Update code cov, include ruff

.github/workflows/python-test.yml

@@ -1,15 +1,35 @@
 # This workflow will install Python dependencies, run tests and lint with a single version of Python
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Python application
+name: Tests and coverage
 
 on:
   push:
     branches: [master]
   pull_request:
     branches: [master]
+  workflow_dispatch:
 
 jobs:
+  lint_and_format_check_with_ruff:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v5
+
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+
+      - uses: astral-sh/ruff-action@v3
+        with:
+          args: "format --check"
+
+      - uses: astral-sh/ruff-action@v3
+        with:
+          args: "check"
+
   test:
     runs-on: ubuntu-latest
     strategy:
@@ -22,20 +42,21 @@ jobs:
         uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install flake8
           pip install -e ".[testing]"
-      - name: Lint with flake8
-        run: |
-          # stop the build if there are Python syntax errors or undefined names
-          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
       - name: Test with pytest
         run: |
-          pytest --cov persim
+          pytest --cov=persim --cov-report=xml
+
       - name: Upload coverage results
-        run: |
-          bash <(curl -s https://codecov.io/bash)
+        if: matrix.python-version == '3.12'
+        uses: codecov/codecov-action@v5
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          name: persim
+          verbose: true
+          fail_ci_if_error: true

docs/conf.py

@@ -1,18 +1,18 @@
 # -*- coding: utf-8 -*-
 import os
 import sys
+from importlib import metadata
 
 sys.path.insert(0, os.path.abspath("."))
 from sktda_docs_config import *
 
-from persim import __version__
 
 project = "Persim"
 copyright = "2019, Nathaniel Saul"
 author = "Nathaniel Saul"
 
-version = __version__
-release = __version__
+version = metadata.version("persim")
+release = metadata.version("persim")
 
 language = "en"
 

docs/notebooks/Classification with persistence images.ipynb

@@ -28,7 +28,6 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from ripser import Rips\n",
-    "from persim import PersImage\n",
     "from persim import PersistenceImager"
    ]
   },
@@ -51,17 +50,23 @@
     "N_per_class = int(N / 2)\n",
     "N_in_class = 400\n",
     "\n",
+    "\n",
     "def noise(N, scale):\n",
     "    return scale * np.random.random((N, 2))\n",
     "\n",
+    "\n",
     "def circle(N, scale, offset):\n",
-    "    return offset + scale * datasets.make_circles(n_samples=N, factor=0.4, noise=0.05)[0]\n",
-    "    \n",
+    "    return (\n",
+    "        offset + scale * datasets.make_circles(n_samples=N, factor=0.4, noise=0.05)[0]\n",
+    "    )\n",
+    "\n",
+    "\n",
     "just_noise = [noise(N_in_class, 150) for _ in range(N_per_class)]\n",
     "\n",
     "half = int(N_in_class / 2)\n",
-    "with_circle = [np.concatenate((circle(half, 50, 70), noise(half, 150)))\n",
-    "               for _ in range(N_per_class)]\n",
+    "with_circle = [\n",
+    "    np.concatenate((circle(half, 50, 70), noise(half, 150))) for _ in range(N_per_class)\n",
+    "]\n",
     "\n",
     "datas = []\n",
     "datas.extend(just_noise)\n",
@@ -93,17 +98,17 @@
    "source": [
     "# Visualize the data\n",
     "fig, axs = plt.subplots(1, 2)\n",
-    "fig.set_size_inches(10,5)\n",
+    "fig.set_size_inches(10, 5)\n",
     "\n",
-    "xs, ys = just_noise[0][:,0], just_noise[0][:,1]\n",
+    "xs, ys = just_noise[0][:, 0], just_noise[0][:, 1]\n",
     "axs[0].scatter(xs, ys)\n",
     "axs[0].set_title(\"Example noise dataset\")\n",
-    "axs[0].set_aspect('equal', 'box')\n",
+    "axs[0].set_aspect(\"equal\", \"box\")\n",
     "\n",
-    "xs_, ys_ = with_circle[0][:,0], with_circle[0][:,1]\n",
+    "xs_, ys_ = with_circle[0][:, 0], with_circle[0][:, 1]\n",
     "axs[1].scatter(xs_, ys_)\n",
     "axs[1].set_title(\"Example noise with circle dataset\")\n",
-    "axs[1].set_aspect('equal', 'box')\n",
+    "axs[1].set_aspect(\"equal\", \"box\")\n",
     "\n",
     "fig.tight_layout()"
    ]
@@ -155,7 +160,7 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(12,6))\n",
+    "plt.figure(figsize=(12, 6))\n",
     "plt.subplot(121)\n",
     "\n",
     "rips.plot(diagrams_h1[0], show=False)\n",
@@ -234,16 +239,16 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(15,7.5))\n",
+    "plt.figure(figsize=(15, 7.5))\n",
     "\n",
     "for i in range(4):\n",
-    "    ax = plt.subplot(240+i+1)\n",
+    "    ax = plt.subplot(240 + i + 1)\n",
     "    pimgr.plot_image(imgs[i], ax)\n",
     "    plt.title(\"PI of $H_1$ for noise\")\n",
     "\n",
     "for i in range(4):\n",
-    "    ax = plt.subplot(240+i+5)\n",
-    "    pimgr.plot_image(imgs[-(i+1)], ax)\n",
+    "    ax = plt.subplot(240 + i + 5)\n",
+    "    pimgr.plot_image(imgs[-(i + 1)], ax)\n",
     "    plt.title(\"PI of $H_1$ for circle w/ noise\")"
    ]
   },
@@ -260,7 +265,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(imgs_array, labels, test_size=0.40, random_state=42)"
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    imgs_array, labels, test_size=0.40, random_state=42\n",
+    ")"
    ]
   },
   {

docs/notebooks/Differentiation with Persistence Landscapes.ipynb

@@ -37,16 +37,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import random\n",
     "\n",
     "from ripser import ripser\n",
     "from persim.landscapes import (\n",
-    "    PersLandscapeApprox, \n",
-    "    average_approx, \n",
-    "    snap_pl, \n",
-    "    plot_landscape, \n",
-    "    plot_landscape_simple\n",
+    "    PersLandscapeApprox,\n",
+    "    average_approx,\n",
+    "    snap_pl,\n",
+    "    plot_landscape,\n",
+    "    plot_landscape_simple,\n",
     ")\n",
     "from tadasets import dsphere"
    ]
@@ -100,15 +99,19 @@
     "sph3_pl1 = []\n",
     "\n",
     "for _ in range(num_runs):\n",
-    "    sph2 = dsphere(n=num_pts, d=2)/1.3333 # sample points, scaling appropriately\n",
-    "    sph2_dgm = ripser(sph2, maxdim=2)['dgms'] # compute PH0, PH1, PH2\n",
-    "    sph2_pl = PersLandscapeApprox(dgms=sph2_dgm, hom_deg=1) # compute persistence landscape\n",
+    "    sph2 = dsphere(n=num_pts, d=2) / 1.3333  # sample points, scaling appropriately\n",
+    "    sph2_dgm = ripser(sph2, maxdim=2)[\"dgms\"]  # compute PH0, PH1, PH2\n",
+    "    sph2_pl = PersLandscapeApprox(\n",
+    "        dgms=sph2_dgm, hom_deg=1\n",
+    "    )  # compute persistence landscape\n",
     "    sph2_pl1.append(sph2_pl)\n",
-    "    \n",
-    "    sph3 = dsphere(n=num_pts, d=3)/1.3581 # sample points, scaling appropriately\n",
-    "    sph3_dgm = ripser(sph3, maxdim=2)['dgms'] # compute PH0, PH1, PH2\n",
-    "    sph3_pl = PersLandscapeApprox(dgms=sph3_dgm, hom_deg=1) # compute persistence landscape\n",
-    "    sph3_pl1.append(sph3_pl)\n"
+    "\n",
+    "    sph3 = dsphere(n=num_pts, d=3) / 1.3581  # sample points, scaling appropriately\n",
+    "    sph3_dgm = ripser(sph3, maxdim=2)[\"dgms\"]  # compute PH0, PH1, PH2\n",
+    "    sph3_pl = PersLandscapeApprox(\n",
+    "        dgms=sph3_dgm, hom_deg=1\n",
+    "    )  # compute persistence landscape\n",
+    "    sph3_pl1.append(sph3_pl)"
    ]
   },
   {
@@ -138,7 +141,7 @@
    "source": [
     "avg_sph2 = average_approx(sph2_pl1)\n",
     "avg_sph3 = average_approx(sph3_pl1)\n",
-    "print(avg_sph2, '\\n') \n",
+    "print(avg_sph2, \"\\n\")\n",
     "print(avg_sph3)"
    ]
   },
@@ -169,7 +172,7 @@
     "true_diff_pl = avg_sph2_snapped - avg_sph3_snapped\n",
     "significance = true_diff_pl.sup_norm()\n",
     "\n",
-    "print(f'The threshold for significance is {significance}.')"
+    "print(f\"The threshold for significance is {significance}.\")"
    ]
   },
   {
@@ -201,9 +204,11 @@
    ],
    "source": [
     "# view figures in notebook\n",
-    "# %matplotlib inline \n",
+    "# %matplotlib inline\n",
     "\n",
-    "plot_landscape_simple(avg_sph2_snapped,title='Average $PL_1$ for $S^2$.',depth_range=range(13))"
+    "plot_landscape_simple(\n",
+    "    avg_sph2_snapped, title=\"Average $PL_1$ for $S^2$.\", depth_range=range(13)\n",
+    ")"
    ]
   },
   {
@@ -226,7 +231,9 @@
     }
    ],
    "source": [
-    "plot_landscape_simple(avg_sph3_snapped,title='Average $PL_1$ for $S^2$.',depth_range=range(13))"
+    "plot_landscape_simple(\n",
+    "    avg_sph3_snapped, title=\"Average $PL_1$ for $S^2$.\", depth_range=range(13)\n",
+    ")"
    ]
   },
   {
@@ -249,7 +256,9 @@
     }
    ],
    "source": [
-    "plot_landscape_simple(true_diff_pl,title='Difference of average PLs',depth_range=range(13))"
+    "plot_landscape_simple(\n",
+    "    true_diff_pl, title=\"Difference of average PLs\", depth_range=range(13)\n",
+    ")"
    ]
   },
   {
@@ -280,7 +289,7 @@
     }
    ],
    "source": [
-    "plot_landscape(true_diff_pl,title='Difference of average PLs')"
+    "plot_landscape(true_diff_pl, title=\"Difference of average PLs\")"
    ]
   },
   {
@@ -310,24 +319,27 @@
     "sig_count = 0\n",
     "\n",
     "for shuffle in range(num_perms):\n",
-    "    A_indices = random.sample(range(2*num_runs),num_runs)\n",
-    "    B_indices = [_ for _ in range(2*num_runs) if _ not in A_indices]\n",
-    "    \n",
+    "    A_indices = random.sample(range(2 * num_runs), num_runs)\n",
+    "    B_indices = [_ for _ in range(2 * num_runs) if _ not in A_indices]\n",
+    "\n",
     "    A_pl = [comb_pl[i] for i in A_indices]\n",
     "    B_pl = [comb_pl[j] for j in B_indices]\n",
-    "    \n",
+    "\n",
     "    A_avg = average_approx(A_pl)\n",
     "    B_avg = average_approx(B_pl)\n",
-    "    [A_avg_sn, B_avg_sn] = snap_pl([A_avg,B_avg])\n",
-    "    \n",
+    "    [A_avg_sn, B_avg_sn] = snap_pl([A_avg, B_avg])\n",
+    "\n",
     "    shuff_diff = A_avg_sn - B_avg_sn\n",
-    "    if (shuff_diff.sup_norm() >= significance): sig_count += 1\n",
+    "    if shuff_diff.sup_norm() >= significance:\n",
+    "        sig_count += 1\n",
     "\n",
-    "pval = sig_count/num_perms\n",
+    "pval = sig_count / num_perms\n",
     "\n",
-    "print(f'There were {sig_count} shuffles out of {num_perms} that',\n",
-    "     'were more significant than the true labelling. Thus, the',\n",
-    "     f'p-value is {pval}.')"
+    "print(\n",
+    "    f\"There were {sig_count} shuffles out of {num_perms} that\",\n",
+    "    \"were more significant than the true labelling. Thus, the\",\n",
+    "    f\"p-value is {pval}.\",\n",
+    ")"
    ]
   },
   {