Diffraction module updates

README.md

@@ -1,4 +1,3 @@
-
 > :warning: **py4DSTEM version 0.14 update** :warning: Warning: this is a major update and we expect some workflows to break.  You can still install previous versions of py4DSTEM [as discussed here](#legacyinstall)
 
 > :warning: **Phase retrieval refactor version 0.14.9** :warning: Warning: The phase-retrieval modules in py4DSTEM (DPC, parallax, and ptychography) underwent a major refactor in version 0.14.9 and as such older tutorial notebooks will not work as expected. Notably, class names have been pruned to remove the trailing "Reconstruction" (`DPCReconstruction` -> `DPC` etc.), and regularization functions have dropped the `_iter` suffix (and are instead specified as boolean flags). See the [updated tutorials](https://github.com/py4dstem/py4DSTEM_tutorials) for more information.

py4DSTEM/process/__init__.py

@@ -19,3 +19,5 @@
 except (ImportError, ModuleNotFoundError) as exc:
     if not is_package_lite:
         raise exc
+
+from py4DSTEM.process.utils import Cluster

py4DSTEM/process/diffraction/crystal.py

@@ -97,7 +97,7 @@ def __init__(
         # [a a a 90 90 90]
         # [a b c 90 90 90]
         # [a b c alpha beta gamma]
-        cell = np.asarray(cell, dtype="float_")
+        cell = np.array(cell, dtype="float")
         if np.size(cell) == 1:
             self.cell = np.hstack([cell, cell, cell, 90, 90, 90])
         elif np.size(cell) == 3:
@@ -653,7 +653,7 @@ def calculate_structure_factors(
         # Calculate single atom scattering factors
         # Note this can be sped up a lot, but we may want to generalize to allow non-1.0 occupancy in the future.
         f_all = np.zeros(
-            (np.size(self.g_vec_leng, 0), self.positions.shape[0]), dtype="float_"
+            (np.size(self.g_vec_leng, 0), self.positions.shape[0]), dtype="float"
         )
         for a0 in range(self.positions.shape[0]):
             atom_sf = single_atom_scatter([self.numbers[a0]], [1], self.g_vec_leng, "A")

py4DSTEM/process/diffraction/crystal_phase.py

@@ -102,6 +102,7 @@ def quantify_single_pattern(
         plot_correlation_radius=False,
         scale_markers_experiment=40,
         scale_markers_calculated=200,
+        max_marker_size=400,
         crystal_inds_plot=None,
         phase_colors=None,
         figsize=(10, 7),
@@ -615,16 +616,22 @@ def quantify_single_pattern(
                 qy0,
                 qx0,
                 # s = scale_markers_experiment * intensity0,
-                s=scale_markers_experiment
-                * bragg_peaks.data["intensity"][np.logical_not(keep)],
+                s=np.minimum(
+                    scale_markers_experiment
+                    * bragg_peaks.data["intensity"][np.logical_not(keep)],
+                    max_marker_size,
+                ),
                 marker="o",
                 facecolor=[0.7, 0.7, 0.7],
             )
             ax.scatter(
                 qy,
                 qx,
                 # s = scale_markers_experiment * intensity,
-                s=scale_markers_experiment * bragg_peaks.data["intensity"][keep],
+                s=np.minimum(
+                    scale_markers_experiment * bragg_peaks.data["intensity"][keep],
+                    max_marker_size,
+                ),
                 marker="o",
                 facecolor=[0.7, 0.7, 0.7],
             )
@@ -799,6 +806,7 @@ def quantify_phase(
         strain_max=0.02,
         include_false_positives=True,
         weight_false_positives=1.0,
+        weight_unmatched_peaks=1.0,
         progress_bar=True,
     ):
         """
@@ -899,6 +907,7 @@ def quantify_phase(
                 strain_max=strain_max,
                 include_false_positives=include_false_positives,
                 weight_false_positives=weight_false_positives,
+                weight_unmatched_peaks=weight_unmatched_peaks,
                 plot_result=False,
                 verbose=False,
                 returnfig=False,
@@ -1220,6 +1229,7 @@ def plot_dominant_phase(
         self,
         use_correlation_scores=False,
         reliability_range=(0.0, 1.0),
+        normalize_exp_intensity=True,
         sigma=0.0,
         phase_colors=None,
         ticks=True,
@@ -1302,6 +1312,11 @@ def plot_dominant_phase(
                     sigma=sigma,
                     mode="nearest",
                 )
+        self.phase_sig = phase_sig
+
+        # # normalize the signal by the intensity of each experimental pattern
+        # if normalize_exp_intensity:
+        #     phase_sig /= self.int_total[None,:,:]
 
         # find highest correlation score for each crystal and match index
         for a0 in range(self.num_crystals):
@@ -1327,6 +1342,11 @@ def plot_dominant_phase(
 
             # Estimate the reliability
             phase_rel = phase_corr - phase_corr_2nd
+
+            # normalize the reliability by the intensity of each experimental pattern
+            if normalize_exp_intensity:
+                phase_rel /= self.int_total
+
             phase_scale = np.clip(
                 (phase_rel - reliability_range[0])
                 / (reliability_range[1] - reliability_range[0]),
@@ -1351,6 +1371,8 @@ def plot_dominant_phase(
             sub = phase_map == a0
             for a1 in range(3):
                 self.phase_rgb[:, :, a1][sub] = phase_colors[a0, a1] * phase_scale[sub]
+
+        self.phase_scale = phase_scale
         # normalize
         # self.phase_rgb = np.clip(
         #     (self.phase_rgb - rel_range[0]) / (rel_range[1] - rel_range[0]),