gage_data_grabber.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ASFPM 2020 Demo\n",
    "\n",
    "## USGS API\n",
    "\n",
    "Examples of how to retrieve data directly from the USGS REST API for:\n",
    "\n",
    "##### I. Peak Streamflow\n",
    "\n",
    "##### II. Daily Streamflow\n",
    "\n",
    "##### III. Instantaneous Streamflow\n",
    "\n",
    "---\n",
    "\n",
    "#### Useful Links:\n",
    "\n",
    "[REST Web Services](https://waterservices.usgs.gov/rest/): REST is a \"URL friendly\" way to retrieve data over the web.\n",
    "\n",
    "[USGS Gages Map](https://maps.waterdata.usgs.gov/mapper/index.html): Retrieve a Gage ID from an online map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from utils import Url\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enter Gage ID and State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gage = \"03221646\"\n",
    "state = \"OH\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# I. Peak Streamflow\n",
    "\n",
    "### Generate URL's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://waterdata.usgs.gov/nwis/inventory?agency_code=USGS&site_no=03221646\">https://waterdata.usgs.gov/nwis/inventory?agency_code=USGS&site_no=03221646</a>"
      ],
      "text/plain": [
       "Url(_Url__url='https://waterdata.usgs.gov/nwis/inventory?agency_code=USGS&site_no=03221646')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Auto generate url string\n",
    "stats_uri = f'https://nwis.waterdata.usgs.gov/{state}/nwis/peak?site_no={gage}&agency_cd=USGS&format=rdb'\n",
    "alt_stats_uri = f'https://nwis.waterdata.usgs.gov/usa/nwis/peak/?site_no={gage}'\n",
    "gage_url = f'https://waterdata.usgs.gov/nwis/inventory?agency_code=USGS&site_no={gage}'\n",
    "\n",
    "# Print Clickable Link\n",
    "Url(gage_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#\n",
      "# U.S. Geological Survey\n",
      "# National Water Information System\n",
      "# Retrieved: 2020-05-30 11:52:58 EDT\n",
      "#\n",
      "# ---------------------------------- WARNING ----------------------------------------\n",
      "# Some of the data that you have obtained from this U.S. Geological Survey database\n",
      "# may not have received Director's approval. Any such data values are qualified\n",
      "# as provisional and are subject to revision. Provisional data are released on the\n",
      "# condition that neither the USGS nor the United States Government may be held liable\n",
      "# for any damages resulting from its use.\n",
      "#\n",
      "# More data may be available offline.\n",
      "# For more information on these data,  contact  Ohio Water Data Inquiries.\n",
      "# This file contains the annual peak streamflow data.\n",
      "#\n",
      "# This information includes the following fields:\n",
      "#\n",
      "#  agency_cd     Agency Code\n",
      "#  site_no       USGS station number\n",
      "#  peak_dt       Date of peak streamflow (format YYYY-MM-DD)\n",
      "#  peak_tm       Time of peak streamflow (24 hour format, 00:00 - 23:59)\n",
      "#  peak_va       Annual peak streamflow value in cfs\n",
      "#  peak_cd       Peak Discharge-Qualification codes (see explanation below)\n",
      "#  gage_ht       Gage height for the associated peak streamflow in feet\n",
      "#  gage_ht_cd    Gage height qualification codes\n",
      "#  year_last_pk  Peak streamflow reported is the highest since this year\n",
      "#  ag_dt         Date of maximum gage-height for water year (if not concurrent with peak)\n",
      "#  ag_tm         Time of maximum gage-height for water year (if not concurrent with peak\n",
      "#  ag_gage_ht    maximum Gage height for water year in feet (if not concurrent with peak\n",
      "#  ag_gage_ht_cd maximum Gage height code\n",
      "#\n",
      "# Sites in this file include:\n",
      "#  USGS 03221646 Scioto River at 5th Ave at Columbus OH\n",
      "#\n",
      "# Peak Streamflow-Qualification Codes(peak_cd):\n",
      "#   1 ... Discharge is a Maximum Daily Average\n",
      "#   2 ... Discharge is an Estimate\n",
      "#   3 ... Discharge affected by Dam Failure\n",
      "#   4 ... Discharge less than indicated value,\n",
      "#           which is Minimum Recordable Discharge at this site\n",
      "#   5 ... Discharge affected to unknown degree by\n",
      "#           Regulation or Diversion\n",
      "#   6 ... Discharge affected by Regulation or Diversion\n",
      "#   7 ... Discharge is an Historic Peak\n",
      "#   8 ... Discharge actually greater than indicated value\n",
      "#   9 ... Discharge due to Snowmelt, Hurricane,\n",
      "#           Ice-Jam or Debris Dam breakup\n",
      "#   A ... Year of occurrence is unknown or not exact\n",
      "#   Bd ... Day of occurrence is unknown or not exact\n",
      "#   Bm ... Month of occurrence is unknown or not exact\n",
      "#   C ... All or part of the record affected by Urbanization,\n",
      "#            Mining, Agricultural changes, Channelization, or other\n",
      "#   F ... Peak supplied by another agency\n",
      "#   O ... Opportunistic value not from systematic data collection\n",
      "#   R ... Revised\n",
      "#\n",
      "# Gage height qualification codes(gage_ht_cd,ag_gage_ht_cd):\n",
      "#   1 ... Gage height affected by backwater\n",
      "#   2 ... Gage height not the maximum for the year\n",
      "#   3 ... Gage height at different site and(or) datum\n",
      "#   4 ... Gage height below minimum recordable elevation\n",
      "#   5 ... Gage height is an estimate\n",
      "#   6 ... Gage datum changed during this year\n",
      "#   7 ... Debris, mud, or hyper-concentrated flow\n",
      "#   8 ... Gage height tidally affected\n",
      "#   Bd ... Day of occurrence is unknown or not exact\n",
      "#   Bm ... Month of occurrence is unknown or not exact\n",
      "#   F ... Peak supplied by another agency\n",
      "#   R ... Revised\n",
      "#\n",
      "#\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(stats_uri)\n",
    "response_text = r.text.split('\\n')\n",
    "\n",
    "if not r.ok or '<!DOCTYPE html>' in response_text:\n",
    "    print(f\"No Content Found!\\n\\n Verify Site info using Link Above\")\n",
    "    r = requests.get(alt_stats_uri)\n",
    "    stats_uri = f'https://nwis.waterdata.usgs.gov/nwis/peak?site_no={gage}&agency_cd=USGS&format=rdb'\n",
    "    if not r.ok:\n",
    "        print(f\"Strike 2: No Peak Flow for this site!\\n Verify Site info using Link Above\") \n",
    "\n",
    "    \n",
    "if r.ok and \"No sites/data found\" not in r.text:\n",
    "    for line in response_text:\n",
    "        if \"#\" in line:\n",
    "            print(line.replace('\\t',''))\n",
    "else:\n",
    "    print(f\"No Data: Please verify site and state info or click link above\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get tabular data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_table(stats_uri, comment = \"#\")\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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>agency_cd</th>\n",
       "      <th>site_no</th>\n",
       "      <th>peak_dt</th>\n",
       "      <th>peak_tm</th>\n",
       "      <th>peak_va</th>\n",
       "      <th>peak_cd</th>\n",
       "      <th>gage_ht</th>\n",
       "      <th>gage_ht_cd</th>\n",
       "      <th>year_last_pk</th>\n",
       "      <th>ag_dt</th>\n",
       "      <th>ag_tm</th>\n",
       "      <th>ag_gage_ht</th>\n",
       "      <th>ag_gage_ht_cd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2015-06-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16100</td>\n",
       "      <td>6</td>\n",
       "      <td>20.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11400</td>\n",
       "      <td>6</td>\n",
       "      <td>17.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2017-05-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8000</td>\n",
       "      <td>6</td>\n",
       "      <td>14.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2018-04-04</td>\n",
       "      <td>03:45</td>\n",
       "      <td>20300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2019-02-08</td>\n",
       "      <td>03:23</td>\n",
       "      <td>16600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  agency_cd   site_no    peak_dt peak_tm  peak_va peak_cd gage_ht gage_ht_cd  \\\n",
       "0      USGS  03221646 2015-06-20     NaN    16100       6   20.64        NaN   \n",
       "1      USGS  03221646 2015-12-29     NaN    11400       6   17.39        NaN   \n",
       "2      USGS  03221646 2017-05-06     NaN     8000       6   14.83        NaN   \n",
       "3      USGS  03221646 2018-04-04   03:45    20300     NaN   22.63        NaN   \n",
       "4      USGS  03221646 2019-02-08   03:23    16600     NaN   20.37        NaN   \n",
       "\n",
       "  year_last_pk ag_dt ag_tm ag_gage_ht ag_gage_ht_cd  \n",
       "0          NaN   NaN   NaN        NaN           NaN  \n",
       "1          NaN   NaN   NaN        NaN           NaN  \n",
       "2          NaN   NaN   NaN        NaN           NaN  \n",
       "3          NaN   NaN   NaN        NaN           NaN  \n",
       "4          NaN   NaN   NaN        NaN           NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table(stats_uri, comment = \"#\")\n",
    "df = df[1:].reset_index(drop=True)\n",
    "\n",
    "if 'peak_va' in df.columns:\n",
    "    df['peak_va'] = pd.to_numeric(df['peak_va'] )\n",
    "    df['peak_dt'] = pd.to_datetime(df['peak_dt'])\n",
    "else:\n",
    "    print(\"No Peak Streamflow Found in Data!\")\n",
    "\n",
    "# Preview Data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if '# Sites in this file include:\\r' in response_text:\n",
    "    idx = response_text.index('# Sites in this file include:\\r') + 1\n",
    "    gage_id_text = response_text[idx].replace('\\r','').replace('#  ','')\n",
    "else:\n",
    "    gage_id_text = gage\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,6))\n",
    "ax.scatter(x= df.peak_dt.values, y = df.peak_va.values,  color=\"none\", edgecolor=\"blue\");ax.grid();\n",
    "ax.set_title(gage_id_text);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## II. Daily Streamflow\n",
    "\n",
    "### Enter Starting Year\n",
    "\n",
    "NOTE: Retrieves data by *Water Year*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://waterdata.usgs.gov/nwis/inventory?agency_code=USGS&site_no=03221646\">https://waterdata.usgs.gov/nwis/inventory?agency_code=USGS&site_no=03221646</a>"
      ],
      "text/plain": [
       "Url(_Url__url='https://waterdata.usgs.gov/nwis/inventory?agency_code=USGS&site_no=03221646')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Auto generate url string\n",
    "daily_uri = f\"https://waterdata.usgs.gov/{state}/nwis/dv?cb_00060=on&format=rdb&site_no={gage}&referred_module=sw&period=&begin_date={year-1}-10-01&end_date={year}-9-30\"\n",
    "\n",
    "# Print Clickable Link\n",
    "Url(gage_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ---------------------------------- WARNING ----------------------------------------\n",
      "# Some of the data that you have obtained from this U.S. Geological Survey database\n",
      "# may not have received Director's approval. Any such data values are qualified\n",
      "# as provisional and are subject to revision. Provisional data are released on the\n",
      "# condition that neither the USGS nor the United States Government may be held liable\n",
      "# for any damages resulting from its use.\n",
      "#\n",
      "# Additional info: https://help.waterdata.usgs.gov/policies/provisional-data-statement\n",
      "#\n",
      "# File-format description:  https://help.waterdata.usgs.gov/faq/about-tab-delimited-output\n",
      "# Automated-retrieval info: https://help.waterdata.usgs.gov/faq/automated-retrievals\n",
      "#\n",
      "# Contact:   gs-w_support_nwisweb@usgs.gov\n",
      "# retrieved: 2020-05-30 11:53:00 EDT       (vaww01)\n",
      "#\n",
      "# Data for the following 1 site(s) are contained in this file\n",
      "#    USGS 03221646 Scioto River at 5th Ave at Columbus OH\n",
      "# -----------------------------------------------------------------------------------\n",
      "#\n",
      "# Data provided for site 03221646\n",
      "#            TS   parameter     statistic     Description\n",
      "#        109046       00060     00003     Discharge, cubic feet per second (Mean)\n",
      "#\n",
      "# Data-value qualification codes included in this output:\n",
      "#     A  Approved for publication -- Processing and review completed.\n",
      "#     e  Value has been estimated.\n",
      "# \n"
     ]
    }
   ],
   "source": [
    "r = requests.get(daily_uri)\n",
    "response_text = r.text.split('\\n')\n",
    "\n",
    "if not r.ok or '<!DOCTYPE html>' in response_text:\n",
    "    print(f\"No Content Found!\\n\\n Verify Site info using Link Above\")\n",
    "    \n",
    "if r.ok and \"No sites/data found\" not in r.text:\n",
    "    for line in response_text:\n",
    "        if \"#\" in line:\n",
    "            print(line.replace('\\t',''))\n",
    "else:\n",
    "    print(f\"No Data: Please verify site and state info or click link above\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get tabular data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>agency_cd</th>\n",
       "      <th>site_no</th>\n",
       "      <th>datetime</th>\n",
       "      <th>109046_00060_00003</th>\n",
       "      <th>109046_00060_00003_cd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>98.9</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2017-10-02</td>\n",
       "      <td>98.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2017-10-03</td>\n",
       "      <td>103</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2017-10-04</td>\n",
       "      <td>105</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2017-10-05</td>\n",
       "      <td>167</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  agency_cd   site_no   datetime 109046_00060_00003 109046_00060_00003_cd\n",
       "0      USGS  03221646 2017-10-01               98.9                     A\n",
       "1      USGS  03221646 2017-10-02               98.0                     A\n",
       "2      USGS  03221646 2017-10-03                103                     A\n",
       "3      USGS  03221646 2017-10-04                105                     A\n",
       "4      USGS  03221646 2017-10-05                167                     A"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table(daily_uri, comment = \"#\")\n",
    "df = df[1:].reset_index(drop=True)\n",
    "\n",
    "if 'datetime' in df.columns:\n",
    "    df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "else:\n",
    "    print(\"No Peak Streamflow Found in Data!\")\n",
    "\n",
    "# Preview Data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Column Name from table/metadata for daily flow vaules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_flow_col = \"109046_00060_00003\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the flow data to numeric\n",
    "df[daily_flow_col] = pd.to_numeric(df[daily_flow_col])\n",
    "\n",
    "# Get Gage ID info\n",
    "if '# Sites in this file include:\\r' in response_text:\n",
    "    idx = response_text.index('# Sites in this file include:\\r') + 1\n",
    "    gage_id_text = response_text[idx].replace('\\r','').replace('#  ','')\n",
    "else:\n",
    "    gage_id_text = gage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(28,4))\n",
    "ax.plot(df.datetime,df[daily_flow_col].values,  color=\"black\");ax.grid();\n",
    "ax.set_title(f'{gage_id_text}\\nDaily Flow for Water Year {year}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## III. Instantaneous Streamflow\n",
    "\n",
    "### Enter Starting and Ending Dates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = \"2018-05-09\"\n",
    "end_date = \"2019-05-31\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Auto generate url string\n",
    "instantaneous_uri = f\"https://waterservices.usgs.gov/nwis/iv/?format=rdb&sites={gage}&startDT={start_date}&endDT={end_date}&parameterCd=00060&siteStatus=all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ---------------------------------- WARNING ----------------------------------------\n",
      "# Some of the data that you have obtained from this U.S. Geological Survey database may not \n",
      "# have received Director's approval.  Any such data values are qualified as provisional and \n",
      "# are subject to revision.  Provisional data are released on the condition that neither the \n",
      "# USGS nor the United States Government may be held liable for any damages resulting from its use.\n",
      "#  Go to http://help.waterdata.usgs.gov/policies/provisional-data-statement for more information.\n",
      "#\n",
      "# File-format description:  http://help.waterdata.usgs.gov/faq/about-tab-delimited-output\n",
      "# Automated-retrieval info: http://help.waterdata.usgs.gov/faq/automated-retrievals\n",
      "#\n",
      "# Contact:   gs-w_support_nwisweb@usgs.gov\n",
      "# retrieved: 2020-05-30 11:53:02 -04:00(nadww02)\n",
      "#\n",
      "# Data for the following 1 site(s) are contained in this file\n",
      "#    USGS 03221646 Scioto River at 5th Ave at Columbus OH\n",
      "# -----------------------------------------------------------------------------------\n",
      "#\n",
      "# TS_ID - An internal number representing a time series.\n",
      "#\n",
      "# Data provided for site 03221646\n",
      "#    TS_ID       Parameter Description\n",
      "#    110317      00060     Discharge, cubic feet per second\n",
      "#\n",
      "# Data-value qualification codes included in this output:\n",
      "#     A  Approved for publication -- Processing and review completed.\n",
      "#     e  Value has been estimated.\n",
      "#\n"
     ]
    }
   ],
   "source": [
    "r = requests.get(instantaneous_uri)\n",
    "response_text = r.text.split('\\n')\n",
    "\n",
    "if not r.ok or '<!DOCTYPE html>' in response_text:\n",
    "    print(f\"No Content Found!\\n\\n Verify Site info using Link Above\")\n",
    "    \n",
    "if r.ok and \"No sites/data found\" not in r.text:\n",
    "    for line in response_text:\n",
    "        if \"#\" in line:\n",
    "            print(line.replace('\\t',''))\n",
    "else:\n",
    "    print(f\"No Data: Please verify site and state info or click link above\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get tabular data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>agency_cd</th>\n",
       "      <th>site_no</th>\n",
       "      <th>datetime</th>\n",
       "      <th>tz_cd</th>\n",
       "      <th>110317_00060</th>\n",
       "      <th>110317_00060_cd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2018-05-09 00:00:00</td>\n",
       "      <td>EDT</td>\n",
       "      <td>279</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2018-05-09 00:15:00</td>\n",
       "      <td>EDT</td>\n",
       "      <td>279</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2018-05-09 00:30:00</td>\n",
       "      <td>EDT</td>\n",
       "      <td>279</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2018-05-09 00:45:00</td>\n",
       "      <td>EDT</td>\n",
       "      <td>283</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>USGS</td>\n",
       "      <td>03221646</td>\n",
       "      <td>2018-05-09 01:00:00</td>\n",
       "      <td>EDT</td>\n",
       "      <td>279</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  agency_cd   site_no            datetime tz_cd 110317_00060 110317_00060_cd\n",
       "0      USGS  03221646 2018-05-09 00:00:00   EDT          279               A\n",
       "1      USGS  03221646 2018-05-09 00:15:00   EDT          279               A\n",
       "2      USGS  03221646 2018-05-09 00:30:00   EDT          279               A\n",
       "3      USGS  03221646 2018-05-09 00:45:00   EDT          283               A\n",
       "4      USGS  03221646 2018-05-09 01:00:00   EDT          279               A"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table(instantaneous_uri, comment = \"#\")\n",
    "df = df[1:].reset_index(drop=True)\n",
    "\n",
    "if 'datetime' in df.columns:\n",
    "    df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "else:\n",
    "    print(\"No Peak Streamflow Found in Data!\")\n",
    "\n",
    "# Preview Data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Column Name from table/metadata for daily flow vaules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "inst_flow_col = \"110317_00060\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the flow data to numeric\n",
    "df[inst_flow_col] = pd.to_numeric(df[inst_flow_col])\n",
    "\n",
    "# Get Gage ID info\n",
    "if '# Sites in this file include:\\r' in response_text:\n",
    "    idx = response_text.index('# Sites in this file include:\\r') + 1\n",
    "    gage_id_text = response_text[idx].replace('\\r','').replace('#  ','')\n",
    "else:\n",
    "    gage_id_text = gage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(28,4))\n",
    "ax.plot(df.datetime,df[inst_flow_col].values,  color=\"blue\");ax.grid();\n",
    "ax.set_title(f'{gage_id_text}\\nInstantaneous Flow');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# END"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}