Improve linting and consistency

.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:

.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]

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.

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",

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()"
    ]
   },

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",

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)"
    ]

docs/examples/polygon_merger_using_rtree.ipynb

@@ -29,17 +29,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "**Implementation summary**\n",
     "\n",
@@ -66,14 +69,17 @@
     "   |_tests/\n",
     "     |_ test_polygon_merger.py\n",
     "```"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -85,13 +91,7 @@
     "from histomicstk.annotations_and_masks.masks_to_annotations_handler import (\n",
     "    get_annotation_documents_from_contours, _discard_nonenclosed_background_group)\n",
     "from histomicstk.annotations_and_masks.annotation_and_mask_utils import parse_slide_annotations_into_tables"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -439,7 +439,7 @@
    "source": [
     "# init & run polygon merger\n",
     "pm = Polygon_merger_v2(contours_df, verbose=1)\n",
-    "pm.unique_groups.remove(\"roi\")\n",
+    "pm.unique_groups.remove('roi')\n",
     "pm.run()"
    ]
   },
@@ -460,13 +460,13 @@
    "source": [
     "# add colors (aesthetic)\n",
     "for group in pm.unique_groups:\n",
-    "    cs = contours_df.loc[contours_df.loc[:, \"group\"] == group, \"color\"]\n",
+    "    cs = contours_df.loc[contours_df.loc[:, 'group'] == group, 'color']\n",
     "    pm.new_contours.loc[\n",
-    "        pm.new_contours.loc[:, \"group\"] == group, \"color\"] = cs.iloc[0]\n",
+    "        pm.new_contours.loc[:, 'group'] == group, 'color'] = cs.iloc[0]\n",
     "\n",
     "# get rid of nonenclosed stroma (aesthetic)\n",
     "pm.new_contours = _discard_nonenclosed_background_group(\n",
-    "    pm.new_contours, background_group=\"mostly_stroma\")"
+    "    pm.new_contours, background_group='mostly_stroma')"
    ]
   },
   {
@@ -663,12 +663,12 @@
     "annotation_docs = get_annotation_documents_from_contours(\n",
     "    pm.new_contours.copy(), separate_docs_by_group=True,\n",
     "    docnamePrefix='test',\n",
-    "    verbose=False, monitorPrefix=POST_SLIDE_ID + \": annotation docs\")\n",
+    "    verbose=False, monitorPrefix=POST_SLIDE_ID + ': annotation docs')\n",
     "\n",
     "# post annotations to slide -- make sure it posts without errors\n",
     "for annotation_doc in annotation_docs:\n",
     "    resp = gc.post(\n",
-    "        \"/annotation?itemId=\" + POST_SLIDE_ID, json=annotation_doc)"
+    "        '/annotation?itemId=' + POST_SLIDE_ID, json=annotation_doc)"
    ]
   },
   {

docs/examples/positive_pixel_count.ipynb

@@ -166,19 +166,19 @@
     "import ipywidgets as widgets\n",
     "from ipywidgets import interactive\n",
     "\n",
-    "params= { \n",
+    "params= {\n",
     "    'hue_value': widgets.FloatSlider(min=0.0,max=1.0,step=0.004,style = {'description_width': 'initial'}, value=0.05),\n",
-    "    'hue_width':widgets.FloatSlider(min=0.0,max=1.0,step=0.05,style = {'description_width': 'initial'}, value=0.15), \n",
+    "    'hue_width':widgets.FloatSlider(min=0.0,max=1.0,step=0.05,style = {'description_width': 'initial'}, value=0.15),\n",
     "    'saturation_minimum': widgets.FloatSlider(min=0.0,max=1.0,step=0.05,style = {'description_width': 'initial'}, value=0.05),\n",
     "    'intensity_upper_limit': widgets.FloatSlider(min=0.0,max=1.0,step=0.05,style = {'description_width': 'initial'}, value=0.95),\n",
     "    'intensity_weak_threshold': widgets.FloatSlider(min=0.0,max=1.0,step=0.05,style = {'description_width': 'initial'}, value=0.65),\n",
     "    'intensity_strong_threshold':widgets.FloatSlider(min=0.0,max=1.0,step=0.05,style = {'description_width': 'initial'}, value=0.35),\n",
-    "    'intensity_lower_limit':widgets.FloatSlider(min=0.0,max=1.0,step=0.05,style = {'description_width': 'initial'}, value=0.05)\n",
+    "    'intensity_lower_limit':widgets.FloatSlider(min=0.0,max=1.0,step=0.05,style = {'description_width': 'initial'}, value=0.05),\n",
     "        }\n",
     "\n",
     "def g(**params):\n",
     "    count_and_label(ppc.Parameters(**params))\n",
-    "    \n",
+    "\n",
     "w = interactive(g,**params)\n",
     "display(w)\n",
     "\n",
@@ -679,7 +679,7 @@
     "ts = large_image.getTileSource(slide_path)\n",
     "im_region = ts.getRegion(region=region, format=large_image.tilesource.TILE_FORMAT_NUMPY)[0]\n",
     "\n",
-    "print(\"The region\")\n",
+    "print('The region')\n",
     "plt.imshow(im_region)\n",
     "plt.show()"
    ]
@@ -817,7 +817,7 @@
     }
    ],
    "source": [
-    "print(\"stats_dask - stats:\")\n",
+    "print('stats_dask - stats:')\n",
     "pp_namedtuple(ppc.Output(**{f: getattr(stats_dask, f) - getattr(stats, f) for f in ppc.Output._fields}))"
    ]
   },

docs/examples/semantic_segmentation_color_thresholding_approach.ipynb

@@ -69,7 +69,7 @@
    "outputs": [],
    "source": [
     "APIURL = 'http://candygram.neurology.emory.edu:8080/api/v1/'\n",
-    "SAMPLE_SLIDE_ID = \"5d8c296cbd4404c6b1fa5572\"\n",
+    "SAMPLE_SLIDE_ID = '5d8c296cbd4404c6b1fa5572'\n",
     "\n",
     "gc = girder_client.GirderClient(apiUrl=APIURL)\n",
     "gc.authenticate(apiKey='kri19nTIGOkWH01TbzRqfohaaDWb6kPecRqGmemb')\n",

docs/examples/semantic_segmentation_superpixel_approach.ipynb

@@ -86,7 +86,7 @@
    "outputs": [],
    "source": [
     "APIURL = 'http://candygram.neurology.emory.edu:8080/api/v1/'\n",
-    "SAMPLE_SLIDE_ID = \"5d586d76bd4404c6b1f286ae\"\n",
+    "SAMPLE_SLIDE_ID = '5d586d76bd4404c6b1f286ae'\n",
     "# SAMPLE_SLIDE_ID = \"5d8c296cbd4404c6b1fa5572\"\n",
     "\n",
     "gc = girder_client.GirderClient(apiUrl=APIURL)\n",
@@ -248,7 +248,7 @@
     "        'deconvolve_first': False,\n",
     "        'n_thresholding_steps': 2,\n",
     "        'sigma': 1.5,\n",
-    "        'min_size': 500, },\n",
+    "        'min_size': 500 },\n",
     "    verbose=2, monitorPrefix='test',\n",
     "    logging_savepath=logging_savepath)"
    ]

docs/examples/simple_tissue_detection.ipynb

@@ -53,7 +53,7 @@
    "source": [
     "APIURL = 'http://candygram.neurology.emory.edu:8080/api/v1/'\n",
     "# SAMPLE_SLIDE_ID = '5d586d57bd4404c6b1f28640'\n",
-    "SAMPLE_SLIDE_ID = \"5d817f5abd4404c6b1f744bb\"\n",
+    "SAMPLE_SLIDE_ID = '5d817f5abd4404c6b1f744bb'\n",
     "\n",
     "gc = girder_client.GirderClient(apiUrl=APIURL)\n",
     "# gc.authenticate(interactive=True)\n",
@@ -311,7 +311,7 @@
     "        ax[0].imshow(thumbnail_rgb)\n",
     "        ax[1].imshow(labeled, cmap=cMap)\n",
     "        ax[2].imshow(mask, cmap=cMap)\n",
-    "        plt.suptitle(\"deconvolve = %s, n_thresholding_steps = %d\" % (deconvolve_first, n_thresholding_steps), fontsize=20)\n",
+    "        plt.suptitle('deconvolve = %s, n_thresholding_steps = %d' % (deconvolve_first, n_thresholding_steps), fontsize=20)\n",
     "        plt.show()"
    ]
   }