# !/usr/bin/env python
# -*- coding: utf-8 -*-
#
#    Project: Azimuthal integration
#             https://github.com/silx-kit/pyFAI
#
#    Copyright (C) 2012-2022 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.

"""This modules contains only one (large) class in charge  of:

* calculating the geometry, i.e. the position in the detector space of each pixel of the detector
* manages caches to store intermediate results

NOTA: The Geometry class is not a "transformation class" which would take a
detector and transform it. It is rather a description of the experimental setup.

"""

__author__ = "Jerome Kieffer"
__contact__ = "Jerome.Kieffer@ESRF.eu"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "16/01/2024"
__status__ = "production"
__docformat__ = 'restructuredtext'

import logging
from math import pi
twopi = 2 * pi
from numpy import arccos, arctan2, sin, cos, sqrt
import numpy
import os
import threading
import json
import gc
from collections import namedtuple, OrderedDict
from .fit2d import convert_to_Fit2d, convert_from_Fit2d
from .. import detectors
from .. import units
from ..utils.decorators import deprecated
from ..utils import crc32, deg2rad
from .. import utils
from ..io import ponifile, integration_config
from ..units import CONST_hc, to_unit

logger = logging.getLogger(__name__)

try:
    import numexpr
except ImportError:
    logger.debug("Backtrace", exc_info=True)
    numexpr = None

try:
    from ..ext import _geometry
except ImportError:
    logger.debug("Backtrace", exc_info=True)
    _geometry = None

try:
    from ..ext import bilinear
except ImportError:
    logger.debug("Backtrace", exc_info=True)
    bilinear = None

PolarizationArray = namedtuple("PolarizationArray", ["array", "checksum"])
PolarizationDescription = namedtuple("PolarizationDescription",
                                     ["polarization_factor", "axis_offset"])


class Geometry(object):
    """This class is the parent-class of azimuthal integrator.

    This class contains a detector (using composition) which provides the
    position of all pixels, or only a limited set of pixel indices.
    The Geometry class is responsible for translating/rotating those pixel to
    their position in reference to the sample position.
    The description of the experimental setup is inspired by the work of P. Boesecke

    Detector is assumed to be corrected from "raster orientation" effect.
    It is not addressed here but rather in the Detector object or at read time.
    Considering there is no tilt:

    - Detector fast dimension (dim2) is supposed to be horizontal
      (dimension X of the image)

    - Detector slow dimension (dim1) is supposed to be vertical, upwards
      (dimension Y of the image)

    - The third dimension is chose such as the referential is
      orthonormal, so dim3 is along incoming X-ray beam

    Demonstration of the equation done using Mathematica:

    .. literalinclude:: ../../mathematica/geometry.txt
        :language: mathematica
    """
    _LAST_POLARIZATION = "last_polarization"

    def __init__(self, dist=1, poni1=0, poni2=0, rot1=0, rot2=0, rot3=0,
                 pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None,
                 orientation=0):
        """
        :param dist: distance sample - detector plan (orthogonal distance, not along the beam), in meter.
        :param poni1: coordinate of the point of normal incidence along the detector's first dimension, in meter
        :param poni2: coordinate of the point of normal incidence along the detector's second dimension, in meter
        :param rot1: first rotation from sample ref to detector's ref, in radians
        :param rot2: second rotation from sample ref to detector's ref, in radians
        :param rot3: third rotation from sample ref to detector's ref, in radians
        :param pixel1: Deprecated. Pixel size of the fist dimension of the detector,  in meter.
            If both pixel1 and pixel2 are not None, detector pixel size is overwritten.
            Prefer defining the detector pixel size on the provided detector object.
            Prefer defining the detector pixel size on the provided detector
            object (``detector.pixel1 = 5e-6``).
        :type pixel1: float
        :param pixel2: Deprecated. Pixel size of the second dimension of the detector,  in meter.
            If both pixel1 and pixel2 are not None, detector pixel size is overwritten.
            Prefer defining the detector pixel size on the provided detector
            object (``detector.pixel2 = 5e-6``).
        :type pixel2: float
        :param splineFile: Deprecated. File containing the geometric distortion of the detector.
            If not None, pixel1 and pixel2 are ignored and detector spline is overwritten.
            Prefer defining the detector spline manually
            (``detector.splineFile = "file.spline"``).
        :type splineFile: str
        :param detector: name of the detector or Detector instance. String
            description is deprecated. Prefer using the result of the detector
            factory: ``pyFAI.detector_factory("eiger4m")``
        :type detector: str or pyFAI.Detector
        :param wavelength: Wave length used in meter
        :type wavelength: float
        :param int orientation: orientation of the detector, see pyFAI.detectors.orientation.Orientation
        """
        self._dist = dist
        self._poni1 = poni1
        self._poni2 = poni2
        self._rot1 = rot1
        self._rot2 = rot2
        self._rot3 = rot3
        self.param = [self._dist, self._poni1, self._poni2,
                      self._rot1, self._rot2, self._rot3]
        self.chiDiscAtPi = True  # chi discontinuity (radians), pi by default
        self._cached_array = {}  # dict for caching all arrays
        self._dssa_order = 3  # by default we correct for 1/cos(2th), fit2d corrects for 1/cos^3(2th)
        self._wavelength = wavelength
        self._oversampling = None
        self._correct_solid_angle_for_spline = True
        self._sem = threading.Semaphore()
        self._transmission_normal = None
        self._parallax = None

        if detector:
            if isinstance(detector, utils.StringTypes):
                self.detector = detectors.detector_factory(detector)
            else:
                self.detector = detector
        else:
            self.detector = detectors.Detector()
        if splineFile:
            self.detector.splineFile = os.path.abspath(splineFile)
        elif pixel1 and pixel2:
            self.detector.pixel1 = pixel1
            self.detector.pixel2 = pixel2
        if orientation:
            self.detector._orientation = detectors.orientation.Orientation(orientation or detector.ORIENTATION)

    def __repr__(self, dist_unit="m", ang_unit="rad", wl_unit="m"):
        """Nice representation of the class

        :param dist_unit: units for distances
        :param ang_unit: units used for angles
        :param wl_unit: units used for wavelengths
        :return: nice string representing the configuration in use
        """
        dist_unit = to_unit(dist_unit, units.LENGTH_UNITS) or units.l_m
        ang_unit = to_unit(ang_unit, units.ANGLE_UNITS) or units.A_rad
        wl_unit = to_unit(wl_unit, units.LENGTH_UNITS) or units.l_m
        self.param = [self._dist, self._poni1, self._poni2,
                      self._rot1, self._rot2, self._rot3]
        lstTxt = [self.detector.__repr__()]
        if self._wavelength:
            lstTxt.append(f"Wavelength= {self._wavelength * wl_unit.scale:.6e} {wl_unit}")
        lstTxt.append(f"SampleDetDist= {self._dist * dist_unit.scale:.6e} {dist_unit}\t"
                       f"PONI= {self._poni1 * dist_unit.scale:.6e}, {self._poni2 * dist_unit.scale:.6e} {dist_unit}\t"
                       f"rot1={self._rot1 * ang_unit.scale:.6f}  "
                       f"rot2={self._rot2 * ang_unit.scale:.6f}  "
                       f"rot3={self._rot3 * ang_unit.scale:.6f} {ang_unit}")
        if self.detector.pixel1:
            lstTxt.append(convert_to_Fit2d(self).__repr__())
        return os.linesep.join(lstTxt)

    def check_chi_disc(self, azimuth_range):
        """Check the position of the :math:`\\chi` discontinuity

        :param range: range of chi for the integration
        :return: True if there is a problem
        """
        lower, upper = azimuth_range
        error_msg = "Azimuthal range issue: Range [%s, %s] not in valid region %s in radians: Expect %s results !"
        if self.chiDiscAtPi:
            txt_range = "[-π; π["
            lower_bound = -pi
            upper_bound = pi
        else:
            txt_range = "[-0; 2π["
            lower_bound = 0
            upper_bound = 2 * pi

        if lower < lower_bound:
            if upper < lower_bound:
                logger.warning(error_msg, lower, upper, txt_range, "empty")
            else:
                logger.warning(error_msg, lower, upper, txt_range, "partial")
            return True
        elif lower > upper_bound:
            logger.warning(error_msg, lower, upper, txt_range, "empty")
            return True
        else:
            if upper > upper_bound:
                logger.warning(error_msg, lower, upper, txt_range, "partial")
                return True
        return False

    def normalize_azimuth_range(self, azimuth_range):
        """Convert the azimuth range from degrees to radians

        This method takes care of the position of the discontinuity and adapts the range accordingly!

        :param azimuth_range: 2-tuple of float in degrees
        :return: 2-tuple of float in radians in a range such to avoid the discontinuity
        """
        if azimuth_range is None:
            return
        azimuth_range = tuple(deg2rad(azimuth_range[i], self.chiDiscAtPi) for i in (0, -1))
        if azimuth_range[1] <= azimuth_range[0]:
            azimuth_range = (azimuth_range[0], azimuth_range[1] + twopi)
            self.check_chi_disc(azimuth_range)
        return azimuth_range

    def _correct_parallax(self, d1, d2, p1, p2):
        """Calculate the displacement of pixels due to parallax effect.

        :param d1: ndarray of dimention 1/2 containing the Y pixel positions
        :param d2: ndarray of dimention 1/2 containing the X pixel positions
        :param p1: ndarray of dimention 1/1 containing the x pixel positions in meter. MODIFIED IN PLACE!
        :param p2: ndarray of dimention 1/2 containing the y pixel positions in meter. MODIFIED IN PLACE!
        :return: 2-arrays of same shape as d1 & d2 with the displacement in meters

        d1, d2, p1 and p2 should all have the same shape !!!
        p1 and p2 get modified in place !
        """
        delta1 = delta2 = 0
        if self._parallax is not None:
                r0 = numpy.vstack((p1.ravel(), p2.ravel()))
                length = numpy.linalg.norm(r0, axis=0)
                length[length == 0] = 1.0  # avoid zero division error
                r0 /= length  # normalize array r0

                displacement = self._parallax(self.sin_incidence(d1.ravel(), d2.ravel()))
                delta1, delta2 = displacement * r0
                delta1.shape = p1.shape
                delta2.shape = p2.shape
                p1 -= delta1
                p2 -= delta2
        return delta1, delta2

    def _calc_cartesian_positions(self, d1, d2, poni1=None, poni2=None, do_parallax=False):
        """
        Calculate the position in cartesian coordinate (centered on the PONI)
        and in meter of a couple of coordinates.
        The half pixel offset is taken into account here !!!

        :param d1: ndarray of dimention 1/2 containing the Y pixel positions
        :param d2: ndarray of dimention 1/2 containing the X pixel positions
        :param poni1: value in the Y direction of the poni coordinate (meter)
        :param poni2: value in the X direction of the poni coordinate (meter)
        :param do_parallax: position should be corrected for parallax effect
        :return: 2-arrays of same shape as d1 & d2 with the position in meters

        d1 and d2 must have the same shape, returned array will have
        the same shape.
        """
        if poni1 is None:
            poni1 = self.poni1
        if poni2 is None:
            poni2 = self.poni2

        p1, p2, p3 = self.detector.calc_cartesian_positions(d1, d2)
        p1 = p1 - poni1
        p2 = p2 - poni2
        if do_parallax and (self._parallax is not None):
            self._correct_parallax(d1, d2, p1, p2)
        return p1, p2, p3

    def calc_pos_zyx(self, d0=None, d1=None, d2=None, param=None, corners=False, use_cython=True, do_parallax=False):
        """Calculate the position of a set of points in space in the sample's centers referential.

        This is usually used for calculating the pixel position in space.

        Nota: dim3 is the same as dim0

        :param d0: altitude on the point compared to the detector (i.e. z), may be None
        :param d1: position on the detector along the slow dimension (i.e. y)
        :param d2: position on the detector along the fastest dimension (i.e. x)
        :param corners: return positions on the corners (instead of center)
        :param use_cython: set to False to validate using pure numpy
        :param do_parallax: position should be corrected for parallax effect
        :return: 3-tuple of nd-array, with dim0=along the beam,
                                           dim1=along slowest dimension
                                           dim2=along fastest dimension
        """
        if param is None:
            dist = self._dist
            poni1 = self._poni1
            poni2 = self._poni2
            rot1 = self._rot1
            rot2 = self._rot2
            rot3 = self._rot3
        else:
            dist, poni1, poni2, rot1, rot2, rot3 = param[:6]

        if d0 is None:
            L = dist
        else:
            L = dist + d0
        if corners:
            tmp = self.detector.get_pixel_corners(correct_binning=True)
            p1 = tmp[..., 1]
            p2 = tmp[..., 2]
            p3 = tmp[..., 0]
        else:
            p1, p2, p3 = self.detector.calc_cartesian_positions(d1, d2)
        if ((not do_parallax) or (self._parallax is None)) and use_cython and (_geometry is not None):
            t3, t1, t2 = _geometry.calc_pos_zyx(L, poni1, poni2, rot1, rot2, rot3, p1, p2, p3,
                                                orientation=self.detector.orientation)
        else:
            shape = p1.shape
            size = p1.size
            p1 = (p1 - poni1).ravel()
            p2 = (p2 - poni2).ravel()
            # we did make copies with the subtraction
            assert size == p2.size
            if do_parallax and self._parallax is not None:
                self._correct_parallax(d1, d2, p1, p2)

            # note the change of sign in the third dimension:
            # Before the rotation we are in the detector's referential,
            # the sample position is at d3= -L <0
            # the sample detector distance is always positive.
            if p3 is None:
                p3 = numpy.zeros(size) + L
            else:
                p3 = (L + p3).ravel()
                assert size == p3.size
            coord_det = numpy.vstack((p1, p2, p3))
            coord_sample = numpy.dot(self.rotation_matrix(param), coord_det)
            t1, t2, t3 = coord_sample
            t1.shape = shape
            t2.shape = shape
            t3.shape = shape
            # correct orientation:
            if self.detector.orientation in (1, 2):
                numpy.negative(t1, out=t1)
            if self.detector.orientation in (1, 4):
                numpy.negative(t2, out=t2)
        return (t3, t1, t2)

    def tth(self, d1, d2, param=None, path="cython"):
        """
        Calculates the 2theta value for the center of a given pixel
        (or set of pixels)

        :param d1: position(s) in pixel in first dimension (c order)
        :type d1: scalar or array of scalar
        :param d2: position(s) in pixel in second dimension (c order)
        :type d2: scalar or array of scalar
        :param path: can be "cos", "tan" or "cython"
        :return: 2theta in radians
        :rtype: float or array of floats.
        """

        if (path == "cython") and (_geometry is not None):
            if param is None:
                dist, poni1, poni2 = self._dist, self._poni1, self._poni2
                rot1, rot2, rot3 = self._rot1, self._rot2, self._rot3
            else:
                dist, poni1, poni2, rot1, rot2, rot3 = param[:6]
            p1, p2, p3 = self._calc_cartesian_positions(d1, d2, poni1, poni2, do_parallax=True)
            tmp = _geometry.calc_tth(L=dist,
                                     rot1=rot1,
                                     rot2=rot2,
                                     rot3=rot3,
                                     pos1=p1,
                                     pos2=p2,
                                     pos3=p3)
        else:
            t3, t1, t2 = self.calc_pos_zyx(d0=None, d1=d1, d2=d2, param=param, do_parallax=True)
            if path == "cos":
                tmp = arccos(t3 / sqrt(t1 * t1 + t2 * t2 + t3 * t3))
            else:
                tmp = arctan2(sqrt(t1 * t1 + t2 * t2), t3)
        return tmp

    def qFunction(self, d1, d2, param=None, path="cython"):
        """
        Calculates the q value for the center of a given pixel (or set
        of pixels) in nm-1

        q = 4pi/lambda sin( 2theta / 2 )

        :param d1: position(s) in pixel in first dimension (c order)
        :type d1: scalar or array of scalar
        :param d2: position(s) in pixel in second dimension (c order)
        :type d2: scalar or array of scalar
        :return: q in in nm^(-1)
        :rtype: float or array of floats.
        """
        if not self.wavelength:
            raise RuntimeError(("Scattering vector q cannot be calculated"
                                " without knowing wavelength !!!"))

        if (_geometry is not None) and (path == "cython"):
            if param is None:
                dist, poni1, poni2 = self._dist, self._poni1, self._poni2
                rot1, rot2, rot3 = self._rot1, self._rot2, self._rot3
            else:
                dist, poni1, poni2, rot1, rot2, rot3 = param[:6]

            p1, p2, p3 = self._calc_cartesian_positions(d1, d2, poni1, poni2, do_parallax=True)
            out = _geometry.calc_q(L=dist,
                                   rot1=rot1,
                                   rot2=rot2,
                                   rot3=rot3,
                                   pos1=p1,
                                   pos2=p2,
                                   pos3=p3,
                                   wavelength=self.wavelength)
        else:
            out = 4.0e-9 * pi / self.wavelength * \
                numpy.sin(self.tth(d1=d1, d2=d2, param=param, path=path) / 2.0)
        return out

    def rFunction(self, d1, d2, param=None, path="cython"):
        """
        Calculates the radius value for the center of a given pixel
        (or set of pixels) in m

          r = distance to the incident beam

        :param d1: position(s) in pixel in first dimension (c order)
        :type d1: scalar or array of scalar
        :param d2: position(s) in pixel in second dimension (c order)
        :type d2: scalar or array of scalar
        :return: r in in m
        :rtype: float or array of floats.
        """

        if (_geometry is not None) and (path == "cython"):
            if param is None:
                dist, poni1, poni2 = self._dist, self._poni1, self._poni2
                rot1, rot2, rot3 = self._rot1, self._rot2, self._rot3
            else:
                dist, poni1, poni2, rot1, rot2, rot3 = param[:6]

            p1, p2, p3 = self._calc_cartesian_positions(d1, d2, poni1, poni2, do_parallax=True)
            out = _geometry.calc_r(L=dist,
                                   rot1=rot1,
                                   rot2=rot2,
                                   rot3=rot3,
                                   pos1=p1,
                                   pos2=p2,
                                   pos3=p3)
        else:
            # Before 03/2016 it was the distance at beam-center
            # cosTilt = cos(self._rot1) * cos(self._rot2)
            # directDist = self._dist / cosTilt  # in m
            # out = directDist * numpy.tan(self.tth(d1=d1, d2=d2, param=param))
            _, t1, t2 = self.calc_pos_zyx(d0=None, d1=d1, d2=d2, param=param, do_parallax=True)
            out = numpy.sqrt(t1 * t1 + t2 * t2)
        return out

    def qArray(self, shape=None):
        """
        Generate an array of the given shape with q(i,j) for all
        elements.
        """
        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)
        if self._cached_array.get("q_center") is None:
            with self._sem:
                if self._cached_array.get("q_center") is None:
                    qa = numpy.fromfunction(self.qFunction, shape,
                                            dtype=numpy.float32)
                    self._cached_array["q_center"] = qa

        return self._cached_array["q_center"]

    def rArray(self, shape=None):
        """Generate an array of the given shape with r(i,j) for all elements;
        The radius r being  in meters.

        :param shape: expected shape of the detector
        :return: 2d array of the given shape with radius in m from beam center on detector.
        """
        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)

        if self._cached_array.get("r_center") is None:
            with self._sem:
                if self._cached_array.get("r_center") is None:
                    self._cached_array["r_center"] = numpy.fromfunction(self.rFunction, shape,
                                                                        dtype=numpy.float32)
        return self._cached_array.get("r_center")

    def rd2Array(self, shape=None):
        """Generate an array of the given shape with (d*(i,j))^2 for all pixels.

        d*^2 is the reciprocal spacing squared in inverse nm squared

        :param shape: expected shape of the detector
        :return: 2d array of the given shape with reciprocal spacing squared
        """
        qArray = self.qArray(shape)
        if self._cached_array.get("d*2_center") is None:
            with self._sem:
                if self._cached_array.get("d*2_center") is None:
                    self._cached_array["d*2_center"] = (qArray / (twopi)) ** 2
        return self._cached_array["d*2_center"]

    @deprecated
    def qCornerFunct(self, d1, d2):
        """Calculate the q_vector for any pixel corner (in nm^-1)

        :param shape: expected shape of the detector
        """
        return self.qFunction(d1 - 0.5, d2 - 0.5)

    @deprecated
    def rCornerFunct(self, d1, d2):
        """
        Calculate the radius array for any pixel corner (in m)
        """
        return self.rFunction(d1 - 0.5, d2 - 0.5)

    @deprecated
    def tth_corner(self, d1, d2):
        """
        Calculates the 2theta value for the corner of a given pixel
        (or set of pixels)

        :param d1: position(s) in pixel in first dimension (c order)
        :type d1: scalar or array of scalar
        :param d2: position(s) in pixel in second dimension (c order)
        :type d2: scalar or array of scalar
        :return: 2theta in radians
        :rtype: floar or array of floats.
        """
        return self.tth(d1 - 0.5, d2 - 0.5)

    def twoThetaArray(self, shape=None):
        """Generate an array of two-theta(i,j) in radians for each pixel in detector

        the 2theta array values are in radians

        :param shape: shape of the detector
        :return: array of 2theta position in radians
        """
        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)

        if self._cached_array.get("2th_center") is None:
            with self._sem:
                if self._cached_array.get("2th_center") is None:
                    ttha = numpy.fromfunction(self.tth,
                                              shape,
                                              dtype=numpy.float32)
                    self._cached_array["2th_center"] = ttha
        return self._cached_array["2th_center"]

    def chi(self, d1, d2, path="cython"):
        """
        Calculate the chi (azimuthal angle) for the centre of a pixel
        at coordinate d1, d2.
        Conversion to lab coordinate system is performed in calc_pos_zyx.

        :param d1: pixel coordinate along the 1st dimention (C convention)
        :type d1: float or array of them
        :param d2: pixel coordinate along the 2nd dimention (C convention)
        :type d2: float or array of them
        :param path: can be "tan" (i.e via numpy) or "cython"
        :return: chi, the azimuthal angle in rad
        """
        if (path == "cython") and (_geometry is not None):
            p1, p2, p3 = self._calc_cartesian_positions(d1, d2, self._poni1, self._poni2, do_parallax=True)
            chi = _geometry.calc_chi(L=self._dist,
                                     rot1=self._rot1, rot2=self._rot2, rot3=self._rot3,
                                     pos1=p1, pos2=p2, pos3=p3,
                                     orientation=self.detector.orientation,
                                     chi_discontinuity_at_pi=self.chiDiscAtPi)
            chi.shape = d1.shape
        else:
            _, t1, t2 = self.calc_pos_zyx(d0=None, d1=d1, d2=d2, corners=False, use_cython=True, do_parallax=True)
            chi = numpy.arctan2(t1, t2)
            if not self.chiDiscAtPi:
                numpy.mod(chi, (twopi), out=chi)
        return chi

    def chi_corner(self, d1, d2):
        """
        Calculate the chi (azimuthal angle) for the corner of a pixel
        at coordinate d1,d2 which in the lab ref has coordinate:

        :param d1: pixel coordinate along the 1st dimention (C convention)
        :type d1: float or array of them
        :param d2: pixel coordinate along the 2nd dimention (C convention)
        :type d2: float or array of them
        :return: chi, the azimuthal angle in rad
        """
        return self.chi(d1 - 0.5, d2 - 0.5)

    def chiArray(self, shape=None):
        """Generate an array of azimuthal angle chi(i,j) for all elements in the detector.

        Azimuthal angles are in radians

        Nota: Refers to the pixel centers !

        :param shape: the shape of the chi array
        :return: the chi array as numpy.ndarray
        """
        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)

        if self._cached_array.get("chi_center") is None:
            with self._sem:
                if self._cached_array.get("chi_center") is None:
                    chia = numpy.fromfunction(self.chi, shape,
                                              dtype=numpy.float32)
                    if not self.chiDiscAtPi:
                        chia = chia % (twopi)
                    self._cached_array["chi_center"] = chia
        return self._cached_array["chi_center"]

    def position_array(self, shape=None, corners=False, dtype=numpy.float64, use_cython=True, do_parallax=False):
        """Generate an array for the pixel position given the shape of the detector.

        if corners is False, the coordinates of the center of the pixel
        is returned in an array of shape: (shape[0], shape[1], 3)
        where the 3 coordinates are:
        * z: along incident beam,
        * y: to the top/sky,
        * x: towards the center of the ring

        If is True, the corner of each pixels are then returned.
        the output shape is then (shape[0], shape[1], 4, 3)

        :param shape: shape of the array expected
        :param corners: set to true to receive a (...,4,3) array of corner positions
        :param dtype: output format requested. Double precision is needed for fitting the geometry
        :param (bool) use_cython: set to false to test the Python path (slower)
        :param do_parallax: correct for parallax effect (if parametrized)
        :return: 3D coodinates as nd-array of size (...,3) or (...,3) (default)

        Nota: this value is not cached and actually generated on demand (costly)
        """
        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)

        pos = numpy.fromfunction(lambda d1, d2: self.calc_pos_zyx(d0=None, d1=d1, d2=d2,
                                                                  corners=corners,
                                                                  use_cython=use_cython,
                                                                  do_parallax=do_parallax),
                                 shape,
                                 dtype=dtype)
        outshape = pos[0].shape + (3,)
        tpos = numpy.empty(outshape, dtype=dtype)
        for idx in range(3):
            tpos[..., idx] = pos[idx]
        return tpos

    @deprecated
    def positionArray(self, *arg, **kwarg):
        """Deprecated version of :meth:`position_array`, left for compatibility see doc of position_array"""
        return self.position_array(*arg, **kwarg)

    def corner_array(self, shape=None, unit=None, use_cython=True, scale=True):
        """
        Generate a 3D array of the given shape with (i,j) (radial
        angle 2th, azimuthal angle chi ) for all elements.

        :param shape: expected shape
        :type shape: 2-tuple of integer
        :param unit: string like "2th_deg" or an instance of pyFAI.units.Unit
        :param use_cython: set to False to use the slower Python path (for tests)
        :param scale: set to False for returning the internal representation
                      (S.I. often) which is faster
        :return: 3d array with shape=(\\*shape,4,2) the two elements are:
            - dim3[0]: radial angle 2th, q, r...
            - dim3[1]: azimuthal angle chi
        """
        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)
        requested_unit = space = unit
        if unit:
            unit = to_unit(unit)
            space = unit.space
            requested_unit = unit
        if unit is None or space == "chi":
            # If no unit is asked, any is OK for extracting the Chi array
            unit = None
            for sp in [u.space for u in units.RADIAL_UNITS.values()]:
                ary = self._cached_array.get(sp + "_corner")
                if (ary is not None) and (shape == ary.shape[:2]):
                    if space == "chi":
                        res = ary.copy()
                        res[..., 0] = ary[..., 1]
                        if scale and requested_unit:
                            res *= requested_unit.scale
                        return res
                    else:
                        return ary
            # This is the fastest to calculate
            space = "r"
            # unit = to_unit("r_m")
        key = space + "_corner"
        if self._cached_array.get(key) is None or shape != self._cached_array.get(key).shape[:2]:
            with self._sem:
                if self._cached_array.get(key) is None or shape != self._cached_array.get(key).shape[:2]:
                    # fix this with #1957
                    nb_corners = 6 if isinstance(self.detector, detectors.HexDetector) else 4

                    corners = None
                    if (_geometry is not None) and use_cython:
                        if self.detector.IS_CONTIGUOUS:
                            d1 = utils.expand2d(numpy.arange(shape[0] + 1.0), shape[1] + 1.0, False)
                            d2 = utils.expand2d(numpy.arange(shape[1] + 1.0), shape[0] + 1.0, True)
                            p1, p2, p3 = self.detector.calc_cartesian_positions(d1, d2, center=False, use_cython=use_cython)
                            # TODO fix test so that this is simpler: issue #2014
                            # p1, p2, p3 = self.detector.calc_cartesian_positions(center=False, use_cython=use_cython)
                        else:
                            det_corners = self.detector.get_pixel_corners()
                            p1 = det_corners[..., 1]
                            p2 = det_corners[..., 2]
                            p3 = det_corners[..., 0]
                        try:
                            res = _geometry.calc_rad_azim(self.dist, self.poni1, self.poni2,
                                                          self.rot1, self.rot2, self.rot3,
                                                          p1, p2, p3,
                                                          space, self._wavelength,
                                                          orientation=self.detector.orientation,
                                                          chi_discontinuity_at_pi=self.chiDiscAtPi)
                        except KeyError:
                            logger.warning("No fast path for space: %s", space)
                        except AttributeError as err:
                            logger.warning("AttributeError: The binary extension _geomety may be missing: %s", err)
                        else:
                            if self.detector.IS_CONTIGUOUS:
                                if bilinear:
                                    # convert_corner_2D_to_4D needs contiguous arrays as input
                                    radi = numpy.ascontiguousarray(res[..., 0], numpy.float32)
                                    azim = numpy.ascontiguousarray(res[..., 1], numpy.float32)
                                    corners = bilinear.convert_corner_2D_to_4D(2, radi, azim)
                                else:
                                    corners = numpy.zeros((shape[0], shape[1], nb_corners, 2),
                                                          dtype=numpy.float32)
                                    corners[:,:, 0,:] = res[:-1,:-1,:]
                                    corners[:,:, 1,:] = res[1:,:-1,:]
                                    corners[:,:, 2,:] = res[1:, 1:,:]
                                    corners[:,:, 3,:] = res[:-1, 1:,:]
                            else:
                                corners = res
                    if corners is None:
                        # In case the fast-path is not implemented
                        pos = self.position_array(shape, corners=True)
                        x = pos[..., 2]
                        y = pos[..., 1]
                        z = pos[..., 0]
                        if numexpr is None:
                            # numpy path
                            chi = numpy.arctan2(y, x)
                            if not self.chiDiscAtPi:
                                numpy.mod(chi, (twopi), out=chi)
                        else:
                            # numexpr path
                            chi = numexpr.evaluate("arctan2(y, x)") if self.chiDiscAtPi else numexpr.evaluate("arctan2(y, x)%twopi")
                        corners = numpy.zeros((shape[0], shape[1], nb_corners, 2),
                                              dtype=numpy.float32)
                        if chi.shape[:2] == shape:
                            corners[..., 1] = chi
                        else:
                            corners[:shape[0],:shape[1],:, 1] = chi[:shape[0],:shape[1],:]
                        if space is not None:
                            rad = unit.equation(x, y, z, self._wavelength)
                            if rad.shape[:2] == shape:
                                corners[..., 0] = rad
                            else:
                                corners[:shape[0],:shape[1],:, 0] = rad[:shape[0],:shape[1],:]
                    self._cached_array[key] = corners

        res = self._cached_array[key]
        if requested_unit:
            if requested_unit.space == "chi":
                res = res.copy()
                if scale:
                    res[..., 0] = res[..., 1] * requested_unit.scale
                else:
                    res[..., 0] = res[..., 1]
            else:
                if scale:
                    res = res.copy()
                    res[..., 0] *= requested_unit.scale
        return res

    @deprecated
    def cornerArray(self, shape=None):
        """Generate a 4D array of the given shape with (i,j) (radial
        angle 2th, azimuthal angle chi ) for all elements.

        :param shape: expected shape
        :type shape: 2-tuple of integer
        :return: 3d array with shape=(\\*shape,4,2) the two elements are:
           * dim3[0]: radial angle 2th
           * dim3[1]: azimuthal angle chi
        """
        return self.corner_array(shape, unit=units.TTH_RAD, scale=False)

    @deprecated
    def cornerQArray(self, shape=None):
        """
        Generate a 3D array of the given shape with (i,j) (azimuthal
        angle) for all elements.

        :param shape: expected shape
        :type shape: 2-tuple of integer
        :return: 3d array with shape=(\\*shape,4,2) the two elements are (scattering vector q, azimuthal angle chi)
        """
        return self.corner_array(shape, unit=units.Q, use_cython=False, scale=False)

    @deprecated
    def cornerRArray(self, shape=None):
        """
        Generate a 3D array of the given shape with (i,j) (azimuthal
        angle) for all elements.

        :param shape: expected shape
        :type shape: 2-tuple of integer
        :return: 3d array with shape=(\\*shape,4,2) the two elements are (radial distance, azimuthal angle chi)
        """
        return self.corner_array(shape, unit=units.R, use_cython=False, scale=False)

    @deprecated
    def cornerRd2Array(self, shape=None):
        """
        Generate a 3D array of the given shape with (i,j) (azimuthal
        angle) for all elements.

        :param shape: expected shape
        :type shape: 2-tuple of integer
        :return: 3d array with shape=(\\*shape,4,2) the two elements are (reciprocal spacing squared, azimuthal angle chi)
        """
        return self.corner_array(shape, unit=units.RecD2_NM, scale=False)

    def center_array(self, shape=None, unit="2th_deg", scale=True):
        """
        Generate a 2D array of the given shape with (i,j) (radial
        angle ) for all elements.

        :param shape: expected shape
        :type shape: 2-tuple of integer
        :param unit: string like "2th_deg" or an instance of pyFAI.units.Unit
        :param scale: set to False for returning the internal representation
                (S.I. often) which is faster.
        :return: 2d array of given shape
        """

        unit = to_unit(unit)
        space = unit.name.split("_")[0]
        key = space + "_center"
        ary = self._cached_array.get(key)

        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)

        if (ary is not None) and (ary.shape == shape):
            if scale and unit:
                return ary * unit.scale
            else:
                return ary

        pos = self.position_array(shape, corners=False)
        x = pos[..., 2]
        y = pos[..., 1]
        z = pos[..., 0]
        ary = unit.equation(x, y, z, self.wavelength)
        if unit.space == "chi" and not self.chiDiscAtPi:
            numpy.mod(ary, twopi, out=ary)
        self._cached_array[key] = ary
        if scale and unit:
                return ary * unit.scale
        else:
            return ary

    def delta_array(self, shape=None, unit="2th_deg", scale=False):
        """
        Generate a 2D array of the given shape with the delta-radius for all elements.

        :param shape: expected shape
        :type shape: 2-tuple of integer
        :param unit: string like "2th_deg" or an instance of pyFAI.units.Unit
        :param scale: set to False for returning the internal representation
                (S.I. often) which is faster
        :return: 2D array
        """

        unit = to_unit(unit)
        space = unit.name.split("_")[0] + "_delta"
        ary = self._cached_array.get(space)

        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)

        if (ary is not None) and (ary.shape == shape):
            if scale and unit:
                return ary * unit.scale
            else:
                return ary
        center = self.center_array(shape, unit=unit, scale=False)
        corners = self.corner_array(shape, unit=unit, scale=False)
        delta = abs(corners[..., 0] - numpy.atleast_3d(center))
        if space == "chi_delta":
            if numexpr:
                delta = numexpr.evaluate("where(delta<twopi-delta, delta, twopi-delta)")
            else:
                numpy.minimum(delta, twopi - delta, out=delta)

        ary = delta.max(axis=-1)
        self._cached_array[space] = ary
        if scale and unit:
            return ary * unit.scale
        else:
            return ary

    def delta2Theta(self, shape=None):
        """
        Generate a 2D array of the given shape with the max distance between the center and any corner in 2 theta angle in radians

        :param shape: The shape of the detector array: 2-tuple of integer
        :return: 2D-array containing the max delta angle between a pixel center and any corner in 2theta-angle (rad)
        """
        return self.delta_array(shape, units.TTH, False)

    def deltaChi(self, shape=None, use_cython=True):
        """
        Generate a 2D array of the given shape with the max distance between the center and any corner in chi-angle (rad)

        :param shape: The shape of the detector array: 2-tuple of integer
        :return: 2D-array  containing the max delta angle between a pixel center and any corner in chi-angle (rad)
        """
        key = "chi_delta"
        if self._cached_array.get(key) is None:
            center = self.chiArray(shape)
            corner = self.corner_array(shape, None)
            with self._sem:
                if self._cached_array.get(key) is None:
                    if use_cython and (_geometry is not None):
                        delta = _geometry.calc_delta_chi(center, corner)
                        self._cached_array[key] = delta
                    else:
                        center = numpy.atleast_3d(center)
                        delta = numpy.minimum(((corner[:,:,:, 1] - center) % twopi),
                                              ((center - corner[:,:,:, 1]) % twopi))
                        self._cached_array[key] = delta.max(axis=-1)
        return self._cached_array[key]

    def deltaQ(self, shape=None):
        """
        Generate a 2D array of the given shape with the max distance between the center
        and any corner in q_vector unit (nm^-1)

        :param shape: The shape of the detector array: 2-tuple of integer
        :return: array 2D containing the max delta Q between a pixel center and any corner in q_vector unit (nm^-1)
        """
        return self.delta_array(shape, units.Q, False)

    def deltaR(self, shape=None):
        """
        Generate a 2D array of the given shape with (i,j) with the max
        distance between the center and any corner in radius unit (m)

        :param shape: The shape of the detector array: 2-tuple of integer
        :return: array 2D containing the max delta Q between a pixel center and any corner in distance (m)
        """
        return self.delta_array(shape, units.R_M, False)

    def deltaRd2(self, shape=None):
        """
        Generate a 2D array of the given shape with the max
        distance between the center and any corner in unit: reciprocal spacing squarred (1/nm^2)

        :param shape: The shape of the detector array: 2-tuple of integer
        :return: array 2D containing the max delta (d*)^2 between a pixel center and any corner in reciprocal spacing squarred (1/nm^2)
        """
        return self.delta_array(shape, units.RecD2_NM, False)

    def array_from_unit(self, shape=None, typ="center", unit=units.TTH, scale=True):
        """
        Generate an array of position in different dimentions (R, Q, 2Theta ...)

        :param shape: shape of the expected array, leave it to None for safety
        :type shape: ndarray.shape
        :param typ: "center", "corner" or "delta"
        :type typ: str
        :param unit: can be any valid unit (found in  units.AZIMUTHAL_UNITS or units.RADIAL_UNITS)
        :type unit: pyFAI.units.Unit or 2-tuple of them (valid only for corner coordinates calculation
        :param scale: set to False for returning the internal representation
                (S.I. often) which is faster
        :return: R, Q or 2Theta array depending on unit
        :rtype: ndarray
        """
        shape = self.get_shape(shape)
        if shape is None:
            logger.error("Shape is neither specified in the method call, "
                         "neither in the detector: %s", self.detector)

        if typ not in ("center", "corner", "delta"):
            logger.warning("Unknown type of array %s,"
                           " defaulting to 'center'" % typ)
            typ = "center"
        if typ == "corner" and isinstance(unit, (tuple, list)) and len(unit) == 2:
            unit2 = tuple(to_unit(u) for u in unit)
            unit = unit2[0]
        else:
            unit2 = None
            unit = to_unit(unit)
        meth_name = unit.get(typ)
        if meth_name and meth_name in dir(Geometry):
            # fast path may be available
            out = Geometry.__dict__[meth_name](self, shape)
            if scale and unit:
                out = out * unit.scale
        else:
            # fast path is definitely not available, use the generic formula
            if typ == "center":
                out = self.center_array(shape, unit, scale=scale)
            elif typ == "corner":
                if unit2 is None:
                    out = self.corner_array(shape, unit, scale=scale)
                else:
                    out = self.corner_array(shape, unit2[0], scale=scale)
                    if unit2[1].space != "chi":
                        out1 = self.corner_array(shape, unit2[1], scale=scale)
                        out[..., 1] = out1[..., 0]
            elif typ == "delta":
                out = self.delta_array(shape, unit, scale=scale)
            else:
                logger.error("Unsupported")
        return out

    def sin_incidence(self, d1, d2, path="cython"):
        """
        Calculate the sinus of the incidence angle (alpha) for current pixels (P).
        The poni being the point of normal incidence,
        it's incidence angle is :math:`{\\alpha} = 0` hence :math:`sin({\\alpha}) = 0`.

        Works also for non-flat detectors where the normal of the pixel is considered.

        :param d1: 1d or 2d set of points in pixel coord
        :param d2:  1d or 2d set of points in pixel coord
        :param path: can be "cython", "numexpr" or "numpy" (fallback).
        :return: cosine of the incidence angle
        """

        if self.detector.IS_FLAT:
            p1, p2, _ = self._calc_cartesian_positions(d1, d2)
            if (_geometry is not None) and (path == "cython"):
                sina = _geometry.calc_sina(self._dist, p1, p2)
            elif (numexpr is not None) and (path != "numpy"):
                sina = numexpr.evaluate("sqrt((p1*p1 + p2*p2) / (dist*dist + (p1*p1 + p2*p2)))",
                                        local_dict={"dist": self._dist, "p1":p1, "p2":p2})
            else:
                sina = numpy.sqrt((p1 * p1 + p2 * p2) / (self._dist * self._dist + (p1 * p1 + p2 * p2)))
        else:
            cosa = self.cos_incidence(d1, d2, path).clip(0.0, 1.0)
            if numexpr is not None and path != "numpy":
                sina = numexpr.evaluate("sqrt(1.0-cosa*cosa)")
            else:
                sina = numpy.sqrt(1.0 - (cosa * cosa))
        return sina

    def cos_incidence(self, d1, d2, path="cython"):
        """
        Calculate the cosinus of the incidence angle (alpha) for current pixels (P).
        The poni being the point of normal incidence,
        it's incidence angle is :math:`{\\alpha} = 0` hence :math:`cos({\\alpha}) = 1`.

        Works also for non-flat detectors where the normal of the pixel is considered.

        :param d1: 1d or 2d set of points in pixel coord
        :param d2:  1d or 2d set of points in pixel coord
        :param path: can be "cython", "numexpr" or "numpy" (fallback).
        :return: cosine of the incidence angle
        """
        p1, p2, p3 = self._calc_cartesian_positions(d1, d2)
        if p3 is not None:
            # case for non-planar detector ...

            # Calculate the sample-pixel vector (center of pixel) and norm it
            z, y, x = self.calc_pos_zyx(d0=None, d1=d1, d2=d2, corners=False)
            t = numpy.zeros((z.size, 3))
            for i, v in enumerate((z, y, x)):
                t[..., i] = v.ravel()
            length = numpy.sqrt((t * t).sum(axis=-1))
            length.shape = (length.size, 1)
            length.strides = (length.strides[0], 0)
            t /= length
            # extract the 4 corners of each pixel and calculate the cross product of the diagonal to get the normal
            z, y, x = self.calc_pos_zyx(d0=None, d1=d1, d2=d2, corners=True)
            corners = numpy.zeros(z.shape + (3,))
            for i, v in enumerate((z, y, x)):
                corners[..., i] = v
            A = corners[..., 0,:]
            B = corners[..., 1,:]
            C = corners[..., 2,:]
            D = corners[..., 3,:]
            A.shape = -1, 3
            B.shape = -1, 3
            C.shape = -1, 3
            D.shape = -1, 3
            orth = numpy.cross(C - A, D - B)
            # normalize the normal vector
            length = numpy.sqrt((orth * orth).sum(axis=-1))
            length.shape = length.shape + (1,)
            length.strides = length.strides[:-1] + (0,)
            orth /= length
            # calculate the cosine of the vector using the dot product
            cosa = (t * orth).sum(axis=-1).reshape(d1.shape)
        else:
            if (_geometry is not None) and (path == "cython"):
                cosa = _geometry.calc_cosa(self._dist, p1, p2)
            elif (numexpr is not None) and (path != "numpy"):
                cosa = numexpr.evaluate("dist/sqrt(dist*dist + (p1*p1 + p2*p2))",
                                        local_dict={"dist": self._dist, "p1":p1, "p2":p2})
            else:
                cosa = self._dist / numpy.sqrt(self._dist * self._dist + (p1 * p1 + p2 * p2))
        return cosa

    def diffSolidAngle(self, d1, d2):
        """
        Calculate the solid angle of the current pixels (P) versus the PONI (C)

        .. math::

            dOmega = \\frac{Omega(P)}{Omega(C)}
                   = \\frac{A \\cdot cos(a)}{SP^2} \\cdot \\frac{SC^2}{A \\cdot cos(0)}
                   = \\frac{3}{cos(a)}
                   = \\frac{SC^3}{SP^3}

            cos(a) = \\frac{SC}{SP}

        :param d1: 1d or 2d set of points
        :param d2: 1d or 2d set of points (same size&shape as d1)
        :return: solid angle correction array
        """
        ds = 1.0

        # #######################################################
        # Nota: the solid angle correction should be done in flat
        # field correction. Here is dual-correction
        # #######################################################

        if self.spline and self._correct_solid_angle_for_spline:
            max1 = d1.max() + 1
            max2 = d2.max() + 1
            sX = self.spline.splineFuncX(numpy.arange(max2 + 1),
                                         numpy.arange(max1) + 0.5)
            sY = self.spline.splineFuncY(numpy.arange(max2) + 0.5,
                                         numpy.arange(max1 + 1))
            dX = sX[:, 1:] - sX[:,:-1]
            dY = sY[1:,:] - sY[:-1,:]
            ds = (dX + 1.0) * (dY + 1.0)

        cosa = self._cached_array.get("cos_incidence")
        if cosa is None:
            cosa = self._cached_array["cos_incidence"] = self.cos_incidence(d1, d2)
        dsa = ds * cosa ** self._dssa_order
        return dsa

    def solidAngleArray(self, shape=None, order=3, absolute=False, with_checksum=False):
        """Generate an array for the solid angle correction
        given the shape of the detector.

        solid_angle = cos(incidence)^3

        :param shape: shape of the array expected
        :param order: should be 3, power of the formula just obove
        :param absolute: the absolute solid angle is calculated as:
        :param with_checksum: returns the array _and_ its checksum as a 2-tuple

        SA = pix1*pix2/dist^2 * cos(incidence)^3

        """
        shape = self.get_shape(shape)
        if order is True:
            self._dssa_order = 3.0
        else:
            self._dssa_order = float(order)

        key = f"solid_angle#{self._dssa_order}"
        key_crc = f"solid_angle#{self._dssa_order}_crc"
        dssa = self._cached_array.get(key)
        if dssa is None:
            dssa = numpy.fromfunction(self.diffSolidAngle,
                                      shape, dtype=numpy.float32)
            self._cached_array[key_crc] = crc32(dssa)
            self._cached_array[key] = dssa

        if absolute:
            # not inplace to avoid mangling  the cache !
            dssa = self.pixel1 * self.pixel2 / (self._dist ** 2) * dssa
        if with_checksum:
            return (dssa, self._cached_array[key_crc])
        else:
            return dssa

    def get_config(self):
        """
        return the configuration as a dictionnary

        :return: dictionary with the current configuration
        """
        with self._sem:
            # TODO: ponifile should not be used here
            #     if it was only used for IO, it would be better to remove
            #     this function
            poni = ponifile.PoniFile(data=self)
            return poni.as_dict()

    def _init_from_poni(self, poni):
        """Init the geometry from a poni object."""
        if poni.detector is not None:
            self.detector = poni.detector
        if poni.dist is not None:
            self._dist = poni.dist
        if poni.poni1 is not None:
            self._poni1 = poni.poni1
        if poni.poni2 is not None:
            self._poni2 = poni.poni2
        if poni.rot1 is not None:
            self._rot1 = poni.rot1
        if poni.rot2 is not None:
            self._rot2 = poni.rot2
        if poni.rot3 is not None:
            self._rot3 = poni.rot3
        if poni.wavelength is not None:
            self._wavelength = poni.wavelength
        self.reset()

    def set_config(self, config):
        """
        Set the config of the geometry and of the underlying detector

        :param config: dictionary with the configuration
        :return: itself
        """
        # TODO: ponifile should not be used here
        #     if it was only used for IO, it would be better to remove
        #     this function
        poni = ponifile.PoniFile(config)
        self._init_from_poni(poni)
        return self

    def save(self, filename):
        """
        Save the geometry parameters.

        :param filename: name of the file where to save the parameters
        :type filename: string
        """
        try:
            with open(filename, "a") as f:
                poni = ponifile.PoniFile(self)
                poni.write(f)
        except IOError:
            logger.error("IOError while writing to file %s", filename)

    write = save

    @classmethod
    def sload(cls, filename):
        """
        A static method combining the constructor and the loader from
        a file

        :param filename: name of the file to load
        :type filename: string
        :return: instance of Geometry of AzimuthalIntegrator set-up with the parameter from the file.
        """
        inst = cls()
        inst.load(filename)
        return inst

    def load(self, filename):
        """
        Load the refined parameters from a file.

        :param filename: name of the file to load
        :type filename: string
        :return: itself with updated parameters
        """
        try:
            if os.path.exists(filename):
                with open(filename) as f:
                    dico = json.load(f)
            else:
                dico = json.loads(filename)
        except Exception:
            logger.info("Unable to parse %s as JSON file, defaulting to PoniParser", filename)
            poni = ponifile.PoniFile(data=filename)
        else:
            config = integration_config.ConfigurationReader(dico)
            poni = config.pop_ponifile()
        self._init_from_poni(poni)

        return self

    read = load

    def getPyFAI(self):
        """
        Export geometry setup with the geometry of PyFAI

        Deprecated, please use get/set_config which is cleaner! when it comes to detector specification

        :return: dict with the parameter-set of the PyFAI geometry
        """
        with self._sem:
            out = self.detector.getPyFAI()
            out["dist"] = self._dist
            out["poni1"] = self._poni1
            out["poni2"] = self._poni2
            out["rot1"] = self._rot1
            out["rot2"] = self._rot2
            out["rot3"] = self._rot3
            if self._wavelength:
                out["wavelength"] = self._wavelength
        return out

    def setPyFAI(self, **kwargs):
        """
        set the geometry from a pyFAI-like dict

        Deprecated, please use get/set_config which is cleaner! when it comes to detector specification
        """
        with self._sem:
            for key in ["dist", "poni1", "poni2",
                        "rot1", "rot2", "rot3",
                        "wavelength"]:
                if key in kwargs:
                    setattr(self, key, kwargs.pop(key))

            if "detector" in kwargs:
                self.detector = detectors.Detector.from_dict(kwargs)
            else:
                self.detector = detectors.Detector(pixel1=kwargs.get("pixel1"),
                                                   pixel2=kwargs.get("pixel2"),
                                                   splineFile=kwargs.get("splineFile"),
                                                   max_shape=kwargs.get("max_shape"))
            self.param = [self._dist, self._poni1, self._poni2,
                          self._rot1, self._rot2, self._rot3]
            self.chiDiscAtPi = True  # position of the discontinuity of chi in radians, pi by default
            self.reset()
            self._oversampling = None
        return self

    def getFit2D(self):
        """
        Export geometry setup with the geometry of Fit2D

        :return: dict with parameters compatible with fit2D geometry
        """
        with self._sem:
            f2d = convert_to_Fit2d(self)
        return f2d._asdict()

    def setFit2D(self, directDist, centerX, centerY,
                 tilt=0., tiltPlanRotation=0.,
                 pixelX=None, pixelY=None, splineFile=None,
                 detector=None, wavelength=None):
        """
        Set the Fit2D-like parameter set: For geometry description see
        HPR 1996 (14) pp-240
        https://doi.org/10.1080/08957959608201408

        Warning: Fit2D flips automatically images depending on their file-format.
        By reverse engineering we noticed this behavour for Tiff and Mar345 images (at least).
        To obtaine correct result you will have to flip images using numpy.flipud.

        :param direct: direct distance from sample to detector along the incident beam (in millimeter as in fit2d)
        :param tilt: tilt in degrees
        :param tiltPlanRotation: Rotation (in degrees) of the tilt plan arround the Z-detector axis
                * 0deg -> Y does not move, +X goes to Z<0
                * 90deg -> X does not move, +Y goes to Z<0
                * 180deg -> Y does not move, +X goes to Z>0
                * 270deg -> X does not move, +Y goes to Z>0

        :param pixelX,pixelY: as in fit2d they ar given in micron, not in meter
        :param centerX, centerY: pixel position of the beam center
        :param splineFile: name of the file containing the spline
        :param detector: name of the detector or detector object
        """
        pixelX = pixelX if pixelX is not None else self.detector.pixel2 * 1e6
        pixelY = pixelY if pixelY is not None else self.detector.pixel1 * 1e6
        splineFile = splineFile if splineFile is not None else self.detector.splineFile
        detector = detector if detector is not None else self.detector
        wavelength = wavelength if wavelength else (
                        self.wavelength * 1e10 if self.wavelength else None)
        poni = convert_from_Fit2d({"directDist":directDist,
                                   "centerX":centerX,
                                   "centerY":centerY,
                                   "tilt":tilt,
                                   "tiltPlanRotation":tiltPlanRotation,
                                   "pixelX":pixelX,
                                   "pixelY":pixelY,
                                   "splineFile":splineFile,
                                   "detector": detector,
                                   'wavelength':wavelength})
        with self._sem:
            self._init_from_poni(poni)
        return self

    def setSPD(self, SampleDistance, Center_1, Center_2, Rot_1=0, Rot_2=0, Rot_3=0,
               PSize_1=None, PSize_2=None, splineFile=None, BSize_1=1, BSize_2=1,
               WaveLength=None):
        """
        Set the SPD like parameter set: For geometry description see
        Peter Boesecke J.Appl.Cryst.(2007).40, s423–s427

        Basically the main difference with pyFAI is the order of the axis which are flipped

        :param SampleDistance: distance from sample to detector at the PONI (orthogonal projection)
        :param Center_1: pixel position of the PONI along fastest axis
        :param Center_2: pixel position of the PONI along slowest axis
        :param Rot_1: rotation around the fastest axis (x)
        :param Rot_2: rotation around the slowest axis (y)
        :param Rot_3: rotation around the axis ORTHOGONAL to the detector plan
        :param PSize_1: pixel size in meter along the fastest dimention
        :param PSize_2: pixel size in meter along the slowst dimention
        :param splineFile: name of the file containing the spline
        :param BSize_1: pixel binning factor along the fastest dimention
        :param BSize_2: pixel binning factor along the slowst dimention
        :param WaveLength: wavelength used
        """
        # first define the detector
        if splineFile:
            # let's assume the spline file is for unbinned detectors ...
            self.detector = detectors.FReLoN(splineFile)
            self.detector.binning = (int(BSize_2), int(BSize_1))
        elif PSize_1 and PSize_2:
            self.detector = detectors.Detector(PSize_2, PSize_1)
            if BSize_2 > 1 or BSize_1 > 1:
                # set binning factor without changing pixel size
                self.detector._binning = (int(BSize_2), int(BSize_1))

        # then the geometry
        self._dist = float(SampleDistance)
        self._poni1 = float(Center_2) * self.detector.pixel1
        self._poni2 = float(Center_1) * self.detector.pixel2
        # This is WRONG ... correct it
        self._rot1 = Rot_2 or 0
        self._rot2 = Rot_1 or 0
        self._rot3 = -(Rot_3 or 0)
        if Rot_1 or Rot_2 or Rot_3:
            # TODO: one-day
            raise NotImplementedError("rotation axis not yet implemented for SPD")
        # and finally the wavelength
        if WaveLength:
            self.wavelength = float(WaveLength)
        self.reset()
        return self

    def getSPD(self):
        """
        get the SPD like parameter set: For geometry description see
        Peter Boesecke J.Appl.Cryst.(2007).40, s423–s427

        Basically the main difference with pyFAI is the order of the axis which are flipped

        :return: dictionnary with those parameters:
            SampleDistance: distance from sample to detector at the PONI (orthogonal projection)
            Center_1, pixel position of the PONI along fastest axis
            Center_2: pixel position of the PONI along slowest axis
            Rot_1: rotation around the fastest axis (x)
            Rot_2: rotation around the slowest axis (y)
            Rot_3: rotation around the axis ORTHOGONAL to the detector plan
            PSize_1: pixel size in meter along the fastest dimention
            PSize_2: pixel size in meter along the slowst dimention
            splineFile: name of the file containing the spline
            BSize_1: pixel binning factor along the fastest dimention
            BSize_2: pixel binning factor along the slowst dimention
            WaveLength: wavelength used in meter
        """

        res = OrderedDict((("PSize_1", self.detector.pixel2),
                           ("PSize_2", self.detector.pixel1),
                           ("BSize_1", self.detector.binning[1]),
                           ("BSize_2", self.detector.binning[0]),
                           ("splineFile", self.detector.splineFile),
                           ("Rot_3", None),
                           ("Rot_2", None),
                           ("Rot_1", None),
                           ("Center_2", self._poni1 / self.detector.pixel1),
                           ("Center_1", self._poni2 / self.detector.pixel2),
                           ("SampleDistance", self.dist)))
        if self._wavelength:
            res["WaveLength"] = self._wavelength
        if abs(self.rot1) > 1e-6 or abs(self.rot2) > 1e-6 or abs(self.rot3) > 1e-6:
            logger.warning("Rotation conversion from pyFAI to SPD is not yet implemented")
        return res

    def getImageD11(self):
        """Export the current geometry in ImageD11 format.
        Please refer to the documentation in doc/source/geometry_conversion.rst
        for the orientation and units of those values.

        :return: an Ordered dict with those parameters:
            distance 294662.658 #in nm
            o11 1
            o12 0
            o21 0
            o22 -1
            tilt_x 0.00000
            tilt_y -0.013173
            tilt_z 0.002378
            wavelength 0.154
            y_center 1016.328171
            y_size 48.0815
            z_center 984.924425
            z_size 46.77648
        """
        f2d = self.getFit2D()
        distance = f2d.get("directDist", 0) * 1e3  # mm -> µm
        y_center = f2d.get("centerX", 0)  # in pixel
        z_center = f2d.get("centerY", 0)  # in pixel

        tilt_x = self.rot3
        tilt_y = self.rot2
        tilt_z = -self.rot1
        out = OrderedDict([("distance", distance),
                           ("o11", 1),
                           ("o12", 0),
                           ("o21", 0),
                           ("o22", -1),
                           ("tilt_x", tilt_x),
                           ("tilt_y", tilt_y),
                           ("tilt_z", tilt_z),
                           ])
        if self._wavelength:
            out["wavelength"] = self.wavelength * 1e9  # nm
        if y_center:
            out["y_center"] = y_center
        out["y_size"] = self.detector.pixel2 * 1e6  # µm
        if z_center:
            out["z_center"] = z_center
        out["z_size"] = self.detector.pixel1 * 1e6  # µm
        return out

    def setImageD11(self, param):
        """Set the geometry from the parameter set which contains distance,
        o11, o12, o21, o22, tilt_x, tilt_y tilt_z, wavelength, y_center, y_size,
        z_center and z_size.
        Please refer to the documentation in doc/source/geometry_conversion.rst
        for the orientation and units of those values.

        :param param: dict with the values to set.
        """
        o11 = param.get("o11")
        if o11 is not None:
            assert o11 == 1, "Only canonical orientation is supported"
        o12 = param.get("o12")
        if o12 is not None:
            assert o12 == 0, "Only canonical orientation is supported"
        o21 = param.get("o21")
        if o21 is not None:
            assert o21 == 0, "Only canonical orientation is supported"
        o22 = param.get("o22")
        if o22 is not None:
            assert o22 == -1, "Only canonical orientation is supported"

        self.rot3 = param.get("tilt_x", 0.0)
        self.rot2 = param.get("tilt_y", 0.0)
        self.rot1 = -param.get("tilt_z", 0.0)
        distance = param.get("distance", 0.0) * 1e-6  # ->m
        self.dist = distance * cos(self.rot2) * cos(self.rot1)
        pixel_v = param.get("z_size", 0.0) * 1e-6
        pixel_h = param.get("y_size", 0.0) * 1e-6
        self.poni1 = -distance * sin(self.rot2) + pixel_v * param.get("z_center", 0.0)
        self.poni2 = +distance * cos(self.rot2) * sin(self.rot1) + pixel_h * param.get("y_center", 0.0)
        self.detector = detectors.Detector(pixel1=pixel_v, pixel2=pixel_h)
        wl = param.get("wavelength")
        if wl:
            self.wavelength = wl * 1e-9
        self.reset()
        return self

    def getCXI(self):
        """Export the geometry in the CXI format as defined in
        https://raw.githubusercontent.com/cxidb/CXI/master/cxi_file_format.pdf

        :return: dict with the structure of a CXI file to be written into HDF5
        """
        cxi = {"cxi_version": 160}
        if self._wavelength:
            cxi["beam"] = {"incident_energy": self.get_energy(),
                           "incident_wavelength": self.get_wavelength(),
                           # "incident_polarization": #TODO
            }
        detector = {"distance": self.dist,
                    "x_pixel_size": self.detector.pixel2,
                    "y_pixel_size": self.detector.pixel1,
                    "description": self.detector.name,
                    "mask": self.detector.mask}
        geometry = {"translation": [-self.poni2, -self.poni1, self.dist]}
        # This is the matrix that transforms the sample's orientation to the detector's orientation
        rot = numpy.linalg.inv(self.rotation_matrix())
        # TODO: double check this with CXI gemetry visualizer. Indices could be transposed.
        geometry["orientation"] = [rot[1, 1],  # x′ · x,
                                   rot[1, 0],  # x′ · y,
                                   rot[1, 2],  # x′ · z,
                                   rot[0, 1],  # y′ · x,
                                   rot[0, 0],  # y′ · y,
                                   rot[0, 2],  # y′ · z]
                                    ]
        detector["geometry_1"] = geometry
        cxi["detector_1"] = detector
        return cxi

    def setCXI(self, dico):
        """Set the geometry of the azimuthal integrator from a CXI data structure (as dict)

        :param dico: dictionary with CXI information
        """
        detector = dico.get("detector_1", {})
        det = detectors.detector_factory(detector.get("description", "Detector"))
        if "x_pixel_size" in detector:
            det.pixel2 = float(detector["x_pixel_size"])
        if "y_pixel_size" in detector:
            det.pixel1 = float(detector["y_pixel_size"])
        if "mask" in detector:
            det.mask = detector["mask"]
        self.detector = det
        if "distance" in detector:
            self.dist = float(detector["distance"])
        if "beam" in dico:
            beam = dico["beam"]
            if "incident_energy" in beam:
                self.energy = float(beam["incident_energy"])
            if "incident_wavelength" in beam:
                self.wavelength = float(beam["incident_wavelength"])
        if "geometry_1" in detector:
            geo = detector["geometry_1"]
            translation = geo.get("translation", [])
            if len(translation) == 3:
                self.dist = translation[2]
                self.poni1 = -translation[1]
                self.poni2 = -translation[0]
            orientation = geo.get("orientation", [])
            if len(orientation) == 6:
                rot = numpy.zeros((3, 3))
                rot[1, 1] = orientation[0]  # x′ · x,
                rot[1, 0] = orientation[1]  # x′ · y,
                rot[1, 2] = orientation[2]  # x′ · z,
                rot[0, 1] = orientation[3]  # y′ · x,
                rot[0, 0] = orientation[4]  # y′ · y,
                rot[0, 2] = orientation[5]  # y′ · z
                rot[2] = numpy.cross(rot[0], rot[1])
                rot = numpy.linalg.inv(rot)
                rot4 = numpy.identity(4)
                rot4[:3,:3] = rot
                from ..third_party.transformations import euler_from_matrix
                euler = euler_from_matrix(rot4, axes='sxyz')
                self._rot1 = -euler[0]
                self._rot2 = -euler[1]
                self._rot3 = euler[2]
        self.reset()

    def set_param(self, param):
        """set the geometry from a 6-tuple with dist, poni1, poni2, rot1, rot2,
        rot3
        """
        if len(param) == 6:
            self._dist, self._poni1, self._poni2, self._rot1, self._rot2, self._rot3 = param
        elif len(param) == 7:
            self._dist, self._poni1, self._poni2, self._rot1, self._rot2, self._rot3, self.wavelength = param
        else:
            raise RuntimeError("Only 6 or 7-uplet are possible")
        self.reset()

    def set_rot_from_quaternion(self, w, x, y, z):
        """Quaternions are convieniant ways to represent 3D rotation
        This method allows to define rot1(left-handed), rot2(left-handed) and
        rot3 (right handed) as definied in the documentation from a quaternion,
        expressed in the right handed (x1, x2, x3) basis set.

        Uses the transformations-library from C. Gohlke

        :param w: Real part of the quaternion (correspond to cos alpha/2)
        :param x: Imaginary part of the quaternion, correspond to u1*sin(alpha/2)
        :param y: Imaginary part of the quaternion, correspond to u2*sin(alpha/2)
        :param z: Imaginary part of the quaternion, correspond to u3*sin(alpha/2)
        """
        from ..third_party.transformations import euler_from_quaternion

        euler = euler_from_quaternion((w, x, y, z), axes='sxyz')
        self._rot1 = -euler[0]
        self._rot2 = -euler[1]
        self._rot3 = euler[2]

    def quaternion(self, param=None):
        """Calculate the quaternion associated to the current rotations
        from rot1, rot2, rot3.

        Uses the transformations-library from C. Gohlke

        :param param: use this set of parameters instead of the default one.
        :return: numpy array with 4 elements [w, x, y, z]
        """
        from ..third_party.transformations import quaternion_from_euler
        if param is None:
            rot1 = self.rot1
            rot2 = self.rot2
            rot3 = self.rot3
        else:
            rot1 = param[3]
            rot2 = param[4]
            rot3 = param[5]

        return quaternion_from_euler(-rot1, -rot2, rot3, axes="sxyz")

    def make_headers(self, type_="list"):
        """Create a configuration / header for the integrated data

        :param type: can be "list" or "dict"
        :return: the header with the proper format
        """
        res = None
        if type_ == "dict":
            res = self.getPyFAI()
        else:  # type_ == "list":
            f2d = self.getFit2D()
            res = ["== pyFAI calibration ==",
                   "Distance Sample to Detector: %s m" % self.dist,
                   "PONI: %.3e, %.3e m" % (self.poni1, self.poni2),
                   "Rotations: %.6f %.6f %.6f rad" % (self.rot1, self.rot2, self.rot3),
                   "",
                   "== Fit2d calibration ==",
                   "Distance Sample-beamCenter: %.3f mm" % f2d["directDist"],
                   "Center: x=%.3f, y=%.3f pix" % (f2d["centerX"], f2d["centerY"]),
                   "Tilt: %.3f deg  TiltPlanRot: %.3f deg" % (f2d["tilt"], f2d["tiltPlanRotation"]),
                   "", str(self.detector),
                   "   Detector has a mask: %s " % (self.detector.mask is not None),
                   "   Detector has a dark current: %s " % (self.detector.darkcurrent is not None),
                   "   detector has a flat field: %s " % (self.detector.flatfield is not None),
                   ""]

            if self._wavelength is not None:
                res.append("Wavelength: %s m" % self._wavelength)
        return res

    def setChiDiscAtZero(self):
        """
        Set the position of the discontinuity of the chi axis between
        0 and 2pi.  By default it is between pi and -pi
        """
        if self.chiDiscAtPi is False:
            return
        with self._sem:
            self.chiDiscAtPi = False
            self._cached_array["chi_center"] = None
            for key in list(self._cached_array.keys()):
                if isinstance(key, str) and "corner" in key:
                    self._cached_array[key] = None

    def setChiDiscAtPi(self):
        """
        Set the position of the discontinuity of the chi axis between
        -pi and +pi.  This is the default behavour
        """
        if self.chiDiscAtPi is True:
            return
        with self._sem:
            self.chiDiscAtPi = True
            self._cached_array["chi_center"] = None
            for key in list(self._cached_array.keys()):
                if isinstance(key, str) and "corner" in key:
                    self._cached_array[key] = None

    @deprecated
    def setOversampling(self, iOversampling):
        """
        set the oversampling factor
        """
        if self._oversampling is None:
            lastOversampling = 1.0
        else:
            lastOversampling = float(self._oversampling)

        self._oversampling = iOversampling
        self.reset()
        self.pixel1 /= self._oversampling / lastOversampling
        self.pixel2 /= self._oversampling / lastOversampling

    @deprecated
    def oversampleArray(self, myarray):
        origShape = myarray.shape
        origType = myarray.dtype
        new = numpy.zeros((origShape[0] * self._oversampling,
                           origShape[1] * self._oversampling)).astype(origType)
        for i in range(self._oversampling):
            for j in range(self._oversampling):
                new[i::self._oversampling, j::self._oversampling] = myarray
        return new

    def polarization(self, shape=None, factor=None, axis_offset=0, with_checksum=False, path="numexpr"):
        """
        Calculate the polarization correction accoding to the
        polarization factor:

        * If the polarization factor is None,
            the correction is not applied (returns 1)
        * If the polarization factor is 0 (circular polarization),
            the correction correspond to (1+(cos2θ)^2)/2
        * If the polarization factor is 1 (linear horizontal polarization),
            there is no correction in the vertical plane  and a node at 2th=90, chi=0
        * If the polarization factor is -1 (linear vertical polarization),
            there is no correction in the horizontal plane and a node at 2th=90, chi=90
        * If the polarization is elliptical, the polarization factor varies between -1 and +1.

        The axis_offset parameter allows correction for the misalignement of
        the polarization plane (or ellipse main axis) and the the detector's X axis.

        :param shape: the shape of the array,
                    can be guessed most of the time from the detector definition
        :param factor: (Ih-Iv)/(Ih+Iv): varies between 0 (circular/random polarization)
                    and 1 (where division by 0 could occure at 2th=90, chi=0)
        :param axis_offset: Angle between the polarization main axis and
                            detector's X direction (in radians !!!)
        :param with_checksum: calculate also the checksum (used with OpenCL integration)
        :param path: set to numpy to enforce the use of numpy, else uses numexpr (mutithreaded)
        :return: 2D array with polarization correction (normalization) array
                 or namedtuple if with_checksum


        """

        shape = self.get_shape(shape)
        if shape is None:
            raise RuntimeError(("You should provide a shape if the"
                                " geometry is not yet initialized"))
        if factor is None:
            if with_checksum:
                one = numpy.ones(shape, dtype=numpy.float32)
                return PolarizationArray(one, crc32(one))
            else:
                return numpy.ones(shape, dtype=numpy.float32)
        elif ((factor is True) and
              (self._LAST_POLARIZATION in self._cached_array)):
            pol = self._cached_array[self._LAST_POLARIZATION]
            return pol if with_checksum else pol.array

        factor = float(factor)
        axis_offset = float(axis_offset)
        desc = PolarizationDescription(factor, axis_offset)
        pol = self._cached_array.get(desc)
        if pol is None or (pol.array.shape != shape):
            tth = self.twoThetaArray(shape)
            chi = self.chiArray(shape)
            with self._sem:
                if pol is None or (pol.array.shape != shape):
                    if path == "numexpr" and numexpr:
                        pola = numexpr.evaluate(
    "0.5 * (1.0 + cos(tth)**2 - factor * cos(2.0 * (chi + axis_offset)) * (1.0 - cos(tth)**2))")
                    else:
                        cos2_tth = numpy.cos(tth) ** 2
                        pola = 0.5 * (1.0 + cos2_tth -
                                      factor * numpy.cos(2.0 * (chi + axis_offset)) * (1.0 - cos2_tth))
                    pola = pola.astype(numpy.float32)
                    polc = crc32(pola)
                    pol = PolarizationArray(pola, polc)
                    self._cached_array[desc] = pol
        self._cached_array[self._LAST_POLARIZATION] = pol
        return pol if with_checksum else pol.array

    def calc_transmission(self, t0, shape=None):
        """
        Defines the absorption correction for a phosphor screen or a scintillator
        from t0, the normal transmission of the screen.

        .. math::

            Icor = \\frac{Iobs(1-t0)}{1-exp(ln(t0)/cos(incidence))}

            let_t = \\frac{1-exp(ln(t0)/cos(incidence))}{1 - t0}

        See reference on:
        J. Appl. Cryst. (2002). 35, 356–359 G. Wu et al.  CCD phosphor

        :param t0: value of the normal transmission (from 0 to 1)
        :param shape: shape of the array
        :return: actual
        """
        shape = self.get_shape(shape)
        if t0 < 0 or t0 > 1:
            logger.error("Impossible value for normal transmission: %s", t0)
            return

        with self._sem:
            if (t0 == self._transmission_normal):
                transmission_corr = self._cached_array.get("transmission_corr")
                if ((shape is None) or (transmission_corr is not None and shape == transmission_corr.shape)):
                    return transmission_corr

            if shape is None:
                raise RuntimeError(("You should provide a shape if the"
                                    " geometry is not yet initiallized"))

        with self._sem:
            self._transmission_normal = t0
            cosa = self._cached_array.get("cos_incidence")
            if cosa is None:
                cosa = numpy.fromfunction(self.cos_incidence,
                                          shape,
                                          dtype=numpy.float32)
                self._cached_array["cos_incidence"] = cosa
            transmission_corr = (1.0 - numpy.exp(numpy.log(t0) / cosa)) / (1 - t0)
            self._cached_array["transmission_crc"] = crc32(transmission_corr)
            self._cached_array["transmission_corr"] = transmission_corr

        return transmission_corr

    def reset(self, collect_garbage=True):
        """
        reset most arrays that are cached: used when a parameter changes.

        :param collect_garbage: set to False to prevent garbage collection, faster
        """
        self.param = [self._dist, self._poni1, self._poni2,
                      self._rot1, self._rot2, self._rot3]
        self._transmission_normal = None
        self._cached_array = {}
        if collect_garbage:
            gc.collect()

    def calcfrom1d(self, tth, I, shape=None, mask=None,
                   dim1_unit=units.TTH, correctSolidAngle=True,
                   dummy=0.0,
                   polarization_factor=None, polarization_axis_offset=0,
                   dark=None, flat=None,
                   ):
        """
        Computes a 2D image from a 1D integrated profile

        :param tth: 1D array with radial unit, this array needs to be ordered
        :param I: scattering intensity, corresponding intensity
        :param shape: shape of the image (if not defined by the detector)
        :param dim1_unit: unit for the "tth" array
        :param correctSolidAngle:
        :param dummy: value for masked pixels
        :param polarization_factor: set to true to use previously used value
        :param polarization_axis_offset: axis_offset to be send to the polarization method
        :param dark: dark current correction
        :param flat: flatfield corrction
        :return: 2D image reconstructed

        """
        dim1_unit = to_unit(dim1_unit)
        tth = tth / dim1_unit.scale

        if shape is None:
            shape = self.detector.max_shape
        try:
            ttha = self.__getattribute__(dim1_unit.center)(shape)
        except Exception:
            raise RuntimeError("in pyFAI.Geometry.calcfrom1d: " +
                               str(dim1_unit) + " not (yet?) Implemented")
        calcimage = numpy.interp(ttha.ravel(), tth, I)
        calcimage.shape = shape
        if correctSolidAngle:
            calcimage *= self.solidAngleArray(shape)
        if polarization_factor is not None:
            calcimage *= self.polarization(shape, polarization_factor,
                                           axis_offset=polarization_axis_offset,
                                           with_checksum=False)
        if flat is not None:
            assert flat.shape == tuple(shape)
            calcimage *= flat
        if dark is not None:
            assert dark.shape == tuple(shape)
            calcimage += dark
        if mask is not None:
            assert mask.shape == tuple(shape)
            calcimage[numpy.where(mask)] = dummy
        return calcimage

    def calcfrom2d(self, I, tth, chi, shape=None, mask=None,
                   dim1_unit=units.TTH, dim2_unit=units.CHI_DEG,
                   correctSolidAngle=True, dummy=0.0,
                   polarization_factor=None, polarization_axis_offset=0,
                   dark=None, flat=None,
                   ):
        """
        Computes a 2D image from a cake / 2D integrated image

        :param I: scattering intensity, as an image n_tth, n_chi
        :param tth: 1D array with radial unit, this array needs to be ordered
        :param chi: 1D array with azimuthal unit, this array needs to be ordered
        :param shape: shape of the image (if not defined by the detector)
        :param dim1_unit: unit for the "tth" array
        :param dim2_unit: unit for the "chi" array
        :param correctSolidAngle:
        :param dummy: value for masked pixels
        :param polarization_factor: set to true to use previously used value
        :param polarization_axis_offset: axis_offset to be send to the polarization method
        :param dark: dark current correction
        :param flat: flatfield corrction
        :return: 2D image reconstructed

        """
        dim1_unit = to_unit(dim1_unit)
        dim2_unit = to_unit(dim2_unit)
        tth = numpy.ascontiguousarray(tth, numpy.float64) / dim1_unit.scale
        chi = numpy.ascontiguousarray(chi, numpy.float64) / dim2_unit.scale
        if shape is None:
            shape = self.detector.max_shape
        try:
            ttha = self.__getattribute__(dim1_unit.center)(shape)
        except Exception:
            raise RuntimeError("in pyFAI.Geometry.calcfrom2d: " +
                               str(dim1_unit) + " not (yet?) Implemented")
        chia = self.chiArray(shape)

        built_mask = numpy.ones(shape, dtype=numpy.int8)
        empty_data = numpy.zeros(shape, dtype=numpy.float32)

        from ..ext.inpainting import polar_interpolate
        calcimage = polar_interpolate(empty_data,
                                      mask=built_mask,
                                      radial=ttha,
                                      azimuthal=chia,
                                      polar=I,
                                      rad_pos=tth,
                                      azim_pos=chi)
        if correctSolidAngle:
            calcimage *= self.solidAngleArray(shape)
        if polarization_factor is not None:
            calcimage *= self.polarization(shape, polarization_factor,
                                           axis_offset=polarization_axis_offset,
                                           with_checksum=False)
        if flat is not None:
            assert flat.shape == tuple(shape)
            calcimage *= flat
        if dark is not None:
            assert dark.shape == tuple(shape)
            calcimage += dark
        if mask is not None:
            assert mask.shape == tuple(shape)
            calcimage[numpy.where(mask)] = dummy
        return calcimage

    def __copy__(self):
        """:return: a shallow copy of itself.
        """
        new = self.__class__(detector=self.detector)
        # transfer numerical values:
        numerical = ["_dist", "_poni1", "_poni2", "_rot1", "_rot2", "_rot3",
                     "chiDiscAtPi", "_wavelength",
                     '_oversampling', '_correct_solid_angle_for_spline',
                     '_transmission_normal',
                     ]
        # array = []
        for key in numerical:
            new.__setattr__(key, self.__getattribute__(key))
        new.param = [new._dist, new._poni1, new._poni2,
                     new._rot1, new._rot2, new._rot3]
        new._cached_array = self._cached_array.copy()
        return new

    def __deepcopy__(self, memo=None):
        """deep copy
        :param memo: dict with modified objects
        :return: a deep copy of itself."""
        numerical = ["_dist", "_poni1", "_poni2", "_rot1", "_rot2", "_rot3",
                     "chiDiscAtPi", "_dssa_order", "_wavelength",
                     '_oversampling', '_correct_solid_angle_for_spline',
                     '_transmission_normal',
                     ]
        if memo is None:
            memo = {}
        new = self.__class__()
        memo[id(self)] = new
        new_det = self.detector.__deepcopy__(memo)
        new.detector = new_det

        for key in numerical:
            old_value = self.__getattribute__(key)
            memo[id(old_value)] = old_value
            new.__setattr__(key, old_value)
        new_param = [new._dist, new._poni1, new._poni2,
                     new._rot1, new._rot2, new._rot3]
        memo[id(self.param)] = new_param
        new.param = new_param
        cached = {}
        memo[id(self._cached_array)] = cached
        for key, old_value in self._cached_array.copy().items():
            if "copy" in dir(old_value):
                new_value = old_value.copy()
                memo[id(old_value)] = new_value
        new._cached_array = cached
        return new

    def rotation_matrix(self, param=None):
        """Compute and return the detector tilts as a single rotation matrix

        Corresponds to rotations about axes 1 then 2 then 3 (=> 0 later on)
        For those using spd (PB = Peter Boesecke), tilts relate to
        this system (JK = Jerome Kieffer) as follows:
        JK1 = PB2 (Y)
        JK2 = PB1 (X)
        JK3 = PB3 (Z)
        ...slight differences will result from the order
        FIXME: make backwards and forwards converter helper function

        axis1 is  vertical and perpendicular to beam
        axis2 is  horizontal and perpendicular to beam
        axis3  is along the beam, becomes axis0
        see:
        http://pyfai.readthedocs.io/en/latest/geometry.html#detector-position
        or ../doc/source/img/PONI.png

        :param param: list of geometry parameters, defaults to self.param
                      uses elements [3],[4],[5]
        :type param: list of float
        :return: rotation matrix
        :rtype: 3x3 float array
        """
        if param is None:
            param = self.param
        cos_rot1 = cos(param[3])
        cos_rot2 = cos(param[4])
        cos_rot3 = cos(param[5])
        sin_rot1 = sin(param[3])
        sin_rot2 = sin(param[4])
        sin_rot3 = sin(param[5])

        # Rotation about axis 1: Note this rotation is left-handed
        rot1 = numpy.array([[1.0, 0.0, 0.0],
                            [0.0, cos_rot1, sin_rot1],
                            [0.0, -sin_rot1, cos_rot1]])
        # Rotation about axis 2. Note this rotation is left-handed
        rot2 = numpy.array([[cos_rot2, 0.0, -sin_rot2],
                            [0.0, 1.0, 0.0],
                            [sin_rot2, 0.0, cos_rot2]])
        # Rotation about axis 3: Note this rotation is right-handed
        rot3 = numpy.array([[cos_rot3, -sin_rot3, 0.0],
                            [sin_rot3, cos_rot3, 0.0],
                            [0.0, 0.0, 1.0]])
        rotation_matrix = numpy.dot(numpy.dot(rot3, rot2),
                                    rot1)  # 3x3 matrix

        return rotation_matrix

    def guess_npt_rad(self):
        """ calculate the number of pixels from the beam-center to the corner the further away from it.

        :return: this distance as a number of pixels.

        It is a good guess of the number of bins to be used without oversampling too much the data for azimuthal integration
        """
        assert self.detector.shape
        with self._sem:
            f2d = convert_to_Fit2d(self)
        x = numpy.atleast_2d([0, self.detector.shape[-1]]) - f2d.centerX
        y = numpy.atleast_2d([0, self.detector.shape[0]]).T - f2d.centerY
        r = ((x ** 2 + y ** 2) ** 0.5).max()
        return int(r)

# ############################################
# Accessors and public properties of the class
# ############################################
    def get_shape(self, shape=None):
        """Guess what is the best shape ....

        :param shape: force this value (2-tuple of int)
        :return: 2-tuple of int
        """
        if shape is None:
            shape = self.detector.shape
        if shape is None:
            for ary in self._cached_array.values():
                if ary is not None:
                    if hasattr(ary, "shape"):
                        shape = ary.shape[:2]
                    elif hasattr(ary, "array"):
                        shape = ary.array.shape[:2]
                    break
        return shape

    def set_dist(self, value):
        if isinstance(value, float):
            self._dist = value
        else:
            self._dist = float(value)
        self.reset()

    def get_dist(self):
        return self._dist

    dist = property(get_dist, set_dist)

    def set_poni1(self, value):
        if isinstance(value, float):
            self._poni1 = value
        elif isinstance(value, (tuple, list)):
            self._poni1 = float(value[0])
        else:
            self._poni1 = float(value)
        self.reset()

    def get_poni1(self):
        return self._poni1

    poni1 = property(get_poni1, set_poni1)

    def set_poni2(self, value):
        if isinstance(value, float):
            self._poni2 = value
        elif isinstance(value, (tuple, list)):
            self._poni2 = float(value[0])
        else:
            self._poni2 = float(value)
        self.reset()

    def get_poni2(self):
        return self._poni2

    poni2 = property(get_poni2, set_poni2)

    def set_rot1(self, value):
        if isinstance(value, float):
            self._rot1 = value
        elif isinstance(value, (tuple, list)):
            self._rot1 = float(value[0])
        else:
            self._rot1 = float(value)
        self.reset()

    def get_rot1(self):
        return self._rot1

    rot1 = property(get_rot1, set_rot1)

    def set_rot2(self, value):
        if isinstance(value, float):
            self._rot2 = value
        elif isinstance(value, (tuple, list)):
            self._rot2 = float(value[0])
        else:
            self._rot2 = float(value)
        self.reset()

    def get_rot2(self):
        return self._rot2

    rot2 = property(get_rot2, set_rot2)

    def set_rot3(self, value):
        if isinstance(value, float):
            self._rot3 = value
        elif isinstance(value, (tuple, list)):
            self._rot3 = float(value[0])
        else:
            self._rot3 = float(value)
        self.reset()

    def get_rot3(self):
        return self._rot3

    rot3 = property(get_rot3, set_rot3)

    def set_wavelength(self, value):
        "Set the wavelength in meter!"
        old_wl = self._wavelength
        if isinstance(value, float):
            self._wavelength = value
        elif isinstance(value, (tuple, list)):
            self._wavelength = float(value[0])
        else:
            self._wavelength = float(value)
        qa = dqa = q_corner = None
        if old_wl and self._wavelength:
            if self._cached_array.get("q_center") is not None:
                qa = self._cached_array["q_center"] * old_wl / self._wavelength

            q_corner = self._cached_array.get("q_corner")
            if q_corner is not None:
                q_corner[..., 0] = q_corner[..., 0] * old_wl / self._wavelength

        self.reset()
        # restore updated values
        self._cached_array["q_delta"] = dqa
        self._cached_array["q_center"] = qa
        self._cached_array["q_corner"] = q_corner

    def get_wavelength(self):
        return self._wavelength

    wavelength = property(get_wavelength, set_wavelength)

    def get_energy(self):
        if self._wavelength:
            return 1e-10 * CONST_hc / self._wavelength

    def set_energy(self, energy):
        "Set the energy in keV"
        wavlength = 1e-10 * CONST_hc / energy
        self.set_wavelength(wavlength)

    energy = property(get_energy, set_energy)

    def get_ttha(self):
        return self._cached_array.get("2th_center")

    def set_ttha(self, _):
        logger.error("You are not allowed to modify 2theta array")

    def del_ttha(self):
        self._cached_array["2th_center"] = None

    ttha = property(get_ttha, set_ttha, del_ttha, "2theta array in cache")

    def get_chia(self):
        return self._cached_array.get("chi_center")

    def set_chia(self, _):
        logger.error("You are not allowed to modify chi array")

    def del_chia(self):
        self._cached_array["chi_center"] = None

    chia = property(get_chia, set_chia, del_chia, "chi array in cache")

    def get_dssa(self):
        key = "solid_angle#%s" % (self._dssa_order)
        return self._cached_array.get(key)

    def set_dssa(self, _):
        logger.error("You are not allowed to modify solid angle array")

    def del_dssa(self):
        self._cached_array["solid_angle#%s" % (self._dssa_order)] = None
        self._cached_array["solid_angle#%s_crc" % (self._dssa_order)] = None

    dssa = property(get_dssa, set_dssa, del_dssa, "solid angle array in cache")

    def get_qa(self):
        return self._cached_array.get("q_center")

    def set_qa(self, _):
        logger.error("You are not allowed to modify Q array")

    def del_qa(self):
        self._cached_array["q_center"] = None

    qa = property(get_qa, set_qa, del_qa, "Q array in cache")

    def get_ra(self):
        return self._cached_array.get("r_center")

    def set_ra(self, _):
        logger.error("You are not allowed to modify R array")

    def del_ra(self):
        self.self._cached_array["r_center"] = None

    ra = property(get_ra, set_ra, del_ra, "R array in cache")

    def get_pixel1(self):
        return self.detector.pixel1

    def set_pixel1(self, pixel1):
        self.detector.pixel1 = pixel1

    pixel1 = property(get_pixel1, set_pixel1)

    def get_pixel2(self):
        return self.detector.pixel2

    def set_pixel2(self, pixel2):
        self.detector.pixel2 = pixel2

    pixel2 = property(get_pixel2, set_pixel2)

    def get_splineFile(self):
        return self.detector.splineFile

    def set_splineFile(self, splineFile):
        self.detector.splineFile = splineFile

    splineFile = property(get_splineFile, set_splineFile)

    def get_spline(self):
        return self.detector.spline

    def set_spline(self, spline):
        self.detector.spline = spline

    spline = property(get_spline, set_spline)

    def get_correct_solid_angle_for_spline(self):
        return self._correct_solid_angle_for_spline

    def set_correct_solid_angle_for_spline(self, value):
        v = bool(value)
        with self._sem:
            if v != self._correct_solid_angle_for_spline:
                self._dssa = None
                self._correct_solid_angle_for_spline = v

    correct_SA_spline = property(get_correct_solid_angle_for_spline,
                                 set_correct_solid_angle_for_spline)

    def set_maskfile(self, maskfile):
        self.detector.set_maskfile(maskfile)

    def get_maskfile(self):
        return self.detector.get_maskfile()

    maskfile = property(get_maskfile, set_maskfile)

    def set_mask(self, mask):
        self.detector.set_mask(mask)

    def get_mask(self):
        return self.detector.get_mask()

    mask = property(get_mask, set_mask)

    def get_parallax(self):
        return self._parallax

    def set_parallax(self, value):
        from ..parallax import Parallax
        if value is not None:
            assert isinstance(value, Parallax)
        self._parallax = value
        self.reset()

    parallax = property(get_parallax, set_parallax)

    # Property to provide _dssa and _dssa_crc and so one to maintain the API
    @property
    def _dssa(self):
        key = "solid_angle#%s" % (self._dssa_order)
        return self._cached_array.get(key)

    @property
    def _dssa_crc(self):
        key = "solid_angle#%s_crc" % (self._dssa_order)
        return self._cached_array.get(key)

    @property
    def _cosa(self):
        return self._cached_array.get("cos_incidence")

    @property
    def _transmission_crc(self):
        return self._cached_array.get("transmission_crc")

    @property
    def _transmission_corr(self):
        return self._cached_array.get("transmission_corr")

    def __getnewargs_ex__(self):
        "Helper function for pickling geometry"
        return (self.dist, self.poni1, self.poni2,
                self.rot1, self.rot2, self.rot3,
                self.pixel1, self.pixel2,
                self.splineFile, self.detector, self.wavelength), {}

    def __getstate__(self):
        """Helper function for pickling geometry

        :return: the state of the object
        """

        state_blacklist = ('_sem',)
        state = self.__dict__.copy()
        for key in state_blacklist:
            if key in state:
                del state[key]
        return state

    def __setstate__(self, state):
        """Helper function for unpickling geometry

        :param state: the state of the object
        """
        for statekey, statevalue in state.items():
            setattr(self, statekey, statevalue)
        self._sem = threading.Semaphore()
