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clean up the code to use the same namedtuple as histogram_engine

This commit is contained in:
Jerome Kieffer 2019-04-24 14:59:21 +02:00
parent 605e1e8ed6
commit 6276da43e8
1 changed files with 27 additions and 33 deletions
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60
pyFAI/engines/CSR_engine.py
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@ -29,7 +29,7 @@ __author__ = "Jerome Kieffer"
__contact__ = "Jerome.Kieffer@ESRF.eu"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "19/03/2019"
__date__ = "24/04/2019"
__status__ = "development"
import logging
@ -45,7 +45,7 @@ except ImportError as err:
else:
preproc = preproc_cy
from . import Integrate1dResult, Integrate2dResult, Integrate1dWithErrorResult, Integrate2dWithErrorResult
from ..containers import Integrate1dtpl, Integrate2dtpl
class CSRIntegrator(object):
@ -202,25 +202,22 @@ class CsrIntegrator1d(CSRIntegrator):
trans = CSRIntegrator.integrate(self, signal, variance, dummy, delta_dummy,
dark, flat, solidangle, polarization,
absorption, normalization_factor)
signal = trans[:, 0]
variance = trans[:, 1]
normalization = trans[:, 2]
count = trans[..., -1] # should be 3
mask = (normalization == 0)
with numpy.errstate(divide='ignore', invalid='ignore'):
norm = trans[:, 2]
intensity = trans[:, 0] / norm
mask = norm == 0
intensity = signal / normalization
intensity[mask] = self.empty
if do_variance:
error = numpy.sqrt(trans[:, 1]) / norm
error = numpy.sqrt(variance) / normalization
error[mask] = self.empty
if do_variance:
result = Integrate1dWithErrorResult(self.bin_centers,
intensity,
error,
trans)
else:
result = Integrate1dResult(self.bin_centers,
intensity,
trans)
return result
else:
variance = error = None
return Integrate1dtpl(self.bin_centers,
intensity, error,
signal, variance, normalization, count)
class CsrIntegrator2d(CSRIntegrator):
@ -293,24 +290,21 @@ class CsrIntegrator2d(CSRIntegrator):
dark, flat, solidangle, polarization,
absorption, normalization_factor)
trans.shape = self.bins + (-1,)
signal = trans[..., 0]
variance = trans[..., 1]
normalization = trans[..., 2]
count = trans[..., -1] # should be 3
mask = (normalization == 0)
with numpy.errstate(divide='ignore', invalid='ignore'):
norm = trans[..., 2]
intensity = trans[..., 0] / norm
mask = norm == 0
intensity = signal / normalization
intensity[mask] = self.empty
if do_variance:
error = numpy.sqrt(trans[..., 1]) / norm
error = numpy.sqrt(variance) / normalization
error[mask] = self.empty
else:
variance = error = None
return Integrate2dtpl(self.bin_centers0, self.bin_centers1,
intensity, error,
signal, variance, normalization, count)
if do_variance:
result = Integrate2dWithErrorResult(intensity,
error,
self.bin_centers0,
self.bin_centers1,
trans)
else:
result = Integrate2dResult(intensity,
self.bin_centers0,
self.bin_centers1,
trans)
return result