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Merge pull request #22 from gnzng/fix-gaussian

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Fix use of gaussian_probe() + typo
This commit is contained in:
allevitan
2025-03-07 16:22:00 +01:00
committed by GitHub
parent db2450c5ae f8bebd0436
commit 54decc486a
3 changed files with 5 additions and 7 deletions
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src/cdtools/models/fancy_ptycho.py
+2 -3
View File
@@ -195,7 +195,7 @@ class FancyPtycho(CDIModel):
@classmethod
def from_dataset(cls,
dataset,
probe_size=None,
probe_shape=None,
randomize_ang=0,
n_modes=1,
n_obj_modes=1,
@@ -277,7 +277,7 @@ class FancyPtycho(CDIModel):
)
# Finally, initialize the probe and object using this information
if probe_size is None:
if probe_shape is None:
probe = tools.initializers.SHARP_style_probe(
dataset,
propagation_distance=propagation_distance,
@@ -288,7 +288,6 @@ class FancyPtycho(CDIModel):
dataset,
obj_basis,
probe_shape,
probe_size,
propagation_distance=propagation_distance,
)
src/cdtools/models/multislice_ptycho.py
+2 -3
View File
@@ -181,7 +181,7 @@ class MultislicePtycho(CDIModel):
dataset,
dz,
nz,
probe_size=None,
probe_shape=None,
randomize_ang=0,
n_modes=1,
n_obj_modes=1,
@@ -262,7 +262,7 @@ class MultislicePtycho(CDIModel):
)
# Finally, initialize the probe and object using this information
if probe_size is None:
if probe_shape is None:
probe = tools.initializers.SHARP_style_probe(
dataset,
propagation_distance=propagation_distance,
@@ -273,7 +273,6 @@ class MultislicePtycho(CDIModel):
dataset,
obj_basis,
probe_shape,
probe_size,
propagation_distance=propagation_distance,
)
src/cdtools/tools/initializers/initializers.py
+1 -1
View File
@@ -234,7 +234,7 @@ def gaussian_probe(dataset, basis, shape, sigma, propagation_distance=0, polariz
polarizer = dataset.polarizer.tolist()
analyzer = dataset.analyzer.tolist()
factors = [(math.cos(math.radians(polarizer[idx] - analyzer[idx])))**2 for idx in range(len(dataset)) if (abs(polarizer[idx] - analyzer[idx]) > 5)]
avg_intensities = [t.sum(dataset[idx][1]) / factor[idx] for idx in range(len(dataset))]
avg_intensities = [t.sum(dataset[idx][1]) / factors[idx] for idx in range(len(dataset))]
avg_intensity = t.mean(t.tensor(avg_intensities))
probe_intensity = t.sum(t.abs(probe)**2)