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139 prepare for magnetism refl1d #140

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May 2, 2024
Merged

139 prepare for magnetism refl1d #140

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729d8ab
clean refnx
andped10 Apr 26, 2024
3b90cd3
test and functionality reuse
andped10 Apr 29, 2024
ecad8a9
satisfy ruff
andped10 Apr 29, 2024
db1a576
pr response
andped10 May 2, 2024
714089c
pr response
andped10 May 2, 2024
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code comment
andped10 May 2, 2024
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194 changes: 111 additions & 83 deletions EasyReflectometry/calculators/refl1d/wrapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,9 +10,10 @@

from ..wrapper_base import WrapperBase

PADDING_RANGE = 3.5
UPSCALE_FACTOR = 21
RESOLUTION_PADDING = 3.5
OVERSAMPLING_FACTOR = 21
MAGNETISM = False
ALL_POLARIZATIONS = False


class Refl1dWrapper(WrapperBase):
Expand All @@ -30,7 +31,7 @@ def create_layer(self, name: str):

:param name: The name of the layer
"""
if MAGNETISM:
if MAGNETISM: # A test with hardcoded magnetism in all layers
magnetism = names.Magnetism(rhoM=0.2, thetaM=270)
else:
magnetism = None
Expand All @@ -47,17 +48,6 @@ def create_item(self, name: str):
)
del self.storage['item'][name].stack[0]

def update_item(self, name: str, **kwargs):
"""
Update a layer.

:param name: The item name
"""
item = self.storage['item'][name]
for key in kwargs.keys():
ii = getattr(item, key)
setattr(ii, 'value', kwargs[key])

def get_item_value(self, name: str, key: str) -> float:
"""
A function to get a given item value
Expand Down Expand Up @@ -155,61 +145,43 @@ def calculate(self, q_array: np.ndarray, model_name: str) -> np.ndarray:
:param model_name: the model name
:return: reflectivity calculated at q
"""
structure = model.Stack()
# -1 to reverse the order
for i in self.storage['model'][model_name]['items'][::-1]:
if i.repeat.value == 1:
# -1 to reverse the order
for j in range(len(i.stack))[::-1]:
structure |= i.stack[j]
else:
stack = model.Stack()
# -1 to reverse the order
for j in range(len(i.stack))[::-1]:
stack |= i.stack[j]
structure |= model.Repeat(stack, repeat=i.repeat.value)

argmin = np.argmin(q_array)
argmax = np.argmax(q_array)
dq_vector = self._resolution_function(q_array)
sample = _build_sample(self.storage, model_name)
dq_array = self._resolution_function(q_array)

if is_constant_resolution_function(self._resolution_function):
# Get percentage of Q and change from sigma to FWHM
dq_vector = dq_vector * q_array / 100 / (2 * np.sqrt(2 * np.log(2)))

if MAGNETISM:
xs = []
for _ in range(4):
q = names.QProbe(
Q=q_array,
dQ=dq_vector,
intensity=self.storage['model'][model_name]['scale'],
background=self.storage['model'][model_name]['bkg'],
)
q.calc_Qo = np.linspace(
q_array[argmin] - PADDING_RANGE * dq_vector[argmin],
q_array[argmax] + PADDING_RANGE * dq_vector[argmax],
UPSCALE_FACTOR * len(q_array),
)
xs.append(q)
probe = names.PolarizedQProbe(xs=xs, name='polarized')

R = names.Experiment(probe=probe, sample=structure).reflectivity()[1]
else:
q = names.QProbe(
Q=q_array,
dQ=dq_vector,
intensity=self.storage['model'][model_name]['scale'],
background=self.storage['model'][model_name]['bkg'],
dq_array = dq_array * q_array / 100 / (2 * np.sqrt(2 * np.log(2)))

if not MAGNETISM:
probe = _get_probe(
q_array=q_array,
dq_array=dq_array,
model_name=model_name,
storage=self.storage,
oversampling_factor=OVERSAMPLING_FACTOR,
)
q.calc_Qo = np.linspace(
q_array[argmin] - PADDING_RANGE * dq_vector[argmin],
q_array[argmax] + PADDING_RANGE * dq_vector[argmax],
UPSCALE_FACTOR * len(q_array),
_, reflectivity = names.Experiment(probe=probe, sample=sample).reflectivity()
else:
polarized_probe = _get_polarized_probe(
q_array=q_array,
dq_array=dq_array,
model_name=model_name,
storage=self.storage,
oversampling_factor=OVERSAMPLING_FACTOR,
all_polarizations=ALL_POLARIZATIONS,
)
R = names.Experiment(probe=q, sample=structure).reflectivity()[1]
polarized_reflectivity = names.Experiment(probe=polarized_probe, sample=sample).reflectivity()

if ALL_POLARIZATIONS:
raise NotImplementedError('Polarized reflectivity not yet implemented')
_, reflectivity_pp = polarized_reflectivity[0]
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_, reflectivity_pm = polarized_reflectivity[1]
_, reflectivity_mp = polarized_reflectivity[2]
_, reflectivity_mm = polarized_reflectivity[3]
else:
_, reflectivity = polarized_reflectivity[0]

return R
return reflectivity

def sld_profile(self, model_name: str) -> Tuple[np.ndarray, np.ndarray]:
"""
Expand All @@ -218,26 +190,82 @@ def sld_profile(self, model_name: str) -> Tuple[np.ndarray, np.ndarray]:
:param model_name: the model name
:return: z and sld(z)
"""
structure = model.Stack()
# -1 to reverse the order
for i in self.storage['model'][model_name]['items'][::-1]:
if i.repeat.value == 1:
# -1 to reverse the order
for j in range(len(i.stack))[::-1]:
structure |= i.stack[j]
else:
stack = model.Stack()
# -1 to reverse the order
for j in range(len(i.stack))[::-1]:
stack |= i.stack[j]
structure |= model.Repeat(stack, repeat=i.repeat.value)

q = names.QProbe(
np.linspace(0.001, 0.3, 10),
np.linspace(0.001, 0.3, 10),
intensity=self.storage['model'][model_name]['scale'],
background=self.storage['model'][model_name]['bkg'],
sample = _build_sample(self.storage, model_name)
probe = _get_probe(
q_array=np.linspace(0.001, 0.3, 10),
dq_array=np.linspace(0.001, 0.3, 10), # TODO why would we use a steadily increasing dq (resolution)?
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Where do these numbers come from?

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Generally speaking, the numbers are a sensible range o be calculating reflectivity over. I am less sure about the linspace though as logspace is used in alot of real experiments. Though it doesn't really matter so much

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Realized that the provided q and dq are not used for anything in the SLD determination. However, they are required and are now changed to 1 as a dummy value and a code comment has been added.

model_name=model_name,
storage=self.storage,
)
z, sld, _ = names.Experiment(probe=q, sample=structure).smooth_profile()
z, sld, _ = names.Experiment(probe=probe, sample=sample).smooth_profile()
# -1 to reverse the order
return z, sld[::-1]


def _get_oversampling_q(q_array: np.ndarray, dq_array: np.ndarray, oversampling_factor: int) -> np.ndarray:
argmin = np.argmin(q_array)
argmax = np.argmax(q_array)
return np.linspace(
q_array[argmin] - RESOLUTION_PADDING * dq_array[argmin], # get dq (resolution) corresponding to the smallest q
q_array[argmax] + RESOLUTION_PADDING * dq_array[argmax], # get dq (resolution) corresponding to the largest q
oversampling_factor * len(q_array),
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)


def _get_probe(
q_array: np.ndarray,
dq_array: np.ndarray,
model_name: str,
storage: dict,
oversampling_factor: int = 1,
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) -> names.QProbe:
probe = names.QProbe(
Q=q_array,
dQ=dq_array,
intensity=storage['model'][model_name]['scale'],
background=storage['model'][model_name]['bkg'],
)
if oversampling_factor > 1:
probe.calc_Qo = _get_oversampling_q(q_array, dq_array, oversampling_factor)
return probe


def _get_polarized_probe(
q_array: np.ndarray,
dq_array: np.ndarray,
model_name: str,
storage: dict,
oversampling_factor: int = 1,
all_polarizations: bool = False,
) -> names.QProbe:
xs = []
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for i in range(4):
if i == 0 or all_polarizations:
probe = _get_probe(
q_array=q_array,
dq_array=dq_array,
model_name=model_name,
storage=storage,
oversampling_factor=oversampling_factor,
)
else:
probe = None
xs.append(probe)
return names.PolarizedQProbe(xs=xs, name='polarized')


def _build_sample(storage: dict, model_name: str) -> model.Stack:
sample = model.Stack()
# -1 to reverse the order
for i in storage['model'][model_name]['items'][::-1]:
if i.repeat.value == 1:
# -1 to reverse the order
for j in range(len(i.stack))[::-1]:
sample |= i.stack[j]
else:
stack = model.Stack()
# -1 to reverse the order
for j in range(len(i.stack))[::-1]:
stack |= i.stack[j]
sample |= model.Repeat(stack, repeat=i.repeat.value)
return sample
1 change: 0 additions & 1 deletion EasyReflectometry/calculators/refnx/calculator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,6 @@ class Refnx(CalculatorBase):
_model_link = {
'scale': 'scale',
'background': 'bkg',
'resolution': 'dq',
}

def __init__(self):
Expand Down
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