Merge branch 'dev' into g1_g2_indexing

.github/workflows/check_install_dev.yml

@@ -16,7 +16,7 @@ jobs:
                 allow_failure: [false]
                 runs-on: [ubuntu-latest]
                 architecture: [x86_64]
-                python-version: ["3.9", "3.10", "3.11",]
+                python-version: ["3.9", "3.10", "3.11", "3.12"]
                 # include:
                 #     - python-version: "3.12.0-beta.4"
                 #       runs-on: ubuntu-latest

.github/workflows/check_install_main.yml

@@ -16,7 +16,7 @@ jobs:
                 allow_failure: [false]
                 runs-on: [ubuntu-latest, windows-latest, macos-latest]
                 architecture: [x86_64]
-                python-version: ["3.9", "3.10", "3.11",]
+                python-version: ["3.9", "3.10", "3.11", "3.12"]
                 #include:
                 #    - python-version: "3.12.0-beta.4"
                 #      runs-on: ubuntu-latest

.github/workflows/check_install_quick.yml

@@ -20,7 +20,7 @@ jobs:
                 allow_failure: [false]
                 runs-on: [ubuntu-latest] 
                 architecture: [x86_64]
-                python-version: ["3.10"]
+                python-version: ["3.9", "3.12"]
                 # Currently no public runners available for this but this or arm64 should work next time
                 # include:
                 #     - python-version: "3.10"

py4DSTEM/__init__.py

@@ -52,17 +52,25 @@
     BraggVectorMap,
 )
 
+from py4DSTEM.process import classification
+
+
+# diffraction
+from py4DSTEM.process.diffraction import Crystal, Orientation
+
+
+# ptycho
+from py4DSTEM.process import phase
+
+
+# polar
+from py4DSTEM.process.polar import PolarDatacube
+
+
 # strain
 from py4DSTEM.process.strain.strain import StrainMap
 
-# TODO - crystal
-# TODO - ptycho
-# TODO - others
-
-# TODO - where
-from py4DSTEM.process import (
-    PolarDatacube,
-)
+from py4DSTEM.process import wholepatternfit
 
 
 ### more submodules

py4DSTEM/braggvectors/braggvector_methods.py

@@ -99,12 +99,12 @@ def histogram(
         # then scale by the sampling factor
         else:
             # get pixel calibration
-            if self.calstate["pixel"] == True:
+            if self.calstate["pixel"] is True:
                 qpix = self.calibration.get_Q_pixel_size()
                 qx /= qpix
                 qy /= qpix
             # origin calibration
-            if self.calstate["center"] == True:
+            if self.calstate["center"] is True:
                 origin = self.calibration.get_origin_mean()
                 qx += origin[0]
                 qy += origin[1]
@@ -153,12 +153,12 @@ def histogram(
         ).reshape(Q_Nx, Q_Ny)
 
         # determine the resampled grid center and pixel size
-        if mode == "cal" and self.calstate["center"] == True:
+        if mode == "cal" and self.calstate["center"] is True:
             x0 = sampling * origin[0]
             y0 = sampling * origin[1]
         else:
             x0, y0 = 0, 0
-        if mode == "cal" and self.calstate["pixel"] == True:
+        if mode == "cal" and self.calstate["pixel"] is True:
             pixelsize = qpix / sampling
         else:
             pixelsize = 1 / sampling

py4DSTEM/braggvectors/braggvectors.py

@@ -272,6 +272,7 @@ def copy(self, name=None):
         braggvector_copy.set_raw_vectors(self._v_uncal.copy())
         for k in self.metadata.keys():
             braggvector_copy.metadata = self.metadata[k].copy()
+        braggvector_copy.setcal()
         return braggvector_copy
 
     # write
@@ -384,7 +385,7 @@ def __getitem__(self, pos):
     def __repr__(self):
         space = " " * len(self.__class__.__name__) + "  "
         string = f"{self.__class__.__name__}( "
-        string += f"Retrieves raw bragg vectors. Get vectors for scan position x,y with [x,y]. )"
+        string += "Retrieves raw bragg vectors. Get vectors for scan position x,y with [x,y]. )"
         return string
 
 

py4DSTEM/braggvectors/diskdetection.py

@@ -231,10 +231,10 @@ def find_Bragg_disks(
         mode = "dc_ml"
 
     elif mode == "datacube":
-        if distributed is None and CUDA == False:
+        if distributed is None and CUDA is False:
             mode = "dc_CPU"
-        elif distributed is None and CUDA == True:
-            if CUDA_batched == False:
+        elif distributed is None and CUDA is True:
+            if CUDA_batched is False:
                 mode = "dc_GPU"
             else:
                 mode = "dc_GPU_batched"
@@ -271,7 +271,7 @@ def find_Bragg_disks(
         kws["data_file"] = data_file
         kws["cluster_path"] = cluster_path
     # ML arguments
-    if ML == True:
+    if ML is True:
         kws["CUDA"] = CUDA
         kws["model_path"] = ml_model_path
         kws["num_attempts"] = ml_num_attempts
@@ -759,7 +759,7 @@ def _parse_distributed(distributed):
     data_file = distributed["data_file"]
 
     if not isinstance(data_file, str):
-        er = f"Expected string for distributed key 'data_file', "
+        er = "Expected string for distributed key 'data_file', "
         er += f"received {type(data_file)}"
         raise TypeError(er)
     if len(data_file.strip()) == 0:
@@ -773,7 +773,7 @@ def _parse_distributed(distributed):
         cluster_path = distributed["cluster_path"]
 
         if not isinstance(cluster_path, str):
-            er = f"distributed key 'cluster_path' must be of type str, "
+            er = "distributed key 'cluster_path' must be of type str, "
             er += f"received {type(cluster_path)}"
             raise TypeError(er)
 
@@ -784,7 +784,7 @@ def _parse_distributed(distributed):
             er = f"distributed key 'cluster_path' does not exist: {cluster_path}"
             raise FileNotFoundError(er)
         elif not os.path.isdir(cluster_path):
-            er = f"distributed key 'cluster_path' is not a directory: "
+            er = "distributed key 'cluster_path' is not a directory: "
             er += f"{cluster_path}"
             raise NotADirectoryError(er)
     else:

py4DSTEM/braggvectors/diskdetection_aiml.py

@@ -528,7 +528,7 @@ def find_Bragg_disks_aiml_serial(
         )
     )
 
-    if global_threshold == True:
+    if global_threshold is True:
         from py4DSTEM.braggvectors import universal_threshold
 
         peaks = universal_threshold(
@@ -559,7 +559,7 @@ def find_Bragg_disks_aiml(
     model_path=None,
     distributed=None,
     CUDA=True,
-    **kwargs
+    **kwargs,
 ):
     """
     Finds the Bragg disks in all diffraction patterns of datacube by AI/ML method. This method

py4DSTEM/braggvectors/diskdetection_aiml_cuda.py

@@ -233,7 +233,7 @@ def find_Bragg_disks_aiml_CUDA(
             datacube.R_N, int(t2 / 3600), int(t2 / 60), int(t2 % 60)
         )
     )
-    if global_threshold == True:
+    if global_threshold is True:
         from py4DSTEM.braggvectors import universal_threshold
 
         peaks = universal_threshold(
@@ -265,7 +265,7 @@ def _find_Bragg_disks_aiml_single_DP_CUDA(
     blocks=None,
     threads=None,
     model_path=None,
-    **kwargs
+    **kwargs,
 ):
     """
     Finds the Bragg disks in single DP by AI/ML method. This method utilizes FCU-Net
@@ -496,7 +496,7 @@ def get_maxima_2D_cp(
         if minSpacing > 0:
             deletemask = np.zeros(len(maxima), dtype=bool)
             for i in range(len(maxima)):
-                if deletemask[i] == False:
+                if deletemask[i] == False:  # noqa: E712
                     tooClose = (
                         (maxima["x"] - maxima["x"][i]) ** 2
                         + (maxima["y"] - maxima["y"][i]) ** 2

py4DSTEM/braggvectors/diskdetection_cuda.py

@@ -482,7 +482,7 @@ def get_maxima_2D(
         if minSpacing > 0:
             deletemask = np.zeros(len(maxima), dtype=bool)
             for i in range(len(maxima)):
-                if deletemask[i] == False:
+                if deletemask[i] == False:  # noqa: E712
                     tooClose = (
                         (maxima["x"] - maxima["x"][i]) ** 2
                         + (maxima["y"] - maxima["y"][i]) ** 2

py4DSTEM/braggvectors/diskdetection_parallel_new.py

@@ -100,7 +100,7 @@ def beta_parallel_disk_detection(
     close_dask_client=False,
     return_dask_client=True,
     *args,
-    **kwargs
+    **kwargs,
 ):
     """
     This is not fully validated currently so may not work, please report bugs on the py4DSTEM github page.
@@ -137,8 +137,8 @@ def beta_parallel_disk_detection(
     #   ... dask stuff.
     # TODO add assert statements and other checks. Think about reordering opperations
 
-    if dask_client == None:
-        if dask_client_params != None:
+    if dask_client is None:
+        if dask_client_params is not None:
             dask.config.set(
                 {
                     "distributed.worker.memory.spill": False,
@@ -201,7 +201,7 @@ def beta_parallel_disk_detection(
         dask_data = da.from_array(
             dataset.data, chunks=(1, 1, dataset.Q_Nx, dataset.Q_Ny)
         )
-    elif dataset.stack_pointer != None:
+    elif dataset.stack_pointer is not None:
         dask_data = da.from_array(
             dataset.stack_pointer, chunks=(1, 1, dataset.Q_Nx, dataset.Q_Ny)
         )
@@ -225,7 +225,7 @@ def beta_parallel_disk_detection(
             probe_kernel_FT=dask_probe_delayed[0, 0],
             # probe_kernel_FT=delayed_probe_kernel_FT,
             *args,
-            **kwargs
+            **kwargs,
         )  # passing through args from earlier or should I use
         # corrPower=corrPower,
         # sigma=sigma_gaussianFilter,
@@ -261,9 +261,9 @@ def beta_parallel_disk_detection(
     if close_dask_client:
         dask_client.close()
         return peaks
-    elif close_dask_client == False and return_dask_client == True:
+    elif close_dask_client is False and return_dask_client is True:
         return peaks, dask_client
-    elif close_dask_client and return_dask_client == False:
+    elif close_dask_client and return_dask_client is False:
         return peaks
     else:
         print(

py4DSTEM/braggvectors/threshold.py

@@ -1,7 +1,6 @@
 # Bragg peaks thresholding fns
 
 import numpy as np
-
 from emdfile import tqdmnd, PointListArray
 
 
@@ -52,7 +51,7 @@ def threshold_Braggpeaks(
             r2 = minPeakSpacing**2
             deletemask = np.zeros(pointlist.length, dtype=bool)
             for i in range(pointlist.length):
-                if deletemask[i] == False:
+                if deletemask[i] == False:  # noqa: E712
                     tooClose = (
                         (pointlist.data["qx"] - pointlist.data["qx"][i]) ** 2
                         + (pointlist.data["qy"] - pointlist.data["qy"][i]) ** 2
@@ -160,7 +159,7 @@ def universal_threshold(
             r2 = minPeakSpacing**2
             deletemask = np.zeros(pointlist.length, dtype=bool)
             for i in range(pointlist.length):
-                if deletemask[i] == False:
+                if deletemask[i] == False:  # noqa: E712
                     tooClose = (
                         (pointlist.data["qx"] - pointlist.data["qx"][i]) ** 2
                         + (pointlist.data["qy"] - pointlist.data["qy"][i]) ** 2

py4DSTEM/data/calibration.py

@@ -234,7 +234,7 @@ def attach(self, data):
         """
         from py4DSTEM.data import Data
 
-        assert isinstance(data, Data), f"data must be a Data instance"
+        assert isinstance(data, Data), "data must be a Data instance"
         self.root.attach(data)
 
     # Register for auto-calibration
@@ -316,7 +316,7 @@ def set_Q_pixel_units(self, x):
             "pixels",
             "A^-1",
             "mrad",
-        ), f"Q pixel units must be 'A^-1', 'mrad' or 'pixels'."
+        ), "Q pixel units must be 'A^-1', 'mrad' or 'pixels'."
         self._params["Q_pixel_units"] = x
 
     def get_Q_pixel_units(self):

py4DSTEM/datacube/virtualdiffraction.py

@@ -114,7 +114,7 @@ def get_virtual_diffraction(
         # Calculate
 
         # ...with no center shifting
-        if shift_center == False:
+        if shift_center is False:
             # ...for the whole pattern
             if mask is None:
                 if method == "mean":

py4DSTEM/datacube/virtualimage.py

@@ -197,13 +197,13 @@ def get_virtual_image(
         # Get mask
         mask = self.make_detector(self.Qshape, mode, g)
         # if return_mask is True, skip computation
-        if return_mask == True and shift_center == False:
+        if return_mask is True and shift_center is False:
             return mask
 
         # Calculate virtual image
 
         # no center shifting
-        if shift_center == False:
+        if shift_center is False:
             # single CPU
             if not dask:
                 # allocate space
@@ -220,7 +220,7 @@ def get_virtual_image(
                     virtual_image[rx, ry] = np.sum(self.data[rx, ry] * mask)
 
             # dask
-            if dask == True:
+            if dask is True:
                 # set up a generalized universal function for dask distribution
                 def _apply_mask_dask(self, mask):
                     virtual_image = np.sum(
@@ -444,7 +444,7 @@ def position_detector(
         # shift center
         if shift_center is None:
             shift_center = False
-        elif shift_center == True:
+        elif shift_center is True:
             assert isinstance(
                 data, tuple
             ), "If shift_center is set to True, `data` should be a 2-tuple (rx,ry). \
@@ -552,7 +552,7 @@ def get_calibrated_detector_geometry(
         # Convert units into detector pixels
 
         # Shift center
-        if centered == True:
+        if centered is True:
             if mode == "point":
                 g = (g[0] + x0_mean, g[1] + y0_mean)
             if mode in ("circle", "circular", "annulus", "annular"):
@@ -561,7 +561,7 @@ def get_calibrated_detector_geometry(
                 g = (g[0] + x0_mean, g[1] + x0_mean, g[2] + y0_mean, g[3] + y0_mean)
 
         # Scale by the detector pixel size
-        if calibrated == True:
+        if calibrated is True:
             if mode == "point":
                 g = (g[0] / unit_conversion, g[1] / unit_conversion)
             if mode in ("circle", "circular"):