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"""
io_tools
Functions to load data
"""
from __future__ import absolute_import
import os
import sys
import json
import pickle
import numpy as np
__author__ = "Xin Wang"
__email__ = "wangxin@nii.ac.jp"
__copyright__ = "Copyright 2021, Xin Wang"
def f_read_raw_mat(filename, col, data_format='f4', end='l'):
"""data = f_read_raw_mat(filename, col, data_format='float', end='l')
Read the binary data from filename
Return data, which is a (N, col) array
input
-----
filename: str, path to the binary data on the file system
col: int, number of column assumed by the data matrix
format: str, please use the Python protocal to write format
default: 'f4', float32
end: str, little endian 'l' or big endian 'b'?
default: 'l'
output
------
data: np.array, shape (N, col), where N is the number of rows
decided by total_number_elements // col
"""
f = open(filename,'rb')
if end=='l':
data_format = '<'+data_format
elif end=='b':
data_format = '>'+data_format
else:
data_format = '='+data_format
datatype = np.dtype((data_format,(col,)))
data = np.fromfile(f,dtype=datatype)
f.close()
if data.ndim == 2 and data.shape[1] == 1:
return data[:,0]
else:
return data
def f_read_raw_mat_length(filename, data_format='f4'):
"""len = f_read_raw_mat_length(filename, data_format='f4')
Read length of data, i.e., number of elements in the data file.
If data is in shape (N, M), then len = N * M
input
-----
filename: str, path to the binary data on the file system
format: str, please use the Python protocal to write format
default: 'f4', float32
output
------
len: int, number of data elements in the data file
"""
f = open(filename,'rb')
tmp = f.seek(0, 2)
bytes_num = f.tell()
f.close()
if data_format == 'f4':
return int(bytes_num / 4)
else:
return bytes_num
def f_read_htk(filename, data_format='f4', end='l'):
"""data = read_htk(filename, data_format='f4', end='l')
Read HTK File and return the data as numpy.array
input
-----
filename: str, path to the binary HTK data on file system
data_format: str, format of the returned data
default: 'f4' float32
end: little endian 'l' or big endian 'b'?
default: 'l'
output
------
data: numpy.array
"""
if end=='l':
data_format = '<'+data_format
data_formatInt4 = '<i4'
data_formatInt2 = '<i2'
elif end=='b':
data_format = '>'+data_format
data_formatInt4 = '>i4'
data_formatInt2 = '>i2'
else:
data_format = '='+data_format
data_formatInt4 = '=i4'
data_formatInt2 = '=i2'
head_type = np.dtype([('nSample',data_formatInt4),
('Period',data_formatInt4),
('SampleSize',data_formatInt2),
('kind',data_formatInt2)])
f = open(filename,'rb')
head_info = np.fromfile(f,dtype=head_type,count=1)
"""if end=='l':
data_format = '<'+data_format
elif end=='b':
data_format = '>'+data_format
else:
data_format = '='+data_format
"""
if 'f' in data_format:
sample_size = int(head_info['SampleSize'][0]/4)
else:
print("Error in read_htk: input should be float32")
return False
datatype = np.dtype((data_format,(sample_size,)))
data = np.fromfile(f,dtype=datatype)
f.close()
return data
def f_read_htk_length(filename, data_format='f4', end='l'):
"""length = read_htk(filename, data_format='f4', end='l')
Read HTK File and return the number of data elements in the file
Read HTK File and return the data as numpy.array
input
-----
filename: str, path to the binary HTK data on file system
data_format: str, format of the returned data
default: 'f4' float32
end: little endian 'l' or big endian 'b'?
default: 'l'
output
------
length: int, number of data elements in the file
"""
if end=='l':
data_format = '<'+data_format
data_formatInt4 = '<i4'
data_formatInt2 = '<i2'
elif end=='b':
data_format = '>'+data_format
data_formatInt4 = '>i4'
data_formatInt2 = '>i2'
else:
data_format = '='+data_format
data_formatInt4 = '=i4'
data_formatInt2 = '=i2'
head_type = np.dtype([('nSample',data_formatInt4),
('Period',data_formatInt4),
('SampleSize',data_formatInt2),
('kind',data_formatInt2)])
f = open(filename,'rb')
head_info = np.fromfile(f,dtype=head_type,count=1)
f.close()
sample_size = int(head_info['SampleSize'][0]/4)
return sample_size
def f_write_raw_mat(data, filename, data_format='f4', end='l'):
"""flag = write_raw_mat(data, filename, data_format='f4', end='l')
Write data to file on the file system as binary data
input
-----
data: np.array, data to be saved
filename: str, path of the file to save the data
data_format: str, data_format for numpy
default: 'f4', float32
end: str little endian 'l' or big endian 'b'?
default: 'l'
output
------
flag: bool, whether the writing is done or not
"""
if not isinstance(data, np.ndarray):
print("Error write_raw_mat: input should be np.array")
return False
f = open(filename,'wb')
if len(data_format)>0:
if end=='l':
data_format = '<'+data_format
elif end=='b':
data_format = '>'+data_format
else:
data_format = '='+data_format
datatype = np.dtype(data_format)
temp_data = data.astype(datatype)
else:
temp_data = data
temp_data.tofile(f,'')
f.close()
return True
def f_append_raw_mat(data, filename, data_format='f4', end='l'):
"""flag = write_raw_mat(data, filename, data_format='f4', end='l')
Append data to an existing file on the file system as binary data
input
-----
data: np.array, data to be saved
filename: str, path of the file to save the data
data_format: str, data_format for numpy
default: 'f4', float32
end: str little endian 'l' or big endian 'b'?
default: 'l'
output
------
flag: bool, whether the writing is done or not
"""
if not isinstance(data, np.ndarray):
print("Error write_raw_mat: input shoul be np.array")
return False
f = open(filename,'ab')
if len(data_format)>0:
if end=='l':
data_format = '<'+data_format
elif end=='b':
data_format = '>'+data_format
else:
data_format = '='+data_format
datatype = np.dtype(data_format)
temp_data = data.astype(datatype)
else:
temp_data = data
temp_data.tofile(f,'')
f.close()
return True
def f_write_htk(data, targetfile,
sampPeriod=50000, sampKind=9, data_format='f4', end='l'):
"""
write_htk(data,targetfile,
sampPeriod=50000,sampKind=9,data_format='f4',end='l')
Write data as HTK-compatible format
input
-----
data: np.array, data to be saved
targetfile: str, path of the file to save the data
...
output
------
"""
if data.ndim==1:
nSamples, vDim = data.shape[0], 1
else:
nSamples, vDim = data.shape
if data_format=='f4':
sampSize = vDim * 4;
else:
sampSize = vDim * 8;
f = open(targetfile,'wb')
if len(data_format)>0:
if end=='l':
data_format1 = '<i4'
data_format2 = '<i2'
elif end=='b':
data_format1 = '>i4'
data_format2 = '>i2'
else:
data_format1 = '=i4'
data_format2 = '=i2'
temp_data = np.array([nSamples, sampPeriod],
dtype=np.dtype(data_format))
temp_data.tofile(f, '')
temp_data = np.array([sampSize, sampKind], dtype=np.dtype(data_format2))
temp_data.tofile(f, '')
if len(data_format)>0:
if end=='l':
data_format = '<'+data_format
elif end=='b':
data_format = '>'+data_format
else:
data_format = '='+data_format
datatype = np.dtype(data_format)
temp_data = data.astype(datatype)
else:
temp_data = data
temp_data.tofile(f, '')
f.close()
return True
def read_dic(file_path):
""" dic = read_dic(file_path)
Read a json file from file_path and return a dictionary
input
-----
file_path: string, path to the file
output
------
dic: a dictionary
"""
try:
data = json.load( open(file_path) )
except IOError:
print("Cannot find %s" % (file_path))
sys.exit(1)
except json.decoder.JSONDecodeError:
print("Cannot parse %s" % (file_path))
sys.exit(1)
return data
def write_dic(dic, file_path):
""" write_dic(dic, file_path)
Write a dictionary to file
input
-----
dic: dictionary to be dumped
file_path: file to store the dictionary
"""
try:
json.dump(dic, open(file_path, 'w'))
except IOError:
print("Cannot write to %s " % (file_path))
sys.exit(1)
def file_exist(file_path):
""" file_exit(file_path)
Whether file exists
"""
return os.path.isfile(file_path) or os.path.islink(file_path)
def pickle_dump(data, file_path):
""" pickle_dump(data, file_path)
Dump data into a pickle file
inputs:
data: python object, data to be dumped
file_path: str, path to save the pickle file
"""
try:
os.mkdir(os.path.dirname(file_path))
except OSError:
pass
with open(file_path, 'wb') as file_ptr:
pickle.dump(data, file_ptr)
return
def pickle_load(file_path):
""" data = pickle_load(file_path)
Load data from a pickle dump file
inputs:
file_path: str, path of the pickle file
output:
data: python object
"""
with open(file_path, 'rb') as file_ptr:
data = pickle.load(file_ptr)
return data
def wrapper_data_load_with_cache(file_path, method_data_load,
cache_dir='__cache',
use_cached_data=True, verbose=False):
"""wrapper_data_load_with_cache(file_path, method_data_load,
cache_dir='__cache',
use_cached_data=True, verbose=False):
Load data from file and save data as pickle file in cache.
input
-----
file_path: str, path of input file
method_data_load: python function, funtion to load the data
cache_dir: str, default __cache, the directory to save cached pickle file
use_cached_data: bool, default True, use cached data when available
verbose: bool, default False, print information on reading/writing
output
------
data: python object decided by method_data_load
This method is useful to load large text file. No need to parse text
everytime because the data will be saved as pickle file in cache after
the first time of execution
Example:
from core_scripts.data_io import io_tools
from core_scripts.other_tools import list_tools
data = io_tools.wrapper_data_load_with_cache('test_file',
list_tools.read_list_from_text)
"""
try:
os.mkdir(cache_dir)
except OSError:
pass
cache_file_path = '_'.join(file_path.split(os.path.sep))
cache_file_path = os.path.join(cache_dir, cache_file_path)
cache_file_path += '.pkl'
if use_cached_data and os.path.isfile(cache_file_path):
if verbose:
print("Load cached data {:s}".format(cache_file_path))
return pickle_load(cache_file_path)
else:
data = method_data_load(file_path)
pickle_dump(data, cache_file_path)
if verbose:
print("Load data {:s}".format(file_path))
print("Save cahced data {:s}".format(cache_file_path))
return data
if __name__ == "__main__":
print("Definition of tools for I/O operation")
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