update tutorial

tutorials/2_tutorial_svd.ipynb

@@ -62,7 +62,7 @@
     "# Select path to SVD config file\n",
     "# An example of this file is available in the path ../config_files/config_svd.yaml\n",
     "config_svd_path = FileChooser(os.path.expanduser(\"~\"))\n",
-    "config_svd_path.filter_pattern = '*.yaml'\n",
+    "config_svd_path.filter_pattern = \"*.yaml\"\n",
     "display(config_svd_path)"
    ]
   },
@@ -93,11 +93,22 @@
     "Here is a brief explanation of each key\n",
     "\n",
     "* path_to_volumes (str): this is the path to your submissions (the result of running the preprocessing). They should be called submission_0.pt, submission_1.pt, ...\n",
+    "\n",
     "* box_size_ds (int): you can choose to downsample the volumes to speed up the analysis, or to get rid of high frequency features.\n",
+    "\n",
     "* submission_list (List): here you can choose which submissions are used for the analysis. If you want to use submissions 0, 3, 6; then this should be [0, 3, 6]\n",
+    "\n",
     "* experiment_mode (str): the options are \"all_vs_all\", \"all_vs_ref\". If you are using ref, then SVD is computed from the refence volumes and the rest of the volumes are projected to it. Otherwise, all volumes are used to do the projection\n",
-    "* path_to_reference (str): path to the reference volumes (only needed if mode is \"all_vs_ref\")\n",
+    "\n",
+    "* reference_options (dict)\n",
+    "    * path_to_reference (str): path to the reference volumes (only needed if mode is \"all_vs_ref\")\n",
+    "    * n_volumes (int): number of volumes to use for analysis\n",
+    "    * random_subset (bool): whether to use a random subset or not\n",
+    "\n",
+    "    If you set `random_set = True`, then SVD will be run with randomly chosen n_volumes Volumes. Otherwise, the volumes are chosen as `skip_vols = total_volumes // n_volumes` and something equivalent to `volumes[::skip_vols, ...]`\n",
+    "\n",
     "* dtype (str): can be float32 or float64\n",
+    "\n",
     "* output_options (dict): dictionary with options to personalize the output\n",
     "    * output_path (str): where the volumes will be saved\n",
     "    * save_volumes (bool): whether or not to save the volumes used (this will save the normalized, downsampled, and mean-removed volumes)\n",
@@ -125,7 +136,7 @@
    "source": [
     "# Select path to SVD results\n",
     "svd_results_path = FileChooser(os.path.expanduser(\"~\"))\n",
-    "svd_results_path.filter_pattern = '*.pt'\n",
+    "svd_results_path.filter_pattern = \"*.pt\"\n",
     "display(svd_results_path)"
    ]
   },
@@ -316,7 +327,7 @@
    "source": [
     "# Select path to SVD results\n",
     "svd_all_vs_all_results_path = FileChooser(os.path.expanduser(\"~\"))\n",
-    "svd_all_vs_all_results_path.filter_pattern = '*.pt'\n",
+    "svd_all_vs_all_results_path.filter_pattern = \"*.pt\"\n",
     "display(svd_all_vs_all_results_path)"
    ]
   },
@@ -425,9 +436,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "gpucryonerf",
+   "display_name": "cryo-challenge-kernel",
    "language": "python",
-   "name": "python3"
+   "name": "cryo-challenge-kernel"
   },
   "language_info": {
    "codemirror_mode": {
@@ -439,7 +450,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.17"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,