Run black formatting (restore default line length of 88)

Author: ehrenfeu

src/imcflibs/imagej/bioformats.py

@@ -159,9 +159,7 @@ def export(imp, filename, overwrite=False):
         log.debug("Detected calibration unit: %s", unit)
     except Exception as err:
         log.error("Unable to detect spatial unit: %s", err)
-        raise RuntimeError(
-            "Error detecting image calibration: %s" % err
-        )
+        raise RuntimeError("Error detecting image calibration: %s" % err)
     if unit == "pixel" and (suffix == "ics" or suffix == "ids"):
         log.warn(
             "Forcing unit to be 'm' instead of 'pixel' to avoid "
@@ -178,9 +176,7 @@ def export(imp, filename, overwrite=False):
     log.debug("Exporting finished.")
 
 
-def export_using_orig_name(
-    imp, path, orig_name, tag, suffix, overwrite=False
-):
+def export_using_orig_name(imp, path, orig_name, tag, suffix, overwrite=False):
     """Export an image to a given path, deriving the name from the input file.
 
     The input filename is stripped to its pure file name, without any path or
@@ -442,12 +438,8 @@ def get_stage_coords(source, filenames):
                 log.debug("no z calibration found, trying to recover")
                 first_plane = omeMeta.getPlanePositionZ(0, 0)
                 next_plane_imagenumber = frame_size_c + frame_size_t - 1
-                second_plane = omeMeta.getPlanePositionZ(
-                    0, next_plane_imagenumber
-                )
-                z_interval = abs(
-                    abs(first_plane.value()) - abs(second_plane.value())
-                )
+                second_plane = omeMeta.getPlanePositionZ(0, next_plane_imagenumber)
+                z_interval = abs(abs(first_plane.value()) - abs(second_plane.value()))
                 log.debug("z-interval seems to be: " + str(z_interval))
 
             # create an image calibration
@@ -480,14 +472,12 @@ def get_stage_coords(source, filenames):
 
             physSizeX_max = (
                 physSizeX.value()
-                if physSizeX.value()
-                >= omeMeta.getPixelsPhysicalSizeX(series).value()
+                if physSizeX.value() >= omeMeta.getPixelsPhysicalSizeX(series).value()
                 else omeMeta.getPixelsPhysicalSizeX(series).value()
             )
             physSizeY_max = (
                 physSizeY.value()
-                if physSizeY.value()
-                >= omeMeta.getPixelsPhysicalSizeY(series).value()
+                if physSizeY.value() >= omeMeta.getPixelsPhysicalSizeY(series).value()
                 else omeMeta.getPixelsPhysicalSizeY(series).value()
             )
             if omeMeta.getPixelsPhysicalSizeZ(series):
@@ -506,9 +496,7 @@ def get_stage_coords(source, filenames):
             pos_y = current_position_y.value()
 
             if current_position_z is None:
-                log.debug(
-                    "the z-position is missing in the ome-xml metadata."
-                )
+                log.debug("the z-position is missing in the ome-xml metadata.")
                 pos_z = 1.0
             else:
                 pos_z = current_position_z.value()
@@ -531,9 +519,7 @@ def get_stage_coords(source, filenames):
         rel_pos_y = (
             stage_coordinates_y[i] - stage_coordinates_y[0]
         ) / physSizeY.value()
-        rel_pos_z = (
-            stage_coordinates_z[i] - stage_coordinates_z[0]
-        ) / z_interval
+        rel_pos_z = (stage_coordinates_z[i] - stage_coordinates_z[0]) / z_interval
 
         relative_coordinates_x_px.append(rel_pos_x)
         relative_coordinates_y_px.append(rel_pos_y)

src/imcflibs/imagej/labelimage.py

@@ -31,9 +31,7 @@ def label_image_to_roi_list(label_image, low_thresh=None):
     max_value = 0
 
     for slice in range(1, label_image.getNSlices() + 1):
-        label_image_slice = Duplicator().run(
-            label_image, 1, 1, slice, slice, 1, 1
-        )
+        label_image_slice = Duplicator().run(label_image, 1, 1, slice, slice, 1, 1)
 
         image_processor = label_image_slice.getProcessor()
         pixels = image_processor.getFloatArray()
@@ -59,9 +57,7 @@ def label_image_to_roi_list(label_image, low_thresh=None):
             elif value == 0:
                 continue
             # print(value)
-            float_processor.setThreshold(
-                value, value, ImageProcessor.NO_LUT_UPDATE
-            )
+            float_processor.setThreshold(value, value, ImageProcessor.NO_LUT_UPDATE)
             roi = ThresholdToSelection.run(img_float_copy)
             roi.setName(str(value))
             roi.setPosition(slice)
@@ -98,9 +94,7 @@ def cookie_cut_labels(label_image_ref, label_image_to_relate):
     Prefs.blackBackground = True
     IJ.run(imp_dup, "Convert to Mask", "")
     IJ.run(imp_dup, "Divide...", "value=255")
-    return ImageCalculator.run(
-        label_image_ref, imp_dup, "Multiply create"
-    )
+    return ImageCalculator.run(label_image_ref, imp_dup, "Multiply create")
 
 
 def relate_label_images(outer_label_imp, inner_label_imp):
@@ -203,9 +197,7 @@ def measure_objects_size_shape_2d(label_image):
     return regions.process(label_image)
 
 
-def binary_to_label(
-    imp, title, min_thresh=1, min_vol=None, max_vol=None
-):
+def binary_to_label(imp, title, min_thresh=1, min_vol=None, max_vol=None):
     """Segment a binary image to get a label image (2D/3D).
 
     Works on: 2D and 3D binary data.
@@ -286,14 +278,10 @@ def dilate_labels_2d(imp, dilation_radius):
     # Iterate over each slice of the input ImagePlus
     for i in range(1, imp.getNSlices() + 1):
         # Duplicate the current slice
-        current_imp = Duplicator().run(
-            imp, 1, 1, i, imp.getNSlices(), 1, 1
-        )
+        current_imp = Duplicator().run(imp, 1, 1, i, imp.getNSlices(), 1, 1)
 
         # Perform a dilation of the labels in the current slice
-        dilated_labels_imp = li.dilateLabels(
-            current_imp, dilation_radius
-        )
+        dilated_labels_imp = li.dilateLabels(current_imp, dilation_radius)
 
         # Append the dilated labels to the list
         dilated_labels_list.append(dilated_labels_imp)

src/imcflibs/imagej/misc.py

@@ -43,9 +43,7 @@ def show_progress(cur, final):
     -----
     `ij.IJ.showProgress` internally increments the given `cur` value by 1.
     """
-    log.info(
-        "Progress: %s / %s (%s)", cur + 1, final, (1.0 + cur) / final
-    )
+    log.info("Progress: %s / %s (%s)", cur + 1, final, (1.0 + cur) / final)
     IJ.showProgress(cur, final)
 
 
@@ -82,9 +80,7 @@ def elapsed_time_since(start, end=None):
 
     hours, rem = divmod(end - start, 3600)
     minutes, seconds = divmod(rem, 60)
-    return "{:0>2}:{:0>2}:{:05.2f}".format(
-        int(hours), int(minutes), seconds
-    )
+    return "{:0>2}:{:0>2}:{:05.2f}".format(int(hours), int(minutes), seconds)
 
 
 def percentage(part, whole):
@@ -125,13 +121,9 @@ def calculate_mean_and_stdv(values_list, round_decimals=0):
     if not filtered_list:
         return 0, 0
 
-    mean = round(
-        sum(filtered_list) / len(filtered_list), round_decimals
-    )
-    variance = sum((x - mean) ** 2 for x in filtered_list) / len(
-        filtered_list
-    )
-    std_dev = round(variance**0.5, round_decimals)
+    mean = round(sum(filtered_list) / len(filtered_list), round_decimals)
+    variance = sum((x - mean) ** 2 for x in filtered_list) / len(filtered_list)
+    std_dev = round(variance ** 0.5, round_decimals)
 
     return mean, std_dev
 
@@ -168,9 +160,7 @@ def find_focus(imp):
     # Check if more than 1 channel
     # FUTURE Could be improved for multi channel
     if imp_dimensions[2] != 1:
-        sys.exit(
-            "Image has more than one channel, please reduce dimensionality"
-        )
+        sys.exit("Image has more than one channel, please reduce dimensionality")
 
     # Loop through each time point
     for plane in range(1, imp_dimensions[4] + 1):
@@ -186,9 +176,7 @@ def find_focus(imp):
             # pix_array = pix_array*pix_array
 
             sumpix_array = sum(pix_array)
-            var = sumpix_array / (
-                imp_dimensions[0] * imp_dimensions[1] * mean
-            )
+            var = sumpix_array / (imp_dimensions[0] * imp_dimensions[1] * mean)
 
             if var > norm_var:
                 norm_var = var
@@ -217,14 +205,10 @@ def send_mail(job_name, recipient, filename, total_execution_time):
 
     # Ensure the sender and server are configured from Prefs
     if not sender:
-        log.info(
-            "Sender email is not configured. Please check IJ_Prefs.txt."
-        )
+        log.info("Sender email is not configured. Please check IJ_Prefs.txt.")
         return
     if not server:
-        log.info(
-            "SMTP server is not configured. Please check IJ_Prefs.txt."
-        )
+        log.info("SMTP server is not configured. Please check IJ_Prefs.txt.")
         return
 
     # Ensure the recipient is provided
@@ -305,12 +289,7 @@ def timed_log(message, as_string=False):
         Flag to request the formatted string to be returned instead of printing
         it to the log. By default False.
     """
-    formatted = (
-        time.strftime("%H:%M:%S", time.localtime())
-        + ": "
-        + message
-        + " "
-    )
+    formatted = time.strftime("%H:%M:%S", time.localtime()) + ": " + message + " "
     if as_string:
         return formatted
     IJ.log(formatted)
@@ -497,23 +476,17 @@ def write_ordereddict_to_csv(out_file, content):
     if not os.path.exists(out_file):
         # If the file does not exist, create it and write the header
         with open(out_file, "wb") as f:
-            dict_writer = csv.DictWriter(
-                f, content[0].keys(), delimiter=";"
-            )
+            dict_writer = csv.DictWriter(f, content[0].keys(), delimiter=";")
             dict_writer.writeheader()
             dict_writer.writerows(content)
     else:
         # If the file exists, append the results
         with open(out_file, "ab") as f:
-            dict_writer = csv.DictWriter(
-                f, content[0].keys(), delimiter=";"
-            )
+            dict_writer = csv.DictWriter(f, content[0].keys(), delimiter=";")
             dict_writer.writerows(content)
 
 
-def save_image_in_format(
-    imp, format, out_dir, series, pad_number, split_channels
-):
+def save_image_in_format(imp, format, out_dir, series, pad_number, split_channels):
     """Save an ImagePlus object in the specified format.
 
     This function provides flexible options for saving ImageJ images in various
@@ -596,9 +569,7 @@ def save_image_in_format(
                     imp.getNFrames(),
                 )
             )
-            dir_to_save.append(
-                os.path.join(out_dir, "C" + str(channel))
-            )
+            dir_to_save.append(os.path.join(out_dir, "C" + str(channel)))
     else:
         imp_to_use.append(imp)
         dir_to_save.append(out_dir)

src/imcflibs/imagej/objects3d.py

@@ -75,9 +75,7 @@ def imgplus_to_population3d(imp):
     return Objects3DPopulation(img)
 
 
-def segment_3d_image(
-    imp, title=None, min_thresh=1, min_vol=None, max_vol=None
-):
+def segment_3d_image(imp, title=None, min_thresh=1, min_vol=None, max_vol=None):
     """Segment a 3D binary image to get a labelled stack.
 
     Parameters
@@ -123,9 +121,7 @@ def segment_3d_image(
     return seg.getImagePlus()
 
 
-def get_objects_within_intensity(
-    obj_pop, imp, min_intensity, max_intensity
-):
+def get_objects_within_intensity(obj_pop, imp, min_intensity, max_intensity):
     """Filter a population for objects within the given intensity range.
 
     Parameters
@@ -152,10 +148,7 @@ def get_objects_within_intensity(
         # Calculate the mean intensity of the object
         mean_intensity = obj.getPixMeanValue(ImageHandler.wrap(imp))
         # Check if the object is within the specified intensity range
-        if (
-            mean_intensity >= min_intensity
-            and mean_intensity < max_intensity
-        ):
+        if mean_intensity >= min_intensity and mean_intensity < max_intensity:
             objects_within_intensity.append(obj)
 
     # Return the new population with the filtered objects
@@ -239,9 +232,7 @@ def seeded_watershed(imp_binary, imp_peaks, threshold=10):
     img_seed = ImagePlusAdapter.convertFloat(imp_peaks).copy()
 
     if threshold:
-        watersheded_result = WatershedLabeling.watershed(
-            img, img_seed, threshold
-        )
+        watersheded_result = WatershedLabeling.watershed(img, img_seed, threshold)
     else:
         watersheded_result = WatershedLabeling.watershed(img, img_seed)