def file_reader(filename, endianess = '<', **kwds):
mapped_parameters={}
dtype_list = get_std_dtype_list(endianess) + get_fei_dtype_list(endianess)
f = open(filename, 'rb')
std_header = np.fromfile(f, dtype = get_std_dtype_list(endianess),
count = 1)
fei_header = None
if std_header['NEXT'] / 1024 == 128:
print "It seems to contain an extended FEI header"
fei_header = np.fromfile(f, dtype = get_fei_dtype_list(endianess),
count = 1024)
if f.tell() == 1024 + std_header['NEXT']:
print "The FEI header was correctly loaded"
else:
print "There was a problem reading the extended header"
f.seek(1024 + std_header['NEXT'])
fei_header = None
NX, NY, NZ = std_header['NX'], std_header['NY'], std_header['NZ']
data = np.memmap(f, mode = 'c', offset = f.tell(),
dtype = get_data_type(std_header['MODE'], endianess)
).squeeze().reshape((NX, NY, NZ), order = 'F').T
original_parameters = { 'std_header' : sarray2dict(std_header)}
if fei_header is not None:
fei_dict = sarray2dict(fei_header,)
del fei_dict['empty']
original_parameters['fei_header'] = fei_dict
dim = len(data.shape)
if fei_header is None:
# The scale is in Amstrongs, we convert it to nm
scales = [ 10 * float(std_header['Zlen']/std_header['MZ'])
if float(std_header['MZ']) != 0 else 1,
10 * float(std_header['Ylen']/std_header['MY'])
if float(std_header['MY']) != 0 else 1,
10 * float(std_header['Xlen']/std_header['MX'])
if float(std_header['MX']) != 0 else 1,]
offsets = [ 10 * float(std_header['ZORIGIN']),
10 * float(std_header['YORIGIN']),
10 * float(std_header['XORIGIN']),]
else:
# FEI does not use the standard header to store the scale
# It does store the spatial scale in pixel_size, one per angle in meters
scales = [1, ] + [fei_header['pixel_size'][0] * 10**9,] * 2
offsets = [0,] * 3
units = ['undefined', 'nm', 'nm']
names = ['z', 'y', 'x']
mapped_parameters = { 'original_filename' : os.path.split(filename)[1],
'record_by' : 'image',
'signal_type' : "",}
#create the axis objects for each axis
axes=[
{
'size': data.shape[i],
'index_in_array' : i,
'name' : names[i + 3 - dim],
'scale' : scales[i + 3 - dim],
'offset': offsets[i + 3 - dim],
'units' : units[i + 3 - dim],}
for i in xrange(dim)]
dictionary = { 'data' : data,
'axes' : axes,
'mapped_parameters' : mapped_parameters,
'original_parameters' : original_parameters,}
return [dictionary,]
def ser_reader(filename, objects = None, *args, **kwds):
"""Reads the information from the file and returns it in the Hyperspy
required format"""
# Determine if it is an emi or a ser file.
header, data = load_ser_file(filename)
record_by = guess_record_by(header['DataTypeID'])
axes = []
ndim = int(header['NumberDimensions'])
if record_by == 'spectrum':
array_shape = [None,] * int(ndim)
i_array = range(ndim)
if len(data['PositionY']) > 1 and \
(data['PositionY'][0] == data['PositionY'][1]):
# The spatial dimensions are stored in the reversed order
# We reverse the shape
i_array.reverse()
# Extra dimensions
for i in xrange(ndim):
if i_array[i] == ndim - 1:
name = 'x'
elif i_array[i] == ndim - 2:
name = 'y'
else:
name = 'undefined_%i' % (i + 1)
axes.append({
'name' : name,
'offset' : header['Dim-%i_CalibrationOffset' % (i + 1)][0],
'scale' : header['Dim-%i_CalibrationDelta' % (i + 1)][0],
'units' : header['Dim-%i_Units' % (i + 1)][0],
'size' : header['Dim-%i_DimensionSize' % (i + 1)][0],
'index_in_array' : i_array[i]
})
array_shape[i_array[i]] = \
header['Dim-%i_DimensionSize' % (i + 1)][0]
# FEI seems to use the international system of units (SI) for the
# spatial scale. However, we prefer to work in nm
for axis in axes:
if axis['units'] == 'meters':
axis['units'] = 'nm'
axis['scale'] *= 10**9
# Spectral dimension
axes.append({
'name' : 'undefined',
'offset' : data['CalibrationOffset'][0],
'scale' : data['CalibrationDelta'][0],
'units' : 'undefined',
'size' : data['ArrayLength'][0],
'index_in_array' : header['NumberDimensions'][0]
})
array_shape.append(data['ArrayLength'][0])
elif record_by == 'image':
array_shape = [None,] * int(ndim - 1)
# Y axis
axes.append({
'name' : 'y',
'offset' : data['CalibrationOffsetY'][0] - \
data['CalibrationElementY'][0] * data['CalibrationDeltaY'][0],
'scale' : data['CalibrationDeltaY'][0],
'units' : 'Unknown',
'size' : data['ArraySizeY'][0],
'index_in_array' : ndim - 1
})
array_shape.append(data['ArraySizeY'][0])
# X axis
axes.append({
'name' : 'x',
'offset' : data['CalibrationOffsetX'][0] - \
data['CalibrationElementX'][0] * data['CalibrationDeltaX'][0],
'scale' : data['CalibrationDeltaX'][0],
'units' : 'undefined',
'size' : data['ArraySizeX'][0],
'index_in_array' : ndim
})
array_shape.append(data['ArraySizeX'][0])
# Extra dimensions
for i in xrange(ndim - 1):
axes.append({
'name' : 'undefined%s' % i,
'offset' : header['Dim-%i_CalibrationOffset' % i + 1][0],
'scale' : header['Dim-%i_CalibrationDelta' % i + 1][0],
'units' : header['Dim-%i_Units' % i + 1][0],
'size' : header['Dim-%i_DimensionSize' % i + 1][0],
'index_in_array' : ndim - 1 -i
})
array_shape.append(header['Dim-%i_DimensionSize' % i + 1][0])
# If the acquisition stops before finishing the job, the stored file will
# report the requested size even though no values are recorded. Therefore if
# the shapes of the retrieved array does not match that of the data
# dimensions we must fill the rest with zeros or (better) nans if the
# dtype is float
if np.cumprod(array_shape)[-1] != np.cumprod(data['Array'].shape)[-1]:
dc = np.zeros(np.cumprod(array_shape)[-1],
dtype = data['Array'].dtype)
if dc.dtype is np.dtype('f') or dc.dtype is np.dtype('f8'):
dc[:] = np.nan
dc[:data['Array'].ravel().shape[0]] = data['Array'].ravel()
else:
dc = data['Array']
dc = dc.reshape(array_shape)
if record_by == 'image':
dc = dc[::-1]
if ordict:
original_parameters = OrderedDict()
else:
print("\nWARNING:")
print("FEI plugin")
print("OrderedDict is not available, using a standard dictionary.\n")
original_parameters = {}
header_parameters = sarray2dict(header)
sarray2dict(data, header_parameters)
if objects is not None:
i = 0
for obj in objects:
original_parameters['emi_xml%i' % i] = xmlreader.readConfig(obj)
# We remove the Array key to save memory avoiding duplication
del header_parameters['Array']
original_parameters['ser_header_parameters'] = header_parameters
dictionary = {
'data' : dc,
'mapped_parameters' : {
'original_filename' : os.path.split(filename)[1],
'record_by' : record_by,
'signal_type' : "",},
'axes' : axes,
'original_parameters' : original_parameters}
return dictionary
def ser_reader(filename, objects=None, *args, **kwds):
"""Reads the information from the file and returns it in the Hyperspy
required format"""
# Determine if it is an emi or a ser file.
header, data = load_ser_file(filename)
record_by = guess_record_by(header["DataTypeID"])
axes = []
ndim = int(header["NumberDimensions"])
if record_by == "spectrum":
array_shape = [None] * int(ndim)
i_array = range(ndim)
if len(data["PositionY"]) > 1 and (data["PositionY"][0] == data["PositionY"][1]):
# The spatial dimensions are stored in the reversed order
# We reverse the shape
i_array.reverse()
# Extra dimensions
for i in xrange(ndim):
if i_array[i] == ndim - 1:
name = "x"
elif i_array[i] == ndim - 2:
name = "y"
else:
name = "undefined_%i" % (i + 1)
axes.append(
{
"name": name,
"offset": header["Dim-%i_CalibrationOffset" % (i + 1)][0],
"scale": header["Dim-%i_CalibrationDelta" % (i + 1)][0],
"units": header["Dim-%i_Units" % (i + 1)][0],
"size": header["Dim-%i_DimensionSize" % (i + 1)][0],
"index_in_array": i_array[i],
}
)
array_shape[i_array[i]] = header["Dim-%i_DimensionSize" % (i + 1)][0]
# FEI seems to use the international system of units (SI) for the
# spatial scale. However, we prefer to work in nm
for axis in axes:
if axis["units"] == "meters":
axis["units"] = "nm"
axis["scale"] *= 10 ** 9
# Spectral dimension
axes.append(
{
"name": "undefined",
"offset": data["CalibrationOffset"][0],
"scale": data["CalibrationDelta"][0],
"units": "undefined",
"size": data["ArrayLength"][0],
"index_in_array": header["NumberDimensions"][0],
}
)
array_shape.append(data["ArrayLength"][0])
elif record_by == "image":
array_shape = [None] * int(ndim - 1)
# Y axis
axes.append(
{
"name": "y",
"offset": data["CalibrationOffsetY"][0] - data["CalibrationElementY"][0] * data["CalibrationDeltaY"][0],
"scale": data["CalibrationDeltaY"][0],
"units": "Unknown",
"size": data["ArraySizeY"][0],
"index_in_array": ndim - 1,
}
)
array_shape.append(data["ArraySizeY"][0])
# X axis
axes.append(
{
"name": "x",
"offset": data["CalibrationOffsetX"][0] - data["CalibrationElementX"][0] * data["CalibrationDeltaX"][0],
"scale": data["CalibrationDeltaX"][0],
"units": "undefined",
"size": data["ArraySizeX"][0],
"index_in_array": ndim,
}
)
array_shape.append(data["ArraySizeX"][0])
# Extra dimensions
for i in xrange(ndim - 1):
axes.append(
{
"name": "undefined%s" % i,
"offset": header["Dim-%i_CalibrationOffset" % i + 1][0],
"scale": header["Dim-%i_CalibrationDelta" % i + 1][0],
"units": header["Dim-%i_Units" % i + 1][0],
"size": header["Dim-%i_DimensionSize" % i + 1][0],
"index_in_array": ndim - 1 - i,
}
)
array_shape.append(header["Dim-%i_DimensionSize" % i + 1][0])
# If the acquisition stops before finishing the job, the stored file will
# report the requested size even though no values are recorded. Therefore if
# the shapes of the retrieved array does not match that of the data
# dimensions we must fill the rest with zeros or (better) nans if the
# dtype is float
if np.cumprod(array_shape)[-1] != np.cumprod(data["Array"].shape)[-1]:
dc = np.zeros(np.cumprod(array_shape)[-1], dtype=data["Array"].dtype)
if dc.dtype is np.dtype("f") or dc.dtype is np.dtype("f8"):
dc[:] = np.nan
dc[: data["Array"].ravel().shape[0]] = data["Array"].ravel()
else:
dc = data["Array"]
dc = dc.reshape(array_shape)
if record_by == "image":
dc = dc[::-1]
if ordict:
original_parameters = OrderedDict()
else:
print ("\nWARNING:")
print ("FEI plugin")
print ("OrderedDict is not available, using a standard dictionary.\n")
original_parameters = {}
header_parameters = sarray2dict(header)
sarray2dict(data, header_parameters)
if objects is not None:
i = 0
for obj in objects:
original_parameters["emi_xml%i" % i] = xmlreader.readConfig(obj)
# We remove the Array key to save memory avoiding duplication
del header_parameters["Array"]
original_parameters["ser_header_parameters"] = header_parameters
dictionary = {
"data": dc,
"mapped_parameters": {"original_filename": filename, "record_by": record_by, "signal": None},
"axes": axes,
"original_parameters": original_parameters,
}
return dictionary
def ser_reader(filename, objects=None, *args, **kwds):
"""Reads the information from the file and returns it in the Hyperspy
required format"""
# Determine if it is an emi or a ser file.
header, data = load_ser_file(filename)
record_by = guess_record_by(header['DataTypeID'])
axes = []
ndim = int(header['NumberDimensions'])
if record_by == 'spectrum':
array_shape = [
None,
] * int(ndim)
i_array = range(ndim)
if len(data['PositionY']) > 1 and \
(data['PositionY'][0] == data['PositionY'][1]):
# The spatial dimensions are stored in the reversed order
# We reverse the shape
i_array.reverse()
# Extra dimensions
for i in xrange(ndim):
if i_array[i] == ndim - 1:
name = 'x'
elif i_array[i] == ndim - 2:
name = 'y'
else:
name = 'undefined_%i' % (i + 1)
axes.append({
'name':
name,
'offset':
header['Dim-%i_CalibrationOffset' % (i + 1)][0],
'scale':
header['Dim-%i_CalibrationDelta' % (i + 1)][0],
'units':
header['Dim-%i_Units' % (i + 1)][0],
'size':
header['Dim-%i_DimensionSize' % (i + 1)][0],
'index_in_array':
i_array[i]
})
array_shape[i_array[i]] = \
header['Dim-%i_DimensionSize' % (i + 1)][0]
# FEI seems to use the international system of units (SI) for the
# spatial scale. However, we prefer to work in nm
for axis in axes:
if axis['units'] == 'meters':
axis['units'] = 'nm'
axis['scale'] *= 10**9
# Spectral dimension
axes.append({
'name': 'undefined',
'offset': data['CalibrationOffset'][0],
'scale': data['CalibrationDelta'][0],
'units': 'undefined',
'size': data['ArrayLength'][0],
'index_in_array': header['NumberDimensions'][0]
})
array_shape.append(data['ArrayLength'][0])
elif record_by == 'image':
array_shape = [
None,
] * int(ndim - 1)
# Y axis
axes.append({
'name' : 'y',
'offset' : data['CalibrationOffsetY'][0] - \
data['CalibrationElementY'][0] * data['CalibrationDeltaY'][0],
'scale' : data['CalibrationDeltaY'][0],
'units' : 'Unknown',
'size' : data['ArraySizeY'][0],
'index_in_array' : ndim - 1
})
array_shape.append(data['ArraySizeY'][0])
# X axis
axes.append({
'name' : 'x',
'offset' : data['CalibrationOffsetX'][0] - \
data['CalibrationElementX'][0] * data['CalibrationDeltaX'][0],
'scale' : data['CalibrationDeltaX'][0],
'units' : 'undefined',
'size' : data['ArraySizeX'][0],
'index_in_array' : ndim
})
array_shape.append(data['ArraySizeX'][0])
# Extra dimensions
for i in xrange(ndim - 1):
axes.append({
'name': 'undefined%s' % i,
'offset': header['Dim-%i_CalibrationOffset' % i + 1][0],
'scale': header['Dim-%i_CalibrationDelta' % i + 1][0],
'units': header['Dim-%i_Units' % i + 1][0],
'size': header['Dim-%i_DimensionSize' % i + 1][0],
'index_in_array': ndim - 1 - i
})
array_shape.append(header['Dim-%i_DimensionSize' % i + 1][0])
# If the acquisition stops before finishing the job, the stored file will
# report the requested size even though no values are recorded. Therefore if
# the shapes of the retrieved array does not match that of the data
# dimensions we must fill the rest with zeros or (better) nans if the
# dtype is float
if np.cumprod(array_shape)[-1] != np.cumprod(data['Array'].shape)[-1]:
dc = np.zeros(np.cumprod(array_shape)[-1], dtype=data['Array'].dtype)
if dc.dtype is np.dtype('f') or dc.dtype is np.dtype('f8'):
dc[:] = np.nan
dc[:data['Array'].ravel().shape[0]] = data['Array'].ravel()
else:
dc = data['Array']
dc = dc.reshape(array_shape)
if record_by == 'image':
dc = dc[::-1]
if ordict:
original_parameters = OrderedDict()
else:
print("\nWARNING:")
print("FEI plugin")
print("OrderedDict is not available, using a standard dictionary.\n")
original_parameters = {}
header_parameters = sarray2dict(header)
sarray2dict(data, header_parameters)
if objects is not None:
i = 0
for obj in objects:
original_parameters['emi_xml%i' % i] = xmlreader.readConfig(obj)
# We remove the Array key to save memory avoiding duplication
del header_parameters['Array']
original_parameters['ser_header_parameters'] = header_parameters
dictionary = {
'data': dc,
'mapped_parameters': {
'original_filename': filename,
'record_by': record_by,
'signal': None
},
'axes': axes,
'original_parameters': original_parameters
}
return dictionary
def file_reader(filename, endianess='<', **kwds):
mapped_parameters = {}
dtype_list = get_std_dtype_list(endianess) + get_fei_dtype_list(endianess)
f = open(filename, 'rb')
std_header = np.fromfile(f, dtype=get_std_dtype_list(endianess), count=1)
fei_header = None
if std_header['NEXT'] / 1024 == 128:
print "It seems to contain an extended FEI header"
fei_header = np.fromfile(f,
dtype=get_fei_dtype_list(endianess),
count=1024)
if f.tell() == 1024 + std_header['NEXT']:
print "The FEI header was correctly loaded"
else:
print "There was a problem reading the extended header"
f.seek(1024 + std_header['NEXT'])
fei_header = None
NX, NY, NZ = std_header['NX'], std_header['NY'], std_header['NZ']
data = np.memmap(f,
mode='c',
offset=f.tell(),
dtype=get_data_type(std_header['MODE'],
endianess)).squeeze().reshape(
(NX, NY, NZ), order='F').T
original_parameters = {'std_header': sarray2dict(std_header)}
if fei_header is not None:
fei_dict = sarray2dict(fei_header, )
del fei_dict['empty']
original_parameters['fei_header'] = fei_dict
dim = len(data.shape)
if fei_header is None:
# The scale is in Amstrongs, we convert it to nm
scales = [
10 * float(std_header['Zlen'] / std_header['MZ'])
if float(std_header['MZ']) != 0 else 1,
10 * float(std_header['Ylen'] / std_header['MY'])
if float(std_header['MY']) != 0 else 1,
10 * float(std_header['Xlen'] / std_header['MX'])
if float(std_header['MX']) != 0 else 1,
]
offsets = [
10 * float(std_header['ZORIGIN']),
10 * float(std_header['YORIGIN']),
10 * float(std_header['XORIGIN']),
]
else:
# FEI does not use the standard header to store the scale
# It does store the spatial scale in pixel_size, one per angle in meters
scales = [
1,
] + [
fei_header['pixel_size'][0] * 10**9,
] * 2
offsets = [
0,
] * 3
units = ['undefined', 'nm', 'nm']
names = ['z', 'y', 'x']
mapped_parameters = {
'original_filename': filename,
'record_by': 'image',
'signal': None,
}
#create the axis objects for each axis
axes = [{
'size': data.shape[i],
'index_in_array': i,
'name': names[i + 3 - dim],
'scale': scales[i + 3 - dim],
'offset': offsets[i + 3 - dim],
'units': units[i + 3 - dim],
} for i in xrange(dim)]
dictionary = {
'data': data,
'axes': axes,
'mapped_parameters': mapped_parameters,
'original_parameters': original_parameters,
}
return [
dictionary,
]
