84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
def load_mat(f,var,squeeze=True,cast_string=True,cast_integer=False):
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try:
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return load_matbin(f,var,squeeze=squeeze)
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except NotImplementedError:
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return load_matv73(f,var,squeeze=squeeze,cast_string=cast_string,cast_integer=cast_integer)
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def load_matbin(f,var,squeeze=True):
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'''Loads variables from a matlab binary mat file (not v7.3).'''
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from scipy.io import loadmat
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import numpy
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data = loadmat(f,variable_names=var,squeeze_me=squeeze,struct_as_record=True)
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def parse(val):
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if not isinstance(val,numpy.ndarray):
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return val
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if val.dtype.names is not None:
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d = {}
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for key in val.dtype.names:
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d[key] = parse(val[key].item())
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return d
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else:
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return val
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if isinstance(var,(tuple,list)):
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return tuple(parse(data[x]) for x in var)
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else:
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return parse(data[var])
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def load_matv73(f,var,squeeze=True,cast_string=True,cast_integer=False):
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'''Loads variables from a matlab hdf5 mat file (v7.3).'''
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import numpy
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import h5py
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if isinstance(f,str):
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f = h5py.File(f,'r')
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if isinstance(var,(list,tuple)):
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return tuple(load_matv73(f,x,cast_string=cast_string,cast_integer=cast_integer) for x in var)
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def parse(val):
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# String data
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if cast_string and val.dtype==numpy.dtype('uint16'):
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return ''.join([chr(x) for x in val.squeeze()])
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# Numeric data
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val = val.transpose()
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if cast_integer:
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val_int = numpy.rint(val)
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if numpy.all(numpy.abs(val-val_int)<1e-8):
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val = val_int.astype('int')
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if squeeze:
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val = val.squeeze()
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if val.shape==():
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val = val.item()
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return val
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t = f[var]
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if isinstance(t,h5py.Dataset):
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val = t[:]
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if val.dtype==numpy.dtype('object'):
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# Cell arrays are stored as references
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for idx,x in numpy.ndenumerate(val):
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val[idx] = parse(f[val[idx]][:])
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return val
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else:
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return parse(val)
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else:
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d = {}
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for key in t:
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d[key] = load_matv73(t,key,
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squeeze=squeeze,
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cast_string=cast_string,
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cast_integer=cast_integer)
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return d
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def h5write(file,arr,name='data',truncate=True):
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import h5py
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is_open = isinstance(file,(h5py.File,h5py.Group))
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flag = 'w' if truncate else 'a'
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f = file if is_open else h5py.File(file,flag)
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f.create_dataset(name,data=arr)
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if not is_open: f.close()
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return
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def h5read(file,name='data'):
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import h5py
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is_open = isinstance(file,(h5py.File,h5py.Group))
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f = file if is_open else h5py.File(file,'r')
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data = f[name][:]
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if not is_open: f.close()
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return data |