Network Hydraulic Scaling Research App.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Hydraulic Scaling Research Tools\n",
    "\n",
    "The following scripts were created to develop the data used in Lapides et al. 2020. Funding for this work was provided by to Leclerc and Hahm by Simon Fraser University and a Natural Sciences and Engineering ResearchCouncil of Canada (NSERC) Undergraduate Student Research Award (USRA). Thank you to Margaret Zimmer for sharing streamflow data. \n",
    "\n",
    "All code, unless otherwise specified, is by Christine D Leclerc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:22:33.715529Z",
     "start_time": "2020-10-16T01:22:33.710578Z"
    },
    "code_folding": []
   },
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-18T18:53:02.710602Z",
     "start_time": "2020-12-18T18:52:57.541991Z"
    }
   },
   "outputs": [],
   "source": [
    "from adjustText import adjust_text\n",
    "import array\n",
    "import fiona\n",
    "import geopandas as gpd\n",
    "import importlib.util\n",
    "import io\n",
    "from io import BytesIO\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.figure import Figure\n",
    "import numpy as np\n",
    "from osgeo import gdal\n",
    "import pandas as pd\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "from pylab import *\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "import re\n",
    "import requests\n",
    "import scipy.optimize\n",
    "from shapely.geometry import Point\n",
    "import ulmo\n",
    "import uncertainties\n",
    "import urllib.request\n",
    "from urllib.request import urlopen\n",
    "import zipfile\n",
    "from zipfile import ZipFile\n",
    "plt.rcParams['font.sans-serif'] = 'Arial'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:23:47.988786Z",
     "start_time": "2020-10-16T01:23:47.982774Z"
    }
   },
   "source": [
    "## Access watershed metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:24:00.825290Z",
     "start_time": "2020-10-16T01:23:58.568238Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create data frame\n",
    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
    "\n",
    "# Create arrays\n",
    "areaM2 = []\n",
    "areaFT2 = []\n",
    "areaMI2 = []\n",
    "\n",
    "# Populate arrays\n",
    "for i in range(len(dMeta)):\n",
    "    areaM2.append(areaKM2[i] * 1000000)   # m^2  = km^2 * 1000000\n",
    "    areaFT2.append(areaKM2[i] * 1.076e7)  # ft^2 = km^2 * 1.076e+7\n",
    "    areaMI2.append(areaKM2[i] * 0.386102) # mi^2 = km^2 * 0.386102\n",
    "\n",
    "# Unit conversion foactors\n",
    "CFSfromCMS   = 35.3146667 # ft^3/s = m^3/s * 35.3146667\n",
    "CMSfromCFS   = 0.0283     # m^3/s  = ft^3/s * 0.0283\n",
    "MMDAYfromFTS = 2.633e7    # mm/day = ft/s * 2.633e7\n",
    "MMDAYfromMS  = 8.64e+7    # mm/day = m/s * 8.64e+7\n",
    "CMSfromLS    = 0.001      # m^3/s  = L/s / 0.001\n",
    "DAYfromS     = 86400      # day    = 86400 * s\n",
    "KMfromMM     = 0.000001   # km     = mm * 0.000001\n",
    "FT2fromkm2   = 1.076e+7   # ft^2   = km^2 * 1.076e+7\n",
    "M2fromKM2    = 1000000    # m^2    = km^2 * 1000000\n",
    "MMfromFT     = 304.8      # mm = ft * 304.8\n",
    "MLSfromMLDAY = 1.1574074074074e-5  # ML/day * 1.1574074074074e-5\n",
    "LSfromMLS    = 1000000    # L/s = ML/s * 1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process watershed discharge time series data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bull Creek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:25:05.850678Z",
     "start_time": "2020-10-16T01:24:48.447530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frames; Discharge units L/s\n",
    "baseURL = 'https://www.hydroshare.org/resource/d6d8b2a6e5604629b1192233646dfea1/data/contents/WY'\n",
    "dBull2004 = pd.read_csv(baseURL + '2004_gap_filled_Bull_discharge_daily.csv')\n",
    "dBull2005 = pd.read_csv(baseURL + '2005_gap_filled_Bull_discharge_daily.csv')\n",
    "dBull2006 = pd.read_csv(baseURL + '2006_gap_filled_Bull_discharge_daily.csv')\n",
    "dBull2007 = pd.read_csv(baseURL + '2007_gap_filled_Bull_discharge_daily.csv')\n",
    "dBull2008 = pd.read_csv(baseURL + '2008_gap_filled_Bull_discharge_daily.csv')\n",
    "dBull2009 = pd.read_csv(baseURL + '2009_gap_filled_Bull_discharge_daily.csv')\n",
    "dBull2010 = pd.read_csv(baseURL + '2010_gap_filled_Bull_discharge_daily.csv')\n",
    "\n",
    "# Scrub data\n",
    "# 2004\n",
    "dBull2004.insert(0, 'date', '0')\n",
    "dBull2004.insert(1, 'q_mm_per_day', '0')\n",
    "dBull2004.rename(columns={dBull2004.columns[3]: 'Year', dBull2004.columns[4]: 'Month', dBull2004.columns[5]: 'Day'}, inplace=True)\n",
    "dBull2004.rename(columns={dBull2004.columns[11]: 'B201', dBull2004.columns[12]: 'B203', dBull2004.columns[13]: 'B204'}, inplace=True)\n",
    "dBull2004.drop(\n",
    "    dBull2004.columns[[2, 6, 7, 8, 9, 10, 15, 16]], axis=1, inplace=True)\n",
    "\n",
    "for i in dBull2004.index:  # Format data\n",
    "    month = str(dBull2004['Month'][i])\n",
    "    if len(str(dBull2004['Month'][i])) != 2:\n",
    "        month = '0' + str(dBull2004['Month'][i])\n",
    "    day = str(dBull2004['Day'][i])\n",
    "    if len(str(dBull2004['Day'][i])) != 2:\n",
    "        day = '0' + str(str(dBull2004['Day'][i]))\n",
    "    date = str(dBull2004['Year'][i]) + '-' + month + '-' + day\n",
    "    dBull2004.at[i, 'date'] = date\n",
    "    q_mm_per_day = dBull2004['B201'][i] + \\\n",
    "        dBull2004['B203'][i] + dBull2004['B204'][i]\n",
    "    dBull2004.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "for i in range(len(dBull2004)):  # Value scan\n",
    "    dBull2004['date'].loc['2004-04-31'] = '2004-03-31'\n",
    "    dBull2004['date'].loc['2004-06-31'] = '2004-05-31'\n",
    "    dBull2004['date'].loc['2004-07-30'] = '2004-06-30'\n",
    "    dBull2004['date'].loc['2004-09-31'] = '2004-08-31'\n",
    "\n",
    "# Unit conversion\n",
    "dBull2004['q_mm_per_day'] = dBull2004['q_mm_per_day'].astype(float)\n",
    "dBull2004['q_mm_per_day'] = dBull2004['q_mm_per_day'] * CMSfromLS\n",
    "dBull2004['q_mm_per_day'] = dBull2004['q_mm_per_day'] * CFSfromCMS\n",
    "dBull2004['q_mm_per_day'] = dBull2004['q_mm_per_day'] / areaFT2[0]\n",
    "dBull2004['q_mm_per_day'] = dBull2004['q_mm_per_day'] * MMDAYfromFTS\n",
    "dBull2004.drop(dBull2004.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "dBull2004.sort_values(by=['date'])\n",
    "dBull2004 = dBull2004.reset_index(drop=True)\n",
    "\n",
    "# 2005\n",
    "dBull2005.insert(0, 'date', '0')\n",
    "dBull2005.insert(1, 'q_mm_per_day', '0')\n",
    "dBull2005.rename(columns={dBull2005.columns[3]: 'Year', dBull2005.columns[4]\n",
    "                 : 'Month', dBull2005.columns[5]: 'Day'}, inplace=True)\n",
    "dBull2005.rename(columns={dBull2005.columns[11]: 'B201', dBull2005.columns[12]\n",
    "                 : 'B203', dBull2005.columns[13]: 'B204'}, inplace=True)\n",
    "dBull2005.drop(\n",
    "    dBull2005.columns[[2, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19]], axis=1, inplace=True)\n",
    "\n",
    "for i in dBull2005.index:  # Format data\n",
    "    month = str(dBull2005['Month'][i]).replace('.0', '')\n",
    "    if len(str(dBull2005['Month'][i]).replace('.0', '')) != 2:\n",
    "        month = '0' + str(dBull2005['Month'][i]).replace('.0', '')\n",
    "    day = str(dBull2005['Day'][i]).replace('.0', '')\n",
    "    if len(str(dBull2005['Day'][i]).replace('.0', '')) != 2:\n",
    "        day = '0' + str(str(dBull2005['Day'][i])).replace('.0', '')\n",
    "    date = str(dBull2005['Year'][i]).replace(\n",
    "        '.0', '') + '-' + month.replace('.0', '') + '-' + day.replace('.0', '')\n",
    "    dBull2005.at[i, 'date'] = date\n",
    "    q_mm_per_day = dBull2005['B201'][i] + \\\n",
    "        dBull2005['B203'][i] + dBull2005['B204'][i]\n",
    "    dBull2005.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dBull2005['q_mm_per_day'] = dBull2005['q_mm_per_day'].astype(float)\n",
    "dBull2005['q_mm_per_day'] = dBull2005['q_mm_per_day'] * CMSfromLS\n",
    "dBull2005['q_mm_per_day'] = dBull2005['q_mm_per_day'] * CFSfromCMS\n",
    "dBull2005['q_mm_per_day'] = dBull2005['q_mm_per_day'] / areaFT2[0]\n",
    "dBull2005['q_mm_per_day'] = dBull2005['q_mm_per_day'] * MMDAYfromFTS\n",
    "dBull2005.drop(dBull2005.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "dBull2005 = dBull2005[0:365]\n",
    "dBull2005.sort_values(by=['date'])\n",
    "dBull2005 = dBull2005.reset_index(drop=True)\n",
    "\n",
    "# 2006\n",
    "dBull2006.insert(0, 'date', '0')\n",
    "dBull2006.insert(1, 'q_mm_per_day', '0')\n",
    "dBull2006.rename(columns={dBull2006.columns[3]: 'Year', dBull2006.columns[4]\n",
    "                 : 'Month', dBull2006.columns[5]: 'Day'}, inplace=True)\n",
    "dBull2006.rename(columns={dBull2006.columns[11]: 'B201', dBull2006.columns[12]\n",
    "                 : 'B203', dBull2006.columns[13]: 'B204'}, inplace=True)\n",
    "dBull2006.drop(\n",
    "    dBull2006.columns[[2, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19]], axis=1, inplace=True)\n",
    "\n",
    "for i in dBull2006.index:  # Format data\n",
    "    month = str(dBull2006['Month'][i]).replace('.0', '')\n",
    "    if len(str(dBull2006['Month'][i]).replace('.0', '')) != 2:\n",
    "        month = '0' + str(dBull2006['Month'][i]).replace('.0', '')\n",
    "    day = str(dBull2006['Day'][i]).replace('.0', '')\n",
    "    if len(str(dBull2006['Day'][i]).replace('.0', '')) != 2:\n",
    "        day = '0' + str(str(dBull2006['Day'][i])).replace('.0', '')\n",
    "    date = str(dBull2006['Year'][i]).replace(\n",
    "        '.0', '') + '-' + month.replace('.0', '') + '-' + day.replace('.0', '')\n",
    "    dBull2006.at[i, 'date'] = date\n",
    "    q_mm_per_day = dBull2006.B201[i] + dBull2006.B203[i] + dBull2006.B204[i]\n",
    "    dBull2006.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dBull2006['q_mm_per_day'] = dBull2006['q_mm_per_day'].astype(float)\n",
    "dBull2006['q_mm_per_day'] = dBull2006['q_mm_per_day'] * CMSfromLS\n",
    "dBull2006['q_mm_per_day'] = dBull2006['q_mm_per_day'] * CFSfromCMS\n",
    "dBull2006['q_mm_per_day'] = dBull2006['q_mm_per_day'] / areaFT2[0]\n",
    "dBull2006['q_mm_per_day'] = dBull2006['q_mm_per_day'] * MMDAYfromFTS\n",
    "dBull2006.drop(dBull2006.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "dBull2006 = dBull2006[0:365]\n",
    "dBull2006.sort_values(by=['date'])\n",
    "dBull2006 = dBull2006.reset_index(drop=True)\n",
    "\n",
    "# 2007\n",
    "dBull2007.insert(0, 'date', '0')\n",
    "dBull2007.insert(1, 'q_mm_per_day', '0')\n",
    "dBull2007.rename(columns={dBull2007.columns[3]: 'Year', dBull2007.columns[4]\n",
    "                 : 'Month', dBull2007.columns[5]: 'Day'}, inplace=True)\n",
    "dBull2007.rename(columns={dBull2007.columns[11]: 'B201', dBull2007.columns[12]\n",
    "                 : 'B203', dBull2007.columns[13]: 'B204'}, inplace=True)\n",
    "dBull2007.drop(\n",
    "    dBull2007.columns[[2, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19]], axis=1, inplace=True)\n",
    "\n",
    "# Format data\n",
    "for i in dBull2007.index:\n",
    "    month = str(dBull2007['Month'][i]).replace('.0', '')\n",
    "    if len(str(dBull2007['Month'][i]).replace('.0', '')) != 2:\n",
    "        month = '0' + str(dBull2007['Month'][i]).replace('.0', '')\n",
    "    day = str(dBull2007['Day'][i]).replace('.0', '')\n",
    "    if len(str(dBull2007['Day'][i]).replace('.0', '')) != 2:\n",
    "        day = '0' + str(str(dBull2007['Day'][i])).replace('.0', '')\n",
    "    date = str(dBull2007['Year'][i]).replace(\n",
    "        '.0', '') + '-' + month.replace('.0', '') + '-' + day.replace('.0', '')\n",
    "    dBull2007.at[i, 'date'] = date\n",
    "    q_mm_per_day = dBull2007.B201[i] + dBull2007.B203[i] + dBull2007.B204[i]\n",
    "    dBull2007.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dBull2007['q_mm_per_day'] = dBull2007['q_mm_per_day'].astype(float)\n",
    "dBull2007['q_mm_per_day'] = dBull2007['q_mm_per_day'] * CMSfromLS    \n",
    "dBull2007['q_mm_per_day'] = dBull2007['q_mm_per_day'] * CFSfromCMS  \n",
    "dBull2007['q_mm_per_day'] = dBull2007['q_mm_per_day'] / areaFT2[0]   \n",
    "dBull2007['q_mm_per_day'] = dBull2007['q_mm_per_day'] * MMDAYfromFTS\n",
    "dBull2007.drop(dBull2007.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "dBull2007 = dBull2007[0:365]\n",
    "dBull2007.sort_values(by=['date'])\n",
    "dBull2007 = dBull2007.reset_index(drop=True)\n",
    "\n",
    "\n",
    "# 2008\n",
    "dBull2008.insert(0, 'date', '0')\n",
    "dBull2008.insert(1, 'q_mm_per_day', '0')\n",
    "dBull2008.rename(columns={dBull2008.columns[3]: 'Year', dBull2008.columns[4]: 'Month', dBull2008.columns[5]: 'Day'}, inplace=True)\n",
    "dBull2008.rename(columns={dBull2008.columns[11]: 'B201', dBull2008.columns[12]: 'B203', dBull2008.columns[13]: 'B204'}, inplace=True)\n",
    "dBull2008.drop(\n",
    "    dBull2008.columns[[2, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19]], axis=1, inplace=True)\n",
    "\n",
    "# Format data\n",
    "for i in dBull2008.index:\n",
    "    month = str(dBull2008['Month'][i]).replace('.0', '')\n",
    "    if len(str(dBull2008['Month'][i]).replace('.0', '')) != 2:\n",
    "        month = '0' + str(dBull2008['Month'][i]).replace('.0', '')\n",
    "    day = str(dBull2008['Day'][i]).replace('.0', '')\n",
    "    if len(str(dBull2008['Day'][i]).replace('.0', '')) != 2:\n",
    "        day = '0' + str(str(dBull2008['Day'][i])).replace('.0', '')\n",
    "    date = str(dBull2008['Year'][i]).replace(\n",
    "        '.0', '') + '-' + month.replace('.0', '') + '-' + day.replace('.0', '')\n",
    "    dBull2008.at[i, 'date'] = date\n",
    "    q_mm_per_day = dBull2008['B201'][i] + \\\n",
    "        dBull2008['B203'][i] + dBull2008['B204'][i]\n",
    "    dBull2008.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "for i in range(len(dBull2008)):  # Value scan\n",
    "    dBull2008.date.loc['2008-04-31'] = '2008-03-31'\n",
    "    dBull2008.date.loc['2008-06-31'] = '2008-05-31'\n",
    "    dBull2008.date.loc['2008-09-31'] = '2008-08-31'\n",
    "\n",
    "# Unit conversion\n",
    "dBull2008['q_mm_per_day'] = dBull2008['q_mm_per_day'].astype(float)\n",
    "dBull2008['q_mm_per_day'] = dBull2008['q_mm_per_day'] * CMSfromLS    \n",
    "dBull2008['q_mm_per_day'] = dBull2008['q_mm_per_day'] * CFSfromCMS   \n",
    "dBull2008['q_mm_per_day'] = dBull2008['q_mm_per_day'] / areaFT2[0]  \n",
    "dBull2008['q_mm_per_day'] = dBull2008['q_mm_per_day'] * MMDAYfromFTS\n",
    "dBull2008.drop(dBull2008.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "dBull2008 = dBull2008[0:365]\n",
    "dBull2008.sort_values(by=['date'])\n",
    "dBull2008 = dBull2008.reset_index(drop=True)\n",
    "\n",
    "# 2009\n",
    "dBull2009.insert(0, 'date', '0')\n",
    "dBull2009.insert(1, 'q_mm_per_day', '0')\n",
    "dBull2009.rename(columns={dBull2009.columns[3]: 'Year', dBull2009.columns[4]: 'Month', dBull2009.columns[5]: 'Day'}, inplace=True)\n",
    "dBull2009.rename(columns={dBull2009.columns[11]: 'B201', dBull2009.columns[14]: 'B203', dBull2009.columns[17]: 'B204'}, inplace=True)\n",
    "dBull2009.drop(dBull2009.columns[[\n",
    "               2, 6, 7, 8, 9, 10, 12, 13, 15, 16, 18, 19, 21, 22]], axis=1, inplace=True)\n",
    "\n",
    "# Format data\n",
    "for i in dBull2009.index:\n",
    "    month = str(dBull2009['Month'][i])\n",
    "    if len(str(dBull2009['Month'][i])) != 2:\n",
    "        month = '0' + str(dBull2009['Month'][i])\n",
    "    day = str(dBull2009['Day'][i])\n",
    "    if len(str(dBull2009['Day'][i])) != 2:\n",
    "        day = '0' + str(str(dBull2009['Day'][i]))\n",
    "    date = str(dBull2009['Year'][i]) + '-' + month + '-' + day\n",
    "    dBull2009.at[i, 'date'] = date\n",
    "    q_mm_per_day = dBull2009.B201[i] + dBull2009.B203[i] + dBull2009.B204[i]\n",
    "    dBull2009.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dBull2009['q_mm_per_day'] = dBull2009['q_mm_per_day'].astype(float)\n",
    "dBull2009['q_mm_per_day'] = dBull2009['q_mm_per_day'] * CMSfromLS    \n",
    "dBull2009['q_mm_per_day'] = dBull2009['q_mm_per_day'] * CFSfromCMS   \n",
    "dBull2009['q_mm_per_day'] = dBull2009['q_mm_per_day'] / areaFT2[0]   \n",
    "dBull2009['q_mm_per_day'] = dBull2009['q_mm_per_day'] * MMDAYfromFTS\n",
    "dBull2009.drop(dBull2009.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "dBull2009 = dBull2009[0:365]\n",
    "dBull2009.sort_values(by=['date'])\n",
    "dBull2009 = dBull2009.reset_index(drop=True)\n",
    "\n",
    "\n",
    "# 2010\n",
    "dBull2010.insert(0, 'date', '0')\n",
    "dBull2010.insert(1, 'q_mm_per_day', '0')\n",
    "dBull2010.rename(columns={dBull2010.columns[3]: 'Year', dBull2010.columns[4]: 'Month', dBull2010.columns[5]: 'Day'}, inplace=True)\n",
    "dBull2010.rename(columns={dBull2010.columns[11]: 'B201', dBull2010.columns[14]: 'B203', dBull2010.columns[17]: 'B204'}, inplace=True)\n",
    "dBull2010.drop(dBull2010.columns[[\n",
    "               2, 6, 7, 8, 9, 10, 12, 13, 15, 16, 18, 19, 21, 22]], axis=1, inplace=True)\n",
    "\n",
    "# Format data\n",
    "for i in dBull2010.index:\n",
    "    month = str(dBull2010['Month'][i])\n",
    "    if len(str(dBull2010['Month'][i])) != 2:\n",
    "        month = '0' + str(dBull2010['Month'][i])\n",
    "    day = str(dBull2010['Day'][i])\n",
    "    if len(str(dBull2010['Day'][i])) != 2:\n",
    "        day = '0' + str(str(dBull2010['Day'][i]))\n",
    "    date = str(dBull2010['Year'][i]) + '-' + month + '-' + day\n",
    "    dBull2010.at[i, 'date'] = date\n",
    "    q_mm_per_day = dBull2010['B201'][i] + \\\n",
    "        dBull2010['B203'][i] + dBull2010['B204'][i]\n",
    "    dBull2010.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dBull2010['q_mm_per_day'] = dBull2010['q_mm_per_day'].astype(float)\n",
    "dBull2010['q_mm_per_day'] = dBull2010['q_mm_per_day'] * CMSfromLS \n",
    "dBull2010['q_mm_per_day'] = dBull2010['q_mm_per_day'] * CFSfromCMS\n",
    "dBull2010['q_mm_per_day'] = dBull2010['q_mm_per_day'] / areaFT2[0] \n",
    "dBull2010['q_mm_per_day'] = dBull2010['q_mm_per_day'] * MMDAYfromFTS\n",
    "dBull2010.drop(dBull2010.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "dBull2010.sort_values(by=['date'])\n",
    "dBull2010 = dBull2010.reset_index(drop=True)\n",
    "\n",
    "# Concatenate data frames for 2004 to 2010\n",
    "dBull = pd.concat([dBull2004, dBull2005, dBull2006, dBull2007,\n",
    "                   dBull2008, dBull2009, dBull2010], axis=0)\n",
    "dBull.q_mm_per_day = round(dBull.q_mm_per_day, 3)\n",
    "dBull.sort_values(by=['date'])\n",
    "dBull = dBull.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "ax.plot(dBull['date'], dBull['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Bull Creek')\n",
    "labels = ['2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011']\n",
    "ticks_to_use = dBull.index[::365]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caspar Creek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:25:11.598073Z",
     "start_time": "2020-10-16T01:25:09.253650Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frames\n",
    "dCasparN = pd.read_csv('https://www.fs.fed.us/psw/topics/water/caspar/data/flow/daily/tribs/nflo8695.txt.gz',\n",
    "                       compression='gzip', header=None, sep=',', quotechar='\"')  # Discharge in cfs\n",
    "dCasparN.insert(0, 'date', '')\n",
    "dCasparN.insert(1, 'q_mm_per_day', '')\n",
    "dCasparS = pd.read_csv('https://www.fs.fed.us/psw/topics/water/caspar/data/flow/daily/tribs/sflo8695.txt.gz',\n",
    "                       compression='gzip', header=None, sep=',', quotechar='\"')  # Discharge in cfs\n",
    "dCasparS.insert(0, 'date', '')\n",
    "dCasparS.insert(1, 'q_mm_per_day', '')\n",
    "\n",
    "# Scrub data\n",
    "# Format data for north tributary\n",
    "dCasparN.rename(columns={0: 'originaldata'}, inplace=True)\n",
    "for i in dCasparN.index:\n",
    "    year = dCasparN['originaldata'][i][6:8]\n",
    "    if year >= str(85):\n",
    "        year = '19' + year\n",
    "    month = dCasparN['originaldata'][i][:2]\n",
    "    day = dCasparN['originaldata'][i][3:5]\n",
    "    date = year + '-' + month + '-' + day\n",
    "    dCasparN.at[i, 'date'] = date\n",
    "    q = dCasparN['originaldata'][i][15:23]\n",
    "    dCasparN.at[i, 'q_mm_per_day'] = q\n",
    "\n",
    "dCasparN.drop(dCasparN.columns[[2]], axis=1, inplace=True)\n",
    "\n",
    "dCasparS.rename(columns={0: 'originaldata'}, inplace=True)\n",
    "\n",
    "# Format data for south tributary\n",
    "for i in dCasparS.index:\n",
    "    year = dCasparS['originaldata'][i][6:8]\n",
    "    if year >= str(85):\n",
    "        year = '19' + year\n",
    "    month = dCasparS['originaldata'][i][:2]\n",
    "    day = dCasparS['originaldata'][i][3:5]\n",
    "    date = year + '-' + month + '-' + day\n",
    "    dCasparS.at[i, 'date'] = date\n",
    "    q = dCasparS['originaldata'][i][15:23]\n",
    "    dCasparS.at[i, 'q_mm_per_day'] = q\n",
    "\n",
    "dCasparS.drop(dCasparS.columns[[2]], axis=1, inplace=True)\n",
    "\n",
    "# Merge data from north and south tributaries\n",
    "dCaspar = pd.merge(left=dCasparN, left_on='date',\n",
    "                   right=dCasparS, right_on='date')\n",
    "dCaspar.insert(1, 'q_mm_per_day', '')\n",
    "\n",
    "for i in dCaspar.index:\n",
    "    q = float(dCaspar['q_mm_per_day_x'][i]) + float(dCaspar['q_mm_per_day_y'][i])\n",
    "    dCaspar.at[i, 'q_mm_per_day'] = q\n",
    "\n",
    "dCaspar.drop(dCaspar.columns[[2, 3]], axis=1, inplace=True)\n",
    "\n",
    "# Unit conversion\n",
    "dCaspar['q_mm_per_day'] = dCaspar['q_mm_per_day'] / areaFT2[1]\n",
    "dCaspar['q_mm_per_day'] = dCaspar['q_mm_per_day'] * MMDAYfromFTS\n",
    "dCaspar['q_mm_per_day'] = pd.to_numeric(dCaspar['q_mm_per_day'])\n",
    "dCaspar['q_mm_per_day'] = round(dCaspar['q_mm_per_day'], 3)\n",
    "dCaspar = dCaspar.sort_values(by=['date'])\n",
    "dCaspar = dCaspar.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "ax.plot(dCaspar['date'], dCaspar['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Caspar Creek')\n",
    "labels = ['1985', '1986', '1987', '1988', '1989', '1990',\n",
    "          '1991', '1992', '1993', '1994', '1995', '1996']\n",
    "ticks_to_use = dCaspar.index[::365]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CWT12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:25:21.213441Z",
     "start_time": "2020-10-16T01:25:13.326951Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame\n",
    "z = urlopen(\n",
    "    'https://www.fs.usda.gov/rds/archive/products/RDS-2016-0025/RDS-2016-0025.zip')\n",
    "myzip = ZipFile(BytesIO(z.read())).extract(\n",
    "    'Data/ws18_daily_flow_1937_2019.csv')\n",
    "dCWT12 = pd.read_csv(myzip)\n",
    "\n",
    "# Scrub data\n",
    "dCWT12.rename(columns={'CSM': 'q_mm_per_day'}, inplace=True)\n",
    "dCWT12.insert(3, 'date', date)\n",
    "\n",
    "# Format data\n",
    "for i in range(len(dCWT12)):\n",
    "    waterYear = str(dCWT12['WYR'].iloc[i])\n",
    "    month = str(dCWT12['MO'].iloc[i])\n",
    "    day = str(dCWT12['DAY'].iloc[i])\n",
    "    if len(month) != 2:  # Check MM format\n",
    "        month = '0' + month\n",
    "    if len(day) != 2:   # Check DD format\n",
    "        day = '0' + day\n",
    "    date = waterYear + '-' + month + '-' + day  # ISO format\n",
    "    dCWT12.at[i, 'date'] = date\n",
    "    if dCWT12['q_mm_per_day'].iloc[i] == '.':\n",
    "        discharge = 0\n",
    "    else:\n",
    "        discharge = dCWT12['q_mm_per_day'].iloc[i]\n",
    "    dCWT12.at[i, 'q_mm_per_day'] = discharge\n",
    "\n",
    "# Unit conversion\n",
    "dCWT12['q_mm_per_day'] = dCWT12['q_mm_per_day'].astype(float)\n",
    "dCWT12['q_mm_per_day'] = dCWT12['q_mm_per_day'] * areaMI2[2]\n",
    "dCWT12['q_mm_per_day'] = dCWT12['q_mm_per_day'] / areaFT2[2]\n",
    "dCWT12['q_mm_per_day'] = dCWT12['q_mm_per_day'] * MMDAYfromFTS\n",
    "dCWT12 = dCWT12.drop(columns=['WYR', 'MO', 'DAY'])\n",
    "dCWT12['q_mm_per_day'] = round(dCWT12['q_mm_per_day'], 3)\n",
    "dCWT12 = dCWT12.sort_values(by=['date'])\n",
    "dCWT12 = dCWT12.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(18, 3))\n",
    "ax.plot(dCWT12['date'], dCWT12['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='CWT12')\n",
    "labels = ['1937', '1947', '1957', '1967',\n",
    "          '1977', '1987', '1997', '2007', '2017']\n",
    "ticks_to_use = dCWT12.index[::3650]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duke Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:25:28.389754Z",
     "start_time": "2020-10-16T01:25:26.038749Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lWbGxlf2mu/NVbPoFWytnhlNkhuu0d2b4fi1wuaRrgP8O32PPcRd63l0pSWeyeSQVNsQpCsN5JLeG778qhyFOtRAISCqTqbAdTjEZrtPeM5IOBd5HkEPyBnCDmS0vl3GVIpNxp7tUZKs2ybRPtRcnhqvanAQsIJg4/C6CmfBulPSRMtlWMTKe5FByUmn/juPEcFWb04F5Ztae3SDpt8AtwF9GWlA4gfgCYDbQAPyAYI6c3xI8m5YBp5lZxX3etPX1B/G+IMUlKx9etYkXwzX/pnJFBMDMWoF0nmWdTJBifzTBrHqXABcCZ4bbBOwS3k72N572Kk7Rya3aGIY8kyQWDCckQz0youSeDMYNwFk56yngCOCBcP1O4Lg8r11UslWblAtJyUim3SOJE8NVbf5ukKZeAQfnU5CZtQFIGk8wNeeZwE+tbwLYVmDiYOdKOhU4FWDWrFn5FD8islWbZDpDY11NycurJrK3O+VCEiuGE5JPDrE97+ZgSTMJprf4pZldI+mCnN3jge2DnWdmlwOXA8ydO7fkboJ51aZkZL/RZDoY/LmYIajOnjRj6l34K8Fwzb8PDLUvHyTtDtwNfN3M7g03PyVpvpndTxA3+Vsxy8yXPo/EhaRUlCLYetDZd3H/GfOZPXVc0a/tDE85Rzv7LrAbcJakbKzkdOCicIqLFwiqPBWnL0bi7nex6ZdHUgI6evJtC3AKoWxCYmanEwjHQOaVy4aoZBPSPNeh+BhGTUIlC7bWJLwVqBLk2wITa7KhEW+1KQEGtQkVTaQ3tnbRnerzQmr8F10RYvu1dxbg4qa9ZaGk1NckSGUyRcmQf8cP7+WS+1b0rvstqwyxFZKDzr6LF9a15HVutmrjwdbiY0BdbYJk2khnjLoiuBDNncneZc9PqQyxFRKAlpwf2EjIBlu9+bf4mEFdTV+MpKG2uD9Bv2eVIdZCku9PKvtjTHqrTUmoq0n0xkiKERw1ywmQ+z2rCPEWkjyVpDfY6lWbomMY9bWJ3gBpsb7hntDD8XtWGeItJHn+TD2PZHiWrNrGX55ek9e5ZrDb2Hq2d4TVziL87w2jOxUKyS5UtTGzqqlqxVpI8iUrJD0pF5LB+PlfX+b0657O69x0xpjaVM+mtm6gOPP/mvXdq10p2HrlopXMOe/uSptRFuItJHn+RrNPkUKakEcLyXSGruTIPmchcY1kOsPkcQ1sbOmmviZBsR7Y2apNVA/AzGjtyi8YH5WVm9tp666OiSljLSTJPH+l2RpNexUIyR1L1/Hzv45s9MzaAUKyZntn5HNTGaO+RnSl0jTUJrAijEZnQHcohlGb7G9+eg2HnDtyb+HkKx7nwnteHvF5cSfWQpJvQlm2atPRE/+nSUtncsSfc6BHctT597G1vSfSucl0hrqaBJPH1tPanSp6sDWqR7KxpTuvchat2MwTK7fkdW6cibWQ5Ftfzma2tnfH3yNp70mP+HuqTez4s9lZYDqVzvDZKx4jmTZqaxIce+B0dp/QEKlq88fFq1m1pX3I/bkxkqgB8nQBnpCP6rYjsRaSnjybAq2KPJKO7lRvi0dUBouR7MwT6EymeXjFFlLpDHU14vyPH8pfTntPJK/xmzc+y3VPrh72mF4hiXjP095MXFRiLST5Vm2yp1WPRzKyP9VgQrKzFq5sGcmMUZtIUJMQ4xpqIge0a4YdAcnK65G4Q7IDsRSSvr4y+cdIxtbXVIdH0pOiJ1V4q83OhKTPY8hQVxucP7a+NvL4ITtrKerubf6NJhA+d1FxiaWQZFPb8+10l8kYTQ3Rf+SjmfbuPDySQR7JudWjwZo8s0LSncpQF8ZYahKKPIfQcEJi1lf+YFWsjS1dfPX3S/ptc4+kuMRSSLL15Pw9EhjfWFtFHskIYyQ1ff+kbPUxmdNq8tZzFvLqprZ+5/SkA1Fu705RW9P/nxilCXhnqSs96aET0ja39bB0TXO/bYXkrXmwdUfiKSRDjHC2dnsn2yI0U6bNaGqsq44YSXd6xEKS+6fOejPZa3SG+RzL1vYfwiHrMbR2pfoNHRD0u9l5+cN5TdlWm/FDeJGdyRQbW7v7CVY+sykO13JU7cRTSMLHzcCMzeMvfICvX/u/g56zaks7Vy5aCQRVmwnV5JFEfDw/t7Z5h/4j2XOz79nvbN2AJLWefkLSp0TjdhInycYyupJpNrZ08YlLH9nhvloYbJ02oYFtHTs+KDp6ArFs6ey7n9kEtm3tPTR3RMtwnfeT+yMdV43EU0jCH9/mtv5JR+096b7OYgP49UOv8v3bngeCp9X4xtqqyGxt74nukfzDRYt4ZVN7Py8ke26vRxJ+Z+uau/qdm93f1p3sl4cytqGG9mHSyFu7gn0dPWle29LB4lXbeGNbR79jghhJmt3HNw4qClmh2tTWZ1P2up+87FE+t+DxnX30fniMZEdiKSTZevKGAdmL2bFCH16xmTnn3d2veTiZ6nvKpjMWPClL3E9i7fbOirYetHen6OhOjSiW1JVM935v7TnnZoWioydNU0Mtawd4JN29QtI/RrLHxDGsHiAMuWQ9jO2dSbaHy+ub+9/XdCbwSHYfwiPJiltuNmtL2M9mc1s3m1pHnuW69I1mHlmxedhjqklwKi4kkhKSfiXpUUn3S9qv0GsuWr6Z9+w3lfUtwRMo+8OvDft4PPLKZpo7k9z13Prec7aEsZOeVIaMQUNdomRd0u9cuo67n1vPkeffx21L15WkjCj83TkLWdvcFalqk40vbO9I9vZhassVknSfkOw7bdyQHklzZ5L6nBjJW3Zv4uX1rQD85uGVvLCuhZ5UpveebevoYc7MSSzf0Mr2cMS77H3Nsq65KxSSxt5exblkPZKNOYKRreYkFL3lKIskvn/783zmiuE9mWpqYa64kAAfBRrN7N3At4GfFXrBYw+czgWfOJTWriQ3P7WGo358H+fd+hzTxzeyta2HB17exMffthdfv+YpLr53OUtWbWPJqq3MmNDI+Xe+yFd+v4TtHUlqE+qtjy9avnnEvWSH4js3LeWsvywDoK1rZF5PKYY2iHLNbJPu1o4eUukgsNnalerXrAvB0//NU8axYcCfPSs0L61vpTZHSN5/8AyufmwVHT0pzrv1ea5/cjUf/PmDfPF3i+lKplnf3MX+05vo6Enz7BvbmTNzEgufW98bi5k8rp4Vm9p4dXM7B71pAj2pDBfdu5x7nt/QG8vp6EkxtamBVVv6PJ+WrmTY+9j6/eG7I+TUdHSn2HPSGGDoFqcHX97ENY+/vtNrwchyWnpSmYI6Om7JEdoNLV3c8szavK+VSzknyBqK9wB3AZjZY5LmFnrB6RMaAbjsc3P5xd9WkM4YKza2sc+0cXzjM2/jN4+s5KwPHcS3TziQqx99jYvuXc47957C54+czfVPvs43jt+ffaY1kUxnOPGSRXz5mH35xg3PcO6HD+YfDt2DDS1dJCT23G0MW9t72NjSRX1tgtlTxpHMZNjWnqS1K8n08Y28uL6FQ/eaRH1tgu/8+VnOO/GtmPVVu17f2sH65q5g5LCaBFvbexhTX8Njr27l1w++ykWfPpzdxtaRzBjj6ms47Hv3cPGnD2fCmDoOmjGeupoECYneFkmDtc2d7DFxDChw3bd39LD31KbeXIx1zZ2cfm3feCK1NWL11g4mNNaFf6xglJCMBdNqNncme8e/fWb1dp5YuZUZExt5eUMru4ff9f0vbeTot0xlc1s3TY217LnbGO5Yuo65s3ejvibBi+tb2WNiI2ubu/oFWw/eYwL/ePievOu/7uUdsydzyzNrmTl5LHvtNoYDz7oLgJ+dNId5B0zj69c8xYWfnMOzbzRz3M8eYEpTA1vbezj3wwdzx7L1HHvQdI5482784m8ruPCel9lz0hgOmDGetds7Oflds7j+ydd5avU2Dps5iY2t3Ry610QWr9oGwCX3LWdsfS3fu+15/v34/QEYW19DQuLAGeP7dS58bm0Li1dtQ4ILFr7Exw7fE0lMbaoPgrpdSf76wobe4598bSvj6msZU1/D5LH1wb0yeGjFJiaPreczVzzO7//lnRyy50TSZlz24Ct85Zh9SeQ0j60OfyffuWkpH3vbnnxtXn/HfbBxXQbqjQFH/OCv/O6L7+CwmZP4/IIneHF9K4ftNYlZU8YO/meKiIrRjbsgA6QrgD+Z2Z3h+uvAPmaWyjkmdxLxI1atWlUW21ZsbOPu59fzwrpWmjuTdCfTtHSlaGqoIZk22rpTNDXUsvuEBrpTGdZs66S2JsHY+hqmjKtnbXMnTTlP7te3djB7yjh60hmmNTXw2pZ2pjQ1kFCQcNWdzDBxTB3d6Qx1CbFsbTOzJo+lriZBbU2C9u4Um9u6mdbUEOa5pHd4opoZknrfBUwaW0dzZ7LfcUftO4XamgTbO5LsO30ctzy9lnTGSEhIgfueUOD6JxIinclwwO4T2N7Rw+Mrt3LBJw7lkvtWkDHjPftNZUx9Ta8X8PkjZ/PWPSdyyX3LWbO9i3QmQzpj/N/3vYUZExo5dK9JQ87Rm0xnyJjRUFvDmu2dzJjQOGgyWkdPilc2trP/jCYaage/VnNHklc3t7FiYxsnHrYHHd1plm9sY9maZl7f2sGX5+3Do69sob42wcaWbja0dDGmvoZJY+qoSYi1zV1sbOkmlckgYNbksRx/8AxmTGxkyaqt7Da2nmfe2M7tz66jvjb4LhMSk8bW0dad4oAZ42msq2FLWzft3Wnae1K9QV6Are09TB5XzwG7j+f1rR29TecrNrax3/Smfp+lO5WmLWw6n9rUMGhca7CYzMCcl60dPUwcUwfAmyY2Mm//acyZOYm3z5486HfY//paYmaDPuh3BSG5EHjMzP4Yrr9hZnsNdfzcuXNt8eLFZbPPcZyA4YRkV4iRPAz8PYCkdwFLK2uO4zgjZVeIkdwEHC/pEYLa4xcqbI/jOCOk4kJiZhngK5W2w3Gc/NkVqjaO44xyXEgcxykYFxLHcQqm4s2/I0XSJiBKIslUYPjOEE6c8ftffN5sZtMG2zHqhCQqkhYP1ebtxB+//+XFqzaO4xSMC4njOAUTZyG5vNIGOBXF738ZiW2MxHGc8hFnj8RxnDIxqoREUp2kqyU9JOkJSSdK2k/SonDbpZISOcfvJ2lZzvpkSZvDkdjul3R6ZT6Jkw9FuP/jJF0VHvu4pHdU5pPEj1ElJMDJwBYzOxo4AbgEuBA4M9wm4CMAkj4HXEeQT5DlbcC1ZjY/fP1PWa13CqXQ+/8fwLLw2C8BB5TR9lgz2oTkBuCsnPUUcATwQLh+J3BcuLwNmDfg/COAt0l6QNINkt5USmOdolPo/f8A0CNpYXidhaUztboYVUJiZm1m1ippPHAjcCZBwDgbMW4FJobH3mZmA2c0ehE4x8zmATcDF5fJdKcIFOH+TwV2M7MPALcCPy2T6bFnVAkJgKSZwN+Aq83sGiB3zLnxwPZhTr8vPBeCcVAOL4mRTsko8P5vAW4Jl28FPPO1SIwqIZG0O3A38C0zWxBufkrS/HD5BOChYS5xBfDxcPl9wJJhjnV2MYpw/xcRjsYHHAM8Vwo7q5FRlUci6X+ATxFUUbKcDlwE1AMvAF8ys3TOOevNbEa4vDewgCAo1w78q5lVbmIZZ0QU4f5PJniYvAlIAv9sZq+Vx/p4M6qExHGcXZNRVbVxHGfXxIXEcZyCcSFxHKdgXEgcxykYFxLHcQrGhaSKkfSzsPPii5JeD5dvkHSYpLOLWM4USZcV63o7KWuGpEvKUZbThzf/Okg6BTjQzL5doutfClxqZs+W4vqDlPcj4C4ze2CnBztFwT0SZwckzZd0Xbi8Iux6/6ikH0q6JOzCf3W4f6akOyX9LXyfOeBaE4C3m9mzkt4v6YacfQ9LepOkk8LrL5J0frhvL0m3SrpH0v9K+mi4fZmkP0u6VtJRkh4LhwW4JeyDA3ANQaKaUy7MzF9V/gJOAc7PWZ8PXBcup4BZQB1Bp7iDCTKDVwKTgOuBE8Jj3wf8YcC135/dFp73IrBbeJ2/AJOB54Gx4TFXA8cT9OKdH247ErgnXH4NODxc/gnwTYIH4keBWeH2GmBDpb/XanpVfO5fZ5dni5m9DiCp3cyeD5ebgUbgEOC7kr5FIBQ9A86fCmwAMKj7JywAAAE7SURBVDOT9Hvg08A+wJXAfsA04A5JEHS824egX8yZkv4FMAIhy/JS+P5fwH8C9wJrgMfDctKSUpISFswt7ZQYr9o4O2NnQbQXCTrRzQe+TNC9P5eNBJ5Llt8AJxF0mruDwLNZDRwfXuNiAkH4PnCVmX2OoLevcq6RFYfPAr81s/cSdMA7FUCBIqVcRMqHeyROoZwBXCqpERjDjrGJx4AfZ1fMbI2kVuAxM0sBmyRdCDwgqYag6vJHgkGMLpK0nkBoprIjTwK/k9RG4AmdGm4/BHi0SJ/PiYC32jglR9KvgMvM7Klw/Tbg/5nZihKVdwFwq5kNN6SAU0S8auOUg7OBr0kaI2kJ8EwJRWQGMMFFpLy4R+I4TsG4R+I4TsG4kDiOUzAuJI7jFIwLieM4BeNC4jhOwbiQOI5TMP8fhi+nKkmWFm8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame based on processed data (original data available by request)\n",
    "\n",
    "dDuke = pd.read_csv(\n",
    "    'https://www.hydroshare.org/resource/1182147d58724a2a84dc3a382636d35e/data/contents/q_dukeforest.csv', index_col=0)\n",
    "\n",
    "### Start code used to transform original data ###\n",
    "\n",
    "# Create data frames\n",
    "#fullQuery = 'zip/duke_WSA_WS1_mmhr_sync_5Dec16.csv'\n",
    "# dDuke = pd.read_csv(fullQuery)  # Discharge in mm/hr\n",
    "\n",
    "# Scrub data\n",
    "#dDuke.insert(0, 'date', '0')\n",
    "#dDuke.insert(1, 'q_mm_per_day', '0')\n",
    "#dDuke.rename(columns={dDuke.columns[2]: 'timestamp', dDuke.columns[3]: 'WSA', dDuke.columns[4]: 'WS1'}, inplace=True)\n",
    "#dDuke.drop(dDuke.columns[[5, 6]], axis=1, inplace=True)\n",
    "\n",
    "# for i in dDuke.index:\n",
    "#    q_mm_per_day = dDuke.WS1[i]  # Use WS 1 data\n",
    "#    dDuke.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "#    timestamp = str(dDuke.timestamp[i])\n",
    "#    timestamp = timestamp[0:10]\n",
    "#    dDuke.at[i, 'date'] = timestamp\n",
    "\n",
    "# Unit conversion\n",
    "#dDuke.drop(dDuke.columns[[2, 3, 4]], axis=1, inplace=True)\n",
    "# dDuke['q_mm_per_day'] = dDuke['q_mm_per_day'] * 24  # Multiply discharge in mm/hr by 24 to get mm/day\n",
    "# dDuke = dDuke.groupby('date')['q_mm_per_day'].mean() # Find average daily discharge\n",
    "#dDuke = dDuke.to_string().split('\\n')\n",
    "#dDuke = dDuke[1:]\n",
    "#date = list()\n",
    "#q_mm_per_day = list()\n",
    "\n",
    "# for line in dDuke:\n",
    "#    date.append(line[:10])\n",
    "#    q_mm_per_day.append(line[14:])\n",
    "\n",
    "#qTimeseries = {'date': date, 'q_mm_per_day': q_mm_per_day}\n",
    "#dDuke = pd.DataFrame(qTimeseries, columns=['date', 'q_mm_per_day'])\n",
    "#dDuke['q_mm_per_day'] = dDukeforest['q_mm_per_day'].astype(float)\n",
    "#dDuke['q_mm_per_day'] = round(dDukeforest['q_mm_per_day'], 3)\n",
    "# dDuke.sort_values(by=['date'])\n",
    "\n",
    "### End code used to transform original data ###\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(4, 3))\n",
    "ax.plot(dDuke['date'], dDuke['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Duke Forest')\n",
    "labels = ['2015', '2016', '2017']\n",
    "ticks_to_use = dDuke.index[::365]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elder Creek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:25:34.855311Z",
     "start_time": "2020-10-16T01:25:30.293462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame\n",
    "dElder = pd.read_csv('https://waterdata.usgs.gov/nwis/dv?cb_00060=on&format=rdb&site_no=11475560&referred_module=sw&period=&begin_date=' +\n",
    "                     start[4] + '&end_date=' + stop[4], header=31, delim_whitespace=True)  # Discharge in cfs\n",
    "\n",
    "# Scrub data\n",
    "dElder.columns = ['usgs', 'site', 'date', 'q_mm_per_day', 'a']\n",
    "dElder = dElder.drop(columns=['usgs', 'site', 'a'])\n",
    "\n",
    "# Unit conversion\n",
    "dElder['q_mm_per_day'] = dElder['q_mm_per_day'] / areaFT2[4]\n",
    "dElder['q_mm_per_day'] = dElder['q_mm_per_day'] * MMDAYfromFTS\n",
    "dElder['q_mm_per_day'] = round(dElder['q_mm_per_day'], 3)\n",
    "dElder.sort_values(by=['date'])\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(12, 3))\n",
    "ax.plot(dElder['date'], dElder['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Elder Creek')\n",
    "labels = ['1967', '1977', '1987', '1997', '2007', '2017']\n",
    "ticks_to_use = dElder.index[::3650]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FNW37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:25:41.995799Z",
     "start_time": "2020-10-16T01:25:36.206057Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame\n",
    "z = urlopen(\n",
    "    'https://www.fs.usda.gov/rds/archive/products/RDS-2011-0015/RDS-2011-0015.zip')\n",
    "myzip = ZipFile(BytesIO(z.read())).extract('Data/Fernow_Streamflow.csv')\n",
    "dFNW37 = pd.read_csv(myzip)  # Discharge in mm/day\n",
    "\n",
    "# Scrub data\n",
    "dFNW37.rename(columns={'Date (mm/dd/yyyy)': 'date',\n",
    "                       'Discharge (mm)': 'q_mm_per_day'}, inplace=True)\n",
    "dFNW37 = dFNW37[92443:116793]  # Get WS 4 records\n",
    "\n",
    "# Format data\n",
    "for i in dFNW37.index:\n",
    "    waterYear = str()\n",
    "    waterYear = str(dFNW37['date'][i])\n",
    "    waterYear = re.findall('/(\\d\\d\\d\\d)', waterYear)\n",
    "    waterYear = ''.join(waterYear)\n",
    "    month = str()\n",
    "    month = dFNW37['date'][i]\n",
    "    month = re.findall('(\\d+)/\\d+/\\d\\d\\d\\d', month)\n",
    "    month = ''.join(month)\n",
    "    day = str()\n",
    "    day = dFNW37['date'][i]\n",
    "    day = re.findall('\\d+/(\\d+)/\\d\\d\\d\\d', day)\n",
    "    day = ''.join(day)\n",
    "    if len(month) != 2:\n",
    "        month = '0' + month\n",
    "    if len(day) != 2:\n",
    "        day = '0' + day\n",
    "    date = waterYear + '-' + month + '-' + day\n",
    "    dFNW37.at[i, 'date'] = date\n",
    "\n",
    "dFNW37 = dFNW37.drop(columns=['Watershed'])\n",
    "dFNW37 = dFNW37.sort_values(by=['date'])\n",
    "\n",
    "# Get average daily discharge\n",
    "dFNW37 = dFNW37.groupby('date')['q_mm_per_day'].mean()\n",
    "dFNW37 = dFNW37.to_string().replace('date', '').split('\\n')\n",
    "dFNW37 = dFNW37[1:]\n",
    "dateList = [i.split(' ')[0] for i in dFNW37]\n",
    "qList = [i.split(' ', 1)[1] for i in dFNW37]\n",
    "\n",
    "# Re-create data frame\n",
    "dFNW37 = pd.DataFrame(list(zip(dateList, qList)),\n",
    "                      columns=['date', 'q_mm_per_day'])\n",
    "dFNW37['q_mm_per_day'] = pd.to_numeric(dFNW37['q_mm_per_day'])\n",
    "dFNW37['q_mm_per_day'] = round(dFNW37['q_mm_per_day'], 3)\n",
    "dFNW37.sort_values(by=['date'])\n",
    "dFNW37 = dFNW37.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(14, 3))\n",
    "ax.plot(dFNW37['date'], dFNW37['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='FNW37')\n",
    "labels = ['1951', '1961', '1971', '1981', '1991', '2001', '2011']\n",
    "ticks_to_use = dFNW37.index[::3650]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HB13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:25:51.853852Z",
     "start_time": "2020-10-16T01:25:46.942090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame; Discharge in mm/day\n",
    "dHB13 = pd.read_csv(\n",
    "    'https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-hbr.2.11&entityid=1254d17cbd381556c05afa740d380e78')\n",
    "\n",
    "# Scrub data\n",
    "dHB13.rename(columns={'DATE': 'date', 'WS': 'watershed',\n",
    "                      'Streamflow': 'q_mm_per_day'}, inplace=True)\n",
    "dHB13 = dHB13.drop(columns=['Flag'])\n",
    "dHB13['q_mm_per_day'] = pd.to_numeric(dHB13['q_mm_per_day'])\n",
    "dHB13 = dHB13.loc[dHB13['watershed'] == 6]  # Get WS 6 records\n",
    "dHB13 = dHB13.drop(columns=['watershed'])\n",
    "dHB13['q_mm_per_day'] = round(dHB13['q_mm_per_day'], 3)\n",
    "dHB13.sort_values(by=['date'])\n",
    "dHB13 = dHB13.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(12, 3))\n",
    "ax.plot(dHB13['date'], dHB13['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='HB13')\n",
    "labels = ['1963', '1973', '1983', '1993', '2003', '2013']\n",
    "ticks_to_use = dHB13.index[::3650]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HB42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-27T19:08:15.013064Z",
     "start_time": "2020-11-27T19:08:09.951809Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame; Discharge in mm/day\n",
    "dHB42 = pd.read_csv(\n",
    "    'https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-hbr.2.11&entityid=1254d17cbd381556c05afa740d380e78')\n",
    "\n",
    "# Scrub data\n",
    "dHB42.rename(columns={'DATE': 'date', 'WS': 'watershed',\n",
    "                      'Streamflow': 'q_mm_per_day'}, inplace=True)\n",
    "dHB42 = dHB42.drop(columns=['Flag'])\n",
    "dHB42['q_mm_per_day'] = pd.to_numeric(dHB42['q_mm_per_day'])\n",
    "dHB42 = dHB42.loc[dHB42['watershed'] == 3]  # Get WS 3 records\n",
    "dHB42 = dHB42.drop(columns=['watershed'])\n",
    "dHB42['q_mm_per_day'] = round(dHB42['q_mm_per_day'], 3)\n",
    "dHB42.sort_values(by=['date'])\n",
    "dHB42 = dHB42.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(14, 3))\n",
    "ax.plot(dHB42['date'], dHB42['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='HB42')\n",
    "labels = ['1957', '1967', '1977', '1987', '1997', '2007', '2017']\n",
    "ticks_to_use = dHB42.index[::3650]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pioneer Creek\n",
    "Based on code by W Jesse Hahm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-19T04:29:08.030539Z",
     "start_time": "2020-12-19T04:24:53.004320Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "processing data from request: https://nwis.waterservices.usgs.gov/nwis/iv/?format=waterml&site=13306336&startDT=2011-11-05T00%3A00%3A00&endDT=2020-05-29T00%3A00%3A00\n",
      "processing data from request: https://nwis.waterservices.usgs.gov/nwis/iv/?format=waterml&site=13310850&startDT=2011-11-05T00%3A00%3A00&endDT=2020-05-29T00%3A00%3A00\n",
      "processing data from request: https://nwis.waterservices.usgs.gov/nwis/iv/?format=waterml&site=13313000&startDT=2011-11-05T00%3A00%3A00&endDT=2020-05-29T00%3A00%3A00\n",
      "processing data from request: https://nwis.waterservices.usgs.gov/nwis/iv/?format=waterml&site=13310199&startDT=2011-11-05T00%3A00%3A00&endDT=2020-05-29T00%3A00%3A00\n",
      "processing data from request: https://nwis.waterservices.usgs.gov/nwis/iv/?format=waterml&site=13297330&startDT=2011-11-05T00%3A00%3A00&endDT=2020-05-29T00%3A00%3A00\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create dictionaries to re-construct discharge for Pioneer Creek\n",
    "# per Whiting and Godsey 2016\n",
    "name_id = {\n",
    "    'Blackbird Ck.': 13306336,\n",
    "    'Meadow Ck.': 13310850,\n",
    "    'Johnson Ck.': 13313000,\n",
    "    'Middle Fork Salmon': 13310199,\n",
    "    'Thompson Ck.': 13297330,\n",
    "}\n",
    "\n",
    "a = 0.016  # Pre-factor per Whiting and Godsey 2016, eq. (3)\n",
    "\n",
    "# Function to get data\n",
    "def getUSGSdata(siteno, startdate, enddate):\n",
    "    sitename = {}\n",
    "    sitename = ulmo.usgs.nwis.get_site_data(\n",
    "        siteno, service='iv', start=startdate, end=enddate, methods='all')\n",
    "    sitename = pd.DataFrame(sitename)\n",
    "    sitename = pd.DataFrame(sitename['00060:00000']['values'])\n",
    "    sitename['date'] = pd.to_datetime(\n",
    "        pd.Series(sitename['datetime']), utc=True).dt.tz_convert('US/Pacific')\n",
    "    sitename.set_index(['date'], inplace=True)\n",
    "    dPio = sitename.value.astype(float)\n",
    "    dPio = dPio.resample('15min').mean()\n",
    "    return dPio\n",
    "\n",
    "# Correct values as per Whiting and Godsey 2016 and convert discharge in CFS to CMS\n",
    "dPios = []\n",
    "for value in name_id.values():\n",
    "    site = str(value)\n",
    "    startdate = pd.to_datetime('2011-11-05')\n",
    "    enddate = pd.to_datetime('2020-05-29')\n",
    "    series = getUSGSdata(site, startdate, enddate)\n",
    "    dPio = pd.DataFrame()\n",
    "    dPio['CMS'] = series \n",
    "    \n",
    "    if value == 13306336:\n",
    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),0.0212,(dPio['CMS']*0.054/35.3146662126613)) \n",
    "\n",
    "    if value == 13310850:\n",
    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),-0.0144,(dPio['CMS']*(-0.174)/35.3146662126613))\n",
    "\n",
    "    if value == 13313000:\n",
    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),np.nan,(dPio['CMS']*0.003/35.3146662126613))\n",
    "\n",
    "    if value == 13310199:\n",
    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),np.nan,(dPio['CMS']*0.001/35.3146662126613))\n",
    "\n",
    "    if value == 13297330:\n",
    "        dPio['CMS'] = np.where(dPio['CMS'].isnull(),np.nan,(dPio['CMS']*(-0.094)/35.3146662126613))\n",
    "                \n",
    "    dPios.append(dPio)\n",
    "\n",
    "# Add corrected values per Whiting and Godsey 2016, eq. (3)\n",
    "final = a + dPios[0] + dPios[1] + dPios[2] + dPios[3] + dPios[4]\n",
    "\n",
    "# Plot reconstructed Pioneer Creek discharge time series in  mm/day\n",
    "final['mm15min'] = final['CMS'] * 1000 * 60 * 15 / (15.8 * 10 ** 6)\n",
    "final['q_mm_per_day'] = final['mm15min'] * 4 * 24\n",
    "final_daily = final['q_mm_per_day'].resample('1D').mean()\n",
    "final_daliy = final_daily.iloc[1:]\n",
    "\n",
    "# Finalize data\n",
    "final_daily_date = list()\n",
    "final_daily_q = list()\n",
    "\n",
    "# Format data\n",
    "for i in range(len(final_daily)):\n",
    "    dateStr = str(final_daily.index[i])\n",
    "    dateStr = dateStr[0:10]\n",
    "    final_daily_date.append(dateStr)\n",
    "    final_daily_q.append(final_daily[i])\n",
    "\n",
    "d = {'date': final_daily_date, 'q_mm_per_day': final_daily_q}\n",
    "dPioDaily = pd.DataFrame(data=d)\n",
    "\n",
    "dPioDaily['q_mm_per_day'] = np.where((dPioDaily['q_mm_per_day'] < 0),np.nan,dPioDaily['q_mm_per_day']) # Convert any negative values to NaN values\n",
    "\n",
    "dPioDaily.to_csv('q_pioneercreek.csv') \n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "ax.plot(dPioDaily['date'], dPioDaily['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Pioneer Creek')\n",
    "labels = ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']\n",
    "ticks_to_use = dPioDaily.index[::365]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Providence Creek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:39:21.132755Z",
     "start_time": "2020-10-16T01:39:17.719201Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frames; Discharge in L/s\n",
    "baseURL = 'https://sndl.ucmerced.edu/files/MHWG/Field/Southern_Sierra_CZO_KREW/Providence_Creek/Discharge/Level_1b/WY'\n",
    "dProv2004 = pd.read_csv(\n",
    "    baseURL + '2004_gap_filled_Providence_discharge_daily.csv', low_memory=False)\n",
    "dProv2005 = pd.read_csv(\n",
    "    baseURL + '2005_gap_filled_Providence_discharge_daily.csv', low_memory=False)\n",
    "dProv2006 = pd.read_csv(\n",
    "    baseURL + '2006_gap_filled_Providence_discharge_daily.csv', low_memory=False)\n",
    "dProv2007 = pd.read_csv(\n",
    "    baseURL + '2007_gap_filled_Providence_discharge_daily.csv', low_memory=False)\n",
    "dProv2008 = pd.read_csv(\n",
    "    baseURL + '2008_gap_filled_Providence_discharge_daily.csv', low_memory=False)\n",
    "dProv2009 = pd.read_csv(\n",
    "    baseURL + '2009_gap_filled_Providence_discharge_daily.csv', low_memory=False)\n",
    "dProv2010 = pd.read_csv(\n",
    "    baseURL + '2010_gap_filled_Providence_discharge_daily.csv', low_memory=False)\n",
    "\n",
    "# Scrub data\n",
    "# 2004\n",
    "dProv2004.insert(0, 'date', '0')\n",
    "dProv2004.insert(1, 'q_mm_per_day', '0')\n",
    "dProv2004.rename(columns={dProv2004.columns[3]: 'Year', dProv2004.columns[4]                          : 'Month', dProv2004.columns[5]: 'Day'}, inplace=True)\n",
    "dProv2004.rename(columns={dProv2004.columns[11]: 'P301', dProv2004.columns[12]: 'P303',\n",
    "                          dProv2004.columns[13]: 'P304', dProv2004.columns[14]: 'D102'}, inplace=True)\n",
    "dProv2004.drop(dProv2004.columns[[2, 6, 7, 8, 9, 10]], axis=1, inplace=True)\n",
    "\n",
    "# Format data\n",
    "for i in dProv2004.index:\n",
    "    month = str(dProv2004['Month'][i])\n",
    "    if len(str(dProv2004['Month'][i])) != 2:\n",
    "        month = '0' + str(dProv2004['Month'][i])\n",
    "    day = str(dProv2004['Day'][i])\n",
    "    if len(str(dProv2004['Day'][i])) != 2:\n",
    "        day = '0' + str(dProv2004['Day'][i])\n",
    "    date = str(dProv2004['Year'][i]) + '-' + month + '-' + day\n",
    "    dProv2004.at[i, 'date'] = date\n",
    "    q_mm_per_day = float(dProv2004.P301[i]) + float(dProv2004.P303[i]) + float(\n",
    "        dProv2004.P304[i]) + float(dProv2004.D102[i])\n",
    "    dProv2004.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Value scan\n",
    "for i in range(len(dProv2004)):\n",
    "    dProv2004['date'].loc['2004-04-31'] = '2004-03-31'\n",
    "    dProv2004['date'].loc['2004-05-30'] = '2004-04-30'\n",
    "    dProv2004['date'].loc['2004-06-31'] = '2004-05-31'\n",
    "    dProv2004['date'].loc['2004-07-30'] = '2004-06-30'\n",
    "    dProv2004['date'].loc['2004-08-31'] = '2004-07-31'\n",
    "    dProv2004['date'].loc['2004-09-31'] = '2004-08-31'\n",
    "\n",
    "# Unit conversion\n",
    "dProv2004['q_mm_per_day'] = dProv2004['q_mm_per_day'].astype(float)\n",
    "dProv2004['q_mm_per_day'] = dProv2004['q_mm_per_day'] * CMSfromLS \n",
    "dProv2004['q_mm_per_day'] = dProv2004['q_mm_per_day'] * CFSfromCMS\n",
    "dProv2004['q_mm_per_day'] = dProv2004['q_mm_per_day'] / areaFT2[9]\n",
    "dProv2004['q_mm_per_day'] = dProv2004['q_mm_per_day'] * MMDAYfromFTS\n",
    "dProv2004.drop(dProv2004.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "\n",
    "# 2005\n",
    "dProv2005.insert(0, 'date', '0')\n",
    "dProv2005.insert(1, 'q_mm_per_day', '0')\n",
    "dProv2005.rename(columns={dProv2005.columns[3]: 'Year', dProv2005.columns[4]\n",
    "                 : 'Month', dProv2005.columns[5]: 'Day'}, inplace=True)\n",
    "dProv2005.rename(columns={dProv2005.columns[11]: 'P301', dProv2005.columns[12]: 'P303',\n",
    "                          dProv2005.columns[13]: 'P304', dProv2005.columns[14]: 'D102'}, inplace=True)\n",
    "dProv2005.drop(\n",
    "    dProv2005.columns[[2, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19]], axis=1, inplace=True)\n",
    "dProv2005 = dProv2005[0:365]\n",
    "\n",
    "# Format data\n",
    "for i in dProv2005.index:\n",
    "    month = str(dProv2005['Month'][i]).replace('.0', '')\n",
    "    if len(month) != 2:\n",
    "        month = '0' + str(dProv2005['Month'][i])\n",
    "    day = str(dProv2005['Day'][i]).replace('.0', '')\n",
    "    if len(day) != 2:\n",
    "        day = '0' + str(dProv2005['Day'][i])\n",
    "    date = str(dProv2005['Year'][i]).replace(\n",
    "        '.0', '') + '-' + month.replace('.0', '') + '-' + day.replace('.0', '')\n",
    "    dProv2005.at[i, 'date'] = date\n",
    "\n",
    "# Add subwatershed values\n",
    "for i in dProv2005.index:\n",
    "    q_mm_per_day = dProv2005.P301[i] + dProv2005.P303[i] + \\\n",
    "        dProv2005.P304[i] + dProv2005.D102[i]\n",
    "    dProv2005.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dProv2005['q_mm_per_day'] = dProv2005['q_mm_per_day'].astype(float)\n",
    "dProv2005.drop(dProv2005.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "dProv2005['q_mm_per_day'] = dProv2005['q_mm_per_day'] * CMSfromLS   \n",
    "dProv2005['q_mm_per_day'] = dProv2005['q_mm_per_day'] * CFSfromCMS\n",
    "dProv2005['q_mm_per_day'] = dProv2005['q_mm_per_day'] / areaFT2[9]\n",
    "dProv2005['q_mm_per_day'] = dProv2005['q_mm_per_day'] * MMDAYfromFTS\n",
    "\n",
    "# 2006\n",
    "dProv2006.insert(0, 'date', '0')\n",
    "dProv2006.insert(1, 'q_mm_per_day', '0')\n",
    "dProv2006.rename(columns={dProv2006.columns[3]: 'Year', dProv2006.columns[4]: 'Month', dProv2006.columns[5]: 'Day'}, inplace=True)\n",
    "dProv2006.rename(columns={dProv2006.columns[11]: 'P301', dProv2006.columns[12]: 'P303',\n",
    "                          dProv2006.columns[13]: 'P304', dProv2006.columns[14]: 'D102'}, inplace=True)\n",
    "dProv2006.drop(\n",
    "    dProv2006.columns[[2, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19]], axis=1, inplace=True)\n",
    "dProv2006 = dProv2006[0:365]\n",
    "\n",
    "# Format data\n",
    "for i in dProv2006.index:\n",
    "    month = str(dProv2006['Month'][i]).replace('.0', '')\n",
    "    if len(month) != 2:\n",
    "        month = '0' + str(dProv2006['Month'][i])\n",
    "    day = str(dProv2006['Day'][i]).replace('.0', '')\n",
    "    if len(day) != 2:\n",
    "        day = '0' + str(dProv2006['Day'][i])\n",
    "    date = str(dProv2006.Year[i]).replace(\n",
    "        '.0', '') + '-' + month.replace('.0', '') + '-' + day.replace('.0', '')\n",
    "    dProv2006.at[i, 'date'] = date\n",
    "\n",
    "# Add subwatershed values\n",
    "for i in dProv2006.index:\n",
    "    q_mm_per_day = dProv2006.P301[i] + dProv2006.P303[i] + \\\n",
    "        dProv2006.P304[i] + dProv2006.D102[i]\n",
    "    dProv2006.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "dProv2006.drop(dProv2006.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "\n",
    "# Unit conversion\n",
    "dProv2006['q_mm_per_day'] = dProv2006['q_mm_per_day'].astype(float)\n",
    "dProv2006['q_mm_per_day'] = dProv2006['q_mm_per_day'] * CMSfromLS\n",
    "dProv2006['q_mm_per_day'] = dProv2006['q_mm_per_day'] * CFSfromCMS\n",
    "dProv2006['q_mm_per_day'] = dProv2006['q_mm_per_day'] / areaFT2[9] \n",
    "dProv2006['q_mm_per_day'] = dProv2006['q_mm_per_day'] * MMDAYfromFTS\n",
    "\n",
    "# 2007\n",
    "dProv2007.insert(0, 'date', '0')\n",
    "dProv2007.insert(1, 'q_mm_per_day', '0')\n",
    "dProv2007.rename(columns={dProv2007.columns[3]: 'Year', dProv2007.columns[4]: 'Month', dProv2007.columns[5]: 'Day'}, inplace=True)\n",
    "dProv2007.rename(columns={dProv2007.columns[11]: 'P301', dProv2007.columns[12]: 'P303',\n",
    "                          dProv2007.columns[13]: 'P304', dProv2007.columns[14]: 'D102'}, inplace=True)\n",
    "dProv2007.drop(\n",
    "    dProv2007.columns[[2, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19]], axis=1, inplace=True)\n",
    "\n",
    "# Format data\n",
    "dProv2007 = dProv2007[0:365]\n",
    "for i in dProv2007.index:\n",
    "    month = str(dProv2007['Month'][i]).replace('.0', '')\n",
    "    if len(month) != 2:\n",
    "        month = '0' + str(dProv2007['Month'][i])\n",
    "    day = str(dProv2007['Day'][i]).replace('.0', '')\n",
    "    if len(day) != 2:\n",
    "        day = '0' + str(dProv2007['Day'][i])\n",
    "    date = str(dProv2007['Year'][i]).replace(\n",
    "        '.0', '') + '-' + month.replace('.0', '') + '-' + day.replace('.0', '')\n",
    "    dProv2007.at[i, 'date'] = date\n",
    "\n",
    "# Add subwatershed values\n",
    "for i in dProv2007.index:\n",
    "    q_mm_per_day = dProv2007.P301[i] + dProv2007.P303[i] + \\\n",
    "        dProv2007.P304[i] + dProv2007.D102[i]\n",
    "    dProv2007.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "dProv2007.drop(dProv2007.columns[[2, 3, 4, 5, 6, 7, 8]], axis=1, inplace=True)\n",
    "\n",
    "# Unit conversion\n",
    "dProv2007['q_mm_per_day'] = dProv2007['q_mm_per_day'].astype(float)\n",
    "dProv2007['q_mm_per_day'] = dProv2007['q_mm_per_day'] * CMSfromLS \n",
    "dProv2007['q_mm_per_day'] = dProv2007['q_mm_per_day'] * CFSfromCMS\n",
    "dProv2007['q_mm_per_day'] = dProv2007['q_mm_per_day'] / areaFT2[9] \n",
    "dProv2007['q_mm_per_day'] = dProv2007['q_mm_per_day'] * MMDAYfromFTS\n",
    "\n",
    "# 2008\n",
    "dProv2008.insert(0, 'date', '0')\n",
    "dProv2008.insert(1, 'q_mm_per_day', '0')\n",
    "dProv2008.rename(columns={dProv2008.columns[3]: 'Year', dProv2008.columns[4]: 'Month', dProv2008.columns[5]: 'Day'}, inplace=True)\n",
    "dProv2008.rename(columns={dProv2008.columns[11]: 'P301', dProv2008.columns[12]: 'P303',\n",
    "                          dProv2008.columns[13]: 'P304', dProv2008.columns[14]: 'D102'}, inplace=True)\n",
    "\n",
    "# Format data\n",
    "for i in dProv2008.index:\n",
    "    month = str(dProv2008['Month'][i])\n",
    "    if len(month) != 2:\n",
    "        month = '0' + str(dProv2008['Month'][i])\n",
    "    day = str(dProv2008['Day'][i])\n",
    "    if len(day) != 2:\n",
    "        day = '0' + str(dProv2008['Day'][i])\n",
    "    date = str(dProv2008['Year'][i]) + '-' + month + '-' + day\n",
    "    dProv2008.at[i, 'date'] = date\n",
    "\n",
    "# Add subwatershed values\n",
    "for i in dProv2008.index:\n",
    "    q_mm_per_day = dProv2008.P301[i] + dProv2008.P303[i] + \\\n",
    "        dProv2008.P304[i] + dProv2008.D102[i]\n",
    "    dProv2008.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dProv2008['q_mm_per_day'] = dProv2008['q_mm_per_day'].astype(float)\n",
    "dProv2008['q_mm_per_day'] = dProv2008['q_mm_per_day'] * CMSfromLS   \n",
    "dProv2008['q_mm_per_day'] = dProv2008['q_mm_per_day'] * CFSfromCMS  \n",
    "dProv2008['q_mm_per_day'] = dProv2008['q_mm_per_day'] / areaFT2[9]   \n",
    "\n",
    "dProv2008['q_mm_per_day'] = dProv2008['q_mm_per_day'] * MMDAYfromFTS\n",
    "dProv2008.drop(dProv2008.columns[[\n",
    "               2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]], axis=1, inplace=True)\n",
    "\n",
    "# Value scan\n",
    "for i in range(len(dProv2008)):\n",
    "    dProv2008['date'].loc['2008-04-31'] = '2008-03-31'\n",
    "    dProv2008['date'].loc['2008-05-30'] = '2008-04-30'\n",
    "    dProv2008['date'].loc['2008-06-31'] = '2008-05-31'\n",
    "    dProv2008['date'].loc['2008-07-30'] = '2008-06-30'\n",
    "    dProv2008['date'].loc['2008-08-31'] = '2008-07-31'\n",
    "    dProv2008['date'].loc['2008-09-31'] = '2008-08-30'\n",
    "\n",
    "# 2009\n",
    "dProv2009.insert(0, 'date', '0')\n",
    "dProv2009.insert(1, 'q_mm_per_day', '0')\n",
    "dProv2009.rename(columns={dProv2009.columns[3]: 'Year', dProv2009.columns[4]: 'Month', dProv2009.columns[5]: 'Day'}, inplace=True)\n",
    "dProv2009.rename(columns={dProv2009.columns[11]: 'P301', dProv2009.columns[14]: 'P303',\n",
    "                          dProv2009.columns[17]: 'P304', dProv2009.columns[20]: 'D102'}, inplace=True)\n",
    "\n",
    "# Format data\n",
    "for i in dProv2009.index:\n",
    "    month = str(dProv2009['Month'][i])\n",
    "    if len(month) != 2:\n",
    "        month = '0' + str(dProv2009['Month'][i])\n",
    "    day = str(dProv2009['Day'][i])\n",
    "    if len(day) != 2:\n",
    "        day = '0' + str(dProv2009['Day'][i])\n",
    "    date = str(dProv2009['Year'][i]) + '-' + month + '-' + day\n",
    "    dProv2009.at[i, 'date'] = date\n",
    "\n",
    "# Add subwatershed values\n",
    "for i in dProv2009.index:\n",
    "    q_mm_per_day = dProv2009.P301[i] + dProv2009.P303[i] + \\\n",
    "        dProv2009.P304[i] + dProv2009.D102[i]\n",
    "    dProv2009.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dProv2009['q_mm_per_day'] = dProv2009['q_mm_per_day'].astype(float)\n",
    "dProv2009['q_mm_per_day'] = dProv2009['q_mm_per_day'] * CMSfromLS    \n",
    "dProv2009['q_mm_per_day'] = dProv2009['q_mm_per_day'] * CFSfromCMS   \n",
    "dProv2009['q_mm_per_day'] = dProv2009['q_mm_per_day'] / areaFT2[9]   \n",
    "dProv2009['q_mm_per_day'] = dProv2009['q_mm_per_day'] * MMDAYfromFTS\n",
    "dProv2009.drop(dProv2009.columns[[2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
    "                                  12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]], axis=1, inplace=True)\n",
    "\n",
    "# 2010\n",
    "dProv2010.insert(0, 'date', '0')\n",
    "dProv2010.insert(1, 'q_mm_per_day', '0')\n",
    "dProv2010.rename(columns={dProv2010.columns[3]: 'Year', dProv2010.columns[4]: 'Month', dProv2010.columns[5]: 'Day'}, inplace=True)\n",
    "dProv2010.rename(columns={dProv2010.columns[11]: 'P301', dProv2010.columns[14]: 'P303',\n",
    "                          dProv2010.columns[17]: 'P304', dProv2010.columns[20]: 'D102'}, inplace=True)\n",
    "\n",
    "# Format data\n",
    "for i in dProv2010.index:\n",
    "    month = str(dProv2010['Month'][i])\n",
    "    if len(month) != 2:\n",
    "        month = '0' + str(dProv2010['Month'][i])\n",
    "    day = str(dProv2010['Day'][i])\n",
    "    if len(day) != 2:\n",
    "        day = '0' + str(dProv2010.Day[i])\n",
    "    date = str(dProv2010['Year'][i]) + '-' + month + '-' + day\n",
    "    dProv2010.at[i, 'date'] = date\n",
    "\n",
    "# Add subwatershed values\n",
    "for i in dProv2010.index:\n",
    "    q_mm_per_day = dProv2010.P301[i] + dProv2010.P303[i] + \\\n",
    "        dProv2010.P304[i] + dProv2010.D102[i]\n",
    "    dProv2010.at[i, 'q_mm_per_day'] = q_mm_per_day\n",
    "\n",
    "# Unit conversion\n",
    "dProv2010['q_mm_per_day'] = dProv2010['q_mm_per_day'].astype(float)\n",
    "dProv2010['q_mm_per_day'] = dProv2010['q_mm_per_day'] * CMSfromLS \n",
    "dProv2010['q_mm_per_day'] = dProv2010['q_mm_per_day'] * CFSfromCMS \n",
    "dProv2010['q_mm_per_day'] = dProv2010['q_mm_per_day'] / areaFT2[9]  \n",
    "dProv2010['q_mm_per_day'] = dProv2010['q_mm_per_day'] * MMDAYfromFTS\n",
    "dProv2010.drop(dProv2010.columns[[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
    "                                  14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]], axis=1, inplace=True)\n",
    "dProv = pd.concat([dProv2004, dProv2005, dProv2006, dProv2007,\n",
    "                   dProv2008, dProv2009, dProv2010], axis=0)\n",
    "dProv['q_mm_per_day'] = round(dProv['q_mm_per_day'], 3)\n",
    "dProv.sort_values(by=['date'])\n",
    "dProv = dProv.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.plot(dProv['date'], dProv['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)',\n",
    "       title='Providence Creek')\n",
    "labels = ['2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010']\n",
    "ticks_to_use = dProv.index[::365]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### River Ray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:40:04.241978Z",
     "start_time": "2020-10-16T01:40:00.191298Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame\n",
    "raycsv = requests.get(\n",
    "    'http://nrfaapps.ceh.ac.uk/nrfa/data/tsData/39017_gdf.csv?stn=39017&dt=gdf', allow_redirects=True)\n",
    "openraycsv = 'zip/riverray.csv'\n",
    "open(openraycsv, 'wb').write(raycsv.content)\n",
    "dRay = pd.read_csv(openraycsv, low_memory=False)  # Discharge in m^3/s\n",
    "\n",
    "# Scrub data\n",
    "dRay = dRay.iloc[19:, :2]\n",
    "dRay.rename(columns={'file': 'date',\n",
    "                     'timestamp': 'q_mm_per_day'}, inplace=True)\n",
    "\n",
    "# Unit conversion\n",
    "dRay['q_mm_per_day'] = dCWT12['q_mm_per_day'].astype(float)\n",
    "dRay['q_mm_per_day'] = dRay['q_mm_per_day'] * CFSfromCMS\n",
    "dRay['q_mm_per_day'] = dRay['q_mm_per_day'] / areaFT2[10]\n",
    "dRay['q_mm_per_day'] = dRay['q_mm_per_day'] * MMDAYfromFTS\n",
    "dRay['q_mm_per_day'] = round(dRay['q_mm_per_day'], 3)\n",
    "dRay.sort_values(by=['date'])\n",
    "dRay = dRay.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(12, 3))\n",
    "ax.plot(dRay['date'], dRay['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='River Ray')\n",
    "labels = ['1962', '1972', '1982', '1992', '2002', '2012']\n",
    "ticks_to_use = dRay.index[::3650]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sagehen Creek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:40:11.381507Z",
     "start_time": "2020-10-16T01:40:06.276828Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame\n",
    "dSagehen = pd.read_csv('https://waterdata.usgs.gov/nwis/dv?cb_00060=on&format=rdb&site_no=10343500&referred_module=sw&period=&begin_date=' +\n",
    "                       start[11] + '&end_date=' + stop[11], header=31, delim_whitespace=True)  # Discharge in cfs\n",
    "\n",
    "# Scrub data\n",
    "dSagehen.columns = ['usgs', 'site', 'date', 'q_mm_per_day', 'a']\n",
    "dSagehen['q_mm_per_day'] = dSagehen['q_mm_per_day'].astype(float)\n",
    "dSagehen['q_mm_per_day'] = dSagehen['q_mm_per_day'] / areaFT2[11]   \n",
    "dSagehen['q_mm_per_day'] = dSagehen['q_mm_per_day'] * MMDAYfromFTS  \n",
    "dSagehen = dSagehen.drop(columns=['usgs', 'site', 'a'])\n",
    "dSagehen['q_mm_per_day'] = round(dSagehen['q_mm_per_day'], 3)\n",
    "dSagehen.sort_values(by=['date'])\n",
    "dSagehen = dSagehen.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(14, 3))\n",
    "ax.plot(dSagehen['date'], dSagehen['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Sagehen Creek')\n",
    "labels = ['1953', '1963', '1973', '1983', '1993', '2003', '2013']\n",
    "ticks_to_use = dSagehen.index[::3650]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upper Studibach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:40:24.774533Z",
     "start_time": "2020-10-16T01:40:18.438094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame\n",
    "dStud = pd.read_excel('https://www.envidat.ch/dataset/4de5d4cd-f265-48b9-98a1-290d6f78fb23/resource/0216a1f8-97b9-4668-b765-621831a63885/download/runoff_daily-mean_alptal_1968-2017.xlsx',\n",
    "                      sheet_name='Erlenbach')  # Discharge in L/s\n",
    "\n",
    "# Scrub data\n",
    "dStud.rename(columns={dStud.columns[0]: 'date',\n",
    "                      dStud.columns[1]: 'q_mm_per_day'}, inplace=True)\n",
    "dStud = dStud[7:]\n",
    "dStud = dStud.drop(\n",
    "    columns={dStud.columns[2], dStud.columns[3], dStud.columns[4]})\n",
    "dStud['date'] = dStud['date'].replace(' 00:00:00', '')\n",
    "\n",
    "# Unit conversion\n",
    "dStud['q_mm_per_day'] = dStud['q_mm_per_day'].astype(float)\n",
    "dStud['q_mm_per_day'] = dStud['q_mm_per_day'] * CMSfromLS # Multiply discharge in L/s by CMSfromLS to get cms\n",
    "dStud['q_mm_per_day'] = dStud['q_mm_per_day'] * CFSfromCMS \n",
    "dStud['q_mm_per_day'] = dStud['q_mm_per_day'] / 0.7 # Uses Erlenbach (proxy) watershed area\n",
    "dStud['q_mm_per_day'] = dStud['q_mm_per_day'] * MMDAYfromFTS # Multiply discharge in ft/s by MMDAYfromFTS to get mm/day\n",
    "dStud['q_mm_per_day'] = round(dStud['q_mm_per_day'], 3)\n",
    "dStud.sort_values(by=['date'])\n",
    "dStud = dStud.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.plot(dStud['date'], dStud['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Upper Studibach')\n",
    "labels = ['1978', '1988', '1998', '2008']\n",
    "ticks_to_use = dStud.index[::3650]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yellow Barn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T01:40:36.072713Z",
     "start_time": "2020-10-16T01:40:33.462129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data frame\n",
    "dYellow = pd.read_csv('https://waterdata.usgs.gov/nwis/dv?cb_00060=on&format=rdb&site_no=04233300&referred_module=sw&period=&begin_date=' +\n",
    "                      start[13]+'&end_date='+stop[13], header=28, delim_whitespace=True)  # Discharge in cfs\n",
    "dYellow = dYellow.drop([0])\n",
    "\n",
    "# Scrub data\n",
    "dYellow.columns = ['usgs', 'site', 'date', 'q_mm_per_day', 'a']\n",
    "\n",
    "# Unit conversion\n",
    "dYellow['q_mm_per_day'] = dYellow['q_mm_per_day'].astype(float)\n",
    "dYellow['q_mm_per_day'] = dYellow['q_mm_per_day'] / (101.01 * FT2fromkm2)  # Uses Six Mile Creek (proxy) watershed area\n",
    "dYellow['q_mm_per_day'] = dYellow['q_mm_per_day'] * MMDAYfromFTS\n",
    "dYellow = dYellow.drop(columns=['usgs', 'site', 'a'])\n",
    "dYellow['q_mm_per_day'] = round(dYellow['q_mm_per_day'], 3)\n",
    "dYellow.sort_values(by=['date'])\n",
    "dYellow = dYellow.reset_index(drop=True)\n",
    "\n",
    "# Plot data\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.plot(dYellow['date'], dYellow['q_mm_per_day'], linewidth=0.9)\n",
    "ax.set(xlabel='Time (years)', ylabel='Discharge (mm/day)', title='Yellow Barn')\n",
    "labels = ['1995', '2005', '2015']\n",
    "ticks_to_use = dYellow.index[::3650]\n",
    "ax.set_xticks(ticks_to_use)\n",
    "ax.set_xticklabels(labels, rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Köppen climate zone classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T18:24:35.712154Z",
     "start_time": "2020-11-06T18:24:13.817021Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watershed        Köppen Class\n",
      "Elder Creek      Csb\n",
      "Caspar Creek     Csb\n",
      "Sagehen Creek    Dsb\n",
      "Providence       Csb\n",
      "Bull Creek       Csa\n",
      "Pioneer Creek    Dfb\n",
      "CWT12            Cfb\n",
      "FNW37            Cfb\n",
      "Duke Forest      Cfa\n",
      "Yellow Barn      Dfb\n",
      "HB13             Dfb\n",
      "HB42             Dfb\n",
      "River Ray        Cfb\n",
      "Upper Studibach  Cfb\n"
     ]
    }
   ],
   "source": [
    "# Köppen climate zone classification\n",
    "\n",
    "# Get watershed metadata\n",
    "dMeta = pd.read_csv(\n",
    "    'https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
    "\n",
    "# Get Köppen Classification System per Chen and Chen, http://hanschen.org/koppen/#data\n",
    "dKoppen = pd.read_csv(\n",
    "    'zip/koppensys/koppen_30year_1901-2010.tsv', delimiter=\"\\t\")\n",
    "\n",
    "# Scrub data\n",
    "dKoppen.drop(dKoppen.columns[[2, 3, 4, 5, 6, 7, 8, 9]], axis=1, inplace=True)\n",
    "\n",
    "# Print table header\n",
    "print('{:<16}'.format('Watershed'), 'Köppen Class')\n",
    "\n",
    "# Find closest Köppen coordinates\n",
    "kLat = list()\n",
    "kLon = list()\n",
    "\n",
    "for i in range(len(dMeta)):\n",
    "    # Get gauge station coordinates\n",
    "    lat = dMeta['gauge_lat_decdeg'][i]\n",
    "    lon = dMeta['gauge_lon_decdeg'][i]\n",
    "    kLat.append(lat)\n",
    "    kLon.append(lon)\n",
    "\n",
    "    # Get fractional and whole portions of float\n",
    "    fracLat, wholeLat = math.modf(float(kLat[i]))\n",
    "    fracLon, wholeLon = math.modf(float(kLon[i]))\n",
    "\n",
    "    # Create Köppen classification latitude\n",
    "    if fracLat == 0.25 or fracLat == 0.75:\n",
    "        break\n",
    "    else:\n",
    "        if fracLat >= 0 and fracLat < 0.5:\n",
    "            kLat[i] = wholeLat + 0.25\n",
    "        else:\n",
    "            kLat[i] = wholeLat + 0.75\n",
    "\n",
    "    # Create Köppen classification longitude\n",
    "    if fracLon == 0.25 or fracLon == 0.75:\n",
    "        break\n",
    "    else:\n",
    "        if wholeLon < 0:\n",
    "            if fracLon <= 0 and fracLon > -0.5:\n",
    "                kLon[i] = wholeLon - 0.25\n",
    "            else:\n",
    "                kLon[i] = wholeLon - 0.75\n",
    "        else:\n",
    "            if fracLon >= 0 and fracLon < 0.5:\n",
    "                kLon[i] = wholeLon + 0.25\n",
    "            else:\n",
    "                kLon[i] = wholeLon + 0.75\n",
    "\n",
    "# Get Köppen class\n",
    "for i in range(len(dKoppen)):\n",
    "    if dKoppen['longitude'][i] == kLon[0]:  # Bull Creek\n",
    "        if dKoppen['latitude'][i] == kLat[0]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [0]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[1]:  # Caspar Creek\n",
    "        if dKoppen['latitude'][i] == kLat[1]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [1]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[2]:  # CWT12\n",
    "        if dKoppen['latitude'][i] == kLat[2]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [2]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[3]:  # Duke\n",
    "        if dKoppen['latitude'][i] == kLat[3]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [3]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[4]:  # Elder Creek\n",
    "        if dKoppen['latitude'][i] == kLat[4]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [4]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[5]:  # FNW37\n",
    "        if dKoppen['latitude'][i] == kLat[5]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [5]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[6]:  # HB13\n",
    "        if dKoppen['latitude'][i] == kLat[6]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [6]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[7]:  # HB42\n",
    "        if dKoppen['latitude'][i] == kLat[7]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [7]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[8]:  # Pioneer Creek\n",
    "        if dKoppen['latitude'][i] == kLat[8]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [8]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[9]:  # Providence Creek\n",
    "        if dKoppen['latitude'][i] == kLat[9]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [9]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[10]:  # River Ray\n",
    "        if dKoppen['latitude'][i] == kLat[10]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [10]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[11]:  # Sagehen Creek\n",
    "        if dKoppen['latitude'][i] == kLat[11]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [11]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[12]:  # Upper Studibach\n",
    "        if dKoppen['latitude'][i] == kLat[12]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [12]), dKoppen['p1981_2010'][i])\n",
    "\n",
    "    if dKoppen['longitude'][i] == kLon[13]:  # Yellow Barn\n",
    "        if dKoppen['latitude'][i] == kLat[13]:\n",
    "            print('{:<16}'.format(dMeta['ws_name']\n",
    "                                  [13]), dKoppen['p1981_2010'][i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot watersheds by climate zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-06T18:25:20.652054Z",
     "start_time": "2020-11-06T18:25:17.973919Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unable to open EPSG support file gcs.csv.  Try setting the GDAL_DATA environment variable to point to the directory containing EPSG csv files.\n"
     ]
    }
   ],
   "source": [
    "# Create data frames\n",
    "dMeta = pd.read_csv(\n",
    "    'https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
    "d = pd.DataFrame(\n",
    "    {'Watershed': ['Bull Creek', 'Caspar Creek', 'CWT12', 'Duke Forest', 'Elder Creek', 'FNW37', 'HB13', 'HB42', 'Pioneer Creek', 'Providence', 'River Ray', 'Sagehen Creek', 'Upper Studibach', 'Yellow Barn'],\n",
    "     'PointColour': ['#fcfe04', '#cecc08', '#63fd32', '#c5fe4b', '#cecc08', '#63fd32', '#3cc4f8', '#3cc4f8', '#3cc4f8', '#cecc08', '#63fd32', '#c900c4', '#63fd32', '#3cc4f8'],\n",
    "     'Label': ['Csa', 'Csb', 'Cfb', 'Cfa', 'Csb', 'Cfb', 'Dfb', 'Dfb', 'Dfb', 'Csb', 'Cfb', 'Dsb', 'Cfb', 'Dfb'],\n",
    "     'Latitude': [dMeta['gauge_lat_decdeg'].loc[0]-2, dMeta['gauge_lat_decdeg'].loc[1], dMeta['gauge_lat_decdeg'].loc[2], dMeta['gauge_lat_decdeg'].loc[3], dMeta['gauge_lat_decdeg'].loc[4], dMeta['gauge_lat_decdeg'].loc[5], dMeta['gauge_lat_decdeg'].loc[6]+2, dMeta['gauge_lat_decdeg'].loc[7], dMeta['gauge_lat_decdeg'].loc[8], dMeta['gauge_lat_decdeg'].loc[9], dMeta['gauge_lat_decdeg'].loc[10], dMeta['gauge_lat_decdeg'].loc[11], dMeta['gauge_lat_decdeg'].loc[12], dMeta['gauge_lat_decdeg'].loc[13]],\n",
    "     'Longitude': [dMeta['gauge_lon_decdeg'].loc[0]-1, dMeta['gauge_lon_decdeg'].loc[1]-2, dMeta['gauge_lon_decdeg'].loc[2], dMeta['gauge_lon_decdeg'].loc[3], dMeta['gauge_lon_decdeg'].loc[4]-1, dMeta['gauge_lon_decdeg'].loc[5], dMeta['gauge_lon_decdeg'].loc[6], dMeta['gauge_lon_decdeg'].loc[7], dMeta['gauge_lon_decdeg'].loc[8], dMeta['gauge_lon_decdeg'].loc[9], dMeta['gauge_lon_decdeg'].loc[10], dMeta['gauge_lon_decdeg'].loc[11], dMeta['gauge_lon_decdeg'].loc[12], dMeta['gauge_lon_decdeg'].loc[13]]})\n",
    "gdf = gpd.GeoDataFrame(d, geometry=gpd.points_from_xy(\n",
    "    d['Longitude'], d['Latitude']))\n",
    "\n",
    "# Set up plot axes\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "ax = world.plot(color='', edgecolor='#c6c6c6', figsize=(6, 3), markersize=8, linewidth=1)\n",
    "ax.margins(x=0, y=0)\n",
    "\n",
    "i = 0\n",
    "\n",
    "# Coordinates for plot\n",
    "for x, y in zip(d.Longitude, d.Latitude):\n",
    "    ax.scatter(x, y, c=d['PointColour'][i], edgecolors='black', linewidths=0.5)\n",
    "    i = i + 1\n",
    "plt.annotate('Providence Creek', xy=(-119.21, 37.06), xytext=(35, -100), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('Caspar Creek', xy=(-123.73, 39.34), xytext=(19, -77), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('Pioneer Creek', xy=(-114.842, 45.25), xytext=(112, -109), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('Duke Forest', xy=(-78.98, 36.02), xytext=(79, -65), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('CWT12', xy=(-83.44, 35.05), xytext=(57, -40), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('Elder Creek', xy=(-123.64-3, 39.73), xytext=(11, -52), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('Bull Creek', xy=(-119.06-1, 36.97-2), xytext=(-1, -23), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('FNW37', xy=(-79.69, 39.05), xytext=(145, -68), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('HB13', xy=(-71.74, 43.95+2), xytext=(50, 19), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('Yellow Barn', xy=(-76.44, 42.40), xytext=(120, -48), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('HB42', xy=(-71.72, 43.96), xytext=(80, -26), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('River Ray', xy=(-1.01, 51.88), xytext=(100, 11), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('Sagehen Creek', xy=(-120.24, 39.43), xytext=(24, 25), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "plt.annotate('Upper Studibach', xy=(8.73, 47.04), xytext=(126, -19), textcoords='offset points', ha='right', va='bottom', bbox=dict(\n",
    "    boxstyle='round,pad=0.3', fc='#ffffff', edgecolor='black', lw=0.5), arrowprops=dict(arrowstyle='-', connectionstyle='arc3,rad=0', lw=0.5))\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Create legend\n",
    "Cfa_patch = Line2D([0], [0], marker='o', color='w', mec='k', mew=0.5, label='Cfa', mfc='#c5fe4b', ms=5)\n",
    "Cfb_patch = Line2D([0], [0], marker='o', color='w', mec='k', mew=0.5, label='Cfb', mfc='#63fd32', ms=5)\n",
    "Csa_patch = Line2D([0], [0], marker='o', color='w', mec='k', mew=0.5, label='Csa', mfc='#fcfe04', ms=5)\n",
    "Csb_patch = Line2D([0], [0], marker='o', color='w', mec='k', mew=0.5, label='Csb', mfc='#cecc08', ms=5)\n",
    "Dfb_patch = Line2D([0], [0], marker='o', color='w', mec='k', mew=0.5, label='Dfb', mfc='#3cc4f8', ms=5)\n",
    "Dsb_patch = Line2D([0], [0], marker='o', color='w', mec='k', mew=0.5, label='Dsb', mfc='#c900c4', ms=5)\n",
    "plt.legend(handles=[Cfa_patch, Cfb_patch, Csa_patch, Csb_patch, Dfb_patch, Dsb_patch],\n",
    "           loc='lower right', ncol=3, title='Watershed Climate Zones', edgecolor='black')\n",
    "plt.setp(plt.gca().get_legend().get_texts(), fontsize='8')\n",
    "\n",
    "# Show plot\n",
    "# plt.savefig('mapgrpbycli.eps', format='eps', dpi=300) # Save print resolution image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find alpha and beta +- standard deviation\n",
    "Based on code by Dana Lapides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-19T04:52:54.691100Z",
     "start_time": "2020-12-19T04:52:21.320346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watershed         alpha              beta\n",
      "Bull Creek        2.94724 +/- 0.07893   0.18699 +/- 0.01948   \n",
      "Caspar Creek      1.27105 +/- 0.08656   0.31253 +/- 0.03715   \n",
      "CWT12             4.70266 +/- 0.1393    0.07534 +/- 0.01836   \n",
      "Duke Forest       3.79121 +/- 0.18244   0.17806 +/- 0.01518   \n",
      "Elder Creek       2.33444 +/- 0.14761   0.17266 +/- 0.03691   \n",
      "FNW37             1.79648 +/- 0.06343   0.17214 +/- 0.02391   \n",
      "HB13              7.67939 +/- 0.44509   0.13925 +/- 0.03066   \n",
      "HB42              5.58181 +/- 0.18499   0.19925 +/- 0.0256    \n",
      "Pioneer Creek     0.83179 +/- 0.01334   0.19785 +/- 0.02881   \n",
      "Providence        1.78529 +/- 0.11339   0.38823 +/- 0.06389   \n",
      "River Ray         2.39902 +/- 0.09345   0.14948 +/- 0.01565   \n",
      "Sagehen Creek     1.11966 +/- 0.07348   0.32018 +/- 0.0757    \n",
      "Upper Studibach   6.12678 +/- 1.67099   0.31193 +/- 0.1418    \n",
      "Yellow Barn       5.40442 +/- 0.1585    0.10407 +/- 0.03143   \n"
     ]
    }
   ],
   "source": [
    "# Access watershed metadata\n",
    "dMeta = pd.read_csv(\n",
    "    'https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
    "\n",
    "# Create arrays\n",
    "alpha = []\n",
    "beta = []\n",
    "survey = []\n",
    "\n",
    "# Print results table header\n",
    "print('{:<18}'.format('Watershed') + '{:<19}'.format('alpha') +\n",
    "      '{:<0}'.format('beta'))\n",
    "\n",
    "# Get data and\n",
    "for i in range(len(dMeta)):\n",
    "\n",
    "    # Import hydrograph data\n",
    "    survey = pd.read_csv('https://www.hydroshare.org/resource/7ade2a53d8b34aba89b69a91ac8834ee/data/contents/surveys_' +\n",
    "                         dMeta['ws_name'][i].replace(' ', '').lower() + '.csv')\n",
    "    Q = survey['q_mm_per_day']\n",
    "    L = survey['dd_km_per_km2']\n",
    "\n",
    "    # Find alpha and beta\n",
    "    def model(Q, alpha, beta):\n",
    "        \"\"\"Return the wetted channel extent (L) as drainage density.\"\"\"\n",
    "        return alpha * Q ** beta\n",
    "\n",
    "    # Find optimal parameter values and estimated parameter covariance\n",
    "    popt, pcov = scipy.optimize.curve_fit(model, Q, L)\n",
    "\n",
    "    # retrieve parameter values\n",
    "    alpha = round(popt[0], 5), '+/-', round(pcov[0, 0]**0.5, 5)\n",
    "    beta = round(popt[1], 5), '+/-', round(pcov[1, 1]**0.5, 5)\n",
    "\n",
    "    # Print results table body rows\n",
    "    print(str('{:<18}'.format(dMeta['ws_name'][i]) +\n",
    "              '{:<28}'.format(str(alpha)) + '{:<28}'.format(str(beta))).replace('(', '').replace(')', '').replace(',', '').replace('\\'', ''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean and standard deviation for Q and L\n",
    "Based on code by Dana Lapides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-19T07:17:36.652130Z",
     "start_time": "2020-12-19T07:17:00.871490Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watershed        Q_mean     Q_std      L_mean     L_std     \n",
      "Bull Creek       1.78775    3.51865    0.78789    0.20841   \n",
      "Caspar Creek     1.3609     4.32294    0.11563    0.07142   \n",
      "CWT12            2.72504    2.54695    39.61816   4.55994   \n",
      "Duke Forest      1.17915    5.3538     65.20698   54.65253  \n",
      "Elder Creek      3.58219    8.21966    0.14304    0.04281   \n",
      "FNW37            1.74482    3.14041    4.43711    1.69469   \n",
      "HB13             2.5776     5.13427    57.40866   13.93659  \n",
      "HB42             2.39261    4.89017    13.1899    4.18283   \n",
      "Pioneer Creek    0.31733    0.27786    0.04071    0.00462   \n",
      "Providence       0.93506    1.69729    0.35742    0.18841   \n",
      "River Ray        0.44117    1.2623     0.07943    0.0437    \n",
      "Sagehen Creek    1.06915    1.97522    0.03545    0.01449   \n",
      "Upper Studibach  4.81301    7.8766     63.67032   28.29222  \n",
      "Yellow Barn      1.50672    2.31656    3.56591    0.39108   \n"
     ]
    }
   ],
   "source": [
    "# Create data frame\n",
    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
    "dMeta = dMeta.drop(['ws_name_sub', 'gauge_id', 'gauge_name', 'gauge_lat_decdeg', 'gauge_lon_decdeg', 'q_url', 'gauge_elev_m', 'q_start', 'q_stop', 'ws_elev_avg_m', 'ws_soil', 'ws_bedrock', 'ws_climate', 'ws_precip_mm_per_yr'], axis=1)\n",
    "dMeta = dMeta.reset_index()\n",
    "\n",
    "Q_series = ['https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_bullcreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_casparcreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_cwt12.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_dukeforest.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_eldercreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_fnw37.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_hb13.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_hb42.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_pioneercreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_providence.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_riverray.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_sagehencreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_upperstudibach.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_yellowbarn.csv']\n",
    "\n",
    "# Print results table header\n",
    "print('{:<16}'.format('Watershed'),\n",
    "      '{:<10}'.format('Q_mean'),\n",
    "      '{:<10}'.format('Q_std'),\n",
    "      '{:<10}'.format('L_mean'),\n",
    "      '{:<10}'.format('L_std'))\n",
    "\n",
    "qMean = list()\n",
    "lMean = list()\n",
    "qStd = list()\n",
    "lStd = list()\n",
    "\n",
    "for i in range(len(dMeta)):\n",
    "    data = pd.read_csv(Q_series[i])\n",
    "    alpha = dMeta['alpha_units_vary'][i]\n",
    "    beta = dMeta['beta'][i]\n",
    "    Q = data['q_mm_per_day']\n",
    "    L = alpha * Q ** beta\n",
    "    qMean.append(np.mean(Q)) \n",
    "    lMean.append(np.mean(L / dMeta['ws_area_km2'][i])) # L as drainage density in km/km^2 \n",
    "    qStd.append(np.std(Q))  \n",
    "    lStd.append(np.std(L / dMeta['ws_area_km2'][i])) # Standard deviation for L as drainage density in km/km^2 \n",
    "\n",
    "    # Print results table body rows\n",
    "    print('{:<16}'.format(dMeta['ws_name'][i]),\n",
    "          '{:<10}'.format(str(round(qMean[i], 5))),\n",
    "          '{:<10}'.format(str(round(qStd[i], 5))),\n",
    "          '{:<10}'.format(str(round(lMean[i], 5))),\n",
    "          '{:<10}'.format(str(round(lStd[i], 5))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-19T08:45:47.003434Z",
     "start_time": "2020-12-19T08:45:03.153534Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watershed        R-squared \n",
      "Bull Creek       0.70553   \n",
      "Caspar Creek     0.65243   \n",
      "CWT12            0.31192   \n",
      "Duke Forest      0.20263   \n",
      "Elder Creek      0.55377   \n",
      "FNW37            0.41202   \n",
      "HB13             0.41343   \n",
      "HB42             0.54601   \n",
      "Pioneer Creek    0.83855   \n",
      "Providence       0.79517   \n",
      "River Ray        0.28341   \n",
      "Sagehen Creek    0.77407   \n",
      "Upper Studibach  0.77213   \n",
      "Yellow Barn      0.58146   \n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "# Create data frame\n",
    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
    "dMeta = dMeta.drop(['ws_name_sub', 'gauge_id', 'gauge_name', 'gauge_lat_decdeg', 'gauge_lon_decdeg', 'q_url', 'gauge_elev_m', 'q_start', 'q_stop', 'ws_elev_avg_m', 'ws_soil', 'ws_bedrock', 'ws_climate', 'ws_precip_mm_per_yr'], axis=1)\n",
    "dMeta = dMeta.reset_index()\n",
    "\n",
    "Q_series = ['https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_bullcreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_casparcreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_cwt12.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_dukeforest.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_eldercreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_fnw37.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_hb13.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_hb42.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_pioneercreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_providence.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_riverray.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_sagehencreek.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_upperstudibach.csv',\n",
    "            'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_yellowbarn.csv']\n",
    "\n",
    "# Print results table header\n",
    "print('{:<16}'.format('Watershed'),\n",
    "      '{:<10}'.format('R-squared'))\n",
    "\n",
    "r_squared = list()\n",
    "\n",
    "for i in range(len(dMeta)):\n",
    "    data = pd.read_csv(Q_series[i])\n",
    "    alpha = dMeta['alpha_units_vary'][i]\n",
    "    beta = dMeta['beta'][i]\n",
    "    Q = data['q_mm_per_day']\n",
    "    Q = Q[np.logical_not(np.isnan(Q))] # Remove NaNs\n",
    "    L = alpha * Q ** beta\n",
    "    \n",
    "    # APPROACH 1 TO FINDING R-SQUARED\n",
    "    correlation_matrix = np.corrcoef(Q, L)\n",
    "    correlation_xy = correlation_matrix[0,1]\n",
    "    r_squared = correlation_xy**2\n",
    "\n",
    "    # Print results table body rows\n",
    "    print('{:<16}'.format(dMeta['ws_name'][i]),\n",
    "          '{:<10}'.format(str(round(r_squared, 5))))\n",
    "    \n",
    "    # APPROACH 2 TO FINDING R-SQUARED\n",
    "    # Creating linear regression model\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(Q,L)\n",
    "    def linefitline(b):\n",
    "        return intercept + slope * b\n",
    "    line1 = linefitline(Q)\n",
    "\n",
    "    # Plot data, linear model, and y-mean line\n",
    "    plt.scatter(Q,L)\n",
    "    plt.plot(Q,line1, c = 'g')\n",
    "    line2 = np.full(len(L),[L.mean()])\n",
    "    plt.plot(Q,line2, c = 'r')\n",
    "    plt.show()\n",
    "    \n",
    "    # Calculate variance in linear model\n",
    "    differences_line1 = linefitline(Q)-L\n",
    "    line1sum = 0\n",
    "    for i in differences_line1:\n",
    "        line1sum = line1sum + (i*i)\n",
    "\n",
    "    # Calculate variance of target variable\n",
    "    differences_line2 = line2 - L\n",
    "    line2sum = 0\n",
    "    for i in differences_line2:\n",
    "        line2sum = line2sum + (i*i)\n",
    "    \n",
    "    # Explained model variance is target variable variance minus variance in linear model\n",
    "    explModVar = line2sum - line1sum\n",
    "    \n",
    "    # Explained model variance of the model / target variable variance\n",
    "    print('r-squared', explModVar / line2sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coefficient of variation for Q and L\n",
    "Based on code by Dana Lapides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-19T09:24:32.156242Z",
     "start_time": "2020-12-19T09:23:22.921505Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watershed         CVQ       CVL_emp   CVL_botter\n",
      "Bull Creek        1.9682    0.2645    0.5069    \n",
      "Caspar Creek      3.1765    0.6177    1.2691    \n",
      "CWT12             0.9346    0.1151    0.084     \n",
      "Duke Forest       4.5404    0.8381    1.3115    \n",
      "Elder Creek       2.2946    0.3214    0.5789    \n",
      "FNW37             1.7998    0.3819    0.4242    \n",
      "HB13              1.9919    0.2428    0.4102    \n",
      "HB42              2.0439    0.3171    0.5574    \n",
      "Pioneer Creek     0.8756    0.1135    0.1934    \n",
      "Providence        1.8152    0.5271    0.8058    \n",
      "River Ray         2.8612    0.5502    0.6874    \n",
      "Sagehen Creek     1.8475    0.4088    0.7081    \n",
      "Upper Studibach   1.6365    0.4444    0.6039    \n",
      "Yellow Barn       1.5375    0.1097    0.2256    \n"
     ]
    }
   ],
   "source": [
    "# Coefficient of variation for Q and L\n",
    "# Based on code by Dana Lapides\n",
    "\n",
    "# CVL formula based on work of Gianluca Botter, derived by David Dralle\n",
    "def botter_CVL(beta, CVQ):\n",
    "    num = math.gamma(1./CVQ**2)*math.gamma(2.*beta+1. /\n",
    "                                           CVQ**2.)-math.gamma(beta+1/CVQ**2)**2\n",
    "    denom = math.gamma(beta+1/CVQ**2)\n",
    "    return (num**0.5/denom)\n",
    "\n",
    "# Remove NaN values from hydrograph\n",
    "def remove_nan(hydrograph):\n",
    "    hydrorange = []\n",
    "    hydrograph.index.drop_duplicates(keep='first')\n",
    "    for j in range(len(hydrograph)):\n",
    "        if math.isnan(hydrograph['q_mm_per_day'][j]):\n",
    "            hydrograph[(hydrograph['q_mm_per_day'] != np.nan)]\n",
    "        else:\n",
    "            hydrorange = hydrorange + [j]\n",
    "    hydrograph = hydrograph.iloc[hydrorange, :]\n",
    "    return hydrograph\n",
    "\n",
    "# Create data frame for watershed metadata \n",
    "dMeta = pd.read_csv('https://www.hydroshare.org/resource/1f97ba4f8ea64812b10c14a10071c69f/data/contents/ws_metadata.csv')\n",
    "dMeta = dMeta.reset_index()\n",
    "\n",
    "# Print results table header\n",
    "print('{:<18}'.format('Watershed') + '{:<10}'.format('CVQ') +\n",
    "      '{:<10}'.format('CVL_emp') + '{:<10}'.format('CVL_botter'))\n",
    "\n",
    "# Create arrays\n",
    "area = [3.58, 8.48, 0.124, 0.033, 16.8, 0.366, 0.134, 0.424, 15.8, 4.01, 18.6, 27.2, 0.7, 101.01] # Areas written out as Upper Studibach and Yellow Barn watersheds use proxy watershed areas for calculations and this is not reflected in the ws_area_km column of the ws_metadata table\n",
    "CVQ = []\n",
    "CVL_emp = []\n",
    "CVL_botter = []\n",
    "#hydroL = []\n",
    "\n",
    "# Find CVQ, CVL_emp, and CVL_botter   \n",
    "for i in range(len(dMeta)):\n",
    "    # Import hydrograph data\n",
    "    hydrograph = pd.read_csv(\n",
    "        'https://www.hydroshare.org/resource/ea4ccadf124b4bed86fe6fc7efd8c779/data/contents/q_' + dMeta['ws_name'][i].replace(' ', '').lower() + '.csv')\n",
    "\n",
    "    # Set to date index\n",
    "    hydrograph = hydrograph.set_index('date')\n",
    "    hydrograph.index = pd.to_datetime(hydrograph.index)\n",
    "\n",
    "    # Remove NaN values that cause error in CV calculation\n",
    "    hydrograph['q_mm_per_day'] = pd.to_numeric(hydrograph['q_mm_per_day'], errors='coerce')\n",
    "    hydrograph = remove_nan(hydrograph)\n",
    "\n",
    "    # Calculate L timeseries\n",
    "    alpha = dMeta['alpha_units_vary'][i]\n",
    "    beta = dMeta['beta'][i]\n",
    "    \n",
    "#    for i in range(len(hydrograph)):\n",
    "#        hydroLItem = (alpha*(hydrograph['q_mm_per_day'][i]**beta)) \n",
    "#        hydroL.append(hydroLItem) \n",
    "        \n",
    "    # Calculate coefficients of variation\n",
    "    CVQ = CVQ + [np.std(hydrograph['q_mm_per_day']) / np.mean(hydrograph['q_mm_per_day'])] # CVQ\n",
    "    CVL_empItem = dMeta['l_sd_km_km2'][i] / dMeta['l_avg_km_km2'][i]\n",
    "    CVL_emp.append(CVL_empItem) # CVL based on empirical values\n",
    "    CVL_botter = CVL_botter + [botter_CVL(beta, CVQ[-1])] # CVL from David Dralle's formula\n",
    "\n",
    "# Print results table body\n",
    "for i in range(len(dMeta)):\n",
    "    print('{:<18}'.format(dMeta['ws_name'][i]) + '{:<10}'.format(str(\n",
    "        round(CVQ[i], 4))) + '{:<10}'.format(str(round(CVL_emp[i], 4))) + '{:<10}'.format(str(round(CVL_botter[i], 4))))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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