def test_register_event():
event = Event()
num_calls = {}
def callback():
num_calls['a'] = 'a'
event.register(callback)
event.notify()
assert event.notify_count == 1
assert num_calls['a'] == 'a'
class DummyWithWatchedMethod:
def __init__(self):
self._notify_count = 0
def callback(notifier, property_name, change):
self._notify_count += 1
self._notified_property = 0
self.event = Event()
self.event.register(callback)
@watched_method('event')
def notified_method(self):
pass
@property
def notified_property(self):
return self._notified_property
@notified_property.setter
@watched_property('event')
def notified_property(self, value):
self._notified_property = value
class CTF(HasAcceleratorMixin):
"""
Contrast transfer function object
The Contrast Transfer Function (CTF) describes the aberrations of the objective lens in HRTEM and specifies how the
condenser system shapes the probe in STEM.
abTEM implements phase aberrations up to 5th order using polar coefficients. See Eq. 2.22 in the reference [1]_.
Cartesian coefficients can be converted to polar using the utility function abtem.transfer.cartesian2polar.
Partial coherence is included as an envelope in the quasi-coherent approximation. See Chapter 3.2 in reference [1]_.
For a more detailed discussion with examples, see our `walkthrough
<https://abtem.readthedocs.io/en/latest/walkthrough/05_contrast_transfer_function.html>`_.
Parameters
----------
semiangle_cutoff: float
The semiangle cutoff describes the sharp Fourier space cutoff due to the objective aperture [mrad].
rolloff: float
Softens the cutoff. A value of 0 gives a hard cutoff, while 1 gives the softest possible cutoff [Å].
focal_spread: float
The 1/e width of the focal spread due to chromatic aberration and lens current instability [Å].
angular_spread: float
The 1/e width of the angular deviations due to source size [Å].
gaussian_spread:
The 1/e width image deflections due to vibrations and thermal magnetic noise [Å].
energy: float
The electron energy of the wave functions this contrast transfer function will be applied to [eV].
parameters: dict
Mapping from aberration symbols to their corresponding values. All aberration magnitudes should be given in Å.
kwargs:
Provide the aberration coefficients as keyword arguments.
References
----------
.. [1] Kirkland, E. J. (2010). Advanced Computing in Electron Microscopy (2nd ed.). Springer.
"""
def __init__(self,
semiangle_cutoff: float = np.inf,
rolloff: float = 0.1,
focal_spread: float = 0.,
angular_spread: float = 0.,
gaussian_spread: float = 0.,
energy: float = None,
parameters: Mapping[str, float] = None,
**kwargs):
for key in kwargs.keys():
if (key not in polar_symbols) and (key
not in polar_aliases.keys()):
raise ValueError('{} not a recognized parameter'.format(key))
self.changed = Event()
self._accelerator = Accelerator(energy=energy)
self._accelerator.changed.register(self.changed.notify)
self._semiangle_cutoff = semiangle_cutoff
self._rolloff = rolloff
self._focal_spread = focal_spread
self._angular_spread = angular_spread
self._gaussian_spread = gaussian_spread
self._parameters = dict(zip(polar_symbols, [0.] * len(polar_symbols)))
if parameters is None:
parameters = {}
parameters.update(kwargs)
self.set_parameters(parameters)
def parametrization_property(key):
def getter(self):
return self._parameters[key]
def setter(self, value):
old = getattr(self, key)
self._parameters[key] = value
self.changed.notify(**{
'notifier': self,
'property_name': key,
'change': old != value
})
return property(getter, setter)
for symbol in polar_symbols:
setattr(self.__class__, symbol, parametrization_property(symbol))
for key, value in polar_aliases.items():
if key != 'defocus':
setattr(self.__class__, key, parametrization_property(value))
@property
def nyquist_sampling(self):
return 1 / (4 * self.semiangle_cutoff / self.wavelength * 1e-3)
@property
def parameters(self):
"""The parameters."""
return self._parameters
@property
def defocus(self) -> float:
"""The defocus [Å]."""
return -self._parameters['C10']
@defocus.setter
def defocus(self, value: float):
self.C10 = -value
@property
def semiangle_cutoff(self) -> float:
"""The semi-angle cutoff [mrad]."""
return self._semiangle_cutoff
@semiangle_cutoff.setter
@watched_property('changed')
def semiangle_cutoff(self, value: float):
self._semiangle_cutoff = value
@property
def rolloff(self) -> float:
"""The fraction of soft tapering of the cutoff."""
return self._rolloff
@rolloff.setter
@watched_property('changed')
def rolloff(self, value: float):
self._rolloff = value
@property
def focal_spread(self) -> float:
"""The focal spread [Å]."""
return self._focal_spread
@focal_spread.setter
@watched_property('changed')
def focal_spread(self, value: float):
"""The angular spread [mrad]."""
self._focal_spread = value
@property
def angular_spread(self) -> float:
return self._angular_spread
@angular_spread.setter
@watched_property('changed')
def angular_spread(self, value: float):
self._angular_spread = value
@property
def gaussian_spread(self) -> float:
"""The Gaussian spread [Å]."""
return self._gaussian_spread
@gaussian_spread.setter
@watched_property('changed')
def gaussian_spread(self, value: float):
self._gaussian_spread = value
@watched_method('changed')
def set_parameters(self, parameters: dict):
"""
Set the phase of the phase aberration.
Parameters
----------
parameters: dict
Mapping from aberration symbols to their corresponding values.
"""
for symbol, value in parameters.items():
if symbol in self._parameters.keys():
self._parameters[symbol] = value
elif symbol == 'defocus':
self._parameters[polar_aliases[symbol]] = -value
elif symbol in polar_aliases.keys():
self._parameters[polar_aliases[symbol]] = value
else:
raise ValueError(
'{} not a recognized parameter'.format(symbol))
return parameters
def evaluate_aperture(
self, alpha: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
xp = get_array_module(alpha)
semiangle_cutoff = self.semiangle_cutoff / 1000
if self.semiangle_cutoff == xp.inf:
return xp.ones_like(alpha)
if self.rolloff > 0.:
rolloff = self.rolloff * semiangle_cutoff
array = .5 * (
1 + xp.cos(np.pi *
(alpha - semiangle_cutoff + rolloff) / rolloff))
array[alpha > semiangle_cutoff] = 0.
array = xp.where(alpha > semiangle_cutoff - rolloff, array,
xp.ones_like(alpha, dtype=xp.float32))
else:
array = xp.array(alpha < semiangle_cutoff).astype(xp.float32)
return array
def evaluate_temporal_envelope(
self, alpha: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
xp = get_array_module(alpha)
return xp.exp(-(.5 * xp.pi / self.wavelength * self.focal_spread *
alpha**2)**2).astype(xp.float32)
def evaluate_gaussian_envelope(
self, alpha: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
xp = get_array_module(alpha)
return xp.exp(-.5 * self.gaussian_spread**2 * alpha**2 /
self.wavelength**2)
def evaluate_spatial_envelope(self, alpha: Union[float, np.ndarray], phi: Union[float, np.ndarray]) -> \
Union[float, np.ndarray]:
xp = get_array_module(alpha)
p = self.parameters
dchi_dk = 2 * xp.pi / self.wavelength * (
(p['C12'] * xp.cos(2. * (phi - p['phi12'])) + p['C10']) * alpha +
(p['C23'] * xp.cos(3. * (phi - p['phi23'])) +
p['C21'] * xp.cos(1. * (phi - p['phi21']))) * alpha**2 +
(p['C34'] * xp.cos(4. * (phi - p['phi34'])) +
p['C32'] * xp.cos(2. *
(phi - p['phi32'])) + p['C30']) * alpha**3 +
(p['C45'] * xp.cos(5. * (phi - p['phi45'])) +
p['C43'] * xp.cos(3. * (phi - p['phi43'])) +
p['C41'] * xp.cos(1. * (phi - p['phi41']))) * alpha**4 +
(p['C56'] * xp.cos(6. * (phi - p['phi56'])) +
p['C54'] * xp.cos(4. * (phi - p['phi54'])) +
p['C52'] * xp.cos(2. * (phi - p['phi52'])) + p['C50']) * alpha**5)
dchi_dphi = -2 * xp.pi / self.wavelength * (
1 / 2. *
(2. * p['C12'] * xp.sin(2. *
(phi - p['phi12']))) * alpha + 1 / 3. *
(3. * p['C23'] * xp.sin(3. * (phi - p['phi23'])) +
1. * p['C21'] * xp.sin(1. *
(phi - p['phi21']))) * alpha**2 + 1 / 4. *
(4. * p['C34'] * xp.sin(4. * (phi - p['phi34'])) +
2. * p['C32'] * xp.sin(2. *
(phi - p['phi32']))) * alpha**3 + 1 / 5. *
(5. * p['C45'] * xp.sin(5. * (phi - p['phi45'])) +
3. * p['C43'] * xp.sin(3. * (phi - p['phi43'])) +
1. * p['C41'] * xp.sin(1. *
(phi - p['phi41']))) * alpha**4 + 1 / 6. *
(6. * p['C56'] * xp.sin(6. * (phi - p['phi56'])) +
4. * p['C54'] * xp.sin(4. * (phi - p['phi54'])) +
2. * p['C52'] * xp.sin(2. * (phi - p['phi52']))) * alpha**5)
return xp.exp(-xp.sign(self.angular_spread) *
(self.angular_spread / 2 / 1000)**2 *
(dchi_dk**2 + dchi_dphi**2))
def evaluate_chi(
self, alpha: Union[float, np.ndarray],
phi: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
xp = get_array_module(alpha)
p = self.parameters
alpha2 = alpha**2
alpha = xp.array(alpha)
array = xp.zeros(alpha.shape, dtype=np.float32)
if any([p[symbol] != 0. for symbol in ('C10', 'C12', 'phi12')]):
array += (1 / 2 * alpha2 *
(p['C10'] + p['C12'] * xp.cos(2 * (phi - p['phi12']))))
if any(
[p[symbol] != 0. for symbol in ('C21', 'phi21', 'C23', 'phi23')]):
array += (1 / 3 * alpha2 * alpha *
(p['C21'] * xp.cos(phi - p['phi21']) +
p['C23'] * xp.cos(3 * (phi - p['phi23']))))
if any([
p[symbol] != 0.
for symbol in ('C30', 'C32', 'phi32', 'C34', 'phi34')
]):
array += (1 / 4 * alpha2**2 *
(p['C30'] + p['C32'] * xp.cos(2 * (phi - p['phi32'])) +
p['C34'] * xp.cos(4 * (phi - p['phi34']))))
if any([
p[symbol] != 0.
for symbol in ('C41', 'phi41', 'C43', 'phi43', 'C45', 'phi41')
]):
array += (1 / 5 * alpha2**2 * alpha *
(p['C41'] * xp.cos((phi - p['phi41'])) +
p['C43'] * xp.cos(3 * (phi - p['phi43'])) +
p['C45'] * xp.cos(5 * (phi - p['phi45']))))
if any([
p[symbol] != 0. for symbol in ('C50', 'C52', 'phi52', 'C54',
'phi54', 'C56', 'phi56')
]):
array += (1 / 6 * alpha2**3 *
(p['C50'] + p['C52'] * xp.cos(2 * (phi - p['phi52'])) +
p['C54'] * xp.cos(4 * (phi - p['phi54'])) +
p['C56'] * xp.cos(6 * (phi - p['phi56']))))
array = 2 * xp.pi / self.wavelength * array
return array
def evaluate_aberrations(self, alpha: Union[float, np.ndarray], phi: Union[float, np.ndarray]) -> \
Union[float, np.ndarray]:
xp = get_array_module(alpha)
complex_exponential = get_device_function(xp, 'complex_exponential')
return complex_exponential(-self.evaluate_chi(alpha, phi))
def evaluate(self, alpha: Union[float, np.ndarray],
phi: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
array = self.evaluate_aberrations(alpha, phi)
if self.semiangle_cutoff < np.inf:
array *= self.evaluate_aperture(alpha)
if self.focal_spread > 0.:
array *= self.evaluate_temporal_envelope(alpha)
if self.angular_spread > 0.:
array *= self.evaluate_spatial_envelope(alpha, phi)
if self.gaussian_spread > 0.:
array *= self.evaluate_gaussian_envelope(alpha)
return array
def evaluate_on_grid(self, grid, xp=np):
kx, ky = spatial_frequencies(grid.gpts, grid.sampling)
kx = kx.reshape((1, -1, 1))
ky = ky.reshape((1, 1, -1))
kx = xp.asarray(kx)
ky = xp.asarray(ky)
alpha, phi = polar_coordinates(xp.asarray(kx * self.wavelength),
xp.asarray(ky * self.wavelength))
return self.evaluate(alpha, phi)
def profiles(self, max_semiangle: float = None, phi: float = 0.):
if max_semiangle is None:
if self._semiangle_cutoff == np.inf:
max_semiangle = 50
else:
max_semiangle = self._semiangle_cutoff * 1.6
alpha = np.linspace(0, max_semiangle / 1000., 500)
aberrations = self.evaluate_aberrations(alpha, phi)
aperture = self.evaluate_aperture(alpha)
temporal_envelope = self.evaluate_temporal_envelope(alpha)
spatial_envelope = self.evaluate_spatial_envelope(alpha, phi)
gaussian_envelope = self.evaluate_gaussian_envelope(alpha)
envelope = aperture * temporal_envelope * spatial_envelope * gaussian_envelope
calibration = Calibration(offset=0.,
sampling=(alpha[1] - alpha[0]) * 1000.,
units='mrad',
name='alpha')
profiles = {}
profiles['ctf'] = Measurement(aberrations.imag * envelope,
calibrations=[calibration],
name='CTF')
profiles['aperture'] = Measurement(aperture,
calibrations=[calibration],
name='Aperture')
profiles['temporal_envelope'] = Measurement(temporal_envelope,
calibrations=[calibration],
name='Temporal')
profiles['spatial_envelope'] = Measurement(spatial_envelope,
calibrations=[calibration],
name='Spatial')
profiles['gaussian_spread'] = Measurement(gaussian_envelope,
calibrations=[calibration],
name='Gaussian')
profiles['envelope'] = Measurement(envelope,
calibrations=[calibration],
name='Envelope')
return profiles
def apply(self, waves, interact=False, sliders=None, throttling=0.):
from abtem.visualize.bqplot import show_measurement_2d
from abtem.visualize.widgets import quick_sliders, throttle
import ipywidgets as widgets
if interact:
image_waves = waves.copy()
def update():
image_waves._array[:] = waves.apply_ctf(self).array
return image_waves.intensity()
figure, callback = show_measurement_2d(update)
if throttling:
callback = throttle(throttling)(callback)
self.changed.register(callback)
if sliders:
sliders = quick_sliders(self, **sliders)
figure = widgets.HBox([figure, widgets.VBox(sliders)])
return image_waves, figure
else:
if sliders:
raise RuntimeError()
return waves.apply_ctf(self)
def interact(self,
max_semiangle: float = None,
phi: float = 0.,
sliders=None,
throttling=False):
import bqplot.pyplot as plt
from abtem.visualize.bqplot import show_measurement_1d
from abtem.visualize.widgets import quick_sliders, throttle
import ipywidgets as widgets
figure = plt.figure(fig_margin={
'top': 0,
'bottom': 50,
'left': 50,
'right': 0
})
figure.layout.height = '250px'
figure.layout.width = '300px'
_, callback = show_measurement_1d(
lambda: self.profiles(max_semiangle, phi).values(), figure)
if throttling:
callback = throttle(throttling)(callback)
self.changed.register(callback)
if sliders:
sliders = quick_sliders(self, **sliders)
return widgets.HBox([figure, widgets.VBox(sliders)])
else:
return figure
def show(self,
max_semiangle: float = None,
phi: float = 0,
ax=None,
**kwargs):
"""
Show the contrast transfer function.
Parameters
----------
max_semiangle: float
Maximum semiangle to display in the plot.
ax: matplotlib Axes, optional
If given, the plot will be added to this matplotlib axes.
phi: float, optional
The contrast transfer function will be plotted along this angle. Default is 0.
n: int, optional
Number of evaluation points to use in the plot. Default is 1000.
title: str, optional
The title of the plot. Default is 'None'.
kwargs:
Additional keyword arguments for the line plots.
"""
import matplotlib.pyplot as plt
if ax is None:
ax = plt.subplot()
for key, profile in self.profiles(max_semiangle, phi).items():
if not np.all(profile.array == 1.):
ax, lines = profile.show(legend=True, ax=ax, **kwargs)
return ax
def copy(self):
parameters = self.parameters.copy()
return self.__class__(semiangle_cutoff=self.semiangle_cutoff,
rolloff=self.rolloff,
focal_spread=self.focal_spread,
angular_spread=self.angular_spread,
gaussian_spread=self.gaussian_spread,
energy=self.energy,
parameters=parameters)
class CrystalPotential(AbstractPotential):
"""
Crystal potential object
The crystal potential may be used to represent a potential consisting of a repeating unit. This may allow
calculations to be performed with lower memory and computational cost.
The crystal potential has an additional function in conjunction with frozen phonon calculations. The number of
frozen phonon configurations are not given by the FrozenPhonon objects, rather the ensemble of frozen phonon
potentials represented by a potential with frozen phonons represent a collection of units, which will be assembled
randomly to represent a random potential. The number of frozen phonon configurations should be given explicitely.
This may save computational cost since a smaller number of units can be combined to a larger frozen phonon ensemble.
Parameters
----------
potential_unit : AbstractPotential
The potential unit that repeated will create the full potential.
repetitions : three int
The repetitions of the potential in x, y and z.
num_frozen_phonon_configs : int
Number of frozen phonon configurations.
"""
def __init__(self,
potential_unit: AbstractPotential,
repetitions: Tuple[int, int, int],
num_frozen_phonon_configs: int = 1):
self._potential_unit = potential_unit
self.repetitions = repetitions
self._num_frozen_phonon_configs = num_frozen_phonon_configs
if (potential_unit.num_frozen_phonon_configs
== 1) & (num_frozen_phonon_configs > 1):
warnings.warn(
'"num_frozen_phonon_configs" is greater than one, but the potential unit does not have'
'frozen phonons')
if (potential_unit.num_frozen_phonon_configs >
1) & (num_frozen_phonon_configs == 1):
warnings.warn(
'the potential unit has frozen phonons, but "num_frozen_phonon_configs" is set to 1'
)
self._cache = Cache(1)
self._changed = Event()
gpts = (self._potential_unit.gpts[0] * self.repetitions[0],
self._potential_unit.gpts[1] * self.repetitions[1])
extent = (self._potential_unit.extent[0] * self.repetitions[0],
self._potential_unit.extent[1] * self.repetitions[1])
self._grid = Grid(extent=extent,
gpts=gpts,
sampling=self._potential_unit.sampling,
lock_extent=True)
self._grid.changed.register(self._changed.notify)
self._changed.register(cache_clear_callback(self._cache))
super().__init__(precalculate=False)
@HasGridMixin.gpts.setter
def gpts(self, gpts):
if not ((gpts[0] % self.repetitions[0] == 0) and
(gpts[1] % self.repetitions[0] == 0)):
raise ValueError(
'gpts must be divisible by the number of potential repetitions'
)
self.grid.gpts = gpts
self._potential_unit.gpts = (gpts[0] // self._repetitions[0],
gpts[1] // self._repetitions[1])
@HasGridMixin.sampling.setter
def sampling(self, sampling):
self.sampling = sampling
self._potential_unit.sampling = sampling
@property
def num_frozen_phonon_configs(self):
return self._num_frozen_phonon_configs
def generate_frozen_phonon_potentials(self, pbar=False):
for i in range(self.num_frozen_phonon_configs):
yield self
@property
def repetitions(self) -> Tuple[int, int, int]:
return self._repetitions
@repetitions.setter
def repetitions(self, repetitions: Tuple[int, int, int]):
repetitions = tuple(repetitions)
if len(repetitions) != 3:
raise ValueError('repetitions must be sequence of length 3')
self._repetitions = repetitions
@property
def num_slices(self) -> int:
return self._potential_unit.num_slices * self.repetitions[2]
def get_slice_thickness(self, i) -> float:
return self._potential_unit.get_slice_thickness(i)
@cached_method('_cache')
def _calculate_configs(self, energy, max_batch=1):
potential_generators = self._potential_unit.generate_frozen_phonon_potentials(
pbar=False)
potential_configs = []
for potential in potential_generators:
if isinstance(potential, AbstractPotentialBuilder):
potential = potential.build(max_batch=max_batch)
elif not isinstance(potential, PotentialArray):
raise RuntimeError()
if energy is not None:
potential = potential.as_transmission_function(
energy=energy, max_batch=max_batch)
potential = potential.tile(self.repetitions[:2])
potential_configs.append(potential)
return potential_configs
def _generate_slices_base(self,
first_slice=0,
last_slice=None,
max_batch=1,
energy=None):
first_layer = first_slice // self._potential_unit.num_slices
if last_slice is None:
last_layer = self.repetitions[2]
else:
last_layer = last_slice // self._potential_unit.num_slices
first_slice = first_slice % self._potential_unit.num_slices
last_slice = None
configs = self._calculate_configs(energy, max_batch)
if len(configs) == 1:
layers = configs * self.repetitions[2]
else:
layers = [
configs[np.random.randint(len(configs))]
for _ in range(self.repetitions[2])
]
for layer_num, layer in enumerate(layers[first_layer:last_layer]):
if layer_num == last_layer:
last_slice = last_slice % self._potential_unit.num_slices
for start, end, potential_slice in layer.generate_slices(
first_slice=first_slice,
last_slice=last_slice,
max_batch=max_batch):
yield layer_num + start, layer_num + end, potential_slice
first_slice = 0
def generate_slices(self, first_slice=0, last_slice=None, max_batch=1):
return self._generate_slices_base(first_slice=first_slice,
last_slice=last_slice,
max_batch=max_batch)
def generate_transmission_functions(self,
energy,
first_slice=0,
last_slice=None,
max_batch=1):
return self._generate_slices_base(first_slice=first_slice,
last_slice=last_slice,
max_batch=max_batch,
energy=energy)