# This file is part of cfelpyutils.
#
# cfelpyutils 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.
#
# cfelpyutils 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 cfelpyutils. If not, see <http://www.gnu.org/licenses/>.
"""
Geometry utilities.
This module contains the implementation of several functions used to
manipulate geometry information."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import collections
import numpy
PixelMaps = collections.namedtuple( # pylint: disable=C0103
typename='PixelMaps',
field_names=['x', 'y', 'r']
)
"""
Pixel maps storing data geometry.
A namedtuple that stores the pixel maps describing the geometry of a
dataset. The first two fields, named "x" and "y" respectively, store
the pixel maps for the x coordinate and the y coordinate. The third
field, named "r", is instead a pixel map storing the distance of each
pixel in the data array from the center of the reference system.
"""
def compute_pix_maps(geometry):
"""
Compute pixel maps from a CrystFEL geometry object.
Take as input a CrystFEL-style geometry object (A dictionary
returned by the function load_crystfel_geometry function in the
crystfel_utils module) and return a PixelMap tuple . The origin the
reference system used by the pixel maps is set at the beam
interaction point.
Args:
geometry (dict): A CrystFEL geometry object (A dictionary
returned by the
:obj:`cfelpyutils.crystfel_utils.load_crystfel_geometry`
function).
Returns:
PixelMaps: A PixelMaps tuple storing the pixel maps (ndarrays
of type float).
"""
max_fs_in_slab = numpy.array([
geometry['panels'][k]['max_fs']
for k in geometry['panels']
]).max()
max_ss_in_slab = numpy.array([
geometry['panels'][k]['max_ss']
for k in geometry['panels']
]).max()
x_map = numpy.zeros(
shape=(max_ss_in_slab + 1, max_fs_in_slab + 1),
dtype=numpy.float32 # pylint: disable=E1101
)
y_map = numpy.zeros(
shape=(max_ss_in_slab + 1, max_fs_in_slab + 1),
dtype=numpy.float32 # pylint: disable=E1101
)
# Iterate over the panels. For each panel, determine the pixel
# indices, then compute the x,y vectors. Finally, copy the
# panel pixel maps into the detector-wide pixel maps.
for pan in geometry['panels']:
ss_grid, fs_grid = numpy.meshgrid(
numpy.arange(
geometry['panels'][pan]['max_ss'] -
geometry['panels'][pan]['min_ss'] +
1
),
numpy.arange(
geometry['panels'][pan]['max_fs'] -
geometry['panels'][pan]['min_fs'] +
1
),
indexing='ij'
)
y_panel = (
ss_grid * geometry['panels'][pan]['ssy'] +
fs_grid * geometry['panels'][pan]['fsy'] +
geometry['panels'][pan]['cny']
)
x_panel = (
ss_grid * geometry['panels'][pan]['ssx'] +
fs_grid * geometry['panels'][pan]['fsx'] +
geometry['panels'][pan]['cnx']
)
x_map[
geometry['panels'][pan]['min_ss']:
geometry['panels'][pan]['max_ss'] + 1,
geometry['panels'][pan]['min_fs']:
geometry['panels'][pan]['max_fs'] + 1
] = x_panel
y_map[
geometry['panels'][pan]['min_ss']:
geometry['panels'][pan]['max_ss'] + 1,
geometry['panels'][pan]['min_fs']:
geometry['panels'][pan]['max_fs'] + 1
] = y_panel
r_map = numpy.sqrt(numpy.square(x_map) + numpy.square(y_map))
return PixelMaps(x_map, y_map, r_map)
def compute_min_array_size(pixel_maps):
"""
Compute the minimum array size storing data with applied geometry.
Return the minimum size of an array that can store data on which
the geometry information described by the pixel maps has been
applied.
The returned array shape is big enough to display all the input
pixel values in the reference system of the physical detector. The
array is also supposed to be centered at the center of the
reference system of the detector (i.e: the beam interaction point).
Args:
pixel_maps [PixelMaps]: a PixelMaps tuple.
Returns:
Tuple[int, int]: a numpy-style shape tuple storing the minimum
array size.
"""
# Find the largest absolute values of x and y in the maps. Since
# the returned array is centered on the origin, the minimum array
# size along a certain axis must be at least twice the maximum
# value for that axis. 2 pixels are added for good measure.
x_map, y_map = pixel_maps.x, pixel_maps.y
y_minimum = 2 * int(max(abs(y_map.max()), abs(y_map.min()))) + 2
x_minimum = 2 * int(max(abs(x_map.max()), abs(x_map.min()))) + 2
return (y_minimum, x_minimum)
def compute_visualization_pix_maps(geometry):
"""
Compute pixel maps for visualization of the data.
The pixel maps can be used for to display the data in a Pyqtgraph
ImageView widget.
Args:
geometry (dict): A CrystFEL geometry object (A dictionary
returned by the
:obj:`cfelpyutils.crystfel_utils.load_crystfel_geometry`
function).
Returns:
PixelMaps: A PixelMaps tuple containing the adjusted pixel
maps. The first two fields, named "x" and "y" respectively,
store the pixel maps for the x coordinate and the y
coordinates (as ndarrays of type int). The third field
("r") is just set to None.
"""
# Shift the origin of the reference system from the beam position
# to the top-left of the image that will be displayed. Compute the
# size of the array needed to display the data, then use this
# information to estimate the magnitude of the shift.
pixel_maps = compute_pix_maps(geometry)
min_shape = compute_min_array_size(pixel_maps)
new_x_map = numpy.array(
object=pixel_maps.x,
dtype=numpy.int
) + min_shape[1] // 2 - 1
new_y_map = numpy.array(
object=pixel_maps.y,
dtype=numpy.int
) + min_shape[0] // 2 - 1
return PixelMaps(new_x_map, new_y_map, None)