDT): r"""ZDT2 test problem. d-dimensional problem evaluated on `[0, 1]^d`: f_0(x) = x_0 f_1(x) = g(x) * (1 - (x_0 / g(x))^2) g(x) = 1 + 9 / (d - 1) * \sum_{i=1}^{d-1} x_i The reference point comes from [Yang2019a]_. The pareto front is concave. """ _max_hv = 120 + 1 / 3 def evaluate_true(self, X: Tensor) -> Tensor: f_0 = X[..., 0] g = self._g(X=X) f_1 = g * (1 - (f_0 / g).pow(2)) return torch.stack([f_0, f_1], dim=-1) def gen_pareto_front(self, n: int) -> Tensor: f_0 = torch.linspace( 0, 1, n, dtype=self.bounds.dtype, device=self.bounds.device ) f_1 = 1 - f_0.pow(2) f_X = torch.stack([f_0, f_1], dim=-1) if self.negate: f_X *= -1 return f_X class ZDT3(ZDT): r"""ZDT3 test problem. d-dimensional problem evaluated on `[0, 1]^d`: f_0(x) = x_0 f_1(x) = 1 - sqrt(x_0 / g(x)) - x_0 / g * sin(10 * pi * x_0) g(x) = 1 + 9 / (d - 1) * \sum_{i=1}^{d-1} x_i The reference point comes from [Yang2019a]_. The pareto front consists of several discontinuous convex parts. """ _max_hv = 128.77811613069076060 _parts = [ # this interval includes both end points [0, 0.0830015349], # this interval includes only the right end points [0.1822287280, 0.2577623634], [0.4093136748, 0.4538821041], [0.6183967944, 0.6525117038], [0.8233317983, 0.8518328654], ] # nugget to make sure linspace returns elements within the specified range _eps = 1e-6 def evaluate_true(self, X: Tensor) -> Tensor: f_0 = X[..., 0] g = self._g(X=X) f_1 = 1 - (f_0 / g).sqrt() - f_0 / g * torch.sin(10 * math.pi * f_0) return torch.stack([f_0, f_1], dim=-1) def gen_pareto_front(self, n: int) -> Tensor: n_parts = len(self._parts) n_per_part = torch.full( torch.Size([n_parts]), n // n_parts, dtype=torch.long, device=self.bounds.device, ) left_over = n % n_parts n_per_part[:left_over] += 1 f_0s = [] for i, p in enumerate(self._parts): left, right = p f_0s.append( torch.linspace( left + self._eps, right - self._eps, n_per_part[i], dtype=self.bounds.dtype, device=self.bounds.device, ) ) f_0 = torch.cat(f_0s, dim=0) f_1 = 1 - f_0.sqrt() - f_0 * torch.sin(10 * math.pi * f_0) f_X = torch.stack([f_0, f_1], dim=-1) if self.negate: f_X *= -1 return f_X # ------ Constrained Multi-Objective Test Problems ----- # class BNH(MultiObjectiveTestProblem, ConstrainedBaseTestProblem): r"""The constrained BNH problem. See [GarridoMerchan2020]_ for more details on this problem. Note that this is a minimization problem. """ dim = 2 num_objectives = 2 num_constraints = 2 _bounds = [(0.0, 5.0), (0.0, 3.0)] _ref_point = [0.0, 0.0] # TODO: Determine proper reference point def evaluate_true(self, X: Tensor) -> Tensor: return torch.stack( [4.0 * (X ** 2).sum(dim=-1), ((X - 5.0) ** 2).sum(dim=-1)], dim=-1 ) def evaluate_slack_true(self, X: Tensor) -> Tensor: c1 = 25.0 - (X[..., 0] - 5.0) ** 2 - X[..., 1] ** 2 c2 = (X[..., 0] - 8.0) ** 2 + (X[..., 1] + 3.0) ** 2 - 7.7 return torch.stack([c1, c2], dim=-1) class SRN(MultiObjectiveTestProblem, ConstrainedBaseTestProblem): r"""The constrained SRN problem. See [GarridoMerchan2020]_ for more details on this problem. Note that this is a minimization problem. """ dim = 2 num_objectives = 2 num_constraints = 2 _bounds = [(-20.0, 20.0), (-20.0, 20.0)] _ref_point = [0.0, 0.0] # TODO: Determine proper reference point def evaluate_true(self, X: Tensor) -> Tensor: obj1 = 2.0 + ((X - 2.0) ** 2).sum(dim=-1) obj2 = 9.0 * X[..., 0] - (X[..., 1] - 1.0) ** 2 return torch.stack([obj1, obj2], dim=-1) def evaluate_slack_true(self, X: Tensor) -> Tensor: c1 = 225.0 - ((X ** 2) ** 2).sum(dim=-1) c2 = -10.0 - X[..., 0] + 3 * X[..., 1] return torch.stack([c1, c2], dim=-1) class CONSTR(MultiObjectiveTestProblem, ConstrainedBaseTestProblem): r"""The constrained CONSTR problem. See [GarridoMerchan2020]_ for more details on this problem. Note that this is a minimization problem. """ dim = 2 num_objectives = 2 num_constraints = 2 _bounds = [(0.1, 10.0), (0.0, 5.0)] _ref_point = [10.0, 10.0] def evaluate_true(self, X: Tensor) -> Tensor: obj1 = X[..., 0] obj2 = (1.0 + X[..., 1]) / X[..., 0] return torch.stack([obj1, obj2], dim=-1) def evaluate_slack_true(self, X: Tensor) -> Tensor: c1 = 9.0 * X[..., 0] + X[..., 1] - 6.0 c2 = 9.0 * X[..., 0] - X[..., 1] - 1.0 return torch.stack([c1, c2], dim=-1) class ConstrainedBraninCurrin(BraninCurrin, ConstrainedBaseTestProblem): r"""Constrained Branin Currin Function. This uses the disk constraint from [Gelbart2014]_. """ dim = 2 num_objectives = 2 num_constraints = 1 _bounds = [(0.0, 1.0), (0.0, 1.0)] _con_bounds = [(-5.0, 10.0), (0.0, 15.0)] _ref_point = [80.0, 12.0] _max_hv = 608.4004237022673 # from NSGA-II with 90k evaluations def __init__(self, noise_std: Optional[float] = None, negate: bool = False) -> None: super().__init__(noise_std=noise_std, negate=negate) con_bounds = torch.tensor(self._con_bounds, dtype=torch.float).transpose(-1, -2) self.register_buffer("con_bounds", con_bounds) def evaluate_slack_true(self, X: Tensor) -> Tensor: X_tf = unnormalize(X, self.con_bounds) return 50 - (X_tf[..., 0:1] - 2.5).pow(2) - (X_tf[..., 1:2] - 7.5).pow(2) class C2DTLZ2(DTLZ2, ConstrainedBaseTestProblem): num_constraints = 1 _r = 0.2 # approximate from nsga-ii, TODO: replace with analytic _max_hv = 0.3996406303723544 def evaluate_slack_true(self, X: Tensor) -> Tensor: if X.ndim > 2: raise NotImplementedError("Batch X is not supported.") f_X = self.evaluate_true(X) term1 = (f_X - 1).pow(2) mask = ~(torch.eye(f_X.shape[-1], device=f_X.device).bool()) indices = torch.arange(f_X.shape[1], device=f_X.device).repeat(f_X.shape[1], 1) indexer = indices[mask].view(f_X.shape[1], f_X.shape[-1] - 1) term2_inner = ( f_X.unsqueeze(1) .expand(f_X.shape[0], f_X.shape[-1], f_X.shape[-1]) .gather(dim=-1, index=indexer.repeat(f_X.shape[0], 1, 1)) ) term2 = (term2_inner.pow(2) - self._r ** 2).sum(dim=-1) min1 = (term1 + term2).min(dim=-1).values min2 = ((f_X - 1 / math.sqrt(f_X.shape[-1])).pow(2) - self._r ** 2).sum(dim=-1) return -torch.min(min1, min2).unsqueeze(-1) class OSY(MultiObjectiveTestProblem, ConstrainedBaseTestProblem): r""" The OSY test problem from [Oszycka1995]_. Implementation from https://github.com/msu-coinlab/pymoo/blob/master/pymoo/problems/multi/osy.py Note that this implementation assumes minimization, so please choose negate=True. """ dim = 6 num_constraints = 6 num_objectives = 2 _bounds = [ (0.0, 10.0), (0.0, 10.0), (1.0, 5.0), (0.0, 6.0), (1.0, 5.0), (0.0, 10.0), ] _ref_point = [-75.0, 75.0] def evaluate_true(self, X: Tensor) -> Tensor: f1 = -( 25 * (X[..., 0] - 2) ** 2 + (X[..., 1] - 2) ** 2 + (X[..., 2] - 1) ** 2 + (X[..., 3] - 4) ** 2 + (X[..., 4] - 1) ** 2 ) f2 = (X ** 2).sum(-1) return torch.stack([f1, f2], dim=-1) def evaluate_slack_true(self, X: Tensor) -> Tensor: g1 = X[..., 0] + X[..., 1] - 2.0 g2 = 6.0 - X[..., 0] - X[..., 1] g3 = 2.0 - X[..., 1] + X[..., 0] g4 = 2.0 - X[..., 0] + 3.0 * X[..., 1] g5 = 4.0 - (X[..., 2] - 3.0) ** 2 - X[..., 3] g6 = (X[..., 4] - 3.0) ** 2 + X[..., 5] - 4.0 return torch.stack([g1, g2, g3, g4, g5, g6], dim=-1)

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from __future__ import annotations import math from typing import Optional import torch from botorch.test_functions.base import ( ConstrainedBaseTestProblem, MultiObjectiveTestProblem, ) from botorch.test_functions.synthetic import Branin from botorch.utils.sampling import sample_hypersphere, sample_simplex from botorch.utils.transforms import unnormalize from scipy.special import gamma from torch import Tensor class BraninCurrin(MultiObjectiveTestProblem): dim = 2 num_objectives = 2 _bounds = [(0.0, 1.0), (0.0, 1.0)] _ref_point = [18.0, 6.0] _max_hv = 59.36011874867746 def __init__(self, noise_std: Optional[float] = None, negate: bool = False) -> None: super().__init__(noise_std=noise_std, negate=negate) self._branin = Branin() def _rescaled_branin(self, X: Tensor) -> Tensor: x_0 = 15 * X[..., 0] - 5 x_1 = 15 * X[..., 1] return self._branin(torch.stack([x_0, x_1], dim=-1)) @staticmethod def _currin(X: Tensor) -> Tensor: x_0 = X[..., 0] x_1 = X[..., 1] factor1 = 1 - torch.exp(-1 / (2 * x_1)) numer = 2300 * x_0.pow(3) + 1900 * x_0.pow(2) + 2092 * x_0 + 60 denom = 100 * x_0.pow(3) + 500 * x_0.pow(2) + 4 * x_0 + 20 return factor1 * numer / denom def evaluate_true(self, X: Tensor) -> Tensor: branin = self._rescaled_branin(X=X) currin = self._currin(X=X) return torch.stack([branin, currin], dim=-1) class DTLZ(MultiObjectiveTestProblem): def __init__( self, dim: int, num_objectives: int = 2, noise_std: Optional[float] = None, negate: bool = False, ) -> None: if dim <= num_objectives: raise ValueError( f"dim must be > num_objectives, but got {dim} and {num_objectives}" ) self.num_objectives = num_objectives self.dim = dim self.k = self.dim - self.num_objectives + 1 self._bounds = [(0.0, 1.0) for _ in range(self.dim)] self._ref_point = [self._ref_val for _ in range(num_objectives)] super().__init__(noise_std=noise_std, negate=negate) class DTLZ1(DTLZ): _ref_val = 400.0 @property def _max_hv(self) -> float: return self._ref_val ** self.num_objectives - 1 / 2 ** self.num_objectives def evaluate_true(self, X: Tensor) -> Tensor: X_m = X[..., -self.k :] X_m_minus_half = X_m - 0.5 sum_term = ( X_m_minus_half.pow(2) - torch.cos(20 * math.pi * X_m_minus_half) ).sum(dim=-1) g_X_m = 100 * (self.k + sum_term) g_X_m_term = 0.5 * (1 + g_X_m) fs = [] for i in range(self.num_objectives): idx = self.num_objectives - 1 - i f_i = g_X_m_term * X[..., :idx].prod(dim=-1) if i > 0: f_i *= 1 - X[..., idx] fs.append(f_i) return torch.stack(fs, dim=-1) def gen_pareto_front(self, n: int) -> Tensor: f_X = 0.5 * sample_simplex( n=n, d=self.num_objectives, qmc=True, dtype=self.ref_point.dtype, device=self.ref_point.device, ) if self.negate: f_X *= -1 return f_X class DTLZ2(DTLZ): _ref_val = 1.1 @property def _max_hv(self) -> float: hypercube_vol = self._ref_val ** self.num_objectives pos_hypersphere_vol = ( math.pi ** (self.num_objectives / 2) / gamma(self.num_objectives / 2 + 1) / 2 ** self.num_objectives ) return hypercube_vol - pos_hypersphere_vol def evaluate_true(self, X: Tensor) -> Tensor: X_m = X[..., -self.k :] g_X = (X_m - 0.5).pow(2).sum(dim=-1) g_X_plus1 = 1 + g_X fs = [] pi_over_2 = math.pi / 2 for i in range(self.num_objectives): idx = self.num_objectives - 1 - i f_i = g_X_plus1.clone() f_i *= torch.cos(X[..., :idx] * pi_over_2).prod(dim=-1) if i > 0: f_i *= torch.sin(X[..., idx] * pi_over_2) fs.append(f_i) return torch.stack(fs, dim=-1) def gen_pareto_front(self, n: int) -> Tensor: f_X = sample_hypersphere( n=n, d=self.num_objectives, dtype=self.ref_point.dtype, device=self.ref_point.device, qmc=True, ).abs() if self.negate: f_X *= -1 return f_X class VehicleSafety(MultiObjectiveTestProblem): _ref_point = [1864.72022, 11.81993945, 0.2903999384] _max_hv = 246.81607081187002 _bounds = [(1.0, 3.0)] * 5 dim = 5 num_objectives = 3 def evaluate_true(self, X: Tensor) -> Tensor: X1, X2, X3, X4, X5 = torch.split(X, 1, -1) f1 = ( 1640.2823 + 2.3573285 * X1 + 2.3220035 * X2 + 4.5688768 * X3 + 7.7213633 * X4 + 4.4559504 * X5 ) f2 = ( 6.5856 + 1.15 * X1 - 1.0427 * X2 + 0.9738 * X3 + 0.8364 * X4 - 0.3695 * X1 * X4 + 0.0861 * X1 * X5 + 0.3628 * X2 * X4 - 0.1106 * X1.pow(2) - 0.3437 * X3.pow(2) + 0.1764 * X4.pow(2) ) f3 = ( -0.0551 + 0.0181 * X1 + 0.1024 * X2 + 0.0421 * X3 - 0.0073 * X1 * X2 + 0.024 * X2 * X3 - 0.0118 * X2 * X4 - 0.0204 * X3 * X4 - 0.008 * X3 * X5 - 0.0241 * X2.pow(2) + 0.0109 * X4.pow(2) ) f_X = torch.cat([f1, f2, f3], dim=-1) return f_X class ZDT(MultiObjectiveTestProblem): _ref_point = [11.0, 11.0] def __init__( self, dim: int, num_objectives: int = 2, noise_std: Optional[float] = None, negate: bool = False, ) -> None: if num_objectives != 2: raise NotImplementedError( f"{type(self).__name__} currently only supports 2 objectives." ) if dim < num_objectives: raise ValueError( f"dim must be >= num_objectives, but got {dim} and {num_objectives}" ) self.num_objectives = num_objectives self.dim = dim self._bounds = [(0.0, 1.0) for _ in range(self.dim)] super().__init__(noise_std=noise_std, negate=negate) @staticmethod def _g(X: Tensor) -> Tensor: return 1 + 9 * X[..., 1:].mean(dim=-1) class ZDT1(ZDT): _max_hv = 120 + 2 / 3 def evaluate_true(self, X: Tensor) -> Tensor: f_0 = X[..., 0] g = self._g(X=X) f_1 = g * (1 - (f_0 / g).sqrt()) return torch.stack([f_0, f_1], dim=-1) def gen_pareto_front(self, n: int) -> Tensor: f_0 = torch.linspace( 0, 1, n, dtype=self.bounds.dtype, device=self.bounds.device ) f_1 = 1 - f_0.sqrt() f_X = torch.stack([f_0, f_1], dim=-1) if self.negate: f_X *= -1 return f_X class ZDT2(ZDT): _max_hv = 120 + 1 / 3 def evaluate_true(self, X: Tensor) -> Tensor: f_0 = X[..., 0] g = self._g(X=X) f_1 = g * (1 - (f_0 / g).pow(2)) return torch.stack([f_0, f_1], dim=-1) def gen_pareto_front(self, n: int) -> Tensor: f_0 = torch.linspace( 0, 1, n, dtype=self.bounds.dtype, device=self.bounds.device ) f_1 = 1 - f_0.pow(2) f_X = torch.stack([f_0, f_1], dim=-1) if self.negate: f_X *= -1 return f_X class ZDT3(ZDT): _max_hv = 128.77811613069076060 _parts = [ [0, 0.0830015349], [0.1822287280, 0.2577623634], [0.4093136748, 0.4538821041], [0.6183967944, 0.6525117038], [0.8233317983, 0.8518328654], ] _eps = 1e-6 def evaluate_true(self, X: Tensor) -> Tensor: f_0 = X[..., 0] g = self._g(X=X) f_1 = 1 - (f_0 / g).sqrt() - f_0 / g * torch.sin(10 * math.pi * f_0) return torch.stack([f_0, f_1], dim=-1) def gen_pareto_front(self, n: int) -> Tensor: n_parts = len(self._parts) n_per_part = torch.full( torch.Size([n_parts]), n // n_parts, dtype=torch.long, device=self.bounds.device, ) left_over = n % n_parts n_per_part[:left_over] += 1 f_0s = [] for i, p in enumerate(self._parts): left, right = p f_0s.append( torch.linspace( left + self._eps, right - self._eps, n_per_part[i], dtype=self.bounds.dtype, device=self.bounds.device, ) ) f_0 = torch.cat(f_0s, dim=0) f_1 = 1 - f_0.sqrt() - f_0 * torch.sin(10 * math.pi * f_0) f_X = torch.stack([f_0, f_1], dim=-1) if self.negate: f_X *= -1 return f_X class BNH(MultiObjectiveTestProblem, ConstrainedBaseTestProblem): dim = 2 num_objectives = 2 num_constraints = 2 _bounds = [(0.0, 5.0), (0.0, 3.0)] _ref_point = [0.0, 0.0] def evaluate_true(self, X: Tensor) -> Tensor: return torch.stack( [4.0 * (X ** 2).sum(dim=-1), ((X - 5.0) ** 2).sum(dim=-1)], dim=-1 ) def evaluate_slack_true(self, X: Tensor) -> Tensor: c1 = 25.0 - (X[..., 0] - 5.0) ** 2 - X[..., 1] ** 2 c2 = (X[..., 0] - 8.0) ** 2 + (X[..., 1] + 3.0) ** 2 - 7.7 return torch.stack([c1, c2], dim=-1) class SRN(MultiObjectiveTestProblem, ConstrainedBaseTestProblem): dim = 2 num_objectives = 2 num_constraints = 2 _bounds = [(-20.0, 20.0), (-20.0, 20.0)] _ref_point = [0.0, 0.0] def evaluate_true(self, X: Tensor) -> Tensor: obj1 = 2.0 + ((X - 2.0) ** 2).sum(dim=-1) obj2 = 9.0 * X[..., 0] - (X[..., 1] - 1.0) ** 2 return torch.stack([obj1, obj2], dim=-1) def evaluate_slack_true(self, X: Tensor) -> Tensor: c1 = 225.0 - ((X ** 2) ** 2).sum(dim=-1) c2 = -10.0 - X[..., 0] + 3 * X[..., 1] return torch.stack([c1, c2], dim=-1) class CONSTR(MultiObjectiveTestProblem, ConstrainedBaseTestProblem): dim = 2 num_objectives = 2 num_constraints = 2 _bounds = [(0.1, 10.0), (0.0, 5.0)] _ref_point = [10.0, 10.0] def evaluate_true(self, X: Tensor) -> Tensor: obj1 = X[..., 0] obj2 = (1.0 + X[..., 1]) / X[..., 0] return torch.stack([obj1, obj2], dim=-1) def evaluate_slack_true(self, X: Tensor) -> Tensor: c1 = 9.0 * X[..., 0] + X[..., 1] - 6.0 c2 = 9.0 * X[..., 0] - X[..., 1] - 1.0 return torch.stack([c1, c2], dim=-1) class ConstrainedBraninCurrin(BraninCurrin, ConstrainedBaseTestProblem): dim = 2 num_objectives = 2 num_constraints = 1 _bounds = [(0.0, 1.0), (0.0, 1.0)] _con_bounds = [(-5.0, 10.0), (0.0, 15.0)] _ref_point = [80.0, 12.0] _max_hv = 608.4004237022673 def __init__(self, noise_std: Optional[float] = None, negate: bool = False) -> None: super().__init__(noise_std=noise_std, negate=negate) con_bounds = torch.tensor(self._con_bounds, dtype=torch.float).transpose(-1, -2) self.register_buffer("con_bounds", con_bounds) def evaluate_slack_true(self, X: Tensor) -> Tensor: X_tf = unnormalize(X, self.con_bounds) return 50 - (X_tf[..., 0:1] - 2.5).pow(2) - (X_tf[..., 1:2] - 7.5).pow(2) class C2DTLZ2(DTLZ2, ConstrainedBaseTestProblem): num_constraints = 1 _r = 0.2 _max_hv = 0.3996406303723544 def evaluate_slack_true(self, X: Tensor) -> Tensor: if X.ndim > 2: raise NotImplementedError("Batch X is not supported.") f_X = self.evaluate_true(X) term1 = (f_X - 1).pow(2) mask = ~(torch.eye(f_X.shape[-1], device=f_X.device).bool()) indices = torch.arange(f_X.shape[1], device=f_X.device).repeat(f_X.shape[1], 1) indexer = indices[mask].view(f_X.shape[1], f_X.shape[-1] - 1) term2_inner = ( f_X.unsqueeze(1) .expand(f_X.shape[0], f_X.shape[-1], f_X.shape[-1]) .gather(dim=-1, index=indexer.repeat(f_X.shape[0], 1, 1)) ) term2 = (term2_inner.pow(2) - self._r ** 2).sum(dim=-1) min1 = (term1 + term2).min(dim=-1).values min2 = ((f_X - 1 / math.sqrt(f_X.shape[-1])).pow(2) - self._r ** 2).sum(dim=-1) return -torch.min(min1, min2).unsqueeze(-1) class OSY(MultiObjectiveTestProblem, ConstrainedBaseTestProblem): dim = 6 num_constraints = 6 num_objectives = 2 _bounds = [ (0.0, 10.0), (0.0, 10.0), (1.0, 5.0), (0.0, 6.0), (1.0, 5.0), (0.0, 10.0), ] _ref_point = [-75.0, 75.0] def evaluate_true(self, X: Tensor) -> Tensor: f1 = -( 25 * (X[..., 0] - 2) ** 2 + (X[..., 1] - 2) ** 2 + (X[..., 2] - 1) ** 2 + (X[..., 3] - 4) ** 2 + (X[..., 4] - 1) ** 2 ) f2 = (X ** 2).sum(-1) return torch.stack([f1, f2], dim=-1) def evaluate_slack_true(self, X: Tensor) -> Tensor: g1 = X[..., 0] + X[..., 1] - 2.0 g2 = 6.0 - X[..., 0] - X[..., 1] g3 = 2.0 - X[..., 1] + X[..., 0] g4 = 2.0 - X[..., 0] + 3.0 * X[..., 1] g5 = 4.0 - (X[..., 2] - 3.0) ** 2 - X[..., 3] g6 = (X[..., 4] - 3.0) ** 2 + X[..., 5] - 4.0 return torch.stack([g1, g2, g3, g4, g5, g6], dim=-1)

true

f7297230ad6a4050958a36c1f28f9d0abd69b6a2

15,799

py

Python

ptf-tests/tests/lib/helper.py

robertmacdavid/up4-abstract

b1d184ef5528b7a96da9c26c3d22ff2616d41fa3

[ "Apache-2.0" ]

3

2021-11-18T00:00:13.000Z

2021-11-18T02:09:19.000Z

ptf-tests/tests/lib/helper.py

robertmacdavid/up4-abstract

b1d184ef5528b7a96da9c26c3d22ff2616d41fa3

[ "Apache-2.0" ]

null

ptf-tests/tests/lib/helper.py

robertmacdavid/up4-abstract

b1d184ef5528b7a96da9c26c3d22ff2616d41fa3

[ "Apache-2.0" ]

null

# Copyright 2020-2021 Open Networking Foundation # Copyright 2021-present Princeton University # # 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 re import google.protobuf.text_format import grpc from ptf import testutils as testutils from p4.config.v1 import p4info_pb2 from p4.v1 import p4runtime_pb2, p4runtime_pb2_grpc, p4data_pb2 from convert import encode def get_match_field_value(match_field): match_type = match_field.WhichOneof("field_match_type") if match_type == 'valid': return match_field.valid.value elif match_type == 'exact': return match_field.exact.value elif match_type == 'lpm': return match_field.lpm.value, match_field.lpm.prefix_len elif match_type == 'ternary': return match_field.ternary.value, match_field.ternary.mask elif match_type == 'range': return match_field.range.low, match_field.range.high else: raise Exception("Unsupported match type with type %r" % match_type) class P4InfoHelper(object): def __init__(self, p4info): self.p4info = p4info self.next_mbr_id = 1 self.next_grp_id = 1 def get_next_mbr_id(self): mbr_id = self.next_mbr_id self.next_mbr_id = self.next_mbr_id + 1 return mbr_id def read_pkt_count(self, c_name, line_id): counter = self.read_counter(c_name, line_id, typ="BOTH") return counter.data.packet_count def read_byte_count(self, c_name, line_id): counter = self.read_counter(c_name, line_id, typ="BYTES") return counter.data.byte_count def read_counter(self, c_name, c_index, typ): # Check counter type with P4Info counter = self.get_obj('counters', c_name) counter_type_unit = p4info_pb2.CounterSpec.Unit.items()[counter.spec.unit][0] if counter_type_unit != "BOTH" and counter_type_unit != typ: raise Exception("Counter " + c_name + " is of type " + counter_type_unit + ", but requested: " + typ) req = self.get_new_read_request() entity = req.entities.add() counter_entry = entity.counter_entry c_id = self.get_id('counters', c_name) counter_entry.counter_id = c_id index = counter_entry.index index.index = c_index for entity in self.read_request(req): if entity.HasField("counter_entry"): return entity.counter_entry return None def clear_counters(self): pass def read_request(self, req): entities = [] grpc_addr = testutils.test_param_get("grpcaddr") channel = grpc.insecure_channel(grpc_addr) stub = p4runtime_pb2_grpc.P4RuntimeStub(channel) try: for resp in stub.Read(req): entities.extend(resp.entities) except grpc.RpcError as e: if e.code() != grpc.StatusCode.UNKNOWN: raise e raise P4RuntimeException(e) return entities def write_request(self, req, store=True): rep = self._write(req) if store: self.reqs.append(req) return rep def get_new_write_request(self): req = p4runtime_pb2.WriteRequest() req.device_id = int(testutils.test_param_get("device_id")) election_id = req.election_id election_id.high = 0 election_id.low = self.election_id return req def get_new_read_request(self): req = p4runtime_pb2.ReadRequest() req.device_id = int(testutils.test_param_get("device_id")) return req def get_next_grp_id(self): grp_id = self.next_grp_id self.next_grp_id = self.next_grp_id + 1 return grp_id def get_enum_member_val(self, enum_name, enum_member): members = self.get_enum_members(name=enum_name) val = members.get(enum_member, None) if val is None: raise Exception("%s not a member of enum %s. Available Members: %s" \ % (enum_member, enum_name, str(list(members.keys())))) return val def get_enum_obj(self, name): if "type_info" in dir(self.p4info): type_info = self.p4info.type_info if "serializable_enums" in dir(type_info): for key, val in type_info.serializable_enums.items(): if key == name: return val raise AttributeError("Could not find enum named %s" % name) def get_enum_members(self, name=None, obj=None): if obj is None: if name is None: raise AssertionError("Must provide either an enum name or enum object") obj = self.get_enum_obj(name) return {member.name: member.value for member in obj.members} def get_enum_width(self, name): return self.get_enum_obj(name).underlying_type.bitwidth def get(self, entity_type, name=None, id=None): if name is not None and id is not None: raise AssertionError("name or id must be None") for o in getattr(self.p4info, entity_type): pre = o.preamble if name: if pre.name == name: return o else: if pre.id == id: return o if name: raise AttributeError("Could not find %r of type %s" % (name, entity_type)) else: raise AttributeError("Could not find id %r of type %s" % (id, entity_type)) def get_id(self, entity_type, name): return self.get(entity_type, name=name).preamble.id def get_name(self, entity_type, id): return self.get(entity_type, id=id).preamble.name def get_obj(self, entity_type, name): return self.get(entity_type, name=name) def __getattr__(self, attr): # Synthesize convenience functions for name to id lookups for top-level # entities e.g. get_tables_id(name_string) or # get_actions_id(name_string) m = re.search(r"^get_(\w+)_id$", attr) if m: primitive = m.group(1) return lambda name: self.get_id(primitive, name) # Synthesize convenience functions for id to name lookups # e.g. get_tables_name(id) or get_actions_name(id) m = re.search(r"^get_(\w+)_name$", attr) if m: primitive = m.group(1) return lambda x: self.get_name(primitive, x) raise AttributeError("%r object has no attribute %r (check your P4Info)" % (self.__class__, attr)) def get_match_field(self, table_name, name=None, id=None): t = None for t in self.p4info.tables: if t.preamble.name == table_name: break if not t: raise AttributeError("No such table %r in P4Info" % table_name) for mf in t.match_fields: if name is not None: if mf.name == name: return mf elif id is not None: if mf.id == id: return mf raise AttributeError("%r has no match field %r (check your P4Info)" % (table_name, name if name is not None else id)) def get_packet_metadata(self, meta_type, name=None, id=None): for t in self.p4info.controller_packet_metadata: pre = t.preamble if pre.name == meta_type: for m in t.metadata: if name is not None: if m.name == name: return m elif id is not None: if m.id == id: return m raise AttributeError("ControllerPacketMetadata %r has no metadata %r (check your P4Info)" % (meta_type, name if name is not None else id)) def get_match_field_id(self, table_name, match_field_name): return self.get_match_field(table_name, name=match_field_name).id def get_match_field_name(self, table_name, match_field_id): return self.get_match_field(table_name, id=match_field_id).name def get_match_field_pb(self, table_name, match_field_name, value): p4info_match = self.get_match_field(table_name, match_field_name) bitwidth = p4info_match.bitwidth p4runtime_match = p4runtime_pb2.FieldMatch() p4runtime_match.field_id = p4info_match.id match_type = p4info_match.match_type if match_type == p4info_pb2.MatchField.EXACT: exact = p4runtime_match.exact exact.value = encode(value, bitwidth) elif match_type == p4info_pb2.MatchField.LPM: if type(value) is str and '/' in value: value = value.split('/') value[1] = int(value[1]) lpm = p4runtime_match.lpm lpm.value = encode(value[0], bitwidth) lpm.prefix_len = value[1] elif match_type == p4info_pb2.MatchField.TERNARY: lpm = p4runtime_match.ternary lpm.value = encode(value[0], bitwidth) lpm.mask = encode(value[1], bitwidth) elif match_type == p4info_pb2.MatchField.RANGE: lpm = p4runtime_match.range lpm.low = encode(value[0], bitwidth) lpm.high = encode(value[1], bitwidth) else: raise Exception("Unsupported match type with type %r" % match_type) return p4runtime_match def get_action_param(self, action_name, name=None, id=None): for a in self.p4info.actions: pre = a.preamble if pre.name == action_name: for p in a.params: if name is not None: if p.name == name: return p elif id is not None: if p.id == id: return p raise AttributeError("Action %r has no param %r (check your P4Info)" % (action_name, name if name is not None else id)) def get_counter(self, counter_name): for a in self.p4info.direct_counters: pre = a.preamble if pre.name == counter_name: return a raise AttributeError("Counter %r doesnt exist (check your P4Info)" % (counter_name)) def get_action_param_id(self, action_name, param_name): return self.get_action_param(action_name, name=param_name).id def get_action_param_name(self, action_name, param_id): return self.get_action_param(action_name, id=param_id).name def get_action_param_pb(self, action_name, param_name, value): p4info_param = self.get_action_param(action_name, param_name) p4runtime_param = p4runtime_pb2.Action.Param() p4runtime_param.param_id = p4info_param.id p4runtime_param.value = encode(value, p4info_param.bitwidth) return p4runtime_param def build_table_entry(self, table_name, match_fields=None, default_action=False, action_name=None, action_params=None, group_id=None, priority=None): table_entry = p4runtime_pb2.TableEntry() table_entry.table_id = self.get_tables_id(table_name) if priority is not None: table_entry.priority = priority if match_fields: table_entry.match.extend([ self.get_match_field_pb(table_name, match_field_name, value) for match_field_name, value in match_fields.items() ]) if default_action: table_entry.is_default_action = True if action_name: action = table_entry.action.action action.CopyFrom(self.build_action(action_name, action_params)) if group_id: table_entry.action.action_profile_group_id = group_id return table_entry def build_action(self, action_name, action_params=None): action = p4runtime_pb2.Action() action.action_id = self.get_actions_id(action_name) if action_params: action.params.extend([ self.get_action_param_pb(action_name, field_name, value) for field_name, value in action_params.items() ]) return action def build_act_prof_member(self, act_prof_name, action_name, action_params=None, member_id=None): member = p4runtime_pb2.ActionProfileMember() member.action_profile_id = self.get_action_profiles_id(act_prof_name) member.member_id = member_id if member_id else self.get_next_mbr_id() member.action.CopyFrom(self.build_action(action_name, action_params)) return member def build_act_prof_group(self, act_prof_name, group_id, actions=()): messages = [] group = p4runtime_pb2.ActionProfileGroup() group.action_profile_id = self.get_action_profiles_id(act_prof_name) group.group_id = group_id for action in actions: action_name = action[0] if len(action) > 1: action_params = action[1] else: action_params = None member = self.build_act_prof_member(act_prof_name, action_name, action_params) messages.extend([member]) group_member = p4runtime_pb2.ActionProfileGroup.Member() group_member.member_id = member.member_id group_member.weight = 1 group.members.extend([group_member]) messages.append(group) return messages def build_packet_out(self, payload, metadata=None): packet_out = p4runtime_pb2.PacketOut() packet_out.payload = bytes(payload) if not metadata: return packet_out for name, value in metadata.items(): p4info_meta = self.get_packet_metadata("packet_out", name) meta = packet_out.metadata.add() meta.metadata_id = p4info_meta.id meta.value = encode(value, p4info_meta.bitwidth) return packet_out def build_packet_in(self, payload, metadata=None): packet_in = p4runtime_pb2.PacketIn() packet_in.payload = bytes(payload) if not metadata: return packet_in for name, value in metadata.items(): p4info_meta = self.get_packet_metadata("packet_in", name) meta = packet_in.metadata.add() meta.metadata_id = p4info_meta.id meta.value = encode(value, p4info_meta.bitwidth) return packet_in def build_digest_entry(self, digest_name, max_timeout_ns, max_list_size, ack_timeout_ns): digest_entry = p4runtime_pb2.DigestEntry() digest_entry.digest_id = self.get_digests_id(digest_name) config = digest_entry.config config.max_timeout_ns = max_timeout_ns config.max_list_size = max_list_size config.ack_timeout_ns = ack_timeout_ns return digest_entry def build_p4data_bitstring(self, value): data = p4data_pb2.P4Data() data.bitstring = value return data def build_p4data_struct(self, members): data = p4data_pb2.P4Data() struct = data.struct for m in members: x = struct.members.add() x.CopyFrom(m) return data

39.009877

100

0.62352

import re import google.protobuf.text_format import grpc from ptf import testutils as testutils from p4.config.v1 import p4info_pb2 from p4.v1 import p4runtime_pb2, p4runtime_pb2_grpc, p4data_pb2 from convert import encode def get_match_field_value(match_field): match_type = match_field.WhichOneof("field_match_type") if match_type == 'valid': return match_field.valid.value elif match_type == 'exact': return match_field.exact.value elif match_type == 'lpm': return match_field.lpm.value, match_field.lpm.prefix_len elif match_type == 'ternary': return match_field.ternary.value, match_field.ternary.mask elif match_type == 'range': return match_field.range.low, match_field.range.high else: raise Exception("Unsupported match type with type %r" % match_type) class P4InfoHelper(object): def __init__(self, p4info): self.p4info = p4info self.next_mbr_id = 1 self.next_grp_id = 1 def get_next_mbr_id(self): mbr_id = self.next_mbr_id self.next_mbr_id = self.next_mbr_id + 1 return mbr_id def read_pkt_count(self, c_name, line_id): counter = self.read_counter(c_name, line_id, typ="BOTH") return counter.data.packet_count def read_byte_count(self, c_name, line_id): counter = self.read_counter(c_name, line_id, typ="BYTES") return counter.data.byte_count def read_counter(self, c_name, c_index, typ): counter = self.get_obj('counters', c_name) counter_type_unit = p4info_pb2.CounterSpec.Unit.items()[counter.spec.unit][0] if counter_type_unit != "BOTH" and counter_type_unit != typ: raise Exception("Counter " + c_name + " is of type " + counter_type_unit + ", but requested: " + typ) req = self.get_new_read_request() entity = req.entities.add() counter_entry = entity.counter_entry c_id = self.get_id('counters', c_name) counter_entry.counter_id = c_id index = counter_entry.index index.index = c_index for entity in self.read_request(req): if entity.HasField("counter_entry"): return entity.counter_entry return None def clear_counters(self): pass def read_request(self, req): entities = [] grpc_addr = testutils.test_param_get("grpcaddr") channel = grpc.insecure_channel(grpc_addr) stub = p4runtime_pb2_grpc.P4RuntimeStub(channel) try: for resp in stub.Read(req): entities.extend(resp.entities) except grpc.RpcError as e: if e.code() != grpc.StatusCode.UNKNOWN: raise e raise P4RuntimeException(e) return entities def write_request(self, req, store=True): rep = self._write(req) if store: self.reqs.append(req) return rep def get_new_write_request(self): req = p4runtime_pb2.WriteRequest() req.device_id = int(testutils.test_param_get("device_id")) election_id = req.election_id election_id.high = 0 election_id.low = self.election_id return req def get_new_read_request(self): req = p4runtime_pb2.ReadRequest() req.device_id = int(testutils.test_param_get("device_id")) return req def get_next_grp_id(self): grp_id = self.next_grp_id self.next_grp_id = self.next_grp_id + 1 return grp_id def get_enum_member_val(self, enum_name, enum_member): members = self.get_enum_members(name=enum_name) val = members.get(enum_member, None) if val is None: raise Exception("%s not a member of enum %s. Available Members: %s" \ % (enum_member, enum_name, str(list(members.keys())))) return val def get_enum_obj(self, name): if "type_info" in dir(self.p4info): type_info = self.p4info.type_info if "serializable_enums" in dir(type_info): for key, val in type_info.serializable_enums.items(): if key == name: return val raise AttributeError("Could not find enum named %s" % name) def get_enum_members(self, name=None, obj=None): if obj is None: if name is None: raise AssertionError("Must provide either an enum name or enum object") obj = self.get_enum_obj(name) return {member.name: member.value for member in obj.members} def get_enum_width(self, name): return self.get_enum_obj(name).underlying_type.bitwidth def get(self, entity_type, name=None, id=None): if name is not None and id is not None: raise AssertionError("name or id must be None") for o in getattr(self.p4info, entity_type): pre = o.preamble if name: if pre.name == name: return o else: if pre.id == id: return o if name: raise AttributeError("Could not find %r of type %s" % (name, entity_type)) else: raise AttributeError("Could not find id %r of type %s" % (id, entity_type)) def get_id(self, entity_type, name): return self.get(entity_type, name=name).preamble.id def get_name(self, entity_type, id): return self.get(entity_type, id=id).preamble.name def get_obj(self, entity_type, name): return self.get(entity_type, name=name) def __getattr__(self, attr): m = re.search(r"^get_(\w+)_id$", attr) if m: primitive = m.group(1) return lambda name: self.get_id(primitive, name) m = re.search(r"^get_(\w+)_name$", attr) if m: primitive = m.group(1) return lambda x: self.get_name(primitive, x) raise AttributeError("%r object has no attribute %r (check your P4Info)" % (self.__class__, attr)) def get_match_field(self, table_name, name=None, id=None): t = None for t in self.p4info.tables: if t.preamble.name == table_name: break if not t: raise AttributeError("No such table %r in P4Info" % table_name) for mf in t.match_fields: if name is not None: if mf.name == name: return mf elif id is not None: if mf.id == id: return mf raise AttributeError("%r has no match field %r (check your P4Info)" % (table_name, name if name is not None else id)) def get_packet_metadata(self, meta_type, name=None, id=None): for t in self.p4info.controller_packet_metadata: pre = t.preamble if pre.name == meta_type: for m in t.metadata: if name is not None: if m.name == name: return m elif id is not None: if m.id == id: return m raise AttributeError("ControllerPacketMetadata %r has no metadata %r (check your P4Info)" % (meta_type, name if name is not None else id)) def get_match_field_id(self, table_name, match_field_name): return self.get_match_field(table_name, name=match_field_name).id def get_match_field_name(self, table_name, match_field_id): return self.get_match_field(table_name, id=match_field_id).name def get_match_field_pb(self, table_name, match_field_name, value): p4info_match = self.get_match_field(table_name, match_field_name) bitwidth = p4info_match.bitwidth p4runtime_match = p4runtime_pb2.FieldMatch() p4runtime_match.field_id = p4info_match.id match_type = p4info_match.match_type if match_type == p4info_pb2.MatchField.EXACT: exact = p4runtime_match.exact exact.value = encode(value, bitwidth) elif match_type == p4info_pb2.MatchField.LPM: if type(value) is str and '/' in value: value = value.split('/') value[1] = int(value[1]) lpm = p4runtime_match.lpm lpm.value = encode(value[0], bitwidth) lpm.prefix_len = value[1] elif match_type == p4info_pb2.MatchField.TERNARY: lpm = p4runtime_match.ternary lpm.value = encode(value[0], bitwidth) lpm.mask = encode(value[1], bitwidth) elif match_type == p4info_pb2.MatchField.RANGE: lpm = p4runtime_match.range lpm.low = encode(value[0], bitwidth) lpm.high = encode(value[1], bitwidth) else: raise Exception("Unsupported match type with type %r" % match_type) return p4runtime_match def get_action_param(self, action_name, name=None, id=None): for a in self.p4info.actions: pre = a.preamble if pre.name == action_name: for p in a.params: if name is not None: if p.name == name: return p elif id is not None: if p.id == id: return p raise AttributeError("Action %r has no param %r (check your P4Info)" % (action_name, name if name is not None else id)) def get_counter(self, counter_name): for a in self.p4info.direct_counters: pre = a.preamble if pre.name == counter_name: return a raise AttributeError("Counter %r doesnt exist (check your P4Info)" % (counter_name)) def get_action_param_id(self, action_name, param_name): return self.get_action_param(action_name, name=param_name).id def get_action_param_name(self, action_name, param_id): return self.get_action_param(action_name, id=param_id).name def get_action_param_pb(self, action_name, param_name, value): p4info_param = self.get_action_param(action_name, param_name) p4runtime_param = p4runtime_pb2.Action.Param() p4runtime_param.param_id = p4info_param.id p4runtime_param.value = encode(value, p4info_param.bitwidth) return p4runtime_param def build_table_entry(self, table_name, match_fields=None, default_action=False, action_name=None, action_params=None, group_id=None, priority=None): table_entry = p4runtime_pb2.TableEntry() table_entry.table_id = self.get_tables_id(table_name) if priority is not None: table_entry.priority = priority if match_fields: table_entry.match.extend([ self.get_match_field_pb(table_name, match_field_name, value) for match_field_name, value in match_fields.items() ]) if default_action: table_entry.is_default_action = True if action_name: action = table_entry.action.action action.CopyFrom(self.build_action(action_name, action_params)) if group_id: table_entry.action.action_profile_group_id = group_id return table_entry def build_action(self, action_name, action_params=None): action = p4runtime_pb2.Action() action.action_id = self.get_actions_id(action_name) if action_params: action.params.extend([ self.get_action_param_pb(action_name, field_name, value) for field_name, value in action_params.items() ]) return action def build_act_prof_member(self, act_prof_name, action_name, action_params=None, member_id=None): member = p4runtime_pb2.ActionProfileMember() member.action_profile_id = self.get_action_profiles_id(act_prof_name) member.member_id = member_id if member_id else self.get_next_mbr_id() member.action.CopyFrom(self.build_action(action_name, action_params)) return member def build_act_prof_group(self, act_prof_name, group_id, actions=()): messages = [] group = p4runtime_pb2.ActionProfileGroup() group.action_profile_id = self.get_action_profiles_id(act_prof_name) group.group_id = group_id for action in actions: action_name = action[0] if len(action) > 1: action_params = action[1] else: action_params = None member = self.build_act_prof_member(act_prof_name, action_name, action_params) messages.extend([member]) group_member = p4runtime_pb2.ActionProfileGroup.Member() group_member.member_id = member.member_id group_member.weight = 1 group.members.extend([group_member]) messages.append(group) return messages def build_packet_out(self, payload, metadata=None): packet_out = p4runtime_pb2.PacketOut() packet_out.payload = bytes(payload) if not metadata: return packet_out for name, value in metadata.items(): p4info_meta = self.get_packet_metadata("packet_out", name) meta = packet_out.metadata.add() meta.metadata_id = p4info_meta.id meta.value = encode(value, p4info_meta.bitwidth) return packet_out def build_packet_in(self, payload, metadata=None): packet_in = p4runtime_pb2.PacketIn() packet_in.payload = bytes(payload) if not metadata: return packet_in for name, value in metadata.items(): p4info_meta = self.get_packet_metadata("packet_in", name) meta = packet_in.metadata.add() meta.metadata_id = p4info_meta.id meta.value = encode(value, p4info_meta.bitwidth) return packet_in def build_digest_entry(self, digest_name, max_timeout_ns, max_list_size, ack_timeout_ns): digest_entry = p4runtime_pb2.DigestEntry() digest_entry.digest_id = self.get_digests_id(digest_name) config = digest_entry.config config.max_timeout_ns = max_timeout_ns config.max_list_size = max_list_size config.ack_timeout_ns = ack_timeout_ns return digest_entry def build_p4data_bitstring(self, value): data = p4data_pb2.P4Data() data.bitstring = value return data def build_p4data_struct(self, members): data = p4data_pb2.P4Data() struct = data.struct for m in members: x = struct.members.add() x.CopyFrom(m) return data

true

f729748acc8620809f6dd195df4ac38d7f1b2eb8

5,457

py

Python

distributed/protocol/compression.py

gjoseph92/distributed

af64e07a01e8ce0d76744099a93ca2155d835ba8

[ "BSD-3-Clause" ]

null

distributed/protocol/compression.py

gjoseph92/distributed

af64e07a01e8ce0d76744099a93ca2155d835ba8

[ "BSD-3-Clause" ]

null

distributed/protocol/compression.py

gjoseph92/distributed

af64e07a01e8ce0d76744099a93ca2155d835ba8

[ "BSD-3-Clause" ]

null

""" Record known compressors Includes utilities for determining whether or not to compress """ from __future__ import print_function, division, absolute_import import logging import random import dask from toolz import identity, partial try: import blosc n = blosc.set_nthreads(2) if hasattr("blosc", "releasegil"): blosc.set_releasegil(True) except ImportError: blosc = False from ..utils import ignoring, ensure_bytes compressions = {None: {"compress": identity, "decompress": identity}} compressions[False] = compressions[None] # alias default_compression = None logger = logging.getLogger(__name__) with ignoring(ImportError): import zlib compressions["zlib"] = {"compress": zlib.compress, "decompress": zlib.decompress} with ignoring(ImportError): import snappy def _fixed_snappy_decompress(data): # snappy.decompress() doesn't accept memoryviews if isinstance(data, (memoryview, bytearray)): data = bytes(data) return snappy.decompress(data) compressions["snappy"] = { "compress": snappy.compress, "decompress": _fixed_snappy_decompress, } default_compression = "snappy" with ignoring(ImportError): import lz4 try: # try using the new lz4 API import lz4.block lz4_compress = lz4.block.compress lz4_decompress = lz4.block.decompress except ImportError: # fall back to old one lz4_compress = lz4.LZ4_compress lz4_decompress = lz4.LZ4_uncompress # helper to bypass missing memoryview support in current lz4 # (fixed in later versions) def _fixed_lz4_compress(data): try: return lz4_compress(data) except TypeError: if isinstance(data, (memoryview, bytearray)): return lz4_compress(bytes(data)) else: raise def _fixed_lz4_decompress(data): try: return lz4_decompress(data) except (ValueError, TypeError): if isinstance(data, (memoryview, bytearray)): return lz4_decompress(bytes(data)) else: raise compressions["lz4"] = { "compress": _fixed_lz4_compress, "decompress": _fixed_lz4_decompress, } default_compression = "lz4" with ignoring(ImportError): import blosc compressions["blosc"] = { "compress": partial(blosc.compress, clevel=5, cname="lz4"), "decompress": blosc.decompress, } default = dask.config.get("distributed.comm.compression") if default != "auto": if default in compressions: default_compression = default else: raise ValueError( "Default compression '%s' not found.\n" "Choices include auto, %s" % (default, ", ".join(sorted(map(str, compressions)))) ) def byte_sample(b, size, n): """ Sample a bytestring from many locations Parameters ---------- b: bytes or memoryview size: int size of each sample to collect n: int number of samples to collect """ starts = [random.randint(0, len(b) - size) for j in range(n)] ends = [] for i, start in enumerate(starts[:-1]): ends.append(min(start + size, starts[i + 1])) ends.append(starts[-1] + size) parts = [b[start:end] for start, end in zip(starts, ends)] return b"".join(map(ensure_bytes, parts)) def maybe_compress(payload, min_size=1e4, sample_size=1e4, nsamples=5): """ Maybe compress payload 1. We don't compress small messages 2. We sample the payload in a few spots, compress that, and if it doesn't do any good we return the original 3. We then compress the full original, it it doesn't compress well then we return the original 4. We return the compressed result """ compression = dask.config.get("distributed.comm.compression") if compression == "auto": compression = default_compression if not compression: return None, payload if len(payload) < min_size: return None, payload if len(payload) > 2 ** 31: # Too large, compression libraries often fail return None, payload min_size = int(min_size) sample_size = int(sample_size) compress = compressions[compression]["compress"] # Compress a sample, return original if not very compressed sample = byte_sample(payload, sample_size, nsamples) if len(compress(sample)) > 0.9 * len(sample): # sample not very compressible return None, payload if type(payload) is memoryview: nbytes = payload.itemsize * len(payload) else: nbytes = len(payload) if default_compression and blosc and type(payload) is memoryview: # Blosc does itemsize-aware shuffling, resulting in better compression compressed = blosc.compress( payload, typesize=payload.itemsize, cname="lz4", clevel=5 ) compression = "blosc" else: compressed = compress(ensure_bytes(payload)) if len(compressed) > 0.9 * nbytes: # full data not very compressible return None, payload else: return compression, compressed def decompress(header, frames): """ Decompress frames according to information in the header """ return [ compressions[c]["decompress"](frame) for c, frame in zip(header["compression"], frames) ]

27.560606

85

0.645226

from __future__ import print_function, division, absolute_import import logging import random import dask from toolz import identity, partial try: import blosc n = blosc.set_nthreads(2) if hasattr("blosc", "releasegil"): blosc.set_releasegil(True) except ImportError: blosc = False from ..utils import ignoring, ensure_bytes compressions = {None: {"compress": identity, "decompress": identity}} compressions[False] = compressions[None] default_compression = None logger = logging.getLogger(__name__) with ignoring(ImportError): import zlib compressions["zlib"] = {"compress": zlib.compress, "decompress": zlib.decompress} with ignoring(ImportError): import snappy def _fixed_snappy_decompress(data): if isinstance(data, (memoryview, bytearray)): data = bytes(data) return snappy.decompress(data) compressions["snappy"] = { "compress": snappy.compress, "decompress": _fixed_snappy_decompress, } default_compression = "snappy" with ignoring(ImportError): import lz4 try: # try using the new lz4 API import lz4.block lz4_compress = lz4.block.compress lz4_decompress = lz4.block.decompress except ImportError: # fall back to old one lz4_compress = lz4.LZ4_compress lz4_decompress = lz4.LZ4_uncompress # helper to bypass missing memoryview support in current lz4 # (fixed in later versions) def _fixed_lz4_compress(data): try: return lz4_compress(data) except TypeError: if isinstance(data, (memoryview, bytearray)): return lz4_compress(bytes(data)) else: raise def _fixed_lz4_decompress(data): try: return lz4_decompress(data) except (ValueError, TypeError): if isinstance(data, (memoryview, bytearray)): return lz4_decompress(bytes(data)) else: raise compressions["lz4"] = { "compress": _fixed_lz4_compress, "decompress": _fixed_lz4_decompress, } default_compression = "lz4" with ignoring(ImportError): import blosc compressions["blosc"] = { "compress": partial(blosc.compress, clevel=5, cname="lz4"), "decompress": blosc.decompress, } default = dask.config.get("distributed.comm.compression") if default != "auto": if default in compressions: default_compression = default else: raise ValueError( "Default compression '%s' not found.\n" "Choices include auto, %s" % (default, ", ".join(sorted(map(str, compressions)))) ) def byte_sample(b, size, n): starts = [random.randint(0, len(b) - size) for j in range(n)] ends = [] for i, start in enumerate(starts[:-1]): ends.append(min(start + size, starts[i + 1])) ends.append(starts[-1] + size) parts = [b[start:end] for start, end in zip(starts, ends)] return b"".join(map(ensure_bytes, parts)) def maybe_compress(payload, min_size=1e4, sample_size=1e4, nsamples=5): compression = dask.config.get("distributed.comm.compression") if compression == "auto": compression = default_compression if not compression: return None, payload if len(payload) < min_size: return None, payload if len(payload) > 2 ** 31: # Too large, compression libraries often fail return None, payload min_size = int(min_size) sample_size = int(sample_size) compress = compressions[compression]["compress"] # Compress a sample, return original if not very compressed sample = byte_sample(payload, sample_size, nsamples) if len(compress(sample)) > 0.9 * len(sample): # sample not very compressible return None, payload if type(payload) is memoryview: nbytes = payload.itemsize * len(payload) else: nbytes = len(payload) if default_compression and blosc and type(payload) is memoryview: # Blosc does itemsize-aware shuffling, resulting in better compression compressed = blosc.compress( payload, typesize=payload.itemsize, cname="lz4", clevel=5 ) compression = "blosc" else: compressed = compress(ensure_bytes(payload)) if len(compressed) > 0.9 * nbytes: # full data not very compressible return None, payload else: return compression, compressed def decompress(header, frames): return [ compressions[c]["decompress"](frame) for c, frame in zip(header["compression"], frames) ]

true

f72974964b34bc8965a973df3379b8aa79b3c1da

1,819

py

Python

src/sas/sasgui/perspectives/calculator/console.py

opendatafit/sasview

c470220eecfc9f6d8a0e27e2ea8919dcb1b38e39

[ "BSD-3-Clause" ]

null

src/sas/sasgui/perspectives/calculator/console.py

opendatafit/sasview

c470220eecfc9f6d8a0e27e2ea8919dcb1b38e39

[ "BSD-3-Clause" ]

1

2021-09-20T13:20:35.000Z

src/sas/sasgui/perspectives/calculator/console.py

opendatafit/sasview

c470220eecfc9f6d8a0e27e2ea8919dcb1b38e39

[ "BSD-3-Clause" ]

null

""" Console Module display message of a dialog """ import wx import sys from sas.sascalc.dataloader.loader import Loader _BOX_WIDTH = 60 CONSOLE_WIDTH = 340 CONSOLE_HEIGHT = 240 if sys.platform.count("win32") > 0: _STATICBOX_WIDTH = 450 PANEL_WIDTH = 500 PANEL_HEIGHT = 550 FONT_VARIANT = 0 else: _STATICBOX_WIDTH = 480 PANEL_WIDTH = 530 PANEL_HEIGHT = 560 FONT_VARIANT = 1 class ConsoleDialog(wx.Dialog): """ Data summary dialog """ def __init__(self, parent=None, manager=None, data=None, title="Data Summary", size=(PANEL_WIDTH, PANEL_HEIGHT)): wx.Dialog.__init__(self, parent=parent, title=title, size=size) self.parent = parent self._manager = manager self._data = data self.sizer = wx.BoxSizer(wx.VERTICAL) self.msg_txt = wx.TextCtrl(self, size=(PANEL_WIDTH - 40, PANEL_HEIGHT - 60), style=wx.TE_MULTILINE) self.msg_txt.SetEditable(False) self.msg_txt.SetValue('No message available') self.sizer.Add(self.msg_txt, 1, wx.EXPAND | wx.ALL, 10) if self._data is not None: self.set_message(msg=self._data.__str__()) self.SetSizer(self.sizer) def set_manager(self, manager): """ Set the manager of this window """ self._manager = manager def set_message(self, msg=""): """ Display the message received """ self.msg_txt.SetValue(str(msg)) if __name__ == "__main__": app = wx.App() # Instantiate a loader loader = Loader() # Load data test_data = loader.load("MAR07232_rest.ASC") dlg = ConsoleDialog(data=test_data) dlg.ShowModal() app.MainLoop()

27.149254

76

0.59978

import wx import sys from sas.sascalc.dataloader.loader import Loader _BOX_WIDTH = 60 CONSOLE_WIDTH = 340 CONSOLE_HEIGHT = 240 if sys.platform.count("win32") > 0: _STATICBOX_WIDTH = 450 PANEL_WIDTH = 500 PANEL_HEIGHT = 550 FONT_VARIANT = 0 else: _STATICBOX_WIDTH = 480 PANEL_WIDTH = 530 PANEL_HEIGHT = 560 FONT_VARIANT = 1 class ConsoleDialog(wx.Dialog): def __init__(self, parent=None, manager=None, data=None, title="Data Summary", size=(PANEL_WIDTH, PANEL_HEIGHT)): wx.Dialog.__init__(self, parent=parent, title=title, size=size) self.parent = parent self._manager = manager self._data = data self.sizer = wx.BoxSizer(wx.VERTICAL) self.msg_txt = wx.TextCtrl(self, size=(PANEL_WIDTH - 40, PANEL_HEIGHT - 60), style=wx.TE_MULTILINE) self.msg_txt.SetEditable(False) self.msg_txt.SetValue('No message available') self.sizer.Add(self.msg_txt, 1, wx.EXPAND | wx.ALL, 10) if self._data is not None: self.set_message(msg=self._data.__str__()) self.SetSizer(self.sizer) def set_manager(self, manager): self._manager = manager def set_message(self, msg=""): self.msg_txt.SetValue(str(msg)) if __name__ == "__main__": app = wx.App() loader = Loader() test_data = loader.load("MAR07232_rest.ASC") dlg = ConsoleDialog(data=test_data) dlg.ShowModal() app.MainLoop()

true

f72974cef8a7f46849e53080deca073565237e4d

305

py

Python

core/libs/sqlsyntax.py

PanDAWMS/panda-bigmon-core-new

4b806af8b0616657dcf293af376c48f61c32b86f

[ "Apache-2.0" ]

null

core/libs/sqlsyntax.py

PanDAWMS/panda-bigmon-core-new

4b806af8b0616657dcf293af376c48f61c32b86f

[ "Apache-2.0" ]

null

core/libs/sqlsyntax.py

PanDAWMS/panda-bigmon-core-new

4b806af8b0616657dcf293af376c48f61c32b86f

[ "Apache-2.0" ]

null

""" A set of functions to handle syntax differences between DBs """ def bind_var(var, db='oracle'): """Format of named bind variable""" if db == 'postgresql': return '%({})s'.format(var) elif db == 'oracle': return ':{}'.format(var) else: return ':{}'.format(var)

23.461538

59

0.567213

def bind_var(var, db='oracle'): if db == 'postgresql': return '%({})s'.format(var) elif db == 'oracle': return ':{}'.format(var) else: return ':{}'.format(var)

true

f729751bd00eae316fcc00d7b6a627402edd0ae3

6,271

py

Python

corelib.py

jaredvann/UntitledLanguage

456f3a636d62028a85ee61eb3a9f04214f799e78

[ "MIT" ]

2

2020-02-07T13:20:03.000Z

2020-06-29T15:58:30.000Z

corelib.py

jaredvann/UntitledLanguage

456f3a636d62028a85ee61eb3a9f04214f799e78

[ "MIT" ]

1

2020-05-09T07:22:42.000Z

corelib.py

jaredvann/Untitled-Language

456f3a636d62028a85ee61eb3a9f04214f799e78

[ "MIT" ]

null

import ctypes import llvmlite.ir as ir from CodeGen import LLVMCodeGenerator from coretypes import * from scopes import Scope from typelib import * ZERO = ir.Constant(ir.IntType(64), 0) def _eq_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered("==", args[0], args[1]) def _neq_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered("!=", args[0], args[1]) def _eq_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed("==", args[0], args[1]) def _neq_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed("!=", args[0], args[1]) def _lt_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered("<", args[0], args[1]) def _gt_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered(">", args[0], args[1]) def _lte_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered("<=", args[0], args[1]) def _gte_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered(">=", args[0], args[1]) def _lt_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed("<", args[0], args[1]) def _gt_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed(">", args[0], args[1]) def _lte_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed("<=", args[0], args[1]) def _gte_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed(">=", args[0], args[1]) def _add_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.add(args[0], args[1]) def _sub_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.sub(args[0], args[1]) def _mul_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.mul(args[0], args[1]) def _div_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.sdiv(args[0], args[1]) def _rem_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.srem(args[0], args[1]) def _add_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fadd(args[0], args[1]) def _sub_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fsub(args[0], args[1]) def _mul_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fmul(args[0], args[1]) def _div_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fdiv(args[0], args[1]) def _rem_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.frem(args[0], args[1]) def _pow_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.call(ir.Function(cg.module, ir.FunctionType(arg_types[0], arg_types), "pow"), args) def _abs_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.call(ir.Function(cg.module, ir.FunctionType(arg_types[0], arg_types), "fabs"), args) def _index(cg: LLVMCodeGenerator, args, arg_types): arr_ptr, index = args return cg.builder.gep(arr_ptr, [ZERO, index]) scope = Scope() scope.add_type(Array) scope.add_type(Bool) scope.add_type(Float) scope.add_type(Int) # Equalities scope.add_function(FunctionType("==", [Bool, Bool], Bool, _eq_Int)) scope.add_function(FunctionType("!=", [Bool, Bool], Bool, _neq_Int)) scope.add_function(FunctionType("==", [Float, Float], Bool, _eq_Float)) scope.add_function(FunctionType("!=", [Float, Float], Bool, _neq_Float)) scope.add_function(FunctionType("==", [Int, Int], Bool, _eq_Int)) scope.add_function(FunctionType("!=", [Int, Int], Bool, _neq_Int)) # Comparisons scope.add_function(FunctionType("<", [Float, Float], Bool, _lt_Float)) scope.add_function(FunctionType(">", [Float, Float], Bool, _gt_Float)) scope.add_function(FunctionType("<=", [Float, Float], Bool, _lte_Float)) scope.add_function(FunctionType(">=", [Float, Float], Bool, _gte_Float)) scope.add_function(FunctionType("<", [Int, Int], Bool, _lt_Int)) scope.add_function(FunctionType(">", [Int, Int], Bool, _gt_Int)) scope.add_function(FunctionType("<=", [Int, Int], Bool, _lte_Int)) scope.add_function(FunctionType(">=", [Int, Int], Bool, _gte_Int)) # Math Operators scope.add_function(FunctionType("+", [Float, Float], Float, _add_Float)) scope.add_function(FunctionType("-", [Float, Float], Float, _sub_Float)) scope.add_function(FunctionType("*", [Float, Float], Float, _mul_Float)) scope.add_function(FunctionType("/", [Float, Float], Float, _div_Float)) scope.add_function(FunctionType("%", [Float, Float], Float, _rem_Float)) scope.add_function(FunctionType("^", [Float, Float], Float, _pow_Float)) scope.add_function(FunctionType("+", [Int, Int], Int, _add_Int)) scope.add_function(FunctionType("-", [Int, Int], Int, _sub_Int)) scope.add_function(FunctionType("*", [Int, Int], Int, _mul_Int)) scope.add_function(FunctionType("/", [Int, Int], Int, _div_Int)) scope.add_function(FunctionType("%", [Int, Int], Int, _rem_Int)) # Math Functions scope.add_function(FunctionType("abs", [Float], Float, _abs_Float, no_mangle=True)) scope.add_function(FunctionType("floor", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("ceil", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("round", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("sqrt", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("exp", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("log", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("log10", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("log2", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("sin", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("cos", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("tan", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("index", [Type("Array", [Bool], ['N']), Int], Bool.to_ref(), _index)) scope.add_function(FunctionType("index", [Type("Array", [Int], ['N']), Int], Int.to_ref(), _index)) scope.add_function(FunctionType("index", [Type("Array", [Float], ['N']), Int], Float.to_ref(), _index)) scope.add_function(FunctionType("putchar", [Int], Int, no_mangle=True)) # scope.add_function(FunctionType("sum", [Type("Array", [Int], ['N'])], Int))

39.19375

106

0.721735

import ctypes import llvmlite.ir as ir from CodeGen import LLVMCodeGenerator from coretypes import * from scopes import Scope from typelib import * ZERO = ir.Constant(ir.IntType(64), 0) def _eq_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered("==", args[0], args[1]) def _neq_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered("!=", args[0], args[1]) def _eq_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed("==", args[0], args[1]) def _neq_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed("!=", args[0], args[1]) def _lt_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered("<", args[0], args[1]) def _gt_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered(">", args[0], args[1]) def _lte_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered("<=", args[0], args[1]) def _gte_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fcmp_ordered(">=", args[0], args[1]) def _lt_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed("<", args[0], args[1]) def _gt_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed(">", args[0], args[1]) def _lte_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed("<=", args[0], args[1]) def _gte_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.icmp_signed(">=", args[0], args[1]) def _add_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.add(args[0], args[1]) def _sub_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.sub(args[0], args[1]) def _mul_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.mul(args[0], args[1]) def _div_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.sdiv(args[0], args[1]) def _rem_Int(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.srem(args[0], args[1]) def _add_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fadd(args[0], args[1]) def _sub_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fsub(args[0], args[1]) def _mul_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fmul(args[0], args[1]) def _div_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.fdiv(args[0], args[1]) def _rem_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.frem(args[0], args[1]) def _pow_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.call(ir.Function(cg.module, ir.FunctionType(arg_types[0], arg_types), "pow"), args) def _abs_Float(cg: LLVMCodeGenerator, args, arg_types): return cg.builder.call(ir.Function(cg.module, ir.FunctionType(arg_types[0], arg_types), "fabs"), args) def _index(cg: LLVMCodeGenerator, args, arg_types): arr_ptr, index = args return cg.builder.gep(arr_ptr, [ZERO, index]) scope = Scope() scope.add_type(Array) scope.add_type(Bool) scope.add_type(Float) scope.add_type(Int) scope.add_function(FunctionType("==", [Bool, Bool], Bool, _eq_Int)) scope.add_function(FunctionType("!=", [Bool, Bool], Bool, _neq_Int)) scope.add_function(FunctionType("==", [Float, Float], Bool, _eq_Float)) scope.add_function(FunctionType("!=", [Float, Float], Bool, _neq_Float)) scope.add_function(FunctionType("==", [Int, Int], Bool, _eq_Int)) scope.add_function(FunctionType("!=", [Int, Int], Bool, _neq_Int)) scope.add_function(FunctionType("<", [Float, Float], Bool, _lt_Float)) scope.add_function(FunctionType(">", [Float, Float], Bool, _gt_Float)) scope.add_function(FunctionType("<=", [Float, Float], Bool, _lte_Float)) scope.add_function(FunctionType(">=", [Float, Float], Bool, _gte_Float)) scope.add_function(FunctionType("<", [Int, Int], Bool, _lt_Int)) scope.add_function(FunctionType(">", [Int, Int], Bool, _gt_Int)) scope.add_function(FunctionType("<=", [Int, Int], Bool, _lte_Int)) scope.add_function(FunctionType(">=", [Int, Int], Bool, _gte_Int)) scope.add_function(FunctionType("+", [Float, Float], Float, _add_Float)) scope.add_function(FunctionType("-", [Float, Float], Float, _sub_Float)) scope.add_function(FunctionType("*", [Float, Float], Float, _mul_Float)) scope.add_function(FunctionType("/", [Float, Float], Float, _div_Float)) scope.add_function(FunctionType("%", [Float, Float], Float, _rem_Float)) scope.add_function(FunctionType("^", [Float, Float], Float, _pow_Float)) scope.add_function(FunctionType("+", [Int, Int], Int, _add_Int)) scope.add_function(FunctionType("-", [Int, Int], Int, _sub_Int)) scope.add_function(FunctionType("*", [Int, Int], Int, _mul_Int)) scope.add_function(FunctionType("/", [Int, Int], Int, _div_Int)) scope.add_function(FunctionType("%", [Int, Int], Int, _rem_Int)) scope.add_function(FunctionType("abs", [Float], Float, _abs_Float, no_mangle=True)) scope.add_function(FunctionType("floor", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("ceil", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("round", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("sqrt", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("exp", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("log", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("log10", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("log2", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("sin", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("cos", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("tan", [Float], Float, no_mangle=True)) scope.add_function(FunctionType("index", [Type("Array", [Bool], ['N']), Int], Bool.to_ref(), _index)) scope.add_function(FunctionType("index", [Type("Array", [Int], ['N']), Int], Int.to_ref(), _index)) scope.add_function(FunctionType("index", [Type("Array", [Float], ['N']), Int], Float.to_ref(), _index)) scope.add_function(FunctionType("putchar", [Int], Int, no_mangle=True))

true

f7297760253eb2b85eb98ece71815fc89a9a9ce5

16

py

Python

mp_sort/virtenv/lib/python3.6/site-packages/transcrypt/development/manual_tests/incidental_tests/indirect_import_error/main.py

ang-jason/fip_powerx_mini_projects-foxtrot

37e3671969b516369e2d1c7cab5890b75c489f56

[ "MIT" ]

2,200

2016-10-12T16:47:13.000Z

2022-03-30T16:40:35.000Z

mp_sort/virtenv/lib/python3.6/site-packages/transcrypt/development/manual_tests/incidental_tests/indirect_import_error/main.py

ang-jason/fip_powerx_mini_projects-foxtrot

37e3671969b516369e2d1c7cab5890b75c489f56

[ "MIT" ]

672

2016-10-12T16:36:48.000Z

2022-03-25T00:57:04.000Z

mp_sort/virtenv/lib/python3.6/site-packages/transcrypt/development/manual_tests/incidental_tests/indirect_import_error/main.py

ang-jason/fip_powerx_mini_projects-foxtrot

37e3671969b516369e2d1c7cab5890b75c489f56

[ "MIT" ]

230

2016-10-20T14:31:40.000Z

2022-03-16T15:57:15.000Z

import module1

8

15

0.8125

import module1

true

f72977954a5e1f601a1400481980aba248b7012f

736

py

Python

bird/tests/test_mdct_tools.py

mmoussallam/bird

6a362de7d3a52dfcddaed13e8c736d039b03fbb4

[ "BSD-3-Clause" ]

11

2015-02-02T21:41:41.000Z

2022-03-12T17:23:01.000Z

bird/tests/test_mdct_tools.py

mmoussallam/bird

6a362de7d3a52dfcddaed13e8c736d039b03fbb4

[ "BSD-3-Clause" ]

1

2021-01-03T20:45:36.000Z

2021-01-04T16:02:49.000Z

bird/tests/test_mdct_tools.py

mmoussallam/bird

6a362de7d3a52dfcddaed13e8c736d039b03fbb4

[ "BSD-3-Clause" ]

5

2016-04-06T20:42:27.000Z

2021-01-03T20:42:53.000Z

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Manuel Moussallam <manuel.moussallam@gmail.com> # # License: BSD (3-clause) import numpy as np from numpy.testing import assert_array_almost_equal from bird.mdct_tools import mdct, imdct def test_mdct(): "Test mdct and imdct tight frame property" sfreq = 1000. # Hz f = 7. # Hz x1 = np.sin(2. * np.pi * f * np.arange(128, dtype=float) / sfreq) x2 = np.sin(2. * np.pi * f * np.arange(512, dtype=float) / sfreq) rng = np.random.RandomState(42) x3 = rng.standard_normal(x1.shape) wsize = 32 for x in [x1, x2, x3]: X = mdct(x, wsize) xp = imdct(X, wsize) assert_array_almost_equal(x, xp, decimal=12)

26.285714

69

0.638587

import numpy as np from numpy.testing import assert_array_almost_equal from bird.mdct_tools import mdct, imdct def test_mdct(): sfreq = 1000. f = 7. x1 = np.sin(2. * np.pi * f * np.arange(128, dtype=float) / sfreq) x2 = np.sin(2. * np.pi * f * np.arange(512, dtype=float) / sfreq) rng = np.random.RandomState(42) x3 = rng.standard_normal(x1.shape) wsize = 32 for x in [x1, x2, x3]: X = mdct(x, wsize) xp = imdct(X, wsize) assert_array_almost_equal(x, xp, decimal=12)

true

f729799211cb994510bf480b3445854fbf4b7f51

230

py

Python

worms/criteria/__init__.py

abiedermann/worms

026c45a88d5c71b0e035ac83de6f4dc107316ed8

[ "Apache-2.0" ]

4

2018-01-30T23:13:43.000Z

2021-02-12T22:36:54.000Z

worms/criteria/__init__.py

abiedermann/worms

026c45a88d5c71b0e035ac83de6f4dc107316ed8

[ "Apache-2.0" ]

9

2018-02-23T00:52:25.000Z

2022-01-26T00:02:32.000Z

worms/criteria/__init__.py

abiedermann/worms

026c45a88d5c71b0e035ac83de6f4dc107316ed8

[ "Apache-2.0" ]

4

2018-06-28T21:30:14.000Z

2022-03-30T17:50:42.000Z

from .base import * from .hash_util import * from .cyclic import * from .bounded import * from .unbounded import * from .dihedral_lattice import * from .null import * from .bridge import * from .stack import * from .f222 import *

20.909091

31

0.73913

from .base import * from .hash_util import * from .cyclic import * from .bounded import * from .unbounded import * from .dihedral_lattice import * from .null import * from .bridge import * from .stack import * from .f222 import *

true

f7297b112e68f422996bc6c06de8e71039f120a1

3,024

py

Python

2018/day12/puzzle2.py

tcmitchell/AdventOfCode

caaac1aa37c999d4804f9f4154bf7033a06e98af

[ "MIT" ]

null

2018/day12/puzzle2.py

tcmitchell/AdventOfCode

caaac1aa37c999d4804f9f4154bf7033a06e98af

[ "MIT" ]

null

2018/day12/puzzle2.py

tcmitchell/AdventOfCode

caaac1aa37c999d4804f9f4154bf7033a06e98af

[ "MIT" ]

null

import argparse import collections import datetime import itertools import logging import re import sys import time # 500 17458 # 5000 179458 # 50000 1799458 # 50B 1799999999458 def parse_args(args): parser = argparse.ArgumentParser() parser.add_argument("input", type=argparse.FileType('r'), metavar="PUZZLE_INPUT") parser.add_argument('-d', '--debug', action='store_true') args = parser.parse_args() return args def init_logging(debug=False): msgFormat = '%(asctime)s %(levelname)s %(message)s' dateFormat = '%m/%d/%Y %H:%M:%S' level = logging.INFO if debug: level = logging.DEBUG logging.basicConfig(format=msgFormat, datefmt=dateFormat, level=level) def load_input(fp): pots = fp.readline().strip().split()[2] fp.readline() rules = [line.strip().split() for line in fp] rules = {r[0]: r[2] for r in rules} return pots, rules def next_gen(rules, pots): pots = ''.join(pots) if pots in rules: # logging.debug('{} => {}'.format(pots, rules[pots])) return rules[pots] else: # logging.debug('{} => . *'.format(pots)) return '.' # Dynamically pad on both left and right. # Track left padding for subtraction in final computation def main(argv): if not argv: argv = sys.argv args = parse_args(argv) # Init logging init_logging(args.debug) pots, rules = load_input(args.input) logging.info('pots: {}'.format(pots)) logging.info('rules: {}'.format(rules)) # Convert pots to a list so elements can be replaced pots = list(pots) lpadding = 0 extension_size = 3 # Maybe pad the pots on either side if '#' in pots[:3]: for i in range(extension_size): pots.insert(0, '.') lpadding += extension_size if '#' in pots[-3:]: pots.extend(['.'] * extension_size) # logging.debug(' 0: {}'.format(''.join(pots))) generations = 50000000000 generations = 500 generations = 5000 generations = 50000 # generations = 20 for gen in range(generations): new_pots = [pots[0], pots[1]] for i in range(len(pots) - 4): new_pots.append(next_gen(rules, pots[i:i + 5])) new_pots.extend(pots[-2:]) pots = new_pots # Maybe pad the pots on either side if '#' in pots[:3]: for i in range(extension_size): pots.insert(0, '.') lpadding += extension_size if '#' in pots[-3:]: pots.extend(['.'] * extension_size) # if gen % 10000 == 0: # logging.debug(gen) # logging.debug('{: >2d}: {}'.format(gen + 1, ''.join(pots))) logging.debug('pots length = {}'.format(len(pots))) potsum = 0 for i in range(len(pots)): if pots[i] == '#': logging.debug('pot[{}] has a plant'.format(i - 20)) potsum += i - lpadding print('Answer is {}'.format(potsum)) if __name__ == '__main__': main(sys.argv)

26.068966

74

0.585317

import argparse import collections import datetime import itertools import logging import re import sys import time def parse_args(args): parser = argparse.ArgumentParser() parser.add_argument("input", type=argparse.FileType('r'), metavar="PUZZLE_INPUT") parser.add_argument('-d', '--debug', action='store_true') args = parser.parse_args() return args def init_logging(debug=False): msgFormat = '%(asctime)s %(levelname)s %(message)s' dateFormat = '%m/%d/%Y %H:%M:%S' level = logging.INFO if debug: level = logging.DEBUG logging.basicConfig(format=msgFormat, datefmt=dateFormat, level=level) def load_input(fp): pots = fp.readline().strip().split()[2] fp.readline() rules = [line.strip().split() for line in fp] rules = {r[0]: r[2] for r in rules} return pots, rules def next_gen(rules, pots): pots = ''.join(pots) if pots in rules: return rules[pots] else: return '.' def main(argv): if not argv: argv = sys.argv args = parse_args(argv) init_logging(args.debug) pots, rules = load_input(args.input) logging.info('pots: {}'.format(pots)) logging.info('rules: {}'.format(rules)) pots = list(pots) lpadding = 0 extension_size = 3 if '#' in pots[:3]: for i in range(extension_size): pots.insert(0, '.') lpadding += extension_size if '#' in pots[-3:]: pots.extend(['.'] * extension_size) generations = 50000000000 generations = 500 generations = 5000 generations = 50000 for gen in range(generations): new_pots = [pots[0], pots[1]] for i in range(len(pots) - 4): new_pots.append(next_gen(rules, pots[i:i + 5])) new_pots.extend(pots[-2:]) pots = new_pots if '#' in pots[:3]: for i in range(extension_size): pots.insert(0, '.') lpadding += extension_size if '#' in pots[-3:]: pots.extend(['.'] * extension_size) logging.debug('pots length = {}'.format(len(pots))) potsum = 0 for i in range(len(pots)): if pots[i] == '#': logging.debug('pot[{}] has a plant'.format(i - 20)) potsum += i - lpadding print('Answer is {}'.format(potsum)) if __name__ == '__main__': main(sys.argv)

true

f7297b3454c8f6b937a03623d2c2441074999c25

4,066

py

Python

src/aks-preview/azext_aks_preview/_completers.py

santosh02iiit/azure-cli-extensions

24247cfa19e2a5894937f19e17fbdc8308b28ef6

[ "MIT" ]

1

2021-08-03T18:32:54.000Z

src/aks-preview/azext_aks_preview/_completers.py

santosh02iiit/azure-cli-extensions

24247cfa19e2a5894937f19e17fbdc8308b28ef6

[ "MIT" ]

4

2020-09-07T12:56:24.000Z

2021-02-04T12:19:20.000Z

src/aks-preview/azext_aks_preview/_completers.py

santosh02iiit/azure-cli-extensions

24247cfa19e2a5894937f19e17fbdc8308b28ef6

[ "MIT" ]

null

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- from azure.cli.core.commands.parameters import get_one_of_subscription_locations from azure.cli.core.decorators import Completer # pylint: disable=line-too-long from azext_aks_preview.vendored_sdks.azure_mgmt_preview_aks.v2021_07_01.models import ContainerServiceVMSizeTypes @Completer def get_k8s_upgrades_completion_list(cmd, prefix, namespace, **kwargs): # pylint: disable=unused-argument """Return Kubernetes versions available for upgrading an existing cluster.""" resource_group = getattr(namespace, 'resource_group_name', None) name = getattr(namespace, 'name', None) return get_k8s_upgrades(cmd.cli_ctx, resource_group, name) if resource_group and name else None def get_k8s_upgrades(cli_ctx, resource_group, name): from ._client_factory import cf_managed_clusters results = cf_managed_clusters(cli_ctx).get_upgrade_profile(resource_group, name).as_dict() return results['control_plane_profile']['upgrades'] @Completer def get_k8s_versions_completion_list(cmd, prefix, namespace, **kwargs): # pylint: disable=unused-argument """Return Kubernetes versions available for provisioning a new cluster.""" location = _get_location(cmd.cli_ctx, namespace) return get_k8s_versions(cmd.cli_ctx, location) if location else None def get_k8s_versions(cli_ctx, location): """Return a list of Kubernetes versions available for a new cluster.""" from ._client_factory import cf_container_services from jmespath import search # pylint: disable=import-error results = cf_container_services(cli_ctx).list_orchestrators(location, resource_type='managedClusters').as_dict() # Flatten all the "orchestrator_version" fields into one array return search('orchestrators[*].orchestrator_version', results) @Completer def get_vm_size_completion_list(cmd, prefix, namespace, **kwargs): # pylint: disable=unused-argument """Return the intersection of the VM sizes allowed by the ACS SDK with those returned by the Compute Service.""" location = _get_location(cmd.cli_ctx, namespace) result = get_vm_sizes(cmd.cli_ctx, location) return set(r.name for r in result) & set(c.value for c in ContainerServiceVMSizeTypes) def get_vm_sizes(cli_ctx, location): from ._client_factory import cf_compute_service return cf_compute_service(cli_ctx).virtual_machine_sizes.list(location) @Completer def get_ossku_completion_list(cmd, prefix, namespace, **kwargs): # pylint: disable=unused-argument """Return the list of allowed os-sku values""" return ["Ubuntu", "CBLMariner"] def _get_location(cli_ctx, namespace): """ Return an Azure location by using an explicit `--location` argument, then by `--resource-group`, and finally by the subscription if neither argument was provided. """ location = None if getattr(namespace, 'location', None): location = namespace.location elif getattr(namespace, 'resource_group_name', None): location = _get_location_from_resource_group(cli_ctx, namespace.resource_group_name) if not location: location = get_one_of_subscription_locations(cli_ctx) return location def _get_location_from_resource_group(cli_ctx, resource_group_name): from ._client_factory import cf_resource_groups from msrestazure.azure_exceptions import CloudError try: rg = cf_resource_groups(cli_ctx).get(resource_group_name) return rg.location except CloudError as err: # Print a warning if the user hit [TAB] but the `--resource-group` argument was incorrect. # For example: "Warning: Resource group 'bogus' could not be found." from argcomplete import warn warn('Warning: {}'.format(err.message))

43.255319

116

0.727496

from azure.cli.core.commands.parameters import get_one_of_subscription_locations from azure.cli.core.decorators import Completer from azext_aks_preview.vendored_sdks.azure_mgmt_preview_aks.v2021_07_01.models import ContainerServiceVMSizeTypes @Completer def get_k8s_upgrades_completion_list(cmd, prefix, namespace, **kwargs): resource_group = getattr(namespace, 'resource_group_name', None) name = getattr(namespace, 'name', None) return get_k8s_upgrades(cmd.cli_ctx, resource_group, name) if resource_group and name else None def get_k8s_upgrades(cli_ctx, resource_group, name): from ._client_factory import cf_managed_clusters results = cf_managed_clusters(cli_ctx).get_upgrade_profile(resource_group, name).as_dict() return results['control_plane_profile']['upgrades'] @Completer def get_k8s_versions_completion_list(cmd, prefix, namespace, **kwargs): location = _get_location(cmd.cli_ctx, namespace) return get_k8s_versions(cmd.cli_ctx, location) if location else None def get_k8s_versions(cli_ctx, location): from ._client_factory import cf_container_services from jmespath import search results = cf_container_services(cli_ctx).list_orchestrators(location, resource_type='managedClusters').as_dict() return search('orchestrators[*].orchestrator_version', results) @Completer def get_vm_size_completion_list(cmd, prefix, namespace, **kwargs): location = _get_location(cmd.cli_ctx, namespace) result = get_vm_sizes(cmd.cli_ctx, location) return set(r.name for r in result) & set(c.value for c in ContainerServiceVMSizeTypes) def get_vm_sizes(cli_ctx, location): from ._client_factory import cf_compute_service return cf_compute_service(cli_ctx).virtual_machine_sizes.list(location) @Completer def get_ossku_completion_list(cmd, prefix, namespace, **kwargs): return ["Ubuntu", "CBLMariner"] def _get_location(cli_ctx, namespace): location = None if getattr(namespace, 'location', None): location = namespace.location elif getattr(namespace, 'resource_group_name', None): location = _get_location_from_resource_group(cli_ctx, namespace.resource_group_name) if not location: location = get_one_of_subscription_locations(cli_ctx) return location def _get_location_from_resource_group(cli_ctx, resource_group_name): from ._client_factory import cf_resource_groups from msrestazure.azure_exceptions import CloudError try: rg = cf_resource_groups(cli_ctx).get(resource_group_name) return rg.location except CloudError as err: from argcomplete import warn warn('Warning: {}'.format(err.message))

true

f7297b8786de15ec51495c45a86897ab6e14ca5f

1,167

py

Python

activators/src/motdactivator.py

alexandruavadanii/cm-plugins

5c3f9f389f46f719579ac4cd4065490b1723ebff

[ "Apache-2.0" ]

null

activators/src/motdactivator.py

alexandruavadanii/cm-plugins

5c3f9f389f46f719579ac4cd4065490b1723ebff

[ "Apache-2.0" ]

null

activators/src/motdactivator.py

alexandruavadanii/cm-plugins

5c3f9f389f46f719579ac4cd4065490b1723ebff

[ "Apache-2.0" ]

1

2021-04-24T16:48:17.000Z

#! /usr/bin/python # Copyright 2019 Nokia # # 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 cmframework.apis import cmactivator class motdactivator(cmactivator.CMGlobalActivator): playbook = '/opt/openstack-ansible/playbooks/motd.yml' def __init__(self): super(motdactivator, self).__init__() def get_subscription_info(self): return 'cloud.motd' def activate_set(self, props): self._activate() def activate_delete(self, props): self._activate() def activate_full(self, target): self._activate(target=target) def _activate(self, target=None): self.run_playbook(self.playbook, target)

30.710526

74

0.725793

from cmframework.apis import cmactivator class motdactivator(cmactivator.CMGlobalActivator): playbook = '/opt/openstack-ansible/playbooks/motd.yml' def __init__(self): super(motdactivator, self).__init__() def get_subscription_info(self): return 'cloud.motd' def activate_set(self, props): self._activate() def activate_delete(self, props): self._activate() def activate_full(self, target): self._activate(target=target) def _activate(self, target=None): self.run_playbook(self.playbook, target)

true

f7297bf8b18239cca9640934586a3c6e0d1b0006

563

py

Python

code/202011/20201119_283_moveZeroes.py

ace7chan/leetcode-daily

d07fe58fdb635dfa2a092515e8e235c476826b09

[ "MIT" ]

null

code/202011/20201119_283_moveZeroes.py

ace7chan/leetcode-daily

d07fe58fdb635dfa2a092515e8e235c476826b09

[ "MIT" ]

null

code/202011/20201119_283_moveZeroes.py

ace7chan/leetcode-daily

d07fe58fdb635dfa2a092515e8e235c476826b09

[ "MIT" ]

null

from typing import List class Solution: def moveZeroes(self, nums: List[int]) -> None: """ Do not return anything, modify nums in-place instead. """ cur_idx = 0 for num in nums: if num == 0: continue nums[cur_idx] = num cur_idx += 1 while cur_idx < len(nums): nums[cur_idx] = 0 cur_idx += 1 # return nums if __name__ == '__main__': solution = Solution() nums = [0, 1, 0, 3, 12] print(solution.moveZeroes(nums))

22.52

61

0.50444

from typing import List class Solution: def moveZeroes(self, nums: List[int]) -> None: cur_idx = 0 for num in nums: if num == 0: continue nums[cur_idx] = num cur_idx += 1 while cur_idx < len(nums): nums[cur_idx] = 0 cur_idx += 1 if __name__ == '__main__': solution = Solution() nums = [0, 1, 0, 3, 12] print(solution.moveZeroes(nums))

true

f7297e07cf922dfa791b82c22a538dc4f2b6e22c

1,588

py

Python

misc/zkbreaker.py

hubo1016/vlcp

61c4c2595b610675ac0cbc4dbc46f70ec40090d3

[ "Apache-2.0" ]

252

2015-11-17T14:21:50.000Z

2022-03-11T10:19:47.000Z

misc/zkbreaker.py

SarahZarei/vlcp

61c4c2595b610675ac0cbc4dbc46f70ec40090d3

[ "Apache-2.0" ]

23

2018-01-09T13:28:52.000Z

2019-12-12T06:11:44.000Z

misc/zkbreaker.py

SarahZarei/vlcp

61c4c2595b610675ac0cbc4dbc46f70ec40090d3

[ "Apache-2.0" ]

37

2016-08-03T04:42:22.000Z

2021-12-30T16:57:10.000Z

''' Created on 2016/10/25 :author: hubo ''' from vlcp.config import config from vlcp.protocol.zookeeper import ZooKeeper import vlcp.protocol.zookeeper from random import random from vlcp.event.core import syscall_clearqueue from logging import getLogger _logger = getLogger(__name__) @config('protocol.zookeeper') class BreakingZooKeeper(ZooKeeper): ''' This evil protocol breaks ZooKeeper connection from time to time to validate your client and service code ''' _default_senddrop = 0.001 _default_receivedrop = 0.01 async def _senddata(self, connection, data, container, priority = 0): if random() < self.senddrop: _logger.warning("Oops, I break a connection when sending") await connection.reset(True) return await ZooKeeper._senddata(self, connection, data, container, priority) async def requests(self, connection, requests, container, callback=None, priority = 0): def evil_callback(request, response): if random() < self.receivedrop: _logger.warning("Oops, I break a connection when receiving") connection.subroutine(connection.reset(True), False) connection.subroutine(connection.syscall_noreturn(syscall_clearqueue(connection.scheduler.queue[('message', connection)]))) if callback: callback(request, response) return await ZooKeeper.requests(self, connection, requests, container, evil_callback, priority) def patch_zookeeper(): vlcp.protocol.zookeeper.ZooKeeper = BreakingZooKeeper

34.521739

139

0.707809

from vlcp.config import config from vlcp.protocol.zookeeper import ZooKeeper import vlcp.protocol.zookeeper from random import random from vlcp.event.core import syscall_clearqueue from logging import getLogger _logger = getLogger(__name__) @config('protocol.zookeeper') class BreakingZooKeeper(ZooKeeper): _default_senddrop = 0.001 _default_receivedrop = 0.01 async def _senddata(self, connection, data, container, priority = 0): if random() < self.senddrop: _logger.warning("Oops, I break a connection when sending") await connection.reset(True) return await ZooKeeper._senddata(self, connection, data, container, priority) async def requests(self, connection, requests, container, callback=None, priority = 0): def evil_callback(request, response): if random() < self.receivedrop: _logger.warning("Oops, I break a connection when receiving") connection.subroutine(connection.reset(True), False) connection.subroutine(connection.syscall_noreturn(syscall_clearqueue(connection.scheduler.queue[('message', connection)]))) if callback: callback(request, response) return await ZooKeeper.requests(self, connection, requests, container, evil_callback, priority) def patch_zookeeper(): vlcp.protocol.zookeeper.ZooKeeper = BreakingZooKeeper

true

f7297e1604cfd737ba606eafbf1451eb0a2c2972

1,504

py

Python

test/functional/feature_blocksdir.py

fancywarlock/bitcoinr

12b4dee6342556c0890218b843f29cadfab06214

[ "MIT" ]

2

2020-05-31T01:06:06.000Z

2021-06-07T22:29:32.000Z

test/functional/feature_blocksdir.py

fancywarlock/bitcoinr

12b4dee6342556c0890218b843f29cadfab06214

[ "MIT" ]

null

test/functional/feature_blocksdir.py

fancywarlock/bitcoinr

12b4dee6342556c0890218b843f29cadfab06214

[ "MIT" ]

3

2020-09-24T16:46:45.000Z

2021-06-07T22:29:33.000Z

#!/usr/bin/env python3 # Copyright (c) 2018 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the blocksdir option. """ import os import shutil from test_framework.test_framework import bitcoinRTestFramework, initialize_datadir class BlocksdirTest(bitcoinRTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 def skip_test_if_missing_module(self): self.skip_if_no_wallet() def run_test(self): self.stop_node(0) shutil.rmtree(self.nodes[0].datadir) initialize_datadir(self.options.tmpdir, 0) self.log.info("Starting with non exiting blocksdir ...") blocksdir_path = os.path.join(self.options.tmpdir, 'blocksdir') self.nodes[0].assert_start_raises_init_error(["-blocksdir=" + blocksdir_path], 'Error: Specified blocks directory "{}" does not exist.'.format(blocksdir_path)) os.mkdir(blocksdir_path) self.log.info("Starting with exiting blocksdir ...") self.start_node(0, ["-blocksdir=" + blocksdir_path]) self.log.info("mining blocks..") self.nodes[0].generate(10) assert os.path.isfile(os.path.join(blocksdir_path, "regtest", "blocks", "blk00000.dat")) assert os.path.isdir(os.path.join(self.nodes[0].datadir, "regtest", "blocks", "index")) if __name__ == '__main__': BlocksdirTest().main()

37.6

167

0.698803

import os import shutil from test_framework.test_framework import bitcoinRTestFramework, initialize_datadir class BlocksdirTest(bitcoinRTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 def skip_test_if_missing_module(self): self.skip_if_no_wallet() def run_test(self): self.stop_node(0) shutil.rmtree(self.nodes[0].datadir) initialize_datadir(self.options.tmpdir, 0) self.log.info("Starting with non exiting blocksdir ...") blocksdir_path = os.path.join(self.options.tmpdir, 'blocksdir') self.nodes[0].assert_start_raises_init_error(["-blocksdir=" + blocksdir_path], 'Error: Specified blocks directory "{}" does not exist.'.format(blocksdir_path)) os.mkdir(blocksdir_path) self.log.info("Starting with exiting blocksdir ...") self.start_node(0, ["-blocksdir=" + blocksdir_path]) self.log.info("mining blocks..") self.nodes[0].generate(10) assert os.path.isfile(os.path.join(blocksdir_path, "regtest", "blocks", "blk00000.dat")) assert os.path.isdir(os.path.join(self.nodes[0].datadir, "regtest", "blocks", "index")) if __name__ == '__main__': BlocksdirTest().main()

true

f7297e96bfe1de8e74a8448f1f7dfe04aa9d2b63

6,235

py

Python

tests/test_stac.py

stactools-packages/cop-dem

25a4bb69eb60caa339e11b8293291cdc192e2de8

[ "Apache-2.0" ]

null

tests/test_stac.py

stactools-packages/cop-dem

25a4bb69eb60caa339e11b8293291cdc192e2de8

[ "Apache-2.0" ]

3

2021-07-29T16:58:52.000Z

2021-08-12T18:18:42.000Z

tests/test_stac.py

stactools-packages/cop-dem

25a4bb69eb60caa339e11b8293291cdc192e2de8

[ "Apache-2.0" ]

1

2021-08-05T23:17:51.000Z

import datetime from unittest import TestCase from pystac import Provider, MediaType from pystac.extensions.projection import ProjectionExtension from pystac.provider import ProviderRole from stactools.cop_dem import stac from tests import test_data class StacTest(TestCase): def setUp(self): self.glo30_path = test_data.get_external_data( "Copernicus_DSM_COG_10_N53_00_W115_00_DEM.tif") self.glo90_path = test_data.get_external_data( "Copernicus_DSM_COG_30_N53_00_W115_00_DEM.tif") def test_create_glo30_item(self): item = stac.create_item(self.glo30_path) self.assertEqual(item.id, "Copernicus_DSM_COG_10_N53_00_W115_00_DEM") self.assertIsNotNone(item.geometry) self.assertEqual(list(item.bbox), [ -115.00020833333333, 53.00013888888889, -114.00020833333333, 54.00013888888889 ]) self.assertEqual( item.datetime, datetime.datetime(2021, 4, 22, tzinfo=datetime.timezone.utc)) common_metadata = item.common_metadata self.assertEqual(common_metadata.platform, "TanDEM-X") self.assertEqual(common_metadata.gsd, 30) expected_providers = [ Provider("European Space Agency", roles=[ProviderRole.LICENSOR], url=("https://spacedata.copernicus.eu/documents/20126/0/" "CSCDA_ESA_Mission-specific+Annex.pdf")), Provider("Sinergise", roles=[ProviderRole.PRODUCER, ProviderRole.PROCESSOR], url="https://registry.opendata.aws/copernicus-dem/"), Provider("OpenTopography", roles=[ProviderRole.HOST], url=("https://portal.opentopography.org/" "datasetMetadata?otCollectionID=OT.032021.4326.1")) ] for expected, actual in zip(expected_providers, common_metadata.providers): self.assertDictEqual(expected.to_dict(), actual.to_dict()) self.assertEqual(common_metadata.license, "proprietary") projection = ProjectionExtension.ext(item) self.assertEqual(projection.epsg, 4326) self.assertEqual(projection.shape, (3600, 2400)) self.assertEqual(list(projection.transform), [ 0.00041666666666666664, 0.0, -115.00020833333333, 0.0, -0.0002777777777777778, 54.00013888888889 ]) handbook = item.get_single_link("handbook") self.assertIsNotNone(handbook) self.assertEqual(handbook.title, "Copernicus DEM User handbook") self.assertEqual(handbook.rel, "handbook") self.assertEqual( handbook.href, "https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata" "/Copernicus_metadata.pdf") self.assertEqual(handbook.media_type, "application/pdf") data = item.assets["data"] self.assertEqual(data.href, self.glo30_path) self.assertEqual(data.title, "N53_00_W115_00") self.assertIsNone(data.description) self.assertEqual(data.media_type, MediaType.COG) self.assertEqual(data.roles, ["data"]) item.validate() def test_create_glo90_item(self): item = stac.create_item(self.glo90_path) self.assertEqual(item.id, "Copernicus_DSM_COG_30_N53_00_W115_00_DEM") self.assertIsNotNone(item.geometry) self.assertEqual( list(item.bbox), [-115.000625, 53.000416666666666, -114.000625, 54.000416666666666]) self.assertEqual( item.datetime, datetime.datetime(2021, 4, 22, tzinfo=datetime.timezone.utc)) common_metadata = item.common_metadata self.assertEqual(common_metadata.platform, "TanDEM-X") self.assertEqual(common_metadata.gsd, 90) expected_providers = [ Provider("European Space Agency", roles=[ProviderRole.LICENSOR], url=("https://spacedata.copernicus.eu/documents/20126/0/" "CSCDA_ESA_Mission-specific+Annex.pdf")), Provider("Sinergise", roles=[ProviderRole.PRODUCER, ProviderRole.PROCESSOR], url="https://registry.opendata.aws/copernicus-dem/"), Provider("OpenTopography", roles=[ProviderRole.HOST], url=("https://portal.opentopography.org/" "datasetMetadata?otCollectionID=OT.032021.4326.1")) ] for expected, actual in zip(expected_providers, common_metadata.providers): self.assertDictEqual(expected.to_dict(), actual.to_dict()) self.assertEqual(common_metadata.license, "proprietary") projection = ProjectionExtension.ext(item) self.assertEqual(projection.epsg, 4326) self.assertEqual(projection.shape, (1200, 800)) self.assertEqual(list(projection.transform), [ 0.00125, 0.0, -115.000625, 0.0, -0.0008333333333333334, 54.000416666666666 ]) handbook = item.get_single_link("handbook") self.assertIsNotNone(handbook) self.assertEqual(handbook.title, "Copernicus DEM User handbook") self.assertEqual(handbook.rel, "handbook") self.assertEqual( handbook.href, "https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata" "/Copernicus_metadata.pdf") self.assertEqual(handbook.media_type, "application/pdf") data = item.assets["data"] self.assertEqual(data.href, self.glo90_path) self.assertEqual(data.title, "N53_00_W115_00") self.assertIsNone(data.description) self.assertEqual(data.media_type, MediaType.COG) self.assertEqual(data.roles, ["data"]) item.validate() def test_create_item_with_read_href_modifier(self): done = False def do_it(href): nonlocal done done = True return href _ = stac.create_item(self.glo30_path, read_href_modifier=do_it) self.assertTrue(done, "Didn't do it")

41.845638

79

0.631275

import datetime from unittest import TestCase from pystac import Provider, MediaType from pystac.extensions.projection import ProjectionExtension from pystac.provider import ProviderRole from stactools.cop_dem import stac from tests import test_data class StacTest(TestCase): def setUp(self): self.glo30_path = test_data.get_external_data( "Copernicus_DSM_COG_10_N53_00_W115_00_DEM.tif") self.glo90_path = test_data.get_external_data( "Copernicus_DSM_COG_30_N53_00_W115_00_DEM.tif") def test_create_glo30_item(self): item = stac.create_item(self.glo30_path) self.assertEqual(item.id, "Copernicus_DSM_COG_10_N53_00_W115_00_DEM") self.assertIsNotNone(item.geometry) self.assertEqual(list(item.bbox), [ -115.00020833333333, 53.00013888888889, -114.00020833333333, 54.00013888888889 ]) self.assertEqual( item.datetime, datetime.datetime(2021, 4, 22, tzinfo=datetime.timezone.utc)) common_metadata = item.common_metadata self.assertEqual(common_metadata.platform, "TanDEM-X") self.assertEqual(common_metadata.gsd, 30) expected_providers = [ Provider("European Space Agency", roles=[ProviderRole.LICENSOR], url=("https://spacedata.copernicus.eu/documents/20126/0/" "CSCDA_ESA_Mission-specific+Annex.pdf")), Provider("Sinergise", roles=[ProviderRole.PRODUCER, ProviderRole.PROCESSOR], url="https://registry.opendata.aws/copernicus-dem/"), Provider("OpenTopography", roles=[ProviderRole.HOST], url=("https://portal.opentopography.org/" "datasetMetadata?otCollectionID=OT.032021.4326.1")) ] for expected, actual in zip(expected_providers, common_metadata.providers): self.assertDictEqual(expected.to_dict(), actual.to_dict()) self.assertEqual(common_metadata.license, "proprietary") projection = ProjectionExtension.ext(item) self.assertEqual(projection.epsg, 4326) self.assertEqual(projection.shape, (3600, 2400)) self.assertEqual(list(projection.transform), [ 0.00041666666666666664, 0.0, -115.00020833333333, 0.0, -0.0002777777777777778, 54.00013888888889 ]) handbook = item.get_single_link("handbook") self.assertIsNotNone(handbook) self.assertEqual(handbook.title, "Copernicus DEM User handbook") self.assertEqual(handbook.rel, "handbook") self.assertEqual( handbook.href, "https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata" "/Copernicus_metadata.pdf") self.assertEqual(handbook.media_type, "application/pdf") data = item.assets["data"] self.assertEqual(data.href, self.glo30_path) self.assertEqual(data.title, "N53_00_W115_00") self.assertIsNone(data.description) self.assertEqual(data.media_type, MediaType.COG) self.assertEqual(data.roles, ["data"]) item.validate() def test_create_glo90_item(self): item = stac.create_item(self.glo90_path) self.assertEqual(item.id, "Copernicus_DSM_COG_30_N53_00_W115_00_DEM") self.assertIsNotNone(item.geometry) self.assertEqual( list(item.bbox), [-115.000625, 53.000416666666666, -114.000625, 54.000416666666666]) self.assertEqual( item.datetime, datetime.datetime(2021, 4, 22, tzinfo=datetime.timezone.utc)) common_metadata = item.common_metadata self.assertEqual(common_metadata.platform, "TanDEM-X") self.assertEqual(common_metadata.gsd, 90) expected_providers = [ Provider("European Space Agency", roles=[ProviderRole.LICENSOR], url=("https://spacedata.copernicus.eu/documents/20126/0/" "CSCDA_ESA_Mission-specific+Annex.pdf")), Provider("Sinergise", roles=[ProviderRole.PRODUCER, ProviderRole.PROCESSOR], url="https://registry.opendata.aws/copernicus-dem/"), Provider("OpenTopography", roles=[ProviderRole.HOST], url=("https://portal.opentopography.org/" "datasetMetadata?otCollectionID=OT.032021.4326.1")) ] for expected, actual in zip(expected_providers, common_metadata.providers): self.assertDictEqual(expected.to_dict(), actual.to_dict()) self.assertEqual(common_metadata.license, "proprietary") projection = ProjectionExtension.ext(item) self.assertEqual(projection.epsg, 4326) self.assertEqual(projection.shape, (1200, 800)) self.assertEqual(list(projection.transform), [ 0.00125, 0.0, -115.000625, 0.0, -0.0008333333333333334, 54.000416666666666 ]) handbook = item.get_single_link("handbook") self.assertIsNotNone(handbook) self.assertEqual(handbook.title, "Copernicus DEM User handbook") self.assertEqual(handbook.rel, "handbook") self.assertEqual( handbook.href, "https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata" "/Copernicus_metadata.pdf") self.assertEqual(handbook.media_type, "application/pdf") data = item.assets["data"] self.assertEqual(data.href, self.glo90_path) self.assertEqual(data.title, "N53_00_W115_00") self.assertIsNone(data.description) self.assertEqual(data.media_type, MediaType.COG) self.assertEqual(data.roles, ["data"]) item.validate() def test_create_item_with_read_href_modifier(self): done = False def do_it(href): nonlocal done done = True return href _ = stac.create_item(self.glo30_path, read_href_modifier=do_it) self.assertTrue(done, "Didn't do it")

true

f7297f1d91ce8e6b39e55cc5ac6ecf497f9f71db

1,068

py

Python

example_workspace/inverted_hierarchy/model.py

anicokatz/PyMultiNestPlus

d223ac90bef7c1b61e337b70c2bdb41ed46cb2b7

[ "OML" ]

null

example_workspace/inverted_hierarchy/model.py

anicokatz/PyMultiNestPlus

d223ac90bef7c1b61e337b70c2bdb41ed46cb2b7

[ "OML" ]

null

example_workspace/inverted_hierarchy/model.py

anicokatz/PyMultiNestPlus

d223ac90bef7c1b61e337b70c2bdb41ed46cb2b7

[ "OML" ]

null

# INVERTED HIERARCHY import prior_handler as phandle import math import numpy as np import os cwd = os.path.dirname(os.path.realpath(__file__)) print(cwd) prior_handler = phandle.PriorHandler(cwd) con = prior_handler.c n_pars = prior_handler.n_pars def prior(cube, n_dims, n_pars): return prior_handler.scale(cube) def observables(pars): # get the nuisances from the par-based seed nui = prior_handler.get_nui(pars) # get mt value c13 = math.cos(pars[4]) a1 = abs(math.cos(pars[3]*c13))**2 a2 = abs(math.sin(pars[3]*c13))**2 a3 = abs(math.sin(pars[4]))**2 dm2 = pars[5] Dm2 = pars[6] m3 = pars[0] m2 = math.sqrt(max([0, m3**2 + Dm2 + dm2/2])) m1 = math.sqrt(max([0, m3**2 + Dm2 - dm2/2])) # with pars, nui, con, start calculation: return [abs(a1*m1*np.exp(-1j*pars[1]) + a2*m2*np.exp(-1j*pars[2]) + a3*m3 )] def loglikelihood(pars, n_dims, n_pars): mval = observables(pars) mval = mval[0] loglikelihood = (-((mval-con[0])**2)/(2*(con[1]**2))) return loglikelihood

26.04878

80

0.627341

import prior_handler as phandle import math import numpy as np import os cwd = os.path.dirname(os.path.realpath(__file__)) print(cwd) prior_handler = phandle.PriorHandler(cwd) con = prior_handler.c n_pars = prior_handler.n_pars def prior(cube, n_dims, n_pars): return prior_handler.scale(cube) def observables(pars): nui = prior_handler.get_nui(pars) c13 = math.cos(pars[4]) a1 = abs(math.cos(pars[3]*c13))**2 a2 = abs(math.sin(pars[3]*c13))**2 a3 = abs(math.sin(pars[4]))**2 dm2 = pars[5] Dm2 = pars[6] m3 = pars[0] m2 = math.sqrt(max([0, m3**2 + Dm2 + dm2/2])) m1 = math.sqrt(max([0, m3**2 + Dm2 - dm2/2])) return [abs(a1*m1*np.exp(-1j*pars[1]) + a2*m2*np.exp(-1j*pars[2]) + a3*m3 )] def loglikelihood(pars, n_dims, n_pars): mval = observables(pars) mval = mval[0] loglikelihood = (-((mval-con[0])**2)/(2*(con[1]**2))) return loglikelihood

true

f7297fc788219d05bf55769bec2813a65e0f1710

1,478

py

Python

tests/test_documentation_eager.py

burgerkingeater/io

f2de208f474d6ba4926e2c7f9e901e102ca5c254

[ "Apache-2.0" ]

1

2021-05-10T10:44:08.000Z

tests/test_documentation_eager.py

burgerkingeater/io

f2de208f474d6ba4926e2c7f9e901e102ca5c254

[ "Apache-2.0" ]

1

2021-02-24T10:37:32.000Z

tests/test_documentation_eager.py

burgerkingeater/io

f2de208f474d6ba4926e2c7f9e901e102ca5c254

[ "Apache-2.0" ]

null

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. # ============================================================================== """Tests for MNIST Dataset with tf.keras.""" import os def extract_block(filename, lang): """extract_block""" source = "" with open(filename) as f: hit = -1 for line in f: if hit < 0: if line.strip().startswith("```" + lang): hit = line.find("```" + lang) else: if line.strip().startswith("```") and line.find("```") == hit: break source += line return source def test_readme(): """test_readme""" # Note: From README.md filename = os.path.join( os.path.dirname(os.path.abspath(__file__)), "..", "README.md" ) source = extract_block(filename, "python") exec(source, globals()) # pylint: disable=exec-used

32.844444

80

0.587957

import os def extract_block(filename, lang): source = "" with open(filename) as f: hit = -1 for line in f: if hit < 0: if line.strip().startswith("```" + lang): hit = line.find("```" + lang) else: if line.strip().startswith("```") and line.find("```") == hit: break source += line return source def test_readme(): filename = os.path.join( os.path.dirname(os.path.abspath(__file__)), "..", "README.md" ) source = extract_block(filename, "python") exec(source, globals())

true

f72980142790efdf80fe4386352f892f576b4b05

664

py

Python

game/combat/effects/moveeffect/avghp.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

5

2021-06-25T16:44:38.000Z

2021-12-31T01:29:00.000Z

game/combat/effects/moveeffect/avghp.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

null

game/combat/effects/moveeffect/avghp.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

1

2021-06-25T20:33:47.000Z

from game.combat.effects.moveeffect.basemoveeffect import BaseMoveEffect class Avghp(BaseMoveEffect): def after_action(self): user_hp = self.scene.board.get_data(self.move.user).current_hp user_max_hp = self.scene.board.get_actor(self.move.user).stats[0] target_hp = self.scene.board.get_data(self.move.target).current_hp target_max_hp = self.scene.board.get_actor(self.move.target).stats[0] average = (user_hp + target_hp) // 2 self.scene.board.set_hp(self.move.user, min(average, user_max_hp)) self.scene.board.set_hp(self.move.target, min(average, target_max_hp)) return True, False, False

44.266667

78

0.716867

from game.combat.effects.moveeffect.basemoveeffect import BaseMoveEffect class Avghp(BaseMoveEffect): def after_action(self): user_hp = self.scene.board.get_data(self.move.user).current_hp user_max_hp = self.scene.board.get_actor(self.move.user).stats[0] target_hp = self.scene.board.get_data(self.move.target).current_hp target_max_hp = self.scene.board.get_actor(self.move.target).stats[0] average = (user_hp + target_hp) // 2 self.scene.board.set_hp(self.move.user, min(average, user_max_hp)) self.scene.board.set_hp(self.move.target, min(average, target_max_hp)) return True, False, False

true

f7298048176e7ee84c55af3bf97f19b8217d90b0

494

py

Python

ProjectEuler.Problem.039.py

jihunroh/ProjectEuler-Python

2fceaf5c3dd61038004b6128c5d9ee7a76142bca

[ "MIT" ]

null

ProjectEuler.Problem.039.py

jihunroh/ProjectEuler-Python

2fceaf5c3dd61038004b6128c5d9ee7a76142bca

[ "MIT" ]

null

ProjectEuler.Problem.039.py

jihunroh/ProjectEuler-Python

2fceaf5c3dd61038004b6128c5d9ee7a76142bca

[ "MIT" ]

null

from ProjectEulerCommons.Base import * def get_triangle_length_pairs(p): return sum([True for a in range(1, p - 2) for b in range(a, p - a - 1) if p - a - b > b and a**2 + b**2 == (p - a - b)**2]) Answer( max_index([(p, get_triangle_length_pairs(p)) for p in range(3, 1000 + 1)])[0] ) """ ------------------------------------------------ ProjectEuler.Problem.039.py The Answer is: 840 Time Elasped: 43.653260707855225sec ------------------------------------------------ """

29.058824

127

0.506073

from ProjectEulerCommons.Base import * def get_triangle_length_pairs(p): return sum([True for a in range(1, p - 2) for b in range(a, p - a - 1) if p - a - b > b and a**2 + b**2 == (p - a - b)**2]) Answer( max_index([(p, get_triangle_length_pairs(p)) for p in range(3, 1000 + 1)])[0] )

true

f72981074d6cef71e7e9323c24ea3c9a0020ffe1

2,773

py

Python

neptune-python-utils/neptune_python_utils/glue_neptune_connection_info.py

Alfian878787/amazon-neptune-tools

a447da238e99612a290babc66878fe772727a19e

[ "Apache-2.0" ]

null

neptune-python-utils/neptune_python_utils/glue_neptune_connection_info.py

Alfian878787/amazon-neptune-tools

a447da238e99612a290babc66878fe772727a19e

[ "Apache-2.0" ]

null

neptune-python-utils/neptune_python_utils/glue_neptune_connection_info.py

Alfian878787/amazon-neptune-tools

a447da238e99612a290babc66878fe772727a19e

[ "Apache-2.0" ]

null

# Copyright 2020 Amazon.com, Inc. or its affiliates. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). # You may not use this file except in compliance with the License. # A copy of the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. # This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, # either express or implied. See the License for the specific language governing permissions # and limitations under the License. import sys, boto3, os, uuid from urllib.parse import urlparse from botocore.credentials import Credentials from neptune_python_utils.endpoints import Endpoints class GlueNeptuneConnectionInfo: def __init__(self, region, role_arn): self.region = region self.role_arn = role_arn def neptune_endpoints(self, connection_name): """Gets Neptune endpoint information from the Glue Data Catalog. You may need to install a Glue VPC Endpoint in your VPC for this method to work. You can store Neptune endpoint information as JDBC connections in the Glue Data Catalog. JDBC connection strings must begin 'jdbc:'. To store a Neptune endpoint, use the following format: 'jdbc:<protocol>://<dns_name>:<port>/<endpoint>' For example, if you store: 'jdbc:wss://my-neptune-cluster.us-east-1.neptune.amazonaws.com:8182/gremlin' – this method will return: 'wss://my-neptune-cluster.us-east-1.neptune.amazonaws.com:8182/gremlin' Example: >>> gremlin_endpoint = GlueNeptuneConnectionInfo(glueContext).neptune_endpoint('neptune') """ glue = boto3.client('glue', region_name=self.region) connection = glue.get_connection(Name=connection_name) neptune_uri = connection['Connection']['ConnectionProperties']['JDBC_CONNECTION_URL'][5:] parse_result = urlparse(neptune_uri) netloc_parts = parse_result.netloc.split(':') host = netloc_parts[0] port = netloc_parts[1] sts = boto3.client('sts', region_name=self.region) role = sts.assume_role( RoleArn=self.role_arn, RoleSessionName=uuid.uuid4().hex, DurationSeconds=3600 ) credentials = Credentials( access_key=role['Credentials']['AccessKeyId'], secret_key=role['Credentials']['SecretAccessKey'], token=role['Credentials']['SessionToken']) return Endpoints(neptune_endpoint=host, neptune_port=port, region_name=self.region, credentials=credentials)

39.614286

116

0.662099

import sys, boto3, os, uuid from urllib.parse import urlparse from botocore.credentials import Credentials from neptune_python_utils.endpoints import Endpoints class GlueNeptuneConnectionInfo: def __init__(self, region, role_arn): self.region = region self.role_arn = role_arn def neptune_endpoints(self, connection_name): glue = boto3.client('glue', region_name=self.region) connection = glue.get_connection(Name=connection_name) neptune_uri = connection['Connection']['ConnectionProperties']['JDBC_CONNECTION_URL'][5:] parse_result = urlparse(neptune_uri) netloc_parts = parse_result.netloc.split(':') host = netloc_parts[0] port = netloc_parts[1] sts = boto3.client('sts', region_name=self.region) role = sts.assume_role( RoleArn=self.role_arn, RoleSessionName=uuid.uuid4().hex, DurationSeconds=3600 ) credentials = Credentials( access_key=role['Credentials']['AccessKeyId'], secret_key=role['Credentials']['SecretAccessKey'], token=role['Credentials']['SessionToken']) return Endpoints(neptune_endpoint=host, neptune_port=port, region_name=self.region, credentials=credentials)

true

f729829b0184b217d4f1bd32504cb211255b7245

4,125

py

Python

indexclient/parser.py

palkeo/indexd

f55ca6ab9f545d4df3e37b7d48a7bff907e6b27f

[ "BSD-3-Clause" ]

7

2016-10-10T09:36:43.000Z

2020-09-11T06:55:42.000Z

indexclient/parser.py

palkeo/indexd

f55ca6ab9f545d4df3e37b7d48a7bff907e6b27f

[ "BSD-3-Clause" ]

null

indexclient/parser.py

palkeo/indexd

f55ca6ab9f545d4df3e37b7d48a7bff907e6b27f

[ "BSD-3-Clause" ]

6

2016-04-11T15:57:05.000Z

2019-01-06T10:12:29.000Z

import nodes from parsers import parse """ This file contains a function to parse a query. It will have to return a rootnode. """ BINARY_OPERATOR, UNARY_OPERATOR, MODIFIER, TERM = range(4) # Contains a tuple : The first element is the node, the second is the priority of the operator (the bigger it is, the more the operator have a big priority) OPERATORS = { 'AND': (nodes.AndNode, 5, BINARY_OPERATOR), 'OR': (nodes.OrNode, 10, BINARY_OPERATOR), '&&': (nodes.AndNode, 5, BINARY_OPERATOR), '||': (nodes.OrNode, 10, BINARY_OPERATOR), #' -': (nodes.NotNode, 20, UNARY_OPERATOR), 'NOT': (nodes.NotNode, 20, UNARY_OPERATOR), } MODIFIERS = { '"': nodes.ExactNode, '`': nodes.ApproxNode, '[': nodes.ApproxNode, ']': nodes.ApproxNode, } def get_type(term): if term in OPERATORS: return OPERATORS[term][2] elif term in MODIFIERS: return MODIFIER return TERM def parse_query(query): def append_operator(term): assert not(lastType in (BINARY_OPERATOR, UNARY_OPERATOR) and get_type(term) == BINARY_OPERATOR) if get_type(term) == UNARY_OPERATOR and lastType == TERM: operators.append('AND') while len(operators) > 0 and OPERATORS[term][1] < OPERATORS[operators[-1]][1]: if get_type(operators[-1]) == UNARY_OPERATOR: terms.append( OPERATORS[ operators.pop() ][0](terms.pop()) ) else: assert get_type(operators[-1]) == BINARY_OPERATOR terms.append( OPERATORS[ operators.pop() ][0] ( terms.pop(), terms.pop() ) ) operators.append(term) for r in list(OPERATORS.keys()) + list(MODIFIERS.keys()) + ['(',')']: query = query.replace(r, ' ' + r + ' ') query = query.split(' ') terms = [] operators = [] lastType = BINARY_OPERATOR parenthesis_level = 0 parenthesis_start = -1 modifier = None modifier_terms = [] for pos, term in enumerate(query): if not term: continue # Parenthesis if term == '(': parenthesis_level += 1 if parenthesis_level == 1: parenthesis_start = pos + 1 elif term == ')': parenthesis_level -= 1 if parenthesis_level == 0: if lastType == TERM: append_operator('AND') terms.append( parse_query(' '.join(query[parenthesis_start:pos])) ) lastType = TERM continue if parenthesis_level > 0: continue # Modifier if get_type(term) == MODIFIER: if modifier is None: modifier = MODIFIERS[term] else: assert MODIFIERS[term] == modifier if lastType == TERM: append_operator('AND') terms.append(modifier(modifier_terms)) lastType = TERM modifier = None modifier_terms = [] continue if modifier is not None: term_list = parse(term) modifier_terms.extend(nodes.KwNode(i) for i in term_list) continue # Operator or terms if get_type(term) in (BINARY_OPERATOR, UNARY_OPERATOR): append_operator(term) else: term_list = tuple(parse(term)) if len(term_list) == 0: continue elif len(term_list) == 1: terms.append(nodes.KwNode(term_list[0])) else: terms.append(nodes.ExactNode([nodes.KwNode(i) for i in term_list])) if lastType == TERM: append_operator('AND') lastType = get_type(term) assert len(terms) > 0 while len(terms) > 1: if get_type(operators[-1]) == UNARY_OPERATOR: terms.append( OPERATORS[ operators.pop() ][0](terms.pop()) ) else: assert get_type(operators[-1]) == BINARY_OPERATOR terms.append( OPERATORS[ operators.pop() ][0] ( terms.pop(), terms.pop() ) ) return terms[0]

30.330882

156

0.557091

import nodes from parsers import parse BINARY_OPERATOR, UNARY_OPERATOR, MODIFIER, TERM = range(4) OPERATORS = { 'AND': (nodes.AndNode, 5, BINARY_OPERATOR), 'OR': (nodes.OrNode, 10, BINARY_OPERATOR), '&&': (nodes.AndNode, 5, BINARY_OPERATOR), '||': (nodes.OrNode, 10, BINARY_OPERATOR), 'NOT': (nodes.NotNode, 20, UNARY_OPERATOR), } MODIFIERS = { '"': nodes.ExactNode, '`': nodes.ApproxNode, '[': nodes.ApproxNode, ']': nodes.ApproxNode, } def get_type(term): if term in OPERATORS: return OPERATORS[term][2] elif term in MODIFIERS: return MODIFIER return TERM def parse_query(query): def append_operator(term): assert not(lastType in (BINARY_OPERATOR, UNARY_OPERATOR) and get_type(term) == BINARY_OPERATOR) if get_type(term) == UNARY_OPERATOR and lastType == TERM: operators.append('AND') while len(operators) > 0 and OPERATORS[term][1] < OPERATORS[operators[-1]][1]: if get_type(operators[-1]) == UNARY_OPERATOR: terms.append( OPERATORS[ operators.pop() ][0](terms.pop()) ) else: assert get_type(operators[-1]) == BINARY_OPERATOR terms.append( OPERATORS[ operators.pop() ][0] ( terms.pop(), terms.pop() ) ) operators.append(term) for r in list(OPERATORS.keys()) + list(MODIFIERS.keys()) + ['(',')']: query = query.replace(r, ' ' + r + ' ') query = query.split(' ') terms = [] operators = [] lastType = BINARY_OPERATOR parenthesis_level = 0 parenthesis_start = -1 modifier = None modifier_terms = [] for pos, term in enumerate(query): if not term: continue # Parenthesis if term == '(': parenthesis_level += 1 if parenthesis_level == 1: parenthesis_start = pos + 1 elif term == ')': parenthesis_level -= 1 if parenthesis_level == 0: if lastType == TERM: append_operator('AND') terms.append( parse_query(' '.join(query[parenthesis_start:pos])) ) lastType = TERM continue if parenthesis_level > 0: continue # Modifier if get_type(term) == MODIFIER: if modifier is None: modifier = MODIFIERS[term] else: assert MODIFIERS[term] == modifier if lastType == TERM: append_operator('AND') terms.append(modifier(modifier_terms)) lastType = TERM modifier = None modifier_terms = [] continue if modifier is not None: term_list = parse(term) modifier_terms.extend(nodes.KwNode(i) for i in term_list) continue # Operator or terms if get_type(term) in (BINARY_OPERATOR, UNARY_OPERATOR): append_operator(term) else: term_list = tuple(parse(term)) if len(term_list) == 0: continue elif len(term_list) == 1: terms.append(nodes.KwNode(term_list[0])) else: terms.append(nodes.ExactNode([nodes.KwNode(i) for i in term_list])) if lastType == TERM: append_operator('AND') lastType = get_type(term) assert len(terms) > 0 while len(terms) > 1: if get_type(operators[-1]) == UNARY_OPERATOR: terms.append( OPERATORS[ operators.pop() ][0](terms.pop()) ) else: assert get_type(operators[-1]) == BINARY_OPERATOR terms.append( OPERATORS[ operators.pop() ][0] ( terms.pop(), terms.pop() ) ) return terms[0]

true

f72982eaa140f4ef236992892c40e97c081380db

9,193

py

Python

src/brouwers/forum_tools/models.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

6

2015-03-03T13:23:07.000Z

2021-12-19T18:12:41.000Z

src/brouwers/forum_tools/models.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

95

2015-02-07T00:55:39.000Z

2022-02-08T20:22:05.000Z

src/brouwers/forum_tools/models.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

2

2016-03-22T16:53:26.000Z

2019-02-09T22:46:04.000Z

import zlib from datetime import datetime from django.conf import settings from django.db import models from django.urls import reverse from django.utils import timezone from django.utils.http import urlencode from django.utils.timesince import timesince from django.utils.translation import ugettext_lazy as _ from dateutil.relativedelta import relativedelta from brouwers.general.utils import clean_username from .fields import ForumToolsIDField class ForumLinkBase(models.Model): link_id = models.CharField( _("link id"), max_length=128, help_text=_("HTML id of the base anchor.") ) short_description = models.CharField( _("short description"), max_length=64, blank=True ) enabled = models.BooleanField( _("enabled"), default=True, help_text=_("Enable the syncing of this link.") ) from_date = models.DateField( _("from date"), help_text=_("Start date from when this link is enabled.") ) to_date = models.DateField( _("to date"), help_text=_("End date from when this link is enabled, this date included."), ) class Meta: verbose_name = _("base forum link") verbose_name_plural = _("base forum links") def __str__(self): if self.short_description: return _("base forum link: %(desc)s") % {"desc": self.short_description} else: return _("base forum link: %(id)s") % {"id": self.link_id} class ForumLinkSynced(models.Model): base = models.ForeignKey( ForumLinkBase, verbose_name=_("base link"), help_text=_("Link this link syncs with."), on_delete=models.CASCADE, ) link_id = models.CharField( _("link id"), max_length=128, help_text=_("HTML id of the anchor to be synced.") ) class Meta: verbose_name = _("synced forum link") verbose_name_plural = _("synced forum links") def __str__(self): return u"%s -- %s" % (self.base, self.link_id) class BuildReportsForum(models.Model): """Model which tells us which forums hold build reports""" forum = ForumToolsIDField(_("forum"), type="forum") class Meta: verbose_name = _(u"build report forum") verbose_name_plural = _(u"build report forums") ordering = ["forum"] def __str__(self): return self.forum.forum_name if self.forum else _("(forum does not exist)") class ForumCategory(models.Model): name = models.CharField(_("name"), max_length=255) forum = ForumToolsIDField(_("forum"), type="forum", blank=True, null=True) icon_class = models.CharField(_("icon class"), max_length=50, blank=True) class Meta: verbose_name = _(u"forum category") verbose_name_plural = _(u"forum categories") ordering = ("name",) def __str__(self): return self.name # Models to interact with the MYSQL database ############################# class ForumUser(models.Model): """MySQL phpBB3 user, managed by phpBB3""" # mediumint(8) unsigned user_id = models.AutoField(primary_key=True, help_text=_("Primary key")) username = models.CharField(_("username"), max_length=255) username_clean = models.CharField(_("username"), max_length=255) user_posts = models.IntegerField() user_email = models.CharField(_("email"), max_length=100) # bigint(20) user_email_hash = models.BigIntegerField( db_column="user_email_hash", default=0, help_text=_("A hash of the user's email address."), ) user_permissions = models.TextField(blank=True) user_sig = models.TextField(blank=True) user_interests = models.TextField(blank=True) user_actkey = models.TextField(blank=True) user_occ = models.TextField(blank=True) class Meta: managed = False verbose_name = _("forum user") verbose_name_plural = _("forum users") ordering = ("username",) db_table = u"%susers" % settings.PHPBB_TABLE_PREFIX def __str__(self): return self.username def get_absolute_url(self): qs = { "mode": "viewprofile", "u": self.user_id, } return "{0}?{1}".format(reverse("phpBB:memberlist"), urlencode(qs)) def get_email_hash(self): email = self.user_email h = zlib.crc32(email.lower().encode("ascii")) & 0xFFFFFFFF return "%s%s" % (h, len(email)) def save(self, *args, **kwargs): self.user_email_hash = self.get_email_hash() if not self.username_clean: self._clean_username() super().save(*args, **kwargs) def _clean_username(self): self.username_clean = clean_username(self.username) class Forum(models.Model): """ MySQL Forum, managed by phpBB3 """ forum_id = models.AutoField(primary_key=True) forum_name = models.CharField(max_length=60) forum_topics = models.IntegerField(default=0) forum_posts = models.IntegerField(default=0) forum_desc = models.TextField() parent = models.ForeignKey( "self", related_name="child", null=True, blank=True, default=None, on_delete=models.CASCADE, ) def __str__(self): return self.forum_name def get_absolute_url(self): qs = {"f": self.forum_id} return "{0}?{1}".format(reverse("phpBB:viewforum"), urlencode(qs)) class Meta: managed = False db_table = settings.PHPBB_TABLE_PREFIX + "forums" ordering = ["forum_name"] class Topic(models.Model): topic_id = models.AutoField(primary_key=True) forum = models.ForeignKey(Forum, on_delete=models.CASCADE) topic_title = models.CharField(max_length=255) last_post_time = models.BigIntegerField(db_column="topic_last_post_time", default=0) create_time = models.BigIntegerField(db_column="topic_time", default=0) author = models.ForeignKey( ForumUser, db_column="topic_poster", null=True, blank=True, on_delete=models.SET_NULL, ) class Meta: managed = False db_table = settings.PHPBB_TABLE_PREFIX + "topics" ordering = ["topic_id"] def __str__(self): return self.topic_title def get_absolute_url(self): qs = {"t": self.topic_id} if self.forum.pk: qs["f"] = self.forum.pk return "{0}?{1}".format(reverse("phpBB:viewtopic"), urlencode(qs)) @property def created(self): return datetime.utcfromtimestamp(self.create_time).replace(tzinfo=timezone.utc) def get_last_post_time(self): return datetime.utcfromtimestamp(self.last_post_time).replace( tzinfo=timezone.utc ) @property def is_dead(self): """ If the last post is older than settings.TOPIC_DEAD_TIME, it's considered dead. """ last = self.get_last_post_time() lower = timezone.now() - relativedelta(months=settings.TOPIC_DEAD_TIME) return last <= lower @property def age(self): return timesince(self.get_last_post_time()) @property def text_dead(self): return _( "This topic has been inactive for: {0}. Please consider " "sending the author a private message instead of replying " "and thus bumping the topic." ).format(self.age) class ForumPostCountRestriction(models.Model): """Model to hold information on the minimum post-count and level of posting rights. Managed by Django.""" POSTING_LEVELS = ( ("T", _("Topic")), ("R", _("Reply")), ) forum = ForumToolsIDField(_("forum id"), type="forum", blank=True, null=True) min_posts = models.PositiveSmallIntegerField(_("minimum number of posts")) posting_level = models.CharField( _("posting level"), max_length=1, choices=POSTING_LEVELS ) class Meta: verbose_name = _("forum post count restriction") verbose_name_plural = _("forum post count restrictions") ordering = ["forum"] def __str__(self): return _("Restriction for %(forum)s") % {"forum": self.forum.forum_name} class Report(models.Model): """MySQL Report model, managed by phpBB3""" report_id = models.AutoField(primary_key=True, help_text="Primary key") # reason_id = FK to reasons, not implement in Django yet report_closed = models.BooleanField( _("closed"), default=False, help_text=_("Closed reports need no more attention."), ) report_time_int = models.IntegerField( _("time"), db_column="report_time", help_text=_("UNIX time when the report was added."), ) report_text = models.TextField("text", blank=True) class Meta: managed = False verbose_name = _("report") verbose_name_plural = _("reports") db_table = u"%sreports" % settings.PHPBB_TABLE_PREFIX permissions = (("can_see_reports", _("Can see (number of) open reports")),) def __str__(self): return _("Report %(id)s" % {"id": self.report_id}) def report_time(self): return datetime.fromtimestamp(self.report_time_int)

30.952862

88

0.643207

import zlib from datetime import datetime from django.conf import settings from django.db import models from django.urls import reverse from django.utils import timezone from django.utils.http import urlencode from django.utils.timesince import timesince from django.utils.translation import ugettext_lazy as _ from dateutil.relativedelta import relativedelta from brouwers.general.utils import clean_username from .fields import ForumToolsIDField class ForumLinkBase(models.Model): link_id = models.CharField( _("link id"), max_length=128, help_text=_("HTML id of the base anchor.") ) short_description = models.CharField( _("short description"), max_length=64, blank=True ) enabled = models.BooleanField( _("enabled"), default=True, help_text=_("Enable the syncing of this link.") ) from_date = models.DateField( _("from date"), help_text=_("Start date from when this link is enabled.") ) to_date = models.DateField( _("to date"), help_text=_("End date from when this link is enabled, this date included."), ) class Meta: verbose_name = _("base forum link") verbose_name_plural = _("base forum links") def __str__(self): if self.short_description: return _("base forum link: %(desc)s") % {"desc": self.short_description} else: return _("base forum link: %(id)s") % {"id": self.link_id} class ForumLinkSynced(models.Model): base = models.ForeignKey( ForumLinkBase, verbose_name=_("base link"), help_text=_("Link this link syncs with."), on_delete=models.CASCADE, ) link_id = models.CharField( _("link id"), max_length=128, help_text=_("HTML id of the anchor to be synced.") ) class Meta: verbose_name = _("synced forum link") verbose_name_plural = _("synced forum links") def __str__(self): return u"%s -- %s" % (self.base, self.link_id) class BuildReportsForum(models.Model): forum = ForumToolsIDField(_("forum"), type="forum") class Meta: verbose_name = _(u"build report forum") verbose_name_plural = _(u"build report forums") ordering = ["forum"] def __str__(self): return self.forum.forum_name if self.forum else _("(forum does not exist)") class ForumCategory(models.Model): name = models.CharField(_("name"), max_length=255) forum = ForumToolsIDField(_("forum"), type="forum", blank=True, null=True) icon_class = models.CharField(_("icon class"), max_length=50, blank=True) class Meta: verbose_name = _(u"forum category") verbose_name_plural = _(u"forum categories") ordering = ("name",) def __str__(self): return self.name ", default=0, help_text=_("A hash of the user's email address."), ) user_permissions = models.TextField(blank=True) user_sig = models.TextField(blank=True) user_interests = models.TextField(blank=True) user_actkey = models.TextField(blank=True) user_occ = models.TextField(blank=True) class Meta: managed = False verbose_name = _("forum user") verbose_name_plural = _("forum users") ordering = ("username",) db_table = u"%susers" % settings.PHPBB_TABLE_PREFIX def __str__(self): return self.username def get_absolute_url(self): qs = { "mode": "viewprofile", "u": self.user_id, } return "{0}?{1}".format(reverse("phpBB:memberlist"), urlencode(qs)) def get_email_hash(self): email = self.user_email h = zlib.crc32(email.lower().encode("ascii")) & 0xFFFFFFFF return "%s%s" % (h, len(email)) def save(self, *args, **kwargs): self.user_email_hash = self.get_email_hash() if not self.username_clean: self._clean_username() super().save(*args, **kwargs) def _clean_username(self): self.username_clean = clean_username(self.username) class Forum(models.Model): forum_id = models.AutoField(primary_key=True) forum_name = models.CharField(max_length=60) forum_topics = models.IntegerField(default=0) forum_posts = models.IntegerField(default=0) forum_desc = models.TextField() parent = models.ForeignKey( "self", related_name="child", null=True, blank=True, default=None, on_delete=models.CASCADE, ) def __str__(self): return self.forum_name def get_absolute_url(self): qs = {"f": self.forum_id} return "{0}?{1}".format(reverse("phpBB:viewforum"), urlencode(qs)) class Meta: managed = False db_table = settings.PHPBB_TABLE_PREFIX + "forums" ordering = ["forum_name"] class Topic(models.Model): topic_id = models.AutoField(primary_key=True) forum = models.ForeignKey(Forum, on_delete=models.CASCADE) topic_title = models.CharField(max_length=255) last_post_time = models.BigIntegerField(db_column="topic_last_post_time", default=0) create_time = models.BigIntegerField(db_column="topic_time", default=0) author = models.ForeignKey( ForumUser, db_column="topic_poster", null=True, blank=True, on_delete=models.SET_NULL, ) class Meta: managed = False db_table = settings.PHPBB_TABLE_PREFIX + "topics" ordering = ["topic_id"] def __str__(self): return self.topic_title def get_absolute_url(self): qs = {"t": self.topic_id} if self.forum.pk: qs["f"] = self.forum.pk return "{0}?{1}".format(reverse("phpBB:viewtopic"), urlencode(qs)) @property def created(self): return datetime.utcfromtimestamp(self.create_time).replace(tzinfo=timezone.utc) def get_last_post_time(self): return datetime.utcfromtimestamp(self.last_post_time).replace( tzinfo=timezone.utc ) @property def is_dead(self): last = self.get_last_post_time() lower = timezone.now() - relativedelta(months=settings.TOPIC_DEAD_TIME) return last <= lower @property def age(self): return timesince(self.get_last_post_time()) @property def text_dead(self): return _( "This topic has been inactive for: {0}. Please consider " "sending the author a private message instead of replying " "and thus bumping the topic." ).format(self.age) class ForumPostCountRestriction(models.Model): POSTING_LEVELS = ( ("T", _("Topic")), ("R", _("Reply")), ) forum = ForumToolsIDField(_("forum id"), type="forum", blank=True, null=True) min_posts = models.PositiveSmallIntegerField(_("minimum number of posts")) posting_level = models.CharField( _("posting level"), max_length=1, choices=POSTING_LEVELS ) class Meta: verbose_name = _("forum post count restriction") verbose_name_plural = _("forum post count restrictions") ordering = ["forum"] def __str__(self): return _("Restriction for %(forum)s") % {"forum": self.forum.forum_name} class Report(models.Model): report_id = models.AutoField(primary_key=True, help_text="Primary key") # reason_id = FK to reasons, not implement in Django yet report_closed = models.BooleanField( _("closed"), default=False, help_text=_("Closed reports need no more attention."), ) report_time_int = models.IntegerField( _("time"), db_column="report_time", help_text=_("UNIX time when the report was added."), ) report_text = models.TextField("text", blank=True) class Meta: managed = False verbose_name = _("report") verbose_name_plural = _("reports") db_table = u"%sreports" % settings.PHPBB_TABLE_PREFIX permissions = (("can_see_reports", _("Can see (number of) open reports")),) def __str__(self): return _("Report %(id)s" % {"id": self.report_id}) def report_time(self): return datetime.fromtimestamp(self.report_time_int)

true

f72984a9a6003802c913b46bb52ecb628180101d

915

py

Python

day_09/part1.py

pawlodkowski/advent_of_code_2020

ca41416c340747d7e37eeab60046b770c240338b

[ "MIT" ]

2

2020-12-02T09:14:14.000Z

2020-12-02T22:14:21.000Z

day_09/part1.py

pawlodkowski/advent_of_code_2020

ca41416c340747d7e37eeab60046b770c240338b

[ "MIT" ]

null

day_09/part1.py

pawlodkowski/advent_of_code_2020

ca41416c340747d7e37eeab60046b770c240338b

[ "MIT" ]

null

""" Part 1 of https://adventofcode.com/2020/day/9 """ def read_data(filename: str) -> list: with open(filename, "r") as f: data = f.read().split("\n") return data def sum_to_n(n, options): """ Helper function adapted from Day 1 :) """ try: for num in options: complement = n - num if complement in options: first = num second = complement break return first, second except UnboundLocalError: return False if __name__ == "__main__": data = [int(i) for i in read_data("input.txt")] for i, num in enumerate(data): prev25 = data[i - 25 : i] if prev25: if not sum_to_n(num, prev25): print( f"Solution: The first number that is not the sum of any two of the 25 numbers before it is {num}." )

22.875

118

0.525683

def read_data(filename: str) -> list: with open(filename, "r") as f: data = f.read().split("\n") return data def sum_to_n(n, options): try: for num in options: complement = n - num if complement in options: first = num second = complement break return first, second except UnboundLocalError: return False if __name__ == "__main__": data = [int(i) for i in read_data("input.txt")] for i, num in enumerate(data): prev25 = data[i - 25 : i] if prev25: if not sum_to_n(num, prev25): print( f"Solution: The first number that is not the sum of any two of the 25 numbers before it is {num}." )

true

f72985e3c3835b49230c4dfe3c6d5af8845591fc

8,470

py

Python

tests/test_txpool.py

rsoliha/LabChain

4694913f52b3d0567f9a289dfc0b7bec30eccfb9

[ "Apache-2.0" ]

null

tests/test_txpool.py

rsoliha/LabChain

4694913f52b3d0567f9a289dfc0b7bec30eccfb9

[ "Apache-2.0" ]

null

tests/test_txpool.py

rsoliha/LabChain

4694913f52b3d0567f9a289dfc0b7bec30eccfb9

[ "Apache-2.0" ]

null

import unittest import os from labchain.datastructure.txpool import TxPool from labchain.datastructure.transaction import Transaction from labchain.util.cryptoHelper import CryptoHelper from labchain.datastructure.blockchain import BlockChain from labchain.util.configReader import ConfigReader from labchain.consensus.consensus import Consensus class TxPoolTestCase(unittest.TestCase): """Class of testcases for the TxPool module""" def init_blockchain(self): test_resources_dic_path = os.path.abspath(os.path.join(os.path.dirname(__file__), './resources')) test_node_config_file = test_resources_dic_path + '/node_configuration.ini' config_reader = ConfigReader(test_node_config_file) tolerance = config_reader.get_config( section='BLOCK_CHAIN', option='TOLERANCE_LEVEL') pruning = config_reader.get_config( section='BLOCK_CHAIN', option='TIME_TO_PRUNE') min_blocks = config_reader.get_config( section='MINING', option='NUM_OF_BLOCKS_FOR_DIFFICULTY') consensus = Consensus() self.block_list = [] self.blockchain_obj = BlockChain(node_id="nodeId1", tolerance_value=tolerance, pruning_interval=pruning, consensus_obj=consensus, txpool_obj=self._txPoolObj, crypto_helper_obj=self.crypto_helper_obj, min_blocks_for_difficulty=min_blocks, db=None, q=None) def setUp(self): self.crypto_helper_obj = CryptoHelper.instance() self.private_key1, self.public_key1 = self.crypto_helper_obj.generate_key_pair() self.private_key2, self.public_key2 = self.crypto_helper_obj.generate_key_pair() self._txPoolObj = TxPool(self.crypto_helper_obj) self.init_blockchain() t1 = Transaction(self.public_key1, self.public_key2, "a") t1.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(t1, self.blockchain_obj) t2 = Transaction(self.public_key1, self.public_key2, "b") t2.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(t2, self.blockchain_obj) t3 = Transaction(self.public_key1, self.public_key2, "c") t3.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(t3, self.blockchain_obj) def tearDown(self): self._txPoolObj._first_time = True def test_add_transaction(self): """Test for add transaction, get transaction count and get transaction""" transaction = Transaction(self.public_key1, self.public_key2, "d") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) txpool_size = self._txPoolObj.get_transaction_count() status = self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) self.assertEqual(status, True) self.assertEqual(txpool_size + 1, self._txPoolObj.get_transaction_count()) self.assertEqual(transaction.get_json(), self._txPoolObj.get_transaction().get_json()) def test_get_transactions(self): """Test to get a set of transactions""" tx_pool_count = self._txPoolObj.get_transaction_count() t1 = Transaction(self.public_key1, self.public_key2, "e") t1.sign_transaction(self.crypto_helper_obj, self.private_key1) t2 = Transaction(self.public_key2, self.public_key1, "f") t2.sign_transaction(self.crypto_helper_obj, self.private_key2) self._txPoolObj.add_transaction_if_not_exist(t1, self.blockchain_obj) self._txPoolObj.add_transaction_if_not_exist(t2, self.blockchain_obj) self.assertEqual(3, tx_pool_count) self.assertEqual(5, self._txPoolObj.get_transaction_count()) transactions = self._txPoolObj.get_transactions(3) self.assertEqual(len(transactions), 3) self.assertEqual(2, self._txPoolObj.get_transaction_count()) def test_remove_transaction(self): """Test remove transaction""" transaction = Transaction(self.public_key1, self.public_key2, "g") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) tx_pool_count = self._txPoolObj.get_transaction_count() transactions = self._txPoolObj.get_transactions(tx_pool_count) self._txPoolObj.return_transactions_to_pool(transactions, self.blockchain_obj) self.assertTrue(transaction in transactions) status = self._txPoolObj.remove_transaction(transaction) self.assertEqual(status, True) tx_pool_count = self._txPoolObj.get_transaction_count() transactions = self._txPoolObj.get_transactions(tx_pool_count) self.assertFalse(transaction in transactions) def test_return_transactions_to_pool(self): """Test for return transactions to pool""" t1 = Transaction(self.public_key1, self.public_key2, "h") t1.sign_transaction(self.crypto_helper_obj, self.private_key1) t2 = Transaction(self.public_key1, self.public_key2, "i") t2.sign_transaction(self.crypto_helper_obj, self.private_key1) t3 = Transaction(self.public_key1, self.public_key2, "j") t3.sign_transaction(self.crypto_helper_obj, self.private_key1) transactions = [t1, t2, t3] tx_pool_count = self._txPoolObj.get_transaction_count() status = self._txPoolObj.return_transactions_to_pool(transactions, self.blockchain_obj) self.assertEqual(status, True) transactions_new = self._txPoolObj.get_transactions(tx_pool_count + 3) status = any(transaction in transactions for transaction in transactions_new) self.assertEqual(status, True) def test_singleton(self): """Test the single behaviour of the class""" transaction = Transaction(self.public_key1, self.public_key2, "s") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) tx_pool_count = self._txPoolObj.get_transaction_count() txpool = TxPool(self.crypto_helper_obj) self.assertEqual(txpool, self._txPoolObj) self.assertEqual(txpool.get_transaction_count(), tx_pool_count) def test_get_transaction_count(self): """Test the transaction count""" transaction = Transaction(self.public_key1, self.public_key2, "g") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) status = self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) self.assertTrue(status) self.assertEqual(4, self._txPoolObj.get_transaction_count()) def test_add_transaction_if_not_exist(self): """Test adding transaction in txpool only when it is empty""" transaction = Transaction(self.public_key1, self.public_key2, "h") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) status = self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) self.assertTrue(status) def test_get_transaction_by_hash(self): """Test getting transaction from txpool by hash""" tx_pool_count = self._txPoolObj.get_transaction_count() t1 = Transaction(self.public_key1, self.public_key2, "e") t1.sign_transaction(self.crypto_helper_obj, self.private_key1) t2 = Transaction(self.public_key1, self.public_key2, "f") t2.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(t1, self.blockchain_obj) self._txPoolObj.add_transaction_if_not_exist(t2, self.blockchain_obj) self.assertEqual(tx_pool_count, 3) tx_pool_count = self._txPoolObj.get_transaction_count() self.assertEqual(tx_pool_count, 5) hash_val = t1.transaction_hash transaction = self._txPoolObj.get_transaction_by_hash(hash_val)[0] self.assertEqual(t1, transaction) if __name__ == '__main__': unittest.main()

52.608696

105

0.710862

import unittest import os from labchain.datastructure.txpool import TxPool from labchain.datastructure.transaction import Transaction from labchain.util.cryptoHelper import CryptoHelper from labchain.datastructure.blockchain import BlockChain from labchain.util.configReader import ConfigReader from labchain.consensus.consensus import Consensus class TxPoolTestCase(unittest.TestCase): def init_blockchain(self): test_resources_dic_path = os.path.abspath(os.path.join(os.path.dirname(__file__), './resources')) test_node_config_file = test_resources_dic_path + '/node_configuration.ini' config_reader = ConfigReader(test_node_config_file) tolerance = config_reader.get_config( section='BLOCK_CHAIN', option='TOLERANCE_LEVEL') pruning = config_reader.get_config( section='BLOCK_CHAIN', option='TIME_TO_PRUNE') min_blocks = config_reader.get_config( section='MINING', option='NUM_OF_BLOCKS_FOR_DIFFICULTY') consensus = Consensus() self.block_list = [] self.blockchain_obj = BlockChain(node_id="nodeId1", tolerance_value=tolerance, pruning_interval=pruning, consensus_obj=consensus, txpool_obj=self._txPoolObj, crypto_helper_obj=self.crypto_helper_obj, min_blocks_for_difficulty=min_blocks, db=None, q=None) def setUp(self): self.crypto_helper_obj = CryptoHelper.instance() self.private_key1, self.public_key1 = self.crypto_helper_obj.generate_key_pair() self.private_key2, self.public_key2 = self.crypto_helper_obj.generate_key_pair() self._txPoolObj = TxPool(self.crypto_helper_obj) self.init_blockchain() t1 = Transaction(self.public_key1, self.public_key2, "a") t1.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(t1, self.blockchain_obj) t2 = Transaction(self.public_key1, self.public_key2, "b") t2.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(t2, self.blockchain_obj) t3 = Transaction(self.public_key1, self.public_key2, "c") t3.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(t3, self.blockchain_obj) def tearDown(self): self._txPoolObj._first_time = True def test_add_transaction(self): transaction = Transaction(self.public_key1, self.public_key2, "d") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) txpool_size = self._txPoolObj.get_transaction_count() status = self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) self.assertEqual(status, True) self.assertEqual(txpool_size + 1, self._txPoolObj.get_transaction_count()) self.assertEqual(transaction.get_json(), self._txPoolObj.get_transaction().get_json()) def test_get_transactions(self): tx_pool_count = self._txPoolObj.get_transaction_count() t1 = Transaction(self.public_key1, self.public_key2, "e") t1.sign_transaction(self.crypto_helper_obj, self.private_key1) t2 = Transaction(self.public_key2, self.public_key1, "f") t2.sign_transaction(self.crypto_helper_obj, self.private_key2) self._txPoolObj.add_transaction_if_not_exist(t1, self.blockchain_obj) self._txPoolObj.add_transaction_if_not_exist(t2, self.blockchain_obj) self.assertEqual(3, tx_pool_count) self.assertEqual(5, self._txPoolObj.get_transaction_count()) transactions = self._txPoolObj.get_transactions(3) self.assertEqual(len(transactions), 3) self.assertEqual(2, self._txPoolObj.get_transaction_count()) def test_remove_transaction(self): transaction = Transaction(self.public_key1, self.public_key2, "g") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) tx_pool_count = self._txPoolObj.get_transaction_count() transactions = self._txPoolObj.get_transactions(tx_pool_count) self._txPoolObj.return_transactions_to_pool(transactions, self.blockchain_obj) self.assertTrue(transaction in transactions) status = self._txPoolObj.remove_transaction(transaction) self.assertEqual(status, True) tx_pool_count = self._txPoolObj.get_transaction_count() transactions = self._txPoolObj.get_transactions(tx_pool_count) self.assertFalse(transaction in transactions) def test_return_transactions_to_pool(self): t1 = Transaction(self.public_key1, self.public_key2, "h") t1.sign_transaction(self.crypto_helper_obj, self.private_key1) t2 = Transaction(self.public_key1, self.public_key2, "i") t2.sign_transaction(self.crypto_helper_obj, self.private_key1) t3 = Transaction(self.public_key1, self.public_key2, "j") t3.sign_transaction(self.crypto_helper_obj, self.private_key1) transactions = [t1, t2, t3] tx_pool_count = self._txPoolObj.get_transaction_count() status = self._txPoolObj.return_transactions_to_pool(transactions, self.blockchain_obj) self.assertEqual(status, True) transactions_new = self._txPoolObj.get_transactions(tx_pool_count + 3) status = any(transaction in transactions for transaction in transactions_new) self.assertEqual(status, True) def test_singleton(self): transaction = Transaction(self.public_key1, self.public_key2, "s") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) tx_pool_count = self._txPoolObj.get_transaction_count() txpool = TxPool(self.crypto_helper_obj) self.assertEqual(txpool, self._txPoolObj) self.assertEqual(txpool.get_transaction_count(), tx_pool_count) def test_get_transaction_count(self): transaction = Transaction(self.public_key1, self.public_key2, "g") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) status = self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) self.assertTrue(status) self.assertEqual(4, self._txPoolObj.get_transaction_count()) def test_add_transaction_if_not_exist(self): transaction = Transaction(self.public_key1, self.public_key2, "h") transaction.sign_transaction(self.crypto_helper_obj, self.private_key1) status = self._txPoolObj.add_transaction_if_not_exist(transaction, self.blockchain_obj) self.assertTrue(status) def test_get_transaction_by_hash(self): tx_pool_count = self._txPoolObj.get_transaction_count() t1 = Transaction(self.public_key1, self.public_key2, "e") t1.sign_transaction(self.crypto_helper_obj, self.private_key1) t2 = Transaction(self.public_key1, self.public_key2, "f") t2.sign_transaction(self.crypto_helper_obj, self.private_key1) self._txPoolObj.add_transaction_if_not_exist(t1, self.blockchain_obj) self._txPoolObj.add_transaction_if_not_exist(t2, self.blockchain_obj) self.assertEqual(tx_pool_count, 3) tx_pool_count = self._txPoolObj.get_transaction_count() self.assertEqual(tx_pool_count, 5) hash_val = t1.transaction_hash transaction = self._txPoolObj.get_transaction_by_hash(hash_val)[0] self.assertEqual(t1, transaction) if __name__ == '__main__': unittest.main()

true

f729864796f49818be1ba8f4a31b3aafd57c5c19

589

py

Python

gdsfactory/simulation/modes/tests/test_find_modes_dispersion.py

simbilod/gdsfactory

4d76db32674c3edb4d16260e3177ee29ef9ce11d

[ "MIT" ]

null

gdsfactory/simulation/modes/tests/test_find_modes_dispersion.py

simbilod/gdsfactory

4d76db32674c3edb4d16260e3177ee29ef9ce11d

[ "MIT" ]

null

gdsfactory/simulation/modes/tests/test_find_modes_dispersion.py

simbilod/gdsfactory

4d76db32674c3edb4d16260e3177ee29ef9ce11d

[ "MIT" ]

null

import numpy as np from gdsfactory.simulation.modes.find_mode_dispersion import find_mode_dispersion def test_find_modes_waveguide_dispersion() -> None: modes = find_mode_dispersion(wg_width=0.45, resolution=20, cache=None) m1 = modes # print(f"neff1 = {m1.neff}") # print(f"ng1 = {m1.ng}") # neff1 = 2.3948 # ng1 = 4.23194 neff1 = 2.362907833437435 ng1 = 4.202169359808116 assert np.isclose(m1.neff, neff1), (m1.neff, neff1) assert np.isclose(m1.ng, ng1), (m1.ng, ng1) if __name__ == "__main__": test_find_modes_waveguide_dispersion()

23.56

81

0.689304

import numpy as np from gdsfactory.simulation.modes.find_mode_dispersion import find_mode_dispersion def test_find_modes_waveguide_dispersion() -> None: modes = find_mode_dispersion(wg_width=0.45, resolution=20, cache=None) m1 = modes neff1 = 2.362907833437435 ng1 = 4.202169359808116 assert np.isclose(m1.neff, neff1), (m1.neff, neff1) assert np.isclose(m1.ng, ng1), (m1.ng, ng1) if __name__ == "__main__": test_find_modes_waveguide_dispersion()

true

f72986a5df5acfae915ca08e7901dfdce218c1f8

45,453

py

Python

Utilities/JSBSimWriteXml.py

elke0011/OpenFlightSim

1e28c54864ffd188f27425c8a71cce8b70a4bd7f

[ "MIT" ]

15

2019-03-15T17:28:23.000Z

2022-03-21T23:52:53.000Z

Utilities/JSBSimWriteXml.py

elke0011/OpenFlightSim

1e28c54864ffd188f27425c8a71cce8b70a4bd7f

[ "MIT" ]

null

Utilities/JSBSimWriteXml.py

elke0011/OpenFlightSim

1e28c54864ffd188f27425c8a71cce8b70a4bd7f

[ "MIT" ]

5

2019-03-28T17:35:50.000Z

2022-03-04T19:38:03.000Z

""" University of Minnesota Aerospace Engineering and Mechanics - UAV Lab Copyright 2019 Regents of the University of Minnesota. See: LICENSE.md for complete license details. Author: Louis Mueller, Chris Regan """ import os.path from xml.etree import ElementTree as ET import numpy as np ft2m = 0.3048 psf2pa = 47.88026 #%% Save the XML in pretty-ish print def SaveXml(elem, saveFile): from xml.dom import minidom uglyXml = ET.tostring(elem, 'utf-8') prettyXml = minidom.parseString(uglyXml).toprettyxml(indent=' ', newl = '\r\n') os.makedirs(os.path.dirname(saveFile), exist_ok=True) with open(saveFile, 'w') as saveXML: saveXML.write(prettyXml) saveXML.close() #%% Function def Aircraft(oFdm, convertFdm2Jsb, saveJsbPath, aircraftName): # Start JSB-ML with etree elemAircraft = ET.Element('fdm_config', version = '2.0', release = 'Alpha') # Create the Pilot input as a seperate XML file, direct the Aircraft definition to use fcsFile = 'FlightControl.xml' ET.SubElement(elemAircraft, 'flight_control', file = fcsFile) SaveXml(FlightControl(oFdm), os.path.join(saveJsbPath, fcsFile)) # Effectors as a seperate XML file, direct the Aircraft definition to use effFile = 'Effectors.xml' ET.SubElement(elemAircraft, 'system', file = effFile) SaveXml(Effectors(oFdm), os.path.join(saveJsbPath, effFile)) # Create the Mass Properties input as a seperate XML file, direct the Aircraft definition to use massFile = 'Mass.xml' ET.SubElement(elemAircraft, 'mass_balance', file = massFile) SaveXml(MassBalance(oFdm), os.path.join(saveJsbPath, massFile)) # Create the Gear input as a seperate XML file, direct the Aircraft definition to use gearFile = 'Gear.xml' ET.SubElement(elemAircraft, 'ground_reactions', file = gearFile) SaveXml(GroundReactions(oFdm), os.path.join(saveJsbPath, gearFile)) # Create the Propulsion input as a seperate XML file, direct the Aircraft definition to use propFile = 'Propulsion.xml' ET.SubElement(elemAircraft, 'propulsion', file = propFile) SaveXml(Propulsion(oFdm), os.path.join(saveJsbPath, propFile)) # Metrics and Aerodynamics as a seperate XML file, direct the Aircraft definition to use # Group the Metrics and Aero by similar naming; the dimensionalization inherent to Aero is provided by the Metrics metricsFile = 'Metrics.xml' ET.SubElement(elemAircraft, 'metrics', file = metricsFile) SaveXml(Metrics(oFdm), os.path.join(saveJsbPath, metricsFile)) aeroFile = 'Aero.xml' ET.SubElement(elemAircraft, 'aerodynamics', file = aeroFile) SaveXml(Aerodynamics(oFdm, convertFdm2Jsb), os.path.join(saveJsbPath, aeroFile)) # Launcher as a seperate XML file, direct the Aircraft definition to use if 'Winch' in oFdm.keys() : winchFile = 'Winch.xml' ET.SubElement(elemAircraft, 'external_reactions', file = winchFile) SaveXml(Winch(oFdm), os.path.join(saveJsbPath, winchFile)) # Imu as a seperate XML file, direct the Aircraft definition to use if 'Imu' in oFdm['Sensor'].keys() : imuFile = 'SensorImu.xml' ET.SubElement(elemAircraft, 'system', file = imuFile) SaveXml(SensorImu(oFdm), os.path.join(saveJsbPath, imuFile)) # Gps as a seperate XML file, direct the Aircraft definition to use if 'Gps' in oFdm['Sensor'].keys() : gpsFile = 'SensorGps.xml' ET.SubElement(elemAircraft, 'system', file = gpsFile) SaveXml(SensorGps(oFdm), os.path.join(saveJsbPath, gpsFile)) # Pitot as a seperate XML file, direct the Aircraft definition to use if 'Pitot' in oFdm['Sensor'].keys() : pitotFile = 'SensorPitot.xml' ET.SubElement(elemAircraft, 'system', file = pitotFile) SaveXml(SensorPitot(oFdm), os.path.join(saveJsbPath, pitotFile)) # 5Hole as a seperate XML file, direct the Aircraft definition to use if '5Hole' in oFdm['Sensor'].keys() : fiveHoleFile = 'Sensor5Hole.xml' ET.SubElement(elemAircraft, 'system', file = fiveHoleFile) SaveXml(Sensor5Hole(oFdm), os.path.join(saveJsbPath, fiveHoleFile)) # Write the Aircraft XML file saveFile = os.path.join(saveJsbPath, aircraftName + '.xml') SaveXml(elemAircraft, saveFile) return(elemAircraft) #%% Table Generator, Wrapper def TableGen(elemParent, tableArray, tableSignals, tableBreakPts): s = tableArray.shape iAxisRemList = [] for iAxis in range(0, len(s)): if s[iAxis] == 1: iAxisRemList.append(iAxis) # for iRem in iAxisRemList: # XXX # tableArray = tableArray.squeeze(axis=iRem) # del tableSignals[iRem] # del tableBreakPts[iRem] if len(tableArray.shape)==3: table = TableGen3D(elemParent, tableArray, tableSignals, tableBreakPts) elif len(tableArray.shape)==2: table = TableGen2D(elemParent, tableArray, tableSignals, tableBreakPts) elif (len(tableArray.shape)==1) & (tableArray.size > 1): table = TableGen1D(elemParent, tableArray, tableSignals, tableBreakPts) else: table = ET.SubElement(elemParent, 'value').text = str(tableArray) return table #%% Table Generator, 3D def TableGen3D(elemParent, tableArray, tableSignals, tableBreakPts): table = ET.SubElement(elemParent, 'table') #table = ET.Element('table') ET.SubElement(table, 'independentVar', lookup = 'row').text = tableSignals[0] ET.SubElement(table, 'independentVar', lookup = 'column').text = tableSignals[1] ET.SubElement(table, 'independentVar', lookup = 'table').text = tableSignals[2] indentSpace = ' '*4 indentLvl = 4 numRows, numColumns, numTables = np.shape(tableArray) columnHeader = indentSpace*(indentLvl) for columnVal in tableBreakPts[1]: columnHeader += ' '*6 + str(columnVal) for iTable in range(0, numTables): tableStr = ['\n' + columnHeader] for iRow in range(0, numRows): rowStr = str(tableArray[iRow, :, iTable]).replace('[','').replace(']','').replace('\n', '') tableStr.append(indentLvl*indentSpace + str(tableBreakPts[0][iRow]) + indentSpace + rowStr) tableStr = '\n'.join(tableStr) + '\n' + indentLvl*indentSpace # Replace list lines with '/n' strings ET.SubElement(table, 'tableData', breakPoint = str(tableBreakPts[2][iTable])).text = tableStr return table #%% Table Generator, 2D def TableGen2D(elemParent, tableArray, tableSignals, tableBreakPts): table = ET.SubElement(elemParent, 'table') ET.SubElement(table, 'independentVar', lookup = 'row').text = tableSignals[0] ET.SubElement(table, 'independentVar', lookup = 'column').text = tableSignals[1] indentSpace = ' '*4 indentLvl = 4 tableArray = tableArray.transpose() numRows, numColumns = np.shape(tableArray) columnHeader = indentSpace*(indentLvl) for columnVal in tableBreakPts[1]: columnHeader += ' '*6 + str(columnVal) tableStr = ['\n' + columnHeader] for iRow in range(0, numRows): rowStr = str(tableArray[iRow]).replace('[','').replace(']','').replace('\n', '') tableStr.append(indentLvl*indentSpace + str(tableBreakPts[0][iRow]) + indentSpace + rowStr) tableStr = '\n'.join(tableStr) + '\n' + indentLvl*indentSpace # Replace list lines with '/n' strings ET.SubElement(table, 'tableData').text = tableStr return table #%% Table Generator, 1D def TableGen1D(elemParent, tableArray, tableSignals, tableBreakPts): table = ET.SubElement(elemParent, 'table') ET.SubElement(table, 'independentVar', lookup = 'row').text = tableSignals indentSpace = ' '*4 indentLvl = 4 numRows = np.shape(tableArray)[0] tableStr = ['\n'] for iRow in range(0, numRows): rowStr = str(tableArray[iRow]).replace('[','').replace(']','').replace('\n', '') tableStr.append(indentLvl*indentSpace + str(tableBreakPts[iRow]) + indentSpace + rowStr) tableStr = '\n'.join(tableStr) + '\n' + indentLvl*indentSpace # Replace list lines with '/n' strings ET.SubElement(table, 'tableData').text = tableStr return table #%% def MassBalance(oFdm): mass_balance = ET.Element('mass_balance') # Mass ET.SubElement(mass_balance, 'emptywt', unit = 'KG').text = str(oFdm['MassProp']['mass_kg']) # CG location = ET.SubElement(mass_balance, 'location', name = 'CG', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['MassProp']['rCG_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['MassProp']['rCG_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['MassProp']['rCG_S_m'][2]) # Inertia ET.SubElement(mass_balance, 'ixx', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][0,0]) ET.SubElement(mass_balance, 'iyy', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][1,1]) ET.SubElement(mass_balance, 'izz', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][2,2]) ET.SubElement(mass_balance, 'ixy', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][0,1]) ET.SubElement(mass_balance, 'ixz', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][0,2]) ET.SubElement(mass_balance, 'iyz', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][1,2]) return(mass_balance) #%% def GroundReactions(oFdm): ground_reactions = ET.Element('ground_reactions') # Loop Each Gear for gear in oFdm['Gear'].keys(): contact = ET.SubElement(ground_reactions, 'contact', type = 'BOGEY', name = gear) location = ET.SubElement(contact, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Gear'][gear]['rGear_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Gear'][gear]['rGear_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Gear'][gear]['rGear_S_m'][2]) ET.SubElement(contact, 'static_friction').text = str(oFdm['Gear'][gear]['FricStatic']) ET.SubElement(contact, 'dynamic_friction').text = str(oFdm['Gear'][gear]['FricDynamic']) ET.SubElement(contact, 'rolling_friction').text = str(oFdm['Gear'][gear]['FricRoll']) ET.SubElement(contact, 'spring_coeff', unit = 'N/M').text = str(oFdm['Gear'][gear]['kSpring_Npm']) ET.SubElement(contact, 'damping_coeff', unit = 'N/M/SEC').text = str(oFdm['Gear'][gear]['dampSpring_Nspm']) ET.SubElement(contact, 'max_steer', unit = 'DEG').text = '0.0' return(ground_reactions) #%% def Metrics(oFdm): metrics = ET.Element('metrics') # Dimensions ET.SubElement(metrics, 'wingarea', unit = 'M2').text = str(oFdm['Aero']['Ref']['S_m2']) ET.SubElement(metrics, 'wingspan', unit = 'M').text = str(oFdm['Aero']['Ref']['b_m']) ET.SubElement(metrics, 'chord', unit = 'M').text = str(oFdm['Aero']['Ref']['cBar_m']) location = ET.SubElement(metrics, 'location', name = 'AERORP', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Aero']['Ref']['rAero_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Aero']['Ref']['rAero_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Aero']['Ref']['rAero_S_m'][2]) location = ET.SubElement(metrics, 'location', name = 'EYEPOINT', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Aero']['Ref']['rAero_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Aero']['Ref']['rAero_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Aero']['Ref']['rAero_S_m'][2]) location = ET.SubElement(metrics, 'location', name = 'VRP', unit = 'M') ET.SubElement(location, 'x').text = '0.0' ET.SubElement(location, 'y').text = '0.0' ET.SubElement(location, 'z').text = '0.0' return(metrics) #%% def Aerodynamics(oFdm, convertFdm2Jsb): import copy # Aero Coef definitions coefNamesFdm = convertFdm2Jsb['Coef']['oFdm'] # Aero Deriv dependencies definitions depNamesFdm = convertFdm2Jsb['Dep']['oFdm'] depNamesJsb = convertFdm2Jsb['Dep']['jsb'] depScale = convertFdm2Jsb['Dep']['scale'] coefNamesFdm = convertFdm2Jsb['Coef']['oFdm'] # Aero Breakpoint Table defintions indVarTable = convertFdm2Jsb['TableDef']['jsb'] breakPtsTable = convertFdm2Jsb['TableDef']['brkPts'] # Aero Table data to use aeroTable = oFdm['Aero']['Coef'] # Define the conversion from oFdm to JSB-ML # FIXIT - switch to a CDo+CDi drag computation coefTable = {'CL': {'axis': 'LIFT', 'scale': None, 'type': 'force', 'deriv': 'dCL'}, \ 'CD': {'axis': 'DRAG', 'scale': None, 'type': 'force', 'deriv': 'dCD'}, \ 'CY': {'axis': 'SIDE', 'scale': None, 'type': 'force', 'deriv': 'dCY'}, \ 'CMl': {'axis': 'ROLL', 'scale': 'metrics/bw-ft', 'type': 'moment', 'deriv': 'dCMl'}, \ 'CMm': {'axis': 'PITCH', 'scale': 'metrics/cbarw-ft', 'type': 'moment', 'deriv': 'dCMm'}, \ 'CMn': {'axis': 'YAW', 'scale': 'metrics/bw-ft', 'type': 'moment', 'deriv': 'dCMn'}} aerodynamics = ET.Element('aerodynamics') # # Create each coefficient individually, just the table look-up coefNames = coefTable.keys() for iCoef, coef in enumerate(coefNames): convertCoef = coefTable[coef] # For each coefficient: create just the table look-up, then the Multiplication, then the summation for iDep, dep in enumerate(coefNamesFdm): function = ET.SubElement(aerodynamics, 'function', name = str('aero/coefficient/' + coef + '__' + dep)) ET.SubElement(function, 'description').text = str(coef + '__' + dep) # Use the Table Generator to create the properly formated Table for JSB-ML tableArray = aeroTable[coef][dep] tableSignals = indVarTable tableBreakPts = breakPtsTable table = TableGen(function, copy.deepcopy(tableArray), copy.deepcopy(tableSignals), copy.deepcopy(tableBreakPts)) # For each derivative: create just the table look-up, then the Multiplication, then the summation deriv = convertCoef['deriv'] for iDep, dep in enumerate(depNamesFdm): function = ET.SubElement(aerodynamics, 'function', name = str('aero/coefficient/' + deriv + '__' + dep)) ET.SubElement(function, 'description').text = str(deriv + '__' + dep) # Use the Table Generator to create the properly formated Table for JSB-ML tableArray = aeroTable[deriv][dep] tableSignals = indVarTable tableBreakPts = breakPtsTable table = TableGen(function, copy.deepcopy(tableArray), copy.deepcopy(tableSignals), copy.deepcopy(tableBreakPts)) # Multiply each derivative by it's dependent variable function = ET.SubElement(aerodynamics, 'function', name = str('aero/coefficient/' + coef + '__' + dep)) ET.SubElement(function, 'description').text = str(coef + '__' + dep + ' = ' + deriv + '__' + dep + ' * ' + dep) #print(coef + '__' + dep + ' = ' + deriv + '__' + dep + ' * ' + dep) product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/coefficient/' + deriv + '__' + dep #print(deriv + '__' + dep) depSignal = depNamesJsb[iDep] #print(depSignal) if depSignal != None: ET.SubElement(product, 'property').text = depSignal # Dependent Variable/Signal scale = depScale[iDep] if scale != None: if isinstance(scale, str): ET.SubElement(product, 'property').text = str(scale) # Dependent Variable Scaling else: ET.SubElement(product, 'value').text = str(scale) # Dependent Variable Scaling # Sum the Coeficients function = ET.SubElement(aerodynamics, 'function', name = str('aero/coefficient/' + coef)) ET.SubElement(function, 'description').text = str(coef + ' summation') #print(coef + ' summation') summation = ET.SubElement(function, 'sum') for iDep, dep in enumerate(coefNamesFdm): ET.SubElement(summation, 'property').text = 'aero/coefficient/' + coef + '__' + dep #print(coef + '__' + dep) for iDep, dep in enumerate(depNamesFdm): ET.SubElement(summation, 'property').text = 'aero/coefficient/' + coef + '__' + dep #print(coef + '__' + dep) # # Dimensionalize the Coefficients into Forces and Moments for iCoef, coef in enumerate(coefNames): convertCoef = coefTable[coef] axis = ET.SubElement(aerodynamics, 'axis', name = convertCoef['axis']) function = ET.SubElement(axis, 'function', name = str('aero/' + convertCoef['type'] + '/' + convertCoef['axis'] + '__' + coef)) ET.SubElement(function, 'description').text = str(convertCoef['axis'] + ' from ' + coef) product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/qbar-area' # qBar * sRef if convertCoef['scale'] != None: ET.SubElement(product, 'property').text = convertCoef['scale'] # Coefficient Scaling ET.SubElement(product, 'property').text = 'aero/coefficient/' + coef return(aerodynamics) #%% def Propulsion(oFdm): propulsion = ET.Element('propulsion') for key in oFdm['Prop'].keys(): prop = oFdm['Prop'][key] # Motor/Engine engine = ET.SubElement(propulsion, 'engine', file = prop['nameMotor']) # location = ET.SubElement(engine, 'location', unit = 'M') # ET.SubElement(location, 'x').text = str(prop['rMotor_S_m'][0]) # ET.SubElement(location, 'y').text = str(prop['rMotor_S_m'][1]) # ET.SubElement(location, 'z').text = str(prop['rMotor_S_m'][2]) # orient = ET.SubElement(engine, 'orient', unit = 'DEG') # ET.SubElement(orient, 'roll').text = str(prop['sMotor_deg'][0]) # ET.SubElement(orient, 'pitch').text = str(prop['sMotor_deg'][1]) # ET.SubElement(orient, 'yaw').text = str(prop['sMotor_deg'][2]) # Thruster/Prop as an element of the Engine thruster = ET.SubElement(engine, 'thruster', file = prop['nameProp']) location = ET.SubElement(thruster, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(prop['rProp_S_m'][0]) ET.SubElement(location, 'y').text = str(prop['rProp_S_m'][1]) ET.SubElement(location, 'z').text = str(prop['rProp_S_m'][2]) orient = ET.SubElement(thruster, 'orient', unit = 'DEG') ET.SubElement(orient, 'roll').text = str(prop['sProp_deg'][0]) ET.SubElement(orient, 'pitch').text = str(prop['sProp_deg'][1]) ET.SubElement(orient, 'yaw').text = str(prop['sProp_deg'][2]) ET.SubElement(thruster, 'sense').text = str(prop['sense']) # 1 = CW as viewed from cockpit, -1 = CCW ET.SubElement(thruster, 'p_factor').text = str(prop['p_factor']) return(propulsion) #%% FCS def FlightControl(oFdm): # Define all the Pilot input definition # Pilot Inputs, us the FG normalized sticks fcsPilotDef = {} fcsPilotDef['summer'] = {} fcsPilotDef['gain'] = {} fcsPilotDef['summer']['pilotRoll_norm'] = {} fcsPilotDef['summer']['pilotRoll_norm']['inputList'] = ['fcs/aileron-cmd-norm', 'fcs/roll-trim-cmd-norm'] fcsPilotDef['summer']['pilotRoll_norm']['min'] = -1.0 fcsPilotDef['summer']['pilotRoll_norm']['max'] = 1.0 fcsPilotDef['gain']['cmdRoll_rps'] = {} fcsPilotDef['gain']['cmdRoll_rps']['input'] = 'fcs/pilotRoll_norm' fcsPilotDef['gain']['cmdRoll_rps']['gain'] = oFdm['FCS']['Pilot']['kRoll'] fcsPilotDef['summer']['pilotPitch_norm'] = {} fcsPilotDef['summer']['pilotPitch_norm']['inputList'] = ['fcs/elevator-cmd-norm', 'fcs/pitch-trim-cmd-norm'] fcsPilotDef['summer']['pilotPitch_norm']['min'] = -1.0 fcsPilotDef['summer']['pilotPitch_norm']['max'] = 1.0 fcsPilotDef['gain']['cmdPitch_rps'] = {} fcsPilotDef['gain']['cmdPitch_rps']['input'] = 'fcs/pilotPitch_norm' fcsPilotDef['gain']['cmdPitch_rps']['gain'] = oFdm['FCS']['Pilot']['kPitch'] fcsPilotDef['summer']['pilotYaw_norm'] = {} fcsPilotDef['summer']['pilotYaw_norm']['inputList'] = ['fcs/rudder-cmd-norm', 'fcs/yaw-trim-cmd-norm'] fcsPilotDef['summer']['pilotYaw_norm']['min'] = -1.0 fcsPilotDef['summer']['pilotYaw_norm']['max'] = 1.0 fcsPilotDef['gain']['cmdYaw_rps'] = {} fcsPilotDef['gain']['cmdYaw_rps']['input'] = 'fcs/pilotYaw_norm' fcsPilotDef['gain']['cmdYaw_rps']['gain'] = oFdm['FCS']['Pilot']['kYaw'] fcsPilotDef['summer']['pilotFlap_norm'] = {} fcsPilotDef['summer']['pilotFlap_norm']['inputList'] = ['fcs/flap-cmd-norm'] fcsPilotDef['summer']['pilotFlap_norm']['min'] = -1.0 fcsPilotDef['summer']['pilotFlap_norm']['max'] = 1.0 fcsPilotDef['gain']['cmdFlap_rad'] = {} fcsPilotDef['gain']['cmdFlap_rad']['input'] = 'fcs/pilotFlap_norm' fcsPilotDef['gain']['cmdFlap_rad']['gain'] = oFdm['FCS']['Pilot']['kFlap'] # Create the JSB-ML elemFCS = ET.Element('flight_control', name = 'Generic Flight Control') pilot = ET.SubElement(elemFCS, 'channel', name = 'Pilot_Inputs') for type in fcsPilotDef: if type == 'summer': for key in fcsPilotDef['summer'].keys(): entry = fcsPilotDef['summer'][key] summer = ET.SubElement(pilot, 'summer', name = key) for input in entry['inputList']: ET.SubElement(summer, 'input').text = input if ('min' in entry.keys()) or ('max' in entry.keys()): clipto = ET.SubElement(summer, 'clipto') if ('min' in entry.keys()): ET.SubElement(clipto, 'min').text = str(entry['min']) if ('max' in entry.keys()): ET.SubElement(clipto, 'max').text = str(entry['max']) ET.SubElement(summer, 'output').text = 'fcs/' + key if type == 'gain': for key in fcsPilotDef['gain'].keys(): entry = fcsPilotDef['gain'][key] gain = ET.SubElement(pilot, 'pure_gain', name = key) ET.SubElement(gain, 'input').text = entry['input'] ET.SubElement(gain, 'gain').text = str(entry['gain']) if ('min' in entry.keys()) or ('max' in entry.keys()): clipto = ET.SubElement(gain, 'clipto') if ('min' in entry.keys()): ET.SubElement(clipto, 'min').text = str(entry['min']) if ('max' in entry.keys()): ET.SubElement(clipto, 'max').text = str(entry['max']) ET.SubElement(gain, 'output').text = 'fcs/' + key # Control System Surface Mixer mixer = ET.SubElement(elemFCS, 'channel', name = 'Control Mixer') fcsMixerDef = oFdm['FCS']['Mixer'] for iSurf, surf in enumerate(fcsMixerDef['surfNames']): cmdSurf = 'cmd' + surf + '_rad' keyList = [] for iInput, input in enumerate(fcsMixerDef['inputs']): val = fcsMixerDef['surfMix'][iSurf][iInput] key = input + '_2_' + surf if val != 0.0: keyList.append(key) gain = ET.SubElement(mixer, 'pure_gain', name = key.replace('fcs/','')) ET.SubElement(gain, 'input').text = 'fcs/' + input ET.SubElement(gain, 'gain').text = str(val) ET.SubElement(gain, 'output').text = 'fcs/' + key if any(keyList): summer = ET.SubElement(mixer, 'summer', name = cmdSurf) for key in keyList: ET.SubElement(summer, 'input').text = 'fcs/' + key ET.SubElement(summer, 'output').text = 'fcs/' + cmdSurf # Inputs for External Commands, this just add property to create the node in the tree for iSurf, surf in enumerate(fcsMixerDef['surfNames']): cmdSurfExt = 'cmd' + surf + '_ext_rad' prop = ET.SubElement(elemFCS, 'property').text = 'fcs/' + cmdSurfExt name = 'Motor' cmdMotorExt = 'cmd' + name + '_ext_nd' motor = ET.SubElement(elemFCS, 'property').text = 'fcs/' + cmdMotorExt # Add the Motor external command # Inputs for External Commands, this just add property to create the node in the tree extern = ET.SubElement(elemFCS, 'channel', name = 'External Input Summations') for iSurf, surf in enumerate(fcsMixerDef['surfNames']): cmdSurf = 'cmd' + surf + '_rad' cmdSurfExt = 'cmd' + surf + '_ext_rad' summer = ET.SubElement(extern, 'summer') ET.SubElement(summer, 'input').text = 'fcs/' + cmdSurf ET.SubElement(summer, 'input').text = 'fcs/' + cmdSurfExt ET.SubElement(summer, 'output').text = 'fcs/' + cmdSurf name = 'Motor' cmdMotor = 'cmd' + name + '_nd' cmdMotorExt = 'cmd' + name + '_ext_nd' summer = ET.SubElement(extern, 'summer') ET.SubElement(summer, 'input').text = 'fcs/throttle-cmd-norm' ET.SubElement(summer, 'input').text = 'fcs/' + cmdMotorExt ET.SubElement(summer, 'output').text = 'fcs/throttle-pos-norm' return(elemFCS) #%% Effectors, for each surface define the 2nd order TF, and an 'actuator' def Effectors(oFdm): sysEffDef = oFdm['Act'] effectors = ET.Element('system', name = 'Effectors') channel = ET.SubElement(effectors, 'channel', name = 'Actuator Models') for surf in sysEffDef.keys(): cmdSurf = 'cmd' + surf + '_rad' posSurf = 'pos' + surf + '_rad' entry = sysEffDef[surf] # Actuator - delay and freeplay actuator = ET.SubElement(channel, 'actuator', name = 'act' + surf) ET.SubElement(actuator, 'input').text = 'fcs/' + cmdSurf ET.SubElement(actuator, 'lag').text = str(entry['lag_nd']) ET.SubElement(actuator, 'hysteresis_width').text = str(entry['freeplay_rad']) ET.SubElement(actuator, 'delay').text = str(entry['delay_s']) if ('min' in entry.keys()) or ('max' in entry.keys()): clipto = ET.SubElement(actuator, 'clipto') if ('min' in entry.keys()): ET.SubElement(clipto, 'min').text = str(entry['min']) if ('max' in entry.keys()): ET.SubElement(clipto, 'max').text = str(entry['max']) ET.SubElement(actuator, 'output').text = 'fcs/' + posSurf return(effectors) #%% def Winch(oFdm): external_reactions = ET.Element('external_reactions') # Winch force = ET.SubElement(external_reactions, 'force', name='hitch' , frame = 'BODY', unit='N') location = ET.SubElement(force, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Winch']['rHook_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Winch']['rHook_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Winch']['rHook_S_m'][2]) direction = ET.SubElement(force, 'direction') ET.SubElement(direction, 'x').text = str(oFdm['Winch']['sHook_deg'][0]) ET.SubElement(direction, 'y').text = str(oFdm['Winch']['sHook_deg'][1]) ET.SubElement(direction, 'z').text = str(oFdm['Winch']['sHook_deg'][2]) return(external_reactions) #%% IMU def SensorImu(oFdm): imu = ET.Element('system', name = 'Sensor - IMU') # Create time in us function = ET.SubElement(imu, 'function', name = 'sensor/imu/time_us') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'simulation/sim-time-sec' ET.SubElement(product, 'value').text = str(1e6) # Accelerometers if 'Accel' in oFdm['Sensor']['Imu'].keys() : channel = ET.SubElement(imu, 'channel', name = 'Temp Accelerometers') axisList = ['X', 'Y', 'Z'] for axisName in axisList: accel = ET.SubElement(channel, 'accelerometer', name = 'Accel' + axisName) ET.SubElement(accel, 'axis').text = axisName location = ET.SubElement(accel, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Sensor']['Imu']['Accel']['r_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Sensor']['Imu']['Accel']['r_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Sensor']['Imu']['Accel']['r_S_m'][2]) orientation = ET.SubElement(accel, 'orientation', unit='DEG') ET.SubElement(orientation, 'roll').text = str(oFdm['Sensor']['Imu']['Accel']['s_deg'][0]) ET.SubElement(orientation, 'pitch').text = str(oFdm['Sensor']['Imu']['Accel']['s_deg'][1]) ET.SubElement(orientation, 'yaw').text = str(oFdm['Sensor']['Imu']['Accel']['s_deg'][2]) ET.SubElement(accel, 'output').text = 'sensor/imu/accel' + axisName + '_true_fps2' # Convert Units Accelerometer to mps2 for axisName in axisList: function = ET.SubElement(imu, 'function', name = 'sensor/imu/accel' + axisName + '_true_mps2') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'sensor/imu/accel' + axisName + '_true_fps2' ET.SubElement(product, 'value').text = str(ft2m) # Accelerometer Error Model channel = ET.SubElement(imu, 'channel', name = 'Accelerometer Error Model') errMod = oFdm['Sensor']['Imu']['Accel'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = 'Accel' + axisName) ET.SubElement(sensor, 'input').text = 'sensor/imu/accel' + axisName + '_true_mps2' ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/imu/accel' + axisName + '_mps2' # Gyros if 'Gyro' in oFdm['Sensor']['Imu'].keys() : errMod = oFdm['Sensor']['Imu']['Gyro'] channel = ET.SubElement(imu, 'channel', name = 'Gyros') for iAxis, axisName in enumerate(axisList): gyro = ET.SubElement(channel, 'gyro', name = 'Gyro' + axisName) ET.SubElement(gyro, 'axis').text = axisName location = ET.SubElement(gyro, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(errMod['r_S_m'][0]) ET.SubElement(location, 'y').text = str(errMod['r_S_m'][1]) ET.SubElement(location, 'z').text = str(errMod['r_S_m'][2]) orientation = ET.SubElement(gyro, 'orientation', unit='DEG') ET.SubElement(orientation, 'roll').text = str(errMod['s_deg'][0]) ET.SubElement(orientation, 'pitch').text = str(errMod['s_deg'][1]) ET.SubElement(orientation, 'yaw').text = str(errMod['s_deg'][2]) ET.SubElement(gyro, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(gyro, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(gyro, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(gyro, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(gyro, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(gyro, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(gyro, 'output').text = 'sensor/imu/gyro' + axisName + '_rps' # Magnetometers if 'Mag' in oFdm['Sensor']['Imu'].keys() : errMod = oFdm['Sensor']['Imu']['Mag'] channel = ET.SubElement(imu, 'channel', name = 'Magnetometers') for iAxis, axisName in enumerate(axisList): mag = ET.SubElement(channel, 'magnetometer', name = 'Mag' + axisName) ET.SubElement(mag, 'axis').text = axisName location = ET.SubElement(mag, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(errMod['r_S_m'][0]) ET.SubElement(location, 'y').text = str(errMod['r_S_m'][1]) ET.SubElement(location, 'z').text = str(errMod['r_S_m'][2]) orientation = ET.SubElement(mag, 'orientation', unit='DEG') ET.SubElement(orientation, 'roll').text = str(errMod['s_deg'][0]) ET.SubElement(orientation, 'pitch').text = str(errMod['s_deg'][1]) ET.SubElement(orientation, 'yaw').text = str(errMod['s_deg'][2]) ET.SubElement(mag, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(mag, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(mag, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(mag, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(mag, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(mag, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(mag, 'output').text = 'sensor/imu/mag' + axisName + '_nT' # Magnetometer unit conversion for axisName in axisList: function = ET.SubElement(imu, 'function', name = 'sensor/imu/mag' + axisName + '_uT') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'sensor/imu/mag' + axisName + '_nT' ET.SubElement(product, 'value').text = str(0.001) return(imu) #%% GPS def SensorGps(oFdm): gps = ET.Element('system', name = 'Sensor - GPS') # Create time in us function = ET.SubElement(gps, 'function', name = 'sensor/gps/time_us') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'simulation/sim-time-sec' ET.SubElement(product, 'value').text = str(1e6) # GPS Position function = ET.SubElement(gps, 'function', name = 'sensor/gps/lat_true_rad') ET.SubElement(function, 'property').text = 'position/lat-geod-rad' function = ET.SubElement(gps, 'function', name = 'sensor/gps/long_true_rad') ET.SubElement(function, 'property').text = 'position/long-gc-rad' function = ET.SubElement(gps, 'function', name = 'sensor/gps/alt_true_m') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'position/h-sl-ft' ET.SubElement(product, 'value').text = str(ft2m) # GPS Velocity function = ET.SubElement(gps, 'function', name = 'sensor/gps/vNorth_true_mps') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'velocities/v-north-fps' ET.SubElement(product, 'value').text = str(ft2m) function = ET.SubElement(gps, 'function', name = 'sensor/gps/vEast_true_mps') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'velocities/v-east-fps' ET.SubElement(product, 'value').text = str(ft2m) function = ET.SubElement(gps, 'function', name = 'sensor/gps/vDown_true_mps') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'velocities/v-down-fps' ET.SubElement(product, 'value').text = str(ft2m) # GPS Error Model channel = ET.SubElement(gps, 'channel', name = 'GPS Error Models') axisList = ['lat_rad', 'long_rad', 'alt_m'] errMod = oFdm['Sensor']['Gps']['Pos'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = axisName) ET.SubElement(sensor, 'input').text = 'sensor/gps/' + axisName.replace('_', '_true_') ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/gps/' + axisName axisList = ['vNorth_mps', 'vEast_mps', 'vDown_mps'] errMod = oFdm['Sensor']['Gps']['Vel'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = axisName) ET.SubElement(sensor, 'input').text = 'sensor/gps/' + axisName.replace('_', '_true_') ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/gps/' + axisName return(gps) #%% def SensorPitot(oFdm): pitot = ET.Element('system', name = 'Sensor - Pitot-Static Probe') # Create time in us function = ET.SubElement(pitot, 'function', name = 'sensor/pitot/time_us') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'simulation/sim-time-sec' ET.SubElement(product, 'value').text = str(1e6) # Airdata Static function = ET.SubElement(pitot, 'function', name = 'sensor/pitot/presStatic_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'atmosphere/P-psf' ET.SubElement(product, 'value').text = str(psf2pa) # Airdata Tip (Dynamic ~= Impact) function = ET.SubElement(pitot, 'function', name = 'sensor/pitot/presTip_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) # Airdata Temperature function = ET.SubElement(pitot, 'function', name = 'sensor/pitot/temp_true_C') product = ET.SubElement(function, 'product') summation = ET.SubElement(product, 'sum') ET.SubElement(summation, 'property').text = 'atmosphere/T-R' ET.SubElement(summation, 'value').text = str(-491.67) ET.SubElement(product, 'value').text = str(5.0/9.0) # Pitot Error Model channel = ET.SubElement(pitot, 'channel', name = 'Pitot Error Models') axisList = ['presStatic_Pa', 'presTip_Pa', 'temp_C'] errMod = oFdm['Sensor']['Gps']['Vel'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = axisName) ET.SubElement(sensor, 'input').text = 'sensor/pitot/' + axisName.replace('_', '_true_') ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/pitot/' + axisName return(pitot) #%% def Sensor5Hole(oFdm): fiveHole = ET.Element('system', name = 'Sensor - 5Hole Probe') # Determine whether method #1 or method #2 if 'alphaK1' and 'betaK1' in oFdm['Sensor']['5Hole'].keys(): method = 1 elif 'alphaK2' and 'betaK2' in oFdm['Sensor']['5Hole'].keys(): method = 2 else: print('5Hole Probe: Need either (alphaK1 and betaK1) or (alphaK2 and betaK2)') # Create time in us function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/time_us') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'simulation/sim-time-sec' ET.SubElement(product, 'value').text = str(1e6) # Airdata Static function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presStatic_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'atmosphere/P-psf' ET.SubElement(product, 'value').text = str(psf2pa) # Airdata Tip (Dynamic ~= Impact) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presTip_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) # Airdata Temperature function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/temp_true_C') product = ET.SubElement(function, 'product') summation = ET.SubElement(product, 'sum') ET.SubElement(summation, 'property').text = 'atmosphere/T-R' ET.SubElement(summation, 'value').text = str(-491.67) ET.SubElement(product, 'value').text = str(5.0/9.0) # [Method 1] if method == 1: axisList = ['presStatic_Pa', 'presTip_Pa', 'presAlphaBot_Pa', 'presAlphaTop_Pa', 'presBetaRight_Pa', 'presBetaLeft_Pa', 'temp_C'] # Alpha Difference (presAlphaBot - presAlphaTop) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presAlphaBot_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/alpha-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['alphaK1'][0]) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presAlphaTop_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/alpha-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['alphaK1'][1]) # [Method 2] Beta Difference (presBetaRight - presBetaLeft) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presBetaRight_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/beta-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['betaK1'][0]) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presBetaLeft_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/beta-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['betaK1'][1]) # [Method 2] elif method == 2: axisList = ['presStatic_Pa', 'presTip_Pa', 'presAlpha_Pa', 'presBeta_Pa', 'temp_C'] # Alpha Difference (presAlphaBot - presAlphaTop) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presAlpha_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/alpha-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['alphaK2']) # [Method 2] Beta Difference (presBetaRight - presBetaLeft) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presBeta_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/beta-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['betaK2']) # 5Hole Error Model channel = ET.SubElement(fiveHole, 'channel', name = '5Hole Error Models') errMod = oFdm['Sensor']['5Hole'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = axisName) ET.SubElement(sensor, 'input').text = 'sensor/fiveHole/' + axisName.replace('_', '_true_') ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/fiveHole/' + axisName return(fiveHole)

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import os.path from xml.etree import ElementTree as ET import numpy as np ft2m = 0.3048 psf2pa = 47.88026 def SaveXml(elem, saveFile): from xml.dom import minidom uglyXml = ET.tostring(elem, 'utf-8') prettyXml = minidom.parseString(uglyXml).toprettyxml(indent=' ', newl = '\r\n') os.makedirs(os.path.dirname(saveFile), exist_ok=True) with open(saveFile, 'w') as saveXML: saveXML.write(prettyXml) saveXML.close() def Aircraft(oFdm, convertFdm2Jsb, saveJsbPath, aircraftName): elemAircraft = ET.Element('fdm_config', version = '2.0', release = 'Alpha') fcsFile = 'FlightControl.xml' ET.SubElement(elemAircraft, 'flight_control', file = fcsFile) SaveXml(FlightControl(oFdm), os.path.join(saveJsbPath, fcsFile)) effFile = 'Effectors.xml' ET.SubElement(elemAircraft, 'system', file = effFile) SaveXml(Effectors(oFdm), os.path.join(saveJsbPath, effFile)) massFile = 'Mass.xml' ET.SubElement(elemAircraft, 'mass_balance', file = massFile) SaveXml(MassBalance(oFdm), os.path.join(saveJsbPath, massFile)) gearFile = 'Gear.xml' ET.SubElement(elemAircraft, 'ground_reactions', file = gearFile) SaveXml(GroundReactions(oFdm), os.path.join(saveJsbPath, gearFile)) propFile = 'Propulsion.xml' ET.SubElement(elemAircraft, 'propulsion', file = propFile) SaveXml(Propulsion(oFdm), os.path.join(saveJsbPath, propFile)) metricsFile = 'Metrics.xml' ET.SubElement(elemAircraft, 'metrics', file = metricsFile) SaveXml(Metrics(oFdm), os.path.join(saveJsbPath, metricsFile)) aeroFile = 'Aero.xml' ET.SubElement(elemAircraft, 'aerodynamics', file = aeroFile) SaveXml(Aerodynamics(oFdm, convertFdm2Jsb), os.path.join(saveJsbPath, aeroFile)) if 'Winch' in oFdm.keys() : winchFile = 'Winch.xml' ET.SubElement(elemAircraft, 'external_reactions', file = winchFile) SaveXml(Winch(oFdm), os.path.join(saveJsbPath, winchFile)) if 'Imu' in oFdm['Sensor'].keys() : imuFile = 'SensorImu.xml' ET.SubElement(elemAircraft, 'system', file = imuFile) SaveXml(SensorImu(oFdm), os.path.join(saveJsbPath, imuFile)) if 'Gps' in oFdm['Sensor'].keys() : gpsFile = 'SensorGps.xml' ET.SubElement(elemAircraft, 'system', file = gpsFile) SaveXml(SensorGps(oFdm), os.path.join(saveJsbPath, gpsFile)) if 'Pitot' in oFdm['Sensor'].keys() : pitotFile = 'SensorPitot.xml' ET.SubElement(elemAircraft, 'system', file = pitotFile) SaveXml(SensorPitot(oFdm), os.path.join(saveJsbPath, pitotFile)) if '5Hole' in oFdm['Sensor'].keys() : fiveHoleFile = 'Sensor5Hole.xml' ET.SubElement(elemAircraft, 'system', file = fiveHoleFile) SaveXml(Sensor5Hole(oFdm), os.path.join(saveJsbPath, fiveHoleFile)) saveFile = os.path.join(saveJsbPath, aircraftName + '.xml') SaveXml(elemAircraft, saveFile) return(elemAircraft) def TableGen(elemParent, tableArray, tableSignals, tableBreakPts): s = tableArray.shape iAxisRemList = [] for iAxis in range(0, len(s)): if s[iAxis] == 1: iAxisRemList.append(iAxis) if len(tableArray.shape)==3: table = TableGen3D(elemParent, tableArray, tableSignals, tableBreakPts) elif len(tableArray.shape)==2: table = TableGen2D(elemParent, tableArray, tableSignals, tableBreakPts) elif (len(tableArray.shape)==1) & (tableArray.size > 1): table = TableGen1D(elemParent, tableArray, tableSignals, tableBreakPts) else: table = ET.SubElement(elemParent, 'value').text = str(tableArray) return table def TableGen3D(elemParent, tableArray, tableSignals, tableBreakPts): table = ET.SubElement(elemParent, 'table') ET.SubElement(table, 'independentVar', lookup = 'row').text = tableSignals[0] ET.SubElement(table, 'independentVar', lookup = 'column').text = tableSignals[1] ET.SubElement(table, 'independentVar', lookup = 'table').text = tableSignals[2] indentSpace = ' '*4 indentLvl = 4 numRows, numColumns, numTables = np.shape(tableArray) columnHeader = indentSpace*(indentLvl) for columnVal in tableBreakPts[1]: columnHeader += ' '*6 + str(columnVal) for iTable in range(0, numTables): tableStr = ['\n' + columnHeader] for iRow in range(0, numRows): rowStr = str(tableArray[iRow, :, iTable]).replace('[','').replace(']','').replace('\n', '') tableStr.append(indentLvl*indentSpace + str(tableBreakPts[0][iRow]) + indentSpace + rowStr) tableStr = '\n'.join(tableStr) + '\n' + indentLvl*indentSpace ET.SubElement(table, 'tableData', breakPoint = str(tableBreakPts[2][iTable])).text = tableStr return table def TableGen2D(elemParent, tableArray, tableSignals, tableBreakPts): table = ET.SubElement(elemParent, 'table') ET.SubElement(table, 'independentVar', lookup = 'row').text = tableSignals[0] ET.SubElement(table, 'independentVar', lookup = 'column').text = tableSignals[1] indentSpace = ' '*4 indentLvl = 4 tableArray = tableArray.transpose() numRows, numColumns = np.shape(tableArray) columnHeader = indentSpace*(indentLvl) for columnVal in tableBreakPts[1]: columnHeader += ' '*6 + str(columnVal) tableStr = ['\n' + columnHeader] for iRow in range(0, numRows): rowStr = str(tableArray[iRow]).replace('[','').replace(']','').replace('\n', '') tableStr.append(indentLvl*indentSpace + str(tableBreakPts[0][iRow]) + indentSpace + rowStr) tableStr = '\n'.join(tableStr) + '\n' + indentLvl*indentSpace ET.SubElement(table, 'tableData').text = tableStr return table def TableGen1D(elemParent, tableArray, tableSignals, tableBreakPts): table = ET.SubElement(elemParent, 'table') ET.SubElement(table, 'independentVar', lookup = 'row').text = tableSignals indentSpace = ' '*4 indentLvl = 4 numRows = np.shape(tableArray)[0] tableStr = ['\n'] for iRow in range(0, numRows): rowStr = str(tableArray[iRow]).replace('[','').replace(']','').replace('\n', '') tableStr.append(indentLvl*indentSpace + str(tableBreakPts[iRow]) + indentSpace + rowStr) tableStr = '\n'.join(tableStr) + '\n' + indentLvl*indentSpace ET.SubElement(table, 'tableData').text = tableStr return table def MassBalance(oFdm): mass_balance = ET.Element('mass_balance') ET.SubElement(mass_balance, 'emptywt', unit = 'KG').text = str(oFdm['MassProp']['mass_kg']) location = ET.SubElement(mass_balance, 'location', name = 'CG', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['MassProp']['rCG_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['MassProp']['rCG_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['MassProp']['rCG_S_m'][2]) ET.SubElement(mass_balance, 'ixx', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][0,0]) ET.SubElement(mass_balance, 'iyy', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][1,1]) ET.SubElement(mass_balance, 'izz', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][2,2]) ET.SubElement(mass_balance, 'ixy', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][0,1]) ET.SubElement(mass_balance, 'ixz', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][0,2]) ET.SubElement(mass_balance, 'iyz', unit = 'KG*M2').text = str(oFdm['MassProp']['inertia_kgm2'][1,2]) return(mass_balance) def GroundReactions(oFdm): ground_reactions = ET.Element('ground_reactions') for gear in oFdm['Gear'].keys(): contact = ET.SubElement(ground_reactions, 'contact', type = 'BOGEY', name = gear) location = ET.SubElement(contact, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Gear'][gear]['rGear_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Gear'][gear]['rGear_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Gear'][gear]['rGear_S_m'][2]) ET.SubElement(contact, 'static_friction').text = str(oFdm['Gear'][gear]['FricStatic']) ET.SubElement(contact, 'dynamic_friction').text = str(oFdm['Gear'][gear]['FricDynamic']) ET.SubElement(contact, 'rolling_friction').text = str(oFdm['Gear'][gear]['FricRoll']) ET.SubElement(contact, 'spring_coeff', unit = 'N/M').text = str(oFdm['Gear'][gear]['kSpring_Npm']) ET.SubElement(contact, 'damping_coeff', unit = 'N/M/SEC').text = str(oFdm['Gear'][gear]['dampSpring_Nspm']) ET.SubElement(contact, 'max_steer', unit = 'DEG').text = '0.0' return(ground_reactions) def Metrics(oFdm): metrics = ET.Element('metrics') ET.SubElement(metrics, 'wingarea', unit = 'M2').text = str(oFdm['Aero']['Ref']['S_m2']) ET.SubElement(metrics, 'wingspan', unit = 'M').text = str(oFdm['Aero']['Ref']['b_m']) ET.SubElement(metrics, 'chord', unit = 'M').text = str(oFdm['Aero']['Ref']['cBar_m']) location = ET.SubElement(metrics, 'location', name = 'AERORP', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Aero']['Ref']['rAero_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Aero']['Ref']['rAero_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Aero']['Ref']['rAero_S_m'][2]) location = ET.SubElement(metrics, 'location', name = 'EYEPOINT', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Aero']['Ref']['rAero_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Aero']['Ref']['rAero_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Aero']['Ref']['rAero_S_m'][2]) location = ET.SubElement(metrics, 'location', name = 'VRP', unit = 'M') ET.SubElement(location, 'x').text = '0.0' ET.SubElement(location, 'y').text = '0.0' ET.SubElement(location, 'z').text = '0.0' return(metrics) def Aerodynamics(oFdm, convertFdm2Jsb): import copy coefNamesFdm = convertFdm2Jsb['Coef']['oFdm'] depNamesFdm = convertFdm2Jsb['Dep']['oFdm'] depNamesJsb = convertFdm2Jsb['Dep']['jsb'] depScale = convertFdm2Jsb['Dep']['scale'] coefNamesFdm = convertFdm2Jsb['Coef']['oFdm'] indVarTable = convertFdm2Jsb['TableDef']['jsb'] breakPtsTable = convertFdm2Jsb['TableDef']['brkPts'] aeroTable = oFdm['Aero']['Coef'] e': None, 'type': 'force', 'deriv': 'dCL'}, \ 'CD': {'axis': 'DRAG', 'scale': None, 'type': 'force', 'deriv': 'dCD'}, \ 'CY': {'axis': 'SIDE', 'scale': None, 'type': 'force', 'deriv': 'dCY'}, \ 'CMl': {'axis': 'ROLL', 'scale': 'metrics/bw-ft', 'type': 'moment', 'deriv': 'dCMl'}, \ 'CMm': {'axis': 'PITCH', 'scale': 'metrics/cbarw-ft', 'type': 'moment', 'deriv': 'dCMm'}, \ 'CMn': {'axis': 'YAW', 'scale': 'metrics/bw-ft', 'type': 'moment', 'deriv': 'dCMn'}} aerodynamics = ET.Element('aerodynamics') coefNames = coefTable.keys() for iCoef, coef in enumerate(coefNames): convertCoef = coefTable[coef] for iDep, dep in enumerate(coefNamesFdm): function = ET.SubElement(aerodynamics, 'function', name = str('aero/coefficient/' + coef + '__' + dep)) ET.SubElement(function, 'description').text = str(coef + '__' + dep) tableArray = aeroTable[coef][dep] tableSignals = indVarTable tableBreakPts = breakPtsTable table = TableGen(function, copy.deepcopy(tableArray), copy.deepcopy(tableSignals), copy.deepcopy(tableBreakPts)) deriv = convertCoef['deriv'] for iDep, dep in enumerate(depNamesFdm): function = ET.SubElement(aerodynamics, 'function', name = str('aero/coefficient/' + deriv + '__' + dep)) ET.SubElement(function, 'description').text = str(deriv + '__' + dep) tableArray = aeroTable[deriv][dep] tableSignals = indVarTable tableBreakPts = breakPtsTable table = TableGen(function, copy.deepcopy(tableArray), copy.deepcopy(tableSignals), copy.deepcopy(tableBreakPts)) function = ET.SubElement(aerodynamics, 'function', name = str('aero/coefficient/' + coef + '__' + dep)) ET.SubElement(function, 'description').text = str(coef + '__' + dep + ' = ' + deriv + '__' + dep + ' * ' + dep) #print(coef + '__' + dep + ' = ' + deriv + '__' + dep + ' * ' + dep) product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/coefficient/' + deriv + '__' + dep #print(deriv + '__' + dep) depSignal = depNamesJsb[iDep] #print(depSignal) if depSignal != None: ET.SubElement(product, 'property').text = depSignal # Dependent Variable/Signal scale = depScale[iDep] if scale != None: if isinstance(scale, str): ET.SubElement(product, 'property').text = str(scale) # Dependent Variable Scaling else: ET.SubElement(product, 'value').text = str(scale) # Dependent Variable Scaling # Sum the Coeficients function = ET.SubElement(aerodynamics, 'function', name = str('aero/coefficient/' + coef)) ET.SubElement(function, 'description').text = str(coef + ' summation') #print(coef + ' summation') summation = ET.SubElement(function, 'sum') for iDep, dep in enumerate(coefNamesFdm): ET.SubElement(summation, 'property').text = 'aero/coefficient/' + coef + '__' + dep #print(coef + '__' + dep) for iDep, dep in enumerate(depNamesFdm): ET.SubElement(summation, 'property').text = 'aero/coefficient/' + coef + '__' + dep #print(coef + '__' + dep) # # Dimensionalize the Coefficients into Forces and Moments for iCoef, coef in enumerate(coefNames): convertCoef = coefTable[coef] axis = ET.SubElement(aerodynamics, 'axis', name = convertCoef['axis']) function = ET.SubElement(axis, 'function', name = str('aero/' + convertCoef['type'] + '/' + convertCoef['axis'] + '__' + coef)) ET.SubElement(function, 'description').text = str(convertCoef['axis'] + ' from ' + coef) product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/qbar-area' # qBar * sRef if convertCoef['scale'] != None: ET.SubElement(product, 'property').text = convertCoef['scale'] # Coefficient Scaling ET.SubElement(product, 'property').text = 'aero/coefficient/' + coef return(aerodynamics) #%% def Propulsion(oFdm): propulsion = ET.Element('propulsion') for key in oFdm['Prop'].keys(): prop = oFdm['Prop'][key] # Motor/Engine engine = ET.SubElement(propulsion, 'engine', file = prop['nameMotor']) # location = ET.SubElement(engine, 'location', unit = 'M') # ET.SubElement(location, 'x').text = str(prop['rMotor_S_m'][0]) # ET.SubElement(location, 'y').text = str(prop['rMotor_S_m'][1]) # ET.SubElement(location, 'z').text = str(prop['rMotor_S_m'][2]) # orient = ET.SubElement(engine, 'orient', unit = 'DEG') # ET.SubElement(orient, 'roll').text = str(prop['sMotor_deg'][0]) # ET.SubElement(orient, 'pitch').text = str(prop['sMotor_deg'][1]) # ET.SubElement(orient, 'yaw').text = str(prop['sMotor_deg'][2]) # Thruster/Prop as an element of the Engine thruster = ET.SubElement(engine, 'thruster', file = prop['nameProp']) location = ET.SubElement(thruster, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(prop['rProp_S_m'][0]) ET.SubElement(location, 'y').text = str(prop['rProp_S_m'][1]) ET.SubElement(location, 'z').text = str(prop['rProp_S_m'][2]) orient = ET.SubElement(thruster, 'orient', unit = 'DEG') ET.SubElement(orient, 'roll').text = str(prop['sProp_deg'][0]) ET.SubElement(orient, 'pitch').text = str(prop['sProp_deg'][1]) ET.SubElement(orient, 'yaw').text = str(prop['sProp_deg'][2]) ET.SubElement(thruster, 'sense').text = str(prop['sense']) # 1 = CW as viewed from cockpit, -1 = CCW ET.SubElement(thruster, 'p_factor').text = str(prop['p_factor']) return(propulsion) #%% FCS def FlightControl(oFdm): # Define all the Pilot input definition # Pilot Inputs, us the FG normalized sticks fcsPilotDef = {} fcsPilotDef['summer'] = {} fcsPilotDef['gain'] = {} fcsPilotDef['summer']['pilotRoll_norm'] = {} fcsPilotDef['summer']['pilotRoll_norm']['inputList'] = ['fcs/aileron-cmd-norm', 'fcs/roll-trim-cmd-norm'] fcsPilotDef['summer']['pilotRoll_norm']['min'] = -1.0 fcsPilotDef['summer']['pilotRoll_norm']['max'] = 1.0 fcsPilotDef['gain']['cmdRoll_rps'] = {} fcsPilotDef['gain']['cmdRoll_rps']['input'] = 'fcs/pilotRoll_norm' fcsPilotDef['gain']['cmdRoll_rps']['gain'] = oFdm['FCS']['Pilot']['kRoll'] fcsPilotDef['summer']['pilotPitch_norm'] = {} fcsPilotDef['summer']['pilotPitch_norm']['inputList'] = ['fcs/elevator-cmd-norm', 'fcs/pitch-trim-cmd-norm'] fcsPilotDef['summer']['pilotPitch_norm']['min'] = -1.0 fcsPilotDef['summer']['pilotPitch_norm']['max'] = 1.0 fcsPilotDef['gain']['cmdPitch_rps'] = {} fcsPilotDef['gain']['cmdPitch_rps']['input'] = 'fcs/pilotPitch_norm' fcsPilotDef['gain']['cmdPitch_rps']['gain'] = oFdm['FCS']['Pilot']['kPitch'] fcsPilotDef['summer']['pilotYaw_norm'] = {} fcsPilotDef['summer']['pilotYaw_norm']['inputList'] = ['fcs/rudder-cmd-norm', 'fcs/yaw-trim-cmd-norm'] fcsPilotDef['summer']['pilotYaw_norm']['min'] = -1.0 fcsPilotDef['summer']['pilotYaw_norm']['max'] = 1.0 fcsPilotDef['gain']['cmdYaw_rps'] = {} fcsPilotDef['gain']['cmdYaw_rps']['input'] = 'fcs/pilotYaw_norm' fcsPilotDef['gain']['cmdYaw_rps']['gain'] = oFdm['FCS']['Pilot']['kYaw'] fcsPilotDef['summer']['pilotFlap_norm'] = {} fcsPilotDef['summer']['pilotFlap_norm']['inputList'] = ['fcs/flap-cmd-norm'] fcsPilotDef['summer']['pilotFlap_norm']['min'] = -1.0 fcsPilotDef['summer']['pilotFlap_norm']['max'] = 1.0 fcsPilotDef['gain']['cmdFlap_rad'] = {} fcsPilotDef['gain']['cmdFlap_rad']['input'] = 'fcs/pilotFlap_norm' fcsPilotDef['gain']['cmdFlap_rad']['gain'] = oFdm['FCS']['Pilot']['kFlap'] # Create the JSB-ML elemFCS = ET.Element('flight_control', name = 'Generic Flight Control') pilot = ET.SubElement(elemFCS, 'channel', name = 'Pilot_Inputs') for type in fcsPilotDef: if type == 'summer': for key in fcsPilotDef['summer'].keys(): entry = fcsPilotDef['summer'][key] summer = ET.SubElement(pilot, 'summer', name = key) for input in entry['inputList']: ET.SubElement(summer, 'input').text = input if ('min' in entry.keys()) or ('max' in entry.keys()): clipto = ET.SubElement(summer, 'clipto') if ('min' in entry.keys()): ET.SubElement(clipto, 'min').text = str(entry['min']) if ('max' in entry.keys()): ET.SubElement(clipto, 'max').text = str(entry['max']) ET.SubElement(summer, 'output').text = 'fcs/' + key if type == 'gain': for key in fcsPilotDef['gain'].keys(): entry = fcsPilotDef['gain'][key] gain = ET.SubElement(pilot, 'pure_gain', name = key) ET.SubElement(gain, 'input').text = entry['input'] ET.SubElement(gain, 'gain').text = str(entry['gain']) if ('min' in entry.keys()) or ('max' in entry.keys()): clipto = ET.SubElement(gain, 'clipto') if ('min' in entry.keys()): ET.SubElement(clipto, 'min').text = str(entry['min']) if ('max' in entry.keys()): ET.SubElement(clipto, 'max').text = str(entry['max']) ET.SubElement(gain, 'output').text = 'fcs/' + key # Control System Surface Mixer mixer = ET.SubElement(elemFCS, 'channel', name = 'Control Mixer') fcsMixerDef = oFdm['FCS']['Mixer'] for iSurf, surf in enumerate(fcsMixerDef['surfNames']): cmdSurf = 'cmd' + surf + '_rad' keyList = [] for iInput, input in enumerate(fcsMixerDef['inputs']): val = fcsMixerDef['surfMix'][iSurf][iInput] key = input + '_2_' + surf if val != 0.0: keyList.append(key) gain = ET.SubElement(mixer, 'pure_gain', name = key.replace('fcs/','')) ET.SubElement(gain, 'input').text = 'fcs/' + input ET.SubElement(gain, 'gain').text = str(val) ET.SubElement(gain, 'output').text = 'fcs/' + key if any(keyList): summer = ET.SubElement(mixer, 'summer', name = cmdSurf) for key in keyList: ET.SubElement(summer, 'input').text = 'fcs/' + key ET.SubElement(summer, 'output').text = 'fcs/' + cmdSurf # Inputs for External Commands, this just add property to create the node in the tree for iSurf, surf in enumerate(fcsMixerDef['surfNames']): cmdSurfExt = 'cmd' + surf + '_ext_rad' prop = ET.SubElement(elemFCS, 'property').text = 'fcs/' + cmdSurfExt name = 'Motor' cmdMotorExt = 'cmd' + name + '_ext_nd' motor = ET.SubElement(elemFCS, 'property').text = 'fcs/' + cmdMotorExt # Add the Motor external command # Inputs for External Commands, this just add property to create the node in the tree extern = ET.SubElement(elemFCS, 'channel', name = 'External Input Summations') for iSurf, surf in enumerate(fcsMixerDef['surfNames']): cmdSurf = 'cmd' + surf + '_rad' cmdSurfExt = 'cmd' + surf + '_ext_rad' summer = ET.SubElement(extern, 'summer') ET.SubElement(summer, 'input').text = 'fcs/' + cmdSurf ET.SubElement(summer, 'input').text = 'fcs/' + cmdSurfExt ET.SubElement(summer, 'output').text = 'fcs/' + cmdSurf name = 'Motor' cmdMotor = 'cmd' + name + '_nd' cmdMotorExt = 'cmd' + name + '_ext_nd' summer = ET.SubElement(extern, 'summer') ET.SubElement(summer, 'input').text = 'fcs/throttle-cmd-norm' ET.SubElement(summer, 'input').text = 'fcs/' + cmdMotorExt ET.SubElement(summer, 'output').text = 'fcs/throttle-pos-norm' return(elemFCS) #%% Effectors, for each surface define the 2nd order TF, and an 'actuator' def Effectors(oFdm): sysEffDef = oFdm['Act'] effectors = ET.Element('system', name = 'Effectors') channel = ET.SubElement(effectors, 'channel', name = 'Actuator Models') for surf in sysEffDef.keys(): cmdSurf = 'cmd' + surf + '_rad' posSurf = 'pos' + surf + '_rad' entry = sysEffDef[surf] # Actuator - delay and freeplay actuator = ET.SubElement(channel, 'actuator', name = 'act' + surf) ET.SubElement(actuator, 'input').text = 'fcs/' + cmdSurf ET.SubElement(actuator, 'lag').text = str(entry['lag_nd']) ET.SubElement(actuator, 'hysteresis_width').text = str(entry['freeplay_rad']) ET.SubElement(actuator, 'delay').text = str(entry['delay_s']) if ('min' in entry.keys()) or ('max' in entry.keys()): clipto = ET.SubElement(actuator, 'clipto') if ('min' in entry.keys()): ET.SubElement(clipto, 'min').text = str(entry['min']) if ('max' in entry.keys()): ET.SubElement(clipto, 'max').text = str(entry['max']) ET.SubElement(actuator, 'output').text = 'fcs/' + posSurf return(effectors) #%% def Winch(oFdm): external_reactions = ET.Element('external_reactions') # Winch force = ET.SubElement(external_reactions, 'force', name='hitch' , frame = 'BODY', unit='N') location = ET.SubElement(force, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Winch']['rHook_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Winch']['rHook_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Winch']['rHook_S_m'][2]) direction = ET.SubElement(force, 'direction') ET.SubElement(direction, 'x').text = str(oFdm['Winch']['sHook_deg'][0]) ET.SubElement(direction, 'y').text = str(oFdm['Winch']['sHook_deg'][1]) ET.SubElement(direction, 'z').text = str(oFdm['Winch']['sHook_deg'][2]) return(external_reactions) #%% IMU def SensorImu(oFdm): imu = ET.Element('system', name = 'Sensor - IMU') # Create time in us function = ET.SubElement(imu, 'function', name = 'sensor/imu/time_us') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'simulation/sim-time-sec' ET.SubElement(product, 'value').text = str(1e6) # Accelerometers if 'Accel' in oFdm['Sensor']['Imu'].keys() : channel = ET.SubElement(imu, 'channel', name = 'Temp Accelerometers') axisList = ['X', 'Y', 'Z'] for axisName in axisList: accel = ET.SubElement(channel, 'accelerometer', name = 'Accel' + axisName) ET.SubElement(accel, 'axis').text = axisName location = ET.SubElement(accel, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(oFdm['Sensor']['Imu']['Accel']['r_S_m'][0]) ET.SubElement(location, 'y').text = str(oFdm['Sensor']['Imu']['Accel']['r_S_m'][1]) ET.SubElement(location, 'z').text = str(oFdm['Sensor']['Imu']['Accel']['r_S_m'][2]) orientation = ET.SubElement(accel, 'orientation', unit='DEG') ET.SubElement(orientation, 'roll').text = str(oFdm['Sensor']['Imu']['Accel']['s_deg'][0]) ET.SubElement(orientation, 'pitch').text = str(oFdm['Sensor']['Imu']['Accel']['s_deg'][1]) ET.SubElement(orientation, 'yaw').text = str(oFdm['Sensor']['Imu']['Accel']['s_deg'][2]) ET.SubElement(accel, 'output').text = 'sensor/imu/accel' + axisName + '_true_fps2' # Convert Units Accelerometer to mps2 for axisName in axisList: function = ET.SubElement(imu, 'function', name = 'sensor/imu/accel' + axisName + '_true_mps2') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'sensor/imu/accel' + axisName + '_true_fps2' ET.SubElement(product, 'value').text = str(ft2m) # Accelerometer Error Model channel = ET.SubElement(imu, 'channel', name = 'Accelerometer Error Model') errMod = oFdm['Sensor']['Imu']['Accel'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = 'Accel' + axisName) ET.SubElement(sensor, 'input').text = 'sensor/imu/accel' + axisName + '_true_mps2' ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/imu/accel' + axisName + '_mps2' # Gyros if 'Gyro' in oFdm['Sensor']['Imu'].keys() : errMod = oFdm['Sensor']['Imu']['Gyro'] channel = ET.SubElement(imu, 'channel', name = 'Gyros') for iAxis, axisName in enumerate(axisList): gyro = ET.SubElement(channel, 'gyro', name = 'Gyro' + axisName) ET.SubElement(gyro, 'axis').text = axisName location = ET.SubElement(gyro, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(errMod['r_S_m'][0]) ET.SubElement(location, 'y').text = str(errMod['r_S_m'][1]) ET.SubElement(location, 'z').text = str(errMod['r_S_m'][2]) orientation = ET.SubElement(gyro, 'orientation', unit='DEG') ET.SubElement(orientation, 'roll').text = str(errMod['s_deg'][0]) ET.SubElement(orientation, 'pitch').text = str(errMod['s_deg'][1]) ET.SubElement(orientation, 'yaw').text = str(errMod['s_deg'][2]) ET.SubElement(gyro, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(gyro, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(gyro, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(gyro, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(gyro, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(gyro, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(gyro, 'output').text = 'sensor/imu/gyro' + axisName + '_rps' # Magnetometers if 'Mag' in oFdm['Sensor']['Imu'].keys() : errMod = oFdm['Sensor']['Imu']['Mag'] channel = ET.SubElement(imu, 'channel', name = 'Magnetometers') for iAxis, axisName in enumerate(axisList): mag = ET.SubElement(channel, 'magnetometer', name = 'Mag' + axisName) ET.SubElement(mag, 'axis').text = axisName location = ET.SubElement(mag, 'location', unit = 'M') ET.SubElement(location, 'x').text = str(errMod['r_S_m'][0]) ET.SubElement(location, 'y').text = str(errMod['r_S_m'][1]) ET.SubElement(location, 'z').text = str(errMod['r_S_m'][2]) orientation = ET.SubElement(mag, 'orientation', unit='DEG') ET.SubElement(orientation, 'roll').text = str(errMod['s_deg'][0]) ET.SubElement(orientation, 'pitch').text = str(errMod['s_deg'][1]) ET.SubElement(orientation, 'yaw').text = str(errMod['s_deg'][2]) ET.SubElement(mag, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(mag, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(mag, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(mag, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(mag, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(mag, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(mag, 'output').text = 'sensor/imu/mag' + axisName + '_nT' # Magnetometer unit conversion for axisName in axisList: function = ET.SubElement(imu, 'function', name = 'sensor/imu/mag' + axisName + '_uT') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'sensor/imu/mag' + axisName + '_nT' ET.SubElement(product, 'value').text = str(0.001) return(imu) #%% GPS def SensorGps(oFdm): gps = ET.Element('system', name = 'Sensor - GPS') # Create time in us function = ET.SubElement(gps, 'function', name = 'sensor/gps/time_us') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'simulation/sim-time-sec' ET.SubElement(product, 'value').text = str(1e6) # GPS Position function = ET.SubElement(gps, 'function', name = 'sensor/gps/lat_true_rad') ET.SubElement(function, 'property').text = 'position/lat-geod-rad' function = ET.SubElement(gps, 'function', name = 'sensor/gps/long_true_rad') ET.SubElement(function, 'property').text = 'position/long-gc-rad' function = ET.SubElement(gps, 'function', name = 'sensor/gps/alt_true_m') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'position/h-sl-ft' ET.SubElement(product, 'value').text = str(ft2m) # GPS Velocity function = ET.SubElement(gps, 'function', name = 'sensor/gps/vNorth_true_mps') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'velocities/v-north-fps' ET.SubElement(product, 'value').text = str(ft2m) function = ET.SubElement(gps, 'function', name = 'sensor/gps/vEast_true_mps') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'velocities/v-east-fps' ET.SubElement(product, 'value').text = str(ft2m) function = ET.SubElement(gps, 'function', name = 'sensor/gps/vDown_true_mps') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'velocities/v-down-fps' ET.SubElement(product, 'value').text = str(ft2m) # GPS Error Model channel = ET.SubElement(gps, 'channel', name = 'GPS Error Models') axisList = ['lat_rad', 'long_rad', 'alt_m'] errMod = oFdm['Sensor']['Gps']['Pos'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = axisName) ET.SubElement(sensor, 'input').text = 'sensor/gps/' + axisName.replace('_', '_true_') ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/gps/' + axisName axisList = ['vNorth_mps', 'vEast_mps', 'vDown_mps'] errMod = oFdm['Sensor']['Gps']['Vel'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = axisName) ET.SubElement(sensor, 'input').text = 'sensor/gps/' + axisName.replace('_', '_true_') ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/gps/' + axisName return(gps) #%% def SensorPitot(oFdm): pitot = ET.Element('system', name = 'Sensor - Pitot-Static Probe') # Create time in us function = ET.SubElement(pitot, 'function', name = 'sensor/pitot/time_us') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'simulation/sim-time-sec' ET.SubElement(product, 'value').text = str(1e6) # Airdata Static function = ET.SubElement(pitot, 'function', name = 'sensor/pitot/presStatic_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'atmosphere/P-psf' ET.SubElement(product, 'value').text = str(psf2pa) # Airdata Tip (Dynamic ~= Impact) function = ET.SubElement(pitot, 'function', name = 'sensor/pitot/presTip_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) # Airdata Temperature function = ET.SubElement(pitot, 'function', name = 'sensor/pitot/temp_true_C') product = ET.SubElement(function, 'product') summation = ET.SubElement(product, 'sum') ET.SubElement(summation, 'property').text = 'atmosphere/T-R' ET.SubElement(summation, 'value').text = str(-491.67) ET.SubElement(product, 'value').text = str(5.0/9.0) # Pitot Error Model channel = ET.SubElement(pitot, 'channel', name = 'Pitot Error Models') axisList = ['presStatic_Pa', 'presTip_Pa', 'temp_C'] errMod = oFdm['Sensor']['Gps']['Vel'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = axisName) ET.SubElement(sensor, 'input').text = 'sensor/pitot/' + axisName.replace('_', '_true_') ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/pitot/' + axisName return(pitot) #%% def Sensor5Hole(oFdm): fiveHole = ET.Element('system', name = 'Sensor - 5Hole Probe') # Determine whether method #1 or method #2 if 'alphaK1' and 'betaK1' in oFdm['Sensor']['5Hole'].keys(): method = 1 elif 'alphaK2' and 'betaK2' in oFdm['Sensor']['5Hole'].keys(): method = 2 else: print('5Hole Probe: Need either (alphaK1 and betaK1) or (alphaK2 and betaK2)') # Create time in us function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/time_us') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'simulation/sim-time-sec' ET.SubElement(product, 'value').text = str(1e6) # Airdata Static function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presStatic_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'atmosphere/P-psf' ET.SubElement(product, 'value').text = str(psf2pa) # Airdata Tip (Dynamic ~= Impact) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presTip_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) # Airdata Temperature function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/temp_true_C') product = ET.SubElement(function, 'product') summation = ET.SubElement(product, 'sum') ET.SubElement(summation, 'property').text = 'atmosphere/T-R' ET.SubElement(summation, 'value').text = str(-491.67) ET.SubElement(product, 'value').text = str(5.0/9.0) # [Method 1] if method == 1: axisList = ['presStatic_Pa', 'presTip_Pa', 'presAlphaBot_Pa', 'presAlphaTop_Pa', 'presBetaRight_Pa', 'presBetaLeft_Pa', 'temp_C'] # Alpha Difference (presAlphaBot - presAlphaTop) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presAlphaBot_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/alpha-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['alphaK1'][0]) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presAlphaTop_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/alpha-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['alphaK1'][1]) # [Method 2] Beta Difference (presBetaRight - presBetaLeft) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presBetaRight_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/beta-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['betaK1'][0]) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presBetaLeft_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/beta-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['betaK1'][1]) # [Method 2] elif method == 2: axisList = ['presStatic_Pa', 'presTip_Pa', 'presAlpha_Pa', 'presBeta_Pa', 'temp_C'] # Alpha Difference (presAlphaBot - presAlphaTop) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presAlpha_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/alpha-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['alphaK2']) # [Method 2] Beta Difference (presBetaRight - presBetaLeft) function = ET.SubElement(fiveHole, 'function', name = 'sensor/fiveHole/presBeta_true_Pa') product = ET.SubElement(function, 'product') ET.SubElement(product, 'property').text = 'aero/beta-deg' ET.SubElement(product, 'property').text = 'aero/qbar-psf' ET.SubElement(product, 'value').text = str(psf2pa) ET.SubElement(product, 'value').text = str(oFdm['Sensor']['5Hole']['betaK2']) # 5Hole Error Model channel = ET.SubElement(fiveHole, 'channel', name = '5Hole Error Models') errMod = oFdm['Sensor']['5Hole'] for iAxis, axisName in enumerate(axisList): sensor = ET.SubElement(channel, 'sensor', name = axisName) ET.SubElement(sensor, 'input').text = 'sensor/fiveHole/' + axisName.replace('_', '_true_') ET.SubElement(sensor, 'lag').text = str(errMod['lag'][iAxis]) ET.SubElement(sensor, 'noise', variation='ABSOLUTE', distribution = 'GAUSSIAN').text = str((1.0 / 3.0) * errMod['noiseVar'][iAxis]) ET.SubElement(sensor, 'drift_rate').text = str(errMod['drift_ps'][iAxis]) ET.SubElement(sensor, 'gain').text = str(errMod['gain_nd'][iAxis]) ET.SubElement(sensor, 'bias').text = str(errMod['bias'][iAxis]) ET.SubElement(sensor, 'delay').text = str(errMod['delay_s'][iAxis]) ET.SubElement(sensor, 'output').text = 'sensor/fiveHole/' + axisName return(fiveHole)

true

f72986e13bc4533e79920b6bd0a416f26e97ee2f

1,190

py

Python

app.py

ajoss/tk-houdini-geometrynode

b732002c0014f78ff13bea2e86cbe23b890bbbf4

[ "MIT" ]

3

2019-04-17T12:39:20.000Z

2019-11-04T07:25:59.000Z

app.py

ajoss/tk-houdini-geometrynode

b732002c0014f78ff13bea2e86cbe23b890bbbf4

[ "MIT" ]

null

app.py

ajoss/tk-houdini-geometrynode

b732002c0014f78ff13bea2e86cbe23b890bbbf4

[ "MIT" ]

5

2018-09-19T08:13:14.000Z

2020-02-15T14:50:01.000Z

# Copyright (c) 2015 Pixomondo # # CONFIDENTIAL AND PROPRIETARY # # This work is provided "AS IS" and subject to the MIT License included in this # distribution package. See LICENSE. # By accessing, using, copying or modifying this work you indicate your # agreement to the MIT License. All rights # not expressly granted therein are reserved by Pixomondo. """ Geometry Output App for Houdini """ import sgtk class GeometryOutputNode(sgtk.platform.Application): def init_app(self): module = self.import_module("tk_houdini_geometrynode") self.handler = module.ToolkitGeometryNodeHandler(self) def convert_to_geometry_nodes(self): """ Convert all Shotgun Geometry nodes found in the current Script to regular Geometry nodes. Additional toolkit information will be stored in user data named 'tk_*' """ self.handler.convert_sg_to_geometry_nodes() def convert_from_geometry_nodes(self): """ Convert all regular Geometry nodes that have previously been converted from Shotgun Geometry nodes, back into Shotgun Geometry nodes. """ self.handler.convert_geometry_to_sg_nodes()

32.162162

81

0.721008

import sgtk class GeometryOutputNode(sgtk.platform.Application): def init_app(self): module = self.import_module("tk_houdini_geometrynode") self.handler = module.ToolkitGeometryNodeHandler(self) def convert_to_geometry_nodes(self): self.handler.convert_sg_to_geometry_nodes() def convert_from_geometry_nodes(self): self.handler.convert_geometry_to_sg_nodes()

true

f729876b03555c0a2ee2deffa22d9c2809f617fd

5,342

py

Python

urbanairship/push/schedule.py

urbanairship/python-library

f59d7140c16db7aec48e8ebbaf26f31e7f02ab26

[ "Apache-2.0" ]

26

2015-01-05T21:08:07.000Z

2021-05-13T07:27:19.000Z

urbanairship/push/schedule.py

urbanairship/python-library

f59d7140c16db7aec48e8ebbaf26f31e7f02ab26

[ "Apache-2.0" ]

32

2015-01-08T23:46:36.000Z

2022-02-02T18:17:58.000Z

urbanairship/push/schedule.py

urbanairship/python-library

f59d7140c16db7aec48e8ebbaf26f31e7f02ab26

[ "Apache-2.0" ]

33

2015-01-21T08:02:40.000Z

2022-03-25T06:02:04.000Z

from datetime import datetime from urbanairship import common from urbanairship.push import ScheduledPush VALID_DAYS = [ "monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday", ] VALID_RECURRING_TYPES = ["hourly", "daily", "weekly", "monthly", "yearly"] class ScheduledList(common.IteratorParent): """ Iterator for listing all scheduled messages. :ivar limit: Number of entries to fetch in a paginated request. :returns Each ``next`` returns a :py:class:`ScheduledPush` object. """ next_url = None data_attribute = "schedules" id_key = "url" instance_class = ScheduledPush def __init__(self, airship, limit=None): self.next_url = airship.urls.get("schedules_url") params = {"limit": limit} if limit else {} super(ScheduledList, self).__init__(airship, params) def scheduled_time(timestamp): """Specify a time for the delivery of this push. :param timestamp: A ``datetime.datetime`` object. """ return {"scheduled_time": timestamp.strftime("%Y-%m-%dT%H:%M:%S")} def local_scheduled_time(timestamp): """Specify a time for the delivery of this push in device local time. :param timestamp: A ``datetime.datetime`` object. """ return {"local_scheduled_time": timestamp.strftime("%Y-%m-%dT%H:%M:%S")} def best_time(timestamp): """Specify a date to send the push at the best time per-device. Only YYYY_MM_DD are needed. Hour/minute/second information is discarded. :param timestamp: A ``datetime.datetime object. """ return {"best_time": {"send_date": timestamp.strftime("%Y-%m-%d")}} def schedule_exclusion( start_hour=None, end_hour=None, start_date=None, end_date=None, days_of_week=None ): """ Date-time ranges when messages are not sent. at least one of start_hour and end_hour, start_date and end_date, or days_of_week must be included. All dates and times are inclusive. :param start_hour: Optional. An integer 0-23 representing the UTC hour to start exclusion. :param end_hour: Optional. An integer 0-23 representing the UTC hour to stop exclusion. Must be included if start_hour is used. :param start_date: Optional. A datetime.datetime object representing the UTC date to start exclusion. Hour/minute/seconds will be excluded. :param start_date: Optional. A datetime.datetime object representing the UTC date to stop exclusion. Hour/minute/seconds will be excluded. Must be included if start_date is used. :param days_of_week: Optional. A list of the days of the week to exclude on. Possible values: monday, tuesday, wednesday, thursday, friday, saturday, sunday """ exclusion = {} if all([(start_hour < 24), (end_hour < 24)]): exclusion["hour_range"] = "{}-{}".format(start_hour, end_hour) else: raise ValueError("start_date and end_date must be datetime.datetime") if all([(type(start_date) == datetime), (type(end_date) == datetime)]): exclusion["date_range"] = "{}/{}".format( start_date.strftime("%Y-%m-%dT%H:%M:%S"), end_date.strftime("%Y-%m-%dT%H:%M:%S"), ) else: raise ValueError("start_date and end_date must be datetime.datetime") if days_of_week: for day in days_of_week: if day not in VALID_DAYS: raise ValueError("days_of_week must be {}".format(VALID_DAYS)) exclusion["days_of_week"] = days_of_week return exclusion def recurring_schedule( count, type, end_time=None, days_of_week=None, exclusions=None, paused=False ): """ Sets the cadence, end time, and excluded times for a recurring scheduled message. :param count: Required. The frequency of messaging corresponding to the type. For example, a count of 2 results in a message every 2 hours, days, weeks, months, etc based on the type. :param type: Required. The unit of measurement for the cadence. Possible values: hourly, daily, monthly, yearly. :param days_of_week: Required when type is weekly. The days of the week on which Airship can send your message. :param end_time: Optional. A datetime.datetime object representing when the scheduled send will end and stop sending messages. :param exclusions: Optional. A list of urbanaiship.schedule_exclusion defining times in which Airship will not send your message. :param paused: Optional. A boolean value respesnting the paused state of the scheduled message. """ if days_of_week is not None: for day in days_of_week: if day not in VALID_DAYS: raise ValueError("days of week can only include {}".format(VALID_DAYS)) if type not in VALID_RECURRING_TYPES: raise ValueError("type must be one of {}".format(VALID_RECURRING_TYPES)) cadence = {"type": type, "count": count} if type == "weekly": cadence["days_of_week"] = days_of_week recurring = {"cadence": cadence} if end_time: recurring["end_time"] = end_time.strftime("%Y-%m-%dT%H:%M:%S") if exclusions: recurring["exclusions"] = exclusions if paused is not None: recurring["paused"] = paused return {"recurring": recurring}

34.24359

87

0.672595

from datetime import datetime from urbanairship import common from urbanairship.push import ScheduledPush VALID_DAYS = [ "monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday", ] VALID_RECURRING_TYPES = ["hourly", "daily", "weekly", "monthly", "yearly"] class ScheduledList(common.IteratorParent): next_url = None data_attribute = "schedules" id_key = "url" instance_class = ScheduledPush def __init__(self, airship, limit=None): self.next_url = airship.urls.get("schedules_url") params = {"limit": limit} if limit else {} super(ScheduledList, self).__init__(airship, params) def scheduled_time(timestamp): return {"scheduled_time": timestamp.strftime("%Y-%m-%dT%H:%M:%S")} def local_scheduled_time(timestamp): return {"local_scheduled_time": timestamp.strftime("%Y-%m-%dT%H:%M:%S")} def best_time(timestamp): return {"best_time": {"send_date": timestamp.strftime("%Y-%m-%d")}} def schedule_exclusion( start_hour=None, end_hour=None, start_date=None, end_date=None, days_of_week=None ): exclusion = {} if all([(start_hour < 24), (end_hour < 24)]): exclusion["hour_range"] = "{}-{}".format(start_hour, end_hour) else: raise ValueError("start_date and end_date must be datetime.datetime") if all([(type(start_date) == datetime), (type(end_date) == datetime)]): exclusion["date_range"] = "{}/{}".format( start_date.strftime("%Y-%m-%dT%H:%M:%S"), end_date.strftime("%Y-%m-%dT%H:%M:%S"), ) else: raise ValueError("start_date and end_date must be datetime.datetime") if days_of_week: for day in days_of_week: if day not in VALID_DAYS: raise ValueError("days_of_week must be {}".format(VALID_DAYS)) exclusion["days_of_week"] = days_of_week return exclusion def recurring_schedule( count, type, end_time=None, days_of_week=None, exclusions=None, paused=False ): if days_of_week is not None: for day in days_of_week: if day not in VALID_DAYS: raise ValueError("days of week can only include {}".format(VALID_DAYS)) if type not in VALID_RECURRING_TYPES: raise ValueError("type must be one of {}".format(VALID_RECURRING_TYPES)) cadence = {"type": type, "count": count} if type == "weekly": cadence["days_of_week"] = days_of_week recurring = {"cadence": cadence} if end_time: recurring["end_time"] = end_time.strftime("%Y-%m-%dT%H:%M:%S") if exclusions: recurring["exclusions"] = exclusions if paused is not None: recurring["paused"] = paused return {"recurring": recurring}

true

f72987cba217a15bd066d0afb902992a53d03c44

344

py

Python

app/forms.py

LoisaKitakaya/The-Blog

6f2abd70e7d65de938f162b6219c468e5736a1da

[ "MIT" ]

null

app/forms.py

LoisaKitakaya/The-Blog

6f2abd70e7d65de938f162b6219c468e5736a1da

[ "MIT" ]

null

app/forms.py

LoisaKitakaya/The-Blog

6f2abd70e7d65de938f162b6219c468e5736a1da

[ "MIT" ]

null

from django import forms from .models import * # create your forms class PostArticle(forms.ModelForm): class Meta: model = Article fieldS = '__all__' exclude = ['article_author', 'slug', 'posted_on'] class PostComment(forms.ModelForm): class Meta: model = Comment fields = ['comment']

17.2

57

0.622093

from django import forms from .models import * class PostArticle(forms.ModelForm): class Meta: model = Article fieldS = '__all__' exclude = ['article_author', 'slug', 'posted_on'] class PostComment(forms.ModelForm): class Meta: model = Comment fields = ['comment']

true

f72987f95fdc25137b7bca9a81ffdf51642c3e8f

3,178

py

Python

django_three/basicforms/basicforms/settings.py

NNDEV1/DjangoStuff

d8b850dce41a18c807a412f7644abae80f3d7c8e

[ "MIT" ]

1

2021-08-14T14:48:37.000Z

django_three/basicforms/basicforms/settings.py

NNDEV1/DjangoStuff

d8b850dce41a18c807a412f7644abae80f3d7c8e

[ "MIT" ]

null

django_three/basicforms/basicforms/settings.py

NNDEV1/DjangoStuff

d8b850dce41a18c807a412f7644abae80f3d7c8e

[ "MIT" ]

null

""" Django settings for basicforms project. Generated by 'django-admin startproject' using Django 2.1.4. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) TEMPLATE_DIR = os.path.join(BASE_DIR, 'templates') # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'r7^%rvbmkoi%v%def+cf3s1a#&7d5w_731ctf0r$3q92hl=1p%' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'basicapp' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'basicforms.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [TEMPLATE_DIR], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'basicforms.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/'

25.837398

91

0.699182

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) TEMPLATE_DIR = os.path.join(BASE_DIR, 'templates') SECRET_KEY = 'r7^%rvbmkoi%v%def+cf3s1a#&7d5w_731ctf0r$3q92hl=1p%' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'basicapp' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'basicforms.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [TEMPLATE_DIR], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'basicforms.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/'

true

f729888ae93196c5c5c069ec12d59f2077f3c962

3,467

py

Python

azure/mgmt/network/v2017_09_01/models/application_gateway_url_path_map.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

azure/mgmt/network/v2017_09_01/models/application_gateway_url_path_map.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

azure/mgmt/network/v2017_09_01/models/application_gateway_url_path_map.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .sub_resource import SubResource class ApplicationGatewayUrlPathMap(SubResource): """UrlPathMaps give a url path to the backend mapping information for PathBasedRouting. :param id: Resource ID. :type id: str :param default_backend_address_pool: Default backend address pool resource of URL path map. :type default_backend_address_pool: ~azure.mgmt.network.v2017_09_01.models.SubResource :param default_backend_http_settings: Default backend http settings resource of URL path map. :type default_backend_http_settings: ~azure.mgmt.network.v2017_09_01.models.SubResource :param default_redirect_configuration: Default redirect configuration resource of URL path map. :type default_redirect_configuration: ~azure.mgmt.network.v2017_09_01.models.SubResource :param path_rules: Path rule of URL path map resource. :type path_rules: list[~azure.mgmt.network.v2017_09_01.models.ApplicationGatewayPathRule] :param provisioning_state: Provisioning state of the backend http settings resource. Possible values are: 'Updating', 'Deleting', and 'Failed'. :type provisioning_state: str :param name: Name of the resource that is unique within a resource group. This name can be used to access the resource. :type name: str :param etag: A unique read-only string that changes whenever the resource is updated. :type etag: str :param type: Type of the resource. :type type: str """ _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'default_backend_address_pool': {'key': 'properties.defaultBackendAddressPool', 'type': 'SubResource'}, 'default_backend_http_settings': {'key': 'properties.defaultBackendHttpSettings', 'type': 'SubResource'}, 'default_redirect_configuration': {'key': 'properties.defaultRedirectConfiguration', 'type': 'SubResource'}, 'path_rules': {'key': 'properties.pathRules', 'type': '[ApplicationGatewayPathRule]'}, 'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'etag': {'key': 'etag', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, } def __init__(self, id=None, default_backend_address_pool=None, default_backend_http_settings=None, default_redirect_configuration=None, path_rules=None, provisioning_state=None, name=None, etag=None, type=None): super(ApplicationGatewayUrlPathMap, self).__init__(id=id) self.default_backend_address_pool = default_backend_address_pool self.default_backend_http_settings = default_backend_http_settings self.default_redirect_configuration = default_redirect_configuration self.path_rules = path_rules self.provisioning_state = provisioning_state self.name = name self.etag = etag self.type = type

48.830986

216

0.674358

from .sub_resource import SubResource class ApplicationGatewayUrlPathMap(SubResource): _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'default_backend_address_pool': {'key': 'properties.defaultBackendAddressPool', 'type': 'SubResource'}, 'default_backend_http_settings': {'key': 'properties.defaultBackendHttpSettings', 'type': 'SubResource'}, 'default_redirect_configuration': {'key': 'properties.defaultRedirectConfiguration', 'type': 'SubResource'}, 'path_rules': {'key': 'properties.pathRules', 'type': '[ApplicationGatewayPathRule]'}, 'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'etag': {'key': 'etag', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, } def __init__(self, id=None, default_backend_address_pool=None, default_backend_http_settings=None, default_redirect_configuration=None, path_rules=None, provisioning_state=None, name=None, etag=None, type=None): super(ApplicationGatewayUrlPathMap, self).__init__(id=id) self.default_backend_address_pool = default_backend_address_pool self.default_backend_http_settings = default_backend_http_settings self.default_redirect_configuration = default_redirect_configuration self.path_rules = path_rules self.provisioning_state = provisioning_state self.name = name self.etag = etag self.type = type

true

f729894909e1053453798c214ec730a6afb2059d

1,694

py

Python

ivi/agilent/agilentDSOX3034A.py

edupo/python-ivi

8105d8064503725dde781f0378d75db58defaecb

[ "MIT" ]

null

ivi/agilent/agilentDSOX3034A.py

edupo/python-ivi

8105d8064503725dde781f0378d75db58defaecb

[ "MIT" ]

null

ivi/agilent/agilentDSOX3034A.py

edupo/python-ivi

8105d8064503725dde781f0378d75db58defaecb

[ "MIT" ]

null

""" Python Interchangeable Virtual Instrument Library Copyright (c) 2012-2016 Alex Forencich Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from .agilent3000A import * class agilentDSOX3034A(agilent3000A): "Agilent InfiniiVision DSOX3034A IVI oscilloscope driver" def __init__(self, *args, **kwargs): self.__dict__.setdefault('_instrument_id', 'DSO-X 3034A') super(agilentDSOX3034A, self).__init__(*args, **kwargs) self._analog_channel_count = 4 self._digital_channel_count = 0 self._channel_count = self._analog_channel_count + self._digital_channel_count self._bandwidth = 350e6 self._init_channels()

37.644444

86

0.755018

from .agilent3000A import * class agilentDSOX3034A(agilent3000A): def __init__(self, *args, **kwargs): self.__dict__.setdefault('_instrument_id', 'DSO-X 3034A') super(agilentDSOX3034A, self).__init__(*args, **kwargs) self._analog_channel_count = 4 self._digital_channel_count = 0 self._channel_count = self._analog_channel_count + self._digital_channel_count self._bandwidth = 350e6 self._init_channels()

true

f72989e816d65be550aa2241f0ca3cd63ca452f2

494

py

Python

test.py

JiamingBai/InteractiveDataScience.github.io

c43b7bda50356a4b16b6804c3eb8bcfe33ce6b13

[ "MIT" ]

null

test.py

JiamingBai/InteractiveDataScience.github.io

c43b7bda50356a4b16b6804c3eb8bcfe33ce6b13

[ "MIT" ]

null

test.py

JiamingBai/InteractiveDataScience.github.io

c43b7bda50356a4b16b6804c3eb8bcfe33ce6b13

[ "MIT" ]

null

import sys import json import cgi fs = cgi.FieldStorage() sys.stdout.write("Content-Type: application/json") sys.stdout.write("\n") sys.stdout.write("\n") result = {} result['success'] = True result['message'] = "The command Completed Successfully" result['keys'] = ",".join(fs.keys()) d = {} for k in fs.keys(): d[k] = fs.getvalue(k) result['data'] = d sys.stdout.write(json.dumps(result,indent=1)) sys.stdout.write("\n") sys.stdout.close() print("python script return value")

15.4375

56

0.672065

import sys import json import cgi fs = cgi.FieldStorage() sys.stdout.write("Content-Type: application/json") sys.stdout.write("\n") sys.stdout.write("\n") result = {} result['success'] = True result['message'] = "The command Completed Successfully" result['keys'] = ",".join(fs.keys()) d = {} for k in fs.keys(): d[k] = fs.getvalue(k) result['data'] = d sys.stdout.write(json.dumps(result,indent=1)) sys.stdout.write("\n") sys.stdout.close() print("python script return value")

true

f72989fb32f8814dbb34e9d1777eb25d5a1ab37f

4,279

py

Python

bcbio/pipeline/merge.py

arvados/bcbio-nextgen

2a5cfa8c3a1d540bb2f2e66f51835042195cbc87

[ "MIT" ]

3

2015-11-18T07:17:54.000Z

2021-04-28T13:58:37.000Z

bcbio/pipeline/merge.py

yong27/bcbio-nextgen

9320479d8f21677b61ed1274b4da23d569c686ae

[ "MIT" ]

null

bcbio/pipeline/merge.py

yong27/bcbio-nextgen

9320479d8f21677b61ed1274b4da23d569c686ae

[ "MIT" ]

null

"""Handle multiple samples present on a single flowcell Merges samples located in multiple lanes on a flowcell. Unique sample names identify items to combine within a group. """ import os import shutil from bcbio import bam, utils from bcbio.distributed.transaction import file_transaction, tx_tmpdir from bcbio.pipeline import config_utils from bcbio.provenance import do, system def combine_fastq_files(in_files, work_dir, config): if len(in_files) == 1: return in_files[0] else: cur1, cur2 = in_files[0] out1 = os.path.join(work_dir, os.path.basename(cur1)) out2 = os.path.join(work_dir, os.path.basename(cur2)) if cur2 else None if not os.path.exists(out1): with open(out1, "a") as out_handle: for (cur1, _) in in_files: with open(cur1) as in_handle: shutil.copyfileobj(in_handle, out_handle) if out2 and not os.path.exists(out2): with open(out2, "a") as out_handle: for (_, cur2) in in_files: with open(cur2) as in_handle: shutil.copyfileobj(in_handle, out_handle) for f1, f2 in in_files: utils.save_diskspace(f1, "fastq merged to %s" % out1, config) if f2: utils.save_diskspace(f2, "fastq merged to %s" % out2, config) return out1, out2 def merge_bam_files(bam_files, work_dir, config, out_file=None, batch=None): """Merge multiple BAM files from a sample into a single BAM for processing. Checks system open file limit and merges in batches if necessary to avoid file handle limits. """ if len(bam_files) == 1: return bam_files[0] else: if out_file is None: out_file = os.path.join(work_dir, os.path.basename(sorted(bam_files)[0])) if batch is not None: base, ext = os.path.splitext(out_file) out_file = "%s-b%s%s" % (base, batch, ext) if not utils.file_exists(out_file) or not utils.file_exists(out_file + ".bai"): sambamba = config_utils.get_program("sambamba", config) resources = config_utils.get_resources("samtools", config) num_cores = config["algorithm"].get("num_cores", 1) max_mem = config_utils.adjust_memory(resources.get("memory", "1G"), 2, "decrease").upper() # sambamba opens 4 handles per file, so try to guess a reasonable batch size batch_size = (system.open_file_limit() // 4) - 100 if len(bam_files) > batch_size: bam_files = [merge_bam_files(xs, work_dir, config, out_file, i) for i, xs in enumerate(utils.partition_all(batch_size, bam_files))] with tx_tmpdir(config) as tmpdir: with utils.chdir(tmpdir): with file_transaction(config, out_file) as tx_out_file: with file_transaction(config, "%s.list" % os.path.splitext(out_file)[0]) as tx_bam_file_list: with open(tx_bam_file_list, "w") as out_handle: for f in sorted(bam_files): out_handle.write("%s\n" % f) cmd = _sambamba_merge(bam_files) do.run(cmd.format(**locals()), "Merge bam files to %s" % os.path.basename(out_file), None) for b in bam_files: utils.save_diskspace(b, "BAM merged to %s" % out_file, config) bam.index(out_file, config) return out_file def _sambamba_merge(bam_files): """Merge multiple BAM files with sambamba. """ if len(bam_files) > system.open_file_limit(): raise IOError("More files to merge (%s) than available open file descriptors (%s)\n" "See documentation on tips for changing file limits:\n" "https://bcbio-nextgen.readthedocs.org/en/latest/contents/" "parallel.html#tuning-systems-for-scale" % (len(bam_files), system.open_file_limit())) return "{sambamba} merge {tx_out_file} -t {num_cores} `cat {tx_bam_file_list}`"

49.183908

117

0.592662

import os import shutil from bcbio import bam, utils from bcbio.distributed.transaction import file_transaction, tx_tmpdir from bcbio.pipeline import config_utils from bcbio.provenance import do, system def combine_fastq_files(in_files, work_dir, config): if len(in_files) == 1: return in_files[0] else: cur1, cur2 = in_files[0] out1 = os.path.join(work_dir, os.path.basename(cur1)) out2 = os.path.join(work_dir, os.path.basename(cur2)) if cur2 else None if not os.path.exists(out1): with open(out1, "a") as out_handle: for (cur1, _) in in_files: with open(cur1) as in_handle: shutil.copyfileobj(in_handle, out_handle) if out2 and not os.path.exists(out2): with open(out2, "a") as out_handle: for (_, cur2) in in_files: with open(cur2) as in_handle: shutil.copyfileobj(in_handle, out_handle) for f1, f2 in in_files: utils.save_diskspace(f1, "fastq merged to %s" % out1, config) if f2: utils.save_diskspace(f2, "fastq merged to %s" % out2, config) return out1, out2 def merge_bam_files(bam_files, work_dir, config, out_file=None, batch=None): if len(bam_files) == 1: return bam_files[0] else: if out_file is None: out_file = os.path.join(work_dir, os.path.basename(sorted(bam_files)[0])) if batch is not None: base, ext = os.path.splitext(out_file) out_file = "%s-b%s%s" % (base, batch, ext) if not utils.file_exists(out_file) or not utils.file_exists(out_file + ".bai"): sambamba = config_utils.get_program("sambamba", config) resources = config_utils.get_resources("samtools", config) num_cores = config["algorithm"].get("num_cores", 1) max_mem = config_utils.adjust_memory(resources.get("memory", "1G"), 2, "decrease").upper() batch_size = (system.open_file_limit() // 4) - 100 if len(bam_files) > batch_size: bam_files = [merge_bam_files(xs, work_dir, config, out_file, i) for i, xs in enumerate(utils.partition_all(batch_size, bam_files))] with tx_tmpdir(config) as tmpdir: with utils.chdir(tmpdir): with file_transaction(config, out_file) as tx_out_file: with file_transaction(config, "%s.list" % os.path.splitext(out_file)[0]) as tx_bam_file_list: with open(tx_bam_file_list, "w") as out_handle: for f in sorted(bam_files): out_handle.write("%s\n" % f) cmd = _sambamba_merge(bam_files) do.run(cmd.format(**locals()), "Merge bam files to %s" % os.path.basename(out_file), None) for b in bam_files: utils.save_diskspace(b, "BAM merged to %s" % out_file, config) bam.index(out_file, config) return out_file def _sambamba_merge(bam_files): if len(bam_files) > system.open_file_limit(): raise IOError("More files to merge (%s) than available open file descriptors (%s)\n" "See documentation on tips for changing file limits:\n" "https://bcbio-nextgen.readthedocs.org/en/latest/contents/" "parallel.html#tuning-systems-for-scale" % (len(bam_files), system.open_file_limit())) return "{sambamba} merge {tx_out_file} -t {num_cores} `cat {tx_bam_file_list}`"

true

f7298bd1ea920b8cff4d35c6845a6988a31d944d

253

py

Python

wave/transformold/transform.py

jedhsu/wave

a05d8f4b0a96722bdc2f5a514646c7a44681982b

[ "Apache-2.0" ]

null

wave/transformold/transform.py

jedhsu/wave

a05d8f4b0a96722bdc2f5a514646c7a44681982b

[ "Apache-2.0" ]

null

wave/transformold/transform.py

jedhsu/wave

a05d8f4b0a96722bdc2f5a514646c7a44681982b

[ "Apache-2.0" ]

null

# from abc import ABCMeta, abstractmethod # from typing import Iterator # class Transform(metaclass=ABCMeta): # def __init__(self): # pass # @property # @abstractmethod # def waveform(self) -> Iterator[float]: # pass

18.071429

44

0.636364

true

f7298c3dae02b6d779bb5e0dde0e7ea1087b0d3e

2,309

py

Python

Module1/Day04/module1_day04_variables.py

puczilka/100DaysPython

95f761ae506b4175e96b87a43f7177dc3597e586

[ "MIT" ]

23

2019-05-31T18:00:26.000Z

2021-11-21T19:08:19.000Z

Module1/Day04/module1_day04_variables.py

btruck552/100DaysPython

1e45a10387da6d4ebdf8aa5fe13843a4509c8b62

[ "MIT" ]

null

Module1/Day04/module1_day04_variables.py

btruck552/100DaysPython

1e45a10387da6d4ebdf8aa5fe13843a4509c8b62

[ "MIT" ]

42

2019-05-31T17:54:28.000Z

2022-02-12T22:09:51.000Z

""" Author: CaptCorpMURICA Project: 100DaysPython File: module1_day04_variables.py Creation Date: 6/2/2019, 8:55 AM Description: Learn about using variables in python. """ # Variables need to start with a letter or an underscore. Numbers can be used in the variable name as long as it is not # the first character. Additionally, python is case sensitive, so the same word can store multiple items as long as the # casing differs. greeting = "Hello" _name = "General Kenobi." Greeting = "There" _bestLine_ep3_ = "You are a bold one." # Using string concatenation: print(greeting + " " + Greeting + "\n\t" + _name + " " + _bestLine_ep3_) # Using string replacement: print("{} {}\n\t{} {}".format(greeting, Greeting, _name, _bestLine_ep3_)) # Variables can also store numeric values. released = 2005 # Using string concatenation: print("Revenge of the Sith was released on May 4, " + str(released) + ".") # Using string replacement: print("Revenge of the Sith was released on May 4, {}.".format(released)) # Variables are commonly used in arithmetic operations. a = 3 b = 4 c = (a ** 2 + b ** 2) ** .5 print("Pythagorean Theorem: a^2 + b^2 = c^2, so when a = {} and b = {}, then c = {}".format(a, b, c)) # You can test for contents in a variable. If the test results **True**, then the tested condition is in the variable. # Otherwise, the test returns **False**. film = "Revenge of the Sith" print("Sith" in film) print("sith" in film) print("sith" in film.lower()) # Python variables get their type with the data that is stored. Unlike other programming languages, you do not declare a # type for the variable. Additionally, the same variable can be overwritten with new data and a different type. This # should be taken into account when creating python programs. var = "Variables are mutable" type(var) var = 3 type(var) var = 3.5 type(var) # If the variable contains a numeric value, it can be converted to an integer type with the int() function. var = int(var) type(var) # The variable can be converted to a string with the str() function regardless of the contents. var = str(var) type(var) # If the variable contains a numeric value, it can be converted to an float type with the float() function. var = float(var) type(var) var = True type(var)

36.078125

120

0.706366

greeting = "Hello" _name = "General Kenobi." Greeting = "There" _bestLine_ep3_ = "You are a bold one." print(greeting + " " + Greeting + "\n\t" + _name + " " + _bestLine_ep3_) print("{} {}\n\t{} {}".format(greeting, Greeting, _name, _bestLine_ep3_)) released = 2005 print("Revenge of the Sith was released on May 4, " + str(released) + ".") print("Revenge of the Sith was released on May 4, {}.".format(released)) a = 3 b = 4 c = (a ** 2 + b ** 2) ** .5 print("Pythagorean Theorem: a^2 + b^2 = c^2, so when a = {} and b = {}, then c = {}".format(a, b, c)) film = "Revenge of the Sith" print("Sith" in film) print("sith" in film) print("sith" in film.lower()) var = "Variables are mutable" type(var) var = 3 type(var) var = 3.5 type(var) var = int(var) type(var) var = str(var) type(var) var = float(var) type(var) var = True type(var)

true

f7298cb548fa7c1de09bf4bf092c51b5d80b4b4f

14,552

py

Python

ansible/modules/cloud/amazon/lambda_event.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

1

2022-01-25T22:52:58.000Z

ansible/modules/cloud/amazon/lambda_event.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

ansible/modules/cloud/amazon/lambda_event.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

#!/usr/bin/python # (c) 2016, Pierre Jodouin <pjodouin@virtualcomputing.solutions> # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'} DOCUMENTATION = ''' --- module: lambda_event short_description: Creates, updates or deletes AWS Lambda function event mappings. description: - This module allows the management of AWS Lambda function event source mappings such as DynamoDB and Kinesis stream events via the Ansible framework. These event source mappings are relevant only in the AWS Lambda pull model, where AWS Lambda invokes the function. It is idempotent and supports "Check" mode. Use module M(lambda) to manage the lambda function itself and M(lambda_alias) to manage function aliases. version_added: "2.2" author: Pierre Jodouin (@pjodouin), Ryan Brown (@ryansb) options: lambda_function_arn: description: - The name or ARN of the lambda function. required: true aliases: ['function_name', 'function_arn'] state: description: - Describes the desired state. required: true default: "present" choices: ["present", "absent"] alias: description: - Name of the function alias. Mutually exclusive with C(version). required: true version: description: - Version of the Lambda function. Mutually exclusive with C(alias). required: false event_source: description: - Source of the event that triggers the lambda function. required: false default: stream choices: ['stream'] source_params: description: - Sub-parameters required for event source. - I(== stream event source ==) - C(source_arn) The Amazon Resource Name (ARN) of the Kinesis or DynamoDB stream that is the event source. - C(enabled) Indicates whether AWS Lambda should begin polling the event source. Default is True. - C(batch_size) The largest number of records that AWS Lambda will retrieve from your event source at the time of invoking your function. Default is 100. - C(starting_position) The position in the stream where AWS Lambda should start reading. Choices are TRIM_HORIZON or LATEST. required: true requirements: - boto3 extends_documentation_fragment: - aws ''' EXAMPLES = ''' --- # Example that creates a lambda event notification for a DynamoDB stream - hosts: localhost gather_facts: no vars: state: present tasks: - name: DynamoDB stream event mapping lambda_event: state: "{{ state | default('present') }}" event_source: stream function_name: "{{ function_name }}" alias: Dev source_params: source_arn: arn:aws:dynamodb:us-east-1:123456789012:table/tableName/stream/2016-03-19T19:51:37.457 enabled: True batch_size: 100 starting_position: TRIM_HORIZON - name: Show source event debug: var: lambda_stream_events ''' RETURN = ''' --- lambda_stream_events: description: list of dictionaries returned by the API describing stream event mappings returned: success type: list ''' import re import sys try: import boto3 from botocore.exceptions import ClientError, ParamValidationError, MissingParametersError HAS_BOTO3 = True except ImportError: HAS_BOTO3 = False from ansible.module_utils.basic import AnsibleModule from ansible.module_utils.ec2 import (HAS_BOTO3, boto3_conn, camel_dict_to_snake_dict, ec2_argument_spec, get_aws_connection_info) # --------------------------------------------------------------------------------------------------- # # Helper Functions & classes # # --------------------------------------------------------------------------------------------------- class AWSConnection: """ Create the connection object and client objects as required. """ def __init__(self, ansible_obj, resources, use_boto3=True): try: self.region, self.endpoint, aws_connect_kwargs = get_aws_connection_info(ansible_obj, boto3=use_boto3) self.resource_client = dict() if not resources: resources = ['lambda'] resources.append('iam') for resource in resources: aws_connect_kwargs.update(dict(region=self.region, endpoint=self.endpoint, conn_type='client', resource=resource )) self.resource_client[resource] = boto3_conn(ansible_obj, **aws_connect_kwargs) # if region is not provided, then get default profile/session region if not self.region: self.region = self.resource_client['lambda'].meta.region_name except (ClientError, ParamValidationError, MissingParametersError) as e: ansible_obj.fail_json(msg="Unable to connect, authorize or access resource: {0}".format(e)) # set account ID try: self.account_id = self.resource_client['iam'].get_user()['User']['Arn'].split(':')[4] except (ClientError, ValueError, KeyError, IndexError): self.account_id = '' def client(self, resource='lambda'): return self.resource_client[resource] def pc(key): """ Changes python key into Pascale case equivalent. For example, 'this_function_name' becomes 'ThisFunctionName'. :param key: :return: """ return "".join([token.capitalize() for token in key.split('_')]) def ordered_obj(obj): """ Order object for comparison purposes :param obj: :return: """ if isinstance(obj, dict): return sorted((k, ordered_obj(v)) for k, v in obj.items()) if isinstance(obj, list): return sorted(ordered_obj(x) for x in obj) else: return obj def set_api_sub_params(params): """ Sets module sub-parameters to those expected by the boto3 API. :param params: :return: """ api_params = dict() for param in params.keys(): param_value = params.get(param, None) if param_value: api_params[pc(param)] = param_value return api_params def validate_params(module, aws): """ Performs basic parameter validation. :param module: :param aws: :return: """ function_name = module.params['lambda_function_arn'] # validate function name if not re.search('^[\w\-:]+$', function_name): module.fail_json( msg='Function name {0} is invalid. Names must contain only alphanumeric characters and hyphens.'.format(function_name) ) if len(function_name) > 64 and not function_name.startswith('arn:aws:lambda:'): module.fail_json(msg='Function name "{0}" exceeds 64 character limit'.format(function_name)) elif len(function_name) > 140 and function_name.startswith('arn:aws:lambda:'): module.fail_json(msg='ARN "{0}" exceeds 140 character limit'.format(function_name)) # check if 'function_name' needs to be expanded in full ARN format if not module.params['lambda_function_arn'].startswith('arn:aws:lambda:'): function_name = module.params['lambda_function_arn'] module.params['lambda_function_arn'] = 'arn:aws:lambda:{0}:{1}:function:{2}'.format(aws.region, aws.account_id, function_name) qualifier = get_qualifier(module) if qualifier: function_arn = module.params['lambda_function_arn'] module.params['lambda_function_arn'] = '{0}:{1}'.format(function_arn, qualifier) return def get_qualifier(module): """ Returns the function qualifier as a version or alias or None. :param module: :return: """ qualifier = None if module.params['version'] > 0: qualifier = str(module.params['version']) elif module.params['alias']: qualifier = str(module.params['alias']) return qualifier # --------------------------------------------------------------------------------------------------- # # Lambda Event Handlers # # This section defines a lambda_event_X function where X is an AWS service capable of initiating # the execution of a Lambda function (pull only). # # --------------------------------------------------------------------------------------------------- def lambda_event_stream(module, aws): """ Adds, updates or deletes lambda stream (DynamoDb, Kinesis) event notifications. :param module: :param aws: :return: """ client = aws.client('lambda') facts = dict() changed = False current_state = 'absent' state = module.params['state'] api_params = dict(FunctionName=module.params['lambda_function_arn']) # check if required sub-parameters are present and valid source_params = module.params['source_params'] source_arn = source_params.get('source_arn') if source_arn: api_params.update(EventSourceArn=source_arn) else: module.fail_json(msg="Source parameter 'source_arn' is required for stream event notification.") # check if optional sub-parameters are valid, if present batch_size = source_params.get('batch_size') if batch_size: try: source_params['batch_size'] = int(batch_size) except ValueError: module.fail_json(msg="Source parameter 'batch_size' must be an integer, found: {0}".format(source_params['batch_size'])) # optional boolean value needs special treatment as not present does not imply False source_param_enabled = module.boolean(source_params.get('enabled', 'True')) # check if event mapping exist try: facts = client.list_event_source_mappings(**api_params)['EventSourceMappings'] if facts: current_state = 'present' except ClientError as e: module.fail_json(msg='Error retrieving stream event notification configuration: {0}'.format(e)) if state == 'present': if current_state == 'absent': starting_position = source_params.get('starting_position') if starting_position: api_params.update(StartingPosition=starting_position) else: module.fail_json(msg="Source parameter 'starting_position' is required for stream event notification.") if source_arn: api_params.update(Enabled=source_param_enabled) if source_params.get('batch_size'): api_params.update(BatchSize=source_params.get('batch_size')) try: if not module.check_mode: facts = client.create_event_source_mapping(**api_params) changed = True except (ClientError, ParamValidationError, MissingParametersError) as e: module.fail_json(msg='Error creating stream source event mapping: {0}'.format(e)) else: # current_state is 'present' api_params = dict(FunctionName=module.params['lambda_function_arn']) current_mapping = facts[0] api_params.update(UUID=current_mapping['UUID']) mapping_changed = False # check if anything changed if source_params.get('batch_size') and source_params['batch_size'] != current_mapping['BatchSize']: api_params.update(BatchSize=source_params['batch_size']) mapping_changed = True if source_param_enabled is not None: if source_param_enabled: if current_mapping['State'] not in ('Enabled', 'Enabling'): api_params.update(Enabled=True) mapping_changed = True else: if current_mapping['State'] not in ('Disabled', 'Disabling'): api_params.update(Enabled=False) mapping_changed = True if mapping_changed: try: if not module.check_mode: facts = client.update_event_source_mapping(**api_params) changed = True except (ClientError, ParamValidationError, MissingParametersError) as e: module.fail_json(msg='Error updating stream source event mapping: {0}'.format(e)) else: if current_state == 'present': # remove the stream event mapping api_params = dict(UUID=facts[0]['UUID']) try: if not module.check_mode: facts = client.delete_event_source_mapping(**api_params) changed = True except (ClientError, ParamValidationError, MissingParametersError) as e: module.fail_json(msg='Error removing stream source event mapping: {0}'.format(e)) return camel_dict_to_snake_dict(dict(changed=changed, events=facts)) def main(): """Produce a list of function suffixes which handle lambda events.""" this_module = sys.modules[__name__] source_choices = ["stream"] argument_spec = ec2_argument_spec() argument_spec.update( dict( state=dict(required=False, default='present', choices=['present', 'absent']), lambda_function_arn=dict(required=True, default=None, aliases=['function_name', 'function_arn']), event_source=dict(required=False, default="stream", choices=source_choices), source_params=dict(type='dict', required=True, default=None), alias=dict(required=False, default=None), version=dict(type='int', required=False, default=0), ) ) module = AnsibleModule( argument_spec=argument_spec, supports_check_mode=True, mutually_exclusive=[['alias', 'version']], required_together=[] ) # validate dependencies if not HAS_BOTO3: module.fail_json(msg='boto3 is required for this module.') aws = AWSConnection(module, ['lambda']) validate_params(module, aws) this_module_function = getattr(this_module, 'lambda_event_{0}'.format(module.params['event_source'].lower())) results = this_module_function(module, aws) module.exit_json(**results) if __name__ == '__main__': main()

34.24

134

0.628848

from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'} DOCUMENTATION = ''' --- module: lambda_event short_description: Creates, updates or deletes AWS Lambda function event mappings. description: - This module allows the management of AWS Lambda function event source mappings such as DynamoDB and Kinesis stream events via the Ansible framework. These event source mappings are relevant only in the AWS Lambda pull model, where AWS Lambda invokes the function. It is idempotent and supports "Check" mode. Use module M(lambda) to manage the lambda function itself and M(lambda_alias) to manage function aliases. version_added: "2.2" author: Pierre Jodouin (@pjodouin), Ryan Brown (@ryansb) options: lambda_function_arn: description: - The name or ARN of the lambda function. required: true aliases: ['function_name', 'function_arn'] state: description: - Describes the desired state. required: true default: "present" choices: ["present", "absent"] alias: description: - Name of the function alias. Mutually exclusive with C(version). required: true version: description: - Version of the Lambda function. Mutually exclusive with C(alias). required: false event_source: description: - Source of the event that triggers the lambda function. required: false default: stream choices: ['stream'] source_params: description: - Sub-parameters required for event source. - I(== stream event source ==) - C(source_arn) The Amazon Resource Name (ARN) of the Kinesis or DynamoDB stream that is the event source. - C(enabled) Indicates whether AWS Lambda should begin polling the event source. Default is True. - C(batch_size) The largest number of records that AWS Lambda will retrieve from your event source at the time of invoking your function. Default is 100. - C(starting_position) The position in the stream where AWS Lambda should start reading. Choices are TRIM_HORIZON or LATEST. required: true requirements: - boto3 extends_documentation_fragment: - aws ''' EXAMPLES = ''' --- # Example that creates a lambda event notification for a DynamoDB stream - hosts: localhost gather_facts: no vars: state: present tasks: - name: DynamoDB stream event mapping lambda_event: state: "{{ state | default('present') }}" event_source: stream function_name: "{{ function_name }}" alias: Dev source_params: source_arn: arn:aws:dynamodb:us-east-1:123456789012:table/tableName/stream/2016-03-19T19:51:37.457 enabled: True batch_size: 100 starting_position: TRIM_HORIZON - name: Show source event debug: var: lambda_stream_events ''' RETURN = ''' --- lambda_stream_events: description: list of dictionaries returned by the API describing stream event mappings returned: success type: list ''' import re import sys try: import boto3 from botocore.exceptions import ClientError, ParamValidationError, MissingParametersError HAS_BOTO3 = True except ImportError: HAS_BOTO3 = False from ansible.module_utils.basic import AnsibleModule from ansible.module_utils.ec2 import (HAS_BOTO3, boto3_conn, camel_dict_to_snake_dict, ec2_argument_spec, get_aws_connection_info) class AWSConnection: def __init__(self, ansible_obj, resources, use_boto3=True): try: self.region, self.endpoint, aws_connect_kwargs = get_aws_connection_info(ansible_obj, boto3=use_boto3) self.resource_client = dict() if not resources: resources = ['lambda'] resources.append('iam') for resource in resources: aws_connect_kwargs.update(dict(region=self.region, endpoint=self.endpoint, conn_type='client', resource=resource )) self.resource_client[resource] = boto3_conn(ansible_obj, **aws_connect_kwargs) if not self.region: self.region = self.resource_client['lambda'].meta.region_name except (ClientError, ParamValidationError, MissingParametersError) as e: ansible_obj.fail_json(msg="Unable to connect, authorize or access resource: {0}".format(e)) try: self.account_id = self.resource_client['iam'].get_user()['User']['Arn'].split(':')[4] except (ClientError, ValueError, KeyError, IndexError): self.account_id = '' def client(self, resource='lambda'): return self.resource_client[resource] def pc(key): return "".join([token.capitalize() for token in key.split('_')]) def ordered_obj(obj): if isinstance(obj, dict): return sorted((k, ordered_obj(v)) for k, v in obj.items()) if isinstance(obj, list): return sorted(ordered_obj(x) for x in obj) else: return obj def set_api_sub_params(params): api_params = dict() for param in params.keys(): param_value = params.get(param, None) if param_value: api_params[pc(param)] = param_value return api_params def validate_params(module, aws): function_name = module.params['lambda_function_arn'] if not re.search('^[\w\-:]+$', function_name): module.fail_json( msg='Function name {0} is invalid. Names must contain only alphanumeric characters and hyphens.'.format(function_name) ) if len(function_name) > 64 and not function_name.startswith('arn:aws:lambda:'): module.fail_json(msg='Function name "{0}" exceeds 64 character limit'.format(function_name)) elif len(function_name) > 140 and function_name.startswith('arn:aws:lambda:'): module.fail_json(msg='ARN "{0}" exceeds 140 character limit'.format(function_name)) if not module.params['lambda_function_arn'].startswith('arn:aws:lambda:'): function_name = module.params['lambda_function_arn'] module.params['lambda_function_arn'] = 'arn:aws:lambda:{0}:{1}:function:{2}'.format(aws.region, aws.account_id, function_name) qualifier = get_qualifier(module) if qualifier: function_arn = module.params['lambda_function_arn'] module.params['lambda_function_arn'] = '{0}:{1}'.format(function_arn, qualifier) return def get_qualifier(module): qualifier = None if module.params['version'] > 0: qualifier = str(module.params['version']) elif module.params['alias']: qualifier = str(module.params['alias']) return qualifier def lambda_event_stream(module, aws): client = aws.client('lambda') facts = dict() changed = False current_state = 'absent' state = module.params['state'] api_params = dict(FunctionName=module.params['lambda_function_arn']) source_params = module.params['source_params'] source_arn = source_params.get('source_arn') if source_arn: api_params.update(EventSourceArn=source_arn) else: module.fail_json(msg="Source parameter 'source_arn' is required for stream event notification.") batch_size = source_params.get('batch_size') if batch_size: try: source_params['batch_size'] = int(batch_size) except ValueError: module.fail_json(msg="Source parameter 'batch_size' must be an integer, found: {0}".format(source_params['batch_size'])) source_param_enabled = module.boolean(source_params.get('enabled', 'True')) try: facts = client.list_event_source_mappings(**api_params)['EventSourceMappings'] if facts: current_state = 'present' except ClientError as e: module.fail_json(msg='Error retrieving stream event notification configuration: {0}'.format(e)) if state == 'present': if current_state == 'absent': starting_position = source_params.get('starting_position') if starting_position: api_params.update(StartingPosition=starting_position) else: module.fail_json(msg="Source parameter 'starting_position' is required for stream event notification.") if source_arn: api_params.update(Enabled=source_param_enabled) if source_params.get('batch_size'): api_params.update(BatchSize=source_params.get('batch_size')) try: if not module.check_mode: facts = client.create_event_source_mapping(**api_params) changed = True except (ClientError, ParamValidationError, MissingParametersError) as e: module.fail_json(msg='Error creating stream source event mapping: {0}'.format(e)) else: api_params = dict(FunctionName=module.params['lambda_function_arn']) current_mapping = facts[0] api_params.update(UUID=current_mapping['UUID']) mapping_changed = False if source_params.get('batch_size') and source_params['batch_size'] != current_mapping['BatchSize']: api_params.update(BatchSize=source_params['batch_size']) mapping_changed = True if source_param_enabled is not None: if source_param_enabled: if current_mapping['State'] not in ('Enabled', 'Enabling'): api_params.update(Enabled=True) mapping_changed = True else: if current_mapping['State'] not in ('Disabled', 'Disabling'): api_params.update(Enabled=False) mapping_changed = True if mapping_changed: try: if not module.check_mode: facts = client.update_event_source_mapping(**api_params) changed = True except (ClientError, ParamValidationError, MissingParametersError) as e: module.fail_json(msg='Error updating stream source event mapping: {0}'.format(e)) else: if current_state == 'present': api_params = dict(UUID=facts[0]['UUID']) try: if not module.check_mode: facts = client.delete_event_source_mapping(**api_params) changed = True except (ClientError, ParamValidationError, MissingParametersError) as e: module.fail_json(msg='Error removing stream source event mapping: {0}'.format(e)) return camel_dict_to_snake_dict(dict(changed=changed, events=facts)) def main(): this_module = sys.modules[__name__] source_choices = ["stream"] argument_spec = ec2_argument_spec() argument_spec.update( dict( state=dict(required=False, default='present', choices=['present', 'absent']), lambda_function_arn=dict(required=True, default=None, aliases=['function_name', 'function_arn']), event_source=dict(required=False, default="stream", choices=source_choices), source_params=dict(type='dict', required=True, default=None), alias=dict(required=False, default=None), version=dict(type='int', required=False, default=0), ) ) module = AnsibleModule( argument_spec=argument_spec, supports_check_mode=True, mutually_exclusive=[['alias', 'version']], required_together=[] ) if not HAS_BOTO3: module.fail_json(msg='boto3 is required for this module.') aws = AWSConnection(module, ['lambda']) validate_params(module, aws) this_module_function = getattr(this_module, 'lambda_event_{0}'.format(module.params['event_source'].lower())) results = this_module_function(module, aws) module.exit_json(**results) if __name__ == '__main__': main()

true

f7298dd321960e183c2e94eec3d29c49fdb011bf

1,181

py

Python

setup.py

mishrasanskriti802/island-backup

6dd0b45ac9cb87418e05ccfeb1150657d8a8964b

[ "MIT" ]

17

2016-01-28T06:09:26.000Z

2020-01-19T08:37:01.000Z

setup.py

mishrasanskriti802/island-backup

6dd0b45ac9cb87418e05ccfeb1150657d8a8964b

[ "MIT" ]

28

2015-12-15T05:08:28.000Z

2017-04-20T02:34:27.000Z

setup.py

mishrasanskriti802/island-backup

6dd0b45ac9cb87418e05ccfeb1150657d8a8964b

[ "MIT" ]

7

2016-06-09T14:02:21.000Z

2020-10-01T13:55:29.000Z

from setuptools import setup, find_packages with open('README.rst', 'r', encoding='utf8') as f: readme = f.read() with open('requirements.txt','r',encoding='utf8') as f: requirements = f.readlines() version = __import__('island_backup').version setup( name='island_backup', version=version, description="backup 4chan.org h.nimingban and kukuku.cc", long_description=readme, classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Web Environment', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3 :: Only', 'License :: OSI Approved :: MIT License', ], url='https://github.com/littlezz/island-backup', author='littlezz', author_email='zz.at.field@gmail.com', license='MIT', packages=find_packages(exclude=['tests*',]), install_requires=requirements, tests_require=['pytest'], # include_package_data=True, package_data={ 'island_backup': ['templates/*', 'templates/static/*'] }, zip_safe=False, entry_points={ 'console_scripts': [ 'island_backup=island_backup.main:cli' ] }, )

26.244444

62

0.629975

from setuptools import setup, find_packages with open('README.rst', 'r', encoding='utf8') as f: readme = f.read() with open('requirements.txt','r',encoding='utf8') as f: requirements = f.readlines() version = __import__('island_backup').version setup( name='island_backup', version=version, description="backup 4chan.org h.nimingban and kukuku.cc", long_description=readme, classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Web Environment', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3 :: Only', 'License :: OSI Approved :: MIT License', ], url='https://github.com/littlezz/island-backup', author='littlezz', author_email='zz.at.field@gmail.com', license='MIT', packages=find_packages(exclude=['tests*',]), install_requires=requirements, tests_require=['pytest'], package_data={ 'island_backup': ['templates/*', 'templates/static/*'] }, zip_safe=False, entry_points={ 'console_scripts': [ 'island_backup=island_backup.main:cli' ] }, )

true

f7298e2e8b7319492030a6159a8f000bb0cc80c2

9,886

py

Python

sphinx/util/i18n.py

rdt12/sphinx

830b3fbe2babcc8df33f767ce3a406b16c0cac1c

[ "BSD-2-Clause" ]

2

2021-11-29T04:16:41.000Z

2021-12-06T14:59:22.000Z

sphinx/util/i18n.py

Blendify/sphinx

dd00bade705c8cb661151aef9f7504c62cbb17ff

[ "BSD-2-Clause" ]

1

2021-10-16T06:34:21.000Z

sphinx/util/i18n.py

Blendify/sphinx

dd00bade705c8cb661151aef9f7504c62cbb17ff

[ "BSD-2-Clause" ]

1

2021-10-24T01:44:26.000Z

""" sphinx.util.i18n ~~~~~~~~~~~~~~~~ Builder superclass for all builders. :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ import os import re from datetime import datetime, timezone from os import path from typing import TYPE_CHECKING, Callable, Generator, List, NamedTuple, Optional, Tuple, Union import babel.dates from babel.messages.mofile import write_mo from babel.messages.pofile import read_po from sphinx.errors import SphinxError from sphinx.locale import __ from sphinx.util import logging from sphinx.util.osutil import SEP, canon_path, relpath if TYPE_CHECKING: from sphinx.environment import BuildEnvironment logger = logging.getLogger(__name__) class LocaleFileInfoBase(NamedTuple): base_dir: str domain: str charset: str class CatalogInfo(LocaleFileInfoBase): @property def po_file(self) -> str: return self.domain + '.po' @property def mo_file(self) -> str: return self.domain + '.mo' @property def po_path(self) -> str: return path.join(self.base_dir, self.po_file) @property def mo_path(self) -> str: return path.join(self.base_dir, self.mo_file) def is_outdated(self) -> bool: return ( not path.exists(self.mo_path) or path.getmtime(self.mo_path) < path.getmtime(self.po_path)) def write_mo(self, locale: str) -> None: with open(self.po_path, encoding=self.charset) as file_po: try: po = read_po(file_po, locale) except Exception as exc: logger.warning(__('reading error: %s, %s'), self.po_path, exc) return with open(self.mo_path, 'wb') as file_mo: try: write_mo(file_mo, po) except Exception as exc: logger.warning(__('writing error: %s, %s'), self.mo_path, exc) class CatalogRepository: """A repository for message catalogs.""" def __init__(self, basedir: str, locale_dirs: List[str], language: str, encoding: str) -> None: self.basedir = basedir self._locale_dirs = locale_dirs self.language = language self.encoding = encoding @property def locale_dirs(self) -> Generator[str, None, None]: if not self.language: return for locale_dir in self._locale_dirs: locale_dir = path.join(self.basedir, locale_dir) locale_path = path.join(locale_dir, self.language, 'LC_MESSAGES') if path.exists(locale_path): yield locale_dir else: logger.verbose(__('locale_dir %s does not exists'), locale_path) @property def pofiles(self) -> Generator[Tuple[str, str], None, None]: for locale_dir in self.locale_dirs: basedir = path.join(locale_dir, self.language, 'LC_MESSAGES') for root, dirnames, filenames in os.walk(basedir): # skip dot-directories for dirname in dirnames: if dirname.startswith('.'): dirnames.remove(dirname) for filename in filenames: if filename.endswith('.po'): fullpath = path.join(root, filename) yield basedir, relpath(fullpath, basedir) @property def catalogs(self) -> Generator[CatalogInfo, None, None]: for basedir, filename in self.pofiles: domain = canon_path(path.splitext(filename)[0]) yield CatalogInfo(basedir, domain, self.encoding) def docname_to_domain(docname: str, compaction: Union[bool, str]) -> str: """Convert docname to domain for catalogs.""" if isinstance(compaction, str): return compaction if compaction: return docname.split(SEP, 1)[0] else: return docname # date_format mappings: ustrftime() to bable.dates.format_datetime() date_format_mappings = { '%a': 'EEE', # Weekday as locale’s abbreviated name. '%A': 'EEEE', # Weekday as locale’s full name. '%b': 'MMM', # Month as locale’s abbreviated name. '%B': 'MMMM', # Month as locale’s full name. '%c': 'medium', # Locale’s appropriate date and time representation. '%-d': 'd', # Day of the month as a decimal number. '%d': 'dd', # Day of the month as a zero-padded decimal number. '%-H': 'H', # Hour (24-hour clock) as a decimal number [0,23]. '%H': 'HH', # Hour (24-hour clock) as a zero-padded decimal number [00,23]. '%-I': 'h', # Hour (12-hour clock) as a decimal number [1,12]. '%I': 'hh', # Hour (12-hour clock) as a zero-padded decimal number [01,12]. '%-j': 'D', # Day of the year as a decimal number. '%j': 'DDD', # Day of the year as a zero-padded decimal number. '%-m': 'M', # Month as a decimal number. '%m': 'MM', # Month as a zero-padded decimal number. '%-M': 'm', # Minute as a decimal number [0,59]. '%M': 'mm', # Minute as a zero-padded decimal number [00,59]. '%p': 'a', # Locale’s equivalent of either AM or PM. '%-S': 's', # Second as a decimal number. '%S': 'ss', # Second as a zero-padded decimal number. '%U': 'WW', # Week number of the year (Sunday as the first day of the week) # as a zero padded decimal number. All days in a new year preceding # the first Sunday are considered to be in week 0. '%w': 'e', # Weekday as a decimal number, where 0 is Sunday and 6 is Saturday. '%-W': 'W', # Week number of the year (Monday as the first day of the week) # as a decimal number. All days in a new year preceding the first # Monday are considered to be in week 0. '%W': 'WW', # Week number of the year (Monday as the first day of the week) # as a zero-padded decimal number. '%x': 'medium', # Locale’s appropriate date representation. '%X': 'medium', # Locale’s appropriate time representation. '%y': 'YY', # Year without century as a zero-padded decimal number. '%Y': 'yyyy', # Year with century as a decimal number. '%Z': 'zzz', # Time zone name (no characters if no time zone exists). '%z': 'ZZZ', # UTC offset in the form ±HHMM[SS[.ffffff]] # (empty string if the object is naive). '%%': '%', } date_format_re = re.compile('(%s)' % '|'.join(date_format_mappings)) def babel_format_date(date: datetime, format: str, locale: Optional[str], formatter: Callable = babel.dates.format_date) -> str: if locale is None: locale = 'en' # Check if we have the tzinfo attribute. If not we cannot do any time # related formats. if not hasattr(date, 'tzinfo'): formatter = babel.dates.format_date try: return formatter(date, format, locale=locale) except (ValueError, babel.core.UnknownLocaleError): # fallback to English return formatter(date, format, locale='en') except AttributeError: logger.warning(__('Invalid date format. Quote the string by single quote ' 'if you want to output it directly: %s'), format) return format def format_date(format: str, date: datetime = None, language: Optional[str] = None) -> str: if date is None: # If time is not specified, try to use $SOURCE_DATE_EPOCH variable # See https://wiki.debian.org/ReproducibleBuilds/TimestampsProposal source_date_epoch = os.getenv('SOURCE_DATE_EPOCH') if source_date_epoch is not None: date = datetime.utcfromtimestamp(float(source_date_epoch)) else: date = datetime.now(timezone.utc).astimezone() result = [] tokens = date_format_re.split(format) for token in tokens: if token in date_format_mappings: babel_format = date_format_mappings.get(token, '') # Check if we have to use a different babel formatter then # format_datetime, because we only want to format a date # or a time. if token == '%x': function = babel.dates.format_date elif token == '%X': function = babel.dates.format_time else: function = babel.dates.format_datetime result.append(babel_format_date(date, babel_format, locale=language, formatter=function)) else: result.append(token) return "".join(result) def get_image_filename_for_language(filename: str, env: "BuildEnvironment") -> str: if not env.config.language: return filename filename_format = env.config.figure_language_filename d = dict() d['root'], d['ext'] = path.splitext(filename) dirname = path.dirname(d['root']) if dirname and not dirname.endswith(path.sep): dirname += path.sep docpath = path.dirname(env.docname) if docpath and not docpath.endswith(path.sep): docpath += path.sep d['path'] = dirname d['basename'] = path.basename(d['root']) d['docpath'] = docpath d['language'] = env.config.language try: return filename_format.format(**d) except KeyError as exc: raise SphinxError('Invalid figure_language_filename: %r' % exc) from exc def search_image_for_language(filename: str, env: "BuildEnvironment") -> str: if not env.config.language: return filename translated = get_image_filename_for_language(filename, env) _, abspath = env.relfn2path(translated) if path.exists(abspath): return translated else: return filename

37.44697

95

0.606312

import os import re from datetime import datetime, timezone from os import path from typing import TYPE_CHECKING, Callable, Generator, List, NamedTuple, Optional, Tuple, Union import babel.dates from babel.messages.mofile import write_mo from babel.messages.pofile import read_po from sphinx.errors import SphinxError from sphinx.locale import __ from sphinx.util import logging from sphinx.util.osutil import SEP, canon_path, relpath if TYPE_CHECKING: from sphinx.environment import BuildEnvironment logger = logging.getLogger(__name__) class LocaleFileInfoBase(NamedTuple): base_dir: str domain: str charset: str class CatalogInfo(LocaleFileInfoBase): @property def po_file(self) -> str: return self.domain + '.po' @property def mo_file(self) -> str: return self.domain + '.mo' @property def po_path(self) -> str: return path.join(self.base_dir, self.po_file) @property def mo_path(self) -> str: return path.join(self.base_dir, self.mo_file) def is_outdated(self) -> bool: return ( not path.exists(self.mo_path) or path.getmtime(self.mo_path) < path.getmtime(self.po_path)) def write_mo(self, locale: str) -> None: with open(self.po_path, encoding=self.charset) as file_po: try: po = read_po(file_po, locale) except Exception as exc: logger.warning(__('reading error: %s, %s'), self.po_path, exc) return with open(self.mo_path, 'wb') as file_mo: try: write_mo(file_mo, po) except Exception as exc: logger.warning(__('writing error: %s, %s'), self.mo_path, exc) class CatalogRepository: def __init__(self, basedir: str, locale_dirs: List[str], language: str, encoding: str) -> None: self.basedir = basedir self._locale_dirs = locale_dirs self.language = language self.encoding = encoding @property def locale_dirs(self) -> Generator[str, None, None]: if not self.language: return for locale_dir in self._locale_dirs: locale_dir = path.join(self.basedir, locale_dir) locale_path = path.join(locale_dir, self.language, 'LC_MESSAGES') if path.exists(locale_path): yield locale_dir else: logger.verbose(__('locale_dir %s does not exists'), locale_path) @property def pofiles(self) -> Generator[Tuple[str, str], None, None]: for locale_dir in self.locale_dirs: basedir = path.join(locale_dir, self.language, 'LC_MESSAGES') for root, dirnames, filenames in os.walk(basedir): for dirname in dirnames: if dirname.startswith('.'): dirnames.remove(dirname) for filename in filenames: if filename.endswith('.po'): fullpath = path.join(root, filename) yield basedir, relpath(fullpath, basedir) @property def catalogs(self) -> Generator[CatalogInfo, None, None]: for basedir, filename in self.pofiles: domain = canon_path(path.splitext(filename)[0]) yield CatalogInfo(basedir, domain, self.encoding) def docname_to_domain(docname: str, compaction: Union[bool, str]) -> str: if isinstance(compaction, str): return compaction if compaction: return docname.split(SEP, 1)[0] else: return docname date_format_mappings = { '%a': 'EEE', '%A': 'EEEE', '%b': 'MMM', '%B': 'MMMM', '%c': 'medium', '%-d': 'd', '%d': 'dd', '%-H': 'H', '%H': 'HH', '%-I': 'h', '%I': 'hh', '%-j': 'D', '%j': 'DDD', '%-m': 'M', '%m': 'MM', '%-M': 'm', '%M': 'mm', '%p': 'a', '%-S': 's', '%S': 'ss', '%U': 'WW', '%w': 'e', '%-W': 'W', '%W': 'WW', '%x': 'medium', '%X': 'medium', '%y': 'YY', '%Y': 'yyyy', '%Z': 'zzz', '%z': 'ZZZ', '%%': '%', } date_format_re = re.compile('(%s)' % '|'.join(date_format_mappings)) def babel_format_date(date: datetime, format: str, locale: Optional[str], formatter: Callable = babel.dates.format_date) -> str: if locale is None: locale = 'en' if not hasattr(date, 'tzinfo'): formatter = babel.dates.format_date try: return formatter(date, format, locale=locale) except (ValueError, babel.core.UnknownLocaleError): return formatter(date, format, locale='en') except AttributeError: logger.warning(__('Invalid date format. Quote the string by single quote ' 'if you want to output it directly: %s'), format) return format def format_date(format: str, date: datetime = None, language: Optional[str] = None) -> str: if date is None: source_date_epoch = os.getenv('SOURCE_DATE_EPOCH') if source_date_epoch is not None: date = datetime.utcfromtimestamp(float(source_date_epoch)) else: date = datetime.now(timezone.utc).astimezone() result = [] tokens = date_format_re.split(format) for token in tokens: if token in date_format_mappings: babel_format = date_format_mappings.get(token, '') if token == '%x': function = babel.dates.format_date elif token == '%X': function = babel.dates.format_time else: function = babel.dates.format_datetime result.append(babel_format_date(date, babel_format, locale=language, formatter=function)) else: result.append(token) return "".join(result) def get_image_filename_for_language(filename: str, env: "BuildEnvironment") -> str: if not env.config.language: return filename filename_format = env.config.figure_language_filename d = dict() d['root'], d['ext'] = path.splitext(filename) dirname = path.dirname(d['root']) if dirname and not dirname.endswith(path.sep): dirname += path.sep docpath = path.dirname(env.docname) if docpath and not docpath.endswith(path.sep): docpath += path.sep d['path'] = dirname d['basename'] = path.basename(d['root']) d['docpath'] = docpath d['language'] = env.config.language try: return filename_format.format(**d) except KeyError as exc: raise SphinxError('Invalid figure_language_filename: %r' % exc) from exc def search_image_for_language(filename: str, env: "BuildEnvironment") -> str: if not env.config.language: return filename translated = get_image_filename_for_language(filename, env) _, abspath = env.relfn2path(translated) if path.exists(abspath): return translated else: return filename

true

f7298e93853808c239027914d843e53976273ae0

804

py

Python

process_exec/parallel_pipe_io_base.py

sreramk/dag_process_exec

7f5493d44b65afa7b010cdc0081fc141d9339eb3

[ "Apache-2.0" ]

null

process_exec/parallel_pipe_io_base.py

sreramk/dag_process_exec

7f5493d44b65afa7b010cdc0081fc141d9339eb3

[ "Apache-2.0" ]

null

process_exec/parallel_pipe_io_base.py

sreramk/dag_process_exec

7f5493d44b65afa7b010cdc0081fc141d9339eb3

[ "Apache-2.0" ]

null

import abc class WriterBase(abc.ABC): @abc.abstractmethod def set_pipe(self, pipe): pass @abc.abstractmethod def parallel_write_end_loop(self) -> None: pass @abc.abstractmethod def is_running(self): pass @abc.abstractmethod def is_stopped(self): pass @abc.abstractmethod def start_running(self): pass @abc.abstractmethod def __call__(self): pass class ReaderBase(abc.ABC): @abc.abstractmethod def set_pipe(self, pipe): pass @abc.abstractmethod def is_running(self): pass @abc.abstractmethod def is_stopped(self): pass @abc.abstractmethod def start_running(self): pass @abc.abstractmethod def __call__(self): pass

15.461538

46

0.61194

import abc class WriterBase(abc.ABC): @abc.abstractmethod def set_pipe(self, pipe): pass @abc.abstractmethod def parallel_write_end_loop(self) -> None: pass @abc.abstractmethod def is_running(self): pass @abc.abstractmethod def is_stopped(self): pass @abc.abstractmethod def start_running(self): pass @abc.abstractmethod def __call__(self): pass class ReaderBase(abc.ABC): @abc.abstractmethod def set_pipe(self, pipe): pass @abc.abstractmethod def is_running(self): pass @abc.abstractmethod def is_stopped(self): pass @abc.abstractmethod def start_running(self): pass @abc.abstractmethod def __call__(self): pass

true

f7298fd9c7a6827bc9841172f616ef3715d70dfa

2,363

py

Python

src/muse-combineRoiMapsIter.py

CBICA/MUSE

edd01964078f957101130993899c7f4de13d48b6

[ "Unlicense" ]

null

src/muse-combineRoiMapsIter.py

CBICA/MUSE

edd01964078f957101130993899c7f4de13d48b6

[ "Unlicense" ]

1

2020-10-22T21:58:32.000Z

2020-12-24T18:09:43.000Z

src/muse-combineRoiMapsIter.py

CBICA/MUSE

edd01964078f957101130993899c7f4de13d48b6

[ "Unlicense" ]

1

2021-02-24T06:38:44.000Z

#!/usr/bin/env python # # @file muse_combineRoiMapsIter.py # @brief Combine roi probability maps for a single subject # # Copyright (c) 2011, 2012 University of Pennsylvania. All rights reserved. # See http://www.cbica.upenn.edu/sbia/software/license.html or COPYING file. # # Contact: SBIA Group <sbia-software at uphs.upenn.edu> # #Usage # ############################################ # # muse_combineRoiMapsIter.py /Path/To/Input/List.txt /Path/To/Destination/outImgName ################################################ # will read roi files listed in 'List.txt' file #The list file must have full paths to the files import nibabel as nib import numpy as np import sys import re import time print(str(sys.argv)) InputList=str(sys.argv[1]) DestFile=str(sys.argv[2]) ### Sanity check on the arguments if not InputList or not DestFile: print("ERROR: Required input options not provided!!!") sys.exit(0) ### Printing input arguments print('\n\n') print('Subject Input List :', InputList) print('Destination File :', DestFile) print('\n\n') ### Reading input file first line f=open(InputList) fline = f.readline() f.close() ### Extract roi no match=re.search('([\w.-]+)ROI_(\d+)_([\w.-]+)', fline) if match: rnos = match.group(2) rno = int(rnos) else: print('ERROR: No ROI_{roino} in file name !') exit(1) ### Read img, vectorize img = nib.load(str.rstrip(fline)) a=img.get_data() b=np.reshape(a,-1) isize = a.shape vsize = b.shape ### Set index of voxels belonging to that roi, set also max values imgMAX = b imgIND = np.zeros(vsize) imgIND[b>0] = rno ### Reading input file list f=open(InputList) lines = f.readlines() f.close() ctr=1 ### Combine roi images for line in lines: print(line) ### Extract roi no match=re.search('([\w.-]+)ROI_(\d+)_([\w.-]+)', line) if match: rnos = match.group(2) rno = int(rnos) else: print('ERROR: No ROI_{roino} in file name !') exit(1) ### Read img, vectorize img = nib.load(str.rstrip(line)) a=img.get_data() b=np.reshape(a,-1) ### Set index of voxels belonging to that roi, set also max values imgIND.put((b>imgMAX).nonzero(), rno) imgMAX = np.maximum(b,imgMAX) ### Write out img imgINDM = np.reshape(imgIND,isize) aff = img.get_affine() hdr = img.get_header() #hdr.set_data_dtype(np.int16) img2 = nib.Nifti1Image(imgINDM, aff, hdr) img2.to_filename(DestFile);

22.084112

84

0.66314

true

f72990938ad2cb42f00c7cdb6eff613f89c9d0de

1,925

py

Python

empower/cli/lomm_lns_commands/list_lenddevs.py

ericbrinckhaus/empower-runtime-modified

ecd7c1e9f1c19a629abdcb5c55257377313246ea

[ "Apache-2.0" ]

null

empower/cli/lomm_lns_commands/list_lenddevs.py

ericbrinckhaus/empower-runtime-modified

ecd7c1e9f1c19a629abdcb5c55257377313246ea

[ "Apache-2.0" ]

null

empower/cli/lomm_lns_commands/list_lenddevs.py

ericbrinckhaus/empower-runtime-modified

ecd7c1e9f1c19a629abdcb5c55257377313246ea

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # # # 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. """List LoRaWAN End Devices in the LNS database.""" import argparse from empower.cli import command def pa_cmd(args, cmd): """List lEndDevs parser method. usage: empower-ctl.py list-lenddevs <options> optional arguments: -h, --help show this help message and exit -g DEVEUI, --devEUI DEVEUI show results for a specified devEUI id only -v, --verbose verbose """ usage = "%s <options>" % command.USAGE.format(cmd) desc = command.DESCS[cmd] parser = argparse.ArgumentParser(usage=usage, description=desc) # required = parser.add_argument_group('required named arguments') parser.add_argument( '-g', '--devEUI', help='show results for a specified devEUI id only', default=None, type=str, dest="devEUI") parser.add_argument( '-v', '--verbose', help='verbose', action="store_true", default=False, dest="config") (args, leftovers) = parser.parse_known_args(args) return args, leftovers def do_cmd(gargs, args, _): """List lEndDevs registered in the LNS.""" url = '/api/v1/lns/lenddevs/' _, data = command.connect(gargs, ('GET', url), 200) for entry in data: if not args.devEUI: print(entry) elif entry['DevEUI'] == args.devEUI: print(entry)

28.731343

77

0.657143

import argparse from empower.cli import command def pa_cmd(args, cmd): usage = "%s <options>" % command.USAGE.format(cmd) desc = command.DESCS[cmd] parser = argparse.ArgumentParser(usage=usage, description=desc) parser.add_argument( '-g', '--devEUI', help='show results for a specified devEUI id only', default=None, type=str, dest="devEUI") parser.add_argument( '-v', '--verbose', help='verbose', action="store_true", default=False, dest="config") (args, leftovers) = parser.parse_known_args(args) return args, leftovers def do_cmd(gargs, args, _): url = '/api/v1/lns/lenddevs/' _, data = command.connect(gargs, ('GET', url), 200) for entry in data: if not args.devEUI: print(entry) elif entry['DevEUI'] == args.devEUI: print(entry)

true

f7299320ac92f29ec4db9777da6d4d97ea370f59

603

py

Python

make_call.py

nfrumkin/ElMingle

6e9fa6bc4a4826188c9752212ec57b7b7a225b21

[ "MIT" ]

1

2019-11-17T00:44:08.000Z

make_call.py

nfrumkin/ElMingle

6e9fa6bc4a4826188c9752212ec57b7b7a225b21

[ "MIT" ]

null

make_call.py

nfrumkin/ElMingle

6e9fa6bc4a4826188c9752212ec57b7b7a225b21

[ "MIT" ]

null

# Download the helper library from https://www.twilio.com/docs/python/install from twilio.rest import Client # Your Account Sid and Auth Token from twilio.com/console # DANGER! This is insecure. See http://twil.io/secure account_sid = 'ACc07d36f2bedb365988af5c81d578bfef' auth_token = 'd7698b135d26730bf84600c176d6815f' client = Client(account_sid, auth_token) call = client.calls.create( record=True, url='http://demo.twilio.com/docs/voice.xml', to='+19392190769', from_='+12512835337' ) print(call.sid)

33.5

77

0.658375

from twilio.rest import Client account_sid = 'ACc07d36f2bedb365988af5c81d578bfef' auth_token = 'd7698b135d26730bf84600c176d6815f' client = Client(account_sid, auth_token) call = client.calls.create( record=True, url='http://demo.twilio.com/docs/voice.xml', to='+19392190769', from_='+12512835337' ) print(call.sid)

true

f7299343251025558429f8634c574058f7031345

1,529

py

Python

generator.py

y3sar/painter_gan

374fb91927ca584b4ef3fd8ba10922c7b5201780

[ "MIT" ]

1

2020-09-10T07:56:10.000Z

generator.py

y3sar/painter_gan

374fb91927ca584b4ef3fd8ba10922c7b5201780

[ "MIT" ]

15

2020-09-26T00:22:47.000Z

2022-03-02T14:59:36.000Z

generator.py

y3sar/painter_gan

374fb91927ca584b4ef3fd8ba10922c7b5201780

[ "MIT" ]

null

import torch import torch.nn as nn from torchvision.transforms import ToTensor, ToPILImage class Generator(nn.Module): def __init__(self): super().__init__() self.conv_block = nn.Sequential( nn.ConvTranspose2d(100, 512, 4, 1, 0), nn.BatchNorm2d(512), nn.ReLU(True), nn.ConvTranspose2d(512, 256, 4, 2, 1), nn.BatchNorm2d(256), nn.ReLU(True), nn.ConvTranspose2d(256, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.ReLU(True), nn.ConvTranspose2d(128, 64, 4, 2, 1), nn.BatchNorm2d(64), nn.ReLU(True), nn.ConvTranspose2d(64, 3, 4, 2, 1), nn.BatchNorm2d(3), nn.ReLU(True), nn.ConvTranspose2d(3, 3, 4, 2, 1), nn.Tanh(), ) def forward(self, x): x = self.conv_block(x) return x if __name__ == '__main__': img = torch.randn(1, 100, 1, 1) gen = Generator() print(gen(img).shape)

26.362069

74

0.35121

import torch import torch.nn as nn from torchvision.transforms import ToTensor, ToPILImage class Generator(nn.Module): def __init__(self): super().__init__() self.conv_block = nn.Sequential( nn.ConvTranspose2d(100, 512, 4, 1, 0), nn.BatchNorm2d(512), nn.ReLU(True), nn.ConvTranspose2d(512, 256, 4, 2, 1), nn.BatchNorm2d(256), nn.ReLU(True), nn.ConvTranspose2d(256, 128, 4, 2, 1), nn.BatchNorm2d(128), nn.ReLU(True), nn.ConvTranspose2d(128, 64, 4, 2, 1), nn.BatchNorm2d(64), nn.ReLU(True), nn.ConvTranspose2d(64, 3, 4, 2, 1), nn.BatchNorm2d(3), nn.ReLU(True), nn.ConvTranspose2d(3, 3, 4, 2, 1), nn.Tanh(), ) def forward(self, x): x = self.conv_block(x) return x if __name__ == '__main__': img = torch.randn(1, 100, 1, 1) gen = Generator() print(gen(img).shape)

true

f7299350b5f08db93723645ff2abf0821f03a950

8,346

py

Python

Engines/Engines.py

whitemike889/Machapi

8c6f4d4d9d788cc0b56643ef054124c04b7e2728

[ "Apache-2.0" ]

null

Engines/Engines.py

whitemike889/Machapi

8c6f4d4d9d788cc0b56643ef054124c04b7e2728

[ "Apache-2.0" ]

null

Engines/Engines.py

whitemike889/Machapi

8c6f4d4d9d788cc0b56643ef054124c04b7e2728

[ "Apache-2.0" ]

null

## @package Engines # The superior method to solving complex problems is by easily consumable and scalable design, not complex code. # # The engines package consists of virtual classes and methods that define the structure of the engine modules. # # New modules are installed simply by dropping a compliant engine module in the Engines package directory. # # The virtual classes and methods are what defines compliance with the engine. One could simply build these out # manually without using these but this is highly discouraged unless you really know what you're doing, so long as # you're okay with future changes in the engine breaking the functionality of the module you are developing. # # Your code scanner is probably going to whine because I deviate from PEP8. That's mostly because the PEP8 is # worthless bullshit and its evangelical adherents are mostly just eating each others' regurgitations instead of # thinking. from abc import ABCMeta from abc import abstractmethod import configparser import requests ## SessionSpec # # The SessionSpec class is the core of the module. This contains the structure of the class you must implement, # prepackaged for you. Exceptions will be thrown as required until you have implemented all the necessary minimum # pieces in the structure required to be used by a multiplexor, which is not required to utilize a module but forces # continuity between modules when all modules use it. You are strongly advised not to build a module that does not # use the base classes defined here if you wish to contribute. # # This file also serves as an excellent atomic, academic example of abstract method usage and OOP inheritance in Python. class SessionSpec: __metaclass__ = ABCMeta ## Sessionspec.__init__ # # The initialization method for SessionSpec. It is recommended that you inherit from SessionSpec for your Session # class and then call the parent initialization method, and then implement your own module-specific calls in your # Session init. This allows commonalities between modules to be implemented here while also allowing modules to # have their own, which will be a necessity as you'll notice while you're building. def __init__( self, config ): # bind the configuration to the Session object self.config = config # handle to use for getting new pages with the Session object self.client = requests.Session() # But wait -- there's more! # Throwing in rudimentary proxy support: if self.config.proxy_enabled: proxies = { 'http', self.config.proxy } self.client.proxies.update( proxies ) ## SessionSpec.SessionError # # The SessionError subclass defines the Generic exception to be the Session class you are implementing. # # In the example modules, we use this for just about everything, but a better practice would be to use it as a base # class for every TYPE of exception you wish to handle to make your engines more robust in error handling. class SessionError( Exception ): def __init__( self, value ): self.value = value ## SessionSpec.login # # The method used to log in to the site your engine module supports. Self-explanatory. # # Note: When implementing in your module, do NOT return a boolean or use any other indicator of success. Instead, # use SessionSpec.Parser.is_logged_in as a conditional in your logic flow for individual actions. This prevents # your contributors from relying on stateful information in a stateless manner. @abstractmethod def login( self ): pass ## SessionSpec.Config # # While the config file can be universal between engines so long as good naming conventions are used by the module # implementations, each SessionSpec child class will want to import the whole config file and bind to local values # that the module will want to implement. Here we have some operations related to that which will be common to all # modules, providing a common set of values that I'd like the modules to have. # # You'll want to call the parent class's initialization and expand upon it for module-specific features. class Config: def __init__( self, config_file ): settings = configparser.ConfigParser( allow_no_value=True ) settings.read( config_file ) self.useragent = settings.get( "general-client", "user_agent" ) self.proxy_enabled = settings.getboolean( "proxy", "enabled" ) if self.proxy_enabled: self.proxy = settings.get( "proxy", "proxy" ) ## SessionSpec.User # # The User class must be implemented by the module and must be implemented by all modules, as different sites # give users different attributes that the module will be interested in. class User: @abstractmethod def __init__(self): pass ## SessionSpec.Parser # # Like all good web browsers, you'll need to interpret the code that the browser retrieves with an engine. Most # browsers call this something else, but the concept is the same: Code goes in that's served by the endpoint, and # usable data comes out that the browser decides how to display. This parser is the component that handles the # conversion of that HTTP response into a usable format for the rest of the engine. The options here are pretty # limitless to the point that building structure around it can only limit the scope of the module, so, feel free # to experiment. Efforts were taken to design the structure in a way that allows you to do this. # # All methods of the Parser class are static, and extract only one datapoint. As you add more datapoints to # extract and identifiers, you will need to implement those abstract methods in your Parser class and bind the # identifier for its dedicated datapoint in the dictionary-based router that associates datapoints with static child # methods to your Parser. class Parser: ## SessionSpec.Parser.scrape # @type datapoint: string # @param datapoint: Identifier representing a datapoint to be extracted from a larger unprocessed body of data. # # @type text: string # @param text: The unprocessed body of data from which the datapoint should be extracted. # # scrape defines and associates datapoint identifiers with their respective static methods that retrieve them. # # Returns whatever the helper method returns, which is intended to be either an extracted piece of information # from the raw response body or an abstracted piece of data, so long as any points of aggregation are done # statelessly. @staticmethod @abstractmethod def scrape( datapoint, text ): # this is expected to take in an identifier for the first argument representing a datapoint and the raw page # source being interpreted from which the datapoint is extracted pass ## SessionSpec.Parser.is_logged_in # @type text: string # @param text: The unprocessed page response to extract information from that identifies whether or not you have # an active session. # # is_logged_in is a vital session management point in the rest of your module. This is a boolean # return that should be checked ad-hoc any time you perform an action that requires a session. # # The manner in which an active session is validated will vary from site to site, so this must be implemented # per-module but will be needed for any module that interfaces with a site that requires a login. # # Returns a boolean value. @staticmethod @abstractmethod def is_logged_in( text ): pass ## SessionSpec.Parser.send_message_failed # @type text: string # @param text: The unprocessed page response to extract information from that identifies whether or not your # attempt to send a message failed. # # Returns a boolean value. @staticmethod @abstractmethod def send_message_failed( text ): pass

50.277108

120

0.711718

okay with future changes in the engine breaking the functionality of the module you are developing. # worthless bullshit and its evangelical adherents are mostly just eating each others' regurgitations instead of from abc import ABCMeta from abc import abstractmethod import configparser import requests s SessionSpec: __metaclass__ = ABCMeta def __init__( self, config ): self.config = config self.client = requests.Session() # Throwing in rudimentary proxy support: if self.config.proxy_enabled: proxies = { 'http', self.config.proxy } self.client.proxies.update( proxies ) ## SessionSpec.SessionError # # The SessionError subclass defines the Generic exception to be the Session class you are implementing. # # In the example modules, we use this for just about everything, but a better practice would be to use it as a base # class for every TYPE of exception you wish to handle to make your engines more robust in error handling. class SessionError( Exception ): def __init__( self, value ): self.value = value ## SessionSpec.login # # The method used to log in to the site your engine module supports. Self-explanatory. # # Note: When implementing in your module, do NOT return a boolean or use any other indicator of success. Instead, # use SessionSpec.Parser.is_logged_in as a conditional in your logic flow for individual actions. This prevents # your contributors from relying on stateful information in a stateless manner. @abstractmethod def login( self ): pass ## SessionSpec.Config # # While the config file can be universal between engines so long as good naming conventions are used by the module # implementations, each SessionSpec child class will want to import the whole config file and bind to local values # that the module will want to implement. Here we have some operations related to that which will be common to all # modules, providing a common set of values that I'd like the modules to have. class Config: def __init__( self, config_file ): settings = configparser.ConfigParser( allow_no_value=True ) settings.read( config_file ) self.useragent = settings.get( "general-client", "user_agent" ) self.proxy_enabled = settings.getboolean( "proxy", "enabled" ) if self.proxy_enabled: self.proxy = settings.get( "proxy", "proxy" ) class User: @abstractmethod def __init__(self): pass wsers call this something else, but the concept is the same: Code goes in that's served by the endpoint, and class Parser: @staticmethod @abstractmethod def scrape( datapoint, text ): pass @staticmethod @abstractmethod def is_logged_in( text ): pass @staticmethod @abstractmethod def send_message_failed( text ): pass

true

f72994670ea9e8c9d9a4c64841e352b8c6248387

64

py

Python

Conteudo das Aulas/129/filho.py

cerberus707/lab-python

ebba3c9cde873d70d4bb61084f79ce30b7f9e047

[ "Apache-2.0" ]

null

Conteudo das Aulas/129/filho.py

cerberus707/lab-python

ebba3c9cde873d70d4bb61084f79ce30b7f9e047

[ "Apache-2.0" ]

null

Conteudo das Aulas/129/filho.py

cerberus707/lab-python

ebba3c9cde873d70d4bb61084f79ce30b7f9e047

[ "Apache-2.0" ]

null

import sys, os print("Ola do filho", os.getpid(), sys.argv[1])

16

47

0.65625

import sys, os print("Ola do filho", os.getpid(), sys.argv[1])

true

f7299518676a99f536019b7f897b01105167f5d1

1,028

py

Python

events/templatetags/append_get.py

yellowjaguar5/lnldb

dea7708f5e4e103ef6ef968c9f3a4deaa58861c5

[ "MIT" ]

5

2017-09-25T21:24:59.000Z

2021-12-18T17:08:13.000Z

events/templatetags/append_get.py

yellowjaguar5/lnldb

dea7708f5e4e103ef6ef968c9f3a4deaa58861c5

[ "MIT" ]

304

2015-03-24T17:44:22.000Z

2022-03-29T14:09:41.000Z

events/templatetags/append_get.py

yellowjaguar5/lnldb

dea7708f5e4e103ef6ef968c9f3a4deaa58861c5

[ "MIT" ]

10

2017-10-24T02:18:12.000Z

2021-09-20T20:40:25.000Z

from django import template register = template.Library() @register.simple_tag(takes_context=True) def append_to_get(context, replace=True, **kwargs): """ Adds/deletes arguments to the current GET value and returns a querystring containing it. @argument replace: If true, any existing argument named in kwargs will have their value overridden. If false, kwargs are appended only. @argument kwargs: key-val pairs to add, with a value of None for deletion. (Use "None" if you don't want that) """ updated = context['request'].GET.copy() if not replace: # duplicates will be appended to the query updated.update(kwargs) else: # duplicates will be replaced for arg, val in kwargs.items(): updated[arg] = val # if the kwarg is None delete it instead for arg, val in kwargs.items(): if val is None: updated.pop(arg) if updated: return "?" + updated.urlencode() else: return ""

28.555556

60

0.642023

from django import template register = template.Library() @register.simple_tag(takes_context=True) def append_to_get(context, replace=True, **kwargs): updated = context['request'].GET.copy() if not replace: updated.update(kwargs) else: for arg, val in kwargs.items(): updated[arg] = val for arg, val in kwargs.items(): if val is None: updated.pop(arg) if updated: return "?" + updated.urlencode() else: return ""

true

f7299528177ca00d16a733958ceae2e9c52189ba

35,826

py

Python

tools/ReportConverter/ReportConverter.py

siemens/drace

2679067783b1d8f39e4c370cd72a7626ebf5f8e8

[ "MIT", "MIT-0", "BSD-3-Clause" ]

32

2019-02-19T11:37:14.000Z

2022-01-07T16:09:27.000Z

tools/ReportConverter/ReportConverter.py

siemens/drace

2679067783b1d8f39e4c370cd72a7626ebf5f8e8

[ "MIT", "MIT-0", "BSD-3-Clause" ]

86

2019-03-29T08:57:37.000Z

2021-06-30T16:13:06.000Z

tools/ReportConverter/ReportConverter.py

siemens/drace

2679067783b1d8f39e4c370cd72a7626ebf5f8e8

[ "MIT", "MIT-0", "BSD-3-Clause" ]

4

2019-04-16T18:35:02.000Z

2021-06-17T16:49:48.000Z

# # ReportConverter: A graphical report generator for DRace # # Copyright 2019 Siemens AG # # Authors: # <Philip Harr> <philip.harr@siemens.com> # # SPDX-License-Identifier: MIT # ## \package ReportConverter ## \brief Python XML to HTML report converter for the better visualization of drace result data import xml.etree.ElementTree as ET import shutil import argparse import pathlib import datetime import html import sys from subprocess import check_call, STDOUT, DEVNULL from functools import lru_cache try: import matplotlib import matplotlib.pyplot as plt from matplotlib.lines import Line2D noMatplotLib = False except ImportError: noMatplotLib = True print("Matplotlib is not installed.") #look for resources path if getattr(sys, 'frozen', False): SCRIPTPATH = pathlib.Path(sys.executable) SCRIPTPATH = pathlib.Path(SCRIPTPATH / "..") else : SCRIPTPATH = pathlib.Path(pathlib.Path(__file__).resolve().parents[0]) if pathlib.Path(SCRIPTPATH / '../resources').is_dir(): resourcesPath = pathlib.Path(SCRIPTPATH / '../resources') else: if pathlib.Path(SCRIPTPATH / 'resources').is_dir(): resourcesPath = pathlib.Path(SCRIPTPATH / 'resources') else: print("path of resources not found") sys.exit(-1) #Paths g_HTMLTEMPLATES = resourcesPath / "entries.xml" g_CSSPATH = resourcesPath / "css" g_JSPATH = resourcesPath / "js" DEBUG = False #info: blacklisting overrules whitelisting SOURCEFILE_BL = list() SOURCEFILE_WL = list() WHITELISTING = False NUMBEROFCODELINES = 400 if NUMBEROFCODELINES % 2: print('Number of maximum of displayed code lines must be even, but is:') print(str(NUMBEROFCODELINES)) sys.exit(-1) #Source Directories SOURCE_DIRECTORIES = list() class ReportCreator: _htmlTemplatesPath = str(g_HTMLTEMPLATES) _topStackGraphFileName = 'topStackBarchart.png' _errorTimesPlot = 'errorTimes.png' try: if check_call(['code', '--version'], stdout=DEVNULL, stderr=STDOUT, shell=True) == 0: #check if vscode is installed, for sourcefile linking _vscodeFlag = True else: _vscodeFlag = False except: _vscodeFlag = False def __init__(self, pathOfReport, target): self.sourcefileList = list() self._callStackNumber = 0 self._errorNumber = 0 self._snippets = str() self.succesfullReportCreation = True try: self._htmlTemplates = (ET.parse(self._htmlTemplatesPath)).getroot() except FileNotFoundError: print("template file is missing") self.succesfullReportCreation = False return self.SCM = SourceCodeManagement() self._pathOfReport = pathOfReport if self._inputValidation(): hasErrors = self._reportRoot.find('error') != None if not noMatplotLib and hasErrors: self._makeHistogramm(target) self._countTopStackOccurences(target) self._createReport() else: print("input file is not valid") self.succesfullReportCreation = False def _inputValidation(self): try: self._reportContent = ET.parse(self._pathOfReport) except ET.ParseError: return 0 self._reportRoot = self._reportContent.getroot() if self._reportRoot.find('protocolversion') != None and \ self._reportRoot.find('protocoltool') != None and \ self._reportRoot.find('preamble') != None and \ self._reportRoot.find('pid') != None and \ self._reportRoot.find('tool') != None and \ self._reportRoot.find('args') != None and \ self._reportRoot.find('status') != None and \ self._reportRoot.tag == 'valgrindoutput': return 1 else: return 0 def _getHeader(self): header = list() status = self._reportRoot.findall('status') if len(status) == 2: status = status[1] ##second status contains finishing values strDatetime = status.find('time').text if "T" in strDatetime: date = strDatetime.split('T')[0] time = (strDatetime.split('T')[1])[0:-1] #last digit is 'Z' -> not needed else: date = "" time = strDatetime header.append(adjText(date)) header.append(adjText(time)) if status.find('duration') != None: header.append(adjText(status.find('duration').text)) header.append(adjText(status.find('duration').get('unit'))) else: header.append("") header.append("") else: header.append("") header.append("") header.append("") header.append("") arguments = str() for arg in self._reportRoot.find('args').find('vargv').findall('arg'): arguments += arg.text arguments += ' ' header.append(adjText(arguments[0:-1])) #remove last ' ' header.append(adjText(self._reportRoot.find('args').find('argv').find('exe').text)) header.append(adjText(self._reportRoot.find('protocolversion').text)) header.append(adjText(self._reportRoot.find('protocoltool').text)) return header def _makeFileEntry(self, frame): strDir = adjText(self._frameValues["dir"]) strFile = adjText(self._frameValues["file"]) strLine = adjText(self._frameValues["line"]) offset = adjText(self._frameValues["offset"]) if self._vscodeFlag: entry = "<a href='vscode://file/" + strDir + "/" + strFile + ":" + strLine + ":" + offset +"'>"+ strFile +":" + strLine + ":" + offset + "</a>" else: entry = "<a href='file://"+ strDir + "/" + strFile + "'>" + strFile + ":" + strLine + "</a>" return entry def _readFrame(self, frame): if frame is None: self._frameValues = {"obj":"", "fn":"", "ip":"", "dir":"", "file":"", "line":"", "offset":""} return obj = frame.find('obj') if obj is None: obj = "" else: if obj.text is None: obj = "" else: obj = obj.text fn = frame.find('fn') if fn is None: fn = "" else: if fn.text is None: fn = "" else: fn = fn.text ip = frame.find('ip') if ip is None: ip = "" else: if ip.text is None: ip = "" else: ip = ip.text direc = frame.find('dir') if direc is None: direc = "" else: if direc.text is None: direc = "" else: direc = direc.text filename = frame.find('file') if filename is None: filename = "" else: if filename.text is None: filename = "" else: filename = filename.text line = frame.find('line') if line is None: line = "0" else: if line.text is None: line = "0" else: line = line.text offset = frame.find('offset') if offset is None: offset = "0" else: if offset.text is None: offset = "0" else: offset = offset.text self._frameValues = {"obj":obj, "fn":fn, "ip":ip, "dir":direc, "file":filename, "line":line, "offset":offset} def _createSnippetEntry(self, frame, elementNumber, tag, codeIndex, buttonID): newSnippet = self._htmlTemplates.find('snippet_entry').text newSnippet = newSnippet.replace('*SNIPPET_VAR*', ("snippet_" + str(self._callStackNumber))) newSnippet = newSnippet.replace('*STACK_NUMBER*', adjText(hex(elementNumber))) newSnippet = newSnippet.replace('*OBJ*', adjText(self._frameValues["obj"])) newSnippet = newSnippet.replace('*FUNCTION*', adjText(self._frameValues["fn"])) newSnippet = newSnippet.replace('*INSTRUCTION_POINTER*', adjText(self._frameValues["ip"])) newSnippet = newSnippet.replace('*CODE_TAG*', tag) newSnippet = newSnippet.replace('*SNIPPET_BUTTON_ID*', buttonID) if (self._frameValues["file"] != ""): newSnippet = newSnippet.replace('*FILE_NAME_ENTRY*', self._makeFileEntry(frame)) newSnippet = newSnippet.replace('*DIRECTORY*', adjText(self._frameValues["dir"])) newSnippet = newSnippet.replace('*SHORT_DIR*', adjText(self._makeShortDir(self._frameValues["dir"]))) newSnippet = newSnippet.replace('*LINE_OF_CODE*', adjText(self._frameValues["line"])) if(codeIndex != -1): newSnippet = newSnippet.replace('*CODE_ID_VAR*', "snippet_"+str(self._callStackNumber)+"_code") newSnippet = newSnippet.replace('*LANGUAGE*', self.SCM.determineLanguage(adjText(self._frameValues["file"]))) newSnippet = newSnippet.replace('*FIRST_LINE*', str(self.SCM.getFirstLineOfCodeSnippet(codeIndex))) else: newSnippet = newSnippet.replace('*CODE_ID_VAR*', "'None'") else: newSnippet = newSnippet.replace('*FILE_NAME_ENTRY*', 'no filename avail.') newSnippet = newSnippet.replace('*DIRECTORY*', 'no directory avail.') newSnippet = newSnippet.replace('*SHORT_DIR*', 'no directory avail.') self._snippets += newSnippet #append referenced code snippet def _makeShortDir(self, strDir): elements = None if "\\" in strDir: elements = strDir.split("\\") else: if "/" in strDir: elements = strDir.split("/") if elements != None: return elements[0] + "/" + elements[1] + "/.../" + elements[-1] else: return "" def _createCallStack(self, errorEntry, position, outputID): callStack = str() stackTemplate = self._htmlTemplates.find('stack_entry').text stackArray = errorEntry.findall('stack') stack = stackArray[position] elementNumber = 0 frames = stack.findall('frame') if frames is None: return "" for frame in frames: self._readFrame(frame) #reads all frame values and fills member var # updates frame dir if valid sourceDirectories are given, otherwise returns same value newDir = self.SCM.searchSourceDirectories(self._frameValues["dir"], self._frameValues["file"]) self._frameValues["dir"] = adjText(newDir) noPreview = False buttonID = "button_" + str(self._errorNumber) + "_" + str(position) + "_" + str(elementNumber) strOutputID = outputID+str(position) if elementNumber == 0: ###make heading for the red box### if len(self._errorHeading) == 0: self._errorHeading += " Obj. 1: " + (adjText(self._frameValues["obj"]) + ': "' + adjText(self._frameValues["fn"])) + '" ' else: self._errorHeading += "Obj. 2: " + (adjText(self._frameValues["obj"]) + ': "' + adjText(self._frameValues["fn"])) + '"' #general entries (always available) newStackElement = stackTemplate.replace('*STACK_NUMBER*', adjText(hex(elementNumber))+":") newStackElement = newStackElement.replace('*SNIPPET_VAR*', ("snippet_" + str(self._callStackNumber))) newStackElement = newStackElement.replace('*OUTPUT_ID*', strOutputID) newStackElement = newStackElement.replace('*FUNCTION*', adjText(self._frameValues['fn'])) newStackElement = newStackElement.replace('*BUTTON_ID*', buttonID) if (self._frameValues["file"]!= ""): #file is in xml report defined codeIndex, tag = self.SCM.handleSourceCode(self._frameValues["file"], self._frameValues["dir"], self._frameValues["line"]) newStackElement = newStackElement.replace('*FILE*', adjText(self._frameValues["file"])) if(codeIndex != -1): newStackElement = newStackElement.replace('*CODE_VAR*', str(codeIndex)) newStackElement = newStackElement.replace('*CODE_ID_VAR*', "'snippet_"+str(self._callStackNumber)+"_code'") newStackElement = newStackElement.replace('*LINE_OF_CODE*', adjText(self._frameValues["line"])) newStackElement = newStackElement.replace('*FIRST_LINE*', str(self.SCM.getFirstLineOfCodeSnippet(codeIndex))) else: #file is not available on device or file is blacklisted or not whitelisted noPreview = True else: #no filepath for file in xml is given codeIndex = -1 tag = self._htmlTemplates.find('no_code_entry').text newStackElement = newStackElement.replace('*FILE*', 'no filename avail.') noPreview = True if noPreview: newStackElement = newStackElement.replace('*CODE_VAR*', "'None'") newStackElement = newStackElement.replace('*CODE_ID_VAR*', "'None'") newStackElement = newStackElement.replace('*LINE_OF_CODE*', "'None'") newStackElement = newStackElement.replace('*FIRST_LINE*', "'NONE'") searchStr = 'class="' insertPosition = newStackElement.find(searchStr)+len(searchStr) #to add the ".grey" class the position before after class #insertPosition += newStackElement[insertPosition:].find('"') newStackElement = newStackElement[:insertPosition] + "grey-button " + newStackElement[insertPosition:] self._createSnippetEntry(frame, elementNumber, tag, codeIndex, buttonID) callStack += newStackElement #append stack element elementNumber += 1 self._callStackNumber += 1 #increase global call stack number (used for reference variables) return callStack def _makeHistogramm(self, target): errorTimes = dict() statusNode = self._reportRoot.findall('status')[1] if statusNode.find('duration') is None: self._errorTimesPlot = "" return totalDuration = int(statusNode.find('duration').text) errors = self._reportRoot.findall('error') for error in errors: timePoint = (round(float(100 * int(error.find('timestamp').text) /totalDuration))) #get occurance in % if errorTimes.get(timePoint) != None: value = errorTimes.pop(timePoint) errorTimes.update({timePoint: int(value)+1}) else: errorTimes.update({timePoint: 1}) x = list(errorTimes.keys()) y = list(errorTimes.values()) #make plot fig = plt.figure(figsize=(10,4)) ax = plt.axes() ax.scatter(x, y, color='#009999', edgecolor='black') xRangeEnd = max(y)+1 if xRangeEnd < 3: #0, 1, 2 shall be always visible, even if max(y) is only 1 xRangeEnd = 3 ax.set_yticks([i for i in range(0, xRangeEnd)]) ax.set_xticks([i for i in range(0, 110, 10)]) plt.title('Error occurrences by time',fontfamily="monospace", fontweight='bold') plt.ylabel('Occurrences', fontfamily="monospace",fontweight='bold') plt.xlabel('Execution of program in %. \n Total execution time = ' + str(totalDuration) + 'ms', fontfamily="monospace",fontweight='bold') fig.add_axes(ax) #plt.show() figPath = pathlib.Path(target+'/'+self._errorTimesPlot) plt.savefig(str(figPath), dpi=300, format='png', bbox_inches='tight', orientation='landscape') # use format='svg' or 'pdf' for vectorial pictures def _countTopStackOccurences(self, target): topStackOccurences = dict() errors = self._reportRoot.findall('error') for error in errors: stacks = error.findall('stack') for i in range(0,2): topFrame = stacks[i].find('frame') #returns first element of with frame tag if(topFrame != None): self._readFrame(topFrame) tmp1 = self._frameValues["file"] tmp2 = self._frameValues["fn"] if(tmp1 != "None" and tmp2 != "None"): if(len(tmp2) > 20): #split function name in half if it is too long tmp2 = tmp2[:len(tmp2)//2] + '\n' + tmp2[len(tmp2)//2:] identifier = tmp1 + ":\n" + tmp2 if topStackOccurences.get(identifier) != None: value = topStackOccurences.pop(identifier) topStackOccurences.update({identifier: int(value)+1}) else: topStackOccurences.update({identifier: 1}) #sort dict sortedOccurences = sorted(topStackOccurences.items(), key=lambda kv: kv[1]) x=list() y=list() for ele in sortedOccurences[-5:]: #append the 5 largest values in ascending order if len(ele[0]) < 250: x.append(ele[0]) #x values (basically the function names) else: x.append(ele[0][:250]+". . .") y.append(ele[1]) #y values occurrences (bar height) #make plot fig = plt.figure(figsize=(10,4)) ax = plt.axes() barWidth = 0.9 # the width of the bars xLoc = list(range(len(y))) # the x locations for the groups ax.barh([loc for loc in xLoc], y, barWidth, color='#009999') ax.set_yticks([loc for loc in xLoc]) ax.set_yticklabels(reversed(['#'+str(rank) for rank in range(1,len(y)+1)]), minor=False) legend_lines = [Line2D([0], [0], color='#009999', lw=rank) for rank in range(len(y)+1, 1, -1)] ax.legend(legend_lines, reversed(x), loc='center', bbox_to_anchor=(0.5, -0.1*(len(y)+2))) plt.title('Top five functions by top of stack occurrences',fontfamily="monospace", fontweight='bold') plt.xlabel('No. of top of stack occurrences', fontfamily="monospace",fontweight='bold') for i,v in enumerate(y): ax.text(v, i, str(v), ha='left',color='black', fontweight='bold') fig.add_axes(ax) #plt.show() figPath = pathlib.Path(target+'/'+self._topStackGraphFileName) plt.savefig(str(figPath), dpi=300, format='png', bbox_inches='tight', orientation='landscape') # use format='svg' or 'pdf' for vectorial pictures def _createErrorList(self): self._strErrors = str() errorTemplate = self._htmlTemplates.find('error_entry').text errorList = self._reportRoot.findall('error') self._numberOfErrors = len(errorList) for error in errorList: outputID = "output_"+str(self._errorNumber)+"_" newError = errorTemplate.replace('*ERROR_ID*', adjText(error.find('unique').text)) newError = newError.replace('*ERROR_TYPE*', adjText(error.find('kind').text)) xwhat = error.findall('xwhat') errortext1 = xwhat[0].find('text').text #fall back to xauxhwaht -> valgrind format if(len(xwhat) == 1): element = error.find('xauxwhat') if element != None: errortext2 = element.find('text').text else: errortext2 = "" else: errortext2 = xwhat[1].find('text').text newError = newError.replace('*XWHAT_TEXT_1*', adjText(errortext1)) newError = newError.replace('*XWHAT_TEXT_2*', adjText(errortext2)) # Resolved Address info resolvedaddress = error.find('resolvedaddress') if resolvedaddress != None: raModname = resolvedaddress.find('modname') resolvedaddressEntry = "<h5>Resolved Address</h5>" + "Module Name: " \ + adjText(raModname.text) + "" raSymname = resolvedaddress.find('symname') if raSymname != None: resolvedaddressEntry = resolvedaddressEntry + "Symbol Name: " \ + adjText(raSymname.text) + "" raFile = resolvedaddress.find('file') if raFile != None: raLine = resolvedaddress.find('line') raOffset = resolvedaddress.find('offset') resolvedaddressEntry = resolvedaddressEntry + "File: " + adjText(raFile.text) + " Line: " \ + adjText(raLine.text) + " Offset: " + adjText(raOffset.text) + "" else: resolvedaddressEntry = "" newError = newError.replace('*RESOLVED_ADDRESS_ENTRY*', resolvedaddressEntry) self._errorHeading = str() #reset errorHeading, will be filled filled by _createCallStack newError = newError.replace('*CALL_STACK_ENTRIES_1*', self._createCallStack(error, 0, outputID)) if errortext2 != "": newError = newError.replace('*CALL_STACK_ENTRIES_2*', self._createCallStack(error, 1, outputID)) else: newError = newError.replace('*CALL_STACK_ENTRIES_2*', "No Callstack Available") newError = newError.replace('*OUTPUT_ID_1*', outputID+'0') newError = newError.replace('*OUTPUT_ID_2*', outputID+'1') newError = newError.replace('*ERROR_HEADING*', self._errorHeading) self._errorNumber += 1 self._strErrors += newError self.SCM.searchSourceDirectories.cache_clear() def _createHeader(self): hasErrors = self._reportRoot.find('error') != None headerInformation = self._getHeader() self.htmlReport = self._htmlTemplates.find('base_entry').text self.htmlReport = self.htmlReport.replace('*DATE*', headerInformation[0]) self.htmlReport = self.htmlReport.replace('*TIME*', headerInformation[1]) self.htmlReport = self.htmlReport.replace('*DURATION*', headerInformation[2]) self.htmlReport = self.htmlReport.replace('*DURATION_UNIT*', headerInformation[3]) self.htmlReport = self.htmlReport.replace('*ARGS*', headerInformation[4]) self.htmlReport = self.htmlReport.replace('*EXE*', headerInformation[5]) self.htmlReport = self.htmlReport.replace('*PROTOCOLVERSION*', headerInformation[6]) self.htmlReport = self.htmlReport.replace('*PROTOCOLTOOL*', headerInformation[7]) self.htmlReport = self.htmlReport.replace('*NUMBER_OF_ERRORS*', str(self._numberOfErrors)) self.htmlReport = self.htmlReport.replace('*ERROR_ENTRIES*', self._strErrors) if not noMatplotLib and hasErrors: matplotlib_snippet = self._htmlTemplates.find('matplotlib_entries').text matplotlib_snippet = matplotlib_snippet.replace('*TOP_OF_STACK_GRAPH*', self._topStackGraphFileName) matplotlib_snippet = matplotlib_snippet.replace('*ERROR_TIMES_PLOT*', self._errorTimesPlot) self.htmlReport = self.htmlReport.replace('*MATPLOTLIB_PICTURES*', matplotlib_snippet) else: self.htmlReport = self.htmlReport.replace('*MATPLOTLIB_PICTURES*', '') def _createReport(self): self._createErrorList() self._createHeader() self.htmlReport = self.htmlReport.replace("*SNIPPET_VARIABLES*", self._snippets) self.htmlReport = self.SCM.createCodeVars(self.htmlReport) class SourceCodeManagement: def __init__(self): self._sourcefilelist = list() self._htmlTemplatesPath = str(g_HTMLTEMPLATES) self._htmlTemplates = (ET.parse(self._htmlTemplatesPath)).getroot() def _createSourcefileEntry(self, path, line): #one entry consists of the full file path the line number of interest sourceFile = open(path, mode='r') sourceLineList = sourceFile.readlines() if len(sourceLineList) > NUMBEROFCODELINES: newElement = [path, int(line), False] else: newElement = [path, int(line), True] self._sourcefilelist.append(newElement) return self._sourcefilelist.index(newElement) def _returnCode(self, fullPath, justExistance, line = 0): returnSrc = False try: #may throw an an exception in earlier version (until 3.6), therefore try-catch fp = pathlib.Path(fullPath).resolve() #returns absolute path except FileNotFoundError: return -1 except OSError: #if path is available, but for any reason not reachable (e.g. locked by bitlocker) OSError is thrown return -1 if fp.is_file(): for element in SOURCEFILE_BL: #blacklisting routine if str(element) in str(fp): #both are absoulte paths, so comparing is valid return -1 if WHITELISTING: for element in SOURCEFILE_WL: if str(element) in str(fullPath): returnSrc = True break if not returnSrc: return -1 if justExistance: sourceCode = self._getLines(fullPath, line) if sourceCode == -1: ##line was not found return -1 return 0 else: return -1 #if we are here we want to return the source code return adjText(self._getLines(fullPath, line)) def _getLines(self, path, line): sourceFile = open(path, mode='r') sourceLineList = sourceFile.readlines() if len(sourceLineList) < line: #the found file contains less lines than the target (e.g. wrong line number from drace) return -1 if len(sourceLineList) > NUMBEROFCODELINES: if line <= NUMBEROFCODELINES//2: begin = 0 end = NUMBEROFCODELINES else: begin = (line - NUMBEROFCODELINES//2) - 1 #-1 because array starts with 0 end = begin + NUMBEROFCODELINES sourceLineList = sourceLineList[begin:end] sourceCode = str() for sourceLine in sourceLineList: sourceCode += sourceLine sourceFile.close() return sourceCode def handleSourceCode(self, filename, directory, line): fullPath = pathlib.Path(directory +'/'+ filename) src = self._returnCode(fullPath, 1, int(line)) if src == -1: return -1, self._htmlTemplates.find('no_code_entry').text index = -1 #check if source file is already in the list for item in self._sourcefilelist: if item[0] == fullPath: if item[2] or (int(line) - NUMBEROFCODELINES//10) <= item[1] <= (int(line) + NUMBEROFCODELINES//10): index = self._sourcefilelist.index(item) #entry = item if index == -1: index = self._createSourcefileEntry(fullPath, line) strIndex = 'code_' + str(index) return strIndex, (self._htmlTemplates.find('code_entry').text) def createCodeVars(self, report): codeString = str() for sourceObject in self._sourcefilelist: src = self._returnCode(sourceObject[0], justExistance=0, line = sourceObject[1]) tmpCode = "code_" + str(self._sourcefilelist.index(sourceObject)) + ' = "' + src + '";\n' codeString += tmpCode report = report.replace("*CODE_VARIABLES*", codeString) return report def determineLanguage(self, filename): fileParts = filename.split('.') if len(fileParts) == 1: return 'cpp' #files without file endigs are treated as cpp files else: ending = fileParts[-1] if ending == 'c': return 'c' elif ending == 'cpp': return 'cpp' elif ending == 'h': return 'cpp' elif ending == 'cs': return 'csharp' elif ending == 'css': return 'css' elif ending == 'js': return 'javascript' elif ending == 'html': return 'markup' else: return 'cpp' def getFirstLineOfCodeSnippet(self, index): codeSnippet = int(index.split("_")[-1]) #index is e.g. code_3 srcObject = self._sourcefilelist[codeSnippet] if srcObject[2]: return 1 else: firstLine = srcObject[1] - NUMBEROFCODELINES//2 return firstLine #srcObject[1] is line of interest of snippet @lru_cache(maxsize=100) def searchSourceDirectories(self, dir, file): if pathlib.Path(pathlib.Path(dir) / file).is_file(): # path to file in xml file is valid return dir else: # path to file in xml file is NOT valid if not SOURCE_DIRECTORIES: # no sourceDirectories args given print(f"Cannot find file '{file}' in directory '{dir}'.") return dir else: print(f"Cannot find file '{file}' in directory '{dir}'. Searching through given source directories ...") # search in sourceDirectories given from args if applicable for customDirPath in SOURCE_DIRECTORIES: customDir = pathlib.Path(customDirPath) fileInstances = customDir.glob(f'**/{file}') # generator for found file instances try: f1 = next(fileInstances) try: f2 = next(fileInstances) # Check if next found file f2 has a parent directory which supersets that of first found file f1 if str(f1.resolve().parent) == str(f2.resolve().parent)[:len(str(f1.resolve().parent))]: return str(f2.resolve().parent) # second valid file instance in customDirPath else: return str(f1.resolve().parent) # first valid file instance in customDirPath except StopIteration: # Only one valid file instance found in customDirPath return str(f1.resolve().parent) except StopIteration: # No file instance found in customDirPath element continue # Search for file instances in given sourceDirectories failed print(f"Cannot find file '{file}' in given source directories.") return dir def adjText(text): #change html symbols e.g. & -> & text = text.replace('`', '\'') text = text.replace('\\', '/') text = text.replace('\n', '\\n') return html.escape(text) def parseArgumentString(fileList, strEntries): strEntries = strEntries.replace("\\","/") listEntries = strEntries.split(',') for entry in listEntries: #remove potential leading and trailing whitespaces while entry[0] == ' ': entry = entry[1:] while entry[-1] == ' ': entry = entry[:-1] newObject = pathlib.Path(entry) newObject = newObject.resolve() fileList.append(newObject) return def returnDateString(): date = datetime.datetime.utcnow() return date.strftime('%Y%m%d_%H%M') def main(): global SOURCEFILE_BL, SOURCEFILE_WL, WHITELISTING, SOURCE_DIRECTORIES, DEBUG parser = argparse.ArgumentParser() parser.add_argument("-i", "--inputFile", help='define <input_file>', type=str) parser.add_argument("-o", "--outputDirectory", help='define <output_directory>', type=str) parser.add_argument("-b", "--blacklist", help='add blacklist entries <entry1,entry2 ...>', type=str) parser.add_argument("-w", "--whitelist", help='add whitelist entries <entry1,entry2 ...>', type=str) parser.add_argument("-s", "--sourceDirectories", help='add source directories <entry1,entry2 ...>', type=str) parser.add_argument("--Debug", help='Debug Mode', action="store_true") args = parser.parse_args() ###args handling if args.Debug: print("Debug Mode is on") inFile = pathlib.Path(SCRIPTPATH / 'test_files/test.xml') targetDirectory = pathlib.Path(SCRIPTPATH / 'test_files/output') else: if args.inputFile != None: inFile = pathlib.Path(args.inputFile) else: print("You must specify an input file") print() parser.print_help() sys.exit(-1) if not inFile.is_file(): print("Your input file does not exist") parser.print_help() sys.exit(-1) strDate = returnDateString() if not args.Debug: if args.outputDirectory != None: targetDirectory = pathlib.Path(args.outputDirectory+'/drace_report_'+strDate) else: targetDirectory = pathlib.Path('./drace_report_'+strDate) if args.blacklist != None: parseArgumentString(SOURCEFILE_BL, args.blacklist) if args.whitelist != None: parseArgumentString(SOURCEFILE_WL, args.whitelist) WHITELISTING = True if args.sourceDirectories != None: parseArgumentString(SOURCE_DIRECTORIES, args.sourceDirectories) #end of args handling if not targetDirectory.is_dir(): targetDirectory.mkdir() #report gets generated here report = ReportCreator(str(inFile), str(targetDirectory)) if report.succesfullReportCreation: #write report to destination output = open(str(targetDirectory)+'/index.html', mode='w') output.write(report.htmlReport) output.close() #copy needed files to destination cssPath = pathlib.Path(str(targetDirectory)+"/css") jsPath = pathlib.Path(str(targetDirectory)+"/js") if cssPath.is_dir(): shutil.rmtree(str(cssPath)) if jsPath.is_dir(): shutil.rmtree(str(jsPath)) shutil.copytree(str(g_CSSPATH.resolve()), str(targetDirectory / "css")) shutil.copytree(str(g_JSPATH.resolve()), str(targetDirectory / "js")) shutil.copy(str((resourcesPath / 'legend.png').resolve()), str(targetDirectory)) print("Report creation successful") print("Report is at:") print(targetDirectory) return 0 else: print("Report creation was NOT successful") targetDirectory.rmdir() return -1 if __name__ == "__main__": main()

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sys from subprocess import check_call, STDOUT, DEVNULL from functools import lru_cache try: import matplotlib import matplotlib.pyplot as plt from matplotlib.lines import Line2D noMatplotLib = False except ImportError: noMatplotLib = True print("Matplotlib is not installed.") if getattr(sys, 'frozen', False): SCRIPTPATH = pathlib.Path(sys.executable) SCRIPTPATH = pathlib.Path(SCRIPTPATH / "..") else : SCRIPTPATH = pathlib.Path(pathlib.Path(__file__).resolve().parents[0]) if pathlib.Path(SCRIPTPATH / '../resources').is_dir(): resourcesPath = pathlib.Path(SCRIPTPATH / '../resources') else: if pathlib.Path(SCRIPTPATH / 'resources').is_dir(): resourcesPath = pathlib.Path(SCRIPTPATH / 'resources') else: print("path of resources not found") sys.exit(-1) g_HTMLTEMPLATES = resourcesPath / "entries.xml" g_CSSPATH = resourcesPath / "css" g_JSPATH = resourcesPath / "js" DEBUG = False SOURCEFILE_BL = list() SOURCEFILE_WL = list() WHITELISTING = False NUMBEROFCODELINES = 400 if NUMBEROFCODELINES % 2: print('Number of maximum of displayed code lines must be even, but is:') print(str(NUMBEROFCODELINES)) sys.exit(-1) SOURCE_DIRECTORIES = list() class ReportCreator: _htmlTemplatesPath = str(g_HTMLTEMPLATES) _topStackGraphFileName = 'topStackBarchart.png' _errorTimesPlot = 'errorTimes.png' try: if check_call(['code', '--version'], stdout=DEVNULL, stderr=STDOUT, shell=True) == 0: _vscodeFlag = True else: _vscodeFlag = False except: _vscodeFlag = False def __init__(self, pathOfReport, target): self.sourcefileList = list() self._callStackNumber = 0 self._errorNumber = 0 self._snippets = str() self.succesfullReportCreation = True try: self._htmlTemplates = (ET.parse(self._htmlTemplatesPath)).getroot() except FileNotFoundError: print("template file is missing") self.succesfullReportCreation = False return self.SCM = SourceCodeManagement() self._pathOfReport = pathOfReport if self._inputValidation(): hasErrors = self._reportRoot.find('error') != None if not noMatplotLib and hasErrors: self._makeHistogramm(target) self._countTopStackOccurences(target) self._createReport() else: print("input file is not valid") self.succesfullReportCreation = False def _inputValidation(self): try: self._reportContent = ET.parse(self._pathOfReport) except ET.ParseError: return 0 self._reportRoot = self._reportContent.getroot() if self._reportRoot.find('protocolversion') != None and \ self._reportRoot.find('protocoltool') != None and \ self._reportRoot.find('preamble') != None and \ self._reportRoot.find('pid') != None and \ self._reportRoot.find('tool') != None and \ self._reportRoot.find('args') != None and \ self._reportRoot.find('status') != None and \ self._reportRoot.tag == 'valgrindoutput': return 1 else: return 0 def _getHeader(self): header = list() status = self._reportRoot.findall('status') if len(status) == 2: status = status[1] time').text if "T" in strDatetime: date = strDatetime.split('T')[0] time = (strDatetime.split('T')[1])[0:-1] else: date = "" time = strDatetime header.append(adjText(date)) header.append(adjText(time)) if status.find('duration') != None: header.append(adjText(status.find('duration').text)) header.append(adjText(status.find('duration').get('unit'))) else: header.append("") header.append("") else: header.append("") header.append("") header.append("") header.append("") arguments = str() for arg in self._reportRoot.find('args').find('vargv').findall('arg'): arguments += arg.text arguments += ' ' header.append(adjText(arguments[0:-1])) header.append(adjText(self._reportRoot.find('args').find('argv').find('exe').text)) header.append(adjText(self._reportRoot.find('protocolversion').text)) header.append(adjText(self._reportRoot.find('protocoltool').text)) return header def _makeFileEntry(self, frame): strDir = adjText(self._frameValues["dir"]) strFile = adjText(self._frameValues["file"]) strLine = adjText(self._frameValues["line"]) offset = adjText(self._frameValues["offset"]) if self._vscodeFlag: entry = "<a href='vscode://file/" + strDir + "/" + strFile + ":" + strLine + ":" + offset +"'>"+ strFile +":" + strLine + ":" + offset + "</a>" else: entry = "<a href='file://"+ strDir + "/" + strFile + "'>" + strFile + ":" + strLine + "</a>" return entry def _readFrame(self, frame): if frame is None: self._frameValues = {"obj":"", "fn":"", "ip":"", "dir":"", "file":"", "line":"", "offset":""} return obj = frame.find('obj') if obj is None: obj = "" else: if obj.text is None: obj = "" else: obj = obj.text fn = frame.find('fn') if fn is None: fn = "" else: if fn.text is None: fn = "" else: fn = fn.text ip = frame.find('ip') if ip is None: ip = "" else: if ip.text is None: ip = "" else: ip = ip.text direc = frame.find('dir') if direc is None: direc = "" else: if direc.text is None: direc = "" else: direc = direc.text filename = frame.find('file') if filename is None: filename = "" else: if filename.text is None: filename = "" else: filename = filename.text line = frame.find('line') if line is None: line = "0" else: if line.text is None: line = "0" else: line = line.text offset = frame.find('offset') if offset is None: offset = "0" else: if offset.text is None: offset = "0" else: offset = offset.text self._frameValues = {"obj":obj, "fn":fn, "ip":ip, "dir":direc, "file":filename, "line":line, "offset":offset} def _createSnippetEntry(self, frame, elementNumber, tag, codeIndex, buttonID): newSnippet = self._htmlTemplates.find('snippet_entry').text newSnippet = newSnippet.replace('*SNIPPET_VAR*', ("snippet_" + str(self._callStackNumber))) newSnippet = newSnippet.replace('*STACK_NUMBER*', adjText(hex(elementNumber))) newSnippet = newSnippet.replace('*OBJ*', adjText(self._frameValues["obj"])) newSnippet = newSnippet.replace('*FUNCTION*', adjText(self._frameValues["fn"])) newSnippet = newSnippet.replace('*INSTRUCTION_POINTER*', adjText(self._frameValues["ip"])) newSnippet = newSnippet.replace('*CODE_TAG*', tag) newSnippet = newSnippet.replace('*SNIPPET_BUTTON_ID*', buttonID) if (self._frameValues["file"] != ""): newSnippet = newSnippet.replace('*FILE_NAME_ENTRY*', self._makeFileEntry(frame)) newSnippet = newSnippet.replace('*DIRECTORY*', adjText(self._frameValues["dir"])) newSnippet = newSnippet.replace('*SHORT_DIR*', adjText(self._makeShortDir(self._frameValues["dir"]))) newSnippet = newSnippet.replace('*LINE_OF_CODE*', adjText(self._frameValues["line"])) if(codeIndex != -1): newSnippet = newSnippet.replace('*CODE_ID_VAR*', "snippet_"+str(self._callStackNumber)+"_code") newSnippet = newSnippet.replace('*LANGUAGE*', self.SCM.determineLanguage(adjText(self._frameValues["file"]))) newSnippet = newSnippet.replace('*FIRST_LINE*', str(self.SCM.getFirstLineOfCodeSnippet(codeIndex))) else: newSnippet = newSnippet.replace('*CODE_ID_VAR*', "'None'") else: newSnippet = newSnippet.replace('*FILE_NAME_ENTRY*', 'no filename avail.') newSnippet = newSnippet.replace('*DIRECTORY*', 'no directory avail.') newSnippet = newSnippet.replace('*SHORT_DIR*', 'no directory avail.') self._snippets += newSnippet def _makeShortDir(self, strDir): elements = None if "\\" in strDir: elements = strDir.split("\\") else: if "/" in strDir: elements = strDir.split("/") if elements != None: return elements[0] + "/" + elements[1] + "/.../" + elements[-1] else: return "" def _createCallStack(self, errorEntry, position, outputID): callStack = str() stackTemplate = self._htmlTemplates.find('stack_entry').text stackArray = errorEntry.findall('stack') stack = stackArray[position] elementNumber = 0 frames = stack.findall('frame') if frames is None: return "" for frame in frames: self._readFrame(frame) newDir = self.SCM.searchSourceDirectories(self._frameValues["dir"], self._frameValues["file"]) self._frameValues["dir"] = adjText(newDir) noPreview = False buttonID = "button_" + str(self._errorNumber) + "_" + str(position) + "_" + str(elementNumber) strOutputID = outputID+str(position) if elementNumber == 0: errorHeading += " Obj. 1: " + (adjText(self._frameValues["obj"]) + ': "' + adjText(self._frameValues["fn"])) + '" ' else: self._errorHeading += "Obj. 2: " + (adjText(self._frameValues["obj"]) + ': "' + adjText(self._frameValues["fn"])) + '"' newStackElement = stackTemplate.replace('*STACK_NUMBER*', adjText(hex(elementNumber))+":") newStackElement = newStackElement.replace('*SNIPPET_VAR*', ("snippet_" + str(self._callStackNumber))) newStackElement = newStackElement.replace('*OUTPUT_ID*', strOutputID) newStackElement = newStackElement.replace('*FUNCTION*', adjText(self._frameValues['fn'])) newStackElement = newStackElement.replace('*BUTTON_ID*', buttonID) if (self._frameValues["file"]!= ""): codeIndex, tag = self.SCM.handleSourceCode(self._frameValues["file"], self._frameValues["dir"], self._frameValues["line"]) newStackElement = newStackElement.replace('*FILE*', adjText(self._frameValues["file"])) if(codeIndex != -1): newStackElement = newStackElement.replace('*CODE_VAR*', str(codeIndex)) newStackElement = newStackElement.replace('*CODE_ID_VAR*', "'snippet_"+str(self._callStackNumber)+"_code'") newStackElement = newStackElement.replace('*LINE_OF_CODE*', adjText(self._frameValues["line"])) newStackElement = newStackElement.replace('*FIRST_LINE*', str(self.SCM.getFirstLineOfCodeSnippet(codeIndex))) else: noPreview = True else: codeIndex = -1 tag = self._htmlTemplates.find('no_code_entry').text newStackElement = newStackElement.replace('*FILE*', 'no filename avail.') noPreview = True if noPreview: newStackElement = newStackElement.replace('*CODE_VAR*', "'None'") newStackElement = newStackElement.replace('*CODE_ID_VAR*', "'None'") newStackElement = newStackElement.replace('*LINE_OF_CODE*', "'None'") newStackElement = newStackElement.replace('*FIRST_LINE*', "'NONE'") searchStr = 'class="' insertPosition = newStackElement.find(searchStr)+len(searchStr) #to add the ".grey" class the position before after class #insertPosition += newStackElement[insertPosition:].find('"') newStackElement = newStackElement[:insertPosition] + "grey-button " + newStackElement[insertPosition:] self._createSnippetEntry(frame, elementNumber, tag, codeIndex, buttonID) callStack += newStackElement elementNumber += 1 self._callStackNumber += 1 return callStack def _makeHistogramm(self, target): errorTimes = dict() statusNode = self._reportRoot.findall('status')[1] if statusNode.find('duration') is None: self._errorTimesPlot = "" return totalDuration = int(statusNode.find('duration').text) errors = self._reportRoot.findall('error') for error in errors: timePoint = (round(float(100 * int(error.find('timestamp').text) /totalDuration))) if errorTimes.get(timePoint) != None: value = errorTimes.pop(timePoint) errorTimes.update({timePoint: int(value)+1}) else: errorTimes.update({timePoint: 1}) x = list(errorTimes.keys()) y = list(errorTimes.values()) fig = plt.figure(figsize=(10,4)) ax = plt.axes() ax.scatter(x, y, color='#009999', edgecolor='black') xRangeEnd = max(y)+1 if xRangeEnd < 3: xRangeEnd = 3 ax.set_yticks([i for i in range(0, xRangeEnd)]) ax.set_xticks([i for i in range(0, 110, 10)]) plt.title('Error occurrences by time',fontfamily="monospace", fontweight='bold') plt.ylabel('Occurrences', fontfamily="monospace",fontweight='bold') plt.xlabel('Execution of program in %. \n Total execution time = ' + str(totalDuration) + 'ms', fontfamily="monospace",fontweight='bold') fig.add_axes(ax) figPath = pathlib.Path(target+'/'+self._errorTimesPlot) plt.savefig(str(figPath), dpi=300, format='png', bbox_inches='tight', orientation='landscape') def _countTopStackOccurences(self, target): topStackOccurences = dict() errors = self._reportRoot.findall('error') for error in errors: stacks = error.findall('stack') for i in range(0,2): topFrame = stacks[i].find('frame') if(topFrame != None): self._readFrame(topFrame) tmp1 = self._frameValues["file"] tmp2 = self._frameValues["fn"] if(tmp1 != "None" and tmp2 != "None"): if(len(tmp2) > 20): tmp2 = tmp2[:len(tmp2)//2] + '\n' + tmp2[len(tmp2)//2:] identifier = tmp1 + ":\n" + tmp2 if topStackOccurences.get(identifier) != None: value = topStackOccurences.pop(identifier) topStackOccurences.update({identifier: int(value)+1}) else: topStackOccurences.update({identifier: 1}) sortedOccurences = sorted(topStackOccurences.items(), key=lambda kv: kv[1]) x=list() y=list() for ele in sortedOccurences[-5:]: if len(ele[0]) < 250: x.append(ele[0]) else: x.append(ele[0][:250]+". . .") y.append(ele[1]) fig = plt.figure(figsize=(10,4)) ax = plt.axes() barWidth = 0.9 xLoc = list(range(len(y))) ax.barh([loc for loc in xLoc], y, barWidth, color='#009999') ax.set_yticks([loc for loc in xLoc]) ax.set_yticklabels(reversed(['#'+str(rank) for rank in range(1,len(y)+1)]), minor=False) legend_lines = [Line2D([0], [0], color='#009999', lw=rank) for rank in range(len(y)+1, 1, -1)] ax.legend(legend_lines, reversed(x), loc='center', bbox_to_anchor=(0.5, -0.1*(len(y)+2))) plt.title('Top five functions by top of stack occurrences',fontfamily="monospace", fontweight='bold') plt.xlabel('No. of top of stack occurrences', fontfamily="monospace",fontweight='bold') for i,v in enumerate(y): ax.text(v, i, str(v), ha='left',color='black', fontweight='bold') fig.add_axes(ax) figPath = pathlib.Path(target+'/'+self._topStackGraphFileName) plt.savefig(str(figPath), dpi=300, format='png', bbox_inches='tight', orientation='landscape') def _createErrorList(self): self._strErrors = str() errorTemplate = self._htmlTemplates.find('error_entry').text errorList = self._reportRoot.findall('error') self._numberOfErrors = len(errorList) for error in errorList: outputID = "output_"+str(self._errorNumber)+"_" newError = errorTemplate.replace('*ERROR_ID*', adjText(error.find('unique').text)) newError = newError.replace('*ERROR_TYPE*', adjText(error.find('kind').text)) xwhat = error.findall('xwhat') errortext1 = xwhat[0].find('text').text if(len(xwhat) == 1): element = error.find('xauxwhat') if element != None: errortext2 = element.find('text').text else: errortext2 = "" else: errortext2 = xwhat[1].find('text').text newError = newError.replace('*XWHAT_TEXT_1*', adjText(errortext1)) newError = newError.replace('*XWHAT_TEXT_2*', adjText(errortext2)) resolvedaddress = error.find('resolvedaddress') if resolvedaddress != None: raModname = resolvedaddress.find('modname') resolvedaddressEntry = "<h5>Resolved Address</h5>" + "Module Name: " \ + adjText(raModname.text) + "" raSymname = resolvedaddress.find('symname') if raSymname != None: resolvedaddressEntry = resolvedaddressEntry + "Symbol Name: " \ + adjText(raSymname.text) + "" raFile = resolvedaddress.find('file') if raFile != None: raLine = resolvedaddress.find('line') raOffset = resolvedaddress.find('offset') resolvedaddressEntry = resolvedaddressEntry + "File: " + adjText(raFile.text) + " Line: " \ + adjText(raLine.text) + " Offset: " + adjText(raOffset.text) + "" else: resolvedaddressEntry = "" newError = newError.replace('*RESOLVED_ADDRESS_ENTRY*', resolvedaddressEntry) self._errorHeading = str() newError = newError.replace('*CALL_STACK_ENTRIES_1*', self._createCallStack(error, 0, outputID)) if errortext2 != "": newError = newError.replace('*CALL_STACK_ENTRIES_2*', self._createCallStack(error, 1, outputID)) else: newError = newError.replace('*CALL_STACK_ENTRIES_2*', "No Callstack Available") newError = newError.replace('*OUTPUT_ID_1*', outputID+'0') newError = newError.replace('*OUTPUT_ID_2*', outputID+'1') newError = newError.replace('*ERROR_HEADING*', self._errorHeading) self._errorNumber += 1 self._strErrors += newError self.SCM.searchSourceDirectories.cache_clear() def _createHeader(self): hasErrors = self._reportRoot.find('error') != None headerInformation = self._getHeader() self.htmlReport = self._htmlTemplates.find('base_entry').text self.htmlReport = self.htmlReport.replace('*DATE*', headerInformation[0]) self.htmlReport = self.htmlReport.replace('*TIME*', headerInformation[1]) self.htmlReport = self.htmlReport.replace('*DURATION*', headerInformation[2]) self.htmlReport = self.htmlReport.replace('*DURATION_UNIT*', headerInformation[3]) self.htmlReport = self.htmlReport.replace('*ARGS*', headerInformation[4]) self.htmlReport = self.htmlReport.replace('*EXE*', headerInformation[5]) self.htmlReport = self.htmlReport.replace('*PROTOCOLVERSION*', headerInformation[6]) self.htmlReport = self.htmlReport.replace('*PROTOCOLTOOL*', headerInformation[7]) self.htmlReport = self.htmlReport.replace('*NUMBER_OF_ERRORS*', str(self._numberOfErrors)) self.htmlReport = self.htmlReport.replace('*ERROR_ENTRIES*', self._strErrors) if not noMatplotLib and hasErrors: matplotlib_snippet = self._htmlTemplates.find('matplotlib_entries').text matplotlib_snippet = matplotlib_snippet.replace('*TOP_OF_STACK_GRAPH*', self._topStackGraphFileName) matplotlib_snippet = matplotlib_snippet.replace('*ERROR_TIMES_PLOT*', self._errorTimesPlot) self.htmlReport = self.htmlReport.replace('*MATPLOTLIB_PICTURES*', matplotlib_snippet) else: self.htmlReport = self.htmlReport.replace('*MATPLOTLIB_PICTURES*', '') def _createReport(self): self._createErrorList() self._createHeader() self.htmlReport = self.htmlReport.replace("*SNIPPET_VARIABLES*", self._snippets) self.htmlReport = self.SCM.createCodeVars(self.htmlReport) class SourceCodeManagement: def __init__(self): self._sourcefilelist = list() self._htmlTemplatesPath = str(g_HTMLTEMPLATES) self._htmlTemplates = (ET.parse(self._htmlTemplatesPath)).getroot() def _createSourcefileEntry(self, path, line): sourceFile = open(path, mode='r') sourceLineList = sourceFile.readlines() if len(sourceLineList) > NUMBEROFCODELINES: newElement = [path, int(line), False] else: newElement = [path, int(line), True] self._sourcefilelist.append(newElement) return self._sourcefilelist.index(newElement) def _returnCode(self, fullPath, justExistance, line = 0): returnSrc = False try: fp = pathlib.Path(fullPath).resolve() except FileNotFoundError: return -1 except OSError: return -1 if fp.is_file(): for element in SOURCEFILE_BL: if str(element) in str(fp): return -1 if WHITELISTING: for element in SOURCEFILE_WL: if str(element) in str(fullPath): returnSrc = True break if not returnSrc: return -1 if justExistance: sourceCode = self._getLines(fullPath, line) if sourceCode == -1: return -1 return 0 else: return -1 return adjText(self._getLines(fullPath, line)) def _getLines(self, path, line): sourceFile = open(path, mode='r') sourceLineList = sourceFile.readlines() if len(sourceLineList) < line: return -1 if len(sourceLineList) > NUMBEROFCODELINES: if line <= NUMBEROFCODELINES//2: begin = 0 end = NUMBEROFCODELINES else: begin = (line - NUMBEROFCODELINES//2) - 1 end = begin + NUMBEROFCODELINES sourceLineList = sourceLineList[begin:end] sourceCode = str() for sourceLine in sourceLineList: sourceCode += sourceLine sourceFile.close() return sourceCode def handleSourceCode(self, filename, directory, line): fullPath = pathlib.Path(directory +'/'+ filename) src = self._returnCode(fullPath, 1, int(line)) if src == -1: return -1, self._htmlTemplates.find('no_code_entry').text index = -1 for item in self._sourcefilelist: if item[0] == fullPath: if item[2] or (int(line) - NUMBEROFCODELINES//10) <= item[1] <= (int(line) + NUMBEROFCODELINES//10): index = self._sourcefilelist.index(item) if index == -1: index = self._createSourcefileEntry(fullPath, line) strIndex = 'code_' + str(index) return strIndex, (self._htmlTemplates.find('code_entry').text) def createCodeVars(self, report): codeString = str() for sourceObject in self._sourcefilelist: src = self._returnCode(sourceObject[0], justExistance=0, line = sourceObject[1]) tmpCode = "code_" + str(self._sourcefilelist.index(sourceObject)) + ' = "' + src + '";\n' codeString += tmpCode report = report.replace("*CODE_VARIABLES*", codeString) return report def determineLanguage(self, filename): fileParts = filename.split('.') if len(fileParts) == 1: return 'cpp' else: ending = fileParts[-1] if ending == 'c': return 'c' elif ending == 'cpp': return 'cpp' elif ending == 'h': return 'cpp' elif ending == 'cs': return 'csharp' elif ending == 'css': return 'css' elif ending == 'js': return 'javascript' elif ending == 'html': return 'markup' else: return 'cpp' def getFirstLineOfCodeSnippet(self, index): codeSnippet = int(index.split("_")[-1]) srcObject = self._sourcefilelist[codeSnippet] if srcObject[2]: return 1 else: firstLine = srcObject[1] - NUMBEROFCODELINES//2 return firstLine @lru_cache(maxsize=100) def searchSourceDirectories(self, dir, file): if pathlib.Path(pathlib.Path(dir) / file).is_file(): return dir else: if not SOURCE_DIRECTORIES: print(f"Cannot find file '{file}' in directory '{dir}'.") return dir else: print(f"Cannot find file '{file}' in directory '{dir}'. Searching through given source directories ...") for customDirPath in SOURCE_DIRECTORIES: customDir = pathlib.Path(customDirPath) fileInstances = customDir.glob(f'**/{file}') try: f1 = next(fileInstances) try: f2 = next(fileInstances) if str(f1.resolve().parent) == str(f2.resolve().parent)[:len(str(f1.resolve().parent))]: return str(f2.resolve().parent) else: return str(f1.resolve().parent) except StopIteration: return str(f1.resolve().parent) except StopIteration: continue print(f"Cannot find file '{file}' in given source directories.") return dir def adjText(text): text = text.replace('`', '\'') text = text.replace('\\', '/') text = text.replace('\n', '\\n') return html.escape(text) def parseArgumentString(fileList, strEntries): strEntries = strEntries.replace("\\","/") listEntries = strEntries.split(',') for entry in listEntries: #remove potential leading and trailing whitespaces while entry[0] == ' ': entry = entry[1:] while entry[-1] == ' ': entry = entry[:-1] newObject = pathlib.Path(entry) newObject = newObject.resolve() fileList.append(newObject) return def returnDateString(): date = datetime.datetime.utcnow() return date.strftime('%Y%m%d_%H%M') def main(): global SOURCEFILE_BL, SOURCEFILE_WL, WHITELISTING, SOURCE_DIRECTORIES, DEBUG parser = argparse.ArgumentParser() parser.add_argument("-i", "--inputFile", help='define <input_file>', type=str) parser.add_argument("-o", "--outputDirectory", help='define <output_directory>', type=str) parser.add_argument("-b", "--blacklist", help='add blacklist entries <entry1,entry2 ...>', type=str) parser.add_argument("-w", "--whitelist", help='add whitelist entries <entry1,entry2 ...>', type=str) parser.add_argument("-s", "--sourceDirectories", help='add source directories <entry1,entry2 ...>', type=str) parser.add_argument("--Debug", help='Debug Mode', action="store_true") args = parser.parse_args() ###args handling if args.Debug: print("Debug Mode is on") inFile = pathlib.Path(SCRIPTPATH / 'test_files/test.xml') targetDirectory = pathlib.Path(SCRIPTPATH / 'test_files/output') else: if args.inputFile != None: inFile = pathlib.Path(args.inputFile) else: print("You must specify an input file") print() parser.print_help() sys.exit(-1) if not inFile.is_file(): print("Your input file does not exist") parser.print_help() sys.exit(-1) strDate = returnDateString() if not args.Debug: if args.outputDirectory != None: targetDirectory = pathlib.Path(args.outputDirectory+'/drace_report_'+strDate) else: targetDirectory = pathlib.Path('./drace_report_'+strDate) if args.blacklist != None: parseArgumentString(SOURCEFILE_BL, args.blacklist) if args.whitelist != None: parseArgumentString(SOURCEFILE_WL, args.whitelist) WHITELISTING = True if args.sourceDirectories != None: parseArgumentString(SOURCE_DIRECTORIES, args.sourceDirectories) #end of args handling if not targetDirectory.is_dir(): targetDirectory.mkdir() #report gets generated here report = ReportCreator(str(inFile), str(targetDirectory)) if report.succesfullReportCreation: #write report to destination output = open(str(targetDirectory)+'/index.html', mode='w') output.write(report.htmlReport) output.close() #copy needed files to destination cssPath = pathlib.Path(str(targetDirectory)+"/css") jsPath = pathlib.Path(str(targetDirectory)+"/js") if cssPath.is_dir(): shutil.rmtree(str(cssPath)) if jsPath.is_dir(): shutil.rmtree(str(jsPath)) shutil.copytree(str(g_CSSPATH.resolve()), str(targetDirectory / "css")) shutil.copytree(str(g_JSPATH.resolve()), str(targetDirectory / "js")) shutil.copy(str((resourcesPath / 'legend.png').resolve()), str(targetDirectory)) print("Report creation successful") print("Report is at:") print(targetDirectory) return 0 else: print("Report creation was NOT successful") targetDirectory.rmdir() return -1 if __name__ == "__main__": main()

true

f72995c0706428d76b90433f3ac5c63e2b41e814

3,104

py

Python

setup.py

PhTrempe/pytest

47200c141a78f06e5d61e183f61c41ef464283ef

[ "MIT" ]

null

setup.py

PhTrempe/pytest

47200c141a78f06e5d61e183f61c41ef464283ef

[ "MIT" ]

1

2017-12-25T20:47:50.000Z

setup.py

PhTrempe/pytest

47200c141a78f06e5d61e183f61c41ef464283ef

[ "MIT" ]

null

"""A setuptools based setup module. See: https://packaging.python.org/en/latest/distributing.html https://github.com/pypa/sampleproject """ # Always prefer setuptools over distutils from setuptools import setup, find_packages # To use a consistent encoding from codecs import open from os import path here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, 'README.rst'), encoding='utf-8') as f: long_description = f.read() setup( name='pytest', # Versions should comply with PEP440. For a discussion on single-sourcing # the version across setup.py and the project code, see # https://packaging.python.org/en/latest/single_source_version.html version='0.0.0.dev1', description='A sample Python project', long_description=long_description, # The project's main homepage. url='https://github.com/phtrempe/pytest', # Author details author='Philippe Trempe', # Choose your license license='MIT', # See https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ # How mature is this project? Common values are # 3 - Alpha # 4 - Beta # 5 - Production/Stable 'Development Status :: 1 - Planning', # Indicate who your project is intended for 'Intended Audience :: Developers', 'Topic :: Software Development :: Build Tools', # Pick your license as you wish (should match "license" above) 'License :: OSI Approved :: MIT License', # Specify the Python versions you support here. In particular, ensure # that you indicate whether you support Python 2, Python 3 or both. 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', ], # What does your project relate to? keywords='pytest sample python project setup setuptools development', # You can just specify the packages manually here if your project is # simple. Or you can use find_packages(). packages=find_packages(exclude=['contrib', 'docs', 'tests']), # Alternatively, if you want to distribute just a my_module.py, uncomment # this: # py_modules=["my_module"], # List run-time dependencies here. These will be installed by pip when # your project is installed. For an analysis of "install_requires" vs pip's # requirements files see: # https://packaging.python.org/en/latest/requirements.html install_requires=['numpy'], # To provide executable scripts, use entry points in preference to the # "scripts" keyword. Entry points provide cross-platform support and allow # pip to create the appropriate form of executable for the target platform. entry_points={ 'console_scripts': [ 'pytest=pytest:main', ], }, )

33.73913

79

0.670103

from setuptools import setup, find_packages from codecs import open from os import path here = path.abspath(path.dirname(__file__)) with open(path.join(here, 'README.rst'), encoding='utf-8') as f: long_description = f.read() setup( name='pytest', version='0.0.0.dev1', description='A sample Python project', long_description=long_description, url='https://github.com/phtrempe/pytest', # Author details author='Philippe Trempe', # Choose your license license='MIT', # See https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ # How mature is this project? Common values are # 3 - Alpha # 4 - Beta # 5 - Production/Stable 'Development Status :: 1 - Planning', # Indicate who your project is intended for 'Intended Audience :: Developers', 'Topic :: Software Development :: Build Tools', # Pick your license as you wish (should match "license" above) 'License :: OSI Approved :: MIT License', # Specify the Python versions you support here. In particular, ensure # that you indicate whether you support Python 2, Python 3 or both. 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', ], # What does your project relate to? keywords='pytest sample python project setup setuptools development', # You can just specify the packages manually here if your project is # simple. Or you can use find_packages(). packages=find_packages(exclude=['contrib', 'docs', 'tests']), # Alternatively, if you want to distribute just a my_module.py, uncomment # this: # py_modules=["my_module"], # List run-time dependencies here. These will be installed by pip when # your project is installed. For an analysis of "install_requires" vs pip's install_requires=['numpy'], entry_points={ 'console_scripts': [ 'pytest=pytest:main', ], }, )

true

f729968b5fa200fe31945c3588835dee308235ac

343

py

Python

app/forms.py

budiryan/ScholarsNet

b6a9f3830c390a4420e361752f0187d8f955acfe

[ "MIT" ]

9

2017-06-08T12:05:03.000Z

2021-11-08T12:19:46.000Z

app/forms.py

budiryan/ScholarsNet

b6a9f3830c390a4420e361752f0187d8f955acfe

[ "MIT" ]

null

app/forms.py

budiryan/ScholarsNet

b6a9f3830c390a4420e361752f0187d8f955acfe

[ "MIT" ]

null

from wtforms import Form, TextField, SelectField from wtforms.validators import DataRequired class QueryForm(Form): search_query = TextField('', validators=[DataRequired()], render_kw={"placeholder": "Your query here"}) search_category = SelectField('Search for', choices=[('pa', 'Paper / Author'), ('p', 'Paper'), ('a', 'Author')])

42.875

116

0.705539

from wtforms import Form, TextField, SelectField from wtforms.validators import DataRequired class QueryForm(Form): search_query = TextField('', validators=[DataRequired()], render_kw={"placeholder": "Your query here"}) search_category = SelectField('Search for', choices=[('pa', 'Paper / Author'), ('p', 'Paper'), ('a', 'Author')])

true

f72996a5cdda64f19e82fe2f13168ab10ac0eae9

1,161

py

Python

test/functional/rpc_deprecated.py

republic-productions/finalcoin

7c0f335ded1e5c662034c822ca2c474b8e62778f

[ "MIT" ]

null

test/functional/rpc_deprecated.py

republic-productions/finalcoin

7c0f335ded1e5c662034c822ca2c474b8e62778f

[ "MIT" ]

null

test/functional/rpc_deprecated.py

republic-productions/finalcoin

7c0f335ded1e5c662034c822ca2c474b8e62778f

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2017-2020 The Finalcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test deprecation of RPC calls.""" from test_framework.test_framework import FinalcoinTestFramework class DeprecatedRpcTest(FinalcoinTestFramework): def set_test_params(self): self.num_nodes = 2 self.setup_clean_chain = True self.extra_args = [[], ['-deprecatedrpc=bumpfee']] def run_test(self): # This test should be used to verify correct behaviour of deprecated # RPC methods with and without the -deprecatedrpc flags. For example: # # In set_test_params: # self.extra_args = [[], ["-deprecatedrpc=generate"]] # # In run_test: # self.log.info("Test generate RPC") # assert_raises_rpc_error(-32, 'The wallet generate rpc method is deprecated', self.nodes[0].rpc.generate, 1) # self.generate(self.nodes[1], 1) self.log.info("No tested deprecated RPC methods") if __name__ == '__main__': DeprecatedRpcTest().main()

38.7

117

0.683032

from test_framework.test_framework import FinalcoinTestFramework class DeprecatedRpcTest(FinalcoinTestFramework): def set_test_params(self): self.num_nodes = 2 self.setup_clean_chain = True self.extra_args = [[], ['-deprecatedrpc=bumpfee']] def run_test(self): self.log.info("No tested deprecated RPC methods") if __name__ == '__main__': DeprecatedRpcTest().main()

true

f72997edfbcf28fc6d5fa7753ddb6011179db888

13,463

py

Python

samples/openapi3/client/petstore/python/petstore_api/model/parent_pet.py

JigarJoshi/openapi-generator

785535b8d6881b358463994823abbda2b26ff42e

[ "Apache-2.0" ]

1

2022-01-03T04:40:07.000Z

samples/openapi3/client/petstore/python/petstore_api/model/parent_pet.py

JigarJoshi/openapi-generator

785535b8d6881b358463994823abbda2b26ff42e

[ "Apache-2.0" ]

28

2021-04-07T07:38:36.000Z

2022-03-31T03:10:56.000Z

samples/openapi3/client/petstore/python/petstore_api/model/parent_pet.py

JigarJoshi/openapi-generator

785535b8d6881b358463994823abbda2b26ff42e

[ "Apache-2.0" ]

2

2021-11-03T10:07:15.000Z

2021-12-17T13:00:53.000Z

""" OpenAPI Petstore This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from petstore_api.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from petstore_api.exceptions import ApiAttributeError def lazy_import(): from petstore_api.model.child_cat import ChildCat from petstore_api.model.grandparent_animal import GrandparentAnimal globals()['ChildCat'] = ChildCat globals()['GrandparentAnimal'] = GrandparentAnimal class ParentPet(ModelComposed): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'pet_type': (str,), # noqa: E501 } @cached_property def discriminator(): lazy_import() val = { 'ChildCat': ChildCat, } if not val: return None return {'pet_type': val} attribute_map = { 'pet_type': 'pet_type', # noqa: E501 } read_only_vars = { } @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """ParentPet - a model defined in OpenAPI Keyword Args: pet_type (str): _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) constant_args = { '_check_type': _check_type, '_path_to_item': _path_to_item, '_spec_property_naming': _spec_property_naming, '_configuration': _configuration, '_visited_composed_classes': self._visited_composed_classes, } composed_info = validate_get_composed_info( constant_args, kwargs, self) self._composed_instances = composed_info[0] self._var_name_to_model_instances = composed_info[1] self._additional_properties_model_instances = composed_info[2] discarded_args = composed_info[3] for var_name, var_value in kwargs.items(): if var_name in discarded_args and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self._additional_properties_model_instances: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', '_composed_instances', '_var_name_to_model_instances', '_additional_properties_model_instances', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """ParentPet - a model defined in OpenAPI Keyword Args: pet_type (str): _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) constant_args = { '_check_type': _check_type, '_path_to_item': _path_to_item, '_spec_property_naming': _spec_property_naming, '_configuration': _configuration, '_visited_composed_classes': self._visited_composed_classes, } composed_info = validate_get_composed_info( constant_args, kwargs, self) self._composed_instances = composed_info[0] self._var_name_to_model_instances = composed_info[1] self._additional_properties_model_instances = composed_info[2] discarded_args = composed_info[3] for var_name, var_value in kwargs.items(): if var_name in discarded_args and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self._additional_properties_model_instances: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.") @cached_property def _composed_schemas(): # we need this here to make our import statements work # we must store _composed_schemas in here so the code is only run # when we invoke this method. If we kept this at the class # level we would get an error because the class level # code would be run when this module is imported, and these composed # classes don't exist yet because their module has not finished # loading lazy_import() return { 'anyOf': [ ], 'allOf': [ GrandparentAnimal, ], 'oneOf': [ ], }

42.203762

174

0.581668

import re import sys from petstore_api.model_utils import ( ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from petstore_api.exceptions import ApiAttributeError def lazy_import(): from petstore_api.model.child_cat import ChildCat from petstore_api.model.grandparent_animal import GrandparentAnimal globals()['ChildCat'] = ChildCat globals()['GrandparentAnimal'] = GrandparentAnimal class ParentPet(ModelComposed): allowed_values = { } validations = { } @cached_property def additional_properties_type(): lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) _nullable = False @cached_property def openapi_types(): lazy_import() return { 'pet_type': (str,), } @cached_property def discriminator(): lazy_import() val = { 'ChildCat': ChildCat, } if not val: return None return {'pet_type': val} attribute_map = { 'pet_type': 'pet_type', } read_only_vars = { } @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) constant_args = { '_check_type': _check_type, '_path_to_item': _path_to_item, '_spec_property_naming': _spec_property_naming, '_configuration': _configuration, '_visited_composed_classes': self._visited_composed_classes, } composed_info = validate_get_composed_info( constant_args, kwargs, self) self._composed_instances = composed_info[0] self._var_name_to_model_instances = composed_info[1] self._additional_properties_model_instances = composed_info[2] discarded_args = composed_info[3] for var_name, var_value in kwargs.items(): if var_name in discarded_args and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self._additional_properties_model_instances: continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', '_composed_instances', '_var_name_to_model_instances', '_additional_properties_model_instances', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) constant_args = { '_check_type': _check_type, '_path_to_item': _path_to_item, '_spec_property_naming': _spec_property_naming, '_configuration': _configuration, '_visited_composed_classes': self._visited_composed_classes, } composed_info = validate_get_composed_info( constant_args, kwargs, self) self._composed_instances = composed_info[0] self._var_name_to_model_instances = composed_info[1] self._additional_properties_model_instances = composed_info[2] discarded_args = composed_info[3] for var_name, var_value in kwargs.items(): if var_name in discarded_args and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self._additional_properties_model_instances: continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.") @cached_property def _composed_schemas(): # loading lazy_import() return { 'anyOf': [ ], 'allOf': [ GrandparentAnimal, ], 'oneOf': [ ], }

true

f72997f84b0fae8cdf9d5355a151756c3d7c8425

1,530

py

Python

test/test_sqlitedriver.py

ahplummer/jutestring

2cb1dfda0152cae4c94be55587f34480518218ca

[ "MIT" ]

1

2019-05-11T20:03:44.000Z

test/test_sqlitedriver.py

ahplummer/jutestring

2cb1dfda0152cae4c94be55587f34480518218ca

[ "MIT" ]

null

test/test_sqlitedriver.py

ahplummer/jutestring

2cb1dfda0152cae4c94be55587f34480518218ca

[ "MIT" ]

null

import pytest, sys, os myPath = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, myPath + '/../src/') from sqlitedriver import sqliteclass dbname = 'test.db' shorturl = "shortdata" longurl = "longdata" @pytest.fixture(scope='module') def resource_setup(request): print('Setting up resources for testing') if os.path.exists(dbname): os.remove(dbname) def resource_teardown(): print('Tearing down resources from testing') if os.path.exists(dbname): os.remove(dbname) request.addfinalizer(resource_teardown) def test_createTable(resource_setup): sqlitedriver = sqliteclass(dbname) result, error = sqlitedriver.createTable() assert None == error assert True == result def test_insertData(resource_setup): sqlitedriver = sqliteclass(dbname) result, error = sqlitedriver.insertData(shorturl, longurl) assert None == error assert True == result def test_readShortUrl(resource_setup): sqlitedriver = sqliteclass(dbname) result, error = sqlitedriver.readShortUrl(longurl) assert None == error assert shorturl == result result, error = sqlitedriver.readShortUrl("blah") assert None == error assert None == result def test_deleteShortUrl(resource_setup): sqlitedriver = sqliteclass(dbname) result, error = sqlitedriver.readShortUrl(longurl) assert None == error assert shorturl == result result, error = sqlitedriver.deleteShortUrl(longurl) assert None == error assert 1 == result

30

62

0.711765

import pytest, sys, os myPath = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, myPath + '/../src/') from sqlitedriver import sqliteclass dbname = 'test.db' shorturl = "shortdata" longurl = "longdata" @pytest.fixture(scope='module') def resource_setup(request): print('Setting up resources for testing') if os.path.exists(dbname): os.remove(dbname) def resource_teardown(): print('Tearing down resources from testing') if os.path.exists(dbname): os.remove(dbname) request.addfinalizer(resource_teardown) def test_createTable(resource_setup): sqlitedriver = sqliteclass(dbname) result, error = sqlitedriver.createTable() assert None == error assert True == result def test_insertData(resource_setup): sqlitedriver = sqliteclass(dbname) result, error = sqlitedriver.insertData(shorturl, longurl) assert None == error assert True == result def test_readShortUrl(resource_setup): sqlitedriver = sqliteclass(dbname) result, error = sqlitedriver.readShortUrl(longurl) assert None == error assert shorturl == result result, error = sqlitedriver.readShortUrl("blah") assert None == error assert None == result def test_deleteShortUrl(resource_setup): sqlitedriver = sqliteclass(dbname) result, error = sqlitedriver.readShortUrl(longurl) assert None == error assert shorturl == result result, error = sqlitedriver.deleteShortUrl(longurl) assert None == error assert 1 == result

true

f72998baf379666c230be32adf84dd43e4101c26

4,709

py

Python

ShicimingjuCrawleAndDisplay/manages.py

zuojilei/ECommerceCrawlers

b92d8c48e4cfe514ef050f78e0a32f952cfef6a6

[ "MIT" ]

null

ShicimingjuCrawleAndDisplay/manages.py

zuojilei/ECommerceCrawlers

b92d8c48e4cfe514ef050f78e0a32f952cfef6a6

[ "MIT" ]

14

2021-03-31T19:34:14.000Z

2022-03-12T00:23:00.000Z

ShicimingjuCrawleAndDisplay/manages.py

zuojilei/ECommerceCrawlers

b92d8c48e4cfe514ef050f78e0a32f952cfef6a6

[ "MIT" ]

null

# -*- coding: UTF-8 -*- __author__ = 'Joynice' import queue import re import threading import requests from faker import Faker from flask_migrate import Migrate, MigrateCommand from flask_script import Manager from lxml import etree from .app import create_app from .exts import db from .models import Poem, Poet user_agent = Faker('zh-CN').user_agent() app = create_app() manager = Manager(app) Migrate(app, db) manager.add_command('db', MigrateCommand) def get_header(): return { 'User-Agent': user_agent, 'Connection': 'close' } # 多线程爬取，由于可能导致数据爬取不全，数据诗词总数约为20w+数据 @manager.command def spider(): class Shici(object): def __init__(self, thread=5): self.poet_queue = queue.Queue() # 诗人 self.thread = thread self.base_url = 'http://www.shicimingju.com' def get_poet_url(self): for i in range(1, 13054): url = 'http://www.shicimingju.com/chaxun/zuozhe/{}.html'.format(i) self.poet_queue.put(url) def Spider(self): while not self.poet_queue.empty(): url = self.poet_queue.get() req = requests.get(url, headers=get_header()) if req.status_code == 200: req.encoding = 'utf-8' html = etree.HTML(req.text) name = html.xpath('/html/body/div[4]/div[2]/div[1]/div[2]/div[1]/h4/a/text()')[0] dynasty = html.xpath('/html/body/div[4]/div[2]/div[1]/div[3]/div[1]/div[2]/a/text()') if len(dynasty) == 0: dynasty = '未知' else: dynasty = dynasty[0] introduction = html.xpath('/html/body/div[4]/div[2]/div[1]/div[2]/div[1]/div[1]')[0].xpath( 'string(.)').strip() with app.app_context(): poet = Poet(name=name, dynasty=dynasty, introduction=introduction) db.session.add(poet) db.session.commit() id = poet.id poem_num = html.xpath('/html/body/div[4]/div[2]/div[1]/div[3]/div[2]/div[2]/a/text()')[0][:-1] poet_url_list = [] for i in range(1, int(int(poem_num) / 40) + 2): poet_id = re.sub("\D", "", url) poet_page_url = 'http://www.shicimingju.com/chaxun/zuozhe/{}_{}.html'.format(poet_id, i) req1 = requests.get(url=poet_page_url, headers=get_header()) if req1.status_code == 200: req1.encoding = 'utf-8' list_html = etree.HTML(req1.text) poet_url = list_html.xpath('//*/h3/a/@href') poet_url_list += poet_url poet_url_list = map(lambda x: self.base_url + x, poet_url_list) for url in poet_url_list: print(url) req2 = requests.get(url, headers=get_header()) if req2.status_code == 200: req2.encoding = 'utf-8' poet_html = etree.HTML(req2.text) title = poet_html.xpath('//*[@class="card"]/h1/text()')[0] content = '\n'.join(poet_html.xpath('//*[@class="item_content"]/text()')).strip() if not content: content = '\n'.join(poet_html.xpath('//*[@class="para"]/text()')).strip() if len(poet_html.xpath('//*[@class="shangxi_content"]')) == 0: analysis = '' else: analysis = poet_html.xpath('//*[@class="shangxi_content"]')[0].xpath( 'string(.)').strip() with app.app_context(): poem = Poem(title=title, content=content, analysis=analysis, author=id) db.session.add(poem) db.session.commit() def run(self): self.get_poet_url() thread_list = [] for i in range(self.thread): t = threading.Thread(target=self.Spider) thread_list.append(t) for t in thread_list: t.setDaemon(True) t.start() for t in thread_list: t.join() self.Spider() a = Shici() a.run() if __name__ == '__main__': manager.run()

39.241667

114

0.475048

__author__ = 'Joynice' import queue import re import threading import requests from faker import Faker from flask_migrate import Migrate, MigrateCommand from flask_script import Manager from lxml import etree from .app import create_app from .exts import db from .models import Poem, Poet user_agent = Faker('zh-CN').user_agent() app = create_app() manager = Manager(app) Migrate(app, db) manager.add_command('db', MigrateCommand) def get_header(): return { 'User-Agent': user_agent, 'Connection': 'close' } @manager.command def spider(): class Shici(object): def __init__(self, thread=5): self.poet_queue = queue.Queue() self.thread = thread self.base_url = 'http://www.shicimingju.com' def get_poet_url(self): for i in range(1, 13054): url = 'http://www.shicimingju.com/chaxun/zuozhe/{}.html'.format(i) self.poet_queue.put(url) def Spider(self): while not self.poet_queue.empty(): url = self.poet_queue.get() req = requests.get(url, headers=get_header()) if req.status_code == 200: req.encoding = 'utf-8' html = etree.HTML(req.text) name = html.xpath('/html/body/div[4]/div[2]/div[1]/div[2]/div[1]/h4/a/text()')[0] dynasty = html.xpath('/html/body/div[4]/div[2]/div[1]/div[3]/div[1]/div[2]/a/text()') if len(dynasty) == 0: dynasty = '未知' else: dynasty = dynasty[0] introduction = html.xpath('/html/body/div[4]/div[2]/div[1]/div[2]/div[1]/div[1]')[0].xpath( 'string(.)').strip() with app.app_context(): poet = Poet(name=name, dynasty=dynasty, introduction=introduction) db.session.add(poet) db.session.commit() id = poet.id poem_num = html.xpath('/html/body/div[4]/div[2]/div[1]/div[3]/div[2]/div[2]/a/text()')[0][:-1] poet_url_list = [] for i in range(1, int(int(poem_num) / 40) + 2): poet_id = re.sub("\D", "", url) poet_page_url = 'http://www.shicimingju.com/chaxun/zuozhe/{}_{}.html'.format(poet_id, i) req1 = requests.get(url=poet_page_url, headers=get_header()) if req1.status_code == 200: req1.encoding = 'utf-8' list_html = etree.HTML(req1.text) poet_url = list_html.xpath('//*/h3/a/@href') poet_url_list += poet_url poet_url_list = map(lambda x: self.base_url + x, poet_url_list) for url in poet_url_list: print(url) req2 = requests.get(url, headers=get_header()) if req2.status_code == 200: req2.encoding = 'utf-8' poet_html = etree.HTML(req2.text) title = poet_html.xpath('//*[@class="card"]/h1/text()')[0] content = '\n'.join(poet_html.xpath('//*[@class="item_content"]/text()')).strip() if not content: content = '\n'.join(poet_html.xpath('//*[@class="para"]/text()')).strip() if len(poet_html.xpath('//*[@class="shangxi_content"]')) == 0: analysis = '' else: analysis = poet_html.xpath('//*[@class="shangxi_content"]')[0].xpath( 'string(.)').strip() with app.app_context(): poem = Poem(title=title, content=content, analysis=analysis, author=id) db.session.add(poem) db.session.commit() def run(self): self.get_poet_url() thread_list = [] for i in range(self.thread): t = threading.Thread(target=self.Spider) thread_list.append(t) for t in thread_list: t.setDaemon(True) t.start() for t in thread_list: t.join() self.Spider() a = Shici() a.run() if __name__ == '__main__': manager.run()

true

f7299913712ad38823cbff706b312ff773b30c29

19,889

py

Python

manila/utils.py

redhat-openstack/manila

bef43561b303a36d99849952ba8c408b19bafd02

[ "Apache-2.0" ]

null

manila/utils.py

redhat-openstack/manila

bef43561b303a36d99849952ba8c408b19bafd02

[ "Apache-2.0" ]

null

manila/utils.py

redhat-openstack/manila

bef43561b303a36d99849952ba8c408b19bafd02

[ "Apache-2.0" ]

null

# Copyright 2010 United States Government as represented by the # Administrator of the National Aeronautics and Space Administration. # Copyright 2011 Justin Santa Barbara # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Utilities and helper functions.""" import contextlib import errno import inspect import os import pyclbr import random import re import shutil import socket import sys import tempfile from eventlet import pools import netaddr from oslo_concurrency import lockutils from oslo_concurrency import processutils from oslo_config import cfg from oslo_log import log from oslo_utils import importutils from oslo_utils import timeutils import paramiko import retrying import six from manila.db import api as db_api from manila import exception from manila.i18n import _ CONF = cfg.CONF LOG = log.getLogger(__name__) synchronized = lockutils.synchronized_with_prefix('manila-') def _get_root_helper(): return 'sudo manila-rootwrap %s' % CONF.rootwrap_config def execute(*cmd, **kwargs): """Convenience wrapper around oslo's execute() function.""" if 'run_as_root' in kwargs and 'root_helper' not in kwargs: kwargs['root_helper'] = _get_root_helper() return processutils.execute(*cmd, **kwargs) def trycmd(*args, **kwargs): """Convenience wrapper around oslo's trycmd() function.""" if 'run_as_root' in kwargs and 'root_helper' not in kwargs: kwargs['root_helper'] = _get_root_helper() return processutils.trycmd(*args, **kwargs) class SSHPool(pools.Pool): """A simple eventlet pool to hold ssh connections.""" def __init__(self, ip, port, conn_timeout, login, password=None, privatekey=None, *args, **kwargs): self.ip = ip self.port = port self.login = login self.password = password self.conn_timeout = conn_timeout if conn_timeout else None self.path_to_private_key = privatekey super(SSHPool, self).__init__(*args, **kwargs) def create(self): ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) look_for_keys = True if self.path_to_private_key: self.path_to_private_key = os.path.expanduser( self.path_to_private_key) look_for_keys = False elif self.password: look_for_keys = False try: ssh.connect(self.ip, port=self.port, username=self.login, password=self.password, key_filename=self.path_to_private_key, look_for_keys=look_for_keys, timeout=self.conn_timeout) # Paramiko by default sets the socket timeout to 0.1 seconds, # ignoring what we set through the sshclient. This doesn't help for # keeping long lived connections. Hence we have to bypass it, by # overriding it after the transport is initialized. We are setting # the sockettimeout to None and setting a keepalive packet so that, # the server will keep the connection open. All that does is send # a keepalive packet every ssh_conn_timeout seconds. if self.conn_timeout: transport = ssh.get_transport() transport.sock.settimeout(None) transport.set_keepalive(self.conn_timeout) return ssh except Exception as e: msg = _("Check whether private key or password are correctly " "set. Error connecting via ssh: %s") % e LOG.error(msg) raise exception.SSHException(msg) def get(self): """Return an item from the pool, when one is available. This may cause the calling greenthread to block. Check if a connection is active before returning it. For dead connections create and return a new connection. """ if self.free_items: conn = self.free_items.popleft() if conn: if conn.get_transport().is_active(): return conn else: conn.close() return self.create() if self.current_size < self.max_size: created = self.create() self.current_size += 1 return created return self.channel.get() def remove(self, ssh): """Close an ssh client and remove it from free_items.""" ssh.close() ssh = None if ssh in self.free_items: self.free_items.pop(ssh) if self.current_size > 0: self.current_size -= 1 def check_ssh_injection(cmd_list): ssh_injection_pattern = ['`', '$', '|', '||', ';', '&', '&&', '>', '>>', '<'] # Check whether injection attacks exist for arg in cmd_list: arg = arg.strip() # Check for matching quotes on the ends is_quoted = re.match('^(?P<quote>[\'"])(?P<quoted>.*)(?P=quote)$', arg) if is_quoted: # Check for unescaped quotes within the quoted argument quoted = is_quoted.group('quoted') if quoted: if (re.match('[\'"]', quoted) or re.search('[^\\\\][\'"]', quoted)): raise exception.SSHInjectionThreat(command=cmd_list) else: # We only allow spaces within quoted arguments, and that # is the only special character allowed within quotes if len(arg.split()) > 1: raise exception.SSHInjectionThreat(command=cmd_list) # Second, check whether danger character in command. So the shell # special operator must be a single argument. for c in ssh_injection_pattern: if c not in arg: continue result = arg.find(c) if not result == -1: if result == 0 or not arg[result - 1] == '\\': raise exception.SSHInjectionThreat(command=cmd_list) class LazyPluggable(object): """A pluggable backend loaded lazily based on some value.""" def __init__(self, pivot, **backends): self.__backends = backends self.__pivot = pivot self.__backend = None def __get_backend(self): if not self.__backend: backend_name = CONF[self.__pivot] if backend_name not in self.__backends: raise exception.Error(_('Invalid backend: %s') % backend_name) backend = self.__backends[backend_name] if isinstance(backend, tuple): name = backend[0] fromlist = backend[1] else: name = backend fromlist = backend self.__backend = __import__(name, None, None, fromlist) LOG.debug('backend %s', self.__backend) return self.__backend def __getattr__(self, key): backend = self.__get_backend() return getattr(backend, key) def delete_if_exists(pathname): """Delete a file, but ignore file not found error.""" try: os.unlink(pathname) except OSError as e: if e.errno == errno.ENOENT: return else: raise def get_from_path(items, path): """Returns a list of items matching the specified path. Takes an XPath-like expression e.g. prop1/prop2/prop3, and for each item in items, looks up items[prop1][prop2][prop3]. Like XPath, if any of the intermediate results are lists it will treat each list item individually. A 'None' in items or any child expressions will be ignored, this function will not throw because of None (anywhere) in items. The returned list will contain no None values. """ if path is None: raise exception.Error('Invalid mini_xpath') (first_token, sep, remainder) = path.partition('/') if first_token == '': raise exception.Error('Invalid mini_xpath') results = [] if items is None: return results if not isinstance(items, list): # Wrap single objects in a list items = [items] for item in items: if item is None: continue get_method = getattr(item, 'get', None) if get_method is None: continue child = get_method(first_token) if child is None: continue if isinstance(child, list): # Flatten intermediate lists for x in child: results.append(x) else: results.append(child) if not sep: # No more tokens return results else: return get_from_path(results, remainder) def is_ipv6_configured(): """Check if system contain IPv6 capable network interface. :rtype: bool :raises: IOError """ try: fd = open('/proc/net/if_inet6') except IOError as e: if e.errno != errno.ENOENT: raise result = False else: result = bool(fd.read(32)) fd.close() return result def is_eventlet_bug105(): """Check if eventlet support IPv6 addresses. See https://bitbucket.org/eventlet/eventlet/issue/105 :rtype: bool """ try: mod = sys.modules['eventlet.support.greendns'] except KeyError: return False try: connect_data = mod.getaddrinfo('::1', 80) except socket.gaierror: return True fail = [x for x in connect_data if x[0] != socket.AF_INET6] return bool(fail) def monkey_patch(): """Patch decorator. If the Flags.monkey_patch set as True, this function patches a decorator for all functions in specified modules. You can set decorators for each modules using CONF.monkey_patch_modules. The format is "Module path:Decorator function". Example: 'manila.api.ec2.cloud:' \ manila.openstack.common.notifier.api.notify_decorator' Parameters of the decorator is as follows. (See manila.openstack.common.notifier.api.notify_decorator) name - name of the function function - object of the function """ # If CONF.monkey_patch is not True, this function do nothing. if not CONF.monkey_patch: return # Get list of modules and decorators for module_and_decorator in CONF.monkey_patch_modules: module, decorator_name = module_and_decorator.split(':') # import decorator function decorator = importutils.import_class(decorator_name) __import__(module) # Retrieve module information using pyclbr module_data = pyclbr.readmodule_ex(module) for key in module_data.keys(): # set the decorator for the class methods if isinstance(module_data[key], pyclbr.Class): clz = importutils.import_class("%s.%s" % (module, key)) # NOTE(vponomaryov): we need to distinguish class methods types # for py2 and py3, because the concept of 'unbound methods' has # been removed from the python3.x if six.PY3: member_type = inspect.isfunction else: member_type = inspect.ismethod for method, func in inspect.getmembers(clz, member_type): setattr( clz, method, decorator("%s.%s.%s" % (module, key, method), func)) # set the decorator for the function if isinstance(module_data[key], pyclbr.Function): func = importutils.import_class("%s.%s" % (module, key)) setattr(sys.modules[module], key, decorator("%s.%s" % (module, key), func)) def read_cached_file(filename, cache_info, reload_func=None): """Read from a file if it has been modified. :param cache_info: dictionary to hold opaque cache. :param reload_func: optional function to be called with data when file is reloaded due to a modification. :returns: data from file """ mtime = os.path.getmtime(filename) if not cache_info or mtime != cache_info.get('mtime'): with open(filename) as fap: cache_info['data'] = fap.read() cache_info['mtime'] = mtime if reload_func: reload_func(cache_info['data']) return cache_info['data'] def file_open(*args, **kwargs): """Open file see built-in file() documentation for more details Note: The reason this is kept in a separate module is to easily be able to provide a stub module that doesn't alter system state at all (for unit tests) """ return file(*args, **kwargs) def service_is_up(service): """Check whether a service is up based on last heartbeat.""" last_heartbeat = service['updated_at'] or service['created_at'] # Timestamps in DB are UTC. elapsed = timeutils.total_seconds(timeutils.utcnow() - last_heartbeat) return abs(elapsed) <= CONF.service_down_time def validate_service_host(context, host): service = db_api.service_get_by_host_and_topic(context, host, 'manila-share') if not service_is_up(service): raise exception.ServiceIsDown(service=service['host']) return service def read_file_as_root(file_path): """Secure helper to read file as root.""" try: out, _err = execute('cat', file_path, run_as_root=True) return out except exception.ProcessExecutionError: raise exception.FileNotFound(file_path=file_path) @contextlib.contextmanager def temporary_chown(path, owner_uid=None): """Temporarily chown a path. :params owner_uid: UID of temporary owner (defaults to current user) """ if owner_uid is None: owner_uid = os.getuid() orig_uid = os.stat(path).st_uid if orig_uid != owner_uid: execute('chown', owner_uid, path, run_as_root=True) try: yield finally: if orig_uid != owner_uid: execute('chown', orig_uid, path, run_as_root=True) @contextlib.contextmanager def tempdir(**kwargs): tmpdir = tempfile.mkdtemp(**kwargs) try: yield tmpdir finally: try: shutil.rmtree(tmpdir) except OSError as e: LOG.debug('Could not remove tmpdir: %s', six.text_type(e)) def walk_class_hierarchy(clazz, encountered=None): """Walk class hierarchy, yielding most derived classes first.""" if not encountered: encountered = [] for subclass in clazz.__subclasses__(): if subclass not in encountered: encountered.append(subclass) # drill down to leaves first for subsubclass in walk_class_hierarchy(subclass, encountered): yield subsubclass yield subclass def ensure_tree(path): """Create a directory (and any ancestor directories required) :param path: Directory to create """ try: os.makedirs(path) except OSError as exc: if exc.errno == errno.EEXIST: if not os.path.isdir(path): raise else: raise def cidr_to_netmask(cidr): """Convert cidr to netmask.""" try: network = netaddr.IPNetwork(cidr) return str(network.netmask) except netaddr.AddrFormatError: raise exception.InvalidInput(_("Invalid cidr supplied %s") % cidr) def is_valid_ip_address(ip_address, ip_version): if int(ip_version) == 4: return netaddr.valid_ipv4(ip_address) elif int(ip_version) == 6: return netaddr.valid_ipv6(ip_address) else: raise exception.ManilaException( _("Provided improper IP version '%s'.") % ip_version) class IsAMatcher(object): def __init__(self, expected_value=None): self.expected_value = expected_value def __eq__(self, actual_value): return isinstance(actual_value, self.expected_value) class ComparableMixin(object): def _compare(self, other, method): try: return method(self._cmpkey(), other._cmpkey()) except (AttributeError, TypeError): # _cmpkey not implemented, or return different type, # so I can't compare with "other". return NotImplemented def __lt__(self, other): return self._compare(other, lambda s, o: s < o) def __le__(self, other): return self._compare(other, lambda s, o: s <= o) def __eq__(self, other): return self._compare(other, lambda s, o: s == o) def __ge__(self, other): return self._compare(other, lambda s, o: s >= o) def __gt__(self, other): return self._compare(other, lambda s, o: s > o) def __ne__(self, other): return self._compare(other, lambda s, o: s != o) def retry(exception, interval=1, retries=10, backoff_rate=2, wait_random=False): """A wrapper around retrying library. This decorator allows to log and to check 'retries' input param. Time interval between retries is calculated in the following way: interval * backoff_rate ^ previous_attempt_number :param exception: expected exception type. When wrapped function raises an exception of this type, the function execution is retried. :param interval: param 'interval' is used to calculate time interval between retries: interval * backoff_rate ^ previous_attempt_number :param retries: number of retries. :param backoff_rate: param 'backoff_rate' is used to calculate time interval between retries: interval * backoff_rate ^ previous_attempt_number :param wait_random: boolean value to enable retry with random wait timer. """ def _retry_on_exception(e): return isinstance(e, exception) def _backoff_sleep(previous_attempt_number, delay_since_first_attempt_ms): exp = backoff_rate ** previous_attempt_number wait_for = max(0, interval * exp) if wait_random: wait_val = random.randrange(interval * 1000.0, wait_for * 1000.0) else: wait_val = wait_for * 1000.0 LOG.debug("Sleeping for %s seconds.", (wait_val / 1000.0)) return wait_val def _print_stop(previous_attempt_number, delay_since_first_attempt_ms): delay_since_first_attempt = delay_since_first_attempt_ms / 1000.0 LOG.debug("Failed attempt %s", previous_attempt_number) LOG.debug("Have been at this for %s seconds", delay_since_first_attempt) return previous_attempt_number == retries if retries < 1: raise ValueError(_('Retries must be greater than or ' 'equal to 1 (received: %s).') % retries) def _decorator(f): @six.wraps(f) def _wrapper(*args, **kwargs): r = retrying.Retrying(retry_on_exception=_retry_on_exception, wait_func=_backoff_sleep, stop_func=_print_stop) return r.call(f, *args, **kwargs) return _wrapper return _decorator

32.983416

79

0.622052

import contextlib import errno import inspect import os import pyclbr import random import re import shutil import socket import sys import tempfile from eventlet import pools import netaddr from oslo_concurrency import lockutils from oslo_concurrency import processutils from oslo_config import cfg from oslo_log import log from oslo_utils import importutils from oslo_utils import timeutils import paramiko import retrying import six from manila.db import api as db_api from manila import exception from manila.i18n import _ CONF = cfg.CONF LOG = log.getLogger(__name__) synchronized = lockutils.synchronized_with_prefix('manila-') def _get_root_helper(): return 'sudo manila-rootwrap %s' % CONF.rootwrap_config def execute(*cmd, **kwargs): if 'run_as_root' in kwargs and 'root_helper' not in kwargs: kwargs['root_helper'] = _get_root_helper() return processutils.execute(*cmd, **kwargs) def trycmd(*args, **kwargs): if 'run_as_root' in kwargs and 'root_helper' not in kwargs: kwargs['root_helper'] = _get_root_helper() return processutils.trycmd(*args, **kwargs) class SSHPool(pools.Pool): def __init__(self, ip, port, conn_timeout, login, password=None, privatekey=None, *args, **kwargs): self.ip = ip self.port = port self.login = login self.password = password self.conn_timeout = conn_timeout if conn_timeout else None self.path_to_private_key = privatekey super(SSHPool, self).__init__(*args, **kwargs) def create(self): ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) look_for_keys = True if self.path_to_private_key: self.path_to_private_key = os.path.expanduser( self.path_to_private_key) look_for_keys = False elif self.password: look_for_keys = False try: ssh.connect(self.ip, port=self.port, username=self.login, password=self.password, key_filename=self.path_to_private_key, look_for_keys=look_for_keys, timeout=self.conn_timeout) # keeping long lived connections. Hence we have to bypass it, by # overriding it after the transport is initialized. We are setting # the sockettimeout to None and setting a keepalive packet so that, # the server will keep the connection open. All that does is send # a keepalive packet every ssh_conn_timeout seconds. if self.conn_timeout: transport = ssh.get_transport() transport.sock.settimeout(None) transport.set_keepalive(self.conn_timeout) return ssh except Exception as e: msg = _("Check whether private key or password are correctly " "set. Error connecting via ssh: %s") % e LOG.error(msg) raise exception.SSHException(msg) def get(self): if self.free_items: conn = self.free_items.popleft() if conn: if conn.get_transport().is_active(): return conn else: conn.close() return self.create() if self.current_size < self.max_size: created = self.create() self.current_size += 1 return created return self.channel.get() def remove(self, ssh): ssh.close() ssh = None if ssh in self.free_items: self.free_items.pop(ssh) if self.current_size > 0: self.current_size -= 1 def check_ssh_injection(cmd_list): ssh_injection_pattern = ['`', '$', '|', '||', ';', '&', '&&', '>', '>>', '<'] # Check whether injection attacks exist for arg in cmd_list: arg = arg.strip() # Check for matching quotes on the ends is_quoted = re.match('^(?P<quote>[\'"])(?P<quoted>.*)(?P=quote)$', arg) if is_quoted: # Check for unescaped quotes within the quoted argument quoted = is_quoted.group('quoted') if quoted: if (re.match('[\'"]', quoted) or re.search('[^\\\\][\'"]', quoted)): raise exception.SSHInjectionThreat(command=cmd_list) else: # We only allow spaces within quoted arguments, and that # is the only special character allowed within quotes if len(arg.split()) > 1: raise exception.SSHInjectionThreat(command=cmd_list) # Second, check whether danger character in command. So the shell # special operator must be a single argument. for c in ssh_injection_pattern: if c not in arg: continue result = arg.find(c) if not result == -1: if result == 0 or not arg[result - 1] == '\\': raise exception.SSHInjectionThreat(command=cmd_list) class LazyPluggable(object): def __init__(self, pivot, **backends): self.__backends = backends self.__pivot = pivot self.__backend = None def __get_backend(self): if not self.__backend: backend_name = CONF[self.__pivot] if backend_name not in self.__backends: raise exception.Error(_('Invalid backend: %s') % backend_name) backend = self.__backends[backend_name] if isinstance(backend, tuple): name = backend[0] fromlist = backend[1] else: name = backend fromlist = backend self.__backend = __import__(name, None, None, fromlist) LOG.debug('backend %s', self.__backend) return self.__backend def __getattr__(self, key): backend = self.__get_backend() return getattr(backend, key) def delete_if_exists(pathname): try: os.unlink(pathname) except OSError as e: if e.errno == errno.ENOENT: return else: raise def get_from_path(items, path): if path is None: raise exception.Error('Invalid mini_xpath') (first_token, sep, remainder) = path.partition('/') if first_token == '': raise exception.Error('Invalid mini_xpath') results = [] if items is None: return results if not isinstance(items, list): # Wrap single objects in a list items = [items] for item in items: if item is None: continue get_method = getattr(item, 'get', None) if get_method is None: continue child = get_method(first_token) if child is None: continue if isinstance(child, list): # Flatten intermediate lists for x in child: results.append(x) else: results.append(child) if not sep: # No more tokens return results else: return get_from_path(results, remainder) def is_ipv6_configured(): try: fd = open('/proc/net/if_inet6') except IOError as e: if e.errno != errno.ENOENT: raise result = False else: result = bool(fd.read(32)) fd.close() return result def is_eventlet_bug105(): try: mod = sys.modules['eventlet.support.greendns'] except KeyError: return False try: connect_data = mod.getaddrinfo('::1', 80) except socket.gaierror: return True fail = [x for x in connect_data if x[0] != socket.AF_INET6] return bool(fail) def monkey_patch(): # If CONF.monkey_patch is not True, this function do nothing. if not CONF.monkey_patch: return # Get list of modules and decorators for module_and_decorator in CONF.monkey_patch_modules: module, decorator_name = module_and_decorator.split(':') # import decorator function decorator = importutils.import_class(decorator_name) __import__(module) # Retrieve module information using pyclbr module_data = pyclbr.readmodule_ex(module) for key in module_data.keys(): # set the decorator for the class methods if isinstance(module_data[key], pyclbr.Class): clz = importutils.import_class("%s.%s" % (module, key)) # NOTE(vponomaryov): we need to distinguish class methods types # for py2 and py3, because the concept of 'unbound methods' has # been removed from the python3.x if six.PY3: member_type = inspect.isfunction else: member_type = inspect.ismethod for method, func in inspect.getmembers(clz, member_type): setattr( clz, method, decorator("%s.%s.%s" % (module, key, method), func)) # set the decorator for the function if isinstance(module_data[key], pyclbr.Function): func = importutils.import_class("%s.%s" % (module, key)) setattr(sys.modules[module], key, decorator("%s.%s" % (module, key), func)) def read_cached_file(filename, cache_info, reload_func=None): mtime = os.path.getmtime(filename) if not cache_info or mtime != cache_info.get('mtime'): with open(filename) as fap: cache_info['data'] = fap.read() cache_info['mtime'] = mtime if reload_func: reload_func(cache_info['data']) return cache_info['data'] def file_open(*args, **kwargs): return file(*args, **kwargs) def service_is_up(service): last_heartbeat = service['updated_at'] or service['created_at'] # Timestamps in DB are UTC. elapsed = timeutils.total_seconds(timeutils.utcnow() - last_heartbeat) return abs(elapsed) <= CONF.service_down_time def validate_service_host(context, host): service = db_api.service_get_by_host_and_topic(context, host, 'manila-share') if not service_is_up(service): raise exception.ServiceIsDown(service=service['host']) return service def read_file_as_root(file_path): try: out, _err = execute('cat', file_path, run_as_root=True) return out except exception.ProcessExecutionError: raise exception.FileNotFound(file_path=file_path) @contextlib.contextmanager def temporary_chown(path, owner_uid=None): if owner_uid is None: owner_uid = os.getuid() orig_uid = os.stat(path).st_uid if orig_uid != owner_uid: execute('chown', owner_uid, path, run_as_root=True) try: yield finally: if orig_uid != owner_uid: execute('chown', orig_uid, path, run_as_root=True) @contextlib.contextmanager def tempdir(**kwargs): tmpdir = tempfile.mkdtemp(**kwargs) try: yield tmpdir finally: try: shutil.rmtree(tmpdir) except OSError as e: LOG.debug('Could not remove tmpdir: %s', six.text_type(e)) def walk_class_hierarchy(clazz, encountered=None): if not encountered: encountered = [] for subclass in clazz.__subclasses__(): if subclass not in encountered: encountered.append(subclass) # drill down to leaves first for subsubclass in walk_class_hierarchy(subclass, encountered): yield subsubclass yield subclass def ensure_tree(path): try: os.makedirs(path) except OSError as exc: if exc.errno == errno.EEXIST: if not os.path.isdir(path): raise else: raise def cidr_to_netmask(cidr): try: network = netaddr.IPNetwork(cidr) return str(network.netmask) except netaddr.AddrFormatError: raise exception.InvalidInput(_("Invalid cidr supplied %s") % cidr) def is_valid_ip_address(ip_address, ip_version): if int(ip_version) == 4: return netaddr.valid_ipv4(ip_address) elif int(ip_version) == 6: return netaddr.valid_ipv6(ip_address) else: raise exception.ManilaException( _("Provided improper IP version '%s'.") % ip_version) class IsAMatcher(object): def __init__(self, expected_value=None): self.expected_value = expected_value def __eq__(self, actual_value): return isinstance(actual_value, self.expected_value) class ComparableMixin(object): def _compare(self, other, method): try: return method(self._cmpkey(), other._cmpkey()) except (AttributeError, TypeError): # _cmpkey not implemented, or return different type, # so I can't compare with "other". return NotImplemented def __lt__(self, other): return self._compare(other, lambda s, o: s < o) def __le__(self, other): return self._compare(other, lambda s, o: s <= o) def __eq__(self, other): return self._compare(other, lambda s, o: s == o) def __ge__(self, other): return self._compare(other, lambda s, o: s >= o) def __gt__(self, other): return self._compare(other, lambda s, o: s > o) def __ne__(self, other): return self._compare(other, lambda s, o: s != o) def retry(exception, interval=1, retries=10, backoff_rate=2, wait_random=False): def _retry_on_exception(e): return isinstance(e, exception) def _backoff_sleep(previous_attempt_number, delay_since_first_attempt_ms): exp = backoff_rate ** previous_attempt_number wait_for = max(0, interval * exp) if wait_random: wait_val = random.randrange(interval * 1000.0, wait_for * 1000.0) else: wait_val = wait_for * 1000.0 LOG.debug("Sleeping for %s seconds.", (wait_val / 1000.0)) return wait_val def _print_stop(previous_attempt_number, delay_since_first_attempt_ms): delay_since_first_attempt = delay_since_first_attempt_ms / 1000.0 LOG.debug("Failed attempt %s", previous_attempt_number) LOG.debug("Have been at this for %s seconds", delay_since_first_attempt) return previous_attempt_number == retries if retries < 1: raise ValueError(_('Retries must be greater than or ' 'equal to 1 (received: %s).') % retries) def _decorator(f): @six.wraps(f) def _wrapper(*args, **kwargs): r = retrying.Retrying(retry_on_exception=_retry_on_exception, wait_func=_backoff_sleep, stop_func=_print_stop) return r.call(f, *args, **kwargs) return _wrapper return _decorator

true

f7299b8c93a212f361068d4db0ea7d318b67843a

17,636

py

Python

src/models/SGNN_EBM_models.py

chao1224/SGNN-EBM

bda4c486e8ecb9775b635757dbe1071878be7b8a

[ "MIT" ]

null

src/models/SGNN_EBM_models.py

chao1224/SGNN-EBM

bda4c486e8ecb9775b635757dbe1071878be7b8a

[ "MIT" ]

1

2022-03-25T01:47:18.000Z

2022-03-25T01:50:12.000Z

src/models/SGNN_EBM_models.py

chao1224/SGNN-EBM

bda4c486e8ecb9775b635757dbe1071878be7b8a

[ "MIT" ]

null

import torch from torch import nn import torch.nn.functional as F from torch_scatter import scatter_add class NCE_C_Parameter(torch.nn.Module): def __init__(self, N): super(NCE_C_Parameter, self).__init__() self.NCE_C = nn.Parameter(torch.zeros(N, requires_grad=True)) class GNN_EBM_Layer_01(torch.nn.Module): def __init__(self, input_dim, output_dim): super(GNN_EBM_Layer_01, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.edge_layer = torch.nn.Linear(input_dim, output_dim) self.node_layer = torch.nn.Linear(input_dim, output_dim) self.mlp = torch.nn.Linear(input_dim, output_dim) def node_message_passing(self, x, x_2nd_agg, edge): T = x.size()[1] node_in, node_out = edge[0], edge[1] # M, M update = (scatter_add(x_2nd_agg, node_out, dim=1, dim_size=T) + scatter_add(x_2nd_agg, node_in, dim=1, dim_size=T)) / 2 # B, T, d x = x + update # B, T, d return x def forward(self, x_1st, x_2nd, edge): ''' :param x: (B, T, 2, d) :param x_2nd: (B, M, 4, d) :param edge: (M, 2) :return: (B, T, 2, d_out) ''' aggregate_indice = torch.LongTensor([0, 0, 1, 1]).to(x_1st.device) node_i_indice = torch.LongTensor([0, 0, 1, 1]).to(x_1st.device) node_j_indice = torch.LongTensor([0, 1, 0, 1]).to(x_1st.device) x_1st_neg = x_1st[:, :, 0, :] # B, T, d x_1st_pos = x_1st[:, :, 1, :] # B, T, d x_2nd_agg = scatter_add(x_2nd, aggregate_indice, dim=2) # B, T, 2, d x_2nd_neg = x_2nd_agg[:, :, 0, :] # B, M, d x_2nd_pos = x_2nd_agg[:, :, 1, :] # B, M, d x_neg = self.node_message_passing(x_1st_neg, x_2nd_neg, edge) # B, T, d x_pos = self.node_message_passing(x_1st_pos, x_2nd_pos, edge) # B, T, d x = torch.stack([x_neg, x_pos], dim=2) # B, T, 2, d x = self.node_layer(x) # B, T, 2, d edge_i = torch.index_select(x_1st, 1, edge[0]) # B, M, 2, dim edge_i = torch.index_select(edge_i, 2, node_i_indice) # B, M, 4, dim edge_j = torch.index_select(x_1st, 1, edge[1]) # B, M, 2, dim edge_j = torch.index_select(edge_j, 2, node_j_indice) # B, M, 4, dim edge = x_2nd + self.mlp(edge_i + edge_j) # B, M, 4, d edge = self.edge_layer(edge) return x, edge class GNN_Energy_Model_1st_Order_01(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, output_dim, dropout=0, concat=False): super(GNN_Energy_Model_1st_Order_01, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.output_dim = output_dim self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = torch.nn.Sequential( torch.nn.Linear(2 * hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, output_dim) ) return def forward(self, x_1st, x_2nd, edge): ''' :param x_1st: B,T,2,dim :param x_2nd: B,M,4,dim :param edge: 2,M :return: B,T,1 ''' B, T = x_1st.size()[:2] h_node_list = [x_1st] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) # x_edge = F.relu(x_edge) x_node = F.dropout(x_node, self.dropout, training=self.training) # x_edge = F.dropout(x_edge, self.dropout, training=self.training) h_node_list.append(x_node) if self.concat: h = torch.cat(h_node_list, dim=3).view(B, T, -1) # B, T, 2*layer_num*d else: h = x_node.view(B, T, -1) # B, T, 2*d h = self.node_readout(h) # B, T, 1 return h class GNN_Energy_Model_1st_Order_02(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, output_dim, dropout=0, concat=False): super(GNN_Energy_Model_1st_Order_02, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.output_dim = output_dim self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = torch.nn.Linear(2 * hidden_dim_sum, output_dim) return def forward(self, x_1st, x_2nd, edge): ''' :param x_1st: B,T,2,dim :param x_2nd: B,M,4,dim :param edge: 2,M :return: B,T,1 ''' B, T = x_1st.size()[:2] h_node_list = [x_1st] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) # x_edge = F.relu(x_edge) x_node = F.dropout(x_node, self.dropout, training=self.training) # x_edge = F.dropout(x_edge, self.dropout, training=self.training) h_node_list.append(x_node) if self.concat: h = torch.cat(h_node_list, dim=3).view(B, T, -1) # B, T, 2*layer_num*d else: h = x_node.view(B, T, -1) # B, T, 2*d h = self.node_readout(h) # B, T, 1 return h class GNN_Energy_Model_2nd_Order_01(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, dropout=0, concat=False): super(GNN_Energy_Model_2nd_Order_01, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = torch.nn.Sequential( torch.nn.Linear(hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, 1) ) self.edge_readout = torch.nn.Sequential( torch.nn.Linear(hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, 1) ) return def forward(self, x_1st, x_2nd, edge): ''' :param x_1st: B,T,2,dim :param x_2nd: B,M,4,dim :param edge: 2,M :return: (B,T,2), (B,M,4) ''' B, T = x_1st.size()[:2] M = edge.size()[1] h_node_list = [x_1st] h_edge_list = [x_2nd] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) # x_edge = F.relu(x_edge) x_node = F.dropout(x_node, self.dropout, training=self.training) # x_edge = F.dropout(x_edge, self.dropout, training=self.training) h_node_list.append(x_node) h_edge_list.append(x_edge) if self.concat: h_node = torch.cat(h_node_list, dim=3) # B, T, 2, layer_num*d h_edge = torch.cat(h_edge_list, dim=3) # B, M, 4, layer_num*d else: h_node = x_node # B, T, 2, d h_edge = x_edge # B, M, 4, d h_node = self.node_readout(h_node) # B, T, 2, 1 h_edge = self.edge_readout(h_edge) # B, M, 4, 1 h_node = h_node.squeeze(3) # B, T, 2 h_edge = h_edge.squeeze(3) # B, M, 4 return h_node, h_edge class GNN_Energy_Model_2nd_Order_02(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, dropout=0, concat=False): super(GNN_Energy_Model_2nd_Order_02, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = torch.nn.Sequential( torch.nn.Linear(2 * hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, 2) ) self.edge_readout = torch.nn.Sequential( torch.nn.Linear(4 * hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, 4) ) return def forward(self, x_1st, x_2nd, edge): ''' :param x_1st: B,T,2,dim :param x_2nd: B,M,4,dim :param edge: 2,M :return: (B,T,2), (B,M,4) ''' B, T = x_1st.size()[:2] M = x_2nd.size()[1] h_node_list = [x_1st] h_edge_list = [x_2nd] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) # x_edge = F.relu(x_edge) x_node = F.dropout(x_node, self.dropout, training=self.training) # x_edge = F.dropout(x_edge, self.dropout, training=self.training) h_node_list.append(x_node) h_edge_list.append(x_edge) if self.concat: h_node = torch.cat(h_node_list, dim=3).view(B, T, -1) # B, T, 2*layer_num*d h_edge = torch.cat(h_edge_list, dim=3).view(B, M, -1) # B, M, 4*layer_num*d else: h_node = x_node.view(B, T, -1) # B, T, 2*d h_edge = x_edge.view(B, M, -1) # B, M, 4*d h_node = self.node_readout(h_node) # B, T, 2 h_edge = self.edge_readout(h_edge) # B, M, 4 return h_node, h_edge class GNN_Energy_Model_2nd_Order_03(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, dropout=0, concat=False): super(GNN_Energy_Model_2nd_Order_03, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = nn.Linear(2 * hidden_dim_sum, 2) self.edge_readout = nn.Linear(4 * hidden_dim_sum, 4) return def forward(self, x_1st, x_2nd, edge): ''' :param x_1st: B,T,2,dim :param x_2nd: B,M,4,dim :param edge: 2,M :return: (B,T,2), (B,M,4) ''' B, T = x_1st.size()[:2] M = edge.size()[1] h_node_list = [x_1st] h_edge_list = [x_2nd] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) # x_edge = F.relu(x_edge) x_node = F.dropout(x_node, self.dropout, training=self.training) # x_edge = F.dropout(x_edge, self.dropout, training=self.training) h_node_list.append(x_node) h_edge_list.append(x_edge) if self.concat: h_node = torch.cat(h_node_list, dim=3) # B, T, 2, layer_num*d h_edge = torch.cat(h_edge_list, dim=3) # B, M, 4, layer_num*d else: h_node = x_node # B, T, 2, d h_edge = x_edge # B, M, 4, d h_node = h_node.view(B, T, -1) # B, T, 2*d h_edge = h_edge.view(B, M, -1) # B, M, 4*d h_node = self.node_readout(h_node) # B, T, 2 h_edge = self.edge_readout(h_edge) # B, M, 4 return h_node, h_edge # class GATNet(torch.nn.Module): # def __init__(self, embedding_dim=10, hidden_dim=10, num_head=8): # super(GATNet, self).__init__() # self.conv1 = GATConv(embedding_dim, hidden_dim, heads=num_head, dropout=0.6) # self.conv2 = GATConv(hidden_dim * num_head, hidden_dim, heads=1, concat=False, dropout=0.6) # def forward(self, data): # x = data.x # x = F.dropout(x, p=0.6, training=self.training) # x = F.elu(self.conv1(x, data.edge_index)) # x = F.dropout(x, p=0.6, training=self.training) # x = self.conv2(x, data.edge_index) # return x # class MLP(nn.Sequential): # def __init__(self, input_dim, output_dim, hidden_dims=[1024, 512], dropout=0.1, use_batch_norm=False): # super(MLP, self).__init__() # self.input_dim = input_dim # self.output_dim = output_dim # self.hidden_dims = hidden_dims # self.use_batch_norm = use_batch_norm # self.dropout = nn.Dropout(0.1) # self.layer_size = len(self.hidden_dims) + 1 # dims = [self.input_dim] + self.hidden_dims + [self.output_dim] # self.predictor = nn.ModuleList([nn.Linear(dims[i], dims[i + 1]) for i in range(self.layer_size)]) # if use_batch_norm: # self.batch_norms = nn.ModuleList([nn.BatchNorm1d(dims[i + 1]) for i in range(self.layer_size)]) # for m in self.modules(): # if isinstance(m, nn.Linear): # nn.init.xavier_uniform_(m.weight.data) # if m.bias is not None: # m.bias.data.fill_(0.0) # def norm(self): # with torch.no_grad(): # norm = 0 # for m in self.modules(): # if isinstance(m, nn.Linear): # norm += torch.norm(m.weight.data).item() # return norm # def forward(self, v): # ''' # : params x: (batch_size, *, input_dim) # : output : (batch_size, *, output_dim) # ''' # B, t, _ = v.size() # v = v.flatten(0, -2) # # print('input norm: %.5f' % (torch.norm(v).item())) # for i, l in enumerate(self.predictor): # v = l(v) # if i != self.layer_size - 1: # if self.use_batch_norm: # v = self.batch_norms[i](v) # v = F.relu(v) # v = self.dropout(v) # # print('layer %d norm: %.5f' % (i, torch.norm(v).item())) # v = v.reshape(B, t, -1) # return v # class GradKnowledgeGraphModel(nn.Module): # def __init__(self, num_tasks, args): # super(GradKnowledgeGraphModel, self).__init__() # self.num_tasks = num_tasks # self.weights = nn.Parameter(torch.ones(self.num_tasks, 1), requires_grad=True) # self.register_parameter('grad_KG', self.weights) # self.softmax = nn.Softmax(dim=0) # self.normalize_method = args.grad_KG_normalize_method # def forward(self, task_repr): # # ########## This won't train ########## # # task_repr = task_repr * self.weights.data # task_repr = task_repr * self.weights # return task_repr # def renormalize(self): # if self.normalize_method == 'sum': # ########## TODO: there might be negatives after backward ########## # normalize_coeff = self.num_tasks / self.weights.data.sum() # self.weights.data *= normalize_coeff # elif self.normalize_method == 'softmax': # self.weights.data = self.softmax(self.weights.data) * self.num_tasks # return # def reset_param(self): # self.weights.data.fill_(1) # return

37.845494

109

0.576435

import torch from torch import nn import torch.nn.functional as F from torch_scatter import scatter_add class NCE_C_Parameter(torch.nn.Module): def __init__(self, N): super(NCE_C_Parameter, self).__init__() self.NCE_C = nn.Parameter(torch.zeros(N, requires_grad=True)) class GNN_EBM_Layer_01(torch.nn.Module): def __init__(self, input_dim, output_dim): super(GNN_EBM_Layer_01, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.edge_layer = torch.nn.Linear(input_dim, output_dim) self.node_layer = torch.nn.Linear(input_dim, output_dim) self.mlp = torch.nn.Linear(input_dim, output_dim) def node_message_passing(self, x, x_2nd_agg, edge): T = x.size()[1] node_in, node_out = edge[0], edge[1] update = (scatter_add(x_2nd_agg, node_out, dim=1, dim_size=T) + scatter_add(x_2nd_agg, node_in, dim=1, dim_size=T)) / 2 x = x + update return x def forward(self, x_1st, x_2nd, edge): aggregate_indice = torch.LongTensor([0, 0, 1, 1]).to(x_1st.device) node_i_indice = torch.LongTensor([0, 0, 1, 1]).to(x_1st.device) node_j_indice = torch.LongTensor([0, 1, 0, 1]).to(x_1st.device) x_1st_neg = x_1st[:, :, 0, :] x_1st_pos = x_1st[:, :, 1, :] x_2nd_agg = scatter_add(x_2nd, aggregate_indice, dim=2) x_2nd_neg = x_2nd_agg[:, :, 0, :] x_2nd_pos = x_2nd_agg[:, :, 1, :] x_neg = self.node_message_passing(x_1st_neg, x_2nd_neg, edge) x_pos = self.node_message_passing(x_1st_pos, x_2nd_pos, edge) x = torch.stack([x_neg, x_pos], dim=2) x = self.node_layer(x) edge_i = torch.index_select(x_1st, 1, edge[0]) edge_i = torch.index_select(edge_i, 2, node_i_indice) edge_j = torch.index_select(x_1st, 1, edge[1]) edge_j = torch.index_select(edge_j, 2, node_j_indice) edge = x_2nd + self.mlp(edge_i + edge_j) edge = self.edge_layer(edge) return x, edge class GNN_Energy_Model_1st_Order_01(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, output_dim, dropout=0, concat=False): super(GNN_Energy_Model_1st_Order_01, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.output_dim = output_dim self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = torch.nn.Sequential( torch.nn.Linear(2 * hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, output_dim) ) return def forward(self, x_1st, x_2nd, edge): B, T = x_1st.size()[:2] h_node_list = [x_1st] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) x_node = F.dropout(x_node, self.dropout, training=self.training) h_node_list.append(x_node) if self.concat: h = torch.cat(h_node_list, dim=3).view(B, T, -1) else: h = x_node.view(B, T, -1) h = self.node_readout(h) return h class GNN_Energy_Model_1st_Order_02(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, output_dim, dropout=0, concat=False): super(GNN_Energy_Model_1st_Order_02, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.output_dim = output_dim self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = torch.nn.Linear(2 * hidden_dim_sum, output_dim) return def forward(self, x_1st, x_2nd, edge): B, T = x_1st.size()[:2] h_node_list = [x_1st] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) x_node = F.dropout(x_node, self.dropout, training=self.training) h_node_list.append(x_node) if self.concat: h = torch.cat(h_node_list, dim=3).view(B, T, -1) else: h = x_node.view(B, T, -1) h = self.node_readout(h) return h class GNN_Energy_Model_2nd_Order_01(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, dropout=0, concat=False): super(GNN_Energy_Model_2nd_Order_01, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = torch.nn.Sequential( torch.nn.Linear(hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, 1) ) self.edge_readout = torch.nn.Sequential( torch.nn.Linear(hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, 1) ) return def forward(self, x_1st, x_2nd, edge): B, T = x_1st.size()[:2] M = edge.size()[1] h_node_list = [x_1st] h_edge_list = [x_2nd] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) x_node = F.dropout(x_node, self.dropout, training=self.training) h_node_list.append(x_node) h_edge_list.append(x_edge) if self.concat: h_node = torch.cat(h_node_list, dim=3) h_edge = torch.cat(h_edge_list, dim=3) else: h_node = x_node h_edge = x_edge h_node = self.node_readout(h_node) h_edge = self.edge_readout(h_edge) h_node = h_node.squeeze(3) h_edge = h_edge.squeeze(3) return h_node, h_edge class GNN_Energy_Model_2nd_Order_02(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, dropout=0, concat=False): super(GNN_Energy_Model_2nd_Order_02, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = torch.nn.Sequential( torch.nn.Linear(2 * hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, 2) ) self.edge_readout = torch.nn.Sequential( torch.nn.Linear(4 * hidden_dim_sum, 2 * hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(2 * hidden_dim_sum, hidden_dim_sum), torch.nn.ReLU(), torch.nn.Linear(hidden_dim_sum, 4) ) return def forward(self, x_1st, x_2nd, edge): B, T = x_1st.size()[:2] M = x_2nd.size()[1] h_node_list = [x_1st] h_edge_list = [x_2nd] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) x_node = F.dropout(x_node, self.dropout, training=self.training) h_node_list.append(x_node) h_edge_list.append(x_edge) if self.concat: h_node = torch.cat(h_node_list, dim=3).view(B, T, -1) h_edge = torch.cat(h_edge_list, dim=3).view(B, M, -1) else: h_node = x_node.view(B, T, -1) h_edge = x_edge.view(B, M, -1) h_node = self.node_readout(h_node) h_edge = self.edge_readout(h_edge) return h_node, h_edge class GNN_Energy_Model_2nd_Order_03(torch.nn.Module): def __init__(self, ebm_GNN_dim, ebm_GNN_layer_num, dropout=0, concat=False): super(GNN_Energy_Model_2nd_Order_03, self).__init__() self.ebm_GNN_dim = ebm_GNN_dim self.ebm_GNN_layer_num = ebm_GNN_layer_num - 1 self.dropout = dropout self.concat = concat hidden_layers_dim = [ebm_GNN_dim] * ebm_GNN_layer_num self.hidden_layers = torch.nn.ModuleList() for in_, out_ in zip(hidden_layers_dim[:-1], hidden_layers_dim[1:]): self.hidden_layers.append(GNN_EBM_Layer_01(in_, out_)) if self.concat: hidden_dim_sum = sum(hidden_layers_dim) else: hidden_dim_sum = ebm_GNN_dim self.node_readout = nn.Linear(2 * hidden_dim_sum, 2) self.edge_readout = nn.Linear(4 * hidden_dim_sum, 4) return def forward(self, x_1st, x_2nd, edge): B, T = x_1st.size()[:2] M = edge.size()[1] h_node_list = [x_1st] h_edge_list = [x_2nd] x_node, x_edge = x_1st, x_2nd for i in range(self.ebm_GNN_layer_num): x_node, x_edge = self.hidden_layers[i](x_node, x_edge, edge) if i < self.ebm_GNN_layer_num - 1: x_node = F.relu(x_node) x_node = F.dropout(x_node, self.dropout, training=self.training) h_node_list.append(x_node) h_edge_list.append(x_edge) if self.concat: h_node = torch.cat(h_node_list, dim=3) h_edge = torch.cat(h_edge_list, dim=3) else: h_node = x_node h_edge = x_edge h_node = h_node.view(B, T, -1) h_edge = h_edge.view(B, M, -1) h_node = self.node_readout(h_node) h_edge = self.edge_readout(h_edge) return h_node, h_edge # : params x: (batch_size, *, input_dim) # : output : (batch_size, *, output_dim) # ''' # self.weights.data = self.softmax(self.weights.data) * self.num_tasks # return # def reset_param(self): # self.weights.data.fill_(1) # return

true

f7299b9017108db2507245642f537176097512d3

3,847

py

Python

calculate_t2.py

kathoma/AutomaticKneeMRISegmentation

72ea3fa96fa5de34461b5999814aa706360f4a79

[ "Apache-2.0" ]

7

2020-12-09T05:34:06.000Z

2022-03-17T10:14:24.000Z

calculate_t2.py

kathoma/AutomaticKneeMRISegmentation

72ea3fa96fa5de34461b5999814aa706360f4a79

[ "Apache-2.0" ]

8

2021-03-31T18:03:52.000Z

2022-02-09T23:54:21.000Z

calculate_t2.py

kathoma/AutomaticKneeMRISegmentation

72ea3fa96fa5de34461b5999814aa706360f4a79

[ "Apache-2.0" ]

1

2022-02-20T16:04:55.000Z

from __future__ import print_function, division import sys sys.path.insert(0, 'lib') import numpy as np import random import scipy.io as sio import os import pandas as pd import scipy.ndimage as ndimage import math import os import scipy.linalg as la from joblib import Parallel, delayed from scipy.optimize import curve_fit from skimage import measure import scipy.stats as ss import skimage ######################################################### # Calculating T2 Values for Segmented Voxels ######################################################### def exp_func(mri_time, A, m, b): return A*np.exp(-m*mri_time) def running_mean(x): kernel = np.ones((3,))/3 conv = np.convolve(x, kernel, mode = 'valid') temp = np.copy(x) temp[1:-1]=conv # Avoid boundary effects of convolution temp[0]=np.mean(x[0:2]) temp[-1]=np.mean(x[-2:]) return temp def strictly_decreasing(vec): return np.all(np.diff(vec)<0) def fit_t2(t2imgs, t2times, segmentation = None, n_jobs = 4, show_bad_pixels = True): ''' Fits T2 curves to the T2_weighted images in each slice. IN: t2imgs - with T2 weighted images in numpy array (nr_slices, time_steps, width, heigth) t2times - list with aquisition times segmentation - segmentation matrix (nr_slices, width, heigth) n_jobs - number of parallel jobs OUT: matrix (nr_slices, width, heigth) with T2 values ''' t2_tensor = np.zeros((t2imgs.shape[0], t2imgs.shape[2], t2imgs.shape[3])) def fit_per_slice(slice_idx, show_bad_pixels): scan = t2imgs[slice_idx,:,:,:] mri_time = np.array(t2times[slice_idx]) - t2times[slice_idx][0] #np.array(t2times[slice_idx])# if not segmentation is None: # if we have a segmentation segmentation_mask = segmentation[slice_idx,:,:] (cartilage_indices_r, cartilage_indices_c) = np.where(segmentation_mask) t2_matrix = np.full((scan.shape[1], scan.shape[2]), np.nan) if len(cartilage_indices_r)> 0: for i in np.arange(len(cartilage_indices_r)): ir = cartilage_indices_r[i] ic = cartilage_indices_c[i] if all(scan[:,ir,ic] == scan[0,ir,ic]): # if constant value, decay is 0 continue try: if strictly_decreasing(scan[1:,ir,ic]): echo_corrected = scan[1:,ir,ic] else: echo_corrected = running_mean(scan[1:,ir,ic]) parameters,_ = curve_fit(exp_func, mri_time[1:], echo_corrected, p0 = [scan[0,ir,ic], .03, 0])#, # bounds = ([-np.inf, 0, -np.inf], [np.inf, 100, np.inf])) m = parameters[1] t2_ = 1./m t2_matrix[ir, ic] = t2_ if show_bad_pixels: if ((t2_ > .100) or (t2_< -.100)): print(t2_) plt.plot(mri_time, scan[:,ir,ic]) plt.plot(mri_time, exp_func(mri_time, *parameters), 'r-') plt.show() except RuntimeError: if show_bad_pixels: plt.plot(mri_time, scan[:,ir,ic]) plt.title("Did not converge") plt.show() return t2_matrix for i in range(t2imgs.shape[0]): t2_tensor[i,:,:] = fit_per_slice(i, show_bad_pixels)*1000 # in ms return t2_tensor

35.953271

102

0.512867

from __future__ import print_function, division import sys sys.path.insert(0, 'lib') import numpy as np import random import scipy.io as sio import os import pandas as pd import scipy.ndimage as ndimage import math import os import scipy.linalg as la from joblib import Parallel, delayed from scipy.optimize import curve_fit from skimage import measure import scipy.stats as ss import skimage

true

f7299c8d741309a0fb151a29fea28020d2131a61

808

py

Python

somedjango/manage.py

dvaldivia/grpc-celery-fork-bug

421eca43daef9e138d53e6f095cf470b98c14f99

[ "MIT" ]

null

somedjango/manage.py

dvaldivia/grpc-celery-fork-bug

421eca43daef9e138d53e6f095cf470b98c14f99

[ "MIT" ]

null

somedjango/manage.py

dvaldivia/grpc-celery-fork-bug

421eca43daef9e138d53e6f095cf470b98c14f99

[ "MIT" ]

1

2019-03-14T04:09:43.000Z

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "somedjango.settings") try: from django.core.management import execute_from_command_line except ImportError: # The above import may fail for some other reason. Ensure that the # issue is really that Django is missing to avoid masking other # exceptions on Python 2. try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

35.130435

77

0.643564

import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "somedjango.settings") try: from django.core.management import execute_from_command_line except ImportError: try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

true

f7299d40298fb4401be8a4ad23a730983ca934ac

14,248

py

Python

src/longbow/scsplit/command.py

broadinstitute/annmas

79da783acf41e5aaca4a14ef991c9ab0aac3c59a

[ "BSD-3-Clause" ]

null

src/longbow/scsplit/command.py

broadinstitute/annmas

79da783acf41e5aaca4a14ef991c9ab0aac3c59a

[ "BSD-3-Clause" ]

12

2021-02-02T17:16:33.000Z

2021-03-15T20:31:28.000Z

src/longbow/scsplit/command.py

broadinstitute/annmas

79da783acf41e5aaca4a14ef991c9ab0aac3c59a

[ "BSD-3-Clause" ]

null

import logging import sys import itertools import time import click import click_log import tqdm import pysam import multiprocessing as mp from inspect import getframeinfo, currentframe, getdoc from ..utils import bam_utils from ..utils.model import LibraryModel from ..annotate.command import get_segments from ..meta import VERSION logging.basicConfig(stream=sys.stderr) logger = logging.getLogger("scsplit") click_log.basic_config(logger) __DEFAULT_DUMMY_CBC = "CTGCCTAACCTGATCC" __DEFAULT_OUT_BASE_NAME = logger.name __DEFAULT_UMI_LENGTH = 10 __OUT_READ_FILE_SUFFIX = "_mates" __OUT_WHITELIST_FILE_SUFFIX = "_whitelist.txt" @click.command(name=logger.name) @click_log.simple_verbosity_option(logger) @click.option( "-t", "--threads", type=int, default=mp.cpu_count() - 1, show_default=True, help="number of threads to use (0 for all)", ) @click.option( "-o", "--output-base-name", default=__DEFAULT_OUT_BASE_NAME, type=str, help=f"base name for output files [default: {__DEFAULT_OUT_BASE_NAME}]", ) @click.option( "-c", "--cell-barcode", default=__DEFAULT_DUMMY_CBC, type=str, help=f"dummy cell barcode to use for the dataset [default: {__DEFAULT_DUMMY_CBC}, " f"length: {len(__DEFAULT_DUMMY_CBC)}]", ) @click.option( "-u", "--umi-length", default=__DEFAULT_UMI_LENGTH, type=int, show_default=True, help=f"length of the UMI from this library", ) @click.option( "-b", "--write-bam", is_flag=True, default=False, show_default=True, help=f"Write out an annotated bam file in addition to the mates files.", ) @click.option( '--force', is_flag=True, default=False, show_default=True, help="Force scsplit to run on the input bam without checking for compatibility." ) @click.option( "-m", "--model", default="mas15", show_default=True, help="The model to use for annotation. If the given value is a pre-configured model name, then that " "model will be used. Otherwise, the given value will be treated as a file name and Longbow will attempt to " "read in the file and create a LibraryModel from it. Longbow will assume the contents are the configuration " "of a LibraryModel as per LibraryModel.to_json()." ) @click.argument("input-bam", default="-" if not sys.stdin.isatty() else None, type=click.File("rb")) def main(threads, output_base_name, cell_barcode, umi_length, force, model, write_bam, input_bam): """Create files for use in `alevin` for single-cell analysis. This tool coerces a set of reads from a single source into a format that `alevin` can ingest. Segment names are assumed to be those in the default model (utils/model.py). INPUT_BAM should contain reads that have been processed by `longbow segment`. The output from this tool consists of several files: OUTPUT_BASE_NAME_mates_1.fastq: A file containing partial sequences for all reads in the given input file. These partial reads consist of the dummy cell barcode + detected UMI for each read in the given input file. OUTPUT_BASE_NAME_mates_2.fastq: A file containing partial sequences for all reads in the given input file. These partial reads consist of the transcript sequences for all reads in the given input file. Transcript sequences include data after the UMI and before the Poly-A tail. All bases outside of this range are excluded from the output. OUTPUT_BASE_NAME_whitelist.txt: A whitelist file for alevin containing the given dummy cell barcode. """ t_start = time.time() logger.info("Invoked via: longbow %s", " ".join(sys.argv[1:])) threads = mp.cpu_count() if threads <= 0 or threads > mp.cpu_count() else threads logger.info(f"Running with {threads} worker subprocess(es)") # Get our model: if LibraryModel.has_prebuilt_model(model): logger.info(f"Using %s", LibraryModel.pre_configured_models[model]["description"]) model = LibraryModel.build_pre_configured_model(model) else: logger.info(f"Loading model from json file: %s", model) model = LibraryModel.from_json_file(model) # Configure process manager: # NOTE: We're using processes to overcome the Global Interpreter Lock. manager = mp.Manager() process_input_data_queue = manager.Queue(threads) results = manager.Queue() # Start worker sub-processes: worker_process_pool = [] for _ in range(threads): p = mp.Process( target=_sub_process_work_fn, args=(process_input_data_queue, results, umi_length, model, write_bam) ) p.start() worker_process_pool.append(p) pysam.set_verbosity(0) # silence message about the .bai file not being found with pysam.AlignmentFile( input_bam, "rb", check_sq=False, require_index=False ) as bam_file, tqdm.tqdm( desc="Progress", unit=" read", colour="green", file=sys.stderr, leave=False, disable=not sys.stdin.isatty(), ) as pbar: if force: logger.info("Force mode - skipping bam header check for compatibility") else: # Make sure we're given an input bam file we can work with: if not _validate_input_bam(bam_file.header): # Bad news - we have to quit. # let's try to do it nicely: for r in (None,) * threads: process_input_data_queue.put(r) # Wait for our input jobs to finish: for p in worker_process_pool: p.join() sys.exit(1) # Get our header from the input bam file: out_header = bam_utils.create_bam_header_with_program_group(logger.name, bam_file.header, models=[model]) # Start output worker: res = manager.dict({"num_reads_processed": 0}) output_worker = mp.Process( target=_sub_process_write_fn, args=( results, output_base_name, cell_barcode, pbar, res, write_bam, out_header ), ) output_worker.start() # Add in a sentinel value at the end of the queue - one for each subprocess - so we guarantee # that all subprocesses will exit: iter_data = itertools.chain(bam_file, (None,) * threads) for r in iter_data: if r is not None: process_input_data_queue.put(r.to_string()) else: process_input_data_queue.put(r) # Wait for our input jobs to finish: for p in worker_process_pool: p.join() # Now that our input processes are done, we can add our exit sentinel onto the output queue and # wait for that process to end: results.put(None) output_worker.join() # Write out our CBC whitelist file: with open(f"{output_base_name}{__OUT_WHITELIST_FILE_SUFFIX}", "w") as f: f.write(f"{cell_barcode}\n") logger.info(f"Processed {res['num_reads_processed']} reads.") logger.info(f"CBC length: {len(cell_barcode)}.") logger.info(f"UMI length: {umi_length}.") logger.info(f"Done. Elapsed time: %2.2fs.", time.time() - t_start) def _validate_input_bam(input_bam_header): """Check that the given input_bam_header contains an `longbow segment` program group.""" in_bam_header_dict = input_bam_header.to_dict() if "PG" not in in_bam_header_dict: logger.warning("Could not find PG entry in header. Cannot confirm that this file is compatible.") else: found_segment_cmd = False for info in [item for item in in_bam_header_dict["PG"]]: if "PN" not in info: continue if info["PN"] == "longbow" and info["ID"].split("-")[1] == "segment": found_segment_cmd = True break if not found_segment_cmd: logger.error( "Input bam file header does not indicate that it was created by longbow segment. " "This tool requires `longbow segment` reads as input data.") return False return True def _get_start_segment_from_list(seg_list, model, read_name): """Get the start segment segment from the list of SegmentInfo objects based on the given model. If no start segment is found, returns None.""" # The start segment should be the first matching segment: for s in seg_list: if s.name in model.start_element_names: return s logger.warning("Could not process read: %s - No start segment found (start names: %s).", read_name, model.start_element_names) return None def _get_end_segment_from_list(seg_list, model, read_name): """Get the end segment segment from the list of SegmentInfo objects based on the given model. If no start segment is found, returns None.""" # The end segment should be the last matching segment, so we # iterate from the end to the start of the list: for s in reversed(seg_list): if s.name in model.end_element_names: return s logger.warning("Could not process read: %s - No end segment found (end names: %s).", read_name, model.start_element_names) return None def _sub_process_work_fn(in_queue, out_queue, umi_length, array_model, do_bam_out): """Function to run in each subprocess. Extracts and returns all segments from an input read.""" while True: # Wait until we get some data. # Note: Because we have a sentinel value None inserted at the end of the input data for each # subprocess, we don't have to add a timeout - we're guaranteed each process will always have # at least one element. raw_data = in_queue.get() # Check for exit sentinel: if raw_data is None: return # Unpack our data here: read = pysam.AlignedSegment.fromstring( raw_data, pysam.AlignmentHeader.from_dict(dict()) ) _, segments = get_segments(read) # Get start element position # (for MAS-seq it's the 10x adapter) start_segment = _get_start_segment_from_list(segments, array_model, read.query_name) if start_segment is None: continue # Get the end element position: # (for MAS-seq it's the Poly-a) end_segment = _get_end_segment_from_list(segments, array_model, read.query_name) if end_segment is None: continue # Now we grab the bases just after the 10x adapter as the UMI # and the bases between the UMI and the poly A for the transcript # Note: Positions are inclusive so we must add 1 to the end position to get that base as well: umi_start = start_segment.end+1 umi_end = umi_start + umi_length umi_bases = read.query_sequence[umi_start:umi_end] umi_quals = "".join([chr(i + 33) for i in read.query_alignment_qualities[umi_start:umi_end]]) transcript_bases = read.query_sequence[umi_end:end_segment.start] transcript_quals = "".join( [chr(i + 33) for i in read.query_alignment_qualities[umi_end:end_segment.start]] ) # Place our data on the output queue: if do_bam_out: out_queue.put( tuple([read.query_name, umi_bases, umi_quals, transcript_bases, transcript_quals, read.to_string()]) ) else: out_queue.put( tuple([read.query_name, umi_bases, umi_quals, transcript_bases, transcript_quals]) ) def _sub_process_write_fn( out_queue, out_base_name, cell_barcode, pbar, res, do_bam_out, out_bam_header ): """Thread / process fn to write out all our data.""" try: if do_bam_out: out_bam_file = pysam.AlignmentFile(f"{out_base_name}.cbc_umi_annotated.bam", "wb", header=out_bam_header) with open(f"{out_base_name}{__OUT_READ_FILE_SUFFIX}1.fastq", "w") as mates1_file, \ open(f"{out_base_name}{__OUT_READ_FILE_SUFFIX}2.fastq", "w") as mates2_file: while True: # Wait for some output data: raw_data = out_queue.get() # Check for exit sentinel: if raw_data is None: break # Unpack data: if do_bam_out: read_name, umi_bases, umi_quals, transcript_bases, transcript_quals, read_string = raw_data else: read_name, umi_bases, umi_quals, transcript_bases, transcript_quals = raw_data # Create mates1 and mates2 records: mates_1_record = pysam.FastxRecord( name=read_name, sequence=cell_barcode + umi_bases, quality=(chr(33 + 60) * len(cell_barcode)) + umi_quals ) mates_2_record = pysam.FastxRecord( name=read_name, sequence=transcript_bases, quality=transcript_quals ) # Write out mates1 and mates2 records: mates1_file.write(str(mates_1_record)) mates1_file.write("\n") mates2_file.write(str(mates_2_record)) mates2_file.write("\n") if do_bam_out: read = pysam.AlignedSegment.fromstring( read_string, pysam.AlignmentHeader.from_dict(dict()) ) read.set_tag("CR", cell_barcode) read.set_tag("UR", umi_bases) out_bam_file.write(read) # Increment our counters: res["num_reads_processed"] += 1 pbar.update(1) # Obligatory log message: logger.debug("Processed read: %s", read_name) finally: if do_bam_out: out_bam_file.close()

36.162437

123

0.629913

import logging import sys import itertools import time import click import click_log import tqdm import pysam import multiprocessing as mp from inspect import getframeinfo, currentframe, getdoc from ..utils import bam_utils from ..utils.model import LibraryModel from ..annotate.command import get_segments from ..meta import VERSION logging.basicConfig(stream=sys.stderr) logger = logging.getLogger("scsplit") click_log.basic_config(logger) __DEFAULT_DUMMY_CBC = "CTGCCTAACCTGATCC" __DEFAULT_OUT_BASE_NAME = logger.name __DEFAULT_UMI_LENGTH = 10 __OUT_READ_FILE_SUFFIX = "_mates" __OUT_WHITELIST_FILE_SUFFIX = "_whitelist.txt" @click.command(name=logger.name) @click_log.simple_verbosity_option(logger) @click.option( "-t", "--threads", type=int, default=mp.cpu_count() - 1, show_default=True, help="number of threads to use (0 for all)", ) @click.option( "-o", "--output-base-name", default=__DEFAULT_OUT_BASE_NAME, type=str, help=f"base name for output files [default: {__DEFAULT_OUT_BASE_NAME}]", ) @click.option( "-c", "--cell-barcode", default=__DEFAULT_DUMMY_CBC, type=str, help=f"dummy cell barcode to use for the dataset [default: {__DEFAULT_DUMMY_CBC}, " f"length: {len(__DEFAULT_DUMMY_CBC)}]", ) @click.option( "-u", "--umi-length", default=__DEFAULT_UMI_LENGTH, type=int, show_default=True, help=f"length of the UMI from this library", ) @click.option( "-b", "--write-bam", is_flag=True, default=False, show_default=True, help=f"Write out an annotated bam file in addition to the mates files.", ) @click.option( '--force', is_flag=True, default=False, show_default=True, help="Force scsplit to run on the input bam without checking for compatibility." ) @click.option( "-m", "--model", default="mas15", show_default=True, help="The model to use for annotation. If the given value is a pre-configured model name, then that " "model will be used. Otherwise, the given value will be treated as a file name and Longbow will attempt to " "read in the file and create a LibraryModel from it. Longbow will assume the contents are the configuration " "of a LibraryModel as per LibraryModel.to_json()." ) @click.argument("input-bam", default="-" if not sys.stdin.isatty() else None, type=click.File("rb")) def main(threads, output_base_name, cell_barcode, umi_length, force, model, write_bam, input_bam): t_start = time.time() logger.info("Invoked via: longbow %s", " ".join(sys.argv[1:])) threads = mp.cpu_count() if threads <= 0 or threads > mp.cpu_count() else threads logger.info(f"Running with {threads} worker subprocess(es)") if LibraryModel.has_prebuilt_model(model): logger.info(f"Using %s", LibraryModel.pre_configured_models[model]["description"]) model = LibraryModel.build_pre_configured_model(model) else: logger.info(f"Loading model from json file: %s", model) model = LibraryModel.from_json_file(model) manager = mp.Manager() process_input_data_queue = manager.Queue(threads) results = manager.Queue() # Start worker sub-processes: worker_process_pool = [] for _ in range(threads): p = mp.Process( target=_sub_process_work_fn, args=(process_input_data_queue, results, umi_length, model, write_bam) ) p.start() worker_process_pool.append(p) pysam.set_verbosity(0) # silence message about the .bai file not being found with pysam.AlignmentFile( input_bam, "rb", check_sq=False, require_index=False ) as bam_file, tqdm.tqdm( desc="Progress", unit=" read", colour="green", file=sys.stderr, leave=False, disable=not sys.stdin.isatty(), ) as pbar: if force: logger.info("Force mode - skipping bam header check for compatibility") else: # Make sure we're given an input bam file we can work with: if not _validate_input_bam(bam_file.header): for r in (None,) * threads: process_input_data_queue.put(r) # Wait for our input jobs to finish: for p in worker_process_pool: p.join() sys.exit(1) # Get our header from the input bam file: out_header = bam_utils.create_bam_header_with_program_group(logger.name, bam_file.header, models=[model]) # Start output worker: res = manager.dict({"num_reads_processed": 0}) output_worker = mp.Process( target=_sub_process_write_fn, args=( results, output_base_name, cell_barcode, pbar, res, write_bam, out_header ), ) output_worker.start() # Add in a sentinel value at the end of the queue - one for each subprocess - so we guarantee # that all subprocesses will exit: iter_data = itertools.chain(bam_file, (None,) * threads) for r in iter_data: if r is not None: process_input_data_queue.put(r.to_string()) else: process_input_data_queue.put(r) # Wait for our input jobs to finish: for p in worker_process_pool: p.join() # Now that our input processes are done, we can add our exit sentinel onto the output queue and # wait for that process to end: results.put(None) output_worker.join() # Write out our CBC whitelist file: with open(f"{output_base_name}{__OUT_WHITELIST_FILE_SUFFIX}", "w") as f: f.write(f"{cell_barcode}\n") logger.info(f"Processed {res['num_reads_processed']} reads.") logger.info(f"CBC length: {len(cell_barcode)}.") logger.info(f"UMI length: {umi_length}.") logger.info(f"Done. Elapsed time: %2.2fs.", time.time() - t_start) def _validate_input_bam(input_bam_header): in_bam_header_dict = input_bam_header.to_dict() if "PG" not in in_bam_header_dict: logger.warning("Could not find PG entry in header. Cannot confirm that this file is compatible.") else: found_segment_cmd = False for info in [item for item in in_bam_header_dict["PG"]]: if "PN" not in info: continue if info["PN"] == "longbow" and info["ID"].split("-")[1] == "segment": found_segment_cmd = True break if not found_segment_cmd: logger.error( "Input bam file header does not indicate that it was created by longbow segment. " "This tool requires `longbow segment` reads as input data.") return False return True def _get_start_segment_from_list(seg_list, model, read_name): # The start segment should be the first matching segment: for s in seg_list: if s.name in model.start_element_names: return s logger.warning("Could not process read: %s - No start segment found (start names: %s).", read_name, model.start_element_names) return None def _get_end_segment_from_list(seg_list, model, read_name): # The end segment should be the last matching segment, so we # iterate from the end to the start of the list: for s in reversed(seg_list): if s.name in model.end_element_names: return s logger.warning("Could not process read: %s - No end segment found (end names: %s).", read_name, model.start_element_names) return None def _sub_process_work_fn(in_queue, out_queue, umi_length, array_model, do_bam_out): while True: # Wait until we get some data. # Note: Because we have a sentinel value None inserted at the end of the input data for each # subprocess, we don't have to add a timeout - we're guaranteed each process will always have # at least one element. raw_data = in_queue.get() # Check for exit sentinel: if raw_data is None: return # Unpack our data here: read = pysam.AlignedSegment.fromstring( raw_data, pysam.AlignmentHeader.from_dict(dict()) ) _, segments = get_segments(read) # Get start element position # (for MAS-seq it's the 10x adapter) start_segment = _get_start_segment_from_list(segments, array_model, read.query_name) if start_segment is None: continue end_segment = _get_end_segment_from_list(segments, array_model, read.query_name) if end_segment is None: continue # Now we grab the bases just after the 10x adapter as the UMI # and the bases between the UMI and the poly A for the transcript # Note: Positions are inclusive so we must add 1 to the end position to get that base as well: umi_start = start_segment.end+1 umi_end = umi_start + umi_length umi_bases = read.query_sequence[umi_start:umi_end] umi_quals = "".join([chr(i + 33) for i in read.query_alignment_qualities[umi_start:umi_end]]) transcript_bases = read.query_sequence[umi_end:end_segment.start] transcript_quals = "".join( [chr(i + 33) for i in read.query_alignment_qualities[umi_end:end_segment.start]] ) # Place our data on the output queue: if do_bam_out: out_queue.put( tuple([read.query_name, umi_bases, umi_quals, transcript_bases, transcript_quals, read.to_string()]) ) else: out_queue.put( tuple([read.query_name, umi_bases, umi_quals, transcript_bases, transcript_quals]) ) def _sub_process_write_fn( out_queue, out_base_name, cell_barcode, pbar, res, do_bam_out, out_bam_header ): try: if do_bam_out: out_bam_file = pysam.AlignmentFile(f"{out_base_name}.cbc_umi_annotated.bam", "wb", header=out_bam_header) with open(f"{out_base_name}{__OUT_READ_FILE_SUFFIX}1.fastq", "w") as mates1_file, \ open(f"{out_base_name}{__OUT_READ_FILE_SUFFIX}2.fastq", "w") as mates2_file: while True: # Wait for some output data: raw_data = out_queue.get() # Check for exit sentinel: if raw_data is None: break # Unpack data: if do_bam_out: read_name, umi_bases, umi_quals, transcript_bases, transcript_quals, read_string = raw_data else: read_name, umi_bases, umi_quals, transcript_bases, transcript_quals = raw_data # Create mates1 and mates2 records: mates_1_record = pysam.FastxRecord( name=read_name, sequence=cell_barcode + umi_bases, quality=(chr(33 + 60) * len(cell_barcode)) + umi_quals ) mates_2_record = pysam.FastxRecord( name=read_name, sequence=transcript_bases, quality=transcript_quals ) # Write out mates1 and mates2 records: mates1_file.write(str(mates_1_record)) mates1_file.write("\n") mates2_file.write(str(mates_2_record)) mates2_file.write("\n") if do_bam_out: read = pysam.AlignedSegment.fromstring( read_string, pysam.AlignmentHeader.from_dict(dict()) ) read.set_tag("CR", cell_barcode) read.set_tag("UR", umi_bases) out_bam_file.write(read) # Increment our counters: res["num_reads_processed"] += 1 pbar.update(1) # Obligatory log message: logger.debug("Processed read: %s", read_name) finally: if do_bam_out: out_bam_file.close()

true

f7299dc9ddc1e115d3f2974bfd99ca7dd9ec3b12

10,844

py

Python

isign/bundle.py

chenchaozhongvip/isign

9561e3c3fc3fe9281792c60503c5a2a6235725ad

[ "Apache-2.0" ]

null

isign/bundle.py

chenchaozhongvip/isign

9561e3c3fc3fe9281792c60503c5a2a6235725ad

[ "Apache-2.0" ]

null

isign/bundle.py

chenchaozhongvip/isign

9561e3c3fc3fe9281792c60503c5a2a6235725ad

[ "Apache-2.0" ]

null

""" Represents a bundle. In the words of the Apple docs, it's a convenient way to deliver software. Really it's a particular kind of directory structure, with one main executable, well-known places for various data files and libraries, and tracking hashes of all those files for signing purposes. For isign, we have two main kinds of bundles: the App, and the Framework (a reusable library packaged along with its data files.) An App may contain many Frameworks, but a Framework has to be re-signed independently. See the Apple Developer Documentation "About Bundles" """ import biplist import code_resources from exceptions import NotMatched import copy import glob import logging import os from os.path import basename, exists, join, splitext from signer import openssl_command import signable import shutil log = logging.getLogger(__name__) def is_info_plist_native(plist): """ If an bundle is for native iOS, it has these properties in the Info.plist """ return ( 'CFBundleSupportedPlatforms' in plist and 'iPhoneOS' in plist['CFBundleSupportedPlatforms'] ) class Bundle(object): """ A bundle is a standard directory structure, a signable, installable set of files. Apps are Bundles, but so are some kinds of Frameworks (libraries) """ helpers = [] signable_class = None entitlements_path = None # Not set for every bundle type def __init__(self, path): self.path = path self.info_path = join(self.path, 'Info.plist') if not exists(self.info_path): raise NotMatched("no Info.plist found; probably not a bundle") self.info = biplist.readPlist(self.info_path) self.orig_info = None if not is_info_plist_native(self.info): # while we should probably not allow this *or* add it ourselves, it appears to work without it log.debug(u"Missing/invalid CFBundleSupportedPlatforms value in {}".format(self.info_path)) # will be added later self.seal_path = None def get_entitlements_path(self): return self.entitlements_path def get_executable_path(self): """ Path to the main executable. For an app, this is app itself. For a Framework, this is the main framework """ executable_name = None if 'CFBundleExecutable' in self.info: executable_name = self.info['CFBundleExecutable'] else: executable_name, _ = splitext(basename(self.path)) executable = join(self.path, executable_name) if not exists(executable): raise Exception( 'could not find executable for {0}'.format(self.path)) return executable def update_info_props(self, new_props): if self.orig_info is None: self.orig_info = copy.deepcopy(self.info) changed = False if ('CFBundleIdentifier' in new_props and 'CFBundleURLTypes' in self.info and 'CFBundleURLTypes' not in new_props): # The bundle identifier changed. Check CFBundleURLTypes for # CFBundleURLName values matching the old bundle # id if it's not being set explicitly old_bundle_id = self.info['CFBundleIdentifier'] new_bundle_id = new_props['CFBundleIdentifier'] for url_type in self.info['CFBundleURLTypes']: if 'CFBundleURLName' not in url_type: continue if url_type['CFBundleURLName'] == old_bundle_id: url_type['CFBundleURLName'] = new_bundle_id changed = True for key, val in new_props.iteritems(): is_new_key = key not in self.info if is_new_key or self.info[key] != val: if is_new_key: log.warn("Adding new Info.plist key: {}".format(key)) self.info[key] = val changed = True if changed: biplist.writePlist(self.info, self.info_path, binary=True) else: self.orig_info = None def info_props_changed(self): return self.orig_info is not None def info_prop_changed(self, key): if not self.orig_info: # No props have been changed return False if key in self.info and key in self.orig_info and self.info[key] == self.orig_info[key]: return False return True def get_info_prop(self, key): return self.info[key] def sign_dylibs(self, signer, path): """ Sign all the dylibs in this directory """ for dylib_path in glob.glob(join(path, '*.dylib')): dylib = signable.Dylib(self, dylib_path, signer) dylib.sign(self, signer) def sign(self, signer): """ Sign everything in this bundle, recursively with sub-bundles """ # log.debug("SIGNING: %s" % self.path) frameworks_path = join(self.path, 'Frameworks') if exists(frameworks_path): # log.debug("SIGNING FRAMEWORKS: %s" % frameworks_path) # sign all the frameworks for framework_name in os.listdir(frameworks_path): framework_path = join(frameworks_path, framework_name) # log.debug("checking for framework: %s" % framework_path) try: framework = Framework(framework_path) # log.debug("resigning: %s" % framework_path) framework.resign(signer) except NotMatched: # log.debug("not a framework: %s" % framework_path) continue # sign all the dylibs under Frameworks self.sign_dylibs(signer, frameworks_path) # sign any dylibs in the main directory (rare, but it happens) self.sign_dylibs(signer, self.path) plugins_path = join(self.path, 'PlugIns') if exists(plugins_path): # sign the appex executables appex_paths = glob.glob(join(plugins_path, '*.appex')) for appex_path in appex_paths: plist_path = join(appex_path, 'Info.plist') if not exists(plist_path): continue plist = biplist.readPlist(plist_path) appex_exec_path = join(appex_path, plist['CFBundleExecutable']) appex = signable.Appex(self, appex_exec_path, singer) appex.sign(self, signer) # then create the seal # TODO maybe the app should know what its seal path should be... self.seal_path = code_resources.make_seal(self.get_executable_path(), self.path) # then sign the app executable = self.signable_class(self, self.get_executable_path(), signer) executable.sign(self, signer) def resign(self, signer): """ signs bundle, modifies in place """ self.sign(signer) log.debug("Resigned bundle at <%s>", self.path) class Framework(Bundle): """ A bundle that comprises reusable code. Similar to an app in that it has its own resources and metadata. Not like an app because the main executable doesn't have Entitlements, or an Application hash, and it doesn't have its own provisioning profile. """ # the executable in this bundle will be a Framework signable_class = signable.Framework def __init__(self, path): super(Framework, self).__init__(path) class App(Bundle): """ The kind of bundle that is visible as an app to the user. Contains the provisioning profile, entitlements, etc. """ # the executable in this bundle will be an Executable (i.e. the main # executable of an app) signable_class = signable.Executable def __init__(self, path): super(App, self).__init__(path) self.entitlements_path = join(self.path, 'Entitlements.plist') self.provision_path = join(self.path, 'embedded.mobileprovision') def provision(self, provision_path): shutil.copyfile(provision_path, self.provision_path) @staticmethod def extract_entitlements(provision_path): """ Given a path to a provisioning profile, return the entitlements encoded therein """ cmd = [ 'smime', '-inform', 'der', '-verify', # verifies content, prints verification status to STDERR, # outputs content to STDOUT. In our case, will be an XML plist '-noverify', # accept self-signed certs. Not the opposite of -verify! '-in', provision_path ] # this command always prints 'Verification successful' to stderr. (profile_text, err) = openssl_command(cmd, data=None, expect_err=True) if err and err.strip() != 'Verification successful': log.error('Received unexpected error from openssl: {}'.format(err)) plist_dict = biplist.readPlistFromString(profile_text) if 'Entitlements' not in plist_dict: log.debug('failed to get entitlements in provisioning profile') raise Exception('could not find Entitlements in {}'.format(provision_path)) return plist_dict['Entitlements'] def write_entitlements(self, entitlements): """ Write entitlements to self.entitlements_path. This actually doesn't matter to the app, it's just used later on by other parts of the signing process. """ biplist.writePlist(entitlements, self.entitlements_path, binary=False) log.debug("wrote Entitlements to {0}".format(self.entitlements_path)) def resign(self, signer, provisioning_profile, alternate_entitlements_path=None): """ signs app in place """ # TODO all this mucking about with entitlements feels wrong. The entitlements_path is # not actually functional, it's just a way of passing it to later stages of signing. # Maybe we should determine entitlements data in isign/archive.py or even isign/isign.py, # and then embed it into Signer? # In the typical case, we add entitlements from the pprof into the app's signature if alternate_entitlements_path is None: # copy the provisioning profile in self.provision(provisioning_profile) entitlements = self.extract_entitlements(provisioning_profile) else: log.info("signing with alternative entitlements: {}".format(alternate_entitlements_path)) entitlements = biplist.readPlist(alternate_entitlements_path) self.write_entitlements(entitlements) # actually resign this bundle now super(App, self).resign(signer)

42.359375

106

0.635835

import biplist import code_resources from exceptions import NotMatched import copy import glob import logging import os from os.path import basename, exists, join, splitext from signer import openssl_command import signable import shutil log = logging.getLogger(__name__) def is_info_plist_native(plist): return ( 'CFBundleSupportedPlatforms' in plist and 'iPhoneOS' in plist['CFBundleSupportedPlatforms'] ) class Bundle(object): helpers = [] signable_class = None entitlements_path = None def __init__(self, path): self.path = path self.info_path = join(self.path, 'Info.plist') if not exists(self.info_path): raise NotMatched("no Info.plist found; probably not a bundle") self.info = biplist.readPlist(self.info_path) self.orig_info = None if not is_info_plist_native(self.info): log.debug(u"Missing/invalid CFBundleSupportedPlatforms value in {}".format(self.info_path)) self.seal_path = None def get_entitlements_path(self): return self.entitlements_path def get_executable_path(self): executable_name = None if 'CFBundleExecutable' in self.info: executable_name = self.info['CFBundleExecutable'] else: executable_name, _ = splitext(basename(self.path)) executable = join(self.path, executable_name) if not exists(executable): raise Exception( 'could not find executable for {0}'.format(self.path)) return executable def update_info_props(self, new_props): if self.orig_info is None: self.orig_info = copy.deepcopy(self.info) changed = False if ('CFBundleIdentifier' in new_props and 'CFBundleURLTypes' in self.info and 'CFBundleURLTypes' not in new_props): old_bundle_id = self.info['CFBundleIdentifier'] new_bundle_id = new_props['CFBundleIdentifier'] for url_type in self.info['CFBundleURLTypes']: if 'CFBundleURLName' not in url_type: continue if url_type['CFBundleURLName'] == old_bundle_id: url_type['CFBundleURLName'] = new_bundle_id changed = True for key, val in new_props.iteritems(): is_new_key = key not in self.info if is_new_key or self.info[key] != val: if is_new_key: log.warn("Adding new Info.plist key: {}".format(key)) self.info[key] = val changed = True if changed: biplist.writePlist(self.info, self.info_path, binary=True) else: self.orig_info = None def info_props_changed(self): return self.orig_info is not None def info_prop_changed(self, key): if not self.orig_info: # No props have been changed return False if key in self.info and key in self.orig_info and self.info[key] == self.orig_info[key]: return False return True def get_info_prop(self, key): return self.info[key] def sign_dylibs(self, signer, path): for dylib_path in glob.glob(join(path, '*.dylib')): dylib = signable.Dylib(self, dylib_path, signer) dylib.sign(self, signer) def sign(self, signer): # log.debug("SIGNING: %s" % self.path) frameworks_path = join(self.path, 'Frameworks') if exists(frameworks_path): # log.debug("SIGNING FRAMEWORKS: %s" % frameworks_path) # sign all the frameworks for framework_name in os.listdir(frameworks_path): framework_path = join(frameworks_path, framework_name) # log.debug("checking for framework: %s" % framework_path) try: framework = Framework(framework_path) # log.debug("resigning: %s" % framework_path) framework.resign(signer) except NotMatched: # log.debug("not a framework: %s" % framework_path) continue # sign all the dylibs under Frameworks self.sign_dylibs(signer, frameworks_path) # sign any dylibs in the main directory (rare, but it happens) self.sign_dylibs(signer, self.path) plugins_path = join(self.path, 'PlugIns') if exists(plugins_path): # sign the appex executables appex_paths = glob.glob(join(plugins_path, '*.appex')) for appex_path in appex_paths: plist_path = join(appex_path, 'Info.plist') if not exists(plist_path): continue plist = biplist.readPlist(plist_path) appex_exec_path = join(appex_path, plist['CFBundleExecutable']) appex = signable.Appex(self, appex_exec_path, singer) appex.sign(self, signer) # then create the seal # TODO maybe the app should know what its seal path should be... self.seal_path = code_resources.make_seal(self.get_executable_path(), self.path) # then sign the app executable = self.signable_class(self, self.get_executable_path(), signer) executable.sign(self, signer) def resign(self, signer): self.sign(signer) log.debug("Resigned bundle at <%s>", self.path) class Framework(Bundle): # the executable in this bundle will be a Framework signable_class = signable.Framework def __init__(self, path): super(Framework, self).__init__(path) class App(Bundle): # the executable in this bundle will be an Executable (i.e. the main # executable of an app) signable_class = signable.Executable def __init__(self, path): super(App, self).__init__(path) self.entitlements_path = join(self.path, 'Entitlements.plist') self.provision_path = join(self.path, 'embedded.mobileprovision') def provision(self, provision_path): shutil.copyfile(provision_path, self.provision_path) @staticmethod def extract_entitlements(provision_path): cmd = [ 'smime', '-inform', 'der', '-verify', # verifies content, prints verification status to STDERR, # outputs content to STDOUT. In our case, will be an XML plist '-noverify', # accept self-signed certs. Not the opposite of -verify! '-in', provision_path ] # this command always prints 'Verification successful' to stderr. (profile_text, err) = openssl_command(cmd, data=None, expect_err=True) if err and err.strip() != 'Verification successful': log.error('Received unexpected error from openssl: {}'.format(err)) plist_dict = biplist.readPlistFromString(profile_text) if 'Entitlements' not in plist_dict: log.debug('failed to get entitlements in provisioning profile') raise Exception('could not find Entitlements in {}'.format(provision_path)) return plist_dict['Entitlements'] def write_entitlements(self, entitlements): biplist.writePlist(entitlements, self.entitlements_path, binary=False) log.debug("wrote Entitlements to {0}".format(self.entitlements_path)) def resign(self, signer, provisioning_profile, alternate_entitlements_path=None): # TODO all this mucking about with entitlements feels wrong. The entitlements_path is # not actually functional, it's just a way of passing it to later stages of signing. if alternate_entitlements_path is None: # copy the provisioning profile in self.provision(provisioning_profile) entitlements = self.extract_entitlements(provisioning_profile) else: log.info("signing with alternative entitlements: {}".format(alternate_entitlements_path)) entitlements = biplist.readPlist(alternate_entitlements_path) self.write_entitlements(entitlements) # actually resign this bundle now super(App, self).resign(signer)

true

f729a0a1a201637f8cee6c4cdabae3ca28bf5f7c

2,036

py

Python

models/servicedefinition_tests.py

elementechemlyn/CareConnectBuilder

c004fa94c1af64d636ee25de8f13e34fe723b5f3

[ "MIT" ]

1

2021-12-24T11:14:38.000Z

models/servicedefinition_tests.py

elementechemlyn/CareConnectBuilder

c004fa94c1af64d636ee25de8f13e34fe723b5f3

[ "MIT" ]

null

models/servicedefinition_tests.py

elementechemlyn/CareConnectBuilder

c004fa94c1af64d636ee25de8f13e34fe723b5f3

[ "MIT" ]

1

2020-09-16T14:47:26.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Generated from FHIR 3.0.0.11832 on 2017-03-22. # 2017, SMART Health IT. import os import io import unittest import json from . import servicedefinition from .fhirdate import FHIRDate class ServiceDefinitionTests(unittest.TestCase): def instantiate_from(self, filename): datadir = os.environ.get('FHIR_UNITTEST_DATADIR') or '' with io.open(os.path.join(datadir, filename), 'r', encoding='utf-8') as handle: js = json.load(handle) self.assertEqual("ServiceDefinition", js["resourceType"]) return servicedefinition.ServiceDefinition(js) def testServiceDefinition1(self): inst = self.instantiate_from("servicedefinition-example.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceDefinition instance") self.implServiceDefinition1(inst) js = inst.as_json() self.assertEqual("ServiceDefinition", js["resourceType"]) inst2 = servicedefinition.ServiceDefinition(js) self.implServiceDefinition1(inst2) def implServiceDefinition1(self, inst): self.assertEqual(inst.date.date, FHIRDate("2015-07-22").date) self.assertEqual(inst.date.as_json(), "2015-07-22") self.assertEqual(inst.description, "Guideline appropriate ordering is used to assess appropriateness of an order given a patient, a proposed order, and a set of clinical indications.") self.assertEqual(inst.id, "example") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "guildeline-appropriate-ordering") self.assertEqual(inst.status, "draft") self.assertEqual(inst.text.status, "generated") self.assertEqual(inst.title, "Guideline Appropriate Ordering Module") self.assertEqual(inst.topic[0].text, "Guideline Appropriate Ordering") self.assertEqual(inst.topic[1].text, "Appropriate Use Criteria") self.assertEqual(inst.version, "1.0.0")

42.416667

192

0.698428

import os import io import unittest import json from . import servicedefinition from .fhirdate import FHIRDate class ServiceDefinitionTests(unittest.TestCase): def instantiate_from(self, filename): datadir = os.environ.get('FHIR_UNITTEST_DATADIR') or '' with io.open(os.path.join(datadir, filename), 'r', encoding='utf-8') as handle: js = json.load(handle) self.assertEqual("ServiceDefinition", js["resourceType"]) return servicedefinition.ServiceDefinition(js) def testServiceDefinition1(self): inst = self.instantiate_from("servicedefinition-example.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceDefinition instance") self.implServiceDefinition1(inst) js = inst.as_json() self.assertEqual("ServiceDefinition", js["resourceType"]) inst2 = servicedefinition.ServiceDefinition(js) self.implServiceDefinition1(inst2) def implServiceDefinition1(self, inst): self.assertEqual(inst.date.date, FHIRDate("2015-07-22").date) self.assertEqual(inst.date.as_json(), "2015-07-22") self.assertEqual(inst.description, "Guideline appropriate ordering is used to assess appropriateness of an order given a patient, a proposed order, and a set of clinical indications.") self.assertEqual(inst.id, "example") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "guildeline-appropriate-ordering") self.assertEqual(inst.status, "draft") self.assertEqual(inst.text.status, "generated") self.assertEqual(inst.title, "Guideline Appropriate Ordering Module") self.assertEqual(inst.topic[0].text, "Guideline Appropriate Ordering") self.assertEqual(inst.topic[1].text, "Appropriate Use Criteria") self.assertEqual(inst.version, "1.0.0")

true

f729a1238c99c70155e183fce2609f5d29e9072a

6,509

py

Python

src/common/receiptmanager/receiptmanager.py

catarinaacsilva/security_auction

f0b76ad47ca8cc211fd90712c2090b8e5ff934a5

[ "MIT" ]

null

src/common/receiptmanager/receiptmanager.py

catarinaacsilva/security_auction

f0b76ad47ca8cc211fd90712c2090b8e5ff934a5

[ "MIT" ]

1

2021-06-01T23:30:44.000Z

src/common/receiptmanager/receiptmanager.py

catarinaacsilva/security_auction

f0b76ad47ca8cc211fd90712c2090b8e5ff934a5

[ "MIT" ]

null

# coding: utf-8 from ..cryptmanager import * from ..utils import * from ..cartaodecidadao import CartaoDeCidadao from ..certmanager import CertManager from Crypto.Hash import SHA256 from hmac import compare_digest import hashlib import json import os import getpass import sys class ReceiptManager: def __init__(self, cc): self.cc = cc self.cc_number = str(self.cc.get_identity()[1]) self.pw = None def validate_receipt(self, receipt): repository_onion = json.dumps(receipt["ONION_2"]).encode('UTF-8') repository_onion_sig = fromBase64(receipt["SIGNATURE"]) repository_cert = CertManager.get_cert_by_name('repository.crt') manager_onion = json.dumps(receipt["ONION_2"]["ONION_1"]).encode('UTF-8') manager_onion_sig = fromBase64(receipt["ONION_2"]["SIGNATURE"]) manager_cert = CertManager.get_cert_by_name('manager.crt') client_onion = json.dumps(receipt["ONION_2"]["ONION_1"]["ONION_0"]).encode('UTF-8') client_onion_sig = fromBase64(receipt["ONION_2"]["ONION_1"]["SIGNATURE"]) client_cert = self.cc.get_certificate_raw() cm = CertManager(cert = repository_cert) valid_repo = cm.verify_signature( repository_onion_sig , repository_onion ) cm = CertManager(cert = manager_cert) valid_mana = cm.verify_signature( manager_onion_sig , manager_onion ) cm = CertManager(cert = client_cert) valid_client = cm.verify_signature( client_onion_sig , client_onion ) return valid_repo and valid_mana and valid_client def save_receipt(self, auction_id, receipt, prev_hash): ''' Save Receipt ''' # Checking for Permissions on Folder self.check_perm() # Checking existence of user dir self.check_dir() # Opening File Where Receipt Will Be Stored file = open('src/common/receiptmanager/receipts/'+self.cc_number+'/'+auction_id+'-'+prev_hash, 'wb') # Getting User Password Key pw = self.get_key() # Building HMAC for receipt hmac = SHA256.new(receipt) hmac = hmac.digest() # Encrypting receipt with key result = encrypt(pw, (hmac+receipt)) # Writting on File file.write(result) file.close() def get_receipt(self, receipt_name, pw = None): ''' Get Receipt ''' # Checking for Permissions on Folder self.check_perm() # Checking existence of user dir self.check_dir() # Checking if such receipt exists if os.path.isfile('src/common/receiptmanager/receipts/'+self.cc_number+'/'+receipt_name): # Opening receipt file file = open('src/common/receiptmanager/receipts/'+self.cc_number+'/'+receipt_name, 'rb') # Getting the key if not pw: pw = self.get_key() # Decrypting Receipt result = decrypt(pw, file.read()) file.close() # Checking integrity of the receipt if(compare_digest(result[:32], SHA256.new(result[32:]).digest())): return result[32:] else: print( colorize("ERROR: Corrupted File Or Unauthorized Access", 'red') ) input("Press any key to continue...") return None else: print( colorize("ERROR: Receipt Not Found", 'red') ) input("Press any key to continue...") return None def get_participated_auctions(self): ''' Get list of participated auctions ids ''' # Checking for Permissions on Folder self.check_perm() # Checking existence of user dir self.check_dir() auctions = [] # For Each Receipt for filename in os.listdir('src/common/receiptmanager/receipts/'+self.cc_number): # Ignore pwd file if filename.startswith('.'): continue # Add receipt to receipts list auctions.append(int(filename.split("-")[0])) return auctions def get_receipt_value(self, auction_id, hidden_value): ''' Get Receipt Value ''' # Checking for Permissions on Folder self.check_perm() # Checking existence of user dir self.check_dir() receipts = [] for filename in os.listdir('src/common/receiptmanager/receipts/'+self.cc_number): if filename.startswith(auction_id+'-'): # Opening receipt file file = open('src/common/receiptmanager/receipts/'+self.cc_number+'/'+filename, 'rb') # Getting the key pw = self.get_key() # Decrypting Receipt result = decrypt(pw, file.read()) file.close() # Checking integrity of the receipt if(compare_digest(result[:32], SHA256.new(result[32:]).digest())): receipt = json.loads(result[32:]) value = receipt["ONION_2"]["ONION_1"]["ONION_0"]["VALUE"] if hidden_value: secret = fromBase64(receipt["KEY"]) receipts.append((decrypt(secret, fromBase64(value)).decode(), receipt["ONION_2"]["PREV_HASH"])) else: receipts.append((fromBase64(value).decode(), receipt["ONION_2"]["PREV_HASH"])) else: print( colorize("ERROR: Corrupted File Or Unauthorized Access", 'red') ) input("Press any key to continue...") return receipts def get_key(self): ''' Getting new password from user ''' if not self.pw is None: return self.pw # Checking if there is a password already set if os.path.isfile("src/common/receiptmanager/receipts/"+self.cc_number+"/.pwd"): # Getting .pwd contents and sign it file = open("src/common/receiptmanager/receipts/"+self.cc_number+"/.pwd", "rb") key = self.cc.sign(file.read()) file.close() else: # Building new random for password new = os.urandom(128) file = open("src/common/receiptmanager/receipts/"+self.cc_number+"/.pwd", "wb") file.write(new) file.close() key = self.cc.sign(new) self.pw = self.password_builder(key, self.cc.get_public_key()[10:26]) # Return Hashing Of Password return self.pw def password_builder(self, password, salt): ''' Hashing of Password ''' password_hash = hashlib.pbkdf2_hmac('sha256', password, salt, 1000, 16) return password_hash def check_dir(self): ''' Check if DIR exists, if it doesn't, create a new one ''' if os.path.isdir("src/common/receiptmanager/receipts/"+self.cc_number): return else: os.mkdir("src/common/receiptmanager/receipts/"+self.cc_number) def check_perm(self): ''' Checks read and write permissions ''' while(not os.access('src/common/receiptmanager/receipts', os.R_OK)): print( colorize("I have no READ permissions, please allow READ permissions at src/common/receiptmanager/receipts", 'red') ) input("Press any key to try again...") clean(lines = 2) while(not os.access('src/common/receiptmanager/receipts', os.W_OK)): print( colorize("I have no WRITE permissions, please allow WRITE permissions at src/common/receiptmanager/receipts", 'red') ) input("Press any key to try again...") clean(lines = 2)

31.75122

128

0.703027

from ..cryptmanager import * from ..utils import * from ..cartaodecidadao import CartaoDeCidadao from ..certmanager import CertManager from Crypto.Hash import SHA256 from hmac import compare_digest import hashlib import json import os import getpass import sys class ReceiptManager: def __init__(self, cc): self.cc = cc self.cc_number = str(self.cc.get_identity()[1]) self.pw = None def validate_receipt(self, receipt): repository_onion = json.dumps(receipt["ONION_2"]).encode('UTF-8') repository_onion_sig = fromBase64(receipt["SIGNATURE"]) repository_cert = CertManager.get_cert_by_name('repository.crt') manager_onion = json.dumps(receipt["ONION_2"]["ONION_1"]).encode('UTF-8') manager_onion_sig = fromBase64(receipt["ONION_2"]["SIGNATURE"]) manager_cert = CertManager.get_cert_by_name('manager.crt') client_onion = json.dumps(receipt["ONION_2"]["ONION_1"]["ONION_0"]).encode('UTF-8') client_onion_sig = fromBase64(receipt["ONION_2"]["ONION_1"]["SIGNATURE"]) client_cert = self.cc.get_certificate_raw() cm = CertManager(cert = repository_cert) valid_repo = cm.verify_signature( repository_onion_sig , repository_onion ) cm = CertManager(cert = manager_cert) valid_mana = cm.verify_signature( manager_onion_sig , manager_onion ) cm = CertManager(cert = client_cert) valid_client = cm.verify_signature( client_onion_sig , client_onion ) return valid_repo and valid_mana and valid_client def save_receipt(self, auction_id, receipt, prev_hash): self.check_perm() self.check_dir() file = open('src/common/receiptmanager/receipts/'+self.cc_number+'/'+auction_id+'-'+prev_hash, 'wb') pw = self.get_key() hmac = SHA256.new(receipt) hmac = hmac.digest() result = encrypt(pw, (hmac+receipt)) file.write(result) file.close() def get_receipt(self, receipt_name, pw = None): self.check_perm() self.check_dir() if os.path.isfile('src/common/receiptmanager/receipts/'+self.cc_number+'/'+receipt_name): file = open('src/common/receiptmanager/receipts/'+self.cc_number+'/'+receipt_name, 'rb') if not pw: pw = self.get_key() result = decrypt(pw, file.read()) file.close() if(compare_digest(result[:32], SHA256.new(result[32:]).digest())): return result[32:] else: print( colorize("ERROR: Corrupted File Or Unauthorized Access", 'red') ) input("Press any key to continue...") return None else: print( colorize("ERROR: Receipt Not Found", 'red') ) input("Press any key to continue...") return None def get_participated_auctions(self): self.check_perm() self.check_dir() auctions = [] for filename in os.listdir('src/common/receiptmanager/receipts/'+self.cc_number): if filename.startswith('.'): continue auctions.append(int(filename.split("-")[0])) return auctions def get_receipt_value(self, auction_id, hidden_value): self.check_perm() self.check_dir() receipts = [] for filename in os.listdir('src/common/receiptmanager/receipts/'+self.cc_number): if filename.startswith(auction_id+'-'): file = open('src/common/receiptmanager/receipts/'+self.cc_number+'/'+filename, 'rb') pw = self.get_key() result = decrypt(pw, file.read()) file.close() if(compare_digest(result[:32], SHA256.new(result[32:]).digest())): receipt = json.loads(result[32:]) value = receipt["ONION_2"]["ONION_1"]["ONION_0"]["VALUE"] if hidden_value: secret = fromBase64(receipt["KEY"]) receipts.append((decrypt(secret, fromBase64(value)).decode(), receipt["ONION_2"]["PREV_HASH"])) else: receipts.append((fromBase64(value).decode(), receipt["ONION_2"]["PREV_HASH"])) else: print( colorize("ERROR: Corrupted File Or Unauthorized Access", 'red') ) input("Press any key to continue...") return receipts def get_key(self): if not self.pw is None: return self.pw if os.path.isfile("src/common/receiptmanager/receipts/"+self.cc_number+"/.pwd"): file = open("src/common/receiptmanager/receipts/"+self.cc_number+"/.pwd", "rb") key = self.cc.sign(file.read()) file.close() else: new = os.urandom(128) file = open("src/common/receiptmanager/receipts/"+self.cc_number+"/.pwd", "wb") file.write(new) file.close() key = self.cc.sign(new) self.pw = self.password_builder(key, self.cc.get_public_key()[10:26]) return self.pw def password_builder(self, password, salt): password_hash = hashlib.pbkdf2_hmac('sha256', password, salt, 1000, 16) return password_hash def check_dir(self): if os.path.isdir("src/common/receiptmanager/receipts/"+self.cc_number): return else: os.mkdir("src/common/receiptmanager/receipts/"+self.cc_number) def check_perm(self): while(not os.access('src/common/receiptmanager/receipts', os.R_OK)): print( colorize("I have no READ permissions, please allow READ permissions at src/common/receiptmanager/receipts", 'red') ) input("Press any key to try again...") clean(lines = 2) while(not os.access('src/common/receiptmanager/receipts', os.W_OK)): print( colorize("I have no WRITE permissions, please allow WRITE permissions at src/common/receiptmanager/receipts", 'red') ) input("Press any key to try again...") clean(lines = 2)

true

f729a24a66e616326b404dc1b3f9fa74a4985595

245

py

Python

happybase/hbase/constants.py

BeeGroup-cimne/happybase

2dacd3045baaaf39c6328b5172eef1dc302ea307

[ "MIT" ]

null

happybase/hbase/constants.py

BeeGroup-cimne/happybase

2dacd3045baaaf39c6328b5172eef1dc302ea307

[ "MIT" ]

null

happybase/hbase/constants.py

BeeGroup-cimne/happybase

2dacd3045baaaf39c6328b5172eef1dc302ea307

[ "MIT" ]

null

# # Autogenerated by Thrift Compiler (0.9.1) # # DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING # # options string: py # from thrift.Thrift import TType, TMessageType, TException, TApplicationException from .ttypes import *

20.416667

80

0.75102

from thrift.Thrift import TType, TMessageType, TException, TApplicationException from .ttypes import *

true

f729a2d781b6d90d31a6e8106f80366f8380d7fc

1,962

py

Python

tests/test.py

alexd-conf/coinmarketcap-scraper

06c60f7c2deba14d876e812d6a94c30f3a8091cb

[ "MIT" ]

null

tests/test.py

alexd-conf/coinmarketcap-scraper

06c60f7c2deba14d876e812d6a94c30f3a8091cb

[ "MIT" ]

null

tests/test.py

alexd-conf/coinmarketcap-scraper

06c60f7c2deba14d876e812d6a94c30f3a8091cb

[ "MIT" ]

null

import unittest from unittest.mock import MagicMock from scraper.scraper import get_table_with_data, row_not_loaded, \ reload_table_rows, get_coin_name, get_coin_symbol, \ get_coin_price, get_coin_change24h, get_coin_change7d, \ get_coin_market_cap, get_coin_volume24h, get_coin_circulating_supply class TestStringMethods(unittest.TestCase): def test_get_table_with_data_raises_error(self): self.assertRaises(AttributeError, get_table_with_data, "") def test_row_not_loaded_true(self): def has_attr(arg): return True row_mock = MagicMock() row_mock.has_attr = has_attr self.assertTrue(row_not_loaded(row_mock)) def test_row_not_loaded_false(self): def has_attr(arg): return False row_mock = MagicMock() row_mock.has_attr = has_attr self.assertFalse(row_not_loaded(row_mock)) def test_reload_table_rows_raises_error(self): driver_mock = MagicMock(page_source="") self.assertRaises(AttributeError, reload_table_rows, driver_mock) def test_get_coin_name_result_none(self): self.assertIsNone(get_coin_name([])) def test_get_coin_symbol_result_none(self): self.assertIsNone(get_coin_symbol([])) def test_get_coin_price_result_none(self): self.assertIsNone(get_coin_price([])) def test_get_coin_change24h_result_none(self): self.assertIsNone(get_coin_change24h([])) def test_get_coin_change7d_result_none(self): self.assertIsNone(get_coin_change7d([])) def test_get_coin_market_cap_result_none(self): self.assertIsNone(get_coin_market_cap([])) def test_get_coin_volume24h_result_none(self): self.assertIsNone(get_coin_volume24h([])) def test_get_coin_circulating_supply_result_none(self): self.assertIsNone(get_coin_circulating_supply([]))

34.421053

96

0.70897

import unittest from unittest.mock import MagicMock from scraper.scraper import get_table_with_data, row_not_loaded, \ reload_table_rows, get_coin_name, get_coin_symbol, \ get_coin_price, get_coin_change24h, get_coin_change7d, \ get_coin_market_cap, get_coin_volume24h, get_coin_circulating_supply class TestStringMethods(unittest.TestCase): def test_get_table_with_data_raises_error(self): self.assertRaises(AttributeError, get_table_with_data, "") def test_row_not_loaded_true(self): def has_attr(arg): return True row_mock = MagicMock() row_mock.has_attr = has_attr self.assertTrue(row_not_loaded(row_mock)) def test_row_not_loaded_false(self): def has_attr(arg): return False row_mock = MagicMock() row_mock.has_attr = has_attr self.assertFalse(row_not_loaded(row_mock)) def test_reload_table_rows_raises_error(self): driver_mock = MagicMock(page_source="") self.assertRaises(AttributeError, reload_table_rows, driver_mock) def test_get_coin_name_result_none(self): self.assertIsNone(get_coin_name([])) def test_get_coin_symbol_result_none(self): self.assertIsNone(get_coin_symbol([])) def test_get_coin_price_result_none(self): self.assertIsNone(get_coin_price([])) def test_get_coin_change24h_result_none(self): self.assertIsNone(get_coin_change24h([])) def test_get_coin_change7d_result_none(self): self.assertIsNone(get_coin_change7d([])) def test_get_coin_market_cap_result_none(self): self.assertIsNone(get_coin_market_cap([])) def test_get_coin_volume24h_result_none(self): self.assertIsNone(get_coin_volume24h([])) def test_get_coin_circulating_supply_result_none(self): self.assertIsNone(get_coin_circulating_supply([]))

true

f729a2d9f63ddeb8e9182a8ef1ee556c40834496

78

py

Python

orionxapi/__init__.py

tomymacmillan/orionx-api-client

7a9f0dc8b86ec451c8482451eba356c5d840c66e

[ "MIT" ]

19

2017-12-27T17:23:08.000Z

2021-08-02T01:13:10.000Z

orionxapi/__init__.py

tomymacmillan/orionx-api-client

7a9f0dc8b86ec451c8482451eba356c5d840c66e

[ "MIT" ]

12

2018-01-02T22:36:56.000Z

2018-07-23T15:52:23.000Z

orionxapi/__init__.py

tomymacmillan/orionx-api-client

7a9f0dc8b86ec451c8482451eba356c5d840c66e

[ "MIT" ]

9

2017-12-27T08:10:46.000Z

2021-05-12T17:03:35.000Z

from .client import client, as_completed __all__ = ['client', 'as_completed']

26

40

0.75641

from .client import client, as_completed __all__ = ['client', 'as_completed']

true

f729a3f83be9c31ba99f040897298ef106279325

1,944

py

Python

src/modules/indel_primer/main.py

AndersenLab/CAENDR

ce4cdb74db736db8226ffc90988959b71b0d5ff5

[ "MIT" ]

3

2022-02-09T07:04:37.000Z

2022-03-11T02:46:35.000Z

src/modules/indel_primer/main.py

AndersenLab/CAENDR

ce4cdb74db736db8226ffc90988959b71b0d5ff5

[ "MIT" ]

4

2022-01-28T22:28:08.000Z

2022-02-11T21:47:15.000Z

src/modules/indel_primer/main.py

AndersenLab/CAENDR

ce4cdb74db736db8226ffc90988959b71b0d5ff5

[ "MIT" ]

1

2022-01-11T03:39:02.000Z

import os import sys from subprocess import Popen, PIPE, STDOUT from logzero import logger from dotenv import load_dotenv from caendr.utils import monitor from caendr.models.error import EnvVarError from caendr.services.cloud.storage import upload_blob_from_file dotenv_file = '.env' load_dotenv(dotenv_file) monitor.init_sentry("indel_primer") MODULE_SITE_BUCKET_PRIVATE_NAME = os.environ.get('MODULE_SITE_BUCKET_PRIVATE_NAME') INDEL_SITE = os.environ.get('INDEL_SITE') INDEL_STRAIN_1 = os.environ.get('INDEL_STRAIN_1') INDEL_STRAIN_2 = os.environ.get('INDEL_STRAIN_2') RESULT_BUCKET = os.environ.get('RESULT_BUCKET') RESULT_BLOB = os.environ.get('RESULT_BLOB') WORMBASE_VERSION = os.environ.get('WORMBASE_VERSION') INDEL_VCF_VERSION = os.environ.get('INDEL_VCF_VERSION') logger.info(f'Indel Primer: INDEL_SITE:{INDEL_SITE} INDEL_STRAIN_1:{INDEL_STRAIN_1} INDEL_STRAIN_2:{INDEL_STRAIN_2} WORMBASE_VERSION:{WORMBASE_VERSION} INDEL_VCF_VERSION:{INDEL_VCF_VERSION} RESULT_BUCKET:{RESULT_BUCKET} RESULT_BLOB:{RESULT_BLOB}') if not INDEL_SITE or not INDEL_STRAIN_1 or not INDEL_STRAIN_2 or not RESULT_BLOB or not RESULT_BUCKET or not WORMBASE_VERSION or not INDEL_VCF_VERSION: raise EnvVarError() cmd = ('conda', 'run', '-n', 'indel-primer', 'vk', 'primer', 'indel', '--region', INDEL_SITE, '--nprimers', '10', '--polymorphic', '--ref', WORMBASE_VERSION, '--samples', f'{INDEL_STRAIN_1},{INDEL_STRAIN_2}', INDEL_VCF_VERSION) with Popen(cmd, stdout=PIPE, stderr=PIPE, bufsize=1) as p, open('results.tsv', 'ab') as file: for line in p.stdout: # b'\n'-separated lines logger.info(line) # pass bytes as is file.write(line) for line in p.stderr: # b'\n'-separated lines logger.error(sys.stdout.buffer.write(line)) # pass bytes as is upload_blob_from_file(RESULT_BUCKET, 'results.tsv', RESULT_BLOB)

31.354839

247

0.727366

import os import sys from subprocess import Popen, PIPE, STDOUT from logzero import logger from dotenv import load_dotenv from caendr.utils import monitor from caendr.models.error import EnvVarError from caendr.services.cloud.storage import upload_blob_from_file dotenv_file = '.env' load_dotenv(dotenv_file) monitor.init_sentry("indel_primer") MODULE_SITE_BUCKET_PRIVATE_NAME = os.environ.get('MODULE_SITE_BUCKET_PRIVATE_NAME') INDEL_SITE = os.environ.get('INDEL_SITE') INDEL_STRAIN_1 = os.environ.get('INDEL_STRAIN_1') INDEL_STRAIN_2 = os.environ.get('INDEL_STRAIN_2') RESULT_BUCKET = os.environ.get('RESULT_BUCKET') RESULT_BLOB = os.environ.get('RESULT_BLOB') WORMBASE_VERSION = os.environ.get('WORMBASE_VERSION') INDEL_VCF_VERSION = os.environ.get('INDEL_VCF_VERSION') logger.info(f'Indel Primer: INDEL_SITE:{INDEL_SITE} INDEL_STRAIN_1:{INDEL_STRAIN_1} INDEL_STRAIN_2:{INDEL_STRAIN_2} WORMBASE_VERSION:{WORMBASE_VERSION} INDEL_VCF_VERSION:{INDEL_VCF_VERSION} RESULT_BUCKET:{RESULT_BUCKET} RESULT_BLOB:{RESULT_BLOB}') if not INDEL_SITE or not INDEL_STRAIN_1 or not INDEL_STRAIN_2 or not RESULT_BLOB or not RESULT_BUCKET or not WORMBASE_VERSION or not INDEL_VCF_VERSION: raise EnvVarError() cmd = ('conda', 'run', '-n', 'indel-primer', 'vk', 'primer', 'indel', '--region', INDEL_SITE, '--nprimers', '10', '--polymorphic', '--ref', WORMBASE_VERSION, '--samples', f'{INDEL_STRAIN_1},{INDEL_STRAIN_2}', INDEL_VCF_VERSION) with Popen(cmd, stdout=PIPE, stderr=PIPE, bufsize=1) as p, open('results.tsv', 'ab') as file: for line in p.stdout: logger.info(line) file.write(line) for line in p.stderr: logger.error(sys.stdout.buffer.write(line)) upload_blob_from_file(RESULT_BUCKET, 'results.tsv', RESULT_BLOB)

true

f729a4340682dd093b68dbd33d955ab4cb402955

800

py

Python

corehq/apps/domain/management/commands/fill_last_modified_date.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

471

2015-01-10T02:55:01.000Z

2022-03-29T18:07:18.000Z

corehq/apps/domain/management/commands/fill_last_modified_date.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

14,354

2015-01-01T07:38:23.000Z

2022-03-31T20:55:14.000Z

corehq/apps/domain/management/commands/fill_last_modified_date.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

175

2015-01-06T07:16:47.000Z

2022-03-29T13:27:01.000Z

from django.core.management.base import BaseCommand from dimagi.utils.couch.database import iter_docs from corehq.apps.domain.models import Domain class Command(BaseCommand): def _get_domains_without_last_modified_date(self): docs = iter_docs(Domain.get_db(), [ domain['id'] for domain in Domain.view( "domain/domains", reduce=False, include_docs=False ) ]) return [x for x in docs if 'last_modified' not in x or not x['last_modified']] def handle(self, **options): for domain_doc in self._get_domains_without_last_modified_date(): print("Updating domain {}".format(domain_doc['name'])) domain = Domain.wrap(domain_doc) domain.save()

30.769231

86

0.625

from django.core.management.base import BaseCommand from dimagi.utils.couch.database import iter_docs from corehq.apps.domain.models import Domain class Command(BaseCommand): def _get_domains_without_last_modified_date(self): docs = iter_docs(Domain.get_db(), [ domain['id'] for domain in Domain.view( "domain/domains", reduce=False, include_docs=False ) ]) return [x for x in docs if 'last_modified' not in x or not x['last_modified']] def handle(self, **options): for domain_doc in self._get_domains_without_last_modified_date(): print("Updating domain {}".format(domain_doc['name'])) domain = Domain.wrap(domain_doc) domain.save()

true

f729a4ab9057a7ce46593c5c5e7bee598b7c3f0f

32,947

py

Python

src/module/DeepFM.py

uchida-takumi/recommender_system_verification

a079e0c8764926e5dc66da01a809c6ba4fde7fb7

[ "MIT" ]

null

src/module/DeepFM.py

uchida-takumi/recommender_system_verification

a079e0c8764926e5dc66da01a809c6ba4fde7fb7

[ "MIT" ]

null

src/module/DeepFM.py

uchida-takumi/recommender_system_verification

a079e0c8764926e5dc66da01a809c6ba4fde7fb7

[ "MIT" ]

null

""" # install the package pip install deepctr # tutorial https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr # github https://github.com/shenweichen/DeepCTR しかし、これは binary しか出来ないので適応不可能。 binary を無理矢理適応させるばあいは、非クリックデータを何らかの方法で生成する必要がある。 # ---- 次のアイデア ---- # github https://github.com/ChenglongChen/tensorflow-DeepFM """ import tensorflow as tf import os import pickle import pandas as pd import numpy as np import copy from sklearn.metrics import roc_auc_score from sklearn.metrics import mean_absolute_error from src.module.tensorflow_DeepFM.DeepFM import DeepFM as DeepFM_ # インターフェース class DeepFM: def __init__(self, set_train_test_users, set_train_test_items, dict_genre=None, first_half_fit_only_fm=False, ctr_prediction=True): """ import pandas as pd DIR_DATA = 'src/module/knowledge_graph_attention_network/Data/ml' df_train = pd.read_csv(os.path.join(DIR_DATA, 'train_rating.csv')) df_test = pd.read_csv(os.path.join(DIR_DATA, 'test_rating.csv')) set_train_test_users = set(np.concatenate([df_train['UserID'], df_test['UserID']])) set_train_test_items = set(np.concatenate([df_train['MovieID'], df_test['MovieID']])) dict_genre = pickle.load(open(os.path.join(DIR_DATA, 'genre.pickle'), 'rb')) self = DeepFM(set_train_test_users, set_train_test_items, dict_genre) self.dfm_params['epoch'] = 10 self.dfm_params['batch_size'] = 64 users = df_train['UserID'].values items = df_train['UserID'].values ratings = df_train['Rating'].values self.fit(users, items, ratings) predicted = self.predict(df_test['UserID'].values, df_test['UserID'].values) # MAE of test-set print( np.mean(np.abs(predicted - df_test['Rating'])) ) # MAE of mean-prediction print( np.mean(np.abs(df_test['Rating'].mean() - df_test['Rating'])) ) ## まぁ、実際のテストをクリアできればOKとする。 """ """ 参考として、Movielens1Mデータで検証されたハイパーパラメータは以下の通り Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems k(hidden-factor) = 8, γ(learning-rate) = 0.01, λ(regularization) = 0.045 K = [9, 3, 3]; Γ= [0.01, 0.01, 0.01]; Λ = [0.1, 0.01, 0.1] """ self.set_train_test_users = set(set_train_test_users) self.set_train_test_items = set(set_train_test_items) self.dict_genre = dict_genre self.first_half_fit_only_fm = first_half_fit_only_fm self.data_manager = Data_manager(set_train_test_users, set_train_test_items, dict_genre) feature_size, field_size = self.data_manager.get_feature_size_field_size() self.dfm_params = { "feature_size" : feature_size, "field_size" : field_size, "loss_type" : "mse", # "logloss" なら {0,1} の判別問題。 "mse" なら　regression。 "use_fm": True, # fm-layer を使用 "use_deep": True, # deep-layer を使用 "embedding_size": 8, "dropout_fm": [1.0, 1.0], "deep_layers": [32, 32], "dropout_deep": [0.5, 0.5, 0.5], "deep_layers_activation": tf.nn.relu, "epoch": 30, "batch_size": 64, "learning_rate": 0.001, "optimizer_type": "adam", "batch_norm": 1, "batch_norm_decay": 0.995, "l2_reg": 0.0001, "l2_reg_embedding": 0.0001, "l2_reg_bias": 0.0001, "verbose": True, "eval_metric": mean_absolute_error, "greater_is_better": False, # 学習における損失スコアが大きい方が良いかどうか "random_seed": 2017, } self.ctr_prediction = ctr_prediction if self.ctr_prediction: self.dfm_params["loss_type"] = "logloss" def fit(self, users, items, ratings, test_users=[], test_items=[], test_ratings=[], **kargs): """ users = [0,0,1] items = [0,3,3] ratings = [3.,4.,5.] """ global_mean_bias_init = np.float32(np.mean(ratings)) global_mean_bias_init = 0.01 self.model = DeepFM_(**self.dfm_params, global_mean_bias_init=global_mean_bias_init, first_half_fit_only_fm=self.first_half_fit_only_fm) # もし、CTR予測の場合は、y=0のデータをランダム生成する。 if self.ctr_prediction: users = list(users) + list(np.random.choice(list(set(users)), size=len(users))) items = list(items) + list(np.random.choice(list(set(items)), size=len(items))) ratings = list((np.array(ratings)>0).astype(int)) + [0]*len(ratings) test_ratings = list((np.array(test_ratings)>0).astype(int)) Xi, Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(users, items) if len(test_users)>0: test_Xi, test_Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(test_users, test_items) self.model.fit(Xi, Xv, ratings, test_Xi, test_Xv, test_ratings, early_stopping=True) else: self.model.fit(Xi, Xv, ratings, early_stopping=True, **kargs) # load data self.trained_users = list(set(users)) self.trained_items = list(set(items)) self.global_mean = self.model.predict(Xi, Xv).mean() def predict(self, users, items, *args, **kargs): Xi, Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(users, items) predicted = self.model.predict(Xi, Xv) return predicted # prepare training and validation data in the required format class Data_manager: def __init__(self, users, items, dict_genre=None): """ users [array like object]: train, test set に含まれる user_id items [array like object]: train, test set に含まれる item_id dict_genre [dictionary]: ex) {item_id: [genre_id1, genre_id2]} tensorflow_DeepFM/example 内部のプログラム、特にDataReader.pyを読み、データの形式を解読した。結論として、 item, user, genre の各IDは以下のように変換すればよい。 1) user = {0,1,2} → {0,1,2} *未変更 2) item = {0,1} → {3,4} *userからのインクリメントID 3) genre = {0,1} → {5,6} *itemからのインクリメントID 4) a interaction-sample [u,i,g] = [0,1,0]→[0,4,5] 5) Xi_train (X-index trainset) = [変換した[u,i,g]1, 変換した[u,i,g]2, ...] 6) Xv_train (X-value trainset) = [[1.,1.,1.], [1.,1.,1.], ...] user,item,genre はカテゴリ変数なのですべて1.となる。 7) y_train = [rating-score1, rating-score2, ...] *変換不要 EXAMPLE ------------- import pandas as pd df_rating = pd.read_csv(os.path.join(DIR_DATA, 'train_rating.csv')) dict_genre = pickle.load(open(os.path.join(DIR_DATA, 'genre.pickle'), 'rb')) users = df_rating['UserID'] items = df_rating['MovieID'] self = Data_manager(users, items, dict_genre=dict_genre) """ self.dict_genre = dict_genre # インクリメントインデックスを生成するオブジェクト self.inclement_index を生成する。 if dict_genre: dict_genre = {i:gs for i,gs in dict_genre.items() if i in items} n_genre = max([max(gs) for i,gs in dict_genre.items() if gs]) + 1 genres = list(range(n_genre)) else: dict_genre = {} n_genre = 0 genres = [] self.inclement_index = inclement_index(users, items, genres) # userとitemをインクリメントIDに変更する dict_genre = {self.inclement_index.transform([i], field='item')[0]:gs for i,gs in dict_genre.items()} # user, itemはそれぞれで2フィールド、ジャンルはジャンルラベルごとに別々のフィールドにわける。 self.re_dict_genre = {} for i,gs in dict_genre.items(): # re_dict は　{item_id:(field_id, genru_id)}となる。 genre_one_hot_vec = [0] * n_genre for g in gs: genre_one_hot_vec[g] = 1 # カテゴリ変数はかならず整数の1とする。 self.re_dict_genre[i] = genre_one_hot_vec self.genre_indexes = self.inclement_index.transform(genres, field='genre') self.feature_size = self.inclement_index.get_feature_size() self.field_size = 2 + n_genre def get_feature_size_field_size(self): return self.feature_size, self.field_size def transform_users_and_items_to_Xi_Xv(self, users, items): """ users = [0,0,1] items = [1,5,5] """ Xi, Xv = [], [] users = self.inclement_index.transform(users, field='user') items = self.inclement_index.transform(items, field='item') for u,i in zip(users, items): if self.dict_genre: Xi.append([u, i] + self.genre_indexes) Xv.append([1, 1] + self.re_dict_genre[i]) else: Xi.append([u, i]) Xv.append([1, 1]) return Xi, Xv class inclement_index: def __init__(self, users, items, genres=[]): """ users = ['u0','u1',3] items = ['i0', 3] genres = ['pop', 'sf'] self = inclement_index(users, items, genres) self.transform(['u0', 'u1', 3], field='user', inverse=False) self.transform(['i0', 3], field='item', inverse=False) self.transform(['pop', 'sf'], field='genre', inverse=False) transformed = self.transform(['u0', 'u1', 3], field='user', inverse=False) self.transform(transformed, field='user', inverse=True) """ users = set(users) items = set(items) genres = set(genres) self.increment_cnt = 0 self.user_dict = {u:self.get_incremate_index() for u in users} self.user_inverse_dict = {v:k for k,v in self.user_dict.items()} self.item_dict = {i:self.get_incremate_index() for i in items} self.item_inverse_dict = {v:k for k,v in self.item_dict.items()} self.genre_dict = {g:self.get_incremate_index() for g in genres} self.genre_inverse_dict = {v:k for k,v in self.genre_dict.items()} def transform(self, xs, field='user', inverse=False): """ xs = [0,2] self.transform(xs, type='user') """ if inverse: if field == 'user': _dict = self.user_inverse_dict elif field == 'item': _dict = self.item_inverse_dict elif field == 'genre': _dict = self.genre_inverse_dict else: if field == 'user': _dict = self.user_dict elif field == 'item': _dict = self.item_dict elif field == 'genre': _dict = self.genre_dict return [_dict[x] for x in xs] def get_incremate_index(self): now_index = copy.deepcopy(self.increment_cnt) self.increment_cnt += 1 return now_index def get_feature_size(self): return self.increment_cnt if __name__ == 'how to use it.': ########################### # --- かなりシンプルなテスト --- sample_size = 1000 users = np.random.choice(range(100), size=sample_size) items = np.random.choice(range(100), size=sample_size) genre_dict = None ratings = users - items self = DeepFM(set(users), set(items)) self.dfm_params['batch_size'] = 64 self.dfm_params['epoch'] = 100 self.fit(users, items, ratings) self.predict([10, 5, 10], [10, 10, 2]) # 正解は [0, -5, 8] である # 十分に小さなbatch_sizeかどうかは非常に重要 # これは学習テストのロス減少によって確認できる。 ########################### # --- シンプルなテスト1 --- sample_size = 1000 n_user = 500 n_item = 20 users = np.random.choice(range(n_user), size=sample_size) items = np.random.choice(range(n_item), size=sample_size) user_embedding = {u:np.random.rand(5)-0.5 for u in range(n_user)} item_embedding = {i:np.random.rand(5)-0.5 for i in range(n_item)} def rating(u, i): return 10*sum(user_embedding[u] * item_embedding[i]) + 3 ratings = [rating(u, i) for u,i in zip(users, items)] self = DeepFM(list(range(n_user)), list(range(n_item))) self.dfm_params['epoch'] = 100 self.dfm_params['embedding_size'] = 200 self.dfm_params['l2_reg'] = 0.0045 self.fit(users, items, ratings) test_users = np.random.choice(range(n_user), size=sample_size) test_items = np.random.choice(range(n_item), size=sample_size) test_ratings = [rating(u, i) for u,i in zip(users, items)] predicted = self.predict(test_users, test_items) print( np.mean(np.abs(test_ratings - predicted)) ) print( np.mean(np.abs(test_ratings - np.mean(ratings))) ) # scaler を導入すると改善されるか？　→　特に改善はされていない。 from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit([[r] for r in ratings]) s_ratings = scaler.transform([[r] for r in ratings])[:,0] self.fit(users, items, s_ratings) predicted = self.predict(test_users, test_items) predicted = scaler.inverse_transform(predicted[:,None]) print( np.mean(np.abs(test_ratings - predicted)) ) print( np.mean(np.abs(test_ratings - np.mean(ratings))) ) ########################### # --- シンプルなテスト2　bias とembedding あり --- sample_size = 1000 n_user = 500 n_item = 20 users = np.random.choice(range(n_user), size=sample_size) items = np.random.choice(range(n_item), size=sample_size) user_embedding = {u:np.random.rand(5)-0.5 for u in range(n_user)} item_embedding = {i:np.random.rand(5)-0.5 for i in range(n_item)} user_bias = {u:u/10 for u in range(n_user)} # 単純にidが大きいほどバイアスが大きい item_bias = {i:i for i in range(n_item)} # 単純にidが大きいほどバイアスが大きい def rating(u, i): return 10*sum(user_embedding[u] * item_embedding[i]) + user_bias[u] + item_bias[i] ratings = [rating(u, i) for u,i in zip(users, items)] test_users = np.random.choice(range(n_user), size=sample_size) test_items = np.random.choice(range(n_item), size=sample_size) test_ratings = [rating(u, i) for u,i in zip(users, items)] self = DeepFM(list(range(n_user)), list(range(n_item))) self.dfm_params['epoch'] = 100 self.dfm_params['embedding_size'] = 200 self.fit(users, items, ratings, test_users, test_items, test_ratings) # 平均性能との比較 predicted = self.predict(test_users, test_items) print( np.mean(np.abs(test_ratings - predicted)) ) print( np.mean(np.abs(test_ratings - np.mean(ratings))) ) # オラクルとの比較 predicted = self.predict([200]*n_item, list(range(n_item))) answer = [rating(200,i) for i in range(n_item)] print(predicted) print(answer) print(predicted - answer) ## 内部の embedding を確認する。 feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) ########################### # --- シンプルなテスト3　head-tail-new ID --- sample_size = 1000 n_user = 200 n_item = 50 ## id が後半になるほど学習セット中の出現率が低くなる。 p_user = 1/np.array(range(1, n_user+1)); p_user /= p_user.sum() p_item = 1/np.array(range(1, n_item+1)); p_item /= p_item.sum() users = np.random.choice(range(n_user), size=sample_size, p=p_user) items = np.random.choice(range(n_item), size=sample_size, p=p_item) user_embedding = {u:np.random.rand(5)-0.5 for u in range(n_user)} item_embedding = {i:np.random.rand(5)-0.5 for i in range(n_item)} user_bias = {u:u/10 for u in range(n_user)} # 単純にidが大きいほどバイアスが大きい item_bias = {i:i for i in range(n_item)} # 単純にidが大きいほどバイアスが大きい def rating(u, i): return 10*sum(user_embedding[u] * item_embedding[i]) + user_bias[u] + item_bias[i] ratings = [rating(u, i) for u,i in zip(users, items)] ## user=500 と item=20 はそれぞれ新規IDとなる test_users = np.random.choice(range(n_user), size=sample_size) test_items = np.random.choice(range(n_item), size=sample_size) test_ratings = [rating(u, i) for u,i in zip(users, items)] self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 300 self.dfm_params['embedding_size'] = 4 self.fit(users, items, ratings, test_users, test_items, test_ratings) # 平均値予測との比較 predicted = self.predict(test_users, test_items) print( np.mean(np.abs(test_ratings - predicted)) ) print( np.mean(np.abs(test_ratings - np.mean(ratings))) ) ## 内部の embedding を確認する。 feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) ## 可視化する(ID=500 まではユーザーで、それ以降はアイテム) import pandas as pd # [正常] 一部のembeddingがIDの増加に合わせて線形に変化している。これらはバイアス効果を一部学習している。 pd.DataFrame(feature_embeddings).plot() # [成功] DeepFM のバイアスの初期値を0付近にすることで、userのバイアスはオラクルに近くなった。 # [?] itemのバイアスはオラクルと逆にidが増加するほど減少している → おそらくembeddingがバイアスを学習してしまったゆえか？ pd.DataFrame(feature_bias).plot() # 新規IDを確認する → ほぼ、初期値の0付近か？ ## 新規ユーザー feature_embeddings[200] feature_bias[200] ## 新規アイテム feature_embeddings[-1] feature_bias[-1] ############################################## # --- IDとは無関係なランダムなバイアスで学習してみる --- sample_size = 1000 n_user = 200 n_item = 50 ## id が後半になるほど学習セット中の出現率が低くなる。 p_user = 1/np.array(range(1, n_user+1)); p_user /= p_user.sum() p_item = 1/np.array(range(1, n_item+1)); p_item /= p_item.sum() users = np.random.choice(range(n_user), size=sample_size, p=p_user) items = np.random.choice(range(n_item), size=sample_size, p=p_item) user_bias = {u:np.random.rand() for u in range(n_user)} item_bias = {i:np.random.rand() for i in range(n_item)} user_embedding = {u:np.random.rand(5)-0.5 for u in range(n_user)} item_embedding = {i:np.random.rand(5)-0.5 for i in range(n_item)} def rating(u, i): return 3*(sum(user_embedding[u] * item_embedding[i]) + user_bias[u] + item_bias[i]) ratings = [rating(u, i) for u,i in zip(users, items)] ## user=500 と item=20 はそれぞれ新規IDとなる test_users = np.random.choice(range(n_user), size=sample_size) test_items = np.random.choice(range(n_item), size=sample_size) test_ratings = [rating(u, i) for u,i in zip(users, items)] # ------------------------------ ############################################## self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 100 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.001 self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) """ デバック self.predict([1]*n_item, range(n_item)) self.predict([0]*n_item, range(n_item)) [rating(1, i) for i in range(n_item)] """ pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() """ 本テストは想定どおりの結果となり、成功したといえる。その成功要因は、以下の変更を加えたことによる。 [1] 各id の embedding, bias の初期値を0付近のものに変更した。 [2] l2_reg の対象として　embedding, bias を追加した。（おそらく、マイナーIDのweightが抑制されると思われるが、詳細は不明） """ # --- パラメータごとの影響を確認する。 self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 10 self.dfm_params['embedding_size'] = 4 self.dfm_params['use_deep'] = False self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 10 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.001 self.dfm_params['learning_rate'] = 0.001 self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 10 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.100 self.dfm_params['learning_rate'] = 0.001 self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 10 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.001 self.dfm_params['learning_rate'] = 0.010 self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=True) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.010 self.dfm_params['learning_rate'] = 0.010 self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() # --- only fm self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=True) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.010 self.dfm_params['learning_rate'] = 0.010 self.dfm_params['use_deep'] = False self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() # ---- high l2-reg self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=True) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.100 self.dfm_params['learning_rate'] = 0.010 self.dfm_params['use_deep'] = False self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() # ---- high learning_rate self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=False) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.0100 self.dfm_params['l2_reg_embedding'] = 0.0100 self.dfm_params['l2_reg_bias'] = 0.0100 self.dfm_params['learning_rate'] = 0.0100 self.dfm_params['use_deep'] = False self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() ## 結論、頻度の違いがバイアスに影響を与えることはない。 # ---- high learning_rate self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=False) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.0100 #self.dfm_params['l2_reg_embedding'] = 0.0100 #self.dfm_params['l2_reg_bias'] = 0.0100 self.dfm_params['learning_rate'] = 0.0020 self.dfm_params['use_deep'] = False self.dfm_params['batch_size'] = 32 self.dfm_params['loss_type'] = 'mse' self.dfm_params['optimizer_type'] = 'sgd' #self.dfm_params['optimizer_type'] = 'adam' self.fit(users, items, ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self.predict([0,0,150,150],[0,10,0,10]) ########################## # MovieLensのCTR問題として定義し直して、性能を比較する import numpy as np ctr_users = list(users) + list(np.random.choice(list(set(users)), size=len(users))) ctr_items = list(items) + list(np.random.choice(list(set(items)), size=len(items))) ctrs = [1]*len(users) + [0]*len(users) self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=True) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.0010 #self.dfm_params['l2_reg_embedding'] = 0.0020 #self.dfm_params['l2_reg_bias'] = 0.0020 self.dfm_params['learning_rate'] = 0.00010 #self.dfm_params['use_deep'] = False self.dfm_params['batch_size'] = 16 self.dfm_params['loss_type'] = 'logloss' self.dfm_params['greater_is_better'] = True self.fit(ctr_users, ctr_items, ctrs) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self.predict([0,0,150,150],[0,10,0,10]) ######################## # CTR 対応型のテスト self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=False, ctr_prediction=True) self.dfm_params['epoch'] = 30 self.dfm_params['embedding_size'] = 4 self.dfm_params['batch_size'] = 32 self.dfm_params['dropout_fm'] = [0.5, 0.5] self.dfm_params['l2_reg'] = 0.0 self.dfm_params['l2_reg_embedding'] = 0.0 self.dfm_params['l2_reg_bias'] = 0.0 self.fit(users, items, ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() pd.DataFrame(self.predict([200]*50,list(range(50)))).plot() # 新規ユーザーだけ常に一定になる。 self.predict([0,0,150,150],[0,10,0,10]) self.predict([50]*50,list(range(50))) self.predict([100]*50,list(range(50))) self.predict([150]*50,list(range(50))) self.predict([200]*50,list(range(50))) # 新規ユーザーだけ常に一定になる。 self.predict([199]*50,list(range(50))) # 新規ユーザーだけ常に一定になる。 self.predict([198]*50,list(range(50))) # 新規ユーザーだけ常に一定になる。 self.predict([197]*50,list(range(50))) # 新規ユーザーだけ常に一定になる。 self.predict(list(range(200)),[50]*200) # 新規ユーザーだけ常に一定になる。 feature_embeddings[200] feature_bias[200] feature_embeddings[150] feature_bias[150] feature_embeddings[220] feature_embeddings[222] feature_embeddings[223] ######################## # tensorflow の動作テスト weight = tf.Variable(initial_value=[[0,1,2,3], [0,10,20,30], [0,100,200,300]], trainable=True, name='test', dtype=tf.float32) sess = tf.Session() sess.run(tf.global_variables_initializer()) sess.run(weight) op = weight[1,3].assign(9999.) sess.run(op) sess.run(weight) ######################## # 上手く行かなかったので、テスト # 実際のデータで確認する ############################################## # --- 疑似データの生成 --- sample_size = 10000 n_user = 2000 n_item = 500 ## id が後半になるほど学習セット中の出現率が低くなる。 p_user = 1/np.array(range(1, n_user+1)); p_user /= p_user.sum() p_item = 1/np.array(range(1, n_item+1)); p_item /= p_item.sum() users = np.random.choice(range(n_user), size=sample_size, p=p_user) items = np.random.choice(range(n_item), size=sample_size, p=p_item) user_bias = {u:np.random.rand() for u in range(n_user)} item_bias = {i:np.random.rand() for i in range(n_item)} user_embedding = {u:np.random.rand(5)-0.5 for u in range(n_user)} item_embedding = {i:np.random.rand(5)-0.5 for i in range(n_item)} def rating(u, i): return 3*(sum(user_embedding[u] * item_embedding[i]) + user_bias[u] + item_bias[i]) ratings = [rating(u, i) for u,i in zip(users, items)] ## user=500 と item=20 はそれぞれ新規IDとなる test_users = np.random.choice(range(n_user), size=sample_size) test_items = np.random.choice(range(n_item), size=sample_size) test_ratings = [rating(u, i) for u,i in zip(users, items)] # ------------------------------ ############################################## for i in range(5): self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=False, ctr_prediction=False) self.dfm_params['epoch'] = 10 self.dfm_params['embedding_size'] = 4 self.dfm_params['deep_layers'] = [16, 16] self.dfm_params['l2_reg'] = 0.0100 #0.0040 self.dfm_params['l2_reg_embedding'] = 0.0000 #0.001 self.dfm_params['l2_reg_bias'] = 0.000 #0.001 self.dfm_params['learning_rate'] = 0.00100 #0.001 self.dfm_params['use_deep'] = False self.dfm_params['batch_size'] = 128 self.dfm_params['loss_type'] = 'mse' #self.dfm_params['optimizer_type'] = 'sgd' self.fit(users, items, ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) concat_projection = self.model.sess.run(self.model.weights["concat_projection"]) # [0,1]がuser,itemのbiasに対する重み #pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() pd.DataFrame(concat_projection).plot() #pd.DataFrame(self.predict([200]*50,list(range(50)))).plot() # 新規ユーザーだけ常に一定になる。 df_result = pd.DataFrame() df_result['u=10'] = self.predict([10]*n_item,list(range(n_item))) df_result['u=100'] = self.predict([100]*n_item,list(range(n_item))) df_result['u=1000'] = self.predict([1000]*n_item,list(range(n_item))) df_result['u=2000'] = self.predict([2000]*n_item,list(range(n_item))) df_result.plot() """ Best setting? self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=False, ctr_prediction=False) self.dfm_params['epoch'] = 10 self.dfm_params['embedding_size'] = 4 self.dfm_params['deep_layers'] = [16, 16] self.dfm_params['l2_reg'] = 0.04 #0.0040 self.dfm_params['l2_reg_embedding'] = 0.001 #0.001 self.dfm_params['l2_reg_bias'] = 0.001 #0.001 self.dfm_params['learning_rate'] = 0.0010 #0.001 self.dfm_params['use_deep'] = True self.dfm_params['batch_size'] = 64 self.dfm_params['loss_type'] = 'mse' #self.dfm_params['optimizer_type'] = 'sgd' self.fit(users, items, ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) #pd.DataFrame(feature_embeddings).plot() #pd.DataFrame(feature_bias).plot() pd.DataFrame(self.predict([200]*50,list(range(50)))).plot() # 新規ユーザーだけ常に一定になる。 """

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144

0.63241

import tensorflow as tf import os import pickle import pandas as pd import numpy as np import copy from sklearn.metrics import roc_auc_score from sklearn.metrics import mean_absolute_error from src.module.tensorflow_DeepFM.DeepFM import DeepFM as DeepFM_ class DeepFM: def __init__(self, set_train_test_users, set_train_test_items, dict_genre=None, first_half_fit_only_fm=False, ctr_prediction=True): self.set_train_test_users = set(set_train_test_users) self.set_train_test_items = set(set_train_test_items) self.dict_genre = dict_genre self.first_half_fit_only_fm = first_half_fit_only_fm self.data_manager = Data_manager(set_train_test_users, set_train_test_items, dict_genre) feature_size, field_size = self.data_manager.get_feature_size_field_size() self.dfm_params = { "feature_size" : feature_size, "field_size" : field_size, "loss_type" : "mse", "use_fm": True, "use_deep": True, "embedding_size": 8, "dropout_fm": [1.0, 1.0], "deep_layers": [32, 32], "dropout_deep": [0.5, 0.5, 0.5], "deep_layers_activation": tf.nn.relu, "epoch": 30, "batch_size": 64, "learning_rate": 0.001, "optimizer_type": "adam", "batch_norm": 1, "batch_norm_decay": 0.995, "l2_reg": 0.0001, "l2_reg_embedding": 0.0001, "l2_reg_bias": 0.0001, "verbose": True, "eval_metric": mean_absolute_error, "greater_is_better": False, "random_seed": 2017, } self.ctr_prediction = ctr_prediction if self.ctr_prediction: self.dfm_params["loss_type"] = "logloss" def fit(self, users, items, ratings, test_users=[], test_items=[], test_ratings=[], **kargs): global_mean_bias_init = np.float32(np.mean(ratings)) global_mean_bias_init = 0.01 self.model = DeepFM_(**self.dfm_params, global_mean_bias_init=global_mean_bias_init, first_half_fit_only_fm=self.first_half_fit_only_fm) if self.ctr_prediction: users = list(users) + list(np.random.choice(list(set(users)), size=len(users))) items = list(items) + list(np.random.choice(list(set(items)), size=len(items))) ratings = list((np.array(ratings)>0).astype(int)) + [0]*len(ratings) test_ratings = list((np.array(test_ratings)>0).astype(int)) Xi, Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(users, items) if len(test_users)>0: test_Xi, test_Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(test_users, test_items) self.model.fit(Xi, Xv, ratings, test_Xi, test_Xv, test_ratings, early_stopping=True) else: self.model.fit(Xi, Xv, ratings, early_stopping=True, **kargs) self.trained_users = list(set(users)) self.trained_items = list(set(items)) self.global_mean = self.model.predict(Xi, Xv).mean() def predict(self, users, items, *args, **kargs): Xi, Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(users, items) predicted = self.model.predict(Xi, Xv) return predicted class Data_manager: def __init__(self, users, items, dict_genre=None): self.dict_genre = dict_genre if dict_genre: dict_genre = {i:gs for i,gs in dict_genre.items() if i in items} n_genre = max([max(gs) for i,gs in dict_genre.items() if gs]) + 1 genres = list(range(n_genre)) else: dict_genre = {} n_genre = 0 genres = [] self.inclement_index = inclement_index(users, items, genres) dict_genre = {self.inclement_index.transform([i], field='item')[0]:gs for i,gs in dict_genre.items()} self.re_dict_genre = {} for i,gs in dict_genre.items(): genre_one_hot_vec = [0] * n_genre for g in gs: genre_one_hot_vec[g] = 1 self.re_dict_genre[i] = genre_one_hot_vec self.genre_indexes = self.inclement_index.transform(genres, field='genre') self.feature_size = self.inclement_index.get_feature_size() self.field_size = 2 + n_genre def get_feature_size_field_size(self): return self.feature_size, self.field_size def transform_users_and_items_to_Xi_Xv(self, users, items): Xi, Xv = [], [] users = self.inclement_index.transform(users, field='user') items = self.inclement_index.transform(items, field='item') for u,i in zip(users, items): if self.dict_genre: Xi.append([u, i] + self.genre_indexes) Xv.append([1, 1] + self.re_dict_genre[i]) else: Xi.append([u, i]) Xv.append([1, 1]) return Xi, Xv class inclement_index: def __init__(self, users, items, genres=[]): users = set(users) items = set(items) genres = set(genres) self.increment_cnt = 0 self.user_dict = {u:self.get_incremate_index() for u in users} self.user_inverse_dict = {v:k for k,v in self.user_dict.items()} self.item_dict = {i:self.get_incremate_index() for i in items} self.item_inverse_dict = {v:k for k,v in self.item_dict.items()} self.genre_dict = {g:self.get_incremate_index() for g in genres} self.genre_inverse_dict = {v:k for k,v in self.genre_dict.items()} def transform(self, xs, field='user', inverse=False): if inverse: if field == 'user': _dict = self.user_inverse_dict elif field == 'item': _dict = self.item_inverse_dict elif field == 'genre': _dict = self.genre_inverse_dict else: if field == 'user': _dict = self.user_dict elif field == 'item': _dict = self.item_dict elif field == 'genre': _dict = self.genre_dict return [_dict[x] for x in xs] def get_incremate_index(self): now_index = copy.deepcopy(self.increment_cnt) self.increment_cnt += 1 return now_index def get_feature_size(self): return self.increment_cnt if __name__ == 'how to use it.': ings) self.predict([10, 5, 10], [10, 10, 2]) ng(u, i): return 10*sum(user_embedding[u] * item_embedding[i]) + 3 ratings = [rating(u, i) for u,i in zip(users, items)] self = DeepFM(list(range(n_user)), list(range(n_item))) self.dfm_params['epoch'] = 100 self.dfm_params['embedding_size'] = 200 self.dfm_params['l2_reg'] = 0.0045 self.fit(users, items, ratings) test_users = np.random.choice(range(n_user), size=sample_size) test_items = np.random.choice(range(n_item), size=sample_size) test_ratings = [rating(u, i) for u,i in zip(users, items)] predicted = self.predict(test_users, test_items) print( np.mean(np.abs(test_ratings - predicted)) ) print( np.mean(np.abs(test_ratings - np.mean(ratings))) ) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit([[r] for r in ratings]) s_ratings = scaler.transform([[r] for r in ratings])[:,0] self.fit(users, items, s_ratings) predicted = self.predict(test_users, test_items) predicted = scaler.inverse_transform(predicted[:,None]) print( np.mean(np.abs(test_ratings - predicted)) ) print( np.mean(np.abs(test_ratings - np.mean(ratings))) ) u:u/10 for u in range(n_user)} item_bias = {i:i for i in range(n_item)} def rating(u, i): return 10*sum(user_embedding[u] * item_embedding[i]) + user_bias[u] + item_bias[i] ratings = [rating(u, i) for u,i in zip(users, items)] test_users = np.random.choice(range(n_user), size=sample_size) test_items = np.random.choice(range(n_item), size=sample_size) test_ratings = [rating(u, i) for u,i in zip(users, items)] self = DeepFM(list(range(n_user)), list(range(n_item))) self.dfm_params['epoch'] = 100 self.dfm_params['embedding_size'] = 200 self.fit(users, items, ratings, test_users, test_items, test_ratings) predicted = self.predict(test_users, test_items) print( np.mean(np.abs(test_ratings - predicted)) ) print( np.mean(np.abs(test_ratings - np.mean(ratings))) ) predicted = self.predict([200]*n_item, list(range(n_item))) answer = [rating(200,i) for i in range(n_item)] print(predicted) print(answer) print(predicted - answer) s = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) ndom.rand(5)-0.5 for u in range(n_user)} item_embedding = {i:np.random.rand(5)-0.5 for i in range(n_item)} user_bias = {u:u/10 for u in range(n_user)} item_bias = {i:i for i in range(n_item)} def rating(u, i): return 10*sum(user_embedding[u] * item_embedding[i]) + user_bias[u] + item_bias[i] ratings = [rating(u, i) for u,i in zip(users, items)] e(range(n_user), size=sample_size) test_items = np.random.choice(range(n_item), size=sample_size) test_ratings = [rating(u, i) for u,i in zip(users, items)] self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 300 self.dfm_params['embedding_size'] = 4 self.fit(users, items, ratings, test_users, test_items, test_ratings) predicted = self.predict(test_users, test_items) print( np.mean(np.abs(test_ratings - predicted)) ) print( np.mean(np.abs(test_ratings - np.mean(ratings))) ) s = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() ture_embeddings[200] feature_bias[200] ture_embeddings[-1] feature_bias[-1] pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 10 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.100 self.dfm_params['learning_rate'] = 0.001 self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1))) self.dfm_params['epoch'] = 10 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.001 self.dfm_params['learning_rate'] = 0.010 self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=True) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.010 self.dfm_params['learning_rate'] = 0.010 self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=True) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.010 self.dfm_params['learning_rate'] = 0.010 self.dfm_params['use_deep'] = False self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=True) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.100 self.dfm_params['learning_rate'] = 0.010 self.dfm_params['use_deep'] = False self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self = DeepFM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=False) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.0100 self.dfm_params['l2_reg_embedding'] = 0.0100 self.dfm_params['l2_reg_bias'] = 0.0100 self.dfm_params['learning_rate'] = 0.0100 self.dfm_params['use_deep'] = False self.fit(users, items, ratings, test_users, test_items, test_ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() FM(list(range(n_user+1)), list(range(n_item+1)), first_half_fit_only_fm=False) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.0100 self.dfm_params['learning_rate'] = 0.0020 self.dfm_params['use_deep'] = False self.dfm_params['batch_size'] = 32 self.dfm_params['loss_type'] = 'mse' self.dfm_params['optimizer_type'] = 'sgd' self.fit(users, items, ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self.predict([0,0,150,150],[0,10,0,10]) rst_half_fit_only_fm=True) self.dfm_params['epoch'] = 20 self.dfm_params['embedding_size'] = 4 self.dfm_params['l2_reg'] = 0.0010 self.dfm_params['learning_rate'] = 0.00010 self.dfm_params['batch_size'] = 16 self.dfm_params['loss_type'] = 'logloss' self.dfm_params['greater_is_better'] = True self.fit(ctr_users, ctr_items, ctrs) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() self.predict([0,0,150,150],[0,10,0,10]) , 0.5] self.dfm_params['l2_reg'] = 0.0 self.dfm_params['l2_reg_embedding'] = 0.0 self.dfm_params['l2_reg_bias'] = 0.0 self.fit(users, items, ratings) feature_embeddings = self.model.sess.run(self.model.weights["feature_embeddings"]) feature_bias = self.model.sess.run(self.model.weights["feature_bias"]) concat_bias = self.model.sess.run(self.model.weights["concat_bias"]) pd.DataFrame(feature_embeddings).plot() pd.DataFrame(feature_bias).plot() pd.DataFrame(self.predict([200]*50,list(range(50)))).plot() self.predict([0,0,150,150],[0,10,0,10]) self.predict([50]*50,list(range(50))) self.predict([100]*50,list(range(50))) self.predict([150]*50,list(range(50))) self.predict([200]*50,list(range(50))) self.predict([199]*50,list(range(50))) self.predict([198]*50,list(range(50))) self.predict([197]*50,list(range(50))) self.predict(list(range(200)),[50]*200) feature_embeddings[200] feature_bias[200] feature_embeddings[150] feature_bias[150] feature_embeddings[220] feature_embeddings[222] feature_embeddings[223] (op) sess.run(weight)

true

f729a5f7dadfdd5df237186e52fbdba21151b043

1,185

py

Python

scripts/quest/q20894s.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

54

2019-04-16T23:24:48.000Z

2021-12-18T11:41:50.000Z

scripts/quest/q20894s.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

3

2019-05-19T15:19:41.000Z

2020-04-27T16:29:16.000Z

scripts/quest/q20894s.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

49

2020-11-25T23:29:16.000Z

2022-03-26T16:20:24.000Z

# 20893 - [Job Adv] (Lv.100) The Empress' Chief Knight sm.setSpeakerID(1101000) sm.sendNext("#h #... what is this?") sm.setPlayerAsSpeaker() sm.sendNext("This, milady, is the report from Neinheart about the activities of the Cygnus Knights.") sm.setSpeakerID(1101000) sm.sendNext("Haha, is that wat Neinheart said? It is a recommendation about you. It's all about the process of you getting stronger and the activities done by you...") sm.setPlayerAsSpeaker() sm.sendNext("What did Neinheart write about me?") sm.setSpeakerID(1101000) if sm.sendAskYesNo("I would like to appoint a title to you for your activities and effort. will you accept this?"): sm.sendSay("#h #, with your braveness and courage, from now on you are a new captain of the knights. Please use your power to protect the Maple World.") if sm.canHold(1142069): chrJobID = sm.getChr().getJob() sm.jobAdvance(chrJobID+1) sm.giveItem(1142069) sm.addAP(3) sm.completeQuest(parentID) else: sm.sendSay("Please make space in your Equip inventory.") sm.dispose() else: sm.sendSay("Please speak to me again when you change your mind.") sm.dispose()

49.375

167

0.709705

sm.setSpeakerID(1101000) sm.sendNext("#h #... what is this?") sm.setPlayerAsSpeaker() sm.sendNext("This, milady, is the report from Neinheart about the activities of the Cygnus Knights.") sm.setSpeakerID(1101000) sm.sendNext("Haha, is that wat Neinheart said? It is a recommendation about you. It's all about the process of you getting stronger and the activities done by you...") sm.setPlayerAsSpeaker() sm.sendNext("What did Neinheart write about me?") sm.setSpeakerID(1101000) if sm.sendAskYesNo("I would like to appoint a title to you for your activities and effort. will you accept this?"): sm.sendSay("#h #, with your braveness and courage, from now on you are a new captain of the knights. Please use your power to protect the Maple World.") if sm.canHold(1142069): chrJobID = sm.getChr().getJob() sm.jobAdvance(chrJobID+1) sm.giveItem(1142069) sm.addAP(3) sm.completeQuest(parentID) else: sm.sendSay("Please make space in your Equip inventory.") sm.dispose() else: sm.sendSay("Please speak to me again when you change your mind.") sm.dispose()

true

f729a757c28a8effadf260089ab2ae44c11f245e

2,029

py

Python

esphome/components/esp32/gpio_esp32_s3.py

OttoWinter/esphomeyaml

6a85259e4d6d1b0a0f819688b8e555efcb99ecb0

[ "MIT" ]

249

2018-04-07T12:04:11.000Z

2019-01-25T01:11:34.000Z

esphome/components/esp32/gpio_esp32_s3.py

OttoWinter/esphomeyaml

6a85259e4d6d1b0a0f819688b8e555efcb99ecb0

[ "MIT" ]

243

2018-04-11T16:37:11.000Z

2019-01-25T16:50:37.000Z

esphome/components/esp32/gpio_esp32_s3.py

OttoWinter/esphomeyaml

6a85259e4d6d1b0a0f819688b8e555efcb99ecb0

[ "MIT" ]

40

2018-04-10T05:50:14.000Z

2019-01-25T15:20:36.000Z

import logging from esphome.const import ( CONF_INPUT, CONF_MODE, CONF_NUMBER, ) import esphome.config_validation as cv _ESP_32S3_SPI_PSRAM_PINS = { 26: "SPICS1", 27: "SPIHD", 28: "SPIWP", 29: "SPICS0", 30: "SPICLK", 31: "SPIQ", 32: "SPID", } _ESP_32_ESP32_S3R8_PSRAM_PINS = { 33: "SPIIO4", 34: "SPIIO5", 35: "SPIIO6", 36: "SPIIO7", 37: "SPIDQS", } _ESP_32S3_STRAPPING_PINS = {0, 3, 45, 46} _LOGGER = logging.getLogger(__name__) def esp32_s3_validate_gpio_pin(value): if value < 0 or value > 48: raise cv.Invalid(f"Invalid pin number: {value} (must be 0-46)") if value in _ESP_32S3_SPI_PSRAM_PINS: raise cv.Invalid( f"This pin cannot be used on ESP32-S3s and is already used by the SPI/PSRAM interface(function: {_ESP_32S3_SPI_PSRAM_PINS[value]})" ) if value in _ESP_32_ESP32_S3R8_PSRAM_PINS: _LOGGER.warning( "GPIO%d is used by the PSRAM interface on ESP32-S3R8 / ESP32-S3R8V and should be avoided on these models", value, ) if value in _ESP_32S3_STRAPPING_PINS: _LOGGER.warning( "GPIO%d is a Strapping PIN and should be avoided.\n" "Attaching external pullup/down resistors to strapping pins can cause unexpected failures.\n" "See https://esphome.io/guides/faq.html#why-am-i-getting-a-warning-about-strapping-pins", value, ) if value in (22, 23, 24, 25): # These pins are not exposed in GPIO mux (reason unknown) # but they're missing from IO_MUX list in datasheet raise cv.Invalid(f"The pin GPIO{value} is not usable on ESP32-S3s.") return value def esp32_s3_validate_supports(value): num = value[CONF_NUMBER] mode = value[CONF_MODE] is_input = mode[CONF_INPUT] if num < 0 or num > 48: raise cv.Invalid(f"Invalid pin number: {num} (must be 0-46)") if is_input: # All ESP32 pins support input mode pass return value

27.053333

143

0.637753

import logging from esphome.const import ( CONF_INPUT, CONF_MODE, CONF_NUMBER, ) import esphome.config_validation as cv _ESP_32S3_SPI_PSRAM_PINS = { 26: "SPICS1", 27: "SPIHD", 28: "SPIWP", 29: "SPICS0", 30: "SPICLK", 31: "SPIQ", 32: "SPID", } _ESP_32_ESP32_S3R8_PSRAM_PINS = { 33: "SPIIO4", 34: "SPIIO5", 35: "SPIIO6", 36: "SPIIO7", 37: "SPIDQS", } _ESP_32S3_STRAPPING_PINS = {0, 3, 45, 46} _LOGGER = logging.getLogger(__name__) def esp32_s3_validate_gpio_pin(value): if value < 0 or value > 48: raise cv.Invalid(f"Invalid pin number: {value} (must be 0-46)") if value in _ESP_32S3_SPI_PSRAM_PINS: raise cv.Invalid( f"This pin cannot be used on ESP32-S3s and is already used by the SPI/PSRAM interface(function: {_ESP_32S3_SPI_PSRAM_PINS[value]})" ) if value in _ESP_32_ESP32_S3R8_PSRAM_PINS: _LOGGER.warning( "GPIO%d is used by the PSRAM interface on ESP32-S3R8 / ESP32-S3R8V and should be avoided on these models", value, ) if value in _ESP_32S3_STRAPPING_PINS: _LOGGER.warning( "GPIO%d is a Strapping PIN and should be avoided.\n" "Attaching external pullup/down resistors to strapping pins can cause unexpected failures.\n" "See https://esphome.io/guides/faq.html#why-am-i-getting-a-warning-about-strapping-pins", value, ) if value in (22, 23, 24, 25): raise cv.Invalid(f"The pin GPIO{value} is not usable on ESP32-S3s.") return value def esp32_s3_validate_supports(value): num = value[CONF_NUMBER] mode = value[CONF_MODE] is_input = mode[CONF_INPUT] if num < 0 or num > 48: raise cv.Invalid(f"Invalid pin number: {num} (must be 0-46)") if is_input: # All ESP32 pins support input mode pass return value

true

f729a7d37d4edca3f86ab33a43e1680548398202

810

py

Python

setup.py

ghuvrons/Jati

b4d056cf38d4770f3bef0f3db93c4b982f4e3da0

[ "MIT" ]

null

setup.py

ghuvrons/Jati

b4d056cf38d4770f3bef0f3db93c4b982f4e3da0

[ "MIT" ]

null

setup.py

ghuvrons/Jati

b4d056cf38d4770f3bef0f3db93c4b982f4e3da0

[ "MIT" ]

null

import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="Jati", version="0.0.2", author="Janoko", author_email="janoko@sandhika.com", description="Jati merupakan modul python untuk restAPI dan websocket", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/ghuvrons/Jati", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], entry_points = { 'console_scripts': ['jati=Jati.CLI:main'], }, python_requires='>=3.7', install_requires=[ "click>=5.1", "PyMySQL==1.0.2" ] )

26.129032

74

0.62963

import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="Jati", version="0.0.2", author="Janoko", author_email="janoko@sandhika.com", description="Jati merupakan modul python untuk restAPI dan websocket", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/ghuvrons/Jati", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], entry_points = { 'console_scripts': ['jati=Jati.CLI:main'], }, python_requires='>=3.7', install_requires=[ "click>=5.1", "PyMySQL==1.0.2" ] )

true

f729a80b6998f64cb1816f1f898fafce7f1fc291

6,848

py

Python

kubernetes/client/models/v1_endpoints_list.py

pllsxyc/python

442ebc019056c2dc246be94f85cf61f1e1d26a88

[ "Apache-2.0" ]

1

2019-10-07T13:54:36.000Z

kubernetes/client/models/v1_endpoints_list.py

pllsxyc/python

442ebc019056c2dc246be94f85cf61f1e1d26a88

[ "Apache-2.0" ]

8

2020-12-21T03:18:50.000Z

2022-03-02T03:06:30.000Z

kubernetes/client/models/v1_endpoints_list.py

pllsxyc/python

442ebc019056c2dc246be94f85cf61f1e1d26a88

[ "Apache-2.0" ]

1

2021-03-16T16:05:33.000Z

# coding: utf-8 """ Kubernetes No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: release-1.16 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from kubernetes.client.configuration import Configuration class V1EndpointsList(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'api_version': 'str', 'items': 'list[V1Endpoints]', 'kind': 'str', 'metadata': 'V1ListMeta' } attribute_map = { 'api_version': 'apiVersion', 'items': 'items', 'kind': 'kind', 'metadata': 'metadata' } def __init__(self, api_version=None, items=None, kind=None, metadata=None, local_vars_configuration=None): # noqa: E501 """V1EndpointsList - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._api_version = None self._items = None self._kind = None self._metadata = None self.discriminator = None if api_version is not None: self.api_version = api_version self.items = items if kind is not None: self.kind = kind if metadata is not None: self.metadata = metadata @property def api_version(self): """Gets the api_version of this V1EndpointsList. # noqa: E501 APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources # noqa: E501 :return: The api_version of this V1EndpointsList. # noqa: E501 :rtype: str """ return self._api_version @api_version.setter def api_version(self, api_version): """Sets the api_version of this V1EndpointsList. APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources # noqa: E501 :param api_version: The api_version of this V1EndpointsList. # noqa: E501 :type: str """ self._api_version = api_version @property def items(self): """Gets the items of this V1EndpointsList. # noqa: E501 List of endpoints. # noqa: E501 :return: The items of this V1EndpointsList. # noqa: E501 :rtype: list[V1Endpoints] """ return self._items @items.setter def items(self, items): """Sets the items of this V1EndpointsList. List of endpoints. # noqa: E501 :param items: The items of this V1EndpointsList. # noqa: E501 :type: list[V1Endpoints] """ if self.local_vars_configuration.client_side_validation and items is None: # noqa: E501 raise ValueError("Invalid value for `items`, must not be `None`") # noqa: E501 self._items = items @property def kind(self): """Gets the kind of this V1EndpointsList. # noqa: E501 Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds # noqa: E501 :return: The kind of this V1EndpointsList. # noqa: E501 :rtype: str """ return self._kind @kind.setter def kind(self, kind): """Sets the kind of this V1EndpointsList. Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds # noqa: E501 :param kind: The kind of this V1EndpointsList. # noqa: E501 :type: str """ self._kind = kind @property def metadata(self): """Gets the metadata of this V1EndpointsList. # noqa: E501 :return: The metadata of this V1EndpointsList. # noqa: E501 :rtype: V1ListMeta """ return self._metadata @metadata.setter def metadata(self, metadata): """Sets the metadata of this V1EndpointsList. :param metadata: The metadata of this V1EndpointsList. # noqa: E501 :type: V1ListMeta """ self._metadata = metadata def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, V1EndpointsList): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, V1EndpointsList): return True return self.to_dict() != other.to_dict()

33.242718

312

0.622079

import pprint import re import six from kubernetes.client.configuration import Configuration class V1EndpointsList(object): openapi_types = { 'api_version': 'str', 'items': 'list[V1Endpoints]', 'kind': 'str', 'metadata': 'V1ListMeta' } attribute_map = { 'api_version': 'apiVersion', 'items': 'items', 'kind': 'kind', 'metadata': 'metadata' } def __init__(self, api_version=None, items=None, kind=None, metadata=None, local_vars_configuration=None): if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._api_version = None self._items = None self._kind = None self._metadata = None self.discriminator = None if api_version is not None: self.api_version = api_version self.items = items if kind is not None: self.kind = kind if metadata is not None: self.metadata = metadata @property def api_version(self): return self._api_version @api_version.setter def api_version(self, api_version): self._api_version = api_version @property def items(self): return self._items @items.setter def items(self, items): if self.local_vars_configuration.client_side_validation and items is None: raise ValueError("Invalid value for `items`, must not be `None`") self._items = items @property def kind(self): return self._kind @kind.setter def kind(self, kind): self._kind = kind @property def metadata(self): return self._metadata @metadata.setter def metadata(self, metadata): self._metadata = metadata def to_dict(self): result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, V1EndpointsList): return False return self.to_dict() == other.to_dict() def __ne__(self, other): if not isinstance(other, V1EndpointsList): return True return self.to_dict() != other.to_dict()

true