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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Project: Fast Azimuthal integration
# https://github.com/silx-kit/pyFAI
#
# Copyright (C) 2017-2018 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.
"""
Detectors manufactured by PSI, those may be different from the one from Dectris
"""
__author__ = "Jerome Kieffer"
__contact__ = "Jerome.Kieffer@ESRF.eu"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "25/06/2020"
__status__ = "production"
import numpy
import logging
import json
from ._common import Detector
from ..utils import mathutil
logger = logging.getLogger(__name__)
class Jungfrau(Detector):
"""
Raw Jungfrau module without sub-module pixel expension applied.
"""
MANUFACTURER = "PSI"
MODULE_SIZE = (256, 256) # number of pixels per module (y, x)
MAX_SHAPE = (512, 1024) # max size of the detector
PIXEL_SIZE = (75e-6, 75e-6)
BORDER_SIZE_RELATIVE = 2.0
force_pixel = True
aliases = ["Jungfrau 500k"]
uniform_pixel = False
@classmethod
def _calc_pixels_size(cls, length, module_size, pixel_size):
"""
given the length (in pixel) of the detector, the size of a
module (in pixels) and the pixel_size (in meter). this method
return the length of each pixels 0..length.
:param length: the number of pixel to compute
:type length: int
:param module_size: the number of pixel of one module
:type module_size: int
:param pixel_size: the size of one pixels (meter per pixel)
:type length: float
:return: the coordinates of each pixels 0..length
:rtype: ndarray
"""
size = numpy.ones(length)
n = length // module_size
for i in range(1, n):
size[i * module_size - 1] = cls.BORDER_SIZE_RELATIVE
size[i * module_size] = cls.BORDER_SIZE_RELATIVE
return pixel_size * size
def __init__(self, pixel1=75e-6, pixel2=75e-6, max_shape=None, module_size=None):
Detector.__init__(self, pixel1=pixel1, pixel2=pixel2, max_shape=max_shape)
self._pixel_edges = None # array of size max_shape+1: pixels are contiguous
if (module_size is None) and ("MODULE_SIZE" in dir(self.__class__)):
self.module_size = tuple(self.MODULE_SIZE)
else:
self.module_size = module_size
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self.pixel1, self.pixel2)
def calc_pixels_edges(self):
"""
Calculate the position of the pixel edges
"""
if self._pixel_edges is None:
pixel_size1 = self._calc_pixels_size(self.max_shape[0], self.module_size[0], self.PIXEL_SIZE[0])
pixel_size2 = self._calc_pixels_size(self.max_shape[1], self.module_size[1], self.PIXEL_SIZE[1])
pixel_edges1 = numpy.zeros(self.max_shape[0] + 1)
pixel_edges2 = numpy.zeros(self.max_shape[1] + 1)
pixel_edges1[1:] = numpy.cumsum(pixel_size1)
pixel_edges2[1:] = numpy.cumsum(pixel_size2)
self._pixel_edges = pixel_edges1, pixel_edges2
return self._pixel_edges
def get_pixel_corners(self, correct_binning=False):
"""
Calculate the position of the corner of the pixels
This should be overwritten by class representing non-contiguous detector (Xpad, ...)
Precision float32 is ok: precision of 1µm for a detector size of 1m
:return: 4D array containing:
pixel index (slow dimension)
pixel index (fast dimension)
corner index (A, B, C or D), triangles or hexagons can be handled the same way
vertex position (z,y,x)
"""
if self._pixel_corners is None:
with self._sem:
if self._pixel_corners is None:
edges1, edges2 = self.calc_pixels_edges()
p1 = mathutil.expand2d(edges1, self.shape[1] + 1, False)
p2 = mathutil.expand2d(edges2, self.shape[0] + 1, True)
# p3 = None
self._pixel_corners = numpy.zeros((self.shape[0], self.shape[1], 4, 3), dtype=numpy.float32)
self._pixel_corners[:, :, 0, 1] = p1[:-1, :-1]
self._pixel_corners[:, :, 0, 2] = p2[:-1, :-1]
self._pixel_corners[:, :, 1, 1] = p1[1:, :-1]
self._pixel_corners[:, :, 1, 2] = p2[1:, :-1]
self._pixel_corners[:, :, 2, 1] = p1[1:, 1:]
self._pixel_corners[:, :, 2, 2] = p2[1:, 1:]
self._pixel_corners[:, :, 3, 1] = p1[:-1, 1:]
self._pixel_corners[:, :, 3, 2] = p2[:-1, 1:]
# if p3 is not None:
# # non flat detector
# self._pixel_corners[:, :, 0, 0] = p3[:-1, :-1]
# self._pixel_corners[:, :, 1, 0] = p3[1:, :-1]
# self._pixel_corners[:, :, 2, 0] = p3[1:, 1:]
# self._pixel_corners[:, :, 3, 0] = p3[:-1, 1:]
if correct_binning and self._pixel_corners.shape[:2] != self.shape:
return self._rebin_pixel_corners()
else:
return self._pixel_corners
def calc_cartesian_positions(self, d1=None, d2=None, center=True, use_cython=True):
"""
Calculate the position of each pixel center in cartesian coordinate
and in meter of a couple of coordinates.
The half pixel offset is taken into account here !!!
:param d1: the Y pixel positions (slow dimension)
:type d1: ndarray (1D or 2D)
:param d2: the X pixel positions (fast dimension)
:type d2: ndarray (1D or 2D)
:return: position in meter of the center of each pixels.
:rtype: ndarray
d1 and d2 must have the same shape, returned array will have
the same shape.
"""
edges1, edges2 = self.calc_pixels_edges()
if (d1 is None) or (d2 is None):
if center:
# Take the center of each pixel
d1 = 0.5 * (edges1[:-1] + edges1[1:])
d2 = 0.5 * (edges2[:-1] + edges2[1:])
else:
# take the lower corner
d1 = edges1[:-1]
d2 = edges2[:-1]
p1 = numpy.outer(d1, numpy.ones(self.shape[1]))
p2 = numpy.outer(numpy.ones(self.shape[0]), d2)
else:
if center:
# Not +=: do not mangle in place arrays
d1 = d1 + 0.5
d2 = d2 + 0.5
p1 = numpy.interp(d1, numpy.arange(self.max_shape[0] + 1), edges1, edges1[0], edges1[-1])
p2 = numpy.interp(d2, numpy.arange(self.max_shape[1] + 1), edges2, edges2[0], edges2[-1])
return p1, p2, None
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