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555 lines
17 KiB
Plaintext
555 lines
17 KiB
Plaintext
6 years ago
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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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"## Investigate how dune toe compares to R_high"
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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": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-12-03T03:38:44.538853Z",
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"start_time": "2018-12-03T03:38:44.189514Z"
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}
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},
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"outputs": [],
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"source": [
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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": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-12-03T03:38:46.213387Z",
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"start_time": "2018-12-03T03:38:44.781382Z"
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}
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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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"\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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"import plotly.io as pio"
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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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"### Load data\n",
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"Load data from the `./data/interim/` folder and parse 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": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-12-03T03:38:53.297184Z",
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"start_time": "2018-12-03T03:38:46.365829Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Importing profiles.csv\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:472: FutureWarning:\n",
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"\n",
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"elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
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"\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Importing profile_features.csv\n",
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"Importing impacts_forecasted_foreshore_slope_sto06.csv\n",
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"Importing impacts_forecasted_mean_slope_sto06.csv\n",
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"Importing impacts_observed.csv\n",
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"Importing twl_foreshore_slope_sto06.csv\n",
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"Importing twl_mean_slope_sto06.csv\n",
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"Done!\n"
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]
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}
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],
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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_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
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"df_profile_features = df_from_csv('profile_features.csv', index_col=[0])\n",
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"\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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"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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"\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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"### Compare underpredicted cases"
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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": 39,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-12-03T04:05:30.984007Z",
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"start_time": "2018-12-03T04:05:30.805508Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>dune_toe_z</th>\n",
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" <th>R_high</th>\n",
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" <th>diff</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>AVOCAn0005</th>\n",
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" <td>3.306</td>\n",
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" <td>3.260440</td>\n",
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" <td>-0.045560</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AVOCAn0008</th>\n",
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" <td>3.507</td>\n",
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" <td>3.220084</td>\n",
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" <td>-0.286916</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>BILG0005</th>\n",
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" <td>4.807</td>\n",
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" <td>3.293445</td>\n",
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" <td>-1.513555</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>BLUEYS0001</th>\n",
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" <td>3.064</td>\n",
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" <td>2.800144</td>\n",
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" <td>-0.263856</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>BLUEYS0002</th>\n",
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" <td>2.929</td>\n",
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" <td>2.470641</td>\n",
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" <td>-0.458359</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" dune_toe_z R_high diff\n",
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"AVOCAn0005 3.306 3.260440 -0.045560\n",
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"AVOCAn0008 3.507 3.220084 -0.286916\n",
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"BILG0005 4.807 3.293445 -1.513555\n",
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"BLUEYS0001 3.064 2.800144 -0.263856\n",
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"BLUEYS0002 2.929 2.470641 -0.458359"
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]
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},
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"execution_count": 39,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Find site_ids where the forecast has been underpredicted\n",
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"set1 = set(impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime == 'swash'\").index.get_level_values('site_id'))\n",
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"set2 = set(impacts['observed'].query(\"storm_regime == 'collision'\").index.get_level_values('site_id'))\n",
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"site_ids = list(set1.intersection(set2))\n",
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"\n",
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"# Get dune toes at these sites and predicted max R_high\n",
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"df_toes = df_profile_features.loc[site_ids].query('profile_type==\"prestorm\"').dune_toe_z\n",
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"df_R_highs = twls['forecasted']['mean_slope_sto06'].loc[site_ids].groupby('site_id')['R_high'].max()\n",
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"\n",
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"# Join into one dataframe\n",
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"df_twl_toes = pd.concat([df_toes, df_R_highs],axis=1,sort=True)\n",
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"df_twl_toes['diff'] = df_twl_toes['R_high'] - df_twl_toes['dune_toe_z']\n",
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"df_twl_toes.head()\n"
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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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"Now let's plot the comparison between our R_high TWL values and the dune toes to see how far off they were."
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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": 41,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-12-03T04:08:15.732169Z",
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"start_time": "2018-12-03T04:08:15.656966Z"
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}
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "35b9331242af473dba2f91761c307022",
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"version_major": 2,
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"version_minor": 0
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},
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"text/html": [
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"<p>Failed to display Jupyter Widget of type <code>FigureWidget</code>.</p>\n",
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"<p>\n",
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" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
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" that the widgets JavaScript is still loading. If this message persists, it\n",
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" likely means that the widgets JavaScript library is either not installed or\n",
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" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
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" Widgets Documentation</a> for setup instructions.\n",
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"</p>\n",
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"<p>\n",
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" If you're reading this message in another frontend (for example, a static\n",
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" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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" it may mean that your frontend doesn't currently support widgets.\n",
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"</p>\n"
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],
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"text/plain": [
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"FigureWidget({\n",
|
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|
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" 'data': [{'type': 'histogram',\n",
|
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|
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" 'uid': '75f0d11f-9242-4fc7-b433-1f04e1e37ba6',\n",
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" 'y': [-0.045560088746212646, -0.28691603912686325,\n",
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" -1.5135547360075963, ..., -0.4613631587476821,\n",
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" -0.5212332930925054, -0.3948507473332721]}],\n",
|
||
|
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" 'layout': {'bargap': 0.2,\n",
|
||
|
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" 'bargroupgap': 0.1,\n",
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" 'title': 'D_low - R_high<br>Observed Collision, Forecasted Swash',\n",
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" 'xaxis': {'title': 'Count'},\n",
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" 'yaxis': {'title': 'z (m AHD)'}}\n",
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"})"
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
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}
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],
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"source": [
|
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"trace1 = go.Histogram(y=df_twl_toes['diff'].tolist())\n",
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"\n",
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"layout = go.Layout(\n",
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" title='D_low - R_high<br>Observed Collision, Forecasted Swash',\n",
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" yaxis=dict(\n",
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" title='z (m AHD)'\n",
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" ),\n",
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" xaxis=dict(\n",
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" title='Count'\n",
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" ),\n",
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" bargap=0.2,\n",
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" bargroupgap=0.1\n",
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")\n",
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"\n",
|
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"g_plot = go.FigureWidget(data=[trace1], layout=layout)\n",
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"g_plot"
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]
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||
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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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||
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"metadata": {},
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||
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"source": [
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"The above plot shows that the R_high value for most of the incorrectly forecasted collision regimes, was typically underpredicted by less than 0.5 m."
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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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||
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"metadata": {},
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||
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"source": [
|
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"### Compare overpredicted cases"
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]
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||
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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": 42,
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||
|
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"metadata": {
|
||
|
|
"ExecuteTime": {
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||
|
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"end_time": "2018-12-03T04:08:56.128806Z",
|
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"start_time": "2018-12-03T04:08:55.894182Z"
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}
|
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},
|
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
|
||
|
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"<style scoped>\n",
|
||
|
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" .dataframe tbody tr th:only-of-type {\n",
|
||
|
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" vertical-align: middle;\n",
|
||
|
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
|
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>dune_toe_z</th>\n",
|
||
|
|
" <th>R_high</th>\n",
|
||
|
|
" <th>diff</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
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" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AVOCAn0004</th>\n",
|
||
|
|
" <td>3.178</td>\n",
|
||
|
|
" <td>3.416988</td>\n",
|
||
|
|
" <td>0.238988</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>BOOM0004</th>\n",
|
||
|
|
" <td>3.065</td>\n",
|
||
|
|
" <td>3.074980</td>\n",
|
||
|
|
" <td>0.009980</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>BOOM0011</th>\n",
|
||
|
|
" <td>2.771</td>\n",
|
||
|
|
" <td>6.491824</td>\n",
|
||
|
|
" <td>3.720824</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>BOOM0012</th>\n",
|
||
|
|
" <td>2.796</td>\n",
|
||
|
|
" <td>3.148087</td>\n",
|
||
|
|
" <td>0.352087</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>CATHIE0001</th>\n",
|
||
|
|
" <td>2.780</td>\n",
|
||
|
|
" <td>3.522792</td>\n",
|
||
|
|
" <td>0.742792</td>\n",
|
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|
|
" </tr>\n",
|
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|
" </tbody>\n",
|
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|
"</table>\n",
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"</div>"
|
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|
|
],
|
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|
|
"text/plain": [
|
||
|
|
" dune_toe_z R_high diff\n",
|
||
|
|
"AVOCAn0004 3.178 3.416988 0.238988\n",
|
||
|
|
"BOOM0004 3.065 3.074980 0.009980\n",
|
||
|
|
"BOOM0011 2.771 6.491824 3.720824\n",
|
||
|
|
"BOOM0012 2.796 3.148087 0.352087\n",
|
||
|
|
"CATHIE0001 2.780 3.522792 0.742792"
|
||
|
|
]
|
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},
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"execution_count": 42,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
|
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|
|
"source": [
|
||
|
|
"# Find site_ids where the forecast has been overpredicted\n",
|
||
|
|
"set1 = set(impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime == 'collision'\").index.get_level_values('site_id'))\n",
|
||
|
|
"set2 = set(impacts['observed'].query(\"storm_regime == 'swash'\").index.get_level_values('site_id'))\n",
|
||
|
|
"site_ids = list(set1.intersection(set2))\n",
|
||
|
|
"\n",
|
||
|
|
"# Get dune toes at these sites and predicted max R_high\n",
|
||
|
|
"df_toes = df_profile_features.loc[site_ids].query('profile_type==\"prestorm\"').dune_toe_z\n",
|
||
|
|
"df_R_highs = twls['forecasted']['mean_slope_sto06'].loc[site_ids].groupby('site_id')['R_high'].max()\n",
|
||
|
|
"\n",
|
||
|
|
"# Join into one dataframe\n",
|
||
|
|
"df_twl_toes = pd.concat([df_toes, df_R_highs],axis=1,sort=True)\n",
|
||
|
|
"df_twl_toes['diff'] = df_twl_toes['R_high'] - df_twl_toes['dune_toe_z']\n",
|
||
|
|
"df_twl_toes.head()\n"
|
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"cell_type": "code",
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"ExecuteTime": {
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"end_time": "2018-12-03T04:14:46.601092Z",
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"start_time": "2018-12-03T04:14:46.522883Z"
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"The errors when we forecast collision but observe swash are much greater than we we forecast swash and observe collision. For this case, errors in excess of 1.0 m common. Why is this?"
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