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1052 lines
47 KiB
Plaintext
1052 lines
47 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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"## Investigate \"collision protection volume\" concept"
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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": "2018-12-03T00:37:28.874517Z",
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"start_time": "2018-12-03T00:37:28.528594Z"
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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": "2018-12-03T00:37:30.229243Z",
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"start_time": "2018-12-03T00:37:28.875519Z"
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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": 83,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-12-03T02:20:29.132099Z",
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"start_time": "2018-12-03T02:20:22.217681Z"
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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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"Lets define a function to calculate the \"collision protection volume\" based on prestorm profiles."
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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": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-12-03T00:37:37.244970Z",
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"start_time": "2018-12-03T00:37:36.924017Z"
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},
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"code_folding": []
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},
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"outputs": [],
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"source": [
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"from shapely.geometry import Point, LineString, Polygon\n",
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"\n",
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"def collision_protection_vol(x,z, d_low_x, d_low_z, lower_z, angle):\n",
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" # First, get the bounding line strings of our protection volume\n",
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" lower_line = LineString([Point(min(x), lower_z), Point(max(x), lower_z)])\n",
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" profile_line = LineString([Point(x_coord, z_coord) for x_coord, z_coord in zip(x,z) \n",
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" if all([not np.isnan(x_coord), not np.isnan(z_coord)])])\n",
|
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" slope_line = LineString([Point(d_low_x, d_low_z), \n",
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" Point(max(x), d_low_z - max(x) * np.sin(np.deg2rad(angle)))])\n",
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"\n",
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" # Work out where our lower line and slope line intersect\n",
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" lower_profile_intersection = lower_line.intersection(profile_line)\n",
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" d_protected_intersection = lower_line.intersection(slope_line)\n",
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" \n",
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" # Define the perimeter of the protection area\n",
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" profile_protected = LineString([Point(x_coord, z_coord) for x_coord, z_coord \n",
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" in zip(profile_line.xy[0],profile_line.xy[1]) \n",
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" if d_low_x < x_coord < lower_profile_intersection.xy[0][0]] \n",
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" + [lower_profile_intersection]\n",
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" + [d_protected_intersection]\n",
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" + [Point(d_low_x, d_low_z)])\n",
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" \n",
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" # Convert to polygon and return the area (m3/m)\n",
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" protection_area_poly = Polygon(profile_protected)\n",
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" protection_area_vol = protection_area_poly.area\n",
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" return protection_area_vol\n",
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"\n",
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"site_id = 'NARRA0018'\n",
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"profile_type = 'prestorm'\n",
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"query = \"site_id == '{}' and profile_type == '{}'\".format(site_id, profile_type)\n",
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"prestorm_profile = df_profiles.query(query)\n",
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"profile_features = df_profile_features.query(query)\n",
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"\n",
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"x = prestorm_profile.index.get_level_values('x')\n",
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"z = prestorm_profile.z\n",
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"d_low_x = profile_features.dune_toe_x.tolist()[0]\n",
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"d_low_z = profile_features.dune_toe_z.tolist()[0]\n",
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"angle = 60 # degrees from the horizontal\n",
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"lower_z = 0.5 # from mhw\n",
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"\n",
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"vol = collision_protection_vol(x,z, d_low_x, d_low_z, lower_z, angle)"
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|
]
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|
},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 58,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-12-03T01:24:18.270476Z",
|
|
"start_time": "2018-12-03T01:24:18.119986Z"
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}
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|
},
|
|
"outputs": [],
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"source": [
|
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"from datetime import timedelta\n",
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"\n",
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"def wl_time(t, wl, z_lower, z_upper):\n",
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" \"\"\"\n",
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" Returns the amount of time the water level is between two elevations.\n",
|
|
" \"\"\"\n",
|
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" df_wl = pd.DataFrame.from_records([(t_val, R2_val) for t_val, R2_val in zip(t,R2)], columns=['datetime','wl'])\n",
|
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" df_wl.set_index(pd.DatetimeIndex(df_wl['datetime']),inplace=True)\n",
|
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" df_wl.drop(columns=['datetime'], inplace=True)\n",
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" \n",
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" # Assumes that each record is one hour... probably need to check this\n",
|
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" hours = len(df_wl.query('{} < wl < {}'.format(z_lower, z_upper)))\n",
|
|
" return timedelta(hours=hours)\n",
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"\n",
|
|
"def wave_power(t, wl, z_lower, z_upper, Hs0, Tp):\n",
|
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" \"\"\"\n",
|
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" Returns the cumulative wave power when the water level is between two elevations.\n",
|
|
" \"\"\"\n",
|
|
" df_wl = pd.DataFrame.from_records([(t_val, R2_val,Hs0_val,Tp_val) for t_val, R2_val,Hs0_val,Tp_val in zip(t,R2,Hs0,Tp)], columns=['datetime','wl', 'Hs0','Tp'])\n",
|
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" df_wl.set_index(pd.DatetimeIndex(df_wl['datetime']),inplace=True)\n",
|
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" df_wl.drop(columns=['datetime'], inplace=True)\n",
|
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" \n",
|
|
" # Assumes that each record is one hour... probably need to check this\n",
|
|
" rho = 1025 # kg/m3\n",
|
|
" g = 9.8 # m/s2\n",
|
|
" df_wl_times = df_wl.query('{} < wl < {}'.format(z_lower, z_upper))\n",
|
|
" power = rho * g ** 2 / 64 / np.pi * df_wl_times.Hs0 ** 2 * df_wl_times.Tp\n",
|
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" return power.sum()\n",
|
|
"\n",
|
|
"df_twl = twls['forecasted']['mean_slope_sto06']\n",
|
|
"df_twl_site = df_twl.query(\"site_id == '{}'\".format(site_id))\n",
|
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"\n",
|
|
"R2 = df_twl_site.R2.tolist()\n",
|
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"t = df_twl_site.index.get_level_values('datetime')\n",
|
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"z_lower = 0.5\n",
|
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"z_upper = d_low_z\n",
|
|
"\n",
|
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"exposed_time = wl_time(t, R2, z_lower,z_upper)\n",
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"\n",
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"Hs0 = df_twl.Hs0.tolist()\n",
|
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"Tp = df_twl.Tp.tolist()\n",
|
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"wave_p = wave_power(t, R2, z_lower,z_upper,Hs0, Tp)\n"
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|
]
|
|
},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 59,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-12-03T01:24:24.215223Z",
|
|
"start_time": "2018-12-03T01:24:24.210209Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"8554715.596323118"
|
|
]
|
|
},
|
|
"execution_count": 59,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
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"wave_p"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-12-03T00:54:57.439956Z",
|
|
"start_time": "2018-12-03T00:54:57.425899Z"
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}
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|
},
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"outputs": [],
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"source": [
|
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"def dune_toe_elevation_change(site_id, df_profile_features):\n",
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" query = \"site_id == '{}'\".format(site_id)\n",
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" profile_features = df_profile_features.query(query)\n",
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" prestorm_dune_toe_z = profile_features.query(\"profile_type=='prestorm'\").dune_toe_z.tolist()[0]\n",
|
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" poststorm_dune_toe_z = profile_features.query(\"profile_type=='poststorm'\").dune_toe_z.tolist()[0]\n",
|
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" return prestorm_dune_toe_z - poststorm_dune_toe_z\n",
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"\n",
|
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"toe_ele_change = dune_toe_elevation_change(\"NARRA0018\", df_profile_features)"
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": 92,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-12-03T03:07:48.328428Z",
|
|
"start_time": "2018-12-03T03:06:54.182343Z"
|
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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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"0 of 204\n",
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"20 of 204\n",
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"40 of 204\n",
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"60 of 204\n",
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"80 of 204\n",
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"100 of 204\n",
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"120 of 204\n",
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"140 of 204\n",
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"160 of 204\n",
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"180 of 204\n",
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"200 of 204\n"
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]
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}
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],
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"source": [
|
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"vols = []\n",
|
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"exposed_times = []\n",
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"toe_ele_changes = []\n",
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"wave_powers = []\n",
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"\n",
|
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"# Get site ids where we observed collision\n",
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"observed_site_ids = impacts['observed'].query(\"storm_regime=='collision'\").index.get_level_values('site_id').unique()\n",
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"\n",
|
|
"# Get site ids where we forecast swash\n",
|
|
"forecasted_site_ids = impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime=='swash'\").index.get_level_values('site_id').unique()\n",
|
|
"\n",
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"site_ids = set(observed_site_ids).intersection(set(forecasted_site_ids))\n",
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"\n",
|
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"# Calculate for each site\n",
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"\n",
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"for n, site_id in enumerate(site_ids):\n",
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" \n",
|
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" if n%20 ==0:\n",
|
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" print('{} of {}'.format(n, len(site_ids)))\n",
|
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" \n",
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" try:\n",
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" query = \"site_id == '{}' and profile_type == '{}'\".format(site_id, 'prestorm')\n",
|
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" prestorm_profile = df_profiles.query(query)\n",
|
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" profile_features = df_profile_features.query(query)\n",
|
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"\n",
|
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" vol = collision_protection_vol(x = prestorm_profile.index.get_level_values('x'),\n",
|
|
" z = prestorm_profile.z,\n",
|
|
" d_low_x = profile_features.dune_toe_x.tolist()[0],\n",
|
|
" d_low_z = profile_features.dune_toe_z.tolist()[0],\n",
|
|
" lower_z = profile_features.dune_toe_z.tolist()[0] - 2, # from mhw\n",
|
|
" angle = 60, # degrees from the horizontal\n",
|
|
" )\n",
|
|
"\n",
|
|
" df_twl = twls['forecasted']['mean_slope_sto06']\n",
|
|
" df_twl_site = df_twl.query(\"site_id == '{}'\".format(site_id))\n",
|
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"\n",
|
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" exposed_time = wl_time(t = df_twl_site.index.get_level_values('datetime'),\n",
|
|
" wl = df_twl_site.R2.tolist(),\n",
|
|
" z_lower = profile_features.dune_toe_z.tolist()[0] -2,\n",
|
|
" z_upper = profile_features.dune_toe_z.tolist()[0],\n",
|
|
" )\n",
|
|
"\n",
|
|
" \n",
|
|
" power = wave_power(t = df_twl_site.index.get_level_values('datetime'),\n",
|
|
" wl = df_twl_site.R2.tolist(),\n",
|
|
" z_lower = profile_features.dune_toe_z.tolist()[0] -2,\n",
|
|
" z_upper = profile_features.dune_toe_z.tolist()[0],\n",
|
|
" Hs0=df_twl_site.Hs0.tolist(),\n",
|
|
" Tp=df_twl_site.Tp.tolist())\n",
|
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" \n",
|
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" toe_ele_change = dune_toe_elevation_change(site_id, df_profile_features)\n",
|
|
" except:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" vols.append(vol)\n",
|
|
" exposed_times.append(exposed_time)\n",
|
|
" toe_ele_changes.append(toe_ele_change)\n",
|
|
" wave_powers.append(power)\n",
|
|
"# if n>100:\n",
|
|
"# break\n",
|
|
"\n",
|
|
" \n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 95,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2018-12-03T03:12:11.598150Z",
|
|
"start_time": "2018-12-03T03:12:11.590128Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
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" 0.46199999999999974<br>LHOUSEn0059,\n",
|
|
" 0.7839999999999998<br>LHOUSEn0057,\n",
|
|
" 0.2939999999999996<br>NINEMs0029,\n",
|
|
" 0.013000000000000345<br>LHOUSEn0030,\n",
|
|
" -0.09400000000000075<br>NINEMs0008,\n",
|
|
" 0.022999999999999687<br>MANNING0092,\n",
|
|
" -0.6230000000000002<br>MONA0012,\n",
|
|
" 0.9040000000000004<br>NSHORE_n0026,\n",
|
|
" 0.027000000000000135<br>DIAMONDn0030,\n",
|
|
" -0.1280000000000001<br>MANNING0091, 0.25<br>STUART0043,\n",
|
|
" 0.488<br>NSHORE_n0020, -0.1200000000000001<br>ELIZA0004,\n",
|
|
" 0.43599999999999994<br>SWRO0005,\n",
|
|
" 0.04999999999999982<br>HARR0050,\n",
|
|
" 0.11599999999999966<br>FOST0001,\n",
|
|
" 1.5290000000000001<br>STOCS0008,\n",
|
|
" 1.0189999999999997<br>NARRA0009,\n",
|
|
" 0.5780000000000003<br>STUART0037,\n",
|
|
" -0.4969999999999999<br>STUART0033,\n",
|
|
" -0.39800000000000013<br>STUART0046,\n",
|
|
" 0.6430000000000002<br>FOST0004,\n",
|
|
" -0.04500000000000037<br>SWRO0001,\n",
|
|
" -0.40700000000000003<br>NINEMn0054,\n",
|
|
" 0.07000000000000028<br>STOCNs0069,\n",
|
|
" -0.18599999999999994<br>STOCNn0004,\n",
|
|
" 1.4900000000000002<br>MANNING0084,\n",
|
|
" 0.2889999999999997<br>STUART0040,\n",
|
|
" -0.3719999999999999<br>NAMB0048,\n",
|
|
" 0.1519999999999997<br>BOOM0007,\n",
|
|
" 0.10899999999999999<br>NINEMn0034,\n",
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|
" -0.3700000000000001<br>ONEMILE0001,\n",
|
|
" 0.7320000000000002<br>ENTRA0075, -0.238<br>STOCNn0011,\n",
|
|
" -0.08900000000000041<br>DIAMONDn0021,\n",
|
|
" 0.20699999999999985<br>LHOUSEn0060,\n",
|
|
" 0.9870000000000001<br>CRESn0064,\n",
|
|
" 1.9829999999999997<br>NARRA0007,\n",
|
|
" 0.6460000000000004<br>MANNING0073,\n",
|
|
" 0.06100000000000039<br>ONEMILE0005,\n",
|
|
" 0.06300000000000017<br>NINEMs0037,\n",
|
|
" -0.03500000000000014<br>LHOUSEn0061,\n",
|
|
" 0.5730000000000004<br>NINEMn0040,\n",
|
|
" 0.7640000000000002<br>AVOCAn0005,\n",
|
|
" -0.7690000000000006<br>STOCNn0041,\n",
|
|
" 0.5750000000000002<br>ELIZA0006,\n",
|
|
" 1.4529999999999998<br>ENTRA0072,\n",
|
|
" 0.07500000000000018<br>MANNING0082, 0.649<br>LHOUSEn0029],\n",
|
|
" 'type': 'scatter',\n",
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|
" 'uid': 'bdcb15ca-50ce-42b2-8441-134aec09d1a6',\n",
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" 'x': [0.0, 62.0, 35.0, 53.0, 4.0, 41.0, 31.0, 98.0, 17.0, 103.0,\n",
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" 81.0, 1.0, 31.0, 0.0, 24.0, 35.0, 67.0, 53.0, 77.0, 130.0,\n",
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" 84.0, 31.0, 2.0, 43.0, 84.0, 421.0, 29.0, 22.0, 54.0, 150.0,\n",
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" 205.0, 16.0, 35.0, 82.0, 49.0, 0.0, 29.0, 58.0, 57.0, 103.0,\n",
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|
" 45.0, 17.0, 426.0, 30.0],\n",
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" 'y': [15.057260981886461, 50.48600703337221, 52.22661963845738,\n",
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|
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" 88.35412756202288, 42.25595601431678, 56.81861249472931,\n",
|
|
" 32.63000580753741, 36.79710517558988, 58.3621888814069,\n",
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|
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|
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" 21.722947051488013, 19.58764895174499, 38.591489138007844,\n",
|
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" 50.57685864743641, 63.74425935533099, 82.20046361640517,\n",
|
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" 44.20822435998208, 26.438775903302503, 50.05321159752885,\n",
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" 54.12827133882625, 24.70991457046442, 60.9358370882951,\n",
|
|
" 54.14071472699229, 27.848684395204568, 52.36273443570386,\n",
|
|
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|
|
" 89.42388399872287, 53.47096959758166]}],\n",
|
|
" 'layout': {'title': 'Dune Collision Protection',\n",
|
|
" 'xaxis': {'autorange': True, 'showgrid': True, 'showline': True, 'title': 'Exposed time', 'zeroline': True},\n",
|
|
" 'yaxis': {'autorange': True,\n",
|
|
" 'showgrid': True,\n",
|
|
" 'showline': True,\n",
|
|
" 'title': 'Collision protection volume',\n",
|
|
" 'zeroline': True}}\n",
|
|
"})"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"trace1 = go.Scatter(\n",
|
|
" x=[x.total_seconds() / 60 / 60 for x in exposed_times],\n",
|
|
" y=vols,\n",
|
|
" text = ['{}<br>{}'.format(ele, site_id) for ele,site_id in zip(toe_ele_changes,site_ids)],\n",
|
|
" mode='markers',\n",
|
|
" marker=dict(\n",
|
|
" size=4,\n",
|
|
"# color = [-1 if x<0 else 1 for x in toe_ele_changes],\n",
|
|
" color = toe_ele_changes,\n",
|
|
"# color = wave_powers,\n",
|
|
"# color = [x.total_seconds() / 60 / 60 for x in exposed_times],\n",
|
|
" colorscale='Viridis',\n",
|
|
" showscale=True\n",
|
|
" ))\n",
|
|
"\n",
|
|
"layout = go.Layout(\n",
|
|
" title='Dune Collision Protection',\n",
|
|
"# height=300,\n",
|
|
"# legend=dict(font={'size': 10}),\n",
|
|
"# margin=dict(t=50, b=50, l=50, r=20),\n",
|
|
" xaxis=dict(\n",
|
|
" title='Exposed time',\n",
|
|
" autorange=True,\n",
|
|
" showgrid=True,\n",
|
|
" zeroline=True,\n",
|
|
" showline=True,\n",
|
|
" ),\n",
|
|
" yaxis=dict(\n",
|
|
" title='Collision protection volume',\n",
|
|
" autorange=True,\n",
|
|
" showgrid=True,\n",
|
|
" zeroline=True,\n",
|
|
" showline=True,\n",
|
|
" ))\n",
|
|
"\n",
|
|
"g_plot = go.FigureWidget(data=[trace1], layout=layout)\n",
|
|
"g_plot"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
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|
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|
"language": "python",
|
|
"name": "python3"
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|
},
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"title_sidebar": "Contents",
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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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|
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},
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"r": {
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"delete_cmd_prefix": "rm(",
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