def slicingInfo2IndexSlice(self, slicingInfo):
"""slicingInfo2Range(slicingInfo) --> slice instance
note: slicing is inclusive
"""
unit = self.unit()
start, end = slicingInfo.start, slicingInfo.end
# if axis has dimensional unit, convert start and end to pure numbers if necessary
if isDimensional(unit):
if isDimensional(start): start = start/unit
if isDimensional(end): end = end/unit
# if axis' unit is a number. it is assumed that the start and end are always given with unit
# so we need to divide them by the unit
if isNumber(unit) and unit not in [1, 1.0]:
if isNumber(start): start = start/unit
if isNumber(end): end = end/unit
# if start and end are special objects, convert to numbers as well
bc = self.binCenters()
if start == front: start = bc[0]
if end == back: end = bc[-1]
# at this point, start and end should all be numbers
if not isNumber(start) or not isNumber(end):
raise RuntimeError, "At this point, start and end should all be numbers: start=%s(%s), end=%s(%s)" % (start, type(start), end, type(end))
#slice. +1 is due to the difference of bin boundaries and bin centers
s = ( self.cellIndexFromValue( start ),
self.cellIndexFromValue( end ) + 1 )
return s
def __getitem__(self, s):
"""Slicing
h[ (3.0, 4.0), () ]
h[ 3.0, () ]
h[ (), () ]
h[ (None, 4.0), (999., None ) ]
"""
if self.errors() is None:
raise NotImplementedError , "__getitem__: errors is None"
# if s is iterable, we should assume that it is trying to do slicing.
# This also means no axis can use iterables as values.
if self.dimension() == 1 and '__iter__' not in dir(s): s = (s,)
# We also allow use of dictionary. This is a convenient and flexible way
# to get or set a slice.
if isinstance(s, dict):
s = _slicingInfosFromDictionary(s, self.axes())
else:
s = _makeSlicingInfos( s, self.dimension() )
#at this point, s is a tuple of SlicingInfo instances.
# check sanity of inputs
if not isinstance(s, tuple) or len(s) != self.dimension():
raise NotImplementedError , "Histogram[ %s ]. my dimension: %s" % (
s, self.dimension())
# a more meaningful name
slicingInfos = s
# slicingInfo tuple --> a tuple of index slices
indexSlices = self.slicingInfos2IndexSlices( slicingInfos )
#the following line will fail if a dataset is None
#should define a special NoneDataset to solve this problem
newdatasets = [dataset[indexSlices] for dataset in self.datasets()]
#if requested for item instead of slice, return the item now.
if isNumber(newdatasets[0]) or isDimensional(newdatasets[0]):
return newdatasets
#meta data need to be passed to the new histogram
newAttrs = self._attributes.copy()
#axes of new histogram
newAxes = []
for slicingInfo, name in zip(slicingInfos, self.axisNameList()):
axis = self.axisFromName( name )
if not isSlicingInfo( slicingInfo ):
# if it is not a slicingInfo instance,
# it must be a value indexable in this axis.
# This is already tested in method "slicingInfo2IndexSlices"
value = '%s' % slicingInfo
name = axis.name()
newAttrs[name] = value
else:
newAxes.append( axis[ slicingInfo ] )
pass
continue
#name of new histogram
newName = "%s in %s" % (
self.name(),
["%s(%s)"%(axisName, slicingInfo) for axisName, slicingInfo \
in zip(self.axisNameList(), slicingInfos)])
#new histogram
new = Histogram(
name = newName, unit = self.unit(),
data = newdatasets[0], errors = newdatasets[1],
axes = newAxes, attributes = newAttrs, slice = True)
#addtional datasets. This is not tested yet!!!
#probably we should really limit histogram to have only two datasets!!!
for i in range(2, len(newdatasets)):
ds = newdatasets[i]
new.addDataset( ds.name(), ds )
continue
return new