nsw-2016-storm-impact/notebooks/12_method_plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup notebook\n",
    "Import our required packages and set default plotting options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable autoreloading of our modules. \n",
    "# Most of the code will be located in the /src/ folder, \n",
    "# and then called from the notebook.\n",
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import decimal\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "import plotly.plotly as py\n",
    "import plotly.tools as tls\n",
    "import plotly.figure_factory as ff\n",
    "from plotly import tools\n",
    "import plotly.io as pio\n",
    "from scipy import stats\n",
    "import math\n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "import colorlover as cl\n",
    "from tqdm import tqdm_notebook\n",
    "from ipywidgets import widgets, Output\n",
    "from IPython.display import display, clear_output, Image, HTML\n",
    "from scipy import stats\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from matplotlib.lines import Line2D\n",
    "from cycler import cycler\n",
    "from scipy.interpolate import interp1d\n",
    "from pandas.api.types import CategoricalDtype\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "from scipy import interpolate\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplot lib default settings\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 6)\n",
    "plt.rcParams['axes.grid'] = True\n",
    "plt.rcParams['grid.alpha'] = 0.3\n",
    "plt.rcParams['grid.color'] = \"grey\"\n",
    "plt.rcParams['grid.linestyle'] = \"--\"\n",
    "plt.rcParams['grid.linewidth'] = 0.5\n",
    "plt.rcParams['axes.grid'] = True\n",
    "\n",
    "# # https://stackoverflow.com/a/20709149\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "matplotlib.rcParams['font.family'] = 'sans-serif'\n",
    "\n",
    "# # matplotlib.rcParams['text.latex.preamble'] = [\n",
    "# #        r'\\usepackage{siunitx}',   # i need upright \\micro symbols, but you need...\n",
    "# #        r'\\sisetup{detect-all}',   # ...this to force siunitx to actually use your fonts\n",
    "# #        r'\\usepackage{helvet}',    # set the normal font here\n",
    "# #        r'\\usepackage{amsmath}',\n",
    "# #        r'\\usepackage{sansmath}',  # load up the sansmath so that math -> helvet\n",
    "# #        r'\\sansmath',              # <- tricky! -- gotta actually tell tex to use!\n",
    "# # ]\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [\n",
    "    r'\\usepackage{siunitx}',   # i need upright \\micro symbols, but you need...\n",
    "    r'\\sisetup{detect-all}',   # ...this to force siunitx to actually use your fonts\n",
    "    r'\\usepackage[default]{sourcesanspro}',\n",
    "    r'\\usepackage{amsmath}',\n",
    "       r'\\usepackage{sansmath}',  # load up the sansmath so that math -> helvet\n",
    "       r'\\sansmath',              # <- tricky! -- gotta actually tell tex to use!\n",
    "]\n",
    "\n",
    "# import matplotlib as mpl\n",
    "# mpl.use(\"pgf\")\n",
    "# pgf_with_custom_preamble = {\n",
    "#     \"font.family\":\"sans-serif\",  # use serif/main font for text elements\n",
    "#     \"text.usetex\":True,  # use inline math for ticks\n",
    "#     \"pgf.rcfonts\":False,  # don't setup fonts from rc parameters\n",
    "#     \"pgf.preamble\": [\n",
    "#         r'\\usepackage{siunitx}',  # i need upright \\micro symbols, but you need...\n",
    "#         r'\\sisetup{detect-all}',  # ...this to force siunitx to actually use your fonts\n",
    "#         r'\\usepackage[default]{sourcesanspro}',\n",
    "#         r'\\usepackage{amsmath}',\n",
    "#         r'\\usepackage[mathrm=sym]{unicode-math}',\n",
    "#         r'\\setmathfont{Fira Math}',\n",
    "#     ]\n",
    "# }\n",
    "# mpl.rcParams.update(pgf_with_custom_preamble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data\n",
    "Import our data from the `./data/interim/` folder and load it into pandas dataframes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
    "    print('Importing {}'.format(csv))\n",
    "    return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
    "\n",
    "df_waves = df_from_csv('waves.csv', index_col=[0, 1])\n",
    "df_tides = df_from_csv('tides.csv', index_col=[0, 1])\n",
    "df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
    "df_sites = df_from_csv('sites.csv', index_col=[0])\n",
    "df_sites_waves = df_from_csv('sites_waves.csv', index_col=[0])\n",
    "df_profile_features_crest_toes = df_from_csv('profile_features_crest_toes.csv', index_col=[0,1])\n",
    "\n",
    "# Note that the forecasted data sets should be in the same order for impacts and twls\n",
    "impacts = {\n",
    "    'forecasted': {\n",
    "    'postintertidal_slope_sto06': df_from_csv('impacts_forecasted_postintertidal_slope_sto06.csv', index_col=[0]),\n",
    "    'postmean_slope_sto06': df_from_csv('impacts_forecasted_postmean_slope_sto06.csv', index_col=[0]),\n",
    "    'preintertidal_slope_sto06': df_from_csv('impacts_forecasted_preintertidal_slope_sto06.csv', index_col=[0]),\n",
    "    'premean_slope_sto06': df_from_csv('impacts_forecasted_premean_slope_sto06.csv', index_col=[0]),\n",
    "        },\n",
    "    'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
    "    }\n",
    "\n",
    "twls = {\n",
    "    'forecasted': {\n",
    "    'postintertidal_slope_sto06': df_from_csv('twl_postintertidal_slope_sto06.csv', index_col=[0,1]),\n",
    "    'postmean_slope_sto06': df_from_csv('twl_postmean_slope_sto06.csv', index_col=[0,1]),\n",
    "    'preintertidal_slope_sto06': df_from_csv('twl_preintertidal_slope_sto06.csv', index_col=[0,1]),\n",
    "    'premean_slope_sto06': df_from_csv('twl_premean_slope_sto06.csv', index_col=[0,1]),\n",
    "    }\n",
    "}\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot example of swash and collision profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Setup grid\n",
    "fig = plt.figure(figsize=(7, 2), dpi=300)\n",
    "gs = gridspec.GridSpec(1, 3)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax2 = fig.add_subplot(gs[0, 1], sharey=ax1)\n",
    "ax3 = fig.add_subplot(gs[0, 2], sharey=ax1)\n",
    "\n",
    "prestorm_color = '#fdae61'\n",
    "poststorm_color = '#d53e4f'\n",
    "\n",
    "plots = [\n",
    "    {\n",
    "        'ax': ax1,\n",
    "        'title': 'Swash regime',\n",
    "        'site_id': 'GRANTSn0009',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Grants\\nBeach',\n",
    "        'subplot_letter': '(a)',\n",
    "        'xlim_left' : 50,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax2,\n",
    "        'title': 'Collision regime',\n",
    "        'site_id': 'MANNING0078',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Manning\\nPoint',\n",
    "        'subplot_letter': '(b)',\n",
    "        'xlim_left' : 100,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax3,\n",
    "        'title': 'Overwash regime',\n",
    "        'site_id': 'AVOCAs0001',\n",
    "        'x_min_fill': 80,\n",
    "        'location': 'Avoca Lagoon\\nEntrance',\n",
    "        'subplot_letter': '(c)',\n",
    "        'xlim_left' : 50,\n",
    "    },\n",
    "]\n",
    "\n",
    "for plot in plots:\n",
    "    ax = plot['ax']\n",
    "    site_id = plot['site_id']\n",
    "    df_pro_pre = df_profiles.xs((site_id, 'prestorm'),\n",
    "                                level=['site_id', 'profile_type'])\n",
    "    df_pro_post = df_profiles.xs((site_id, 'poststorm'),\n",
    "                                 level=['site_id', 'profile_type'])\n",
    "    df_feat_pre = df_profile_features_crest_toes.xs(\n",
    "        (site_id, 'prestorm'), level=['site_id', 'profile_type'])\n",
    "    df_feat_post = df_profile_features_crest_toes.xs(\n",
    "        (site_id, 'poststorm'), level=['site_id', 'profile_type'])\n",
    "\n",
    "    ax.plot(\n",
    "        df_pro_post.index.get_level_values('x'),\n",
    "        df_pro_post.z.values,\n",
    "        label='Poststorm profile',\n",
    "        color=poststorm_color,\n",
    "        linestyle='--')\n",
    "    ax.plot(\n",
    "        df_pro_pre.index.get_level_values('x'),\n",
    "        df_pro_pre.z.values,\n",
    "        label='Prestorm profile',\n",
    "        color=prestorm_color)\n",
    "    ax.scatter(\n",
    "        df_feat_post.dune_crest_x,\n",
    "        df_feat_post.dune_crest_z,\n",
    "        label='Poststorm dune crest',\n",
    "        facecolor=poststorm_color,\n",
    "        edgecolor='black',\n",
    "        linewidth='0.75',\n",
    "        marker='v',\n",
    "        zorder=3)\n",
    "    ax.scatter(\n",
    "        df_feat_pre.dune_crest_x,\n",
    "        df_feat_pre.dune_crest_z,\n",
    "        label='Prestorm dune crest',\n",
    "        facecolor=prestorm_color,\n",
    "        edgecolor='black',\n",
    "        linewidth='0.75',\n",
    "        marker='v',\n",
    "        zorder=3)\n",
    "\n",
    "    if not np.isnan(df_feat_post.dune_toe_x.values[0]):\n",
    "        ax.scatter(\n",
    "            df_feat_post.dune_toe_x,\n",
    "            df_feat_post.dune_toe_z,\n",
    "            label='Poststorm dune toe',\n",
    "            facecolor=poststorm_color,\n",
    "            edgecolor='black',\n",
    "            linewidth='0.75',\n",
    "            marker='^',\n",
    "            zorder=3)\n",
    "\n",
    "    if not np.isnan(df_feat_pre.dune_toe_x.values[0]):\n",
    "        ax.scatter(\n",
    "            df_feat_pre.dune_toe_x,\n",
    "            df_feat_pre.dune_toe_z,\n",
    "            label='Prestorm dune toe',\n",
    "            facecolor=prestorm_color,\n",
    "            edgecolor='black',\n",
    "            linewidth='0.75',\n",
    "            marker='^',\n",
    "            zorder=3)\n",
    "\n",
    "    # Fill to zero elevation if there is no post profile value\n",
    "    z_post = df_pro_post.z.values\n",
    "    z_post[np.isnan(z_post)] = 0\n",
    "\n",
    "    # Only want to fill the difference between pre/post past a certain x-val\n",
    "    mask = df_pro_post.index.get_level_values('x') > plot['x_min_fill']\n",
    "\n",
    "    ax.fill_between(\n",
    "        df_pro_post.index.get_level_values('x')[mask],\n",
    "        df_pro_pre.z.values[mask],\n",
    "        z_post[mask],\n",
    "        color='#f46d43',\n",
    "        label='Eroded volume',\n",
    "        alpha=0.4)\n",
    "\n",
    "    # Set lims\n",
    "    ax.set_ylim([0, 10])\n",
    "    ax.set_xlim(left=plot['xlim_left'])\n",
    "\n",
    "    # Set ticks to 50 m\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "    \n",
    "    # Change tick label size\n",
    "    ax.tick_params(axis='both', which='major', labelsize=7)\n",
    "\n",
    "    # Add titles showing regimes\n",
    "    ax.set_title(plot['title'], size=8)\n",
    "\n",
    "    # Add subplot letters\n",
    "    ax.text(\n",
    "        0.02,\n",
    "        0.90,\n",
    "        plot['subplot_letter'],\n",
    "        transform=ax.transAxes,\n",
    "        size=10,\n",
    "        bbox=dict(\n",
    "            facecolor='white',\n",
    "            alpha=0.9,\n",
    "            edgecolor='white',\n",
    "            boxstyle='square,pad=0'))\n",
    "\n",
    "    # Add text showing location\n",
    "    ax.text(\n",
    "        0.95,\n",
    "        0.95,\n",
    "        plot['location'],\n",
    "        transform=ax.transAxes,\n",
    "        size=8,\n",
    "        horizontalalignment='right',\n",
    "        verticalalignment='top',\n",
    "        zorder=3,\n",
    "        bbox=dict(\n",
    "            facecolor='white',\n",
    "            alpha=0.9,\n",
    "            edgecolor='white',\n",
    "            boxstyle='square,pad=0'))\n",
    "\n",
    "# Remove tick parameters from shared axis\n",
    "ax2.tick_params(labelleft=False)\n",
    "ax3.tick_params(labelleft=False)\n",
    "\n",
    "# Add y-label\n",
    "ax1.set_ylabel('Elevation (mAHD)', size=8)\n",
    "\n",
    "# Add common x-label\n",
    "fig.text(\n",
    "    0.5,\n",
    "    -0.04,\n",
    "    'Cross-shore position (m)',\n",
    "    ha='center',\n",
    "    size=8,\n",
    "    horizontalalignment='center',\n",
    "    verticalalignment='top')\n",
    "\n",
    "# Add legend\n",
    "ax1.legend(\n",
    "    prop={'size': 6},\n",
    "    ncol=4,\n",
    "    bbox_to_anchor=(0.90, 1.0),\n",
    "    loc=\"lower right\",\n",
    "    bbox_transform=fig.transFigure)\n",
    "\n",
    "# Add title\n",
    "fig.text(\n",
    "0.06,1.01,\n",
    "'Examples of observed\\nstorm regimes',\n",
    "horizontalalignment='left',\n",
    "verticalalignment='bottom',\n",
    "size=10)\n",
    "\n",
    "\n",
    "fig.savefig('12_observed_storm_impacts.png',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## For coasts and ports\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Setup grid\n",
    "fig = plt.figure(figsize=(3, 1.2), dpi=300)\n",
    "gs = gridspec.GridSpec(1, 3)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax2 = fig.add_subplot(gs[0, 1], sharey=ax1)\n",
    "ax3 = fig.add_subplot(gs[0, 2], sharey=ax1)\n",
    "\n",
    "prestorm_color = '#fdae61'\n",
    "poststorm_color = '#d53e4f'\n",
    "scatter_marker_size=12\n",
    "scatter_line_width = 0.6\n",
    "plots = [\n",
    "    {\n",
    "        'ax': ax1,\n",
    "        'title': 'Swash regime',\n",
    "        'site_id': 'GRANTSn0009',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Grants\\nBeach',\n",
    "        'subplot_letter': '(a)',\n",
    "        'xlim_left' : 50,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax2,\n",
    "        'title': 'Collision regime',\n",
    "        'site_id': 'MANNING0078',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Manning\\nPoint',\n",
    "        'subplot_letter': '(b)',\n",
    "        'xlim_left' : 100,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax3,\n",
    "        'title': 'Overwash regime',\n",
    "        'site_id': 'AVOCAs0001',\n",
    "        'x_min_fill': 80,\n",
    "        'location': 'Avoca Lagoon\\nEntrance',\n",
    "        'subplot_letter': '(c)',\n",
    "        'xlim_left' : 50,\n",
    "    },\n",
    "]\n",
    "\n",
    "for plot in plots:\n",
    "    ax = plot['ax']\n",
    "    site_id = plot['site_id']\n",
    "    df_pro_pre = df_profiles.xs((site_id, 'prestorm'),\n",
    "                                level=['site_id', 'profile_type'])\n",
    "    df_pro_post = df_profiles.xs((site_id, 'poststorm'),\n",
    "                                 level=['site_id', 'profile_type'])\n",
    "    df_feat_pre = df_profile_features_crest_toes.xs(\n",
    "        (site_id, 'prestorm'), level=['site_id', 'profile_type'])\n",
    "    df_feat_post = df_profile_features_crest_toes.xs(\n",
    "        (site_id, 'poststorm'), level=['site_id', 'profile_type'])\n",
    "\n",
    "    ax.plot(\n",
    "        df_pro_post.index.get_level_values('x'),\n",
    "        df_pro_post.z.values,\n",
    "        label='Poststorm profile',\n",
    "        color=poststorm_color,\n",
    "        linestyle='--')\n",
    "    ax.plot(\n",
    "        df_pro_pre.index.get_level_values('x'),\n",
    "        df_pro_pre.z.values,\n",
    "        label='Prestorm profile',\n",
    "        color=prestorm_color)\n",
    "    ax.scatter(\n",
    "        df_feat_post.dune_crest_x,\n",
    "        df_feat_post.dune_crest_z,\n",
    "        scatter_marker_size,\n",
    "        label='Poststorm dune crest',\n",
    "        facecolor=poststorm_color,\n",
    "        edgecolor='black',\n",
    "        linewidth=scatter_line_width,\n",
    "        marker='v',\n",
    "        zorder=3)\n",
    "    ax.scatter(\n",
    "        df_feat_pre.dune_crest_x,\n",
    "        df_feat_pre.dune_crest_z,\n",
    "        scatter_marker_size,\n",
    "        label='Prestorm dune crest',\n",
    "        facecolor=prestorm_color,\n",
    "        edgecolor='black',\n",
    "        linewidth=scatter_line_width,\n",
    "        marker='v',\n",
    "        zorder=3)\n",
    "\n",
    "    if not np.isnan(df_feat_post.dune_toe_x.values[0]):\n",
    "        ax.scatter(\n",
    "            df_feat_post.dune_toe_x,\n",
    "            df_feat_post.dune_toe_z,\n",
    "            scatter_marker_size,\n",
    "            label='Poststorm dune toe',\n",
    "            facecolor=poststorm_color,\n",
    "            edgecolor='black',\n",
    "            linewidth=scatter_line_width,\n",
    "            marker='^',\n",
    "            zorder=3)\n",
    "\n",
    "    if not np.isnan(df_feat_pre.dune_toe_x.values[0]):\n",
    "        ax.scatter(\n",
    "            df_feat_pre.dune_toe_x,\n",
    "            df_feat_pre.dune_toe_z,\n",
    "            scatter_marker_size,\n",
    "            label='Prestorm dune toe',\n",
    "            facecolor=prestorm_color,\n",
    "            edgecolor='black',\n",
    "            linewidth=scatter_line_width,\n",
    "            marker='^',\n",
    "            zorder=3)\n",
    "\n",
    "    # Fill to zero elevation if there is no post profile value\n",
    "    z_post = df_pro_post.z.values\n",
    "    z_post[np.isnan(z_post)] = 0\n",
    "\n",
    "    # Only want to fill the difference between pre/post past a certain x-val\n",
    "    mask = df_pro_post.index.get_level_values('x') > plot['x_min_fill']\n",
    "\n",
    "    ax.fill_between(\n",
    "        df_pro_post.index.get_level_values('x')[mask],\n",
    "        df_pro_pre.z.values[mask],\n",
    "        z_post[mask],\n",
    "        color='#f46d43',\n",
    "        label='Eroded volume',\n",
    "        alpha=0.4)\n",
    "\n",
    "    # Set lims\n",
    "    ax.set_ylim([0, 10])\n",
    "    ax.set_xlim(left=plot['xlim_left'])\n",
    "\n",
    "    # Set ticks to 50 m\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "    \n",
    "    # Change tick label size\n",
    "    ax.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "    # Add titles showing regimes\n",
    "    ax.set_title(plot['title'], size=6)\n",
    "\n",
    "    # Add subplot letters\n",
    "    ax.text(\n",
    "        0.05,\n",
    "        0.90,\n",
    "        plot['subplot_letter'],\n",
    "        transform=ax.transAxes,\n",
    "        size=6,\n",
    "        bbox=dict(\n",
    "            facecolor='white',\n",
    "            alpha=0.9,\n",
    "            edgecolor='white',\n",
    "            boxstyle='square,pad=0'))\n",
    "\n",
    "    # Add text showing location\n",
    "    ax.text(\n",
    "        0.95,\n",
    "        0.95,\n",
    "        plot['location'],\n",
    "        transform=ax.transAxes,\n",
    "        size=5,\n",
    "        horizontalalignment='right',\n",
    "        verticalalignment='top',\n",
    "        zorder=3,\n",
    "        bbox=dict(\n",
    "            facecolor='white',\n",
    "            alpha=0.9,\n",
    "            edgecolor='white',\n",
    "            boxstyle='square,pad=0'))\n",
    "\n",
    "# Remove tick parameters from shared axis\n",
    "ax2.tick_params(labelleft=False)\n",
    "ax3.tick_params(labelleft=False)\n",
    "\n",
    "# Add y-label\n",
    "ax1.set_ylabel('Elevation (mAHD)', size=8)\n",
    "\n",
    "# Add common x-label\n",
    "fig.text(\n",
    "    0.5,\n",
    "    -0.04,\n",
    "    'Cross-shore position (m)',\n",
    "    ha='center',\n",
    "    size=8,\n",
    "    horizontalalignment='center',\n",
    "    verticalalignment='top')\n",
    "\n",
    "plt.tight_layout(pad=0.1,w_pad=0.1,h_pad=0.1)\n",
    "\n",
    "# Add legend\n",
    "ax1.legend(\n",
    "    prop={'size': 4},\n",
    "    ncol=2,\n",
    "    bbox_to_anchor=(1.0, 1.05),\n",
    "    loc=\"lower right\",\n",
    "    bbox_transform=fig.transFigure)\n",
    "\n",
    "# Add title\n",
    "fig.text(\n",
    "0.01,1.1,\n",
    "'Examples of observed\\nstorm regimes',\n",
    "horizontalalignment='left',\n",
    "verticalalignment='bottom',\n",
    "size=8)\n",
    "\n",
    "# Adjust width of border\n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax1.spines[axis].set_linewidth(0.7)\n",
    "    ax2.spines[axis].set_linewidth(0.7)\n",
    "    ax3.spines[axis].set_linewidth(0.7)\n",
    "\n",
    "\n",
    "fig.savefig('12_c&p_observed_storm_impacts.png',dpi=600, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Swash zone volume change histogram\n",
    "How much does the beach width change variation can we expect in the swash regime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "impacts['observed'][impacts['observed'].storm_regime=='swash'].width_msl_change_pct.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Setup grid\n",
    "fig = plt.figure(figsize=(6, 3), dpi=300)\n",
    "gs = gridspec.GridSpec(2, 3)\n",
    "ax1 = fig.add_subplot(gs[0:2, 0:2])\n",
    "ax2 = fig.add_subplot(gs[0, 2])\n",
    "ax3 = fig.add_subplot(gs[1, 2], sharex=ax2)\n",
    "\n",
    "# Histogram\n",
    "ax1.hist(\n",
    "    impacts['observed'][impacts['observed'].storm_regime=='swash'].width_msl_change_pct \n",
    ")\n",
    "\n",
    "# Format axes, ticks and labels\n",
    "ax1.set_xlabel('$\\Delta$ beach width', size=8)\n",
    "ax1.set_ylabel('Count', size=8)\n",
    "ax1.xaxis.set_major_locator(ticker.MultipleLocator(25))\n",
    "ax1.xaxis.set_major_formatter(ticker.PercentFormatter())\n",
    "ax1.tick_params(axis='both', which='major', labelsize=7)\n",
    "\n",
    "# Show erosion and accretion regions\n",
    "ax1.autoscale(False)\n",
    "ax1.fill([-100,0,0,-100], [0,0,500,500], '#f46d43', alpha=0.2,zorder=0)\n",
    "ax1.fill([100,0,0,100], [0,0,500,500], '#abdda4', alpha=0.2,zorder=0)\n",
    "\n",
    "ax1.text(\n",
    "    0.95,\n",
    "    0.95,\n",
    "    'Accretion',\n",
    "    transform=ax1.transAxes,\n",
    "    size=8,\n",
    "    horizontalalignment='right',\n",
    "    verticalalignment='top',\n",
    "    zorder=3,\n",
    "    bbox=dict(\n",
    "        facecolor='white',\n",
    "        alpha=0.0,\n",
    "        edgecolor='white',\n",
    "        boxstyle='square,pad=0'))\n",
    "\n",
    "ax1.text(\n",
    "    0.05,\n",
    "    0.95,\n",
    "    'Erosion',\n",
    "    transform=ax1.transAxes,\n",
    "    size=8,\n",
    "    horizontalalignment='left',\n",
    "    verticalalignment='top',\n",
    "    zorder=3,\n",
    "    bbox=dict(\n",
    "        facecolor='white',\n",
    "        alpha=0.0,\n",
    "        edgecolor='white',\n",
    "        boxstyle='square,pad=0'))\n",
    "\n",
    "# Add subplot letters\n",
    "ax1.text(\n",
    "    0.93,\n",
    "    0.05,\n",
    "    '(a)',\n",
    "    transform=ax1.transAxes,\n",
    "    size=10,\n",
    "    bbox=dict(\n",
    "        facecolor='white',\n",
    "        alpha=0.0,\n",
    "        edgecolor='white',\n",
    "        boxstyle='square,pad=0'))\n",
    "\n",
    "# Profile subplots\n",
    "prestorm_color = '#fdae61'\n",
    "poststorm_color = '#d53e4f'\n",
    "\n",
    "plots = [\n",
    "    {\n",
    "        'ax': ax2,\n",
    "        'title': '$\\Delta$ beach width = $+83\\%$',\n",
    "        'site_id': 'MANNING0125',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Grants\\nBeach',\n",
    "        'subplot_letter': '(b)',\n",
    "        'xlim_left' : 50,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax3,\n",
    "        'title': '$\\Delta$ beach width = $-73\\%$',\n",
    "        'site_id': 'DIAMONDn0024',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Manning\\nPoint',\n",
    "        'subplot_letter': '(c)',\n",
    "        'xlim_left' : 100,\n",
    "    },\n",
    "]\n",
    "\n",
    "for plot in plots:\n",
    "    \n",
    "    ax = plot['ax']\n",
    "    site_id = plot['site_id']\n",
    "    df_pro_pre = df_profiles.xs((site_id, 'prestorm'),\n",
    "                                level=['site_id', 'profile_type'])\n",
    "    df_pro_post = df_profiles.xs((site_id, 'poststorm'),\n",
    "                                 level=['site_id', 'profile_type'])\n",
    "    \n",
    "    ax.plot(\n",
    "        df_pro_post.index.get_level_values('x'),\n",
    "        df_pro_post.z.values,\n",
    "        label='Poststorm profile',\n",
    "        color=poststorm_color,\n",
    "        linestyle='--')\n",
    "    ax.plot(\n",
    "        df_pro_pre.index.get_level_values('x'),\n",
    "        df_pro_pre.z.values,\n",
    "        label='Prestorm profile',\n",
    "        color=prestorm_color)\n",
    "    \n",
    "    if ax == ax2:\n",
    "        z_pre = df_pro_pre.z.values\n",
    "        z_pre[np.isnan(z_pre)] = 0\n",
    "        \n",
    "        # Only want to fill the difference between pre/post past a certain x-val\n",
    "        mask = df_pro_pre.index.get_level_values('x') > plot['x_min_fill']\n",
    "    \n",
    "        ax.fill_between(\n",
    "            df_pro_post.index.get_level_values('x')[mask],\n",
    "            df_pro_post.z.values[mask],\n",
    "            z_pre[mask],\n",
    "            color='#abdda4',\n",
    "            label='Accreted volume',\n",
    "            alpha=0.4)\n",
    "        \n",
    "    \n",
    "    if ax == ax3:\n",
    "        # Fill to zero elevation if there is no post profile value\n",
    "        z_post = df_pro_post.z.values\n",
    "        z_post[np.isnan(z_post)] = 0\n",
    "\n",
    "        # Only want to fill the difference between pre/post past a certain x-val\n",
    "        mask = df_pro_post.index.get_level_values('x') > plot['x_min_fill']\n",
    "\n",
    "        ax.fill_between(\n",
    "            df_pro_post.index.get_level_values('x')[mask],\n",
    "            df_pro_pre.z.values[mask],\n",
    "            z_post[mask],\n",
    "            color='#f46d43',\n",
    "            label='Eroded volume',\n",
    "            alpha=0.4)\n",
    "        \n",
    "        # Add dummy legend entry\n",
    "        ax.fill_between(\n",
    "            [0,1],\n",
    "            [0,0],\n",
    "            [0,0],\n",
    "            color='#abdda4',\n",
    "            label='Accreted volume',\n",
    "            alpha=0.4)\n",
    "    \n",
    "    # Set lims\n",
    "    ax.set_ylim([0, 12])\n",
    "    ax.set_xlim(left=plot['xlim_left'])\n",
    "\n",
    "    # Set ticks to 50 m\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "    \n",
    "    # Change tick label size\n",
    "    ax.tick_params(axis='both', which='major', labelsize=7)\n",
    "\n",
    "    # Add titles showing regimes\n",
    "    ax.set_title(plot['title'], size=8)\n",
    "    \n",
    "    # Add subplot letters\n",
    "    ax.text(\n",
    "        0.04,\n",
    "        0.08,\n",
    "        plot['subplot_letter'],\n",
    "        transform=ax.transAxes,\n",
    "        size=10,\n",
    "        bbox=dict(\n",
    "            facecolor='white',\n",
    "            alpha=0.9,\n",
    "            edgecolor='white',\n",
    "            boxstyle='square,pad=0'))\n",
    "    \n",
    "# Remove tick parameters from shared axis\n",
    "ax2.tick_params(labelbottom=False)\n",
    "\n",
    "# Axis labels\n",
    "ax3.set_xlabel('Cross-shore position (m)', size=8)\n",
    "\n",
    "# Change y-axis to right hand side\n",
    "ax2.yaxis.tick_right()\n",
    "ax3.yaxis.tick_right()\n",
    "\n",
    "\n",
    "# Add common x-label\n",
    "fig.text(\n",
    "    0.98,\n",
    "    0.5,\n",
    "    'Elevation (mAHD)',\n",
    "    ha='center',\n",
    "    size=8,\n",
    "    horizontalalignment='center',\n",
    "    verticalalignment='center',\n",
    "    rotation=90)\n",
    "\n",
    "# Needs to be called before legend\n",
    "fig.tight_layout()\n",
    "\n",
    "# Add legend\n",
    "ax3.legend(\n",
    "    prop={'size': 6},\n",
    "    ncol=2,\n",
    "    bbox_to_anchor=(1.0, 0.97),\n",
    "    loc=\"lower right\",\n",
    "    bbox_transform=fig.transFigure\n",
    ")\n",
    "\n",
    "# Add title\n",
    "fig.text(\n",
    "0.06,1.00,\n",
    "'Variation in volume change for swash regime profiles',\n",
    "horizontalalignment='left',\n",
    "verticalalignment='bottom',\n",
    "size=10)\n",
    "\n",
    "fig.savefig('12_var_vol_change_swash_regime.png',dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     43
    ]
   },
   "outputs": [],
   "source": [
    "# For Coasts and Ports\n",
    "\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Setup grid\n",
    "fig = plt.figure(figsize=(3, 1.5), dpi=300)\n",
    "gs = gridspec.GridSpec(2, 3)\n",
    "ax1 = fig.add_subplot(gs[0:2, 0:2])\n",
    "ax2 = fig.add_subplot(gs[0, 2])\n",
    "ax3 = fig.add_subplot(gs[1, 2], sharex=ax2)\n",
    "\n",
    "# Histogram\n",
    "ax1.hist(\n",
    "    impacts['observed'][impacts['observed'].storm_regime ==\n",
    "                        'swash'].width_msl_change_pct\n",
    ")\n",
    "\n",
    "# Format axes, ticks and labels\n",
    "ax1.set_xlabel('$\\Delta$ beach width', size=6)\n",
    "ax1.set_ylabel('Count', size=6)\n",
    "ax1.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "ax1.xaxis.set_major_formatter(ticker.PercentFormatter())\n",
    "ax1.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "# Show erosion and accretion regions\n",
    "ax1.autoscale(False)\n",
    "ax1.fill([-100, 0, 0, -100], [0, 0, 500, 500], '#f46d43', alpha=0.2, zorder=0)\n",
    "ax1.fill([100, 0, 0, 100], [0, 0, 500, 500], '#abdda4', alpha=0.2, zorder=0)\n",
    "\n",
    "ax1.text(\n",
    "    0.95,\n",
    "    0.95,\n",
    "    'Accretion',\n",
    "    transform=ax1.transAxes,\n",
    "    size=6,\n",
    "    horizontalalignment='right',\n",
    "    verticalalignment='top',\n",
    "    zorder=3,\n",
    "    bbox=dict(\n",
    "        facecolor='white',\n",
    "        alpha=0.0,\n",
    "        edgecolor='white',\n",
    "        boxstyle='square,pad=0'))\n",
    "\n",
    "ax1.text(\n",
    "    0.05,\n",
    "    0.95,\n",
    "    'Erosion',\n",
    "    transform=ax1.transAxes,\n",
    "    size=6,\n",
    "    horizontalalignment='left',\n",
    "    verticalalignment='top',\n",
    "    zorder=3,\n",
    "    bbox=dict(\n",
    "        facecolor='white',\n",
    "        alpha=0.0,\n",
    "        edgecolor='white',\n",
    "        boxstyle='square,pad=0'))\n",
    "\n",
    "# Add subplot letters\n",
    "ax1.text(\n",
    "    0.90,\n",
    "    0.04,\n",
    "    '(a)',\n",
    "    transform=ax1.transAxes,\n",
    "    size=8,\n",
    "    bbox=dict(\n",
    "        facecolor='white',\n",
    "        alpha=0.0,\n",
    "        edgecolor='white',\n",
    "        boxstyle='square,pad=0'))\n",
    "\n",
    "# Profile subplots\n",
    "prestorm_color = '#fdae61'\n",
    "poststorm_color = '#d53e4f'\n",
    "\n",
    "plots = [\n",
    "    {\n",
    "        'ax': ax2,\n",
    "        'title': '$\\Delta$ beach width = $+83\\%$',\n",
    "        'site_id': 'MANNING0125',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Grants\\nBeach',\n",
    "        'subplot_letter': '(b)',\n",
    "        'xlim_left': 50,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax3,\n",
    "        'title': '$\\Delta$ beach width = $-73\\%$',\n",
    "        'site_id': 'DIAMONDn0024',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Manning\\nPoint',\n",
    "        'subplot_letter': '(c)',\n",
    "        'xlim_left': 100,\n",
    "    },\n",
    "]\n",
    "\n",
    "for plot in plots:\n",
    "\n",
    "    ax = plot['ax']\n",
    "    site_id = plot['site_id']\n",
    "    df_pro_pre = df_profiles.xs((site_id, 'prestorm'),\n",
    "                                level=['site_id', 'profile_type'])\n",
    "    df_pro_post = df_profiles.xs((site_id, 'poststorm'),\n",
    "                                 level=['site_id', 'profile_type'])\n",
    "\n",
    "    ax.plot(\n",
    "        df_pro_post.index.get_level_values('x'),\n",
    "        df_pro_post.z.values,\n",
    "        label='Poststorm profile',\n",
    "        color=poststorm_color,\n",
    "        linestyle='--',\n",
    "        lw=1)\n",
    "    ax.plot(\n",
    "        df_pro_pre.index.get_level_values('x'),\n",
    "        df_pro_pre.z.values,\n",
    "        label='Prestorm profile',\n",
    "        color=prestorm_color,\n",
    "        lw=1)\n",
    "\n",
    "    if ax == ax2:\n",
    "        z_pre = df_pro_pre.z.values\n",
    "        z_pre[np.isnan(z_pre)] = 0\n",
    "\n",
    "        # Only want to fill the difference between pre/post past a certain x-val\n",
    "        mask = df_pro_pre.index.get_level_values('x') > plot['x_min_fill']\n",
    "\n",
    "        ax.fill_between(\n",
    "            df_pro_post.index.get_level_values('x')[mask],\n",
    "            df_pro_post.z.values[mask],\n",
    "            z_pre[mask],\n",
    "            color='#abdda4',\n",
    "            label='Accreted volume',\n",
    "            alpha=0.4)\n",
    "\n",
    "    if ax == ax3:\n",
    "        # Fill to zero elevation if there is no post profile value\n",
    "        z_post = df_pro_post.z.values\n",
    "        z_post[np.isnan(z_post)] = 0\n",
    "\n",
    "        # Only want to fill the difference between pre/post past a certain x-val\n",
    "        mask = df_pro_post.index.get_level_values('x') > plot['x_min_fill']\n",
    "\n",
    "        ax.fill_between(\n",
    "            df_pro_post.index.get_level_values('x')[mask],\n",
    "            df_pro_pre.z.values[mask],\n",
    "            z_post[mask],\n",
    "            color='#f46d43',\n",
    "            label='Eroded volume',\n",
    "            alpha=0.4)\n",
    "\n",
    "        # Add dummy legend entry\n",
    "        ax.fill_between(\n",
    "            [0, 1],\n",
    "            [0, 0],\n",
    "            [0, 0],\n",
    "            color='#abdda4',\n",
    "            label='Accreted volume',\n",
    "            alpha=0.4)\n",
    "\n",
    "    # Set lims\n",
    "    ax.set_ylim([0, 12])\n",
    "    ax.set_xlim(left=plot['xlim_left'])\n",
    "\n",
    "    # Set ticks to 50 m\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(5))\n",
    "\n",
    "    # Change tick label size\n",
    "    ax.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "    # Add titles showing regimes\n",
    "    ax.set_title(plot['title'], size=5)\n",
    "\n",
    "    # Add subplot letters\n",
    "    ax.text(\n",
    "        0.04,\n",
    "        0.15,\n",
    "        plot['subplot_letter'],\n",
    "        transform=ax.transAxes,\n",
    "        size=8,\n",
    "        bbox=dict(\n",
    "            facecolor='white',\n",
    "            alpha=0.9,\n",
    "            edgecolor='white',\n",
    "            boxstyle='square,pad=0'))\n",
    "\n",
    "\n",
    "# Remove tick parameters from shared axis\n",
    "ax2.tick_params(labelbottom=False)\n",
    "\n",
    "# Remove tick lines but keep labels\n",
    "ax2.tick_params(axis='y', which='both', length=0)\n",
    "ax3.tick_params(axis='y', which='both', length=0)\n",
    "\n",
    "# Axis labels\n",
    "ax3.set_xlabel('Cross-shore position (m)', size=6)\n",
    "\n",
    "# Change y-axis to right hand side\n",
    "ax2.yaxis.tick_right()\n",
    "ax3.yaxis.tick_right()\n",
    "\n",
    "# Pad tick parameter labels\n",
    "ax1.xaxis.set_tick_params(pad=-3)\n",
    "ax2.xaxis.set_tick_params(pad=-3)\n",
    "ax3.xaxis.set_tick_params(pad=-3)\n",
    "\n",
    "\n",
    "# Add common x-label\n",
    "fig.text(\n",
    "    1.75,\n",
    "    0.5,\n",
    "    'Elevation (mAHD)',\n",
    "    size=6,\n",
    "    horizontalalignment='center',\n",
    "    verticalalignment='center',\n",
    "    rotation=90,\n",
    "    transform=ax1.transAxes)\n",
    "\n",
    "# Needs to be called before legend\n",
    "fig.tight_layout(pad=0.5, h_pad=0.1, w_pad=0.2)\n",
    "\n",
    "# Add legend\n",
    "ax3.legend(\n",
    "    prop={'size': 4.5},\n",
    "    ncol=2,\n",
    "    bbox_to_anchor=(1.0, 0.97),\n",
    "    loc=\"lower right\",\n",
    "    bbox_transform=fig.transFigure\n",
    ")\n",
    "\n",
    "# Add title\n",
    "fig.text(\n",
    "    0.06, 1.14,\n",
    "    'Variation in beach width\\nchange for swash regime\\nprofiles',\n",
    "    horizontalalignment='left',\n",
    "    verticalalignment='top',\n",
    "    size=8)\n",
    "\n",
    "# Adjust width of border\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax1.spines[axis].set_linewidth(0.7)\n",
    "    ax2.spines[axis].set_linewidth(0.7)\n",
    "    ax3.spines[axis].set_linewidth(0.7)\n",
    "    ax2.yaxis.set_tick_params(width=0.7)\n",
    "    ax3.yaxis.set_tick_params(width=0.7)\n",
    "\n",
    "fig.savefig('12_c&p_var_vol_change_swash_regime.png',\n",
    "            dpi=600, bbox_inches=\"tight\", pad_inches=0.01)\n",
    "# fig.savefig('12_c&p_var_vol_change_swash_regime.svg',bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Narrabeen section plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Setup grid\n",
    "fig = plt.figure(figsize=(3, 5), dpi=150)\n",
    "gs = gridspec.GridSpec(3, 1)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax2 = fig.add_subplot(gs[1, 0], sharex=ax1)\n",
    "ax3 = fig.add_subplot(gs[2, 0], sharex=ax1)\n",
    "\n",
    "prestorm_color = '#fdae61'\n",
    "poststorm_color = '#d53e4f'\n",
    "\n",
    "plots = [\n",
    "    {\n",
    "        'ax': ax1,\n",
    "        'title': '$\\Delta$ Dune Vol = \\SI{-13}{\\m^{3}\\per\\m}',\n",
    "        'site_id': 'NARRA0005',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Grants\\nBeach',\n",
    "        'subplot_letter': '(a)',\n",
    "        'xlim_left' : 150,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax2,\n",
    "        'title': '$\\Delta$ Dune Vol = \\SI{-10}{\\m^{3}\\per\\m}',\n",
    "        'site_id': 'NARRA0026',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Manning\\nPoint',\n",
    "        'subplot_letter': '(b)',\n",
    "        'xlim_left' : 150,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax3,\n",
    "        'title': '$\\Delta$ Dune Vol = \\SI{-21}{\\m^{3}\\per\\m}',\n",
    "        'site_id': 'NARRA0028',\n",
    "        'x_min_fill': 190,\n",
    "        'location': 'Avoca Lagoon\\nEntrance',\n",
    "        'subplot_letter': '(c)',\n",
    "        'xlim_left' : 150,\n",
    "    },\n",
    "]\n",
    "\n",
    "for plot in plots:\n",
    "    ax = plot['ax']\n",
    "    site_id = plot['site_id']\n",
    "    df_pro_pre = df_profiles.xs((site_id, 'prestorm'),\n",
    "                                level=['site_id', 'profile_type'])\n",
    "    df_pro_post = df_profiles.xs((site_id, 'poststorm'),\n",
    "                                 level=['site_id', 'profile_type'])\n",
    "    df_feat_pre = df_profile_features_crest_toes.xs(\n",
    "        (site_id, 'prestorm'), level=['site_id', 'profile_type'])\n",
    "    df_feat_post = df_profile_features_crest_toes.xs(\n",
    "        (site_id, 'poststorm'), level=['site_id', 'profile_type'])\n",
    "\n",
    "    ax.plot(\n",
    "        df_pro_post.index.get_level_values('x'),\n",
    "        df_pro_post.z.values,\n",
    "        label='Poststorm profile',\n",
    "        color=poststorm_color,\n",
    "        linestyle='--')\n",
    "    ax.plot(\n",
    "        df_pro_pre.index.get_level_values('x'),\n",
    "        df_pro_pre.z.values,\n",
    "        label='Prestorm profile',\n",
    "        color=prestorm_color)\n",
    "#     ax.scatter(\n",
    "#         df_feat_post.dune_crest_x,\n",
    "#         df_feat_post.dune_crest_z,\n",
    "#         label='Poststorm dune crest',\n",
    "#         facecolor=poststorm_color,\n",
    "#         edgecolor='black',\n",
    "#         linewidth='0.75',\n",
    "#         marker='v',\n",
    "#         zorder=3)\n",
    "#     ax.scatter(\n",
    "#         df_feat_pre.dune_crest_x,\n",
    "#         df_feat_pre.dune_crest_z,\n",
    "#         label='Prestorm dune crest',\n",
    "#         facecolor=prestorm_color,\n",
    "#         edgecolor='black',\n",
    "#         linewidth='0.75',\n",
    "#         marker='v',\n",
    "#         zorder=3)\n",
    "\n",
    "#     if not np.isnan(df_feat_post.dune_toe_x.values[0]):\n",
    "#         ax.scatter(\n",
    "#             df_feat_post.dune_toe_x,\n",
    "#             df_feat_post.dune_toe_z,\n",
    "#             label='Poststorm dune toe',\n",
    "#             facecolor=poststorm_color,\n",
    "#             edgecolor='black',\n",
    "#             linewidth='0.75',\n",
    "#             marker='^',\n",
    "#             zorder=3)\n",
    "\n",
    "#     if not np.isnan(df_feat_pre.dune_toe_x.values[0]):\n",
    "#         ax.scatter(\n",
    "#             df_feat_pre.dune_toe_x,\n",
    "#             df_feat_pre.dune_toe_z,\n",
    "#             label='Prestorm dune toe',\n",
    "#             facecolor=prestorm_color,\n",
    "#             edgecolor='black',\n",
    "#             linewidth='0.75',\n",
    "#             marker='^',\n",
    "#             zorder=3)\n",
    "\n",
    "    # Fill to zero elevation if there is no post profile value\n",
    "    z_post = df_pro_post.z.values\n",
    "    z_post[np.isnan(z_post)] = 0\n",
    "\n",
    "    # Only want to fill the difference between pre/post past a certain x-val\n",
    "    mask = df_pro_post.index.get_level_values('x') > plot['x_min_fill']\n",
    "\n",
    "    ax.fill_between(\n",
    "        df_pro_post.index.get_level_values('x')[mask],\n",
    "        df_pro_pre.z.values[mask],\n",
    "        z_post[mask],\n",
    "        color='#f46d43',\n",
    "        label='Eroded volume',\n",
    "        alpha=0.4)\n",
    "\n",
    "    # Set lims\n",
    "    ax.set_ylim([0, 14])\n",
    "    ax.set_xlim(left=plot['xlim_left'])\n",
    "\n",
    "    # Set ticks to 50 m\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "    \n",
    "    # Change tick label size\n",
    "    ax.tick_params(axis='both', which='major', labelsize=7)\n",
    "\n",
    "    # Add titles showing regimes\n",
    "    ax.set_title(plot['title'], size=8)\n",
    "\n",
    "    # Add subplot letters\n",
    "    ax.text(\n",
    "        0.02,\n",
    "        0.05,\n",
    "        plot['subplot_letter'],\n",
    "        transform=ax.transAxes,\n",
    "        size=10,\n",
    "        bbox=dict(\n",
    "            facecolor='white',\n",
    "            alpha=0.9,\n",
    "            edgecolor='white',\n",
    "            boxstyle='square,pad=0'))\n",
    "\n",
    "#     # Add text showing location\n",
    "#     ax.text(\n",
    "#         0.95,\n",
    "#         0.95,\n",
    "#         plot['location'],\n",
    "#         transform=ax.transAxes,\n",
    "#         size=8,\n",
    "#         horizontalalignment='right',\n",
    "#         verticalalignment='top',\n",
    "#         zorder=3,\n",
    "#         bbox=dict(\n",
    "#             facecolor='white',\n",
    "#             alpha=0.9,\n",
    "#             edgecolor='white',\n",
    "#             boxstyle='square,pad=0'))\n",
    "\n",
    "# Remove tick parameters from shared axis\n",
    "ax1.tick_params(labelbottom=False)\n",
    "ax2.tick_params(labelbottom=False)\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Add y-label\n",
    "ax3.set_xlabel('Cross-shore position (m)', size=8)\n",
    "\n",
    "# Add common x-label\n",
    "fig.text(\n",
    "    0.0,\n",
    "    0.5,\n",
    "    'Elevation (mAHD)',\n",
    "    ha='center',\n",
    "    size=8,\n",
    "    rotation=90,\n",
    "    horizontalalignment='center',\n",
    "    verticalalignment='center')\n",
    "\n",
    "# # Add legend\n",
    "# ax1.legend(\n",
    "#     prop={'size': 6},\n",
    "#     ncol=4,\n",
    "#     bbox_to_anchor=(0.90, 1.0),\n",
    "#     loc=\"lower right\",\n",
    "#     bbox_transform=fig.transFigure)\n",
    "\n",
    "# # Add title\n",
    "# fig.text(\n",
    "# 0.06,1.01,\n",
    "# 'Examples of observed\\nstorm regimes',\n",
    "# horizontalalignment='left',\n",
    "# verticalalignment='bottom',\n",
    "# size=10)\n",
    "\n",
    "\n",
    "fig.savefig('12_narrabeen_triple_plot.png',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## For coasts and ports\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# Setup grid\n",
    "fig = plt.figure(figsize=(1, 1.5), dpi=150)\n",
    "gs = gridspec.GridSpec(3, 1)\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax2 = fig.add_subplot(gs[1, 0], sharex=ax1)\n",
    "ax3 = fig.add_subplot(gs[2, 0], sharex=ax1)\n",
    "\n",
    "prestorm_color = '#fdae61'\n",
    "poststorm_color = '#d53e4f'\n",
    "\n",
    "plots = [\n",
    "    {\n",
    "        'ax': ax1,\n",
    "        'title': '$\\Delta$ Dune Vol = \\SI{-13}{\\m^{3}\\per\\m}',\n",
    "        'site_id': 'NARRA0005',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Grants\\nBeach',\n",
    "        'subplot_letter': 'Profile A',\n",
    "        'xlim_left': 150,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax2,\n",
    "        'title': '$\\Delta$ Dune Vol = \\SI{-10}{\\m^{3}\\per\\m}',\n",
    "        'site_id': 'NARRA0026',\n",
    "        'x_min_fill': 150,\n",
    "        'location': 'Manning\\nPoint',\n",
    "        'subplot_letter': 'Profile B',\n",
    "        'xlim_left': 150,\n",
    "    },\n",
    "    {\n",
    "        'ax': ax3,\n",
    "        'title': '$\\Delta$ Dune Vol = \\SI{-21}{\\m^{3}\\per\\m}',\n",
    "        'site_id': 'NARRA0028',\n",
    "        'x_min_fill': 190,\n",
    "        'location': 'Avoca Lagoon\\nEntrance',\n",
    "        'subplot_letter': 'Profile C',\n",
    "        'xlim_left': 150,\n",
    "    },\n",
    "]\n",
    "\n",
    "for plot in plots:\n",
    "    ax = plot['ax']\n",
    "    site_id = plot['site_id']\n",
    "    df_pro_pre = df_profiles.xs((site_id, 'prestorm'),\n",
    "                                level=['site_id', 'profile_type'])\n",
    "    df_pro_post = df_profiles.xs((site_id, 'poststorm'),\n",
    "                                 level=['site_id', 'profile_type'])\n",
    "    df_feat_pre = df_profile_features_crest_toes.xs(\n",
    "        (site_id, 'prestorm'), level=['site_id', 'profile_type'])\n",
    "    df_feat_post = df_profile_features_crest_toes.xs(\n",
    "        (site_id, 'poststorm'), level=['site_id', 'profile_type'])\n",
    "\n",
    "    ax.plot(\n",
    "        df_pro_post.index.get_level_values('x'),\n",
    "        df_pro_post.z.values,\n",
    "        label='Poststorm profile',\n",
    "        color=poststorm_color,\n",
    "        linestyle='--')\n",
    "    ax.plot(\n",
    "        df_pro_pre.index.get_level_values('x'),\n",
    "        df_pro_pre.z.values,\n",
    "        label='Prestorm profile',\n",
    "        color=prestorm_color)\n",
    "    #     ax.scatter(\n",
    "    #         df_feat_post.dune_crest_x,\n",
    "    #         df_feat_post.dune_crest_z,\n",
    "    #         label='Poststorm dune crest',\n",
    "    #         facecolor=poststorm_color,\n",
    "    #         edgecolor='black',\n",
    "    #         linewidth='0.75',\n",
    "    #         marker='v',\n",
    "    #         zorder=3)\n",
    "    #     ax.scatter(\n",
    "    #         df_feat_pre.dune_crest_x,\n",
    "    #         df_feat_pre.dune_crest_z,\n",
    "    #         label='Prestorm dune crest',\n",
    "    #         facecolor=prestorm_color,\n",
    "    #         edgecolor='black',\n",
    "    #         linewidth='0.75',\n",
    "    #         marker='v',\n",
    "    #         zorder=3)\n",
    "\n",
    "    #     if not np.isnan(df_feat_post.dune_toe_x.values[0]):\n",
    "    #         ax.scatter(\n",
    "    #             df_feat_post.dune_toe_x,\n",
    "    #             df_feat_post.dune_toe_z,\n",
    "    #             label='Poststorm dune toe',\n",
    "    #             facecolor=poststorm_color,\n",
    "    #             edgecolor='black',\n",
    "    #             linewidth='0.75',\n",
    "    #             marker='^',\n",
    "    #             zorder=3)\n",
    "\n",
    "    #     if not np.isnan(df_feat_pre.dune_toe_x.values[0]):\n",
    "    #         ax.scatter(\n",
    "    #             df_feat_pre.dune_toe_x,\n",
    "    #             df_feat_pre.dune_toe_z,\n",
    "    #             label='Prestorm dune toe',\n",
    "    #             facecolor=prestorm_color,\n",
    "    #             edgecolor='black',\n",
    "    #             linewidth='0.75',\n",
    "    #             marker='^',\n",
    "    #             zorder=3)\n",
    "\n",
    "    # Fill to zero elevation if there is no post profile value\n",
    "    z_post = df_pro_post.z.values\n",
    "    z_post[np.isnan(z_post)] = 0\n",
    "\n",
    "    # Only want to fill the difference between pre/post past a certain x-val\n",
    "    mask = df_pro_post.index.get_level_values('x') > plot['x_min_fill']\n",
    "\n",
    "    ax.fill_between(\n",
    "        df_pro_post.index.get_level_values('x')[mask],\n",
    "        df_pro_pre.z.values[mask],\n",
    "        z_post[mask],\n",
    "        color='#f46d43',\n",
    "        label='Eroded volume',\n",
    "        alpha=0.4)\n",
    "\n",
    "    # Set lims\n",
    "    ax.set_ylim([0, 14])\n",
    "    ax.set_xlim(left=plot['xlim_left'])\n",
    "\n",
    "    # Set ticks to 50 m\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(5))\n",
    "    ax.yaxis.set_tick_params(pad=-3)\n",
    "    ax.xaxis.set_tick_params(pad=-3)\n",
    "\n",
    "    # Change tick label size\n",
    "    ax.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "    # Add titles showing regimes\n",
    "    ax.set_title(plot['title'], size=6)\n",
    "\n",
    "    # Add subplot letters\n",
    "    ax.text(\n",
    "        0.60,\n",
    "        0.6,\n",
    "        plot['subplot_letter'],\n",
    "        transform=ax.transAxes,\n",
    "        size=6,\n",
    "        bbox=dict(\n",
    "            facecolor='white',\n",
    "            alpha=0.9,\n",
    "            edgecolor='white',\n",
    "            boxstyle='square,pad=0'))\n",
    "\n",
    "#     # Add text showing location\n",
    "#     ax.text(\n",
    "#         0.95,\n",
    "#         0.95,\n",
    "#         plot['location'],\n",
    "#         transform=ax.transAxes,\n",
    "#         size=8,\n",
    "#         horizontalalignment='right',\n",
    "#         verticalalignment='top',\n",
    "#         zorder=3,\n",
    "#         bbox=dict(\n",
    "#             facecolor='white',\n",
    "#             alpha=0.9,\n",
    "#             edgecolor='white',\n",
    "#             boxstyle='square,pad=0'))\n",
    "\n",
    "# Remove tick parameters from shared axis\n",
    "ax1.tick_params(labelbottom=False)\n",
    "ax2.tick_params(labelbottom=False)\n",
    "\n",
    "plt.tight_layout(pad=0.1, w_pad=0.1, h_pad=0.1)\n",
    "\n",
    "# Add x-label\n",
    "ax3.set_xlabel('Cross-shore position (m)', size=6)\n",
    "\n",
    "# Add common y-label\n",
    "fig.text(\n",
    "    -0.05,\n",
    "    0.5,\n",
    "    'Elevation (mAHD)',\n",
    "    ha='center',\n",
    "    size=6,\n",
    "    rotation=90,\n",
    "    horizontalalignment='center',\n",
    "    verticalalignment='center')\n",
    "\n",
    "# # Add legend\n",
    "# ax1.legend(\n",
    "#     prop={'size': 6},\n",
    "#     ncol=4,\n",
    "#     bbox_to_anchor=(0.90, 1.0),\n",
    "#     loc=\"lower right\",\n",
    "#     bbox_transform=fig.transFigure)\n",
    "\n",
    "# # Add title\n",
    "# fig.text(\n",
    "# 0.06,1.01,\n",
    "# 'Examples of observed\\nstorm regimes',\n",
    "# horizontalalignment='left',\n",
    "# verticalalignment='bottom',\n",
    "# size=10)\n",
    "\n",
    "# Adjust width of border\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax1.spines[axis].set_linewidth(0.7)\n",
    "    ax2.spines[axis].set_linewidth(0.7)\n",
    "    ax3.spines[axis].set_linewidth(0.7)\n",
    "\n",
    "fig.savefig(\n",
    "    '12_c&p_narrabeen_triple_plot.png',\n",
    "    dpi=600,\n",
    "    bbox_inches=\"tight\",\n",
    "    pad_inches=0.01)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sites.groupby('beach').mean()[['lat','lon']].to_dict('index')"
   ]
  }
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