{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Development of Algorithms for ERDDAP Dataset from ONC CTD Data\n",
    "\n",
    "Figuring out how to transform a day's ONC CTD data from a SoG node into\n",
    "a netCDF file that is part of an ERDDAP dataset:\n",
    "\n",
    "* filter the ONC data to include only `qaqcFlag == 1` samples\n",
    "* remove the `qaqcFlag` arrays as variable attributes\n",
    "* aggregate the data into 15 minute time bins;\n",
    "mean, variance, and count for each variable in each time bin\n",
    "* store dataset as netCDF file\n",
    "* generate ERDDAP `/opt/tomcat/content/erddap/datasets.xml` fragment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "import os\n",
    "\n",
    "import arrow\n",
    "from lxml import etree\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "from salishsea_tools import data_tools\n",
    "from salishsea_tools.places import PLACES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data from ONC `scalardata` Web Service\n",
    "\n",
    "Access to the ONC web services requires a user token which you can generate on the \n",
    "`Web Services API` tab of your [ONC account profile page](http://dmas.uvic.ca/Profile).\n",
    "I have stored mine in an environment variable so as not to publish it to the world\n",
    "in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TOKEN = os.environ['ONC_USER_TOKEN']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Request a day's worth of CTD salinity and temperature data\n",
    "and parse them into an `xarray.Dataset`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "onc_data = data_tools.get_onc_data(\n",
    "    'scalardata', 'getByStation', TOKEN,\n",
    "    station='SCVIP', deviceCategory='CTD',\n",
    "    sensors='salinity,temperature',\n",
    "    dateFrom=data_tools.onc_datetime('2015-12-27 00:00', 'utc'),\n",
    ")\n",
    "\n",
    "ctd_data = data_tools.onc_json_to_dataset(onc_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (sampleTime: 86398)\n",
       "Coordinates:\n",
       "  * sampleTime   (sampleTime) datetime64[ns] 2015-12-27T00:00:00.259000 ...\n",
       "Data variables:\n",
       "    salinity     (sampleTime) float64 31.23 31.23 31.23 31.23 31.23 31.23 ...\n",
       "    temperature  (sampleTime) float64 9.959 9.959 9.959 9.959 9.959 9.959 ...\n",
       "Attributes:\n",
       "    dateFrom: 2015-12-27T00:00:00.000Z\n",
       "    deviceCategory: CTD\n",
       "    dateTo: None\n",
       "    rowLimit: None\n",
       "    outputFormat: None\n",
       "    sensors: salinity,temperature\n",
       "    nextDateFrom: 2015-12-28T00:00:00.090Z\n",
       "    totalActualSamples: 172796\n",
       "    station: SCVIP"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctd_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'salinity' (sampleTime: 86398)>\n",
       "array([ 31.22826354,  31.22836401,  31.22826354, ...,  28.26344878,\n",
       "        28.26375019,  28.26415208])\n",
       "Coordinates:\n",
       "  * sampleTime  (sampleTime) datetime64[ns] 2015-12-27T00:00:00.259000 ...\n",
       "Attributes:\n",
       "    unitOfMeasure: g/kg\n",
       "    qaqcFlag: [1 1 1 ..., 4 4 4]\n",
       "    sensorName: Reference Salinity\n",
       "    actualSamples: 86398"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctd_data.data_vars['salinity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter Data Based on `qaqcFlag` Values\n",
    "\n",
    "Filter the salinity data to exclude samples for which `qaqcFlag != 1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'salinity' (sampleTime: 86398)>\n",
       "array([ 31.22826354,  31.22836401,  31.22826354, ...,  28.26344878,\n",
       "        28.26375019,  28.26415208])\n",
       "Coordinates:\n",
       "  * sampleTime  (sampleTime) datetime64[ns] 2015-12-27T00:00:00.259000 ...\n",
       "Attributes:\n",
       "    unitOfMeasure: g/kg\n",
       "    qaqcFlag: [1 1 1 ..., 4 4 4]\n",
       "    sensorName: Reference Salinity\n",
       "    actualSamples: 86398"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctd_data.salinity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "salinity_qaqc_mask = ctd_data.salinity.attrs['qaqcFlag'] == 1\n",
    "salinity = xr.DataArray(\n",
    "    name='salinity',\n",
    "    data=ctd_data.salinity[salinity_qaqc_mask].values,\n",
    "    coords={'time': ctd_data.salinity.sampleTime[salinity_qaqc_mask].values},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'salinity' (time: 54393)>\n",
       "array([ 31.22826354,  31.22836401,  31.22826354, ...,  31.22977061,\n",
       "        31.22967014,  31.22977061])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2015-12-27T00:00:00.259000 ..."
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salinity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter the temperature data to exclude samples for which `qaqcFlag != 1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'temperature' (sampleTime: 86398)>\n",
       "array([ 9.959 ,  9.959 ,  9.9591, ...,  9.9531,  9.9533,  9.9531])\n",
       "Coordinates:\n",
       "  * sampleTime  (sampleTime) datetime64[ns] 2015-12-27T00:00:00.259000 ...\n",
       "Attributes:\n",
       "    unitOfMeasure: C\n",
       "    qaqcFlag: [1 1 1 ..., 1 1 1]\n",
       "    sensorName: Temperature\n",
       "    actualSamples: 86398"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctd_data.temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temperature_qaqc_mask = ctd_data.temperature.attrs['qaqcFlag'] == 1\n",
    "temperature = xr.DataArray(\n",
    "    name='temperature',\n",
    "    data=ctd_data.temperature[temperature_qaqc_mask].values,\n",
    "    coords={'time': ctd_data.temperature.sampleTime[temperature_qaqc_mask].values},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'temperature' (time: 86395)>\n",
       "array([ 9.959 ,  9.959 ,  9.9591, ...,  9.9531,  9.9533,  9.9531])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2015-12-27T00:00:00.259000 ..."
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create 15 Minute Resampled Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Station-specific metadata for the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xr_metadata = {\n",
    "    'SCVIP': {\n",
    "        'place_name': 'Central node',\n",
    "        'ONC_station': 'Central',\n",
    "        'ONC_stationCode': PLACES['Central node']['ONC stationCode'],\n",
    "        'ONC_stationDescription': \n",
    "            'Pacific, Salish Sea, Strait of Georgia, Central, Strait of Georgia VENUS Instrument Platform',\n",
    "        'ONC_data_product_url': 'http://dmas.uvic.ca/DataSearch?location=SCVIP&deviceCategory=CTD',\n",
    "    },\n",
    "    'SEVIP': {\n",
    "        'place_name': 'East node',\n",
    "        'ONC_station': 'East',\n",
    "        'ONC_stationCode': PLACES['East node']['ONC stationCode'],\n",
    "        'ONC_stationDescription': \n",
    "            'Pacific, Salish Sea, Strait of Georgia, East, Strait of Georgia VENUS Instrument Platform',\n",
    "        'ONC_data_product_url': 'http://dmas.uvic.ca/DataSearch?location=SEVIP&deviceCategory=CTD',\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define an aggregation function to count the samples in each\n",
    "resampling interval:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count(values, axis):\n",
    "    return values.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataset of resampled data and their statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "onc_station = 'SCVIP'\n",
    "\n",
    "ds = xr.Dataset(\n",
    "    data_vars={\n",
    "        'salinity': xr.DataArray(\n",
    "            name='salinity',\n",
    "            data=salinity.resample('15Min', 'time', how='mean'),\n",
    "            attrs={\n",
    "                'ioos_category': 'Salinity',\n",
    "                'standard_name': 'sea_water_reference_salinity',\n",
    "                'long_name': 'reference salinity',\n",
    "                'units': 'g/kg',\n",
    "                'aggregation_operation': 'mean',\n",
    "                'aggregation_interval': 15*60,\n",
    "                'aggregation_interval_units': 'seconds',\n",
    "            },\n",
    "        ),\n",
    "        'salinity_std_dev': xr.DataArray(\n",
    "            name='salinity_std_dev',\n",
    "            data=salinity.resample('15Min', 'time', how='std'),\n",
    "            attrs={\n",
    "                'ioos_category': 'Salinity',\n",
    "                'standard_name':b 'sea_water_reference_salinity_standard_deviation',\n",
    "                'long_name': 'reference salinity standard deviation',\n",
    "                'units': 'g/kg',\n",
    "                'aggregation_operation': 'standard deviation',\n",
    "                'aggregation_interval': 15*60,\n",
    "                'aggregation_interval_units': 'seconds',\n",
    "            },\n",
    "        ),\n",
    "        'salinity_sample_count': xr.DataArray(\n",
    "            name='salinity_sample_count',\n",
    "            data=salinity.resample('15Min', 'time', how=count),\n",
    "            attrs={\n",
    "                'standard_name': 'sea_water_reference_salinity_sample_count',\n",
    "                'long_name': 'reference salinity sample count',\n",
    "                'aggregation_operation': 'count',\n",
    "                'aggregation_interval': 15*60,\n",
    "                'aggregation_interval_units': 'seconds',\n",
    "            },\n",
    "        ),\n",
    "        'temperature': xr.DataArray(\n",
    "            name='temperature',\n",
    "            data=temperature.resample('15Min', 'time', how='mean'),\n",
    "            attrs={\n",
    "                'ioos_category': 'Temperature',\n",
    "                'standard_name': 'sea_water_temperature',\n",
    "                'long_name': 'temperature',\n",
    "                'units': 'degrees_Celcius',\n",
    "                'aggregation_operation': 'mean',\n",
    "                'aggregation_interval': 15*60,\n",
    "                'aggregation_interval_units': 'seconds',\n",
    "            },\n",
    "        ),\n",
    "        'temperature_std_dev': xr.DataArray(\n",
    "            name='temperature_std_dev',\n",
    "            data=temperature.resample('15Min', 'time', how='std'),\n",
    "            attrs={\n",
    "                'ioos_category': 'Temperature',\n",
    "                'standard_name': 'sea_water_temperature_standard_deviation',\n",
    "                'long_name': 'temperature standard deviation',\n",
    "                'units': 'degrees_Celcius',\n",
    "                'aggregation_operation': 'standard deviation',\n",
    "                'aggregation_interval': 15*60,\n",
    "                'aggregation_interval_units': 'seconds',\n",
    "            },\n",
    "        ),\n",
    "        'temperature_sample_count': xr.DataArray(\n",
    "            name='temperature_sample_count',\n",
    "            data=temperature.resample('15Min', 'time', how=count),\n",
    "            attrs={\n",
    "                'standard_name': 'sea_water_temperature_sample_count',\n",
    "                'long_name': 'temperature sample count',\n",
    "                'aggregation_operation': 'count',\n",
    "                'aggregation_interval': 15*60,\n",
    "                'aggregation_interval_units': 'seconds',\n",
    "            },\n",
    "        ),\n",
    "    },\n",
    "    coords={\n",
    "        'depth': PLACES[xr_metadata[onc_station]['place_name']]['depth'],\n",
    "        'longitude': PLACES[xr_metadata[onc_station]['place_name']]['lon lat'][0],\n",
    "        'latitude': PLACES[xr_metadata[onc_station]['place_name']]['lon lat'][1],\n",
    "    },\n",
    "    attrs={\n",
    "        'history': \"\"\"\n",
    "{0} Download raw data from ONC scalardata API.\n",
    "{0} Filter to exclude data with qaqcFlag != 1.\n",
    "{0} Resample data to 15 minute intervals using mean, standard deviation and count as aggregation functions.\n",
    "{0} Store as netCDF4 file.\n",
    "        \"\"\".format(arrow.now().format('YYYY-MM-DD HH:mm:ss')),\n",
    "        'ONC_station': xr_metadata[onc_station]['ONC_station'],\n",
    "        'ONC_stationCode': PLACES[xr_metadata[onc_station]['place_name']]['ONC stationCode'],\n",
    "        'ONC_stationDescription': xr_metadata[onc_station]['ONC_stationDescription'],\n",
    "        'ONC_data_product_url': xr_metadata[onc_station]['ONC_data_product_url'],\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If any of the `DataArray`s are short compared to the others the missing values\n",
    "are filled with `NaN`s.\n",
    "That makes sense for temperature and salinity values,\n",
    "and their standard deviations,\n",
    "but not their sample counts.\n",
    "So, we change `NaN`s to zeros in the sample count `DataArray`s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds.salinity_sample_count.values = np.nan_to_num(ds.salinity_sample_count.values)\n",
    "ds.temperature_sample_count.values = np.nan_to_num(ds.temperature_sample_count.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (time: 96)\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2015-12-27 ...\n",
       "    longitude                 float64 -123.4\n",
       "    depth                     int64 294\n",
       "    latitude                  float64 49.04\n",
       "Data variables:\n",
       "    salinity                  (time) float64 31.23 31.23 31.23 31.23 31.23 ...\n",
       "    salinity_std_dev          (time) float64 0.0001472 0.0001813 0.0001711 ...\n",
       "    temperature_sample_count  (time) int64 900 900 900 900 900 900 900 900 ...\n",
       "    temperature_std_dev       (time) float64 0.0001285 0.0001598 0.000142 ...\n",
       "    salinity_sample_count     (time) float64 900.0 899.0 900.0 900.0 900.0 ...\n",
       "    temperature               (time) float64 9.959 9.959 9.959 9.959 9.958 ...\n",
       "Attributes:\n",
       "    history: \n",
       "2016-09-19 13:07:14 Download raw data from ONC scalardata API.\n",
       "2016-09-19 13:07:14 Filter to exclude data with qaqcFlag != 1.\n",
       "2016-09-19 13:07:14 Resample data to 15 minute intervals using mean, standard deviation and count as aggregation functions.\n",
       "2016-09-19 13:07:14 Store as netCDF4 file.\n",
       "        \n",
       "    ONC_stationDescription: Pacific, Salish Sea, Strait of Georgia, Central, Strait of Georgia VENUS Instrument Platform\n",
       "    ONC_stationCode: SCVIP\n",
       "    ONC_station: Central\n",
       "    ONC_data_product_url: http://dmas.uvic.ca/DataSearch?location=SCVIP&deviceCategory=CTD"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'salinity_sample_count' (time: 96)>\n",
      "array([ 900.,  899.,  900.,  900.,  900.,  899.,  899.,  900.,  900.,\n",
      "        900.,  899.,  897.,  900.,  896.,  899.,  900.,  899.,  899.,\n",
      "        900.,  900.,  899.,  900.,  899.,  899.,  900.,  899.,  899.,\n",
      "        899.,  899.,  897.,  900.,  899.,  900.,  900.,  900.,  899.,\n",
      "        899.,  899.,  899.,  896.,  895.,  899.,  899.,  900.,  898.,\n",
      "        898.,  900.,  899.,  899.,  900.,  900.,  899.,  900.,  899.,\n",
      "        900.,  899.,  898.,  898.,  900.,  900.,  446.,    0.,    0.,\n",
      "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
      "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
      "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
      "          0.,    0.,    0.,    0.,    0.,    0.])\n",
      "Coordinates:\n",
      "  * time       (time) datetime64[ns] 2015-12-27 2015-12-27T00:15:00 ...\n",
      "    longitude  float64 -123.4\n",
      "    depth      int64 294\n",
      "    latitude   float64 49.04\n",
      "Attributes:\n",
      "    standard_name: sea_water_reference_salinity_sample_count\n",
      "    aggregation_operation: count\n",
      "    aggregation_interval: 900\n",
      "    aggregation_interval_units: seconds\n",
      "    long_name: reference salinity sample count\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fab0b9b0e10>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Md6+LPys0Dks5DBwIl18ekqLS4Mcfw7/JPvvAH/4QdzTpo7FYROrNsGFwwgnw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\n/wALGXxT2unmfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab0bac06a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(ds.salinity_sample_count)\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 6))\n",
    "salinity.plot(ax=ax1)\n",
    "ax1.set_title('Raw Data')\n",
    "ds.salinity.plot(ax=ax2)\n",
    "ax2.set_title('15 min Averaged')\n",
    "ds.salinity_std_dev.plot(ax=ax3)\n",
    "ax3.set_title('15 min Std Dev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'temperature_sample_count' (time: 96)>\n",
      "array([900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900,\n",
      "       900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900,\n",
      "       900, 900, 900, 899, 901, 899, 900, 900, 900, 900, 900, 900, 900,\n",
      "       900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900,\n",
      "       900, 900, 900, 900, 900, 900, 900, 900, 900, 897, 900, 900, 900,\n",
      "       900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900,\n",
      "       900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900, 900,\n",
      "       900, 900, 899, 900, 900])\n",
      "Coordinates:\n",
      "  * time       (time) datetime64[ns] 2015-12-27 2015-12-27T00:15:00 ...\n",
      "    longitude  float64 -123.4\n",
      "    depth      int64 294\n",
      "    latitude   float64 49.04\n",
      "Attributes:\n",
      "    standard_name: sea_water_temperature_sample_count\n",
      "    aggregation_operation: count\n",
      "    aggregation_interval: 900\n",
      "    aggregation_interval_units: seconds\n",
      "    long_name: temperature sample count\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fab0b8d7be0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KMevHgOh5IPAt4Pac7f3MrHu0fBLwtLsvdvc5wEwz2yradU/g9Wi/9WOnOJQw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j6RW78e6NzZu+1++y3f2bEKRGvdGti2zbv9ASy2bNwIdOjg7XYFwYQtAPT3fdvHphmX6WCyTJ0U\n61YQUSul1HYiag2gUluIiNoDeBfAlUqp9akGpxcqrKis5IuzsKOVf3gVrmhFlIUKff5Cuo4fgLlQ\nkSp7wktHRWGhtw4Dorirwihib9gQ70plJVQ4Kf2orubfScvX0DrrmOFXRoXmqNi8OS5UFBTw/nbu\nTPxd/ODRR4H33weWLUsWSceNG2e5npR+CIIgCIKQhB85FVZlHxqao8JLdu4E6tTx9qRPECyYC6Ar\nER1FRHUAXALgfcMy7wO4CgCIaCiA3bGyjlTrvg8OywSA0QAmx9YvAfABgLuVUrO9+AVywVEBZKfz\nx5EjfLc5VW5DrmLMX0jX8QNgoUL/nqfq+AHw32jdOm8cFX5gVf6hOSoA8w4p+/bxZ0P/ndK5M/+u\nSiGJq64C/vSn+Gu7jgo/Sz80slH+UVMDvP46i2EffuhsXREqBEEQBEFIwo+ciq++yr5QIUGaQrZQ\nStUAuBXAVABLAbyplFpORDcS0Q2xZT4EsI6IVgN4EcDNqdaNbXo8gBFEtBLAaQCeiE2/BcDRAB4g\novlENI+IMvIY5IpQkY1ATa3jh5flb2HBeAFut/Rj9+7463SlH02a8DJOhIqzz/a2xCMVboUKzU2h\n/1w0asTCzNatydtbvBh47rl4qcmRI9adZPwSKsxKP4DsBGrOmMHHlPHjgSefdLauCBWCIAiCICTh\nh6Piq6+s8ykAf4SKtWtFqBCyh1LqY6VUD6VUN6XUE7FpLyql/qJb5lalVFelVH+l1LxU68am71RK\nnR6bd4ZSands+qNKqcZKqWOVUgNjPzO6P5orQkXTpsAzzwCvvgosXcrOB7vs38/rpSOqZR8A5wBt\n2hQPJLVT+tGjB190a+zcmb70A3AmVPz0p4BJdIovmAkVhw7x/4B2MW8lVJh1IOnVK3nZgwf5fS4u\nBj77LB6kaSV+ae6dykpvP3sNGrCzYfXqRKEiG46K114DrrgCuOgidp3MnWt/XREqBEEQBEFIwmuh\n4tAhYMkS4NhjrZdp08ZboaK2lu/gnH++d9sUhKiiVOrWiWHi6aeBoUOBjz8GRo3iO9RdugCnn87T\nUvHpp8C111p3fdDQHBVRpE4dfr+WLuXXdko/jj+ew5A1UchOmCaQWswIEjOhYssWFiGKYimO3bqx\n2H3kSHwZY5CmRu/enMGgZ80aFspvu43LP1LlU2iUlrKrw+tMjmbN+DvYWPrhxlGxbBkLH+nYvx+Y\nNAm45BIWa371K+Cpp+Lz9+1Lvb4IFYIgCIIgJHH00Ry85VULwEWLgK5dU9+189pRMXEi/7z6au+2\nKQhRpbqaA/bS3VkPAz17Ar/+NfDGG8CqVSwqfPIJ8POfA3fcYZ4VoDFjBl+4TZqUeh/V1dF1VADA\niBFxUcdO6UdJCWcxLFjAr+2UfgDOHBXZxEyo0Jd9AOxwaN2aQ0E1jEGaGmZCxYoV/Fm9/HLg3/9m\noSedY8kPoULb7rZtyaUfbhwV55zDDpF0TJnCLsq2bfn1ddcB06axs+Ktt9iFkgoRKgRBEARBSKK4\nmO8E2e0Nn450ZR+At0LFzp3AfffxXawCOdsRhLTkStmHGXXq8N3v66/nDhHTp1svW14O/PKXfKGU\niiiXfgBcZqGFG9op/QCAYcOAmTP5ebrSD+29y2WhAkgu/6ioMC/9MBMqVq7kkpmGDYErrwSeeMKe\no+K777wXKpo14/8NvcjipvRj3z4WGr78Mv2yWtmHRuPGLFYMGcKdQP75z9Try1e3IAiCIAimdO/O\ndyu9IF3HD4AvkioruWQjU+6/H/jZz1KXmgiCECeXhQoNIuCWWzi80Izdu7lN8oMP8t3tykrz5YDo\nCxUnn8xOt5077ZV+ACxUzJrFz+06KsJa+lFWZl+o0LdydeOoAICbb+bt23FUAN5/9po14/LKwsL4\nNDelH8tjEb/phIodO9hFcsEFidPvuotLt775BjjppNTbEKFCEAQh4hDR7US0OPa4zWR+CRG9S0QL\niWg2EfXWzVsfmz6fiL7STZ9ARMuJaAERvUNE0vwxgnTrxif1XmDHUVG3Lp+cZZpC/vXXwHvvAQ8/\nnNl2BCGfiIJQAfAd3BkzuOOPkZkzWTBt2pQ7TLz7rvV2oi5U1KvHwZVTp9p3VJx4IgsVStnPqAiz\no8LoJjATKoytXK0cFa1acW6DXvzQHBUAC/8jRpiLHHq099SP0g992QfgrvRj6VLOgpkzJ/VNheee\n4/8xY85L8+bsLtFyQFIhQoUgCEKEIaI+AK4FMBjAAADnEFEXw2L3AZivlOoPYDSAZ3XzagEMjyXK\n6++HTwXQRyk1AMAqAPf69TsIwdGtmzeOiqoqPgHs2zf9sl6Uf7z2Glu7U51EC0I+8tRTwDXXmM+L\nilDRqBFfCL3wQvK8GTOAH/+Yn//858C//pU4f86ceL5F1IUKgC8kP/zQvqPiqKO4lG7t2vSlH9oF\napiFCjuOij592Blw4AC/tnJUELGrQgsoVSrRUQEA//gHcMMNqcfll1DRrFmyUOGm9GPZMuCUU3h7\nxi4nGs8/D/z978Djj7sbq4YIFYIgCNGmF4A5SqmDSqkaAF8AMBjx0BvA5wCglFoJoBMRaVWUBJPv\nCqXUp0opTUufDaC9cRkh9/Gq9OObb7iLSHFx+mW96Pwxdy4n1AuCEKemhlt6Tp3KIZRGoiJUAGyz\nf/ll4IcfEqfPmBFvf3nmmcD8+Xy8UQr43e+4k0h5Oc/PB6HirLM4ULOqyp6jgihe/pHOUVFYyKJx\nSYl34/USu0LFsGHAoEEsftXWWgsVQGL5R2UlvwdlZfH5rVqxmycVpaXsNqhXz9nvk46yMqBDh8Rp\nzZs7dzAuXcq/5/HHm5d/vPwyZ3F89lnye+kUESoEQRCizRIAJxFRKRE1AHA2AMNXFRYiJl4Q0RAA\nHREXHhSAaUQ0l4iut9jHGAAfeT5yIXC8Kv345hs+0bNDpo6KI0e4rapkUwhCIlOn8oXS++8Dt9/O\nXX30REmo6N4dGDgw0TGxdy9fRGpZOfXqcfeCt98G7r0XmDwZGDuW7wYD+SFUHHUU/82/+MJ+t5cT\nT+QSmnRCBcDdLogyH6cflJSwk+TQIX5dWwts2pR8MV9QALzyCjsP7rzTuvQDSBQqjG4Ku5SW8ufO\n6/ft5ps5u0mPW0dFnz7mQsVbbwHjxrFI0cXo3XWBjeoQQRAEIVdRSq0govEApgGoBjAfgLH79RMA\nniGieQAWG5YZppTaFnNYTCOi5UqpmdqKRHQ/gMNKqdetxjB27Nj/Ph8+fDiGa7ezhNDToQPfbdm/\n354t2Io1a/gEzg6ZChXLlnGf+HR3rYT8o7y8HOXa7fI85KWXuCvGoEFcGnXNNdzSU+uKU1mZPvA2\nl7jrLmDMGOC88/jib9YsYPDgxDvVF18MXHopCxvTp7Prq1MnYOtWFirSdWiIAj/9KTBhAn8m7DBs\nGJcQ1anDuUK5SkFBPKOhbVv+/DdubC7Y1K3L7WyHDWP3jTF3QaN3bxYCgcR8CieUlnpf9qFt10hJ\nCX/Ojxyxlxmxbx9/P3fpwkKFJuoB7Ni6+24WB7t182bMIlQIgiBEHKXURAATAYCIHgWwyTC/CuyK\nQGyZdQDWxuZti/3cQUSTAAwBMDO23NVgh8apqfavFyqE3KKwkE9IVq8GjjnG/XbWruU7l3Zo3Zov\nEtwyd2760E4hPzEKpePGjQtuMAEwfTrw6qv8/N57gf/7P77QuPVWnhYlRwUAnHYaMGoUZwK89VZi\nPoXGGWcAv/gF32nWLuQuuYRFnerq6DsqAM6pmDDBvqOif38WsKOQAaSVf7Rta172oae0lPM8XnnF\n2u3Qp483jgo/hAozCgtZrNi5097//vLlLOoVFvI5wcaNcWfNBx+wY+uEE7wbn5R+CIIgRBwtb4KI\nOgI4H8DrhvlNiag49vx6ADOUUtVE1ICIGsWmNwRwBriUBER0JoA7AZynlDqYtV9GyDpelH+sXQt0\n7mxv2UwdFV9/zXdNBUFI5MIL4xfeRUUsWowbxzX3QPSECgAYP55zdv76V86eMBr66tYFnnwy8aL7\nppuAv/yFL8DyQag44QS+MLYrVBQVcZZHFIQKfYvSdEIFwG6bhx6ynt+2LYdufv+9e0fFUUdlnu3g\nBCflH1rZB8Cfg0GDOIAW4Pyb22/3dmwiVAiCIESfd4hoCYDJAG5WSu0lohuJSMue7gVgCREtB/AT\nANpXTSsAM4loPjgwc4pSamps3h8BNAKXg8wjIp0BUIgSmQZq1tTwXZdOnewtn6lQIY4KQTDnekPK\nUI8ewOjRwH338esoChX16nFw6D33AEuW8AV2Ovr1A44+mkMm80GoKC4GHn2Uf2+7DBuWuuNHrtCi\nBbB+PfDHP3KpkJ3PRyq0zh/Ll7t3VPTvD0yZktk4nOAkUFML0tTQcioWLWJh5sILvR2blH4IgiBE\nHKXUySbTXtQ9nw0gSfdXSq0DtzQ126ZHFYhC2OnWzTzZ2y5bt/KJUP369pbPRKg4eJDv+Aww/dQK\nQn5jlj/xu9/xxdRXX/HFir5DQVTo1YtLGyZPtn8cuvlmzq7IB6ECiJf/2OX8873vShEELVty6c/I\nkcCbb2YuVAB8IT9vHrBlizeBkn7j1FFx7bXx18cfDzz3HP+uN93EuSVeIkKFIAiCIAiWdOsWr2t3\ng5OyDyCz9qSLFvF4Mwn+FISoYlZX37Qp8MgjHDrZtKm9FsK5yJgxHB5qlwsu4GNR8+b+jSmXGTAg\nGoLwvfcC//M/7KDxCi1Qs1On3Ph/shIqjhwBFixILKVcujRe+gGwsHPFFexkXLnS+7FJ6YcgCIIg\nCJZkWvqxbp2zu0qlpRxid9BF8omUfQiCc665hu+OR63sw4iTdo916vCFV69e/o1HCJ527bwVKQAW\nKsrL3eVTBIFZ6cecOezAGjoUmD2bp+k7fmi0bMmPUaP8OX6Io0IQBEEQBEvatOETlD173LX8dOqo\nKCjg5PDt24GOHZ3t6+uvgR/9yNk6gpDvFBRw949Jk4IeSbjIl7IPwVt69+ZsJjf5FEFQVsb5NAB/\nz999N5dJ/e//AocOAXfeCXzxBWduaB0/9Dz6qH83CMRRIQiCIAiCJURA167uXRVr1zqv023dGti2\nzfm+5s6Vjh+C4IYhQ4DHHw96FIKQ+3TowB1Ucs1RMWMGB3kqxWGgV1zBYbu7d3MpizFIU+Pii/3L\n4hChQhAEQRCElGRS/uG09ANwF6i5bx+LIk6S6wVBEATBSwoKuOXrsccGPRJ7lJWxg+Kyy9hZ9eKL\nQEkJzyss5CDau+8GFi5MzKfIBiJUCIIgCIKQkm7dMnNUOCn9ANwJFfPnA337ep86LgiCIAhOmDo1\nd8JGBw5kV8TChcDZZyfPP/NMzvJ44QURKgRBEARBCBluhYr9+4Fdu4C2bZ2t56bzh5R9CIIgCIIz\nOnYE/vxn69bERJxXsX+/eemHn4hQIQiCIAhCSrp3B7791vl669dzi7YCh2cbbhwV8+YBgwY5W0cQ\nBEEQhNQceyy7Frt1y+5+RagQBEEQBCEl3bqxUKGUs/XclH0A7oSKhQs5CEwQBEEQBG8ZMMBZi18v\nEKFCEARBEISUlJWxeDBrlrP13ARpAs6FikOHgNWrs18/KwiCIAiCP4hQIQiCIAhCSoiA664DXn7Z\n2XqZOCqctCddvpz3U6+e830JgiAIghA+RKj4/+3debxd473H8c8vo5KIlpi55nkI1QhaaW/UWGNx\nUQSpOeirrSl6q62pzVVjDVeDosUNVdNF0QpVVWoIiqKGlpKaUlykJL/7x/OcZuXkDOucs/dev7P3\n9/16rVf2Gvbe373Os38nr+c861kiIiLSrX33Tbcwmzmz/HOef773IypmzICPPy53/PTpsN56PX8f\nERERiUkdFSIiItKtkSNhyy3hyivLP6e3l34suCCsskqaILOMxx7T/BQiIiLNRB0VIiIiUspXvwo/\n/nG5STXde3/pB8C4cXDnneWO1YgKERGR5qKOChERESll3Dj4xz/KjXR4/XUYOuZO6T8AABkwSURB\nVBRGjOjde22xBfzqV+WO1YgKERGR5qKOChERESllwACYMCGNqujOCy/0fjQFwOabwwMPwAcfdH3c\na6/B7Nmw9NK9fy8RERGJRR0VIiIiUtr++8PUqd1PqtnbiTTbLLwwrLsu3Hdf18e1XfbR6Pu7i4iI\nSP2oo0JERERKW3pp2HNPOOKIro/ra0cFlJunQpd9iIiINB91VIiIiEiPTJ6cLsuYOrXj/bNnwzXX\nwJgxfXufMvNUTJ+ujgoREZFmo44KERER6ZGFFoIrrkijKl55Zf79U6akSzd23LFv7zNmDDz9dNeX\nmeiOHyIiIs1HHRUiIiLSY6NHw+GHwwEHwJw5c7e//TZ8+9twzjl9nzdi6FDYZBOYNq3j/bNmwXPP\nwVpr9e19REREJBZ1VIiIiEivTJqUOgt22AFmzEjbTjwRdt4ZRo2qzXt0NU/FU0+leTAWWKA27yUi\nIiIxqKNCREREemXQILj99jRHxKhRcNZZcNVVcPLJtXuPruap0ESaIiIizUkdFSIiItJrQ4bAKafA\ntdfCuefC974Hiy1Wu9cfNQreeANefHH+fZpIU0REpDmpo0JERET6bLPN0nwRhx5a29cdMAB22w1+\n+tN5t7unkRajR9f2/URERKR66qgQERGRmujr5Jmd2XdfuPzy1DnR5oEH4L33YOzY+ryniIiIVEcd\nFSIiIhLaxhunTpD775+77YIL4OCD04gLERERaS769S4iIiKhmcH48XDZZWn9rbfg+uthv/0qjSUi\nIiJ1oo4KERERCW/vveGaa+DDD9NlINttByNHVp1KRERE6qHuHRVmdpSZPZ6XIzvYv4iZXWdm083s\nfjNbq93+AWb2sJndWNh2opm9nLc/bGZb1/tz1Mq0adOqjjAfZSpHmcqJlilanir0pQ6b2Yt5+yNm\n9kBh+yfN7HYz+5OZ/dLMRjTq89RCxHahTF1bfvl0B5AbboAzzpjGIYdUnWhekc5Vm4iZIG6uWjCz\nrc3saTN7xsyO7eSYc8zsWTN71MxGdffcruqtmR2fX+spM9uyvp+utqK2g4i5lKm8iLmUqXfq2lFh\nZmsDE4CNgFHAl8xspXaHTQIecff1gfHAOe32HwU82cHLn+HuG+blthpHr5uIjUKZylGmcqJlipan\n0WpQh+cAn3f3Ddy9eH+F44A73X114NfA8fX6DPUQsV0oU/fGj4djjoEPP5zGZz9bdZp5RTtXEDMT\nxM3VV2Y2APgRsBWwNrCnma3R7phtgJXdfVXgYODCEs/tsN7mTuXdgTWBbYDzzeo1pW3tRW0HEXMp\nU3kRcylT79R7RMWawO/dfZa7zwbuAXZpd8xapKKLu/8JWMHMRgKY2bLAtsCUDl673xRiEZEK9akO\nk2ptR78rdgTyjAFcBuxU6+Ai7e2yC7z5Jmy0Uf3uMCLSB6OBZ939JXf/CLiaVCuLdgQuB3D33wMj\nzGyJbp7bWb3dAbja3T929xeBZ/PriIj0e/XuqHgC+FwesrYgqdNhuXbHTCf/p9nMRgPLA8vmfWcC\nRwPO/CbmIXNT+tuQYxGRBuprHXbgDjN70MwOLDxncXefAeDurwGL1/EziAAwbBjcdhtssEHVSUQ6\ntAzw18L6y3lbmWO6eu4SndTb9s95pYP3ExHpl8y9oz6AGr6B2f7A4cB7wB+BWe7+9cL+4cDZpCHJ\njwNrAAeS/iO9jbtPNLPPA99w9+3zc0YCb7i7m9nJwFLuPqGD967vhxMR6SV3b9jfg3tbh939MTNb\nyt1fzXX3DmCiu99rZm+5+6cKr/Gmuy/awXurDotIWLWsxWb2ZWArdz8or+8NjHb3IwvH3ASc5u73\n5fU7gWOAFTt7rpm97e6fLLzGm+6+qJmdC/zO3a/M26cAt7j7dR1kUy0WkZA6q8ODGvDGlwKXApjZ\nKczb84u7vwsc0LZuZs8DzwN7ADuY2bbAJ4DhZna5u+/r7q8XXuLHwE2dvLcGhopIy+tFHX6BVIdx\n91fzv6+b2S9Iw4rvBWaY2RLuPsPMlgT+3sl7qw6LSKt4hTQirc2yeVv7Y5br4JghXTz3tU7qbWev\nNR/VYhHpbxpx14+2+SaWB3YGrmy3f4SZDc6PDwTucff33H2Suy/v7iuROi1+7e775uOWLLzELqSh\nzSIi0oFe1OG73f09M1vQzIbl7QsBWzK33t4I7JcfjwduqPfnEBEJ7kFgFTP7NzMbQvr/643tjrkR\naPv/7BhgZr6so6vndlZvbwT2MLMhZrYisArwr7sziYj0Z3UfUQH83Mw+BXwEHObu75jZwYC7+0Wk\nid4uM7M5pCHJ813C0YHJ+XZOc4AXSbMmi4hIx3pbh5cAfpGHDA8Cfubut+d9PwCmmtkBwEukmedF\nRFqWu882s4nA7aQ/Bl7s7k8V662732Jm25rZc8D/Aft39dz80h3WW3d/0symku6O11bfdYmHiDSF\nus9RISIiIiIiIiJSVt0v/WhVEe9jrUxdi5SljTKVFzVXq4h6/iPmUqbuRcsDMTNB3FwQO1uzinjO\nlam8aLmi5QFl6qnI2bqjjooaMrNhZnaEma0MLJC3Vdo4lKl0pqHRhksqU/citqVWEvX8R8ylTOVF\nqzMQMxPEzBW1XTWziOdcmcoLnCtifVGmEqK2qZ7SpR81YmZfAC4gTTT3Jun2f0d2/SxlCpLpKNI1\nojcAD7r7zWZmVRYdZSqVJ1xbaiVRz3/EXMpUXrQ6EzVT1FxR21Uzi3jOlam8wLki1hdlKpcpZJvq\nDY2oqJ1lgKvdfVfgP4HNzGwCgJkNVKaYmcxsHLAnaTbtp4HvmtnG7u5mVsn3Q5lKC9WWWlDU8x8x\nlzKVELHORMwUORcB21ULiHjOlakf54pYX5SpR8K1qd5SR0UvmdlyZrZhYdMapNmbcfe/A8cCJ+X1\n2coUKlPxbjeLAbe6+6PufhVwGXBhzjOnEXmUqXSecG2plUQ9/xFzKVOPcoWqM1EzRc0VtV01s4jn\nXJmaIlfE+qJM5TKFbFM14e5aergAJwN/Jd1CajKwCLAp8Hy7424AvpUfmzJVmwkYDPwQOAsYl7ft\nAtzV7rgngP0adI6UqR+2pVZbop7/iLmUqXSmiHUmXKbgucK1q2ZfIp5zZerfuSLWF2Xq322qlotG\nVPSQmS0GrAqsTLqP9WzgRHe/D3jSzE4tHH4JsKSZDfbcMpSpmkx5CNZ5wEjgIeA4MzvY3a8DRprZ\nXoXDTwB2g3TT83rkUaYeZQrVllpN1PMfMZcylc4Usc6EyxQ8V7h21ewinnNl6t+5ItYXZepRrnBt\nqtbUUdFz/wQ2ARZ395nA/wBuZnsDBwNfMbPN87GrAy+7+0fKVHmmEcB6wEHufgVwBjDKzMYCE4FT\nzWxIPvZvpC/4QLO6zpCrTOVEa0utJur5j5hLmcqJWGciZoqcK2K7anYRz7ky9e9cEeuLMpUXsU3V\nlDoqSrI8+Yi7v0NqCG29Z48DvyM1lBnAd4C9zOweYG/gQWVqbKb2hcHMzN3fBl4CDsib7wX+AOzh\n7tOAO4BzzGxX4DhgmLvPrlWvozL1Kl/lbamVRT3/EXMpU5c5wtWZiJki52qXKUS7aiURz7ky9b9c\nEeuLMvU6Y4g21RAe4PqTiAtwCLA+MLyDfTsAFwPr5vWNgOuBEXl9AWBbZaos08DC47Zb8A4gzcw7\nhdTz2JbnXGAF0jVd2wPXAd9VpsZnitiWWmmJev4j5lKmHuUKVWeiZoqaK2q7auYl4jlXpqbIFbG+\nKFM/blONWCoPEG0B1gYeAW4mzdz6k8K+y3MDWBqYBEwp7LsHWF2ZKs20F+nasTOB3Qvbv0SaAXc5\n4HTgmMK++4AxhfXBytTYTBHbUistUc9/xFzK1KNcoepM1ExRc0VtV828RDznytQUuSLWF2Xqx22q\nkUvlAaItwOeBC/LjYcBNwOS8vmThuCVyQ7gI+D1p6M0IZaomUy4ifwA2J/Vq3g3slfftk/cPAsYC\nvwV2AlYBfgV8uk7nSJn6YVtqtSXq+Y+YS5lKZ4pYZ8JlCp4rXLtq9iXiOVem/p0rYn1Rpv7dphq9\nVB6g6oU0XOcz5F4w0vCacwr7VwDeBpbJ61bYNxL4IrCPMlWSqTg8ayxwdmF9a+CVTp63PWn22z8B\nhypTYzNFbEuttEQ9/xFzKVOPcoWqM1EzRc0VtV018xLxnCtTU+SKWF+UqR+3qSqXygNU+uHhINJk\nI/8LXAYsCywDvAosWjjuTOYdbjMBWFaZKs30beB8YLe8/mngkXbH3Aac1m5b2/VmQ4EBytTYTBHb\nUistUc9/xFzK1KNcoepM1ExRc0VtV828RDznytQUuSLWF2Xqx22q6qVl7/phZguQZkX9nLtvB/yF\nNFPru8CVpOEzbS4HBprZInn9n8A/a33bGWUqnekEYFNSEZloZt9w94eAV8zspMKh3wQ2N7MR+Xmn\nAXsAuPssd5+jTI3LFLEttZKo5z9iLmXqUa5QdSZqpqi5orarZhbxnCtTU+SKWF+UqVymkG0qhKp7\nSqpcgKdJjQJgVeC7wLHAQOA5YNe8bzfgXGWqPhPpGrFfAuvl9c1J9zPeC1geeIPcs0iaYOYCYOG8\nPt9sucrUuEzR2lIrLlHPf8RcylQqT7g6EzFT5FwR21UrLBHPuTL131wR64sy9e82FWVpuREVZjbQ\nzNo+9yXAjgDu/izp3rMrAosCE4FxZnYHcBJpcpJa5hgcLVO7fJVn6uBexgPd/WPgCdJtgiDNuHsf\nMA54CzgLON3M9gS+RRo69X7O/m6tsilTj/IMqLottbII3+XIuVSLS+dQPW6iXKrLjRfluxwxU/Q6\nHCmXanFTZVId7k7VPSX1XoB9gS3JE4+027cp6Z64W+T1lYCfk2/pAgwGtgKG1TjT14BryPe8DZLp\nK8Bm5J7DIJmGFh4PKDz+Iun6rbXz+uqkWwZtSup53Aq4lHR/41pn+nRHr1lxpiXbzg/zTg5USSZg\ntS72VdKWWmnJNW8sHcz4XOX5R7W4bKZwtTi/tupx+VyhanJ+L9XlBi8ErMWoDvckl2px+UzhajGq\nw02xVB6gbh8sFZffALfmBjiVucN3TgN2BRYGDgOuBgblfbcA/16HPJYb2WnA7bS7nU0VmfJrr0i6\n1c4twCnAT4DF8r7JFWXaMv/cLqIwey0whvRLfzhwIvD9wr4bgD0L67W+l/G43J4uABYqbB8dINPP\ngQsL2zeuIhMwCngReAZYsd2+U6toS62y5PqyFHAX6XZZ/w38rOrvcn591eJyucLV4vz6qsc9zxWi\nJufXU11u4ELQWozqcE+yqRaXzxSuFqM63FRL0136kYdmDQa2Ac5y921Ivyhmwr8+72R3v9bd3yFN\nUjIHuNrMbgUWIt1yppaZBnlqcYOAdYE93P2htglastMbnSk/XAuY5u7buvsJ+X3Pzvt+0KhMlgwy\ns2NIX9ofAdOAbcxsx3zYcNKMu++SZsVdx8y+nieUGQy80/Z67v5RjTINNLPDgJ8C57n7oe7+f4XD\nFm5kpkK21Ujn6WzgUGB5MxuXdzf6PA3MD9cBvk8akrajmQ0pHNbQ71wrMbPBub4MJ91Oaxzpl91b\nzJ2AqWHf5UIu1eKSmfLDELU4Z1I97nm+MDU551FdbrCItVh1uGe58kPV4u4zhazFqsNNqOqeklot\npIJ3OmmozmeZd2jUFGA6achM271nBxX2DyZNqHJgnTKdDWxBusftGcBqpAZ7F3AxsEk+3hqY6RxS\n7+J3gJ8V9h8DfACMyesDG5BpAHOHZ+0JrJofDwP+i3z7oA6eN4o0POsx4KQ6Zhqff25L5PVtgU8W\n21AFmXYnT6ZD+oVwDelauvl6geuViTRE7lTSXxjGFs7PGODXwKgunluXttRKS7vv8ubADsAl7drL\na8DYtuMbcf5RLe7Nzy9ELS60G9XjnueqvCa3tRNUlxu6ELAWozrc25+fanH5TCFqMarDTb203Q+2\nX8sTy5xHapS3AvsB15N6sHcjFZ5pwIbA+u6+Q37eTsDf3P2BOme6jXSt2x3ATqRivCBpSNlBwM7u\nvkl+3s6knvh6Z/ol8GXSZC3HA0cDQ4A1SL15KzboPO1PHlrn7pPM7BPALNIvgo/M7ErgDne/tN3z\nhrv7u7lXcqC7f1DHTCNJE9lsCKxMGro1C3jW3b/V4EyXuvsJZrYGaeKdP5KGA75Eup2Ru/s+9c5k\nZmNJ/9n4HfAQcCBwtLvfk/efATip+M8sPK9ubamVdPBd3h24h/Q93sLdH8vHHQL8h7t/Ia/X9fyr\nFvcqU4hanF9f9bjnuULU5PzaqssNFrEWqw73Opdqcc8yVV6LVYdbQNU9JbVYSAXmPvKtY0i9xOeS\nb+VSOG4R0i+QttvS7Ais2aBM2wGTSL80pgNfLRz7BLB9frxTAzN9idRTfCmwN3At6RfYahQmkanX\neSL1Cl8PHAU8DKzSbv8Q4DpgnXbbJwLH1ekctc+0Wt6+JWnI3fp5fd38c1wnrx/WwExtE+uMJP0i\nPTSvLwC8zty/Rhxex0yfY95rJM8GTiusL0v6j9BmeX1E/vfL9WrfrbR08l3+BukvA7fnbQNIk0lN\nBVbI2+pW8zrJpVpc/udXWS3Or6163Ptcldfk/Pqqyw1eItZi1eE+//xUi7vPVHktVh1ujaXyADVs\nGFcCR+THw0g9yOcDSxaOGQNcQR0m0SqRaThptuXTgXvzF2uZ/AWaCqxcUabx+TwtWjhmd9L1gY3I\ns3z+9/sUhtrlbYsDt+XHyzD3HsILNDDTVfnxAAozP5OGZv2Eub/gP1FRpovJ913O234EbFfvTKS/\nfgwlD4EkDUucnB+3TQK0N3AjcDOpt7vu7amVlnbf5YWBfUjDDGeQhwwCG7W1l4pyqRaXz1RpLc7v\np3rc91yV1OT8+qrLFSwRa7HqcJ9yqRZ3nylELVYdbv6lmSbT/AUwysyWcvf3SL3ZHwJLm9mKZnYC\naQKhP3iNJ9Eqmeld4GnSBEbnk3pEzyRNrPKMu/+5okyPk4ZqrWBmi5rZSaQv/P0w//2aa83d/5If\nngWsamZbFnavBIwws6NIX+Yl8/ZZDcy0splt5e5zgOJEQceQekVfzs+p6bDiEpm2yZmeAy4ys9XN\nbBLpWtQn653J3d9391nuPjtv2gr4a973cd62NrA1MN3d969XlhZW/C6/Qxp2+Rbw7bz9ZtJ/wB6G\n+n+XO8mlWlw+U6W1GFSP+5ir0pqcX191uRoRa7HqcO9zqRZ3nylELVYdbgFV95TUaiHdDuoHwPGF\nbfcCG5CuebsUWC5Apt8Co/PjDSn0bld8nj5Dup3QqeShiRX8DA8G7i6sfw34CLiw0T+7LjJtB9xN\n+k/HfPchb2Cm3xTWTyfd/uyqCtr4QFLv9a3kv4AAa+a2fUJVbakVlk6+y/eRb/MGfKGKNqpa3Ofz\nVHktzllUj3uWK0RNzu+vutzY8x2uFqsO1+RcqRaXz1R5LVYdbs6lKSbTbGNmm5J6Pc8FHiQV4qOA\nx72iD9pBpouBE9z9/irydJLpEuCb7v5whZkGuPscM7uWNDv2W6Qe2ac9T0BTcaZXgfeAR0kTBVVy\nrtpl+jvwPmmY5OPegL8idpDHSH8JmUL6q8QE0rk62t3fbnSeVhOxvnSSS7W4XKbKa3HOpXrcu1yV\n1+ScSXW5wfpJfVEdLp9LtbhcphC1WHW4iVXdU1LrhXSv6EtIQ8omVp1HmXqUaUHSxE5vAEdVnaeD\nTEdWnSdiJtJ1rnNIf4GYUHWeVlsifpej5lKmHuVSPe7HuVSXKznn4b7LytQUuVSL+28m1eE+Lk01\noqKNmQ0m3Yrm424PbhBl6p6ZfZN0bdux7l7Xa+3KUqZSeZYlTR52RoQ8rSjad7lNxFzKVE60OgMx\nM0HMXKrL1Qj6XVamkiLmClpflKkE1eG+a8qOCumf2oZuVZ2jSJlEpBVFrDMRM0HcXCLS/0WsL8ok\njaKOChEREREREREJo5luTyoiIiIiIiIi/Zw6KkREREREREQkDHVUiIiIiIiIiEgY6qgQERERERER\nkTDUUSEiIiIiIiIiYaijQlqamY0ws0Pz46XMbGrVmUREWonqsIhI9VSLJRrdnlRampmtANzk7utW\nHEVEpCWpDouIVE+1WKIZVHUAkYqdBqxkZg8DzwFruvu6ZjYe2AlYCFgF+CEwBNgH+BDY1t1nmtlK\nwHnAYsD7wIHu/kwFn0NEpL9SHRYRqZ5qsYSiSz+k1R0H/NndNwSOBopDjNYmFebRwCnAe/m4+4F9\n8zEXARPd/TP5+Rc0KriISJNQHRYRqZ5qsYSiERUinbvL3d8H3jezmcDNefvjwLpmthCwKXCNmVne\nN7iCnCIizUp1WESkeqrF0nDqqBDp3KzCYy+szyF9dwYAb+ceZRERqT3VYRGR6qkWS8Pp0g9pde8C\nw/Nj6+rA9tz9XeAFM9u1bZuZrVfDbCIirUB1WESkeqrFEoo6KqSluftbwG/N7DFgMvNejzfPoZ1s\n3xuYYGaPmtkTwA51iCki0rRUh0VEqqdaLNHo9qQiIiIiIiIiEoZGVIiIiIiIiIhIGOqoEBERERER\nEZEw1FEhIiIiIiIiImGoo0JEREREREREwlBHhYiIiIiIiIiEoY4KEREREREREQlDHRUiIiIiIiIi\nEsb/A9wBYD/ajbkBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab0b9bb550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(ds.temperature_sample_count)\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 6))\n",
    "temperature.plot(ax=ax1)\n",
    "ax1.set_title('Raw Data')\n",
    "ds.temperature.plot(ax=ax2)\n",
    "ax2.set_title('15 min Averaged')\n",
    "ds.temperature_std_dev.plot(ax=ax3)\n",
    "ax3.set_title('15 min Std Dev')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Store the Dataset as a netCDF4 File\n",
    "\n",
    "ERDDAP requires that all files in a dataset have the same units for their `time` variable.\n",
    "On the other hand,\n",
    "`xarray` defaults to using the 1st `time` value in the dataset as the time-base for the units.\n",
    "So, we have to explicitly define the `time` units as an encoding when the dataset is stored\n",
    "as a netCDF4 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds.to_netcdf(\n",
    "    '/results/observations/ONC/CTD/{station}/{station}_CTD_15m_20151227.nc'\n",
    "    .format(station=onc_station),\n",
    "    encoding={'time': {'units': 'minutes since 1970-01-01 00:00'}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate an ERDDAP Dataset XML Fragment\n",
    "\n",
    "Use the `/opt/tomcat/webapps/erddap/WEB-INF/GenerateDatasetsXml.sh` script\n",
    "generate the initial version of an XML fragment for a dataset:\n",
    "```\n",
    "$ cd /opt/tomcat/webapps/erddap/WEB-INF/\n",
    "$ bash GenerateDatasetsXml.sh EDDTableFromNcFiles /results/observations/ONC/CTD/SCVIP/\n",
    "```\n",
    "The `EDDTableFromNcFiles` and `/results/observations/ONC/CTD/SCVIP/` arguments\n",
    "tell the script which `EDDType` and what parent directory to use,\n",
    "avoiding having to type those in answer to prompts.\n",
    "Answer the remaining prompts,\n",
    "for example:\n",
    "```\n",
    "File name regex (e.g., \".*\\.nc\") (default=\"\")\n",
    "? .*SCVIP_CTD_15m_\\d{8}\\.nc$ \n",
    "A sample full file name (default=\"\")\n",
    "? /results/observations/ONC/CTD/SCVIP/SCVIP_CTD_15m_20160724.nc\n",
    "DimensionsCSV (or \"\" for default) (default=\"\")\n",
    "? \n",
    "ReloadEveryNMinutes (e.g., 10080) (default=\"\")\n",
    "? 10080\n",
    "PreExtractRegex (default=\"\")\n",
    "? \n",
    "PostExtractRegex (default=\"\")\n",
    "? \n",
    "ExtractRegex (default=\"\")\n",
    "? \n",
    "Column name for extract (default=\"\")\n",
    "? \n",
    "Sorted column source name (default=\"\")\n",
    "? \n",
    "Sort files by sourceName (default=\"\")\n",
    "? \n",
    "infoUrl (default=\"\")\n",
    "? https://salishsea-meopar-tools.readthedocs.org/en/latest/results_server/\n",
    "institution (default=\"\")\n",
    "? UBC EOAS\n",
    "summary (default=\"\")\n",
    "? \n",
    "title (default=\"\")\n",
    "? ONC, Strait of Georgia, Central Node, Salinity and Temperature, 15min, v1\n",
    "```\n",
    "\n",
    "The output is written to `/results/erddap/logs/GenerateDatasetsXml.out`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `metadata` dictionary below contains information for dataset\n",
    "attribute tags whose values need to be changed,\n",
    "or that need to be added for all datasets.\n",
    "\n",
    "The keys are the dataset attribute names.\n",
    "\n",
    "The values are dicts containing a required `text` item\n",
    "and perhaps an optional `after` item.\n",
    "\n",
    "The value associated with the `text` key is the text content\n",
    "for the attribute tag.\n",
    "\n",
    "When present,\n",
    "the value associated with the `after` key is the name\n",
    "of the dataset attribute after which a new attribute tag\n",
    "containing the `text` value is to be inserted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "metadata = OrderedDict([\n",
    "    ('cdm_data_type', {'text': 'TimeSeries'}),\n",
    "    ('cdm_timeseries_variables', {\n",
    "        'text': 'depth, longitude, latitude',\n",
    "        'after': 'cdm_data_type',\n",
    "    }),\n",
    "    ('institution_fullname', {\n",
    "        'text': 'Earth, Ocean & Atmospheric Sciences, University of British Columbia',\n",
    "        'after': 'institution',\n",
    "    }),\n",
    "    ('license', {\n",
    "        'text': '''The Salish Sea MEOPAR observation datasets are copyright 2013 – present\n",
    "by the Salish Sea MEOPAR Project Contributors, The University of British Columbia, and Ocean Networks Canada.\n",
    "\n",
    "They are licensed under the Apache License, Version 2.0. http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Raw instrument data on which this dataset is based were provided by Ocean Networks Canada.''',\n",
    "    }),\n",
    "    ('project', {\n",
    "        'text':'Salish Sea MEOPAR NEMO Model',\n",
    "        'after': 'title',\n",
    "    }),\n",
    "    ('creator_name', {\n",
    "        'text': 'Salish Sea MEOPAR Project Contributors',\n",
    "    }),\n",
    "    ('creator_email', {\n",
    "        'text': 'sallen@eos.ubc.ca',\n",
    "        'after': 'creator_name',\n",
    "    }),\n",
    "    ('creator_url', {\n",
    "        'text': 'https://salishsea-meopar-docs.readthedocs.org/',\n",
    "    }),\n",
    "    ('acknowledgement', {\n",
    "        'text': 'MEOPAR, ONC, Compute Canada',\n",
    "        'after': 'creator_url',\n",
    "    }),\n",
    "    ('drawLandMask', {\n",
    "        'text': 'over',\n",
    "        'after': 'acknowledgement',\n",
    "    }),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `datasets` dictionary below provides the content\n",
    "for the dataset `title` and `summary` attributes.\n",
    "\n",
    "The `title` attribute content appears in the the datasets list table\n",
    "(among other places).\n",
    "\n",
    "The `summary` atribute content appears\n",
    "(among other places)\n",
    "when a user hovers the cursor over the `?` icon beside the `title`\n",
    "content in the datasets list table.\n",
    "The text that is inserted into the `summary` attribute tag\n",
    "by code later in this notebook is the\n",
    "`title` content followed by the `summary` content,\n",
    "separated by a blank line.\n",
    "\n",
    "The keys of the `datasets` dict are the `datasetID` strings that\n",
    "are used in many places by the ERDDAP server.\n",
    "They are structured as follows:\n",
    "\n",
    "* `ubc` to indicate that the dataset was produced at UBC\n",
    "* `ONC` to indicate that the dataset is a product of filter, resampling, etc.\n",
    "raw instrument data provided by Ocean Networks Canada (ONC)\n",
    "* a description of the dataset variables; e.g. `SCVIPCTD`\n",
    "* the time interval of values in the dataset; e.g. `15m`\n",
    "* the dataset version; e.g. `V1`\n",
    "\n",
    "So:\n",
    "\n",
    "* `ubcONCSCVIPCTD15mV1` is the version 1 dataset of 15 minute resampled CTD temperature and salinity data\n",
    "from the ONC Strait of Georgia Central node VENUS instrument platform\n",
    "\n",
    "The dataset version part of the `datasetID` is used to indicate changes in the variables\n",
    "contained in the dataset.\n",
    "\n",
    "All datasets start at `V1` and their `summary` ends with a notation about the variables\n",
    "that they contain; e.g.\n",
    "```\n",
    "v1: reference salinity, reference salinity standard deviation, reference salinity sample counts, \n",
    "temperature, temperature standard deviation, temperature sample counts variables\n",
    "```\n",
    "When the a dataset version is incremented a line describing the change is added\n",
    "to the end of its `summary`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datasets = {\n",
    "    'ubcONCSCVIPCTD15mV1' :{\n",
    "        'type': 'resampled CTD',\n",
    "        'title': 'ONC, Strait of Georgia, Central Node, Salinity and Temperature, 15min, v1',\n",
    "        'summary':'''Temperature and salinity data from the Ocean Networks Canada (ONC)\n",
    "Strait of Georgia Central Node VENUS Instrument Platform CTD.\n",
    "The data are resampled from the raw instrument data to 15 minute mean values.\n",
    "They are accompanied by standard deviations and sample counts for each of the 15 minute\n",
    "aggregation intervals.\n",
    "\n",
    "v1: reference salinity, reference salinity standard deviation, reference salinity sample counts, \n",
    "temperature, temperature standard deviation, temperature sample counts variables''',\n",
    "        'keywords': '''15min aggregation, ONC Central Node VENUS Instrument Platform, Ocean Networks Canada,\n",
    "depth, UBC EOAS, Strait of Georgia, latitude, longitude, ocean, SCVIP, observations, CTD,\n",
    "Oceans &gt; Ocean Temperature &gt; Water Temperature,\n",
    "reference salinity, salinity_sample_count, salinity_std_dev, sea_water_reference_salinity, \n",
    "sea_water_reference_salinity_sample_count, sea_water_reference_salinity_standard_deviation, \n",
    "sea_water_temperature, sea_water_temperature_sample_count, sea_water_temperature_standard_deviation, \n",
    "temperature, temperature_sample_count, temperature_std_dev, time''',\n",
    "        'fileNameRegex': '.*SCVIP_CTD_15m_\\d{8}\\.nc$'\n",
    "    },\n",
    "    \n",
    "    'ubcONCSEVIPCTD15mV1' :{\n",
    "        'type': 'resampled CTD',\n",
    "        'title': 'ONC, Strait of Georgia, East Node, Salinity and Temperature, 15min, v1',\n",
    "        'summary':'''Temperature and salinity data from the Ocean Networks Canada (ONC)\n",
    "Strait of Georgia East Node VENUS Instrument Platform CTD.\n",
    "The data are resampled from the raw instrument data to 15 minute mean values.\n",
    "They are accompanied by standard deviations and sample counts for each of the 15 minute\n",
    "aggregation intervals.\n",
    "\n",
    "v1: reference salinity, reference salinity standard deviation, reference salinity sample counts, \n",
    "temperature, temperature standard deviation, temperature sample counts variables''',\n",
    "        'keywords': '''15min aggregation, ONC East Node VENUS Instrument Platform, Ocean Networks Canada,\n",
    "depth, UBC EOAS, Strait of Georgia, latitude, longitude, ocean, SEVIP, observations, CTD,\n",
    "Oceans &gt; Ocean Temperature &gt; Water Temperature,\n",
    "reference salinity, salinity_sample_count, salinity_std_dev, sea_water_reference_salinity, \n",
    "sea_water_reference_salinity_sample_count, sea_water_reference_salinity_standard_deviation, \n",
    "sea_water_temperature, sea_water_temperature_sample_count, sea_water_temperature_standard_deviation, \n",
    "temperature, temperature_sample_count, temperature_std_dev, time''',\n",
    "        'fileNameRegex': '.*SEVIP_CTD_15m_\\d{8}\\.nc$'\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few convenience functions to reduce code repetition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_tree(root):\n",
    "    \"\"\"Display an XML tree fragment with indentation.\n",
    "    \"\"\"\n",
    "    print(etree.tostring(root, pretty_print=True).decode('ascii'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_att(root, att):\n",
    "    \"\"\"Return the dataset attribute element named att\n",
    "    or raise a ValueError exception if it cannot be found.\n",
    "    \"\"\"\n",
    "    e = root.find('.//att[@name=\"{}\"]'.format(att))\n",
    "    if e is None:\n",
    "        raise ValueError('{} attribute element not found'.format(att))\n",
    "    return e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* parse the output of `GenerateDatasetsXml.sh` into an XML tree data structure\n",
    "* set the `datasetID` dataset attribute value\n",
    "* change the `recursive` dataset attribute value to `false`\n",
    "* re-set the `fileNameRegex` dataset attribute value because it looses its `\\` characters during parsing(?)\n",
    "* add a `cf_role` attribute element with value `timeseries_id` to the `time` variable\n",
    "* update the metadata elements with the contents of the `datasets` dict defined above\n",
    "* set the colour map limit variables for the variables that they make sense for,\n",
    "and delete from the variables for which they are nonsensical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parser = etree.XMLParser(remove_blank_text=True)\n",
    "tree = etree.parse('/results/erddap/logs/GenerateDatasetsXml.out', parser)\n",
    "root = tree.getroot()\n",
    "\n",
    "datasetID = 'ubcONCSEVIPCTD15mV1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "root.attrib['datasetID'] = datasetID\n",
    "root.find('.//recursive').text = 'false'\n",
    "root.find('.//fileNameRegex').text = datasets[datasetID]['fileNameRegex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['time',\n",
       " 'salinity',\n",
       " 'temperature',\n",
       " 'temperature_std_dev',\n",
       " 'salinity_std_dev',\n",
       " 'salinity_sample_count',\n",
       " 'temperature_sample_count',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'depth']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars = [e.text for e in tree.findall('//sourceName')]\n",
    "vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "e = etree.Element('att', name='cf_role')\n",
    "e.text = 'timeseries_id'\n",
    "tree.find('//dataVariable[{}]/addAttributes'.format(vars.index('time')+1)).append(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for att, info in metadata.items():\n",
    "    e = etree.Element('att', name=att)\n",
    "    e.text = info['text']\n",
    "    try:\n",
    "        root.find('.//att[@name=\"{}\"]'.format(info['after'])).addnext(e)\n",
    "    except KeyError:\n",
    "        find_att(root, att).text = info['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title = datasets[datasetID]['title']\n",
    "find_att(root, 'title').text = title\n",
    "find_att(root, 'summary').text = '{0}\\n\\n{1}'.format(title, datasets[datasetID]['summary'])\n",
    "find_att(root, 'keywords').text = datasets[datasetID]['keywords']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Salinity colour map limits\n",
    "e = tree.find(\n",
    "    '//dataVariable[{}]/addAttributes/att[@name=\"colorBarMinimum\"]'\n",
    "    .format(vars.index('salinity')+1))\n",
    "e.text = '0.0'\n",
    "e = tree.find(\n",
    "    '//dataVariable[{}]/addAttributes/att[@name=\"colorBarMaximum\"]'\n",
    "    .format(vars.index('salinity')+1))\n",
    "e.text = '34.0'\n",
    "\n",
    "# Temperature colour map limits\n",
    "e = tree.find(\n",
    "    '//dataVariable[{}]/addAttributes/att[@name=\"colorBarMinimum\"]'\n",
    "    .format(vars.index('temperature')+1))\n",
    "e.text = '4.0'\n",
    "e = tree.find(\n",
    "    '//dataVariable[{}]/addAttributes/att[@name=\"colorBarMaximum\"]'\n",
    "    .format(vars.index('temperature')+1))\n",
    "e.text = '20.0'\n",
    "\n",
    "# Depth colour map limits\n",
    "e = tree.find(\n",
    "    '//dataVariable[{}]/addAttributes/att[@name=\"colorBarMinimum\"]'\n",
    "    .format(vars.index('depth')+1))\n",
    "e.text = '0.0'\n",
    "e = tree.find(\n",
    "    '//dataVariable[{}]/addAttributes/att[@name=\"colorBarMaximum\"]'\n",
    "    .format(vars.index('depth')+1))\n",
    "e.text = '450.0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Delete nonsensical colourBar* attributes\n",
    "no_cbar_vars = [\n",
    "    'temperature_sample_count', 'temperature_std_dev',\n",
    "    'salinity_sample_count', 'salinity_std_dev']\n",
    "for var in no_cbar_vars:\n",
    "    for att in ('colorBarMinimum', 'colorBarMaximum'):\n",
    "        e = tree.find(\n",
    "            '//dataVariable[{0}]/addAttributes/att[@name=\"{1}\"]'\n",
    "            .format(vars.index(var)+1, att))\n",
    "        e.getparent().remove(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the resulting dataset XML fragment below and edit the dicts and\n",
    "code cell above until it is what is required for the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<dataset type=\"EDDTableFromNcFiles\" datasetID=\"ubcONCSEVIPCTD15mV1\" active=\"true\">\n",
      "  <reloadEveryNMinutes>10080</reloadEveryNMinutes>\n",
      "  <updateEveryNMillis>10000</updateEveryNMillis>\n",
      "  <fileDir>/results/observations/ONC/CTD/SEVIP/</fileDir>\n",
      "  <recursive>false</recursive>\n",
      "  <fileNameRegex>.*SEVIP_CTD_15m_\\d{8}\\.nc$</fileNameRegex>\n",
      "  <metadataFrom>last</metadataFrom>\n",
      "  <preExtractRegex/>\n",
      "  <postExtractRegex/>\n",
      "  <extractRegex/>\n",
      "  <columnNameForExtract/>\n",
      "  <sortedColumnSourceName>time</sortedColumnSourceName>\n",
      "  <sortFilesBySourceNames>time</sortFilesBySourceNames>\n",
      "  <fileTableInMemory>false</fileTableInMemory>\n",
      "  <accessibleViaFiles>false</accessibleViaFiles>\n",
      "  <!-- sourceAttributes>\n",
      "        <att name=\"_NCProperties\">version=1|netcdflibversion=4.4.1|hdf5libversion=1.8.17</att>\n",
      "        <att name=\"coordinates\">latitude longitude depth</att>\n",
      "        <att name=\"history\">\n",
      "2016-09-10 15:28:46 Download raw data from ONC scalardata API.\n",
      "2016-09-10 15:28:46 Filter to exclude data with qaqcFlag != 1.\n",
      "2016-09-10 15:28:46 Resample data to 15 minute intervals using mean, standard deviation and count as aggregation functions.\n",
      "2016-09-10 15:28:46 Store as netCDF4 file.\n",
      "        </att>\n",
      "        <att name=\"ONC_data_product_url\">http://dmas.uvic.ca/DataSearch?location=SEVIP&amp;deviceCategory=CTD</att>\n",
      "        <att name=\"ONC_station\">East</att>\n",
      "        <att name=\"ONC_stationCode\">SEVIP</att>\n",
      "        <att name=\"ONC_stationDescription\">Pacific, Salish Sea, Strait of Georgia, East, Strait of Georgia VENUS Instrument Platform</att>\n",
      "    </sourceAttributes -->\n",
      "  <!-- Please specify the actual cdm_data_type (TimeSeries?) and related info below, for example...\n",
      "        <att name=\"cdm_timeseries_variables\">station, longitude, latitude</att>\n",
      "        <att name=\"subsetVariables\">station, longitude, latitude</att>\n",
      "    -->\n",
      "  <addAttributes>\n",
      "    <att name=\"cdm_data_type\">TimeSeries</att>\n",
      "    <att name=\"cdm_timeseries_variables\">depth, longitude, latitude</att>\n",
      "    <att name=\"Conventions\">COARDS, CF-1.6, ACDD-1.3</att>\n",
      "    <att name=\"creator_name\">Salish Sea MEOPAR Project Contributors</att>\n",
      "    <att name=\"creator_email\">sallen@eos.ubc.ca</att>\n",
      "    <att name=\"creator_url\">https://salishsea-meopar-docs.readthedocs.org/</att>\n",
      "    <att name=\"acknowledgement\">MEOPAR, ONC, Compute Canada</att>\n",
      "    <att name=\"drawLandMask\">over</att>\n",
      "    <att name=\"infoUrl\">https://salishsea-meopar-tools.readthedocs.org/en/latest/results_server/</att>\n",
      "    <att name=\"institution\">UBC EOAS</att>\n",
      "    <att name=\"institution_fullname\">Earth, Ocean &amp; Atmospheric Sciences, University of British Columbia</att>\n",
      "    <att name=\"keywords\">15min aggregation, ONC East Node VENUS Instrument Platform, Ocean Networks Canada,\n",
      "depth, UBC EOAS, Strait of Georgia, latitude, longitude, ocean, SEVIP, observations, CTD,\n",
      "Oceans &amp;gt; Ocean Temperature &amp;gt; Water Temperature,\n",
      "reference salinity, salinity_sample_count, salinity_std_dev, sea_water_reference_salinity, \n",
      "sea_water_reference_salinity_sample_count, sea_water_reference_salinity_standard_deviation, \n",
      "sea_water_temperature, sea_water_temperature_sample_count, sea_water_temperature_standard_deviation, \n",
      "temperature, temperature_sample_count, temperature_std_dev, time</att>\n",
      "    <att name=\"keywords_vocabulary\">GCMD Science Keywords</att>\n",
      "    <att name=\"license\">The Salish Sea MEOPAR observation datasets are copyright 2013 – present\n",
      "by the Salish Sea MEOPAR Project Contributors, The University of British Columbia, and Ocean Networks Canada.\n",
      "\n",
      "They are licensed under the Apache License, Version 2.0. http://www.apache.org/licenses/LICENSE-2.0\n",
      "\n",
      "Raw instrument data on which this dataset is based were provided by Ocean Networks Canada.</att>\n",
      "    <att name=\"sourceUrl\">(local files)</att>\n",
      "    <att name=\"standard_name_vocabulary\">CF Standard Name Table v29</att>\n",
      "    <att name=\"summary\">ONC, Strait of Georgia, East Node, Salinity and Temperature, 15min, v1\n",
      "\n",
      "Temperature and salinity data from the Ocean Networks Canada (ONC)\n",
      "Strait of Georgia East Node VENUS Instrument Platform CTD.\n",
      "The data are resampled from the raw instrument data to 15 minute mean values.\n",
      "They are accompanied by standard deviations and sample counts for each of the 15 minute\n",
      "aggregation intervals.\n",
      "\n",
      "v1: reference salinity, reference salinity standard deviation, reference salinity sample counts, \n",
      "temperature, temperature standard deviation, temperature sample counts variables</att>\n",
      "    <att name=\"title\">ONC, Strait of Georgia, East Node, Salinity and Temperature, 15min, v1</att>\n",
      "    <att name=\"project\">Salish Sea MEOPAR NEMO Model</att>\n",
      "  </addAttributes>\n",
      "  <dataVariable>\n",
      "    <sourceName>time</sourceName>\n",
      "    <destinationName>time</destinationName>\n",
      "    <dataType>long</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "            <att name=\"calendar\">proleptic_gregorian</att>\n",
      "            <att name=\"units\">minutes since 1970-01-01</att>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes>\n",
      "      <att name=\"long_name\">Time</att>\n",
      "      <att name=\"standard_name\">time</att>\n",
      "      <att name=\"cf_role\">timeseries_id</att>\n",
      "    </addAttributes>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>salinity</sourceName>\n",
      "    <destinationName>salinity</destinationName>\n",
      "    <dataType>double</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "            <att name=\"aggregation_interval\" type=\"long\">900</att>\n",
      "            <att name=\"aggregation_interval_units\">seconds</att>\n",
      "            <att name=\"aggregation_operation\">mean</att>\n",
      "            <att name=\"ioos_category\">Salinity</att>\n",
      "            <att name=\"long_name\">reference salinity</att>\n",
      "            <att name=\"standard_name\">sea_water_reference_salinity</att>\n",
      "            <att name=\"units\">g/kg</att>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes>\n",
      "      <att name=\"colorBarMaximum\" type=\"double\">34.0</att>\n",
      "      <att name=\"colorBarMinimum\" type=\"double\">0.0</att>\n",
      "    </addAttributes>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>temperature</sourceName>\n",
      "    <destinationName>temperature</destinationName>\n",
      "    <dataType>double</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "            <att name=\"aggregation_interval\" type=\"long\">900</att>\n",
      "            <att name=\"aggregation_interval_units\">seconds</att>\n",
      "            <att name=\"aggregation_operation\">mean</att>\n",
      "            <att name=\"ioos_category\">Temperature</att>\n",
      "            <att name=\"long_name\">temperature</att>\n",
      "            <att name=\"standard_name\">sea_water_temperature</att>\n",
      "            <att name=\"units\">degrees_Celcius</att>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes>\n",
      "      <att name=\"colorBarMaximum\" type=\"double\">20.0</att>\n",
      "      <att name=\"colorBarMinimum\" type=\"double\">4.0</att>\n",
      "    </addAttributes>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>temperature_std_dev</sourceName>\n",
      "    <destinationName>temperature_std_dev</destinationName>\n",
      "    <dataType>double</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "            <att name=\"aggregation_interval\" type=\"long\">900</att>\n",
      "            <att name=\"aggregation_interval_units\">seconds</att>\n",
      "            <att name=\"aggregation_operation\">standard deviation</att>\n",
      "            <att name=\"ioos_category\">Temperature</att>\n",
      "            <att name=\"long_name\">temperature standard deviation</att>\n",
      "            <att name=\"standard_name\">sea_water_temperature_standard_deviation</att>\n",
      "            <att name=\"units\">degrees_Celcius</att>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes/>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>salinity_std_dev</sourceName>\n",
      "    <destinationName>salinity_std_dev</destinationName>\n",
      "    <dataType>double</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "            <att name=\"aggregation_interval\" type=\"long\">900</att>\n",
      "            <att name=\"aggregation_interval_units\">seconds</att>\n",
      "            <att name=\"aggregation_operation\">standard deviation</att>\n",
      "            <att name=\"ioos_category\">Salinity</att>\n",
      "            <att name=\"long_name\">reference salinity standard deviation</att>\n",
      "            <att name=\"standard_name\">sea_water_reference_salinity_standard_deviation</att>\n",
      "            <att name=\"units\">g/kg</att>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes/>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>salinity_sample_count</sourceName>\n",
      "    <destinationName>salinity_sample_count</destinationName>\n",
      "    <dataType>long</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "            <att name=\"aggregation_interval\" type=\"long\">900</att>\n",
      "            <att name=\"aggregation_interval_units\">seconds</att>\n",
      "            <att name=\"aggregation_operation\">count</att>\n",
      "            <att name=\"long_name\">reference salinity sample count</att>\n",
      "            <att name=\"standard_name\">sea_water_reference_salinity_sample_count</att>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes/>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>temperature_sample_count</sourceName>\n",
      "    <destinationName>temperature_sample_count</destinationName>\n",
      "    <dataType>long</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "            <att name=\"aggregation_interval\" type=\"long\">900</att>\n",
      "            <att name=\"aggregation_interval_units\">seconds</att>\n",
      "            <att name=\"aggregation_operation\">count</att>\n",
      "            <att name=\"long_name\">temperature sample count</att>\n",
      "            <att name=\"standard_name\">sea_water_temperature_sample_count</att>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes/>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>latitude</sourceName>\n",
      "    <destinationName>latitude</destinationName>\n",
      "    <dataType>double</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes>\n",
      "      <att name=\"colorBarMaximum\" type=\"double\">90.0</att>\n",
      "      <att name=\"colorBarMinimum\" type=\"double\">-90.0</att>\n",
      "      <att name=\"long_name\">Latitude</att>\n",
      "      <att name=\"standard_name\">latitude</att>\n",
      "      <att name=\"units\">degrees_north</att>\n",
      "    </addAttributes>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>longitude</sourceName>\n",
      "    <destinationName>longitude</destinationName>\n",
      "    <dataType>double</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes>\n",
      "      <att name=\"colorBarMaximum\" type=\"double\">180.0</att>\n",
      "      <att name=\"colorBarMinimum\" type=\"double\">-180.0</att>\n",
      "      <att name=\"long_name\">Longitude</att>\n",
      "      <att name=\"standard_name\">longitude</att>\n",
      "      <att name=\"units\">degrees_east</att>\n",
      "    </addAttributes>\n",
      "  </dataVariable>\n",
      "  <dataVariable>\n",
      "    <sourceName>depth</sourceName>\n",
      "    <destinationName>depth</destinationName>\n",
      "    <dataType>long</dataType>\n",
      "    <!-- sourceAttributes>\n",
      "        </sourceAttributes -->\n",
      "    <addAttributes>\n",
      "      <att name=\"colorBarMaximum\" type=\"double\">450.0</att>\n",
      "      <att name=\"colorBarMinimum\" type=\"double\">0.0</att>\n",
      "      <att name=\"colorBarPalette\">OceanDepth</att>\n",
      "      <att name=\"long_name\">Depth</att>\n",
      "      <att name=\"standard_name\">depth</att>\n",
      "      <att name=\"units\">m</att>\n",
      "    </addAttributes>\n",
      "  </dataVariable>\n",
      "</dataset>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_tree(root)"
   ]
  },
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   "metadata": {
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   },
   "source": [
    "Store the XML fragment for the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('/results/erddap_datasets_xml/{}.xml'.format(datasetID), 'wb') as f:\n",
    "    f.write(etree.tostring(root, pretty_print=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Edit `/opt/tomcat/content/erddap/datasets.xml` to include the XML fragment for the dataset that was stored by the abave cell.\n",
    "\n",
    "Create a flag file to signal the ERDDAP server process to load the dataset:\n",
    "```bash\n",
    "$ cd /results/erddap/flag/\n",
    "$ touch <datasetID>\n",
    "```\n",
    "\n",
    "Confirm that the dataset and its metadata were correctly added to ERDDAP by inspecting\n",
    "https://salishsea.eos.ubc.ca/erddap/tabledap/.\n",
    "If there is a problem,\n",
    "error messages can be found in `/results/erddap/logs/log.txt`."
   ]
  }
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