diff --git a/README.md b/README.md
index ff383ac..7030c3d 100644
--- a/README.md
+++ b/README.md
@@ -1,119 +1,86 @@
 # model_seg_ms_mp2rage
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8376754.svg)](https://doi.org/10.5281/zenodo.8376754)
-
-Model repository for MS lesion segmentation on MP2RAGE data as discussed [here](https://github.com/ivadomed/ivadomed/issues/821).
-This repository contains two models, one for spinal cord (SC) segmentation and one for MS lesion segmentation.
+Model repository for MS lesion segmentation on MP2RAGE data (UNIT1 contrast)
 
 ## Model overview 
 
-![model_overview](https://github.com/ivadomed/model_seg_ms_mp2rage/releases/download/r20211223/model_overview.png)
+![uniseg_update](https://github.com/ivadomed/model_seg_ms_mp2rage/assets/77469192/e4b5fbcc-03e9-4bc4-829e-604c32dbdd68)
 
-As of https://github.com/ivadomed/model_seg_ms_mp2rage/releases/tag/r20230210, five models are trained (with dropout and different seeds), to perform ensemble/bagging during inference and compute epistemic uncertainty.
+3D model trained with [nnUNetv2](https://github.com/MIC-DKFZ/nnUNet) framework.
 
 ## Dependencies
+[SCT 6.4](https://spinalcordtoolbox.com/)
 
-- [SCT](https://spinalcordtoolbox.com/) commit: 7fd2ea718751dd858840c3823c0830a910d9777c
-- [ivadomed](https://ivadomed.org) commit: git-master-93ba03df76d229d8a190c694f757da6c00efa545
-
-## Clone this repository
-
-~~~
-git clone https://github.com/ivadomed/model_seg_ms_mp2rage.git
-~~~
-
-## Get the data
-
-- git@data.neuro.polymtl.ca:datasets/basel-mp2rage
-- Commit: 93110d8ebb65398dcc6e4528bf548eb7828332f1
-
-### Example calls to get the data
-
-~~~
-git clone git@data.neuro.polymtl.ca:datasets/basel-mp2rage
-cd basel-mp2rage
-git annex get .
-cd ..
-~~~
- 
-## Prepare the data
+## Datasets for training 
 
-The data need to be preprocessed before training. The preprocessing command is:
+- `basel-mp2rage`
+- `nih-ms-mp2rage`
+- `marseille-3t-mp2rage`
 
-~~~
-sct_run_batch -script <PATH_TO_REPOSITORY>/preprocessing/preprocess_data.sh -path-data <PATH_TO_DATA>/basel-mp2rage/ -path-output basel-mp2rage-preprocessed -jobs <JOBS>
-~~~
-
-This command will create a `data_processed_scseg` folder for the SC segmentation task and a 
-`data_processed_lesionseg` folder for the lesion segmentation task inside the `basel-mp2rage-preprocessed` 
-you specified. Each of these two folders contain only the required files for their respective task.
-
-After running the preprocessing, you can also run the quality-control (QC) script:
+## Installation 
+```bash
+sct_deepseg -install-task seg_sc_contrast_agnostic
 ```
-python preprocessing/qc_preprocess.py -s basel-mp2rage-preprocessed
+```bash
+sct_deepseg -install-task seg_ms_lesion_mp2rage 
 ```
-which i) logs resolutions and sizes for each subject image (both raw and cropped) for data exploration, 
-ii) performs basic shape checks for spinal cord (SC) cropped images and ground-truths (GTs), and 
-most importantly iii) checks if the dilated SC mask leaves out any lesions from the GT of each rater.
 
-## Training
+**Warning:** When running the MS lesion segmentation model, the image first need to be cropped around the spinal cord mask with a dilation of 30 mm in the axial plane and 5 mm in the Z-axis. 
 
-Spinal cord segmentation training was carried out with
-```
-ivadomed --train -c config/seg_sc.json
+## Launch MS lesion segmentation:
+1. Spinal cord segmentation 
+```bash
+sct_deepseg -i IMAGE_UNIT1 -task seg_sc_contrast_agnostic -o IMAGE_UNIT1_sc
 ```
-
-Lesion segmentation training was carried out with
+2. Cropping with dilation (for axial orientation images with 1mm isotropic resolution)
+```bash
+sct_crop_image -i IMAGE_UNIT1 -m IMAGE_UNIT1_sc -dilate 30x30x5 -o IMAGE_UNIT1_cropped
 ```
-ivadomed --train -c config/seg_lesion.json
+3. MS lesion segmentation 
+```bash
+sct_deepseg -i IMAGE_UNIT1_cropped -task seg_ms_lesion_mp2rage 
 ```
 
-> **Note**
-> See [hyperparameter optimization](https://github.com/ivadomed/model_seg_ms_mp2rage/issues/58)
+## Model implementation on Slicer
 
+1. Install [3D Slicer version 5.7.0](https://download.slicer.org/), then install the module [SlicerNNUnet](https://github.com/KitwareMedical/SlicerNNUnet) from the extensions explorer.
 
-## Get trained models
+<img src="https://github.com/spinalcordtoolbox/spinalcordtoolbox/assets/77469192/9d7964d2-66e3-464d-ac1a-04caaaced63b" width="300px;" alt=""/>
 
-```
-cp -r ~/duke/temp/uzay/saved_models_basel/seg_sc_output .
-cp -r ~/duke/temp/uzay/saved_models_basel/seg_lesion_output .
-```
 
-## Performance evaluation
+2. Download and unzip the [ nnUNetTrainer_seg_ms_lesion_mp2rage__nnUNetPlans__3d_fullres.zip](https://github.com/ivadomed/model_seg_ms_mp2rage/releases/tag/r20240610) file. (~120 Mb)
 
-To test a spinal cord segmentation model run
-```
-ivadomed --test -c config/seg_sc.json
-```
-
-To test a lesion segmentation model independently on the two rater's annotations
-```
-ivadomed --test -c config/test_on_rater1.json
-ivadomed --test -c config/test_on_rater2.json
-```
-
-Visualize predictions
-
-Update variable below:
-```
-SUBJECT_LIST=(002 017 025 058)
-PATH_MODEL=model_seg_lesion_mp2rage_20230124_204632
-```
-Then launch:
-```
-for SUBJECT in ${SUBJECT_LIST[@]}; do fsleyes -S ~/data.neuro/basel-mp2rage/sub-P${SUBJECT}/anat/sub-P${SUBJECT}_UNIT1.nii.gz ~/data.neuro/basel-mp2rage/derivatives/labels/sub-P${SUBJECT}/anat/sub-P${SUBJECT}_UNIT1_lesion-manualNeuroPoly.nii.gz -cm yellow ${PATH_MODEL}/pred_masks/sub-P${SUBJECT}_UNIT1_pred.nii.gz -cm red -a 50; done
+3. Unzip the `.zip` file and place it inside a folder named `Dataset403_seg_ms_lesion_mp2rage_1mm_322subj`. The final directory structure should look like this:
 ```
+Dataset403_seg_ms_lesion_mp2rage_1mm_322subj
+└── nnUNetTrainer_seg_ms_lesion_mp2rage__nnUNetPlans__3d_fullres
+    ├── dataset_fingerprint.json
+    ├── dataset.json
+    ├── dataset_split.md
+    ├── datasplits
+    │   ├── datasplit_basel-mp2rage.yaml
+    │   ├── datasplit_marseille-3t-mp2rage.yaml
+    │   └── datasplit_nih-ms-mp2rage.yaml
+    ├── fold_3
+    │   ├── checkpoint_final.pth
+    │   ├── debug.json
+    │   ├── progress.png
+    │   └── training_log_2024_3_14_20_35_08.txt
+    └── plans.json
+``` 
 
-## Segment an image
+4. nnUNet Install: Follow the instructions on first row of:
+![Slicer-UNIseg](https://github.com/ivadomed/model_seg_ms_mp2rage/assets/77469192/90207a02-f640-4624-b10d-1abbd6433ba6)
 
-To run inference:
-```
-ivadomed_segment_image -i IMAGE -m MODEL
-```
+5. Implementation
+- Navigate and select the Slicer friendly folder on `Model path`
+- Folds: Choose the fold 3
+- Apply `model_seg_ms_mp2rage` !! 🚀🚀🚀
 
-> **Warning**
-> When running the MS lesion segmentation model, the image first need to be cropped around the spinal cord with a dilation of 32 in the axial plane.
 
-## Ensemble
+## Acknowledgments
 
-Multiple models were trained with different seeds, and they can all be used at test-time to produce slightly different segmentations that can then be everaged (ensemble/bagging). See more details [here](https://github.com/ivadomed/model_seg_ms_mp2rage/issues/63#issuecomment-1409250538).
+- Charidimos Tsagkas (Translational Neuroradiology Section, National Institutes of Health, Bethesda, USA)
+- Daniel Reich (Translational Neuroradiology Section, National Institutes of Health, Bethesda, USA)
+- Cristina Granziera (Neurologic Clinic and Policlinic, University Hospital Basel and University of Basel, Basel, Switzerland)
+- Virginie Callot (Center for Magnetic Resonance in Biology and Medicine, CRMBM-CEMEREM, UMR 7339, CNRS, Aix-Marseille University, Marseille, France)
diff --git a/data_analysis/test_metrics_nnunet.ipynb b/data_analysis/test_metrics_nnunet.ipynb
new file mode 100644
index 0000000..880fb21
--- /dev/null
+++ b/data_analysis/test_metrics_nnunet.ipynb
@@ -0,0 +1,5673 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "00bb4dd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import nibabel as nib\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "from statannotations.Annotator import Annotator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "22e8f839",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dice(A_binary, B_binary):\n",
+    "    AB = A_binary * B_binary\n",
+    "    DC = 2*(np.sum(AB))/(np.sum(A_binary) + np.sum(B_binary))\n",
+    "    return DC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96bf8c97",
+   "metadata": {},
+   "source": [
+    "# 3T Data test Dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "42c7a37d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#subjects= [\"sub-C010-ses-M0\", \"sub-C012-ses-M0\", \"sub-C015-ses-M0\", \"sub-MRS00-ses-M0\", \"sub-MRS15-ses-M0\", \"sub-MRS15-ses-M24\", \"sub-MRS18-ses-M0\", \"sub-MRS23-ses-M0\", \"sub-MRS23-ses-M24\", \"sub-nih003-ses-M0\", \"sub-nih009-ses-M0\", \"sub-nih017-ses-M0\", \"sub-nih021-ses-M0\", \"sub-nih037-ses-M0\", \"sub-nih042-ses-M0\", \"sub-nih043-ses-M0\", \"sub-nih045-ses-M0\", \"sub-nih051-ses-M0\", \"sub-nih052-ses-M0\", \"sub-nih054-ses-M0\", \"sub-nih056-ses-M0\", \"sub-nih067-ses-M0\", \"sub-nih073-ses-M0\", \"sub-nih075-ses-M0\", \"sub-nih084-ses-M0\", \"sub-nih087-ses-M0\", \"sub-nih089-ses-M0\", \"sub-nih092-ses-M0\", \"sub-nih100-ses-M0\", \"sub-nih101-ses-M0\", \"sub-nih110-ses-M0\", \"sub-nih112-ses-M0\", \"sub-nih117-ses-M0\", \"sub-nih120-ses-M0\", \"sub-nih134-ses-M0\", \"sub-nih136-ses-M0\", \"sub-nih144-ses-M0\", \"sub-nih147-ses-M0\", \"sub-nih149-ses-M0\", \"sub-nih155-ses-M0\", \"sub-nih173-ses-M0\", \"sub-nih179-ses-M0\", \"sub-nih183-ses-M0\", \"sub-nih194-ses-M0\", \"sub-nih195-ses-M0\", \"sub-nih199-ses-M0\", \"sub-P002-ses-M0\", \"sub-P016-ses-M0\", \"sub-P021-ses-M0\", \"sub-P037-ses-M0\", \"sub-P038-ses-M0\", \"sub-P044-ses-M0\", \"sub-P046-ses-M0\", \"sub-P051-ses-M0\", \"sub-P058-ses-M0\", \"sub-P061-ses-M0\", \"sub-P063-ses-M0\", \"sub-P066-ses-M0\", \"sub-P069-ses-M0\", \"sub-P077-ses-M0\", \"sub-P079-ses-M0\", \"sub-P080-ses-M0\", \"sub-P084-ses-M0\", \"sub-P085-ses-M0\", \"sub-P094-ses-M0\", \"sub-P109-ses-M0\", \"sub-P120-ses-M0\", \"sub-P126-ses-M0\", \"sub-P130-ses-M0\", \"sub-P132-ses-M0\", \"sub-P133-ses-M0\", \"sub-P136-ses-M0\", \"sub-P149-ses-M0\", \"sub-P165-ses-M0\", \"sub-P175-ses-M0\", \"sub-P178-ses-M0\", \"sub-P183-ses-M0\", \"sub-P189-ses-M0\", \"sub-P199-ses-M0\", \"sub-P249-ses-M0\"]\n",
+    "subjects= [\"sub-MRS00-ses-M0\", \"sub-MRS15-ses-M0\", \"sub-MRS15-ses-M24\", \"sub-MRS18-ses-M0\", \"sub-MRS23-ses-M0\", \"sub-MRS23-ses-M24\", \"sub-nih003-ses-M0\", \"sub-nih009-ses-M0\", \"sub-nih021-ses-M0\", \"sub-nih037-ses-M0\", \"sub-nih042-ses-M0\", \"sub-nih043-ses-M0\", \"sub-nih045-ses-M0\", \"sub-nih051-ses-M0\", \"sub-nih054-ses-M0\",  \"sub-nih067-ses-M0\", \"sub-nih073-ses-M0\", \"sub-nih075-ses-M0\", \"sub-nih089-ses-M0\", \"sub-nih092-ses-M0\",  \"sub-nih101-ses-M0\", \"sub-nih110-ses-M0\", \"sub-nih112-ses-M0\", \"sub-nih117-ses-M0\", \"sub-nih120-ses-M0\", \"sub-nih134-ses-M0\", \"sub-nih136-ses-M0\", \"sub-nih144-ses-M0\", \"sub-nih147-ses-M0\", \"sub-nih149-ses-M0\", \"sub-nih173-ses-M0\", \"sub-nih179-ses-M0\", \"sub-nih183-ses-M0\", \"sub-nih194-ses-M0\", \"sub-nih195-ses-M0\", \"sub-nih199-ses-M0\", \"sub-P002-ses-M0\", \"sub-P016-ses-M0\", \"sub-P021-ses-M0\", \"sub-P037-ses-M0\", \"sub-P038-ses-M0\", \"sub-P044-ses-M0\", \"sub-P046-ses-M0\", \"sub-P051-ses-M0\", \"sub-P058-ses-M0\", \"sub-P061-ses-M0\", \"sub-P063-ses-M0\", \"sub-P066-ses-M0\", \"sub-P069-ses-M0\",  \"sub-P079-ses-M0\", \"sub-P080-ses-M0\", \"sub-P084-ses-M0\", \"sub-P085-ses-M0\", \"sub-P094-ses-M0\", \"sub-P109-ses-M0\", \"sub-P120-ses-M0\",  \"sub-P130-ses-M0\", \"sub-P132-ses-M0\", \"sub-P149-ses-M0\", \"sub-P165-ses-M0\", \"sub-P175-ses-M0\", \"sub-P178-ses-M0\", \"sub-P183-ses-M0\",  \"sub-P199-ses-M0\", \"sub-P249-ses-M0\"]\n",
+    "#subjects= [\"sub-C010-ses-M0\",\"sub-C012-ses-M0\",\"sub-C015-ses-M0\",\"sub-nih017-ses-M0\",\"sub-nih052-ses-M0\",\"sub-nih056-ses-M0\",\"sub-nih084-ses-M0\",\"sub-nih087-ses-M0\",\"sub-nih100-ses-M0\",\"sub-nih155-ses-M0\",\"sub-P077-ses-M0\",\"sub-P126-ses-M0\",\"sub-P133-ses-M0\",\"sub-P136-ses-M0\",\"sub-P189-ses-M0\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "id": "8b405833",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>dice_lesion_algo1</th>\n",
+       "      <th>dice_lesion_single</th>\n",
+       "      <th>dice_lesion_multi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS00-ses-M0</td>\n",
+       "      <td>0.272853</td>\n",
+       "      <td>0.635678</td>\n",
+       "      <td>0.548431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS15-ses-M0</td>\n",
+       "      <td>0.090409</td>\n",
+       "      <td>0.721868</td>\n",
+       "      <td>0.656020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS15-ses-M24</td>\n",
+       "      <td>0.122134</td>\n",
+       "      <td>0.763158</td>\n",
+       "      <td>0.661224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS18-ses-M0</td>\n",
+       "      <td>0.401709</td>\n",
+       "      <td>0.517857</td>\n",
+       "      <td>0.175824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS23-ses-M0</td>\n",
+       "      <td>0.077063</td>\n",
+       "      <td>0.746186</td>\n",
+       "      <td>0.537415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>sub-P175-ses-M0</td>\n",
+       "      <td>0.834286</td>\n",
+       "      <td>0.846626</td>\n",
+       "      <td>0.677165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>sub-P178-ses-M0</td>\n",
+       "      <td>0.635838</td>\n",
+       "      <td>0.262948</td>\n",
+       "      <td>0.173913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>sub-P183-ses-M0</td>\n",
+       "      <td>0.681733</td>\n",
+       "      <td>0.550588</td>\n",
+       "      <td>0.510309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>sub-P199-ses-M0</td>\n",
+       "      <td>0.849968</td>\n",
+       "      <td>0.846255</td>\n",
+       "      <td>0.799429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>sub-P249-ses-M0</td>\n",
+       "      <td>0.741395</td>\n",
+       "      <td>0.721144</td>\n",
+       "      <td>0.690367</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>65 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              subject  dice_lesion_algo1  dice_lesion_single  \\\n",
+       "0    sub-MRS00-ses-M0           0.272853            0.635678   \n",
+       "1    sub-MRS15-ses-M0           0.090409            0.721868   \n",
+       "2   sub-MRS15-ses-M24           0.122134            0.763158   \n",
+       "3    sub-MRS18-ses-M0           0.401709            0.517857   \n",
+       "4    sub-MRS23-ses-M0           0.077063            0.746186   \n",
+       "..                ...                ...                 ...   \n",
+       "60    sub-P175-ses-M0           0.834286            0.846626   \n",
+       "61    sub-P178-ses-M0           0.635838            0.262948   \n",
+       "62    sub-P183-ses-M0           0.681733            0.550588   \n",
+       "63    sub-P199-ses-M0           0.849968            0.846255   \n",
+       "64    sub-P249-ses-M0           0.741395            0.721144   \n",
+       "\n",
+       "    dice_lesion_multi  \n",
+       "0            0.548431  \n",
+       "1            0.656020  \n",
+       "2            0.661224  \n",
+       "3            0.175824  \n",
+       "4            0.537415  \n",
+       "..                ...  \n",
+       "60           0.677165  \n",
+       "61           0.173913  \n",
+       "62           0.510309  \n",
+       "63           0.799429  \n",
+       "64           0.690367  \n",
+       "\n",
+       "[65 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 298,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=[]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/'\n",
+    "for subject in subjects: \n",
+    "    gt_lesion_data= nib.load(path_database + '403/GT_sc/'+ subject +'_UNIT1_GT.nii.gz')\n",
+    "    gt_lesion = np.array(gt_lesion_data.get_fdata())\n",
+    "    \n",
+    "    gt_lesion_data_brain= nib.load(path_database + '401/sc_lesion/'+ subject +'_UNIT1_GT.nii.gz')\n",
+    "    gt_lesion_brain = np.array(gt_lesion_data_brain.get_fdata())\n",
+    "    th, gt_lesion_brain = cv2.threshold(gt_lesion_brain, 0.5, 1, cv2.THRESH_BINARY)\n",
+    "\n",
+    "    pred_lesion_data_single_fov= nib.load(path_database + '401/sc_lesion/'+ subject +'_UNIT1_algo-3-fov.nii.gz')\n",
+    "    pred_lesion_single_fov = np.array(pred_lesion_data_single_fov.get_fdata())\n",
+    "    th, pred_lesion_single_fov = cv2.threshold(pred_lesion_single_fov, 0.5, 1, cv2.THRESH_BINARY)\n",
+    "    \n",
+    "    pred_lesion_data_algo1 = nib.load(path_database + '403/masks_cropped/'+ subject +'_UNIT1_algo-1.nii.gz')\n",
+    "    pred_lesion_algo1 = np.array(pred_lesion_data_algo1.get_fdata())\n",
+    "\n",
+    "    pred_lesion_data_single = nib.load(path_database + '403/masks_cropped/'+ subject +'_UNIT1_algo-3-singleclass.nii.gz')\n",
+    "    pred_lesion_single = np.array(pred_lesion_data_single.get_fdata())\n",
+    "\n",
+    "    pred_lesion_data_multi = nib.load(path_database + '405/masks_cropped/lesions/'+ subject +'_UNIT1_algo-3-multiclass.nii.gz')\n",
+    "    pred_lesion_multi = np.array(pred_lesion_data_multi.get_fdata())\n",
+    "    \n",
+    "    dice_lesion_algo1 = dice(gt_lesion,pred_lesion_algo1)\n",
+    "    dice_lesion_single = dice(gt_lesion,pred_lesion_single)\n",
+    "    dice_lesion_single_fov = dice(gt_lesion_brain,pred_lesion_single_fov)\n",
+    "    dice_lesion_multi = dice(gt_lesion,pred_lesion_multi)\n",
+    "    \n",
+    "    d = {'subject': subject,'dice_lesion_algo1':dice_lesion_algo1,'dice_lesion_single':dice_lesion_single,\n",
+    "         'dice_lesion_multi':dice_lesion_multi}\n",
+    "    df.append(d)\n",
+    "data=pd.DataFrame(data=df)\n",
+    "data  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "id": "be71ebe1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>subject</th>\n",
+       "      <th>dice_lesion_algo1</th>\n",
+       "      <th>dice_lesion_single</th>\n",
+       "      <th>dice_lesion_multi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>sub-MRS00-ses-M0</td>\n",
+       "      <td>0.272853</td>\n",
+       "      <td>0.635678</td>\n",
+       "      <td>0.548431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>sub-MRS15-ses-M0</td>\n",
+       "      <td>0.090409</td>\n",
+       "      <td>0.721868</td>\n",
+       "      <td>0.656020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>sub-MRS15-ses-M24</td>\n",
+       "      <td>0.122134</td>\n",
+       "      <td>0.763158</td>\n",
+       "      <td>0.661224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>sub-MRS18-ses-M0</td>\n",
+       "      <td>0.401709</td>\n",
+       "      <td>0.517857</td>\n",
+       "      <td>0.175824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>sub-MRS23-ses-M0</td>\n",
+       "      <td>0.077063</td>\n",
+       "      <td>0.746186</td>\n",
+       "      <td>0.537415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>60</td>\n",
+       "      <td>sub-P175-ses-M0</td>\n",
+       "      <td>0.834286</td>\n",
+       "      <td>0.846626</td>\n",
+       "      <td>0.677165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>61</td>\n",
+       "      <td>sub-P178-ses-M0</td>\n",
+       "      <td>0.635838</td>\n",
+       "      <td>0.262948</td>\n",
+       "      <td>0.173913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>62</td>\n",
+       "      <td>sub-P183-ses-M0</td>\n",
+       "      <td>0.681733</td>\n",
+       "      <td>0.550588</td>\n",
+       "      <td>0.510309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>63</td>\n",
+       "      <td>sub-P199-ses-M0</td>\n",
+       "      <td>0.849968</td>\n",
+       "      <td>0.846255</td>\n",
+       "      <td>0.799429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>64</td>\n",
+       "      <td>sub-P249-ses-M0</td>\n",
+       "      <td>0.741395</td>\n",
+       "      <td>0.721144</td>\n",
+       "      <td>0.690367</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>65 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0            subject  dice_lesion_algo1  dice_lesion_single  \\\n",
+       "0            0   sub-MRS00-ses-M0           0.272853            0.635678   \n",
+       "1            1   sub-MRS15-ses-M0           0.090409            0.721868   \n",
+       "2            2  sub-MRS15-ses-M24           0.122134            0.763158   \n",
+       "3            3   sub-MRS18-ses-M0           0.401709            0.517857   \n",
+       "4            4   sub-MRS23-ses-M0           0.077063            0.746186   \n",
+       "..         ...                ...                ...                 ...   \n",
+       "60          60    sub-P175-ses-M0           0.834286            0.846626   \n",
+       "61          61    sub-P178-ses-M0           0.635838            0.262948   \n",
+       "62          62    sub-P183-ses-M0           0.681733            0.550588   \n",
+       "63          63    sub-P199-ses-M0           0.849968            0.846255   \n",
+       "64          64    sub-P249-ses-M0           0.741395            0.721144   \n",
+       "\n",
+       "    dice_lesion_multi  \n",
+       "0            0.548431  \n",
+       "1            0.656020  \n",
+       "2            0.661224  \n",
+       "3            0.175824  \n",
+       "4            0.537415  \n",
+       "..                ...  \n",
+       "60           0.677165  \n",
+       "61           0.173913  \n",
+       "62           0.510309  \n",
+       "63           0.799429  \n",
+       "64           0.690367  \n",
+       "\n",
+       "[65 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 343,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/'\n",
+    "#data.to_excel(path_database + \"dices_3T.xlsx\") \n",
+    "data = pd.read_excel(path_database + \"dices_3T.xlsx\")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "id": "531bb923",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dice algo 1 mean: 0.5766179279972311     dice STD: 0.23888111583666977\n",
+      "dice algo 2 single-class mean: 0.6694275795193678     dice STD: 0.20733123150750932\n",
+      "dice algo 2 multi-class mean: 0.55761346815888     dice STD: 0.21561584859352326\n",
+      "p-value annotation legend:\n",
+      "      ns: p <= 1.00e+00\n",
+      "       *: 1.00e-02 < p <= 5.00e-02\n",
+      "      **: 1.00e-03 < p <= 1.00e-02\n",
+      "     ***: 1.00e-04 < p <= 1.00e-03\n",
+      "    ****: p <= 1.00e-04\n",
+      "\n",
+      "dice_lesion_algo1 vs. dice_lesion_single: Wilcoxon test (paired samples), P_val:1.100e-03 Stat=5.110e+02\n",
+      "dice_lesion_single vs. dice_lesion_multi: Wilcoxon test (paired samples), P_val:4.283e-10 Stat=8.600e+01\n",
+      "dice_lesion_algo1 vs. dice_lesion_multi: Wilcoxon test (paired samples), P_val:1.667e-01 Stat=8.060e+02\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<AxesSubplot:xlabel='Group', ylabel='Dice'>,\n",
+       " [<statannotations.Annotation.Annotation at 0x7f6d1c284e50>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1c284950>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1c284c90>])"
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = data\n",
+    "df_lesion_algo_1 = data.filter(['dice_lesion_algo1'], axis=1)\n",
+    "df_lesion_algo_1.insert(0, 'Group', 'dice_lesion_algo1')\n",
+    "df_lesion_algo_1.rename(columns={'dice_lesion_algo1': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_mono = data.filter(['dice_lesion_single'], axis=1)\n",
+    "df_lesion_mono.insert(0, 'Group', 'dice_lesion_single')\n",
+    "df_lesion_mono.rename(columns={'dice_lesion_single': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_mono_fov = data.filter(['dice_lesion_single_fov'], axis=1)\n",
+    "df_lesion_mono_fov.insert(0, 'Group', 'dice_lesion_single_fov')\n",
+    "df_lesion_mono_fov.rename(columns={'dice_lesion_single_fov': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_multi = data.filter(['dice_lesion_multi'], axis=1)\n",
+    "df_lesion_multi.insert(0, 'Group', 'dice_lesion_multi')\n",
+    "df_lesion_multi.rename(columns={'dice_lesion_multi': 'Dice'}, inplace=True)\n",
+    "\n",
+    "frames = [ df_lesion_algo_1, df_lesion_mono ,df_lesion_multi]\n",
+    "result_gm = pd.concat(frames, axis=0)\n",
+    "\n",
+    "plt.figure(figsize=(2,5))\n",
+    "plt.xticks(rotation=90)\n",
+    "colores = [\"#1f77b4\", \"#FF8C00\", \"#FF1C00\"]\n",
+    "ax_gm = sns.boxplot(data=result_gm, x=\"Group\", palette=colores, y=\"Dice\")\n",
+    "print (\"dice algo 1 mean:\", np.mean(data[\"dice_lesion_algo1\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_algo1\"])) \n",
+    "print (\"dice algo 2 single-class mean:\", np.mean(data[\"dice_lesion_single\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_single\"])) \n",
+    "print (\"dice algo 2 multi-class mean:\", np.mean(data[\"dice_lesion_multi\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_multi\"])) \n",
+    "\n",
+    "\n",
+    "\n",
+    "pairs=[\n",
+    "           ((\"dice_lesion_algo1\"), (\"dice_lesion_single\")),\n",
+    "           ((\"dice_lesion_algo1\"), (\"dice_lesion_multi\")),\n",
+    "           ((\"dice_lesion_single\"), (\"dice_lesion_multi\")) ]\n",
+    "annotator_csa_gm = Annotator(ax_gm, pairs, data=result_gm, x=\"Group\", y=\"Dice\")#, order=order)\n",
+    "annotator_csa_gm.configure(test='Wilcoxon', text_format='star', loc='inside').apply_test()\n",
+    "annotator_csa_gm.apply_and_annotate()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "id": "cbb02e15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columna_1 = data[\"dice_lesion_algo1\"]\n",
+    "columna_2 = data[\"dice_lesion_single\"]\n",
+    "columna_3 = data[\"dice_lesion_multi\"]\n",
+    "\n",
+    "plt.figure(figsize=((10,4)))\n",
+    "plt.fill_between(range(0, 7), 0, 1, color='gray', alpha=0.9, label='sub-MRS')\n",
+    "plt.fill_between(range(7, 36), 0, 1, color='gray', alpha=0.3, label='sub-NIH')\n",
+    "plt.fill_between(range(36, 66), 0, 1, color='green', alpha=0.2, label='sub-Basel')\n",
+    "#plt.fill_between(range(6, 81), min(columna_1[:80]), max(columna_1[:80]), color='gray', alpha=0.2, label='sub-Basel')\n",
+    "plt.scatter(range(1, len(columna_1) + 1), columna_1, marker='o', color='#1f77b4', label='algo 1')\n",
+    "plt.scatter(range(1, len(columna_2) + 1), columna_2, marker='*', color='#FF8C00', label='algo 3')\n",
+    "plt.scatter(range(1, len(columna_3) + 1), columna_3, marker='+', color='#FF1C00', label='algo 3 MC')\n",
+    "\n",
+    "#plt.title('UNIT1-den VS UNIT1-nonden')\n",
+    "plt.xlabel('Subject')\n",
+    "plt.ylabel('Dice')\n",
+    "#plt.legend(loc='best')\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0821e56d",
+   "metadata": {},
+   "source": [
+    "# 7T Data test Dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "de4c8a60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>dice_lesion_algo1</th>\n",
+       "      <th>dice_lesion_single</th>\n",
+       "      <th>dice_lesion_multi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS01_ses-M0</td>\n",
+       "      <td>0.576490</td>\n",
+       "      <td>0.787402</td>\n",
+       "      <td>0.606452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS04_ses-M0</td>\n",
+       "      <td>0.245312</td>\n",
+       "      <td>0.637191</td>\n",
+       "      <td>0.485465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS05_ses-M0</td>\n",
+       "      <td>0.157846</td>\n",
+       "      <td>0.237895</td>\n",
+       "      <td>0.166852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS05_ses-M24</td>\n",
+       "      <td>0.167464</td>\n",
+       "      <td>0.229508</td>\n",
+       "      <td>0.108999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS06_ses-M0</td>\n",
+       "      <td>0.138391</td>\n",
+       "      <td>0.356432</td>\n",
+       "      <td>0.340871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sub-MRS08_ses-M0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.661111</td>\n",
+       "      <td>0.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sub-MRS09_ses-M0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.590476</td>\n",
+       "      <td>0.351145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sub-MRS10_ses-M0</td>\n",
+       "      <td>0.398210</td>\n",
+       "      <td>0.653846</td>\n",
+       "      <td>0.462222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sub-MRS11_ses-M0</td>\n",
+       "      <td>0.219887</td>\n",
+       "      <td>0.626999</td>\n",
+       "      <td>0.535012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sub-MRS12_ses-M0</td>\n",
+       "      <td>0.383358</td>\n",
+       "      <td>0.497479</td>\n",
+       "      <td>0.142857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>sub-MRS14_ses-M0</td>\n",
+       "      <td>0.473431</td>\n",
+       "      <td>0.467446</td>\n",
+       "      <td>0.161074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>sub-MRS14_ses-M24</td>\n",
+       "      <td>0.339936</td>\n",
+       "      <td>0.513694</td>\n",
+       "      <td>0.142412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sub-MRS16_ses-M0</td>\n",
+       "      <td>0.388595</td>\n",
+       "      <td>0.566667</td>\n",
+       "      <td>0.390107</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              subject  dice_lesion_algo1  dice_lesion_single  \\\n",
+       "0    sub-MRS01_ses-M0           0.576490            0.787402   \n",
+       "1    sub-MRS04_ses-M0           0.245312            0.637191   \n",
+       "2    sub-MRS05_ses-M0           0.157846            0.237895   \n",
+       "3   sub-MRS05_ses-M24           0.167464            0.229508   \n",
+       "4    sub-MRS06_ses-M0           0.138391            0.356432   \n",
+       "5    sub-MRS08_ses-M0           0.000000            0.661111   \n",
+       "6    sub-MRS09_ses-M0           0.000000            0.590476   \n",
+       "7    sub-MRS10_ses-M0           0.398210            0.653846   \n",
+       "8    sub-MRS11_ses-M0           0.219887            0.626999   \n",
+       "9    sub-MRS12_ses-M0           0.383358            0.497479   \n",
+       "10   sub-MRS14_ses-M0           0.473431            0.467446   \n",
+       "11  sub-MRS14_ses-M24           0.339936            0.513694   \n",
+       "12   sub-MRS16_ses-M0           0.388595            0.566667   \n",
+       "\n",
+       "    dice_lesion_multi  \n",
+       "0            0.606452  \n",
+       "1            0.485465  \n",
+       "2            0.166852  \n",
+       "3            0.108999  \n",
+       "4            0.340871  \n",
+       "5            0.428571  \n",
+       "6            0.351145  \n",
+       "7            0.462222  \n",
+       "8            0.535012  \n",
+       "9            0.142857  \n",
+       "10           0.161074  \n",
+       "11           0.142412  \n",
+       "12           0.390107  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subjects=[\"sub-MRS01_ses-M0\",\"sub-MRS04_ses-M0\",\"sub-MRS05_ses-M0\",\"sub-MRS05_ses-M24\",\"sub-MRS06_ses-M0\",\"sub-MRS08_ses-M0\",\"sub-MRS09_ses-M0\",\"sub-MRS10_ses-M0\",\"sub-MRS11_ses-M0\",\"sub-MRS12_ses-M0\",\"sub-MRS14_ses-M0\",\"sub-MRS14_ses-M24\",\"sub-MRS16_ses-M0\"]\n",
+    "#subjects=[\"sub-MRS02_ses-M0\",\"sub-MRS07_ses-M0\",\"sub-MRS07_ses-M24\",\"sub-MRS15_ses-M0\",\"sub-MRS15_ses-M24\",,\"sub-MRS17_ses-M0\"\n",
+    "df=[]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/7T/'\n",
+    "for subject in subjects: \n",
+    "    gt_lesion_data= nib.load(path_database + 'GT_iso/' + subject +'_UNIT1_desc-rater1_label-lesion_seg.nii.gz')\n",
+    "    gt_lesion = np.array(gt_lesion_data.get_fdata())\n",
+    "    \n",
+    "    pred_lesion_data_algo1 = nib.load(path_database + 'algo-1_iso/'+ subject +'_UNIT1_label-lesion_algo1_reg.nii.gz')\n",
+    "    pred_lesion_algo1 = np.array(pred_lesion_data_algo1.get_fdata())\n",
+    "\n",
+    "    pred_lesion_data_single = nib.load(path_database + '403_iso/'+ subject +'_UNIT1_algo-3-singleclass.nii.gz')\n",
+    "    pred_lesion_single = np.array(pred_lesion_data_single.get_fdata())\n",
+    "\n",
+    "    pred_lesion_data_multi = nib.load(path_database + '405_iso/'+ subject +'_UNIT1_algo-3-multiclass.nii.gz')\n",
+    "    pred_lesion_multi = np.array(pred_lesion_data_multi.get_fdata())\n",
+    "    \n",
+    "    dice_lesion_algo1 = dice(gt_lesion,pred_lesion_algo1)\n",
+    "    dice_lesion_single = dice(gt_lesion,pred_lesion_single)\n",
+    "    dice_lesion_multi = dice(gt_lesion,pred_lesion_multi)\n",
+    "    \n",
+    "    d = {'subject': subject,'dice_lesion_algo1':dice_lesion_algo1,'dice_lesion_single':dice_lesion_single,\n",
+    "         'dice_lesion_multi':dice_lesion_multi}\n",
+    "    df.append(d)\n",
+    "data_iso=pd.DataFrame(data=df)\n",
+    "data_iso  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c818a5a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data=data_iso\n",
+    "columna_1 = data[\"dice_lesion_algo1\"]\n",
+    "columna_2 = data[\"dice_lesion_single\"]\n",
+    "columna_3 = data[\"dice_lesion_multi\"]\n",
+    "\n",
+    "plt.figure(figsize=((4,4)))\n",
+    "#plt.fill_between(range(0, 7), 0, 1, color='gray', alpha=0.9, label='sub-MRS')\n",
+    "#plt.fill_between(range(7, 36), 0, 1, color='gray', alpha=0.3, label='sub-NIH')\n",
+    "#plt.fill_between(range(36, 66), 0, 1, color='green', alpha=0.2, label='sub-Basel')\n",
+    "#plt.fill_between(range(6, 81), min(columna_1[:80]), max(columna_1[:80]), color='gray', alpha=0.2, label='sub-Basel')\n",
+    "plt.scatter(range(1, len(columna_1) + 1), columna_1, marker='o', color='#1f77b4', label='algo 1')\n",
+    "plt.scatter(range(1, len(columna_2) + 1), columna_2, marker='*', color='#FF8C00', label='algo 2 singleclass')\n",
+    "plt.scatter(range(1, len(columna_3) + 1), columna_3, marker='+', color='#FF1C00', label='algo 2 multiclass')\n",
+    "\n",
+    "#plt.title('UNIT1-den VS UNIT1-nonden')\n",
+    "plt.xlabel('Subject')\n",
+    "plt.ylabel('Dice')\n",
+    "#plt.legend(loc='best')\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "801dac31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dice algo 1 mean: 0.2683784682890462     dice STD: 0.1693570504836527\n",
+      "dice algo 2 single-class mean: 0.5250881632404787     dice STD: 0.16061272489564407\n",
+      "dice algo 2 multi-class mean: 0.33246463383992136     dice STD: 0.1636434689599618\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\npairs=[\\n           ((\"dice algo 1\"), (\"dice algo 2 single\")),\\n           ((\"dice algo 1\"), (\"dice algo 2 MC\")),\\n           ((\"dice algo 2 MC\"), (\"dice algo 2 single\")) ]\\nannotator_csa_gm = Annotator(ax_gm, pairs, data=result_gm, x=\"Group\", y=\"Dice\")#, order=order)\\nannotator_csa_gm.configure(test=\\'t-test_paired\\', text_format=\\'star\\', loc=\\'inside\\').apply_test()\\nannotator_csa_gm.apply_and_annotate()\\n'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = data_iso\n",
+    "df_lesion_algo_1 = data.filter(['dice_lesion_algo1'], axis=1)\n",
+    "df_lesion_algo_1.insert(0, 'Group', 'dice_lesion_algo1')\n",
+    "df_lesion_algo_1.rename(columns={'dice_lesion_algo1': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_mono = data.filter(['dice_lesion_single'], axis=1)\n",
+    "df_lesion_mono.insert(0, 'Group', 'dice_lesion_single')\n",
+    "df_lesion_mono.rename(columns={'dice_lesion_single': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_multi = data.filter(['dice_lesion_multi'], axis=1)\n",
+    "df_lesion_multi.insert(0, 'Group', 'dice_lesion_multi')\n",
+    "df_lesion_multi.rename(columns={'dice_lesion_multi': 'Dice'}, inplace=True)\n",
+    "\n",
+    "frames = [ df_lesion_algo_1, df_lesion_mono ,df_lesion_multi]\n",
+    "result_gm = pd.concat(frames, axis=0)\n",
+    "\n",
+    "plt.figure(figsize=(2,5))\n",
+    "plt.xticks(rotation=90)\n",
+    "colores = [\"#1f77b4\", \"#FF8C00\", \"#FF1C00\"]\n",
+    "ax_gm = sns.boxplot(data=result_gm, x=\"Group\", palette=colores, y=\"Dice\")\n",
+    "print (\"dice algo 1 mean:\", np.mean(data[\"dice_lesion_algo1\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_algo1\"])) \n",
+    "print (\"dice algo 2 single-class mean:\", np.mean(data[\"dice_lesion_single\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_single\"])) \n",
+    "print (\"dice algo 2 multi-class mean:\", np.mean(data[\"dice_lesion_multi\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_multi\"])) \n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "pairs=[\n",
+    "           ((\"dice algo 1\"), (\"dice algo 2 single\")),\n",
+    "           ((\"dice algo 1\"), (\"dice algo 2 MC\")),\n",
+    "           ((\"dice algo 2 MC\"), (\"dice algo 2 single\")) ]\n",
+    "annotator_csa_gm = Annotator(ax_gm, pairs, data=result_gm, x=\"Group\", y=\"Dice\")#, order=order)\n",
+    "annotator_csa_gm.configure(test='t-test_paired', text_format='star', loc='inside').apply_test()\n",
+    "annotator_csa_gm.apply_and_annotate()\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3f28f820",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>dice_lesion_algo1</th>\n",
+       "      <th>dice_lesion_single</th>\n",
+       "      <th>dice_lesion_multi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS00_ses-M0_run-1</td>\n",
+       "      <td>0.508227</td>\n",
+       "      <td>0.601405</td>\n",
+       "      <td>0.454433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS00_ses-M24_run-1</td>\n",
+       "      <td>0.367181</td>\n",
+       "      <td>0.483068</td>\n",
+       "      <td>0.491018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS00_ses-M24_run-2</td>\n",
+       "      <td>0.405126</td>\n",
+       "      <td>0.567731</td>\n",
+       "      <td>0.686071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS01_ses-M0_run-1</td>\n",
+       "      <td>0.586035</td>\n",
+       "      <td>0.779359</td>\n",
+       "      <td>0.777778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS01_ses-M0_run-2</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.768696</td>\n",
+       "      <td>0.739040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sub-MRS03_ses-M0_run-1</td>\n",
+       "      <td>0.349933</td>\n",
+       "      <td>0.660300</td>\n",
+       "      <td>0.648622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sub-MRS03_ses-M0_run-2</td>\n",
+       "      <td>0.206578</td>\n",
+       "      <td>0.573577</td>\n",
+       "      <td>0.573010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sub-MRS04_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.623501</td>\n",
+       "      <td>0.501370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sub-MRS04_ses-M0_run-2</td>\n",
+       "      <td>0.140228</td>\n",
+       "      <td>0.620370</td>\n",
+       "      <td>0.445040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sub-MRS04_ses-M24_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.480000</td>\n",
+       "      <td>0.460568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>sub-MRS05_ses-M0_run-1</td>\n",
+       "      <td>0.185185</td>\n",
+       "      <td>0.123974</td>\n",
+       "      <td>0.069680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>sub-MRS05_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.215078</td>\n",
+       "      <td>0.147094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sub-MRS05_ses-M24_run-1</td>\n",
+       "      <td>0.135747</td>\n",
+       "      <td>0.110384</td>\n",
+       "      <td>0.070949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>sub-MRS05_ses-M24_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.095618</td>\n",
+       "      <td>0.077994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>sub-MRS06_ses-M0_run-1</td>\n",
+       "      <td>0.195396</td>\n",
+       "      <td>0.730769</td>\n",
+       "      <td>0.587764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>sub-MRS06_ses-M0_run-2</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>0.775972</td>\n",
+       "      <td>0.588774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>sub-MRS08_ses-M0_run-1</td>\n",
+       "      <td>0.024973</td>\n",
+       "      <td>0.256637</td>\n",
+       "      <td>0.057143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>sub-MRS08_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>sub-MRS09_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.048193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>sub-MRS09_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.009434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>sub-MRS10_ses-M0_run-1</td>\n",
+       "      <td>0.253927</td>\n",
+       "      <td>0.477064</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>sub-MRS10_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>sub-MRS11_ses-M0_run-1</td>\n",
+       "      <td>0.259048</td>\n",
+       "      <td>0.368466</td>\n",
+       "      <td>0.356855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>sub-MRS11_ses-M0_run-2</td>\n",
+       "      <td>0.209246</td>\n",
+       "      <td>0.547347</td>\n",
+       "      <td>0.480583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>sub-MRS11_ses-M24_run-1</td>\n",
+       "      <td>0.531014</td>\n",
+       "      <td>0.678940</td>\n",
+       "      <td>0.553633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>sub-MRS11_ses-M24_run-2</td>\n",
+       "      <td>0.382470</td>\n",
+       "      <td>0.592506</td>\n",
+       "      <td>0.544737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>sub-MRS12_ses-M0_run-1</td>\n",
+       "      <td>0.158332</td>\n",
+       "      <td>0.714384</td>\n",
+       "      <td>0.641363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>sub-MRS12_ses-M0_run-2</td>\n",
+       "      <td>0.261854</td>\n",
+       "      <td>0.608917</td>\n",
+       "      <td>0.485714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>sub-MRS13_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.533873</td>\n",
+       "      <td>0.476366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>sub-MRS14_ses-M0_run-1</td>\n",
+       "      <td>0.336891</td>\n",
+       "      <td>0.277819</td>\n",
+       "      <td>0.172598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>sub-MRS14_ses-M0_run-2</td>\n",
+       "      <td>0.277176</td>\n",
+       "      <td>0.531339</td>\n",
+       "      <td>0.222417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>sub-MRS14_ses-M24_run-1</td>\n",
+       "      <td>0.532560</td>\n",
+       "      <td>0.459082</td>\n",
+       "      <td>0.240808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>sub-MRS14_ses-M24_run-2</td>\n",
+       "      <td>0.335290</td>\n",
+       "      <td>0.528416</td>\n",
+       "      <td>0.267511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>sub-MRS16_ses-M0_run-1</td>\n",
+       "      <td>0.307692</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.516908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>sub-MRS16_ses-M0_run-2</td>\n",
+       "      <td>0.433031</td>\n",
+       "      <td>0.484490</td>\n",
+       "      <td>0.438538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>sub-MRS18_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.505495</td>\n",
+       "      <td>0.096386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>sub-MRS18_ses-M24_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    subject  dice_lesion_algo1  dice_lesion_single  \\\n",
+       "0    sub-MRS00_ses-M0_run-1           0.508227            0.601405   \n",
+       "1   sub-MRS00_ses-M24_run-1           0.367181            0.483068   \n",
+       "2   sub-MRS00_ses-M24_run-2           0.405126            0.567731   \n",
+       "3    sub-MRS01_ses-M0_run-1           0.586035            0.779359   \n",
+       "4    sub-MRS01_ses-M0_run-2           0.600000            0.768696   \n",
+       "5    sub-MRS03_ses-M0_run-1           0.349933            0.660300   \n",
+       "6    sub-MRS03_ses-M0_run-2           0.206578            0.573577   \n",
+       "7    sub-MRS04_ses-M0_run-1           0.000000            0.623501   \n",
+       "8    sub-MRS04_ses-M0_run-2           0.140228            0.620370   \n",
+       "9   sub-MRS04_ses-M24_run-1           0.000000            0.480000   \n",
+       "10   sub-MRS05_ses-M0_run-1           0.185185            0.123974   \n",
+       "11   sub-MRS05_ses-M0_run-2           0.000000            0.215078   \n",
+       "12  sub-MRS05_ses-M24_run-1           0.135747            0.110384   \n",
+       "13  sub-MRS05_ses-M24_run-2           0.000000            0.095618   \n",
+       "14   sub-MRS06_ses-M0_run-1           0.195396            0.730769   \n",
+       "15   sub-MRS06_ses-M0_run-2           0.692308            0.775972   \n",
+       "16   sub-MRS08_ses-M0_run-1           0.024973            0.256637   \n",
+       "17   sub-MRS08_ses-M0_run-2           0.000000            0.000000   \n",
+       "18   sub-MRS09_ses-M0_run-1           0.000000            0.000000   \n",
+       "19   sub-MRS09_ses-M0_run-2           0.000000            0.000000   \n",
+       "20   sub-MRS10_ses-M0_run-1           0.253927            0.477064   \n",
+       "21   sub-MRS10_ses-M0_run-2           0.000000            0.000000   \n",
+       "22   sub-MRS11_ses-M0_run-1           0.259048            0.368466   \n",
+       "23   sub-MRS11_ses-M0_run-2           0.209246            0.547347   \n",
+       "24  sub-MRS11_ses-M24_run-1           0.531014            0.678940   \n",
+       "25  sub-MRS11_ses-M24_run-2           0.382470            0.592506   \n",
+       "26   sub-MRS12_ses-M0_run-1           0.158332            0.714384   \n",
+       "27   sub-MRS12_ses-M0_run-2           0.261854            0.608917   \n",
+       "28   sub-MRS13_ses-M0_run-1           0.000000            0.533873   \n",
+       "29   sub-MRS14_ses-M0_run-1           0.336891            0.277819   \n",
+       "30   sub-MRS14_ses-M0_run-2           0.277176            0.531339   \n",
+       "31  sub-MRS14_ses-M24_run-1           0.532560            0.459082   \n",
+       "32  sub-MRS14_ses-M24_run-2           0.335290            0.528416   \n",
+       "33   sub-MRS16_ses-M0_run-1           0.307692            0.444444   \n",
+       "34   sub-MRS16_ses-M0_run-2           0.433031            0.484490   \n",
+       "35   sub-MRS18_ses-M0_run-1           0.000000            0.505495   \n",
+       "36  sub-MRS18_ses-M24_run-1           0.000000            0.000000   \n",
+       "\n",
+       "    dice_lesion_multi  \n",
+       "0            0.454433  \n",
+       "1            0.491018  \n",
+       "2            0.686071  \n",
+       "3            0.777778  \n",
+       "4            0.739040  \n",
+       "5            0.648622  \n",
+       "6            0.573010  \n",
+       "7            0.501370  \n",
+       "8            0.445040  \n",
+       "9            0.460568  \n",
+       "10           0.069680  \n",
+       "11           0.147094  \n",
+       "12           0.070949  \n",
+       "13           0.077994  \n",
+       "14           0.587764  \n",
+       "15           0.588774  \n",
+       "16           0.057143  \n",
+       "17           0.000000  \n",
+       "18           0.048193  \n",
+       "19           0.009434  \n",
+       "20           0.000000  \n",
+       "21           0.000000  \n",
+       "22           0.356855  \n",
+       "23           0.480583  \n",
+       "24           0.553633  \n",
+       "25           0.544737  \n",
+       "26           0.641363  \n",
+       "27           0.485714  \n",
+       "28           0.476366  \n",
+       "29           0.172598  \n",
+       "30           0.222417  \n",
+       "31           0.240808  \n",
+       "32           0.267511  \n",
+       "33           0.516908  \n",
+       "34           0.438538  \n",
+       "35           0.096386  \n",
+       "36           0.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subjects=[\"sub-MRS00_ses-M0_run-1\", \"sub-MRS00_ses-M24_run-1\", \"sub-MRS00_ses-M24_run-2\", \"sub-MRS01_ses-M0_run-1\", \"sub-MRS01_ses-M0_run-2\",\"sub-MRS03_ses-M0_run-1\", \"sub-MRS03_ses-M0_run-2\", \"sub-MRS04_ses-M0_run-1\", \"sub-MRS04_ses-M0_run-2\", \"sub-MRS04_ses-M24_run-1\", \"sub-MRS05_ses-M0_run-1\", \"sub-MRS05_ses-M0_run-2\", \"sub-MRS05_ses-M24_run-1\", \"sub-MRS05_ses-M24_run-2\", \"sub-MRS06_ses-M0_run-1\", \"sub-MRS06_ses-M0_run-2\", \"sub-MRS08_ses-M0_run-1\", \"sub-MRS08_ses-M0_run-2\", \"sub-MRS09_ses-M0_run-1\", \"sub-MRS09_ses-M0_run-2\", \"sub-MRS10_ses-M0_run-1\", \"sub-MRS10_ses-M0_run-2\", \"sub-MRS11_ses-M0_run-1\", \"sub-MRS11_ses-M0_run-2\", \"sub-MRS11_ses-M24_run-1\", \"sub-MRS11_ses-M24_run-2\", \"sub-MRS12_ses-M0_run-1\", \"sub-MRS12_ses-M0_run-2\", \"sub-MRS13_ses-M0_run-1\", \"sub-MRS14_ses-M0_run-1\", \"sub-MRS14_ses-M0_run-2\", \"sub-MRS14_ses-M24_run-1\", \"sub-MRS14_ses-M24_run-2\", \"sub-MRS16_ses-M0_run-1\", \"sub-MRS16_ses-M0_run-2\", \"sub-MRS18_ses-M0_run-1\", \"sub-MRS18_ses-M24_run-1\"]\n",
+    "#subjects=[\"sub-MRS02_ses-M0_run-1\", \"sub-MRS07_ses-M0_run-1\", \"sub-MRS07_ses-M0_run-2\", \"sub-MRS07_ses-M24_run-1\", \"sub-MRS07_ses-M24_run-2\", \"sub-MRS15_ses-M0_run-1\", \"sub-MRS15_ses-M0_run-2\", \"sub-MRS15_ses-M24_run-1\", \"sub-MRS15_ses-M24_run-2\",  \"sub-MRS17_ses-M0_run-1\", \"sub-MRS17_ses-M0_run-2\", \"sub-MRS17_ses-M24_run-1\", \"sub-MRS18_ses-M0_run-2\"\n",
+    "df=[]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/7T/'\n",
+    "for subject in subjects: \n",
+    "    gt_lesion_data= nib.load(path_database + 'GT_aniso/' + subject +'_UNIT1_desc-rater1_label-lesion_seg.nii.gz')\n",
+    "    gt_lesion = np.array(gt_lesion_data.get_fdata())\n",
+    "    \n",
+    "    pred_lesion_data_algo1 = nib.load(path_database + 'algo-1_aniso/'+ subject +'_UNIT1_label-lesion_algo1.nii.gz')\n",
+    "    pred_lesion_algo1 = np.array(pred_lesion_data_algo1.get_fdata())\n",
+    "\n",
+    "    pred_lesion_data_single = nib.load(path_database + '403_aniso/lesions/'+ subject +'_UNIT1_algo-3-singleclass.nii.gz')\n",
+    "    pred_lesion_single = np.array(pred_lesion_data_single.get_fdata())\n",
+    "\n",
+    "    pred_lesion_data_multi = nib.load(path_database + '405_aniso/lesions/'+ subject +'_UNIT1_algo-3-multiclass.nii.gz')\n",
+    "    pred_lesion_multi = np.array(pred_lesion_data_multi.get_fdata())\n",
+    "    \n",
+    "    dice_lesion_algo1 = dice(gt_lesion,pred_lesion_algo1)\n",
+    "    dice_lesion_single = dice(gt_lesion,pred_lesion_single)\n",
+    "    dice_lesion_multi = dice(gt_lesion,pred_lesion_multi)\n",
+    "    \n",
+    "    d = {'subject': subject,'dice_lesion_algo1':dice_lesion_algo1,'dice_lesion_single':dice_lesion_single,\n",
+    "         'dice_lesion_multi':dice_lesion_multi}\n",
+    "    df.append(d)\n",
+    "data_aniso=pd.DataFrame(data=df)\n",
+    "data_aniso  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c2aed74b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data=data_aniso\n",
+    "columna_1 = data[\"dice_lesion_algo1\"]\n",
+    "columna_2 = data[\"dice_lesion_single\"]\n",
+    "columna_3 = data[\"dice_lesion_multi\"]\n",
+    "\n",
+    "plt.figure(figsize=((8,4)))\n",
+    "#plt.fill_between(range(0, 7), 0, 1, color='gray', alpha=0.9, label='sub-MRS')\n",
+    "#plt.fill_between(range(7, 36), 0, 1, color='gray', alpha=0.3, label='sub-NIH')\n",
+    "#plt.fill_between(range(36, 66), 0, 1, color='green', alpha=0.2, label='sub-Basel')\n",
+    "#plt.fill_between(range(6, 81), min(columna_1[:80]), max(columna_1[:80]), color='gray', alpha=0.2, label='sub-Basel')\n",
+    "plt.scatter(range(1, len(columna_1) + 1), columna_1, marker='o', color='#1f77b4', label='algo 1')\n",
+    "plt.scatter(range(1, len(columna_2) + 1), columna_2, marker='*', color='#FF8C00', label='algo 2 singleclass')\n",
+    "plt.scatter(range(1, len(columna_3) + 1), columna_3, marker='+', color='#FF1C00', label='algo 2 multiclass')\n",
+    "\n",
+    "#plt.title('UNIT1-den VS UNIT1-nonden')\n",
+    "plt.xlabel('Subject')\n",
+    "plt.ylabel('Dice')\n",
+    "#plt.legend(loc='best')\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "id": "108393c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dice algo 1 mean: 0.23447152565900828     dice STD: 0.2045401362270218\n",
+      "dice algo 2 single-class mean: 0.4383519536162025     dice STD: 0.24596057087667095\n",
+      "dice algo 2 multi-class mean: 0.3494159821329882     dice STD: 0.2444740159840532\n",
+      "p-value annotation legend:\n",
+      "      ns: p <= 1.00e+00\n",
+      "       *: 1.00e-02 < p <= 5.00e-02\n",
+      "      **: 1.00e-03 < p <= 1.00e-02\n",
+      "     ***: 1.00e-04 < p <= 1.00e-03\n",
+      "    ****: p <= 1.00e-04\n",
+      "\n",
+      "dice_lesion_algo1 vs. dice_lesion_single: Wilcoxon test (paired samples), P_val:2.687e-06 Stat=1.300e+01\n",
+      "dice_lesion_single vs. dice_lesion_multi: Wilcoxon test (paired samples), P_val:4.552e-05 Stat=5.900e+01\n",
+      "dice_lesion_algo1 vs. dice_lesion_multi: Wilcoxon test (paired samples), P_val:3.187e-03 Stat=1.250e+02\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<AxesSubplot:xlabel='Group', ylabel='Dice'>,\n",
+       " [<statannotations.Annotation.Annotation at 0x7f6d4d457450>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4d457c90>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4d457c50>])"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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V7u7uiI2NRYsWLXDp0iWUlZXJVL19MXWsGnNU3561t80bg/9O8ZCmpqZCp9MhLS0Nfn5+AABnZ2fExMRg+vTp0Gg0da67fPlylJSU4IsvvjDcCb5Xr16y1G2PSkpKMHToUJttv+rO/NayY8cO3jwcArS7Bw8ehFarNQQUAIYMGQI3NzccOHCg3nU3b96M559/vsGhGojsmeJH0szMTIwaNcpompubG9q0aYPMzMw617ty5QpycnKgVqvxt7/9DUeOHMFf/vIXDBs2DK+//jqD24DEgVfg7ixZZVv/N5IjrNGZllaoMHNf68ZvyIEoHlKdTge1Wl1julqtRmFhYZ3r5eTkAADef/99hIWFYeXKlcjIyMB//vMflJWVIS4uzqJ6JElCUVGRReuaovr7tuLiYsNYpXKo/tjuzhLcXeR7bEso+fzY+rElSTL5/bbiIa1LQztRWVkJANBoNIiPjwcAaLValJeX4/3338err74Kf39/sx+3rKwM6enplhVtgtLSUsPP58+fh7u7u80eq77HtgdKPj9yPLabm5tJyykeUrVaDZ1OV2P67du36z1p1Lx5cwBAaGio0fTQ0FBUVlYiMzPTopC6urqiffv2Zq9nquqv1kFBQbK25dY++2prSj4/tn7sjIwMk5dVPKQajabGe0+9Xo/Lly/XeK9aXevWreHq6lpjelWL4uRk2TkxlUoFLy8vi9Y1dftVPD09ZT17aW8fZyj5/Nj6sc35t1D87G6/fv1w/Phx5OfnG6bt2bMHer0e/fv3r3M9Nzc39OnTB8eOHTOafuzYMbi4uNj0aEgkJ8VDGhERAW9vb0RGRuLQoUNIS0vDvHnzMHz4cKN2NzY2Fp06dTJad8aMGbhw4QL++c9/4vDhw1i3bh2SkpIwbtw4o490iOyZ4u2uWq3G+vXrERcXh+joaHh4eCA8PBwxMTFGy1VWVqKiosJo2iOPPILly5dj4cKFmDZtGpo3b46XXnoJr776qpy7QGRTVglpSUkJCgsL0aJFC7i4mL/Jdu3aYfXq1fUus2DBAixYsKDG9D59+qBPnz5mPyaRvWhUu3v8+HG8+OKL6N69O5588klcuHABAPDOO+9g9+7dVimQqKmzOKTHjh3D5MmTUVpaikmTJhk+twQAX19ffPnll1YpkKipszikiYmJ6NevH9LS0vDaa68ZzQsKCsL58+cbWxsRoREhTU9PR0REBICan/n4+fkhNze3cZUREYBGhNTZ2bnO723m5ubiL3/5i8VFEdEfLA5pSEgIvvrqq1rn7dq1C127drV000RUjcUfwUydOhWTJ0/GjBkzMGLECKhUKpw+fRqbN2/Grl27sH79emvWSdRkWRzS3r17Y8GCBZg/fz727dsHAHj33XehVqsRHx+PRx991GpFEjVljbqY4dlnn8WQIUNw8uRJ5OTkwNfXF927d7fpBepETU2jrzjy8PCAVqu1Ri1EZrH2jdVseVM1wPIbq1kc0s2bN+PatWuIjo6uMS8pKQmtW7fGiBEjLN08UYNseWM1a99UDbD8xmoWn939+OOPa73tCfD7FUcpKSmWbpqIqrH4SHrp0iU8/PDDtc7TaDS4dOmSxUURmSux6Arc0fh7ElVtwVpfjy+FCjO9GndjtUa9J719+3at0+/cuVPja2VEtuQOySohFZHF7W6HDh2wbdu2Wudt3bq1zqMsEZnH4pCOGzcOu3btwuuvv47Tp0/jt99+w+nTpzFnzhzs3r0bL730kjXrJGqyLG53hw8fjqysLKxYscLo8kAnJydMnz4dzzzzjFUKJGrqGvWe9NVXX8WoUaNw9OhR5OXlwc/PD3369MH9999vrfqImrxGX8zwwAMPYPTo0daohYhqofjdAomofmYdSTt27IjPPvsMjzzyCIKCguq9xEmlUuGnn35qdIFETZ1ZIZ0xYwbuuecew8/2dkd0IntkVkijoqIMP9d2zS7Zj9JyMV9gRa1LSRadOMrLy0Nqaiq+++473Lx5EwDQqlUr9OrVC6NHj4avr69ViyTrqD6U38z94o8BKuewhyIzO6THjh1DdHQ07ty5A2dnZ/j6+kKSJGRnZ+Po0aNYs2YNkpOT0bNnT1vUS9TkmBXSvLw8vPbaa/D29kZcXBz69+9v+OpNcXEx/vvf/+L999/HzJkzsX37dh5RBVP9HELigCtCDiJcWq4yHOV5zuN3ZoV006ZNqKysxMaNG3HvvfcazfP09MSwYcPQtWtXPPvss9i0aROmTJli1WLJetxdxB/pm35n1uekhw8fxqhRo2oEtLqAgACMHDkShw4danRxRGRmSLOystCjR48Gl3v00UeRlZVlcVFE9AezQqrT6Uwa99PPzw86nc7ioojoD2aFVK/Xw9XVtcHlXFxc6ry7PRGZx+yPYLKysuDs7NzgMkRkHWaH9I033mhwGUmSePqcyErMCml8fLyt6iCiOpgVUlvci5SI6sfvkxIJjiElEhxDSiQ4hpRIcAwpkeAYUiLBMaREgmNIiQTX6JtjE4mg1GqDFVqXNepiSMluGd1YrZFjgMrB0hursd0lEhyPpGS3jG6sZqWRvq2t+kjfln4zjCElh8CRvolIMQwpkeAYUiLBMaREgmNIiQTHkBIJToiQZmdnY/LkyejatSu0Wi3i4uJQUlJi1jb27NmDDh06IDw83EZVEilD8c9JdTodJkyYgICAACQmJiIvLw/x8fEoKChAQkKCSdsoKSlBfHw8WrZsaeNqieSneEhTU1Oh0+mQlpZmGMLC2dkZMTExmD59OjQaTYPbWL58OQICAvDAAw/gxx9/tHXJRLJSvN09ePAgtFqt0RgzQ4YMgZubGw4cONDg+pcvX8batWvx5ptv2rJMIsUoHtLMzMwaR0s3Nze0adMGmZmZDa7/73//G88++yyCgoJsVSKRohRvd3U6HdRqdY3parUahYWF9a67f/9+nDx5Ejt37rRaPZIkoaioyGrb+7PqJ8SKi4st/vpSYx/bHjT0/Njz/pgzFIviIa1LQztRWlqK+fPnIzo62qThGE1VVlaG9PR0q23vz0pLSw0/nz9/Hu7u7jZ7rPoe2x409PzY+/64ubmZtJ7iIVWr1bWOZXr79u16TxqtX78eTk5OePrppw3rl5WVobKyEjqdDh4eHiY/CdW5urqiffv2Zq9nquqv/kFBQfDw8LDZY9X32PagoefHnvcnIyPD5PUUD6lGo6nx3lOv1+Py5csYNWpUnetlZWXh0qVL0Gq1Neb17NkTb7/9NsaMGWN2PSqVCl5eXmavZ872q3h6esLT09Nmj1XfY9uDhp4fe94fc2pXPKT9+vXDRx99hPz8fPj6+gL4/cIEvV6P/v3717nelClTagwgtWLFCmRnZyM+Ph6BgYG2LJtINoqf3Y2IiIC3tzciIyNx6NAhpKWlYd68eRg+fLhRuxsbG4tOnToZftdoNOjVq5fRf/7+/vDy8kKvXr1wzz33KLE7RFan+JFUrVZj/fr1iIuLQ3R0NDw8PBAeHo6YmBij5SorK1FRUaFQlUTKUTykANCuXTusXr263mUWLFiABQsWNLgMkaNRvN0lovoxpESCE6LdFZUkSVb/LK769mzxOZ+Hh4fdfTRB9WNI61FSUoKhQ4fabPt//gjJGnbs2CHrZ69ke2x3iQTHI6mJ7nQdA8nJSk9X1UXjVmpLVZXlaHZqo1W2ReJhSE0kObkAzq5Kl1Erx7xvO1Vhu0skOIaUSHAMKZHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcBxFuokorrDPKOGDdgcutWZejYEibqJn7WitdApmI7S6R4HgkbUI8PDywY8cOq26zpKQEzz33HABgy5Yt8PDwsNq2rbkte8aQNiEqlQqenp42276Hh4dNt99Usd0lEhxDSiQ4hpRIcAwpkeAYUiLBMaREgmNIiQTHkBIJjiElEhxDSiQ4IS4LzM7ORlxcHL7//nt4enri6aefRkxMTL3Xbt65cwdr167FwYMHkZ2dDRcXFwQHB2P27NkIDg6WsXoi21I8pDqdDhMmTEBAQAASExORl5eH+Ph4FBQUICEhoc71rl27hs8++wyjRo3CzJkzUV5ejpSUFERERCA1NdX6Qa0os+72rEnk2qjRFA9pamoqdDod0tLS4OfnBwBwdnZGTEwMpk+fDo1GU+t6DzzwAPbs2WN0QXfv3r0xcOBAfPLJJ4iPj290bVLVt5kBeJ9ObfT25FC9ZnIMir8nPXjwILRarSGgADBkyBC4ubnhwIEDda7n5eVV4xsX7u7u0Gg0uHnzps3qJZKb4kfSzMxMjBo1ymiam5sb2rRpg8zMTLO2VVRUhPT0dDz77LNWqU1V7X4gt7tEAM6uVtmu1VWUGY70Kmvcw4SEonhIdTod1Gp1jelqtRqFhYVmbWvx4sUoLi7GSy+9ZHE9kiShqKgIwO9faDZwdhU3pNUUFxfL2vJWf46UfGx7UP35kSTJ5BdUxUNaF3N2AgC+/vprrF+/HnPnzkXbtm0tftyysjKkp6cDAEpLSy3ejlLOnz8Pd3d32R6v+nOk5GPbgz8/P25ubiatp3hI1Wo1dDpdjem3b9+u86TRnx05cgRvvPEGJk+ejHHjxjWqHldXV7Rv3x6A/b1SA0BQUJCstx2p/hwp+dj2oPrzk5GRYfJ6iodUo9HUeO+p1+tx+fLlGu9Va3PmzBlERUUhLCwM//jHPxpdj0qlgpeXl+Fne+Pp6SnrLUyqP0dKPrY9qP78mFO74md3+/Xrh+PHjyM/P98wbc+ePdDr9ejfv3+962ZmZmLKlCno3r074uPj7e4fjcgUioc0IiIC3t7eiIyMxKFDh5CWloZ58+Zh+PDhRu1ubGwsOnXqZPg9NzcXkydPhqurK1555RWcO3cOp06dwqlTp/DTTz8psStENqF4u6tWq7F+/XrExcUhOjoaHh4eCA8PR0xMjNFylZWVqKioMPyekZGB69evAwAmTpxotOz999+P/fv327x2IjkoHlIAaNeuHVavXl3vMgsWLMCCBQsMv/fq1QsXLlywdWlEilO83SWi+jGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHghBhEmKixSqGyynak//u/dbZmnboYUnIIM71aK12CzbDdJRIcj6Rktzw8PLBjxw6rba+kpATPPfccAGDLli3w8PCw2rYBWLw9hpTslkqlgqenp0227eHhYbNtm4vtLpHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHgGFIiwTGkRIJjSIkEx5ASCY4hJRIcQ0okOIaUSHAMKZHghAhpdnY2Jk+ejK5du0Kr1SIuLg4lJSUmrbtlyxaEhYUhJCQE4eHhVr2dBpEIFL99ik6nw4QJExAQEIDExETk5eUhPj4eBQUFSEhIqHfdnTt3Ys6cOZg6dSr69OmDvXv3YtasWfD29kbfvn1l2gMi21I8pKmpqdDpdEhLS4Ofnx8AwNnZGTExMZg+fTo0Gk2d6y5ZsgRhYWH4+9//DgAIDQ1FdnY2EhMTGVJyGIq3uwcPHoRWqzUEFACGDBkCNzc3HDhwoM71rly5gqysLISHhxtNDw8Px5kzZ5CXl2ezmonkpPiRNDMzE6NGjTKa5ubmhjZt2iAzM7PO9bKysgAADz74oNF0jUYDSZKQlZVlFPzGUlWWG+5uXidJAirLrfaYAAAnF0BV/13QVdZ+TACSJJl0XqD6MqYs7+HhAVUD+2MLpuyPufsCyLM/iodUp9NBrVbXmK5Wq1FYWFjnelXz/ryuj4+P0XxzSZKEoqIiAMb/UM1ObbRoe3IrLi6GJDX4clIvSZIQExOD9PR0s9arumdtfTp16oQPPvhA1qBasj+m7Atg+f5IkmTyOoqHtC6m7sSfl6n6A7X0j6CsrMzwj1laWmrRNpR0/vx5uLu7N2obkiShuLjYShUZKyoqQnp6uuwhFXF/3NzcTFpO8ZCq1WrodLoa02/fvl3vSaPqR8yWLVsapldtq7ajsylcXV3Rvn17AL//43755ZcmrytJktWD7e7ubtYfgLnL12Xp0qUm74s5L4zWqs9cpu6PuS/ylu5PRkaGycsqHlKNRlPjvader8fly5drvFetruq9aFZWllGYMzMzoVKparxXNZVKpYKXl5fh97/85S8WbccRONq+i7Q/5gRb8bO7/fr1w/Hjx5Gfn2+YtmfPHuj1evTv37/O9Vq3bo0HH3wQ27dvN5q+detWPPLII1Y9aUSkJMVDGhERAW9vb0RGRuLQoUNIS0vDvHnzMHz4cKMjZGxsLDp16mS07syZM7Fjxw4sWrQIJ06cwPz583HkyBHMnDlT7t0gshnF2121Wo3169cjLi4O0dHR8PDwQHh4OGJiYoyWq6ysREVFhdG0oUOHoqSkBMuWLcPq1avRtm1bLFq0iBcykENRSY09X+9Azp49CwAICQlRuBJydOb8rSne7hJR/RhSIsExpESCY0iJBMeQEglO8Y9gRFJWVgZJkgxn3ohsRa/X2/8F9kpQ4ppSappUKpXJf2/8nJRIcHxPSiQ4hpRIcAwpkeAYUiLBMaREgmNIiQTHkBIJjiElEhxDSiQ4hpRIcAwpkeAYUiLBMaREgmNIyWKFhYX47rvv8PXXXxsGyCotLUVlZaXClTkWfp9UYd9++y2SkpKQkpKidCkmq6ysxOLFi/Hxxx+juLgYKpUKmzZtgo+PD6KiotClSxdERUUpXWad1q5di+HDh6Nly5ZYu3ZtvcuqVCpMnDhRnsLqwJAqLC8vD99++63SZZhlyZIl+OSTT/CPf/wDvXr1wtNPP22YN2DAAHzxxRdCh/S9995Djx490LJlS7z33nv1LsuQOrDdu3ebtNypU6dsW4gNbNmyBbNnz8bYsWNrjCrQpk0bXLlyRaHKTHP+/PlafxYVQ2ojM2fOhEqlMmlAX3u7bUtBQUGdw1JWVlaivNz6I4/byrVr1+Dv7w9XV9ca88rLy3Hz5k0EBAQoUNkfGFIb8fX1xRNPPNFg23fgwAHMmzdPpqqsIzAwEEeOHIFWq60x78SJE3jooYcUqMoyAwcOxGeffYZHHnmkxrzz58/jhRdeMHvEc2tjSG2kS5cuyM7Oxv3331/vci1atJCpIuuZOHEi/vWvf8HFxQVhYWEAgBs3buDUqVP4+OOPER8fr3CFpquv09Hr9SaPxm1LDKmNPP744/jqq68aXO7+++/HiBEjbF+QFY0cORKFhYVISkrC8uXLAQAzZsyAp6cnXnvtNQwbNkzhCuuXmZlpNHD1iRMncOPGDaNlSktLsW3bNrRu3Vru8mrg3QLJYnfv3sXJkyeRn58PHx8fdO/eHc2aNVO6rAYlJycjOTkZAOo9b6BWqzF//nwMGjRIzvJqYEipybl9+zZ0Oh0kScKgQYOQnJyMjh07Gi3j6uoKf39/IU7qMaQy0ev12L59O7777jsUFhbCx8cHjz76KIYNGybE+56GmPqRUpWnnnrKRpVY19WrV+Hv7y/0vwFDKoPc3FxMnDgRFy9eREBAAPz9/XHr1i1cu3YNDz30ENatWyf8CaSgoCCTl1WpVIqfEa1PQUGBWcs3b97cJnWYiiGVwT//+U8cPXoUycnJ6Nq1q2H66dOnER0dDa1W2+CVL0q7evWqWcs3dFZbSUFBQWa1sUq/4PDsrgwOHDiAN954wyigwO8f08yePdsuPrIQOXTmmj9/vhDvNU3FkMqgpKQEvr6+tc5r3rw5SkpKZK6oaRs5cqTSJZiFIZVBcHAwUlJS0LdvXzg7OxumV1RUICUlBcHBwQpWZ7762kWVSgVvb28EBQVhwoQJGDBggMzVOR6+J5XBd999h5dffhktW7bEwIED4e/vj5ycHOzbtw85OTlYs2YNHn30UaXLNNmKFSvw6aefwtXVFU888QRatGiBnJwc/Pe//0VFRQWeeeYZfPvttzh16hQSEhKMviUjmvHjxze4jNJfI+SRVAaPPvooNm7ciGXLlmHbtm3Q6XTw8fFBjx49MG3aNLs7khYWFiI4OBhJSUlwcvrjvgGxsbGIiopCWVkZNm7ciFmzZmHlypVCh9TT07NGV1BQUIALFy5ArVbX+PxUERKRmbRarXTgwIFa5x04cEAKDQ2VJEmS9u7dK4WEhMhZmtXk5ORIL7zwgrR9+3alS5F4+xQyW0lJCa5fv17rvGvXrqG0tBQA4OXlVetXwOxBixYtMGXKFCxevFjpUtjuyqG+9z1OTk7w9vZGx44dMWrUKNxzzz0yVmaZAQMGYOHChfDy8sKTTz6JZs2a4c6dO9i3bx8WLlxouNb1woULaNu2rcLVWq6yshK3bt1SugyeOJLDtGnT8NNPPyEnJwcPPfQQWrRogdzcXFy8eBH+/v5o3bo1zp8/D0mS7OJs7507dzBnzhzs3bsXKpUKLi4uKC8vhyRJGDx4MOLj49GsWTPs3r0bXl5e6Nu3r9Il1+ncuXM1ppWVlSEzMxNLly5FmzZtsG7dOvkLq4YhlUFaWhpWrVqFjz76yOirT5cvX0ZkZCReeeUVPPHEE5g0aRJ8fX2xevVqBas1XWZmJs6cOYNbt26hVatWCAkJqfOODaKq7eOkqkh06dIFCQkJin9djSGVwVNPPYWYmJhaLzrfuXMnFi5ciD179mDr1q1466238P333ytQZdP0zTff1Jjm7u6Oe++9V5i3HnxPKoMbN27U+eG/k5MTfvvtNwBAq1atatzYS1QVFRU4ffo0bty4Ab1eX2O+vXyR/bHHHlO6hAYxpDIICQlBYmIigoODjW5qdfXqVSQmJhrur3P16lVhXr3rc+7cOURHR+P69eu1fmFapVLZTUir3LlzBzdu3DCcma5O6XMEbHdlkJGRgZdffhl5eXno0KEDfH19kZ+fjwsXLqBFixZYu3YtNBoNVq5cCWdnZ0yaNEnpkuv1wgsvoKSkBLGxsdBoNLV+zFLXtcqi+e233xAbG4ujR4/WmCdJkhBfu2NIZVJaWopNmzbhxx9/xK1bt+Dv74+QkBCMGjUK7u7uSpdnlm7dumHx4sXo37+/0qU02oQJE3Dp0iVMmTIFgYGBtb7gKN0Ss92Vibu7O8aNG6d0GVYRGBiIu3fvKl2GVZw5cwYffPCB4vcxqg+vOCKzvfHGG1i+fLnRHffsVdu2bYW/mTfbXRvp1q2byV8sVqlUdvWxy/Dhw3Hr1i3odDq0atUK3t7eRvNVKpVJtzMVwbFjx7BgwQIsXrwY7dq1U7qcWrHdtZFJkybZ1bf/zREcHOww+6bVahEaGoqnn35a2BccHkmpSXvvvfewdu1aBAcHIzAwsNa7Bip9exuGlJq0nj174uWXX0ZkZKTSpdSJ7a5MdDoddu3ahezs7Fqv0HnzzTcVqMp0cXFxmDRpEgICAhAXF9fg8qLvTxUXFxd06dJF6TLqxSOpDH755RdERERAr9ejuLgYfn5+KCwsRHl5OXx8fNCsWTPs27dP6TLrNWDAAHz44YcICgpq8L5FKpVK+P2psnDhQuTk5Cje0taHIZXBtGnTIEkSlixZgq5du2Lz5s0ICgrC9u3bsWjRIiQmJqJz585Kl9kkrVy5Ep9++inuueceaLVaqNVqo/kijPTNkMqgd+/e+Pe//43+/fujU6dOSE1NNdyD9+OPP8a2bduQmpqqbJFNVEN35hfhskC+J5WBXq9Hs2bN4OTkBB8fH9y8edMwr3379nYxJHx1P/74I27fvm0YRLiwsBAffPABMjMz0bt3b8yYMcPoBmUis4fn3j6eSTsXGBhoGKahU6dO+PTTT3Hnzh2UlJTgs88+Q6tWrRSu0Dzx8fFGF1/Mnz8fO3bsgL+/P9asWYOPPvpIwepsp7KyEuPHj8cvv/wi6+MypDJ4+umnDa/Yr776Ks6ePYvHHnsMPXr0wO7duzFjxgyFKzRPRkYGQkJCAPx+U7Jdu3YhNjYWiYmJiImJUfzDf1uRJAnffPON7Ncts92Vwcsvv2z4uWvXrti6dSsOHTqEkpIShIaG4uGHH1awOvOVlJTA09MTAPDDDz9Ar9dj4MCBAIAOHTrUGDWbGodHUgXcd999GD16NMaPH18joEq1VOZo3bo1Dh48CAD4+uuvERwcbBgeMDc31y5G+7YnDKlglGqpzDFx4kSsWrUKoaGhSEtLM7pl6TfffIMOHTooWJ3jYbtLZnv++efRtm1bnD17Fp06dUJoaKhhXvPmzU0aX4VMx5CSRXr27ImePXvWmB4dHW30e2VlJSZOnIh3330XgYGBMlXnWNjukk3ZQ/suOh5JiUzk7OyMffv2yf65NkNKTd7hw4exa9euWm/pqVKpsH79esPv999/v9zlMaTUtK1atQoJCQm477778OCDD8LLy0vpkmpgSAWjVEvVVH366acYO3Ys/vWvfwl7SxiGVCait1RNVUFBAQYPHixsQAGe3ZXFqlWr8Morr+Dw4cOorKyEl5eX0X9Vl9iR/J588knh79TI75PKYMCAAXjiiSeEbqls6erVq2jVqpWQo34fOXIE77zzDoYMGYLevXvX+NI3wLFgmoTu3btj6dKlhu9fOgJz2neR/flL39VfREUZC4bvSWVQ1VI5Skjt4YyoqVJSUpQuoUE8ksrAHloqczT19l1uDKkM7KGlMocjtu/ffvstvvvuOxQWFsLHxwc9e/bEo48+qnRZANjuysIeWipzOFL7XlRUhKioKBw9ehQuLi5o3rw5CgoKUFFRgd69eyM5OVnxs+8MqQyUHt/S2kaOHIl33nkHpaWldt++JyQk4PTp01i4cCHCwsLg7OyMiooK7Nq1C3PnzsXChQsVv9E3210ZidxSmcOR2ve+ffsiMjISY8eOrTFvw4YN+Oijj3D48GEFKvsDj6QysIeWyhyO1L7rdDq0bt261nlt2rSBTqeTuaKaGFIZ2ENLZQ5Hat81Gg3S0tLw+OOP15iXlpaG9u3bK1CVMba7MrCHlsoSjtC+7927F9HR0ejSpQvCwsLQsmVL5OTkYOfOnThz5gwSExMxaNAgRWvkkVQG9tBSmcOR2vdBgwYhOTkZS5cuxXvvvWd4T92xY0ckJyc3ODiVLCSyuREjRkizZ8+udd7s2bOl5557TuaKGuedd96RunfvLm3dulUqLy+XJEmSysvLpW3btkk9evSQ5s2bp3CFlrl7965048YN6e7du0qXYoTtrgzsoaUyh6O276JiuysDu2ipzGDv7bu9DYjMkMpk4MCBGDhwIIqKinD79m14e3vb7YXp9nBGtD779+/H888/j4CAAOzfv7/eZVUqleIhZbtLZnO09l10DKmN2FtLZa59+/Zh6dKlSE9PN2rfo6Ki7K59Fx1DaiMDBgzAhx9+iKCgoAb/aFUqFfbt2ydTZdZl7+27PQyIzJBSkzZu3DhotVpERUUBAF5//XXs3bsXffr0waFDh/DKK68oPn4sTxyRSRy1fc/IyMDUqVMB/DEg8r/+9S+MGjUKGzZsQEpKCkPaFNhDS9UQezsjaip7GBCZIZVBfHw8tFqtIaTz5883tFRr1qyBk5OT4q/WDakezIZCak+qBkR+7LHHhB0QWeyXbweRkZGBkJAQAH+0VLGxsUhMTERMTAy++uorhStsuuxhQGQeSWVgDy2VORyhfa9iDwMiM6QysIeWyhyO0L5XZ+qAyEqxj5c7O2cPLZU5HK19Lysrw8aNGxEbG4tJkybhl19+AQBs374dmZmZyhYHHkllYQ8tlTkcqX2/cuUKJk6ciLy8PAQFBeHUqVOGUcm//fZbHDp0CPHx8YrWyJDKRPSWyhyO1L7HxcXBz88PX3zxBdRqNTp37myY17NnT/znP/9RsLrfsd2ViegtlTkcqX3/5ptvMH36dPj5+dW4G7+/vz9u3bqlUGV/4JFUBvbQUpnDkdp3Z2dn1HVlbE5OjhDXIzOkMrCHlspcjtK+9+zZE2vXrkW/fv0MHxupVCpIkoTPP/9ciLv0M6Qy+Oabb7Bw4UL4+fmhoqLCaJ4oLZW5ysrKsGnTJpw9exY3btzA3LlzERgYiO3bt6NDhw7QaDRKl2iSmJgYjBkzBsOGDcPAgQOhUqmwYcMGXLx4EZcuXcIXX3yhdIl8TyoHe2ipzHHlyhWEhYXh/fffR3Z2No4dO2bUvq9atUrhCk2n0WiwefNmdO/eHVu3boWzszP+97//oU2bNvjiiy/Qpk0bpUvkkVQO9tBSmcPR2vfWrVvjvffeU7qMOjGkMrCHlsocjti+i4whlUFVS5WcnGzUUmm1WiQkJAjRUpnD3tv3adOmmbysSqXCRx99ZMNqGsaQykT0lsoc9t6+V71/the8fQqZLTMzE2PGjIGPjw8GDhyI9evXY+TIkUbtu711ByJjSG3E3loqc125cgXJyck4cuQICgoK4OPjA61Wi5kzZzpsQCsrKzF48GAsW7YMDz30kGyPy3bXRuytpTKXI7XvppIkCVevXoVer5f1cRlSG/n444+VLoEcBEMqGKVaqoY4evsuMoZUMEq1VA1x9PZdZAwpmYTtu3J47S7ZVGVlJQYOHIiLFy8qXYpV/Pk7p3JgSMmmRG3fLaXEJ5Zsd4n+z/Xr13H9+nUEBQXVemmjs7Mzzp8/L3tdPJIKSImWqin77LPP8Pjjj2PAgAEYN24csrOzAQAzZszA+vXrFa6OIRUSLwKTz7p16zBv3jw888wzWLFihdFz/9hjj2HHjh0KVvc7trsyE7Wlaqo++eQTREZGIjIyssbX7tq1a2c4qiqJR1KZiN5S2ZLI7ftvv/2Gbt261TrP1dUVxcXFMldUE0MqA3toqWxJ5PY9ICAAZ8+erXXe6dOnERgYKG9BtWC7KwN7aKksZe/t++jRo5GcnAxfX1889dRTAIDy8nL873//w+rVq/Haa68pWyAYUlnYQ0tlrs8++wzJycnIyckBAGzatAnBwcGYMWMGHnvsMUyYMEHhCk0zefJkXL9+HXPnzsVbb70FABgzZgwAYOzYsRg3bpyS5QFgSGVR1VLVdscCUVoqc6xbtw4JCQmYMGECQkNDMWXKFMO8qvbdXkIKAG+++SYmTJiAo0ePIj8/3/DdWFH+XRhSGdhDS2UOR2zfW7dujRdffFHpMmrFkMrAHloqczhS+759+3Zcu3YNr7zySo15q1evRkBAAIYOHapAZX9gSGUiektlDkdq31esWIGRI0fWOs/DwwMrV65kSJsSkVsqczhS+/7LL7/U+eV6jUYjROvOkMrAHloqczhS++7u7o7c3Nxa5926dQsuLspHRPkKmgB7aKnM5Sjte8+ePbFixQoMGDDA6HPeoqIirFq1Co899piC1f2OIZWBPbRUlnCE9n3WrFmIiIjA4MGDMWTIELRq1Qo3b97Erl27UFZWJsS4NgypDOyhpTKHI7XvGo0GmzZtQmJiInbv3o2CggI0b94cvXv3RlRUFNq2bat0iQypHOyhpTKHo7Xvbdu2xcKFC5Uuo04MqQzsoaUyh6O276JiSGVgDy2VOey9fZ82bRrmzJmDwMDABu8nLMI9hMV+Nh2I6C2VOey9fb97967hckZ7uJ8wB2wis2VmZiIiIgJubm61tu8bN26ERqNRukyHwZDaiL21VOa6dOkSEhMTceLECUP7rtVq7aJ9T0tLM2v5ESNG2KQOU7HdtRF7a6nMZc/t+5w5c4x+r7q9S/XjVfVbvigdUh5JqcnJz883/Hz58mXMmjULw4cPx5AhQ9CiRQvk5uZi586d2Lp1KxYtWoQuXbooWC1DajP21lI1xFHb95dffhmhoaH429/+VmPesmXLcOzYMcVvFMd210bsraVqiKO27ydPnqz1yikA6Ny5M5YtWyZzRTUxpDZy7Ngxw8+mtFSiqz6qmiONsObn54ft27ejT58+NeZt27YNfn5+ClRljO2uDOyhpWqIo7XvVT7//HPMnTsXPXv2xKBBgwwvoHv37sW3336Ld999F6NHj1a0RoZUBl27dsXSpUtrfbU+fPgwoqKicOrUKfkLM0NQUJDR7w217+np6fIUZgX//e9/sWzZMpw7dw7l5eVwcXFBp06dMG3aNAwYMEDp8tjuysEeWqqGOFr7Xt2TTz6JJ598EpWVlcjLy4Ofnx+cnMS5bzyPpDKwh5bKHI7QvtsTHkllMHr0aPj7+2PZsmX44IMPjFqqDz/8UIiWyhz2cEbUkTCkMhG9pTKHI7Tv9oQhlZmTkxNatmypdBmNMm3aNMydOxeXL1+us30n6+F7UrKI6GdEHQlDSo3iCO276BhSIsHxpY9IcAwpkeAYUiLBMaREguPnpE3M+fPnkZKSgm+++QY3b94EANx7770IDQ3FCy+8gJCQEIUrpD/j2d0mJDU1FfPmzUO7du0wevRoww2us7KysHXrVvzwww/Ys2cP2rRpo3ClVB1D2kR8//33eOmll9C/f38kJibCzc2txjI7duxA9+7dcc8999S6jeLiYnh6etq6VPoTvidtIpYvXw5nZ2e8++67tQYUAIYOHWoI6Jw5c9CtWzdcuHABkyZNQrdu3TBx4kQAQEFBAd5++208/vjj6Ny5MwYOHIhFixZBr9cbtvXrr7+iQ4cO+PLLL2s8TocOHZCUlGT4PSkpCR06dMBPP/2EqKgodO/eHT169EBMTAzy8vKs+CzYJ74nbQIqKipw4sQJdO7cGa1atTJ5vbKyMkyfPh0RERGYMmUKKioqUFpaivHjx+PKlSuIjo5Ghw4d8N1332HFihVIT0/HihUrLK4zKioKYWFhiIiIQEZGBpYsWYLMzEx8/vnncHV1tXi79o4hbQLy8/NRUlKCgICAGvMqKiqM7q7g7OxsuMNCWVkZZsyYgVGjRhnmp6am4sKFC1i8eLFh5LQ+ffrAy8sLCQkJOHLkSK3fjjHF4MGD8c9//hMA0LdvX7Ro0QIxMTHYsWMHnnnmGYu26QjY7jZxI0eORHBwsOG/NWvWGM0fMmSI0e/Hjx+Hl5cXwsLCamwHML6Dg7mGDx9u9PvQoUPh4uKCEydOWLxNR8AjaRPg6+sLDw8PXLt2rca8hQsXori4GLdu3cL06dON5nl6eqJZs2ZG0woKCtCyZUuj+xkBQIsWLeDi4oKCggKL6/T39zf63cXFBc2bN2/UNh0Bj6RNgLOzM0JDQ/Hjjz8aPhut0r59e4SEhODhhx+usd6fgwgAzZs3R05ODv78oUBubi7Ky8vh6+sL4PfhEQEYnUwCjO8e/2e3bt0y+r28vNwwzkxTxpA2EVOnTkVFRQXeeustlJWVWbwdrVaLoqIi7N2712h61S0/tVotAKBly5Zwd3fHhQsXjJbbt29fndv++uuvjX7fsWMHysvLhR9K0dbY7jYRPXr0wNy5cxEXF4eRI0di9OjRaN++PZycnHDr1i3s3r0bAGq0t382YsQIbNiwAa+//jquXr2Khx9+GN9//z2WL1+O/v37o3fv3gB+Pwo/88wz2Lx5M9q0aYOgoCCcOXMGW7durXPbe/bsgbOzM/r06YOLFy9iyZIlCAoKMpygaqoY0iZkzJgx6NatG9avX49169bh5s2bUKlUuPfee9GtWzesW7fOcCSsi7u7O1JSUrBo0SKsWrUK+fn5uOeeezBp0iRERUUZLVs11MaqVatQVFSEXr16YdmyZXXeuSEpKQlJSUnYuHEjVCoVBgwYgNjY2Do/120qeMURKS4pKQnJyck4duwYb2JWC74nJRIcQ0okOLa7RILjkZRIcAwpkeAYUiLBMaREgmNIiQTHkBIJjiElEhxDSiQ4hpRIcP8f3PwvRtDDmMEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = data_aniso\n",
+    "df_lesion_algo_1 = data.filter(['dice_lesion_algo1'], axis=1)\n",
+    "df_lesion_algo_1.insert(0, 'Group', 'dice_lesion_algo1')\n",
+    "df_lesion_algo_1.rename(columns={'dice_lesion_algo1': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_mono = data.filter(['dice_lesion_single'], axis=1)\n",
+    "df_lesion_mono.insert(0, 'Group', 'dice_lesion_single')\n",
+    "df_lesion_mono.rename(columns={'dice_lesion_single': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_multi = data.filter(['dice_lesion_multi'], axis=1)\n",
+    "df_lesion_multi.insert(0, 'Group', 'dice_lesion_multi')\n",
+    "df_lesion_multi.rename(columns={'dice_lesion_multi': 'Dice'}, inplace=True)\n",
+    "\n",
+    "frames = [ df_lesion_algo_1, df_lesion_mono ,df_lesion_multi]\n",
+    "result_gm = pd.concat(frames, axis=0)\n",
+    "\n",
+    "plt.figure(figsize=(2,5))\n",
+    "plt.xticks(rotation=90)\n",
+    "colores = [\"#1f77b4\", \"#FF8C00\", \"#FF1C00\"]\n",
+    "ax_gm = sns.boxplot(data=result_gm, x=\"Group\", palette=colores, y=\"Dice\")\n",
+    "print (\"dice algo 1 mean:\", np.mean(data[\"dice_lesion_algo1\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_algo1\"])) \n",
+    "print (\"dice algo 2 single-class mean:\", np.mean(data[\"dice_lesion_single\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_single\"])) \n",
+    "print (\"dice algo 2 multi-class mean:\", np.mean(data[\"dice_lesion_multi\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_multi\"])) \n",
+    "\n",
+    "\n",
+    "pairs=[\n",
+    "           ((\"dice_lesion_algo1\"), (\"dice_lesion_single\")),\n",
+    "           ((\"dice_lesion_algo1\"), (\"dice_lesion_multi\")),\n",
+    "           ((\"dice_lesion_single\"), (\"dice_lesion_multi\")) ]\n",
+    "annotator_csa_gm = Annotator(ax_gm, pairs, data=result_gm, x=\"Group\", y=\"Dice\")#, order=order)\n",
+    "annotator_csa_gm.configure(test='Wilcoxon', text_format='star', loc='inside').apply_test()\n",
+    "annotator_csa_gm.apply_and_annotate()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d94b1f3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>dice_lesion_algo1</th>\n",
+       "      <th>dice_lesion_single</th>\n",
+       "      <th>dice_lesion_multi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS01_ses-M0</td>\n",
+       "      <td>0.576490</td>\n",
+       "      <td>0.787402</td>\n",
+       "      <td>0.606452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS04_ses-M0</td>\n",
+       "      <td>0.245312</td>\n",
+       "      <td>0.637191</td>\n",
+       "      <td>0.485465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS05_ses-M0</td>\n",
+       "      <td>0.157846</td>\n",
+       "      <td>0.237895</td>\n",
+       "      <td>0.166852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS05_ses-M24</td>\n",
+       "      <td>0.167464</td>\n",
+       "      <td>0.229508</td>\n",
+       "      <td>0.108999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS06_ses-M0</td>\n",
+       "      <td>0.138391</td>\n",
+       "      <td>0.356432</td>\n",
+       "      <td>0.340871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sub-MRS08_ses-M0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.661111</td>\n",
+       "      <td>0.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sub-MRS09_ses-M0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.590476</td>\n",
+       "      <td>0.351145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sub-MRS10_ses-M0</td>\n",
+       "      <td>0.398210</td>\n",
+       "      <td>0.653846</td>\n",
+       "      <td>0.462222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sub-MRS11_ses-M0</td>\n",
+       "      <td>0.219887</td>\n",
+       "      <td>0.626999</td>\n",
+       "      <td>0.535012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sub-MRS12_ses-M0</td>\n",
+       "      <td>0.383358</td>\n",
+       "      <td>0.497479</td>\n",
+       "      <td>0.142857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>sub-MRS14_ses-M0</td>\n",
+       "      <td>0.473431</td>\n",
+       "      <td>0.467446</td>\n",
+       "      <td>0.161074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>sub-MRS14_ses-M24</td>\n",
+       "      <td>0.339936</td>\n",
+       "      <td>0.513694</td>\n",
+       "      <td>0.142412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sub-MRS16_ses-M0</td>\n",
+       "      <td>0.388595</td>\n",
+       "      <td>0.566667</td>\n",
+       "      <td>0.390107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS00_ses-M0_run-1</td>\n",
+       "      <td>0.508227</td>\n",
+       "      <td>0.601405</td>\n",
+       "      <td>0.454433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS00_ses-M24_run-1</td>\n",
+       "      <td>0.367181</td>\n",
+       "      <td>0.483068</td>\n",
+       "      <td>0.491018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS00_ses-M24_run-2</td>\n",
+       "      <td>0.405126</td>\n",
+       "      <td>0.567731</td>\n",
+       "      <td>0.686071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS01_ses-M0_run-1</td>\n",
+       "      <td>0.586035</td>\n",
+       "      <td>0.779359</td>\n",
+       "      <td>0.777778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS01_ses-M0_run-2</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.768696</td>\n",
+       "      <td>0.739040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sub-MRS03_ses-M0_run-1</td>\n",
+       "      <td>0.349933</td>\n",
+       "      <td>0.660300</td>\n",
+       "      <td>0.648622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sub-MRS03_ses-M0_run-2</td>\n",
+       "      <td>0.206578</td>\n",
+       "      <td>0.573577</td>\n",
+       "      <td>0.573010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sub-MRS04_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.623501</td>\n",
+       "      <td>0.501370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sub-MRS04_ses-M0_run-2</td>\n",
+       "      <td>0.140228</td>\n",
+       "      <td>0.620370</td>\n",
+       "      <td>0.445040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sub-MRS04_ses-M24_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.480000</td>\n",
+       "      <td>0.460568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>sub-MRS05_ses-M0_run-1</td>\n",
+       "      <td>0.185185</td>\n",
+       "      <td>0.123974</td>\n",
+       "      <td>0.069680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>sub-MRS05_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.215078</td>\n",
+       "      <td>0.147094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sub-MRS05_ses-M24_run-1</td>\n",
+       "      <td>0.135747</td>\n",
+       "      <td>0.110384</td>\n",
+       "      <td>0.070949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>sub-MRS05_ses-M24_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.095618</td>\n",
+       "      <td>0.077994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>sub-MRS06_ses-M0_run-1</td>\n",
+       "      <td>0.195396</td>\n",
+       "      <td>0.730769</td>\n",
+       "      <td>0.587764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>sub-MRS06_ses-M0_run-2</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>0.775972</td>\n",
+       "      <td>0.588774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>sub-MRS08_ses-M0_run-1</td>\n",
+       "      <td>0.024973</td>\n",
+       "      <td>0.256637</td>\n",
+       "      <td>0.057143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>sub-MRS08_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>sub-MRS09_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.048193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>sub-MRS09_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.009434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>sub-MRS10_ses-M0_run-1</td>\n",
+       "      <td>0.253927</td>\n",
+       "      <td>0.477064</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>sub-MRS10_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>sub-MRS11_ses-M0_run-1</td>\n",
+       "      <td>0.259048</td>\n",
+       "      <td>0.368466</td>\n",
+       "      <td>0.356855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>sub-MRS11_ses-M0_run-2</td>\n",
+       "      <td>0.209246</td>\n",
+       "      <td>0.547347</td>\n",
+       "      <td>0.480583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>sub-MRS11_ses-M24_run-1</td>\n",
+       "      <td>0.531014</td>\n",
+       "      <td>0.678940</td>\n",
+       "      <td>0.553633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>sub-MRS11_ses-M24_run-2</td>\n",
+       "      <td>0.382470</td>\n",
+       "      <td>0.592506</td>\n",
+       "      <td>0.544737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>sub-MRS12_ses-M0_run-1</td>\n",
+       "      <td>0.158332</td>\n",
+       "      <td>0.714384</td>\n",
+       "      <td>0.641363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>sub-MRS12_ses-M0_run-2</td>\n",
+       "      <td>0.261854</td>\n",
+       "      <td>0.608917</td>\n",
+       "      <td>0.485714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>sub-MRS13_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.533873</td>\n",
+       "      <td>0.476366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>sub-MRS14_ses-M0_run-1</td>\n",
+       "      <td>0.336891</td>\n",
+       "      <td>0.277819</td>\n",
+       "      <td>0.172598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>sub-MRS14_ses-M0_run-2</td>\n",
+       "      <td>0.277176</td>\n",
+       "      <td>0.531339</td>\n",
+       "      <td>0.222417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>sub-MRS14_ses-M24_run-1</td>\n",
+       "      <td>0.532560</td>\n",
+       "      <td>0.459082</td>\n",
+       "      <td>0.240808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>sub-MRS14_ses-M24_run-2</td>\n",
+       "      <td>0.335290</td>\n",
+       "      <td>0.528416</td>\n",
+       "      <td>0.267511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>sub-MRS16_ses-M0_run-1</td>\n",
+       "      <td>0.307692</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.516908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>sub-MRS16_ses-M0_run-2</td>\n",
+       "      <td>0.433031</td>\n",
+       "      <td>0.484490</td>\n",
+       "      <td>0.438538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>sub-MRS18_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.505495</td>\n",
+       "      <td>0.096386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>sub-MRS18_ses-M24_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    subject  dice_lesion_algo1  dice_lesion_single  \\\n",
+       "0          sub-MRS01_ses-M0           0.576490            0.787402   \n",
+       "1          sub-MRS04_ses-M0           0.245312            0.637191   \n",
+       "2          sub-MRS05_ses-M0           0.157846            0.237895   \n",
+       "3         sub-MRS05_ses-M24           0.167464            0.229508   \n",
+       "4          sub-MRS06_ses-M0           0.138391            0.356432   \n",
+       "5          sub-MRS08_ses-M0           0.000000            0.661111   \n",
+       "6          sub-MRS09_ses-M0           0.000000            0.590476   \n",
+       "7          sub-MRS10_ses-M0           0.398210            0.653846   \n",
+       "8          sub-MRS11_ses-M0           0.219887            0.626999   \n",
+       "9          sub-MRS12_ses-M0           0.383358            0.497479   \n",
+       "10         sub-MRS14_ses-M0           0.473431            0.467446   \n",
+       "11        sub-MRS14_ses-M24           0.339936            0.513694   \n",
+       "12         sub-MRS16_ses-M0           0.388595            0.566667   \n",
+       "0    sub-MRS00_ses-M0_run-1           0.508227            0.601405   \n",
+       "1   sub-MRS00_ses-M24_run-1           0.367181            0.483068   \n",
+       "2   sub-MRS00_ses-M24_run-2           0.405126            0.567731   \n",
+       "3    sub-MRS01_ses-M0_run-1           0.586035            0.779359   \n",
+       "4    sub-MRS01_ses-M0_run-2           0.600000            0.768696   \n",
+       "5    sub-MRS03_ses-M0_run-1           0.349933            0.660300   \n",
+       "6    sub-MRS03_ses-M0_run-2           0.206578            0.573577   \n",
+       "7    sub-MRS04_ses-M0_run-1           0.000000            0.623501   \n",
+       "8    sub-MRS04_ses-M0_run-2           0.140228            0.620370   \n",
+       "9   sub-MRS04_ses-M24_run-1           0.000000            0.480000   \n",
+       "10   sub-MRS05_ses-M0_run-1           0.185185            0.123974   \n",
+       "11   sub-MRS05_ses-M0_run-2           0.000000            0.215078   \n",
+       "12  sub-MRS05_ses-M24_run-1           0.135747            0.110384   \n",
+       "13  sub-MRS05_ses-M24_run-2           0.000000            0.095618   \n",
+       "14   sub-MRS06_ses-M0_run-1           0.195396            0.730769   \n",
+       "15   sub-MRS06_ses-M0_run-2           0.692308            0.775972   \n",
+       "16   sub-MRS08_ses-M0_run-1           0.024973            0.256637   \n",
+       "17   sub-MRS08_ses-M0_run-2           0.000000            0.000000   \n",
+       "18   sub-MRS09_ses-M0_run-1           0.000000            0.000000   \n",
+       "19   sub-MRS09_ses-M0_run-2           0.000000            0.000000   \n",
+       "20   sub-MRS10_ses-M0_run-1           0.253927            0.477064   \n",
+       "21   sub-MRS10_ses-M0_run-2           0.000000            0.000000   \n",
+       "22   sub-MRS11_ses-M0_run-1           0.259048            0.368466   \n",
+       "23   sub-MRS11_ses-M0_run-2           0.209246            0.547347   \n",
+       "24  sub-MRS11_ses-M24_run-1           0.531014            0.678940   \n",
+       "25  sub-MRS11_ses-M24_run-2           0.382470            0.592506   \n",
+       "26   sub-MRS12_ses-M0_run-1           0.158332            0.714384   \n",
+       "27   sub-MRS12_ses-M0_run-2           0.261854            0.608917   \n",
+       "28   sub-MRS13_ses-M0_run-1           0.000000            0.533873   \n",
+       "29   sub-MRS14_ses-M0_run-1           0.336891            0.277819   \n",
+       "30   sub-MRS14_ses-M0_run-2           0.277176            0.531339   \n",
+       "31  sub-MRS14_ses-M24_run-1           0.532560            0.459082   \n",
+       "32  sub-MRS14_ses-M24_run-2           0.335290            0.528416   \n",
+       "33   sub-MRS16_ses-M0_run-1           0.307692            0.444444   \n",
+       "34   sub-MRS16_ses-M0_run-2           0.433031            0.484490   \n",
+       "35   sub-MRS18_ses-M0_run-1           0.000000            0.505495   \n",
+       "36  sub-MRS18_ses-M24_run-1           0.000000            0.000000   \n",
+       "\n",
+       "    dice_lesion_multi  \n",
+       "0            0.606452  \n",
+       "1            0.485465  \n",
+       "2            0.166852  \n",
+       "3            0.108999  \n",
+       "4            0.340871  \n",
+       "5            0.428571  \n",
+       "6            0.351145  \n",
+       "7            0.462222  \n",
+       "8            0.535012  \n",
+       "9            0.142857  \n",
+       "10           0.161074  \n",
+       "11           0.142412  \n",
+       "12           0.390107  \n",
+       "0            0.454433  \n",
+       "1            0.491018  \n",
+       "2            0.686071  \n",
+       "3            0.777778  \n",
+       "4            0.739040  \n",
+       "5            0.648622  \n",
+       "6            0.573010  \n",
+       "7            0.501370  \n",
+       "8            0.445040  \n",
+       "9            0.460568  \n",
+       "10           0.069680  \n",
+       "11           0.147094  \n",
+       "12           0.070949  \n",
+       "13           0.077994  \n",
+       "14           0.587764  \n",
+       "15           0.588774  \n",
+       "16           0.057143  \n",
+       "17           0.000000  \n",
+       "18           0.048193  \n",
+       "19           0.009434  \n",
+       "20           0.000000  \n",
+       "21           0.000000  \n",
+       "22           0.356855  \n",
+       "23           0.480583  \n",
+       "24           0.553633  \n",
+       "25           0.544737  \n",
+       "26           0.641363  \n",
+       "27           0.485714  \n",
+       "28           0.476366  \n",
+       "29           0.172598  \n",
+       "30           0.222417  \n",
+       "31           0.240808  \n",
+       "32           0.267511  \n",
+       "33           0.516908  \n",
+       "34           0.438538  \n",
+       "35           0.096386  \n",
+       "36           0.000000  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frames_7T = [data_iso, data_aniso]\n",
+    "result_7T = pd.concat(frames_7T)\n",
+    "result_7T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "id": "6939eefd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/'\n",
+    "result_7T.to_excel(path_database + \"dices_7T.xlsx\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "id": "118cc053",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = result_7T\n",
+    "columna_1 = data[\"dice_lesion_algo1\"]\n",
+    "columna_2 = data[\"dice_lesion_single\"]\n",
+    "columna_3 = data[\"dice_lesion_multi\"]\n",
+    "\n",
+    "plt.figure(figsize=((10,4)))\n",
+    "plt.fill_between(range(0, 14), 0, 1, color='gray', alpha=0.7, label='7T_iso')\n",
+    "plt.fill_between(range(14, 51), 0, 1, color='gray', alpha=0.1, label='7T_aniso')\n",
+    "#plt.fill_between(range(36, 66), 0, 1, color='green', alpha=0.2, label='sub-Basel')\n",
+    "#plt.fill_between(range(6, 81), min(columna_1[:80]), max(columna_1[:80]), color='gray', alpha=0.2, label='sub-Basel')\n",
+    "plt.scatter(range(1, len(columna_1) + 1), columna_1, marker='o', color='#1f77b4', label='algo 1')\n",
+    "plt.scatter(range(1, len(columna_2) + 1), columna_2, marker='*', color='#FF8C00', label='algo 3')\n",
+    "plt.scatter(range(1, len(columna_3) + 1), columna_3, marker='+', color='#FF1C00', label='algo 3 MC')\n",
+    "\n",
+    "#plt.title('UNIT1-den VS UNIT1-nonden')\n",
+    "plt.xlabel('Subject')\n",
+    "plt.ylabel('Dice')\n",
+    "#plt.legend(loc='best')\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "id": "2941ae0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dice algo 1 mean: 0.24328733074281822     dice STD: 0.19656450122823108\n",
+      "dice algo 2 single-class mean: 0.4609033681185143     dice STD: 0.23004786466809465\n",
+      "dice algo 2 multi-class mean: 0.3450086315767909     dice STD: 0.22637547747417813\n",
+      "p-value annotation legend:\n",
+      "      ns: p <= 1.00e+00\n",
+      "       *: 1.00e-02 < p <= 5.00e-02\n",
+      "      **: 1.00e-03 < p <= 1.00e-02\n",
+      "     ***: 1.00e-04 < p <= 1.00e-03\n",
+      "    ****: p <= 1.00e-04\n",
+      "\n",
+      "dice_lesion_algo1 vs. dice_lesion_single: Wilcoxon test (paired samples), P_val:1.835e-08 Stat=1.900e+01\n",
+      "dice_lesion_single vs. dice_lesion_multi: Wilcoxon test (paired samples), P_val:1.288e-07 Stat=6.500e+01\n",
+      "dice_lesion_algo1 vs. dice_lesion_multi: Wilcoxon test (paired samples), P_val:2.653e-03 Stat=2.800e+02\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<AxesSubplot:xlabel='Group', ylabel='Dice'>,\n",
+       " [<statannotations.Annotation.Annotation at 0x7f6d1d878750>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1d8780d0>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1d893910>])"
+      ]
+     },
+     "execution_count": 306,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = result_7T\n",
+    "df_lesion_algo_1 = data.filter(['dice_lesion_algo1'], axis=1)\n",
+    "df_lesion_algo_1.insert(0, 'Group', 'dice_lesion_algo1')\n",
+    "df_lesion_algo_1.rename(columns={'dice_lesion_algo1': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_mono = data.filter(['dice_lesion_single'], axis=1)\n",
+    "df_lesion_mono.insert(0, 'Group', 'dice_lesion_single')\n",
+    "df_lesion_mono.rename(columns={'dice_lesion_single': 'Dice'}, inplace=True)\n",
+    "\n",
+    "df_lesion_multi = data.filter(['dice_lesion_multi'], axis=1)\n",
+    "df_lesion_multi.insert(0, 'Group', 'dice_lesion_multi')\n",
+    "df_lesion_multi.rename(columns={'dice_lesion_multi': 'Dice'}, inplace=True)\n",
+    "\n",
+    "frames = [ df_lesion_algo_1, df_lesion_mono ,df_lesion_multi]\n",
+    "result_gm = pd.concat(frames, axis=0)\n",
+    "\n",
+    "plt.figure(figsize=(2,5))\n",
+    "plt.xticks(rotation=90)\n",
+    "colores = [\"#1f77b4\", \"#FF8C00\", \"#FF1C00\"]\n",
+    "ax_gm = sns.boxplot(data=result_gm, x=\"Group\", palette=colores, y=\"Dice\")\n",
+    "print (\"dice algo 1 mean:\", np.mean(data[\"dice_lesion_algo1\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_algo1\"])) \n",
+    "print (\"dice algo 2 single-class mean:\", np.mean(data[\"dice_lesion_single\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_single\"])) \n",
+    "print (\"dice algo 2 multi-class mean:\", np.mean(data[\"dice_lesion_multi\"]) ,\"    dice STD:\", np.std(data[\"dice_lesion_multi\"])) \n",
+    "\n",
+    "\n",
+    "pairs=[\n",
+    "           ((\"dice_lesion_algo1\"), (\"dice_lesion_single\")),\n",
+    "           ((\"dice_lesion_algo1\"), (\"dice_lesion_multi\")),\n",
+    "           ((\"dice_lesion_single\"), (\"dice_lesion_multi\")) ]\n",
+    "annotator_csa_gm = Annotator(ax_gm, pairs, data=result_gm, x=\"Group\", y=\"Dice\")#, order=order)\n",
+    "annotator_csa_gm.configure(test='Wilcoxon', text_format='star', loc='inside').apply_test()\n",
+    "annotator_csa_gm.apply_and_annotate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df5a3efd",
+   "metadata": {},
+   "source": [
+    "# Zero lesion patients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "id": "25b99c5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>lesions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-C010-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-C012-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-C015-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-nih017-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-nih052-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sub-nih056-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sub-nih084-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sub-nih087-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sub-nih100-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sub-nih155-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>sub-P077-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>sub-P126-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sub-P133-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>sub-P136-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>sub-P189-ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              subject  lesions\n",
+       "0     sub-C010-ses-M0      0.0\n",
+       "1     sub-C012-ses-M0      0.0\n",
+       "2     sub-C015-ses-M0      0.0\n",
+       "3   sub-nih017-ses-M0      0.0\n",
+       "4   sub-nih052-ses-M0      0.0\n",
+       "5   sub-nih056-ses-M0      0.0\n",
+       "6   sub-nih084-ses-M0      0.0\n",
+       "7   sub-nih087-ses-M0      0.0\n",
+       "8   sub-nih100-ses-M0      0.0\n",
+       "9   sub-nih155-ses-M0      0.0\n",
+       "10    sub-P077-ses-M0      0.0\n",
+       "11    sub-P126-ses-M0      0.0\n",
+       "12    sub-P133-ses-M0      0.0\n",
+       "13    sub-P136-ses-M0      0.0\n",
+       "14    sub-P189-ses-M0      0.0"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1=[]\n",
+    "subjects = [\"sub-C010-ses-M0\", \"sub-C012-ses-M0\", \"sub-C015-ses-M0\", \"sub-MRS00-ses-M0\", \"sub-MRS15-ses-M0\", \"sub-MRS15-ses-M24\", \"sub-MRS18-ses-M0\", \"sub-MRS23-ses-M0\", \"sub-MRS23-ses-M24\", \"sub-nih003-ses-M0\", \"sub-nih009-ses-M0\", \"sub-nih017-ses-M0\", \"sub-nih021-ses-M0\", \"sub-nih037-ses-M0\", \"sub-nih042-ses-M0\", \"sub-nih043-ses-M0\", \"sub-nih045-ses-M0\", \"sub-nih051-ses-M0\", \"sub-nih052-ses-M0\", \"sub-nih054-ses-M0\", \"sub-nih056-ses-M0\", \"sub-nih067-ses-M0\", \"sub-nih073-ses-M0\", \"sub-nih075-ses-M0\", \"sub-nih084-ses-M0\", \"sub-nih087-ses-M0\", \"sub-nih089-ses-M0\", \"sub-nih092-ses-M0\", \"sub-nih100-ses-M0\", \"sub-nih101-ses-M0\", \"sub-nih110-ses-M0\", \"sub-nih112-ses-M0\", \"sub-nih117-ses-M0\", \"sub-nih120-ses-M0\", \"sub-nih134-ses-M0\", \"sub-nih136-ses-M0\", \"sub-nih144-ses-M0\", \"sub-nih147-ses-M0\", \"sub-nih149-ses-M0\", \"sub-nih155-ses-M0\", \"sub-nih173-ses-M0\", \"sub-nih179-ses-M0\", \"sub-nih183-ses-M0\", \"sub-nih194-ses-M0\", \"sub-nih195-ses-M0\", \"sub-nih199-ses-M0\", \"sub-P002-ses-M0\", \"sub-P016-ses-M0\", \"sub-P021-ses-M0\", \"sub-P037-ses-M0\", \"sub-P038-ses-M0\", \"sub-P044-ses-M0\", \"sub-P046-ses-M0\", \"sub-P051-ses-M0\", \"sub-P058-ses-M0\", \"sub-P061-ses-M0\", \"sub-P063-ses-M0\", \"sub-P066-ses-M0\", \"sub-P069-ses-M0\", \"sub-P077-ses-M0\", \"sub-P079-ses-M0\", \"sub-P080-ses-M0\", \"sub-P084-ses-M0\", \"sub-P085-ses-M0\", \"sub-P094-ses-M0\", \"sub-P109-ses-M0\", \"sub-P120-ses-M0\", \"sub-P126-ses-M0\", \"sub-P130-ses-M0\", \"sub-P132-ses-M0\", \"sub-P133-ses-M0\", \"sub-P136-ses-M0\", \"sub-P149-ses-M0\", \"sub-P165-ses-M0\", \"sub-P175-ses-M0\", \"sub-P178-ses-M0\", \"sub-P183-ses-M0\", \"sub-P189-ses-M0\", \"sub-P199-ses-M0\", \"sub-P249-ses-M0\"]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/'\n",
+    "for subject in subjects: \n",
+    "    gt_lesion_data= nib.load(path_database + '403/GT_sc/'+ subject +'_UNIT1_GT.nii.gz')\n",
+    "    gt_lesion = np.array(gt_lesion_data.get_fdata())\n",
+    "    if  np.max(gt_lesion) == 0 :\n",
+    "        d = {'subject': subject,'lesions':np.max(gt_lesion)}\n",
+    "        df1.append(d)\n",
+    "data_zero = pd.DataFrame(data=df1)\n",
+    "data_zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "id": "64b4228f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>lesions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS02_ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS07_ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS07_ses-M24</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS15_ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS15_ses-M24</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sub-MRS17_ses-M0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sub-MRS17_ses-M24</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             subject  lesions\n",
+       "0   sub-MRS02_ses-M0      0.0\n",
+       "1   sub-MRS07_ses-M0      0.0\n",
+       "2  sub-MRS07_ses-M24      0.0\n",
+       "3   sub-MRS15_ses-M0      0.0\n",
+       "4  sub-MRS15_ses-M24      0.0\n",
+       "5   sub-MRS17_ses-M0      0.0\n",
+       "6  sub-MRS17_ses-M24      0.0"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1=[]\n",
+    "subjects=[\"sub-MRS01_ses-M0\",\"sub-MRS02_ses-M0\",\"sub-MRS04_ses-M0\",\"sub-MRS05_ses-M0\",\"sub-MRS05_ses-M24\",\"sub-MRS06_ses-M0\",\"sub-MRS07_ses-M0\",\"sub-MRS07_ses-M24\",\"sub-MRS08_ses-M0\",\"sub-MRS09_ses-M0\",\"sub-MRS10_ses-M0\",\"sub-MRS11_ses-M0\",\"sub-MRS12_ses-M0\",\"sub-MRS14_ses-M0\",\"sub-MRS14_ses-M24\",\"sub-MRS15_ses-M0\",\"sub-MRS15_ses-M24\",\"sub-MRS16_ses-M0\",\"sub-MRS17_ses-M0\",\"sub-MRS17_ses-M24\"]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/7T/'\n",
+    "for subject in subjects: \n",
+    "    gt_lesion_data= nib.load(path_database + 'GT_iso/' + subject +'_UNIT1_desc-rater1_label-lesion_seg.nii.gz')\n",
+    "    gt_lesion = np.array(gt_lesion_data.get_fdata())\n",
+    "    if  np.max(gt_lesion) == 0 :\n",
+    "        d = {'subject': subject,'lesions':np.max(gt_lesion)}\n",
+    "        df1.append(d)\n",
+    "data_zero = pd.DataFrame(data=df1)\n",
+    "data_zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "id": "17dcaab2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>lesions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS02_ses-M0_run-1</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS07_ses-M0_run-1</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS07_ses-M0_run-2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS07_ses-M24_run-1</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS07_ses-M24_run-2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sub-MRS15_ses-M0_run-1</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sub-MRS15_ses-M0_run-2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sub-MRS15_ses-M24_run-1</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sub-MRS15_ses-M24_run-2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sub-MRS17_ses-M0_run-1</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>sub-MRS17_ses-M0_run-2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>sub-MRS17_ses-M24_run-1</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sub-MRS18_ses-M0_run-2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    subject  lesions\n",
+       "0    sub-MRS02_ses-M0_run-1      0.0\n",
+       "1    sub-MRS07_ses-M0_run-1      0.0\n",
+       "2    sub-MRS07_ses-M0_run-2      0.0\n",
+       "3   sub-MRS07_ses-M24_run-1      0.0\n",
+       "4   sub-MRS07_ses-M24_run-2      0.0\n",
+       "5    sub-MRS15_ses-M0_run-1      0.0\n",
+       "6    sub-MRS15_ses-M0_run-2      0.0\n",
+       "7   sub-MRS15_ses-M24_run-1      0.0\n",
+       "8   sub-MRS15_ses-M24_run-2      0.0\n",
+       "9    sub-MRS17_ses-M0_run-1      0.0\n",
+       "10   sub-MRS17_ses-M0_run-2      0.0\n",
+       "11  sub-MRS17_ses-M24_run-1      0.0\n",
+       "12   sub-MRS18_ses-M0_run-2      0.0"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1=[]\n",
+    "subjects=[\"sub-MRS00_ses-M0_run-1\", \"sub-MRS00_ses-M24_run-1\", \"sub-MRS00_ses-M24_run-2\", \"sub-MRS01_ses-M0_run-1\", \"sub-MRS01_ses-M0_run-2\",\"sub-MRS02_ses-M0_run-1\", \"sub-MRS03_ses-M0_run-1\", \"sub-MRS03_ses-M0_run-2\", \"sub-MRS04_ses-M0_run-1\", \"sub-MRS04_ses-M0_run-2\", \"sub-MRS04_ses-M24_run-1\", \"sub-MRS05_ses-M0_run-1\", \"sub-MRS05_ses-M0_run-2\", \"sub-MRS05_ses-M24_run-1\", \"sub-MRS05_ses-M24_run-2\", \"sub-MRS06_ses-M0_run-1\", \"sub-MRS06_ses-M0_run-2\", \"sub-MRS07_ses-M0_run-1\", \"sub-MRS07_ses-M0_run-2\", \"sub-MRS07_ses-M24_run-1\", \"sub-MRS07_ses-M24_run-2\", \"sub-MRS08_ses-M0_run-1\", \"sub-MRS08_ses-M0_run-2\", \"sub-MRS09_ses-M0_run-1\", \"sub-MRS09_ses-M0_run-2\", \"sub-MRS10_ses-M0_run-1\", \"sub-MRS10_ses-M0_run-2\", \"sub-MRS11_ses-M0_run-1\", \"sub-MRS11_ses-M0_run-2\", \"sub-MRS11_ses-M24_run-1\", \"sub-MRS11_ses-M24_run-2\", \"sub-MRS12_ses-M0_run-1\", \"sub-MRS12_ses-M0_run-2\", \"sub-MRS13_ses-M0_run-1\", \"sub-MRS14_ses-M0_run-1\", \"sub-MRS14_ses-M0_run-2\", \"sub-MRS14_ses-M24_run-1\", \"sub-MRS14_ses-M24_run-2\", \"sub-MRS15_ses-M0_run-1\", \"sub-MRS15_ses-M0_run-2\", \"sub-MRS15_ses-M24_run-1\", \"sub-MRS15_ses-M24_run-2\", \"sub-MRS16_ses-M0_run-1\", \"sub-MRS16_ses-M0_run-2\", \"sub-MRS17_ses-M0_run-1\", \"sub-MRS17_ses-M0_run-2\", \"sub-MRS17_ses-M24_run-1\", \"sub-MRS18_ses-M0_run-1\", \"sub-MRS18_ses-M0_run-2\", \"sub-MRS18_ses-M24_run-1\"]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/7T/'\n",
+    "for subject in subjects: \n",
+    "    gt_lesion_data= nib.load(path_database + 'GT_aniso/' + subject +'_UNIT1_desc-rater1_label-lesion_seg.nii.gz')\n",
+    "    gt_lesion = np.array(gt_lesion_data.get_fdata())\n",
+    "    if  np.max(gt_lesion) == 0 :\n",
+    "        d = {'subject': subject,'lesions':np.max(gt_lesion)}\n",
+    "        df1.append(d)\n",
+    "data_zero = pd.DataFrame(data=df1)\n",
+    "data_zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "id": "b005fdd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>lesions</th>\n",
+       "      <th>GT</th>\n",
+       "      <th>field</th>\n",
+       "      <th>algo 1</th>\n",
+       "      <th>algo 3</th>\n",
+       "      <th>algo 3 MC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>sub-C010-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>sub-C012-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>sub-C015-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>sub-nih017-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>sub-nih052-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>sub-nih056-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>sub-nih084-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>sub-nih087-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>sub-nih100-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>sub-nih155-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>sub-P077-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>sub-P126-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>sub-P133-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>sub-P136-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>sub-P189-ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>sub-MRS02_ses-M0_run-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>sub-MRS07_ses-M0_run-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>sub-MRS07_ses-M0_run-2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>sub-MRS07_ses-M24_run-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>sub-MRS07_ses-M24_run-2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>sub-MRS15_ses-M0_run-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>sub-MRS15_ses-M0_run-2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>sub-MRS15_ses-M24_run-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>sub-MRS15_ses-M24_run-2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>sub-MRS17_ses-M0_run-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>sub-MRS17_ses-M0_run-2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>sub-MRS17_ses-M24_run-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>sub-MRS18_ses-M0_run-2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>sub-MRS02_ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>sub-MRS07_ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>sub-MRS07_ses-M24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>sub-MRS15_ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>sub-MRS15_ses-M24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>sub-MRS17_ses-M0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>sub-MRS17_ses-M24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    subject                  lesions  GT field  algo 1  algo 3  algo 3 MC\n",
+       "0         1          sub-C010-ses-M0   0    3T       0       0          0\n",
+       "1         2          sub-C012-ses-M0   0    3T       0       0          0\n",
+       "2         3          sub-C015-ses-M0   0    3T       0       0          0\n",
+       "3         4        sub-nih017-ses-M0   0    3T       2       0          0\n",
+       "4         5        sub-nih052-ses-M0   0    3T       1       0          0\n",
+       "5         6        sub-nih056-ses-M0   0    3T       0       0          0\n",
+       "6         7        sub-nih084-ses-M0   0    3T       0       0          0\n",
+       "7         8        sub-nih087-ses-M0   0    3T       0       0          0\n",
+       "8         9        sub-nih100-ses-M0   0    3T       0       0          0\n",
+       "9        10        sub-nih155-ses-M0   0    3T       1       0          0\n",
+       "10       11          sub-P077-ses-M0   0    3T       0       0          0\n",
+       "11       12          sub-P126-ses-M0   0    3T       0       0          0\n",
+       "12       13          sub-P133-ses-M0   0    3T       0       0          0\n",
+       "13       14          sub-P136-ses-M0   0    3T       0       0          0\n",
+       "14       15          sub-P189-ses-M0   0    3T       0       0          0\n",
+       "15       16   sub-MRS02_ses-M0_run-1   0    7T       1       1          0\n",
+       "16       17   sub-MRS07_ses-M0_run-1   0    7T       1       0          0\n",
+       "17       18   sub-MRS07_ses-M0_run-2   0    7T       1       1          1\n",
+       "18       19  sub-MRS07_ses-M24_run-1   0    7T       1       0          0\n",
+       "19       20  sub-MRS07_ses-M24_run-2   0    7T       1       0          0\n",
+       "20       21   sub-MRS15_ses-M0_run-1   0    7T       1       0          0\n",
+       "21       22   sub-MRS15_ses-M0_run-2   0    7T       1       0          0\n",
+       "22       23  sub-MRS15_ses-M24_run-1   0    7T       1       0          0\n",
+       "23       24  sub-MRS15_ses-M24_run-2   0    7T       1       0          0\n",
+       "24       25   sub-MRS17_ses-M0_run-1   0    7T       3       1          0\n",
+       "25       26   sub-MRS17_ses-M0_run-2   0    7T       1       0          0\n",
+       "26       27  sub-MRS17_ses-M24_run-1   0    7T       1       1          1\n",
+       "27       28   sub-MRS18_ses-M0_run-2   0    7T       1       0          0\n",
+       "28       29         sub-MRS02_ses-M0   0    7T       1       0          0\n",
+       "29       30         sub-MRS07_ses-M0   0    7T       0       0          0\n",
+       "30       31        sub-MRS07_ses-M24   0    7T       1       0          0\n",
+       "31       32         sub-MRS15_ses-M0   0    7T       3       0          0\n",
+       "32       33        sub-MRS15_ses-M24   0    7T       0       0          0\n",
+       "33       34         sub-MRS17_ses-M0   0    7T       0       1          0\n",
+       "34       35       sub-MRS17_ses-M24    0    7T       2       2          0"
+      ]
+     },
+     "execution_count": 331,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/'\n",
+    "data_zero_lesions = pd.read_excel(path_database + \"zero_lesions.xlsx\")\n",
+    "data_zero_lesions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "id": "e561e81a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/'\n",
+    "data_zero_lesions = pd.read_excel(path_database + \"zero_lesions.xlsx\")\n",
+    "\n",
+    "data = data_zero_lesions\n",
+    "columna_1 = data[\"algo 1\"]\n",
+    "columna_2 = data[\"algo 3\"]\n",
+    "columna_3 = data[\"algo 3 MC\"]\n",
+    "\n",
+    "# Definir los nombres de los sujetos\n",
+    "nombres_sujetos = data[\"lesions\"].tolist()\n",
+    "\n",
+    "plt.figure(figsize=((15,4)))\n",
+    "plt.fill_between(range(1, 16), 0, 3, color='orange', alpha=0.1, label='3T')\n",
+    "plt.fill_between(range(16, 36), 0, 3, color='red', alpha=0.1, label='7T')\n",
+    "#plt.fill_between(range(16, 35), 0, 3, color='green', alpha=0.2, label='sub-Basel')\n",
+    "#plt.fill_between(range(6, 81), min(columna_1[:80]), max(columna_1[:80]), color='gray', alpha=0.2, label='sub-Basel')\n",
+    "plt.scatter(range(1, len(columna_1) + 1), columna_1, marker='o', color='#1f77b4', label='algo 1')\n",
+    "plt.scatter(range(1, len(columna_2) + 1), columna_2, marker='*', color='#FF8C00', label='algo 3')\n",
+    "plt.scatter(range(1, len(columna_3) + 1), columna_3, marker='+', color='#FF1C00', label='algo 3 MC')\n",
+    "\n",
+    "# Establecer los nombres de los sujetos en el eje x\n",
+    "plt.xticks(range(1, len(nombres_sujetos) + 1), nombres_sujetos, rotation=55, ha='right')\n",
+    "\n",
+    "#plt.title('UNIT1-den VS UNIT1-nonden')\n",
+    "plt.xlabel('Subject')\n",
+    "plt.ylabel('Nb of lesions')\n",
+    "#plt.legend(loc='best')\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "plt.tight_layout()  # Ajustar el diseño para que no se corten las etiquetas\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c772e67",
+   "metadata": {},
+   "source": [
+    "# Lesion detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c490010c",
+   "metadata": {},
+   "source": [
+    "# 3T test\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "id": "8fe563b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=[]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/'\n",
+    "data_excel = pd.read_excel(path_database + \"3t_metrics_F1_sensitivity_mp2rage.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "id": "b51d92a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/scipy/stats/morestats.py:3155: UserWarning: Sample size too small for normal approximation.\n",
+      "  warnings.warn(\"Sample size too small for normal approximation.\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sensitivity algo 1 mean: 0.801055611055611 Sensitivity STD: 0.23870182272596754\n",
+      "Sensitivity algo 3 mean: 0.8087479187479187 Sensitivity STD: 0.2211765776015126\n",
+      "Sensitivity algo 3 MC mean: 0.7653779553779552 Sensitivity STD: 0.24308886784059408\n",
+      "Precision algo 1 mean: 0.868070818070818 Precision STD: 0.23870182272596754\n",
+      "Precision algo 3 mean: 0.9298534798534799 Precision STD: 0.24308886784059408\n",
+      "Precision algo 3 MC mean: 0.9229304029304028 Precision STD: 0.2211765776015126\n",
+      "F1 score algo 1 mean: 0.81014545283776 F1 score STD: 0.21411196719058345\n",
+      "F1 score algo 3 mean: 0.8496257588565275 F1 score STD: 0.2272806047119562\n",
+      "F1 score algo 3 MC mean: 0.8208356600664287 F1 score STD: 0.25292030512671937\n",
+      "p-value annotation legend:\n",
+      "      ns: p <= 1.00e+00\n",
+      "       *: 1.00e-02 < p <= 5.00e-02\n",
+      "      **: 1.00e-03 < p <= 1.00e-02\n",
+      "     ***: 1.00e-04 < p <= 1.00e-03\n",
+      "    ****: p <= 1.00e-04\n",
+      "\n",
+      "Sensitivity algo 1 vs. Sensitivity algo 3: Wilcoxon test (paired samples), P_val:6.698e-01 Stat=8.450e+01\n",
+      "Sensitivity algo 3 vs. Sensitivity algo 3 MC: Wilcoxon test (paired samples), P_val:3.385e-02 Stat=1.900e+01\n",
+      "Precision algo 1 vs. Precision algo 3: Wilcoxon test (paired samples), P_val:1.532e-02 Stat=3.500e+01\n",
+      "Precision algo 3 vs. Precision algo 3 MC: Wilcoxon test (paired samples), P_val:8.927e-01 Stat=7.000e+00\n",
+      "F1 score algo 1 vs. F1 score algo 3: Wilcoxon test (paired samples), P_val:1.325e-02 Stat=1.420e+02\n",
+      "F1 score algo 3 vs. F1 score algo 3 MC: Wilcoxon test (paired samples), P_val:1.022e-01 Stat=4.800e+01\n",
+      "Sensitivity algo 1 vs. Sensitivity algo 3 MC: Wilcoxon test (paired samples), P_val:3.441e-01 Stat=8.850e+01\n",
+      "Precision algo 1 vs. Precision algo 3 MC: Wilcoxon test (paired samples), P_val:2.260e-02 Stat=3.850e+01\n",
+      "F1 score algo 1 vs. F1 score algo 3 MC: Wilcoxon test (paired samples), P_val:1.258e-01 Stat=2.080e+02\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<AxesSubplot:xlabel='Group', ylabel='Metric'>,\n",
+       " [<statannotations.Annotation.Annotation at 0x7f6d4dca1dd0>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4dca1d10>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4dca1c50>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4dca1f10>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4dca1ad0>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4dca1b50>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4dca1b90>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4dca1a90>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4dca19d0>])"
+      ]
+     },
+     "execution_count": 323,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = data_excel\n",
+    "df_Sensitivity_algo_1 = data.filter(['Sensitivity algo 1'], axis=1)\n",
+    "df_Sensitivity_algo_1.insert(0, 'Group', 'Sensitivity algo 1')\n",
+    "df_Sensitivity_algo_1.rename(columns={'Sensitivity algo 1': 'Metric'}, inplace=True)\n",
+    "df_Sensitivity_algo_3 = data.filter(['Sensitivity algo 3'], axis=1)\n",
+    "df_Sensitivity_algo_3.insert(0, 'Group', 'Sensitivity algo 3')\n",
+    "df_Sensitivity_algo_3.rename(columns={'Sensitivity algo 3': 'Metric'}, inplace=True)\n",
+    "df_Sensitivity_algo_3_mc = data.filter(['Sensitivity algo 3 MC'], axis=1)\n",
+    "df_Sensitivity_algo_3_mc.insert(0, 'Group', 'Sensitivity algo 3 MC')\n",
+    "df_Sensitivity_algo_3_mc.rename(columns={'Sensitivity algo 3 MC': 'Metric'}, inplace=True)\n",
+    "\n",
+    "\n",
+    "df_Precision_algo_1 = data.filter(['Precision algo 1'], axis=1)\n",
+    "df_Precision_algo_1.insert(0, 'Group', 'Precision algo 1')\n",
+    "df_Precision_algo_1.rename(columns={'Precision algo 1': 'Metric'}, inplace=True)\n",
+    "df_Precision_algo_3 = data.filter(['Precision algo 3'], axis=1)\n",
+    "df_Precision_algo_3.insert(0, 'Group', 'Precision algo 3')\n",
+    "df_Precision_algo_3.rename(columns={'Precision algo 3': 'Metric'}, inplace=True)\n",
+    "df_Precision_algo_3_mc = data.filter(['Precision algo 3 MC'], axis=1)\n",
+    "df_Precision_algo_3_mc.insert(0, 'Group', 'Precision algo 3 MC')\n",
+    "df_Precision_algo_3_mc.rename(columns={'Precision algo 3 MC': 'Metric'}, inplace=True)\n",
+    "\n",
+    "df_F1_algo_1 = data.filter(['F1 score algo 1'], axis=1)\n",
+    "df_F1_algo_1.insert(0, 'Group', 'F1 score algo 1')\n",
+    "df_F1_algo_1.rename(columns={'F1 score algo 1': 'Metric'}, inplace=True)\n",
+    "df_F1_algo_3 = data.filter(['F1 score algo 3'], axis=1)\n",
+    "df_F1_algo_3.insert(0, 'Group', 'F1 score algo 3')\n",
+    "df_F1_algo_3.rename(columns={'F1 score algo 3': 'Metric'}, inplace=True)\n",
+    "df_F1_algo_3_mc = data.filter(['F1 score algo 3 MC'], axis=1)\n",
+    "df_F1_algo_3_mc.insert(0, 'Group', 'F1 score algo 3 MC')\n",
+    "df_F1_algo_3_mc.rename(columns={'F1 score algo 3 MC': 'Metric'}, inplace=True)\n",
+    "\n",
+    "frames = [df_Sensitivity_algo_1, df_Sensitivity_algo_3, df_Sensitivity_algo_3_mc, df_Precision_algo_1, df_Precision_algo_3, \n",
+    "          df_Precision_algo_3_mc , df_F1_algo_1, df_F1_algo_3, df_F1_algo_3_mc]\n",
+    "result_gm = pd.concat(frames, axis=0)\n",
+    "\n",
+    "plt.figure(figsize=(5,5))\n",
+    "plt.xticks(rotation=90)\n",
+    "colores = [\"#1f77b4\", \"#FF8C00\", \"#FF1C00\"]\n",
+    "ax_gm = sns.boxplot(data=result_gm, x=\"Group\", palette=colores,  y=\"Metric\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "print (\"Sensitivity algo 1 mean:\", np.mean(data[\"Sensitivity algo 1\"]) ,\"Sensitivity STD:\", np.std(data[\"Precision algo 1\"])) \n",
+    "print (\"Sensitivity algo 3 mean:\", np.mean(data[\"Sensitivity algo 3\"]) ,\"Sensitivity STD:\", np.std(data[\"Precision algo 3\"])) \n",
+    "print (\"Sensitivity algo 3 MC mean:\", np.mean(data[\"Sensitivity algo 3 MC\"]) ,\"Sensitivity STD:\", np.std(data[\"Precision algo 3 MC\"])) \n",
+    "print (\"Precision algo 1 mean:\", np.mean(data[\"Precision algo 1\"]) ,\"Precision STD:\", np.std(data[\"Precision algo 1\"]))\n",
+    "print (\"Precision algo 3 mean:\", np.mean(data[\"Precision algo 3\"]) ,\"Precision STD:\", np.std(data[\"Precision algo 3 MC\"])) \n",
+    "print (\"Precision algo 3 MC mean:\", np.mean(data[\"Precision algo 3 MC\"]) ,\"Precision STD:\", np.std(data[\"Precision algo 3\"])) \n",
+    "print (\"F1 score algo 1 mean:\", np.mean(data[\"F1 score algo 1\"]) ,\"F1 score STD:\", np.std(data[\"F1 score algo 1\"])) \n",
+    "print (\"F1 score algo 3 mean:\", np.mean(data[\"F1 score algo 3\"]) ,\"F1 score STD:\", np.std(data[\"F1 score algo 3\"])) \n",
+    "print (\"F1 score algo 3 MC mean:\", np.mean(data[\"F1 score algo 3 MC\"]) ,\"F1 score STD:\", np.std(data[\"F1 score algo 3 MC\"])) \n",
+    "\n",
+    "pairs=[\n",
+    "           ((\"Sensitivity algo 1\"), (\"Sensitivity algo 3\")),\n",
+    "           ((\"Sensitivity algo 1\"), (\"Sensitivity algo 3 MC\")),\n",
+    "           ((\"Sensitivity algo 3\"), (\"Sensitivity algo 3 MC\")),\n",
+    "            ((\"Precision algo 1\"), (\"Precision algo 3\")),\n",
+    "            ((\"Precision algo 1\"), (\"Precision algo 3 MC\")),\n",
+    "            ((\"Precision algo 3\"), (\"Precision algo 3 MC\")),\n",
+    "           ((\"F1 score algo 1\"), (\"F1 score algo 3\")),\n",
+    "            ((\"F1 score algo 1\"), (\"F1 score algo 3 MC\")),\n",
+    "            ((\"F1 score algo 3\"), (\"F1 score algo 3 MC\"))\n",
+    "]\n",
+    "annotator_csa_gm = Annotator(ax_gm, pairs, data=result_gm, x=\"Group\", y=\"Metric\")#, order=order)\n",
+    "annotator_csa_gm.configure(test='Wilcoxon', text_format='star', loc='inside').apply_test()\n",
+    "annotator_csa_gm.apply_and_annotate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffb8d6e5",
+   "metadata": {},
+   "source": [
+    "# 7T aniso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "id": "379acfd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>dice algo1</th>\n",
+       "      <th>dice algo 3</th>\n",
+       "      <th>dice algo 3 MC</th>\n",
+       "      <th>Sensitivity algo 1</th>\n",
+       "      <th>Sensitivity algo 3</th>\n",
+       "      <th>Sensitivity algo 3 MC</th>\n",
+       "      <th>Precision algo 1</th>\n",
+       "      <th>Precision algo 3</th>\n",
+       "      <th>Precision algo 3 MC</th>\n",
+       "      <th>F1 score algo 1</th>\n",
+       "      <th>F1 score algo 3</th>\n",
+       "      <th>F1 score algo 3 MC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS00_ses-M0_run-1</td>\n",
+       "      <td>0.508227</td>\n",
+       "      <td>0.601405</td>\n",
+       "      <td>0.454433</td>\n",
+       "      <td>0.727273</td>\n",
+       "      <td>0.727273</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.727273</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.727273</td>\n",
+       "      <td>0.761905</td>\n",
+       "      <td>0.705882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS00_ses-M24_run-1</td>\n",
+       "      <td>0.367181</td>\n",
+       "      <td>0.483068</td>\n",
+       "      <td>0.491018</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.941176</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS00_ses-M24_run-2</td>\n",
+       "      <td>0.405126</td>\n",
+       "      <td>0.567731</td>\n",
+       "      <td>0.686071</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.933333</td>\n",
+       "      <td>0.833333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS01_ses-M0</td>\n",
+       "      <td>0.576490</td>\n",
+       "      <td>0.787402</td>\n",
+       "      <td>0.606452</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.857143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS01_ses-M0_run-1</td>\n",
+       "      <td>0.586035</td>\n",
+       "      <td>0.779359</td>\n",
+       "      <td>0.777778</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sub-MRS01_ses-M0_run-2</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.768696</td>\n",
+       "      <td>0.739040</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sub-MRS03_ses-M0_run-1</td>\n",
+       "      <td>0.349933</td>\n",
+       "      <td>0.660300</td>\n",
+       "      <td>0.648622</td>\n",
+       "      <td>0.272727</td>\n",
+       "      <td>0.454545</td>\n",
+       "      <td>0.454545</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.625000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sub-MRS03_ses-M0_run-2</td>\n",
+       "      <td>0.206578</td>\n",
+       "      <td>0.573577</td>\n",
+       "      <td>0.573010</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.461538</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sub-MRS04_ses-M0</td>\n",
+       "      <td>0.245312</td>\n",
+       "      <td>0.637191</td>\n",
+       "      <td>0.485465</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sub-MRS04_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.623501</td>\n",
+       "      <td>0.501370</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>sub-MRS04_ses-M0_run-2</td>\n",
+       "      <td>0.140228</td>\n",
+       "      <td>0.620370</td>\n",
+       "      <td>0.445040</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>sub-MRS04_ses-M24_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.480000</td>\n",
+       "      <td>0.460568</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sub-MRS05_ses-M0</td>\n",
+       "      <td>0.157846</td>\n",
+       "      <td>0.237895</td>\n",
+       "      <td>0.166852</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.181818</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.461538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>sub-MRS05_ses-M0_run-1</td>\n",
+       "      <td>0.185185</td>\n",
+       "      <td>0.123974</td>\n",
+       "      <td>0.069680</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>0.153846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>sub-MRS05_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.215078</td>\n",
+       "      <td>0.147094</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.181818</td>\n",
+       "      <td>0.181818</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.307692</td>\n",
+       "      <td>0.307692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>sub-MRS05_ses-M24</td>\n",
+       "      <td>0.167464</td>\n",
+       "      <td>0.229508</td>\n",
+       "      <td>0.108999</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.461538</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>sub-MRS05_ses-M24_run-1</td>\n",
+       "      <td>0.135747</td>\n",
+       "      <td>0.110384</td>\n",
+       "      <td>0.070949</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.222222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>sub-MRS05_ses-M24_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.095618</td>\n",
+       "      <td>0.077994</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>sub-MRS06_ses-M0</td>\n",
+       "      <td>0.138391</td>\n",
+       "      <td>0.356432</td>\n",
+       "      <td>0.340871</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>sub-MRS06_ses-M0_run-1</td>\n",
+       "      <td>0.195396</td>\n",
+       "      <td>0.730769</td>\n",
+       "      <td>0.587764</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>sub-MRS06_ses-M0_run-2</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>0.775972</td>\n",
+       "      <td>0.588774</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>sub-MRS08_ses-M0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.661111</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>sub-MRS08_ses-M0_run-1</td>\n",
+       "      <td>0.024973</td>\n",
+       "      <td>0.256637</td>\n",
+       "      <td>0.057143</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>sub-MRS08_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>sub-MRS09_ses-M0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.590476</td>\n",
+       "      <td>0.351145</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>sub-MRS09_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.048193</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>sub-MRS09_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.009434</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>sub-MRS10_ses-M0</td>\n",
+       "      <td>0.398210</td>\n",
+       "      <td>0.653846</td>\n",
+       "      <td>0.462222</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>sub-MRS10_ses-M0_run-1</td>\n",
+       "      <td>0.253927</td>\n",
+       "      <td>0.477064</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>sub-MRS10_ses-M0_run-2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>sub-MRS11_ses-M0</td>\n",
+       "      <td>0.219887</td>\n",
+       "      <td>0.626999</td>\n",
+       "      <td>0.535012</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>sub-MRS11_ses-M0_run-1</td>\n",
+       "      <td>0.259048</td>\n",
+       "      <td>0.368466</td>\n",
+       "      <td>0.356855</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>sub-MRS11_ses-M0_run-2</td>\n",
+       "      <td>0.209246</td>\n",
+       "      <td>0.547347</td>\n",
+       "      <td>0.480583</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>sub-MRS11_ses-M24_run-1</td>\n",
+       "      <td>0.531014</td>\n",
+       "      <td>0.678940</td>\n",
+       "      <td>0.553633</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>sub-MRS11_ses-M24_run-2</td>\n",
+       "      <td>0.382470</td>\n",
+       "      <td>0.592506</td>\n",
+       "      <td>0.544737</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>sub-MRS12_ses-M0</td>\n",
+       "      <td>0.383358</td>\n",
+       "      <td>0.497479</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.769231</td>\n",
+       "      <td>0.923077</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>sub-MRS12_ses-M0_run-1</td>\n",
+       "      <td>0.158332</td>\n",
+       "      <td>0.714384</td>\n",
+       "      <td>0.641363</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>0.888889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>sub-MRS12_ses-M0_run-2</td>\n",
+       "      <td>0.261854</td>\n",
+       "      <td>0.608917</td>\n",
+       "      <td>0.485714</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>sub-MRS13_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.533873</td>\n",
+       "      <td>0.476366</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.758621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>sub-MRS14_ses-M0</td>\n",
+       "      <td>0.473431</td>\n",
+       "      <td>0.467446</td>\n",
+       "      <td>0.161074</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.923077</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>sub-MRS14_ses-M0_run-1</td>\n",
+       "      <td>0.336891</td>\n",
+       "      <td>0.277819</td>\n",
+       "      <td>0.172598</td>\n",
+       "      <td>0.529412</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.294118</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>sub-MRS14_ses-M0_run-2</td>\n",
+       "      <td>0.277176</td>\n",
+       "      <td>0.531339</td>\n",
+       "      <td>0.222417</td>\n",
+       "      <td>0.312500</td>\n",
+       "      <td>0.437500</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.384615</td>\n",
+       "      <td>0.608696</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>sub-MRS14_ses-M24</td>\n",
+       "      <td>0.339936</td>\n",
+       "      <td>0.513694</td>\n",
+       "      <td>0.142412</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>sub-MRS14_ses-M24_run-1</td>\n",
+       "      <td>0.532560</td>\n",
+       "      <td>0.459082</td>\n",
+       "      <td>0.240808</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.187500</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.640000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.315789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>sub-MRS14_ses-M24_run-2</td>\n",
+       "      <td>0.335290</td>\n",
+       "      <td>0.528416</td>\n",
+       "      <td>0.267511</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.588235</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>0.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>sub-MRS16_ses-M0</td>\n",
+       "      <td>0.388595</td>\n",
+       "      <td>0.566667</td>\n",
+       "      <td>0.390107</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.769231</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>sub-MRS16_ses-M0_run-1</td>\n",
+       "      <td>0.307692</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.516908</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>sub-MRS16_ses-M0_run-2</td>\n",
+       "      <td>0.433031</td>\n",
+       "      <td>0.484490</td>\n",
+       "      <td>0.438538</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>sub-MRS18_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.505495</td>\n",
+       "      <td>0.096386</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>sub-MRS18_ses-M24_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    subject  dice algo1  dice algo 3  dice algo 3 MC  \\\n",
+       "0    sub-MRS00_ses-M0_run-1    0.508227     0.601405        0.454433   \n",
+       "1   sub-MRS00_ses-M24_run-1    0.367181     0.483068        0.491018   \n",
+       "2   sub-MRS00_ses-M24_run-2    0.405126     0.567731        0.686071   \n",
+       "3          sub-MRS01_ses-M0    0.576490     0.787402        0.606452   \n",
+       "4    sub-MRS01_ses-M0_run-1    0.586035     0.779359        0.777778   \n",
+       "5    sub-MRS01_ses-M0_run-2    0.600000     0.768696        0.739040   \n",
+       "6    sub-MRS03_ses-M0_run-1    0.349933     0.660300        0.648622   \n",
+       "7    sub-MRS03_ses-M0_run-2    0.206578     0.573577        0.573010   \n",
+       "8          sub-MRS04_ses-M0    0.245312     0.637191        0.485465   \n",
+       "9    sub-MRS04_ses-M0_run-1    0.000000     0.623501        0.501370   \n",
+       "10   sub-MRS04_ses-M0_run-2    0.140228     0.620370        0.445040   \n",
+       "11  sub-MRS04_ses-M24_run-1    0.000000     0.480000        0.460568   \n",
+       "12         sub-MRS05_ses-M0    0.157846     0.237895        0.166852   \n",
+       "13   sub-MRS05_ses-M0_run-1    0.185185     0.123974        0.069680   \n",
+       "14   sub-MRS05_ses-M0_run-2    0.000000     0.215078        0.147094   \n",
+       "15        sub-MRS05_ses-M24    0.167464     0.229508        0.108999   \n",
+       "16  sub-MRS05_ses-M24_run-1    0.135747     0.110384        0.070949   \n",
+       "17  sub-MRS05_ses-M24_run-2    0.000000     0.095618        0.077994   \n",
+       "18         sub-MRS06_ses-M0    0.138391     0.356432        0.340871   \n",
+       "19   sub-MRS06_ses-M0_run-1    0.195396     0.730769        0.587764   \n",
+       "20   sub-MRS06_ses-M0_run-2    0.692308     0.775972        0.588774   \n",
+       "21         sub-MRS08_ses-M0    0.000000     0.661111        0.428571   \n",
+       "22   sub-MRS08_ses-M0_run-1    0.024973     0.256637        0.057143   \n",
+       "23   sub-MRS08_ses-M0_run-2    0.000000     0.000000        0.000000   \n",
+       "24         sub-MRS09_ses-M0    0.000000     0.590476        0.351145   \n",
+       "25   sub-MRS09_ses-M0_run-1    0.000000     0.000000        0.048193   \n",
+       "26   sub-MRS09_ses-M0_run-2    0.000000     0.000000        0.009434   \n",
+       "27         sub-MRS10_ses-M0    0.398210     0.653846        0.462222   \n",
+       "28   sub-MRS10_ses-M0_run-1    0.253927     0.477064        0.000000   \n",
+       "29   sub-MRS10_ses-M0_run-2    0.000000     0.000000        0.000000   \n",
+       "30         sub-MRS11_ses-M0    0.219887     0.626999        0.535012   \n",
+       "31   sub-MRS11_ses-M0_run-1    0.259048     0.368466        0.356855   \n",
+       "32   sub-MRS11_ses-M0_run-2    0.209246     0.547347        0.480583   \n",
+       "33  sub-MRS11_ses-M24_run-1    0.531014     0.678940        0.553633   \n",
+       "34  sub-MRS11_ses-M24_run-2    0.382470     0.592506        0.544737   \n",
+       "35         sub-MRS12_ses-M0    0.383358     0.497479        0.142857   \n",
+       "36   sub-MRS12_ses-M0_run-1    0.158332     0.714384        0.641363   \n",
+       "37   sub-MRS12_ses-M0_run-2    0.261854     0.608917        0.485714   \n",
+       "38   sub-MRS13_ses-M0_run-1    0.000000     0.533873        0.476366   \n",
+       "39         sub-MRS14_ses-M0    0.473431     0.467446        0.161074   \n",
+       "40   sub-MRS14_ses-M0_run-1    0.336891     0.277819        0.172598   \n",
+       "41   sub-MRS14_ses-M0_run-2    0.277176     0.531339        0.222417   \n",
+       "42        sub-MRS14_ses-M24    0.339936     0.513694        0.142412   \n",
+       "43  sub-MRS14_ses-M24_run-1    0.532560     0.459082        0.240808   \n",
+       "44  sub-MRS14_ses-M24_run-2    0.335290     0.528416        0.267511   \n",
+       "45         sub-MRS16_ses-M0    0.388595     0.566667        0.390107   \n",
+       "46   sub-MRS16_ses-M0_run-1    0.307692     0.444444        0.516908   \n",
+       "47   sub-MRS16_ses-M0_run-2    0.433031     0.484490        0.438538   \n",
+       "48   sub-MRS18_ses-M0_run-1    0.000000     0.505495        0.096386   \n",
+       "49  sub-MRS18_ses-M24_run-1    0.000000     0.000000        0.000000   \n",
+       "\n",
+       "    Sensitivity algo 1  Sensitivity algo 3  Sensitivity algo 3 MC  \\\n",
+       "0             0.727273            0.727273               0.545455   \n",
+       "1             0.750000            1.000000               0.500000   \n",
+       "2             0.714286            1.000000               0.714286   \n",
+       "3             0.750000            1.000000               0.750000   \n",
+       "4             0.666667            0.666667               0.666667   \n",
+       "5             0.666667            0.666667               0.666667   \n",
+       "6             0.272727            0.454545               0.454545   \n",
+       "7             0.300000            0.500000               0.500000   \n",
+       "8             0.400000            0.600000               0.600000   \n",
+       "9             0.000000            0.500000               0.500000   \n",
+       "10            0.333333            0.666667               0.666667   \n",
+       "11            0.000000            0.500000               0.250000   \n",
+       "12            0.100000            0.400000               0.300000   \n",
+       "13            0.166667            0.166667               0.083333   \n",
+       "14            0.000000            0.181818               0.181818   \n",
+       "15            0.300000            0.200000               0.200000   \n",
+       "16            0.250000            0.125000               0.125000   \n",
+       "17            0.000000            0.142857               0.142857   \n",
+       "18            0.400000            0.800000               0.600000   \n",
+       "19            0.800000            0.600000               0.600000   \n",
+       "20            1.000000            1.000000               1.000000   \n",
+       "21            0.000000            0.333333               0.333333   \n",
+       "22            0.333333            0.333333               0.333333   \n",
+       "23            0.000000            0.000000               0.000000   \n",
+       "24            0.500000            0.500000               0.500000   \n",
+       "25            0.000000            0.000000               0.000000   \n",
+       "26            0.000000            0.000000               0.000000   \n",
+       "27            0.500000            0.500000               0.500000   \n",
+       "28            0.666667            0.333333               0.000000   \n",
+       "29            0.000000            0.000000               0.000000   \n",
+       "30            0.333333            0.666667               0.666667   \n",
+       "31            0.600000            0.400000               0.200000   \n",
+       "32            0.333333            0.666667               0.666667   \n",
+       "33            0.600000            0.400000               0.400000   \n",
+       "34            0.666667            1.000000               0.666667   \n",
+       "35            0.833333            1.000000               0.500000   \n",
+       "36            0.600000            0.800000               0.800000   \n",
+       "37            0.333333            1.000000               0.666667   \n",
+       "38            0.000000            0.666667               0.611111   \n",
+       "39            0.833333            1.000000               0.333333   \n",
+       "40            0.529412            0.411765               0.294118   \n",
+       "41            0.312500            0.437500               0.250000   \n",
+       "42            0.500000            0.666667               0.333333   \n",
+       "43            0.500000            0.250000               0.187500   \n",
+       "44            0.500000            0.800000               0.600000   \n",
+       "45            0.625000            0.750000               0.500000   \n",
+       "46            0.666667            0.666667               0.666667   \n",
+       "47            0.666667            0.333333               0.333333   \n",
+       "48            0.000000            0.500000               0.000000   \n",
+       "49            0.000000            0.000000               0.000000   \n",
+       "\n",
+       "    Precision algo 1  Precision algo 3  Precision algo 3 MC  F1 score algo 1  \\\n",
+       "0           0.727273          0.800000             1.000000         0.727273   \n",
+       "1           0.857143          0.888889             0.666667         0.800000   \n",
+       "2           0.714286          0.875000             1.000000         0.714286   \n",
+       "3           0.750000          1.000000             1.000000         0.750000   \n",
+       "4           1.000000          1.000000             1.000000         0.800000   \n",
+       "5           1.000000          1.000000             1.000000         0.800000   \n",
+       "6           1.000000          1.000000             1.000000         0.428571   \n",
+       "7           1.000000          1.000000             1.000000         0.461538   \n",
+       "8           1.000000          1.000000             1.000000         0.571429   \n",
+       "9           0.000000          1.000000             1.000000         0.000000   \n",
+       "10          1.000000          1.000000             1.000000         0.500000   \n",
+       "11          0.000000          1.000000             1.000000         0.000000   \n",
+       "12          1.000000          1.000000             1.000000         0.181818   \n",
+       "13          1.000000          1.000000             1.000000         0.285714   \n",
+       "14          0.000000          1.000000             1.000000         0.000000   \n",
+       "15          1.000000          1.000000             1.000000         0.461538   \n",
+       "16          1.000000          1.000000             1.000000         0.400000   \n",
+       "17          0.000000          1.000000             1.000000         0.000000   \n",
+       "18          0.666667          0.800000             0.750000         0.500000   \n",
+       "19          0.571429          1.000000             1.000000         0.666667   \n",
+       "20          1.000000          1.000000             1.000000         1.000000   \n",
+       "21          0.000000          1.000000             1.000000         0.000000   \n",
+       "22          1.000000          1.000000             1.000000         0.500000   \n",
+       "23          0.000000          0.000000             0.000000         0.000000   \n",
+       "24          1.000000          0.500000             1.000000         0.666667   \n",
+       "25          0.000000          0.000000             0.000000         0.000000   \n",
+       "26          0.000000          0.000000             0.000000         0.000000   \n",
+       "27          1.000000          1.000000             1.000000         0.666667   \n",
+       "28          0.500000          1.000000             0.000000         0.571429   \n",
+       "29          0.000000          0.000000             0.000000         0.000000   \n",
+       "30          1.000000          1.000000             1.000000         0.500000   \n",
+       "31          1.000000          1.000000             1.000000         0.750000   \n",
+       "32          0.666667          1.000000             1.000000         0.444444   \n",
+       "33          0.600000          1.000000             1.000000         0.600000   \n",
+       "34          0.666667          1.000000             1.000000         0.666667   \n",
+       "35          0.714286          0.857143             1.000000         0.769231   \n",
+       "36          0.428571          1.000000             1.000000         0.500000   \n",
+       "37          0.500000          1.000000             1.000000         0.400000   \n",
+       "38          0.000000          1.000000             1.000000         0.000000   \n",
+       "39          0.500000          0.857143             1.000000         0.625000   \n",
+       "40          0.900000          1.000000             1.000000         0.666667   \n",
+       "41          0.500000          1.000000             1.000000         0.384615   \n",
+       "42          0.375000          0.571429             1.000000         0.428571   \n",
+       "43          0.888889          1.000000             1.000000         0.640000   \n",
+       "44          0.714286          1.000000             1.000000         0.588235   \n",
+       "45          1.000000          1.000000             1.000000         0.769231   \n",
+       "46          0.666667          1.000000             1.000000         0.666667   \n",
+       "47          0.666667          1.000000             0.500000         0.666667   \n",
+       "48          0.000000          1.000000             0.000000         0.000000   \n",
+       "49          0.000000          0.000000             0.000000         0.000000   \n",
+       "\n",
+       "    F1 score algo 3  F1 score algo 3 MC  \n",
+       "0          0.761905            0.705882  \n",
+       "1          0.941176            0.571429  \n",
+       "2          0.933333            0.833333  \n",
+       "3          1.000000            0.857143  \n",
+       "4          0.800000            0.800000  \n",
+       "5          0.800000            0.800000  \n",
+       "6          0.625000            0.625000  \n",
+       "7          0.666667            0.666667  \n",
+       "8          0.750000            0.750000  \n",
+       "9          0.666667            0.666667  \n",
+       "10         0.800000            0.800000  \n",
+       "11         0.666667            0.400000  \n",
+       "12         0.571429            0.461538  \n",
+       "13         0.285714            0.153846  \n",
+       "14         0.307692            0.307692  \n",
+       "15         0.333333            0.333333  \n",
+       "16         0.222222            0.222222  \n",
+       "17         0.250000            0.250000  \n",
+       "18         0.800000            0.666667  \n",
+       "19         0.750000            0.750000  \n",
+       "20         1.000000            1.000000  \n",
+       "21         0.500000            0.500000  \n",
+       "22         0.500000            0.500000  \n",
+       "23         0.000000            0.000000  \n",
+       "24         0.500000            0.666667  \n",
+       "25         0.000000            0.000000  \n",
+       "26         0.000000            0.000000  \n",
+       "27         0.666667            0.666667  \n",
+       "28         0.500000            0.000000  \n",
+       "29         0.000000            0.000000  \n",
+       "30         0.800000            0.800000  \n",
+       "31         0.571429            0.333333  \n",
+       "32         0.800000            0.800000  \n",
+       "33         0.571429            0.571429  \n",
+       "34         1.000000            0.800000  \n",
+       "35         0.923077            0.666667  \n",
+       "36         0.888889            0.888889  \n",
+       "37         1.000000            0.800000  \n",
+       "38         0.800000            0.758621  \n",
+       "39         0.923077            0.500000  \n",
+       "40         0.583333            0.454545  \n",
+       "41         0.608696            0.400000  \n",
+       "42         0.615385            0.500000  \n",
+       "43         0.400000            0.315789  \n",
+       "44         0.888889            0.750000  \n",
+       "45         0.857143            0.666667  \n",
+       "46         0.800000            0.800000  \n",
+       "47         0.500000            0.400000  \n",
+       "48         0.666667            0.000000  \n",
+       "49         0.000000            0.000000  "
+      ]
+     },
+     "execution_count": 321,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=[]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/'\n",
+    "data_excel_7t = pd.read_excel(path_database + \"7t_metrics_F1_sensitivity_mp2rage.xlsx\")\n",
+    "data_excel_7t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "id": "84e95373",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sensitivity algo 1 mean: 0.400623949579832 Sensitivity STD: 0.38714110281898106\n",
+      "Sensitivity algo 3 mean: 0.5262818309141839 Sensitivity STD: 0.30475897826389226\n",
+      "Sensitivity algo 3 MC mean: 0.4078004626092861 Sensitivity STD: 0.34976579465566804\n",
+      "Precision algo 1 mean: 0.6114898989898991 Precision STD: 0.38714110281898106\n",
+      "Precision algo 3 mean: 0.8629920634920635 Precision STD: 0.34976579465566804\n",
+      "Precision algo 3 MC mean: 0.8383333333333333 Precision STD: 0.30475897826389226\n",
+      "F1 score algo 1 mean: 0.4503918232094703 F1 score STD: 0.29047797970223105\n",
+      "F1 score algo 3 mean: 0.6159296768874776 F1 score STD: 0.28720250975714295\n",
+      "F1 score algo 3 MC mean: 0.5232138534336143 F1 score STD: 0.28353123239126327\n",
+      "p-value annotation legend:\n",
+      "      ns: p <= 1.00e+00\n",
+      "       *: 1.00e-02 < p <= 5.00e-02\n",
+      "      **: 1.00e-03 < p <= 1.00e-02\n",
+      "     ***: 1.00e-04 < p <= 1.00e-03\n",
+      "    ****: p <= 1.00e-04\n",
+      "\n",
+      "Sensitivity algo 1 vs. Sensitivity algo 3: Wilcoxon test (paired samples), P_val:8.818e-04 Stat=1.215e+02\n",
+      "Sensitivity algo 3 vs. Sensitivity algo 3 MC: Wilcoxon test (paired samples), P_val:3.959e-05 Stat=0.000e+00\n",
+      "Precision algo 1 vs. Precision algo 3: Wilcoxon test (paired samples), P_val:1.779e-05 Stat=1.950e+01\n",
+      "Precision algo 3 vs. Precision algo 3 MC: Wilcoxon test (paired samples), P_val:7.553e-01 Stat=2.950e+01\n",
+      "F1 score algo 1 vs. F1 score algo 3: Wilcoxon test (paired samples), P_val:2.276e-05 Stat=8.650e+01\n",
+      "F1 score algo 3 vs. F1 score algo 3 MC: Wilcoxon test (paired samples), P_val:9.052e-05 Stat=1.300e+01\n",
+      "Sensitivity algo 1 vs. Sensitivity algo 3 MC: Wilcoxon test (paired samples), P_val:7.451e-01 Stat=2.785e+02\n",
+      "Precision algo 1 vs. Precision algo 3 MC: Wilcoxon test (paired samples), P_val:9.171e-05 Stat=2.650e+01\n",
+      "F1 score algo 1 vs. F1 score algo 3 MC: Wilcoxon test (paired samples), P_val:4.774e-02 Stat=2.340e+02\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<AxesSubplot:xlabel='Group', ylabel='Metric'>,\n",
+       " [<statannotations.Annotation.Annotation at 0x7f6d4d838550>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4d8383d0>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4d838910>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d4d838650>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1d395410>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1d395750>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1d395710>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1d395450>,\n",
+       "  <statannotations.Annotation.Annotation at 0x7f6d1d395390>])"
+      ]
+     },
+     "execution_count": 320,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = data_excel_7t\n",
+    "df_Sensitivity_algo_1 = data.filter(['Sensitivity algo 1'], axis=1)\n",
+    "df_Sensitivity_algo_1.insert(0, 'Group', 'Sensitivity algo 1')\n",
+    "df_Sensitivity_algo_1.rename(columns={'Sensitivity algo 1': 'Metric'}, inplace=True)\n",
+    "df_Sensitivity_algo_3 = data.filter(['Sensitivity algo 3'], axis=1)\n",
+    "df_Sensitivity_algo_3.insert(0, 'Group', 'Sensitivity algo 3')\n",
+    "df_Sensitivity_algo_3.rename(columns={'Sensitivity algo 3': 'Metric'}, inplace=True)\n",
+    "df_Sensitivity_algo_3_mc = data.filter(['Sensitivity algo 3 MC'], axis=1)\n",
+    "df_Sensitivity_algo_3_mc.insert(0, 'Group', 'Sensitivity algo 3 MC')\n",
+    "df_Sensitivity_algo_3_mc.rename(columns={'Sensitivity algo 3 MC': 'Metric'}, inplace=True)\n",
+    "\n",
+    "\n",
+    "df_Precision_algo_1 = data.filter(['Precision algo 1'], axis=1)\n",
+    "df_Precision_algo_1.insert(0, 'Group', 'Precision algo 1')\n",
+    "df_Precision_algo_1.rename(columns={'Precision algo 1': 'Metric'}, inplace=True)\n",
+    "df_Precision_algo_3 = data.filter(['Precision algo 3'], axis=1)\n",
+    "df_Precision_algo_3.insert(0, 'Group', 'Precision algo 3')\n",
+    "df_Precision_algo_3.rename(columns={'Precision algo 3': 'Metric'}, inplace=True)\n",
+    "df_Precision_algo_3_mc = data.filter(['Precision algo 3 MC'], axis=1)\n",
+    "df_Precision_algo_3_mc.insert(0, 'Group', 'Precision algo 3 MC')\n",
+    "df_Precision_algo_3_mc.rename(columns={'Precision algo 3 MC': 'Metric'}, inplace=True)\n",
+    "\n",
+    "df_F1_algo_1 = data.filter(['F1 score algo 1'], axis=1)\n",
+    "df_F1_algo_1.insert(0, 'Group', 'F1 score algo 1')\n",
+    "df_F1_algo_1.rename(columns={'F1 score algo 1': 'Metric'}, inplace=True)\n",
+    "df_F1_algo_3 = data.filter(['F1 score algo 3'], axis=1)\n",
+    "df_F1_algo_3.insert(0, 'Group', 'F1 score algo 3')\n",
+    "df_F1_algo_3.rename(columns={'F1 score algo 3': 'Metric'}, inplace=True)\n",
+    "df_F1_algo_3_mc = data.filter(['F1 score algo 3 MC'], axis=1)\n",
+    "df_F1_algo_3_mc.insert(0, 'Group', 'F1 score algo 3 MC')\n",
+    "df_F1_algo_3_mc.rename(columns={'F1 score algo 3 MC': 'Metric'}, inplace=True)\n",
+    "\n",
+    "frames = [df_Sensitivity_algo_1, df_Sensitivity_algo_3, df_Sensitivity_algo_3_mc, df_Precision_algo_1, df_Precision_algo_3, \n",
+    "          df_Precision_algo_3_mc , df_F1_algo_1, df_F1_algo_3, df_F1_algo_3_mc]\n",
+    "result_gm = pd.concat(frames, axis=0)\n",
+    "\n",
+    "plt.figure(figsize=(5,5))\n",
+    "plt.xticks(rotation=90)\n",
+    "colores = [\"#1f77b4\", \"#FF8C00\", \"#FF1C00\"]\n",
+    "ax_gm = sns.boxplot(data=result_gm, x=\"Group\", palette=colores,  y=\"Metric\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "print (\"Sensitivity algo 1 mean:\", np.mean(data[\"Sensitivity algo 1\"]) ,\"Sensitivity STD:\", np.std(data[\"Precision algo 1\"])) \n",
+    "print (\"Sensitivity algo 3 mean:\", np.mean(data[\"Sensitivity algo 3\"]) ,\"Sensitivity STD:\", np.std(data[\"Precision algo 3\"])) \n",
+    "print (\"Sensitivity algo 3 MC mean:\", np.mean(data[\"Sensitivity algo 3 MC\"]) ,\"Sensitivity STD:\", np.std(data[\"Precision algo 3 MC\"])) \n",
+    "print (\"Precision algo 1 mean:\", np.mean(data[\"Precision algo 1\"]) ,\"Precision STD:\", np.std(data[\"Precision algo 1\"]))\n",
+    "print (\"Precision algo 3 mean:\", np.mean(data[\"Precision algo 3\"]) ,\"Precision STD:\", np.std(data[\"Precision algo 3 MC\"])) \n",
+    "print (\"Precision algo 3 MC mean:\", np.mean(data[\"Precision algo 3 MC\"]) ,\"Precision STD:\", np.std(data[\"Precision algo 3\"])) \n",
+    "print (\"F1 score algo 1 mean:\", np.mean(data[\"F1 score algo 1\"]) ,\"F1 score STD:\", np.std(data[\"F1 score algo 1\"])) \n",
+    "print (\"F1 score algo 3 mean:\", np.mean(data[\"F1 score algo 3\"]) ,\"F1 score STD:\", np.std(data[\"F1 score algo 3\"])) \n",
+    "print (\"F1 score algo 3 MC mean:\", np.mean(data[\"F1 score algo 3 MC\"]) ,\"F1 score STD:\", np.std(data[\"F1 score algo 3 MC\"])) \n",
+    "\n",
+    "pairs=[\n",
+    "           ((\"Sensitivity algo 1\"), (\"Sensitivity algo 3\")),\n",
+    "           ((\"Sensitivity algo 1\"), (\"Sensitivity algo 3 MC\")),\n",
+    "           ((\"Sensitivity algo 3\"), (\"Sensitivity algo 3 MC\")),\n",
+    "            ((\"Precision algo 1\"), (\"Precision algo 3\")),\n",
+    "            ((\"Precision algo 1\"), (\"Precision algo 3 MC\")),\n",
+    "            ((\"Precision algo 3\"), (\"Precision algo 3 MC\")),\n",
+    "           ((\"F1 score algo 1\"), (\"F1 score algo 3\")),\n",
+    "            ((\"F1 score algo 1\"), (\"F1 score algo 3 MC\")),\n",
+    "            ((\"F1 score algo 3\"), (\"F1 score algo 3 MC\"))\n",
+    "]\n",
+    "annotator_csa_gm = Annotator(ax_gm, pairs, data=result_gm, x=\"Group\", y=\"Metric\")#, order=order)\n",
+    "annotator_csa_gm.configure(test='Wilcoxon', text_format='star', loc='inside').apply_test()\n",
+    "annotator_csa_gm.apply_and_annotate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "id": "755206f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/'\n",
+    "data_3T = pd.read_excel(path_database + \"dices_3T.xlsx\") \n",
+    "data_7T = pd.read_excel(path_database + \"dices_7T.xlsx\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dd9a5fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "220ae994",
+   "metadata": {},
+   "source": [
+    "## 3T 7T dices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "28d4722a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>field</th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>subject</th>\n",
+       "      <th>dice_lesion_algo1</th>\n",
+       "      <th>dice_lesion_single</th>\n",
+       "      <th>dice_lesion_multi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sub-MRS00-ses-M0</td>\n",
+       "      <td>0.272853</td>\n",
+       "      <td>0.635678</td>\n",
+       "      <td>0.548431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sub-MRS15-ses-M0</td>\n",
+       "      <td>0.090409</td>\n",
+       "      <td>0.721868</td>\n",
+       "      <td>0.656020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3T</td>\n",
+       "      <td>2</td>\n",
+       "      <td>sub-MRS15-ses-M24</td>\n",
+       "      <td>0.122134</td>\n",
+       "      <td>0.763158</td>\n",
+       "      <td>0.661224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3T</td>\n",
+       "      <td>3</td>\n",
+       "      <td>sub-MRS18-ses-M0</td>\n",
+       "      <td>0.401709</td>\n",
+       "      <td>0.517857</td>\n",
+       "      <td>0.175824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3T</td>\n",
+       "      <td>4</td>\n",
+       "      <td>sub-MRS23-ses-M0</td>\n",
+       "      <td>0.077063</td>\n",
+       "      <td>0.746186</td>\n",
+       "      <td>0.537415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>7T</td>\n",
+       "      <td>32</td>\n",
+       "      <td>sub-MRS14_ses-M24_run-2</td>\n",
+       "      <td>0.335290</td>\n",
+       "      <td>0.528416</td>\n",
+       "      <td>0.267511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>7T</td>\n",
+       "      <td>33</td>\n",
+       "      <td>sub-MRS16_ses-M0_run-1</td>\n",
+       "      <td>0.307692</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.516908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>7T</td>\n",
+       "      <td>34</td>\n",
+       "      <td>sub-MRS16_ses-M0_run-2</td>\n",
+       "      <td>0.433031</td>\n",
+       "      <td>0.484490</td>\n",
+       "      <td>0.438538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>7T</td>\n",
+       "      <td>35</td>\n",
+       "      <td>sub-MRS18_ses-M0_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.505495</td>\n",
+       "      <td>0.096386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>7T</td>\n",
+       "      <td>36</td>\n",
+       "      <td>sub-MRS18_ses-M24_run-1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>115 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   field  Unnamed: 0                  subject  dice_lesion_algo1  \\\n",
+       "0     3T           0         sub-MRS00-ses-M0           0.272853   \n",
+       "1     3T           1         sub-MRS15-ses-M0           0.090409   \n",
+       "2     3T           2        sub-MRS15-ses-M24           0.122134   \n",
+       "3     3T           3         sub-MRS18-ses-M0           0.401709   \n",
+       "4     3T           4         sub-MRS23-ses-M0           0.077063   \n",
+       "..   ...         ...                      ...                ...   \n",
+       "45    7T          32  sub-MRS14_ses-M24_run-2           0.335290   \n",
+       "46    7T          33   sub-MRS16_ses-M0_run-1           0.307692   \n",
+       "47    7T          34   sub-MRS16_ses-M0_run-2           0.433031   \n",
+       "48    7T          35   sub-MRS18_ses-M0_run-1           0.000000   \n",
+       "49    7T          36  sub-MRS18_ses-M24_run-1           0.000000   \n",
+       "\n",
+       "    dice_lesion_single  dice_lesion_multi  \n",
+       "0             0.635678           0.548431  \n",
+       "1             0.721868           0.656020  \n",
+       "2             0.763158           0.661224  \n",
+       "3             0.517857           0.175824  \n",
+       "4             0.746186           0.537415  \n",
+       "..                 ...                ...  \n",
+       "45            0.528416           0.267511  \n",
+       "46            0.444444           0.516908  \n",
+       "47            0.484490           0.438538  \n",
+       "48            0.505495           0.096386  \n",
+       "49            0.000000           0.000000  \n",
+       "\n",
+       "[115 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_3T.insert(0, 'field', '3T')\n",
+    "data_7T.insert(0, 'field', '7T')\n",
+    "frames_3T7T = [data_3T, data_7T]\n",
+    "result_3T7T = pd.concat(frames_3T7T)\n",
+    "result_3T7T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "4adbffc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    field                  subject            Methods       DSC\n",
+      "0      3T         sub-MRS00-ses-M0  dice_lesion_algo1  0.272853\n",
+      "1      3T         sub-MRS15-ses-M0  dice_lesion_algo1  0.090409\n",
+      "2      3T        sub-MRS15-ses-M24  dice_lesion_algo1  0.122134\n",
+      "3      3T         sub-MRS18-ses-M0  dice_lesion_algo1  0.401709\n",
+      "4      3T         sub-MRS23-ses-M0  dice_lesion_algo1  0.077063\n",
+      "..    ...                      ...                ...       ...\n",
+      "340    7T  sub-MRS14_ses-M24_run-2  dice_lesion_multi  0.267511\n",
+      "341    7T   sub-MRS16_ses-M0_run-1  dice_lesion_multi  0.516908\n",
+      "342    7T   sub-MRS16_ses-M0_run-2  dice_lesion_multi  0.438538\n",
+      "343    7T   sub-MRS18_ses-M0_run-1  dice_lesion_multi  0.096386\n",
+      "344    7T  sub-MRS18_ses-M24_run-1  dice_lesion_multi  0.000000\n",
+      "\n",
+      "[345 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(result_3T7T)\n",
+    "# Eliminar la columna 'Unnamed: 0'\n",
+    "df.drop(columns=['Unnamed: 0'], inplace=True)\n",
+    "# Crear una nueva columna 'DICE' y apilar las columnas 'dice_lesion_algo1', 'dice_lesion_single' y 'dice_lesion_multi'\n",
+    "df = df.melt(id_vars=['field', 'subject'], var_name='Methods', value_name='DSC')\n",
+    "# Mostrar el DataFrame resultante\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c18cacc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1, 2]),\n",
+       " [Text(0, 0, 'dice_lesion_algo1'),\n",
+       "  Text(1, 0, 'dice_lesion_single'),\n",
+       "  Text(2, 0, 'dice_lesion_multi')])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colores = [\"#0f77c4\",  \"#FF1C00\"]\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "plt.figure(figsize=(5,4))\n",
+    "plt.title(\"T1 values in WM maks\")\n",
+    "#ax_csa_wm = sns.violinplot(data=result, x=\"Region\", y=\"T1\", hue=\"Laterality\", split=True) #swarmplot\n",
+    "ax_csa_gm = sns.violinplot(data=df, palette=colores, x=\"Methods\", y=\"DSC\", hue=\"field\", split=True)\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "plt.xticks(rotation=55)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "id": "6c3b3586",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     field                  subject             Methods   Metrics\n",
+      "0       3T         sub-MRS00-ses-M0          dice algo1  0.272853\n",
+      "1       3T         sub-MRS15-ses-M0          dice algo1  0.090409\n",
+      "2       3T        sub-MRS15-ses-M24          dice algo1  0.122134\n",
+      "3       3T         sub-MRS18-ses-M0          dice algo1  0.401709\n",
+      "4       3T         sub-MRS23-ses-M0          dice algo1  0.077063\n",
+      "...    ...                      ...                 ...       ...\n",
+      "1375    7T         sub-MRS16_ses-M0  F1 score algo 3 MC  0.666667\n",
+      "1376    7T   sub-MRS16_ses-M0_run-1  F1 score algo 3 MC  0.800000\n",
+      "1377    7T   sub-MRS16_ses-M0_run-2  F1 score algo 3 MC  0.400000\n",
+      "1378    7T   sub-MRS18_ses-M0_run-1  F1 score algo 3 MC  0.000000\n",
+      "1379    7T  sub-MRS18_ses-M24_run-1  F1 score algo 3 MC  0.000000\n",
+      "\n",
+      "[1380 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "path_database  = '/mnt/d/Poly/project_ms_seg/'\n",
+    "data_3T = pd.read_excel(path_database + \"3t_metrics_F1_sensitivity_mp2rage.xlsx\")\n",
+    "data_7T = pd.read_excel(path_database + \"7t_metrics_F1_sensitivity_mp2rage.xlsx\")\n",
+    "data_3T.insert(0, 'field', '3T')\n",
+    "data_7T.insert(0, 'field', '7T')\n",
+    "frames_3T7T = [data_3T, data_7T]\n",
+    "result_3T7T = pd.concat(frames_3T7T)\n",
+    "df = pd.DataFrame(result_3T7T)\n",
+    "# Eliminar la columna 'Unnamed: 0'\n",
+    "#df.drop(columns=['Unnamed: 0'], inplace=True)\n",
+    "# Crear una nueva columna 'DICE' y apilar las columnas 'dice_lesion_algo1', 'dice_lesion_single' y 'dice_lesion_multi'\n",
+    "df = df.melt(id_vars=['field', 'subject'], var_name='Methods', value_name='Metrics')\n",
+    "# Mostrar el DataFrame resultante\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "id": "81039e3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
+       " [Text(0, 0, 'dice algo1'),\n",
+       "  Text(1, 0, 'dice algo 3'),\n",
+       "  Text(2, 0, 'dice algo 3 MC'),\n",
+       "  Text(3, 0, 'Sensitivity algo 1'),\n",
+       "  Text(4, 0, 'Sensitivity algo 3'),\n",
+       "  Text(5, 0, 'Sensitivity algo 3 MC'),\n",
+       "  Text(6, 0, 'Precision algo 1'),\n",
+       "  Text(7, 0, 'Precision algo 3'),\n",
+       "  Text(8, 0, 'Precision algo 3 MC'),\n",
+       "  Text(9, 0, 'F1 score algo 1'),\n",
+       "  Text(10, 0, 'F1 score algo 3'),\n",
+       "  Text(11, 0, 'F1 score algo 3 MC')])"
+      ]
+     },
+     "execution_count": 325,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ixpY5lrCwsCKV9XfOLC86Olq+vr5OKw8AyoJx48Zp7969Cg4O1iuvvGJ1OG4pJiZGCxcu1MSJExUaGmp1OFm4cmzugjpASeFcsx51ACsVajEd0zTVv39/vfbaa+rZs6ezYwLKBB+ZLpOoLE41d71ldQgAgFIuNjZWe/fulSTt3btXsbGxCgoKsjgq92K32xUREaEzZ84oIiJC7du3l81mszosSa4dm7ugDlBSONesRx3AaoVKVJYrV07VqlVjrjsALs0ZvVozji5qP9TMvVoBAFmNGTMm298bNmywKBr3tGLFCsXFxUmS4uLiFBUVpXvvvdfiqK5w5djcBXWAksK5Zj3qAFYrVKJSkvr3769169apV69eTgwHQGlgs9kUHR3tlLLsdrtjWPXatWuL/Gtd5vLcpVdrbkzTzHN+zYLMxcn8mgVnZR0kFTnF7jyuFAtcy7vvvquEhIQs2xISEvTuu+/qrrvusigq93L8+HFFRUU5OiGYpqmoqCj17dtXgYGBxObmqAPncKXroCvFkllxn2vOeN3O7MTgivi8wxUUOlHZrFkzbdiwQSNHjlTfvn1VvXr1bF+c+vbtW+QAAbgewzCKZY5Fm83G3I1OZJqmxo8f7xhOmR95zcUZHBysyMhIkpX5ZEUdZB7t4Kq9eBmRgQypqalasmTJVR9bsmSJ7rjjDpUrV+jbVeSDaZpauHBhjtvnz59vWZvvyrG5C+qgaLgm519JnGuuWgeugs87XEWh7/ymTp0qSTp16pS+/fbbbI8bhqEDBw4UPjIAAACUacuXL8/zcYabFa/Y2Fjt2LEj2/a0tDTt2LFDsbGxql+/vgWRuXZs7oI6QEnhXLMedQBXUehE5bJly8imA4ALMwxDkZGReQ4llv76NT2vdp2h3wVjRR1kfswZ87Q6S+Z5WjmHkGHkyJG5JitHjhxZgtG4p6CgIHXq1Ek7d+5UWlqaY7unp6c6dOhg6aJGrhybu6AOioZrcv4V17nmylNWZXCVhWr4vMNVFDpR2aVLF2fGUSoZ6SlFLySjq30RLxBOiQVAmVNcw/SRf1bWAfO0wtWVK1dODz744FWHf48dO5Zh3yXAMAxNnDhRo0aNuup2K5MYrhybu6AOnIdrcu6K61xjyqr84/MOV1Hou78bbrhBixcvVrNmzbI9dujQIY0dO1afffZZkYJzdTV3vWV1CAAAAKXaXXfdpX//+99ZFtTx8/PTkCFDLIzKvQQGBio8PFzvvPOOTNOUYRgKDw9X3bp1rQ7NpWNzF9QBSgrnmvWoA7gCj8IeeOLECSUnJ1/1saSkJJ08ebLQQQEAAJQFSTJc6p+rev3113P9G8Vv2LBhqlq1qiSpWrVqCg8Ptziiv7hybO6COkBJ4VyzHnUAqxXLeJpjx46pfPnyxVG05ZjjAgAA5IZVXgsuKChIwcHB2rt3r4KDg5kHywI2m02TJ0/WwoULNXHiRJe6n3Tl2NwFdYCSwrlmPeoAVitQonLt2rVau3at4++ZM2fK398/yz5JSUk6ePCgOnXq5JwIXQxzXJQupmnmuohF5sfys9gFC4kAAFA8XnnlFatDcHuhoaEKDQ21OoyrcuXY3AV1gJLCuWY96gBWKlCiMjExUefOnZN0JWF38eJFpaRkXcTFy8tLt9xyi8aPH++8KIFCME1T48eP1969e/O1f0bv1twEBwcrMjKSZCUAIEes8goAAAAUToESleHh4Y75CXr37q3IyMirLqYDAAAAVnkFAAAACqLQc1Ru3rzZmXEATmcYhiIjI/Mc0p0xT1d+epcw9BsAAAAAAKB4FGkxneTkZK1Zs0bffvutzp07pxkzZqhBgwbatGmTmjZtqnr1XHMCebiP4ppTFAAAAAAAAM5V6ETl2bNnNWrUKP3888+qVq2a4uLidPnyZUnSZ599pi+//FIzZ850VpwAAAAAAAAAyjCPwh744osvKj4+XqtXr9aWLVscw2clqUuXLtqxY4dTAgQAAAAAAABQ9hU6UbllyxZNmDBBLVu2zDZnX82aNfXHH38UOTgAAAAAAAAA7qHQicpLly6pTp06V30sNTVVaWlphQ4KAAAAAAAAgHspdKIyMDBQu3btuupju3fvVsOGDQtbNAAAANzM0qVL1bt3by1dutTqUAAAAGCRQicqBw4cqDfffFObNm1yzE9pGIZ2796t5cuX69Zbb3VakAAAACi7zp8/rxUrVig9PV0rVqzQ+fPnrQ4JAAAAFih0ovL+++9X+/btNW7cOHXr1k2SNHr0aA0ZMkRt2rTRyJEjnRYkAAAAyq6nn35a6enpkqT09HQ988wzFkcEAAAAK5Qr7IFeXl568803tWHDBn3++ec6e/asKleurF69eql///7y8Ch0DhQAAABu4rvvvtOePXuybNu9e7e+++47dezY0aKoAAAAYIVCJyqlK0O9+/fvr/79+zsrHgAAALiJ9PR0zZo166qPzZo1S+vWrePHbwAAADdSoERlQYZzG4ahZcuWFTggAAAAuIft27crPj7+qo/Fx8dr+/btCgkJKeGoAAAAYJUCJSq//fZb+fv7q3bt2sUVDwAAANxEly5dFBAQcNVkZcWKFdWlSxcLogIAAIBVCpSorFevno4dOyZ/f38NHjxYt9xyi/z8/IocxNGjRzVnzhx9//338vX1Vf/+/TVlyhTZbLZ8l7Fx40aNGzdO1157rdavX1/kmAAAAFC8PDw89Mwzz2jKlCnZHpsxYwbDvgEAANxMge7+Nm7cqOXLl6tevXqaM2eOunfvrqeeeko7d+4sdADx8fEaNWqULl++rEWLFmnq1Kn6+OOPNX369HyXYbfbNXfuXFWrVq3QcQAoe5JkuNQ/AEB2HTt2VKtWrbJsa926tdq3b29RRAAAK8XExGjIkCGKiYmxOhQAFijwYjqdO3dW586d9cwzz+jjjz/WmjVrFB4eroYNG2rw4MG67bbbCpQwXLlypeLj47Vu3TpVqVJFkuTp6akpU6Zo7NixatSoUZ5lLFmyRHXq1FFgYKD27t1b0JcEoAwxTdPx/xP86lkYSc4yxwgAkKZPn64hQ4Y4/n7qqacsjAYAYBW73a6IiAidOXNGERERat++fYFGWgIo/Qo9nsbf31933XWXVq1apY8++kghISFasGCBnn322QKVs23bNoWEhDiSlJJ00003ydvbW1u3bs3z+NjYWL399tsF6oEJAAAA1/H3aXs++eQTiyIBAFhpxYoViouLkyTFxcUpKirK4ogAlLQC96j8u8OHD2vt2rX6z3/+I9M01bBhwwIfP3jw4CzbvL29FRQUpMOHD+d5/HPPPadbb71VzZo1K9DzAiibDOOvIdaLEo7JR67RezFJhqOHZ+YYyzpXGvLuSrEA+Mvx48ezfRGNiopS3759FRgYaFFUAMoi0zRlt9tzfDzzY7ntl8Fms7nVfV1xy7geZIw+Mk2T6wHghgqVqLx8+bI++eQTrV69Wj/++KOCgoI0YsQIhYWFqWbNmgUqKz4+XgEBAdm2BwQE6MKFC7keu3nzZv3www/69NNPC/ScuTFNUwkJCU4rLzeZL36JiYkuNRzUlWNzZfm5obGaq9Wns8+1zOX5yHSZRGVmrlYHzpaYmOj4f1cdfp+QkFBm64B2qHCc2RZRB/lnmqYiIiKybU9PT1dERIRmz55dqCQAdQDg70zT1JQpU3TgwIF87R8WFpbnPi1atNCLL76YYztFW5R/xXU9cCa+IxdOWfgcmKZp+fnnTgqUqNyxY4c++OAD/fe//5Vpmrrpppv06KOPqnPnzk4PLK8TISkpSc8//7zGjx+fZdh4UaWkpOT74lVUSUlJjv8/ePCgfHx8SuR588OVY3Nlmd83V+Vq9ensc406sF5puBk5ePBgmZ3viM9A4TizLaIO8u/UqVNXXZQxPT1dO3fu1NatWwv8I7hEHQDIzjTNLD+mOkNCQoIOHDiQ4/dW2qL8K67rgTPxHblwysrnwNvbu4SiQYESlSNGjJC/v78GDhyo/v37y9/fX5K0b9++q+7fsmXLPMsMCAhQfHx8tu0XL17MdSGdZcuWycPDQ/3793ccn5KSovT0dMXHx8tmsxXqRPLy8lLjxo0LfFxhZP4y36xZM5f60uzKsbmy0pCgcbX6dPa5Rh1YL3MduOrw++bNm5fZOuAzUDjObIuog4LFsWnTJu3atUvp6emO7R4eHmrXrp169uxZ5B6Vzpj2IaMVK2pJmWNxlToA3MnixYvzTJpk9KrKT9vj4+OT635cDwoWR3FcD5yJ78iFUxY+B7/88ksJRoMCD/2+dOmSVq1apVWrVuW4T0ZvyPz0TGzUqFG2uSiTk5MVGxubbe7KzI4cOaLffvtNISEh2R7r1KmTZs6cqbvuuivP5/87wzDk5+dX4OMKI3ND6+vrK19f3yKVl9ecKwWROTbDMJx2USjr87iUhtfmjHPNmZz9OaAOrJe5Dlx1+H1ZrgM+A4XjzLaIOiiYyZMna9SoUVm2eXh4aPLkySpfvnyhysxcB646BYUr1QHgTgrbrhQG14OCKY7rgTM5+3uLuygLn4PS8BrKkgIlKufOnev0AHr06KHXXntN586dU+XKlSVJGzduVHJysnr27Jnjcffff3+2eUPeeOMNHT16VHPnzlWDBg2cHqurs9vtuvnmm51ebn7mZ8mv6OhoGnQAAOAQGBio8PBwvfPOO44fu8PDw1W3bl2rQwMAlCCuBwCkAiYqnZmwyjB06FC98847euihh/TQQw8pLi5O8+bN08CBA7MM/Z42bZrWrVun/fv3S7rSE/PvQ8PXrl2rU6dOqUuXLk6PEwAAAMVj2LBhio6O1pkzZ1StWjWFh4cXqTybzabo6GinxGa32x33wGvXrnXaUD+GDAJAds6+HgAofQq16rczBQQEaNmyZZozZ47Gjx8vm82mAQMGaMqUKVn2S09PV1pamkVRlj7OmBfOmfMxueqwKwAAYD2bzabJkydr4cKFmjhxYpGTeIZhFMsIDpvNxsgQAChGzr4eACh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\n",
+      "text/plain": [
+       "<Figure size 1500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colores = [\"#0f77c4\",  \"#FF1C00\"]\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "plt.figure(figsize=(15,4))\n",
+    "plt.title(\"Voxel and Lesion wise metrics\")\n",
+    "#ax_csa_wm = sns.violinplot(data=result, x=\"Region\", y=\"T1\", hue=\"Laterality\", split=True) #swarmplot\n",
+    "#ax_csa_gm = sns.violinplot(data=df, palette=colores, x=\"Methods\", y=\"Metrics\", hue=\"field\", split=True)\n",
+    "ax_csa_gm = sns.boxplot(data=df, palette=colores, x=\"Methods\", y=\"Metrics\", hue=\"field\")\n",
+    "\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "plt.xticks(rotation=55)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "id": "2d411623",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 8.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 35.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 6.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 27.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 55.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 22.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 72.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 56.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 62.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 18.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 30.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 26.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f6d4d0e1110>"
+      ]
+     },
+     "execution_count": 328,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#colores = [\"#0f77c4\",  \"#FF1C00\"]\n",
+    "colores = [ \"#FFD700\", \"#FF4500\"]\n",
+    "\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "plt.figure(figsize=(24,8))\n",
+    "plt.title(\"Voxel and Lesion wise metrics\")\n",
+    "#ax_csa_wm = sns.violinplot(data=result, x=\"Region\", y=\"T1\", hue=\"Laterality\", split=True) #swarmplot\n",
+    "ax_csa_gm = sns.violinplot(data=df, palette=colores, x=\"Methods\", y=\"Metrics\", hue=\"field\", split=True , inner=None,alpha=0.3)\n",
+    "\n",
+    "# Agregar scatter plot\n",
+    "sns.swarmplot(data=df, x=\"Methods\", y=\"Metrics\", hue=\"field\", dodge=True, palette=\"gray\", alpha=0.9)\n",
+    "#ax_csa_gm = sns.violinplot(data=df, palette=colores, x=\"Methods\", y=\"Metrics\", hue=\"field\", split=True, showfliers=False)\n",
+    "\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "#plt.xticks(rotation=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "71c32135",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    field                  subject          Methods  Nb of lesions\n",
+      "0      3T         sub-MRS00-ses-M0         Total GT              7\n",
+      "1      3T         sub-MRS15-ses-M0         Total GT              6\n",
+      "2      3T        sub-MRS15-ses-M24         Total GT              6\n",
+      "3      3T         sub-MRS18-ses-M0         Total GT              2\n",
+      "4      3T         sub-MRS23-ses-M0         Total GT              4\n",
+      "..    ...                      ...              ...            ...\n",
+      "455    7T  sub-MRS14_ses-M24_run-2  Total algo 3 MC              6\n",
+      "456    7T   sub-MRS16_ses-M0_run-1  Total algo 3 MC              2\n",
+      "457    7T   sub-MRS16_ses-M0_run-2  Total algo 3 MC              2\n",
+      "458    7T   sub-MRS18_ses-M0_run-1  Total algo 3 MC              0\n",
+      "459    7T  sub-MRS18_ses-M24_run-1  Total algo 3 MC              0\n",
+      "\n",
+      "[460 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "path_database  = '/mnt/d/Poly/project_ms_seg/'\n",
+    "lesions = pd.read_excel(path_database + \"lesions.xlsx\")\n",
+    "\n",
+    "df = pd.DataFrame(lesions)\n",
+    "# Eliminar la columna 'Unnamed: 0'\n",
+    "#df.drop(columns=['Unnamed: 0'], inplace=True)\n",
+    "# Crear una nueva columna 'DICE' y apilar las columnas 'dice_lesion_algo1', 'dice_lesion_single' y 'dice_lesion_multi'\n",
+    "df = df.melt(id_vars=['field', 'subject'], var_name='Methods', value_name='Nb of lesions')\n",
+    "# Mostrar el DataFrame resultante\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "d8d5c2dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 7.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f6d1e3e1150>"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1400x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#colores = [\"#0f77c4\",  \"#FF1C00\"]\n",
+    "colores = [ \"#FFD700\", \"#FF4500\"]\n",
+    "\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "plt.figure(figsize=(14,5))\n",
+    "plt.title(\"Voxel and Lesion wise metrics\")\n",
+    "#ax_csa_wm = sns.violinplot(data=result, x=\"Region\", y=\"T1\", hue=\"Laterality\", split=True) #swarmplot\n",
+    "ax_csa_gm = sns.violinplot(data=df, palette=colores, x=\"Methods\", y=\"Nb of lesions\", hue=\"field\", split=True , inner=None,alpha=0.3)\n",
+    "\n",
+    "# Agregar scatter plot\n",
+    "sns.swarmplot(data=df, x=\"Methods\", y=\"Nb of lesions\", hue=\"field\", dodge=True, palette=\"gray\", alpha=0.9)\n",
+    "#ax_csa_gm = sns.violinplot(data=df, palette=colores, x=\"Methods\", y=\"Metrics\", hue=\"field\", split=True, showfliers=False)\n",
+    "\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
+    "#plt.xticks(rotation=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "821efdca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>field</th>\n",
+       "      <th>Total GT</th>\n",
+       "      <th>Total algo 1</th>\n",
+       "      <th>Total algo 3</th>\n",
+       "      <th>Total algo 3 MC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-MRS00-ses-M0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-MRS15-ses-M0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-MRS15-ses-M24</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS18-ses-M0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS23-ses-M0</td>\n",
+       "      <td>3T</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>sub-MRS14_ses-M24_run-2</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>sub-MRS16_ses-M0_run-1</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>sub-MRS16_ses-M0_run-2</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>sub-MRS18_ses-M0_run-1</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>sub-MRS18_ses-M24_run-1</td>\n",
+       "      <td>7T</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>115 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     subject field  Total GT  Total algo 1  Total algo 3  \\\n",
+       "0           sub-MRS00-ses-M0    3T         7             9             5   \n",
+       "1           sub-MRS15-ses-M0    3T         6             5             6   \n",
+       "2          sub-MRS15-ses-M24    3T         6             3             5   \n",
+       "3           sub-MRS18-ses-M0    3T         2             5             1   \n",
+       "4           sub-MRS23-ses-M0    3T         4             7             4   \n",
+       "..                       ...   ...       ...           ...           ...   \n",
+       "110  sub-MRS14_ses-M24_run-2    7T        10             7             8   \n",
+       "111   sub-MRS16_ses-M0_run-1    7T         3             3             2   \n",
+       "112   sub-MRS16_ses-M0_run-2    7T         3             3             1   \n",
+       "113   sub-MRS18_ses-M0_run-1    7T         2             0             1   \n",
+       "114  sub-MRS18_ses-M24_run-1    7T         4             0             0   \n",
+       "\n",
+       "     Total algo 3 MC  \n",
+       "0                  5  \n",
+       "1                  6  \n",
+       "2                  5  \n",
+       "3                  1  \n",
+       "4                  3  \n",
+       "..               ...  \n",
+       "110                6  \n",
+       "111                2  \n",
+       "112                2  \n",
+       "113                0  \n",
+       "114                0  \n",
+       "\n",
+       "[115 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lesions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "66d06fe1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      9\n",
+       "1      5\n",
+       "2      3\n",
+       "3      5\n",
+       "4      7\n",
+       "      ..\n",
+       "110    7\n",
+       "111    3\n",
+       "112    3\n",
+       "113    0\n",
+       "114    0\n",
+       "Name: Total algo 1, Length: 115, dtype: int64"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lesions[\"Total algo 1\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "ab231a4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p1 = lesions[\"Total GT\"]\n",
+    "p2 = lesions[\"Total algo 1\"]\n",
+    "p3 = lesions[\"Total algo 3\"]\n",
+    "p4 = lesions[\"Total algo 3 MC\"]\n",
+    "pairs = list(zip(p1, p2))\n",
+    "#subjects = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "plt.figure(figsize=(4, 4))\n",
+    "for i, pair in enumerate(pairs):\n",
+    "\n",
+    "    plt.plot((\"GT\", \"algo 1\"), pair)\n",
+    "    plt.scatter((\"GT\", \"algo 1\"), pair)\n",
+    "plt.legend(loc=\"best\")\n",
+    "plt.grid()\n",
+    "plt.title(\"Nb of lesions method GT algo 1\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "65a7efb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bt27lmWee4cCBA9xyyy3k5OQQCoUSYZcXG8uXvvQl3n77bT7xiU/wpS99idTUVF555RX+9Kc/8Y1vfAOr1Tql8b799tv85je/4bbbbmPOnDlIksQbb7zB4OAg69evv6xrcSm+853v8NGPfpSPfexjfOQjHyEvLw+fz0dLSwtvvfUWTz75JCAvoxkMBl555RVKSkowmUxkZmYm+SAuxWQ//w0bNrB69Wp+8IMfEAgEKC8v58iRI7z00kuTep2UlBT++7//m7/8y7/knnvu4SMf+QhLly7FbDbj8Xg4fPgwvb29M5YNfcsttySig8rKyjh9+jSPP/74pJIQ/+qv/oq/+Iu/4LOf/Syf/OQnicViPP7445jNZgYGBhLHrV+/ng0bNvDDH/4Qr9fLihUrqKmp4Sc/+QkLFy68ZB7AAw88wPz581mwYAE2m42enh5eeOEFDh06xD/+4z+i0VxfU+v1NdrrlIULF7Jt27YJ78A2b95MaWkpP/7xj+ns7GTOnDn88Ic/HJOwciF6vZ4nn3yShx56iMcee4z+/n6ysrL4zGc+kwgxnAnUajU//elPefLJJ3nppZd49NFHUavVZGdns3r1apxOJyA7i/fu3cvDDz9Mb28vJpMJp9PJT3/6UzZs2DDh+YuLi/ntb3/Lj370Ix588EGCwSAlJSV8//vfH+PMmwyFhYWkpKTw2GOP0dPTg1arpaioiH/7t3/jAx/4wLSvw2QoLS3l+eef55FHHuHHP/4xfX19WK1WCgsLk8IvjUYj3/ve9/iv//ovPvvZzxKJRPjyl7/MV77ylUm/1mQ/f5VKxU9/+lO+//3v89hjjxGJRFixYgWPPvoo73nPeyb1WsuWLWPHjh08+eST7Ny5k1/+8pdEIhHS0tJYtGgR//Iv/3LZzudhvvOd76DRaHj00Ufx+/0sXLiQhx9+mP/8z/+85HOrqqoSx371q18lIyODj3zkI/T09CTd/QuCwCOPPMLDDz/M888/z89+9jNsNhv33HMPf/M3f3PJpa4VK1bw+uuv8/TTT+P1erFarZSXl/Pzn/88EQ58PSFIkiRd7UEoKCgozBaRSIT3v//9ZGVl8Ytf/OJqD+eaRLEIFBQUbii+/e1vs379ejIyMnC5XDzzzDPU19fzne9852oP7ZpFEQIFBYUbCp/Px7//+7/T19eHVqtl4cKFPProozPmM7sRUZaGFBQUFG5ylPBRBQUFhZscRQgUFBQUbnIUIVBQUFC4ybnhncVHjx5FkqRL1pdRUFBQuF6IRCIIgjBjSXw3vBBIkoTiD1dQULiRmOk57YYXgmFLYLpFpBQUFBSuNU6ePDmj51N8BAoKCgo3OYoQKCgoKNzkKEKgoKCgcJOjCIGCgoLCTc4N7yxWUFBQuNaIRCLEYrEJ92u1WtRq9RUbjyIECgoKCleIwcFBXC7XRXsvg9wvITU1lezs7DE9qWcDRQgUFBQUrgCDg4O0t7djsVhwOBxotdpxJ3lJkvD5fPT29mI0Gq9I20tFCMZBEiUCbQPEfBHUZi3G/FQE1eyrsoKCwo2Ly+XCYrGQn59/ybt8o9FIKBSip6eH1NTUWbcKFCG4AG+ti963Goh6R5pfayw6MjYXY3E6ruLIFBQUrlcikQihUAiHwzHpST0lJYXBwUFisdis90BWooZG4a110fnyuSQRAIh6w3S+fA5vresqjUxBQeF6ZtgxPJWaZ8OTfzQanZUxjUYRgjiSKNH7VsNFj+l9uwFJVOoWKSgoTI+pLPFcCSfxMIoQxAm0DYyxBC4kOhQm0DZwhUakoKCgcGVQfARxYr7IpI7r29+KFJMwFaQiqBUdVVBQuP5RhCCO2jy5tbtA6wCB1gFUBg2W0nQsTgemQpsiCgoKCtctUxaC5uZmHn/8cY4fP05dXR3FxcXs2LEj6ZiysrIJn797924yMzMn3D/ecx0OB3v37p3qUKeEMT8VjUV30eUhlVGDxenAV+cm5o8weKqHwVM9qPRqzKV2rGWKKCgoKFx/TFkI6urq2LVrF0uXLkUUxXEbJDz77LNjtn3zm9/EaDReVASGuf/++7n77rsTj69EdzFBJZCxuZjOl89NeEzW1lIsTgfSlhICbQN4a914a13E/BGGTvcwdDouCiV2LHFRUGkUUVBQUJCZSkOZK9lQa8pCsHnzZm677TYAtm/fzqlTp8Ycs2zZsqTHbW1tNDU18Y1vfGNSr5GTkzPmHFcCi9NBzvvmj80jsOrIuHUkj0BQCZgKbJgKbGRsLibYPshQrQtvrZuYL8zQmR6GzvSg0qkxDy8fzU1TREFB4SZlOIvY5/NhNBon9Ry/35947mwzZSFQqaY+me3YsQNBEJLu8q9VLE4H5lL7pDOLBZWAcU4qxjmpI6JQ48Jb5ybmDTN0ppehM72yKJSky5aCIgoKCjcVarWa1NRUent7CYVCpKSkoNFoJiwx4ff76enpwWazXZHic1fEWfzqq6+yevVqsrOzJ3X8o48+yo9+9COMRiMbNmzg7/7u78jNzZ3lUY4wfMc/5ecJAsb8VIz5cVHoGMJb48Jb6yLqDTN0tpehs70IWlkUrGV2WRS0V67KoIKCwtUhOzsbo9FIT08Pg4ODlzzeZrNNes68XGZdCM6dO0dtbS0PPvjgpI5///vfzy233ILD4aC2tpaf/vSnfPSjH+Wll14iNTV1lkc7cwiCgDEvBWNeCo5bi2RRiC8fRYdCeM/14j03LAppsiVSpIiCgsKNiiAI2Gw2UlNTicViF80YvuHKUL/yyitotVruuOOOSR3/7//+74m/V69ezcqVK7n33nv53e9+x+c///nZGuaskiQKtxQR7By2FIZFwYX3nAtBq8JcnD4iCjpFFBQUbjQEQUCj0cx6/aCpMKsjkSSJ1157jY0bN067lOr8+fMpKiri9OnTMzu4q4QgCBhzUzDmyqIQ6vLKPoVaF9HBkCwQNS4EjQpzcdxSKE5XREFBQWHWmFUhePfdd+no6Jh0tNBEXMkwKgBRFGlvb8fr9WKxWMjLy5uWk/xSCIKAIceKIceKY9NcQl1evLUuhmpdRAdC8fBUtywKRWlYyhRRUFBQmHlmVQheeeUVTCYTt95667TPcfbsWZqamvjgBz84gyObmLq6Ot566y28Xm9im8ViYfPmzcybN2/WXne0KNir5hLq9sk+hRoXkYEg3jo33jpZFExFaVidDswlaah01455qaCgcH0y5VkkEAiwa9cugMRd8x/+8AcA1qxZQ3p6OiCXTn399de57bbbJoyb3bp1K7m5uTzxxBMAPP7447S2tibOU1dXx89+9jOys7O57777pvUGp0JdXR0vv/zymO1er5eXX36Z973vfbMqBsMIgoAh24Ih24J9YyGhnlGi4Aniq3Pjq3MjqAVMRfHlo5J01HpFFBQUFKbOlGcOt9vNX//1XydtG3785JNPsnbtWgD27NlDf3//RXMHYrEYoigmHhcVFfHGG2/w2muv4fP5SEtLY9OmTXz1q18lJSVlqkOdEqIo8tZbb130mLfffpuSkpJZWSaaCEEQMGRZMGRZsG8oJNzrY6jWLYtCfwDf+T585/tkUZgbF4VSRRQUFBQmjyBd6QX4K8zJkycBWLx48UWPa21t5Xe/+90lz/ehD32IOXPmzMjYLgdJkgi7/HhrZJ9CpC+Q2CeoBUyFNtmnUGJHbVBEQUHhRmKy89pkUWaIOKN9AjNx3GwjCAL6DDP6DDPp6wtkUYgvH4X7Avga+vE19IPqPKZCG9YyOWNaEQUFBYULUWaFOBaLZVLHXUuxv8OMFgX7+kJCLp8ccVTjIuz242/sx984IgoWpx1LqR21cfZrmCgoKFz7XHuz2lUiLy8Pi8VyyTv+P/zhD/T19bFixYorUgxqOugdZvQOM/bKAsJufyJPIewaEYWeN+sxFaRiKXMooqCgcJOj+AhGMVHU0DCpqakMDMitKq1WK+vXr2fhwoVXtLfo5RB2+xN5CuFe/8gOAUwFthFRMCmioKBwLTPTPgJFCC5gvDwCq9XKrbfeSmlpKWfPnmXPnj0MDQ0BkJmZSVVVFYWFhTM/+Fkk3BeI1z5yEerxjewQwFhgw+q0Y57nQKOIgoLCNYciBFNkOhfsUpnFkUiEo0ePcvDgQcJhuW9BUVERVVVVOByOmX0DV4BwfyDhaB4jCnNSsTgdWObZ0Zh1V2+QCgoKCRQhmCIzfcFG4/f7OXDgAMePH0cURQRBoLy8nMrKykk7n681wp4A3hq581qoe5S/RJDbeVqcDixORRQUFK4mihBMkdkUgmH6+/vZvXs3dXV1gBxZtHr1alatWoVOd/1OmBFPMOFTCHUlO9GN+SlxUXCgsVy/71FB4XpEEYIpciWEYJj29nZ27dpFZ2cnAGazmcrKSsrLy69oNvJsEBmIi0LNWFEw5KdgHbYULPqrNEIFhZsHRQimyJUUApAzfuvq6ti9ezcejwcAu91OVVUVRUVF102E0cVIFMGrcRHsHEraZ8hLkfMUnA60VkUUFBRmA0UIpsiVFoJhYrEYx48fZ//+/QSDQQDmzJnDpk2byMrKuqJjmU0igyG8dbKjOdhxgSjkWuWQ1HkOtCmKKCgozBSKEEyR6VywmBjjRPN5+v0+0kxmlhSWolZNrwdAMBjk0KFDHDlyhFgsBsCCBQvYsGHDrBfSu9JEhkKJdpzB9uSerIacuCg47WhTDFdphAoKNwaKEEyRqV6w6nPHeaHRi1caKZ1tEQJ8oMhC1fyl0x7H4OAge/bs4ezZswCo1WpWrFjBmjVrMBhuvIkxOhTCW+dmqNZFsC1ZFPTZFqxlsqNZm3rjvXcFhdlGEYIpMpULVn3uOE81DDeUHr2WL1+i+4s1lyUGAF1dXVRXV9Pa2gqAwWCgoqKCpUuXXtFm1VeSqHe425qLwHiiEI8+0toUUVBQmAyKEEyRyV6wmBjj668fwCsZkEVAQk2MWKIck4RFCPLDO9ZNe5loGEmSaGhooLq6mr6+PgBsNhsbN25k3rx5N4RDeSKivvAoURgY1lgA9FkWLE4H1jJFFBQULoYiBFNkshfsaGMNj5wdyarNppsussinkyB6XMid1764wMzyorIZGZsoipw8eZJ9+/bh98u1f3Jycti0aRN5eXkz8hrXMlFfWI4+qnURaL1AFDLNcZ+CA13a+B3uFBRuVpR+BLNEv9+X9FhCDUi0kYMZP6uEE7RIufT7x3/+dFCpVCxdupQFCxZw+PBh3nnnHTo7O/ntb3/LvHnz2LhxI2lpaTP3gtcYGrMO27IcbMtyiPrC+M67Gap1E2jxEOrxEerx4d7dLItCfPlIl66IgoLCTKNYBHEutAgATPiREAggTz5OoYH1li6WLL4bi23ujI/V6/Wyd+9eTp8+jSRJCaFYt24dJpNpxl/vWiXqj+A7L+cp+Fs8SZaCLsMk+xTKHOjSb55roqAwGmVpaIpM30cwgo4wYbSAgAk/VarDLM4wk1u8FUta0YyPube3l+rqapqamgDQ6/WsWbOGFStWXJONcWaTWCCCNyEKAyCOfF11DlPCp6CzK6KgcPOgCMEUmcmooRSNxGBULhUxT2hivepdMuwF5JRsxZpWPJPDBqC5uZldu3bR29sLyOWwN2zYwIIFC25oh/JEJESh1o2/2ZMsCnZTIk9B7zBfvUEqKFwBFCGYIjOZR7Bu3mJeruvgjYYeJMBIgCrVYYpU7VjTS8kp3oo1fWYFQZIkzpw5w549exI9EjIzM9m0aRMFBQUz+lrXE7FgVPYp1LjGikK6ccTR7DDdlKKpcGOjCMEUmY3M4gaPj1+daKbTK5eOKBVaWK86jFEIY0krIbdkK9b0khl9H5FIhCNHjnDo0KFED4Ti4mKqqqqw2+0z+lrXG7FgFF99fPmo2YMUG/lKa9ONIz4FRRQUbhAUIZgis1VrKBITeeV8J3+o70YCTKoYG4SDFAvNAFjSiskt2YolrWRGJx+/38/+/fs5ceJEogfC4sWLqaysxGxWlkRioSi+8314a134m/qTRSFNthSsTju6DLMiCgrXLYoQTJHZLjrXFLcO2uPWwQKzjzWhnRiR40wttiLZh5BeOqMTT19fH7t37+b8+fMAaLVaVq1axerVq9FqlfaSEBeF+rgoNF4gCjZDYvlIn6mIgsL1hSIEU+RKVB+NxERere/i9/VdiBJYtCput/WQ6XkbJNn5bLbNjS8ZzWzmcFtbG9XV1TdkD4SZJBaK4mvokx3Njf1IUTGxT2szJPIU9FmKKChc+yhCMEWuZBnq5gE/vzrRTNtQAIBlGWZuNdYQ7NqPJMYFIbVQFgS7c8YmHEmSqK2tZffu3QwMDAA3Xg+EmUQMR/E19MuO5gtFIdWQ6Kegz7Yo107hmuSqC0FzczOPP/44x48fp66ujuLiYnbs2JF0zPbt23nhhRfGPPd//ud/qKqquuj5I5EIP/nJT3jhhRcYGhpiyZIlfOc732H+/PlTGWaCK92PICqKvHa+i9fqu4hJYNaquW+enTmBd3G1HxglCHLYaYq9bMYmm2g0yvHjxzlw4ECiB0JBQQFVVVU3VA+EmUQMx+KWggtfQ7IoaFL0iTwFRRQUriWuuhDs3LmTf/mXf2Hp0qU0NjYiSdK4QvDOO+/wwx/+MGl7SUkJVqv1oud/8MEHefHFF9m+fTt5eXk89thjnD17lldeeYWMjIypDBW4eo1pWgZl66B1ULYOlmam8mFnGoHOPfS2HkASI0BcEIpvI8Uxf8YmmmAwyMGDBzl69GiiB8LChQtZv379DdcDYSYRwzF8jf2yKNT3JYuCVS9bCmUODDlWRRQUripXXQhEUUysPW/fvp1Tp06NKwTjbb8U3d3d3HrrrXznO9/hYx/7GCCXXdiyZQv33XcfX//616d0Prh6QgAQFSX+0NDFjrouYpKESaPmw4vyWWnX0N1cTW/r/oQgmFLmkFuydUYFYWBggD179nDu3DkANBpNogeCXq90DLsYYiQuCjUufA19SJFxRMHpwJCriILCleeqF52bTQfknj17iMVibNu2LbHNYrGwefNmdu3aNS0huJpoVAJ3l+awLNPGr0400zzo5xfHmzmckcL9i+8ge+4tdDftord1H/7BVs4f/QWmlHxyireSmnH52cOpqals27aNlStXsmvXLtra2jh06BAnT56koqKCJUuW3LA9EC4XlVaN1enA6nQgRmL4m/oZqnHjq+8jOhTC824Hnnc70Fh1WObJeQqKKChcr8xa4ZqWlhZWrVpFMBjE6XTyxS9+kdtuu+2iz6mvr8fhcGCz2ZK2l5SU8MorryRZI9cT+SlGvlVZxusN3bxyvpOTvYP8U/VZ/nxBPpXObWTNvYXu5mFBaKP+2C8xWfPIKbmN1IxFlz25ZGdn86EPfYj6+np2795NX18fb731FkePHmXjxo2Uls5saOuNhkqrlif7ecOi4EksH0WHwniOdOA50oHaosMyz461zIEhL0W5pgrXDbMiBAsWLGDx4sWUlpYyNDTEM888w5e+9CX+8z//kzvvvHPC5w0ODo7rQ0hNTSUSieD3+7FYLLMx5FlHrRK4qzSbpVmpPHGimcYBP7862cw7Xf3cX15AvnMbWXM30d1UTW/rXvxD7dQfewKjNZfckq2XLQiCIFBaWkpxcXGiB0J/fz8vv/wyubm5bNq0idzc3Bl8xzcmsijYscyzI0ZF/E1xn8L5PmLeMANHOxk42onarMPitGN1xkVBpYiCwrXLrAjBJz/5yaTHmzdv5sMf/jA/+clPLioEwLiT3Y0U4ZpnNfLNijLebOzmpbpOTvUO8k+7z/ChBflsyLeT77yL7Lmb6G6upqdlL4Ghjrgg5JBTvBVb5iIEYfpW0Xg9EDo6OnjmmWdwOp1s3LhxjEWmMD4qjQpLqR1LaVwUmj2yT6HeTcw3WhS0skXhtGPMT1VEQeGa44rUNFapVNx+++384Ac/IBgMTtisPSUlhcHBwTHbBwcH0Wq1N0xNfrVK4M6SbJZmyb6DBo+PJ0+28E5nP59YXIDdaCZv3nvIKqyiu3k3PS17CAx10nD8SYyWHHKKb8OWVX5ZgqDT6Vi/fj1LlixJ9ECora3l/PnzLFu2jHXr1mE0Kk1gJotKo8JSko6lJB0xKhJo9jBU68J33k3MF2HgWCcDxzpRm7SyRVHmUERB4ZrhihW3n8xdfUlJCW63G4/Hk3RXWl9fT1FR0XXpH7gYORYD36xwsrOxhxdrOzjjGuKfqs9y34I8quY40OjM5M27k6y5VSMWgreThhNPYbBkk1t8G7asxZclCFarlTvvvJOVK1cmeiAcOXKE06dPs3btWpYvX37T9UC4XFQaFeaSdMwl6UixuKVQ68J7vo+YP8LA8S4GjnehNmoxx5ePjHMUUVC4elyRX7goirz++uvMmzdvQmsAYMOGDahUKn7/+9/zkY98BACfz8dbb73FfffddyWGesVRCQK3F2exJDOVJ042c77fx69PtfJOp4dPLC4gw6RHozWRV3onWYVV9DTvprtlD0FvFw0nfo3BnEVOyW2kZS25LEHIyMjggx/8IE1NTVRXVyea4xw7dowNGzYwf/7MhbXeTAhqFebidMzF6WTGRPwtA7Io1LmJBSIMHu9i8HgXaqMG8zw5JNVUYFNEQeGKMuU8gkAgwK5duwB4+umnaW1tZfv27QCsWbOGQCDA9u3bufvuuykoKGBgYIBnnnmGgwcP8vDDD7N169bEubZu3Upubi5PPPFEYtuDDz7ISy+9xPbt28nNzeUXv/hFIifhekoomw6iJPFWUy8v1LQTFiX0ahUfLMtjU6ED1ahJOBrx09O8h56W3cSicgaxwZxJTvFtpGUvvSxBAFm4z5w5w969exM9ELKysti0aRNz5sy5rHMryEgxEX/rAN4aF97zbsRANLFPZdTIvgenA1NBKoL6xrKEFS6fq55Q1tbWxpYtW8bd9+STT1JWVsa3vvUtTp8+TV9fH1qtlvLycr7whS+wcePGpOM3b95MXl4eTz31VGJbOBxOKjGxdOnS66rExEzQ4wvyxMkWavvkSdiZbuGTiwvJNCcngUUjAXpa9tDTvJtYVM5gnklBiEQivPvuuxw6dIhIRE58U3ogzDySKBFoHWCoxoWvzkVstCgY4qJQpoiCwghXXQiuN65HIQDZOvhTcy/P1XQQjono1CruLcvl1sKMJOsAIBYJ0NO6l+6m6oQg6E0Z5BRvIT17GYLq8pLG/H4/+/bt48SJE0iShCAILFmyhIqKCqUHwgwjiRKBtrilUOcm5o8k9qkMGswl6VjLHJgKbYoo3MQoQjBFrlchGKbXH+KJk83UuGXroDTNzKeWFJJlHutriUWD9LTspbu5mlhE7oegNznigrD8sgXB7Xaze/du6uvrAbkHwurVq1m1apXSA2EWuKgo6NWYS+KWQqENlUYRhZsJRQimyPUuBCBbB9UtLv7vXDuhmIhWJfCBsly2zM0cYx3AsCDso7t5V7IgFG0hPefyBaGtrY1du3bR1dUFyGVAKisrWbRo0Q0X2XWtIIkSgfbBuCi4iPkuFIX0uCikKaJwE6AIwRS5EYRgGJc/xJMnWzjrHgKgxCZbB9mW8SOxYtEgva376W7aRTTiA0BvtJNdvAV7zorLEgRJkqipqWH37t2J3A+Hw8GmTZuYO3futM+rcGkkUSLYMchQjQtvrZy8NoxKN0oU5iqicKOiCMEUuZGEAOQJeHerm/8910YwKqJRCbzfmcvWovGtA4BYNERv674kQdAZ08kp2oI9d+VlCUI0GuXYsWMcOHCAUCgEQGFhIVVVVWRmZk77vAqTQ5Ikgu2DckhqrZuodxxRcNplUdAqBQZvFBQhmCI3mhAM4w6EeepkC6dd8t14kc3EpxYXkmudOBs4Fg3T2xYXhLDsc9AZ0mQfQu5KVKrpp5UEAgEOHjzIsWPHEj0QFi1axPr16y/Zg0JhZpAkiWDHUFwUXESHRkRB0Koxl6RhdTowFSmicL2jCMEUuVGFAOQf/t42N78720Ygbh28b14Otxdlob5IQpIYC9Pbup+upj8lCUJ28WbsuasuSxA8Hg979uyhpqYGkHsgrFy5ktWrVys9EK4gkiQR7BySfQq1bqJDocQ+QSsnuVmcDszFiihcjyhCMEVuZCEYpi8Q5tenWjjZK1sHhakmPr2kkLyLWAcQF4S2A3Q1/oloWPY76Aw2sos2Y89bfVmC0NnZya5du2hvbwfAaDRSWVnJ4sWLlR4IVxhJkgh1eeM+BRfRwVGioBkWBTvm4nRUOuWzuR5QhGCK3AxCAPKPfX97H8+eacMfjaEWBN47L5s7irPRXKJcgRiL0Nt2gO6mPxEJyWKiNdjIKboVe96aaQuCJEnU19dTXV1Nf38/AGlpaUoPhKvIsCh4a10M1YwjCkVpWMociihc4yhCMEVuFiEYxhMM8+tTrRzvGQCgIMXIp5cUkp9y6cqtYiyCq/0gXY1vjwiCPpXsoltx5K1BpZ5erkAsFkv0QAgE5IS3vLw8Nm3aRE5OzrTOqXD5SJJEqNuLt9aNt8ZFZCCY2CdoVJiKZJ+CuSQNlU4pPHgtoQjBFLnZhADkH/jBjn6eOdOKPxJDLcBdpTncVZKFZhJx/rIgHIoLgiwoMyEIoVCIw4cP8+677xKNymUUysrK2LBhg9ID4SojSRKhHl/cp+Ai4hklCmoBU1Ga7FMoSUetV0ThaqMIwRS5GYVgmIFQhF+fauFYtzyZ51tl66AgdXJ9HUQxiqvtEF2Nb40ShBSy596KI3/ttAVhaGgo0QMB5H4Vy5cvZ+3atUoPhGsASZII9/oSPoVI/wWiMDe+fKSIwlVDEYIpcjMLAcg/6sOd/TxzuhVv3Dp4T0k220qzJ2UdgCwI7vbDdDa+RSToAUCjs5JddCsZ+eumLQg9PT1UV1fT3NwMgF6vZ926dSxbtkzpgXCNMCwK3lo3Q7UuIn2BxD5BLWAqtMVFwY7aoHxmVwpFCKbIzS4EwwyGIvzmdCvvdnkAyLMa+PSSuRRO0jqAEUHoanyLcJIg3BIXBN20xtbU1MSuXbtwuVyA3KN6w4YNlJWVKQ7lawhJkgi7/HhrXGNEAZWAaa5N9imUKqIw2yhCMEUUIUjmnc5+fnO6laFwFJUAdxZncXdpDtopVLIUxSjujnfpavgj4aAcDaTRWcieewuO/ArUmqkLgiiKnD59mn379iV6IGRnZ7Np0yby8/OnfD6F2Sfk8iXyFMJu/8gOVdxScDqwlKajNioFCWcaRQimiCIEYxkKRXjmTBuHO+VJPMdi4NNLCimyTa2ktCTGcHe8S2fDzhFB0JrJmnsLGXMqpyUIkUiEd955h8OHDyd6IJSUlFBVVUV6evqUz6dwZQi5/ImM5rDrAlEoSMVS5sBSaldEYYZQhGCKKEIwMUe6+vn1Kdk6EIDbi7O4Z97UrAOIC0Lnu3Q2/JFwoA8YFoRNcUGYekaxz+dj//79Y3ogVFZWYjJNfjlL4coTdvsZqnXhrblAFARGWQp21CZFFKaLIgRTRBGCi+MNR3nmTCuHOuQ7+myznk8tKaQkzTLlc8mCcISuhj8SCriBYUGoigvCxP2qJ8LtdlNdXU1DQwMAOp2O1atXs3LlSqUHwnVAuM8vO5prXIR7fSM7BDAWxH0K8+xoFFGYEooQTBFFCCbHsW4Pvz7VwkBItg62FmVyjzMX3TS6YElijL6uo3Q2/JGQX3YAq7UmsgqryCxYPy1BaG1tZdeuXXR3dwNyD4T169ezcOFCpQfCdUK4P5DIUwj1XCAKc1JlS2GeHY15ekEHNxOKEEwRRQgmjy8c5dmzbexvl5d3ssx6PrW4kNL0qVsHMCwIx+KC0AtcniBIksS5c+fYs2dPogdCRkYGVVVVSg+E64xwfyDhUwh1XyAK+XFRcCqiMBGKEEwRRQimzomeAZ462YInFEEAtszN5P1luein2SNXkkRZEOp3jgiCxkhW4UYyCzag1k4tiSwajXL06FEOHjyY1ANh06ZNZGRkTGuMClePsCeQKHMR6vYm7TPmp8iO5nkONBZFFIZRhGCKKEIwPfyRKL87287eNnmtP8Ok51NLCnCmT7+3gCSJ9Hcdp7NhJ0FfDyALQmbhBjILNqKZoiAEAgEOHDjAsWPHEEURUHogXO9EPEG5IF6ti1BXsigY8lOwDlsKlpu7pLkiBFNEEYLL41TvAE+ebKE/KIdy3lqYwb1luRg0l9PmUqS/60RcEOQ1f7XGQGbBRjILpy4IHo+H3bt3U1tbC8g9EFatWsXq1avR6ZS7yOuVyEAw0Xkt2DmUtM+Ql4LFacfqdKCx3nyioAjBFFGE4PLxR2L837k2drfK1oHDqOOTSwqZb7+8u25JEunvPkln/ZsJQVBpDGQVbIgLwtTCRDs6Oti1axcdHR0AmEwmKioqWLJkieJQvs6JDAbl5aNaF8GOC0Qh15pYPtKm3ByioAjBFFGEYOY43TvIkydb6AvKLRBvKXDwwfl5l2UdgCwInu5TdDS8SdDbBYBKrSezYANZc6umJAiSJHH+/Hl2796d6IGQnp7Oxo0bKSkpUUpW3ABEBkN46+Q8hTGikBMXBacdbcrUo9OuFxQhmCKKEMwsgUiM52ra2dUih4XajTo+sbiAhY6Uyz63JIl4ek7RWb+TgLcTGBaE9WQVVqHRTT7zORaLceLECfbv35/ogZCfn8+mTZvIzs6+7LEqXBtEh0J46+Q8hWD7YNI+Q44Vi9OOxelAm3pjiYIiBFNEEYLZ4axrkCdOtuAOyNZB1RwHfzY/D+MM9L+VBeE0nQ07CQzJyzyyIFSSVbhpSoIQCoU4dOgQR44cSfRAmD9/Phs2bCA1NfWyx6pw7RD1hhLLR4G2ZFHQZ1vijmYHWtv1LwpXXQiam5t5/PHHOX78OHV1dRQXF7Njx47E/lgsxi9+8Qt27drF+fPnicViOJ1OvvzlL1NRUXHJ85eVlY3Z5nA42Lt371SGmUARgtkjGI3xfE0HbzfLIaHpBi33Ly6kPOPyrQOQl3kGek/TUb+TwJDc+1il1pExp5KsuZvQ6iaf3zA4OMjevXs5c+YMAGq1OtEDwWC4/icGhWSi3nBi+WiMKGRZsDgdWMuuX1G46kKwc+dO/uVf/oWlS5fS2NiIJElJQuDz+di0aRPvf//7Wb9+PRqNhhdeeIHXXnuNn/70p9x6660XPX9ZWRn3338/d999d2KbVqtl0aJFU3xrMooQzD417iGeONlMr1+2Djbk27lvQT6mGbAOYFgQztBZ/yb+YUFQackoqCSr8Ba0+skLQnd3N9XV1bS0tABgMBhYt24dS5cuVXog3KBEfWG8de64KAzAqBlPn2WOJ6850KVdP02RrroQiKKYiMDYvn07p06dGmMReL3eJLNbkiQ++MEPYjabeeqppy56/rKyMv7u7/6Oz372s1MZ1oQoQnBlCEVjvFDbwR+bZOvAZtDyifICFmfO3PKLJEkMuM7KgjDYBsQFYU4FWXNvQaufXBSTJEmJHghutxwJlZqaysaNG3E6nYpD+QYmIQq1LgKtF4hCZlwUyq59UZjpeW3Kt0CXCsNTq9Vj1l4FQWD+/Pm8++67U305hesEvUbNhxfOYUV2Gk+caKbHH+In79RTkZfOny/Mx6y9/LttQRCwZSwk1bGAQdc5OurfxD/YSndzNT2t+8mYs47subeg1V98aUoQBIqKiigsLOTUqVPs27ePgYEBduzYQU5ODlVVVUoPhBsUjVmHbVkOtmU5RP0RfHVy57VAi4dQj49Qjw/3nmZ0GSbZp1DmQJd+41e7vSxn8XgWwXiIosi2bdsoKirikUceueixZWVl2Gw2vF4vRqORDRs28Hd/93fk5uZOa4yKRXDlCcVEXqzp4I9NPUhAql7L/eVzWJplm9HXkSSJQVcNnQ1v4huQl3oElYaM/Aqyiy4tCMOM1wOhtLSUjRs3Kj0QbhJi/gje87Kl4G/2JFkKOocJS5kDq9OBzn5tiMJVXxoazWSF4IknnuD73/8+Tz31FKtXr77osd/85je55ZZbcDgc1NbW8tOf/hSNRsNLL700rSgPRQiuHuf7vfzqRDPdPrke0LrcdD68MB+zbmbX4iVJYtBdS2f9m/gG5P7HsiCsI2vuLegMk/ve+Hw+9u3bx8mTJ5EkCZVKxZIlS6ioqFB6INxExAJxUahx42/xgDgyRerspnieggO94+p9J647ITh06BCf+cxn+MQnPsHf/d3fTfk1zp07x7333svXvvY1Pv/5z0/5+YoQXF3CMZGX6zp4o0G2DlJ0Gj5eXsDybNuMv5YkSQz11dFR/wY+z4ggOPLWkl1066QFYbweCGvWrGHFihVKD4SbDFkU+kYshdGikG5MiILOYbqivqXrSgjOnTvHxz/+cTZs2MBDDz007Qu1bds25s2bx49//OMpP1cRgmuDBo+PX51optMbBGBNThofXjQH6wxbBzAiCJ31O/F6GgEQBDWO/GFBsE3qPC0tLezatYueHrlAnsViYcOGDSxcuFBxKN+ExIJRfOdln4K/yZMkCtp044hP4QqIwnUjBC0tLXz0ox+luLiYxx577LKKf9111104nc4rJgSSJOLtbyQSGkSrT8GSVoQgKLVqLpdITOSVuk7+0NCNBFh1Gj62aA4rc9Jm5fVkQains+FNvP3y3b0gqHHkrSG7ePOkBGG4B8Lu3bsZGpLLGWRkZLBp0yYKCwtnZdwK1z6xYBRfvRtvrRt/Uz9SbJQopBnjPgU7ugzzhKIgiRKBtgFivghqsxZjfiqCanICctWjhiZDb28vn/nMZ3A4HDzyyCOXJQJnz56lqamJD37wgzM4wonp7z5J67mXiIQGEtu0+lTmzL+HtCzFqrgctGoV987PY3m2jV+daKbDG+RnRxtZ2dnPRxfNIUU/s8sugiCQYi8lxV7KUF89HfVv4O1voLdtP672Q9jzVpNTtBmdcWIhEgSBBQsWMG/ePI4cOcKhQ4fo7e3l//7v/5g7dy6bNm3C4XDM6LgVrn3UBg0pi7JIWZRFLBTFV9+Ht8aFv6mfSH+A/gOt9B9oRZtmSOQp6DNHRMFb66L3rQai3nDinBqLjozNxVicV/77NGWLIBAIsGvXLgCefvppWltb2b59OwBr1qzBZDLx53/+57S0tPCDH/xgzI9k2bJlib+3bt1Kbm4uTzzxBACPP/44ra2trFmzhvT0dOrq6vjZz36GXq/nxRdfJCVl6hmrU1HO/u6TNBx/csL9xUs/oYjBDBGJiew438UfGroQJbDoNHx0YT6rctJm1awe6quns/5NhvrrAdlCsOetIrtoC/qLCMIwfr+fgwcPJnogCIKQ6IFgsUyvk5vCjUMsFMXXEBeFxgssBZssCmqDBld104TnyHnf/EuKwVVfGmpra2PLli3j7nvyySfJy8ubcD9ATU1N4u/NmzeTl5eXSDJ76623+PnPf05jYyM+n4+0tDSqqqr46le/SmZm5lSGmWCyF0ySRE5Wfy/JErgQrd7G4qpvKctEM0jzgJ9fnmiifUj2HazIsvGx8pm3Di5kqK+BzoY3Geo7L28QVDhyV5NdvBm98dIho/39/ezZs0fpgaAwIWI4iq++X/YpNPYjRcVJPU9j1TH386svukx01YXgemOyF2yor57ad352yfM5V/0l1vSSGRmbgkxUFHntfBev1XcRk8CsVfORRXNYM8vWAYC3v5GO+jcZ6quTNwgq7LmryCnajN5kv+Tz29vb2bVrF52dcrVUk8lEZWUlixcvVnogKCQQwzF8DX14jnUSvKD20XjkfagcU4Ftwv2KEEyRyV6wvs6jNJ78zSXPV7T4o6TnLJ+RsSkk0zLo51cnmmkdlMtGL8tK5WOLCrAZZj9k0+tporP+TQbd8h0+ggp7zkpyijejN13cTJckibq6Onbv3o3H4wHkHghVVVUUFxcrEUYKCYbO9tL1as0lj8veVoZ1wcT9t68LZ/H1yKSzUMPeSx+kMC0KUkx8u3I+f6jvYsf5Lo51D1Dbd4YPL8xnXW76rE6oFttc5q38fJIguDsO4+58F3vOCrKLt2CYQBAEQcDpdFJSUsLx48fZv38/fX19vPjii8yZM4dNmzaRlZU1a2NXuH5Qmyd3UzPZ42YKxSKIMxkfwTCpGQvJd27DYJ6e30Lh0rQNBvjliSZa4tbBkswUPl5eQJrhyqy/+zwtdDS8yaDrnLxBUJGevZyc4i0YzBPfqQEEg8FED4RYLAbAggUL2LBhw7QCHhRuHCRRounRw0nRQhei+AhmgZmMGkqxOxnsOw+SKDsX89aQW3L7pKteKkyNqCjxekM3O853EhUljBo1f74wn8q82bUORuMbaKGzficDrrPxLQLpOcOCcPEbgcHBQfbs2cPZs/JzlR4ICiCHjna+fG7C/ddF1ND1xlQv2Ph5BDbmzH8faVmLCfp6aKt9lYFeucGJSq0nu+gWsgqrUKmVaJHZoH0owK9ONNM04AegPCOF+8sLSDdeuevtG2ils/7NCwRhGTnFt11SELq7u9m1axetra2A3AOhoqKCpUuXolbPTM8GheuLcfMIrDoybp1cHoEiBFNktjKLh/rqaat9Ff+g/OPW6lPILb0Te+5KJbx0FoiJEm82dvNS3bB1oOK+BflsyLdfUWesb7BNFoT4jQAIpGUvJaf4NoyWif0AkiTR2NhIdXV1ogeCzWZj48aNzJs3T3Eo34RcS5nFihBcBpIk0t91nPa63xMO9gNgtGST57ybVMfYlpsKl0+nN8CvTrTQ4PEBsNBh5ROLC7FfQesAwD/YTmfDm3h6Tse3CKRlL4kLQvaEzxNFkVOnTrF37178ftnCycnJYdOmTeTl5V2BkSvcCChCMEWuRNE5UYzS27KXzoY/EovKzk2rfR75zrsxWafXR0FhYkRJYmdjDy/WdhARJfRqFfctyKNqjuOK31nLgrATT8+p+BaBtKy4IFgnFoRwOJzogRCNRgGYN28eGzduJC1tdmovKdw4KEIwRa5k9dFoxE9nw056W/YhSTFAwJ67ktzSOyZd8VJh8nR5gzxxspnz/bJ1sMBu5ROLC3CY9Fd8LP6hDjrrd+LpOZnYZstaQm7xbRitORM+z+v1sm/fPk6dOpXogbB06VLWrVun9EBQmBBFCKbI1ShDHfK7aK/7A/3dxwEQVFqyCqvILroFtUaJFplJREniraZeXqhpJxy3Dj44P49NBQ5UV2HdPTDUSWfDTvq7TyS22TLLySnZelHr0OVyUV1dTWOjXDZbp9Oxdu1ali9frvRAUBiDIgRT5Gr2I/B5Wmir3ZGoia/RmskpuZ2M/LUIKiVaZCbp8QX51YkW6vrlhL+ydAufWFxIpvnKWwcAgaGuUYIg/8RsmeXkFN+GKWViX0BzczO7du2it7cXAKvVyoYNG1iwYIHiUFZIoAjBFJnOBQuHQvzpjR14Bv3YUkzccvvd6PTTm1AkSWKg9zRtta8S8rsA0JsyyHfeRWrGIuXHPYOIksSfmnt5rqaDcExEp1Zxb1kutxZmXBXrACDg7ZYFoes4w4KQmrGI3JLbMKXkj/scSZI4e/Yse/bsSfRAyMzMZNOmTRQUFFypoStcwyhCMEWmesGef/Zpdqny8JtHMkBNvkE2ie3c++cfm/Y4JDFGb9sBOuvfJBqR17QttiLyy+7GnKr8uGeSXn+IJ040U9MnWwfz0ix8ckkBWeartywX8HbT1fBH+rqOMSIIC8kp2Yp5AkGIRCKJHgjhsBxvXlRURFVVldID4SZHEYIpMpUL9vyzT/N7y3z5weg7yPgleo/33GWJAUAsEqCr6U90N1cjiXK0SFr2MvJK34PedOnyxwqTQ5Qkqltc/N+5dkIxEZ1K4P1luWyZm3nVrAOAoK+Hzoad9HUeIyEIjgWyIKTOGfc5fr+fAwcOcPz48UQPhPLyciorK5UeCDcpihBMkclesHAoxDde3o/fZAVBwBAaZG5vLRglggYLIUEPYfib2yuwpmag1l5e3Ho46KHj/B9wdxwBJARBTWbBerKLt6DRKtEiM4XLH+LJky2cdctLLCVpZj61uJBsy9V12gd9vXFBOMqwIKQ45pNbsnVCC7Gvr489e/ZQVyeXzNZqtaxatYpVq1YpPRBuMhQhmCKTvWBvvPIc/6suSjx2thyjtmAZRt8QS5rfYYG2Hn2RAcEgO3ljMQkxBqIkIEkqQA1oEFRaVCodKo0etVqPRmtErTOh1ZnQGSzoDVb0xhR0xlSM5lTCARdtta8m6uGrNUZyireQUbAelUopDjsTSJLE7lY3/3uujWBURKsSuMeZy9aiq2sdgCwIXY1/xN15VK5hBaTYy8gp2YrFNn5P5At7IJjNZiorKykvL1d6INwkKEIwRSZ7wX739FO8mbYo8Xhu9yl6LAUJX4HR56X8xH4WDJ5FV6RHXWxOiMLlIMYkYiJIkoBGA8O/Y0kSiIlmBJUVtcaAWmNEozOh1ZvR6c3oDFb0RllUDGYbWp3xssdyo+MOhHnyZDNnXLJ1UGQz8eklheRYrv61C/pddDX8EXfnkVGC4IwLwtwxx0uSRG1tLbt372ZgQK6LZbfbqaqqoqioSAlCuMFRhGCKTNciGMbg8xLTaIjo5aUEg9/L4mP7KTt3DEtRHoZFRahLs4lpRKJhP9FogFg0iBgNIYphJDECRIEYgiCiEiRUalCrZ/aHKooSsRhIooAoCSQsFEGDMGyhaAyoNQY0WhNavQmd3oLOYEFnsGIw2TCYUtHoDDf0XaUkSextc/O7s20EoiIalcD75uVwe1EW6knWeZlNQn4XnY1v4e54NyEIVvs8cou3Ykkb+/2MxWKJHgjBoNzus6CggKqqKqUHwg2MIgRTZLo+gjFIEqpYDFEjL9cY/F7Kjx+g7MwRtGKM1MXlOCorsFesRZuaeslxxSJhgoFBgv4BQoFBwkEvkZCXSMhHOOQlFu5EEvsR4uvHsZiaaFSFJIkIxFCpZk9QxBiIooAkCUioATWCoJUFRa0bJShGNHozer0ZrcEaF5RUDKYUtHrzNS0ofYEwT51q4VSv3DZwbqqJTy0pJM969a0DgJC/j67GP+LqeGdEENJLySnZijWteMzxwWCQgwcPcvToUaUHwk2AIgRTZCajhu7wniOr8g5ePd+FOyCH8xnDQRYe2cv80++ijUZApSK1fBH2ygrsFevQ2S4tChMRCQ3SUf8mrraDgASCioz8teQU345WL0eLxGJRQoFBgr4BQsEhwoEBwkEfkZCPSMRPLCxbKLFYCCkWQpQiIMUtFEQElYhaBSo1M7qcIEmyhSLGQJJUFwiKFpVah0ptQKOVLRSNzhRf8rKgM6SgN1nRm1LRG6yzJiiSJLG/vY/fnmkjEI2hUQncXZrNHcXZaK4B6wAgFOijq+EtXB2HkwWheCvW9LGCMDAwwJ49ezh3Tq53r1arWbFiBWvXrkU/zVwYhWsPRQimyGzkEURFiQPtbl6r76LXLwuCSYqx9PxJiqvfkAUBQKUiZeECHOsrsVesRTfNYmIBbzftta8mauHLPRBuJatw44z1QBBjUUJBL0GfJ2GhhINDCUGJhgPEogFi0RCSGEYUhwUlmhAUlQrUsyUoIkiiCgkVoAFBg0qlRVDLTnm1xoBGZ0SrjftRDFbZOW+yYTCloDdYUKnHd757grJ1cKJHtg4KUox8ekkh+SnXTvRWKNBPV+NbuNsPx+tYgSWthNySrVjTS8Yc39XVRXV1tdID4QZFEYIpMpuZxVFR4mBHH6+e76LXHwLArFaxNthH6b4/Ej43qguRIJCyaCGOynXYKyrQpU9dFOQeCDvwD7YBoNWnklt6xzXVA0EURULBIUL+AUL+IcLBuKiEfETCPmKRANFIEDEWRIzJPhRJiiBbKHE/inp2BEWMITvmE4KiBkGLStCASk+jKp99YgkhNKiQqLD6qHKoMJpS0BusGMyp6I0pqCcQlCtBONBPV+PbuNoPjRKEYnJLtmJJK0m6ZpIk0dDQQHV1NX19fYDSA+FGQRGCKXIlag3FRglCz7AgaNXcmmGmvPEM3r378MZjvwFZFBbMx76+EnvFOvT2ySeSjfRAeI1w0AOA0ZpDvvNuUuzOmXxbVxVRFImEfAT9sh8lHByS/4V8REM+opH4kldUFhRRDEOSoEgJP4pqCss8PsnAbnEVTZKc3GWnn1vVB3AInsQxidBhccSHgqBBEOTQYbVGjyruQ9HGI720cce83pgi+1HMtssSlHDQQ1fjW7jaRgmCrUj2IaSXJk3yoihy8uRJ9u3bl+iBkJuby6ZNm8jNVcqkX48oQjBFrmTRuZgocahTFoRunywIJq2arUWZrDcJ+A8fwr13P0M1tSNPEgSs88twrK/AXlGB3mGf1GuJsQg9LXvpavwjsagcLZJiLyPfue2iZY9vNkRRJBoOEvT1EwwMxgXFSzjkJRLyE434RwRleNlLitIo5HBQtZyQoEeQRJYLp1mpPoNaEGdsbJPKRdEY0CRCh0civfQmG3pjCiqVhKttH672g4lMdbNtbnzJKPmuPxwOc/jwYd55551EDwSn08mGDRuUHgjXGYoQTJGrUX1UlCQOdfTz6vlOuoYFQaPmtqJMtszNQO3x4N5/ANfefQydq0l6riwKcUsh49L1ZKJhn9wDoXX/SA+EvFXkltyBzjB9R7UCDIYiPH26lSNdHgByLXr+vMhKpjpAKDBioURC3iQfSiwmC4o8MY8KHVZJccf8zC7JxGISoigvpclLavJ2UVQTk0yoVBbUGiPquGM+io7zLQM0t8ktM1UqFUsWl1O5fgNG47URNaVwcRQhmCJXswy1KEkc7uhnxyhBMGrU3DY3g9uKMjFpNYRcbtz79+Pau5+hs+eSnm8tc2JfX4GjsgJ9RsZFX0vugfD7RB18QaUla24V2XOVHgiXyzud/Tx9uhVvOIpKgDuLs7i7NAetenp+mUg4SNA/SDgwIP8fkq2USNhPLOyXl71ioVnPRfEGNTR0Wunzyv4vtUok3+4lO9Uf9zmNivRSj4QOyxaKEa1u1JKXQY7yMphS0RmuHSf7jcpVF4Lm5mYef/xxjh8/Tl1dHcXFxezYsWPMcbt27eKhhx6ivr6e7OxsPvWpT/Gxj126YFskEuEnP/kJL7zwAkNDQyxZsoTvfOc7zJ8/fyrDTDCdCxYMhXnh5d30uH1k2s184H0bMeinH50jShLvdPaz43wXnV55GceoUbMlLghmrbxWHHK7ce8/iHvvPgbPnkuErQJY5s2Tl48qKzBkZU74Wl5PE221O/B5mgHQ6CzkltyOI2+N0gPhMhgKRfjNmVbe6fQAkGsx8KklhRTZzFdtTLFImEA8DyV0gaBEQgPEQp1I0mAiF0UUVUSjgly4jhEfyoBfT32XFV9QboCj18Yozhoi0xYcN6XmUkwquTER6WVCqzOj05vQxsuvGOLZ8hqd8ZrORblcYrEozWd3E/C6MFocFC7YOGm/0VUXgp07d/Iv//IvLF26lMbGRiRJGiMER48e5eMf/zj33HMP73vf+zhy5AgPP/wwDz74IPfdd99Fz//ggw/y4osvsn37dvLy8njsscc4e/Ysr7zyChmXuCsej6lesJ8+/io7z0QIM/IL0CFx20ItD3x225RffzSiJPFul4cddZ10JARBxea5mWydm4lZN/IlCLn76DtwANe+AwyePnOBKJRir6zAsb4CwzjZo5Ik4ek5RXvda4keCAZzJnnztpGaoTQ4uRzejVsHQ+EoAnBHcRbvmzd962C2iYQG6WraJS8dinJYsyllDrklW0lxzEcQBGKxKAFvP6dPn+LIsdP4A7L1mmLRUZpvJNUYi+eihBFjITnKK5GLEkOIL3mpNVcmuRFBi0qlRaXWo9Lo0cSz5TXD5Vf0FnRGa0JQrsXkxrOHX8TTvQedbrQPR8KWtYEFq99/yedfdSEQRTFxUbdv386pU6fGCMHnPvc5BgYG+N///d/Etn/4h3/g7bffprq6esIPpbu7m1tvvZXvfOc7CevB6/WyZcsW7rvvPr7+9a9P6c3B1C7YTx9/ldfORCfcf9dCzWWLAciCcCQuCO1xQTBoVGwuzGRrUSYWXfJdQbi/H/f+g7j27mPwzFk5qD6OuaQEx/q4KGQnN0sXxSiutgN01L9JLCJHi1jSisl33j1hyWOFSzMUjvLbM60c6ugHINus51NL5lKSdvWsg0sRCQ3R3bSL3tZ9cg4IYErJJ6d4a9LNwXg9EIqLi6mqqsJuv3gggxiLEgoMEfQPyI75wCDhkJdoyE8kPNoxP5zcGB0V6XUFclESyY1yLsqwhSKodSMFIhORXhZZVIxW9MZUOXR4hpIbzx5+EV/fHoAx4b4A5vRLi8FVF4LRjCcE4XCYFStW8PWvf51PfepTie2HDh3i/vvv57nnnqO8vHzc8z333HN8+9vf5uDBg9hstsT2b33rW5w6dYpXXnllymOc7AULhsJ87NuvJVkCF6JD4unv3XVZy0SjESWJo10edpzvom0oAIBerWLz3Ay2FmVh1Y01E8Mej7x8tG8/A6dOJ4tCcZHsaF5fgTFnJHIoFgnQ2fg2PS27E5El6dnLyZ13J3qj0gNhuhzt8vDrUy0Mxq2DrUWZ3OPMRXeNWgcAkZCX7ua4IMTiyZDWPHJKtpKasTAxMfn9fvbv38/x48eRJAlBEFi8eDGVlZWYzbMreInkRv8gocAA4cCIUz4S9hMN+0dly4dlx/xVS24csVCEeOhwop6XzoQmHumlN6agN6ag1Vs4Uf19dDph3LFJkkQkAmve828XXSaaaSGY8cyYlpYWIpEIxcXJ6e+lpaUA1NfXTygE9fX1OByOJBEAKCkp4ZVXXkmyRmaaF17enSQCReZBPEETKpVIIKrGL6kJI/DzX/+JP3//euypRrSayxuLShBYmZPG8mwbx7oHeKWuk7ahAL+v7+atpl5uLczg9qJMrPqR5uU6m42c99xBznvuIOwZoO/AQVz79jNw8hS+hkZ8DY00P/U05qKihKPZmJdLvvMuMudU0H7+D/R1HqGv6yj93SfILNhAdvFmpQfCNFiebcOZbuG3Z9s40N7HG409HO8Z4FOLCylNvzYbxmj1FvKd28iau4nupmp6W/fiH2qn/tivMFrzyC25jdSMRZhMJrZs2cLy5cvZvXs358+f58SJE5w9e5bVq1ezatUqtFrtpV9wGqjUGoxmG0az7bLOk8hF8XniFsoQ4ZCXcNArF4iM+IkOF4iMheLZ8mNzUdQaWVAEQa4QLCMBsfi/8MiLSvIpIhGIG+Fj0OtVxKJiPNpLIhIWMVk0qNXya+h00Hx2N8Xlt17W+58KMy4EwyVxLyx0Nfx4eP94DA4OYrVax2xPTU0lEong9/tnrSNTj9uX9FgUtfTHNPLnPIqdZwLsPLMTAJtVjyPVgD3VSIbNiN1mlB/b4o9TDWg1l3bQqgSBFdk2lmWlcrx7gB3nO2kZDPCHhm7ebu7llsIM7rhAEAB0tlSy77yd7DtvJzIwgPugnKfgOXESX2MjvsZGWn79G0xzC2VLobKCosUfIatwY7wHwnm6m3fh6jhMTvFtZMypUHogTBGzTsNnl85lVXYavz7VQrcvxP87UMuWuZm8vywX/TVqHWh1FvKdd5E9dxPdzdX0tOwlMNRO/bEnMFpzyCneii1zEenp6dxzzz20tbWxa9cuurq62LdvH8ePH2f9+vUsWrTomlt/H0alUsVLtVu5nEBqORclkBCUUGCQSHCIcMhPJOxLslCGc1GGLRSIoRIk2Y8yKrlRrVExfMNvMKoJBmKojSNzRcDruowRT51Z+9VPZJJdylSbyFyabTLtZqjzJh63BnSUpw6AqMbjMxEQ1YSRCCPrvwR4hkJ4hkKcb5tY3FItuhGhSDXgsBlHCYcsInqt/AVQCQLLhwWhR7YQWgYDvD4sCAUO7ijOIkU/9k5Mm5pK9u1byb59K5HBIfoOHsS1dz8DJ07ib2qmpamZlqefwVRYgL2ygrzKu4gWemmrfZWgr5u2mpfpadlD/ry7sGUtURzKU2RpViql6Qv43Zk29rX3sbOphxM9A3xySQHO9LE3N9cKGp2ZvHnvIauwiu7m3fS07CEw1EnD8ScxWnLIKbkNW2Y5+fn5fPSjH6WmpoY9e/YwMDDAG2+8wZEjR6iqqmLu3Lk37HdGpVKhM5jRGcykkHdZ56o99jp9bW8krAFRlFCrBYym5KnYaLmyPalnXAhS4yWYL7zzHxyUC3pdrCRuSkpK4rgLn6vVajGZZm/54gPv28j/HRjxEYioOTWQioDEggwXt2S5EcIGel3p9PWlEEZFGIgAYSS0Fh06q56oWsAbjOLyBAhHRQa8YQa8YRraJxYLq0mXEAZHqvx/hs3IezPtuO0x9nT30+YP8kZjD39q7mVTYQZ3FGeROo4gAGhTrGRtvY2srbcRGRqi7+Ah3Pv24zl2An9zC/7mFlqfeRZTwRzS11dgW7gQV987hAN9NJz4NebUAvKdd49b/15hYsxaDZ9eOpdVOWk8eaqFHn+IHxyoY3NhBveW5aKfhHV4tZAF4U6y5laNWAjeThqOP4XBkk1u8W3YshYzf/58SktLOXbsGAcPHsTlcvH8889TUFDApk2byMycOLRZAUoWb8Hd9iZ6w8V9BIULNl7Rcc24EBQUFKDVamloaKCqqiqx/fz584C83j8RJSUluN1uPB5Pkp+gvr6eoqKiWTVBDXodty3UjokakhA405vBmd4M/mxpgDu2RXF37KevP5VeVxo9rnT8fhN4o/I/IEOvZsuCbHKL0knJshISRVwDQVyeAC5PAPdAAJcniGsgQCgcY8gfZsgfpqFjYrEwGjQIejUxjcD/nXHzsuE8C3NtbJqXRWGGBUeqEYN+7MeptVrJum0LWbdtIer1jiwfHT+Bv6UVf0u8OmVhPqZbCwlYevENtFBz+BFsmeXkzbsLg3nqYbs3M4szU/nuxoX837k2dre6eau5l5O9A3xycSFl9mvXOgDQaE3kld5JVmEVPXELIejtouHErzGYs8gpuY20rCWsWrWK8vJyDhw4wLFjx2hpaeGpp55i4cKFrF+/XumBMAFqtQZb1gZ8fXsSTvhhhlc+bFkbrnhhwxmPGgI5fHRoaIhnn302se0f//EfeeuttyYVPvoP//APfOQjHwHA5/OxefPmKxI+CpPLIwgHB+ht3Udv635i0QCBgJ7u3nTaOh0MDqYiicl3fvZMMyVlGZSUZVBYbEcXn7AlScIXiCSJhGsggNsTTPzt8gQIhi9wVEyA2ajFEV9+Gl6CSn5swGSQrYio10ffocO49u3Dc/Q4Urz2DCY1hlvzoVANAvEeCBXklNyGVndtOkCvZU71DvLUyWb6gnLI5i0FDj44Pw/DNWwdjCYaCdDTspue5t2JmlYGcyY5xVtJy16CIKgYGBhg9+7d1NTI5VI0Gg0rVqxgzZo1Sg+ECbju8wgCgQC7du0C4Omnn6a1tZXt27cDsGbNGtLT0xMJZR/4wAd473vfy5EjR/jJT34yJqFs69at5Obm8sQTTyS2Pfjgg7z00kts376d3NxcfvGLXyTE5koklMHkM4vFWBh3x7t0N+8m5O+Vt4nQ3OmgpcOBf8iKGDHCKFFRq1XMKUqThWF+Jlk51ouurUqShD++1OQatiQ8fs53D3G+axCvL0QsGEOKTe5jNBk0sjCkysKQYTOSZlSh62xGOHcSTr6LLhJESNeiqUhHPVcOFVSpdGQXbyarsAqVenaiRW5UApEY/3eunepW2QFoN+r45OICFjiun7tmWRD2xAVBDnWWBeE20rKXIggqurq62LVrF21tcpl0o9FIRUUFS5YsUXogjMN1nVnc1tbGli1bxt335JNPsnbtWkAuMfGjH/0oUWLi05/+9JgSE5s3byYvL4+nnnoqsS0cDieVmFi6dOkVLzExVSRJZNBVQ3fzbob6RspNh0ULdW0GurpsRENmYiEzUix5EjVb9ZQ4HZSUZVDszMBsnfwdlCRJnHEN8cr5Tup6hxCDMYjEKDYZydXp8PsiCavC7QngC06cLDcagwZSpBAmbx+p2gCp+ZCaHiPFEMami1FSuJLilbdf1br81yNnXYM8cbIl0d2uao6DP5ufh1F7/UySsUiAnta9dDdVJwRBb8ogp3gL6dnLQFBRX19PdXU1/f1ywl1aWhobN26ktLT0hnUoX2muuhBcb1zponOBoU66W3bT13k0kbwlqM0MhDI5dT5EMKAmFpRFQQyb4uWHR8jOS5FFoSyDgrnpqCeRqyBJEmfdQ7xS18n5fjkMVqMS2JBv5z0l2aQbZWvGH4zgji9DuQcCF/gt5L+9gcik3qdWFSNNJ5LpsJGVnS4vPdmSo6MsRq3yw7+AYDTGc+fa+VOLbB2kG7R8YnEhizKuH+sAIBYN0tOyl+7m6kTWut7kIKf4NtKzlyFKJHogBAKyYOTl5bFp0yZycpQy6ZeLIgRT5GpVH42EvPS2yX6EaFgOSxVUWjTmebS5zdTVdxKJxBDDBtlSiKQQDSVbC1qdmrmldkqcGZTMzyDdYb7kMtI5t5dX6jqp65dfc1gQ7izJxm68dEZ0MBQd8VMMjCxH9bq99HT14fKFCcQmZwnodepEnsWwn2I4z8IR32Y13Zxicc49xBMnmnHFrYMN+XbuW5CP6TqyDmBYEPbR3bwrWRCKtpCes5xwJMrhw4d59913k3ogbNy4cUziqMLkUYRgilzNMtQgN5Dp6zpGT3M1AW9XfKuA1V5GVFNCQ+sQDQ2NRCIRpJiaWMiMIKYSC5qJhJM/Glu6kWKn7HQumufAYBx/rV6SJGr6ZEGo7ZMFQS0IrM+3c1dpFnbj5TnwBt3dnNz3Ep3eHgaCegaDOgY6BQbcarzaFIZ0FnyxyUV46TSqUcJgGNfJnWLW3ZBiEYrGeL6mg7eaZf9SmkHL/eUFLM68/vpIxKJBelv30920i2hEtkr1RjvZxVuw56zA6/Ozd+9eTp8+Dcix+cuXL2ft2rVKD4RpoAjBFLnaQjCMJEkM9Z2np3l3ogk9gNGaiz2vkoFgKnV19dTX18uiIIEY1aOR0lBLaQx5JMRRDmFBJZBXYEtEI+XOsY3bkrHGLfsQatwjglCZn85dJdk4TJcnCEFfL201OxhwnZHfY0QkdnSA6DEPkaiKcGYeLF5JrKgMrzF1ZAkqbnF4vKFJvY5Wo0osNw07uUcsDDnvItWsn1JLymuJ2r4hfnWiJdH3ujIvnQ8tzE+UJ7+eiEVDcUH4U0IQdMZ0coq3YM9ZSa/LTXV1Nc3Ncpl0vV7P2rVrWb58ORrN9fd+rxaKEEyRa0UIRhP09dDTvAdXxzuJ0sAanZXMORXYslfR1uGitraWhoaGRAVISRTQqdIx6rLxD6gZ6E+eRA1GLcVOR8JiSE1Lvsuq7RvilbouzrmHAFALUBn3IWRcpiB4PU201byCb6AFACGiInKwj+iJPuKl8NE5HDgq12GvrMBa5kRQqQhHYvQNDofKBhNObdco/4VnaHJioVGrRmVuGxJLTw7bSAmQVMu1KxahmMiLNR38sakHCUjVa7m/fA5Ls2xXe2jTIhYN09u2T7YQ4kujOmM6OUWbSc9dSUtLG9XV1fT2ytZQSkoKGzZsYP78+Tek9TceoiRR1+fFE4pg02uZl25BNcn3rgjBFLkWhWCYaMSPq+0APS37iITkZDJBpcGes4LMwo1oDQ6ampqora2lvr4+IQoABl0KNksBsaCJzjYfoQsighxZFkqcstN5bokdrU5ee66LLxmdHSUI6/LsbCu9PEGQJAlP90m5B0JAboGoVaWgbtAw+MdTiMFg4lid3Y69ch2O9ZUJUZiISFQcEYtxndwB+odCTOZbrFYJ2C9RG8pmNaC+imJxvs/Lr042J3per8tL58ML8pN6VVxPiLEwva376Wr604ggGNLILt5MWvYKzp2rZe/evXi98r6srCw2bdrEnDk3dpn0I139/PZMG/3BkeCMNIOWDy/MZ0X2pftHK0IwRa5lIRhGEmP0d5+gu7ka/2BbYnuK3Ulm4UZS7GXEYjGam5upra3l/PnzSaJgMpnJzihGLabh6grR3uJJmhjVahUFxemJZaTMHCv1Hh+v1HVyxiULgkqAdbnpbCvNJtM8/daWohilt3U/nQ07R3og2IqwRkvwHjhH36HDxOJRJAC69HTsFeuwr68gZcH8i4rCRERjIn2jlpyGQ2ZHP+4fDCJO4puuUgmkpxgmrA3lSDWSZtWjnsVicuGYyEu1HbzZOGwdaPjYogKWZ9tm7TVnGzEWprftIF2NbxMNy985ncFGdtFmUjKXcfTocQ4dOkQkIk+MJSUlbNy48ZI9EK5HjnT189MjjRPuf2BF0SXFQBGCKXI9CMEwkiTh8zTR3VyNp+c0w+sqBnMmmYUbseesRKXWEo1GaWlpSYhCKDSyfGIymSiaW4pJl8WAW6Kh1sVAfyDpdSxWPcVxURCyTOxsd3PaJdd4UgmwNi4IWZchCNFIgK7Gt+hp2TPSAyFnOTmFW/CdbcG9b78sCv6RWr3atDR5+Wh9BSnz5yPMYBJSNCbSPxjCPRCg94IyH8NLUn1TEQurPin6aXgJavhxesrli0V9v49fnWhK9Ltek5PGhxfNGbdPxfWCGIvQ23aA7qY/EQnJ3zmtwUZO0a0YbIs4ePAwJ06cSJRfWLJkCRUVFbPeA+FKERNFtr99Ck9o4pyeNIOWf7u1/KLLRIoQTJHrSQhGE/K76WnZi6v9EGJMngjUWhMZ+RVkFlSi1ctx57FYjJaWFmpqaqivryc4agnGZDJRUlJCdkYh/iEtDbUumuvdRC4oWZGTn0qKM51Gs0C9X36+AKzNS2dbSTbZlukLQijQT8f539PXeVQ+r0oj90Ao2owKDZ5jx3Ht3U/foUPEfKNFwYZ93Toc6ytIWbhgRkVhImIxEY83JAvFaMtitJN7IIg4CbVQCWCzGhKRULKT25j0OD3VgOYSYhGJibxc18nrDd1IgFWn4WOL5rAy59LLB9cyYiyCq122EBKCoE8lu+hWBGMpe/fup76+Xt6u1bJmzRpWrlw5az0QLoUkSYRFiUAkRjAaIxAd/l+84HGMQGT0NjGxLxiN4Y/GJnWz8fW18y5al0oRgilyvQrBMLFIAFfHYXqa9xAOypmagqAmLXspWYVVmFJGyuIOi8KwpTBaFIxGI/PmzaOkuBQpZqax1k1DTS9dHcnVXkW7nuD8dNwG+W5EANbkprGtNIecyxAE32AbbTU78PbLP2611kRu8VYcc9ahUmkQIxE8x0/g3rsf98FDxHwj/SG0qanYK9ZiX19J6qKFV0QUJiImSniGgokEvOE8C/eo2lDugSCxSfzaBQFsFn1SLSjHqJyLYX+GVqOi0ePjlyea6Yy3Nl2VY+OjC+eM6VFxvSELwqG4IMh+smFBCJFH9e69dHXJYdcWi4X169ezcOHCSReglCSJUExMnrQjYyfp0Y/H2xac5AQ+U3xu2VzW5k7cPVARgilyvQvBMJIYw9N7mu7manye5sR2S1oJWYUb431nR34csViM1tZWamtrqaurSxIFg8HAvHnzcDqdpNkyaT7fR31tLw01vfi8su8hZNUyWGQlmDEy+a/KSuV9ZbnkWKYX9y1JEgOus7TXvkrQ1wPIyUd5pe/BlrU4ES0iRiIMnDgpWwoHDxH1jvSJ0KamkB63FFLLF11VUZgIUZQY8IZGWRTBsUtRAwGik6wPNdwAKT3VwBASbaEQKr0ai1nHvUvmcEtpJrrrMNR0NHJ/7UN0Nb6VEASNLoWU/I10DqZw/PA7+L2yb8GYmkZ2+XI09uzEnXbSRB5JvlufyQlOAIwaNQaNGqNGhUGrjj9WYdSoR+0b2WbQqDFq5ePbh4L8/OjE/oFhFItghrlRhGA0voEWupt30999AiS5Z7He5CCzYAP23FWoNcnRP7FYjLa2toQoBEY5aw0GA6WlpTidTubkz6G320dDbS/1Nb20NPYRNGkYKLIQzIhP/pLEXJWWu4qzWDovc1rhmJIYw9V+iI76NxKRJObUQvLL7sZim5t0rBiNjhKFg0SHRkRBk5KCfd0aHOsrSV1cfk2KwkSIosSgLzzKqT0qGmqUkzsSFS99MiDFrMNhm1wDpNlElCSCo5ZLxiyhjJqkx7vzDkfC5MdqKZdOYxXkpUKfZOB4bD6dbgM2VxvquM/Ja06jN7OUkOHSVXFVAokJeswkPXoi146a5JMmdnm/Tq26rPBWUZLY/vappGihC1F8BLPAjSgEw4SDHtmP0HYwUQBMrTHiyF9DZsEGdAbbmOeIophkKVwoCiUlJZSVlVFQUEAsKtFULy8hnWx202rTEMgcEQSLO8QStZ5lpbLjOcU2NUshFg3S3bSL7qZdcr9YwJa5mDznXRhMYzs0idEog6dO49q7D/f+g0SHhhL7NFYr6WvXyJbCksWoboDkJEkaEQv3QHCUk1t2eLf0ehn0hpnsmsVEDZDsqUbSUgxYLDokFUlr3BddQomM3RaKTU64LoWKGGVCIytUI4IQxEiLZhE9PRqiHc0Mh8alzikmr3wZqSkpoyZ7VeJO3KBRo1ON3wjmaqBEDV0FbmQhGCYWDeHueIeelj2E/PFep4KKtKzFZBVUYbYVjPs8URSTLAX/qAgevV6fsBQKCwtRq9V4+vwcPN1Jda+HHsPIj8rYHSClcYg8iyERjVRYPJK7cCnCwQE669/A1X4YkEBQkTmnkpzi29Doxo8WkWIxBk6ewrVvvywKozrbaayWuCjIloLqKjkYrwQN/V4ef7eRdrePWCiGXaUmS6vD6wvhGQgx6A0xNBQmNskJWtCoUBvUqPVqVHo1aoMalV4zss2gRjWJaCiNSkheQrnwznv03bh2grtzjRoVMfo6j9DV8MeEj0yjs5CatZbT9SHO1cgNrzQaDStXrmT16tXXRQ8EJY/gCnMzCMEwkiQy0HuOnuZqhuJOWZCXXbIKN2LLLEdQjT85i6JIe3t7QhR8o5y1er2ekpKShChoNBqa+308d6qVs0Mj4mHskQVB542i1qgoKIrnLszPIDP74n0XAAJDXbTVvcqg6xwAao2B7KLNZBZsuGgPBCkWY+D0GdxxSyEyqk2qxmIhfc1q7OsrsC1dcs2IQlQU5eWRS0ahXHA3HkmOQglPwhqQJAkpKhILxhBDMWKhWPzv6Ki/J9/TQq9XY7HosVn1pKXIvosMm5GsNBM56SZy082kmC9d4HAqSGIMd8e7dDbsHBEErRmTfQVHzgzR1i47lI1GI5WVlSxevPia74GgZBZfQW4mIRiNf7Cdnpbd9HUeQ5LkcFGdIY3MgvU48tag1k68jCOKIh0dHdTU1IwRBZ1OlxCFuXPn0uWPsON8J+92eRLHWPvDmGo96LwjsdKWFL1cRbUsgyKnA7Nl4ru2QXctbbWvEhjqiI/bRm7pnaTnLE9yiI+HFIsxeOYsrr37ce8/QMQzMi612UT6Gnn5yLZs6ZRFQZIkoqI07qR94SQ9fhTKyPHRGQ5B0alV6FQCwZiYOHeKTsO8dAupeu34696j7sQNahViVMQzOJ6TeyQiKhCaXLe8CxsgXVgbypFqxDxB0cSLIYkx3J3v0tnwR8KBPkAWBLVlEe+cGsDdJ98EpKWlUVVVRUlJyTWzJDSTKEIwRW5WIRgmEhqkt3W/XA47XgRMpdbjyFtNZsF69OOsxY9GkqQkS8E7KoJHp9NRXFyM0+lE58jmD029vNvpSURpFKq1ZHT6cZ9zE42MWpoQICcvNdF3YU5h2pi+C5Ik0td5hPa6PySiSEzWPPKcd5NiL53Ue5diMQbPnpN9CvuSRUEwGlEtXUZs2QqCpWUEBdWYu/HxolBiM/xz0atVSc7IpOURrfoiDs7kSX24LEZUlHi9oZtX6jqJSRImjZoPLcynMi99RiZEfzCSEIlhJ3dvUjHByTdAMuo1SbWghvMsRpcAMRs04zd5F2O440tGwyVN1Fozoq6UQyc8+OIF/PLz89m0aRPZ2dmX/d6vJRQhmCI3uxAMI8Yi9HUeobt5N0Ffd3yrgC1zIZmFVVhsRZecKCRJoqOjg9raWmpra5NEQavVUlJSQnphCSdCWt7tHkgIwpKMFFbqTfiaPTTUuui+IHdBp1czt8SRWEZKd5gTrxcMh+hq3k1/6y6keGId1lJCmbcQUKeN3G1PcDc+vIQixkQyutuY23CWwoZzmP0jYw9rdbQWOmkqmU9HfgmxSTiaDZda975g0h49qY+eyCe7FDBV2ocC/PJEM80D8tJdeUYK95cXJJoUzSYXNkDqHWVZDDu9fZNsgGTQqceU+BhdG8qeoiM8cIquxrcS/jG1xkRQKOSdUwOEwvINSFlZGRs3biQ19for8T0eihBMEUUIkpEkiSF3Ld3Nuxl01yS2m1LyySzcSFrWElSqS0+EkiTR2dmZEIWh0RE8Wi3pxWV0WLM57xtZSihMMVKekYIuItHT5MHTMoC3fVBusTkK0aQhbNfjTdMRTNMjaVQYCLJSdYqFwnnUgoQoCZyTijksLibA5KKVBOIhhGrI7u4g7/wZMmpPoRsaESZRb0AsX4xqxUoMi5dgNBmT7r6NGjV6jWrWJvCZJCZKvNHYzct1nURFCaNGxX0L8tmQb7/qyyWBUHRk2WlYKC4oJjjkn5xYDDdASjFGMUg9WLSDpBhC2Eyg1qTQ2DyICtBo1Cxbtox169ZhMEw/OfJaQBGCKTKdCxaKRPm/6tN09vvJSTPxZ1WL0F/nCTvjEfB209O8G3fnu0hiFEkCSZ+ONWcdpoylRATdWOdl5EKHpnzn7Q2G8IbChGISMUElp81OBklCOxTB0BfC4A6hHwgjjPpGSgKEU3TEMgwIWWbS00KUS0fIjMpJdTFBy4B1BaG0NRh0huS7c23yEopOPXYCl0SRoZpa2aewbz9htzuxT2UwkL56JY71ldhWLEd9HUSjjEenV7YOGj2ydbDQYeUTiwsn1bHuahIMR5Nbq3rG5lkM+sKXPhGgVonoVFH0qigmnURxQRYLnYVkppmvWgOkcFTk93sb6XT7yLGbec/6InSTaE0LihBMmalesIdfOsQfD3QTC4+saat1Krasy+Ir96yZlTFeLnIST2wSUSgTpNBHogQiEULiDP8IJAlVLConAEkiolpLTK1NiITDqGOBw0qe1ZhYLtGIMNA2SE9jP+0NfXhc/qRTGk1aip0ZFBWH0XOQoE+u1qrVp5Bbcjv2vNWXdChPOFxRxFt3Pu5T2E+o15XYpzIYSFu5Asf6StJWrbjuREGUJN5s7OGl2g4iooRBo+LP5udRNcdx1a2DyyEUieGOC8PoPAvXQIDu3j5cAwF8ocndxF3JBki/fOUUL+6qT0oBUQnw/k0lfPq95Zd8viIEU2QqF+zhlw7xRnXnhPtvr8qZUTGIihKhcSZt/wXOypGMzPGjUGYqiWcYFSI6ImiJoCOCUashxWzDYkxJyrxMXhMfm8SjFaCnpyexfDQwMEBYa8TlKGQwNTshCAvsFu5x5lKSNjZDtN/tT2Q6N9a5Lui7IFFW5qWooA61Sl6aMpizyHduI8VxeQ1OJEnCW1sn5yns20+op3fk+uj1pK1agaOygrRVK1FfR8sMXd4gvzrZTH2/HDiwwG7lE4sLLrtb3bWKJIl0tx2n9uwuevuGGAzqGQwZ6fCY6R3UE4hpiEo6gtHJ3TzMRAOkX75yiuf/VD/h/ntvubQYKEIwRSZ7wUKRKH/+j79PsgQuRK1X8ex334NaPTbCZGw25vh346PT7CcTAz4VJkriGTelXnuhg3PkeLUAPk8j3c3VDPSeZbgcttGSTWbhRtKzl180rn88JElKEoUeXwi3o5CB1CyI38EXGlX82aK5zM+0jXsOMSbS1uKhoUYWho5Wue+CShApKOhgXkkzOq0sFHpzEUXl78Ocmj/t6zl67N7z9bj37sO1dz+hnp7EPpVOR9rKFdjXV5K+agXq66D/rihJvNXUyws17YRFCb1axQfn57GpwHFd+D6mgySJ9HefpLP+zUSwhISGNpeJ5m4D4ZiKnPxSSuYvISxqR5agRvktPN7Lb4Bks+r51n/tuWj9I5UA//tv773oMpEiBFNkshfs6T8e57evNSUemywSgl6LzqZDZzeARgNqNWqdGpGZ/bFokybwi2dajh8PLseBa2ehWUrQ10tPy17cHYcRY/J6rEZnISO/gow5FWj1ExfGmohhUairq+NEfRMNWhsDqdkJQXCIATbnplK1aN5Fs0QD/jCNdS7qz8nC4PcNUVrcwtzCdtQqCUkCf7iItOzNlCwoxmS5/DVxSZLw1Tcklo+CXd2JfSqdDtuK5TjWV5C2ahUa07UtCt2+IE+caKGuX46gKku38MklhZfduvRaRpJEPN2n6Gh4k6BXTkIT0dDaY6DVZSImqlmyZAmVlZWYTKak50aiIv2DwbF5FqMe9w8FJyUWl+Jz7yvnnk0lE+5XhGCKTPaC/fD/9rNr/8idntWuYsgtWweCVoUx24Qx14x21GSikkS0SOhUcjy4SaPGpNNg1uuwGvWYtNrkO/NxC1qp0EyjK9eVRm6reYie1r1Egh5ALoednrOCrMKNGK050zqvJEn09vby7rk69vT46TGmJQTB7O9nsSZEhXMuxcXFF430kCQJV49XLpZ3vgmT5l1ysuXPMxZT0dichy9cztzSXErKMsifm3bZjWMkScLX0Ih7335ce/cR7OxK7BO0WtJWLMdeWUH6mlVoLphUrhVESeLt5l6er+kgHBPRqVXcW5bLrYUZN6x1AHFB6DlFZ/1OAl55OViU1LT2Gmh1mUHQU1i6CHvuPEIR8IciBIJRfMEo/qD8tz8k/+2P/x0IRvAFIjNSrnrb+iL+8t4lE+5XhGCKTNciUKslDGaBoE8iFhv5QVh0YbJNg2QZhtCqLp5laTQasVqtWK1WLBZL4u/hxxaL5ao12pgukhijv+ckPc27E83qAazppWQVVpHiKJu+s1aSqGvv4qWzrdSF1Ujxicjk6yfD3cyirDScTiclJSWXDP+LRmI0nDtNX/ubaFXyBB0Ka6mrL6SlNQetTsfcUnuifedw7sJ0kSQJX2OTvHy0bz/BjhFfk6DVkrZ8Gfb1FaSvXoXmGuy21eML8eTJZmr6ZOtgXpqFTy0puKy2pVcCUZQIhqMEQlF5Qg5G8AWj8kQdjMQn6/jkHf/bNzyRByP4QxHyzF2sndNITorsN4nGBNrdJtpcZrxhLQ2DdroDVpjCSoAgyAlzJr0Go0GL2aDBZNDSPxSk8YI8mvFQLIIZZiZ9BKPRaVQsKU5hyVw9dmMYn8/H0NAQQ0NDeL1eotHJZVcaDIYxAnHh39eqWHg9TfQ076a/+yTDfgS9KYOswg2k56xCrZn+UozLH+KF08280zuUWIoz+vpxuJqwBgcpLCzE6XRSWlp6SUthoPcMred2EA7KUUD+gImz5+bS1eNg+MedZjclRGFuqR29YfrXXJIk/M3Nckjq3n0E2jsS+wSNBtvypTgqK0lfsxqN5doRBVGS2NXi4rlz7YRiIjqVwPvLctkyN3PGrQNRlEYm79DwxCz/PTxx+4Mjk7h8xz1qcg/Et4WiM7IUAxJlGX3cUtqSJAgdbhOtLjNhwYoubR5mWxYmgxaTQZ7kTQYtRoMmMdGbDBqMeg0GnWZch3E4KnLf9lcuajXcMD6C+++/n0OHDo2770c/+hHbtm2b0vNee+01SkomVseLMZNRQ7esy6QkM4M3DrbQ2j2SQJXjMLN1TQGbV83BnmqUM2KDwSRhGO/vqYrFeCIx/O9qikUo0E9vyx562w8hRuUGOGqNkYw568iYsx6dYfrZnO5AmD/Ud7G71cVwTTSjz4PD1YjJ70GtUlFQUJAQBeMEztrxeiDEyKaptYzac0JS+0mVSiB/bppcAsOZQW5+KsI0QwZlUWiJLx/tJ9DWltgnaDTYli3FXrkO+9o1aCyXrqt/Jej1h3jyZAvn3PJ3vCTNzKcWF5JtMRBLTODJE3YgaRIfvWQy/v7ARXr2TgeVSsBskO++5Qk6PjHrNRjjf8v7NZj08Yk8vt0YP95s0KLTqhh0naWz/k38Q+0AxETZQmjtNZE3p4SqqioyMjKmPdabJmro/PnzSeUHAJ544gneeOMNdu/eTXr6+C3Y7r//fqLRKN/85jeTti9YsGDapWVnJI9Ar2LL2pE8AkmSqGnpZ+ehFqqPtiUKcakEWLkgi61rClm9MOui/WiHxWJYGEYLxOjHkcgksyv1+jEicaFY6HSzm0AUiwZxt79Dd8vuREEwBBXp2UvJLKzCnDL9CJ6+QJjf13exp809UlQt4iO1ow6Tvx8BUKlUzJkzJyEKFzr7hsfY1fQnupuqkeI9EFIci4mq19JUH6a+xkWfy5f0nOHcBbk2koOU1Ok7gf0tLbj2yj6FQOsForB0MfbKStLXrkZrnboT/mJEY2LS8knyZC2vb/tHrXv7glHcapGhNC2oBCRRItg0yEDjpZc1poJaJYzcYY+6qx49WSdP3hccG5/o9Vr1jOZDDHfT66x/E/+g/DnFRIEOt5FWlwXnfNmhbJ3m53TT5hFs2bKFkpISHn300QmPuf/++zGZTPz85z+fsded7cziYCjKnuMdvHmomTONfYntNquezSvncNuaAuZkTe/LIkkSoVBoQpEY/jdZsdDpdBf1WVit1hmp5S6Xwz5Dd1M1Xs9IAw6LrYjMwo3YMhdN24/QFwjzh4Zudre6EoKQqRHJdDcT6WhKrOIKgpBkKVwoCuHgAB31r+NufweQEAQ1GXMqySnewtCQQH1NLw214+UuQEa2NbGMVFCcjnaanb/8La2JPAV/84i/RVCrSV2yGMf6CqyrVhPRGpIn8fjfgfiEfVHnZfwOPByZXNXQC1EZ1KTOT0dvl5ffwgMhBs70oQqLiQnbODwx67WYjCNLJsPLJ8aLTPRazeV1/JptJEli0HWOjvo38Q+2AhATocNtoqM/laXL17J69epp3WTddJnFR44c4SMf+Qg//OEPee973zvhcdeKEEyXtp4hdh5q4Y/vtOIZCiW2L5ibztY1BWxYlodRP/OlKkKh0EWtCq/XSygUuvSJkMXiYktQFosFvV4/6R+vb7CNnuZq+rqOJ9pq6ozpZBZswJG3GrVmes7I/mCYP9R3Uz1KEAosehYKPryN53D1jiSACYKQsBTmzZuXJAr+oQ7aa19l0F0LDPdA2EJmwXpUai2xmEh7s4f62pHchdFB4BqNioJiOyXzMyhxOsiI912IRGOJZZBk52Sy89IXX+sWeruxtZwlq7MGm2+kzIWIQJMxmxpLIbWWAgLqy3Pe6jQqTMbk5ROj/oJJ+oI1cINezXl/gLc6+wjFRDQqgffNy+H2oqxE1dMbHVkQauhseDMRJBEToaPPhMvrYPXaKhYvXozqCkUAXpdC8OCDD/L888+zb9++cc31Ye6//35OnTqFJEnEYjGWLl3KX//1X7N69eppv/bVKDoXjYm8c7abNw+28M657sT6s1GvZsPSPG5fW0hZYdqVrWsSDl/SZzFZsdBqtRNaFMN/GwyGpPcXDg7Q27qP3rYDxCJy2QiVxoAjb41cDts4/nLhpfAEZQuhusVFJH6di2wmbs22IvS0UVdXS8+oBDBBEMjPz0+IgjkewTPoqpF7IMRDCdW6VHSOW4jonfiDMQLx9e2hwRB9XYN4e/2E+wNIkeTggogAg0h4JIkBJKZzH54WHmS+t4n53maywv2J7SIC3al5dGQ78eQ50aSmJk/i+uGlFO0Y5+XwhK+d5B3nePQFwjx1qoVTvfLy0NxUE59aUkie9drOl5hJJEli0F1LZ/2b+Abi9a5E6OwzMRjJo3LDFoqLi2f9t33dCUE0GqWqqoqKigr+4z/+46LH/uQnPyE3N5e5c+fS09PD448/Tk1NDU899RTLly+f1utf7eqjfYNB/ni4hZ2HWugYtfY8J8vC1jWF3LpyDjbrtZHAEw6HJxSJ4b+DweCkzqXRaMYVCbPJQCzQis99BDHcG68yIWDLWkxW4UbMqYVT/hFJkkTPUJA/NHRzoLOfaPwr7dBpWWgwovMFaevopqunjwFvgJikIiqqiEkqVBo9kqAlKqoIhiKUZ3ezeV4zKQY5ea5jwMIbNXNp6reN+9oGIBWBFARSANWoEEMJiYAgENKqiJk0aK06TAZdPMpEO87698iEbY7ftav7e/G9+w59+/fjaxjV51alIrV8EfbKCuwV69DZrkx5ZUmS2Nfex7Nn2ghEY2hUAneXZnNHcTaam8Q6gHgV3746Os6/kRAEMW4hRDSlbKjaOqs9EK47Idi1axdf+MIX+NnPfsatt946pef6/X7uvvtuSkpK+J//+Z9pvf7VFoJhJEniTGMfbxxsZs/xjsSarVolsGZRNrevLWR5WeY1b2pHIpExy04XLkcFAoFJnUulUiEHO0XkUDsBtHorJlshBmsekkpHJAaBUGxslMoF6+HDVpdKp8JckIIp34wQd9ZHBsN4GwYIuScnYlpVjA0lXVQUtKDTyJ9Tpy+bRv8yJE16YvI2GzUYR03ierWaIZePnrYB2hv7cHUnB0zo9BqKSuPLSGUZpNmnFjoa6OzEve8Arr378dWPijpRqUhdtDAuCmvRpV265+3l0h8M89TJFk7GrYOCFCOfXlJIfsq1mTg3WwwLQnvdG/gHRwShs9+ENmUJlRtum5UeCNedEHzjG9+gurqaPXv2TCvE8bvf/S6vv/46+/btm9brXytCMBp/MEL10XbePNRMbYsnsd2eamDzqjlsXVNIzmUmOM0mw0k8Fzovhydmry+EZ8jPoDfAoDeILxDGF5DXxIMRkXBUIhITiEkqppKkcykEgYRz0mjWosoyEk7VIsXF1SwJFKu1zDEYMBu1CGKEPlcX3Z1tePp6UKtENIKIWpDIz89jXmkBqdpWBnqOyP4NQYUjbw25JbdPqrTG0EAwUTCvodaF/4KSycm5Cw70hsn7j4JdXbj2HcC9dx/e86NEQRBIWbQQx7ClkD57oiBJEgc6+vjtmTb8kRhqQWBbaTbvKbm5rAMYFoR62mp/T2BI9iGIInR5zKRmr2PNultmtAfCdSUEwWCQyspK3vve9/Ld7353Wuf453/+Z954440rKgRDQ15e+sV/E/N7UZss3POZL2G1zk6Md3PnIG8caubtd9oY8o9MFItLHGxdW0Dlklz004xKuZDRMeBjE3iiBEIRfIELEnzGiVKZ6RhwQQC9VkCrklATRSCMChEVEprhyfnC/wURjUr+X68VSLUaSbVaSElJXo4SDCYO94fZ0zlAOF6ltSDFyHvn5bA0MzWxDDU4OEhdXR01NTV0dibnkhTmp1OUOQAROa5cpdaTPXcTmYWbJp00J4kSne0D1McL5rU19U+Yu1BSlkFO3uRzF4LdPYk8BW9dXdKFTVkwH/v6SuwV69Dbp+eHuRSeYIRfn2rheI/cUnROipFPLSmk4CazDoYZ6qun6ewOwvES6aIIvYNWsubewrKV61Gr5d+zd8jLrueeRor5EdQmNn3wY1gmOc9cV0Lw2muv8bWvfY2nn36aVatWTfn5fr+fbdu24XQ6px1JNNUL9sS//xOLw/XYVCPLGx7RyEldCZ/85vTEbDJEojEOnu7izYMtHK3tSWRLmg0aNi7PY+PSPLLSTQTCscTddVL2ZaIWykQJPJFJNx6fLHIMeLJzcjznZVLs9+hww/gxel1yDLgYi9DXdYzOxmoGPL2EIipCEQ2CLhtBl00ook4sSfl8vouMcARRq2coq5geS6bcOAdwaGBDhoFlWTZSU1IwmUyoVKqEKNTW1tLRMZIVnGoKM78giFErO7vlHgh3YM9bNeVw2FAwStN5VyJMtW+CvgvDwmBNndzdZLCnB/f+A7j37meopnZkx7AoVK7DXlmB3m6f0ngvhSRJHOro55kzrfgiMdQCvKckm22l2ddFLa3ZYNB9nsYzrxANyN8hUYQ+XyoF8++kbv9uHNnd6HUjv8lQWI27O5v3fvarlzz3dSUEDzzwAOfOneOtt94a4wD89re/zYsvvsiZM2cAeOedd3j88cfZunUrubm59PT08Mtf/pK6ujp+85vfsGTJxAWYLsZULtgT//5PbIicApIbbA1foT3a8imJQTQmjqlzMlILJTmBZ/S6t8cbpm8gMGMFrC5Eox6VxKPXjuO8TJ68jfH18GGn5nCmpm6WY8Blc/s8Pc27GXCdTWw3WnPJKthIWs4yJElIKu8xnpN7dHJjVK2lP30O/Wl5iGp5KUYf9GJ3NZHqc2Mxm5MsCp1Ox+DgID09PfT29gISmalBirK9GOM/Yp0pk4L57yPVUTbt99rv9iWshcY6N+ELrK7MHGtCGAqL09FMwkoM9fbGl4/2M1RTk7TPOr8Mx7ClkOGY9rgvZDAU4elTrRzp9gCQZzXw6SVzKUy9Oa0DgEF3PedPvogUjlc7lcA9qEeviZFiHvmch+eZzra8S4rBdSMEAwMDrF+/nk9+8pN84xvfGLN/+/btvPDCC9TEv6DNzc08+OCD1NTU4PF4MBqNLF++nC9/+cvTFgGY/AUbGvJS9+O/JFUIIAjyh1Ur2oiKGiRJQ1jS4hEtWNffhySo4xN3LFHvJBCKxf+P4g/JMeSR6Mw2jBmN2aDBnqonw2aI34mr5fXx4ZR6vVr+W6+R9w0n91xmCOHVIuR30dd5gIGe40ii/ONRay2k5awiLXsVGu3EPpVYLEYgEIiLgg+fz4fb6+NUUEOT2kJMkCfVYUGwDvVe1HOhUqmQpBh5dj+FGV60GvknFJbSsedvJrdgwWXFk8diIp2tgzTV99N0vo+ujqExuQv5c1OZW5rO3NJ07BmmSwpy2N2H59A7eA4ewldbl7TPVFpC2to12NauRue4fEtBkiSO9Xr53/M9+CIxVMBtBencUZh+01oHAIN9DTSdfRUtcuKpKEHfoB6NIGJLkZNCJQnCETVLNv39RZeJrhshuFaY7AX79X/+O5Veuc6RKMGemJMqTS0dURtHw3M5Hi6gW7RNawyq4TXtUevaY/4fbx181Hq4BPQGrXT5UvBGR8JN9eoIOaZBso1DGDQzu3Z/LaJRi+Sm+8mz+9FrZaGNidDtMdLmMuEPTS0gIabS0GefQ39a/hgL4VKCMDyewgwveXY/KpX8Q+72GGjsshKKzoxvRxJVxEJmYkEzsZAJSUx+j4IqgtrgQ633o9b7EFQXvwHRhkLYXX2k97qxDg4mvcchq4W+DDt9Djuhy3RuRtXa/5+9Pw+TLL3rO9HPec8a+5p7ZmXtS+/d6lZ3S2pAsgQImOuxn4EBXws8AwZshBlzjVnmAsZmLr72vRhjjw0YzLAYDNeD8AASICQsGrWkbvW+1L7kvkVk7BFnfd/7x4mMzKzMrMqsrupF1Pd54jkRJ2PLExHv5/x2VkZP0MqOAGC5bcaWzpFwWzd55DtXSip0JdClwJICQxkYSsNUOgYCE4GBjqnFW0PTMTUdQzMwNQNDMwhTdbpj50lm+j2vIo1GNUFxeNM9uLx4nG/8O9+z5/u4C4IDar8H7Nd+5sd5SsZuKqngM/IMHxTnMbTNH9VKlOVcNMF5OUZdS+8ZvLx+e7sTKFq+zVI3w0ovQ6Q2FhtFwe4xlmxSdtq3/TXfadJQDOVcJssdsslNAK63LOYqKWpti4NkJEXCYL04Sa04NQCC5XXibqfN1Zs+k2OGHB1tM5yPU1QjCfOVFLNrKSJ5+86ClQIVWjEYvBSRlwC2Pr9CmC66HYNBWD1uZCyYnk+xUqVUqZJpbIdCO5OmWu5DIXHrUGhmhlgZPUlkWKAUxeos5co1hLpzFvP1ihdwDUPqmFLHlAJj2+ItMDcWbkR/ATcw+ot4fDExNBNxi+1RrlczuUh7+CzC9AhcjVxhM1lkaW6cb/rOf7jnY283CG5/v4N3qfRkGvquZKHBXxNn8ZRGV5nomiJByIjeZERv8tWcxUfHRyBUhEABWmy+K2CP73dX12gYOi0haBk6DV3QMjSahqBpCFq6TrhPn7shDIb0JFF9hO7qCJ16mpqXpOYlMS3J4WnJqRMGE0MOCdPBMR0ShoVjOCSMBI5pkTAcTN18R/d6uZmUUvRac6wvfIHW+jmKGZ9ixsdKDFEcf5zc0AMHGqvZDSI+t1Djc/N1enaKxYl7GTnxMF83XeThocxN2zGvr15i6fIfo1NlerjDWLHHtZUUS+tJdMNE13WCIEDK/S2CiUSCdDpFOpUmtbFNpeJ96TS25bA03+bapXWuXapRWe0ggwQySBC0wbJ1Dh0tcPh4gcPHi+QLe1cBB/U69eeep/6lZ2mfPU+61SbdajN9dYbEkcMD95E9Mrzv47mhdhDxf15a5YXVFuvlacxDJ/hbp0Y4nL1xVXIUhnTrXTr1Fr1mF7/l4ndcQtdHugHSjyCMIFRoEnSpIZSG3l/UDTYWcANNiO3MfBNSShGqIL4QEqqIiAipKSKhUAIwNDB0hKUjHBPDsViYuUC6UEW3fQzbx7B8LAuSie3fK01/a2Mqdy2Cvq6PEVwvpaCrLApH78Gfew2izbNQkR9FDp2k7YzQdkOCZhXVqWG4dZywSVq1sbT9Zex0NIOWbtEyTdqmScvSaZqCugnrumJNiwh2Od2XboKoMkG4NgnB5tmblqpjDC2gF5fQdnEd6UInacSwSJgJkqZDYtvt7fuSZiL+m+GQtBL9xyaw3gFA8bpVVmf/ksrCc8gobpdhmCnKU08wPPU+TDu77+fqBiGfubbGn11dpRvGn91oyuYbj4/x3vHCDYEQz0B4nbnzf4jfi/sGdT2dK8sZKk0b0CiVSkxOTlLqZ+/sVqQXRfv7zmwdgGQaKby2RaMqWV108dztz1EsJ/tdVIc5fKy0Z+2CX6/H2UfPfJHGa6/HKS99pY4djesU3v8+EmO7V88Gfkin1qS93qJXb+E2u3hdj0uB5POJJK6uoynFmfUmD63UsaRClwK9fzaus3kGfju/V1JJQhUQqXBzAdfiBVwKULoGhoZm6gjbxHBMjKSNnU6QyKZI5tOkixmS+fQgDfQgarfavPK5n8Yyoz3XmbsxgjugO5E1JN0OnYvP0Tn7BbpXXtoGBbM4TurMk6TOvA9rOG6XIKWkVa9TXVygubpCd32NoFFBtdcxvDp20CSj2lja/nz8XWXRFilcK41vpwiTacJUCplO4qcc5loJLl4VLM5rKBn/I5qQpIfr2CNLRMk13Gh/Vbb7ldBEDIo+GLZCYzfQJK1EvN0AS/9+trH/hnZ7KQp6VBaeZXX28/hu3KtH03QKow8xMv0UyezEvp+rG0R8dmaVT19dpduvBh9J2Xzj8VHeO1a8YSW4khFr819i6fKfEgZxmqsbZnhjxqLZ3bRSyuUyJ0+e5OTJkwMwKKXo9Xo37A11swFISoEMbCIvhQoyhG4MoQ1pAobHUhw5UeL0feNMTZcI/YDWeryAu80OvVYXv7JOcOk8cvYSVJbQtkauUwW00jRG6ShmorDNhbKXegI+M2rxRj6GUMGTfMOiz0RvbytpYwHfOAOPVESkyS0LOGDoaJaObhvojoWZdLDTCZxskmQhTaaYI5FN3NICfjv1B7/yc4xNxjUpu60zX1FZQ+8U3Y46gppM8pp1dNfUUel26Fx6ns7ZZ+hdfgkVbbaENotjpE4/SerMk1gjR264wEkpadfrVJeW+rBYxW+sodo1dLeOEzRJqxb2PmHRUxarFHkhPMar7gTVLVbCUNbkg49O8sEnpkgkoBe4dIMevbC/DVy6gUtv43ro0uvf3ti/uc9Fcfu+QpqmbQPEVohs3N6AyAAsG3DZAiDbsNCUor76Oiuzf0GnPjN4jXThGCPTT5EbOrPv/P/eFiB0+kAYTsZAeHz8xkCIQpflq/+NlZnPDTKehHOY2UqeKzOr29xEu0FhL20dgLReXae6XKFeq9NqNel2u7i+ixf6yP7nEwedk/1LChVtL4bTRIRtuWTskLytyOomDhaJ/sXBQvgeXvUybuUCQX2OrelMRmoIu3wCu3wCIxkXr0kV9RfwkFCFRFpEhCTSFLPZBF8cL9EzDFCKe3s93hsFJFI2djpJIpskmc+QKWexU87bvoDfTv3Br/wcpZHlvxp1BO8EvZWVxdLr0r34PO1zX6B3+UVUuBn8MQqjpM88Ser0k1ijt9adUEpJp9miurRIc2WFTnWFoFFBdtbRezXsIHZDOdr2+QRKwUxU5oveCV7wDuMRn60JJMfMNe5LVpjOR5jpIma+TKo4QnZkhNLYOJmbNDNTSuGFHt0tEBmAJXD7cNkOkc19m1DpBj1u51dRQ8MxbZJGDIdxQ+e05jMqu4NzYl9P0MtOo/LHSNgZkub1FkoCx7C3pYK6YcRnr63x6asrtLcA4RuOj/LETYDgu3UWL/0J1cXn2ZiBkB16D9XaMHNXFqmtrGNILc46UQLHsEiYDrYwEEpDSBBSi7NWENuCmLq2u4tHoQiI6OHj4ve3Hj18mmFEy9PpeTahn+ifVm85hoaHYXfQnU4/6BxnzFiajoWBEyly61XSa8uY1VW0LZ+fNT5O+X1PMvLBryI5ufdAok4Q8rtvzPPMwvrgWH7HA9OcLL4zprXdSf2VqSx+J+jt6jUkvR7dS8/TPvvMTijkR0ideZL06Sexxo7ddt96u9GgurhIY3WFbnUVv1FBttcRbg3hd7jsFnnBP8yVcGTwmIzW4zH7Mk/YlxjRN6dQucqko6VxzSyRk0dLF7FyZZLFYbLDI5TGJ0jnsm+6D7tSCi/yt1se4SZUdgPL1tubEOoR3SAbJaNpPOKYPGSZOP1F25WKl/2A592A1i4/B8ewB5ZG0nBwhIOjknT9cVbUEEF/EU6GAaebdY40Whgh6FF/4ZYglEBXsf9bJLqo8SuobH/uQGhir57Eqh5BU2/urDdSIcGGD1yFREREmkKK2IWCroG5GcA0EzZmwkJzDKqdiNU1j5Vll8b6dcOONIludTdTVA1/m1vDCAIK1XVKa1Vy9cY2KFAuYT9wP7nHH6N44sRgpsVWvbra4Ndfm6XuBmjAhw4P8TdOjmMbXzlWwO3UXRAcUO+EpnPS79G99EIcU7j0/HYo5IZJnXmC1On3YY8ff8sCru1mk7Nnr/HfXlzk+WsunWDzdSeNdR63LvK4fXlfrihPGbS1NJ6ZJXTyaKkiZq5MqjRMZmiE0vg4mXz+LRnaoZQiiIId7qxu0KPjdui2u/RqbYJ6D6vZINH10AMNIhMinSgykFJHVwYWOiZx/vdGDrg5SCGMFyhfgxeLBs+WTHpGfAxzvuTJSsC99YgbLWNhehV37HVkog9ez0FbPEpUKxIiCVVEqCJ8GRKoiBBJQIRm6iTzGQojJQpDpX4AM02qkMF+E2meW9Vt+1y52G+Yd36NVnP7rIpEUqcwbJDMhuh2l567OQBJD0KK1XWKlQq5WgOxZYnpJhOsl0s0x0cxR0dJb2lVbqXSfKmj83Ij/n2UExZ/54FpTpVu79jOrwTdBcEB9U4AwVZJ36V7eQsUgs0fmJEbGsQU7PETbxkU9hqk41g6jxzN8MBwREmu4zcryNY6ou+GSsoWKW1/w2x8ZdDWUrjGBiwKmLkyieIwuaFRCmOj5EqlXWERRRHdRod2rUW31o4zUNo9gk6P0A2QfojyQwglWkTsQlHa4Ax8awbK7coBHxw76ROqkK4W8mrR4eWhDG6//UPS8zm6ukypvkpAgKsH9ERATw/oGB4dy6NjehzORHwgZZLp/++LYcSf9zzm+5XpuhTke0mKvTR5N4nYkgMZWgpZEBhDNk4uScpMbrq2zDhVeDMwvyVobzgY+v6yx5VSrC23+i0wKsxeqRJurZrXYHwq329/kSdT0Ol04tYezbU13Fdfg/MXsRaX0LbEQ3qJBNWhEtWhEr1kchA5baeKLI+dIjRjqE0FDR6xfArZnbO4DzIt7ytJd0FwQL3TQLBVMvA2oXDxeVSwmcmjZ8ukTz9B6sz7sCdO3PKM34PqoIN03G6PyuICjZVl2tVV/HqFqFVF9GoYbgNThugY+CpBSIIQB4mN0izAQsNE00yEZiI0A72f861fdwZ+O///63PANzJQ4kzwkFB5SC1CihAMsDNZkoURnEwaJ5skkUuRLmbIFLPo182y9iLJ52bW+JMrKzT92JoqOiYfmi5w35BFEHlb3F6b1orrdUi3ZhjuLaP3C1EWMHku0FkOfXpBDz8K0KVG3k1R7O6Egmv4rCc6VBNtuqZ303o6UxjbYyJmYkvqcN8VtmVfsg8RU1k0Fn1WrnaZvVTfMXfBdgyOnCjHaaonhyiU4pz4sNNh/dnnWHv68zRefgW1JeNJ5vN0pyaoDQ9RFRqdIGRt+Bj1QpzhZfo9RpfOk+rWtr3W1gFIu03Ky2QyO6blfSXoLggOqFs5YCtLSzz/C7+Ag8RF8J7v/V5Gxsbu1FsEYij0Lr9E+9wzdC9+GeVvgUKmROr0E6TPvA978uQdh0IUhLRqTV56fZnPvbrMy3PtwRhIARxPwf0pxWFDoisQEejEPnB9WxHP7c0BV4MUwnDLNiLsF/JIXUOYOsKxNlMIUzZWKtHPQEmTKmRJ51M7FvCtCrwmq3PPUJn74iDtU+g25YnHGD70AezkzfvxeJHkL2ZjIDS8TSB89Ngo758sYeq7f4aB12Lx8p9SWXh2MANhaOJxxo59LZqZwB1kcfVodVvMzcyxNLNEfamK2tKhUHMEsqjjZSPaprcZbwldvHB/Vtx+5QQp8u1R0o0h7FoOLdh+bM2MIjMlKB2yGTmUIp1KkAg0xBuX8Z9/jd4rr6OCTSg442MUnngc54H7OW+l+IP5Fq1+cs1U1GKyPofbau57ANIGLDbAsBssEonEWw6LXq/Fn3zqV+l0O6SSKb7uo/8TicT+3GB3QXBAHfSA/dlP/jiHrRkEm2fDkhTX/Gk+/FP/7I68x+slA4/elZfonPsinQvPofzNL7yeKZI6/QSp00/iTJ3eBoXAD2mvN+nUthfxhF2PyA2QXhDP2I36LhTFliIeo7+AGxhie1qhKyUvuj5f6nrMbvnB5oTgsaTN4wmb0g2CeltzwKNBEc/2HHCla0QoIhUSqQAZuaioiwhb2FGdtKqSp85+YoehErS1FD09S2Dn0FIFjGyZRHGIdHmE4tgYheHhm6YjyiiguvQCqzNP43ZW+ns18sP3Mjz9FOn8jVOCAfxI8hezFf74ygoNLw7AFvpA+MANgNBrr7Bw8ZM01uK2J0K3GT3yQUamn0LoO2cg+L7PlStXuHjxIleuXNlWX5DL5Thx4gSnTp1iZGQEqSRu6G1PFQ63pg3Ht7v+9jTh69OGe+EutSgKEp0c6cYQ6WaZZLuApjb/R6lJuuka7dwa7dwabrKJFUqOLPicmPOYXvQxos0lqZdPULlnijfufYolERevJQzJk2MhhzMmIgTlSiIvJOj5eD2PXqc7qLXYLyx0Xd8TEhuX2wmL//xb/4K1NfC39KOyjIihIfjWv/WPb/r4uyA4oA5ywP7sJ3+cI1b8w9v6cW8coKv+PXcEBoHrD4p4eo02biv2fwc9n6jXw+rMkvCukQjnEWxmc0gSBNoRpDiGEFMY4vbOPo4XcH8zB5yIpUjykguv9zR6avMoHUoJHh62eGAyTTqXxMmlSOZSpG9jEY/veVSXlqmvLNGurOLW1ohaVbRuDctvkIhapOnuq89SpDRapOgZWUI7h0oWMbMlnMIQmaFhCqPjFEaGMQxjMLB8deYvaFY3e/wns5MMTz9FYeQBhLixv92PJE/PVfjjyyvU+0DIOyYfPTrCU1PlPYEQT736Q7rNeMiJaecYP/51lMbfs6dlGAQBV65c4cKFCzugkM1mB1AYHR19UwublH2ghFvThjch0gtdWh2X+rxPe17iLhuozvbjFJk+rWwMhXa2gqb1BlA4vOhhbCmMvnjsMM++/5sIEnFKs++fo+d9Edie4bStuFFPkMIhoSys0MCM9Dg5wFcoLyJ0Q0LvugypPaTr+g6r4npYJJM37wT7n3/rX7CwtPHZ7VxpJsbkTWFwFwQH1H4P2MrSEq3/+MNodNDofyRbVpS4jVCS1Lf9OEMjcdql7/q01lt06x3cZg+v7RJ0PYLe5tm3CiRapAYBTKE2uxJu+MNvVIW5TSoEOYsmL6DJK2hsmviKJEqcQOkniBjpZ50ERH3XSXwGLvsphKAMgTB1NMvAcEzMpIWdcXDSCZL5FOlCGitp7bmAB6HkuXMV/uz5JV6+tD6oikw6Bk89MMyH3zPO0fH0W25uh77P+uoqzdU1OtVVvEaVqL2O1m1g+k0SUZsUPYR28699pDQ6WpKeSBPYWUjmMXIp7HwP9CobTaUMK0N5/FGKo+/BMG/cOyeIJJ9favKnszXq/UFBOUvna6cLfGAsuysQlFLU115n+dpnCbx4CpiTGmbsyIfJFI7d+PWCkJnZOS5evsK1a7MEW6CQSac5fuwIx48dYXRk+C35rNarPa5eqnH1Yo2ZK3V8f3sLjMKQzfC0TX5Kx8m6ROcuwcsXMM/PIYKIwDB54b0f5Oz9jwGgR12U+jJeOHvLxY2aAisydl5CA0daWJGOHgm0fTQx1DQNJ2mTTCVJp1JkMxnymSyZdJp0OoVlavze/+93CKLNMa2BEnGZNwAKy5B813f/vRu6ie6C4IDa7wH75E/+JKetuL2EAl4XJxgPNDTNRok0kEZoWYTIYAjnhkU8t6po4PsOtp2F95dyIhEitQhECMInwRoZuUrKX0dXmz8oaej4hRRBMUWYcbbXsd8B1Xs2Ly0M89LiCPXeZvriSKbNIxMr3D+2RtJ6B7XIVgotiBB+uOWy5XYQovnRDX/20hB4Q1m84SzK6n8PpELvuAg3AF0gTQNpGShL3/EZhEpwTh3lRXkPHeJZCkm6PCTOco92GWOfvakOokjCestmreFQbdnbuqLaZsRQ1mUo55JNBnf6KwOAlBq1epa1aoFKpUijmWbrGbIQEaVinaFyjXKuSmKtgrzSQV7rsjw0yee/+pto5eIK5hOrb/Ck/gL6kIYP+Ao8pfCVwlP0t2qwf+t1n+338RQ7cKIpMHeBhb0FGqbU9wULUAhdohsRpi6xzIBaPYG2ZT05dsTkv/+b/2DPZ7gLggNqvwfssz/54xzuu4UUUI+OU9Av7XpfRRK0DGhpFBmkliAkSahsAuXgKZsQ+rnfEYGKCJCEWkgkIqQeoYwQzQgwHA874ZNIeWSTAbc0nlgqjGYPq9bGrHUR0WaKnjQEQSGFX0zfcShIBVfXc7w4P8rZ1dJgodGF5MxwlYcnlzlSbLw7WmTvgEU0gITwY1CIfrzEL6bxRnJEqU3XnFHv4qw0MJqxj1qZOtIykFZ/24dEYFm8YZ/iBXE/7QEQejwoznKPdgnzDgABbgwFy4gYysVQyL1FUADwfJNKNU+lUmStWsDztrs6HcdlqFSjXKhS7CwgZzp8eei9vHHvY6BpJDstnnzujznsrKAfT6GN3FpqqVKKkBgMoYIQRaRi+0/2/05/yde0OIEiNrQ1CHVkqBOFBn4g8AK9fxF4YbzdLZ1L2D2kt2lNjo74/N//9o/u+R7vguCAuhWLAOCV7hTjloVJF4MupuZhCG9fLgWAKIAg0PFCEzdy6MoUHZWmpbI0KFLXSoS7+PSlFhHpIbIPDCkipJAoXYKuwABhgGEKLFvHcQxSCYdc2qGcyzCayzDqrqHPv0rv0gtIdzPoLRJpEsffQ/LkY9hTp9HEnavabHUDnn5lhc88v8TVpc30wqG8w4ceGeVDj4wxlL89xU9vl6IwolGt0FhZpVVZxu/OI4xV9ORm1a3o+jgrDaxqe3u17XXy0Xmh9B6eHX2CjhW7BBzp85Bq8GjeojQ8RHF4GHPLsBivt87ytT+nUenHtYTB0MQTDE2+D93Yf7woDENm5xa4ePkKV6/N4PubPvNUKsnxo7H7aGx05C0pCoR+7cJKl6uXaly5uM7cTIMo3JIVpcHYRIbDR7OEqsFnlE7djkF67MIrvPeZT5NKWmQeOkHy/qNYU0Wk8olCHxm5RKGHjHyiyEOGXryNPKLQY6c98Gb+D4ikRhhpRFIQhBpuIHADHTcQeJGGG2l02w7allTguxbBbdatxgiulwIiaUB5GrW6gHJdlIz3a1rs4hMG6OYWd99NFAUQhDp+sAGLJB2ZpkWOBgXqWolA3NpiKbUIpQeMmIscNmeZ1hdIaJsVzT1MrupjXLMnqSRHcJIOqaRDLpOklMswUigwNVSklMliGG/OBXZpvs6nvzTD516Yp+PGZ9GaBg+dGOIjj0/zxH2jmF9BrQTczhqrs5+nuvgcMoqPuaY5KDWGW3cI6g3o1DC8OomoRUa1BwOQIk3wavEBnhl5Pw07D0AyaPPE6hd5uPICXmTQ1TP4ZhaVLKBnStgFGyOxgIrifj2GlWb82EcoTzx+YNiHYcjMzAwXLlzg0qVL+P7mdyaVSnHixAlOnjzJxMTEbYGCUjJekMN4cY5CFxm6m7ej+LrneizN+8zPKRYXdZqN7XE1YUW4p21WykOgaSS6bZ78i09xaKYf3E/p6MdS6MfSaGM3txSUAolOFGkEIYSRRig1okj0txqhFP2tRhgJIqmh6w52Ik0ylSWRzJHJ5Mlks4OgsmHAr/3qL+OHu1sGd2MEd0h3KmsobFZxFy/gLVzAnb+Av3wFFfr9CVJxZ2oZgdQMIiUAhRASQ5cYhmK/v88ohCAQeJEe9/1RDk2SNMlQ13LURImAFLrUEZGBLndftDUkI8Yy09ZVpqwZHLEZaPakzVxwiBn/MMvBOGrLmYnSZByb0KPYrSUipC6RIrZQtL6FolsC0zJwEiappE0ulaSYSzNSzDM1NMxwLkukNL7wyiKffnaWVy5VBq+RSVp88D2TfOTxaQ6P7X9mwDtdYdClMv8sq3OfJ3DrQNwOuzj+CCOHniKRiWtTpJTU1yqsLy/RWl2mV1vDa1S5rKd5vXicdt9CSAYdHl/9Io9UnseS1zUWBIJ8kt5UEenE6aXSBXc9DWIUKz9EujRMbnSM8tg4TvLGQW3oWwqzswMoeN7mdyaZTHLs2BGOHz3EyFAepTYX882FfOti7m1f4PvXN+ZGHFQ91xq4kCrVAkEQg8HLmqzfkydMxbcnV+Z58s9/n1SjsXmskhbB9BCdsTz1dAI/UNsW8zDSkErj+oU6HhKUvmEBm2nePPHjbtbQ26C3qo5ARQHeykwMhoXzeAsXCOurO+4n7CTG6DFkcohux6fTahM2ami9JkboYmoBpogwzIPBIgw1gkgn0Ex8kaBnpejYWZrJPJXkEC3NwnNDAj9C+iGjwTrH5SJHWSK5xVLwlMWcP8mMf5SVYBx5w245+5dCEulxtW6kR7hIapFBIzQJt+SZJ6yQUj5ktKyRy9jkMkmK2QzDhRwT5RLjxeKbtlDeaikZUVt9ldWZp+k0Zgf7M8UTjEw/RbZ8as9U0FAqvrhQ5Y8uLVPpxZ+Tg+Rev8Lx6kWMdnXbACRTRHhDWdzxAqofcDKaPRLz6xidzUW3q2xcPUlgpVGJFCKVwkylMDNJrEwCK2UjdDlYuAO/x9q6z1JFsloXhNHm+zWNiKGsF8cUUv6txYA0gW442y+6jW44CMO+7m82QrcJQvB8Sbvjs7jQY2GmR2U5oNVSNI5kaU2nQdMQfsTQ+SUOzZ5nvHqZfG9zFrVvmayXS3QmJ9CmJslks7umhaZSqX0t8vvV3TqCt1hvZ2Vx2K7hLVzsg+Ei3tKlbb2FNmSWJrAnTuFMnMCZPIVZniSSisbsVSpnX6Q9f42otgLdxi3DQl4Hi1BPoJI5jMIQmUKGpNYlWjhL1KlvPshK0B05zXLuMHN6mUbXo9318HoBgR8RBRIZAhFokYYWCYQUfetER5cGIjJumEmhUDSACpI6m9aXAApolNHIwOA5FAqp94PufahsxFCUrkBXCENDmALL0rETJqmETSadoJhNM1TIMV4qMlkqYVs7C7PutNr1a6zMPE195dXBf2snhxiZforS+Ht2LRYDCMKQL86v8skrFSp991rSgK8agscLHoZyCYMeXquBW68StGpImqiEGiQIGLVODAR3f3nzUheDrKc40B1fQkOnGqZZ6aWotBLboGCZMDZkMzWWZmQog2klEHq8eO9YzA0Hvf83TRgDd41Sim53syhst4E87XZ7zwluG3MXumaJtaOTBKn4mCZWehTON0iokLJWp7BygULjGnYUB/TNfJ7Sk49Tet+T5O69B+0Ozz+4W1n8Fuqd1GtIyQh/dQZ3/gLewnnchQuEteUd99OsBM74ceyJkzgTp7AnTqAnd3eZ1Oausvrai7TnLhGur6J1GxhRD1MFmHoUu6H2eQItI4giDTTQdcVWF7ASBtah+8k98mEyJ96DZuzv7CgMQ5brNRYqVZarddabLRqtLt2ej+sG+F5IFEhUCIGv0XJtGr6FLzd/hDaKMhplBNYBBtLfSAq1BSTRluC86gfmQZigmwLDEti2SSppkU4lKGTSDOWzTA2VGC8PkbD3BoqU4Ra/tzc4w3Z7FZprZ2nXrw2G1WiajuUUMKwUSkVb3Cvu4D5SaVxU0zwv76NJpn98PB4Q57hfu3DzKXdKxRlRnkIPAF+CG4EXoHkBuu9hhj6G2l+2UkfZLOpDLJslalqSrUm3lmly9OhR7r3vPqamptB1HSnlYJHfurhfP6pzvzOdU6nUDjfN1rN5K5HgE68v8t+W1lGA8CWF83WSq5tV0XnTJ1+7QqFxjXxvBYHEzOUoPvE45fc/Se6+e+84FA6quyA4oN5JINhNUaeBu3gRb/58vF28uK3P0IbM4lgfDCexJ05hDR/adyCwPn+NlddfpDN3hbC63Lcsuhh9WJgHgAX0MyEiLc6I0pLIRA69MExq/DBD9z5MfvrYm3LfKKU4P1vj01+a5emX5un1C6+EBsemUhyZsEinfNqdHu2uuwkUP7ZQVNi3UKSGkDoiEn3rJLZStrY8uB2KRIjSNy+aESH0EGEEGEaEboQYRoRlRJhGiG1E2GZEwgxJ6CFZJ8LW1b7SNIUwEYaDpie4KCf5knuIWhT7+x0R8WShx/tKESl78+xbGHbcP2nm83Qa1wDQjQRjxz7M0NT7dlRF3+oAJIlGRWRZECUW9RL+lkJJQ4UUZZuM6mKoEFez6WkWvf42vK4mR9O0wSK/V9uHVCq174r1mUaXX335Ggvt+Lc1ic7QpSaVmca2++mapOCuUGjOUuoukAwaWLlsDIX3PUnu/vveEVC4C4ID6p0OguulZIS/NtePNcSWQ1Bd3HE/zXSwx48NwOBMnERP3Xia2I1UX5xl7Y2XaM9eIqgsQ6eBHnb7lkWIeRA3VBS7ocJIECiT0HCQThajMExy4jCl0w9QPHpqX7DoeSGff3mBP/3SLGevrQ/25zM2H3rPFB9+7yGmRuIzY6UUSgbXBSS9OF0wcIkiF9/r0uq0qTSbNNo92q5Pz43wfIkf9N93KIiiOBc8Cg1kaKAiHRUZaKGBFsUX8SaHyGyXYnhklSPTC5QLrcHe1WaS8yslFprpuBrc0DAsgeOYJBIW6ZRDL5ljXiYGjdmShs6Hjwzz1w4Pk9xSmKKUolk5z/zFP8Jtx5aolSgyceIbKIw8cPNRqlvO2FutFvXKGp3KMmGzgug1MMM2SeXhKB9b+fQ0mxVR2AEFUwWMRetMyCrDsoFA4SmDjpbENTKEdgEtU8bKD93WAUihlPzRpWU+dXmZSEHa1Pkbx8bI13yuXqhw+fwa7dZ2160TdSi25yl2Fyj2lkikbEpPPE7p/TEUxNsUr7oLggPq3QaC3RT1Wv1YQz9LafEiyuvuuJ+RH+mDIbYcrJHDaPvsOb8ftVYWWXn9BboXX0Bfv4YVttDFpgmvNt3RN5WMIAo1gg1Y6DbSySByBezhUTLT06QnR9EI4zzvsMdCxeXzZwO+eFGj7W4uCNPFDo9MrnF6eBlbv50VzFocqNTtgV87PsOOr7uBTq0nWe9FNLuSVi+i7Up6nsLzFL4fpwjLUEOLQIsEmhQIqaNHen+7EyjZbIuj0/OMja4hRPzz7HQdrs1MMLcwShTt/EwV0B1J0DySHmTMaGGEs1TDWq6gqbAfRwFhKI4O1bl/ZJWEER+vVphmMTwOepGkIbCA0PMGC3+n09nXKFEhxDZ3TcIyMAKfVrPBer1BreexpRwAQ4WMy3UmoirDso64QQ7/XgOQksUhssOj+x6ANNvo8quvzDDfimMDD43k+Nv3HSJrGawubcxdWGP26jrR1rkLSpH11ih1Fyh1FyiYLuXHH6P8/veRe+D+txQKd0FwQH0lgOB6KSUJKvMxGOYv4C5eIFib5/pCGM2wsMeObXMpGZnCbXsfUoa4c2fjcZwXvkzU2jxjl2j4moMfCKQvMQgwtThmoe+3tZKEMIBQCgJlEAqd0DIJHYsFUeLl3hSv10eRfVePpYfcO1rhkclVpks+urkZmIyDkluClFuDl/rW/VsWfN16S+ZA1Ftt5qtVlqrrrDWaNFsdWh0X6beZdJY5kl3H7ndf80PBldUiFxeGcHvOlrRhHSENFNAbcWgczhCm+0AIJJm5Dpm5NmLLKqzrEUcOz3Hs8ByGES94yyslzl04SqebjGcy6HHqsBIhqh9LUUKiBunDGsLUMG0TJ2GTyabIp1OUcxmGS3kOlYcopFMD609KycLCAhcuXODixYt0OpvZeYauU0w45ERI2m1iuLcyAEmnraVvOgBJAp+6vMIfXVoiUpA0db7tnkkeHy8OLKPAj5i5Uh2A4fq5C0bkUegtUeouMEydicfui2MKD9yPuI0ZRrvpXQGC3/u93+NHf3RnefTf/bt/l3/0j/7RDR/7iU98gl/8xV9kYWGB6elpvu/7vo+PfvSjt/xebuWA1RsNfvY3f4+OG5JyDH7wb/9N8rlbd7u8FYrcDt7ixUFdg7d4YVtV8YaMbBl74iTW2FGM0Wn0whCS6MD53xsVmQP4KIXe8bDWO5i1Drq/eWauhEaQS+IXUwS5JGEgkbUI2fARnRDdC9DDEFOFmJrEMOS+YSEldCOLispSURnqMkldJomEw9Rolsceu4djDz+MYd3ezqxvpWTkU118npWZp/G6a/29Grmhe0mX30NAjna7TaVaZbFaZb3eoNnpUrOyNIpThE5ccatFIc7aComVFUQISB2kjqNL7j20wuGxCpoW9wCanR/j4qVp/ODNZ1bFzQ43alHCfh1KDBTTCEiYPo7w0LeeyGgCO5ujNDbG0cPTjORzaK0W7WplxwAky49hkdZ2aYu9iwKl09LSuEaGtcwhvjD+GDUrHhp/3IH/8eQ4h8aHd1gWjVqPKxf64zsvVHB722MkSb9OsbvIkKxy/IEpRp56kvyDD+wJhbVqnd/5t/8F6SqEo/E/fvx/YKiU39f/8K4CwS//8i+TyWymQ42MjDB2gzTMP/7jP+YHfuAH+O7v/m7e//7382d/9mf85m/+Jr/8y7/MBz7wgVt6Lwc9YD/0s7+MvZrHCjYren3TxRuu8y9/8Ltu6T3cLiklb1B9uX0hDwMX2aggq6tQq6LVG2id7o6cG6VpREmLMO0Qpm3ClIOyD2jibssBt9GFjd7xEKursLQI3S1A0g3MqRM4xx/COfoQZjI7eKwmNgfZ9JoNll99nuaVs/hri8hWDd3vYCovTp01JLqxP1dUpDT8QKBCCJVBIGyknUbkSjijUxSO38vwmYewbtO839uhIAgGaZKDzJpmk7A3R0KbI21vnp02uybzlSRrDQd13SesgE5uhOrQEXr9zqgmigfSGu8fSTOczw167budVRYu/BGNytn4sZpJTRznWneEVsen0/XxvIDQl3HqcEA/dVggpIbou7s2XF+6PMhZsUJYPQynhZ5oI7a4+JQURG6KwE3jBxZSV5vFjVtShzVdoesRphZiaSE2HgnpkZJdcrJDUbYYpoWjwm21DhGCL448yV+OPoUUOk7Y42vmP8N07SquniF08pDsWxaF2A2VGxml55pcvVjlyvk15mdqbF1JNRWR761QDtc4erzE8a95hMIjDw+g8PP/6y/R7eTx9eTgMVbUJZmq8w/+t+++6dF6V4HgC1/4AsVicd+P++hHP8rJkyf51//6Xw/2fed3fietVovf/d3fvaX3cpAD9kM/+8ukF+IW01tz3zda27YnVm4JBkpGRJG3LR1Qbj277i/m2/dtKbmPNrb+zV/sRookRsfDaLvobQ+j4yLCXdL0nARasYwoj6EPT2AOT2HY6e0+ct3esoAbewYalVL4S5dpn/sCnbNfIKyvDP6mGRaJYw+TPvMkyeOPIuybV7tuVa/VYOW1F2hcOYe3ugCtGsJvI2SANASGoUjvsz+Ukv0q7jB2QwXCQdopRLaEMzJJ4cS9DN/zyG2BRRAEO9Ilr0+jvNlAlZQdMFnuMpLvDdJ8I2UjrWPYuXvI5srbpm8p4MXlOn94aXngG7d1wYcOD/GRIyNkrE34t9YvMX/+D+m2FoB4BsLE8a+nOP7IgVxlnu8zX62yWF1nrdZgvdmm1e7R6cW1KL4fIQOJDBVE2pZaFA1bi7BND8vuIvTNVFYlNSI3TehmiNwUcHDX3UYtitzI9hIhmgiQKYPGkWn8VHzymmmscnT+eTJBEwefBD5JfJLKJ42HKSN6Ih6A1DOLNBin5heoNSx6wfa4jxV2KfmrTE8kmF9ZY1E/hRTXncn0l+J8YummMPiKBcHc3Bwf/vCH+bf/9t/ykY98ZLD/E5/4BD/6oz/KM888cyCobGi/B6zeaPD/+ZlPYwY2GhoKhab7WAYYRoRhhGi2yzf8d6dxbH2zUVXoDxpWSekjQ++6v/kotb8Cnv1K0wx03RoEMoVhIXQbTY/PxoVh9f/u9LcWuuj7vLc8RtN0osYawcol/OUr+MuXCaoL8aq4VULHLE9hjR7DGjmKOXIMPVs6cGdHpRTR2gzepWfxLj2HbGypvNZNrOn7sY6/F+vIQwjrYFDYS5evLvOXf/kqMwsVEqpLXnTJiw5j1BmhTlq4GOb+UjeV2ugPFQe4A2ERmSnIFDDK46SnT5A/eg+BUrTaHbrdDu1Ol26nTbsdbzvdLp67P3+3bhik0inSyRSpdIrUjm0SXQTUVl+ktvI80cZYTWGRG3qA4th7sRPbfzNSKV6vtvnTuXUW+5XGltB4/3ier5kokO6P8FRK0aq+SnX2s4R+nGJpJ0cpT3+EZO7ofg//m5ZSisWlJV4/f56F+Tl8b+sMDo1Qt3CVjRsaqAjUlqB8HJgXfUslTh/WbgIOpUF7KkXzSAaEhhZKcpeaJJd6O61pVBw70cN+a/gAISKEFiCUQoQmumsSyDTquvTYtLeOLWsEuqBuDCNEauMfxop6fOdPfO0N3UTvKhCUSiVqtRrj4+N8y7d8C9/1Xd+1Z97v5z73Ob77u7+bT37ykxw7tjls45VXXuGbv/mb+U//6T/x6KOPHvi97PeA/cT//qsYV4aA+ANeGV9leKkMGn0zVBIJhTI0lKWjTBNlmCjDQpkCqQuUoSENgdQ1lCGQRn+ra7w7ei+/hVKK4d4Kp+tnOVM/R9HbDDSHms6V7FHO5c9wKXcCT3/zZ+FKKry1Ht3FDv76pi9ZMzRSQw4T+R4TYYVir0q+VyMTNElHHRzlYYvgYLAINTxp4OLQEUlaVoa6XWA9VaKSHmYtO0JkvPVVzbcinYj7tfM8LN7A7tcNzMoxvigfYp382/vm3i2SCrvuUZyr4lRdQrX9JMeKuvTMOrocH+wrZpf5+E/u7X243SC4I/lOQ0NDfP/3fz8PPvggmqbx2c9+lp/7uZ9jZWWFn/iJn9j1MY1+U6hsdnsFba4fpG00GjseczvVcUO2hYMlaEqgqbgoaXcFXD8mby9JwQAKG5DYhIeG0vv7jP6+HWCJr3/FAEXTWE2Ospoc5S/Gvobh3iqn62c5XT9HyatysnGRk42LhJrO1cxRzuVPczF3Es+4NShoQsMZSeKMJInckN5ih+5SB+lGtJd6nF+Cy+kxEuPHSRxNIq4bDCGikGJ7jaHWCsVOlbxbI+s3SUUdEsqNYWEoNAGGqTAISBFQogVqBVziS3UTFr40cJVNR0/SNDM0nALrySKVzAhrmVFC8+2HRYTOS+oezkXHeES8xr3aRQ6JJSa1Zc6rIzwn76dL8uZP9FdZQsMrOiwVJ+KbXsjQ1WWyy00i3+7389puhUv3rU3mvCMgeOqpp3jqqacGtz/wgQ9g2za/9mu/xvd+7/cyPDy852OvdzdsGCx3eoxeytk8FBoaI8vDyGyNTqJDV/cIROxT1IRER4vzvyNjsBWRGV8PzM3rUkfv54gLCfgbLdxufdiIrmtYtoFl64Otve32bvt0bGf7bV0Xt3xMVejjr14jWLkycCnJ7k5QCye96U4aPYY5fBhh7bWQnwKeit1H1Xm8S8/hX3oWakucaF7kRPNi7KI6dB/28fdiHX0Y0e8/f8P3qhS+H8RumnaHTqdDp+3SsV1aJZdrax6X1wRr3QRhO6B1oU7nQo1yosNYsk3BcrdZAqZlofLD+Mmj9NJJtGQakUmhJ1OYloG/PEdv7jLeyhy01tH9NkbkYWkBhh5h9NuUb8AiSUCRNrC6CYu+cTRwQ0mdQLMJzSSk8ujFUVJTxyideZBEbu90YK9bYX35Weprr6L63UoNM01h9D0URh7BMDePn1KKs7UOfzpbZa4du19MofG+0TxfM1kgaxnA/fjuOtXZz9Bef4Mz2hXuMeYojD1JYfz9e/ZJutNSSrFWqXD16hUuX71Kq7VZkGcYBpOTkxw7coRDh6axbiGtc6Xr8zsXl5lpxZbkiXySbzk+QtE5+HP9+s/9Z2rt2PPgkSEwXJBgyMlt9xPOW3vC95bVEWy4eH7pl36Jr/7qr97x97fbNXR9jOB6KRSB6fGDP/zXaEUhL81e4cLKLLO1edaDCr7RQjN2sQ5UnE2h9VIIL4EILXSpoxNP7tKVPoCJHVpYkY0ZWRjSREgDpI6KBNFtnvQodA3bNrAdE9sxNi/X77M3/rb7/Qwz9rlGzQruoBr6At7yFXa8aU1gDR3CntysazCLYzcEkr82S/vsF+icfYagMr/lHzBIHLkf69ijRKOnaQdyz941QXBzqy2QgvWgwGInS8PdtAbyaYMn7ynx4cemODw5hPUmG9WFvsfaxbOsn3uF3tIMUaOCcFsYkYuphXF/qAPPtNAIpEGoWYRWMs6dL4+RnT7B6AOPYWcSrM1/ibW5zxN4TSAeYlMaew/D00+RSI8Mnk8pxWtrTf6vi0tca8RFi5bQ+KpDQ3z9sRFydrz4tevXmL/wh3TqM8DGDISvpTzx3js68OhmUkqxurrKhQsXuHDhAvV6ffA3wzA4fPgwJ0+e5NixYwf6LKVSfObaKp84v0ggFbYu+B9OT/BVh8qIA5xQrVXr/Mo/+1N8kdg95e1tihG8Y/r5Hj0aB6CuXLmyDQSXL19G07TB3++U8rkc3nAdc2EkDhTvkjXkDdcpFgoUgenyEPD4tueYrVR4ee4qF1bnmG8ssu5X6Gk1pOFDpkGU2XnWrHw7vkgdUGhGgGb30PTrArYKRGSQCpJkwyRpmSStkiS1BEk9jWOkcIw0lplGKQPPDeOLF+D3r7tuiO/1m5dFil43oNd9c4FsIbRtcLCcU9jOvdjjAkP20P06eq+C1l5Bd2uY8wHmwquY2guYWoCdMEmPHyY9dRxn6iTO+AmEnUQpheu6tJRDa+JR2rnT9JavIRZeI1W9TMJv0Lv8Ir3LLyLRWBU5FkWJJb24rZ3BhhzH2bOH/MZ10zRRSnF5vsGnn40H6dTbIZ96doU/fm7ltgzSMSybsXsfYuzeh/a8TxiGVC+8RuX8q/QWZ4kaa4jeBiwCTF3GMQsRD0LSTYUzcFN2wFuDhQuw8DlWnvllon7rDKSB0A00W0NL6Kxf/BMqmT8neegU4/d8HdnSSTRN4/7hHPcNZXltrckfXFriar3Ln11b5XOza3zVoTJff3SUfP4wpx77Puqrr7Jw8VN43QqzZ3+P1dm/ZOLkN5Irn7njVvxu0jSNkZERRkZG+MAHPsDq6ioXL17kwoUL1Go1Ll26xKVLl9B1ncOHD3Pq1CmOHj2Kbd+4zkRoGh85MsIDwzn+j1dmuFTr8J9en+PLSzW+44FphpL7q1MZKuVJpur4vcTOUvz+OXkyXd93PcHt0ltmEfzzf/7P+fVf/3U+97nPMTQ0tOt9PvrRj3L69Gn+1b/6V4N9b2X6KNyZOoL5apWX565yfnWW+cYSVS8GBMbe2SPKtxF+Aj0y0YDQ8JB2B824uVvJkSZZmSCHQ15PkLfTlBJZytkSQ8UxyqUJbDPbB0UfGG6wCY/B/mDzem/nvts40Q8NiakFGFoQD24XkkhTREIj1DQQEk1INE2CiEjgMqRqjKoqBa0VL5Caj65JwuIhtOkHcY4/SnZobN8DQ66XF0Tv2EE6YRiyfuU81XOv0Jm/SlRfQ7ibMy0OOgBJhnEjwUDqeMoiMpKodA6zNEpj7CRfcia43Is/cENofNVUma8/NkLBsZAypDL3RRavfJooiK2IdOEYk6e+iVR28kYv+5ZJKcXa2trAUqjVaoO/6brO9PQ0p06d4tixYzeFglSKP59Z4/fOL+JHEksX/M1T43xwemjf1sGudQRhl2T6K6iO4Du/8zt54oknOHnyJACf+cxn+N3f/V2+/du/nR/7sR8D4Md+7Mf4/d//fd54443B4z71qU/xD//hP+R7vud7eN/73sdnPvMZfv3Xf/0tLSiDt66yeLFW46XZK5xfmWO+uUjVXaNLDcwbpBcGDlaYxokcHAxsTaLpLj2jR1O4eGJ/Z/i2NMlKhzwJcnqCvJmilMxRypYYKY0wPjJNOlvcM8tLKUXgR31LYytEAlrNLq1mh1arR7ft0u16uL24Q2jgS8JQoaRASQFqr5F9tyrVh0J8sR2DRDZDsljCSSWwnC2uLtvAScRxlYHrq+8Ks2wDsSUwv1Tp8GfPzfKZ52apNjazjk5M5fnI49N81UMTpBJ3tq3AQRWGIfWZy6y9/iKdxWtEtdU3NQAplIKGlaOeLNI0M7TNDE4QMWXqlCanKZ+5j557kdXZpwdts4tjDzN+/KPYidvX2uTNSilFpVIZQGF9fTNjbQMKG+4jx9k7OWG14/Frr85wYT0u7jtZTPMd9x9iOLW/hIav+Mrin/7pn+bpp59meXkZKSWHDx/mm7/5m/nYxz42MBd/5Ed+hE984hOcP39+22M/8YlP8Au/8AuDFhMf//jH3/IWE2+3luo1Xp69yvmVWeYaS1QGgLhBCX3gkCRPwSxS0FMUdIOsHuJHXepem3rUo4FLU/Rw9wkLSxrkZILshmVhpijYaTJ2hqSdxtAThD60u519DQy5XqlUilQqTSqVIWGnsO0ktlLonTqysY5fq9Kt1QeN6TYvFqGeJtRTBFj4kY7nS5S8vV9ly74+JhIDou2FLFQ7LFa7BCgiQOiC00eLvPe+MU4dLeI4Jk7C3AGUd5rCMKQxe5W1N56jO/sqorOM7nmIQCJUhEChH9CyCKVApky0nIW0DCJTJ1JFEuWHGLnvvRSmjtzZf+oAUkpRrVYHUKhWq4O/CSEGUDh+/PiuUJBK8bmZCv/n+QW8SGIJjb9xaoIPHd6/dXAreleA4J2kdyMI9tJqs8FLM1c5tzzLXGNxAAhl3qAKNbRJqgJFe4ip3CinRqY5XixAr8lKdYlKs0q1W6fud6iH3QEsegeARVY6pKVFWpoklIEtDWxMEmaKdKpALlMi229lsHV4yH56yasoxF+dHbTk3muYD2YCc+wE2vAptOIRVH6KXqtF8/IbNGcv49br20ASJYeQiTKhmSEIGLjIXDdARrf3J2Fa+k0C7+Z1cRYD57r7WLaBrt/5BnhKRtRXX2dl9i8GgWCAhD6CamVwVxqEtVXoNggNjTBp42hBXH27z/nDMoIwiN1QG23K42l5I6QnD1M+8zC5Q0felpGk1WqV8+fP7wqFQ4cODaCQSGyvBVjrxtbB+WpsHRwvpPiO+6cZTd+ZtiV3QXBAfSWBYC9VW01emLnC+eVZZjYAoW4CiMDCCNLYYZJkYJHwIRtCqp+uIjSJaYEwFZEhCYSkJ0I6IqCl+bSES1ffX8sLU+pkNywLEVsWxUSWcqbIcHGYsZFpCsWRfQ8ZOdgwn3gEqJ4p41dm6Z77It7S5S330nAO3UPqzJOkTj+BkS4QhtGWmMh1sRJ39321eo/1Wo9uL64q1QFxW11eYJhiF5fWppvLdnYBjb0TOLqxP6B0GrOszDxNbeWVQbW5nSwzfOgDlMYfRTdiX/r5aivOMlqrkgla5PwGR+uzTKxeIek1sOwIK6EQUYSI9jd5bFdYJLK3dQDSzbTVUqhUNmNEG1A4ceIEJ06cGEBBKsVfzFb4L+di68AUGv/9yXE+fGT4tlsHd0FwQH2lg2C3gSEb29V6jWvNOpWwS9fw8SyXyO6h2Xu7mFRgYvhpkipD2coznRvm3vFDHBsdI5vNkk6nB10Zu90Wi4vXWKkuUmlUWe/WWffaNKIeDdWjqbl09P2dJRpKJxs55EiQE84WWBQYKowwNjRFaWh8V1gcdJiPWZpAhQH+yjX8latb74Fz6Ayp030oZA7e0mTrIJ1z19bjNGEgl7R49OQQDxwtkXXM66ByXSD+un1hsL/Fc78yDLENGjtcYIntYNE1l27zHJ3aqwi6GGaEbZuMTL+X4UPvx3LyQAyEP7i0NDgr1jWN900W+YZjo5Qcg8ril1k4+weobgsRRKi2IlwK0Nr90aoixDAV+x2hsdcAJD0/RGryyIEGIN1M6+vrAyisra0N9muats1SSCaTVHsev/bKLGercT3D0XyKv/PANGO30Tq4C4ID6t0MgiiK6HQ6NxzifSsDQ3THoaICKmGXatCiIWv0aKCsncNuBgotHJWnaJWZyIxxcmSKB6eOcKhUvuEgkG63zfLyNVYqS6zV16h2G9S8No2oS0P1aBwAFroScTaUcsiJBAUzRcHJUMoUGMoPMz5yiNLQGIZubhnmcz7e7jHMR8+W0RMZpNsmbKxt+YuGM3U6thROPYGRLe3rPW7V3EqLP3t2ls9+eY56e/N/PHO4yNc+foj3PzhBYh+dXqNI4nsh7nWZW/5NrJTrbwf+rRcy7iahyXgEpy1IpFMkkklsx8AXsNDzWQ9DpKGBITg+lOHRqRKljEFn/WUaq8+iCxfDiCiO3svkyY/ipOJswtbKIitvvEjr6kWC6hJ06uhBD1P5MSwOPNNi6wAkB+lk0PNDJMYPUT51P6WT9x0IFuvr61y8eJHz58/vgMLU1NQACi+s9/jds/O4ocQQGv+3E2N87ZER9NsQM7oLggPqVg7YhZfP8We/9lsQmaAHfPg7/hYnHzx9W99XFEU7FvXrr28d2nEjCSF2Hdy99XYymbzp5KZGp8NLs9c4uzzDtdoCa701OmqdyOjs3WcnNLFVnoJZZjI7xomhSR46dJTp8tC+xwq6vS7LyzMsVxZYq1fimIXXphZ2aCqXhubS1vfXa15Xgox0yKlEDAsjScHJUEznKZsOxcgl0VgmXLq4vUBtQ0JHMyyUv92tZk+eIn3mfaROP3lgKISR5Lk3Vvj0szM8f3aFjZh2wtb5wIMTfO3j05yaLtzxvHsZyU1IbAGIvzXzywsHdSeeF2zWn2yAxQvxvdsLFK0PFNs2SGYyOAlrm0WyNavL6e+TXpv23EW81RlUfQm9V8X2W5jEsDBvYQBSEOnxAKQNWOTKJMamKZ5+gKETZ3adaVGr1QaWwurqZhPFDSiMHj3Os36Sc7X4t3w4l+TvPDDNRCbBwuwC//XXfhvfSmH5Hf76d3wbE4cm9vWe74LggDroAfv3/8tP0VCn8Y3N8nsr7JDTzvH3fu4n9/UcYRjesMVwq9Wi273B2fcW6bq+6yK/9Xoymbyji0ij2+WV2Wu8sTzDTG2B1e4q7ZsBIjKxoxwFq8xEdowTQ1M8OHWYI0MjtzR31vO6LC/NstKHRaVTiy2LsEtDuTS0Hm3hovYzAF5pccxCOeSURV7q5MKIXLdD1muTCzpkojZ7RSzsiROkzryf9OknMHK718TspWqjx2e/PMenn51lqbIJ+qmRDB957yE++J4p8pl39hAdKRV+HySN6gJLMy9QW7lCEArCUEeRwU4dRrdG8H2otlwWax263QARKbRQYkpQoby9tShC2wYQQweCNnhthN/CCDtYsoelejjCx9E9bDOuP9moQzG1AINwx/d6MC0v0vGVEY9WtTOIXCmGxcn7sEanuHJthgsXLrCysrLljWmIw2e4lBjBV3EdRmnhGpXCONGWNhWGG3Dm/LP8gx/9/pv+r3dBcEAd5ID9+//lp1gTj8Q3dqn4G5Iv8F3/8sd2Dgy5bsG/WS/5Dem6vmNhv/6MPpFIvC0VmvtRy+3x8sw13liaYaa2yEp3hbZcJzLbNwCEgR3lyVslJjJjHC9P8cDUYY6PjL6pweQAvueysjLDcmWR1doq1U6dmhvDot6PWbSEi9rHfAKhNDKRQy40yYYa+bBLLmiTC13yYUQulKQjiV0YI33P+8k8/Ncwc3v30LpeSilev1Ll08/O8pcvL+IH8Vm2LjTee+8oX/v4NA+fGr4tboS3QoHXZHXuGdbmvjAoKhO6TXniMYYPfQA7WeJyrc0fXFzm9Urc5kKgeGwozwcnSojmPPOXnqbTrMUw0bIk8/egW6P4XrTDzXW9FXM7gQKqP1rVx+rXo1hiOyx2va4ChPQhkqAiInR6wqKl2bSNBNVkicvTD9PJlOOXkRK2fuf768z9rzxzUxjcBcEBtd8DduHlc/zef3w5rvTTNNLaOTBMdM3FUF1QILGQQiCFQhmKyJRo1u7njboQJCwHJ+GQtB1sJ0Ey4eBYNgknQSLhYBnm/qe9v4vUDQIu1atca1ZZdNdZDxv0RJPI6qDtsQirSMcMsmTIUjYLTCaLnMiXmcpk3zQgtioMA1rtOvVulVavRcPv0ApdGtKljUdLuLSEh9wHLDSlyESSXCjJhhHZyCCtZcikR8nkJ8mlSuQyeQzjxmf4rh/x0tUOz11qMl/djCVkkzqPHsvy6LEMpew7q1htT6kQv3sNr3MWGW60VNEwnUns1Bl0a4hlX/FMI+KaK/t/hdMpweMZnUx4jV7rZVQUW0u6WcDJvgfDHt37JZUiDBVBoAgDSRAo/EASBoqgfzvw+9tg53br/W73amjSh4XwMYkr52vDOa4dP0JommhScmjmIjMTR8CyQCkML+T/+ejkDd1Ed0FwQO33gP27H/wJKtp7AZDSY6J7maX0PQBoSpL2amTdVXLeGjl3lUTQus3JgV/58oRgtphjvphiNW/RyGq46RDpuGhib0AYHZtkW6fQkIzUXQ5VW4w327cwm+qu7mpvKUBqOqGwCIVJ1N9efz0UFtEu1yO9fx/NQt2ka2BkCdZP53CH4tTTo2ef58qZ9wz+/vDzn+Xv//je892/YpvOve2KzG1Ho9y9QKQSNBLDeEaKllOi5ZRY4AwAZuSSc1fJumvk3DWy7hqGus0tQr/CZEvJiUqNE5Xatv2+EMwUsywU0gNA9NIhMuGi6RFRtksrC61xmAWeI4mKMhhdm0RLp9CUjNRcDq23mWi07gLirm5JGqCrCD3qYUf7c+/upk2g7IRJZJpI00KaJuFzJivj43gpmytj26utfevmLdZvp+6CYEP6ZiWtEDYvlz+KRR1oI2QHdA2hCcAilCkC3aGSOkQldaj/KIUwXHTTRTe7CLOH0P29PT8yRIQBehhgBD6W72G5Po7r4/Q8kl0X4xYqXKUQBKZFYJoEhkVoGPGXUTcJdYNIGH33lo4SAk0INN3AtAXJhEOykKE4XiY7VMK6SfOt26ndWpP5UcTl+jpXmlUWuuusR3U6WiOOQegRUaZLOwPtcZgDvkwCJVMYfoY0OUpGnolkgeO5MoezeYx9Fqy9abktmH0Zufg63W6FliFoGIKmodPQbepGkpZp09QlTcMl2ocbCoDARPoOyndQgU1aORxLJbhnKMtIrkguVcA64Nznt1IqcvE7F/G6F1Cyv9BqOlbiKHbqDBWZ5plmxJXeZt3EqaTgsZRLuvcigTvbf4yBnTqDk74HxLvEZdbXp//L/8Xl49vP4uvZEcR1lcqWv7+Mwdulu66hvq6PEeyQUlhRl7/5Pz/IoVNHeePlZS68vsLS/Drtlo+SOx+jaQGm0UY3Owizi2b5KEPff7N5GaLJMK7IDCP0KMIIIiwvwOn5pLo9Eq6PGfqYwZscbH/9S2uCwLIITJvAsPpQ6QNFmEjdINJ0lDBQukAIA2HpWLZFOpukMFpk/OQRxk8d3VGO/2blBwGvLczy2vw1rqwvsNxZoRlWCYwWmti9+EpJgRlmyRklRlOjHCtPcv/ENPdMHsK8g4CIOg06579E++wzuDOv75gHHQFd3aFhZGmYDg07QcO2aeiKhghoaB5NvUeo7a+oLBXZcTaUFjcTLNppisk85VyJkdI44+OHSSYzd+A/3b+kDKktv8TKzNP0WpuFf7nyGYann6Kmj/OHl5d5aSWOMWjAe8byfGhYEs1/ik4jBoJhZRg//nWUxx99W2cgHEQLswv89JfnCW1jz3XmbozgDuh2Zw3tlUK6NN/gtRcXmLlcpbLawfcCdnbVVGiGh2F0sY06ttHEEB2UphFpOpG2ccZugrb/Ll+6CjGJMFAIqRBSg1CgAh3lG2iRhiYjhIzQZYCIQgwZYIQBZuRj9EFiBj5WsL/irv1KaloME9MiMGOYBLpFpJuEukkkTCJhoISOEjqaLtBNHdu2SGQS5IcKjJ88wtR9J24KFD8IeH1xPgZEdYHlzjKNsEpgNG8KiKxRZCw1ytHSBPdOTHPfxKFbmmZ1I0XdJp3zz9I59wy9q69ug4JmWKgo3AEKNIFRnsIfOko9maOmGawHPVY6Ta502qxGPXzTRbPcPf/H65WMrAEs8nqSgpWmmMxRyhYZHZpgfHSaVPr2d9u9Xkop2rUrrMz8BY21s2yk/iTSYwxPP0UneYo/urLGiyubczzeM5rnqwpNovlP4ffirqFOaoTJk99Itnz6HZtht1U//zP/hlcfeF98Y5d15m7W0B3Q21FHAOD7IWdfWeLC6ysszjVoNVzkbt0xNYlu9WJXkuWiWz00QgwVYEoXQ0UYhkAiCJVGqAShphNpJvtvCRmhqwATiWNA0jbJpFPkCyWGxiYZO3yC7NAYAN1Gk5nXz7N0ZYHmWo1Ou4fvBsgwQkXRAChChugyxIiC+BL2LZPQi6Hie7e5uTQEVh8ohk1gmH0LxeoDxUAKo+/y6gPFMDAdEzvl0Mnb1DOCdcNj1a3SCCv7AESGrF5iJD3CkeIE940f5r6pQzi3ARBRt0XnwrN0zn6B3rVX4n4JfempPCKRJuq1kZ36jseKRBp7PJ7yZk2cYIFRPv38Ep975Squ6qBZLsJyKeV8CkkXYbg0+80EA7G/YrCEtMhJh6yWiDvPWilKyTzlbImR0hhjo9NksrevtbTbWWN19i+pLjyH3BiraaUZmnoSL/ce/ni2wQvLm8fikZEc70svoRY3ZyBkiseZPPmNJN8hMxBupJ//mX/D2VPvJbxbR/DW6J1UWbyy1OT1Fxa4erlKZaWN5+4eXBZ6iOH4CLODZnbQDG+HFSmEIJ+ySOugq4AoCPCCADeI8CIIELcMC1vfhEWhUKQ8OsHI4RNky6P7bgzX6/WYe+0iS5euUV+r0W108TyfKIhQkUSTG1CJrRNdhjFMrrNQzMBD3OavqN+3TnqGzVI+w0IuQTUtaKckXtJDJro7J8T1paSG7qfJ6EXGMqMcLU5yz/g0Dxw6fMuAiHotuheeo332C/SuvhL3cu7LLI5hjhxGGDZBbRl/6TIqur4zrIY5NIUYPckr4RH+Yt7itZnNub0bg3Q+9OgkhaTH8vJc3Hm2tc56r0k96FCPejTp0RAuvthf0sONBiANF8cYH58me9Aq7KBLZf5LrM5+nsDru4aEQXHsYaLSk3x6MeD5LUB4aDjD4/ZlxMrntsxAeISJ41+P9Q6agbCb7lYWv4V6J/caCv2Qs6+tcOH1ZRZm6zTre1gNKExHkkhHmAmXQK0Tyd1dOJqmkc/nKZVKlMtlkoaG7NXpNes06jVarTYdz8cNN2BhgNh/ly9dhRhE2DqkbJN0Kkm+UKA0MsH44RNkh3dvDHer6vV6LF28xsK5y9SW12k3u/iej/QjpIxg4PIKN11efQtlYKX0g/HierfLXv8msJDLMFtMs5p3qOUE3XREmPLQ9N3PqJXUEG4Cs2uT7Jnkujr5nk7Z09CFgSZ0hKFjWiZOyiZXyjEyPc70/afIDZcHzxP12nQvPkfn7BfoXnl5OxTKk6ROvhdz6BBRt4G3eBFv/gJhY3XH+6kaw3xZf5gv1IapuZtnETcbpBNFEe3mOovLM6ysL1Ntrsf9oYJ+M8G+ZeHtExYbA5BiWCS3DEAqMlIa3XMAkpIRtZVXWZn5C7rNucH+TPEEcuj9fK6a4Pnl+qCO7IFykkf117GqXwRieAwfeoqxIx9EN9+5AfRb1V0QHFDvZBDspspKm9deXODqxQprK23c3u5zAYSAVFaQKQhSuRApmtRq63jezQGx9VIoFHBbNZZnLlJdnGe9WqXZbtF1A9xI4iv9YLBQEiEDzD4skpZBOpWkUChSGhln9NBx8mOTtxUW+1Gv16NybZ75s5epLFfoNDp4XZ8oCJF9l9fWOIoebbi9+pZJ6KMHHpWEyUI+yXLBoZbTbw4IpSF6Nk7bJNdUDNV9Jtc7TK83sGUMpsAwN+MnZuzuCgwLZQhyiR6lRJOi1UJsyS7qqiQNfQwvf4zC+CSjZYHlVfAWLuAtXkKFcfKAVBpng3G+6B3ntWAK2U+utQyN9/f7HN17tHRg33qjXmVpeYbV9SXWGlXWe414pkXUo0E8Le/NDEAqJnNxm/LCCLm0jrv+Go2119mIIzipYdTwUzzTGuLLy40BEO4rWjwsXyDVjn/3uplk/OhHKE89gdjvd/hdoLsgOKDebSC4XmEoufDGCudfja2GRq1LtEdaqe0YFMoOQ2MWhWGBHzapVqtUq9UbAiKXy+0ARLFY3Dbnt12vsTpzjtWFOWrVKs1Wq29ZKAIVWxbqFmBh9WGRSSXJ5fKUR8YYOXSM4sThtxwW+9XypWvMvHGJ6uIq7UabbtujonzWrIhmwqfj+HgJF5nooRm7nzlvBUS2FQNiYr3D4fUGzi49+zUdnDw4RbBz2xPPQhfcdWjXdbqhRWjaKF2gC4mlhTiahy0COrrDK9ZRvmwcZ03LDx6fFz3uS3d59OQQ9z52P6PHD9+W49RsVllenmO5skSlVWG9uwGLuD9UU7j0xP6y3SxpkFU2aR3Spk9Gh6wQZIVJInMvV4z7ebXGIPh6Jid4KPwiOS+ePWEny0yc+Cj54fvfFQHlm+kuCA6odzsIdlOt2uHVFxa4cqHC2nKLXncvq0Ejk3MYm8wxfSxHaVSn2aoP4FCtVnHdvbt6Xg+Icrm8AxBb1W3WWL56gbXFOdarazEs3A1YaESaeWBYGAPLQiedTJDLFSiPjDNy6CiliWl0452bRy6l5PLqCq/MXeVSZZ6F5hI1v4Kn10HfCxCgeQmMXoJE1yLX1Sl3JGNtj2QQYIY+lnTJO12yaZdUJtoVCr11CPfoa6iARafMy5kTnMscxu/n4mtKcrS7wAOtyxz2l4lMG9dKxKnDG0F53SAS5iAwjxAIXUc3DeykRSafojQ+xNSZExSnxm6a6dVq1lhenmW5uth3Q9Wp+ZszLRoHgIWBhkGaUE+jaWmESDEkdE7ri0yKOhkhKOenmTr935HOH97Xc75TdRcEB9StHLDFpWX+62//SwJcTBz++rf9EONje/c6ebslQ8nFc6uce3WZ+Zka9VqPKNzdH27ZOqXhNIePlbj3oXFyRXMbGKrVKpVK5YaAyGazAyiUy+WBBWFZ1k3fa7fVYHXmIqsLM6xX+rDoebihxB9YFvvvHyxkiEGILSDRh0U+l6c0PMrwoaMMTR19x8FCSsnVtVVembvKxbU55pvLWwCxO9SVAj1MkdaKDCWGOVyc4PToIR4cHaP36udpvvqX6LWrCDZdVL3IZtUvsuYV6AQWuoz6Li8fIwpIBB20wOeKOcZrqaPMO5tN81Jhj/tal3mgeYlS0Lyl/zMSYjN1uF+HEhhWvxWDQaQbW4CiI4SObulYjkU6l6I4WmLs5BGy40Xq9VVWqousNSqsdxp9WHSpa20aokeP/cV/dCAjNHLKJi8LFIzsvgcg3W4tzS3wx7/4b1FhF81I8vXf83HGpu4Gi++IDnrA/s3Pfj8vD3s0zc0vQjaIeHDV5vt/8N/ckfd4J9SodXn1hUWuXFhjdalFt7P7WZWmQSYbWw0n7hnhnodGcRyLbre7DQzr6+tUKpUbdlbNZrM7XEylUmlfgNgqr9tm+doFVudmWK+u0mw26bgevSC2LELNQGl7FORcry2wsIQiYRmkE05s7QyPMnLoGOXJI5i79Jp/qyWlZKayxstzV7m4Os98a4mav4ar1cG4MSBSWoEhp8y4ZjLdrnBk6RXS4eZnZeSH48lrZ96HPXZsV/fI6y+d45OfeZXnlzU6chOe02qVR+VF7g1nsKIQV1p0lYOnLAJlICPimpR+6rB124sbt9ai2JsWihGnDYfCxCxqpI66yGKXtlK0lGItsFiM0rS0AKm6KLXPrsAHGIB0q/qPP/qD+GGVcMvYUCOUWEaJ//lnfvamj78LggPqIAfs3/zs9/P0eP8Ht0uhx1OL5rsKBlslpeTy+QrnXl1i7lqNerVLuIfVYFo6paEU00djq2H8UG7LeMruAArVanVw/UbzFTKZzK6AsN9ECwuv22Zl5iKr87PUqis0Gk3aPQ83jPClOCAsFNqGG0ooEqYgnUyQzeYoDY8wPHmE4enjbxsspJTMrVd5ee4KF1bmmG8use5X+oDYe9HV/ASJwCHfjRj1fY4EHU6FDYq5Qh8KT2KPn9gBhcEgnS9d4/lzq4NBOrYW8rB1lSesixw2KoNDqxkW9tgx7ImTOBOnkNkxZi8vsXx1gUa1Qa/VI/ACojAcpA6LQepwiN4PysdZXn2g+HHq8IG9+Skd4/4s+r1ZNCc+mat7SZ5vnOJi7ihSc5GyQz5cYES/BqpNK1K0PJMW8kDT8nYbgFTKFBgujDA6PMnQ8MSusPiPP/qDdNV6/+DtXGeSWvGmMLgLggNqvwdscWmZn/jMT9A0RPzhRJJUkNy8g1KkQ8nfe+/3MFQ++NjCd6LanZBL59aYu9pkvdKl2wnZWQ0NmqZIpgzKwykOHctx7HSZxHXtt10voNmoUavXaTaaNBt1avU6Xm9vF5OTTFDI58lm82TzOQr5HNl8Hvs2VfQGnkdt6RrrKws0axU6rTZdP3ZDhUoQ0g9w7xsWsWVhaBLHECQtm1Q2TTZfpjgyQWHiCKZ5MOvnzWq5Xufs0hzXasusdFdpBOu4orGnBQGAZ5FwTbKBxgiCo8Ux7jn5MOVDOytzay2fL72+zDOvLrNW34T9SFLyaHaVB8NXSIaN618BkSliDB/ZvJQPoR2wnXi302PtygyV+WVa1SZe2yUI+sWNMkLrZ3vpKuy7vMI4rhT5WCogNSmwT9no2Tgu1YySvNI+zpXEEVT/vYwGyzzknGdEW0e1ArwXm3Qqgm7aoZtx6CQMuklBy4a2HdE2PTq6v+8BSOnIIR3aJCOLRGSQlCZGEKB5PqHbRgU+SvY2h9srhREp/vo/+H/d0E10FwQH1H4P2L//2f8Hfz7R/6JHkq+65vHalIktFelAkg4kqVCRCCSJQJEIFFagUFIjRCNUxG4LtH4FMIRoqHdRs2qlQJIilEVCVSJURSKVgx39PCW6VsfQ1jG0KoZYR9D9ShytcFd3dcdl+ZLAyqPYjMWk9FH+p5/+mT0fc7cN9R1SwPYzVzehsW71D4+z9+PSoYzHHG7Z5sOwf1uSCiO0m8Aivr55n3CX+8i3ACiaBjoddL2DTVzEo5ROqAqEqjgAhMIhUkUiVcTjOESg4cZgENU+IGpo2u2dbXtXd/WVKIVCt7KE/iYI1F4pX3dId0HQl4kD9A++LvhS0eA96yNIEzwjwhMRrhbhigBPC+iJEKlB2xC0DcHCDZ47FUlyQUQu2gqN+Ho89jDCvIldptBRmEgMpGYgMYg0g1CLew+Fmk4odCKhExpxz3PMBCKZxErnSeTLZEtjpDMFLOPWMyKkUiwvdrl8bo2lxRaNmk8YSBQOgRoniMYH79gwNDJ5m9GJNMdOlpmYSm+awHvID0Ia9Tr1RoNmvUGjWaNeb9Dr7P3DsG2bfCFPNpcnm8tRKOTJ5Qo49luXLRTJiNrSLLWlWRq1Cu1mi67v4vpRnDpLnCGzr86zSqGpuJmgoUlsXZC0LFLpFNlcicLwGMWJo9jJ5M2fa59aq63z+vnnubJ8lRWvSdOSuI4P1g1cTIGFLXMIL02n6RC2E0gvCZHJyUMFnrxvlEdODWHqGrK5RrhyjWD1CuHqVaL1BXaMAxMCo3QIY+QwxvBRjOEjiHThtuf9h0GXxtoLtFdfIgq7dFSCV9UZLspDgxOuEa3Cg9pZxkSVTPlBChPvxzDf/IyA3/93/zsdfxVDWQiRQDcShFYb5c9vu59m3L7Pdj+6I66hT33qU/zBH/wBr7/+Oo1Gg6mpKb7t276Nb/3Wb73h2MGPfexjPPvsszv2f/KTn+TYsWO39F5uOUZwvZQiG0r+16/5J2jpHHONDmeXlrhWW2E9aNGVPZTqIGUbqToo2UGqDrC/s+KEVGSj+DXyQR8SQUQ+DAbQsG7DJ6UQKCyUZiI1g6gPlEgzNkEi9Hh2gWGibBvsBHoqjZ3NkykOMzQ2TaE4MsgG6nZ9Xn9pkUtnV1leaNBuenuO/EulLYbHsxw7NcR9D0+Qzd3A3Noi3/d3pLlWq1Wazb1TGxOJxI46iFKp9LbNgY7CgLW5a6zOX6G6skSjUaPV6dHr94cKDwILQJNh3B9KUyQMjZRjkclkKJaGGJqYZvTISZKZg3cRVWFA98pLdM59gbnzL3FOmsyYSZYtk7qj4doeWHsHVZVvI3spVC+N8DMcH5rir7/3IZ44c2hw3KXv4i1dxls4j7twAW/hAlFnZ6xBT+WxJ0/hTJzEnjiJPXYMYd6eoL2MAtaXX2Rl5mnc9jItleRFeQ/n1LFBBfYYqzwqXmPSqDF65GsYmf5qdOPW40BLcwv815//MUJd27sN9VdKjOBbvuVbGB8f5yMf+QilUokvfelL/NIv/RLf/u3fzg//8A/v+biPfexjhGG44z5nzpy55QyTtypryAsjljsei+0eSy2XxbbLQrPLWq+F7ENBynYfFn1IbEBjn7BwgLQUpKRGKhKkQ41MoJENNXKBohAoklJhqBBdRQgVIFSIho/G/sr996s49mEhNTO+DCyUGCYtmWQ9zFPzcjSCDB0/idoRa1DohqBQSjE5XeDMA2McO1FGGPsPKvq+z/r6+g5ANBo7F5UNOY4zgMLWSzKZfNurTqMoYn3hGiuzl6msLFGv12h3e3T9sA8Lvd+m/CCwCDG1CMcQpGyLbDZDoVhieGKK4enTpPN7N2dTYUDv6iu0zz1D98JzSDcemFLXTC4mRpkrTLJsGVRliy41lLl3iqYKLMwwx1BiiEP5CU6NTPHQ1FEmS6V47nB9BW/hIu7C+bhVxsq1bV1ZARA61vBhnIkTA0AY+ZE39bkppWitX2Rl5mmalXO0VZIX5RnOqeNE/e/saB8Ih50eE8e/jtLEo2j7nStynf7KZA2tr69TLBa37fuZn/kZfvu3f5svf/nLe+aVf+xjHyOZTPKLv/iLt+29vN11BH4kWW7HYFhs91hquyy2XNa6Hor4Swh+Hw5tlOyAbKHLBsgWkg6+8gjZ38dko5FCJ6EMHOWQJEnazFGwC+StBHndwAhc/FYD1euA7yF8Dz2MU/mMKMLYyMRQG9sQQYCmAsC/pWhFpAS1qMhaOEwlHKYSDtGR6R33MzWfolknb7bImU3SdgfNhFAYSMNEWhbKdtBTaaxMnnS+TGl0ktLoBAlr05wOgmAAiK2prvV6fc/36DjOrmmuqVTqbQfEVkVRRH1pnuWZi1RWFmNYdLoDWAQHhMXGTAtLi3B0QcoxyaQzFEslSuOTjB0+RbpQRkUBvauv0j77BboXnkW67cFT6KkcyVOPExx+hLORybmVeWbriyy1l+lpdTT7Bjn8oYWj8hStMlPZMU6MTPHg1FEmMmnC1Wu481ushnZtx8NFMtu3GE7FgBg/jrBurdGc21llZeZpqovP04oMXpL3cFYdIyJeD0ZY41HxGsczMHXyG8mWT93Sd+OvbB3B7//+7/PDP/zDPP300wwPD+96n3cCCOCtqSz2I8lKJ4bCYttlqd1jse2y2vF2LPkbsBCqgyOb6GENwjqoBhEdPDw6ROy3jMeCGBaYJKRNQkuS1nMUkmUmSpOcmT7B2NAwhrEzhNTzu1SXF6guz9OuV/BbdWSng/JcdN9DhMEmUFSELvtbFcYWCvEWfHrSoRIOsxYOUQmHWA/LRDvCVoqcXqdsrDFkrFLW18jp9R1Wter/Z0ozkZgDd9fGwJ9w4PKK3V6hbhBqBj46vUjR9iQ9peFJgbiudbdt27sCIp1Ov6MAsVVRFFFfnWf52mWqKwvU12u0Ol26fvDmYIHENjTSls6IGVCWDRKtZbRgM9lCJLOkTj1O6syTJKbvQxM6c2s1/svnX+T5a5fpqBoi0UZLtBHOzQCRo2gNMZEZ4/jwJPcXCwx3VvGXLuHNn8dbvrqtQysAmsAaOoQ9Gdc12BMnMYtjB/qsQr/D2vyXWJv7PDU34CV5hrPq+A4gnCllmDr1jSSz+6sI3qq/kpXFP/7jP86f/umf8swzz+xZvv2xj32M1157DaUUURTx4IMP8gM/8AM89thjt/y677ZeQ8EGINruwHpYbPdY7Xrs2qEasHXBeNohqwfIboWwt4bfW8UPavTo4mouXS2gS8R+54+ZQHoAC4uEliKtZ8kny4wXJjh16DhTo2O7wmI/8n2f6toilZVZ2tU13FadoNmiXYdO26LtJmn5adxoZwzB1AJKRpUhfY2ysUzZWMXeZz+a/UhhorDioLxmEmk6YR8qoaYT9AP0gaYT6DrSNMFJYmVyZIbiArSx8SMkbmMw904oiiKalWVWrl1kbWmeeq1Gq9Oh6wW4fTdU3Hn2xskFmpIMySYT0Rpjch17i6szFCZ+fhLr8IOMPPJBssMTXJ5v8KfPzvAXL8zT8V00p4NItEkXPLRkm0BvEBmdvdORQxNb5SmYZSYywxy1U5ykx3BtBn/pElGzsuMhg2E+k6ewJ07gjJ9A2Df/fKQMqa28wurM06w1qrwkz/CGOjY4YRmmwqPidR4cH2Py5NdjOfmbPuft0LsSBK+++irf+q3fyvd93/fx9//+39/zfj//8z/P+Pg4hw8fZnV1lV/5lV/h/Pnz/MZv/AYPP/zwLb82vHtAsJeCSLLa9WIXU6vvYmq7rHZc9ppxb+uC0bTDeP8ylk5geC2Wl66xUJ1jvbtGK2zSUx16mktPC+kQsdMm2V0mG5aFMbAsUnqWolNirDjJyUPHmB6buGVYwP6H+Rh6RCoVkEv3yCVbpM0WeuDHlyjElH3rZKvbq2+daCrox1H2169mv4qB0o+haFuC8lszvIRBZBhEpoWyHLREEjOdIZkvkx8aY3TiCOl09ra+r4OqubbE4tULVJYXqNeqNFsxLHYbgKQpSVk2mZBVxqMqNpufl4/BoiiwpOVoaCmEEFSjIjPdEkudTeBnkibvf3CEsUmouKtcqy2y2l2hLWtEZntvQEQmdpQjbxQY1ZMcikKOdZcZWb2EJncf5rMRhHYmT2GWxvf0+yulaNevsjrzNAsrl3lZnuYNdZxwKxCMszx2+CRjRz+IbuwvCeJW9a4DwdraGt/yLd/CyMgIv/Ebv7Fn58rd1O12+aZv+iaOHTvGf/gP/+GWXv8rBQR7KZSS1U4fEBsuppbLSscj2uOjtYQWAyKT2AaJctJCaBpLa6ucu3aJhcoc1c4arbBOV3VwNY+eFtAhwt0nLAxiWCQHsEiQElnyiRJjhXGOTx7j2OShfcNi/8N8wEkYDI1kOHyizP0PT1Ae2RmT2JDv+7QaVVaWrtGqrOG21gnbLfB6aJ6HHvr96tUNmGy6vGK3V4im4qC8ts/g/36lMPoWylag6ANLJdT7YNFNpGkiLXsAFCdXpDA8wcjYEbK5/G19X1vVXFtieeYSa0vz1NartNpteq5PMmgzrBqMyfoOKCzpRRZEiVWRoxtZLHWzLHez+HLzu5A3u5zINrhnOGCkXCBZHqFqJrjWbTFTX2K1t0IrWr8JIAysMENOpRgKYbLT5Fh7iSnZQWx5jHBS2OMn+q0yTmKPn0BP7PzOeN0qq7N/yez8K7wQHNsGhCGqvNe6xJMnHmB46gm0/U4IPKDeVSBotVp87GMfw/M8fuu3fotC4eCj437qp36KP/mTP+GZZ565pffwlQ6CvRRKxVo3di0tbQlWr3Q8wj0Wzg1AjKUdxtMJxjPx9aGkvSP/f3W9yrmrF5mrzFJpr9EONmCx6YbqHQQWmk5SGTjSii0LkSHvlBjNj3Pi0DGOT07vCYv9D/PRyOYdxqfynLpvlDP3jWBYt7eUJooiZq5dYmHmEo3VZfxWA83toUc+lpKYSmKqCGOw3RpD2cj0CvpA2d8UsP0qrkWJ4yiRZgygslGHEonN+Ik0TZRlg5PESGdIZotkS6MMTRyiWBw5+GvLiPXXn6H20p+jFs+hh5tOygCdJS3HvF5mWRRZ99MsdbNU3dSgMl9okiGnzViySc5y0Yh7FW0MQDJMg5ZtU7N06npEU+vRpUFottG0Pb6HkY7pp8gEJkNdj0m/y/GgzbRsDwBhlif7LqUYDmZ5crC4R0GPysKzXLv2LM/1xnldndgGhCcSc3zgzKMURm7/DIR3DQg8z+M7v/M7uXbtGr/zO7/DxMTBgykA/+Sf/JNBbOFW9FcVBHspkoq1DRfTllTX5Y67JyAMoTGachjPbFoP431A6GLvL3ilXuf81YvMrF6j0lmjFdTpyQ03lE/nALDQgRRi07IgQVLPkLNLjOXHOTZxmBOHjmBZ1rZhPvOzNZq13g2H+ZSH0xw5XuLeRyYYGbszbpgoiqjX6zvSXGu1GlG0uwVhaBqFrEPa1LCExIh89ChAhEHs8trI9Lre5dW3UMTA5XW7gbJRi2JtqUXR406gfbdXKPTY5WVYsYXiJDBSaexsgUxpiDIRzL+Be/E5ok598NyalUAOHaFhl7jWtXl5xeBiI0sr3HS1JHSf0WSL0WQTe4/JcPEblcgooKVLWgZ0TEnPCvCtHtLqoondvxMq0jE9h0xPUPYlk4HLiaDFIdnGtBP9zKQYDvb4SYSTpL76GlevfpEv1FO8pk4QEns+yqzz/swqX3Pf+0kXpm/L8Yd3CQjCMOTjH/84X/7yl/nN3/xNTp++tcHv3W6Xb/zGb+TkyZO3nEl0KwdsZW6Oz/7Cv8PwIkJb50Pf+/cZmZq6pdd/tyiSikrP25HFtNx2CW4CiLGNOEQmhsTwTQCxVfVWkzcuX2B2dYZKe5VmUKPbh4WrBXQI6e4TFgJIaYKUMnCURULFsMjbJfLmMFo7R2dFo7Lauekwn/HJHCfvG+HMA2NYt9lq2Cop5a6AWF9f3xsQhkGhUNhRC5HL5XYt2Gy3m6wuXqNWWaRbWydoN+PUYc9FBPGMZz3aCpSoX4uyFSjBHahFiYECGgoZpyhvidNINFw9Q8fMsSKKzPYyzLYc2pGFqyw8ZVBKhUwlGxTMOn60/2l5oVI0NEVThHSNANfyCG0XZfduAAiB6TqkPZ2yFwPieNjmRC5Nql/TEBVyLNSv8LkVuQ0IJWp8VaHJBx94ikQqnlF97fIF/vCTv4GmQpRm8E3f8DEOHzu5r2P3rgDBT/zET/A7v/M7/NAP/RCPPvrotr8dP36cdDrNj/3Yj/H7v//7vPHGGwB8+ctf5ld+5Vf4yEc+wvj4OKurq/zqr/4qFy9e5Ld+67d44IEHbum9HPSA/e4P/WOGrs1h+ZtZKL5lsXZ4im/5l//ilt7Du1lSKSpdf1sNxGK7x3Lbxd8DELqmMZqyGcv0XUx9SAwlHYx9AmKrmu02Z69c4NrKLGut5QEsXK1HTwvoEtI5CCwQJJVBspvHbpXQ2zm0dhrlW2i7VElYtkFpOMXhYyXue3iCscmDV+seVFsBsbXtd61WIwx3P8PXdZ1isbgjzTWfz9+won+/6nW7rC7PUF1boLveTx3uddA8DxH46GEwiKEYcsM6uX21KHspUhqeMlGahS5MtA13F4JAE/gqRoyHho/AQ8fTDHxhEBDHWQJ0IgQh0NQUDT2iY4R4pkdo91D2jSwIgbEBCF8yGUacKmYpjuf5ohzmNXWcYAsQvqbscfXZv+Tx9iXyW2Bf13WeSZ7g4//gf7vp//yuAMGHPvQhFhZ2777z67/+6zz++OP8yI/8CJ/4xCc4f/48ADMzM/zTf/pPOX/+PPV6nUQiwcMPP8zHP/7xW4YAHOyA/e4P/WPGL8QzTrd+UTcO0OLJY38lYbCbpFJUe/4ADEvtzZRXf5eZuwC6BsOpzSym8UyCsbTDSMrGeJMLVbvb5ezVi1xduspac6UPi3bfsvAHlsWNvuwiNEh08qTaedLtAk67gIh2SW4QknTGYWKqwIkzm8N83gpJKWk0GrtaEDcCRKFQ2BUQb8dc6J7fZW1xjtrKAu3aGn67QdTtoHkuwvcRYYAZhThhBydqYcnutslr8Wco4Da3YlTEPaFC4piJj46PGFzvaDotIWiJ2M3UNSNc28czNFyh4Yl4G2gaStNQUiDcBI5vYRp5XGsC3RnGSOQpiyaPt57jgctfRvSX4I3v5qcyp28Kg3cFCN5J2u8BW5mb440f/MeYfny2UsnYtMYOERERSp/A72KimP7QRzh++l4yb3NK3ztVUinqgWTVjVjxQla9iBU3YtUL8ffIzhRA2dYZtnWGHZ0R22DE0Slb+i1ZEHup5/tcXpplvrbCem+ddtigSz8bSvh0tYiOkpuwUGC5KZLtAsl2nkS7gNPL7LAalCZRiTZawsVOS8plh6NTo5wcP0zigNPZblVSStrdDs16nUarRb3ZoNGo02o29wSEEIJ0Nks+myeXzZDLZsnlCmTS6bcFEHtJKUlUmSW49hLBzMuobn3zb8LAzU1wTQ7xSj1HtaXhaAGOFpAUPmN2jxHbI6cF/aD8lhgKwaC4UdunRbkfScDXNFy9DwdNw9UF3gYsNIErbTyShCKJKRy+vvYqOjEMGrpO8Zt/+oZuorsgOKD2e8B++3/9UQ69dgGIP8j65BTF+bk97y+FQOo6UhdEukYoFIEuiQyF0jWkQRzhjMexohtg6GAJhSMkCSFJCbDeOb+3OyoFNM0slcQQFadMxdnYlvH13ftIaUpS8NYpu5X+ZY2hXoWiV8VQd6bFta80qnaaipNg3bJomIK2qdHRJR09ohtpyE6WRB8MyXYeI9z5/gPTpZuuEabriHQTK9EhpVTcG8qX5H2fIa/HsNfCuo2L0F91+UrnrD/Op3oPsiQ329yM6nUety7xmH2FjNjecl4SF8/5wiYQVhxjwEBpAtDR0BCAUCCUQkci+lCJK+U3UodvHSifzpzmI61zg9ufzN7Dx7//n+15/7vzCO6QDG/7wqJlJe18ERH6iChEhBF6FKHL+LRWSImQkluNn7n9i9Tog0QjMuLrka4hdZBGvEUHDA2hK3Q9BoqpK5w+VNJCYb/DgaIBuaBJLmhyrHl5sF8BLTOzBQz9baKMpzusO2XWnTIXtj6XkuS9GkNbAFF2KxTdKqZ6cxkylqYY81uM+a097xOoRap2irVckvWyRY0UHT9H0MtCJ4vezWAGDrnaGNTGAJCapJtsUk3X6KbrdHM1AkuB5pBEI41OSupxM8EAsoGk4AWU/R5DbgvnNhe7faXK0iIetOd4wJqjo2xe8A/zx90HWI7y/Nfeo/xB7xHuM+d4wr7EGXMRocWtEC0iLNkFeetzAGKgCDzhEAqTUDPjlF1NRykdpTQipeihCAiQIkDXQtrC5GRrZttzaW/ye3xQ3QVBX+GWlVQAuTcWCNJxCbrSBWEug5fO0c7m0ZwUQeATBSFEEVIGyNBDRGEMiyiKU/uiCH0DIIPrYbzdAIoCEcY1qPtuFrRFPrBOHyhCI+yDJNQ1Al0Q6BqhIQgNndDQicw4RxzLRk8ksNNpktk8hfIw4+NTZPNFrLd43GIeuD4nSylFM5R9t1LEqhuy0nczuVJQc0rUnBIXODV4jAYULcFw37U0YusMOwZDto51G11MAGXY8srb1en4XDhX4fyVFRp1n9A1EZFBspMn2clTWonvF5puDIVUjXa6zlqqgTIj2BaWsAGbJBpJdBLKxJE2SeIq7oJdYCw3wrHRKfLpvQvm9iulFJ1ul0ajTrPZotZq0KzXaDZbBHsMpdc0jXQ2Qz4Tu5iyuRy5bIFsJn3gqnKlFEoGyMhHKi/eSh/V38rIR0kfGXnxftnfH3rQqqOvVTCrDYQfkNY8vso5z1OJC6xaBb7UPcbTjeO8EkzzSjBNxvZ4aGyFh8eXKSb3Hqna/y8RuoUmTIRuI4SF0C2EsNCEhRA2QrcwhYW9sb+/je8XP+YTn/g/eKLbX/T75562amJe3ztLe2uX5ruuob6ujxFcLwUEts09/9//966ppGEYUpuZZe3yFRorVdrNNh03wI3iKlxXk/S0ELlR3KLUJhj6kDC2wMIKFWYo+/tCRBggogAxuB7DRL/NJ4obQNmASQwU7f/f3pkHyVned/7z3m/fc1+a0YAkdBhG4QhnbKCKxUQLwalKajFFUcTrgkUGKwlQKRkTttgQvJWC2CYOhSCBZB0XxHEtrOMCe0O8wgfYxghisBFCGl1zaK6e6enzPZ/9o3t6pjUz0ow0IwnN86nq6nnffp+ep3uk5/s+vxNf1/A0tSIqellQLBPFstFjMaxEkkRDE01tHXR2r6G1vXPeKrMngxCCjOPNqsU0kCtR8OY2FylAU9SsJsq1x21WJSK0xSysk2jSs1DCMKTvwDi/fneAQ/vTpEcLeO7suQoEIpLHj2dwEmmy8TQZK0ewQA2LVCvPGuXKs0qMhFFHU7yFrubVbFqzgaa6uhP6DEIIcrncnD0hHMcBBJoq0DWBpoboWvnnZCJKMm6TiNtEIga2pWHoSnkB90sEfonAd8rPgVNe0E/WVCYEWsHFSOcxx/NozvS2PVRUBo12fjx5LrsKXTgV1d3YqXN1T5zLNtYRiUTQdBtNt1E1q/JsLklS2IF9e0j/y0OkgmDedUb6CJaBMylqyPd9Mn19jOztZeLICNlMjkLJoxiElAgpKSElJSBQFra6G0LDDlVsX1Qad/sQeAShi+8X8fwCiu+ieT66H6AHIaYfoAcCIwjRgnITDC0QaOFcbetPHAEEGviqgq+pZVHRlYqYVETF0AiMcmlpLAs9GsWKJ4nXN9DY1s6qrnNZ1b1mQYIihGDS9WuimAYrdZly8wgEQGPErIa3TolEe9zGXmaBqDbz+c0wRwaO3czHjCmYKRclmcWLjVFQMuVigqpbCZ0NFpwyZs8Qi4iwiSgxEnqS+kgDHXVNrFvVTn3MJAimFukS4dRCXV2wZ54r4XslRLh0Rf8AUNTqYqzpFppmTy/OujXrtfK5SPl46rxug6LjDR8g/8Gb5He/iZcerP4Koeoc1rt5fbyd95xOHExits7VF3fy6cu6WduZWpbqst948stsqfgD5lpnZNTQMrAkeQSWxUh35ykJHfV9n+zgIMMf9TI+OEQuk6NQdCmGIUUR4iiCouIvWCx0oWILHVso2IpKRNeIRUwSqQR17a20bVxHsq2dYiFP/+ED9B/Yz/jIILmJNE42S1AoorgOiuuiTwmKH1R2KyFGINCDcHkFRWXGDqVi7tJUPF3F0zUCXcM3dIRpIkwLLRrBSiSI1zVQ37qK+o7ViLoW0p5S3T0M5kpk3fmXz8aIOZ0oF49UciKWTyDCMGTgUIZfvzvAwd4xxkbyc+4aAHRdpa4xQufqBOdtStHeaXDoyCD7BvoYnhwh42YoiEKlNpRLUfHJnYBY2ELHDnSs0CQiNGKKQr0BHdESSXvuf3+KolUXaVSDIFTxAwXHDSmWfPIFF8cN8AOl+loQKviBShAqRGMpUvXNNDY00TgjYW4xNcrmQwiBO3yQ/AdvkP/gTbz0QPW1UNHYG3by83wn73tdlITJuR1Jrr+sm2sv6SQRXdrd7Tee/DJXFT46u/MIziTO1sziib4+hvfuY3xgmGwmWxaLIKQkQkqKoKT4+IsWCxVbUYhoKlHbJFGXoL6jhZZ1a4m3tS3I3uu6Lv0He+k/vJ+xI4PkxtM4uUn8QgFK5UxWzfPQvQAjCMuiEoTovsAIBFoQoocCLVheQQk0BVfXcU0b14rg2HGcSAInksS1Y3imhWtaeIY142eTWDzKqlS8WodpahcRNRYmEGHo19xN195lzzj2HZxSkcE+hyNHBONpg4lMBMeZHaFk6B51dZPU101Sl8pSl5rEMGpFJAgE6ZLOkaJFxlfKVWYVn5Lm46gehUrl2YXGPsxsgBQRFpFKA6TGaAudTZ1sPGc9HS2zaxIJISgUCnOamIrF+XsTJJPJWW1HGxoaTtj8KITAGzlEMvZ3dQAAHHRJREFUbkoUxqbzngI0dnvtvON0877Xha/ZXHlBO9dfvprN65pRl8jfdNZnFp9JrORaQ5mBfoY/6mVicJjJiUnyRZeSH1ASgqIS4igBnrKwMExt5s4CtSwWEZNEKk59WzMt69aQWHVyJadn4rouQ4N99B3sZfTIANn0KKXJDEGhgHBKqM6UyctH90OMiunLqJi7pnYoeiCWVFAAPMPENUw808YzTVzTwtc0Ak0QqgKh+qB56JqLZgk0S0WPaBgRMG0VO6JhRzX0E9hdlEom45kkExNJxieSZCbjhOHR7yOIRDxSdT7NLRrd50RZ1Z3EMKO1ZhWtbEJRdQtV1fF9n76hI+w5uJf+8X7GC6PkggxFUaCoOhTxTqgBUlQY2KLSAEmvoyHSSEelAVJXe0f1+vkEolCYP5InkUhURWGq5EZDQ8OiWtuWReEwud1vkv/gDbzR6UbyASq73Xbedbt5z+siUV/Pf7p0Nddd2kVL/enrNyGFYJGsZCFYCJNDQ4x89BHjfUNVsShWxGLKZ7E4sdDKOwsUIppG1DZIpOKkWptoOW8tqc7OJROL4yGEwCkVGB06zKHeD0mPlGvtuLksQbEAjovieqhegO4FaH6I7odovqg8M8PkJZinwsAJE6i1jvlAr/hTdAXfUCtRXhqhoSNMA8WOoEVj2Ik6Us2ttK1aQ+uqcznS77Ln/SEG+jJkM/OX5Y5EDZrbEqxZ30TPxauob4yd0Lz7hgbZvf8j+tP9jBVGyHsZCifYAGnubnnlBkid9Z2s715HQyLJ5ORkte3oVMmN4wnEXF3lFiIQ7shh8h+8SW73G3gj07lEgVD40CuLwvv+atav6+T6y7u54oI2jFMQdDATKQSLRArByZMdHmF4b0UsxjPkC05VLBwlpLgIsVCFQkToWEIlgoKtaURtnXgiTl1bE81r11C3ugtVEYTBTPNJsWo2CX1nhjPTmcOpWY5ACfwSiKULq3LcAKeoUCoKnEKAVwzwSwF+CbzARAQGSqCjhCpaqGAEIYbrYLgOputgeC6G66AHSxsjHlRyUabCh4tmhEmrhazRTMFowtEa5g5HVEqYdkBjS4yeC8/lt6/ahK6ffE0igIHhIXYf+Ii+0aVrgBTFmC5TriWpsxpIWXWkrASK6zNRKb2Rz+fnfa94PD6nQNj23I1k3NG+iqP5DdzhQ9XzgVDY47XzrtdNr7aWKy5Zx/WXd3POMlWvPRopBItECsGpIZ8eZ+jDD0n39ZfFouhS8sPyzgJBCR93oWKBgo2BrarYmoKlCywjwDI9TLuEGSmim86C2+2CUjGB2GhabVRJOQplxnHVXGLXRqZUQwgXvlAWvKBSg6k2imkiX8LwHAzXxXRLGK5bOXawSnmsQgazlMUqZbFKBUyniOH7lWivyg4lEGgL/J8bopA368nYLWTsZjJ2C0Vz9oKlhR4JZ4S4O0rEHyHij4HmVfJRKrkolUgvYVooto0Ri2InUyQbmmhqW8XqNeto61iYP21odJTf7N8zowFShoLInXADpPiMbnm2sDFFFFNEMHwDUQhw80W0eWpaxWKxWf6HxsZGIpFI9Rp3rL8sCh+8iTt8oHo+EAof+W2863aTa+7hU1ds5OoLVxGLnLyDez6kECySE/nC0mMZnn/2/1AKfGxN53N3foaGxuWvOHm6ECIkDNwZDszynXbVeXnU3ffc8d/l42PFgAe+ileM4JYiOK6J6+o4nkopUHCCkJIIcBYY26IKBRsdS9GwAVtTiJgqsahFsjFJU3cnDeeuwYokUTVjUQv4clP0AgbzpUoviOkoprHi/Nb3hKnXRDF1JGwSYYnRg70MDRxiIj1CYWIcL5cnLBXBcSpOeb8S5RWUfSmVKC8hTAp6EzmzmazVzKTdTKDOXrgi7iSp0nDlMULMHUc9zuJ8vFwUr5LcGFZzUSLosShWIkmysZmm9g66utfS3NrBRC675A2QIkLHDgzMwELzDFRXR3UNNF9BD6gRi5kCMfOhFcbJ736T3G/ewDtKFPb6rbwfnEt0w+Vcc+Umzl/TOGcY6p4P9vJ/v/0NrMDB0Sw+/V/uZf2mdQv6LFIIFsliv7CvPPY8v8rGyLnTtsS46bA5kedLD35uWeZ4oggRzl6wvVJl4XZqF+xg2sRydFx4GCzUortApmLAtZnx3jPjv6fvvmtjwm28kk96fz8T/SNMpifJ5x2Knj/DZxGWxWIBHmBFKNPRUChEVJWIZRBPRKhraaTpnNU0rlmDMY9Z4FRT8oPpnUOl5ehgrsToMQQibmjVKq7VpkEJm6SpLzgGPp/P0bvnQz74j14G+7PkJ1UCP4pgtjNUET4Rf4y4O0LSGSFVHCbiF8u+lCVObiznoij4GvhqORfF06dCh7Vqxnxg6AjbILB1PEvFtaBkCfJ2SNYMmNRCiguMGKg2QBI6Vmhg+Aa6Z5WFwtPRPQ0tFCRmmJiabIVU5iDi8K8Qo9Pmo7AiCvvN82i76FNcfdUnaEyVdxg7/vsXOGfPCPHC9JeWi6ocWN/Mf3vkqePOUwrBIlnMF/aVx57njbGpdpqzUz2uahxfEjEQYVC1Yc9K2JmxmM8VZhjOHBcsbRLPVAx4bWTJPCaUqrmk1syi6TaKuvBF6EQoZbOMfLSXsUN9ZEYnyOeLFL2AYjgVOhssTizQy4l5KNiqSsTUSSSiJJsaaDqni+bz1p1WsXD8gCN5h/5ssUYoRgvuvPfCMUOb1XK0Ix4hZS38b5MezfP+O/307hll5Ej2uM18Wttj1DX7GHqaydFBchPjuPksQaGE4pRQZuxQpnJRjEoOyrLnomjg6yqBUXbEe6aKa6o4hkLJhLyhkDcU3Oqj/Jo74+HpCihKVSwi6FiBjumb6J6J5hnEAo01OJxTGKTOHa3OIRQK+/xWxhsv4OCRPq54/0M0xJwJZb++sPW4YiCFYJEs9AtLj2W45/EfkHPLHZMQASllpplCEDVc7vjPG4hEdBAuIvTKz8KF0EOIynHoVp79Ga+7CFE+ZskLSmmgGiiKCYqBopqglOuioFTOqyaKYsw4P+MZA1QLRTnDK9ctAt8pURgawkmnKeXzuK6HGwR4hLiV0NnSgsUCLAysUMMUCoaiYmo6pmlgx2LYjQ1E2lrRjYWHLC4FnhBM+CFpPyDth6T9kHE/JDNPPwgAS4F6XaNBVysPjXpdJaYqxxWIMAgZHswyeChLerREMe8RhMwuy41A1xXiKYOm1hgd5ySpX0SopeM6jI8NMpEepJAZx81nCJwilIqobqUBjudjBEE1D8XwZ4YOh8smKFOi4MwQDPcoAXENBc9QUAwVTdfQNA1D07BQiIcKycBDFE0mRi2YCDln9ADWjN+Rj6qc+/BXjmkmkkKwSBb6hT3xP/8XO0cqfgAREDcEOV/W5DubURE06CENRkhCD4jpPrbmo6k+iuoRKB4uLmIhq4kAEwNdGIjQIAgNnECnEGhMehppTyPtq8xdYWaJURX0qI4eMyqP8s9adP7dQOiF+HlvxsPHz3uEzrEd/OW7Y4ijEEMhDuhzfEYHQR5BDsghKHDSFYWOjwiwlSIRckQpEBFFoqKILRzs0MEKPazAxwo8zDDACKZFRQ8Eejidj7LUfzVHL4uFZygUkwaFks660Vz19Xcu7uLe//61ecfLMtTLROmokD5T90EKwVlNiMKorzHqaxxV8rOKMkMsknpATPOxdB+9Riw8hCJw8XAVr1y+lvLdaKzyaIcasSA08EMdN9DJBzpZTyPta6Q9Ff9kl51Q4Oc8/NxRphwV9GitOOgxAy2ioxoqZp2FWVe7qwn9cJY4+LlpgQiASWASwdTSbkNVFOIoRChnIlsoTHUICCtikEOQQ5DnhIrvHhtFo0ScEnHGofwHmfpqF7P5FQEmDlElR1TkK6JSwg4dbDElKB5W6GMGPmYQYAqBLmaYu/xy9rxa2axZvsDyBRShfjLg7Qu6YIYQWEvttzsOcqWrYGszvgpFY6IUsql+hFAouL6G62t4vsa6eIEtN1572uYpOfMIfI/SyBCF0TRONlc2Q/kenhBlM5RaNkPNJRYwLRZtAAIsdCyhYQoVExVDVTFMAzsaxa6vJ9bejm5H5p7Micy/amIqm5nGKz9nghB0FTNlYaZqBcJQoH6GaalBV2nQNBLabBNTqehxeH+G4cEc2XEH1wlRZwjFNAIropFqsGjpSNB1ThLDPLuWqFf/5Xnq0hOUTAijOoYVQqhyyXu1TbCceZo1LRfSNFRhlo9gFoKE5fKN+284q0NJJcuD77mM7dvP6IGDTAyNkZvMU3A9SkFYLVNemlmm/DiYU9FQU/WhDI1Y1CbVkKK+q4PW9ecRra8//hsda85hyFDeqeY/TIW5DuVLBPNM09JU2uLTfamnopgaIyZqRSDCMOTA3jE++NUgh/anGR8r4Htz+zUMU6OhKcrqcxs4/8IOOs+pRz3J/tankz0f7GX///gSscLc/ZZPl4/g7JLbk6ChMcXmRJ43xkzKf47Z/vyeeF6KgOSE0A2T1o0baN04XzubcuXZ9P79jPYeYPzIGPlsnoJTKSZ4lFi4io+rlM0yQNlGk81BdhQO7oOf/BhTaNMlPxSlXHk2ZpOsT1K/qo3WjRuJNcwvFrqqsioRYVUiAu3T1/mhYKQw1QtiOlHuSN7BCUIOZgoczNSWfzBVpSwQiUhZIFI2l27ZwJZoWSBy2RLvvzPAvg9HGOqfJJdzQIDnBgwNZBkayPLWTw+iKBCLW7SuSrJuYws9F60iGj+1jZROhvWb1vH/1jdz/rtD86wycGB9MzcsMJ9gqZA7gqOYK48gYTn0xM+8PALJymPRDZCOgzGXWESsslh0tdGy7jwSLc0Leq8gFAwXHAYrSXJTeRBH8iX8eeofGVMCUdk9rKqEujbaBn0HJvjgPwY41JsmPTZ3Mx8A3VCpbyzvGj7xWx10r20443cNMo/gFCMziyUrjWVpgHSUWEQjJqm6JPUdrTRvOI9k6+yS01MEoWCk4EwnylVE4lgCoasKbTG72jSoPR6hQdMY3jPKvg9Gjt3MR4FYzKSlI8m6jc30XLSKePLMSBqcicwsPoXIWkMSyWyWvwGSSixiVRsgta5fQ6pjVc2YUAhGC07N7mGqLpN3DIFojVl0xMutRqMFn8y+CUYPTpA+bjOfKF3n1LOxp521G5rO+F3DsZBCsEikEEgkJ87paICkahqjBXfaxJQr12UazJdw50mW0xSFtphFq21gjDm4AzkKAzmKmdK8LUCjMZOW9gRr1jfTc3EHqdPYX2CxSCFYJFIIJJLl5VQ1QKprbcbo7mYyXsdQwa06qgdzJZx5BQKahEpswoXhIsFYkaAwd6aGpqvU1Ufo7K5nY08b521sQV2istxLjRSCRSKFQCI5/SxnAyRL01DiUby6ekqpevKJOsaExmAlimkWocDMeaQmfYy0gzrhoMwTvrpUzXyWmo+NEOzfv59HH32Ut99+m0gkwo033sgDDzwwbwOImbz00kvs2LGD/v5+uru7ueeee9iyZcsJzUMKgUTy8WCpGyDZQkfRbXwrhhuJUYxGydsxMoaFS+2dvuoEmJMu1oRHJOOgZz3msnhpmkKqPsqq1XVs6Glj/Sdal6yZz2L4WOQRTE5Ocscdd9DR0cGTTz5JOp3mK1/5ChMTEzz++OPHHPv973+f7du3c9ddd/E7v/M7vPbaa/zpn/4piUSCT37yk8sxXYlEcgaQaGk+bqhqPj3O8J49pPsGmUxnyBdKFL3anYWrBISKoKB4EHpQzEIR7HS5/EUD4OsWnhkjsBJ4ZgzHipBviFJqjpABCAVG3sfKuJiVh1EMCAJBejRPejTPe7vKDe/tiEFza5xzz2vigotW0dQaX9DnzWfz/PO3dpIpBqQiGrfcdi2xxOnZcSzLjuCZZ57hqaee4oc//CENDeXqIv/6r//KAw88wCuvvMLatWvnHbtlyxbWr1/P17/+9eq5z3/+82SzWb797W8vei5yRyCRrCwK4+OM7NnL2OF+MuOTFPIlip5PsSIWjhLiKLMrAAvA101cK4ZjxnCtKI4Vx7ViBJqO6gaYk960OEx6qHOkWCu6QjJp07m6jvXnt7Hpglb0o0plPPm17zI2pKO509uOwFRpbPXZ9ic3H/czfix2BD/60Y+48sorqyIAcMMNN/Dggw/y+uuvzysEhw8fpre3l/vuu6/m/E033cSXvvQl0ul0zXtKJBLJ0UTr6+m+/FK6L7903muK2UlGdu9h7HA/k2MZchWxKDmCkpunpGSrPS0EEOgmjhnDsaK4DTFy7TEcI4lWUjAzLtaki5nxMAo+whdk0kUy6SK/fneQ/62AGtFpaI6xbk0jv/7NXrJDKiq1tifVDRk/rPLk1767IDFYSpZFCPbt28cf/MEf1JwzTZPVq1ezb9++ecf19vYCsGbNmprza9euRQhBb2+vFAKJRHLSRBJJVl/626y+9LfnvWbOBkj5cYrhGCVFUFQCCpqCY8dwG2KU2mNMqhEomej5ECvjYWZc1EAgCj5jBzOMHcwAKsJSUGwwNB/TLZLPlcvvCWBsWCOfzZ9SM9Gy+QiSydnNsZPJJJlMZt5xU68dPTaVStW8LpFIJMuNnUjQdfFFdF180bzXOLkcI3t7GTt4qCwWuUEKXsCkojJeFyHbapMXNsK1UAoqZjZAz/uojgAHPDRcJU4iniNXEQPNEfzzt3byX+++8ZR91lNadE4IsaA2eUdfM+XGWM72hxKJRLJYrHiczgs303nh5nmv8YpFRj76iJEDh/n17j4ydVEyxCj6FmFJRUHg5GuX4kxxYdFRS8WyxD0lk0kmJydnnc9ms3PuFKaY785/6r2ONVYikUjORIxIhI7Nm/mtm2+klOxkpL8et99EGxIYmQA9E+L5tWH1qcipbRu7LEKwdu3aWb4A13U5dOjQMSOGpnwDU76CKfbt24eiKLN8BxKJRPJx4pbbriUw1XnbdAogsBRuue3aUzirZRKCq6++mp/97GeMj49Xz/3bv/0brutyzTXXzDuuq6uLNWvW8Morr9Sc/973vsfmzZulo1gikXysiSViNLaWQ1ePFoOp48aW4JTnEyyLEHz2s58lkUjwhS98gR//+Me8/PLL/MVf/AW/93u/V7MjePDBB/nEJz5RM3bbtm28+uqrfPWrX+XnP/85jz32GD/96U/Ztm3bckxVIpFITinb/uRm6rtCQrN2+Q0thfqu8JSHjsIyOYuTyST/+I//yKOPPsoXv/hFbNvmpptu4oEHHqi5LgxDgqDWKbJlyxZKpRJPP/00f//3f093dzdf/epXZVaxRCI5a9j2Jzef/ZnFZxIys1gikZxtLPW6dmbWWJVIJBLJKUMKgUQikaxwpBBIJBLJCkcKgUQikaxwpBBIJBLJCkcKgUQikaxwTmnRudOB53kIIarhVhKJRPJxx3XdJS3CedYLgaxYKpFIzjYURVnSte2sTyiTSCQSybGRPgKJRCJZ4UghkEgkkhWOFAKJRCJZ4UghkEgkkhWOFAKJRCJZ4UghkEgkkhWOFAKJRCJZ4UghkEgkkhWOFAKJRCJZ4UghkEgkkhWOFAKJRCJZ4UghkEgkkhXOWV99dCG8/vrr/NM//RPvvfce2WyWVCrF5s2b+exnP8urr77Kyy+/fMzxl112Gd/85jdPzWQlEskZSV9fH9dddx1f//rX+d3f/d1T8jufe+45vvvd79LX14fv+3R1dXHLLbdw2223Lao66YoXgr/+679mx44dXH/99Tz88MM0NzczOjrKa6+9xtatW3nooYe49dZbq9c/9dRT9Pb28vjjj1fPxePx0zF1iUSywslms9x0002cd955GIbBm2++yaOPPkoul+Puu+9e8PusaCHYuXMnO3bs4N577+WLX/xizWtbtmzhjjvuQFVVLrjggur5hoYGBgYGuPDCC0/xbCUSiaSWP/7jP645vuqqqxgYGOCll15alBCsaB/B888/T3NzM1u3bp3z9c2bN9eIgEQiWZm888473H333Xzyk5/kwgsv5DOf+cxxTcZQ7iT26KOPctlll3HJJZfw4IMP8tJLL7Fhwwb6+vqq101MTPDlL3+ZK664gs2bN/OHf/iH/OQnPzmhudbX1+N53qLGrNgdge/77Nq1ixtuuAFdX7Ffg0QiWQADAwNcfPHF3HrrrZimya5du3jooYcA+P3f//15xz3xxBO8+OKLbNu2jU2bNvHqq6/yta99reaaIAi48847OXToEPfddx9tbW288MIL3HXXXTz33HNcccUVx52f7/s4jsNbb73Fyy+/zL333ruoz7diV8CJiQlc16W9vb3mvBCCIAiqx6qqoqoreuMkkax4brzxxurPQgguvfRShoaGePHFF+cVgomJCV544QW2bt3KXXfdBcCnPvUpbr/9do4cOVK9bufOnfzqV7/imWee4Zprrqled9NNN/G3f/u3xxWCgwcP8ulPf7p6vHXrVv7oj/5oUZ9vxQrBVIfOoz3rP/jBD2rsbrfddhsPP/zwKZ2bRCI5s8hkMvzN3/wN//7v/87Q0FD1ZrGurm7eMXv27MFxHK677rqa89dddx2/+MUvqse//OUvicViVRGA8g3oli1b2LFjB0EQoGnavL+nvb2d73znOxQKBd566y2effZZVFVl27ZtC/58K1YI6uvrMU2zRpkBrrzySr7zne8AzOs7kEgkK4vt27fzzjvvcM8997Bu3Tri8TgvvPACr7766rxjRkZGgPJaM5OGhoaa48nJSZqammaNb2pqwvM8CoUCiURi3t9jmiY9PT0AXH755USjUR5//HFuvfVWmpubF/T5VqzNQ9d1Lr74Yt58880aU1AqlaKnp4eenh5M0zyNM5RIJGcCjuPw+uuvs3XrVm6//XauvPJKenp6qlaF+ZhahMfHx2vOp9PpmuNUKsXo6Ois8aOjoxiGQTQaXdR8zz//fIIgoL+/f8FjVqwQAHzuc59jeHiYp59++nRPRSKRnKG4rksQBBiGUT2Xy+X44Q9/eMxx69evx7IsXnvttZrzRx9fcskl5PN5fvSjH1XPhWHI97//fS666KJjmoXm4u2330ZRFDo7Oxc8ZsWahgCuvfZa7rrrLp588kl2797Nli1baGlpIZvN8stf/pKRkRFisdjpnqZEIjmNJBIJenp6ePbZZ2loaEDXdZ555hni8fisu/uZ1NXVceutt/L0009jWRabNm3ilVde4fDhwwDVIJRrr72WzZs382d/9mfcd999tLa28uKLL7J///5j+iez2Sx33nknN998M93d3fi+z89+9jO++c1vcsstt8xpbpqPFS0EAPfffz+XXHIJ3/rWt3jkkUfI5XKkUinOP/98HnvssZpoAYlEsjJ54okn+PM//3O2b99OXV0dt99+O4VCgeeee+6Y4+6//3583+eZZ54hDEOuv/56Pv/5z/OXf/mXVbu/pmk8++yz/NVf/RVPPPEEhUKBDRs2sGPHDi6//PJ539uyLM4991z+4R/+gaGhIWzbZvXq1TzyyCPHDGmdC0Ucz9AlkUgkkiXjgQceYNeuXcc1LZ1KVvyOQCKRSJaLX/ziF+zatYvzzz+fMAzZuXMn3/ve99i+ffvpnloNUggkEolkmYhGo+zcuZO/+7u/o1QqsWrVKrZv377ohK/lRpqGJBKJZIWzosNHJRKJRCKFQCKRSFY8UggkEolkhSOFQCKRSFY4UggkEolkhSOFQCKRSFY4UggkEolkhSOFQCKRSFY4/x8/eL1RspeDugAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p1 = lesions[\"Total GT\"]\n",
+    "p2 = lesions[\"Total algo 1\"]\n",
+    "p3 = lesions[\"Total algo 3\"]\n",
+    "p4 = lesions[\"Total algo 3 MC\"]\n",
+    "pairs = list(zip(p1, p3))\n",
+    "#subjects = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "plt.figure(figsize=(4, 4))\n",
+    "for i, pair in enumerate(pairs):\n",
+    "\n",
+    "    plt.plot((\"GT\", \"algo 3\"), pair)\n",
+    "    plt.scatter((\"GT\", \"algo 3\"), pair)\n",
+    "plt.legend(loc=\"best\")\n",
+    "plt.grid()\n",
+    "plt.title(\"Nb of lesions method GT algo 3\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "725a67ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p1 = lesions[\"Total GT\"]\n",
+    "p2 = lesions[\"Total algo 1\"]\n",
+    "p3 = lesions[\"Total algo 3\"]\n",
+    "p4 = lesions[\"Total algo 3 MC\"]\n",
+    "pairs = list(zip(p1, p4))\n",
+    "#subjects = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "plt.figure(figsize=(4, 4))\n",
+    "for i, pair in enumerate(pairs):\n",
+    "\n",
+    "    plt.plot((\"GT\", \"algo 3 MC\"), pair)\n",
+    "    plt.scatter((\"GT\", \"algo 3 MC\"), pair)\n",
+    "plt.legend(loc=\"best\")\n",
+    "plt.grid()\n",
+    "plt.title(\"Nb of lesions method GT algo 3 MC\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "7583dad9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Calcular el promedio de p1, p2 y p3 para cada punto de control\n",
+    "promedio_p1 = np.mean(p1)\n",
+    "promedio_p2 = np.mean(p2)\n",
+    "promedio_p3 = np.mean(p3)\n",
+    "\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "\n",
+    "# Trazar líneas y puntos de dispersión entre los puntos de control\n",
+    "plt.plot((\"algo 1\", \"algo 2\", \"algo 3\"), [promedio_p1, promedio_p2, promedio_p3], label=\"Promedio\", marker='o', color='black')\n",
+    "\n",
+    "# Agregar puntos de dispersión para cada conjunto de datos\n",
+    "plt.scatter([\"algo 1\"] * len(p1), p1, color='blue', alpha=0.5)\n",
+    "plt.scatter([\"algo 2\"] * len(p2), p2, color='orange', alpha=0.5)\n",
+    "plt.scatter([\"algo 3\"] * len(p3), p3, color='green', alpha=0.5)\n",
+    "\n",
+    "plt.legend(loc=\"best\")\n",
+    "plt.grid()\n",
+    "plt.title(\"Nb of lesions method\")\n",
+    "plt.xlabel(\"Methods\")\n",
+    "plt.ylabel(\"Number of Lesions\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "0dbfe4c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Suponiendo que p1, p2 y p3 son las listas de valores para cada método.\n",
+    "# Asumiendo que p1, p2 y p3 son listas de igual longitud.\n",
+    "\n",
+    "p1 = lesions[\"Total GT\"]\n",
+    "p2 = lesions[\"Total algo 1\"]\n",
+    "p3 = lesions[\"Total algo 3\"]\n",
+    "p4 = lesions[\"Total algo 3 MC\"]\n",
+    "\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "\n",
+    "# Trazar líneas y puntos de dispersión entre los puntos de control\n",
+    "for pair in zip(p1, p2, p3, p4):\n",
+    "    plt.plot([\"GT\", \"algo 1\", \"algo 3\", \"algo 3 MC\"], pair, marker='o')\n",
+    "    plt.scatter([\"GT\", \"algo 1\", \"algo 3\", \"algo 3 MC\"], pair)\n",
+    "\n",
+    "plt.legend([\"Total GT\", \"Total algo 1\", \"Total algo 3\"], loc=\"best\")\n",
+    "#plt.grid()\n",
+    "plt.title(\"Nb of lesions by method\")\n",
+    "plt.xlabel(\"Methods\")\n",
+    "plt.ylabel(\"Number of Lesions by subject\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d5b6827",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_database  = '/mnt/d/Poly/project_ms_seg/'\n",
+    "data_excel = pd.read_excel(path_database + \"3t_metrics_F1_sensitivity_mp2rage.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9f15a6e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nlaines/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>subject</th>\n",
+       "      <th>dice_lesion_algo1</th>\n",
+       "      <th>dice_lesion_single</th>\n",
+       "      <th>dice_lesion_multi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sub-C010-ses-M0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sub-C012-ses-M0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sub-C015-ses-M0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sub-MRS00-ses-M0</td>\n",
+       "      <td>0.272853</td>\n",
+       "      <td>0.635678</td>\n",
+       "      <td>0.548431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sub-MRS15-ses-M0</td>\n",
+       "      <td>0.090409</td>\n",
+       "      <td>0.721868</td>\n",
+       "      <td>0.656020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>sub-P178-ses-M0</td>\n",
+       "      <td>0.635838</td>\n",
+       "      <td>0.262948</td>\n",
+       "      <td>0.173913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>sub-P183-ses-M0</td>\n",
+       "      <td>0.681733</td>\n",
+       "      <td>0.550588</td>\n",
+       "      <td>0.510309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>sub-P189-ses-M0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>sub-P199-ses-M0</td>\n",
+       "      <td>0.849968</td>\n",
+       "      <td>0.846255</td>\n",
+       "      <td>0.799429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>sub-P249-ses-M0</td>\n",
+       "      <td>0.741395</td>\n",
+       "      <td>0.721144</td>\n",
+       "      <td>0.690367</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>80 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             subject  dice_lesion_algo1  dice_lesion_single  dice_lesion_multi\n",
+       "0    sub-C010-ses-M0                NaN                 NaN                NaN\n",
+       "1    sub-C012-ses-M0                NaN                 NaN                NaN\n",
+       "2    sub-C015-ses-M0                NaN                 NaN                NaN\n",
+       "3   sub-MRS00-ses-M0           0.272853            0.635678           0.548431\n",
+       "4   sub-MRS15-ses-M0           0.090409            0.721868           0.656020\n",
+       "..               ...                ...                 ...                ...\n",
+       "75   sub-P178-ses-M0           0.635838            0.262948           0.173913\n",
+       "76   sub-P183-ses-M0           0.681733            0.550588           0.510309\n",
+       "77   sub-P189-ses-M0                NaN                 NaN                NaN\n",
+       "78   sub-P199-ses-M0           0.849968            0.846255           0.799429\n",
+       "79   sub-P249-ses-M0           0.741395            0.721144           0.690367\n",
+       "\n",
+       "[80 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=[]\n",
+    "path_database  = '/mnt/d/Poly/project_ms_seg/for_dice/'\n",
+    "for subject in subjects: \n",
+    "    gt_lesion_data= nib.load(path_database + '401/brain_lesion/'+ subject +'_UNIT1_GT.nii.gz')\n",
+    "    gt_lesion = np.array(gt_lesion_data.get_fdata())\n",
+    "    \n",
+    "    pred_lesion_data_algo1 = nib.load(path_database + '401/brain_lesion/'+ subject +'_UNIT1_algo-1.nii.gz')\n",
+    "    pred_lesion_algo1 = np.array(pred_lesion_data_algo1.get_fdata())\n",
+    "\n",
+    "    pred_lesion_data_single = nib.load(path_database + '401/brain_lesion/'+ subject +'_UNIT1_algo-3-singleclass.nii.gz')\n",
+    "    pred_lesion_single = np.array(pred_lesion_data_single.get_fdata())\n",
+    "\n",
+    "    pred_lesion_data_multi = nib.load(path_database + '401/brain_lesion/'+ subject +'_UNIT1_algo-3-multiclass.nii.gz')\n",
+    "    pred_lesion_multi = np.array(pred_lesion_data_multi.get_fdata())\n",
+    "    \n",
+    "    dice_lesion_algo1 = dice(gt_lesion,pred_lesion_algo1)\n",
+    "    dice_lesion_single = dice(gt_lesion,pred_lesion_single)\n",
+    "    dice_lesion_multi = dice(gt_lesion,pred_lesion_multi)\n",
+    "    \n",
+    "    d = {'subject': subject,'dice_lesion_algo1':dice_lesion_algo1,'dice_lesion_single':dice_lesion_single,\n",
+    "         'dice_lesion_multi':dice_lesion_multi}\n",
+    "    df.append(d)\n",
+    "data=pd.DataFrame(data=df)\n",
+    "data  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "5a85c174",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(columna_3[:6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "792f59b0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f70819ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "d5040dd1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "df=[]\n",
+    "path_database  = '/mnt/d/Poly/region_based/'\n",
+    "data_excel = pd.read_excel(path_database + \"metrics_F1_sensitivity_mp2rage.xlsx\")\n",
+    "#data_excel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "535f9684",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nb_train_cal=141\n",
+    "nb_train_edm=24\n",
+    "nb_train_mon=11\n",
+    "\n",
+    "nb_test_cal=39\n",
+    "nb_test_edm=3\n",
+    "nb_test_mon=3\n",
+    "\n",
+    "\n",
+    "nb_train_cal=67/82*100\n",
+    "nb_test_cal=15/82*100\n",
+    "\n",
+    "nb_train_edm=46/59*100\n",
+    "nb_test_edm=13/59*100\n",
+    "\n",
+    "nb_train_mon=78/94*100\n",
+    "nb_test_mon=16/94*100\n",
+    "\n",
+    "\n",
+    "data = pd.DataFrame({'Train_Validation' : [nb_train_cal , nb_train_edm, nb_train_mon],\n",
+    "                     'Test': [ nb_test_cal,nb_test_edm , nb_test_mon]},\n",
+    "                    index=('Basel', 'Marseille M0', 'Marseille M24'))\n",
+    "total = data.sum(axis=1)\n",
+    "plt.figure(figsize=(2,5))\n",
+    "plt.bar(data.index, data.Train_Validation+ data.Test, label='Test')\n",
+    "plt.bar(data.index, data.Train_Validation, label='Train / Validation')\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.xticks(rotation=90)\n",
+    "\n",
+    "plt.savefig('color_bar.pdf')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "046da1c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Basel            18.292683\n",
+       "Marseille M0     22.033898\n",
+       "Marseille M24    17.021277\n",
+       "Name: Test, dtype: float64"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "1329560c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 300x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nb_total_m0=27\n",
+    "nb_total_m24=14\n",
+    "nb_total_basel=198\n",
+    "nb_total_nih=163\n",
+    "\n",
+    "nb_train_m0=23\n",
+    "nb_train_m24=12\n",
+    "nb_train_basel=161\n",
+    "nb_train_nih=126\n",
+    "\n",
+    "nb_test_m0=4\n",
+    "nb_test_m24=2\n",
+    "nb_test_basel=37\n",
+    "nb_test_nih=37\n",
+    "\n",
+    "test_m0=nb_train_m0/nb_total_m0 * 100\n",
+    "test_m24=nb_train_m24/nb_total_m24 *100\n",
+    "test_basel=nb_train_basel/nb_total_basel *100\n",
+    "test_nih=nb_train_nih/nb_total_nih *100\n",
+    "\n",
+    "train_m0=nb_test_m0/nb_total_m0 *100\n",
+    "train_m24=nb_test_m24/nb_total_m24 *100\n",
+    "train_basel=nb_test_basel/nb_total_basel *100\n",
+    "train_nih=nb_test_nih/nb_total_nih *100\n",
+    "\n",
+    "\n",
+    "data = pd.DataFrame({'Train_Validation' : [train_m0 , train_m24, train_basel,train_nih ],\n",
+    "                     'Test': [ test_m0, test_m24 , test_basel,test_nih]},\n",
+    "                    index=('Marseille M0', 'Marseille M24', 'Basel', 'NIH'))\n",
+    "total = data.sum(axis=1)\n",
+    "plt.figure(figsize=(3,4))\n",
+    "plt.bar(data.index, data.Test + data.Train_Validation, label='Test')\n",
+    "plt.bar(data.index, data.Test, label='Train / Validation')\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.xticks(rotation=90)\n",
+    "\n",
+    "plt.savefig('color_bar.pdf')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/dataset_split.md b/dataset_split.md
new file mode 100644
index 0000000..1b43304
--- /dev/null
+++ b/dataset_split.md
@@ -0,0 +1,18 @@
+### Datasets and version (commit) used (n=3):
+
+| Dataset   |   Version | 
+|:-----------|--------------------:|
+|  `datasplit_basel-mp2rage.yaml`         | 961a0c0afc753ba960155548e14c227d14703c1b       |
+|  `datasplit_nih-ms-mp2rage.yaml`        | 8187361e4f5143ffc6c8d93750a34a57e424a3a8       |
+|  `datasplit_marseille-3t-mp2rage.yaml`  | 372955118e04e6b2dd5e85b30c06d45eb65b1ee8       |
+
+### Training/Validation/Test splits across all datasets:
+
+* Number of training/validation samples (i.e. images, not subjects): 322
+* Number of testing samples (i.e. images, not subjects): 80
+
+### Training/Validation/Test across each contrast (n=9):
+
+| Contrast   |   #train/validation |   #test        |
+|:-----------|--------------------:|---------------:|
+| UNIT1      |           322       |             80 |
\ No newline at end of file
diff --git a/datasplits/datasplit_basel-mp2rage.yaml b/datasplits/datasplit_basel-mp2rage.yaml
new file mode 100644
index 0000000..8cc0608
--- /dev/null
+++ b/datasplits/datasplit_basel-mp2rage.yaml
@@ -0,0 +1,200 @@
+test:
+- sub-C010
+- sub-C012
+- sub-C015
+- sub-P002
+- sub-P016
+- sub-P021
+- sub-P037
+- sub-P038
+- sub-P044
+- sub-P046
+- sub-P051
+- sub-P058
+- sub-P061
+- sub-P063
+- sub-P066
+- sub-P069
+- sub-P077
+- sub-P079
+- sub-P080
+- sub-P084
+- sub-P085
+- sub-P094
+- sub-P109
+- sub-P120
+- sub-P126
+- sub-P130
+- sub-P132
+- sub-P133
+- sub-P136
+- sub-P149
+- sub-P165
+- sub-P175
+- sub-P178
+- sub-P183
+- sub-P189
+- sub-P199
+- sub-P249
+train/validation:
+- sub-C001
+- sub-C002
+- sub-C003
+- sub-C005
+- sub-C006
+- sub-C007
+- sub-C008
+- sub-C009
+- sub-C011
+- sub-C013
+- sub-C014
+- sub-C016
+- sub-C018
+- sub-C019
+- sub-C020
+- sub-C021
+- sub-C022
+- sub-P001
+- sub-P003
+- sub-P004
+- sub-P005
+- sub-P006
+- sub-P007
+- sub-P010
+- sub-P011
+- sub-P012
+- sub-P013
+- sub-P014
+- sub-P015
+- sub-P017
+- sub-P019
+- sub-P022
+- sub-P023
+- sub-P024
+- sub-P025
+- sub-P026
+- sub-P027
+- sub-P028
+- sub-P029
+- sub-P030
+- sub-P031
+- sub-P033
+- sub-P034
+- sub-P035
+- sub-P036
+- sub-P039
+- sub-P040
+- sub-P041
+- sub-P042
+- sub-P043
+- sub-P045
+- sub-P047
+- sub-P048
+- sub-P049
+- sub-P050
+- sub-P052
+- sub-P053
+- sub-P054
+- sub-P055
+- sub-P056
+- sub-P057
+- sub-P059
+- sub-P062
+- sub-P065
+- sub-P067
+- sub-P068
+- sub-P070
+- sub-P071
+- sub-P072
+- sub-P073
+- sub-P074
+- sub-P075
+- sub-P076
+- sub-P078
+- sub-P081
+- sub-P082
+- sub-P083
+- sub-P086
+- sub-P087
+- sub-P088
+- sub-P089
+- sub-P091
+- sub-P092
+- sub-P095
+- sub-P096
+- sub-P097
+- sub-P098
+- sub-P099
+- sub-P100
+- sub-P101
+- sub-P102
+- sub-P103
+- sub-P104
+- sub-P105
+- sub-P106
+- sub-P108
+- sub-P110
+- sub-P111
+- sub-P112
+- sub-P113
+- sub-P114
+- sub-P115
+- sub-P116
+- sub-P117
+- sub-P118
+- sub-P119
+- sub-P121
+- sub-P122
+- sub-P123
+- sub-P124
+- sub-P125
+- sub-P129
+- sub-P134
+- sub-P135
+- sub-P137
+- sub-P138
+- sub-P139
+- sub-P140
+- sub-P141
+- sub-P142
+- sub-P144
+- sub-P145
+- sub-P146
+- sub-P147
+- sub-P148
+- sub-P151
+- sub-P153
+- sub-P156
+- sub-P157
+- sub-P159
+- sub-P160
+- sub-P161
+- sub-P162
+- sub-P163
+- sub-P169
+- sub-P170
+- sub-P172
+- sub-P173
+- sub-P174
+- sub-P176
+- sub-P179
+- sub-P180
+- sub-P181
+- sub-P182
+- sub-P184
+- sub-P185
+- sub-P187
+- sub-P188
+- sub-P191
+- sub-P192
+- sub-P193
+- sub-P194
+- sub-P197
+- sub-P198
+- sub-P200
+- sub-P241
+- sub-P242
+- sub-P243
+- sub-P244
+- sub-P246
+- sub-P250
\ No newline at end of file
diff --git a/datasplits/datasplit_marseille-3t-mp2rage.yaml b/datasplits/datasplit_marseille-3t-mp2rage.yaml
new file mode 100644
index 0000000..1352271
--- /dev/null
+++ b/datasplits/datasplit_marseille-3t-mp2rage.yaml
@@ -0,0 +1,43 @@
+test:
+- sub-MRS00-ses-M0
+- sub-MRS15-ses-M0
+- sub-MRS15-ses-M24
+- sub-MRS18-ses-M0
+- sub-MRS23-ses-M0
+- sub-MRS23-ses-M24
+train/validation:
+- sub-MRS01-ses-M0
+- sub-MRS01-ses-M24
+- sub-MRS02-ses-M0
+- sub-MRS02-ses-M24
+- sub-MRS03-ses-M0
+- sub-MRS03-ses-M24
+- sub-MRS04-ses-M0
+- sub-MRS05-ses-M0
+- sub-MRS06-ses-M0
+- sub-MRS06-ses-M24
+- sub-MRS07-ses-M0
+- sub-MRS08-ses-M0
+- sub-MRS08-ses-M24
+- sub-MRS09-ses-M0
+- sub-MRS09-ses-M24
+- sub-MRS10-ses-M0
+- sub-MRS11-ses-M0
+- sub-MRS11-ses-M24
+- sub-MRS12-ses-M0
+- sub-MRS12-ses-M24
+- sub-MRS13-ses-M0
+- sub-MRS14-ses-M0
+- sub-MRS14-ses-M24
+- sub-MRS16-ses-M0
+- sub-MRS17-ses-M0
+- sub-MRS19-ses-M0
+- sub-MRS19-ses-M24
+- sub-MRS20-ses-M0
+- sub-MRS21-ses-M0
+- sub-MRS21-ses-M24
+- sub-MRS22-ses-M0
+- sub-MRS24-ses-M0
+- sub-MRS24-ses-M24
+- sub-MRS25-ses-M0
+- sub-MRS26-ses-M0
\ No newline at end of file
diff --git a/datasplits/datasplit_nih-ms-mp2rage.yaml b/datasplits/datasplit_nih-ms-mp2rage.yaml
new file mode 100644
index 0000000..caf092d
--- /dev/null
+++ b/datasplits/datasplit_nih-ms-mp2rage.yaml
@@ -0,0 +1,165 @@
+test:
+- sub-nih003
+- sub-nih009
+- sub-nih017
+- sub-nih021
+- sub-nih037
+- sub-nih042
+- sub-nih043
+- sub-nih045
+- sub-nih051
+- sub-nih052
+- sub-nih054
+- sub-nih056
+- sub-nih067
+- sub-nih073
+- sub-nih075
+- sub-nih084
+- sub-nih087
+- sub-nih089
+- sub-nih092
+- sub-nih100
+- sub-nih101
+- sub-nih110
+- sub-nih112
+- sub-nih117
+- sub-nih120
+- sub-nih134
+- sub-nih136
+- sub-nih144
+- sub-nih147
+- sub-nih149
+- sub-nih155
+- sub-nih173
+- sub-nih179
+- sub-nih183
+- sub-nih194
+- sub-nih195
+- sub-nih199
+train/validation:
+- sub-nih004
+- sub-nih005
+- sub-nih006
+- sub-nih007
+- sub-nih008
+- sub-nih010
+- sub-nih012
+- sub-nih013
+- sub-nih015
+- sub-nih018
+- sub-nih020
+- sub-nih022
+- sub-nih023
+- sub-nih024
+- sub-nih026
+- sub-nih027
+- sub-nih028
+- sub-nih029
+- sub-nih030
+- sub-nih031
+- sub-nih033
+- sub-nih034
+- sub-nih035
+- sub-nih036
+- sub-nih038
+- sub-nih039
+- sub-nih040
+- sub-nih041
+- sub-nih044
+- sub-nih046
+- sub-nih047
+- sub-nih048
+- sub-nih049
+- sub-nih053
+- sub-nih055
+- sub-nih057
+- sub-nih058
+- sub-nih059
+- sub-nih060
+- sub-nih061
+- sub-nih062
+- sub-nih063
+- sub-nih064
+- sub-nih065
+- sub-nih066
+- sub-nih068
+- sub-nih069
+- sub-nih070
+- sub-nih071
+- sub-nih072
+- sub-nih074
+- sub-nih076
+- sub-nih078
+- sub-nih079
+- sub-nih081
+- sub-nih082
+- sub-nih083
+- sub-nih086
+- sub-nih090
+- sub-nih091
+- sub-nih093
+- sub-nih095
+- sub-nih096
+- sub-nih097
+- sub-nih098
+- sub-nih099
+- sub-nih102
+- sub-nih103
+- sub-nih104
+- sub-nih105
+- sub-nih107
+- sub-nih108
+- sub-nih114
+- sub-nih116
+- sub-nih118
+- sub-nih119
+- sub-nih121
+- sub-nih122
+- sub-nih123
+- sub-nih124
+- sub-nih127
+- sub-nih128
+- sub-nih130
+- sub-nih131
+- sub-nih133
+- sub-nih135
+- sub-nih138
+- sub-nih141
+- sub-nih142
+- sub-nih143
+- sub-nih148
+- sub-nih150
+- sub-nih152
+- sub-nih153
+- sub-nih156
+- sub-nih157
+- sub-nih158
+- sub-nih159
+- sub-nih160
+- sub-nih161
+- sub-nih165
+- sub-nih166
+- sub-nih167
+- sub-nih168
+- sub-nih169
+- sub-nih170
+- sub-nih171
+- sub-nih172
+- sub-nih174
+- sub-nih175
+- sub-nih176
+- sub-nih177
+- sub-nih178
+- sub-nih180
+- sub-nih181
+- sub-nih182
+- sub-nih185
+- sub-nih186
+- sub-nih187
+- sub-nih188
+- sub-nih189
+- sub-nih191
+- sub-nih192
+- sub-nih196
+- sub-nih198
+- sub-nih200
\ No newline at end of file
diff --git a/nnunet/README.md b/nnunet/README.md
new file mode 100644
index 0000000..0f4fb0a
--- /dev/null
+++ b/nnunet/README.md
@@ -0,0 +1,92 @@
+# Installation instructions
+
+## Installation of required libraries
+
+Create a virtual invironment: 
+~~~
+conda create -n venv_nnunet python=3.9
+~~~
+
+Activate the environment with the following command:
+~~~
+conda activate venv_nnunet
+~~~
+
+To install required libraries to train an nnUNet v2:
+
+```
+pip install -r requirements_nnunet.txt
+```
+
+Install SpinalCordToolbox 6.2 :
+
+Installation link : https://spinalcordtoolbox.com/user_section/installation.html
+
+
+# Data preparation
+
+Create the following folders:
+
+~~~
+mkdir nnUNet_raw
+mkdir nnUNet_preprocessed
+mkdir nnUNet_results
+~~~
+
+~~~
+ python convert_bids_to_nnUnetv2.py --path-data data-mp2rage/  --path-out nnUNet_raw --dataset-name ms-seg --label-suffix desc-rater1_label-lesion_seg 
+    --dataset-number 401 --contrasts UNIT1 --seed 90 --split 0.8 0.2  --labels-path-name data-mp2rage/derivatives/labels/  --session-name ses-M0
+~~~
+
+> **Note**
+> The test ratio is 0.2 for 20% (train ratio is therefore 80%). For M0 images, the time point is ses-M0.
+
+
+# Model training on Compute Canada
+
+Send nnUNet_raw to Compute Canada (see https://docs.alliancecan.ca/wiki/Transferring_data)
+
+Before training the model, nnU-Net performs data preprocessing and checks the integrity of the dataset:
+
+~~~
+export nnUNet_raw="/path/to/nnUNet_raw"
+export nnUNet_preprocessed="/path/to/nnUNet_preprocessed"
+export nnUNet_results="/path/to/nnUNet_results"
+
+nnUNetv2_plan_and_preprocess -d DATASET-ID --verify_dataset_integrity
+~~~
+
+You will get the configuration plan for all four configurations (2d, 3d_fullres, 3d_lowres).
+
+To train the model, use the following command:
+
+~~~
+CUDA_VISIBLE_DEVICES=Nb nnUNetv2_train DATASET-ID CONFIG FOLD --npz
+~~~
+
+## Examples for our training
+
+nnunet/run_pre.sh
+nnunet/run_train.sh
+
+## Run :
+sbatch run_pre.sh
+sbatch run_train.sh
+
+# After training, best configuration found
+
+~~~
+nnUNetv2_find_best_configuration DATASET_NAME_OR_ID -c CONFIGURATIONS 
+~~~
+
+## For our venv on compute canada
+sbatch run_post.sh
+
+# Inference evaluation on test dataset split 
+
+~~~
+nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_NAME_OR_ID -c CONFIGURATION --save_probabilities
+~~~
+
+# Evaluate segmentation
+`data_analysis/test_metrics_nnunet.ipynb`
\ No newline at end of file
diff --git a/nnunet/convert_bids_to_nnUnetv2.py b/nnunet/convert_bids_to_nnUnetv2.py
new file mode 100644
index 0000000..8239e7a
--- /dev/null
+++ b/nnunet/convert_bids_to_nnUnetv2.py
@@ -0,0 +1,305 @@
+"""
+Converts the BIDS-structured MP2RAGE dataset to the nnUNetv2 dataset format. 
+Full details about the format can be found here: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/dataset_format.md
+Currently supports for different sessions 
+Usage example:
+    python convert_bids_to_nnUnetv2.py --path-data data-mp2rage/  --path-out nnUNet_raw --dataset-name ms-seg --label-suffix desc-rater1_label-lesion_seg 
+    --dataset-number 401 --contrasts UNIT1 --seed 90 --split 0.8 0.2  --labels-path-name data-mp2rage/derivatives/labels/  --session-name ses-M0
+    
+"""
+
+import argparse
+import pathlib
+from pathlib import Path
+import json
+import os
+from collections import OrderedDict
+import pandas as pd
+from loguru import logger
+from sklearn.model_selection import train_test_split
+
+import nibabel as nib
+import numpy as np
+
+
+def binarize_label(subject_path, label_path):
+    label_npy = nib.load(label_path).get_fdata()
+    threshold = 1e-8
+    label_npy = np.where(label_npy > threshold, 1, 0)
+    ref = nib.load(subject_path)
+    label_bin = nib.Nifti1Image(label_npy, ref.affine, ref.header)
+    # overwrite the original label file with the binarized version
+    nib.save(label_bin, label_path)
+
+
+# parse command line arguments
+parser = argparse.ArgumentParser(description='Convert BIDS-structured dataset to nnUNetV2 database format.')
+parser.add_argument('--path-data', help='Path to BIDS dataset.', required=True)
+parser.add_argument('--path-out', help='Path to output directory.', required=True)
+parser.add_argument('--dataset-name', '-dname', default='MSSpineLesion', type=str,
+                    help='Specify the task name - usually the anatomy to be segmented, e.g. Hippocampus',)
+parser.add_argument('--label-suffix', default='seg-manual', type=str,
+                    help='Specify name of the segmentation to use.',)
+parser.add_argument('--dataset-number', '-dnum', default=501,type=int, 
+                    help='Specify the task number, has to be greater than 500 but less than 999. e.g 502')
+parser.add_argument("--contrasts", required=True, nargs="*", help="Contrasts to build our dataset from.")
+parser.add_argument('--seed', default=42, type=int, 
+                    help='Seed to be used for the random number generator split into training and test sets.')
+# argument that accepts a list of floats as train val test splits
+parser.add_argument('--split', nargs='+', required=True, type=float, default=[0.8, 0.2],
+                    help='Ratios of training (includes validation) and test splits lying between 0-1. Example: --split 0.8 0.2')
+parser.add_argument('--labels-path-name', default='labels', type=str,
+                    help='Specify name labels folder in the derivative repository.',)
+parser.add_argument('--session-name', default='', type=str,
+                    help='name of the session to add before anat.',)
+
+args = parser.parse_args()
+
+root = Path(args.path_data)
+train_ratio, test_ratio = args.split
+path_out = Path(os.path.join(os.path.abspath(args.path_out), f'Dataset{args.dataset_number}_{args.dataset_name}'))
+
+# create individual directories for train and test images and labels
+path_out_imagesTr = Path(os.path.join(path_out, 'imagesTr'))
+path_out_imagesTs = Path(os.path.join(path_out, 'imagesTs'))
+path_out_labelsTr = Path(os.path.join(path_out, 'labelsTr'))
+path_out_labelsTs = Path(os.path.join(path_out, 'labelsTs'))
+
+train_images, train_labels, test_images, test_labels = [], [], [], []
+if args.session_name != '':
+    anat_path = args.session_name + '/anat'
+else:
+    anat_path = 'anat'
+
+
+if __name__ == '__main__':
+
+    # make the directories
+    pathlib.Path(path_out).mkdir(parents=True, exist_ok=True)
+    pathlib.Path(path_out_imagesTr).mkdir(parents=True, exist_ok=True)
+    pathlib.Path(path_out_imagesTs).mkdir(parents=True, exist_ok=True)
+    pathlib.Path(path_out_labelsTr).mkdir(parents=True, exist_ok=True)
+    pathlib.Path(path_out_labelsTs).mkdir(parents=True, exist_ok=True)
+
+    # set the random number generator seed
+    rng = np.random.default_rng(args.seed)
+
+    # # Get all subjects from participants.tsv
+    # subjects_df = pd.read_csv(os.path.join(root, 'participants.tsv'), sep='\t')
+    # subjects_tsv = subjects_df['participant_id'].values.tolist()
+    # logger.info(f"Total number of subjects in the tsv file: {len(subjects_tsv)}")
+
+    # get the list of subjects in the root directory 
+    subjects = [subject for subject in os.listdir(root) if subject.startswith('sub-')]
+    logger.info(f"Total number of subjects in the root directory: {len(subjects)}")
+    # sort the subjects list
+    subjects.sort()
+
+    # Get the training and test splits
+    train_subjects, test_subjects = train_test_split(subjects, test_size=test_ratio, random_state=args.seed)
+    # rng.shuffle(train_subjects)
+
+    # # list of contrasts: T2w, T1w, T2star, MTon, MToff, DWI
+    # contrasts = ['dwi', 'flip-1_mt-on_MTS', 'flip-2_mt-off_MTS', 'T1w', 'T2star', 'T2w']
+
+    train_ctr, test_ctr = 0, 0
+    for subject in subjects:
+
+        if subject in train_subjects:
+
+            internal_ctr = 0
+            # loop over all contrasts
+            for contrast in args.contrasts:
+                if args.session_name != '':
+                    contrast = args.session_name + '_' + contrast
+                if contrast != 'dwi':
+                    subject_images_path = os.path.join(root, subject, anat_path)
+                    subject_labels_path = os.path.join(root, 'derivatives', args.labels_path_name, subject, anat_path)
+
+                    subject_image_file = os.path.join(subject_images_path, f"{subject}_{contrast}.nii.gz")
+                    subject_label_file = os.path.join(subject_labels_path, f"{subject}_{contrast}_{args.label_suffix}.nii.gz")
+
+                    # check if both image and label files exist
+                    if not (os.path.exists(subject_image_file) and os.path.exists(subject_label_file)):
+                        print(f"Skipping train subject {subject}'s contrast {contrast} as either image or label does not exist.")
+                        continue
+
+                    # create the new convention names for nnunet
+                    sub_name = str(Path(subject_image_file).name).split('_')[0] # + '_' + str(Path(subject_image_file).name).split('_')[1]
+                    subject_image_file_nnunet = os.path.join(path_out_imagesTr,f"{args.dataset_name}_{sub_name}-{contrast}_{train_ctr:03d}_0000.nii.gz")
+                    subject_label_file_nnunet = os.path.join(path_out_labelsTr,f"{args.dataset_name}_{sub_name}-{contrast}_{train_ctr:03d}.nii.gz")
+
+                    # copy the files to new structure using symbolic links (prevents duplication of data and saves space)
+                    os.symlink(os.path.abspath(subject_image_file), subject_image_file_nnunet)
+                    os.symlink(os.path.abspath(subject_label_file), subject_label_file_nnunet)
+
+                    # binarize the label file
+                    binarize_label(subject_image_file_nnunet, subject_label_file_nnunet)
+
+                    # increment the counters
+                    train_ctr += 1
+                    internal_ctr += 1
+
+                else:
+                    subject_images_path = os.path.join(root, subject, 'dwi')
+                    subject_labels_path = os.path.join(root, 'derivatives', args.labels_path_name, subject, 'dwi')
+
+                    subject_image_file = os.path.join(subject_images_path, f"{subject}_rec-average_{contrast}.nii.gz")
+                    subject_label_file = os.path.join(subject_labels_path, f"{subject}_rec-average_{contrast}_{args.label_suffix}.nii.gz")
+
+                    # check if both image and label files exist
+                    if not (os.path.exists(subject_image_file) and os.path.exists(subject_label_file)):
+                        print(f"Skipping train subject {subject}'s contrast {contrast} as either image or label does not exist.")
+                        continue
+
+                    # create the new convention names for nnunet
+                    sub_name = str(Path(subject_image_file).name).split('_')[0] # + '_' + str(Path(subject_image_file).name).split('_')[1]
+                    subject_image_file_nnunet = os.path.join(path_out_imagesTr,f"{args.dataset_name}_{sub_name}-{contrast}_{train_ctr:03d}_0000.nii.gz")
+                    subject_label_file_nnunet = os.path.join(path_out_labelsTr,f"{args.dataset_name}_{sub_name}-{contrast}_{train_ctr:03d}.nii.gz")
+
+                    # copy the files to new structure using symbolic links (prevents duplication of data and saves space)
+                    os.symlink(os.path.abspath(subject_image_file), subject_image_file_nnunet)
+                    os.symlink(os.path.abspath(subject_label_file), subject_label_file_nnunet)
+
+                    # binarize the label file
+                    binarize_label(subject_image_file_nnunet, subject_label_file_nnunet)
+
+                    # increment the counters
+                    train_ctr += 1
+                    internal_ctr += 1
+
+            # print(f"Found {internal_ctr} contrasts for Subject {subject}.")
+
+        elif subject in test_subjects:
+
+            internal_ctr = 0
+            # loop over all contrasts
+            for contrast in args.contrasts:
+                if args.session_name != '':
+                    contrast = args.session_name + '_' + contrast
+                if contrast != 'dwi':
+                    subject_images_path = os.path.join(root, subject, anat_path)
+                    subject_labels_path = os.path.join(root, 'derivatives', args.labels_path_name, subject, anat_path)
+
+                    subject_image_file = os.path.join(subject_images_path, f"{subject}_{contrast}.nii.gz")
+                    subject_label_file = os.path.join(subject_labels_path, f"{subject}_{contrast}_{args.label_suffix}.nii.gz")
+
+                    # check if both image and label files exist
+                    if not (os.path.exists(subject_image_file) and os.path.exists(subject_label_file)):
+                        print(f"Skipping test subject {subject}'s contrast {contrast} as either image or label does not exist.")
+                        continue
+
+                    # create the new convention names for nnunet
+                    sub_name = str(Path(subject_image_file).name).split('_')[0] # + '_' + str(Path(subject_image_file).name).split('_')[1]
+                    subject_image_file_nnunet = os.path.join(path_out_imagesTs,f"{args.dataset_name}_{sub_name}-{contrast}_{test_ctr:03d}_0000.nii.gz")
+                    subject_label_file_nnunet = os.path.join(path_out_labelsTs,f"{args.dataset_name}_{sub_name}-{contrast}_{test_ctr:03d}.nii.gz")
+
+                    # copy the files to new structure using symbolic links (prevents duplication of data and saves space)
+                    os.symlink(os.path.abspath(subject_image_file), subject_image_file_nnunet)
+                    os.symlink(os.path.abspath(subject_label_file), subject_label_file_nnunet)
+
+                    # binarize the label file
+                    binarize_label(subject_image_file_nnunet, subject_label_file_nnunet)
+
+                    # increment the counters
+                    test_ctr += 1
+                    internal_ctr += 1
+
+                else:
+                    subject_images_path = os.path.join(root, subject, 'dwi')
+                    subject_labels_path = os.path.join(root, 'derivatives', args.labels_path_name, subject, 'dwi')
+
+                    subject_image_file = os.path.join(subject_images_path, f"{subject}_rec-average_{contrast}.nii.gz")
+                    subject_label_file = os.path.join(subject_labels_path, f"{subject}_rec-average_{contrast}_{args.label_suffix}.nii.gz")
+
+                    # check if both image and label files exist
+                    if not (os.path.exists(subject_image_file) and os.path.exists(subject_label_file)):
+                        print(f"Skipping test subject {subject}'s contrast {contrast} as either image or label does not exist.")
+                        continue
+
+                    # create the new convention names for nnunet
+                    sub_name = str(Path(subject_image_file).name).split('_')[0] # + '_' + str(Path(subject_image_file).name).split('_')[1]
+                    subject_image_file_nnunet = os.path.join(path_out_imagesTs,f"{args.dataset_name}_{sub_name}-{contrast}_{test_ctr:03d}_0000.nii.gz")
+                    subject_label_file_nnunet = os.path.join(path_out_labelsTs,f"{args.dataset_name}_{sub_name}-{contrast}_{test_ctr:03d}.nii.gz")
+
+                    # copy the files to new structure using symbolic links (prevents duplication of data and saves space)
+                    os.symlink(os.path.abspath(subject_image_file), subject_image_file_nnunet)
+                    os.symlink(os.path.abspath(subject_label_file), subject_label_file_nnunet)
+
+                    # binarize the label file
+                    binarize_label(subject_image_file_nnunet, subject_label_file_nnunet)
+
+                    # increment the counters
+                    test_ctr += 1
+                    internal_ctr += 1
+
+        else:
+            print("Skipping file, could not be located in the Train or Test splits split.", subject)
+
+    logger.info(f"Number of training and validation subjects (including all contrasts): {train_ctr}")
+    logger.info(f"Number of test subjects (including all contrasts): {test_ctr}")
+    # assert train_ctr == len(train_subjects), 'No. of train/val images do not match'
+    # assert test_ctr == len(test_subjects), 'No. of test images do not match'
+
+    # c.f. dataset json generation
+    # In nnUNet V2, dataset.json file has become much shorter. The description of the fields and changes
+    # can be found here: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/dataset_format.md#datasetjson
+    # this file can be automatically generated using the following code here:
+    # https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunetv2/dataset_conversion/generate_dataset_json.py
+    # example: https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/dataset_conversion/Task055_SegTHOR.py
+
+    json_dict = OrderedDict()
+    json_dict['name'] = args.dataset_name
+    json_dict['description'] = args.dataset_name
+    json_dict['reference'] = "TBD"
+    json_dict['licence'] = "TBD"
+    json_dict['release'] = "0.0"
+    json_dict['numTraining'] = train_ctr
+    json_dict['numTest'] = test_ctr
+
+    # The following keys are the most important ones. 
+    """
+    channel_names:
+        Channel names must map the index to the name of the channel. For BIDS, this refers to the contrast suffix.
+        {
+            0: 'T1',
+            1: 'CT'
+        }
+    Note that the channel names may influence the normalization scheme!! Learn more in the documentation.
+    labels:
+        This will tell nnU-Net what labels to expect. Important: This will also determine whether you use region-based training or not.
+        Example regular labels:
+        {
+            'background': 0,
+            'left atrium': 1,
+            'some other label': 2
+        }
+        Example region-based training: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/region_based_training.md
+        {
+            'background': 0,
+            'whole tumor': (1, 2, 3),
+            'tumor core': (2, 3),
+            'enhancing tumor': 3
+        }
+        Remember that nnU-Net expects consecutive values for labels! nnU-Net also expects 0 to be background!
+    """
+
+    json_dict['channel_names'] = {
+        0: "acq-sag_UNIT1",
+    }
+
+    json_dict['labels'] = {
+        "background": 0,
+        "label-lesion_seg": 1,
+    }
+
+    # Needed for finding the files correctly. IMPORTANT! File endings must match between images and segmentations!
+    json_dict['file_ending'] = ".nii.gz"
+
+    # create dataset_description.json
+    json_object = json.dumps(json_dict, indent=4)
+    # write to dataset description
+    # nn-unet requires it to be "dataset.json"
+    dataset_dict_name = f"dataset.json"
+    with open(os.path.join(path_out, dataset_dict_name), "w") as outfile:
+        outfile.write(json_object)
\ No newline at end of file
diff --git a/nnunet/inference.sh b/nnunet/inference.sh
new file mode 100644
index 0000000..81c4194
--- /dev/null
+++ b/nnunet/inference.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+#SBATCH --account=def-jcohen
+#SBATCH --time=00-00:20:00
+#SBATCH --cpus-per-task=32
+#SBATCH --mem=128G
+#SBATCH --gpus-per-node=a100:1
+#SBATCH --mail-user=nilser.laines@gmail.com
+#SBATCH --mail-type=ALL
+
+
+module load python/3.10
+
+source ~/scratch/nnunet-ms/bin/activate
+
+export nnUNet_raw=$(realpath nnUNet_raw)
+export nnUNet_preprocessed=$(realpath nnUNet_preprocessed)
+export nnUNet_results=$(realpath nnUNet_results)
+
+#nnUNetv2_train 405 3d_fullres $SLURM_ARRAY_TASK_ID --npz
+#salloc --account=def-jcohen --time=0:20:00 --cpus-per-task=32 --mem=128G --gpus-per-node=a100:1
+nnUNetv2_predict -d Dataset403_crop-sc-ms -i nnUNet_raw/Dataset403_crop-sc-ms/imagesTs -o results_nnunet/403 -f  0 1 2 3 4 -tr nnUNetTrainer -c 3d_fullres -p nnUNetPlans
+
+
+
+
diff --git a/nnunet/run_post.sh b/nnunet/run_post.sh
new file mode 100644
index 0000000..1c59f00
--- /dev/null
+++ b/nnunet/run_post.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+#SBATCH --account=def-jcohen
+#SBATCH --time=07-00:00:00
+#SBATCH --cpus-per-task=16
+#SBATCH --mem=128G
+#SBATCH --gpus-per-node=1
+#SBATCH --mail-user=nilser.laines@gmail.com
+#SBATCH --mail-type=ALL
+
+
+source ~/scratch/nnunet-ms/bin/activate
+export nnUNet_raw=$(realpath nnUNet_raw)
+export nnUNet_preprocessed=$(realpath nnUNet_preprocessed)
+export nnUNet_results=$(realpath nnUNet_results)
+
+nnUNetv2_predict -d Dataset401_fov-sc-ms -i nnUNet_raw/Dataset401_fov-sc-ms/imagesTs -o results_nnunet/401 -f  0 1 2 3 4 -tr nnUNetTrainer -c 3d_fullres -p nnUNetPlans
+
diff --git a/nnunet/run_pre.sh b/nnunet/run_pre.sh
new file mode 100644
index 0000000..9feaeb8
--- /dev/null
+++ b/nnunet/run_pre.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+#SBATCH --account=def-jcohen
+#SBATCH --time=07-00:00:00
+#SBATCH --cpus-per-task=16
+#SBATCH --mem=128G
+#SBATCH --gpus-per-node=1
+#SBATCH --mail-user=nilser.laines@gmail.com
+#SBATCH --mail-type=ALL
+
+source ~/scratch/nnunet-ms/bin/activate
+export nnUNet_raw=$(realpath nnUNet_raw)
+export nnUNet_preprocessed=$(realpath nnUNet_preprocessed)
+export nnUNet_results=$(realpath nnUNet_results)
+
+nnUNetv2_plan_and_preprocess -d 401 --verify_dataset_integrity
diff --git a/nnunet/run_train.sh b/nnunet/run_train.sh
new file mode 100644
index 0000000..7a92cec
--- /dev/null
+++ b/nnunet/run_train.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+#SBATCH --account=def-jcohen
+#SBATCH --time=07-00:00:00
+#SBATCH --cpus-per-task=16
+#SBATCH --mem=128G
+#SBATCH --gpus-per-node=1
+#SBATCH --mail-user=nilser.laines@gmail.com
+#SBATCH --mail-type=ALL
+#SBATCH --array=0-4
+
+module load python/3.10
+
+source ~/scratch/nnunet-ms/bin/activate
+
+export nnUNet_raw=$(realpath nnUNet_raw)
+export nnUNet_preprocessed=$(realpath nnUNet_preprocessed)
+export nnUNet_results=$(realpath nnUNet_results)
+
+nnUNetv2_train 405 3d_fullres $SLURM_ARRAY_TASK_ID --npz
+
+
+
+