qc_flags/qc_flags.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/petercable/virtenvs/ooi/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import seaborn as sbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = 'deployment0003_RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample_20161110T000000.935191-20161110T102128.580840.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I don't want to see all the attributes\n",
    "ds.attrs = {}\n",
    "# And let's focus on just one parameter for now, convert to dataframe since this is all scalar data\n",
    "df = ds[['seawater_temperature', 'seawater_temperature_qc_executed', 'seawater_temperature_qc_results']].to_dataframe()\n",
    "# Re-index by time\n",
    "df = df.set_index('time')\n",
    "# drop lat/lon\n",
    "df = df.drop(['lat', 'lon'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seawater_temperature</th>\n",
       "      <th>seawater_temperature_qc_executed</th>\n",
       "      <th>seawater_temperature_qc_results</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:00.935191</th>\n",
       "      <td>8.391116</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:01.935922</th>\n",
       "      <td>8.391242</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:02.935403</th>\n",
       "      <td>8.391430</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:03.935715</th>\n",
       "      <td>8.392496</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:04.935821</th>\n",
       "      <td>8.393562</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            seawater_temperature  \\\n",
       "time                                               \n",
       "2016-11-10 00:00:00.935191              8.391116   \n",
       "2016-11-10 00:00:01.935922              8.391242   \n",
       "2016-11-10 00:00:02.935403              8.391430   \n",
       "2016-11-10 00:00:03.935715              8.392496   \n",
       "2016-11-10 00:00:04.935821              8.393562   \n",
       "\n",
       "                            seawater_temperature_qc_executed  \\\n",
       "time                                                           \n",
       "2016-11-10 00:00:00.935191                                29   \n",
       "2016-11-10 00:00:01.935922                                29   \n",
       "2016-11-10 00:00:02.935403                                29   \n",
       "2016-11-10 00:00:03.935715                                29   \n",
       "2016-11-10 00:00:04.935821                                29   \n",
       "\n",
       "                            seawater_temperature_qc_results  \n",
       "time                                                         \n",
       "2016-11-10 00:00:00.935191                               21  \n",
       "2016-11-10 00:00:01.935922                               21  \n",
       "2016-11-10 00:00:02.935403                               21  \n",
       "2016-11-10 00:00:03.935715                               21  \n",
       "2016-11-10 00:00:04.935821                               21  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106d3ff10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFZCAYAAADZ6SWdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFQRBEAQTE5OvIAiCIJjY/wC9a64oEfIqbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102f48410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.seawater_temperature.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QC table\n",
    "```\n",
    "Test name              Bit position\n",
    "                         15 14 13 12 11 10  9  8  7  6  5  4  3  2  1  0\n",
    "global_range_test         0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1\n",
    "dataqc_localrangetest     0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0\n",
    "dataqc_spiketest          0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0\n",
    "dataqc_polytrendtest      0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0\n",
    "dataqc_stuckvaluetest     0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0\n",
    "dataqc_gradienttest       0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0\n",
    "dataqc_propagateflags     0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0\n",
    "```\n",
    "\n",
    "The QC flags for all tests are OR'd together to produce a single value for each data point. So, given a qc_executed value of 29 we can see which tests were run by reversing the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 0, 1], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unpackbits(np.array(29).astype('uint8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you compare this result to the table above you can see that the following tests were executed:\n",
    "\n",
    "```\n",
    "global_range_test\n",
    "dataqc_spiketest\n",
    "dataqc_polytrendtest\n",
    "dataqc_stuckvaluetest\n",
    "```\n",
    "\n",
    "We can craft a function to create new booleans variables for each test run containing the pass/fail results from that test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def parse_qc(dataframe):\n",
    "    names = {\n",
    "        0: 'global_range_test',\n",
    "        1: 'dataqc_localrangetest',\n",
    "        2: 'dataqc_spiketest',\n",
    "        3: 'dataqc_polytrendtest',\n",
    "        4: 'dataqc_stuckvaluetest',\n",
    "        5: 'dataqc_gradienttest',\n",
    "        7: 'dataqc_propagateflags',\n",
    "    }\n",
    "    # Just in case a different set of tests were run on some datapoint\n",
    "    # *this should never happen*\n",
    "    executed = np.bitwise_or.reduce(dataframe.seawater_temperature_qc_executed.values)\n",
    "    executed_bits = np.unpackbits(executed.astype('uint8'))\n",
    "    for index, value in enumerate(executed_bits[::-1]):\n",
    "        if value:\n",
    "            name = names.get(index)\n",
    "            mask = 2**index\n",
    "            values = (dataframe.seawater_temperature_qc_results.values & mask) > 0\n",
    "            df[name] = values\n",
    "    dataframe.drop(['seawater_temperature_qc_executed', 'seawater_temperature_qc_results'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parse_qc(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seawater_temperature</th>\n",
       "      <th>global_range_test</th>\n",
       "      <th>dataqc_spiketest</th>\n",
       "      <th>dataqc_polytrendtest</th>\n",
       "      <th>dataqc_stuckvaluetest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:00.935191</th>\n",
       "      <td>8.391116</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:01.935922</th>\n",
       "      <td>8.391242</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:02.935403</th>\n",
       "      <td>8.391430</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:03.935715</th>\n",
       "      <td>8.392496</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10 00:00:04.935821</th>\n",
       "      <td>8.393562</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            seawater_temperature global_range_test  \\\n",
       "time                                                                 \n",
       "2016-11-10 00:00:00.935191              8.391116              True   \n",
       "2016-11-10 00:00:01.935922              8.391242              True   \n",
       "2016-11-10 00:00:02.935403              8.391430              True   \n",
       "2016-11-10 00:00:03.935715              8.392496              True   \n",
       "2016-11-10 00:00:04.935821              8.393562              True   \n",
       "\n",
       "                           dataqc_spiketest dataqc_polytrendtest  \\\n",
       "time                                                               \n",
       "2016-11-10 00:00:00.935191             True                False   \n",
       "2016-11-10 00:00:01.935922             True                False   \n",
       "2016-11-10 00:00:02.935403             True                False   \n",
       "2016-11-10 00:00:03.935715             True                False   \n",
       "2016-11-10 00:00:04.935821             True                False   \n",
       "\n",
       "                           dataqc_stuckvaluetest  \n",
       "time                                              \n",
       "2016-11-10 00:00:00.935191                  True  \n",
       "2016-11-10 00:00:01.935922                  True  \n",
       "2016-11-10 00:00:02.935403                  True  \n",
       "2016-11-10 00:00:03.935715                  True  \n",
       "2016-11-10 00:00:04.935821                  True  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aea76d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFZCAYAAADZ6SWdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQN8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106d11210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}