diff --git a/images/txt.txt b/images/txt.txt
deleted file mode 100644
index 8b13789..0000000
--- a/images/txt.txt
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/src/results_explorer.ipynb b/src/results_explorer.ipynb
index 63bdc2d..eaf43af 100644
--- a/src/results_explorer.ipynb
+++ b/src/results_explorer.ipynb
@@ -8,26 +8,312 @@
    "outputs": [],
    "source": [
     "from variability import calculate_indicators\n",
+    "from ti_mask_generation import gen_ti_frame_squares\n",
     "from utils import load_pickle_file\n",
+    "from display_functions import plot_realizations\n",
+    "import geone as gn\n",
     "import pickle\n",
     "import matplotlib.pyplot as plt\n",
     "from sklearn import manifold\n",
     "from matplotlib import cm\n",
     "from matplotlib.lines import Line2D\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import zipfile\n",
+    "import csv\n",
+    "import ast\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import glob\n",
+    "from itertools import product\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "456dff81",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import zipfile\n",
-    "import csv\n",
-    "import ast\n",
-    "import numpy as np\n",
+    "SEED_LIST=[852,123,456,789,254,\n",
+    "           #648,675,585,654,157\n",
+    "           ]\n",
+    "TI_PCT_AREA_LIST=[25,\n",
+    "                  55,\n",
+    "                  #75,\n",
+    "                  90]\n",
+    "NUM_SHAPE_LIST=[#1,\n",
+    "                5,\n",
+    "                10,\n",
+    "                15,\n",
+    "                50]\n",
+    "AUX_VARS_LIST=[\n",
+    "    \"grid_grv\",\n",
+    "    \"grid_lmp\",\n",
+    "    \"grid_mag\",\n",
+    "    \"grid_grv,grid_lmp\",\n",
+    "    \"grid_grv,grid_mag\",\n",
+    "    \"grid_lmp,grid_mag\",\n",
+    "    # \"grid_grv,grid_lmp,grid_mag\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ad481fe",
+   "metadata": {},
+   "source": [
+    "# THE CELLS BELOW ARE FOR DISPLAYING THE IMAGES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "edd69bba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_all_simulation_folders(ti_pct, num_shape, ti_aux_var, combinations_dict=None,\n",
+    "                                seed=None, zip_path=r'K:\\CET\\Resultats\\results.zip', output_dir=None):\n",
+    "    norm = lambda s: s.strip().lower()\n",
+    "    aux_set = {norm(x) for x in ti_aux_var.split(\",\")}\n",
+    "    result = {}\n",
+    "\n",
+    "    seed_filter = set(map(int, seed)) if seed is not None else (\n",
+    "        set(int(v[\"seed\"]) for v in combinations_dict.values()) if combinations_dict else None\n",
+    "    )\n",
+    "\n",
+    "    def parse_folder(folder):\n",
+    "        # output_mps_clf_{ti_pct}_{aux_vars}_{seed}/\n",
+    "        name = folder.strip('/').split('/')[-1]\n",
+    "        if not name.startswith('output_mps_clf_'):\n",
+    "            return None\n",
+    "        parts = name[len('output_mps_clf_'):].rsplit('_', 1)\n",
+    "        if len(parts) != 2:\n",
+    "            return None\n",
+    "        left, seed_str = parts\n",
+    "        try:\n",
+    "            seed_val = int(seed_str)\n",
+    "        except ValueError:\n",
+    "            return None\n",
+    "        sub = left.split('_', 1)\n",
+    "        if len(sub) != 2:\n",
+    "            return None\n",
+    "        ti_pct_str, aux_vars_str = sub\n",
+    "        try:\n",
+    "            ti_pct_val = int(ti_pct_str)\n",
+    "        except ValueError:\n",
+    "            return None\n",
+    "        return ti_pct_val, aux_vars_str, seed_val\n",
+    "\n",
+    "    import zipfile, csv, ast\n",
+    "    with zipfile.ZipFile(zip_path, 'r') as zipf:\n",
+    "        folders = [f for f in zipf.namelist() if f.startswith('output_mps_clf_') and f.endswith('/')]\n",
+    "        for folder in folders:\n",
+    "            parsed = parse_folder(folder)\n",
+    "            if not parsed:\n",
+    "                continue\n",
+    "            ti_pct_in_folder, aux_vars_in_folder, seed_in_folder = parsed\n",
+    "\n",
+    "            if seed_filter is not None and seed_in_folder not in seed_filter:\n",
+    "                continue\n",
+    "\n",
+    "            log_path = folder + 'deesse_output/simulation_log.csv'\n",
+    "            try:\n",
+    "                with zipf.open(log_path) as log_file:\n",
+    "                    reader = csv.DictReader((line.decode('utf-8') for line in log_file))\n",
+    "                    for row in reader:\n",
+    "                        try:\n",
+    "                            row_aux_set = {norm(x) for x in ast.literal_eval(row['auxTI_var'])}\n",
+    "                        except Exception:\n",
+    "                            continue\n",
     "\n",
+    "                        if (int(row['ti_pct_area']) == int(ti_pct)\n",
+    "                            and int(row['ti_nshapes']) == int(num_shape)\n",
+    "                            and row_aux_set == aux_set\n",
+    "                            and {norm(x) for x in aux_vars_in_folder.split(',')} == aux_set):\n",
+    "                            if combinations_dict:\n",
+    "                                for idx, combo in combinations_dict.items():\n",
+    "                                    if (int(combo['seed']) == seed_in_folder\n",
+    "                                        and int(combo['ti_pct_area']) == int(ti_pct)\n",
+    "                                        and int(combo['num_shape']) == int(num_shape)\n",
+    "                                        and {norm(x) for x in str(combo['aux_vars']).split(',')} == aux_set):\n",
+    "                                        result[idx] = folder\n",
+    "                                        break\n",
+    "                            else:\n",
+    "                                result[seed_in_folder] = folder\n",
+    "                            break\n",
+    "            except KeyError:\n",
+    "                continue\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8ee2b1bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combinations_dict = {\n",
+    "    i: {\"seed\": s, \"ti_pct_area\": t, \"num_shape\": n, \"aux_vars\": a}\n",
+    "    for i, (s, t, n, a) in enumerate(\n",
+    "        product(SEED_LIST, TI_PCT_AREA_LIST+[75], NUM_SHAPE_LIST+[1], AUX_VARS_LIST+[\"grid_grv,grid_lmp,grid_mag\"]), \n",
+    "        1\n",
+    "    )\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ed81aaf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_folders= {}\n",
+    "for ti_pct_i in TI_PCT_AREA_LIST+[75]:\n",
+    "    for num_shape_i in NUM_SHAPE_LIST+[1]:\n",
+    "        for ti_aux_var_i in AUX_VARS_LIST+[\"grid_grv,grid_lmp,grid_mag\"]:\n",
+    "            folders = find_all_simulation_folders(\n",
+    "                                ti_pct=ti_pct_i,\n",
+    "                                num_shape=num_shape_i,\n",
+    "                                ti_aux_var=ti_aux_var_i,\n",
+    "                                seed=[852,123,456,789,254],\n",
+    "                                combinations_dict = combinations_dict\n",
+    "                            )\n",
+    "            all_folders |= folders\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd9d40cb",
+   "metadata": {},
+   "source": [
+    "### The cell below is for displaying the TIs wih their mask."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "00907c17",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "123 25 5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image = np.load(r\"..\\data\\grid_geo.npy\")\n",
+    "for (key, item) in all_folders.items():\n",
+    "    ti_frame, need_to_cut = gen_ti_frame_squares(529,337, \n",
+    "                                                 ti_pct_area = combinations_dict[key]['ti_pct_area'], \n",
+    "                                                 ti_nsquares = combinations_dict[key]['num_shape'], \n",
+    "                                                 seed = combinations_dict[key]['seed'])\n",
+    "    masked_ti = np.ma.masked_array(image, mask = ~(ti_frame[0].astype(bool)))\n",
+    "    plt.imshow(masked_ti)\n",
+    "    print(combinations_dict[key]['seed'],combinations_dict[key]['ti_pct_area'],combinations_dict[key]['num_shape'])\n",
+    "    break\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "552a7428",
+   "metadata": {},
+   "source": [
+    "### The cell below is for displaying the Simulated Images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d363f3dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QZ3a8cNa4GHDcm/HuyMwqn6S9evoey934vWmDx5Z95t2RmRWWSH7itY0s4QCrUTZwWtoRgHZaZ9icaOxZFu/tlM6vIGvf0T2uOvfIeK5xxU3pXtvjN/HeAQ2r3K/uyQt3WqHUBVbP/A1dw9mzt4kT3hoZl0zfu8PPVVGfj6vOPTJOuP60uKe297LXKzPlcd/Yn8Xc4SsvaJtqMpGtycePZ32xwzNCZ2AOB5JSvSAXZ1x1aty0aEDaUaDkmb9LQyafz7foEZbc7GEdnaXgdj3z1ChrWv6vN3O/bPQasCRe2PF3BTnHiMmHRcUN/SOzkpWZ3j9yabz6mZtW+rmNHzs21r6j+7I/vzsyE78/9HJLxsAnMLFA6xXj/L3nywfG7IW9ot8tPVPL8M4hjfHK53794dO8/2On80ZFt8UrX3Jx0dcWxfMF+h0CugrzN7RNMc7hn+STro070pyj6uKVkTcv99pj9REn3jEq1p60fJiFQ8rjhbOsggEtZQ6H1ivG+XvPlw+MpdevG+WN6a1k1VIzv5iLGftdn3YMKGnm79LQ6Z6cPXjaPrHRHafGRnecGpnsihPO4AfLo89NvZcds8+U/Vc4ZlJDU2x0x6lxzBu7R0TEsEePi7nZJSs938St7458+cqfnl3rju4x5E8nrvS9Fz9zQww5Z8qyn9sOuUoxC0CX8Mjm90eP+3uv/sAOtO7vu8Um97V+/9jud/XpgDQAUPo+6do4abtVR1x7xK9iyDlTYuGQVS9zDABdwSOb3x/N1cUxR6/OGpMr4+zZ26QdA6DDdZpy9qZFA2KrS0fHO78ZGus+lo91H8uv+hu7+Vh2zILfrhdHzvjccm9vXpmNNTeZG69ev1mcPXubqJ5cE4tzbfhmUT5i0F/KY8j9J6/wVlWmMm7a4LFlPztWVbZ+fAAoUWPPvz3tCDHokYhP3XZqqz5TuTQfn/pd6z4DAF3FV37wYOLn7PXnnnHue1uv8Pqe3bNx0waPxZXfuC6W9u80tz4AoE2K4Rq8JXq+k42n5myYdgyADtcprlBealwa1154WPR5PRtVC1u3hlK3xbmYuXiN5V7rWVYd//z076L2i7Xxbn2feHDMz2L9ipqIiGjINy137FaXjl5h6eSPy2Qjqt9dxeZ1ANBFHdpz7gr7rycuH9FjZlk05bPLvXzbjy5Z5b6zkY8Y+GQ+Rpxzanx60lEFjdOQb4psPuG1IAGggL6xxtvxuysujXW+9Vpi56xekIt//XREjF+01kr3ht+9ey7++P1L4pJLx0XeQ7QAdFH/d3Vhr187UvONa8cFc7ZIOwZAhyr5cvaZhsaYuHRIVCxt+5r585d0j9pc/XKvVWUqY8quN8ctGz4ae916drybrYvJDQ2x7dVnxIym2mXHVSxZ/XnLGiPeaq5d7XEA0JUUw1Msvd/Mxha/HRMzPzZPb1TZM6782VWr/EwmF1G5JB+LF3aP+dm6gmXZ4rbT45vv7lSw8QAgDYMresaQHvOiqUdy38Iqb8jHDd/+Unz+m6et9P11KnrGiOryePGb9psFoGt66rwrom6tsqhfI/3r8NUpb8zH338wMq6av0HaUQA6TPH/a7wao35wRow//6B2jdHvlp7xf3N3WOX7U792bfxu0afjivf2in6vZGPv350dzzR8+I3cBZvlV/vkT79XsrHnxz4DAF1dZaY8fn3+5WnHiIiItZ/OxW73fyumNLauaB10X2WcMXPfguV47ehr48p1ny7YeACQlp+uPTnqDluY+HkzuYjLPhia+HkBoNhVZSrjmQuujaO+9VDaUVrszu/uG+e/t1Vct2BQ2lEACq7ky9lCuW3yjrHHSwfFvq/st+y1GxYOjD1eOij2eOmguPeiz8drP908IiJq3svEQ4uHR0TE64dfFzO/mF3pmB83cGIujrjv9GXjWbYQgK5uQHlTzNw/G3M/nf4agzVvl8eTS/97M3ft8sZ4d5fV/5r0r+c3jelNVscAgP/1hQ2mxOLByc7xmVzEnf+3d+zx0kFx8dxNEj03AFB4/7pw5/j1ZQfGHi8dpKQFOhXl7P836A8V0TxuYCy+dnAMf+ory16f88igaB43MMobPrZ8cT4i97HHZZ/c+/IWnWPdx/LRPG5gNI8bGMP+dmKhogNASRpc0TNmfOHX8dOjx8ecrdMvaD8uFxGZtu+YAABd2q2L+8edz2yfylxaueTD6+7rn9ot+ZMDQBGb2Vwb4/68T9oxWq37vFw0jxsYV//2oLhh4cC04wAUhHL2f5Q1R/S8s3ds+vhX44Q+s6NunRWfcO39Zjbu+dXnlk0GA8q7xz4//GcsOHZxi8+z7v2VMeT+kyMi4pDXPh+b/Wp0PLK0uG5MA0ASDuxRF40b1q/+wA7Ud2o2rvzlIfFwXWVERPQrq4iv7fuPeOeQlW9JMHd4eezzw3/Gn/a9PDaq7JlkVAAoejtUvxXRXBY9Z61+lamO0v+pijjp7V1TOz8AFJu52cpY58nSXc2x79Rs/HPBxmnHACiITD6fb9F3WXOzh3V0ljbZ8dujompR4SeVbLdMNPTJRNWi/PJPzX5MQ5+yuPqCK2PHqg9v5C7MLY3t/jk61rm7W4vOkS+PWNqvLCrr8lG5JB/1fcvi3h9cEoMr3OSFlSkbOC3tCFByCjl/v9tcGyd8/rjlXvvs3c/Huf3b/3+bQ//69Rh0X2W7x2mv+r5lcc8PLon1//9cPDe7JHZ44Jux81bT4tXfbho1cz78nSNblYnmr86LSdvclWZcKAnmb2ibYr0Gb4lz39s6/nnpiKhcku4yFI09y+LL5z4UY/u9nmoOKFXmcGi9Yp6/Jzc0xGljT087Rrs09C6LbFXEH77rHjqsivm7NJT8k7N//fFl8bEVhgumvDEfNXNyqyxmIyKqFuZi7NljYsdvj4qIiD5l3ePV3W+Iv119dSzYaPVPwWayETVzcssuWKvn5+KIc86Kmc32rmurE94aGcNuHpV2DIBO6cXGvpF99bXlfv6xbd+4fXHfdo/96l7Xx/vbpv9rSfX8XBx59n/n4gHlPWLqgdfGzRs+Ek9ecGX87eqr429XXx3Dxz4fDU0VKacFgOLUkKuIsqa0U0RUHP1enNH3tbRjAAAFUrUoFzVzcpGLcA8dKGnp3wVtp5pMt1i4YXrLAWeyHxa5D9ZVR0REeaYsqjKVUTc416KC9n+VN+TjSz84u2BLHDfls3Hr4v7L/SzMLS3I2MXohvUfj2lfvTbtGACdzjUL1otfbLndCq/nmxrjN5sNjYvmbho3LRrQ5vErM+Ud8mWrtihvzMfBPzp72Z8rM+XL5vePfn45eEI8v+PvUkwJAMXr8nX+Hb1PeTsae6Z7y6E8k4/yTMnf9gCAgqgpa47aQZ1jW72bFmxf0HvoAEkr+auUykx53HTaL1LNUFGfj+//7Pi47IOhy16bfsR18ew3r4rZI1r/P3H1/FyMufmUNucZv2itOPbN3WJ+ti7eyy6N6847bLmfzz33tajLrXwPPQBYmQeO3DVy9avYFzaXjX9tVR03jv1Su85RteWCaK4ujoa2vCHiormbph0DAErWXzZ9MCqPfi/Vgvbt19eMlxo775eTAaA1Nq7sERedeWO8s1sm3tktE/V9S7caeOiCz0b1/FyMveqUuGHhwLTjALRa6f4L/P9l87k49PFT044RNXNy8dvr940L5myx7LXKTHncfsQVbRqv++x8bPbEV+OaBeut9timfDY2e+Kry35+Pv6wmPGzzWK7v4+J3R8fs2LWG/vG1v86qU25AGBVal77IPZ46aA2f/6FHX9XNBeH3Rbn4tZ7Ppd2DAAoacPWmBPZbumdf/DDmTjggTPjLcseAkBEROxXUx/Tv3xdTP/ydbFo6OqPL3a938zGL8Yf0qJ76ADFpDjugLZDLvKx7r0pXu19TO+3snHHq9su99qnKrOx4NjFrR6rx3u5GHBbTdzx9varPbYpn40Bt9Us++n7ajYiIgbdVxnr3FW10s+sfVd1DHlAQQtA4WSnTo+5Dw9KO0bB9HozH5+fckDaMQCgZP1z0ubR/YNcqhkGPRLxdnNNqhkAoBidcvBDRfMF6fboOzXbonvoAMWk5P/1rcyUxz4/+GfaMZbp9eeecfbsbZb9uU9Z9/jH9tfH3K/UtWm8ursGxlY/Hx1b/Xx0NOSbChUzIh8x6OGyGHL/yYUbE4BO67A7Ho2y6uoOP89FY2+MXEWHn6ZFqhbm4rXXLY8EAG11+wFXx6L1098L7huXjyns9TQAdAJj+71eNFsLtVfdXQM9PQuUlJIvZ5vy2Xjgp7unHWOZ6gW5eOKnO8UNCwdGba4+anP10ausWzyz8w0x84ut/8Zw93m56DMjG31mZGPXH5y+0r1id/3JmW3KmslGVL9b4SIVgNU6oc/s+PbLE1d73KArn4mNHjn+E4+pzdXHu821se8BR8fC3NLlfnap+iD6/H1aZHL5QkVvl3UfLovPTznAXAkALbDvgccs9+cdqyrjj9/6War7zkZE9Ho7GwtzjSu9ngaAruyhb18SucrSL2i7z8vFrRfuF5MaXLsDpaHky9mIiMZexTWBlDfmY/z5B8UXTjsjvnDaGbH1r86ImrJuERXtW86p+9xc7PKzM2NK4/JP4T7x7cvbPOaAF7Kx6V9GtSsXAF3D0IrV79eWb2iIXOMnPyFz8FdHx3Hrj4z8My/FEYN3Xu7nyPV2iey8D6LH36dERV02yhvTXQqxrDninQW9Y4vbT4tnGgp3Q3dyQ0Nk8+n+3QCg0P5y/y0rvLbf1edEt9r057xDTx+70uvpiA+/9P1C44dfHnusPmJhbmkKCQEgeWuV94glA8tiycCyqF+jtKuC8oZ8PL5kk7RjALRIaf+LGx8ua3zjuZenHSMxvd7Oxv6/H7vca+WZTMwdXh6NPcvi/W3Lom7N1v1/a8XcSt8qAqBgal7rFke8vme8n13S5jFyixdHt4f+HT3+MzvK69O9odvvlp5x9F7/iu2qCrfH/Zf+Nibmu/ELAIn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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "output_mps_clf_145_grid_grv_123/\n"
+     ]
+    }
+   ],
+   "source": [
+    "with zipfile.ZipFile(r\"K:\\CET\\Resultats\\results.zip\") as zip:\n",
+    "    for (key, item) in all_folders.items():\n",
+    "        deesse_output = pickle.load(zip.open(item + 'deesse_output/deesse_output.pkl', mode='r'))\n",
+    "        plot_realizations(deesse_output, n_real=10, show=True)\n",
+    "        print(item)\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "848ddedd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "deesse_input = gn.deesseinterface.DeesseInput(\n",
+    "    nx=337, ny=529, nz=1,\n",
+    "    sx=1, sy=1, sz=1,\n",
+    "    ox=0, oy=0, oz=0,\n",
+    "    nv=2, varname=[['grid_geo', 'grid_grv'],['grid_geo', 'grid_grv']],\n",
+    "    TI=ti_list,\n",
+    "    #pdfTI = pdf_ti,\n",
+    "    mask=simgrid_mask,\n",
+    "    dataImage=cd_list,\n",
+    "    distanceType=distance_types,\n",
+    "    nneighboringNode=nvar*[nn],\n",
+    "    distanceThreshold=nvar*[dt],\n",
+    "    maxScanFraction=nTI*[ms],\n",
+    "    outputVarFlag=outputFlag,\n",
+    "    expMax= expMax,\n",
+    "    npostProcessingPathMax=1,\n",
+    "    seed=seed,\n",
+    "    nrealization=numberofmpsrealizations\n",
+    ") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "764f81c2",
+   "metadata": {},
+   "source": [
+    "# THE CELLS BELOW ARE FOR DISPLAYING THE ANALYSIS OF THE RESULTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a860e8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "def find_all_simulation_folders(ti_pct, num_shape, ti_aux_var, seed=[852,123,456,789,254,648], zip_path=r'K:\\CET\\Resultats\\results.zip', output_dir=None):\n",
     "    aux_var_str = \",\".join(sorted(ti_aux_var.split(\",\")))  # Normalise les variables\n",
     "    matched_folders = []\n",
@@ -58,44 +344,13 @@
     "                            break\n",
     "            except KeyError:\n",
     "                continue\n",
-    "    return matched_folders\n",
-    "    # if matched_folders:\n",
-    "    #     # Choix du dossier de sortie\n",
-    "    #     if output_dir is None:\n",
-    "    #         # Dossier par défaut = un dossier temporaire\n",
-    "    #         extract_dir = os.path.join(os.getcwd(), f\"mps_sim_{next(tempfile._get_candidate_names())}\")\n",
-    "    #     else:\n",
-    "    #         # On utilise directement le dossier fourni\n",
-    "    #         extract_dir = os.path.abspath(output_dir)\n",
-    "\n",
-    "    #     os.makedirs(extract_dir, exist_ok=True)\n",
-    "\n",
-    "    #     with zipfile.ZipFile(zip_path, 'r') as zipf:\n",
-    "    #         for folder in matched_folders:\n",
-    "    #             for file in zipf.namelist():\n",
-    "    #                 if file.startswith(folder):\n",
-    "    #                     zipf.extract(file, extract_dir)\n",
-    "\n",
-    "    #     # Construire la liste des chemins extraits\n",
-    "    #     extracted_paths = [os.path.join(extract_dir, f) for f in matched_folders]\n",
-    "\n",
-    "    #     print(\"✅ Dossiers extraits :\")\n",
-    "    #     for p in extracted_paths:\n",
-    "    #         print(\"   \", p)\n",
-    "\n",
-    "    #     # Ouvre le dossier global qui contient tout\n",
-    "    #     subprocess.run(['explorer', os.path.realpath(extract_dir)], check=False)\n",
-    "\n",
-    "    #     return extracted_paths\n",
-    "    # else:\n",
-    "    #     print(\"❌ Aucun dossier correspondant trouvé.\")\n",
-    "    #     return []"
+    "    return matched_folders\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "43cc3a7a",
+   "id": "27efa3ec",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,34 +376,38 @@
     "    # \"grid_grv,grid_lmp,grid_mag\",\n",
     "]\n",
     "\n",
-    "reference_folder = find_all_simulation_folders(\n",
-    "                    ti_pct=75,\n",
-    "                    num_shape=1,\n",
-    "                    ti_aux_var=\"grid_grv,grid_lmp,grid_mag\",\n",
-    "                    seed=[852,123,456,789,254]\n",
-    "                )\n",
     "\n",
-    "compared_folders = []\n",
     "\n",
-    "for aux_var in AUX_VARS_LIST:\n",
-    "    matched_folders = find_all_simulation_folders(\n",
-    "                        ti_pct=75,\n",
-    "                        num_shape=1,\n",
-    "                        ti_aux_var=aux_var,\n",
-    "                        output_dir = r\"K:\\CET\\Resultats\\TEST\",\n",
-    "                        seed=[852,123,456,789,254]\n",
-    "                    )\n",
     "\n",
-    "    compared_folders.extend(matched_folders)\n",
+    "# reference_folders_dict = find_all_simulation_folders(\n",
+    "#                     ti_pct=75,\n",
+    "#                     num_shape=1,\n",
+    "#                     ti_aux_var=\"grid_grv,grid_lmp,grid_mag\",\n",
+    "#                     output_dir = r\"K:\\CET\\Resultats\\TEMP2\",\n",
+    "#                     seed=[852,123,456,789,254],\n",
+    "#                 )\n",
     "\n",
-    "deesse_outputs = []\n",
-    "with zipfile.ZipFile(r\"K:\\CET\\Resultats\\results.zip\") as zip:\n",
-    "    for matched_folder in compared_folders:\n",
-    "        log_path = matched_folder + 'deesse_output/deesse_output.pkl'\n",
-    "        deesse_output = pickle.load(zip.open(log_path, mode='r'))\n",
-    "        deesse_outputs.append(deesse_output)\n",
-    "    for matched_folder in reference_folder:\n",
-    "        deesse_outputs.append(pickle.load(zip.open(matched_folder + 'deesse_output/deesse_output.pkl', mode='r')))\n",
+    "# compared_folders = []\n",
+    "\n",
+    "# for aux_var in AUX_VARS_LIST:\n",
+    "#     matched_folders_dict = find_all_simulation_folders(\n",
+    "#                             ti_pct=75,\n",
+    "#                             num_shape=1,\n",
+    "#                             ti_aux_var=aux_var,\n",
+    "#                             output_dir = r\"K:\\CET\\Resultats\\TEMP2\",\n",
+    "#                             seed=[852,123,456,789,254]\n",
+    "#                         )\n",
+    "\n",
+    "#     compared_folders.extend(matched_folders)\n",
+    "\n",
+    "# deesse_outputs = []\n",
+    "# with zipfile.ZipFile(r\"K:\\CET\\Resultats\\results.zip\") as zip:\n",
+    "#     for matched_folder in compared_folders:\n",
+    "#         log_path = matched_folder + 'deesse_output/deesse_output.pkl'\n",
+    "#         deesse_output = pickle.load(zip.open(log_path, mode='r'))\n",
+    "#         deesse_outputs.append(deesse_output)\n",
+    "#     for matched_folder in reference_folder:\n",
+    "#         deesse_outputs.append(pickle.load(zip.open(matched_folder + 'deesse_output/deesse_output.pkl', mode='r')))\n",
     "\n",
     "\n",
     "\n"
@@ -156,60 +415,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "554b1e93",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reference_folder = find_all_simulation_folders(\n",
-    "                    ti_pct=90,\n",
-    "                    num_shape=1,\n",
-    "                    ti_aux_var=\"grid_grv,grid_lmp,grid_mag\",\n",
-    "                    seed=[852,123,456,789,254]\n",
-    "                )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "cfcf24b1",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*** Img object ***\n",
-      "name = ''\n",
-      "(nx, ny, nz) = (337, 529, 1) # number of cells along each axis\n",
-      "(sx, sy, sz) = (1.0, 1.0, 1.0) # cell size (spacing) along each axis\n",
-      "(ox, oy, oz) = (0.0, 0.0, 0.0) # origin (coordinates of bottom-lower-left corner)\n",
-      "nv = 1  # number of variable(s)\n",
-      "varname = ['grid_geo_real00000']\n",
-      "val: (1, 1, 529, 337)-array\n",
-      "*****\n"
+     "ename": "KeyboardInterrupt",
+     "evalue": "decoding with 'cp437' codec failed (KeyboardInterrupt: )",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\Axel (Travail)\\.conda\\envs\\PythonEnvGeoclassifMPS\\lib\\encodings\\cp437.py:14\u001b[0m, in \u001b[0;36mCodec.decode\u001b[1;34m(self, input, errors)\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m codecs\u001b[38;5;241m.\u001b[39mcharmap_encode(\u001b[38;5;28minput\u001b[39m,errors,encoding_map)\n\u001b[1;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m,\u001b[38;5;28minput\u001b[39m,errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m codecs\u001b[38;5;241m.\u001b[39mcharmap_decode(\u001b[38;5;28minput\u001b[39m,errors,decoding_table)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: ",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[42], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdisplay_functions\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m plot_realizations\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mzipfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mZipFile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mK:\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mCET\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mResultats\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mresults.zip\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m z:\n\u001b[0;32m      3\u001b[0m     deesse_output \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(z\u001b[38;5;241m.\u001b[39mopen(reference_folder[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeesse_output/deesse_output.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(deesse_output[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msim\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n",
+      "File \u001b[1;32mc:\\Users\\Axel (Travail)\\.conda\\envs\\PythonEnvGeoclassifMPS\\lib\\zipfile.py:1268\u001b[0m, in \u001b[0;36mZipFile.__init__\u001b[1;34m(self, file, mode, compression, allowZip64, compresslevel, strict_timestamps)\u001b[0m\n\u001b[0;32m   1266\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1267\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m-> 1268\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_RealGetContents\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1269\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m   1270\u001b[0m         \u001b[38;5;66;03m# set the modified flag so central directory gets written\u001b[39;00m\n\u001b[0;32m   1271\u001b[0m         \u001b[38;5;66;03m# even if no files are added to the archive\u001b[39;00m\n\u001b[0;32m   1272\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_didModify \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Axel (Travail)\\.conda\\envs\\PythonEnvGeoclassifMPS\\lib\\zipfile.py:1373\u001b[0m, in \u001b[0;36mZipFile._RealGetContents\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1370\u001b[0m     filename \u001b[38;5;241m=\u001b[39m filename\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m   1371\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1372\u001b[0m     \u001b[38;5;66;03m# Historical ZIP filename encoding\u001b[39;00m\n\u001b[1;32m-> 1373\u001b[0m     filename \u001b[38;5;241m=\u001b[39m \u001b[43mfilename\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcp437\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1374\u001b[0m \u001b[38;5;66;03m# Create ZipInfo instance to store file information\u001b[39;00m\n\u001b[0;32m   1375\u001b[0m x \u001b[38;5;241m=\u001b[39m ZipInfo(filename)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: decoding with 'cp437' codec failed (KeyboardInterrupt: )"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -224,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "id": "323d5598",
    "metadata": {},
    "outputs": [],
@@ -234,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "id": "48c8c44d",
    "metadata": {},
    "outputs": [],
@@ -244,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "id": "09d4f5e0",
    "metadata": {},
    "outputs": [],
@@ -378,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "id": "74d3ff7b",
    "metadata": {},
    "outputs": [