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# -*- coding: utf-8 -*-
#cython: embedsignature=True, language_level=3
#cython: boundscheck=False, wraparound=False, cdivision=True, initializedcheck=False,
## This is for developping:
##cython: profile=True, warn.undeclared=True, warn.unused=True, warn.unused_result=False, warn.unused_arg=True
#
# Project: Fast Azimuthal integration
# https://github.com/silx-kit/pyFAI
#
# Copyright (C) 2012-2020 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.
"""
Module providing inversion transformation from pixel coordinate to radial/azimuthal
coordinate.
"""
__author__ = "Jerome Kieffer"
__license__ = "MIT"
__date__ = "30/04/2020"
__copyright__ = "2018-2018, ESRF"
__contact__ = "jerome.kieffer@esrf.fr"
include "regrid_common.pxi"
import logging
logger = logging.getLogger("pyFAI.ext.invert_geometry")
cdef class InvertGeometry:
"""
Class to inverse the geometry: takes the nearest pixel then use linear interpolation
:param radius: 2D array with radial position
:param angle: 2D array with azimuthal position
Call it with (r,chi) to retrieve the pixel where it comes from.
"""
cdef:
position_t[:, ::1] radius, angle
int dim0, dim1
position_t rad_min, rad_max, rad_scale, ang_min, ang_max, ang_scale
def __cinit__(self, radius, angle):
"""Constructor of the class
:param radius: 2D array with the radius for every position
:param angle: 2D array with the azimuth for every position
"""
cdef:
int id0, id1
position_t rad_min, rad_max, ang_min, ang_max, ang, rad
self.dim0 = radius.shape[0]
self.dim1 = radius.shape[1]
assert angle.shape[0] == self.dim0, "the two array have the same shape"
assert angle.shape[1] == self.dim1, "the two array have the same shape"
self.radius = numpy.ascontiguousarray(radius, position_d)
self.angle = numpy.ascontiguousarray(angle, position_d)
rad_min = rad_max = self.radius[0, 0]
ang_min = ang_max = self.angle[0, 0]
for id0 in range(self.dim0):
for id1 in range(self.dim1):
ang = self.angle[id0, id1]
rad = self.radius[id0, id1]
if ang > ang_max:
ang_max = ang
elif ang < ang_min:
ang_min = ang
if rad > rad_max:
rad_max = rad
elif rad < rad_min:
rad_min = rad
self.rad_min = rad_min
self.rad_max = rad_max
self.rad_scale = 1.0 / (rad_max - rad_min) ** 2
self.ang_min = ang_min
self.ang_max = ang_max
self.ang_scale = 1.0 / (ang_max - ang_min) ** 2
def __dealloc__(self):
self.radius = None
self.angle = None
def __call__(self, position_t rad, position_t ang, bint refined=True):
"""Calculate the pixel coordinate leading to the value (rad, angle)
:param rad: radial value
:param ang: angular value
:param refined: if True: use linear interpolation, else provide nearest pixel
:return p0, p1: pixel coordinates
"""
cdef:
int id0, id1, best0, best1
position_t cost, min_cost, gr0, ga0, gr1, ga1, cor0, cor1, target_ang, target_rad, det
with nogil:
best0 = best1 = 0
cor0 = cor1 = 0.0
cost = self.ang_scale * (self.angle[0, 0] - ang) ** 2 \
+ self.rad_scale * (self.radius[0, 0] - rad) ** 2
min_cost = cost
for id0 in range(self.dim0):
for id1 in range(self.dim1):
cost = self.ang_scale * (self.angle[id0, id1] - ang) ** 2 \
+ self.rad_scale * (self.radius[id0, id1] - rad) ** 2
if cost < min_cost:
min_cost = cost
best0 = id0
best1 = id1
if refined and \
(best0 > 0) and (best0 < self.dim0 - 1) and\
(best1 > 0) and (best1 < self.dim1 - 1):
# First order Taylor expansion
gr0 = 0.5 * (self.radius[best0 + 1, best1] - self.radius[best0 - 1, best1])
ga0 = 0.5 * (self.angle[best0 + 1, best1] - self.angle[best0 - 1, best1])
gr1 = 0.5 * (self.radius[best0, best1 + 1] - self.radius[best0, best1 - 1])
ga1 = 0.5 * (self.angle[best0, best1 + 1] - self.angle[best0, best1 - 1])
target_ang = ang - self.angle[best0, best1]
target_rad = rad - self.radius[best0, best1]
# inversion of the matrix
det = ga1 * gr0 - ga0 * gr1
if det == 0.0:
with gil:
logger.info("Impossible to invert the matrix")
else:
cor0 = (target_rad * ga1 - target_ang * gr1) / det
cor1 = (-target_rad * ga0 + target_ang * gr0) / det
return (best0 + cor0, best1 + cor1)
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