pep 8 - Comparisons to singletons complience

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/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

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(

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(
@@ -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] is False:
                     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] is False:
                     tooClose = (
                         (maxima["x"] - maxima["x"][i]) ** 2
                         + (maxima["y"] - maxima["y"][i]) ** 2

py4DSTEM/braggvectors/diskdetection_parallel_new.py

@@ -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

@@ -52,7 +52,7 @@ 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] is False:
                     tooClose = (
                         (pointlist.data["qx"] - pointlist.data["qx"][i]) ** 2
                         + (pointlist.data["qy"] - pointlist.data["qy"][i]) ** 2
@@ -160,7 +160,7 @@ 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] is False:
                     tooClose = (
                         (pointlist.data["qx"] - pointlist.data["qx"][i]) ** 2
                         + (pointlist.data["qy"] - pointlist.data["qy"][i]) ** 2

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"):

py4DSTEM/io/filereaders/read_K2.py

@@ -336,9 +336,9 @@ def _find_offsets(self):
             for i in range(8):
                 sync = False
                 frame = 0
-                while sync == False:
+                while sync is False:
                     sync = self._bin_files[i][frame]["block"] == block_id
-                    if sync == False:
+                    if sync is False:
                         frame += 1
                 self._shutter_offsets[i] += frame
             print("Offsets are currently ", self._shutter_offsets)
@@ -358,20 +358,20 @@ def _find_offsets(self):
                     sync = False
                     next_frame = stripe[j]["frame"]
 
-        if sync == False:
+        if sync is False:
             # the first frame is incomplete, so we need to seek the next one
             print(
                 f"First frame ({first_frame}) incomplete, seeking frame {next_frame}..."
             )
             for i in range(8):
                 sync = False
                 frame = 0
-                while sync == False:
+                while sync is False:
                     sync = (
                         self._bin_files[i][self._shutter_offsets[i] + frame]["frame"]
                         == next_frame
                     )
-                    if sync == False:
+                    if sync is False:
                         frame += 1
                 self._shutter_offsets[i] += frame
             print("Offsets are now ", self._shutter_offsets)
@@ -387,7 +387,7 @@ def _find_offsets(self):
                 ]
                 if np.any(stripe[:]["frame"] != first_frame):
                     sync = False
-            if sync == True:
+            if sync is True:
                 print("New frame is complete!")
             else:
                 print("Next frame also incomplete!!!! Data may be corrupt?")
@@ -397,7 +397,7 @@ def _find_offsets(self):
         for i in range(8):
             shutter = False
             frame = 0
-            while shutter == False:
+            while shutter is False:
                 offset = self._shutter_offsets[i] + (frame * 32)
                 stripe = self._bin_files[i][offset : offset + 32]
                 shutter = stripe[0]["shutter"]

py4DSTEM/preprocess/electroncount.py

@@ -402,7 +402,7 @@ def counted_pointlistarray_to_datacube(counted_pointlistarray, shape, subpixel=F
         (4D array of bools): a 4D array of bools, with true indicating an electron strike.
     """
     assert len(shape) == 4
-    assert subpixel == False, "subpixel mode not presently supported."
+    assert subpixel is False, "subpixel mode not presently supported."
     R_Nx, R_Ny, Q_Nx, Q_Ny = shape
     counted_datacube = np.zeros((R_Nx, R_Nx, Q_Nx, Q_Ny), dtype=bool)
 

py4DSTEM/preprocess/radialbkgrd.py

@@ -80,7 +80,7 @@ def get_1D_polar_background(
     # Crop polar data to maximum distance which contains information from original image
     if (polarData.mask.sum(axis=(0)) == polarData.shape[0]).any():
         ii = polarData.data.shape[1] - 1
-        while polarData.mask[:, ii].all() == True:
+        while polarData.mask[:, ii].all() is True:
             ii = ii - 1
         maximalDistance = ii
         polarData = polarData[:, 0:maximalDistance]
@@ -105,16 +105,16 @@ def get_1D_polar_background(
 
     background1D = np.maximum(background1D, min_background_value)
 
-    if smoothing == True:
-        if smoothing_log == True:
+    if smoothing is True:
+        if smoothing_log is True:
             background1D = np.log(background1D)
 
         background1D = savgol_filter(
             background1D, smoothingWindowSize, smoothingPolyOrder
         )
-        if smoothing_log == True:
+        if smoothing_log is True:
             background1D = np.exp(background1D)
-    if return_polararr == True:
+    if return_polararr is True:
         return (background1D, r_bins, polarData)
     else:
         return (background1D, r_bins)

py4DSTEM/preprocess/utils.py

@@ -293,7 +293,7 @@ def filter_2D_maxima(
     if minSpacing > 0:
         deletemask = np.zeros(len(maxima), dtype=bool)
         for i in range(len(maxima)):
-            if deletemask[i] == False:
+            if deletemask[i] is False:
                 tooClose = (
                     (maxima["x"] - maxima["x"][i]) ** 2
                     + (maxima["y"] - maxima["y"][i]) ** 2

py4DSTEM/process/calibration/ellipse.py

@@ -63,7 +63,7 @@ def fit_ellipse_1D(ar, center=None, fitradii=None, mask=None):
     rr = np.sqrt((xx - x0) ** 2 + (yy - y0) ** 2)
     _mask = (rr > ri) * (rr <= ro)
     if mask is not None:
-        _mask *= mask == False
+        _mask *= mask is False
     xs, ys = np.nonzero(_mask)
     vals = ar[_mask]
 

py4DSTEM/process/calibration/origin.py

@@ -154,15 +154,15 @@ def fit_origin(
                 robust=robust,
                 robust_steps=robust_steps,
                 robust_thresh=robust_thresh,
-                data_mask=mask == True,
+                data_mask=mask is True,
             )
             popt_y, pcov_y, qy0_fit, _ = fit_2D(
                 f,
                 qy0_meas,
                 robust=robust,
                 robust_steps=robust_steps,
                 robust_thresh=robust_thresh,
-                data_mask=mask == True,
+                data_mask=mask is True,
             )
 
     # Compute residuals

py4DSTEM/process/calibration/qpixelsize.py

@@ -60,6 +60,6 @@ def get_dq_from_indexed_peaks(qs, hkl, a):
     # Get pixel size
     dq = 1 / (c * a)
     qs_fit = d_inv[mask] / a
-    hkl_fit = [hkl[i] for i in range(len(hkl)) if mask[i] == True]
+    hkl_fit = [hkl[i] for i in range(len(hkl)) if mask[i] is True]
 
     return dq, qs_fit, hkl_fit

py4DSTEM/process/classification/braggvectorclassification.py

@@ -594,8 +594,8 @@ def merge_iterative(self, threshBPs=0.1, threshScanPosition=0.1):
             W_merge = W_merge[:, 1:]
             H_merge = H_merge[1:, :]
 
-            W_ = np.hstack((W_[:, merged == False], W_merge))
-            H_ = np.vstack((H_[merged == False, :], H_merge))
+            W_ = np.hstack((W_[:, merged is False], W_merge))
+            H_ = np.vstack((H_[merged is False, :], H_merge))
             Nc_ = W_.shape[1]
 
             if len(merge_candidates) == 0: