initial bare exception fix

py4DSTEM/braggvectors/diskdetection.py

@@ -221,7 +221,7 @@ def find_Bragg_disks(
                 mode = "dp"
             elif data.ndim == 3:
                 mode = "dp_stack"
-        except:
+        except Exception:
             er = f"entry {data} for `data` could not be parsed"
             raise Exception(er)
 

py4DSTEM/braggvectors/diskdetection_aiml.py

@@ -103,12 +103,12 @@ def find_Bragg_disks_aiml_single_DP(
     """
     try:
         import crystal4D
-    except:
-        raise ImportError("Import Error: Please install crystal4D before proceeding")
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError("Import Error: Please install crystal4D before proceeding")
     try:
         import tensorflow as tf
-    except:
-        raise ImportError(
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError(
             "Please install tensorflow before proceeding - please check "
             + "https://www.tensorflow.org/install"
             + "for more information"
@@ -256,8 +256,8 @@ def find_Bragg_disks_aiml_selected(
 
     try:
         import crystal4D
-    except:
-        raise ImportError("Import Error: Please install crystal4D before proceeding")
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError("Import Error: Please install crystal4D before proceeding")
 
     assert len(Rx) == len(Ry)
     peaks = []
@@ -433,8 +433,8 @@ def find_Bragg_disks_aiml_serial(
 
     try:
         import crystal4D
-    except:
-        raise ImportError("Import Error: Please install crystal4D before proceeding")
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError("Import Error: Please install crystal4D before proceeding")
 
     # Make the peaks PointListArray
     # dtype = [('qx',float),('qy',float),('intensity',float)]
@@ -643,8 +643,8 @@ def find_Bragg_disks_aiml(
     """
     try:
         import crystal4D
-    except:
-        raise ImportError("Please install crystal4D before proceeding")
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError("Please install crystal4D before proceeding")
 
     def _parse_distributed(distributed):
         import os
@@ -840,7 +840,8 @@ def _integrate_disks(DP, maxima_x, maxima_y, maxima_int, int_window_radius=1):
         disks.append(np.average(disk))
     try:
         disks = disks / max(disks)
-    except:
+    # TODO work out what exception would go here
+    except Exception:
         pass
     return (maxima_x, maxima_y, disks)
 
@@ -878,8 +879,8 @@ def _get_latest_model(model_path=None):
 
     try:
         import tensorflow as tf
-    except:
-        raise ImportError(
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError(
             "Please install tensorflow before proceeding - please check "
             + "https://www.tensorflow.org/install"
             + "for more information"
@@ -891,8 +892,12 @@ def _get_latest_model(model_path=None):
     if model_path is None:
         try:
             os.mkdir("./tmp")
-        except:
+        except FileExistsError:
             pass
+        except Exception as e:
+            # TODO work out if I want to pass or raise 
+            pass
+            # raise e
         # download the json file with the meta data
         download_file_from_google_drive("FCU-Net", "./tmp/model_metadata.json")
         with open("./tmp/model_metadata.json") as f:
@@ -905,9 +910,13 @@ def _get_latest_model(model_path=None):
             with open("./tmp/model_metadata_old.json") as f_old:
                 metaold = json.load(f_old)
                 file_id_old = metaold["file_id"]
-        except:
+        # TODO Double check this is correct Error 
+        except FileNotFoundError:
+            file_id_old = file_id
+        except Exception:
             file_id_old = file_id
 
+
         if os.path.exists(file_path) and file_id == file_id_old:
             print(
                 "Latest model weight is already available in the local system. Loading the model... \n"
@@ -921,7 +930,8 @@ def _get_latest_model(model_path=None):
             download_file_from_google_drive(file_id, filename)
             try:
                 shutil.unpack_archive(filename, "./tmp", format="zip")
-            except:
+            # TODO Work work what specific exception
+            except Exception:
                 pass
             model_path = file_path
             os.rename("./tmp/model_metadata.json", "./tmp/model_metadata_old.json")

py4DSTEM/braggvectors/diskdetection_aiml_cuda.py

@@ -22,8 +22,8 @@
 
 try:
     import tensorflow as tf
-except:
-    raise ImportError(
+except ModuleNotFoundError:
+    raise ModuleNotFoundError(
         "Please install tensorflow before proceeding - please check "
         + "https://www.tensorflow.org/install"
         + "for more information"
@@ -637,7 +637,8 @@ def upsampled_correlation_cp(imageCorr, upsampleFactor, xyShift):
         )
         dx = (icc[2, 1] - icc[0, 1]) / (4 * icc[1, 1] - 2 * icc[2, 1] - 2 * icc[0, 1])
         dy = (icc[1, 2] - icc[1, 0]) / (4 * icc[1, 1] - 2 * icc[1, 2] - 2 * icc[1, 0])
-    except:
+    # TODO I think this is just the Index Error 
+    except IndexError or Exception:
         dx, dy = (
             0,
             0,
@@ -733,6 +734,7 @@ def _integrate_disks_cp(DP, maxima_x, maxima_y, maxima_int, int_window_radius=1)
         disks.append(np.average(disk))
     try:
         disks = disks / max(disks)
-    except:
+    # TODO work out what exception to use
+    except Exception:
         pass
     return (maxima_x, maxima_y, disks)

py4DSTEM/braggvectors/diskdetection_cuda.py

@@ -618,7 +618,8 @@ def upsampled_correlation(imageCorr, upsampleFactor, xyShift):
             dy = (icc[1, 2] - icc[1, 0]) / (
                 4 * icc[1, 1] - 2 * icc[1, 2] - 2 * icc[1, 0]
             )
-        except:
+        # TODO Work out what exception to use 
+        except IndexError or Exception:
             dx, dy = (
                 0,
                 0,

py4DSTEM/datacube/virtualimage.py

@@ -414,7 +414,8 @@ def position_detector(
                 try:
                     image = self.tree(k)
                     break
-                except:
+                # TODO check what error is produced when passing nonexistant key to tree
+                except KeyError or AttributeError:
                     pass
             if image is None:
                 image = self[0, 0]

py4DSTEM/io/filereaders/read_K2.py

@@ -124,7 +124,8 @@ def __init__(
             # this may be wrong for binned data... in which case the reader doesn't work anyway!
             Q_Nx = gtg.allTags[".SI Image Tags.Acquisition.Parameters.Detector.height"]
             Q_Ny = gtg.allTags[".SI Image Tags.Acquisition.Parameters.Detector.width"]
-        except:
+        # TODO check this is the correct error type
+        except ValueError:
             print("Warning: diffraction pattern shape not detected!")
             print("Assuming 1920x1792 as the diffraction pattern size!")
             Q_Nx = 1792

py4DSTEM/io/legacy/read_utils.py

@@ -100,7 +100,8 @@ def get_N_dataobjects(filepath, topgroup="4DSTEM_experiment"):
         N_pla = len(f[topgroup]["data/pointlistarrays"].keys())
         try:
             N_coords = len(f[topgroup]["data/coordinates"].keys())
-        except:
+        # TODO work out what exception will be raised 
+        except ValueError or AttributeError or Exception:
             N_coords = 0
         N_do = N_dc + N_cdc + N_ds + N_rs + N_pl + N_pla + N_coords
         return N_dc, N_cdc, N_ds, N_rs, N_pl, N_pla, N_coords, N_do

py4DSTEM/process/classification/featurization.py

@@ -477,7 +477,7 @@ def get_class_DPs(self, datacube, method, thresh):
                     datacube.data.shape[2],
                     datacube.data.shape[3],
                 )
-            except:
+            except ValueError:
                 raise ValueError(
                     "Datacube must have same R_Nx and R_Ny dimensions as Featurization instance."
                 )

py4DSTEM/process/polar/polar_peaks.py

@@ -602,8 +602,8 @@ def refine_peaks_local(
                         self.peaks[rx, ry]["qr"][a0] = p0[2] / q_step
                         self.peaks[rx, ry]["sigma_annular"][a0] = p0[3] / t_step
                         self.peaks[rx, ry]["sigma_radial"][a0] = p0[4] / q_step
-
-                    except:
+                    # TODO work out what error is raised 
+                    except Exception:
                         pass
 
             else:
@@ -643,8 +643,8 @@ def refine_peaks_local(
                         self.peaks[rx, ry]["qr"][a0] = p0[2] / q_step
                         self.peaks[rx, ry]["sigma_annular"][a0] = p0[3] / t_step
                         self.peaks[rx, ry]["sigma_radial"][a0] = p0[4] / q_step
-
-                    except:
+                    # TODO work out what exception is raised 
+                    except Exception:
                         pass
 
 
@@ -1044,7 +1044,8 @@ def fit_image(basis, *coefs):
                 ),
                 name="peaks_polar",
             )
-        except:
+        # TODO work out what exception is raised
+        except Exception:
             # if fitting has failed, we will still output the last iteration
             # TODO - add a flag for unconverged fits
             coefs_peaks = np.reshape(coefs_all[(3 * num_rings + 3) :], (5, num_peaks)).T

py4DSTEM/process/utils/multicorr.py

@@ -99,7 +99,8 @@ def upsampled_correlation(imageCorr, upsampleFactor, xyShift, device="cpu"):
         )
         dx = (icc[2, 1] - icc[0, 1]) / (4 * icc[1, 1] - 2 * icc[2, 1] - 2 * icc[0, 1])
         dy = (icc[1, 2] - icc[1, 0]) / (4 * icc[1, 1] - 2 * icc[1, 2] - 2 * icc[1, 0])
-    except:
+    # TODO work out what error is raised 
+    except IndexError or Exception:
         dx, dy = (
             0,
             0,

py4DSTEM/utils/configuration_checker.py

@@ -304,7 +304,7 @@ def import_tester(m: str) -> bool:
     # try and import the module
     try:
         importlib.import_module(m)
-    except:
+    except ModuleNotFoundError:
         state = False
 
     return state
@@ -391,7 +391,8 @@ def check_cupy_gpu(gratuitously_verbose: bool, **kwargs):
         try:
             d = cp.cuda.Device(i)
             hasattr(d, "attributes")
-        except:
+        # TODO work out what error is raised
+        except AttributeError or Exception:
             num_gpus_detected = i
             break