3- Disharge - connect to odm2 SQLite and USGS with ulmo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Plot Luquillo CZO discharge data and USGS discharge data</h1>\n",
    "<p>1) First load standard python libraries</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/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 sys\n",
    "import os\n",
    "from datetime import datetime\n",
    "import pprint\n",
    "import numpy as np\n",
    "import getpass\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import DateFormatter\n",
    "from IPython.display import display, HTML\n",
    "import ipywidgets as widgets\n",
    "from utilities import hydroshare\n",
    "from odm2api.ODM2.services.readService import *\n",
    "from odm2api.ODM2.services.createService import *\n",
    "import ulmo\n",
    "import json\n",
    "from ulmo.usgs import nwis\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>2) Next load odm2api components we will use </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from odm2api.ODMconnection import dbconnection\n",
    "import odm2api.ODM2.services.readService as odm2\n",
    "from odm2api.ODM2.models import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>3) now connect to the database and instantiate the read service </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding the following system variables:\n",
      "   HS_USR_NAME = miguelcleon\n",
      "   HS_RES_ID = a5669d17013445c896a8397a8e22ece3\n",
      "   HS_RES_TYPE = timeseriesresource\n",
      "   JUPYTER_HUB_IP = jupyter.cuahsi.org\n",
      "\n",
      "These can be accessed using the following command: \n",
      "   os.environ[key]\n",
      "\n",
      "   (e.g.)\n",
      "   os.environ[\"HS_USR_NAME\"]  => miguelcleon\n",
      "\n",
      "The hs_utils library requires a secure connection to your HydroShare account.\n",
      "Enter the HydroShare password for user 'miguelcleon': ········\n",
      "Successfully established a connection with HydroShare\n",
      "This resource already exists in your userspace.\n",
      "Would you like to overwrite this data [Y/n]? n\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b>Found the following file(s) associated with this HydroShare resource.</b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "LCZO-ODM2-MariaTimeSeries.sqlite"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "These files are stored in a dictionary called <b>hs.content</b> for your convenience.  To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content[\"MY_FILE\"] </pre> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#session_factory = dbconnection.createConnection('postgresql', database_address_text.value, database_text.value, \n",
    "#                                                username_text.value, p) \n",
    "#read = odm2.ReadODM2(session_factory)\n",
    "\n",
    "# get resource content.  Returns a dictionary of filenames and their paths \n",
    "hs = hydroshare.hydroshare()\n",
    "content = hs.getResourceFromHydroShare('e049f19dc8ba46c98754711da2ab6030')\n",
    "\n",
    "LCZOSensorDB = hs.content['LCZO-ODM2-MariaTimeSeries.sqlite']\n",
    "\n",
    "session_factory = dbconnection.createConnection('sqlite', LCZOSensorDB)\n",
    "read = ReadODM2(session_factory)\n",
    "write = CreateODM2(session_factory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h3>Retrieve USGS data </h3>\n",
    "<p>4) use ulmo to connect to USGS data. </p>\n",
    "\n",
    "<p>5) setup a pandas dataframe and show some basic statistics about it.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "processing data from request: https://waterservices.usgs.gov/nwis/dv/?endDT=2017-09-30&startDT=2017-09-01&site=50067000&format=waterml\n",
      "processing data from request: https://nwis.waterservices.usgs.gov/nwis/iv/?endDT=2017-09-30T00%3A00%3A00&startDT=2017-09-01T00%3A00%3A00&site=50067000&format=waterml\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             value\n",
      "count  2403.000000\n",
      "mean     73.890354\n",
      "std     335.053196\n",
      "min       9.220000\n",
      "25%      12.900000\n",
      "50%      14.400000\n",
      "75%      20.800000\n",
      "max    4700.000000\n"
     ]
    }
   ],
   "source": [
    "# nwis.hdf5.update_site_data('50067000',start='2017-09-01', end='2017-09-30')#   12189500\n",
    "\n",
    "# read daily mean discharge data from cache (statistics code 00003)\n",
    "#Rio Sabana\n",
    "data = nwis.get_site_data('50067000',start='2017-09-01', end='2017-09-30') #, parameter_code='00060:00003')['00060:00003']\n",
    "\n",
    "# print(data['value'])\n",
    "#for vals in data['values']:\n",
    "#    print(vals['value'] + ' ' + vals['datetime'])\n",
    "#for keys,values in data.items():\n",
    "#    print(keys)\n",
    "#    print(values)\n",
    "\n",
    "sabanaDf = pd.DataFrame(data['00060:00000']['values']).drop(['qualifiers'], axis=1).set_index('datetime')\n",
    "sabanaDf.value = sabanaDf.value.apply(np.float)\n",
    "sabanaDf.index = pd.to_datetime(sabanaDf.index).to_period('min')\n",
    "# sabanaDf['datetime'] = pd.Series(pd.to_datetime(data['00060:00000']['datetime']).to_period('min'), index = sabanaDf.index)\n",
    "#  Series(np.random.randn(sLength), index=df1.index)\n",
    "\n",
    "\n",
    "# mark bad data as NaN\n",
    "sabanaDf[sabanaDf.values == -999999] = np.nan\n",
    "print(sabanaDf.describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Retrieve LCZO Data </h3>\n",
    "<p>6) Retrieve LCZO Discharge data from SQLite file we have loaded in the local environment. These data are for the Quebrada Sonadora a discontinued USGS gage the LCZO is now mangaing.</p>\n",
    "<p>7) Show basic statistics about the LCZO discharge data. </p> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    2780.000000\n",
      "mean       22.126960\n",
      "std        44.040375\n",
      "min         7.430561\n",
      "25%         9.092660\n",
      "50%        11.239964\n",
      "75%        16.278315\n",
      "max       787.825819\n",
      "Name: datavalue, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# print(resultWidget.value)\n",
    "# print(resultWidget2.value)\n",
    "resultids = []\n",
    "resultnames = {}\n",
    "selectedResult = read.getDetailedResultInfo(resultTypeCV = 'Time series coverage',resultID=17274)\n",
    "resultidlist = list()\n",
    "resultidlist.append(17274)\n",
    "DischargeResultValues = read.getResultValues(resultids=resultidlist, starttime='2017-09-01', \n",
    "                                                 endtime= '2017-09-30')\n",
    "print(DischargeResultValues['datavalue'].describe())\n",
    "\n",
    "# get second time series \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>8) Format a plot and plot the selected time series</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3pDaztZx6wDiGDV1z6uRhQwdz6gHjWpQia4V+PRaTmTVHqSLarZg6mwOEmVU1cc9RDggdzkVMZmZWlQOEmZlV5QBhZmZVOUCYmVlVDhBmZlaVA4SZmVXlAGFmZlW5H4SZVeXhvs0BwszW4uG+DVzEZGZVeLhvAwcIM6vCw30bOECYWRW1hvX2cN+dxQHCzNbi4b4NmhggJI2TdHvZ37OSTpE0XNINku7P/zfP20vSdyUtkHSnpL2alTYzq2/inqM4a9JujOoahoBRXcM4a9JurqDuMOqLaZ8lDQa6gQnAicAzEXG2pMnA5hHxeUkHAZ8CDsrbfSciJtTb7/jx42POnDlNTr2Z2cAiaW5EjG+0XV8VMe0HPBARDwOHAVPz8qnAxPz4MODCSGYBXZK26aP0mZlZhb4KEEcBl+bHW0fEYwD5/1Z5+ShgYdlrFuVlZmbWAk0PEJLWAw4Frmy0aZVla5V/STpB0hxJcxYvXtwbSTQzsyr64g7iQOBPEfFEfv5Eqego/38yL18EjC573bbAo5U7i4jzImJ8RIwfMWJEE5NtZtbZ+iJAHM3q4iWA6cCx+fGxwDVly4/JrZn2BpaViqLMzKzvNXUsJkkbAvsDHytbfDZwhaTjgUeAI/Ly60ktmBYAzwPHNTNtZmZWX1MDREQ8D2xRsexpUqumym2D1ATWzMzagHtSm5lZVQ4QZmZWlQOEmZlV5QBhZmZVOUCYmVlVDhBmZlaVA4SZmVXlAGFmZlU5QJiZWVUOEGZmVpUDhJmZVeUAYWZmVTlAmJlZVQ4QZmZWlQOEmZlV5QBhZmZVOUCYmVlVTQ0Qkrok/VLSnyXdK+ktkoZLukHS/fn/5nlbSfqupAWS7pS0VzPTZmZm9TX7DuI7wG8iYmdgD+BeYDJwY0TsBNyYnwMcCOyU/04AftjktJmZWR1NCxCSNgXeDlwAEBEvRsRS4DBgat5sKjAxPz4MuDCSWUCXpG2alT4zM6tvSK0VkuYDUWt9ROzeYN+vBRYDP5O0BzAXOBnYOiIey/t4TNJWeftRwMKy1y/Kyx6rSNcJpDsMxowZ0yAJZma2rmoGCOCQ/P/E/P+i/P+DwPMF970X8KmImC3pO6wuTqpGVZatFaAi4jzgPIDx48fXDGBmZvbq1CxiioiHI+JhYJ+I+LeImJ//JgMHFNj3ImBRRMzOz39JChhPlIqO8v8ny7YfXfb6bYFHe/Z2zMystxSpg9hI0ttKTyS9Fdio0Ysi4nFgoaRxedF+wD3AdODYvOxY4Jr8eDpwTG7NtDewrFQUZWZmfa9eEVPJ8cBPJW2Wny8FPlpw/58CLpG0HvAgcBwpKF0h6XjgEeCIvO31wEHAAlIR1nEFj2FmZk3QMEBExFxgj9wqSRGxrOjOI+J2YHyVVftV2TZYXd9hZmYt1jBASFofeB8wFhgipbrkiPhyU1NmZmYtVaSI6RpgGamZ6gvNTY6ZmbWLIgFi24h4T9NTYmZmbaVIK6ZbJe3W9JSYmVlbKXIH8TbgI5L+SipiEqlOuVFPajMz68eKBIgDm54KMzNrOw2LmHJv6i7gvfmvKy8zM7MBrGGAkHQycAmwVf67WNKnmp0wMzNrraI9qSdExHMAkr4O3Ab8VzMTZmatNW1eN+fMuI9Hl65gZNcwTj1gHBP3HNXqZFkfKhIgBKwqe76K6iOvmtkAMW1eN6ddPZ8VK9NPv3vpCk67ej6Ag0QHKdLM9WfAbElTJE0BZpEnATKzgemcGfe9EhxKVqxcxTkz7mtRiqwViozF9C1JM0nNXQUcFxHzmp0wM2udR5eu6NFyG5iKjMW0N3B3RPwpP99E0oSyeR7MbIAZ2TWM7irBYGTXsBakxlqlSBHTD4G/lT1/Li8zswHq1APGMWzo4DWWDRs6mFMPGFfjFTYQFaqkzkNxAxARL0sq8joz66dKFdFuxdTZipzoH5T0aVbfNXySNPmPmQ1gE/cc5YDQ4YoUMX0ceCvQTZo3egJwQpGdS3pI0nxJt0uak5cNl3SDpPvz/83zckn6rqQFku6UtNe6vSUzM+sNRYbaeDIijoqIrSJi64j4QEQ82YNjvCsi3hgRpZnlJgM3RsROwI35OaQxn3bKfyfgeg4zs5YqMtTG6yTdKOmu/Hx3Sae/imMeBkzNj6cCE8uWXxjJLKBL0jav4jhm9ipMm9fNPmffxPaTr2Ofs29i2rzuVifJ+liRIqbzgdOAlQARcSdwVMH9B/BbSXMllYqlto6Ix/K+HiON7wQwClhY9tpFedkaJJ0gaY6kOYsXLy6YDDPriVJP6u6lKwhW96R2kOgsRQLEhhHxh4plLxXc/z4RsRep+OhESW+vs2214TtirQUR50XE+IgYP2LEiILJMLOeqNWT+oxr725RiqwVigSIpyTtQD5ZSzoceKzIziPi0fz/SeBXwJuBJ0pFR/l/qT5jETC67OXbAo8WOY6Z9a5aPaaXPL/SdxEdpEiAOBH4MbCzpG7gFOATjV4kaSNJm5QeA/8E3AVMB47Nmx0LXJMfTweOya2Z9gaWlYqizKxv1esx7fGYOkeRsZgeBN6dT/KDImJ5wX1vDfxKUuk4v4iI30j6I3CFpOOBR4Aj8vbXAwcBC4DngeN69E7MrNecesA4Trn89qrrPB5T5ygyFtPJpBFdlwPn5/4JkyPit/VelwPLHlWWPw3sV2V5kO5WzKzFJu45iinT72bpipVrrfN4TJ2jSBHTRyPiWVIR0VakK/uzm5oqM2u5KYfu4vGYOlzRCYMgFf/8LCLuUC43MrOBy+MxWZEAMVfSb4HtgdNyxfPLzU2WmbUDj8fU2YrOSf1G4MGIeF7SFrgC2WzA85zUVqQV08vAn8qePw083cxEmVlreU5qg2KV1GbWYTwntYEDhJlV4TmpDRoECEmDSqO4mlnnqNXXwX0gOkvdAJHrH+6QNKaP0mNmbcBzUhsUa8W0DXC3pD8Az5UWRsShTUuVmbWU+0AYFAsQZzQ9FWbWdtwHwoo0c/2dpO2AnSLifyRtCAxu9Doz6/9OnzafS2cvZFUEgyWOnjCar0zcrdXJsj5SZMrRfwV+SRryG9Isb9OamSgza73Tp83n4lmPsCrSvF2rIrh41iOcPm1+i1NmfaXofBD7AM8CRMT9rJ4m1MwGqEtmPVJ1+aWzF1ZdbgNPkQDxQkS8WHoiaQhVpgI1s4Fj2rzumj/y0h2FDXxFAsTvJH0BGCZpf+BK4NrmJsvMWqlej+nBHsy5YxQJEJOBxcB84GOkmd9OL3oASYMlzZP06/x8e0mzJd0v6XJJ6+Xl6+fnC/L6sT19M2bWO7rr9Jg+esLomutsYGkYIHJnuanAmaQmr1Pz7G9FnQzcW/b868C5EbETsIQ0Wiz5/5KI2BE4N29nZi1Q6y5B4FZMHaRIK6aDgQeA7wLfAxZIOrDIziVtCxwM/CQ/F7AvqVUUpMAzMT8+LD8nr9/PExOZtUategbXPnSWIkVM3wTeFRHvjIh3AO8iXeEX8W3g31g9wdAWwNKIeCk/X0RqNkv+vxAgr1+WtzezPjaqxphLtZbbwFQkQDwZEQvKnj8IPNnoRZIOya+dW764yqZRYF35fk+QNEfSnMWLFzdKhpmtA4/FZFBsqI27JV0PXEE6YR8B/FHSJICIuLrG6/YBDpV0ELABsCnpjqJL0pB8l7At8GjefhEwGliUm9JuBjxTudOIOA84D2D8+PG+4zVrAo/FZABqVN8s6Wd1VkdEfLThQaR3Ap+LiEMkXQlcFRGXSfoRcGdE/EDSicBuEfFxSUcBkyLi/fX2O378+JgzZ06jw5uZWRlJcyNifKPtiozF1NvzT38euEzSV4B5wAV5+QXARZIWkO4cjurl45pZD3hOaitSxPSqRcRMYGZ+/CDw5irb/J1UfGVmLeY5qQ085aiZVVFrTuozrr27RSmyVijSD2L7IsvMbOCo1ZN6yfMrmTavu49TY61S5A7iqirLflllmZkNANPmdVdtc15Sb5wmG1hq1kFI2hnYBdis1KQ125TUbNXMBqBzZtxXt8f0o3XGabKBpV4l9TjgEKALeG/Z8uXAvzYzUWbWOo0CwEj3pu4YNQNERFwDXCPpLRFxWx+mycxaaGTXsJp1EO5N3VmK1EE8LelGSXcBSNpdUuHhvs2sf3nXziOqLh82dBBnTdrNzVw7SJEAcT5wGrASICLuxJ3YzAasm/9cfYyz4Rut7+DQYYoEiA0j4g8Vy16quqWZ9Xu16iBcOd15igSIpyTtQB5ZVdLhwGNNTZWZtUytSmhXTneeIgHiRODHwM6SuoFTgE80NVVm1jIe6ttKigzW9yDwbkkbAYMiYnnzk2VmreKhvq2kYYCQtDXwNWBkRBwo6Q3AWyLiggYvNbN+auKeoxwQrFAR08+BGcDI/PwvpGImMzMbwIoEiC0j4gryvNJ5JrhV9V9iZmb9XZEA8ZykLVjdimlvYFlTU2VmZi1XZMKgzwDTgR0k3QKMAA5vaqqspTyTmJlBnTsISaXZ3ZYA7wDeCnwM2CX3pq5L0gaS/iDpDkl3SzojL99e0mxJ90u6XNJ6efn6+fmCvH7sq3xvtg5KM4l1L11BsHomMc8BYNZ56hUxnZb/XxURL0XE3RFxV0SsLLjvF4B9I2IP4I3Ae3Lx1NeBcyNiJ1LwOT5vfzywJCJ2BM7N21kfqzWTmOcAMOs89YqYnpZ0M7C9pOmVKyPi0Ho7jogA/pafDs1/AewLfCAvnwpMAX4IHJYfQ5qQ6HuSlPdjfcTDLJhZSb0AcTCwF3AR8M112bmkwcBcYEfg+8ADwNLcEgpgEVAq3B4FLITUUkrSMmAL4KmKfZ4AnAAwZsyYdUmW1VFrqGcPs2DWeWoWMUXEixExC3hrRPyu8q/IziNiVUS8EdgWeDPw+mqb5f/VZjlc6+4hIs6LiPERMX7EiOrDEtu68zALZlZSZKiN6mP/9kBELJU0E9gb6JI0JN9FbAs8mjdbBIwGFkkaAmwGPPNqj20942EWzKykSDPXdSJpBLAyB4dhwLtJFc83k5rJXgYcC1yTXzI9P78tr7/J9Q+t4WEWOlt5M+euDYcSActWrPTFQgdqWoAAtgGm5nqIQcAVEfFrSfcAl0n6CjAPKI3pdAFwkaQFpDsHT0pk1sdKzZxLLdmWPL+60WKpyTPgINEhigzWNxU4OSKW5uebA9+MiI/We13uK7FnleUPkuojKpf/HTiicrmZ9Z1qzZzLlZo8O0B0hiJDbexeCg4AEbGEKid+M+v/ijRndpPnzlGkiGmQpM1zYEDS8IKvszbhoTOsqFrNnCu3sc5Q5A7im8Ctks6UdCZwK/CN5ibLeouHzrCeqNbMuZybPHeWhgEiIi4E3gc8ATwJTIqIi5qdMOsdHjrDemLinqN435tGVe2UNHQQnDVpN999dpB6g/Vtmv8PBx4HfgFcAjyel1k/4KEzrKdu/vPitXuoAitfhjkPu2tSJ6l3B/GL/H8uMCf/n1v23PqBWuXFLke2WurVQVw6e2EfpsRard5QG4fk/9tHxGvz/9Lfa/suifZqeOgM64lp87qrFi+VrHLf1Y5SpJIaSZMkfUvSNyVNbHairPeUypQHK/3sB0u8703uKW3VnTPjvqrFS+XcwKFzNAwQkn4AfByYD9wFfFzS95udMOsd0+Z1c9Xc7leu/FZFcNXcbv/IraoidVNu4NA5ivRneAewa2lcpNyzen5TU2W9pl4rJt9FWKUi/SDcwKFzFCliug8on3hhNNBwylFrD27FZD1x6gHj6tZBgBs4dJJ6zVyvzTPJbQHcK2lmnmHuXsATMfQTbsVkPTFxz1F16yDcwKGz1Cti+s8+S4U1zakHjFtjdE7wj9zq6xo2lKUr1p56XrijXKepGSCKzhpn7c0TAFlPvfhS9dFcuzYc6u9Nhyky3PdyVk/9uR4wFHguIjZtZsKs93gCICtq2rxunl/5ctV15XNDWGcoMuXoJuXPcz+IteZzsIHDo792rjOuvbvmulJfGuschTrKlYuIacC+jbaTNFrSzZLulXS3pJPz8uGSbpB0f/6/eV4uSd+VtEDSnZL26vG7sVfNo792tnp3Ce5F3XmKdJSbVPZ3uKSzoWFnS4CXgM9GxOuBvYETJb0BmAzcGBE7ATfm5wAXxq4ZAAAT00lEQVQHAjvlvxOAH/b87dir5dFfrZZRbvnWcYp0lHtv2eOXgIeAwxq9KCIeAx7Lj5dLuhcYlV/7zrzZVGAm8Pm8/MLcIW+WpC5J2+T9WB9xv4nOVqsFE6TvwNjJ1zHKxY4do0gdxHGv9iCSxpKmKZ0NbF066UfEY5K2ypuNAsqHilyUl60RICSdQLrDYMyY8v571htq9aR1v4nOMOXQXfjMFbfzcpUygtKiUrEj4CAxwBUpYpoqqavs+eaSflr0AJI2Bq4CTomIZ+ttWmXZWl/TiDgvIsZHxPgRI9xfr4hp87rZ5+yb2H7ydexz9k116xM8+mtnm7jnKDbdYGjD7Vzs2BmKFDHtHhFLS08iYomkPYvsXNJQUnC4JCKuzoufKBUdSdqGNEsdpDuG0WUv3xZ4tMhxrLZSpXOpXqHR1Z/7TdiyGkVMlVzsOPAVCRCDJG0eEUvglRnmivSfEHABcG9EfKts1XTgWODs/P+asuUnSboMmAAsc/3Dq7cug/W530RnKzJgX2k7G9iKNHP9JnCrpDMlfRm4FfhGgdftA3wY2FfS7fnvIFJg2F/S/cD++TnA9cCDwALgfOCTPXsrVo0rna2nTj1gHEMH1e/zMHSQXOzYAYpUUl8oaQ6p74OASRFxT4HX/Z7q9QoA+1XZPoATG+3XesaVztZTcx5+hpXVaqnLbLzBEN9ldoAiRUzkgNAwKFj7GbtF9QAxdgsHCFvbtHndXDzrkYbbLfWwGx2hxz2prX+Z9eCSHi23ztaTlknuXT/wFbqDsP6r1vAI9YZN8FhMnatI5TSk9ufuCzHw+Q6iQ9UaeG3avG5O/eUda4zFdOov7/DVoq3FfSEGPgeIAWz/b82suW7v125edfkZ197NylVr3l2sXBV1R/m0zlX0jsP6JweIAez+J5+rue6hp6v/sGuN5um5AKwaDwE+sDlAdCj3g7De4CHABzYHiA612bDq4+109XC5DSw9HdLbQ4APbA4QA9im6w+uua5y+I2SKYfuslYv2qGDxJRDd+nVtFl7qjZYYz3v2tkDZg5kDhAD2HMvVp9bGOCFl17m9Gnz11o+cc9RnHPEHozqGoZIV4jnHLGHmzJ2iIl7juJ9bxpVuG7h5j8vbnKKrJXcD2IAa1Q+fPGsR/jKxN3WWu7B+jrXtHndXDW3u3DdguuyBjbfQQxgbmFiPTVl+t01ix+rqVWXZQODA8QAdvSE0Y03omcTCtnANW1ed83pRmvxNcjA5iKmAWz8dsPrDrwmVvecLnWOK/WchvYcQsHDgDTPuvSK9qB9A5vvIAawRj/4t+4wvGbP6VMuv52xk6/jjWf8tm3uKDwMSHOtS32Ch40f2BwgBrBGP/iHnl7RsIf00hUrOfXK9jgJexiQ5urpyd6TBg18TQsQkn4q6UlJd5UtGy7pBkn35/+b5+WS9F1JCyTdKWmvZqWrkzT6wRe9Ylz5crTFoGweBqS5avVpqHWSGDRILt4b4Jp5B/Fz4D0VyyYDN0bETsCN+TnAgcBO+e8E4IdNTFfHaHR1t9mwoYV7SHtQtoGvVp+GWr1pavWlsYGjaQEiIv4XeKZi8WHA1Px4KjCxbPmFkcwCuiRt06y0WbJy1ctMOXQXGkw/DFBom2bbcGj1r2ut5dYz61IHcenshU1IibWLvv5lbR0RjwHk/1vl5aOA8m/aorzMXoVGxULPvbiKOQ8/U6i/RIMpivvEekOqDwFRa7n1zAbrEGg9WN/A1i6XXtXOUFW/eZJOkDRH0pzFi93Nv54iV4SXzl7YcIL6drGsRhv9WsutZ154qfbQLNaZ+jpAPFEqOsr/n8zLFwHlvbq2BR6ttoOIOC8ixkfE+BEjPFBYPUVapfSnK8Ba78dNLXvHul4ntEMLN2uOvg4Q04Fj8+NjgWvKlh+TWzPtDSwrFUXZuisy0mbRqoXNN2z9kAqnHjCOoYMrRpod7KaWvaVWUWOjIsh2aOFmzdHMZq6XArcB4yQtknQ8cDawv6T7gf3zc4DrgQeBBcD5wCebla5OUmSkzaIXjQfv3iZtBioT3H9ugNperaFZjp4wum5DALdwG7ia2Yrp6IjYJiKGRsS2EXFBRDwdEftFxE75/zN524iIEyNih4jYLSLmNCtdnaQ3f7jtMKzzOTPuW6u+pF36aAwE47cbzuCK5mqDB4nx2w3na5N2r/vaD55/WzOTZi3SLpXU1sumzesuXHxURDtcJdaqdPeQ073jnBn3saoiAK/KAbhRh7hbHnjGfSIGIAeIAeqcGff1aulLaWC/VuqqUQ9Sa7n1TK2LgKIXB/UGhrT+yQFigOrtq+qg9ZWRtRpc9aOGWGb9iof7HqCacc5sdTFTrbkKejqHgfW906fN59LZC1kVwWCJoyeMrjqbobUXBwgrrBXDbZSfWGpph2FArLbTp81fo/hpVcQrzx0k2psDhBXW1x2uK08stfSTjuD9Vin+rjdYvLiqfmaPnXzdK48HAd868o38Ynb1z/AXs6vPiW7tw3UQ1rZc6dl36rVAKoWEbxy+R4/2+TJwyuW31wzgDuztzwHCBoRWt7Dqz6bN664bjEfloUyaMfeDP7f25gBhA8IXf+U2+OtqyvT6M/KVD2Wyzw7De/XY/tzamwPEAFVkCO91sf+3ZjZlv6/Wcy+uanUS+q1GrcDK7xwu+de39Oqxn3txFWMnX8fYyde5N3YbcoAYoJo1Suv9Tz7HhK/e0JR9W//w7SPf2JT93vLAM2tUclvrOUAMQM0u131i+YsOEgNIT2fka/Y81L6baB9u5toHps3r5nNX3sFLZc02dtpqI274zDvXaX+7f+k3PPvC6iKVTdcfzJ1nrJ7+u1GZcm94YvmLjJ183at6H40MGzqIFSuLT2Kz/eTr+OvZBzclLda3SncT++wwvNeLtaw4RT8ep2D8+PExZ057D/w6bV43p1x+e831D/XwhNZfbsFF7d7clQGtmkb51kgzA1d/VrRvScmH9h5Tta9CT/fTLLXSZ/VJmhsR4xtu5wBRTOVVe4mg6lXr9pOv69FwF7X20+j41rva4Yq11me99SbrMfuL+6/zfnf+4vX8vUFHt0r1LmDWZX/9XV8GpB1Pu46XyrJ3iGDBWb1zh+wAUUO7XPmY9QeN7nD7yx3tQLYuQatogOioSmoHh3QVYtZbHjr7YDYY7C9VK10865GmzcXRVpXUkt4DfAcYDPwkIs5u8JIeuXT2wt7cXb+04KyDqxZhbDBY/PmrB1V9ja8SrZ4/f/Ugps3r5pwZ9/Ho0hWM7BrG2C2GccsDz7Q6aR3j0tkLm1L01TYBQtJg4PukuaoXAX+UND0i7umtYzSrb0B/UbrOa1RBXOmhsw/uyPLmTteTXtMT9xxVtfnrhK/ewBPLX1xjmYCtNllvreW27pp1bmubAAG8GVgQEQ8CSLoMOAzotQAxWOroIPHBvces82tLdxfVfvA28PRWZX1PK9XdGGPdNGvkhHYKEKOA8jKgRcCEyo0knQCcADBmTM9OeEdPGD3g6iC23mQ9Nh02lPuffK7udr3V+qLWD/6D59/W9CKFes1jHbjWXU+bWjdTo7tb38lWd/SE0U3Zb9u0YpJ0BHBARPxLfv5h4M0R8alar+kvrZh6qylsuXZojjkQdPIVq/uK1Lf/t2Y2vPBqB81sxdROAeItwJSIOCA/Pw0gIs6q9Zr+0FHOzKzd9Mdmrn8EdpK0vaT1gKOA6S1Ok5lZx2qbOoiIeEnSScAMUjPXn0ZE8wcVMjOzqtomQABExPXA9a1Oh5mZtVcRk5mZtREHCDMzq6ptWjGtC0mLgYf74FBbAk/1wXH6M+dRMc6nxpxHxbyafNouIkY02qhfB4i+ImlOkSZhncx5VIzzqTHnUTF9kU8uYjIzs6ocIMzMrCoHiGLOa3UC+gHnUTHOp8acR8U0PZ9cB2FmZlX5DsLMzKpygDAzs6ocIMzM+ilJTT2HO0AU1OwPYqBwPllv8XepNilNIRcRL1cu69XjuJK6PkkKZ1KPlX2BnXeApK2BoRGxqNVpaXeSNoiIv5c992+wjKR9gH8ADgVuBS6PiPnNOJYjdB2SJgCfkTRN0lGSBpdf1fgKJ5H0Fkkfl3SjpHMk7RJZq9PWRk4HToJ0wpM0XNJrJL2mtKylqWsTkg4CzpX0kKR3ODhUNRV4Hvg2MBq4VNJteVbOXuU7iDok/RG4HHga2Bc4JyLubG2q2o+k24EzgWeA/wJeA9wFnBURM/wjB0l/Bv4pIh6R9CngSGAxcA9wbkR47CFA0nzgRGA7YDcggL2AayPiu61MWzvIdw/fiIh9KpYfC0wC/iMi7uit4/kKuAZJhwDPRMR/AheSZrz7Ydn6CyWNbFX62oWk/YAlEXFVRNwMTAQuyn9HS+pycNDWwPwcHDYD/oVUPPB1YHvgZElDW5nGdiDpYOCJiPhfUtHJ50izSp4HfETSpFamr008Ajwg6UPl35mImApcC5zWmwdzgKjt9aTZ7YiIVfnq5a+SPiLpDcD2EfFoS1PYHp4i5ct2+Qt7GLBpRFxAukL+REtT1wYi4gngbknXAMcCv4+IZyJiFqnoaZ+IWNnSRLaHbYAL8uNdgC9FxC0RcSXpxHd0y1LWJiJiISmPjgZ+IOk9uahyHPBW4L7ePF5bzSjXZq4BNpc0hFQUtxL4Lunqb1/gulYmrl1ExB2SHiblxwJgGfCTvHoTUrFTx4uIKZI+AewK7CPpS6T82hf4v5Ymrn1cAzybH88CZpatOwCY29cJajeSNiDlzXHAx4EvAM8BT5CK477fq8fr8Lv/uiRtEhHLy56vR7rdPQLYISIeb1ni2oykTYGxEXFnrnDdGLgJODwi+mLOjrYlaUiec30T0lXeXsBGwIbAX4GpEfFsvX10CkmbRcSyimWbA78FJuUr6I6U665GA13A50rfGUldpBZyi3v9mA4Qa5P0MVLZ8BhgM9LV8S8iYqmknYFPRcSJrUxjO5B0ArADqUJxEPAb4FcRsUTSFsAuuTzZKlQ7EXYySceQKqXXB34dEb/NywXsBLwpIi5tYRJbStK2pDqGr5AuMkS629oVuAX4XjOKKV0HUUHSGOBTwJ+A/wQuBt4JTJd0TET82cHhlXz6NOm2/xuk4oH3ANdI+lBEPN3pwUHSGEl3S/qspO3Klisilkl6XSvT1y4kjQZOBh4gteqaLOnt8Eo/mic7OThkHwR+FxFXkQLCwcDdwA9IfSJ2b8ZBHSDW9h5gQURcAdyRv5hHAl8G3i1pr5amrn1U5tMlrM6n/SW9saWpaw8fJNXz7QHMlPTfuTniBnn9D3KxZac7BpgZET+KiB8BV5Aq80tFl99xKy9Gk5rcQyqiPCMiroyIm4CFpCauvc4BYm2/AhZLOioiVkG6iomI/yFVBB3Z0tS1j3r59CRucQIpHz4WEceQWuX8CvgoMCf3i3g+Il5sZQLbxCjgXnil8+klwKh8F/FOYH238uJzwOz8+CzgyrJ1ryc1C+51roOoQtLhwBRSq4D/Jn1h/0a6sjkjIqa3LnXtw/lUX24BtznwdMWYORuQWncdHhHXtyp97ULSMGCr8sYMkv4Z+DCp6euUiJjRqvS1sxxEfxwRr2/K/h0gapP0j6QWS/9Eit53RcQ5rU1V+3E+NVbemzwHiF9GxCEtTlbbqcini4B/joiNW5ystiFpUES8LOn1wF+AscCuEXFNU47nALE2SYMhdZCTtHmpVU5EPN3qtLUT51MxVX7UGwKviYj7W5y0tlKWT7uSKqt3BfaOiI6fgjS35hqUf2sideI9vNnNo10HUaE0aFr+IAYBV0va1Ce9NTmfGlMyOJ/0BHwH2Cgiljs4rFYln74FbBwRd3ZycCj9xuCV+r1V+enRwMqIeDYXYzaNe1Kz5m1t/l/6II4iVSQ+W7q6aVki24DzqbE6eVT+ox5c9mPvSAXzaWiHV04rD6GxO6ko6YaImEcaF25B3qapRUAOEEmRD8LDMTufiiiSR1Ysnzr2QiN7P6m57wxSHv1K0gLSMN/XQ7qDb2YCXMSUvB84FxjJ6g/if4BxwBxo/gfRTzifGnMeFeN8auwk0tDe3yY1bZ1G6k39XlJfiKbzHURyEvDvEXFz7tX5ImmMnPcCj5O/sOZ8KsB5VIzzqY7cgfI+YAWkUVwlvYk0FtzTwEmSTomIpc1MR8ffQVT7IIA3ATeQRtk8KQ+G1dGcT405j4pxPjWWO1BeDVyrNKPlN4DFEXFPRFwMTCDnXzN1/B1ERLwoqfRB3EJqhrg4Iu4B7pH0Rfrgg2h3zqfGnEfFOJ8akzSedFd1OvAhYCXwgbzuRODOiHih2enwHcSaH8QWtOiDaHfOp8acR8U4n+rL+fNN0nwPXaQhWrpId1bDSaO4fqNP0tLJHeXKPoingD+Qvqg7kMpCf0oaMfGeiOjoiUqcT405j4pxPjUm6cfAoog4U2mwwm1IeXQIsCwienVa0bpp6fAA0TYfRDtzPjXmPCrG+dSYpPeRAuUXI+KxsuVbkOZ6PzcibuiLtHR6EdNvge0lbRMRz0bEfXnwtH8H9pC0f4vT1y6cT405j4pxPjV2A6k/0Y8l/bukfSUNy6MUvA5Y0lcJ6fQ7iE1Jwx9sQeqgcwtwW0SsyB1SjoqIjm5uB86nIpxHxTifipO0L/AW0uyWe5Katy6MiOP7LA2dHCBK2uGD6A+cT405j4pxPhWjNPLvFsBgYEtgfl8OP+IAkbX6g+gvnE+NOY+KcT61PwcIMzOrqtMrqc3MrAYHCDMzq8oBwszMqnKAMDOzqhwgzMysKgcIMzOr6v8DC77V1e0YWEIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dateFmt = DateFormatter('%m-%d')\n",
    "fig, ax = plt.subplots()\n",
    "# pprint.pprint(BaroPressureResultValues.__dict__)\n",
    "ax.plot_date(DischargeResultValues['valuedatetime'], DischargeResultValues['datavalue'])\n",
    "ax.xaxis.set_major_formatter(dateFmt)\n",
    "plt.xticks(rotation=70)\n",
    "for result in selectedResult:\n",
    "    plt.title(str(result.VariableCode) + ' Quebrada Sonadora September 2017')\n",
    "    plt.ylabel(result.UnitsName)\n",
    "\n",
    "    \n",
    "    \n",
    "#time_format = '%Y-%m-%d %H:%M'\n",
    "\n",
    "#time = [datetime.strptime(i, ti#me_format) for i in sabanaDf.index.to_series().astype(str)]\n",
    "# print(time)\n",
    "#ax.plot_date(time, sabanaDf['value'])\n",
    "#ax.xaxis.set_major_formatter(dateFmt)\n",
    "#plt.xticks(rotation=70)\n",
    "#for result in selectedResult2:\n",
    "#    plt.title(str(result.VariableCode) + ' September 2017')\n",
    "#    plt.ylabel(result.UnitsName)\n",
    "    \n",
    "# QS = 375 meters RI = 618 meters elevation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>9) Use the numpy libaray to calculate some statistics about the time series</p> \n",
    "<p>10) Dynamically create histogram bins based on the time series standard deviation and graph the histogram</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dateFmt = DateFormatter('%m-%d')\n",
    "fig, ax = plt.subplots()\n",
    "# pprint.pprint(BaroPressureResultValues.__dict__)\n",
    "time_format = '%Y-%m-%d %H:%M'\n",
    "\n",
    "time = [datetime.strptime(i, time_format) for i in sabanaDf.index.to_series().astype(str)]\n",
    "# print(time)\n",
    "ax.plot_date(time, sabanaDf['value'])\n",
    "ax.xaxis.set_major_formatter(dateFmt)\n",
    "plt.xticks(rotation=70)\n",
    "for result in selectedResult:\n",
    "    plt.title(str(result.VariableCode) + ' Rio Sabana September 2017')\n",
    "    plt.ylabel(result.UnitsName)\n",
    "    \n",
    "# QS = 375 meters RI = 618 meters elevation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2.7",
   "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"
  },
  "widgets": {
   "state": {
    "fcb15f2d06154012851b3a8d210d70a4": {
     "views": [
      {
       "cell_index": 9
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}