def save(self, filename, only_view = False, **kwds):
"""Saves the signal in the specified format.
The function gets the format from the extension. You can use:
- hdf5 for HDF5
- nc for NetCDF
- msa for EMSA/MSA single spectrum saving.
- bin to produce a raw binary file
- Many image formats such as png, tiff, jpeg...
Please note that not all the formats supports saving datasets of
arbitrary dimensions, e.g. msa only suports 1D data.
Parameters
----------
filename : str
msa_format : {'Y', 'XY'}
'Y' will produce a file without the energy axis. 'XY' will also
save another column with the energy axis. For compatibility with
Gatan Digital Micrograph 'Y' is the default.
only_view : bool
If True, only the current view will be saved. Otherwise the full
dataset is saved. Please note that not all the formats support this
option at the moment.
"""
io.save(filename, self, **kwds)
def save(self, filename, only_view=False, **kwds):
"""Saves the signal in the specified format.
The function gets the format from the extension. You can use:
- hdf5 for HDF5
- nc for NetCDF
- msa for EMSA/MSA single spectrum saving.
- bin to produce a raw binary file
- Many image formats such as png, tiff, jpeg...
Please note that not all the formats supports saving datasets of
arbitrary dimensions, e.g. msa only suports 1D data.
Parameters
----------
filename : str
msa_format : {'Y', 'XY'}
'Y' will produce a file without the energy axis. 'XY' will also
save another column with the energy axis. For compatibility with
Gatan Digital Micrograph 'Y' is the default.
only_view : bool
If True, only the current view will be saved. Otherwise the full
dataset is saved. Please note that not all the formats support this
option at the moment.
"""
io.save(filename, self, **kwds)
def compareSaveLoad(self,
testShape,
dtype='int8',
compressor=None,
clevel=1,
lazy=False,
do_async=False,
**kwargs):
# This is the main function which reads and writes from disk.
mrcName = os.path.join(tmpDir,
"testMage_{}_lazy_{}.mrcz".format(dtype, lazy))
dtype = np.dtype(dtype)
if dtype == 'float32' or dtype == 'float64':
testData = np.random.normal(size=testShape).astype(dtype)
elif dtype == 'complex64' or dtype == 'complex128':
testData = np.random.normal(size=testShape).astype(
dtype) + 1.0j * np.random.normal(size=testShape).astype(dtype)
else: # integers
testData = np.random.randint(10, size=testShape).astype(dtype)
testSignal = signals.Signal2D(testData)
if lazy:
testSignal = testSignal.as_lazy()
# Unfortunately one cannot iterate over axes_manager in a Pythonic way
# for axis in testSignal.axes_manager:
testSignal.axes_manager[0].name = 'z'
testSignal.axes_manager[0].scale = np.random.uniform(low=0.0, high=1.0)
testSignal.axes_manager[0].units = 'nm'
testSignal.axes_manager[1].name = 'x'
testSignal.axes_manager[1].scale = np.random.uniform(low=0.0, high=1.0)
testSignal.axes_manager[1].units = 'nm'
testSignal.axes_manager[2].name = 'y'
testSignal.axes_manager[2].scale = np.random.uniform(low=0.0, high=1.0)
testSignal.axes_manager[2].units = 'nm'
# Meta-data that goes into MRC fixed header
testSignal.metadata.set_item('Acquisition_instrument.TEM.beam_energy',
300.0)
# Meta-data that goes into JSON extended header
testSignal.metadata.set_item(
'Acquisition_instrument.TEM.magnification', 25000)
testSignal.metadata.set_item(
'Signal.Noise_properties.Variance_linear_model.gain_factor', 1.0)
save(mrcName,
testSignal,
compressor=compressor,
clevel=clevel,
do_async=do_async,
**kwargs)
if do_async:
# Poll file on disk since we don't return the
# concurrent.futures.Future
t_stop = perf_counter() + MAX_ASYNC_TIME
sleep(0.005)
while (perf_counter() < t_stop):
try:
fh = open(mrcName, 'a')
fh.close()
break
except IOError:
sleep(0.001)
print("Time to save file: {} s".format(perf_counter() -
(t_stop - MAX_ASYNC_TIME)))
sleep(0.1)
reSignal = load(mrcName)
try:
os.remove(mrcName)
except IOError:
print("Warning: file {} left on disk".format(mrcName))
npt.assert_array_almost_equal(testSignal.data.shape,
reSignal.data.shape)
npt.assert_array_almost_equal(testSignal.data, reSignal.data)
npt.assert_almost_equal(
testSignal.metadata.Acquisition_instrument.TEM.beam_energy,
reSignal.metadata.Acquisition_instrument.TEM.beam_energy)
npt.assert_almost_equal(
testSignal.metadata.Acquisition_instrument.TEM.magnification,
reSignal.metadata.Acquisition_instrument.TEM.magnification)
for aName in ['x', 'y', 'z']:
npt.assert_equal(testSignal.axes_manager[aName].size,
reSignal.axes_manager[aName].size)
npt.assert_almost_equal(testSignal.axes_manager[aName].scale,
reSignal.axes_manager[aName].scale)
if dtype == 'complex64':
assert isinstance(reSignal, signals.ComplexSignal2D)
else:
assert isinstance(reSignal, signals.Signal2D)
assert_deep_almost_equal(testSignal.axes_manager.as_dictionary(),
reSignal.axes_manager.as_dictionary())
assert_deep_almost_equal(testSignal.metadata.as_dictionary(),
reSignal.metadata.as_dictionary())
return reSignal
def compareSaveLoad(self, testShape, dtype='int8', compressor=None,
clevel=1, lazy=False, do_async=False, **kwargs):
# This is the main function which reads and writes from disk.
mrcName = os.path.join(
tmpDir, "testMage_{}_lazy_{}.mrcz".format(dtype, lazy))
dtype = np.dtype(dtype)
if dtype == 'float32' or dtype == 'float64':
testData = np.random.normal(size=testShape).astype(dtype)
elif dtype == 'complex64' or dtype == 'complex128':
testData = np.random.normal(size=testShape).astype(
dtype) + 1.0j * np.random.normal(size=testShape).astype(dtype)
else: # integers
testData = np.random.randint(10, size=testShape).astype(dtype)
testSignal = signals.Signal2D(testData)
if lazy:
testSignal = testSignal.as_lazy()
# Unfortunately one cannot iterate over axes_manager in a Pythonic way
# for axis in testSignal.axes_manager:
testSignal.axes_manager[0].name = 'z'
testSignal.axes_manager[0].scale = np.random.uniform(low=0.0, high=1.0)
testSignal.axes_manager[0].units = 'nm'
testSignal.axes_manager[1].name = 'x'
testSignal.axes_manager[1].scale = np.random.uniform(low=0.0, high=1.0)
testSignal.axes_manager[1].units = 'nm'
testSignal.axes_manager[2].name = 'y'
testSignal.axes_manager[2].scale = np.random.uniform(low=0.0, high=1.0)
testSignal.axes_manager[2].units = 'nm'
# Meta-data that goes into MRC fixed header
testSignal.metadata.set_item(
'Acquisition_instrument.TEM.beam_energy', 300.0)
# Meta-data that goes into JSON extended header
testSignal.metadata.set_item(
'Acquisition_instrument.TEM.magnification', 25000)
testSignal.metadata.set_item(
'Signal.Noise_properties.Variance_linear_model.gain_factor', 1.0)
save(mrcName, testSignal, compressor=compressor,
clevel=clevel, do_async=do_async, **kwargs)
if do_async:
# Poll file on disk since we don't return the
# concurrent.futures.Future
t_stop = perf_counter() + MAX_ASYNC_TIME
sleep(0.005)
while(perf_counter() < t_stop):
try:
fh = open(mrcName, 'a')
fh.close()
break
except IOError:
sleep(0.001)
print("Time to save file: {} s".format(
perf_counter() - (t_stop - MAX_ASYNC_TIME)))
sleep(0.005)
reSignal = load(mrcName)
try:
os.remove(mrcName)
except IOError:
print("Warning: file {} left on disk".format(mrcName))
npt.assert_array_almost_equal(
testSignal.data.shape,
reSignal.data.shape)
npt.assert_array_almost_equal(testSignal.data, reSignal.data)
npt.assert_almost_equal(
testSignal.metadata.Acquisition_instrument.TEM.beam_energy,
reSignal.metadata.Acquisition_instrument.TEM.beam_energy)
npt.assert_almost_equal(
testSignal.metadata.Acquisition_instrument.TEM.magnification,
reSignal.metadata.Acquisition_instrument.TEM.magnification)
for aName in ['x', 'y', 'z']:
npt.assert_equal(
testSignal.axes_manager[aName].size,
reSignal.axes_manager[aName].size)
npt.assert_almost_equal(
testSignal.axes_manager[aName].scale,
reSignal.axes_manager[aName].scale)
if dtype == 'complex64':
assert isinstance(reSignal, signals.ComplexSignal2D)
else:
assert isinstance(reSignal, signals.Signal2D)
assert_deep_almost_equal(testSignal.axes_manager.as_dictionary(),
reSignal.axes_manager.as_dictionary())
assert_deep_almost_equal(testSignal.metadata.as_dictionary(),
reSignal.metadata.as_dictionary())
return reSignal