Update notebooks
Chris Leaman
6 years ago
parent
6efae1c0da
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{
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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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"# Evaluate prediction metrics \n",
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"- This notebook is used to check out each of our storm impact prediction models performed in comparison to our observed storm impacts."
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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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"heading_collapsed": true
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},
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"source": [
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"## Setup notebook\n",
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"Import our required packages and set default plotting options."
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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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"hidden": true
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},
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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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"hidden": true
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},
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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 matplotlib.lines import Line2D\n",
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"from cycler import cycler\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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"hidden": true
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},
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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\n",
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"Import our data from the `./data/interim/` folder and load it into pandas dataframes. "
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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_hol86': df_from_csv('impacts_forecasted_mean_slope_hol86.csv',index_col=[0]),\n",
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" 'mean_slope_nie91': df_from_csv('impacts_forecasted_mean_slope_nie91.csv',index_col=[0]),\n",
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" 'mean_slope_pow18': df_from_csv('impacts_forecasted_mean_slope_pow18.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_hol86': df_from_csv('twl_mean_slope_hol86.csv',index_col=[0,1]),\n",
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" 'mean_slope_nie91': df_from_csv('twl_mean_slope_nie91.csv',index_col=[0,1]),\n",
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" 'mean_slope_pow18': df_from_csv('twl_mean_slope_pow18.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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"heading_collapsed": true
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},
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"source": [
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"## Generate longshore plots 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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"hidden": true
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},
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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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"df_obs_impacts = impacts['observed'].loc[impacts['observed'].index.str.\n",
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" contains(beach)]\n",
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"\n",
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"# Get index for each site on the beach\n",
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"n = [x for x in range(len(df_obs_impacts))][::-1]\n",
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"n_sites = [x for x in df_obs_impacts.index][::-1]\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_obs_impacts.storm_regime = df_obs_impacts.storm_regime.astype(cat_type)\n",
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"\n",
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"# Create figure\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=(18, 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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"# ax1: Impacts\n",
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"\n",
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"# Define colors for storm regime\n",
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"cmap = {'swash': '#1a9850', 'collision': '#fee08b', 'overwash': '#d73027'}\n",
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"\n",
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"# Common marker style\n",
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"marker_style = {\n",
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" 's': 60,\n",
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" 'linewidths': 0.7,\n",
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" 'alpha': 1,\n",
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" 'edgecolors': 'k',\n",
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" 'marker': 'o',\n",
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"}\n",
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"\n",
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"# Plot observed impacts\n",
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"colors = [cmap.get(x) for x in df_obs_impacts.storm_regime]\n",
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"colors = ['#d73027' if c is None else c for c in colors]\n",
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"ax1.scatter([0 for x in n], n, color=colors, **marker_style)\n",
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"\n",
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"# Plot model impacts\n",
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"for i, model in enumerate(impacts['forecasted']):\n",
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"\n",
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" # Only get model results for this beach\n",
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" df_model = impacts['forecasted'][model].loc[impacts['forecasted'][model].\n",
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" index.str.contains(beach)]\n",
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"\n",
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" # Recast storm regimes as categorical data\n",
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" df_model.storm_regime = df_model.storm_regime.astype(cat_type)\n",
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"\n",
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" # Assign colors\n",
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" colors = [cmap.get(x) for x in df_model.storm_regime]\n",
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" colors = ['#aaaaaa' if c is None else c for c in colors]\n",
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" ax1.scatter([i + 1 for x in n], n, color=colors, **marker_style)\n",
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"\n",
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"# Add model names to each impact on x axis\n",
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"ax1.set_xticks(range(len(impacts['forecasted']) + 1))\n",
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"ax1.set_xticklabels(['observed'] +\n",
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" [x.replace('_', '\\_') for x in impacts['forecasted']])\n",
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"ax1.xaxis.set_tick_params(rotation=90)\n",
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"\n",
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"# Add title\n",
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"ax1.set_title('Storm regime')\n",
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"\n",
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"# Create custom legend\n",
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"legend_elements = [\n",
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" Line2D([0], [0],\n",
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" marker='o',\n",
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" color='w',\n",
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" label='Swash',\n",
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" markerfacecolor='#1a9850',\n",
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" markersize=8,\n",
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" markeredgewidth=1.0,\n",
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" markeredgecolor='k'),\n",
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" Line2D([0], [0],\n",
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" marker='o',\n",
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" color='w',\n",
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" label='Collision',\n",
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" markerfacecolor='#fee08b',\n",
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" markersize=8,\n",
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" markeredgewidth=1.0,\n",
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" markeredgecolor='k'),\n",
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" Line2D([0], [0],\n",
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" marker='o',\n",
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" color='w',\n",
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" label='Overwash',\n",
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" markerfacecolor='#d73027',\n",
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" markersize=8,\n",
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" markeredgewidth=1.0,\n",
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" markeredgecolor='k'),\n",
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"]\n",
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"ax1.legend(\n",
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" handles=legend_elements, loc='lower center', bbox_to_anchor=(0.5, 1.1))\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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"# ax2: elevations\n",
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"\n",
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"# Dune elevations\n",
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"df_feats = df_profile_features_crest_toes.xs(['prestorm'],\n",
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" level=['profile_type'])\n",
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"df_feats = df_feats.loc[df_feats.index.str.contains(beach)]\n",
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"\n",
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"ax2.plot(df_feats.dune_crest_z, n, color='#fdae61')\n",
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"ax2.plot(df_feats.dune_toe_z, n, color='#fdae61')\n",
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"ax2.fill_betweenx(\n",
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" n,\n",
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" df_feats.dune_toe_z,\n",
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" df_feats.dune_crest_z,\n",
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" alpha=0.2,\n",
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" color='#fdae61',\n",
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" label='$D_{low}$ to $D_{high}$')\n",
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"\n",
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"model_colors = [\n",
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" '#1f78b4',\n",
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" '#33a02c',\n",
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" '#e31a1c',\n",
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" '#6a3d9a',\n",
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" '#a6cee3',\n",
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" '#b2df8a',\n",
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" '#fb9a99',\n",
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" '#cab2d6',\n",
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" '#ffff99',\n",
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" ]\n",
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"\n",
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"# Define colors to cycle through for our R_high\n",
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"ax2.set_prop_cycle(cycler('color', model_colors))\n",
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"\n",
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"# Plot R_high values\n",
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"for model in impacts['forecasted']:\n",
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"\n",
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" # Only get model results for this beach\n",
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" df_model = impacts['forecasted'][model].loc[impacts['forecasted'][model].\n",
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" index.str.contains(beach)]\n",
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"\n",
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" # Recast storm regimes as categorical data\n",
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" ax2.plot(df_model.R_high, n, label=model.replace('_', '\\_'))\n",
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"\n",
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"# Set title, legend and labels\n",
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"ax2.set_title('TWL \\& dune\\nelevations')\n",
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"ax2.legend(loc='lower center', bbox_to_anchor=(0.5, 1.1))\n",
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"ax2.set_xlabel('Elevation (m AHD)')\n",
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"\n",
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"\n",
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"# ax3: Plot R_high - D_low\n",
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"\n",
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"# Define colors to cycle through for our R_high\n",
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"ax3.set_prop_cycle(cycler('color', model_colors))\n",
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"\n",
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"# Plot R_high values\n",
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"for model in impacts['forecasted']:\n",
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" \n",
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" df_model = impacts['forecasted'][model].loc[impacts['forecasted'][model].\n",
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" index.str.contains(beach)]\n",
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" # R_high - D_low\n",
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" ax3.plot(df_model.R_high - df_feats.dune_toe_z, n, label=model.replace('_', '\\_'))\n",
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"\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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"\n",
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"\n",
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"# Define colors to cycle through for our R2\n",
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"ax4.set_prop_cycle(cycler('color', model_colors))\n",
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"\n",
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"# R_high - D_low\n",
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"for model in impacts['forecasted']:\n",
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" df_R2 = impacts['forecasted'][model].merge(twls['forecasted'][model],on=['site_id','datetime'])\n",
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" df_R2 = df_R2.loc[df_R2.index.str.contains(beach)]\n",
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" ax4.plot(df_R2.R2, n, label=model.replace('_', '\\_'))\n",
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"\n",
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"ax4.set_title(r'$R_{2\\%}$')\n",
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"ax4.set_xlabel('Height (m)')\n",
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"\n",
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"# Need to chose a model to extract environmental parameters at maximum R_high time\n",
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"model = 'mean_slope_sto06'\n",
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"df_beach = impacts['forecasted'][model].merge(twls['forecasted'][model], on=['site_id','datetime'])\n",
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"df_beach = df_beach.loc[df_beach.index.str.contains(beach)]\n",
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"\n",
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"# Wave height, wave period, beach slope\n",
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"ax5.plot(df_beach.beta, n,color='#4daf4a')\n",
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"ax5.set_title(r'$\\beta$')\n",
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"ax5.set_xlabel('Mean prestorm\\nbeach slope')\n",
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"ax5.set_xlim([0,0.15])\n",
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"\n",
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"ax6.plot(df_beach.Hs0, n,color='#999999')\n",
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"ax6.set_title('$H_{s0}$')\n",
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"ax6.set_xlabel('Sig. wave height (m)')\n",
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"ax6.set_xlim([3,5])\n",
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"\n",
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"ax7.plot(df_beach.Tp, n,color='#999999')\n",
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"ax7.set_title('$T_{p}$')\n",
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"ax7.set_xlabel('Peak wave period (s)')\n",
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"ax7.set_xlim([8,14])\n",
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"\n",
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"ax8.plot(df_beach.tide, n,color='#999999')\n",
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"ax8.set_title('Tide \\& surge')\n",
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"ax8.set_xlabel('Elevation (m AHD)')\n",
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"ax8.set_xlim([0,2])\n",
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"\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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"# # 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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]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Generate classification reports for each model\n",
|
||||
"Use sklearn metrics to generate classification reports for each forecasting model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sklearn.metrics\n",
|
||||
"\n",
|
||||
"# Get observed impacts\n",
|
||||
"df_obs = impacts['observed']\n",
|
||||
"\n",
|
||||
"# Convert storm regimes to categorical datatype\n",
|
||||
"cat_type = CategoricalDtype(\n",
|
||||
" categories=['swash', 'collision', 'overwash', 'inundation'], ordered=True)\n",
|
||||
"df_obs.storm_regime = df_obs_impacts.storm_regime.astype(cat_type)\n",
|
||||
"\n",
|
||||
"for model in impacts['forecasted']:\n",
|
||||
" df_for = impacts['forecasted'][model]\n",
|
||||
" df_for.storm_regime = df_for.storm_regime.astype(cat_type)\n",
|
||||
"\n",
|
||||
" m = sklearn.metrics.classification_report(\n",
|
||||
" df_obs.storm_regime.astype(cat_type).cat.codes.values,\n",
|
||||
" df_for.storm_regime.astype(cat_type).cat.codes.values,\n",
|
||||
" labels=[0, 1, 2, 3],\n",
|
||||
" target_names=['swash', 'collision', 'overwash', 'inundation'])\n",
|
||||
" print(model)\n",
|
||||
" print(m)\n",
|
||||
" print()\n",
|
||||
" "
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"hide_input": false,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": false,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
},
|
||||
"varInspector": {
|
||||
"cols": {
|
||||
"lenName": 16,
|
||||
"lenType": 16,
|
||||
"lenVar": 40
|
||||
},
|
||||
"kernels_config": {
|
||||
"python": {
|
||||
"delete_cmd_postfix": "",
|
||||
"delete_cmd_prefix": "del ",
|
||||
"library": "var_list.py",
|
||||
"varRefreshCmd": "print(var_dic_list())"
|
||||
},
|
||||
"r": {
|
||||
"delete_cmd_postfix": ") ",
|
||||
"delete_cmd_prefix": "rm(",
|
||||
"library": "var_list.r",
|
||||
"varRefreshCmd": "cat(var_dic_list()) "
|
||||
}
|
||||
},
|
||||
"types_to_exclude": [
|
||||
"module",
|
||||
"function",
|
||||
"builtin_function_or_method",
|
||||
"instance",
|
||||
"_Feature"
|
||||
],
|
||||
"window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -1,348 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Longshore plots of each beach\n",
|
||||
"- Need to create a longshore plot of each beach to see how the variables change alongshore."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup notebook"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 scipy.interpolate import interp1d\n",
|
||||
"from pandas.api.types import CategoricalDtype"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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'] = 0.5\n",
|
||||
"plt.rcParams['grid.color'] = \"grey\"\n",
|
||||
"plt.rcParams['grid.linestyle'] = \"--\"\n",
|
||||
"plt.rcParams['axes.grid']=True\n",
|
||||
"\n",
|
||||
"# https://stackoverflow.com/a/20709149\n",
|
||||
"matplotlib.rcParams['text.usetex'] = True\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",
|
||||
"] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Import data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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_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",
|
||||
" 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
|
||||
" 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
|
||||
" },\n",
|
||||
" 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"twls = {\n",
|
||||
" 'forecasted': {\n",
|
||||
" 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
|
||||
" 'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
|
||||
" }\n",
|
||||
"}\n",
|
||||
"print('Done!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Generate plot for each beach"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"beach = 'NARRA'\n",
|
||||
"\n",
|
||||
"# Get the dataframe\n",
|
||||
"df = impacts['forecasted']['mean_slope_sto06']\n",
|
||||
"df = df.rename(columns={'storm_regime': 'forecasted_regime'})\n",
|
||||
"\n",
|
||||
"df_beach = df.loc[df.index.str.contains(beach)]\n",
|
||||
"\n",
|
||||
"# Add information about hydrodynamics at max(R_high) time\n",
|
||||
"df_beach = df_beach.merge(\n",
|
||||
" twls['forecasted']['mean_slope_sto06'].drop(columns=['R_high', 'R_low']),\n",
|
||||
" left_on=['site_id', 'datetime'],\n",
|
||||
" right_on=['site_id', 'datetime'])\n",
|
||||
"\n",
|
||||
"# Add information about observed impacts\n",
|
||||
"obs_impacts = impacts['observed'].rename(columns={\n",
|
||||
" 'storm_regime': 'observed_regime'\n",
|
||||
"}).observed_regime.to_frame()\n",
|
||||
"df_beach = df_beach.merge(obs_impacts, left_on='site_id', right_on='site_id')\n",
|
||||
"\n",
|
||||
"# Convert storm regimes to categorical datatype\n",
|
||||
"cat_type = CategoricalDtype(\n",
|
||||
" categories=['swash', 'collision', 'overwash', 'inundation'], ordered=True)\n",
|
||||
"df_beach.forecasted_regime = df_beach.forecasted_regime.astype(cat_type)\n",
|
||||
"df_beach.observed_regime = df_beach.observed_regime.astype(cat_type)\n",
|
||||
"\n",
|
||||
"# Get index\n",
|
||||
"n = [x for x in range(len(df_beach))][::-1]\n",
|
||||
"n_sites = [x for x in df_beach.index][::-1]\n",
|
||||
"\n",
|
||||
"f, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8) = plt.subplots(\n",
|
||||
" 1,\n",
|
||||
" 8,\n",
|
||||
" sharey=True,\n",
|
||||
" figsize=(14, 8),\n",
|
||||
" gridspec_kw={'width_ratios': [4, 4, 2, 2, 2, 2,2,2]})\n",
|
||||
"\n",
|
||||
"# Specify colors for storm regimes\n",
|
||||
"cmap = {\n",
|
||||
" 'swash': '#1a9850',\n",
|
||||
" 'collision': '#fee08b',\n",
|
||||
" 'overwash': '#d73027'\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"colors = [cmap.get(x) for x in df_beach.observed_regime]\n",
|
||||
"colors = ['#d73027' if c is None else c for c in colors]\n",
|
||||
"\n",
|
||||
"# Plot forecasted and observed storm regime\n",
|
||||
"ax1.scatter(\n",
|
||||
" df_beach.observed_regime.cat.codes.replace(-1,np.NaN),\n",
|
||||
" n,\n",
|
||||
" color=colors,\n",
|
||||
" marker='o',\n",
|
||||
" label='Observed regime')\n",
|
||||
"\n",
|
||||
"ax1.scatter(\n",
|
||||
" df_beach.forecasted_regime.cat.codes.replace(-1,np.NaN),\n",
|
||||
" n,\n",
|
||||
" color='b',\n",
|
||||
" marker='o',\n",
|
||||
" edgecolors='black',\n",
|
||||
" facecolors='none',\n",
|
||||
" label='Forecasted regime')\n",
|
||||
"\n",
|
||||
"ax1.set_title('Storm\\nregime')\n",
|
||||
"ax1.set_xticks([0,1,2,3])\n",
|
||||
"ax1.set_xticklabels(['swash','collision','overwash','inundation'])\n",
|
||||
"ax1.tick_params(axis='x', rotation=45)\n",
|
||||
"ax1.legend(loc='center', bbox_to_anchor=(0.5, -0.15))\n",
|
||||
"\n",
|
||||
"# Replace yticks with site_ids\n",
|
||||
"yticks = ax1.get_yticks().tolist()\n",
|
||||
"yticks = [n_sites[int(y)] if 0 <= y <= len(n_sites) else y for y in yticks ]\n",
|
||||
"ax1.set_yticklabels(yticks)\n",
|
||||
"\n",
|
||||
"# Water levels\n",
|
||||
"ax2.plot(df_beach.R_high, n, color='#2c7bb6')\n",
|
||||
"ax2.plot(df_beach.R_low, n, color='#2c7bb6')\n",
|
||||
"ax2.fill_betweenx(\n",
|
||||
" n, df_beach.R_low, df_beach.R_high, alpha=0.2, color='#2c7bb6', label='$R_{low}$ to $R_{high}$')\n",
|
||||
"\n",
|
||||
"# Dune elevations\n",
|
||||
"ax2.plot(df_beach.dune_crest_z, n, color='#fdae61')\n",
|
||||
"ax2.plot(df_beach.dune_toe_z, n, color='#fdae61')\n",
|
||||
"ax2.fill_betweenx(\n",
|
||||
" n, df_beach.dune_toe_z, df_beach.dune_crest_z, alpha=0.2, color='#fdae61', label='$D_{low}$ to $D_{high}$')\n",
|
||||
"\n",
|
||||
"ax2.set_title('TWL \\& Dune\\nElevations')\n",
|
||||
"ax2.legend(loc='center',bbox_to_anchor=(0.5,-0.15))\n",
|
||||
"ax2.set_xlabel('Elevation (m AHD)')\n",
|
||||
"\n",
|
||||
"# Plot R_high - D_low\n",
|
||||
"ax3.plot(df_beach.R_high - df_beach.dune_toe_z,n,color='#999999')\n",
|
||||
"ax3.axvline(x=0,color='black',linestyle=':')\n",
|
||||
"ax3.set_title('$R_{high}$ - $D_{low}$')\n",
|
||||
"ax3.set_xlabel('Height (m)')\n",
|
||||
"ax3.set_xlim([-2,2])\n",
|
||||
"\n",
|
||||
"# Wave height, wave period, beach slope\n",
|
||||
"ax4.plot(df_beach.Hs0, n,color='#377eb8')\n",
|
||||
"ax4.set_title('$H_{s0}$')\n",
|
||||
"ax4.set_xlabel('Sig. wave height (m)')\n",
|
||||
"ax4.set_xlim([3,5])\n",
|
||||
"\n",
|
||||
"ax5.plot(df_beach.Tp, n,color='#e41a1c')\n",
|
||||
"ax5.set_title('$T_{p}$')\n",
|
||||
"ax5.set_xlabel('Peak wave period (s)')\n",
|
||||
"ax5.set_xlim([8,14])\n",
|
||||
"\n",
|
||||
"ax6.plot(df_beach.tide, n,color='#a6cee3')\n",
|
||||
"ax6.set_title('Tide')\n",
|
||||
"ax6.set_xlabel('Elevation (m AHD)')\n",
|
||||
"ax6.set_xlim([0,2])\n",
|
||||
"\n",
|
||||
"ax7.plot(df_beach.beta, n,color='#4daf4a')\n",
|
||||
"ax7.set_title(r'$\\beta$')\n",
|
||||
"ax7.set_xlabel('Mean prestorm\\nbeach slope')\n",
|
||||
"ax7.set_xlim([0,0.15])\n",
|
||||
"\n",
|
||||
"ax8.plot(df_beach.R2, n,color='#6a3d9a')\n",
|
||||
"ax8.set_title(r'$R_{2\\%}$')\n",
|
||||
"ax8.set_xlabel('Height (m)')\n",
|
||||
"\n",
|
||||
"plt.tight_layout()\n",
|
||||
"f.subplots_adjust(top=0.88)\n",
|
||||
"f.suptitle(beach)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Print to figure\n",
|
||||
"plt.savefig('07-{}.png'.format(beach), dpi=600, bbox_inches='tight') \n",
|
||||
"\n",
|
||||
"plt.show()\n",
|
||||
"plt.close()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"hide_input": false,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": false,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
},
|
||||
"varInspector": {
|
||||
"cols": {
|
||||
"lenName": 16,
|
||||
"lenType": 16,
|
||||
"lenVar": 40
|
||||
},
|
||||
"kernels_config": {
|
||||
"python": {
|
||||
"delete_cmd_postfix": "",
|
||||
"delete_cmd_prefix": "del ",
|
||||
"library": "var_list.py",
|
||||
"varRefreshCmd": "print(var_dic_list())"
|
||||
},
|
||||
"r": {
|
||||
"delete_cmd_postfix": ") ",
|
||||
"delete_cmd_prefix": "rm(",
|
||||
"library": "var_list.r",
|
||||
"varRefreshCmd": "cat(var_dic_list()) "
|
||||
}
|
||||
},
|
||||
"types_to_exclude": [
|
||||
"module",
|
||||
"function",
|
||||
"builtin_function_or_method",
|
||||
"instance",
|
||||
"_Feature"
|
||||
],
|
||||
"window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,186 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from dtaidistance import dtw\n",
|
||||
"from dtaidistance import dtw_visualisation as dtwvis"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Import data\n",
|
||||
"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_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
|
||||
"\n",
|
||||
"print('Done!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use dtaidistance package"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"p1 = df_profiles.dropna(subset=['z']).xs(['AVOCAn0003','prestorm'],level=['site_id','profile_type']).z.values\n",
|
||||
"p2 = df_profiles.dropna(subset=['z']).xs(['AVOCAn0004','prestorm'],level=['site_id','profile_type']).z.values\n",
|
||||
"path = dtw.warping_path(p1,p2)\n",
|
||||
"dtwvis.plot_warping(p1,p2,path)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use kshape package"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"profiles = df_profiles.dropna(subset=['z'])\\\n",
|
||||
" .xs(['prestorm'],level=['profile_type'])\\\n",
|
||||
" .groupby('site_id').z\\\n",
|
||||
" .apply(list).tolist()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# profiles = [x[-50:] for x in profiles]\n",
|
||||
"# print(min(len(x) for x in profiles))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from kshape.core import kshape, zscore\n",
|
||||
"\n",
|
||||
"time_series = [[1,2,3,4], [0,1,2,3], [0,1,2,3], [1,2,2,3]]\n",
|
||||
"cluster_num = 4\n",
|
||||
"clusters = kshape(zscore(profiles, axis=1), cluster_num)\n",
|
||||
"# print(clusters)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cluster_no = 0\n",
|
||||
"\n",
|
||||
"# Plot shape of all clusters\n",
|
||||
"plt.figure(0)\n",
|
||||
"for n,cluster in enumerate(clusters):\n",
|
||||
" plt.plot(cluster[0],label=n)\n",
|
||||
"plt.legend()\n",
|
||||
"\n",
|
||||
"plt.figure(1)\n",
|
||||
"# Plot all profiles in partiuclar cluster\n",
|
||||
"for profile_no in clusters[cluster_no][1]:\n",
|
||||
" plt.plot(profiles[profile_no])\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"a = [1,2,3,4,5,6]\n",
|
||||
"a[-1:]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"hide_input": false,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": false,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"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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"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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},
|
||||
"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": [
|
||||
"module",
|
||||
"function",
|
||||
"builtin_function_or_method",
|
||||
"instance",
|
||||
"_Feature"
|
||||
],
|
||||
"window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue