oc4_algo_recreating.ipynb

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    "colab": {
      "name": "oc4_algo_recreating.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNrfcwkSA7fADUxmJNjSkK/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/marrs-lab/ocean_color/blob/main/oc4_algo_recreating.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhwGblZ0kom1"
      },
      "source": [
        "## NASA OC4 Algorithm from NOMAD\n",
        "\n",
        "This notebook aims to recreate the OC4 ocean color band ratio algorithm from NASA's chlorophyll ATBD (https://oceancolor.gsfc.nasa.gov/atbd/chlor_a). The most current of the OCx algorithm series which was first outlined in O'Reilly et al's *Ocean color chlorophyll algorithms for SeaWiFS*. https://doi.org/10.1029/98JC02160\n",
        "\n",
        "Author of this notebook is Patrick Gray (patrick.c.gray@duke.edu) at the Duke University Marine Lab in the Marine Robotics and Remote Sensing Lab (http://marineuas.net/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubYDdP2-KO-2"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import operator\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cz1ATt6P-qq5"
      },
      "source": [
        "Pull in the NOMAD dataset\n",
        "\n",
        "This is just the NOMAD dataset but with all the header removed except the column names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "cHZsvm34RN65",
        "outputId": "d6ed5cea-7ff9-409c-8531-7c051b25a66c"
      },
      "source": [
        "df = pd.read_csv('https://seabass.gsfc.nasa.gov/wiki/NOMAD/nomad_seabass_v2.a_2008200.txt', skiprows=list(range(0,88)) + [89,90])\n",
        "df = df.rename(columns={'/fields=year': 'year'})\n",
        "\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4459, 217)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>17</td>\n",
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              "      <td>3.70</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.91</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
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              "      <td>-999.0</td>\n",
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              "      <td>-999.0</td>\n",
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              "      <td>-999.0</td>\n",
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              "      <td>-999.0</td>\n",
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              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>38.6367</td>\n",
              "      <td>-76.32</td>\n",
              "      <td>1568</td>\n",
              "      <td>3.70</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>2.6900</td>\n",
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              "      <td>-999.0</td>\n",
              "      <td>1.6130</td>\n",
              "      <td>1.4441</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1.2004</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.9776</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.8857</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.8577</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1.0546</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>1.5343</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.051578</td>\n",
              "      <td>0.066430</td>\n",
              "      <td>0.092185</td>\n",
              "      <td>-999.0</td>\n",
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              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>20675</td>\n",
              "      <td>ace0301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2003</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>18</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>38.3047</td>\n",
              "      <td>-76.44</td>\n",
              "      <td>1559</td>\n",
              "      <td>22.03</td>\n",
              "      <td>1.0</td>\n",
              "      <td>23.55</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>3.0950</td>\n",
              "      <td>2.3966</td>\n",
              "      <td>2.1689</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1.6452</td>\n",
              "      <td>1.3757</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1.1533</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.9311</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.8597</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.8625</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1.0355</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>1.2731</td>\n",
              "      <td>1.2541</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.056001</td>\n",
              "      <td>0.068530</td>\n",
              "      <td>0.090152</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.108838</td>\n",
              "      <td>0.132581</td>\n",
              "      <td>...</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>20691</td>\n",
              "      <td>ace0302</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 217 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month  day  hour  ...  alpha-car  alpha-beta-car   flag   cruise\n",
              "0  2003      4   15    15  ...     -999.0          -999.0  20691  ace0301\n",
              "1  2003      4   15    16  ...     -999.0          -999.0  20675  ace0301\n",
              "2  2003      4   15    17  ...     -999.0          -999.0  20691  ace0301\n",
              "3  2003      4   17    18  ...     -999.0          -999.0  20675  ace0301\n",
              "4  2003      7   21    18  ...     -999.0          -999.0  20691  ace0302\n",
              "\n",
              "[5 rows x 217 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbYkEW50-tLm"
      },
      "source": [
        "Assign the specific wavelengths we'll be using as well as the blue band maximum, a merged HPLC and fluorometric chla field, and then filter out any fields with missing data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dTtfFpB5acL",
        "outputId": "a0f6e88b-1b08-459e-ce87-b6982394432b"
      },
      "source": [
        "# blue_lw =   'lw489'\n",
        "# blue_es =   'es489'\n",
        "# blue_rrs =  'rrs489'\n",
        "\n",
        "blue_rrs = 'blue_rrs'\n",
        "\n",
        "lw_443 = 'lw443'\n",
        "es_443 = 'es443'\n",
        "rrs_443 = 'rrs443'\n",
        "\n",
        "lw_510 = 'lw510'\n",
        "es_510 = 'es510'\n",
        "rrs_510 = 'rrs510'\n",
        "\n",
        "lw_489 = 'lw489'\n",
        "es_489 = 'es489'\n",
        "rrs_489 = 'rrs489'\n",
        "\n",
        "green_lw =  'lw560'\n",
        "green_es =  'es560'\n",
        "green_rrs = 'rrs560'\n",
        "\n",
        "red_lw =    'lw670'\n",
        "red_es =    'es670'\n",
        "red_rrs =   'rrs670'\n",
        "\n",
        "\n",
        "def chl_fcn(row):\n",
        "  if row['chl_a'] != -999 :\n",
        "    return (row['chl_a']) # HPLC\n",
        "  else:\n",
        "    return (row['chl']) # flu\n",
        "\n",
        "df['chl_merge'] = df.apply(lambda row: chl_fcn(row), axis=1)\n",
        "\n",
        "# whether to use only HPLC or merged chl\n",
        "# Hu et al 2012 only use HPLC which is chl_a\n",
        "chl = 'chl_merge' # 'chl_merge'\n",
        "\n",
        "# make a copy to subset\n",
        "df_chl = df.copy()\n",
        "\n",
        "# create the rrs columns\n",
        "df_chl[rrs_443] = df_chl[lw_443]/df_chl[es_443]\n",
        "df_chl[rrs_489] = df_chl[lw_489]/df_chl[es_489]\n",
        "df_chl[rrs_510] = df_chl[lw_510]/df_chl[es_510]\n",
        "\n",
        "df_chl[green_rrs] = df_chl[green_lw]/df_chl[green_es]\n",
        "df_chl[red_rrs] = df_chl[red_lw]/df_chl[red_es]\n",
        "\n",
        "# subset it to delete any measurements that are missing\n",
        "df_chl = df_chl[df_chl['lw443'] != -999]\n",
        "df_chl = df_chl[df_chl['lw555'] != -999]\n",
        "#df_chl = df_chl[df_chl['lw670'] != -999]\n",
        "df_chl = df_chl[df_chl['es443'] != -999]\n",
        "df_chl = df_chl[df_chl['es555'] != -999]\n",
        "#df_chl = df_chl[df_chl['es670'] != -999]\n",
        "#df_chl = df_chl[df_chl['lat'] != -999]\n",
        "df_chl = df_chl[df_chl['etopo2'] != -999]\n",
        "#df_chl = df_chl[df_chl['lw411'] != -999]\n",
        "#df_chl = df_chl[df_chl['es411'] != -999]\n",
        "df_chl = df_chl[df_chl['lw489'] != -999]\n",
        "df_chl = df_chl[df_chl['es489'] != -999]\n",
        "df_chl = df_chl[df_chl['lw510'] != -999]\n",
        "df_chl = df_chl[df_chl['es510'] != -999]\n",
        "\n",
        "df_chl = df_chl[df_chl['lw560'] != -999]\n",
        "df_chl = df_chl[df_chl['es560'] != -999]\n",
        "\n",
        "def blue_fcn(row):\n",
        "  #my_list = [row[rrs_443], row[rrs_489], row[rrs_510]]\n",
        "  my_list = [row[rrs_489]]\n",
        "  max_value = max(my_list)\n",
        "  return (max_value)\n",
        "\n",
        "df_chl[blue_rrs] = df_chl.apply(lambda row: blue_fcn(row), axis=1)\n",
        "\n",
        "df_chl = df_chl[df_chl[chl] != -999]\n",
        "\n",
        "#df_chl = df_chl[df_chl[blue_rrs] > 0]\n",
        "#df_chl = df_chl[df_chl[green_rrs] > 0]\n",
        "\n",
        "#df_chl = df_chl[df_chl[chl] < 50]\n",
        "\n",
        "#df_chl = df_chl[df_chl[chl] > .05]\n",
        "\n",
        "# subset to only rows that were deeper than x meters\n",
        "#df_chl = df_chl[df_chl['etopo2'] > 5]\n",
        "\n",
        "# subset to lat between 60N and 60S\n",
        "#df_chl = df_chl[(df_chl['lat'] < 60) & (df_chl['lat'] > -60)]\n",
        "\n",
        "print(\"Number of rows remaning after filters: \", len(df_chl))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows remaning after filters:  756\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VAL2zhN5FMJ"
      },
      "source": [
        "#df = df.replace(-999, np.nan)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "yOWMYaHtRb6X",
        "outputId": "706dc5f9-5ad1-44fc-ac12-c400bef6d3e2"
      },
      "source": [
        "fig,ax = plt.subplots()\n",
        "\n",
        "df_chl.plot(ax=ax, kind='scatter',x=chl, y='lat')\n",
        "ax.set_xlim(-1,20)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.0, 20.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "JzOg3gpCANs1",
        "outputId": "709fb37d-5681-447a-de69-d5fff4294766"
      },
      "source": [
        "fig,ax = plt.subplots()\n",
        "df_chl.hist(blue_rrs, bins=30,ax=ax, color='blue', alpha=0.7)\n",
        "df_chl.hist(green_rrs, bins=30,ax=ax, color='green', alpha=0.7)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f13051abf10>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0Sw79qvo68LIF2h8DXjXKoCRJy8Nv5EpSQwx9SWqIt1aeIMe7/BRg6ylb2b5zu7dglrRkHulLUkMMfUlqiKEvSQ1xTn8NGjT3f5hz/5KOZuifwIb9cAA/IKRWOL0jSQ0x9CWpIYa+JDXEOf0Jcqwbxx3r/vuStFge6UtSQzzSXwXeClrSavFIX5IaYuhLUkOc3tGi+G1gaW3zSF+SGmLoS1JDDH1Jaohz+gIWd3M2SWuXR/qS1BCP9Nc4b90gaTEMfS0LL+2UJpOhvwYcPpr/+Ytg7/7xbOto/mYgtcHQP0F5fx9JC/FEriQ1xCN9AZM77bOl79TA1q2wfXvv9W5PBUhLYuhrySb1g0LSsRn6WlWDrvLZu6H3vGluuEN7rxqSjs/Q19j5G4A0uZYt9JNcDPxn4CTgv1bVtcu1Ly2fcV4FNMqHwd4NW/j5525l74bepP66f7P0bcHy3HbC3x60FixL6Cc5CfgvwC8ADwFfSbKrqu5Zjv1pbVvtD5Zx7X8LTi1p8i3Xkf4mYK6qvg6QZBbYChj6WhWT+r2FLcf5nNi74ciF77loK9t3bl/mES3uQ2nc51COt72tpzxb/1r44JzU80upqvFvNHkDcHFV/cvu/ZuAl1fV2/r6bAO2dW//HnDfEJs+A/jWmIe7lli/9Vt/uxaq/6eq6oWL2ciqncitqh3AjsWsk2RfVU0v05AmnvVbv/Vb/6jbWa5v5B4Azul7f3bXJklaRcsV+l8BNiY5N8mPApcBu5ZpX5KkIS3L9E5VPZ3kbcCf0Ltk8+NVdfcYNr2o6aATkPW3zfrbNpb6l+VEriRpMnmXTUlqiKEvSQ2ZmNBPcnGS+5LMJbl6geWnJPl0t/zPkqzvW/burv2+JK9ZyXGPy1LrT/KCJF9IMp/kwys97nEZof5fSLI/yZ3d8z9Z6bGPwwj1b0pyR/f4apLXrfTYx2GU///d8p/s/g/8+kqNeZxG+PmvT/Ldvn8DHx24s6pa9Qe9k71/CbwE+FHgq8B5R/X5VeCj3evLgE93r8/r+p8CnNtt56TVrmkF6z8V+EfAW4EPr3Ytq1D/PwBe3L2+ADiw2vWscP3PA07uXp8JPHr4/Vp5jFJ/3/KbgP8O/Ppq17PCP//1wF2L2d+kHOn/8LYNVfV94PBtG/ptBW7oXt8EvCpJuvbZqvpeVd0PzHXbW0uWXH9VHaqqLwF/vXLDHbtR6v/zqvqrrv1u4O8kOWVFRj0+o9T/VFU93bU/F1iLV2aM8v+fJJcC99P7+a9FI9W/WJMS+mcB3+x7/1DXtmCf7h/5E8ALhlx30o1S/4lgXPW/Hri9qr63TONcLiPVn+TlSe4G7gTe2vchsFYsuf4kU8C7gPeuwDiXy6j//s9N8udJvpjk5wbtzPvp64SQ5Hzg/cCrV3ssK62q/gw4P8lLgRuSfL6q1vJvfotxDfDBqppf4oHvWvcw8JNV9ViSi4D/keT8qnryWCtMypH+MLdt+GGfJCcDPwE8NuS6k26U+k8EI9Wf5GzgM8AVVfWXyz7a8RvLz7+q7gXm6Z3bWEtGqf/lwAeSPAC8A3hP98XQtWTJ9XfT2o8BVNV+eucGfvp4O5uU0B/mtg27gCu7128A/rR6ZzJ2AZd1Z7fPBTYCE3oj3WMapf4TwZLrT3Ia8MfA1VX1v1dsxOM1Sv3ndiFAkp8CfgZ4YGWGPTZLrr+qfq6q1lfVeuB3gf9QVWvtKrZRfv4vTO/vl5DkJfTy7+vH3dtqn7nuOzt9CfB/6X1S/UbX9u+AX+xeP5fe2fk5eqH+kr51f6Nb7z7gtatdyyrU/wBwkN5R3kMcdeZ/LTyWWj/wm8Ah4I6+x4tWu54VrP9N9E5g3gHcDly62rWsZP1HbeMa1uDVOyP+/F9/1M9/y6B9eRsGSWrIpEzvSJJWgKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGvL/Aa5RqRRjJZLSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "Q29M1bds4w2t",
        "outputId": "d491be12-2008-4eab-d66d-d9209d6f2f39"
      },
      "source": [
        "df_chl.hist(chl, bins=30)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1303288e10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "RfA3TdGCWWQL",
        "outputId": "72a30afa-770a-47ed-f122-c31c38ed6028"
      },
      "source": [
        "fig,ax = plt.subplots()\n",
        "\n",
        "ax.scatter(df_chl[blue_rrs]/df_chl[green_rrs], df_chl[chl])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f1304075410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qRTOr6CTWXp6",
        "outputId": "4776f6e7-7667-4fcd-f62d-651a9b2afc81"
      },
      "source": [
        "fig,ax = plt.subplots()\n",
        "\n",
        "ax.scatter(x = np.log10(df_chl[blue_rrs]/df_chl[green_rrs]), y = np.log10(df_chl[chl]))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f13036d5110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0h0eLQFa5JYW"
      },
      "source": [
        "### Run the polynomial regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "tC8B0L0nNy9I",
        "outputId": "9156067a-9c1f-40fd-dcd6-9010c57c7ba0"
      },
      "source": [
        "x = np.log10( df_chl[blue_rrs]/df_chl[green_rrs] )\n",
        "y = np.log10(df_chl[chl])\n",
        "\n",
        "# transforming the data to include another axis\n",
        "x = x[:, np.newaxis]\n",
        "y = y[:, np.newaxis]\n",
        "\n",
        "polynomial_features= PolynomialFeatures(degree=4, include_bias=True)\n",
        "x_poly = polynomial_features.fit_transform(x)\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x_poly, y)\n",
        "y_poly_pred = model.predict(x_poly)\n",
        "\n",
        "rmse = np.sqrt(mean_squared_error(y,y_poly_pred))\n",
        "r2 = r2_score(y,y_poly_pred)\n",
        "print(\"RMSE = \", rmse)\n",
        "print(\"R2 = \", r2)\n",
        "\n",
        "plt.scatter(x, y, s=10)\n",
        "# sort the values of x before line plot\n",
        "sort_axis = operator.itemgetter(0)\n",
        "sorted_zip = sorted(zip(x,y_poly_pred), key=sort_axis)\n",
        "x, y_poly_pred = zip(*sorted_zip)\n",
        "\n",
        "plt.plot(x, y_poly_pred, color='m')\n",
        "plt.ylabel('log10(chla)')\n",
        "plt.xlabel('log10(MBR)')\n",
        "plt.xlim(-0.5,1.1)\n",
        "plt.ylim(-2,2)\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "RMSE =  0.30414031642920103\n",
            "R2 =  0.8178755860683482\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMXUt8NQJJ0-"
      },
      "source": [
        "#Correct coefs for SeaWiFS are\n",
        "# 0.3272\t-2.9940,\t2.7218,\t-1.2259,\t-0.5683"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZS0rAEZ-N2CA",
        "outputId": "a92e0b02-b8ac-41c9-e47c-90e4f4664a18"
      },
      "source": [
        "model.intercept_, model.coef_"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.21918403]),\n",
              " array([[ 0.        , -2.2121782 ,  1.28636224, -0.32947542, -1.88032039]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1venGDnOhIL",
        "outputId": "81119c12-6de8-4457-c50d-ee61a8e0a7b6"
      },
      "source": [
        "polynomial_features.get_feature_names()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1', 'x0', 'x0^2', 'x0^3', 'x0^4']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxWDHbIR1nOP"
      },
      "source": [
        ""
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVnR1xUh1ou9"
      },
      "source": [
        "### Evaulate Uncertainty for Various Coefficients\n",
        "\n",
        "Using the landsat 8 OC2 bands what do we expect as for RSME and R2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5O0DM5l1r6S",
        "outputId": "de80026e-151e-48f2-ed26-ce7f8634e733",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# for the landsat 8 OC2 algorithm which most closely corresponds to the Altum/Micasense bands:\n",
        "# coefficients are 0.1977\t-1.8117\t1.9743\t-2.5635\t-0.7218\n",
        "# bands are 482\t561\n",
        "poly_func = np.poly1d([-0.7218,-2.5635,1.9743, -1.8117, 0.1977])\n",
        "poly_func"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "poly1d([-0.7218, -2.5635,  1.9743, -1.8117,  0.1977])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HNPWvbY6yI9",
        "outputId": "23254e4e-fd6f-4a62-db7e-e558b6895a58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(756, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BRLgb88557n",
        "outputId": "99280fce-e642-4434-eba2-6f2e2f7aea75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "x = np.log10( df_chl[blue_rrs]/df_chl[green_rrs] )\n",
        "y = np.log10(df_chl[chl])\n",
        "\n",
        "# transforming the data to include another axis\n",
        "x = x[:, np.newaxis]\n",
        "y = y[:, np.newaxis]\n",
        "\n",
        "y_poly_pred = poly_func(x)\n",
        "\n",
        "rmse = np.sqrt(mean_squared_error(y,y_poly_pred))\n",
        "r2 = r2_score(y,y_poly_pred)\n",
        "print(\"RMSE = \", rmse)\n",
        "print(\"R2 = \", r2)\n",
        "\n",
        "plt.scatter(x, y, s=10)\n",
        "# sort the values of x before line plot\n",
        "sort_axis = operator.itemgetter(0)\n",
        "sorted_zip = sorted(zip(x,y_poly_pred), key=sort_axis)\n",
        "x, y_poly_pred = zip(*sorted_zip)\n",
        "\n",
        "plt.plot(x, y_poly_pred, color='m')\n",
        "plt.ylabel('log10(chla)')\n",
        "plt.xlabel('log10(MBR)')\n",
        "plt.xlim(-0.5,1.1)\n",
        "plt.ylim(-2,2)\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE =  0.314736707124328\n",
            "R2 =  0.8049639135527913\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbGHFmcg8mRJ",
        "outputId": "f789f613-6f9e-43d4-b4d0-c3c004f5fddd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.array(x).shape"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(756, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeJndF738u_Z",
        "outputId": "b1d9c758-4c97-456a-eeb0-878c8ab58911",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#RMSE\n",
        "np.sqrt(mean_squared_error(10**np.array(y),10**np.array(y_poly_pred)))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.310379722745715"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CirS57s6vT8",
        "outputId": "23471fae-e296-442e-a145-8705ee6f7f38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "plt.plot(10**np.array(x), 10**np.array(y_poly_pred), color='m')\n",
        "plt.scatter(df_chl[blue_rrs]/df_chl[green_rrs], df_chl[chl], s=10)\n",
        "plt.ylabel('log10(chla)')\n",
        "plt.xlabel('log10(MBR)')\n",
        "#plt.xlim(-0.5,1.1)\n",
        "#plt.ylim(-2,2)\n",
        "plt.show()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}