diff --git a/notebooks/4.0-eo-initial-eurosat-data-exploration.ipynb b/notebooks/4.0-eo-initial-eurosat-data-exploration.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EuroSat Imagery - EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: altair in /usr/local/lib/python3.6/dist-packages (4.1.0)\n",
+      "Requirement already satisfied: vega_datasets in /usr/local/lib/python3.6/dist-packages (0.9.0)\n",
+      "Requirement already satisfied: entrypoints in /usr/local/lib/python3.6/dist-packages (from altair) (0.3)\n",
+      "Requirement already satisfied: pandas>=0.18 in /usr/local/lib/python3.6/dist-packages (from altair) (1.1.5)\n",
+      "Requirement already satisfied: jsonschema in /usr/local/lib/python3.6/dist-packages (from altair) (3.2.0)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from altair) (1.17.3)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.6/dist-packages (from altair) (2.11.2)\n",
+      "Requirement already satisfied: toolz in /usr/local/lib/python3.6/dist-packages (from altair) (0.11.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.18->altair) (2.8.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.18->altair) (2021.1)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.18->altair) (1.15.0)\n",
+      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->altair) (1.1.1)\n",
+      "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.6/dist-packages (from jsonschema->altair) (1.7.0)\n",
+      "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from jsonschema->altair) (0.16.0)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from jsonschema->altair) (49.6.0)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from jsonschema->altair) (20.2.0)\n",
+      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata->jsonschema->altair) (3.1.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install altair vega_datasets\n",
+    "%pylab inline\n",
+    "\n",
+    "# import gdal\n",
+    "import rasterio\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "import pandas as pd\n",
+    "import altair as alt\n",
+    "import random\n",
+    "\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Images in the dataset are in in Geotif format with all the bands (13) in the same file. The code below is an example of a patch labeled as \"Industrial\". Each band has 60x60 pixeles. In a visual inspection, we can note note that each image has the same covered area. The resolution varies for different bands. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sr_path = '/workspace/app/data/raw/EuroSat/fulldata/'\n",
+    "# os.listdir(sr_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "band_ds = rasterio.open(sr_path+'Forest/Forest_1000.tif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 13 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,15))\n",
+    "\n",
+    "bands_l =['B01-Aerosols-60m','B02-Blue-10m','B03-Green-10m',\n",
+    "          'B04-Red-10m','B05-Red edge 1-20m','B06-Red edge 2-20m',\n",
+    "          'B07-Red edge 3-20','B08-NIR-10m','B09-Water vapor-60m',\n",
+    "          'B10-Cirrus-60m','B11-SWIR 1-20m','B12-SWIR 2-20m',\n",
+    "          'B08A-Red edge 4-20m']\n",
+    "\n",
+    "for idx in range(13): # 13 bands\n",
+    "    raster_band = np.array(band_ds.read(idx+1))\n",
+    "    img1 = np.array(raster_band)\n",
+    "\n",
+    "    plt.subplots_adjust(hspace=.1)\n",
+    "    plt.subplot(4,4,idx+1)\n",
+    "    plt.imshow(img1,interpolation='nearest',cmap='gray')\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.grid(False)\n",
+    "    plt.title(bands_l[idx], fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RGB Images\n",
+    "\n",
+    "The data set has 10 different labels. Images are saved in different folders according to its label. Each folder has the name of the corresponding label. Here, we plot one example per each label considereing only the RGB (B04,B03,B02, Respectively)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Pasture', 'Industrial', 'PermanentCrop', 'AnnualCrop', 'Highway', 'HerbaceousVegetation', 'Residential', 'Forest', 'River', 'SeaLake']\n"
+     ]
+    }
+   ],
+   "source": [
+    "base_path = '/workspace/app/data/raw/EuroSat/'\n",
+    "rgbpath = os.path.join(base_path,'rgbdata/')\n",
+    "labels = os.listdir(rgbpath)\n",
+    "print(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lcount={}\n",
+    "\n",
+    "plt.figure(figsize=(20,8))\n",
+    "\n",
+    "cont = 1\n",
+    "for lbl in labels:\n",
+    "  figpath = os.path.join(rgbpath,lbl+'/')\n",
+    "  lcount[lbl] = len(os.listdir(figpath))\n",
+    "  figname = figpath+lbl+'_1000.jpg'\n",
+    "  img2 = mpimg.imread(figname)\n",
+    "\n",
+    "  plt.subplots_adjust(hspace=.1)\n",
+    "  plt.subplot(2,5,cont)\n",
+    "\n",
+    "  imgplot = plt.imshow(img2)\n",
+    "  plt.xticks([])\n",
+    "  plt.yticks([])\n",
+    "  plt.grid(False)\n",
+    "  plt.title(labels[cont-1], fontsize=16)\n",
+    "  cont = cont + 1\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([   0.,  500., 1000., 1500., 2000., 2500., 3000., 3500.]),\n",
+       " [Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, '')])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(15,5))\n",
+    "ax = fig.add_axes([0,0,1,1])\n",
+    "labels = lcount.keys()\n",
+    "count = lcount.values()\n",
+    "ax.bar(labels,count)\n",
+    "\n",
+    "for bar in ax.patches:\n",
+    "    bar.set_facecolor('royalblue')\n",
+    "    \n",
+    "ax.set_ylabel('Patches',fontsize=24)\n",
+    "plt.xticks(rotation = 90,fontsize=20)\n",
+    "plt.yticks(fontsize=18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bands_l2 =['B01','B02','B03','B04','B05','B06','B07','B08',\n",
+    "          'B09','B10','B11','B12','B8A']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pasture',\n",
+       " 'Industrial',\n",
+       " 'PermanentCrop',\n",
+       " 'AnnualCrop',\n",
+       " 'Highway',\n",
+       " 'HerbaceousVegetation',\n",
+       " 'Residential',\n",
+       " 'Forest',\n",
+       " 'River',\n",
+       " 'SeaLake']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cont = 0\n",
+    "label_list = os.listdir(sr_path)\n",
+    "label_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Pasture\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " Industrial\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " PermanentCrop\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " AnnualCrop\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " Highway\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " HerbaceousVegetation\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " Residential\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " Forest\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " River\n",
+      " 95/100 [===========================>..] - ETA: 0s\n",
+      " SeaLake\n",
+      " 95/100 [===========================>..] - ETA: 0s"
+     ]
+    }
+   ],
+   "source": [
+    "nexamp = 100\n",
+    "\n",
+    "df_mean = pd.DataFrame(columns=bands_l2)\n",
+    "df_mean['Label'] = None\n",
+    "df_mean['File'] = None\n",
+    "blist = list(df_mean.columns)\n",
+    "df_std = df_mean.copy()\n",
+    "\n",
+    "new_mean={}\n",
+    "new_std={}\n",
+    "for i in blist:\n",
+    "    new_mean[i]=''\n",
+    "    new_std[i]=''\n",
+    "    \n",
+    "for lbl in label_list:\n",
+    "    print('\\n {}'.format(lbl))\n",
+    "    label_path = os.path.join(sr_path,lbl)\n",
+    "    file_list = os.listdir(label_path)\n",
+    "    randomlist = random.sample(range(len(file_list)), nexamp)\n",
+    "    new_mean['Label']=lbl\n",
+    "    new_std['Label']=lbl\n",
+    "    file_list = [file_list[x] for x in randomlist]\n",
+    "    progress_bar = tf.keras.utils.Progbar(target = len(file_list))\n",
+    "    for findex,fin in enumerate(file_list):\n",
+    "        new_mean['File']=fin\n",
+    "        new_std['File']=fin\n",
+    "        band_ds = rasterio.open(os.path.join(label_path,fin))\n",
+    "        for idx in range(13): # 13 bands\n",
+    "            band_name = bands_l2[idx]\n",
+    "            raster_band = np.array(band_ds.read(idx+1))\n",
+    "            bidx = np.array(raster_band)\n",
+    "            new_mean[band_name]=bidx.mean()\n",
+    "            new_std[band_name]=bidx.std()           \n",
+    "        df_mean = df_mean.append(new_mean, ignore_index=True)\n",
+    "        df_std = df_std.append(new_std, ignore_index=True)\n",
+    "        progress_bar.update(findex)\n",
+    "        \n",
+    "df_dic = {}\n",
+    "for lbl in bands_l2:\n",
+    "    df_std2 = df_std[[lbl,'Label','File']].copy()\n",
+    "    df_std2 = df_std2.rename(columns={lbl: \"std\"})\n",
+    "    df_mean2 = df_mean[[lbl,'Label','File']].copy()\n",
+    "    df_mean2 = df_mean2.rename(columns={lbl: \"mean\"})\n",
+    "\n",
+    "    df_dic[lbl] = pd.merge(df_mean2,df_std2,how='left',on=['File','Label'])\n",
+    "    df_dic[lbl].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figura={}\n",
+    "for band in bands_l2:\n",
+    "    figura[band] = alt.Chart(df_dic[band]).mark_circle(size=60).encode(\n",
+    "        x='std',\n",
+    "        y='mean',\n",
+    "        color='Label',\n",
+    "        opacity=alt.value(0.5),\n",
+    "    ).properties(\n",
+    "        title=band+' ('+str(nexamp)+' random examples per label)',\n",
+    "        width=600,\n",
+    "        height=400\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-bd28ed26d89c4b3c83ada0ca9c4e5e67\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-bd28ed26d89c4b3c83ada0ca9c4e5e67\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-bd28ed26d89c4b3c83ada0ca9c4e5e67\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function loadScript(lib) {\n",
+       "      return new Promise(function(resolve, reject) {\n",
+       "        var s = document.createElement('script');\n",
+       "        s.src = paths[lib];\n",
+       "        s.async = true;\n",
+       "        s.onload = () => resolve(paths[lib]);\n",
+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "      });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-000509fd7ecfafbee94c24c3ee109c1f\"}, \"mark\": {\"type\": \"circle\", \"size\": 60}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Label\"}, \"opacity\": {\"value\": 0.5}, \"x\": {\"type\": \"quantitative\", \"field\": \"std\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"mean\"}}, \"height\": 400, \"title\": \"B01 (100 random examples per label)\", \"width\": 600, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-000509fd7ecfafbee94c24c3ee109c1f\": [{\"mean\": 1105.248291015625, \"Label\": \"Pasture\", \"File\": \"Pasture_405.tif\", \"std\": 20.95105955538848}, {\"mean\": 1214.87890625, \"Label\": \"Pasture\", \"File\": \"Pasture_950.tif\", \"std\": 16.173286000492013}, {\"mean\": 1184.078125, \"Label\": \"Pasture\", \"File\": \"Pasture_1451.tif\", \"std\": 18.89535455682494}, {\"mean\": 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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B01']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-543760a6e3254755b2ca083c2a814871\"></div>\n",
+       "<script type=\"text/javascript\">\n",
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+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
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+       "      outputDiv = document.getElementById(\"altair-viz-543760a6e3254755b2ca083c2a814871\");\n",
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+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
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+       "\n",
+       "    function loadScript(lib) {\n",
+       "      return new Promise(function(resolve, reject) {\n",
+       "        var s = document.createElement('script');\n",
+       "        s.src = paths[lib];\n",
+       "        s.async = true;\n",
+       "        s.onload = () => resolve(paths[lib]);\n",
+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "      });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B02']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-46deb00dc81a440097efc9296a7f7729\"></div>\n",
+       "<script type=\"text/javascript\">\n",
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+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
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+       "    const paths = {\n",
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+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
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+       "\n",
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+       "        s.src = paths[lib];\n",
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+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "      });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
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+      "text/plain": [
+       "alt.Chart(...)"
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+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
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+   "source": [
+    "figura['B03']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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+       "      loadScript(\"vega\")\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
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+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
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+   ],
+   "source": [
+    "figura['B04']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-b01b350ac2204c03be978bf768174f5f\"></div>\n",
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+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
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+       "    }\n",
+       "\n",
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+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
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+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
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+    "figura['B05']"
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+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
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+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function loadScript(lib) {\n",
+       "      return new Promise(function(resolve, reject) {\n",
+       "        var s = document.createElement('script');\n",
+       "        s.src = paths[lib];\n",
+       "        s.async = true;\n",
+       "        s.onload = () => resolve(paths[lib]);\n",
+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "      });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B06']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-cb62328d32ce4c35bd292ebde0c750ac\"></div>\n",
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+       "    function showError(err) {\n",
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+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B07']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-f92b0945b6834c8e97850729c510ee0d\"></div>\n",
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+       "      });\n",
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+       "\n",
+       "    function showError(err) {\n",
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+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B08']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-1748b9590cac4a8bb209d34abdddf2b4\"></div>\n",
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+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
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+       "\n",
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+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
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+       "        .then(() => displayChart(vegaEmbed));\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B8A']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-fe115b5a6a8544c180cbe45176972468\"></div>\n",
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+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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+       "\n",
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+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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+       "      displayChart(vegaEmbed);\n",
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+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
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+       "        .then(() => displayChart(vegaEmbed));\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B09']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-656c4bfa7dc248489044e3d092a1a2f0\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
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+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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+       "\n",
+       "    function loadScript(lib) {\n",
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+       "        s.onload = () => resolve(paths[lib]);\n",
+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "      });\n",
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+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-20e378ad4a6cf28c8517516bfe4a9f36\"}, \"mark\": {\"type\": \"circle\", \"size\": 60}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Label\"}, \"opacity\": {\"value\": 0.5}, \"x\": {\"type\": \"quantitative\", \"field\": \"std\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"mean\"}}, \"height\": 400, \"title\": \"B10 (100 random examples per label)\", \"width\": 600, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-20e378ad4a6cf28c8517516bfe4a9f36\": [{\"mean\": 15.222412109375, \"Label\": \"Pasture\", \"File\": \"Pasture_405.tif\", \"std\": 0.9978471171619243}, {\"mean\": 14.08984375, \"Label\": \"Pasture\", \"File\": \"Pasture_950.tif\", \"std\": 1.2638816600401865}, {\"mean\": 11.42138671875, \"Label\": \"Pasture\", \"File\": \"Pasture_1451.tif\", \"std\": 0.833075785574823}, {\"mean\": 16.123046875, 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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B10']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-09872d3c3dde4a159574a249d2c8acc2\"></div>\n",
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+       "      outputDiv = document.getElementById(\"altair-viz-09872d3c3dde4a159574a249d2c8acc2\");\n",
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+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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+       "        s.onload = () => resolve(paths[lib]);\n",
+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
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+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
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+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B11']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-530252fa0c924514916bed109729d17d\"></div>\n",
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+       "      });\n",
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+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
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+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
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+       "        .then(() => displayChart(vegaEmbed));\n",
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\"std\": 2.8627086030337034}, {\"mean\": 62.646484375, \"Label\": \"SeaLake\", \"File\": \"SeaLake_2283.tif\", \"std\": 19.74694713706731}, {\"mean\": 6.47607421875, \"Label\": \"SeaLake\", \"File\": \"SeaLake_935.tif\", \"std\": 2.2274069272343517}, {\"mean\": 39.59765625, \"Label\": \"SeaLake\", \"File\": \"SeaLake_956.tif\", \"std\": 6.637167068436347}, {\"mean\": 11.0869140625, \"Label\": \"SeaLake\", \"File\": \"SeaLake_2340.tif\", \"std\": 3.942551899086396}, {\"mean\": 145.99951171875, \"Label\": \"SeaLake\", \"File\": \"SeaLake_23.tif\", \"std\": 19.549916074284855}, {\"mean\": 42.9169921875, \"Label\": \"SeaLake\", \"File\": \"SeaLake_804.tif\", \"std\": 3.2122118572043106}, {\"mean\": 129.254638671875, \"Label\": \"SeaLake\", \"File\": \"SeaLake_1274.tif\", \"std\": 16.141702014112724}, {\"mean\": 40.8603515625, \"Label\": \"SeaLake\", \"File\": \"SeaLake_339.tif\", \"std\": 14.922440443260406}, {\"mean\": 68.149658203125, \"Label\": \"SeaLake\", \"File\": \"SeaLake_80.tif\", \"std\": 135.73644907791575}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "figura['B12']"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/references/ML_pipeline.md b/references/ML_pipeline.md
index 88e64cc..84d32dc 100644
--- a/references/ML_pipeline.md
+++ b/references/ML_pipeline.md
@@ -5,8 +5,14 @@
 - Train images:269695
 - Test images:125866
 - Validation images:123723
-- Balanced irrigation: 6606
-- Balanced vineyards: 4758
+
+- Balanced train images:13932
+- Balanced test images:6604
+- Balanced val images:6606
+
+- Balanced vineyard train images:9790
+- Balanced vineyard test images:4500
+- Balanced vineyard val images:4758
 
 #### EDA 
 # Integrating and Exploring the Combined MSI Dataset for California
diff --git a/src/data/bemodels b/src/data/bemodels
new file mode 120000
index 0000000..ed72a09
--- /dev/null
+++ b/src/data/bemodels
@@ -0,0 +1 @@
+/workspace/app/data/raw/bigearthnet-models/
\ No newline at end of file
diff --git a/src/data/count_tfrecords.py b/src/data/count_tfrecords.py
new file mode 100644
index 0000000..7a628c7
--- /dev/null
+++ b/src/data/count_tfrecords.py
@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import pandas as pd
+import tensorflow as tf
+
+print(pd.__version__)
+print(tf.__version__)
+
+tf_file_path_list = ['balanced_train.tfrecord', 'balanced_test.tfrecord','balanced_val.tfrecord',
+                     'balanced_vy_train.tfrecord', 'balanced_vy_test.tfrecord', 'balanced_vy_val.tfrecord'
+                     ]
+for csv_file in tf_file_path_list:
+    tf_records_filename = '/workspace/app/data/processed/'+csv_file
+    c = 0
+    for record in tf.python.python_io.tf_record_iterator(tf_records_filename):
+        c += 1
+    print (csv_file,c)
+
diff --git a/src/data/make_data_eurosat.ipynb b/src/data/make_data_eurosat.ipynb
new file mode 100644
index 0000000..986d5c0
--- /dev/null
+++ b/src/data/make_data_eurosat.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!/usr/bin/env python\n",
+    "# coding: utf-8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "import csv\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import rasterio\n",
+    "import tensorflow as tf\n",
+    "from glob import glob\n",
+    "from tqdm import tqdm\n",
+    "from random import choices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to the BigEarthNet extracted files\n",
+    "eurosat_path = '/workspace/app/data/raw/EuroSat/fulldata/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Models folder is already checkin. No need to download the models\n",
+    "eurosat_folder = '/workspace/app/data/raw/eurosat-models/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Stores the TFRecords\n",
+    "out_folder = '/workspace/app/data/processed/EuroSat'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(eurosat_path):\n",
+    "    print('folder', eurosat_path, 'does not exist')\n",
+    "    print('Downloading Data...')\n",
+    "    # Downloads the data from EuroSat website\n",
+    "    # os.system(\"curl http://madm.dfki.de/files/sentinel/EuroSAT.zip -o /data/raw/eurosat_rgb.zip\")\n",
+    "#     os.system(\"unzip /data/raw/eurosat_rgb.zip -d /data/raw\")\n",
+    "#     os.mkdir(eurosat_path)\n",
+    "#     os.rename(\"/data/raw/ds/..../*)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(eurosat_folder):\n",
+    "    print('ERROR: folder', eurosat_folder, 'does not exist')\n",
+    "    os.mkdir(eurosat_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(out_folder):\n",
+    "    print('ERROR: folder', out_folder, 'does not exist')\n",
+    "    os.mkdir(out_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using Python Version: 1.1.5\n",
+      "Using TensorFlow Version: 2.3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Using Python Version: {pd.__version__}')\n",
+    "print(f'Using TensorFlow Version: {tf.__version__}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cont = 0\n",
+    "label_list = os.listdir(eurosat_path)\n",
+    "label_indices = {'original_labels':{}}\n",
+    "for lbl in label_list:\n",
+    "    label_indices['original_labels'][lbl] = cont\n",
+    "    cont += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prep_example_eurosat(bands, original_labels, original_labels_multi_hot, patch_name):\n",
+    "    return tf.train.Example(\n",
+    "            features=tf.train.Features(\n",
+    "                feature={\n",
+    "                    'B01': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B01']))),\n",
+    "                    'B02': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B02']))),\n",
+    "                    'B03': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B03']))),\n",
+    "                    'B04': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B04']))),\n",
+    "                    'B05': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B05']))),\n",
+    "                    'B06': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B06']))),\n",
+    "                    'B07': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B07']))),\n",
+    "                    'B08': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B08']))),\n",
+    "                    'B8A': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B8A']))),\n",
+    "                    'B09': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B09']))),\n",
+    "                    'B11': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B11']))),\n",
+    "                    'B12': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=np.ravel(bands['B12']))),\n",
+    "                    'original_labels': tf.train.Feature(\n",
+    "                        bytes_list=tf.train.BytesList(\n",
+    "                            value=[i.encode('utf-8') for i in original_labels])),\n",
+    "                    'original_labels_multi_hot': tf.train.Feature(\n",
+    "                        int64_list=tf.train.Int64List(value=original_labels_multi_hot)),\n",
+    "                    'patch_name': tf.train.Feature(\n",
+    "                        bytes_list=tf.train.BytesList(value=[patch_name.encode('utf-8')]))\n",
+    "                }))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Label: Pasture \n",
+      "\n",
+      "Folder Pasture exist already\n",
+      "1996/2000 [============================>.] - ETA: 0s\n",
+      " Label: Industrial \n",
+      "\n",
+      "Folder Industrial exist already\n",
+      "2499/2500 [============================>.] - ETA: 0s\n",
+      " Label: PermanentCrop \n",
+      "\n",
+      "Folder PermanentCrop exist already\n",
+      "2496/2500 [============================>.] - ETA: 0s\n",
+      " Label: AnnualCrop \n",
+      "\n",
+      "Folder AnnualCrop exist already\n",
+      "2998/3000 [============================>.] - ETA: 0s\n",
+      " Label: Highway \n",
+      "\n",
+      "Folder Highway exist already\n",
+      "2497/2500 [============================>.] - ETA: 0s\n",
+      " Label: HerbaceousVegetation \n",
+      "\n",
+      "Folder HerbaceousVegetation exist already\n",
+      "2996/3000 [============================>.] - ETA: 0s\n",
+      " Label: Residential \n",
+      "\n",
+      "Folder Residential exist already\n",
+      "2999/3000 [============================>.] - ETA: 0s\n",
+      " Label: Forest \n",
+      "\n",
+      "Folder Forest exist already\n",
+      "2997/3000 [============================>.] - ETA: 0s\n",
+      " Label: River \n",
+      "\n",
+      "Folder River exist already\n",
+      "2498/2500 [============================>.] - ETA: 0s\n",
+      " Label: SeaLake \n",
+      "\n",
+      "Folder SeaLake exist already\n",
+      "2998/3000 [============================>.] - ETA: 0sCompleted!!!\n"
+     ]
+    }
+   ],
+   "source": [
+    "bands_l =['B01','B02','B03','B04','B05','B06','B07','B08',\n",
+    "          'B09','B10','B11','B12','B8A']\n",
+    "\n",
+    "# Split options [Train = 0, Test= 1, Validation= 2]\n",
+    "split_option = [0,1,2]\n",
+    "# Split Probabilities [Train = 50%, Test= 25%, Validation= 25%]\n",
+    "weights = [0.5,0.25, 0.25]\n",
+    "\n",
+    "# TFRecords Writers\n",
+    "TFRec_writer_train = tf.io.TFRecordWriter(os.path.join(out_folder, 'train.tfrecord'))\n",
+    "TFRec_writer_test = tf.io.TFRecordWriter(os.path.join(out_folder, 'test.tfrecord'))\n",
+    "TFRec_writer_val = tf.io.TFRecordWriter(os.path.join(out_folder, 'val.tfrecord'))\n",
+    "patch_name = out_folder\n",
+    "for tifile in label_list:\n",
+    "    # create Folder\n",
+    "    print('\\n Label: {} \\n'.format(tifile))\n",
+    "\n",
+    "    try:\n",
+    "        os.mkdir(patch_name)\n",
+    "    except:\n",
+    "        print('Folder {} exist already'.format(tifile))\n",
+    "        \n",
+    "    # create labels\n",
+    "    original_labels = [tifile]\n",
+    "    \n",
+    "    # hot encode label\n",
+    "    original_labels_multi_hot = np.zeros(len(label_list),dtype=int)\n",
+    "    lidx = label_indices['original_labels'][tifile]\n",
+    "    original_labels_multi_hot[lidx] = 1\n",
+    "\n",
+    "    # loop in the folder\n",
+    "    files_list = os.listdir(os.path.join(eurosat_path,tifile))\n",
+    "   \n",
+    "    # write holder tfrecord\n",
+    "    progress_bar = tf.keras.utils.Progbar(target = len(files_list))\n",
+    "    for findex,fex in enumerate(files_list):\n",
+    "        sname = fex.split('_')[1][:-4]\n",
+    "        band_ds = rasterio.open(os.path.join(eurosat_path,tifile,fex))\n",
+    "        bands = {}\n",
+    "        for idx in range(13): # 13 bands\n",
+    "            if bands_l[idx] == 'B10':\n",
+    "                continue\n",
+    "            bands[bands_l[idx]] = np.array(band_ds.read(idx+1))\n",
+    "\n",
+    "        # prep example dev example\n",
+    "        example = prep_example_eurosat(bands,\n",
+    "                                       original_labels, \n",
+    "                                       original_labels_multi_hot, \n",
+    "                                       patch_name)\n",
+    "        \n",
+    "        pick = choices(split_option,weights)[0]\n",
+    "        if pick == 0:\n",
+    "            TFRec_writer_train.write(example.SerializeToString()) # This is the full path to tfrecord train\n",
+    "        elif pick == 1:\n",
+    "            TFRec_writer_test.write(example.SerializeToString()) # This is the full path to tfrecord test\n",
+    "        else:\n",
+    "            TFRec_writer_val.write(example.SerializeToString()) # This is the full path to tfrecord val           \n",
+    "            \n",
+    "        progress_bar.update(findex)\n",
+    "\n",
+    "print('Completed!!!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/data/make_dataset.py b/src/data/make_dataset.py
index 03103bc..c05fbcd 100755
--- a/src/data/make_dataset.py
+++ b/src/data/make_dataset.py
@@ -89,15 +89,40 @@ def preprocess_tfrecords():
         writer = tf.data.experimental.TFRecordWriter(f"{out_folder}/val-part-{i}.tfrecord")
         writer.write(raw_dataset.shard(shards, i))
 
+# Shards the data
+# tf_main_file is a main tf file name. The function will look for corresponding train, test, and val files
+# Ex: balanced, balanced_vy
+def shard_tfrecords(tf_main_file):
 
-def preprocess_tfrecords_labelled():
+    # Shard the Train data
+    raw_dataset = tf.data.TFRecordDataset(out_folder + "/"+tf_main_file+"_train.tfrecord")
+    shards = 50
+    for i in range(shards):
+        writer = tf.data.experimental.TFRecordWriter(f"{out_folder}/{tf_main_file}_train-part-{i}.tfrecord")
+        writer.write(raw_dataset.shard(shards, i))
+
+    # Shard the Test data
+    raw_dataset = tf.data.TFRecordDataset(out_folder + "/"+tf_main_file+"_test.tfrecord")
+    shards = 20
+    for i in range(shards):
+        writer = tf.data.experimental.TFRecordWriter(f"{out_folder}/{tf_main_file}_test-part-{i}.tfrecord")
+        writer.write(raw_dataset.shard(shards, i))
+
+    # Shard the Val data
+    raw_dataset = tf.data.TFRecordDataset(out_folder + "/"+tf_main_file+"_val.tfrecord")
+    shards = 20
+    for i in range(shards):
+        writer = tf.data.experimental.TFRecordWriter(f"{out_folder}/{tf_main_file}_val-part-{i}.tfrecord")
+        writer.write(raw_dataset.shard(shards, i))
+
+def preprocess_tfrecords_labelled(split):
     with open(big_earth_models_folder + 'label_indices.json', 'rb') as f:
         label_indices = json.load(f)
 
     root_folder = big_earth_path
 
     # splits = glob(f'/workspace/app/data/raw/bigearthnet-models/splits/val.csv')
-    splits = glob(f'{big_earth_models_folder}splits/val.csv')
+    splits = glob(f'{big_earth_models_folder}splits/{split}.csv')
 
     # Checks the existence of patch folders and populate the list of patch folder paths
     folder_path_list = []
@@ -165,13 +190,13 @@ def preprocess_tfrecords_labelled():
     neg_df = pd.DataFrame(nonirrigated_examples, columns=['file'])
     # pos_df.to_csv('/workspace/app/data/raw/bigearthnet-models/splits/positive_train.csv')
     # neg_df.to_csv('/workspace/app/data/raw/bigearthnet-models/splits/negative_train.csv')
-    pos_df.to_csv(big_earth_models_folder + 'splits/positive_val.csv')
-    neg_df.to_csv(big_earth_models_folder + 'splits/negative_val.csv')
+    pos_df.to_csv(big_earth_models_folder + 'splits/positive_'+split+'.csv')
+    neg_df.to_csv(big_earth_models_folder + 'splits/negative_'+split+'.csv')
 
     # pos_irr_df = pd.read_csv('/workspace/app/data/raw/bigearthnet-models/splits/positive_train.csv')
     # neg_irr_df = pd.read_csv('/workspace/app/data/raw/bigearthnet-models/splits/negative_train.csv')
-    pos_irr_df = pd.read_csv(big_earth_models_folder + 'splits/positive_val.csv')
-    neg_irr_df = pd.read_csv(big_earth_models_folder + 'splits/negative_val.csv')
+    pos_irr_df = pd.read_csv(big_earth_models_folder + 'splits/positive_'+split+'.csv')
+    neg_irr_df = pd.read_csv(big_earth_models_folder + 'splits/negative_'+split+'.csv')
 
     len(pos_irr_df)
 
@@ -208,9 +233,9 @@ def preprocess_tfrecords_labelled():
     balanced_df = pd.concat([pos_df, neg_ir_df])
     # Shuffle the examples
     balanced_df = balanced_df.sample(frac=1)
-    balanced_df.to_csv(f'{big_earth_models_folder}splits/final_balanced_val.csv')
+    balanced_df.to_csv(f'{big_earth_models_folder}splits/balanced_{split}.csv')
 
-    splits = glob(f'{big_earth_models_folder}splits/final_balanced_val.*')
+    splits = glob(f'{big_earth_models_folder}splits/balanced_{split}.csv')
     patch_names_list = []
     split_names = []
     for csv_file in splits:
@@ -225,10 +250,11 @@ def preprocess_tfrecords_labelled():
         split_names, patch_names_list,
         label_indices, False, True)
 
+    # Start for vineyards data
     pos_df = pd.DataFrame(vy_examples, columns=['file'])
     neg_df = pd.DataFrame(nonvy_examples, columns=['file'])
-    pos_df.to_csv(big_earth_models_folder + 'splits/positive_val.csv')
-    neg_df.to_csv(big_earth_models_folder + 'splits/negative_val.csv')
+    pos_df.to_csv(big_earth_models_folder + 'splits/positive_vy_'+split+'.csv')
+    neg_df.to_csv(big_earth_models_folder + 'splits/negative_vy_'+split+'.csv')
 
     # # Create Data sets for finetuning. Make total dataset size divisible by 32 or 64 for easy batching
 
@@ -270,9 +296,9 @@ def preprocess_tfrecords_labelled():
     balanced_df = pd.concat([pos_df, neg_vy_df])
     # Shuffle the examples
     balanced_df = balanced_df.sample(frac=1)
-    balanced_df.to_csv(f'{big_earth_models_folder}splits/final_balanced_val_vy.csv')
+    balanced_df.to_csv(f'{big_earth_models_folder}splits/balanced_vy_{split}.csv')
 
-    splits = glob(f'{big_earth_models_folder}splits/final_balanced_val_vy.*')
+    splits = glob(f'{big_earth_models_folder}splits/balanced_vy_{split}.csv')
     patch_names_list = []
     split_names = []
     for csv_file in splits:
@@ -293,10 +319,20 @@ def preprocess_tfrecords_labelled():
         description='This script creates TFRecord files for the BigEarthNet train, validation and test splits. It also shards the files.')
     parser.add_argument('-d', '--download', default=False, type=bool,
                         help="whether to download bigearthnet data")
-    parser.add_argument('-tf', '--tfrecords', default=True, type=bool,
+    parser.add_argument('-tf', '--tfrecords', default=False, type=bool,
                         help="whether to create tfrecords")
-    parser.add_argument('-tfl', '--tfrecordslabeled', default=True, type=bool,
+
+    # process tf records labelled for balanced data
+    parser.add_argument('-tfl', '--tfrecordslabeled', default=False, type=bool,
                         help="whether to create tfrecords with labelled")
+    parser.add_argument('-s', '--split', default='train', type=str,
+                        help="which dataset split to create (train,val,test)")
+
+    # Shard the data
+    parser.add_argument('-sd', '--sharddata', default=False, type=bool,
+                        help="whether to shard the data or not")
+    parser.add_argument('-sdn', '--shardname', default='balanced', type=str,
+                        help="which main file to shard")
     args = parser.parse_args()
 
     if args.download:
@@ -311,5 +347,10 @@ def preprocess_tfrecords_labelled():
 
     if args.tfrecordslabeled:
         print('preprocess_tfrecords_labelled---START')
-        preprocess_tfrecords_labelled()
+        preprocess_tfrecords_labelled(args.split)
         print('preprocess_tfrecords_labelled---END')
+
+    if args.sharddata:
+        print('shard_tfrecords---START')
+        shard_tfrecords(args.shardname)
+        print('shard_tfrecords---END')
diff --git a/src/data/preprocess_tfrecords.py b/src/data/preprocess_tfrecords.py
index 98897ff..25a6ac0 100755
--- a/src/data/preprocess_tfrecords.py
+++ b/src/data/preprocess_tfrecords.py
@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 # coding: utf-8
+# %%
 
 # # Big Earth Net Preprocessing
 # ## Irrigation Capstone Fall 2020
@@ -7,7 +8,7 @@
 # 
 # This notebook is used to preprocess the GeoTiff files that contain the Sentinel-2 MSI data comprising the BigEarthNet dataset into TFRecords files. It is based on the preprocessing scripts from the BigEarthNet repo, but has been updated to work in Colaboratory with Python3.7+ and TensorFlow 2.3.
 
-# In[1]:
+# %%
 
 
 import pandas as pd
@@ -21,7 +22,7 @@
 import json
 
 
-# In[2]:
+# %%
 
 
 print(pd.__version__)
@@ -30,7 +31,7 @@
 
 # ## Mount Google Drive and Set Paths
 
-# In[3]:
+# %%
 
 
 # base_path = '/content/gdrive/My Drive/Capstone Project'
@@ -41,13 +42,13 @@
 # ## Create Symbolic Link(s)
 # Set up a symbolic link to allow for easy Python module imports. Then check to make sure the link works (it is a Unix link so check from shell)
 
-# In[ ]:
+# %%
 
 
 os.system("ln -s '/workspace/app/data/raw/bigearthnet-models/' bemodels")
 
 
-# In[ ]:
+# %%
 
 
 os.system('ls bemodels')
@@ -59,13 +60,13 @@
 # ### Note: This processing takes a really long time. 
 # We need to determine if there is a better way to get this data ready for ingestion into our models.
 
-# In[4]:
+# %%
 
 
 from bemodels import tensorflow_utils
 
 
-# In[ ]:
+# %%
 
 
 
diff --git a/src/data/preprocess_tfrecords_eurosat.py b/src/data/preprocess_tfrecords_eurosat.py
new file mode 100644
index 0000000..ce4ece0
--- /dev/null
+++ b/src/data/preprocess_tfrecords_eurosat.py
@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+# coding: utf-8
+# %%
+# # Big Earth Net Preprocessing
+# ## Irrigation Capstone Fall 2020
+# ### TP Goter
+# 
+# This notebook is used to preprocess the GeoTiff files that contain the Sentinel-2 MSI data comprising the BigEarthNet dataset into TFRecords files. It is based on the preprocessing scripts from the BigEarthNet repo, but has been updated to work in Colaboratory with Python3.7+ and TensorFlow 2.3.
+
+# %%
+import pandas as pd
+import tensorflow as tf
+from glob import glob
+import os
+import numpy as np
+from tqdm import tqdm
+import rasterio
+import csv
+import json
+
+
+# %%
+print(pd.__version__)
+print(tf.__version__)
+
+# ## Mount Google Drive and Set Paths
+
+# %%
+# base_path = '/content/gdrive/My Drive/Capstone Project'
+big_earth_path ='/workspace/app/data/raw/BigEarthNet/'
+
+big_earth_models_folder ='/workspace/app/data/raw/bigearthnet-models/'
+
+# ## Create Symbolic Link(s)
+# Set up a symbolic link to allow for easy Python module imports. Then check to make sure the link works (it is a Unix link so check from shell)
+
+# %%
+os.system("ln -s '/workspace/app/data/raw/bigearthnet-models/' bemodels")
+
+
+# %%
+os.system('ls bemodels')
+
+# ## Process All of the BigEarthNet data
+# This simple script will loop over all of the subfolders in the BigEarthNet-v1.0 folder. Currently this folder does not contain the entirety of the BigEarthNet Dataset. Due to this issue, the original scripting was modified to run through the train, test, val sets and only process files if they exist. The previous script simply aborted if a file was listed in the train.csv file and was not in the directory.
+# 
+# ### Note: This processing takes a really long time. 
+# We need to determine if there is a better way to get this data ready for ingestion into our models.
+
+# %%
+from bemodels import tensorflow_utils
+
+
+# %%
+# Index labels
+
+# %%
+splitst = glob(f"{big_earth_models_folder}{'splits/train.csv'}")
+splitst
+
+# %%
+with open(big_earth_models_folder+'label_indices.json', 'rb') as f:
+    label_indices = json.load(f)
+
+root_folder = big_earth_path
+out_folder = '/workspace/app/data/processed'
+#splits = glob(f'/workspace/app/data/raw/bigearthnet-models/splits/train.csv')
+
+# Checks the existence of patch folders and populate the list of patch folder paths
+csv_file_path_list = ['splits/train.csv', 'splits/test.csv', 'splits/val.csv']
+if not os.path.exists(root_folder):
+    print('ERROR: folder', root_folder, 'does not exist')
+
+try:
+    for csv_file in csv_file_path_list:
+        splits = glob(f"{big_earth_models_folder}{csv_file}") # all patch folders 
+        print(csv_file)
+        
+        patch_names_list = []
+        split_names = []
+        for csv_file in splits:
+            
+            print(csv_file)
+   
+            patch_names_list.append([])
+            print(patch_names_list)
+        
+            split_names.append(os.path.basename(csv_file).split('.')[0])
+            print(split_names)
+            with open(csv_file, 'r') as fp:
+                csv_reader = csv.reader(fp, delimiter=',')
+
+                for row in csv_reader:
+                    patch_names_list[-1].append(row[0].strip())
+
+            print(len(patch_names_list[0]))
+            print(root_folder)
+            print(out_folder)
+            print(split_names)
+#         tensorflow_utils.prep_tf_record_files(
+#             root_folder, out_folder,
+#             split_names, patch_names_list,
+#             label_indices, False, True)
+except:
+    print('ERROR: some csv files either do not exist or have been corrupted')
+
+label_indices # Dictionary with labels
+
+# %%
+# Shard the Train data
+raw_dataset = tf.data.TFRecordDataset(out_folder+"/train.tfrecord")
+
+shards = 50
+
+for i in range(shards):
+    writer = tf.data.experimental.TFRecordWriter(f"{out_folder}/train-part-{i}.tfrecord")
+    writer.write(raw_dataset.shard(shards, i))
+
+# Shard the Test data
+raw_dataset = tf.data.TFRecordDataset(out_folder+"/test.tfrecord")
+
+shards = 20
+
+for i in range(shards):
+    writer = tf.data.experimental.TFRecordWriter(f"{out_folder}/test-part-{i}.tfrecord")
+    writer.write(raw_dataset.shard(shards, i))
+
+# Shard the Val data
+raw_dataset = tf.data.TFRecordDataset(out_folder+"/val.tfrecord")
+
+shards = 20
+
+for i in range(shards):
+    writer = tf.data.experimental.TFRecordWriter(f"{out_folder}/val-part-{i}.tfrecord")
+    writer.write(raw_dataset.shard(shards, i))
diff --git a/src/models/utils.py b/src/models/utils.py
index 2b6b9ba..d42198f 100755
--- a/src/models/utils.py
+++ b/src/models/utils.py
@@ -190,7 +190,7 @@ def standardize_feature(data, band_name):
     img = tf.concat([bands_10m, bands_20m], axis=2)
     
     return img, 0
-  
+
 def get_batched_dataset(filenames, batch_size, augment=False, simclr=False, ca=False):
     '''
     This function is used to return a batch generator for training our tensorflow model.