Interfaces

Python interfaces to HDF5 include h5py and pytables (for which the module name is tables). While we actively use and support h5py, we will not discuss pytables as it is not a tool that we presently work with.

H5py

Many of our datasets are one dimensional arrays of a compound type. A compound type is a well defined object in the HDF5 library - it is very much like a C struct. In hdfview (a useful tool for viewing hdf5 files provided by the hdf5 group) they often look like two dimensional arrays, but the columns are really the field names of the compound type. When you load a dataset into h5py, it will return a  numpy array (with extra metadata attached) that is close to the original hdf5 layout.  It will be a 1D array with a numpy dtype that exactly represents the compound type in the hdf5 file.

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• The compound type
  • when the number of fields gets large, this does not print well when interactively exploring the data in h5py
  • Field names are far from the data
  • dtypes, especially with enums in them, can be very complicated
• enums - these are well defined objects in hdf5 - in the data they are stored as integers.  A dictionary that maps symbolic names to integers is stored in one spot in the dataset.
  • In h5py they are displayed as ints, extra steps are required to obtain the enum dictionary and translate ints to strings, or use strings to test the values of the enum.
• vlen data
  • h5py (as of version 2.2) presently only supports variable length strings
  • Our EvrData uses general vlen data to represent the variable number of event codes that occur with each event
  • We have patched h5py to be able to read  general vlen data, such as what is in our EvrData

The current version of h5py works fine with vlen data, but older versions did not support it.

vlen data

Here is an example of how one might work with vlen data. An example of vlen data is the EvrData. During each event, the EvrData includes fifoCodes - this is a variable length list. Each element in the list has three parts, timestampHigh, timestampLow and eventCode. A lot of users will need to examine the eventCode's. Starting with EvrData::DataV4, one can access event codes without working with vlen data - one uses the dataset 'present' that has been translated into the hdf5. However we want to demonstrate how to work with vlen data. So let's write an example that takes the EvrData and flattens it out into a table, where each entry in the table is 0 or 1 depending on whether or not that eventCode fired (this is exactly the content of the 'present' dataset in the hdf5).

import h5py
import numpy as np

f=h5py.File('/reg/d/psdm/xpp/xpptut13/hdf5/xpptut13-r0179.h5','r')
evrData=f['/Configure:0000/Run:0000/CalibCycle:0000/EvrData::DataV3/NoDetector.0:Evr.0/data']
numberOfEvents = len(evrData)    # this gives 483
largestEventCode = max([max([fifoEvent['eventCode'] for fifoEvent in fifoEvents]) for fifoEvents in  evrData['fifoEvents']])
# this gives 162, this is the largest eventCode that occurs in this calib cycle.
eventCodes = np.zeros((numberOfEvents, largestEventCode+1), np.int8)
for eventIndex,fifoEvents in enumerate(evrData['fifoEvents']):
    for fifoEvent in fifoEvents:
        eventCodes[eventIndex, fifoEvent['eventCode']]=1

At this point, eventCodes is a 483 x 162 table of 0/1 - the rows are the events, and the columns are the event codes. If we wanted to find what event codes were present and in what frequency, one could do

eventCodesInData = np.where(np.sum(eventCodes,0)!=0)[0]
numberOfTimesEachEventCodeFired = dict(zip(eventCodesInData, np.sum(eventCodes,0)[eventCodesInData]))
# this dict will be
# {41: 242, 
#  42: 121, 
#  67: 98, 
# 140: 483, 
# 162: 69}

One could then construct a logical index array to quickly average the cspad over the 121 events where event code 42 fired:

eventsWith42 = eventCodes[:,42]==1
cspad=f['/Configure:0000/Run:0000/CalibCycle:0000/CsPad::ElementV2/XppGon.0:Cspad.0/data']
assert len(eventsWith42)==len(cspad), "There are cases when datasets for different types " + \
        "are not aligned due to damage, it is best to do more than this and check that the times datasets are the same between cspad and the evrdata"
cspadAt42 = cspad[eventsWith42]
cspadAt42.shape    # this returns  (121, 32, 185, 388)
avgAt42 = np.average(cspadAt42,0)
avgAt42.shape      # this returns  (32, 185, 388)

 To work with the vlen field in the EvrData using the high level h5py functions, you will need to obtain the patched version of h5py from LCLS.  One can also use the low level interface to the HDF5 library that h5py provides (our patched version is not needed in this case).

Code to Print Datasets

Below is code that provides a function, printds to display h5py datasets. The output has the following features:

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