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Python
parser/python/validate.py
jnorwood/NNEF-Tools
5eb3755b5322040d42893e41b15093337abe04ce
[ "Apache-2.0" ]
null
null
null
parser/python/validate.py
jnorwood/NNEF-Tools
5eb3755b5322040d42893e41b15093337abe04ce
[ "Apache-2.0" ]
null
null
null
parser/python/validate.py
jnorwood/NNEF-Tools
5eb3755b5322040d42893e41b15093337abe04ce
[ "Apache-2.0" ]
null
null
null
# Copyright (c) 2017 The Khronos Group Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import nnef import argparse if __name__ == '__main__': ap = argparse.ArgumentParser() ap.add_argument('path', type=str, help='path to the model to validate') ap.add_argument('--stdlib', type=str, help='file name of alternate standard operation definitions ' '(defaults to all-primitive definitions)', default='') ap.add_argument('--lower', type=str, help='comma separated list of operations to lower (if defined as compound)', default='') ap.add_argument('--shapes', action="store_true", help='perform shape validation as well') args = ap.parse_args() stdlib = '' if args.stdlib: try: with open(args.stdlib) as file: stdlib = file.read() except FileNotFoundError as e: print('Could not open file: ' + args.stdlib) exit(-1) try: graph = nnef.load_graph(args.path, stdlib=stdlib, lowered=args.lower.split(',')) except nnef.Error as err: print('Parse error: ' + str(err)) exit(-1) if args.shapes: try: nnef.infer_shapes(graph) except nnef.Error as err: print('Shape error: ' + str(err)) exit(-1) print(nnef.format_graph(graph.name, graph.inputs, graph.outputs, graph.operations)) print('Validation succeeded')
36.444444
117
0.643293
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py
Python
armi/physics/neutronics/latticePhysics/latticePhysicsInterface.py
celikten/armi
4e100dd514a59caa9c502bd5a0967fd77fdaf00e
[ "Apache-2.0" ]
null
null
null
armi/physics/neutronics/latticePhysics/latticePhysicsInterface.py
celikten/armi
4e100dd514a59caa9c502bd5a0967fd77fdaf00e
[ "Apache-2.0" ]
null
null
null
armi/physics/neutronics/latticePhysics/latticePhysicsInterface.py
celikten/armi
4e100dd514a59caa9c502bd5a0967fd77fdaf00e
[ "Apache-2.0" ]
null
null
null
# Copyright 2019 TerraPower, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # 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. """" Lattice Physics Interface Parent classes for codes responsible for generating broad-group cross sections """ import os import shutil from armi import nuclearDataIO from armi import interfaces, runLog from armi.utils import codeTiming from armi.physics import neutronics from armi.physics.neutronics.const import CONF_CROSS_SECTION from armi.utils.customExceptions import important LATTICE_PHYSICS = "latticePhysics" @important def SkippingXsGen_BuChangedLessThanTolerance(tolerance): return "Skipping XS Generation this cycle because median block burnups changes less than {}%".format( tolerance ) def setBlockNeutronVelocities(r, neutronVelocities): """ Set the ``mgNeutronVelocity`` parameter for each block using the ``neutronVelocities`` dictionary data. Parameters ---------- neutronVelocities : dict Dictionary that is keyed with the ``representativeBlock`` XS IDs with values of multigroup neutron velocity data computed by MC2. Raises ------ ValueError Multi-group neutron velocities was not computed during the cross section calculation. """ for b in r.core.getBlocks(): xsID = b.getMicroSuffix() if xsID not in neutronVelocities: raise ValueError( "Cannot assign multi-group neutron velocity to {} because it does not exist in " "the neutron velocities dictionary with keys: {}. The XS library does not contain " "data for the {} xsid.".format(b, neutronVelocities.keys(), xsID) ) b.p.mgNeutronVelocity = neutronVelocities[b.getMicroSuffix()] class LatticePhysicsInterface(interfaces.Interface): """Class for interacting with lattice physics codes.""" function = LATTICE_PHYSICS def __init__(self, r, cs): interfaces.Interface.__init__(self, r, cs) # Set to True by default, but should be disabled when perturbed cross sections are generated. self._updateBlockNeutronVelocities = True # Geometry options available through the lattice physics interfaces self._ZERO_DIMENSIONAL_GEOM = "0D" self._ONE_DIMENSIONAL_GEOM = "1D" self._TWO_DIMENSIONAL_GEOM = "2D" self._SLAB_MODEL = " slab" self._CYLINDER_MODEL = " cylinder" self._HEX_MODEL = " hex" self._burnupTolerance = self.cs["tolerateBurnupChange"] self._oldXsIdsAndBurnup = {} self.executablePath = self._getExecutablePath() self.executableRoot = os.path.dirname(self.executablePath) self.includeGammaXS = neutronics.gammaTransportIsRequested( cs ) or neutronics.gammaXsAreRequested(cs) def _getExecutablePath(self): raise NotImplementedError @codeTiming.timed def interactBOC(self, cycle=0): """ Run the lattice physics code if ``genXS`` is set and update burnup groups. Generate new cross sections based off the case settings and the current state of the reactor. Notes ----- :py:meth:`armi.physics.fuelCycle.fuelHandlers.FuelHandler.interactBOC` also calls this if the ``runLatticePhysicsBeforeShuffling``setting is True. This happens because branch searches may need XS. """ self.updateXSLibrary(cycle) def updateXSLibrary(self, cycle): """ Update the current XS library, either by creating or reloading one. Parameters ---------- cycle : int The cycle that is being processed. Used to name the library. See Also -------- computeCrossSections : run lattice physics on the current reactor state no matter weather needed or not. """ runLog.important("Preparing XS for cycle {}".format(cycle)) representativeBlocks, xsIds = self._getBlocksAndXsIds() if self._newLibraryShouldBeCreated(cycle, representativeBlocks, xsIds): if self.cs["clearXS"]: self.clearXS() self.computeCrossSections( blockList=representativeBlocks, xsLibrarySuffix=self._getSuffix(cycle) ) self._renameExistingLibrariesForCycle(cycle) else: self.readExistingXSLibraries(cycle) self._checkInputs() def _renameExistingLibrariesForCycle(self, cycle): """Copy the existing neutron and/or gamma libraries into cycle-dependent files.""" shutil.copy(neutronics.ISOTXS, nuclearDataIO.getExpectedISOTXSFileName(cycle)) if self.includeGammaXS: shutil.copy( neutronics.GAMISO, nuclearDataIO.getExpectedGAMISOFileName( cycle=cycle, suffix=self._getSuffix(cycle) ), ) shutil.copy( neutronics.PMATRX, nuclearDataIO.getExpectedPMATRXFileName( cycle=cycle, suffix=self._getSuffix(cycle) ), ) def _checkInputs(self): pass def readExistingXSLibraries(self, cycle): raise NotImplementedError def makeCycleXSFilesAsBaseFiles(self, cycle): raise NotImplementedError @staticmethod def _copyLibraryFilesForCycle(cycle, libFiles): runLog.extra("Current library files: {}".format(libFiles)) for baseName, cycleName in libFiles.items(): if not os.path.exists(cycleName): if not os.path.exists(baseName): raise ValueError( "Neither {} nor {} libraries exist. Either the " "current cycle library for cycle {} should exist " "or a base library is required to continue.".format( cycleName, baseName, cycle ) ) runLog.info( "Existing library {} for cycle {} does not exist. " "The active library is {}".format(cycleName, cycle, baseName) ) else: runLog.info("Using {} as an active library".format(baseName)) if cycleName != baseName: shutil.copy(cycleName, baseName) def _readGammaBinaries(self, lib, gamisoFileName, pmatrxFileName): raise NotImplementedError( "Gamma cross sections not implemented in {}".format(self.cs["xsKernel"]) ) def _writeGammaBinaries(self, lib, gamisoFileName, pmatrxFileName): raise NotImplementedError( "Gamma cross sections not implemented in {}".format(self.cs["xsKernel"]) ) def _getSuffix(self, cycle): # pylint: disable=unused-argument, no-self-use return "" def interactCoupled(self, iteration): """ Runs on secondary coupled iterations. After a coupled iteration, the cross sections need to be regenerated. This will bring in the spectral effects of changes in densities, as well as changes in Doppler. Parameters ---------- iteration : int This is unused since cross sections are generated on a per-cycle basis. """ self.r.core.lib = None self.updateXSLibrary(self.r.p.cycle) def clearXS(self): raise NotImplementedError def interactEOC(self, cycle=None): """ Interact at the end of a cycle. Force updating cross sections at the start of the next cycle. """ self.r.core.lib = None def computeCrossSections( self, baseList=None, forceSerial=False, xsLibrarySuffix="", blockList=None ): """ Prepare a batch of inputs, execute them, and store results on reactor library. Parameters ---------- baseList : list a user-specified set of bases that will be run instead of calculating all of them forceSerial : bool, optional Will run on 1 processor in sequence instead of on many in parallel Useful for optimization/batch runs where every processor is on a different branch xsLibrarySuffix : str, optional A book-keeping suffix used in Doppler calculations blockList : list, optional List of blocks for which to generate cross sections. If None, representative blocks will be determined """ self.r.core.lib = self._generateXsLibrary( baseList, forceSerial, xsLibrarySuffix, blockList ) def _generateXsLibrary( self, baseList, forceSerial, xsLibrarySuffix, blockList, writers=None, purgeFP=True, ): raise NotImplementedError def _executeLatticePhysicsCalculation(self, returnedFromWriters, forceSerial): raise NotImplementedError def generateLatticePhysicsInputs( self, baseList, xsLibrarySuffix, blockList, xsWriters=None ): """ Write input files for the generation of cross section libraries. Parameters ---------- baseList : list A list of cross-section id strings (e.g. AA, BC) that will be generated. Default: all in reactor xsLibrarySuffix : str A suffix added to the end of the XS file names such as 'voided' for voided XS. Default: Empty blockList : list The blocks to write inputs for. xsWriters : list, optional The specified writers to write the input files Returns ------- returnedFromWriters: list A list of what this specific writer instance returns for each representative block. It is the responsibility of the subclassed interface to implement. In many cases, it is the executing agent. See Also -------- :py:meth:`terrapower.physics.neutronics.mc2.mc2Writers.Mc2V2Writer.write` :py:meth:`armi.physics.neutronics.latticePhysics.serpentWriters.SerpentWriter.write` """ returnedFromWriters = [] baseList = set(baseList or []) representativeBlocks = blockList or self.getRepresentativeBlocks() for repBlock in representativeBlocks: xsId = repBlock.getMicroSuffix() if not baseList or xsId in baseList: # write the step number to the info log runLog.info( "Creating input writer(s) for {0} with {1:65s} BU (%FIMA): {2:10.2f}".format( xsId, repBlock, repBlock.p.percentBu ) ) writers = self.getWriters(repBlock, xsLibrarySuffix, xsWriters) for writer in writers: fromWriter = writer.write() returnedFromWriters.append(fromWriter) return returnedFromWriters def getWriters(self, representativeBlock, xsLibrarySuffix, writers=None): """ Return valid lattice physics writer subclass(es). Parameters ---------- representativeBlock : Block A representative block object that can be created from a block collection. xsLibrarySuffix : str A suffix added to the end of the XS file names such as 'voided' for voided XS. Default: Empty writers : list of lattice physics writer objects, optional If the writers are known, they can be provided and constructed. Returns ------- writers : list A list of writers for the provided representative block. """ xsID = representativeBlock.getMicroSuffix() if writers: # Construct the writers that are provided writers = [ w( representativeBlock, r=self.r, externalCodeInterface=self, xsLibrarySuffix=xsLibrarySuffix, ) for w in writers ] else: geom = self.cs[CONF_CROSS_SECTION][xsID].geometry writers = self._getGeomDependentWriters( representativeBlock, xsID, geom, xsLibrarySuffix ) return writers def _getGeomDependentWriters( self, representativeBlock, xsID, geom, xsLibrarySuffix ): raise NotImplementedError def getReader(self): raise NotImplementedError def _newLibraryShouldBeCreated(self, cycle, representativeBlockList, xsIDs): """ Determines whether the cross section generator should be executed at this cycle. Criteria include: #. genXS setting is turned on #. We are beyond any requested skipCycles (restart cycles) #. The blocks have changed burnup beyond the burnup threshold #. Lattice physics kernel (e.g. MC2) hasn't already been executed for this cycle (possible if it runs during fuel handling) """ executeXSGen = bool(self.cs["genXS"] and cycle >= self.cs["skipCycles"]) idsChangedBurnup = self._checkBurnupThresholds(representativeBlockList) if executeXSGen and not idsChangedBurnup: executeXSGen = False if self.r.core._lib is not None: # pylint: disable=protected-access # justification=r.core.lib property can raise exception or load pre-generated # ISOTXS, but the interface should have responsibilty of loading # XS's have already generated for this cycle (maybe during fuel management). Should we update due to # changes that occurred during fuel management? missing = set(xsIDs) - set(self.r.core.lib.xsIDs) if missing and not executeXSGen: runLog.warning( "Even though XS generation is not activated, new XS {0} are needed. " "Perhaps a booster came in.".format(missing) ) elif missing: runLog.important( "New XS sets {0} will be generated for this cycle".format(missing) ) else: runLog.important( "No new XS needed for this cycle. {0} exist. Skipping".format( self.r.core.lib.xsIDs ) ) executeXSGen = False # no newXs return executeXSGen def _checkBurnupThresholds(self, blockList): """ Check to see if burnup has changed meaningfully. If there are, then the xs sets should be regenerated. Otherwise then go ahead and skip xs generation. This is motivated by the idea that during very long explicit equilibrium runs, it might save time to turn off xs generation at a certain point. Parameters ---------- blockList: iterable List of all blocks to examine Returns ------- idsChangedBurnup: bool flag regarding whether or not burnup changed substantially """ idsChangedBurnup = True if self._burnupTolerance > 0: idsChangedBurnup = False for b in blockList: xsID = b.getMicroSuffix() if xsID not in self._oldXsIdsAndBurnup: # Looks like a new ID was found that was not in the old ID's # have to regenerate the cross-sections this time around self._oldXsIdsAndBurnup[xsID] = b.p.percentBu idsChangedBurnup = True else: # The id was found. Now it is time to compare the burnups to determine # if there has been enough meaningful change between the runs buOld = self._oldXsIdsAndBurnup[xsID] buNow = b.p.percentBu if abs(buOld - buNow) > self._burnupTolerance: idsChangedBurnup = True # update the oldXs burnup to be the about to be newly generated xsBurnup self._oldXsIdsAndBurnup[xsID] = buNow runLog.important( "Burnup has changed in xsID {} from {} to {}. " "Recalculating Cross-sections".format(xsID, buOld, buNow) ) if not idsChangedBurnup: SkippingXsGen_BuChangedLessThanTolerance(self._burnupTolerance) return idsChangedBurnup def _getProcessesPerNode(self): raise NotImplementedError def getRepresentativeBlocks(self): """Return a list of all blocks in the problem.""" xsGroupManager = self.getInterface("xsGroups") return xsGroupManager.representativeBlocks.values() # OrderedDict def _getBlocksAndXsIds(self): """Return blocks and their xsIds.""" blocks = self.getRepresentativeBlocks() return blocks, [b.getMicroSuffix() for b in blocks] def updatePhysicsCouplingControl(self): """ Disable XS update in equilibrium cases after a while. Notes ----- This is only relevant for equilibrium cases. We have to turn off XS updates after several cyclics or else the number densities will never converge. """ if self.r.core.p.cyclics >= self.cs["numCyclicsBeforeStoppingXS"]: self.enabled(False) runLog.important( "Disabling {} because numCyclics={}".format(self, self.r.core.p.cyclics) )
37.985386
112
0.614784
afa51d355786df98b0025450846ad5820c6447a0
761
py
Python
code/exercises/07_DataStructures/ex_07_05_sequence.py
chiachang100/LearnToCodeWithPython
fe16115cb3be612d5abd8ffdbd6a14a37d6b4d52
[ "Apache-2.0" ]
null
null
null
code/exercises/07_DataStructures/ex_07_05_sequence.py
chiachang100/LearnToCodeWithPython
fe16115cb3be612d5abd8ffdbd6a14a37d6b4d52
[ "Apache-2.0" ]
null
null
null
code/exercises/07_DataStructures/ex_07_05_sequence.py
chiachang100/LearnToCodeWithPython
fe16115cb3be612d5abd8ffdbd6a14a37d6b4d52
[ "Apache-2.0" ]
null
null
null
#!/usr/bin/python # Filename: ex_sequence.py shoplist = ['apple', 'mango', 'carrot', 'banana'] name = 'swaroop' # Indexing or 'Subscription' operation print('Item 0 is', shoplist[0]) print('Item 1 is', shoplist[1]) print('Item 2 is', shoplist[2]) print('Item 3 is', shoplist[3]) print('Item -1 is', shoplist[-1]) print('Item -2 is', shoplist[-2]) print('Character 0 is', name[0]) # Slicing on a list print('Item 1 to 3 is', shoplist[1:3]) print('Item 2 to end is', shoplist[2:]) print('Item 1 to -1 is', shoplist[1:-1]) print('Item start to end is', shoplist[:]) # Slicing on a string print('characters 1 to 3 is', name[1:3]) print('characters 2 to end is', name[2:]) print('characters 1 to -1 is', name[1:-1]) print('characters start to end is', name[:])
27.178571
49
0.658344
7727c3a4fe50654ffd803485c0e0c94d2c96db3c
121,553
py
Python
salt/utils/parsers.py
eyj/salt
5232d7f60cb78591d8bad25d796b0a945fc6350c
[ "Apache-2.0" ]
2
2015-09-21T14:13:30.000Z
2016-02-12T11:33:46.000Z
salt/utils/parsers.py
eyj/salt
5232d7f60cb78591d8bad25d796b0a945fc6350c
[ "Apache-2.0" ]
null
null
null
salt/utils/parsers.py
eyj/salt
5232d7f60cb78591d8bad25d796b0a945fc6350c
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- ''' :codeauthor: :email:`Pedro Algarvio ([email protected])` salt.utils.parsers ~~~~~~~~~~~~~~~~~~ This is where all the black magic happens on all of salt's CLI tools. ''' # pylint: disable=missing-docstring,protected-access,too-many-ancestors,too-few-public-methods # pylint: disable=attribute-defined-outside-init,no-self-use # Import python libs from __future__ import absolute_import, print_function import os import sys import types import signal import getpass import logging import optparse import traceback import yaml from functools import partial # Import salt libs import salt.config as config import salt.defaults.exitcodes import salt.log.setup as log import salt.syspaths as syspaths import salt.version as version import salt.utils import salt.utils.args import salt.utils.xdg import salt.utils.jid from salt.utils import kinds from salt.defaults import DEFAULT_TARGET_DELIM from salt.utils.validate.path import is_writeable from salt.utils.verify import verify_files import salt.exceptions import salt.ext.six as six from salt.ext.six.moves import range # pylint: disable=import-error,redefined-builtin def _sorted(mixins_or_funcs): return sorted( mixins_or_funcs, key=lambda mf: getattr(mf, '_mixin_prio_', 1000) ) class MixInMeta(type): # This attribute here won't actually do anything. But, if you need to # specify an order or a dependency within the mix-ins, please define the # attribute on your own MixIn _mixin_prio_ = 0 def __new__(mcs, name, bases, attrs): instance = super(MixInMeta, mcs).__new__(mcs, name, bases, attrs) if not hasattr(instance, '_mixin_setup'): raise RuntimeError( 'Don\'t subclass {0} in {1} if you\'re not going to use it ' 'as a salt parser mix-in.'.format(mcs.__name__, name) ) return instance class OptionParserMeta(MixInMeta): def __new__(mcs, name, bases, attrs): instance = super(OptionParserMeta, mcs).__new__(mcs, name, bases, attrs) if not hasattr(instance, '_mixin_setup_funcs'): instance._mixin_setup_funcs = [] if not hasattr(instance, '_mixin_process_funcs'): instance._mixin_process_funcs = [] if not hasattr(instance, '_mixin_after_parsed_funcs'): instance._mixin_after_parsed_funcs = [] if not hasattr(instance, '_mixin_before_exit_funcs'): instance._mixin_before_exit_funcs = [] for base in _sorted(bases + (instance,)): func = getattr(base, '_mixin_setup', None) if func is not None and func not in instance._mixin_setup_funcs: instance._mixin_setup_funcs.append(func) func = getattr(base, '_mixin_after_parsed', None) if func is not None and func not in \ instance._mixin_after_parsed_funcs: instance._mixin_after_parsed_funcs.append(func) func = getattr(base, '_mixin_before_exit', None) if func is not None and func not in \ instance._mixin_before_exit_funcs: instance._mixin_before_exit_funcs.append(func) # Mark process_<opt> functions with the base priority for sorting for func in dir(base): if not func.startswith('process_'): continue func = getattr(base, func) if getattr(func, '_mixin_prio_', None) is not None: # Function already has the attribute set, don't override it continue if six.PY2: func.__func__._mixin_prio_ = getattr( base, '_mixin_prio_', 1000 ) else: func._mixin_prio_ = getattr( base, '_mixin_prio_', 1000 ) return instance class CustomOption(optparse.Option, object): def take_action(self, action, dest, *args, **kwargs): # see https://github.com/python/cpython/blob/master/Lib/optparse.py#L786 self.explicit = True return optparse.Option.take_action(self, action, dest, *args, **kwargs) class OptionParser(optparse.OptionParser, object): VERSION = version.__saltstack_version__.formatted_version usage = '%prog' epilog = ('You can find additional help about %prog issuing "man %prog" ' 'or on http://docs.saltstack.com') description = None # Private attributes _mixin_prio_ = 100 # Setup multiprocessing logging queue listener _setup_mp_logging_listener_ = False def __init__(self, *args, **kwargs): kwargs.setdefault('version', '%prog {0}'.format(self.VERSION)) kwargs.setdefault('usage', self.usage) if self.description: kwargs.setdefault('description', self.description) if self.epilog: kwargs.setdefault('epilog', self.epilog) kwargs.setdefault('option_class', CustomOption) optparse.OptionParser.__init__(self, *args, **kwargs) if self.epilog and '%prog' in self.epilog: self.epilog = self.epilog.replace('%prog', self.get_prog_name()) def add_option_group(self, *args, **kwargs): option_group = optparse.OptionParser.add_option_group(self, *args, **kwargs) option_group.option_class = CustomOption return option_group def parse_args(self, args=None, values=None): options, args = optparse.OptionParser.parse_args(self, args, values) if 'args_stdin' in options.__dict__ and options.args_stdin is True: # Read additional options and/or arguments from stdin and combine # them with the options and arguments from the command line. new_inargs = sys.stdin.readlines() new_inargs = [arg.rstrip('\r\n') for arg in new_inargs] new_options, new_args = optparse.OptionParser.parse_args( self, new_inargs) options.__dict__.update(new_options.__dict__) args.extend(new_args) if options.versions_report: self.print_versions_report() self.options, self.args = options, args # Let's get some proper sys.stderr logging as soon as possible!!! # This logging handler will be removed once the proper console or # logfile logging is setup. log.setup_temp_logger( getattr(self.options, 'log_level', 'error') ) # Gather and run the process_<option> functions in the proper order process_option_funcs = [] for option_key in options.__dict__: process_option_func = getattr( self, 'process_{0}'.format(option_key), None ) if process_option_func is not None: process_option_funcs.append(process_option_func) for process_option_func in _sorted(process_option_funcs): try: process_option_func() except Exception as err: # pylint: disable=broad-except logging.getLogger(__name__).exception(err) self.error( 'Error while processing {0}: {1}'.format( process_option_func, traceback.format_exc(err) ) ) # Run the functions on self._mixin_after_parsed_funcs for mixin_after_parsed_func in self._mixin_after_parsed_funcs: # pylint: disable=no-member try: mixin_after_parsed_func(self) except Exception as err: # pylint: disable=broad-except logging.getLogger(__name__).exception(err) self.error( 'Error while processing {0}: {1}'.format( mixin_after_parsed_func, traceback.format_exc(err) ) ) if self.config.get('conf_file', None) is not None: # pylint: disable=no-member logging.getLogger(__name__).debug( 'Configuration file path: {0}'.format( self.config['conf_file'] # pylint: disable=no-member ) ) # Retain the standard behavior of optparse to return options and args return options, args def _populate_option_list(self, option_list, add_help=True): optparse.OptionParser._populate_option_list( self, option_list, add_help=add_help ) for mixin_setup_func in self._mixin_setup_funcs: # pylint: disable=no-member mixin_setup_func(self) def _add_version_option(self): optparse.OptionParser._add_version_option(self) self.add_option( '--versions-report', '-V', action='store_true', help='Show program\'s dependencies version number and exit.' ) def print_versions_report(self, file=sys.stdout): # pylint: disable=redefined-builtin print('\n'.join(version.versions_report()), file=file) self.exit(salt.defaults.exitcodes.EX_OK) def exit(self, status=0, msg=None): # Run the functions on self._mixin_after_parsed_funcs for mixin_before_exit_func in self._mixin_before_exit_funcs: # pylint: disable=no-member try: mixin_before_exit_func(self) except Exception as err: # pylint: disable=broad-except logger = logging.getLogger(__name__) logger.exception(err) logger.error( 'Error while processing {0}: {1}'.format( mixin_before_exit_func, traceback.format_exc(err) ) ) if self._setup_mp_logging_listener_ is True: # Stop the logging queue listener process log.shutdown_multiprocessing_logging_listener(daemonizing=True) if isinstance(msg, six.string_types) and msg and msg[-1] != '\n': msg = '{0}\n'.format(msg) optparse.OptionParser.exit(self, status, msg) def error(self, msg): """error(msg : string) Print a usage message incorporating 'msg' to stderr and exit. This keeps option parsing exit status uniform for all parsing errors. """ self.print_usage(sys.stderr) self.exit(salt.defaults.exitcodes.EX_USAGE, '{0}: error: {1}\n'.format(self.get_prog_name(), msg)) class MergeConfigMixIn(six.with_metaclass(MixInMeta, object)): ''' This mix-in will simply merge the CLI-passed options, by overriding the configuration file loaded settings. This mix-in should run last. ''' _mixin_prio_ = six.MAXSIZE def _mixin_setup(self): if not hasattr(self, 'setup_config') and not hasattr(self, 'config'): # No configuration was loaded on this parser. # There's nothing to do here. return # Add an additional function that will merge the shell options with # the config options and if needed override them self._mixin_after_parsed_funcs.append(self.__merge_config_with_cli) def __merge_config_with_cli(self, *args): # pylint: disable=unused-argument # Merge parser options for option in self.option_list: if option.dest is None: # --version does not have dest attribute set for example. # All options defined by us, even if not explicitly(by kwarg), # will have the dest attribute set continue # Get the passed value from shell. If empty get the default one default = self.defaults.get(option.dest) value = getattr(self.options, option.dest, default) if option.dest not in self.config: # There's no value in the configuration file if value is not None: # There's an actual value, add it to the config self.config[option.dest] = value elif value is not None and getattr(option, "explicit", False): # Only set the value in the config file IF it was explicitly # specified by the user, this makes it possible to tweak settings # on the configuration files bypassing the shell option flags' # defaults self.config[option.dest] = value elif option.dest in self.config: # Let's update the option value with the one from the # configuration file. This allows the parsers to make use of # the updated value by using self.options.<option> setattr(self.options, option.dest, self.config[option.dest]) # Merge parser group options if any for group in self.option_groups: for option in group.option_list: if option.dest is None: continue # Get the passed value from shell. If empty get the default one default = self.defaults.get(option.dest) value = getattr(self.options, option.dest, default) if option.dest not in self.config: # There's no value in the configuration file if value is not None: # There's an actual value, add it to the config self.config[option.dest] = value elif value is not None and getattr(option, "explicit", False): # Only set the value in the config file IF it was explicitly # specified by the user, this makes it possible to tweak # settings on the configuration files bypassing the shell # option flags' defaults self.config[option.dest] = value elif option.dest in self.config: # Let's update the option value with the one from the # configuration file. This allows the parsers to make use # of the updated value by using self.options.<option> setattr(self.options, option.dest, self.config[option.dest]) class SaltfileMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = -20 def _mixin_setup(self): self.add_option( '--saltfile', default=None, help='Specify the path to a Saltfile. If not passed, one will be ' 'searched for in the current working directory.' ) def process_saltfile(self): if self.options.saltfile is None: # No one passed a Saltfile as an option, environment variable!? self.options.saltfile = os.environ.get('SALT_SALTFILE', None) if self.options.saltfile is None: # If we're here, no one passed a Saltfile either to the CLI tool or # as an environment variable. # Is there a Saltfile in the current directory? try: # cwd may not exist if it was removed but salt was run from it saltfile = os.path.join(os.getcwd(), 'Saltfile') except OSError: saltfile = '' if os.path.isfile(saltfile): self.options.saltfile = saltfile else: saltfile = self.options.saltfile if not self.options.saltfile: # There's still no valid Saltfile? No need to continue... return if not os.path.isfile(self.options.saltfile): self.error( '\'{0}\' file does not exist.\n'.format(self.options.saltfile ) ) # Make sure we have an absolute path self.options.saltfile = os.path.abspath(self.options.saltfile) # Make sure we let the user know that we will be loading a Saltfile logging.getLogger(__name__).info( 'Loading Saltfile from \'{0}\''.format(self.options.saltfile) ) try: saltfile_config = config._read_conf_file(saltfile) except salt.exceptions.SaltConfigurationError as error: self.error(error.message) self.exit(salt.defaults.exitcodes.EX_GENERIC, '{0}: error: {1}\n'.format(self.get_prog_name(), error.message)) if not saltfile_config: # No configuration was loaded from the Saltfile return if self.get_prog_name() not in saltfile_config: # There's no configuration specific to the CLI tool. Stop! return # We just want our own configuration cli_config = saltfile_config[self.get_prog_name()] # If there are any options, who's names match any key from the loaded # Saltfile, we need to update its default value for option in self.option_list: if option.dest is None: # --version does not have dest attribute set for example. continue if option.dest not in cli_config: # If we don't have anything in Saltfile for this option, let's # continue processing right now continue # Get the passed value from shell. If empty get the default one default = self.defaults.get(option.dest) value = getattr(self.options, option.dest, default) if value != default: # The user passed an argument, we won't override it with the # one from Saltfile, if any continue # We reached this far! Set the Saltfile value on the option setattr(self.options, option.dest, cli_config[option.dest]) option.explicit = True # Let's also search for options referred in any option groups for group in self.option_groups: for option in group.option_list: if option.dest is None: continue if option.dest not in cli_config: # If we don't have anything in Saltfile for this option, # let's continue processing right now continue # Get the passed value from shell. If empty get the default one default = self.defaults.get(option.dest) value = getattr(self.options, option.dest, default) if value != default: # The user passed an argument, we won't override it with # the one from Saltfile, if any continue setattr(self.options, option.dest, cli_config[option.dest]) option.explicit = True # Any left over value in the saltfile can now be safely added for key in cli_config: setattr(self.options, key, cli_config[key]) class HardCrashMixin(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 40 _config_filename_ = None def _mixin_setup(self): hard_crash = os.environ.get('SALT_HARD_CRASH', False) self.add_option( '--hard-crash', action='store_true', default=hard_crash, help=('Raise any original exception rather than exiting gracefully. ' 'Default: %default.') ) class ConfigDirMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = -10 _config_filename_ = None _default_config_dir_ = syspaths.CONFIG_DIR _default_config_dir_env_var_ = 'SALT_CONFIG_DIR' def _mixin_setup(self): config_dir = os.environ.get(self._default_config_dir_env_var_, None) if not config_dir: config_dir = self._default_config_dir_ logging.getLogger(__name__).debug('SYSPATHS setup as: {0}'.format(syspaths.CONFIG_DIR)) self.add_option( '-c', '--config-dir', default=config_dir, help=('Pass in an alternative configuration directory. Default: ' '\'%default\'.') ) def process_config_dir(self): if not os.path.isdir(self.options.config_dir): # No logging is configured yet sys.stderr.write( 'WARNING: CONFIG \'{0}\' directory does not exist.\n'.format( self.options.config_dir ) ) # Make sure we have an absolute path self.options.config_dir = os.path.abspath(self.options.config_dir) if hasattr(self, 'setup_config'): if not hasattr(self, 'config'): self.config = {} try: self.config.update(self.setup_config()) except (IOError, OSError) as exc: self.error( 'Failed to load configuration: {0}'.format(exc) ) def get_config_file_path(self, configfile=None): if configfile is None: configfile = self._config_filename_ return os.path.join(self.options.config_dir, configfile) class LogLevelMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 10 _default_logging_level_ = 'warning' _default_logging_logfile_ = None _logfile_config_setting_name_ = 'log_file' _loglevel_config_setting_name_ = 'log_level' _logfile_loglevel_config_setting_name_ = 'log_level_logfile' # pylint: disable=invalid-name _skip_console_logging_config_ = False def _mixin_setup(self): if self._default_logging_logfile_ is None: # This is an attribute available for programmers, so, raise a # RuntimeError to let them know about the proper usage. raise RuntimeError( 'Please set {0}._default_logging_logfile_'.format( self.__class__.__name__ ) ) group = self.logging_options_group = optparse.OptionGroup( self, 'Logging Options', 'Logging options which override any settings defined on the ' 'configuration files.' ) self.add_option_group(group) if not getattr(self, '_skip_console_logging_config_', False): group.add_option( '-l', '--log-level', choices=list(log.LOG_LEVELS), help='Console logging log level. One of {0}. ' 'Default: \'{1}\'.'.format( ', '.join([repr(l) for l in log.SORTED_LEVEL_NAMES]), getattr(self, '_default_logging_level_', 'warning') ) ) group.add_option( '--log-file', default=None, help='Log file path. Default: \'{0}\'.'.format( self._default_logging_logfile_ ) ) group.add_option( '--log-file-level', dest=self._logfile_loglevel_config_setting_name_, choices=list(log.LOG_LEVELS), help='Logfile logging log level. One of {0}. ' 'Default: \'{1}\'.'.format( ', '.join([repr(l) for l in log.SORTED_LEVEL_NAMES]), getattr(self, '_default_logging_level_', 'warning') ) ) def process_log_level(self): if not self.options.log_level: cli_log_level = 'cli_{0}_log_level'.format( self.get_prog_name().replace('-', '_') ) if self.config.get(cli_log_level, None) is not None: self.options.log_level = self.config.get(cli_log_level) elif self.config.get(self._loglevel_config_setting_name_, None): self.options.log_level = self.config.get( self._loglevel_config_setting_name_ ) else: self.options.log_level = self._default_logging_level_ # Setup extended logging right before the last step self._mixin_after_parsed_funcs.append(self.__setup_extended_logging) # Setup the console and log file configuration before the MP logging # listener because the MP logging listener may need that config. self._mixin_after_parsed_funcs.append(self.__setup_logfile_logger_config) self._mixin_after_parsed_funcs.append(self.__setup_console_logger_config) # Setup the multiprocessing log queue listener if enabled self._mixin_after_parsed_funcs.append(self._setup_mp_logging_listener) # Setup the console as the last _mixin_after_parsed_func to run self._mixin_after_parsed_funcs.append(self.__setup_console_logger) def process_log_file(self): if not self.options.log_file: cli_setting_name = 'cli_{0}_log_file'.format( self.get_prog_name().replace('-', '_') ) if self.config.get(cli_setting_name, None) is not None: # There's a configuration setting defining this log file path, # i.e., `key_log_file` if the cli tool is `salt-key` self.options.log_file = self.config.get(cli_setting_name) elif self.config.get(self._logfile_config_setting_name_, None): # Is the regular log file setting set? self.options.log_file = self.config.get( self._logfile_config_setting_name_ ) else: # Nothing is set on the configuration? Let's use the cli tool # defined default self.options.log_file = self._default_logging_logfile_ def process_log_file_level(self): if not self.options.log_file_level: cli_setting_name = 'cli_{0}_log_file_level'.format( self.get_prog_name().replace('-', '_') ) if self.config.get(cli_setting_name, None) is not None: # There's a configuration setting defining this log file # logging level, i.e., `key_log_file_level` if the cli tool is # `salt-key` self.options.log_file_level = self.config.get(cli_setting_name) elif self.config.get( self._logfile_loglevel_config_setting_name_, None): # Is the regular log file level setting set? self.options.log_file_level = self.config.get( self._logfile_loglevel_config_setting_name_ ) else: # Nothing is set on the configuration? Let's use the cli tool # defined default self.options.log_level = self._default_logging_level_ def __setup_logfile_logger_config(self, *args): # pylint: disable=unused-argument if self._logfile_loglevel_config_setting_name_ in self.config and not \ self.config.get(self._logfile_loglevel_config_setting_name_): # Remove it from config so it inherits from log_level self.config.pop(self._logfile_loglevel_config_setting_name_) loglevel = self.config.get( self._logfile_loglevel_config_setting_name_, self.config.get( # From the config setting self._loglevel_config_setting_name_, # From the console setting self.config['log_level'] ) ) cli_log_path = 'cli_{0}_log_file'.format( self.get_prog_name().replace('-', '_') ) if cli_log_path in self.config and not self.config.get(cli_log_path): # Remove it from config so it inherits from log_level_logfile self.config.pop(cli_log_path) if self._logfile_config_setting_name_ in self.config and not \ self.config.get(self._logfile_config_setting_name_): # Remove it from config so it inherits from log_file self.config.pop(self._logfile_config_setting_name_) logfile = self.config.get( # First from the config cli setting cli_log_path, self.config.get( # From the config setting self._logfile_config_setting_name_ ) ) if self.config['verify_env']: # Verify the logfile if it was explicitly set but do not try to # verify the default if logfile is not None and not logfile.startswith(('tcp://', 'udp://', 'file://')): # Logfile is not using Syslog, verify current_umask = os.umask(0o027) verify_files([logfile], self.config['user']) os.umask(current_umask) if logfile is None: # Use the default setting if the logfile wasn't explicity set logfile = self._default_logging_logfile_ cli_log_file_fmt = 'cli_{0}_log_file_fmt'.format( self.get_prog_name().replace('-', '_') ) if cli_log_file_fmt in self.config and not \ self.config.get(cli_log_file_fmt): # Remove it from config so it inherits from log_fmt_logfile self.config.pop(cli_log_file_fmt) if self.config.get('log_fmt_logfile', None) is None: # Remove it from config so it inherits from log_fmt_console self.config.pop('log_fmt_logfile', None) log_file_fmt = self.config.get( cli_log_file_fmt, self.config.get( 'cli_{0}_log_fmt'.format( self.get_prog_name().replace('-', '_') ), self.config.get( 'log_fmt_logfile', self.config.get( 'log_fmt_console', self.config.get( 'log_fmt', config._DFLT_LOG_FMT_CONSOLE ) ) ) ) ) cli_log_file_datefmt = 'cli_{0}_log_file_datefmt'.format( self.get_prog_name().replace('-', '_') ) if cli_log_file_datefmt in self.config and not \ self.config.get(cli_log_file_datefmt): # Remove it from config so it inherits from log_datefmt_logfile self.config.pop(cli_log_file_datefmt) if self.config.get('log_datefmt_logfile', None) is None: # Remove it from config so it inherits from log_datefmt_console self.config.pop('log_datefmt_logfile', None) if self.config.get('log_datefmt_console', None) is None: # Remove it from config so it inherits from log_datefmt self.config.pop('log_datefmt_console', None) log_file_datefmt = self.config.get( cli_log_file_datefmt, self.config.get( 'cli_{0}_log_datefmt'.format( self.get_prog_name().replace('-', '_') ), self.config.get( 'log_datefmt_logfile', self.config.get( 'log_datefmt_console', self.config.get( 'log_datefmt', '%Y-%m-%d %H:%M:%S' ) ) ) ) ) if not is_writeable(logfile, check_parent=True): # Since we're not be able to write to the log file or its parent # directory (if the log file does not exit), are we the same user # as the one defined in the configuration file? current_user = salt.utils.get_user() if self.config['user'] != current_user: # Yep, not the same user! # Is the current user in ACL? acl = self.config.get('publisher_acl') or self.config.get('client_acl', {}) if salt.utils.check_whitelist_blacklist(current_user, whitelist=six.iterkeys(acl)): # Yep, the user is in ACL! # Let's write the logfile to its home directory instead. xdg_dir = salt.utils.xdg.xdg_config_dir() user_salt_dir = (xdg_dir if os.path.isdir(xdg_dir) else os.path.expanduser('~/.salt')) if not os.path.isdir(user_salt_dir): os.makedirs(user_salt_dir, 0o750) logfile_basename = os.path.basename( self._default_logging_logfile_ ) logging.getLogger(__name__).debug( 'The user \'{0}\' is not allowed to write to \'{1}\'. ' 'The log file will be stored in ' '\'~/.salt/\'{2}\'.log\''.format( current_user, logfile, logfile_basename ) ) logfile = os.path.join( user_salt_dir, '{0}.log'.format(logfile_basename) ) # If we haven't changed the logfile path and it's not writeable, # salt will fail once we try to setup the logfile logging. # Save the settings back to the configuration self.config[self._logfile_config_setting_name_] = logfile self.config[self._logfile_loglevel_config_setting_name_] = loglevel self.config['log_fmt_logfile'] = log_file_fmt self.config['log_datefmt_logfile'] = log_file_datefmt def setup_logfile_logger(self): logfile = self.config[self._logfile_config_setting_name_] loglevel = self.config[self._logfile_loglevel_config_setting_name_] log_file_fmt = self.config['log_fmt_logfile'] log_file_datefmt = self.config['log_datefmt_logfile'] log.setup_logfile_logger( logfile, loglevel, log_format=log_file_fmt, date_format=log_file_datefmt ) for name, level in six.iteritems(self.config['log_granular_levels']): log.set_logger_level(name, level) def __setup_extended_logging(self, *args): # pylint: disable=unused-argument log.setup_extended_logging(self.config) def _get_mp_logging_listener_queue(self): return log.get_multiprocessing_logging_queue() def _setup_mp_logging_listener(self, *args): # pylint: disable=unused-argument if self._setup_mp_logging_listener_: log.setup_multiprocessing_logging_listener( self.config, self._get_mp_logging_listener_queue() ) def __setup_console_logger_config(self, *args): # pylint: disable=unused-argument # Since we're not going to be a daemon, setup the console logger cli_log_fmt = 'cli_{0}_log_fmt'.format( self.get_prog_name().replace('-', '_') ) if cli_log_fmt in self.config and not self.config.get(cli_log_fmt): # Remove it from config so it inherits from log_fmt_console self.config.pop(cli_log_fmt) logfmt = self.config.get( cli_log_fmt, self.config.get( 'log_fmt_console', self.config.get( 'log_fmt', config._DFLT_LOG_FMT_CONSOLE ) ) ) cli_log_datefmt = 'cli_{0}_log_datefmt'.format( self.get_prog_name().replace('-', '_') ) if cli_log_datefmt in self.config and not \ self.config.get(cli_log_datefmt): # Remove it from config so it inherits from log_datefmt_console self.config.pop(cli_log_datefmt) if self.config.get('log_datefmt_console', None) is None: # Remove it from config so it inherits from log_datefmt self.config.pop('log_datefmt_console', None) datefmt = self.config.get( cli_log_datefmt, self.config.get( 'log_datefmt_console', self.config.get( 'log_datefmt', '%Y-%m-%d %H:%M:%S' ) ) ) # Save the settings back to the configuration self.config['log_fmt_console'] = logfmt self.config['log_datefmt_console'] = datefmt def __setup_console_logger(self, *args): # pylint: disable=unused-argument # If daemon is set force console logger to quiet if getattr(self.options, 'daemon', False) is True: return log.setup_console_logger( self.config['log_level'], log_format=self.config['log_fmt_console'], date_format=self.config['log_datefmt_console'] ) for name, level in six.iteritems(self.config['log_granular_levels']): log.set_logger_level(name, level) class RunUserMixin(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 20 def _mixin_setup(self): self.add_option( '-u', '--user', help='Specify user to run {0}.'.format(self.get_prog_name()) ) class DaemonMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 30 def _mixin_setup(self): self.add_option( '-d', '--daemon', default=False, action='store_true', help='Run the {0} as a daemon.'.format(self.get_prog_name()) ) self.add_option( '--pid-file', dest='pidfile', default=os.path.join( syspaths.PIDFILE_DIR, '{0}.pid'.format(self.get_prog_name()) ), help=('Specify the location of the pidfile. Default: \'%default\'.') ) def _mixin_before_exit(self): if hasattr(self, 'config') and self.config.get('pidfile', ''): # We've loaded and merged options into the configuration, it's safe # to query about the pidfile if self.check_pidfile(): os.unlink(self.config['pidfile']) def set_pidfile(self): from salt.utils.process import set_pidfile set_pidfile(self.config['pidfile'], self.config['user']) def check_pidfile(self): ''' Report whether a pidfile exists ''' from salt.utils.process import check_pidfile return check_pidfile(self.config['pidfile']) def get_pidfile(self): ''' Return a pid contained in a pidfile ''' from salt.utils.process import get_pidfile return get_pidfile(self.config['pidfile']) def daemonize_if_required(self): if self.options.daemon: if self._setup_mp_logging_listener_ is True: # Stop the logging queue listener for the current process # We'll restart it once forked log.shutdown_multiprocessing_logging_listener(daemonizing=True) # Late import so logging works correctly salt.utils.daemonize() # Setup the multiprocessing log queue listener if enabled self._setup_mp_logging_listener() def check_running(self): ''' Check if a pid file exists and if it is associated with a running process. ''' # There is no os.getppid method for windows if salt.utils.is_windows(): from salt.utils.win_functions import get_parent_pid ppid = get_parent_pid() else: ppid = os.getppid() if self.check_pidfile(): pid = self.get_pidfile() if not salt.utils.is_windows(): if self.check_pidfile() and self.is_daemonized(pid) and not os.getppid() == pid: return True else: # We have no os.getppid() on Windows. Best effort. if self.check_pidfile() and self.is_daemonized(pid): return True return False def is_daemonized(self, pid): from salt.utils.process import os_is_running return os_is_running(pid) # Common methods for scripts which can daemonize def _install_signal_handlers(self): signal.signal(signal.SIGTERM, self._handle_signals) signal.signal(signal.SIGINT, self._handle_signals) def prepare(self): self.parse_args() def start(self): self.prepare() self._install_signal_handlers() def _handle_signals(self, signum, sigframe): # pylint: disable=unused-argument msg = self.__class__.__name__ if signum == signal.SIGINT: msg += ' received a SIGINT.' elif signum == signal.SIGTERM: msg += ' received a SIGTERM.' logging.getLogger(__name__).warning('{0} Exiting.'.format(msg)) self.shutdown(exitmsg='{0} Exited.'.format(msg)) def shutdown(self, exitcode=0, exitmsg=None): self.exit(exitcode, exitmsg) class PidfileMixin(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 40 def _mixin_setup(self): salt.utils.warn_until( 'Nitrogen', 'Please stop sub-classing PidfileMix and instead subclass ' 'DaemonMixIn which contains the same behavior. PidfileMixin ' 'will be supported until Salt {version}.' ) try: self.add_option( '--pid-file', dest='pidfile', default=os.path.join( syspaths.PIDFILE_DIR, '{0}.pid'.format(self.get_prog_name()) ), help=('Specify the location of the pidfile. Default: \'%default\'.') ) # Since there was no colision with DaemonMixin, let's add the # pidfile mixin methods. This is used using types.MethodType # because if we had defined these at the class level, they would # have overridden the exact same methods from the DaemonMixin. def set_pidfile(self): from salt.utils.process import set_pidfile set_pidfile(self.config['pidfile'], self.config['user']) self.set_pidfile = types.MethodType(set_pidfile, self) def check_pidfile(self): ''' Report whether a pidfile exists ''' from salt.utils.process import check_pidfile return check_pidfile(self.config['pidfile']) self.check_pidfile = types.MethodType(check_pidfile, self) def get_pidfile(self): ''' Return a pid contained in a pidfile ''' from salt.utils.process import get_pidfile return get_pidfile(self.config['pidfile']) self.get_pidfile = types.MethodType(get_pidfile, self) except optparse.OptionConflictError: # The option was already added by the DaemonMixin pass class TargetOptionsMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 20 selected_target_option = None def _mixin_setup(self): group = self.target_options_group = optparse.OptionGroup( self, 'Target Options', 'Target selection options.' ) self.add_option_group(group) group.add_option( '-E', '--pcre', default=False, action='store_true', help=('Instead of using shell globs to evaluate the target ' 'servers, use pcre regular expressions.') ) group.add_option( '-L', '--list', default=False, action='store_true', help=('Instead of using shell globs to evaluate the target ' 'servers, take a comma or space delimited list of ' 'servers.') ) group.add_option( '-G', '--grain', default=False, action='store_true', help=('Instead of using shell globs to evaluate the target ' 'use a grain value to identify targets, the syntax ' 'for the target is the grain key followed by a glob' 'expression: "os:Arch*".') ) group.add_option( '-P', '--grain-pcre', default=False, action='store_true', help=('Instead of using shell globs to evaluate the target ' 'use a grain value to identify targets, the syntax ' 'for the target is the grain key followed by a pcre ' 'regular expression: "os:Arch.*".') ) group.add_option( '-N', '--nodegroup', default=False, action='store_true', help=('Instead of using shell globs to evaluate the target ' 'use one of the predefined nodegroups to identify a ' 'list of targets.') ) group.add_option( '-R', '--range', default=False, action='store_true', help=('Instead of using shell globs to evaluate the target ' 'use a range expression to identify targets. ' 'Range expressions look like %cluster.') ) group = self.additional_target_options_group = optparse.OptionGroup( self, 'Additional Target Options', 'Additional options for minion targeting.' ) self.add_option_group(group) group.add_option( '--delimiter', default=DEFAULT_TARGET_DELIM, help=('Change the default delimiter for matching in multi-level ' 'data structures. Default: \'%default\'.') ) self._create_process_functions() def _create_process_functions(self): for option in self.target_options_group.option_list: def process(opt): if getattr(self.options, opt.dest): self.selected_target_option = opt.dest funcname = 'process_{0}'.format(option.dest) if not hasattr(self, funcname): setattr(self, funcname, partial(process, option)) def _mixin_after_parsed(self): group_options_selected = [ option for option in self.target_options_group.option_list if getattr(self.options, option.dest) is True ] if len(group_options_selected) > 1: self.error( 'The options {0} are mutually exclusive. Please only choose ' 'one of them'.format('/'.join( [option.get_opt_string() for option in group_options_selected])) ) self.config['selected_target_option'] = self.selected_target_option class ExtendedTargetOptionsMixIn(TargetOptionsMixIn): def _mixin_setup(self): TargetOptionsMixIn._mixin_setup(self) group = self.target_options_group group.add_option( '-C', '--compound', default=False, action='store_true', help=('The compound target option allows for multiple target ' 'types to be evaluated, allowing for greater granularity in ' 'target matching. The compound target is space delimited, ' 'targets other than globs are preceded with an identifier ' 'matching the specific targets argument type: salt ' '\'G@os:RedHat and webser* or E@database.*\'.') ) group.add_option( '-I', '--pillar', default=False, dest='pillar_target', action='store_true', help=('Instead of using shell globs to evaluate the target ' 'use a pillar value to identify targets, the syntax ' 'for the target is the pillar key followed by a glob ' 'expression: "role:production*".') ) group.add_option( '-J', '--pillar-pcre', default=False, action='store_true', help=('Instead of using shell globs to evaluate the target ' 'use a pillar value to identify targets, the syntax ' 'for the target is the pillar key followed by a pcre ' 'regular expression: "role:prod.*".') ) group.add_option( '-S', '--ipcidr', default=False, action='store_true', help=('Match based on Subnet (CIDR notation) or IP address.') ) self._create_process_functions() def process_pillar_target(self): if self.options.pillar_target: self.selected_target_option = 'pillar' class TimeoutMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 10 def _mixin_setup(self): if not hasattr(self, 'default_timeout'): raise RuntimeError( 'You need to define the \'default_timeout\' attribute ' 'on {0}'.format(self.__class__.__name__) ) self.add_option( '-t', '--timeout', type=int, default=self.default_timeout, help=('Change the timeout, if applicable, for the running ' 'command (in seconds). Default: %default.') ) class ArgsStdinMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 10 def _mixin_setup(self): self.add_option( '--args-stdin', default=False, dest='args_stdin', action='store_true', help=('Read additional options and/or arguments from stdin. ' 'Each entry is newline separated.') ) class ProxyIdMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio = 40 def _mixin_setup(self): self.add_option( '--proxyid', default=None, dest='proxyid', help=('Id for this proxy.') ) class OutputOptionsMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 40 _include_text_out_ = False selected_output_option = None def _mixin_setup(self): group = self.output_options_group = optparse.OptionGroup( self, 'Output Options', 'Configure your preferred output format.' ) self.add_option_group(group) group.add_option( '--out', '--output', dest='output', help=( 'Print the output from the \'{0}\' command using the ' 'specified outputter.'.format( self.get_prog_name(), ) ) ) group.add_option( '--out-indent', '--output-indent', dest='output_indent', default=None, type=int, help=('Print the output indented by the provided value in spaces. ' 'Negative values disables indentation. Only applicable in ' 'outputters that support indentation.') ) group.add_option( '--out-file', '--output-file', dest='output_file', default=None, help='Write the output to the specified file.' ) group.add_option( '--out-file-append', '--output-file-append', action='store_true', dest='output_file_append', default=False, help='Append the output to the specified file.' ) group.add_option( '--no-color', '--no-colour', default=False, action='store_true', help='Disable all colored output.' ) group.add_option( '--force-color', '--force-colour', default=False, action='store_true', help='Force colored output.' ) group.add_option( '--state-output', '--state_output', default=None, help=('Override the configured state_output value for minion ' 'output. One of \'full\', \'terse\', \'mixed\', \'changes\' or \'filter\'. ' 'Default: \'%default\'.') ) group.add_option( '--state-verbose', '--state_verbose', default=None, help=('Override the configured state_verbose value for minion ' 'output. Set to True or False. Default: %default.') ) for option in self.output_options_group.option_list: def process(opt): default = self.defaults.get(opt.dest) if getattr(self.options, opt.dest, default) is False: return self.selected_output_option = opt.dest funcname = 'process_{0}'.format(option.dest) if not hasattr(self, funcname): setattr(self, funcname, partial(process, option)) def process_output(self): self.selected_output_option = self.options.output def process_output_file(self): if self.options.output_file is not None and self.options.output_file_append is False: if os.path.isfile(self.options.output_file): try: with salt.utils.fopen(self.options.output_file, 'w') as ofh: # Make this a zero length filename instead of removing # it. This way we keep the file permissions. ofh.write('') except (IOError, OSError) as exc: self.error( '{0}: Access denied: {1}'.format( self.options.output_file, exc ) ) def process_state_verbose(self): if self.options.state_verbose == "True" or self.options.state_verbose == "true": self.options.state_verbose = True elif self.options.state_verbose == "False" or self.options.state_verbose == "false": self.options.state_verbose = False def _mixin_after_parsed(self): group_options_selected = [ option for option in self.output_options_group.option_list if ( getattr(self.options, option.dest) and (option.dest.endswith('_out') or option.dest == 'output')) ] if len(group_options_selected) > 1: self.error( 'The options {0} are mutually exclusive. Please only choose ' 'one of them'.format('/'.join([ option.get_opt_string() for option in group_options_selected ])) ) self.config['selected_output_option'] = self.selected_output_option class ExecutionOptionsMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 10 def _mixin_setup(self): group = self.execution_group = optparse.OptionGroup( self, 'Execution Options', # Include description here as a string ) group.add_option( '-L', '--location', default=None, help='Specify which region to connect to.' ) group.add_option( '-a', '--action', default=None, help='Perform an action that may be specific to this cloud ' 'provider. This argument requires one or more instance ' 'names to be specified.' ) group.add_option( '-f', '--function', nargs=2, default=None, metavar='<FUNC-NAME> <PROVIDER>', help='Perform an function that may be specific to this cloud ' 'provider, that does not apply to an instance. This ' 'argument requires a provider to be specified (i.e.: nova).' ) group.add_option( '-p', '--profile', default=None, help='Create an instance using the specified profile.' ) group.add_option( '-m', '--map', default=None, help='Specify a cloud map file to use for deployment. This option ' 'may be used alone, or in conjunction with -Q, -F, -S or -d. ' 'The map can also be filtered by a list of VM names.' ) group.add_option( '-H', '--hard', default=False, action='store_true', help='Delete all VMs that are not defined in the map file. ' 'CAUTION!!! This operation can irrevocably destroy VMs! It ' 'must be explicitly enabled in the cloud config file.' ) group.add_option( '-d', '--destroy', default=False, action='store_true', help='Destroy the specified instance(s).' ) group.add_option( '--no-deploy', default=True, dest='deploy', action='store_false', help='Don\'t run a deploy script after instance creation.' ) group.add_option( '-P', '--parallel', default=False, action='store_true', help='Build all of the specified instances in parallel.' ) group.add_option( '-u', '--update-bootstrap', default=False, action='store_true', help='Update salt-bootstrap to the latest stable bootstrap release.' ) group.add_option( '-y', '--assume-yes', default=False, action='store_true', help='Default "yes" in answer to all confirmation questions.' ) group.add_option( '-k', '--keep-tmp', default=False, action='store_true', help='Do not remove files from /tmp/ after deploy.sh finishes.' ) group.add_option( '--show-deploy-args', default=False, action='store_true', help='Include the options used to deploy the minion in the data ' 'returned.' ) group.add_option( '--script-args', default=None, help='Script arguments to be fed to the bootstrap script when ' 'deploying the VM.' ) group.add_option( '-b', '--bootstrap', nargs=1, default=False, metavar='<HOST> [MINION_ID] [OPTIONS...]', help='Bootstrap an existing machine.' ) self.add_option_group(group) def process_function(self): if self.options.function: self.function_name, self.function_provider = self.options.function if self.function_provider.startswith('-') or \ '=' in self.function_provider: self.error( '--function expects two arguments: <function-name> ' '<provider>' ) class CloudQueriesMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 20 selected_query_option = None def _mixin_setup(self): group = self.cloud_queries_group = optparse.OptionGroup( self, 'Query Options', # Include description here as a string ) group.add_option( '-Q', '--query', default=False, action='store_true', help=('Execute a query and return some information about the ' 'nodes running on configured cloud providers.') ) group.add_option( '-F', '--full-query', default=False, action='store_true', help=('Execute a query and return all information about the ' 'nodes running on configured cloud providers.') ) group.add_option( '-S', '--select-query', default=False, action='store_true', help=('Execute a query and return select information about ' 'the nodes running on configured cloud providers.') ) group.add_option( '--list-providers', default=False, action='store_true', help='Display a list of configured providers.' ) group.add_option( '--list-profiles', default=None, action='store', help='Display a list of configured profiles. Pass in a cloud ' 'provider to view the provider\'s associated profiles, ' 'such as digital_ocean, or pass in "all" to list all the ' 'configured profiles.' ) self.add_option_group(group) self._create_process_functions() def _create_process_functions(self): for option in self.cloud_queries_group.option_list: def process(opt): if getattr(self.options, opt.dest): query = 'list_nodes' if opt.dest == 'full_query': query += '_full' elif opt.dest == 'select_query': query += '_select' elif opt.dest == 'list_providers': query = 'list_providers' if self.args: self.error( '\'--list-providers\' does not accept any ' 'arguments' ) elif opt.dest == 'list_profiles': query = 'list_profiles' option_dict = vars(self.options) if option_dict.get('list_profiles') == '--list-providers': self.error( '\'--list-profiles\' does not accept ' '\'--list-providers\' as an argument' ) self.selected_query_option = query funcname = 'process_{0}'.format(option.dest) if not hasattr(self, funcname): setattr(self, funcname, partial(process, option)) def _mixin_after_parsed(self): group_options_selected = [ option for option in self.cloud_queries_group.option_list if getattr(self.options, option.dest) is not False and getattr(self.options, option.dest) is not None ] if len(group_options_selected) > 1: self.error( 'The options {0} are mutually exclusive. Please only choose ' 'one of them'.format('/'.join([ option.get_opt_string() for option in group_options_selected ])) ) self.config['selected_query_option'] = self.selected_query_option class CloudProvidersListsMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 30 def _mixin_setup(self): group = self.providers_listings_group = optparse.OptionGroup( self, 'Cloud Providers Listings', # Include description here as a string ) group.add_option( '--list-locations', default=None, help=('Display a list of locations available in configured cloud ' 'providers. Pass the cloud provider that available ' 'locations are desired on, aka "linode", or pass "all" to ' 'list locations for all configured cloud providers.') ) group.add_option( '--list-images', default=None, help=('Display a list of images available in configured cloud ' 'providers. Pass the cloud provider that available images ' 'are desired on, aka "linode", or pass "all" to list images ' 'for all configured cloud providers.') ) group.add_option( '--list-sizes', default=None, help=('Display a list of sizes available in configured cloud ' 'providers. Pass the cloud provider that available sizes ' 'are desired on, aka "AWS", or pass "all" to list sizes ' 'for all configured cloud providers.') ) self.add_option_group(group) def _mixin_after_parsed(self): list_options_selected = [ option for option in self.providers_listings_group.option_list if getattr(self.options, option.dest) is not None ] if len(list_options_selected) > 1: self.error( 'The options {0} are mutually exclusive. Please only choose ' 'one of them'.format( '/'.join([ option.get_opt_string() for option in list_options_selected ]) ) ) class ProfilingPMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 130 def _mixin_setup(self): group = self.profiling_group = optparse.OptionGroup( self, 'Profiling support', # Include description here as a string ) group.add_option( '--profiling-path', dest='profiling_path', default='/tmp/stats', help=('Folder that will hold all stats generations path. Default: \'%default\'.') ) group.add_option( '--enable-profiling', dest='profiling_enabled', default=False, action='store_true', help=('Enable generating profiling stats. See also: --profiling-path.') ) self.add_option_group(group) class CloudCredentialsMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 30 def _mixin_setup(self): group = self.cloud_credentials_group = optparse.OptionGroup( self, 'Cloud Credentials', # Include description here as a string ) group.add_option( '--set-password', default=None, nargs=2, metavar='<USERNAME> <PROVIDER>', help=('Configure password for a cloud provider and save it to the keyring. ' 'PROVIDER can be specified with or without a driver, for example: ' '"--set-password bob rackspace" or more specific ' '"--set-password bob rackspace:openstack" ' 'Deprecated.') ) self.add_option_group(group) def process_set_password(self): if self.options.set_password: raise RuntimeError( 'This functionality is not supported; ' 'please see the keyring module at http://docs.saltstack.com/en/latest/topics/sdb/' ) class EAuthMixIn(six.with_metaclass(MixInMeta, object)): _mixin_prio_ = 30 def _mixin_setup(self): group = self.eauth_group = optparse.OptionGroup( self, 'External Authentication', # Include description here as a string ) group.add_option( '-a', '--auth', '--eauth', '--external-auth', default='', dest='eauth', help=('Specify an external authentication system to use.') ) group.add_option( '-T', '--make-token', default=False, dest='mktoken', action='store_true', help=('Generate and save an authentication token for re-use. The ' 'token is generated and made available for the period ' 'defined in the Salt Master.') ) group.add_option( '--username', dest='username', nargs=1, help=('Username for external authentication.') ) group.add_option( '--password', dest='password', nargs=1, help=('Password for external authentication.') ) self.add_option_group(group) class MasterOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, MergeConfigMixIn, LogLevelMixIn, RunUserMixin, DaemonMixIn, SaltfileMixIn)): description = 'The Salt master, used to control the Salt minions.' # ConfigDirMixIn config filename attribute _config_filename_ = 'master' # LogLevelMixIn attributes _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'master') _setup_mp_logging_listener_ = True def setup_config(self): return config.master_config(self.get_config_file_path()) class MinionOptionParser(six.with_metaclass(OptionParserMeta, MasterOptionParser)): # pylint: disable=no-init description = ( 'The Salt minion, receives commands from a remote Salt master.' ) # ConfigDirMixIn config filename attribute _config_filename_ = 'minion' # LogLevelMixIn attributes _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'minion') _setup_mp_logging_listener_ = True def setup_config(self): opts = config.minion_config(self.get_config_file_path(), # pylint: disable=no-member cache_minion_id=True, ignore_config_errors=False) # Optimization: disable multiprocessing logging if running as a # daemon, without engines and without multiprocessing if not opts.get('engines') and not opts.get('multiprocessing', True) \ and self.options.daemon: # pylint: disable=no-member self._setup_mp_logging_listener_ = False return opts class ProxyMinionOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, ProxyIdMixIn, ConfigDirMixIn, MergeConfigMixIn, LogLevelMixIn, RunUserMixin, DaemonMixIn, SaltfileMixIn)): # pylint: disable=no-init description = ( 'The Salt proxy minion, connects to and controls devices not able to run a minion. ' 'Receives commands from a remote Salt master.' ) # ConfigDirMixIn config filename attribute _config_filename_ = 'proxy' # LogLevelMixIn attributes _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'proxy') def setup_config(self): try: minion_id = self.values.proxyid except AttributeError: minion_id = None return config.minion_config(self.get_config_file_path(), cache_minion_id=False, minion_id=minion_id) class SyndicOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, MergeConfigMixIn, LogLevelMixIn, RunUserMixin, DaemonMixIn, SaltfileMixIn)): description = ( 'A seamless master of masters. Scale Salt to thousands of hosts or ' 'across many different networks.' ) # ConfigDirMixIn config filename attribute _config_filename_ = 'master' # LogLevelMixIn attributes _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'master') _setup_mp_logging_listener_ = True def setup_config(self): return config.syndic_config( self.get_config_file_path(), self.get_config_file_path('minion')) class SaltCMDOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, MergeConfigMixIn, TimeoutMixIn, ExtendedTargetOptionsMixIn, OutputOptionsMixIn, LogLevelMixIn, HardCrashMixin, SaltfileMixIn, ArgsStdinMixIn, EAuthMixIn)): default_timeout = 5 usage = '%prog [options] \'<target>\' <function> [arguments]' # ConfigDirMixIn config filename attribute _config_filename_ = 'master' # LogLevelMixIn attributes _default_logging_level_ = 'warning' _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'master') _loglevel_config_setting_name_ = 'cli_salt_log_file' try: os.getcwd() except OSError: sys.exit("Cannot access current working directory. Exiting!") def _mixin_setup(self): self.add_option( '-s', '--static', default=False, action='store_true', help=('Return the data from minions as a group after they ' 'all return.') ) self.add_option( '-p', '--progress', default=False, action='store_true', help=('Display a progress graph. Requires "progressbar" python package.') ) self.add_option( '--failhard', default=False, action='store_true', help=('Stop batch execution upon first "bad" return.') ) self.add_option( '--async', default=False, dest='async', action='store_true', help=('Run the salt command but don\'t wait for a reply.') ) self.add_option( '--subset', default=0, type=int, help=('Execute the routine on a random subset of the targeted ' 'minions. The minions will be verified that they have the ' 'named function before executing.') ) self.add_option( '-v', '--verbose', default=False, action='store_true', help=('Turn on command verbosity, display jid and active job ' 'queries.') ) self.add_option( '--hide-timeout', dest='show_timeout', default=True, action='store_false', help=('Hide minions that timeout.') ) self.add_option( '--show-jid', default=False, action='store_true', help=('Display jid without the additional output of --verbose.') ) self.add_option( '-b', '--batch', '--batch-size', default='', dest='batch', help=('Execute the salt job in batch mode, pass either the number ' 'of minions to batch at a time, or the percentage of ' 'minions to have running.') ) self.add_option( '--batch-wait', default=0, dest='batch_wait', type=float, help=('Wait the specified time in seconds after each job is done ' 'before freeing the slot in the batch for the next one.') ) self.add_option( '--return', default='', metavar='RETURNER', help=('Set an alternative return method. By default salt will ' 'send the return data from the command back to the master, ' 'but the return data can be redirected into any number of ' 'systems, databases or applications.') ) self.add_option( '--return_config', default='', metavar='RETURNER_CONF', help=('Set an alternative return method. By default salt will ' 'send the return data from the command back to the master, ' 'but the return data can be redirected into any number of ' 'systems, databases or applications.') ) self.add_option( '--return_kwargs', default={}, metavar='RETURNER_KWARGS', help=('Set any returner options at the command line.') ) self.add_option( '--module-executors', dest='module_executors', default=None, metavar='EXECUTOR_LIST', help=('Set an alternative list of executors to override the one ' 'set in minion config.') ) self.add_option( '-d', '--doc', '--documentation', dest='doc', default=False, action='store_true', help=('Return the documentation for the specified module or for ' 'all modules if none are specified.') ) self.add_option( '--args-separator', dest='args_separator', default=',', help=('Set the special argument used as a delimiter between ' 'command arguments of compound commands. This is useful ' 'when one wants to pass commas as arguments to ' 'some of the commands in a compound command.') ) self.add_option( '--summary', dest='cli_summary', default=False, action='store_true', help=('Display summary information about a salt command.') ) self.add_option( '--metadata', default='', metavar='METADATA', help=('Pass metadata into Salt, used to search jobs.') ) self.add_option( '--output-diff', dest='state_output_diff', action='store_true', default=False, help=('Report only those states that have changed.') ) self.add_option( '--config-dump', dest='config_dump', action='store_true', default=False, help=('Dump the master configuration values') ) def _mixin_after_parsed(self): if len(self.args) <= 1 and not self.options.doc: try: self.print_help() except Exception: # pylint: disable=broad-except # We get an argument that Python's optparser just can't deal # with. Perhaps stdout was redirected, or a file glob was # passed in. Regardless, we're in an unknown state here. sys.stdout.write('Invalid options passed. Please try -h for ' 'help.') # Try to warn if we can. sys.exit(salt.defaults.exitcodes.EX_GENERIC) # Dump the master configuration file, exit normally at the end. if self.options.config_dump: cfg = config.master_config(self.get_config_file_path()) sys.stdout.write(yaml.dump(cfg, default_flow_style=False)) sys.exit(salt.defaults.exitcodes.EX_OK) if self.options.doc: # Include the target if not self.args: self.args.insert(0, '*') if len(self.args) < 2: # Include the function self.args.insert(1, 'sys.doc') if self.args[1] != 'sys.doc': self.args.insert(1, 'sys.doc') if len(self.args) > 3: self.error('You can only get documentation for one method at one time.') if self.options.list: try: if ',' in self.args[0]: self.config['tgt'] = self.args[0].replace(' ', '').split(',') else: self.config['tgt'] = self.args[0].split() except IndexError: self.exit(42, '\nCannot execute command without defining a target.\n\n') else: try: self.config['tgt'] = self.args[0] except IndexError: self.exit(42, '\nCannot execute command without defining a target.\n\n') # Detect compound command and set up the data for it if self.args: try: if ',' in self.args[1]: self.config['fun'] = self.args[1].split(',') self.config['arg'] = [[]] cmd_index = 0 if (self.args[2:].count(self.options.args_separator) == len(self.config['fun']) - 1): # new style parsing: standalone argument separator for arg in self.args[2:]: if arg == self.options.args_separator: cmd_index += 1 self.config['arg'].append([]) else: self.config['arg'][cmd_index].append(arg) else: # old style parsing: argument separator can be inside args for arg in self.args[2:]: if self.options.args_separator in arg: sub_args = arg.split(self.options.args_separator) for sub_arg_index, sub_arg in enumerate(sub_args): if sub_arg: self.config['arg'][cmd_index].append(sub_arg) if sub_arg_index != len(sub_args) - 1: cmd_index += 1 self.config['arg'].append([]) else: self.config['arg'][cmd_index].append(arg) if len(self.config['fun']) != len(self.config['arg']): self.exit(42, 'Cannot execute compound command without ' 'defining all arguments.\n') # parse the args and kwargs before sending to the publish # interface for i in range(len(self.config['arg'])): self.config['arg'][i] = salt.utils.args.parse_input( self.config['arg'][i]) else: self.config['fun'] = self.args[1] self.config['arg'] = self.args[2:] # parse the args and kwargs before sending to the publish # interface self.config['arg'] = \ salt.utils.args.parse_input(self.config['arg']) except IndexError: self.exit(42, '\nIncomplete options passed.\n\n') def setup_config(self): return config.client_config(self.get_config_file_path()) class SaltCPOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, OutputOptionsMixIn, ConfigDirMixIn, MergeConfigMixIn, TimeoutMixIn, TargetOptionsMixIn, LogLevelMixIn, HardCrashMixin, SaltfileMixIn)): description = ( 'salt-cp is NOT intended to broadcast large files, it is intended to ' 'handle text files.\nsalt-cp can be used to distribute configuration ' 'files.' ) default_timeout = 5 usage = '%prog [options] \'<target>\' SOURCE DEST' # ConfigDirMixIn config filename attribute _config_filename_ = 'master' # LogLevelMixIn attributes _default_logging_level_ = 'warning' _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'master') _loglevel_config_setting_name_ = 'cli_salt_cp_log_file' def _mixin_after_parsed(self): # salt-cp needs arguments if len(self.args) <= 1: self.print_help() self.error('Insufficient arguments') if self.options.list: if ',' in self.args[0]: self.config['tgt'] = self.args[0].split(',') else: self.config['tgt'] = self.args[0].split() else: self.config['tgt'] = self.args[0] self.config['src'] = self.args[1:-1] self.config['dest'] = self.args[-1] def setup_config(self): return config.master_config(self.get_config_file_path()) class SaltKeyOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, MergeConfigMixIn, LogLevelMixIn, OutputOptionsMixIn, RunUserMixin, HardCrashMixin, SaltfileMixIn, EAuthMixIn)): description = 'Salt key is used to manage Salt authentication keys' usage = '%prog [options]' # ConfigDirMixIn config filename attribute _config_filename_ = 'master' # LogLevelMixIn attributes _skip_console_logging_config_ = True _logfile_config_setting_name_ = 'key_logfile' _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'key') def _mixin_setup(self): actions_group = optparse.OptionGroup(self, 'Actions') actions_group.set_conflict_handler('resolve') actions_group.add_option( '-l', '--list', default='', metavar='ARG', help=('List the public keys. The args ' '\'pre\', \'un\', and \'unaccepted\' will list ' 'unaccepted/unsigned keys. ' '\'acc\' or \'accepted\' will list accepted/signed keys. ' '\'rej\' or \'rejected\' will list rejected keys. ' '\'den\' or \'denied\' will list denied keys. ' 'Finally, \'all\' will list all keys.') ) actions_group.add_option( '-L', '--list-all', default=False, action='store_true', help='List all public keys. Deprecated: use "--list all".' ) actions_group.add_option( '-a', '--accept', default='', help='Accept the specified public key (use --include-rejected and ' '--include-denied to match rejected and denied keys in ' 'addition to pending keys). Globs are supported.', ) actions_group.add_option( '-A', '--accept-all', default=False, action='store_true', help='Accept all pending keys.' ) actions_group.add_option( '-r', '--reject', default='', help='Reject the specified public key. Use --include-accepted and ' '--include-denied to match accepted and denied keys in ' 'addition to pending keys. Globs are supported.' ) actions_group.add_option( '-R', '--reject-all', default=False, action='store_true', help='Reject all pending keys.' ) actions_group.add_option( '--include-all', default=False, action='store_true', help='Include rejected/accepted keys when accepting/rejecting. ' 'Deprecated: use "--include-rejected" and "--include-accepted".' ) actions_group.add_option( '--include-accepted', default=False, action='store_true', help='Include accepted keys when rejecting.' ) actions_group.add_option( '--include-rejected', default=False, action='store_true', help='Include rejected keys when accepting.' ) actions_group.add_option( '--include-denied', default=False, action='store_true', help='Include denied keys when accepting/rejecting.' ) actions_group.add_option( '-p', '--print', default='', help='Print the specified public key.' ) actions_group.add_option( '-P', '--print-all', default=False, action='store_true', help='Print all public keys.' ) actions_group.add_option( '-d', '--delete', default='', help='Delete the specified key. Globs are supported.' ) actions_group.add_option( '-D', '--delete-all', default=False, action='store_true', help='Delete all keys.' ) actions_group.add_option( '-f', '--finger', default='', help='Print the specified key\'s fingerprint.' ) actions_group.add_option( '-F', '--finger-all', default=False, action='store_true', help='Print all keys\' fingerprints.' ) self.add_option_group(actions_group) self.add_option( '-q', '--quiet', default=False, action='store_true', help='Suppress output.' ) self.add_option( '-y', '--yes', default=False, action='store_true', help='Answer "Yes" to all questions presented. Default: %default.' ) self.add_option( '--rotate-aes-key', default=True, help=('Setting this to False prevents the master from refreshing ' 'the key session when keys are deleted or rejected, this ' 'lowers the security of the key deletion/rejection operation. ' 'Default: %default.') ) key_options_group = optparse.OptionGroup( self, 'Key Generation Options' ) self.add_option_group(key_options_group) key_options_group.add_option( '--gen-keys', default='', help='Set a name to generate a keypair for use with salt.' ) key_options_group.add_option( '--gen-keys-dir', default='.', help=('Set the directory to save the generated keypair, only ' 'works with "gen_keys_dir" option. Default: \'%default\'.') ) key_options_group.add_option( '--keysize', default=2048, type=int, help=('Set the keysize for the generated key, only works with ' 'the "--gen-keys" option, the key size must be 2048 or ' 'higher, otherwise it will be rounded up to 2048. ' 'Default: %default.') ) key_options_group.add_option( '--gen-signature', default=False, action='store_true', help=('Create a signature file of the masters public-key named ' 'master_pubkey_signature. The signature can be send to a ' 'minion in the masters auth-reply and enables the minion ' 'to verify the masters public-key cryptographically. ' 'This requires a new signing-key-pair which can be auto-created ' 'with the --auto-create parameter.') ) key_options_group.add_option( '--priv', default='', type=str, help=('The private-key file to create a signature with.') ) key_options_group.add_option( '--signature-path', default='', type=str, help=('The path where the signature file should be written.') ) key_options_group.add_option( '--pub', default='', type=str, help=('The public-key file to create a signature for.') ) key_options_group.add_option( '--auto-create', default=False, action='store_true', help=('Auto-create a signing key-pair if it does not yet exist.') ) def process_config_dir(self): if self.options.gen_keys: # We're generating keys, override the default behavior of this # function if we don't have any access to the configuration # directory. if not os.access(self.options.config_dir, os.R_OK): if not os.path.isdir(self.options.gen_keys_dir): # This would be done at a latter stage, but we need it now # so no errors are thrown os.makedirs(self.options.gen_keys_dir) self.options.config_dir = self.options.gen_keys_dir super(SaltKeyOptionParser, self).process_config_dir() # Don't change its mixin priority! process_config_dir._mixin_prio_ = ConfigDirMixIn._mixin_prio_ def setup_config(self): keys_config = config.master_config(self.get_config_file_path()) if self.options.gen_keys: # Since we're generating the keys, some defaults can be assumed # or tweaked keys_config['key_logfile'] = os.devnull keys_config['pki_dir'] = self.options.gen_keys_dir return keys_config def process_rotate_aes_key(self): if hasattr(self.options, 'rotate_aes_key') and isinstance(self.options.rotate_aes_key, str): if self.options.rotate_aes_key.lower() == 'true': self.options.rotate_aes_key = True elif self.options.rotate_aes_key.lower() == 'false': self.options.rotate_aes_key = False def process_list(self): # Filter accepted list arguments as soon as possible if not self.options.list: return if not self.options.list.startswith(('acc', 'pre', 'un', 'rej', 'den', 'all')): self.error( '\'{0}\' is not a valid argument to \'--list\''.format( self.options.list ) ) def process_keysize(self): if self.options.keysize < 2048: self.error('The minimum value for keysize is 2048') elif self.options.keysize > 32768: self.error('The maximum value for keysize is 32768') def process_gen_keys_dir(self): # Schedule __create_keys_dir() to run if there's a value for # --create-keys-dir self._mixin_after_parsed_funcs.append(self.__create_keys_dir) # pylint: disable=no-member def _mixin_after_parsed(self): # It was decided to always set this to info, since it really all is # info or error. self.config['loglevel'] = 'info' def __create_keys_dir(self, *args): # pylint: disable=unused-argument if not os.path.isdir(self.config['gen_keys_dir']): os.makedirs(self.config['gen_keys_dir']) class SaltCallOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, MergeConfigMixIn, LogLevelMixIn, OutputOptionsMixIn, HardCrashMixin, SaltfileMixIn, ArgsStdinMixIn, ProfilingPMixIn)): description = ('Salt call is used to execute module functions locally ' 'on a minion') usage = '%prog [options] <function> [arguments]' # ConfigDirMixIn config filename attribute _config_filename_ = 'minion' # LogLevelMixIn attributes _default_logging_level_ = 'info' _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'minion') def _mixin_setup(self): self.add_option( '-g', '--grains', dest='grains_run', default=False, action='store_true', help='Return the information generated by the salt grains.' ) self.add_option( '-m', '--module-dirs', default=[], action='append', help=('Specify an additional directory to pull modules from. ' 'Multiple directories can be provided by passing ' '`-m/--module-dirs` multiple times.') ) self.add_option( '-d', '--doc', '--documentation', dest='doc', default=False, action='store_true', help=('Return the documentation for the specified module or for ' 'all modules if none are specified.') ) self.add_option( '--master', default='', dest='master', help=('Specify the master to use. The minion must be ' 'authenticated with the master. If this option is omitted, ' 'the master options from the minion config will be used. ' 'If multi masters are set up the first listed master that ' 'responds will be used.') ) self.add_option( '--return', default='', metavar='RETURNER', help=('Set salt-call to pass the return data to one or many ' 'returner interfaces.') ) self.add_option( '--local', default=False, action='store_true', help='Run salt-call locally, as if there was no master running.' ) self.add_option( '--file-root', default=None, help='Set this directory as the base file root.' ) self.add_option( '--pillar-root', default=None, help='Set this directory as the base pillar root.' ) self.add_option( '--states-dir', default=None, help='Set this directory to search for additional states.' ) self.add_option( '--retcode-passthrough', default=False, action='store_true', help=('Exit with the salt call retcode and not the salt binary ' 'retcode.') ) self.add_option( '--metadata', default=False, dest='print_metadata', action='store_true', help=('Print out the execution metadata as well as the return. ' 'This will print out the outputter data, the return code, ' 'etc.') ) self.add_option( '--set-metadata', dest='metadata', default=None, metavar='METADATA', help=('Pass metadata into Salt, used to search jobs.') ) self.add_option( '--id', default='', dest='id', help=('Specify the minion id to use. If this option is omitted, ' 'the id option from the minion config will be used.') ) self.add_option( '--skip-grains', default=False, action='store_true', help=('Do not load grains.') ) self.add_option( '--refresh-grains-cache', default=False, action='store_true', help=('Force a refresh of the grains cache.') ) self.add_option( '-t', '--timeout', default=60, dest='auth_timeout', type=int, help=('Change the timeout, if applicable, for the running ' 'command. Default: %default.') ) self.add_option( '--output-diff', dest='state_output_diff', action='store_true', default=False, help=('Report only those states that have changed.') ) def _mixin_after_parsed(self): if not self.args and not self.options.grains_run and not self.options.doc: self.print_help() self.error('Requires function, --grains or --doc') elif len(self.args) >= 1: if self.options.grains_run: self.error('-g/--grains does not accept any arguments') if self.options.doc and len(self.args) > 1: self.error('You can only get documentation for one method at one time') self.config['fun'] = self.args[0] self.config['arg'] = self.args[1:] def setup_config(self): opts = config.minion_config(self.get_config_file_path(), cache_minion_id=True) if opts.get('transport') == 'raet': if not self._find_raet_minion(opts): # must create caller minion opts['__role'] = kinds.APPL_KIND_NAMES[kinds.applKinds.caller] return opts def _find_raet_minion(self, opts): ''' Returns true if local RAET Minion is available ''' yardname = 'manor' dirpath = opts['sock_dir'] role = opts.get('id') if not role: emsg = ("Missing role required to setup RAET SaltCaller.") logging.getLogger(__name__).error(emsg + "\n") raise ValueError(emsg) kind = opts.get('__role') # application kind 'master', 'minion', etc if kind not in kinds.APPL_KINDS: emsg = ("Invalid application kind = '{0}' for RAET SaltCaller.".format(kind)) logging.getLogger(__name__).error(emsg + "\n") raise ValueError(emsg) if kind in [kinds.APPL_KIND_NAMES[kinds.applKinds.minion], kinds.APPL_KIND_NAMES[kinds.applKinds.caller], ]: lanename = "{0}_{1}".format(role, kind) else: emsg = ("Unsupported application kind '{0}' for RAET SaltCaller.".format(kind)) logging.getLogger(__name__).error(emsg + '\n') raise ValueError(emsg) if kind == kinds.APPL_KIND_NAMES[kinds.applKinds.minion]: # minion check from raet.lane.yarding import Yard ha, dirpath = Yard.computeHa(dirpath, lanename, yardname) # pylint: disable=invalid-name if (os.path.exists(ha) and not os.path.isfile(ha) and not os.path.isdir(ha)): # minion manor yard return True return False def process_module_dirs(self): for module_dir in self.options.module_dirs: # Provide some backwards compatibility with previous comma # delimited format if ',' in module_dir: self.config.setdefault('module_dirs', []).extend( os.path.abspath(x) for x in module_dir.split(',')) continue self.config.setdefault('module_dirs', []).append(os.path.abspath(module_dir)) class SaltRunOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, MergeConfigMixIn, TimeoutMixIn, LogLevelMixIn, HardCrashMixin, SaltfileMixIn, OutputOptionsMixIn, ArgsStdinMixIn, ProfilingPMixIn, EAuthMixIn)): default_timeout = 1 usage = '%prog [options]' # ConfigDirMixIn config filename attribute _config_filename_ = 'master' # LogLevelMixIn attributes _default_logging_level_ = 'warning' _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'master') _loglevel_config_setting_name_ = 'cli_salt_run_log_file' def _mixin_setup(self): self.add_option( '-d', '--doc', '--documentation', dest='doc', default=False, action='store_true', help=('Display documentation for runners, pass a runner or ' 'runner.function to see documentation on only that runner ' 'or function.') ) self.add_option( '--async', default=False, action='store_true', help=('Start the runner operation and immediately return control.') ) group = self.output_options_group = optparse.OptionGroup( self, 'Output Options', 'Configure your preferred output format.' ) self.add_option_group(group) group.add_option( '--quiet', default=False, action='store_true', help='Do not display the results of the run.' ) def _mixin_after_parsed(self): if self.options.doc and len(self.args) > 1: self.error('You can only get documentation for one method at one time') if len(self.args) > 0: self.config['fun'] = self.args[0] else: self.config['fun'] = '' if len(self.args) > 1: self.config['arg'] = self.args[1:] else: self.config['arg'] = [] def setup_config(self): return config.client_config(self.get_config_file_path()) class SaltSSHOptionParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, MergeConfigMixIn, LogLevelMixIn, TargetOptionsMixIn, OutputOptionsMixIn, SaltfileMixIn, HardCrashMixin)): usage = '%prog [options]' # ConfigDirMixIn config filename attribute _config_filename_ = 'master' # LogLevelMixIn attributes _default_logging_level_ = 'warning' _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'ssh') _loglevel_config_setting_name_ = 'cli_salt_run_log_file' def _mixin_setup(self): self.add_option( '-r', '--raw', '--raw-shell', dest='raw_shell', default=False, action='store_true', help=('Don\'t execute a salt routine on the targets, execute a ' 'raw shell command.') ) self.add_option( '--roster', dest='roster', default='flat', help=('Define which roster system to use, this defines if a ' 'database backend, scanner, or custom roster system is ' 'used. Default: \'flat\'.') ) self.add_option( '--roster-file', dest='roster_file', default='', help=('Define an alternative location for the default roster ' 'file location. The default roster file is called roster ' 'and is found in the same directory as the master config ' 'file.') ) self.add_option( '--refresh', '--refresh-cache', dest='refresh_cache', default=False, action='store_true', help=('Force a refresh of the master side data cache of the ' 'target\'s data. This is needed if a target\'s grains have ' 'been changed and the auto refresh timeframe has not been ' 'reached.') ) self.add_option( '--max-procs', dest='ssh_max_procs', default=25, type=int, help='Set the number of concurrent minions to communicate with. ' 'This value defines how many processes are opened up at a ' 'time to manage connections, the more running processes the ' 'faster communication should be. Default: %default.' ) self.add_option( '--extra-filerefs', dest='extra_filerefs', default=None, help='Pass in extra files to include in the state tarball.' ) self.add_option('--min-extra-modules', dest='min_extra_mods', default=None, help='One or comma-separated list of extra Python modules' 'to be included into Minimal Salt.') self.add_option( '--thin-extra-modules', dest='thin_extra_mods', default=None, help='One or comma-separated list of extra Python modules' 'to be included into Thin Salt.') self.add_option( '-v', '--verbose', default=False, action='store_true', help=('Turn on command verbosity, display jid.') ) self.add_option( '-s', '--static', default=False, action='store_true', help=('Return the data from minions as a group after they ' 'all return.') ) self.add_option( '-w', '--wipe', default=False, action='store_true', dest='ssh_wipe', help='Remove the deployment of the salt files when done executing.', ) self.add_option( '-W', '--rand-thin-dir', default=False, action='store_true', help=('Select a random temp dir to deploy on the remote system. ' 'The dir will be cleaned after the execution.')) self.add_option( '-t', '--regen-thin', '--thin', dest='regen_thin', default=False, action='store_true', help=('Trigger a thin tarball regeneration. This is needed if ' 'custom grains/modules/states have been added or updated.')) self.add_option( '--python2-bin', default='python2', help='Path to a python2 binary which has salt installed.' ) self.add_option( '--python3-bin', default='python3', help='Path to a python3 binary which has salt installed.' ) self.add_option( '--jid', default=None, help='Pass a JID to be used instead of generating one.' ) ports_group = optparse.OptionGroup( self, 'Port Forwarding Options', 'Parameters for setting up SSH port forwarding.' ) ports_group.add_option( '--remote-port-forwards', dest='ssh_remote_port_forwards', help='Setup remote port forwarding using the same syntax as with ' 'the -R parameter of ssh. A comma separated list of port ' 'forwarding definitions will be translated into multiple ' '-R parameters.' ) self.add_option_group(ports_group) auth_group = optparse.OptionGroup( self, 'Authentication Options', 'Parameters affecting authentication.' ) auth_group.add_option( '--priv', dest='ssh_priv', help='Ssh private key file.' ) auth_group.add_option( '-i', '--ignore-host-keys', dest='ignore_host_keys', default=False, action='store_true', help='By default ssh host keys are honored and connections will ' 'ask for approval. Use this option to disable ' 'StrictHostKeyChecking.' ) auth_group.add_option( '--no-host-keys', dest='no_host_keys', default=False, action='store_true', help='Removes all host key checking functionality from SSH session.' ) auth_group.add_option( '--user', dest='ssh_user', default='root', help='Set the default user to attempt to use when ' 'authenticating.' ) auth_group.add_option( '--passwd', dest='ssh_passwd', default='', help='Set the default password to attempt to use when ' 'authenticating.' ) auth_group.add_option( '--askpass', dest='ssh_askpass', default=False, action='store_true', help='Interactively ask for the SSH password with no echo - avoids ' 'password in process args and stored in history.' ) auth_group.add_option( '--key-deploy', dest='ssh_key_deploy', default=False, action='store_true', help='Set this flag to attempt to deploy the authorized ssh key ' 'with all minions. This combined with --passwd can make ' 'initial deployment of keys very fast and easy.' ) auth_group.add_option( '--identities-only', dest='ssh_identities_only', default=False, action='store_true', help='Use the only authentication identity files configured in the ' 'ssh_config files. See IdentitiesOnly flag in man ssh_config.' ) auth_group.add_option( '--sudo', dest='ssh_sudo', default=False, action='store_true', help='Run command via sudo.' ) self.add_option_group(auth_group) scan_group = optparse.OptionGroup( self, 'Scan Roster Options', 'Parameters affecting scan roster.' ) scan_group.add_option( '--scan-ports', default='22', dest='ssh_scan_ports', help='Comma-separated list of ports to scan in the scan roster.', ) scan_group.add_option( '--scan-timeout', default=0.01, dest='ssh_scan_timeout', help='Scanning socket timeout for the scan roster.', ) self.add_option_group(scan_group) def _mixin_after_parsed(self): if not self.args: self.print_help() self.error('Insufficient arguments') if self.options.list: if ',' in self.args[0]: self.config['tgt'] = self.args[0].split(',') else: self.config['tgt'] = self.args[0].split() else: self.config['tgt'] = self.args[0] self.config['argv'] = self.args[1:] if not self.config['argv'] or not self.config['tgt']: self.print_help() self.error('Insufficient arguments') if self.options.ssh_askpass: self.options.ssh_passwd = getpass.getpass('Password: ') def setup_config(self): return config.master_config(self.get_config_file_path()) def process_jid(self): if self.options.jid is not None: if not salt.utils.jid.is_jid(self.options.jid): self.error('\'{0}\' is not a valid JID'.format(self.options.jid)) class SaltCloudParser(six.with_metaclass(OptionParserMeta, OptionParser, LogLevelMixIn, MergeConfigMixIn, OutputOptionsMixIn, ConfigDirMixIn, CloudQueriesMixIn, ExecutionOptionsMixIn, CloudProvidersListsMixIn, CloudCredentialsMixIn, HardCrashMixin, SaltfileMixIn)): # ConfigDirMixIn attributes _config_filename_ = 'cloud' # LogLevelMixIn attributes _default_logging_level_ = 'info' _logfile_config_setting_name_ = 'log_file' _loglevel_config_setting_name_ = 'log_level_logfile' _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'cloud') def print_versions_report(self, file=sys.stdout): # pylint: disable=redefined-builtin print('\n'.join(version.versions_report(include_salt_cloud=True)), file=file) self.exit(salt.defaults.exitcodes.EX_OK) def parse_args(self, args=None, values=None): try: # Late import in order not to break setup from salt.cloud import libcloudfuncs libcloudfuncs.check_libcloud_version() except ImportError as exc: self.error(exc) return super(SaltCloudParser, self).parse_args(args, values) def _mixin_after_parsed(self): if 'DUMP_SALT_CLOUD_CONFIG' in os.environ: import pprint print('Salt cloud configuration dump(INCLUDES SENSIBLE DATA):') pprint.pprint(self.config) self.exit(salt.defaults.exitcodes.EX_OK) if self.args: self.config['names'] = self.args def setup_config(self): try: return config.cloud_config(self.get_config_file_path()) except salt.exceptions.SaltCloudConfigError as exc: self.error(exc) class SPMParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, LogLevelMixIn, MergeConfigMixIn, SaltfileMixIn)): ''' The cli parser object used to fire up the salt spm system. ''' description = 'SPM is used to manage 3rd party formulas and other Salt components' usage = '%prog [options] <function> [arguments]' # ConfigDirMixIn config filename attribute _config_filename_ = 'spm' # LogLevelMixIn attributes _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'spm') def _mixin_setup(self): self.add_option( '-y', '--assume-yes', default=False, action='store_true', help='Default "yes" in answer to all confirmation questions.' ) self.add_option( '-f', '--force', default=False, action='store_true', help='Default "yes" in answer to all confirmation questions.' ) self.add_option( '-v', '--verbose', default=False, action='store_true', help='Display more detailed information.' ) def _mixin_after_parsed(self): # spm needs arguments if len(self.args) <= 1: if not self.args or self.args[0] not in ('update_repo',): self.print_help() self.error('Insufficient arguments') def setup_config(self): return salt.config.spm_config(self.get_config_file_path()) class SaltAPIParser(six.with_metaclass(OptionParserMeta, OptionParser, ConfigDirMixIn, LogLevelMixIn, DaemonMixIn, MergeConfigMixIn)): ''' The Salt API cli parser object used to fire up the salt api system. ''' # ConfigDirMixIn config filename attribute _config_filename_ = 'master' # LogLevelMixIn attributes _default_logging_logfile_ = os.path.join(syspaths.LOGS_DIR, 'api') def setup_config(self): return salt.config.api_config(self.get_config_file_path()) # pylint: disable=no-member
38.735819
110
0.543787
026962c395159a92f5db4dc41caa8e06f959058f
443
py
Python
ml/data_utils.py
wds-seu/Aceso
70fca2b3ff810fa32b7ebe6edf0d264da126d521
[ "MIT" ]
1
2019-10-08T10:54:52.000Z
2019-10-08T10:54:52.000Z
ml/data_utils.py
wds-seu/Aceso
70fca2b3ff810fa32b7ebe6edf0d264da126d521
[ "MIT" ]
null
null
null
ml/data_utils.py
wds-seu/Aceso
70fca2b3ff810fa32b7ebe6edf0d264da126d521
[ "MIT" ]
null
null
null
#!/usr/bin/env python # -*- coding:utf-8 _*- from sklearn.model_selection import train_test_split from utils.DataHelper import DataHelper from config import DefaultConfig opt = DefaultConfig() def build_data(): x_text, y, vocabulary, vocabulary_inv = DataHelper(opt.train_data_root, train=True).load_text_data(opt.use_umls) x_train, x_val, y_train, y_val = train_test_split(x_text, y, test_size=0.3, random_state=1, shuffle=True)
34.076923
116
0.772009
161fc216cd97b68346d08db33a110ef3a04fb9bd
5,738
py
Python
betka/utils.py
phracek/betka
12c920bd76d33f81bbf8cda6f27d673c8826cf9e
[ "MIT" ]
1
2020-11-05T21:16:28.000Z
2020-11-05T21:16:28.000Z
betka/utils.py
phracek/betka
12c920bd76d33f81bbf8cda6f27d673c8826cf9e
[ "MIT" ]
16
2020-03-20T11:23:27.000Z
2022-03-08T17:09:11.000Z
betka/utils.py
phracek/betka
12c920bd76d33f81bbf8cda6f27d673c8826cf9e
[ "MIT" ]
1
2020-03-11T09:29:48.000Z
2020-03-11T09:29:48.000Z
# MIT License # # Copyright (c) 2020 SCL team at Red Hat # # 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 contextlib import contextmanager import logging import shutil import os import json import jinja2 import subprocess from pathlib import Path from betka.constants import HOME logger = logging.getLogger(__name__) def run_cmd(cmd, return_output=False, ignore_error=False, shell=False, **kwargs): """ Run provided command on host system using the same user as invoked this code. Raises subprocess.CalledProcessError if it fails. :param cmd: list or str :param return_output: bool, return output of the command :param ignore_error: bool, do not fail in case nonzero return code :param shell: bool, run command in shell :param kwargs: pass keyword arguments to subprocess.check_* functions; for more info, please check `help(subprocess.Popen)` :return: None or str """ logger.debug("command: %r", cmd) try: if return_output: return subprocess.check_output( cmd, stderr=subprocess.STDOUT, universal_newlines=True, shell=shell, **kwargs, ) else: return subprocess.check_call(cmd, shell=shell, **kwargs) except subprocess.CalledProcessError as cpe: if ignore_error: if return_output: return cpe.output else: return cpe.returncode else: logger.error(f"failed with code {cpe.returncode} and output:\n{cpe.output}") raise cpe def text_from_template(template_dir, template_filename, template_data): """ Create text based on template in path template_dir/template_filename :param template_dir: string, directory containing templates :param template_filename: template for text in jinja :param template_data: dict, data for substitution in template :return: string """ if not os.path.exists(os.path.join(template_dir, template_filename)): raise FileNotFoundError("Path to template not found.") template_loader = jinja2.FileSystemLoader(searchpath=template_dir) template_env = jinja2.Environment(loader=template_loader) template = template_env.get_template(template_filename) output_text = template.render(template_data=template_data) logger.debug("Text from template created:") logger.debug(output_text) return output_text def copy_upstream2downstream(src_parent: Path, dest_parent: Path): """Copies content from upstream repo to downstream repo Copies all files/dirs/symlinks from upstream source to dist-git one by one, while removing previous if exists. :param src_parent: path to source directory :param dest_parent: path to destination directory """ for f in src_parent.iterdir(): if f.name.startswith(".git"): continue dest = dest_parent / f.name src = src_parent / f.name logger.debug(f"Copying {str(src)} to {str(dest)}.") # First remove the dest only if it is not symlink. if dest.is_dir() and not dest.is_symlink(): logger.debug("rmtree %s", dest) shutil.rmtree(dest) else: if dest.exists(): dest.unlink() # Now copy the src to dest if src.is_symlink() or not src.is_dir(): logger.debug("cp %s %s", src, dest) shutil.copy2(src, dest, follow_symlinks=False) else: logger.debug("cp -r %s %s", src, dest) shutil.copytree(src, dest, symlinks=True) def clean_directory(path: Path): """ Function cleans directory except itself :param path: directory path which is cleaned """ for d in path.iterdir(): src = path / d if src.is_dir(): logger.debug("rmtree %s", str(src)) shutil.rmtree(src) else: src.unlink() def list_dir_content(dir_name: Path): """ Lists all content of dir_name :param dir_name: Directory for showing files """ logger.info("Look for a content in '%s' directory", str(dir_name)) for f in dir_name.rglob("*"): if str(f).startswith(".git"): continue logger.debug(f"{f.parent / f.name}") def load_config_json(): with open(f"{HOME}/config.json") as config_file: data = json.load(config_file) return data @contextmanager def cwd(path): """ Switch to Path directory and once action is done returns back :param path: :return: """ prev_cwd = Path.cwd() os.chdir(path) try: yield finally: os.chdir(prev_cwd)
32.788571
89
0.66626
5d0138a06f2443ef3fc96673c74b3944b37a3723
1,782
py
Python
torch_glow/tests/nodes/avgpool1d_test.py
YaronBenAtar/glow
a13706a4239fa7eaf059c670dc573e3eb0768f86
[ "Apache-2.0" ]
2,838
2018-05-02T16:57:22.000Z
2022-03-31T14:35:26.000Z
torch_glow/tests/nodes/avgpool1d_test.py
YaronBenAtar/glow
a13706a4239fa7eaf059c670dc573e3eb0768f86
[ "Apache-2.0" ]
4,149
2018-05-02T17:50:14.000Z
2022-03-31T23:56:43.000Z
torch_glow/tests/nodes/avgpool1d_test.py
LaudateCorpus1/glow-1
cda5383b1609ebad1a3631ca77b41b8a863443d4
[ "Apache-2.0" ]
685
2018-05-02T16:54:09.000Z
2022-03-24T01:12:24.000Z
# Copyright (c) Glow Contributors. See CONTRIBUTORS file. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import, division, print_function, unicode_literals import torch import torch.nn.functional as F from tests import utils class SimpleAvgPool1dModule(torch.nn.Module): def __init__(self, kernel_size, stride=None, padding=0): super(SimpleAvgPool1dModule, self).__init__() self.kernel_size = kernel_size self.padding = padding self.stride = stride def forward(self, inputs): return F.avg_pool1d( inputs, self.kernel_size, padding=self.padding, stride=self.stride ) class TestAvgPool1d(utils.TorchGlowTestCase): def test_avg_pool1d_basic(self): """Basic test of the PyTorch avg_pool1d Node on Glow.""" inputs = torch.randn(1, 4, 9) utils.compare_tracing_methods( SimpleAvgPool1dModule(3), inputs, fusible_ops={"aten::avg_pool1d"} ) def test_avg_pool1d_with_args(self): """Test of the PyTorch avg_pool1d Node with arguments on Glow.""" inputs = torch.randn(1, 4, 10) utils.compare_tracing_methods( SimpleAvgPool1dModule(3, stride=7), inputs, fusible_ops={"aten::avg_pool1d"} )
34.269231
88
0.708193
b1c5f4cfef4234cf455db73d71bf20f2ddeb87c5
15,306
py
Python
python/ccxt/async_support/btcbox.py
atoll6/ccxt
3c5fd65a32073bfbe1a7e0f5a02d7d3f93850780
[ "MIT" ]
1
2018-08-20T09:38:13.000Z
2018-08-20T09:38:13.000Z
python/ccxt/async_support/btcbox.py
atoll6/ccxt
3c5fd65a32073bfbe1a7e0f5a02d7d3f93850780
[ "MIT" ]
null
null
null
python/ccxt/async_support/btcbox.py
atoll6/ccxt
3c5fd65a32073bfbe1a7e0f5a02d7d3f93850780
[ "MIT" ]
1
2021-09-16T08:33:31.000Z
2021-09-16T08:33:31.000Z
# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.async_support.base.exchange import Exchange # ----------------------------------------------------------------------------- try: basestring # Python 3 except NameError: basestring = str # Python 2 import json from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import DDoSProtection from ccxt.base.errors import InvalidNonce from ccxt.base.precise import Precise class btcbox(Exchange): def describe(self): return self.deep_extend(super(btcbox, self).describe(), { 'id': 'btcbox', 'name': 'BtcBox', 'countries': ['JP'], 'rateLimit': 1000, 'version': 'v1', 'has': { 'cancelOrder': True, 'CORS': None, 'createOrder': True, 'fetchBalance': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchOrders': True, 'fetchTicker': True, 'fetchTickers': None, 'fetchTrades': True, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/51840849/87327317-98c55400-c53c-11ea-9a11-81f7d951cc74.jpg', 'api': 'https://www.btcbox.co.jp/api', 'www': 'https://www.btcbox.co.jp/', 'doc': 'https://blog.btcbox.jp/en/archives/8762', 'fees': 'https://support.btcbox.co.jp/hc/en-us/articles/360001235694-Fees-introduction', }, 'api': { 'public': { 'get': [ 'depth', 'orders', 'ticker', ], }, 'private': { 'post': [ 'balance', 'trade_add', 'trade_cancel', 'trade_list', 'trade_view', 'wallet', ], }, }, 'markets': { 'BTC/JPY': {'id': 'btc', 'symbol': 'BTC/JPY', 'base': 'BTC', 'quote': 'JPY', 'baseId': 'btc', 'quoteId': 'jpy', 'taker': 0.05 / 100, 'maker': 0.05 / 100}, 'ETH/JPY': {'id': 'eth', 'symbol': 'ETH/JPY', 'base': 'ETH', 'quote': 'JPY', 'baseId': 'eth', 'quoteId': 'jpy', 'taker': 0.10 / 100, 'maker': 0.10 / 100}, 'LTC/JPY': {'id': 'ltc', 'symbol': 'LTC/JPY', 'base': 'LTC', 'quote': 'JPY', 'baseId': 'ltc', 'quoteId': 'jpy', 'taker': 0.10 / 100, 'maker': 0.10 / 100}, 'BCH/JPY': {'id': 'bch', 'symbol': 'BCH/JPY', 'base': 'BCH', 'quote': 'JPY', 'baseId': 'bch', 'quoteId': 'jpy', 'taker': 0.10 / 100, 'maker': 0.10 / 100}, }, 'exceptions': { '104': AuthenticationError, '105': PermissionDenied, '106': InvalidNonce, '107': InvalidOrder, # price should be an integer '200': InsufficientFunds, '201': InvalidOrder, # amount too small '202': InvalidOrder, # price should be [0 : 1000000] '203': OrderNotFound, '401': OrderNotFound, # cancel canceled, closed or non-existent order '402': DDoSProtection, }, }) async def fetch_balance(self, params={}): await self.load_markets() response = await self.privatePostBalance(params) result = {'info': response} codes = list(self.currencies.keys()) for i in range(0, len(codes)): code = codes[i] currency = self.currency(code) currencyId = currency['id'] free = currencyId + '_balance' if free in response: account = self.account() used = currencyId + '_lock' account['free'] = self.safe_string(response, free) account['used'] = self.safe_string(response, used) result[code] = account return self.parse_balance(result) async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() market = self.market(symbol) request = {} numSymbols = len(self.symbols) if numSymbols > 1: request['coin'] = market['baseId'] response = await self.publicGetDepth(self.extend(request, params)) return self.parse_order_book(response, symbol) def parse_ticker(self, ticker, market=None): timestamp = self.milliseconds() symbol = None if market is not None: symbol = market['symbol'] last = self.safe_number(ticker, 'last') return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_number(ticker, 'high'), 'low': self.safe_number(ticker, 'low'), 'bid': self.safe_number(ticker, 'buy'), 'bidVolume': None, 'ask': self.safe_number(ticker, 'sell'), 'askVolume': None, 'vwap': None, 'open': None, 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': None, 'average': None, 'baseVolume': self.safe_number(ticker, 'vol'), 'quoteVolume': self.safe_number(ticker, 'volume'), 'info': ticker, } async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) request = {} numSymbols = len(self.symbols) if numSymbols > 1: request['coin'] = market['baseId'] response = await self.publicGetTicker(self.extend(request, params)) return self.parse_ticker(response, market) def parse_trade(self, trade, market=None): timestamp = self.safe_timestamp(trade, 'date') symbol = None if market is not None: symbol = market['symbol'] id = self.safe_string(trade, 'tid') priceString = self.safe_string(trade, 'price') amountString = self.safe_string(trade, 'amount') price = self.parse_number(priceString) amount = self.parse_number(amountString) cost = self.parse_number(Precise.string_mul(priceString, amountString)) type = None side = self.safe_string(trade, 'type') return { 'info': trade, 'id': id, 'order': None, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'type': type, 'side': side, 'takerOrMaker': None, 'price': price, 'amount': amount, 'cost': cost, 'fee': None, } async def fetch_trades(self, symbol, since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) request = {} numSymbols = len(self.symbols) if numSymbols > 1: request['coin'] = market['baseId'] response = await self.publicGetOrders(self.extend(request, params)) return self.parse_trades(response, market, since, limit) async def create_order(self, symbol, type, side, amount, price=None, params={}): await self.load_markets() market = self.market(symbol) request = { 'amount': amount, 'price': price, 'type': side, 'coin': market['baseId'], } response = await self.privatePostTradeAdd(self.extend(request, params)) # # { # "result":true, # "id":"11" # } # return self.parse_order(response, market) async def cancel_order(self, id, symbol=None, params={}): await self.load_markets() # a special case for btcbox – default symbol is BTC/JPY if symbol is None: symbol = 'BTC/JPY' market = self.market(symbol) request = { 'id': id, 'coin': market['baseId'], } response = await self.privatePostTradeCancel(self.extend(request, params)) # # {"result":true, "id":"11"} # return self.parse_order(response, market) def parse_order_status(self, status): statuses = { # TODO: complete list 'part': 'open', # partially or not at all executed 'all': 'closed', # fully executed 'cancelled': 'canceled', 'closed': 'closed', # never encountered, seems to be bug in the doc 'no': 'closed', # not clarified in the docs... } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): # # { # "id":11, # "datetime":"2014-10-21 10:47:20", # "type":"sell", # "price":42000, # "amount_original":1.2, # "amount_outstanding":1.2, # "status":"closed", # "trades":[] # } # id = self.safe_string(order, 'id') datetimeString = self.safe_string(order, 'datetime') timestamp = None if datetimeString is not None: timestamp = self.parse8601(order['datetime'] + '+09:00') # Tokyo time amount = self.safe_number(order, 'amount_original') remaining = self.safe_number(order, 'amount_outstanding') price = self.safe_number(order, 'price') # status is set by fetchOrder method only status = self.parse_order_status(self.safe_string(order, 'status')) # fetchOrders do not return status, use heuristic if status is None: if remaining is not None and remaining == 0: status = 'closed' trades = None # todo: self.parse_trades(order['trades']) symbol = None if market is not None: symbol = market['symbol'] side = self.safe_string(order, 'type') return self.safe_order({ 'id': id, 'clientOrderId': None, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': None, 'amount': amount, 'remaining': remaining, 'filled': None, 'side': side, 'type': None, 'timeInForce': None, 'postOnly': None, 'status': status, 'symbol': symbol, 'price': price, 'stopPrice': None, 'cost': None, 'trades': trades, 'fee': None, 'info': order, 'average': None, }) async def fetch_order(self, id, symbol=None, params={}): await self.load_markets() # a special case for btcbox – default symbol is BTC/JPY if symbol is None: symbol = 'BTC/JPY' market = self.market(symbol) request = self.extend({ 'id': id, 'coin': market['baseId'], }, params) response = await self.privatePostTradeView(self.extend(request, params)) return self.parse_order(response, market) async def fetch_orders_by_type(self, type, symbol=None, since=None, limit=None, params={}): await self.load_markets() # a special case for btcbox – default symbol is BTC/JPY if symbol is None: symbol = 'BTC/JPY' market = self.market(symbol) request = { 'type': type, # 'open' or 'all' 'coin': market['baseId'], } response = await self.privatePostTradeList(self.extend(request, params)) orders = self.parse_orders(response, market, since, limit) # status(open/closed/canceled) is None # btcbox does not return status, but we know it's 'open' as we queried for open orders if type == 'open': for i in range(0, len(orders)): orders[i]['status'] = 'open' return orders async def fetch_orders(self, symbol=None, since=None, limit=None, params={}): return await self.fetch_orders_by_type('all', symbol, since, limit, params) async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): return await self.fetch_orders_by_type('open', symbol, since, limit, params) def nonce(self): return self.milliseconds() def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'] + '/' + self.version + '/' + path if api == 'public': if params: url += '?' + self.urlencode(params) else: self.check_required_credentials() nonce = str(self.nonce()) query = self.extend({ 'key': self.apiKey, 'nonce': nonce, }, params) request = self.urlencode(query) secret = self.hash(self.encode(self.secret)) query['signature'] = self.hmac(self.encode(request), self.encode(secret)) body = self.urlencode(query) headers = { 'Content-Type': 'application/x-www-form-urlencoded', } return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, httpCode, reason, url, method, headers, body, response, requestHeaders, requestBody): if response is None: return # resort to defaultErrorHandler # typical error response: {"result":false,"code":"401"} if httpCode >= 400: return # resort to defaultErrorHandler result = self.safe_value(response, 'result') if result is None or result is True: return # either public API(no error codes expected) or success code = self.safe_value(response, 'code') feedback = self.id + ' ' + body self.throw_exactly_matched_exception(self.exceptions, code, feedback) raise ExchangeError(feedback) # unknown message async def request(self, path, api='public', method='GET', params={}, headers=None, body=None, config={}, context={}): response = await self.fetch2(path, api, method, params, headers, body, config, context) if isinstance(response, basestring): # sometimes the exchange returns whitespace prepended to json response = self.strip(response) if not self.is_json_encoded_object(response): raise ExchangeError(self.id + ' ' + response) response = json.loads(response) return response
39.65285
170
0.531948
dbc4bc7d40e97ad6c5ca88fbb4c028df75f0f9d1
6,860
py
Python
raasbot/api/views.py
kneeraazon01/raasbot
932648a6017cc5f888038ad3c54bb59c35deadf8
[ "Apache-2.0" ]
null
null
null
raasbot/api/views.py
kneeraazon01/raasbot
932648a6017cc5f888038ad3c54bb59c35deadf8
[ "Apache-2.0" ]
null
null
null
raasbot/api/views.py
kneeraazon01/raasbot
932648a6017cc5f888038ad3c54bb59c35deadf8
[ "Apache-2.0" ]
null
null
null
import json from django.http import JsonResponse from raasbot.settings import BASE_DIR from random import choice from os import path from core.models import ( User, Bot, ScrapedUser, UserMessageData, Message, Text, LogData ) def get_essentials(request): data = json.loads(request.body) token = data.get('token') user = User.objects.get(token=token) return (data, token, user) # Create your views here. def add_scraped_users(request): try: data, token, user = get_essentials(request) users = data.get('users') #for i in users: # a, b = ScrapedUser.objects.get_or_create(scrap_id = i['id'], name=i['username'], user=user, person_id = f"{i['id']}_{user.token}") # print(a, b) ScrapedUser.objects.bulk_create( [ScrapedUser(scrap_id = i['id'], name=i['username'], user=user, person_id = f"{i['id']}_{user.token}") for i in users], ignore_conflicts=True ) return JsonResponse({ "success": True, "message": "Users scraped and saved in database" }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def get_next_user(request): try: data, token, user = get_essentials(request) target = user.scraped_users.filter(texted=False).first() if not target: return JsonResponse({ 'success': True, 'continue': False }) target.texted=True target.save() return JsonResponse({ "success": True, "continue": True, "id": target.scrap_id, "username": target.name, "person_id": target.person_id }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def get_init_data(request): try: data, token, user = get_essentials(request) text_data = user.text_data.first() restricted_roles = text_data.roles.all() return JsonResponse({ "success": True, "guild_id": text_data.guild, "channel_id": text_data.channel, "invite_link": text_data.guild_invite, "restricted_roles": [role.name for role in restricted_roles] }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def get_text(request): try: data, token, user = get_essentials(request) message = user.dms.first() with open(path.join(BASE_DIR, 'texts.json'), 'r', encoding='utf-8') as texts: text = [] var = json.loads(texts.read()) text.append(choice(var.get('greetings'))) text.append(choice(var.get('relevance'))) text.append(choice(var.get("approach")).replace('<topic>', message.topic).replace('<invite>', message.guild_invite).replace('<info>', message.invite_info)) text.append(choice(var.get("link")).replace('<invite>', message.guild_invite).replace('<topic>', message.topic).replace('<info>', message.invite_info)) text.append(choice(var.get("info")).replace('<info>', message.invite_info).replace('<topic>', message.topic).replace('<invite>', message.guild_invite)) text.append(choice(var.get("ending"))) text.append(choice(var.get("conclusion"))) return JsonResponse({ "success": True, "text": text }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def get_tokens(request): try: data, token, user = get_essentials(request) bots = user.bots.filter(is_alive=True) return JsonResponse({ "success": True, "tokens": [bot.token for bot in bots] }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def should_scrape(request): try: data, token, user = get_essentials(request) count = user.scraped_users.filter(texted=False).count() return JsonResponse({ 'success': True, 'scrape': not (count > 1000) }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def log_result(request): try: data, token, user = get_essentials(request) target_id = data.get('target') target = ScrapedUser.objects.get(user=user, scrap_id=target_id) LogData.objects.create( user = user, target = target, success = data.get('success') ) return JsonResponse({ 'success': True }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def log_dup_result(request): try: data, token, user = get_essentials(request) target_id = data.get('target') target = ScrapedUser.objects.get(person_id=target_id) LogData.objects.create( user = user, target = target, success = data.get('success') ) return JsonResponse({ 'success': True }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def mark_dead(request): try: data, token, user = get_essentials(request) bot_token = data.get('bot_token') bot = Bot.objects.get(token=bot_token) bot.is_alive = False bot.save() return JsonResponse({ 'success': True }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" }) def add_bulk_token(request): try: data, token, user = get_essentials(request) tokens = data.get('tokens') Bot.objects.bulk_create( [Bot(token = bot, is_alive=True, user=user) for bot in tokens], ignore_conflicts=True ) return JsonResponse({ 'success': True }) except Exception as e: print(e) return JsonResponse({ "success": False, "message": f"{e}" })
31.46789
168
0.528134
947964009551475601d73b7ef519a2e87594ff86
39
py
Python
packages/jsii-rosetta/test/translations/calls/method_call.py
NGL321/jsii
a31ebf5ef676391d97f2286edc21e5859c38c96c
[ "Apache-2.0" ]
1,639
2019-07-05T07:21:00.000Z
2022-03-31T09:55:01.000Z
packages/jsii-rosetta/test/translations/calls/method_call.py
NGL321/jsii
a31ebf5ef676391d97f2286edc21e5859c38c96c
[ "Apache-2.0" ]
2,704
2019-07-01T23:10:28.000Z
2022-03-31T23:40:12.000Z
packages/jsii-rosetta/test/translations/calls/method_call.py
NGL321/jsii
a31ebf5ef676391d97f2286edc21e5859c38c96c
[ "Apache-2.0" ]
146
2019-07-02T14:36:25.000Z
2022-03-26T00:21:27.000Z
some_object.call_some_function(1, 2, 3)
39
39
0.820513
0ef02d4cfc1faf8314ad3d3f2641e5dc2eb22651
9,230
py
Python
teste/lstm.py
joandesonandrade/nebulosa
5bc157322ed0bdb81f6f00f6ed1ea7f7a5cadfe0
[ "MIT" ]
null
null
null
teste/lstm.py
joandesonandrade/nebulosa
5bc157322ed0bdb81f6f00f6ed1ea7f7a5cadfe0
[ "MIT" ]
null
null
null
teste/lstm.py
joandesonandrade/nebulosa
5bc157322ed0bdb81f6f00f6ed1ea7f7a5cadfe0
[ "MIT" ]
null
null
null
# Código desenvolvido pelo Pedro Piassa # Artigo http://www.uel.br/cce/dc/wp-content/uploads/PRELIMINAR-PEDRO-VITOR-PIASSA.pdf todos os direitos reservados. import os from math import * import pandas as pd import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.layers import LSTM from tensorflow.keras.utils import plot_model from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from matplotlib import pyplot FILE_PATH = "folder/training_data/" MODEL_PATH = "folder/model/lstm-{0}.model" PLOT_PATH = "folder/lstm-{0}.model" class DataType(dict): def __getattr__(self, name): if name in self: return self[name] raise AttributeError(f"Data type '{name}' not found") def __setattr__(self, name, value): raise AttributeError("Can't assign an attribute for a DataType") def __delattr__(self, name): if name in self: return self.pop(name, None) raise AttributeError(f"Data type '{name}' not found") def prepare_data(data_type_folder, folders, files): data_dict = {} whole = None for folder in folders: single = None for file in files: full_path = FILE_PATH + data_type_folder + folder + "/" + file with open(full_path) as data_file: data_as_float = np.array([[line.rstrip("\r\n") for line in data_file]], dtype=np.float32) if single is None: single = data_as_float else: single = np.concatenate((single, data_as_float), axis=0) if whole is None: whole = single.T else: whole = np.concatenate((whole, single.T), axis=0) return whole def series_to_supervised(data, n_in=1, n_out=1, dropnan=True, drop_cols=None): n_vars = 1 if type(data) is list else data.shape[1] df = pd.DataFrame(data) cols = [] names = [] for i in range(n_in, 0, -1): cols.append(df.shift(i)) names += [f'var{j+1}(t-{i})' for j in range(n_vars)] for i in range(n_out): cols.append(df.shift(-i)) if i == 0: names += [f'var{j+1}(t)' for j in range(n_vars)] else: names += [f'var{j+1}(t+{i})' for j in range(n_vars)] agg = pd.concat(cols, axis=1) agg.columns = names if dropnan: agg.dropna(inplace=True) if drop_cols is not None: index_offset = n_in * n_vars drop_range = [j + index_offset + (n_vars * i) for i in range(n_out) for j in drop_cols] agg.drop(agg.columns[drop_range], axis=1, inplace=True) return agg def training_data(files, data_type): data = [] for _, v in files[data_type].items(): data.append(v) return np.array(data, dtype=np.float32).T def lstm_model(train_X, output_amount, activation): model = Sequential() model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), activation=activation)) model.add(Dense(output_amount)) return model def get_forecast(test_data, forecast_data, scaler, drop_cols): inv_forecast_data = np.empty(test_data.shape) j = 0 for i in range(test_data.shape[1]): if i in drop_cols: inv_forecast_data[:, i] = test_data[:, i] else: inv_forecast_data[:, i] = forecast_data[:, j] j += 1 inv_forecast_data = scaler.inverse_transform(inv_forecast_data) res = np.empty(forecast_data.shape) j = 0 for i in range(test_data.shape[1]): if i not in drop_cols: res[:, j] = inv_forecast_data[:, i] j += 1 return res def plot_data(ip_data_forecast, port_data_forecast, name=None): ip_data_orig = values[:86400, 1] port_data_orig = values[:86400, 2] ip_data_ddos = ddos_vals[:, 1] port_data_ddos = ddos_vals[:, 2] fig, axs = plt.subplots(3, 2) plt.subplots_adjust(hspace=0.4) axs[0, 0].plot(ip_data_orig) axs[0, 0].set_title('IP orig') axs[0, 1].plot(port_data_orig) axs[0, 1].set_title('PORT orig') axs[1, 0].plot(ip_data_forecast) axs[1, 0].set_title('IP forecast') axs[1, 1].plot(port_data_forecast) axs[1, 1].set_title('PORT forecast') axs[2, 0].plot(ip_data_ddos) axs[2, 0].set_title('IP DDoS') axs[2, 1].plot(port_data_ddos) axs[2, 1].set_title('PORT DDoS') tick_pos = [i * 3600 for i in range(25)] tick_label = [i for i in range(25)] for i in range(3): for j in range(2): pyplot.sca(axs[i, j]) pyplot.xticks(tick_pos, tick_label) pyplot.legend() pyplot.show() if name is not None: fig.savefig(name + '.png') def sketch_data(data, title): print('[*] ' + title.upper()) if type(data) == pd.DataFrame: print(data.head()) else: print(data) print(data.shape) print("--------------------------------------------------------------------------------\n\n") if __name__ == "__main__": no_attack_data_folders = ['021218', '031218', '051218', '071218', '091218', '121218'] ddos_first_data_folders = ['071218'] ddos_sec_data_folders = ['071218'] data_files = ['bytes1.txt', 'EntropiaDstIP1.txt', 'EntropiaDstPort1.txt', 'EntropiaScrIP1.txt', 'EntropiaSrcPort1.txt', 'packets1.txt'] prediction_data = ['EntropiaDstIP1.txt', 'EntropiaDstPort1.txt'] data_type = DataType(NO_ATTACK='without-attack/', DDOS_PORTSCAN='ddos-portscan/', PORTSCAN_DDOS='portscan-ddos/') values = prepare_data(data_type.NO_ATTACK, no_attack_data_folders, data_files) sketch_data(values, "raw traffic data, without attack, taken from the data files") ddos_vals = prepare_data(data_type.DDOS_PORTSCAN, ddos_first_data_folders, data_files) sketch_data(ddos_vals, "raw traffic data containing a ddos attack, taken from the data files") scaler = MinMaxScaler(feature_range=(0, 1)) scaled = scaler.fit_transform(values) lag_input = 1 lag_output = 1 n_features = 6 drop_cols = [j for i, j in zip(data_files, range(len(data_files))) if i not in prediction_data] # Takes only the columns that are represented by the prediction_data variable scaled_data = series_to_supervised(scaled, n_in=lag_input, n_out=lag_output, drop_cols=drop_cols) sketch_data(scaled_data, "timeseries converted to a supervised learning problem") scaled_values = scaled_data.values testing_len = 86400 # size of the testing set training_len = scaled_values.shape[0] - testing_len # size of the training set n_obs = n_features * lag_input # number of observed variables layer_out = (n_features - len(drop_cols)) * lag_output # output variables train = scaled_values[:training_len, :] test = scaled_values[training_len:, :] sketch_data(train, "training data") sketch_data(test, "testing data") train_X = train[:, :n_obs] train_y = train[:, -layer_out:] test_X = test[:, :n_obs] test_y = test[:, -layer_out:] # print(train_X.shape, len(train_X), train_y.shape) train_X = train_X.reshape((train_X.shape[0], lag_input, n_features)) test_X = test_X.reshape((test_X.shape[0], lag_input, n_features)) # print(train_X.shape, test_X.shape) activation = tf.nn.softmax epochs = 4 batch_size = 60 model_name = f'activation={activation.__name__}-epochs={epochs}-batch_size={batch_size}' model_fullpath = MODEL_PATH.format(model_name) plot_fullpath = PLOT_PATH.format(model_name) if not os.path.isfile(model_fullpath): model = lstm_model(train_X=train_X, output_amount=layer_out, activation=activation) print(model.summary()) plot_model(model, to_file='./model_lstm.png', show_shapes=True, show_layer_names=True) model.compile(loss='mae', optimizer='adam', metrics=['accuracy', 'mse']) history = model.fit(train_X, train_y, epochs=epochs, batch_size=batch_size, validation_data=(test_X, test_y), verbose=1, shuffle=False) model.save(model_fullpath) else: model = tf.keras.models.load_model(model_fullpath) yhat = model.predict(test_X) sketch_data(yhat, "model prediction data") test_X = test_X.reshape((test_X.shape[0], lag_input*n_features)) sketch_data(test_X, "reshaped testing data") res = get_forecast(test_X, yhat, scaler, drop_cols) # Here is the predicted traffic sketch_data(res, "Predicted traffic data") plot_data(ip_data_forecast=res[:, 0], port_data_forecast=res[:, 1], name=plot_fullpath)
32.614841
177
0.614951
3879967ddb860fa780d0041379d64132aabcb634
473
py
Python
Lib/test/test_difflib.py
deadsnakes/python2.3
0b4a6871ca57123c10aa48cc2a5d2b7c0ee3c849
[ "PSF-2.0" ]
1
2020-11-26T18:53:46.000Z
2020-11-26T18:53:46.000Z
Lib/test/test_difflib.py
deadsnakes/python2.3
0b4a6871ca57123c10aa48cc2a5d2b7c0ee3c849
[ "PSF-2.0" ]
null
null
null
Lib/test/test_difflib.py
deadsnakes/python2.3
0b4a6871ca57123c10aa48cc2a5d2b7c0ee3c849
[ "PSF-2.0" ]
1
2019-04-11T11:27:01.000Z
2019-04-11T11:27:01.000Z
import difflib from test import test_support import unittest import doctest class TestSFbugs(unittest.TestCase): def test_ratio_for_null_seqn(self): # Check clearing of SF bug 763023 s = difflib.SequenceMatcher(None, [], []) self.assertEqual(s.ratio(), 1) self.assertEqual(s.quick_ratio(), 1) self.assertEqual(s.real_quick_ratio(), 1) Doctests = doctest.DocTestSuite(difflib) test_support.run_unittest(TestSFbugs, Doctests)
24.894737
49
0.718816
e10900f18bbb495e2228594da2923c849a55de50
943
py
Python
BMSS/models/model_functions/BMSS_LogicGate_ANDgate_BasalLeakinessP1_BasalLeakinessP3_MaturationTime.py
EngBioNUS/BMSS2
41163c61a4e0ef3c6430e5954d81a77832e49a9d
[ "Apache-2.0" ]
null
null
null
BMSS/models/model_functions/BMSS_LogicGate_ANDgate_BasalLeakinessP1_BasalLeakinessP3_MaturationTime.py
EngBioNUS/BMSS2
41163c61a4e0ef3c6430e5954d81a77832e49a9d
[ "Apache-2.0" ]
null
null
null
BMSS/models/model_functions/BMSS_LogicGate_ANDgate_BasalLeakinessP1_BasalLeakinessP3_MaturationTime.py
EngBioNUS/BMSS2
41163c61a4e0ef3c6430e5954d81a77832e49a9d
[ "Apache-2.0" ]
4
2020-08-24T13:35:55.000Z
2022-03-07T16:48:12.000Z
import numpy as np from numpy import log as ln from numpy import log10 as log from numpy import exp from numba import jit @jit(nopython=True) def model_BMSS_LogicGate_ANDgate_BasalLeakinessP1_BasalLeakinessP3_MaturationTime(y, t, params): m1 = y[0] p1 = y[1] m2 = y[2] p2 = y[3] m3 = y[4] p3 = y[5] p3_mat = y[6] k_leak1 = params[0] k_leak = params[1] synm1 = params[2] degm = params[3] synp = params[4] degp = params[5] synm2 = params[6] synm3 = params[7] p1_max = params[8] p2_max = params[9] k_mat = params[10] state1 = params[11] state2 = params[12] dm1 = k_leak1 + synm1*state1 - degm*m1 dp1 = synp*m1 - degp*p1 dm2 = synm2*state2 - degm*m2 dp2 = synp*m2 - degp*p2 dm3 = k_leak + synm3*(p1/p1_max)*(p2/p2_max) - degm*m3 dp3 = synp*m3 - k_mat*p3 dp3_mat = k_mat*m3 - degp*p3_mat return np.array([dm1, dp1, dm2, dp2, dm3, dp3, dp3_mat])
24.179487
96
0.622481
465a5b849ec43b60bdd1fb5c18a038cf6ba281c3
2,555
py
Python
Vuld_SySe/clone_analysis/analyze_clone.py
bstee615/ReVeal
fc22d0d54a3a23d4e0bc45a249b7eea22749685e
[ "MIT" ]
63
2020-09-24T07:51:45.000Z
2022-03-24T03:34:43.000Z
Vuld_SySe/clone_analysis/analyze_clone.py
smartcontract-detect-yzu/ReVeal
ca31b783384b4cdb09b69950e48f79fa0748ef1d
[ "MIT" ]
16
2020-08-04T16:26:46.000Z
2022-03-08T03:13:33.000Z
Vuld_SySe/clone_analysis/analyze_clone.py
smartcontract-detect-yzu/ReVeal
ca31b783384b4cdb09b69950e48f79fa0748ef1d
[ "MIT" ]
29
2020-09-08T17:27:07.000Z
2022-03-31T22:32:39.000Z
import argparse import json import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.cluster import KMeans from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.svm import LinearSVC from sklearn.metrics import accuracy_score as acc, precision_score as pr, recall_score as rc, f1_score as f1 def clone_analysis(data_paths): code = [] labels = [] positives = 0 for file_name in data_paths: data = json.load(open(file_name)) for example in data: code.append(example['tokenized']) l = 0 if 'label' in example.keys(): l = int(example['label']) elif 'lebel' in example.keys(): l = int(example['lebel']) elif 'leble' in example.keys(): l = int(example['leble']) elif 'lable' in example.keys(): l = int(example['lable']) if l > 1: l = 1 positives += l labels.append(l) print(len(code), len(labels), positives, len(labels) - positives) vectorizer = TfidfVectorizer(input=code, lowercase=False, ngram_range=(1, 3)) X = vectorizer.fit_transform(code) model = KMeans(n_clusters=10, max_iter=100) model.fit(X) y = model.predict(X) cluster_to_positive = [0] * 10 cluster_to_negative = [0] * 10 for pred, label in zip(y, labels): if label == 1: cluster_to_positive[pred] += 1 else: cluster_to_negative[pred] += 1 print(cluster_to_positive) print(cluster_to_negative) percentages = [float(p) / (p + n) for p, n in zip(cluster_to_positive, cluster_to_negative)] for p in percentages: print(p) for _ in range(5): XTrain, XTest, YTrain, YTest = train_test_split(X, labels, test_size=0.2) model = RandomForestClassifier() model.fit(XTrain, YTrain) predicted = model.predict(XTest) print('%.3f\t%.3f\t%.3f\t%.3f' % ( acc(YTest, predicted) * 100, pr(YTest, predicted) * 100, rc(YTest, predicted) * 100, f1(YTest, predicted) * 100) ) pass if __name__ == '__main__': data = '../../data/draper/chrome_debian.json' parser = argparse.ArgumentParser() parser.add_argument( '--data_source', help='Path of the json file of tha data', nargs='+', default=[ data ] ) args = parser.parse_args() clone_analysis(args.data_source) pass
34.066667
124
0.610176
3f2c58be4306c6ae33b80bf1d1e7afde5a7ee7c7
8,191
py
Python
unittests/testerrors.py
Serhiy1/archivist-python
70c7acf29eecd303bb1517d3636663d83f36cc2c
[ "MIT" ]
null
null
null
unittests/testerrors.py
Serhiy1/archivist-python
70c7acf29eecd303bb1517d3636663d83f36cc2c
[ "MIT" ]
null
null
null
unittests/testerrors.py
Serhiy1/archivist-python
70c7acf29eecd303bb1517d3636663d83f36cc2c
[ "MIT" ]
null
null
null
""" Test archivist """ # pylint: disable=attribute-defined-outside-init # pylint: disable=missing-docstring # pylint: disable=too-few-public-methods import json from unittest import TestCase from requests_toolbelt.multipart.encoder import MultipartEncoder from archivist.errors import ( _parse_response, Archivist4xxError, Archivist5xxError, ArchivistBadRequestError, ArchivistError, ArchivistForbiddenError, ArchivistNotFoundError, ArchivistNotImplementedError, ArchivistPaymentRequiredError, ArchivistTooManyRequestsError, ArchivistUnauthenticatedError, ArchivistUnavailableError, ) from .mock_response import MockResponse class TestErrors(TestCase): """ Test exceptions for archivist """ def test_errors_200(self): """ Test errors """ response = MockResponse(200) error = _parse_response(response) self.assertEqual( error, None, msg="error should be None", ) def test_errors_300(self): """ Test errors """ response = MockResponse(300) error = _parse_response(response) self.assertEqual( error, None, msg="error should be None", ) def test_errors_400(self): """ Test errors """ response = MockResponse(400, error="some error") error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistBadRequestError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (400)', msg="incorrect error", ) def test_errors_401(self): """ Test errors """ response = MockResponse(401, error="some error") error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistUnauthenticatedError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (401)', msg="incorrect error", ) def test_errors_402(self): """ Test errors """ response = MockResponse( 402, error="Entity QuotaReached", description="Current quota is 10" ) error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistPaymentRequiredError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "Entity QuotaReached", "description": "Current quota is 10"} (402)', msg="incorrect error", ) def test_errors_403(self): """ Test errors """ response = MockResponse(403, error="some error") error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistForbiddenError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (403)', ) def test_errors_403_multipartencoder(self): """ Test errors """ class Object: pass request = Object() request.body = MultipartEncoder( fields={ "file": ("filename", "filecontents", "image/jpg"), } ) response = MockResponse( 403, request=request, error="some error", ) error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistForbiddenError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (403)', ) def test_errors_404(self): """ Test errors """ class Object: pass request = Object() request.body = json.dumps({"identity": "entity/xxxxx"}) response = MockResponse( 404, request=request, error="some error", ) error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistNotFoundError) as ex: raise error self.assertEqual( str(ex.exception), "entity/xxxxx not found (404)", msg="incorrect error", ) def test_errors_429(self): """ Test errors """ class Object: pass request = Object() request.body = json.dumps({"identity": "entity/xxxxx"}) response = MockResponse( 429, request=request, error="some error", ) error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistTooManyRequestsError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (429)', msg="incorrect error", ) def test_errors_4xx(self): """ Test errors """ response = MockResponse(405, error="some error") error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(Archivist4xxError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (405)', msg="incorrect error", ) def test_errors_500(self): """ Test errors """ response = MockResponse(500, error="some error") error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(Archivist5xxError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (500)', msg="incorrect error", ) def test_errors_501(self): """ Test errors """ response = MockResponse(501, error="some error") error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistNotImplementedError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (501)', msg="incorrect error", ) def test_errors_503(self): """ Test errors """ response = MockResponse(503, error="some error") error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistUnavailableError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (503)', msg="incorrect error", ) def test_errors_600(self): """ Test errors """ response = MockResponse(600, error="some error") error = _parse_response(response) self.assertIsNotNone( error, msg="error should not be None", ) with self.assertRaises(ArchivistError) as ex: raise error self.assertEqual( str(ex.exception), '{"error": "some error"} (600)', msg="incorrect error", )
25.359133
91
0.53046
c21edd9c4d9fc3d01afae0497d3eab3e9c31ff09
506
py
Python
pypy/jit/codegen/i386/test/test_genc_virtualizable.py
camillobruni/pygirl
ddbd442d53061d6ff4af831c1eab153bcc771b5a
[ "MIT" ]
12
2016-01-06T07:10:28.000Z
2021-05-13T23:02:02.000Z
pypy/jit/codegen/i386/test/test_genc_virtualizable.py
camillobruni/pygirl
ddbd442d53061d6ff4af831c1eab153bcc771b5a
[ "MIT" ]
null
null
null
pypy/jit/codegen/i386/test/test_genc_virtualizable.py
camillobruni/pygirl
ddbd442d53061d6ff4af831c1eab153bcc771b5a
[ "MIT" ]
2
2016-07-29T07:09:50.000Z
2016-10-16T08:50:26.000Z
from pypy.jit.codegen.i386.test.test_genc_portal import I386PortalTestMixin from pypy.jit.timeshifter.test import test_virtualizable class TestVirtualizableExplicit(I386PortalTestMixin, test_virtualizable.TestVirtualizableExplicit): pass class TestVirtualizableImplicit(I386PortalTestMixin, test_virtualizable.TestVirtualizableImplicit): pass # for the individual tests see # ====> ../../../timeshifter/test/test_virtualizable.py
33.733333
78
0.727273
aee193b572fa6bb5a397563697e9c0175fc63ba2
16,074
py
Python
tests/regressiontests/admin_views/models.py
five3/django
fa3f0aa021bea85b9196ef154a32c7bb1023a1e9
[ "BSD-3-Clause" ]
3
2015-10-14T09:13:48.000Z
2021-01-01T06:31:25.000Z
tests/regressiontests/admin_views/models.py
econchick/django
86c5c0154f69728eba4aad6204621f07cdd3459d
[ "BSD-3-Clause" ]
null
null
null
tests/regressiontests/admin_views/models.py
econchick/django
86c5c0154f69728eba4aad6204621f07cdd3459d
[ "BSD-3-Clause" ]
1
2019-07-15T02:35:16.000Z
2019-07-15T02:35:16.000Z
# -*- coding: utf-8 -*- from __future__ import unicode_literals import datetime import tempfile import os from django.contrib.auth.models import User from django.contrib.contenttypes import generic from django.contrib.contenttypes.models import ContentType from django.core.files.storage import FileSystemStorage from django.db import models class Section(models.Model): """ A simple section that links to articles, to test linking to related items in admin views. """ name = models.CharField(max_length=100) class Article(models.Model): """ A simple article to test admin views. Test backwards compatibility. """ title = models.CharField(max_length=100) content = models.TextField() date = models.DateTimeField() section = models.ForeignKey(Section, null=True, blank=True) def __unicode__(self): return self.title def model_year(self): return self.date.year model_year.admin_order_field = 'date' model_year.short_description = '' class Book(models.Model): """ A simple book that has chapters. """ name = models.CharField(max_length=100, verbose_name='¿Name?') def __unicode__(self): return self.name class Promo(models.Model): name = models.CharField(max_length=100, verbose_name='¿Name?') book = models.ForeignKey(Book) def __unicode__(self): return self.name class Chapter(models.Model): title = models.CharField(max_length=100, verbose_name='¿Title?') content = models.TextField() book = models.ForeignKey(Book) def __unicode__(self): return self.title class Meta: # Use a utf-8 bytestring to ensure it works (see #11710) verbose_name = '¿Chapter?' class ChapterXtra1(models.Model): chap = models.OneToOneField(Chapter, verbose_name='¿Chap?') xtra = models.CharField(max_length=100, verbose_name='¿Xtra?') def __unicode__(self): return '¿Xtra1: %s' % self.xtra class ChapterXtra2(models.Model): chap = models.OneToOneField(Chapter, verbose_name='¿Chap?') xtra = models.CharField(max_length=100, verbose_name='¿Xtra?') def __unicode__(self): return '¿Xtra2: %s' % self.xtra class RowLevelChangePermissionModel(models.Model): name = models.CharField(max_length=100, blank=True) class CustomArticle(models.Model): content = models.TextField() date = models.DateTimeField() class ModelWithStringPrimaryKey(models.Model): string_pk = models.CharField(max_length=255, primary_key=True) def __unicode__(self): return self.string_pk def get_absolute_url(self): return '/dummy/%s/' % self.string_pk class Color(models.Model): value = models.CharField(max_length=10) warm = models.BooleanField() def __unicode__(self): return self.value # we replicate Color to register with another ModelAdmin class Color2(Color): class Meta: proxy = True class Thing(models.Model): title = models.CharField(max_length=20) color = models.ForeignKey(Color, limit_choices_to={'warm': True}) def __unicode__(self): return self.title class Actor(models.Model): name = models.CharField(max_length=50) age = models.IntegerField() def __unicode__(self): return self.name class Inquisition(models.Model): expected = models.BooleanField() leader = models.ForeignKey(Actor) country = models.CharField(max_length=20) def __unicode__(self): return "by %s from %s" % (self.leader, self.country) class Sketch(models.Model): title = models.CharField(max_length=100) inquisition = models.ForeignKey(Inquisition, limit_choices_to={'leader__name': 'Palin', 'leader__age': 27, 'expected': False, }) def __unicode__(self): return self.title class Fabric(models.Model): NG_CHOICES = ( ('Textured', ( ('x', 'Horizontal'), ('y', 'Vertical'), ) ), ('plain', 'Smooth'), ) surface = models.CharField(max_length=20, choices=NG_CHOICES) class Person(models.Model): GENDER_CHOICES = ( (1, "Male"), (2, "Female"), ) name = models.CharField(max_length=100) gender = models.IntegerField(choices=GENDER_CHOICES) age = models.IntegerField(default=21) alive = models.BooleanField() def __unicode__(self): return self.name class Persona(models.Model): """ A simple persona associated with accounts, to test inlining of related accounts which inherit from a common accounts class. """ name = models.CharField(blank=False, max_length=80) def __unicode__(self): return self.name class Account(models.Model): """ A simple, generic account encapsulating the information shared by all types of accounts. """ username = models.CharField(blank=False, max_length=80) persona = models.ForeignKey(Persona, related_name="accounts") servicename = 'generic service' def __unicode__(self): return "%s: %s" % (self.servicename, self.username) class FooAccount(Account): """A service-specific account of type Foo.""" servicename = 'foo' class BarAccount(Account): """A service-specific account of type Bar.""" servicename = 'bar' class Subscriber(models.Model): name = models.CharField(blank=False, max_length=80) email = models.EmailField(blank=False, max_length=175) def __unicode__(self): return "%s (%s)" % (self.name, self.email) class ExternalSubscriber(Subscriber): pass class OldSubscriber(Subscriber): pass class Media(models.Model): name = models.CharField(max_length=60) class Podcast(Media): release_date = models.DateField() class Meta: ordering = ('release_date',) # overridden in PodcastAdmin class Vodcast(Media): media = models.OneToOneField(Media, primary_key=True, parent_link=True) released = models.BooleanField(default=False) class Parent(models.Model): name = models.CharField(max_length=128) class Child(models.Model): parent = models.ForeignKey(Parent, editable=False) name = models.CharField(max_length=30, blank=True) class EmptyModel(models.Model): def __unicode__(self): return "Primary key = %s" % self.id temp_storage = FileSystemStorage(tempfile.mkdtemp(dir=os.environ['DJANGO_TEST_TEMP_DIR'])) UPLOAD_TO = os.path.join(temp_storage.location, 'test_upload') class Gallery(models.Model): name = models.CharField(max_length=100) class Picture(models.Model): name = models.CharField(max_length=100) image = models.FileField(storage=temp_storage, upload_to='test_upload') gallery = models.ForeignKey(Gallery, related_name="pictures") class Language(models.Model): iso = models.CharField(max_length=5, primary_key=True) name = models.CharField(max_length=50) english_name = models.CharField(max_length=50) shortlist = models.BooleanField(default=False) class Meta: ordering = ('iso',) # a base class for Recommender and Recommendation class Title(models.Model): pass class TitleTranslation(models.Model): title = models.ForeignKey(Title) text = models.CharField(max_length=100) class Recommender(Title): pass class Recommendation(Title): recommender = models.ForeignKey(Recommender) class Collector(models.Model): name = models.CharField(max_length=100) class Widget(models.Model): owner = models.ForeignKey(Collector) name = models.CharField(max_length=100) class DooHickey(models.Model): code = models.CharField(max_length=10, primary_key=True) owner = models.ForeignKey(Collector) name = models.CharField(max_length=100) class Grommet(models.Model): code = models.AutoField(primary_key=True) owner = models.ForeignKey(Collector) name = models.CharField(max_length=100) class Whatsit(models.Model): index = models.IntegerField(primary_key=True) owner = models.ForeignKey(Collector) name = models.CharField(max_length=100) class Doodad(models.Model): name = models.CharField(max_length=100) class FancyDoodad(Doodad): owner = models.ForeignKey(Collector) expensive = models.BooleanField(default=True) class Category(models.Model): collector = models.ForeignKey(Collector) order = models.PositiveIntegerField() class Meta: ordering = ('order',) def __unicode__(self): return '%s:o%s' % (self.id, self.order) class Link(models.Model): posted = models.DateField( default=lambda: datetime.date.today() - datetime.timedelta(days=7) ) url = models.URLField() post = models.ForeignKey("Post") class PrePopulatedPost(models.Model): title = models.CharField(max_length=100) published = models.BooleanField() slug = models.SlugField() class PrePopulatedSubPost(models.Model): post = models.ForeignKey(PrePopulatedPost) subtitle = models.CharField(max_length=100) subslug = models.SlugField() class Post(models.Model): title = models.CharField(max_length=100, help_text="Some help text for the title (with unicode ŠĐĆŽćžšđ)") content = models.TextField(help_text="Some help text for the content (with unicode ŠĐĆŽćžšđ)") posted = models.DateField( default=datetime.date.today, help_text="Some help text for the date (with unicode ŠĐĆŽćžšđ)" ) public = models.NullBooleanField() def awesomeness_level(self): return "Very awesome." class Gadget(models.Model): name = models.CharField(max_length=100) def __unicode__(self): return self.name class Villain(models.Model): name = models.CharField(max_length=100) def __unicode__(self): return self.name class SuperVillain(Villain): pass class FunkyTag(models.Model): "Because we all know there's only one real use case for GFKs." name = models.CharField(max_length=25) content_type = models.ForeignKey(ContentType) object_id = models.PositiveIntegerField() content_object = generic.GenericForeignKey('content_type', 'object_id') def __unicode__(self): return self.name class Plot(models.Model): name = models.CharField(max_length=100) team_leader = models.ForeignKey(Villain, related_name='lead_plots') contact = models.ForeignKey(Villain, related_name='contact_plots') tags = generic.GenericRelation(FunkyTag) def __unicode__(self): return self.name class PlotDetails(models.Model): details = models.CharField(max_length=100) plot = models.OneToOneField(Plot) def __unicode__(self): return self.details class SecretHideout(models.Model): """ Secret! Not registered with the admin! """ location = models.CharField(max_length=100) villain = models.ForeignKey(Villain) def __unicode__(self): return self.location class SuperSecretHideout(models.Model): """ Secret! Not registered with the admin! """ location = models.CharField(max_length=100) supervillain = models.ForeignKey(SuperVillain) def __unicode__(self): return self.location class CyclicOne(models.Model): name = models.CharField(max_length=25) two = models.ForeignKey('CyclicTwo') def __unicode__(self): return self.name class CyclicTwo(models.Model): name = models.CharField(max_length=25) one = models.ForeignKey(CyclicOne) def __unicode__(self): return self.name class Topping(models.Model): name = models.CharField(max_length=20) class Pizza(models.Model): name = models.CharField(max_length=20) toppings = models.ManyToManyField('Topping') class Album(models.Model): owner = models.ForeignKey(User) title = models.CharField(max_length=30) class Employee(Person): code = models.CharField(max_length=20) class WorkHour(models.Model): datum = models.DateField() employee = models.ForeignKey(Employee) class Question(models.Model): question = models.CharField(max_length=20) class Answer(models.Model): question = models.ForeignKey(Question, on_delete=models.PROTECT) answer = models.CharField(max_length=20) def __unicode__(self): return self.answer class Reservation(models.Model): start_date = models.DateTimeField() price = models.IntegerField() DRIVER_CHOICES = ( ('bill', 'Bill G'), ('steve', 'Steve J'), ) RESTAURANT_CHOICES = ( ('indian', 'A Taste of India'), ('thai', 'Thai Pography'), ('pizza', 'Pizza Mama'), ) class FoodDelivery(models.Model): reference = models.CharField(max_length=100) driver = models.CharField(max_length=100, choices=DRIVER_CHOICES, blank=True) restaurant = models.CharField(max_length=100, choices=RESTAURANT_CHOICES, blank=True) class Meta: unique_together = (("driver", "restaurant"),) class Paper(models.Model): title = models.CharField(max_length=30) author = models.CharField(max_length=30, blank=True, null=True) class CoverLetter(models.Model): author = models.CharField(max_length=30) date_written = models.DateField(null=True, blank=True) def __unicode__(self): return self.author class Story(models.Model): title = models.CharField(max_length=100) content = models.TextField() class OtherStory(models.Model): title = models.CharField(max_length=100) content = models.TextField() class ComplexSortedPerson(models.Model): name = models.CharField(max_length=100) age = models.PositiveIntegerField() is_employee = models.NullBooleanField() class PrePopulatedPostLargeSlug(models.Model): """ Regression test for #15938: a large max_length for the slugfield must not be localized in prepopulated_fields_js.html or it might end up breaking the javascript (ie, using THOUSAND_SEPARATOR ends up with maxLength=1,000) """ title = models.CharField(max_length=100) published = models.BooleanField() slug = models.SlugField(max_length=1000) class AdminOrderedField(models.Model): order = models.IntegerField() stuff = models.CharField(max_length=200) class AdminOrderedModelMethod(models.Model): order = models.IntegerField() stuff = models.CharField(max_length=200) def some_order(self): return self.order some_order.admin_order_field = 'order' class AdminOrderedAdminMethod(models.Model): order = models.IntegerField() stuff = models.CharField(max_length=200) class AdminOrderedCallable(models.Model): order = models.IntegerField() stuff = models.CharField(max_length=200) class Report(models.Model): title = models.CharField(max_length=100) def __unicode__(self): return self.title class MainPrepopulated(models.Model): name = models.CharField(max_length=100) pubdate = models.DateField() status = models.CharField( max_length=20, choices=(('option one', 'Option One'), ('option two', 'Option Two'))) slug1 = models.SlugField() slug2 = models.SlugField() class RelatedPrepopulated(models.Model): parent = models.ForeignKey(MainPrepopulated) name = models.CharField(max_length=75) pubdate = models.DateField() status = models.CharField( max_length=20, choices=(('option one', 'Option One'), ('option two', 'Option Two'))) slug1 = models.SlugField(max_length=50) slug2 = models.SlugField(max_length=60) class UnorderedObject(models.Model): """ Model without any defined `Meta.ordering`. Refs #16819. """ name = models.CharField(max_length=255) bool = models.BooleanField(default=True) class UndeletableObject(models.Model): """ Model whose show_delete in admin change_view has been disabled Refs #10057. """ name = models.CharField(max_length=255)
25.884058
110
0.689188
853c025e7d5e8a53de7685c1b072eb1ce8ff162a
1,342
py
Python
templates/agent_NOP.py
trer/bombots
16f0360236185e104f219127770e62799eb3c4ab
[ "MIT" ]
null
null
null
templates/agent_NOP.py
trer/bombots
16f0360236185e104f219127770e62799eb3c4ab
[ "MIT" ]
null
null
null
templates/agent_NOP.py
trer/bombots
16f0360236185e104f219127770e62799eb3c4ab
[ "MIT" ]
null
null
null
import random import sys import numpy as np sys.path.append("..") from bombots.environment import Bombots from collections import Counter class NOPAgent: # Feel free to remove the comments from this file, they # can be found in the source on GitHub at any time # https://github.com/CogitoNTNU/bombots # This is an example agent that can be used as a learning resource or as # a technical base for your own agents. There are few limitations to how # this should be structured, but the functions '__init__' and 'act' need # to have the exact parameters that they have in the original source. # Action reference: # Bombots.NOP - No Operation # Bombots.UP - Move up # Bombots.DOWN - Move down # Bombots.LEFT - Move left # Bombots.RIGHT - Move right # Bombots.BOMB - Place bomb # State reference: # env_state['agent_pos'] - Coordinates of the controlled bot - (x, y) tuple # env_state['enemy_pos'] - Coordinates of the opponent bot - (x, y) tuple # env_state['bomb_pos'] - List of bomb coordinates - [(x, y), (x, y)] list of tuples def __init__(self, env): self.env = env self.hasprinted = False self.counter = 0 def act(self, env_state): action = Bombots.NOP return action
28.553191
98
0.648286
a1207a43231398e66b31d93db3c6a745292b4259
766
py
Python
var/spack/repos/builtin/packages/libxprintapputil/package.py
HaochengLIU/spack
26e51ff1705a4d6234e2a0cf734f93f7f95df5cb
[ "ECL-2.0", "Apache-2.0", "MIT" ]
2
2018-11-27T03:39:44.000Z
2021-09-06T15:50:35.000Z
var/spack/repos/builtin/packages/libxprintapputil/package.py
HaochengLIU/spack
26e51ff1705a4d6234e2a0cf734f93f7f95df5cb
[ "ECL-2.0", "Apache-2.0", "MIT" ]
1
2019-01-11T20:11:52.000Z
2019-01-11T20:11:52.000Z
var/spack/repos/builtin/packages/libxprintapputil/package.py
HaochengLIU/spack
26e51ff1705a4d6234e2a0cf734f93f7f95df5cb
[ "ECL-2.0", "Apache-2.0", "MIT" ]
1
2020-10-14T14:20:17.000Z
2020-10-14T14:20:17.000Z
# Copyright 2013-2018 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class Libxprintapputil(AutotoolsPackage): """Xprint application utility routines.""" homepage = "https://cgit.freedesktop.org/xorg/lib/libXprintAppUtil/" url = "https://www.x.org/archive/individual/lib/libXprintAppUtil-1.0.1.tar.gz" version('1.0.1', '3adb71fa34a2d4e75d8b840310318f76') depends_on('libx11') depends_on('libxp') depends_on('libxprintutil') depends_on('libxau') depends_on('printproto', type='build') depends_on('pkgconfig', type='build') depends_on('util-macros', type='build')
30.64
87
0.71671
ff652cb01eb0f230a4c418594a9a254d3ce42ebb
2,973
py
Python
tests/test_client.py
jaebradley/nba_data
30d817bbc1c5474774f97f3800354492e382d206
[ "MIT" ]
8
2017-01-07T13:32:16.000Z
2019-08-08T17:36:26.000Z
tests/test_client.py
jaebradley/nba_data
30d817bbc1c5474774f97f3800354492e382d206
[ "MIT" ]
72
2016-09-01T01:21:07.000Z
2021-03-25T21:41:38.000Z
tests/test_client.py
jaebradley/nba_data
30d817bbc1c5474774f97f3800354492e382d206
[ "MIT" ]
4
2016-12-06T10:30:59.000Z
2021-09-08T21:23:43.000Z
from datetime import date from unittest import TestCase from nba_data.client import Client from nba_data.data.box_scores import GameBoxScore from nba_data.data.game import LoggedGame from nba_data.data.players import Player, DetailedPlayer from nba_data.data.season import Season from nba_data.data.team import Team class TestClient(TestCase): def test_instantiation(self): self.assertIsNotNone(Client()) def test_get_all_nba_players(self): players = Client.get_all_nba_players() self.assertIsNotNone(players) self.assertIsInstance(players, list) self.assertGreater(len(players), 0) def test_get_players_for_season(self): players = Client.get_players_for_season(season=Season.season_2015) self.assertIsNotNone(players) self.assertGreater(len(players), 0) self.assertIsInstance(players[0], Player) def test_get_players_for_all_seasons(self): for season in Season: players = Client.get_players_for_season(season=season) self.assertIsNotNone(players) self.assertGreater(len(players), 0) def test_get_games_for_team(self): games = Client.get_games_for_team(season=Season.season_2015, team=Team.boston_celtics) self.assertIsNotNone(games) self.assertEqual(len(games), 82) self.assertIsInstance(games[0], LoggedGame) def test_get_player_info(self): player_details = Client.get_player_info(player_id=201566) self.assertIsNotNone(player_details) self.assertIsInstance(player_details, DetailedPlayer) # def test_all_player_info(self): # for player in Client.get_players_for_season(season=Season.season_2015): # player_details = Client.get_player_info(player_id=player.nba_id) # self.assertIsNotNone(player_details) # self.assertIsInstance(player_details, PlayerDetails) def test_get_advanced_box_score(self): advanced_box_score = Client.get_advanced_box_score(game_id="0021501205") self.assertIsNotNone(advanced_box_score) self.assertIsInstance(advanced_box_score, GameBoxScore) def test_get_traditional_box_score(self): traditional_box_score = Client.get_traditional_box_score(game_id="0021501205") self.assertIsNotNone(traditional_box_score) self.assertIsInstance(traditional_box_score, GameBoxScore) def test_get_game_counts_in_date_range(self): game_counts = Client.get_game_counts_in_date_range() self.assertIsNotNone(game_counts) def test_get_games_for_date(self): date_value = date(year=2016, month=1, day=1) games = Client.get_games_for_date(date_value=date_value) self.assertIsNotNone(games) self.assertIsNot(len(games), 0) def test_get_players(self): players = Client.get_players(season=Season.season_2016) self.assertIsNotNone(players) self.assertGreater(len(players), 0)
39.118421
94
0.734612
422248934e888289363b595c08543c6b63446d3a
32,398
py
Python
net/xception_body.py
rickyHong/Light-Head-RCNN-enhanced-Xdetector
1b19e15709635e007494648c4fb519b703a29d84
[ "Apache-2.0" ]
116
2018-03-12T19:38:06.000Z
2021-02-23T16:15:34.000Z
net/xception_body.py
rickyHong/Light-Head-RCNN-enhanced-Xdetector
1b19e15709635e007494648c4fb519b703a29d84
[ "Apache-2.0" ]
11
2018-04-25T15:46:21.000Z
2019-03-15T11:25:48.000Z
net/xception_body.py
rickyHong/Light-Head-RCNN-enhanced-Xdetector
1b19e15709635e007494648c4fb519b703a29d84
[ "Apache-2.0" ]
37
2018-03-31T03:38:42.000Z
2020-09-18T09:04:32.000Z
# Copyright 2018 Changan Wang # 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 tensorflow as tf from . import resnet_v2 from utility import eval_helper USE_FUSED_BN = True BN_EPSILON = 0.0001#0.001 BN_MOMENTUM = 0.99 #initializer_to_use = tf.glorot_uniform_initializer initializer_to_use = tf.glorot_normal_initializer conv_bn_initializer_to_use = tf.glorot_normal_initializer#lambda : tf.truncated_normal_initializer(mean=0.0, stddev=0.005) def get_shape(x, rank=None): if x.get_shape().is_fully_defined(): return x.get_shape().as_list() else: static_shape = x.get_shape() if rank is None: static_shape = static_shape.as_list() rank = len(static_shape) else: static_shape = x.get_shape().with_rank(rank).as_list() dynamic_shape = tf.unstack(tf.shape(x), rank) return [s if s is not None else d for s, d in zip(static_shape, dynamic_shape)] def _pad_axis(x, offset, size, axis=0, name=None): with tf.name_scope(name, 'rpn_pad_axis'): shape = get_shape(x) rank = len(shape) # Padding description. new_size = tf.maximum(size-offset-shape[axis], 0) pad1 = tf.stack([0]*axis + [offset] + [0]*(rank-axis-1)) pad2 = tf.stack([0]*axis + [new_size] + [0]*(rank-axis-1)) paddings = tf.stack([pad1, pad2], axis=1) x = tf.pad(x, paddings, mode='CONSTANT') # Reshape, to get fully defined shape if possible. # TODO: fix with tf.slice shape[axis] = tf.maximum(size, shape[axis]) x = tf.reshape(x, tf.stack(shape)) return x def _bboxes_nms(scores, bboxes, nms_threshold=0.5, keep_top_k=200, mode=None, scope=None): with tf.name_scope(scope, 'bboxes_nms_single', [scores, bboxes]): # Apply NMS algorithm. idxes = tf.image.non_max_suppression(bboxes, scores, keep_top_k, nms_threshold) scores = tf.gather(scores, idxes) bboxes = tf.gather(bboxes, idxes) # Pad results. scores = _pad_axis(scores, 0, keep_top_k, axis=0) bboxes = _pad_axis(bboxes, 0, keep_top_k, axis=0) return [scores, bboxes] def _bboxes_nms1(scores, bboxes, nms_threshold = 0.5, keep_top_k = 200, mode = 'union', scope=None): with tf.name_scope(scope, 'rpn_bboxes_nms', [scores, bboxes]): num_bboxes = tf.shape(scores)[0] #keep_top_k=tf.minimum(num_bboxes, rpn_post_nms_top_n) def nms_proc(scores, bboxes): # sort all the bboxes #scores, idxes = tf.nn.top_k(scores, k = num_bboxes, sorted = True) #bboxes = tf.gather(bboxes, idxes) ymin = bboxes[:, 0] xmin = bboxes[:, 1] ymax = bboxes[:, 2] xmax = bboxes[:, 3] vol_anchors = (xmax - xmin) * (ymax - ymin) nms_mask = tf.cast(tf.ones_like(scores, dtype=tf.int8), tf.bool) keep_mask = tf.cast(tf.zeros_like(scores, dtype=tf.int8), tf.bool) def safe_divide(numerator, denominator): return tf.where(tf.greater(denominator, 0), tf.divide(numerator, denominator), tf.zeros_like(denominator)) def get_scores(bbox, nms_mask): # the inner square inner_ymin = tf.maximum(ymin, bbox[0]) inner_xmin = tf.maximum(xmin, bbox[1]) inner_ymax = tf.minimum(ymax, bbox[2]) inner_xmax = tf.minimum(xmax, bbox[3]) h = tf.maximum(inner_ymax - inner_ymin, 0.) w = tf.maximum(inner_xmax - inner_xmin, 0.) inner_vol = h * w this_vol = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) if mode == 'union': union_vol = vol_anchors - inner_vol + this_vol elif mode == 'min': union_vol = tf.minimum(vol_anchors, this_vol) else: raise ValueError('unknown mode to use for nms.') return safe_divide(inner_vol, union_vol) * tf.cast(nms_mask, tf.float32) def condition(index, nms_mask, keep_mask): return tf.logical_and(tf.reduce_sum(tf.cast(nms_mask, tf.int32)) > 0, tf.less(index, keep_top_k)) def body(index, nms_mask, keep_mask): # at least one True in nms_mask indices = tf.where(nms_mask)[0][0] bbox = bboxes[indices] this_mask = tf.one_hot(indices, num_bboxes, on_value=False, off_value=True, dtype=tf.bool) keep_mask = tf.logical_or(keep_mask, tf.logical_not(this_mask)) nms_mask = tf.logical_and(nms_mask, this_mask) nms_scores = get_scores(bbox, nms_mask) nms_mask = tf.logical_and(nms_mask, nms_scores < nms_threshold) return [index+1, nms_mask, keep_mask] index = 0 [index, nms_mask, keep_mask] = tf.while_loop(condition, body, [index, nms_mask, keep_mask]) return [_pad_axis(tf.boolean_mask(scores, keep_mask), 0, keep_top_k), _pad_axis(tf.boolean_mask(bboxes, keep_mask), 0, keep_top_k)] #return tf.cond(tf.less(num_bboxes, 1), lambda: [_pad_axis(tf.constant([1.]), 0, keep_top_k), _pad_axis(tf.constant([[0.2, 0.2, 0.8, 0.8]]), 0, keep_top_k)], lambda: nms_proc(scores, bboxes)) return nms_proc(scores, bboxes) def _filter_and_sort_boxes(scores, bboxes, min_size, keep_topk, scope=None):#, keep_top_k = 100 #scores = tf.Print(scores,[tf.shape(scores)]) # scores = tf.Print(scores,[tf.shape(scores), min_size]) # bboxes = tf.Print(bboxes,[tf.shape(bboxes), bboxes]) with tf.name_scope(scope, 'rpn_filter_sort_boxes', [scores, bboxes]): ymin = bboxes[:, 0] #ymin = tf.Print(ymin, [tf.shape(ymin)]) xmin = bboxes[:, 1] ymax = bboxes[:, 2] xmax = bboxes[:, 3] ws = xmax - xmin hs = ymax - ymin x_ctr = xmin + ws / 2. y_ctr = ymin + hs / 2. keep_mask = tf.logical_and(tf.greater(ws, min_size), tf.greater(hs, min_size)) keep_mask = tf.logical_and(keep_mask, tf.greater(x_ctr, 0.)) keep_mask = tf.logical_and(keep_mask, tf.greater(y_ctr, 0.)) keep_mask = tf.logical_and(keep_mask, tf.less(x_ctr, 1.)) keep_mask = tf.logical_and(keep_mask, tf.less(y_ctr, 1.)) scores = tf.boolean_mask(scores, keep_mask) scores, idxes = tf.nn.top_k(scores, k=tf.minimum(tf.shape(scores)[0], keep_topk), sorted=True) return [_pad_axis(scores, 0, keep_topk, axis=0), _pad_axis(tf.gather(tf.boolean_mask(bboxes, keep_mask), idxes), 0, keep_topk, axis=0)] # scores = _pad_axis(tf.boolean_mask(scores, keep_mask), 0, keep_topk, axis=0) # bboxes = _pad_axis(tf.boolean_mask(bboxes, keep_mask), 0, keep_topk, axis=0) #return [_pad_axis(tf.boolean_mask(scores, keep_mask), 0, keep_topk, axis=0), _pad_axis(tf.boolean_mask(bboxes, keep_mask), 0, keep_topk, axis=0)] #return [tf.boolean_mask(scores, keep_mask), tf.boolean_mask(bboxes, keep_mask)] # def _bboxes_sort(scores, bboxes, top_k=100, scope=None): # #scores = tf.Print(scores,[tf.shape(scores)]) # with tf.name_scope(scope, 'rpn_bboxes_sort', [scores, bboxes]): # # Sort scores... # scores, idxes = tf.nn.top_k(scores, k=top_k, sorted=True) # return [scores, tf.gather(bboxes, idxes)] # #return [_pad_axis(scores, 0, top_k, axis=0), _pad_axis(tf.gather(bboxes, idxes), 0, top_k, axis=0)] def _bboxes_clip(bbox_ref, bboxes, scope=None): #bboxes = tf.Print(bboxes,[tf.shape(bboxes)]) with tf.name_scope(scope, 'rpn_bboxes_clip'): # Easier with transposed bboxes. Especially for broadcasting. bbox_ref = tf.transpose(bbox_ref) bboxes = tf.transpose(bboxes) # Intersection bboxes and reference bbox. ymin = tf.maximum(bboxes[0], bbox_ref[0]) xmin = tf.maximum(bboxes[1], bbox_ref[1]) ymax = tf.minimum(bboxes[2], bbox_ref[2]) xmax = tf.minimum(bboxes[3], bbox_ref[3]) # Double check! Empty boxes when no-intersection. # _____ # | | # |_____| # ______ # | | # |______| ymin = tf.minimum(ymin, ymax) xmin = tf.minimum(xmin, xmax) bboxes = tf.transpose(tf.stack([ymin, xmin, ymax, xmax], axis=0)) return bboxes def _upsample_rois(scores, bboxes, keep_top_k): # upsample with replacement # filter out paddings bboxes = tf.boolean_mask(bboxes, scores > 0.) scores = tf.boolean_mask(scores, scores > 0.) scores, bboxes = tf.cond(tf.less(tf.shape(scores)[0], 1), lambda: (tf.constant([1.]), tf.constant([[0.2, 0.2, 0.8, 0.8]])), lambda: (scores, bboxes)) #scores = tf.Print(scores,[scores]) def upsampel_impl(): num_bboxes = tf.shape(scores)[0] left_count = keep_top_k - num_bboxes select_indices = tf.random_shuffle(tf.range(num_bboxes))[:tf.floormod(left_count, num_bboxes)] #### zero select_indices = tf.concat([tf.tile(tf.range(num_bboxes), [tf.floor_div(left_count, num_bboxes) + 1]), select_indices], axis = 0) return [tf.gather(scores, select_indices), tf.gather(bboxes, select_indices)] return tf.cond(tf.shape(scores)[0] < keep_top_k, lambda : upsampel_impl(), lambda : [scores, bboxes]) def _point2center(proposals_bboxes): ymin, xmin, ymax, xmax = proposals_bboxes[:, :, 0], proposals_bboxes[:, :, 1], proposals_bboxes[:, :, 2], proposals_bboxes[:, :, 3] height, width = (ymax - ymin), (xmax - xmin) return tf.stack([ymin + height / 2., xmin + width / 2., height, width], axis=-1) def relu_separable_bn_block(inputs, filters, name_prefix, is_training, data_format): bn_axis = -1 if data_format == 'channels_last' else 1 inputs = tf.nn.relu(inputs, name=name_prefix + '_act') inputs = tf.layers.separable_conv2d(inputs, filters, (3, 3), strides=(1, 1), padding='same', data_format=data_format, activation=None, use_bias=False, depthwise_initializer=conv_bn_initializer_to_use(), pointwise_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer(), name=name_prefix, reuse=None) inputs = tf.layers.batch_normalization(inputs, momentum=BN_MOMENTUM, name=name_prefix + '_bn', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) return inputs def XceptionBody(input_image, num_classes, is_training = False, data_format='channels_last'): # one question: is there problems with the unaligned 'valid-conv' when mapping ROI from input image to last feature maps # modify the input size to 481 bn_axis = -1 if data_format == 'channels_last' else 1 # (481-3+0*2)/2 + 1 = 240 inputs = tf.layers.conv2d(input_image, 32, (3, 3), use_bias=False, name='block1_conv1', strides=(2, 2), padding='valid', data_format=data_format, activation=None, kernel_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer()) inputs = tf.layers.batch_normalization(inputs, momentum=BN_MOMENTUM, name='block1_conv1_bn', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) inputs = tf.nn.relu(inputs, name='block1_conv1_act') # (240-3+0*2)/1 + 1 = 238 inputs = tf.layers.conv2d(inputs, 64, (3, 3), use_bias=False, name='block1_conv2', strides=(1, 1), padding='valid', data_format=data_format, activation=None, kernel_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer()) inputs = tf.layers.batch_normalization(inputs, momentum=BN_MOMENTUM, name='block1_conv2_bn', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) inputs = tf.nn.relu(inputs, name='block1_conv2_act') # (238-1+0*2)/2 + 1 = 119 residual = tf.layers.conv2d(inputs, 128, (1, 1), use_bias=False, name='conv2d_1', strides=(2, 2), padding='same', data_format=data_format, activation=None, kernel_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer()) residual = tf.layers.batch_normalization(residual, momentum=BN_MOMENTUM, name='batch_normalization_1', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) inputs = tf.layers.separable_conv2d(inputs, 128, (3, 3), strides=(1, 1), padding='same', data_format=data_format, activation=None, use_bias=False, depthwise_initializer=conv_bn_initializer_to_use(), pointwise_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='block2_sepconv1', reuse=None) inputs = tf.layers.batch_normalization(inputs, momentum=BN_MOMENTUM, name='block2_sepconv1_bn', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) inputs = relu_separable_bn_block(inputs, 128, 'block2_sepconv2', is_training, data_format) # (238-3+1*2)/2 + 1 = 119 inputs = tf.layers.max_pooling2d(inputs, pool_size=(3, 3), strides=(2, 2), padding='same', data_format=data_format, name='block2_pool') # 119 #inputs = inputs + residual inputs = tf.add(inputs, residual, name='residual_add_0') #inputs = tf.Print(inputs,[tf.shape(inputs), inputs,residual]) # (119-1+0*2)/2 + 1 = 60 residual = tf.layers.conv2d(inputs, 256, (1, 1), use_bias=False, name='conv2d_2', strides=(2, 2), padding='same', data_format=data_format, activation=None, kernel_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer()) residual = tf.layers.batch_normalization(residual, momentum=BN_MOMENTUM, name='batch_normalization_2', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) inputs = relu_separable_bn_block(inputs, 256, 'block3_sepconv1', is_training, data_format) inputs = relu_separable_bn_block(inputs, 256, 'block3_sepconv2', is_training, data_format) # (119-3+1*2)/2 + 1 = 60 inputs = tf.layers.max_pooling2d(inputs, pool_size=(3, 3), strides=(2, 2), padding='same', data_format=data_format, name='block3_pool') # 60 #inputs = inputs + residual inputs = tf.add(inputs, residual, name='residual_add_1') # (119-1+0*2)/2 + 1 = 30 residual = tf.layers.conv2d(inputs, 728, (1, 1), use_bias=False, name='conv2d_3', strides=(2, 2), padding='same', data_format=data_format, activation=None, kernel_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer()) residual = tf.layers.batch_normalization(residual, momentum=BN_MOMENTUM, name='batch_normalization_3', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) inputs = relu_separable_bn_block(inputs, 728, 'block4_sepconv1', is_training, data_format) inputs = relu_separable_bn_block(inputs, 728, 'block4_sepconv2', is_training, data_format) # (119-3+1*2)/2 + 1 = 30 inputs = tf.layers.max_pooling2d(inputs, pool_size=(3, 3), strides=(2, 2), padding='same', data_format=data_format, name='block4_pool') # 30 #inputs = inputs + residual inputs = tf.add(inputs, residual, name='residual_add_2') for index in range(8): residual = inputs prefix = 'block' + str(index + 5) inputs = relu_separable_bn_block(inputs, 728, prefix + '_sepconv1', is_training, data_format) inputs = relu_separable_bn_block(inputs, 728, prefix + '_sepconv2', is_training, data_format) inputs = relu_separable_bn_block(inputs, 728, prefix + '_sepconv3', is_training, data_format) #inputs = inputs + residual inputs = tf.add(inputs, residual, name=prefix + '_residual_add') mid_outputs = tf.nn.relu(inputs, name='before_block13_act') # remove stride 2 for the residual connection residual = tf.layers.conv2d(inputs, 1024, (1, 1), use_bias=False, name='conv2d_4', strides=(1, 1), padding='same', data_format=data_format, activation=None, kernel_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer()) residual = tf.layers.batch_normalization(residual, momentum=BN_MOMENTUM, name='batch_normalization_4', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) inputs = relu_separable_bn_block(inputs, 728, 'block13_sepconv1', is_training, data_format) inputs = relu_separable_bn_block(inputs, 1024, 'block13_sepconv2', is_training, data_format) #inputs = inputs + residual inputs = tf.add(inputs, residual, name='residual_add_3') # use atrous algorithm at last two conv inputs = tf.layers.separable_conv2d(inputs, 1536, (3, 3), strides=(1, 1), dilation_rate=(2, 2), padding='same', data_format=data_format, activation=None, use_bias=False, depthwise_initializer=conv_bn_initializer_to_use(), pointwise_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='block14_sepconv1', reuse=None) inputs = tf.layers.batch_normalization(inputs, momentum=BN_MOMENTUM, name='block14_sepconv1_bn', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) inputs = tf.nn.relu(inputs, name='block14_sepconv1_act') inputs = tf.layers.separable_conv2d(inputs, 2048, (3, 3), strides=(1, 1), dilation_rate=(2, 2), padding='same', data_format=data_format, activation=None, use_bias=False, depthwise_initializer=conv_bn_initializer_to_use(), pointwise_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='block14_sepconv2', reuse=None) inputs = tf.layers.batch_normalization(inputs, momentum=BN_MOMENTUM, name='block14_sepconv2_bn', axis=bn_axis, epsilon=BN_EPSILON, training=is_training, reuse=None, fused=USE_FUSED_BN) outputs = tf.nn.relu(inputs, name='block14_sepconv2_act') # the output size here is 30 x 30 return mid_outputs, outputs def get_rpn(net_input, num_anchors, is_training, data_format, var_scope): with tf.variable_scope(var_scope): rpn_relu = tf.layers.conv2d(inputs=net_input, filters=512, kernel_size=(3, 3), strides=1, padding='SAME', use_bias=True, activation=tf.nn.relu, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), data_format=data_format) rpn_cls_score = tf.layers.conv2d(inputs=rpn_relu, filters=2 * num_anchors, kernel_size=(1, 1), strides=1, padding='SAME', use_bias=True, activation=None, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), data_format=data_format) rpn_bbox_pred = tf.layers.conv2d(inputs=rpn_relu, filters=4 * num_anchors, kernel_size=(1, 1), strides=1, padding='SAME', use_bias=True, activation=None, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), data_format=data_format) #rpn_bbox_pred = tf.Print(rpn_bbox_pred,[tf.shape(rpn_bbox_pred), net_input, rpn_bbox_pred]) return rpn_cls_score, rpn_bbox_pred def get_proposals(object_score, bboxes_pred, encode_fn, rpn_pre_nms_top_n, rpn_post_nms_top_n, nms_threshold, rpn_min_size, is_training, data_format): '''About the input: object_score: N x num_bboxes bboxes_pred: N x num_bboxes x 4 decode_fn: accept location_pred and return the decoded bboxes in format 'batch_size, feature_h, feature_w, num_anchors, 4' rpn_min_size: the absolute pixels a bbox must have in both side data_format: specify the input format this function do the following process: 1. for each (H, W) location i generate A anchor boxes centered on cell i apply predicted bbox deltas at cell i to each of the A anchors (this is done before this function) 2. clip predicted boxes to image 3. remove predicted boxes with either height or width < rpn_min_size 4. sort all (proposal, score) pairs by score from highest to lowest 5. take top pre_nms_topN rois before NMS 6. apply NMS with threshold 0.7 to remaining rois 7. take after_nms_topN rois after NMS (if number of bboxes if less than after_nms_topN, the upsample them) 8. take both the top rois and all the ground truth bboxes as all_rois 9. rematch all_rois to get regress and classification target 10.sample all_rois as proposals ''' # it's better to run these task on GPU to avoid lots of Host->Device memory copy with tf.device('/cpu:0'): #bboxes_pred = decode_fn(location_pred) bboxes_pred = tf.map_fn(lambda _bboxes : _bboxes_clip([0., 0., 1., 1.], _bboxes), bboxes_pred, back_prop=False) object_score, bboxes_pred = tf.map_fn(lambda _score_bboxes : _filter_and_sort_boxes(_score_bboxes[0], _score_bboxes[1], rpn_min_size, keep_topk=rpn_pre_nms_top_n), [object_score, bboxes_pred], back_prop=False, infer_shape=True) #object_score, bboxes_pred = tf.map_fn(lambda _score_bboxes : _bboxes_sort(_score_bboxes[0], _score_bboxes[1], top_k=rpn_pre_nms_top_n), [object_score, bboxes_pred], back_prop=False) # object_score.set_shape([None, rpn_pre_nms_top_n]) # bboxes_pred.set_shape([None, rpn_pre_nms_top_n, 4]) object_score, bboxes_pred = tf.map_fn(lambda _score_bboxes : _bboxes_nms(_score_bboxes[0], _score_bboxes[1], nms_threshold = nms_threshold, keep_top_k=rpn_post_nms_top_n, mode = 'union'), [object_score, bboxes_pred], back_prop=False)#, dtype=[tf.float32, tf.float32], infer_shape=True # padding to fix the size of rois # the object_score is not in descending order when the upsample padding happened #object_score = tf.Print(object_score, [object_score[0],object_score[1],object_score[2],object_score[3]], message='object_score0:', summarize=1000) #bboxes_pred = tf.Print(bboxes_pred, [bboxes_pred[0],bboxes_pred[1],bboxes_pred[2],bboxes_pred[3]], message='bboxes_pred0:', summarize=1000) object_score, bboxes_pred = tf.map_fn(lambda _score_bboxes : _upsample_rois(_score_bboxes[0], _score_bboxes[1], keep_top_k= rpn_post_nms_top_n), [object_score, bboxes_pred], back_prop=False) # match and sample to get proposals and targets #print(encode_fn(bboxes_pred)) # object_score = tf.Print(object_score, [object_score[0],object_score[1],object_score[2],object_score[3]], message='object_score1:', summarize=1000) # bboxes_pred = tf.Print(bboxes_pred, [bboxes_pred[0],bboxes_pred[1],bboxes_pred[2],bboxes_pred[3]], message='bboxes_pred1:', summarize=1000) if not is_training: return tf.stop_gradient(bboxes_pred) proposals_bboxes, proposals_targets, proposals_labels, proposals_scores = encode_fn(bboxes_pred) return tf.stop_gradient(proposals_bboxes), tf.stop_gradient(proposals_targets), tf.stop_gradient(proposals_labels), tf.stop_gradient(proposals_scores) def large_sep_kernel(net_input, depth_mid, depth_output, is_training, data_format, var_scope): with tf.variable_scope(var_scope): with tf.variable_scope('Branch_0'): branch_0a = tf.layers.conv2d(inputs=net_input, filters=depth_mid, kernel_size=(15, 1), strides=1, padding='SAME', use_bias=True, activation=None, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), data_format=data_format) branch_0b = tf.layers.conv2d(inputs=branch_0a, filters=depth_output, kernel_size=(1, 15), strides=1, padding='SAME', use_bias=True, activation=None, kernel_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer(), data_format=data_format) with tf.variable_scope('Branch_1'): branch_1a = tf.layers.conv2d(inputs=net_input, filters=depth_mid, kernel_size=(15, 1), strides=1, padding='SAME', use_bias=True, activation=None, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), data_format=data_format) branch_1b = tf.layers.conv2d(inputs=branch_1a, filters=depth_output, kernel_size=(1, 15), strides=1, padding='SAME', use_bias=True, activation=None, kernel_initializer=conv_bn_initializer_to_use(), bias_initializer=tf.zeros_initializer(), data_format=data_format) return resnet_v2.batch_norm_relu(branch_0b + branch_1b, is_training, data_format) def get_head(net_input, pooling_op, grid_width, grid_height, loss_func, proposals_bboxes, num_classes, is_training, using_ohem, ohem_roi_one_image, data_format, var_scope): # proposals_bboxes = tf.Print(proposals_bboxes, [tf.shape(proposals_bboxes), proposals_bboxes]) with tf.variable_scope(var_scope): # two pooling op here in original r-fcn # rfcn_cls = tf.layers.conv2d(inputs=net_input, filters=10 * grid_width * grid_height, kernel_size=(1, 1), strides=1, # padding='SAME', use_bias=True, activation=tf.nn.relu, # kernel_initializer=initializer_to_use(), # bias_initializer=tf.zeros_initializer(), # data_format=data_format) # rfcn_bbox = tf.layers.conv2d(inputs=net_input, filters=10 * grid_width * grid_height, kernel_size=(1, 1), strides=1, # padding='SAME', use_bias=True, activation=tf.nn.relu, # kernel_initializer=initializer_to_use(), # bias_initializer=tf.zeros_initializer(), # data_format=data_format) # {num_per_batch, num_rois, grid_size, bank_size} yxhw_bboxes = _point2center(proposals_bboxes) if data_format == 'channels_last': net_input = tf.transpose(net_input, [0, 3, 1, 2]) psroipooled_rois, _ = pooling_op(net_input, yxhw_bboxes, grid_width, grid_height) psroipooled_rois = tf.map_fn(lambda pooled_feat: tf.reshape(pooled_feat, [-1, 10 * grid_width * grid_height]), psroipooled_rois) #psroipooled_rois = tf.reshape(psroipooled_rois, [-1, proposals_labels.get_shape().as_list()[-1], 10 * grid_width * grid_height]) select_indices = None if using_ohem: subnet_fc_feature = tf.layers.dense(psroipooled_rois, 2048, activation=tf.nn.relu, use_bias=True, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='subnet_fc', reuse=False) cls_score = tf.layers.dense(subnet_fc_feature, num_classes, activation=None, use_bias=True, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='fc_cls', reuse=False) bboxes_reg = tf.layers.dense(subnet_fc_feature, 4, activation=None, use_bias=True, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='fc_loc', reuse=False) # the input of loss_func is (batch, num_rois, num_classes), (batch, num_rois, 4) # the output should be (batch, num_rois) ohem_loss = loss_func(tf.stop_gradient(cls_score), tf.stop_gradient(bboxes_reg), None) ohem_select_num = tf.minimum(ohem_roi_one_image, tf.shape(ohem_loss)[1]) _, select_indices = tf.nn.top_k(ohem_loss, k=ohem_select_num) select_indices = tf.stop_gradient(select_indices) psroipooled_rois = tf.gather(psroipooled_rois, select_indices, axis=1) # proposals_bboxes = tf.gather(proposals_bboxes, select_indices, axis=1) # proposals_targets = tf.gather(proposals_targets, select_indices, axis=1) # proposals_labels = tf.gather(proposals_labels, select_indices, axis=1) # proposals_scores = tf.gather(proposals_scores, select_indices, axis=1) subnet_fc_feature = tf.layers.dense(psroipooled_rois, 2048, activation=tf.nn.relu, use_bias=True, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='subnet_fc', reuse=using_ohem) cls_score = tf.layers.dense(subnet_fc_feature, num_classes, activation=None, use_bias=True, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='fc_cls', reuse=using_ohem) bboxes_reg = tf.layers.dense(subnet_fc_feature, 4, activation=None, use_bias=True, kernel_initializer=initializer_to_use(), bias_initializer=tf.zeros_initializer(), name='fc_loc', reuse=using_ohem) return tf.reduce_mean(loss_func(cls_score, bboxes_reg, select_indices)) if is_training else (cls_score, bboxes_reg)
57.443262
292
0.620316
acb165dcd72bc31a14763c6569e915f4373b10fb
411
py
Python
build/ros_control/hardware_interface/catkin_generated/pkg.installspace.context.pc.py
Jam-cpu/Masters-Project---Final
0b266b1f117a579b96507249f0a128d0e3cc082a
[ "BSD-3-Clause-Clear" ]
null
null
null
build/ros_control/hardware_interface/catkin_generated/pkg.installspace.context.pc.py
Jam-cpu/Masters-Project---Final
0b266b1f117a579b96507249f0a128d0e3cc082a
[ "BSD-3-Clause-Clear" ]
null
null
null
build/ros_control/hardware_interface/catkin_generated/pkg.installspace.context.pc.py
Jam-cpu/Masters-Project---Final
0b266b1f117a579b96507249f0a128d0e3cc082a
[ "BSD-3-Clause-Clear" ]
null
null
null
# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "${prefix}/include".split(';') if "${prefix}/include" != "" else [] PROJECT_CATKIN_DEPENDS = "roscpp".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else [] PROJECT_NAME = "hardware_interface" PROJECT_SPACE_DIR = "/workspace/install" PROJECT_VERSION = "0.19.5"
45.666667
101
0.717762
2e7c6965bed99f7cfa627e7992ca7b5f54a9c738
1,305
py
Python
aoc_cas/aoc2021/day11.py
TedCassirer/advent-of-code
fb87dfdbb48b44f864337750aa58a809dcf72392
[ "MIT" ]
1
2020-11-30T19:17:50.000Z
2020-11-30T19:17:50.000Z
aoc_cas/aoc2021/day11.py
TedCassirer/advent-of-code
fb87dfdbb48b44f864337750aa58a809dcf72392
[ "MIT" ]
null
null
null
aoc_cas/aoc2021/day11.py
TedCassirer/advent-of-code
fb87dfdbb48b44f864337750aa58a809dcf72392
[ "MIT" ]
null
null
null
def step(grid): M, N = len(grid), len(grid[0]) allFlashes = set() flashed = set() for y in range(M): for x in range(N): grid[y][x] += 1 if grid[y][x] == 10: flashed.add((y, x)) while flashed: allFlashes.update(flashed) nxt = set() for y, x in flashed: for iy in range(max(0, y - 1), min(M, y + 2)): for ix in range(max(0, x - 1), min(N, x + 2)): if iy == y and ix == x: continue grid[iy][ix] += 1 if grid[iy][ix] == 10: nxt.add((iy, ix)) flashed = nxt for y, x in allFlashes: grid[y][x] = 0 return len(allFlashes) def part1(data): grid = [[int(n) for n in row] for row in data.splitlines()] flashes = 0 for t in range(100): flashes += step(grid) return flashes def part2(data): grid = [[int(n) for n in row] for row in data.splitlines()] tentacleBoys = len(grid) * len(grid[0]) for t in range(1, 1 << 31): if step(grid) == tentacleBoys: return t if __name__ == "__main__": from aocd import get_data data = get_data(year=2021, day=11) print(part1(data)) print(part2(data))
25.588235
63
0.479693
9e3c44abdf87e00e1e8968c006431a091de697ef
930
py
Python
src/lexers/common_lexer/lexer.py
cezarschroeder/language-applications
b9ad61f7b472c7939997a210837586464866c698
[ "MIT" ]
null
null
null
src/lexers/common_lexer/lexer.py
cezarschroeder/language-applications
b9ad61f7b472c7939997a210837586464866c698
[ "MIT" ]
null
null
null
src/lexers/common_lexer/lexer.py
cezarschroeder/language-applications
b9ad61f7b472c7939997a210837586464866c698
[ "MIT" ]
null
null
null
from abc import ABC, abstractmethod class Lexer(ABC): EOF: str = '<EOF>' EOF_TYPE: int = 1 input_string: str = '' current_position: int = 0 current_char: str = '' def __init__(self, input_string: str): self.input_string = input_string self.current_char = input_string[self.current_position] def consume(self): self.current_position += 1 if self.current_position >= len(self.input_string): self.current_char = self.EOF else: self.current_char = self.input_string[self.current_position] def match(self, char: str): if self.current_char == char: self.consume() else: raise Exception('Error: Expecting: {}. Found: {}.'.format(char, self.current_char)) @abstractmethod def next_token(self): pass @abstractmethod def get_token_name(self, token_type: int): pass
26.571429
95
0.62043
8b5bf68362f6096d3e3be5e36c8755369879ab6d
3,231
py
Python
dmm/dmm_input.py
clinfo/DeepKF
ee4f1be28e5f3bfa46bb47dbdc4d5f678eed36c1
[ "MIT" ]
5
2019-12-19T13:33:36.000Z
2021-06-01T06:08:16.000Z
dmm/dmm_input.py
clinfo/DeepKF
ee4f1be28e5f3bfa46bb47dbdc4d5f678eed36c1
[ "MIT" ]
24
2020-03-03T19:40:55.000Z
2021-05-26T15:27:38.000Z
dmm/dmm_input.py
clinfo/DeepKF
ee4f1be28e5f3bfa46bb47dbdc4d5f678eed36c1
[ "MIT" ]
1
2019-12-19T13:35:07.000Z
2019-12-19T13:35:07.000Z
# ============================================================================== # Load data # Copyright 2017 Kyoto Univ. Okuno lab. . All Rights Reserved. # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import numpy as np import tensorflow as tf class dotdict(dict): """dot.notation access to dictionary attributes""" __getattr__ = dict.get __setattr__ = dict.__setitem__ __delattr__ = dict.__delitem__ def load_data( config, with_shuffle=True, with_train_test=True, test_flag=False, output_dict_flag=True, ): time_major = config["time_major"] l = None if not test_flag: x = np.load(config["data_train_npy"]) if config["mask_train_npy"] is None: m = np.ones_like(x) else: m = np.load(config["mask_train_npy"]) if config["steps_train_npy"] is None: s = [len(x[i]) for i in range(len(x))] s = np.array(s) else: s = np.load(config["steps_train_npy"]) if config["label_train_npy"] is not None: l = np.load(config["label_train_npy"]) else: x = np.load(config["data_test_npy"]) if config["mask_test_npy"] is None: m = np.ones_like(x) else: m = np.load(config["mask_test_npy"]) if config["steps_test_npy"] is None: s = [len(x[i]) for i in range(len(x))] s = np.array(s) else: s = np.load(config["steps_test_npy"]) if config["label_test_npy"] is not None: l = np.load(config["label_test_npy"]) if not time_major: x = x.transpose((0, 2, 1)) m = m.transpose((0, 2, 1)) # train / validatation/ test data_num = x.shape[0] data_idx = list(range(data_num)) if with_shuffle: np.random.shuffle(data_idx) # split train/test sep = [0.0, 1.0] if with_train_test: sep = config["train_test_ratio"] prev_idx = 0 sum_r = 0.0 sep_idx = [] for r in sep: sum_r += r idx = int(data_num * sum_r) sep_idx.append([prev_idx, idx]) prev_idx = idx print("#training data:", sep_idx[0][1] - sep_idx[0][0]) print("#valid data:", sep_idx[1][1] - sep_idx[1][0]) tr_idx = data_idx[sep_idx[0][0] : sep_idx[0][1]] te_idx = data_idx[sep_idx[1][0] : sep_idx[1][1]] # storing data train_data = dotdict({}) valid_data = dotdict({}) tr_x = x[tr_idx] te_x = x[te_idx] train_data.x = tr_x valid_data.x = te_x tr_m = m[tr_idx] te_m = m[te_idx] train_data.m = tr_m valid_data.m = te_m tr_s = s[tr_idx] te_s = s[te_idx] train_data.s = tr_s valid_data.s = te_s if l is not None: tr_l = l[tr_idx] te_l = l[te_idx] train_data.l = tr_l valid_data.l = te_l train_data.num = tr_x.shape[0] valid_data.num = te_x.shape[0] train_data.n_steps = tr_x.shape[1] valid_data.n_steps = te_x.shape[1] train_data.dim = tr_x.shape[2] valid_data.dim = tr_x.shape[2] return train_data, valid_data
29.108108
80
0.561127
5ee21cab062fbb17ab361df8708b6a99cb4faeb3
2,993
py
Python
sources/experiments/baseline/extract_metrics3.py
JohannOberleitner/pdesolver
f01f83bde44e9f5aae424a4daa13219f986c5884
[ "Apache-2.0" ]
null
null
null
sources/experiments/baseline/extract_metrics3.py
JohannOberleitner/pdesolver
f01f83bde44e9f5aae424a4daa13219f986c5884
[ "Apache-2.0" ]
null
null
null
sources/experiments/baseline/extract_metrics3.py
JohannOberleitner/pdesolver
f01f83bde44e9f5aae424a4daa13219f986c5884
[ "Apache-2.0" ]
null
null
null
import getopt import json import sys def make_filename(baseFilename, gridSize, architectureType, count, epochs, index, charges_count, postfix): return '{0}_{1}_{2}_{3}_{4}_{5}_{6}_{7}'.format(baseFilename, gridSize, architectureType, charges_count, epochs, count, index, postfix) def parseArguments(argv): supportedOptions = "hf:s:a:N:e:p:c:" usage = 'extract_metrics3.py -f <basefileName> -s <gridSize> -a <architectureType> -c <charges_count> -N <count> -e <epochs> -p <postfix> --start <start> --end <end>' baseFileName = None label = None count = 20 persistFile = None readFile = None try: opts, args = getopt.getopt(argv, supportedOptions, ["start=", "end="]) except getopt.GetoptError: print(usage) sys.exit(2) for opt, arg in opts: if opt == '-h': print(usage) sys.exit() elif opt in ("-f"): baseFileName = arg elif opt in ("-s"): gridSize = int(arg) elif opt in ("-a"): architectureType = arg elif opt in ("-N"): count = int(arg) elif opt in ("-e"): epochs = int(arg) elif opt in ("-p"): postfix = arg elif opt in ("--start"): startIndex = int(arg) elif opt in ("--end"): endIndex = int(arg) elif opt in ("-c"): charges_count = int(arg) return baseFileName, gridSize, architectureType, count, epochs, startIndex, endIndex, charges_count, postfix if __name__ == '__main__': baseFilename, gridSize, architectureType, count, epochs, startIndex, endIndex, charges_count, postfix = parseArguments(sys.argv[1:]) s = '' for index in range(startIndex, endIndex+1): filename = make_filename(baseFilename, gridSize, architectureType, count, epochs, index, charges_count, postfix) with open(filename, "r") as read_file: data = json.load(read_file) avg_error_avg = data['avg_error_avg'] avg_error_variance = data['avg_error_variance'] learning_duration= data['learning_duration'] #s = '{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(gridSize, count, architectureType, epochs, learning_duration, avg_error_avg, avg_error_variance, variances_avg, variances_variance, median_values, median_values_variance, max_values, max_values_variance) #s1 = '{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t'.format(learning_duration, avg_error_avg, avg_error_variance, variances_avg, variances_variance, median_values, median_values_variance, max_values, max_values_variance) #print(index, s1) s += '{}\t{}\t{}\t'.format(learning_duration, avg_error_avg, avg_error_variance) print(s) # gridSize, count, architectureType, epochs, learning_duration, avg_error_avg, avg_error_variance, variances_avg, variances_variance, median_values, median_values_variance, max_values, max_values_variance) #print(data)
39.381579
277
0.638824
c6d9c40151b223d4e1eb6c76179e222f8444f3da
15,451
py
Python
pyblp/results/bootstrapped_results.py
smgim/pyblp
ade94928d840f14c44aca845db8d63b8056faaea
[ "MIT" ]
null
null
null
pyblp/results/bootstrapped_results.py
smgim/pyblp
ade94928d840f14c44aca845db8d63b8056faaea
[ "MIT" ]
null
null
null
pyblp/results/bootstrapped_results.py
smgim/pyblp
ade94928d840f14c44aca845db8d63b8056faaea
[ "MIT" ]
null
null
null
"""Economy-level structuring of bootstrapped BLP problem results.""" import itertools from pathlib import Path import pickle import time from typing import Any, Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union import numpy as np from .problem_results import ProblemResults from .results import Results from .. import exceptions, options from ..configurations.integration import Integration from ..markets.results_market import ResultsMarket from ..primitives import Agents from ..utilities.basics import ( Array, Error, SolverStats, format_seconds, format_table, generate_items, get_indices, output, output_progress ) class BootstrappedResults(Results): r"""Bootstrapped results of a solved problem. This class has the same methods as :class:`ProblemResults` that compute post-estimation outputs in one or more markets, but not other methods like :meth:`ProblemResults.compute_optimal_instruments` that do not make sense in a bootstrapped dataset. The only other difference is that methods return arrays with an extra first dimension along which bootstrapped results are stacked (these stacked results can be used to construct, for example, confidence intervals for post-estimation outputs). Similarly, arrays of data (except for firm IDs and ownership matrices) passed as arguments to methods should have an extra first dimension of size :attr:`BootstrappedResults.draws`. Attributes ---------- problem_results : `ProblemResults` :class:`ProblemResults` that was used to compute these bootstrapped results. bootstrapped_sigma : `ndarray` Bootstrapped Cholesky decomposition of the covariance matrix for unobserved taste heterogeneity, :math:`\Sigma`. bootstrapped_pi : `ndarray` Bootstrapped parameters that measures how agent tastes vary with demographics, :math:`\Pi`. bootstrapped_rho : `ndarray` Bootstrapped parameters that measure within nesting group correlations, :math:`\rho`. bootstrapped_beta : `ndarray` Bootstrapped demand-side linear parameters, :math:`\beta`. bootstrapped_gamma : `ndarray` Bootstrapped supply-side linear parameters, :math:`\gamma`. bootstrapped_prices : `ndarray` Bootstrapped prices, :math:`p`. If a supply side was not estimated, these are unchanged prices. Otherwise, they are equilibrium prices implied by each draw. bootstrapped_shares : `ndarray` Bootstrapped market shares, :math:`s`, implied by each draw. bootstrapped_delta : `ndarray` Bootstrapped mean utility, :math:`\delta`, implied by each draw. computation_time : `float` Number of seconds it took to compute the bootstrapped results. draws : `int` Number of bootstrap draws. fp_converged : `ndarray` Flags for convergence of the iteration routine used to compute equilibrium prices in each market. Rows are in the same order as :attr:`Problem.unique_market_ids` and column indices correspond to draws. fp_iterations : `ndarray` Number of major iterations completed by the iteration routine used to compute equilibrium prices in each market for each draw. Rows are in the same order as :attr:`Problem.unique_market_ids` and column indices correspond to draws. contraction_evaluations : `ndarray` Number of times the contraction used to compute equilibrium prices was evaluated in each market for each draw. Rows are in the same order as :attr:`Problem.unique_market_ids` and column indices correspond to draws. Examples -------- - :doc:`Tutorial </tutorial>` """ problem_results: ProblemResults bootstrapped_sigma: Array bootstrapped_pi: Array bootstrapped_rho: Array bootstrapped_beta: Array bootstrapped_gamma: Array bootstrapped_prices: Array bootstrapped_shares: Array bootstrapped_delta: Array computation_time: float draws: int fp_converged: Array fp_iterations: Array contraction_evaluations: Array def __init__( self, problem_results: ProblemResults, bootstrapped_sigma: Array, bootstrapped_pi: Array, bootstrapped_rho: Array, bootstrapped_beta: Array, bootstrapped_gamma: Array, bootstrapped_prices: Array, bootstrapped_shares: Array, bootstrapped_delta: Array, start_time: float, end_time: float, draws: int, iteration_stats: Mapping[Hashable, SolverStats]) -> None: """Structure bootstrapped problem results.""" super().__init__(problem_results.problem, problem_results._parameters) self.problem_results = problem_results self.bootstrapped_sigma = bootstrapped_sigma self.bootstrapped_pi = bootstrapped_pi self.bootstrapped_rho = bootstrapped_rho self.bootstrapped_beta = bootstrapped_beta self.bootstrapped_gamma = bootstrapped_gamma self.bootstrapped_prices = bootstrapped_prices self.bootstrapped_shares = bootstrapped_shares self.bootstrapped_delta = bootstrapped_delta self.computation_time = end_time - start_time self.draws = draws unique_market_ids = problem_results.problem.unique_market_ids self.fp_converged = np.array( [[iteration_stats[(d, t)].converged for d in range(self.draws)] for t in unique_market_ids], dtype=np.bool_, ) self.fp_iterations = np.array( [[iteration_stats[(d, t)].iterations for d in range(self.draws)] for t in unique_market_ids], dtype=np.int64, ) self.contraction_evaluations = np.array( [[iteration_stats[(d, t)].evaluations for d in range(self.draws)] for t in unique_market_ids], dtype=np.int64, ) def __str__(self) -> str: """Format bootstrapped problem results as a string.""" header = [("Computation", "Time"), ("Bootstrap", "Draws")] values = [format_seconds(self.computation_time), self.draws] if self.fp_iterations.sum() > 0 or self.contraction_evaluations.sum() > 0: header.extend([("Fixed Point", "Iterations"), ("Contraction", "Evaluations")]) values.extend([self.fp_iterations.sum(), self.contraction_evaluations.sum()]) return format_table(header, values, title="Bootstrapped Results Summary") def _combine_arrays( self, compute_market_results: Callable, market_ids: Array, fixed_args: Sequence = (), market_args: Sequence = (), agent_data: Optional[Mapping] = None, integration: Optional[Integration] = None) -> Array: """Compute arrays for one or all markets and stack them into a single tensor. An array for a single market is computed by passing fixed_args (identical for all markets) and market_args (matrices with as many rows as there are products that are restricted to the market) to compute_market_results, a ResultsMarket method that returns the output for the market and any errors encountered during computation. Agent data and an integration configuration can be optionally specified to override agent data. """ errors: List[Error] = [] # keep track of how long it takes to compute the arrays start_time = time.time() # structure or construct different agent data if agent_data is None and integration is None: agents = self._economy.agents agents_market_indices = self._economy._agent_market_indices else: agents = Agents(self._economy.products, self._economy.agent_formulation, agent_data, integration) agents_market_indices = get_indices(agents.market_ids) def market_factory(pair: Tuple[int, Hashable]) -> tuple: """Build a market with bootstrapped data along with arguments used to compute arrays.""" c, s = pair data_override_c = { 'prices': self.bootstrapped_prices[c], 'shares': self.bootstrapped_shares[c] } market_cs = ResultsMarket( self._economy, s, self._parameters, self.bootstrapped_sigma[c], self.bootstrapped_pi[c], self.bootstrapped_rho[c], self.bootstrapped_beta[c], self.bootstrapped_gamma[c], self.bootstrapped_delta[c], data_override=data_override_c, agents_override=agents[agents_market_indices[s]] ) args_cs: List[Optional[Array]] = [] for market_arg in market_args: if market_arg is None: args_cs.append(market_arg) elif len(market_arg.shape) == 2: if market_ids.size == 1: args_cs.append(market_arg) else: args_cs.append(market_arg[self._economy._product_market_indices[s]]) else: assert len(market_arg.shape) == 3 if market_ids.size == 1: args_cs.append(market_arg[c]) else: args_cs.append(market_arg[c, self._economy._product_market_indices[s]]) return (market_cs, *fixed_args, *args_cs) # construct a mapping from draws and market IDs to market-specific arrays and compute the full matrix size array_mapping: Dict[Tuple[int, Hashable], Array] = {} pairs = itertools.product(range(self.draws), market_ids) generator = generate_items(pairs, market_factory, compute_market_results) if self.draws > 1 or market_ids.size > 1: generator = output_progress(generator, self.draws * market_ids.size, start_time) for (d, t), (array_dt, errors_dt) in generator: array_mapping[(d, t)] = np.c_[array_dt] errors.extend(errors_dt) # output a warning about any errors if errors: output("") output(exceptions.MultipleErrors(errors)) output("") # determine the sizes of dimensions dimension_sizes = [] for dimension in range(len(array_mapping[(0, market_ids[0])].shape)): if dimension == 0: dimension_sizes.append(sum(array_mapping[(0, t)].shape[dimension] for t in market_ids)) else: dimension_sizes.append(max(array_mapping[(0, t)].shape[dimension] for t in market_ids)) # preserve the original product order or the sorted market order when stacking the arrays combined = np.full((self.draws, *dimension_sizes), np.nan, options.dtype) for (d, t), array_dt in array_mapping.items(): slices = (slice(0, s) for s in array_dt.shape[1:]) if dimension_sizes[0] == market_ids.size: combined[(d, market_ids == t, *slices)] = array_dt elif dimension_sizes[0] == self._economy.N: combined[(d, self._economy._product_market_indices[t], *slices)] = array_dt else: assert market_ids.size == 1 combined[d] = array_dt # output how long it took to compute the arrays end_time = time.time() output(f"Finished after {format_seconds(end_time - start_time)}.") output("") return combined def _coerce_matrices(self, matrices: Any, market_ids: Array) -> Array: """Coerce array-like stacked matrix tensors into a stacked matrix tensor and validate it.""" matrices = np.atleast_3d(np.asarray(matrices, options.dtype)) rows = sum(i.size for t, i in self._economy._product_market_indices.items() if t in market_ids) columns = max(i.size for t, i in self._economy._product_market_indices.items() if t in market_ids) if matrices.shape != (self.draws, rows, columns): raise ValueError(f"matrices must be {self.draws} by {rows} by {columns}.") return matrices def _coerce_optional_delta(self, delta: Optional[Any], market_ids: Array) -> Array: """Coerce optional array-like mean utilities into a column vector tensor and validate it.""" if delta is None: return None delta = np.atleast_3d(np.asarray(delta, options.dtype)) rows = sum(i.size for t, i in self._economy._product_market_indices.items() if t in market_ids) if delta.shape != (self.draws, rows, 1): raise ValueError(f"delta must be None or {self.draws} by {rows}.") return delta def _coerce_optional_costs(self, costs: Optional[Any], market_ids: Array) -> Array: """Coerce optional array-like costs into a column vector tensor and validate it.""" if costs is None: return None costs = np.atleast_3d(np.asarray(costs, options.dtype)) rows = sum(i.size for t, i in self._economy._product_market_indices.items() if t in market_ids) if costs.shape != (self.draws, rows, 1): raise ValueError(f"costs must be None or {self.draws} by {rows}.") return costs def _coerce_optional_prices(self, prices: Optional[Any], market_ids: Array) -> Array: """Coerce optional array-like prices into a column vector tensor and validate it.""" if prices is None: return None prices = np.atleast_3d(np.asarray(prices, options.dtype)) rows = sum(i.size for t, i in self._economy._product_market_indices.items() if t in market_ids) if prices.shape != (self.draws, rows, 1): raise ValueError(f"prices must be None or {self.draws} by {rows}.") return prices def _coerce_optional_shares(self, shares: Optional[Any], market_ids: Array) -> Array: """Coerce optional array-like shares into a column vector tensor and validate it.""" if shares is None: return shares shares = np.atleast_3d(np.asarray(shares, options.dtype)) rows = sum(i.size for t, i in self._economy._product_market_indices.items() if t in market_ids) if shares.shape != (self.draws, rows, 1): raise ValueError(f"shares must be None or {self.draws} by {rows}.") return shares def to_pickle(self, path: Union[str, Path]) -> None: """Save these results as a pickle file. Parameters ---------- path: `str or Path` File path to which these results will be saved. """ with open(path, 'wb') as handle: pickle.dump(self, handle) def to_dict( self, attributes: Sequence[str] = ( 'bootstrapped_sigma', 'bootstrapped_pi', 'bootstrapped_rho', 'bootstrapped_beta', 'bootstrapped_gamma', 'bootstrapped_prices', 'bootstrapped_shares', 'bootstrapped_delta', 'computation_time', 'draws', 'fp_converged', 'fp_iterations', 'contraction_evaluations' )) -> dict: """Convert these results into a dictionary that maps attribute names to values. Parameters ---------- attributes : `sequence of str, optional` Name of attributes that will be added to the dictionary. By default, all :class:`BootstrappedResults` attributes are added except for :attr:`BootstrappedResults.problem_results`. Returns ------- `dict` Mapping from attribute names to values. """ return {k: getattr(self, k) for k in attributes}
50.32899
120
0.663193
9e533bcf077d35b39e5d1b1f565d9562861ff2ea
1,576
py
Python
ede/fixtures.py
FrancoisConstant/elm-django-example
bcee0b364467e1ad367397aa576b6000a206d738
[ "Unlicense" ]
5
2018-10-08T09:04:42.000Z
2020-11-21T13:17:56.000Z
ede/fixtures.py
FrancoisConstant/elm-django-example
bcee0b364467e1ad367397aa576b6000a206d738
[ "Unlicense" ]
null
null
null
ede/fixtures.py
FrancoisConstant/elm-django-example
bcee0b364467e1ad367397aa576b6000a206d738
[ "Unlicense" ]
1
2020-07-31T20:07:43.000Z
2020-07-31T20:07:43.000Z
from ede.models import TweetAuthor, Tweet def load_some_random_tweets(): jesus = TweetAuthor.objects.create( name="Jesus", username="jesus" ) for tweet in ( """I will be out of the office until Monday with no phone or email access. If you need immediate attention please contact my dad directly""", """They say abstinence is 100% effective for birth control. Tell that to my mom""", """YOLO!\n\nUnless you're me.""", """Thanks guys, but I think I'm just going to spend my birthday alone this year.""", """Don't listen to the shit my Dad says""", """Let the healing begin""" ): Tweet.objects.create(author=jesus, content=tweet) jim = TweetAuthor.objects.create( name="Jim Jefferies", username="jimjefferies" ) for tweet in ( """It feels like there is a World Cup camera man who's only job is to find hot girls in the crowd""", """If Russia wins the world cup it's fake news""", """If you are going to say the word cunt, you better say it with the right accent.""", """We would like to make it clear that Kanye West only ever worked for the Jim Jefferies Show in a freelance capacity and his views do not represent the views of us, Jim Jefferies, or Comedy Central. As of today, he is no longer an employee of this production.""", """With all this time spent protesting, when are the teachers going to learn how to shoot their guns?""" ): Tweet.objects.create(author=jim, content=tweet)
45.028571
149
0.647843
284973a49d15540d8e64e7fe575d07be290cae46
2,686
py
Python
onnxmltools/convert/sparkml/operator_converters/imputer.py
xhochy/onnxmltools
cb2782b155ff67dc1e586f36a27c5d032070c801
[ "Apache-2.0" ]
623
2018-02-16T20:43:01.000Z
2022-03-31T05:00:17.000Z
onnxmltools/convert/sparkml/operator_converters/imputer.py
xhochy/onnxmltools
cb2782b155ff67dc1e586f36a27c5d032070c801
[ "Apache-2.0" ]
339
2018-02-26T21:27:04.000Z
2022-03-31T03:16:50.000Z
onnxmltools/convert/sparkml/operator_converters/imputer.py
xhochy/onnxmltools
cb2782b155ff67dc1e586f36a27c5d032070c801
[ "Apache-2.0" ]
152
2018-02-24T01:20:22.000Z
2022-03-31T07:41:35.000Z
# SPDX-License-Identifier: Apache-2.0 import copy from ...common.data_types import Int64TensorType, FloatTensorType from ...common.utils import check_input_and_output_numbers, check_input_and_output_types from ...common._registration import register_converter, register_shape_calculator def convert_imputer(scope, operator, container): op = operator.raw_operator op_type = 'Imputer' name = scope.get_unique_operator_name(op_type) attrs = {'name': name} input_type = operator.inputs[0].type surrogates = op.surrogateDF.toPandas().values[0].tolist() value = op.getOrDefault('missingValue') if isinstance(input_type, FloatTensorType): attrs['imputed_value_floats'] = surrogates attrs['replaced_value_float'] = value elif isinstance(input_type, Int64TensorType): attrs['imputed_value_int64s'] = [int(x) for x in surrogates] attrs['replaced_value_int64'] = int(value) else: raise RuntimeError("Invalid input type: " + input_type) if len(operator.inputs) > 1: concatenated_output = scope.get_unique_variable_name('concat_tensor') container.add_node('Concat', operator.input_full_names, concatenated_output, name=scope.get_unique_operator_name('Concat'), op_version=4, axis=1) imputed_output = scope.get_unique_variable_name('imputed_tensor') container.add_node(op_type, concatenated_output, imputed_output, op_domain='ai.onnx.ml', **attrs) container.add_node('Split', imputed_output, operator.output_full_names, name=scope.get_unique_operator_name('Split'), op_version=2, axis=1, split=range(1, len(operator.output_full_names))) else: container.add_node(op_type, operator.inputs[0].full_name, operator.output_full_names[0], op_domain='ai.onnx.ml', **attrs) register_converter('pyspark.ml.feature.ImputerModel', convert_imputer) def calculate_imputer_output_shapes(operator): check_input_and_output_numbers(operator, output_count_range=[1, len(operator.outputs)]) check_input_and_output_types(operator, good_input_types=[FloatTensorType, Int64TensorType], good_output_types=[FloatTensorType, Int64TensorType]) input_type = copy.deepcopy(operator.inputs[0].type) for output in operator.outputs: output.type = input_type register_shape_calculator('pyspark.ml.feature.ImputerModel', calculate_imputer_output_shapes)
44.766667
105
0.675726
6108520198937274eb787e68d28c67c21609c223
12,074
py
Python
wikiconv/ingest_revisions/dataflow_main.py
ipavlopoulos/wikidetox
16fb2fe900964aa27d0cf10c7316c41dcb8ff9a2
[ "Apache-2.0" ]
null
null
null
wikiconv/ingest_revisions/dataflow_main.py
ipavlopoulos/wikidetox
16fb2fe900964aa27d0cf10c7316c41dcb8ff9a2
[ "Apache-2.0" ]
14
2020-11-13T18:58:29.000Z
2022-03-02T09:31:46.000Z
wikiconv/ingest_revisions/dataflow_main.py
cameronaaron/wikidetox
8dec18ba2c0e19efc3ed1dd3f40dd0b8d9f3b4fa
[ "Apache-2.0" ]
1
2020-09-25T15:46:02.000Z
2020-09-25T15:46:02.000Z
""" Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ------------------------------------------------------------------------------- Dataflow Main A dataflow pipeline to ingest the Wikipedia dump from 7zipped xml files to json. Run with: python dataflow_main.py --setup_file ./setup.py Args: ingestFrom: choose from the three options : {wikipedia, local, cloud}: - wikipedia: performs the downloading job from Wikipedia, run with: [python dataflow_main.py --setup_file ./setup.py\ --ingestFrom=wikipedia --download --language=YourLanguage\ --dumpdate=YourDumpdate --cloudBucket=YourCloudBucket\ --project=YourGoogleCloudProject --bucket=TemporaryFileBucket] - local: Tests the pipeline locally, run the code with [python dataflow_main.py --setup_file ./setup.py\ --ingestFrom=local --localStorage=YourLocalStorage --testmode\ --output=YourOutputStorage --project=YourGoogleCloudProject\ --bucket=TemporaryFileBucket] - cloud: Reads from downloaded bz2 files on cloud, performs the ingestion job, run the code with [python dataflow_main.py --setup_file ./setup.py\ [--ingestFrom=cloud --output=YourOutputStorage --cloudBucket=YourCloudBucket\ --project=YourGoogleCloudProject --bucket=TemporaryFileBucket] output: the data storage where you want to store the ingested results language: the language of the wikipedia data you want to extract, e.g. en, fr, zh dumpdate: the dumpdate of the wikipedia data, e.g. latest testmode: if turned on, the pipeline runs on DirectRunner. localStorage: the location of the local test file. download: if turned on, the pipeline only performs downloading job from Wikipedia. cloudBucket: the cloud bucket where the ingestion reads from or the download stores to. """ from __future__ import absolute_import import logging import subprocess from threading import Timer import json import sys import zlib import copy import bz2 from os import path from ingest_utils.wikipedia_revisions_ingester import parse_stream import math import os import time import urllib import urllib2 import subprocess import StringIO import lxml from HTMLParser import HTMLParser import re import argparse import boto import gcs_oauth2_boto_plugin # boto needs to be configured, see here: # https://cloud.google.com/storage/docs/boto-plugin#setup-python import apache_beam as beam from apache_beam.metrics.metric import Metrics from apache_beam.options.pipeline_options import PipelineOptions from apache_beam.options.pipeline_options import SetupOptions from apache_beam.io import ReadFromText from apache_beam.io import WriteToText from apache_beam.io import filesystems from datetime import datetime GOOGLE_STORAGE = 'gs' LOCAL_STORAGE = 'file' MEMORY_THERESHOLD = 1000000 def run(known_args, pipeline_args, sections): """Main entry point; defines and runs the ingestion pipeline.""" if known_args.testmode: # In testmode, disable cloud storage backup and run on directRunner pipeline_args.append('--runner=DirectRunner') else: pipeline_args.append('--runner=DataflowRunner') pipeline_args.extend([ '--project={project}'.format(project=known_args.project), '--staging_location=gs://{bucket}/staging'.format(bucket=known_args.bucket), '--temp_location=gs://{bucket}/tmp'.format(bucket=known_args.bucket), '--job_name=ingest-latest-revisions-{lan}'.format(lan=known_args.language), '--num_workers=80', ]) pipeline_options = PipelineOptions(pipeline_args) pipeline_options.view_as(SetupOptions).save_main_session = True with beam.Pipeline(options=pipeline_options) as p: pcoll = (p | "GetDataDumpList" >> beam.Create(sections)) if known_args.download: pcoll = (pcoll | "DownloadDataDumps" >> beam.ParDo(DownloadDataDumps(), known_args.cloudBucket)) else: pcoll = ( pcoll | "Ingestion" >> beam.ParDo(WriteDecompressedFile(), known_args.cloudBucket, known_args.ingestFrom) | "AddGroupByKey" >> beam.Map(lambda x: (x['year'], x)) | "ShardByYear" >>beam.GroupByKey() | "WriteToStorage" >> beam.ParDo(WriteToStorage(), known_args.output, known_args.dumpdate, known_args.language)) class DownloadDataDumps(beam.DoFn): def process(self, element, bucket): """Downloads a data dump file, store in cloud storage. Returns the cloud storage location. """ mirror, chunk_name = element logging.info('USERLOG: Download data dump %s to store in cloud storage.' % chunk_name) # Download data dump from Wikipedia and upload to cloud storage. url = mirror + "/" + chunk_name write_path = path.join('gs://', bucket, chunk_name) urllib.urlretrieve(url, chunk_name) os.system("gsutil cp %s %s" % (chunk_name, write_path)) os.system("rm %s" % chunk_name) yield chunk_name return class WriteDecompressedFile(beam.DoFn): def __init__(self): self.processed_revisions = Metrics.counter(self.__class__, 'processed_revisions') self.large_page_revision_count = Metrics.counter(self.__class__, 'large_page_revision_cnt') def process(self, element, bucket, ingestFrom): """Ingests the xml dump into json, returns the josn records """ # Decompress the data dump chunk_name = element logging.info('USERLOG: Running ingestion process on %s' % chunk_name) if ingestFrom == 'local': input_stream = chunk_name else: os.system("gsutil -m cp %s %s" % (path.join('gs://', bucket, chunk_name), chunk_name)) input_stream = chunk_name # Running ingestion on the xml file last_revision = 'None' last_completed = time.time() cur_page_id = None page_size = 0 cur_page_revision_cnt = 0 for i, content in enumerate(parse_stream(bz2.BZ2File(chunk_name))): self.processed_revisions.inc() # Add the year field for sharding dt = datetime.strptime(content['timestamp'], "%Y-%m-%dT%H:%M:%SZ") content['year'] = dt.isocalendar()[0] last_revision = content['rev_id'] yield content logging.info('CHUNK {chunk}: revision {revid} ingested, time elapsed: {time}.'.format(chunk=chunk_name, revid=last_revision, time=time.time() - last_completed)) last_completed = time.time() if content['page_id'] == cur_page_id: page_size += len(json.dumps(content)) cur_page_revision_cnt += 1 else: if page_size >= MEMORY_THERESHOLD: self.large_page_revision_count.inc(cur_page_revision_cnt) cur_page_id = content['page_id'] page_size = len(json.dumps(content)) cur_page_revision_cnt = 1 if page_size >= MEMORY_THERESHOLD: self.large_page_revision_count.inc(cur_page_revision_cnt) if ingestFrom != 'local': os.system("rm %s" % chunk_name) logging.info('USERLOG: Ingestion on file %s complete! %s lines emitted, last_revision %s' % (chunk_name, i, last_revision)) class WriteToStorage(beam.DoFn): def process(self, element, outputdir, dumpdate, language): (key, val) = element year = int(key) cnt = 0 # Creates writing path given the year pair date_path = "{outputdir}/{date}-{lan}/date-{year}/".format(outputdir=outputdir, date=dumpdate, lan=language, year=year) file_path = 'revisions-{cnt:06d}.json' write_path = path.join(date_path, file_path.format(cnt=cnt)) while filesystems.FileSystems.exists(write_path): cnt += 1 write_path = path.join(date_path, file_path.format(cnt=cnt)) # Writes to storage logging.info('USERLOG: Write to path %s.' % write_path) outputfile = filesystems.FileSystems.create(write_path) for output in val: outputfile.write(json.dumps(output) + '\n') outputfile.close() class ParseDirectory(HTMLParser): def __init__(self): self.files = [] HTMLParser.__init__(self) def handle_starttag(self, tag, attrs): self.files.extend(attr[1] for attr in attrs if attr[0] == 'href') def files(self): return self.files def directory(mirror): """Download the directory of files from the webpage. This is likely brittle based on the format of the particular mirror site. """ # Download the directory of files from the webpage for a particular language. parser = ParseDirectory() directory = urllib2.urlopen(mirror) parser.feed(directory.read().decode('utf-8')) # Extract the filenames of each XML meta history file. meta = re.compile('^[a-zA-Z-]+wiki-latest-pages-meta-history.*\.bz2$') return [(mirror, filename) for filename in parser.files if meta.match(filename)] if __name__ == '__main__': logging.getLogger().setLevel(logging.INFO) # Define parameters parser = argparse.ArgumentParser() # Options: local, cloud parser.add_argument('--project', dest='project', help='Your google cloud project.') parser.add_argument('--bucket', dest='bucket', help='Your google cloud bucket for temporary and staging files.') parser.add_argument('--ingestFrom', dest='ingestFrom', default='none') parser.add_argument('--download', dest='download', action='store_true') parser.add_argument('--output', dest='output', help='Specify the output storage in cloud.') parser.add_argument('--cloudBucket', dest='cloudBucket', help='Specify the cloud storage location to store/stores the raw downloads.') parser.add_argument('--language', dest='language', help='Specify the language of the Wiki Talk Page you want to ingest.') parser.add_argument('--dumpdate', dest='dumpdate', help='Specify the date of the Wikipedia data dump.') parser.add_argument('--localStorage', dest='localStorage', help='If ingest from local storage, please specify the location of the input file.') parser.add_argument('--testmode', dest='testmode', action='store_true') known_args, pipeline_args = parser.parse_known_args() if known_args.download: # If specified downloading from Wikipedia dumpstatus_url = 'https://dumps.wikimedia.org/{lan}wiki/{date}/dumpstatus.json'.format(lan=known_args.language, date=known_args.dumpdate) try: response = urllib2.urlopen(dumpstatus_url) dumpstatus = json.loads(response.read()) url = 'https://dumps.wikimedia.org/{lan}wiki/{date}'.format(lan=known_args.language, date=known_args.dumpdate) sections = [(url, filename) for filename in dumpstatus['jobs']['metahistorybz2dump']['files'].keys()] except: # In the case dumpdate is not specified or is invalid, download the # latest version. mirror = 'http://dumps.wikimedia.your.org/{lan}wiki/latest'.format(lan=known_args.language) sections = directory(mirror) if known_args.ingestFrom == "cloud": sections = [] uri = boto.storage_uri(known_args.cloudBucket, GOOGLE_STORAGE) prefix = known_args.cloudBucket[known_args.cloudBucket.find('/')+1:] for obj in uri.list_bucket(prefix=prefix): sections.append(obj.name[obj.name.rfind('/') + 1:]) if known_args.ingestFrom == 'local': sections = [known_args.localStorage] run(known_args, pipeline_args, sections)
42.069686
166
0.688256
08adfe91f6a227885367bf7dbfb2c5eab63c1380
3,791
py
Python
zcrmsdk/src/com/zoho/crm/api/org/api_exception.py
zoho/zohocrm-python-sdk-2.0
3a93eb3b57fed4e08f26bd5b311e101cb2995411
[ "Apache-2.0" ]
null
null
null
zcrmsdk/src/com/zoho/crm/api/org/api_exception.py
zoho/zohocrm-python-sdk-2.0
3a93eb3b57fed4e08f26bd5b311e101cb2995411
[ "Apache-2.0" ]
null
null
null
zcrmsdk/src/com/zoho/crm/api/org/api_exception.py
zoho/zohocrm-python-sdk-2.0
3a93eb3b57fed4e08f26bd5b311e101cb2995411
[ "Apache-2.0" ]
null
null
null
try: from zcrmsdk.src.com.zoho.crm.api.exception import SDKException from zcrmsdk.src.com.zoho.crm.api.util import Choice, Constants from zcrmsdk.src.com.zoho.crm.api.org.action_response import ActionResponse from zcrmsdk.src.com.zoho.crm.api.org.response_handler import ResponseHandler except Exception: from ..exception import SDKException from ..util import Choice, Constants from .action_response import ActionResponse from .response_handler import ResponseHandler class APIException(ResponseHandler, ActionResponse): def __init__(self): """Creates an instance of APIException""" super().__init__() self.__status = None self.__code = None self.__message = None self.__details = None self.__key_modified = dict() def get_status(self): """ The method to get the status Returns: Choice: An instance of Choice """ return self.__status def set_status(self, status): """ The method to set the value to status Parameters: status (Choice) : An instance of Choice """ if status is not None and not isinstance(status, Choice): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: status EXPECTED TYPE: Choice', None, None) self.__status = status self.__key_modified['status'] = 1 def get_code(self): """ The method to get the code Returns: Choice: An instance of Choice """ return self.__code def set_code(self, code): """ The method to set the value to code Parameters: code (Choice) : An instance of Choice """ if code is not None and not isinstance(code, Choice): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: code EXPECTED TYPE: Choice', None, None) self.__code = code self.__key_modified['code'] = 1 def get_message(self): """ The method to get the message Returns: Choice: An instance of Choice """ return self.__message def set_message(self, message): """ The method to set the value to message Parameters: message (Choice) : An instance of Choice """ if message is not None and not isinstance(message, Choice): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: message EXPECTED TYPE: Choice', None, None) self.__message = message self.__key_modified['message'] = 1 def get_details(self): """ The method to get the details Returns: dict: An instance of dict """ return self.__details def set_details(self, details): """ The method to set the value to details Parameters: details (dict) : An instance of dict """ if details is not None and not isinstance(details, dict): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: details EXPECTED TYPE: dict', None, None) self.__details = details self.__key_modified['details'] = 1 def is_key_modified(self, key): """ The method to check if the user has modified the given key Parameters: key (string) : A string representing the key Returns: int: An int representing the modification """ if key is not None and not isinstance(key, str): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: key EXPECTED TYPE: str', None, None) if key in self.__key_modified: return self.__key_modified.get(key) return None def set_key_modified(self, key, modification): """ The method to mark the given key as modified Parameters: key (string) : A string representing the key modification (int) : An int representing the modification """ if key is not None and not isinstance(key, str): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: key EXPECTED TYPE: str', None, None) if modification is not None and not isinstance(modification, int): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: modification EXPECTED TYPE: int', None, None) self.__key_modified[key] = modification
24.458065
100
0.718808
9cef3a306d331d5b0f37fc9fa5a1bcb2a32029bd
870
py
Python
scratch/sonar.py
wbkang/rpi-repo
fc2b770f99cc2405fbf6855f9f961c4f6aed99cb
[ "MIT" ]
null
null
null
scratch/sonar.py
wbkang/rpi-repo
fc2b770f99cc2405fbf6855f9f961c4f6aed99cb
[ "MIT" ]
null
null
null
scratch/sonar.py
wbkang/rpi-repo
fc2b770f99cc2405fbf6855f9f961c4f6aed99cb
[ "MIT" ]
null
null
null
#!/usr/bin/env python import RPi.GPIO as GPIO import time GPIO.setmode(GPIO.BCM) trigger = 25 echo = 12 GPIO.setup(trigger, GPIO.OUT) GPIO.setup(echo, GPIO.IN) while True: GPIO.output(trigger, False) print("Waiting for trigger to settle") time.sleep(1) GPIO.output(trigger, True) time.sleep(0.00001) GPIO.output(trigger, False) pulse_start = -1 ch = GPIO.wait_for_edge(echo, GPIO.RISING, timeout=1000) if not ch: print("time out for up") continue pulse_start = time.time() ch = GPIO.wait_for_edge(echo, GPIO.FALLING, timeout=1000) if not ch: print("time out for down") continue pulse_end = time.time() duration = pulse_end - pulse_start print("duration:%s" % duration) distance = duration * 1000000 / 58 print("distance is %dcm" % distance) GPIO.cleanup()
18.913043
61
0.647126
1af8d8122ed656e879deda0e0916ecb5e9dcd587
107,999
py
Python
test/integration/component/test_vpc.py
ksowmya/cloudstack-1
f8f779158da056be7da669884ae4ddd109cec044
[ "Apache-2.0" ]
1
2020-03-27T22:21:20.000Z
2020-03-27T22:21:20.000Z
test/integration/component/test_vpc.py
ksowmya/cloudstack-1
f8f779158da056be7da669884ae4ddd109cec044
[ "Apache-2.0" ]
6
2020-11-16T20:46:14.000Z
2022-02-01T01:06:16.000Z
test/integration/component/test_vpc.py
pkoistin/cloudstack
fd43cf151663c48fe29f97323490d53a7c0f9d5b
[ "ECL-2.0", "Apache-2.0" ]
1
2019-12-26T07:16:06.000Z
2019-12-26T07:16:06.000Z
# 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. """ Component tests for VPC functionality """ #Import Local Modules from nose.plugins.attrib import attr from marvin.cloudstackTestCase import * from marvin.cloudstackException import cloudstackAPIException from marvin.cloudstackAPI import * from marvin.integration.lib.utils import * from marvin.integration.lib.base import * from marvin.integration.lib.common import * class Services: """Test VPC services """ def __init__(self): self.services = { "account": { "email": "[email protected]", "firstname": "Test", "lastname": "User", "username": "test", # Random characters are appended for unique # username "password": "password", }, "domain_admin": { "email": "[email protected]", "firstname": "Domain", "lastname": "Admin", "username": "DoA", # Random characters are appended for unique # username "password": "password", }, "service_offering": { "name": "Tiny Instance", "displaytext": "Tiny Instance", "cpunumber": 1, "cpuspeed": 100, "memory": 128, }, "network_offering": { "name": 'VPC Network offering', "displaytext": 'VPC Network off', "guestiptype": 'Isolated', "supportedservices": 'Vpn,Dhcp,Dns,SourceNat,PortForwarding,Lb,UserData,StaticNat,NetworkACL', "traffictype": 'GUEST', "availability": 'Optional', "useVpc": 'on', "serviceProviderList": { "Vpn": 'VpcVirtualRouter', "Dhcp": 'VpcVirtualRouter', "Dns": 'VpcVirtualRouter', "SourceNat": 'VpcVirtualRouter', "PortForwarding": 'VpcVirtualRouter', "Lb": 'VpcVirtualRouter', "UserData": 'VpcVirtualRouter', "StaticNat": 'VpcVirtualRouter', "NetworkACL": 'VpcVirtualRouter' }, }, "network_offering_no_lb": { "name": 'VPC Network offering', "displaytext": 'VPC Network off', "guestiptype": 'Isolated', "supportedservices": 'Vpn,Dhcp,Dns,SourceNat,PortForwarding,UserData,StaticNat,NetworkACL', "traffictype": 'GUEST', "availability": 'Optional', "useVpc": 'on', "serviceProviderList": { "Vpn": 'VpcVirtualRouter', "Dhcp": 'VpcVirtualRouter', "Dns": 'VpcVirtualRouter', "SourceNat": 'VpcVirtualRouter', "PortForwarding": 'VpcVirtualRouter', "UserData": 'VpcVirtualRouter', "StaticNat": 'VpcVirtualRouter', "NetworkACL": 'VpcVirtualRouter' }, }, "vpc_offering": { "name": 'VPC off', "displaytext": 'VPC off', "supportedservices": 'Dhcp,Dns,SourceNat,PortForwarding,Vpn,Lb,UserData,StaticNat,NetworkACL', }, "vpc": { "name": "TestVPC", "displaytext": "TestVPC", "cidr": '10.0.0.1/24' }, "vpc_no_name": { "displaytext": "TestVPC", "cidr": '10.0.0.1/24' }, "network": { "name": "Test Network", "displaytext": "Test Network", "netmask": '255.255.255.0' }, "lbrule": { "name": "SSH", "alg": "leastconn", # Algorithm used for load balancing "privateport": 22, "publicport": 2222, "openfirewall": False, "startport": 22, "endport": 2222, "protocol": "TCP", "cidrlist": '0.0.0.0/0', }, "natrule": { "privateport": 22, "publicport": 22, "startport": 22, "endport": 22, "protocol": "TCP", "cidrlist": '0.0.0.0/0', }, "fw_rule": { "startport": 1, "endport": 6000, "cidr": '0.0.0.0/0', # Any network (For creating FW rule) "protocol": "TCP" }, "icmp_rule": { "icmptype": -1, "icmpcode": -1, "cidrlist": '0.0.0.0/0', "protocol": "ICMP" }, "virtual_machine": { "displayname": "Test VM", "username": "root", "password": "password", "ssh_port": 22, "hypervisor": 'XenServer', # Hypervisor type should be same as # hypervisor type of cluster "privateport": 22, "publicport": 22, "protocol": 'TCP', }, "domain": { "name": "TestDomain" }, "ostype": 'CentOS 5.3 (64-bit)', # Cent OS 5.3 (64 bit) "sleep": 60, "timeout": 10, "mode": 'advanced' } class TestVPC(cloudstackTestCase): @classmethod def setUpClass(cls): cls.api_client = super( TestVPC, cls ).getClsTestClient().getApiClient() cls.services = Services().services # Get Zone, Domain and templates cls.domain = get_domain(cls.api_client, cls.services) cls.zone = get_zone(cls.api_client, cls.services) cls.template = get_template( cls.api_client, cls.zone.id, cls.services["ostype"] ) cls.services["virtual_machine"]["zoneid"] = cls.zone.id cls.services["virtual_machine"]["template"] = cls.template.id cls.service_offering = ServiceOffering.create( cls.api_client, cls.services["service_offering"] ) cls.vpc_off = VpcOffering.create( cls.api_client, cls.services["vpc_offering"] ) cls.vpc_off.update(cls.api_client, state='Enabled') cls._cleanup = [ cls.service_offering, ] return @classmethod def tearDownClass(cls): try: #Cleanup resources used cleanup_resources(cls.api_client, cls._cleanup) except Exception as e: raise Exception("Warning: Exception during cleanup : %s" % e) return def setUp(self): self.apiclient = self.testClient.getApiClient() self.dbclient = self.testClient.getDbConnection() self.account = Account.create( self.apiclient, self.services["account"], admin=True, domainid=self.domain.id ) self.cleanup = [] self.cleanup.insert(0, self.account) return def tearDown(self): try: cleanup_resources(self.apiclient, self.cleanup) except Exception as e: self.debug("Warning: Exception during cleanup : %s" % e) #raise Exception("Warning: Exception during cleanup : %s" % e) return def validate_vpc_offering(self, vpc_offering): """Validates the VPC offering""" self.debug("Check if the VPC offering is created successfully?") vpc_offs = VpcOffering.list( self.apiclient, id=vpc_offering.id ) self.assertEqual( isinstance(vpc_offs, list), True, "List VPC offerings should return a valid list" ) self.assertEqual( vpc_offering.name, vpc_offs[0].name, "Name of the VPC offering should match with listVPCOff data" ) self.debug( "VPC offering is created successfully - %s" % vpc_offering.name) return def validate_vpc_network(self, network, state=None): """Validates the VPC network""" self.debug("Check if the VPC network is created successfully?") vpc_networks = VPC.list( self.apiclient, id=network.id ) self.assertEqual( isinstance(vpc_networks, list), True, "List VPC network should return a valid list" ) self.assertEqual( network.name, vpc_networks[0].name, "Name of the VPC network should match with listVPC data" ) if state: self.assertEqual( vpc_networks[0].state, state, "VPC state should be '%s'" % state ) self.debug("VPC network validated - %s" % network.name) return #list_vpc_apis should be the first case otherwise the vpc counts would be wrong @attr(tags=["advanced", "intervlan"]) def test_01_list_vpc_apis(self): """ Test list VPC APIs """ # Validate the following # 1. Create multiple VPCs # 2. listVPCs() by name. VPC with the provided name should be listed. # 3. listVPCs() by displayText. VPC with the provided displayText # should be listed. # 4. listVPCs() by cidr. All the VPCs with the provided cidr should # be listed. # 5. listVPCs() by vpcofferingId.All the VPCs with the vpcofferingId # should be listed. # 6. listVPCs() by supported Services(). All the VPCs that provide the # list of services should be listed. # 7. listVPCs() by restartRequired (set to true). All the VPCs that # require restart should be listed. self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc_1 = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc_1) self.services["vpc"]["cidr"] = "10.1.46.1/16" vpc_2 = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc_2) self.debug("Check list VPC API by Name?") vpcs = VPC.list( self.apiclient, name=vpc_1.name, listall=True ) self.assertEqual( isinstance(vpcs, list), True, "List VPC shall return a valid resposne" ) vpc = vpcs[0] self.assertEqual( vpc.name, vpc_1.name, "VPC name should match with the existing one" ) self.debug("Check list VPC API by displayText?") vpcs = VPC.list( self.apiclient, displaytext=vpc_1.displaytext, listall=True ) self.assertEqual( isinstance(vpcs, list), True, "List VPC shall return a valid resposne" ) vpc = vpcs[0] self.assertEqual( vpc.displaytext, vpc_1.displaytext, "VPC displaytext should match with the existing one" ) self.debug("Check list VPC API by cidr?") vpcs = VPC.list( self.apiclient, cidr=vpc_2.cidr, listall=True ) self.assertEqual( isinstance(vpcs, list), True, "List VPC shall return a valid resposne" ) vpc = vpcs[0] self.assertEqual( vpc.cidr, vpc_2.cidr, "VPC cidr should match with the existing one" ) self.debug("Validating list VPC by Id") self.validate_vpc_network(vpc_1) self.debug("Validating list VPC by vpcofferingId") vpcs = VPC.list( self.apiclient, vpcofferingid=self.vpc_off.id, listall=True ) self.assertEqual( isinstance(vpcs, list), True, "List VPC by vpcofferingId should return a valid response" ) self.debug("Length of list VPC response: %s" % len(vpcs)) self.assertEqual( len(vpcs), 2, "List VPC should return 2 enabled VPCs" ) for vpc in vpcs: self.assertEqual( vpc.vpcofferingid, self.vpc_off.id, "VPC offering ID should match with that of resposne" ) self.debug("Validating list VPC by supportedservices") vpcs = VPC.list( self.apiclient, supportedservices='Vpn,Dhcp,Dns,SourceNat,PortForwarding,Lb,UserData,StaticNat,NetworkACL', listall=True, account=self.account.name, domainid=self.account.domainid ) self.assertEqual( isinstance(vpcs, list), True, "List VPC by vpcofferingId should return a valid response" ) for vpc in vpcs: self.assertIn( vpc.id, [vpc_1.id, vpc_2.id], "VPC offering ID should match with that of resposne" ) self.debug("Validating list VPC by restart required") vpcs = VPC.list( self.apiclient, restartrequired=True, listall=True, account=self.account.name, domainid=self.account.domainid ) if vpcs is not None: for vpc in vpcs: self.assertEqual( vpc.restartrequired, True, "RestartRequired should be set as True" ) self.debug("Validating list VPC by restart required") vpcs = VPC.list( self.apiclient, restartrequired=False, listall=True, account=self.account.name, domainid=self.account.domainid ) self.assertEqual( isinstance(vpcs, list), True, "List VPC by vpcofferingId should return a valid response" ) if vpcs is not None: for vpc in vpcs: self.assertEqual( vpc.restartrequired, False, "RestartRequired should be set as False" ) return @attr(tags=["advanced", "intervlan"]) def test_02_restart_vpc_no_networks(self): """ Test restart VPC having no networks """ # Validate the following # 1. Create a VPC with cidr - 10.1.1.1/16 # 2. Restart VPC. Restart VPC should be successful self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.debug("Restarting the VPC with no network") try: vpc.restart(self.apiclient) except Exception as e: self.fail("Failed to restart VPC network - %s" % e) self.validate_vpc_network(vpc, state='Enabled') return @attr(tags=["advanced", "intervlan"]) def test_03_restart_vpc_with_networks(self): """ Test restart VPC having networks """ # Validate the following # 1. Create a VPC with cidr - 10.1.1.1/16 # 2. Add couple of networks to VPC. # 3. Restart VPC. Restart network should be successful self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network_1 = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) self.debug("Created network with ID: %s" % network_1.id) self.network_offering_no_lb = NetworkOffering.create( self.apiclient, self.services["network_offering_no_lb"], conservemode=False ) # Enable Network offering self.network_offering_no_lb.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering_no_lb) gateway = '10.1.2.1' # New network -> different gateway self.debug("Creating network with network offering: %s" % self.network_offering_no_lb.id) network_2 = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering_no_lb.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) self.debug("Created network with ID: %s" % network_2.id) self.debug("Restarting the VPC with no network") try: vpc.restart(self.apiclient) except Exception as e: self.fail("Failed to restart VPC network - %s" % e) self.validate_vpc_network(vpc, state='Enabled') return @attr(tags=["advanced", "intervlan"]) def test_04_delete_vpc_no_networks(self): """ Test delete VPC having no networks """ # Validate the following # 1. Create a VPC with cidr - 10.1.1.1/16 # 2. Delete VPC. Delete VPC should be successful self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.debug("Restarting the VPC with no network") try: vpc.delete(self.apiclient) except Exception as e: self.fail("Failed to delete VPC network - %s" % e) self.debug("Check if the VPC offering is deleted successfully?") vpcs = VPC.list( self.apiclient, id=vpc.id ) self.assertEqual( vpcs, None, "List VPC offerings should not return anything" ) return @attr(tags=["advanced", "intervlan"]) def test_05_delete_vpc_with_networks(self): """ Test delete VPC having networks """ # Validate the following # 1. Create a VPC with cidr - 10.1.1.1/16 # 2. Add couple of networks to VPC. # 3. Delete VPC. Delete network should be successful # 4. Virtual Router should be deleted # 5. Source NAT should be released back to pool self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network_1 = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) self.debug("Created network with ID: %s" % network_1.id) self.network_offering_no_lb = NetworkOffering.create( self.apiclient, self.services["network_offering_no_lb"], conservemode=False ) # Enable Network offering self.network_offering_no_lb.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering_no_lb) gateway = '10.1.2.1' # New network -> different gateway self.debug("Creating network with network offering: %s" % self.network_offering_no_lb.id) network_2 = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering_no_lb.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) self.debug("Created network with ID: %s" % network_2.id) self.debug("Deleting the VPC with no network") with self.assertRaises(Exception): vpc.delete(self.apiclient) self.debug("Delete VPC failed as there are still networks in VPC") self.debug("Deleting the networks in the VPC") try: network_1.delete(self.apiclient) network_2.delete(self.apiclient) except Exception as e: self.fail("failed to delete the VPC networks: %s" % e) self.debug("Now trying to delete VPC") try: vpc.delete(self.apiclient) except Exception as e: self.fail("Delete to restart VPC network - %s" % e) self.debug("Check if the VPC offering is deleted successfully?") vpcs = VPC.list( self.apiclient, id=vpc.id ) self.assertEqual( vpcs, None, "List VPC offerings should not return anything" ) self.debug("Waiting for network.gc.interval to cleanup network resources") interval = list_configurations( self.apiclient, name='network.gc.interval' ) wait = list_configurations( self.apiclient, name='network.gc.wait' ) # Sleep to ensure that all resources are deleted time.sleep(int(interval[0].value) + int(wait[0].value)) self.debug("Check if VR is deleted or not?") routers = Router.list( self.apiclient, account=self.account.name, domainid=self.account.domainid, listall=True ) self.assertEqual( routers, None, "List Routers for the account should not return any response" ) return @attr(tags=["advanced", "intervlan"]) def test_06_list_vpc_apis_admin(self): """ Test list VPC APIs for different user roles """ # Validate the following # 1. list VPCS as admin User to view all the Vpcs owned by admin user # 2. list VPCS as regular User to view all the Vpcs owned by user # 3. list VPCS as domain admin User to view all the Vpcs owned by admin self.user = Account.create( self.apiclient, self.services["account"], ) self.cleanup.append(self.user) self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc_1 = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc_1) self.services["vpc"]["cidr"] = "10.1.46.1/16" vpc_2 = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.user.name, domainid=self.user.domainid ) self.validate_vpc_network(vpc_2) self.debug("Validating list VPCs call by passing account and domain") vpcs = VPC.list( self.apiclient, account=self.user.name, domainid=self.user.domainid, listall=True ) self.assertEqual( isinstance(vpcs, list), True, "List VPC should return a valid response" ) vpc = vpcs[0] self.assertEqual( vpc.id, vpc_2.id, "List VPC should return VPC belonging to that account" ) return @attr(tags=["advanced", "intervlan", "multiple"]) def test_07_restart_network_vm_running(self): """ Test Restart VPC when there are multiple networks associated """ # Validate the following # 1. Create a VPC with cidr - 10.1.1.1/16 # 2. Add network1(10.1.1.1/24) and network2(10.1.2.1/24) to this VPC # 3. Deploy vm1 and vm2 in network1 and vm3 and vm4 in network2 # 4. Create a PF rule using TCP protocol on port 22 for vm1 # 5. Create a Static Nat rule for vm2 # 6. Create an LB rule for vm3 and vm4 # 7. Create ingress network ACL for allowing all the above rules from # public ip range on network1 and network2. # 8. Create egress network ACL for network1 and network2 to access # google.com # 9. Create a private gateway for this VPC and add a static route to # this gateway # 10. Create a VPN gateway for this VPC and add static route to gateway # 11. Make sure that all the PF, LB and Static NAT rules work # 12. Make sure that we are able to access google.com from all VM # 13. Make sure that the newly added private gateway's and VPN # gateway's static routes work as expected. self.debug("Creating a VPC offering..") vpc_off = VpcOffering.create( self.apiclient, self.services["vpc_offering"] ) self.cleanup.append(vpc_off) self.validate_vpc_offering(vpc_off) self.debug("Enabling the VPC offering created") vpc_off.update(self.apiclient, state='Enabled') self.debug("creating a VPC network in the account: %s" % self.account.name) self.services["vpc"]["cidr"] = '10.1.1.1/16' vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) self.network_offering_no_lb = NetworkOffering.create( self.apiclient, self.services["network_offering_no_lb"], conservemode=False ) # Enable Network offering self.network_offering_no_lb.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering_no_lb) # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering_no_lb.id) network_1 = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering_no_lb.id, zoneid=self.zone.id, gateway='10.1.1.1', vpcid=vpc.id ) self.debug("Created network with ID: %s" % network_1.id) # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network_2 = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway='10.1.2.1', vpcid=vpc.id ) self.debug("Created network with ID: %s" % network_2.id) self.debug("deploying VMs in network: %s" % network_1.name) # Spawn an instance in that network vm_1 = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network_1.id)] ) self.debug("Deployed VM in network: %s" % network_1.id) vm_2 = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network_1.id)] ) self.debug("Deployed VM in network: %s" % network_1.id) self.debug("deploying VMs in network: %s" % network_2.name) # Spawn an instance in that network vm_3 = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network_2.id)] ) self.debug("Deployed VM in network: %s" % network_2.id) vm_4 = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network_2.id)] ) self.debug("Deployed VM in network: %s" % network_2.id) self.debug("Associating public IP for network: %s" % network_1.name) public_ip_1 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network_1.id, vpcid=vpc.id ) self.debug("Associated %s with network %s" % ( public_ip_1.ipaddress.ipaddress, network_1.id )) nat_rule = NATRule.create( self.apiclient, vm_1, self.services["natrule"], ipaddressid=public_ip_1.ipaddress.id, openfirewall=False, networkid=network_1.id, vpcid=vpc.id ) self.debug("Adding NetwrokACl rules to make NAT rule accessible") nwacl_nat = NetworkACL.create( self.apiclient, networkid=network_1.id, services=self.services["natrule"], traffictype='Ingress' ) self.debug("Associating public IP for network: %s" % network_1.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network_1.id, vpcid=vpc.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network_1.id )) self.debug("Enabling static NAT for IP: %s" % public_ip_2.ipaddress.ipaddress) try: StaticNATRule.enable( self.apiclient, ipaddressid=public_ip_2.ipaddress.id, virtualmachineid=vm_2.id, networkid=network_1.id ) self.debug("Static NAT enabled for IP: %s" % public_ip_2.ipaddress.ipaddress) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip_2.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, networkid=network_1.id, listall=True, isstaticnat=True, account=self.account.name, domainid=self.account.domainid ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip_2.ipaddress.ipaddress, "List public Ip for network should list the Ip addr" ) self.debug("Associating public IP for network: %s" % vpc.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network_2.id, vpcid=vpc.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network_2.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network_2.id, vpcid=vpc.id, domainid=self.account.domainid ) self.debug("Adding virtual machines %s and %s to LB rule" % ( vm_3.name, vm_4.name)) lb_rule.assign(self.apiclient, [vm_3, vm_4]) self.debug("Adding NetwrokACl rules to make PF and LB accessible") nwacl_lb = NetworkACL.create( self.apiclient, networkid=network_2.id, services=self.services["lbrule"], traffictype='Ingress' ) self.debug("Adding Egress rules to network %s and %s to allow access to internet") nwacl_internet_1 = NetworkACL.create( self.apiclient, networkid=network_1.id, services=self.services["icmp_rule"], traffictype='Egress' ) nwacl_internet_2 = NetworkACL.create( self.apiclient, networkid=network_2.id, services=self.services["icmp_rule"], traffictype='Egress' ) self.debug("Checking if we can SSH into VM_1?") try: ssh_1 = vm_1.get_ssh_client( ipaddress=public_ip_1.ipaddress.ipaddress, reconnect=True, port=self.services["natrule"]["publicport"] ) self.debug("SSH into VM is successfully") self.debug("Verifying if we can ping to outside world from VM?") # Ping to outsite world res = ssh_1.execute("ping -c 1 www.google.com") # res = 64 bytes from maa03s17-in-f20.1e100.net (74.125.236.212): # icmp_req=1 ttl=57 time=25.9 ms # --- www.l.google.com ping statistics --- # 1 packets transmitted, 1 received, 0% packet loss, time 0ms # rtt min/avg/max/mdev = 25.970/25.970/25.970/0.000 ms except Exception as e: self.fail("Failed to SSH into VM - %s, %s" % (public_ip_1.ipaddress.ipaddress, e)) result = str(res) self.debug("Result: %s" % result) self.assertEqual( result.count("1 received"), 1, "Ping to outside world from VM should be successful" ) self.debug("Checking if we can SSH into VM_2?") try: ssh_2 = vm_2.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule"]["publicport"] ) self.debug("SSH into VM is successfully") self.debug("Verifying if we can ping to outside world from VM?") res = ssh_2.execute("ping -c 1 www.google.com") except Exception as e: self.fail("Failed to SSH into VM - %s, %s" % (public_ip_2.ipaddress.ipaddress, e)) result = str(res) self.debug("Result: %s" % result) self.assertEqual( result.count("1 received"), 1, "Ping to outside world from VM should be successful" ) self.debug("Checking if we can SSH into VM using LB rule?") try: ssh_3 = vm_3.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH into VM is successfully") self.debug("Verifying if we can ping to outside world from VM?") res = ssh_3.execute("ping -c 1 www.google.com") except Exception as e: self.fail("Failed to SSH into VM - %s, %s" % (public_ip_3.ipaddress.ipaddress, e)) result = str(res) self.debug("Result: %s" % result) self.assertEqual( result.count("1 received"), 1, "Ping to outside world from VM should be successful" ) return @attr(tags=["advanced", "intervlan"]) def test_08_delete_vpc(self): """ Test vpc deletion after account deletion """ # Validate the following # 1. Create a VPC with cidr - 10.1.1.1/16 # 2. Add network1(10.1.1.1/24) and network2(10.1.2.1/24) to this VPC # 3. Deploy vm1 and vm2 in network1 and vm3 and vm4 in network2 # 4. Create a PF rule using TCP protocol on port 22 for vm1 # 5. Create a Static Nat rule for vm2 # 6. Create an LB rule for vm3 and vm4 # 7. Create ingress network ACL for allowing all the above rules from # public ip range on network1 and network2. # 8. Create egress network ACL for network1 and network2 to access # google.com # 9. Delete account self.debug("Removing account from cleanup list") self.cleanup = [] self.debug("Creating a VPC offering..") vpc_off = VpcOffering.create( self.apiclient, self.services["vpc_offering"] ) self.cleanup.append(vpc_off) self.validate_vpc_offering(vpc_off) self.debug("Enabling the VPC offering created") vpc_off.update(self.apiclient, state='Enabled') self.debug("creating a VPC network in the account: %s" % self.account.name) self.services["vpc"]["cidr"] = '10.1.1.1/16' vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) self.network_offering_no_lb = NetworkOffering.create( self.apiclient, self.services["network_offering_no_lb"], conservemode=False ) # Enable Network offering self.network_offering_no_lb.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering_no_lb) # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network_1 = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering_no_lb.id, zoneid=self.zone.id, gateway='10.1.1.1', vpcid=vpc.id ) self.debug("Created network with ID: %s" % network_1.id) # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering_no_lb.id) network_2 = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway='10.1.2.1', vpcid=vpc.id ) self.debug("Created network with ID: %s" % network_2.id) self.debug("deploying VMs in network: %s" % network_1.name) # Spawn an instance in that network vm_1 = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network_1.id)] ) self.debug("Deployed VM in network: %s" % network_1.id) vm_2 = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network_1.id)] ) self.debug("Deployed VM in network: %s" % network_1.id) self.debug("deploying VMs in network: %s" % network_2.name) # Spawn an instance in that network vm_3 = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network_2.id)] ) self.debug("Deployed VM in network: %s" % network_2.id) vm_4 = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network_2.id)] ) self.debug("Deployed VM in network: %s" % network_2.id) self.debug("Associating public IP for network: %s" % network_1.name) public_ip_1 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network_1.id, vpcid=vpc.id ) self.debug("Associated %s with network %s" % ( public_ip_1.ipaddress.ipaddress, network_1.id )) nat_rule = NATRule.create( self.apiclient, vm_1, self.services["natrule"], ipaddressid=public_ip_1.ipaddress.id, openfirewall=False, networkid=network_1.id, vpcid=vpc.id ) self.debug("Adding NetwrokACl rules to make NAT rule accessible") nwacl_nat = NetworkACL.create( self.apiclient, networkid=network_1.id, services=self.services["natrule"], traffictype='Ingress' ) self.debug("Associating public IP for network: %s" % network_1.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network_1.id, vpcid=vpc.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network_1.id )) self.debug("Enabling static NAT for IP: %s" % public_ip_2.ipaddress.ipaddress) try: StaticNATRule.enable( self.apiclient, ipaddressid=public_ip_2.ipaddress.id, virtualmachineid=vm_2.id, networkid=network_1.id ) self.debug("Static NAT enabled for IP: %s" % public_ip_2.ipaddress.ipaddress) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip_2.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, networkid=network_1.id, listall=True, isstaticnat=True, account=self.account.name, domainid=self.account.domainid ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip_2.ipaddress.ipaddress, "List public Ip for network should list the Ip addr" ) self.debug("Associating public IP for network: %s" % vpc.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network_2.id, vpcid=vpc.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network_2.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network_2.id, vpcid=vpc.id, domainid=self.account.domainid ) self.debug("Adding virtual machines %s and %s to LB rule" % ( vm_3.name, vm_4.name)) lb_rule.assign(self.apiclient, [vm_3, vm_4]) self.debug("Adding NetwrokACl rules to make PF and LB accessible") nwacl_lb = NetworkACL.create( self.apiclient, networkid=network_2.id, services=self.services["lbrule"], traffictype='Ingress' ) self.debug("Adding Egress rules to network %s and %s to allow access to internet") nwacl_internet_1 = NetworkACL.create( self.apiclient, networkid=network_1.id, services=self.services["icmp_rule"], traffictype='Egress' ) nwacl_internet_2 = NetworkACL.create( self.apiclient, networkid=network_2.id, services=self.services["icmp_rule"], traffictype='Egress' ) self.debug("Checking if we can SSH into VM_1?") try: ssh_1 = vm_1.get_ssh_client( ipaddress=public_ip_1.ipaddress.ipaddress, reconnect=True, port=self.services["natrule"]["publicport"]) self.debug("SSH into VM is successfully") self.debug("Verifying if we can ping to outside world from VM?") # Ping to outsite world res = ssh_1.execute("ping -c 1 www.google.com") # res = 64 bytes from maa03s17-in-f20.1e100.net (74.125.236.212): # icmp_req=1 ttl=57 time=25.9 ms # --- www.l.google.com ping statistics --- # 1 packets transmitted, 1 received, 0% packet loss, time 0ms # rtt min/avg/max/mdev = 25.970/25.970/25.970/0.000 ms except Exception as e: self.fail("Failed to SSH into VM - %s, %s" % (public_ip_1.ipaddress.ipaddress, e)) result = str(res) self.debug("result: %s" % result) self.assertEqual( result.count("1 received"), 1, "Ping to outside world from VM should be successful" ) self.debug("Checking if we can SSH into VM_2?") try: ssh_2 = vm_2.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule"]["publicport"]) self.debug("SSH into VM is successfully") self.debug("Verifying if we can ping to outside world from VM?") res = ssh_2.execute("ping -c 1 www.google.com") except Exception as e: self.fail("Failed to SSH into VM - %s, %s" % (public_ip_2.ipaddress.ipaddress, e)) result = str(res) self.debug("Result: %s" % result) self.assertEqual( result.count("1 received"), 1, "Ping to outside world from VM should be successful" ) self.debug("Checking if we can SSH into VM using LB rule?") try: ssh_3 = vm_3.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH into VM is successfully") self.debug("Verifying if we can ping to outside world from VM?") res = ssh_3.execute("ping -c 1 www.google.com") except Exception as e: self.fail("Failed to SSH into VM - %s, %s" % (public_ip_3.ipaddress.ipaddress, e)) result = str(res) self.debug("Result: %s" % result) self.assertEqual( result.count("1 received"), 1, "Ping to outside world from VM should be successful" ) self.debug("Deleting the account") self.account.delete(self.apiclient) self.debug("Waiting for account to cleanup") interval = list_configurations( self.apiclient, name='account.cleanup.interval' ) # Sleep to ensure that all resources are deleted time.sleep(int(interval[0].value)) self.debug("Checking if VPC is deleted after account deletion") vpcs = VPC.list( self.apiclient, id=vpc.id, listall=True ) self.assertEqual( vpcs, None, "List VPC should not return any response" ) return @attr(tags=["advanced", "intervlan"]) def test_09_vpc_create(self): """ Test to create vpc and verify VPC state, VR and SourceNatIP """ # Validate the following: # 1. VPC should get created with "Enabled" state. # 2. The VR should start when VPC is created. # 3. SourceNatIP address should be allocated to the VR self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.debug("Verify if the VPC was created with enabled state") self.assertEqual( vpc.state, 'Enabled', "VPC after creation should be in enabled state but the " "state is %s" % vpc.state ) self.debug("Verify if the Router has started") routers = Router.list( self.apiclient, account=self.account.name, domainid=self.account.domainid, listall=True ) self.assertEqual( isinstance(routers, list), True, "List Routers should return a valid list" ) self.assertEqual(routers[0].state, 'Running', "Router should be in running state" ) src_nat_list = PublicIPAddress.list( self.apiclient, account=self.account.name, domainid=self.account.domainid, listall=True, issourcenat=True, vpcid=vpc.id ) self.assertEqual(src_nat_list[0].ipaddress, routers[0].publicip, "Source Nat IP address was not allocated to VR" ) @attr(tags=["advanced", "intervlan"]) def test_10_nonoverlaping_cidrs(self): """ Test creation of multiple VPCs with non-overlapping CIDRs """ self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("Creating a VPC network in the account: %s" % self.account.name) vpc_1 = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc_1) self.services["vpc"]["cidr"] = "10.2.1.1/16" self.debug( "Creating a non-overlapping VPC network in the account: %s" % self.account.name) vpc_2 = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc_2) self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("Creating a overlapping VPC network in the account: %s" % self.account.name) try: vpc_3 = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.debug("%s" % vpc_3) except Exception as e: self.debug("%s" % e) pass else: assert("VPC created with overlapping CIDR") return @attr(tags=["advanced", "intervlan"]) def test_11_deploy_vm_wo_network_netdomain(self): """ Test deployment of vm in a VPC without network domain """ # 1. Create VPC without providing networkDomain. # 2. Add network without networkDomain to this VPC. # 3. Deploy VM in this network. if self.zone.domain == None: cmd = updateZone.updateZoneCmd() cmd.id = self.zone.id cmd.domain = "test.domain.org" self.apiclient.updateZone(cmd) self.zone = Zone.list(self.apiclient, id=self.zone.id)[0] self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id, ) self.debug("Created network with ID: %s" % network.id) # Spawn an instance in that network virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) self.validate_vm_netdomain(virtual_machine, vpc, network, self.zone.domain) def validate_vm_netdomain(self, vm, vpc, network, expected_netdomain): self.debug("Associating public IP for network: %s" % network.name) src_nat_ip_addr = PublicIPAddress.create( self.apiclient, zoneid=self.zone.id, accountid=self.account.name, domainid=self.account.domainid, networkid=network.id, vpcid=vpc.id ) self.debug("Associated %s with network %s" % ( src_nat_ip_addr.ipaddress.ipaddress, network.id )) self.debug("Public IP %s" % src_nat_ip_addr.__dict__) # Create NAT rule nat_rule = NATRule.create( self.apiclient, vm, self.services["natrule"], src_nat_ip_addr.ipaddress.id, openfirewall=False, networkid=network.id, vpcid=vpc.id ) list_nat_rule_response = NATRule.list( self.apiclient, id=nat_rule.id ) self.assertEqual( isinstance(list_nat_rule_response, list), True, "Check list response returns a valid list" ) self.assertNotEqual( len(list_nat_rule_response), 0, "Check Port Forwarding Rule is created" ) self.assertEqual( list_nat_rule_response[0].id, nat_rule.id, "Check Correct Port forwarding Rule is returned" ) self.debug("Adding NetworkACl rules to make NAT rule accessible") nwacl_nat = NetworkACL.create( self.apiclient, networkid=network.id, services=self.services["natrule"], traffictype='Ingress' ) self.debug("SSHing into VM with IP address %s with NAT IP %s" % ( vm.ipaddress, src_nat_ip_addr.ipaddress.ipaddress)) try: ssh_1 = vm.get_ssh_client( ipaddress=src_nat_ip_addr.ipaddress.ipaddress) self.debug("SSH into VM is successfully") # Ping to outsite world res = ssh_1.execute("cat /etc/resolv.conf") except Exception as e: self.fail("Failed to SSH into VM - %s, %s" % (vm.ssh_ip, e)) vm_domain = res[1].split(" ")[1] self.assertEqual( vm_domain, expected_netdomain, "The network domain assigned to virtual machine " "is %s expected domain was %s" % (vm_domain, expected_netdomain) ) @attr(tags=["advanced", "intervlan"]) def test_12_deploy_vm_with_netdomain(self): """ Test deployment of vm in a VPC with network domain """ # 1. Create VPC without providing networkDomain. # 2. Add network with networkDomain to this VPC. # 3. It should fail. self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) # Creation of network with different network domain than the one # specified in VPC should fail. with self.assertRaises(Exception): Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id, networkdomain='test.netdomain' ) @attr(tags=["advanced", "intervlan"]) def test_13_deploy_vm_with_vpc_netdomain(self): """ Test deployment of vm in a VPC with network domain """ # 1. Create VPC with providing networkDomain. # 2. Add network without networkDomain to this VPC. # 3. Deploy VM in this network, it should get VPC netdomain self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) netdomain = "cl2.internal" vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid, networkDomain=netdomain ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id, ) self.debug("Created network with ID: %s" % network.id) # Spawn an instance in that network virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) self.validate_vm_netdomain(virtual_machine, vpc, network, netdomain) @attr(tags=["advanced", "intervlan"]) def test_14_deploy_vm_1(self): """ Test vm deploy in network by a user where VPC was created without account/domain ID """ # 1. Create VPC without providing account/domain ID. # 2. Add network with using user account to this VPC. # 3. Deploy VM in this network user = Account.create( self.apiclient, self.services["account"] ) self.debug("Created account: %s" % user.name) self.cleanup.append(user) self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % user.name) userapiclient = self.testClient.createUserApiClient( UserName=user.name, DomainName=user.domain, acctType=0) vpc = VPC.create( userapiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( userapiclient, self.services["network"], networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) self.debug("Created network with ID: %s" % network.id) # Spawn an instance in that network virtual_machine = VirtualMachine.create( userapiclient, self.services["virtual_machine"], serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) self.assertNotEqual(virtual_machine, None, "VM creation in the network failed") return @attr(tags=["advanced", "intervlan"]) def test_15_deploy_vm_2(self): """ Test deployment of vm in a network in a domain admin account where VPC is created without account/domain ID """ # 1. Create VPC without providing account/domain ID. # 2. Add network with using domain admin account to this VPC. # 3. Deploy VM in this network domain = Domain.create( self.api_client, self.services["domain"], ) user = Account.create( self.apiclient, self.services["account"] ) self.debug("Created account: %s" % user.name) self.cleanup.append(user) self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % user.name) #0 - User, 1 - Root Admin, 2 - Domain Admin userapiclient = self.testClient.getUserApiClient( account=user.name, domain=self.services["domain"]["name"], type=2) vpc = VPC.create( userapiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( userapiclient, self.services["network"], networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) self.debug("Created network with ID: %s" % network.id) # Spawn an instance in that network virtual_machine = VirtualMachine.create( userapiclient, self.services["virtual_machine"], serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) self.assertNotEqual(virtual_machine, None, "VM creation in the network failed") return @attr(tags=["advanced", "intervlan"]) def test_16_deploy_vm_for_user_by_admin(self): """ Test deployment of vm in a network by root admin for user. """ #1. As root admin account , # Create VPC(name,zoneId,cidr,vpcOfferingId,networkDomain by passing user Account/domain ID. #2. As the user account used in step1 , create a network as part of this VPC. #3. Deploy Vms as part of this network. user = Account.create( self.apiclient, self.services["account"] ) self.debug("Created account: %s" % user.name) self.cleanup.append(user) self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % user.name) userapiclient = self.testClient.getUserApiClient( account=user.name, domain=user.domain, type=0) vpc = VPC.create( self.apiclient, self.services["vpc"], account=user.name, domainid=user.domainid, vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( userapiclient, self.services["network"], networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) self.debug("Created network with ID: %s" % network.id) # Spawn an instance in that network virtual_machine = VirtualMachine.create( userapiclient, self.services["virtual_machine"], serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) self.assertNotEqual(virtual_machine, None, "VM creation in the network failed") return @attr(tags=["advanced", "intervlan"]) def test_17_deploy_vm_for_user_by_domain_admin(self): """ Test deployment of vm in a network by domain admin for user. """ #1. As domain admin account , Create # VPC(name,zoneId,cidr,vpcOfferingId,networkDomain # by passing user Account/domain ID. #2. As the user account used in step1, create network as part of this VPC #3. Deploy Vms as part of this network. domain = Domain.create( self.api_client, self.services["domain"], ) domain_admin = Account.create( self.apiclient, self.services["domain_admin"] ) self.debug("Created account: %s" % domain_admin.name) self.cleanup.append(domain_admin) da_apiclient = self.testClient.getUserApiClient( account=domain_admin.name, domain=domain_admin.domain, type=2) user = Account.create( self.apiclient, self.services["account"] ) self.debug("Created account: %s" % user.name) self.cleanup.append(user) self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % user.name) #0 - User, 1 - Root Admin, 2 - Domain Admin userapiclient = self.testClient.getUserApiClient( account=user.name, domain=user.domain, type=0) with self.assertRaises(cloudstackAPIException): vpc = VPC.create( da_apiclient, self.services["vpc"], account=user.name, domainid=user.domainid, vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, ) @attr(tags=["advanced", "intervlan"]) def test_18_create_net_for_user_diff_domain_by_doadmin(self): """ Test creation of network by domain admin for user from different domain """ #1. As domain admin account , Create VPC(name,zoneId,cidr,vpcOfferingId,networkDomain) without passing Account/domain ID. #2. As any User account that is not under this domain , create a network as part of this VPC. domain = Domain.create( self.api_client, self.services["domain"], ) domain_admin = Account.create( self.apiclient, self.services["domain_admin"] ) self.debug("Created account: %s" % domain_admin.name) self.cleanup.append(domain_admin) da_apiclient = self.testClient.getUserApiClient( account=domain_admin.name, domain=self.services["domain"]["name"], type=2) user = Account.create( self.apiclient, self.services["account"] ) self.debug("Created account: %s" % user.name) self.cleanup.append(user) self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % user.name) #0 - User, 1 - Root Admin, 2 - Domain Admin userapiclient = self.testClient.getUserApiClient( account=user.name, domain=user.domain, type=0) vpc = VPC.create( da_apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) with self.assertRaises(Exception): network = Network.create( userapiclient, self.services["network"], networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) @attr(tags=["advanced", "intervlan"]) def test_19_create_vpc_wo_params(self): """ Test creation of VPC without mandatory parameters """ # Validate the following # 1. Create a VPC with cidr - 10.1.1.1/16 # 2. Delete VPC. Delete VPC should be successful self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) # Create VPC without vpcOffering param with self.assertRaises(Exception): vpc = VPC.create( self.apiclient, self.services["vpc"], zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.services["vpc_no_name"]["cidr"] = "10.1.1.1/16" # Create VPC without name param with self.assertRaises(Exception): vpc = VPC.create( self.apiclient, self.services["vpc_no_name"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) # Create VPC without zoneid param with self.assertRaises(Exception): vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, account=self.account.name, domainid=self.account.domainid ) vpc_wo_cidr = {"name": "TestVPC_WO_CIDR", "displaytext": "TestVPC_WO_CIDR" } # Create VPC without CIDR with self.assertRaises(Exception): vpc = VPC.create( self.apiclient, vpc_wo_cidr, vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) @attr(tags=["advanced", "intervlan"]) def test_20_update_vpc_name_display_text(self): """ Test to verify updation of vpc name and display text """ # Validate the following: # 1. VPC should get created with "Enabled" state. # 2. The VR should start when VPC is created. # 3. SourceNatIP address should be allocated to the VR self.services["vpc"]["cidr"] = "10.1.1.1/16" self.debug("creating a VPC network in the account: %s" % self.account.name) vpc = VPC.create( self.apiclient, self.services["vpc"], vpcofferingid=self.vpc_off.id, zoneid=self.zone.id, account=self.account.name, domainid=self.account.domainid ) self.validate_vpc_network(vpc) self.network_offering = NetworkOffering.create( self.apiclient, self.services["network_offering"], conservemode=False ) # Enable Network offering self.network_offering.update(self.apiclient, state='Enabled') self.cleanup.append(self.network_offering) gateway = vpc.cidr.split('/')[0] # Split the cidr to retrieve gateway # for eg. cidr = 10.0.0.1/24 # Gateway = 10.0.0.1 # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id, gateway=gateway, vpcid=vpc.id ) self.debug("Created network with ID: %s" % network.id) new_name = "New VPC" new_display_text = "New display text" vpc.update( self.apiclient, name=new_name, displaytext=new_display_text ) vpc_networks = VPC.list( self.apiclient, id=vpc.id ) self.assertEqual( isinstance(vpc_networks, list), True, "List VPC network should return a valid list" ) self.assertEqual(vpc_networks[0].name, new_name, "Updation of VPC name failed.") self.assertEqual(vpc_networks[0].displaytext, new_display_text, "Updation of VPC display text failed.")
44.646135
130
0.43631
a4e611d14d4270f4f75dd1d02c9d363a287c4957
157
py
Python
authlib/oauth2/rfc8414/__init__.py
jonathanunderwood/authlib
3834a2a80876a87cdaab4240d77185179970c3ab
[ "BSD-3-Clause" ]
1
2021-12-09T07:11:05.000Z
2021-12-09T07:11:05.000Z
authlib/oauth2/rfc8414/__init__.py
jonathanunderwood/authlib
3834a2a80876a87cdaab4240d77185179970c3ab
[ "BSD-3-Clause" ]
null
null
null
authlib/oauth2/rfc8414/__init__.py
jonathanunderwood/authlib
3834a2a80876a87cdaab4240d77185179970c3ab
[ "BSD-3-Clause" ]
2
2021-05-24T20:34:12.000Z
2022-03-26T07:46:17.000Z
from .models import AuthorizationServerMetadata from .well_known import get_well_known_url __all__ = ['AuthorizationServerMetadata', 'get_well_known_url']
26.166667
63
0.847134
6873c21cb8bf8647565adb655e8124bfb1ce1947
644
py
Python
code/Test/test_PortscanAttack.py
TomasMadeja/ID2T
77f51c074d1ff83c7d648ae62ecaed3e5cfde80c
[ "MIT" ]
33
2018-11-21T12:50:52.000Z
2022-01-12T05:38:12.000Z
code/Test/test_PortscanAttack.py
TomasMadeja/ID2T
77f51c074d1ff83c7d648ae62ecaed3e5cfde80c
[ "MIT" ]
108
2018-11-21T12:33:47.000Z
2022-02-09T15:56:59.000Z
code/Test/test_PortscanAttack.py
TomasMadeja/ID2T
77f51c074d1ff83c7d648ae62ecaed3e5cfde80c
[ "MIT" ]
20
2018-11-22T13:03:20.000Z
2022-01-12T00:19:28.000Z
import Test.ID2TAttackTest as Test class UnitTestPortscanAttack(Test.ID2TAttackTest): def test_portscan_basic(self): self.order_test([['PortscanAttack']]) def test_portscan_revers_ports(self): self.order_test([['PortscanAttack', 'port.dst.order-desc=1']]) def test_portscan_shuffle_dst_ports(self): self.order_test([['PortscanAttack', 'port.dst.shuffle=1']]) def test_portscan_shuffle_src_ports(self): self.order_test([['PortscanAttack', 'port.dst.shuffle=1']]) def test_portscan_ips_not_in_pcap(self): self.order_test([['PortscanAttack', 'ip.src=1.1.1.1', 'ip.dst=2.2.2.2']])
32.2
81
0.700311
2df59a1151bf6c50c6aa8504fdecff8af387ed5f
9,667
py
Python
rosiepi/run_rosiepi.py
sommersoft/RosiePi
45d7de65e924ae6a9fb804e8795b169eb1085243
[ "MIT" ]
null
null
null
rosiepi/run_rosiepi.py
sommersoft/RosiePi
45d7de65e924ae6a9fb804e8795b169eb1085243
[ "MIT" ]
null
null
null
rosiepi/run_rosiepi.py
sommersoft/RosiePi
45d7de65e924ae6a9fb804e8795b169eb1085243
[ "MIT" ]
null
null
null
# The MIT License (MIT) # # Copyright (c) 2019 Michael Schroeder # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # import pathlib ACTIVATE_THIS = f'{pathlib.Path().home()}/rosie_pi/rosie_venv/bin/activate_this.py' with open(ACTIVATE_THIS) as file_: exec(file_.read(), dict(__file__=ACTIVATE_THIS)) # pylint: disable=exec-used # pylint: disable=wrong-import-position import argparse import dataclasses import datetime import logging import json import traceback from configparser import ConfigParser from socket import gethostname import requests from .rosie import test_controller # pylint: disable=invalid-name rosiepi_logger = logging.getLogger(__name__) cli_parser = argparse.ArgumentParser(description="RosieApp") cli_parser.add_argument( "commit", help="Commit of circuitpython firmware to build" ) cli_parser.add_argument( "check_run_id", help="ID of the check run that requested the test" ) # TODO: update to adafruit github GIT_URL_COMMIT = "https://github.com/sommersoft/circuitpython/commit/" _STATIC_CONFIG_FILE = pathlib.Path("/etc/opt/physaci_sub/conf.ini") class PhysaCIConfig(): """ Container class for holding local configuration results. """ def __init__(self): self.config = ConfigParser(allow_no_value=True, default_section="local") read_config = self.config.read(_STATIC_CONFIG_FILE) if not read_config: raise RuntimeError("Failed to read physaCI subscription info.") self.config_location = self.config.get("local", "config_file", fallback=_STATIC_CONFIG_FILE) if self.config_location != _STATIC_CONFIG_FILE.resolve(): alt_conf_file = pathlib.Path(self.config_location) read_config = self.config.read([_STATIC_CONFIG_FILE, alt_conf_file]) @property def physaci_url(self): """ URL to send physaCI requests. """ return self.config.get("local", "physaci_url") @property def physaci_api_key(self): """ API key to send with physaCI requests. """ return self.config.get("physaci", "api_access_key") @property def supported_boards(self): """ The boards connected to this RosiePi node. """ boards = self.config.get("rosie_pi", "boards") board_list = [board.strip() for board in boards.split(",")] return board_list @dataclasses.dataclass class GitHubData(): """ Dataclass to contain data formatted to update the GitHub check run. """ conclusion: str = "" completed_at: str = "" output: dict = dataclasses.field(default_factory=dict) # pylint: disable=too-few-public-methods @dataclasses.dataclass class NodeTestData(): """ Dataclass to contain test data stored by physaCI. """ board_tests: list = dataclasses.field(default_factory=list) class TestResultPayload(): """ Container to hold the test result payload """ def __init__(self): self.github_data = GitHubData() self.node_test_data = NodeTestData() @property def payload_json(self): """ Format the contents into a JSON string. """ payload_dict = { "github_data": dataclasses.asdict(self.github_data), "node_test_data": dataclasses.asdict(self.node_test_data) } return json.dumps(payload_dict) def markdownify_results(results, results_url): """ Puts test results into a Markdown table for use with the GitHub Check Run API for the output text. :param: results: Iterable of dicts with info. """ mdown = [ "| Board | Result | Tests Passed | Tests Failed |", "| :---: | :---: | :---: | :---: |" ] for board in results: board_mdown = [ "", board["board_name"], board["outcome"], board["tests_passed"], board["tests_failed"], "", ] mdown.append("|".join(board_mdown)) mdown.extend([ "", f"Full test log(s) available [here]({results_url})." ]) return "\n".join(mdown) def run_rosie(commit, check_run_id, boards, payload): """ Runs rosiepi for each board. Returns results as a JSON for sending to GitHub. :param: commit: The commit of circuitpython to pass to rosiepi. :param: check_run_id: The ID of the GitHub Check Run :param: boards: The boards connected to the RosiePi node to run tests on. Supplied by the node's config file. :param: payload: The ``TestResultPayload`` container to hold incremental result data. """ app_conclusion = "" rosiepi_logger.info("Starting tests...") for board in boards: board_results = { "board_name": board, "outcome": None, "tests_passed": 0, "tests_failed": 0, "rosie_log": "", } try: rosie_test = test_controller.TestController(board, commit) # check if connection to board was successful if rosie_test.state != "error": rosie_test.start_test() else: board_results["outcome"] = "Error" #print(rosie_test.log.getvalue()) app_conclusion = "failure" except Exception: # pylint: disable=broad-except rosie_test.log.write(traceback.format_exc()) break finally: # now check the result of each board test if rosie_test.result: # everything passed! board_results["outcome"] = "Passed" if app_conclusion != "failure": app_conclusion = "success" else: if rosie_test.state != "error": board_results["outcome"] = "Failed" else: board_results["outcome"] = "Error" app_conclusion = "failure" board_results["tests_passed"] = str(rosie_test.tests_passed) board_results["tests_failed"] = str(rosie_test.tests_failed) board_results["rosie_log"] = rosie_test.log.getvalue() payload.node_test_data.board_tests.append(board_results) app_output_summary = [ f"RosiePi Node: {gethostname()}", f"Overall Outcome: {app_conclusion.title()}" ] results_url = ( f"https://www.physaci.com/job?node={gethostname()}&job-id={check_run_id}" ) payload.github_data.output.update( { "title": "RosiePi Test Results", "summary": "\n\n".join(app_output_summary), "text": markdownify_results( payload.node_test_data.board_tests, results_url ), } ) payload.github_data.conclusion = app_conclusion payload.github_data.completed_at = ( datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ") ) rosiepi_logger.info("Tests completed...") def send_results(check_run_id, physaci_config, results_payload): """ Send the results to physaCI. :param: check_run_id: The check run ID of the initiating check. :param: physaci_config: A ``PhysaCIConfig()`` instance :param: results_payload: A JSON string with the test results. """ rosiepi_logger.info("Sending test results to physaCI.") physaci_url = physaci_config.physaci_url + "/testresult/update" header = {"x-functions-key": physaci_config.physaci_api_key} payload = json.loads(results_payload) payload["node_name"] = gethostname() payload["check_run_id"] = check_run_id response = requests.post(physaci_url, headers=header, json=payload) if not response.ok: rosiepi_logger.warning( "Failed to send results to physaCI.\n" "Response code: %s\n" "Response: %s", response.status_code, response.text ) raise RuntimeError( f"RosiePi failed to send results. Results payload: {results_payload}" ) rosiepi_logger.info("Test results sent successfully.") def main(): """ Run RosiePi tests. """ cli_arg = cli_parser.parse_args() commit = cli_arg.commit check_run_id = cli_arg.check_run_id rosiepi_logger.info("Initiating RosiePi test(s).") rosiepi_logger.info("Testing commit: %s", commit) rosiepi_logger.info("Check run id: %s", check_run_id) config = PhysaCIConfig() payload = TestResultPayload() run_rosie(commit, check_run_id, config.supported_boards, payload) send_results(check_run_id, config, payload.payload_json)
32.880952
83
0.644771
b1208a88f57bf3e9dc48493a4ad89161dd97a2f3
4,367
py
Python
builds/min-v1.0/21Lane-min-v1.0-windows/21Lane/ftpclient.py
jarvis004/21Lane
2da4ae4825f165148fbc4762b5eeb43283a7546a
[ "Apache-1.1" ]
1
2017-04-24T21:49:45.000Z
2017-04-24T21:49:45.000Z
builds/min-v1.0/21Lane-min-v1.0-windows/21Lane/ftpclient.py
jarvis004/21Lane
2da4ae4825f165148fbc4762b5eeb43283a7546a
[ "Apache-1.1" ]
null
null
null
builds/min-v1.0/21Lane-min-v1.0-windows/21Lane/ftpclient.py
jarvis004/21Lane
2da4ae4825f165148fbc4762b5eeb43283a7546a
[ "Apache-1.1" ]
null
null
null
import sys, os import ftplib from pprint import pprint from copy import deepcopy # make sure port number is an integer configFile = 'ftp-client.conf' class FTPClient: def __init__(self, hostname='localhost', port=2121): self.hostname = hostname self.port = int(port) self.pwd = '/' self.filelist = None self.getDownloadPath() def ping(self): # return alive status ftp = ftplib.FTP() try: ftp.connect(host=self.hostname, port=self.port, timeout=2) ftp.quit() return True except Exception as e: raise e return False def ftplist(self): # remark : directory is also a file try: l = [] ftp = ftplib.FTP() ftp.connect(host=self.hostname, port=self.port) ftp.login() ftp.dir(self.pwd, l.append) ftp.quit() m = deepcopy(l) for i in range(len(l)): l[i] = l[i].split() tmplist = {} for i in range(len(l)): # l[i] = [ x for x in l[i] if x!= '' ] # no need if using string.split(), which was string.split(' ') earlier isDir = l[i][0].startswith('d') filesize = l[i][4] for item in l[i][:8]: m[i] = m[i].replace(item, '', 1) filename = m[i].strip() tmplist[i] = {'isDir':isDir, 'filesize':filesize, 'filename':filename, 'pathname':self.pwd } except Exception as e: ftp.close() print('error occured') return None else: return tmplist def ftplistrecur(self, path): # self.ftplist should be typecasted to an empty lists # self.pwd has to be set to blank and absolute path should be provided try: self.pwd = path pprint(self.pwd) retval = self.ftplist() if not len(retval) > 0: return # dictionary append would lose duplicate keys for val in list(retval.values()): if val['isDir']: self.ftplistrecur(os.path.join(path, val['filename'])) else: self.filelist.append(val) self.pwd = path except Exception as e: raise e return False def ftprecur(self, path): self.pwd = '' self.filelist = [] return self.ftplistrecur(path) def getDownloadPath(self): try: conf = open(configFile, 'r') self.downloadPath = conf.readline().strip() conf.close() if (len(self.downloadPath) == 0): raise IOError except IOError: self.downloadPath = os.path.join(os.path.expanduser('~'), 'Downloads') def downloadFile(self, pathname, filename, destpath=''): if (len(filename) == 0 or len(pathname) == 0): return False ftp = ftplib.FTP() if destpath.startswith('/'): destpath = destpath[1:] downloadPath = self.downloadPath if (destpath != ''): downloadPath = os.path.join(downloadPath, destpath) # counter = 0 # dummy = downloadPath # while (os.path.exists(dummy)): # dummy = downloadPath+'_'+str(counter) # counter+=1 # if (counter != 0): # downloadPath = dummy try: os.makedirs(downloadPath) except Exception as e: pass targetFile = os.path.join(downloadPath, filename) dummy = targetFile basename = os.path.splitext(targetFile)[0] extension = os.path.splitext(targetFile)[1] counter = 0 while (os.path.exists(dummy)): dummy = basename+'_'+str(counter)+extension counter+=1 if (counter != 0): targetFile = dummy write_stream = open(targetFile, 'wb') try: print('trying to connect to', self.hostname, self.port) ftp.connect(host=self.hostname, port=self.port) ftp.login() print('retrieving ', pathname,'/', filename) ftp.retrbinary('RETR %s' % pathname+'/'+filename, write_stream.write) ftp.quit() write_stream.close() return True except Exception as e: write_stream.close() ftp.close() os.remove(targetFile) print(pathname+'/'+filename) raise e return False # def downloadFolder(self, pathname, dirname): # if (len(pathname) == 0): # return False # ftp = ftplib.FTP() # self.getDownloadPath() # downloadDir = os.path.join(self.downloadPath, dirname) # try: # tries = 0 # if (os.path.exists(downloadDir)): # while(os.path.exists(downloadDir+'_'+str(tries))): # tries += 1 # if (tries != 0): # downloadDir += str(tries) # os.mkdir(downloadDir) # self.pwd = pathname # self.ftplist() # entrylist = [ entry for entry in list(self.filelist.values()) if not entry['isDir'] ] # files = [] # for entry in entrylist: # files.append(entry['filename']) # return # except Exception as e: # raise e
25.538012
113
0.643233
880c7fcc6c15c9ae213369eaf2b6be0c0cbe49c6
5,726
py
Python
tools/profiling/ios_bin/binary_size.py
goodarzysepideh/grpc
5a4ba15346f2dc75960e91148f5f77aa682e0f6a
[ "Apache-2.0" ]
5
2019-11-12T04:30:55.000Z
2021-08-11T23:04:12.000Z
tools/profiling/ios_bin/binary_size.py
goodarzysepideh/grpc
5a4ba15346f2dc75960e91148f5f77aa682e0f6a
[ "Apache-2.0" ]
10
2015-03-03T06:51:51.000Z
2022-03-23T14:10:56.000Z
tools/profiling/ios_bin/binary_size.py
goodarzysepideh/grpc
5a4ba15346f2dc75960e91148f5f77aa682e0f6a
[ "Apache-2.0" ]
2
2021-09-13T15:17:48.000Z
2022-02-04T21:54:39.000Z
#!/usr/bin/env python3 # # Copyright 2018 gRPC authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import glob import multiprocessing import os import shutil import subprocess import sys from parse_link_map import parse_link_map sys.path.append( os.path.join(os.path.dirname(sys.argv[0]), '..', '..', 'run_tests', 'python_utils')) import check_on_pr # Only show diff 1KB or greater diff_threshold = 1000 size_labels = ('Core', 'ObjC', 'BoringSSL', 'Protobuf', 'Total') argp = argparse.ArgumentParser( description='Binary size diff of gRPC Objective-C sample') argp.add_argument('-d', '--diff_base', type=str, help='Commit or branch to compare the current one to') args = argp.parse_args() def dir_size(dir): total = 0 for dirpath, dirnames, filenames in os.walk(dir): for f in filenames: fp = os.path.join(dirpath, f) total += os.stat(fp).st_size return total def get_size(where, frameworks): build_dir = 'src/objective-c/examples/Sample/Build/Build-%s/' % where if not frameworks: link_map_filename = 'Build/Intermediates.noindex/Sample.build/Release-iphoneos/Sample.build/Sample-LinkMap-normal-arm64.txt' return parse_link_map(build_dir + link_map_filename) else: framework_dir = 'Build/Products/Release-iphoneos/Sample.app/Frameworks/' boringssl_size = dir_size(build_dir + framework_dir + 'openssl.framework') core_size = dir_size(build_dir + framework_dir + 'grpc.framework') objc_size = dir_size(build_dir + framework_dir + 'GRPCClient.framework') + \ dir_size(build_dir + framework_dir + 'RxLibrary.framework') + \ dir_size(build_dir + framework_dir + 'ProtoRPC.framework') protobuf_size = dir_size(build_dir + framework_dir + 'Protobuf.framework') app_size = dir_size(build_dir + 'Build/Products/Release-iphoneos/Sample.app') return core_size, objc_size, boringssl_size, protobuf_size, app_size def build(where, frameworks): subprocess.check_call(['make', 'clean']) shutil.rmtree('src/objective-c/examples/Sample/Build/Build-%s' % where, ignore_errors=True) subprocess.check_call( 'CONFIG=opt EXAMPLE_PATH=src/objective-c/examples/Sample SCHEME=Sample FRAMEWORKS=%s ./build_one_example.sh' % ('YES' if frameworks else 'NO'), shell=True, cwd='src/objective-c/tests') os.rename('src/objective-c/examples/Sample/Build/Build', 'src/objective-c/examples/Sample/Build/Build-%s' % where) text = 'Objective-C binary sizes\n' for frameworks in [False, True]: build('new', frameworks) new_size = get_size('new', frameworks) old_size = None if args.diff_base: old = 'old' where_am_i = subprocess.check_output( ['git', 'rev-parse', '--abbrev-ref', 'HEAD']).decode().strip() subprocess.check_call(['git', 'checkout', '--', '.']) subprocess.check_call(['git', 'checkout', args.diff_base]) subprocess.check_call(['git', 'submodule', 'update', '--force']) try: build('old', frameworks) old_size = get_size('old', frameworks) finally: subprocess.check_call(['git', 'checkout', '--', '.']) subprocess.check_call(['git', 'checkout', where_am_i]) subprocess.check_call(['git', 'submodule', 'update', '--force']) text += ('***************FRAMEWORKS****************\n' if frameworks else '*****************STATIC******************\n') row_format = "{:>10}{:>15}{:>15}" + '\n' text += row_format.format('New size', '', 'Old size') if old_size == None: for i in range(0, len(size_labels)): text += ('\n' if i == len(size_labels) - 1 else '') + row_format.format('{:,}'.format(new_size[i]), size_labels[i], '') else: has_diff = False for i in range(0, len(size_labels) - 1): if abs(new_size[i] - old_size[i]) < diff_threshold: continue if new_size[i] > old_size[i]: diff_sign = ' (>)' else: diff_sign = ' (<)' has_diff = True text += row_format.format('{:,}'.format(new_size[i]), size_labels[i] + diff_sign, '{:,}'.format(old_size[i])) i = len(size_labels) - 1 if new_size[i] > old_size[i]: diff_sign = ' (>)' elif new_size[i] < old_size[i]: diff_sign = ' (<)' else: diff_sign = ' (=)' text += ('\n' if has_diff else '') + row_format.format( '{:,}'.format(new_size[i]), size_labels[i] + diff_sign, '{:,}'.format(old_size[i])) if not has_diff: text += '\n No significant differences in binary sizes\n' text += '\n' print(text) check_on_pr.check_on_pr('Binary Size', '```\n%s\n```' % text)
38.173333
132
0.585225
4df0fdcfb86d9c81fa006b0a64c16033dd466b40
471
py
Python
ex30.py
EiEiKyaw/python-exercise
63dcc073f55f125de784eb61aa7c82a50ca706ed
[ "MIT" ]
null
null
null
ex30.py
EiEiKyaw/python-exercise
63dcc073f55f125de784eb61aa7c82a50ca706ed
[ "MIT" ]
null
null
null
ex30.py
EiEiKyaw/python-exercise
63dcc073f55f125de784eb61aa7c82a50ca706ed
[ "MIT" ]
null
null
null
people = 30 cars = 40 trucks = 15 if cars > people: print("We should take the cars.") elif cars < people: print("We should not take the cars.") else: print("We can't decide.") if trucks > cars: print("That's too many trucks.") elif trucks < cars: print("Maybe we could take the trucks.") else: print("We still can't decide.") if people > trucks: print("Alright, let's just take the trucks.") else: print("Fine, let's stay home then.")
19.625
49
0.639066
e58c4709959f2a79d40786e239c8ae523dd3714a
1,620
py
Python
TensorFlow_implementation/Summary_Generator/Text_Preprocessing_Helpers/pickling_tools.py
shashankk24/natural-language-summary-generation-from-structured-data-master
a8bd083685ff7d5c0228588c47ddfcecba4cf78b
[ "MIT" ]
187
2017-12-16T06:22:52.000Z
2022-03-13T03:46:55.000Z
TensorFlow_implementation/Summary_Generator/Text_Preprocessing_Helpers/pickling_tools.py
shashankk24/natural-language-summary-generation-from-structured-data-master
a8bd083685ff7d5c0228588c47ddfcecba4cf78b
[ "MIT" ]
12
2017-12-19T08:51:12.000Z
2021-11-16T18:55:20.000Z
TensorFlow_implementation/Summary_Generator/Text_Preprocessing_Helpers/pickling_tools.py
shashankk24/natural-language-summary-generation-from-structured-data-master
a8bd083685ff7d5c0228588c47ddfcecba4cf78b
[ "MIT" ]
54
2017-12-18T21:52:10.000Z
2022-01-25T12:51:51.000Z
from __future__ import print_function import _pickle as pickle # pickle module in python import os # for path related operations ''' Simple function to perform pickling of the given object. This fucntion may fail if the size of the object exceeds the max size of the pickling protocol used. Although this is highly rare, One might then have to resort to some other strategy to pickle the data. The second function available is to unpickle a file located at the specified path ''' # coded by botman # function to pickle an object def pickleIt(obj, save_path): ''' function to pickle the given object. @param obj => the python object to be pickled save_path => the path where the pickled file is to be saved @return => nothing (the pickle file gets saved at the given location) ''' if(not os.path.isfile(save_path)): with open(save_path, 'wb') as dumping: pickle.dump(obj, dumping) print("The file has been pickled at:", save_path) else: print("The pickle file already exists: ", save_path) # function to unpickle the given file and load the obj back into the python environment def unPickleIt(pickle_path): # might throw the file not found exception ''' function to unpickle the object from the given path @param pickle_path => the path where the pickle file is located @return => the object extracted from the saved path ''' with open(pickle_path, 'rb') as dumped_pickle: obj = pickle.load(dumped_pickle) return obj # return the unpickled object
34.468085
121
0.691358
c73235ac97b242309e20ba2204439aca1dd108f5
1,135
py
Python
nvd3/__init__.py
timgates42/python-nvd3
f9cb41b5deaa0d7c338764d7008a4c9a5ad50dca
[ "MIT" ]
442
2015-01-12T10:13:52.000Z
2022-01-11T15:18:48.000Z
nvd3/__init__.py
timgates42/python-nvd3
f9cb41b5deaa0d7c338764d7008a4c9a5ad50dca
[ "MIT" ]
106
2015-01-11T20:27:50.000Z
2021-11-05T17:18:15.000Z
nvd3/__init__.py
timgates42/python-nvd3
f9cb41b5deaa0d7c338764d7008a4c9a5ad50dca
[ "MIT" ]
161
2015-01-06T13:31:18.000Z
2022-03-09T05:22:30.000Z
#!/usr/bin/python # -*- coding: utf-8 -*- """ Python-nvd3 is a Python wrapper for NVD3 graph library. NVD3 is an attempt to build re-usable charts and chart components for d3.js without taking away the power that d3.js gives you. Project location : https://github.com/areski/python-nvd3 """ __version__ = '0.14.2' __all__ = ['lineChart', 'pieChart', 'lineWithFocusChart', 'stackedAreaChart', 'multiBarHorizontalChart', 'linePlusBarChart', 'cumulativeLineChart', 'scatterChart', 'discreteBarChart', 'multiBarChart', 'bulletChart', 'multiChart'] from .lineChart import lineChart from .pieChart import pieChart from .lineWithFocusChart import lineWithFocusChart from .stackedAreaChart import stackedAreaChart from .multiBarHorizontalChart import multiBarHorizontalChart from .linePlusBarChart import linePlusBarChart from .cumulativeLineChart import cumulativeLineChart from .scatterChart import scatterChart from .discreteBarChart import discreteBarChart from .multiBarChart import multiBarChart from .bulletChart import bulletChart from .multiChart import multiChart from . import ipynb
34.393939
65
0.778855
bae6673b12509c4e956367e597d5ffbab7a00d33
1,303
py
Python
id_spider/generate_lua_table.py
AceticAcid-89/TakusMorphCatalog
c8ff0d394f8050424f0bde587e5fcf9c850f746e
[ "MIT" ]
2
2020-07-20T09:34:09.000Z
2020-08-05T14:41:49.000Z
id_spider/generate_lua_table.py
AceticAcid-89/TakusMorphCatalog
c8ff0d394f8050424f0bde587e5fcf9c850f746e
[ "MIT" ]
null
null
null
id_spider/generate_lua_table.py
AceticAcid-89/TakusMorphCatalog
c8ff0d394f8050424f0bde587e5fcf9c850f746e
[ "MIT" ]
1
2020-08-05T14:44:52.000Z
2020-08-05T14:44:52.000Z
# coding=utf-8 import json NPC_JSON_FILE = "npc_id_display_id.json" MOUNT_JSON_FILE = "mount_spell_id_display_id.json" target_lua = "..\\database\\npc_%s.lua" with open(NPC_JSON_FILE, encoding="utf-8") as f: data = json.load(f) with open(MOUNT_JSON_FILE, encoding="utf-8") as f: data.update(json.load(f)) full_list = [] for npc_id in data: display_id = data[npc_id]["display_id"] en_name = data[npc_id]["en_name"] if '"' in en_name: en_name = en_name.replace('"', '') cn_name = data[npc_id]["cn_name"] if '"' in cn_name: cn_name = cn_name.replace('"', '') npc_str = ' npc_id_%s = {display_id = "%s",' \ ' en_name = "%s", cn_name = "%s"}' % \ (npc_id, display_id, en_name, cn_name) full_list.append(npc_str) full_content = "local _, ns = ...\nns.npc_id_table_%s = {\n" # for wow interface constants memory limit, split to 3 tables parts = 3 nums = len(full_list) // parts for i in range(0, parts): if i == parts - 1: part_list = full_list[i * nums: (i + 2) * nums] else: part_list = full_list[i * nums: (i + 1) * nums] file_name = target_lua % i prefix = full_content % i with open(file_name, "w", encoding="utf-8") as f: f.write(prefix + ",\n".join(part_list) + "}")
28.955556
61
0.606293
5b3f063aa6752a360cbe14bbc7a063995f258cd4
269
py
Python
tests/artificial/transf_Quantization/trend_LinearTrend/cycle_30/ar_/test_artificial_32_Quantization_LinearTrend_30__20.py
shaido987/pyaf
b9afd089557bed6b90b246d3712c481ae26a1957
[ "BSD-3-Clause" ]
377
2016-10-13T20:52:44.000Z
2022-03-29T18:04:14.000Z
tests/artificial/transf_Quantization/trend_LinearTrend/cycle_30/ar_/test_artificial_32_Quantization_LinearTrend_30__20.py
ysdede/pyaf
b5541b8249d5a1cfdc01f27fdfd99b6580ed680b
[ "BSD-3-Clause" ]
160
2016-10-13T16:11:53.000Z
2022-03-28T04:21:34.000Z
tests/artificial/transf_Quantization/trend_LinearTrend/cycle_30/ar_/test_artificial_32_Quantization_LinearTrend_30__20.py
ysdede/pyaf
b5541b8249d5a1cfdc01f27fdfd99b6580ed680b
[ "BSD-3-Clause" ]
63
2017-03-09T14:51:18.000Z
2022-03-27T20:52:57.000Z
import pyaf.Bench.TS_datasets as tsds import tests.artificial.process_artificial_dataset as art art.process_dataset(N = 32 , FREQ = 'D', seed = 0, trendtype = "LinearTrend", cycle_length = 30, transform = "Quantization", sigma = 0.0, exog_count = 20, ar_order = 0);
38.428571
169
0.736059
fa3ffc6288247c5f6f065c89572cbfdd8f74bcd9
818
py
Python
gmail_att_example.py
mattepasztor/Mikroelektro--GKLB_INTM020-_Beadando
7336160dcd09228f501b895154159be8e40b4ac1
[ "MIT" ]
null
null
null
gmail_att_example.py
mattepasztor/Mikroelektro--GKLB_INTM020-_Beadando
7336160dcd09228f501b895154159be8e40b4ac1
[ "MIT" ]
null
null
null
gmail_att_example.py
mattepasztor/Mikroelektro--GKLB_INTM020-_Beadando
7336160dcd09228f501b895154159be8e40b4ac1
[ "MIT" ]
null
null
null
import smtplib import mimetypes from email.message import EmailMessage message = EmailMessage() sender = "[email protected]" recipient = "[email protected]" message['From'] = sender message['To'] = recipient message['Subject'] = 'Learning to send email from medium.com' body = """Hello I am learning to send emails using Python!!!""" message.set_content(body) mime_type, _ = mimetypes.guess_type('something.pdf') mime_type, mime_subtype = mime_type.split('/') with open('something.pdf', 'rb') as file: message.add_attachment(file.read(), maintype=mime_type, subtype=mime_subtype, filename='something.pdf') print(message) mail_server = smtplib.SMTP_SSL('smtp.gmail.com') mail_server.set_debuglevel(1) mail_server.login("[email protected]", 'Your password') mail_server.send_message(message) mail_server.quit()
32.72
61
0.768949
ec303d9e3981e66605633aef4f8af6b12b0c3094
673
py
Python
tests/pymath/test_fibonacci.py
BrianLusina/PyCharm
144dd4f6b2d254507237f46c8ee175c407fe053d
[ "Apache-2.0", "MIT" ]
null
null
null
tests/pymath/test_fibonacci.py
BrianLusina/PyCharm
144dd4f6b2d254507237f46c8ee175c407fe053d
[ "Apache-2.0", "MIT" ]
null
null
null
tests/pymath/test_fibonacci.py
BrianLusina/PyCharm
144dd4f6b2d254507237f46c8ee175c407fe053d
[ "Apache-2.0", "MIT" ]
null
null
null
import unittest from pymath.xbonacci.fibonacci import fib class FibonacciTestCase(unittest.TestCase): def test1(self): self.assertEquals(fib(0, 1, 1), [0, 1, 1]) def test5(self): self.assertEqual(fib(5, 8, 89), [5, 8, 13, 21, 34, 55, 89]) def test2(self): self.assertEquals(fib(0, 1, 34), [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]) def test3(self): self.assertEquals( fib(0, 1, 610), [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610], ) def test4(self): self.assertEquals(fib(5, 8, 144), [5, 8, 13, 21, 34, 55, 89, 144]) if __name__ == "__main__": unittest.main()
24.035714
75
0.542348
c517fdf57720b17e3e1245b562b4c47849cbbb1d
247
py
Python
Leetcode/950. Reveal Cards In Increasing Order/solution2.py
asanoviskhak/Outtalent
c500e8ad498f76d57eb87a9776a04af7bdda913d
[ "MIT" ]
51
2020-07-12T21:27:47.000Z
2022-02-11T19:25:36.000Z
Leetcode/950. Reveal Cards In Increasing Order/solution2.py
CrazySquirrel/Outtalent
8a10b23335d8e9f080e5c39715b38bcc2916ff00
[ "MIT" ]
null
null
null
Leetcode/950. Reveal Cards In Increasing Order/solution2.py
CrazySquirrel/Outtalent
8a10b23335d8e9f080e5c39715b38bcc2916ff00
[ "MIT" ]
32
2020-07-27T13:54:24.000Z
2021-12-25T18:12:50.000Z
from collections import deque class Solution: def deckRevealedIncreasing(self, deck: List[int]) -> List[int]: d = deque() for x in sorted(deck)[::-1]: d.rotate() d.appendleft(x) return list(d)
22.454545
67
0.562753
d026e0d8de7ab4cf39a71bd3db4b611b26924e7e
1,221
bzl
Python
tools/bazel/rust_cxx_bridge.bzl
wqfish/cxx
45a1f88bf9e11ea0682f285a8dd353d866ff3cb8
[ "Apache-2.0", "MIT" ]
null
null
null
tools/bazel/rust_cxx_bridge.bzl
wqfish/cxx
45a1f88bf9e11ea0682f285a8dd353d866ff3cb8
[ "Apache-2.0", "MIT" ]
null
null
null
tools/bazel/rust_cxx_bridge.bzl
wqfish/cxx
45a1f88bf9e11ea0682f285a8dd353d866ff3cb8
[ "Apache-2.0", "MIT" ]
null
null
null
# buildifier: disable=module-docstring load("@bazel_skylib//rules:run_binary.bzl", "run_binary") load("@rules_cc//cc:defs.bzl", "cc_library") def rust_cxx_bridge(name, src, deps = []): """A macro defining a cxx bridge library Args: name (string): The name of the new target src (string): The rust source file to generate a bridge for deps (list, optional): A list of dependencies for the underlying cc_library. Defaults to []. """ native.alias( name = "%s/header" % name, actual = src + ".h", ) native.alias( name = "%s/source" % name, actual = src + ".cc", ) run_binary( name = "%s/generated" % name, srcs = [src], outs = [ src + ".h", src + ".cc", ], args = [ "$(location %s)" % src, "-o", "$(location %s.h)" % src, "-o", "$(location %s.cc)" % src, ], tool = "//:codegen", ) cc_library( name = name, srcs = [src + ".cc"], deps = deps + [":%s/include" % name], ) cc_library( name = "%s/include" % name, hdrs = [src + ".h"], )
24.42
100
0.472563
525169d272984fdd6103a849e5a39bb8f6f0db9f
9,083
bzl
Python
go/private/rules/binary.bzl
chancila/rules_go
3b6e69e730f98308b2f922682443b5d51fd61825
[ "Apache-2.0" ]
null
null
null
go/private/rules/binary.bzl
chancila/rules_go
3b6e69e730f98308b2f922682443b5d51fd61825
[ "Apache-2.0" ]
1
2021-09-10T08:34:28.000Z
2021-09-10T08:34:28.000Z
go/private/rules/binary.bzl
xflagstudio/rules_go
70b8365a90e226b98f66bc35d3abb3e335883d81
[ "Apache-2.0" ]
null
null
null
# Copyright 2014 The Bazel 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. load( "//go/private:context.bzl", "go_context", ) load( "//go/private:common.bzl", "asm_exts", "cgo_exts", "go_exts", ) load( "//go/private:providers.bzl", "GoLibrary", "GoSDK", ) load( "//go/private/rules:transition.bzl", "go_transition_rule", ) load( "//go/private:mode.bzl", "LINKMODE_C_ARCHIVE", "LINKMODE_C_SHARED", "LINKMODE_PLUGIN", "LINKMODE_SHARED", ) load( "//go/private:rpath.bzl", "rpath", ) _EMPTY_DEPSET = depset([]) def new_cc_import( go, hdrs = _EMPTY_DEPSET, defines = _EMPTY_DEPSET, local_defines = _EMPTY_DEPSET, dynamic_library = None, static_library = None, alwayslink = False, linkopts = []): if dynamic_library: linkopts = linkopts + rpath.flags(go, dynamic_library) return CcInfo( compilation_context = cc_common.create_compilation_context( defines = defines, local_defines = local_defines, headers = hdrs, includes = depset([hdr.root.path for hdr in hdrs.to_list()]), ), linking_context = cc_common.create_linking_context( linker_inputs = depset([ cc_common.create_linker_input( owner = go.label, libraries = depset([ cc_common.create_library_to_link( actions = go.actions, cc_toolchain = go.cgo_tools.cc_toolchain, feature_configuration = go.cgo_tools.feature_configuration, dynamic_library = dynamic_library, static_library = static_library, alwayslink = alwayslink, ), ]), user_link_flags = depset(linkopts), ), ]), ), ) def _go_binary_impl(ctx): """go_binary_impl emits actions for compiling and linking a go executable.""" go = go_context(ctx) is_main = go.mode.link not in (LINKMODE_SHARED, LINKMODE_PLUGIN) library = go.new_library(go, importable = False, is_main = is_main) source = go.library_to_source(go, ctx.attr, library, ctx.coverage_instrumented()) name = ctx.attr.basename if not name: name = ctx.label.name executable = None if ctx.attr.out: # Use declare_file instead of attr.output(). When users set output files # directly, Bazel warns them not to use the same name as the rule, which is # the common case with go_binary. executable = ctx.actions.declare_file(ctx.attr.out) archive, executable, runfiles = go.binary( go, name = name, source = source, gc_linkopts = gc_linkopts(ctx), version_file = ctx.version_file, info_file = ctx.info_file, executable = executable, ) providers = [ library, source, archive, OutputGroupInfo( cgo_exports = archive.cgo_exports, compilation_outputs = [archive.data.file], ), DefaultInfo( files = depset([executable]), runfiles = runfiles, executable = executable, ), ] # If the binary's linkmode is c-archive or c-shared, expose CcInfo if go.cgo_tools and go.mode.link in (LINKMODE_C_ARCHIVE, LINKMODE_C_SHARED): cc_import_kwargs = { "linkopts": { "darwin": [], "windows": ["-mthreads"], }.get(go.mode.goos, ["-pthread"]), } cgo_exports = archive.cgo_exports.to_list() if cgo_exports: header = ctx.actions.declare_file("{}.h".format(name)) ctx.actions.symlink( output = header, target_file = cgo_exports[0], ) cc_import_kwargs["hdrs"] = depset([header]) if go.mode.link == LINKMODE_C_SHARED: cc_import_kwargs["dynamic_library"] = executable elif go.mode.link == LINKMODE_C_ARCHIVE: cc_import_kwargs["static_library"] = executable cc_import_kwargs["alwayslink"] = True ccinfo = new_cc_import(go, **cc_import_kwargs) ccinfo = cc_common.merge_cc_infos( cc_infos = [ccinfo] + [d[CcInfo] for d in source.cdeps], ) providers.append(ccinfo) return providers _go_binary_kwargs = { "implementation": _go_binary_impl, "attrs": { "srcs": attr.label_list(allow_files = go_exts + asm_exts + cgo_exts), "data": attr.label_list(allow_files = True), "deps": attr.label_list( providers = [GoLibrary], ), "embed": attr.label_list( providers = [GoLibrary], ), "embedsrcs": attr.label_list(allow_files = True), "importpath": attr.string(), "gc_goopts": attr.string_list(), "gc_linkopts": attr.string_list(), "x_defs": attr.string_dict(), "basename": attr.string(), "out": attr.string(), "cgo": attr.bool(), "cdeps": attr.label_list(), "cppopts": attr.string_list(), "copts": attr.string_list(), "cxxopts": attr.string_list(), "clinkopts": attr.string_list(), "_go_context_data": attr.label(default = "//:go_context_data"), }, "executable": True, "toolchains": ["@io_bazel_rules_go//go:toolchain"], } go_binary = rule(**_go_binary_kwargs) go_transition_binary = go_transition_rule(**_go_binary_kwargs) def _go_tool_binary_impl(ctx): sdk = ctx.attr.sdk[GoSDK] name = ctx.label.name if sdk.goos == "windows": name += ".exe" cout = ctx.actions.declare_file(name + ".a") if sdk.goos == "windows": cmd = "@echo off\n {go} tool compile -o {cout} -trimpath=%cd% {srcs}".format( go = sdk.go.path.replace("/", "\\"), cout = cout.path, srcs = " ".join([f.path for f in ctx.files.srcs]), ) bat = ctx.actions.declare_file(name + ".bat") ctx.actions.write( output = bat, content = cmd, ) ctx.actions.run( executable = bat, inputs = sdk.libs + sdk.headers + sdk.tools + ctx.files.srcs + [sdk.go], outputs = [cout], env = {"GOROOT": sdk.root_file.dirname}, # NOTE(#2005): avoid realpath in sandbox mnemonic = "GoToolchainBinaryCompile", ) else: cmd = "{go} tool compile -o {cout} -trimpath=$PWD {srcs}".format( go = sdk.go.path, cout = cout.path, srcs = " ".join([f.path for f in ctx.files.srcs]), ) ctx.actions.run_shell( command = cmd, inputs = sdk.libs + sdk.headers + sdk.tools + ctx.files.srcs + [sdk.go], outputs = [cout], env = {"GOROOT": sdk.root_file.dirname}, # NOTE(#2005): avoid realpath in sandbox mnemonic = "GoToolchainBinaryCompile", ) out = ctx.actions.declare_file(name) largs = ctx.actions.args() largs.add_all(["tool", "link"]) largs.add("-o", out) largs.add(cout) ctx.actions.run( executable = sdk.go, arguments = [largs], inputs = sdk.libs + sdk.headers + sdk.tools + [cout], outputs = [out], mnemonic = "GoToolchainBinary", ) return [DefaultInfo( files = depset([out]), executable = out, )] go_tool_binary = rule( implementation = _go_tool_binary_impl, attrs = { "srcs": attr.label_list( allow_files = True, doc = "Source files for the binary. Must be in 'package main'.", ), "sdk": attr.label( mandatory = True, providers = [GoSDK], doc = "The SDK containing tools and libraries to build this binary", ), }, executable = True, doc = """Used instead of go_binary for executables used in the toolchain. go_tool_binary depends on tools and libraries that are part of the Go SDK. It does not depend on other toolchains. It can only compile binaries that just have a main package and only depend on the standard library and don't require build constraints. """, ) def gc_linkopts(ctx): gc_linkopts = [ ctx.expand_make_variables("gc_linkopts", f, {}) for f in ctx.attr.gc_linkopts ] return gc_linkopts
33.029091
94
0.580095
8ab9f25d840054dbc9dafa7c148423e6cd6cdbf5
24
py
Python
src/cog/__init__.py
Hen676/FadedDiscordBot.PY
ec83ebf928071759e2fcf74d0ad8ce84f18cf3ca
[ "MIT" ]
null
null
null
src/cog/__init__.py
Hen676/FadedDiscordBot.PY
ec83ebf928071759e2fcf74d0ad8ce84f18cf3ca
[ "MIT" ]
1
2021-04-03T21:34:16.000Z
2021-04-03T21:34:16.000Z
src/cog/__init__.py
Hen676/FadedDiscordBot.PY
ec83ebf928071759e2fcf74d0ad8ce84f18cf3ca
[ "MIT" ]
1
2021-02-24T14:50:14.000Z
2021-02-24T14:50:14.000Z
#!/usr/bin/env python3.7
24
24
0.708333
d38bf96fd14fe5edd2c227ddbe293af9533fca74
947
py
Python
tests/test_game_converter.py
mervynn/RocAlphaGo
7b3ea100e9602088b9ca9f7e5eff9c593e667649
[ "MIT" ]
1
2017-02-08T15:11:59.000Z
2017-02-08T15:11:59.000Z
tests/test_game_converter.py
mervynn/RocAlphaGo
7b3ea100e9602088b9ca9f7e5eff9c593e667649
[ "MIT" ]
null
null
null
tests/test_game_converter.py
mervynn/RocAlphaGo
7b3ea100e9602088b9ca9f7e5eff9c593e667649
[ "MIT" ]
null
null
null
from AlphaGo.preprocessing.game_converter import run_game_converter from AlphaGo.util import sgf_to_gamestate import unittest import os class TestSGFLoading(unittest.TestCase): def test_ab_aw(self): with open('tests/test_data/sgf/ab_aw.sgf', 'r') as f: sgf_to_gamestate(f.read()) class TestCmdlineConverter(unittest.TestCase): def test_directory_conversion(self): args = ['--features', 'board,ones,turns_since', '--outfile', '.tmp.testing.h5', '--directory', 'tests/test_data/sgf/'] run_game_converter(args) os.remove('.tmp.testing.h5') def test_directory_walk(self): args = ['--features', 'board,ones,turns_since', '--outfile', '.tmp.testing.h5', '--directory', 'tests/test_data', '--recurse'] run_game_converter(args) os.remove('.tmp.testing.h5') if __name__ == '__main__': unittest.main()
30.548387
67
0.635692
35ad33be6a1e6225ef205d621b1ec1a269636b44
17,762
py
Python
fromHTMLtoVagrant/VagrantTopologyOSPF.py
SuperboGiuseppe/dncs_lab2
c340169f3133c4fa1574f5be82268e2958e57975
[ "MIT" ]
2
2021-01-24T11:19:04.000Z
2021-01-24T16:36:41.000Z
fromHTMLtoVagrant/VagrantTopologyOSPF.py
SuperboGiuseppe/dncs_lab2
c340169f3133c4fa1574f5be82268e2958e57975
[ "MIT" ]
null
null
null
fromHTMLtoVagrant/VagrantTopologyOSPF.py
SuperboGiuseppe/dncs_lab2
c340169f3133c4fa1574f5be82268e2958e57975
[ "MIT" ]
3
2021-01-24T11:16:51.000Z
2021-03-20T10:18:00.000Z
import ipcalc import codecs import yaml #this function writes the beginning of the VagrantFile def BeginVagrantFile(f): f.write("# -*- mode: ruby -*- \n# vi: set ft=ruby :\n\n") f.write("#All Vagrant configuration is done below. The 2 in Vagrant.configure\n#configures the configuration version we support older styles for\n#backwards compatibility. Please don't change it unless you know what\n#you're doing.\n") f.write("Vagrant.configure(\"2\") do |config|\n") f.write("config.vm.box_check_update = true\n") f.write("config.vm.provider \"virtualbox\" do |vb|\n") f.write("vb.customize [\"modifyvm\", :id, \"--usb\", \"on\"]\n") f.write("vb.customize [\"modifyvm\", :id, \"--usbehci\", \"off\"]\n") f.write("vb.customize [\"modifyvm\", :id, \"--nicpromisc2\", \"allow-all\"]\n") f.write("vb.customize [\"modifyvm\", :id, \"--nicpromisc3\", \"allow-all\"]\n") f.write("vb.customize [\"modifyvm\", :id, \"--nicpromisc4\", \"allow-all\"]\n") f.write("vb.customize [\"modifyvm\", :id, \"--nicpromisc5\", \"allow-all\"]\n") f.write("vb.cpus = 1\n") f.write("end\n") #this function write in the vagrant file a new PC host def writeHost(f,Host, edges): Id = Host["id"] Name = Host["label"] Os = Host["vm_image"] Ram = Host["ram"] N_Cpus = Host["n_cpus"] Ip = Host["network_interfaces"][0]["ip_address"] Netmask = Host["network_interfaces"][0]["netmask"] Interface = Host["network_interfaces"][0]["name_interface"] EdgeReference = Host["network_interfaces"][0]["edge"] UplinkBandwidth = 0 DownlinkBandwidth = 0 for edge in edges: if EdgeReference[0] == edge["from"] and EdgeReference[1] == edge["to"]: UplinkBandwidth = edge["bandwidth_up"] DownlinkBandwidth = edge["bandwidth_down"] IpNoSub = Ip.split("/")[0] Network = ipcalc.Network(Ip) IpNet = Network.network() CustumScript = Host["custom_script"] for x in Network: Gateway = str(x) f.write("config.vm.define \"" + Name + "\" do |" + Name + "|\n") f.write(Name + ".vm.box = \"" + Os + "\"\n") f.write(Name + ".vm.hostname = \"" + Name + "\"\n") if Id == 4: f.write(Name + ".vm.network \"private_network\", ip: \"" + IpNoSub +"\", netmask: \"" + Netmask + "\", virtualbox__intnet: \"broadcast_router-south-1\", auto_config: true\n") if Id == 5: f.write(Name + ".vm.network \"private_network\", ip: \"" + IpNoSub +"\", netmask: \"" + Netmask + "\", virtualbox__intnet: \"broadcast_router-south-2\", auto_config: true\n") if Id == 6: f.write(Name + ".vm.network \"private_network\", ip: \"" + IpNoSub +"\", netmask: \"" + Netmask + "\", virtualbox__intnet: \"broadcast_router-south-3\", auto_config: true\n") f.write(Name + '.vm.provision "file", source: \"../Dashboard_Server/telegraf.conf\", destination: \"/tmp/telegraf.conf\"\n') f.write(Name + ".vm.provision \"shell\", run: \"always\", inline: <<-SHELL\n") f.write("echo \"Static Routig configuration Started for " + Name + "\"\n") f.write("sudo sysctl -w net.ipv4.ip_forward=1\n") f.write("sudo route add -net " + str(IpNet) + " netmask " + Netmask + " gw " + Gateway + " dev " + Interface + "\n") f.write('cd /home/vagrant\n') f.write('git clone https://github.com/magnific0/wondershaper.git\n') f.write('cd wondershaper\n') if UplinkBandwidth > 0 or DownlinkBandwidth > 0: f.write('sudo ./wondershaper -a ' + Interface) if DownlinkBandwidth > 0: f.write(' -d ' + str(DownlinkBandwidth)) if UplinkBandwidth > 0: f.write(' -u ' + str(UplinkBandwidth)) f.write('\n') f.write('wget https://dl.influxdata.com/telegraf/releases/telegraf_1.17.3-1_amd64.deb\n') f.write('sudo dpkg -i telegraf_1.17.3-1_amd64.deb\n') f.write('sudo mv /tmp/telegraf.conf /etc/telegraf/telegraf.conf\n') f.write('sudo systemctl restart telegraf\n') f.write('sudo systemctl enable telegraf\n') f.write(CustumScript + " \n")#here there is the custum script f.write("echo \"Configuration END\"\n") f.write("echo \"" + Name + " is ready to Use\"\n") f.write("SHELL\n") f.write(Name + ".vm.provider \"virtualbox\" do |vb|\n") f.write("vb.memory = " + str(Ram) + "\n") f.write("vb.cpus = " + str(N_Cpus) + "\n") f.write("end\n") f.write("end\n") #this function write in the vagrant file a new Router def writeRouter(f,Router, edges): Id = Router["id"] Name = Router["label"] Os = Router["vm_image"] Ram = Router["ram"] N_Cpus = Router["n_cpus"] Ip1 = Router["network_interfaces"][0]["ip_address"] Netmask1 = Router["network_interfaces"][0]["netmask"] Interface1 = Router["network_interfaces"][0]["name_interface"] EdgeReference1 = Router["network_interfaces"][0]["edge"] UplinkBandwidth1 = 0 DownlinkBandwidth1 = 0 for edge in edges: if EdgeReference1[0] == edge["from"] and EdgeReference1[1] == edge["to"]: UplinkBandwidth1 = edge["bandwidth_up"] DownlinkBandwidth1 = edge["bandwidth_down"] IpNoSub1 = Ip1.split("/")[0] NetmaskAbbr1 = Ip1.split("/")[1] Ip2 = Router["network_interfaces"][1]["ip_address"] Netmask2 = Router["network_interfaces"][1]["netmask"] Interface2 = Router["network_interfaces"][1]["name_interface"] EdgeReference2 = Router["network_interfaces"][1]["edge"] UplinkBandwidth2 = 0 DownlinkBandwidth2 = 0 for edge in edges: if EdgeReference2[0] == edge["from"] and EdgeReference2[1] == edge["to"]: UplinkBandwidth2 = edge["bandwidth_up"] DownlinkBandwidth2 = edge["bandwidth_down"] IpNoSub2 = Ip2.split("/")[0] NetmaskAbbr2 = Ip2.split("/")[1] Ip3 = Router["network_interfaces"][2]["ip_address"] Netmask3 = Router["network_interfaces"][2]["netmask"] Interface3 = Router["network_interfaces"][2]["name_interface"] EdgeReference3 = Router["network_interfaces"][2]["edge"] UplinkBandwidth3 = 0 DownlinkBandwidth3 = 0 for edge in edges: if EdgeReference3[0] == edge["from"] and EdgeReference3[1] == edge["to"]: UplinkBandwidth3 = edge["bandwidth_up"] DownlinkBandwidth3 = edge["bandwidth_down"] IpNoSub3 = Ip3.split("/")[0] NetmaskAbbr3 = Ip3.split("/")[1] Network1 = ipcalc.Network(Ip1) IpNet1 = Network1.network() for x in Network1: Gateway1 = str(x) Network2 = ipcalc.Network(Ip2) IpNet2 = Network2.network() for x in Network2: Gateway2 = str(x) Network3 = ipcalc.Network(Ip3) IpNet3 = Network3.network() for x in Network3: Gateway3 = str(x) CustomScript = Router["custom_script"] f.write("config.vm.define \""+ Name +"\" do |" + Name + "|\n") f.write(Name + ".vm.box = \"" + Os + "\"\n") f.write(Name + ".vm.hostname = \""+ Name +"\"\n") if Id == 1: f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-south-1\", auto_config: false\n") f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-inter-1\", auto_config: false\n") f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-inter-3\", auto_config: false\n") if Id == 2: f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-south-2\", auto_config: false\n") f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-inter-2\", auto_config: false\n") f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-inter-1\", auto_config: false\n") if Id == 3: f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-south-3\", auto_config: false\n") f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-inter-3\", auto_config: false\n") f.write(Name + ".vm.network \"private_network\", virtualbox__intnet: \"broadcast_router-inter-2\", auto_config: false\n") f.write(Name + '.vm.provision "file", source: \"../Dashboard_Server/telegraf.conf\", destination: \"/tmp/telegraf.conf\"\n') f.write(Name + ".vm.provision \"shell\", inline: <<-SHELL\n") f.write("echo \" Quagga "+ Name +" start installing\"\n") f.write("#sudo sysctl -w net.ipv4.ip_forward=1\n") f.write("sudo apt-get update\n") f.write("sudo apt-get install quagga quagga-doc traceroute\n") f.write("sudo cp /usr/share/doc/quagga/examples/zebra.conf.sample /etc/quagga/zebra.conf\n") f.write("sudo cp /usr/share/doc/quagga/examples/ospfd.conf.sample /etc/quagga/ospfd.conf\n") f.write("sudo chown quagga.quaggavty /etc/quagga/*.conf\n") f.write("sudo /etc/init.d/quagga start\n") f.write("sudo sed -i s'/zebra=no/zebra=yes/' /etc/quagga/daemons\n") f.write("sudo sed -i s'/ospfd=no/ospfd=yes/' /etc/quagga/daemons\n") f.write("sudo echo 'VTYSH_PAGER=more' >>/etc/environment\n") f.write("sudo echo 'export VTYSH_PAGER=more' >>/etc/bash.bashrc\n") f.write("sudo /etc/init.d/quagga restart\n") f.write("echo \"Routing Protocol ospf Configuration Started\"\n") f.write("sudo vtysh -c '\n") f.write("configure terminal\n") f.write("router ospf\n") f.write("network " + str(IpNet1) + "/" + NetmaskAbbr1 + " area 0.0.0.0\n") f.write("network " + str(IpNet2) + "/" + NetmaskAbbr2 + " area 0.0.0.0\n") f.write("network " + str(IpNet3) + "/" + NetmaskAbbr3 + " area 0.0.0.0\n") f.write("exit\n") f.write("interface " + Interface1 + "\n") f.write("ip address " + IpNoSub1 + "/" + NetmaskAbbr1 + "\n") f.write("exit\n") f.write("interface " + Interface2 + "\n") f.write("ip address " + IpNoSub2 + "/" + NetmaskAbbr2 + "\n") f.write("exit\n") f.write("interface " + Interface3 + "\n") f.write("ip address " + IpNoSub3 + "/" + NetmaskAbbr3 + "\n") f.write("do write\n") f.write("exit\n") f.write("exit\n") f.write("ip forwarding\n") f.write("exit'\n") f.write('cd /home/vagrant\n') f.write('git clone https://github.com/magnific0/wondershaper.git\n') f.write('cd wondershaper\n') if UplinkBandwidth1 > 0 or DownlinkBandwidth1 > 0: f.write('sudo ./wondershaper -a ' + Interface1) if DownlinkBandwidth1 > 0: f.write(' -d ' + str(DownlinkBandwidth1)) if UplinkBandwidth1 > 0: f.write(' -u ' + str(UplinkBandwidth1)) f.write('\n') if UplinkBandwidth2 > 0 or DownlinkBandwidth2 > 0: f.write('sudo ./wondershaper -a ' + Interface2) if DownlinkBandwidth2 > 0: f.write(' -d ' + str(DownlinkBandwidth2)) if UplinkBandwidth2 > 0: f.write(' -u ' + str(UplinkBandwidth2)) f.write('\n') if UplinkBandwidth3 > 0 or DownlinkBandwidth3 > 0: f.write('sudo ./wondershaper -a ' + Interface3) if DownlinkBandwidth3 > 0: f.write(' -d ' + str(DownlinkBandwidth3)) if UplinkBandwidth3 > 0: f.write(' -u ' + str(UplinkBandwidth3)) f.write('\n') f.write('wget https://dl.influxdata.com/telegraf/releases/telegraf_1.17.3-1_amd64.deb\n') f.write('sudo dpkg -i telegraf_1.17.3-1_amd64.deb\n') f.write('sudo mv /tmp/telegraf.conf /etc/telegraf/telegraf.conf\n') f.write('sudo systemctl restart telegraf\n') f.write('sudo systemctl enable telegraf\n') f.write(CustomScript + " \n") #here there is the custum script f.write("echo \"Configuration END\"\n") f.write("echo \"" + Name + " is ready to Use\"\n") f.write("SHELL\n") f.write("# " + Name + ".vm.provision \"shell\", path: \"common.sh\"\n") f.write(Name + ".vm.provider \"virtualbox\" do |vb|\n") f.write("vb.memory = " + str(Ram) + "\n") f.write("vb.cpus = " + str(N_Cpus) + "\n") f.write("end\n") f.write("end\n") """ #the following is a fake graph that i used for testing #instead of typing everytime the input in the command line host1 = (4,{ "Id" : 4, "Name":"host1", "Type": "Host", "Ram": "1024", "Os": "bento/ubuntu-16.04", "custom_script":"echo 'THIS IS CUSTUM SCRIPT'", "Network" : [{ "Ip": "192.168.1.1/24", "Netmask": "255.255.255.0", "Interface" : "eth1" }] }) host2 = (5,{ "Id" : 5, "Name":"host2", "Type": "Host", "Ram": "1024", "Os": "bento/ubuntu-16.04", "custom_script":"echo 'THIS IS CUSTUM SCRIPT'", "Network" : [{ "Ip": "192.168.2.1/24", "Netmask": "255.255.255.0", "Interface" : "eth1" }] }) host3 = (6,{ "Id" : 6, "Name":"host3", "Type": "Host", "Ram": "1024", "Os": "bento/ubuntu-16.04", "custom_script":"echo 'THIS IS CUSTUM SCRIPT'", "Network" : [{ "Ip": "192.168.3.1/24", "Netmask": "255.255.255.0", "Interface" : "eth1" }] }) rout1 = (1,{ "Id" : 1, "Name":"router1", "Type": "Router", "Ram": "1024", "Os": "bento/ubuntu-16.04", "custom_script": "echo 'THIS IS CUSTUM SCRIPT'", "Network" : [{ "Ip": "192.168.1.254/24", "Netmask": "255.255.255.0", "Interface" : "eth1" },{ "Ip": "192.168.100.1/24", "Netmask": "255.255.255.0", "Interface" : "eth2" },{ "Ip": "192.168.101.2/24", "Netmask": "255.255.255.0", "Interface" : "eth3" }] }) rout2 = (2,{ "Id" : 2, "Name":"router2", "Type": "Router", "Ram": "1024", "Os": "bento/ubuntu-16.04", "custom_script": "echo 'THIS IS CUSTUM SCRIPT'", "Network" : [{ "Ip": "192.168.2.254/24", "Netmask": "255.255.255.0", "Interface" : "eth1" },{ "Ip": "192.168.100.2/24", "Netmask": "255.255.255.0", "Interface" : "eth2" },{ "Ip": "192.168.102.2/24", "Netmask": "255.255.255.0", "Interface" : "eth3" }] }) rout3 = (3,{ "Id" : 3, "Name":"ruoter3", "Type": "Router", "Ram": "1024", "Os": "bento/ubuntu-16.04", "custom_script": "echo 'THIS IS CUSTUM SCRIPT'", "Network" : [{ "Ip": "192.168.3.254/24", "Netmask": "255.255.255.0", "Interface" : "eth1" },{ "Ip": "192.168.101.1/24", "Netmask": "255.255.255.0", "Interface" : "eth2" },{ "Ip": "192.168.102.1/24", "Netmask": "255.255.255.0", "Interface" : "eth3" }] }) MyNet = [host1,host2,host3,rout1,rout2,rout3] def remap(newList): print("-------------------") for item in newList: print("Looking at device " + str(item)) print("the TYPE is " + item["type"]) if item["type"] == "router" : for device in MyNet: if device[1]["Id"] is item["id"]: print("remap of device " + str(device[1]["Id"]) + " to device " + str(item["id"])) device[1]["Name"] = item["label"] device[1]["Ram"] = item["ram"] device[1]["Os"] = item["vm_image"] device[1]["Network"][0]["Ip"] = item["network_interfaces"][0]["ip_address"] device[1]["Network"][0]["Netmask"] = item["network_interfaces"][0]["netmask"] device[1]["Network"][0]["Interface"] = item["network_interfaces"][0]["name_interface"] device[1]["Network"][1]["Ip"] = item["network_interfaces"][1]["ip_address"] device[1]["Network"][1]["Netmask"] = item["network_interfaces"][1]["netmask"] device[1]["Network"][1]["Interface"] = item["network_interfaces"][1]["name_interface"] device[1]["Network"][2]["Ip"] = item["network_interfaces"][2]["ip_address"] device[1]["Network"][2]["Netmask"] = item["network_interfaces"][2]["netmask"] device[1]["Network"][2]["Interface"] = item["network_interfaces"][2]["name_interface"] for item in newList: if item["type"] == "host" : for device in MyNet: if device[1]["Id"] is item["id"]: print("remap of device " + str(device[1]["Id"]) + " to device " + str(item["id"])) device[1]["Name"] = item["label"] device[1]["Ram"] = item["ram"] device[1]["Os"] = item["vm_image"] device[1]["Network"][0]["Ip"] = item["network_interfaces"][0]["ip_address"] device[1]["Network"][0]["Netmask"] = item["network_interfaces"][0]["netmask"] device[1]["Network"][0]["Interface"] = item["network_interfaces"][0]["name_interface"] return MyNet """ def html_to_vagrantfile(nodes, edges): VagrantFile = open("Vagrantfile", "w") BeginVagrantFile(VagrantFile) for node in nodes: if node["type"] == "router": writeRouter(VagrantFile, node, edges) if node["type"] == "host": writeHost(VagrantFile, node, edges) VagrantFile.write("end\n") VagrantFile.close() #read the data structure from input #Network = G.nodes.data(): #file = codecs.open(network_path, "r", "utf-8") #html = file.read() #if "nodes = new vis.DataSet(" in html: #listOfDevice = find_between(html, "nodes = new vis.DataSet(" , ")") #print(listOfDevice) #listOfDevice = yaml.load(listOfDevice) #Network = remap(listOfDevice) #Network = listOfDevice #N.B per Luca, Network è già la lista dei nodi che puoi esplorare #first, let's write the beginnig of the VagrantFile #second, let's write each device with his feature #this topology has 3 hosts and 3 routers #call the respective function to "populate" the vagrant file #BeginVagrantFile(VagrantFile) #for device in Network: # typeOfDevice = device[1]["Type"] #print("the device is a " + typeOfDevice) # if typeOfDevice is "Router": # writeRouter(VagrantFile,device) #for device in Network: # typeOfDevice = device[1]["Type"] #print("the device is a " + typeOfDevice) # if typeOfDevice is "Host": # writeHost(VagrantFile,device)
38.197849
239
0.599201
19a064d12c2e513890077a2a3b62adfba76c2966
3,248
py
Python
airflow/dags/iotexetl_airflow/bigquery_utils.py
blockchain-etl/iotex-etl
bd350c3190acac35d17532eff383e05d08011e24
[ "MIT" ]
3
2020-07-04T13:53:38.000Z
2020-07-30T15:07:35.000Z
airflow/dags/iotexetl_airflow/bigquery_utils.py
blockchain-etl/iotex-etl
bd350c3190acac35d17532eff383e05d08011e24
[ "MIT" ]
13
2020-07-16T06:07:33.000Z
2020-08-20T10:35:10.000Z
airflow/dags/iotexetl_airflow/bigquery_utils.py
blockchain-etl/iotex-etl
bd350c3190acac35d17532eff383e05d08011e24
[ "MIT" ]
1
2021-01-20T10:06:20.000Z
2021-01-20T10:06:20.000Z
import json import logging from google.cloud import bigquery from google.api_core.exceptions import Conflict, NotFound, Forbidden from iotexetl_airflow.file_utils import read_file def create_dataset(client, dataset_name, project=None): dataset = client.dataset(dataset_name, project=project) try: logging.info('Creating new dataset ...') dataset = client.create_dataset(dataset) logging.info('New dataset created: ' + dataset_name) except Conflict as error: logging.info('Dataset already exists') except Forbidden as error: logging.info('User does not have bigquery.datasets.create permission in project') return dataset def submit_bigquery_job(job, configuration): try: logging.info('Creating a job: ' + json.dumps(configuration.to_api_repr())) result = job.result() logging.info(result) assert job.errors is None or len(job.errors) == 0 return result except Exception: logging.info(job.errors) raise def read_bigquery_schema_from_file(filepath): file_content = read_file(filepath) json_content = json.loads(file_content) return read_bigquery_schema_from_json_recursive(json_content) def read_bigquery_schema_from_json_recursive(json_schema): """ CAUTION: Recursive function This method can generate BQ schemas for nested records """ result = [] for field in json_schema: if field.get('type').lower() == 'record' and field.get('fields'): schema = bigquery.SchemaField( name=field.get('name'), field_type=field.get('type', 'STRING'), mode=field.get('mode', 'NULLABLE'), description=field.get('description'), fields=read_bigquery_schema_from_json_recursive(field.get('fields')) ) else: schema = bigquery.SchemaField( name=field.get('name'), field_type=field.get('type', 'STRING'), mode=field.get('mode', 'NULLABLE'), description=field.get('description') ) result.append(schema) return result def query(bigquery_client, sql, destination=None, priority=bigquery.QueryPriority.INTERACTIVE): job_config = bigquery.QueryJobConfig() job_config.destination = destination job_config.priority = priority logging.info('Executing query: ' + sql) query_job = bigquery_client.query(sql, location='US', job_config=job_config) submit_bigquery_job(query_job, job_config) assert query_job.state == 'DONE' def create_view(bigquery_client, sql, table_ref): table = bigquery.Table(table_ref) table.view_query = sql logging.info('Creating view: ' + json.dumps(table.to_api_repr())) try: table = bigquery_client.create_table(table) except Conflict: # https://cloud.google.com/bigquery/docs/managing-views table = bigquery_client.update_table(table, ['view_query']) assert table.table_id == table_ref.table_id return table def does_table_exist(bigquery_client, table_ref): try: table = bigquery_client.get_table(table_ref) except NotFound: return False return True
32.808081
95
0.673337
df678a6400bf8bf73819affa31fede801c9adee3
4,381
py
Python
src/erc20ABI.py
georgedaviesr3/web3-flashbots
3cbb266f32e215ca0ecf003c0c184735a4efa57a
[ "MIT" ]
null
null
null
src/erc20ABI.py
georgedaviesr3/web3-flashbots
3cbb266f32e215ca0ecf003c0c184735a4efa57a
[ "MIT" ]
null
null
null
src/erc20ABI.py
georgedaviesr3/web3-flashbots
3cbb266f32e215ca0ecf003c0c184735a4efa57a
[ "MIT" ]
null
null
null
abi = """ [{"inputs":[{"internalType":"string","name":"name","type":"string"},{"internalType":"string","name":"symbol","type":"string"},{"internalType":"uint256","name":"initialBalance","type":"uint256"},{"internalType":"address payable","name":"feeReceiver","type":"address"}],"stateMutability":"payable","type":"constructor"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"spender","type":"address"},{"indexed":false,"internalType":"uint256","name":"value","type":"uint256"}],"name":"Approval","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"previousOwner","type":"address"},{"indexed":true,"internalType":"address","name":"newOwner","type":"address"}],"name":"OwnershipTransferred","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"from","type":"address"},{"indexed":true,"internalType":"address","name":"to","type":"address"},{"indexed":false,"internalType":"uint256","name":"value","type":"uint256"}],"name":"Transfer","type":"event"},{"inputs":[{"internalType":"address","name":"owner","type":"address"},{"internalType":"address","name":"spender","type":"address"}],"name":"allowance","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"spender","type":"address"},{"internalType":"uint256","name":"amount","type":"uint256"}],"name":"approve","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"account","type":"address"}],"name":"balanceOf","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"decimals","outputs":[{"internalType":"uint8","name":"","type":"uint8"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"spender","type":"address"},{"internalType":"uint256","name":"subtractedValue","type":"uint256"}],"name":"decreaseAllowance","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[],"name":"generator","outputs":[{"internalType":"string","name":"","type":"string"}],"stateMutability":"pure","type":"function"},{"inputs":[],"name":"getOwner","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"spender","type":"address"},{"internalType":"uint256","name":"addedValue","type":"uint256"}],"name":"increaseAllowance","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[],"name":"name","outputs":[{"internalType":"string","name":"","type":"string"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"owner","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"renounceOwnership","outputs":[],"stateMutability":"nonpayable","type":"function"},{"inputs":[],"name":"symbol","outputs":[{"internalType":"string","name":"","type":"string"}],"stateMutability":"view","type":"function"},{"inputs":[],"name":"totalSupply","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"recipient","type":"address"},{"internalType":"uint256","name":"amount","type":"uint256"}],"name":"transfer","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"sender","type":"address"},{"internalType":"address","name":"recipient","type":"address"},{"internalType":"uint256","name":"amount","type":"uint256"}],"name":"transferFrom","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"newOwner","type":"address"}],"name":"transferOwnership","outputs":[],"stateMutability":"nonpayable","type":"function"},{"inputs":[],"name":"version","outputs":[{"internalType":"string","name":"","type":"string"}],"stateMutability":"view","type":"function"}]"""
2,190.5
4,371
0.65921
03972863b1066e4187056dbc2a7e47c642010636
375
py
Python
app/YtManagerApp/migrations/0013_auto_20190922_1834.py
cyberjacob/ytsm
7e0bd75945b2bade0d38233e7fe890971d7909f2
[ "MIT" ]
1
2022-02-07T07:43:49.000Z
2022-02-07T07:43:49.000Z
app/YtManagerApp/migrations/0013_auto_20190922_1834.py
girlpunk/ytsm
7e0bd75945b2bade0d38233e7fe890971d7909f2
[ "MIT" ]
null
null
null
app/YtManagerApp/migrations/0013_auto_20190922_1834.py
girlpunk/ytsm
7e0bd75945b2bade0d38233e7fe890971d7909f2
[ "MIT" ]
null
null
null
# Generated by Django 2.2.5 on 2019-09-22 18:34 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('YtManagerApp', '0012_auto_20190819_1615'), ] operations = [ migrations.AlterField( model_name='video', name='name', field=models.TextField(), ), ]
19.736842
52
0.589333
0bb05c453490ab91c445164f878bfe14d7e386fb
725
py
Python
tests/integration/test_artist_searcher.py
LimaGuilherme/what-is-your-band-favorite-word-api
dff4a9ed120bdd9dca1ad72167aa963d15d810ca
[ "MIT" ]
null
null
null
tests/integration/test_artist_searcher.py
LimaGuilherme/what-is-your-band-favorite-word-api
dff4a9ed120bdd9dca1ad72167aa963d15d810ca
[ "MIT" ]
null
null
null
tests/integration/test_artist_searcher.py
LimaGuilherme/what-is-your-band-favorite-word-api
dff4a9ed120bdd9dca1ad72167aa963d15d810ca
[ "MIT" ]
null
null
null
from unittest import TestCase from src.lyrics.searchers import ArtistSearcher from src import configurations as config_module class TestArtistSearcher(TestCase): def setUp(self) -> None: self.config = config_module.get_config(config_type='full') def test_should_return_false_for_nonexistent_artist(self): artist_searcher = ArtistSearcher(self.config) is_valid = artist_searcher.is_this_artist_valid('Random Unknown Artist') self.assertEqual(is_valid, False) def test_should_return_true_for_existing_artist(self): artist_searcher = ArtistSearcher(self.config) is_valid = artist_searcher.is_this_artist_valid('Queen') self.assertEqual(is_valid, True)
31.521739
80
0.761379
2773d9879273afd833af34f3f64e90035bda8932
3,705
py
Python
rotkehlchen/tests/fixtures/db.py
sponnet/rotki
ff706784fbc80cc66035a5355418c5ecf93f13ba
[ "BSD-3-Clause" ]
null
null
null
rotkehlchen/tests/fixtures/db.py
sponnet/rotki
ff706784fbc80cc66035a5355418c5ecf93f13ba
[ "BSD-3-Clause" ]
1
2020-01-14T04:37:15.000Z
2020-01-14T04:37:15.000Z
rotkehlchen/tests/fixtures/db.py
sponnet/rotki
ff706784fbc80cc66035a5355418c5ecf93f13ba
[ "BSD-3-Clause" ]
null
null
null
import os from typing import Any, Dict, List, Optional import pytest from rotkehlchen.assets.asset import Asset from rotkehlchen.db.dbhandler import DBHandler from rotkehlchen.db.settings import ModifiableDBSettings from rotkehlchen.tests.utils.constants import DEFAULT_TESTS_MAIN_CURRENCY from rotkehlchen.typing import FilePath from rotkehlchen.user_messages import MessagesAggregator @pytest.fixture def username(): return 'testuser' @pytest.fixture(scope='session') def session_ignored_assets() -> Optional[List[Asset]]: return None @pytest.fixture def ignored_assets() -> Optional[List[Asset]]: return None @pytest.fixture(scope='session') def session_username(): return 'session_test_user' @pytest.fixture def data_dir(tmpdir_factory) -> FilePath: return FilePath(tmpdir_factory.mktemp('data')) @pytest.fixture(scope='session') def session_data_dir(tmpdir_factory) -> FilePath: return FilePath(tmpdir_factory.mktemp('session_data')) @pytest.fixture def user_data_dir(data_dir, username) -> FilePath: """Create and return the user data directory""" user_data_dir = os.path.join(data_dir, username) if not os.path.exists(user_data_dir): os.mkdir(user_data_dir) return FilePath(user_data_dir) @pytest.fixture(scope='session') def session_user_data_dir(session_data_dir, session_username): """Create and return the session scoped user data directory""" user_data_dir = os.path.join(session_data_dir, session_username) if not os.path.exists(user_data_dir): os.mkdir(user_data_dir) return user_data_dir def _init_database( data_dir: FilePath, password: str, msg_aggregator: MessagesAggregator, db_settings: Optional[Dict[str, Any]], ignored_assets: Optional[List[Asset]], ) -> DBHandler: db = DBHandler(data_dir, password, msg_aggregator) settings = { # DO not submit usage analytics during tests 'submit_usage_analytics': False, 'main_currency': DEFAULT_TESTS_MAIN_CURRENCY, } # Set the given db_settings. The pre-set values have priority unless overriden here if db_settings is not None: for key, value in db_settings.items(): settings[key] = value db.set_settings(ModifiableDBSettings(**settings)) if ignored_assets: for asset in ignored_assets: db.add_to_ignored_assets(asset) return db @pytest.fixture def database( user_data_dir, function_scope_messages_aggregator, db_password, db_settings, start_with_logged_in_user, ignored_assets, ) -> Optional[DBHandler]: if not start_with_logged_in_user: return None return _init_database( data_dir=user_data_dir, msg_aggregator=function_scope_messages_aggregator, password=db_password, db_settings=db_settings, ignored_assets=ignored_assets, ) @pytest.fixture(scope='session') def session_database( session_user_data_dir, messages_aggregator, session_db_password, session_db_settings, session_start_with_logged_in_user, session_ignored_assets, ) -> Optional[DBHandler]: if not session_start_with_logged_in_user: return None return _init_database( data_dir=session_user_data_dir, msg_aggregator=messages_aggregator, password=session_db_password, db_settings=session_db_settings, ignored_assets=session_ignored_assets, ) @pytest.fixture def db_settings() -> Optional[Dict[str, Any]]: return None @pytest.fixture(scope='session') def session_db_settings() -> Optional[Dict[str, Any]]: return None
26.847826
87
0.721727
7b3e1dfb834c67ebe41c25676a768dd032b10f78
11,568
py
Python
train_multi_step.py
ahsan-muzaheed/MTGNN
1d82bdddbbaca57e5c9634e13605f80f0d8dd3c6
[ "MIT" ]
null
null
null
train_multi_step.py
ahsan-muzaheed/MTGNN
1d82bdddbbaca57e5c9634e13605f80f0d8dd3c6
[ "MIT" ]
null
null
null
train_multi_step.py
ahsan-muzaheed/MTGNN
1d82bdddbbaca57e5c9634e13605f80f0d8dd3c6
[ "MIT" ]
null
null
null
import torch import numpy as np import argparse import time from util import * from trainer import Trainer from net import gtnet def str_to_bool(value): if isinstance(value, bool): return value if value.lower() in {'false', 'f', '0', 'no', 'n'}: return False elif value.lower() in {'true', 't', '1', 'yes', 'y'}: return True raise ValueError(f'{value} is not a valid boolean value') parser = argparse.ArgumentParser() #parser.add_argument('--device',type=str,default='cuda:1',help='') parser.add_argument('--device',type=str,default='cuda:0',help='') parser.add_argument('--data',type=str,default='data/METR-LA',help='data path') parser.add_argument('--adj_data', type=str,default='data/sensor_graph/adj_mx.pkl',help='adj data path') parser.add_argument('--gcn_true', type=str_to_bool, default=True, help='whether to add graph convolution layer') parser.add_argument('--buildA_true', type=str_to_bool, default=True,help='whether to construct adaptive adjacency matrix') parser.add_argument('--load_static_feature', type=str_to_bool, default=False,help='whether to load static feature') parser.add_argument('--cl', type=str_to_bool, default=True,help='whether to do curriculum learning') parser.add_argument('--gcn_depth',type=int,default=2,help='graph convolution depth') parser.add_argument('--num_nodes',type=int,default=207,help='number of nodes/variables') parser.add_argument('--dropout',type=float,default=0.3,help='dropout rate') parser.add_argument('--subgraph_size',type=int,default=20,help='k') parser.add_argument('--node_dim',type=int,default=40,help='dim of nodes') parser.add_argument('--dilation_exponential',type=int,default=1,help='dilation exponential') parser.add_argument('--conv_channels',type=int,default=32,help='convolution channels') parser.add_argument('--residual_channels',type=int,default=32,help='residual channels') parser.add_argument('--skip_channels',type=int,default=64,help='skip channels') parser.add_argument('--end_channels',type=int,default=128,help='end channels') parser.add_argument('--in_dim',type=int,default=2,help='inputs dimension') parser.add_argument('--seq_in_len',type=int,default=12,help='input sequence length') parser.add_argument('--seq_out_len',type=int,default=12,help='output sequence length') parser.add_argument('--layers',type=int,default=3,help='number of layers') parser.add_argument('--batch_size',type=int,default=64,help='batch size') parser.add_argument('--learning_rate',type=float,default=0.001,help='learning rate') parser.add_argument('--weight_decay',type=float,default=0.0001,help='weight decay rate') parser.add_argument('--clip',type=int,default=5,help='clip') parser.add_argument('--step_size1',type=int,default=2500,help='step_size') parser.add_argument('--step_size2',type=int,default=100,help='step_size') parser.add_argument('--epochs',type=int,default=100,help='') parser.add_argument('--print_every',type=int,default=50,help='') parser.add_argument('--seed',type=int,default=101,help='random seed') parser.add_argument('--save',type=str,default='./save/',help='save path') parser.add_argument('--expid',type=int,default=1,help='experiment id') parser.add_argument('--propalpha',type=float,default=0.05,help='prop alpha') parser.add_argument('--tanhalpha',type=float,default=3,help='adj alpha') parser.add_argument('--num_split',type=int,default=1,help='number of splits for graphs') parser.add_argument('--runs',type=int,default=10,help='number of runs') args = parser.parse_args() torch.set_num_threads(3) def main(runid): # torch.manual_seed(args.seed) # torch.backends.cudnn.deterministic = True # torch.backends.cudnn.benchmark = False # np.random.seed(args.seed) #load data device = torch.device(args.device) dataloader = load_dataset(args.data, args.batch_size, args.batch_size, args.batch_size) scaler = dataloader['scaler'] predefined_A = load_adj(args.adj_data) predefined_A = torch.tensor(predefined_A)-torch.eye(args.num_nodes) predefined_A = predefined_A.to(device) # if args.load_static_feature: # static_feat = load_node_feature('data/sensor_graph/location.csv') # else: # static_feat = None model = gtnet(args.gcn_true, args.buildA_true, args.gcn_depth, args.num_nodes, device, predefined_A=predefined_A, dropout=args.dropout, subgraph_size=args.subgraph_size, node_dim=args.node_dim, dilation_exponential=args.dilation_exponential, conv_channels=args.conv_channels, residual_channels=args.residual_channels, skip_channels=args.skip_channels, end_channels= args.end_channels, seq_length=args.seq_in_len, in_dim=args.in_dim, out_dim=args.seq_out_len, layers=args.layers, propalpha=args.propalpha, tanhalpha=args.tanhalpha, layer_norm_affline=True) print(args) print('The recpetive field size is', model.receptive_field) nParams = sum([p.nelement() for p in model.parameters()]) print('Number of model parameters is', nParams) engine = Trainer(model, args.learning_rate, args.weight_decay, args.clip, args.step_size1, args.seq_out_len, scaler, device, args.cl) print("start training...",flush=True) his_loss =[] val_time = [] train_time = [] minl = 1e5 for i in range(1,args.epochs+1): train_loss = [] train_mape = [] train_rmse = [] t1 = time.time() dataloader['train_loader'].shuffle() for iter, (x, y) in enumerate(dataloader['train_loader'].get_iterator()): trainx = torch.Tensor(x).to(device) trainx= trainx.transpose(1, 3) trainy = torch.Tensor(y).to(device) trainy = trainy.transpose(1, 3) if iter%args.step_size2==0: perm = np.random.permutation(range(args.num_nodes)) num_sub = int(args.num_nodes/args.num_split) for j in range(args.num_split): if j != args.num_split-1: id = perm[j * num_sub:(j + 1) * num_sub] else: id = perm[j * num_sub:] id = torch.tensor(id).to(device) tx = trainx[:, :, id, :] ty = trainy[:, :, id, :] metrics = engine.train(tx, ty[:,0,:,:],id) train_loss.append(metrics[0]) train_mape.append(metrics[1]) train_rmse.append(metrics[2]) if iter % args.print_every == 0 : log = 'Iter: {:03d}, Train Loss: {:.4f}, Train MAPE: {:.4f}, Train RMSE: {:.4f}' print(log.format(iter, train_loss[-1], train_mape[-1], train_rmse[-1]),flush=True) t2 = time.time() train_time.append(t2-t1) #validation valid_loss = [] valid_mape = [] valid_rmse = [] s1 = time.time() for iter, (x, y) in enumerate(dataloader['val_loader'].get_iterator()): testx = torch.Tensor(x).to(device) testx = testx.transpose(1, 3) testy = torch.Tensor(y).to(device) testy = testy.transpose(1, 3) metrics = engine.eval(testx, testy[:,0,:,:]) valid_loss.append(metrics[0]) valid_mape.append(metrics[1]) valid_rmse.append(metrics[2]) s2 = time.time() log = 'Epoch: {:03d}, Inference Time: {:.4f} secs' print(log.format(i,(s2-s1))) val_time.append(s2-s1) mtrain_loss = np.mean(train_loss) mtrain_mape = np.mean(train_mape) mtrain_rmse = np.mean(train_rmse) mvalid_loss = np.mean(valid_loss) mvalid_mape = np.mean(valid_mape) mvalid_rmse = np.mean(valid_rmse) his_loss.append(mvalid_loss) log = 'Epoch: {:03d}, Train Loss: {:.4f}, Train MAPE: {:.4f}, Train RMSE: {:.4f}, Valid Loss: {:.4f}, Valid MAPE: {:.4f}, Valid RMSE: {:.4f}, Training Time: {:.4f}/epoch' print(log.format(i, mtrain_loss, mtrain_mape, mtrain_rmse, mvalid_loss, mvalid_mape, mvalid_rmse, (t2 - t1)),flush=True) if mvalid_loss<minl: torch.save(engine.model.state_dict(), args.save + "exp" + str(args.expid) + "_" + str(runid) +".pth") minl = mvalid_loss print("Average Training Time: {:.4f} secs/epoch".format(np.mean(train_time))) print("Average Inference Time: {:.4f} secs".format(np.mean(val_time))) bestid = np.argmin(his_loss) engine.model.load_state_dict(torch.load(args.save + "exp" + str(args.expid) + "_" + str(runid) +".pth")) print("Training finished") print("The valid loss on best model is", str(round(his_loss[bestid],4))) #valid data outputs = [] realy = torch.Tensor(dataloader['y_val']).to(device) realy = realy.transpose(1,3)[:,0,:,:] for iter, (x, y) in enumerate(dataloader['val_loader'].get_iterator()): testx = torch.Tensor(x).to(device) testx = testx.transpose(1,3) with torch.no_grad(): preds = engine.model(testx) preds = preds.transpose(1,3) outputs.append(preds.squeeze()) yhat = torch.cat(outputs,dim=0) yhat = yhat[:realy.size(0),...] pred = scaler.inverse_transform(yhat) vmae, vmape, vrmse = metric(pred,realy) #test data outputs = [] realy = torch.Tensor(dataloader['y_test']).to(device) realy = realy.transpose(1, 3)[:, 0, :, :] for iter, (x, y) in enumerate(dataloader['test_loader'].get_iterator()): testx = torch.Tensor(x).to(device) testx = testx.transpose(1, 3) with torch.no_grad(): preds = engine.model(testx) preds = preds.transpose(1, 3) outputs.append(preds.squeeze()) yhat = torch.cat(outputs, dim=0) yhat = yhat[:realy.size(0), ...] mae = [] mape = [] rmse = [] for i in range(args.seq_out_len): pred = scaler.inverse_transform(yhat[:, :, i]) real = realy[:, :, i] metrics = metric(pred, real) log = 'Evaluate best model on test data for horizon {:d}, Test MAE: {:.4f}, Test MAPE: {:.4f}, Test RMSE: {:.4f}' print(log.format(i + 1, metrics[0], metrics[1], metrics[2])) mae.append(metrics[0]) mape.append(metrics[1]) rmse.append(metrics[2]) return vmae, vmape, vrmse, mae, mape, rmse if __name__ == "__main__": vmae = [] vmape = [] vrmse = [] mae = [] mape = [] rmse = [] for i in range(args.runs): vm1, vm2, vm3, m1, m2, m3 = main(i) vmae.append(vm1) vmape.append(vm2) vrmse.append(vm3) mae.append(m1) mape.append(m2) rmse.append(m3) mae = np.array(mae) mape = np.array(mape) rmse = np.array(rmse) amae = np.mean(mae,0) amape = np.mean(mape,0) armse = np.mean(rmse,0) smae = np.std(mae,0) smape = np.std(mape,0) srmse = np.std(rmse,0) print('\n\nResults for 10 runs\n\n') #valid data print('valid\tMAE\tRMSE\tMAPE') log = 'mean:\t{:.4f}\t{:.4f}\t{:.4f}' print(log.format(np.mean(vmae),np.mean(vrmse),np.mean(vmape))) log = 'std:\t{:.4f}\t{:.4f}\t{:.4f}' print(log.format(np.std(vmae),np.std(vrmse),np.std(vmape))) print('\n\n') #test data print('test|horizon\tMAE-mean\tRMSE-mean\tMAPE-mean\tMAE-std\tRMSE-std\tMAPE-std') for i in [2,5,11]: log = '{:d}\t{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}' print(log.format(i+1, amae[i], armse[i], amape[i], smae[i], srmse[i], smape[i]))
39.889655
178
0.634941
e4fd6a1d25a14612f71157f2be50ad791d58941f
3,077
py
Python
cgcrepair/utils/parse/cwe.py
epicosy/cgc-repair
f347a29ef482019563402684e585080836d3d093
[ "MIT" ]
null
null
null
cgcrepair/utils/parse/cwe.py
epicosy/cgc-repair
f347a29ef482019563402684e585080836d3d093
[ "MIT" ]
null
null
null
cgcrepair/utils/parse/cwe.py
epicosy/cgc-repair
f347a29ef482019563402684e585080836d3d093
[ "MIT" ]
null
null
null
import itertools from typing import Union, List import pandas as pd import re CWE_REGEX = r'CWE-\d{1,4}' PARENT_CWE = r"^::NATURE:ChildOf:CWE ID:(\d{1,4}):" PRECEDE_CWE = r"::NATURE:CanPrecede:CWE ID:(\d{1,4}):" PEER_CWE = r"::NATURE:PeerOf:CWE ID:(\d{1,4}):" ALIAS_CWE = r"::NATURE:CanAlsoBe:CWE ID:(\d{1,4}):" # TODO: fix this path, can not remain like this cwe_dict = pd.read_csv('/usr/local/share/cwe_dict.csv', index_col=False) cwe_dict.rename(columns={'CWE-ID': 'cwe_id', 'Name': 'name', 'Related Weaknesses': 'relatives'}, inplace=True) no_null_relatives = cwe_dict[cwe_dict.relatives.notnull()] cwe_alias = {} cwe_precedents = {} def cwe_from_info(description: str) -> list: return re.findall(CWE_REGEX, description) def most_common(lst: list): return max(itertools.groupby(sorted(lst)), key=lambda tup: (len(list(tup[1])), -lst.index(tup[0])))[0] def populate_relatives(): def get_aliases(relatives: str): return re.findall(ALIAS_CWE, relatives) def get_peers(relatives: str): return re.findall(PEER_CWE, relatives) def get_precedents(relatives: str): return re.findall(PRECEDE_CWE, relatives) for i, row in no_null_relatives.iterrows(): aliases = get_aliases(row.relatives) peers = get_peers(row.relatives) precedents = get_precedents(row.relatives) if aliases: for alias in aliases: cwe_alias[int(alias)] = row.cwe_id if peers: for peer in peers: cwe_alias[int(peer)] = row.cwe_id if precedents: for precedent in precedents: cwe_precedents[int(precedent)] = row.cwe_id def match_parent_cwe(related_weakness: str) -> Union[int, None]: match = re.match(PARENT_CWE, related_weakness) if match: return int(match.group(1)) else: return None def get_parent(cwe_id: int) -> Union[int, None]: if not cwe_id: return None cwe_row = no_null_relatives[no_null_relatives.cwe_id == cwe_id] if cwe_row.empty: return None return match_parent_cwe(cwe_row.relatives.values[0]) def top_parent(cwe_id: int, previous: int = None, count: int = 2, depth: int = 0) -> int: if not cwe_id: return previous if depth == count: if cwe_id: return cwe_id return previous parent = get_parent(cwe_id) alias = cwe_alias.get(cwe_id) if parent: return top_parent(parent, cwe_id, count, depth + 1) elif alias: return top_parent(alias, cwe_id, count, depth + 1) else: return cwe_id def get_name(cwe_id: int) -> str: if not cwe_id: return "" cwe_row = cwe_dict[cwe_dict.cwe_id == cwe_id] if cwe_row.empty: return "" return cwe_row.name.values[0] def main_cwe(cwe_ids: List[int], count: int): if not cwe_ids: return None parents = [top_parent(cwe_id, count=count) for cwe_id in cwe_ids] if len(parents) > 1: return most_common(parents) return parents[0] populate_relatives()
25.221311
110
0.646409
7cc1ba6fcd7109c4840283fc4721084c9585eba6
2,651
py
Python
suii_mux_manager_comm/src/suii_mux_manager_comm/yaml_handler.py
RoboHubEindhoven/suii_control
312114ca878d8659e04a1ae8f1cfe7454dd9d060
[ "BSD-3-Clause" ]
null
null
null
suii_mux_manager_comm/src/suii_mux_manager_comm/yaml_handler.py
RoboHubEindhoven/suii_control
312114ca878d8659e04a1ae8f1cfe7454dd9d060
[ "BSD-3-Clause" ]
null
null
null
suii_mux_manager_comm/src/suii_mux_manager_comm/yaml_handler.py
RoboHubEindhoven/suii_control
312114ca878d8659e04a1ae8f1cfe7454dd9d060
[ "BSD-3-Clause" ]
null
null
null
from yaml_objects import * import yaml import string ## ===== YAMLReader ===== ## # Input: Path # Output: A parsed YAML Object class YAMLReader(): @staticmethod def load(path): yaml_file = None with open(path, 'r') as stream: try: yaml_file = yaml.safe_load(stream) except yaml.YAMLError as exc: print(exc) return None return yaml_file ## ===== YAMLHandler ===== ## # Get specific data from YAML class YAMLHandler(): def __init__(self, path=""): self.path = path self.data = None def load(self): if (self.path != ""): self.data = YAMLReader.load(self.path) return self.data != None def get_table_height_for (self, destination_str): # Data is a dictionary of waypoints # With baby dictionaries inside for waypoint_key, waypoint_value in self.data.items(): if (waypoint_value['location']['description'] == destination_str): return waypoint_value['service_area']['height'] return -1 def get_pose_for(self, destination_str): # Data is a dictionary of waypoints # With baby dictionaries inside for waypoint_key, waypoint_value in self.data.items(): if (waypoint_value['location']['description'] == destination_str): orientation = [waypoint_value['pose']['orientation']['w'], waypoint_value['pose']['orientation']['x'], waypoint_value['pose']['orientation']['y'], waypoint_value['pose']['orientation']['z']] position = [waypoint_value['pose']['position']['x'], waypoint_value['pose']['position']['y'], waypoint_value['pose']['position']['z']] return orientation, position return None, None # for task in self.data: # if (type(task) == NavigationTask): # lower_string = string.lower(task.destination.description) # if (lower_string == string.lower(destination_str)): # # w, x, y, z # orientation = [task.destination.orientation.w, task.destination.orientation.x, # task.destination.orientation.y, task.destination.orientation.z] # # x, y, z # position = [task.destination.position.x, task.destination.position.y, task.destination.position.z] # return orientation, position # return None, None
40.784615
120
0.550358
ff4b18b3c9d2186f572be37fecdb37eb10a9c7c1
704
py
Python
day7/day7.py
hmcc/advent-of-code-2021
6f9a2fc713901ca03eecf90fb0c2fa183a2b323f
[ "MIT" ]
null
null
null
day7/day7.py
hmcc/advent-of-code-2021
6f9a2fc713901ca03eecf90fb0c2fa183a2b323f
[ "MIT" ]
null
null
null
day7/day7.py
hmcc/advent-of-code-2021
6f9a2fc713901ca03eecf90fb0c2fa183a2b323f
[ "MIT" ]
null
null
null
def read_input(filename): with open(filename) as file: positions = [int(n) for n in file.readline().strip().split(',')] return positions def triangle(n): return int(n * (n + 1) / 2) def cost_one(positions, position): return sum(abs(x - position) for x in positions) def cost_two(positions, position): return sum(triangle(abs(x - position)) for x in positions) def solve(positions, cost_fn): costs = [cost_fn(positions, idx) for idx, _ in enumerate(positions)] return min(costs) def part_one(filename): return solve(read_input(filename), cost_one) def part_two(filename): return solve(read_input(filename), cost_two) print(part_two('day7/input'))
21.333333
72
0.68608
98a0b099b8c881469b789098bd6c6fb63efcdf6c
23,453
py
Python
oss2/resumable.py
perfectworks/aliyun-oss-python-sdk
026257304c23e9717c608486f9fa8b981ef569fc
[ "MIT" ]
null
null
null
oss2/resumable.py
perfectworks/aliyun-oss-python-sdk
026257304c23e9717c608486f9fa8b981ef569fc
[ "MIT" ]
null
null
null
oss2/resumable.py
perfectworks/aliyun-oss-python-sdk
026257304c23e9717c608486f9fa8b981ef569fc
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- """ oss2.resumable ~~~~~~~~~~~~~~ 该模块包含了断点续传相关的函数和类。 """ import os from . import utils from . import iterators from . import exceptions from . import defaults from .models import PartInfo from .compat import json, stringify, to_unicode from .task_queue import TaskQueue import functools import threading import random import string import logging import shutil _MAX_PART_COUNT = 10000 _MIN_PART_SIZE = 100 * 1024 logger = logging.getLogger(__name__) def resumable_upload(bucket, key, filename, store=None, headers=None, multipart_threshold=None, part_size=None, progress_callback=None, num_threads=None): """断点上传本地文件。 实现中采用分片上传方式上传本地文件,缺省的并发数是 `oss2.defaults.multipart_num_threads` ,并且在 本地磁盘保存已经上传的分片信息。如果因为某种原因上传被中断,下次上传同样的文件,即源文件和目标文件路径都 一样,就只会上传缺失的分片。 缺省条件下,该函数会在用户 `HOME` 目录下保存断点续传的信息。当待上传的本地文件没有发生变化, 且目标文件名没有变化时,会根据本地保存的信息,从断点开始上传。 :param bucket: :class:`Bucket <oss2.Bucket>` 对象 :param key: 上传到用户空间的文件名 :param filename: 待上传本地文件名 :param store: 用来保存断点信息的持久存储,参见 :class:`ResumableStore` 的接口。如不指定,则使用 `ResumableStore` 。 :param headers: 传给 `put_object` 或 `init_multipart_upload` 的HTTP头部 :param multipart_threshold: 文件长度大于该值时,则用分片上传。 :param part_size: 指定分片上传的每个分片的大小。如不指定,则自动计算。 :param progress_callback: 上传进度回调函数。参见 :ref:`progress_callback` 。 :param num_threads: 并发上传的线程数,如不指定则使用 `oss2.defaults.multipart_num_threads` 。 """ size = os.path.getsize(filename) multipart_threshold = defaults.get(multipart_threshold, defaults.multipart_threshold) if size >= multipart_threshold: uploader = _ResumableUploader(bucket, key, filename, size, store, part_size=part_size, headers=headers, progress_callback=progress_callback, num_threads=num_threads) result = uploader.upload() else: with open(to_unicode(filename), 'rb') as f: result = bucket.put_object(key, f, headers=headers, progress_callback=progress_callback) return result def resumable_download(bucket, key, filename, multiget_threshold=None, part_size=None, progress_callback=None, num_threads=None, store=None): """断点下载。 实现的方法是: #. 在本地创建一个临时文件,文件名由原始文件名加上一个随机的后缀组成; #. 通过指定请求的 `Range` 头按照范围并发读取OSS文件,并写入到临时文件里对应的位置; #. 全部完成之后,把临时文件重命名为目标文件 (即 `filename` ) 在上述过程中,断点信息,即已经完成的范围,会保存在磁盘上。因为某种原因下载中断,后续如果下载 同样的文件,也就是源文件和目标文件一样,就会先读取断点信息,然后只下载缺失的部分。 缺省设置下,断点信息保存在 `HOME` 目录的一个子目录下。可以通过 `store` 参数更改保存位置。 使用该函数应注意如下细节: #. 对同样的源文件、目标文件,避免多个程序(线程)同时调用该函数。因为断点信息会在磁盘上互相覆盖,或临时文件名会冲突。 #. 避免使用太小的范围(分片),即 `part_size` 不宜过小,建议大于或等于 `oss2.defaults.multiget_part_size` 。 #. 如果目标文件已经存在,那么该函数会覆盖此文件。 :param bucket: :class:`Bucket <oss2.Bucket>` 对象。 :param str key: 待下载的远程文件名。 :param str filename: 本地的目标文件名。 :param int multiget_threshold: 文件长度大于该值时,则使用断点下载。 :param int part_size: 指定期望的分片大小,即每个请求获得的字节数,实际的分片大小可能有所不同。 :param progress_callback: 下载进度回调函数。参见 :ref:`progress_callback` 。 :param num_threads: 并发下载的线程数,如不指定则使用 `oss2.defaults.multiget_num_threads` 。 :param store: 用来保存断点信息的持久存储,可以指定断点信息所在的目录。 :type store: `ResumableDownloadStore` :raises: 如果OSS文件不存在,则抛出 :class:`NotFound <oss2.exceptions.NotFound>` ;也有可能抛出其他因下载文件而产生的异常。 """ multiget_threshold = defaults.get(multiget_threshold, defaults.multiget_threshold) result = bucket.head_object(key) if result.content_length >= multiget_threshold: downloader = _ResumableDownloader(bucket, key, filename, _ObjectInfo.make(result), part_size=part_size, progress_callback=progress_callback, num_threads=num_threads, store=store) downloader.download() else: bucket.get_object_to_file(key, filename, progress_callback=progress_callback) _MAX_MULTIGET_PART_COUNT = 100 def determine_part_size(total_size, preferred_size=None): """确定分片上传是分片的大小。 :param int total_size: 总共需要上传的长度 :param int preferred_size: 用户期望的分片大小。如果不指定则采用defaults.part_size :return: 分片大小 """ if not preferred_size: preferred_size = defaults.part_size return _determine_part_size_internal(total_size, preferred_size, _MAX_PART_COUNT) def _determine_part_size_internal(total_size, preferred_size, max_count): if total_size < preferred_size: return total_size if preferred_size * max_count < total_size: if total_size % max_count: return total_size // max_count + 1 else: return total_size // max_count else: return preferred_size def _split_to_parts(total_size, part_size): parts = [] num_parts = utils.how_many(total_size, part_size) for i in range(num_parts): if i == num_parts - 1: start = i * part_size end = total_size else: start = i * part_size end = part_size + start parts.append(_PartToProcess(i + 1, start, end)) return parts class _ResumableOperation(object): def __init__(self, bucket, key, filename, size, store, progress_callback=None): self.bucket = bucket self.key = key self.filename = filename self.size = size self._abspath = os.path.abspath(filename) self.__store = store self.__record_key = self.__store.make_store_key(bucket.bucket_name, key, self._abspath) logger.info('key is {0}'.format(self.__record_key)) # protect self.__progress_callback self.__plock = threading.Lock() self.__progress_callback = progress_callback def _del_record(self): self.__store.delete(self.__record_key) def _put_record(self, record): self.__store.put(self.__record_key, record) def _get_record(self): return self.__store.get(self.__record_key) def _report_progress(self, consumed_size): if self.__progress_callback: with self.__plock: self.__progress_callback(consumed_size, self.size) class _ObjectInfo(object): def __init__(self): self.size = None self.etag = None self.mtime = None @staticmethod def make(head_object_result): objectInfo = _ObjectInfo() objectInfo.size = head_object_result.content_length objectInfo.etag = head_object_result.etag objectInfo.mtime = head_object_result.last_modified return objectInfo class _ResumableDownloader(_ResumableOperation): def __init__(self, bucket, key, filename, objectInfo, part_size=None, store=None, progress_callback=None, num_threads=None): super(_ResumableDownloader, self).__init__(bucket, key, filename, objectInfo.size, store or ResumableDownloadStore(), progress_callback=progress_callback) self.objectInfo = objectInfo self.__part_size = defaults.get(part_size, defaults.multiget_part_size) self.__part_size = _determine_part_size_internal(self.size, self.__part_size, _MAX_MULTIGET_PART_COUNT) self.__tmp_file = None self.__num_threads = defaults.get(num_threads, defaults.multiget_num_threads) self.__finished_parts = None self.__finished_size = None # protect record self.__lock = threading.Lock() self.__record = None def download(self): self.__load_record() parts_to_download = self.__get_parts_to_download() # create tmp file if it is does not exist open(self.__tmp_file, 'a').close() q = TaskQueue(functools.partial(self.__producer, parts_to_download=parts_to_download), [self.__consumer] * self.__num_threads) q.run() utils.force_rename(self.__tmp_file, self.filename) self._report_progress(self.size) self._del_record() def __producer(self, q, parts_to_download=None): for part in parts_to_download: q.put(part) def __consumer(self, q): while q.ok(): part = q.get() if part is None: break self.__download_part(part) def __download_part(self, part): self._report_progress(self.__finished_size) with open(self.__tmp_file, 'rb+') as f: f.seek(part.start, os.SEEK_SET) headers = {'If-Match': self.objectInfo.etag, 'If-Unmodified-Since': utils.http_date(self.objectInfo.mtime)} result = self.bucket.get_object(self.key, byte_range=(part.start, part.end - 1), headers=headers) utils.copyfileobj_and_verify(result, f, part.end - part.start, request_id=result.request_id) self.__finish_part(part) def __load_record(self): record = self._get_record() if record and not self.is_record_sane(record): self._del_record() record = None if record and not os.path.exists(self.filename + record['tmp_suffix']): self._del_record() record = None if record and self.__is_remote_changed(record): utils.silently_remove(self.filename + record['tmp_suffix']) self._del_record() record = None if not record: record = {'mtime': self.objectInfo.mtime, 'etag': self.objectInfo.etag, 'size': self.objectInfo.size, 'bucket': self.bucket.bucket_name, 'key': self.key, 'part_size': self.__part_size, 'tmp_suffix': self.__gen_tmp_suffix(), 'abspath': self._abspath, 'parts': []} self._put_record(record) self.__tmp_file = self.filename + record['tmp_suffix'] self.__part_size = record['part_size'] self.__finished_parts = list(_PartToProcess(p['part_number'], p['start'], p['end']) for p in record['parts']) self.__finished_size = sum(p.size for p in self.__finished_parts) self.__record = record def __get_parts_to_download(self): assert self.__record all_set = set(_split_to_parts(self.size, self.__part_size)) finished_set = set(self.__finished_parts) return sorted(list(all_set - finished_set), key=lambda p: p.part_number) @staticmethod def is_record_sane(record): try: for key in ('etag', 'tmp_suffix', 'abspath', 'bucket', 'key'): if not isinstance(record[key], str): logger.info('{0} is not a string: {1}, but {2}'.format(key, record[key], record[key].__class__)) return False for key in ('part_size', 'size', 'mtime'): if not isinstance(record[key], int): logger.info('{0} is not an integer: {1}, but {2}'.format(key, record[key], record[key].__class__)) return False for key in ('parts'): if not isinstance(record['parts'], list): logger.info('{0} is not a list: {1}, but {2}'.format(key, record[key], record[key].__class__)) return False except KeyError as e: logger.info('Key not found: {0}'.format(e.args)) return False return True def __is_remote_changed(self, record): return (record['mtime'] != self.objectInfo.mtime or record['size'] != self.objectInfo.size or record['etag'] != self.objectInfo.etag) def __finish_part(self, part): logger.debug('finishing part: part_number={0}, start={1}, end={2}'.format(part.part_number, part.start, part.end)) with self.__lock: self.__finished_parts.append(part) self.__finished_size += part.size self.__record['parts'].append({'part_number': part.part_number, 'start': part.start, 'end': part.end}) self._put_record(self.__record) def __gen_tmp_suffix(self): return '.tmp-' + ''.join(random.choice(string.ascii_lowercase) for i in range(12)) class _ResumableUploader(_ResumableOperation): """以断点续传方式上传文件。 :param bucket: :class:`Bucket <oss2.Bucket>` 对象 :param key: 文件名 :param filename: 待上传的文件名 :param size: 文件总长度 :param store: 用来保存进度的持久化存储 :param headers: 传给 `init_multipart_upload` 的HTTP头部 :param part_size: 分片大小。优先使用用户提供的值。如果用户没有指定,那么对于新上传,计算出一个合理值;对于老的上传,采用第一个 分片的大小。 :param progress_callback: 上传进度回调函数。参见 :ref:`progress_callback` 。 """ def __init__(self, bucket, key, filename, size, store=None, headers=None, part_size=None, progress_callback=None, num_threads=None): super(_ResumableUploader, self).__init__(bucket, key, filename, size, store or ResumableStore(), progress_callback=progress_callback) self.__headers = headers self.__part_size = defaults.get(part_size, defaults.part_size) self.__mtime = os.path.getmtime(filename) self.__num_threads = defaults.get(num_threads, defaults.multipart_num_threads) self.__upload_id = None # protect below fields self.__lock = threading.Lock() self.__record = None self.__finished_size = 0 self.__finished_parts = None def upload(self): self.__load_record() parts_to_upload = self.__get_parts_to_upload(self.__finished_parts) parts_to_upload = sorted(parts_to_upload, key=lambda p: p.part_number) q = TaskQueue(functools.partial(self.__producer, parts_to_upload=parts_to_upload), [self.__consumer] * self.__num_threads) q.run() self._report_progress(self.size) result = self.bucket.complete_multipart_upload(self.key, self.__upload_id, self.__finished_parts) self._del_record() return result def __producer(self, q, parts_to_upload=None): for part in parts_to_upload: q.put(part) def __consumer(self, q): while True: part = q.get() if part is None: break self.__upload_part(part) def __upload_part(self, part): with open(to_unicode(self.filename), 'rb') as f: self._report_progress(self.__finished_size) f.seek(part.start, os.SEEK_SET) result = self.bucket.upload_part(self.key, self.__upload_id, part.part_number, utils.SizedFileAdapter(f, part.size)) self.__finish_part(PartInfo(part.part_number, result.etag, size=part.size)) def __finish_part(self, part_info): with self.__lock: self.__finished_parts.append(part_info) self.__finished_size += part_info.size self.__record['parts'].append({'part_number': part_info.part_number, 'etag': part_info.etag}) self._put_record(self.__record) def __load_record(self): record = self._get_record() if record and not _is_record_sane(record): self._del_record() record = None if record and self.__file_changed(record): logger.debug('{0} was changed, clear the record.'.format(self.filename)) self._del_record() record = None if record and not self.__upload_exists(record['upload_id']): logger.debug('{0} upload not exist, clear the record.'.format(record['upload_id'])) self._del_record() record = None if not record: part_size = determine_part_size(self.size, self.__part_size) upload_id = self.bucket.init_multipart_upload(self.key, headers=self.__headers).upload_id record = {'upload_id': upload_id, 'mtime': self.__mtime, 'size': self.size, 'parts': [], 'abspath': self._abspath, 'bucket': self.bucket.bucket_name, 'key': self.key, 'part_size': part_size} logger.debug('put new record upload_id={0} part_size={1}'.format(upload_id, part_size)) self._put_record(record) self.__record = record self.__part_size = self.__record['part_size'] self.__upload_id = self.__record['upload_id'] self.__finished_parts = self.__get_finished_parts() self.__finished_size = sum(p.size for p in self.__finished_parts) def __get_finished_parts(self): last_part_number = utils.how_many(self.size, self.__part_size) parts = [] for p in self.__record['parts']: part_info = PartInfo(int(p['part_number']), p['etag']) if part_info.part_number == last_part_number: part_info.size = self.size % self.__part_size else: part_info.size = self.__part_size parts.append(part_info) return parts def __upload_exists(self, upload_id): try: list(iterators.PartIterator(self.bucket, self.key, upload_id, '0', max_parts=1)) except exceptions.NoSuchUpload: return False else: return True def __file_changed(self, record): return record['mtime'] != self.__mtime or record['size'] != self.size def __get_parts_to_upload(self, parts_uploaded): all_parts = _split_to_parts(self.size, self.__part_size) if not parts_uploaded: return all_parts all_parts_map = dict((p.part_number, p) for p in all_parts) for uploaded in parts_uploaded: if uploaded.part_number in all_parts_map: del all_parts_map[uploaded.part_number] return all_parts_map.values() _UPLOAD_TEMP_DIR = '.py-oss-upload' _DOWNLOAD_TEMP_DIR = '.py-oss-download' class _ResumableStoreBase(object): def __init__(self, root, dir): self.dir = os.path.join(root, dir) if os.path.isdir(self.dir): return utils.makedir_p(self.dir) def get(self, key): pathname = self.__path(key) logger.debug('get key={0}, pathname={1}'.format(key, pathname)) if not os.path.exists(pathname): return None # json.load()返回的总是unicode,对于Python2,我们将其转换 # 为str。 try: with open(to_unicode(pathname), 'r') as f: content = json.load(f) except ValueError: os.remove(pathname) return None else: return stringify(content) def put(self, key, value): pathname = self.__path(key) with open(to_unicode(pathname), 'w') as f: json.dump(value, f) logger.debug('put key={0}, pathname={1}'.format(key, pathname)) def delete(self, key): pathname = self.__path(key) os.remove(pathname) logger.debug('del key={0}, pathname={1}'.format(key, pathname)) def __path(self, key): return os.path.join(self.dir, key) def _normalize_path(path): return os.path.normpath(os.path.normcase(path)) class ResumableStore(_ResumableStoreBase): """保存断点上传断点信息的类。 每次上传的信息会保存在 `root/dir/` 下面的某个文件里。 :param str root: 父目录,缺省为HOME :param str dir: 子目录,缺省为 `_UPLOAD_TEMP_DIR` """ def __init__(self, root=None, dir=None): super(ResumableStore, self).__init__(root or os.path.expanduser('~'), dir or _UPLOAD_TEMP_DIR) @staticmethod def make_store_key(bucket_name, key, filename): filepath = _normalize_path(filename) oss_pathname = 'oss://{0}/{1}'.format(bucket_name, key) return utils.md5_string(oss_pathname) + '-' + utils.md5_string(filepath) class ResumableDownloadStore(_ResumableStoreBase): """保存断点下载断点信息的类。 每次下载的断点信息会保存在 `root/dir/` 下面的某个文件里。 :param str root: 父目录,缺省为HOME :param str dir: 子目录,缺省为 `_DOWNLOAD_TEMP_DIR` """ def __init__(self, root=None, dir=None): super(ResumableDownloadStore, self).__init__(root or os.path.expanduser('~'), dir or _DOWNLOAD_TEMP_DIR) @staticmethod def make_store_key(bucket_name, key, filename): filepath = _normalize_path(filename) oss_pathname = 'oss://{0}/{1}'.format(bucket_name, key) return utils.md5_string(oss_pathname) + '-' + utils.md5_string(filepath) + '-download' def make_upload_store(root=None, dir=None): return ResumableStore(root=root, dir=dir) def make_download_store(root=None, dir=None): return ResumableDownloadStore(root=root, dir=dir) def _rebuild_record(filename, store, bucket, key, upload_id, part_size=None): abspath = os.path.abspath(filename) mtime = os.path.getmtime(filename) size = os.path.getsize(filename) store_key = store.make_store_key(bucket.bucket_name, key, abspath) record = {'upload_id': upload_id, 'mtime': mtime, 'size': size, 'parts': [], 'abspath': abspath, 'key': key} for p in iterators.PartIterator(bucket, key, upload_id): record['parts'].append({'part_number': p.part_number, 'etag': p.etag}) if not part_size: part_size = p.size record['part_size'] = part_size store.put(store_key, record) def _is_record_sane(record): try: for key in ('upload_id', 'abspath', 'key'): if not isinstance(record[key], str): logger.info('{0} is not a string: {1}, but {2}'.format(key, record[key], record[key].__class__)) return False for key in ('size', 'part_size'): if not isinstance(record[key], int): logger.info('{0} is not an integer: {1}'.format(key, record[key])) return False if not isinstance(record['mtime'], int) and not isinstance(record['mtime'], float): logger.info('mtime is not a float or an integer: {0}'.format(record['mtime'])) return False if not isinstance(record['parts'], list): logger.info('parts is not a list: {0}'.format(record['parts'].__class__.__name__)) return False except KeyError as e: logger.info('Key not found: {0}'.format(e.args)) return False return True class _PartToProcess(object): def __init__(self, part_number, start, end): self.part_number = part_number self.start = start self.end = end @property def size(self): return self.end - self.start def __hash__(self): return hash(self.__key()) def __eq__(self, other): return self.__key() == other.__key() def __key(self): return (self.part_number, self.start, self.end)
33.504286
122
0.617405
745667a9b9bb8b69e1c8090a4c931640a3539d7b
2,156
py
Python
tools/c7n_azure/c7n_azure/resources/key_vault_certificate.py
dnouri/cloud-custodian
4e8b3b45f60731df942ffe6b61645416d7a67daa
[ "Apache-2.0" ]
8
2021-05-18T02:22:03.000Z
2021-09-11T02:49:04.000Z
tools/c7n_azure/c7n_azure/resources/key_vault_certificate.py
dnouri/cloud-custodian
4e8b3b45f60731df942ffe6b61645416d7a67daa
[ "Apache-2.0" ]
79
2019-03-20T12:27:06.000Z
2019-08-14T14:07:04.000Z
tools/c7n_azure/c7n_azure/resources/key_vault_certificate.py
dnouri/cloud-custodian
4e8b3b45f60731df942ffe6b61645416d7a67daa
[ "Apache-2.0" ]
3
2017-09-21T13:36:46.000Z
2021-09-20T16:38:29.000Z
# Copyright 2019 Microsoft Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.from c7n_azure.provider import resources import logging from c7n_azure import constants from c7n_azure.provider import resources from c7n_azure.query import ChildResourceManager, ChildTypeInfo from c7n_azure.utils import generate_key_vault_url log = logging.getLogger('custodian.azure.keyvault.certificates') @resources.register('keyvault-certificate') class KeyVaultCertificate(ChildResourceManager): """Key Vault Certificate Resource :example: This policy will find all certificates that will expire in next 30 days .. code-block:: yaml policies: - name: keyvault-certificates description: List all certificates expiring in next 30 days resource: azure.keyvault-certificate filters: - type: value key: attributes.exp value_type: expiration op: lt value: 30 """ class resource_type(ChildTypeInfo): doc_groups = ['Security'] resource = constants.RESOURCE_VAULT service = 'azure.keyvault' client = 'KeyVaultClient' enum_spec = (None, 'get_certificates', None) parent_manager_name = 'keyvault' raise_on_exception = False default_report_fields = ( 'id', 'attributes.expires' ) @classmethod def extra_args(cls, parent_resource): return {'vault_base_url': generate_key_vault_url(parent_resource['name'])}
31.705882
87
0.661874
856b23fdd78fd53a8d4f18a2322f7c480d672bbd
2,199
py
Python
tests/test_misty2py_skills.py
ChrisScarred/misty2py-skills
30557d246b91fb525866fe8b92e280d2609ca26b
[ "MIT" ]
null
null
null
tests/test_misty2py_skills.py
ChrisScarred/misty2py-skills
30557d246b91fb525866fe8b92e280d2609ca26b
[ "MIT" ]
null
null
null
tests/test_misty2py_skills.py
ChrisScarred/misty2py-skills
30557d246b91fb525866fe8b92e280d2609ca26b
[ "MIT" ]
null
null
null
from misty2py.utils.utils import get_misty def test_angry_expression(capsys): from misty2py_skills.expressions import angry_expression with capsys.disabled(): result = angry_expression(get_misty()) print(result) assert result.get("overall_success") def test_battery_printer(capsys): from misty2py_skills.demonstrations.battery_printer import battery_printer with capsys.disabled(): result = battery_printer(get_misty(), 2) print(result) assert result.get("overall_success") def test_explore(capsys): from misty2py_skills.demonstrations import explore with capsys.disabled(): explore.explore() # assert True to show that the program does not crash during the interaction assert True def test_face_recognition(capsys): from misty2py_skills import face_recognition with capsys.disabled(): result = face_recognition.face_recognition(get_misty()) print(result) assert result.get("overall_success") def test_free_memory(capsys): from misty2py.basic_skills.free_memory import free_memory with capsys.disabled(): result = free_memory(get_misty(), "data") print(result) assert result.get("overall_success") def test_hey_misty(capsys): from misty2py_skills import hey_misty with capsys.disabled(): result = hey_misty.greet() print(result) assert result.get("overall_success") def test_remote_control(capsys): from misty2py_skills import remote_control with capsys.disabled(): result = remote_control.remote_control() print(result) assert result.get("overall_success") def test_listening_expression(capsys): from misty2py_skills.expressions import listening_expression with capsys.disabled(): result = listening_expression(get_misty()) print(result) assert result.get("overall_success") def test_question_answering(capsys): from misty2py_skills import question_answering with capsys.disabled(): result = question_answering.question_answering() print(result) assert result.get("overall_success")
26.493976
84
0.712597
237e1d8f0f92c344aa087bd9421beeff3f4fcb17
1,265
py
Python
model-optimizer/extensions/front/onnx/affine_ext.py
zhoub/dldt
e42c01cf6e1d3aefa55e2c5df91f1054daddc575
[ "Apache-2.0" ]
3
2020-02-09T23:25:37.000Z
2021-01-19T09:44:12.000Z
model-optimizer/extensions/front/onnx/affine_ext.py
zhoub/dldt
e42c01cf6e1d3aefa55e2c5df91f1054daddc575
[ "Apache-2.0" ]
null
null
null
model-optimizer/extensions/front/onnx/affine_ext.py
zhoub/dldt
e42c01cf6e1d3aefa55e2c5df91f1054daddc575
[ "Apache-2.0" ]
2
2020-04-18T16:24:39.000Z
2021-01-19T09:42:19.000Z
""" Copyright (c) 2018-2019 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import numpy as np from mo.front.extractor import FrontExtractorOp from mo.front.onnx.extractors.utils import onnx_attr class AffineFrontExtractor(FrontExtractorOp): # Affine operation will be transformed to ImageScalar and further will be converted to Mul->Add seq op = 'Affine' enabled = True @staticmethod def extract(node): dst_type = lambda x: np.array(x) scale = onnx_attr(node, 'alpha', 'f', default=None, dst_type=dst_type) bias = onnx_attr(node, 'beta', 'f', default=None, dst_type=dst_type) node['scale'] = scale node['bias'] = bias node['op'] = 'ImageScaler' return __class__.enabled
31.625
103
0.716996
2a3e28b6d197a36d057d95175237844dd85ecc45
1,086
py
Python
python/demos/binomialBetaPosteriorDemo.py
qyxiao/pmt
87513794fc43f8aa1f4f3d7588fa45ffc75d1a44
[ "MIT" ]
null
null
null
python/demos/binomialBetaPosteriorDemo.py
qyxiao/pmt
87513794fc43f8aa1f4f3d7588fa45ffc75d1a44
[ "MIT" ]
null
null
null
python/demos/binomialBetaPosteriorDemo.py
qyxiao/pmt
87513794fc43f8aa1f4f3d7588fa45ffc75d1a44
[ "MIT" ]
null
null
null
#!/usr/bin/env python # Plots Beta-Binomial distribution along with the prior and likelihood. import matplotlib.pyplot as pl import numpy as np import scipy from scipy.stats import beta alphas = [2, 2, 1, 1] betas = [2, 2, 1, 1] Ns = [4, 40, 4, 40] ks = [1, 10, 1, 10] plots = ['betaPostInfSmallSample', 'betaPostInfLargeSample', 'betaPostUninfSmallSample', 'betaPostUninfLargeSample'] x = np.linspace(0.001, 0.999, 50) for i in range(len(plots)): alpha_prior = alphas[i] beta_prior = betas[i] N = Ns[i] k = ks[i] alpha_post = alpha_prior + N - k beta_post = beta_prior + k alpha_lik = N - k + 1 beta_lik = k + 1 pl.plot(x, beta.pdf(x, alpha_prior, beta_prior), 'r-', label='prior Be(%2.1f, %2.1f)' % (alpha_prior, beta_prior)) pl.plot(x, beta.pdf(x, alpha_lik, beta_lik), 'k:', label='lik Be(%2.1f, %2.1f)' % (alpha_lik, beta_lik)) pl.plot(x, beta.pdf(x, alpha_post, beta_post), 'b-', label='post Be(%2.1f, %2.1f)' % (alpha_post, beta_post)) pl.legend(loc='upper left') pl.savefig(plots[i] + '.png') pl.show()
29.351351
71
0.631676
76e88d20f46632206ded5388ace04ed05d17a536
1,867
py
Python
test/test_list_unspent_transaction_outputs_by_address_e400.py
Crypto-APIs/Crypto_APIs_2.0_SDK_Python
c59ebd914850622b2c6500c4c30af31fb9cecf0e
[ "MIT" ]
5
2021-05-17T04:45:03.000Z
2022-03-23T12:51:46.000Z
test/test_list_unspent_transaction_outputs_by_address_e400.py
Crypto-APIs/Crypto_APIs_2.0_SDK_Python
c59ebd914850622b2c6500c4c30af31fb9cecf0e
[ "MIT" ]
null
null
null
test/test_list_unspent_transaction_outputs_by_address_e400.py
Crypto-APIs/Crypto_APIs_2.0_SDK_Python
c59ebd914850622b2c6500c4c30af31fb9cecf0e
[ "MIT" ]
2
2021-06-02T07:32:26.000Z
2022-02-12T02:36:23.000Z
""" CryptoAPIs Crypto APIs 2.0 is a complex and innovative infrastructure layer that radically simplifies the development of any Blockchain and Crypto related applications. Organized around REST, Crypto APIs 2.0 can assist both novice Bitcoin/Ethereum enthusiasts and crypto experts with the development of their blockchain applications. Crypto APIs 2.0 provides unified endpoints and data, raw data, automatic tokens and coins forwardings, callback functionalities, and much more. # noqa: E501 The version of the OpenAPI document: 2.0.0 Contact: [email protected] Generated by: https://openapi-generator.tech """ import sys import unittest import cryptoapis from cryptoapis.model.banned_ip_address_details import BannedIpAddressDetails from cryptoapis.model.invalid_pagination import InvalidPagination from cryptoapis.model.limit_greater_than_allowed import LimitGreaterThanAllowed from cryptoapis.model.uri_not_found import UriNotFound globals()['BannedIpAddressDetails'] = BannedIpAddressDetails globals()['InvalidPagination'] = InvalidPagination globals()['LimitGreaterThanAllowed'] = LimitGreaterThanAllowed globals()['UriNotFound'] = UriNotFound from cryptoapis.model.list_unspent_transaction_outputs_by_address_e400 import ListUnspentTransactionOutputsByAddressE400 class TestListUnspentTransactionOutputsByAddressE400(unittest.TestCase): """ListUnspentTransactionOutputsByAddressE400 unit test stubs""" def setUp(self): pass def tearDown(self): pass def testListUnspentTransactionOutputsByAddressE400(self): """Test ListUnspentTransactionOutputsByAddressE400""" # FIXME: construct object with mandatory attributes with example values # model = ListUnspentTransactionOutputsByAddressE400() # noqa: E501 pass if __name__ == '__main__': unittest.main()
41.488889
484
0.80075
35485b3f9dcfb6cea090bfa650ee857cd81cb7ab
8,137
py
Python
gewittergefahr/gg_io/grib_io_test.py
dopplerchase/GewitterGefahr
4415b08dd64f37eba5b1b9e8cc5aa9af24f96593
[ "MIT" ]
26
2018-10-04T01:07:35.000Z
2022-01-29T08:49:32.000Z
gewittergefahr/gg_io/grib_io_test.py
liuximarcus/GewitterGefahr
d819874d616f98a25187bfd3091073a2e6d5279e
[ "MIT" ]
4
2017-12-25T02:01:08.000Z
2018-12-19T01:54:21.000Z
gewittergefahr/gg_io/grib_io_test.py
liuximarcus/GewitterGefahr
d819874d616f98a25187bfd3091073a2e6d5279e
[ "MIT" ]
11
2017-12-10T23:05:29.000Z
2022-01-29T08:49:33.000Z
"""Unit tests for grib_io.py.""" import copy import unittest import numpy from gewittergefahr.gg_io import grib_io TOLERANCE = 1e-6 GRIB1_FILE_NAME = 'foo.grb' GRIB2_FILE_NAME = 'foo.grb2' NON_GRIB_FILE_NAME = 'foo.txt' SPECIFIC_HUMIDITY_NAME_GRIB1 = 'SPFH:500 mb' SPECIFIC_HUMIDITY_NAME_GRIB2 = 'SPFH:500 mb' HEIGHT_NAME_GRIB1 = 'HGT:sfc' HEIGHT_NAME_GRIB2 = 'HGT:surface' TEMPERATURE_NAME_GRIB1 = 'TMP:2 m above gnd' TEMPERATURE_NAME_GRIB2 = 'TMP:2 m above ground' SENTINEL_VALUE = 9.999e20 DATA_MATRIX_WITH_SENTINELS = numpy.array([ [999.25, 1000., 1001., 1001.5, SENTINEL_VALUE], [999.5, 1000.75, 1002., 1002.2, SENTINEL_VALUE], [999.9, 1001.1, 1001.7, 1002.3, 1003.], [SENTINEL_VALUE, 1001.5, 1001.6, 1002.1, 1002.5], [SENTINEL_VALUE, SENTINEL_VALUE, 1002., 1002., 1003.3] ]) DATA_MATRIX_NO_SENTINELS = numpy.array([ [999.25, 1000., 1001., 1001.5, numpy.nan], [999.5, 1000.75, 1002., 1002.2, numpy.nan], [999.9, 1001.1, 1001.7, 1002.3, 1003.], [numpy.nan, 1001.5, 1001.6, 1002.1, 1002.5], [numpy.nan, numpy.nan, 1002., 1002., 1003.3] ]) U_WIND_NAME_GRIB1 = 'UGRD:500 mb' V_WIND_NAME_GRIB1 = 'VGRD:500 mb' NON_WIND_NAME_GRIB1 = 'TMP:500 mb' NON_GRIB_FILE_TYPE = 'text' class GribIoTests(unittest.TestCase): """Each method is a unit test for grib_io.py.""" def test_field_name_grib1_to_grib2_humidity(self): """Ensures correct output from _field_name_grib1_to_grib2. In this case the field is 500-mb specific humidity. """ self.assertEqual( grib_io._field_name_grib1_to_grib2(SPECIFIC_HUMIDITY_NAME_GRIB1), SPECIFIC_HUMIDITY_NAME_GRIB2) def test_field_name_grib1_to_grib2_height(self): """Ensures correct output from _field_name_grib1_to_grib2. In this case the field is surface geopotential height. """ self.assertEqual( grib_io._field_name_grib1_to_grib2(HEIGHT_NAME_GRIB1), HEIGHT_NAME_GRIB2) def test_field_name_grib1_to_grib2_temperature(self): """Ensures correct output from _field_name_grib1_to_grib2. In this case the field is 2-metre temperature. """ self.assertEqual( grib_io._field_name_grib1_to_grib2(TEMPERATURE_NAME_GRIB1), TEMPERATURE_NAME_GRIB2) def test_sentinel_value_to_nan_defined(self): """Ensures correct output from _sentinel_value_to_nan. In this case the sentinel value is defined. """ this_data_matrix = grib_io._sentinel_value_to_nan( data_matrix=copy.deepcopy(DATA_MATRIX_WITH_SENTINELS), sentinel_value=SENTINEL_VALUE) self.assertTrue(numpy.allclose( this_data_matrix, DATA_MATRIX_NO_SENTINELS, atol=TOLERANCE, equal_nan=True)) def test_sentinel_value_to_nan_none(self): """Ensures correct output from _sentinel_value_to_nan. In this case the sentinel value is None. """ this_data_matrix = grib_io._sentinel_value_to_nan( data_matrix=copy.deepcopy(DATA_MATRIX_WITH_SENTINELS), sentinel_value=None) self.assertTrue(numpy.allclose( this_data_matrix, DATA_MATRIX_WITH_SENTINELS, atol=TOLERANCE)) def test_check_file_type_grib1(self): """Ensures correct output from check_file_type. Here, file type is grib1. """ grib_io.check_file_type(grib_io.GRIB1_FILE_TYPE) def test_check_file_type_grib2(self): """Ensures correct output from check_file_type. Here, file type is grib2. """ grib_io.check_file_type(grib_io.GRIB2_FILE_TYPE) def test_check_file_type_invalid(self): """Ensures correct output from check_file_type. Here, file type is invalid. """ with self.assertRaises(ValueError): grib_io.check_file_type(NON_GRIB_FILE_TYPE) def test_file_name_to_type_grib1(self): """Ensures correct output from file_name_to_type. Here, file type is grib1. """ self.assertEqual(grib_io.file_name_to_type(GRIB1_FILE_NAME), grib_io.GRIB1_FILE_TYPE) def test_file_name_to_type_grib2(self): """Ensures correct output from file_name_to_type. Here, file type is grib2. """ self.assertEqual(grib_io.file_name_to_type(GRIB2_FILE_NAME), grib_io.GRIB2_FILE_TYPE) def test_file_name_to_type_invalid(self): """Ensures correct output from file_name_to_type. Here, file type is invalid. """ with self.assertRaises(ValueError): grib_io.file_name_to_type(NON_GRIB_FILE_NAME) def test_is_u_wind_field_true(self): """Ensures correct output from is_u_wind_field. In this case the input field is u-wind, so the answer is True. """ self.assertTrue(grib_io.is_u_wind_field(U_WIND_NAME_GRIB1)) def test_is_u_wind_field_v_wind(self): """Ensures correct output from is_u_wind_field. In this case the input field is v-wind, so the answer is False. """ self.assertFalse(grib_io.is_u_wind_field(V_WIND_NAME_GRIB1)) def test_is_u_wind_field_non_wind(self): """Ensures correct output from is_u_wind_field. In this case the input field is not wind-related, so the answer is False. """ self.assertFalse(grib_io.is_u_wind_field(NON_WIND_NAME_GRIB1)) def test_is_v_wind_field_true(self): """Ensures correct output from is_v_wind_field. In this case the input field is v-wind, so the answer is True. """ self.assertTrue(grib_io.is_v_wind_field(V_WIND_NAME_GRIB1)) def test_is_v_wind_field_u_wind(self): """Ensures correct output from is_v_wind_field. In this case the input field is u-wind, so the answer is False. """ self.assertFalse(grib_io.is_v_wind_field(U_WIND_NAME_GRIB1)) def test_is_v_wind_field_non_wind(self): """Ensures correct output from is_v_wind_field. In this case the input field is not wind-related, so the answer is False. """ self.assertFalse(grib_io.is_v_wind_field(NON_WIND_NAME_GRIB1)) def test_switch_uv_in_field_name_input_u(self): """Ensures correct output from switch_uv_in_field_name. In this case the input field is u-wind. """ self.assertTrue(grib_io.switch_uv_in_field_name(U_WIND_NAME_GRIB1) == V_WIND_NAME_GRIB1) def test_switch_uv_in_field_name_input_v(self): """Ensures correct output from switch_uv_in_field_name. In this case the input field is v-wind. """ self.assertTrue(grib_io.switch_uv_in_field_name(V_WIND_NAME_GRIB1) == U_WIND_NAME_GRIB1) def test_switch_uv_in_field_name_input_non_wind(self): """Ensures correct output from switch_uv_in_field_name. In this case the input field is not wind-related, so it should be unchanged. """ self.assertTrue(grib_io.switch_uv_in_field_name(NON_WIND_NAME_GRIB1) == NON_WIND_NAME_GRIB1) def test_file_type_to_extension_grib1(self): """Ensures correct output from file_type_to_extension. Here, file type is grib1. """ self.assertEqual( grib_io.file_type_to_extension(grib_io.GRIB1_FILE_TYPE), grib_io.GRIB1_FILE_EXTENSION) def test_file_type_to_extension_grib2(self): """Ensures correct output from file_type_to_extension. Here, file type is grib2. """ self.assertEqual( grib_io.file_type_to_extension(grib_io.GRIB2_FILE_TYPE), grib_io.GRIB2_FILE_EXTENSION) def test_file_type_to_extension_invalid(self): """Ensures correct output from file_type_to_extension. Here, file type is invalid. """ with self.assertRaises(ValueError): grib_io.file_type_to_extension(NON_GRIB_FILE_TYPE) if __name__ == '__main__': unittest.main()
30.590226
81
0.678506
9afc911dda00ed813996836b0ac624986b272fa8
681,532
py
Python
platform/python/MailChecker.py
polydice/mailchecker
85df82a90399ca6e4b320fe619b6199e836b4447
[ "MIT" ]
null
null
null
platform/python/MailChecker.py
polydice/mailchecker
85df82a90399ca6e4b320fe619b6199e836b4447
[ "MIT" ]
null
null
null
platform/python/MailChecker.py
polydice/mailchecker
85df82a90399ca6e4b320fe619b6199e836b4447
[ "MIT" ]
null
null
null
import re import sys if sys.version_info[0] >= 3: xrange = range class MailChecker(object): blacklist = 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valid_matcher = re.compile(r"\A(?!(?:(?:\x22?\x5C[\x00-\x7E]\x22?)|(?:\x22?[^\x5C\x22]\x22?)){255,})(?!(?:(?:\x22?\x5C[\x00-\x7E]\x22?)|(?:\x22?[^\x5C\x22]\x22?)){65,}@)(?:(?:[\x21\x23-\x27\x2A\x2B\x2D\x2F-\x39\x3D\x3F\x5E-\x7E]+)|(?:\x22(?:[\x01-\x08\x0B\x0C\x0E-\x1F\x21\x23-\x5B\x5D-\x7F]|(?:\x5C[\x00-\x7F]))*\x22))(?:\.(?:(?:[\x21\x23-\x27\x2A\x2B\x2D\x2F-\x39\x3D\x3F\x5E-\x7E]+)|(?:\x22(?:[\x01-\x08\x0B\x0C\x0E-\x1F\x21\x23-\x5B\x5D-\x7F]|(?:\x5C[\x00-\x7F]))*\x22)))*@(?:(?:(?!.*[^.]{64,})(?:(?:(?:xn--)?[a-z0-9]+(?:-[a-z0-9]+)*\.){1,126}){1,}(?:(?:[a-z][a-z0-9]*)|(?:(?:xn--)[a-z0-9]+))(?:-[a-z0-9]+)*)|(?:\[(?:(?:IPv6:(?:(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){7})|(?:(?!(?:.*[a-f0-9][:\]]){7,})(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){0,5})?::(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){0,5})?)))|(?:(?:IPv6:(?:(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){5}:)|(?:(?!(?:.*[a-f0-9]:){5,})(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){0,3})?::(?:[a-f0-9]{1,4}(?::[a-f0-9]{1,4}){0,3}:)?)))?(?:(?:25[0-5])|(?:2[0-4][0-9])|(?:1[0-9]{2})|(?:[1-9]?[0-9]))(?:\.(?:(?:25[0-5])|(?:2[0-4][0-9])|(?:1[0-9]{2})|(?:[1-9]?[0-9]))){3}))\]))\Z") @classmethod def is_valid(cls, email): email = email.lower() return (cls.is_valid_email_format(email) and not cls.is_blacklisted(email)) @classmethod def all_domain_suffixes(cls, email): domain = email.split("@")[-1] components = domain.split(".") return (".".join(components[n:]) for n in xrange(0, len(components))) @classmethod def is_blacklisted(cls, email): all_domain_suffixes = cls.all_domain_suffixes(email) return any(domain in cls.blacklist for domain in all_domain_suffixes) @classmethod def is_valid_email_format(cls, email): return bool(email) and cls.valid_matcher.search(email) is not None @classmethod def add_custom_domains(cls, domains = []): cls.blacklist.update(domains)
17,038.3
679,502
0.762927
8a537a23132b1659d27af4ab18f1a55d32ddd338
927
py
Python
ctestgen/runner/basic_test_runner.py
VolandTymim/ctestgen
e2567714fcc312a76af082733b10e5054c4cc6f0
[ "MIT" ]
2
2019-01-12T01:13:09.000Z
2020-06-14T15:32:48.000Z
ctestgen/runner/basic_test_runner.py
VolandTymim/ctestgen
e2567714fcc312a76af082733b10e5054c4cc6f0
[ "MIT" ]
null
null
null
ctestgen/runner/basic_test_runner.py
VolandTymim/ctestgen
e2567714fcc312a76af082733b10e5054c4cc6f0
[ "MIT" ]
null
null
null
import os from abc import abstractmethod from ctestgen.runner import TestRunner, get_program_response, \ find_environment_variables, TestRunResult class BasicTestRunner(TestRunner): def __init__(self, run_arguments, *args, print_test_info=True, **kwargs): super().__init__(*args, **kwargs) self.run_arguments = run_arguments self.print_test_info = print_test_info def _get_env(self): return find_environment_variables() @abstractmethod def _process_program_response(self, test_dir, test_filename, program_response) -> TestRunResult: pass def _on_test(self, test_dir, test_filename, env): if self.print_test_info: print(test_filename) program_response = get_program_response(self.run_arguments + [os.path.join(test_dir, test_filename)], env) return self._process_program_response(test_dir, test_filename, program_response)
37.08
114
0.740022
b72ae0cfecd2f3e41bdb6ad202f279d0c5609d98
1,274
py
Python
cal_setup.py
shourya5/Calendar_bot
1da1aad1c06ad7ecf0263ac341e7ece4569beb4c
[ "MIT" ]
1
2021-06-09T13:06:50.000Z
2021-06-09T13:06:50.000Z
cal_setup.py
shourya5/Calendar_bot
1da1aad1c06ad7ecf0263ac341e7ece4569beb4c
[ "MIT" ]
1
2021-06-08T05:49:07.000Z
2021-06-30T11:27:27.000Z
cal_setup.py
shourya5/Calendar_bot
1da1aad1c06ad7ecf0263ac341e7ece4569beb4c
[ "MIT" ]
1
2021-07-07T15:21:48.000Z
2021-07-07T15:21:48.000Z
import os.path from googleapiclient.discovery import build from google_auth_oauthlib.flow import InstalledAppFlow from google.auth.transport.requests import Request from google.oauth2.credentials import Credentials # If modifying these scopes, delete the file token.json. SCOPES = ['https://www.googleapis.com/auth/calendar'] CREDENTIALS_FILE = 'credentials.json' def get_calendar_service(): creds = None # The file token.pickle stores the user's access and refresh tokens, and is # created automatically when the authorization flow completes for the first # time. if os.path.exists('token.json'): creds = Credentials.from_authorized_user_file('token.json', SCOPES) # If there are no (valid) credentials available, let the user log in. if not creds or not creds.valid: if creds and creds.expired and creds.refresh_token: creds.refresh(Request()) else: flow = InstalledAppFlow.from_client_secrets_file( CREDENTIALS_FILE, SCOPES) creds = flow.run_console() # Save the credentials for the next run with open('token.json', 'w') as token: token.write(creds.to_json()) service = build('calendar', 'v3', credentials=creds) return service
39.8125
79
0.705651
9545b239457bd1e4ee06260010737580602c3f23
10,448
py
Python
pyClarion/components/rules.py
jlichter/pyClarion
326a9b7ac03baaaf8eba49a42954f88542c191e9
[ "MIT" ]
25
2018-09-21T17:51:09.000Z
2022-03-08T12:24:35.000Z
pyClarion/components/rules.py
jlichter/pyClarion
326a9b7ac03baaaf8eba49a42954f88542c191e9
[ "MIT" ]
9
2018-07-01T00:44:02.000Z
2022-02-10T10:56:30.000Z
pyClarion/components/rules.py
jlichter/pyClarion
326a9b7ac03baaaf8eba49a42954f88542c191e9
[ "MIT" ]
10
2018-09-21T17:51:13.000Z
2022-03-03T07:58:37.000Z
"""Tools for creating, managing, and processing rules.""" __all__ = ["Rule", "Rules", "AssociativeRules", "ActionRules"] from ..base.symbols import ConstructType, Symbol, rule, chunk from ..base.components import Process from .. import numdicts as nd from typing import ( Mapping, MutableMapping, TypeVar, Generic, Type, Dict, FrozenSet, Set, Tuple, overload, cast ) from types import MappingProxyType class Rule(object): """Represents a rule form.""" __slots__ = ("_conc", "_weights") def __init__( self, conc: chunk, *conds: chunk, weights: Dict[chunk, float] = None ): """ Initialize a new rule. If conditions contains items that do not appear in weights, weights is extended to map each of these items to a weight of 1. If weights is None, every cond is assigned a weight of 1. If the weights sum to more than 1.0, they are renormalized such that each weight w is mapped to w / sum(weights.values()). :param conclusion: A chunk symbol for the rule conclusion. :param conditions: A sequence of chunk symbols representing rule conditions. :param weights: An optional mapping from condition chunk symbols to condition weights. """ # preconditions if weights is not None: if not set(conds).issuperset(weights): ValueError("Keys of arg `weights` do not match conds.") if not all(0 < v for v in weights.values()): ValueError("Weights must be strictly positive.") ws = nd.MutableNumDict(weights) ws.extend(conds, value=1.0) w_sum = nd.val_sum(ws) if w_sum > 1.0: ws /= w_sum self._conc = conc self._weights = nd.freeze(ws) # postconditions assert set(self._weights) == set(conds), "Each cond must have a weight." assert nd.val_sum(ws) <= 1, "Inferred weights must sum to one or less." def __repr__(self) -> str: return "Rule(conc={}, weights={})".format(self.conc, self.weights) def __eq__(self, other) -> bool: if isinstance(other, Rule): b = ( self.conc == other.conc and nd.isclose(self.weights, other.weights) ) return b else: return NotImplemented @property def conc(self) -> chunk: """Conclusion of rule.""" return self._conc @property def weights(self) -> nd.NumDict: """Conditions and condition weights of rule.""" return self._weights def strength(self, strengths: nd.NumDict) -> float: """ Compute rule strength given condition strengths. The rule strength is computed as the weighted sum of the condition strengths in strengths. Implementation based on p. 60 and p. 73 of Anatomy of the Mind. """ weighted = nd.keep(strengths, keys=self.weights) * self.weights return nd.val_sum(weighted) Rt = TypeVar("Rt", bound="Rule") class Rules(MutableMapping[rule, Rt], Generic[Rt]): """A simple rule database.""" @overload def __init__(self: "Rules[Rule]") -> None: ... @overload def __init__(self: "Rules[Rule]", *, max_conds: int) -> None: ... @overload def __init__(self, *, rule_type: Type[Rt]) -> None: ... @overload def __init__(self, *, max_conds: int, rule_type: Type[Rt]) -> None: ... @overload def __init__( self, data: Mapping[rule, Rt], max_conds: int, rule_type: Type[Rt] ) -> None: ... def __init__( self, data: Mapping[rule, Rt] = None, max_conds: int = None, rule_type: Type[Rt] = None ) -> None: if data is None: data = dict() else: data = dict(data) self._data = data self.max_conds = max_conds self.Rule = rule_type if rule_type is not None else Rule self._add_promises: MutableMapping[rule, Rt] = dict() self._del_promises: Set[rule] = set() def __repr__(self): repr_ = "{}({})".format(type(self).__name__, repr(self._data)) return repr_ def __len__(self): return len(self._data) def __iter__(self): yield from iter(self._data) def __getitem__(self, key): return self._data[key] def __setitem__(self, key, val): self._validate_rule_form(val) if isinstance(val, self.Rule): self._data[key] = val else: msg = "This rule database expects rules of type '{}'." TypeError(msg.format(type(self.Rule.__name__))) def __delitem__(self, key): del self._data[key] @property def add_promises(self): """A view of promised additions.""" return MappingProxyType(self._add_promises) @property def del_promises(self): """A view of promised deletions.""" return frozenset(self._del_promises) def define( self, r: rule, conc: chunk, *conds: chunk, weights: Dict[chunk, float] = None ) -> rule: """ Add a new rule. Returns the rule symbol. """ self[r] = self.Rule(conc, *conds, weights=weights) return r def contains_form(self, form): """ Check if the rule set contains a given rule form. See Rule for details on rule forms. """ return any(form == entry for entry in self.values()) def request_add(self, r, form): """ Inform self of a new rule to be applied at a later time. Adds the new rule on call to self.step(). If r is already member of self, will overwrite the existing rule. Does not check for duplicate forms. If an update is already registered for rule r, will throw an error. Does not validate the rule form before registering the request. Validation occurs at update time. """ if r in self._add_promises or r in self._del_promises: msg = "Rule {} already registered for a promised update." raise ValueError(msg.format(r)) else: self._add_promises[r] = form def request_del(self, r: rule) -> None: """ Inform self of an existing rule to be removed at update time. The rule will be removed on call to step(). If r is not already a member of self, will raise an error. """ if r in self._add_promises or r in self._del_promises: msg = "Rule {} already registered for a promised update." raise ValueError(msg.format(r)) elif r not in self: raise ValueError("Cannot delete non-existent rule.") else: self._del_promises.add(r) def step(self): """Apply any promised updates.""" for r in self._del_promises: del self[r] self._del_promises.clear() self.update(self._add_promises) self._add_promises.clear() def _validate_rule_form(self, form): if self.max_conds is not None and len(form.weights) > self.max_conds: msg = "Received rule with {} conditions; maximum allowed is {}." raise ValueError(msg.format(len(form.weights), self.max_conds)) class RuleDBUpdater(Process): """Applies requested updates to a client Rules instance.""" _serves = ConstructType.updater def __init__(self, rules: "Rules") -> None: """Initialize a Rules.Updater instance.""" super().__init__() self.rules = rules def __call__( self, inputs: Mapping[Tuple[Symbol, ...], nd.NumDict] ) -> nd.NumDict: """Resolve all outstanding rule database update requests.""" self.rules.step() return super().call(inputs) class AssociativeRules(Process): """ Propagates activations among chunks through associative rules. The strength of the conclusion is calculated as a weighted sum of condition strengths. In cases where there are multiple rules with the same conclusion, the maximum is taken. Implementation based on p. 73-74 of Anatomy of the Mind. """ _serves = ConstructType.flow_tt def __init__(self, source: Symbol, rules: Rules) -> None: super().__init__(expected=(source,)) self.rules = rules def call( self, inputs: Mapping[Tuple[Symbol, ...], nd.NumDict] ) -> nd.NumDict: strengths, = self.extract_inputs(inputs) d = nd.MutableNumDict(default=0.0) for r, form in self.rules.items(): s_r = form.strength(strengths) d[form.conc] = max(d[form.conc], s_r) d[r] = s_r d.squeeze() assert d.default == 0, "Unexpected output default." return d class ActionRules(Process): """ Propagates activations from condition to action chunks using action rules. Action rules compete to be selected based on their rule strengths, which is equal to the product of an action rule's weight and the strength of its condition chunk. The rule strength of the selected action is then propagated to its conclusion. """ _serves = ConstructType.flow_tt def __init__( self, source: Symbol, rules: Rules, temperature: float = .01 ) -> None: if rules.max_conds is None or rules.max_conds > 1: msg = "Rule database must not accept multiple condition rules." raise ValueError(msg) super().__init__(expected=(source,)) self.rules = rules self.temperature = temperature def call( self, inputs: Mapping[Tuple[Symbol, ...], nd.NumDict] ) -> nd.NumDict: strengths, = self.extract_inputs(inputs) d = nd.MutableNumDict(default=0) for r, form in self.rules.items(): d[r] = form.strength(strengths) probabilities = nd.boltzmann(d, self.temperature) selection = nd.draw(probabilities, n=1) d *= selection d.squeeze() d.max(nd.transform_keys(d, func=lambda r: self.rules[r].conc)) # postcondition assert d.default == 0, "Unexpected output default." return d
27.787234
81
0.594755
a7165b4597820f8f5b50d3babff2c918d3022479
1,081
py
Python
xls/ir/python/bits_test.py
hafixo/xls
21009ec2165d04d0037d9cf3583b207949ef7a6d
[ "Apache-2.0" ]
null
null
null
xls/ir/python/bits_test.py
hafixo/xls
21009ec2165d04d0037d9cf3583b207949ef7a6d
[ "Apache-2.0" ]
null
null
null
xls/ir/python/bits_test.py
hafixo/xls
21009ec2165d04d0037d9cf3583b207949ef7a6d
[ "Apache-2.0" ]
null
null
null
# Lint as: python3 # # Copyright 2020 The XLS Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for xls.ir.python.bits.""" from xls.ir.python import bits from absl.testing import absltest class BitsTest(absltest.TestCase): def test_bits(self): self.assertEqual(43, bits.UBits(43, 7).to_uint()) self.assertEqual(53, bits.UBits(53, 7).to_int()) self.assertEqual(33, bits.SBits(33, 8).to_uint()) self.assertEqual(83, bits.SBits(83, 8).to_int()) self.assertEqual(-83, bits.SBits(-83, 8).to_int()) if __name__ == '__main__': absltest.main()
30.885714
74
0.725254
4883069c51bde589111025b699efbc790eb17ba5
337
py
Python
estilos.py
Labbbbas/tkinter_py
bf388625bd5890434375884e30c84a2d4f771d1c
[ "MIT" ]
null
null
null
estilos.py
Labbbbas/tkinter_py
bf388625bd5890434375884e30c84a2d4f771d1c
[ "MIT" ]
null
null
null
estilos.py
Labbbbas/tkinter_py
bf388625bd5890434375884e30c84a2d4f771d1c
[ "MIT" ]
null
null
null
import tkinter as tk ventana = tk.Tk() labelMessage = tk.Label(ventana, text='Hola a todos', fg='blue').pack() labelMessage2 = tk.Label(ventana, text="Nueva etiqueta", fg='red', font='Helvetica 10 bold').pack() labelMessage3 = tk.Label(ventana, text='Another window', fg='white', bg='red', font='Times 18').pack() ventana.mainloop()
30.636364
102
0.700297
0a348a35b0ca9cf6c37ea59b7a5164257d35065c
7,926
py
Python
plugins/modules/env_idbroker_info.py
nmarian85/cloudera.cloud
817fc1a5400c1f43614c886bce1770076c1e91d1
[ "Apache-2.0" ]
11
2021-05-05T19:44:14.000Z
2021-08-23T20:22:55.000Z
plugins/modules/env_idbroker_info.py
nmarian85/cloudera.cloud
817fc1a5400c1f43614c886bce1770076c1e91d1
[ "Apache-2.0" ]
19
2021-05-18T11:02:05.000Z
2022-03-19T17:25:56.000Z
plugins/modules/env_idbroker_info.py
nmarian85/cloudera.cloud
817fc1a5400c1f43614c886bce1770076c1e91d1
[ "Apache-2.0" ]
18
2021-05-05T17:29:49.000Z
2022-02-10T10:46:54.000Z
#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2021 Cloudera, Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ansible.module_utils.basic import AnsibleModule from ansible_collections.cloudera.cloud.plugins.module_utils.cdp_common import CdpModule ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'} DOCUMENTATION = r''' --- module: env_idbroker_info short_description: Gather information about CDP ID Broker description: - Gather information about the ID Broker mappings for a CDP Environment. - The module supports check_mode. author: - "Webster Mudge (@wmudge)" - "Dan Chaffelson (@chaffelson)" requirements: - cdpy options: name: description: - The name of the Environment. aliases: - environment type: str required: True extends_documentation_fragment: - cloudera.cloud.cdp_sdk_options - cloudera.cloud.cdp_auth_options ''' EXAMPLES = r''' # Note: These examples do not set authentication details. # Gather information about the ID Broker mappings - cloudera.cloud.env_idbroker_info: name: example-environment ''' RETURN = r''' idbroker: description: Returns the mappings and sync status for the ID Broker for the Environment. returned: when supported type: dict contains: mappingsVersion: description: The version of the mappings. returned: always type: str sample: AWS dataAccessRole: description: The cloud provider role to which data access services will be mapped (e.g. an ARN in AWS, a Resource ID in Azure). returned: always type: str rangerAuditRole: description: - The cloud provider role to which services that write to Ranger audit logs will be mapped (e.g. an ARN in AWS, a Resource ID in Azure). - Note that some data access services also write to Ranger audit logs; such services will be mapped to the 'dataAccessRole', not the 'rangerAuditRole'. returned: always type: str rangerCloudAccessAuthorizerRole: description: The cloud provider role to which the Ranger RAZ service will be mapped (e.g. an ARN in AWS, a Resource ID in Azure). returned: when supported type: str mappings: description: ID Broker mappings for individual actors and groups. Does not include mappings for data access services. returned: when supported type: list elements: dict contains: accessorCrn: description: The CRN of the actor (group or user) mapped to the cloud provider role. returned: on success type: str role: description: The cloud provider identitier for the role. returned: on success type: str syncStatus: description: The status of the most recent ID Broker mappings sync operation, if any. Not present if there is no Datalake associated with the Environment. returned: when supported type: dict contains: globalStatus: description: The overall mappings sync status for all Datalake clusters in the Environment. returned: always type: str sample: - NEVER_RUN - REQUESTED - REJECTED - RUNNING - COMPLETED - FAILED - TIMEDOUT syncNeeded: description: Flag indicating whether a sync is needed to bring in-cluster mappings up-to-date. returned: always type: bool statuses: description: Map of Datalake cluster CRN-to-mappings sync status for each Datalake cluster in the environment. returned: always type: dict contains: __datalake CRN__: description: The Datalake cluster CRN returned: always type: dict contains: endDate: description: The date when the mappings sync completed or was terminated. Omitted if status is NEVER_RUN or RUNNING. returned: when supported type: str errorDetail: description: The detail of the error. Omitted if status is not FAILED. returned: when supported type: str startDate: description: The date when the mappings sync started executing. Omitted if status is NEVER_RUN. returned: when supported type: str status: description: The mappings sync summary status. returned: always type: str sample: - NEVER_RUN - REQUESTED - REJECTED - RUNNING - COMPLETED - FAILED - TIMEDOUT sdk_out: description: Returns the captured CDP SDK log. returned: when supported type: str sdk_out_lines: description: Returns a list of each line of the captured CDP SDK log. returned: when supported type: list elements: str ''' class EnvironmentIdBrokerInfo(CdpModule): def __init__(self, module): super(EnvironmentIdBrokerInfo, self).__init__(module) # Set variables self.name = self._get_param('name') # Initialize the return values self.idbroker = dict() # Execute logic process self.process() @CdpModule._Decorators.process_debug def process(self): self.idbroker = self.cdpy.environments.gather_idbroker_mappings(self.name) def main(): module = AnsibleModule( argument_spec=CdpModule.argument_spec( name=dict(required=True, type='str', aliases=['environment']) ), supports_check_mode=True ) result = EnvironmentIdBrokerInfo(module) output = dict( changed=False, idbroker=result.idbroker, ) if result.debug: output.update( sdk_out=result.log_out, sdk_out_lines=result.log_lines ) module.exit_json(**output) if __name__ == '__main__': main()
36.525346
120
0.54214
1ab748c92590974afd15925eebb4136cd0606e06
58
py
Python
exoatlas/visualizations/axes/__init__.py
zkbt/exopop
5e8b9d391fe9e2d39c623d7ccd7eca8fd0f0f3f8
[ "MIT" ]
4
2020-06-24T16:38:27.000Z
2022-01-23T01:57:19.000Z
exoatlas/visualizations/axes/__init__.py
zkbt/exopop
5e8b9d391fe9e2d39c623d7ccd7eca8fd0f0f3f8
[ "MIT" ]
4
2018-09-20T23:12:30.000Z
2019-05-15T15:31:58.000Z
exoatlas/visualizations/axes/__init__.py
zkbt/exopop
5e8b9d391fe9e2d39c623d7ccd7eca8fd0f0f3f8
[ "MIT" ]
null
null
null
from .plottable import * from .preset_plottables import *
19.333333
32
0.793103
ead5fdfc155eb7a30483049266b00d8cb7f0e242
495
py
Python
drf_admin/apps/monitor/serializers/ip.py
guohaihan/myproject
0ec105d0bd48477faddf93bd62a8ede800419ae6
[ "MIT" ]
228
2020-06-20T10:07:03.000Z
2022-03-29T07:11:01.000Z
drf_admin/apps/monitor/serializers/ip.py
guohaihan/myproject
0ec105d0bd48477faddf93bd62a8ede800419ae6
[ "MIT" ]
25
2020-07-16T12:29:04.000Z
2022-02-16T06:31:06.000Z
drf_admin/apps/monitor/serializers/ip.py
guohaihan/myproject
0ec105d0bd48477faddf93bd62a8ede800419ae6
[ "MIT" ]
82
2020-10-26T07:14:15.000Z
2022-03-29T07:53:23.000Z
# -*- coding: utf-8 -*- """ @author : Wang Meng @github : https://github.com/tianpangji @software : PyCharm @file : ip.py @create : 2020/10/4 10:22 """ from rest_framework import serializers from monitor.models import IpBlackList class IpBlackListSerializer(serializers.ModelSerializer): """ IP黑名单管理序列化器 """ create_time = serializers.DateTimeField(format='%Y-%m-%d %H:%M:%S', read_only=True) class Meta: model = IpBlackList fields = '__all__'
21.521739
87
0.654545
e891f886d79b397170ddabf82850e364cc01a1b2
5,532
py
Python
forms-flow-data-analysis-api/tests/conftest.py
sreehari-aot/forms-flow-ai
11e2fdd6da792aaa9dd46c0cec38564fe5916b58
[ "Apache-2.0" ]
null
null
null
forms-flow-data-analysis-api/tests/conftest.py
sreehari-aot/forms-flow-ai
11e2fdd6da792aaa9dd46c0cec38564fe5916b58
[ "Apache-2.0" ]
null
null
null
forms-flow-data-analysis-api/tests/conftest.py
sreehari-aot/forms-flow-ai
11e2fdd6da792aaa9dd46c0cec38564fe5916b58
[ "Apache-2.0" ]
null
null
null
"""Common setup and fixtures for the pytest suite used by this service.""" import pytest from flask_migrate import Migrate, upgrade from sqlalchemy import event, text from sqlalchemy.schema import DropConstraint, MetaData from api import create_app, setup_jwt_manager from api.models import db as _db from api.utils import jwt as _jwt from api.utils.enumerator import Service @pytest.fixture(scope="session") def app(): """Return a session-wide application configured in TEST mode.""" _app = create_app("testing") return _app @pytest.fixture(scope="function") def app_ctx(event_loop): # pylint: disable=unused-argument # def app_ctx(): """Return a session-wide application configured in TEST mode.""" _app = create_app("testing") with _app.app_context(): yield _app @pytest.fixture def config(app): # pylint: disable=redefined-outer-name """Return the application config.""" return app.config @pytest.fixture(scope="function") def app_request(): """Return a session-wide application configured in TEST mode.""" _app = create_app("testing") return _app @pytest.fixture(scope="session") def client(app): # pylint: disable=redefined-outer-name """Return a session-wide Flask test client.""" return app.test_client() @pytest.fixture(scope="session") def client_ctx(app): # pylint: disable=redefined-outer-name """Return session-wide Flask test client.""" with app.test_client() as _client: yield _client @pytest.fixture(scope="session") def db(app): # pylint: disable=redefined-outer-name, invalid-name """Return a session-wide initialised database. Drops all existing tables - Meta follows Postgres FKs """ if app.config["DATABASE_SUPPORT"] == Service.ENABLED.value: with app.app_context(): # Clear out any existing tables metadata = MetaData(_db.engine) metadata.reflect() for table in metadata.tables.values(): for fk in table.foreign_keys: # pylint: disable=invalid-name _db.engine.execute(DropConstraint(fk.constraint)) metadata.drop_all() _db.drop_all() sequence_sql = """SELECT sequence_name FROM information_schema.sequences WHERE sequence_schema='public' """ sess = _db.session() for seq in [name for (name,) in sess.execute(text(sequence_sql))]: try: sess.execute(text("DROP SEQUENCE public.%s ;" % seq)) print("DROP SEQUENCE public.%s " % seq) except Exception as err: # pylint: disable=broad-except print(f"Error: {err}") sess.commit() # ############################################ # There are 2 approaches, an empty database, or the same one that the app # will use create the tables # _db.create_all() # or # Use Alembic to load all of the DB revisions including supporting lookup data # This is the path we'll use in selfservice_api!! # even though this isn't referenced directly, # it sets up the internal configs that upgrade needs Migrate(app, _db) upgrade() return _db @pytest.fixture(scope="function") def session(app, db): # pylint: disable=redefined-outer-name, invalid-name """Return a function-scoped session.""" with app.app_context(): if app.config["DATABASE_SUPPORT"] == Service.ENABLED.value: conn = db.engine.connect() txn = conn.begin() options = dict(bind=conn, binds={}) sess = db.create_scoped_session(options=options) # establish a SAVEPOINT just before beginning the test # (http://docs.sqlalchemy.org/en/latest/orm/session_transaction.html#using-savepoint) sess.begin_nested() @event.listens_for(sess(), "after_transaction_end") def restart_savepoint(sess2, trans): # pylint: disable=unused-variable # Detecting whether this is indeed the nested transaction of the test if ( trans.nested and not trans._parent.nested ): # pylint: disable=protected-access # Handle where test DOESN'T session.commit(), sess2.expire_all() sess.begin_nested() db.session = sess sql = text("select 1") sess.execute(sql) yield sess # Cleanup sess.remove() # This instruction rollsback any commit that were executed in the tests. txn.rollback() conn.close() else: yield app @pytest.fixture(scope="session") def jwt(app): """Return session-wide jwt manager.""" return _jwt @pytest.fixture(scope="session", autouse=True) def auto(docker_services, app): """Spin up a keycloak instance and initialize jwt.""" if app.config.get("USE_DOCKER_MOCK"): docker_services.start("keycloak") docker_services.wait_for_service("keycloak", 8081) setup_jwt_manager(app, _jwt) @pytest.fixture(scope="session") def docker_compose_files(pytestconfig): """Get the docker-compose.yml absolute path.""" import os return [ os.path.join(str(pytestconfig.rootdir), "tests/docker", "docker-compose.yml") ]
33.325301
97
0.615871
1bad50821a15281504e5d47fd1a85eec9219ceaf
2,491
py
Python
test/python/WMCore_t/WorkQueue_t/WorkQueueProfile_t.py
hufnagel/WMCore
b150cc725b68fc1cf8e6e0fa07c826226a4421fa
[ "Apache-2.0" ]
21
2015-11-19T16:18:45.000Z
2021-12-02T18:20:39.000Z
test/python/WMCore_t/WorkQueue_t/WorkQueueProfile_t.py
hufnagel/WMCore
b150cc725b68fc1cf8e6e0fa07c826226a4421fa
[ "Apache-2.0" ]
5,671
2015-01-06T14:38:52.000Z
2022-03-31T22:11:14.000Z
test/python/WMCore_t/WorkQueue_t/WorkQueueProfile_t.py
hufnagel/WMCore
b150cc725b68fc1cf8e6e0fa07c826226a4421fa
[ "Apache-2.0" ]
67
2015-01-21T15:55:38.000Z
2022-02-03T19:53:13.000Z
#!/usr/bin/env python """ WorkQueue tests """ from __future__ import absolute_import import tempfile import unittest import cProfile import pstats from WMQuality.Emulators.WMSpecGenerator.WMSpecGenerator import WMSpecGenerator from WMCore.WorkQueue.WorkQueue import globalQueue from .WorkQueueTestCase import WorkQueueTestCase class WorkQueueProfileTest(WorkQueueTestCase): """ _WorkQueueTest_ """ def setUp(self): """ If we dont have a wmspec file create one Warning: For the real profiling test including spec generation. need to use real spec instead of using emulator generated spec which doesn't include couchDB access and cmssw access """ WorkQueueTestCase.setUp(self) self.cacheDir = tempfile.mkdtemp() self.specGenerator = WMSpecGenerator(self.cacheDir) self.specNamePrefix = "TestReReco_" self.specs = self.createReRecoSpec(5, "file") # Create queues self.globalQueue = globalQueue(DbName=self.globalQDB, InboxDbName=self.globalQInboxDB, NegotiationTimeout=0) def tearDown(self): """tearDown""" WorkQueueTestCase.tearDown(self) try: self.specGenerator.removeSpecs() except Exception: pass def createReRecoSpec(self, numOfSpec, kind="spec"): specs = [] for i in range(numOfSpec): specName = "%s%s" % (self.specNamePrefix, (i + 1)) specs.append(self.specGenerator.createReRecoSpec(specName, kind)) return specs def createProfile(self, name, function): filename = name prof = cProfile.Profile() prof.runcall(function) prof.dump_stats(filename) p = pstats.Stats(filename) p.strip_dirs().sort_stats('cumulative').print_stats(0.1) p.strip_dirs().sort_stats('time').print_stats(0.1) p.strip_dirs().sort_stats('calls').print_stats(0.1) # p.strip_dirs().sort_stats('name').print_stats(10) def testQueueElementProfile(self): self.createProfile('queueElementProfile.prof', self.multipleQueueWorkCall) def multipleQueueWorkCall(self): i = 0 for wmspec in self.specs: i += 1 self.globalQueue.queueWork(wmspec, self.specNamePrefix + str(i), 'test_team') if __name__ == "__main__": unittest.main()
30.753086
89
0.634685
df648e6d103cb75c7eb8e15d919a7dd76a0f1fa3
37,607
py
Python
invenio_app_ils/config.py
jrcastro2/invenio-app-ils
502b9e7bac737863905976a1d07e2cd924f5d779
[ "MIT" ]
null
null
null
invenio_app_ils/config.py
jrcastro2/invenio-app-ils
502b9e7bac737863905976a1d07e2cd924f5d779
[ "MIT" ]
null
null
null
invenio_app_ils/config.py
jrcastro2/invenio-app-ils
502b9e7bac737863905976a1d07e2cd924f5d779
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- # # Copyright (C) 2018-2020 CERN. # # invenio-app-ils is free software; you can redistribute it and/or modify it # under the terms of the MIT License; see LICENSE file for more details. """Default configuration for invenio-app-ils. You overwrite and set instance-specific configuration by either: - Configuration file: ``<virtualenv prefix>/var/instance/invenio.cfg`` - Environment variables: ``APP_<variable name>`` """ from datetime import timedelta from invenio_accounts.config import \ ACCOUNTS_REST_AUTH_VIEWS as _ACCOUNTS_REST_AUTH_VIEWS from invenio_app.config import APP_DEFAULT_SECURE_HEADERS from invenio_oauthclient.contrib import cern from invenio_records_rest.facets import terms_filter from invenio_records_rest.utils import allow_all, deny_all from invenio_stats.aggregations import StatAggregator from invenio_stats.processors import EventsIndexer from invenio_stats.queries import ESTermsQuery from invenio_app_ils.document_requests.indexer import DocumentRequestIndexer from invenio_app_ils.documents.indexer import DocumentIndexer from invenio_app_ils.eitems.indexer import EItemIndexer from invenio_app_ils.internal_locations.indexer import InternalLocationIndexer from invenio_app_ils.items.indexer import ItemIndexer from invenio_app_ils.literature.api import LITERATURE_PID_FETCHER, \ LITERATURE_PID_MINTER, LITERATURE_PID_TYPE from invenio_app_ils.literature.covers_builder import build_ils_demo_cover_urls from invenio_app_ils.literature.search import LiteratureSearch from invenio_app_ils.locations.indexer import LocationIndexer from invenio_app_ils.patrons.indexer import PatronIndexer from invenio_app_ils.series.indexer import SeriesIndexer from invenio_app_ils.vocabularies.indexer import VocabularyIndexer from .document_requests.api import DOCUMENT_REQUEST_PID_FETCHER, \ DOCUMENT_REQUEST_PID_MINTER, DOCUMENT_REQUEST_PID_TYPE, DocumentRequest from .document_requests.search import DocumentRequestSearch from .documents.api import DOCUMENT_PID_FETCHER, DOCUMENT_PID_MINTER, \ DOCUMENT_PID_TYPE, Document from .documents.search import DocumentSearch from .eitems.api import EITEM_PID_FETCHER, EITEM_PID_MINTER, EITEM_PID_TYPE, \ EItem from .eitems.search import EItemSearch from .facets import default_value_when_missing_filter, keyed_range_filter, \ not_empty_object_or_list_filter from .internal_locations.api import INTERNAL_LOCATION_PID_FETCHER, \ INTERNAL_LOCATION_PID_MINTER, INTERNAL_LOCATION_PID_TYPE, \ InternalLocation from .internal_locations.search import InternalLocationSearch from .items.api import ITEM_PID_FETCHER, ITEM_PID_MINTER, ITEM_PID_TYPE, Item from .items.search import ItemSearch from .locations.api import LOCATION_PID_FETCHER, LOCATION_PID_MINTER, \ LOCATION_PID_TYPE, Location from .locations.search import LocationSearch from .patrons.api import PATRON_PID_FETCHER, PATRON_PID_MINTER, \ PATRON_PID_TYPE, Patron from .patrons.search import PatronsSearch from .permissions import PatronOwnerPermission, \ authenticated_user_permission, backoffice_permission, \ views_permissions_factory from .records.permissions import record_read_permission_factory from .series.api import SERIES_PID_FETCHER, SERIES_PID_MINTER, \ SERIES_PID_TYPE, Series from .series.search import SeriesSearch from .views import UserInfoResource from .vocabularies.api import VOCABULARY_PID_FETCHER, VOCABULARY_PID_MINTER, \ VOCABULARY_PID_TYPE, Vocabulary from .vocabularies.search import VocabularySearch def _(x): """Identity function used to trigger string extraction.""" return x ############################################################################### # Debug ############################################################################### DEBUG = True DEBUG_TB_ENABLED = True DEBUG_TB_INTERCEPT_REDIRECTS = False ############################################################################### # OAuth ############################################################################### OAUTH_REMOTE_APP = cern.REMOTE_REST_APP OAUTH_REMOTE_APP["authorized_redirect_url"] = "/login" OAUTH_REMOTE_APP["error_redirect_url"] = "/login" OAUTHCLIENT_REST_REMOTE_APPS = dict(cern=OAUTH_REMOTE_APP) CERN_APP_CREDENTIALS = dict( consumer_key="CHANGE_ME", consumer_secret="CHANGE_ME" ) # Rate limiting # ============= #: Storage for ratelimiter. RATELIMIT_STORAGE_URL = "redis://localhost:6379/3" # I18N # ==== #: Default language BABEL_DEFAULT_LANGUAGE = "en" #: Default time zone BABEL_DEFAULT_TIMEZONE = "Europe/Zurich" # Email configuration # =================== #: Email address for support. SUPPORT_EMAIL = "[email protected]" #: Disable email sending by default. MAIL_SUPPRESS_SEND = True #: Email address for email notification sender. MAIL_NOTIFY_SENDER = "[email protected]" #: Email CC address(es) for email notifications. MAIL_NOTIFY_CC = [] #: Email BCC address(es) for email notification. MAIL_NOTIFY_BCC = [] # Enable sending mail to test recipients. ILS_MAIL_ENABLE_TEST_RECIPIENTS = False #: When ILS_MAIL_ENABLE_TEST_RECIPIENTS=True, all emails are sent here ILS_MAIL_NOTIFY_TEST_RECIPIENTS = ["[email protected]"] #: Document request message creator class ILS_DOCUMENT_REQUEST_MAIL_MSG_CREATOR = "invenio_app_ils.document_requests.mail.factory:default_document_request_message_creator" #: Document request email templates ILS_DOCUMENT_REQUEST_MAIL_TEMPLATES = {} # Assets # ====== #: Static files collection method (defaults to copying files). COLLECT_STORAGE = "flask_collect.storage.file" # Accounts # ======== #: Email address used as sender of account registration emails. SECURITY_EMAIL_SENDER = SUPPORT_EMAIL #: Email subject for account registration emails. SECURITY_EMAIL_SUBJECT_REGISTER = _("Welcome to invenio-app-ils!") #: Redis session storage URL. ACCOUNTS_SESSION_REDIS_URL = "redis://localhost:6379/1" _ACCOUNTS_REST_AUTH_VIEWS.update(user_info=UserInfoResource) ACCOUNTS_REST_AUTH_VIEWS = _ACCOUNTS_REST_AUTH_VIEWS ACCOUNTS_REST_CONFIRM_EMAIL_ENDPOINT = "/accounts/confirm-email" # Celery configuration # ==================== BROKER_URL = "amqp://guest:guest@localhost:5672/" #: URL of message broker for Celery (default is RabbitMQ). CELERY_BROKER_URL = "amqp://guest:guest@localhost:5672/" #: URL of backend for result storage (default is Redis). CELERY_RESULT_BACKEND = "redis://localhost:6379/2" #: Scheduled tasks configuration (aka cronjobs). CELERY_BEAT_SCHEDULE = { "indexer": { "task": "invenio_indexer.tasks.process_bulk_queue", "schedule": timedelta(minutes=5), }, "accounts": { "task": "invenio_accounts.tasks.clean_session_table", "schedule": timedelta(minutes=60), }, "send_expiring_loans_loans": { "task": "invenio_app_ils.circulation.mail.tasks.send_expiring_loans_mail_reminder", "schedule": timedelta(days=1), }, "cancel_expired_loan": { "task": "invenio_app_ils.circulation.tasks.cancel_expired_loan_requests", "schedule": timedelta(days=1), }, "send_overdue_loan_reminders": { "task": "invenio_app_ils.circulation.mail.tasks.send_overdue_loans_mail_reminder", "schedule": timedelta(days=1), }, "stats-process-events": { "task": "invenio_stats.tasks.process_events", "schedule": timedelta(minutes=30), "args": [("record-view", "file-download")], }, "stats-aggregate-events": { "task": "invenio_stats.tasks.aggregate_events", "schedule": timedelta(hours=3), "args": [("record-view-agg", "file-download-agg")], }, } # Database # ======== #: Database URI including user and password SQLALCHEMY_DATABASE_URI = "postgresql+psycopg2://test:psw@localhost/ils" # JSONSchemas # =========== #: Hostname used in URLs for local JSONSchemas. JSONSCHEMAS_HOST = "127.0.0.1:5000" # CORS # ==== REST_ENABLE_CORS = True # change this only while developing CORS_SEND_WILDCARD = True CORS_SUPPORTS_CREDENTIALS = False # Flask configuration # =================== # See details on # http://flask.pocoo.org/docs/0.12/config/#builtin-configuration-values #: Secret key - each installation (dev, production, ...) needs a separate key. #: It should be changed before deploying. SECRET_KEY = "CHANGE_ME" #: Max upload size for form data via application/mulitpart-formdata. MAX_CONTENT_LENGTH = 100 * 1024 * 1024 # 100 MiB #: Sets cookie with the secure flag by default SESSION_COOKIE_SECURE = True #: Since HAProxy and Nginx route all requests no matter the host header #: provided, the allowed hosts variable is set to localhost. In production it #: should be set to the correct host and it is strongly recommended to only #: route correct hosts to the application. APP_ALLOWED_HOSTS = ["localhost", "127.0.0.1"] APP_DEFAULT_SECURE_HEADERS["content_security_policy"] = {} #: Single Page Application host and routes, useful in templates/emails SPA_HOST = "http://localhost:3000" SPA_PATHS = dict(profile="/profile") # OAI-PMH # ======= OAISERVER_ID_PREFIX = "oai:invenio_app_ils.org:" _DOCID_CONVERTER = ( 'pid(docid, record_class="invenio_app_ils.documents.api:Document")' ) _PITMID_CONVERTER = ( 'pid(pitmid, record_class="invenio_app_ils.items.api:Item")' ) _EITMID_CONVERTER = ( 'pid(eitmid, record_class="invenio_app_ils.eitems.api:EItem")' ) _LOCID_CONVERTER = ( 'pid(locid, record_class="invenio_app_ils.locations.api:Location")' ) _ILOCID_CONVERTER = ( 'pid(ilocid, record_class="invenio_app_ils.internal_locations.api:InternalLocation")' ) _DREQID_CONVERTER = 'pid(dreqid, record_class="invenio_app_ils.document_requests.api:DocumentRequest")' _SERID_CONVERTER = ( 'pid(serid, record_class="invenio_app_ils.series.api:Series")' ) ############################################################################### # RECORDS REST ############################################################################### _RECORDS_REST_MAX_RESULT_WINDOW = 10000 PIDSTORE_RECID_FIELD = "pid" # name of the URL arg to choose response serializer REST_MIMETYPE_QUERY_ARG_NAME = "format" RECORDS_REST_ENDPOINTS = dict( docid=dict( pid_type=DOCUMENT_PID_TYPE, pid_minter=DOCUMENT_PID_MINTER, pid_fetcher=DOCUMENT_PID_FETCHER, search_class=DocumentSearch, record_class=Document, indexer_class=DocumentIndexer, record_loaders={ "application/json": ( "invenio_app_ils.documents.loaders:document_loader" ) }, record_serializers={ "application/json": ( "invenio_app_ils.literature.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_app_ils.literature.serializers:json_v1_search" ), "text/csv": ( "invenio_app_ils.literature.serializers:csv_v1_search" ), }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, list_route="/documents/", item_route="/documents/<{0}:pid_value>".format(_DOCID_CONVERTER), default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=record_read_permission_factory, list_permission_factory_imp=allow_all, # auth via search filter create_permission_factory_imp=backoffice_permission, update_permission_factory_imp=backoffice_permission, delete_permission_factory_imp=backoffice_permission, ), pitmid=dict( pid_type=ITEM_PID_TYPE, pid_minter=ITEM_PID_MINTER, pid_fetcher=ITEM_PID_FETCHER, search_class=ItemSearch, record_class=Item, indexer_class=ItemIndexer, record_loaders={ "application/json": ("invenio_app_ils.items.loaders:item_loader") }, record_serializers={ "application/json": ( "invenio_app_ils.items.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_app_ils.items.serializers:json_v1_search" ), "text/csv": ("invenio_app_ils.items.serializers:csv_v1_search"), }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, list_route="/items/", item_route="/items/<{0}:pid_value>".format(_PITMID_CONVERTER), default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=backoffice_permission, list_permission_factory_imp=backoffice_permission, create_permission_factory_imp=backoffice_permission, update_permission_factory_imp=backoffice_permission, delete_permission_factory_imp=backoffice_permission, ), eitmid=dict( pid_type=EITEM_PID_TYPE, pid_minter=EITEM_PID_MINTER, pid_fetcher=EITEM_PID_FETCHER, search_class=EItemSearch, record_class=EItem, indexer_class=EItemIndexer, record_loaders={ "application/json": ( "invenio_app_ils.eitems.loaders:eitem_loader" ) }, record_serializers={ "application/json": ( "invenio_app_ils.records.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_records_rest.serializers:json_v1_search" ), "text/csv": ("invenio_app_ils.records.serializers:csv_v1_search"), }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, list_route="/eitems/", item_route="/eitems/<{0}:pid_value>".format(_EITMID_CONVERTER), default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=backoffice_permission, list_permission_factory_imp=backoffice_permission, create_permission_factory_imp=backoffice_permission, update_permission_factory_imp=backoffice_permission, delete_permission_factory_imp=backoffice_permission, ), locid=dict( pid_type=LOCATION_PID_TYPE, pid_minter=LOCATION_PID_MINTER, pid_fetcher=LOCATION_PID_FETCHER, search_class=LocationSearch, record_class=Location, indexer_class=LocationIndexer, record_loaders={ "application/json": ( "invenio_app_ils.locations.loaders:location_loader" ) }, record_serializers={ "application/json": ( "invenio_app_ils.records.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_records_rest.serializers:json_v1_search" ) }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, list_route="/locations/", item_route="/locations/<{0}:pid_value>".format(_LOCID_CONVERTER), default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=backoffice_permission, list_permission_factory_imp=backoffice_permission, create_permission_factory_imp=backoffice_permission, update_permission_factory_imp=backoffice_permission, delete_permission_factory_imp=backoffice_permission, ), serid=dict( pid_type=SERIES_PID_TYPE, pid_minter=SERIES_PID_MINTER, pid_fetcher=SERIES_PID_FETCHER, search_class=SeriesSearch, record_class=Series, indexer_class=SeriesIndexer, record_loaders={ "application/json": ( "invenio_app_ils.series.loaders:series_loader" ) }, record_serializers={ "application/json": ( "invenio_app_ils.literature.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_app_ils.literature.serializers:json_v1_search" ), "text/csv": ( "invenio_app_ils.literature.serializers:csv_v1_search" ), }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, list_route="/series/", item_route="/series/<{0}:pid_value>".format(_SERID_CONVERTER), default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=record_read_permission_factory, list_permission_factory_imp=allow_all, create_permission_factory_imp=backoffice_permission, update_permission_factory_imp=backoffice_permission, delete_permission_factory_imp=backoffice_permission, ), ilocid=dict( pid_type=INTERNAL_LOCATION_PID_TYPE, pid_minter=INTERNAL_LOCATION_PID_MINTER, pid_fetcher=INTERNAL_LOCATION_PID_FETCHER, search_class=InternalLocationSearch, record_class=InternalLocation, indexer_class=InternalLocationIndexer, record_loaders={ "application/json": ( "invenio_app_ils.internal_locations.loaders:internal_location_loader" ) }, record_serializers={ "application/json": ( "invenio_app_ils.records.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_records_rest.serializers:json_v1_search" ) }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, list_route="/internal-locations/", item_route="/internal-locations/<{0}:pid_value>".format( _ILOCID_CONVERTER ), default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=record_read_permission_factory, list_permission_factory_imp=backoffice_permission, create_permission_factory_imp=backoffice_permission, update_permission_factory_imp=backoffice_permission, delete_permission_factory_imp=backoffice_permission, ), patid=dict( pid_type=PATRON_PID_TYPE, pid_minter=PATRON_PID_MINTER, pid_fetcher=PATRON_PID_FETCHER, search_class=PatronsSearch, record_class=Patron, indexer_class=PatronIndexer, record_serializers={ "application/json": ( "invenio_records_rest.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_records_rest.serializers:json_v1_search" ), "text/csv": ("invenio_app_ils.records.serializers:csv_v1_search"), }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, item_route="/patrons/<pid({}):pid_value>".format(PATRON_PID_TYPE), list_route="/patrons/", default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=deny_all, list_permission_factory_imp=backoffice_permission, create_permission_factory_imp=deny_all, update_permission_factory_imp=deny_all, delete_permission_factory_imp=deny_all, ), dreqid=dict( pid_type=DOCUMENT_REQUEST_PID_TYPE, pid_minter=DOCUMENT_REQUEST_PID_MINTER, pid_fetcher=DOCUMENT_REQUEST_PID_FETCHER, search_class=DocumentRequestSearch, record_class=DocumentRequest, indexer_class=DocumentRequestIndexer, search_factory_imp="invenio_app_ils.search_permissions" ":search_factory_filter_by_patron", record_loaders={ "application/json": ( "invenio_app_ils.document_requests.loaders:document_request_loader" ) }, record_serializers={ "application/json": ( "invenio_app_ils.records.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_app_ils.records.serializers:json_v1_search" ), "text/csv": "invenio_app_ils.records.serializers:csv_v1_search", }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, list_route="/document-requests/", item_route="/document-requests/<{0}:pid_value>".format( _DREQID_CONVERTER ), default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=PatronOwnerPermission, list_permission_factory_imp=authenticated_user_permission, # auth via search_factory create_permission_factory_imp=authenticated_user_permission, update_permission_factory_imp=backoffice_permission, delete_permission_factory_imp=backoffice_permission, ), vocid=dict( pid_type=VOCABULARY_PID_TYPE, pid_minter=VOCABULARY_PID_MINTER, pid_fetcher=VOCABULARY_PID_FETCHER, search_class=VocabularySearch, indexer_class=VocabularyIndexer, record_class=Vocabulary, record_serializers={ "application/json": ( "invenio_app_ils.records.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_app_ils.records.serializers:json_v1_search" ), "text/csv": ("invenio_app_ils.records.serializers:csv_v1_search"), }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, item_route="/vocabularies/<pid({}):pid_value>".format( VOCABULARY_PID_TYPE ), list_route="/vocabularies/", default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=deny_all, list_permission_factory_imp=backoffice_permission, create_permission_factory_imp=deny_all, update_permission_factory_imp=deny_all, delete_permission_factory_imp=deny_all, ), litid=dict( # Literature is a search endpoint that allows the user to search in # both the documents and series index. pid_type=LITERATURE_PID_TYPE, pid_minter=LITERATURE_PID_MINTER, pid_fetcher=LITERATURE_PID_FETCHER, search_class=LiteratureSearch, search_factory_imp="invenio_app_ils.literature.search" ":search_factory_literature", record_serializers={ "application/json": ( "invenio_app_ils.literature.serializers:json_v1_response" ) }, search_serializers={ "application/json": ( "invenio_app_ils.literature.serializers:json_v1_search" ), "text/csv": ( "invenio_app_ils.literature.serializers:csv_v1_search" ), }, search_serializers_aliases={ "csv": "text/csv", "json": "application/json", }, item_route="/literature/<pid({}):pid_value>".format( LITERATURE_PID_TYPE ), list_route="/literature/", default_media_type="application/json", max_result_window=_RECORDS_REST_MAX_RESULT_WINDOW, error_handlers=dict(), read_permission_factory_imp=deny_all, list_permission_factory_imp=allow_all, # auth via search filter create_permission_factory_imp=deny_all, update_permission_factory_imp=deny_all, delete_permission_factory_imp=deny_all, ), ) # RECORDS REST sort options # ========================= RECORDS_REST_SORT_OPTIONS = dict( document_requests=dict( # DocumentRequestSearch.Meta.index mostrecent=dict( fields=["_updated"], title="Newest", default_order="desc", order=1 ), bestmatch=dict( fields=["-_score"], title="Best match", default_order="asc", order=2, ), ), documents=dict( # DocumentSearch.Meta.index mostrecent=dict( fields=["_updated"], title="Newest", default_order="desc", order=1 ), bestmatch=dict( fields=["-_score"], title="Best match", default_order="asc", order=2, ), available_items=dict( fields=["-circulation.has_items_for_loan"], title="Available Items", default_order="asc", order=3, ), mostloaned=dict( fields=["circulation.past_loans_count"], title="Most loaned", default_order="desc", order=4, ), publication_year=dict( fields=["publication_year"], title="Publication year", default_order="desc", order=5, ), ), eitems=dict( # ItemSearch.Meta.index mostrecent=dict( fields=["_updated"], title="Newest", default_order="desc", order=1 ), bestmatch=dict( fields=["-_score"], title="Best match", default_order="asc", order=2, ), ), items=dict( # ItemSearch.Meta.index mostrecent=dict( fields=["_updated"], title="Newest", default_order="desc", order=1 ), bestmatch=dict( fields=["-_score"], title="Best match", default_order="asc", order=2, ), ), patrons=dict( # PatronsSearch.Meta.index bestmatch=dict( fields=["-_score"], title="Best match", default_order="asc", order=1, ) ), series=dict( # SeriesSearch.Meta.index mostrecent=dict( fields=["_updated"], title="Newest", default_order="desc", order=1 ), bestmatch=dict( fields=["-_score"], title="Best match", default_order="asc", order=2, ), ), ) # RECORDS REST facets # ========================= #: Number of records to fetch by default RECORDS_REST_DEFAULT_RESULTS_SIZE = 15 #: Number of tags to display in the DocumentsSearch facet FACET_TAG_LIMIT = 5 RECORDS_REST_FACETS = dict( documents=dict( # DocumentSearch.Meta.index aggs=dict( access=dict(terms=dict(field="restricted")), tag=dict(terms=dict(field="tags", size=FACET_TAG_LIMIT)), language=dict(terms=dict(field="languages")), doctype=dict(terms=dict(field="document_type")), relation=dict(terms=dict(field="relation_types")), availability=dict( range=dict( field="circulation.has_items_for_loan", ranges=[{"key": "available for loan", "from": 1}], ) ), medium=dict(terms=dict(field="stock.mediums")), ), post_filters=dict( access=terms_filter("restricted"), doctype=terms_filter("document_type"), language=terms_filter("languages"), tag=terms_filter("tags"), availability=keyed_range_filter( "circulation.has_items_for_loan", {"available for loan": {"gt": 0}}, ), relation=terms_filter("relation_types"), medium=terms_filter("stock.mediums"), ), ), document_requests=dict( # DocumentRequestSearch.Meta.index aggs=dict( state=dict(terms=dict(field="state")), reject_reason=dict(terms=dict(field="reject_reason")), ), post_filters=dict( state=terms_filter("state"), reject_reason=terms_filter("reject_reason"), ), ), items=dict( # ItemSearch.Meta.index aggs=dict( status=dict(terms=dict(field="status")), medium=dict(terms=dict(field="medium")), circulation=dict( terms=dict(field="circulation.state", missing="NOT_ON_LOAN") ), restrictions=dict(terms=dict(field="circulation_restriction")), location=dict(terms=dict(field="internal_location.location.name")), internal_location=dict(terms=dict(field="internal_location.name")), ), filters=dict(), post_filters=dict( circulation=default_value_when_missing_filter( "circulation.state", "NOT_ON_LOAN" ), status=terms_filter("status"), medium=terms_filter("medium"), restrictions=terms_filter("circulation_restriction"), location=terms_filter("internal_location.location.name"), internal_location=terms_filter("internal_location.name"), ), ), eitems=dict( aggs=dict( access=dict(terms=dict(field="open_access")), has_files=dict( filters=dict( filters=dict( has_files=dict(exists=dict(field="files.file_id")), no_files=dict( bool=dict( must_not=dict( exists=dict(field="files.file_id") ) ) ), ) ) ), ), post_filters=dict( access=terms_filter("open_access"), has_files=not_empty_object_or_list_filter("files.file_id"), ), ), series=dict( # SeriesSearch.Meta.index aggs=dict( moi=dict(terms=dict(field="mode_of_issuance")), language=dict(terms=dict(field="languages")), relation=dict(terms=dict(field="relation_types")), ), post_filters=dict( moi=terms_filter("mode_of_issuance"), language=terms_filter("languages"), relation=terms_filter("relation_types"), ), ), ) # ILS # === ILS_VIEWS_PERMISSIONS_FACTORY = views_permissions_factory """Permissions factory for ILS views to handle all ILS actions.""" ILS_INDEXER_TASK_DELAY = timedelta(seconds=5) """Time delay that indexers spawning their asynchronous celery tasks.""" # Accounts REST # ============== ACCOUNTS_REST_READ_USER_PROPERTIES_PERMISSION_FACTORY = backoffice_permission """Default read user properties permission factory: reject any request.""" ACCOUNTS_REST_UPDATE_USER_PROPERTIES_PERMISSION_FACTORY = backoffice_permission """Default modify user properties permission factory: reject any request.""" ACCOUNTS_REST_READ_USERS_LIST_PERMISSION_FACTORY = backoffice_permission """Default list users permission factory: reject any request.""" # The HTML tags allowed with invenio_records_rest.schemas.fields.sanitizedhtml ALLOWED_HTML_TAGS = [] # Stats # ===== STATS_EVENTS = { "file-download": { "signal": "invenio_files_rest.signals.file_downloaded", "templates": "invenio_app_ils.stats.file_download", "event_builders": [ "invenio_app_ils.eitems.api:eitem_event_builder", "invenio_stats.contrib.event_builders.file_download_event_builder", ], "cls": EventsIndexer, "params": { "preprocessors": [ "invenio_stats.processors:flag_robots", lambda doc: doc if not doc["is_robot"] else None, "invenio_stats.processors:flag_machines", "invenio_stats.processors:anonymize_user", "invenio_stats.contrib.event_builders:build_file_unique_id", ], "double_click_window": 30, "suffix": "%Y-%m", }, }, "record-view": { "signal": "invenio_app_ils.signals.record_viewed", "templates": "invenio_stats.contrib.record_view", "event_builders": [ "invenio_stats.contrib.event_builders.record_view_event_builder" ], "cls": EventsIndexer, "params": { "preprocessors": [ "invenio_stats.processors:flag_robots", lambda doc: doc if not doc["is_robot"] else None, "invenio_stats.processors:flag_machines", "invenio_stats.processors:anonymize_user", "invenio_stats.contrib.event_builders:build_record_unique_id", ], "double_click_window": 30, "suffix": "%Y-%m", }, }, } STATS_AGGREGATIONS = { "file-download-agg": dict( templates="invenio_app_ils.stats.aggregations.aggr_file_download", cls=StatAggregator, params=dict( event="file-download", field="file_id", interval="day", index_interval="month", copy_fields=dict( bucket_id="bucket_id", file_id="file_id", file_key="file_key", size="size", eitem_pid="eitem_pid", document_pid="document_pid", ), metric_fields=dict( unique_count=( "cardinality", "unique_session_id", {"precision_threshold": 1000}, ) ), ), ), "record-view-agg": dict( templates="invenio_stats.contrib.aggregations.aggr_record_view", cls=StatAggregator, params=dict( event="record-view", field="pid_value", interval="day", index_interval="month", copy_fields=dict(pid_type="pid_type", pid_value="pid_value"), metric_fields=dict( unique_count=( "cardinality", "unique_session_id", {"precision_threshold": 1000}, ) ), ), ), } STATS_QUERIES = { "file-download-by-document": dict( cls=ESTermsQuery, permission_factory=None, params=dict( index="stats-file-download", copy_fields=dict( bucket_id="bucket_id", file_id="file_id", file_key="file_key", size="size", eitem_pid="eitem_pid", document_pid="document_pid", ), required_filters=dict(document_pid="document_pid"), metric_fields=dict( count=("sum", "count", {}), unique_count=("sum", "unique_count", {}), ), ), ), "record-view": dict( cls=ESTermsQuery, permission_factory=None, params=dict( index="stats-record-view", copy_fields=dict(pid_type="pid_type", pid_value="pid_value"), required_filters=dict(pid_value="pid_value"), metric_fields=dict( count=("sum", "count", {}), unique_count=("sum", "unique_count", {}), ), ), ), } # List of available vocabularies ILS_VOCABULARIES = [ "acq_medium", "acq_order_line_payment_mode", "acq_order_line_purchase_type", "acq_payment_mode", "acq_recipient", "affiliation_identifier_scheme", "alternative_identifier_scheme", "alternative_title_type", "author_identifier_scheme", "author_role", "author_type", "conference_identifier_scheme", "country", "currencies", "identifier_scheme", "ill_item_type", "ill_payment_mode", "language", "license", "series_identifier_scheme", "series_url_access_restriction", "tag", ] ILS_VOCABULARY_SOURCES = { "json": "invenio_app_ils.vocabularies.sources:json_source", "opendefinition": "invenio_app_ils.vocabularies.sources:opendefinition_source", } OPENDEFINITION_JSONRESOLVER_HOST = "inveniosoftware.org" FILES_REST_PERMISSION_FACTORY = "invenio_app_ils.permissions:files_permission" ILS_RECORDS_EXPLICIT_PERMISSIONS_ENABLED = False """Enable records restrictions by `_access` field. When enabled, it allows to define explicit permissions for each record to provide read access to specific users or roles. When disabled, it will avoid checking for user ids and roles on each search query and record fetch. """ ILS_LITERATURE_COVER_URLS_BUILDER = build_ils_demo_cover_urls """Default implementation for building cover urls in document serializer.""" # Namespaces for fields added to the metadata schema ILS_RECORDS_METADATA_NAMESPACES = {} # Fields added to the metadata schema. ILS_RECORDS_METADATA_EXTENSIONS = {}
36.022031
129
0.639668
4192e3f3711fc4842e698ac261cc3cb47c066da1
427
py
Python
get_hash.py
mwzhu/twitter-telegram
968924faf0410bf58c1f3a977eefe6de4676090e
[ "MIT" ]
null
null
null
get_hash.py
mwzhu/twitter-telegram
968924faf0410bf58c1f3a977eefe6de4676090e
[ "MIT" ]
null
null
null
get_hash.py
mwzhu/twitter-telegram
968924faf0410bf58c1f3a977eefe6de4676090e
[ "MIT" ]
null
null
null
import telebot from telethon.sync import TelegramClient from telethon.tl.types import InputPeerUser, InputPeerChannel from telethon import TelegramClient, sync, events api_id = '13944982' api_hash = '0947f1da826caa937c5ccb1cbafb9264' client = TelegramClient('session', api_id, api_hash) client.connect() response = client.invoke(ResolveUsernameRequest("harrymcmoney")) print(response.chats[0].access_hash) client.disconnect()
32.846154
64
0.826698
634fed2ed60843f10c62e76d00af20e77f6c1399
400
py
Python
Desafio_056.py
roggnarok/Curso_em_video_Python
337287662e241a56a8a20ea0981a063c08dfab4d
[ "MIT" ]
null
null
null
Desafio_056.py
roggnarok/Curso_em_video_Python
337287662e241a56a8a20ea0981a063c08dfab4d
[ "MIT" ]
null
null
null
Desafio_056.py
roggnarok/Curso_em_video_Python
337287662e241a56a8a20ea0981a063c08dfab4d
[ "MIT" ]
null
null
null
# Curso em vídeo - Desafio 056 - FOR ''' Desenvolva um programa que leia o nome, idade e sexo de 4 pessoas. No final do programa mostre, A média de idade do grupo, Qual o nome do homem mais velho, Quantas mulheres têm menos de 20 anos. ''' idade = [] nome = [] for i in range(4): nome.append(input('Digite o nome: ')) idade.append(int(input('Digite a idade: ')))
26.666667
59
0.635
53702c484bccdc2dfd00870dd2211f36baf2f5da
18,490
py
Python
module/sunggu_module.py
babbu3682/MRI-Net
e3b6aeb44991bf45a4884a46af48a99ac656ac52
[ "MIT" ]
3
2021-10-04T11:00:23.000Z
2021-12-13T12:31:28.000Z
module/sunggu_module.py
babbu3682/MRI-Net
e3b6aeb44991bf45a4884a46af48a99ac656ac52
[ "MIT" ]
null
null
null
module/sunggu_module.py
babbu3682/MRI-Net
e3b6aeb44991bf45a4884a46af48a99ac656ac52
[ "MIT" ]
null
null
null
import torch.nn as nn import torch class TimeDistributed(nn.Module): def __init__(self, module, batch_first=False): super(TimeDistributed, self).__init__() self.module = module self.batch_first = batch_first def forward(self, x): if len(x.size()) <= 2: return self.module(x) # print("0# =", x.shape) 0# = torch.Size([5, 1024, 2, 40, 40]) # Squash samples and timesteps into a single axis batch, channel, time, height, width = x.shape x_reshape = x.contiguous().view(batch*time, channel, height, width) # (samples * timesteps, input_size) # print("2# =", x_reshape.shape) torch.Size([10, 1024, 40, 40]) y = self.module(x_reshape) # print("1", y.shape) [10, 512, 40, 40]) # print("self.module.out_channels", self.module.out_channels) 512 # We have to reshape Y if self.batch_first: y = y.contiguous().view(batch, self.module.out_channels, time, height, width) # (samples, timesteps, output_size) else: y = y.view(-1, x.size(1), y.size(-1)) # (timesteps, samples, output_size) return y class ConvLSTMCell(nn.Module): def __init__(self, input_dim, hidden_dim, kernel_size, bias): super(ConvLSTMCell, self).__init__() self.input_dim = input_dim self.hidden_dim = hidden_dim self.kernel_size = kernel_size self.padding = kernel_size[0] // 2, kernel_size[1] // 2 self.bias = bias self.conv = nn.Conv2d(in_channels=self.input_dim + self.hidden_dim, out_channels=4 * self.hidden_dim, kernel_size=self.kernel_size, padding=self.padding, bias=self.bias) def forward(self, input_tensor, cur_state): h_cur, c_cur = cur_state combined = torch.cat([input_tensor, h_cur], dim=1) # concatenate along channel axis combined_conv = self.conv(combined) cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1) i = torch.sigmoid(cc_i) f = torch.sigmoid(cc_f) o = torch.sigmoid(cc_o) g = torch.tanh(cc_g) c_next = f * c_cur + i * g h_next = o * torch.tanh(c_next) return h_next, c_next def init_hidden(self, batch_size, image_size): height, width = image_size return (torch.zeros(batch_size, self.hidden_dim, height, width, device=self.conv.weight.device), torch.zeros(batch_size, self.hidden_dim, height, width, device=self.conv.weight.device)) class ConvLSTM2d(nn.Module): def __init__(self, input_dim, hidden_dim, kernel_size, num_layers, batch_first=False, bias=True, return_all_layers=False): super(ConvLSTM2d, self).__init__() self._check_kernel_size_consistency(kernel_size) # Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers kernel_size = self._extend_for_multilayer(kernel_size, num_layers) hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers) if not len(kernel_size) == len(hidden_dim) == num_layers: raise ValueError('Inconsistent list length.') self.input_dim = input_dim self.hidden_dim = hidden_dim self.kernel_size = kernel_size self.num_layers = num_layers self.batch_first = batch_first self.bias = bias self.return_all_layers = return_all_layers cell_list = [] for i in range(0, self.num_layers): cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1] cell_list.append(ConvLSTMCell(input_dim=cur_input_dim, hidden_dim=self.hidden_dim[i], kernel_size=self.kernel_size[i], bias=self.bias)) self.cell_list = nn.ModuleList(cell_list) def forward(self, input_tensor, hidden_state=None): if not self.batch_first: # (t, b, c, h, w) -> (b, t, c, h, w) input_tensor = input_tensor.permute(1, 0, 2, 3, 4) # print("input_tensor.shape", input_tensor.shape) # (b, c, t, h, w) -> (b, t, c, h, w) input_tensor = input_tensor.permute(0, 2, 1, 3, 4) b, _, _, h, w = input_tensor.size() # Implement stateful ConvLSTM if hidden_state is not None: raise NotImplementedError() else: # Since the init is done in forward. Can send image size here hidden_state = self._init_hidden(batch_size=b, image_size=(h, w)) layer_output_list = [] last_state_list = [] seq_len = input_tensor.size(1) cur_layer_input = input_tensor for layer_idx in range(self.num_layers): h, c = hidden_state[layer_idx] output_inner = [] for t in range(seq_len): h, c = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :], cur_state=[h, c]) output_inner.append(h) layer_output = torch.stack(output_inner, dim=1) cur_layer_input = layer_output layer_output_list.append(layer_output) last_state_list.append([h, c]) if not self.return_all_layers: # (b, t, c, h, w) -> (b, c, t, h, w) layer_output_list = layer_output_list[-1].permute(0, 2, 1, 3, 4) last_state_list = last_state_list[-1] return layer_output_list, last_state_list def _init_hidden(self, batch_size, image_size): init_states = [] for i in range(self.num_layers): init_states.append(self.cell_list[i].init_hidden(batch_size, image_size)) return init_states @staticmethod def _check_kernel_size_consistency(kernel_size): if not (isinstance(kernel_size, tuple) or (isinstance(kernel_size, list) and all([isinstance(elem, tuple) for elem in kernel_size]))): raise ValueError('`kernel_size` must be tuple or list of tuples') @staticmethod def _extend_for_multilayer(param, num_layers): if not isinstance(param, list): param = [param] * num_layers return param # ######################################################################################################################## # ##################### 모델 ########################## # ######################################################################################################################## # class Flatten(torch.nn.Module): # def forward(self, x): # return x.view(x.shape[0], -1) # class conv_block(nn.Module): # def __init__(self, ch_in, ch_out): # super(conv_block,self).__init__() # self.convlstm1 = ConvLSTM2d(input_dim=ch_in, hidden_dim=ch_out, kernel_size=(3, 3), num_layers=1, batch_first=True, bias=True, return_all_layers=False) # self.norm1 = nn.BatchNorm3d(ch_out) # self.relu1 = nn.ReLU(inplace=True) # self.convlstm2 = ConvLSTM2d(input_dim=ch_out, hidden_dim=ch_out, kernel_size=(3, 3), num_layers=1, batch_first=True, bias=True, return_all_layers=False) # self.norm2 = nn.BatchNorm3d(ch_out) # self.relu2 = nn.ReLU(inplace=True) # def forward(self,x): # x = self.convlstm1(x) # x = self.norm1(x[0]) # x = self.relu1(x) # # x = self.convlstm2(x) # # x = self.norm2(x[0]) # # x = self.relu2(x) # return x # class up_conv(nn.Module): # def __init__(self, ch_in, ch_out): # super(up_conv,self).__init__() # self.up = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=False) # self.timedistributed = TimeDistributed(module=nn.Conv2d(in_channels=ch_in, out_channels=ch_out, kernel_size=3, stride=1, padding=1, bias=True), batch_first=True) # self.norm = nn.BatchNorm3d(ch_out) # self.relu = nn.ReLU(inplace=True) # def forward(self,x): # x = self.up(x) # # print("check1",x.shape) check1 torch.Size([5, 1024, 2, 40, 40]) # x = self.timedistributed(x) # # print("check2",x.shape) torch.Size([5, 512, 2, 40, 40]) # x = self.norm(x) # x = self.relu(x) # return x # class U_Net(nn.Module): # def __init__(self, img_ch=1, output_ch=1): # super(U_Net,self).__init__() # self.Maxpool = nn.MaxPool3d(kernel_size=2, stride=2) # self.Conv1 = conv_block(ch_in=img_ch, ch_out=64) # self.Conv2 = conv_block(ch_in=64, ch_out=128) # self.Conv3 = conv_block(ch_in=128, ch_out=256) # self.Conv4 = conv_block(ch_in=256, ch_out=512) # self.Conv5 = conv_block(ch_in=512, ch_out=1024) # self.Up5 = up_conv(ch_in=1024, ch_out=512) # self.Up_conv5 = conv_block(ch_in=1024, ch_out=512) # self.Up4 = up_conv(ch_in=512,ch_out=256) # self.Up_conv4 = conv_block(ch_in=512, ch_out=256) # self.Up3 = up_conv(ch_in=256,ch_out=128) # self.Up_conv3 = conv_block(ch_in=256, ch_out=128) # self.Up2 = up_conv(ch_in=128,ch_out=64) # self.Up_conv2 = conv_block(ch_in=128, ch_out=64) # self.Conv_1x1 = nn.Conv3d(64, output_ch, kernel_size=1, stride=1, padding=0) # self.linear = nn.Sequential(nn.AdaptiveAvgPool2d(1), Flatten(), nn.Linear(1024, 1, bias=True)) # def forward(self, x): # # encoding path # # print("x = " ,x.shape) torch.Size([5, 1, 16, 320, 320]) # x1 = self.Conv1(x) # # print("1 = " ,x1.shape) 1 = torch.Size([5, 64, 16, 320, 320]) # x2 = self.Maxpool(x1) # # print("2 = " ,x2.shape) 2 = torch.Size([5, 64, 8, 160, 160]) # x2 = self.Conv2(x2) # # print("3 = " ,x2.shape) 3 = torch.Size([5, 128, 8, 160, 160]) # x3 = self.Maxpool(x2) # # print("4 = " ,x3.shape) 4 = torch.Size([5, 128, 4, 80, 80]) # x3 = self.Conv3(x3) # # print("5 = " ,x3.shape) 5 = torch.Size([5, 256, 4, 80, 80]) # x4 = self.Maxpool(x3) # # print("6 = " ,x4.shape) 6 = torch.Size([5, 256, 2, 40, 40]) # x4 = self.Conv4(x4) # # print("7 = " ,x4.shape) 7 = torch.Size([5, 512, 2, 40, 40]) # x5 = self.Maxpool(x4) # # print("8 = " ,x5.shape) 8 = torch.Size([5, 512, 1, 20, 20]) # x5 = self.Conv5(x5) # # print("9 = ", x5.shape) # 9 = torch.Size([5, 1024, 1, 20, 20]) # # Aux 붙이기 # cls = self.linear(x5.squeeze()) # # print("10 = " ,cls.shape) 10 = torch.Size([5, 1]) # # decoding + concat path # d5 = self.Up5(x5) # # print("11 = " ,d5.shape) 11 = torch.Size([5, 512, 2, 40, 40]) # d5 = torch.cat((x4,d5),dim=1) # # print("12 = " ,d5.shape) torch.Size([5, 1024, 2, 40, 40]) # d5 = self.Up_conv5(d5) # # print("13 = " ,d5.shape) torch.Size([5, 512, 2, 40, 40]) # d4 = self.Up4(d5) # # print("14 = " ,d4.shape) torch.Size([5, 256, 4, 80, 80]) # d4 = torch.cat((x3,d4),dim=1) # # print("15 = " ,d4.shape) torch.Size([5, 512, 4, 80, 80]) # d4 = self.Up_conv4(d4) # # print("16 = " ,d4.shape) torch.Size([5, 256, 4, 80, 80]) # d3 = self.Up3(d4) # # print("17 = " ,d3.shape) torch.Size([5, 128, 8, 160, 160]) # d3 = torch.cat((x2,d3),dim=1) # # print("18 = " ,d3.shape) torch.Size([5, 256, 8, 160, 160]) # d3 = self.Up_conv3(d3) # # print("19 = " ,d3.shape) torch.Size([5, 128, 8, 160, 160]) # d2 = self.Up2(d3) # # print("20 = " ,d2.shape) torch.Size([5, 64, 16, 320, 320]) # d2 = torch.cat((x1,d2),dim=1) # # print("21 = " ,d2.shape) torch.Size([5, 128, 16, 320, 320]) # d2 = self.Up_conv2(d2) # # print("22 = " ,d2.shape) torch.Size([5, 64, 16, 320, 320]) # d1 = self.Conv_1x1(d2) # # print("1 = " ,d1.shape) torch.Size([5, 1, 16, 320, 320]) # return d1, cls ######################################################################################################################## ##################### 모델 2########################## ######################################################################################################################## class Flatten(torch.nn.Module): def forward(self, x): return x.view(x.shape[0], -1) class conv_block(nn.Module): def __init__(self, ch_in, ch_out): super(conv_block,self).__init__() self.timedistributed1 = TimeDistributed(module=nn.Conv2d(in_channels=ch_in, out_channels=ch_out, kernel_size=3, stride=1, padding=1, bias=True), batch_first=True) self.norm1 = nn.BatchNorm3d(ch_out) self.relu1 = nn.ReLU(inplace=True) self.timedistributed2 = TimeDistributed(module=nn.Conv2d(in_channels=ch_out, out_channels=ch_out, kernel_size=3, stride=1, padding=1, bias=True), batch_first=True) self.norm2 = nn.BatchNorm3d(ch_out) self.relu2 = nn.ReLU(inplace=True) def forward(self,x): x = self.timedistributed1(x) x = self.norm1(x) x = self.relu1(x) x = self.timedistributed2(x) x = self.norm2(x) x = self.relu2(x) return x class up_conv(nn.Module): def __init__(self, ch_in, ch_out): super(up_conv,self).__init__() self.up = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=False) self.convlstm = ConvLSTM2d(input_dim=ch_in, hidden_dim=ch_out, kernel_size=(3, 3), num_layers=1, batch_first=True, bias=True, return_all_layers=False) self.norm = nn.BatchNorm3d(ch_out) self.relu = nn.ReLU(inplace=True) def forward(self,x): x = self.up(x) # print("check1",x.shape) check1 torch.Size([5, 1024, 2, 40, 40]) x = self.convlstm(x) # print("check2",x.shape) torch.Size([5, 512, 2, 40, 40]) x = self.norm(x[0]) x = self.relu(x) return x class U_Net(nn.Module): def __init__(self, img_ch=1, output_ch=1): super(U_Net,self).__init__() self.Maxpool = nn.MaxPool3d(kernel_size=2, stride=2) self.Conv1 = conv_block(ch_in=img_ch, ch_out=64) self.Conv2 = conv_block(ch_in=64, ch_out=128) self.Conv3 = conv_block(ch_in=128, ch_out=256) self.Conv4 = conv_block(ch_in=256, ch_out=512) self.Conv5 = conv_block(ch_in=512, ch_out=1024) self.Up5 = up_conv(ch_in=1024, ch_out=512) self.Up_conv5 = conv_block(ch_in=1024, ch_out=512) self.Up4 = up_conv(ch_in=512,ch_out=256) self.Up_conv4 = conv_block(ch_in=512, ch_out=256) self.Up3 = up_conv(ch_in=256,ch_out=128) self.Up_conv3 = conv_block(ch_in=256, ch_out=128) self.Up2 = up_conv(ch_in=128,ch_out=64) self.Up_conv2 = conv_block(ch_in=128, ch_out=64) self.Conv_1x1 = nn.Conv3d(64, output_ch, kernel_size=1, stride=1, padding=0) self.linear = nn.Sequential(nn.AdaptiveAvgPool2d(1), Flatten(), nn.Linear(1024, 1, bias=True)) def forward(self, x): # encoding path # print("x = " ,x.shape) torch.Size([5, 1, 16, 320, 320]) x1 = self.Conv1(x) # print("1 = " ,x1.shape) 1 = torch.Size([5, 64, 16, 320, 320]) x2 = self.Maxpool(x1) # print("2 = " ,x2.shape) 2 = torch.Size([5, 64, 8, 160, 160]) x2 = self.Conv2(x2) # print("3 = " ,x2.shape) 3 = torch.Size([5, 128, 8, 160, 160]) x3 = self.Maxpool(x2) # print("4 = " ,x3.shape) 4 = torch.Size([5, 128, 4, 80, 80]) x3 = self.Conv3(x3) # print("5 = " ,x3.shape) 5 = torch.Size([5, 256, 4, 80, 80]) x4 = self.Maxpool(x3) # print("6 = " ,x4.shape) 6 = torch.Size([5, 256, 2, 40, 40]) x4 = self.Conv4(x4) # print("7 = " ,x4.shape) 7 = torch.Size([5, 512, 2, 40, 40]) x5 = self.Maxpool(x4) # print("8 = " ,x5.shape) 8 = torch.Size([5, 512, 1, 20, 20]) x5 = self.Conv5(x5) # print("9 = ", x5.shape) # 9 = torch.Size([5, 1024, 1, 20, 20]) # Aux 붙이기 cls = self.linear(x5.squeeze()) # print("10 = " ,cls.shape) 10 = torch.Size([5, 1]) # decoding + concat path d5 = self.Up5(x5) # print("11 = " ,d5.shape) 11 = torch.Size([5, 512, 2, 40, 40]) d5 = torch.cat((x4,d5),dim=1) # print("12 = " ,d5.shape) torch.Size([5, 1024, 2, 40, 40]) d5 = self.Up_conv5(d5) # print("13 = " ,d5.shape) torch.Size([5, 512, 2, 40, 40]) d4 = self.Up4(d5) # print("14 = " ,d4.shape) torch.Size([5, 256, 4, 80, 80]) d4 = torch.cat((x3,d4),dim=1) # print("15 = " ,d4.shape) torch.Size([5, 512, 4, 80, 80]) d4 = self.Up_conv4(d4) # print("16 = " ,d4.shape) torch.Size([5, 256, 4, 80, 80]) d3 = self.Up3(d4) # print("17 = " ,d3.shape) torch.Size([5, 128, 8, 160, 160]) d3 = torch.cat((x2,d3),dim=1) # print("18 = " ,d3.shape) torch.Size([5, 256, 8, 160, 160]) d3 = self.Up_conv3(d3) # print("19 = " ,d3.shape) torch.Size([5, 128, 8, 160, 160]) d2 = self.Up2(d3) # print("20 = " ,d2.shape) torch.Size([5, 64, 16, 320, 320]) d2 = torch.cat((x1,d2),dim=1) # print("21 = " ,d2.shape) torch.Size([5, 128, 16, 320, 320]) d2 = self.Up_conv2(d2) # print("22 = " ,d2.shape) torch.Size([5, 64, 16, 320, 320]) d1 = self.Conv_1x1(d2) # print("1 = " ,d1.shape) torch.Size([5, 1, 16, 320, 320]) return d1, cls
40.997783
172
0.526609
85745eff7370191db37cedb357e531f79e34951e
1,477
py
Python
analyzer/certificate_validator.py
Gr1ph00n/staticwebanalyzer
8bf6337a77192b85913d75778830ccbb9006081f
[ "MIT" ]
null
null
null
analyzer/certificate_validator.py
Gr1ph00n/staticwebanalyzer
8bf6337a77192b85913d75778830ccbb9006081f
[ "MIT" ]
null
null
null
analyzer/certificate_validator.py
Gr1ph00n/staticwebanalyzer
8bf6337a77192b85913d75778830ccbb9006081f
[ "MIT" ]
null
null
null
#!/usr/bin/env python # file name: certificate_validator.py # created by: Ventura Del Monte # purpose: SSL certificate validation # last edited by: Ventura Del Monte 17-10-14 # more details here: # http://curl.haxx.se/libcurl/c/CURLOPT_SSL_VERIFYPEER.html # http://curl.haxx.se/libcurl/c/CURLOPT_SSL_VERIFYHOST.html # https://github.com/pycurl/pycurl/blob/master/tests/certinfo_test.py import pycurl import utils def dummy(*args): pass class CertificateValidator: defaultCurlOptions = [ (pycurl.SSL_VERIFYPEER, 1), (pycurl.SSL_VERIFYHOST, 2), (pycurl.FOLLOWLOCATION, 1), (pycurl.CAINFO, "./ssl/curl-ca-bundle.crt"), (pycurl.OPT_CERTINFO, 1), (pycurl.WRITEFUNCTION, dummy) ] def __init__(self, userAgent = 'libcurl'): self.curl = pycurl.Curl() assert hasattr(self.curl, 'OPT_CERTINFO') for opt in self.defaultCurlOptions: #print opt self.curl.setopt(*opt) self.curl.setopt(pycurl.USERAGENT, userAgent) def close(self): self.curl.close() def setUserAgent(self, userAgent): self.curl.setopt(pycurl.USERAGENT, userAgent) def validate(self, url): try: self.curl.setopt(pycurl.URL, url) self.curl.perform() cert = self.curl.getinfo(pycurl.INFO_CERTINFO) except pycurl.error as e: print e return e[1], {} return "SSL Certificate is valid", self.parseCertificate(cert) def parseCertificate(self, data): return {entry[0] : entry[1] for entry in data[0] if entry[0] != "Signature" and entry[0] != "Cert"}
24.616667
101
0.717671
0f0b5e3b5c78a458cbb155934954ef186c3e4df6
625
py
Python
rail_object_detector/scripts/test_detections_topic.py
GT-RAIL/rail_object_detector
896940ebf127577203ff5e0b62374d9f6fda10f3
[ "MIT" ]
5
2017-01-08T21:52:52.000Z
2018-03-18T18:47:17.000Z
rail_object_detector/scripts/test_detections_topic.py
GT-RAIL/rail_object_detector
896940ebf127577203ff5e0b62374d9f6fda10f3
[ "MIT" ]
5
2017-06-06T18:07:24.000Z
2018-03-16T02:30:17.000Z
rail_object_detector/scripts/test_detections_topic.py
GT-RAIL/rail_object_detector
896940ebf127577203ff5e0b62374d9f6fda10f3
[ "MIT" ]
3
2017-06-02T19:55:58.000Z
2017-06-22T17:16:18.000Z
#!/usr/bin/env python # This script is designed to test the functionality of the object detections # topic import rospy from rail_object_detection_msgs.msg import Detections def detections_callback(data): rospy.loginfo("*****************New Message:*********************") rospy.loginfo( "Frame@Timestamp: %s@%s" % (data.header.frame_id, data.header.stamp,) ) rospy.loginfo("Objects: %s" % ",".join([x.label for x in data.objects])) def main(): rospy.init_node('test_detections_topic') rospy.Subscriber('/darknet_node/detections', Detections, detections_callback) rospy.spin() if __name__ == '__main__': main()
28.409091
78
0.7008
27a423ea46ca88773e5e4221d56078e230f6a970
9,159
py
Python
multi_task_helpers.py
gcormier/hpc-dfo
ac1ab24b116eddd9ce2fe2c4bfdb81b6b26349eb
[ "MIT", "Unlicense" ]
1
2019-04-22T12:39:32.000Z
2019-04-22T12:39:32.000Z
multi_task_helpers.py
gcormier/hpc-dfo
ac1ab24b116eddd9ce2fe2c4bfdb81b6b26349eb
[ "MIT", "Unlicense" ]
null
null
null
multi_task_helpers.py
gcormier/hpc-dfo
ac1ab24b116eddd9ce2fe2c4bfdb81b6b26349eb
[ "MIT", "Unlicense" ]
null
null
null
from __future__ import print_function import datetime import sys import time import azure.batch.batch_service_client as batch import azure.batch.models as batchmodels import os sys.path.append('.') import common.helpers # noqa def create_pool_and_wait_for_vms( batch_service_client, pool_id, publisher, offer, sku, vm_size, target_dedicated_nodes, command_line=None, resource_files=None, elevation_level=batchmodels.ElevationLevel.admin, enable_inter_node_communication=True): """ Creates a pool of compute nodes with the specified OS settings. :param batch_service_client: A Batch service client. :type batch_service_client: `azure.batch.BatchServiceClient` :param str pool_id: An ID for the new pool. :param str publisher: Marketplace Image publisher :param str offer: Marketplace Image offer :param str sku: Marketplace Image sku :param str vm_size: The size of VM, eg 'Standard_A1' or 'Standard_D1' per https://azure.microsoft.com/en-us/documentation/articles/ virtual-machines-windows-sizes/ :param int target_dedicated_nodes: Number of target VMs for the pool :param str command_line: command line for the pool's start task. :param list resource_files: A collection of resource files for the pool's start task. :param str elevation_level: Elevation level the task will be run as; either 'admin' or 'nonadmin'. """ print('Creating pool [{}]...'.format(pool_id)) sku_to_use, image_ref_to_use = \ common.helpers.select_latest_verified_vm_image_with_node_agent_sku( batch_service_client, publisher, offer, sku) user = batchmodels.AutoUserSpecification( scope=batchmodels.AutoUserScope.pool, elevation_level=elevation_level) new_pool = batch.models.PoolAddParameter( id=pool_id, virtual_machine_configuration=batchmodels.VirtualMachineConfiguration( image_reference=image_ref_to_use, node_agent_sku_id=sku_to_use), vm_size=vm_size, target_dedicated_nodes=target_dedicated_nodes, resize_timeout=datetime.timedelta(minutes=15), enable_inter_node_communication=enable_inter_node_communication, max_tasks_per_node=1, start_task=batch.models.StartTask( command_line=command_line, user_identity=batchmodels.UserIdentity(auto_user=user), wait_for_success=False, resource_files=resource_files) if command_line else None, ) common.helpers.create_pool_if_not_exist(batch_service_client, new_pool) # because we want all nodes to be available before any tasks are assigned # to the pool, here we will wait for all compute nodes to reach idle nodes = common.helpers.wait_for_all_nodes_state( batch_service_client, new_pool, frozenset( (batchmodels.ComputeNodeState.start_task_failed, batchmodels.ComputeNodeState.unusable, batchmodels.ComputeNodeState.idle) ) ) # ensure all node are idle if any(node.state != batchmodels.ComputeNodeState.idle for node in nodes): raise RuntimeError('node(s) of pool {} not in idle state'.format( pool_id)) def add_task( batch_service_client, job_id, task_id, num_instances, application_cmdline, input_files, elevation_level, output_file_names, output_container_sas, coordination_cmdline, common_files): """ Adds a task for each input file in the collection to the specified job. :param batch_service_client: A Batch service client. :type batch_service_client: `azure.batch.BatchServiceClient` :param str job_id: The ID of the job to which to add the task. :param str task_id: The ID of the task to be added. :param str application_cmdline: The application commandline for the task. :param list input_files: A collection of input files. :param elevation_level: Elevation level used to run the task; either 'admin' or 'nonadmin'. :type elevation_level: `azure.batch.models.ElevationLevel` :param int num_instances: Number of instances for the task :param str coordination_cmdline: The application commandline for the task. :param list common_files: A collection of common input files. """ print('Adding {} task to job [{}]...'.format(task_id, job_id)) multi_instance_settings = None if coordination_cmdline or (num_instances and num_instances > 1): multi_instance_settings = batchmodels.MultiInstanceSettings( number_of_instances=num_instances, coordination_command_line=coordination_cmdline, common_resource_files=common_files) user = batchmodels.AutoUserSpecification( scope=batchmodels.AutoUserScope.pool, elevation_level=elevation_level) output_file = batchmodels.OutputFile( file_pattern=output_file_names, destination=batchmodels.OutputFileDestination( container=batchmodels.OutputFileBlobContainerDestination( container_url=output_container_sas)), upload_options=batchmodels.OutputFileUploadOptions( upload_condition=batchmodels. OutputFileUploadCondition.task_completion)) task = batchmodels.TaskAddParameter( id=task_id, command_line=application_cmdline, user_identity=batchmodels.UserIdentity(auto_user=user), resource_files=input_files, multi_instance_settings=multi_instance_settings, output_files=[output_file]) batch_service_client.task.add(job_id, task) def wait_for_subtasks_to_complete( batch_service_client, job_id, task_id, timeout): """ Returns when all subtasks in the specified task reach the Completed state. :param batch_service_client: A Batch service client. :type batch_service_client: `azure.batch.BatchServiceClient` :param str job_id: The id of the job whose tasks should be to monitored. :param str task_id: The id of the task whose subtasks should be monitored. :param timedelta timeout: The duration to wait for task completion. If all tasks in the specified job do not reach Completed state within this time period, an exception will be raised. """ timeout_expiration = datetime.datetime.now() + timeout print("Monitoring all tasks for 'Completed' state, timeout in {}..." .format(timeout), end='') while datetime.datetime.now() < timeout_expiration: print('.', end='') sys.stdout.flush() subtasks = batch_service_client.task.list_subtasks(job_id, task_id) incomplete_subtasks = [subtask for subtask in subtasks.value if subtask.state != batchmodels.TaskState.completed] if not incomplete_subtasks: print("Subtask complete!") return True else: time.sleep(10) print("Subtasks did not reach completed state within timeout period!") raise RuntimeError( "ERROR: Subtasks did not reach 'Completed' state within " "timeout period of " + str(timeout)) def wait_for_tasks_to_complete(batch_service_client, job_id, timeout): """ Returns when all tasks in the specified job reach the Completed state. :param batch_service_client: A Batch service client. :type batch_service_client: `azure.batch.BatchServiceClient` :param str job_id: The id of the job whose tasks should be to monitored. :param timedelta timeout: The duration to wait for task completion. If all tasks in the specified job do not reach Completed state within this time period, an exception will be raised. """ timeout_expiration = datetime.datetime.now() + timeout print("Monitoring all tasks for 'Completed' state, timeout in {}..." .format(timeout), end='') while datetime.datetime.now() < timeout_expiration: print('.', end='') sys.stdout.flush() tasks = batch_service_client.task.list(job_id) for task in tasks: if task.state == batchmodels.TaskState.completed: # Pause execution until subtasks reach Completed state. wait_for_subtasks_to_complete(batch_service_client, job_id, task.id, datetime.timedelta(minutes=10)) incomplete_tasks = [task for task in tasks if task.state != batchmodels.TaskState.completed] if not incomplete_tasks: print("Taskss complete!") return True else: time.sleep(10) print("Tasks did not reach completed state within timeout period!") raise RuntimeError("ERROR: Tasks did not reach 'Completed' state within " "timeout period of " + str(timeout))
42.799065
79
0.678458
f0568a73ae6bc8aef3fe6533df41f319311a6312
197
py
Python
strategy/web_copy.py
Pobux/pattern_conception
a4aa15e910c4ec55abda7c833562a3cc41dcb325
[ "MIT" ]
null
null
null
strategy/web_copy.py
Pobux/pattern_conception
a4aa15e910c4ec55abda7c833562a3cc41dcb325
[ "MIT" ]
null
null
null
strategy/web_copy.py
Pobux/pattern_conception
a4aa15e910c4ec55abda7c833562a3cc41dcb325
[ "MIT" ]
null
null
null
#-*- coding: utf-8 -*- # Creation Date : 2016-10-18 # Created by : Antoine LeBel from location_copy import LocationCopy class WebCopy(LocationCopy): def copy(self): print("Copie web")
21.888889
38
0.680203
94f315501a9360e5a9e67ab0c5d8cda7581c206a
2,122
py
Python
NoteBooks/Curso de Flask/Ex_Files_Full_Stack_Dev_Flask/Exercise Files/Section 5/5.2, 5.3 Start/application/routes.py
Alejandro-sin/Learning_Notebooks
161d6bed4c7b1d171b45f61c0cc6fa91e9894aad
[ "MIT" ]
1
2021-02-26T13:12:22.000Z
2021-02-26T13:12:22.000Z
NoteBooks/Curso de Flask/Ex_Files_Full_Stack_Dev_Flask/Exercise Files/Section 5/5.2, 5.3 Start/application/routes.py
Alejandro-sin/Learning_Notebooks
161d6bed4c7b1d171b45f61c0cc6fa91e9894aad
[ "MIT" ]
null
null
null
NoteBooks/Curso de Flask/Ex_Files_Full_Stack_Dev_Flask/Exercise Files/Section 5/5.2, 5.3 Start/application/routes.py
Alejandro-sin/Learning_Notebooks
161d6bed4c7b1d171b45f61c0cc6fa91e9894aad
[ "MIT" ]
null
null
null
from application import app, db from flask import render_template, request, json, Response from application.models import User, Course, Enrollment courseData = [{"courseID":"1111","title":"PHP 111","description":"Intro to PHP","credits":"3","term":"Fall, Spring"}, {"courseID":"2222","title":"Java 1","description":"Intro to Java Programming","credits":"4","term":"Spring"}, {"courseID":"3333","title":"Adv PHP 201","description":"Advanced PHP Programming","credits":"3","term":"Fall"}, {"courseID":"4444","title":"Angular 1","description":"Intro to Angular","credits":"3","term":"Fall, Spring"}, {"courseID":"5555","title":"Java 2","description":"Advanced Java Programming","credits":"4","term":"Fall"}] @app.route("/") @app.route("/index") @app.route("/home") def index(): return render_template("index.html", index=True ) @app.route("/login") def login(): return render_template("login.html", login=True ) @app.route("/courses/") @app.route("/courses/<term>") def courses(term="Spring 2019"): return render_template("courses.html", courseData=courseData, courses = True, term=term ) @app.route("/register") def register(): return render_template("register.html", register=True) @app.route("/enrollment", methods=["GET","POST"]) def enrollment(): id = request.form.get('courseID') title = request.form['title'] term = request.form.get('term') return render_template("enrollment.html", enrollment=True, data={"id":id,"title":title,"term":term}) @app.route("/api/") @app.route("/api/<idx>") def api(idx=None): if(idx == None): jdata = courseData else: jdata = courseData[int(idx)] return Response(json.dumps(jdata), mimetype="application/json") @app.route("/user") def user(): #User(user_id=1, first_name="Christian", last_name="Hur", email="[email protected]", password="abc1234").save() #User(user_id=2, first_name="Mary", last_name="Jane", email="[email protected]", password="password123").save() users = User.objects.all() return render_template("user.html", users=users)
43.306122
558
0.65787
775506d0be721b999a7ad969f0e1deda7d9a4952
2,855
py
Python
prepare.py
barbmarques/classification-exercises
368cde67365141fc4148d3fee14881dd210e2f8f
[ "MIT" ]
1
2021-02-22T23:54:03.000Z
2021-02-22T23:54:03.000Z
prepare.py
barbmarques/classification-exercises
368cde67365141fc4148d3fee14881dd210e2f8f
[ "MIT" ]
null
null
null
prepare.py
barbmarques/classification-exercises
368cde67365141fc4148d3fee14881dd210e2f8f
[ "MIT" ]
null
null
null
import pandas as pd from pandas import DataFrame from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split def clean_titanic(df): ''' clean_titanic will take a dataframe acquired as df and remove columns that are: duplicates, have too many nulls, and will fill in smaller amounts of nulls in embark_town encode sex and embark_town columns return: single cleaned dataframe ''' df.drop_duplicates df['embark_town'] = df['embark_town'].fillna('Southampton') dummies = pd.get_dummies(df[['embark_town', 'sex']], drop_first=True) dropcols = ['deck', 'class', 'embarked', 'sex', 'embark_town'] df.drop(columns=dropcols, inplace = True) return pd.concat([df, dummies], axis=1) def handle_missing_values(df): return df.assign( embark_town=df.embark_town.fillna('Southampton'), embarked=df.embarked.fillna('O'), ) def remove_columns(df): return df.drop(columns=['deck']) def encode_embarked(df): encoder = LabelEncoder() encoder.fit(df.embarked) return df.assign(embarked_encode = encoder.transform(df.embarked)) def prep_titanic_data(df): df = df\ .pipe(handle_missing_values)\ .pipe(remove_columns)\ .pipe(encode_embarked) return df import pandas as pd def clean_iris(df): """ clean_iris will take an acquired df and remove `species_id` and `measurement_id` columns and rename `species_name` column to just `species` and encode 'species_name' column into TWO new columns return: single cleaned dataframe """ dropcols = ['species_id', 'measurement_id'] df = df.drop(columns= dropcols) df = df.rename(columns={'species_name': 'species'}) dummy_sp = pd.get_dummies(df[['species']], drop_first=True) return pd.concat([df, dummy_sp], axis =1) def prep_iris(df): """ prep_iris will take one argument(df) and run clean_iris to remove/rename/encode columns then split our data into 20/80, then split the 80% into 30/70 perform a train, validate, test split return: the three split pandas dataframes-train/validate/test """ iris_df = clean_iris(df) train_validate, test = train_test_split(iris_df, test_size=0.2, random_state=3210, stratify=iris_df.species) train, validate = train_test_split(train_validate, train_size=0.7, random_state=3210, stratify=train_validate.species) return train, validate, test def train_validate_test_split(df, seed=123): train_and_validate, test = train_test_split( df, test_size=0.2, random_state=seed, stratify=None ) train, validate = train_test_split( train_and_validate, test_size=0.3, random_state=seed, stratify=train_and_validate ) return train, validate, test
31.032609
122
0.69317
2fac3537b4e361b91f268526947ad8c4e81fca28
423
py
Python
bugaderia/conftest.py
jaleo56/bugaderia
fd29f516692af79e024945abcf7d7a413698ab3e
[ "MIT" ]
null
null
null
bugaderia/conftest.py
jaleo56/bugaderia
fd29f516692af79e024945abcf7d7a413698ab3e
[ "MIT" ]
null
null
null
bugaderia/conftest.py
jaleo56/bugaderia
fd29f516692af79e024945abcf7d7a413698ab3e
[ "MIT" ]
null
null
null
import pytest from django.conf import settings from django.test import RequestFactory from bugaderia.users.tests.factories import UserFactory @pytest.fixture(autouse=True) def media_storage(settings, tmpdir): settings.MEDIA_ROOT = tmpdir.strpath @pytest.fixture def user() -> settings.AUTH_USER_MODEL: return UserFactory() @pytest.fixture def request_factory() -> RequestFactory: return RequestFactory()
20.142857
55
0.787234
7fa75fc54ab2a818bb0e1223e1d5b06327fe89ce
16,649
py
Python
targetcli/ui_backstore.py
Datera/targetcli
a1251821e1f6effeccb196a7a70544ab44fe2108
[ "Apache-2.0" ]
44
2015-04-02T21:44:31.000Z
2022-01-12T03:28:01.000Z
targetcli/ui_backstore.py
Datera/targetcli
a1251821e1f6effeccb196a7a70544ab44fe2108
[ "Apache-2.0" ]
22
2015-03-29T20:08:19.000Z
2020-03-19T14:31:40.000Z
targetcli/ui_backstore.py
Datera/targetcli
a1251821e1f6effeccb196a7a70544ab44fe2108
[ "Apache-2.0" ]
23
2015-06-18T14:29:16.000Z
2021-12-07T00:52:01.000Z
''' Implements the targetcli backstores related UI. This file is part of LIO(tm). Copyright (c) 2011-2014 by Datera, Inc Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' import os from ui_node import UINode, UIRTSLibNode from rtslib import RTSRoot from rtslib import FileIOBackstore, IBlockBackstore from rtslib import PSCSIBackstore, RDMCPBackstore from rtslib import FileIOStorageObject, IBlockStorageObject from rtslib import PSCSIStorageObject, RDMCPStorageObject from rtslib.utils import get_block_type, is_disk_partition from rtslib.utils import convert_human_to_bytes, convert_bytes_to_human from configshell import ExecutionError def dedup_so_name(storage_object): ''' Useful for migration from ui_backstore_legacy to new style with 1:1 hba:so mapping. If name is a duplicate in a backstore, returns name_X where X is the HBA index. ''' names = [so.name for so in RTSRoot().storage_objects if so.backstore.plugin == storage_object.backstore.plugin] if names.count(storage_object.name) > 1: return "%s_%d" % (storage_object.name, storage_object.backstore.index) else: return storage_object.name class UIBackstores(UINode): ''' The backstores container UI. ''' def __init__(self, parent): UINode.__init__(self, 'backstores', parent) self.cfs_cwd = "%s/core" % self.cfs_cwd self.refresh() def refresh(self): self._children = set([]) UIPSCSIBackstore(self) UIRDMCPBackstore(self) UIFileIOBackstore(self) UIIBlockBackstore(self) class UIBackstore(UINode): ''' A backstore UI. Abstract Base Class, do not instantiate. ''' def __init__(self, plugin, parent): UINode.__init__(self, plugin, parent) self.cfs_cwd = "%s/core" % self.cfs_cwd self.refresh() def refresh(self): self._children = set([]) for so in RTSRoot().storage_objects: if so.backstore.plugin == self.name: ui_so = UIStorageObject(so, self) ui_so.name = dedup_so_name(so) def summary(self): no_storage_objects = len(self._children) if no_storage_objects > 1: msg = "%d Storage Objects" % no_storage_objects else: msg = "%d Storage Object" % no_storage_objects return (msg, None) def prm_buffered(self, buffered): buffered = \ self.ui_eval_param(buffered, 'bool', True) if buffered: self.shell.log.info("Using buffered mode.") else: self.shell.log.info("Not using buffered mode.") return buffered def ui_command_delete(self, name): ''' Recursively deletes the storage object having the specified I{name}. If there are LUNs using this storage object, they will be deleted too. EXAMPLE ======= B{delete mystorage} ------------------- Deletes the storage object named mystorage, and all associated LUNs. ''' self.assert_root() try: child = self.get_child(name) except ValueError: self.shell.log.error("No storage object named %s." % name) else: hba = child.rtsnode.backstore child.rtsnode.delete() if not list(hba.storage_objects): hba.delete() self.remove_child(child) self.shell.log.info("Deleted storage object %s." % name) self.parent.parent.refresh() def ui_complete_delete(self, parameters, text, current_param): ''' Parameter auto-completion method for user command delete. @param parameters: Parameters on the command line. @type parameters: dict @param text: Current text of parameter being typed by the user. @type text: str @param current_param: Name of parameter to complete. @type current_param: str @return: Possible completions @rtype: list of str ''' if current_param == 'name': names = [child.name for child in self.children] completions = [name for name in names if name.startswith(text)] else: completions = [] if len(completions) == 1: return [completions[0] + ' '] else: return completions def next_hba_index(self): self.shell.log.debug("%r" % [(backstore.plugin, backstore.index) for backstore in RTSRoot().backstores]) indexes = [backstore.index for backstore in RTSRoot().backstores if backstore.plugin == self.name] self.shell.log.debug("Existing %s backstore indexes: %r" % (self.name, indexes)) for index in range(1048576): if index not in indexes: backstore_index = index break if backstore_index is None: raise ExecutionError("Cannot find an available backstore index.") else: self.shell.log.debug("First available %s backstore index is %d." % (self.name, backstore_index)) return backstore_index def assert_available_so_name(self, name): names = [child.name for child in self.children] if name in names: raise ExecutionError("Storage object %s/%s already exist." % (self.name, name)) class UIPSCSIBackstore(UIBackstore): ''' PSCSI backstore UI. ''' def __init__(self, parent): UIBackstore.__init__(self, 'pscsi', parent) def ui_command_create(self, name, dev): ''' Creates a PSCSI storage object, with supplied name and SCSI device. The SCSI device I{dev} can either be a path name to the device, in which case it is recommended to use the /dev/disk/by-id hierarchy to have consistent naming should your physical SCSI system be modified, or an SCSI device ID in the H:C:T:L format, which is not recommended as SCSI IDs may vary in time. ''' self.assert_root() self.assert_available_so_name(name) backstore = PSCSIBackstore(self.next_hba_index(), mode='create') if get_block_type(dev) is not None or is_disk_partition(dev): self.shell.log.info("Note: block backstore recommended for " "SCSI block devices") try: so = PSCSIStorageObject(backstore, name, dev) except Exception, exception: backstore.delete() raise exception ui_so = UIStorageObject(so, self) self.shell.log.info("Created pscsi storage object %s using %s" % (name, dev)) return self.new_node(ui_so) class UIRDMCPBackstore(UIBackstore): ''' RDMCP backstore UI. ''' def __init__(self, parent): UIBackstore.__init__(self, 'rd_mcp', parent) def ui_command_create(self, name, size, nullio=None): ''' Creates an RDMCP storage object. I{size} is the size of the ramdisk, and the optional I{nullio} parameter is a boolean specifying whether or not we should use a stub nullio instead of a real ramdisk. SIZE SYNTAX =========== - If size is an int, it represents a number of bytes. - If size is a string, the following units can be used: - B{B} or no unit present for bytes - B{k}, B{K}, B{kB}, B{KB} for kB (kilobytes) - B{m}, B{M}, B{mB}, B{MB} for MB (megabytes) - B{g}, B{G}, B{gB}, B{GB} for GB (gigabytes) - B{t}, B{T}, B{tB}, B{TB} for TB (terabytes) ''' self.assert_root() self.assert_available_so_name(name) backstore = RDMCPBackstore(self.next_hba_index(), mode='create') nullio = self.ui_eval_param(nullio, 'bool', False) try: so = RDMCPStorageObject(backstore, name, size, nullio=nullio) except Exception, exception: backstore.delete() raise exception ui_so = UIStorageObject(so, self) self.shell.log.info("Created rd_mcp ramdisk %s with size %s." % (name, size)) if nullio and not so.nullio: self.shell.log.warning("nullio ramdisk is not supported by this " "kernel version, created with nullio=false") return self.new_node(ui_so) class UIFileIOBackstore(UIBackstore): ''' FileIO backstore UI. ''' def __init__(self, parent): UIBackstore.__init__(self, 'fileio', parent) def _create_file(self, filename, size, sparse=True): f = open(filename, "w+") try: if sparse: os.ftruncate(f.fileno(), size) else: self.shell.log.info("Writing %s bytes" % size) while size > 0: write_size = min(size, 1024) f.write("\0" * write_size) size -= write_size except IOError: f.close() os.remove(filename) raise ExecutionError("Could not expand file to size") f.close() def ui_command_create(self, name, file_or_dev, size=None, buffered=None, sparse=None): ''' Creates a FileIO storage object. If I{file_or_dev} is a path to a regular file to be used as backend, then the I{size} parameter is mandatory. Else, if I{file_or_dev} is a path to a block device, the size parameter B{must} be ommited. If present, I{size} is the size of the file to be used, I{file} the path to the file or I{dev} the path to a block device. The I{buffered} parameter is a boolean stating whether or not to enable buffered mode. It is enabled by default (asynchronous mode). The I{sparse} parameter is only applicable when creating a new backing file. It is a boolean stating if the created file should be created as a sparse file (the default), or fully initialized. SIZE SYNTAX =========== - If size is an int, it represents a number of bytes. - If size is a string, the following units can be used: - B{B} or no unit present for bytes - B{k}, B{K}, B{kB}, B{KB} for kB (kilobytes) - B{m}, B{M}, B{mB}, B{MB} for MB (megabytes) - B{g}, B{G}, B{gB}, B{GB} for GB (gigabytes) - B{t}, B{T}, B{tB}, B{TB} for TB (terabytes) ''' self.assert_root() self.assert_available_so_name(name) self.shell.log.debug("Using params size=%s buffered=%s" " sparse=%s" % (size, buffered, sparse)) sparse = self.ui_eval_param(sparse, 'bool', True) backstore = FileIOBackstore(self.next_hba_index(), mode='create') is_dev = get_block_type(file_or_dev) is not None \ or is_disk_partition(file_or_dev) if size is None and is_dev: backstore = FileIOBackstore(self.next_hba_index(), mode='create') try: so = FileIOStorageObject( backstore, name, file_or_dev, buffered_mode=self.prm_buffered(buffered)) except Exception, exception: backstore.delete() raise exception self.shell.log.info("Created fileio %s with size %s." % (name, size)) self.shell.log.info("Note: block backstore preferred for " " best results.") ui_so = UIStorageObject(so, self) return self.new_node(ui_so) elif size is not None and not is_dev: backstore = FileIOBackstore(self.next_hba_index(), mode='create') try: so = FileIOStorageObject( backstore, name, file_or_dev, size, buffered_mode=self.prm_buffered(buffered)) except Exception, exception: backstore.delete() raise exception self.shell.log.info("Created fileio %s." % name) ui_so = UIStorageObject(so, self) return self.new_node(ui_so) else: # use given file size only if backing file does not exist if os.path.isfile(file_or_dev): new_size = str(os.path.getsize(file_or_dev)) if size: self.shell.log.info("%s exists, using its size (%s bytes)" " instead" % (file_or_dev, new_size)) size = new_size elif os.path.exists(file_or_dev): raise ExecutionError("Path %s exists but is not a file" % file_or_dev) else: # create file and extend to given file size if not size: raise ExecutionError("Attempting to create file for new" + " fileio backstore, need a size") self._create_file(file_or_dev, convert_human_to_bytes(size), sparse) class UIIBlockBackstore(UIBackstore): ''' IBlock backstore UI. ''' def __init__(self, parent): UIBackstore.__init__(self, 'iblock', parent) def ui_command_create(self, name, dev): ''' Creates an IBlock Storage object. I{dev} is the path to the TYPE_DISK block device to use. ''' self.assert_root() self.assert_available_so_name(name) backstore = IBlockBackstore(self.next_hba_index(), mode='create') try: so = IBlockStorageObject(backstore, name, dev) except Exception, exception: backstore.delete() raise exception ui_so = UIStorageObject(so, self) self.shell.log.info("Created iblock storage object %s using %s." % (name, dev)) return self.new_node(ui_so) class UIStorageObject(UIRTSLibNode): ''' A storage object UI. Abstract Base Class, do not instantiate. ''' def __init__(self, storage_object, parent): name = storage_object.name UIRTSLibNode.__init__(self, name, storage_object, parent) self.cfs_cwd = storage_object.path self.refresh() def ui_command_version(self): ''' Displays the version of the current backstore's plugin. ''' backstore = self.rtsnode.backstore self.shell.con.display("Backstore plugin %s %s" % (backstore.plugin, backstore.version)) def summary(self): so = self.rtsnode errors = [] if so.backstore.plugin.startswith("rd"): path = "ramdisk" else: path = so.udev_path if not path: errors.append("BROKEN STORAGE LINK") legacy = [] if self.rtsnode.name != self.name: legacy.append("ADDED SUFFIX") if len(list(self.rtsnode.backstore.storage_objects)) > 1: legacy.append("SHARED HBA") if legacy: errors.append("LEGACY: " + ", ".join(legacy)) size = convert_bytes_to_human(getattr(so, "size", 0)) if so.status == "activated": status = "in use" else: status = "not in use" nullio_str = "" try: if so.nullio: nullio_str = "nullio" except AttributeError: pass if errors: info = ", ".join(errors) if path: info += " (%s %s)" % (path, status) return (info, False) else: info = ", ".join(["%s" % str(data) for data in (size, path, status, nullio_str) if data]) return (info, True)
37.329596
86
0.576912
7edf1dc7d60a9a3c670b195f91dff4eb1026ee1d
919
py
Python
tests/components/default_config/test_init.py
squirrel289/core
6c5bcbfc3ee40927458e9188d6b79bf63933d3f9
[ "Apache-2.0" ]
5
2020-09-17T21:47:23.000Z
2021-06-04T04:37:29.000Z
tests/components/default_config/test_init.py
SicAriuSx83/core
162c39258e68ae42fe4e1560ae91ed54f5662409
[ "Apache-2.0" ]
47
2020-07-23T07:13:11.000Z
2022-03-31T06:01:46.000Z
tests/components/default_config/test_init.py
SicAriuSx83/core
162c39258e68ae42fe4e1560ae91ed54f5662409
[ "Apache-2.0" ]
2
2017-09-03T16:06:02.000Z
2021-01-12T15:07:52.000Z
"""Test the default_config init.""" import pytest from homeassistant.setup import async_setup_component from tests.async_mock import patch @pytest.fixture(autouse=True) def mock_zeroconf(): """Mock zeroconf.""" with patch("homeassistant.components.zeroconf.HaZeroconf"): yield @pytest.fixture(autouse=True) def mock_ssdp(): """Mock ssdp.""" with patch("homeassistant.components.ssdp.Scanner.async_scan"): yield @pytest.fixture(autouse=True) def mock_updater(): """Mock updater.""" with patch("homeassistant.components.updater.get_newest_version"): yield @pytest.fixture(autouse=True) def recorder_url_mock(): """Mock recorder url.""" with patch("homeassistant.components.recorder.DEFAULT_URL", "sqlite://"): yield async def test_setup(hass): """Test setup.""" assert await async_setup_component(hass, "default_config", {"foo": "bar"})
22.975
78
0.70185
c9f7ca1ee5df68789c6d3210640c890e5019a12c
6,125
py
Python
test/cpython/test_ucn.py
aisk/pyston
ac69cfef0621dbc8901175e84fa2b5cb5781a646
[ "BSD-2-Clause", "Apache-2.0" ]
1
2020-02-06T14:28:45.000Z
2020-02-06T14:28:45.000Z
test/cpython/test_ucn.py
aisk/pyston
ac69cfef0621dbc8901175e84fa2b5cb5781a646
[ "BSD-2-Clause", "Apache-2.0" ]
null
null
null
test/cpython/test_ucn.py
aisk/pyston
ac69cfef0621dbc8901175e84fa2b5cb5781a646
[ "BSD-2-Clause", "Apache-2.0" ]
1
2020-02-06T14:29:00.000Z
2020-02-06T14:29:00.000Z
""" Test script for the Unicode implementation. Written by Bill Tutt. Modified for Python 2.0 by Fredrik Lundh ([email protected]) (c) Copyright CNRI, All Rights Reserved. NO WARRANTY. """#" import unittest import sys from test import test_support try: from _testcapi import INT_MAX, PY_SSIZE_T_MAX, UINT_MAX except ImportError: INT_MAX = PY_SSIZE_T_MAX = UINT_MAX = 2**64 - 1 class UnicodeNamesTest(unittest.TestCase): def checkletter(self, name, code): # Helper that put all \N escapes inside eval'd raw strings, # to make sure this script runs even if the compiler # chokes on \N escapes res = eval(ur'u"\N{%s}"' % name) self.assertEqual(res, code) return res def test_general(self): # General and case insensitivity test: chars = [ "LATIN CAPITAL LETTER T", "LATIN SMALL LETTER H", "LATIN SMALL LETTER E", "SPACE", "LATIN SMALL LETTER R", "LATIN CAPITAL LETTER E", "LATIN SMALL LETTER D", "SPACE", "LATIN SMALL LETTER f", "LATIN CAPITAL LeTtEr o", "LATIN SMaLl LETTER x", "SPACE", "LATIN SMALL LETTER A", "LATIN SMALL LETTER T", "LATIN SMALL LETTER E", "SPACE", "LATIN SMALL LETTER T", "LATIN SMALL LETTER H", "LATIN SMALL LETTER E", "SpAcE", "LATIN SMALL LETTER S", "LATIN SMALL LETTER H", "LATIN small LETTER e", "LATIN small LETTER e", "LATIN SMALL LETTER P", "FULL STOP" ] string = u"The rEd fOx ate the sheep." self.assertEqual( u"".join([self.checkletter(*args) for args in zip(chars, string)]), string ) def test_ascii_letters(self): import unicodedata for char in "".join(map(chr, xrange(ord("a"), ord("z")))): name = "LATIN SMALL LETTER %s" % char.upper() code = unicodedata.lookup(name) self.assertEqual(unicodedata.name(code), name) def test_hangul_syllables(self): self.checkletter("HANGUL SYLLABLE GA", u"\uac00") self.checkletter("HANGUL SYLLABLE GGWEOSS", u"\uafe8") self.checkletter("HANGUL SYLLABLE DOLS", u"\ub3d0") self.checkletter("HANGUL SYLLABLE RYAN", u"\ub7b8") self.checkletter("HANGUL SYLLABLE MWIK", u"\ubba0") self.checkletter("HANGUL SYLLABLE BBWAEM", u"\ubf88") self.checkletter("HANGUL SYLLABLE SSEOL", u"\uc370") self.checkletter("HANGUL SYLLABLE YI", u"\uc758") self.checkletter("HANGUL SYLLABLE JJYOSS", u"\ucb40") self.checkletter("HANGUL SYLLABLE KYEOLS", u"\ucf28") self.checkletter("HANGUL SYLLABLE PAN", u"\ud310") self.checkletter("HANGUL SYLLABLE HWEOK", u"\ud6f8") self.checkletter("HANGUL SYLLABLE HIH", u"\ud7a3") import unicodedata self.assertRaises(ValueError, unicodedata.name, u"\ud7a4") def test_cjk_unified_ideographs(self): self.checkletter("CJK UNIFIED IDEOGRAPH-3400", u"\u3400") self.checkletter("CJK UNIFIED IDEOGRAPH-4DB5", u"\u4db5") self.checkletter("CJK UNIFIED IDEOGRAPH-4E00", u"\u4e00") self.checkletter("CJK UNIFIED IDEOGRAPH-9FA5", u"\u9fa5") self.checkletter("CJK UNIFIED IDEOGRAPH-20000", u"\U00020000") self.checkletter("CJK UNIFIED IDEOGRAPH-2A6D6", u"\U0002a6d6") def test_bmp_characters(self): import unicodedata count = 0 for code in xrange(0x10000): char = unichr(code) name = unicodedata.name(char, None) if name is not None: self.assertEqual(unicodedata.lookup(name), char) count += 1 def test_misc_symbols(self): self.checkletter("PILCROW SIGN", u"\u00b6") self.checkletter("REPLACEMENT CHARACTER", u"\uFFFD") self.checkletter("HALFWIDTH KATAKANA SEMI-VOICED SOUND MARK", u"\uFF9F") self.checkletter("FULLWIDTH LATIN SMALL LETTER A", u"\uFF41") def test_errors(self): import unicodedata self.assertRaises(TypeError, unicodedata.name) self.assertRaises(TypeError, unicodedata.name, u'xx') self.assertRaises(TypeError, unicodedata.lookup) self.assertRaises(KeyError, unicodedata.lookup, u'unknown') def test_strict_eror_handling(self): # bogus character name self.assertRaises( UnicodeError, unicode, "\\N{blah}", 'unicode-escape', 'strict' ) # long bogus character name self.assertRaises( UnicodeError, unicode, "\\N{%s}" % ("x" * 100000), 'unicode-escape', 'strict' ) # missing closing brace self.assertRaises( UnicodeError, unicode, "\\N{SPACE", 'unicode-escape', 'strict' ) # missing opening brace self.assertRaises( UnicodeError, unicode, "\\NSPACE", 'unicode-escape', 'strict' ) @test_support.cpython_only @unittest.skipUnless(INT_MAX < PY_SSIZE_T_MAX, "needs UINT_MAX < SIZE_MAX") @unittest.skipUnless(UINT_MAX < sys.maxint, "needs UINT_MAX < sys.maxint") @test_support.bigmemtest(minsize=UINT_MAX + 1, memuse=2 + 4 // len(u'\U00010000')) def test_issue16335(self, size): func = self.test_issue16335 if size < func.minsize: raise unittest.SkipTest("not enough memory: %.1fG minimum needed" % (func.minsize * func.memuse / float(1024**3),)) # very very long bogus character name x = b'\\N{SPACE' + b'x' * int(UINT_MAX + 1) + b'}' self.assertEqual(len(x), len(b'\\N{SPACE}') + (UINT_MAX + 1)) self.assertRaisesRegexp(UnicodeError, 'unknown Unicode character name', x.decode, 'unicode-escape' ) def test_main(): test_support.run_unittest(UnicodeNamesTest) if __name__ == "__main__": test_main()
36.029412
80
0.596571
26deb0e9e89ddc92315de78d1e8a495cade067bd
4,599
py
Python
tests/test_jsonify.py
sergiobrr/tg2
401d77d82bd9daacb9444150c63bb039bf003436
[ "MIT" ]
812
2015-01-16T22:57:52.000Z
2022-03-27T04:49:40.000Z
tests/test_jsonify.py
sergiobrr/tg2
401d77d82bd9daacb9444150c63bb039bf003436
[ "MIT" ]
74
2015-02-18T17:55:31.000Z
2021-12-13T10:41:08.000Z
tests/test_jsonify.py
sergiobrr/tg2
401d77d82bd9daacb9444150c63bb039bf003436
[ "MIT" ]
72
2015-06-10T06:02:45.000Z
2022-03-27T08:37:24.000Z
from tg import jsonify, lurl from datetime import datetime from decimal import Decimal from nose.tools import raises from nose import SkipTest from webob.multidict import MultiDict import json from tg.util import LazyString from tg.util.webtest import test_context class Foo(object): def __init__(self, bar): self.bar = bar class Bar(object): def __init__(self, bar): self.bar = bar def __json__(self): return 'bar-%s' % self.bar class Baz(object): pass def test_string(): d = "string" encoded = jsonify.encode(d) assert encoded == '"string"' @raises(jsonify.JsonEncodeError) def test_list(): d = ['a', 1, 'b', 2] encoded = jsonify.encode(d) assert encoded == '["a", 1, "b", 2]' def test_list_allowed_iter(): lists_encoder = jsonify.JSONEncoder(allow_lists=True) d = ['a', 1, 'b', 2] encoded = jsonify.encode(d, lists_encoder) assert encoded == '["a", 1, "b", 2]' @raises(jsonify.JsonEncodeError) def test_list_iter(): d = list(range(3)) encoded = jsonify.encode_iter(d) assert ''.join(jsonify.encode_iter(d)) == jsonify.encode(d) def test_list_allowed_iter(): lists_encoder = jsonify.JSONEncoder(allow_lists=True) d = list(range(3)) encoded = jsonify.encode_iter(d, lists_encoder) assert ''.join(encoded) == '[0, 1, 2]' def test_dictionary(): d = {'a': 1, 'b': 2} encoded = jsonify.encode(d) expected = json.dumps(json.loads('{"a": 1, "b": 2}')) assert encoded == expected @raises(jsonify.JsonEncodeError) def test_nospecificjson(): b = Baz() try: encoded = jsonify.encode(b) except TypeError as e: pass assert "is not JSON serializable" in e.message def test_exlicitjson(): b = Bar("bq") encoded = jsonify.encode(b) assert encoded == '"bar-bq"' @raises(jsonify.JsonEncodeError) def test_exlicitjson_in_list(): b = Bar("bq") d = [b] encoded = jsonify.encode(d) assert encoded == '["bar-bq"]' def test_exlicitjson_in_dict(): b = Bar("bq") d = {"b": b} encoded = jsonify.encode(d) assert encoded == '{"b": "bar-bq"}' def test_datetime(): d = datetime.utcnow() encoded = jsonify.encode({'date':d}) assert str(d.year) in encoded, (str(d), encoded) def test_datetime_iso(): isodates_encoder = jsonify.JSONEncoder(isodates=True) d = datetime.utcnow() encoded = jsonify.encode({'date': d}, encoder=isodates_encoder) isoformat_without_millis = json.dumps({'date': d.isoformat()[:19]}) assert isoformat_without_millis == encoded, (isoformat_without_millis, encoded) assert 'T' in encoded, encoded def test_date_iso(): isodates_encoder = jsonify.JSONEncoder(isodates=True) d = datetime.utcnow().date() encoded = jsonify.encode({'date': d}, encoder=isodates_encoder) isoformat_without_millis = json.dumps({'date': d.isoformat()}) assert isoformat_without_millis == encoded, (isoformat_without_millis, encoded) loaded_date = json.loads(encoded) assert len(loaded_date['date'].split('-')) == 3 def test_datetime_time(): d = datetime.utcnow().time() encoded = jsonify.encode({'date':d}) assert str(d.hour) in encoded, (str(d), encoded) def test_datetime_time_iso(): isodates_encoder = jsonify.JSONEncoder(isodates=True) d = datetime.utcnow().time() encoded = jsonify.encode({'date': d}, encoder=isodates_encoder) isoformat_without_millis = json.dumps({'date': d.isoformat()[:8]}) assert isoformat_without_millis == encoded, (isoformat_without_millis, encoded) def test_decimal(): d = Decimal('3.14') encoded = jsonify.encode({'dec':d}) assert '3.14' in encoded def test_objectid(): try: from bson import ObjectId except: raise SkipTest() d = ObjectId('507f1f77bcf86cd799439011') encoded = jsonify.encode({'oid':d}) assert encoded == '{"oid": "%s"}' % d, encoded def test_multidict(): d = MultiDict({'v':1}) encoded = jsonify.encode({'md':d}) assert encoded == '{"md": {"v": 1}}', encoded def test_json_encode_lazy_url(): with test_context(None, '/'): url = lurl('/test') encoded = jsonify.encode({'url': url}) assert encoded == '{"url": "/test"}', encoded def test_json_encode_lazy_string(): text = LazyString(lambda: 'TEST_STRING') encoded = jsonify.encode({'text': text}) assert encoded == '{"text": "TEST_STRING"}', encoded def test_json_encode_generators(): encoded = jsonify.encode({'values': (v for v in [1, 2, 3])}) assert encoded == '{"values": [1, 2, 3]}', encoded
28.565217
83
0.652316
92fc90ba3cfb3449fc9a936c05ebca6e56dfc9b7
3,697
py
Python
src/data/dataloader.py
wozniakmikolaj/painting-style-classification
77c400a1c54ca5385ad0d4ed6c7f33aa720af832
[ "MIT" ]
null
null
null
src/data/dataloader.py
wozniakmikolaj/painting-style-classification
77c400a1c54ca5385ad0d4ed6c7f33aa720af832
[ "MIT" ]
null
null
null
src/data/dataloader.py
wozniakmikolaj/painting-style-classification
77c400a1c54ca5385ad0d4ed6c7f33aa720af832
[ "MIT" ]
null
null
null
"""Data Loader""" # standard library # internal # external import numpy as np import tensorflow as tf from sklearn.preprocessing import LabelEncoder class DataLoader: """Data Loader class""" @staticmethod def etl_load_dataset(data_config): """Performs the whole data pipeline, from load and processing to returning split data ready for analysis. Args: data_config(config): Config class passed with the model. Returns: train_dataset(tf.data.Dataset): transformed train dataset validation_dataset(tf.data.Dataset): transformed validation dataset """ dataset = DataLoader()._load_data(data_config) train_dataset, validation_dataset = DataLoader().create_datasets(dataset, data_config) return train_dataset, validation_dataset @staticmethod def create_datasets(dataset, data_config): """Splits the dataset, shuffles, batches and returns the data ready for analysis. Args: dataset(tf.data.Dataset): Processed dataset. data_config(config): Config class passed with the model. Returns: train_dataset(tf.data.Dataset): transformed train dataset validation_dataset(tf.data.Dataset): transformed validation dataset """ dataset_length = tf.data.experimental.cardinality(dataset).numpy() train_take_size = int(dataset_length * data_config.data.train_split) validation_take_size = int(dataset_length * data_config.data.validation_split) test_take_size = int(dataset_length * data_config.data.test_split) train_dataset = dataset.take(train_take_size) validation_dataset = dataset.skip(train_take_size) train_dataset = train_dataset.cache().shuffle(data_config.train.buffer_size).batch(data_config.train.batch_size, drop_remainder=True).repeat() train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE) validation_dataset = validation_dataset.batch(data_config.train.batch_size) return train_dataset, validation_dataset @staticmethod def _load_data(data_config): """"Loads in the raw data, processes it, encodes string labels to numeric. Args: data_config(config): Config class passed with the model. Returns: dataset(tf.data.Dataset): A processed dataset. """ data = np.load(data_config.paths.path_processed_data) labels = np.load(data_config.paths.path_processed_labels) data, labels = DataLoader()._prepare_data(data, labels, data_config) le = LabelEncoder() labels = le.fit_transform(labels).astype('int8') return tf.data.Dataset.from_tensor_slices((data, labels)) @staticmethod def _prepare_data(data, labels, data_config): """Shuffles, reshapes (adding an extra dimension) and normalizes data and label arrays. Args: data(np.array): Raw data array. labels(np.array): Raw labels array. data_config(config): Config class passed with the model. Returns: data(np.array): transformed data array labels(np.array): transformed labels array """ data = np.random.RandomState(7).permutation(data) labels = np.random.RandomState(7).permutation(labels) data = data.reshape(-1, data_config.data.image_size, data_config.data.image_size, data_config.data.num_channels).astype('float32') data = data / 255. return data, labels
36.60396
120
0.664052
d54f8977bce27028246b64092808cce8ec90df49
10,392
py
Python
saas/backend/service/resource.py
nannan00/bk-iam-saas
217600fa6e5fd466fff9c33c20c4dbd7c69f77d9
[ "MIT" ]
null
null
null
saas/backend/service/resource.py
nannan00/bk-iam-saas
217600fa6e5fd466fff9c33c20c4dbd7c69f77d9
[ "MIT" ]
null
null
null
saas/backend/service/resource.py
nannan00/bk-iam-saas
217600fa6e5fd466fff9c33c20c4dbd7c69f77d9
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- """ TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-权限中心(BlueKing-IAM) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT 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 logging from typing import Dict, List, Optional, Tuple from redis.exceptions import RedisError from backend.component import iam, resource_provider from backend.util.basic import chunked from backend.util.cache import redis_region, region from .models import ( ResourceAttribute, ResourceAttributeValue, ResourceInstanceBaseInfo, ResourceInstanceInfo, ResourceTypeProviderConfig, SystemProviderConfig, ) # 只暴露ResourceProvider,其他只是辅助ResourceProvider的 __all__ = ["ResourceProvider"] logger = logging.getLogger(__name__) class SystemProviderConfigService: """提供系统配置""" @region.cache_on_arguments(expiration_time=60) # 缓存1分钟 def get_provider_config(self, system_id) -> SystemProviderConfig: """获取接入系统的回调信息,包括鉴权和Host""" system_info = iam.get_system(system_id, fields="provider_config") provider_config = system_info["provider_config"] return SystemProviderConfig(**provider_config) class ResourceTypeProviderConfigService: """提供资源类型配置""" # TODO: 这里需要由后台提供查询某个系统某个资源类型的API,而不是使用批量查询系统资源类型 @region.cache_on_arguments(expiration_time=60) # 一分钟 def _list_resource_type_provider_config(self, system_id: str) -> Dict[str, Dict]: """提供给provider_config使用的获取某个系统所有资源类型""" resource_types = iam.list_resource_type([system_id], fields="id,provider_config")[system_id] provider_config_dict = {i["id"]: i["provider_config"] for i in resource_types} return provider_config_dict def get_provider_config(self, system_id: str, resource_type_id: str) -> ResourceTypeProviderConfig: """获取资源类型的回调配置""" provider_config_dict = self._list_resource_type_provider_config(system_id) return ResourceTypeProviderConfig(**provider_config_dict[resource_type_id]) class ResourceProviderConfig: """资源提供者配置""" def __init__(self, system_id: str, resource_type_id: str): self.system_id = system_id self.resource_type_id = resource_type_id self.auth_info, self.host = self._get_auth_info_and_host() self.path = self._get_path() def _get_auth_info_and_host(self) -> Tuple[Dict[str, str], str]: """iam后台获取系统的provider config""" provider_config = SystemProviderConfigService().get_provider_config(self.system_id) return {"auth": provider_config.auth, "token": provider_config.token}, provider_config.host def _get_path(self) -> str: """iam后台获取请求该资源类型所需的URL Path""" provider_config = ResourceTypeProviderConfigService().get_provider_config( self.system_id, self.resource_type_id ) return provider_config.path class ResourceIDNameCache: """资源的ID和Name缓存""" def __init__(self, system_id: str, resource_type_id: str): self.system_id = system_id self.resource_type_id = resource_type_id def _generate_id_cache_key(self, resource_id: str) -> str: """ 生成Cache Key # TODO: 后面重构整个Cache模块时,将这些Key统一到一处,便于管理和避免key冲突 """ prefix = "bk_iam:rp:id_name" return f"{prefix}:{self.system_id}:{self.resource_type_id}:{resource_id}" def set(self, id_name_map: Dict[str, str]): """Cache所有短时间内使用list_instance/fetch_instance/search_instance的数据,用于校验和查询id与name使用""" # 缓存有问题,不影响正常逻辑 try: with redis_region.backend.client.pipeline() as pipe: for _id, name in id_name_map.items(): # 只缓存name是字符串的,其他非法的不缓存 if isinstance(name, str): pipe.set(self._generate_id_cache_key(_id), name, ex=5 * 60) # 缓存5分钟 pipe.execute() except RedisError as error: logger.exception(f"set resource id name cache error: {error}") def get(self, ids: List[str]) -> Dict[str, Optional[str]]: """ 获取缓存内容,对于缓存不存在的,则返回为空 """ # 缓存有问题,不影响正常逻辑 try: with redis_region.backend.client.pipeline() as pipe: for _id in ids: # 如果不存在,则None pipe.get(self._generate_id_cache_key(_id)) # 有序的列表 result = pipe.execute() except RedisError as error: logger.exception(f"get resource id name cache error: {error}") result = [None] * len(ids) return {_id: name for _id, name in zip(ids, result)} class ResourceProvider: """资源提供者""" name_attribute = "display_name" def __init__(self, system_id: str, resource_type_id: str): """初始化:认证信息、请求客户端""" self.system_id = system_id self.resource_type_id = resource_type_id # 根据系统和资源类型获取相关认证信息和Host、URL_PATH provider_config = ResourceProviderConfig(system_id, resource_type_id) auth_info, host, url_path = provider_config.auth_info, provider_config.host, provider_config.path url = f"{host}{url_path}" self.client = resource_provider.ResourceProviderClient(system_id, resource_type_id, url, auth_info) # 缓存服务 self.id_name_cache = ResourceIDNameCache(system_id, resource_type_id) def list_attr(self) -> List[ResourceAttribute]: """查询某个资源类型可用于配置权限的属性列表""" return [ ResourceAttribute(**i) for i in self.client.list_attr() # 由于存在接入系统将内置属性_bk_xxx,包括_bk_iam_path_或将id的返回,防御性过滤掉 if not i["id"].startswith("_bk_") and i["id"] != "id" ] def list_attr_value( self, attr: str, keyword: str = "", limit: int = 10, offset: int = 0 ) -> Tuple[int, List[ResourceAttributeValue]]: """获取一个资源类型某个属性的值列表""" filter_condition = {"keyword": keyword} page = {"limit": limit, "offset": offset} count, results = self.client.list_attr_value(attr, filter_condition, page) return count, [ResourceAttributeValue(**i) for i in results] def list_instance( self, parent_type: str = "", parent_id: str = "", limit: int = 10, offset: int = 0 ) -> Tuple[int, List[ResourceInstanceBaseInfo]]: """根据上级资源获取某个资源实例列表""" filter_condition = {} if parent_type and parent_id: filter_condition["parent"] = {"type": parent_type, "id": parent_id} page = {"limit": limit, "offset": offset} count, results = self.client.list_instance(filter_condition, page) # 转换成需要的数据 instance_results = [ResourceInstanceBaseInfo(**i) for i in results] # Cache 查询到的信息 if instance_results: self.id_name_cache.set({i.id: i.display_name for i in instance_results}) return count, instance_results def search_instance( self, keyword: str, parent_type: str = "", parent_id: str = "", limit: int = 10, offset: int = 0 ) -> Tuple[int, List[ResourceInstanceBaseInfo]]: """根据上级资源和Keyword搜索某个资源实例列表""" # Note: 虽然与list_instance很相似,但在制定回调接口协议时特意分开为两个API,这样方便后续搜索的扩展 filter_condition: Dict = {"keyword": keyword} if parent_type and parent_id: filter_condition["parent"] = {"type": parent_type, "id": parent_id} page = {"limit": limit, "offset": offset} count, results = self.client.search_instance(filter_condition, page) # 转换成需要的数据 instance_results = [ResourceInstanceBaseInfo(**i) for i in results] # Cache 查询到的信息 if instance_results: self.id_name_cache.set({i.id: i.display_name for i in instance_results}) return count, instance_results def fetch_instance_info( self, ids: List[str], attributes: Optional[List[str]] = None ) -> List[ResourceInstanceInfo]: """批量查询资源实例属性,包括display_name等""" # fetch_instance_info 接口的批量限制 fetch_limit = 1000 # 分页查询资源实例属性 results = [] page_ids_list = chunked(ids, fetch_limit) for page_ids in page_ids_list: filter_condition = {"ids": page_ids, "attrs": attributes} if attributes else {"ids": page_ids} page_results = self.client.fetch_instance_info(filter_condition) results.extend(page_results) # Dict转为struct instance_results = [] for i in results: instance_results.append( ResourceInstanceInfo( id=i["id"], # 容错处理:接入系统实现的回调接口可能将所有属性都返回,所以只过滤需要的属性即可 attributes={k: v for k, v in i.items() if not attributes or k in attributes}, ) ) # IDNameCache,对于查询所有属性或者包括name属性,则进行缓存 if instance_results and (not attributes or self.name_attribute in attributes): self.id_name_cache.set( { i.id: i.attributes[self.name_attribute] # 只有包括name属性才进行缓存 for i in instance_results if self.name_attribute in i.attributes } ) return instance_results def fetch_instance_name(self, ids: List[str]) -> List[ResourceInstanceBaseInfo]: """批量查询资源实例的Name属性""" # 先从缓存取,取不到的则再查询 cache_id_name_map = self.id_name_cache.get(ids) results = [ ResourceInstanceBaseInfo(id=_id, display_name=name) for _id, name in cache_id_name_map.items() if name ] # 未被缓存的需要实时查询 not_cached_ids = [_id for _id, name in cache_id_name_map.items() if name is None] not_cached_results = self.fetch_instance_info(not_cached_ids, [self.name_attribute]) results.extend( [ ResourceInstanceBaseInfo(id=i.id, display_name=i.attributes[self.name_attribute]) for i in not_cached_results ] ) return results
39.816092
115
0.654927
d2d3aa0ed8ca8e2ce1addd5aaf167e526bc55aea
2,230
py
Python
kalmanFilter.py
sbabich/Multi-Object-Tracking-with-Kalman-Filter
a08e9bc16f555a3d35743949a3a8cce7432cf02b
[ "MIT" ]
89
2018-08-11T10:01:35.000Z
2022-03-31T16:00:21.000Z
kalmanFilter.py
sbabich/Multi-Object-Tracking-with-Kalman-Filter
a08e9bc16f555a3d35743949a3a8cce7432cf02b
[ "MIT" ]
2
2019-11-26T09:20:12.000Z
2021-12-19T22:28:38.000Z
kalmanFilter.py
sbabich/Multi-Object-Tracking-with-Kalman-Filter
a08e9bc16f555a3d35743949a3a8cce7432cf02b
[ "MIT" ]
28
2019-01-23T03:16:46.000Z
2022-03-29T02:26:36.000Z
#################### Import Section of the code ############################# try: import numpy as np except Exception as e: print(e,"\nPlease Install the package") #################### Import Section ends here ################################ class KalmanFilter(object): """docstring for KalmanFilter""" def __init__(self, dt=1,stateVariance=1,measurementVariance=1, method="Velocity" ): super(KalmanFilter, self).__init__() self.method = method self.stateVariance = stateVariance self.measurementVariance = measurementVariance self.dt = dt self.initModel() """init function to initialise the model""" def initModel(self): if self.method == "Accerelation": self.U = 1 else: self.U = 0 self.A = np.matrix( [[1 ,self.dt, 0, 0], [0, 1, 0, 0], [0, 0, 1, self.dt], [0, 0, 0, 1]] ) self.B = np.matrix( [[self.dt**2/2], [self.dt], [self.dt**2/2], [self.dt]] ) self.H = np.matrix( [[1,0,0,0], [0,0,1,0]] ) self.P = np.matrix(self.stateVariance*np.identity(self.A.shape[0])) self.R = np.matrix(self.measurementVariance*np.identity( self.H.shape[0])) self.Q = np.matrix( [[self.dt**4/4 ,self.dt**3/2, 0, 0], [self.dt**3/2, self.dt**2, 0, 0], [0, 0, self.dt**4/4 ,self.dt**3/2], [0, 0, self.dt**3/2,self.dt**2]]) self.erroCov = self.P self.state = np.matrix([[0],[1],[0],[1]]) """Predict function which predicst next state based on previous state""" def predict(self): self.predictedState = self.A*self.state + self.B*self.U self.predictedErrorCov = self.A*self.erroCov*self.A.T + self.Q temp = np.asarray(self.predictedState) return temp[0], temp[2] """Correct function which correct the states based on measurements""" def correct(self, currentMeasurement): self.kalmanGain = self.predictedErrorCov*self.H.T*np.linalg.pinv( self.H*self.predictedErrorCov*self.H.T+self.R) self.state = self.predictedState + self.kalmanGain*(currentMeasurement - (self.H*self.predictedState)) self.erroCov = (np.identity(self.P.shape[0]) - self.kalmanGain*self.H)*self.predictedErrorCov
33.283582
79
0.590135
13d6db5947e2a0cc41aebef3eb17287b3618d372
24,060
py
Python
qiskit_machine_learning/kernels/quantum_kernel.py
Anthem-Quantum/qiskit-machine-learning
2edc1f106c72a41ae2e99b58ee1edb85aa97d8bf
[ "Apache-2.0" ]
null
null
null
qiskit_machine_learning/kernels/quantum_kernel.py
Anthem-Quantum/qiskit-machine-learning
2edc1f106c72a41ae2e99b58ee1edb85aa97d8bf
[ "Apache-2.0" ]
null
null
null
qiskit_machine_learning/kernels/quantum_kernel.py
Anthem-Quantum/qiskit-machine-learning
2edc1f106c72a41ae2e99b58ee1edb85aa97d8bf
[ "Apache-2.0" ]
null
null
null
# This code is part of Qiskit. # # (C) Copyright IBM 2021, 2022. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Quantum Kernel Algorithm""" from typing import Optional, Union, Sequence, Mapping, List import copy import numbers import numpy as np from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit.circuit import Parameter, ParameterVector, ParameterExpression from qiskit.circuit.parameterexpression import ParameterValueType from qiskit.circuit.library import ZZFeatureMap from qiskit.providers import Backend, BaseBackend from qiskit.utils import QuantumInstance from ..exceptions import QiskitMachineLearningError class QuantumKernel: r"""Quantum Kernel. The general task of machine learning is to find and study patterns in data. For many algorithms, the datapoints are better understood in a higher dimensional feature space, through the use of a kernel function: .. math:: K(x, y) = \langle f(x), f(y)\rangle. Here K is the kernel function, x, y are n dimensional inputs. f is a map from n-dimension to m-dimension space. :math:`\langle x, y \rangle` denotes the dot product. Usually m is much larger than n. The quantum kernel algorithm calculates a kernel matrix, given datapoints x and y and feature map f, all of n dimension. This kernel matrix can then be used in classical machine learning algorithms such as support vector classification, spectral clustering or ridge regression. """ def __init__( self, feature_map: Optional[QuantumCircuit] = None, enforce_psd: bool = True, batch_size: int = 900, quantum_instance: Optional[Union[QuantumInstance, BaseBackend, Backend]] = None, user_parameters: Optional[Union[ParameterVector, Sequence[Parameter]]] = None, ) -> None: """ Args: feature_map: Parameterized circuit to be used as the feature map. If None is given, the `ZZFeatureMap` is used with two qubits. enforce_psd: Project to closest positive semidefinite matrix if x = y. Only enforced when not using the state vector simulator. Default True. batch_size: Number of circuits to batch together for computation. Default 900. quantum_instance: Quantum Instance or Backend user_parameters: Iterable containing ``Parameter`` objects which correspond to quantum gates on the feature map circuit which may be tuned. If users intend to tune feature map parameters to find optimal values, this field should be set. """ # Class fields self._feature_map = None self._unbound_feature_map = None self._user_parameters = None self._user_param_binds = None self._enforce_psd = enforce_psd self._batch_size = batch_size self._quantum_instance = quantum_instance # Setters self.feature_map = feature_map if feature_map is not None else ZZFeatureMap(2) if user_parameters is not None: self.user_parameters = user_parameters @property def feature_map(self) -> QuantumCircuit: """Return feature map""" return self._feature_map @feature_map.setter def feature_map(self, feature_map: QuantumCircuit) -> None: """ Set feature map. The ``unbound_feature_map`` field will be automatically updated when this field is set, and ``user_parameters`` and ``user_param_binds`` fields will be reset to ``None``. """ self._feature_map = feature_map self._unbound_feature_map = copy.deepcopy(self._feature_map) self._user_parameters = None self._user_param_binds = None @property def unbound_feature_map(self) -> QuantumCircuit: """Return unbound feature map""" return copy.deepcopy(self._unbound_feature_map) @property def quantum_instance(self) -> QuantumInstance: """Return quantum instance""" return self._quantum_instance @quantum_instance.setter def quantum_instance( self, quantum_instance: Union[Backend, BaseBackend, QuantumInstance] ) -> None: """Set quantum instance""" if isinstance(quantum_instance, (BaseBackend, Backend)): self._quantum_instance = QuantumInstance(quantum_instance) else: self._quantum_instance = quantum_instance @property def user_parameters(self) -> Optional[Union[ParameterVector, Sequence[Parameter]]]: """Return the vector of user parameters.""" return copy.copy(self._user_parameters) @user_parameters.setter def user_parameters(self, user_params: Union[ParameterVector, Sequence[Parameter]]) -> None: """Set the user parameters""" self._user_param_binds = {user_params[i]: user_params[i] for i, _ in enumerate(user_params)} self._user_parameters = copy.deepcopy(user_params) def assign_user_parameters( self, values: Union[Mapping[Parameter, ParameterValueType], Sequence[ParameterValueType]] ) -> None: """ Assign user parameters in the ``QuantumKernel`` feature map. Args: values (dict or iterable): Either a dictionary or iterable specifying the new parameter values. If a dict, it specifies the mapping from ``current_parameter`` to ``new_parameter``, where ``new_parameter`` can be a parameter expression or a numeric value. If an iterable, the elements are assigned to the existing parameters in the order of ``QuantumKernel.user_parameters``. Raises: ValueError: Incompatible number of user parameters and values """ if self._user_parameters is None: raise ValueError( f""" The number of parameter values ({len(values)}) does not match the number of user parameters tracked by the QuantumKernel (None). """ ) # Get the input parameters. These should remain unaffected by assigning of user parameters. input_params = list(set(self._unbound_feature_map.parameters) - set(self._user_parameters)) # If iterable of values is passed, the length must match length of user_parameters field if isinstance(values, (Sequence, np.ndarray)): if len(values) != len(self._user_parameters): raise ValueError( f""" The number of parameter values ({len(values)}) does not match the number of user parameters tracked by the QuantumKernel ({len(self._user_parameters)}). """ ) values = {p: values[i] for i, p in enumerate(self._user_parameters)} else: if not isinstance(values, dict): raise ValueError( f""" 'values' must be of type Dict or Sequence. Type {type(values)} is not supported. """ ) # All input keys must exist in the circuit # This check actually catches some well defined assignments; # however; we throw an error to be consistent with the behavior # of QuantumCircuit's parameter binding. unknown_parameters = list(set(values.keys()) - set(self._user_parameters)) if len(unknown_parameters) > 0: raise ValueError( f"Cannot bind parameters ({unknown_parameters}) not tracked by the quantum kernel." ) # Because QuantumKernel supports parameter rebinding, entries of the `values` dictionary must # be handled differently depending on whether they represent numerical assignments or parameter # reassignments. However, re-ordering the values dictionary inherently changes the expected # behavior of parameter binding, as entries in the values dict do not commute with one another # in general. To resolve this issue, we handle each entry of the values dict one at a time. for param, bind in values.items(): if isinstance(bind, ParameterExpression): self._unbound_feature_map.assign_parameters({param: bind}, inplace=True) # User params are all non-input params in the unbound feature map # This list comprehension ensures that self._user_parameters is ordered # in a way that is consistent with self.feature_map.parameters self._user_parameters = [ p for p in self._unbound_feature_map.parameters if (p not in input_params) ] # Remove param if it was overwritten if param not in self._user_parameters: del self._user_param_binds[param] # Add new parameters for sub_param in bind.parameters: if sub_param not in self._user_param_binds.keys(): self._user_param_binds[sub_param] = sub_param # If parameter is being set to expression of itself, user_param_binds # reflects a self-bind if param in bind.parameters: self._user_param_binds[param] = param # If assignment is numerical, update the param_binds elif isinstance(bind, numbers.Number): self._user_param_binds[param] = bind else: raise ValueError( f""" Parameters can only be bound to numeric values, Parameters, or ParameterExpressions. Type {type(bind)} is not supported. """ ) # Reorder dict according to self._user_parameters self._user_param_binds = { param: self._user_param_binds[param] for param in self._user_parameters } # Update feature map with numerical parameter assignments self._feature_map = self._unbound_feature_map.assign_parameters(self._user_param_binds) @property def user_param_binds(self) -> Optional[Mapping[Parameter, float]]: """Return a copy of the current user parameter mappings for the feature map circuit.""" return copy.deepcopy(self._user_param_binds) def bind_user_parameters( self, values: Union[Mapping[Parameter, ParameterValueType], Sequence[ParameterValueType]] ) -> None: """ Alternate function signature for ``assign_user_parameters`` """ self.assign_user_parameters(values) def get_unbound_user_parameters(self) -> List[Parameter]: """Return a list of any unbound user parameters in the feature map circuit.""" unbound_user_params = [] if self._user_param_binds is not None: # Get all user parameters not associated with numerical values unbound_user_params = [ val for val in self._user_param_binds.values() if not isinstance(val, numbers.Number) ] return unbound_user_params def construct_circuit( self, x: ParameterVector, y: ParameterVector = None, measurement: bool = True, is_statevector_sim: bool = False, ) -> QuantumCircuit: r""" Construct inner product circuit for given datapoints and feature map. If using `statevector_simulator`, only construct circuit for :math:`\Psi(x)|0\rangle`, otherwise construct :math:`Psi^dagger(y) x Psi(x)|0>` If y is None and not using `statevector_simulator`, self inner product is calculated. Args: x: first data point parameter vector y: second data point parameter vector, ignored if using statevector simulator measurement: include measurement if not using statevector simulator is_statevector_sim: use state vector simulator Returns: QuantumCircuit Raises: ValueError: - x and/or y have incompatible dimension with feature map - unbound user parameters in the feature map circuit """ # Ensure all user parameters have been bound in the feature map circuit. unbound_params = self.get_unbound_user_parameters() if unbound_params: raise ValueError( f""" The feature map circuit contains unbound user parameters ({unbound_params}). All user parameters must be bound to numerical values before constructing inner product circuit. """ ) if len(x) != self._feature_map.num_parameters: raise ValueError( "x and class feature map incompatible dimensions.\n" f"x has {len(x)} dimensions, but feature map has {self._feature_map.num_parameters}." ) q = QuantumRegister(self._feature_map.num_qubits, "q") c = ClassicalRegister(self._feature_map.num_qubits, "c") qc = QuantumCircuit(q, c) x_dict = dict(zip(self._feature_map.parameters, x)) psi_x = self._feature_map.assign_parameters(x_dict) qc.append(psi_x.to_instruction(), qc.qubits) if not is_statevector_sim: if y is not None and len(y) != self._feature_map.num_parameters: raise ValueError( "y and class feature map incompatible dimensions.\n" f"y has {len(y)} dimensions, but feature map has {self._feature_map.num_parameters}." ) if y is None: y = x y_dict = dict(zip(self._feature_map.parameters, y)) psi_y_dag = self._feature_map.assign_parameters(y_dict) qc.append(psi_y_dag.to_instruction().inverse(), qc.qubits) if measurement: qc.barrier(q) qc.measure(q, c) return qc def _compute_overlap(self, idx, results, is_statevector_sim, measurement_basis) -> float: """ Helper function to compute overlap for given input. """ if is_statevector_sim: # |<0|Psi^dagger(y) x Psi(x)|0>|^2, take the amplitude v_a, v_b = [results[int(i)] for i in idx] tmp = np.vdot(v_a, v_b) kernel_value = np.vdot(tmp, tmp).real # pylint: disable=no-member else: result = results.get_counts(idx) kernel_value = result.get(measurement_basis, 0) / sum(result.values()) return kernel_value def evaluate(self, x_vec: np.ndarray, y_vec: np.ndarray = None) -> np.ndarray: r""" Construct kernel matrix for given data and feature map If y_vec is None, self inner product is calculated. If using `statevector_simulator`, only build circuits for :math:`\Psi(x)|0\rangle`, then perform inner product classically. Args: x_vec: 1D or 2D array of datapoints, NxD, where N is the number of datapoints, D is the feature dimension y_vec: 1D or 2D array of datapoints, MxD, where M is the number of datapoints, D is the feature dimension Returns: 2D matrix, NxM Raises: QiskitMachineLearningError: - A quantum instance or backend has not been provided ValueError: - unbound user parameters in the feature map circuit - x_vec and/or y_vec are not one or two dimensional arrays - x_vec and y_vec have have incompatible dimensions - x_vec and/or y_vec have incompatible dimension with feature map and and feature map can not be modified to match. """ # Ensure all user parameters have been bound in the feature map circuit. unbound_params = self.get_unbound_user_parameters() if unbound_params: raise ValueError( f""" The feature map circuit contains unbound user parameters ({unbound_params}). All user parameters must be bound to numerical values before evaluating the kernel matrix. """ ) if self._quantum_instance is None: raise QiskitMachineLearningError( "A QuantumInstance or Backend must be supplied to evaluate a quantum kernel." ) if isinstance(self._quantum_instance, (BaseBackend, Backend)): self._quantum_instance = QuantumInstance(self._quantum_instance) if not isinstance(x_vec, np.ndarray): x_vec = np.asarray(x_vec) if y_vec is not None and not isinstance(y_vec, np.ndarray): y_vec = np.asarray(y_vec) if x_vec.ndim > 2: raise ValueError("x_vec must be a 1D or 2D array") if x_vec.ndim == 1: x_vec = np.reshape(x_vec, (-1, len(x_vec))) if y_vec is not None and y_vec.ndim > 2: raise ValueError("y_vec must be a 1D or 2D array") if y_vec is not None and y_vec.ndim == 1: y_vec = np.reshape(y_vec, (-1, len(y_vec))) if y_vec is not None and y_vec.shape[1] != x_vec.shape[1]: raise ValueError( "x_vec and y_vec have incompatible dimensions.\n" f"x_vec has {x_vec.shape[1]} dimensions, but y_vec has {y_vec.shape[1]}." ) if x_vec.shape[1] != self._feature_map.num_parameters: try: self._feature_map.num_qubits = x_vec.shape[1] except AttributeError: raise ValueError( "x_vec and class feature map have incompatible dimensions.\n" f"x_vec has {x_vec.shape[1]} dimensions, " f"but feature map has {self._feature_map.num_parameters}." ) from AttributeError if y_vec is not None and y_vec.shape[1] != self._feature_map.num_parameters: raise ValueError( "y_vec and class feature map have incompatible dimensions.\n" f"y_vec has {y_vec.shape[1]} dimensions, but feature map " f"has {self._feature_map.num_parameters}." ) # determine if calculating self inner product is_symmetric = True if y_vec is None: y_vec = x_vec elif not np.array_equal(x_vec, y_vec): is_symmetric = False # initialize kernel matrix kernel = np.zeros((x_vec.shape[0], y_vec.shape[0])) # set diagonal to 1 if symmetric if is_symmetric: np.fill_diagonal(kernel, 1) # get indices to calculate if is_symmetric: mus, nus = np.triu_indices(x_vec.shape[0], k=1) # remove diagonal else: mus, nus = np.indices((x_vec.shape[0], y_vec.shape[0])) mus = np.asarray(mus.flat) nus = np.asarray(nus.flat) is_statevector_sim = self._quantum_instance.is_statevector measurement = not is_statevector_sim measurement_basis = "0" * self._feature_map.num_qubits # calculate kernel if is_statevector_sim: # using state vector simulator if is_symmetric: to_be_computed_data = x_vec else: # not symmetric to_be_computed_data = np.concatenate((x_vec, y_vec)) feature_map_params = ParameterVector("par_x", self._feature_map.num_parameters) parameterized_circuit = self.construct_circuit( feature_map_params, feature_map_params, measurement=measurement, is_statevector_sim=is_statevector_sim, ) parameterized_circuit = self._quantum_instance.transpile(parameterized_circuit)[0] statevectors = [] for min_idx in range(0, len(to_be_computed_data), self._batch_size): max_idx = min(min_idx + self._batch_size, len(to_be_computed_data)) circuits = [ parameterized_circuit.assign_parameters({feature_map_params: x}) for x in to_be_computed_data[min_idx:max_idx] ] results = self._quantum_instance.execute(circuits, had_transpiled=True) for j in range(max_idx - min_idx): statevectors.append(results.get_statevector(j)) offset = 0 if is_symmetric else len(x_vec) matrix_elements = [ self._compute_overlap(idx, statevectors, is_statevector_sim, measurement_basis) for idx in list(zip(mus, nus + offset)) ] for i, j, value in zip(mus, nus, matrix_elements): kernel[i, j] = value if is_symmetric: kernel[j, i] = kernel[i, j] else: # not using state vector simulator feature_map_params_x = ParameterVector("par_x", self._feature_map.num_parameters) feature_map_params_y = ParameterVector("par_y", self._feature_map.num_parameters) parameterized_circuit = self.construct_circuit( feature_map_params_x, feature_map_params_y, measurement=measurement, is_statevector_sim=is_statevector_sim, ) parameterized_circuit = self._quantum_instance.transpile(parameterized_circuit)[0] for idx in range(0, len(mus), self._batch_size): to_be_computed_data_pair = [] to_be_computed_index = [] for sub_idx in range(idx, min(idx + self._batch_size, len(mus))): i = mus[sub_idx] j = nus[sub_idx] x_i = x_vec[i] y_j = y_vec[j] if not np.all(x_i == y_j): to_be_computed_data_pair.append((x_i, y_j)) to_be_computed_index.append((i, j)) circuits = [ parameterized_circuit.assign_parameters( {feature_map_params_x: x, feature_map_params_y: y} ) for x, y in to_be_computed_data_pair ] results = self._quantum_instance.execute(circuits, had_transpiled=True) matrix_elements = [ self._compute_overlap(circuit, results, is_statevector_sim, measurement_basis) for circuit in range(len(circuits)) ] for (i, j), value in zip(to_be_computed_index, matrix_elements): kernel[i, j] = value if is_symmetric: kernel[j, i] = kernel[i, j] if self._enforce_psd and is_symmetric: # Find the closest positive semi-definite approximation to symmetric kernel matrix. # The (symmetric) matrix should always be positive semi-definite by construction, # but this can be violated in case of noise, such as sampling noise, thus the # adjustment is only done if NOT using the statevector simulation. D, U = np.linalg.eig(kernel) # pylint: disable=invalid-name kernel = U @ np.diag(np.maximum(0, D)) @ U.transpose() return kernel
43.273381
105
0.613134
66bfff33bd42019afbec91fc3d1d513597425236
172
py
Python
www/uwsgi.py
oldhawaii/oldhawaii-metadata
838de68bb5e3aa137fe3dab4a02509fedfe829ac
[ "MIT" ]
1
2015-08-16T07:08:03.000Z
2015-08-16T07:08:03.000Z
www/uwsgi.py
oldhawaii/oldhawaii-metadata
838de68bb5e3aa137fe3dab4a02509fedfe829ac
[ "MIT" ]
null
null
null
www/uwsgi.py
oldhawaii/oldhawaii-metadata
838de68bb5e3aa137fe3dab4a02509fedfe829ac
[ "MIT" ]
null
null
null
#!/usr/bin/env python # -*- coding: utf-8 -*- from oldhawaii_metadata.app import get_app app = get_app() if __name__ == "__main__": app.run() # vim: filetype=python
15.636364
42
0.662791
6ef0e51a394d4ad880ea089053a995611e0d2c2e
128
py
Python
serialtest.py
madisoncooney/HPR_Titan
b50bb63e038c665c138fd6a5eff59dbc165d3c92
[ "MIT" ]
null
null
null
serialtest.py
madisoncooney/HPR_Titan
b50bb63e038c665c138fd6a5eff59dbc165d3c92
[ "MIT" ]
2
2020-03-24T16:59:58.000Z
2020-03-31T02:55:04.000Z
serialtest.py
madisoncooney/HPR_Titan
b50bb63e038c665c138fd6a5eff59dbc165d3c92
[ "MIT" ]
null
null
null
import serial ser = serial.Serial(port='COM5', baudrate=115200, timeout=1) while 1: line = ser.readline() print(line)
16
60
0.679688
fd44ee2535aa63acf73171d903fdd6832652b305
29,239
py
Python
PFinal/PFinal.py
aalonso99/Machine-Learning-UGR
f5276c93ce62c20ca09f49658390ee939344dfee
[ "MIT" ]
null
null
null
PFinal/PFinal.py
aalonso99/Machine-Learning-UGR
f5276c93ce62c20ca09f49658390ee939344dfee
[ "MIT" ]
null
null
null
PFinal/PFinal.py
aalonso99/Machine-Learning-UGR
f5276c93ce62c20ca09f49658390ee939344dfee
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from collections import Counter from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.feature_selection import mutual_info_classif from sklearn.model_selection import cross_val_predict from sklearn.metrics import confusion_matrix, precision_score, recall_score from sklearn.linear_model import LogisticRegression from imblearn.ensemble import BalancedBaggingClassifier, BalancedRandomForestClassifier from sklearn.svm import SVC from joblib import Parallel from joblib import delayed as delay from sklearn.neural_network import MLPClassifier from scipy.stats import normaltest #from sklearn.manifold import TSNE seed = 1 np.random.seed(seed) datadir = './datos/' #Si está a True se enseñan todas las gráficas mostradas en la memoria. En caso # contrario solo se muestran los resultados numéricos. show = True def plt_deactivate_xticks(): plt.tick_params( axis='x', # changes apply to the x-axis which='both', # both major and minor ticks are affected bottom=False, # ticks along the bottom edge are off top=False, # ticks along the top edge are off labelbottom=False) # labels along the bottom edge are off #threshold es el umbral de probabilidad para pertenecer o no a la primera clase. # Si method no es 'predict_proba' se ignora este parámetro def test_model(estimator, X, y, cv=5, method='predict',threshold=0.5): if method == 'predict': y_pred = cross_val_predict(estimator, X, y, cv=cv, method=method) if method == 'predict_proba': y_probs = cross_val_predict(estimator, X, y, cv=cv, method=method) y_pred = np.empty_like(y) y_pred[y_probs[:,0]>threshold] = 'neg' y_pred[y_probs[:,0]<=threshold] = 'pos' return y_pred, confusion_matrix(y, y_pred) def prec_from_conf_mat(conf_mat): return round( ( np.sum(conf_mat.diagonal()) )/np.sum(conf_mat), 4 ) def my_scorer(confmat): tn, fp, fn, tp = confmat.ravel() cost = 10*fp+500*fn return cost def print_conf_mat(conf_mat): group_counts = ["{0:0.0f}".format(value) for value in conf_mat.flatten()] norm_cm = (conf_mat.T/np.sum(conf_mat, axis=1)).T group_percentages = ["{0:.3%}".format(value) for value in norm_cm.flatten()] labels = [f"{v1}\n{v2}" for v1, v2 in zip(group_counts,group_percentages)] labels = np.asarray(labels).reshape(2,2) plt.figure(figsize=(4,4)) sns.heatmap(norm_cm, annot=labels, fmt=''); plt.show(); '''Funciones para búsqueda de modelos''' #Regresión Logística def test_LR(solver, c, reg): y_pred, conf_mat = test_model( LogisticRegression( random_state=seed, C=c, dual=False, solver=solver, penalty=reg, max_iter=10000), X_train, y_train, cv=5 ) prec = prec_from_conf_mat(conf_mat) return y_pred, conf_mat, {'Solver':solver, 'C':c, 'Reg':reg, 'AC':prec, 'Score':my_scorer(conf_mat)} #Regresión Logística con Bagging def test_EnsembleLR(solver, c, reg): y_pred, conf_mat = test_model( BalancedBaggingClassifier( base_estimator=LogisticRegression( random_state=seed, C=c, dual=False, solver=solver, penalty=reg, max_iter=1000), n_estimators=300, max_samples=1500, max_features=1.0, sampling_strategy=1.0, replacement=False, random_state=seed), X_train, y_train, cv=5 ) prec = prec_from_conf_mat(conf_mat) return y_pred, conf_mat, {'Solver':solver, 'C':c, 'Reg':reg, 'AC':prec, 'Score':my_scorer(conf_mat)} #Bagging de SVC con kernel RBF def test_SVC(c, gamma): y_pred, conf_mat = test_model( BalancedBaggingClassifier( base_estimator=SVC( random_state=seed, C=c, kernel = 'rbf', gamma = gamma, max_iter=1000), n_estimators=300, max_samples=1500, max_features=1.0, sampling_strategy=1.0, replacement=False, random_state=seed), X_train, y_train,cv=5) prec = prec_from_conf_mat(conf_mat) return y_pred, conf_mat, {'C':c, 'G':gamma, 'AC':prec, 'Score': my_scorer(conf_mat) } #Random Forest con submuestro balanceado def test_RF(n_estim, alpha): model = BalancedRandomForestClassifier(n_estimators=n_estim, bootstrap=True, criterion='entropy', max_features='sqrt', min_impurity_decrease=0.0, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, oob_score=False, ccp_alpha=alpha, n_jobs=-1, verbose=0, random_state=seed) y_pred, conf_mat = test_model( model, X_train, y_train, cv=5 ) prec = prec_from_conf_mat(conf_mat) return y_pred, conf_mat, {'Estimators':n_estim,'Alpha':alpha,'AC':prec, 'Score':my_scorer(conf_mat)} # Random Forest con Clasificación Ponderada def test_RF_filtered(n_estim, threshold): model = BalancedRandomForestClassifier(n_estimators=n_estim, bootstrap=True, criterion='entropy', max_features='sqrt', min_impurity_decrease=0.0, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, oob_score=False, ccp_alpha=0.0, n_jobs=-1, verbose=0, random_state=seed) y_pred, conf_mat = test_model( model, X_train, y_train, cv=5, method='predict_proba', threshold=threshold ) prec = prec_from_conf_mat(conf_mat) return y_pred, conf_mat, {'Estimators':n_estim,'Thres':threshold, 'AC':prec, 'Score':my_scorer(conf_mat)} #Bagging de Neural Network def test_EnsembleNN(hidden_layer_sizes, alpha): NNmodel = MLPClassifier(hidden_layer_sizes=hidden_layer_sizes, activation='relu', solver='lbfgs', alpha=alpha, max_iter=1000, tol=0.0001, verbose=False, warm_start=False, max_fun=15000, random_state=seed) y_pred, conf_mat = test_model( BalancedBaggingClassifier( base_estimator=NNmodel, n_estimators=300, max_samples=1500, max_features=1.0, sampling_strategy=1.0, replacement=False, random_state=seed), X_train, y_train, cv=5 ) prec = prec_from_conf_mat(conf_mat) return y_pred, conf_mat, {'Layers':hidden_layer_sizes, 'Alpha':alpha , 'AC':prec, 'Score':my_scorer(conf_mat)} #Cambia el tamaño de las figuras sns.set(rc={'figure.figsize':(20,10)}) #Leemos el conjunto de entrenamiento a la vez que lo barajamos raw_train = pd.read_csv( datadir+'aps_failure_training_set.csv', header=14, na_values="na" ).sample(frac=1, random_state=seed).reset_index(drop=True) raw_test = pd.read_csv( datadir+'aps_failure_test_set.csv', header=14, na_values="na" ) '''PREPROCESADO DE DATOS''' # Indicamos aquellas vaiables que tienen mas del 50% de los datos missing para deshacernos de ellas missing = raw_train.isna().sum().div(raw_train.shape[0]).mul(100).to_frame().sort_values(by=0, ascending = False) umbral_nan_col = 50 cols_missing = missing[missing[0]>umbral_nan_col] if show: '''Mostramos el histograma con el % de missing values de cada variable''' print("Hay {} columnas con un {}% o más de valores perdidos.".format(len(cols_missing), umbral_nan_col)) fig, ax = plt.subplots(figsize=(15,5)) ax.bar(missing.index, missing.values.T[0]) plt.xticks([]) plt.ylabel("Percentage missing") plt.show() input('-------------- Pulse Intro para continuar --------------') #Guardamos las etiquetas y los datos sin las columnas eliminadas. y_train = raw_train['class'].to_numpy() y_test = raw_test['class'].to_numpy() cols_to_drop = list(cols_missing.index)+['class'] X_train = raw_train.drop(cols_to_drop, axis=1).to_numpy() X_test = raw_test.drop(cols_to_drop, axis=1).to_numpy() #Eliminamos ahora los datos que tienen missing values en mas del 20% de sus variables n_columns = X_train.shape[1] umbral_nan_row = 0.2*n_columns valid_examples = np.isnan(X_train).sum(axis=1) < umbral_nan_row X_train = X_train[valid_examples] y_train = y_train[valid_examples] if show: '''Mostramos el histograma con el número de datos etiquetados con cada clase''' count_tr = Counter(y_train) plt.bar(range(len(count_tr)), list(count_tr.values()), align='center', label='Instancias en D_train') plt.xticks(range(len(count_tr)), list(count_tr.keys())) plt.legend() plt.show() input('-------------- Pulse Intro para continuar --------------') means = np.nanmean(X_train, axis=0) stds = np.nanstd(X_train, axis=0) if show: m = sns.barplot(x=list(range(len(means))), y=means, palette="muted") m.set_yscale("log") m.set(xticklabels=[]); plt.show() s = sns.barplot(x=list(range(len(stds))), y=stds, palette="muted") s.set_yscale("log") s.set(xticklabels=[]); plt.show() input('-------------- Pulse Intro para continuar --------------') #Eliminamos las columnas con std=0 valid_cols = ~(stds==0) X_train = X_train[:,valid_cols] X_test = X_test[:,valid_cols] means = means[valid_cols] stds = stds[valid_cols] # Guardamos las posiciones con valores perdidos nan_values = np.isnan(X_train) nan_values_test = np.isnan(X_test) #Arreglamos los missing values randoms = (np.random.rand(*X_train.shape)-0.5) * 3*stds X_train[nan_values] = 0 X_train += nan_values*(means+randoms) randoms_test = (np.random.rand(*X_test.shape)-0.5) * 3*stds X_test[nan_values_test] = 0 X_test += nan_values_test*(means+randoms_test) #Normalización de los datos scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) if show: fig = plt.figure() ax = fig.add_subplot() ax.boxplot( X_train ); plt.show() input('-------------- Pulse Intro para continuar --------------') #Comprobamos si nuestros datos siguen una distribución normal. Aplicamos un test de hipótesis tomando # como hipótesis nula que los atributos de nuestro dataset sigan una distribución normal. _,pvalues = normaltest(X_train) pvalues # Con un p-valor de 0, la hipótesis nula se rechaza para todos los atributos de nuestro dataset. Los datos no siguen una distribución normal. # Ahora vemos a estudiar la correlación entre las distintas variables. (EXPLICAR EN QUÉ CONSISTE ESTO, Y EL TEST DE PEARSON) if show: pearson_corr_mat = np.corrcoef(X_train.T) plt.figure(figsize=(8,8)) plt.imshow(np.abs(pearson_corr_mat)) plt.title("Matriz de correlación entre variables") plt.colorbar() plt.show() input('-------------- Pulse Intro para continuar --------------') # Observamos algunos grupos de variables altamente correladas que indican variables redundantes. pca = PCA(0.98) pca.fit(X_train); if show: #print("\nPCA: Peso por componente\n", pca.explained_variance_ratio_) evr_plot = sns.barplot( x=list(range(len(pca.explained_variance_ratio_))), y=pca.explained_variance_ratio_, palette="muted").set_title('PCA: Peso por componente') plt.show() input('-------------- Pulse Intro para continuar --------------') X_train = pca.transform(X_train) X_test = pca.transform(X_test) if show: pearson_corr_mat = np.corrcoef(X_train.T) plt.figure(figsize=(8,8)) plt.title("Matriz de correlación entre variables (tras PCA)") plt.imshow(np.abs(pearson_corr_mat)) plt.colorbar() plt.show() input('-------------- Pulse Intro para continuar --------------') # Las nuevas variables, obtenidas mediante PCA, son incorreladas por construcción. # Al contar con un dataset con un ratio instancias/atributos relativamente alto, es posible aplicar métodos no paramétricos para estimar la dependencia entre las etiquetas y cada uno de los atributos. Esto es lo próximo que vamos a probar. #Es independiente de la escala y del orden de las etiquetas (las toma como categóricas por defecto) mutual_info_vec = mutual_info_classif(X_train, y_train, discrete_features='auto', random_state=seed) if show: sns.set(rc={'figure.figsize':(11.7,8.27)}) bp = sns.barplot(x=np.arange(len(mutual_info_vec)), y=mutual_info_vec, palette="muted"); bp.set(xticklabels=[]); plt.show(); '''Búsqueda de mejores modelos''' '''Regresión Logística''' results_lr = Parallel(n_jobs=5)( delay(test_LR)(solver, c, reg) for solver,reg in [('saga','l1'),('lbfgs','l2')] for c in [1,10,100,1000,10000] ) for _,_,r in results_lr: print("Solver ->",r['Solver'],"; C ->", r['C'],"; Reg. type ->", r['Reg'], "; AC ->", r['AC'], "; Score ->", r['Score']) best_y_pred_lr = results_lr[0][0] best_conf_mat_lr = results_lr[0][1] print("Precision 'neg' ->",precision_score(y_train, best_y_pred_lr, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_train, best_y_pred_lr, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_train, best_y_pred_lr, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_train, best_y_pred_lr, pos_label='pos')) if show: print_conf_mat(best_conf_mat_lr) data2d = X_train[:,:2] y = best_y_pred_lr for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') '''Regresión Logística con Bagging''' results_elr = Parallel(n_jobs=5)( delay(test_EnsembleLR)(solver, c, reg) for solver,reg in [('saga','l1'),('lbfgs','l2')] for c in [1,10,100,1000,10000] ) for _,_,r in results_elr: print("Solver ->",r['Solver'],"; C ->", r['C'],"; Reg. type ->", r['Reg'], "; AC ->", r['AC'], "; Score ->", r['Score']) best_y_pred_elr = results_elr[9][0] best_conf_mat_elr = results_elr[9][1] print("Precision 'neg' ->",precision_score(y_train, best_y_pred_elr, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_train, best_y_pred_elr, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_train, best_y_pred_elr, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_train, best_y_pred_elr, pos_label='pos')) if show: print_conf_mat(best_conf_mat_elr) data2d = X_train[:,:2] y = best_y_pred_elr for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') '''Bagging de SVC con kernel RBF''' results_svc = Parallel(n_jobs=5)( delay(test_SVC)( c, gamma) for c in [1,10,100,1000,10000] for gamma in [0.05,0.01,0.005] ) for _,_,r in results_svc: print(" C ->", r['C'],"; G ->", r['G'], "; AC ->", r['AC'], "; Score ->", r['Score']) best_y_pred_svc = results_svc[11][0] best_conf_mat_svc = results_svc[11][1] print("Precision 'neg' ->",precision_score(y_train, best_y_pred_svc, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_train, best_y_pred_svc, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_train, best_y_pred_svc, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_train, best_y_pred_svc, pos_label='pos')) if show: print_conf_mat(best_conf_mat_svc) data2d = X_train[:,:2] y = best_y_pred_svc for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') '''Random Forest con submuestreo balanceado''' results_rf = Parallel(n_jobs=5)( delay(test_RF)(n_estim, alpha) for n_estim in [100,200,400,750,1000] for alpha in [0.01,0.001,0.0001,0.0] ) for _,_,r in results_rf: print("Estimators ->", r['Estimators'],"; α ->", r['Alpha'], "; AC ->", r['AC'], "; Score ->", r['Score']) #El mejor resultado se da con 400 estimadores, y criterio de separación de entropía best_y_pred_rf = results_rf[11][0] best_conf_mat_rf = results_rf[11][1] print("Precision 'neg' ->",precision_score(y_train, best_y_pred_rf, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_train, best_y_pred_rf, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_train, best_y_pred_rf, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_train, best_y_pred_rf, pos_label='pos')) if show: print_conf_mat(best_conf_mat_rf) data2d = X_train[:,:2] y = best_y_pred_rf for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') '''Random Forest con Clasificación Ponderada''' results_rf_filt = Parallel(n_jobs=6)( delay(test_RF_filtered)(n_estim, thres) for n_estim in [200,400,750] for thres in [0.3,0.35,0.4,0.45] ) for _,_,r in results_rf_filt: print("Estimators ->", r['Estimators'], "; Umbral Prob. ->", r['Thres'], "; AC ->", r['AC'], "; Score ->", r['Score']) best_y_pred_rf_filt = results_rf_filt[9][0] best_conf_mat_rf_filt = results_rf_filt[9][1] print("Precision 'neg' ->",precision_score(y_train, best_y_pred_rf_filt, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_train, best_y_pred_rf_filt, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_train, best_y_pred_rf_filt, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_train, best_y_pred_rf_filt, pos_label='pos')) if show: print_conf_mat(best_conf_mat_rf_filt) data2d = X_train[:,:2] y = best_y_pred_rf_filt for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') '''Bagging de Redes Neuronales''' results_nn = Parallel(n_jobs=6)( delay(test_EnsembleNN)(hidden_layer_sizes, alpha) for hidden_layer_sizes in [ (5,5), (10,10), (25,25), (50,50) ] for alpha in [ 0.0001, 0.001, 0.01 ] ) for _,_,r in results_nn: print("Layers ->", r['Layers'], "; α ->", r['Alpha'], "; AC ->", r['AC'], "; Score ->", r['Score']) best_y_pred_nn = results_nn[4][0] best_conf_mat_nn = results_nn[4][1] print("Precision 'neg' ->",precision_score(y_train, best_y_pred_nn, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_train, best_y_pred_nn, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_train, best_y_pred_nn, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_train, best_y_pred_nn, pos_label='pos')) if show: print_conf_mat(best_conf_mat_nn) data2d = X_train[:,:2] y = best_y_pred_nn for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') '''Datos originales''' if show: data2d = X_train[:,:2] y = y_train for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); '''ELECCIÓN DE MEJORES MODELOS EN BASE A LOS RESULTADOS OBTENIDOS''' bb_lr = BalancedBaggingClassifier( base_estimator=LogisticRegression( random_state=seed, C=10000, dual=False, solver='lbfgs', penalty='l2', max_iter=1000), n_estimators=300, max_samples=1500, max_features=1.0, sampling_strategy=1.0, replacement=False, random_state=seed) bb_svc = BalancedBaggingClassifier( base_estimator=SVC( random_state=seed, C=1000, kernel = 'rbf', gamma = 0.005, class_weight = 'balanced', max_iter=10000), n_estimators=300, max_samples=1500, max_features=1.0, sampling_strategy=1.0, replacement=False, random_state=seed) b_rf = BalancedRandomForestClassifier(n_estimators=750, bootstrap=True, criterion='entropy', max_features='sqrt', min_impurity_decrease=0.0, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, oob_score=False, n_jobs=-1, verbose=0, random_state=seed) bb_nn = BalancedBaggingClassifier( base_estimator=MLPClassifier(hidden_layer_sizes=(10,10), activation='relu', solver='lbfgs', alpha=0.001, batch_size=32, learning_rate_init=0.001, momentum=0.9, nesterovs_momentum=True, early_stopping=True, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, max_iter=1000, shuffle=True, tol=0.0001, verbose=False, warm_start=False, n_iter_no_change=4, max_fun=15000, random_state=seed), n_estimators=300, max_samples=1500, max_features=1.0, sampling_strategy=1.0, replacement=False, random_state=seed ) # Ajuste bb_lr.fit(X_train, y_train); bb_svc.fit(X_train, y_train); b_rf.fit(X_train, y_train); bb_nn.fit(X_train, y_train); '''RESULTADOS''' bb_lr_pred = bb_lr.predict(X_test) bb_svc_pred = bb_svc.predict(X_test) b_rf_probs = b_rf.predict_proba(X_test) b_rf_pred = np.empty_like(y_test) b_rf_pred[b_rf_probs[:,0]>0.35] = 'neg' b_rf_pred[b_rf_probs[:,0]<=0.35] = 'pos' bb_nn_pred = bb_nn.predict(X_test) cm_bb_lr = confusion_matrix(y_test, bb_lr_pred) cm_bb_svc = confusion_matrix(y_test, bb_svc_pred) cm_b_rf = confusion_matrix(y_test, b_rf_pred) cm_bb_nn = confusion_matrix(y_test, bb_nn_pred) # Logistic Regression print("Resultados en test del Bagging de Regresión Logística.") print("AC ->", prec_from_conf_mat(cm_bb_lr), "; Score ->", my_scorer(cm_bb_lr)) print("Precision 'neg' ->",precision_score(y_test, bb_lr_pred, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_test, bb_lr_pred, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_test, bb_lr_pred, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_test, bb_lr_pred, pos_label='pos')) if show: print_conf_mat(cm_bb_lr) data2d = X_test[:,:2] y = bb_lr_pred for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') # SVC-RBF print("Resultados en test del Bagging de SVD-RBF.") print("AC ->", prec_from_conf_mat(cm_bb_svc), "; Score ->", my_scorer(cm_bb_svc)) print("Precision 'neg' ->",precision_score(y_test, bb_svc_pred, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_test, bb_svc_pred, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_test, bb_svc_pred, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_test, bb_svc_pred, pos_label='pos')) if show: print_conf_mat(cm_bb_svc) data2d = X_test[:,:2] y = bb_svc_pred for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') # MLP print("Resultados en test del Bagging de Redes Neuronales.") print("AC ->", prec_from_conf_mat(cm_bb_nn), "; Score ->", my_scorer(cm_bb_nn)) print("Precision 'neg' ->",precision_score(y_test, bb_nn_pred, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_test, bb_nn_pred, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_test, bb_nn_pred, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_test, bb_nn_pred, pos_label='pos')) if show: print_conf_mat(cm_bb_nn) data2d = X_test[:,:2] y = bb_nn_pred for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') # Random Forest print("Resultados en test del Random Forest con clasificación ponderada.") print("AC ->", prec_from_conf_mat(cm_b_rf), "; Score ->", my_scorer(cm_b_rf)) print("Precision 'neg' ->",precision_score(y_test, b_rf_pred, pos_label='neg')) print("Recall 'neg' ->",recall_score(y_test, b_rf_pred, pos_label='neg')) print("Precision 'pos' ->",precision_score(y_test, b_rf_pred, pos_label='pos')) print("Recall 'pos' ->",recall_score(y_test, b_rf_pred, pos_label='pos')) if show: print_conf_mat(cm_b_rf) data2d = X_test[:,:2] y = b_rf_pred for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show(); input('-------------- Pulse Intro para continuar --------------') '''DATOS EN CONJUNTO TEST''' print("Datos de TEST:") if show: data2d = X_test[:,:2] y = y_test for i in ['neg', 'pos']: plt.scatter(x=data2d[y==i,0], y=data2d[y==i,1], label=str(i)) plt.legend() plt.xlim(-5,+30) plt.ylim(-15,+20) plt.show();
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