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# coding: utf-8
#
# Project: Azimuthal integration
# https://github.com/silx-kit/pyFAI
#
# Copyright (C) 2015-2018 European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""Common Look-Up table/CSR object creation tools"""
__author__ = "Jerome Kieffer"
__contact__ = "Jerome.kieffer@esrf.fr"
__date__ = "14/01/2021"
__status__ = "stable"
__license__ = "MIT"
import cython
from cython.parallel import prange
import numpy
from .preproc import preproc
from ..containers import Integrate1dtpl
cdef class CsrIntegrator(object):
"""Abstract class which implements only the integrator...
Now uses CSR (Compressed Sparse raw) with main attributes:
* nnz: number of non zero elements
* data: coefficient of the matrix in a 1D vector of float32
* indices: Column index position for the data (same size as
* indptr: row pointer indicates the start of a given row. len nrow+1
Nota: nnz = indptr[-1]+1 = len(indices) = len(data)
"""
cdef:
readonly index_t input_size, output_size, nnz
readonly data_t empty
readonly data_t[::1] _data
readonly index_t[::1] _indices, _indptr
def __init__(self,
tuple lut,
int image_size,
data_t empty=0.0):
"""Constructor for a CSR generic integrator
:param lut: Sparse matrix in CSR format, tuple of 3 arrays with (data, indices, indptr)
:param size: input image size
:param empty: value for empty pixels
"""
self.empty = empty
self.input_size = image_size
assert len(lut) == 3, "Sparse matrix is expected as 3-tuple CSR with (data, indices, indptr)"
assert len(lut[1]) == len(lut[0]), "Sparse matrix in CSR format is expected to have len(data) == len(indices) is expected as 3-tuple CSR with (data, indices, indptr)"
self._data = numpy.ascontiguousarray(lut[0], dtype=data_d)
self._indices = numpy.ascontiguousarray(lut[1], dtype=numpy.int32)
self._indptr = numpy.ascontiguousarray(lut[2], dtype=numpy.int32)
self.nnz = len(lut[1])
self.output_size = len(lut[2])-1
def __dealloc__(self):
self._data = None
self._indices = None
self._indpts = None
self.empty = 0
self.input_size = 0
self.output_size = 0
self.nnz = 0
@property
def data(self):
return numpy.asarray(self._data)
@property
def indices(self):
return numpy.asarray(self._indices)
@property
def indptr(self):
return numpy.asarray(self._indptr)
def integrate_legacy(self,
weights,
dummy=None,
delta_dummy=None,
dark=None,
flat=None,
solidAngle=None,
polarization=None,
double normalization_factor=1.0,
int coef_power=1):
"""
Actually perform the integration which in this case looks more like a matrix-vector product
:param weights: input image
:type weights: ndarray
:param dummy: value for dead pixels (optional)
:type dummy: float
:param delta_dummy: precision for dead-pixel value in dynamic masking
:type delta_dummy: float
:param dark: array with the dark-current value to be subtracted (if any)
:type dark: ndarray
:param flat: array with the dark-current value to be divided by (if any)
:type flat: ndarray
:param solidAngle: array with the solid angle of each pixel to be divided by (if any)
:type solidAngle: ndarray
:param polarization: array with the polarization correction values to be divided by (if any)
:type polarization: ndarray
:param normalization_factor: divide the valid result by this value
:param coef_power: set to 2 for variance propagation, leave to 1 for mean calculation
:return: positions, pattern, weighted_histogram and unweighted_histogram
:rtype: 4-tuple of ndarrays
"""
cdef:
index_t i = 0, j = 0, idx = 0, bins = self.output_size, size = self.input_size
acc_t acc_data = 0.0, acc_count = 0.0, epsilon = 1e-10, coef = 0.0
data_t data = 0.0, cdummy = 0.0, cddummy = 0.0
bint do_dummy = False, do_dark = False, do_flat = False, do_polarization = False, do_solidAngle = False
acc_t[::1] sum_data = numpy.zeros(self.bins, dtype=acc_d)
acc_t[::1] sum_count = numpy.zeros(self.bins, dtype=acc_d)
data_t[::1] merged = numpy.zeros(self.bins, dtype=data_d)
data_t[::1] cdata, tdata, cflat, cdark, csolidAngle, cpolarization
assert weights.size == size, "weights size"
if dummy is not None:
do_dummy = True
cdummy = <data_t> float(dummy)
if delta_dummy is None:
cddummy = <data_t> 0.0
else:
cddummy = <data_t> float(delta_dummy)
else:
do_dummy = False
cdummy = <data_t> self.empty
if flat is not None:
do_flat = True
assert flat.size == size, "flat-field array size"
cflat = numpy.ascontiguousarray(flat.ravel(), dtype=data_d)
if dark is not None:
do_dark = True
assert dark.size == size, "dark current array size"
cdark = numpy.ascontiguousarray(dark.ravel(), dtype=data_d)
if solidAngle is not None:
do_solidAngle = True
assert solidAngle.size == size, "Solid angle array size"
csolidAngle = numpy.ascontiguousarray(solidAngle.ravel(), dtype=data_d)
if polarization is not None:
do_polarization = True
assert polarization.size == size, "polarization array size"
cpolarization = numpy.ascontiguousarray(polarization.ravel(), dtype=data_d)
if (do_dark + do_flat + do_polarization + do_solidAngle):
tdata = numpy.ascontiguousarray(weights.ravel(), dtype=data_d)
cdata = numpy.empty(size, dtype=data_d)
if do_dummy:
for i in prange(size, nogil=True, schedule="static"):
data = tdata[i]
if ((cddummy != 0) and (fabs(data - cdummy) > cddummy)) or ((cddummy == 0) and (data != cdummy)):
# Nota: -= and /= operatore are seen as reduction in cython parallel.
if do_dark:
data = data - cdark[i]
if do_flat:
data = data / cflat[i]
if do_polarization:
data = data / cpolarization[i]
if do_solidAngle:
data = data / csolidAngle[i]
cdata[i] = data
else: # set all dummy_like values to cdummy. simplifies further processing
cdata[i] = cdummy
else:
for i in prange(size, nogil=True, schedule="static"):
data = tdata[i]
if do_dark:
data = data - cdark[i]
if do_flat:
data = data / cflat[i]
if do_polarization:
data = data / cpolarization[i]
if do_solidAngle:
data = data / csolidAngle[i]
cdata[i] = data
else:
if do_dummy:
tdata = numpy.ascontiguousarray(weights.ravel(), dtype=data_d)
cdata = numpy.zeros(size, dtype=data_d)
for i in prange(size, nogil=True, schedule="static"):
data = tdata[i]
if ((cddummy != 0) and (fabs(data - cdummy) > cddummy)) or ((cddummy == 0) and (data != cdummy)):
cdata[i] = data
else:
cdata[i] = cdummy
else:
cdata = numpy.ascontiguousarray(weights.ravel(), dtype=data_d)
for i in prange(bins, nogil=True, schedule="guided"):
acc_data = 0.0
acc_count = 0.0
for j in range(self._indptr[i], self._indptr[i + 1]):
idx = self._indices[j]
coef = self._data[j]
if coef == 0.0:
continue
data = cdata[idx]
if do_dummy and (data == cdummy):
continue
acc_data = acc_data + (coef ** coef_power) * data
acc_count = acc_count + coef
sum_data[i] = acc_data
sum_count[i] = acc_count
if acc_count > epsilon:
merged[i] = acc_data / acc_count / normalization_factor
else:
merged[i] = cdummy
return (self.bin_centers,
numpy.asarray(merged),
numpy.asarray(sum_data),
numpy.asarray(sum_count))
integrate = integrate_legacy
def integrate_ng(self,
weights,
variance=None,
dummy=None,
delta_dummy=None,
dark=None,
flat=None,
solidangle=None,
polarization=None,
absorption=None,
data_t normalization_factor=1.0,
):
"""
Actually perform the integration which in this case consists of:
* Calculate the signal, variance and the normalization parts
* Perform the integration which is here a matrix-vector product
:param weights: input image
:type weights: ndarray
:param variance: the variance associate to the image
:type variance: ndarray
:param dummy: value for dead pixels (optional)
:type dummy: float
:param delta_dummy: precision for dead-pixel value in dynamic masking
:type delta_dummy: float
:param dark: array with the dark-current value to be subtracted (if any)
:type dark: ndarray
:param flat: array with the dark-current value to be divided by (if any)
:type flat: ndarray
:param solidAngle: array with the solid angle of each pixel to be divided by (if any)
:type solidAngle: ndarray
:param polarization: array with the polarization correction values to be divided by (if any)
:type polarization: ndarray
:param absorption: Apparent efficiency of a pixel due to parallax effect
:type absorption: ndarray
:param normalization_factor: divide the valid result by this value
:return: positions, pattern, weighted_histogram and unweighted_histogram
:rtype: Integrate1dtpl 4-named-tuple of ndarrays
"""
cdef:
int32_t i, j, idx = 0, bins = self.bins, size = self.size
acc_t acc_sig = 0.0, acc_var = 0.0, acc_norm = 0.0, acc_count = 0.0, coef = 0.0
data_t empty
acc_t[::1] sum_sig = numpy.empty(bins, dtype=acc_d)
acc_t[::1] sum_var = numpy.empty(bins, dtype=acc_d)
acc_t[::1] sum_norm = numpy.empty(bins, dtype=acc_d)
acc_t[::1] sum_count = numpy.empty(bins, dtype=acc_d)
data_t[::1] merged = numpy.empty(bins, dtype=data_d)
data_t[::1] error = numpy.empty(bins, dtype=data_d)
data_t[:, ::1] preproc4
assert weights.size == size, "weights size"
empty = dummy if dummy is not None else self.empty
#Call the preprocessor ...
preproc4 = preproc(weights.ravel(),
dark=dark,
flat=flat,
solidangle=solidangle,
polarization=polarization,
absorption=absorption,
mask=self.cmask if self.check_mask else None,
dummy=dummy,
delta_dummy=delta_dummy,
normalization_factor=normalization_factor,
empty=self.empty,
split_result=4,
variance=variance,
dtype=data_d)
for i in prange(bins, nogil=True, schedule="guided"):
acc_sig = 0.0
acc_var = 0.0
acc_norm = 0.0
acc_count = 0.0
for j in range(self._indptr[i], self._indptr[i + 1]):
idx = self._indices[j]
coef = self._data[j]
if coef == 0.0:
continue
acc_sig = acc_sig + coef * preproc4[idx, 0]
acc_var = acc_var + coef * coef * preproc4[idx, 1]
acc_norm = acc_norm + coef * preproc4[idx, 2]
acc_count = acc_count + coef * preproc4[idx, 3]
sum_sig[i] = acc_sig
sum_var[i] = acc_var
sum_norm[i] = acc_norm
sum_count[i] = acc_count
if acc_count > 0.0:
merged[i] = acc_sig / acc_norm
error[i] = sqrt(acc_var) / acc_norm
else:
merged[i] = empty
error[i] = empty
#"position intensity error signal variance normalization count"
return Integrate1dtpl(self.bin_centers,
numpy.asarray(merged),numpy.asarray(error) ,
numpy.asarray(sum_sig),numpy.asarray(sum_var),
numpy.asarray(sum_norm), numpy.asarray(sum_count))
|
