Improve linting and consistency (#1084)

DigitalSlideArchive · Dec 5, 2023 · 4b3391a · 4b3391a
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diff --git a/.circleci/config.yml b/.circleci/config.yml
@@ -203,7 +203,7 @@ jobs:
           name: Install dependencies
           command: sudo apt-get update -yq && sudo apt install -yq pandoc
       - tox:
-          env: docs,flake8
+          env: docs,lint
       - store_artifacts:
           path: docs/_build/html
       - persist_to_workspace:

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -51,7 +51,7 @@ jobs:
           pip install tox
       - name: Run tox for lint and docs
         run: |
-          tox -e flake8,docs
+          tox -e lint,docs
       - uses: actions/upload-artifact@v3
         with:
           name: docs

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -70,3 +70,10 @@ repos:
   rev: 6.1.0
   hooks:
     - id: flake8
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.1.3
+  hooks:
+    - id: ruff
+      args: [--fix, --exit-non-zero-on-fix]
+      types_or: [python, pyi]
diff --git a/docs/conf.py b/docs/conf.py
@@ -261,7 +261,7 @@
 man_pages = [
     ('index', 'HistomicsTK',
      'HistomicsTK Documentation',
-     ['Kitware, Inc.'], 1)
+     ['Kitware, Inc.'], 1),
 ]
 
 # If true, show URL addresses after external links.

diff --git a/docs/examples/annotation_database_backup_and_sql_parser.ipynb b/docs/examples/annotation_database_backup_and_sql_parser.ipynb
diff --git a/docs/examples/annotations_to_object_segmentation_masks.ipynb b/docs/examples/annotations_to_object_segmentation_masks.ipynb
diff --git a/docs/examples/annotations_to_semantic_segmentation_masks.ipynb b/docs/examples/annotations_to_semantic_segmentation_masks.ipynb
diff --git a/docs/examples/color_deconvolution.ipynb b/docs/examples/color_deconvolution.ipynb
@@ -27,14 +27,12 @@
     "import histomicstk as htk\n",
     "\n",
     "import numpy as np\n",
-    "import scipy as sp\n",
     "\n",
     "import skimage.io\n",
     "import skimage.measure\n",
     "import skimage.color\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib.patches as mpatches\n",
     "%matplotlib inline\n",
     "\n",
     "#Some nice default configuration for plots\n",
@@ -230,7 +228,7 @@
     "\n",
     "# perform sparse color deconvolution\n",
     "imDeconvolved = htk.preprocessing.color_deconvolution.color_deconvolution(\n",
-    "    imInput, \n",
+    "    imInput,\n",
     "    htk.preprocessing.color_deconvolution.complement_stain_matrix(W_est),\n",
     "    I_0,\n",
     ")\n",

diff --git a/docs/examples/color_normalization_and_augmentation.ipynb b/docs/examples/color_normalization_and_augmentation.ipynb
@@ -54,7 +54,7 @@
    "outputs": [],
    "source": [
     "APIURL = 'http://candygram.neurology.emory.edu:8080/api/v1/'\n",
-    "SAMPLE_SLIDE_ID = \"5d817f5abd4404c6b1f744bb\"\n",
+    "SAMPLE_SLIDE_ID = '5d817f5abd4404c6b1f744bb'\n",
     "\n",
     "gc = girder_client.GirderClient(apiUrl=APIURL)\n",
     "# gc.authenticate(interactive=True)\n",
@@ -75,7 +75,7 @@
     "W_target = np.array([\n",
     "    [0.5807549,  0.08314027,  0.08213795],\n",
     "    [0.71681094,  0.90081588,  0.41999816],\n",
-    "    [0.38588316,  0.42616716, -0.90380025]\n",
+    "    [0.38588316,  0.42616716, -0.90380025],\n",
     "])\n",
     "\n",
     "# visualization color map\n",
@@ -105,9 +105,9 @@
    "source": [
     "# get RGB image at a small magnification\n",
     "slide_info = gc.get('item/%s/tiles' % SAMPLE_SLIDE_ID)\n",
-    "getStr = \"/item/%s/tiles/region?left=%d&right=%d&top=%d&bottom=%d\" % (\n",
-    "    SAMPLE_SLIDE_ID, 0, slide_info['sizeX'], 0, slide_info['sizeY']\n",
-    "    ) + \"&magnification=%.2f\" % MAG\n",
+    "getStr = '/item/%s/tiles/region?left=%d&right=%d&top=%d&bottom=%d' % (\n",
+    "    SAMPLE_SLIDE_ID, 0, slide_info['sizeX'], 0, slide_info['sizeY'],\n",
+    "    ) + '&magnification=%.2f' % MAG\n",
     "tissue_rgb = get_image_from_htk_response(\n",
     "    gc.get(getStr, jsonResp=False))\n",
     "\n",
@@ -317,7 +317,7 @@
     "    ax[0].imshow(tissue_rgb[ymin:ymax, xmin:xmax, :])\n",
     "    ax[1].imshow(tissue_rgb_normalized[ymin:ymax, xmin:xmax, :])\n",
     "    plt.show()\n",
-    "    \n",
+    "\n",
     "vis_result()"
    ]
   },
@@ -854,7 +854,7 @@
     "    ax[0].imshow(rgb)\n",
     "    ax[1].imshow(augmented_rgb)\n",
     "    plt.show()\n",
-    "    \n",
+    "\n",
     "vis_augmentation()"
    ]
   },

diff --git a/docs/examples/creating_gallery_images_review.ipynb b/docs/examples/creating_gallery_images_review.ipynb
@@ -108,9 +108,9 @@
     "SAMPLE_FOLDER_ID = '5e2a2da8ddda5f83986d18a2'\n",
     "\n",
     "# This is the girder folder where galleries will be visualized\n",
-    "POST_FOLDERID = \"5e3ce440ddda5f839875b33e\"\n",
+    "POST_FOLDERID = '5e3ce440ddda5f839875b33e'\n",
     "\n",
-    "# Connect to an authenticated girder API. You \n",
+    "# Connect to an authenticated girder API. You\n",
     "gc = girder_client.GirderClient(apiUrl=APIURL)\n",
     "gc.authenticate(interactive=True)  # need this to post!\n",
     "# gc.authenticate(apiKey='kri19nTIGOkWH01TbzRqfohaaDWb6kPecRqGmemb')\n",

diff --git a/docs/examples/nuclei_segmentation.ipynb b/docs/examples/nuclei_segmentation.ipynb
@@ -151,7 +151,7 @@
     "    'hematoxylin': [0.65, 0.70, 0.29],\n",
     "    'eosin':       [0.07, 0.99, 0.11],\n",
     "    'dab':         [0.27, 0.57, 0.78],\n",
-    "    'null':        [0.0, 0.0, 0.0]\n",
+    "    'null':        [0.0, 0.0, 0.0],\n",
     "}\n",
     "\n",
     "# specify stains of input image\n",
@@ -160,8 +160,8 @@
     "stain_3 = 'null'          # set to null of input contains only two stains\n",
     "\n",
     "# create stain matrix\n",
-    "W = np.array([stainColorMap[stain_1], \n",
-    "              stainColorMap[stain_2], \n",
+    "W = np.array([stainColorMap[stain_1],\n",
+    "              stainColorMap[stain_2],\n",
     "              stainColorMap[stain_3]]).T\n",
     "\n",
     "# perform standard color deconvolution\n",
@@ -226,7 +226,7 @@
     "im_log_max, im_sigma_max = htk.filters.shape.cdog(\n",
     "    im_nuclei_stain, im_fgnd_mask,\n",
     "    sigma_min=min_radius * np.sqrt(2),\n",
-    "    sigma_max=max_radius * np.sqrt(2)\n",
+    "    sigma_max=max_radius * np.sqrt(2),\n",
     ")\n",
     "\n",
     "# detect and segment nuclei using local maximum clustering\n",
@@ -266,14 +266,14 @@
     "    height = objProps[i].bbox[2] - objProps[i].bbox[0] + 1\n",
     "\n",
     "    cur_bbox = {\n",
-    "        \"type\":        \"rectangle\",\n",
-    "        \"center\":      c,\n",
-    "        \"width\":       width,\n",
-    "        \"height\":      height,\n",
+    "        'type':        'rectangle',\n",
+    "        'center':      c,\n",
+    "        'width':       width,\n",
+    "        'height':      height,\n",
     "    }\n",
     "\n",
     "    plt.plot(c[0], c[1], 'g+')\n",
-    "    mrect = mpatches.Rectangle([c[0] - 0.5 * width, c[1] - 0.5 * height] , \n",
+    "    mrect = mpatches.Rectangle([c[0] - 0.5 * width, c[1] - 0.5 * height] ,\n",
     "                               width, height, fill=False, ec='g', linewidth=2)\n",
     "    plt.gca().add_patch(mrect)"
    ]