"""Python script to convert the HDF5 data file in the Electronic
Supplemental Material for the following paper to a text file:

Weidemann, C. T. & Kahana, M. J. (2016). Assessing recognition
memory using confidence ratings and response times. Royal Society
Open Science, 3, 150670.

Not all information from the HDF5 file is transferred to the .tsv
file with the current script, but the below script can be adapted
to extract further information from the HDF5 file.

This script was written by Christoph T. Weidemann (ctw@cogsci.info),
and is released into the public domain (see comment below for details
and disclaimer).

"""

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#### This is free and unencumbered software released into the public domain.
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import numpy as np
import h5py

# path to input data file in HDF5 format:
hdf5_file = 'WeidemannKahana2016_data.hdf5'

# path to output data file in text format:
txt_file = 'WeidemannKahana2016_data.tsv'

# Formats for the columns in the text file:
fmts = ['%s', '%d', '%d', '%f', '%s', '%s', '%f',  '%f', '%s',
        '%d', '%s', '%s']

# Names of the columns in the text file:
colnames = ['participant', 'session', 'test_list', 'study_list', 'type',
            'recog_resp', 'recog_conf', 'recog_rt', 'item', 'itemno',
            'recalled', 'finalrecalled']

# Character to delimit the columns:
delimchar = '\t'

np.savetxt(txt_file, h5py.File(hdf5_file)['WeidemannKahana2016_data'],
           fmt=fmts, delimiter=delimchar, header=delimchar.join(colnames))