def test_export_import_measurement(tmp_path):
d = tmp_path / 'sub'
d.mkdir()
path = d / 'measurement.hdf5'
calibrations = calibrations_from_grid((512, 256), (.1, .3), ['x', 'y'],
'Å')
measurement = Measurement(np.random.rand(512, 256), calibrations)
measurement.write(path)
imported_measurement = Measurement.read(path)
assert np.allclose(measurement.array, imported_measurement.array)
assert measurement.calibrations[0] == imported_measurement.calibrations[0]
assert measurement.calibrations[1] == imported_measurement.calibrations[1]
def allocate_measurement(self, waves, scan: AbstractScan) -> Measurement:
"""
Allocate a Measurement object or an hdf5 file.
Parameters
----------
waves : Waves or SMatrix object
The wave function that will define the shape of the diffraction patterns.
scan: Scan object
The scan object that will define the scan dimensions the measurement.
Returns
-------
Measurement object or str
The allocated measurement or path to hdf5 file with the measurement data.
"""
waves.grid.check_is_defined()
calibrations = calibrations_from_grid(waves.gpts,
waves.sampling,
names=['x', 'y'],
units='Å')
array = np.zeros(scan.shape + waves.gpts, dtype=np.complex64)
measurement = Measurement(array,
calibrations=scan.calibrations +
calibrations)
if isinstance(self.save_file, str):
measurement = measurement.write(self.save_file)
return measurement
def allocate_measurement(self, grid: Grid, wavelength: float,
scan: AbstractScan) -> Measurement:
inner, outer, nbins_radial, nbins_azimuthal = self._get_bins(
grid.antialiased_sampling, wavelength)
shape = scan.shape
calibrations = scan.calibrations
if nbins_radial > 1:
shape += (nbins_radial, )
calibrations += (Calibration(offset=inner,
sampling=self._radial_steps,
units='mrad'), )
if nbins_azimuthal > 1:
shape += (nbins_azimuthal, )
calibrations += (Calibration(offset=0,
sampling=self._azimuthal_steps,
units='rad'), )
array = np.zeros(shape, dtype=np.float32)
measurement = Measurement(array, calibrations=calibrations)
if isinstance(self.save_file, str):
measurement = measurement.write(self.save_file)
return measurement
def allocate_measurement(self,
waves,
scan: AbstractScan = None) -> Measurement:
"""
Allocate a Measurement object or an hdf5 file.
Parameters
----------
waves : Waves or SMatrix object
The wave function that will define the shape of the diffraction patterns.
scan: Scan object
The scan object that will define the scan dimensions the measurement.
Returns
-------
Measurement object or str
The allocated measurement or path to hdf5 file with the measurement data.
"""
waves.grid.check_is_defined()
waves.accelerator.check_is_defined()
check_max_angle_exceeded(waves, self.max_angle)
gpts = waves.downsampled_gpts(self.max_angle)
gpts, new_angular_sampling = self._resampled_gpts(
gpts, angular_sampling=waves.angular_sampling)
sampling = (1 / new_angular_sampling[0] / gpts[0] * waves.wavelength *
1000, 1 / new_angular_sampling[1] / gpts[1] *
waves.wavelength * 1000)
calibrations = calibrations_from_grid(gpts,
sampling,
names=['alpha_x', 'alpha_y'],
units='mrad',
scale_factor=waves.wavelength *
1000,
fourier_space=True)
if scan is None:
scan_shape = ()
scan_calibrations = ()
elif isinstance(scan, tuple):
scan_shape = scan
scan_calibrations = (None, ) * len(scan)
else:
scan_shape = scan.shape
scan_calibrations = scan.calibrations
array = np.zeros(scan_shape + gpts)
measurement = Measurement(array,
calibrations=scan_calibrations +
calibrations)
if isinstance(self.save_file, str):
measurement = measurement.write(self.save_file)
return measurement
def allocate_measurement(self,
waves,
scan: AbstractScan = None) -> Measurement:
"""
Allocate a Measurement object or an hdf5 file.
Parameters
----------
waves : Waves object
An example of the
scan : Scan object
The scan object that will define the scan dimensions the measurement.
Returns
-------
Measurement object or str
The allocated measurement or path to hdf5 file with the measurement data.
"""
waves.grid.check_is_defined()
waves.accelerator.check_is_defined()
if scan is None:
shape = ()
calibrations = ()
else:
shape = scan.shape
calibrations = scan.calibrations
nbins_radial, nbins_azimuthal, inner, _ = self._get_bins(
min(waves.cutoff_scattering_angles))
if nbins_radial > 1:
shape += (nbins_radial, )
calibrations += (Calibration(offset=inner,
sampling=self._radial_steps,
units='mrad'), )
if nbins_azimuthal > 1:
shape += (nbins_azimuthal, )
calibrations += (Calibration(offset=0,
sampling=self._azimuthal_steps,
units='rad'), )
array = np.zeros(shape, dtype=np.float32)
measurement = Measurement(array, calibrations=calibrations)
if isinstance(self.save_file, str):
measurement = measurement.write(self.save_file)
return measurement
def allocate_measurement(self, grid: Grid, wavelength: float,
scan: AbstractScan) -> Measurement:
grid.check_is_defined()
calibrations = calibrations_from_grid(grid.gpts,
grid.sampling,
names=['x', 'y'],
units='Å')
array = np.zeros(scan.shape + grid.gpts, dtype=np.complex64)
measurement = Measurement(array,
calibrations=scan.calibrations +
calibrations)
if isinstance(self.save_file, str):
measurement = measurement.write(self.save_file)
return measurement
def allocate_measurement(self, grid: Grid, wavelength: float,
scan: AbstractScan) -> Measurement:
grid.check_is_defined()
shape = (grid.gpts[0] // 2, grid.gpts[1] // 2)
calibrations = calibrations_from_grid(grid.antialiased_gpts,
grid.antialiased_sampling,
names=['alpha_x', 'alpha_y'],
units='mrad',
scale_factor=wavelength * 1000,
fourier_space=True)
array = np.zeros(scan.shape + shape)
measurement = Measurement(array,
calibrations=scan.calibrations +
calibrations)
if isinstance(self.save_file, str):
measurement = measurement.write(self.save_file)
return measurement
