add occupancy to structure factors

py4dstem · Dec 12, 2023 · 924f0e1 · 924f0e1
1 parent 7f7cf46
commit 924f0e1
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Showing 2 changed files with 10 additions and 7 deletions.
diff --git a/py4DSTEM/process/diffraction/crystal.py b/py4DSTEM/process/diffraction/crystal.py
@@ -662,10 +662,14 @@ def calculate_structure_factors(
         # Calculate structure factors
         self.struct_factors = np.zeros(np.size(self.g_vec_leng, 0), dtype="complex64")
         for a0 in range(self.positions.shape[0]):
-            self.struct_factors += f_all[:, a0] * np.exp(
-                (2j * np.pi * self.occupancy[a0])
-                * np.sum(
-                    self.hkl * np.expand_dims(self.positions[a0, :], axis=1), axis=0
+            self.struct_factors += (
+                f_all[:, a0]
+                * self.occupancy[a0]
+                * np.exp(
+                    (2j * np.pi)
+                    * np.sum(
+                        self.hkl * np.expand_dims(self.positions[a0, :], axis=1), axis=0
+                    )
                 )
             )
 

diff --git a/py4DSTEM/process/diffraction/crystal_bloch.py b/py4DSTEM/process/diffraction/crystal_bloch.py
@@ -27,7 +27,6 @@ def calculate_dynamical_structure_factors(
     tol_structure_factor: float = 0.0,
     recompute_kinematic_structure_factors=True,
     g_vec_precision=None,
-    verbose=True,
 ):
     """
     Calculate and store the relativistic corrected structure factors used for Bloch computations
@@ -92,7 +91,7 @@ def calculate_dynamical_structure_factors(
 
     # Calculate the reciprocal lattice points to include based on k_max
 
-    k_max = np.asarray(k_max)
+    k_max: np.ndarray = np.asarray(k_max)
 
     if recompute_kinematic_structure_factors:
         if hasattr(self, "struct_factors"):
@@ -215,7 +214,7 @@ def get_f_e(q, Z, thermal_sigma, method):
 
     # Calculate structure factors
     struct_factors = np.sum(
-        f_e * np.exp(2.0j * np.pi * np.squeeze(self.positions[:, None, :] @ hkl)),
+        f_e * self.occupancy[:,None] * np.exp(2.0j * np.pi * np.squeeze(self.positions[:, None, :] @ hkl)),
         axis=0,
     )