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Objective
LCLS Data Management 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:
...
where item
stands for file, group of dataset.
Check if the HDF5 item is "File", "Group", or "Dataset"
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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
or convert the group in dictionary and iterate over their key and values,Code Block list_of_item_names = group.items() print list_of_item_names
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
...
- from the
time
data recordCode 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
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#!/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
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Similar code plots the dataset as an image using the matplotlib library
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, andcspad-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:
Code Block |
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import numpy as np
ped_fname = |
Code Block |
#!/usr/bin/env python import h5py import numpy as np import matplotlib.pyplot as plt def plotImage(arr) : fig = plt.figure(figsize=(8,8), 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) plt.show() eventNumber = 5 file = h5py.File('/reg/d/psdm/XPP<INS>/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) file.close() |
Example 2: Extract and print the time variables
<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)
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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:
Code Block |
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if ds1ev.shape == ped_arr.shape : ds1ev -= ped_arr
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Note |
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This operation will only be valid if the CSPad data array is completely filled (all sensors are available) and its shape is equal to (32, 185, 388). Otherwise, the pedestal subtraction can be done in a loop over available sensors, taking into account the CSPad configuration. |
Code examples
Example 1: Basic operations
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#!/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
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Similar code plots the dataset as image or histogram using the matplotlib library
Code Block |
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#!/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()
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Example 2: Extract and print the time variables
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#!/usr/bin/env python
import h5py
import time |
Code Block |
#!/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) 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 = dataset[ind] t_sec = time[0] t_nsec = time[1] print_time(t_sec, t_nsec) f.close() # 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----- |
Example 3: Print entire file/group structure using recursive method
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)
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()
#----------------------------------------------------
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Example 3: Print entire file/group structure using recursive method
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#!/usr/bin/env python
import h5py
import sys
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/Run:0000"]
print_hdf5_item_structure(item)
file.close()
def print_hdf5_item_structure(g, offset=' ') :
"""Prints the input file/group/dataset (g) name and begin iterations on its content"""
if isinstance(g,h5py.File) :
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Code Block |
#!/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_item_structure(g,offset=' '): """Prints the input file/group/dataset (g) name and begin iterations on its content""" print "Structure of the", if isinstance(g,h5py.File): print "'File'", elif isinstance(g,h5py.Group): print "'Group' from file", elif isinstance(g,h5py.Dataset): print "'Dataset' from file", print g.file,"\n",g.name if isinstance(g,h5py.Dataset): print offset, "(Dateset) len =", g.shape #, subg.dtype else: print g.file, '(File)', g.name elif print_group_contentisinstance(g,offseth5py.Dataset) : def print_group_content(g,offset=' print '(Dataset): """Prints content of the file/group/dataset iteratively, starting from the sub-groups of g"""', g.name, ' len =', g.shape #, g.dtype elif isinstance(g,h5py.Group) : for key,val in dict(g).iteritems(): print '(Group)', g.name else : subg = val print 'WORNING: UNKNOWN ITEM IN printHDF5 offsetFILE', key, #,"g.name ", subg.name #, val, subgsys.len(), type(subg),exit ( "EXECUTION IS TERMINATED" ) if isinstance(subgif isinstance(g, h5py.File) or isinstance(g, h5py.DatasetGroup) : for key,val in print " (Dateset)dict(g).iteritems() : len =", subg.shape #, subg.dtype subg = val elif isinstance(subg, h5py.Group): print offset, key, #," print " (Group) len =",len, subg.name #, val, subg.len(), type(subg), print_hdf5_groupitem_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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#!/usr/bin/env python import os import sys import h5py import numpy as np class TwoDatasetSynchronization ( object ) : """Matching elements of two datasets using their time stamps""" def __init__ ( self, file, Xdsname, Ydsname ) : """Initialization""" self.dsX = file[Xdsname] self.dsY = file[Ydsname] XTimedsname = get_item_path_to_last_name(Xdsname) + '/time' YTimedsname = get_item_path_to_last_name(Ydsname) + '/time' self.dsXT = file[XTimedsname] self.dsYT = file[YTimedsname] self.XTarr = 0.000000001 * self.dsXT['nanoseconds'] + self.dsXT['seconds'] self.YTarr = 0.000000001 * self.dsYT['nanoseconds'] + self.dsYT['seconds'] self._nXpoints = self.dsX.shape[0] self._nYpoints = self.dsY.shape[0] self._indX = 0 self._indY = 0 self._tmapXlist = [] self._tmapYlist = [] print 'Xdsname =',Xdsname print 'Ydsname =',Ydsname print 'XTimedsname =',XTimedsname print 'YTimedsname =',YTimedsname print 'Initialization: datasets X and Y have length =', self._nXpoints, self._nYpoints def twoDatasetSynchronizationIterations( self ) : """Iteration over time indexes and appending of syncronized arrays.""" while self._indX < self._nXpoints and self._indY < self._nYpoints : if self.XTarr[self._indX] == self.YTarr[self._indY] : # Time is the same self._tmapXlist.append(self.dsX[self._indX]) self._tmapYlist.append(self.dsY[self._indY]) self._indX += 1 self._indY += 1 elif self.XTarr[self._indX] > self.YTarr[self._indY] : # Time X > Time Y self._indY += 1 self.printMissingSynchronization() else : # Time X < Time Y self._indX += 1 self.printMissingSynchronization() def printMissingSynchronization( self ) : print 'Missing of syncronization for X,Y indexes ',self._indX,self._indY def runSynchronization( self ) : """Executes synchronization and makes the references for synchronized arrays.""" self.twoDatasetSynchronizationIterations() self.Xarr = np.array(self._tmapXlist) self.Yarr = np.array(self._tmapYlist) print 'Number of synchronized in time X and Y array elements =', self.Xarr.shape, self.Yarr.shape def get_item_path_to_last_name(dsname): """Returns the path to the last part of the item name""" path,name = os.path.split(str(dsname)) return path def main() : """EXAMPLE: Time synchronization of two datasets. In this example we open the file, which contains normalcorrect dataset ("Y)" and the dataset with lost records ("X)". We access these arrays and associated time arrays through the class TwoDatasetSynchronization. Then we iterate over indexes of these arrays and appendsappend the lists of syncronized arrays. Program prints the message in case of missing synchronization. """ file = h5py.File('/reg/d/psdm/CXI/cxi80410/hdf5/cxi80410-r0730.h5', 'r') Xdsname = '/Configure:0000/Run:0000/CalibCycle:0000/Bld::BldDataFEEGasDetEnergy/NoDetector.0:NoDevice.2/data' Ydsname = '/Configure:0000/Run:0000/CalibCycle:0000/Ipimb::DataV1/CxiDg1.0:Ipimb.0/data' synchro = TwoDatasetSynchronization (file, Xdsname, Ydsname) synchro.runSynchronization() #-------------------------------- if __name__ == "__main__" : main() print('Exit') sys.exit ( "That's it!" ) #-------------------------------- |