def getPackagePath2(self, name, var, force=False):
""" Return a path to the package for specified environment variable.
For example: _getPackagePath('x','LD_LIBRARY_PATH') returns a LD_LIBRARY_PATH for the package (if defined).
The value may be a string or a list of strings - in the later case the individual path components will
be separated by colons (':').
If the path is not defined (see getPackagePath()) then return an empty string.
"""
import os
path, tarball = self.getPackagePath(name, force)
if path and var in self.packages[name]:
from Ganga.Utility.util import isStringLike
ppath = self.packages[name][var]
if isStringLike(ppath):
ppath = [ppath]
return ':'.join(map(lambda p: os.path.join(path, p), ppath))
# for p in ppath:
## result += os.path.join(path,p)
# return result
else:
return ''
def __makeList__(self, element):
if isStringLike(element):
myList = [element]
elif canLoopOver(element):
myList = element
else:
myList = [element]
return myList
def __makeList__(self,element):
if isStringLike(element):
myList = [element]
elif canLoopOver(element):
myList = element
else:
myList = [element]
return myList
def __defaultSubtitle__(self, titleAttr):
""" compose the subtitle according to the given titleAttr. """
if isStringLike(titleAttr): ## a string
return titleAttr
elif canLoopOver(titleAttr): ## a list
return self.__defaultSubtitle__(titleAttr[0])
elif inspect.isfunction(
titleAttr): ## other things: function, object ...
return 'user function %s' % titleAttr.__name__
def __defaultSubtitle__(self,titleAttr):
""" compose the subtitle according to the given titleAttr. """
if isStringLike(titleAttr): ## a string
return titleAttr
elif canLoopOver(titleAttr): ## a list
return self.__defaultSubtitle__(titleAttr[0])
elif inspect.isfunction(titleAttr): ## other things: function, object ...
return 'user function %s' % titleAttr.__name__
def __makeJobInfo__(job, attrNameList):
'''make complete jobinfo according to the given list of job attributs'''
jobInfo = []
for attrName in attrNameList:
try:
if isStringLike(attrName):
attrValue, attrClass = __getJobAttribute__(job, attrName)
else:
attrValue, attrClass = attrName(job), None
jobInfo.append(attrValue)
except Exception as x:
logger.warning('Ignoring job %d: %s' % (job.id, str(x)))
jobInfo.append(NOT_APPLICABLE)
return jobInfo
def __makeJobInfo__(job,attrNameList):
'''make complete jobinfo according to the given list of job attributs'''
jobInfo = []
for attrName in attrNameList:
try:
if isStringLike(attrName):
attrValue, attrClass = __getJobAttribute__(job,attrName)
else:
attrValue, attrClass = attrName(job), None
jobInfo.append(attrValue)
except Exception as x:
logger.warning('Ignoring job %d: %s'%(job.id,str(x)))
jobInfo.append(NOT_APPLICABLE)
return jobInfo
def getPackagePath2(self, name, var, force=False):
""" Return a path to the package for specified environment variable.
For example: _getPackagePath('x','LD_LIBRARY_PATH') returns a LD_LIBRARY_PATH for the package (if defined).
The value may be a string or a list of strings - in the later case the individual path components will
be separated by colons (':').
If the path is not defined (see getPackagePath()) then return an empty string.
"""
import os
path, tarball = self.getPackagePath(name, force)
if path and var in self.packages[name]:
from Ganga.Utility.util import isStringLike
ppath = self.packages[name][var]
if isStringLike(ppath):
ppath = [ppath]
return ':'.join(map(lambda p: os.path.join(path, p), ppath))
# for p in ppath:
## result += os.path.join(path,p)
# return result
else:
return ''
def histogram(self, jobs, attr, **keywords):
"""
The plotter's interface for generating one or multiple histograms in one chart.
usage:
>>> plotter.histogram(jobs,attr,**keywords)
Generate a histogram catogorized by the attr among the given jobs.
required arguments:
jobs: A GANGA's job table
attr: A string or a user defined function, the corresponding value of which
will be extracted from the given jobs to generate the histogram.
optional arguments:
colormap: A list of values representing the colors that will be picked to
paint the histogram bars. The supported description of color can be found
at http://matplotlib.sourceforge.net/matplotlib.pylab.html#-colors
normalize: Sets if doing histogram normalization. Default is "False"
numbins: Sets the number of bins in the histogram. Default is 50
title: A string specifying the title of the histogram.
xlabel: A string specifying the xlabel of the histogram.
label: A string specifying the lable of the histogram displayed on the legend.
xmin: A number specifying the lower bound of the histogram range.
xmax: A number specifying the upper bound of the histogram range.
output: A name of file where the pie chart plot will be exported. The format
is auto-determinated by the extension of the given name.
dataproc: An user-defined function which will be applied to process the value
of attr before plotting the histogram.
deep: Sets if looping over all the subjob levels. Default is "True"
"""
# default keyword arguments
subtitle = self.__defaultSubtitle__(attr) # default subtitle
xlabel = self.__defaultSubtitle__(attr) # default xlabel
label = self.__defaultSubtitle__(attr) # default label
colormap = None # colormap
output = None # the output file of the picture
attrext = None # trigger the build-in data processing function
dataproc = None # function for data processing (cannot work together with 'attrext')
deep = True # deep looping over all the subjob levels
normalize = False # histogram normalization
numbins = 50 # number of bins in the histogram
xmin = -1 # lower bound of the histogram
xmax = -1 # upper bound of the histogram
# update keyword arguments with the given values
if 'title' in keywords: subtitle = keywords['title']
if 'colormap' in keywords: colormap = keywords['colormap']
if 'xlabel' in keywords: xlabel = keywords['xlabel']
if 'label' in keywords: label = keywords['label']
if 'output' in keywords: output = keywords['output']
if 'dataproc' in keywords: dataproc = keywords['dataproc']
if 'deep' in keywords: deep = keywords['deep']
if 'normalize' in keywords: normalize = keywords['normalize']
if 'numbins' in keywords: numbins = keywords['numbins']
if 'xmin' in keywords: xmin = keywords['xmin']
if 'xmax' in keywords: xmax = keywords['xmax']
jlist = []
for j in jobs:
jlist.append(j)
attr_spec = self.__makeList__(attr)
logger.debug('attribute specification: %s' % repr(attr_spec))
# special build-in dataprocs for the CE-based pie chart
dataproc = self.__setDataProcessor__(attr, attrext, dataproc)
dataTable = getJobInfoTable(jlist, attr_spec, deep)
logger.debug('internal data table: %s' % repr(dataTable))
# make the plot title
title = __makePlotTitle__(len(dataTable[1:]), deep, subtitle)
pltColIdList = range(len(dataTable[0]))
if not canLoopOver(dataproc):
dataproc = len(dataTable[0]) * [dataproc]
elif len(dataproc) != len(dataTable[0]):
logger.error(
'dataproc requires a list with %d elements, %d are given' %
(len(dataTable[0]), len(dataproc)))
return False
if (not canLoopOver(label)) or (isStringLike(label)):
label = len(dataTable[0]) * [label]
elif len(label) != len(dataTable[0]):
logger.error(
'label requires a list with %d elements, %d are given' %
(len(dataTable[0]), len(label)))
return False
# make the plot
self.__makeMultiHistograms__(dataTable,pltColIdList=pltColIdList,pltDataProcList=dataproc,pltLabelList=label, \
pltTitle=title,pltNumBins=numbins,pltRange=(xmin,xmax), pltXLabel=xlabel, \
pltColorMap=colormap,pltNormalize=normalize, \
pltOutput=output)
def histogram(self,jobs,attr,**keywords):
"""
The plotter's interface for generating one or multiple histograms in one chart.
usage:
>>> plotter.histogram(jobs,attr,**keywords)
Generate a histogram catogorized by the attr among the given jobs.
required arguments:
jobs: A GANGA's job table
attr: A string or a user defined function, the corresponding value of which
will be extracted from the given jobs to generate the histogram.
optional arguments:
colormap: A list of values representing the colors that will be picked to
paint the histogram bars. The supported description of color can be found
at http://matplotlib.sourceforge.net/matplotlib.pylab.html#-colors
normalize: Sets if doing histogram normalization. Default is "False"
numbins: Sets the number of bins in the histogram. Default is 50
title: A string specifying the title of the histogram.
xlabel: A string specifying the xlabel of the histogram.
label: A string specifying the lable of the histogram displayed on the legend.
xmin: A number specifying the lower bound of the histogram range.
xmax: A number specifying the upper bound of the histogram range.
output: A name of file where the pie chart plot will be exported. The format
is auto-determinated by the extension of the given name.
dataproc: An user-defined function which will be applied to process the value
of attr before plotting the histogram.
deep: Sets if looping over all the subjob levels. Default is "True"
"""
# default keyword arguments
subtitle = self.__defaultSubtitle__(attr) # default subtitle
xlabel = self.__defaultSubtitle__(attr) # default xlabel
label = self.__defaultSubtitle__(attr) # default label
colormap = None # colormap
output = None # the output file of the picture
attrext = None # trigger the build-in data processing function
dataproc = None # function for data processing (cannot work together with 'attrext')
deep = True # deep looping over all the subjob levels
normalize = False # histogram normalization
numbins = 50 # number of bins in the histogram
xmin = -1 # lower bound of the histogram
xmax = -1 # upper bound of the histogram
# update keyword arguments with the given values
if 'title' in keywords: subtitle = keywords['title']
if 'colormap' in keywords: colormap = keywords['colormap']
if 'xlabel' in keywords: xlabel = keywords['xlabel']
if 'label' in keywords: label = keywords['label']
if 'output' in keywords: output = keywords['output']
if 'dataproc' in keywords: dataproc = keywords['dataproc']
if 'deep' in keywords: deep = keywords['deep']
if 'normalize' in keywords: normalize= keywords['normalize']
if 'numbins' in keywords: numbins = keywords['numbins']
if 'xmin' in keywords: xmin = keywords['xmin']
if 'xmax' in keywords: xmax = keywords['xmax']
jlist = []
for j in jobs:
jlist.append(j)
attr_spec = self.__makeList__(attr)
logger.debug('attribute specification: %s' % repr(attr_spec))
# special build-in dataprocs for the CE-based pie chart
dataproc = self.__setDataProcessor__(attr,attrext,dataproc)
dataTable = getJobInfoTable(jlist,attr_spec,deep)
logger.debug('internal data table: %s' % repr(dataTable))
# make the plot title
title = __makePlotTitle__(len(dataTable[1:]),deep,subtitle)
pltColIdList = range(len(dataTable[0]))
if not canLoopOver(dataproc):
dataproc = len( dataTable[0] ) * [dataproc]
elif len(dataproc) != len(dataTable[0]):
logger.error('dataproc requires a list with %d elements, %d are given' % (len(dataTable[0]),len(dataproc)) )
return False
if (not canLoopOver(label)) or (isStringLike(label)):
label = len( dataTable[0] ) * [label]
elif len(label) != len(dataTable[0]):
logger.error('label requires a list with %d elements, %d are given' % (len(dataTable[0]), len(label)) )
return False
# make the plot
self.__makeMultiHistograms__(dataTable,pltColIdList=pltColIdList,pltDataProcList=dataproc,pltLabelList=label, \
pltTitle=title,pltNumBins=numbins,pltRange=(xmin,xmax), pltXLabel=xlabel, \
pltColorMap=colormap,pltNormalize=normalize, \
pltOutput=output)
