#!/usr/bin/env python ################################################## ## DEPENDENCIES import sys import os import os.path try: import builtins as builtin except ImportError: import __builtin__ as builtin from os.path import getmtime, exists import time import types from Cheetah.Version import MinCompatibleVersion as RequiredCheetahVersion from Cheetah.Version import MinCompatibleVersionTuple as RequiredCheetahVersionTuple from Cheetah.Template import Template from Cheetah.DummyTransaction import * from Cheetah.NameMapper import NotFound, valueForName, valueFromSearchList, valueFromFrameOrSearchList from Cheetah.CacheRegion import CacheRegion import Cheetah.Filters as Filters import Cheetah.ErrorCatchers as ErrorCatchers from xpdeint.Vectors._NoiseVector import _NoiseVector ################################################## ## MODULE CONSTANTS VFFSL=valueFromFrameOrSearchList VFSL=valueFromSearchList VFN=valueForName currentTime=time.time __CHEETAH_version__ = '2.4.4' __CHEETAH_versionTuple__ = (2, 4, 4, 'development', 0) __CHEETAH_genTime__ = 1413234541.036245 __CHEETAH_genTimestamp__ = 'Tue Oct 14 08:09:01 2014' __CHEETAH_src__ = '/Users/graham/Library/XMDS/src/xmds2/admin/staging/xmds-2.2.2/xpdeint/Vectors/NoiseVector.tmpl' __CHEETAH_srcLastModified__ = 'Sat Feb 4 18:39:07 2012' __CHEETAH_docstring__ = 'Autogenerated by Cheetah: The Python-Powered Template Engine' if __CHEETAH_versionTuple__ < RequiredCheetahVersionTuple: raise AssertionError( 'This template was compiled with Cheetah version' ' %s. Templates compiled before version %s must be recompiled.'%( __CHEETAH_version__, RequiredCheetahVersion)) ################################################## ## CLASSES class NoiseVector(_NoiseVector): ################################################## ## CHEETAH GENERATED METHODS def __init__(self, *args, **KWs): super(NoiseVector, self).__init__(*args, **KWs) if not self._CHEETAH__instanceInitialized: cheetahKWArgs = {} allowedKWs = 'searchList namespaces filter filtersLib errorCatcher'.split() for k,v in KWs.items(): if k in allowedKWs: cheetahKWArgs[k] = v self._initCheetahInstance(**cheetahKWArgs) def description(self, **KWS): ## Generated from @def description: noise vector $name at line 27, col 1. trans = KWS.get("trans") if (not trans and not self._CHEETAH__isBuffering and not callable(self.transaction)): trans = self.transaction # is None unless self.awake() was called if not trans: trans = DummyTransaction() _dummyTrans = True else: _dummyTrans = False write = trans.response().write SL = self._CHEETAH__searchList _filter = self._CHEETAH__currentFilter ######################################## ## START - generated method body write(u'''noise vector ''') _v = VFFSL(SL,"name",True) # u'$name' on line 27, col 32 if _v is not None: write(_filter(_v, rawExpr=u'$name')) # from line 27, col 32. ######################################## ## END - generated method body return _dummyTrans and trans.response().getvalue() or "" def evaluateFunctionContents(self, function, **KWS): """ This function returns the code for the calculated vector's ``evaluate()`` function. The ``evaluate()`` function executes the code that defines the calculated vector. """ ## CHEETAH: generated from @def evaluateFunctionContents($function) at line 29, col 1. trans = KWS.get("trans") if (not trans and not self._CHEETAH__isBuffering and not callable(self.transaction)): trans = self.transaction # is None unless self.awake() was called if not trans: trans = DummyTransaction() _dummyTrans = True else: _dummyTrans = False write = trans.response().write SL = self._CHEETAH__searchList _filter = self._CHEETAH__currentFilter ######################################## ## START - generated method body # For distributed MPI drivers, we may need to share the noise if it is defined on a field # that is not distributed. featureOrdering = ['Driver'] featureDict = {'extraIndent': 0} _v = VFFSL(SL,"insertCodeForFeatures",False)('evaluateNoiseVectorBegin', featureOrdering, featureDict) # u"${insertCodeForFeatures('evaluateNoiseVectorBegin', featureOrdering, featureDict)}" on line 39, col 1 if _v is not None: write(_filter(_v, rawExpr=u"${insertCodeForFeatures('evaluateNoiseVectorBegin', featureOrdering, featureDict)}")) # from line 39, col 1. extraIndent = featureDict['extraIndent'] # _v = VFFSL(SL,"evaluateFunctionContentsInner",False)(function) # u'${evaluateFunctionContentsInner(function), extraIndent=extraIndent}' on line 42, col 1 if _v is not None: write(_filter(_v, extraIndent=extraIndent, rawExpr=u'${evaluateFunctionContentsInner(function), extraIndent=extraIndent}')) # from line 42, col 1. # _v = VFFSL(SL,"insertCodeForFeaturesInReverseOrder",False)('evaluateNoiseVectorEnd', featureOrdering, featureDict) # u"${insertCodeForFeaturesInReverseOrder('evaluateNoiseVectorEnd', featureOrdering, featureDict)}" on line 44, col 1 if _v is not None: write(_filter(_v, rawExpr=u"${insertCodeForFeaturesInReverseOrder('evaluateNoiseVectorEnd', featureOrdering, featureDict)}")) # from line 44, col 1. # if VFFSL(SL,"needsTransforms",True): # generated from line 46, col 3 write(u'''_''') _v = VFFSL(SL,"id",True) # u'${id}' on line 47, col 2 if _v is not None: write(_filter(_v, rawExpr=u'${id}')) # from line 47, col 2. write(u'''_basis = ''') _v = VFFSL(SL,"basisIndexForBasis",False)(self.initialBasis) # u'$basisIndexForBasis(self.initialBasis)' on line 47, col 16 if _v is not None: write(_filter(_v, rawExpr=u'$basisIndexForBasis(self.initialBasis)')) # from line 47, col 16. write(u'''; ''') # ######################################## ## END - generated method body return _dummyTrans and trans.response().getvalue() or "" def evaluateFunctionContentsInner(self, function, **KWS): ## CHEETAH: generated from @def evaluateFunctionContentsInner($function) at line 52, col 1. trans = KWS.get("trans") if (not trans and not self._CHEETAH__isBuffering and not callable(self.transaction)): trans = self.transaction # is None unless self.awake() was called if not trans: trans = DummyTransaction() _dummyTrans = True else: _dummyTrans = False write = trans.response().write SL = self._CHEETAH__searchList _filter = self._CHEETAH__currentFilter ######################################## ## START - generated method body # write(u'''const ptrdiff_t _vector_size = ''') _v = VFFSL(SL,"sizeInBasisInReals",False)(self.initialBasis) # u'${sizeInBasisInReals(self.initialBasis)}' on line 54, col 32 if _v is not None: write(_filter(_v, rawExpr=u'${sizeInBasisInReals(self.initialBasis)}')) # from line 54, col 32. write(u'''; ''') _v = VFFSL(SL,"randomVariable.makeNoises",True) # u'${randomVariable.makeNoises}' on line 55, col 1 if _v is not None: write(_filter(_v, rawExpr=u'${randomVariable.makeNoises}')) # from line 55, col 1. # ######################################## ## END - generated method body return _dummyTrans and trans.response().getvalue() or "" def splitFunctionContents(self, function, **KWS): """ This function is responsible for splitting the noise due to a failed step. As the implementation of this function is dependent on the random variable, it is the responsibility of the random variable to implement this. """ ## CHEETAH: generated from @def splitFunctionContents($function) at line 59, col 1. trans = KWS.get("trans") if (not trans and not self._CHEETAH__isBuffering and not callable(self.transaction)): trans = self.transaction # is None unless self.awake() was called if not trans: trans = DummyTransaction() _dummyTrans = True else: _dummyTrans = False write = trans.response().write SL = self._CHEETAH__searchList _filter = self._CHEETAH__currentFilter ######################################## ## START - generated method body # For distributed MPI drivers, we may need to share the split noise. So we perform the split # on only one of the nodes. featureOrdering = ['Driver'] featureDict = {'extraIndent': 0} _v = VFFSL(SL,"insertCodeForFeatures",False)('evaluateNoiseVectorBegin', featureOrdering, featureDict) # u"${insertCodeForFeatures('evaluateNoiseVectorBegin', featureOrdering, featureDict)}" on line 70, col 1 if _v is not None: write(_filter(_v, rawExpr=u"${insertCodeForFeatures('evaluateNoiseVectorBegin', featureOrdering, featureDict)}")) # from line 70, col 1. extraIndent = featureDict['extraIndent'] # _v = VFFSL(SL,"splitFunctionContentsInner",False)(function) # u'${splitFunctionContentsInner(function), extraIndent = extraIndent}' on line 73, col 1 if _v is not None: write(_filter(_v, extraIndent = extraIndent, rawExpr=u'${splitFunctionContentsInner(function), extraIndent = extraIndent}')) # from line 73, col 1. # _v = VFFSL(SL,"insertCodeForFeaturesInReverseOrder",False)('evaluateNoiseVectorEnd', featureOrdering, featureDict) # u"${insertCodeForFeaturesInReverseOrder('evaluateNoiseVectorEnd', featureOrdering, featureDict)}" on line 75, col 1 if _v is not None: write(_filter(_v, rawExpr=u"${insertCodeForFeaturesInReverseOrder('evaluateNoiseVectorEnd', featureOrdering, featureDict)}")) # from line 75, col 1. # if VFFSL(SL,"needsTransforms",True): # generated from line 77, col 3 write(u'''_''') _v = VFFSL(SL,"id",True) # u'${id}' on line 78, col 2 if _v is not None: write(_filter(_v, rawExpr=u'${id}')) # from line 78, col 2. write(u'''_basis = ''') _v = VFFSL(SL,"basisIndexForBasis",False)(self.initialBasis) # u'${basisIndexForBasis(self.initialBasis)}' on line 78, col 16 if _v is not None: write(_filter(_v, rawExpr=u'${basisIndexForBasis(self.initialBasis)}')) # from line 78, col 16. write(u'''; ''') # ######################################## ## END - generated method body return _dummyTrans and trans.response().getvalue() or "" def splitFunctionContentsInner(self, function, **KWS): ## CHEETAH: generated from @def splitFunctionContentsInner($function) at line 83, col 1. trans = KWS.get("trans") if (not trans and not self._CHEETAH__isBuffering and not callable(self.transaction)): trans = self.transaction # is None unless self.awake() was called if not trans: trans = DummyTransaction() _dummyTrans = True else: _dummyTrans = False write = trans.response().write SL = self._CHEETAH__searchList _filter = self._CHEETAH__currentFilter ######################################## ## START - generated method body # write(u'''const ptrdiff_t _vector_size = ''') _v = VFFSL(SL,"sizeInBasisInReals",False)(self.initialBasis) # u'${sizeInBasisInReals(self.initialBasis)}' on line 85, col 32 if _v is not None: write(_filter(_v, rawExpr=u'${sizeInBasisInReals(self.initialBasis)}')) # from line 85, col 32. write(u'''; ''') _v = VFN(VFFSL(SL,"randomVariable",True),"splitNoise",False)(function) # u'${randomVariable.splitNoise(function)}' on line 86, col 1 if _v is not None: write(_filter(_v, rawExpr=u'${randomVariable.splitNoise(function)}')) # from line 86, col 1. # ######################################## ## END - generated method body return _dummyTrans and trans.response().getvalue() or "" def writeBody(self, **KWS): ## CHEETAH: main method generated for this template trans = KWS.get("trans") if (not trans and not self._CHEETAH__isBuffering and not callable(self.transaction)): trans = self.transaction # is None unless self.awake() was called if not trans: trans = DummyTransaction() _dummyTrans = True else: _dummyTrans = False write = trans.response().write SL = self._CHEETAH__searchList _filter = self._CHEETAH__currentFilter ######################################## ## START - generated method body # # NoiseVector.tmpl # # Created by Joe Hope on 2009-08-17. # # Copyright (c) 2009-2012, Joe Hope # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . # write(u''' ''') # # Description of template write(u''' ''') ######################################## ## END - generated method body return _dummyTrans and trans.response().getvalue() or "" ################################################## ## CHEETAH GENERATED ATTRIBUTES _CHEETAH__instanceInitialized = False _CHEETAH_version = __CHEETAH_version__ _CHEETAH_versionTuple = __CHEETAH_versionTuple__ _CHEETAH_genTime = __CHEETAH_genTime__ _CHEETAH_genTimestamp = __CHEETAH_genTimestamp__ _CHEETAH_src = __CHEETAH_src__ _CHEETAH_srcLastModified = __CHEETAH_srcLastModified__ _mainCheetahMethod_for_NoiseVector= 'writeBody' ## END CLASS DEFINITION if not hasattr(NoiseVector, '_initCheetahAttributes'): templateAPIClass = getattr(NoiseVector, '_CHEETAH_templateClass', Template) templateAPIClass._addCheetahPlumbingCodeToClass(NoiseVector) # CHEETAH was developed by Tavis Rudd and Mike Orr # with code, advice and input from many other volunteers. # For more information visit http://www.CheetahTemplate.org/ ################################################## ## if run from command line: if __name__ == '__main__': from Cheetah.TemplateCmdLineIface import CmdLineIface CmdLineIface(templateObj=NoiseVector()).run()