Merge pull request #691 from tgwoodcock/detector_orientation

Detector orientation with euler angles: simulations from master patterns and GeometricalKikuchiPatternSimulation

src/kikuchipy/_utils/_detector_coordinates.py

@@ -0,0 +1,357 @@
+# Copyright 2019-2024 The kikuchipy developers
+#
+# This file is part of kikuchipy.
+#
+# kikuchipy is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# kikuchipy is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with kikuchipy. If not, see <http://www.gnu.org/licenses/>.
+
+"""Functions for converting between pixel and gnomonic detector coordinates."""
+
+from typing import Union
+
+import numpy as np
+
+
+def get_coordinate_conversions(gnomonic_bounds: np.ndarray, bounds: np.ndarray) -> dict:
+    """
+    Get factors for converting between pixel and gnomonic coordinates.
+
+    Return a dict 'conversions' containing the keys
+    "pix_to_gn", containing factors for converting
+    pixel to gnomonic coordinates, and "gn_to_pix",
+    containing factors for converting gnomonic to pixel
+    coordinates.
+    Under each of these keys is a further dict with the
+    keys: "m_x", "c_x", "m_y" and "c_y". These are the
+    slope (m) and y-intercept (c) corresponding to
+    y = mx + c, which describes the linear conversion
+    of the coordinates. A (different) linear relationship
+    is required for x (column) and y(row) coordinates,
+    hence the two sets of m and c parameters.
+
+    Parameters
+    ----------
+    gnomonic_bounds
+        Array of shape at least (4,) containing the
+        gnomonic bounds of the EBSD detector screen.
+        Typically obtained as the "gnomonic_bounds"
+        property of an EBSDDetector.
+    bounds
+        Array of four ints giving the detector bounds
+        [x0, x1, y0, y1] in pixel coordinates. Typically
+        obtained from the "bounds" property of an
+        EBSDDetector.
+
+    Returns
+    -------
+    conversions
+        Contains the keys "pix_to_gn", containing factors
+        for converting pixel to gnomonic coordinates, and
+        "gn_to_pix", containing factors for converting
+        gnomonic to pixel coordinates.
+        Under each of these keys is a further dict with the
+        keys: "m_x", "c_x", "m_y" and "c_y". These are the
+        slope (m) and y-intercept (c) corresponding to
+        y = mx + c, which describes the linear conversion
+        of the coordinates. A (different) linear relationship
+        is required for x (column) and y(row) coordinates,
+        hence the two sets of m and c parameters.
+
+    Examples
+    --------
+    Create an EBSD detector and get the coordinate conversion factors.
+
+    >>> import numpy as np
+    >>> import kikuchipy as kp
+    >>> from kikuchipy._utils._detector_coordinates import get_coordinate_conversions
+    >>> det = kp.detectors.EBSDDetector(
+    ...     shape=(60, 60),
+    ...     pc=np.ones((10, 20, 3)) * (0.421, 0.779, 0.505),
+    ...     convention="edax",
+    ...     px_size=70,
+    ...     binning=8,
+    ...     tilt=5,
+    ...     sample_tilt=70,
+    ... )
+    >>> det
+    EBSDDetector(shape=(60, 60), pc=(0.421, 0.221, 0.505), sample_tilt=70.0, tilt=5.0, azimuthal=0.0, twist=0.0, binning=8.0, px_size=70.0 um)
+    >>> det.navigation_shape
+    (10, 20)
+    >>> det.bounds
+    array([ 0, 59,  0, 59])
+    >>> det.gnomonic_bounds[0, 0]
+    array([-0.83366337,  1.14653465, -1.54257426,  0.43762376])
+    >>> conversions = get_coordinate_conversions(det.gnomonic_bounds, det.bounds)
+    >>> conversions["pix_to_gn"]["m_x"].shape
+    (10, 20)
+    """
+    gnomonic_bounds = np.atleast_2d(gnomonic_bounds)
+
+    m_pix_to_gn_x = (gnomonic_bounds[..., 1] - gnomonic_bounds[..., 0]) / (
+        bounds[1] + 1
+    )
+    c_pix_to_gn_x = gnomonic_bounds[..., 0]
+
+    m_pix_to_gn_y = (gnomonic_bounds[..., 2] - gnomonic_bounds[..., 3]) / (
+        bounds[3] + 1
+    )
+    c_pix_to_gn_y = gnomonic_bounds[..., 3]
+
+    m_gn_to_pix_x = 1 / m_pix_to_gn_x
+    c_gn_to_pix_x = -c_pix_to_gn_x / m_pix_to_gn_x
+
+    m_gn_to_pix_y = 1 / m_pix_to_gn_y
+    c_gn_to_pix_y = -c_pix_to_gn_y / m_pix_to_gn_y
+
+    conversions = {
+        "pix_to_gn": {
+            "m_x": m_pix_to_gn_x,
+            "c_x": c_pix_to_gn_x,
+            "m_y": m_pix_to_gn_y,
+            "c_y": c_pix_to_gn_y,
+        },
+        "gn_to_pix": {
+            "m_x": m_gn_to_pix_x,
+            "c_x": c_gn_to_pix_x,
+            "m_y": m_gn_to_pix_y,
+            "c_y": c_gn_to_pix_y,
+        },
+    }
+
+    return conversions
+
+
+def convert_coordinates(
+    coords: np.ndarray,
+    direction: str,
+    conversions: dict,
+    detector_index: Union[None, tuple, int] = None,
+) -> np.ndarray:
+    """
+    Convert between gnomonic and pixel coordinates.
+
+    Parameters
+    ----------
+    coords
+        An array of coordinates of any shape whereby the
+        x and y coordinates to be converted are stored in
+        the last axis.
+    direction
+        Either "pix_to_gn" or "gn_to_pix", depending on the
+        direction of conversion needed.
+    conversions
+        Dict containing the conversion parameters. Usually
+        the output of get_coordinate_conversions().
+        Contains the keys "pix_to_gn", containing factors
+        for converting pixel to gnomonic coordinates, and
+        "gn_to_pix", containing factors for converting
+        gnomonic to pixel coordinates.
+        Under each of these keys is a further dict with the
+        keys: "m_x", "c_x", "m_y" and "c_y". These are the
+        slope (m) and y-intercept (c) corresponding to
+        y = mx + c, which describes the linear conversion
+        of the coordinates. A (different) linear relationship
+        is required for x (column) and y(row) coordinates,
+        hence the two sets of m and c parameters.
+        The shape of each array of conversion factors
+        typically corresponds to the navigation shape
+        of an EBSDDetector.
+    detector_index
+        Index showing which conversion factors in *conversions[direction]*
+        should be applied to *coords*.
+        If None, **all** conversion factors in *conversions[direction]*
+        are applied to *coords*.
+        If an int is supplied, this refers to an index in a 1D dataset.
+        A 1D tuple *e.g.* (3,) can also be passed for a 1D dataset.
+        A 2D index can be specified by supplying a tuple *e.g.* (2, 3).
+        The default value is None.
+
+    Returns
+    -------
+    coords_out
+        Array of coords but with values converted as specified
+        by direction. The shape is either the same as the input
+        or is the navigation shape then the shape of the input.
+
+    Examples
+    --------
+
+    Convert 300 xy coordinates for all patterns in a dataset.
+
+    >>> import numpy as np
+    >>> import kikuchipy as kp
+    >>> from kikuchipy._utils._detector_coordinates import (get_coordinate_conversions, convert_coordinates)
+    >>> s = kp.data.nickel_ebsd_small()
+    >>> det = s.detector
+    >>> det.navigation_shape
+    (3, 3)
+    >>> coords_2d = np.random.randint(0, 60, (300, 2))
+    >>> coords_2d.shape
+    (300, 2)
+    >>> conv = get_coordinate_conversions(det.gnomonic_bounds, det.bounds)
+    >>> coords_out = convert_coordinates(coords_2d, "pix_to_gn", conv, None)
+    >>> coords_out.shape
+    (3, 3, 300, 2)
+
+    Convert 300 xy coordinates for the pattern at index (1, 2) in a dataset.
+
+    >>> import numpy as np
+    >>> import kikuchipy as kp
+    >>> from kikuchipy._utils._detector_coordinates import (get_coordinate_conversions, convert_coordinates)
+    >>> s = kp.data.nickel_ebsd_small()
+    >>> det = s.detector
+    >>> det.navigation_shape
+    (3, 3)
+    >>> coords_2d = np.random.randint(0, 60, (300, 2))
+    >>> coords_2d.shape
+    (300, 2)
+    >>> conv = get_coordinate_conversions(det.gnomonic_bounds, det.bounds)
+    >>> coords_out = convert_coordinates(coords_2d, "pix_to_gn", conv, (1, 2))
+    >>> coords_out.shape
+    (300, 2)
+
+    Convert 17 sets of 300 xy coordinates, different for each pattern in a dataset.
+
+    >>> import numpy as np
+    >>> import kikuchipy as kp
+    >>> from kikuchipy._utils._detector_coordinates import (get_coordinate_conversions, convert_coordinates)
+    >>> s = kp.data.nickel_ebsd_small()
+    >>> det = s.detector
+    >>> det.navigation_shape
+    (3, 3)
+    >>> coords_2d = np.random.randint(0, 60, (3, 3, 17, 300, 2))
+    >>> coords_2d.shape
+    (3, 3, 17, 300, 2)
+    >>> conv = get_coordinate_conversions(det.gnomonic_bounds, det.bounds)
+    >>> coords_out = convert_coordinates(coords_2d, "pix_to_gn", conv, None)
+    >>> coords_out.shape
+    (3, 3, 17, 300, 2)
+
+    Convert 17 sets of 300 xy coordinates, the same for all pattern in a dataset.
+
+    >>> import numpy as np
+    >>> import kikuchipy as kp
+    >>> from kikuchipy._utils._detector_coordinates import (get_coordinate_conversions, convert_coordinates)
+    >>> s = kp.data.nickel_ebsd_small()
+    >>> det = s.detector
+    >>> det.navigation_shape
+    (3, 3)
+    >>> coords_2d = np.random.randint(0, 60, (17, 300, 2))
+    >>> coords_2d.shape
+    (17, 300, 2)
+    >>> conv = get_coordinate_conversions(det.gnomonic_bounds, det.bounds)
+    >>> coords_out = convert_coordinates(coords_2d, "pix_to_gn", conv, None)
+    >>> coords_out.shape
+    (3, 3, 17, 300, 2)
+    """
+    coords = np.atleast_2d(coords)
+
+    nav_shape = conversions[direction]["m_x"].shape
+    nav_ndim = len(nav_shape)
+
+    if isinstance(detector_index, type(None)):
+        detector_index = ()
+        if coords.ndim >= nav_ndim + 2 and coords.shape[:nav_ndim] == nav_shape:
+            # one or more sets of coords, different for each image
+            out_shape = coords.shape
+        else:
+            # one or more sets of coords, the same for each image
+            out_shape = nav_shape + coords.shape
+
+        extra_axes = list(range(nav_ndim, len(out_shape) - 1))
+
+        coords_out = _convert_coordinates(
+            coords,
+            out_shape,
+            detector_index,
+            np.expand_dims(conversions[direction]["m_x"], extra_axes),
+            np.expand_dims(conversions[direction]["c_x"], extra_axes),
+            np.expand_dims(conversions[direction]["m_y"], extra_axes),
+            np.expand_dims(conversions[direction]["c_y"], extra_axes),
+        )
+
+    else:
+        if isinstance(detector_index, int):
+            detector_index = tuple([detector_index])
+
+        out_shape = coords.shape
+
+        coords_out = _convert_coordinates(
+            coords,
+            out_shape,
+            detector_index,
+            conversions[direction]["m_x"],
+            conversions[direction]["c_x"],
+            conversions[direction]["m_y"],
+            conversions[direction]["c_y"],
+        )
+
+    return coords_out
+
+
+def _convert_coordinates(
+    coords: np.ndarray,
+    out_shape: tuple,
+    detector_index: tuple,
+    m_x: Union[np.ndarray, float],
+    c_x: Union[np.ndarray, float],
+    m_y: Union[np.ndarray, float],
+    c_y: Union[np.ndarray, float],
+) -> np.ndarray:
+    """
+    Return converted coordinate depending on arguments.
+
+    This function is usually called by convert_coordinates().
+
+    Parameters
+    ----------
+    coords
+        An array of coordinates whereby the x and y coordinates
+        to be converted are stored in the last axis
+    direction
+        Either "pix_to_gn" or "gn_to_pix", depending on the
+        direction of conversion needed.
+    conversions
+        dict
+
+
+    Parameters
+    ----------
+    coords
+        An array of coordinates whereby the x and y coordinates
+        to be converted are stored in the last axis.
+    out_shape
+        Tuple of ints giving the output shape.
+    detector_index
+        Tuple giving the detector index..
+    m_x
+        Conversion factor m for x coordinate.
+    c_x
+        Conversion factor c for x coordinate.
+    m_y
+        Conversion factor m for y coordinate.
+    c_y
+        Conversion factor c for y coordinate.
+
+    Returns
+    -------
+    coords_out
+        Array of coords the same shape as the input but
+        with converted values.
+    """
+    coords_out = np.zeros(out_shape, dtype=float)
+
+    coords_out[..., 0] = m_x[detector_index] * coords[..., 0] + c_x[detector_index]
+    coords_out[..., 1] = m_y[detector_index] * coords[..., 1] + c_y[detector_index]
+
+    return coords_out