NotebookPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Webinar: NASA ORNL DAAC MODIS and VIIRS Data Tools and Services at your Fingertips\n",
    "-----\n",
    "\n",
    "### Table of Contents\n",
    "*Note: Links will not work when viewed in GitHub*\n",
    "\n",
    "* [**Introduction**](#Introduction)           \n",
    "* [**Helpful Links**](#Helpful-Links)           \n",
    "* [**Prerequisites**](#Prerequisites)                                                                \n",
    "* [**GET products**](#GET-products)                                                              \n",
    "* [**GET dates**](#GET-dates)                                                                    \n",
    "* [**GET subset**](#GET-subset)                                                                  \n",
    "* [**Request a time series**](#Make-a-time-series-from-multiple-subset-requests)                  \n",
    "* [**Quality filtering**](#Apply-quality-filter-to-the-time-series)                                          \n",
    "* [**Bonus Content**](#Bonus-Content)                                                                   \n",
    "    \n",
    "    [QC Table for MODIS Land Surface Temp/Emissivity (MOD11A2)](#Build-the-QC-Table-for-VNP13A1)     \n",
    "\n",
    "\n",
    "# Introduction\n",
    "-----\n",
    "\n",
    "In this demo, we'll learn how to access the MODIS/VIIRS Web Service using R and efficiently prepare a time series of data for further analysis. The methods in this demo can be applied to subsets that are retrieved through the MODIS/VIIRS Global Subsetting Tool or Web Service.\n",
    "\n",
    "First, we'll see example usage for the **dates** and **subset** web service functions. Then we'll request a time series of Enhanced Vegetation Index (EVI) from the [**VIIRS Vegetation Indices (VNP13A1)**](https://dx.doi.org/10.5067/VIIRS/VNP13A1.001) product and learn how to apply custom filtering based on user-specified quality criteria.  \n",
    "\n",
    "**We will start with this plot EVI that assumes all data quality is satisfactory:**    \n",
    "<img src=\"pictures/evi2_unfiltered_py.png\" style=\"width: 750px;\">\n",
    "\n",
    "**And finish with this plot of filtered EVI that indicates the percentage of pixels meeting our custom quality criteria:**\n",
    "<img src=\"pictures/evi2_filtered_py.png\" style=\"width: 750px;\">\n",
    "\n",
    "\n",
    "# Helpful Links\n",
    "-----\n",
    "\n",
    "[ORNL DAAC MODIS Website](https://modis.ornl.gov/)     \n",
    "\n",
    "[MODIS/VIIRS Web Service](https://modis.ornl.gov/data/modis_webservice.html)     \n",
    "\n",
    "[VNP13A1 User Guide](https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp13_user_guide_atbd_v2.1.2.pdf)\n",
    "\n",
    "# Prerequisites\n",
    "-----\n",
    "\n",
    "**Libraries:**       \n",
    "\n",
    "* **requests**   --- for submitting GET requests    \n",
    "* **pandas**     --- for convenient data frame structure\n",
    "* **numpy**      --- for fast arrays\n",
    "* **json**       --- for translating json response to R data frame (optional)  \n",
    "* **datetime**   --- for convenient reformatting of dates (optional)\n",
    "* **matplotlib** --- for plotting     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## GET products\n",
    "------\n",
    "\n",
    "Visit the ORNL DAAC MODIS/VIIRS Web Service help page for descriptions and usage examples of each web service function:\n",
    "https://modis.ornl.gov/data/modis_webservice.html\n",
    "\n",
    "Set the REST API endpoint and test connectivity using **requests.get**.\n",
    "\n",
    "**GET products:**\n",
    "https://modis.ornl.gov/rst/api/v1/products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response status: 200\n",
      "<class 'list'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'product': 'MCD12Q1',\n",
       " 'description': 'MODIS/Terra+Aqua Land Cover Type (LC) Yearly L3 Global 500 m SIN Grid',\n",
       " 'frequency': 'Yearly',\n",
       " 'resolution_meters': 500}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://modis.ornl.gov/rst/api/v1/\"\n",
    "header = {'Accept': 'application/json'} # JSON response; text response: header = {'Accept': 'text/csv'}\n",
    "\n",
    "response = requests.get(\"https://modis.ornl.gov/rst/api/v1/products\", headers=header)\n",
    "print(\"Response status: \" + str(response.status_code))\n",
    "\n",
    "products = json.loads(response.text)['products']\n",
    "print(type(products))\n",
    "products[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will submit a subset request for the **VIIRS Enhanced Vegetation Index 2 (VNP13A1)** for a site in Great Smoky Mountains National Park near Gatlinburg, TN (35.71326, -83.51047). Around 11,000 acres burned during the [Chimney Tops 2 Fire](https://www.nps.gov/grsm/learn/news/nps-releases-review-of-chimney-tops-2-fire.htm) in November and December 2016.\n",
    "\n",
    "\n",
    "## GET dates\n",
    "------\n",
    "\n",
    "```https://modis.ornl.gov/rst/api/v1/PRODUCT/dates?latitude=LATITUDE&longitude=LONGITUDE```\n",
    "\n",
    "The MODIS/VIIRS Web Service has a ten tile limit per request to ensure responsivity of the service and to avoid time outs. We need to make a list of available dates to iterate through while we submit our subset requests. The **dates** function returns a list of available dates for the specified coordinate and MODIS/VIIRS product.\n",
    "\n",
    "| Parameter          | Description                                                   |\n",
    "|:-------------------|:--------------------------------------------------------------|\n",
    "| **product**        | MODIS/VIIRS product code as listed by **products** function   |\n",
    "| **latitude**       | latitude                                                      |\n",
    "| **longitude**      | longitude                                                     |\n",
    "\n",
    "The response contains a list of two strings per available MODIS date for the requested MODIS/VIIRS product and coordinate:\n",
    "* modis_date (\"AYYYYDOY\")    \n",
    "* calendar_date (\"YYYY-MM-DD\")\n",
    "\n",
    "Get the available dates for VNP13A1 at our site:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A2012017', 'A2012025', 'A2012033', 'A2012041', 'A2012049', 'A2012057', 'A2012065', 'A2012073', 'A2012081']\n"
     ]
    }
   ],
   "source": [
    "# Set dates request parameters\n",
    "prod = 'VNP13A1'     # MODIS product\n",
    "lat = '35.71326'   # Input latitude\n",
    "lon = '-83.51047'   # Input longitude\n",
    "\n",
    "response = requests.get('https://modis.ornl.gov/rst/api/v1/'+prod+'/dates?latitude='+lat+'&longitude='+lon, headers=header)\n",
    "dates = json.loads(response.text)['dates']\n",
    "modis_dates = [i['modis_date'] for i in dates]\n",
    "calendar_dates = [i['calendar_date'] for i in dates]\n",
    "doys = [i[-3:] for i in modis_dates]\n",
    "\n",
    "# Print the first ten dates\n",
    "print(modis_dates[0:9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## GET subset\n",
    "------\n",
    "\n",
    "The **subset** function returns the subset in either json or text/csv format. *kmAboveBelow* and *kmLeftRightight* indicate the number of kilometers above and below, left and right of the center pixel of the subset; e.g. for a 500-m product (MOD09A1) 10, 10 will result in a subset with a spatial extent covering 20.5- x 20.5-km. The subset command takes the following inputs:\n",
    "\n",
    "| Parameter          | Description                                                   |\n",
    "|:-------------------|:--------------------------------------------------------------|\n",
    "| **product**        | MODIS product code as listed by **products**                  |\n",
    "| **latitude**       | latitude                                                      |\n",
    "| **longitude**      | longitude                                                     |\n",
    "| **band**           | band ID as listed by **bands**                                |\n",
    "| **startDate**      | MODIS start date as listed by **dates** (\"AYYYYDOY\")          |\n",
    "| **endDate**        | MODIS end date as listed by **dates** (\"AYYYYDOY\")            |\n",
    "| **kmAboveBelow**   | number of kilometers to subset above and below center pixel   |\n",
    "| **kmLeftRight**    | number of kilometers to subset left and right of center pixel |\n",
    "\n",
    "\n",
    "The response includes helpful metadata about the subset:\n",
    "\n",
    "* band --- requested band\n",
    "* cellsize --- cell size in meters\n",
    "* header --- request url\n",
    "* latitude --- latitude of center pixel of subset\n",
    "* longitude --- longitude of center pixel of subset\n",
    "* ncols --- number of columns in the subset\n",
    "* nrows --- number of rows in the subset\n",
    "* scale --- scale factor\n",
    "* **subset --- subset data; one per timestep - see below**\n",
    "* units --- product data units\n",
    "* xllcorner --- MODIS sinusoidal x-coordinate of the lower left corner of the subset\n",
    "* yllcorner --- MODIS sinusoidal y-coordinate of the lower left corner of the subset\n",
    "\n",
    "**The subset data are stored within the subset element of the response. We'll take a look at its contents in the next step.**\n",
    "\n",
    "In the following code block we set our subset parameters, make a simple function that combines all of them into a URL string, and submit a subset request for the first date in our list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'xllcorner': '-7541804.45',\n",
       " 'yllcorner': '3969200.01',\n",
       " 'cellsize': 463.312716528,\n",
       " 'nrows': 9,\n",
       " 'ncols': 9,\n",
       " 'band': '500_m_16_days_EVI2',\n",
       " 'units': 'EVI2 ratio - No units',\n",
       " 'scale': '0.0001',\n",
       " 'latitude': 35.71326,\n",
       " 'longitude': -83.51047,\n",
       " 'header': 'https://modis.ornl.gov/rst/api/v1/VNP13A1/subset?latitude=35.71326&longitude=-83.51047&band=500_m_16_days_EVI2&startDate=A2012017&endDate=A2012017&kmAboveBelow=2&kmLeftRight=2',\n",
       " 'subset': [{'modis_date': 'A2012017',\n",
       "   'calendar_date': '2012-01-17',\n",
       "   'band': '500_m_16_days_EVI2',\n",
       "   'tile': 'h11v05',\n",
       "   'proc_date': '2018137160532',\n",
       "   'data': [1920,\n",
       "    2032,\n",
       "    2042,\n",
       "    1744,\n",
       "    1887,\n",
       "    1588,\n",
       "    1588,\n",
       "    1932,\n",
       "    2154,\n",
       "    2048,\n",
       "    1778,\n",
       "    1448,\n",
       "    1493,\n",
       "    1493,\n",
       "    1388,\n",
       "    1698,\n",
       "    2326,\n",
       "    2385,\n",
       "    1593,\n",
       "    1772,\n",
       "    1593,\n",
       "    2186,\n",
       "    1779,\n",
       "    1519,\n",
       "    1760,\n",
       "    2001,\n",
       "    2375,\n",
       "    1922,\n",
       "    2152,\n",
       "    2090,\n",
       "    2090,\n",
       "    1554,\n",
       "    1720,\n",
       "    2169,\n",
       "    1954,\n",
       "    2244,\n",
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       "    1497,\n",
       "    1778,\n",
       "    2060,\n",
       "    1421,\n",
       "    1372,\n",
       "    1501,\n",
       "    1757,\n",
       "    1106,\n",
       "    2426,\n",
       "    2239,\n",
       "    2235,\n",
       "    1814,\n",
       "    1697,\n",
       "    1653,\n",
       "    1621,\n",
       "    1577,\n",
       "    1417,\n",
       "    1812,\n",
       "    1576,\n",
       "    1473,\n",
       "    1686,\n",
       "    2385,\n",
       "    2876,\n",
       "    2059,\n",
       "    2059,\n",
       "    1997,\n",
       "    1767,\n",
       "    2030,\n",
       "    1809,\n",
       "    1694,\n",
       "    900,\n",
       "    1354,\n",
       "    2570,\n",
       "    2024,\n",
       "    1905,\n",
       "    1958,\n",
       "    1958,\n",
       "    2643,\n",
       "    1569,\n",
       "    1128,\n",
       "    1851,\n",
       "    2955,\n",
       "    3292,\n",
       "    2222]}]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the remaining subset request parameters\n",
    "data_band = '500_m_16_days_EVI2'   # Daytime LST band name\n",
    "qc_band = '500_m_16_days_VI_Quality' # Daytime QC band name\n",
    "above_below = 2            # km above/below\n",
    "left_right = 2            # km left/right\n",
    "\n",
    "def requestURL(latitude, longitude, product, band, start_date, end_date, kmAB, kmLR):\n",
    "    return(\n",
    "        str(\n",
    "            url+product+\"/subset?\"+ \\\n",
    "            \"latitude=\"+str(latitude)+ \\\n",
    "            \"&longitude=\"+str(longitude)+ \\\n",
    "            \"&band=\"+band+ \\\n",
    "            \"&startDate=\"+start_date+ \\\n",
    "            \"&endDate=\"+end_date+ \\\n",
    "            \"&kmAboveBelow=\"+str(kmAB)+ \\\n",
    "            \"&kmLeftRight=\"+str(kmLR)\n",
    "        )\n",
    "    )\n",
    "\n",
    "response = requests.get(requestURL(lat,lon,prod,data_band,modis_dates[0],modis_dates[0],above_below,left_right), headers=header)\n",
    "subset = json.loads(response.text)\n",
    "subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format subset response as familiar \"CSV-like\" structure\n",
    "\n",
    "We can reformat the time series into a structure more suitable for plotting by converting the **data** column into a data frame and transposing it such that each row is one step in the time series and each column is a pixel. \n",
    "\n",
    "The resulting structure matches what we would get by reading a CSV from the Global Tool into a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1920</td>\n",
       "      <td>2032</td>\n",
       "      <td>2042</td>\n",
       "      <td>1744</td>\n",
       "      <td>1887</td>\n",
       "      <td>1588</td>\n",
       "      <td>1588</td>\n",
       "      <td>1932</td>\n",
       "      <td>2154</td>\n",
       "      <td>2048</td>\n",
       "      <td>...</td>\n",
       "      <td>1905</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>2643</td>\n",
       "      <td>1569</td>\n",
       "      <td>1128</td>\n",
       "      <td>1851</td>\n",
       "      <td>2955</td>\n",
       "      <td>3292</td>\n",
       "      <td>2222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 81 columns</p>\n",
       "</div>"
      ],
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       "     0     1     2     3     4     5     6     7     8     9   ...     71  \\\n",
       "0  1920  2032  2042  1744  1887  1588  1588  1932  2154  2048  ...   1905   \n",
       "\n",
       "     72    73    74    75    76    77    78    79    80  \n",
       "0  1958  1958  2643  1569  1128  1851  2955  3292  2222  \n",
       "\n",
       "[1 rows x 81 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data as a pandas data frame so that rows are timesteps and columns are pixels\n",
    "format_subset = pd.DataFrame([subset['subset'][0]['data']])\n",
    "format_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're not going to plot the raster grids in the Python version of the demo. But here's how you capture the subset metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get subset metadata\n",
    "nrow = int(subset['nrows']) # number of rows\n",
    "ncol = int(subset['ncols']) # number of columns\n",
    "xllcorner = float(subset['xllcorner']) # lower left hand corner x-coordinate\n",
    "yllcorner = float(subset['yllcorner']) # lower left hand corner y-coordinate\n",
    "cellsize = float(subset['cellsize']) # pixel size\n",
    "scale = float(subset['scale']) # scale factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "## Make a time series from multiple subset requests\n",
    "\n",
    "Because of the ten tile limit imposed by the web service, we have to submit multiple requests to get a complete time series. \n",
    "\n",
    "Use the same input parameters as above and loop through the *dates* variable, submitting requests for the **2-band Enhanced Vegetation Index (500_m_16_days_EVI2)** and **VI Quality (500_m_16_days_VI_Quality)** layers for each timestep:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize lists to store the returned LST and QC data\n",
    "proddata = []\n",
    "qcdata = []\n",
    "\n",
    "# Iterate through the list of dates and submit subset requests.\n",
    "for dt in range(0,len(modis_dates), 1):\n",
    "    date = modis_dates[dt]\n",
    "    \n",
    "    # Submit request\n",
    "    subset = requests.get(\n",
    "        requestURL(lat,lon,prod,data_band,date,date,above_below,left_right), \n",
    "        headers=header\n",
    "    )\n",
    "    # Append data column (subset element) to list\n",
    "    proddata.append(json.loads(subset.text)['subset'][0]['data'])\n",
    "        \n",
    "    # Build QC request url and submit request    \n",
    "    qcsubset = requests.get(\n",
    "        requestURL(lat,lon,prod,qc_band,date,date,above_below,left_right),\n",
    "        headers=header\n",
    "    )\n",
    "    # Loop through list of dictionaries inside the subset key and append data to qcdata\n",
    "    qcdata.append(json.loads(qcsubset.text)['subset'][0]['data'])\n",
    "    \n",
    "# Make a pandas data frame from each list\n",
    "prodvalues = pd.DataFrame(proddata, index=calendar_dates)\n",
    "qcvalues = pd.DataFrame(qcdata, index=calendar_dates)\n",
    "\n",
    "# Write the data to CSVs\n",
    "prodvalues.to_csv('data/prodvalues.csv', sep=',', header=False)\n",
    "qcvalues.to_csv('data/qcvalues.csv', sep=',', header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Apply the scale factor    \n",
    "2. Calculate the mean and standard deviation of each timestep as a new data frame called **prodstats**    \n",
    "3. Plot the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prodvalues_scaled = prodvalues*float(scale)\n",
    "prodstats = pd.DataFrame(\n",
    "    np.column_stack(\n",
    "        [\n",
    "            prodvalues_scaled.mean(axis=1), \n",
    "            prodvalues_scaled.std(axis=1), \n",
    "            prodvalues_scaled.apply(lambda row: (float(row.count())/(ncol*nrow))*100, axis=1)\n",
    "        ]\n",
    "    ), \n",
    "    columns=['mean', 'sd', 'quality'], \n",
    "    index=pd.to_datetime(calendar_dates)\n",
    ")\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (15,5)\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_axisbelow(True)\n",
    "ax1.grid(color='gray', linestyle='dashed', alpha=0.5)\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('EVI')\n",
    "ax1.plot(prodstats.index, prodstats['mean'], 'k-')\n",
    "ax1.fill_between(prodstats.index, prodstats['mean']-prodstats['sd'], prodstats['mean']+prodstats['sd'])\n",
    "ax1.tick_params(axis='y')\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "\n",
    "plt.title(\"VNP13A1 Enhanced Vegetation Index\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Apply quality filter to the time series\n",
    "------\n",
    "\n",
    "So let's figure out what the quality layer values mean. Here is the QC table from the [VNP13A1 User Guide](https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/vnp13_user_guide_atbd_v2.1.2.pdf) (p. 43):\n",
    "\n",
    "<img src=\"pictures/VNP13A1_QC.png\" style=\"width: 600px;\">\n",
    "\n",
    "Get a list of unique values from the QC layer time series. Iterate through the unique QC values, get the binary representation of each and determine quality descriptions for each group in the QC Table from the User Guide:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the unique QC values: [2061, 2065, 2116, 2120, 2124, 2185, 2189, 2257, 2261, 2325, 2445, 2517, 2521, 3358, 3362, 3366, 3418, 18713, 34893, 34961, 34965, 35033, 35037, 35097, 35101, 35105, 35157, 35221, 35225, 35293, 35297, 51229, 51485, 51489, 51493]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Integer_Value</th>\n",
       "      <th>Bit15</th>\n",
       "      <th>Bit14</th>\n",
       "      <th>Bit13</th>\n",
       "      <th>Bit12</th>\n",
       "      <th>Bit11</th>\n",
       "      <th>Bit10</th>\n",
       "      <th>Bit9</th>\n",
       "      <th>Bit8</th>\n",
       "      <th>Bit7</th>\n",
       "      <th>...</th>\n",
       "      <th>Bit0</th>\n",
       "      <th>MODLAND_QA</th>\n",
       "      <th>VI_usefulness</th>\n",
       "      <th>Aerosol_quantity</th>\n",
       "      <th>Adjacent_cloud_detected</th>\n",
       "      <th>Atmosphere_BRDF_correction_performed</th>\n",
       "      <th>Mixed_clouds</th>\n",
       "      <th>Land_Water_flag</th>\n",
       "      <th>Possible_snow_ice</th>\n",
       "      <th>Possible_shadow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2061</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 2</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2065</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 3</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Lower quality</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Decreasing quality 1</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Decreasing quality 2</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Integer_Value  Bit15  Bit14  Bit13  Bit12  Bit11  Bit10  Bit9  Bit8  Bit7  \\\n",
       "0           2061      0      0      0      0      1      0     0     0     0   \n",
       "1           2065      0      0      0      0      1      0     0     0     0   \n",
       "2           2116      0      0      0      0      1      0     0     0     0   \n",
       "3           2120      0      0      0      0      1      0     0     0     0   \n",
       "4           2124      0      0      0      0      1      0     0     0     0   \n",
       "\n",
       "        ...        Bit0                  MODLAND_QA         VI_usefulness  \\\n",
       "0       ...           1  LST Produced,Other Quality  Decreasing quality 2   \n",
       "1       ...           1  LST Produced,Other Quality  Decreasing quality 3   \n",
       "2       ...           0                    LST GOOD         Lower quality   \n",
       "3       ...           0                    LST GOOD  Decreasing quality 1   \n",
       "4       ...           0                    LST GOOD  Decreasing quality 2   \n",
       "\n",
       "   Aerosol_quantity  Adjacent_cloud_detected  \\\n",
       "0       Climatology                       No   \n",
       "1       Climatology                       No   \n",
       "2               Low                       No   \n",
       "3               Low                       No   \n",
       "4               Low                       No   \n",
       "\n",
       "   Atmosphere_BRDF_correction_performed  Mixed_clouds Land_Water_flag  \\\n",
       "0                                    No            No  land no desert   \n",
       "1                                    No            No  land no desert   \n",
       "2                                    No            No  land no desert   \n",
       "3                                    No            No  land no desert   \n",
       "4                                    No            No  land no desert   \n",
       "\n",
       "  Possible_snow_ice Possible_shadow  \n",
       "0                No              No  \n",
       "1                No              No  \n",
       "2                No              No  \n",
       "3                No              No  \n",
       "4                No              No  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qcvals = sorted(pd.unique(qcvalues.values.ravel()))\n",
    "print(\"These are the unique QC values: \"+ str(qcvals))\n",
    "\n",
    "# Create empty list to store QC bit information\n",
    "QC_Data = []\n",
    "\n",
    "# Iterate through the list of unique QC integers and create a list of bits and descriptions for each.\n",
    "for integer in qcvals:\n",
    "    bits = list(map(int, list(\"{0:b}\".format(integer).zfill(16))))\n",
    "    \n",
    "    # MODLAND_QA bits description\n",
    "    if (bits[14] == 0 and bits[15] == 0): MODLAND_QA = 'LST GOOD'\n",
    "    if (bits[14] == 0 and bits[15] == 1): MODLAND_QA = 'LST Produced,Other Quality'\n",
    "    if (bits[14] == 1 and bits[15] == 0): MODLAND_QA = 'No Pixel,clouds'\n",
    "    if (bits[14] == 1 and bits[15] == 1): MODLAND_QA = 'No Pixel, Other QA'\n",
    "        \n",
    "    # VI_usefulness bits description\n",
    "    if (bits[10] == 0 and bits[11] == 0 and bits[12] == 0 and bits[13] == 0): VI_usefulness = 'Highest quality'\n",
    "    if (bits[10] == 0 and bits[11] == 0 and bits[12] == 0 and bits[13] == 1): VI_usefulness = 'Lower quality'\n",
    "    if (bits[10] == 0 and bits[11] == 0 and bits[12] == 1 and bits[13] == 0): VI_usefulness = 'Decreasing quality 1'\n",
    "    if (bits[10] == 0 and bits[11] == 0 and bits[12] == 1 and bits[13] == 1): VI_usefulness = 'Decreasing quality 2'\n",
    "    if (bits[10] == 0 and bits[11] == 1 and bits[12] == 0 and bits[13] == 0): VI_usefulness = 'Decreasing quality 3'\n",
    "    if (bits[10] == 0 and bits[11] == 1 and bits[12] == 0 and bits[13] == 1): VI_usefulness = 'Decreasing quality 4'\n",
    "    if (bits[10] == 0 and bits[11] == 1 and bits[12] == 1 and bits[13] == 0): VI_usefulness = 'Decreasing quality 5'\n",
    "    if (bits[10] == 0 and bits[11] == 1 and bits[12] == 1 and bits[13] == 1): VI_usefulness = 'Decreasing quality 6'\n",
    "    if (bits[10] == 1 and bits[11] == 0 and bits[12] == 0 and bits[13] == 0): VI_usefulness = 'Decreasing quality 7'\n",
    "    if (bits[10] == 1 and bits[11] == 0 and bits[12] == 0 and bits[13] == 1): VI_usefulness = 'Decreasing quality 8'\n",
    "    if (bits[10] == 1 and bits[11] == 0 and bits[12] == 1 and bits[13] == 0): VI_usefulness = 'Decreasing quality 9'\n",
    "    if (bits[10] == 1 and bits[11] == 0 and bits[12] == 1 and bits[13] == 1): VI_usefulness = 'Decreasing quality 10'\n",
    "    if (bits[10] == 1 and bits[11] == 1 and bits[12] == 0 and bits[13] == 0): VI_usefulness = 'Decreasing quality 11'\n",
    "    if (bits[10] == 1 and bits[11] == 1 and bits[12] == 0 and bits[13] == 1): VI_usefulness = 'Quality so low that it is not useful'\n",
    "    if (bits[10] == 1 and bits[11] == 1 and bits[12] == 1 and bits[13] == 0): VI_usefulness = 'L1B data faulty'\n",
    "    if (bits[10] == 1 and bits[11] == 1 and bits[12] == 1 and bits[13] == 1): VI_usefulness = 'Not useful for any other reason/not processed'\n",
    "\n",
    "    # Aerosol_quantity bits description\n",
    "    if (bits[8] == 0 and bits[9] == 0): Aerosol_quantity = 'Climatology'\n",
    "    if (bits[8] == 0 and bits[9] == 1): Aerosol_quantity = 'Low'\n",
    "    if (bits[8] == 1 and bits[9] == 0): Aerosol_quantity = 'Average'\n",
    "    if (bits[8] == 1 and bits[9] == 1): Aerosol_quantity = 'High'\n",
    "    \n",
    "    # Adjacent_cloud_detected bits description\n",
    "    if (bits[7] == 0): Adjacent_cloud_detected = 'No'\n",
    "    if (bits[7] == 1): Adjacent_cloud_detected = 'Yes'\n",
    "    \n",
    "    # Atmosphere_BRDF_correction_performed bits description\n",
    "    if (bits[6] == 0): Atmosphere_BRDF_correction_performed = 'No'\n",
    "    if (bits[6] == 1): Atmosphere_BRDF_correction_performed = 'Yes'\n",
    "    \n",
    "    # Mixed_clouds bits description\n",
    "    if (bits[5] == 0): Mixed_clouds = 'No'\n",
    "    if (bits[5] == 1): Mixed_clouds = 'Yes'\n",
    "\n",
    "    # Aerosol_quantity bits description\n",
    "    if (bits[2] == 0 and bits[3] == 0 and bits[4] == 0): Land_Water_flag = 'Land and desert'\n",
    "    if (bits[2] == 0 and bits[3] == 0 and bits[4] == 1): Land_Water_flag = 'land no desert'\n",
    "    if (bits[2] == 0 and bits[3] == 1 and bits[4] == 0): Land_Water_flag = 'inland water'\n",
    "    if (bits[2] == 0 and bits[3] == 1 and bits[4] == 1): Land_Water_flag = 'sea water'\n",
    "    if (bits[2] == 1 and bits[3] == 0 and bits[4] == 1): Land_Water_flag = 'coastal'\n",
    "\n",
    "    # Possible_snow_ice bits description\n",
    "    if (bits[1] == 0): Possible_snow_ice = 'No'\n",
    "    if (bits[1] == 1): Possible_snow_ice = 'Yes'\n",
    "\n",
    "    # Possible_shadow bits description\n",
    "    if (bits[0] == 0): Possible_shadow = 'No'\n",
    "    if (bits[0] == 1): Possible_shadow = 'Yes'\n",
    "\n",
    "    \n",
    "    # Append this integers bit values and descriptions to list\n",
    "    QC_Data.append(\n",
    "        [integer] + \\\n",
    "        bits + \\\n",
    "        [\n",
    "            MODLAND_QA, \n",
    "            VI_usefulness, \n",
    "            Aerosol_quantity, \n",
    "            Adjacent_cloud_detected,\n",
    "            Atmosphere_BRDF_correction_performed,\n",
    "            Mixed_clouds,\n",
    "            Land_Water_flag,\n",
    "            Possible_snow_ice,\n",
    "            Possible_shadow\n",
    "        ]\n",
    "    )\n",
    "\n",
    "# Convert QC bits and descriptions to pandas data frame\n",
    "QC_Data = pd.DataFrame(\n",
    "    QC_Data, \n",
    "    columns=[\n",
    "        'Integer_Value', \n",
    "        'Bit15', 'Bit14', 'Bit13', 'Bit12', 'Bit11', 'Bit10', 'Bit9', 'Bit8', \n",
    "        'Bit7', 'Bit6', 'Bit5', 'Bit4', 'Bit3', 'Bit2', 'Bit1', 'Bit0', \n",
    "        'MODLAND_QA', 'VI_usefulness', 'Aerosol_quantity', 'Adjacent_cloud_detected',\n",
    "        'Atmosphere_BRDF_correction_performed','Mixed_clouds','Land_Water_flag',\n",
    "        'Possible_snow_ice','Possible_shadow'\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Print the first five rows\n",
    "QC_Data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now decide what pixels to exclude from the analysis using the information provided in the QC group columns. We'll keep only the rows for integers that represent pixels of unacceptable quality: \n",
    "\n",
    "1. **MODLAND_QA** must not be *Pixel produced, but most probably cloudy* or *Pixel not produced due to other reasons than clouds*  \n",
    "2. **VI_usefulness** must not be *Quality Lv 4 (or worse)*\n",
    "3. **Aerosol_quantity** must not be *High*\n",
    "4. **Adjacent_cloud_detected** must not be *Yes*\n",
    "5. **Possible_shadow** must not be *Yes*\n",
    "\n",
    "We use the integers from the remaining rows to make a mask from the QC layer at each timestep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Integer_Value</th>\n",
       "      <th>Bit15</th>\n",
       "      <th>Bit14</th>\n",
       "      <th>Bit13</th>\n",
       "      <th>Bit12</th>\n",
       "      <th>Bit11</th>\n",
       "      <th>Bit10</th>\n",
       "      <th>Bit9</th>\n",
       "      <th>Bit8</th>\n",
       "      <th>Bit7</th>\n",
       "      <th>...</th>\n",
       "      <th>Bit0</th>\n",
       "      <th>MODLAND_QA</th>\n",
       "      <th>VI_usefulness</th>\n",
       "      <th>Aerosol_quantity</th>\n",
       "      <th>Adjacent_cloud_detected</th>\n",
       "      <th>Atmosphere_BRDF_correction_performed</th>\n",
       "      <th>Mixed_clouds</th>\n",
       "      <th>Land_Water_flag</th>\n",
       "      <th>Possible_snow_ice</th>\n",
       "      <th>Possible_shadow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2257</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 3</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 4</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2325</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 4</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2517</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 4</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2521</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 5</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3358</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Decreasing quality 6</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3362</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Decreasing quality 7</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3366</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Decreasing quality 8</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Decreasing quality 5</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18713</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 5</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>34965</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 4</td>\n",
       "      <td>Average</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>35033</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 5</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35037</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 6</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>35097</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 5</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>35101</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 6</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>35105</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 7</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>35157</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 4</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>35221</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 4</td>\n",
       "      <td>Average</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 5</td>\n",
       "      <td>Average</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>35293</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 6</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35297</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 7</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>51229</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 6</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>51485</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 6</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>51489</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 7</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>51493</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Decreasing quality 8</td>\n",
       "      <td>Climatology</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>land no desert</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Integer_Value  Bit15  Bit14  Bit13  Bit12  Bit11  Bit10  Bit9  Bit8  Bit7  \\\n",
       "7            2257      0      0      0      0      1      0     0     0     1   \n",
       "8            2261      0      0      0      0      1      0     0     0     1   \n",
       "9            2325      0      0      0      0      1      0     0     1     0   \n",
       "11           2517      0      0      0      0      1      0     0     1     1   \n",
       "12           2521      0      0      0      0      1      0     0     1     1   \n",
       "13           3358      0      0      0      0      1      1     0     1     0   \n",
       "14           3362      0      0      0      0      1      1     0     1     0   \n",
       "15           3366      0      0      0      0      1      1     0     1     0   \n",
       "16           3418      0      0      0      0      1      1     0     1     0   \n",
       "17          18713      0      1      0      0      1      0     0     1     0   \n",
       "20          34965      1      0      0      0      1      0     0     0     1   \n",
       "21          35033      1      0      0      0      1      0     0     0     1   \n",
       "22          35037      1      0      0      0      1      0     0     0     1   \n",
       "23          35097      1      0      0      0      1      0     0     1     0   \n",
       "24          35101      1      0      0      0      1      0     0     1     0   \n",
       "25          35105      1      0      0      0      1      0     0     1     0   \n",
       "26          35157      1      0      0      0      1      0     0     1     0   \n",
       "27          35221      1      0      0      0      1      0     0     1     1   \n",
       "28          35225      1      0      0      0      1      0     0     1     1   \n",
       "29          35293      1      0      0      0      1      0     0     1     1   \n",
       "30          35297      1      0      0      0      1      0     0     1     1   \n",
       "31          51229      1      1      0      0      1      0     0     0     0   \n",
       "32          51485      1      1      0      0      1      0     0     1     0   \n",
       "33          51489      1      1      0      0      1      0     0     1     0   \n",
       "34          51493      1      1      0      0      1      0     0     1     0   \n",
       "\n",
       "         ...        Bit0                  MODLAND_QA         VI_usefulness  \\\n",
       "7        ...           1  LST Produced,Other Quality  Decreasing quality 3   \n",
       "8        ...           1  LST Produced,Other Quality  Decreasing quality 4   \n",
       "9        ...           1  LST Produced,Other Quality  Decreasing quality 4   \n",
       "11       ...           1  LST Produced,Other Quality  Decreasing quality 4   \n",
       "12       ...           1  LST Produced,Other Quality  Decreasing quality 5   \n",
       "13       ...           0             No Pixel,clouds  Decreasing quality 6   \n",
       "14       ...           0             No Pixel,clouds  Decreasing quality 7   \n",
       "15       ...           0             No Pixel,clouds  Decreasing quality 8   \n",
       "16       ...           0             No Pixel,clouds  Decreasing quality 5   \n",
       "17       ...           1  LST Produced,Other Quality  Decreasing quality 5   \n",
       "20       ...           1  LST Produced,Other Quality  Decreasing quality 4   \n",
       "21       ...           1  LST Produced,Other Quality  Decreasing quality 5   \n",
       "22       ...           1  LST Produced,Other Quality  Decreasing quality 6   \n",
       "23       ...           1  LST Produced,Other Quality  Decreasing quality 5   \n",
       "24       ...           1  LST Produced,Other Quality  Decreasing quality 6   \n",
       "25       ...           1  LST Produced,Other Quality  Decreasing quality 7   \n",
       "26       ...           1  LST Produced,Other Quality  Decreasing quality 4   \n",
       "27       ...           1  LST Produced,Other Quality  Decreasing quality 4   \n",
       "28       ...           1  LST Produced,Other Quality  Decreasing quality 5   \n",
       "29       ...           1  LST Produced,Other Quality  Decreasing quality 6   \n",
       "30       ...           1  LST Produced,Other Quality  Decreasing quality 7   \n",
       "31       ...           1  LST Produced,Other Quality  Decreasing quality 6   \n",
       "32       ...           1  LST Produced,Other Quality  Decreasing quality 6   \n",
       "33       ...           1  LST Produced,Other Quality  Decreasing quality 7   \n",
       "34       ...           1  LST Produced,Other Quality  Decreasing quality 8   \n",
       "\n",
       "    Aerosol_quantity  Adjacent_cloud_detected  \\\n",
       "7               High                       No   \n",
       "8               High                       No   \n",
       "9        Climatology                      Yes   \n",
       "11              High                      Yes   \n",
       "12              High                      Yes   \n",
       "13       Climatology                      Yes   \n",
       "14       Climatology                      Yes   \n",
       "15       Climatology                      Yes   \n",
       "16               Low                      Yes   \n",
       "17       Climatology                      Yes   \n",
       "20           Average                       No   \n",
       "21              High                       No   \n",
       "22              High                       No   \n",
       "23       Climatology                      Yes   \n",
       "24       Climatology                      Yes   \n",
       "25       Climatology                      Yes   \n",
       "26               Low                      Yes   \n",
       "27           Average                      Yes   \n",
       "28           Average                      Yes   \n",
       "29              High                      Yes   \n",
       "30              High                      Yes   \n",
       "31       Climatology                       No   \n",
       "32       Climatology                      Yes   \n",
       "33       Climatology                      Yes   \n",
       "34       Climatology                      Yes   \n",
       "\n",
       "    Atmosphere_BRDF_correction_performed  Mixed_clouds Land_Water_flag  \\\n",
       "7                                     No            No  land no desert   \n",
       "8                                     No            No  land no desert   \n",
       "9                                     No            No  land no desert   \n",
       "11                                    No            No  land no desert   \n",
       "12                                    No            No  land no desert   \n",
       "13                                    No           Yes  land no desert   \n",
       "14                                    No           Yes  land no desert   \n",
       "15                                    No           Yes  land no desert   \n",
       "16                                    No           Yes  land no desert   \n",
       "17                                    No            No  land no desert   \n",
       "20                                    No            No  land no desert   \n",
       "21                                    No            No  land no desert   \n",
       "22                                    No            No  land no desert   \n",
       "23                                    No            No  land no desert   \n",
       "24                                    No            No  land no desert   \n",
       "25                                    No            No  land no desert   \n",
       "26                                    No            No  land no desert   \n",
       "27                                    No            No  land no desert   \n",
       "28                                    No            No  land no desert   \n",
       "29                                    No            No  land no desert   \n",
       "30                                    No            No  land no desert   \n",
       "31                                    No            No  land no desert   \n",
       "32                                    No            No  land no desert   \n",
       "33                                    No            No  land no desert   \n",
       "34                                    No            No  land no desert   \n",
       "\n",
       "   Possible_snow_ice Possible_shadow  \n",
       "7                 No              No  \n",
       "8                 No              No  \n",
       "9                 No              No  \n",
       "11                No              No  \n",
       "12                No              No  \n",
       "13                No              No  \n",
       "14                No              No  \n",
       "15                No              No  \n",
       "16                No              No  \n",
       "17               Yes              No  \n",
       "20                No             Yes  \n",
       "21                No             Yes  \n",
       "22                No             Yes  \n",
       "23                No             Yes  \n",
       "24                No             Yes  \n",
       "25                No             Yes  \n",
       "26                No             Yes  \n",
       "27                No             Yes  \n",
       "28                No             Yes  \n",
       "29                No             Yes  \n",
       "30                No             Yes  \n",
       "31               Yes             Yes  \n",
       "32               Yes             Yes  \n",
       "33               Yes             Yes  \n",
       "34               Yes             Yes  \n",
       "\n",
       "[25 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MODLAND_QA = ['Pixel produced, but most probably cloudy','Pixel not produced due to other reasons than clouds']\n",
    "VI_usefulness = [\n",
    "    \"Decreasing quality 4\",\n",
    "    \"Decreasing quality 5\",\n",
    "    \"Decreasing quality 6\",\n",
    "    \"Decreasing quality 7\",\n",
    "    \"Decreasing quality 8\",\n",
    "    \"Decreasing quality 9\",\n",
    "    \"Decreasing quality 10\",\n",
    "    \"Decreasing quality 11\",\n",
    "    \"Quality so low that it is not useful\",\n",
    "    \"L1B data faulty\",\n",
    "    \"Not useful for any other reason/not processed\"\n",
    "]\n",
    "Aerosol_quantity = \"High\"\n",
    "\n",
    "QC_Data = QC_Data.loc[QC_Data['MODLAND_QA'].isin(MODLAND_QA) | (QC_Data['VI_usefulness'].isin(VI_usefulness)) | (QC_Data['Aerosol_quantity'] == Aerosol_quantity)]\n",
    "QC_Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can filter the data using the remaining integers.    \n",
    "\n",
    "<img src=\"pictures/filtering_example.png\" style=\"width: 800px;\">\n",
    "\n",
    "We can use **pandas.mask** to filter the remaining QC integer values from our LST time series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter = QC_Data['Integer_Value'].tolist()\n",
    "proddata_filt = prodvalues.mask(qcvalues.isin(filter))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The EVI time series is filtered now! We'll check by plotting again. This time we'll add a couple additional columns (year and day-of-the-year) to our stats table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "proddata_filt_scale = proddata_filt*float(scale)\n",
    "\n",
    "prodstats = pd.DataFrame(\n",
    "    np.column_stack(\n",
    "        [\n",
    "            proddata_filt_scale.mean(axis=1), \n",
    "            proddata_filt_scale.std(axis=1), \n",
    "            proddata_filt_scale.apply(lambda row: (float(row.count())/(ncol*nrow))*100, axis=1)\n",
    "        ]\n",
    "    ), \n",
    "    columns=['mean', 'sd', 'quality'], \n",
    "    index=pd.to_datetime(calendar_dates)\n",
    ")\n",
    "\n",
    "prodstats['year'] = [dt[0:4] for dt in calendar_dates]\n",
    "prodstats['doy'] = doys\n",
    "years = list(set([dt[0:4] for dt in calendar_dates]))\n",
    "colors = ['blue', 'green','red','cyan','magenta','yellow','black']\n",
    "\n",
    "# Plot time series\n",
    "plt.rcParams['figure.figsize'] = (15,5)\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_axisbelow(True)\n",
    "ax1.grid(color='gray', linestyle='dashed', alpha=0.5)\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('EVI')\n",
    "ax1.plot(prodstats.index, prodstats['mean'], 'k-')\n",
    "ax1.fill_between(prodstats.index, prodstats['mean']-prodstats['sd'], prodstats['mean']+prodstats['sd'])\n",
    "ax1.tick_params(axis='y')\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('% Good Pixels')\n",
    "ax2.bar(prodstats.index, prodstats['quality'], 5, alpha = 0.5)\n",
    "ax2.tick_params(axis='y')\n",
    "\n",
    "# Plot by year\n",
    "plt.rcParams['figure.figsize'] = (15,5)\n",
    "fig2, ax2 = plt.subplots()\n",
    "\n",
    "ax2.set_axisbelow(True)\n",
    "ax2.grid(color='gray', linestyle='dashed', alpha=0.5)\n",
    "ax2.set_xlabel('Date')\n",
    "ax2.set_ylabel('EVI')\n",
    "for i, yr in enumerate(years):\n",
    "    ax2.plot(prodstats[prodstats['year'] == yr]['doy'], prodstats[prodstats['year'] == yr]['mean'], 'k-', color = colors[i],  label=yr)\n",
    "ax2.tick_params(axis='y')\n",
    "plt.xticks(rotation=90)\n",
    "fig.autofmt_xdate()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus Content\n",
    "-----\n",
    "\n",
    "### MOD11A2 QC Table\n",
    "Land Surface Temperature/Emissivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>Integer_Value</th>\n",
       "      <th>Bit7</th>\n",
       "      <th>Bit6</th>\n",
       "      <th>Bit5</th>\n",
       "      <th>Bit4</th>\n",
       "      <th>Bit3</th>\n",
       "      <th>Bit2</th>\n",
       "      <th>Bit1</th>\n",
       "      <th>Bit0</th>\n",
       "      <th>Mandatory_QA</th>\n",
       "      <th>Data_Quality</th>\n",
       "      <th>Emiss_Err</th>\n",
       "      <th>LST_Err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .01</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .02</td>\n",
       "      <td>LST Err &lt;= 1K</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>228</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>229</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>235</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &lt;= .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>240</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>241</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>242</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>243</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>Good Data</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>247</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>Other Quality</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>248</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>251</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>252</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LST GOOD</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>253</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LST Produced,Other Quality</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No Pixel,clouds</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No Pixel, Other QA</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Emiss Err &gt; .04</td>\n",
       "      <td>LST Err &gt; 3K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>256 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Integer_Value  Bit7  Bit6  Bit5  Bit4  Bit3  Bit2  Bit1  Bit0  \\\n",
       "0                0     0     0     0     0     0     0     0     0   \n",
       "1                1     0     0     0     0     0     0     0     1   \n",
       "2                2     0     0     0     0     0     0     1     0   \n",
       "3                3     0     0     0     0     0     0     1     1   \n",
       "4                4     0     0     0     0     0     1     0     0   \n",
       "5                5     0     0     0     0     0     1     0     1   \n",
       "6                6     0     0     0     0     0     1     1     0   \n",
       "7                7     0     0     0     0     0     1     1     1   \n",
       "8                8     0     0     0     0     1     0     0     0   \n",
       "9                9     0     0     0     0     1     0     0     1   \n",
       "10              10     0     0     0     0     1     0     1     0   \n",
       "11              11     0     0     0     0     1     0     1     1   \n",
       "12              12     0     0     0     0     1     1     0     0   \n",
       "13              13     0     0     0     0     1     1     0     1   \n",
       "14              14     0     0     0     0     1     1     1     0   \n",
       "15              15     0     0     0     0     1     1     1     1   \n",
       "16              16     0     0     0     1     0     0     0     0   \n",
       "17              17     0     0     0     1     0     0     0     1   \n",
       "18              18     0     0     0     1     0     0     1     0   \n",
       "19              19     0     0     0     1     0     0     1     1   \n",
       "20              20     0     0     0     1     0     1     0     0   \n",
       "21              21     0     0     0     1     0     1     0     1   \n",
       "22              22     0     0     0     1     0     1     1     0   \n",
       "23              23     0     0     0     1     0     1     1     1   \n",
       "24              24     0     0     0     1     1     0     0     0   \n",
       "25              25     0     0     0     1     1     0     0     1   \n",
       "26              26     0     0     0     1     1     0     1     0   \n",
       "27              27     0     0     0     1     1     0     1     1   \n",
       "28              28     0     0     0     1     1     1     0     0   \n",
       "29              29     0     0     0     1     1     1     0     1   \n",
       "..             ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "226            226     1     1     1     0     0     0     1     0   \n",
       "227            227     1     1     1     0     0     0     1     1   \n",
       "228            228     1     1     1     0     0     1     0     0   \n",
       "229            229     1     1     1     0     0     1     0     1   \n",
       "230            230     1     1     1     0     0     1     1     0   \n",
       "231            231     1     1     1     0     0     1     1     1   \n",
       "232            232     1     1     1     0     1     0     0     0   \n",
       "233            233     1     1     1     0     1     0     0     1   \n",
       "234            234     1     1     1     0     1     0     1     0   \n",
       "235            235     1     1     1     0     1     0     1     1   \n",
       "236            236     1     1     1     0     1     1     0     0   \n",
       "237            237     1     1     1     0     1     1     0     1   \n",
       "238            238     1     1     1     0     1     1     1     0   \n",
       "239            239     1     1     1     0     1     1     1     1   \n",
       "240            240     1     1     1     1     0     0     0     0   \n",
       "241            241     1     1     1     1     0     0     0     1   \n",
       "242            242     1     1     1     1     0     0     1     0   \n",
       "243            243     1     1     1     1     0     0     1     1   \n",
       "244            244     1     1     1     1     0     1     0     0   \n",
       "245            245     1     1     1     1     0     1     0     1   \n",
       "246            246     1     1     1     1     0     1     1     0   \n",
       "247            247     1     1     1     1     0     1     1     1   \n",
       "248            248     1     1     1     1     1     0     0     0   \n",
       "249            249     1     1     1     1     1     0     0     1   \n",
       "250            250     1     1     1     1     1     0     1     0   \n",
       "251            251     1     1     1     1     1     0     1     1   \n",
       "252            252     1     1     1     1     1     1     0     0   \n",
       "253            253     1     1     1     1     1     1     0     1   \n",
       "254            254     1     1     1     1     1     1     1     0   \n",
       "255            255     1     1     1     1     1     1     1     1   \n",
       "\n",
       "                   Mandatory_QA   Data_Quality         Emiss_Err  \\\n",
       "0                      LST GOOD      Good Data  Emiss Err <= .01   \n",
       "1    LST Produced,Other Quality      Good Data  Emiss Err <= .01   \n",
       "2               No Pixel,clouds      Good Data  Emiss Err <= .01   \n",
       "3            No Pixel, Other QA      Good Data  Emiss Err <= .01   \n",
       "4                      LST GOOD  Other Quality  Emiss Err <= .01   \n",
       "5    LST Produced,Other Quality  Other Quality  Emiss Err <= .01   \n",
       "6               No Pixel,clouds  Other Quality  Emiss Err <= .01   \n",
       "7            No Pixel, Other QA  Other Quality  Emiss Err <= .01   \n",
       "8                      LST GOOD            TBD  Emiss Err <= .01   \n",
       "9    LST Produced,Other Quality            TBD  Emiss Err <= .01   \n",
       "10              No Pixel,clouds            TBD  Emiss Err <= .01   \n",
       "11           No Pixel, Other QA            TBD  Emiss Err <= .01   \n",
       "12                     LST GOOD            TBD  Emiss Err <= .01   \n",
       "13   LST Produced,Other Quality            TBD  Emiss Err <= .01   \n",
       "14              No Pixel,clouds            TBD  Emiss Err <= .01   \n",
       "15           No Pixel, Other QA            TBD  Emiss Err <= .01   \n",
       "16                     LST GOOD      Good Data  Emiss Err <= .02   \n",
       "17   LST Produced,Other Quality      Good Data  Emiss Err <= .02   \n",
       "18              No Pixel,clouds      Good Data  Emiss Err <= .02   \n",
       "19           No Pixel, Other QA      Good Data  Emiss Err <= .02   \n",
       "20                     LST GOOD  Other Quality  Emiss Err <= .02   \n",
       "21   LST Produced,Other Quality  Other Quality  Emiss Err <= .02   \n",
       "22              No Pixel,clouds  Other Quality  Emiss Err <= .02   \n",
       "23           No Pixel, Other QA  Other Quality  Emiss Err <= .02   \n",
       "24                     LST GOOD            TBD  Emiss Err <= .02   \n",
       "25   LST Produced,Other Quality            TBD  Emiss Err <= .02   \n",
       "26              No Pixel,clouds            TBD  Emiss Err <= .02   \n",
       "27           No Pixel, Other QA            TBD  Emiss Err <= .02   \n",
       "28                     LST GOOD            TBD  Emiss Err <= .02   \n",
       "29   LST Produced,Other Quality            TBD  Emiss Err <= .02   \n",
       "..                          ...            ...               ...   \n",
       "226             No Pixel,clouds      Good Data  Emiss Err <= .04   \n",
       "227          No Pixel, Other QA      Good Data  Emiss Err <= .04   \n",
       "228                    LST GOOD  Other Quality  Emiss Err <= .04   \n",
       "229  LST Produced,Other Quality  Other Quality  Emiss Err <= .04   \n",
       "230             No Pixel,clouds  Other Quality  Emiss Err <= .04   \n",
       "231          No Pixel, Other QA  Other Quality  Emiss Err <= .04   \n",
       "232                    LST GOOD            TBD  Emiss Err <= .04   \n",
       "233  LST Produced,Other Quality            TBD  Emiss Err <= .04   \n",
       "234             No Pixel,clouds            TBD  Emiss Err <= .04   \n",
       "235          No Pixel, Other QA            TBD  Emiss Err <= .04   \n",
       "236                    LST GOOD            TBD  Emiss Err <= .04   \n",
       "237  LST Produced,Other Quality            TBD  Emiss Err <= .04   \n",
       "238             No Pixel,clouds            TBD  Emiss Err <= .04   \n",
       "239          No Pixel, Other QA            TBD  Emiss Err <= .04   \n",
       "240                    LST GOOD      Good Data   Emiss Err > .04   \n",
       "241  LST Produced,Other Quality      Good Data   Emiss Err > .04   \n",
       "242             No Pixel,clouds      Good Data   Emiss Err > .04   \n",
       "243          No Pixel, Other QA      Good Data   Emiss Err > .04   \n",
       "244                    LST GOOD  Other Quality   Emiss Err > .04   \n",
       "245  LST Produced,Other Quality  Other Quality   Emiss Err > .04   \n",
       "246             No Pixel,clouds  Other Quality   Emiss Err > .04   \n",
       "247          No Pixel, Other QA  Other Quality   Emiss Err > .04   \n",
       "248                    LST GOOD            TBD   Emiss Err > .04   \n",
       "249  LST Produced,Other Quality            TBD   Emiss Err > .04   \n",
       "250             No Pixel,clouds            TBD   Emiss Err > .04   \n",
       "251          No Pixel, Other QA            TBD   Emiss Err > .04   \n",
       "252                    LST GOOD            TBD   Emiss Err > .04   \n",
       "253  LST Produced,Other Quality            TBD   Emiss Err > .04   \n",
       "254             No Pixel,clouds            TBD   Emiss Err > .04   \n",
       "255          No Pixel, Other QA            TBD   Emiss Err > .04   \n",
       "\n",
       "           LST_Err  \n",
       "0    LST Err <= 1K  \n",
       "1    LST Err <= 1K  \n",
       "2    LST Err <= 1K  \n",
       "3    LST Err <= 1K  \n",
       "4    LST Err <= 1K  \n",
       "5    LST Err <= 1K  \n",
       "6    LST Err <= 1K  \n",
       "7    LST Err <= 1K  \n",
       "8    LST Err <= 1K  \n",
       "9    LST Err <= 1K  \n",
       "10   LST Err <= 1K  \n",
       "11   LST Err <= 1K  \n",
       "12   LST Err <= 1K  \n",
       "13   LST Err <= 1K  \n",
       "14   LST Err <= 1K  \n",
       "15   LST Err <= 1K  \n",
       "16   LST Err <= 1K  \n",
       "17   LST Err <= 1K  \n",
       "18   LST Err <= 1K  \n",
       "19   LST Err <= 1K  \n",
       "20   LST Err <= 1K  \n",
       "21   LST Err <= 1K  \n",
       "22   LST Err <= 1K  \n",
       "23   LST Err <= 1K  \n",
       "24   LST Err <= 1K  \n",
       "25   LST Err <= 1K  \n",
       "26   LST Err <= 1K  \n",
       "27   LST Err <= 1K  \n",
       "28   LST Err <= 1K  \n",
       "29   LST Err <= 1K  \n",
       "..             ...  \n",
       "226   LST Err > 3K  \n",
       "227   LST Err > 3K  \n",
       "228   LST Err > 3K  \n",
       "229   LST Err > 3K  \n",
       "230   LST Err > 3K  \n",
       "231   LST Err > 3K  \n",
       "232   LST Err > 3K  \n",
       "233   LST Err > 3K  \n",
       "234   LST Err > 3K  \n",
       "235   LST Err > 3K  \n",
       "236   LST Err > 3K  \n",
       "237   LST Err > 3K  \n",
       "238   LST Err > 3K  \n",
       "239   LST Err > 3K  \n",
       "240   LST Err > 3K  \n",
       "241   LST Err > 3K  \n",
       "242   LST Err > 3K  \n",
       "243   LST Err > 3K  \n",
       "244   LST Err > 3K  \n",
       "245   LST Err > 3K  \n",
       "246   LST Err > 3K  \n",
       "247   LST Err > 3K  \n",
       "248   LST Err > 3K  \n",
       "249   LST Err > 3K  \n",
       "250   LST Err > 3K  \n",
       "251   LST Err > 3K  \n",
       "252   LST Err > 3K  \n",
       "253   LST Err > 3K  \n",
       "254   LST Err > 3K  \n",
       "255   LST Err > 3K  \n",
       "\n",
       "[256 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create empty list to store QC bit information\n",
    "QC_Data = []\n",
    "\n",
    "# Iterate through the list of 8-bit integers and populate QC table with bit definitions.\n",
    "for integer in range(0, 256, 1):\n",
    "    bits = list(map(int, list(\"{0:b}\".format(integer).zfill(8))))\n",
    "    \n",
    "    # Mandatory_QA bits description\n",
    "    if (bits[6] == 0 and bits[7] == 0): Mandatory_QA = 'LST GOOD'\n",
    "    if (bits[6] == 0 and bits[7] == 1): Mandatory_QA = 'LST Produced,Other Quality'\n",
    "    if (bits[6] == 1 and bits[7] == 0): Mandatory_QA = 'No Pixel,clouds'\n",
    "    if (bits[6] == 1 and bits[7] == 1): Mandatory_QA = 'No Pixel, Other QA'\n",
    "        \n",
    "    # Data_Quality bits description\n",
    "    if (bits[4] == 0 and bits[5] == 0): Data_Quality = 'Good Data'\n",
    "    if (bits[4] == 0 and bits[5] == 1): Data_Quality = 'Other Quality'\n",
    "    if (bits[4] == 1 and bits[5] == 0): Data_Quality = 'TBD'\n",
    "    if (bits[4] == 1 and bits[5] == 1): Data_Quality = 'TBD'\n",
    "        \n",
    "    # Emiss_Err bits description\n",
    "    if (bits[2] == 0 and bits[3] == 0): Emiss_Err = 'Emiss Err <= .01'\n",
    "    if (bits[2] == 0 and bits[3] == 1): Emiss_Err = 'Emiss Err <= .02'\n",
    "    if (bits[2] == 1 and bits[3] == 0): Emiss_Err = 'Emiss Err <= .04'\n",
    "    if (bits[2] == 1 and bits[3] == 1): Emiss_Err = 'Emiss Err > .04'\n",
    "        \n",
    "    # LST_Err bits description\n",
    "    if (bits[0] == 0 and bits[1] == 0): LST_Err = 'LST Err <= 1K'\n",
    "    if (bits[0] == 0 and bits[1] == 1): LST_Err = 'LST Err <= 3K'\n",
    "    if (bits[0] == 1 and bits[1] == 0): LST_Err = 'LST Err <= 2K'\n",
    "    if (bits[0] == 1 and bits[1] == 1): LST_Err = 'LST Err > 3K' \n",
    "    \n",
    "    # Append this integers bit values and descriptions to list\n",
    "    QC_Data.append([integer] + bits + [Mandatory_QA, Data_Quality, Emiss_Err, LST_Err])\n",
    "    \n",
    "\n",
    "# Convert QC bits and descriptions to pandas data frame\n",
    "QC_Data = pd.DataFrame(QC_Data, columns=['Integer_Value', 'Bit7', 'Bit6', 'Bit5', 'Bit4', 'Bit3', 'Bit2', 'Bit1', 'Bit0', 'Mandatory_QA', 'Data_Quality', 'Emiss_Err', 'LST_Err'])\n",
    "QC_Data"
   ]
  }
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