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Test: scipp-analysis|estia|analyze_reduced_data|fit_model_reasonable
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load_data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
@pytest.fixture(scope="module")
def fit_model(load_data):
data = load_data
# Rescale data
reflectivity = data["data"]["R_0"].values
scale_factor = 1 / np.max(reflectivity)
data["data"]["R_0"].values *= scale_factor
# Create a model for the sample
si = Material(sld=2.07, isld=0.0, name="Si")
sio2 = Material(sld=3.47, isld=0.0, name="SiO2")
d2o = Material(sld=6.33, isld=0.0, name="D2O")
dlipids = Material(sld=5.0, isld=0.0, name="DLipids")
superphase = Layer(material=si, thickness=0, roughness=0, name="Si superphase")
sio2_layer = Layer(material=sio2, thickness=20, roughness=4, name="SiO2 layer")
dlipids_layer = Layer(
material=dlipids, thickness=40, roughness=4, name="DLipids layer"
)
subphase = Layer(material=d2o, thickness=0, roughness=5, name="D2O subphase")
multi_sample = Sample(
Multilayer(superphase),
Multilayer(sio2_layer),
Multilayer(dlipids_layer),
Multilayer(subphase),
name="Multilayer Structure",
)
multi_layer_model = Model(
sample=multi_sample,
scale=1,
background=0.000001,
resolution_function=PercentageFwhm(0),
name="Multilayer Model",
)
# Set the fitting parameters
sio2_layer.roughness.bounds = (3, 12)
sio2_layer.material.sld.bounds = (3.47, 5)
sio2_layer.thickness.bounds = (10, 30)
subphase.material.sld.bounds = (6, 6.35)
dlipids_layer.thickness.bounds = (30, 60)
dlipids_layer.roughness.bounds = (3, 10)
dlipids_layer.material.sld.bounds = (4, 6)
multi_layer_model.scale.bounds = (0.8, 1.2)
multi_layer_model.background.bounds = (1e-6, 1e-3)
# Run the model and plot the results
multi_layer_model.interface = CalculatorFactory()
fitter1 = MultiFitter(multi_layer_model)
fitter1.switch_minimizer(AvailableMinimizers.Bumps_simplex)
> analysed = fitter1.fit(data)
^^^^^^^^^^^^^^^^^
tests/scipp-analysis/estia/ort_file_test.py:99:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
id = 0
def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
"""
Perform the fitting and populate the DataGroups with the result.
:param data: DataGroup to be fitted to and populated
:param method: Optimisation method
:note: Points with zero variance in the data will be automatically masked
out during fitting. A warning will be issued if any such points
are found, indicating the number of points masked per reflectivity.
"""
refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]]
x = []
y = []
dy = []
# Process each reflectivity dataset
for i in refl_nums:
x_vals = data['coords'][f'Qz_{i}'].values
y_vals = data['data'][f'R_{i}'].values
variances = data['data'][f'R_{i}'].variances
# Find points with non-zero variance
zero_variance_mask = (variances == 0.0)
num_zero_variance = np.sum(zero_variance_mask)
if num_zero_variance > 0:
> warnings.warn(
f"Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.",
UserWarning
)
E UserWarning: Masked 11 data point(s) in reflectivity 0 due to zero variance during fitting.
.tox/scipp-analysis-estia/lib/python3.12/site-packages/easyreflectometry/fitting.py:62: UserWarning
load_data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
@pytest.fixture(scope="module")
def fit_model(load_data):
data = load_data
# Rescale data
reflectivity = data["data"]["R_0"].values
scale_factor = 1 / np.max(reflectivity)
data["data"]["R_0"].values *= scale_factor
# Create a model for the sample
si = Material(sld=2.07, isld=0.0, name="Si")
sio2 = Material(sld=3.47, isld=0.0, name="SiO2")
d2o = Material(sld=6.33, isld=0.0, name="D2O")
dlipids = Material(sld=5.0, isld=0.0, name="DLipids")
superphase = Layer(material=si, thickness=0, roughness=0, name="Si superphase")
sio2_layer = Layer(material=sio2, thickness=20, roughness=4, name="SiO2 layer")
dlipids_layer = Layer(
material=dlipids, thickness=40, roughness=4, name="DLipids layer"
)
subphase = Layer(material=d2o, thickness=0, roughness=5, name="D2O subphase")
multi_sample = Sample(
Multilayer(superphase),
Multilayer(sio2_layer),
Multilayer(dlipids_layer),
Multilayer(subphase),
name="Multilayer Structure",
)
multi_layer_model = Model(
sample=multi_sample,
scale=1,
background=0.000001,
resolution_function=PercentageFwhm(0),
name="Multilayer Model",
)
# Set the fitting parameters
sio2_layer.roughness.bounds = (3, 12)
sio2_layer.material.sld.bounds = (3.47, 5)
sio2_layer.thickness.bounds = (10, 30)
subphase.material.sld.bounds = (6, 6.35)
dlipids_layer.thickness.bounds = (30, 60)
dlipids_layer.roughness.bounds = (3, 10)
dlipids_layer.material.sld.bounds = (4, 6)
multi_layer_model.scale.bounds = (0.8, 1.2)
multi_layer_model.background.bounds = (1e-6, 1e-3)
# Run the model and plot the results
multi_layer_model.interface = CalculatorFactory()
fitter1 = MultiFitter(multi_layer_model)
fitter1.switch_minimizer(AvailableMinimizers.Bumps_simplex)
> analysed = fitter1.fit(data)
^^^^^^^^^^^^^^^^^
tests/scipp-analysis/estia/ort_file_test.py:99:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
id = 0
def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
"""
Perform the fitting and populate the DataGroups with the result.
:param data: DataGroup to be fitted to and populated
:param method: Optimisation method
:note: Points with zero variance in the data will be automatically masked
out during fitting. A warning will be issued if any such points
are found, indicating the number of points masked per reflectivity.
"""
refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]]
x = []
y = []
dy = []
# Process each reflectivity dataset
for i in refl_nums:
x_vals = data['coords'][f'Qz_{i}'].values
y_vals = data['data'][f'R_{i}'].values
variances = data['data'][f'R_{i}'].variances
# Find points with non-zero variance
zero_variance_mask = (variances == 0.0)
num_zero_variance = np.sum(zero_variance_mask)
if num_zero_variance > 0:
> warnings.warn(
f"Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.",
UserWarning
)
E UserWarning: Masked 11 data point(s) in reflectivity 0 due to zero variance during fitting.
.tox/scipp-analysis-estia/lib/python3.12/site-packages/easyreflectometry/fitting.py:62: UserWarning
load_data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
@pytest.fixture(scope="module")
def fit_model(load_data):
data = load_data
# Rescale data
reflectivity = data["data"]["R_0"].values
scale_factor = 1 / np.max(reflectivity)
data["data"]["R_0"].values *= scale_factor
# Create a model for the sample
si = Material(sld=2.07, isld=0.0, name="Si")
sio2 = Material(sld=3.47, isld=0.0, name="SiO2")
d2o = Material(sld=6.33, isld=0.0, name="D2O")
dlipids = Material(sld=5.0, isld=0.0, name="DLipids")
superphase = Layer(material=si, thickness=0, roughness=0, name="Si superphase")
sio2_layer = Layer(material=sio2, thickness=20, roughness=4, name="SiO2 layer")
dlipids_layer = Layer(
material=dlipids, thickness=40, roughness=4, name="DLipids layer"
)
subphase = Layer(material=d2o, thickness=0, roughness=5, name="D2O subphase")
multi_sample = Sample(
Multilayer(superphase),
Multilayer(sio2_layer),
Multilayer(dlipids_layer),
Multilayer(subphase),
name="Multilayer Structure",
)
multi_layer_model = Model(
sample=multi_sample,
scale=1,
background=0.000001,
resolution_function=PercentageFwhm(0),
name="Multilayer Model",
)
# Set the fitting parameters
sio2_layer.roughness.bounds = (3, 12)
sio2_layer.material.sld.bounds = (3.47, 5)
sio2_layer.thickness.bounds = (10, 30)
subphase.material.sld.bounds = (6, 6.35)
dlipids_layer.thickness.bounds = (30, 60)
dlipids_layer.roughness.bounds = (3, 10)
dlipids_layer.material.sld.bounds = (4, 6)
multi_layer_model.scale.bounds = (0.8, 1.2)
multi_layer_model.background.bounds = (1e-6, 1e-3)
# Run the model and plot the results
multi_layer_model.interface = CalculatorFactory()
fitter1 = MultiFitter(multi_layer_model)
fitter1.switch_minimizer(AvailableMinimizers.Bumps_simplex)
> analysed = fitter1.fit(data)
^^^^^^^^^^^^^^^^^
tests/scipp-analysis/estia/ort_file_test.py:99:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
id = 0
def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
"""
Perform the fitting and populate the DataGroups with the result.
:param data: DataGroup to be fitted to and populated
:param method: Optimisation method
:note: Points with zero variance in the data will be automatically masked
out during fitting. A warning will be issued if any such points
are found, indicating the number of points masked per reflectivity.
"""
refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]]
x = []
y = []
dy = []
# Process each reflectivity dataset
for i in refl_nums:
x_vals = data['coords'][f'Qz_{i}'].values
y_vals = data['data'][f'R_{i}'].values
variances = data['data'][f'R_{i}'].variances
# Find points with non-zero variance
zero_variance_mask = (variances == 0.0)
num_zero_variance = np.sum(zero_variance_mask)
if num_zero_variance > 0:
> warnings.warn(
f"Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.",
UserWarning
)
E UserWarning: Masked 11 data point(s) in reflectivity 0 due to zero variance during fitting.
.tox/scipp-analysis-estia/lib/python3.12/site-packages/easyreflectometry/fitting.py:62: UserWarning
load_data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
@pytest.fixture(scope="module")
def fit_model(load_data):
data = load_data
# Rescale data
reflectivity = data["data"]["R_0"].values
scale_factor = 1 / np.max(reflectivity)
data["data"]["R_0"].values *= scale_factor
# Create a model for the sample
si = Material(sld=2.07, isld=0.0, name="Si")
sio2 = Material(sld=3.47, isld=0.0, name="SiO2")
d2o = Material(sld=6.33, isld=0.0, name="D2O")
dlipids = Material(sld=5.0, isld=0.0, name="DLipids")
superphase = Layer(material=si, thickness=0, roughness=0, name="Si superphase")
sio2_layer = Layer(material=sio2, thickness=20, roughness=4, name="SiO2 layer")
dlipids_layer = Layer(
material=dlipids, thickness=40, roughness=4, name="DLipids layer"
)
subphase = Layer(material=d2o, thickness=0, roughness=5, name="D2O subphase")
multi_sample = Sample(
Multilayer(superphase),
Multilayer(sio2_layer),
Multilayer(dlipids_layer),
Multilayer(subphase),
name="Multilayer Structure",
)
multi_layer_model = Model(
sample=multi_sample,
scale=1,
background=0.000001,
resolution_function=PercentageFwhm(0),
name="Multilayer Model",
)
# Set the fitting parameters
sio2_layer.roughness.bounds = (3, 12)
sio2_layer.material.sld.bounds = (3.47, 5)
sio2_layer.thickness.bounds = (10, 30)
subphase.material.sld.bounds = (6, 6.35)
dlipids_layer.thickness.bounds = (30, 60)
dlipids_layer.roughness.bounds = (3, 10)
dlipids_layer.material.sld.bounds = (4, 6)
multi_layer_model.scale.bounds = (0.8, 1.2)
multi_layer_model.background.bounds = (1e-6, 1e-3)
# Run the model and plot the results
multi_layer_model.interface = CalculatorFactory()
fitter1 = MultiFitter(multi_layer_model)
fitter1.switch_minimizer(AvailableMinimizers.Bumps_simplex)
> analysed = fitter1.fit(data)
^^^^^^^^^^^^^^^^^
tests/scipp-analysis/estia/ort_file_test.py:99:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
id = 0
def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
"""
Perform the fitting and populate the DataGroups with the result.
:param data: DataGroup to be fitted to and populated
:param method: Optimisation method
:note: Points with zero variance in the data will be automatically masked
out during fitting. A warning will be issued if any such points
are found, indicating the number of points masked per reflectivity.
"""
refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]]
x = []
y = []
dy = []
# Process each reflectivity dataset
for i in refl_nums:
x_vals = data['coords'][f'Qz_{i}'].values
y_vals = data['data'][f'R_{i}'].values
variances = data['data'][f'R_{i}'].variances
# Find points with non-zero variance
zero_variance_mask = (variances == 0.0)
num_zero_variance = np.sum(zero_variance_mask)
if num_zero_variance > 0:
> warnings.warn(
f"Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.",
UserWarning
)
E UserWarning: Masked 11 data point(s) in reflectivity 0 due to zero variance during fitting.
.tox/scipp-analysis-estia/lib/python3.12/site-packages/easyreflectometry/fitting.py:62: UserWarning
load_data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
@pytest.fixture(scope="module")
def fit_model(load_data):
data = load_data
# Rescale data
reflectivity = data["data"]["R_0"].values
scale_factor = 1 / np.max(reflectivity)
data["data"]["R_0"].values *= scale_factor
# Create a model for the sample
si = Material(sld=2.07, isld=0.0, name="Si")
sio2 = Material(sld=3.47, isld=0.0, name="SiO2")
d2o = Material(sld=6.33, isld=0.0, name="D2O")
dlipids = Material(sld=5.0, isld=0.0, name="DLipids")
superphase = Layer(material=si, thickness=0, roughness=0, name="Si superphase")
sio2_layer = Layer(material=sio2, thickness=20, roughness=4, name="SiO2 layer")
dlipids_layer = Layer(
material=dlipids, thickness=40, roughness=4, name="DLipids layer"
)
subphase = Layer(material=d2o, thickness=0, roughness=5, name="D2O subphase")
multi_sample = Sample(
Multilayer(superphase),
Multilayer(sio2_layer),
Multilayer(dlipids_layer),
Multilayer(subphase),
name="Multilayer Structure",
)
multi_layer_model = Model(
sample=multi_sample,
scale=1,
background=0.000001,
resolution_function=PercentageFwhm(0),
name="Multilayer Model",
)
# Set the fitting parameters
sio2_layer.roughness.bounds = (3, 12)
sio2_layer.material.sld.bounds = (3.47, 5)
sio2_layer.thickness.bounds = (10, 30)
subphase.material.sld.bounds = (6, 6.35)
dlipids_layer.thickness.bounds = (30, 60)
dlipids_layer.roughness.bounds = (3, 10)
dlipids_layer.material.sld.bounds = (4, 6)
multi_layer_model.scale.bounds = (0.8, 1.2)
multi_layer_model.background.bounds = (1e-6, 1e-3)
# Run the model and plot the results
multi_layer_model.interface = CalculatorFactory()
fitter1 = MultiFitter(multi_layer_model)
fitter1.switch_minimizer(AvailableMinimizers.Bumps_simplex)
> analysed = fitter1.fit(data)
^^^^^^^^^^^^^^^^^
tests/scipp-analysis/estia/ort_file_test.py:99:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
id = 0
def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
"""
Perform the fitting and populate the DataGroups with the result.
:param data: DataGroup to be fitted to and populated
:param method: Optimisation method
:note: Points with zero variance in the data will be automatically masked
out during fitting. A warning will be issued if any such points
are found, indicating the number of points masked per reflectivity.
"""
refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]]
x = []
y = []
dy = []
# Process each reflectivity dataset
for i in refl_nums:
x_vals = data['coords'][f'Qz_{i}'].values
y_vals = data['data'][f'R_{i}'].values
variances = data['data'][f'R_{i}'].variances
# Find points with non-zero variance
zero_variance_mask = (variances == 0.0)
num_zero_variance = np.sum(zero_variance_mask)
if num_zero_variance > 0:
> warnings.warn(
f"Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.",
UserWarning
)
E UserWarning: Masked 11 data point(s) in reflectivity 0 due to zero variance during fitting.
.tox/scipp-analysis-estia/lib/python3.12/site-packages/easyreflectometry/fitting.py:62: UserWarning
load_data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
@pytest.fixture(scope="module")
def fit_model(load_data):
data = load_data
# Rescale data
reflectivity = data["data"]["R_0"].values
scale_factor = 1 / np.max(reflectivity)
data["data"]["R_0"].values *= scale_factor
# Create a model for the sample
si = Material(sld=2.07, isld=0.0, name="Si")
sio2 = Material(sld=3.47, isld=0.0, name="SiO2")
d2o = Material(sld=6.33, isld=0.0, name="D2O")
dlipids = Material(sld=5.0, isld=0.0, name="DLipids")
superphase = Layer(material=si, thickness=0, roughness=0, name="Si superphase")
sio2_layer = Layer(material=sio2, thickness=20, roughness=4, name="SiO2 layer")
dlipids_layer = Layer(
material=dlipids, thickness=40, roughness=4, name="DLipids layer"
)
subphase = Layer(material=d2o, thickness=0, roughness=5, name="D2O subphase")
multi_sample = Sample(
Multilayer(superphase),
Multilayer(sio2_layer),
Multilayer(dlipids_layer),
Multilayer(subphase),
name="Multilayer Structure",
)
multi_layer_model = Model(
sample=multi_sample,
scale=1,
background=0.000001,
resolution_function=PercentageFwhm(0),
name="Multilayer Model",
)
# Set the fitting parameters
sio2_layer.roughness.bounds = (3, 12)
sio2_layer.material.sld.bounds = (3.47, 5)
sio2_layer.thickness.bounds = (10, 30)
subphase.material.sld.bounds = (6, 6.35)
dlipids_layer.thickness.bounds = (30, 60)
dlipids_layer.roughness.bounds = (3, 10)
dlipids_layer.material.sld.bounds = (4, 6)
multi_layer_model.scale.bounds = (0.8, 1.2)
multi_layer_model.background.bounds = (1e-6, 1e-3)
# Run the model and plot the results
multi_layer_model.interface = CalculatorFactory()
fitter1 = MultiFitter(multi_layer_model)
fitter1.switch_minimizer(AvailableMinimizers.Bumps_simplex)
> analysed = fitter1.fit(data)
^^^^^^^^^^^^^^^^^
tests/scipp-analysis/estia/ort_file_test.py:99:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
id = 0
def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
"""
Perform the fitting and populate the DataGroups with the result.
:param data: DataGroup to be fitted to and populated
:param method: Optimisation method
:note: Points with zero variance in the data will be automatically masked
out during fitting. A warning will be issued if any such points
are found, indicating the number of points masked per reflectivity.
"""
refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]]
x = []
y = []
dy = []
# Process each reflectivity dataset
for i in refl_nums:
x_vals = data['coords'][f'Qz_{i}'].values
y_vals = data['data'][f'R_{i}'].values
variances = data['data'][f'R_{i}'].variances
# Find points with non-zero variance
zero_variance_mask = (variances == 0.0)
num_zero_variance = np.sum(zero_variance_mask)
if num_zero_variance > 0:
> warnings.warn(
f"Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.",
UserWarning
)
E UserWarning: Masked 11 data point(s) in reflectivity 0 due to zero variance during fitting.
.tox/scipp-analysis-estia/lib/python3.12/site-packages/easyreflectometry/fitting.py:62: UserWarning
load_data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
@pytest.fixture(scope="module")
def fit_model(load_data):
data = load_data
# Rescale data
reflectivity = data["data"]["R_0"].values
scale_factor = 1 / np.max(reflectivity)
data["data"]["R_0"].values *= scale_factor
# Create a model for the sample
si = Material(sld=2.07, isld=0.0, name="Si")
sio2 = Material(sld=3.47, isld=0.0, name="SiO2")
d2o = Material(sld=6.33, isld=0.0, name="D2O")
dlipids = Material(sld=5.0, isld=0.0, name="DLipids")
superphase = Layer(material=si, thickness=0, roughness=0, name="Si superphase")
sio2_layer = Layer(material=sio2, thickness=20, roughness=4, name="SiO2 layer")
dlipids_layer = Layer(
material=dlipids, thickness=40, roughness=4, name="DLipids layer"
)
subphase = Layer(material=d2o, thickness=0, roughness=5, name="D2O subphase")
multi_sample = Sample(
Multilayer(superphase),
Multilayer(sio2_layer),
Multilayer(dlipids_layer),
Multilayer(subphase),
name="Multilayer Structure",
)
multi_layer_model = Model(
sample=multi_sample,
scale=1,
background=0.000001,
resolution_function=PercentageFwhm(0),
name="Multilayer Model",
)
# Set the fitting parameters
sio2_layer.roughness.bounds = (3, 12)
sio2_layer.material.sld.bounds = (3.47, 5)
sio2_layer.thickness.bounds = (10, 30)
subphase.material.sld.bounds = (6, 6.35)
dlipids_layer.thickness.bounds = (30, 60)
dlipids_layer.roughness.bounds = (3, 10)
dlipids_layer.material.sld.bounds = (4, 6)
multi_layer_model.scale.bounds = (0.8, 1.2)
multi_layer_model.background.bounds = (1e-6, 1e-3)
# Run the model and plot the results
multi_layer_model.interface = CalculatorFactory()
fitter1 = MultiFitter(multi_layer_model)
fitter1.switch_minimizer(AvailableMinimizers.Bumps_simplex)
> analysed = fitter1.fit(data)
^^^^^^^^^^^^^^^^^
tests/scipp-analysis/estia/ort_file_test.py:99:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self =
data = DataGroup(sizes={}, keys=[
data: {'R_0':
])
id = 0
def fit(self, data: sc.DataGroup, id: int = 0) -> sc.DataGroup:
"""
Perform the fitting and populate the DataGroups with the result.
:param data: DataGroup to be fitted to and populated
:param method: Optimisation method
:note: Points with zero variance in the data will be automatically masked
out during fitting. A warning will be issued if any such points
are found, indicating the number of points masked per reflectivity.
"""
refl_nums = [k[3:] for k in data['coords'].keys() if 'Qz' == k[:2]]
x = []
y = []
dy = []
# Process each reflectivity dataset
for i in refl_nums:
x_vals = data['coords'][f'Qz_{i}'].values
y_vals = data['data'][f'R_{i}'].values
variances = data['data'][f'R_{i}'].variances
# Find points with non-zero variance
zero_variance_mask = (variances == 0.0)
num_zero_variance = np.sum(zero_variance_mask)
if num_zero_variance > 0:
> warnings.warn(
f"Masked {num_zero_variance} data point(s) in reflectivity {i} due to zero variance during fitting.",
UserWarning
)
E UserWarning: Masked 11 data point(s) in reflectivity 0 due to zero variance during fitting.
.tox/scipp-analysis-estia/lib/python3.12/site-packages/easyreflectometry/fitting.py:62: UserWarning