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Use the public storage_type attribute of boost-histogram #46

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Use the public storage_type attribute of boost-histogram #46

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8 changes: 4 additions & 4 deletions narf/histutils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ def make_array_interface_view(boost_hist):

underflow = [axis.traits.underflow for axis in boost_hist.axes]

acc_type = convert_storage_type(boost_hist._storage_type)
acc_type = convert_storage_type(boost_hist.storage_type)
arrview = ROOT.narf.array_interface_view[acc_type, len(shape)](arr, shape, strides, underflow)

return arrview
Expand All @@ -132,9 +132,9 @@ def hist_to_pyroot_boost(hist_hist, tensor_rank = 0, force_atomic = False):
scalar_type = ROOT.double
dimensions = ROOT.Eigen.Sizes[tuple(tensor_sizes)]

if issubclass(hist_hist._storage_type, bh.storage.Double):
if issubclass(hist_hist.storage_type, bh.storage.Double):
cppstoragetype = ROOT.narf.tensor_accumulator[scalar_type, dimensions]
elif issubclass(hist_hist._storage_type, bh.storage.Weight):
elif issubclass(hist_hist.storage_type, bh.storage.Weight):
cppstoragetype = ROOT.narf.tensor_accumulator[ROOT.boost.histogram.accumulators.weighted_sum[scalar_type], dimensions]
else:
raise TypeError("Requested storage type is not supported with tensor weights currently")
Expand All @@ -143,7 +143,7 @@ def hist_to_pyroot_boost(hist_hist, tensor_rank = 0, force_atomic = False):
cppstoragetype = ROOT.narf.atomic_adaptor[cppstoragetype]
else:
python_axes = hist_hist.axes
cppstoragetype = convert_storage_type(hist_hist._storage_type, force_atomic = force_atomic)
cppstoragetype = convert_storage_type(hist_hist.storage_type, force_atomic = force_atomic)

cppaxes = [ROOT.std.move(convert_axis(axis)) for axis in python_axes]

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