parse_process.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parse Raw Data\n",
    "\n",
    "Make sure you have followed the instructions in the README to get your packages installed and environment set up.\n",
    "\n",
    "The mi-instrument toolbox, used to parse raw datafiles, is set up to be run from the shell. To keep things contained within the notebook, we will execute the commands using subprocess.\n",
    "\n",
    "NOTE: This example parses cabled data. Cabled data parsers are referred to as `drivers` and the parsing process as `playback`. However, the process is analagous to the way un-cabled data can be parsed and processed. A follow up example for un-cabled data can be provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import subprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mi-instrument has two basic operations\n",
    "\n",
    "```\n",
    "analyze <dir>\n",
    "playback <type> <module> <refdes> log:// csv:// <file>\n",
    "```\n",
    "\n",
    "First let's analyze the directory containing the example raw data file, obtained at\n",
    "\n",
    "https://rawdata.oceanobservatories.org/files/RS01SBPS/SF01A/CTDPFA102/2017/10/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing 1 files in ./raw_data: \n",
      "CTDPFA102\n",
      "chunky (0, 0, None, None)\n",
      "ascii (0, 0, None, None)\n",
      "binary (1, 3801512, datetime.datetime(2017, 10, 5, 0, 0), datetime.datetime(2017, 10, 6, 0, 0))\n",
      "\n"
     ]
    }
   ],
   "source": [
    "analyze = 'analyze ./raw_data'\n",
    "print subprocess.check_output(analyze, shell=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We see that this folder contains 1 binary (datalog) file. Next we will playback (parse) this data. The playback option accepts three data types, which is why you want to analyze what you are working with first.\n",
    "\n",
    "`<type>`\n",
    "```\n",
    "chunky\n",
    "ascii\n",
    "datalog (binary)\n",
    "```\n",
    "Next you will need to specify the correct module (driver) for this data type, which reflects the path to the parser in the mi-instrument repo. How do you know which driver to use? \n",
    "\n",
    "This information is contained in the provenance json file that is served up on THREDDS alongside the NetCDF in an asynchronous data download. \n",
    "\n",
    "https://opendap.oceanobservatories.org/thredds/catalog/ooi/friedrich-knuth-rutgers/20171007T204818-RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample/catalog.html\n",
    "\n",
    "This file has been placed in the /ooinet_data folder [here](https://github.com/friedrichknuth/ooi_local_processing/blob/master/ooinet_data/deployment0004_RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample_aggregate_provenance.json#L4770) for reference. This information is also being listed for quick reference on the Data Team Portal page for RS01SBPS-SF01A-2A-CTDPFA102 ctdpf_sbe43_sample http://ooi.visualocean.net/data-streams/view/6114.\n",
    "\n",
    "`<module>`\n",
    "```\n",
    "mi.instrument.seabird.sbe16plus_v2.ctdpf_sbe43.driver\n",
    "```\n",
    "\n",
    "Next, specify the reference designator this data came from and to produce the log:// output, along with one of the following parsed dataset output options:\n",
    "\n",
    "```\n",
    "csv://\n",
    "pandas://\n",
    "xarray://\n",
    "```\n",
    "\n",
    "For this example, we will choose the csv:// output type, but if you are parsing a directory with multiple files, you may want to consider a more efficient output type. Finally, specify the path to the raw data file. See fully qualified playback command below.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "playback = 'playback datalog mi.instrument.seabird.sbe16plus_v2.ctdpf_sbe43.driver \\\n",
    "RS01SBPS-SF01A-2A-CTDPFA102 log:// csv:// ./raw_data/CTDPFA102_10.33.3.195_2101_20171005T0000_UTC.dat'\n",
    "print subprocess.call(playback, shell=True) # if output 0 then it ran without error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Running this command should have produce the following two files in your ooi_local_processing working directory:\n",
    "```\n",
    "ctdpf_sbe43_sample.csv\n",
    "mi-drivers.log\n",
    "\n",
    "```\n",
    "Now lets inspect what is in the csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dim_0</th>\n",
       "      <th>conductivity</th>\n",
       "      <th>driver_timestamp</th>\n",
       "      <th>ext_volt0</th>\n",
       "      <th>pkt_format_id</th>\n",
       "      <th>pkt_version</th>\n",
       "      <th>port_timestamp</th>\n",
       "      <th>preferred_timestamp</th>\n",
       "      <th>pressure</th>\n",
       "      <th>pressure_temp</th>\n",
       "      <th>quality_flag</th>\n",
       "      <th>stream_name</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1376076</td>\n",
       "      <td>3.718126e+09</td>\n",
       "      <td>14738</td>\n",
       "      <td>JSON_Data</td>\n",
       "      <td>1</td>\n",
       "      <td>3.716150e+09</td>\n",
       "      <td>port_timestamp</td>\n",
       "      <td>712870</td>\n",
       "      <td>24791</td>\n",
       "      <td>ok</td>\n",
       "      <td>ctdpf_sbe43_sample</td>\n",
       "      <td>441232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1376078</td>\n",
       "      <td>3.718126e+09</td>\n",
       "      <td>14737</td>\n",
       "      <td>JSON_Data</td>\n",
       "      <td>1</td>\n",
       "      <td>3.716150e+09</td>\n",
       "      <td>port_timestamp</td>\n",
       "      <td>712869</td>\n",
       "      <td>24790</td>\n",
       "      <td>ok</td>\n",
       "      <td>ctdpf_sbe43_sample</td>\n",
       "      <td>441230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1376079</td>\n",
       "      <td>3.718126e+09</td>\n",
       "      <td>14741</td>\n",
       "      <td>JSON_Data</td>\n",
       "      <td>1</td>\n",
       "      <td>3.716150e+09</td>\n",
       "      <td>port_timestamp</td>\n",
       "      <td>712869</td>\n",
       "      <td>24789</td>\n",
       "      <td>ok</td>\n",
       "      <td>ctdpf_sbe43_sample</td>\n",
       "      <td>441228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1376081</td>\n",
       "      <td>3.718126e+09</td>\n",
       "      <td>14742</td>\n",
       "      <td>JSON_Data</td>\n",
       "      <td>1</td>\n",
       "      <td>3.716150e+09</td>\n",
       "      <td>port_timestamp</td>\n",
       "      <td>712868</td>\n",
       "      <td>24786</td>\n",
       "      <td>ok</td>\n",
       "      <td>ctdpf_sbe43_sample</td>\n",
       "      <td>441227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1376081</td>\n",
       "      <td>3.718126e+09</td>\n",
       "      <td>14741</td>\n",
       "      <td>JSON_Data</td>\n",
       "      <td>1</td>\n",
       "      <td>3.716150e+09</td>\n",
       "      <td>port_timestamp</td>\n",
       "      <td>712868</td>\n",
       "      <td>24786</td>\n",
       "      <td>ok</td>\n",
       "      <td>ctdpf_sbe43_sample</td>\n",
       "      <td>441225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dim_0  conductivity  driver_timestamp  ext_volt0 pkt_format_id  \\\n",
       "0      0       1376076      3.718126e+09      14738     JSON_Data   \n",
       "1      1       1376078      3.718126e+09      14737     JSON_Data   \n",
       "2      2       1376079      3.718126e+09      14741     JSON_Data   \n",
       "3      3       1376081      3.718126e+09      14742     JSON_Data   \n",
       "4      4       1376081      3.718126e+09      14741     JSON_Data   \n",
       "\n",
       "   pkt_version  port_timestamp preferred_timestamp  pressure  pressure_temp  \\\n",
       "0            1    3.716150e+09      port_timestamp    712870          24791   \n",
       "1            1    3.716150e+09      port_timestamp    712869          24790   \n",
       "2            1    3.716150e+09      port_timestamp    712869          24789   \n",
       "3            1    3.716150e+09      port_timestamp    712868          24786   \n",
       "4            1    3.716150e+09      port_timestamp    712868          24786   \n",
       "\n",
       "  quality_flag         stream_name  temperature  \n",
       "0           ok  ctdpf_sbe43_sample       441232  \n",
       "1           ok  ctdpf_sbe43_sample       441230  \n",
       "2           ok  ctdpf_sbe43_sample       441228  \n",
       "3           ok  ctdpf_sbe43_sample       441227  \n",
       "4           ok  ctdpf_sbe43_sample       441225  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_data = pd.read_csv('ctdpf_sbe43_sample.csv')\n",
    "parsed_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the raw CTD data as it came off the instrument and through the cabled network. Note that the preferred timestamp for this data is identified as the port_timestamp.\n",
    "\n",
    "Now lets run this parsed L0 data, as it would be stored in Cassandra, through the applicable OOI Data Product Algorithm (also known as `processor` or `ion-function`) along with with the correct calibration values applied.\n",
    "\n",
    "We are looking at data from 2017-10-05, which means that on that date, the Unique instrument Identifier (UID) ATAPL-66662-00008 was in the water, according to the RS01SBPS platform deployment sheet on GitHub.\n",
    "\n",
    "https://github.com/ooi-integration/asset-management/blob/master/deployment/RS01SBPS_Deploy.csv\n",
    "\n",
    "This then lets us identify and pull the correct calibration sheet, which has been placed in the /calibration_data folder, and can be found here:\n",
    "\n",
    "https://github.com/ooi-integration/asset-management/blob/master/calibration/CTDPFA/ATAPL-66662-00008__20170125.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serial</th>\n",
       "      <th>value</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CC_a0</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>1.220000e-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_a1</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>2.800000e-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_a2</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>-1.460000e-06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_a3</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>2.000000e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_cpcor</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>-9.570000e-08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ctcor</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>3.250000e-06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_g</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>-9.770000e-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_h</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>1.540000e-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_i</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>-1.890000e-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_j</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>3.940000e-05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_pa0</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>9.150000e-02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_pa1</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>1.550000e-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_pa2</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>6.730000e-12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptca0</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>5.240000e+05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptca1</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>5.250000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptca2</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>-1.160000e-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptcb0</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptcb1</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>5.000000e-05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptcb2</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptempa0</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>-8.090000e+01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptempa1</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>4.720000e+01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_ptempa2</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>-2.370000e-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_offset</th>\n",
       "      <td>16-50115</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              serial         value  notes\n",
       "name                                     \n",
       "CC_a0       16-50115  1.220000e-03    NaN\n",
       "CC_a1       16-50115  2.800000e-04    NaN\n",
       "CC_a2       16-50115 -1.460000e-06    NaN\n",
       "CC_a3       16-50115  2.000000e-07    NaN\n",
       "CC_cpcor    16-50115 -9.570000e-08    NaN\n",
       "CC_ctcor    16-50115  3.250000e-06    NaN\n",
       "CC_g        16-50115 -9.770000e-01    NaN\n",
       "CC_h        16-50115  1.540000e-01    NaN\n",
       "CC_i        16-50115 -1.890000e-04    NaN\n",
       "CC_j        16-50115  3.940000e-05    NaN\n",
       "CC_pa0      16-50115  9.150000e-02    NaN\n",
       "CC_pa1      16-50115  1.550000e-03    NaN\n",
       "CC_pa2      16-50115  6.730000e-12    NaN\n",
       "CC_ptca0    16-50115  5.240000e+05    NaN\n",
       "CC_ptca1    16-50115  5.250000e+00    NaN\n",
       "CC_ptca2    16-50115 -1.160000e-01    NaN\n",
       "CC_ptcb0    16-50115  2.500000e+01    NaN\n",
       "CC_ptcb1    16-50115  5.000000e-05    NaN\n",
       "CC_ptcb2    16-50115  0.000000e+00    NaN\n",
       "CC_ptempa0  16-50115 -8.090000e+01    NaN\n",
       "CC_ptempa1  16-50115  4.720000e+01    NaN\n",
       "CC_ptempa2  16-50115 -2.370000e-01    NaN\n",
       "CC_offset   16-50115  0.000000e+00    NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_values = pd.read_csv('./calibration_data/ATAPL-66662-00008__20170125.csv', index_col= 'name')\n",
    "cal_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process Parsed Data\n",
    "Now that we have our parsed L0 and calibration data loaded, we are ready to import the correct ion-fuction to process the data. For this example, we will process the L1 `seawater_temperature` data product from the L0 `temperature` values in the csv.\n",
    "\n",
    "http://ooi.visualocean.net/parameters/view/908\n",
    "\n",
    "Again, the correct function being used to calculate a given data product can be identified in the provenance json file [here](https://github.com/friedrichknuth/ooi_local_processing/blob/master/ooinet_data/deployment0004_RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample_aggregate_provenance.json#L5047) or on the Data Team Portal under :\n",
    "\n",
    "http://ooi.visualocean.net/parameter-functions/view/35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from ion_functions.data.ctd_functions import ctd_sbe16plus_tempwat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# prepare your inputs for the function\n",
    "t0 = parsed_data['temperature'].values\n",
    "a1 = cal_values.loc['CC_a1'].value\n",
    "a0 = cal_values.loc['CC_a0'].value\n",
    "a3 = cal_values.loc['CC_a3'].value\n",
    "a2 = cal_values.loc['CC_a2'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# run the function to get the seawater_temperature and grab the time stamps\n",
    "seawater_temperature = ctd_sbe16plus_tempwat(t0,a0,a1,a2,a3)\n",
    "time_ntp = parsed_data['port_timestamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert those ntp epoch timestamps (seconds since 1900)\n",
    "ntp_epoch = datetime.datetime(1900, 1, 1)\n",
    "unix_epoch = datetime.datetime(1970, 1, 1)\n",
    "ntp_ordinal = ntp_epoch.toordinal()\n",
    "ntp_delta = (unix_epoch - ntp_epoch).total_seconds()\n",
    "\n",
    "def ntp_seconds_to_datetime(ntp_seconds):\n",
    "    return datetime.datetime.utcfromtimestamp(ntp_seconds - ntp_delta).replace(microsecond=0)\n",
    "\n",
    "time = []\n",
    "for i in time_ntp:\n",
    "    time.append(ntp_seconds_to_datetime(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a human readable timestamp and the L1 data product, time to compare these data to the data output by OOI net."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import OOINet Processed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import the data from an asynchronously produced NetCDF file served up on THREDDS.\n",
    "ds = xr.open_dataset('./ooinet_data/deployment0004_RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample_20171005T000000.721350-20171005T235959.013428.nc',decode_times=False)\n",
    "\n",
    "# extract the ooinet sewater_temp and time\n",
    "seawater_temperature_ooinet = ds.seawater_temperature.values\n",
    "time_ooinet = []\n",
    "for i in ds.port_timestamp.values:\n",
    "    time_ooinet.append(ntp_seconds_to_datetime(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Locally Processed Data to OOINet Output\n",
    "Let's start by visually comparing two data frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# df will be the dataframe that contains the locally parsed time stamps and temperature values\n",
    "df = pd.DataFrame(seawater_temperature, columns=['seawater_temperature'])\n",
    "df['time'] = time\n",
    "\n",
    "# df_nc will contain the time stamps and temperature values produced by ooinet\n",
    "df_nc = pd.DataFrame(seawater_temperature_ooinet, columns=['seawater_temperature_ooinet'])\n",
    "df_nc['time_ooinet'] = time_ooinet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86398\n",
      "86398\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# data frame lengths are identical\n",
    "print len(df)\n",
    "print len(df_nc)\n",
    "\n",
    "# neither contain NaNs or null values\n",
    "print df.isnull().values.any()\n",
    "print df_nc.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seawater_temperature</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.719425</td>\n",
       "      <td>2017-10-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.719550</td>\n",
       "      <td>2017-10-05 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.719674</td>\n",
       "      <td>2017-10-05 00:00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.719736</td>\n",
       "      <td>2017-10-05 00:00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.719861</td>\n",
       "      <td>2017-10-05 00:00:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seawater_temperature                time\n",
       "0              7.719425 2017-10-05 00:00:00\n",
       "1              7.719550 2017-10-05 00:00:01\n",
       "2              7.719674 2017-10-05 00:00:02\n",
       "3              7.719736 2017-10-05 00:00:03\n",
       "4              7.719861 2017-10-05 00:00:04"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visually they look the same\n",
    "df.head() # locally processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seawater_temperature_ooinet</th>\n",
       "      <th>time_ooinet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.719425</td>\n",
       "      <td>2017-10-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.719550</td>\n",
       "      <td>2017-10-05 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.719674</td>\n",
       "      <td>2017-10-05 00:00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.719736</td>\n",
       "      <td>2017-10-05 00:00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.719861</td>\n",
       "      <td>2017-10-05 00:00:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seawater_temperature_ooinet         time_ooinet\n",
       "0                     7.719425 2017-10-05 00:00:00\n",
       "1                     7.719550 2017-10-05 00:00:01\n",
       "2                     7.719674 2017-10-05 00:00:02\n",
       "3                     7.719736 2017-10-05 00:00:03\n",
       "4                     7.719861 2017-10-05 00:00:04"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nc.head() # ooinet processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A final check reveals that one data point appears to have been swapped between the raw data file that was written directly to the OOINet Cassandra database, and the files that were written out to the raw data archive. Raw cabled data streaming in live gets written directly to the Cassandra database, while the raw data files are written out to the archive via a seperate pipeline. There is likely a bug in one of these two pipelines. Ideally, the raw data files should be written out from the Cassandra database, but we haven't gotten there yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82999    8.302190\n",
      "83000    8.302761\n",
      "83001    8.302507\n",
      "83002    8.302317\n",
      "Name: seawater_temperature, dtype: float64\n",
      "82999    8.302190\n",
      "83000    8.302507\n",
      "83001    8.302761\n",
      "83002    8.302317\n",
      "Name: seawater_temperature_ooinet, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# get the numerical diff\n",
    "test = seawater_temperature - seawater_temperature_ooinet\n",
    "\n",
    "# find all values that are not 0\n",
    "[i for i,x in enumerate(test) if x != 0]\n",
    "\n",
    "# observe the swap in the center values. likely a bug when writing out the cabled raw data to file.\n",
    "print df['seawater_temperature'][82999:83003]\n",
    "print df_nc['seawater_temperature_ooinet'][82999:83003]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# some plots for good measure\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD7CAYAAACfQGjDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXd8HNW1x39nm3q15CbLlns3Nhib3mzAlEAooSUP3oNg\nEkiBkJdAeGkkEJJACEkgCTx4EDoEAiQ0G7BpMQZ3XGTcbcm2JKt3aXfv++PO3R2tts3uzJ3d1f1+\nPvpomzRnd2fuuacTYwwKhUKhUITDYbcACoVCoUhdlJJQKBQKRUSUklAoFApFRJSSUCgUCkVElJJQ\nKBQKRUSUklAoFApFRJSSUCgUCkVElJJQKBQKRUSUklAoFApFRFx2C5AsZWVlrKqqym4xFAqFIq1Y\nu3btEcZYeazXpb2SqKqqwpo1a+wWQ6FQKNIKItoXz+uUu0mhUCgUEVFKQqFQKBQRUUpCoVAoFBFR\nSkKhUCgUEVFKQqFQKBQRUUpCoVAoFBFRSkKR8nT1eXH0L5Zj5fZ6u0VRKIYcSkkoUp7dDZ1o6uzD\nr9/abrcoCsWQQykJRcpDZLcECsXQRSkJRdrAGLNbBIViyKGUhCIuth9ux77GTluOTVCmhEJhF7Yp\nCSJ6jIjqiWiz7rGfEVEtEW3Qfs61Sz7FQM7+/Qc49bcrbTm2cDcpQ0KhkI+dlsTjAJaEefx+xthc\n7ecNyTIpUhChHBiUllAoZGObkmCMfQCgya7jK9KHuvYeAMCh1h6bJVEohh6pGJP4FhFt0txRJXYL\no7Cfv6+tAQC093htlkShGHqkmpL4M4CJAOYCOATgvnAvIqKlRLSGiNY0NDTIlE9hA5cePQYAUDUs\n12ZJFIrINLT3os/rt1sM00kpJcEYq2OM+RhjfgCPAFgQ4XUPM8bmM8bml5fHHKyUkWyubUXVba+j\ntqXbblEsRwSu87LSfkaWIoM59q53cM1jn9othumklJIgolG6uxcB2BzptUOdbzy1FgBw+8uf2yyJ\n9XT1+ewWQaGIitfHLYhVuxttlsR8bNuaEdGzAE4DUEZENQB+CuA0IpoLgAHYC+AGu+RLdWqauQWx\nbl+zzZJYz8//uQUAsOVgm82SKBTh8Wdw4p1tSoIxdmWYhx+VLkia4/Vnng80lLq2XrtFUCiiksnp\n2SnlblLET1GOGwBQXpBlsyQKhSKTCz2VkkhTrlo4FgDwlWMqbZZEoVD4M1hLKCWRpjiHUGvU28+Z\nBgA4eXKZzZIoFOHJ5JiEUhJpyhDSEfC4+GlKQ+lNK9KKTO5QrJREmpPB52aAofAeFemNsiQUKYfY\nU2dyVoUg89+hIt1RloQi9dBcLxl8bgYQF6ByNimiUdfWg5rmLluOncmWhOpzkKYELYnMZygoQkXy\nLLz7XQDA3nvOk35sZUkoUo5ADDeDT07BUHCpKdKbTLYklJJQpDziAlTJTYpURVkSitRlCKycGXz9\nKTIEZUkoYvLpniZsO6Qa0FmBcjcpUp1MPkdV4NokLvvrKgDygmZDaXct3mvm20yKdEVZEoqUQ5yT\nQ2HhzGR/ryIz+GhH5k7IVEoiTRFDToZASGJIWU2K9KSxs89uESzDNiVBRI8RUT0RDZo+R0S3EhEj\nItXRLQIPrdwFAGjsyNyTUxDMbhoCGlGRlmTyRsZOS+JxAEtCHySiSgBnAdgvW6B0pLW7324RLCeT\ng4IKRapjm5JgjH0AoCnMU/cD+AGGRjFx0gyFzXUm79IUmUEmx81SKiZBRBcCqGWMbYzxuqVEtIaI\n1jQ0ZG7ASMERF2AmX4iK9GbS8AK7RbCMlFESRJQL4EcAfhLrtYyxhxlj8xlj88vLy60XTmErLOS3\nQpFqjCnJAZCZln3KKAkAEwGMB7CRiPYCGANgHRGNtFWqFCcDz8lBCANCGRKKVCcTz9GUKaZjjH0O\nYLi4rymK+YyxI7YJlQa4nNbrebvdPGJ+cAZefwpFymNnCuyzAFYBmEpENUR0nV2ypCMXzh0NAJg0\nPN9mSawn4G5K421ae08/3tlaZ7cYCotI41MzJrZZEoyxK2M8XyVJlLRkZGG2tGPZfQHYfXwzuOX5\njXhnWx0+/MHpqCzNtVschclkcpp2KsUkFAaQeUraffoHs5tsFiQJ9jV2AgC6+302S6KwgnQ+N2Oh\nlIQi5QlmN6X/lZjJi8lQJpO/VqUk0hSZWU12xwIywZIQqZGZoOgUQwulJBQpjz+DUmBpSCQtDz3s\n3khZiVISipjYffoHU2DtliRxMngNUcD+a8RKlJJQpDyZtMBmYkWuIrPO0VCUkkhzZJycdl8APn/6\nxyQUmU7mnpxKSaQrEnekdrt5MqniWik6RbqhlES6MoQWG3+geZO9ciRDYNyscjdlJJms/JWSUMTE\n7gvA79fkSGMtsbO+A8DQaMg4FEnfMzM2SkmkK0NotfFnQJ2EIAPegiIMmXBuRkIpCUXK48ugmIQ/\nk1eTIYyqk1AMaew+/4PzJNL/QsyAt6AYYiglYQKZsHilMoEUWJvlMANlSWQmmfyt2jlP4jEiqiei\nzbrHfkFEm4hoAxEtI6LRdslnBH8mnyGwP2CcUTGJDHgPisFk8vdqpyXxOIAlIY/9ljE2hzE2F8C/\nEMe861Tg0z1NdouQ0WRCncToIj7/I5MXk6GM3RspK7FNSTDGPgDQFPJYm+5uHtJkXahv77FbBEux\ne2ETKbC2C2ICyt2UoWTw15oyM64FRHQXgKsBtAI4PcJrlgJYCgBjx46VJ1wEMv26t/vtiYU1nd16\npFXRKSWhSDdSLnDNGLuDMVYJ4GkA34rwmocZY/MZY/PLy8vlChgGOy/8TDZzBUElkf7vNf3fgSIc\nmfy9ppyS0PE0gEvsFiIevOm8xY0Du7O3xMebCR+z3Z+lwhoy+WtNKSVBRJN1dy8EUG2XLEbw+jL4\nDEkBRAqsP421hOjZlMZvQRGFTLbobYtJENGzAE4DUEZENQB+CuBcIpoKwA9gH4Bv2CWfEdxO+T0y\nZE44s/v0zyh3U/q/BUUYMvl7tU1JMMauDPPwo9IFMYGJw/NtO/ZQmCchlIPPbkFMIBMUnWJokVLu\npnRFXffWEugCm8afc9DdJPdNXPaXVfjzyl3Sjtfv8+OT3Y3SjpcqpPGpGROlJEwg44ORqWJJZIJD\nX/Jb+HRvE379lrzQ3r3LtuOKhz/BhgMt0o6ZCmTyGqCUhAnYuXZl8skpyISYhIghZYKei8bOOj43\n40h7r82SyCWTv1alJEzA1joJCYe2e3EWC2sa6wjsb+oCYP9naTXCrZbZ7zIMGfyGlZIwAf11n4k7\ne7sXNuFmygR3k92fpfWoyvJMQykJE9ArBtnXhozD2Z1VxDLA3SRI/3cQnYAlkelvNIRMrpNQSsIE\n9Btc2QvZUEiB9WWSksiA9xCNITRVdwCZ/LUqJWEC+l2ErHMl6Pu1/oh2u3lECmwGeJuCHW0zlExv\nURMJpSQUUbHDkpB5Utq9g8+kFNj0fwfRea+6HgDQ2t1nsyRyyeTvVSkJE/DbGZOQkd1k8+43E1Jg\nBZnwHuKhrdtrtwgKk1BKwgzsjElIOEafz14tIQyI9p70X3gyPSYhGF6YZbcIUsnk71UpCRPQKwbp\nHhEJJ+fTq/dZfoxo6Lu/1rel9xTADF5LAADlBVw5lOR6bJZELpn8tSolYQK2xCQknpa1zd3SjhUO\n/Wdan+aVvBkQVolKg/b9DBW3miCT365SEiYwoE5ClmdG4iCeU6faO/1PX6eR63HaKEnyyFw87Zi/\nMWNUIT92Jq+aYcnc92ubkiCix4ionog26x77LRFVE9EmIvoHERXbJZ8RbK2TkHBy2j3sx+8PKge7\n4yPJIvOTtDMd1e5kB4V52GlJPA5gSchjywHMYozNAfAFgNtlC5UIbEBMQs6FGcz4sf5YdqeeMsaQ\n7daUhDe9Vx+ZAU47vrdMmv1hhEx+u7YpCcbYBwCaQh5bxhgTKSyfABgjXbAE8NkQuA7Ofbb+gHYX\nSB1s7UFXHz8t0nVWQUEWn+8l09L02rCdz4RRs4mQye82lWMS1wJ4M9wTRLSUiNYQ0ZqGhgbJYg1G\nv2OTtVMUx8z0LrCNHTwQ2tPPF7y736hGW0+/bfIki9QiSBuMLp9ECzeVUJaEZIjoDgBeAE+He54x\n9jBjbD5jbH55ub1BVcCeFNhA0zsJB/TaoAQF4d5eb38aupwCk+nkHdIOS0Kcj0PO3ZTBtkTKKQki\n+k8A5wP4KkuTChV9LFXWrlvmjm3N3ubAbdnfiLCYbjhlAvJtcNmYhkT3oMCOmIRP4uZFIYeUUhJE\ntATADwBcwBjrslueeFm/P7iIyloE6tvk5aOLfjyAfN9rv6aBJw7Px0/OnwHA/hhJUki1JGwIXGsb\nJruTHWTT2Zv+3QAiYWcK7LMAVgGYSkQ1RHQdgD8BKACwnIg2ENFf7JLPCE+v3h+4LWujuGxrnXY8\nuRfjRzuPSD2eWGzcToLLyX02Pl/6LkAZb0mIwLX0lvn2nhMrt9sfG7UKl10HZoxdGebhR6ULYjKy\nztWCLBfae73SA4Q76ztw6hR5cSDhV3c6HIGFwA5fe7KIr0luTEL+wiksPzGuVd5x7VUSJ0wqw5ub\nD9sqg1WklLspE5C1gzppcpnU4wmKc9xSjycWOreD4HLw0zWdXRkyA5w+G5SpUBIHW+T22LL7nPCm\neZFnNJSSMIHvnDEpcFvWoi12TrKvDadD7uwxr/Y+nQ4KHDudYxKZbkm4nXxJmTOmSOpx+222Lvt1\nSiKdU7TDoZSECRAFF05Z16XYJcr2xTokKwlx8bmdDriEkkjjmITM78uOz0lkN8ne2dsdp9K7uw62\n2NsQ02xsi0lkEgMa/ElaBLw2BQhdkpWEWGycDgrUGqRjTEIg8+uypS2H3x4lobck/H4mfTOT7u1i\noqGUhAnoLwdZ14bXJndTQbbcU0Y09HM5CZQB6ZVy23LI/5zatMFQsovp9OeEnzE4YI/FC6RpsWcU\nlLvJBPTXg7RiOpssCdkxCbFDy3Y7VUzCIHYqU9nH1rvW7Kj21p+TO+o7pB/fSpSSMIGBbTkkBa4D\nMQkphwsg+3i9mpLIcjnSOrtJuCGlxiQku+UGdEOW7W7y6d1NUg8NYKC76fsvbpQvgIUoJWEC+stB\n1hogFso9RzrlHFBDtuUSVBLpbUmI95HJMQn99yL7Owp1N8mm3+dHjtbO/uyZI6Qf30qUkjABO9xN\nIptiw4EWKccTyF6fe/t9ALgl4XaK7Kb08/nakWgge6EesJuXvFD32+xuWruvGd3aufr2ljrpx7cS\npSRMYODQIeuP5/X5Ud/Gi5WOnzDM8uMVZLkwbywfEmifJeFIa0tC8Ks3q6UdS3ZaqN7lIj0FVt+p\n2IY9RPXh9gH37W4TYiZKSZjAXz/YHbgtYxH9yWtb0NjZBwBo7uqz/Hg+xuDW4gGyT36hJDy6mMQv\nX9+K+na5Fb3JsFeyS1AgW5nubQy24rAzBdYOS2LcsFwAvFsxEJx/kgkoJWEyr204aPkx3tL1iAnd\nwViB18/gdvFdvOygoL6YTlgSB5q6cesL6RMcfGLV3sBtGZafQPZCre/XZKclYUdMorIkF0ePLcZy\nrfGm7N5VVqKUhMm8sqHW8mN4nHK/Nr+fBXbxsi9Ab5iKa0D+Z5AMpMvZH1WULe24srObJpTlBW7L\n3s0PzG6SryT6vH5kuZwBy7ejN3Nac6TPlZYmyDhBiyQ32fMxFggay77+REDS7aQBNRrHT5S3I0+W\n/U1Bd1OfxKC7nbt5O+sk7AhZ9Xp98Lgc+P7ZUwAAJbke+UJYhJ3zJB4jonoi2qx77CtEtIWI/EQ0\n3y7ZjPD+FwP7yMs4P4tz5SkJv5+BMQQsCdkxCa/fD6eDQBScJwHY3xraCO9sCw5t6peoJKSnoTL7\nlMQABWWDu6mxsw9EgMfJ02DT6fyMhZ2WxOMAloQ8thnAxQA+kC5NgtS1DgygyrAkxBhPAIEdvlWI\nC87tEu4mSw83CK+PBdxMo4py8Nq3TtQeT5/A4H+dWAUAGFuaK3XxGEqWhN3upprmbqzc3hDYyMjc\nDFiNbUqCMfYBgKaQx7YxxrbbJFJihKzRY4flhX+diYzVMim+unCs5bvFwGQ4h3A3yc9/d+viD7Mr\nikCUXhdheUEWAN73KpMtCb3LZ6gV0wlErCydzs9YqAZ/SRK6j794XoXlx/T7GYpz3SgvyAJj1na9\nFBecy2mXkvAPsJaICG6HA31pZM7vrOO9fHLcTqmLx4c6VyhjbEBLeyt4a/OhwG35KbD2xiTcTsL1\nJ08IXCfpXMsTSloGroloKRGtIaI1DQ32zpYNbXgnwx/q9TM4iQJuGCuPKS72li6erfHQil2WHSsc\nXr8frpBMJreT0srd9PJ6nvGW7XZKdTe9Wx2MhchYs5797EDgtnxLwr5CPnFMp4MCVm9/BrUOT0sl\nwRh7mDE2nzE2v7xc3rzlcDhCdmcyFi9xQjolNLwT/7tJK97bXmd9XYaeF9fUoKG9d8BjbpdDapaQ\nWbidJNWSuHnR5MBtGQunf0BMQu73o1e+spMrGGPwM74WuDKgK0AoaakkUok8LYh890WzAcg5OXx+\nHsyVcUKKxWVYvj0pfeHem9vpSMvsEbfTIXU4jd7CbJcwUnNAgz/J34+d2U3i2C4HBazedOxUHAk7\nU2CfBbAKwFQiqiGi64joIiKqAXA8gNeJ6G275IsX4aM/qrIIDpJzcvj8DE5d3YCVPXrEBbdgPK9L\nuOn0iZYdK148TkdaBQavWjgWZflZcLvkyq1fqEMtXiv4tjbrPdfjtCFobmPfKO0aceg2bul0fsbC\ntsA1Y+zKCE/9Q6ogSRLcRfDeQjIuDq9WAR0Mkll3Qta3cVdPtlvUSVh2qLCUF2Rh8fThAx5r6+5H\nXVv69G7aVd+BIx29mnKT9wHqXYMyzkuRxTUs32NDB1q9u0nqoQeM2BXXpLIkFAHEjoHHCOQEVH1+\nBgcFg+ZWnpBf+tNHABBo8PfQSrmB64b23kA8RNDe68WHO45IlSMZVu/hmd6yYxKijxAgZ9ESC3W2\nyyk9JmFnjUZASaiYhCIcA/2RJMmS8MPlcMBJ1p+QYlcme7A8ALR2cz96pvTnd9voJpPRx0lskLLd\nTukxCX0XWNlp2uLQDl0yiey+WVailESSiAXa5SS093jx5Kp9lh/T5w9aLvy+9ReFfBUBtGpptz8+\nf4YNRzcfOwPuMs4RcS1kux3yd/MDejfZE5NwEoKWRBomVkRCKYkk+emrWwAEexvJyW7yw+WU6/9k\nAI4ZV4KTJpVZfizBYS3uMHl4/oDHF09Pz/GQst1NeqRYuNrCmOWSH7jWF9NtPdgm9didvV4AQK7H\nFbC4/72rUaoMVqKURBIwxgIjC0VgVwbekDoJGRekXzumTDP6zn9xBTyicGB77Xe2cfdT1W2vS5Ml\nGWZXFOH0qeXS3U2nTw3WEMmxJPy8yZ3LId3d8qf3dgRuv7TO+nb9el5cw4sIm7v6Am05/rFergxW\nopREEhzUNfcrltga2BdacS1hAXhhzQG4HCTNjcAYw+ZaviMMtSROm2pvAaVR+n28aly4m+wYbSnD\n/dHv4xMMeQKH5LiA7nAyuyQf6ejFH97bCQA4dWp5IMMLQFpl4EVDKYkkWHTfSgDAl44aDYAPXSnI\nsj6rOFhxbX0KrODMGSM0S0LOxb9bN/IzNGj++H8twJKZIwGkxyzh6sPt2HOkEx6XaP4mX+Y2GcV0\nPu4GdTvlbSYABOa9C644dqy0Y8//5TuB29NGFgIArj1xPABg4d3vSpPDSpSSSAIxx/bui2YBAI4e\nV4KCbGuVRE+/D6v3NAVaEwMDx5mazcRy3tX2hlMnwinRkujR3HiReGsLf8+pngorWlXsrO8INCq0\n2uV0pKMX//Ho6gGpwwdbui095oGmLvzvR3vgchB6+/2BeJIMFoQsxp/stjceMH1UQeB2JtRLKCWR\nBCWaWVuQzX/nZ7nQoQWxrOJNrdNmbUs3qg9zd8z/fbzXsuN5XM5AoNjlkJedE6+7QqTJpirLtgYV\nuFtSG+mHVuzChzuOYGNNa+Cx71k8E/zk36wAALT1ePFudT3ae7zYdkhuAFnw+L/32nJcgb7b7uLf\nvW+jJOaglEQSNHcNXKByPU509vksdYHc8nzwYr/n4jkAgP85b7plx/P5g626ZXZfjXch7U2Tbpu/\nuXQOvtAqoL/QWodbxWMf7wncLs0Lxsqs6t/U6x1o9U3SYkjnPPChJcfTc9frWwO3R0ucHx6NZVuC\nG4M9OrdpuqKUhInkZbng8zNpC1euh49KtLLQzetjgdiHyymn7QiAuBvhdfdZa7klyzeeWgeA95t6\nTmul/cbnh6L9iakM0ymJli5rlETo/y3P58FbGQHkRz4MKkR9Iklolb5MvnfWlAH3093lpJREkkwb\nGfQ/5mmLdqdFLqdDrUG/8iVHj0GWlnb7g79vsuR49W092H2kM6AkNh5okbYz6vFGj0kIfvzqFilB\n2WQhAqq0qYVZLnmXXYku684qAzc0QCu8Ld198X2HVrDniLXWmiDH7Rz0WFXIdMp0b/anlESCiN1B\n9eFgEzXRNrzLootDf9GdMqUMWc7BJ6iZ3LfsCwDAp1rvof1NXQBiB5XNINZnqB/29DtNzlTkP0+o\nAgCcPm04fn/5XAC8KNFKPDolxBDUDKFuIbOYH/J+hJKw0xXY1mO9hdnT7wvUSenJDlEcMq4XK1FK\nIkEaO3oHPZavKQmrgtf6ds9nzRgJt8vaZhliNy9M9wu0VF8ZO8RYx/jqwmCa48gU8UWH0t3nCwRR\nc9zOgHuw3eIFbERhMFf/s73NgdvtFp2Xk0cUDLhPEpu4FGS5MKuCp57qXWsffGH9xMrthyMP4Lr6\n+HGB68Xq79tqlJJIELGDmFtZHHgsV1MSVrmb9PGAHI8TuR5r023FoiZ2hCdO4jMlwu2ezEbsvqqG\n5YZ9/ucXzMQpU3hRnVWWW7L88KWgG9BJFNhh3vqitZlGB5rCp7sebrUmLfXZT/cPuC+rd5LPz9De\n60VFcQ6/rztuqYTi1ic/CfZpE4pKcOeFs3DenFEAUj8DLxZ2Dh16jIjqiWiz7rFSIlpORDu039ba\n5UkgrIVvnBocwpOfpcUkLFq0RNHchPK8GK80B5HaK8jxWOtO0yMU0cs3nhj2eSLC365dwDPKLE47\nTpQjOmvT4aCApWklGw60DLh/2znTAreF29BsZlcUDbhvda2QQFjzQvnpA+hWXYN6hJvtuaXH4fml\nxw96XhTWKksicR4HsCTksdsAvMsYmwzgXe1+StLZy0/CvKyg/1Hs7K1atMSs58vnVwYeO37CMMsu\nyjV7+aLyt2sXAAgG6WT4WNu6vXAQUJwTPUMmL8uVskoitMlbYYz3YgaPfLB7wP1qXa2CPvHBTAqy\nXQFXz7hhufjVxXPgIB6bsDIdXLjPRKWzntc/P2jZcQUdgeMXBOKResQmS8boWCuxTUkwxj4AELq1\nuRDAE9rtJwB8WapQBujqC3Z+FFgdkxDuipmjgzu3UUXZlqUazhvLd0rCrSPcTzIsiT+t2MmHy8dI\n783PcknZNSbCpceMAQBs+tlZAAa2d7dq8ZwZ4vb45UWzA+fHqKIcS475712NaOzsg9tJuP2caSjN\n8+AHS6aBMWtdk71ax4PTp5XjuaXHYfPPz8aq288AENnlZia/fH0bAIRVEECw6eeLa2ssl8VKUi0m\nMYIxJpLIDwMI2xOaiJYS0RoiWtPQYH2AKhxiN61PgQtkN1mkJMQxs3QdZ7PcjrhrCozi9flRqLNS\nhJJqs9jHaiRlMC8rdd1Nfj/DmJIcFOrcdrdr7h+rsm/aur1wOwnv3noq9vzqXORnubDhJ1xJWV2J\nvOOuc7FkFvfDBzZMFrpahALK8bhw3IRhyM9yYVRRDo6tKsFxE0otO24oopI+lCLNcly+tU5aEaoV\npJqSCMD4Vivsdosx9jBjbD5jbH55uT0dQcUFJ3bX+ttW7WzbuvkFl+0KHnNTTSvq2gZnWpnBodae\nAW04Wrt5ltPzWmtkq6ht5rvAOy+cGfO1m2vb8F51Pd7eYl3/qkR5eX0tapoH7mjLtEKzI2Gy48yg\no7cfBdluTCzPH9AeAuCuIKs4e+bA/ZxwgVqVUQUEM+BCaxW4C9J669LjdOCcWSMjPj9c1+L+5uc3\nWC6PVaSakqgjolEAoP2ut1meiHyym3vK9LngWS4HXA6ybGd7i3ai6XPfnRZWWy/bWjfAXTCvkruf\nFo63dpcmmtFNCmkRHo0bnlxrlTimElAS7dYoic5eX9gA+ayKQkwsj//zNEKO24mxpQMVkAxLIujy\nDackrLUuGWPwMRYzieT5pccBGJwEkk6kmpJ4DcA12u1rALxqoyxxMWVE8MIjIktPUOFv1gfqTplc\nDgcFu42aRTifeWGOGw6y3t1UqymJeHzo1b8IzX1IDURcat7Y4gGPlxXwAO+RDmvaRrT3eMMqiRy3\nM7ComonfzwdvuUJcLlbH5wDoBn4NVBL5Husbbe5r7ILPzwItSCKxcMIwVJbmpHVBnZ0psM8CWAVg\nKhHVENF1AO4BcCYR7QCwWLufcggzNz/LNcikz/M40WGRqVuS68GsisIBFbV7GjvhZzC9NbPogyOC\nrwC3Wopy3GjqsrYvjlASo4tjF8llu534zqLJAFKrsvWrj3wCYPCoVRnupvww2W6f7W0OWL9mslXL\nngrt35QnIf1z/X6e7muHJbGplnfYXThhWMzXluZ60GhjL6lksTO76UrG2CjGmJsxNoYx9ihjrJEx\ntogxNpkxtpgxZk1id5I0a4vkHWG6rx5s7cFne80XmzGG96rrA9PaBMdrJ6nZ/Ys2avn2Z88c6HMt\nyfMM6n5rNrsaOlFRnIMsV3xtR97UGuZN+/FbVoplCGGHff3k8QMeL8n1wEHWKYn69t6wacMjC62p\nShc1CpccXTHgcRGTsHJHL+KCoUoxP4t3YzbbutYjBh2NjsPa3VjTig++aEjbjrCp5m5KC3Y38C87\nUqdJ0ePITHbUh29YJnyiZne9fElL29M3MAT4ItdisSWx5WDroArWaPzo3KCyTpWOm63d/Th1Svkg\nRed0EErga/sGAAAgAElEQVTzsixREl19Xuxu6BxgaQounDcaHpfD9NTbzQdbQQRMGzXw+8q3uPsA\nEGw/Uhji7xfZRl0WWpaf7G6Ey0EozIm/Run0e1da4vKzGqUkEkCU/y8IE8A9Y9pwzBwd/wIXLyLQ\n+ZwWCBMMy+MXitlK4t1qnjNQGRKQLMl1o6nTOkvC6/Njd0Mn1u5rjv1ijdOnDccJE7lF9czqfTFe\nbT1tPf3Y19gV9vwAgLJ8DxrazVe0q7TivVDFDnDl3uf1m1638PaWOswcXTgoDiICtVbFr7w+P1q6\n+vH1k8YPeu4fG2oBALsibKySpaffh3e21cPrZ4PczeEQPZwAa6dIWoVSEgkgdtIlYfrDFOW4LenV\nIlIJQ6urxX0zd2zRduNWWxIioHvmjLAlMhER6bI/fnWL6TIZZYvmEpwRYbNQXmCNJXHdE2sAAOfN\nGT3oOdHLyMzNRGevF9WH27Bo2uDvyuNyINfjtKxv0foDLej1+jF91ODP+Dtn8BjV57Wtg54zg3Xa\nBuasOM/RP1w5L7C5s3pCoBUoJZEA+xq5O2lUmO6jlikJLQBYkBXST8ltfhV0p2YSLz1lwqDnSvI8\nlg502dfIXXmThg/eDUdj0vCCQDrw8q11pstlhNc/P4gctxPzKovDPt/Z6x3UY8lMxpcNTsvcfJAv\nmO+Y+Nms398Cxvhs93AU57jRYpGS+Ov7vP3IlBGDz5PZY3hHAqt6ZT20chcA4BJdUkcsrE4btxKl\nJBLgox1HUFGcE7YcvzDHjfYer+m+caF4Qi2JHC2zw0w3gshtnxBmsSnMdqHX67dskIrodDsrAZed\nyHK5/m9rTJXJKLvqOzFjdCGKI3QiFckHVvY1CmWJVvRVUWJeQd2mWq7o5o4JrwyLcj2WTcObOJyf\nm+FcuyJwb5UVs7uBu7GMzAUhokAF9uub5E0mNAOlJBKgpbsv4nQxcSKY3dTrF//is3xDMzmyXA44\nyNwZDyKdtixMDni2BZaLnp2aHzlcGmcsjLqorKKurSfqjIubz+TuECv6GkWa8yws0GUmVqaLMaxF\nEXqHFee4A1X6ZtPY0YeRhdlhe3uJa9AqBXX+UaOR5XKEvT6i8fKNJwAA9jamV5aTUhIJcKi1B8dP\nDJ8fXWTxLia0T4wo4DMzH124a8aHqSYVFeZWZa2IC0gE5I3wu8vm4oZTJoTN7pEFYwy7j3RieEFk\n+Xs0BXvExOC1ON/C+egBoLKUp2o2mBgLCU3HDqU4123ZQt3Q3ovhheE/Y5fTgYIsF1osUlBvfH4o\noal7E8rykJ/lCnRzTheUkjBIa1c/2nu8qCgJnx9dmseVhEiTNQPhpz9fG2Iy+Jjmxgn+rPlcK8O4\nJoSLxKrJX919PgzL8yQ8ba4wx40+r9+2wjphCa3ZGzk7S7ihjnSat1is2nUEAHDB3MFBa3HMwmzX\noPYZiXJAS/OO1qa+ONcTqCkym0Ot3VGrnYty3Wi1SEGF9uOKFyJCWb61MT0rUErCIK9o6XXHjA3v\njxxbynff2+sijzY0yn//nU84+8HZ08I+b7aSEITbkZ85g/u2rSoM2t3QmdRQpUBjOZsGvYiixmu0\n2dbhEL7sJhNbczy4giv2+VWRA6RmZlWJSuv7L5sb8TWF2eZauHqau/ojWhKAZsVY2D7mpEllCf1d\nKs8/iYRSEgYRec7HRrgYx5flwe0kU81sMVFsbIQunsPyzCv7F4u/mCERitiJRgrKJsvuI51hs3Pi\nRRRWmV2BHi+f1/AsojljiiK+plxzRdWb6HYQ6Z6jolRWO4hMa80hzpNoCj3H40Sv1296EgdjDK1d\n/SjKiXwOFudYk6otKshPmpygkpDQV8pslJIwiMjqiTQMx+kgjCzKxmGLpoCFg1sS5iw4727j8Yhv\n6say6hHZVL9+q9qU4+nx+xkaO3sxIokWEnZbEj/7J08wiNaWWwQ869vN6bcluuYC0Yc07ajvMM3i\nFO6maE0Ycy3IvBP/r8/njzpsq8giS0K04xgRxYqJRl6WM2VnskdCKQmDHGrtGVBBGY7iHI9pgWvG\nGLLdDlwXprJUUJLnQXNnvykplWLalpG2GGbR2t0Pxnif/kQRC8eXH/zYLLESIlrfKY/LgdI8j2mW\nxHOf7gcAvHpT+HnggvkGUjZjITKbcjyR32ewyZ+5i7UI/EazUIpzrIlJCIsttF4pXrLdzpRqRBkP\nSkkYoLvPh9qWbkyOMeegONeNJpNO0IOtPejp90ctDBqW50Gfz2+KGevSdqJ29L8Xfu5k4jmzK4I5\n++v2x9/awwyMxGmGF2Sh3qRhUV/U8WB5pArv4Ov459psgjXR3NUf0y0oMv0OtZrboXhXQ+x2Gx6X\nA42dfabXorygDdwKl/kXD14/w476Dnz9CXtreYyglIQBxCIQ2s8oFKeDAl1UkyV0sH04Sk3s3+R0\nUMzq0ERN7ViINjhXLRib8P/wuBz417dPAhDMCpPFdY9/BgBYNG14zNeWF2ShwSR3U0evF7MqCiOO\n0RSItuX/1no8JYpYpMdEyPATiI2N3h1mBqLjwelTI3/OwkozO6Hj4538swtXaBoPohvvO9vqTK1t\nspKUUxJE9F0i2kxEW4joZrvl0fO+lvYZqXhIsHI7f93O+uQznER/qAsjpDYC3JIAkHTw2u9n8DMW\nsc2CwKpxqWI8a6zPNxYiMCzb97tb20T89T+Oifna9h4vNtaY01tob2NnXFPnrtCUr5HOpeGoPsTP\n6wvnVkR93eji2G20E2HboTYUZLkwfVTk1i1CgZitJISlHU9jv3D8/IKZ+OZpPN5n9gwYq0gpJUFE\nswBcD2ABgKMAnE9Ek+yVKkid9qWG67IZDjMWqT6fD04HRc1vL9GURLIplUc6etHvYxGrdq1GZCSF\ntn42ivCTd0mYcyzo6eff03UnjR80pS0condTsjMP/H6GQ609qIhjQTar0FPEGCIVlApEEoFQ/mbx\nwpoaZLmdURdqUcxodvC6ONeNKxdUJvz3DgcFZsAcMtnCsoqUUhIApgNYzRjrYox5AbwP4GKbZQog\nLrLhBdEX0Qeu4Lnjscz/eKhp7saoouyoC4+wJJKdGCf+fliMdgM3njYxsKMyE9FWujDM0Bwj5Lqt\nyaqJxoEmPs4yWuqrnlsWTwEAdCQ5X2D9gRb4/GzQdLZwiKB+st1RhcUqzrtIiLiWmYFrEfSNVe8h\n3qsZ8RdBfXsPjnT0JZV9BwTn0l/1v6txSGIWZKKkmpLYDOBkIhpGRLkAzgUwSG0T0VIiWkNEaxoa\nrKn8DccD7+4AEPySI9GmpV9uPRi9bUE81DR3x/T9lmoX6w+0ortEadQskWiphQBvyeH1M9Ob/NW1\n9SDL5UBhAn2b9LicDnicDqnuJrFjLY2xcApEB+FkM3DufXs7AKDfF9siEbtr0UE1UZo7+5Djdg6a\nLR1KnscJl4NMbVEjFNQ9F8+O+jqxoWszMRX6xTV8EFekGqJ4OV438vQhrQgylUkpJcEY2wbg1wCW\nAXgLwAYAg650xtjDjLH5jLH55eXJfWFWMLuC7ybDdYk1AmMMO+s7MK40epBMHCfbndzXKbJfJsXw\nb/dpC5LZ7pyPdjaiONedsL9XT5/Pb2ozu1iIxT5eV1mhSa6fqjLuhrx58eSYrzXjcwWAA81dYdvk\nhzue2ZXPXxzm52gslRhQEiYe+1BrN4pz3Tg6QreFeHE4CHvvOQ8A8OQn+/Betb2t7WORUkoCALRZ\n18cwxk4B0AzgC7tlAmCoKZcww5M9QT/b24zW7v5Af/xY5MTY2cViR30HinLcgcBvJOaN5WmmZlc1\n93l9Sb8HPT6JrbhF47x4LYngTje5z/BAUzdmji40TQHEw94jXaiKM7vnSEcfPkkym2oA2tuMlYYu\nXF1mWjFPfbLfkoaF1z6e2umwKackiGi49nsseDziGXsl4sSTmy0QFbUrv6hP6pgf7eCutLNnjoz5\n2hy3E81JnsA76zowZUR+zAVHpDaaecEwxnC4tQenx5E+Gg/zx5VgX2MXbnpmnSn/LxZ7GzvhcTri\nzugxY6fLGMNHO48YamMiChV7vYlZgR29Xmyva0dlDBeont0m9vlar02FK4oRt3I6CAXZLtOUhFdz\nrcYT+4kXEZcCEv8+ZJBySgLAS0S0FcA/AdzEGLNuhJcBnlzFZyd/78wpMV4ZzK554/PE3R1tPf14\n7OO9ABBzZw8A158yAQ5CwoPWGWOoPtwW10Q40dnzw53mxYPaur3o7PPFlaUTD8J3LWvAy676TlSW\n5sSMVwlEGmoyi9gOreOskdboYvjQjrrE5j//bdVeAJGn0YVy5YKxcVtX8fCH93YCiC+9tr3Hi8f/\nvdeU44oW9ndeOMuU/wcA3108Gb+5dA4AoK41dduHp5ySYIydzBibwRg7ijH2rt3yCF7X2hD814lV\ncb0+2eSfdfua0dHrxe8uOyqu1x81pgh+FrvHfySWba1DW483rqptUWxn1oIOADUtXab+z+e1mcKy\n2FTTgpmj43MLAuako27S6ixONRBIFS3u39ycmPLcptVIxGpNIygvyEJzV59pTf5EWm2y8T6jiOr4\nsnxzG1uKTKk6kworrSDllESqE2+7iqsWjo2ZIhiNXdo8inhHJM7RRkhuqknM8BKFgpfGMbdXdIA1\n0997sIVfJGYVYA0vzA6ko1rdK6etpx/17b0RB/6EIz/LBaeDkqohEC0n5lXGH0gVRWb5CfYe2lXf\ngTOmDY87BlKW7wFj5hS1McbQ3uPFWXFOIDxj2vCw400T4WmtP5Y4T81CZJyl8iAipSTiQOyCjoow\n2D4chdlutPUk3nRPnDThBv+Eo7wgCyW57oTnPDRqgddT4miBLHbBzZ3mKYlqrW+TmVW6oinifq1j\nqVVs1nb048viH+hDRPD5GV7dWJvwcf++lqdklhrY3Yr4RV5WYr71w209huoERHyu0YQuxaJCOR73\nK8DPU7M2MmJ+zNkzzR2RG2gbn8LV10pJxIEoeDl3VuwAsqAwx41+H0NPf2K1BLUt3Rg3LDdq6+dQ\nHEQJ98nZWd+Bs2aMiGuH6HY6UJDtMq09OQD85X2eL56M9RWKqFLfZFL7i0iIhoTxtMYIJRlf9Gpt\nzki05o+hlGg1MK9uOGj4eEc6etHU2Rd1Gl0oouZG9FtKhl31fAN0WpSeTXrMVBLvaC30zZ6jUprr\ngctBpo6VNRulJOLgox18NKSRwefJDr+pPtRmePhOY2cfVmw3Hkzu6fdhz5FOTDPgLhlRmG1a75n1\n+5vR2efDl+eONqQUYzGhjC/a339xo2n/Mxyf7W1CRXEOJo+Ir12LYM6YopitLSJR08wXXaMT0kTl\nfiLWlahRmGigA6roPWZGMzuRKDFlRHzKuCzfg/Yeb8LJHHpEU8R4ExPixeEgeP0MD67YFfhOUw2l\nJOJALFwzDcxYENkriaQ41rZ0Y0d9B46bYGwBmTGq0JAiE+yo64CfAdPj7EkFcLfQ21vMKQISk/e+\nuzh25pgR9I0CrYxLNLT3orLUuJusINuVcMuKWm3O8g2nTjD8t4unj0jIYtunKZYTJsavmETRnRlp\nsHu1/zFuWHxKSgxESrYle5+XewO+dbo1beTEd3HL8xss+f/JopREHIhq2lg9m/QUJGFJiB3bghgt\nu0OZPqoQHqfxnc62wzweYMSS2K3VjeysTyyVUs9L62owtjQXVVGmuSWK6DGVbMuSaHT0+gy5fAQF\nWe6EJ+gJxVoV54KpZ0xJDmqauw3Hy/61ibuojGSgifjVH7SWNslwoKkbp0+NP5PLrDGxYoJghYHa\nECN8dsdinDBxmGVz45NFKYk4uOsNPq3NSL53h3bxJ1IrIZqXleUZswpKEmyBIBbQaJ1mQ7lJ21WZ\nUXV9sKUHi6bHnzFjhJe+eQIAYP0B6wYQbTvUhurDxtvCF+a4ElISjDHct5w3Iog12yQcY0py0NHr\nNeyvF/OxjbgEiQj5WS7MMLABCYfPz7D1UJuhjdrwQnPGxG7RerB5Te5VJnA4CPPHlaCp07xUYTNR\nSiJOjOZHC/eDkUInQaDLpsFjluR50NXnM1S9KdwwDjLmbxXxkmR9zV5tol6sCtpEOaqyGF86ajRc\nDmtOddHksKbZeMJAgZYBZxSxuF9z/DjDfwsAY7SMOSPBZJ+fYXhBFpbEUf0fytzK4qSmDQLAy+t4\nJpeRa2KEplCSdTeJTEOzugGEo7wwG35mThaY2SglEQNRXHbdScZ8v6IpXyK9iOraepDrcRpuAfCB\nVuvwxeH4XUAif/2ui6J31Qwlz8PdK8l2WhVdOostUhIAkJ/lTNitE4sH3kncjVKY7UZXn89wvOTN\nzdw6XTA+saD3SC1O8NHOI3H/zSe7G3GotQfnHzXK8PGSHSLFGMOftey3mw3ErYpz3fA4HUkXqm2q\naUFJrtvU4tFQyvNTt15CKYkYiGBZPF0v9Yg0wd8tN9afsLvPh//7eC9yPdGHqoTjK/OND0MRw2+8\nBs3cwGCfJDNHRBqt2amFevKzXOg0Yf53KNsPt+NPK3ibiOW3nGL478Wc5PsNnCOMMdz+8ucAEs/Z\nF9lBRmISYgNy8mTjXZdnVxTB52cJz2DfUd+B3Q2duPPCmYYscyJCn8+PJ5JozVHf3oMX1tTA62eW\nNlEU8ZOv/GUVdiRpdZmNUhIxWK8torMMZDYBxvy2eu55k8c/xsRZRKdHBH6NDDL54Us8HjF3TPyF\ngkCw0VkibhY9Nz7NG/CZnVqop7mrH939vkBVuVmIQO7/Xj3fcPorACyezt0Xj3wY/3yHD7V07KMq\ni+OagBeOXM0KvHdZfMppf2MX/qrNWk/ELSgKQvcmGJh96hPeN23R9MSUYqK1SgCw4C7eGSieJpvJ\nIK7drj4fzrz/A0uPZRSlJGKw70gnst2OQM69EWaMKox71KngtY184XnivxYYPp7Ivlj65Nq4Xt/e\n0x9wwxhtXyCKpJI1j7/QGs0tnGAsk8sIwqq7/glzWzL/UWs2tzjONhGh5HpcOHVKOfwsvl29389w\n9WOfAgB+c8mchI5pFL+f4ZTfrgAAnDgpMffWxOHcYko0DfZvWnPNRNw93z5jEpwOSigFWj9U67eX\nWvt5D8vPwjPXLwzctypInghKScTg37saMbo4JyHLoKos15AbZ39jF5q7+vGjc6cl5McVeeHx8tyn\nBwK3jb6/XI8Lo4qyk6po1WedGMlaMcoPl0wDwAcRvbI+8TYYesQCkqwFJHr3bIyjKlxYQsdWlWCq\nwc1HKCKVNJaSf2r1vsDtR66en9CxqoblgQjY02BcSdRqHQTOm2M8FgIAszRX19ZDxhtf6gP7MuZ1\nnDCxDL++hMcGD7WmTpsOpSSi8PHOI9h6qA27Ezi5Ad7+emd9R9y+3wc1//YZFmZR6BGpvZt+dlZC\nfz95REFgml0i/F4L+l6eQCzFCNluJ+ZqfbduNqlgSdSH/DrJHf03TpsIAHg/jkr55z7jTeaevG5h\njFfG5jLtM4/VEHLjAa68Pr1jUcBNZZRstxOji3Kw+4jxmpo3te7Lt8bRoj8cIvVWdE0wwjptdsVr\n3zoxoWMngujUsHJ7crNozCQllQQR3UJEW4hoMxE9S0TWbTOj8IzW+fGlbx6f0N+LQF28ZvbGmhac\nNrU8rpkOySICuWfNGBH3yM1QJpTlYX9jV8JNDJ9ZzT/fW88yt9I6HK/cZO6FLuaXz62Mvz14OMZp\ndQ6xhlq9tfkQ3t5Sh8rSnJizpeNBZDhd98SaiLPKDzR14aV1NVgyc2TSlt6E8jzDmy3GGJ5evR+z\nK4owIYG+WEDQRbUjgaLP5Vq/pikJxJsSRVSzL9uaOiNN5TZljwMiqgDwHQAzGGPdRPQCgCsAPC5b\nlob2XswbW4xjxiXmL7/mhHHY8HwLPtndGLP5W7/Pj10NHXE3L4vE108aj//9aA98fhbVFSLmY5yc\nxFD3iuIctPd60dTZh2EJtAMRDDfQVTQZLj66Ai+vqwVjyWeq3Kr1gxqfQKxKj8vpQEVxTtRRq34/\nwzee4gH+v3ztmKSOJ9AH2iff8SYAoDDbhYqSXLgcBIeDsFFL2jjLhM6nPf0+fF7bitbu/riD36f8\ndgUONHUnbEUA3I3qchD+ufEg7v3KHGS54lOwfj/D8q11GJbnMUUpx4twM3+YgOVjFSlpSYArrxwi\ncgHIBWC8ZWWS+P0MW2pbMaci8Z3iLG0ITTwzKHY3dKLfxwwHukMRO8RYqamPaNkqybh6XtnA/fvX\n/y21Z/QKhOsh2TYNfl2cyYysLIcj+gS9FZrr4dtnTDI02Cga+Vku3K3Vxhyv9Qhr6/GiojgH5QVZ\nKMpxY8qIfFw0rwIXzatI+njXnsjbtj+nWeex2HqwDQeaeDzi6hOqkjr2SVr7eyPT+ITlcd3J45M6\nthn0ef341Rvb8PUn1mD+L5fjyw9+jKrbXsf3nt+AFdXWu6VSzpJgjNUS0b0A9gPoBrCMMbZM/xoi\nWgpgKQCMHTvWEjn2Nnais8+HmUkoCbFjqo0jTbRa65+UbEBS1C909vqiKicxjjGRinDBH6+chzPu\nex91CVS0+m1oPyAW9IMt3YZmIoTy0EoeO/r2GeY0fBOL4bWPf4a5lcW4bH4l6tt7cMOTa1Gc60FN\ncxfK8rPwnUWTTTme4KqFY3HVQmuun1DOmc0Dz/H2JxLut7duPjnpavyzZozEyu0NeOzjPfjdZXPj\n+pt1+3k84rzZiQXMk+H0qeVYsb0BZ9//wYBK9Ry3Ey4HBTr4vry+FhOH51taCQ6koJIgohIAFwIY\nD6AFwItE9DXG2FPiNYyxhwE8DADz58+3ZLURE6iM9DMKpVA7uePp9Ln9cDtcDkpoJoGe4hxelLaz\nviNgVYTi8zP0+1ggjTVRhJ+4NoEZFm9v4VXDs5NQwkYRwWsxoztRRH3Bt88wZ9F+7Vsn4oI/fYz3\nquvxXnX9gALMieX5mDm6EEtmjoQ7wbqIVOK5zw7gh0umweEguJ0El8MRcG/p+XRPEzwuR1LXn+Dc\n2SPxo398jpfX1catJPY1dsHtpLiHfpnJ2TNHYsX2hoCCWDRtOPr9DI9cfUzAXbazvh1Eya8X8ZBy\nSgLAYgB7GGMNAEBELwM4AcBTUf/KZBo6uJIYHucUrHBku53IcTvjKubZfrgdE8vzk9rZA7zZHAB8\n7dHV2HvPeWFfIzJzfnL+jKSOlQyiJcTVCfYfSgRR1bp8az3OmJaYn10EeceX5SX9XQnmjCnG7rvP\nxZ7GTjR19mH51jq09/TjonljDHcCTmUWji/F6j1NmPeL5YOecxC3avXXysXzKhLOqNKjr+avuu11\nrPz+aRhbGnmgl8/PsGp34qnvyXLFgrEYU5KLO/+1BX+4ch6mjRxcwyQjuUWQikpiP4DjiCgX3N20\nCIB0p7fIU07GLQHwhmTxVEC/W11vSm+Yq08Yhz+t2BnwAYdjvWZKzzUwjjUSi6ePCEztMoLI3vjS\nUaOTliFexHcZbrjL7oYO5Hpcg6yvxo5eOB0UWGg+28s7of5wyVRTZXNoVuTEcuDYqsxRDHr+/LVj\n8Mr6WhDxhdjnZ/D6Gfp9fvj8DEc6+vDsp/uxoKoUx00oNdwvLRr/vu0M/OHdHXjuswM47d6VAIKJ\nDHecOx19Pj+qhuVhRGEWHv/3Xmw80IJbTJ5vYoSTJpdh2S2n2nZ8PSmnJBhjq4no7wDWAfACWA/N\ntSSL5s4+PP/ZAVQU5yAvgTkBemqau2O2rhDVoPFO3IqGaC/+2Md78JMvhbcUNhxoQVGO2/Dku3B4\nXHyndaCpK+621a3d/YEiLpmZI8JdI/pVMcawalcj/vjeTqzazSePHVtVguEF2cjxOOH3M7y68SAI\nwHEThmHqyAK8tfkwst2OhHoYDXVK8zy49qTogeBfXWys0WS8jC7OwT2XzMGFcyvw6Z4mPLhiJ15e\nxxMvRL2QHgcBN54+0RJZ0o2UUxIAwBj7KYCfyjpec2cfnl9zIFAR+vwaXon8wg2J1UeE47WNB3GB\ntmtmjGFjTStqm7uxs74D97/DfdBHmbCz15vHPj/DxpoWTB9ZGAhoA8Dafc2YW1lsShWp6AZ78m94\n64aLj66Az8/wtePGRdwRi8Dv0lPM2ykaob3Hi5+9tgWf7G5E9eF2lBdk4bZzpqGtux+r9zSh+nAb\n2nq88Pr8uHheBYpz3Vi+tQ6rdjdi6ogC3HPJ7KQ3Dwp7OH7iMBw/cRi+u5jHk15aW4MTJ5Whob0X\njZ29YAzYeqgNi6ePyIgYkBlQooVQqcL8+fPZmjXGvVHvVdfhey9sREtXPxwE+BlQkOWC00lo6erH\n5fMr8WsT+rUcaOoKLKCxqP7FElN21ovuW4ldYQqXnrxuAaoPteOuN7bh9nOm4YZTk98peX1+TNLy\n7MMxtjQXlaU5mDOmGMdWleCEiWWY9uO3AAC77z5Xus/3pmfW4fVNh5DjdmL2mCJccNRoXHrMmLg+\n936fHy4HSWnRoFBYDRGtZYzF7LUyZJXE5tpW/PjVzdhR14FLjxmDc2ePsixIWHXb62Efv+Co0fjJ\nl2YkNJc6Gm98fijQXXVuZXHAvSLwOB1Y8d+nmd4f3+9nWLa1DvubOnH3G9UxXx8psG4lPf0+7G7o\nxKThyScJKBTpjFISQ5w+r3/AIrhufzOaOvpQkufB2NLcQKaPlXT1ebF8ax121nfg+AnD8OHOI/jz\nyl04b84oPHD53IRbXSsUiuRRSkKhUCgUEYlXSaitnEKhUCgiopSEQqFQKCKilIRCoVAoIqKUhEKh\nUCgiopSEQqFQKCKilIRCoVAoIqKUhEKhUCgikvZ1EkTUAGCfdrcMQOrM/Qui5DKGkssYSi5jKLk4\n4xhjMTtVpr2S0ENEa+IpDpGNkssYSi5jKLmMoeQyhnI3KRQKhSIiSkkoFAqFIiKZpiSkDicygJLL\nGEouYyi5jKHkMkBGxSQUCoVCYS6ZZkkoFAqFwkSUklAoFApFRNJKSRBRkd0yhIOI8uyWIRxENI6I\nknYxItQAAArTSURBVB+cbTJEFDM3WxGEiMbZLUM4iGgmEd1mtxyhEFGJ3TKEg4iG2S1DIqSFkiCi\nfCL6HYAXieh6Ippqt0xAQK77ATxFRF9LlYtZ93m9DmC03fIINLnuA/AWEd1FRCfaLRMQkOshIjrb\nbllCIaKTAOwhoiV2yyLQfY//AnA5ESU/LN0EiCiXiB4EP7++TUTztMdtXed0n9drRHQ7ER1npzxG\nSXklQUSnAngHgA/AXQBOArDYVqEQuHg/BNAN4DEAJwO40lahABDRsQA+BlAKYB5jbKvNIgEAiMgF\n4EEALgBXA2AAFtkqFAAiGg3gEQCXAbgqBa2cQgBNAL5JRGS3MER0FoD3AfQC+BqAagBttgoV5HsA\nhgG4BkA2gL8CAGPMb5dARHQMgH9qd78J/n1eZpc8iZDySgJADYAbGGP/zRgTJ+deu4QhIrd2sxnA\nQ4yxHzHG/glgA/gJCjsuZp1cvQB2AbifMdZPRHOJqEpbpKWjO+4IAOMZY99ljG0BkAtgmx0yhdAF\n4PcAKgGUAFhCRE47BSIih+4cYuCLcSGAm7Xn7VQWOwFcoZ33HwOYBU3Z23TeO7TfLgBuAM8wxqoZ\nY78F0KBZ+tKtCd05dBjATYyxWxljmwB4AKyTKUuypJySIKLJRPQAEX2DiEoYY7sYYxuJqISIngJw\nNoD/IKLvE1GpRLmmEdH/Afg5EY3TFrrHdYtgLYBxAMAk5hWHkWsTgBUAbiGilQD+COB+AL+R6RPV\nyXWnJlctgB4ieoKIVgM4H8AFRPQKEY2SKNdkIvqBuM8YawGwiTHWDeAJAJdD+x5lopcrZOc7D8B4\nADcC+DoRLdDuS5dLYw9jbAcRebT7TwGYCUg/7ycT0V8B/A8RTWCMecE3aQt1L/sG+FoxRpY1oZPr\nDk2uWsbYViIqIqIHwK2IszT35ggZMiVLSikJLQj2D/AF9zQAf9Vp5FYALzDGxgH4GYAx4CeBDLmG\nAfg/AJsBOMEXvssYY/3ayQkAcwFskSFPFLnuIqLzADwKgAC8whg7GcDPteevs0muuzU3xdkAHgdQ\nzRibAuDr4M0ZfyJJrqsAvAfgv4loqfaYU1MQYIy9BKAFwBU6y8wWucDdcgDfuW9kjG0D/yw/Ad+9\n2yWXAwAYY33afbd4TNZunYhuAfAKgK0AhgO4h3iCxgPgbrlhmowHwJXY9TbJdRcRCYXeBn49VgJY\nCqAP/LpMeWxxQYSDeIZQB4DLGWNbiCgb3CybBX6R+AG8BgCMsWoiaoG8jonTAHQxxu7TLoSrACwi\nou2MsY3aa0YBeFl7L4sAbGaM1dkg10UAtgP4OmOsHwAYYxuIqB1Ao8XyRJPrUnCFkA9+gYAx1ktE\nH0HSogfuurwGPI70IBE9xRjr0twkpJ1jvwe/eF/Sdu2fMsa22yGX9txkAD8lon4AGwFkAVhrsTxR\n5dK+U6ZZDh+Cb0rukOj7bwJwLWNstabM3wVwNGPsPSJ6CcCfEfT7fwGgH+DuMIutnXByVYJbXwzc\nwgdjrIeI1gMYa6EsppFKlkQXgJc0BZHFGOsBVxKD/MNENAfAmeAWhwzWAcgiomO0C+Fj8Avoy5o8\nBJ5FNJWI3gAPzMq4YMLJtR/AVUJBaPLNAXA6gEMSZIokVy24AlsF4FwiOpuI5oMHG6V8j4yxDwB8\nyBhbBeBzcIsUABxigWOMrQH/7taA70D7w/wrWXIBPIPocwDfZIxdDuB5AN+xWqYYcukX288BbCaL\nM3ZC4h2vAvhUWyf6wa2tAu25GwGMJKKfENFl4NaqsBRNVxBxyFUY5m/mglsTu8yWxxIYY9J/wIOY\npTFe4wQ328brHhsFnimwGjx4ZoVcJ4d5vBDAjwD8UvfYhQB+C6AIwBTwhWVlCslVAu6jfRHApykg\n15cB3KfdvhbA0wDWg1uOUuQKec0s8AVudsg5dwWAAwCuTAW5wBWY/nlHKsile3wCeBbRGDvk0l63\nDsBRuvvTwS2gZQC+mkJyDdfO+8+sOO+t+pF/QL5wtQH4JYCcKK87GcC/dPdHar9NX+y0//s/4D70\nsIsDeNrtowDO1+5P0b7sLO3+LSkmV7Z2/7IUkmuNTq5cO+QKee2dAB7Vbi8Ej+NUpKBcTt1zlEJy\nua34rOKVS/u+ZgB4Q7vvAHd1mq5Ik5SLAEzVbp9rlWyWvWfpBwQmAtgNvsOdF+4D1n6fD+DHAC4B\ntyiutUieEgDPaF98YZjnHdrvQnA30jpwP+Ol4EH2ESkq1/AUlavMDrki/I0bPK7VAR6HcKWgXD+1\nYjE2Qa6faffNVlpxyaVbJ45HMAaxCcBN0CnVFJLrO2Z/VrJ+LA9cE1EhY0xfbNMDnpI5GcB1RHQz\nC2YIgWmfMLjb5DoAL4HXSXxokVw9AD4CsI8x1kZEU8BTIDczxg6B56lDe+3fiFdV3w2ezbSUmRyc\nNlGu+hSVy9Rkg3jl0gctNT9yGbilswfcCvxIyZU+cunWiXMA3ABePPotxuMoGS+XVKzSPuCa9xEA\nbwL4CoBK7fHztMddAN4Cz0s/EyEmNbiC+E+L5boc/OSvAPAH8KygT7Xn1wI4VfsbF3S7AFjgKlFy\nSZFLf455ACxRcqWnXAju2M8EcN1QkcuOH8vmSWgFJQDwHLjraCRj7KtEVAngYsbYA0T0OrjGvYsx\n9mNLBIku1wXgAfRriOhCcPfXz7TX3Qi+852r5MosuaxMhVRyyZPLSlJVLluwQvOAp6M9BmCsdr8U\nPJVvKXgFaQ24Jn4NvC+TJcHVOOV6HcCN2n2X7rUTATwJIE/JpeRScim57JbLrp+k6yRC8oQBAIyx\ndnB/3SXa/SYA94L76HYBeAPA3YyxCwDcB2Axmdxu24BcvwFvd1DItNiIViX5RwA1jLFOJZeSS8ml\n5JIpV0qRpMb1hNwnaH5M8G6tW6BpWPCujA8DuNRqzZeAXH+GltIG4FZoWRJKLiWXkkvJJVuuVPtJ\n2JIgom+DVxfeSUQX6J5img/zI/D0x3sBXooO3o6hRft7SzKrEpTLh2CLjw8BHMcYe1DJpeRScim5\nZMqVkiSogU8Fr3o+GrxC9TMAJ4a8phI8V34vgP8Ez2paC2CRVRovSbnOVHIpuZRcSi675ErVH6Mf\nriiUugTAz3WPfxO83TLA08SeA/Cqdn8RgO+D9+y5xKIvXcml5FJyKbnSUq5U/4nng3VpH1Kl7rFL\nAawIed1G8G6fJ4B3hLRWcCWXkkvJpeRKU7nS6SfWBzwb3C9XB+DZkOeqAfyH7v750PVa0h4zvTxe\nyaXkUnIpudJZrnT7iRW4PgJeYTgNQBXxwTGC7wH4JfG5DwBwEMB2InKTNn6RMeaL8f8TRcml5FJy\nKbnSVa60ImbFNRHlMMa6iegG8PSv03TPPQ4+U/kd8NYbrYwxWVOglFxKLiWXkist5UorDJhuOeC9\nlr6je6wIPOr/dwC/sMMUUnIpuZRcSq50lSsdfox+0GcDWK3dng2gXLvtMVOoBE4AJZeSS8ml5EpL\nuVL9x1AxHWPsbQDNRNQL4FcYPBTdFpRcSi4ll5IrXeVKeQxoYQf4NLl9AK63W7spuZRcSi4lV7rL\nlQ4/hlqFE9E5AN5jjPUa0kQWo+QyhpLLGEouYyi5MgvL5kkoFAqFIv1JulW4QqFQKDIXpSQUCoVC\nERGlJBQKhUIREaUkFAqFQhERpSQUCoVCERGlJBQKhUIREaUkFAqFQhGR/wcaQzqThf82+gAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142f4910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# locally processed\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(time,seawater_temperature)\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD7CAYAAACfQGjDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1177d2810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ooinet processed\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(time_ooinet,seawater_temperature_ooinet)\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise demonstrates the processing chain from raw to processed data product using publicly available code, raw data files and metadata. These data and additional data products could be further assembled into a NetCDF, as is produced by OOINet. This was never necessary for data evaluation purposes and should not prevent scientists from validating the output of OOINet. The \"first-in-class\" review effort revealed all steps in the data product processing chain, which allowed the Data Team to diagnose the root cause of issues and work with the appropriate team to find a fix.\n",
    "\n",
    "Stream Engine (uFrame) can be considered an assembly engine that brings together raw data, parsers, algorithms and metadata on demand and in real-time to produce a final NetCDF file (asynchronously) or a real-time JSON data response (synchronously). The Stream Engine code is available on GitHub, but was not used in this exercise, because it is not necessary to recreate the processing pipeline from raw, through parsed and finally processed data.\n",
    "\n",
    "https://github.com/oceanobservatories/stream_engine"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mi",
   "language": "python",
   "name": "mi"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}