2. SIG Heat Flow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xTgW9Tjig1rn"
   },
   "source": [
    "# SIG "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6226,
     "status": "ok",
     "timestamp": 1607916046039,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "HLHYJN6fCMKr",
    "outputId": "4cb84839-b31b-40ba-dc89-bf06bb2b09e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: rasterio in /usr/local/lib/python3.6/dist-packages (1.1.8)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from rasterio) (1.18.5)\n",
      "Requirement already satisfied: snuggs>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from rasterio) (1.4.7)\n",
      "Requirement already satisfied: click-plugins in /usr/local/lib/python3.6/dist-packages (from rasterio) (1.1.1)\n",
      "Requirement already satisfied: affine in /usr/local/lib/python3.6/dist-packages (from rasterio) (2.3.0)\n",
      "Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from rasterio) (7.1.2)\n",
      "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.6/dist-packages (from rasterio) (0.7.1)\n",
      "Requirement already satisfied: attrs in /usr/local/lib/python3.6/dist-packages (from rasterio) (20.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.6/dist-packages (from snuggs>=1.4.1->rasterio) (2.4.7)\n",
      "Requirement already satisfied: earthpy in /usr/local/lib/python3.6/dist-packages (0.9.2)\n",
      "Requirement already satisfied: scikit-image in /usr/local/lib/python3.6/dist-packages (from earthpy) (0.16.2)\n",
      "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from earthpy) (1.18.5)\n",
      "Requirement already satisfied: geopandas in /usr/local/lib/python3.6/dist-packages (from earthpy) (0.8.1)\n",
      "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from earthpy) (3.2.2)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from earthpy) (2.23.0)\n",
      "Requirement already satisfied: rasterio in /usr/local/lib/python3.6/dist-packages (from earthpy) (1.1.8)\n",
      "Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->earthpy) (1.4.1)\n",
      "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->earthpy) (2.5)\n",
      "Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->earthpy) (7.0.0)\n",
      "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->earthpy) (2.4.1)\n",
      "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->earthpy) (1.1.1)\n",
      "Requirement already satisfied: pandas>=0.23.0 in /usr/local/lib/python3.6/dist-packages (from geopandas->earthpy) (1.1.5)\n",
      "Requirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (from geopandas->earthpy) (1.7.1)\n",
      "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from geopandas->earthpy) (3.0.0.post1)\n",
      "Requirement already satisfied: fiona in /usr/local/lib/python3.6/dist-packages (from geopandas->earthpy) (1.8.18)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->earthpy) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->earthpy) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->earthpy) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->earthpy) (1.3.1)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->earthpy) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->earthpy) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->earthpy) (2020.12.5)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->earthpy) (1.24.3)\n",
      "Requirement already satisfied: snuggs>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from rasterio->earthpy) (1.4.7)\n",
      "Requirement already satisfied: attrs in /usr/local/lib/python3.6/dist-packages (from rasterio->earthpy) (20.3.0)\n",
      "Requirement already satisfied: click-plugins in /usr/local/lib/python3.6/dist-packages (from rasterio->earthpy) (1.1.1)\n",
      "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.6/dist-packages (from rasterio->earthpy) (0.7.1)\n",
      "Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from rasterio->earthpy) (7.1.2)\n",
      "Requirement already satisfied: affine in /usr/local/lib/python3.6/dist-packages (from rasterio->earthpy) (2.3.0)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->scikit-image->earthpy) (4.4.2)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas->earthpy) (2018.9)\n",
      "Requirement already satisfied: munch in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas->earthpy) (2.5.0)\n",
      "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas->earthpy) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install rasterio\n",
    "! pip install earthpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 6546,
     "status": "ok",
     "timestamp": 1607916046369,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "SZ_xYova3DGe"
   },
   "outputs": [],
   "source": [
    "import rasterio as rio\n",
    "import earthpy.plot as ep\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Av8AP2aKSbLX"
   },
   "source": [
    "## Cargamos el Raster Grosor sedimentario\n",
    "---\n",
    "Para esto utlizamos en paquete Rasterio y con numpy hacemos las correciones necesarias como quitar valores núlos y en el caso de las edades corregir los decimales de las edades para que queden en un rango global 0 - 280"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 6541,
     "status": "ok",
     "timestamp": 1607916046370,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "FRaXTYuo95pW"
   },
   "outputs": [],
   "source": [
    "#Cargamos el tiff de grosor sedimentarios de la cuenca Panamá\n",
    "thickness_sed = rio.open('thickness_sed1.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6534,
     "status": "ok",
     "timestamp": 1607916046372,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "8JEJyB2UAszT",
    "outputId": "c3179c08-bc5c-409b-8c82-e689d49d0450"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoundingBox(left=-102.88206326184, bottom=-2.766261613718939, right=-76.632063261945, top=7.983738386238061)"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#miramos las cordenadas del raster cargado grosor sedimentario\n",
    "thickness_sed.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 185
    },
    "executionInfo": {
     "elapsed": 6522,
     "status": "ok",
     "timestamp": 1607916046373,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "OUpcG4QMCajV",
    "outputId": "d40041bb-36d1-4f1f-aa59-f97ea10d5121"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#también puede ser viasualizado con rasterio\n",
    "from rasterio.plot import show\n",
    "show(thickness_sed);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6510,
     "status": "ok",
     "timestamp": 1607916046374,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "Q6Ar5-LyFjzf",
    "outputId": "7fd6e231-7bb9-4feb-d046-fd1e295b16f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 232.97592\n",
      "Max: 4627.339\n",
      "Min: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Masked = True sets no data values to np.nan if they are in the metadata\n",
    "thickness_sed_data = thickness_sed.read(1, masked=True) #convierte nuestro raster en un arreglo numpy\n",
    "thickness_sed_meta = thickness_sed.profile\n",
    "# EXPLORE: View summary statistics of canopy height model\n",
    "# Notice the mean value with 0's included in the data\n",
    "print('Mean:', np.nanmean(thickness_sed_data))\n",
    "print('Max:', np.nanmax(thickness_sed_data))\n",
    "print('Min:', np.nanmin(thickness_sed_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 601
    },
    "executionInfo": {
     "elapsed": 6793,
     "status": "ok",
     "timestamp": 1607916046670,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "Jr1LY61rC8NP",
    "outputId": "2be9b5ca-8453-4454-b771-ef8a3bfb1607"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the data by plotting a histogram with earthpy\n",
    "ax=ep.hist(thickness_sed_data,\n",
    "           figsize=(8,8), \n",
    "           colors=\"blue\", \n",
    "           xlabel=\"Grosor sedimentario (m)\", \n",
    "           ylabel=\"Total Pixels\", \n",
    "           title=\"Histograma de Grosor Sedimentario\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6783,
     "status": "ok",
     "timestamp": 1607916046671,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "lXAKQYUqQFyp",
    "outputId": "419602bb-2dca-4a14-a331-39c1b5813226"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 233.00099\n",
      "Max: 4627.339\n",
      "Min: 7.730707e-12\n"
     ]
    }
   ],
   "source": [
    "# CLEANUP: Set CHM values of 0 to NAN (no data or not a number)\n",
    "thickness_sed_data[thickness_sed_data == 0] = np.nan\n",
    "\n",
    "# View summary statistics of canopy height model after cleaning up the data\n",
    "print('Mean:', np.nanmean(thickness_sed_data))\n",
    "print('Max:', np.nanmax(thickness_sed_data))\n",
    "print('Min:', np.nanmin(thickness_sed_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 601
    },
    "executionInfo": {
     "elapsed": 7211,
     "status": "ok",
     "timestamp": 1607916047114,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "I4aaUi3IQgkL",
    "outputId": "a1d1b13b-0507-4025-8e6c-25870d0051c2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Este histograma muestra los valores de px habiendo quitado los valores nulos\n",
    "ax=ep.hist(thickness_sed_data,\n",
    "           figsize=(8,8), \n",
    "           colors=\"purple\", \n",
    "           xlabel=\"Grosor sedimentario (m)\", \n",
    "           ylabel=\"Total Pixels\", \n",
    "           title=\"Histograma de Grosor Sedimentario\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d1CleBxfQ5n5"
   },
   "source": [
    "Sin embargo no se observa gran diferencia al remover los valores nulos entre el primer histograma (Azul) y el segundo (Purpura)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fZtiNNXGtiux"
   },
   "source": [
    "## Cargamos el Raster de Edad\n",
    "---\n",
    "Para esto utlizamos en paquete Rasterio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 7205,
     "status": "ok",
     "timestamp": 1607916047117,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "qdh-lpwHi-AC"
   },
   "outputs": [],
   "source": [
    "#Cargamos el tiff de edades de piso oceánico de la cuenca Panamá\n",
    "Edad = rio.open('edad1.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7200,
     "status": "ok",
     "timestamp": 1607916047120,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "d72h6MB1jE5b",
    "outputId": "ee29d24e-fd34-4aec-c1a2-2b43dbd255b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoundingBox(left=-102.88206326184, bottom=-2.766261613718939, right=-76.58206326184, top=8.033738386281062)"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#miramos las cordenadas del raster cargado edades\n",
    "Edad.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 189
    },
    "executionInfo": {
     "elapsed": 7506,
     "status": "ok",
     "timestamp": 1607916047439,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "RFYWDo3KjmCq",
    "outputId": "e6c15eae-835f-4d64-cdd4-0e499f89b9b8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from rasterio.plot import show\n",
    "show(Edad);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7496,
     "status": "ok",
     "timestamp": 1607916047441,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "3wurnUUVtxjl",
    "outputId": "09f4cd97-fe8e-4f57-cf84-da599d9bc328"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: -3490.327594704971\n",
      "Max: 5209\n",
      "Min: -32768\n"
     ]
    }
   ],
   "source": [
    "# Masked = True sets no data values to np.nan if they are in the metadata\n",
    "Edad_data = Edad.read(1, masked=False) #convierte nuestro raster en un arreglo numpy\n",
    "Edad_meta = Edad.profile\n",
    "# EXPLORE: View summary statistics of canopy height model\n",
    "# Notice the mean value with 0's included in the data\n",
    "print('Mean:', np.nanmean(Edad_data))\n",
    "print('Max:', np.nanmax(Edad_data))\n",
    "print('Min:', np.nanmin(Edad_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "executionInfo": {
     "elapsed": 7485,
     "status": "ok",
     "timestamp": 1607916047443,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "jBvrDS1hyfcw",
    "outputId": "17dd1edd-2dff-4331-af5d-e4d276debb04"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the data by plotting a histogram with earthpy\n",
    "ax=ep.hist(Edad_data,\n",
    "           figsize=(8,8), \n",
    "           colors=\"blue\", \n",
    "           xlabel=\"Edad sin corrección\", \n",
    "           ylabel=\"Total Pixels\", \n",
    "           title=\"Histograma de Edades piso Oceánico\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7475,
     "status": "ok",
     "timestamp": 1607916047446,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "57YHv_VWyyE0",
    "outputId": "53f3a64a-5d72-41c8-886d-aa48b3eb7979"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 7.493006266722997\n",
      "Max: 52.09\n",
      "Min: 0.0\n"
     ]
    }
   ],
   "source": [
    "# CLEANUP: Set CHM values of 0 to NAN (no data or not a number)\n",
    "Edad_data[Edad_data< 0] = 0\n",
    "Edad_data = Edad_data/100\n",
    "\n",
    "# View summary statistics of canopy height model after cleaning up the data\n",
    "print('Mean:', np.nanmean(Edad_data))\n",
    "print('Max:', np.nanmax(Edad_data))\n",
    "print('Min:', np.nanmin(Edad_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "executionInfo": {
     "elapsed": 7680,
     "status": "ok",
     "timestamp": 1607916047664,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "XMkVSMkD3gMT",
    "outputId": "7109bb40-94bb-4b60-81f5-2e5efc7eee8a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Este histograma muestra los valores de px habiendo quitado los valores nulos\n",
    "ax=ep.hist(Edad_data,\n",
    "           figsize=(8,8), \n",
    "           colors=\"purple\", \n",
    "           xlabel=\"Edad (Ma)\", \n",
    "           ylabel=\"Total Pixels\", \n",
    "           title=\"Histograma de Edad piso oceánico\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xR6X00-H701M"
   },
   "source": [
    "### Convirtiendo un Arreglo a Raster\n",
    "---\n",
    "\n",
    "Aquí lo que se hizo depues de un día entero de intentos fue:\n",
    "Haivendo convertido los valores de edades de arreglo .read que estaban en un formato que se salía de los valores normales y quitando los valores negativos\n",
    "\n",
    "Importar el arreglo modificado a un nuevo raster.tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 7672,
     "status": "ok",
     "timestamp": 1607916047665,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "DOaPX6ElAGEv"
   },
   "outputs": [],
   "source": [
    "from rasterio.transform import from_origin\n",
    "\n",
    "#################################\n",
    "# MUY IMPORTANTE CAMBIAR LOS PARAMETRO IMPUESTO POR EL ARCHIVO PROFILE!!!!! \n",
    "#ME GASTE TODO UN DÍA ENCONTRANDO LA SOLUCIÓN!\n",
    "Edad_meta['nodata']= 0\n",
    "Edad_meta['dtype']= 'float64'\n",
    "#################################\n",
    "\n",
    "#sistema de referencia del raster\n",
    "Sist_ref = Edad.crs\n",
    "#tamaño de pixel en x & y del raster\n",
    "pxsz, pysz = Edad.res\n",
    "#bordes del raster\n",
    "west, south, east, north = Edad.bounds\n",
    "\n",
    "#.from_origin(esquina izq west, esquina supe north, xsize, ysize)\n",
    "transform = from_origin(west, north, pxsz, pysz)\n",
    "\n",
    "#height = Edad_data.shape[0], width = Edad_data.shape[1]\n",
    "new_Edad = rio.open('new_Edad.tif', 'w', **Edad_meta)\n",
    "\n",
    "new_Edad.write(Edad_data, 1)\n",
    "# ES NECESARIO ESTE ÚLTIMO CODIGO PARA QUE SE PUEDA VISALIZAR EL RASTER\n",
    "new_Edad.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 189
    },
    "executionInfo": {
     "elapsed": 8027,
     "status": "ok",
     "timestamp": 1607916048032,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "gqAUxZIjLcnr",
    "outputId": "0d81d92f-12f3-4a09-d245-4fc7bb288381"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cargamos el tiff de nueva edad de la cuenca Panamá\n",
    "new_Edad = rio.open('new_Edad.tif')\n",
    "\n",
    "from rasterio.plot import show\n",
    "show(new_Edad);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 8020,
     "status": "ok",
     "timestamp": 1607916048034,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "00vVlMTSCTZ7"
   },
   "outputs": [],
   "source": [
    "# Masked = True sets no data values to np.nan if they are in the metadata\n",
    "Edad_data = new_Edad.read(1, masked=False) #convierte nuestro raster en un arreglo numpy\n",
    "Edad_meta = new_Edad.profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 271
    },
    "executionInfo": {
     "elapsed": 8355,
     "status": "ok",
     "timestamp": 1607916048386,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "TOJ74Og-C57w",
    "outputId": "9d7e72e5-49f4-43c3-9af4-e10f19ca7a73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fe04f914d30>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Edad_data)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bu4K3ZCxSlk5"
   },
   "source": [
    "## Cargamos los Puntos de flujo de calor\n",
    "---\n",
    "Para esto usamos geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "executionInfo": {
     "elapsed": 8339,
     "status": "ok",
     "timestamp": 1607916048388,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "qJHnxoGDYR_y",
    "outputId": "db57a3e4-b6f5-4896-a708-107773f1b01d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Profundidad</th>\n",
       "      <th>Flujo_calor</th>\n",
       "      <th>Pesos</th>\n",
       "      <th>Flux_x_wts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27HF23</td>\n",
       "      <td>-86.86670</td>\n",
       "      <td>3.537222</td>\n",
       "      <td>-2136.0</td>\n",
       "      <td>65.85</td>\n",
       "      <td>0.942231</td>\n",
       "      <td>62.045937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28HF24</td>\n",
       "      <td>-86.99115</td>\n",
       "      <td>2.950989</td>\n",
       "      <td>-2870.0</td>\n",
       "      <td>267.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>211.536033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29HF25</td>\n",
       "      <td>-86.90680</td>\n",
       "      <td>2.720539</td>\n",
       "      <td>-2690.0</td>\n",
       "      <td>211.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>167.168925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31HF27</td>\n",
       "      <td>-87.01960</td>\n",
       "      <td>2.150989</td>\n",
       "      <td>-2824.0</td>\n",
       "      <td>187.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>123.548114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32HF28</td>\n",
       "      <td>-87.05290</td>\n",
       "      <td>2.034306</td>\n",
       "      <td>-2503.0</td>\n",
       "      <td>234.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>154.600314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Codigo       lon       lat  Profundidad  Flujo_calor     Pesos  Flux_x_wts\n",
       "0  27HF23 -86.86670  3.537222      -2136.0        65.85  0.942231   62.045937\n",
       "1  28HF24 -86.99115  2.950989      -2870.0       267.00  0.792270  211.536033\n",
       "2  29HF25 -86.90680  2.720539      -2690.0       211.00  0.792270  167.168925\n",
       "3  31HF27 -87.01960  2.150989      -2824.0       187.00  0.660685  123.548114\n",
       "4  32HF28 -87.05290  2.034306      -2503.0       234.00  0.660685  154.600314"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Flujo_calor = pd.read_csv('df_Flujo_calor1.csv', index_col= 0)\n",
    "df_Flujo_calor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "executionInfo": {
     "elapsed": 8325,
     "status": "ok",
     "timestamp": 1607916048389,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "Xp4Ril9YRH_e",
    "outputId": "77679274-505f-4aa2-c5ea-dfd5d3d6e038"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Profundidad</th>\n",
       "      <th>Flujo_calor</th>\n",
       "      <th>Pesos</th>\n",
       "      <th>Flux_x_wts</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27HF23</td>\n",
       "      <td>-86.86670</td>\n",
       "      <td>3.537222</td>\n",
       "      <td>-2136.0</td>\n",
       "      <td>65.85</td>\n",
       "      <td>0.942231</td>\n",
       "      <td>62.045937</td>\n",
       "      <td>POINT (-86.86670 3.53722)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28HF24</td>\n",
       "      <td>-86.99115</td>\n",
       "      <td>2.950989</td>\n",
       "      <td>-2870.0</td>\n",
       "      <td>267.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>211.536033</td>\n",
       "      <td>POINT (-86.99115 2.95099)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29HF25</td>\n",
       "      <td>-86.90680</td>\n",
       "      <td>2.720539</td>\n",
       "      <td>-2690.0</td>\n",
       "      <td>211.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>167.168925</td>\n",
       "      <td>POINT (-86.90680 2.72054)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31HF27</td>\n",
       "      <td>-87.01960</td>\n",
       "      <td>2.150989</td>\n",
       "      <td>-2824.0</td>\n",
       "      <td>187.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>123.548114</td>\n",
       "      <td>POINT (-87.01960 2.15099)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32HF28</td>\n",
       "      <td>-87.05290</td>\n",
       "      <td>2.034306</td>\n",
       "      <td>-2503.0</td>\n",
       "      <td>234.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>154.600314</td>\n",
       "      <td>POINT (-87.05290 2.03431)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Codigo       lon       lat  ...     Pesos  Flux_x_wts                   geometry\n",
       "0  27HF23 -86.86670  3.537222  ...  0.942231   62.045937  POINT (-86.86670 3.53722)\n",
       "1  28HF24 -86.99115  2.950989  ...  0.792270  211.536033  POINT (-86.99115 2.95099)\n",
       "2  29HF25 -86.90680  2.720539  ...  0.792270  167.168925  POINT (-86.90680 2.72054)\n",
       "3  31HF27 -87.01960  2.150989  ...  0.660685  123.548114  POINT (-87.01960 2.15099)\n",
       "4  32HF28 -87.05290  2.034306  ...  0.660685  154.600314  POINT (-87.05290 2.03431)\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#con este codigo se crea la columna geometry a partir de las columnas lon y lat de las coordenadas del df\n",
    "Puntos_Flujo_calor = gpd.GeoDataFrame(df_Flujo_calor, geometry=gpd.points_from_xy(df_Flujo_calor.lon, df_Flujo_calor.lat))\n",
    "\n",
    "#Verificamos que se haya creado la columna geometry\n",
    "Puntos_Flujo_calor.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 8311,
     "status": "ok",
     "timestamp": 1607916048391,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "oaWS39mdVka5",
    "outputId": "610bc2f7-6b7d-4745-c3ef-c2c5e411931c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'geometry'"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verificamos que columna esta tomando como geometry\n",
    "Puntos_Flujo_calor.geometry.name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "executionInfo": {
     "elapsed": 8479,
     "status": "ok",
     "timestamp": 1607916048572,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "5WV5h3SaV42k",
    "outputId": "190ea469-824c-4e08-bd38-5ce8024c7eae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploteamos los puntos para visualizar el geodataframe\n",
    "ax = Puntos_Flujo_calor.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8470,
     "status": "ok",
     "timestamp": 1607916048574,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "ny1JNaCjTZC8",
    "outputId": "12b087dc-cbae-4319-f77a-6b0aac65f3e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Point\n",
       "1    Point\n",
       "2    Point\n",
       "3    Point\n",
       "4    Point\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensure this is a points layer as you think it is\n",
    "Puntos_Flujo_calor.geom_type.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fhzL4CqJB_Ru"
   },
   "source": [
    "## Adicionamos grosor Sedimentario\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "executionInfo": {
     "elapsed": 8827,
     "status": "ok",
     "timestamp": 1607916048945,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "bjQqkXT-Tuqw",
    "outputId": "f686f6d7-cedd-468b-bd75-086fe3152b30"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#miramos a que pixel en el reaster de grosores sedimentarios corresponde nuestro puntos de flujo de calor\n",
    "from rasterio.plot import plotting_extent\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "ep.plot_bands(thickness_sed_data,\n",
    "              extent=plotting_extent(thickness_sed), # Set spatial extent \n",
    "              cmap='BrBG',\n",
    "              title=\"Cuenca Panamá \\n Puntos de calor en grosor sedimentario\",\n",
    "              scale=False,\n",
    "              ax=ax)\n",
    "\n",
    "Puntos_Flujo_calor.plot(ax=ax,\n",
    "                       marker='s',\n",
    "                       markersize=45,\n",
    "                       color='blue')\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "executionInfo": {
     "elapsed": 8991,
     "status": "ok",
     "timestamp": 1607916049122,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "ZWxVjqDffdcq",
    "outputId": "3e1615e9-1277-452c-8610-2031de4f3b34"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Profundidad</th>\n",
       "      <th>Flujo_calor</th>\n",
       "      <th>Pesos</th>\n",
       "      <th>Flux_x_wts</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Sed_thickness (m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27HF23</td>\n",
       "      <td>-86.86670</td>\n",
       "      <td>3.537222</td>\n",
       "      <td>-2136.0</td>\n",
       "      <td>65.85</td>\n",
       "      <td>0.942231</td>\n",
       "      <td>62.045937</td>\n",
       "      <td>POINT (-86.86670 3.53722)</td>\n",
       "      <td>279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28HF24</td>\n",
       "      <td>-86.99115</td>\n",
       "      <td>2.950989</td>\n",
       "      <td>-2870.0</td>\n",
       "      <td>267.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>211.536033</td>\n",
       "      <td>POINT (-86.99115 2.95099)</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29HF25</td>\n",
       "      <td>-86.90680</td>\n",
       "      <td>2.720539</td>\n",
       "      <td>-2690.0</td>\n",
       "      <td>211.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>167.168925</td>\n",
       "      <td>POINT (-86.90680 2.72054)</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31HF27</td>\n",
       "      <td>-87.01960</td>\n",
       "      <td>2.150989</td>\n",
       "      <td>-2824.0</td>\n",
       "      <td>187.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>123.548114</td>\n",
       "      <td>POINT (-87.01960 2.15099)</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32HF28</td>\n",
       "      <td>-87.05290</td>\n",
       "      <td>2.034306</td>\n",
       "      <td>-2503.0</td>\n",
       "      <td>234.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>154.600314</td>\n",
       "      <td>POINT (-87.05290 2.03431)</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Codigo       lon  ...                   geometry  Sed_thickness (m)\n",
       "0  27HF23 -86.86670  ...  POINT (-86.86670 3.53722)              279.0\n",
       "1  28HF24 -86.99115  ...  POINT (-86.99115 2.95099)              211.0\n",
       "2  29HF25 -86.90680  ...  POINT (-86.90680 2.72054)              203.0\n",
       "3  31HF27 -87.01960  ...  POINT (-87.01960 2.15099)              159.0\n",
       "4  32HF28 -87.05290  ...  POINT (-87.05290 2.03431)              145.0\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraemos las coordenadas del Dataframe en una lista\n",
    "coords = [(x,y) for x, y in zip(df_Flujo_calor.lon, df_Flujo_calor.lat)]\n",
    "\n",
    "# Con este codigo muestramos los vaores de px en el raster y se lo agregamos al data frame\n",
    "df_Flujo_calor['Sed_thickness (m)'] = [x for x in thickness_sed.sample(coords)]\n",
    "df_Flujo_calor = df_Flujo_calor.astype({'Sed_thickness (m)': float})\n",
    "df_Flujo_calor['Sed_thickness (m)'] = list(map(lambda x : round(x, 1), df_Flujo_calor['Sed_thickness (m)']))\n",
    "\n",
    "df_Flujo_calor.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GaTBtxfZBlTy"
   },
   "source": [
    "## Adicionamos Edad\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 288
    },
    "executionInfo": {
     "elapsed": 9361,
     "status": "ok",
     "timestamp": 1607916049509,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "OpfSuCtiCKrf",
    "outputId": "fb380c8c-eba3-48ff-ecb8-f410023a80f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#miramos a que pixel en el reaster de grosores sedimentarios corresponde nuestro puntos de flujo de calor\n",
    "from rasterio.plot import plotting_extent\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "ep.plot_bands(Edad_data,\n",
    "              extent=plotting_extent(new_Edad), # Set spatial extent \n",
    "              cmap='BrBG',\n",
    "              title=\"Cuenca Panamá \\n Puntos de calor en Edad del piso oceánico\",\n",
    "              scale=False,\n",
    "              ax=ax)\n",
    "\n",
    "Puntos_Flujo_calor.plot(ax=ax,\n",
    "                       marker='s',\n",
    "                       markersize=45,\n",
    "                       color='blue')\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "executionInfo": {
     "elapsed": 9351,
     "status": "ok",
     "timestamp": 1607916049513,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "fVPUfQxzFVcg",
    "outputId": "801b4503-63f1-419a-d664-4ad56792de3d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Profundidad</th>\n",
       "      <th>Flujo_calor</th>\n",
       "      <th>Pesos</th>\n",
       "      <th>Flux_x_wts</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Sed_thickness (m)</th>\n",
       "      <th>Age (Ma)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27HF23</td>\n",
       "      <td>-86.86670</td>\n",
       "      <td>3.537222</td>\n",
       "      <td>-2136.0</td>\n",
       "      <td>65.85</td>\n",
       "      <td>0.942231</td>\n",
       "      <td>62.045937</td>\n",
       "      <td>POINT (-86.86670 3.53722)</td>\n",
       "      <td>279.0</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28HF24</td>\n",
       "      <td>-86.99115</td>\n",
       "      <td>2.950989</td>\n",
       "      <td>-2870.0</td>\n",
       "      <td>267.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>211.536033</td>\n",
       "      <td>POINT (-86.99115 2.95099)</td>\n",
       "      <td>211.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29HF25</td>\n",
       "      <td>-86.90680</td>\n",
       "      <td>2.720539</td>\n",
       "      <td>-2690.0</td>\n",
       "      <td>211.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>167.168925</td>\n",
       "      <td>POINT (-86.90680 2.72054)</td>\n",
       "      <td>203.0</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31HF27</td>\n",
       "      <td>-87.01960</td>\n",
       "      <td>2.150989</td>\n",
       "      <td>-2824.0</td>\n",
       "      <td>187.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>123.548114</td>\n",
       "      <td>POINT (-87.01960 2.15099)</td>\n",
       "      <td>159.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32HF28</td>\n",
       "      <td>-87.05290</td>\n",
       "      <td>2.034306</td>\n",
       "      <td>-2503.0</td>\n",
       "      <td>234.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>154.600314</td>\n",
       "      <td>POINT (-87.05290 2.03431)</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Codigo       lon  ...  Sed_thickness (m)  Age (Ma)\n",
       "0  27HF23 -86.86670  ...              279.0      10.1\n",
       "1  28HF24 -86.99115  ...              211.0       7.6\n",
       "2  29HF25 -86.90680  ...              203.0       5.9\n",
       "3  31HF27 -87.01960  ...              159.0       3.6\n",
       "4  32HF28 -87.05290  ...              145.0       3.1\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraemos las coordenadas del Dataframe en una lista\n",
    "coords = [(x,y) for x, y in zip(df_Flujo_calor.lon, df_Flujo_calor.lat)]\n",
    "\n",
    "# Con este codigo muestramos los vaores de px en el raster y se lo agregamos al data frame\n",
    "df_Flujo_calor['Age (Ma)'] = [x for x in new_Edad.sample(coords)]\n",
    "df_Flujo_calor = df_Flujo_calor.astype({'Age (Ma)': float})\n",
    "df_Flujo_calor['Age (Ma)'] = list(map(lambda x : round(x, 1), df_Flujo_calor['Age (Ma)']))\n",
    "\n",
    "df_Flujo_calor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9342,
     "status": "ok",
     "timestamp": 1607916049515,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "N75ktg0aKP0o",
    "outputId": "7e277b0e-b390-4af5-b720-3ab3347ca634"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Codigo               0\n",
       "lon                  0\n",
       "lat                  0\n",
       "Profundidad          0\n",
       "Flujo_calor          0\n",
       "Pesos                0\n",
       "Flux_x_wts           0\n",
       "geometry             0\n",
       "Sed_thickness (m)    0\n",
       "Age (Ma)             3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sin embargo quedaron 3 edades en cero dado que algunos valores de flujo de calor se salían del raster de edades\n",
    "zeros = df_Flujo_calor == 0\n",
    "zeros.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9333,
     "status": "ok",
     "timestamp": 1607916049518,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "zFh4ePCqPFas",
    "outputId": "f6386aa1-d71b-48f9-d920-e960468f06dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1052, 10)"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Flujo_calor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9571,
     "status": "ok",
     "timestamp": 1607916049766,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "kH5nIVt7NWl_",
    "outputId": "26b26c34-95bf-4fad-d3a0-61f83d497c35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Codigo               0\n",
       "lon                  0\n",
       "lat                  0\n",
       "Profundidad          0\n",
       "Flujo_calor          0\n",
       "Pesos                0\n",
       "Flux_x_wts           0\n",
       "geometry             0\n",
       "Sed_thickness (m)    0\n",
       "Age (Ma)             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Elimina las filas con valores iguales a cero en este caso elimina 3 filas\n",
    "df_Flujo_calor = df_Flujo_calor[(df_Flujo_calor != 0).all(1)]\n",
    "zeros = df_Flujo_calor == 0\n",
    "zeros.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9563,
     "status": "ok",
     "timestamp": 1607916049769,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "JqbfxyXCQkxJ",
    "outputId": "d81f2810-039e-4b3e-cbfd-3d7d90c44480"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1049, 10)"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filas eliminadas: 3\n",
    "df_Flujo_calor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "executionInfo": {
     "elapsed": 9554,
     "status": "ok",
     "timestamp": 1607916049771,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "8X1uFUFkOfx-",
    "outputId": "eb0ff126-08dc-4602-87a6-719e9a16a10a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Codigo</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Profundidad</th>\n",
       "      <th>Flujo_calor</th>\n",
       "      <th>Pesos</th>\n",
       "      <th>Flux_x_wts</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Sed_thickness (m)</th>\n",
       "      <th>Age (Ma)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27HF23</td>\n",
       "      <td>-86.86670</td>\n",
       "      <td>3.537222</td>\n",
       "      <td>-2136.0</td>\n",
       "      <td>65.85</td>\n",
       "      <td>0.942231</td>\n",
       "      <td>62.045937</td>\n",
       "      <td>POINT (-86.86670 3.53722)</td>\n",
       "      <td>279.0</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28HF24</td>\n",
       "      <td>-86.99115</td>\n",
       "      <td>2.950989</td>\n",
       "      <td>-2870.0</td>\n",
       "      <td>267.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>211.536033</td>\n",
       "      <td>POINT (-86.99115 2.95099)</td>\n",
       "      <td>211.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29HF25</td>\n",
       "      <td>-86.90680</td>\n",
       "      <td>2.720539</td>\n",
       "      <td>-2690.0</td>\n",
       "      <td>211.00</td>\n",
       "      <td>0.792270</td>\n",
       "      <td>167.168925</td>\n",
       "      <td>POINT (-86.90680 2.72054)</td>\n",
       "      <td>203.0</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31HF27</td>\n",
       "      <td>-87.01960</td>\n",
       "      <td>2.150989</td>\n",
       "      <td>-2824.0</td>\n",
       "      <td>187.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>123.548114</td>\n",
       "      <td>POINT (-87.01960 2.15099)</td>\n",
       "      <td>159.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32HF28</td>\n",
       "      <td>-87.05290</td>\n",
       "      <td>2.034306</td>\n",
       "      <td>-2503.0</td>\n",
       "      <td>234.00</td>\n",
       "      <td>0.660685</td>\n",
       "      <td>154.600314</td>\n",
       "      <td>POINT (-87.05290 2.03431)</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Codigo       lon  ...  Sed_thickness (m)  Age (Ma)\n",
       "0  27HF23 -86.86670  ...              279.0      10.1\n",
       "1  28HF24 -86.99115  ...              211.0       7.6\n",
       "2  29HF25 -86.90680  ...              203.0       5.9\n",
       "3  31HF27 -87.01960  ...              159.0       3.6\n",
       "4  32HF28 -87.05290  ...              145.0       3.1\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Flujo_calor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "executionInfo": {
     "elapsed": 9545,
     "status": "ok",
     "timestamp": 1607916049773,
     "user": {
      "displayName": "Giovanny Alejandro C. Londono",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gg8qR994zMuGAo8zu82tYlP-H_HfxV5MXsT1ZI0kg=s64",
      "userId": "17663649836641339783"
     },
     "user_tz": 300
    },
    "id": "72CK9K3uqmiU",
    "outputId": "0fd4ec44-2f3d-4f5a-8f64-fe7b49e8e42f"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "    async function download(id, filename, size) {\n",
       "      if (!google.colab.kernel.accessAllowed) {\n",
       "        return;\n",
       "      }\n",
       "      const div = document.createElement('div');\n",
       "      const label = document.createElement('label');\n",
       "      label.textContent = `Downloading \"${filename}\": `;\n",
       "      div.appendChild(label);\n",
       "      const progress = document.createElement('progress');\n",
       "      progress.max = size;\n",
       "      div.appendChild(progress);\n",
       "      document.body.appendChild(div);\n",
       "\n",
       "      const buffers = [];\n",
       "      let downloaded = 0;\n",
       "\n",
       "      const channel = await google.colab.kernel.comms.open(id);\n",
       "      // Send a message to notify the kernel that we're ready.\n",
       "      channel.send({})\n",
       "\n",
       "      for await (const message of channel.messages) {\n",
       "        // Send a message to notify the kernel that we're ready.\n",
       "        channel.send({})\n",
       "        if (message.buffers) {\n",
       "          for (const buffer of message.buffers) {\n",
       "            buffers.push(buffer);\n",
       "            downloaded += buffer.byteLength;\n",
       "            progress.value = downloaded;\n",
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       "      }\n",
       "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
       "      const a = document.createElement('a');\n",
       "      a.href = window.URL.createObjectURL(blob);\n",
       "      a.download = filename;\n",
       "      div.appendChild(a);\n",
       "      a.click();\n",
       "      div.remove();\n",
       "    }\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "download(\"download_1cb2f75c-1950-429c-aa64-ad5d93408e76\", \"df_Flujo_calor2.csv\", 139075)"
      ],
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from google.colab import files\n",
    "\n",
    "df_Flujo_calor.to_csv('df_Flujo_calor2.csv')\n",
    "files.download('df_Flujo_calor2.csv')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPRbJsdSQJz9ZI88HuvcsyJ",
   "name": "SIG Heat Flow.ipynb",
   "provenance": []
  },
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 },
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