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%matplotlib inline

Load Dataset

Conventionally, the h5py package is used to create, read, write, and modify h5 files.

Pycroscopy uses h5py to read hdf5 files and the ioHDF5 subpackage (a light wrapper around h5py) within pycroscopy to create / write back to the file. Please see the example on writing hdf5 files for more information on creating and writing to h5 files using pycroscopy.

In the event that modification / addition of data to the existing file is of interest, it is recommended that the file be opened using ioHDF5. The same h5py handles can be obtained easily from ioHDF5. Note that ioHDF5 always reads the files in the 'r+' mode that allows modification of the file.

In this example, we will be loading the Raw_Data dataset from the hdf5 file.



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# Code source: pycroscopy
# Liscense: MIT

from __future__ import division, print_function, absolute_import, unicode_literals
import h5py
try:
    # This package is not part of anaconda and may need to be installed.
    import wget
except ImportError:
    import pip
    pip.main(['install', 'wget'])
    import wget

from os import remove
import pycroscopy as px

# Downloading the file from the pycroscopy Github project
url = 'https://raw.githubusercontent.com/pycroscopy/pycroscopy/master/data/BELine_0004.h5'
h5_path = 'temp.h5'
_ = wget.download(url, h5_path)

# h5_path = px.io_utils.uiGetFile(caption='Select .h5 file', filter='HDF5 file (*.h5)')

# Read the file using using h5py:
h5_file1 = h5py.File(h5_path, 'r')

# Look at the contents of the file:
px.hdf_utils.print_tree(h5_file1)

# Access the "Raw_Data" dataset from its absolute path
h5_raw1 = h5_file1['Measurement_000/Channel_000/Raw_Data']
print('h5_raw1: ', h5_raw1)

# We can get to the same dataset through relative paths:

# Access the Measurement_000 group first
h5_meas_grp = h5_file1['Measurement_000']
print('h5_meas_grp:', h5_meas_grp)

# Now we can access the "Channel_000" group via the h5_meas_grp object
h5_chan_grp = h5_meas_grp['Channel_000']

# And finally, the same raw dataset can be accessed as:
h5_raw1_alias_1 = h5_chan_grp['Raw_Data']
print('h5_raw1_alias_1:', h5_raw1_alias_1)

# Another way to get this dataset is via functions written in pycroscopy:
h5_dsets = px.hdf_utils.getDataSet(h5_file1, 'Raw_Data')
print('h5_dsets:', h5_dsets)

# In this case, there is only a single Raw_Data, so we an access it simply as:
h5_raw1_alias_2 = h5_dsets[0]
print('h5_raw1_alias_2:', h5_raw1_alias_2)

# Let's just check to see if these are indeed aliases of the same dataset:
print('All aliases of the same dataset?', h5_raw1 == h5_raw1_alias_1 and h5_raw1 == h5_raw1_alias_2)

# Let's close this file
h5_file1.close()

# Load the dataset with pycroscopy
hdf = px.ioHDF5(h5_path)

# Getting the same h5py handle to the file:
h5_file2 = hdf.file

h5_raw2 = h5_file2['Measurement_000/Channel_000/Raw_Data']
print('h5_raw2:', h5_raw2)

h5_file2.close()

# Delete the temporarily downloaded h5 file:
remove(h5_path)