{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "90465e37-a1ad-4a68-9d16-c00d6594cee4",
   "metadata": {},
   "source": [
    "# Demonstration of WellResponse Class\n",
    "\n",
    "Notebook to demonstrate using the Well Class to \n",
    "estimate drawdown or stream depletion using the \n",
    "solutions available in the python module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95dbea60-dd79-408f-bb66-6bb84386187c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(1, '../../')\n",
    "import pycap\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ec807f-632c-49ff-a703-da6b757f05d5",
   "metadata": {},
   "source": [
    "# Example Depletion\n",
    "The Well class will estimate depletions for streams.  The stream distances and\n",
    "streambed conductances, if needed, are passed through dictionaries keyed by \n",
    "a stream name or ID.  In this notebook a table of values will be made manually from \n",
    "Table 2 from Reeves, H.W., Hamilton, D.A., Seelbach, P.W., and Asher, A.J., 2009, Ground-water-withdrawal component of the Michigan water-withdrawal screening tool: U.S. Geological Survey Scientific Investigations Report 2009–5003, 36 p.\n",
    "[https://pubs.usgs.gov/sir/2009/5003/]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d858624-c165-413d-bcc4-3e74f5b6362b",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_table = pd.DataFrame(({'id': 8, 'distance': 14802},\n",
    "                            {'id': 9, 'distance': 12609.2},\n",
    "                            {'id': 11, 'distance': 15750.5},\n",
    "                            {'id': 27, 'distance': 22567.6},\n",
    "                            {'id': 9741, 'distance': 27565.2},\n",
    "                            {'id': 10532, 'distance': 33059.5},\n",
    "                            {'id': 11967, 'distance': 14846.3},\n",
    "                            {'id': 12515, 'distance': 17042.55},\n",
    "                            {'id': 12573, 'distance': 11959.5},\n",
    "                            {'id': 12941, 'distance': 19070.8},\n",
    "                            {'id': 13925, 'distance': 10028.9}))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "449df4b2-def0-4e06-8b37-db85108d0a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c27baa-391a-4ee3-95c6-0ab52c1f06e4",
   "metadata": {},
   "source": [
    "# Compute apportionment\n",
    "\n",
    "The Michigan screening tool uses inverse distance weighting apportionment.  Other\n",
    "apportionment approaches that may be used as a simple way to extend the analytical\n",
    "solution are discussed by Zipper and others (2019).  [https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018WR024403]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd49a471-83e2-403a-9695-5b315eedbcd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "invers =np.array([1/x for x in stream_table['distance']])\n",
    "stream_table['apportionment'] = (1./stream_table['distance'])/np.sum(invers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba408c90-1c58-4ac0-a56b-bc568b048810",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853cb92d-6873-48a0-a622-64678d024494",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sum(stream_table['apportionment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "479dcb70-36b1-4cab-968e-1e7e785c05ee",
   "metadata": {},
   "source": [
    "make dictionaries from the distances and apportionment values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "060425be-8fef-4fdb-ada6-f08cfcfd9c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = dict(zip(stream_table.id.values, stream_table.distance.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eabbb8d3-9263-4cc6-9195-1c89ce5c8557",
   "metadata": {},
   "outputs": [],
   "source": [
    "apportion = dict(zip(stream_table.id.values, stream_table.apportionment.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478737c3-0e68-46a6-976b-eacf47820223",
   "metadata": {},
   "source": [
    "# set aquifer properties and streambed conductances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4010787c-a172-4366-92c9-2d56791c7ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_table = pd.DataFrame(({'id': 8, 'distance': 14802},\n",
    "                            {'id': 9, 'distance': 12609.2},\n",
    "                            {'id': 11, 'distance': 15750.5},\n",
    "                            {'id': 27, 'distance': 22567.6},\n",
    "                            {'id': 9741, 'distance': 27565.2},\n",
    "                            {'id': 10532, 'distance': 33059.5},\n",
    "                            {'id': 11967, 'distance': 14846.3},\n",
    "                            {'id': 12515, 'distance': 17042.5},\n",
    "                            {'id': 12573, 'distance': 11959.5},\n",
    "                            {'id': 12941, 'distance': 19070.8},\n",
    "                            {'id': 13925, 'distance': 10028.9}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6de231c4-280f-4351-a6c5-d4dc939b9777",
   "metadata": {},
   "outputs": [],
   "source": [
    "T= 7211.  # ft^2/day\n",
    "S= 0.01\n",
    "Q = 70  # 70 gpm in cubic feet per day\n",
    "well_name = 'demo'\n",
    "pumpdays = int(5. * 365)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9aef4c4-3ab7-4fa2-9f4c-69955c054733",
   "metadata": {},
   "source": [
    "## call a utility to convert Q to a series with appropriate formatting\n",
    "\n",
    "also convert to CFD from GPM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00d5ddce-a8e5-4343-b35a-e9fc73c58520",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = pycap.Q2ts(pumpdays, 5, Q) * pycap.GPM2CFD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0686c301-9569-436e-8e9d-fea657deae7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be2435a8-bf30-4ceb-b9f4-8b0f94ac927f",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_table['conductance'] = 7.11855  # example uses constant streambed_conductance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4d10dca-be43-4292-b3d5-3705669a7975",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e925f15-7afe-4a7e-bf52-65aa41149de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = dict(zip(stream_table.id.values, stream_table.conductance.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ba31012-76ab-4c1e-9e16-be545dce05b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_well = pycap.Well(T=T,\n",
    "                 S=S,\n",
    "                 Q=Q,\n",
    "                 depletion_years=5,\n",
    "                 depl_method='hunt99',\n",
    "                 drawdown_dist={'test0':50.},\n",
    "                 streambed_conductance=cond,\n",
    "                 stream_dist=distances,\n",
    "                 stream_apportionment=apportion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84ad8495-19bd-4b22-a67e-b3440c0af154",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_well.drawdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d13cdfa-02b2-4605-a916-f26398696292",
   "metadata": {},
   "outputs": [],
   "source": [
    "deltaQ = pycap._calc_deltaQ(Q)\n",
    "deltaQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06bbd502-45ad-490b-ac65-d8d2989363d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_well.depletion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45c8ee4e-0a96-4812-8503-0fab8a99b54f",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_depl = pd.DataFrame(test_well.depletion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef9d8276-f8e6-4a99-b08b-8c4766feb941",
   "metadata": {},
   "source": [
    "## need to convert to GPM to compare results to Table 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79602e05-ab9d-46b8-88be-7feaf90eb98f",
   "metadata": {},
   "outputs": [],
   "source": [
    "stream_depl = stream_depl/pycap.GPM2CFD\n",
    "stream_depl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ad4a0b3-be39-4a3f-81f0-83701421be75",
   "metadata": {},
   "outputs": [],
   "source": [
    "five_year= pd.DataFrame(stream_depl.loc[1824].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd435288-e684-4b97-874d-336b682df562",
   "metadata": {},
   "outputs": [],
   "source": [
    "five_year.rename(columns={1824: 'Depletion'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "293efd67-3acc-47f4-be9a-c4e0de0d74db",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "five_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a56842b-a1b5-4311-82c3-4aa14edffa3d",
   "metadata": {},
   "source": [
    "# Matches Table 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06d7dbb5-41c9-4f14-a2a9-2d0535276d46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42bdf026-21c5-4194-ac20-ea78afe218bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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