diff --git a/examples/trajs-files.ipynb b/examples/trajs-files.ipynb
index 98b0b600472525c22595f1d7d0a28369052ea8b7..f156f84cbbb9b0ef9350dbaf6119085ee084cc68 100644
--- a/examples/trajs-files.ipynb
+++ b/examples/trajs-files.ipynb
@@ -104,6 +104,8 @@
        "      <th></th>\n",
        "      <th>file</th>\n",
        "      <th>arbitrary_condition</th>\n",
+       "      <th>arbitrary_condition_copy</th>\n",
+       "      <th>other</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -111,32 +113,46 @@
        "      <th>0</th>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                file arbitrary_condition\n",
-       "0  /Users/hverdier/palm_tools_data/export_folder/...                   A\n",
-       "1  /Users/hverdier/palm_tools_data/export_folder/...                   B\n",
-       "2  /Users/hverdier/palm_tools_data/export_folder/...                   A\n",
-       "3  /Users/hverdier/palm_tools_data/export_folder/...                   B"
+       "                                                file arbitrary_condition  \\\n",
+       "0  /Users/hverdier/palm_tools_data/export_folder/...                   A   \n",
+       "1  /Users/hverdier/palm_tools_data/export_folder/...                   B   \n",
+       "2  /Users/hverdier/palm_tools_data/export_folder/...                   A   \n",
+       "3  /Users/hverdier/palm_tools_data/export_folder/...                   B   \n",
+       "\n",
+       "  arbitrary_condition_copy other  \n",
+       "0                        C     E  \n",
+       "1                        D     E  \n",
+       "2                        C     E  \n",
+       "3                        D     F  "
       ]
      },
      "execution_count": 4,
@@ -149,6 +165,12 @@
     "    \"file\":TRACKS_FILES,\n",
     "    \"arbitrary_condition\":[\n",
     "        \"A\" if i%2 == 0 else \"B\" for i, f in enumerate(TRACKS_FILES)\n",
+    "    ],\n",
+    "    \"arbitrary_condition_copy\":[\n",
+    "        \"C\" if i%2 == 0 else \"D\" for i, f in enumerate(TRACKS_FILES)\n",
+    "    ],\n",
+    "    \"other\":[\n",
+    "        \"E\" if i <= 2 else \"F\" for i, f in enumerate(TRACKS_FILES)\n",
     "    ]})\n",
     "index_df.head()"
    ]
@@ -166,7 +188,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tss = TrackSets.from_files(TRACKS_FILES,root_folder=EXPORT_FOLDER,index_df=index_df)"
+    "for f in glob(os.path.join(EXPORT_FOLDER,\"*.trajs\"),recursive=True):\n",
+    "    os.remove(f)"
    ]
   },
   {
@@ -174,6 +197,22 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "tss = TrackSets.from_files(TRACKS_FILES,root_folder=EXPORT_FOLDER,index_df=index_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ts = tss[0]"
    ]
@@ -201,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,26 +270,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-11-03 13:20:33.303605: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "2022-11-04 19:02:46.873394: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
       "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dropping h cols\n",
-      "Dropping h cols\n",
-      "Dropping h cols\n",
-      "Dropping h cols\n"
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
+      "WARNING:root:Set of 2529 trajs coming from file /Users/hverdier/palm_tools_data/export_folder/66e67e9052c35e8d.trxyt\n",
+      "WARNING:root:There were null rows (9345 / 14801) in added dataframe\n",
+      "WARNING:root:Set of 1468 trajs coming from file /Users/hverdier/palm_tools_data/export_folder/1d372d16b9d77091.trxyt\n",
+      "WARNING:root:There were null rows (4738 / 7708) in added dataframe\n",
+      "WARNING:root:Set of 693 trajs coming from file /Users/hverdier/palm_tools_data/export_folder/02e0de7a4caebc82.trxyt\n",
+      "WARNING:root:There were null rows (2903 / 4475) in added dataframe\n",
+      "WARNING:root:Set of 595 trajs coming from file /Users/hverdier/palm_tools_data/export_folder/10fd51e9edc4d570.trxyt\n",
+      "WARNING:root:There were null rows (2711 / 4142) in added dataframe\n"
      ]
     }
    ],
@@ -260,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,12 +307,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 3 Axes>"
       ]
@@ -287,7 +324,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 3 Axes>"
       ]
@@ -299,7 +336,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 3 Axes>"
       ]
@@ -311,7 +348,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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uoye5rItW95EX+egvLxoErK/+8yYHi9gnOKc3zMKOZfsb//7wB7usb9UyFOoGVtNulVyeClTkul2HhTxPkirXz252QR7quvKmz/m0DHSXSj92iPsQqnpjl0o56zAH0ri/IAaLz3WnPrF1T3y6hOGgG6Rud0iT3xmWUogzFNvLx7btWpeY1bJmV5EDblV53aFum1bmw1I/QemTj+rNlybh1OarVcr42rZd6G6xPGCxXKogpQq3TKptnd4wK/nqPHT7cuwfH8XnemtrdW69XGnvWRO4PEGoybXl0w9osiEJpN++gVTDvGm7ulyNF/k8ULepu8XXsqS5V1UUOsyBNCveFNs0SWphnlp7cua7Os850H22vdOfYJcwTyU8m7YjxgM4ilIJT5d2sBofzfe2zTHQfbc3jb3JgetDml0CLXaYx56/q9j95jHn3Zb+8XFCrNtBoKcc6qHal32QW3AN89CB6jrP2NX4sBiB6jpPVuPlQm7X1AI9dHvM1rSIzBaRn4rIt6ymWZVrVQ64h1uIQLeYR2ohPhCyOk+lW6cLQq/r2FV6rHlbXkd+MYB1ABYZTrMyizs+615jPkoxaC2uPx+epotUQ7xosOP7uDbZKlRcq/G2d6sM87lNJykGqu/r0WN/GzAJchFZCuD3AXwcwPstphmLRZgPuIR6jv3fs39j12bLnZ8VeBpi3hhWd9yeJtOMyaoi/xSADwKYHvcGEbkIwEUAMH/WQqPZPpXVOCyWYT4QO5hDVeNL1s9g63K7u1iLIVw3BHwEOPvG3aRyl++4EB4X8CmF9ijOQS4iZwN4RFVvFZHTxr2v+DDXxXMP9ZZqKYd5LKG7VKzDfICVdTukEuajpB7Y41jsGS8HcI6IbATwFQCvFpEvGUy3MYuTn0AefcplYi1DGytXi2XqWv/4ODwo23Jem6r6IVVdqqrHADgfwPdU9c3OLUtEzmEeu+1tCvM2LUsqYt9H0CatXYtWVTnQC8TYoVhXbu2ldFmexB6FYe7OdA2q6g9U9WzLabqwDHMgn3BMqZ1tqGStloHdKuMxzN20fu11LcxTbF/OYZ5z2y2FWA8M8+a45hpIsaslxTYVMRDzxzBPVyfWmnVVPpBKeKbQhipyC3PL9ralW4VhnqbOrDFfYQ7EC/QQ87UeQ2fJ+pksAj2HNsbCME9Pp9aWzzAHwgZ6wAPHYAwdUykHZcptSwXDPC2dW1O+wxzwF+iD6YYK8cIYOp/3Mf0UA9NHm9rSrRIDw7yaTq6lEGEOPDV4m4Zv6PAe8in0xtAZez+1iFwkIqtFZPXemV21Z5BSmKfUlhyEWl8M83KWw9hSiVxOSgLNxtCZXry00Z0jvsZmqdsGqi/Utkt5fJZQJh3QOnuoC1WVZyzoGDoxT4L6nG8XulVYmcfX6TXDMB8v1hg6ocO8q5V4zCd6uWCYj9b5tcIwT0+o6tz3PBKvxk2vRgp5QOximJctc/fWyAgM88lijaHjKxxyuZbdF19XIzHM4+Ha6GOYp8k6dLsc4AWfguerkUJgmD+Ja6KAYZ6uQaA3CWKX320q1W6V4tVIk96nqqtUdaWqrpw7dWDl6Yc+UDLMe3j54RCrR8WRP6yqnQyuRjoLwHwAi0TkSzk/DIaXJrIiH4mVObVViKuReKANj0E+RtvDvO3LF1Oq3Spt1uYulirL1t6lN9DWsGvrclE9Pq9GilGVtznMy3R3yStqW+i1bXlSw2qcYmCQV8DwI6qPVXk43VzqBtoQ5m1YBqIyXQzz7i2xg5yDMOe254LdKk/HK1jcVbm0kkFeU46BGKrNs3bvDTIfojJdq8q7tbRGcgrznNpK7cSq3D8GeUM5BGSMNna1e6Gry52yLlXl3VlSD3IIcyJqP+cgF5EjReT7IvIzEVkrIhdbNCwXqYZ5zHZ1rTrt2vI2we4VN2UnPC0q8n0APqCqJwA4GcCfisgJBtPNRmphnlp7iGLpSveK81Kq6kOqelv/7zvQe+rIEa7TzU0q4ZlKO1ilEoVjergSkWMAvATAzSNee2Kg+pn9uy1nm4zYIRp7/l3EA1Z17F7xxyzIRWQhgK8DeK+qbh9+vThQ/dSsBVazTU6MMJ26e3OSIc6QoxS0pXtlUj+5yRKKyFz0QvwqVf2GxTRzFjJUUwxwIgrL4qoVAXA5gHWq+kn3JrVDiCqZIR4Xv3FQKiwq8pcDuADAq0Xk9v7PWQbTbQVfYZtLiDPsqIj95H44P7NTVX8EQAza0lqD0LV4FmguAU6UkrY/17MdZwEy4RLCqZ7QJKL4nCtyqqcYxlUq9DaE94I1W7B7RbtuLWCXEaWEQR5RG0KaiOJj1woRBcUTnvYY5BREm7oi2rQs1A4MciKizDHIaaSuD09MlBMGOY1jPjwxuyRoIEY/eVvGXBmlvUtGTjg8MVE+GORUynJ44tyr8tzbT+3EIKeJODwxUfoY5DQWhycmygODnEbyOTwxuycI4I1BlhjkNA6HJybKBMdaoZE4PHF7iciRAK4AcBgABbBKVT8dt1VhtHU4W1bkRN1jfo9AU+xescEgJ+oY3iPQPgxyog4bd49A8f6AvTO7orSNqmOQUxS8ciW+SfcIFO8PmDt1YJwGUmUMcqIO4j0C7cIgJ+oYn/cIUBwMcqLu4T0CLcPryIk6hvcItA8rciKizDHIiYgyxyAnoqh4d6c7kyAXkTNFZL2I3Csil1hMk4iIqnEOchGZDeCzAF4L4AQAfxhr3AYi33av4J3slB6LivwkAPeq6n2qOgPgKwDONZguERFVYBHkRwB4sPDvzRgxAE/dZzsSEVE1wU528tmORER+WAT5FgBHFv69tP9/REQUgEWQ3wLgOBE5VkSmAJwP4DqD6RIRUQXOQa6q+wC8B8B30Bug/mpVXes6XaJU8coVe7yW3I3JWCuqej2A6y2mRURE9fDOTiKizDHIiYgyxyAnIsocg5yoAZ7wpJQwyImIMscgJyLKHIOciJLAa8mbY5ATEWWOQU7UEE94UioY5DSW7yc/LVjDsdWILDDIaSQ++YkoHwxyGodPfqLgQpzwnN7Qvthr3xKRFT75iSgTDHJy0vUnP3XhhOes3XtjN4FKMMhpHD75iSgTDHIah09+IsqEyYMlqH1UdZ+IDJ78NBvAF/jkJ6I0MchpLD75iWJYsn4GW5dPeZ3H9IZZ2LFsv9d5hMSuFYqKNwURuWOQEznK8cqVunft8oCbNgY5Ucfwrt32YZATdQ/v2m0ZBjlR95TetRv7jl3eql9Pe5aEiMyMumOX/eTpYpATdQ/v2m0ZBjlFx0ovON612zJOQS4i/yAid4vInSJyrYgcZNQuIvJEVfcBGNy1uw7A1Snetct+8upcl+ImACtU9YUA7gHwIfcmEZFvqnq9qj5PVZep6sdjt4fcOAW5qt7YP7oDwI/R62sjopZiN1iaLL9XXAjghnEvxr6cidLGgKBY2tC9Ujpoloh8F8DhI166VFW/2X/PpQD2Abhq3HRUdRWAVQCweO6h2qi1RNQpIQbQaoPSIFfVMya9LiJvA3A2gNNVlQFNRBSY61UrZwL4IIBzVPXXNk0iopS1sRss9+4V19b/C4BpADeJyO0i8jmDNlFHtTEgiEJwerCEqj7XqiFERKOwn7xc3t8niIiM5Ny9km/LqZXYvZIHbqe0MMiJKHkhbtcH8q3K82w1ERE9gUFOyeHX9jy0dTvlWJXn12Ii6qRQ3StAfmGeV2upM3Kq9nJqK7UTg5yIGmvzQSynqjyflhJR54XsXgHyCfM8Wkmd1OZqj8gSg5zIAQ824bEqf7r0W0idxqBMXxe2UephnnbrKAo+VJtSF7oqB9IO83RbRjEl9VDtVCu+VNtF/qQa5mm2iqJK8aHaDM20xdg+MapyIM0wT69FlBo+VHsEHli6LbUwT6s1FMwtv7wOAF4gImuGfs4dvKfqQ7VVdaWqrpyatcBrmxmeaetSVQ70wjyVQE+jFRTcS59xDgCsVdUVQz/fBJ7yUO03pfRQ7RTCPIU2UDpSCPP4LaDk8KHalJOYVflA7OqcQU6jJP1Q7ZgVMavxyWKtnxTCHIgX6E4PX6Z2yuGh2gvWbMHuFUfEbgbRSMUw37Fsv/f5sSKnbIWu/liNV9P1qnzYoEp3rdQn/T4rcspaqMqcIU4WfHW7sCKn7PkOWYZ4PlKtyn1jkFMrLFizxUvgti3EQ42jE3O9dTHMTYJcRD4gIioiB1tMj6gpqwDxdWBIQFLj6PjStTB3DnIRORLAawBscm8OkTvXEG5pgAMIO45O7PXYpTC3ONn5T+jdPPJNg2kRmSkGSdkJ0dihE8mFAL466gURuQjARQAwf/Z0yDZRA05B3h+XY4uq3iEiRk0isteloL7lsWuB/jg6Qy9dWhiCYeI4Oqq6CsAqAFg8dVjjIRpiX++/ZP0Mti6fijb/UEqDXES+C+DwES9dCuAv0etWKfWUI/yshTWaSER1vPTg1+PbWz6zVlVXjnq9MI7O6SmNo+NLF8K8NMhV9YxR/y8ivwXgWACDanwpgNtE5CRVfXjEdJ48ws89tPUfHqIUFcbReVWocXRiV+VA+8O88clOVb1LVQ9V1WNU9RgAmwGcOCrEiSgZUcbRSaFrq80nP3lnJ1GH5DCOjk9trczNbgjqV+aPWU2PiNolhaoc6IV526pziXGuQ0QeBfCAp8kfDCDmASX2/Ou04WhVPcRqpp62awrrE8ivHWbbtgX7a6htF2I+I7drlCD3SURWjztb34X5p9IGK6ksC9vhR4jlCbXOYm4bjrVCRJQ5BjkRUebaGOSrOj5/II02WEllWdgOP0IsT6h1Fm3btK6PnIioa9pYkRMRdQqDnIgoc9kHedUnnojIRhG5q39b8mqD+Z4pIutF5F4RuWTE6/NE5Kv9128WkWNc5zk0/SNF5Psi8jMRWSsiF494z2kisq2/zLeLyIct22Ap9vrszyOZdVr2eZWef+6vjztF5EQf7fBNRN7YX9f7RcT80r2yz5XRPL4gIo+MGG0yHFXN+ge90Rfn9P/+CQCfGPO+jQAONprnbAAbADwHwBSAOwCcMPSedwP4XP/v5wP4qvFyPwu9sW2A3tgZ94xow2kAvhV7G+WwPlNbp2WfVwBnAbgBgAA4GcDNsbdjw+V8PoDlAH4AYGXoz5XRfF4J4EQAa2Ktx+wrcg34xJOCkwDcq6r3qeoMgK8AOHfoPecC+GL/79cAOF0MB21X1YdU9bb+33cAWAcg7hBzzUVfn0B26/RcAFdoz48BHCQiz4rdqLpUdZ2qrvc0+SqfK2eq+kMAv7Sebh3ZB/mQC9GrUkZRADeKyK39sdFdHAHgwcK/N+PpO/wT7+kfaLYBeKbjfEfqdzO8BMDNI15+mYjcISI3iMgLfMzfQFLrE0hinZZ9Xquss67rzDrKYvTDSQ+30IpPPAFwqqpuEZFD0RvC8+7+kTRrIrIQwNcBvFdVtw+9fBt6YzPsFJGzAPwngOMCNzE7iazT1nxeq+y/5CaLINcxD7cYqPLEE1Xd0v/zERG5Fr2vXU13jC0Ajiz8e2n//0a9Z7OIzAGwGMAvGs5vJBGZi17gXKWq3xh+vRhCqnq9iPyriBys6Y1SmcT6BNJZpxU+r1XWWRLK9l+PsllHrrLvWik88eQcHfPEExE5UESmB39H7wSpyxnmWwAcJyLHisgUeiffrht6z3UA3tr/+3kAvjfuINNEv3/4cgDrVPWTY95z+KAfWUROQm97m4efgejrE0hnnVb8vF4H4C39q1dOBrBNVR+ybEcLVPlctUOss6xWPwDuRa8f7Pb+z+DKhmcDuL7/9+egd8b6DgBr0ftK5zrfs9C7qmHDYHoAPobeAQUA5gP4Wr99PwHwHOPlPhW9ftQ7C8t+FoB3AXhX/z3v6S/vHeidCD4l9vZKdX2mtE7HfV6H2iEAPttfX3fB+IqPgNv99ej1Xe8B8H8AvuP7c+VhGb4M4CEAe/vL8o7Q65G36BMRZS77rhUioq5jkBMRZY5BTkSUOQY5EVHmGORERJljkBMRZY5BTkSUuf8HOpP+lAVMlZ8AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 3 Axes>"
       ]
@@ -340,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -350,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -388,69 +425,69 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4958</th>\n",
-       "      <td>7.606736</td>\n",
-       "      <td>5.722661</td>\n",
-       "      <td>4366</td>\n",
-       "      <td>1045</td>\n",
-       "      <td>1045.0</td>\n",
-       "      <td>-3.389960</td>\n",
-       "      <td>0.000407</td>\n",
-       "      <td>0.018004</td>\n",
-       "      <td>21</td>\n",
-       "      <td>1057.0</td>\n",
+       "      <th>86165</th>\n",
+       "      <td>6.753912</td>\n",
+       "      <td>6.087762</td>\n",
+       "      <td>70368</td>\n",
+       "      <td>16942</td>\n",
+       "      <td>16942.0</td>\n",
+       "      <td>-2.953002</td>\n",
+       "      <td>0.001114</td>\n",
+       "      <td>0.028958</td>\n",
+       "      <td>18</td>\n",
+       "      <td>16947.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>66566</th>\n",
-       "      <td>4.741368</td>\n",
-       "      <td>2.253081</td>\n",
-       "      <td>54270</td>\n",
-       "      <td>13137</td>\n",
-       "      <td>13137.0</td>\n",
-       "      <td>-3.653682</td>\n",
-       "      <td>0.000222</td>\n",
-       "      <td>0.015809</td>\n",
-       "      <td>17</td>\n",
-       "      <td>13143.0</td>\n",
+       "      <th>38349</th>\n",
+       "      <td>5.865186</td>\n",
+       "      <td>2.555524</td>\n",
+       "      <td>31955</td>\n",
+       "      <td>7729</td>\n",
+       "      <td>7729.0</td>\n",
+       "      <td>-3.673458</td>\n",
+       "      <td>0.000212</td>\n",
+       "      <td>0.018863</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7732.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>52339</th>\n",
-       "      <td>7.432901</td>\n",
-       "      <td>4.655774</td>\n",
-       "      <td>43380</td>\n",
-       "      <td>10488</td>\n",
-       "      <td>10488.0</td>\n",
-       "      <td>-2.954638</td>\n",
-       "      <td>0.001110</td>\n",
-       "      <td>0.029165</td>\n",
-       "      <td>9</td>\n",
-       "      <td>10490.0</td>\n",
+       "      <th>63335</th>\n",
+       "      <td>2.782804</td>\n",
+       "      <td>4.770445</td>\n",
+       "      <td>51730</td>\n",
+       "      <td>12529</td>\n",
+       "      <td>12529.0</td>\n",
+       "      <td>-2.375042</td>\n",
+       "      <td>0.004217</td>\n",
+       "      <td>0.065913</td>\n",
+       "      <td>26</td>\n",
+       "      <td>12542.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>95964</th>\n",
-       "      <td>2.137731</td>\n",
-       "      <td>4.047159</td>\n",
-       "      <td>78031</td>\n",
-       "      <td>18734</td>\n",
-       "      <td>18734.0</td>\n",
-       "      <td>-2.285468</td>\n",
-       "      <td>0.005182</td>\n",
-       "      <td>0.076432</td>\n",
-       "      <td>7</td>\n",
-       "      <td>18737.0</td>\n",
+       "      <th>60569</th>\n",
+       "      <td>4.655547</td>\n",
+       "      <td>3.378312</td>\n",
+       "      <td>49668</td>\n",
+       "      <td>12044</td>\n",
+       "      <td>12044.0</td>\n",
+       "      <td>-3.191435</td>\n",
+       "      <td>0.000644</td>\n",
+       "      <td>0.027745</td>\n",
+       "      <td>32</td>\n",
+       "      <td>12072.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>51917</th>\n",
-       "      <td>2.408715</td>\n",
-       "      <td>3.403530</td>\n",
-       "      <td>43066</td>\n",
-       "      <td>10403</td>\n",
-       "      <td>10403.0</td>\n",
-       "      <td>-2.652811</td>\n",
-       "      <td>0.002224</td>\n",
-       "      <td>0.063109</td>\n",
-       "      <td>12</td>\n",
-       "      <td>10412.0</td>\n",
+       "      <th>4204</th>\n",
+       "      <td>5.751816</td>\n",
+       "      <td>5.162678</td>\n",
+       "      <td>3761</td>\n",
+       "      <td>901</td>\n",
+       "      <td>901.0</td>\n",
+       "      <td>-2.265528</td>\n",
+       "      <td>0.005426</td>\n",
+       "      <td>0.069322</td>\n",
+       "      <td>22</td>\n",
+       "      <td>920.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -458,21 +495,21 @@
       ],
       "text/plain": [
        "              x         y      n  frame        t     log_D         D  \\\n",
-       "4958   7.606736  5.722661   4366   1045   1045.0 -3.389960  0.000407   \n",
-       "66566  4.741368  2.253081  54270  13137  13137.0 -3.653682  0.000222   \n",
-       "52339  7.432901  4.655774  43380  10488  10488.0 -2.954638  0.001110   \n",
-       "95964  2.137731  4.047159  78031  18734  18734.0 -2.285468  0.005182   \n",
-       "51917  2.408715  3.403530  43066  10403  10403.0 -2.652811  0.002224   \n",
+       "86165  6.753912  6.087762  70368  16942  16942.0 -2.953002  0.001114   \n",
+       "38349  5.865186  2.555524  31955   7729   7729.0 -3.673458  0.000212   \n",
+       "63335  2.782804  4.770445  51730  12529  12529.0 -2.375042  0.004217   \n",
+       "60569  4.655547  3.378312  49668  12044  12044.0 -3.191435  0.000644   \n",
+       "4204   5.751816  5.162678   3761    901    901.0 -2.265528  0.005426   \n",
        "\n",
        "       est_sigma  n_points  duration  \n",
-       "4958    0.018004        21    1057.0  \n",
-       "66566   0.015809        17   13143.0  \n",
-       "52339   0.029165         9   10490.0  \n",
-       "95964   0.076432         7   18737.0  \n",
-       "51917   0.063109        12   10412.0  "
+       "86165   0.028958        18   16947.0  \n",
+       "38349   0.018863        10    7732.0  \n",
+       "63335   0.065913        26   12542.0  \n",
+       "60569   0.027745        32   12072.0  \n",
+       "4204    0.069322        22     920.0  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -483,19 +520,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['n', 'L', 'h_1', 'h_2', 'h_3', 'h_4', 'h_5', 'h_6', 'h_7', 'h_8', 'h_9',\n",
-       "       'h_10', 'h_11', 'h_12', 'h_13', 'h_14', 'h_15', 'h_16', 'alpha', 'U_1',\n",
-       "       'U_2', 'best_model', 'p_fBM', 'p_LW', 'p_sBM', 'p_OU', 'p_CTRW'],\n",
+       "Index(['n', 'L', 'alpha', 'U_1', 'U_2', 'best_model', 'p_fBM', 'p_LW', 'p_sBM',\n",
+       "       'p_OU', 'p_CTRW'],\n",
        "      dtype='object')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -506,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -514,22 +550,6 @@
       "text/plain": [
        "n                int64\n",
        "L                int64\n",
-       "h_1            float64\n",
-       "h_2            float64\n",
-       "h_3            float64\n",
-       "h_4            float64\n",
-       "h_5            float64\n",
-       "h_6            float64\n",
-       "h_7            float64\n",
-       "h_8            float64\n",
-       "h_9            float64\n",
-       "h_10           float64\n",
-       "h_11           float64\n",
-       "h_12           float64\n",
-       "h_13           float64\n",
-       "h_14           float64\n",
-       "h_15           float64\n",
-       "h_16           float64\n",
        "alpha          float32\n",
        "U_1            float32\n",
        "U_2            float32\n",
@@ -542,7 +562,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -560,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "tags": []
    },
@@ -582,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -591,7 +611,7 @@
        "'/Users/hverdier/palm_tools_data/export_folder'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -602,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -611,7 +631,7 @@
        "'/Users/hverdier/palm_tools_data/export_folder/MMD_inter_units/example'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -622,14 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -646,16 +659,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "242760"
+       "297154"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -666,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -676,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -701,18 +714,6 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th>h_1</th>\n",
-       "      <th>h_2</th>\n",
-       "      <th>h_3</th>\n",
-       "      <th>h_4</th>\n",
-       "      <th>h_5</th>\n",
-       "      <th>h_6</th>\n",
-       "      <th>h_7</th>\n",
-       "      <th>h_8</th>\n",
-       "      <th>h_9</th>\n",
-       "      <th>h_10</th>\n",
-       "      <th>...</th>\n",
-       "      <th>h_16</th>\n",
        "      <th>alpha</th>\n",
        "      <th>U_1</th>\n",
        "      <th>U_2</th>\n",
@@ -735,36 +736,12 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <th>9</th>\n",
-       "      <td>1.946118</td>\n",
-       "      <td>1.859288</td>\n",
-       "      <td>-0.179611</td>\n",
-       "      <td>-0.811196</td>\n",
-       "      <td>-1.317777</td>\n",
-       "      <td>-0.737733</td>\n",
-       "      <td>-0.148952</td>\n",
-       "      <td>0.290119</td>\n",
-       "      <td>-0.892004</td>\n",
-       "      <td>0.486939</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.339782</td>\n",
        "      <td>0.527137</td>\n",
        "      <td>-1.546970</td>\n",
        "      <td>7.623091</td>\n",
@@ -778,18 +755,6 @@
        "    <tr>\n",
        "      <th>71</th>\n",
        "      <th>9</th>\n",
-       "      <td>1.453888</td>\n",
-       "      <td>3.342568</td>\n",
-       "      <td>1.928414</td>\n",
-       "      <td>-1.690647</td>\n",
-       "      <td>-1.888805</td>\n",
-       "      <td>0.163103</td>\n",
-       "      <td>-1.004605</td>\n",
-       "      <td>1.269271</td>\n",
-       "      <td>-2.759497</td>\n",
-       "      <td>2.366122</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.047722</td>\n",
        "      <td>0.509454</td>\n",
        "      <td>0.719309</td>\n",
        "      <td>6.514616</td>\n",
@@ -801,20 +766,20 @@
        "      <td>0.990664</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>72</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">72</th>\n",
+       "      <th>7</th>\n",
+       "      <td>0.619606</td>\n",
+       "      <td>0.681230</td>\n",
+       "      <td>-2.400732</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.001964</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>0.005308</td>\n",
+       "      <td>0.001737</td>\n",
+       "      <td>0.990848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>-0.462752</td>\n",
-       "      <td>1.376618</td>\n",
-       "      <td>-0.734024</td>\n",
-       "      <td>-1.277834</td>\n",
-       "      <td>0.005935</td>\n",
-       "      <td>0.590233</td>\n",
-       "      <td>-2.147841</td>\n",
-       "      <td>-0.313310</td>\n",
-       "      <td>-2.610162</td>\n",
-       "      <td>0.673430</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-1.057808</td>\n",
        "      <td>0.627733</td>\n",
        "      <td>0.278495</td>\n",
        "      <td>-3.214053</td>\n",
@@ -827,53 +792,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>137</th>\n",
-       "      <th>11</th>\n",
-       "      <td>1.717639</td>\n",
-       "      <td>2.638570</td>\n",
-       "      <td>0.559084</td>\n",
-       "      <td>-1.049830</td>\n",
-       "      <td>-1.500272</td>\n",
-       "      <td>-1.146517</td>\n",
-       "      <td>0.052630</td>\n",
-       "      <td>1.150047</td>\n",
-       "      <td>-1.321636</td>\n",
-       "      <td>1.851419</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.446702</td>\n",
-       "      <td>0.478315</td>\n",
-       "      <td>-0.451064</td>\n",
-       "      <td>7.991570</td>\n",
+       "      <th>8</th>\n",
+       "      <td>0.489349</td>\n",
+       "      <td>-0.385968</td>\n",
+       "      <td>7.451241</td>\n",
        "      <td>CTRW</td>\n",
-       "      <td>0.002973</td>\n",
+       "      <td>0.002006</td>\n",
        "      <td>0.000191</td>\n",
-       "      <td>0.013875</td>\n",
-       "      <td>0.004268</td>\n",
-       "      <td>0.978693</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <th>7</th>\n",
-       "      <td>2.137385</td>\n",
-       "      <td>2.999132</td>\n",
-       "      <td>1.251117</td>\n",
-       "      <td>-1.766266</td>\n",
-       "      <td>-3.122079</td>\n",
-       "      <td>-0.992646</td>\n",
-       "      <td>-0.011700</td>\n",
-       "      <td>1.637186</td>\n",
-       "      <td>-0.560027</td>\n",
-       "      <td>2.498473</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.144387</td>\n",
-       "      <td>0.520929</td>\n",
-       "      <td>-0.098388</td>\n",
-       "      <td>7.354497</td>\n",
-       "      <td>CTRW</td>\n",
-       "      <td>0.001215</td>\n",
-       "      <td>0.000265</td>\n",
-       "      <td>0.010317</td>\n",
-       "      <td>0.001594</td>\n",
-       "      <td>0.986608</td>\n",
+       "      <td>0.009205</td>\n",
+       "      <td>0.003089</td>\n",
+       "      <td>0.985509</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -887,34 +815,10 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>83099</th>\n",
        "      <th>13</th>\n",
-       "      <td>-0.405523</td>\n",
-       "      <td>1.663221</td>\n",
-       "      <td>1.479237</td>\n",
-       "      <td>-1.668656</td>\n",
-       "      <td>-0.494702</td>\n",
-       "      <td>-0.890797</td>\n",
-       "      <td>-0.804132</td>\n",
-       "      <td>0.145606</td>\n",
-       "      <td>-1.672988</td>\n",
-       "      <td>2.146663</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.900320</td>\n",
        "      <td>0.575170</td>\n",
        "      <td>2.163353</td>\n",
        "      <td>-0.334597</td>\n",
@@ -928,18 +832,6 @@
        "    <tr>\n",
        "      <th>83155</th>\n",
        "      <th>7</th>\n",
-       "      <td>2.043290</td>\n",
-       "      <td>1.023442</td>\n",
-       "      <td>-0.493413</td>\n",
-       "      <td>-0.457658</td>\n",
-       "      <td>-1.069538</td>\n",
-       "      <td>-1.194567</td>\n",
-       "      <td>0.823568</td>\n",
-       "      <td>0.287785</td>\n",
-       "      <td>1.032906</td>\n",
-       "      <td>1.139173</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.413723</td>\n",
        "      <td>0.535924</td>\n",
        "      <td>-5.228890</td>\n",
        "      <td>5.770625</td>\n",
@@ -953,18 +845,6 @@
        "    <tr>\n",
        "      <th>83177</th>\n",
        "      <th>8</th>\n",
-       "      <td>-0.052085</td>\n",
-       "      <td>1.450502</td>\n",
-       "      <td>-0.070146</td>\n",
-       "      <td>-0.787487</td>\n",
-       "      <td>-0.530743</td>\n",
-       "      <td>0.602304</td>\n",
-       "      <td>-1.351848</td>\n",
-       "      <td>-0.099467</td>\n",
-       "      <td>-1.265877</td>\n",
-       "      <td>1.045710</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.437743</td>\n",
        "      <td>0.704709</td>\n",
        "      <td>1.096030</td>\n",
        "      <td>-0.988632</td>\n",
@@ -978,18 +858,6 @@
        "    <tr>\n",
        "      <th>83238</th>\n",
        "      <th>8</th>\n",
-       "      <td>2.559482</td>\n",
-       "      <td>2.711824</td>\n",
-       "      <td>0.012999</td>\n",
-       "      <td>0.037514</td>\n",
-       "      <td>-0.612741</td>\n",
-       "      <td>0.090219</td>\n",
-       "      <td>-0.016747</td>\n",
-       "      <td>1.037080</td>\n",
-       "      <td>-1.797913</td>\n",
-       "      <td>1.120737</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.099108</td>\n",
        "      <td>0.501822</td>\n",
        "      <td>-0.616167</td>\n",
        "      <td>5.909852</td>\n",
@@ -1003,18 +871,6 @@
        "    <tr>\n",
        "      <th>83291</th>\n",
        "      <th>7</th>\n",
-       "      <td>2.478641</td>\n",
-       "      <td>3.320015</td>\n",
-       "      <td>2.501287</td>\n",
-       "      <td>-2.108029</td>\n",
-       "      <td>-3.442969</td>\n",
-       "      <td>-0.837099</td>\n",
-       "      <td>-0.063611</td>\n",
-       "      <td>2.021823</td>\n",
-       "      <td>-1.038982</td>\n",
-       "      <td>3.187314</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.347009</td>\n",
        "      <td>0.517916</td>\n",
        "      <td>0.823193</td>\n",
        "      <td>7.136736</td>\n",
@@ -1027,70 +883,42 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2909 rows × 25 columns</p>\n",
+       "<p>5456 rows × 9 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "               h_1       h_2       h_3       h_4       h_5       h_6  \\\n",
-       "n     L                                                                \n",
-       "56    9   1.946118  1.859288 -0.179611 -0.811196 -1.317777 -0.737733   \n",
-       "71    9   1.453888  3.342568  1.928414 -1.690647 -1.888805  0.163103   \n",
-       "72    10 -0.462752  1.376618 -0.734024 -1.277834  0.005935  0.590233   \n",
-       "137   11  1.717639  2.638570  0.559084 -1.049830 -1.500272 -1.146517   \n",
-       "142   7   2.137385  2.999132  1.251117 -1.766266 -3.122079 -0.992646   \n",
-       "...            ...       ...       ...       ...       ...       ...   \n",
-       "83099 13 -0.405523  1.663221  1.479237 -1.668656 -0.494702 -0.890797   \n",
-       "83155 7   2.043290  1.023442 -0.493413 -0.457658 -1.069538 -1.194567   \n",
-       "83177 8  -0.052085  1.450502 -0.070146 -0.787487 -0.530743  0.602304   \n",
-       "83238 8   2.559482  2.711824  0.012999  0.037514 -0.612741  0.090219   \n",
-       "83291 7   2.478641  3.320015  2.501287 -2.108029 -3.442969 -0.837099   \n",
+       "             alpha       U_1       U_2 best_model     p_fBM      p_LW  \\\n",
+       "n     L                                                                 \n",
+       "56    9   0.527137 -1.546970  7.623091       CTRW  0.042417  0.001741   \n",
+       "71    9   0.509454  0.719309  6.514616       CTRW  0.000857  0.000132   \n",
+       "72    7   0.619606  0.681230 -2.400732       CTRW  0.001964  0.000143   \n",
+       "      10  0.627733  0.278495 -3.214053       CTRW  0.000686  0.000036   \n",
+       "137   8   0.489349 -0.385968  7.451241       CTRW  0.002006  0.000191   \n",
+       "...            ...       ...       ...        ...       ...       ...   \n",
+       "83099 13  0.575170  2.163353 -0.334597       CTRW  0.002649  0.000226   \n",
+       "83155 7   0.535924 -5.228890  5.770625         OU  0.338967  0.018285   \n",
+       "83177 8   0.704709  1.096030 -0.988632       CTRW  0.023502  0.003831   \n",
+       "83238 8   0.501822 -0.616167  5.909852       CTRW  0.004591  0.000265   \n",
+       "83291 7   0.517916  0.823193  7.136736       CTRW  0.000186  0.000053   \n",
        "\n",
-       "               h_7       h_8       h_9      h_10  ...      h_16     alpha  \\\n",
-       "n     L                                           ...                       \n",
-       "56    9  -0.148952  0.290119 -0.892004  0.486939  ...  0.339782  0.527137   \n",
-       "71    9  -1.004605  1.269271 -2.759497  2.366122  ...  0.047722  0.509454   \n",
-       "72    10 -2.147841 -0.313310 -2.610162  0.673430  ... -1.057808  0.627733   \n",
-       "137   11  0.052630  1.150047 -1.321636  1.851419  ... -0.446702  0.478315   \n",
-       "142   7  -0.011700  1.637186 -0.560027  2.498473  ...  0.144387  0.520929   \n",
-       "...            ...       ...       ...       ...  ...       ...       ...   \n",
-       "83099 13 -0.804132  0.145606 -1.672988  2.146663  ... -0.900320  0.575170   \n",
-       "83155 7   0.823568  0.287785  1.032906  1.139173  ... -0.413723  0.535924   \n",
-       "83177 8  -1.351848 -0.099467 -1.265877  1.045710  ... -0.437743  0.704709   \n",
-       "83238 8  -0.016747  1.037080 -1.797913  1.120737  ...  0.099108  0.501822   \n",
-       "83291 7  -0.063611  2.021823 -1.038982  3.187314  ...  0.347009  0.517916   \n",
+       "             p_sBM      p_OU    p_CTRW  \n",
+       "n     L                                 \n",
+       "56    9   0.053380  0.049106  0.853357  \n",
+       "71    9   0.007037  0.001310  0.990664  \n",
+       "72    7   0.005308  0.001737  0.990848  \n",
+       "      10  0.002368  0.000952  0.995958  \n",
+       "137   8   0.009205  0.003089  0.985509  \n",
+       "...            ...       ...       ...  \n",
+       "83099 13  0.050670  0.001921  0.944534  \n",
+       "83155 7   0.144248  0.401111  0.097389  \n",
+       "83177 8   0.052769  0.027425  0.892473  \n",
+       "83238 8   0.010502  0.013251  0.971392  \n",
+       "83291 7   0.003787  0.000255  0.995720  \n",
        "\n",
-       "               U_1       U_2  best_model     p_fBM      p_LW     p_sBM  \\\n",
-       "n     L                                                                  \n",
-       "56    9  -1.546970  7.623091        CTRW  0.042417  0.001741  0.053380   \n",
-       "71    9   0.719309  6.514616        CTRW  0.000857  0.000132  0.007037   \n",
-       "72    10  0.278495 -3.214053        CTRW  0.000686  0.000036  0.002368   \n",
-       "137   11 -0.451064  7.991570        CTRW  0.002973  0.000191  0.013875   \n",
-       "142   7  -0.098388  7.354497        CTRW  0.001215  0.000265  0.010317   \n",
-       "...            ...       ...         ...       ...       ...       ...   \n",
-       "83099 13  2.163353 -0.334597        CTRW  0.002649  0.000226  0.050670   \n",
-       "83155 7  -5.228890  5.770625          OU  0.338967  0.018285  0.144248   \n",
-       "83177 8   1.096030 -0.988632        CTRW  0.023502  0.003831  0.052769   \n",
-       "83238 8  -0.616167  5.909852        CTRW  0.004591  0.000265  0.010502   \n",
-       "83291 7   0.823193  7.136736        CTRW  0.000186  0.000053  0.003787   \n",
-       "\n",
-       "              p_OU    p_CTRW  \n",
-       "n     L                       \n",
-       "56    9   0.049106  0.853357  \n",
-       "71    9   0.001310  0.990664  \n",
-       "72    10  0.000952  0.995958  \n",
-       "137   11  0.004268  0.978693  \n",
-       "142   7   0.001594  0.986608  \n",
-       "...            ...       ...  \n",
-       "83099 13  0.001921  0.944534  \n",
-       "83155 7   0.401111  0.097389  \n",
-       "83177 8   0.027425  0.892473  \n",
-       "83238 8   0.013251  0.971392  \n",
-       "83291 7   0.000255  0.995720  \n",
-       "\n",
-       "[2909 rows x 25 columns]"
+       "[5456 rows x 9 columns]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1101,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1110,7 +938,7 @@
        "'/Users/hverdier/palm_tools_data/export_folder'"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1121,16 +949,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<palm_tools.analysis.mmd_analysis.MMDInterUnitAnalysis at 0x7fb4f780bb50>"
+       "<palm_tools.analysis.mmd_analysis.MMDInterUnitAnalysis at 0x7f7c331cb400>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1148,7 +976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1167,7 +995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1186,7 +1014,7 @@
        " '/Users/hverdier/palm_tools_data/export_folder/MMD_inter_units/example/D_null']"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1198,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1207,7 +1035,7 @@
        "(4, 4, 20)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1218,40 +1046,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-0.00055281])"
+       "(4, 4, 1)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mmd._D_null[2,1]"
+    "mmd._D_null.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 1.33056868e-03,  1.20708312e-03,  9.11523991e-04,  1.14537912e-03,\n",
-       "        9.35027329e-04,  5.65662189e-04,  1.15828358e-03,  2.23024950e-04,\n",
-       "        7.38644558e-04,  1.90574620e-03,  1.10820711e-03,  2.07458175e-03,\n",
-       "        9.15455277e-04,  5.40977099e-04, -7.29663651e-07,  2.27029252e-04,\n",
-       "        1.80546469e-03,  7.98029449e-05,  3.63200385e-04,  1.67052435e-04])"
+       "array([-0.00018631,  0.00114822,  0.00170548,  0.00249988,  0.00267076,\n",
+       "        0.0025095 ,  0.00182496,  0.00103207,  0.00163953,  0.00215703,\n",
+       "        0.0014566 , -0.00012938,  0.00241349,  0.00262012,  0.00251539,\n",
+       "        0.00239118,  0.00126461,  0.00051409,  0.00076343,  0.00077009])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1262,26 +1089,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([3., 3., 2., 1., 3., 4., 1., 0., 1., 2.]),\n",
-       " array([-7.29663651e-07,  2.06801478e-04,  4.14332619e-04,  6.21863760e-04,\n",
-       "         8.29394901e-04,  1.03692604e-03,  1.24445718e-03,  1.45198833e-03,\n",
-       "         1.65951947e-03,  1.86705061e-03,  2.07458175e-03]),\n",
+       "(array([2., 0., 1., 2., 2., 2., 2., 1., 1., 7.]),\n",
+       " array([-1.86306898e-04,  9.93999268e-05,  3.85106751e-04,  6.70813576e-04,\n",
+       "         9.56520400e-04,  1.24222722e-03,  1.52793405e-03,  1.81364087e-03,\n",
+       "         2.09934770e-03,  2.38505452e-03,  2.67076135e-03]),\n",
        " <BarContainer object of 10 artists>)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1299,7 +1126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1307,11 +1134,11 @@
       "text/plain": [
        "array([[1.   , 0.025, 0.025, 0.025],\n",
        "       [0.025, 1.   , 0.025, 0.025],\n",
-       "       [0.025, 0.025, 1.   , 0.025],\n",
-       "       [0.025, 0.025, 0.025, 1.   ]])"
+       "       [0.025, 0.025, 1.   , 1.   ],\n",
+       "       [0.025, 0.025, 1.   , 1.   ]])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1324,7 +1151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1336,7 +1163,7 @@
        "       [0.001, 0.001, 0.322,   nan]])"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1347,7 +1174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1356,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1366,7 +1193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1375,7 +1202,7 @@
        "{'file': '/Users/hverdier/palm_tools_data/export_folder/10fd51e9edc4d570.trxyt'}"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1386,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1412,158 +1239,158 @@
        "      <th></th>\n",
        "      <th>n</th>\n",
        "      <th>L</th>\n",
-       "      <th>h_1</th>\n",
-       "      <th>h_2</th>\n",
-       "      <th>h_3</th>\n",
-       "      <th>h_4</th>\n",
-       "      <th>h_5</th>\n",
-       "      <th>h_6</th>\n",
-       "      <th>h_7</th>\n",
-       "      <th>h_8</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>U_1</th>\n",
+       "      <th>U_2</th>\n",
+       "      <th>best_model</th>\n",
+       "      <th>p_fBM</th>\n",
+       "      <th>p_LW</th>\n",
+       "      <th>p_sBM</th>\n",
+       "      <th>p_OU</th>\n",
        "      <th>...</th>\n",
-       "      <th>t</th>\n",
-       "      <th>log_D</th>\n",
        "      <th>D</th>\n",
        "      <th>est_sigma</th>\n",
        "      <th>n_points</th>\n",
        "      <th>duration</th>\n",
        "      <th>file</th>\n",
        "      <th>arbitrary_condition</th>\n",
+       "      <th>arbitrary_condition_copy</th>\n",
+       "      <th>other</th>\n",
        "      <th>unit</th>\n",
        "      <th>traj_ID</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>881</th>\n",
-       "      <td>26187</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0.363305</td>\n",
-       "      <td>2.294458</td>\n",
-       "      <td>1.591987</td>\n",
-       "      <td>-2.311217</td>\n",
-       "      <td>-2.572532</td>\n",
-       "      <td>-1.910674</td>\n",
-       "      <td>-0.515501</td>\n",
-       "      <td>0.313068</td>\n",
+       "      <th>2416</th>\n",
+       "      <td>9002</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.460829</td>\n",
+       "      <td>0.520039</td>\n",
+       "      <td>7.888745</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000180</td>\n",
+       "      <td>0.000025</td>\n",
+       "      <td>0.005772</td>\n",
+       "      <td>0.000209</td>\n",
        "      <td>...</td>\n",
-       "      <td>6324.0</td>\n",
-       "      <td>-2.584207</td>\n",
-       "      <td>0.002605</td>\n",
-       "      <td>0.041359</td>\n",
-       "      <td>9</td>\n",
-       "      <td>6332.0</td>\n",
+       "      <td>0.000343</td>\n",
+       "      <td>0.018655</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2897.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>17994</td>\n",
+       "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>E</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>1097</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1.648949</td>\n",
-       "      <td>2.339597</td>\n",
-       "      <td>1.271079</td>\n",
-       "      <td>-1.968347</td>\n",
-       "      <td>-2.614970</td>\n",
-       "      <td>-1.134896</td>\n",
-       "      <td>-0.186167</td>\n",
-       "      <td>0.901542</td>\n",
-       "      <td>...</td>\n",
-       "      <td>253.0</td>\n",
-       "      <td>-3.105005</td>\n",
-       "      <td>0.000785</td>\n",
-       "      <td>0.037235</td>\n",
-       "      <td>7</td>\n",
-       "      <td>259.0</td>\n",
+       "      <th>2919</th>\n",
+       "      <td>45565</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.416501</td>\n",
+       "      <td>-0.424838</td>\n",
+       "      <td>8.802402</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>0.007808</td>\n",
+       "      <td>0.000295</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000606</td>\n",
+       "      <td>0.025547</td>\n",
+       "      <td>41</td>\n",
+       "      <td>15214.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>15573</td>\n",
+       "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>E</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>229</th>\n",
-       "      <td>6765</td>\n",
-       "      <td>15</td>\n",
-       "      <td>1.980902</td>\n",
-       "      <td>2.615177</td>\n",
-       "      <td>1.895203</td>\n",
-       "      <td>-1.163154</td>\n",
-       "      <td>-0.437727</td>\n",
-       "      <td>-0.721868</td>\n",
-       "      <td>-0.053685</td>\n",
-       "      <td>1.327911</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1628.0</td>\n",
-       "      <td>-2.982662</td>\n",
-       "      <td>0.001041</td>\n",
-       "      <td>0.031273</td>\n",
-       "      <td>15</td>\n",
-       "      <td>1642.0</td>\n",
+       "      <th>1104</th>\n",
+       "      <td>44290</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.455066</td>\n",
+       "      <td>0.328819</td>\n",
+       "      <td>-0.166943</td>\n",
+       "      <td>sBM</td>\n",
+       "      <td>0.067619</td>\n",
+       "      <td>0.006231</td>\n",
+       "      <td>0.662004</td>\n",
+       "      <td>0.045443</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.015896</td>\n",
+       "      <td>0.060797</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14764.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>16139</td>\n",
+       "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>E</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2170</th>\n",
-       "      <td>61198</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.513623</td>\n",
-       "      <td>-0.126454</td>\n",
-       "      <td>1.026302</td>\n",
-       "      <td>-1.077608</td>\n",
-       "      <td>-0.524660</td>\n",
-       "      <td>-1.566746</td>\n",
-       "      <td>-0.081190</td>\n",
-       "      <td>-0.829638</td>\n",
+       "      <th>2371</th>\n",
+       "      <td>11617</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.446666</td>\n",
+       "      <td>0.937838</td>\n",
+       "      <td>6.204060</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.002799</td>\n",
+       "      <td>0.000560</td>\n",
+       "      <td>0.095445</td>\n",
+       "      <td>0.002611</td>\n",
        "      <td>...</td>\n",
-       "      <td>14781.0</td>\n",
-       "      <td>-1.691363</td>\n",
-       "      <td>0.020353</td>\n",
-       "      <td>0.072659</td>\n",
-       "      <td>8</td>\n",
-       "      <td>14788.0</td>\n",
+       "      <td>0.001263</td>\n",
+       "      <td>0.025111</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3759.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
-       "      <td>A</td>\n",
-       "      <td>3</td>\n",
-       "      <td>19953</td>\n",
+       "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>E</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11468</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>4 rows × 40 columns</p>\n",
+       "<p>4 rows × 26 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "          n   L       h_1       h_2       h_3       h_4       h_5       h_6  \\\n",
-       "881   26187   9  0.363305  2.294458  1.591987 -2.311217 -2.572532 -1.910674   \n",
-       "36     1097   7  1.648949  2.339597  1.271079 -1.968347 -2.614970 -1.134896   \n",
-       "229    6765  15  1.980902  2.615177  1.895203 -1.163154 -0.437727 -0.721868   \n",
-       "2170  61198   8 -0.513623 -0.126454  1.026302 -1.077608 -0.524660 -1.566746   \n",
+       "          n   L     alpha       U_1       U_2 best_model     p_fBM      p_LW  \\\n",
+       "2416   9002  13  0.460829  0.520039  7.888745       CTRW  0.000180  0.000025   \n",
+       "2919  45565  31  0.416501 -0.424838  8.802402       CTRW  0.000169  0.000012   \n",
+       "1104  44290   8  0.455066  0.328819 -0.166943        sBM  0.067619  0.006231   \n",
+       "2371  11617  13  0.446666  0.937838  6.204060       CTRW  0.002799  0.000560   \n",
        "\n",
-       "           h_7       h_8  ...        t     log_D         D  est_sigma  \\\n",
-       "881  -0.515501  0.313068  ...   6324.0 -2.584207  0.002605   0.041359   \n",
-       "36   -0.186167  0.901542  ...    253.0 -3.105005  0.000785   0.037235   \n",
-       "229  -0.053685  1.327911  ...   1628.0 -2.982662  0.001041   0.031273   \n",
-       "2170 -0.081190 -0.829638  ...  14781.0 -1.691363  0.020353   0.072659   \n",
+       "         p_sBM      p_OU  ...         D  est_sigma  n_points  duration  \\\n",
+       "2416  0.005772  0.000209  ...  0.000343   0.018655        13    2897.0   \n",
+       "2919  0.007808  0.000295  ...  0.000606   0.025547        41   15214.0   \n",
+       "1104  0.662004  0.045443  ...  0.015896   0.060797         8   14764.0   \n",
+       "2371  0.095445  0.002611  ...  0.001263   0.025111        13    3759.0   \n",
        "\n",
-       "      n_points  duration                                               file  \\\n",
-       "881          9    6332.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "36           7     259.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "229         15    1642.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "2170         8   14788.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "                                                   file  arbitrary_condition  \\\n",
+       "2416  /Users/hverdier/palm_tools_data/export_folder/...                    B   \n",
+       "2919  /Users/hverdier/palm_tools_data/export_folder/...                    B   \n",
+       "1104  /Users/hverdier/palm_tools_data/export_folder/...                    B   \n",
+       "2371  /Users/hverdier/palm_tools_data/export_folder/...                    B   \n",
        "\n",
-       "      arbitrary_condition  unit  traj_ID  \n",
-       "881                     A     3    17994  \n",
-       "36                      A     3    15573  \n",
-       "229                     A     3    16139  \n",
-       "2170                    A     3    19953  \n",
+       "      arbitrary_condition_copy  other unit  traj_ID  \n",
+       "2416                         D      E    2    11213  \n",
+       "2919                         D      E    2    14014  \n",
+       "1104                         D      E    2    13950  \n",
+       "2371                         D      E    2    11468  \n",
        "\n",
-       "[4 rows x 40 columns]"
+       "[4 rows x 26 columns]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1574,7 +1401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1601,22 +1428,22 @@
        "      <th>Unnamed: 0</th>\n",
        "      <th>n</th>\n",
        "      <th>L</th>\n",
-       "      <th>h_1</th>\n",
-       "      <th>h_2</th>\n",
-       "      <th>h_3</th>\n",
-       "      <th>h_4</th>\n",
-       "      <th>h_5</th>\n",
-       "      <th>h_6</th>\n",
-       "      <th>h_7</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>U_1</th>\n",
+       "      <th>U_2</th>\n",
+       "      <th>best_model</th>\n",
+       "      <th>p_fBM</th>\n",
+       "      <th>p_LW</th>\n",
+       "      <th>p_sBM</th>\n",
        "      <th>...</th>\n",
-       "      <th>t</th>\n",
-       "      <th>log_D</th>\n",
        "      <th>D</th>\n",
        "      <th>est_sigma</th>\n",
        "      <th>n_points</th>\n",
        "      <th>duration</th>\n",
        "      <th>file</th>\n",
        "      <th>arbitrary_condition</th>\n",
+       "      <th>arbitrary_condition_copy</th>\n",
+       "      <th>other</th>\n",
        "      <th>unit</th>\n",
        "      <th>traj_ID</th>\n",
        "    </tr>\n",
@@ -1625,122 +1452,122 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>63</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1.010392</td>\n",
-       "      <td>2.946148</td>\n",
-       "      <td>2.346593</td>\n",
-       "      <td>-2.590131</td>\n",
-       "      <td>-2.969108</td>\n",
-       "      <td>-1.057501</td>\n",
-       "      <td>-0.861557</td>\n",
-       "      <td>...</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>-3.272126</td>\n",
-       "      <td>0.000534</td>\n",
-       "      <td>0.031619</td>\n",
-       "      <td>9</td>\n",
-       "      <td>61.0</td>\n",
+       "      <td>16872</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.417854</td>\n",
+       "      <td>1.300084</td>\n",
+       "      <td>6.819479</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000315</td>\n",
+       "      <td>9.779975e-05</td>\n",
+       "      <td>0.044585</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000648</td>\n",
+       "      <td>0.029692</td>\n",
+       "      <td>76</td>\n",
+       "      <td>14224.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
        "      <td>0</td>\n",
-       "      <td>5158</td>\n",
+       "      <td>6872</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>72</td>\n",
+       "      <td>144</td>\n",
        "      <td>7</td>\n",
-       "      <td>1.524720</td>\n",
-       "      <td>1.761242</td>\n",
-       "      <td>1.946277</td>\n",
-       "      <td>-1.502578</td>\n",
-       "      <td>-2.658366</td>\n",
-       "      <td>-1.744368</td>\n",
-       "      <td>0.614263</td>\n",
+       "      <td>0.539603</td>\n",
+       "      <td>1.155300</td>\n",
+       "      <td>6.883584</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.007937</td>\n",
+       "      <td>2.202557e-03</td>\n",
+       "      <td>0.057203</td>\n",
        "      <td>...</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>-2.689572</td>\n",
-       "      <td>0.002044</td>\n",
-       "      <td>0.064027</td>\n",
-       "      <td>7</td>\n",
-       "      <td>66.0</td>\n",
+       "      <td>0.000804</td>\n",
+       "      <td>0.030363</td>\n",
+       "      <td>50</td>\n",
+       "      <td>155.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
        "      <td>0</td>\n",
-       "      <td>5160</td>\n",
+       "      <td>5166</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>144</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0.401657</td>\n",
-       "      <td>1.804630</td>\n",
-       "      <td>2.545321</td>\n",
-       "      <td>-2.192113</td>\n",
-       "      <td>-1.143842</td>\n",
-       "      <td>-1.711132</td>\n",
-       "      <td>-0.090239</td>\n",
+       "      <td>4857</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.501809</td>\n",
+       "      <td>-0.378443</td>\n",
+       "      <td>7.737951</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.003461</td>\n",
+       "      <td>5.241008e-04</td>\n",
+       "      <td>0.032163</td>\n",
        "      <td>...</td>\n",
-       "      <td>106.0</td>\n",
-       "      <td>-3.094710</td>\n",
-       "      <td>0.000804</td>\n",
-       "      <td>0.030363</td>\n",
+       "      <td>0.000438</td>\n",
+       "      <td>0.024121</td>\n",
        "      <td>50</td>\n",
-       "      <td>155.0</td>\n",
+       "      <td>4377.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
        "      <td>0</td>\n",
-       "      <td>5166</td>\n",
+       "      <td>5656</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>144</td>\n",
-       "      <td>50</td>\n",
-       "      <td>1.579205</td>\n",
-       "      <td>2.078706</td>\n",
-       "      <td>1.804325</td>\n",
-       "      <td>-2.040661</td>\n",
-       "      <td>-0.164231</td>\n",
-       "      <td>-2.083222</td>\n",
-       "      <td>0.255794</td>\n",
-       "      <td>...</td>\n",
-       "      <td>106.0</td>\n",
-       "      <td>-3.094710</td>\n",
-       "      <td>0.000804</td>\n",
-       "      <td>0.030363</td>\n",
-       "      <td>50</td>\n",
-       "      <td>155.0</td>\n",
+       "      <td>21141</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.484193</td>\n",
+       "      <td>0.887727</td>\n",
+       "      <td>7.403677</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.001061</td>\n",
+       "      <td>2.475591e-04</td>\n",
+       "      <td>0.044009</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000709</td>\n",
+       "      <td>0.027906</td>\n",
+       "      <td>45</td>\n",
+       "      <td>17612.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
        "      <td>0</td>\n",
-       "      <td>5166</td>\n",
+       "      <td>7281</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
-       "      <td>211</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2.882183</td>\n",
-       "      <td>2.797885</td>\n",
-       "      <td>0.990547</td>\n",
-       "      <td>-1.221976</td>\n",
-       "      <td>-2.175724</td>\n",
-       "      <td>-0.817759</td>\n",
-       "      <td>0.186972</td>\n",
-       "      <td>...</td>\n",
-       "      <td>175.0</td>\n",
-       "      <td>-2.897075</td>\n",
-       "      <td>0.001267</td>\n",
-       "      <td>0.056375</td>\n",
-       "      <td>8</td>\n",
-       "      <td>182.0</td>\n",
+       "      <td>14836</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.538855</td>\n",
+       "      <td>0.593154</td>\n",
+       "      <td>7.512572</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.005647</td>\n",
+       "      <td>2.016661e-03</td>\n",
+       "      <td>0.045361</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001112</td>\n",
+       "      <td>0.037777</td>\n",
+       "      <td>38</td>\n",
+       "      <td>12553.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
        "      <td>0</td>\n",
-       "      <td>5171</td>\n",
+       "      <td>6658</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1767,187 +1594,187 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6064</th>\n",
-       "      <td>2904</td>\n",
-       "      <td>83099</td>\n",
-       "      <td>13</td>\n",
-       "      <td>-0.405523</td>\n",
-       "      <td>1.663221</td>\n",
-       "      <td>1.479237</td>\n",
-       "      <td>-1.668656</td>\n",
-       "      <td>-0.494702</td>\n",
-       "      <td>-0.890797</td>\n",
-       "      <td>-0.804132</td>\n",
-       "      <td>...</td>\n",
-       "      <td>19946.0</td>\n",
-       "      <td>-2.558354</td>\n",
-       "      <td>0.002765</td>\n",
-       "      <td>0.029245</td>\n",
-       "      <td>13</td>\n",
-       "      <td>19958.0</td>\n",
+       "      <th>11424</th>\n",
+       "      <td>5451</td>\n",
+       "      <td>7644</td>\n",
+       "      <td>87</td>\n",
+       "      <td>0.545019</td>\n",
+       "      <td>2.134504</td>\n",
+       "      <td>-2.059474</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000096</td>\n",
+       "      <td>1.344549e-06</td>\n",
+       "      <td>0.007093</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.002423</td>\n",
+       "      <td>0.031340</td>\n",
+       "      <td>87</td>\n",
+       "      <td>1935.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
        "      <td>3</td>\n",
-       "      <td>20595</td>\n",
+       "      <td>16217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6065</th>\n",
-       "      <td>2905</td>\n",
-       "      <td>83155</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2.043290</td>\n",
-       "      <td>1.023442</td>\n",
-       "      <td>-0.493413</td>\n",
-       "      <td>-0.457658</td>\n",
-       "      <td>-1.069538</td>\n",
-       "      <td>-1.194567</td>\n",
-       "      <td>0.823568</td>\n",
-       "      <td>...</td>\n",
-       "      <td>19957.0</td>\n",
-       "      <td>-2.079879</td>\n",
-       "      <td>0.008320</td>\n",
-       "      <td>0.117126</td>\n",
-       "      <td>7</td>\n",
-       "      <td>19963.0</td>\n",
+       "      <th>11425</th>\n",
+       "      <td>5452</td>\n",
+       "      <td>48130</td>\n",
+       "      <td>103</td>\n",
+       "      <td>0.465437</td>\n",
+       "      <td>-1.435776</td>\n",
+       "      <td>6.551026</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.001109</td>\n",
+       "      <td>2.761263e-05</td>\n",
+       "      <td>0.024464</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000822</td>\n",
+       "      <td>0.031445</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11760.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
        "      <td>3</td>\n",
-       "      <td>20596</td>\n",
+       "      <td>19327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6066</th>\n",
-       "      <td>2906</td>\n",
-       "      <td>83177</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.052085</td>\n",
-       "      <td>1.450502</td>\n",
-       "      <td>-0.070146</td>\n",
-       "      <td>-0.787487</td>\n",
-       "      <td>-0.530743</td>\n",
-       "      <td>0.602304</td>\n",
-       "      <td>-1.351848</td>\n",
-       "      <td>...</td>\n",
-       "      <td>19963.0</td>\n",
-       "      <td>-1.975675</td>\n",
-       "      <td>0.010576</td>\n",
-       "      <td>0.031108</td>\n",
-       "      <td>8</td>\n",
-       "      <td>19970.0</td>\n",
+       "      <th>11426</th>\n",
+       "      <td>5453</td>\n",
+       "      <td>4420</td>\n",
+       "      <td>105</td>\n",
+       "      <td>0.456256</td>\n",
+       "      <td>1.728149</td>\n",
+       "      <td>-0.177973</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000019</td>\n",
+       "      <td>4.760843e-07</td>\n",
+       "      <td>0.001840</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000423</td>\n",
+       "      <td>0.022897</td>\n",
+       "      <td>105</td>\n",
+       "      <td>1157.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
        "      <td>3</td>\n",
-       "      <td>20597</td>\n",
+       "      <td>15909</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6067</th>\n",
-       "      <td>2907</td>\n",
-       "      <td>83238</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2.559482</td>\n",
-       "      <td>2.711824</td>\n",
-       "      <td>0.012999</td>\n",
-       "      <td>0.037514</td>\n",
-       "      <td>-0.612741</td>\n",
-       "      <td>0.090219</td>\n",
-       "      <td>-0.016747</td>\n",
-       "      <td>...</td>\n",
-       "      <td>19976.0</td>\n",
-       "      <td>-2.272262</td>\n",
-       "      <td>0.005342</td>\n",
-       "      <td>0.080637</td>\n",
-       "      <td>8</td>\n",
-       "      <td>19983.0</td>\n",
+       "      <th>11427</th>\n",
+       "      <td>5454</td>\n",
+       "      <td>74486</td>\n",
+       "      <td>111</td>\n",
+       "      <td>0.457883</td>\n",
+       "      <td>1.209557</td>\n",
+       "      <td>0.095334</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000044</td>\n",
+       "      <td>1.540801e-06</td>\n",
+       "      <td>0.004179</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000489</td>\n",
+       "      <td>0.021835</td>\n",
+       "      <td>145</td>\n",
+       "      <td>18047.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
        "      <td>3</td>\n",
-       "      <td>20598</td>\n",
+       "      <td>20365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6068</th>\n",
-       "      <td>2908</td>\n",
-       "      <td>83291</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2.478641</td>\n",
-       "      <td>3.320015</td>\n",
-       "      <td>2.501287</td>\n",
-       "      <td>-2.108029</td>\n",
-       "      <td>-3.442969</td>\n",
-       "      <td>-0.837099</td>\n",
-       "      <td>-0.063611</td>\n",
-       "      <td>...</td>\n",
-       "      <td>19993.0</td>\n",
-       "      <td>-3.811961</td>\n",
-       "      <td>0.000154</td>\n",
-       "      <td>0.015613</td>\n",
-       "      <td>7</td>\n",
-       "      <td>19999.0</td>\n",
+       "      <th>11428</th>\n",
+       "      <td>5455</td>\n",
+       "      <td>74486</td>\n",
+       "      <td>145</td>\n",
+       "      <td>0.469349</td>\n",
+       "      <td>-0.487998</td>\n",
+       "      <td>4.797823</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000147</td>\n",
+       "      <td>1.088522e-05</td>\n",
+       "      <td>0.008062</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000489</td>\n",
+       "      <td>0.021835</td>\n",
+       "      <td>145</td>\n",
+       "      <td>18047.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
        "      <td>3</td>\n",
-       "      <td>20599</td>\n",
+       "      <td>20365</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>6069 rows × 41 columns</p>\n",
+       "<p>11429 rows × 27 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      Unnamed: 0      n   L       h_1       h_2       h_3       h_4       h_5  \\\n",
-       "0              0     63   9  1.010392  2.946148  2.346593 -2.590131 -2.969108   \n",
-       "1              1     72   7  1.524720  1.761242  1.946277 -1.502578 -2.658366   \n",
-       "2              2    144  22  0.401657  1.804630  2.545321 -2.192113 -1.143842   \n",
-       "3              3    144  50  1.579205  2.078706  1.804325 -2.040661 -0.164231   \n",
-       "4              4    211   8  2.882183  2.797885  0.990547 -1.221976 -2.175724   \n",
-       "...          ...    ...  ..       ...       ...       ...       ...       ...   \n",
-       "6064        2904  83099  13 -0.405523  1.663221  1.479237 -1.668656 -0.494702   \n",
-       "6065        2905  83155   7  2.043290  1.023442 -0.493413 -0.457658 -1.069538   \n",
-       "6066        2906  83177   8 -0.052085  1.450502 -0.070146 -0.787487 -0.530743   \n",
-       "6067        2907  83238   8  2.559482  2.711824  0.012999  0.037514 -0.612741   \n",
-       "6068        2908  83291   7  2.478641  3.320015  2.501287 -2.108029 -3.442969   \n",
+       "       Unnamed: 0      n    L     alpha       U_1       U_2 best_model  \\\n",
+       "0               0  16872    7  0.417854  1.300084  6.819479       CTRW   \n",
+       "1               1    144    7  0.539603  1.155300  6.883584       CTRW   \n",
+       "2               2   4857    7  0.501809 -0.378443  7.737951       CTRW   \n",
+       "3               3  21141    7  0.484193  0.887727  7.403677       CTRW   \n",
+       "4               4  14836    7  0.538855  0.593154  7.512572       CTRW   \n",
+       "...           ...    ...  ...       ...       ...       ...        ...   \n",
+       "11424        5451   7644   87  0.545019  2.134504 -2.059474       CTRW   \n",
+       "11425        5452  48130  103  0.465437 -1.435776  6.551026       CTRW   \n",
+       "11426        5453   4420  105  0.456256  1.728149 -0.177973       CTRW   \n",
+       "11427        5454  74486  111  0.457883  1.209557  0.095334       CTRW   \n",
+       "11428        5455  74486  145  0.469349 -0.487998  4.797823       CTRW   \n",
        "\n",
-       "           h_6       h_7  ...        t     log_D         D  est_sigma  \\\n",
-       "0    -1.057501 -0.861557  ...     53.0 -3.272126  0.000534   0.031619   \n",
-       "1    -1.744368  0.614263  ...     60.0 -2.689572  0.002044   0.064027   \n",
-       "2    -1.711132 -0.090239  ...    106.0 -3.094710  0.000804   0.030363   \n",
-       "3    -2.083222  0.255794  ...    106.0 -3.094710  0.000804   0.030363   \n",
-       "4    -0.817759  0.186972  ...    175.0 -2.897075  0.001267   0.056375   \n",
-       "...        ...       ...  ...      ...       ...       ...        ...   \n",
-       "6064 -0.890797 -0.804132  ...  19946.0 -2.558354  0.002765   0.029245   \n",
-       "6065 -1.194567  0.823568  ...  19957.0 -2.079879  0.008320   0.117126   \n",
-       "6066  0.602304 -1.351848  ...  19963.0 -1.975675  0.010576   0.031108   \n",
-       "6067  0.090219 -0.016747  ...  19976.0 -2.272262  0.005342   0.080637   \n",
-       "6068 -0.837099 -0.063611  ...  19993.0 -3.811961  0.000154   0.015613   \n",
+       "          p_fBM          p_LW     p_sBM  ...         D  est_sigma  n_points  \\\n",
+       "0      0.000315  9.779975e-05  0.044585  ...  0.000648   0.029692        76   \n",
+       "1      0.007937  2.202557e-03  0.057203  ...  0.000804   0.030363        50   \n",
+       "2      0.003461  5.241008e-04  0.032163  ...  0.000438   0.024121        50   \n",
+       "3      0.001061  2.475591e-04  0.044009  ...  0.000709   0.027906        45   \n",
+       "4      0.005647  2.016661e-03  0.045361  ...  0.001112   0.037777        38   \n",
+       "...         ...           ...       ...  ...       ...        ...       ...   \n",
+       "11424  0.000096  1.344549e-06  0.007093  ...  0.002423   0.031340        87   \n",
+       "11425  0.001109  2.761263e-05  0.024464  ...  0.000822   0.031445       103   \n",
+       "11426  0.000019  4.760843e-07  0.001840  ...  0.000423   0.022897       105   \n",
+       "11427  0.000044  1.540801e-06  0.004179  ...  0.000489   0.021835       145   \n",
+       "11428  0.000147  1.088522e-05  0.008062  ...  0.000489   0.021835       145   \n",
        "\n",
-       "      n_points  duration                                               file  \\\n",
-       "0            9      61.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "1            7      66.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "2           50     155.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "3           50     155.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "4            8     182.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "...        ...       ...                                                ...   \n",
-       "6064        13   19958.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "6065         7   19963.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "6066         8   19970.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "6067         8   19983.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
-       "6068         7   19999.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "       duration                                               file  \\\n",
+       "0       14224.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "1         155.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "2        4377.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "3       17612.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "4       12553.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "...         ...                                                ...   \n",
+       "11424    1935.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "11425   11760.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "11426    1157.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "11427   18047.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
+       "11428   18047.0  /Users/hverdier/palm_tools_data/export_folder/...   \n",
        "\n",
-       "      arbitrary_condition  unit  traj_ID  \n",
-       "0                       B     0     5158  \n",
-       "1                       B     0     5160  \n",
-       "2                       B     0     5166  \n",
-       "3                       B     0     5166  \n",
-       "4                       B     0     5171  \n",
-       "...                   ...   ...      ...  \n",
-       "6064                    A     3    20595  \n",
-       "6065                    A     3    20596  \n",
-       "6066                    A     3    20597  \n",
-       "6067                    A     3    20598  \n",
-       "6068                    A     3    20599  \n",
+       "       arbitrary_condition  arbitrary_condition_copy  other  unit  traj_ID  \n",
+       "0                        B                         D      F     0     6872  \n",
+       "1                        B                         D      F     0     5166  \n",
+       "2                        B                         D      F     0     5656  \n",
+       "3                        B                         D      F     0     7281  \n",
+       "4                        B                         D      F     0     6658  \n",
+       "...                    ...                       ...    ...   ...      ...  \n",
+       "11424                    A                         C      E     3    16217  \n",
+       "11425                    A                         C      E     3    19327  \n",
+       "11426                    A                         C      E     3    15909  \n",
+       "11427                    A                         C      E     3    20365  \n",
+       "11428                    A                         C      E     3    20365  \n",
        "\n",
-       "[6069 rows x 41 columns]"
+       "[11429 rows x 27 columns]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1958,22 +1785,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fb4f706c520>"
+       "<matplotlib.legend.Legend at 0x7f7c341e2610>"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEICAYAAAB25L6yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAThUlEQVR4nO3df5BdZX3H8c/HTcgGjZuwSacxS9ylolSkDXTbSshQBTtGKCKDTlMFxOqk1moQ+8NQ2rp2xjEzWFFLf2XQDlZdLVHRioLoRiQgSAKBAAkKuIRN07IJJhibAGa+/eOcxZu9d3fP5t5z77O779dMhrvPOfecb549fO7Jc859jiNCAIB0vaDVBQAAxkdQA0DiCGoASBxBDQCJI6gBIHGzytjowoULo7u7u4xNA8C0tGXLlj0RsajWslKCuru7W5s3by5j0wAwLdl+fKxlDH0AQOIIagBIHEENAIkrZYwaAFrhueee09DQkA4dOtTqUsbU3t6urq4uzZ49u/B7CGoA08bQ0JDmzZun7u5u2W51OVUiQnv37tXQ0JB6enoKv4+hDwDTxqFDh9TZ2ZlkSEuSbXV2dk76jJ+gBjCtpBrSI46mPoIaABLHGDWAaeuMdQPate9gw7a3ZP5c3b72rAnXu+GGG3TBBRdo+/btOumkk+reL0E9FV19irR/Z3V7x1Lp8m3NrwdI1K59BzW47tyGba977Y2F1uvv79eKFSvU39+vD3/4w3Xvl6CeivbvlPr2V7f3dTS/FgBHOHDggDZt2qSNGzfqvPPOa0hQM0YNAA30ta99TStXrtTLX/5ydXZ2asuWLXVvk6AGgAbq7+/XqlWrJEmrVq1Sf39/3dtk6AMAGuSpp57SwMCAtm3bJts6fPiwbOuqq66q67ZBzqgBoEE2bNigiy++WI8//rgGBwf1xBNPqKenR7fddltd2+WMGsC0tWT+3MJ3ahTd3nj6+/v1wQ9+8Ii2Cy+8UP39/TrzzDOPer8ENYBpq8g9z420cePGqrY1a9bUvV2GPgAgcQQ1ACSOoAaAxBHUAJA4ghoAEkdQA0DiuD0PwPQ11kyTR2uCGSrb2tp0yimnKCLU1tama665RsuXL697twQ1gOlrrJkmj9YEM1TOnTtXW7dulSTdfPPNuuKKK3TrrbfWvVuGPgCgBE8//bQWLFjQkG0VOqO2fbmkd0kKSdskvSMi0n0eOwC0wMGDB7Vs2TIdOnRIu3fv1sDAQEO2O+EZte0lktZI6o2IV0lqk7SqIXsHgGlkZOhjx44duummm3TJJZcoIurebtGhj1mS5tqeJelYSf9d954BYBo7/fTTtWfPHg0PD9e9rQmDOiJ2SfqYpJ2SdkvaHxHfHr2e7dW2N9ve3IjCAGAq27Fjhw4fPqzOzs66tzXhGLXtBZLOl9QjaZ+k621fFBGfq1wvItZLWi9Jvb299Z/rY1y1pm4cbG9BIUDKOpY29lmiHUvHXTwyRi1JEaHrrrtObW1tde+2yMXE10n6SUQMS5Ltr0haLulz474Lpar5ZOW+ppcBpG2ce57LcPjw4VK2W2SMeqekV9s+1tmzZM6WtL2UagAAVYqMUd8laYOke5TdmvcC5UMcAIDyFbqPOiI+JOlDJdcCAHWLiLoeJFu2o7ldj28mApg22tvbtXfv3obcu1yGiNDevXvV3j65K//M9QFg2ujq6tLQ0FBD7l0uS3t7u7q6uib1HoIawLQxe/Zs9fT0tLqMhmPoAwASR1ADQOIIagBIHEENAIkjqAEgcQQ1ACSOoAaAxBHUAJA4ghoAEkdQA0DiCGoASBxBDQCJI6gBIHEENQAkjqAGgMQR1ACQOIIaABJHUANA4ghqAEgcQQ0AiSOoASBxBDUAJI6gBoDEEdQAkDiCGgASR1ADQOIIagBIHEENAIkjqAEgcQQ1ACSOoAaAxBHUAJA4ghoAEkdQA0DiCgW17fm2N9jeYXu77dPLLgwAkJlVcL1PSropIt5s+xhJx5ZYEwCgwoRBbbtD0pmSLpWkiHhW0rPllgUAGFFk6KNH0rCkf7d9r+1rbb+w5LoAALkiQT1L0mmS/iUiTpX0c0lrR69ke7XtzbY3Dw8PN7hMAJi5igT1kKShiLgr/3mDsuA+QkSsj4jeiOhdtGhRI2sEgBltwqCOiP+R9ITtV+RNZ0t6qNSqAADPK3rXx/skfT6/4+MxSe8oryQAQKVCQR0RWyX1llsKAKAWvpkIAIkjqAEgcQQ1ACSOoAaAxBHUAJA4ghoAEkdQA0DiCGoASBxBDQCJI6gBIHEENQAkjqAGgMQR1ACQOIIaABJHUANA4oo+OABTRPfaG6valsyfq9vXntWCagA0AkE9zQyuO7eqrVZ4A5g6GPoAgMQR1ACQOIIaABJHUANA4ghqAEgcd31MJx1Lpb6OquZNcxZKqr4bBMDUQFBPJ5dvq9ncVSO8AUwdDH0AQOIIagBIHEENAIkjqAEgcQQ1ACSOoAaAxBHUAJA4ghoAEkdQA0DiCGoASBxBDQCJI6gBIHEENQAkjqAGgMQVDmrbbbbvtf2NMgsCABxpMmfUl0naXlYhAIDaCj04wHaXskeEfETSB0qtCL909SnS/p1VzUOxUF0tKAdAaxR9wssnJP2VpHljrWB7taTVkrR06dK6C4OykO7bX9W8Yu2NGmx+NQBaZMKhD9t/IOnJiNgy3noRsT4ieiOid9GiRQ0rEABmuiJj1GdIeqPtQUlflHSW7c+VWhUA4HkTBnVEXBERXRHRLWmVpIGIuKj0ygAAkriPGgCSV/RioiQpIr4n6XulVAIAqIkzagBI3KTOqNF83WtvrGpbMn9uCyoB0CoEdeIG153b6hIAtBhDHwCQOIIaABJHUANA4ghqAEgcQQ0AiSOoASBxBDUAJI6gBoDEEdQAkDiCGgASR1ADQOIIagBIHEENAIkjqAEgcQQ1ACSOoAaAxBHUAJA4ghoAEkdQA0DiCGoASBxBDQCJI6gBIHEENQAkjqAGgMQR1ACQOIIaABJHUANA4ghqAEgcQQ0AiSOoASBxBDUAJI6gBoDEEdQAkDiCGgASR1ADQOImDGrbx9veaPsh2w/avqwZhQEAMrMKrPMLSX8eEffYnidpi+1bIuKhkmsDAKjAGXVE7I6Ie/LXP5O0XdKSsgsDAGQmNUZtu1vSqZLuqrFste3NtjcPDw83qDwAQOGgtv0iSV+W9P6IeHr08ohYHxG9EdG7aNGiRtYIADNaoaC2PVtZSH8+Ir5SbkkAgEoTXky0bUmflrQ9Ij5efkkzzxnrBrRr38Gq9sH2xu2je+2NVW1L5s/V7WvPatxOAJSiyF0fZ0i6WNI221vztr+OiG+WVtUMs2vfQQ2uO7d6QV/j9lFr+7XCG0B6JgzqiNgkyU2oZcbaNGeN1PfW6gUdS5tfDIDkFDmjRsm6vEfq29/qMgAkiqCeCTqWSn0dVc2b5iyUVGPIBUBSCOqZ4PJtNZu7aoQ3gPQwKRMAJI6gBoDEEdQAkDiCGgASR1ADQOIIagBIHEENAInjPuoZjsmagPQR1DMckzUB6WPoAwASR1ADQOIIagBIHEENAIkjqAEgcdz1MZMxTzUwJRDUMxnzVANTAkMfAJA4zqhRE99YBNJBUKMmvrEIpIOhDwBIHGfUqMbdIEBSCGpU424QICkMfQBA4jijbqIz1g1o176DVe2D7S0oBsCUQVA30a59B2veTaG+ppcCYAohqJto05w1Ut9bqxd0LG1+MQCmDIK6ibq8R+rb3+oyAEwxXEwEgMRxRo3CdmuRFte4RS9rf6QFFQEzA0GNwsYK41rhDaBxGPoAgMQR1ACQOIY+0BBMiwqUh6BGQzAtKlAegrrBdve9TIs1XHuZFmlxk+sBMPUVCmrbKyV9UlKbpGsjYl2pVU1hizU85pdaZlpI/6D9strfxJQ0FAu14plPVbUzXAJUmzCobbdJ+idJvy9pSNLdtr8eEQ+VXRymiDHmr17csVS6vPaHVldfR83hkt19L5P6qv9F0rJgv/oUaf/O6vaOpTWngx1r4i0+gFCPImfUvyPpkYh4TJJsf1HS+ZJmdFCPNcQxI4c3xpi/elyTDPexgv2MdQM1x8I3zVmTfWW/oLE+CAbbd6r70Beq2/XWmvv9QftlWtxeY+jrkBo2+VbZXzCa7IcNH07lc0SMv4L9ZkkrI+Jd+c8XS/rdiHjvqPVWS1qd//gKSQ8fZU0LJRX/P6x5qGtyqGtyqGtypmNdL42IRbUWNOxiYkSsl7S+3u3Y3hwRvQ0oqaGoa3Koa3Koa3JmWl1FvvCyS9LxFT935W0AgCYoEtR3SzrRdo/tYyStkvT1cssCAIyYcOgjIn5h+72SblZ2e95nIuLBEmuqe/ikJNQ1OdQ1OdQ1OTOqrgkvJgIAWotJmQAgcQQ1ACSuaUFte6Xth20/YnttjeVzbH8pX36X7e6KZVfk7Q/bfn2T6/qA7Yds32/7u7ZfWrHssO2t+Z+GXmAtUNeltocr9v+uimVvt/3j/M/bm1zX1RU1/cj2voplZfbXZ2w/afuBMZbb9qfyuu+3fVrFsjL7a6K63pbXs832HbZ/s2LZYN6+1fbmJtf1Gtv7K35ff1exbNxjoOS6/rKipgfyY+q4fFmZ/XW87Y15Fjxo+7Ia65R3jEVE6X+UXYR8VNIJko6RdJ+kV45a5z2S/jV/vUrSl/LXr8zXnyOpJ99OWxPreq2kY/PXfzpSV/7zgRb216WSrqnx3uMkPZb/d0H+ekGz6hq1/vuUXXwutb/ybZ8p6TRJD4yx/BxJ35JkSa+WdFfZ/VWwruUj+5P0hpG68p8HJS1sUX+9RtI36j0GGl3XqHXPkzTQpP5aLOm0/PU8ST+q8f9kacdYs86on/8aekQ8K2nka+iVzpd0Xf56g6SzbTtv/2JEPBMRP5H0SL69ptQVERsj4v/yH+9Udh952Yr011heL+mWiHgqIn4q6RZJK1tU1x9J6m/QvscVEd+X9NQ4q5wv6bORuVPSfNuLVW5/TVhXRNyR71dq3vFVpL/GUs+x2ei6mnl87Y6Ie/LXP5O0XdKSUauVdow1K6iXSHqi4uchVf8ln18nIn4hab+kzoLvLbOuSu9U9ok5ot32Ztt32n5Tg2qaTF0X5v/E2mB75EtJSfRXPkTUI2mgorms/ipirNrL7K/JGn18haRv297ibIqGZjvd9n22v2X75Lwtif6yfayysPtyRXNT+svZsOypku4atai0Y4z5qAuyfZGkXkm/V9H80ojYZfsESQO2t0XEo00q6b8k9UfEM7b/RNm/RlKaAWeVpA0RcbiirZX9lTTbr1UW1Csqmlfk/fUrkm6xvSM/42yGe5T9vg7YPkfSDZJObNK+izhP0u0RUXn2XXp/2X6Rsg+H90fE043c9niadUZd5Gvoz69je5akDkl7C763zLpk+3WSrpT0xoh4ZqQ9Inbl/31M0veUfco2pa6I2FtRy7WSfqvoe8usq8IqjfpnaYn9VcRYtbd8igTbv6Hsd3h+ROwdaa/oryclfVWNG/KbUEQ8HREH8tfflDTb9kIl0F+58Y6vUvrL9mxlIf35iPhKjVXKO8bKGHivMRA/S9kAeo9+eQHi5FHr/JmOvJj4n/nrk3XkxcTH1LiLiUXqOlXZxZMTR7UvkDQnf71Q0o/VoIsqBetaXPH6Akl3xi8vXPwkr29B/vq4ZtWVr3eSsgs7bkZ/VeyjW2NfHDtXR17o+WHZ/VWwrqXKrrssH9X+QknzKl7foWwWy2bV9asjvz9lgbcz77tCx0BZdeXLO5SNY7+wWf2V/90/K+kT46xT2jHWsM4t8Bc9R9mV0kclXZm3/b2ys1RJapd0fX7Q/lDSCRXvvTJ/38OS3tDkur4j6X8lbc3/fD1vXy5pW36gbpP0zibX9VFJD+b73yjppIr3/nHej49Iekcz68p/7pO0btT7yu6vfkm7JT2nbAzwnZLeLend+XIrewDGo/n+e5vUXxPVda2kn1YcX5vz9hPyvrov/z1f2eS63ltxfN2pig+SWsdAs+rK17lU2Q0Gle8ru79WKBsDv7/id3VOs44xvkIOAInjm4kAkDiCGgASR1ADQOIIagBIHEENAIkjqAEgcQQ1ACSOoEZLVcxR/YDt6/PJdo52W322/yJ/fcc46823/Z56tg80E0GNVjsYEcsi4lWSnlX2Ta/n5ZOxT/o4jYjl4yyer2z+c2BKIKiRktskvcx2d/4Ekc9KekDS8bYvsv3D/Oz732y3SZLtK509SWaTpFeMbMj2gYrXl+TTwd5n+z8krZP0a/m2rsrXmdT2R7N9su3v5Ov+re1/tP3bJfQRZiCmOUUS8hkT3yDpprzpRElvj4g7bf+6pD+UdEZEPGf7nyW9zfaDyibwWqbsWL5H0pZR2z1Z0t8om6tiT/7YphdLelVELMvXOert5+8fmafmLcomLNohaUtE3N2IvgEIarTaXNtb89e3Sfq0pJdIejyyp2RI0tnKpnG9O3voj+ZKelLZrGRfjfwJPK79HMazJF0fEXskKSKesv3iUevUs31Jep2keyPiwXy9YyT9Q9EOACZCUKPVDo6c2Y7Iw/LnlU2SrouIK0at9/4G1VDv9pdJujd/z0uUPRvy9gbVBjBGjSnhu5LenD+5Q7aPyx/19X1Jb7I91/Y8ZU/9GG1A0ltsd468V9LPlD2gtBHbl7KLoCOPVvqosnmagYYhqJG8iHhI2Tjzt23fr+zhoIsje9jol5TNQfwtSVVjwvlwxEck3Wr7Pkkfj+wpKrfntwReVc/2c1+QdKbth/N1f2D7Ew366wPMRw0AqeOMGgASR1ADQOIIagBIHEENAIkjqAEgcQQ1ACSOoAaAxP0/rxB9YxMp55kAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1993,22 +1820,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fb4f7a96760>"
+       "<matplotlib.legend.Legend at 0x7f7c341f04f0>"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2029,22 +1856,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fb4f926eeb0>"
+       "<matplotlib.legend.Legend at 0x7f7c34051d90>"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2067,7 +1894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -2098,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2107,14 +1934,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1000/1000 [00:00<00:00, 2537.56it/s, loss=5.35e-6]\n"
+      "100%|██████████| 1000/1000 [00:00<00:00, 2472.86it/s, loss=3.46e-5]\n"
      ]
     },
     {
@@ -2131,12 +1958,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x432 with 4 Axes>"
       ]
@@ -2151,16 +1978,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<palm_tools.analysis.mds_analysis.MDSAnalysis at 0x7fb4f9597100>"
+       "<palm_tools.analysis.mds_analysis.MDSAnalysis at 0x7f7c33d9efd0>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2171,7 +1998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -2197,23 +2024,23 @@
        "      <th></th>\n",
        "      <th>n</th>\n",
        "      <th>L</th>\n",
-       "      <th>h_1</th>\n",
-       "      <th>h_2</th>\n",
-       "      <th>h_3</th>\n",
-       "      <th>h_4</th>\n",
-       "      <th>h_5</th>\n",
-       "      <th>h_6</th>\n",
-       "      <th>h_7</th>\n",
-       "      <th>h_8</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>best_model</th>\n",
+       "      <th>p_fBM</th>\n",
+       "      <th>p_LW</th>\n",
+       "      <th>p_sBM</th>\n",
+       "      <th>p_OU</th>\n",
+       "      <th>p_CTRW</th>\n",
+       "      <th>x</th>\n",
        "      <th>...</th>\n",
-       "      <th>D</th>\n",
-       "      <th>est_sigma</th>\n",
        "      <th>n_points</th>\n",
        "      <th>duration</th>\n",
        "      <th>file</th>\n",
        "      <th>unit</th>\n",
        "      <th>traj_ID</th>\n",
        "      <th>arbitrary_condition</th>\n",
+       "      <th>arbitrary_condition_copy</th>\n",
+       "      <th>other</th>\n",
        "      <th>X_1</th>\n",
        "      <th>X_2</th>\n",
        "    </tr>\n",
@@ -2221,134 +2048,134 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>63</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1.010392</td>\n",
-       "      <td>2.946148</td>\n",
-       "      <td>2.346593</td>\n",
-       "      <td>-2.590131</td>\n",
-       "      <td>-2.969108</td>\n",
-       "      <td>-1.057501</td>\n",
-       "      <td>-0.861557</td>\n",
-       "      <td>1.024147</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000534</td>\n",
-       "      <td>0.031619</td>\n",
-       "      <td>9</td>\n",
-       "      <td>61.0</td>\n",
+       "      <td>16872</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.417854</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000315</td>\n",
+       "      <td>0.000098</td>\n",
+       "      <td>0.044585</td>\n",
+       "      <td>0.000198</td>\n",
+       "      <td>0.954804</td>\n",
+       "      <td>5.929249</td>\n",
+       "      <td>...</td>\n",
+       "      <td>76</td>\n",
+       "      <td>14224.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>0</td>\n",
-       "      <td>5158</td>\n",
+       "      <td>6872</td>\n",
        "      <td>B</td>\n",
-       "      <td>0.037688</td>\n",
-       "      <td>-0.012737</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
+       "      <td>-0.055998</td>\n",
+       "      <td>-0.008513</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>23</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.964172</td>\n",
-       "      <td>2.530523</td>\n",
-       "      <td>0.598008</td>\n",
-       "      <td>-1.038586</td>\n",
-       "      <td>-1.432702</td>\n",
-       "      <td>-1.241982</td>\n",
-       "      <td>0.254620</td>\n",
-       "      <td>1.149425</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.003247</td>\n",
-       "      <td>0.055952</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18.0</td>\n",
+       "      <td>26260</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.491596</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.002267</td>\n",
+       "      <td>0.000488</td>\n",
+       "      <td>0.015299</td>\n",
+       "      <td>0.004114</td>\n",
+       "      <td>0.977832</td>\n",
+       "      <td>4.678056</td>\n",
+       "      <td>...</td>\n",
+       "      <td>80</td>\n",
+       "      <td>16467.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>1</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2546</td>\n",
        "      <td>A</td>\n",
-       "      <td>0.015171</td>\n",
-       "      <td>0.003542</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
+       "      <td>-0.019613</td>\n",
+       "      <td>-0.009517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>9</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0.115493</td>\n",
-       "      <td>1.404299</td>\n",
-       "      <td>0.270990</td>\n",
-       "      <td>-1.160955</td>\n",
-       "      <td>-0.935690</td>\n",
-       "      <td>-1.629945</td>\n",
-       "      <td>0.087874</td>\n",
-       "      <td>0.225354</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.006020</td>\n",
-       "      <td>0.071946</td>\n",
-       "      <td>13</td>\n",
-       "      <td>25.0</td>\n",
+       "      <td>3705</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.659744</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000635</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>0.010715</td>\n",
+       "      <td>0.000588</td>\n",
+       "      <td>0.988050</td>\n",
+       "      <td>6.554890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1239.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>2</td>\n",
-       "      <td>10300</td>\n",
+       "      <td>10688</td>\n",
        "      <td>B</td>\n",
-       "      <td>-0.013350</td>\n",
-       "      <td>0.013050</td>\n",
+       "      <td>D</td>\n",
+       "      <td>E</td>\n",
+       "      <td>0.027003</td>\n",
+       "      <td>-0.011830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>56</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1.946118</td>\n",
-       "      <td>1.859288</td>\n",
-       "      <td>-0.179611</td>\n",
-       "      <td>-0.811196</td>\n",
-       "      <td>-1.317777</td>\n",
-       "      <td>-0.737733</td>\n",
-       "      <td>-0.148952</td>\n",
-       "      <td>0.290119</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.004331</td>\n",
-       "      <td>0.074693</td>\n",
-       "      <td>9</td>\n",
-       "      <td>18.0</td>\n",
+       "      <td>4420</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.508250</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000600</td>\n",
+       "      <td>0.000129</td>\n",
+       "      <td>0.005971</td>\n",
+       "      <td>0.000916</td>\n",
+       "      <td>0.992385</td>\n",
+       "      <td>5.557804</td>\n",
+       "      <td>...</td>\n",
+       "      <td>105</td>\n",
+       "      <td>1157.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>3</td>\n",
-       "      <td>15457</td>\n",
+       "      <td>15909</td>\n",
        "      <td>A</td>\n",
-       "      <td>-0.042065</td>\n",
-       "      <td>0.026334</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
+       "      <td>0.045053</td>\n",
+       "      <td>-0.002676</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>4 rows × 40 columns</p>\n",
+       "<p>4 rows × 26 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    n   L       h_1       h_2       h_3       h_4       h_5       h_6  \\\n",
-       "0  63   9  1.010392  2.946148  2.346593 -2.590131 -2.969108 -1.057501   \n",
-       "1  23  10  1.964172  2.530523  0.598008 -1.038586 -1.432702 -1.241982   \n",
-       "2   9  13  0.115493  1.404299  0.270990 -1.160955 -0.935690 -1.629945   \n",
-       "3  56   9  1.946118  1.859288 -0.179611 -0.811196 -1.317777 -0.737733   \n",
+       "       n  L     alpha best_model     p_fBM      p_LW     p_sBM      p_OU  \\\n",
+       "0  16872  7  0.417854       CTRW  0.000315  0.000098  0.044585  0.000198   \n",
+       "1  26260  7  0.491596       CTRW  0.002267  0.000488  0.015299  0.004114   \n",
+       "2   3705  7  0.659744       CTRW  0.000635  0.000012  0.010715  0.000588   \n",
+       "3   4420  7  0.508250       CTRW  0.000600  0.000129  0.005971  0.000916   \n",
        "\n",
-       "        h_7       h_8  ...         D  est_sigma  n_points  duration  \\\n",
-       "0 -0.861557  1.024147  ...  0.000534   0.031619         9      61.0   \n",
-       "1  0.254620  1.149425  ...  0.003247   0.055952        10      18.0   \n",
-       "2  0.087874  0.225354  ...  0.006020   0.071946        13      25.0   \n",
-       "3 -0.148952  0.290119  ...  0.004331   0.074693         9      18.0   \n",
+       "     p_CTRW         x  ...  n_points  duration  \\\n",
+       "0  0.954804  5.929249  ...        76   14224.0   \n",
+       "1  0.977832  4.678056  ...        80   16467.0   \n",
+       "2  0.988050  6.554890  ...        84    1239.0   \n",
+       "3  0.992385  5.557804  ...       105    1157.0   \n",
        "\n",
        "                                                file  unit  traj_ID  \\\n",
-       "0  /Users/hverdier/palm_tools_data/export_folder/...     0     5158   \n",
-       "1  /Users/hverdier/palm_tools_data/export_folder/...     1        3   \n",
-       "2  /Users/hverdier/palm_tools_data/export_folder/...     2    10300   \n",
-       "3  /Users/hverdier/palm_tools_data/export_folder/...     3    15457   \n",
+       "0  /Users/hverdier/palm_tools_data/export_folder/...     0     6872   \n",
+       "1  /Users/hverdier/palm_tools_data/export_folder/...     1     2546   \n",
+       "2  /Users/hverdier/palm_tools_data/export_folder/...     2    10688   \n",
+       "3  /Users/hverdier/palm_tools_data/export_folder/...     3    15909   \n",
        "\n",
-       "   arbitrary_condition       X_1       X_2  \n",
-       "0                    B  0.037688 -0.012737  \n",
-       "1                    A  0.015171  0.003542  \n",
-       "2                    B -0.013350  0.013050  \n",
-       "3                    A -0.042065  0.026334  \n",
+       "   arbitrary_condition  arbitrary_condition_copy  other       X_1       X_2  \n",
+       "0                    B                         D      F -0.055998 -0.008513  \n",
+       "1                    A                         C      E -0.019613 -0.009517  \n",
+       "2                    B                         D      E  0.027003 -0.011830  \n",
+       "3                    A                         C      E  0.045053 -0.002676  \n",
        "\n",
-       "[4 rows x 40 columns]"
+       "[4 rows x 26 columns]"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2359,7 +2186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -2385,23 +2212,23 @@
        "      <th></th>\n",
        "      <th>n</th>\n",
        "      <th>L</th>\n",
-       "      <th>h_1</th>\n",
-       "      <th>h_2</th>\n",
-       "      <th>h_3</th>\n",
-       "      <th>h_4</th>\n",
-       "      <th>h_5</th>\n",
-       "      <th>h_6</th>\n",
-       "      <th>h_7</th>\n",
-       "      <th>h_8</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>best_model</th>\n",
+       "      <th>p_fBM</th>\n",
+       "      <th>p_LW</th>\n",
+       "      <th>p_sBM</th>\n",
+       "      <th>p_OU</th>\n",
+       "      <th>p_CTRW</th>\n",
+       "      <th>x</th>\n",
        "      <th>...</th>\n",
-       "      <th>D</th>\n",
-       "      <th>est_sigma</th>\n",
        "      <th>n_points</th>\n",
        "      <th>duration</th>\n",
        "      <th>file</th>\n",
        "      <th>unit</th>\n",
        "      <th>traj_ID</th>\n",
        "      <th>arbitrary_condition</th>\n",
+       "      <th>arbitrary_condition_copy</th>\n",
+       "      <th>other</th>\n",
        "      <th>X_1</th>\n",
        "      <th>X_2</th>\n",
        "    </tr>\n",
@@ -2409,134 +2236,134 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>63</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1.010392</td>\n",
-       "      <td>2.946148</td>\n",
-       "      <td>2.346593</td>\n",
-       "      <td>-2.590131</td>\n",
-       "      <td>-2.969108</td>\n",
-       "      <td>-1.057501</td>\n",
-       "      <td>-0.861557</td>\n",
-       "      <td>1.024147</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000534</td>\n",
-       "      <td>0.031619</td>\n",
-       "      <td>9</td>\n",
-       "      <td>61.0</td>\n",
+       "      <td>16872</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.417854</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000315</td>\n",
+       "      <td>0.000098</td>\n",
+       "      <td>0.044585</td>\n",
+       "      <td>0.000198</td>\n",
+       "      <td>0.954804</td>\n",
+       "      <td>5.929249</td>\n",
+       "      <td>...</td>\n",
+       "      <td>76</td>\n",
+       "      <td>14224.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>0</td>\n",
-       "      <td>5158</td>\n",
+       "      <td>6872</td>\n",
        "      <td>B</td>\n",
-       "      <td>0.037688</td>\n",
-       "      <td>-0.012737</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
+       "      <td>-0.055998</td>\n",
+       "      <td>-0.008513</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>23</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.964172</td>\n",
-       "      <td>2.530523</td>\n",
-       "      <td>0.598008</td>\n",
-       "      <td>-1.038586</td>\n",
-       "      <td>-1.432702</td>\n",
-       "      <td>-1.241982</td>\n",
-       "      <td>0.254620</td>\n",
-       "      <td>1.149425</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.003247</td>\n",
-       "      <td>0.055952</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18.0</td>\n",
+       "      <td>26260</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.491596</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.002267</td>\n",
+       "      <td>0.000488</td>\n",
+       "      <td>0.015299</td>\n",
+       "      <td>0.004114</td>\n",
+       "      <td>0.977832</td>\n",
+       "      <td>4.678056</td>\n",
+       "      <td>...</td>\n",
+       "      <td>80</td>\n",
+       "      <td>16467.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>1</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2546</td>\n",
        "      <td>A</td>\n",
-       "      <td>0.015171</td>\n",
-       "      <td>0.003542</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
+       "      <td>-0.019613</td>\n",
+       "      <td>-0.009517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>9</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0.115493</td>\n",
-       "      <td>1.404299</td>\n",
-       "      <td>0.270990</td>\n",
-       "      <td>-1.160955</td>\n",
-       "      <td>-0.935690</td>\n",
-       "      <td>-1.629945</td>\n",
-       "      <td>0.087874</td>\n",
-       "      <td>0.225354</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.006020</td>\n",
-       "      <td>0.071946</td>\n",
-       "      <td>13</td>\n",
-       "      <td>25.0</td>\n",
+       "      <td>3705</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.659744</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000635</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>0.010715</td>\n",
+       "      <td>0.000588</td>\n",
+       "      <td>0.988050</td>\n",
+       "      <td>6.554890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1239.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>2</td>\n",
-       "      <td>10300</td>\n",
+       "      <td>10688</td>\n",
        "      <td>B</td>\n",
-       "      <td>-0.013350</td>\n",
-       "      <td>0.013050</td>\n",
+       "      <td>D</td>\n",
+       "      <td>E</td>\n",
+       "      <td>0.027003</td>\n",
+       "      <td>-0.011830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>56</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1.946118</td>\n",
-       "      <td>1.859288</td>\n",
-       "      <td>-0.179611</td>\n",
-       "      <td>-0.811196</td>\n",
-       "      <td>-1.317777</td>\n",
-       "      <td>-0.737733</td>\n",
-       "      <td>-0.148952</td>\n",
-       "      <td>0.290119</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.004331</td>\n",
-       "      <td>0.074693</td>\n",
-       "      <td>9</td>\n",
-       "      <td>18.0</td>\n",
+       "      <td>4420</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.508250</td>\n",
+       "      <td>CTRW</td>\n",
+       "      <td>0.000600</td>\n",
+       "      <td>0.000129</td>\n",
+       "      <td>0.005971</td>\n",
+       "      <td>0.000916</td>\n",
+       "      <td>0.992385</td>\n",
+       "      <td>5.557804</td>\n",
+       "      <td>...</td>\n",
+       "      <td>105</td>\n",
+       "      <td>1157.0</td>\n",
        "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
        "      <td>3</td>\n",
-       "      <td>15457</td>\n",
+       "      <td>15909</td>\n",
        "      <td>A</td>\n",
-       "      <td>-0.042065</td>\n",
-       "      <td>0.026334</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
+       "      <td>0.045053</td>\n",
+       "      <td>-0.002676</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>4 rows × 40 columns</p>\n",
+       "<p>4 rows × 26 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    n   L       h_1       h_2       h_3       h_4       h_5       h_6  \\\n",
-       "0  63   9  1.010392  2.946148  2.346593 -2.590131 -2.969108 -1.057501   \n",
-       "1  23  10  1.964172  2.530523  0.598008 -1.038586 -1.432702 -1.241982   \n",
-       "2   9  13  0.115493  1.404299  0.270990 -1.160955 -0.935690 -1.629945   \n",
-       "3  56   9  1.946118  1.859288 -0.179611 -0.811196 -1.317777 -0.737733   \n",
+       "       n  L     alpha best_model     p_fBM      p_LW     p_sBM      p_OU  \\\n",
+       "0  16872  7  0.417854       CTRW  0.000315  0.000098  0.044585  0.000198   \n",
+       "1  26260  7  0.491596       CTRW  0.002267  0.000488  0.015299  0.004114   \n",
+       "2   3705  7  0.659744       CTRW  0.000635  0.000012  0.010715  0.000588   \n",
+       "3   4420  7  0.508250       CTRW  0.000600  0.000129  0.005971  0.000916   \n",
        "\n",
-       "        h_7       h_8  ...         D  est_sigma  n_points  duration  \\\n",
-       "0 -0.861557  1.024147  ...  0.000534   0.031619         9      61.0   \n",
-       "1  0.254620  1.149425  ...  0.003247   0.055952        10      18.0   \n",
-       "2  0.087874  0.225354  ...  0.006020   0.071946        13      25.0   \n",
-       "3 -0.148952  0.290119  ...  0.004331   0.074693         9      18.0   \n",
+       "     p_CTRW         x  ...  n_points  duration  \\\n",
+       "0  0.954804  5.929249  ...        76   14224.0   \n",
+       "1  0.977832  4.678056  ...        80   16467.0   \n",
+       "2  0.988050  6.554890  ...        84    1239.0   \n",
+       "3  0.992385  5.557804  ...       105    1157.0   \n",
        "\n",
        "                                                file  unit  traj_ID  \\\n",
-       "0  /Users/hverdier/palm_tools_data/export_folder/...     0     5158   \n",
-       "1  /Users/hverdier/palm_tools_data/export_folder/...     1        3   \n",
-       "2  /Users/hverdier/palm_tools_data/export_folder/...     2    10300   \n",
-       "3  /Users/hverdier/palm_tools_data/export_folder/...     3    15457   \n",
+       "0  /Users/hverdier/palm_tools_data/export_folder/...     0     6872   \n",
+       "1  /Users/hverdier/palm_tools_data/export_folder/...     1     2546   \n",
+       "2  /Users/hverdier/palm_tools_data/export_folder/...     2    10688   \n",
+       "3  /Users/hverdier/palm_tools_data/export_folder/...     3    15909   \n",
        "\n",
-       "   arbitrary_condition       X_1       X_2  \n",
-       "0                    B  0.037688 -0.012737  \n",
-       "1                    A  0.015171  0.003542  \n",
-       "2                    B -0.013350  0.013050  \n",
-       "3                    A -0.042065  0.026334  \n",
+       "   arbitrary_condition  arbitrary_condition_copy  other       X_1       X_2  \n",
+       "0                    B                         D      F -0.055998 -0.008513  \n",
+       "1                    A                         C      E -0.019613 -0.009517  \n",
+       "2                    B                         D      E  0.027003 -0.011830  \n",
+       "3                    A                         C      E  0.045053 -0.002676  \n",
        "\n",
-       "[4 rows x 40 columns]"
+       "[4 rows x 26 columns]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2554,14 +2381,102 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 56,
+   "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",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>file</th>\n",
+       "      <th>arbitrary_condition</th>\n",
+       "      <th>arbitrary_condition_copy</th>\n",
+       "      <th>other</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
+       "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>E</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>E</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>/Users/hverdier/palm_tools_data/export_folder/...</td>\n",
+       "      <td>B</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                file arbitrary_condition  \\\n",
+       "0  /Users/hverdier/palm_tools_data/export_folder/...                   A   \n",
+       "1  /Users/hverdier/palm_tools_data/export_folder/...                   B   \n",
+       "2  /Users/hverdier/palm_tools_data/export_folder/...                   A   \n",
+       "3  /Users/hverdier/palm_tools_data/export_folder/...                   B   \n",
+       "\n",
+       "  arbitrary_condition_copy other  \n",
+       "0                        C     E  \n",
+       "1                        D     E  \n",
+       "2                        C     E  \n",
+       "3                        D     F  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tss.index_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
     "mmd = MMDInterGroupAnalysis(track_sets=tss,\n",
     "    unit_key=[\"file\"],\n",
     "    null_mode=\"mix\",\n",
-    "    group_by_keys=[\"arbitrary_condition\"],\n",
+    "    group_by_keys=tss.index_df.columns[1:],\n",
     "    # it is possible to delimit units by a column of the trajectories files (if they have additional information)\n",
     "    # or to consider a cartesian product of two columns. \n",
     "    # example of valid values : [\"file\",\"time_bin\"], [\"file\",\"organelle\"], [\"organelle\"] (spanned across files)\n",
@@ -2574,21 +2489,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'arbitrary_condition': 'A', 'arbitrary_condition_copy': 'C', 'other': 'E'},\n",
+       " {'arbitrary_condition': 'B', 'arbitrary_condition_copy': 'D', 'other': 'E'},\n",
+       " {'arbitrary_condition': 'B', 'arbitrary_condition_copy': 'D', 'other': 'F'},\n",
+       " {'arbitrary_condition': 'B', 'arbitrary_condition_copy': 'D'},\n",
+       " {'other': 'E'}]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mmd.groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Il y a un problème dans la façon dont on définit les comparaisons / groupes.\n",
+    "# Il faut "
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "data": {
+      "text/plain": [
+       "[({'arbitrary_condition': 'B', 'arbitrary_condition_copy': 'D', 'other': 'E'},\n",
+       "  {'arbitrary_condition': 'B', 'arbitrary_condition_copy': 'D', 'other': 'F'})]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mmd.comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'arbitrary_condition': 'A', 'arbitrary_condition_copy': 'C', 'other': 'E'},\n",
+       " {'arbitrary_condition': 'B', 'arbitrary_condition_copy': 'D', 'other': 'E'},\n",
+       " {'arbitrary_condition': 'B', 'arbitrary_condition_copy': 'D', 'other': 'F'},\n",
+       " {'arbitrary_condition': 'B', 'arbitrary_condition_copy': 'D'},\n",
+       " {'other': 'E'}]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mmd.groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 5)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mmd._p_val.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Saved dict to /Users/hverdier/palm_tools_data/export_folder/MMD_inter_groups/example/params.json\n"
+      "WARNING:root:Not adding group {'arbitrary_condition_copy': 'C', 'other': 'E'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition_copy': 'D', 'other': 'E'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition_copy': 'D', 'other': 'F'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition': 'A', 'other': 'E'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition': 'B', 'other': 'E'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition': 'B', 'other': 'F'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition': 'A', 'arbitrary_condition_copy': 'C'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'other': 'F'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition_copy': 'C'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition_copy': 'D'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition': 'A'} because it already has an equivalent\n",
+      "WARNING:root:Not adding group {'arbitrary_condition': 'B'} because it already has an equivalent\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m/Users/hverdier/palm-tools/examples/trajs-files.ipynb Cellule 62\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/hverdier/palm-tools/examples/trajs-files.ipynb#Y105sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m mmd\u001b[39m.\u001b[39;49mprocess(force_recompute\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n",
+      "File \u001b[0;32m~/palm-tools/src/palm_tools/analysis/analysis_classes.py:67\u001b[0m, in \u001b[0;36mAnalysisStep.process\u001b[0;34m(self, force_recompute)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mis_already_processed) \u001b[39mor\u001b[39;00m force_recompute:\n\u001b[1;32m     66\u001b[0m     logging\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mStarting to process...\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 67\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process()\n\u001b[1;32m     68\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msave()\n\u001b[1;32m     69\u001b[0m \u001b[39melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/palm-tools/src/palm_tools/analysis/mmd_analysis.py:1789\u001b[0m, in \u001b[0;36mMMDInterGroupAnalysis._process\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1787\u001b[0m     np\u001b[39m.\u001b[39mfill_diagonal(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_p_val, \u001b[39m1.0\u001b[39m)\n\u001b[1;32m   1788\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_p_val[np\u001b[39m.\u001b[39msum(np\u001b[39m.\u001b[39misnan(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_D_null), axis\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mnan\n\u001b[0;32m-> 1789\u001b[0m \u001b[39massert\u001b[39;00m (np\u001b[39m.\u001b[39mtranspose(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_p_val) \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_p_val)\u001b[39m.\u001b[39mall()\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
      ]
     }
    ],
@@ -2598,12 +2629,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 288x288 with 1 Axes>"
       ]
@@ -2620,12 +2651,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": "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",
       "text/plain": [
        "<Figure size 172.8x259.2 with 2 Axes>"
       ]
@@ -2642,27 +2673,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/hverdier/palm-tools/src/palm_tools/analysis/mmd_analysis.py:587: SettingWithCopyWarning: \n",
+      "/Users/hverdier/palm-tools/src/palm_tools/analysis/mmd_analysis.py:606: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  df.sort_values(\"distance\", inplace=True)\n",
-      "/Users/hverdier/palm-tools/src/palm_tools/analysis/mmd_analysis.py:932: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+      "/Users/hverdier/palm-tools/src/palm_tools/analysis/mmd_analysis.py:951: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
       "  plt.tight_layout()\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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5jbXcCqN7+IhdBpEkMDBTh7v6k71IKdKP+xwV5Y29WZUYI+GD9sBQDww87y53gn5VOdyYEIQHx0lzSa5XZvbCigN5kgzLANA70BVfzh2AaD9nONkq4CmhYRiX8tD4KDw0PkrsMoiIuiTLOM2mTnPzVwcNYfnwU+NQXNuCQWEecHWQ9iXe8wkAwrwc8frsPpDJpDVMoc0tg0Pw18OjxC7DpCbF+iHE09EiwjIRERnHwEwdqpf/uVn1vx4tQlZ5IybGWNaanxHejpJdXYCIiMgScUgGdagnr+qJO0eGY9HKJLy1KQMAMN7CAjMvaxMREUkLAzN1OE8nW6xcOAgvrT+JplaNZMe5EhERkWVgYCaTsFXI8eqs3mKXQURERHTFOIaZiIiIiMgIBmYiIiIiIiMYmImIiIiIjGBgJiIiIiIygoGZiIiIiMgIBmYiIiIiIiMYmImIiIiIjGBgJiIiIiIygoGZiIiIiMgIBmYiIiIiIiMYmImIiIiIjGBgJiIiIiIygoGZiIiIiMgIBmYiIiIiIiMYmImIiIiIjGBgJiIiIiIygoGZiIiIiMgImSAIYtdAIpLJZBUA8sWuo4OFCILg/fedltJWibYTsJy28vMrvXYCltPWi35+icwdAzMRERERkREckkFEREREZAQDMxERERGREQzMRERERERGKP7Li728vITQ0FATlSKOpKSkyr9PUJBiOwHLaevF2glYTlul2E7ActrKz6/02glYTlstpZ2A5bT1UsckS/OfAnNoaCgSExNNVYsoZDLZBTOUpdhOwHLaerF2ApbTVim2E7CctvLzK712ApbTVktpJ2A5bb3UMcnScEgGEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATJ3mQHYVMkobxC6DiIiI6D/psoF5T2YFhr2xHdVNqos+36BU47nfU1FzieepazleWItbvj6EqR/twbt/ZaBVoxW7JCIiIqJ/pUsGZpVGh+d+T0NxbQuKa1oueP50WQNmfLoPqw4V4FBulQgVdo73tpxGwqtbzf6koFmlwcM/HYevsy2u7uuPj7dnYdpHe3GsoEbs0ogui1YnYOjr2zD23Z3Q6gSxyyEiIhNTiF3AxSzfn4fcyiYAQIu6fU/k+uQS/O+3FDjYKLBq4SAMDvcUo0STe+yXZPyaVAQAcLCVi1zNlXltYzryqprw/cLBGBLhielx/nhq9Qlc8/l+LBgehkcm9IC9jXm38b9qUWlhZ20FmUwmdilXrKlVA1uFFRTyLnn+3eHO1LVgyOvbDdtyK/N/D4mIyLgu9wtX2diKj7ZlopurHYBzgVmt1eHF9Wm4/4djiOnmgj8eGG72YXnJ7mwsXH7kgv03LjloCMs7HxsNW4X5hskdGeVYebAAC4eHYUiE/v0a08MHfz08EnMSgrF0Ty6u+mgP6pVqkSu9Mot/S8EviYVIzKu+4DmdTsAbf57CF7uy8czaE5j60R70emEz8quaRai0Y/2WVITY5zfj3u+Pttuv0uhw7ef7Ebr4DxzKkc5VoM1ppYawbC2XIevVKSJX1HkEgT3pRGS5ulwP87t/ZaBFrcXr1/TGfd8fQ4tKi/J6Je5ZdRSJ+TW4fVgonrqqJ6zNvDdLqdbitY2n2u0TBAHD39yB4lr9MJTDT4+Dj7OdGOVdllaNtl24r25S4YlfUxDt54zHJvVo91pnO2u8Nqs3lCotNqScgTn/FmeVN+LHI4X48UghAGD1PUPRP9gdgP7SfcRTGw2vdbJVIC7IDXePioCttXl/htVaHR79JRkADOFfpxPw0oaTWLY/z/C6EE9HMcrrUEq1Fi9tOInvDxUAACbF+uKLWwZI4grBP9FodbjmiwNILqzF+vuGo3egq9glERF1ui4VmNNK6vDjkULMHxaG0LM/svd+f9QwRvCjG/vh6r7+YpbYYW7+6hAA4MaEIAD6oBF+XrA68cJEONtZi1Lb5UgrqcPUj/ZiZJQ3bh8WilGR3nhydQrqmtVYMT/hor3kSrUW206VY1IvP7jam09b/25DSkm77dVHi9A/2B0qjQ5Rz/xp2D+7fwBen93brK8YnK9ZdW641J0jw7HqUD6eXpNq2De1dzd8fGM/WJn5kIXsikYsWHYEeWdPCsZG+2DhiHCLCMv5VU0Y9fZOw3a4t/mf/Kw+WoSNJ0oxIMQd8aHu6B3gCjtraXwn/04QBCjVunZD3kpqW1De0Iq4IDfxCiMyQ6IGZkEQUN7QitzKJuRUNOGpNScAAL8kFuLrvbkAYAjLVjJIJiw3KNVIytdPeHt1Zu8LgtWplyeb3QE8wtsJ/YLdsPt0BXafrjDsf3BcJHp2c7non9mcVoq6FjVuiA/qrDJN4o+UM4bH3s62WJ98Bo9PikbfF/8y7H98Ug/cMzpCUiFr3fFiw+NHfk42PA72cMAfDww3qxO+S/n9eDEe/PG4YXtYd09sP1WO1OI6HH56vHiFmYBKo8PUj/bg8Uk9MCHGFz8cLjQckwH9SbyjbZfqY7ksrRodcioasTW9DIB+aE2svysGhLhjwfAw+LvZi1xhx7lp6SEo5DJ8t2AQVBodvtmXi4+2ZSLAzR5/PTxSUsejNjqdgOLaFgR5OIhdCklMpx/9BEHAC+vSkJhfg7zKJjSpLlxebFQPHxRUNSG5qM6wb919wzuzTJO6celBAMBtQ0PRrNai1/ObDc9lvTrFLCdP2VnL8dW8eMz+fH+7sblf781Fs0qDeUNCLziA/ZxYiEB3ewyNMN+x6BmlDcgsbzRs9w5wxfZT5e3C8qc39cfUPt3EKM9kBEHAs7+nXbB/52OjEepl/r2QbZ5afS4w+rrYYl+Wfjz2B3PiRKrIdHZklCOzvBF3fpeEsdE+2H6qHAAQ6G6PTQ+NhJMEwjIA3JgQjHE9fbBifz5KaltQ3azCzowKHC+sRQ8/Z1xv5ifw5/N1scWRvBok5lVj8eoTyCpvxPievnh+eozkwnJKUS3e/es0In2c8NXeXKy9d5ike9H3Z1WirEGJWf0CxS7FYogQmIG1x0ug1upwfXwQwr0d0c3VHnesSESUrxP+fHAk5FYybEsvw4LliQCAZ6b2RK8AaYybq2xsRWpxPQCgf4i7ISzbW8uR9uIks7587elki69vHYjx7+0CALw0IxaHc6vxzb48fL03FxNifLFoVAT6BbujoKoZ+7Kq8OiEKLNuc9twjBGRXjhd1gCVRtfueSketPMqmzD1oz3t9i2dF48JMb4iVWQa2RWNsDovVJTVtwIAZvULwNAIL7HKMpnC6nMnum1hOSHUA9/ePlASPctt6lrUWPRdEo4W1Br2eTra4PmrYzFdYie2YV5OWHu8BNd+cQAA8PWt8RjXU1rf0za5lU3YdboCu85e4ewtkcxwMT8eLsDisyfz0X4ul7yKSx2r07syraxkmD8sDM0qLa6PD8K8IaHIKNUHyBeujjUs0dTYqjH8mfnDwjq7TJO54csDhscP/HDM8Hjro6PMOji22XKyzPD49+MleOe6vtj7vzFYNCoCh3Krcezsj9TPiYWwkgHXxpvv2bEgCNiQcga2Ciu8MrMXgj0csDer0vD8vsVjJReWKxpaMfqdnRdcGQrzktblzxNFdRj37i40tGrgbHcuLDrbKfDC9FgRKzOdg39bzWRIuCeWzZdWWAaAF9entQvLtwwOxtZHRuHqvv6S63W1UZz7iX9ofKRkwzIAxPqfC42TYn0lvdxjpK+z4THDcucR5dr/bUND4WSrwKc7s1BWr8RnO7MxKdbX0GvTqtHixfUnzxUpkQ9+UU0zsiuaLvpcanHdRfebk7SSOry3JQNTe3fD5zf3x9GCmrM3LLHDE5OjcWDxONw0KBgarQ6/JBViVJQ3urma73jBVo0OBdXNeHhCFGSQ4UjeuRuxnHhhIgIkNBayzfk/wOcrP9v7KgV7Mysx/ZO9hu1W9bmrBi9Mj4Wrg/mPzf47nU7A1vRyw/bw7l745raBcLCRVlgGgCen9IT92TkiM+P88crM3nB3tBG5KtM4U6dfcal/sBseGh8lcjWm1d3HGS/NiMXqe4biy7nxYpdjUgNC3JH+0mTkvTFV7FIsiihHQ1cHa8wbEoLPd2WjuKYFGq2Ap6+KMTz/1qaMS94S25wNf3OH4bGbgzVemdkL0X7OsFXI4e1sK2JlV06p1uLhn47DzlqORydGIcDdHk9f1ROv/JGO1zam45lpMYaZ2ltPlqGsvhUvXh0sctVXxs5ajqRnxqOwugUj3z733kplwtvFuNpbY//isRj6xrkbd9w3pjsGhnmIWFXH2ZlRjjtXJAEArhsQiAUjwjD5gz2G7dn9A8Qsz2T2nHdlZESkF5bOize7icf/lrezLTydbFBU04JRPbzFLseknpgcDbVWh0WjIsQupVPMGxIqdgmdxtJu9tUViNZ9sGB4GD7bmY3jhbW4e3QEgj31l3R3ZpTj6725uG1oaLu1XKUk0N0e3y8cbGizFOw6XYHTZfrJb2Pf1Y9htpbrrwx8tTcX/YLdDRPfvtydjQA3e4zr6SNOsR0oMa8GC1ckGra7udohRqKXyGqbVfh8ZzaW7c+DtVyGWwaH4N4x3eHlZN4ne+fr4eeMCTG+eHVWL7g52LQbpvDyzF6Su2TfZsfZMcvdfZwkHZbbvDA9Fo/+koxRUeZ/DDLGyVaB12f3EbsMIkkQLTB7nvcjO6WXHwD9+MjHfklGD19nLJ4SLanAnFOhD5Ox/i5YdnuC2fco/924aB98NS8eNc0qNLVq0NiqQWOrFk2tGjSrtAg5e3JwJK8aR/Jq8ML0GLO/+cynO7Lw9uYMAMDiKdH4cGsmxvf0lWyoyixvxJI9OZjVLwAPj4+S5LJN3Vzt8enN/Q3b9tZyONjI8euioZIOkc52CsyM88eb1/aRzDrhxoyP8UXy8xPFLoOIzIioA9SWz0/AgmVHcM+qo/h+4WA8ty4VDUoNVi0cDDtrOVzsFOjh5/zP/yMz0M3VHi/P7IWZcf6SvFyvkFth/L9YJWHryTJ4ONrghoHmPRxDpxMMYfnOkeHo7u2EFrUWE2OlO6lmYKgHdj8+RpJB+VL6Brnh5EuTxS7D5B6d2OOfX0REZMFEDcyjoryx+p6huOWrQ5jy4W40qbR4eUasISSnvDBJzPI6lL2NHHMHh4hdhugWT4nG7cPCzH78lZWVDK/P7o3n16Xhm7252JVRAWdbBQaFme+a0v+GJYVlIiKiNqJfE+8T6IYf7tT3KE+K9cUtDJWSJpPJ4OdqJ3YZHeLGhGAkPjMeQyI8kVHWgNHRPpdcRYKIiIjMV5dYMyjW3xX7Fo+FjdxKsuM/SZpc7Kzx7W0D8cPhAoyMkvaMeyIiIkvVJQIzAElPqCFpU8itMNeCljMiIiKyNLx+TERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkhEwQhH//YpmsAkC+6coRRYggCN7n75BoOwHLaesF7QQsp60SbSdgOW3l51d67QQsp62W0k7Actp60WOSpflPgZmIiIiIyNJwSAYRERERkREMzERERERERjAwExEREREZofgvL/by8hJCQ0NNVIo4kpKSKv8+mF2K7QQsp60XaydgOW2VYjsBy2krP7/SaydgOW21lHYCltNWSz8mtflPgTk0NBSJiYkdU1UXIZPJLpjNKsV2ApbT1ou1E7CctkqxnYDltJWfX+m1E7CctlpKOwHLaaulH5PacEgGEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBNRpytvUCKzrEHsMoiIiP4VBmbqMsrqldhxqlzsMsjENqeVIuHVbZjw/m6xSyEiIvpXGJipy3h5w0nctTIJ5fVKsUshE/l8Zzbu+i4JAPDarN4iV0NERPTvMDBTl7D7dAU2pJzBvaO7w8fFTuxyqAOoNDrc9u1hHMypAgA88MMxvLnpFABg1cJBuGlQsJjlERER/WsKsQsgUqq1ePb3VIR5OWLR6HCxy6EOIAgCop75EwAQH+KOJ35NQUF1MwBg26OjEOHtJGZ5He61jek4mFOFdfcNF7sUIiIyAQZmEt1nO7ORX9WMlQsGwVYhF7sc6gD3/XDM8Pidv04bHh97dgLcHW3EKMmkFFYynCyph1qrg7WcF+6IiKSmyx7ZdToBW06WQacTxC7FpDalnkFeZZPYZYgmp6IRX+zMxow4fwyP9BK7HJMqrm3B02tOGIYoSNWPhwvwR8qZC/affmWKJMMyAHT3cYJGJyC/ynK/y0REUtZlA/PG1DO4Y0UiUorr2u3X6QQo1VqRqupY+7IqsWjlUbzx5ymxSxGFIAh49vdU2Fpb4empPcUux2TagvKwN7bj58RCnDpTL3ZJJnMguwqLV59ot8/D0Qa5r18FG0WXPdxcVKtGi8S8ahwrqEFqcR1OlzUgt7IJRTXNKK9XoqZJhcZWDfKrmnCyRP+eZpUzMFPX16zSYPn+PLRqpPFbStQZRBmSodbq8OORQtjIZbhuQBCsrGTtnj+UU4VX/0gHAOiE9j3Mp8sbMO2jvfhy7gCM6+nbaTV3tKZWDRavTgEA7DpdAaVaCztrOWqaVPj+cAGu7uuPIA8Hkas0rXXJJdiXVYWXZ8TCx1l6E/2Ka1vw2Y4s/JxYCLVW/zl+8epekp3sllnWgBuXHmy3b2y0D765baBIFV2Z7w7k45Wzx6F/69Gfj2NS7CTIZLJ/fjGRSL7ak4v3tpxGrwBXDAhxF7ucDnc4txq9AlzgYMNRp9RxOvXTJAgCtqaX4/WN6cg5Owzh9+MleOvaPgh014dDnU7ADUvO/ejanDceUKsTMPmDPQCAcDOfNPT25gwU1bTggXGR+GhbJnZmlKOqSYW3N2egtlmNnt2cJR2Y61rUeHlDOvoGuuKmQSFil9OhmlUavL7xFH48UgAACPJwQE5FE4I9HDBCosNOyuuVmP7J3nb77h/bHY9O7CFSRVduTkIwyuqVWLY/D2qtgJhuLnCyU+BwbnW719lby9Gq0UInAFfH+TMsU5dW3qDEF7uyMaWXnyTDcn5VE67/8gBenhGLuUNCxS6HOpggCKIdYzvtGmlaSR1u/uoQ7liRCJkM+PrWeLw+uzeSC2sx+YM9+PlIIQRBQFJBTbs/p5Dr/2GqGlsR8dRGw/5QT/MNk0fyqrH8QB5uHRKK+8d2BwAsWnkUT69JRW2zGuN7+mBMDx+RqzStdzZnoLqpFa/O6g25lbQCxnt/ncbKQ/m4Pj4Iz0yNQVFNC3r4OuPXRUMkeRLU1KrB/OVHoFTrDPs+uCHOrMMyADjZKvD01BhseXgUJsX64uSZehzOrUa4lyMeGNsdfz08EnlvTMWTV0VDJwDT+nTDKzOls7a0VifgydUn8GtSkdildBqNVofGVo3YZZjU+1syodLo8L/J0WKXYhIHsvVzRNannMHwN7ej8OzqPGT+ssobEfbkRqw5Js4xqVN6mL87kIfn1qXBzd4aL82IxY0JwYaZ5MO7e+HxX5PxxG8p+DP1zAUHK4WVFY4W1GD2Z/sN+4Z19zTbXhylWov//ZqCADd7LBgehmfXprZ73sPRBq/P7mO27fs3jhfWYuWhfNw6JBS9AlzFLqdDZZQ24Nv9eZgzMBjxoe547JcU9ApwxfLbB8LNwXwmvDW2avDMmhMY1cMbs/oFXvJ1Gq0O9/9wDKnF58Zl/7poCOJDPTqjzE4R6uWIL+fGI7W4DjIZENPNxfD9/DmxEM/9noYJMb54/4Y4yZz8FVY3Y8RbOwAA2eWNuHbApT8D5qq4tgWejjZoVeuw83Q5tqWXY2dGOeYOCcHjk6QZJk+XNeCnIwW4dWgoQr0cxS7HJNomVbddCTpeWCvJjgpL06rRYvx7uwBAtJWIOiUw21rLIQhArwBXXDcgqF1jgzwc8P3CwVh+IA+vbUw3jPVss/JgPpbtzwMAjIryxr6sSsQFuXVG2Sbx/tbTyKlswlW9/TDt471obNUgxNMB+VX6s+BXZ/aCt7OtyFWajkarw9NrTsDbyRaPTowSu5wOJQgCnvs9Fc52CnRztcPDPyVjaIQnlsyLh5OteY2lc7SRI7O8EUcLajGtj/9FD1CCIOD5dWnYft7tzHc/PgbBZnz1x5i/n9ytSy7B/35LwYhIL3xyUz+zX06uWaVBUn4NSmpb8L/fzk3cXLEgQcSqTCO7ohHj3t3Vbp+now0mxvphWIQ0h00BwOsb0+Foq8ADYyPFLsUkBEHAwZz2Q6bczaij4r+oblLhnb8yEOvvgpslNqzx7wTh3HDcADd7TOvjL0odnfIrfn18EDRaAU+vPYF53xzCV7cOhKu9teF5KysZbh8WBoWVDM/+ntbuz7aF5ccn9cDgcE/sOl2BPoFunVF2h0surMWXu3IAABtPlGJwuAdemtEL1U0qzDk7bntK725ilvifKdVaNKu08LjEcmHHCmrwZ2ophnf3wqBwD3x/qABpJfX45KZ+cLazvuifMVfrkktwKLcaAW72eG/LaYzv6YtPbuoHO2vzW1taJpPhkQlRWLA8Eb8lFWFOwoUTFb/YlYNVhwqwaFQEbkwIgpeTLRzN7MTgcv2VVoqHfzqOgaEeWDI3XhLrh3+4LdNwfGpz+KlxZvn5vRhBEPDgj8exLrnkgud+u3so4oLcJHOF4GL2ZVViR0YFnpwSLYnlHVcfLYJOAGb1CzC8b/lVzSitVxpe89a1fSS5XOkXu7Lbra4l1cDcoFTD2c4ab23OQO7ZeW9bHxklWj2d9ut206BgONsp8MjPx3HjkoNYPj/hgp7U8y/rnu/DOXGYEReAb/flAoBZ9jCX1ikx49N9AAAHGzneuKYPpvfpBqVah0Urkwz7dTrhglVDurKn1pzA6qPFiPV3wcgob4yM9MaAEHfDEmIZpQ1YsjsHS3bnwNFGDq0gYGSUN6aa2YnBP2lQqg0rKhTXtgAANDodbM1sKbXzjY32Qd8gN3y8PQv9Q9xxrKAGNwzUB+d1ySV4c9MpTO/rjycm9TCrz+yV2nW6Avd9fwy9AlzxzW0DYW8jjUD597C87r5hkrtN/flXQ9rcmBAsuclvORWNmP7xXjSp9MvG3TEiDEv35MLLyQa3Dg0Vt7gOoNUJeOTnZADAY78k4+7REZjWpxtmfbq/3euujw8SozyTOz8sPzJBWldq29z/wzEU1zRjVJQPPt+ZDQD4/d5hoh5vO/XXfHpffyydF4+cykZc/+UBFNWcG4yv0ujwZ+qFNzsAgF0ZFahrViO5sBa+LrbwNaODuFYn4LsDeRj8+jYA+hscHH56PK7uq59N/+amU8ipaIKzrQLNKi2Si2rFLfg/enh8FK7pH4hTpQ34fGc2blx6EP1e+gsLlx/BigN5SAjzQEKYfjxr/xB3RPu54OUZsZIbo/3+lkxUNLS227czo8Ks29nWy1xc24KJ7+82XKY/lFOFx35ORkKoB96+to9FheWDOVW467tEdPdxworbE8xuqI0x539Un5jcw2yv5F2KTCZD6ouTkPfGVNx83tKOr87sJWJVpnG8sNYQlgFg6R59Z9OYHj6SuGIgt5Jhy8MjDduf78zG1I/2QqXVTzxu+yxLdZ3p2f0CAOjvMPrAOGkOr4kLcsPRglq8v1V/p9jXZvVGX5E7Szu9+2t0Dx+sXDAIlY2tuO6LA8gqbwQA7MgoR73ywtnJC4aH4ffkEkz8YBf2ZlWir5kdxCsbWw09j70CXLD1kVGGH9l9WZWGIScNZyc7FpjZjN4gDwe8e31f7HxsNOYNCYGNwgpNKi22ppfjud/TMPbdXUg/e1OHFpUWvy4aghBPaU02SS2uwzdnr360eWBsd+S9MVWkijrOyEgvxP+t9+3hn44j0N0eS+YNkMSP7791tKAGC5YdQaC7A75bkABXB2kNKfrlriEA9Cuc3DO6u8jVmI4gCFh1SL/k41sSPeGb3T8Qua9fhdn9AnBjQjB2PDYafzwwHK/Mks7JQaSvM44/NwF+LnZwtlXgySnnJmq23b6hRSXNwPzOdX2x939jcPqVKWKXYjLXx5+baNyzmwvmDBT/aoEo3SPxoR746c4hmPfNYVz/5QG8d31f3PVdUrvX9PB1RkZZAx6f1AMz4vzx6M/JyCxvFP0M47/ydbHDxgdHIMTDAa2ac8tu1bWo8dgv+ktKcisZnpwSjSm9u8Hf1Xx6z88X5OGAl2b0wn1ju+ObvXlYeTDfsOKJXC6Dg40cifk1SD/TgN6B0lkZo7KxFdM+Prf+cHcfJ2y4f7hkgqRMJsMjE6Nw09JDAPQ9V9XNKswbEmpWq35cqbSSOtz2zWF4Odti1cJB8HSS3sTc+FAP7Hp8NIIlvqLAyoP5hsdSvWQP6L+7790QJ3YZJuXmYIP3b4jDTV8dxN6sSgDA3aMjMC7aB1ZWMskeo6ysZIZ7V0iVs501TrwwEflVzQh0t+8SJ7aiXU+M8XfBL4uG4JavDuG2b48Y9ndztcOZOiUqG/WXt63lVugT6Ib19w/H+uQSTOrlJ1bJly3i7E1WFOfNon9xfRrO1Cnh7mCNT2/qj6HdpTExwcfZDounROPu0RH47kAevtmXh+omFfoFu+HZaTHoFeAidokdQqsT8MPhAjxz3rKA2x8dZfY31LmYoRFeeP+GvnhrUwbmLDkApVoHnU745z9oBnQ6AePf3wVbhRyje3hjdJQ3+oe4t1vxIrOsAXO/PgwnWwVWLRxkVkPC/iupXf35u+yKRry6MR0jo7yx/HbzvAMltTckwhP3ju6OT3ZkAQCuHRBo+M0l8+ZsZ92llp4VdQBemJcjfrt7KKZ8uBs1zWp8f8cgDI3wwrA3tqO4tgVWMhhmv9pZy3GdRHoD0krqsPpoMWK6ueDLuQMkuUakq7017hsbifnDw/Dj4UIk5degX5CbWY/pbXMkrxrP/Z6G9DP6oSbWchlOvTxF0jPsZ/ULxIhIbyxYnojkwlqsSy7BM9NixC7rislkgFKlRWF1MzLL9OPwne0UGN7dCyOjvNHUqsGS3TmQW8mw6o7Bku/VkToHGznGRvvg+enSm0dhyR4cH4l92ZUor29FuETXlybxiT6F38/VDoPCPOHtbItBYZ4AgEHh+kliCjNf1/RSIryd8Na1ffDb3UMlGZbP52CjwPzhYfj05v6S+IESBAG3fnPYEJYB4JdFQyUdltt4OdnihzsGAQDKG1qx5WSZyBVdOZlMhnE9faGwssLy+fr1hhVWMhwrqMWTq0/glT/SDT3LYfwhNnvdXO3x2c0DJH2VwBJZy62wcsEg/Hb3UEn8zlDXJPoU7walGtszynFTQrAhdAwO98Tqo8WwlmgIsbOWS3rsnJTJZDKsXDgIX+/Jxaa0UswZGGSWyxxeLgcbBeRWMmh1Au5ddRTr7x+OHn7OYpd1Rcb29MF3B/Px8oaTsJbLsO3R0XB3sEZOZRNc7KwlfSMhIqlwtFVYzDrwJA7RP11Z5Y1wtJFjet9zd24ZfLanWao9zGTe+ge7o//N7qhXquFoI/pXqNO52ClQ06yGrbUVHG3Nf3LjkHBP2FvLcaq0AVN6+RluwsNxkERE1Eb0RNov2B2Hnx6P/sFuhn1BHvbo5mpn9reaJWlzsbO2iKEYfye3ksFaLsOXcwdIYkyvnbXccDcwXvkhIqKL6RLdY38PxjKZDKOivHG0oEakiojoUhaOCEeEtxOGRkhjZRcAuG1oKGzkVhghwdvoEhHRlesSgflinp0WgybVhTcyISJxLRoVIXYJHW5Ydy8Mk8jSjkRE1PG6bGDmAH4iIiIi6go4SJiIiIiIyAgGZiIiIiIiIxiYiYiIiIiMYGAmIiIiIjKCgZmIiIiIyAgGZiIiIiIiIxiYiYiIiIiMYGAmIiIiIjKCgZmIiIiIyAgGZiIiIiIiIxiYiYiIiIiMYGAmIiIiIjKCgZmIiIiIyAgGZiIiIiIiI2SCIPz7F8tkFQDyTVeOKEIEQfA+f4dE2wlYTlsvaCdgOW2VaDsBy2krP7/SaydgOW21lHYCltNWiz4mtflPgZmIiIiIyNJwSAYRERERkREMzERERERERjAwExEREREZofgvL/by8hJCQ0NNVIo4kpKSKv8+wFuK7QQsp60XaydgOW2VYjsBy2krP7/SaydgOW21lHYCltNWSz8mtflPgTk0NBSJiYkdU1UXIZPJLpjhKcV2ApbT1ou1E7CctkqxnYDltJWfX+m1E7CctlpKOwHLaaulH5PacEgGEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxdTF5lEw7nVotdBhERERGdxcDcxby04SSeWXtC7DKIiIiI6CwG5i5EqxNwJK8ajrYKsUuhDqLS6PDk6hR8tSdH7FKIiIjoMjEwdyGnSuvRoNTAw8FG7FKog1jLZSivb8W7f51GcW2L2OUQkRE7TpVj+f48qLU6sUshoi6GgbkLaRu77O7IwCwVMpkML86IBQA8/3sqBEEQuSIyFaVai9DFf+CFdWlil0KX6cvd2fh2Xy4UVjKxSyGiLoaBuQs5kqcPzB4MzJIS6O6AhydEYmt6OTanlYldDpmATicg+tlNAIDSOqXI1dDlKKppxsGcaszuHwiZjIGZiNozu8AsCAI2pZ7BvauOoq5FLXY5HUYQhHM9zBySITm3DwtDtJ8zXliXhsZWjdjlUAcLf2qj4fEXcweIWAldrjVHiwEAs/oFiFwJEXVFZhWYT5c14JavD2HRyqPYcrIMOp10Lm/nVDahslEFAPBwtBa5GvFodQL2Zlbi8V+SMfKtHUg/Uy92SR3CWm6F12b3RlmDEu/+lSF2OdSBZnyy1/A457WrRKykcxwrqMGcJQfw+/FisUvpMIIgYPWxYgwK80CQh4PY5RB1iMd+SeaE8w5kFssx1LWo8cHW01hxIB9tQ8um9/WX1FjfI+etvWxpPcyCICClqA6/Hy/B+pQSVDS0QiYDrurVDcES+vHqH+yOWwaFYPn+PMzuF4jega5il2RyGq0OVjIZrCQ6JvSpNSeQXFQHAEh/abJk2wkADUo13t6cge8O5kMQgAfGRopdUoc5WlCL3Mom3D06QuxSiK6YIAiY/MEeZJQ1wNlWgYUjwsUuSRK6dGDW6gT8kliItzdnoLpZhZsSguFsZ40vdmVj/vBQscvrUOffrMRSxjDnVDTi9+MlWJdcgtzKJgD6VSVuiA/CXaPCEe7tJHKFHe/xyT2wKa0UT65Jwdp7hkEhN6uLPJfUNplRqxOQVlKPAzlVOJBdhcS8aiSEeeDb2xNErrDjfbM3F98fKgAAHH5qHOxt5CJXZDqbUkvx/LpUlNW3AgDuGR2Bod29RK6q4/x2tAh21la4qnc3sUshuiI6nYA5Sw8io6wBAHDwqXEiVyQdXS4wny5rQHFNC1zsrfHCujScKK7DwFB3LJ+egGg/Z4x6eycGh3sg1l9avXOHcqshkwGCIO1VMsrqlVifrA/JKWd75gDA3lqOmwYFY+GIMHRztRexQtNysbPG89NjcN/3x7DiQD7mDw8Tu6QrVteixg1fHsCp0oYLnnOyVeDWoaGdX5SJbUsvw0sbTgIANtw/HD4udiJX1DEEQUB9iwaVTa2obGjFieI6vPJHervXDAx1xyMTokSqsOMp1VpsSC7B5Fg/OHENfDJjWp2A+cuOGDrgDj81TnL3dWjVaHE0vxb7syvR2KrBs1NjOu3KXpf6l2zVaDHz031oVmkBAH4udvhwThyu7usPmUyGDSklKK5twQtXx4pcaccqrm1BcW0LQjwdkF/VDE+JBeZ6pRqbTpTi9+Ri7M+uwvkrq7naW+PWoaG4bWioxfSsj+/pC3cHa/x4pEASgbmiodUQlucMDEJORRMO51Uj3MsRS+bFo7uPtK4UpBbXYcHyRADAl3MHoFeANE7ei2qacdWHe1CvvPSkVA9HG3x0Yz/JXBkBgG3p5ahXanDNgECxS+kwWp2AqR/twS2DQ3DL4BCxyzGpbelleHPTKSy7PQH+btLtbPknKo0Od69Mwq7TFQCAjQ+MkMyJvEqjw4oDedh1ugJH8qqhVOvXSQ9ws8dz02I6rY4uE5hrm1WIf2UrNGcn8t0xIgwPjY9qd3b0zd5chHg6YGy0j1hlmkTb+OXu3k4orG6Gi535T/pTqrXYmVGOtcdKsD2jHCpN+xsB+LrYYuHwcNw4KNisenU0Wh3qWtTwdLK9rD8vCAKeWn0CNc1qvH1t3w6uThzvbdFPYvzzwRFYti8Ph/OqMTbaBx/MiZPEZ/l8xbUtmPaxfpLf4inRmBTrJ3JFHed4YS3qlRqM7uGNnRkV7Z57eUYsssobMSbaR3JXgFYfLYKfix2GRkhniImVDMgoa0B5/YVLHNY2q/DVnlz07OaCqX3MfwhKY6sGp8sa0aLWXvBcanEdvtiVjVdn9oarg3kfi7Q6AVqdABvFhSerSrUWi1YmGb63X82LR4y/S2eXaDJLdmfjnb9OI9LHCXMGBqOophlb08vxzNSenboEpOhJpbFVg2/35uLdLafb7Z83JLRdWD5WUIOjBbV4YXoM5GY+saa4tgU+zrawPttLcyi3Gs52Cvi42MLdweaCywuCIEAQYFYTiu77/hi2pl98zeHXZ/fG7P4BsFWY35jP34+X4NFfkjE43AM3DQrBpFjf/9SOb/flYfWxYjw8PgrjY3xNWGnn2JRaio0nSnHrkBA8teYEjhXU4r4x3fHIhCiz+rz+G7XNKgx7YzsAYGacPxaNktYEsbYVac4Py89Pj8GNCcGwsza/7+q/UdHQip2nK3DHiHCz/105n0wmg43cCq3n3bGwrkWNr/fm4tu9uWho1eCukeGSCMy2ZwPk+Z0yJ0vq8cHW0/jrZBlc7BRIL63H4HBPsUq8YhqtDgtXJOJwbjUmx/phRr8ADIvwhEJuhcZWDe5YnogDOVUAgGem9pTEb0ubuhY1luzOwfiePlg6Lx5f783FyoP5GBHphcm9OrfDQrTArFRrsepQAT7bkYWqJpVh/3PTYvDShpMorVe2W97nm315cLZV4Nr4IDHK7TDpZ+ox7eO9iPJ1xpvX9EafQDcczq3CwFAP1LWoYS23QlJ+NdLPNOBUaT1OnWlARmkDvpg7AMPMaJLNolHhUKq12JtV2W7/0nnxmGDGX+bxPX3RN9AVB3OqcTCnGu4O1rh2QCDmJAQj4h8mKe7PrsSrG9MxMcYX94/t3kkVm05dsxrP/p4KAFifcgZKtRaf3dxfkhOnWlRaxL20xbD96qzeIlbT8ZbszsanO7IN24tGReCBcd3hYCN6n4pJ/X68GFqdgGv6S2/tZRu5FdQaARqtDp/tzMbSPTloUGowpZcfHhwfiWg/afRAtnU8qbU6FFY34/U/07HxRCmc7RR4eHwUbh8eavZXut7cdAo7MyowNtoHW9LLsPpYMbycbDEyygvrjpcYrszfmBCMBRIY5ne+r/fmol6pwR0jwnHPqqP4M7UUE2J88c51fTv9BkOdfjRUa3X4NakIH23LxJk6JYZ398LUPt3w9JoTuHVoqCEUlp13KamktgUbT5zB/GGhZnX5/mI+2pYJe2s5KhpacfUn+2Ajt4JKq0N2RZPhNdd8fgAA4GynQLSfM2b2CzC78b3xoR5YuXAQtpwswzNrT6CsvhUDQ93NMixXNraivkWNcG8nuDpYY+XCQViwXH+2r1Tr8O2+PCzdk4tBYR64aVAwJvfyu6DXuaimGfd9fwxhXo549/q+kuh9fW1jOioa9KsmONkq8P0dgyTzI3w+nU5Az+c2GbbnDAyS1ESans9uanc5O+WFiWYfMP6t1UeL0SfQFZG+zmKX0uGsFVbILG/AD0cK8d7ZK7jv39AXs/pJZ6w2cC4wJxfWYkt6OXafroCviy1+XTRUEmtqrz5ahKV7cnHrkBC8OKOXYbjjmmPFWH303FroQyM88dKMWEndpbKmSYVv9uYi3MsRi1efQEF1M56cEo07R4aL0s5OPervOFWOF9anIb+qGf2D3fDu9X0xNMILWeWNmBjjh4fGnZt5ff7tZVccyIcgCJg3JLQzy+0wlY2tOHWmAb8fL8afqaUAYLjbm0rbfmzv9L7+mBnnj+huLvB3tTP7D/+EGF+cOlOPd7ecxtNTO29wfkd696/T+DWpEHeODMe9Y7rD2c4ay29PwJ3fJWJPZiXuG9MdDrZy/Hi4EA/+eBzuDta4pn8gbhyk73VuUWlx13dJUGt1WDJ3AJwlEEZ2ZpTjp8RCAMCISC98fGM/uElk/fATRXVwslOgWaWBUq3DDV8eMDx3Y0IwXp8tnd5lQRAwMMwDu89OFLoxIcisw3JdixrXfr4fmeWNcHOwRqC7PQLc7BHg5oAAd3sEutvjpyOF2H6q3PBnPBxt8M3eXPQJdEV8qIeI1Xesa/oHYOmeXOzJPHeV7+GfknGiqB7PTTfPY/HF9A1yQ4S3I579Pc2wr6y+FSPe2oGf7xqChDDzfU9Ti+uwePUJDAn3xDNnJ7fZWcsxuVc3tGp02JxWhihfJ8SHeuB/k6INJw9SsGDZEZworkNjqwaNrRp4O9vi+4WDMEjEoTWdGpg/3p6J/KpmxHRzwRdzB8DHWT+Ds7uPk+F2soIgwFZhZehhblZp8MPhAkzu5WdWZ4uCIOCRn5OxJ7MSlY2t7Z7rE+iKQWEeaFJpDeu4AtLs2alpUmHJ7hxMjPFFXJCb2OVclocnRKJVrcWnO7Kx5mgxnpseg0mxfvjq1njc//0xfLIjC09M7oGdj43G/uwqfH84H8v25+Hb/XnY9fhovLM5AyfP1OPrW+PNcm1pnU7AprRSDIvwgquDNQqrm3Hbt0cAAPOGhOC5aTGSWTVBqxMw/bw7950v0N1eUmEZ0I91XTE/AdVNKvR/eQtCPR3FLumK1DarkFneePaxGrXNaqQWG79baHWTCi9tOIkFw8MkFZifnhqDgaEeuPO7pHb7B4S4i1SRabjaW2PzQyNxx4pE7Dhv/L2/qx26uZr3KhFhXo64cWAQHhwf1S4MC4KAB388DgB497o4yd0Eq16pxrbzTmoHhXng45v6GTKjWDo1MH9160B8tC0TKw/mY/TbO3HXyAjcMTKs3Ti5jLIGqLU62J+dZPLb0WLUtagxf5h5jcsRBH3PcmVjK+yt5bh1aCi+2JWNe8dE4IFxkXh/SyZ+PJwLhZXMMP5o04lSXD/QvMdo/90Xu7PRqNLg0Yk9xC7lsvk42+G9G+IwJyEYz/2eikUrj2JklDdemB6DT2/uj8d+ScZbmzLQ3KrFoxOjMDzSCysP5uOZtan4em8u1h4vwaMTojA22vyGowD6Sar3rDoKAPhwTpzhQH3ToGC8NKOXiJWZxte3xqOuRY2mVk27Xqvq8+ZaSEl1kwq3LzsCKxkw0Ix74wAgxNMReW9MhSAIqGpSobhGv2Rn23+LalpQVNN80TXDHxgnnTsXthkSca43btntAzG6h7RWmGqjkFthbLSPITCfenmyJCaqOtoq8OJFjrE/HSk0PJZaWAaA/Mpmw+PRPbzx1bz4LtEp06mB2cPRBi9cHYtbh4birU2n8P7W01h1KB+PTozCtQOCYCUDXlx3Eq721pg/PAxpJXV4768M9A1yM7uzYisrGZbfnoDlB/Lw5qZT+GJXNhxt5BgW4YUZn+wzHLB1ggBruQzXDgiU1BJVgH4c+rJ9eZgVF4AefuY/RjAhzAMb7h+OFQfy8f6W05j8wR7cMTIMr87qDXtrOT7ZkYVmlRbPTuuJeqUagH5VjMmxfrh3jPlO8gvycMDw7l7Ym1VpCMt9g9zwmsQmvgGA3EqGcT31JzbvbNYvl3f7sFDEBbmZ/fCoiymsbsat3xxGcW0LPr9lAPoHm9dx9lJkMhm8nGzh5WSLvv9wZauktgU2Ciu42kvr6h4AONtZ49lpMejh64zhkeYzafxyTOvjj6LaFiwYHiaJsHwpgiBg8eoTAIDtj44SuZqOJwgCZn++z7D97W0Du8yxV5SZK2Fejvj8lgFIyq/Gq3+k43+/ncC3+/IwNtoHB3Kq8PLMXiiqacEtXx+CvbUcH94Q12X+wf4LKysZbh8WhjE9fPDShpNILa7Drd8ehlqr71G2kVvhhoFBWDQ6AgESXHD94+2Z0OoEPDReOncFU8itMH94GKb17YY3Np7CpzuysfZYCZ6Z2hP2NnJ8sy8XLWoN7K31X61IHye8I4FJfs9Nj8HE93cbtlcukN6trv/O18UWNyYE47lpMWZ5/Pknn+3MwlubMuBip8DKhYMwUELDEf4Lqd/sQmqrJlyKu6MNnpzSU+wyTO6bfXkAADtrK7Mc4vdP1iWXGDLSnw+O6FLHXlGneg8I8cBvdw/Fn6mleHPTKXy2M1t/++tIb0z/ZC+cbBX44Y7BCPY0n7HLF6MTBNQ0q1B+dkUBW4UVbhoUjLtGRsDPzMdYXUpBVTN+PFyIOQlBZv/+Xczfh2ncvUo/TGNqn2744bD+cpmTrQJL5sWb/couABDh7YRFoyLwxS790mMtKq0kJi8aM3dIKARB6FIH7I7SrNLgrU36HvRf7x6KKAmuEkEkNYIg4OUNJwEAOx8bI3I1pnFV725oatVibLRPl8tHov+Sy2QyXNW7G8b39MX65BL0C3ZDkIc95g8Lw+z+AWY10e/vdDoB3x3Mx+t/pkOp1o/LvmVwMO4YGS764HVTe3/raSjkMjwwVnrjAs/392EaTapztxVesSABYV7mPYmqjdxKhsVTojF3SAiOFdTA2/ny7nRobqQYlgH91S0A6OZqx7BMZCY+3p4FQH/1q6uFyY5iLdd3KHZFogfmNjYKK1wz4Nz6kA+ON++gpdHqcPuyI9iTWQlHGznuHh2BhcPDLvuWyubkr7RSrDlWjDtHhkvmXvbGnD9M49t9eXB3sMaoKB9JjNv+O/0SXdK+hG0J5FYyONsqMNEM10UnskSCIBjW09704EiRq7FMXSYwS01TqxYNSg0eGNsdtw8Lg7uZ3XjkSiQV1AAArhsgrQXy/4mPsx3+Nzla7DKI/lFVkwoNrRqESuQKCJHUvfpHOgAgytfJovJEV8LAbCKuDtZYe+8wscsQxROTorFoZAS/1ERdVH6V/s6i5r7uMpGlaFtffM09lpkrugIGZupwcisZwzJRFxYX5I69/xsDD35PiczCknkD0KrRwVECk8jNFf/liYgsjNxKhkB3851QTWRpbBVy2Cqku760ORD/1ilERERERF0YAzMRERERkREMzERERERERjAwExEREREZwcBMRERERGQEAzMRERERkREMzERERERERjAwExEREREZwcBMRERERGQEAzMRERERkREMzERERERERjAwExEREREZwcBMRERERGQEAzMRERERkREMzERERERERsgEQfj3L5bJKgDkm64cUYQIguB9/g6JthOwnLZe0E7Actoq0XYCltNWfn6l107ActpqKe0ELKetFn1MavOfAjMRERERkaXhkAwiIiIiIiMYmImIiIiIjGBgJiIiIiIyQvFfXuzl5SWEhoaaqBRxJCUlVV5qgLclsKT31FLaKsV2ApbTVn5+pddOwHLaaintBCynrZZ+TGrznwJzaGgoEhMTO6aqLkImk0lthud/YknvqaW0VYrtBCynrfz8Sq+dgOW01VLaCVhOWy39mNSGQzKIiIiIiIxgYCYiIiIiMoKBmYiIiIjICAZmIiIiIiIjGJiJiIiIiIxgYCYiIiIiMuI/LStHRERElqOuRY19WZXYlVEBJzsFnp0WI3ZJRKJgYCYiIiIDnU7AN/ty8WdqKY4X1kKrEwAAzrYMzGS5OCSDurRmlcZwsCYiItNbsicHr/yRDpVGh3tGR+DXRUMwq18AnO3Yx0aWi4GZurQPt2ZiwCtbGJqJiDpBUn4N3t6cgam9u2HdfcPw6MQeiA/1QItKCycGZrJgZheYjxfWoqqxVewyqJOkltQhyN0BciuZ2KUQWby6FjVe25iOeqVa7FLIBOqa1Xjgh2Pwd7PD69f0hkx27rj7xOQeePe6OPGKIxKZWQVmQRBw/w9H8dgvyWKXQp1AEASkFtejV4CL2KUQEYDPdmRhye4c7M2sFLsU6mCCIOCJ35JRVq/Exzf2h4uddbvnw72d0DvQVaTqiMRnVoH5VGkDCqtbMDHWT+xSqBMU1bSgrkWNXgE8SBOJ7UxdC5btzwMAlNS2iFsMdbjvDuZjc1oZ/jc5GnFBbmKXQ9TlmFVg/iutDDIZML6nr9ilUCdILa4DAPTyZ2AmEtuHWzMhCICNwgrFDMySklZSh1c2pGNMD28sGB4mdjlEXZJZBebNaaUYEOwOb2dbsUuhTpBaUge5lQw9/JzFLoXIomWVN+LnxELcPDgYoZ4OKK5hYJaKplYN7v/+GNwdrfHu9XGw4nwRoosym8BcWN2Mk2fqMTGWvcuWIrW4HpE+TrCzlotdCpFFe/evDNhby3HDwCCcqVXyOykhz65NRV5VEz6c0w8ejjZil0PUZZlNYN5ysgwAMCGG45fN2fZTZcirbPrH1+kn/NWht8TGL/98pBD5Vf/cfinIKm8QuwST2pBSgjXHilBerxS7FJNKLqzFn6mlWDgiHEt350Kp0eKh8ZFil2VSdc1qnCqtF7sMk/s1qQirjxXjgXGRGBzuKXY5RF2a2Syq+NfJUkT5OiHMy1HsUugyJeZVY/6yRNhZW+HJKT0xd3DIJS//ldYrUdWkktSEv81ppfjf6hTclBCMV2f1FrsckxEEAZ/tzMY7f2VgxfwEjIj0FrukDncopwr3fX/MsN3dxwmjorzxyIQoONqazWH1X3lr8yl4ONqgf4g7PtyWibtHRyDc20nsskwmr7IJ8745DK1OwL7FY8Uux2Syyhvx7NpUDA73wP1jpX0CRNQRzKKHuaZJhcO51ZjI3mWzpdMJeHnDSfi62GJIuCeeX5eGud8cuuTkodRife+OVJaUSy2uw0M/HkffQDdJ31pWqxPw7O+peHtzBmb09cegMOn1Wqk0Ojy9NhUAMDbaB4A+fKw6lI/yBmmtEb8nswL7sqpwz+gIvLXpFLq52uH+sd3FLstkThTV4dov9qOguhmPT+ohdjkmo1Rrcd/3R+FgI8eHc/pxnXuif0HUrpDfjxdj1cEC/HTX4HYLpP/dtlPl0AnAJC4nZ7bWHi9GclEd3r2uL2b3D8CPRwrxyoaTmPz+bjx/dSyu6R/Q7jOQWlwHKxnQs5t5BmZBEFDR2IrTpY3Yk1mBL3fnwNPRBkvmDZDk+E+1VgeNVsD9PxzD1vQy3D06Ao9P7CG5CUS1zSrc9u0RZJU3AgC2nypHQpgHZvcLwJRe3eDqYP0P/wfzIQgC3tqUgQA3e8hkMqSV1OOTm/rBwUZaPeht9mRWYNF3SWhSaXFDfBBm9gsQuySTeeWPkzhV2oBltw+Er4ud2OUQmQXRjnwarQ5vbcpAq0ZrNCwDwF9ppejmaieZ3kZLU69U44lfU2BvLYe/mz02p5UCAOYPD8PBnCo89ksyNqeV4rVZvQ0roKQW1yHC28nsfpxrm1V47JdkJOXXoKa5/d3Q3r2+L3ycpffjVFqnxODXtwEArGTAyzNiMXdIqLhFdSClWosdp8qx5lgxdmSUQ63V36b98Uk9MCPOH4HuDiJXaBqb08pworgOT10VjY+2ZWJIuCem9u4mdlkmsfZYMR77JRkanYAoXye8cHWs2CWZzJ8nzmDlwQLcNTIco3v4iF2OSRwrqEGIpyMnMVKHEi2N/HHiDIprWzA43MPo61pUWuzOrMAN8UH/GKypc+l0AnZlVqCktgU1TSrUNKvP/leF6vMeNyg1AACNTosblx5s9//4cE4cJsX64a3NGZj0wW68OrMXpvTuhtSSOgyN8BKjWVfk67252JpejhvigxDl54yXN5wEAMzqFyDZH6ffjhYZHn9xywBJ3FhIpxNwOK8aa48V448TZ9Cg1MDb2Ra3DgnFV3tzMSLSC/eOke7QBK1OwLt/ZSDMyxFZ5Y1obNXgxRmxkjwGf7UnB6/8kQ4AsLeW49Ob+sPeRnpXgQD9alNP/JaCuCA3PCbRISc6nYCbvzqE6+ODJH3iQ51PlMAsCAKW7M4BoJ8sY8yezAoo1TpJ/AhLTUpxHW7/9ohh28FGDncHG3g42sDNwRqhng5oVeuw6WyP8t2jI/D5zmwAQEKoB0ZHe2NstA+c7awxKsobD/98HHevOorJsX4oq29FrL95XVGoV6qxbH8eJsf64c1r+yB08R8AABc7Bd6/IU7c4kwkMa8ab2/OAACM6eFt9t/TFpUWn+7IwppjxSiubYGDjRyTY/0wq38AhkZ4IeKpjQCAvoFu4hZqYrHPb4JSrcONCcH48UgB5g8LQ5SvdNZDL6tXwt3BBm9vPoWle3IN+1+aEYtICbXzfDqdgId+Og4A+PjGfrCWm8UUpv8sv7oZzSotYszs94OMyypvQHcfcb+bogTmfVlVSCvRT+qK/Id/gPUpZ+Bqb42EMOM90dT5mlX6nuMvbumP0T18Ljo295GfjsNGYYU7RoRh6Z5c+LnY4ZOb+iE+tP37GenrjDX3DMMn27PwyY4sADC7FTJWHsxHg1KDe8d0x8fbMg37k5+fKGJVprMtvQwLlicatqdI4HL97swKfLIjC8O6e+KJyT0wIcbXMCzoud9TDa8b1UN6K3+0OZRTBaVaBwD44XAB3B2s8aCElpErrG7GiLd2ID7EHYn5Neju44Ss8kbM7h+A6+KDxC7PZNYcK0ZSfg3evrYPgjykOYwI0J/EA0DkP3TGmaucikaMfXdXu31Hn50gyeEnp0rrkVHagDN1Sry9OQNvXdMH1wwIFK0eUQLzl7uzDY8v1cNcXNuCD7acxvrkEgS62+OzHdm4Nj4QAW72nVUm/QOd/jcVnk62Fw3LxwtrsfpYMRRWMny6IxsjIr3wwQ1x8HS6+J0areVWeHhCFMb19MHW9HIMCHE3ZfkdqkWlxddne6qmf7LXsD/txUmSvIz9/aECPLXmBADghvggPDQhEi525j/hraZJBQB4+9q+8D/vWNOs0mDFgXwAwMmXJpnd2Pp/S6cTcMOS9sOm7hwZIYn3FgCqm1QY8dYOAEBifg0AoKxOiQhvR7w8o5eYpZlUY6sGb246hb5Bbrimv3iBw1RaVFq8tCENPxwuNOyrPvtdlpq2e1K0cXOwhoNEhxBtTi3D+1tPAwD6Brlhci9xr2B2+lE/raQOezIr4edih9J65QVngeX1Sny6Iws/HC6ESqtPZEU1LXh/62n4u9lJugfA3OgE/eSniy2EIAiCYfyuVhDw0PhI3D828l8tX9Qn0A19zOyS96pD+ag67wDt5mCNL24ZILk1eQVBwHtbTuPj7fqrAON7+uKNa3pL5qSgrkU/UbNeqYY/zgXm8Wd7dOYODpFsWAaA9Skl7bbDvRyxcESYSNV0vMLq5gv2tWp0+PTm/pL7rp7v0x1ZKG9oxZdzB0hu5RoA+Gh7ZruwDADDupvfHJh/465REYjxd8Hcrw8DAP54YIQkV14CgIUjwnC0oAa7TuvnsYn9He30v33J7hw42siREOaBHRnlhlURKhtb8cXObHx3MB9anYDr4oNw39ju+tuxfnkAja0aTO/r39nlkhHas4H5YmHpr5NlSMqvgaejDT6YEyfJm1e0qWxsNUwaAoAwL0f8smgIvC7Rk26u1FodFv92wjDJr1+wGz65qZ9kwjIAjIzyxmc7szH5gz14ZmpPTIr1w7f78lBSp7+b30szzHMS0aGcKjSrtBgTfemJp2X1Sjz443HD9oAQd/x299BOqK7z9A1yw5dzB+Cu75IM+16Z2QvRftId75pf1YSv9+Ridv8A9As2n6t2/8WEGF8cyqnC0YJaBLrbY/fjYyR5YtBmxYF82Cis8MzUnpK+6u5oq8BnN/fHtlPlhjXvxdSpgbmophkbUs5g/rBQnCiuQ6SPE2qb1ViyJwfL9+dBqdZidv9APDA2EsGe+jFWG1JKkFneiLev7SPZsyhzpT27vNbu0xXo/7cDsYONHFP7dMOzU2Pg5yq9pdTa5Fc1YdTbOw3bg8I88MMdgyV3sK5XqtHnhb8M2yGeDvj61oGS+0727OaCXxcNwYT3d+OVP9LbnQj9dKfx9eK7stc2piO1pB7f3jYQI6MuPHndlFqKxatTAAABbvZYPj/hHydkm6tJsX7Ie2MqfjxcgO4+ThfMp5CSplYNnl+XBoVchv9Njha7HJPpH+yO1fcMg1Ktldwx6WKemdoTz02LkfRY9DaOtgpc3UU6Szs1MH+9NxcyALcPC8Pod3ZCpdFhxFs70KTS4Oq+/nhwXCSs5VY4XliLFQfycLywFinFdYj2c8ZsCY67MnfC2f9uTivDQ+Oj2j03ItJb0r3KAJCUX4NrPt9v2B4R6YXvFgwSsSLTyChtwKQPdhu2ne0U+Pa2gZKcZALoJ6DO6heANceKDftm9QvAoHDzvGuhIAjILG+EVifg3lVHsfqeoe1WgvhoWybe23IavQJc8OuioZINyn83JyFY7BJMprZZhWX787Bsfx5qm9V4dlqMRdygxBLCMgCEeDqKXYJF6rTAXNOkwo+HCzGupw++2ZsLlUY/PlkGYFoffzQqNbj+ywOobNSPA7VRWKGXvwtuHhSMeUNCeevOLqhtDHN+VZPIlXS+v68Q0T/YDctvTxCxItM5PywDwFfz4hHuLe1Q1dSqMTy2kul7dMxVcW0LmlVa3DemO348Uoj5y49g7T3D4Olki6T8Gnyw9TSu7uuPd67rCxuFNJcasxTl9Up8tTcXKw/mo1mlxYQYX9wzOkKyQzGIOlOnBeaVB/PRotbir5NlEIRz+xtaNVifXIIIb0eMivJBXJAr4oLc0cPPmQfvLk44+0Y2q7QiV9K5fj5SiCd+SzFsh3s54tvbEiQ3DKPNivkJmPeNfoLJ+zf0Ndue1v/iQHYVACDW3wXzh4VdcmUXc5B59jbeo3p4Y3yML2748gBu+/YIhkd64euzSz2+MqsXj7dm7ERRHa75fL9hojwArLtvmNlNnibqyjotMP94RD+DdUSkNxYMD8Om1DMIcLNHXJA7+gS5SmbZIkty3rEZdS1quNpbxnt4+Ow6n0vnxWNstA90giDZmwAA+nkEgH7Iyax+ljE0au/isXCyVUjiylZWmT4wd/d2grujDa4dEIhVhwpworgO0/v649mpPXn8NWO3fnMYu05XXLC/m6t0J4MRiaHTAvP/pkTD39XOMMFi1EUmnpB50Z13qSC3sglxQW7iFdOJ3pjdG29f28cwAUwO8w9VxsyMC4CDjQLPT48Ru5ROI6WTv8zyBng52UKjE/DQj8ew9ngJXO2t8fGN/S46AZDMy8Ec/dWQ56bFYGqfbnC0VcDBWi7ZK15EYum0wNxVZjlSx2kfmBstJjArJNybfDFDu3thqETXNLUEp8saodHpMO7dnWhRa/HA2O64Z0x3i5kgJXWnXp5stqu3EJkT6a7UTibXLjBXWN7EPyJzkF3eiIZWDQaHe+CVmb0tZhUMS8GwTNQ5GJjpsp0/hjmnkoGZqCu6eXAIonydMKtfAMMVEdFlYmCmy+Zkq7+ka28tRy4DM1GXtHiKdG9YQUTUWSxrMCZ1qNE9fOBqb40WtRa5lU2GZeaIiIiIpISBmS6bnbUc1w3QLzPWrNKirL5V5IqIiIiIOh4DM12RmweHGB7nVDaKWAkRERGRaTAw0xUJ83LEiEj9kmMcx0xERERSxMBMV+yWs73MXFqOiIiIpIiBma7YuGgf+LvaoaKRY5iJiIhIerisHF0xhdwK398xGNYKnn8RERGR9DAwU4cI9XIUuwQiIiIik2CXIBERERGREQzMRERERERGMDATERERERnBwExEREREZAQDMxERERGREQzMRERERERGyARB+PcvlskqAOSbrhxRhAiC4C12EWKxpPfUUtoq0XYCltNWfn6l107ActpqKe0ELKetFn1MavOfAjMRERERkaXhkAwiIiIiIiMYmImIiIiIjGBgJiIiIiIygoGZiIiIiMgIhdgFkLi8vLyE0NBQscvoUElJSZUXm+VqKW2VYjsBy2krP7/SaydgOW21lHYCltNWSz8mtWFgtnChoaFITEwUu4wOJZPJLrrMjaW0VYrtBCynrfz8Sq+dgOW01VLaCVhOWy39mNSGQzKIiIiIiIxgYCYiIiIiMoKBmYiIiIjICAZmIiIiIjIbf544gzHv7MSZupZO+zsZmImIiIjIbFQ3q5Bb2QQrmazT/k4GZiIiIiIyG61qHQDAVtF5MZaBmYiIiIjMRqtGH5jtrOWd9ncyMBMRERGR2WjVaAEANnL2MBMRERERXaBVo4ON3ApWVhzDTEQSptbqxC6BiIjMVKta16njlwEGZiKTaWrVdOqSN+aioKoZ497dhX1ZlWKXQkREZkip0cLWmoGZSBI+3ZGFie/tRnm9UuxSuoy6ZjVuX3YYdS1qdHO1E7scIiIyQ/oe5s6b8AcwMBOZTDc3ezS0avD6n6fELqVLUGl0uHtVEgqqm7Fk7gCEezuJXRIRSVhmWQNOltSLXUanqmpsxeLfUpCUXyN2KSbVqtFySAaRVMQFugEA1hwrxqGcKnGLEZkgCHh6zQnsz67Cm9f0waBwT7FLIiKJ+3xnNuYvOyJ2GZ3K3kaOTWml+HJXttilmFSrRgfbTlxSDmBgpi5KqdZi9+kKKNVasUu5bD38nGFz9gz4ud/TLHqi22c7s/FLUhEeHBeJ2f0DxS6HCABwvLAWlY2tYpdBJnK6vAGRvpZ1JcvBRoF5g0OwJb0M2RWNYpdjMq0aTvojC6PVCcitbMJPRwow+7N9CF38B0IX/4HoZzdh3jeH8damDLFLvGw2CivE+rtAYSVDRlkDlu/PE7skUaxPLsHbmzMwM84fD42PFLscIgBAanEd5n51CIt/OyF2KWQCOp2ArPJGRPo4i11Kp5s3NBQ2cit8tSdH7FJMplXd+UMyFJ36t5HFEgQBZ+qUyDg7pmx9cglOlTYY/TMjIr3wwLjunVShafQNdMOpMw0Y2t0D7285jel9/eHrYjmT3ZLyq/HoL8kYGOqON6/tA5ms89bM7EytGi1s5FaSbZ/UZJU3Yt43h+Fib42XZsSKXQ6ZQFFNC5RqHaIsrIcZALycbHHNgED8mliERyb0gLezrdgldbhWjQ4u9tad+ncyMJPJZJU34uu9uThd1oDTpQ1oaNVc8rWTYn1x3YAgDI7whJOtdD6WcUFuWLY/DzclBONgThVe/SMdH93YT+yyOkV+VRPuWJGEADd7LJkb3+kzmk2pqVWDI3nVOJBdhQM5VUgtrsPLM3vh5kEhYpdmMoIg4GhBLeKC3CDvxJsFdLSimmbM/foQrGQyrFw4CP5u9hd93c6Mcmw8cQZPXxUDV4fO/WGmK3e6TN8hY2lDMgD9kMas8kYIENCgVEs2MLOHmSTjYE4VfjhcAAC4dkAgov2c8cof6ZgZ548x0T7oF+SOIA97SffK9Q1yAwDUNqtw96gIfLgtE3MSgjA0wkvcwjqIRqvDdwfzccPAIDjYnDuc6JePOwKdIOCb2wbC3dFGxCo7RkZpA9Ynl+BAThWSC2uh0QmQyQBBAJxtFRgmkff0YtRaHSKf/hMAsGrhIAzrbp5trWhoxS1fHUJTqwY/3TUEYV6Ol3zttvRy/JFyBq/N6t2JFVJHySzXj9/tbmFDMnQ6AY/8fByHc6vx4Zw4ya5GZGdthaP5NdiTWYERkd6d8ndyDDOZzM2DgvHklGjIZPqwMa2PP/LemIoP5vTDjLgABHs6SDosA0DA2d6rHRnluHt0BII9HPDh1kyRq+oY1U0qPLXmBF5cfxIrD+Yb9qs0Oty1MhFF1S1YMjfeaCgxF0q1Ftd+vh+f78qGThBw58hwrFwwCNcN0E9gfPu6PgiVQDsvpkGpNoRlABhspiuc1DWrMffrQyirb8W3tyegZzcXo68/nFuNAaEeUMil8TNZ16zGb0lFWLg8ETM+2QutThC7JJPKLGuAn4sdXDv5sr2YBEHASxtOYuOJUjx9VU/MiAsQuySTeWN2H3g42mDu14fx2sZ0qDSmn1TPHmYyGZlMhrtGRSDC2wkP/ngMV3+yF0vnxRt6XS3Bb0eLAACb08pgZy3HF7cMMIRoc1av1IePtLNrnP50pBB3jAgHADy5+gQO5lTjgxvikBDmIWaZHWZfViUaWjVYPj8Bo6L0vRnrk0vwc2IRFg4Pw+Re3USu0DRKalsw9I3thu2c166ClRkOx2hq1eD2ZYeRU9GEr2+Lx4AQd6Ovr2lSIaOsAdP7mvf7WtXYii0ny/Bnain2ZVVCczYkDwx1N+thNf+GJa6QsXRPDpbtz8P8YWFYOCJM7HJMqoefM9bdNxyvbjyJJbtzsD+7Eh/O6YcIE/aoS+PUmbq08TG++O2eobBRWOH6Lw9gXXKJ2CV1CqVaa+hNtpLpt2P8Xcx+PKRSrcXC5YlIK6mH+9m2ZFc04WhBLT7ZnoXfjhbhofGRmNlPOr0bW9PL4GSrwJCzvatZ5Y1Y/FsKBoS4439TokWu7sroVxNouKDHMa2kzhCWvZxskW2mYRkAHvrpOI4X1uKjG/v9q8u3R/KqAQAJYebXm17brMJ3B/Jw09KDGPjqVixefQK5lU2YPzwMvi62cLSR493r4sQu06TaVsgIdLdHUn61RSzp+fvxYry28RSm9umGZ6b2lPzVW0C/5vQrM3tjydwBKKppwbSP9uKnIwUQBNNcPWEPM3WKaD8X/H7vMNy98ige+OEYMssa8MiEKMl+qZtVGgx4eSta1FoEedijsLoFtc1q+Lma98Q3tVaHe1cdxeHcasitZFg4Ihxvb9Yv/ffU6hPIKGvArH4BeHCcdJaP0+kEbE0vx6gob9gorNCs0uCeVUmwtZbjk5v6wdrML9l/uz8PL284adiO6eaC4ZFeWLJbvyRVlK8TNj4wwqx7JPdkVuCmQcGY3Mvvkq8RBAFpJfXYcrIMvx8vho3CCn0CXTuxyo7x+K8p2HKyzLAd7eeM6X39YSO3Qll9K968pjeCPR1ErNB0imtbsCm1FH+eOAOlWocfDhfih8OF+OOB4Yj1N7/38t/al1WJx35JxqAwD7x7XV+zPbG9XBNj/dAn0A2P/Hwc//vtBHadrsDrs/p0eOcUAzN1GpVWh/4h7jicV42Pt2fhhoFBCHSX1oFbEASsSy7BM2tS0XL2piuVDSoAMGybK51OwOO/JGPbqXIAwMy4AMMSeQlhHjicW42EMA+8cU1vSZ0IpRTXoaKhFeNjfM7esTAVmeWNWDE/Ad1czX94zQ0Dg7DmWBFSi/XDa06eqcfJM/rH8SHu+PHOwWY/jtdKJoP9Re4KplRrcSCnCltPlmFbejlK65WQyYD+we54fFI07Dr5TmId4YWrYzG6hzcyyxqRXdGIzLJGvL05AzYKK4zv6Yvr44PELrHD5VY24fOdWVh9tNgw7ATQL01665BQBHtI63fmfCdL6rHouySEeTliybz4LveZTSmqxeqjxXh+eoxJfxf8XO2wcsEgLNmTg3c2Z+B4wW68f0Nch95VloGZTEqnE7A3qxIrD+Zja3oZBADjon1w69BQyYXllKJavLj+JJLyawz7enZzQfrZ8NGiMt/ALAgCXlyfhrXHS2BvLUerRov7xnbHvqxKAMBjE3tgzbFiPDGph6SWjwOArSfLILeSYUwPH/xwuBBrjhXj4fFRnTYz29ScbBXYcP8IAPqxu3d+l4gjeTUY39MXn9/S3+zDMqAPzG05qqqxFdtPlWNrehn2ZFaiWaWFg40cIyO9Ma6nD8ZE+8DLyXyX4Qpws79gecP4V7aisrFVciezGaUN+HRHFjaklMBaboVbBodgwfAwZJU34vZlR+DrYofRPbwl8Rm+lHf+yoC1wgrLbk/okhMcU4vrsWx/HgaGemBqH9POCbCykmHRqAgMCffEgz8ew41LD+L9G+I6bPIjAzOZRG2zCr8kFmHVoXzkVTXD09EGi0ZF4MaEYASZ8dn+D4cL8OTqE3C0kWPZ/ATEh7ijorEVCa9uAwC42Cnw5JRofLgtEyMjvXHbsFDMWXIQgHn3ML+/NRPLD+RjVr8A/Jl6BjP7BSDMyxHbz/Y29/B1xuuzpbP8llqrw49HCnEguxK7T1diQIg7Cqtb8MK6NIyM8sb9Y837hjqXsimt9GxY9sFnN/c3++EmbRpbNfh6by6OF9biaEENBAHwc7HD7P4BGNfTF0PCPbtcz1xHWbj8iOH234420vjJTynSz5f462QZHG3kuGNkOBYODzesNxzobo+Hxkfig62ZcLGzxnPTY0Su2HRc7a1hp7C65HriYrthYBC+O5iP1zamY1xPn075npXVK9HYqoHcSgabDjyGSePbQ12CIAhILqrDyoP5WJ9cglaNDgND3fHwhChM7uUniZ7HuLMrfDSptLjuiwMXPB/i6YiC6ma0qLV4dGIUIrydEOBmj+LaFijNNDB/szcXH23LhKu9NdYcKwag73Ud9NpWVDaqIJMBdjbSCFZtVh3Mxwvr9eN6Z8b545bBIbjn+yR4OtnggxviJDlG8OfEQjy5+gTG9PDGpzf3h00n3xTAVOqa1YbH1U0qPDguEuN7+iLW30VSva2XMijME1vTy+HlZP5roR85O5xv9+kKuNgp8MC4SNw+NPSCdd5lMplhtYRuruZ3Z1WtTvhXcwYEQYC1XIaSOn1A7Io3/ZJbyfD89BjMWXIQS3fn4H4Tzm+pa1bjxfVpWH2sGDHdXPDdgkH/uHzkf9H1/nXJ7LSotFiXXIyVBwtworgOjjZyXBcfiFsGhyDar+M+rF1Bz24uyH39KoQ9ufGiz58orsOJ4jrM7h+ASF/9gvkz4vzx2c5ssxySIQgCPtquX+mjruVc8OgX7A4fZ1v4utihV4CrJE6GzrcnsxIKKxl+vXso4oLc8Mn2TJypVeKnu4bAQwI3Yfm71UeL8L/fUjAi0guf3zJAUu/n+RN/ciub8OG2TMOQix5+zpLpRb+UO0aG446R4WKXccVSi+sMnRRjenjjhatjEexx8bX8Kxpa8dzvqegb5Ibbh4V2cqVXpqZJhcGvb0OAuz1GdPfCiEjvi94BN7OsAU+vTTXMHenIntSONjjcE1N6+eGzndm4Lj4IfiY4idmRUY7Fv6WgslGFB8ZF4r4x3Tv8pJ+BmS5bdkUjVh7Mx29JRahXatDD1xkvz+yFWf0CuuSZbkfZlFpqeDx3cAi+O3vTjghvRzwwLhK/JhXh0Yk9DK+5ZkAgvtmXa5bLyclkMmy4fzgKqpvx+C8pUMhl2PzQSMlevm7z2uzesJFbGXqu7hndHcO6e6FfsPH1e83R6qNFeOyXZAwJ98TSLjhpqCOsuWcovtmXh/XJJRAE4Jm1qQCA/sFuWH3PMJGro38j2s8Zj0yIwtI9OdiRUYEdb++Ek60CwR4OCPF0QLCnA0I8HBHi6YBl+/PQpNLi3ev6mN34ZRd7a4R7OyH9TD0Kqpqx/EA+FFYyDInwxIr5CWjV6PDx9kws2Z0DBxsF3pjdG9fHB3X5q15PXdUT206V481Np/D+DXEd9v9tUKrxyoZ0/JRYiChfJ3w1byB6m2h1G+mmGjKp/VmVuOmrQ4bt3gGuuHZAIEI8HFDTpIK9tdysl6EyZnC4J16b1RvXDAjA4t9OANDf1fDZaTGws5ZfMMEgwtsJKc9PMttL3HmVzbjl63Pv9YniOgwMlcYNSS6lbfWPNlZWMsmFZUEQcMOSgzicW41BYR74+taBkgzLgP6KyMfB7vjwhjisOlyAlzechEqjg72NNNsrRQq5FR4YF4lbh4YiMa8a+VXNKKhuRn5VEzLKGrAtvRyq89ZbXjwl2ixviy23kuHZqT1x01eHcO+Y7hgU7oE9mZVoUWmx63QFnvs9DQXVzZjdPwBPX9UTnmYyQTXIwwF3jAjDpzuyMXdICPp3wPF0X1Ylnvg1BWfqWnD36Ag8ND7SpFfHGJjpsvTs5oIHxkUiu6IRuRVNyK5oxPPr0gzP28itEO7tiM9u7i+5e9m7O9rgpkHBAPQH5XvHRPzjgdlcwzIARPg44rGJUXjnr9MAgLc3ZeDnRUNEroqulEwmw5RefvByssHb1/a1iPBoZSXD3MEhmDs4BHmVTXC240+guXG1t8a4nr4X7NfqBJTWK5Ff1YT6Fg0mxFz4GnMxtLsXJsT44qs9Obh5cDC6ezvhxQ0ncdu3RxDu7Yjv7xiEoRFeYpf5n90zujt+SSzCE7+m4KmrojEqyueyOtaaWjV4/c90rDxYgHBvR/x699AOCeD/hEcLuizujjZ4ZEKUYVsQBFQ0tCK3sgm5lU3IKm/EV3tzsTOjQnKB+Xy+LnYX9EZKTTdXe9w3NhL3jY1EeYNSsr2Qluj2YWG4fZi0b6F7KaFejmKXQB1IbiVDgJs9ArroahH/1VNX9cTE93dh0XdJyCxrRKtWh0cmROGuUeFmO8fA0VaBt67tgyd+TcH8ZYkI9nDA3MEhuC4+EG4O/25uyKGcKjz+awoKa5qxcHgYHpvUo9N+kxiYqUPIZDL4uNjBx8XOsFD42uPFhjWISRp8nKV9ckBE1BWEeTni1iGh+GpvLoZ398LLM3shTAIneaN7+GDf4rHYnFaKFfvz8erGdLy7JQMz4wIwd0jIJe/IKAgCvt6bi9c2piPIwwE/3TkECWGdOzSQgZlMpmc3F6SXMjATERH9V/+bEo1pff3RN9BVUksgWsutMK2PP6b18Uf6mXqsOJCPNceK8OORQgwMdce8IaGYFOtnGMqo0ujw7NpU/JRYiMmxfnj3+r5wFGFhAQZmMpme3VywbH8eNFqd2c1UJiIiEpO13Mqw9r9U9ezmgtdn98biydH4JakQ3x3Mx/0/HIO3sy1uSgjGlN5+eG5tGg7nVeP+sd3x8Pgo0VYEYWAmk+kV4IpwL0dUN6ngI/FxvkRERHR5XB2ssXBEOOYPC8OuzAqs2J+HD7dl4sNtmbBRWOHDOR13i+vLxcBMJnN1X39c3ddf7DKIiIjIDFhZyTCmhw/G9PBBXmUT1hwrxriePugT6CZ2aQzMRERERNS1hHo54uHzVuMSGweWEhEREREZwcBMRERERGQEAzMRERERkREMzERERERERjAwExEREREZwcBMRERERGQEAzMRERERkREMzERERERERjAwExEREREZwcBMRERERGQEAzMRERERkREMzERERERERjAwExEREREZIRMEQewaSEQymawCQL7YdXSwEEEQvP++01LaKtF2ApbTVn5+pddOwHLaaintBCynrRZ9TGrDwExEREREZASHZBARERERGcHATERERERkBAMzEREREZERCrELIHF5eXkJoaGhYpfRoZKSkiovNmjfUtoqxXYCltNWfn6l107ActpqKe0ELKetln5MasPAbOFCQ0ORmJgodhkdSiaTXXTWrqW0VYrtBCynrfz8Sq+dgOW01VLaCVhOWy39mNSGQzKIiIiIiIxgYCYiIiIiMoKBmYiIiIjICAZmIiIiIiIjGJiJiIiIiIxgYCYiIiIiMoKBmYiIiEiilGqt2CVIAgMzERERkQS1qLTo++Jf+HZfrtilmD0GZiIiIiIJOl5Yi1aNDiGeDmKXYvYYmImICGX1Stz27WEU1TSLXQoRdZCk/GoAQP9gd5ErMX8MzET0nyXlV6NZpRG7DOpA9S1qJOXXYP6yI6hXqsUuh4g6wJG8GkT5OsHNwUbsUsweAzMR/Sc1TSpc8/kBTHx/N3acKr+i/1djqwaCIHRQZXQlIn2d8cUtA5BT0YR7Vh6FWqsTuyQiugJanYCjBTUYEOIhdimSwMBMJAKlWoszdS1il3FZ3B1tENPNBUU1Lbh92RHcu+ooyuqV/+n/IQgCfj9ejDHv7MTa48UmqpT+q2HdvfD67N7Ym1WJZ9ak8mSGyIydLmtAg1KDgaEcjtERGJiJOlmrRosblx7E6Ld3YvupMrHLuSyz+gUAAK7pH4it6WUY/+4urDiQB63unwNWZlkDblx6EA/+eBzdXO3Q3dvZ1OV2Gp1OwKc7slDV2Cp2KZftuvgg3D+2O35KLMRnO7PFLqdL0P2LzzVRV5OYXwMAiGcPc4dgYCbqJGqtDnXNaixcnohjBbUAgDtXJGF9com4hV2Gq+P8IZMBAe722PzQSMQFu+G539Mw+7N9SCupu+ifaWrV4PWN6Zjy4R6kn2nAq7N6Yc09w9A70LWTqzcNjVaHJ35LwdubM7D2uPm9p+d7ZEIUZsT54+3NGVhnhp/PjjTvm8MIf2ojx+yT2UnKq4aPsy2CPOzFLkUSFGIXQGSucioasfZ4CZpaNWhWadGs0qCp9ex/VVo0n93fpNKguVUL1d/GhLZq9NsvbTiJaX26QSaTidGMy+LrYoehEZ5Ye6wYD4+PxIr5CViXXIKXN5zE1Z/sw/xhoXhofBQcbRUQBAEbT5Ti5Q0nUVqvxPXxgfjf5Gh4OtmK3YwOo9Lo8NBPx7DxRCkeHh+F+cNCxS7pishkMrx1bR+cqVXisV+S4e9qh/hQy+ulWr4/D7tPVwAA7BRykash+m+O5NUgPtTdrH5bujIGZqLL9MzaVOzProKjjRwOtgr9f20UcLSVw83eGgFudvptGznsbRRIKarF/uwqAMCHc+LgZKuAg40CIZ4OZnlAmxkXgMd/TcHRgloMCHHHjLgAjIryxpubTmHpnlz8kXIGD02IwvrkEuzJrERMNxd8enN/DAiR1ni6FpUWd69Kws6MCjwztScWjggXu6QOYauQ48u5AzD78/24Y0Ui1twzDKFejmKX1Wk2pZbi+XVpAIDv7xgEKyvz+46S5TpT14Li2hYsGB4mdimdRqcToBUEWMtNM3iCgZm6BI1Wh2a1Fi521mKX8q+kldRhf3YVnpwSjbtGRfzj6wurm/HTkQJ093HCmnuGwtlM2mnM5F5+eGZtKtYeKzaEYDcHG7w+uw9m9w/EU6tP4IlfU+Bsq8AL02Nwy+AQKEx0IBNLg1KNBcsTcSSvGq/P7o0bE4LFLqlDuTva4NvbBmL25/tx+7IjWH33ULg7Sn95qn1ZlVi0MgkAML6nL4ZGeIlcEdF/k3R2/HLfIDdxC+lAbat+nKlTorSuBaV1rSirV6K0XonSOiXKG5R48epeuGmQaY7DDMzU6dLP1EOp1qKopgU7Myrw29Eiw3PHnp1gFj/IX+/JhaONHHP+RUBqVmlw53dJ0OoELJ0XL4mwDADOdtaYEOOLDSkleHZaDGwU58LwwFAP/PHACGxOK8WgcA/4ONuJWKlp1DarcOs3h5FWUo8P5/TD1X39xS7JJEK9HLF03gDcuPQQ7vwuEd8tGAQ7a+kOTzhWUIM7ViQatl+aEStiNUSXJ9jDAXIrGT7dkYWl8+Ihl8AVkufXpWLlwQLDtr21HH6udvBzsUNCmAd8XewQ3c10k8gZmKlTHcmrxs1LD10wnhcAZvcPMIuwXFqnxLrkEswdEgJXe+PhVxAEPPFrCjJK6/HNbQMRJrFL2rP6BWBDyhnsPl2B8TG+7Z6zUVhhukRDZHmDEgmvbgMAfDUv/oK2S82AEA+8d31f3Pf9MTzxawo+uCHOrIco1DWrcSSvGoPCPS44gV19tBjNKi0A4OHxUfB344QpMj99At3wwtWxeHZtKl7ecBIvXG3eJ35n6lrw05FCzOoXgLtHR8DXxQ4udopOHc7IwEydokGpxkfbMrF0T+4Fz4V4OmDdfcP/MXx2FcsP5EEnCJg/7J/Hhn2+KxsbUs5g8ZRojO7h0wnVda6RUd5wd7DG2uPFkg+NbZLyq3HN5wcA6NtvKe2e1scfBdXNeGtTBoI9HPDYpB5il/SfCYKA9Sln8NL6k6hsbIWDjRwz4gIwMtILvQJcEehuj+emx2BzWikUVjLcOVIa49HJ8giCABc7BWwUVlh7vBjPTYsx65PcpbtzoRP0K/gEeTiIUgMDM5nUlpNl+O5gvmGm+d/tXzzWrHpwmlo1WHUwH5N7+f3jl3b7qTK8vTkDV/f1x10S/eG1lut7kX86UogGpVoyw00uRq3V4bnf0/DD4XOXBD+7ub+IFXW+u0dFoKCqGZ/syEKwhwOuHxgkdkn/WmF1M55Zm4pdpyvQJ9AVr8yMxbb0cqw+WmR4T90drBHo7oDyhlZ8fGM/2NtIa+hJY6sGPx4uQP8Qd/QPltbk2zbNKg0cbCw72lQ0tOKZtSewOa0M/YLd8Pa1fc06LFc3qfDD4QLM6OsvWlgGGJjJhAqrm3H3yiRozlv0f3KsHzallQLQrxRhTmEZAH5NKkK9UoMFw40H4OyKRjz4w3HEdHPBm9f0MctVMIzR6gR8tiMLHk42mNkvACsO5GNTaimuizefAPVfHMmrxryvD6NFrTXsO/z0ODjZSvsQWl6vxPIDefh0h/4GJhNifDEu2ge7T1fgqTUn4O9mj+GRXXtCnEarw9d7c/H+1tOQy2R4fnoM5g0JhdxKhsm9uuHlmb1wqrQBJ4rrkFpUhxPFdbiqtx+m9ekmdukdol6pxvb0cqxLLsH2s7eyH9bdE6sWDha5so4nCAJu/eYwQj0d8fZ1fcUup9MJgoB1ySV4fl0amlVaPDklGgtHhJv9+OVl+/PQotZi0eh/nmBvStI+2pOoPt6eaQjLvi62GBXljZ8T9RP8npjcAzPiAsQs77KsSy6BlQz488QZuNgpEOl74QSDeqUad6xIhLXCCl/OHSC5XqraZhXu/+EY9mRWYmiEJ25KCEaIpwPWHi+WZGB+YV0alu3Pa7dvx2OjJTmREdDfTvfVP9Kx6yJXhbacLMOWk+fuTnnv90eR+Mx4ky3jdCW2nCzDopVJ6O7thIyyBkyI8cWLV8decJJuZy1HXJAb4iS0mkAbrU5Anxf+arcvwtsRX86NF6ki01qfcgZH8mrQN9ANvyYV4doBgWKX1GnO71WOC3LDO9f1QXcf87+Lak2TCt/uzcWkWF9EXeT3tjMxMJNJ5Fc1GcKxl5MtpvTqZggdtw0Nxd3/Yim2rujZaTH4clc2lu3Pw1d7c9E/2A1zBgZjap9ucLRVQKcT8PCPx1FQ1YyVCwch0F28y0emcKq0HneuSEJpnRJ21lZwtbeGTCbDzLgAfLQ9E6V1Svi5SitIhnu3n6i59t5hkpu8CQCv/5mOL3flXPS5p6/qiVsGh0BuJUNJbQsKqptRWNMMhZWsS4ZlAFi6OwdanYCMsgZ8ccsATO7lJ3ZJne6l9Wnttt+5rq9kQ2S9Uo0HfjgGAPhqby5s5FaYEONrNnNjLldxbQu+3pOLH48UQKMTJNOr3ObzXdloVGnw6ETx50wwMJNJLP7thOHxNQMCDD/EU/t0w3PTYsx2iEJckBs+v2UAKhtbsfpoEX46UognfkvBi+vTML2vP0rrldiZUYGXZsRicLin2OV2qD9SzuCxX5LhbKfAj3cNxt0rkwzrZk+I8cWH2zKxJb0McweHiFxpx4rp5mJ4/O3tAyXZEwkAx8/err1NgJs9XpnVC6OjvNt9X0O9HM3iBibvz4nDsDe2o0+gq0WGZQAI9mz/Pk2Kle4E1c93ZhseJ4R64JOb+kk6LJ8sqceS3dlYn3IGMgDT+/rjvrHdEeHtJHZpHeZMXQuW7NZnB7F7lwEGZjKBgzlVOJCjv6Pdg+Mi8eG2TADAoDAPvH+9eS9H1cbLyRZ3jozAHSPCkZRfg5+OFOLHI4WG59VaAdVNKniYwTJ5/8Z7W07jo22Z6B/shi9uGQAfFzvUt2jgYq+/9fXnu7Iht5KhT4Cr2KVekfJ6JbydbQ0BsalVg2u/0K+I8ey0GIyR2EongiDgt6PFKKppRn5VMwD9qjV3jYzA9fGBZn2jmQA3eyyeEo03/jyFjNIG9PC78AdXEAQ0qbSSHYs+KOzc7cy/uKW/pCflPjA2EptSSzG8uxdevDpWEr8zfycIAvZmVWLJ7hzsyayEo40ctw0NxfzhYQgws/lAxuh0Av46O6Tq/H1iv6fSPEqQaMoblJiz5CAA/dCLtrDc3ccJ39w2sN3NLaRAJpMhPtQD8aEesFFYYdUh/Wz7lzecxMsbTmLzQyMv+kNtTnQ6AV/vyUGkjxN+uHMwbBVyqDQ6tJy9M+P3hwvwR8oZ/G9ytFnfVUqrE5Dw2jaMjPLGivkJAIDY5zcDAIZ395LcLWaLa1sw7+tDyK5oAgCMiPTCq7N6YUwPH9F/mDrKDfFBeH/Laby9+RQ+u3lAu+OPIAgIe3IjACDvjalilWhS0X7OuG9Md9w2LBReTrZil2NS9jZy7HhstNhlmIRGq8MfJ87gy105OHmmHt7Otnhicg/cnBACVwfpnARptDqsSy7B5zuzkVneaNh/9NkJXeKYxMBMHUal0WH4GzsM221jlj0dbfDTnYPhKNFeHEA/EW7tsWJMivXF1vRyaM9OdnSQwIQ/KysZbhkSgiW7c1BY3YLuPk6oV6oBACV1Lfh4RxZGRnmb/dJ5bWP+dp+ugCAIGPTaNsNz3y1IEKssk1BrdViw7IghLAPAdwsGiViRabg72uCxiT3w6sZ0zP36EL6cqw/N3+7LM6wYIWUKuZVZrpdNek2tGvx0pBBf781FcW0LIrwd8eY1vTGzXwBsFeb/29JGqdbi16QifLErG0U1Lejh64wP58Rhau9uXeoql3QTDHW6snrlBXfws5IBv983DJ4S6d3IrWxCdZMKrWotWtRaqLU6uDnY4MfDBWhSafHQ+Ci8c11fvL8lE8v252Lmp/vw1FU9Mbt/gNmO2waAO0eEY8X+fHyyPRMfzOmH+hZ9YP7hcCF8nG3x3vXmvc7n37X1PALAqZcnm/V7dzHPr0vDqdIGw/bux8eIWI1p3TEyXN8j92sKZn+2H4tGR+DtzRmG50+8MFHE6ogu7a7vkrA3qxIDQ93x4tWxGBstnas/53v0l2T8kXIGfYPc8ML0rttOBmbqMMrz1qhts/HBEZJaKeKZtSewL6vqks/P/mw/fFxs4eNsi1AvR+RUNOHRX5Lxc2Ih3rymj1lMlroYTydbzBsSgqV7cvDAuEjUKzUA9CdEH87pJ5nLvdf0D8RvR4sM2+kvTYadtXR6cgCgoKoZ358dOjS+pw+Uah0C3KUz/vFiZvYLQIC7Pe5ckYgnfk0x7P/ilgGSHtdL5kcQBDz6SzKm9OqGqiYVRvfwxrLbpXWF6+8mxvjij5QzCPV06LJhGWBgpg705qZT7bZ/WTQE0X4ul3i1eXpkQg/cMUINe2s57KzlUMhleHNTBnafrsBVvf3QzdUe5Q2tqGhQAgCcbRVoaNXgUG41PtmRhXfMeDH9O0aGY8WBfHyyPQs3DgoGADwwLhJDIqSzGsjdoyMMgTn5uYmSW0MbAGwUVhjf0xeLRoUjPtTjn/+ARAwM9cDae4dh1Ns7AQAvXh1rsatnUNd18kw9Vh8tRqy/K+qaVZKazHcpM+ICUFTTgrc3Z8DPxQ5PXtVT7JIuioGZOsz0vv7Ymq4fF7h0XjwGSvDHeEBI+9vJanUCssoaMLy7Fz67ecBF/0yLSovyBiV8XbrW+sSrjxbh0V+SIZy9EWPbqAMZ9JMZ287xZTJABhkg049TX3u8GM9Mi8GqhYMkt3Redx8nJD8/EfbWcslNUG3j52qHr26V5o0r/kmIpyM23D8c0z7eC08naaxgQ9Ky9lgxrOUyHMiuQkmdEi/N6CV2SZ3intEROFPXgi9358DP1Q63D+t6k6wZmKnDzIgLQG5lEyK8nTAhRrrrfZ5PbiXDz4uGXHQ4Sht7GzlCPLveUIyEMA/cmBCMpLwaZJQ1GIKzAACCgChfJwwI8YCbg/V5zwnwdLSBs50Cw7p37VsiXy4pr91KgLOdArYKK5ydl0vUZWh1An4/XgKtTsDW9DI8MC4S4y3kt1Qmk+HFq3uhvL4VSfk1uG1oaJebO8LATB3qofFRYpfQ6cx1jHaguwNem9UbgH6Vj6T8GhzJq0FiXjVSiupwuqwRp8sa0d3HCQND3REf4oFB4R5m214iQN/LnPHKFLHLILrAgewqlDe0AtDPL3hoXKTIFXUuuZUMH93YDzZyqy4XlgEGZiIC4OZgg3E9fTGup743Q6nW4kRxHY7kVSMxrwZ/pJzBD4cLcfOgYLx6NmQTEVHHWXOsGAAQ4e2I92+Qxk2+/quuPMmagZmILmBnLcfAUA/DOHSdTkBmeSOs5ZZ3ACciMrUWlRabUs/A2VaBJfPiuXpLF8TATET/yMpKZvZ3LCQi6qpaNVr4udrh6ak9EeHtJHY5dBEMzEREREQicnOwwbZHR4tdBhkhzXWTiIiIiIg6CAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkBAMzEREREZERDMxEREREREYwMBMRERERGcHATERERERkhEwQBLFrIBHJZLIKAPli19HBQgRB8P77Tktpq0TbCVhOW/n5lV47Actpq6W0E7Cctlr0MakNAzMRERERkREckkFEREREZAQDMxERERGREQzMRERERERGKP7Li728vITQ0FATlSKOpKSkyksN8LYElvSeWkpbpdhOwHLays+v9NoJWE5bLaWdgOW01dKPSW3+U2AODQ1FYmJix1TVRchkMqnN8PxPLOk9tZS2SrGdgOW0lZ9f6bUTsJy2Wko7Actpq6Ufk9pwSAYRERERkREMzERERERERjAwExEREREZwcBMRERERGQEAzMRERGRxKg0Osz8dB9+SyoSuxRJYGAmIiIikhiZDMgobcBjvybjx8MFYpdj9hiYiYiIiCTGWm6FfsFu/2/vvsOjKrMHjn8nvfeEkEIqLZRQQm8ixQKIoqJiAxv2de0F6+rq6lrWnw0VbKsIKmIBVFB6DyUEEkgF0ntPJtPu749AFIUsJeHm3jmf5+ExMxPCec3Mvee+97znRVHg0aWpfLbVrrvonjVJmIUQQoiTsNkUtUMQ4owNiQ4AYFhMAE8u28dHm3JVjki7JGEWQggh/sRstfHksn0kPvsL76/PxmK1qR2SEKdtWEwAQV4uPHpRLy7o04Vnf0jjg/U5aoelSZIwCyGEEH9Q3WjixoXb+WzrYUJ8XPnnigP8tL9Y7bCEOG3DYwPZ8cREBnbz561Zg5jSrysvrEjnnbVZaoemOae1NbYQQgihZ1ml9dzyyQ4Kq428emUim7LLKahuYnhsoNqhCXHaHBwMrV87Ozrwn6sH4ORo4OWfDmKxKtw7obuK0WlLp59h3ptfzZIdeZgscjtMjxRF4Znv97M1p0LtUIRodbiigSMVjWqHIc6x9RllXPbOJuqbLSy6bRhJ0f58t6eQa4dFEeTlqnZ4Qpw1J0cHXps5gMsGhvPaqgw+3yYLAU9Vp0+Yv91dwMPf7GXcK2tYuDGXRpNF7ZBEO1p7sIyPNx9i4UZZiGCPqhpMfJ9SyINfpVBU06R2OEBL7eqsD7bxzA/72+XnKYpCQ7Mct7TgcGUj4X7uLLtrFIOjAnhnTTaODgbmjo1VOzQh2k1xrZEDxXU4GMDX3VntcDrEweI6rn5/C+X1ze32Mzt9ScZTUxMY1yOYd9Zm89yPaby1Jos5I6O5YUQ0vh76/EXbk3fXZQOwObsCs9WGs2Onv4YTZ8FstbEnr5r1GWWszyhjb0ENitJy0J4xMJyuvu5qh8jyvUUUVDfRM9S7XX7et7sLeGnlAb66fQRRgZ7t8jPVpigKd36+i7E9grlmaDe1w2k31w+PYmZSBK5OjuRXNfLNrnxmDetGiI+b2qEJ0S52Hq5i7mc7aTZbWTB7CON7hqgdUrtbl1HGXZ/vwt3FkbK65na7O9TpE2aDwcB5PUM4r2cIyYcqeWdtNq+uymD++hyuHd6Nm0fHEOItBzMt2nWkiu25lQyPDWBrTiW7j1QzNCZA7bA6nMVqY+W+Yi7qG4qTHVwg5FU2sj6zJUHenFVBXbMFBwMM7ObPfRN6MLZHEP0j/HD8Q62dWhRF4b2jF3Fuzmf/u6kzmnlx5QHC/NyJ9Pc465/XGVhtCnGPrwDgSGWjrhJmAFcnRwDeW5eNwQC3j4tTOaKOpygtrfMMBvU/g6LjfLs7n0e+TqWrnxtf3jaM+JD2mRToTD7bephnvt9Pjy7eLLgxiTC/9puE6fQJ8x8lRQewcHYAaYW1vLsumw/W5/DRpkPMTIpg7tg4IgP0cUKyF++tzcbX3ZlxPULYmlPJhsyyTpUwv7E6g4p6E3PHxRLRjsnO8tQi/vblHt64agCXDgxvt5/bWTQ0W9iWW8H6jHLWZ5SRU94AQLifO1MTuzK2ezAj44M65a3AtRllHCiuA8DN2fGsf96bv2ZSXt/MhzckHbf4RquaLVZ6zvup9fF3d41SMZqOU1xjZMmOfK4YHNGuJ9zOaNnuAu5bvId5U3pzyxgpPdEjm03hlV8O8u7abIbHBvDutYPx93RRO6x2ZbUpvLA8nYWbcjm/VwhvXjMQL9f2TXE1lTAfkxDmw/9dM5D7J/Vg/rpsFu/IY9H2PKYnhvHcpX3b/X+SaH9ZpfWsSi/B08WJf/10AGhZcPPA5J4qR/a7OqOFL3ccYdH2I1w6MJw7zosjLtjrrH/uDylFAKzcV6SLhFlRFNKKalsT5OTDlZitCm7ODgyPDeS64VGM7RFMXLBnp5/BWplaRFdfNxpN1rNOmLNK61ou6AdHkhjp1z4Bqqi+2ULfp39ufZz5wkW6vUMyf302VkXhjnHxaofSoRRF4b7FewCIDdZHuZA4XkOzhfsW72FVWgnXDO3Gs5f0wcVJP59bk8VGSa2RZ77fz68HSrlpVAxPTOndIXcsNZ1ZKopCqK8bwV6uFNYY+TG1iLnj4tqt9lB0nA/W56AoLSdhL1cn6pstpOTXUNlgIqCTXPk+OTWBm0fH8P76HL7ccYRvduVzcb+u3HVePAlhPmf0M2uNZtZnlOHi6MC6jDIaTRY8XLTzMcwoqcOmKPQK/X38NyzczobMcgB6hXozZ1QMY7sHkxTt3y6ztOfSSzP6U1jTxIVvbMD9LGJv6f6ShoeLIw9f2HkuAs9URX0zg59f3fr4wD8u1O16g7K6Zr7YdoRLB4TTLVDfdy1T8msAuHpIJOf36qJyNKK91TSZuWr+FjJK6nh6WgKzR0Z3+kmLPzKarew4VElZXfPvf+qP/7q60QyAo4OBf0zvw/UjojssHu2cqY8qqmnix5Qivk8pJLWgBoOhZSebeyZ056K+ofh5dI5kS5xcSa2Rxcl5rY+X3jmSya+vB2D53sIOfcOfrjA/d565pA93nx/Pgo25fLblMMv3FjGxdwh3jY9nYDf/0/p5q9NKMFltPHRBT37ZX0xxjZHYdpi1PhfMVhtzPtpBuL87S+aOAFrqkzdklnPDiCjuGh9PF40vjnJwMBDh70GT2XpWNcw/7y9mY1Y5z0xLIFDj7cjyKhsZ8/Ka1sf7n71AcxdCp+PDDTmYrTbuGq/v2mWz1caj3+wl1MeNx6f0VjucDpVX2UiQlyvuLvp9357Ikh15HCiuY8GNSUzorb0LovfWZfPG6szWx+7OjoT4uBLs5UpcsBfDYwMJ9nYl2NuV/hG+9Anz7dB4NJEwVzWYWLGviO/3FLL9UCWKAv0jfJk3pTdT+4cR6qvtk7S9+eMHAFo+BN/cMZLL393MjkNVnSphPibIy5VHLuzF7WPj+GTLIRZuyuWydzYzKj6Qu8bHMyI28JSu3JfvLSLcz507z4vjrvHaut37Q0ohBdVNx9Ueb8pqmVm+fniU5pPlY8xWG1abcsYzzE0mK//4MZ1eod5cNzyqnaM7tw4W13HBG+tbH6c8PRlPHZe8VTaY+GzrYaYlhmnmQvZMvb8+hwPFdXxwQxI+bp1vPUF7UBSFL3fk8fyPaVw7PIrHL9b3hcEftYz9CIO6+WkyWQY4uhaVXx8YR6iPm+rHnk575GtotrAqrYTvUwpZn1GGxaYQG+zJfRN6cMmAMGKCpN5Ki6obTSzafqT18YReIUQGeBAZ4MFP940h0LNzz8b5ejhz74Tu3Dw6hi+2HeH9DTnM+mAbg7r5cff58YzvGXLSxLmm0cz6zDLmjIrR1G0xaDn4zl+XA4DRYm19fmNWOV18XIkP0U9y0WRuGd+ZzqK+ty6bguomvrxtuKZrfHceruTyd7f8/njexE65ULM9LdiYQ5PZyt0au5g9XTll9fzn10wu7hfKpARtJlP/S0mtkUe/2cuag2WMiA3khhHavng9XTsPV5Fd1sDLl/dXO5Qz5u3WkqIGebmqnixDJ0uY9xXUsPtIFdtyK1mdXoLRbKOrrxs3j45hWmIYfcJ8NJdoiOMNeG5V69c9unixYPaQ1sd/rIvt7Dxdnbh1bCzXj4jiq+Q83luXw00fJ5PQ1Ye7xsdzYd/Qvyw6+CWtGLNVYUq/ripFfebWHCzlYEkdvu7ONJtbdt202RQ2Z1dwXs9gXX0ujWeRMOdVNvLuumymJYZpeivlNQdKmfPxDgAcDLDlsQmaLy35X2oazXyy+TAX9Q2lexf9roOx2RQeW5qKm5MDz1zSR+1wOsQPKYXMW7YPo9nKM9MSuGFEtC661JyOL3fk4eXqxJT+2jvfHHMsYa5vtnSKi/VOkzBnldYz9f82AuDv4cwVgyO4JDGcpCh/u3uj69WCP+zm5+HiyM/3jVUxmvbh5uzI9SOiuXpoN5btLuDdtdnc9cUu4oI9ueO8eGYMDG99/y5PLSIywJ3+ER1bZ9UR3lubQ7ifO2O6B/FLWgkAaUW1VDaYGNM9SOXo2pfR1HJBcCYJ8z9+TMPJwcDjF/dq77A6XJPJisli49cDJdy/JAUAF0cHfn1gnG7KbdqyPrOM+mYLs0fGAC0LplydHHRXr704OY9tuZX86/J+utvDwGZT+NfPB5i/LocBkX68OjOxXTobaU2t0cyy3QVYbArpRbUkRXeedq2nw8u1JUm+/bOd/HDPaJWj6SQJc6PJwp2f72x9/OVtI47rdFFaZ9TdB9vebM4q5x8/prU+3vPUZF3NSjo7OnBlUiQzBkXw495CHvp6Lw9+lYKPmxOT+4RS1WBiY2Y5t4yJ1dy4dx+pYvuhSp6amkBhdVPrDOyx+uVRcTpLmI+WnJxuDfO2nAp+SSvh4Qt7doodC0+Vzabwwor04y5ooWXjluX3jrGb/vZhfi3nmJbNZtKZvy6Hv03ozt8n9VA5svZTUmvknyvSGREbyMykSLXDaTeF1U38d+thVqeXkFFSz7XDWtqnabkk6kxVN5qY8uZGLLaWAuB312azYLY2E2br0SLm1IIaFEVhT141XX3dVVu3pnrCrCgK877dR2Zpfetz/p7Ora99sf0Iz/+YzmszE7lIg7eyRUu93KwPt7U+3vvMZF31gfyjopomPttyGJPFxoxB4YzpHgy0lGNYbApTNXh7rKC6CYChMQH8tK8Yo9mKoihszCqnRxcv3W0b3GQ6VpJxeu/Rd9ZmE+Tlwk2jYjoirA5jtFhZc7D0uOeOLcTV6+xcaa2RlPwa9uZXk1ZYi6+HM/5HOyzd/Ely6/dN7qOv+t6nv9uPyWLjnzP6ae7CvS1phbW8sza79fGTUxPsLlk+XNHAwo25LEnOb12H4eRg4K1Zg1SO7Mx9uvlQ69djXl5DflUTf5/Yg79N7K5KPKonzF/uyGPp7gLmjIrmo02HgJbyDAeDgUe+3suvB0oZ0z2IQVGn175LqO9IRSPLU4taNyYBmDelt25XZP+QUsjj36aiKPCfqwcwfcDvm5L8uLeIqEAP+pxh/2Y19Tp6tyejpA43ZwdsR/tnb8+t5Nph+ltIc2wG/XRmmPcV1LAuo4yHLuipuVv4OWUN5JQ1HPfcF7cOO+Ne453R3vxqNmSWk5JXzd78GoprjUBL79a4YE/2Fpgpq2s+7u+sf2i8rvowf77tMD/tL+bhC3vqbtH8sNjjZ1A/WJ/DPRPUSao6ytJd+SzbU8inNw097vmdhyv5YH0uP6cV4+RgYFr/MJbuLsDHzYmdT07SZL/0zJI63luXQ/Lhqtbn4kO8uG9iD1UvYlVNmPcV1PD09/sZ0z2IhK6/H5w/33aEbTkV1BotPDW1pdm21DFrR05ZPW+vyWbZngKsR28LHTNHY7Nvp6Kh2cIz3+/nq535DIj0482rBx53oq1sMLE5u4Lbx2mvHAMgOtATFycHDhTXEeLdsvBrU1YFzRYbo7trd2HbyRybnXE9jcT3vXXZeLk6abKNnMFA6+ZBxyhtfL/WHCiu5dK3N2FTICbIk2GxAfSP8CPxaN9WdxdHEp/95bi/s/2JCZosAzRbbXy7u4AFG3Jxc3HkmiGRTO4TyvM/prF0dwEjYgO5VYfbX3u7OePt6kTd0ffwlToqNwH4bMshnvxuP7FHL3QKq5v4aV8x36UUkpJXja+7M3eMi+PGkdF08XHj2el98HJ10tT5pslkZeGmXH5IKeRAcd1xr107rBsvXNZPpch+p1rCXNNk5s7PdxHo6cIbVw3gsaWphPq40dBsYfneInqFevP5LcNl1z6NOVTewOTX17fWT/3R85f27ZDtKtWUml/DvV/u5lBFA/ecH8+9E7r/5Yr+p33FWG0KU/qFqRTl2XFydKBHFy/Si2qJDAgFYHV6CU4OBobF6C9hNh7tAnKqM8yHyhtYkVrEbWPjOsVK7tPVJ8yX5feO5p5Fu9mbX8Pw2AC8O0ELp/ayaNsRnBwdWPfQeSesLV+RWkRNk7n18c55EzXZEeSHlELuWbT7uOdS8qp5dGkqDgb424Tu3HN+vG5LFVKenswX248wvleIrvZmmL8umxdXttyldXZ0YPpbG1t3aOwV6s2zl/ThyqSI43aM9dbgXdw1B0t55eeD9Av35aqkSBYn5zF3XCxzRsa0TtSoTZWjoqIoPPhVCoXVTSyeOwJ3F0fWZZRxzdBuBHu7YjRbufv8eFydtHVrU0CorxuPX9yb/KomFm76fRHR0OgArhgcoWJk7ctmU/hgQw7//uUgQV6uLLp1+EnbiC1PLSQ2yJPeXbV78dcr1Id1GWVcktiS9P92oJRB3fw7RW/Ms1FrNJNeWEtaUS1pR/+bWdKynuJYS6P/Zf76HJwcHbhpVHQHRtqxogI9+fbOUdQZzbraLbXJZGXp7gIu6ht6wmTZalO48/NdrY93PzkJf09tjn9FatEJnw/3c+f1qwYwNEabC79OlYODQZN3eE5GURRu/XQnq9NLWp87WFJHYoQvD1/Ykwv7hOpqc51jZUK3jInh290F+Lo7c+e4eHw9Ok/yr8rZ7v31OaxKK+GaoZEMjvJnZWoRzRYbF/QJZUSc/mas7ImbsyM3jY7hwj/sDnbopSkqRtT+SmuN3L8khY1Z5VzYJ5SXLu930iSjvL6ZLdkV3DU+XlO3x/6sV6g3X+/Mp/HogrjKBhOjRmi3O0ZWaT23fZpMTnnDCV/v4uPKgo25jO0RxPm9Tl4zV1pr5Jud+VyRFKH5xY+ODgZdJcvQ0sqxzmjhmqHdTvj6Hf/9vTtTylOTO9XJ+XS9e91gAD7ZfIj9hTXcN7EHYX7a6dYiWiiKwvULtrPxaBeiY/qF+/Le9YMJ1+nvND7ECxcnBz7YkMO+gloev7hXp/s8nvOEeUt2Revtha935tPV152DxXX4ezgzJFoW9nV2jSYL23IqqWwwUdV47I+ZqgYTlQ0mqhvNHCz5vf7owD8uVDHajnHDwu3kljfw4ox+XD0kss1E+Kd9xdgUmNpfm+UYx/Q+usbgUMXvCeZoDfdf9nBxpG+4L90CPWg22zBarDSbbTRbrDRbbBjNVj7efIic8oY2E+YFG3Ox2GzMHau/ulA9+HL7EWKDPBl2ktnVYyViKU9P1mQ5zYncODJa7RDEWbDalL8kyzFBnnx750jdltNAS7lJr1Bv9ubXEObrxg0jotUO6S/OacJcWN3ENR9sBWD2yGgqGky8tioDgJlJEbp+M+hFZYOpdQcwaDnh+Hs44+fhQoCHC9FBHq0J89bHJmiuY8CpiAvxoqC6iUsSw9pMlsvrm/li2xHiQ7zo0UXbt86Odco4dHRG1tvViUQNbsByTJifO29eM/Ckr6/LKOPGhduZMTD8pN9T02jmv1sPM6V/GFGB+uo6oAcZJXUkH67iiYt7n/Rz+vasQbKgXHQaFquNh7/eC8Dt4+JYuCkXk8Wm69rzY2w2hb1Ha7Pvn9w5uw2d04R55Eu/AXBVUmTrlpyXJIbx1posXban0qMuPm4svXMkAR4u+Hu64O3qdMITjsVq0+0HfM7IaJbvLWLp7gKuP0HNnKIofJ9SyDPf76eh2cq/ZyZquhxDUZTWlo/HZuGGxwXq9vcLLbe1vVydSIz0o6bRjKer41/G+99th2kwWbl9nMwuqyH5UCVbsiuOax+mKArZZfX8klbCwo25uDo5MGPQyS96JFkWnYXJYuO+xbtZkVrMA5N6cNf4eN5b19Jb+tjaET1bn1nW+vVlbUxUqOmcJsz/uLQv9UYLd5wX1/rcpIQuTErQV3N4PXN2dGBQt/9dOqPnZGpwlD/9wn35eFMu1w3rdlwyXFxj5IlvU/n1QCkDIv145Yr+dO+i3cV+0FI69daaLK4eEsl1w6NYtqeQUTpea6AoCuszyrDYFMb/e23r865ODni5OuF59M+RigbO6xlMnzDtzrRr2ZbsCl5dlcH4XiEcrmhkfUYZGzLLKKxp6bE8Mi6Qhy/spcmOF8K+GM1W7vjvTtYcLGPelN7cMiYWm00h3M+dG0dG6fp8esyIuEAW3TqcoTEBnbab1jlNmE80GyeE1hgMBuaMiub+JSlsyCxnbI9gFEVh8Y48XliejtlmY96U3swZFdNpP/inY1piGE1mK9cdvQv0zLQE3fU5/SODwcB3d48it7yBhmYLdUYLDc1WGkwW6pstNBz9E+TlwoOTe6odrt26bFA4r67KYOr/bQRaupqMigvi7vODGdM9yG629Bba1tBs4dZPk9mSU8E/L+vHrGEtC1QdHAxsevR8laM7d1ydHDt90wdt94QSQiVT+nflnysO8PHmQ8QEefLY0lQ2ZpUzLCaAf13en2gd7aTl5ux43AKM2TrcfObP+oT5ysxxJxfh78G8Kb2pNVoY1yOIxAg/u5iJE/pR02Tmpo93sPtIFa/NTOSygfppvapHkjALcQZcnRy5dlg3/vNrJltzKjDQsjHLrKHdpC5SiHPkFh3uWifsQ2WDiRsWbuNgcR1vzxrERf26qh2S+B/kclyIM3Tt8G74uDmRFB3AL/eP47rhUZIsCyGE+J9u+WQHGcX1vH99kiTLGiEzzEKcoRBvN5LnTcLFSa47hRBCnLroIE9251VjUxS1QxGnSM70QpwFSZaFEEKcrhcu7UefMB8e+SaVpqM7qIrOTWaYhRBCCCHOIXcXR96/Pony+mbcXTrfJh3iryRhFkIIIYQ4x8L83Anzc1c7DHGK5H6yEEIIIYQQbZCEWQghhBBCiDZIwiyEEEIIIUQbJGEWQgghhBCiDZIwCyGEEEII0QZJmIUQQgghhGiDJMxCCCGEEEK0QRJmIYQQQggh2iAJsxBCCCGEEG0wKIpy6t9sMJQBhzsuHFVEKYoSrHYQarGn36m9jFWn4wT7Gau8f/U3TrCfsdrLOMF+xmrXx6RjTithFkIIIYQQwt5ISYYQQgghhBBtkIRZCCGEEEKINkjCLIQQQgghRBskYRZCCCGEEKINTqfzzUFBQUp0dHQHhaKOnTt3lttzlwx7Yi/vXz2OE+xnrCc7JtnLWPU4TrCfsdrLOMF+xip5UovTSpijo6NJTk7uqFhUYTAY9NYSRZyEvbx/9ThOsJ+xnuyYZC9j1eM4wX7Gai/jBPsZq+RJLaQkQwghhBBCiDZIwiyEEEIIIUQbJGEWQgghhBCiDZIwCyGEEEII0QZJmIUQQgghhGiDJMxCCCGEEEK04bTaygkhhBCnQlEUao0WqhtNVDaYqG40U9VooqrRTFWDiUkJXdQOUQghTpkmEmaz1UajyYqvu7PaoQghhF16d2021Y0mBkf5owDVx5LfRhNVDS1fV/8hIa5uMmO1KSf8WQ4GCPNzP7cDEEKIs6CJhHnkS78xsXcXXpzRT+1QhOgQ23Iq+PcvB5mZFMmVSZFqhyPEcYxmK6/8fIAT5b8uTg74ezjj7+GCn4czPbp44efh0vqcv4cL/p7OR59zIcDDBW83JxwcDFx77ocihBBnRBMJc3SgB1mldWqHIUS7K6k18s8V6Xy3pxCDAe6b2EPtkDqc2Wpj6a58BkT60zPUW+1wxCnILW/ApsArV/QnNtgLVycH/DycCfB0wd3ZEYPBoHaIQgjRoTSRMMeHeLNyXxGKosiBWeiC2Wrj402HeGN1Bg0mKwAPTu7JqPgglSPrOCZLS6L81pos8quamDsulscu6q12WOIUZJS0TFj0i/ClV6iPytGop7LBxPx12TwwuScuTrJmXgh7opGE2YvqRjMVDSaCvFzVDkeIs7I5q5ynvt9PVmk93m4tH8GL+4Vy53lxKkfWMSxWG1/tzOet37IoqG4iMcKX56b3YXzPELVDE6coq7QeRwcDMUGeaoeimppGM9d9uI3ssnqmJYbRN9xX7ZCEEOeQJhLm7iFeQMtBWxJmoVVFNU08vzyd5XuLiAxw56mpCfzn10x6hXrzyhWJurx7criigb8v3sOuI9UkRvrx/GV9Oa9HsC7HCmCzKTg46G9smSX1RAV64OrkqHYoqqgzmrnho+1kldbz/g2DJVkWwg5pImGOP5owZ5bWMzw2UOVohDg9JouNDzfm8H+/ZmFTFO6b2J0bRkQz64OtAMy/fjCerpr4KJ4yRVH4emc+z3y/HwcHA/+5egCXJIbpNlEGaDRZuPuL3YztHsTsUTFqh9OuMkrrWicu7E1Ds4U5H+1gf0EN7143mPN0eGfEZlNIL65lS3YFW7IrGBITwO3j9HnHS4gzpYmzdFdfNzxdHMkurVc7FCFOy/qMMp75fj855Q1M7N2Fp6clEOHvzt1f7CajpI6P5wwlKlBft7mrGkw8sSyVFanFDI8N4NWZAwjvhC3Eyuub8XJ1ws357GdNK+qbuemTZFLzq5nQW38JVZPJirOj/dXsGs1WbvkkmV1Hqvi/awbpsnf0T/uKeWzpXqoazQDEBHkyIk4mprQss6SOzNJ6Lu7XVe1QdEUTCbPBYCA+xItM6ZQhNCK/qpHnf0znp/3FRAd68NHsIYzv1ZJIvbM2i+WpRTx2US/G9ghWOdL2tTGznAe+2kNlg4lHL+rFrWNiceykJQqv/HSQNQdL2fb4hLOa+a5vtnDle1soqG7i3esGc0Gf0HaMsnMYGhPApqxyu1p43WyxMveznWzNreC1mYlM6a+/5ONAcS1/X7yH2GBP7p/Ug4kJXejq2/kubsXp+XzbERbvyGNSQhe7vNDtKJr5Pxkf4k1micwwi87v822HmfjaOtZmlPLg5B78dN/Y1mR57cFSXvn5INMSw7htbKzKkbYfo9nKP35M47oF2/B2c+bbO0dx+7i4TpssK4rChswyBnXzP+sEcGNmGTnlDbw1a5Auk2WAUfFBlNebOFhiH5MWZquNu7/YzbqMMl6a0Y/LBkaoHVK7q2k0M/eznTSZrRwqb+CFFen4e7ioHZZoB0OiA2gyW9lfWKt2KLqimYS5excvSuuaqWkyqx2KECfVZGpJHPtH+LH6/nHcfX731lv+ueUN3LtoN71DfXj58v66mak7WFzHpW9vYsHGXG4YEcUPd4/u9IuicsobKKwxMrr72bfx25ZbiZuzA+N0drfgj461O9yUVaFyJB3vUHkD136wjVVpJTw3vQ9XDemmdkgd4sWV6RyuaASgwWTlisER7VKe1BllltQx+6PtVDWY1A7lnPBxbykekDLW9qWZhDk++PdOGUJ0VpuyyjGabdxzfjwR/h6tz9c3W7jt02QcHQzMv34w7i7aPzHZbAoLN+Yy7a2NlNeb+Gj2EJ6b3lcTY9uQUQbA2O5nn+Ruz61kUDd/XfflDfdzJzbIk01Z5WqH0mGsNoUPN+Rw4X/Wk15cy+tXJXLDiGi1w+owkxK6MProhdD5vUJ4ZloflSPqGAXVTVy/YDv7C2upb7aoHU6HqzOaefzbVML93Jmow5p7NWmihhlaZpih5YppcJS/ytEIcWK/HijBy9WJYTG/L5qx2RQeWLKHnPIGPr1pKJEBHm38BG0oqTXy4FcpbMgsZ2LvEF66vL+mWj5uyCwnKtCDboFn97uoNZpJK6rlbxO6t1NkndfI+ECW7irAbLXpri4yq7Seh79OYdeRaib2DuGFy/rRxcdN7bA6VIi3G7uOVNE33If/u2YgTjr7nULLRjM3LNhGg8nCkrkjdHHsbYuiKMxbto/CaiNL5g7H191Z7ZB0RTMJc4S/By5ODrLwT3RaNpvC6vRSxvUIPm62cUNWOT/vL+Gxi3rpYie/Y6vqm8xWXrisL7OGdtNUeYnJYmNrTgWXDgw/65+181AVitKyKE7vRscH8d+tR9iTV82QaH2M12K18cGGXF5fnYGHiyNvXDWA6QP03f4QWhYl3/TJDvw9XFh44xDdtbWElnaAN328g7yqJj67aSi9u+p/h8qluwr4bk8hD0zqweAofXxGOxPNfEocHQzEBXtJSYbotFILaiira/5LW7GAowtpwjpha7XT0dBs4dkf9rMkOZ/+Eb68ftUA4oK115t395EqGkxWxrRDOca23EqcHQ0MjNT3XS+bTeHn/SUAdNJ1nKftYHEdD3+dQkp+DRf1DeW56X0J9tbOXZIzVdNkZs5HOzCarXx+yzBCdDiTbrLYuOPzXezNr+a96wYzzA72b8gtb+DJ7/YxLCaAO8fHqx2OLmkmYYaWDUx2H6lSOwwhTujX9BIcDPxly+feXb3xcHEk+VAl0xLDVIru7GSV1nHzJ8kcqWzkrvFx3Dexh2Zvy2/MKsfRwdAuvWa351bQP8JPE3XbZ0pRFP65Ip1vdxfw4GTtz1yZrTbeW5vNm79l4uPmzNuzBumyZdyJmCw2bv9sJ4cqGvjkpqH06OKtdkjtzmZTeOjrFNZnlPGvy/sxWQeda5otVowmG74eJy6xMFls3LtoNy5ODrxx9YBO251I6zSTMNcazaTmV5Nf1UT0o8vZ9eQkAjylBY7oPFall5IUFYD/n96XTo4ODI8NpMlsVSmys1NaZ+TGhTtotthYfNsIzZcfrM8sJzHC96zr+5pMVvbm13CrjtoDnsj89Tl8uDGX2SOjuUvDM1dZpfV8vDmXTVkV5JY3MC0xjGemJRCoodr7s6EoCo9+s5ctOS19pUfGab887I+aLVYKq418uuUQ3+0p5KELeuqmw8kDS1L4cW8RB/5x4V86mZgsNv7xYxqpBTXMv36w9NHuQJ02YVYUhYMldaw5UMZP+4tJyas+7nUPHc/oCO0pqG4ivaiWxy7qdcLXF9yYpMm6yEaThVs+SaaywcSSuSPoF9G528X9L9WNJvbmV3Pv+We/SG/3kSosNkXzFxBtWZKcx0srDzAtMYynpiZo8j0MLUnFrA+2UlrXDLRsR6/Xntkn8/rqTJbuLuD+ST2YMUh/faV7zvup9esbR0Rx53n62dr7UEUDAF/tzOfKo+3/6pstfLMzn/nrsimsMTJ7ZLTdvafPtU6RMDdbrFTUmyiobmL53iK+21PQuk3nn7k6ObDt8Qm67RcptOm39Jb6zpO18dFiomG1Kfztyz3sK6jh/euTNJ8sA2zOrkBRYEw79V82GNBt157VaSU8tjSVMd2DePXKRBw0epu3oLqJmz7a0Zosr/zbGF0uACutNVLRYDrh2JYk5/Hmr5nMTIrgnvO1e5egLT27eLdurPP0tD6aPOaezLT+YewrqOXlnw7w5LJ9x72WFOXPi5f3Z2w7HNNE2855wqwoCg99vZfiGiOldUYyTnH3visGRzBvSm/8ZCci0QmtSi8lJshTk4vgTub55WmsSivhmWkJf7kQKK9v5seUQsb3CiEq0FOlCE/fzsMtayDSimoZ2M3/rGr9tudWktDVBx83/bVu2nGokru+2EXfMB/eu26wpntMHyiq5WBJHZEB7uRVNul2N7tXfj7It7sLuH1cHPdO6I6LkwOKorAkOY8nvt3HmO5BvHBZP10lkn/089/HUt9swdPFUXdjPLYYtc7Y0kfaz8OZ28bGkhQVwJDos9+tVJyac54wGwwGMkrq2Jtf0+b3JUX5M3NIJFP6ddVlyxuhH/XNFrZmV3DDiCi1Q2k3H23K5aNNh5gzKprZo2Jan0/Nr+HjzYf4IaUQk9WGh4uTphLma4d1IzW/hqe+288X247w1LSEM6rlNFls7DpSxaxh2qqR/GLbEfYV1nD72Lg2e1C/8vNBfN2dWThb+y3HRncPwt/DmbzKJrVD6VCPX9ybkrpm3lqTRc9Qb3p39eHxb1PZnlvJsJgA3r52kGYX6p4qL42/V0/mz91blswdocsFm52dKu+ur28fSeKzvxy3CCrA04XJCV24oG8oI+MCcXWSkguhDRszyzBZbbrZVWlVWgnP/ZjGpIQuzJuSgNlq4+f9xXy86RDJh3/vUjMyLpDLB2urFjI22IvFc4ezIrWYf65IZ9YH27igTxeeuDjhtDYxSS2optliY5hG6petNoXnftjPJ1sOYzDAkh15XDUkknvO706o71/bisWHeLGvoEbzyTKAq5Mj0weE8/HmQwDodTLO2cmB3PJ6uvi4sje/mgeWpODm7MBLM/oxMylSsyU1guM2hZqc0EWSZZWocjR0cXJg7rhYlu4qYELvEC7oE8qQ6ABphSI0aVVaKb7uziTpoJY1Nb+Gexftpl+4L09PS+DdtVn8d+sRimuNdAvw4IFJPfh062EA/nP1QE1+Zg0GA1P6d2VC7xA+3JDDO2uzmfjaOm4aHcPd58efcJaquMZIiLdra9KxLbcSQBMbeNQ3W7h30W5+O1AKwPqHxjN/fTaLtufx+bYjTOgVwpVJkYBCRYOJynoT+VVNNJqsbMmuYHyvkLb/AQ3o3VX/CcYLy9NaZ9E/2JDLJYlhPDk1wS56S+vdsd+hq5MD86YkqByN/VJt+uC+iT24b2IPtf55IU7IZLGxJDmPC/qEntKJxmpTWHOwlPE9gzW/teyx3b+aLVYCPF04/9V1mCy2o7WPfTmvZwj3L9lDRX0zn98yXPMnYjdnR+4+vztXJkXyr58O8N66bL7emc/DF/TkisERrclxflUjo/+1hqhAD2YN7caVSZFsz60kPsSr07ckK6pp4qaPk8ko+X2H1AmvrcPb1QmrTQHg1wOl/Ho0mT7G29WJ2GBP3bTu/OOmQXqsYf41vYRF2/OOe657iJfmP6OiRYCHC5EB7lw/POq07oSJ9qX9+21CtKPkQ5XMW7aPp77bx4i4QKb2D+OCPqEnTRz25FVR2WBiQm9tl2PUGs3c9PEOyo52EtiWU8nMpAhuHBFN96O3/37aV9S67Wp7bPrRWXTxceO1mQO4YUQ0z/2wn4e/2cunWw/x1NQ+DI0JINTHjUBPF/IqG3lx5QFe/SUDBeXorGznta+ghps/2UFDs5UFNyYR7ufOpNfXY7LYmDwolEBPFwI8XahqNPF/v2UBLbv4pf/jQt2VxI3pHsy71w6irL5Z0wsYT8RotvLIN6kAxAZ5cl7PEErrjO2y9bvoHBwcDKx/aLws7lOZJMxC/MHI+CB+vm8sP+4t5Me9RTy2NJV5y/YxKj6Iqf27ckGf0OM2vFiVVoqTg4FxPc9+m2U1vbc2m4ySeiID3LlxRDRXJkUeN06j2crzy9PpFerNHTrqb/pHAyL9+OaOkXyfUshLKw8wc/4WpvTvymMX9eLifl35amcei24dzrLdBfy8v4SL+3be3eFWp5Vwz6Ld+Hs48/UdI+gV2tJqrHuIFwGeLrw4ox8A23IqeGtNS7Ls6+7MXePjdJcsH3NRv877+zpbY3sEMTIuiEsHhGn+Tpc4MUmW1ScJsxB/0jPUm56hPbl/Ug/2F9by494iftxbyMNf72Xet/sY2yOIqf3DmNA7hNXpJQyLDdB8a7FZw7oxIi6QkXFBJ6xLTsmrpry+mZev6K/rE7LBYGD6gHAmJXThvXU5zF+Xzeq0EobHBmI02zhS2ciz0/vy7PS+aod6Uoqi8OnWw3Tv4sWHNyQR4vP7or6L+nXl/37LZOmufL7cnsf2Q5UEebnw6EW9uG54lG67DOiZm7Mjr80coHYYQuieHB2FOAmDwUDfcF/6hvvyyIU9Scmv4ceUQpanFrE6vRSDARQFZo+MVjvUsxbh70GE/8lr44bFBrLpkfM7fc1ue/FwceL+ST24akgk/1p5gO9TCgH4IaWQ6QM6961ug8HAW7MG4uRgwMPl90O8yWJjyY48FAXuX5JCqI8bz0xL4Koh3XCXnVOFEKJNkjALcQoMBgMDIv0YEOnH4xf3ZteRKn49UEqPLl5MT+zcCVR7sZdk+Y/C/dx585qB3Dgyiud+SKOoxqh2SKfkRHc8rl+wjeLalvifnpbArGHddFt+IYQQ7U0SZiFOk4ODgaToAJI00FJMtI/BUQF8d/fo1s4SWvS3id3Zml3BvRO667qsRgghOoIkzEIIcYq02Hf6mJFxQWe0q6EQQgiQaQYhhBBCCCHaIAmzEEIIIYQQbZCEWQghhBBCiDZIwiyEEEIIIUQbJGEWQgghhBCiDZIwCyGEEEII0QZJmIUQQgghhGiDJMxCCCGEEEK0QRJmIYQQQggh2iAJsxBCCCGEEG2QhFkIIYQQQog2SMIshBBCCCFEGyRhFkIIIYQQog0GRVFO/ZsNhjLgcMeFo4ooRVGC1Q5CdDx7ef/qdJxgP2M94THJXsaq03GC/YzVXsYJ9jNWyZM4zYRZCCGEEEIIeyMlGUIIIYQQQrRBEmYhhBBCCCHaIAmzEEIIIYQQbXBSOwAhRPsKCgpSoqOj1Q6j3e3cubP8zwtP9DjWE40T7Gesehwn2M9Y7WWcYD9jPdkxyd5IwiyEzkRHR5OcnKx2GO3OYDD8ZeW5Hsd6onGC/YxVj+ME+xmrvYwT7GesJzsm2RspyRBCCCGEEKINkjALIYQQQgjRBkmYhRBCCCGEaIMkzEIIIYQQdqaqwaR2CJoiCbMQQgghhB0pqmli2Iu/cufnO9mTV612OJogCbMQQgghhB1xdXLk5tExbMgs59K3N3HXF7vUDqnTk4RZCCGEEMKOBHi68NDknlw2MBwAR4NB5Yg6P0mYhRCiAymKwsbMcoxmq9qhCCEEAEazlbu+2MWnWw5z/fAoXpuZqHZInZ4kzEKIc0pRFJot9pE8KorCC8vTuW7BNjZmlqsdjhBCUF7fzNXvb+Wn/cXMm9Kb56b3wclR0sH/RXb6E0K0K5tNoay+mfyqRvKrmsivaqKguomCqibyqxopqG5CUeC3B88j3M9d7XA7jKIovLjyAB9uzCXE25XR3YPUDkkIYefqmy1c+8E2Dlc28O61g7iwb1e1Q9IMSZiFEO1q/voc/vXTgeOeC/B0IdzPnR5dvKkzWmgyWQnwcFEpwo6nKAov/XSA99fnADB3XBxuzo4qRyWEsGdWm8LfFu0mq6yeT+YMlYv40yQJsxCiXY3tEYSXW18i/NyJ8HcnzM8dT9eWQ01Nk5mhL6zmisERuLvoM4FUFIWXfz7I/HUtyXKQlwuzhnZTOaqO9/aaLD7dcogV944h0MtV7XCEEH/y8k8H+PVAKf+Y3keS5TMgCbMQol31CfOlT5jvCV/7fk8BzRYbVw/RZwKpKAqv/HyQd9dmk9DVh7SiWm4bG6vbi4Nj7v5iFz/uLQLQ/ViFPtQ0mVvLxI6Vjjk7GXjsot5qh9YhvkrOY/76HK4fHsX1I6LVDkeTJGEWQpwzX+7II6GrD33DfdQOpd0pisKrv2TwztpsrhkayVNT+/DVzjwuHxShdmjtqtlipbHZir+nC4qiMOblNeRXNQHw2wPj8HCR04rofFallbAkOe9octxIndFy3OseLo70jzjxhb7W7ThUyePfpjIqPpCnpiWoHY5myZFNCHFO7CuoYX9hLc9e0geDDnt+vr46k7fWZHFVUiQvXNoPBwcDN+hsJqe60cSA51YR5OXK1sfOJ/6Jla2v7XlqEn46rksX2vbiinRqmswMiPRjaLQ/Ef4ehPu3lI1F+Hvg7+Gsy+NSXmUjcz/bSYS/B+/MGoyzdMM4Y5IwCyHOicU78nBxcuDSAeFqh9Lu3lidwZu/ZjIzKYIXZ7Qky3pjttoY8NwqAIbG+B+XLGc8fxEuTnIiFp1TTlk9OeUNPDe9j+4uYttS32zhlk+SsVhtfHhjEr4ezmqHpGlyhBNCdDij2cqyPQVc3DdUdwft/6zO5I3VmVwxOIKXZvTXZbKsKAoXvLG+9fGK1GKgZUFj7osXS7KsUYqisCevGqtNUTuUDvXbgVIAxvcMUTmSc8dqU7jvy5aOGG9fO4i4YC+1Q9I8OcoJITrcyn1F1BktzBwSqXYo7eqt3zJ5fXUGlw+K4F+X6zNZBnhx5QFyyhqOe25i7xCS503S5W1saKnVzimrx6bjZPKCN9Zz6dub2JBZpnYoHeq3A6X06OJFZICH2qGcMy//fIDV6aU8NTWBMd2D1Q5HF6QkQwjR4b7cnkdUoAfDYwLVDqXdvL0mi3//ksGMgeG8fEV/HHWaLH+zM7+1n/Qx954fz/2Te6oUUfvLq2xkx6FKQrzdWJycxw8pha2vfTR7CON76WtmcmNmOdct2Nb6eHS8fluM1RnNbM+t5JYxsWqHcs58vTOf+etyuHZYN24YEaV2OLohCbMQokPlljewLbeShy7oqZsZ2Pnrsnnl54NcOiCMV65M1G2yvCmrnAe+Sjnuuf9cPYDpOqpDP1zRwOXvbqa83vSX14ZGB+iuX62iKMz5eHvr49X3j9P1tsgbMsux2BQm9NbXRc/J7DxcyeNLUxkZF8gzOl1grRZJmIUQHeaX/cU88/1+XBwduGKwPtqrfbghhxdXHmBaYhj/1nGyfLC4jms/3Hbcc9/cMYLBUQEqRdS+6oxmvth2hBdXHvjLay5ODmx8ZDwh3m4qRNaxLDaFCH8Pcssbjj62qRxRx/o1vRQ/D2cGRvqpHUqHy69q5LZPdxLm58Y71w6SjhjtTBJmIUS7y69q5Jnv97M6vZReod7836xBdPHRfvKxcGMuzy9PZ0q/rrw+M1FzM3O1RjOHyxvp9z/6zZbUGrn2w63HPbfh4fG6qAHdcaiS//sti/UZJ67b1cs4T+bDDbnkljfwxMW9ySytI8Jfu2NduDGXnPJ6Hr6wFz5uf11MvGj7EZbtKWDGwHDNfVZPl9Fs5dZPd2Ky2vjwxiHS4rEDSMIshGg3ZquNDzfk8uavmRgM8MTFvZk9KloXMx2fbD7Ecz+mcWGfUN64eoAmT8B3f7GbrJI61jx0Hq5OJ96Rr77ZwpyPdhxXopDy9GR83bXf3cRotnL3F7soq2tufe6qpEgWJ+cBcPu4OF0ny7nlDbyxOoML+4Ry61jt1/Sm5Ffz3Z5C1hwo49WZiQyPbVkjYbO1bE//3rpsxvUI5ulL+qgcacf7108HSC+qZeHsJOJDpCNGR9DeEV8I0Sltz61kypsb+NdPBxjTPYhV94/j1rGxukiWP9t6mKe/38+khC68ec1AzY7pltExFNYYWbwj74Svm6027vx8FwdL6pjSrytT+nfl4PMX6iJZBvhy+xFKapuxKeDu7MjskdGtyfIliWE8cqF+FjL+mc2m8Og3e3FxcuC56fpIIPuFt9wpaTJbueaDra2bk9y9aBfvrcvm2mHdWHBjEl6u+p4bXJ1WwkebDnHjiCjO79VF7XB0S9/vIiFEh6tsMPHiinS+2plPuJ87H96QxMQE/Ry0v9h2hCeX7WNi7xDenjVI0z2Hx3QPYki0P2/9lsXMpEicHR1aa7AVReHJZftYn1HGizP6cc3QbipH276MZmtrvbKrkwM3jIhi/tHuHyNiA3l1ZqKuF0gtTs5jW24lL83oR4gOyqMA+kf4AfDsJX3YklPB/PU5rb/TeVN6c/PoGN3+Ti1WG6vSSvho8yG251bSo4sXj13cW+2wdE0SZiHEGbHZFL7amceLKw9Qb7Rw+7g47p0Qj4eLfg4rS3bk8fi3qYzvGczb12o7WQYwGAzcP6kn13ywlVs+SWZjVjnL7x1NnzBf3lmbzZc78rhrfJzukmWA99fn0GxpWeB2y5gY3l6TDUCPLl4snD1Es3cNTkVJrZF/rkhnRGwgV+moF3qfMB8AXl+dwXvXDeaLbUdaX4sL8dJlslzZYOLLHUf475bDFNYYifB35/GLe3HVkG64OZ+4zEq0D/2c2YQQ58yB4lrmfbuP5MNVDI0O4PnL+tKji7faYbWrr3fm88jSvYztEcy71w0+ac2v1oyIC2RkXCAbs8oBsNlgRWoRr/x8kOkDwnhQR/2Vj6mob+a1VRkA/G1Cd/7zayYAgZ4uLJk7AncXffxuoaWs5uGv93Ld8G6tHU2e+m4fJouNF2f001US6Xm01CKnrIHJr6/H192ZN64awKr0EgZH+ascXfvKq2zkrd+yWLangGaLrbVt3ITeXXTbqaezkYRZCHHKGk0W/rM6kwUbc/F2c+KVK/pzxeAIXZ2EAb7dnc9DX6cwOj6I968frLuZmwcm92Dzu1sAiAxw5521WYT6uPHyFf1197tsMlkZ/PxqoKUk5ViybDDAj/eO1k03gc3Z5Xy3u5AVR3fVnHS0LGpJch4/7y/h0Yt6ER3kqXKUHWtq/66M7xWiu41mAO5etJuDxbVcPjiCG0dE0zNUXxMUWiAJsxB2xmK1Ud9sOe1EYVVaCc98v5+C6iauHhLJIxf2wt9TH8nGH323p4AHlqQwIjaQ969P0l2yDDA4KoDYYE9yyhqY8e5mPFwc8XJz0s0s+h/d8fnO1q+3ZFe0fr3q72Pp6uuuRkjtzmi2cv2C7VhtClP7d2Vq/64MivLnri92sXxvEYOj/LlldIzaYXa4Kf26qh1Ch6hqMJGSV82tY2J4YkqC2uHYLf0WbQkhTmhDVjlDX/iVO/67k1VpJZgs/3vjgq935nPrp8kUVDeR0NWH3l19dHUb+5gfUgr5++I9DI0J4MMbk3Q5xmN+e+A8Ft82nIp6E/sKaskqrVc7pA6x9uDv/ZYtNgWApXeOJD5EPzN0bs6O/H1idwCsNoWPNx9i4qvrWJVWwoOTe7Do1uGabIN4KmaPjAbg/kk9GKnTLb5/SSsG4IMNudQ0mVWOxn7JDLMQdiY60JPrR0Tx3Z4CVu4rJsDThWn9uzJjUAT9I3xPeEt+WEwAc0ZFc6Cojn0FNTz9/X66+roxuU+oCiPoGKvTSrhv8R6SogJYcOMQXS1ePJlhsYEsvXMkE15dB0BGSZ2uatEbTZa/PPfRnCEM6qav+laAu8bHU9lgZuGmXACGxgTw4ox+xAXruyfvM5f04elpCborJTomNb+GR75JBWBmUoRuWjxqkf7PCEKI48QEefLk1AQevagXGzPL+WZXPot25PHJlsPEBXsyY1AElw0MJ8zv99vVkQEePD2tD1abwpQ3N9BgsjCuZ7CKo2gfxTVG3vwtk30FNezNr6FvuA8L5wxpXUxkD+KCvUiM9CMlr5rrPtzGkrkjdFPr+vXO/OMevzYzkfE99VffCi0dUJ6c2rs1Yf7y1uE42MliMD0my4qi8OmWw7ywPL31uRdn9FcxImE/ZwUhxHGcHR1aF8jUNJlZkVrE0l35vPLzQf79y0FGxAYyY1AEF/YNbW38/+3uAg4U1/HmNQN1Ue86b1kq6zPLSYry59YxMdw0Okb3mxycSGyQJyl51ZTWNfP5tsO6qZMcEh3Q+vUTF/dmxqAIFaPpeAaDgeR5Ewn0dNFlEmkPFEVheWoRd3+x+7jnH7uol3TDUJn9nRmEEH/h6+7MNUO7cc3QbhypaOTb3QUs3Z3Pg1+l8OSyfVzQpwsX9+vKiyvSSYzwZVp/7S+u2Xm4ktXppTx0QU/uGh+vdjiqOpZbRfi7c9vYOHWDaUe9u/qw4MYk8quauPForaveBXm5qh2COEOHyhuY/vamv9Qpj+kexGWDwlWKShwjCbMQ4jjdAj3428Tu3Dshnl1HqvhmVwE/phSybE8hAG9fO0jzs1eKovDyTwcJ8nJlzqhotcNRnZ+7Cz5uTnw8ZwjB3vpKuCb01s+uk0Kfmi1W5q/Lae0Vfkx8iBdfzR2hy25EWiQJsxDihAwGA4OjAhgcFcBTUxNYn1GGTVEYHhuodmhnbX1mOdtyK3lueh+7WNz3vzxyUU/uOT9eTsxCnGNbcyp44ttUsssajns+3M+dz24eKp/JTkTOFEKI/8nN2VE3HTFsNoVXfj5AZIA7Vw/R3xbQZ8LVyVEXNelCaEVlg4l/rkj/y8JUZ0cDE3p14dGLeummT7heSMIshLArK/cVs6+gltdmJuLipM/etEKIzklRFL7amc+LK9Kpavy9VnlwlD8zBoUzpV9X3ew+qTeSMAsh7IbFauPVVQfp0cWL6QNkEY0Q4typNZq55ZNktudWAtAtwIMZg8K5bGA4UYH6aOWoZ5IwCyHsxje78skpa+D96wdLiyYhxDlVUNVEWV0zs4Z14/JB4Qzq5q/5BdT2RBJmIYRdaLZY+c/qTAZE+jEpQTonCCHOrd5dfVjz4HlqhyHOkCTMQgi74OLowLPT+xIgmzoIIYQ4TZIwCyHsgsFgkJllIYQQZ0SWiAshhBBCCNEGSZiFEEIIIYRogyTMQgghhBBCtEESZiGEEEIIIdogCbMQQgghhBBtkIRZCCGEEEKINkjCLIQQQgghRBskYRZCCCGEEKINkjALIYQQQgjRBoOiKGrHIIRoRwaDoQw4rHYcHSBKUZTgPz6h07H+ZZxgP2PV6TjBfsZqL+ME+xnrCY9J9kYSZiGEEEIIIdogJRlCCCGEEEK0QRJmIYQQQggh2iAJsxBCCCGEEG1wUjsAIUT7CgoKUqKjo9UOo93t3Lmz/M8LT/Q41hONE+xnrHocJ9jPWO1lnHDyz6rQJ0mYhdCZ6OhokpOT1Q7jjGzOKuf11Rm8NnMAkQEex71mMBj+svJcy2M9mRONE+xnrHocJ9jPWO1lnHDyz6rQJynJEEJ0Gl/uyONgcR3B3q5qhyKEEEK0koRZCNEpVDWY+GlfMZcNDMfN2VHtcIQQQohWkjALITqFZXsKMFltXDWkm9qhCCGEEMeRhFkIoTpFUfhyex79I3xJCPNRO5x2JZtDCSGE9knCLIRQXUp+DQdL6rhqSKTaobSrJTvymP3RDoxmq9qhCCGEOAuSMAshVLd4xxHcnR25JDFM7VDazbLdBTyydC8KYDCoHY0QQoizIQmzEEJVDc0Wvt9TyJT+XfF2c1Y7nHaxIrWIB75KYXhMIPOvG4yrkyxiFEIILZM+zEIIVS3fW0SDycrVOinHWJ1Wwr2LdjMw0o8Pb0zC3UWSZb1TFIX9hbU890MaAB/fNAQPFzm9CqEn8okWQqjqyx1HiAv2ZHCUv9qhnLV1GWXc+fku+oT58NGcIXi6yiFWr/KrGtmUVc4bqzMpqjEe91qjySoJsxA6I59oIYRqMkrq2HWkmicu7o1B44W+m7PLue3TZOJDvPj0pmG6KS8RLcrrm9mSXcHm7HIWbc874ff86/J+XDE4EkcHbb+XhRB/JQmzEEI1i3fk4exo4LJB4WqHclaSD1VyyyfJRAV68NnNQ/H1kGRZ62qNZrbnVLIpu5zNWRUcLKn7y/eE+brxyEW9mJwQKqU3QuicJMxCCFU0W6ws3ZXPpIQuBHlpdyvsPXnVzP5oB6E+bvz3lmEEangsZ6O01sgPe4sI9nbVZLcTk8XGjkOVbMoqZ3N2BXvzq7Ep4OrkwJDoAJrMVo5UNtIr1JsrBkdwyYAwQrzd1A5bCHGOSMIshFDFqrQSqhrNmt7Zb39hDTcs2EaApwtf3Drc7hKo6kYTK/cV8/2eQrbmVqAocNnAcM0lzDabwg0Lt7E1pxJHBwMDIv24e3w8I+KCGBTlh6uTI3VGM0U1Rnp08VY73A7RaLKQklfDiLhAtUMRolOShFkIcc7VGs18sD6HcD93RscHqR3OGckoqeO6D7fh5erEF7cOI9TXPpLlRpOFVWkl/JBSyLqMMsxWhZggT+45vzuXJIYRH+KldoinbdGOI2zNqeTRi3px3fAovE6wWNPbzVm3delVDSZmf7SdlPwaNj16PuF+7mqH1CFMFhsp+dVsza5ga24FDc1WbhwZxbT+YTg5Spdd0TZJmIUQ51RWaT23fZbM4YpGXpuZqMkFUjll9cz6YBvOjg58cetwIvw91A6pQzVbrKzPKOf7lEJWp5XQZLYS6uPG7JHRXJIYTt9wH80u2iytNfLSygOMiA1k7thYzY7jTJXWGrn2w21kltYD8OnmQ9wzofsJLxq0xmSxsTe/mq05FWzJqWDn4SqMZhsGA/QO9cFstfH3xSm8tiqD28bGceXgCNycpRZdnJj2PxFCCM1YlVbC3xfvwdXJgc9vGcbwWO3d/j1S0cisD7YBCl/cOoLoIE+1Q+oQVpvC1pwKvt9TyMp9RdQaLfh7ODNjUEvJxZDoABw0eLHzZ8/+kEazxcY/Z/Szu2S5zmhm9kc7KKhuwt3ZkSazlfnrc0grquWzm4epHd5pM1lspBZUszWnkq05FSQfqqLp6Lb0vbv6cM3QbgyPDWRYTAB+Hi7YbAq/HSjlrTVZPLlsH2/+mskto2O49iR3GYR9k3eEEKLD2WwKb/6WyRurM+kX7sv86wcTpsHbvgXVTVzzwVaMFitf3jZck+UHbVEUhT151Xy3p5DlqUWU1TXj6eLI5D6hXJIYxujuQTjr6Nb1r+klLE8t4sHJPYjR6YXPyZgsNu747y4ySur48MYkzusZgsliY+fhKrR4HfT1znyeXLavNUHuFerNVUMiWxNkf0+Xv/wdBwcDExO6MKF3CFtyKnh3bTYvrjxARYOJxy/ufa6HIDo5SZiFEB2qzmjm74tTWJ1ewoxB4fzzsn6ave35zposyuqaWXrnSHqF+qgdTrtbtD2Px79NxcXRgfG9grkkMZzze4XosmVaQ7OFJ5fto0cXL24bG6d2OOeUzabw8NcpbMwq599XJnJezxAAXJwcNLHob+fhSrbmVHLX+HgAjGYrL61MJz7Ei7vGxzE0JpCAEyTIJ2MwGBgZF8TIuCBS8qrp4mMf6xHE6ZGEWQjRYbLL6rnt02QOVTTy9LQEZo+M1vRt70BPFyw2G3HB+ppZPia9qBZvNyc2PXo+Pjpd4HbMq79kUFhj5JtZI3Bx0s+s+al4+eeDLNtTyEMX9OSKwRFqh3NassvqmfPRDmqNFmYmRRLs7crSXQWU15t485qBjIw7u0XEiZF+7ROo0B37OkoIIc6Z1WklXPrWJqoazfz35mHMGRWj6WQZICHMB5vCCTex0IOiGiPhfu66T5b35lfz8eZcrhvejcFRAWqHc059vCmX99Zlc93wbtx5nrZm1huaLdz88Q6aLTagZabZalP4YEMO/SN8GaHBNRFCOyRhFkK0K5tN4T+rM7nl02Sigjz44Z7RmrjNeyoSuvoCLTOxelRU00RXnbfHs1htPPpNKkFerjx8YS+1wzmnVqYW8eyPaUxO6MKzl/TV3AVsdZOZwmojvbr64OhgIPlQFavSisktb2Du2DjNjUdoi5RkCCHaTZ3RzP1LUliVVsKMgeH8c4Z265VPxNW5ZY7BpigqR9IximuM9I/wUzuMDrVwUy5pRbW8e+0g3c+k/9H23Er+tngPg7r58+Y1AzXZzjHcz50XLuvLQ1/vBSD5cBXJh6voFuDBhX1DVY5O6J0kzEKIdqG3euUTqTNaAHTZcspotlLRYCJMxzPMeZWNvL4qk4m9Q+wqwcosqeOWT3YQ4e/Ohzckafoi9sqkSNKL6li4KZc9edUA/OPSvpq8ABDaIiUZQoiz9mu6/uqVT6TOaAbQ5cxkYXUTgG53LFQUhXnL9uFggOema68c4UwV1xi5ceF2XJ0d+WTO0BO2V9Oaxy8+vpTmSo0tXBTapL9pEiHEGUkrrCWjpA6j2UqT2YrRbMNotrb+OfZc0x+eO/Y9WWX19Anz4b3rBut617v65qMzzG76OnQ2maw88s1enBwMDNBpl4Af9haxLqOMp6claLIH+JmoNZqZ/dF2ao0WFs8dTmSAPj6bTo4OrH3wPM7791oi/N01PWMutENfR30hxBm7+ZMdFNUY//K8q5MD7i6OuDk54u7ieNzjYG9n3JwdmJTQhXsndNf9iatehyUZzRYrt32WzM7DVfzfNYPo3sVb7ZDaXUZJHfcu2g3A1P5hKkdzbjRbrMz9dCdZpfV8PGcofcJ81Q6pXUUHeRIX7ElUoH1tOCPUo5+jvhDirHQL8MDbzYlPbhramhy7ODroYvvjM6UcXdx37Pb9sRpmb53MMFusNu5dtJsNmeW8fHl/pvTvqnZIHeLDDTmtX09/ayObH5ugYjQdz2ZTePCrvWzJqeD1qxIZ3f3sehN3VklRAazcV0Sd0Yy3DsukROciNcxCCAAGdPPjUHkjAZ4u+Hu64ObsaNfJcpPJyvULtnPBG+vJKm3pu1x3tCTD21X7J+eW3d728vP+Ep6amsDMIZFqh9RhHpjcs/Xreyd0VzGSc+PJ7/bxQ0ohj17Ui8sG6re+9+qhkdQ3W/j74j289VsmtUfXGAjRESRhFkIAMCDCD5PVRnqRPjflOB1Gc0uZwubsckrrmpn+1iZWpBb9XpKhgxnmt9dksXR3AaCP8bTFz+P3C5xHl6ZSUd+sYjQdo7jGyPvrs4l+dDmfbztCzy7ezB0bq3ZYHWpgN3/uHh/P6vRS/v1LBkOeX025Dn+3onPQ91FSCHHKjm0Jm5JXrduFXzabQoPJQqPJSkOzhYZm69HHFuqbrTQ2W2gwWVl7sJQNmeX8+8pERsUHcsd/d3Hn57sAcHFy0EULq56h3iR09SGtqJavk/OZmaTfGeaCqqbjHt/6aTJL7xylUjTta2NmOe+ty2ZjVvlxz380Z4hddAK5Z0J33vwtC4Bmi42Z723h05uH6nrxsVCHJMxCCAC6+roR4u1KytHeplpV3Wjib1/uaUmITVYaTZbW5LjJbD2ln+Hi6MALl/XliqPtqhbPHc5zP6Tx+bYjmCw2FEXRfDIyuU8ok/uEcuEb6/H10H6JSVuOVDa2fp0U5c9T0xJUjKZ9fbrl0HHJso+bE6sfGEeItz7bA/6Zs6MDy+4axaVvbwKgrL6ZK9/bwmc3DyU+RH8LWIV6JGEWQgAtC9sSI/1aNwPQKkcHA9WNJjxcnAj3c8HT1REPFye8Wv/rhIerI54uTni6OuHp4oiH6++ve7q0vO7s+HvFmquTIy9c1o/Ptx0BoLrRrIt+ttCykDFQJ2M5mYKjPaZvHh3Dk1P1kywDvHPtIDZklTPnox0AzJuaYDfJ8jF/vCP235uHcfMnyaxILebeCZIwi/YjCbMQotWASD9WpZVQ02jW7Kyjt5sz3909ukP/DT+N/r85kY2PjMds1edW38eM6xHMvCm9mTMqRu1Q2p2To8NxG+k4O2r7zseZMhhAUVpKy1b8bTTBXq5qhyR0RhJmIUSrYzM1b/6WSe+uPq0lCeJ4Wi/H+CODwYCLk37GcyIR/h7cMka/C+D6hPnwyhX98XR1YkLvELXDUcWWRydgsdkA7G6GXZwbkjALIVr1i/DFYIAFG3OJC/aUhPlPfrxn9HGlGkJ0Bm7Ojlyp40Wbp0KvW7qLzkMSZiFEKx83Z+KCvcgqrcfPQ991rWeib7i+dksTQghxamSqRAhxnMQIPwD83PVTpyuEEEKcDZlhFkIc57rh3ahoaGb6wHC1QxFCCCE6BUmYhRDHGdjNn4/nDFU7DCGEEKLTkJIMIYQQQggh2iAJsxBCCCGEEG2QhFkIIYQQQog2SMIshBBCCCFEGyRhFkIIIYQQog2SMAshhBBCCNEGSZiFEEIIIYRogyTMQgghhBBCtEESZiGEEEIIIdogCbMQQgghhBBtkIRZCCGEEEKINkjCLIQQQgghRBsMiqKoHYMQoh0ZDIYy4LDacXSAKEVRgv/4hE7H+pdxgv2MVafjBPsZq72ME07yWRX6JAmzEEIIIYQQbZCSDCGEEEIIIdogCbMQQgghhBBtkIRZCCGEEEKINkjCLIQQQgghRBskYRZCCCGEEKINkjALIYQQQgjRBkmYhRBCCCGEaIMkzEIIIYQQQrRBEmYhhBBCCCHa8P9seXqVG/UqiAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x720 with 106 Axes>"
+       "<Figure size 720x720 with 112 Axes>"
       ]
      },
      "metadata": {
@@ -2677,20 +2708,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "67 critic_1\n",
-      "133 critic_2\n"
+      "4 critic_1\n",
+      "128 critic_2\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 576x900 with 8 Axes>"
       ]
diff --git a/src/palm_tools/analysis/mds_analysis.py b/src/palm_tools/analysis/mds_analysis.py
index 53c73f66bdfda0bb20f36f3fb3e7b75cc20c00dd..44732dd120ddf82dd2d5b0dd7a72c49d784e8aa2 100644
--- a/src/palm_tools/analysis/mds_analysis.py
+++ b/src/palm_tools/analysis/mds_analysis.py
@@ -96,6 +96,8 @@ class MDSAnalysis(AnalysisStep):
         else:
             D = np.mean(self.mmd._D_true[np.ix_(indices, indices)], axis=2)
             D_sigma = np.std(self.mmd._D_true[np.ix_(indices, indices)], axis=2)
+            np.fill_diagonal(D, 0)
+            np.fill_diagonal(D_sigma, 1)
             self._mds = MDS_prob(target_dim=self.dim)
             X = self._mds.fit_transform(
                 D,
diff --git a/src/palm_tools/analysis/mmd_analysis.py b/src/palm_tools/analysis/mmd_analysis.py
index 4e4b5ee085f8841f3d88d768cb221bc693f8e6c0..ed6aca17f4c124d1997a50468e9d9df019e220a2 100644
--- a/src/palm_tools/analysis/mmd_analysis.py
+++ b/src/palm_tools/analysis/mmd_analysis.py
@@ -412,6 +412,8 @@ class GroupBasedAnalysisStep(LatentVecsBasedAnalysisStep):
             for l in range(self.min_granularity - 1, len(valid_group_by_keys)):
                 key_sets += list(combinations(valid_group_by_keys, l + 1))
             # key_sets = [("activation"),("activation","cell_type"), ("cell_type")]
+            key_sets_lengths = [len(s) for s in key_sets]
+            key_sets = [key_sets[i] for i in np.argsort(key_sets_lengths)[::-1]]
             self._groups = []
             group_units = []
             for key_set in key_sets:
@@ -1740,8 +1742,11 @@ class MMDInterGroupAnalysis(GroupBasedAnalysisStep):
                 self._D_true[i1, i2] = D_true
                 self._D_true[i2, i1] = D_true
                 self._infos["min_n"][i1, i2] = infos["min_n"]
+                self._infos["min_n"][i2, i1] = infos["min_n"]
                 self._infos["n_units_1"][i1, i2] = infos["n_units_1"]
                 self._infos["n_units_2"][i1, i2] = infos["n_units_2"]
+                self._infos["n_units_1"][i2, i1] = infos["n_units_1"]
+                self._infos["n_units_2"][i2, i1] = infos["n_units_2"]
                 self._n_bootstraps[i1, i2] = infos["n_bootstraps"]
                 self._n_bootstraps[i2, i1] = infos["n_bootstraps"]
                 tqdm_.desc = "(%d bootstraps) : %s VS %s" % (
@@ -1774,15 +1779,14 @@ class MMDInterGroupAnalysis(GroupBasedAnalysisStep):
             p.close()
             p.join()
 
-        mean_D_true = np.mean(self._D_true, axis=2)
-        self._p_val = np.clip(
-            np.nanmean(self._D_null > np.expand_dims(mean_D_true, 2), axis=2),
-            a_min=0.5 / self._n_bootstraps,
-            a_max=1.0,
-        )
+        mean_D_true = np.mean(self._D_true, axis=2, keepdims=True)
+        self._p_val = np.mean(self._D_null > mean_D_true, axis=2)
+        self._p_val = np.where(self._p_val == 0, 0.5 / self._n_bootstraps, self._p_val)
+
         if not self.compare_groups_with_self:
             np.fill_diagonal(self._p_val, 1.0)
         self._p_val[np.sum(np.isnan(self._D_null), axis=2) > 0] = np.nan
+        assert (np.transpose(self._p_val) == self._p_val).all()
 
     def _save(self):
         super()._save()
diff --git a/src/palm_tools/utils/read_file.py b/src/palm_tools/utils/read_file.py
index 7aad8fa1b9408f619cf4fc5607c3b330c5aa0f69..633c884b9918d8eba416f35e39719a9acf71141d 100644
--- a/src/palm_tools/utils/read_file.py
+++ b/src/palm_tools/utils/read_file.py
@@ -286,7 +286,11 @@ def add_t_or_frame(df: pd.DataFrame, DT: float = None):
         return df, DT
 
 
-def check_file_and_read(file_path: str, short_version: bool = False, DT: float = None):
+def check_file_and_read(file_path: str, 
+                        short_version: bool = False, 
+                        DT: float = None,
+                        is3D: bool = False,
+                        ):
     """
     Checks whether a file is readable or not.
     Returns a boolean indicating that
@@ -301,7 +305,7 @@ def check_file_and_read(file_path: str, short_version: bool = False, DT: float =
     for sep in [",", "\t", ";", " "]:
         try:
             df, DT = load_csv_file(
-                file_path=file_path, sep=sep, DT=DT, short_version=short_version
+                file_path=file_path, sep=sep, DT=DT, short_version=short_version, is3D=is3D
             )
             df["n"] = df["n"].astype(int)
             return df, DT
@@ -316,7 +320,7 @@ def check_file_and_read(file_path: str, short_version: bool = False, DT: float =
         mat = loadmat(file_path)
         try:
             mat_df = pd.DataFrame(data=mat)
-            col_mapping = map_df_columns(mat_df)
+            col_mapping = map_df_columns(mat_df,is3D=is3D)
             mat_df = mat_df.rename(columns=col_mapping)
             mat_df, DT = add_t_or_frame(mat_df, DT)
             mat_df["n"] = mat_df["n"].astype(int)
@@ -325,7 +329,7 @@ def check_file_and_read(file_path: str, short_version: bool = False, DT: float =
             for key, matrice in mat.items():
                 try:
                     mat_df = pd.DataFrame(data=matrice)
-                    col_mapping = map_df_columns(mat_df)
+                    col_mapping = map_df_columns(mat_df,is3D=is3D)
                     mat_df = mat_df.rename(columns=col_mapping)
                     mat_df, DT = add_t_or_frame(mat_df, DT)
                     mat_df["n"] = mat_df["n"].astype(int)
@@ -341,7 +345,7 @@ def check_file_and_read(file_path: str, short_version: bool = False, DT: float =
 
     # trackmate file
     try:
-        trackmate_file, DT = load_trackmate_file(file_path)
+        trackmate_file, DT = load_trackmate_file(file_path,is3D=is3D)
         return trackmate_file, DT
     except Exception as e:
         logging.debug("Error in opening Trackmate file")