notebooks/USGS_Gages.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Gage Datum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys; sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fcast import GageUSGS\n",
    "from fcast import new_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class GageUSGS in module fcast.GageUSGS:\n",
      "\n",
      "class GageUSGS(builtins.object)\n",
      " |  GageUSGS(gage: str, get_rc: bool = True)\n",
      " |  \n",
      " |  USGS stream gage class containing data scraped from USGS websites.\n",
      " |  \n",
      " |  The class currently supports scraping of stream gage metadata \n",
      " |  (i.e. vertical datum, lat/lon, etc.), currently available data\n",
      " |  at a gage, as well as rating curves and their metadata.\n",
      " |  \n",
      " |  Parameters:\n",
      " |      gage (str): A string representing a USGS gage id (e.g. '01400000').\n",
      " |      get_rc (bool, optional): Get the rating curve for the USGS gage. Defaults to True.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, gage: str, get_rc: bool = True)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  HUC8\n",
      " |      HUC8 that the gage is within\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  available_data\n",
      " |      The data available from the metadata url\n",
      " |  \n",
      " |  drainage_area_sqmi\n",
      " |      Drainage area upstream of the gage in sq miles\n",
      " |  \n",
      " |  feet_above_vertical_datum\n",
      " |      Feet above the vertical datum, for stage to elevation conversions\n",
      " |  \n",
      " |  gage\n",
      " |      Gage id string\n",
      " |  \n",
      " |  horizontal_datum\n",
      " |      Horizontal Datum of the gage\n",
      " |  \n",
      " |  lat\n",
      " |      Latitude of the gage\n",
      " |  \n",
      " |  lon\n",
      " |      Longitude of the gage\n",
      " |  \n",
      " |  metadata\n",
      " |      Dictionary of general gage metadata\n",
      " |  \n",
      " |  metadata_url\n",
      " |      Url that the gage metadata is gathered from\n",
      " |  \n",
      " |  rating_curve\n",
      " |      USGS Rating curve at gage location\n",
      " |  \n",
      " |  rating_curve_metadata\n",
      " |      USGS Rating curve metadata at gage location\n",
      " |  \n",
      " |  rating_curve_url\n",
      " |  \n",
      " |  vertical_datum\n",
      " |      Vertical datum of the gage\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(GageUSGS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gage = '01651000'\n",
    "test_station = GageUSGS(gage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Lat': 38.952333333333335,\n",
       " 'Lon': 76.96605555555556,\n",
       " 'horizontal datum': 'NAD83',\n",
       " 'HUC8': '02070010',\n",
       " 'drainage area sqmi': 49.4,\n",
       " 'vertical datum': 'NAVD88',\n",
       " 'feet above vertical datum': 16.35,\n",
       " 'gage': '01651000'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>INDEP</th>\n",
       "      <th>SHIFT</th>\n",
       "      <th>DEP</th>\n",
       "      <th>INDEP_SHIFT</th>\n",
       "      <th>INDEP_m</th>\n",
       "      <th>SHIFT_m</th>\n",
       "      <th>DEP_cms</th>\n",
       "      <th>INDEP_SHIFT_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.252984</td>\n",
       "      <td>0.006096</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.259080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.256032</td>\n",
       "      <td>0.006096</td>\n",
       "      <td>0.007646</td>\n",
       "      <td>0.262128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.259080</td>\n",
       "      <td>0.006096</td>\n",
       "      <td>0.015008</td>\n",
       "      <td>0.265176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.262128</td>\n",
       "      <td>0.006096</td>\n",
       "      <td>0.022653</td>\n",
       "      <td>0.268224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.265176</td>\n",
       "      <td>0.006096</td>\n",
       "      <td>0.030299</td>\n",
       "      <td>0.271272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   INDEP  SHIFT   DEP  INDEP_SHIFT   INDEP_m   SHIFT_m   DEP_cms  \\\n",
       "0   0.83   0.02  0.00         0.85  0.252984  0.006096  0.000000   \n",
       "1   0.84   0.02  0.27         0.86  0.256032  0.006096  0.007646   \n",
       "2   0.85   0.02  0.53         0.87  0.259080  0.006096  0.015008   \n",
       "3   0.86   0.02  0.80         0.88  0.262128  0.006096  0.022653   \n",
       "4   0.87   0.02  1.07         0.89  0.265176  0.006096  0.030299   \n",
       "\n",
       "   INDEP_SHIFT_m  \n",
       "0       0.259080  \n",
       "1       0.262128  \n",
       "2       0.265176  \n",
       "3       0.268224  \n",
       "4       0.271272  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usgs_rc = test_station.rating_curve\n",
    "usgs_rc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = new_plot()\n",
    "ax.plot(usgs_rc.DEP_cms, usgs_rc.INDEP_SHIFT_m, label='USGS Shifted')\n",
    "ax.plot(usgs_rc.DEP_cms, usgs_rc.INDEP_m, label='USGS Base')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Edge Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gage = '01651750'\n",
    "# metaData = get_gage_meta(gage)\n",
    "# usgs_rc = get_USGS_ratingcurve(gage)\n",
    "# fig, ax = new_plot()\n",
    "# ax.plot(usgs_rc.DEP_cms, usgs_rc.INDEP_SHIFT_m, label='USGS Shifted')\n",
    "# ax.plot(usgs_rc.DEP_cms, usgs_rc.INDEP_m, label='USGS Base')\n",
    "# ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gage = '01651827'\n",
    "# metaData = get_gage_meta(gage)\n",
    "# usgs_rc = get_USGS_ratingcurve(gage)\n",
    "# fig, ax = new_plot()\n",
    "# ax.plot(usgs_rc.DEP_cms, usgs_rc.INDEP_SHIFT_m, label='USGS Shifted')\n",
    "# ax.plot(usgs_rc.DEP_cms, usgs_rc.INDEP_m, label='USGS Base')\n",
    "# ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gage = '0165258890'\n",
    "# metaData = get_gage_meta(gage)\n",
    "# usgs_rc = get_USGS_ratingcurve(gage)\n",
    "# fig, ax = new_plot()\n",
    "# ax.plot(usgs_rc.DEP_cms, usgs_rc.INDEP_SHIFT_m, label='USGS Shifted')\n",
    "# ax.plot(usgs_rc.DEP_cms, usgs_rc.INDEP_m, label='USGS Base')\n",
    "# ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgecase1 = '01651750'\n",
    "edgecase2 = '01651827'\n",
    "edgecase3 = '0165258890'\n",
    "\n",
    "notimplemented1 = GageUSGS(edgecase1)\n",
    "# notimplemented2 = GageUSGS(edgecase2)\n",
    "# notimplemented3 = GageUSGS(edgecase3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:gis]",
   "language": "python",
   "name": "conda-env-gis-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}