# -*- coding: utf-8 -*-
#_____________________________________________________________________________
#
# Copyright (c) 2012 Berlin Institute of Technology
# All rights reserved.
#
# Developed by: Philipp Meier <pmeier82@gmail.com>
# Neural Information Processing Group (NI)
# School for Electrical Engineering and Computer Science
# Berlin Institute of Technology
# MAR 5-6, Marchstr. 23, 10587 Berlin, Germany
# http://www.ni.tu-berlin.de/
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal with the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimers.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimers in the documentation
# and/or other materials provided with the distribution.
# * Neither the names of Neural Information Processing Group (NI), Berlin
# Institute of Technology, nor the names of its contributors may be used to
# endorse or promote products derived from this Software without specific
# prior written permission.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# WITH THE SOFTWARE.
#_____________________________________________________________________________
#
# Acknowledgements:
# Philipp Meier <pmeier82@gmail.com>
#_____________________________________________________________________________
#
"""datafile implementation for gdf file format"""
__docformat__ = 'restructuredtext'
__all__ = ['GdfFile']
##---IMPORTS
import scipy as sp
from .datafile import DataFile
from ..funcs_general import sortrows, dict_list_to_ndarray
##---CLASSES
[docs]class GdfFile(DataFile):
"""GDF file format - Chen Sorter"""
## implementation
def _initialize_file(self, filename, **kwargs):
self.data = GdfFile.read_gdf(filename)
@staticmethod
[docs] def read_gdf(filename):
"""reads a GDF file and stores to a dict
:type filename: string[path]
:param filename: path to the file to read
:rtype: dict of numpy.ndarray
:return: one sequence per unit
"""
rval = {}
with open(filename, 'r') as f:
for line in f:
data = line.strip().split()
if len(data) != 2:
continue
else:
key, sample = map(int, data)
if key not in rval:
rval[key] = []
rval[key].append(sample)
return dict_list_to_ndarray(rval)
def _closed(self):
return False
def _close(self):
pass
@staticmethod
[docs] def write_gdf(filename, gdf):
"""Writes GDF data file.
:type filename: str
:param filename: valid path on the filesystem
:type gdf: dict or ndarray
:param gdf: either a dict mapping unit ids to spike trains or a
ndarray with the keys in the first column and the samples in the
second column"""
if isinstance(gdf, dict):
gdf = GdfFile.convert_dict_to_matrix(gdf)
sp.savetxt(filename, gdf, fmt='%05d %d')
@staticmethod
[docs] def convert_dict_to_matrix(gdf):
"""converts a GDF dict representation to a matrix
:type gdf: dict
:param gdf: mapping unit ids to spike trains
:rtype: ndarray
:returns: first column ids, second column samples
"""
samples = sp.concatenate(gdf.values())
rval = sp.zeros((len(samples), 2))
rval[:, 0] = samples
idx = 0
ids = sp.zeros(0)
for key in sorted(gdf.keys()):
ids = sp.concatenate((ids, sp.ones((len(gdf[key]))) * idx))
idx += 1
rval[:, 1] = ids
rval = sortrows(rval)[:, [1, 0]]
return rval
@staticmethod
[docs] def convert_matrix_to_dict(gdf):
"""converts a GDF ndarray representation to a dict
:type gdf: ndarray
:param gdf: first column ids, second column samples
:rtype: dict
:returns: mapping unit ids to spike trains
"""
rval = {}
for i in xrange(gdf.shape[0]):
if not gdf[i, 0] in rval.keys():
rval[gdf[i, 0]] = []
rval[gdf[i, 0]].append(gdf[i, 1])
return rval
def _get_data(self, **kwargs):
"""returns the GDF data as ndarray
:rtype: dict
:returns: mapping unit ids to spike trains
"""
return GdfFile.convert_dict_to_matrix(self.data)
if __name__ == '__main__':
import os
try:
fname1 = './test1.gdf'
fname2 = './test2.gdf'
test_data = [
'0001 1000',
'0001 1200',
'0002 1210',
'0001 1280',
'0003 1291',
'0001 1350',
'0002 1400',
]
with open(fname1, 'w') as f:
f.write('\n'.join(test_data))
g1 = GdfFile(fname1)
gdf1 = g1.get_data()
print gdf1
GdfFile.convert_dict_to_matrix(gdf1)
GdfFile.write_gdf(fname2, gdf1)
g2 = GdfFile(fname2)
gdf2 = g2.get_data()
print gdf2
is_same = True
for i in xrange(len(gdf1.keys())):
if not sp.allclose(gdf1[sorted(gdf1.keys())[i]],
gdf2[sorted(gdf2.keys())[i]]):
is_same = False
break
print 'data is same:', is_same
finally:
if os.path.exists(fname1):
os.remove(fname1)
if os.path.exists(fname2):
os.remove(fname2)