def test_save(cleanup=True):
tables = import_tables()
fname = 'foo.hdf5'
h = d.histogram.hist1d(numpy.linspace(0, 1, 11))
# save to opened file
with tables.open_file(fname, 'w') as hdf:
d.histsave(h, hdf, '/', 'foo')
# let histsave open the file for you
d.histsave(h, fname, '/', 'foo', overwrite=True)
if cleanup:
os.unlink(fname)
else:
return fname
def Finish(self):
for i in range(len(self.zenbins_fine)):
for j in range(len(self.depthbins)):
self.multiplicity_slices[i][j].flush()
self.multiplicity._h_bincontent[
i + 1,
j + 1, :] += self.multiplicity_slices[i][j]._h_bincontent
self.multiplicity._h_squaredweights[
i + 1, j +
1, :] += self.multiplicity_slices[i][j]._h_squaredweights
for i in range(len(self.zenbins)):
for j in range(len(self.depthbins)):
self.radius_slices[i][j].flush()
self.energy_slices[i][j].flush()
self.radius._h_bincontent[
i + 1,
j + 1, :, :] += self.radius_slices[i][j]._h_bincontent
self.radius._h_squaredweights[
i + 1,
j + 1, :, :] += self.radius_slices[i][j]._h_squaredweights
self.energy._h_bincontent[
i + 1,
j + 1, :, :, :] += self.energy_slices[i][j]._h_bincontent
self.energy._h_squaredweights[
i + 1, j +
1, :, :, :] += self.energy_slices[i][j]._h_squaredweights
self.primary.flush()
with tables.openFile(outfile, 'w') as hdf:
dashi.histsave(self.primary, hdf, '/', 'primary')
dashi.histsave(self.multiplicity, hdf, '/', 'multiplicity')
dashi.histsave(self.radius, hdf, '/', 'radius')
dashi.histsave(self.energy, hdf, '/', 'energy')
def Finish(self):
# return
with tables.openFile(self.outfile, 'a') as hdf:
if self.where != '/':
hdf.createGroup('/', self.where[1:])
dashi.histsave(self.binner.primary.to_dashi(), hdf, self.where, 'primary')
dashi.histsave(self.binner.multiplicity.to_dashi(), hdf, self.where, 'multiplicity')
dashi.histsave(self.binner.radius.to_dashi(), hdf, self.where, 'radius')
dashi.histsave(self.binner.energy.to_dashi(), hdf, self.where, 'energy')
def save(self, hdf, hist, where): # save in single precision hist._h_bincontent = hist._h_bincontent.astype(numpy.float32) hist._h_squaredweights = hist._h_squaredweights.astype(numpy.float32) dashi.histsave(hist, hdf, self.where, where)