spectro_and_lux_9_colors.ipynb

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    "colab": {
      "name": "spectro_and_lux_9_colors.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMyCDeUx0O4OYyakxc4zbFb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/darkmatter2222/Nuclear-Battery/blob/main/spectro_and_lux_9_colors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9-k23erU27F",
        "outputId": "dab8afed-aaa4-4302-f11c-1b3010a9441f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pymongo in /usr/local/lib/python3.7/dist-packages (4.0.1)\n",
            "Collecting dnspython\n",
            "  Downloading dnspython-2.1.0-py3-none-any.whl (241 kB)\n",
            "\u001b[K     |████████████████████████████████| 241 kB 5.0 MB/s \n",
            "\u001b[?25hInstalling collected packages: dnspython\n",
            "Successfully installed dnspython-2.1.0\n",
            "Requirement already satisfied: pymongo[srv] in /usr/local/lib/python3.7/dist-packages (4.0.1)\n",
            "Requirement already satisfied: dnspython<3.0.0,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from pymongo[srv]) (2.1.0)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install pymongo\n",
        "!pip3 install dnspython\n",
        "!pip3 install pymongo[srv]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pymongo import MongoClient\n",
        "import pymongo\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import json\n",
        "from datetime import datetime\n",
        "import itertools\n",
        "from scipy import stats\n",
        "import seaborn as sns\n",
        "import matplotlib.dates as mdates\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "8TLgfqphU6_P"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "myclient = pymongo.MongoClient('mongodb+srv://google_colab_read_only_nuc_battery:OwzNzRPXuk4Bq9x7@cluster0.kbukv.mongodb.net/test')\n",
        "mydb = myclient[\"nuclear_battery\"]\n",
        "testing_data_col = mydb[\"spectro_and_lux_9_colors\"]"
      ],
      "metadata": {
        "id": "CpUwiBUUU7Rb"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "WL3TZh9kVCEl"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from re import L\n",
        "df = pd.DataFrame()\n",
        "\n",
        "for document in tqdm(testing_data_col.find()):\n",
        "\n",
        "  for trial in document:\n",
        "    if trial != '_id':\n",
        "      values = {}\n",
        "\n",
        "      for wavelength in document[trial]['target_wavelengths']:\n",
        "        measurmenet = document[trial]['target_wavelengths'][wavelength]\n",
        "        values['color_name'] = measurmenet['color_name']\n",
        "        values['color_wavelength'] = wavelength\n",
        "        values['color_intensity'] = measurmenet['color_intensity']\n",
        "\n",
        "        values['vial_color'] = document[trial]['vial_color']\n",
        "        values['vial_age_years'] = document[trial]['vial_age_years']\n",
        "        values['timestamp_of_measurment'] = document[trial]['unix_time_ns']\n",
        "        values['lux'] = document[trial]['lux']\n",
        "\n",
        "        values['clear_intensity'] = document[trial]['special_measurements']['Clear']['color_intensity']\n",
        "        values['near-ir_intensity'] = document[trial]['special_measurements']['NIR']['color_intensity']\n",
        "\n",
        "        values['adafruit_VEML7700_integration_time_value'] = document[trial]['sensor_configs']['adafruit_VEML7700']['integration_time_value']\n",
        "        values['adafruit_VEML7700_gain_value'] = document[trial]['sensor_configs']['adafruit_VEML7700']['gain_value']\n",
        "\n",
        "        values['adafruit_AS7341_gain'] = document[trial]['sensor_configs']['adafruit_AS7341']['gain']\n",
        "        df = df.append(values, ignore_index =True)\n",
        "\n",
        "df['tick'] = 1\n",
        "df = df.reset_index()\n",
        "del(df['index'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qCCZcWQVEPB",
        "outputId": "1a9da356-ff7d-4c2b-bd43-c022a323f281"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "9it [02:48, 18.69s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "fETPyfG3VGQA",
        "outputId": "962fd6f5-c8f5-4d68-897a-17cfecf55b09"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>adafruit_AS7341_gain</th>\n",
              "      <th>adafruit_VEML7700_gain_value</th>\n",
              "      <th>adafruit_VEML7700_integration_time_value</th>\n",
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              "      <th>color_wavelength</th>\n",
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              "<p>19008 rows × 13 columns</p>\n",
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            ],
            "text/plain": [
              "       adafruit_AS7341_gain  adafruit_VEML7700_gain_value  ...  vial_color  tick\n",
              "0                       0.0                         2.000  ...        blue     1\n",
              "1                       0.0                         2.000  ...        blue     1\n",
              "2                       0.0                         2.000  ...        blue     1\n",
              "3                       0.0                         2.000  ...        blue     1\n",
              "4                       0.0                         2.000  ...        blue     1\n",
              "...                     ...                           ...  ...         ...   ...\n",
              "19003                  10.0                         0.125  ...      yellow     1\n",
              "19004                  10.0                         0.125  ...      yellow     1\n",
              "19005                  10.0                         0.125  ...      yellow     1\n",
              "19006                  10.0                         0.125  ...      yellow     1\n",
              "19007                  10.0                         0.125  ...      yellow     1\n",
              "\n",
              "[19008 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['vial_color'] = df['vial_color'].str.replace('ice blue','cyan')"
      ],
      "metadata": {
        "id": "6PGLt0NM2ecg"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(25,8), constrained_layout=True)\n",
        "fig.patch.set_facecolor('white')\n",
        "plt.rcParams['axes.facecolor'] = 'white'\n",
        "new_df = pd.DataFrame()\n",
        "\n",
        "color_names = df['vial_color'].unique()\n",
        "percent_df = pd.DataFrame()\n",
        "\n",
        "for color_name in color_names:\n",
        "  working_df = df[df['vial_color'] == color_name]\n",
        "  sd_df = working_df[(np.abs(stats.zscore(working_df['lux'])) < 0.75)]\n",
        "  sd_df['mean_lux_after_3sd'] = sum(sd_df['lux']) * len(sd_df.index)\n",
        "  new_df = new_df.append(sd_df)\n",
        "  percent_df = percent_df.append(sd_df)\n",
        "\n",
        "new_df = new_df.sort_values(by='mean_lux_after_3sd', ascending=False)\n",
        "sns.boxplot(x=\"vial_color\", y=\"lux\", data=new_df, palette=new_df['vial_color'].unique())\n",
        "\n",
        "fig.suptitle('Lux output of various tritium vials (new 3x25mm) measured by Adafruit VEML7700 at 2mm distance from source w/ all possible gain and intergration time combinations (0.75 standard deviation filter)', fontsize=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "guyLjfdtx55R",
        "outputId": "04710372-17ae-462f-9014-66c12b0ed36d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, 'Lux output of various tritium vials (new 3x25mm) measured by Adafruit VEML7700 at 2mm distance from source w/ all possible gain and intergration time combinations (0.75 standard deviation filter)')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_aggros = percent_df.groupby(['vial_color'])['mean_lux_after_3sd'].mean().reset_index()\n",
        "final_aggros['percent'] = round((final_aggros['mean_lux_after_3sd'] * 1) / max(final_aggros['mean_lux_after_3sd']), 4)\n",
        "\n",
        "final_aggros.sort_values(by='percent', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "hpdP7CD61jt7",
        "outputId": "e5c58a26-1e2a-4d84-daae-951039c7b18b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-de99360b-9d43-45d3-b2a2-a69092c11298\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>vial_color</th>\n",
              "      <th>mean_lux_after_3sd</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>green</td>\n",
              "      <td>2.955663e+06</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>yellow</td>\n",
              "      <td>1.328068e+06</td>\n",
              "      <td>0.4493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cyan</td>\n",
              "      <td>1.224697e+06</td>\n",
              "      <td>0.4144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>white</td>\n",
              "      <td>1.107676e+06</td>\n",
              "      <td>0.3748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>blue</td>\n",
              "      <td>4.797794e+05</td>\n",
              "      <td>0.1623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>orange</td>\n",
              "      <td>2.893889e+05</td>\n",
              "      <td>0.0979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>purple</td>\n",
              "      <td>2.418214e+05</td>\n",
              "      <td>0.0818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>red</td>\n",
              "      <td>6.422815e+04</td>\n",
              "      <td>0.0217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pink</td>\n",
              "      <td>4.400594e+04</td>\n",
              "      <td>0.0149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de99360b-9d43-45d3-b2a2-a69092c11298')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "  vial_color  mean_lux_after_3sd  percent\n",
              "2      green        2.955663e+06   1.0000\n",
              "8     yellow        1.328068e+06   0.4493\n",
              "1       cyan        1.224697e+06   0.4144\n",
              "7      white        1.107676e+06   0.3748\n",
              "0       blue        4.797794e+05   0.1623\n",
              "3     orange        2.893889e+05   0.0979\n",
              "5     purple        2.418214e+05   0.0818\n",
              "6        red        6.422815e+04   0.0217\n",
              "4       pink        4.400594e+04   0.0149"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vial_color_names = df['vial_color'].unique()\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(len(vial_color_names), 1, figsize=(20, len(vial_color_names) * 8), constrained_layout=True)\n",
        "fig.patch.set_facecolor('white')\n",
        "plt.rcParams['axes.facecolor'] = 'white'\n",
        "\n",
        "percent_df = pd.DataFrame()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "spectro_color_names = df['color_name'].unique()\n",
        "x = 0\n",
        "\n",
        "for vial_color_name in vial_color_names:\n",
        "  master_working_df = df[df['vial_color'] == vial_color_name]\n",
        "  new_df = pd.DataFrame()\n",
        "  for spectro_color_name in spectro_color_names:\n",
        "    working_df = master_working_df[(master_working_df['color_name'] == spectro_color_name)]\n",
        "    #sd_df = working_df[(np.abs(stats.zscore(working_df['color_intensity'])) < 0.75)]\n",
        "    sd_df = working_df\n",
        "    sd_df['mean_color_intensity_after_3sd'] = sum(sd_df['color_intensity']) * len(sd_df.index)\n",
        "    new_df = new_df.append(sd_df)\n",
        "    percent_df = percent_df.append(sd_df)\n",
        "  ax[x].set_title(f'{vial_color_name} 3x25mm Tritium Vial (new)')\n",
        "  sns.boxplot(x=\"color_name\", y=\"color_intensity\", data=new_df, palette=new_df['color_name'].unique(), ax=ax[x])\n",
        "  x += 1\n",
        "\n",
        "fig.suptitle('Spectrogram of various tritium vials (new 3x25mm) measured by Adafruit AS7341 at 2mm distance from source w/ all possible gain combinations (0.75 standard deviation filter)', fontsize=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uECUjRP7x57g",
        "outputId": "e4b40a5b-b587-4b6c-eb52-a7eb74a6a3ec"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, 'Spectrogram of various tritium vials (new 3x25mm) measured by Adafruit AS7341 at 2mm distance from source w/ all possible gain combinations (0.75 standard deviation filter)')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x5184 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "percent_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "qvYkWLom8Cja",
        "outputId": "7715adf2-fd6b-4781-de87-d16f0edc5459"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7321955a-c293-4d06-8a1f-dc1ecaa4ac5b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>adafruit_AS7341_gain</th>\n",
              "      <th>adafruit_VEML7700_gain_value</th>\n",
              "      <th>adafruit_VEML7700_integration_time_value</th>\n",
              "      <th>clear_intensity</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>color_name</th>\n",
              "      <th>color_wavelength</th>\n",
              "      <th>lux</th>\n",
              "      <th>near-ir_intensity</th>\n",
              "      <th>timestamp_of_measurment</th>\n",
              "      <th>vial_age_years</th>\n",
              "      <th>vial_color</th>\n",
              "      <th>tick</th>\n",
              "      <th>mean_color_intensity_after_3sd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Violet</td>\n",
              "      <td>415</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>blue</td>\n",
              "      <td>1</td>\n",
              "      <td>252912.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.000</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Violet</td>\n",
              "      <td>415</td>\n",
              "      <td>0.3456</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>blue</td>\n",
              "      <td>1</td>\n",
              "      <td>252912.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.000</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Violet</td>\n",
              "      <td>415</td>\n",
              "      <td>0.3456</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>blue</td>\n",
              "      <td>1</td>\n",
              "      <td>252912.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2.000</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Violet</td>\n",
              "      <td>415</td>\n",
              "      <td>0.3456</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>blue</td>\n",
              "      <td>1</td>\n",
              "      <td>252912.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>4.0</td>\n",
              "      <td>2.000</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Violet</td>\n",
              "      <td>415</td>\n",
              "      <td>0.3456</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>blue</td>\n",
              "      <td>1</td>\n",
              "      <td>252912.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18975</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>800.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>680</td>\n",
              "      <td>0.1728</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>yellow</td>\n",
              "      <td>1</td>\n",
              "      <td>70752.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18983</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>800.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>680</td>\n",
              "      <td>0.1728</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>yellow</td>\n",
              "      <td>1</td>\n",
              "      <td>70752.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18991</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>800.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>680</td>\n",
              "      <td>0.1728</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>yellow</td>\n",
              "      <td>1</td>\n",
              "      <td>70752.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18999</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>800.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>680</td>\n",
              "      <td>0.1728</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>yellow</td>\n",
              "      <td>1</td>\n",
              "      <td>70752.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19007</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.125</td>\n",
              "      <td>800.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Red</td>\n",
              "      <td>680</td>\n",
              "      <td>0.1728</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.640915e+18</td>\n",
              "      <td>0</td>\n",
              "      <td>yellow</td>\n",
              "      <td>1</td>\n",
              "      <td>70752.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19008 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7321955a-c293-4d06-8a1f-dc1ecaa4ac5b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7321955a-c293-4d06-8a1f-dc1ecaa4ac5b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7321955a-c293-4d06-8a1f-dc1ecaa4ac5b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       adafruit_AS7341_gain  ...  mean_color_intensity_after_3sd\n",
              "0                       0.0  ...                        252912.0\n",
              "8                       1.0  ...                        252912.0\n",
              "16                      2.0  ...                        252912.0\n",
              "24                      3.0  ...                        252912.0\n",
              "32                      4.0  ...                        252912.0\n",
              "...                     ...  ...                             ...\n",
              "18975                   6.0  ...                         70752.0\n",
              "18983                   7.0  ...                         70752.0\n",
              "18991                   8.0  ...                         70752.0\n",
              "18999                   9.0  ...                         70752.0\n",
              "19007                  10.0  ...                         70752.0\n",
              "\n",
              "[19008 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['vial_color'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jzhXTZpI9TY1",
        "outputId": "670ea1e5-8bb8-4b1a-e3e0-a04fcc07ab32"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['blue', 'green', 'cyan', 'orange', 'pink', 'purple', 'red',\n",
              "       'white', 'yellow'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vial_color = ''\n",
        "final_aggros = percent_df[percent_df['vial_color'] == 'white'].groupby(['color_name', 'color_wavelength'])['mean_color_intensity_after_3sd'].mean().reset_index()\n",
        "final_aggros['percent'] = round((final_aggros['mean_color_intensity_after_3sd'] * 1) / max(final_aggros['mean_color_intensity_after_3sd']), 4)\n",
        "final_aggros[['color_name', 'color_wavelength', 'percent']].rename(columns={\"color_name\": \"Spectral Name\", \"color_wavelength\": \"Spectral Wavelength (nm)\", \"percent\":\"Relative Intensity\"}).sort_values(by='Spectral Wavelength (nm)', ascending=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "oaOvvJbz7ob2",
        "outputId": "e9f1457a-b6d7-4793-9c56-c6dc7c1d88ba"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8a73797c-b2c6-415f-a7e7-aa981b8ab847\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Spectral Name</th>\n",
              "      <th>Spectral Wavelength (nm)</th>\n",
              "      <th>Relative Intensity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Red</td>\n",
              "      <td>680</td>\n",
              "      <td>0.0653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Orange</td>\n",
              "      <td>630</td>\n",
              "      <td>0.4423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Yellow</td>\n",
              "      <td>590</td>\n",
              "      <td>0.8735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Green</td>\n",
              "      <td>555</td>\n",
              "      <td>1.0000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cyan</td>\n",
              "      <td>515</td>\n",
              "      <td>0.5160</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>480</td>\n",
              "      <td>0.3996</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Indigo</td>\n",
              "      <td>445</td>\n",
              "      <td>0.3219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>415</td>\n",
              "      <td>0.0252</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-8a73797c-b2c6-415f-a7e7-aa981b8ab847');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Spectral Name Spectral Wavelength (nm)  Relative Intensity\n",
              "5           Red                      680              0.0653\n",
              "4        Orange                      630              0.4423\n",
              "7        Yellow                      590              0.8735\n",
              "2         Green                      555              1.0000\n",
              "1          Cyan                      515              0.5160\n",
              "0          Blue                      480              0.3996\n",
              "3        Indigo                      445              0.3219\n",
              "6        Violet                      415              0.0252"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_aggros = percent_df.groupby(['vial_color'])['mean_color_intensity_after_3sd'].mean().reset_index()\n",
        "final_aggros['percent'] = (final_aggros['mean_color_intensity_after_3sd'] / final_aggros['mean_color_intensity_after_3sd'].sum()) * 100\n",
        "final_aggros"
      ],
      "metadata": {
        "colab": {
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          "height": 331
        },
        "id": "KoUNSEmKzHAL",
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      },
      "execution_count": 14,
      "outputs": [
        {
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          "data": {
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              "      <th>vial_color</th>\n",
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              "      <td>12.155309</td>\n",
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              "      <td>357489.0</td>\n",
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              "      <td>446787.0</td>\n",
              "      <td>16.504937</td>\n",
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            ],
            "text/plain": [
              "  vial_color  mean_color_intensity_after_3sd    percent\n",
              "0       blue                        271821.0  10.041448\n",
              "1       cyan                        329043.0  12.155309\n",
              "2      green                        427383.0  15.788126\n",
              "3     orange                        240405.0   8.880897\n",
              "4       pink                        149754.0   5.532122\n",
              "5     purple                        210837.0   7.788614\n",
              "6        red                        273471.0  10.102402\n",
              "7      white                        357489.0  13.206144\n",
              "8     yellow                        446787.0  16.504937"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.groupby(['mean_color_intensity_after_3sd', 'vial_color'])"
      ],
      "metadata": {
        "id": "i_XfDWW5x59O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc444dc7-523a-487a-db45-2f768075522c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f8707e167d0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "oNtLOAVKx5_A"
      },
      "execution_count": 15,
      "outputs": []
    }
  ]
}