diff --git a/cfel_cxi.py b/cfel_cxi.py
index 33f27720e3beeecb0447b91525d53101c0ca3a08..f3174b2d39c6ac15845cdd992e9829c4c0b3e3b3 100644
--- a/cfel_cxi.py
+++ b/cfel_cxi.py
@@ -273,8 +273,7 @@ class CXIWriter:
if self._initialized is True:
raise RuntimeError('Adding stacks to the writer is not possible after initialization.')
- for entry in self._cxi_stacks:
- if path == entry.path:
+ if name in self._cxi_stacks:
if overwrite is True:
del (self._cxi_stacks[name])
else:
diff --git a/cfel_geom.py b/cfel_geom.py
index 41ef64482e2ee9c9d3827134cabd40876ea3f203..4048220a9174bf42bd5aa859649a8578277f50d0 100644
--- a/cfel_geom.py
+++ b/cfel_geom.py
@@ -24,9 +24,23 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
+from collections import namedtuple
import numpy
+
from cfelpyutils.cfel_crystfel import load_crystfel_geometry
+PixelMaps = namedtuple('PixelMaps', ['x', 'y', 'r'])
+ImageShape = namedtuple('ImageShape', ['ss', 'fs'])
+
+
+def _find_minimum_image_shape(x, y):
+
+ # find the smallest size of cspad_geom that contains all
+ # xy values but is symmetric about the origin
+ n = 2 * int(max(abs(y.max()), abs(y.min()))) + 2
+ m = 2 * int(max(abs(x.max()), abs(x.min()))) + 2
+
+ return n, m
def apply_geometry_from_file(data_as_slab, geometry_filename):
"""Parses a geometry file and applies the geometry to data.
@@ -47,14 +61,14 @@ def apply_geometry_from_file(data_as_slab, geometry_filename):
detector, with the origin of the reference system at the beam interaction point.
"""
- yx, slab_shape, img_shape = pixel_maps_for_image_view(geometry_filename)
+ x, y, slab_shape, img_shape = pixel_maps_for_image_view(geometry_filename)
im_out = numpy.zeros(img_shape, dtype=data_as_slab.dtype)
- im_out[yx[0], yx[1]] = data_as_slab.ravel()
+ im_out[y, x] = data_as_slab.ravel()
return im_out
-def apply_geometry_from_pixel_maps(data_as_slab, yx, im_out=None):
+def apply_geometry_from_pixel_maps(data_as_slab, y, x, im_out=None):
"""Applies geometry in pixel map format to data.
Applies geometry, in the form of pixel maps, to detector data in 'slab' format. Turns a 2d array of pixel values
@@ -65,7 +79,9 @@ def apply_geometry_from_pixel_maps(data_as_slab, yx, im_out=None):
data_as_slab (numpy.ndarray): the pixel values to which geometry is to be applied.
- yx (tuple): the yx pixel maps describing the geometry of the detector; each map is a numpy.ndarray.
+ y (numpy.ndarray): the y pixel map describing the geometry of the detector
+
+ x (numpy.ndarray): the x pixel map describing the geometry of the detector
im_out (Optional[numpy.ndarray]): array to hold the output; if not provided, one will be generated
automatically.
@@ -79,17 +95,18 @@ def apply_geometry_from_pixel_maps(data_as_slab, yx, im_out=None):
if im_out is None:
im_out = numpy.zeros(data_as_slab.shape, dtype=data_as_slab.dtype)
- im_out[yx[0], yx[1]] = data_as_slab.ravel()
+ im_out[y, x] = data_as_slab.ravel()
return im_out
def pixel_maps_for_image_view(geometry_filename):
"""Parses a geometry file and creates pixel maps for pyqtgraph visualization.
- Parse the geometry file and creates pixel maps for an array in 'slab' format containing pixel values. The pixel
+ Parses the geometry file and creates pixel maps for an array in 'slab' format containing pixel values. The pixel
maps can be used to create a representation of the physical layout of the detector in a pyqtgraph ImageView
widget (i.e. they apply the detector geometry setting the origin of the reference system is in the top left corner
- of the output array).
+ of the output array). The representation is centered at the point where the beam hits the detector according to
+ the geometry in the file.
Args:
@@ -97,31 +114,52 @@ def pixel_maps_for_image_view(geometry_filename):
Returns:
- (y, x) (numpy.ndarray int, numpy.ndarray int): pixel maps
+ x (numpy.ndarray int): pixel map for x coordinate
+
+ y (numpy.ndarray int): pixel map for x coordinate
+ """
+
+ pixm = pixel_maps_from_geometry_file(geometry_filename)
+ x, y = pixm.x, pixm.y
+
+ n, m = _find_minimum_image_shape(x, y)
+
+ # convert y x values to i j values
+ i = numpy.array(y, dtype=numpy.int) + n // 2 - 1
+ j = numpy.array(x, dtype=numpy.int) + m // 2 - 1
- slab_shape tuple (int, int): shape of the original geometry uncorrected array (the pixel values in "slab"
- format).
+ y = i.flatten()
+ x = j.flatten()
+
+ return PixelMaps(x, y, None)
+
+
+def get_image_shape(geometry_filename):
+ """Parses a geometry file and returns the minimum size of an image that can represent the detector.
+
+ Parses the geometry file and return a numpy shape object representing the minimum size of an image that
+ can contain the physical representation of the detector. The representation is centered at the point where the beam
+ hits the detector according to the geometry in the file.
+
+ Args:
+
+ geometry_filename (str): geometry filename.
+
+ Returns:
img_shape tuple (int, int): shape of the array needed to contain the representation of the physical layout
of the detector.
"""
pixm = pixel_maps_from_geometry_file(geometry_filename)
- x, y = pixm[0], pixm[1]
- slab_shape = x.shape
+ x, y = pixm.x, pixm.y
- # find the smallest size of cspad_geom that contains all
- # xy values but is symmetric about the origin
- n = 2 * int(max(abs(y.max()), abs(y.min()))) + 2
- m = 2 * int(max(abs(x.max()), abs(x.min()))) + 2
+ n, m = _find_minimum_image_shape(x, y)
+
+ img_shape = ImageShape(n, m)
+ return img_shape
- # convert y x values to i j values
- i = numpy.array(y, dtype=numpy.int) + n//2 - 1
- j = numpy.array(x, dtype=numpy.int) + m//2 - 1
- yx = (i.flatten(), j.flatten())
- img_shape = (n, m)
- return yx, slab_shape, img_shape
def pixel_maps_from_geometry_file(fnam):
@@ -146,8 +184,8 @@ def pixel_maps_from_geometry_file(fnam):
max_slab_fs = numpy.array([detector['panels'][k]['max_fs'] for k in detector['panels']]).max()
max_slab_ss = numpy.array([detector['panels'][k]['max_ss'] for k in detector['panels']]).max()
- x = numpy.zeros((max_slab_ss+1, max_slab_fs+1), dtype=numpy.float32)
- y = numpy.zeros((max_slab_ss+1, max_slab_fs+1), dtype=numpy.float32)
+ x = numpy.zeros((max_slab_ss + 1, max_slab_fs + 1), dtype=numpy.float32)
+ y = numpy.zeros((max_slab_ss + 1, max_slab_fs + 1), dtype=numpy.float32)
for p in detector['panels']:
# get the pixel coords for this asic
@@ -163,12 +201,11 @@ def pixel_maps_from_geometry_file(fnam):
r = i * dy + j * dx + r_0
#
y[detector['panels'][p]['min_ss']: detector['panels'][p]['max_ss'] + 1,
- detector['panels'][p]['min_fs']: detector['panels'][p]['max_fs'] + 1] = r.real
+ detector['panels'][p]['min_fs']: detector['panels'][p]['max_fs'] + 1] = r.real
x[detector['panels'][p]['min_ss']: detector['panels'][p]['max_ss'] + 1,
- detector['panels'][p]['min_fs']: detector['panels'][p]['max_fs'] + 1] = r.imag
+ detector['panels'][p]['min_fs']: detector['panels'][p]['max_fs'] + 1] = r.imag
r = numpy.sqrt(numpy.square(x) + numpy.square(y))
- return x, y, r
-
+ return PixelMaps(x, y, r)
diff --git a/cfel_hdf5.py b/cfel_hdf5.py
index a789c314dc4746bbec49fbb0569ee8468520f655..e961a6657dd1fe2b4ae3eedf7f5c5fc63e890885 100644
--- a/cfel_hdf5.py
+++ b/cfel_hdf5.py
@@ -42,6 +42,7 @@ def load_nparray_from_hdf5_file(data_filename, data_group):
nparray (numpy.ndarray): numpy array with the data read from the file.
"""
try:
+
with h5py.File(data_filename, 'r') as hdfile:
nparray = numpy.array(hdfile[data_group])
hdfile.close()