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Analysis & Applications group works on PSANA project - generic framework for analysis of any experimental data. Though this framework is going to be universal, most likely it will not be simple. In this page we discuss a simple but flexible approach to analysis of data stored in HDF5 files. It is based on Python code with extensive exploitation of standard libraries. A few code examples of how to access and process data are presented at the end of this page.

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• Flexibility; HDF5 file has indexed structure, that means direct access to any data of any file from your code.
• Python is a high-level scripting language allows to write transparent and compact code based on well-elaborated standard libraries.
• In general, code in Python works slow comparing to C++, but there are libraries like NumPy written on C++, which solve this problem for manipulation with large arrays.

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• you have to know or learn Python
• current version of the h5py library works quite slow with long HDF5 files

The first issue about Python is not really a drawback. Basic concept of this high-level language can be learned from scratches for about a couple of days. In a week you will feel yourself as an expert and will enjoy programming on this powerful language. Second issue about slow h5py library is really annoying, but we hope that authors will account for our comments and its performance can be improved soon.

Below we assume that everything is setup to work on LCLS analysis farm, otherwise see Computing (including Analysis) and Account Setup.

Libraries

Here is a list of Python libraries which we use in examples below:

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where item stands for file, group of dataset.

Check if the HDF5 item is "File", "Group", or "Dataset"

isFile    = isinstance(item, h5py.File)
isGroup   = isinstance(item, h5py.Group)
isDataset = isinstance(item, h5py.Dataset)

In this example the standard Python method isinstance(...) returns True or False in each case, respectively.

Get information about HDF5 item

• For all HDF5 items:
  these parameters are available:

  Code Block
  item.id # for example: <GroupID [1] (U) 33554473> item.ref # for example: <HDF5 object reference> item.parent # for example: <HDF5 group "/Configure:0000/Run:0000/CalibCycle:0000" (5 members)> item.file # for example: <HDF5 file "cxi80410-r0587.h5" (mode r, 3.5G)> item.name # for example: /Configure:0000/Run:0000/CalibCycle:0000/Camera::FrameV1

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• Get the list of daughters in the group

  Code Block
  list_of_item_names = group.items() print list_of_item_names

  or convert the group in dictionary and iterate over their key and values,

  Code Block
  for key,val in dict(group).iteritems(): print key, val

Extract time

Time variable is stored in HDF5 as a tuple of two long integer numbers representing the seconds since 01/01/1970 and nanoseconds as a fraction of the second. Time is usually stored in the group attributes and/or in the data record with name "time", which can be extracted as shown below

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• from the time data record

  Code Block
  time_dataset = file['/Configure:0000/Run:0000/CalibCycle:0002/Acqiris::DataDescV1/XppLas.0:Acqiris.0/time'] index = 0 # this is an index in the dataset time_arr = time_dataset[index] # get the time tuple consisting of seconds and nanoseconds time_sec = time_arr[0] time_nsec = time_arr[1]

Code examples

Example 1: Basic operations

#!/usr/bin/env python

import h5py
import numpy as np

eventNumber = 5

file    = h5py.File('/reg/d/psdm/XPP/xppcom10/hdf5/xppcom10-r0546.h5', 'r')
dataset = file['/Configure:0000/Run:0000/CalibCycle:0000/Camera::FrameV1/XppSb4Pim.1:Tm6740.1/image']
arr1ev  = dataset[eventNumber]
file.close()

print 'arr1ev.shape =', arr1ev.shape
print 'arr1ev =\n',     arr1ev

Operations with CSPad pedestals

Most generic way to subtract the CSPad pedestals is to use Translator, as described in CsPad calibration in translator. If calibration is requested in the Translator the output HDF5 file has the CSPad image data with already subtracted pedestals. Otherwise, Translator saves raw CSPad data in HDF5 file. If the job execution time is not an issue, the pedestals can be subtracted from raw data directly in code, as explained in this section.

How to find the files with CSPad pedestals

CSPad pedestals are usually calibrated using the "dark" runs. If they were calibrated, the files for appropriate run range, <run-range>.dat, can be found in the directory
/reg/d/psdm/<INSTRUMENT>/<experiment>/calib/<calib-version>/<source>/pedestals/
If the pedestal file was available at translation time, the dataset
/Configure:0000/CsPad::CalibV1/XppGon.0:Cspad.0/pedestals
is saved in the HDF5 file and can be accessed directly.
One may prefer to calibrate and keep pedestal files in the local directory, as explained below.

How to calibrate CSPad pedestals

If the CSPad pedestals were not calibrated, they can be calibrated, as explained in
the description of the CsPadPedestals psana - Original Documentation module. Essentially, one need to run the psana for cspad_mod.CsPadPedestals module, using command
psana -m cspad_mod.CsPadPedestals input-files.xtc
which by default produce two files:

• cspad-pedestals.dat – for average values, and
• cspad-noise.dat – for standard deviation values.
  These files can be loaded in code as explained below.

Get CSPad pedestal array

The file with pedestal values can be read in code as a numpy array:

import numpy as np
ped_fname = '/reg/d/psdm/<INS>/<experiment>/calib/<calib-version>/<source>/pedestals/<run-range>.dat'
ped_arr = np.loadtxt(ped_fname, dtype=np.float32)
ped_arr.shape = (32, 185, 388) # raw shape is (5920, 388)

In this example the pedestal file is loaded from the standard calib directory. For your own pedestal file the path name should be changed.

Subtract CSPad pedestals

Assuming that the CSPad event array ds1ev and the pedestal array ped_arr are available,
the pedestals can be subtracted by the single operation for numpy arrays:

if ds1ev.shape == ped_arr.shape : ds1ev -= ped_arr

Code examples

Example 1: Basic operations

#!/usr/bin/env python

import h5py
import numpy as np

eventNumber = 5

file    = h5py.File('/reg/d/psdm/XPP/xppcom10/hdf5/xppcom10-r0546.h5', 'r')
dataset = file['/Configure:0000/Run:0000/CalibCycle:0000/Camera::FrameV1/XppSb4Pim.1:Tm6740.1/image']
arr1ev  = dataset[eventNumber]
file.close()

print 'arr1ev.shape =', arr1ev.shape
print 'arr1ev =\n',     arr1ev

Similar code plots the dataset as image or histogram using the Similar code plots the dataset as image or histogram using the matplotlib library

#!/usr/bin/env python
import h5py
import numpy as np
import matplotlib.pyplot as plt

def plotImage(arr) :
    fig  = plt.figure(figsize=(5,5), dpi=80, facecolor='w',edgecolor='w',frameon=True)
    imAx = plt.imshow(arr, origin='lower', interpolation='nearest')
    fig.colorbar(imAx, pad=0.01, fraction=0.1, shrink=1.00, aspect=20)

def plotHistogram(arr) :
    fig  = plt.figure(figsize=(5,5), dpi=80, facecolor='w',edgecolor='w',frameon=True)
    plt.hist(arr.flatten(), bins=100)

eventNumber = 5
file    = h5py.File('/reg/d/psdm/XPP/xppcom10/hdf5/xppcom10-r0546.h5', 'r')
dataset = file['/Configure:0000/Run:0000/CalibCycle:0000/Camera::FrameV1/XppSb4Pim.1:Tm6740.1/image']
arr1ev  = dataset[eventNumber]

plotImage(arr1ev)
plotHistogram(arr1ev)
plt.show()
file.close()

...

#!/usr/bin/env python

import h5py
import time

#-----------------------------------------------------

def print_time(t_sec, t_nsec):
    """Converts seconds in human-readable time and prints formatted time"""

    tloc = time.localtime(t_sec) # converts sec to the tuple struct_time in local
    print 'Input time :',t_sec,'sec,',  t_nsec,'nsec, '
    print 'Local time :', time.strftime('%Y-%m-%d %H:%M:%S',tloc)

#-----------------------------------------------------
file_name = '/reg/d/psdm/xpp/xpp22510/hdf5/xpp22510-r0100.h5'
file = h5py.File(file_name, 'r') # open read-only

print "EXAMPLE: Get time from the group attributes:"

group = file["/Configure:0000"]
t_sec  = group.attrs.values()[0]
t_nsec = group.attrs.values()[1]
print_time(t_sec, t_nsec)------
file_name = '/reg/d/psdm/xpp/xpp22510/hdf5/xpp22510-r0100.h5'
file = h5py.File(file_name, 'r') # open read-only

print "EXAMPLE: Get time from the data record 'time'group attributes:"

datasetgroup = file['"/Configure:0000/Run:0000/CalibCycle:0002/Acqiris::DataDescV1/XppLas.0:Acqiris.0/time']
index = 0
time_arr = dataset[ind"]
t_sec  = time_arr group.attrs.values()[0]
t_nsec = time_arrgroup.attrs.values()[1]
print_time(t_sec, t_nsec)

file.close()
#----------------------------------------------------

Example 3: Print entire file/group structure using recursive method

_time(t_sec, t_nsec)

print "EXAMPLE: Get time from the data record 'time':"

dataset = file['/Configure:0000/Run:0000/CalibCycle:0002/Acqiris::DataDescV1/XppLas.0:Acqiris.0/time']
index = 0
time_arr = dataset[ind]
t_sec  = time_arr[0]
t_nsec = time_arr[1]
print_time(t_sec, t_nsec)

file.close()
#----------------------------------------------------

Example 3: Print entire file/group structure using recursive method

#!/usr/bin/env python
import h5py
import sys

#!/usr/bin/env python
import h5py

def print_hdf5_file_structure(file_name):
    """Prints the HDF5 file structure"""
    file = h5py.File(file_name, 'r') # open read-only
    item = file #["/Configure:0000/EvrData::ConfigV4"]
    print_hdf5_item_structure(item)
    file.close()
    print '=== EOF ==='

def print_hdf5_itemfile_structure(g,offset='    '):
    """Prints the input file/group/dataset (g) name and begin iterations on its contentfile_name) :
    """Prints the HDF5 file structure"""
    file  print "Structure of the",= h5py.File(file_name, 'r') # open read-only
    ifitem =  isinstance(g,h5py.File):file #["/Configure:0000/Run:0000"]
    print "'File'",_hdf5_item_structure(item)
    elif isinstance(g,h5py.Group):file.close()

def print_hdf5_item_structure(g, offset='   print "'Group') from file",
:
    """Prints elif isinstance(g,h5py.Dataset): print "'Dataset' from file",
    print g.file,"\n",g.namethe input file/group/dataset (g) name and begin iterations on its content"""
    if   isinstance(g,h5py.DatasetFile) :
  print offset, "(Dateset)   len =",print g.shape #, subg.dtype
    else:              file, '(File)', g.name

    elif isinstance(g,h5py.Dataset) :
        print_group_content(g,offset)

def print_group_content(g,offset='    '):
 '(Dataset)', g.name, '    """Prints content of the file/group/dataset iteratively, starting from the sub-groups of g"""len =', g.shape #, g.dtype

    elif isinstance(g,h5py.Group) :
    for   key,val inprint dict'(Group)', g).iteritems():.name

    else :
   subg = val
   print 'WORNING: UNKNOWN ITEM IN printHDF5 offsetFILE', key, #,"g.name
    ", subg.name #, val, subg.len(), type(subg),
    sys.exit ( "EXECUTION IS TERMINATED" )

    if isinstance(g, h5py.File) or isinstance(subgg, h5py.DatasetGroup) :
        for key,val in  print " (Dateset)   len =", subg.shape #, subg.dtype
dict(g).iteritems() :
           elif isinstance(subg, h5py.Group): = val
            print offset, key, #," (Group)   len =",len(subg)", subg.name #, val, subg.len(), type(subg),
            print_grouphdf5_item_contentstructure(subg, offset + '    ')

if __name__ == "__main__" :
    print_hdf5_file_structure('/reg/d/psdm/XPP/xppcom10/hdf5/xppcom10-r0546.h5')
    sys.exit ( "End of test" )

Example 4: Time-based syncronization of two datasets

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