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Table of Contents |
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Introduction
In this note we describe the python
-based PyCSPadImage package which is intended to evaluate and provide access to the pixel coordinate arrays. In addition, it converts the CSPAD raw data from HDF5 file to the geometry-corrected 2-D image array. This package can be used in a stand-alone python
code.
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Status | Module | For | Description |
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| 2x1 | defines the 2x1 section pixel coordinates | |
| CSPAD2x2 | defines default CSPAD2x2 calibration parameters | |
| CSPAD2x2 | provides access to the CSPAD2x2 calibration parameters | |
| CSPAD2x2 | evaluates the CSPAD2x2 pixel coordinates | |
| CSPAD2x2 | CSPAD2x2 examples | |
| CSPAD | defines CSPAD configuration parameters | |
| CSPAD | defines default CSPAD calibration parameters | |
| CSPAD | evaluates the center_global from other quad parametes | |
| CSPAD | provides access to the CSPAD calibration parameters | |
| CSPAD | evaluates the CSPAD pixel coordinates | |
| CSPAD | CSPAD examples | |
| CSPAD | Depricated | |
| Utils | Graphical utils | |
| Utils | Global methods | |
| Utils | Common methods for operations with HDF5 files | |
| Internal | It is used for alignment purpose |
How to get this package
Below we assume that all standard environment variable settings are done (otherwise see Analysis Workbook. Account Setup). In order to copy the PyCSPadImage package from SVN
repository and run a simple test one has to use commands:
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log in to psana<XXXX>
kinit
cd <your-favorite-directory>
newrel ana-current <your-release-directory-name>
cd <your-release-directory-name>
sit_setup
addpkg PyCSPadImage HEAD
python PyCSPadImage/src/Examples.py 1
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Import modules
External parameters
CSPAD geometry is varying for different detectors, experiments, or even runs. In order to keep track on all these variations LCLS offline has a simple calibration data base, which works as explained in CSPad Alignment. In order to get correct CSPAD alignment parameters the pass to the calibration directory should be specified like this:
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path_calib = '/reg/d/psdm/CXI/cxi80410/calib/CsPad::CalibV1/CxiDs1.0:Cspad.0'
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If the detector configuration was changed during the experiment, then more than one calibration file should be available for the run ranges with stable configuration.
In order to access correct calibration file the run number should be provided, for example
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runnum = 628
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Data for CSPAD image and the detector configuration can be obtained from the HDF5 file, dataset name, and event number for example
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fname = '/reg/d/psdm/CXI/cxi80410/hdf5/cxi80410-r0628.h5'
dsname = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/CxiDs1.0:Cspad.0/data'
event = 34
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Note |
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In further description we assume that this set of external parameters is defined.
Import modules
In code snippets below we use definitions of modules and libraries as follows
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import numpy as np
import CalibPars as calp
import CalibParsEvaluated as cpe
import CSPadConfigPars as ccp
import CSPadImageProducer as cip
import GlobalGraphics as gg
import HDF5Methods as hm
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Reconstruction of CSPAD image
Entire code example for image reconstruction is
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calp.calibpars.setCalibParsForPath ( run=runnum, path=path_calib )
ds1ev = hm.getOneCSPadEventForTest( fname, dsname, event )
cspadimg = cip.CSPadImageProducer(rotation=0, tiltIsOn=True, mirror=True)
arr = cspadimg.getCSPadImage( ds1ev )
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First, one has to set the correct version of the calibration parameters
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calp.calibpars.setCalibParsForPath ( run=runnum, path=path_calib )
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Then, one need in CSPAD dataset for event,
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ds1ev = hm.getOneCSPadEventForTest( fname, dsname, event )
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this method returns the CSPAD data as a numpy array for one event, ds1ev.shape=(Nseg, 185, 388), where Nseg?32.
Note |
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It is recommended to use this method, which also loads correct configuration parameters from HDF5 file. |
Then, one has to initialize the object of the class CSPadImageProducer
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cspadimg = cip.CSPadImageProducer(rotation=0, tiltIsOn=True, mirror=True)
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with optional parameters
rotation
integer from 0 to 3 parameters for CSPAD orientation as 90*rotation
degree.tiltIsOn
=True
orFalse
- to account or not the tiny tilt angle of 2x1 sections.mirror
=True
orFalse
- to mirror reflect or not the image.
Finally the method
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arr = cspadimg.getCSPadImage( ds1ev )
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returns the 2-d numpy array with CSPAD image, which can be plotted using for example matplotlib
.
CSPAD pixel coordinate arrays
CSPAD pixel coordinate arrays can be evaluated/returned in two different shapes:
- for entire CSPAD with shape=(4,8,185,388)
- for data-driven shape=(Nseg,185,388), where Nseg?32 if some quads/segments are missing in data.
To get pixel coordinate arrays shaped for entire CSPAD use code:
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calp.calibpars.setCalibParsForPath ( run=runnum, path=path_calib )
cpe.cpeval.evaluateCSPadPixCoordinates (rotation=0, mirror=False)
xpix, ypix = cpe.cpeval.getCSPadPixCoordinates_pix()
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where the parameters rotation
and mirror
have the same meaning as before,
xpix and ypix are the coordinate (in pixels) arrays with shape = (4,8,185,388).
Method xpix_um, ypix_um = cpe.cpeval.getCSPadPixCoordinates_um()
returns pixel coordinates in micrometer.
To get CSPAD pixel coordinate arrays shaped as in data use code:
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calp.calibpars.setCalibParsForPath ( run=runnum, path=path_calib )
cpe.cpeval.evaluateCSPadPixCoordinatesShapedAsData(fname,dsname,rotation=0,mirror=False)
xpix, ypix = cpe.cpeval.getCSPadPixCoordinatesShapedAsData_pix()
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where xpix and ypix are the coordinate (in pixels) arrays with shape = (Nseg,185,388).
Note that the fname
and dsname
need to be specified in order to get configuration of the data array.
Method xpix_um, ypix_um = cpe.cpeval.getCSPadPixCoordinatesShapedAsData_um()
returns pixel coordinates in micrometer.
The coordinate arrays extracted for both shapes are tested in module Examples.py
by the methods
example_of_image_built_from_pix_coordinate_array_shaped_as_data()
and
example_of_image_built_from_pix_coordinate_array_for_entire_cspad()
,
where images are reconstructed through the pixel coordinate arrays in
cpe.cpeval.getTestImageShapedAsData(ds1ev)
and
cpe.cpeval.getTestImageForEntireArray(ds1ev)
, respectively.
Note |
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The last two methods use implicit loops over all pixels, that works pretty slow in Python. These modules are used for test only and are not recommended for real applications. |
Examples
Module Examples.py
contains a few examples of how to use the PyCSPadImage package.
Get and plot CSPAD image and spectrum
There are two equivalent examples defined by the methods main_example_xpp()
and main_example_cxi()
for XPP and CXI experimental data, respectively.
The essential part of these examples can be presented as:
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import sys
import os
import CalibPars as calp
import CSPadConfigPars as ccp
import CSPadImageProducer as cip
import GlobalGraphics as gg # For test purpose in main only
import HDF5Methods as hm # For test purpose in main only
#----------------------------------------------
def main_example_xpp() :
print 'Start test in main_example_xpp()'
path_calib = '/reg/d/psdm/xpp/xpp47712/calib/CsPad::CalibV1/XppGon.0:Cspad.0'
fname, runnum = '/reg/d/psdm/xpp/xpp47712/hdf5/xpp47712-r0043.h5', 43
dsname = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/XppGon.0:Cspad.0/data'
event = 0
print 'Load calibration parameters from', path_calib
calp.calibpars.setCalibParsForPath ( run=runnum, path=path_calib )
print 'Get raw CSPad event %d from file %s \ndataset %s' % (event, fname, dsname)
ds1ev = hm.getOneCSPadEventForTest( fname, dsname, event )
print 'ds1ev.shape = ',ds1ev.shape
print 'Make the CSPad image from raw array'
cspadimg = cip.CSPadImageProducer(rotation=0, tiltIsOn=True, mirror=False)
arr = cspadimg.getCSPadImage( ds1ev )
print 'Plot CSPad image'
gg.plotImage(arr,range=(0,2000),figsize=(11.6,10))
gg.move(200,100)
gg.plotSpectrum(arr,range=(0,2000))
gg.move(50,50)
print 'To EXIT the test click on "x" in the top-right corner of each plot window.'
gg.show()
#----------------------------------------------
if __name__ == "__main__" :
main_example_xpp()
sys.exit ( 'End of test.' )
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This is working example, which can be copied, pasted in <file-name>.py file and executed.
Note |
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Appropriate permission is required to access particular experimental data. |
In addition to the description above, the statements
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gg.plotImage(arr,range=(0,2000),figsize=(11.6,10))
gg.move(200,100)
gg.plotSpectrum(arr,range=(0,2000))
gg.move(50,50)
gg.show()
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allow to plot the CSPAD 2-d array as image and spectrum, move graphical windows to specified position and show all graphics.
Get CSPAD pixel coordinate arrays
Here we reproduce the test method
example_of_image_built_from_pix_coordinate_array_shaped_as_data()
from the module Examples.py
.
This example shows how to get the CSPAD pixel coordinate arrays. In addition, it uses the getTestImageShapedAsData(ds1ev)
(slow) method to produce image from the pixel coordinate arrays and data and plots this image.
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import sys
import CalibPars as calp
import CalibParsEvaluated as cpe
import GlobalGraphics as gg # For test purpose in main only
import HDF5Methods as hm # For test purpose in main only
#----------------------------------------------
def example_of_image_built_from_pix_coordinate_array_shaped_as_data() :
"""Some CSPAD segments may be missing in the dataset
"""
fname, runnum = '/reg/d/psdm/CXI/cxi80410/hdf5/cxi80410-r0628.h5', 628
dsname = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/CxiDs1.0:Cspad.0/data'
path_calib = '/reg/d/psdm/CXI/cxi80410/calib/CsPad::CalibV1/CxiDs1.0:Cspad.0'
Range = (1000,3500)
calp.calibpars.setCalibParsForPath (run=runnum, path=path_calib)
#cpe.cpeval.printCalibParsEvaluated('center_global')
cpe.cpeval.evaluateCSPadPixCoordinatesShapedAsData(fname,dsname,rotation=0)
# At this point pixel coordinates are available and can be extracted:
xpix, ypix = cpe.cpeval.getCSPadPixCoordinatesShapedAsData_pix()
print 'xpix =\n', xpix
# Test image from pixel coordinate and data arrays can be produced and plotted:
ds1ev = hm.getOneCSPadEventForTest( fname, dsname, event=0 ) # returns array with shape=(29, 185, 388)
arr = cpe.cpeval.getTestImageShapedAsData(ds1ev)
gg.plotImage(arr,range=Range,figsize=(11.6,10))
gg.move(200,100)
gg.show()
#----------------------------------------------
if __name__ == "__main__" :
example_of_image_built_from_pix_coordinate_array_shaped_as_data()
sys.exit ( 'End of test.' )
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Get CSPAD pixel coordinate arrays without data base.
Use class CSPadPixCoordsWODB.py
Essentialy user has to provide 4 arrays for 2x1 alignment. For example see
def main_test_cspad()
:
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xc_um = 109.92 * np.array(
[[ 473.38, 685.26, 155.01, 154.08, 266.81, 53.95, 583.04, 582.15],
[ 989.30, 987.12, 1096.93, 884.11, 1413.16, 1414.94, 1500.83, 1288.02],
[1142.59, 930.23, 1459.44, 1460.67, 1347.57, 1559.93, 1032.27, 1033.44],
[ 626.78, 627.42, 516.03, 729.15, 198.28, 198.01, 115.31, 327.66]])
yc_um = 109.92 * np.array(
[[1028.07, 1026.28, 1139.46, 926.91, 1456.78, 1457.35, 1539.71, 1327.89],
[1180.51, 967.36, 1497.74, 1498.54, 1385.08, 1598.19, 1069.65, 1069.93],
[ 664.89, 666.83, 553.60, 765.91, 237.53, 236.06, 152.17, 365.47],
[ 510.38, 722.95, 193.33, 193.41, 308.04, 95.25, 625.28, 624.14]])
orient_deg = np.array(
[[ 90., 90., 0., 0., 270., 270., 0., 0.],
[ 0., 0., 270., 270., 180., 180., 270., 270.],
[ 90., 90., 0., 0., 270., 270., 0., 0.],
[ 0., 0., 270., 270., 180., 180., 270., 270.]])
tilt_deg = np.array(
[[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.]])
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Note |
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ALL arrays are shown here FOR TEST PURPOSE ONLY !!! and they are not consistent with any real detector. The |
These arrays of alignment parameters are used for initialization of the CSPadPixCoordsWODB
class object, which can be used to get X,Y-coordinate arrays as shown below:
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import sys
import os
import numpy as np
import PyCSPadImage.CalibPars as calp
import PyCSPadImage.CSPadConfigPars as ccp
import PyCSPadImage.CSPadImageProducer as cip
import PyCSPadImage.CSPADPixCoords as pixcoor
import PyCSPadImage.PixCoords2x1 as pixcoor2x1
import PyCSPadImage.GlobalGraphics as gg
import PyCSPadImage.GlobalMethods as gm
import PyCSPadImage.HDF5Methods as hm
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External parameters
HDF5 data file:
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fname = '/reg/d/psdm/xpp/xpptut13/hdf5/xpptut13-r0150.h5' # to test XPP data
fname = '/reg/d/psdm/cxi/cxitut13/hdf5/cxitut13-r1150.h5' # to test CXI data
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Path to the directory with calibration types:
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path_calib = '/reg/d/psdm/CXI/cxi80410/calib/CsPad::CalibV1/CxiDs1.0:Cspad.0/'
path_calib = '/reg/d/psdm/xpp/xpptut13/calib/CsPad::CalibV1/XppGon.0:Cspad.0/'
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HDF5 dataset names for known CSPADs:
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dsname = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/CxiDs1.0:Cspad.0/data'
dsname = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/CxiDsd.0:Cspad.0/data'
dsname = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/XppGon.0:Cspad.0/data'
dsname = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/XcsEndstation.0:Cspad.0/data'
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Interface to CalibPars.py
This class provides access to the CSPAD calibration parameters.
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Regular instantiation:
ALL parameters are OPTIONAL NAMED parameters;
path = '/reg/d/psdm/xpp/xpptut13/calib/CsPad::CalibV1/XppGon.0:Cspad.0/'
run = 123
calib = CalibPars(path, list_of_clib_types=['center', 'tilt', 'pedestals'])
arr_pedestals = calib.getCalibPars('pedestals', run)
Other option for instantiation:
calib = CalibPars()
run = 123 - is an optional, named
calibdir = '/reg/d/psdm/CXI/cxi35711/calib'
group = 'CsPad::CalibV1'
source = 'CxiDs1.0:Cspad.0'
calib.setCalibPars (run, calibdir, group, source)
Get array of calibration parameters for specified type and run number:
type = 'center'
arr = calib.getCalibPars (type[,run])
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Interface to CSPadConfigPars.py
This class provides access to the CSPAD configuration parameters.
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1. Sets a bunch of default configuration parameters,
loads current coniguration parameters from hdf5 file or from external parameters.
2. Provides access to current coniguration parameters
3. Contains conversion methods for arrays between raw data and entire cspad.
Interface
=========
1. Instatiation
1.1 Default constructor sets default parameters for indPairsInQuads & quadNumsInEvent:
config = CSPadConfigPars()
1.2 Initialization of configuration parameters using hdf5 file, for example:
fname = '/reg/d/psdm/xpp/xpp66213/hdf5/xpp66213-r0150.h5'
dsname = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/XppGon.0:Cspad.0/data'
config.setCSPadConfiguration( fname, dsname, event=0 ):
1.3 Initialization of configuration parameters using external arrays, for example:
indPairs = np.arange(32)
indPairs.shape = (4,8)
quadNums = np.arange(4)
config.setCSPadConfigArrays( indPairsInQuads=indPairs, quadNumsInEvent=quadNums )
2. Access methods:
2.1 Access to indPairsInQuads and quadNumsInEvent:
quadNums = config.getQuadNumsInEvent()
indPairs = config.getIndPairsInQuads()
config.printCSPadConfigPars()
2.2 Access to static class parameters:
import CSPadConfigPars as ccp
my_wid2x1 = ccp.CSPadConfigPars().wid2x1
etc...
3. Conversions between entire (4,8,185,388) and shaped as data (N<32,185,388) cspad pixel array shapes:
3.1 Conversion of the entire cspad pixel array arr_entire_cspad with shape (4,8,185,388)
in to the arr_raw_data, shaped as data (N<32,185,388):
arr_raw_data = config.getCSPadPixArrayShapedAsData(arr_entire_cspad)
3.2 Conversion of the cspad pixel array arr_raw_data shaped as data (N<32,185,388)
in to the entire cspad pixel array arr_entire_cspad with shape (4,8,185,388):
arr_entire_cspad = getCSPadPixArrayFromArrayShapedAsData(arr_raw_data)
4. Tests
To test CSPadConfigPars from release directory use command:
python PyCSPadImage/src/CSPadConfigPars.py <test-number>
where <test-number> stands for 0, 1, 2, or 3
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Interface to CSPADPixCoords.py
Class for generation of CSPad pixel coordinate array with and without data base
Code Block |
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Interface
=========
1.0 Instantiation with default parameters taken from optical measurement for XPP on 2013-01-29:
coord = CSPADPixCoords()
1.1 Instantiation with external geometry parameters:
All parameters optional. Default values will be used if parameters are not specified.
xc - np.array(...), shape=(3, 4, 8)) [um]
yc - np.array(...), shape=(3, 4, 8)) [um]
tilt - np.array(...), shape=(4, 8)) [deg]
coord = CSPADPixCoords(xc_um=xc, yc_um=yc, tilt_deg=tilt)
1.2 Instantiation with regular calibration parameters:
path = '/reg/neh/home1/dubrovin/LCLS/CSPadAlignment-v01/calib-xpp-2013-01-29'
run = 123
calib = CalibPars(path, run)
coord = CSPADPixCoords(calib)
1.2 Access methods:
Get arrays of pixel coordinates in mu with shape: [2,185,388]
X, Y = coord |
Code Block |
w = CSPadPixCoordsWODB(xc_um, yc_um, orient_deg, tilt_deg) X,Y = w.get_cspad_pix_coordinate_arrays_pixum () |
where X.shape = Y.shape = (4, 8, 185, 388) and obviously can be re-shaped to (32, 185, 388). Then, it is easy to produce image of coordinate arrays using numpy
2D histogramming method:
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xsize = X.max() + 1
ysize = Y.max() + 1
Img, Xedges, Yedges = np.histogram2d(X.flatten(), Y.flatten(), bins=[xsize,ysize], range=[[0,xsize],[0,ysize]], normed=False, weights=None)
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config=None)
or in integer pixels:
iX,iY = coord.get_cspad_pix_coordinate_arrays_pix (config=None)
1.3 Get image
img = coord.get_cspad_image(data,config)
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Examples
Module Examples.py
contains a few examples of how to use the PyCSPadImage packageIn order to get an image of intensity the weights array needs to be provided.
References
- CSPad image producer in Python (DEPRICATED) - earlier old version descriptionof this page.
- CSPad CSPAD AlignmentPsana CSPad Geometry
- CSPAD Geometry Software (depricated)
- Package CSPadPixCoords for Psana