Fix notebooks

applications/care/denoising/2D/U2OS_CARE.ipynb

@@ -22,12 +22,11 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import tifffile\n",
-    "from PIL import Image\n",
-    "\n",
     "from careamics import CAREamist\n",
     "from careamics.config import create_care_configuration\n",
     "from careamics.utils.metrics import scale_invariant_psnr\n",
-    "from careamics_portfolio import PortfolioManager"
+    "from careamics_portfolio import PortfolioManager\n",
+    "from PIL import Image"
    ]
   },
   {
@@ -204,7 +203,7 @@
    ],
    "source": [
     "config = create_care_configuration(\n",
-    "    experiment_name=\"care_n2n\",\n",
+    "    experiment_name=\"care_U20S\",\n",
     "    data_type=\"tiff\",\n",
     "    axes=\"YX\",\n",
     "    patch_size=(128, 128),\n",
@@ -417,7 +416,7 @@
     "\n",
     "for i in range(3):\n",
     "    pred_image = prediction[images[i]].squeeze()\n",
-    "    \n",
+    "\n",
     "    psnr_noisy = scale_invariant_psnr(test_GT[images[i]], test_low[images[i]])\n",
     "    psnr_result = scale_invariant_psnr(test_GT[images[i]], pred_image)\n",
     "\n",
@@ -428,7 +427,7 @@
     "    ax[i, 1].title.set_text(f\"Prediction\\nPSNR: {psnr_result:.2f}\")\n",
     "\n",
     "    ax[i, 2].imshow(test_GT[images[i]], cmap=\"gray\")\n",
-    "    ax[i, 2].title.set_text(\"Ground-truth\")\n"
+    "    ax[i, 2].title.set_text(\"Ground-truth\")"
    ]
   },
   {
@@ -490,24 +489,28 @@
     "cv_image_pred = prediction[im_idx].squeeze()\n",
     "\n",
     "# create image\n",
-    "cover = np.zeros_like(cv_image_noisy, dtype=np.float32)   \n",
+    "cover = np.zeros_like(cv_image_noisy, dtype=np.float32)\n",
     "width = cover.shape[1]\n",
     "\n",
     "# # normalize train and prediction\n",
-    "norm_noise = (cv_image_noisy - cv_image_noisy.min()) / (cv_image_noisy.max() - cv_image_noisy.min())\n",
-    "norm_pred = (cv_image_pred - cv_image_pred.min()) / (cv_image_pred.max() - cv_image_pred.min())\n",
+    "norm_noise = (cv_image_noisy - cv_image_noisy.min()) / (\n",
+    "    cv_image_noisy.max() - cv_image_noisy.min()\n",
+    ")\n",
+    "norm_pred = (cv_image_pred - cv_image_pred.min()) / (\n",
+    "    cv_image_pred.max() - cv_image_pred.min()\n",
+    ")\n",
     "\n",
     "# fill in halves\n",
-    "cover[:, :width // 2] = norm_noise[:, :width // 2]\n",
-    "cover[:, width // 2:] = norm_pred[:, width // 2:]\n",
+    "cover[:, : width // 2] = norm_noise[:, : width // 2]\n",
+    "cover[:, width // 2 :] = norm_pred[:, width // 2 :]\n",
     "\n",
     "# plot the single image\n",
     "plt.imshow(cover)\n",
     "\n",
     "# save the image\n",
     "im = Image.fromarray(cover * 255)\n",
-    "im = im.convert('L')\n",
-    "im.save(\"U2OS_CARE.jpeg\")\n"
+    "im = im.convert(\"L\")\n",
+    "im.save(\"U2OS_CARE.jpeg\")"
    ]
   },
   {

applications/n2v/2D/SEM_N2V2.ipynb

@@ -21,12 +21,12 @@
     "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "import tifffile\n",
     "import numpy as np\n",
-    "from PIL import Image\n",
+    "import tifffile\n",
     "from careamics import CAREamist\n",
     "from careamics.config import create_n2v_configuration\n",
-    "from careamics_portfolio import PortfolioManager"
+    "from careamics_portfolio import PortfolioManager\n",
+    "from PIL import Image"
    ]
   },
   {
@@ -709,9 +709,9 @@
     "\n",
     "fig, ax = plt.subplots(2, 2, figsize=(10, 10))\n",
     "ax[0, 0].imshow(train_image, cmap=\"gray\")\n",
-    "ax[0, 1].imshow(prediction.squeeze(), cmap=\"gray\")\n",
+    "ax[0, 1].imshow(prediction[0].squeeze(), cmap=\"gray\")\n",
     "ax[1, 0].imshow(train_image[y_start:y_end, x_start:x_end], cmap=\"gray\")\n",
-    "ax[1, 1].imshow(prediction.squeeze()[y_start:y_end, x_start:x_end], cmap=\"gray\")"
+    "ax[1, 1].imshow(prediction[0].squeeze()[y_start:y_end, x_start:x_end], cmap=\"gray\")"
    ]
   },
   {
@@ -736,7 +736,7 @@
     "y_start, height = 1400, 512\n",
     "\n",
     "# create image\n",
-    "cover = np.zeros((height, width))   \n",
+    "cover = np.zeros((height, width))\n",
     "\n",
     "# normalize train and prediction\n",
     "norm_train = (train_image - train_image.min()) / (train_image.max() - train_image.min())\n",
@@ -745,16 +745,20 @@
     "norm_pred = (pred - pred.min()) / (pred.max() - pred.min())\n",
     "\n",
     "# fill in halves\n",
-    "cover[:, :width // 2] = norm_train[y_start:y_start + height, x_start:x_start + width // 2]\n",
-    "cover[:, width // 2:] = norm_pred[y_start:y_start + height, x_start + width // 2:x_start + width]\n",
+    "cover[:, : width // 2] = norm_train[\n",
+    "    y_start : y_start + height, x_start : x_start + width // 2\n",
+    "]\n",
+    "cover[:, width // 2 :] = norm_pred[\n",
+    "    y_start : y_start + height, x_start + width // 2 : x_start + width\n",
+    "]\n",
     "\n",
     "# plot the single image\n",
     "plt.imshow(cover, cmap=\"gray\")\n",
     "\n",
     "# save the image\n",
     "im = Image.fromarray(cover * 255)\n",
-    "im = im.convert('L')\n",
-    "im.save(\"SEM_N2V2.jpeg\")\n"
+    "im = im.convert(\"L\")\n",
+    "im.save(\"SEM_N2V2.jpeg\")"
    ]
   },
   {

scripts/run_all_notebooks.py

@@ -12,7 +12,6 @@
     notebooks.extend(list(Path(folder).rglob("*.ipynb")))
 
 # run each notebook
-last_failed = False
 for nb in notebooks:
     print(f"Running {nb}")
     subprocess.run(["python", "scripts/run_notebook.py", "-n", str(nb)])

scripts/run_notebook.py

@@ -36,7 +36,7 @@
     print(f"{datetime.now()} Done running {nb}")
 
 # save notebook
-if not last_failed and n_cells == len(nb_out.cells):
+if not last_failed and len(nb_out.cells) > 0:
     # make sure that the notebook has the same number of cells
     # this can avoid overwriting the notebook with an empty one
     print(f"{datetime.now()} Writing {nb}")