/usr/lib/python2.7/dist-packages/pyFAI/detectors.py is in pyfai 0.10.2-1.
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# -*- coding: utf-8 -*-
#
# Project: Azimuthal integration
# https://github.com/kif/pyFAI
#
# Copyright (C) European Synchrotron Radiation Facility, Grenoble, France
#
# Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
from __future__ import print_function
__author__ = "Jérôme Kieffer"
__contact__ = "Jerome.Kieffer@ESRF.eu"
__license__ = "GPLv3+"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "23/10/2014"
__status__ = "stable"
__doc__ = """
Module containing the description of all detectors with a factory to instanciate them
"""
import logging
import numpy
import os
import posixpath
import threading
from . import io
from . import spline
from .utils import binning
logger = logging.getLogger("pyFAI.detectors")
try:
from .fastcrc import crc32
except ImportError:
from zlib import crc32
try:
from . import bilinear
except ImportError:
bilinear = None
try:
import fabio
except ImportError:
fabio = None
epsilon = 1e-6
class DetectorMeta(type):
"""
Metaclass used to register all detector classes inheriting from Detector
"""
# we use __init__ rather than __new__ here because we want
# to modify attributes of the class *after* they have been
# created
def __init__(cls, name, bases, dct):
# "Detector" is a bit peculiar: while abstract it may be needed by the GUI, so adding it to the repository
if hasattr(cls, 'MAX_SHAPE') or name == "Detector":
cls.registry[name.lower()] = cls
if hasattr(cls, "aliases"):
for alias in cls.aliases:
cls.registry[alias.lower().replace(" ", "_")] = cls
cls.registry[alias.lower().replace(" ", "")] = cls
super(DetectorMeta, cls).__init__(name, bases, dct)
class Detector(object):
"""
Generic class representing a 2D detector
"""
__metaclass__ = DetectorMeta
force_pixel = False # Used to specify pixel size should be defined by the class itself.
aliases = [] # list of alternative names
registry = {} # list of detectors ...
uniform_pixel = True # tells all pixels have the same size
@classmethod
def factory(cls, name, config=None):
"""
A kind of factory...
@param name: name of a detector
@type name: str
@param config: configuration of the detector
@type config: dict or JSON representation of it.
@return: an instance of the right detector, set-up if possible
@rtype: pyFAI.detectors.Detector
"""
if os.path.isfile(name):
return NexusDetector(name)
name = name.lower()
names = [name, name.replace(" ", "_")]
for name in names:
if name in cls.registry:
mydet = cls.registry[name]()
if config is not None:
mydet.set_config(config)
return mydet
else:
msg = ("Detector %s is unknown !, "
"please check if the filename exists or select one from %s" % (name, cls.registry.keys()))
logger.error(msg)
raise RuntimeError(msg)
def __init__(self, pixel1=None, pixel2=None, splineFile=None):
"""
@param pixel1: size of the pixel in meter along the slow dimension (often Y)
@type pixel1: float
@param pixel2: size of the pixel in meter along the fast dimension (often X)
@type pixel2: float
@param splineFile: path to file containing the geometric correction.
@type splineFile: str
"""
self._pixel1 = None
self._pixel2 = None
if pixel1:
self._pixel1 = float(pixel1)
if pixel2:
self._pixel2 = float(pixel2)
if "MAX_SHAPE" in dir(self.__class__):
self.max_shape = tuple(self.MAX_SHAPE)
else:
self.max_shape = None
self.shape = self.max_shape
self._binning = (1, 1)
self._mask = False
self._mask_crc = None
self._maskfile = None
self._splineFile = None
self.spline = None
self._dx = None
self._dy = None
self.flat = None
self.dark = None
self._splineCache = {} # key=(dx,xpoints,ypoints) value: ndarray
self._sem = threading.Semaphore()
if splineFile:
self.set_splineFile(splineFile)
def __repr__(self):
if (self._pixel1 is None) or (self._pixel2 is None):
return "Undefined detector"
return "Detector %s\t Spline= %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self.splineFile, self._pixel1, self._pixel2)
def set_config(self, config):
"""
Sets the configuration of the detector. This implies:
- Orientation: integers
- Binning
- ROI
The configuration is either a python dictionnary or a JSON string or a file containing this JSON configuration
keys in that dictionnary are :
"orientation": integers from 0 to 7
"binning": integer or 2-tuple of integers. If only one integer is provided,
"offset": coordinate (in pixels) of the start of the detector
"""
raise NotImplementedError
def get_splineFile(self):
return self._splineFile
def set_splineFile(self, splineFile):
if splineFile is not None:
self._splineFile = os.path.abspath(splineFile)
self.spline = spline.Spline(self._splineFile)
# NOTA : X is axis 1 and Y is Axis 0
self._pixel2, self._pixel1 = self.spline.getPixelSize()
self._splineCache = {}
self.uniform_pixel = False
else:
self._splineFile = None
self.spline = None
self.uniform_pixel = True
splineFile = property(get_splineFile, set_splineFile)
def set_dx(self, dx=None):
"""
set the pixel-wise displacement along X (dim2):
"""
if dx is not None:
assert dx.shape == self.max_shape
self._dx = dx
self.uniform_pixel = False
else:
self._dx = None
self.uniform_pixel = True
def set_dy(self, dy=None):
"""
set the pixel-wise displacement along Y (dim1):
"""
if dy is not None:
assert dy.shape == self.max_shape
self._dy = dy
self.uniform_pixel = False
else:
self._dy = None
self.uniform_pixel = True
def get_binning(self):
return self._binning
def set_binning(self, bin_size=(1, 1)):
"""
Set the "binning" of the detector,
@param bin_size: binning as integer or tuple of integers.
@type bin_size: (int, int)
"""
if "__len__" in dir(bin_size) and len(bin_size) >= 2:
bin_size = int(round(float(bin_size[0]))), int(round(float(bin_size[1])))
else:
b = int(round(float(bin_size)))
bin_size = (b, b)
if bin_size != self._binning:
ratioX = bin_size[1] / self._binning[1]
ratioY = bin_size[0] / self._binning[0]
if self.spline is not None:
self.spline.bin((ratioX, ratioY))
self._pixel2, self._pixel1 = self.spline.getPixelSize()
self._splineCache = {}
else:
self._pixel1 *= ratioY
self._pixel2 *= ratioX
self._binning = bin_size
self.shape = (self.max_shape[0] // bin_size[0],
self.max_shape[1] // bin_size[1])
binning = property(get_binning, set_binning)
def getPyFAI(self):
"""
Helper method to serialize the description of a detector using the pyFAI way
with everything in S.I units.
@return: representation of the detector easy to serialize
@rtype: dict
"""
return {"detector": self.name,
"pixel1": self._pixel1,
"pixel2": self._pixel2,
"splineFile": self._splineFile}
def getFit2D(self):
"""
Helper method to serialize the description of a detector using the Fit2d units
@return: representation of the detector easy to serialize
@rtype: dict
"""
return {"pixelX": self._pixel2 * 1e6,
"pixelY": self._pixel1 * 1e6,
"splineFile": self._splineFile}
def setPyFAI(self, **kwarg):
"""
Twin method of getPyFAI: setup a detector instance according to a description
@param kwarg: dictionary containing detector, pixel1, pixel2 and splineFile
"""
if "detector" in kwarg:
self = detector_factory(kwarg["detector"])
for kw in kwarg:
if kw in ["pixel1", "pixel2"]:
setattr(self, kw, kwarg[kw])
elif kw == "splineFile":
self.set_splineFile(kwarg[kw])
def setFit2D(self, **kwarg):
"""
Twin method of getFit2D: setup a detector instance according to a description
@param kwarg: dictionary containing pixel1, pixel2 and splineFile
"""
for kw, val in kwarg.items():
if kw == "pixelX":
self.pixel2 = val * 1e-6
elif kw == "pixelY":
self.pixel1 = val * 1e-6
elif kw == "splineFile":
self.set_splineFile(kwarg[kw])
def calc_cartesian_positions(self, d1=None, d2=None):
"""
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.
"""
if self.shape:
if (d1 is None) or (d2 is None):
# d1, d2 = numpy.ogrid[:self.shape[0], :self.shape[1]]
d1 = numpy.outer(numpy.arange(self.shape[0]), numpy.ones(self.shape[1]))
d2 = numpy.outer(numpy.ones(self.shape[0]), numpy.arange(self.shape[1]))
elif "ndim" in dir(d1):
if d1.ndim == 2:
self.shape = d1.shape
elif "ndim" in dir(d2):
if d2.ndim == 2:
self.shape = d2.shape
if self.spline is not None:
if d2.ndim == 1:
keyX = ("dX", tuple(d1), tuple(d2))
keyY = ("dY", tuple(d1), tuple(d2))
if keyX not in self._splineCache:
self._splineCache[keyX] = \
numpy.array([self.spline.splineFuncX(i2, i1)
for i1, i2 in zip(d1 + 0.5, d2 + 0.5)],
dtype="float64")
if keyY not in self._splineCache:
self._splineCache[keyY] = \
numpy.array([self.spline.splineFuncY(i2, i1)
for i1, i2 in zip(d1 + 0.5, d2 + 0.5)],
dtype="float64")
dX = self._splineCache[keyX]
dY = self._splineCache[keyY]
else:
dX = self.spline.splineFuncX(d2 + 0.5, d1 + 0.5)
dY = self.spline.splineFuncY(d2 + 0.5, d1 + 0.5)
elif self._dx is not None:
if self._binning == (1, 1):
binned_x = self._dx
binned_y = self._dy
else:
binned_x = binning(self._dx, self._binning)
binned_y = binning(self._dy, self._binning)
dX = numpy.interp(d2, numpy.arange(binned_x.shape[1]), binned_x, left=0, right=0)
dY = numpy.interp(d1, numpy.arange(binned_y.shape[0]), binned_y, left=0, right=0)
else:
dX = 0.
dY = 0.
p1 = (self._pixel1 * (dY + 0.5 + d1))
p2 = (self._pixel2 * (dX + 0.5 + d2))
return p1, p2
def calc_mask(self):
"""
Detectors with gaps should overwrite this method with
something actually calculating the mask!
"""
# logger.debug("Detector.calc_mask is not implemented for generic detectors")
return None
############################################################################
# Few properties
############################################################################
def get_mask(self):
if self._mask is False:
with self._sem:
if self._mask is False:
self._mask = self.calc_mask() # gets None in worse cases
if self._mask is not None:
self._mask_crc = crc32(self._mask)
return self._mask
def set_mask(self, mask):
with self._sem:
self._mask = mask
if mask is not None:
self._mask_crc = crc32(mask)
else:
self._mask_crc = None
mask = property(get_mask, set_mask)
def set_maskfile(self, maskfile):
if fabio:
with self._sem:
self._mask = numpy.ascontiguousarray(fabio.open(maskfile).data,
dtype=numpy.int8)
self._mask_crc = crc32(self._mask)
self._maskfile = maskfile
else:
logger.error("FabIO is not available, unable to load the image to set the mask.")
def get_maskfile(self):
return self._maskfile
maskfile = property(get_maskfile, set_maskfile)
def get_pixel1(self):
return self._pixel1
def set_pixel1(self, value):
if isinstance(value, float):
value = value
elif isinstance(value, (tuple, list)):
value = float(value[0])
else:
value = float(value)
if self._pixel1:
err = abs(value - self._pixel1) / self._pixel1
if self.force_pixel and (err > epsilon):
logger.warning("enforcing pixel size 2 for a detector %s" %
self.__class__.__name__)
self._pixel1 = value
pixel1 = property(get_pixel1, set_pixel1)
def get_pixel2(self):
return self._pixel2
def set_pixel2(self, value):
if isinstance(value, float):
value = value
elif isinstance(value, (tuple, list)):
value = float(value[0])
else:
value = float(value)
if self._pixel2:
err = abs(value - self._pixel2) / self._pixel2
if self.force_pixel and (err > epsilon):
logger.warning("enforcing pixel size 2 for a detector %s" %
self.__class__.__name__)
self._pixel2 = value
pixel2 = property(get_pixel2, set_pixel2)
def get_name(self):
"""
Get a meaningful name for detector
"""
if self.aliases:
name = self.aliases[0]
else:
name = self.__class__.__name__
return name
name = property(get_name)
def get_pixel_corners(self):
"""
Calculate the position of the corner of the pixels
This should be overwritten by class representing non-contiguous detector (Xpad, ...)
@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)
"""
#float32 is ok: precision of 1µm for a detector size of 1m
corners = numpy.zeros((self.shape[0], self.shape[1], 4, 3), dtype=numpy.float32)
d1 = numpy.outer(numpy.arange(self.shape[0] + 1), numpy.ones(self.shape[1] + 1)) - 0.5
d2 = numpy.outer(numpy.ones(self.shape[0] + 1), numpy.arange(self.shape[1] + 1)) - 0.5
p1, p2 = self.calc_cartesian_positions(d1, d2)
corners[:, :, 0, 1] = p1[:-1, :-1]
corners[:, :, 0, 2] = p2[:-1, :-1]
corners[:, :, 1, 1] = p1[1:, :-1]
corners[:, :, 1, 2] = p2[1:, :-1]
corners[:, :, 2, 1] = p1[1:, 1:]
corners[:, :, 2, 2] = p2[1:, 1:]
corners[:, :, 3, 1] = p1[:-1, 1:]
corners[:, :, 3, 2] = p2[:-1, 1:]
return corners
def save(self, filename):
"""
Saves the detector description into a NeXus file, adapted from:
http://download.nexusformat.org/sphinx/classes/base_classes/NXdetector.html
Main differences:
* differentiate pixel center from pixel corner offsets
* store all offsets are ndarray according to slow/fast dimention (not x, y)
@param filename: name of the file on the disc
"""
if not io.h5py:
logger.error("h5py module missing: NeXus detectors not supported")
raise RuntimeError("H5py module is missing")
# did_exist = os.path.exists(path)
nxs = io.Nexus(filename, "+")
det_grp = nxs.new_detector(name=self.name.replace(" ", "_"))
det_grp["pixel_size"] = numpy.array([self.pixel1, self.pixel2], dtype=numpy.float32)
if self.max_shape is not None:
det_grp["max_shape"] = numpy.array(self.max_shape, dtype=numpy.int32)
if self.shape is not None:
det_grp["shape"] = numpy.array(self.shape, dtype=numpy.int32)
if self.binning is not None:
det_grp["binning"] = numpy.array(self._binning, dtype=numpy.int32)
if self.mask is not None:
det_grp["mask"] = self.mask
if not self.uniform_pixel:
#Get ready for the worse case: 4 corner per pixel, position 3D: z,y,x
det_grp["pixel_corners"] = self.get_pixel_corners()
det_grp["pixel_corners"].attrs["interpretation"] = "vertex"
nxs.close()
def guess_binning(self, data):
"""
Guess the binning/mode depending on the image shape
@param data: 2-tuple with the shape of the image or the image with a .shape attribute.
"""
if self.force_pixel:
if "shape" in dir(data):
shape = data.shape
else:
shape = tuple(data[:2])
if shape != self.max_shape:
logger.warning("guess_binning is not implemented for %s detectors!\
and image size %s! is wrong, expected %s!" % (self.name, shape, self.shape))
else:
logger.debug("guess_binning is not implemented for generic detectors !")
class NexusDetector(Detector):
"""
Class representing a 2D detector loaded from a NeXus file
"""
def __init__(self, filename=None):
Detector.__init__(self)
self._pixel_corners = None
if filename is not None:
self.load(filename)
self._filename = filename
self.uniform_pixel = True
def __repr__(self):
return "%s detector from NeXus file: %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._filename, self._pixel1, self._pixel2)
def load(self, filename):
"""
Loads the detector description from a NeXus file, adapted from:
http://download.nexusformat.org/sphinx/classes/base_classes/NXdetector.html
@param filename: name of the file on the disc
"""
if not io.h5py:
logger.error("h5py module missing: NeXus detectors not supported")
raise RuntimeError("H5py module is missing")
nxs = io.Nexus(filename, "r")
det_grp = nxs.find_detector()
name = posixpath.split(det_grp.name)[-1]
self.aliases = [name.replace("_", " "), det_grp.name]
if "pixel_size" in det_grp:
self.pixel1, self.pixel2 = det_grp["pixel_size"]
if "binning" in det_grp:
self._binning = tuple(i for i in det_grp["binning"].value)
for what in ("max_shape", "shape"):
if what in det_grp:
self.__setattr__(what, tuple(i for i in det_grp[what].value))
if "mask" in det_grp:
self.mask = det_grp["mask"].value
if "pixel_corners" in det_grp:
self._pixel_corners = det_grp["pixel_corners"].value
self.uniform_pixel = False
else:
self.uniform_pixel = True
def get_pixel_corners(self, use_cython=True):
"""
Calculate the position of the corner of the pixels
This should be overwritten by class representing non-contiguous detector (Xpad, ...)
@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:
if bilinear and use_cython:
d1 = numpy.outer(numpy.arange(self.shape[0] + 1), numpy.ones(self.shape[1] + 1))
d2 = numpy.outer(numpy.ones(self.shape[0] + 1), numpy.arange(self.shape[1] + 1))
p1 = self._pixel1 * d1
p2 = self._pixel2 * d2
corners = bilinear.convert_corner_2D_to_4D(3, p1, p2)
else:
p1 = numpy.arange(self.shape[0] + 1) * self._pixel1
p2 = numpy.arange(self.shape[1] + 1) * self._pixel2
p1.shape = -1, 1
p1.strides = p1.strides[0], 0
p2.shape = 1, -1
p2.strides = 0, p2.strides[1]
corners = numpy.zeros((self.shape[0], self.shape[1], 4, 3), dtype=numpy.float32)
corners[:, :, 0, 1] = p1[:-1, :]
corners[:, :, 0, 2] = p2[:, :-1]
corners[:, :, 1, 1] = p1[1:, :]
corners[:, :, 1, 2] = p2[:, :-1]
corners[:, :, 2, 1] = p1[1:, :]
corners[:, :, 2, 2] = p2[:, 1:]
corners[:, :, 3, 1] = p1[:-1, :]
corners[:, :, 3, 2] = p2[:, 1:]
self._pixel_corners = corners
return self._pixel_corners
def calc_cartesian_positions(self, d1=None, d2=None, center=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 !!!
Adapted to Nexus detector definition
@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)
@param center: retrieve the coordinate of the center of the pixel
@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.
"""
if (d1 is None) or d2 is None:
# d1, d2 = numpy.ogrid[:self.shape[0], :self.shape[1]]
d1 = numpy.outer(numpy.arange(self.shape[0]), numpy.ones(self.shape[1]))
d2 = numpy.outer(numpy.ones(self.shape[0]), numpy.arange(self.shape[1]))
corners = self.get_pixel_corners()
if center:
d1 += 0.5
d2 += 0.5
if bilinear:
p1, p2 = bilinear.calc_cartesian_positions(d1.ravel(), d2.ravel(), corners)
p1.shape = d1.shape
p2.shape = d2.shape
else:
i1 = d1.astype(int)
i2 = d2.astype(int)
delta1 = d1 - i1
delta2 = d2 - i2
pixels = corners[i1, i2]
A1 = pixels[:, :, 0, 1]
A2 = pixels[:, :, 0, 2]
B1 = pixels[:, :, 1, 1]
B2 = pixels[:, :, 1, 2]
C1 = pixels[:, :, 2, 1]
C2 = pixels[:, :, 2, 2]
D1 = pixels[:, :, 3, 1]
D2 = pixels[:, :, 3, 2]
#points A and D are on the same dim1 (Y), they differ in dim2 (X)
#points B and C are on the same dim1 (Y), they differ in dim2 (X)
#p1 = mean(A1,D1) + delta1 * (mean(C2,D2)-mean(A2,C2))
p1 = 0.5 * ((A1 + D1) * (1.0 - delta1) + delta1 * (B1 + C1))
#points A and B are on the same dim2 (X), they differ in dim1
#points A and B are on the same dim2 (X), they differ in dim1
#p2 = mean(A2,B2) + delta2 * (mean(C2,D2)-mean(A2,C2))
p2 = 0.5 * ((A2 + B2) * (1.0 - delta2) + delta2 * (C2 + D2))
return p1, p2
class Pilatus(Detector):
"""
Pilatus detector: generic description containing mask algorithm
Sub-classed by Pilatus1M, Pilatus2M and Pilatus6M
"""
MODULE_SIZE = (195, 487)
MODULE_GAP = (17, 7)
force_pixel = True
def __init__(self, pixel1=172e-6, pixel2=172e-6, x_offset_file=None, y_offset_file=None):
super(Pilatus, self).__init__(pixel1=pixel1, pixel2=pixel2)
self.x_offset_file = x_offset_file
self.y_offset_file = y_offset_file
if self.x_offset_file and self.y_offset_file:
if fabio:
self.offset1 = fabio.open(self.y_offset_file).data
self.offset2 = fabio.open(self.x_offset_file).data
else:
logging.error("FabIO is not available: no distortion correction for Pilatus detectors, sorry.")
self.offset1 = None
self.offset2 = None
else:
self.offset1 = None
self.offset2 = None
def __repr__(self):
txt = "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self.pixel1, self.pixel2)
if self.x_offset_file:
txt += "\t delta_x= %s" % self.x_offset_file
if self.y_offset_file:
txt += "\t delta_y= %s" % self.y_offset_file
return txt
def get_splineFile(self):
if self.x_offset_file and self.y_offset_file:
return "%s,%s" % (self.x_offset_file, self.y_offset_file)
def set_splineFile(self, splineFile=None):
"In this case splinefile is a couple filenames"
if splineFile is not None:
try:
files = splineFile.split(",")
self.x_offset_file = [os.path.abspath(i) for i in files if "x" in i.lower()][0]
self.y_offset_file = [os.path.abspath(i) for i in files if "y" in i.lower()][0]
self.uniform_pixel = False
except Exception as error:
logger.error("set_splineFile with %s gave error: %s" % (splineFile, error))
self.x_offset_file = self.y_offset_file = self.offset1 = self.offset2 = None
self.uniform_pixel = True
return
if fabio:
self.offset1 = fabio.open(self.y_offset_file).data
self.offset2 = fabio.open(self.x_offset_file).data
else:
logging.error("FabIO is not available: no distortion correction for Pilatus detectors, sorry.")
self.offset1 = None
self.offset2 = None
else:
self._splineFile = None
self.uniform_pixel = True
splineFile = property(get_splineFile, set_splineFile)
def calc_mask(self):
"""
Returns a generic mask for Pilatus detectors...
"""
if self.max_shape is None:
raise NotImplementedError("Generic Pilatus detector does not know "
"its max size ...")
mask = numpy.zeros(self.max_shape, dtype=numpy.int8)
# workinng in dim0 = Y
for i in range(self.MODULE_SIZE[0], self.max_shape[0],
self.MODULE_SIZE[0] + self.MODULE_GAP[0]):
mask[i: i + self.MODULE_GAP[0], :] = 1
# workinng in dim1 = X
for i in range(self.MODULE_SIZE[1], self.max_shape[1],
self.MODULE_SIZE[1] + self.MODULE_GAP[1]):
mask[:, i: i + self.MODULE_GAP[1]] = 1
return mask
def calc_cartesian_positions(self, d1=None, d2=None):
"""
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.
"""
if (d1 is None):
d1 = numpy.outer(numpy.arange(self.max_shape[0]), numpy.ones(self.max_shape[1]))
if (d2 is None):
d2 = numpy.outer(numpy.ones(self.max_shape[0]), numpy.arange(self.max_shape[1]))
if self.offset1 is None or self.offset2 is None:
delta1 = delta2 = 0.
else:
if d2.ndim == 1:
d1n = d1.astype(numpy.int32)
d2n = d2.astype(numpy.int32)
delta1 = self.offset1[d1n, d2n] / 100.0 # Offsets are in percent of pixel
delta2 = self.offset2[d1n, d2n] / 100.0
else:
if d1.shape == self.offset1.shape:
delta1 = self.offset1 / 100.0 # Offsets are in percent of pixel
delta2 = self.offset2 / 100.0
elif d1.shape[0] > self.offset1.shape[0]: # probably working with corners
s0, s1 = self.offset1.shape
delta1 = numpy.zeros(d1.shape, dtype=numpy.int32) # this is the natural type for pilatus CBF
delta2 = numpy.zeros(d2.shape, dtype=numpy.int32)
delta1[:s0, :s1] = self.offset1
delta2[:s0, :s1] = self.offset2
mask = numpy.where(delta1[-s0:, :s1] == 0)
delta1[-s0:, :s1][mask] = self.offset1[mask]
delta2[-s0:, :s1][mask] = self.offset2[mask]
mask = numpy.where(delta1[-s0:, -s1:] == 0)
delta1[-s0:, -s1:][mask] = self.offset1[mask]
delta2[-s0:, -s1:][mask] = self.offset2[mask]
mask = numpy.where(delta1[:s0, -s1:] == 0)
delta1[:s0, -s1:][mask] = self.offset1[mask]
delta2[:s0, -s1:][mask] = self.offset2[mask]
delta1 = delta1 / 100.0 # Offsets are in percent of pixel
delta2 = delta2 / 100.0 # former arrays were integers
else:
logger.warning("Surprizing situation !!! please investigate: offset has shape %s and input array have %s" % (self.offset1.shape, d1.shape))
delta1 = delta2 = 0.
# For pilatus,
p1 = (self._pixel1 * (delta1 + 0.5 + d1))
p2 = (self._pixel2 * (delta2 + 0.5 + d2))
return p1, p2
class Pilatus100k(Pilatus):
"""
Pilatus 100k detector
"""
MAX_SHAPE = 195, 487
def __init__(self, pixel1=172e-6, pixel2=172e-6):
super(Pilatus100k, self).__init__(pixel1=pixel1, pixel2=pixel2)
class Pilatus200k(Pilatus):
"""
Pilatus 200k detector
"""
MAX_SHAPE = (407, 487)
def __init__(self, pixel1=172e-6, pixel2=172e-6):
super(Pilatus200k, self).__init__(pixel1=pixel1, pixel2=pixel2)
class Pilatus300k(Pilatus):
"""
Pilatus 300k detector
"""
MAX_SHAPE = (619, 487)
def __init__(self, pixel1=172e-6, pixel2=172e-6):
super(Pilatus300k, self).__init__(pixel1=pixel1, pixel2=pixel2)
class Pilatus300kw(Pilatus):
"""
Pilatus 300k-wide detector
"""
MAX_SHAPE = (195, 1475)
def __init__(self, pixel1=172e-6, pixel2=172e-6):
super(Pilatus300kw, self).__init__(pixel1=pixel1, pixel2=pixel2)
class Pilatus1M(Pilatus):
"""
Pilatus 1M detector
"""
MAX_SHAPE = (1043, 981)
def __init__(self, pixel1=172e-6, pixel2=172e-6):
super(Pilatus1M, self).__init__(pixel1=pixel1, pixel2=pixel2)
class Pilatus2M(Pilatus):
"""
Pilatus 2M detector
"""
MAX_SHAPE = 1679, 1475
def __init__(self, pixel1=172e-6, pixel2=172e-6):
super(Pilatus2M, self).__init__(pixel1=pixel1, pixel2=pixel2)
class Pilatus6M(Pilatus):
"""
Pilatus 6M detector
"""
MAX_SHAPE = (2527, 2463)
def __init__(self, pixel1=172e-6, pixel2=172e-6):
super(Pilatus6M, self).__init__(pixel1=pixel1, pixel2=pixel2)
class Eiger(Detector):
"""
Eiger detector: generic description containing mask algorithm
"""
MODULE_SIZE = (1065, 1030)
MODULE_GAP = (37, 10)
force_pixel = True
def __init__(self, pixel1=75e-6, pixel2=75e-6):
Detector.__init__(self, pixel1=pixel1, pixel2=pixel2)
self.offset1 = self.offset2 = None
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._pixel1, self._pixel2)
def calc_mask(self):
"""
Returns a generic mask for Pilatus detectors...
"""
if self.max_shape is None:
raise NotImplementedError("Generic Pilatus detector does not know"
"the max size ...")
mask = numpy.zeros(self.max_shape, dtype=numpy.int8)
# workinng in dim0 = Y
for i in range(self.MODULE_SIZE[0], self.max_shape[0],
self.MODULE_SIZE[0] + self.MODULE_GAP[0]):
mask[i: i + self.MODULE_GAP[0], :] = 1
# workinng in dim1 = X
for i in range(self.MODULE_SIZE[1], self.max_shape[1],
self.MODULE_SIZE[1] + self.MODULE_GAP[1]):
mask[:, i: i + self.MODULE_GAP[1]] = 1
return mask
def calc_cartesian_positions(self, d1=None, d2=None):
"""
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.
"""
if (d1 is None):
d1 = numpy.outer(numpy.arange(self.max_shape[0]), numpy.ones(self.max_shape[1]))
if (d2 is None):
d2 = numpy.outer(numpy.ones(self.max_shape[0]), numpy.arange(self.max_shape[1]))
if self.offset1 is None or self.offset2 is None:
delta1 = delta2 = 0.
else:
if d2.ndim == 1:
d1n = d1.astype(numpy.int32)
d2n = d2.astype(numpy.int32)
delta1 = self.offset1[d1n, d2n] / 100.0 # Offsets are in percent of pixel
delta2 = self.offset2[d1n, d2n] / 100.0
else:
if d1.shape == self.offset1.shape:
delta1 = self.offset1 / 100.0 # Offsets are in percent of pixel
delta2 = self.offset2 / 100.0
elif d1.shape[0] > self.offset1.shape[0]: # probably working with corners
s0, s1 = self.offset1.shape
delta1 = numpy.zeros(d1.shape, dtype=numpy.int32) # this is the natural type for pilatus CBF
delta2 = numpy.zeros(d2.shape, dtype=numpy.int32)
delta1[:s0, :s1] = self.offset1
delta2[:s0, :s1] = self.offset2
mask = numpy.where(delta1[-s0:, :s1] == 0)
delta1[-s0:, :s1][mask] = self.offset1[mask]
delta2[-s0:, :s1][mask] = self.offset2[mask]
mask = numpy.where(delta1[-s0:, -s1:] == 0)
delta1[-s0:, -s1:][mask] = self.offset1[mask]
delta2[-s0:, -s1:][mask] = self.offset2[mask]
mask = numpy.where(delta1[:s0, -s1:] == 0)
delta1[:s0, -s1:][mask] = self.offset1[mask]
delta2[:s0, -s1:][mask] = self.offset2[mask]
delta1 = delta1 / 100.0 # Offsets are in percent of pixel
delta2 = delta2 / 100.0 # former arrays were integers
else:
logger.warning("Surprising situation !!! please investigate: offset has shape %s and input array have %s" % (self.offset1.shape, d1.shape))
delta1 = delta2 = 0.
# For pilatus,
p1 = (self._pixel1 * (delta1 + 0.5 + d1))
p2 = (self._pixel2 * (delta2 + 0.5 + d2))
return p1, p2
class Eiger1M(Eiger):
"""
Eiger 1M detector
"""
MAX_SHAPE = (1065, 1030)
def __init__(self, pixel1=75e-6, pixel2=75e-6):
Eiger.__init__(self, pixel1=pixel1, pixel2=pixel2)
class Eiger4M(Eiger):
"""
Eiger 4M detector
"""
MAX_SHAPE = (2167, 2070)
def __init__(self, pixel1=75e-6, pixel2=75e-6):
Eiger.__init__(self, pixel1=pixel1, pixel2=pixel2)
class Eiger9M(Eiger):
"""
Eiger 9M detector
"""
MAX_SHAPE = (3269, 3110)
def __init__(self, pixel1=75e-6, pixel2=75e-6):
Eiger.__init__(self, pixel1=pixel1, pixel2=pixel2)
class Eiger16M(Eiger):
"""
Eiger 16M detector
"""
MAX_SHAPE = (4371, 4150)
def __init__(self, pixel1=75e-6, pixel2=75e-6):
Eiger.__init__(self, pixel1=pixel1, pixel2=pixel2)
class Fairchild(Detector):
"""
Fairchild Condor 486:90 detector
"""
force_pixel = True
uniform_pixel = True
aliases = ["Fairchild", "Condor", "Fairchild Condor 486:90"]
MAX_SHAPE = (4096, 4096)
def __init__(self, pixel1=15e-6, pixel2=15e-6):
Detector.__init__(self, pixel1=pixel1, pixel2=pixel2)
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._pixel1, self._pixel2)
class Titan(Detector):
"""
Titan CCD detector from Agilent. Mask not handled
"""
force_pixel = True
MAX_SHAPE = (2048, 2048)
aliases = ["Titan 2k x 2k", "OXD Titan", "Agilent Titan"]
uniform_pixel = True
def __init__(self, pixel1=60e-6, pixel2=60e-6):
Detector.__init__(self, pixel1=pixel1, pixel2=pixel2)
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._pixel1, self._pixel2)
class Dexela2923(Detector):
"""
Dexela CMOS family detector
"""
force_pixel = True
aliases = ["Dexela 2923"]
MAX_SHAPE = (3888, 3072)
def __init__(self, pixel1=75e-6, pixel2=75e-6):
super(Dexela2923, self).__init__(pixel1=pixel1, pixel2=pixel2)
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._pixel1, self._pixel2)
class FReLoN(Detector):
"""
FReLoN detector:
The spline is mandatory to correct for geometric distortion of the taper
TODO: create automatically a mask that removes pixels out of the "valid reagion"
"""
def __init__(self, splineFile=None):
super(FReLoN, self).__init__(splineFile=splineFile)
if splineFile:
self.max_shape = (int(self.spline.ymax - self.spline.ymin),
int(self.spline.xmax - self.spline.xmin))
self.uniform_pixel = False
else:
self.max_shape = (2048, 2048)
self.pixel1 = 50e-6
self.pixel2 = 50e-6
self.shape = self.max_shape
def calc_mask(self):
"""
Returns a generic mask for Frelon detectors...
All pixels which (center) turns to be out of the valid region are by default discarded
"""
if not self._splineFile:
return
d1 = numpy.outer(numpy.arange(self.shape[0]), numpy.ones(self.shape[1])) + 0.5
d2 = numpy.outer(numpy.ones(self.shape[0]), numpy.arange(self.shape[1])) + 0.5
dX = self.spline.splineFuncX(d2, d1)
dY = self.spline.splineFuncY(d2, d1)
p1 = dY + d1
p2 = dX + d2
below_min = numpy.logical_or((p2 < self.spline.xmin), (p1 < self.spline.ymin))
above_max = numpy.logical_or((p2 > self.spline.xmax), (p1 > self.spline.ymax))
mask = numpy.logical_or(below_min, above_max)
return mask
class Basler(Detector):
"""
Basler camera are simple CCD camara over GigaE
"""
force_pixel = True
aliases = ["aca1300"]
MAX_SHAPE = (966, 1296)
def __init__(self, pixel=3.75e-6):
super(Basler, self).__init__(pixel1=pixel, pixel2=pixel)
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._pixel1, self._pixel2)
class Mar345(Detector):
"""
Mar345 Imaging plate detector
In this detector, pixels are always square
The valid image size are 2300, 2000, 1600, 1200, 3450, 3000, 2400, 1800
"""
force_pixel = True
MAX_SHAPE = (3450, 3450)
#Valid image width with corresponding pixel size
VALID_SIZE = {2300:150e-6,
2000:150e-6,
1600:150e-6,
1200:150e-6,
3450:100e-6,
3000:100e-6,
2400:100e-6,
1800:100e-6}
aliases = ["MAR 345", "Mar3450"]
def __init__(self, pixel1=100e-6, pixel2=100e-6):
Detector.__init__(self, pixel1, pixel2)
self.max_shape = (int(self.MAX_SHAPE[0] * 100e-6 / self.pixel1),
int(self.MAX_SHAPE[1] * 100e-6 / self.pixel2))
self.shape = self.max_shape
# self.mode = 1
def calc_mask(self):
c = [i // 2 for i in self.shape]
x, y = numpy.ogrid[:self.shape[0], :self.shape[1]]
mask = ((x + 0.5 - c[0]) ** 2 + (y + 0.5 - c[1]) ** 2) > (c[0]) ** 2
return mask
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._pixel1, self._pixel2)
def guess_binning(self, data):
"""
Guess the binning/mode depending on the image shape
@param data: 2-tuple with the shape of the image or the image with a .shape attribute.
"""
if "shape" in dir(data):
shape = data.shape
else:
shape = data[:2]
dim1, dim2 = shape
self._pixel1 = self.VALID_SIZE[dim1]
self._pixel2 = self.VALID_SIZE[dim1]
self.max_shape = shape
self.shape = shape
self._binning = (1, 1)
self._mask = False
self._mask_crc = None
class ImXPadS10(Detector):
"""
ImXPad detector: ImXPad s10 detector with 1x1modules
"""
MODULE_SIZE = (120, 80) # number of pixels per module (y, x)
MAX_SHAPE = (120, 80) # max size of the detector
PIXEL_SIZE = (130e-6, 130e-6)
BORDER_SIZE_RELATIVE = 2.5
force_pixel = True
aliases = ["Imxpad S10"]
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
size[0] = cls.BORDER_SIZE_RELATIVE
size[-1] = cls.BORDER_SIZE_RELATIVE
return pixel_size * size
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 calc_mask(self):
"""
Calculate the mask
"""
dims = []
for dim in [0, 1]:
pos = numpy.zeros(self.MAX_SHAPE[dim], dtype=numpy.int8)
n = self.MAX_SHAPE[dim] // self.MODULE_SIZE[dim]
for i in range(1, n):
pos[i * self.MODULE_SIZE[dim] - 1] = 1
pos[i * self.MODULE_SIZE[dim]] = 1
pos[0] = 1
pos[-1] = 1
dims.append(pos)
#This is just an "outer sum"
dim1, dim2 = dims
dim1.shape = -1, 1
dim1.strides = dim1.strides[0], 0
dim2.shape = 1, -1
dim2.strides = 0, dim2.strides[-1]
return (dim1 + dim2) > 0
def __init__(self, pixel1=130e-6, pixel2=130e-6):
Detector.__init__(self, pixel1=pixel1, pixel2=pixel2)
self._pixel_edges = None # array of size max_shape+1: pixels are contiguous
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self.pixel1, self.pixel2)
def calc_cartesian_positions(self, d1=None, d2=None):
"""
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):
#Take the center of each pixel
d1 = 0.5 * (edges1[:-1] + edges1[1:])
d2 = 0.5 * (edges2[:-1] + edges2[1:])
p1 = numpy.outer(d1, numpy.ones(self.shape[1]))
p2 = numpy.outer(numpy.ones(self.shape[0]), d2)
else:
p1 = numpy.interp(d1 + 0.5, numpy.arange(self.MAX_SHAPE[0] + 1), edges1, edges1[0], edges1[-1])
p2 = numpy.interp(d2 + 0.5, numpy.arange(self.MAX_SHAPE[1] + 1), edges2, edges2[0], edges2[-1])
return p1, p2
class ImXPadS70(ImXPadS10):
"""
ImXPad detector: ImXPad s70 detector with 1x7modules
"""
MODULE_SIZE = (120, 80) # number of pixels per module (y, x)
MAX_SHAPE = (120, 560) # max size of the detector
PIXEL_SIZE = (130e-6, 130e-6)
BORDER_SIZE_RELATIVE = 2.5
force_pixel = True
aliases = ["Imxpad S70"]
PIXEL_EDGES = None # array of size max_shape+1: pixels are contiguous
def __init__(self, pixel1=130e-6, pixel2=130e-6):
ImXPadS10.__init__(self, pixel1=pixel1, pixel2=pixel2)
class ImXPadS140(ImXPadS10):
"""
ImXPad detector: ImXPad s140 detector with 2x7modules
"""
MODULE_SIZE = (120, 80) # number of pixels per module (y, x)
MAX_SHAPE = (240, 560) # max size of the detector
PIXEL_SIZE = (130e-6, 130e-6)
BORDER_PIXEL_SIZE_RELATIVE = 2.5
force_pixel = True
aliases = ["Imxpad S140"]
def __init__(self, pixel1=130e-6, pixel2=130e-6):
ImXPadS10.__init__(self, pixel1=pixel1, pixel2=pixel2)
class Xpad_flat(ImXPadS10):
"""
Xpad detector: generic description for
ImXPad detector with 8x7modules
"""
MODULE_SIZE = (120, 80)
MODULE_GAP = (3.57e-3, 0) # in meter
force_pixel = True
MAX_SHAPE = (960, 560)
uniform_pixel = False
aliases = ["Xpad S540 flat"]
MODULE_SIZE = (120, 80) # number of pixels per module (y, x)
PIXEL_SIZE = (130e-6, 130e-6)
BORDER_PIXEL_SIZE_RELATIVE = 2.5
def __init__(self, pixel1=130e-6, pixel2=130e-6):
super(Xpad_flat, self).__init__(pixel1=pixel1, pixel2=pixel2)
self._pixel_corners = None
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self.pixel1, self.pixel2)
def calc_mask(self):
"""
Returns a generic mask for Xpad detectors...
discards the first line and raw form all modules:
those are 2.5x bigger and often mis - behaving
"""
if self.max_shape is None:
raise NotImplementedError("Generic Xpad detector does not"
" know the max size ...")
mask = numpy.zeros(self.max_shape, dtype=numpy.int8)
# workinng in dim0 = Y
for i in range(0, self.max_shape[0], self.MODULE_SIZE[0]):
mask[i, :] = 1
mask[i + self.MODULE_SIZE[0] - 1, :] = 1
# workinng in dim1 = X
for i in range(0, self.max_shape[1], self.MODULE_SIZE[1]):
mask[:, i ] = 1
mask[:, i + self.MODULE_SIZE[1] - 1] = 1
return mask
def calc_cartesian_positions(self, d1=None, d2=None, center=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 !!!
Adapted to Nexus detector definition
@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)
@param center: retrieve the coordinate of the center of the pixel
@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.
"""
if (d1 is None) or d2 is None:
# d1, d2 = numpy.ogrid[:self.shape[0], :self.shape[1]]
d1 = numpy.outer(numpy.arange(self.shape[0]), numpy.ones(self.shape[1]))
d2 = numpy.outer(numpy.ones(self.shape[0]), numpy.arange(self.shape[1]))
corners = self.get_pixel_corners()
if center:
d1 += 0.5
d2 += 0.5
if bilinear:
p1, p2 = bilinear.calc_cartesian_positions(d1.ravel(), d2.ravel(), corners)
p1.shape = d1.shape
p2.shape = d2.shape
else:
i1 = d1.astype(int)
i2 = d2.astype(int)
delta1 = d1 - i1
delta2 = d2 - i2
pixels = corners[i1, i2]
A1 = pixels[:, :, 0, 1]
A2 = pixels[:, :, 0, 2]
B1 = pixels[:, :, 1, 1]
B2 = pixels[:, :, 1, 2]
C1 = pixels[:, :, 2, 1]
C2 = pixels[:, :, 2, 2]
D1 = pixels[:, :, 3, 1]
D2 = pixels[:, :, 3, 2]
#points A and D are on the same dim1 (Y), they differ in dim2 (X)
#points B and C are on the same dim1 (Y), they differ in dim2 (X)
#p1 = mean(A1,D1) + delta1 * (mean(C2,D2)-mean(A2,C2))
p1 = 0.5 * ((A1 + D1) * (1.0 - delta1) + delta1 * (B1 + C1))
#points A and B are on the same dim2 (X), they differ in dim1
#points A and B are on the same dim2 (X), they differ in dim1
#p2 = mean(A2,B2) + delta2 * (mean(C2,D2)-mean(A2,C2))
p2 = 0.5 * ((A2 + B2) * (1.0 - delta2) + delta2 * (C2 + D2))
return p1, p2
def get_pixel_corners(self):
"""
Calculate the position of the corner of the pixels
@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:
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])
# half pixel offset
pixel_center1 = pixel_size1 / 2.0 # half pixel offset
pixel_center2 = pixel_size2 / 2.0
#size of all preceeding pixels
pixel_center1[1:] += numpy.cumsum(pixel_size1[:-1])
pixel_center2[1:] += numpy.cumsum(pixel_size2[:-1])
#gaps
for i in range(self.MAX_SHAPE[0] // self.MODULE_SIZE[0]):
pixel_center1[i * self.MODULE_SIZE[0]:
(i + 1) * self.MODULE_SIZE[0]] += i * self.MODULE_GAP[0]
for i in range(self.MAX_SHAPE[1] // self.MODULE_SIZE[1]):
pixel_center2[i * self.MODULE_SIZE[1]:
(i + 1) * self.MODULE_SIZE[1]] += i * self.MODULE_GAP[1]
pixel_center1.shape = -1, 1
pixel_center1.strides = pixel_center1.strides[0], 0
pixel_center2.shape = 1, -1
pixel_center2.strides = 0, pixel_center2.strides[1]
pixel_size1.shape = -1, 1
pixel_size1.strides = pixel_size1.strides[0], 0
pixel_size2.shape = 1, -1
pixel_size2.strides = 0, pixel_size2.strides[1]
corners = numpy.zeros((self.shape[0], self.shape[1], 4, 3), dtype=numpy.float32)
corners[:, :, 0, 1] = pixel_center1 - pixel_size1 / 2.0
corners[:, :, 0, 2] = pixel_center2 - pixel_size2 / 2.0
corners[:, :, 1, 1] = pixel_center1 + pixel_size1 / 2.0
corners[:, :, 1, 2] = pixel_center2 - pixel_size2 / 2.0
corners[:, :, 2, 1] = pixel_center1 + pixel_size1 / 2.0
corners[:, :, 2, 2] = pixel_center2 + pixel_size2 / 2.0
corners[:, :, 3, 1] = pixel_center1 - pixel_size1 / 2.0
corners[:, :, 3, 2] = pixel_center2 + pixel_size2 / 2.0
self._pixel_corners = corners
return self._pixel_corners
class Perkin(Detector):
"""
Perkin detector
"""
aliases = ["Perkin detector"]
force_pixel = True
MAX_SHAPE = (2048, 2048)
def __init__(self, pixel=200e-6):
super(Perkin, self).__init__(pixel1=pixel, pixel2=pixel)
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._pixel1, self._pixel2)
class Rayonix(Detector):
force_pixel = True
BINNED_PIXEL_SIZE = {}
def __init__(self, pixel1=None, pixel2=None):
Detector.__init__(self, pixel1=pixel1, pixel2=pixel2)
def get_binning(self):
return self._binning
def set_binning(self, bin_size=(1, 1)):
"""
Set the "binning" of the detector,
@param bin_size: set the binning of the detector
@type bin_size: int or (int, int)
"""
if "__len__" in dir(bin_size) and len(bin_size) >= 2:
bin_size = int(round(float(bin_size[0]))), int(round(float(bin_size[1])))
else:
b = int(round(float(bin_size)))
bin_size = (b, b)
if bin_size != self._binning:
if (bin_size[0] in self.BINNED_PIXEL_SIZE) and (bin_size[1] in self.BINNED_PIXEL_SIZE):
self._pixel1 = self.BINNED_PIXEL_SIZE[bin_size[0]]
self._pixel2 = self.BINNED_PIXEL_SIZE[bin_size[1]]
else:
logger.warning("Binning factor (%sx%s) is not an official value for Rayonix detectors" % (bin_size[0], bin_size[1]))
self._pixel1 = self.BINNED_PIXEL_SIZE[1] / float(bin_size[0])
self._pixel2 = self.BINNED_PIXEL_SIZE[1] / float(bin_size[1])
self._binning = bin_size
self.shape = (self.max_shape[0] // bin_size[0],
self.max_shape[1] // bin_size[1])
binning = property(get_binning, set_binning)
def __repr__(self):
return "Detector %s\t PixelSize= %.3e, %.3e m" % \
(self.name, self._pixel1, self._pixel2)
def guess_binning(self, data):
"""
Guess the binning/mode depending on the image shape
@param data: 2-tuple with the shape of the image or the image with a .shape attribute.
"""
if "shape" in dir(data):
shape = data.shape
else:
shape = tuple(data[:2])
bin1 = self.MAX_SHAPE[0] // shape[0]
bin2 = self.MAX_SHAPE[1] // shape[1]
self._binning = (bin1, bin2)
self.shape = shape
self.max_shape = shape
self._pixel1 = self.BINNED_PIXEL_SIZE[bin1]
self._pixel2 = self.BINNED_PIXEL_SIZE[bin2]
self._mask = False
self._mask_crc = None
class Rayonix133(Rayonix):
"""
Rayonix 133 2D CCD detector detector also known as mar133
Personnal communication from M. Blum
What should be the default binning factor for those cameras ?
Circular detector
"""
force_pixel = True
BINNED_PIXEL_SIZE = {1: 32e-6,
2: 64e-6,
4: 128e-6,
8: 256e-6,
}
MAX_SHAPE = (4096 , 4096)
aliases = ["MAR133"]
def __init__(self):
Rayonix.__init__(self, pixel1=64e-6, pixel2=64e-6)
self.shape = (2048, 2048)
self._binning = (2, 2)
def calc_mask(self):
"""Circular mask"""
c = [i // 2 for i in self.shape]
x, y = numpy.ogrid[:self.shape[0], :self.shape[1]]
mask = ((x + 0.5 - c[0]) ** 2 + (y + 0.5 - c[1]) ** 2) > (c[0]) ** 2
return mask
class RayonixSx165(Rayonix):
"""
Rayonix sx165 2d Detector also known as MAR165.
Circular detector
"""
BINNED_PIXEL_SIZE = {1: 39.5e-6,
2: 79e-6,
3: 118.616e-6, # image shape is then 1364 not 1365 !
4: 158e-6,
8: 316e-6,
}
MAX_SHAPE = (4096 , 4096)
aliases = ["MAR165", "Rayonix Sx165"]
force_pixel = True
def __init__(self):
Rayonix.__init__(self, pixel1=39.5e-6, pixel2=39.5e-6)
def calc_mask(self):
"""Circular mask"""
c = [i // 2 for i in self.shape]
x, y = numpy.ogrid[:self.shape[0], :self.shape[1]]
mask = ((x + 0.5 - c[0]) ** 2 + (y + 0.5 - c[1]) ** 2) > (c[0]) ** 2
return mask
class RayonixSx200(Rayonix):
"""
Rayonix sx200 2d CCD Detector.
Pixel size are personnal communication from M. Blum.
"""
BINNED_PIXEL_SIZE = {1: 48e-6,
2: 96e-6,
3: 144e-6,
4: 192e-6,
8: 384e-6,
}
MAX_SHAPE = (4096 , 4096)
aliases = ["Rayonix sx200"]
def __init__(self):
Rayonix.__init__(self, pixel1=48e-6, pixel2=48e-6)
class RayonixLx170(Rayonix):
"""
Rayonix lx170 2d CCD Detector (2x1 CCDs).
Nota: this is the same for lx170hs
"""
BINNED_PIXEL_SIZE = {1: 44.2708e-6,
2: 88.5417e-6,
3: 132.8125e-6,
4: 177.0833e-6,
5: 221.3542e-6,
6: 265.625e-6,
8: 354.1667e-6,
10:442.7083e-6
}
MAX_SHAPE = (1920, 3840)
force_pixel = True
aliases = ["Rayonix lx170"]
def __init__(self):
Rayonix.__init__(self, pixel1=44.2708e-6, pixel2=44.2708e-6)
class RayonixMx170(Rayonix):
"""
Rayonix mx170 2d CCD Detector (2x2 CCDs).
Nota: this is the same for mx170hs
"""
BINNED_PIXEL_SIZE = {1: 44.2708e-6,
2: 88.5417e-6,
3: 132.8125e-6,
4: 177.0833e-6,
5: 221.3542e-6,
6: 265.625e-6,
8: 354.1667e-6,
10:442.7083e-6
}
MAX_SHAPE = (3840, 3840)
aliases = ["Rayonix mx170"]
def __init__(self):
Rayonix.__init__(self, pixel1=44.2708e-6, pixel2=44.2708e-6)
class RayonixLx255(Rayonix):
"""
Rayonix lx255 2d Detector (3x1 CCDs)
Nota: this detector is also called lx255hs
"""
BINNED_PIXEL_SIZE = {1: 44.2708e-6,
2: 88.5417e-6,
3: 132.8125e-6,
4: 177.0833e-6,
5: 221.3542e-6,
6: 265.625e-6,
8: 354.1667e-6,
10:442.7083e-6
}
MAX_SHAPE = (1920 , 5760)
aliases = [ "Rayonix lx225"]
def __init__(self):
Rayonix.__init__(self, pixel1=44.2708e-6, pixel2=44.2708e-6)
class RayonixMx225(Rayonix):
"""
Rayonix mx225 2D CCD detector detector
Nota: this is the same definition for mx225he
Personnal communication from M. Blum
"""
force_pixel = True
BINNED_PIXEL_SIZE = {1: 36.621e-6,
2: 73.242e-6,
3: 109.971e-6,
4: 146.484e-6,
8: 292.969e-6
}
MAX_SHAPE = (6144, 6144)
aliases = ["Rayonix mx225"]
def __init__(self):
Rayonix.__init__(self, pixel1=73.242e-6, pixel2=73.242e-6)
self.shape = (3072, 3072)
self._binning = (2, 2)
class RayonixMx225hs(Rayonix):
"""
Rayonix mx225hs 2D CCD detector detector
Pixel size from a personnal communication from M. Blum
"""
force_pixel = True
BINNED_PIXEL_SIZE = {1: 39.0625e-6,
2: 78.125e-6,
3: 117.1875e-6,
4: 156.25e-6,
5: 195.3125e-6,
6: 234.3750e-6,
8: 312.5e-6,
10:390.625e-6,
}
MAX_SHAPE = (5760 , 5760)
aliases = ["Rayonix mx225hs"]
def __init__(self):
Rayonix.__init__(self, pixel1=78.125e-6, pixel2=78.125e-6)
self.shape = (2880, 2880)
self._binning = (2, 2)
class RayonixMx300(Rayonix):
"""
Rayonix mx300 2D detector (4x4 CCDs)
Pixel size from a personnal communication from M. Blum
"""
force_pixel = True
BINNED_PIXEL_SIZE = {1: 36.621e-6,
2: 73.242e-6,
3: 109.971e-6,
4: 146.484e-6,
8: 292.969e-6
}
MAX_SHAPE = (8192, 8192)
aliases = ["Rayonix mx300"]
def __init__(self):
Rayonix.__init__(self, pixel1=73.242e-6, pixel2=73.242e-6)
self.shape = (4096, 4096)
self._binning = (2, 2)
class RayonixMx300hs(Rayonix):
"""
Rayonix mx300hs 2D detector (4x4 CCDs)
Pixel size from a personnal communication from M. Blum
"""
force_pixel = True
BINNED_PIXEL_SIZE = {1: 39.0625e-6,
2: 78.125e-6,
3: 117.1875e-6,
4: 156.25e-6,
5: 195.3125e-6,
6: 234.3750e-6,
8: 312.5e-6,
10: 390.625e-6
}
MAX_SHAPE = (7680, 7680)
aliases = ["Rayonix mx300hs"]
def __init__(self):
Rayonix.__init__(self, pixel1=78.125e-6, pixel2=78.125e-6)
self.shape = (3840, 3840)
self._binning = (2, 2)
class RayonixMx340hs(Rayonix):
"""
Rayonix mx340hs 2D detector (4x4 CCDs)
Pixel size from a personnal communication from M. Blum
"""
force_pixel = True
BINNED_PIXEL_SIZE = {1: 44.2708e-6,
2: 88.5417e-6,
3: 132.8125e-6,
4: 177.0833e-6,
5: 221.3542e-6,
6: 265.625e-6,
8: 354.1667e-6,
10: 442.7083e-6
}
MAX_SHAPE = (7680 , 7680)
aliases = ["Rayonix mx340hs"]
def __init__(self):
Rayonix.__init__(self, pixel1=88.5417e-6, pixel2=88.5417e-6)
self.shape = (3840, 3840)
self._binning = (2, 2)
class RayonixSx30hs(Rayonix):
"""
Rayonix sx30hs 2D CCD camera (1 CCD chip)
Pixel size from a personnal communication from M. Blum
"""
BINNED_PIXEL_SIZE = {1: 15.625e-6,
2: 31.25e-6,
3: 46.875e-6,
4: 62.5e-6,
5: 78.125e-6,
6: 93.75e-6,
8: 125.0e-6,
10:156.25e-6
}
MAX_SHAPE = (1920 , 1920)
aliases = ["Rayonix Sx30hs"]
def __init__(self):
Rayonix.__init__(self, pixel1=15.625e-6, pixel2=15.625e-6)
class RayonixSx85hs(Rayonix):
"""
Rayonix sx85hs 2D CCD camera (1 CCD chip)
Pixel size from a personnal communication from M. Blum
"""
BINNED_PIXEL_SIZE = {1: 44.2708e-6,
2: 88.5417e-6,
3: 132.8125e-6,
4: 177.0833e-6,
5: 221.3542e-6,
6: 265.625e-6,
8: 354.1667e-6,
10: 442.7083e-6
}
MAX_SHAPE = (1920 , 1920)
aliases = ["Rayonix Sx85hs"]
def __init__(self):
Rayonix.__init__(self, pixel1=44.2708e-6, pixel2=44.2708e-6)
class RayonixMx425hs(Rayonix):
"""
Rayonix mx425hs 2D CCD camera (5x5 CCD chip)
Pixel size from a personnal communication from M. Blum
"""
BINNED_PIXEL_SIZE = {1: 44.2708e-6,
2: 88.5417e-6,
3: 132.8125e-6,
4: 177.0833e-6,
5: 221.3542e-6,
6: 265.625e-6,
8: 354.1667e-6,
10: 442.7083e-6
}
MAX_SHAPE = (9600 , 9600)
aliases = ["Rayonix mx425hs"]
def __init__(self):
Rayonix.__init__(self, pixel1=44.2708e-6, pixel2=44.2708e-6)
class RayonixMx325(Rayonix):
"""
Rayonix mx325 and mx325he 2D detector (4x4 CCD chips)
Pixel size from a personnal communication from M. Blum
"""
BINNED_PIXEL_SIZE = {1: 39.673e-6,
2: 79.346e-6,
3: 119.135e-6,
4: 158.691e-6,
8: 317.383e-6
}
MAX_SHAPE = (8192 , 8192)
aliases = ["Rayonix mx325"]
def __init__(self):
Rayonix.__init__(self, pixel1=79.346e-6, pixel2=79.346e-6)
self.shape = (4096, 4096)
self._binning = (2, 2)
ALL_DETECTORS = Detector.registry
detector_factory = Detector.factory
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