docs: fix issues flagged by linter

Author: g-turri

examples/ala2_nys_tutorial.ipynb

@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-05T12:52:36.732175427Z",
@@ -175,7 +175,9 @@
     "x_tickslabels = [f\"{x:.0f}\" for x in (time_ns[::subsample])[::2000]]\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(4, 4))\n",
-    "ax.scatter(test_dihedrals[::subsample, 0], test_dihedrals[::subsample, 1], s=2, color = 'k', alpha = 0.2)\n",
+    "ax.scatter(\n",
+    "    test_dihedrals[::subsample, 0], test_dihedrals[::subsample, 1], s=2, color='k', alpha=0.2\n",
+    "    )\n",
     "ax.set_xlabel(r\"Dihedral $\\phi$\")\n",
     "ax.set_ylabel(r\"Dihedral $\\psi$\")\n",
     "ax.set_xticks([-np.pi, 0, np.pi], [r\"$-\\pi$\", \"$0$\", r\"$\\pi$\"])\n",

examples/fluid_flow_dmd.ipynb

@@ -146,12 +146,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "dde80382",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Custom scaler. Can't use sklearn StandardScaler, as it improperly averages over every dimension, while we want to keep the field-wise means and stds separate.\n",
+    "# Custom scaler. Can't use sklearn StandardScaler, as it improperly averages over every dimension,\n",
+    "# while we want to keep the field-wise means and stds separate.\n",
     "class FlowScaler(BaseEstimator, TransformerMixin):\n",
     "    def fit(self, X, y=None):\n",
     "        # Save shape for inverse_transform\n",
@@ -183,14 +184,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "84804b38",
    "metadata": {},
    "outputs": [],
    "source": [
     "from kooplearn.kernel import KernelRidge\n",
     "\n",
-    "model = KernelRidge(n_components=128, reduced_rank=False, alpha=1e-5).fit(data_pipe.fit_transform(X))\n",
+    "model = KernelRidge(n_components=128, reduced_rank=False, alpha=1e-5)\n",
+    "model.fit(data_pipe.fit_transform(X))\n",
     "# Compute the dynamical modes\n",
     "dmd = model.dynamical_modes(data_pipe.fit_transform(X))"
    ]
@@ -347,6 +349,7 @@
    "source": [
     "# Some nice plots\n",
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\") # Suppress warning from annoying sklearn pipelines (see https://stackoverflow.com/questions/67943229/sklearn-pipeline-instance-is-not-fitted-yet-error-even-though-it-is)\n",
     "t_id = 100\n",
     "fig, axs = plt.subplots(ncols=2, nrows=2, figsize=(12, 3), dpi=200)\n",

examples/kernel_methods.ipynb

@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -85,7 +85,15 @@
     "\n",
     "fig = plt.figure(figsize=(6,6))\n",
     "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.plot(train_set[:, 0], train_set[:, 1], train_set[:, 2], lw=1, label='Training Set', color='k', alpha=0.3)\n",
+    "ax.plot(\n",
+    "    train_set[:, 0],\n",
+    "    train_set[:, 1],\n",
+    "    train_set[:, 2],\n",
+    "    lw=1,\n",
+    "    label='Training Set',\n",
+    "    color='k',\n",
+    "    alpha=0.3\n",
+    "    )\n",
     "ax.plot(test_set[:, 0], test_set[:, 1], test_set[:, 2], lw=2, label='Test Set', color='#e60049')\n",
     "\n",
     "plt.legend(frameon=False)\n",
@@ -416,7 +424,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "kooplearn",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -430,7 +438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.13.7"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

examples/linear_system.ipynb

@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "a8d750ebf6efec3f",
    "metadata": {
     "ExecuteTime": {
@@ -61,7 +61,9 @@
     "# Dataset generation and splitting\n",
     "train_samples = int(1e5)\n",
     "test_samples = int(1e3)\n",
-    "data = make_linear_system(X0 = X0, A=A, n_steps=train_samples + 100 + train_samples, noise=0.1, random_state=seed)\n",
+    "data = make_linear_system(\n",
+    "    X0 = X0, A=A, n_steps=train_samples + 100 + train_samples, noise=0.1, random_state=seed\n",
+    "    )\n",
     "train_set = data[: train_samples + 1]\n",
     "test_set = data[: test_samples + 1]"
    ]
@@ -188,7 +190,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "kooplearn",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -202,7 +204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

examples/logistic_map.ipynb

@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -390,7 +390,8 @@
     "\n",
     "        if (epoch + 1) % 5 == 0 or (epoch == 0):\n",
     "            print(\n",
-    "                f\"EPOCH {epoch + 1:>2}  Loss: {np.mean(train_loss):.2f} (train) -  {np.mean(val_loss):.2f} (val)\"\n",
+    "                f\"EPOCH {epoch + 1:>2}  Loss: {np.mean(train_loss):.2f} (train) -  \" \\\n",
+    "                    f\"{np.mean(val_loss):.2f} (val)\"\n",
     "            )\n",
     "\n",
     "    embedder = FeatureMapEmbedder(encoder=model)\n",
@@ -600,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -625,7 +626,8 @@
     "    spectral_dist = directed_hausdorff_distance(OLS_eigs, ref_eigs)\n",
     "    empirical_spectral_dist = directed_hausdorff_distance(empirical_OLS_eigs, ref_eigs)\n",
     "    print(\n",
-    "        f\"{key} - Spectral Dist: {spectral_dist:.4f} - Empirical Spectral Dist: {empirical_spectral_dist:.4f}\"\n",
+    "        f\"{key} - Spectral Dist: {spectral_dist:.4f} - Empirical Spectral Dist: \" \\\n",
+    "            f\"{empirical_spectral_dist:.4f}\"\n",
     "    )"
    ]
   }
@@ -636,7 +638,7 @@
    "version": ""
   },
   "kernelspec": {
-   "display_name": "kooplearn",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -650,7 +652,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

examples/ordered_mnist_jax.ipynb

@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "7ba18357",
    "metadata": {},
    "outputs": [
@@ -544,7 +544,8 @@
     "from kooplearn.jax.nn import spectral_contrastive_loss, vamp_loss\n",
     "\n",
     "_vamp_loss = partial(vamp_loss, center_covariances=False)\n",
-    "for name, criterion in zip([\"VAMPNets\", \"Spectral Contrastive Loss\"], [ _vamp_loss, spectral_contrastive_loss]):\n",
+    "for name, criterion in zip([\"VAMPNets\", \"Spectral Contrastive Loss\"],\n",
+    "                           [ _vamp_loss, spectral_contrastive_loss]):\n",
     "    print(f\"Fitting {name}\")\n",
     "    trained_models[name] = train_encoder_only(criterion)"
    ]
@@ -784,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "9a43f6cd",
    "metadata": {},
    "outputs": [
@@ -802,7 +803,9 @@
    "source": [
     "nun_models = len(report)\n",
     "num_cols = len(report['Linear']['times'])\n",
-    "fig, axes = plt.subplots(nun_models, num_cols, figsize=(num_cols, nun_models), sharex=True, sharey=True)\n",
+    "fig, axes = plt.subplots(\n",
+    "    nun_models, num_cols, figsize=(num_cols, nun_models), sharex=True, sharey=True\n",
+    "    )\n",
     "\n",
     "test_seed_idx = 0\n",
     "# Remove margins between columns\n",
@@ -815,7 +818,9 @@
     "    ax.set_axis_off()\n",
     "    for prediction_step in range(num_cols - 1):\n",
     "        pred_label = report[model_name]['label'][prediction_step][test_seed_idx]\n",
-    "        true_label = (test_labels[test_seed_idx] + report[model_name]['times'][prediction_step])%num_digits\n",
+    "        true_label = (\n",
+    "            test_labels[test_seed_idx] + report[model_name]['times'][prediction_step]\n",
+    "            )%num_digits\n",
     "        img = report[model_name]['image'][prediction_step][test_seed_idx]\n",
     "        logit = report[model_name]['logits'][prediction_step][test_seed_idx]\n",
     "\n",
@@ -848,7 +853,15 @@
     "\n",
     "# Display the model names on the left of each row\n",
     "for model_idx, model_name in enumerate(report.keys()):\n",
-    "    axes[model_idx, 0].text(-0.1, 0.5, model_name.replace('_', ' '), fontsize=14, ha='right', va='center', transform=axes[model_idx, 0].transAxes)\n",
+    "    axes[model_idx, 0].text(\n",
+    "        -0.1,\n",
+    "        0.5,\n",
+    "        model_name.replace('_', ' '),\n",
+    "        fontsize=14,\n",
+    "        ha='right',\n",
+    "        va='center',\n",
+    "        transform=axes[model_idx, 0].transAxes\n",
+    "        )\n",
     "\n",
     "for class_idx in range(num_cols):\n",
     "    title = (test_labels[test_seed_idx] + class_idx)%num_digits\n",
@@ -879,7 +892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "617e1987",
    "metadata": {},
    "outputs": [
@@ -906,9 +919,13 @@
     "    ax.title.set_text(model_name.replace('_', ' '))\n",
     "    fitted_model = trained_models[model_name]['model']\n",
     "    embedder = trained_models[model_name]['embedder']\n",
-    "    vals, lfuncs, rfuncs = fitted_model.eig(eval_right_on=embedder.transform(test_data), eval_left_on=embedder.transform(test_data))\n",
+    "    vals, lfuncs, rfuncs = fitted_model.eig(\n",
+    "        eval_right_on=embedder.transform(test_data),\n",
+    "        eval_left_on=embedder.transform(test_data)\n",
+    "        )\n",
     "\n",
-    "    unique_vals, idx_start = np.unique(np.abs(vals), return_index=True) # returns the unique values and the index of the first occurrence of a value\n",
+    "    unique_vals, idx_start = np.unique(np.abs(vals), return_index=True) # returns the unique values\n",
+    "    # and the index of the first occurrence of a value\n",
     "\n",
     "    vals, lfuncs, rfuncs = vals[idx_start], lfuncs[:, idx_start], rfuncs[:, idx_start]\n",
     "    top_vals, top_indices = stable_topk(np.abs(vals), 2)\n",
@@ -923,7 +940,9 @@
     "\n",
     "# remove last axis and add legend\n",
     "ax = axes[n_models-1]\n",
-    "legend = ax.legend(*scatter.legend_elements(num=4), title=\"Digits\", frameon=True, bbox_to_anchor=(1.3, 1))\n",
+    "legend = ax.legend(\n",
+    "    *scatter.legend_elements(num=4), title=\"Digits\", frameon=True, bbox_to_anchor=(1.3, 1)\n",
+    "    )\n",
     "ax.add_artist(legend)\n",
     "fig.delaxes(axes[n_models])\n",
     "\n",
@@ -943,7 +962,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "kooplearn",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -957,7 +976,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.1"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,