diff --git a/1_prepare_data.ipynb b/1_prepare_data.ipynb
index 517ddf53441d7a4196967dc298f065670b00f54f..c4d9a8fe8c1273a1250c4b3099ba50e7595e76dd 100644
--- a/1_prepare_data.ipynb
+++ b/1_prepare_data.ipynb
@@ -16,8 +16,7 @@
    "source": [
     "DATADIR = 'era5/' # directory containing downloaded era5 data\n",
     "FIREDATADIR = 'fire_danger/' # directory containing fire data\n",
-    "DESTDIR = 'processed_era5/' # directory to save .npy files for each time step and variable\n",
-    "FIREDESTDIR = 'processed_fire_data/' # directory to save .npy files for each time step and variable for fire data"
+    "DESTDIR = 'processed_data/' # directory to save .npy files for each time step and variable"
    ]
   },
   {
@@ -54,10 +53,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vars = ['u10','v10','t2m','lai_hv','lai_lv','tp'] #considered variables (see 0_download_data.ipynb for long names)\n",
+    "vars = ['u10','v10','t2m','lai_hv','lai_lv','tp','fdimrk'] #considered variables (see 0_download_data.ipynb for long names)\n",
     "months = [(1,31),(2,28),(12,31)] # months + days in month in dowloaded era5 .nc files\n",
-    "years = np.arange(2002,2023) # downloaded years\n",
-    "fire_vars = ['fdimrk'] # fire data variables"
+    "years = np.arange(2002,2023) # downloaded years"
    ]
   },
   {
@@ -65,107 +63,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Procesar datos ERA5"
+    "# Procesamiento de datos para crear archivos .npy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77f93c51256047b981775b4b6469c9d7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9a27a43175944c7aad21cf81d8c3b31f",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5ef1a176e6204c19a63852d952442609",
-       "version_major": 2,
-       "version_minor": 0
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fbf30b91845a4c9bb5ca2ee543ea20ec",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6dc83680a62a42f78ba7cb7e056ac54e",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b4f23446188144a389cf64ca317e40cc",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Processing ERA5 data\n",
+    "# Processing Data to create .npy files\n",
     "for var in vars:\n",
     "  if not os.path.exists(DESTDIR + f\"{var}\"):\n",
     "    os.makedirs(DESTDIR + f\"{var}\")\n",
     "  \n",
     "  for year in tqdm(years):\n",
-    "    root = nc.Dataset(DATADIR + f\"{year:d}.nc\", 'r')\n",
+    "    if var=='fdimrk':\n",
+    "      root = nc.Dataset(FIREDATADIR + f\"{year:d}.nc\", 'r')\n",
+    "    else:\n",
+    "      root = nc.Dataset(DATADIR + f\"{year:d}.nc\", 'r')\n",
     "    v = root.variables[var][:,:-9,:-5] #crop to get to a size suitable for the considered Unet-like model, here 140x140\n",
     "    v = v.data\n",
     "    root.close()\n",
@@ -186,44 +102,6 @@
     "# Procesar datos de índice de peligro de incendio"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b3a75dfe23fa44d7a96e2ff92f9ed211",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Processing Fire danger index data\n",
-    "for var in fire_vars:\n",
-    "  if not os.path.exists(FIREDESTDIR + f\"{var}\"):\n",
-    "    os.makedirs(FIREDESTDIR + f\"{var}\")\n",
-    "  \n",
-    "  for year in tqdm(years):\n",
-    "    root = nc.Dataset(FIREDATADIR + f\"{year:d}.nc\", 'r')\n",
-    "    v = root.variables[var][:,:-9,:-5] #crop to get to a size suitable for the considered Unet-like model, here 140x140\n",
-    "    v = v.data\n",
-    "    root.close()\n",
-    "    t = 0 # time step within v array that I am working on\n",
-    "    for month, days in months:\n",
-    "      for day in range(days):\n",
-    "        np.save(FIREDESTDIR + f\"{var}/{year}_{month:02d}_{day+1:02d}.npy\",v[t])\n",
-    "        t += 1"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",