Applied linting

TJHeeringa · Aug 13, 2024 · 4ffa9b8 · 4ffa9b8
1 parent 89de684
commit 4ffa9b8
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Showing 21 changed files with 265 additions and 316 deletions.
diff --git a/.flake8 b/.flake8
@@ -1,3 +1,3 @@
 [flake8]
-max-line-length=88
-extend-ignore=E230,W503,W504
+max-line-length=130
+extend-ignore=F401
diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
@@ -0,0 +1,36 @@
+name: lint
+on:
+  - push
+  - pull_request
+
+jobs:
+  isort:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out source repository
+        uses: actions/checkout@v3
+      - name: Run isort
+        uses: isort/isort-action@v1
+  black:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out source repository
+        uses: actions/checkout@v4
+      - name: Run black
+        uses: psf/black@stable
+        with:
+          jupyter: true
+  flake8:
+    runs-on: ubuntu-latest
+    name: Lint
+    steps:
+      - name: Check out source repository
+        uses: actions/checkout@v3
+      - name: Set up Python environment
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+      - name: flake8 Lint
+        uses: py-actions/flake8@v2
+        with:
+          plugins: "flake8-nb flake8-black"
diff --git a/notebooks/advection.ipynb b/notebooks/advection.ipynb
@@ -11,7 +11,8 @@
    },
    "source": [
     "import sys\n",
-    "sys.path.insert(0, '../')"
+    "\n",
+    "sys.path.insert(0, \"../\")"
    ],
    "outputs": [],
    "execution_count": 22
@@ -53,7 +54,7 @@
     "from tqdm import tqdm\n",
     "\n",
     "from src.data.generate_datasets import BASE_ADVECTION_CONFIG as CONFIG, generate_advection_dataset\n",
-    "from src.training.advection.train import TRAIN_PARAMETERS, TEST_PARAMETERS\n"
+    "from src.training.advection.train import TRAIN_PARAMETERS, TEST_PARAMETERS"
    ],
    "metadata": {
     "collapsed": false,
@@ -77,7 +78,7 @@
    "cell_type": "code",
    "source": [
     "base_storage_path = Path(\"../data/advection\")\n",
-    "results_path = Path(\"../results/advection\")\n"
+    "results_path = Path(\"../results/advection\")"
    ],
    "metadata": {
     "collapsed": false,
@@ -124,7 +125,7 @@
     "stacked_test_datasets = generate_advection_dataset(TEST_PARAMETERS, base_storage_path=base_storage_path, return_stacked=True)\n",
     "\n",
     "train_dataset = torch.utils.data.TensorDataset(stacked_train_datasets)\n",
-    "test_dataset = torch.utils.data.TensorDataset(stacked_test_datasets)\n"
+    "test_dataset = torch.utils.data.TensorDataset(stacked_test_datasets)"
    ],
    "metadata": {
     "collapsed": false,
@@ -269,7 +270,7 @@
     "TTx, XX = torch.meshgrid(\n",
     "    [time, domain],\n",
     "    indexing=\"ij\",\n",
-    ")\n"
+    ")"
    ],
    "metadata": {
     "collapsed": false,
@@ -299,10 +300,9 @@
    },
    "cell_type": "code",
    "source": [
-    "\n",
     "fig, axs = plt.subplots(1, 3)\n",
     "for i in range(len(TRAIN_PARAMETERS)):\n",
-    "    axs[i].pcolormesh(TTx, XX, stacked_train_datasets[i*CONFIG.Nt:(i+1)*CONFIG.Nt].data.detach())\n"
+    "    axs[i].pcolormesh(TTx, XX, stacked_train_datasets[i * CONFIG.Nt : (i + 1) * CONFIG.Nt].data.detach())"
    ],
    "outputs": [
     {
@@ -330,31 +330,31 @@
   {
    "cell_type": "code",
    "source": [
-    "snapshot_matrix = stacked_train_datasets[:CONFIG.Nt].data\n",
+    "snapshot_matrix = stacked_train_datasets[: CONFIG.Nt].data\n",
     "\n",
     "plt.figure()\n",
     "# plt.pcolormesh(TTx, XX, snapshot_matrix.detach(), cmap=mpl.colormaps[\"magma\"])\n",
     "plt.pcolormesh(TTx, XX, snapshot_matrix.detach())\n",
     "plt.colorbar()\n",
-    "plt.rc('xtick', labelsize=12)  # fontsize of the tick labels\n",
-    "plt.rc('ytick', labelsize=12)  # fontsize of the tick labels\n",
+    "plt.rc(\"xtick\", labelsize=12)  # fontsize of the tick labels\n",
+    "plt.rc(\"ytick\", labelsize=12)  # fontsize of the tick labels\n",
     "plt.ylabel(\"Space\", fontsize=16)\n",
     "plt.xlabel(\"Time\", fontsize=16)\n",
     "# plt.savefig(results_path / \"advection_snapshot.png\", bbox_inches='tight')\n",
     "\n",
     "plt.figure()\n",
     "plt.semilogy(torch.linalg.svdvals(snapshot_matrix), lw=5)\n",
     "# plt.semilogy(torch.linalg.svdvals(snapshot_matrix), lw=5, c=\"magenta\")\n",
-    "plt.rc('xtick', labelsize=14)  # fontsize of the tick labels\n",
-    "plt.rc('ytick', labelsize=14)  # fontsize of the tick labels\n",
+    "plt.rc(\"xtick\", labelsize=14)  # fontsize of the tick labels\n",
+    "plt.rc(\"ytick\", labelsize=14)  # fontsize of the tick labels\n",
     "plt.ylabel(\"Singular value\", fontsize=16)\n",
     "plt.xlabel(\"Index\", fontsize=16)\n",
     "# plt.savefig(results_path / \"advection_singular.png\", bbox_inches='tight')\n",
     "\n",
     "\n",
     "plt.figure()\n",
     "for k in [0, 10, 100, -1]:\n",
-    "    plt.plot(domain, snapshot_matrix[k])\n"
+    "    plt.plot(domain, snapshot_matrix[k])"
    ],
    "metadata": {
     "collapsed": false,
@@ -416,13 +416,15 @@
    "cell_type": "code",
    "source": [
     "fig = plt.figure()\n",
-    "line, = plt.plot(domain, snapshot_matrix.data[0,:].detach(), lw=4)\n",
+    "(line,) = plt.plot(domain, snapshot_matrix.data[0, :].detach(), lw=4)\n",
+    "\n",
     "\n",
     "def animate(k):\n",
-    "    line.set_ydata(snapshot_matrix.data[k,:].detach())\n",
-    "    return line,\n",
+    "    line.set_ydata(snapshot_matrix.data[k, :].detach())\n",
+    "    return (line,)\n",
+    "\n",
     "\n",
-    "# ani = FuncAnimation(fig, animate, interval=40, blit=True, repeat=True, frames=CONFIG.Nt)    \n",
+    "# ani = FuncAnimation(fig, animate, interval=40, blit=True, repeat=True, frames=CONFIG.Nt)\n",
     "# ani.save(results_path / \"advection.gif\", dpi=300, writer=PillowWriter(fps=25))"
    ],
    "outputs": [
@@ -479,7 +481,7 @@
     "plt.figure()\n",
     "plt.semilogy(relative_error, lw=7)\n",
     "plt.ylabel(\"Singular values\")\n",
-    "plt.xlabel(\"Index\")\n"
+    "plt.xlabel(\"Index\")"
    ],
    "metadata": {
     "collapsed": false,
@@ -539,9 +541,9 @@
     "\n",
     "plt.figure()\n",
     "plt.semilogy(relative_error, lw=3)\n",
-    "plt.xlim((0,50))\n",
+    "plt.xlim((0, 50))\n",
     "plt.ylabel(\"Relative error\")\n",
-    "plt.xlabel(\"index\")\n"
+    "plt.xlabel(\"index\")"
    ],
    "outputs": [
     {
@@ -616,16 +618,18 @@
    "cell_type": "code",
    "outputs": [],
    "execution_count": 37,
-   "source": "Vhr = Vh[:ldim,:]"
+   "source": [
+    "Vhr = Vh[:ldim, :]"
+   ]
   },
   {
    "cell_type": "code",
    "source": [
     "plt.figure()\n",
-    "plt.plot(domain, Vh[1,:], label=\"mode 1\")\n",
-    "plt.plot(domain, Vh[2,:], label=\"mode 2\")\n",
-    "plt.plot(domain, Vh[3,:], label=\"mode 3\")\n",
-    "plt.plot(domain, Vh[4,:], label=\"mode 4\")\n"
+    "plt.plot(domain, Vh[1, :], label=\"mode 1\")\n",
+    "plt.plot(domain, Vh[2, :], label=\"mode 2\")\n",
+    "plt.plot(domain, Vh[3, :], label=\"mode 3\")\n",
+    "plt.plot(domain, Vh[4, :], label=\"mode 4\")"
    ],
    "metadata": {
     "collapsed": false,
@@ -671,7 +675,12 @@
     }
    },
    "cell_type": "code",
-   "source": "print(torch.norm(stacked_train_datasets - torch.einsum(\"nm,bn->bm\", Vhr.T @ Vhr, stacked_train_datasets))**2 / torch.norm(stacked_train_datasets)**2)",
+   "source": [
+    "print(\n",
+    "    torch.norm(stacked_train_datasets - torch.einsum(\"nm,bn->bm\", Vhr.T @ Vhr, stacked_train_datasets)) ** 2\n",
+    "    / torch.norm(stacked_train_datasets) ** 2\n",
+    ")"
+   ],
    "outputs": [
     {
      "name": "stdout",
@@ -737,7 +746,7 @@
    "source": [
     "plt.figure()\n",
     "plt.plot(snapshot_matrix[-1].detach(), lw=5)\n",
-    "plt.plot((Vhr.T @ Vhr @ snapshot_matrix[-1]).detach())\n"
+    "plt.plot((Vhr.T @ Vhr @ snapshot_matrix[-1]).detach())"
    ],
    "metadata": {
     "collapsed": false,