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349 lines
11 KiB
Plaintext
349 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Longshore plots of each beach\n",
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"- Need to create a longshore plot of each beach to see how the variables change alongshore."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup notebook"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Enable autoreloading of our modules. \n",
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"# Most of the code will be located in the /src/ folder, \n",
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"# and then called from the notebook.\n",
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"%matplotlib inline\n",
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"%reload_ext autoreload\n",
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"%autoreload"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.core.debugger import set_trace\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"import decimal\n",
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"import plotly\n",
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"import plotly.graph_objs as go\n",
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"import plotly.plotly as py\n",
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"import plotly.tools as tls\n",
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"import plotly.figure_factory as ff\n",
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"from plotly import tools\n",
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"import plotly.io as pio\n",
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"from scipy import stats\n",
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"import math\n",
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"import matplotlib\n",
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"from matplotlib import cm\n",
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"import colorlover as cl\n",
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"from tqdm import tqdm_notebook\n",
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"from ipywidgets import widgets, Output\n",
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"from IPython.display import display, clear_output, Image, HTML\n",
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"from scipy import stats\n",
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"from sklearn.metrics import confusion_matrix\n",
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"import matplotlib.pyplot as plt\n",
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"from scipy.interpolate import interp1d\n",
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"from pandas.api.types import CategoricalDtype"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Matplot lib default settings\n",
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"plt.rcParams[\"figure.figsize\"] = (10,6)\n",
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"plt.rcParams['axes.grid']=True\n",
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"plt.rcParams['grid.alpha'] = 0.5\n",
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"plt.rcParams['grid.color'] = \"grey\"\n",
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"plt.rcParams['grid.linestyle'] = \"--\"\n",
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"plt.rcParams['axes.grid']=True\n",
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"\n",
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"# https://stackoverflow.com/a/20709149\n",
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"matplotlib.rcParams['text.usetex'] = True\n",
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"\n",
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"matplotlib.rcParams['text.latex.preamble'] = [\n",
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" r'\\usepackage{siunitx}', # i need upright \\micro symbols, but you need...\n",
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" r'\\sisetup{detect-all}', # ...this to force siunitx to actually use your fonts\n",
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" r'\\usepackage{helvet}', # set the normal font here\n",
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" r'\\usepackage{amsmath}',\n",
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" r'\\usepackage{sansmath}', # load up the sansmath so that math -> helvet\n",
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" r'\\sansmath', # <- tricky! -- gotta actually tell tex to use!\n",
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"] "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
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" print('Importing {}'.format(csv))\n",
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" return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
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"\n",
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"df_waves = df_from_csv('waves.csv', index_col=[0, 1])\n",
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"df_tides = df_from_csv('tides.csv', index_col=[0, 1])\n",
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"df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
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"df_sites = df_from_csv('sites.csv', index_col=[0])\n",
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"df_profile_features_crest_toes = df_from_csv('profile_features_crest_toes.csv', index_col=[0,1])\n",
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"\n",
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"# Note that the forecasted data sets should be in the same order for impacts and twls\n",
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"impacts = {\n",
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" 'forecasted': {\n",
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" 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
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" 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
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" },\n",
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" 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
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" }\n",
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"\n",
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"\n",
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"twls = {\n",
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" 'forecasted': {\n",
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" 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
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" 'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
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" }\n",
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"}\n",
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"print('Done!')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate plot for each beach"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"beach = 'NARRA'\n",
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"\n",
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"# Get the dataframe\n",
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"df = impacts['forecasted']['mean_slope_sto06']\n",
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"df = df.rename(columns={'storm_regime': 'forecasted_regime'})\n",
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"\n",
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"df_beach = df.loc[df.index.str.contains(beach)]\n",
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"\n",
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"# Add information about hydrodynamics at max(R_high) time\n",
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"df_beach = df_beach.merge(\n",
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" twls['forecasted']['mean_slope_sto06'].drop(columns=['R_high', 'R_low']),\n",
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" left_on=['site_id', 'datetime'],\n",
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" right_on=['site_id', 'datetime'])\n",
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"\n",
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"# Add information about observed impacts\n",
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"obs_impacts = impacts['observed'].rename(columns={\n",
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" 'storm_regime': 'observed_regime'\n",
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"}).observed_regime.to_frame()\n",
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"df_beach = df_beach.merge(obs_impacts, left_on='site_id', right_on='site_id')\n",
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"\n",
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"# Convert storm regimes to categorical datatype\n",
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"cat_type = CategoricalDtype(\n",
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" categories=['swash', 'collision', 'overwash', 'inundation'], ordered=True)\n",
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"df_beach.forecasted_regime = df_beach.forecasted_regime.astype(cat_type)\n",
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"df_beach.observed_regime = df_beach.observed_regime.astype(cat_type)\n",
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"\n",
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"# Get index\n",
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"n = [x for x in range(len(df_beach))][::-1]\n",
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"n_sites = [x for x in df_beach.index][::-1]\n",
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"\n",
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"f, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8) = plt.subplots(\n",
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" 1,\n",
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" 8,\n",
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" sharey=True,\n",
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" figsize=(14, 8),\n",
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" gridspec_kw={'width_ratios': [4, 4, 2, 2, 2, 2,2,2]})\n",
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"\n",
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"# Specify colors for storm regimes\n",
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"cmap = {\n",
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" 'swash': '#1a9850',\n",
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" 'collision': '#fee08b',\n",
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" 'overwash': '#d73027'\n",
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"}\n",
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"\n",
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"colors = [cmap.get(x) for x in df_beach.observed_regime]\n",
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"colors = ['#d73027' if c is None else c for c in colors]\n",
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"\n",
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"# Plot forecasted and observed storm regime\n",
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"ax1.scatter(\n",
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" df_beach.observed_regime.cat.codes.replace(-1,np.NaN),\n",
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" n,\n",
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" color=colors,\n",
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" marker='o',\n",
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" label='Observed regime')\n",
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"\n",
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"ax1.scatter(\n",
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" df_beach.forecasted_regime.cat.codes.replace(-1,np.NaN),\n",
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" n,\n",
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" color='b',\n",
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" marker='o',\n",
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" edgecolors='black',\n",
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" facecolors='none',\n",
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" label='Forecasted regime')\n",
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"\n",
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"ax1.set_title('Storm\\nregime')\n",
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"ax1.set_xticks([0,1,2,3])\n",
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"ax1.set_xticklabels(['swash','collision','overwash','inundation'])\n",
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"ax1.tick_params(axis='x', rotation=45)\n",
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"ax1.legend(loc='center', bbox_to_anchor=(0.5, -0.15))\n",
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"\n",
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"# Replace yticks with site_ids\n",
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"yticks = ax1.get_yticks().tolist()\n",
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"yticks = [n_sites[int(y)] if 0 <= y <= len(n_sites) else y for y in yticks ]\n",
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"ax1.set_yticklabels(yticks)\n",
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"\n",
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"# Water levels\n",
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"ax2.plot(df_beach.R_high, n, color='#2c7bb6')\n",
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"ax2.plot(df_beach.R_low, n, color='#2c7bb6')\n",
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"ax2.fill_betweenx(\n",
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" n, df_beach.R_low, df_beach.R_high, alpha=0.2, color='#2c7bb6', label='$R_{low}$ to $R_{high}$')\n",
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"\n",
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"# Dune elevations\n",
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"ax2.plot(df_beach.dune_crest_z, n, color='#fdae61')\n",
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"ax2.plot(df_beach.dune_toe_z, n, color='#fdae61')\n",
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"ax2.fill_betweenx(\n",
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" n, df_beach.dune_toe_z, df_beach.dune_crest_z, alpha=0.2, color='#fdae61', label='$D_{low}$ to $D_{high}$')\n",
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"\n",
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"ax2.set_title('TWL \\& Dune\\nElevations')\n",
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"ax2.legend(loc='center',bbox_to_anchor=(0.5,-0.15))\n",
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"ax2.set_xlabel('Elevation (m AHD)')\n",
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"\n",
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"# Plot R_high - D_low\n",
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"ax3.plot(df_beach.R_high - df_beach.dune_toe_z,n,color='#999999')\n",
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"ax3.axvline(x=0,color='black',linestyle=':')\n",
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"ax3.set_title('$R_{high}$ - $D_{low}$')\n",
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"ax3.set_xlabel('Height (m)')\n",
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"ax3.set_xlim([-2,2])\n",
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"\n",
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"# Wave height, wave period, beach slope\n",
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"ax4.plot(df_beach.Hs0, n,color='#377eb8')\n",
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"ax4.set_title('$H_{s0}$')\n",
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"ax4.set_xlabel('Sig. wave height (m)')\n",
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"ax4.set_xlim([3,5])\n",
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"\n",
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"ax5.plot(df_beach.Tp, n,color='#e41a1c')\n",
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"ax5.set_title('$T_{p}$')\n",
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"ax5.set_xlabel('Peak wave period (s)')\n",
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"ax5.set_xlim([8,14])\n",
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"\n",
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"ax6.plot(df_beach.tide, n,color='#a6cee3')\n",
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"ax6.set_title('Tide')\n",
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"ax6.set_xlabel('Elevation (m AHD)')\n",
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"ax6.set_xlim([0,2])\n",
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"\n",
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"ax7.plot(df_beach.beta, n,color='#4daf4a')\n",
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"ax7.set_title(r'$\\beta$')\n",
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"ax7.set_xlabel('Mean prestorm\\nbeach slope')\n",
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"ax7.set_xlim([0,0.15])\n",
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"\n",
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"ax8.plot(df_beach.R2, n,color='#6a3d9a')\n",
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"ax8.set_title(r'$R_{2\\%}$')\n",
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"ax8.set_xlabel('Height (m)')\n",
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"\n",
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"plt.tight_layout()\n",
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"f.subplots_adjust(top=0.88)\n",
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"f.suptitle(beach)\n",
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"\n",
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"\n",
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"# Print to figure\n",
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"plt.savefig('07-{}.png'.format(beach), dpi=600, bbox_inches='tight') \n",
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"\n",
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"plt.show()\n",
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"plt.close()"
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]
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}
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],
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"metadata": {
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"hide_input": false,
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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"window_display": false
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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