hdf5/xpptut13-r0150.h5' # to test XPP data
   dsnamefname = '/reg/d/psdm/cxi/cxitut13/hdf5/cxitut13-r1150.h5' # to test CXI data

    runnum = 628
    event  = 34

import sys
import os
import CalibPars    run   = 123
           calib = CalibPars(path, list_of_clib_types=['center', 'tilt', 'pedestals'])
           arr_pedestals = calib.getCalibPars('pedestals', run)
 
       Other option for instantiation:
 as calp
import CSPadConfigPars    as ccp
import CSPadImageProducer as cip
import GlobalGraphics calib    = CalibPars()
           asrun gg # For test purpose in= main123 only
import HDF5Methods- is an optional, named
    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/XppGoncalibdir = '/reg/d/psdm/CXI/cxi35711/calib'
           group    = 'CsPad::CalibV1'
           source   = 'CxiDs1.0:Cspad.0'
    fname, runnum = '/reg/d/psdm/xpp/xpp47712/hdf5/xpp47712-r0043.h5', 43     calib.setCalibPars (run, calibdir, group, source)

    dsname        = '/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/XppGon.0:Cspad.0/data'
    event   Get array of calibration parameters for specified type and run number:
           type = 0'center'

     print 'Load calibration parameters from', path_calib
 arr  = calpcalib.calibpars.setCalibParsForPathgetCalibPars (type[,run])

Appropriate permission is required to access particular experimental data.

    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()

import sys
import CalibPars= 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   as calp
import CalibParsEvaluated as cpe
import GlobalGraphics= config.getQuadNumsInEvent()
         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'
    RangeindPairs = 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 = (1000,3500config.getCSPadPixArrayShapedAsData(arr_entire_cspad)
 
    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 plotted3.2 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:
    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.' )

ALL arrays are shown here FOR TEST PURPOSE ONLY !!! and they are not consistent with any real detector. The orient_deg is not consistent with real orientation as well...

    w = CSPadPixCoordsWODB(xc_um, yc_um, orient_deg, tilt_deg)
    X,Y = wAccess methods:
           Get arrays of pixel coordinates in mu with shape: [2,185,388]
            X, Y = coord.get_cspad_pix_coordinate_arrays_pixum  ()

    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)