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 LCSL data makes use of several HDF5 features that can be awkward to handle in h5py

• 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:

• The header lines give the field names, their types, and identify any vlen data
• The enum fields are translated to integers
• multiple lines are used to print vlen data.
• Optional arguments let you control which rows and columns are printed.
• An optional argument lets you control the formatting of different fields.you control the formatting of different fields.

If you find that the h5py interface (which is basically a numpy interface) for displaying datasets is producing output that is hard to manage, try printds.  At LCLS, this code is in the package h5tools that is part of analysis release 0.9.16 and later.  One does

from h5tools import printds

To use the function. One can also obtain the code by downloading

printds.py

is part of analysis release 0.10.6 and later.  One does

from h5tools import printds

To use the function. One can also obtain the code by downloading

printds.py

putting it in your python path, and importing the function.  Note, the file printds.py is a module that contains a function named printds. When downloading the code and adding it to your python path, one would do

from printds import prinds
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printds(dataset)

to use putting it in your python path, and after loading a dataset ds using h5py, passing it to the function. See the example below, as well as further examples in the code. Note, this function is only for printing datasets. It produces an error when passed any other h5py object (such as a group directory along the way to the dataset).

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Below are some tips for working with enums, vlen strings, and vlen data in h5py. We'll assume that the five datasets in a EvrData::ConfigV7 group have been loaded into the following h5py dataset objects:

From a EvrData::ConfigV7 group, the dataset: output_maps
From a EvrData::DataV3 group, the dataset: data
From a Ipimb::ConfigV2 group, the dataset config

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The EvrData::Data dataset has a very complicated dtype that is not easy to navigate;

In [293]: data.dtype
Out[293]: dtype([('fifoEvents', [(({'type': dtype([('timestampHigh', '<u4'), ('timestampLow', '<u4'), ('eventCode', '<u4')])}, 'vlen'), '|O8')])])

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From which one sees there are two fifoEvents in the first event, one in the second, two in the third, etc.  To work with the data in h5py, one would do

In [297]: data[0,'fifoEvents']
Out[297]:
array([(118401L, 12966L, 41L), (118401L, 11852L, 140L)],
      dtype=[('timestampHigh', '<u4'), ('timestampLow', '<u4'), ('eventCode', '<u4')])

In [298]: data[0,'fifoEvents']['eventCode']
Out[298]: array([ 41, 140], dtype=uint32)

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output_maps is 1d an array of a compound type with several rows. Each row has two different enums: source and conn. Suppose you want to test the source enum in row 0 against the symbolic strings. These are the steps:

  # you first obtain the Python dict that translates strings to ints with this
  # special h5py function for handling enums:

  output_maps_source_enum = h5py.check_dtype(enum=output_maps.dtype['source'])

  # next test as follows:

  rowToTest = 0
  if output_maps_source_enum['Pulse'] == output_maps['source'][rowToTest]:
    # deal with pulse case
  elif output_maps_source_enum['Prescaler'] == output_maps['source'][rowToTest]:
    # deal with Prescaler case
  elif output_maps_source_enum['Force_High'] == output_maps['source'][rowToTest]:
    # deal with Force_High case
  elif output_maps_source_enum['Force_High'] == output_maps['source'][rowToTest]:
    # deal with Force_High case
  elif output_maps_source_enum['Force_Low'] == output_maps['source'][rowToTest]:
    # deal with Force_Low case
  elif output_maps_source_enum['DBus'] == output_maps['source'][rowToTest]:
    # deal with DBus case

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If you want to print the source enum as a string, you need to do something like invert the enum dict:

  output_maps_source_int2str = {[(val,sym) for sym,val in output_maps_source_enum.iteritems()]}
  print "%s" % output_maps_source_int2str[output_maps['source'][rowToTest] ]

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The compound types we store can print as a great deal of data when being explored interactively, which is why we provide the function printds above. However here are a few techniques for working with the datasets directly.

• Use printto hide the dtype. Doing the following:

  Code Block

  In [5]: output_maps.value Out[5]: array([(0, 0, 1, 0, 0), (0, 1, 1, 3, 0), (0, 2, 1, 4, 0), (0, 3, 1, 6, 0), (0, 3, 1, 7, 0), (0, 3, 1, 8, 0), (0, 4, 1, 9, 0), (0, 5, 1, 5, 0), (0, 6, 1, 1, 0), (0, 6, 1, 2, 0), (0, 6, 1, 10, 0), (0, 6, 1, 11, 0)], dtype=[('source', [(({'vals': {'Force_Low': 4L, 'DBus': 1L, 'Force_High': 3L, 'Prescaler': 2L, 'Pulse': 0L}} 'enum'), '<i2')]), ('source_id', '<i2'), ('conn', [(({'vals': {'UnivIO': 1L, 'FrontPanel': 0L}}, 'enum'), '<i2')]), ('conn_id', '<i2'), ('module', '<i2')])

  Can be a lot to look at. If you use print though, you are printing the string representation as opposed to the object representation:

  Code Block
  In [8]: print output_maps.value Out[9]: [(0, 0, 1, 0, 0) (0, 1, 1, 3, 0) (0, 2, 1, 4, 0) (0, 3, 1, 6, 0) (0, 3, 1, 7, 0) (0, 3, 1, 8, 0) (0, 4, 1, 9, 0) (0, 5, 1, 5, 0) (0, 6, 1, 1, 0) (0, 6, 1, 2, 0) (0, 6, 1, 10, 0) (0, 6, 1, 11, 0)]

• Convert from a numpy array to a python list:

 In [9]: output_maps.value.tolist()
 Out[9]:
 [(0, 0, 1, 0, 0),
  (0, 1, 1, 3, 0),
  (0, 2, 1, 4, 0),
  (0, 3, 1, 6, 0),
  (0, 3, 1, 7, 0),
  (0, 3, 1, 8, 0),
  (0, 4, 1, 9, 0),
  (0, 5, 1, 5, 0),
  (0, 6, 1, 1, 0),
  (0, 6, 1, 2, 0),
  (0, 6, 1, 10, 0),
  (0, 6, 1, 11, 0)]

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• Display the field names without the type information by using the dtype.names attribute

 In [15]: output_maps.dtype.names
 Out[15]: ('source', 'source_id', 'conn', 'conn_id', 'module')

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The names in the dtype are very useful for slicing the array. For example, one can do

In [5]: output_maps['source_id']
Out[5]: array([0, 1, 2, 3, 3, 3, 4, 5, 6, 6, 6, 6], dtype=int16)

or

In [6]: output_maps['source']
Out[6]: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int16)

Above though, the enum is returned to us as an array of ints. With the output_maps_source_int2str dict defined above, we can create a list of strings to represent it as follows:

In [15]: [ output_maps_source_int2str[x] for x in output_maps['source']]
Out[15]:
['Pulse',
 'Pulse',
 'Pulse',
 'Pulse',
 'Pulse',
 'Pulse',
 'Pulse',
 'Pulse',
 'Pulse',
 'Pulse',
 'Pulse',
 'Pulse']