def createDensity(
Filename_in,
#MeanNormTuple,
inbranches,
modes,
nevents,
dimension1,
dimension2,
counterbranch,
offsets=None):
from DeepJetCore.compiled import c_meanNormZeroPad
layerbranch = ''
maxlayers = 1
layeroffset = 0
norms = [1 for x in range(len(inbranches))]
means = []
if not offsets:
means = [0 for x in range(len(inbranches))]
else:
means = offsets
x_branch, x_center, x_bins, x_width = dimension1
y_branch, y_center, y_bins, y_width = dimension2
array = numpy.zeros((nevents, x_bins, y_bins, maxlayers, len(inbranches)),
dtype='float32')
c_meanNormZeroPad.fillDensityLayers(
array,
norms,
means,
inbranches,
modes,
layerbranch,
maxlayers,
layeroffset,
Filename_in,
counterbranch,
x_branch,
x_center,
x_bins,
x_width,
y_branch,
y_center,
y_bins,
y_width,
)
array = numpy.reshape(array, (nevents, x_bins, y_bins, len(inbranches)))
return array
def createDensityLayers(Filename_in,
MeanNormTuple,
inbranches,
modes,
layerbranch,
maxlayers,
layeroffset,
nevents,
dimension1,
dimension2,
counterbranch,
scales=None):
from DeepJetCore.compiled import c_meanNormZeroPad
if not scales:
norms = [1 for x in range(len(inbranches))]
else:
norms = scales
if not len(scales) == len(inbranches):
raise ValueError('Scales length must match number of branches')
means = [0 for x in range(len(inbranches))]
x_branch, x_center, x_bins, x_width = dimension1
y_branch, y_center, y_bins, y_width = dimension2
array = numpy.zeros((nevents, x_bins, y_bins, maxlayers, len(inbranches)),
dtype='float32')
c_meanNormZeroPad.fillDensityLayers(
array,
norms,
means,
inbranches,
modes,
layerbranch,
maxlayers,
layeroffset,
Filename_in,
counterbranch,
x_branch,
x_center,
x_bins,
x_width,
y_branch,
y_center,
y_bins,
y_width,
)
return array
def createDensityLayers(Filename_in, MeanNormTuple, inbranches, modes,
layerbranch, maxlayers, layeroffset, nevents,
dimension1, dimension2, counterbranch):
from DeepJetCore.compiled import c_meanNormZeroPad
norms = [1 for x in range(len(inbranches))]
means = [0 for x in range(len(inbranches))]
x_branch, x_center, x_bins, x_width = dimension1
y_branch, y_center, y_bins, y_width = dimension2
array = numpy.zeros((nevents, x_bins, y_bins, maxlayers, len(inbranches)),
dtype='float32')
c_meanNormZeroPad.fillDensityLayers(
array,
norms,
means,
inbranches,
modes,
layerbranch,
maxlayers,
layeroffset,
Filename_in,
counterbranch,
x_branch,
x_center,
x_bins,
x_width,
y_branch,
y_center,
y_bins,
y_width,
)
return array