def file_reader(filename,
record_by=None,
order=None,
lazy=False,
optimize=True):
"""Reads a DM3 file and loads the data into the appropriate class.
data_id can be specified to load a given image within a DM3 file that
contains more than one dataset.
Parameters
----------
record_by: Str
One of: SI, Signal2D
order : Str
One of 'C' or 'F'
lazy : bool, default False
Load the signal lazily.
%s
"""
with open(filename, "rb") as f:
dm = DigitalMicrographReader(f)
dm.parse_file()
images = [
ImageObject(imdict, f, order=order, record_by=record_by)
for imdict in dm.get_image_dictionaries()
]
imd = []
del dm.tags_dict['ImageList']
dm.tags_dict['ImageList'] = {}
for image in images:
dm.tags_dict['ImageList'][
'TagGroup0'] = image.imdict.as_dictionary()
axes = image.get_axes_dict()
mp = image.get_metadata()
mp['General']['original_filename'] = os.path.split(filename)[1]
post_process = []
if image.to_spectrum is True:
post_process.append(lambda s: s.to_signal1D(optimize=optimize))
post_process.append(lambda s: s.squeeze())
if lazy:
image.filename = filename
from dask.array import from_delayed
import dask.delayed as dd
val = dd(image.get_data, pure=True)()
data = from_delayed(val, shape=image.shape, dtype=image.dtype)
else:
data = image.get_data()
imd.append({
'data': data,
'axes': axes,
'metadata': mp,
'original_metadata': dm.tags_dict,
'post_process': post_process,
'mapping': image.get_mapping(),
})
return imd
file_reader.__doc__ %= (OPTIMIZE_ARG.replace('False', 'True'))
class CommonSignal1D:
"""Common functions for 1-dimensional signals."""
def to_signal2D(self, optimize=True):
"""Returns the one dimensional signal as a two dimensional signal.
By default ensures the data is stored optimally, hence often making a
copy of the data. See `transpose` for a more general method with more
options.
%s
See Also
--------
transpose, as_signal1D, as_signal2D, hs.transpose
Raises
------
DataDimensionError
When data.ndim < 2
"""
if self.data.ndim < 2:
raise DataDimensionError(
"A Signal dimension must be >= 2 to be converted to Signal2D")
nat = self.axes_manager._get_axes_in_natural_order()
im = self.transpose(signal_axes=nat[:2],
navigation_axes=nat[2:],
optimize=optimize)
return im
to_signal2D.__doc__ %= (OPTIMIZE_ARG.replace('False', 'True'))
def file_reader(filename, record_by=None, order=None, lazy=False,
optimize=True):
"""Reads a DM3 file and loads the data into the appropriate class.
data_id can be specified to load a given image within a DM3 file that
contains more than one dataset.
Parameters
----------
record_by: Str
One of: SI, Signal2D
order : Str
One of 'C' or 'F'
lazy : bool, default False
Load the signal lazily.
%s
"""
with open(filename, "rb") as f:
dm = DigitalMicrographReader(f)
dm.parse_file()
images = [ImageObject(imdict, f, order=order, record_by=record_by)
for imdict in dm.get_image_dictionaries()]
imd = []
del dm.tags_dict['ImageList']
dm.tags_dict['ImageList'] = {}
for image in images:
dm.tags_dict['ImageList'][
'TagGroup0'] = image.imdict.as_dictionary()
axes = image.get_axes_dict()
mp = image.get_metadata()
mp['General']['original_filename'] = os.path.split(filename)[1]
post_process = []
if image.to_spectrum is True:
post_process.append(lambda s: s.to_signal1D(optimize=optimize))
post_process.append(lambda s: s.squeeze())
if lazy:
image.filename = filename
from dask.array import from_delayed
import dask.delayed as dd
val = dd(image.get_data, pure=True)()
data = from_delayed(val, shape=image.shape,
dtype=image.dtype)
else:
data = image.get_data()
imd.append(
{'data': data,
'axes': axes,
'metadata': mp,
'original_metadata': dm.tags_dict,
'post_process': post_process,
'mapping': image.get_mapping(),
})
return imd
file_reader.__doc__ %= (OPTIMIZE_ARG.replace('False', 'True'))
class CommonSignal2D(object):
"""Common functions for 2-dimensional signals."""
def to_signal1D(self, optimize=True):
"""Returns the image as a spectrum.
%s
See Also
--------
as_signal1D : a method for the same purpose with more options.
signals.Signal1D.to_signal1D : performs the inverse operation on one
dimensional signals.
as_signal2D, transpose, hs.transpose
"""
return self.as_signal1D(0 + 3j, optimize=optimize)
to_signal1D.__doc__ %= (OPTIMIZE_ARG.replace('False', 'True'))