Last checks on notebooks

src/tapenade/analysis/deformation/additional_regionprops_properties.py

@@ -200,7 +200,7 @@ def prop_true_strain_tensor(regionprops_prop, scale):
     principal_lengths = np.array(regionprops_prop.principal_lengths)
     principal_vectors = regionprops_prop.principal_vectors
 
-    denominator = np.power(np.product(principal_lengths), 1 / 3)
+    denominator = np.power(np.prod(principal_lengths), 1 / 3)
     true_strains = np.log(principal_lengths / denominator)
 
     tensor = (

src/tapenade/notebooks/deformation_analysis_notebook.ipynb

@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,17 +103,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 5139/5139 [00:06<00:00, 841.33it/s] \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# props is made of objects 'prop' with many morphological properties\n",
     "# like volume, centroid, etc...\n",
@@ -159,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,7 +354,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "napari-mine",
+   "display_name": "test-tapenade",
    "language": "python",
    "name": "python3"
   },
@@ -376,7 +368,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.10.18"
   }
  },
  "nbformat": 4,

src/tapenade/notebooks/preprocessing_notebook.ipynb

@@ -75,7 +75,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before starting, specify if you wish to display each intermediate result in a Napari viewer:"
+    "Before starting, specify if you wish to display each intermediate result in a Napari viewer\n",
+    "\n",
+    "(IF YES,KEEP THE NAPARI WINDOW OPEN)"
    ]
   },
   {
@@ -439,7 +441,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "napari-mine",
+   "display_name": "test-tapenade",
    "language": "python",
    "name": "python3"
   },
@@ -453,7 +455,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.10.18"
   }
  },
  "nbformat": 4,

src/tapenade/notebooks/registration_notebook.ipynb

@@ -39,6 +39,9 @@
    "metadata": {},
    "source": [
     "### <font color='red'> Install the registration package by following the user manual available on Tapenade's GitHub page\n",
+    "conda install vt -c morpheme\n",
+    "\n",
+    "pip install 3D-registration\n",
     "</font>"
    ]
   },
@@ -170,7 +173,7 @@
     "filename_ref = list_ref[i]\n",
     "filename_float = list_float[i]\n",
     "input_voxel = [0.3,0.3,2] #voxel size of your input image (XYZ)\n",
-    "output_voxel = [0.3,0.3,2] #voxel size of the output registered image (XYZ)\n",
+    "output_voxel = [1,1,1] #voxel size of the output registered image (XYZ)\n",
     "channel_reference = 'hoechst'  #name of the ubiquitous channel\n",
     "##if you have a first idea of your tranformations (rotation, translation), you can input them here:\n",
     "rot = [180,0,0] #XYZ in degrees. ([180,0,0] is a rotation of 180 degrees around the X axis, that we use when flipping the sample)\n",
@@ -195,7 +198,7 @@
     "    rot=rot,\n",
     "    trans2=trans2,\n",
     "    # input_init_trsf_from_plugin=... #path of the json file saved from the plugin\n",
-    "    save_json=Path(path_to_data) / filename_ref, #to save all parameters\n",
+    "    save_json=Path(path_to_data) / filename_ref, #to save all parameters;\n",
     ")\n",
     "\n",
     "##applying the same transformation to the other channels\n",
@@ -209,7 +212,7 @@
     "            floating_image=f\"{filename_float}_{channel}.tif\",\n",
     "            input_voxel=input_voxel,\n",
     "            output_voxel=output_voxel,\n",
-    "            compute_trsf=0,\n",
+    "            compute_trsf=0\n",
     "        )"
    ]
   },
@@ -295,12 +298,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "reconstruction.write_hyperstacks(\n",
+    "registered_image = reconstruction.write_hyperstacks(\n",
     "    path=Path(path_to_data) / filename_ref / \"fused\",\n",
     "    sample_id=\"fusion\",\n",
     "    channels=channels\n",
     ")\n",
-    "#the result is saved under the name 'sample_id'_registered.tif in the folder 'fused'."
+    "#the result is saved under the name 'fusion_registered.tif in the folder 'fused'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eaf7c4ab-9a65-477e-8cc5-c3075542eb96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import napari\n",
+    "    napari_installed = True\n",
+    "except ImportError:\n",
+    "    print(\"napari is not installed, skipping visualization step.\")\n",
+    "    napari_installed = False\n",
+    "\n",
+    "if napari_installed:\n",
+    "    viewer=napari.Viewer()\n",
+    "    viewer.add_image(registered_image)\n",
+    "    napari.run()"
    ]
   },
   {
@@ -374,7 +397,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "napari-mine",
+   "display_name": "test-tapenade",
    "language": "python",
    "name": "python3"
   },
@@ -388,7 +411,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.10.18"
   }
  },
  "nbformat": 4,

src/tapenade/notebooks/spectral_filtering_notebook.ipynb

@@ -4,15 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<h1 style=\"font-size: 48px;\">🏗️</h1> \n",
-    "<big>This notebook is still under construction !</big>\n",
-    "<h1 style=\"font-size: 48px;\">⚠️</h1>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "\n",
+    "## Spectral filtering of multichannels images\n",
+    "\n",
     "### <font color='red'> After clicking on a code cell, press \"Shift+Enter\" to run the code, or click on the \"Run\" button in the toolbar above.<br>\n",
     "\n",
     "### Replace \"...\" signs with the appropriate path to your data.\n",
@@ -68,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,24 +107,33 @@
    "source": [
     "z_to_plot = 50 # index of the z-slice to plot\n",
     "\n",
-    "fout, axarrout = plt.subplots(1,channels, figsize=(20, 5))            \n",
+    "fig1, ax1 = plt.subplots(1,channels, figsize=(20, 5))            \n",
     "\n",
     "for i in range(channels):\n",
-    "    axarrout[i].imshow(img[z_to_plot,i,:,:])\n",
+    "    ax1[i].imshow(img[z_to_plot,i,:,:])\n",
     "\n",
-    "fout2, axarrout2 = plt.subplots(1,channels, figsize=(20, 5))\n",
+    "fig1.suptitle('Channels before spectral filtering')\n",
+    "fig2, ax2 = plt.subplots(1,channels, figsize=(20, 5))\n",
     "\n",
     "for i in range(channels):\n",
-    "    axarrout2[i].imshow(image_filtered[z_to_plot,i,:,:])\n",
+    "    ax2[i].imshow(image_filtered[z_to_plot,i,:,:])\n",
     "\n",
-    "fout.tight_layout()\n",
-    "fout2.tight_layout()"
+    "fig2.suptitle('Chanels after spectral filtering')\n",
+    "fig1.tight_layout()\n",
+    "fig2.tight_layout()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "napari-mine",
+   "display_name": "test-tapenade",
    "language": "python",
    "name": "python3"
   },
@@ -144,9 +147,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.10.18"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

src/tapenade/reconstruction/_reconstruct.py

@@ -806,10 +806,8 @@ def write_hyperstacks(
     tifffile.imwrite(
         Path(path) / f"{sample_id}_registered.tif",
         new_image.astype(dtype),
-        imagej=True,
-        compression=("zlib", 1),
-    )  # float16 not compatible with Fiji
-
+    ) 
+    return new_image.astype(dtype)
 
 def add_centermass(landmarks, radius: int = 10, centermass_label: int = 10):
     """