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Matlab_IO.py
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Matlab_IO.py
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'''
Created on 8 Jun 2015
@author: philip knaute
------------------------------------------------------------------------------
Copyright (C) 2015, Philip Knaute <[email protected]>,
This work is licensed under the Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of
this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send
a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View,
California, 94041, USA.
------------------------------------------------------------------------------
'''
import scipy.io as sio
def read_calc_times(mat_file_path):
""" Read the average calculation times for each operation from a HCTSA_loc.mat file
Parameters:
-----------
mat_file_path : string
Path to the HCTSA_loc.mat file
Returns:
--------
op_ids_times_lst : list
List of two lists. First being the operation ids and second the average times over all
calculated timeseries for each respective operation.
"""
mat_file = sio.loadmat(mat_file_path)
op_id = lambda i : int(mat_file['Operations'][i][0][3][0])
calc_times = mat_file['TS_CalcTime'].sum(axis=0)/mat_file['TS_CalcTime'].shape[0]
op_ids = [op_id(i) for i in range(mat_file['Operations'].shape[0])]
return [op_ids,calc_times.tolist()]
def read_from_mat_file(mat_file_path,hctsa_struct_names,is_from_old_matlab = False):
"""
read .mat files into appropriate python data structures
Parameters:
----------
mat_file_path : string
Path to the matlab file to be read
hctsa_struct_names : list
List of strings of identifiers for which structures are to be read from the mat_file.
Possible values are : 'TimeSeries','Operations','TS_DataMat'
is_from_old_matlab : bool
If the HCTSA_loc.mat files are saved from an older version of the comp engine. The order of entries is different.
Returns:
--------
retval : tuple
Tuple of the imported values in the order given by hctsa_struct_names
"""
mat_file = sio.loadmat(mat_file_path)
retval = tuple()
for item in hctsa_struct_names:
if item == 'TimeSeries':
timeseries = dict()
if is_from_old_matlab:
# lambda function used to populate the dictionary with the appropriate data lists
ts_id = lambda i : int(mat_file['TimeSeries'][i][0][0][0])
ts_filename = lambda i : str(mat_file['TimeSeries'][i][0][1][0])
ts_kw = lambda i : str(mat_file['TimeSeries'][i][0][2][0])
ts_n_samples = lambda i : int(mat_file['TimeSeries'][i][0][3][0])
# data is not included in the returned dictionary as it seem a waste of space
#ts_data = lambda i : mat_file['TimeSeries'][i][0][4]
for extractor,key in zip([ts_id,ts_filename ,ts_kw,ts_n_samples],['id','filename','keywords','n_samples']):
timeseries[key] =[extractor(i) for i in range(mat_file['TimeSeries'].shape[0])]
else:
# -- currently there seems to be a bug in the creation of those files. Need to
# read them differently
# lambda function used to populate the dictionary with the appropriate data lists
#ts_id = lambda i : int(mat_file['TimeSeries'][i][0][0][0])
# ----------- this is the original version, I (Carl) somehow need to switch the first two dimensions ----
# ts_filename = lambda i : str(mat_file['TimeSeries'][0][i][0][0])
# ts_kw = lambda i : str(mat_file['TimeSeries'][0][i][1][0])
# ts_n_samples = lambda i : int(mat_file['TimeSeries'][0][i][2][0])
# # -- data is not included in the returned dictionary as it seem a waste of space
# #ts_data = lambda i : mat_file['TimeSeries'][i][0][3]
# for extractor,key in zip([ts_filename ,ts_kw,ts_n_samples],['filename','keywords','n_samples']):
# timeseries[key] =[extractor(i) for i in range(mat_file['TimeSeries'].shape[1])]
# ------------ to this
ts_filename = lambda i: str(mat_file['TimeSeries'][i][0][0][0])
ts_kw = lambda i: str(mat_file['TimeSeries'][i][0][1][0])
ts_n_samples = lambda i: int(mat_file['TimeSeries'][i][0][2][0])
# -- data is not included in the returned dictionary as it seem a waste of space
# ts_data = lambda i : mat_file['TimeSeries'][i][0][3]
for extractor, key in zip([ts_filename, ts_kw, ts_n_samples], ['filename', 'keywords', 'n_samples']):
timeseries[key] = [extractor(i) for i in range(mat_file['TimeSeries'].shape[0])]
retval = retval + (timeseries,)
if item == 'Operations':
operations = dict()
if is_from_old_matlab:
op_id = lambda i : int(mat_file['Operations'][i][0][0][0])
op_name = lambda i : str(mat_file['Operations'][i][0][1][0])
op_kw = lambda i : str(mat_file['Operations'][i][0][2][0])
op_code = lambda i : str(mat_file['Operations'][i][0][3][0])
op_mopid = lambda i : int(mat_file['Operations'][i][0][4][0])
else:
# lambda function used to populate the dictionary with the appropriate data lists
op_id = lambda i : int(mat_file['Operations'][i][0][3][0])
op_name = lambda i : str(mat_file['Operations'][i][0][1][0])
op_kw = lambda i : str(mat_file['Operations'][i][0][2][0])
op_code = lambda i : str(mat_file['Operations'][i][0][0][0])
op_mopid = lambda i : int(mat_file['Operations'][i][0][4][0])
for extractor,key in zip([op_id,op_name ,op_kw,op_code,op_mopid],['id','name','keywords','code_string','master_id']):
operations[key] =[extractor(i) for i in range(mat_file['Operations'].shape[0])]
retval = retval + (operations,)
if item == 'TS_DataMat':
retval = retval+(mat_file['TS_DataMat'],)
if item == 'MasterOperations':
m_operations = dict()
if is_from_old_matlab:
raise NameError('Don''t know how to get MasterOperations from old Matlab version.')
else:
# lambda function used to populate the dictionary with the appropriate data lists
m_op_id = lambda i: int(mat_file['MasterOperations'][i][0][2][0][0])
m_op_name = lambda i: str(mat_file['MasterOperations'][i][0][1][0])
for extractor, key in zip([m_op_id, m_op_name],
['id', 'name']):
m_operations[key] = [extractor(i) for i in range(mat_file['MasterOperations'].shape[0])]
retval = retval + (m_operations,)
return retval