docs/A-chapter-5-figs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 5 figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from context import RiverNetwork\n",
    "from RiverNetwork import RiverNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure2 = RiverNetwork('../data/single-segment-karahan.xlsx',dt=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inflow = np.array(pd.read_excel('../data/example-inflow-karahan.xlsx').Inflow)\n",
    "structure2.set_shape('S.1',21,inflow-22)\n",
    "structure2.calc_flow_propagation(22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()\n",
    "sns.set_context(\"paper\", rc={\"font.size\":8.0, \n",
    "                             'lines.linewidth':1, \n",
    "                             'patch.linewidth':0.5, \n",
    "                             \"axes.titlesize\":8, \n",
    "                             \"axes.labelsize\":8, \n",
    "                             'xtick.labelsize':8, \n",
    "                             'ytick.labelsize':8, \n",
    "                             'legend.fontsize':8 ,  \n",
    "                             'pgf.rcfonts' : False})\n",
    "(fig,ax) = structure2.draw_Qin(figsize=(5,2))\n",
    "#fig.set_size_inches(5,3)\n",
    "fig.set_dpi(150)\n",
    "fig.patch.set_alpha(0)\n",
    "#plt.title('Single reach example')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('../../thesis/report/figs/karahan.pgf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Figure 5.2](A-ijssel/ijssel_muskingum.ipynb#Figure-5.2-for-thesis)\n",
    "[Link](A-ijssel/ijssel_muskingum.ipynb#Figure-5.2-for-thesis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from context import fit_muskingum\n",
    "from fit_muskingum import getParams\n",
    "from fit_muskingum import calc_Out\n",
    "from fit_muskingum import calc_C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../data/example-inflow-karahan-adjusted.xlsx')\n",
    "df = df.set_index('Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_context(\"paper\", rc={\"font.size\":8.0, \n",
    "                             'lines.linewidth':1, \n",
    "                             'patch.linewidth':0.5, \n",
    "                             \"axes.titlesize\":8, \n",
    "                             \"axes.labelsize\":8, \n",
    "                             'xtick.labelsize':8, \n",
    "                             'ytick.labelsize':8, \n",
    "                             'legend.fontsize':8 ,  \n",
    "                             'pgf.rcfonts' : False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = df.index.values\n",
    "I = np.array(df['Inflow'])\n",
    "\n",
    "fig = plt.figure(figsize=(5,2.5),dpi=150)\n",
    "fig.patch.set_alpha(0)\n",
    "ax = fig.add_subplot(111)\n",
    "    \n",
    "plt.plot(t,I,linewidth = 1 , label = 'inflow')\n",
    "    \n",
    "for x in [0,0.25,0.5]:\n",
    "    k = 1\n",
    "    dt = 1\n",
    "    out = calc_Out(I,calc_C(k,x,dt))\n",
    "    plt.plot(t, out,linewidth = 1, label = 'outflow $x$ = ' + '{:1.2f}'.format(x))    \n",
    "\n",
    "    \n",
    "plt.ylabel('Flow, $Q$ [m$^3$/s]')\n",
    "plt.xlabel('Time [h]')\n",
    "plt.legend()\n",
    "plt.xlim(2,20);\n",
    "plt.tight_layout()\n",
    "#plt.savefig('../../thesis/report/figs/1reachx.pgf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = df.index.values\n",
    "I = np.array(df['Inflow'])\n",
    "\n",
    "length = 50\n",
    "t = range(0,length,1)\n",
    "I = np.append(I,np.full((1,length - len(I)),22))\n",
    "\n",
    "fig = plt.figure(figsize=(5,2.5),dpi=150)\n",
    "fig.patch.set_alpha(0)\n",
    "ax = fig.add_subplot(111)\n",
    "    \n",
    "plt.plot(t,I,linewidth = 1 , label = 'inflow')\n",
    "\n",
    "klist = [1,3,5,10,25,50]\n",
    "for k in klist:\n",
    "    x = 0.01\n",
    "    dt = 1 \n",
    "    out = calc_Out(I,calc_C(k,x,dt))\n",
    "    plt.plot(t, out,linewidth = 1, label = 'outflow $k$ = ' + '{:02d}'.format(k)) \n",
    "    \n",
    "plt.ylabel('Flow, $Q$ [m$^3$/s]')\n",
    "plt.xlabel('Time [h]')\n",
    "plt.legend();\n",
    "plt.tight_layout()\n",
    "#plt.savefig('../../thesis/report/figs/1reachk.pgf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../data/example-inflow-karahan-adjusted.xlsx')\n",
    "df = df.set_index('Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = df.index.values\n",
    "I = np.array(df['Inflow'])\n",
    "I2 = np.array(df['Inflow'])*0.4\n",
    "I2 = np.append(I2[28:37],I2[0:28])\n",
    "fig = plt.figure(figsize=(5,2.5),dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "fig.patch.set_alpha(0)\n",
    "\n",
    "plt.plot(t,I,linewidth = 1 , label = 'outflow reach 3, $Q^{out}_3$')\n",
    "plt.plot(t,I2,linewidth = 1 , label = 'outflow reach 4, $Q^{out}_4$')\n",
    "plt.plot(t,I+I2,'--',linewidth = 1 , label = 'inflow after confluence, $Q^{in}_5$')\n",
    "plt.rcParams.update({'font.size': 8, 'pgf.rcfonts' : False})\n",
    "\n",
    "x = 0.1\n",
    "k = 5\n",
    "dt = 1\n",
    "\n",
    "C0 = calc_C(k,x,dt) # k,x,dt\n",
    "O0 = calc_Out(I+I2,C0)\n",
    "plt.plot(t, O0 ,'r',linewidth = 1, label = 'outflow reach 5, $Q^{out}_5$')\n",
    "\n",
    "plt.ylabel('Flow, $Q$ [m$^3$/s]')\n",
    "plt.xlabel('Time [h]')\n",
    "plt.legend()\n",
    "# save to file\n",
    "plt.tight_layout()\n",
    "#plt.savefig('../../thesis/report/figs/1confluence.pgf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../data/example-inflow-karahan-adjusted.xlsx')\n",
    "df = df.set_index('Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = df.index.values\n",
    "I = np.array(df['Inflow'])\n",
    "\n",
    "frac = 0.4\n",
    "I1 = np.array(df['Inflow'])*frac\n",
    "I2 = np.array(df['Inflow'])*(1-frac)\n",
    "fig = plt.figure(figsize=(5,2.5),dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "fig.patch.set_alpha(0)\n",
    "\n",
    "plt.plot(t,I,linewidth = 1 , label = 'outflow before bifurcation, $Q^{out}_6$')\n",
    "\n",
    "plt.plot(t,I1,'--',linewidth = 1 , label = 'inflow 7 after bifurcation, $Q^{in}_7$, $w_{7,6}=0.4$ ')\n",
    "plt.plot(t,I2,'--',linewidth = 1 , label = 'inflow 8 after bifurcation, $Q^{in}_8$, $w_{8,6}=0.6$ ')\n",
    "\n",
    "x = 0.1\n",
    "k = 5\n",
    "dt = 1\n",
    "C1 = calc_C(k,x,dt) # k,x,dt\n",
    "O1 = calc_Out(I1,C1)\n",
    "plt.plot(t, O1 ,linewidth = 1, label = 'outflow 7, $Q^{out}_7$, $x=0.1$, $k=5$')\n",
    "\n",
    "x = 0.2\n",
    "k = 2\n",
    "dt = 1\n",
    "C2 = calc_C(k,x,dt) # k,x,dt\n",
    "O2 = calc_Out(I2,C2)\n",
    "plt.plot(t, O2 ,linewidth = 1, label = 'outflow 8, $Q^{out}_8$, $x=0.2$, $k=2$')\n",
    "\n",
    "plt.ylabel('Flow, $Q$ [m$^3$/s]')\n",
    "plt.xlabel('Time [h]')\n",
    "plt.legend()\n",
    "# save to file\n",
    "plt.tight_layout()\n",
    "#plt.savefig('../../thesis/report/figs/1bifurcation.pgf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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