IFT_Step01_determine_MAF_WYT_WMT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths, input, and output file names\n",
    "\n",
    "outer_dir = os.path.abspath(os.path.join(os.getcwd() ,\"..\"))\n",
    "refpth = os.path.abspath(os.path.join(os.getcwd() ,\"Reference Files\"))\n",
    "sfe_char_csv = os.path.join(refpth, 'TNC SFE LOI Characteristics with MAF.csv')\n",
    "sfe_char_xls= os.path.join(refpth, 'TNC SFE LOI Characteristics.xlsx')\n",
    "subset_xls = os.path.join(refpth, 'TNC Subset SFE LOIs.xlsx')\n",
    "\n",
    "\n",
    "unimpath = os.path.join(outer_dir, \"IFT_files\", 'Unimpaired Flow')\n",
    "comid_csv = os.path.join(refpth, 'TNC-POI-COMID-20210804.csv')\n",
    "wytdir = os.path.join(unimpath, \"Water Year Types\")\n",
    "wmtdir = os.path.join(unimpath, \"Water Month Types\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original filename: \"00_add_ts_col.py\"\n",
    "def add_ts_col(tab):\n",
    "#adds column for TS as first column of DataFrame as formatted date\n",
    "    tab['TS'] = tab.index.strftime('%m/%d/%Y')\n",
    "    cols = tab.columns.tolist()\n",
    "    cols = cols[-1:] + cols[:-1]\n",
    "    tab = tab[cols]\n",
    "    return tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original filename: \"00_get_all_sfe_lois.py\"\n",
    "# Read in SFE LOI characteristics table and calculate bankfull flow using cont. Area relation\n",
    "\n",
    "def get_all_sfe_lois():\n",
    "    sfelois = pd.read_excel(sfe_char_xls,index_col=0)\n",
    "    loi = [str(i) for i in sfelois['LOI']]\n",
    "    sfelois['Outlet LOI'] = sfelois['LOI']\n",
    "    sfelois['Contributing Area (mi^2)'] = sfelois['Contributing Area']\n",
    "    #sfelois['MAF'] = sfelois['Mean Annual Flow (cfs)']\n",
    "    sfelois['Qbf'] = 71.5 * sfelois['Contributing Area (mi^2)'] #71.5 cfs/mi^2 according to Darren\n",
    "\n",
    "    subset = pd.read_excel(subset_xls)\n",
    "    sublois = sfelois.loc[subset['SWSID'],:]\n",
    "    return sublois, loi, sfelois"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original filename: \"00_read_loi_paradigm_flow_v3.py\"\n",
    "def read_loi_paradigm_flow(p):\n",
    "    unimpflowfile = os.path.join(unimpath, str(p) + '.csv')\n",
    "    unimp = pd.read_csv(unimpflowfile,index_col=0)\n",
    "    unimp.index = pd.DatetimeIndex(unimp.index)\n",
    "    return unimp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOI</th>\n",
       "      <th>dnLOI</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Contributing Area</th>\n",
       "      <th>Geomorphic Class</th>\n",
       "      <th>Geologic Setting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2664961</th>\n",
       "      <td>Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.486536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8285498</th>\n",
       "      <td>Sproul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.992762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LOI  dnLOI  Lat  Lon  Contributing Area  Geomorphic Class  \\\n",
       "2664961  Indian    NaN  NaN  NaN          39.486536               NaN   \n",
       "8285498  Sproul    NaN  NaN  NaN           4.992762               NaN   \n",
       "\n",
       "         Geologic Setting  \n",
       "2664961               NaN  \n",
       "8285498               NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loitab, loi, fultab = get_all_sfe_lois()\n",
    "loipar = pd.read_excel(sfe_char_xls,index_col=0)\n",
    "loipar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original filename: \"01_determine_paradigm_maf_v3.py\"\n",
    "#calculate mean annual flow for locations in version 2 of hydrology model\n",
    "\n",
    "comids = pd.read_csv(comid_csv,index_col = 0)\n",
    "loitab, loi, fultab = get_all_sfe_lois() #full list of SFE LOIs\n",
    "loipar = pd.read_excel(sfe_char_xls,index_col=0) #read table to add characteristics to\n",
    "\n",
    "for p in loi: #loop through all SFE LOIs\n",
    "    if p in loipar.LOI.unique().tolist():\n",
    "        #read unimpaired flow and calculate mean annual flow\n",
    "        unimp = read_loi_paradigm_flow(p)\n",
    "        loipar.loc[loipar['LOI'] == p,'Mean Annual Flow (cfs)'] = np.mean(unimp['flow'].groupby(unimp.index.year).mean())\n",
    "    else: #If LOI isn't in list\n",
    "        print(p + ' not valid')\n",
    "loipar.loc[comids.index,'COMID']=comids['COMID'] #store COMID in the characteristics table too.\n",
    "\n",
    "loipar.to_csv(sfe_char_csv, encoding='utf-8', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Water Year</th>\n",
       "      <th>flow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTTM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-02</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-03</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-04</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-05</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-06</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-07</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-08</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-09</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-10</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.692813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-11</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-12</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-13</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-14</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-15</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-16</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-17</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-18</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-19</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-20</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-21</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-22</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-23</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-24</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-25</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-26</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-27</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-28</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-29</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-30</th>\n",
       "      <td>1960</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.615833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-11</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-12</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-13</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-14</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-15</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-16</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-17</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-18</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-19</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-20</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-21</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-25</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-26</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-27</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-28</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-29</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.000378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21792 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year  Month  Day  Water Year      flow\n",
       "DTTM                                              \n",
       "1960-10-01  1960     10    1        1961  0.692813\n",
       "1960-10-02  1960     10    2        1961  0.692813\n",
       "1960-10-03  1960     10    3        1961  0.692813\n",
       "1960-10-04  1960     10    4        1961  0.692813\n",
       "1960-10-05  1960     10    5        1961  0.692813\n",
       "1960-10-06  1960     10    6        1961  0.692813\n",
       "1960-10-07  1960     10    7        1961  0.692813\n",
       "1960-10-08  1960     10    8        1961  0.692813\n",
       "1960-10-09  1960     10    9        1961  0.692813\n",
       "1960-10-10  1960     10   10        1961  0.692813\n",
       "1960-10-11  1960     10   11        1961  0.615833\n",
       "1960-10-12  1960     10   12        1961  0.615833\n",
       "1960-10-13  1960     10   13        1961  0.615833\n",
       "1960-10-14  1960     10   14        1961  0.615833\n",
       "1960-10-15  1960     10   15        1961  0.615833\n",
       "1960-10-16  1960     10   16        1961  0.615833\n",
       "1960-10-17  1960     10   17        1961  0.615833\n",
       "1960-10-18  1960     10   18        1961  0.615833\n",
       "1960-10-19  1960     10   19        1961  0.615833\n",
       "1960-10-20  1960     10   20        1961  0.615833\n",
       "1960-10-21  1960     10   21        1961  0.615833\n",
       "1960-10-22  1960     10   22        1961  0.615833\n",
       "1960-10-23  1960     10   23        1961  0.615833\n",
       "1960-10-24  1960     10   24        1961  0.615833\n",
       "1960-10-25  1960     10   25        1961  0.615833\n",
       "1960-10-26  1960     10   26        1961  0.615833\n",
       "1960-10-27  1960     10   27        1961  0.615833\n",
       "1960-10-28  1960     10   28        1961  0.615833\n",
       "1960-10-29  1960     10   29        1961  0.615833\n",
       "1960-10-30  1960     10   30        1961  0.615833\n",
       "...          ...    ...  ...         ...       ...\n",
       "2021-09-01  2021      9    1        2021  0.000051\n",
       "2021-09-02  2021      9    2        2021  0.000051\n",
       "2021-09-03  2021      9    3        2021  0.000051\n",
       "2021-09-04  2021      9    4        2021  0.000051\n",
       "2021-09-05  2021      9    5        2021  0.000013\n",
       "2021-09-06  2021      9    6        2021  0.000013\n",
       "2021-09-07  2021      9    7        2021  0.000000\n",
       "2021-09-08  2021      9    8        2021  0.000000\n",
       "2021-09-09  2021      9    9        2021  0.000000\n",
       "2021-09-10  2021      9   10        2021  0.000000\n",
       "2021-09-11  2021      9   11        2021  0.000000\n",
       "2021-09-12  2021      9   12        2021  0.000000\n",
       "2021-09-13  2021      9   13        2021  0.000000\n",
       "2021-09-14  2021      9   14        2021  0.000000\n",
       "2021-09-15  2021      9   15        2021  0.000000\n",
       "2021-09-16  2021      9   16        2021  0.000000\n",
       "2021-09-17  2021      9   17        2021  0.000000\n",
       "2021-09-18  2021      9   18        2021  0.000378\n",
       "2021-09-19  2021      9   19        2021  0.000378\n",
       "2021-09-20  2021      9   20        2021  0.000294\n",
       "2021-09-21  2021      9   21        2021  0.000294\n",
       "2021-09-22  2021      9   22        2021  0.000220\n",
       "2021-09-23  2021      9   23        2021  0.000154\n",
       "2021-09-24  2021      9   24        2021  0.000098\n",
       "2021-09-25  2021      9   25        2021  0.000051\n",
       "2021-09-26  2021      9   26        2021  0.000013\n",
       "2021-09-27  2021      9   27        2021  0.000220\n",
       "2021-09-28  2021      9   28        2021  0.000378\n",
       "2021-09-29  2021      9   29        2021  0.000378\n",
       "2021-09-30  2021      9   30        2021  0.000378\n",
       "\n",
       "[21792 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unimp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Original filename: \"01_get_wyt_paradigm_por.py\"\n",
    "#calculate WYTs and plot (if selected)\n",
    "\n",
    "\n",
    "make_plot = 1\n",
    "sublois, loitab, loi = get_all_sfe_lois()\n",
    "for l in loitab:\n",
    "    unimp = read_loi_paradigm_flow(l)\n",
    "    maflow = unimp.groupby('Water Year').mean()\n",
    "    maflow['WYT'] = ''\n",
    "    maflow = maflow[['flow','WYT']]\n",
    "    dyth = maflow['flow'].quantile(0.33) #dry years are < 33rd percentile\n",
    "    wtth = maflow['flow'].quantile(0.66) #wet years are > 66th percentile\n",
    "    maflow.loc[maflow[maflow['flow'] < dyth].index, 'WYT'] = 'Dry'\n",
    "    maflow.loc[maflow[(maflow['flow'] >= dyth) & (maflow['flow'] < wtth)].index, 'WYT'] = 'Moderate'\n",
    "    maflow.loc[maflow[maflow['flow'] >= wtth].index, 'WYT'] = 'Wet'\n",
    "    # maflow.index = maflow.index.strftime('%Y')\n",
    "    maflow.to_csv(os.path.join(wytdir, 'LOI ' + l + ' WYT.csv'), encoding='utf-8', index=True)\n",
    "\n",
    "    if make_plot == 1: #makes a bar plot of each Water Year and it's mean annual flow\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(1,1,1)\n",
    "        wyts = ['Dry','Moderate','Wet']\n",
    "        cmap = ['orangered','forestgreen','dodgerblue']\n",
    "        for w in range(len(wyts)):\n",
    "            wyt = wyts[w]\n",
    "            mafwyt = maflow.loc[maflow['WYT'] == wyt,:]\n",
    "            plt.bar(x=mafwyt.index,height=mafwyt['flow'],color=cmap[w])\n",
    "\n",
    "        ax.set_xlabel('Water Year')\n",
    "        ax.set_ylabel('Mean Annual Flow (cfs)')\n",
    "        ax.set_title('Water Year Type for Each Year in Period of Record\\nLOI '+l)\n",
    "        plt.legend(wyts)\n",
    "        plt.savefig(os.path.join(wytdir,'Plots','LOI ' + l + ' WYT.png'))\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original filename \"01_get_wmt_paradigm_por.py\"\n",
    "#calculate WMTs at each location\n",
    "\n",
    "\n",
    "sublois, loi, sfelois = get_all_sfe_lois()\n",
    "\n",
    "for l in loi:\n",
    "    unimp = read_loi_paradigm_flow(l)\n",
    "    mmflow = unimp.resample('1M').mean() #calculate resampled unimpaired mean monthly flows\n",
    "    mmflow['Month'] = mmflow.index.month\n",
    "    mmflow['Year'] = mmflow.index.year\n",
    "    mmflow = mmflow[['Month','Year','flow']]\n",
    "    mmflow['WMT'] = ''\n",
    "    for m in range(1,13): #loop through each calendar month\n",
    "        singlemmflow = mmflow[mmflow.index.month == m] #filter to calendar months\n",
    "        #calculate WMT from percentiles\n",
    "        edth = singlemmflow['flow'].quantile(0.1)\n",
    "        dyth = singlemmflow['flow'].quantile(0.3)\n",
    "        bmth = singlemmflow['flow'].quantile(0.5)\n",
    "        amth = singlemmflow['flow'].quantile(0.7)\n",
    "        wtth = singlemmflow['flow'].quantile(0.9)\n",
    "        mmflow.loc[singlemmflow[singlemmflow['flow'] < edth].index,'WMT'] = 'Critically Dry'\n",
    "        mmflow.loc[singlemmflow[(singlemmflow['flow'] >= edth) & (singlemmflow['flow'] < dyth)].index,'WMT'] = 'Dry'\n",
    "        mmflow.loc[singlemmflow[(singlemmflow['flow'] >= dyth) & (singlemmflow['flow'] < bmth)].index,'WMT'] = 'Below Median'\n",
    "        mmflow.loc[singlemmflow[(singlemmflow['flow'] >= bmth) & (singlemmflow['flow'] < amth)].index,'WMT'] = 'Above Median'\n",
    "        mmflow.loc[singlemmflow[(singlemmflow['flow'] >= amth) & (singlemmflow['flow'] < wtth)].index,'WMT'] = 'Wet'\n",
    "        mmflow.loc[singlemmflow[singlemmflow['flow'] >= wtth].index,'WMT'] = 'Extremely Wet'\n",
    "    mmflow.index = mmflow.index.strftime('%b-%Y')\n",
    "    mmflow.to_csv(os.path.join(wmtdir, 'LOI ' + l + ' WMT.csv'), encoding='utf-8', index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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