Ruff Format

Author: lambda-science

deploy_hf_space.py

@@ -1,4 +1,3 @@
-
 from huggingface_hub import HfApi
 
 api = HfApi()

src/impatient/app/dashapp/callbacks.py

@@ -76,11 +76,11 @@ def color_to_class(ontology, color_hex):
 
 
 def make_default_figure(
-        images=None,
-        stroke_color="",
-        stroke_width=3,
-        shapes=[],
-        source_img=None,
+    images=None,
+    stroke_color="",
+    stroke_width=3,
+    shapes=[],
+    source_img=None,
 ):
     fig = plot_common.dummy_fig()
     plot_common.add_layout_images_to_fig(fig, images)
@@ -113,12 +113,12 @@ def save_img_classifier(clf, label_to_colors_args, segmenter_args):
 
 
 def show_segmentation(
-        image_path,
-        mask_shapes,
-        features,
-        segmenter_args,
-        class_label_colormap,
-        onto_tree_imgannot,
+    image_path,
+    mask_shapes,
+    features,
+    segmenter_args,
+    class_label_colormap,
+    onto_tree_imgannot,
 ):
     """adds an image showing segmentations to a figure's layout"""
     # add 1 because classifier takes 0 to mean no mask
@@ -176,14 +176,14 @@ def register_callbacks(dashapp):
         ],
     )
     def annotation_react(
-            href,
-            graph_relayoutData,
-            any_label_class_button_value,
-            stroke_width_value,
-            show_segmentation_value,
-            download_button_n_clicks,
-            sigma_range_slider_value,
-            masks_data,
+        href,
+        graph_relayoutData,
+        any_label_class_button_value,
+        stroke_width_value,
+        show_segmentation_value,
+        download_button_n_clicks,
+        sigma_range_slider_value,
+        masks_data,
     ):
         classified_image_store_data = dash.no_update
         classifier_store_data = dash.no_update
@@ -216,8 +216,8 @@ def annotation_react(
                     ]
 
         if cbcontext in ["sigma-range-slider.value"] or (
-                ("Show segmentation" in show_segmentation_value)
-                and (len(masks_data["shapes"]) > 0)
+            ("Show segmentation" in show_segmentation_value)
+            and (len(masks_data["shapes"]) > 0)
         ):
             segmentation_features_dict = {
                 "intensity": True,
@@ -257,7 +257,7 @@ def annotation_react(
 
         # We want the segmentation to be computed
         if ("Show segmentation" in show_segmentation_value) and (
-                len(masks_data["shapes"]) > 0
+            len(masks_data["shapes"]) > 0
         ):
             segimgpng = None
             try:

src/impatient/app/dashapp/common_func.py

@@ -4,7 +4,7 @@
 
 def load_onto():
     with open(
-            os.path.join("/home/impatient/data/ontology/", "ontology.json"), "r"
+        os.path.join("/home/impatient/data/ontology/", "ontology.json"), "r"
     ) as fp:
         onto_tree = json.load(fp)
     onto_tree_imgannot = [i for i in onto_tree if i["data"]["image_annotation"] == True]

src/impatient/app/dashapp/shapes_to_segmentations.py

@@ -28,7 +28,7 @@ def fromhex(n):
 
 
 def label_to_colors(
-        img, colormap=px.colors.qualitative.Light24, alpha=128, color_class_offset=0
+    img, colormap=px.colors.qualitative.Light24, alpha=128, color_class_offset=0
 ):
     """
     Take MxN matrix containing integers representing labels and return an MxNx4
@@ -44,7 +44,7 @@ def label_to_colors(
     for key, value in colormap.items():
         hex_value = value.replace("#", "")
         colormap_converted[key] = tuple(
-            [fromhex(hex_value[s: s + 2]) for s in range(0, len(hex_value), 2)]
+            [fromhex(hex_value[s : s + 2]) for s in range(0, len(hex_value), 2)]
         )
     cimg = np.zeros(img.shape[:2] + (3,), dtype="uint8")
     label_on_img = np.unique(img).tolist()
@@ -69,11 +69,11 @@ def grey_labels(img):
 
 
 def compute_segmentations(
-        shapes,
-        img_path=None,
-        features=None,
-        shape_layers=None,
-        label_to_colors_args={},
+    shapes,
+    img_path=None,
+    features=None,
+    shape_layers=None,
+    label_to_colors_args={},
 ):
     # load original image
     img = img_to_ubyte_array(img_path)

src/impatient/app/dashapp/trainable_segmentation.py

@@ -27,7 +27,7 @@ def _texture_filter(gaussian_filtered):
 
 
 def _mutiscale_basic_features_singlechannel(
-        img, intensity=True, edges=True, texture=True, sigma_min=0.5, sigma_max=16
+    img, intensity=True, edges=True, texture=True, sigma_min=0.5, sigma_max=16
 ):
     """Features for a single channel nd image.
 
@@ -63,13 +63,13 @@ def _mutiscale_basic_features_singlechannel(
 
 
 def multiscale_basic_features(
-        image,
-        multichannel=True,
-        intensity=True,
-        edges=True,
-        texture=True,
-        sigma_min=0.5,
-        sigma_max=16,
+    image,
+    multichannel=True,
+    intensity=True,
+    edges=True,
+    texture=True,
+    sigma_min=0.5,
+    sigma_max=16,
 ):
     """Local features for a single- or multi-channel nd image.
 

src/impatient/app/dashapp/use_ml_image_segmentation_classifier.py

@@ -38,7 +38,7 @@ def getenv(e):
 
 
 def use_img_classifier_in_mem(
-        clf, segmenter_args, label_to_colors_args, img_path, out_img
+    clf, segmenter_args, label_to_colors_args, img_path, out_img
 ):
     img = skimage.io.imread(img_path)
 

src/impatient/app/historeport/boqa.py

@@ -35,10 +35,10 @@ def boqa(alpha, beta, query, items_stat):
         # Cardinality calculation of terms between H and Q
         m = matrix_m(query, hidden)
         a[disease] = (
-                pow(beta, m[0, 1])
-                * pow(1 - beta, m[1, 1])
-                * pow(1 - alpha, m[0, 0])
-                * pow(alpha, m[1, 0])
+            pow(beta, m[0, 1])
+            * pow(1 - beta, m[1, 1])
+            * pow(1 - alpha, m[0, 0])
+            * pow(alpha, m[1, 0])
         )
         a_init += a[disease]
     for disease in items_stat:

src/impatient/app/historeport/onto_func.py

@@ -165,8 +165,8 @@ def update_ontology(self, dest_onto: object) -> list:
                 updated_jstree_as_list.append(i)
             elif i["id"] in dest_onto.jstree_as_dict.keys():
                 if (
-                        i["text"] != dest_onto.jstree_as_dict[i["id"]]["text"]
-                        or i["data"] != dest_onto.jstree_as_dict[i["id"]]["data"]
+                    i["text"] != dest_onto.jstree_as_dict[i["id"]]["text"]
+                    or i["data"] != dest_onto.jstree_as_dict[i["id"]]["data"]
                 ):
                     # If destination has modified data or name: update
                     i["text"] = dest_onto.jstree_as_dict[i["id"]]["text"]
@@ -213,8 +213,8 @@ def update_ontology(self, dest_onto: object) -> list:
         # If destination has different parent ID: change it.
         for i in dest_onto.jstree_as_dict.keys():
             if (
-                    dest_onto.jstree_as_dict[i]["parent"]
-                    != self.jstree_as_dict[i]["parent"]
+                dest_onto.jstree_as_dict[i]["parent"]
+                != self.jstree_as_dict[i]["parent"]
             ):
                 self.jstree_as_dict[i]["parent"] = dest_onto.jstree_as_dict[i]["parent"]
 

src/impatient/app/historeport/routes.py

@@ -54,7 +54,7 @@ def repredict_reports():
       redirect: Redirect to the historeports index HTML page
     """
     with open(
-            os.path.join("/home/impatient/data/ontology/", "ontology.json"), "r"
+        os.path.join("/home/impatient/data/ontology/", "ontology.json"), "r"
     ) as fp:
         onto_tree = json.load(fp)
     df = db_to_df()
@@ -81,7 +81,7 @@ def repredict_reports():
 @bp.route("/historeport/download", methods=["GET"])
 def histo_download():
     with open(
-            os.path.join("/home/impatient/data/ontology/", "ontology.json"), "r"
+        os.path.join("/home/impatient/data/ontology/", "ontology.json"), "r"
     ) as fp:
         onto_tree = json.load(fp)
     df = db_to_df()
@@ -126,7 +126,7 @@ def historeport():
     # If no args: empty form
     else:
         with open(
-                os.path.join(current_app.config["ONTOLOGY_FOLDER"], "ontology.json")
+            os.path.join(current_app.config["ONTOLOGY_FOLDER"], "ontology.json")
         ) as f:
             empty_json_tree = json.load(f)
         form = ReportForm(ontology_tree=empty_json_tree)

src/impatient/app/histostats/routes.py

@@ -13,7 +13,7 @@ def statsindex():
     """
     # Up to stat_per_diag to move in the historeport part to not always regenerate
     with open(
-            os.path.join("/home/impatient/data/ontology/", "ontology.json"), "r"
+        os.path.join("/home/impatient/data/ontology/", "ontology.json"), "r"
     ) as fp:
         onto_tree = json.load(fp)
     df = db_to_df()