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117cd8a1fc1b6cb288bd166e5b1de8219dbef713 | 1,968 | py | 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 |
82654da5b66d8c54bff2b307bcff5b804bd3119c | 18,195 | 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 (pedro@algarvio.me)`
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": "test@test.com",
"firstname": "Test",
"lastname": "User",
"username": "test",
# Random characters are appended for unique
# username
"password": "password",
},
"domain_admin": {
"email": "domain@admin.com",
"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 = "your-name@gmail.com"
recipient = "example@example.com"
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("your-name@gmail.com", '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: developers@cryptoapis.io
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 = "info@inveniosoftware.org"
#: Disable email sending by default.
MAIL_SUPPRESS_SEND = True
#: Email address for email notification sender.
MAIL_NOTIFY_SENDER = "noreply@inveniosoftware.org"
#: 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 = ["onlyme@inveniosoftware.org"]
#: 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="christian@uta.com", password="abc1234").save()
#User(user_id=2, first_name="Mary", last_name="Jane", email="mary.jane@uta.com", 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 (fredrik@pythonware.com)
(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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