{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing WOFpy LBR sample DB\n",
    "Emilio Mayorga. Run on my conda environment `uwapl_em_mc_1aui`.  \n",
    "3/5,4/2017. Test Don's Amazon cloud deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/filipe/miniconda3/envs/BiG-CZ/lib/python2.7/site-packages/ulmo/twc/kbdi/core.py:20: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n",
      "You can access Timestamp as pandas.Timestamp\n",
      "  CSV_SWITCHOVER = pandas.tslib.Timestamp('2016-10-01')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pytz\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import ulmo\n",
    "from ulmo.util import convert_datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CUAHSI WaterOneFlow: ulmo, SOAP endpoint, and other general info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    ulmo.cuahsi.wof\n",
      "    ~~~~~~~~~~~~~~~\n",
      "\n",
      "    `CUAHSI WaterOneFlow`_ web services\n",
      "\n",
      "    .. _CUAHSI WaterOneFlow: http://his.cuahsi.org/wofws.html\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(ulmo.cuahsi.wof.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['absolute_import', 'core', 'get_site_info', 'get_sites', 'get_values', 'get_variable_info']\n"
     ]
    }
   ],
   "source": [
    "print([obj for obj in dir(ulmo.cuahsi.wof) if not obj.startswith('__')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WaterML/WOF WSDL endpoints\n",
    "wsdlurl = 'http://54.186.36.247:8080/mysqlodm2timeseries/soap/cuahsi_1_0/.wsdl'  # WOF 1.0\n",
    "\n",
    "# 'network code'\n",
    "networkcd = 'mysqlodm2timeseries'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get site information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "one of two sites in the LBR sample DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sitecd = 'USU-LBR-Mendon'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "siteinfo = ulmo.cuahsi.wof.get_site_info(wsdlurl, networkcd+':'+sitecd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dict,\n",
       " ['code', 'name', 'series', 'notes', 'network', 'location', 'timezone_info'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(siteinfo), siteinfo.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('mysqlodm2timeseries',\n",
       " 'USU-LBR-Mendon',\n",
       " 'Little Bear River at Mendon Road near Mendon, Utah')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siteinfo['network'], siteinfo['code'], siteinfo['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'latitude': '41.718473', 'srs': 'EPSG:CUAHSI:4269', 'longitude': '-111.946402'}\n"
     ]
    }
   ],
   "source": [
    "print(siteinfo['location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dict,\n",
       " 18,\n",
       " ['mysqlodm2timeseries:USU7',\n",
       "  'mysqlodm2timeseries:USU6',\n",
       "  'mysqlodm2timeseries:USU5',\n",
       "  'mysqlodm2timeseries:USU4',\n",
       "  'mysqlodm2timeseries:USU3',\n",
       "  'mysqlodm2timeseries:USU48',\n",
       "  'mysqlodm2timeseries:USU47',\n",
       "  'mysqlodm2timeseries:USU9',\n",
       "  'mysqlodm2timeseries:USU8',\n",
       "  'mysqlodm2timeseries:USU13',\n",
       "  'mysqlodm2timeseries:USU10',\n",
       "  'mysqlodm2timeseries:USU35',\n",
       "  'mysqlodm2timeseries:USU34',\n",
       "  'mysqlodm2timeseries:USU37',\n",
       "  'mysqlodm2timeseries:USU36',\n",
       "  'mysqlodm2timeseries:USU44',\n",
       "  'mysqlodm2timeseries:USU33',\n",
       "  'mysqlodm2timeseries:USU32'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(siteinfo['series']), len(siteinfo['series']), siteinfo['series'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['{http://www.cuahsi.org/water_ml/1.0/}variable_time_interval',\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}_source',\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}_quality_control_level',\n",
       " 'variable',\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}_method',\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}value_count']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siteinfo['series']['mysqlodm2timeseries:USU33'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'variable': {'code': 'USU33',\n",
       "  'data_type': 'Average',\n",
       "  'general_category': 'Water Quality',\n",
       "  'id': '33',\n",
       "  'name': 'Oxygen, dissolved percent of saturation',\n",
       "  'no_data_value': '-9999.0000000000',\n",
       "  'sample_medium': 'Unknown',\n",
       "  'time': {},\n",
       "  'units': {'abbreviation': '%',\n",
       "   'code': '1',\n",
       "   'name': 'percent',\n",
       "   'type': 'Dimensionless'},\n",
       "  'value_type': 'Unknown',\n",
       "  'vocabulary': 'mysqlodm2timeseries'},\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}_method': {'method_description': 'Dissolved oxygen measured using a Hydrolab MS5 Water Quality Multiprobe.',\n",
       "  'method_id': '19',\n",
       "  'method_link': 'http://www.hydrolab.com'},\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}_quality_control_level': {'quality_control_level': '0',\n",
       "  'quality_control_level_id': '0'},\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}_source': {'organization': 'Utah State University Utah Water Research Laboratory',\n",
       "  'source_description': 'Continuous water quality monitoring by Utah State University as part of the USDA CEAP Grant',\n",
       "  'source_id': '1',\n",
       "  'source_link': 'http://www.bearriverinfo.org'},\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}value_count': {'value_count': '1440'},\n",
       " '{http://www.cuahsi.org/water_ml/1.0/}variable_time_interval': {'begin_date_time': '2007-09-01T00:00:00',\n",
       "  'end_date_time': '2007-09-30T23:30:00',\n",
       "  'variable_time_interval_type': 'TimeIntervalType'}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siteinfo['series']['mysqlodm2timeseries:USU33']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def site_series_values_to_df(series_values, variable_name):\n",
    "    # Create a clean timeseries list of (dt, val) tuples\n",
    "    tsdt_tuplst = [\n",
    "        (convert_datetime(valdict['datetime']).replace(tzinfo=pytz.utc),\n",
    "         float(valdict['value'])) for valdict in series_values['values']\n",
    "    ]\n",
    "\n",
    "    dt, val = zip(*tsdt_tuplst)\n",
    "    ts_df = pd.DataFrame({'time': dt, variable_name: val})\n",
    "    ts_df.set_index('time', inplace=True)\n",
    "    ts_df.sort_index(ascending=True, inplace=True)\n",
    "    return ts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Retrieves site values from a WaterOneFlow service using a GetValues request.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    wsdl_url : str\n",
      "        URL of a service's web service definition language (WSDL) description.\n",
      "        All WaterOneFlow services publish a WSDL description and this url is the\n",
      "        entry point to the service.\n",
      "    site_code : str\n",
      "        Site code of the site you'd like to get values for. Site codes MUST\n",
      "        contain the network and be of the form network:site_code, as is\n",
      "        required by WaterOneFlow.\n",
      "    variable_code : str\n",
      "        Variable code of the variable you'd like to get values for. Variable\n",
      "        codes MUST contain the network and be of the form\n",
      "        vocabulary:variable_code, as is required by WaterOneFlow.\n",
      "    start : ``None`` or datetime (see :ref:`dates-and-times`)\n",
      "        Start of a date range for a query. If both start and end parameters are\n",
      "        omitted, the entire time series available will be returned.\n",
      "    end : ``None`` or datetime (see :ref:`dates-and-times`)\n",
      "        End of a date range for a query. If both start and end parameters are\n",
      "        omitted, the entire time series available will be returned.\n",
      "    suds_cache: ``None`` or tuple\n",
      "        SOAP local cache duration for WSDL description and client object.\n",
      "        Pass a cache duration tuple like ('days', 3) to set a custom duration.\n",
      "        Duration may be in months, weeks, days, hours, or seconds.\n",
      "        If unspecified, the default duration (1 day) will be used.\n",
      "        Use ``None`` to turn off caching.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    site_values : dict\n",
      "        a python dict containing values\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    ulmo.cuahsi.wof.get_values.__doc__.replace('<', '').replace('>', '')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`'odm2timeseries:USU33'` is 'Oxygen, dissolved percent of saturation'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "variablecd = 'USU33'\n",
    "\n",
    "site_values = ulmo.cuahsi.wof.get_values(wsdlurl, networkcd+':'+sitecd, networkcd+':'+variablecd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sources', 'quality_control_levels', 'values', 'methods', 'variable', 'site']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_values.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'code': 'USU33',\n",
       " 'data_type': 'Average',\n",
       " 'general_category': 'Water Quality',\n",
       " 'id': '33',\n",
       " 'name': 'Oxygen, dissolved percent of saturation',\n",
       " 'no_data_value': '-9999.0000000000',\n",
       " 'sample_medium': 'Unknown',\n",
       " 'time': {'interval': '30',\n",
       "  'units': {'abbreviation': 'min', 'name': 'minute', 'type': 'Time'}},\n",
       " 'units': {'abbreviation': '%',\n",
       "  'code': '1',\n",
       "  'name': 'percent',\n",
       "  'type': 'Dimensionless'},\n",
       " 'value_type': 'Unknown',\n",
       " 'vocabulary': 'mysqlodm2timeseries'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sitevariable = site_values['variable']\n",
    "sitevariable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`site_values['values']` is a list of individual time series values (timestamp and data value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(list,\n",
       " ['method_id',\n",
       "  'censor_code',\n",
       "  'quality_control_level',\n",
       "  'source_id',\n",
       "  'value',\n",
       "  'datetime'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(site_values['values']), site_values['values'][0].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start and end timestamps (local time with time offset vs utc; iso8601 format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2007-09-01T00:00:00', '2007-09-30T23:30:00')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_values['values'][0]['datetime'], site_values['values'][-1]['datetime']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set a nice, user-friendly variable name string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Oxygen, dissolved percent of saturation (Unknown)'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_name = '%s (%s)' % (sitevariable['name'], sitevariable['value_type'])\n",
    "variable_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/filipe/miniconda3/envs/BiG-CZ/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: to_datetime is deprecated. Use self.to_pydatetime()\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2007, 9, 30, 23, 30, tzinfo=<UTC>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtstr_last = site_values['values'][-1]['datetime']\n",
    "convert_datetime(dtstr_last).replace(tzinfo=pytz.utc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm, this failed:\n",
    "```python\n",
    "convert_datetime(dtstr_last).astimezone(pytz.utc)\n",
    "ValueError: astimezone() cannot be applied to a naive datetime\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/filipe/miniconda3/envs/BiG-CZ/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: to_datetime is deprecated. Use self.to_pydatetime()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Oxygen, dissolved percent of saturation (Unknown)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-09-30 21:30:00+00:00</th>\n",
       "      <td>94.99999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-30 22:00:00+00:00</th>\n",
       "      <td>94.18334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-30 22:30:00+00:00</th>\n",
       "      <td>93.28333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-30 23:00:00+00:00</th>\n",
       "      <td>92.41666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-30 23:30:00+00:00</th>\n",
       "      <td>91.58334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Oxygen, dissolved percent of saturation (Unknown)\n",
       "time                                                                        \n",
       "2007-09-30 21:30:00+00:00                                           94.99999\n",
       "2007-09-30 22:00:00+00:00                                           94.18334\n",
       "2007-09-30 22:30:00+00:00                                           93.28333\n",
       "2007-09-30 23:00:00+00:00                                           92.41666\n",
       "2007-09-30 23:30:00+00:00                                           91.58334"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_df = site_series_values_to_df(site_values, variable_name)\n",
    "ts_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(pandas.core.frame.DataFrame,\n",
       " Index([u'Oxygen, dissolved percent of saturation (Unknown)'], dtype='object'),\n",
       " datetime64[ns, UTC],\n",
       " Timestamp('2007-09-01 00:00:00+0000', tz='UTC'),\n",
       " Timestamp('2007-09-30 23:30:00+0000', tz='UTC'))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ts_df), ts_df.columns, ts_df.index.dtype, ts_df.index.min(), ts_df.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WNA00h1OaM2F1oH+cYSSxd4I5MzgfYgzy+WgHdsxZo56lNQFzs1K5qMCr8fSq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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55ac78bb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "varlabel = ts_df.columns[0]\n",
    "ts_df[varlabel].plot(style='-', ax=ax)\n",
    "ax.set_ylabel(varlabel + ', ' + sitevariable['units']['abbreviation']);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}