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nsw-2016-storm-impact
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Update notebooks

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Christopher Leaman 6 years ago
parent 5776111fdb
commit efa94e6069
13 changed files with 2382 additions and 21 deletions
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5
.gitignore vendored
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@ -2,7 +2,10 @@
.ipynb_checkpoints/ .ipynb_checkpoints/
/notebooks/*.png /notebooks/*.png
/notebooks/*.csv /notebooks/*.csv
/notebooks/14_nearshore_waves/
/notebooks/15_nearshore_waves/
/notebooks/*.pkl
/notebooks/15_data/
# exclude data from source control by default # exclude data from source control by default
/data/ /data/

3
environment.yml
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@ -5,6 +5,7 @@ dependencies:
- python=3.6 - python=3.6
- attrs - attrs
- pre_commit - pre_commit
- beautifulsoup4
- autopep8 - autopep8
- black - black
- cartopy - cartopy
@ -23,6 +24,7 @@ dependencies:
- notebook - notebook
- numpy - numpy
- pandas - pandas
- geopandas
- pandoc - pandoc
- pip - pip
- plotly - plotly
@ -43,6 +45,7 @@ dependencies:
- tqdm - tqdm
- yaml - yaml
- yapf - yapf
- jupyterlab
- pip: - pip:
- blackcellmagic - blackcellmagic
- mat4py - mat4py

9
notebooks/01_exploration.ipynb
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@ -604,6 +604,13 @@
"])" "])"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -899,7 +906,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.7"
}, },
"toc": { "toc": {
"base_numbering": 1, "base_numbering": 1,

20
notebooks/07_evaluate_model_performance.ipynb
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@ -655,10 +655,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"code_folding": [ "code_folding": []
23,
223
]
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -921,6 +918,19 @@
"fig.savefig('07_c&p_confusion_matrix',dpi=600,bbox_inches = \"tight\")\n", "fig.savefig('07_c&p_confusion_matrix',dpi=600,bbox_inches = \"tight\")\n",
"plt.show()" "plt.show()"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pd.options.display.max_columns = 500\n",
"print(\"Couldn't get forecast for: {} sites\".format(df_for.storm_regime.isna().sum()))\n",
"print(\"Couldn't get observations for: {} sites\".format(df_obs.storm_regime.isna().sum()))\n",
"\n",
"# df_obs[df_obs.storm_regime.isna()]"
]
} }
], ],
"metadata": { "metadata": {
@ -940,7 +950,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.7"
}, },
"toc": { "toc": {
"base_numbering": 1, "base_numbering": 1,

42
notebooks/11_investigate.ipynb
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@ -399,6 +399,46 @@
"f.savefig('11_change_in_beach_width.png',dpi=600)" "f.savefig('11_change_in_beach_width.png',dpi=600)"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check prestorm and post storm width"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax.get_xaxis().get_major_ticks()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_col = \"width_msl_prestorm\"\n",
"y_col = \"width_msl_poststorm\"\n",
"\n",
"with sns.axes_style(\"white\"):\n",
" g = sns.jointplot(x=x_col,\n",
" y=y_col,\n",
" data=df.dropna(subset=[x_col, y_col]),\n",
" kind=\"hex\",\n",
" ylim=(0, 150),\n",
" xlim=(0, 150))\n",
"\n",
" x0, x1 = g.ax_joint.get_xlim()\n",
" y0, y1 = g.ax_joint.get_ylim()\n",
" lims = [max(x0, y0), min(x1, y1)]\n",
" g.ax_joint.plot(lims, lims, ':k') \n",
" "
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -824,7 +864,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.7"
}, },
"toc": { "toc": {
"base_numbering": 1, "base_numbering": 1,

14
notebooks/12_method_plots.ipynb
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@ -1108,7 +1108,7 @@
"# Add title\n", "# Add title\n",
"fig.text(\n", "fig.text(\n",
" 0.06, 1.14,\n", " 0.06, 1.14,\n",
" 'Variation in volume change\\nfor swash regime profiles',\n", " 'Variation in beach width\\nchange for swash regime\\nprofiles',\n",
" horizontalalignment='left',\n", " horizontalalignment='left',\n",
" verticalalignment='top',\n", " verticalalignment='top',\n",
" size=8)\n", " size=8)\n",
@ -1592,6 +1592,18 @@
"language": "python", "language": "python",
"name": "python3" "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": "3.6.7"
},
"toc": { "toc": {
"base_numbering": 1, "base_numbering": 1,
"nav_menu": {}, "nav_menu": {},

29
notebooks/13_locality_plot.ipynb
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@ -459,7 +459,7 @@
" label='Beaches included')\n", " label='Beaches included')\n",
"\n", "\n",
"handles = [legend_buoy, legend_gauge, legend_beaches]\n", "handles = [legend_buoy, legend_gauge, legend_beaches]\n",
"names = ['Wave buoys', 'Tide gauges', 'Beaches']\n", "names = ['Wave buoys', 'Tide gauges', 'Surveyed\\nbeaches']\n",
"\n", "\n",
"# create legend\n", "# create legend\n",
"ax1.legend(handles, names, title=r'\\underline{Legend}', loc='lower left')\n", "ax1.legend(handles, names, title=r'\\underline{Legend}', loc='lower left')\n",
@ -471,6 +471,13 @@
"ax1.text(153.111704-0.1, -30.300466, r'\\textsc{Coffs Harbour}', transform=ccrs.Geodetic(),ha='right',zorder=4)\n", "ax1.text(153.111704-0.1, -30.300466, r'\\textsc{Coffs Harbour}', transform=ccrs.Geodetic(),ha='right',zorder=4)\n",
"ax1.text(150.891708-0.1, -34.433129, r'\\textsc{Wollongong}', transform=ccrs.Geodetic(),ha='right',zorder=4)\n", "ax1.text(150.891708-0.1, -34.433129, r'\\textsc{Wollongong}', transform=ccrs.Geodetic(),ha='right',zorder=4)\n",
"\n", "\n",
"ax1.plot(151.204325, -33.869810, transform=ccrs.Geodetic(),zorder=3,color='k',marker='.')\n",
"ax1.plot(151.784937, -32.928103, transform=ccrs.Geodetic(),zorder=3,color='k',marker='.')\n",
"ax1.plot(152.909329, -31.440207, transform=ccrs.Geodetic(),zorder=3,color='k',marker='.')\n",
"ax1.plot(153.111704, -30.300466, transform=ccrs.Geodetic(),zorder=3,color='k',marker='.')\n",
"ax1.plot(150.891708, -34.433129,transform=ccrs.Geodetic(),zorder=3,color='k',marker='.')\n",
"\n",
"\n",
"ax2.text(133.729975, -25.173095, r'\\textsc{Australia}', transform=ccrs.Geodetic(),ha='center',zorder=4,va='bottom', fontsize=6, bbox=dict(facecolor=cartopy.feature.COLORS['land'],pad=0.1,linewidth=0, alpha=0.9))\n", "ax2.text(133.729975, -25.173095, r'\\textsc{Australia}', transform=ccrs.Geodetic(),ha='center',zorder=4,va='bottom', fontsize=6, bbox=dict(facecolor=cartopy.feature.COLORS['land'],pad=0.1,linewidth=0, alpha=0.9))\n",
"\n", "\n",
"# # Add inset for Narrabeen\n", "# # Add inset for Narrabeen\n",
@ -492,19 +499,19 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# Try using overpass api\n", "# # Try using overpass api\n",
"# Hard because coastline is given as line string, not shapefile.\n", "# # Hard because coastline is given as line string, not shapefile.\n",
"\n", "\n",
"import overpass\n", "# import overpass\n",
"api = overpass.API()\n", "# api = overpass.API()\n",
"response = api.get('way[\"natural\"=\"coastline\"](-34, 151.0, -33, 152);out geom;')\n", "# response = api.get('way[\"natural\"=\"coastline\"](-34, 151.0, -33, 152);out geom;')\n",
"coords = [x['geometry']['coordinates'] for x in response['features']]\n", "# coords = [x['geometry']['coordinates'] for x in response['features']]\n",
"\n", "\n",
"\n", "\n",
"for line in coords:\n", "# for line in coords:\n",
" lats = [x[1] for x in line]\n", "# lats = [x[1] for x in line]\n",
" lons = [x[0] for x in line]\n", "# lons = [x[0] for x in line]\n",
" ax3.plot(lons,lats, transform=ccrs.Geodetic())" "# ax3.plot(lons,lats, transform=ccrs.Geodetic())"
] ]
}, },
{ {

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notebooks/14_beaches_Australia.geojson
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notebooks/14_polygons_Australia.geojson
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}

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notebooks/14_shoreline_position_vs_wave_energy.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shoreline position v.s. wave energy\n",
"This notebook looks at the relationship between shoreline position and wave energy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import datetime\n",
"import pickle\n",
"import fiona\n",
"import shapely\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import geopandas\n",
"from scipy.stats import percentileofscore\n",
"from shapely.geometry import Point\n",
"import numpy as np\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"import urllib.parse\n",
"import itertools\n",
"from tqdm import tqdm\n",
"import glob\n",
"from scipy.interpolate import griddata, SmoothBivariateSpline\n",
"from scipy.ndimage.filters import gaussian_filter\n",
"import colorcet as cc"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shoreline positions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import Killian's data\n",
"shorelines = pickle.load(open(\"14_timeseries_Australia_2.pkl\", \"rb\"))\n",
"beaches = fiona.open(\"14_beaches_Australia.geojson\")\n",
"polygons = fiona.open(\"14_polygons_Australia.geojson\")\n",
"transects = fiona.open(\"14_transects_Australia.geojson\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [
0
]
},
"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",
"# Import Chris' data\n",
"df_sites = df_from_csv('sites.csv', index_col=[0])\n",
"df_obs_impacts = df_from_csv('impacts_observed.csv', index_col=[0])\n",
"df_waves = df_from_csv('waves.csv', index_col=[0,1])\n",
"df_waves.index = df_waves.index.set_levels([df_waves.index.levels[0], pd.to_datetime(df_waves.index.levels[1])])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get coorindates of transects where Killian has given shoreline data\n",
"transect_data = [x for x in transects if x['properties']['id'] in shorelines.keys()]\n",
"transect_dict = [{'name':x['properties']['name'],\n",
" 'id':x['properties']['id'],\n",
" 'orientation':x['properties']['orientation'],\n",
" 'start_coords': Point(x['geometry']['coordinates'][0][0], x['geometry']['coordinates'][0][1]),\n",
" 'end_coords': Point(x['geometry']['coordinates'][1][0], x['geometry']['coordinates'][1][1])} for x in transect_data]\n",
"df_transects = pd.DataFrame(transect_dict)\n",
"gdf_transects = geopandas.GeoDataFrame(df_transects, geometry='start_coords',crs={'init':'epsg:4326'})\n",
"\n",
"# Find closest Chris transect to each one of Kilian's transects\n",
"# First transform coords using geopandas\n",
"df_sites['coords'] = list(zip(df_sites.lon, df_sites.lat))\n",
"df_sites['coords'] = df_sites['coords'].apply(Point)\n",
"gdf_sites = geopandas.GeoDataFrame(df_sites, geometry='coords',crs={'init':'epsg:4326'})\n",
"gdf_sites['site_id'] = gdf_sites.index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Find nearest Chris transect for each of Kilian's transect\n",
"\n",
"from shapely.ops import nearest_points\n",
"\n",
"def nearest(row,\n",
" geom_union,\n",
" df1,\n",
" df2,\n",
" geom1_col='geometry',\n",
" geom2_col='geometry',\n",
" src_column=None):\n",
" \"\"\"Find the nearest point and return the corresponding value from specified column.\"\"\"\n",
" # Find the geometry that is closest\n",
" nearest = df2[geom2_col] == nearest_points(row[geom1_col], geom_union)[1]\n",
" # Get the corresponding value from df2 (matching is based on the geometry)\n",
" value = df2[nearest][src_column].get_values()[0]\n",
" return value\n",
"\n",
"unary_union = gdf_sites.unary_union\n",
"gdf_transects['chris_site_id'] = gdf_transects.apply(nearest,\n",
" geom_union=unary_union,\n",
" df1=gdf_transects,\n",
" df2=gdf_sites,\n",
" geom1_col='start_coords',\n",
" geom2_col='coords',\n",
" src_column='site_id',\n",
" axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Got the closests site_id, now check the distance. If distance too far between these sites, then probably not a good match.\n",
"gdf_transects = gdf_transects.merge(gdf_sites[['coords']], left_on='chris_site_id', right_on='site_id')\n",
"gdf_transects = gdf_transects.rename({'coords': 'chris_coords'},axis='columns')\n",
"gdf_transects\n",
"distances = gdf_transects[['start_coords']].to_crs(epsg=28356).distance(\n",
" geopandas.GeoDataFrame(gdf_transects[['chris_coords']],\n",
" geometry='chris_coords',\n",
" crs={\n",
" 'init': 'epsg:4326'\n",
" }).to_crs(epsg=28356))\n",
"\n",
"gdf_transects['transect_to_chris_dist'] = distances"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Limit used transects to 300 m max distance\n",
"gdf_transects = gdf_transects[gdf_transects.transect_to_chris_dist < 300]\n",
"gdf_transects"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate the change to the percentile of shoreline right after the storm\n",
"# Kilian's shorelines are given for z=0 MSL, so find change in shoreline due to storm\n",
"gdf_transects=gdf_transects.merge(df_obs_impacts.width_msl_change_m,left_on=['chris_site_id'], right_on=['site_id'])\n",
"gdf_transects"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# At each beach calculate percentile of shoreline right before the storm\n",
"data = []\n",
"\n",
"for row in gdf_transects.iterrows():\n",
"\n",
" # Get shoreline records\n",
" id_shorelines = shorelines[row[1].id]['chainage']\n",
" id_dates = shorelines[row[1].id]['dates']\n",
"\n",
" # Find last date before June 2016 storm\n",
" dt_storm = datetime.datetime(2016, 6, 3)\n",
" dt_storm = dt_storm.replace(tzinfo=datetime.timezone.utc)\n",
" mask = pd.Series([x < dt_storm for x in id_dates])\n",
" i_last_obs = mask[::-1].idxmax()\n",
"\n",
" last_obs_ch = id_shorelines[i_last_obs]\n",
" last_obs_date = id_dates[i_last_obs]\n",
" post_storm_ch = last_obs_ch + row[1].width_msl_change_m\n",
"\n",
" prestorm_shoreline_pctile = percentileofscore(id_shorelines[~np.isnan(id_shorelines)], last_obs_ch)\n",
" poststorm_shoreline_pctile = percentileofscore(id_shorelines[~np.isnan(id_shorelines)],\n",
" post_storm_ch)\n",
" change_shoreline_pctile = poststorm_shoreline_pctile - prestorm_shoreline_pctile\n",
"\n",
" rel_change_shoreline_pctile = (poststorm_shoreline_pctile- prestorm_shoreline_pctile)/prestorm_shoreline_pctile *100\n",
" \n",
" # Calculate percentile of shoreline score\n",
" data.append({\n",
" 'prestorm_shoreline_pctile': prestorm_shoreline_pctile,\n",
" 'poststorm_shoreline_pctile': poststorm_shoreline_pctile,\n",
" 'change_shoreline_pctile': change_shoreline_pctile,\n",
" 'rel_change_shoreline_pctile': rel_change_shoreline_pctile,\n",
" 'index': row[0]\n",
" })\n",
"\n",
"data = pd.DataFrame(data).set_index('index')\n",
"gdf_transects = gdf_transects.join(data)\n",
"gdf_transects"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Grab data from NSW Nearshore wave transformation tool.\n",
"# Need to relate Kilian's site id\n",
"sites = [{\n",
" 'id': 'way4042355',\n",
" 'site_id': 'DEEWHYs0003',\n",
" 'nsw_nearshore_id': 1007832\n",
"}, {\n",
" 'id': 'way13858409',\n",
" 'site_id': 'DEEWHYn0003',\n",
" 'nsw_nearshore_id': 1007822, \n",
"}, {\n",
" 'id': 'way13858412',\n",
" 'site_id': 'MONA0011',\n",
" 'nsw_nearshore_id': 1007726, \n",
"},\n",
"{\n",
" 'id': 'way14040821',\n",
" 'site_id': 'NARRA0007',\n",
" 'nsw_nearshore_id': 1007760, \n",
"},{\n",
" 'id': 'way14040977',\n",
" 'site_id': 'NARRA0018',\n",
" 'nsw_nearshore_id': 1007770, \n",
"},{\n",
" 'id': 'way14041013',\n",
" 'site_id': 'NARRA0030',\n",
" 'nsw_nearshore_id': 1007778, \n",
"},{\n",
" 'id': 'way25005079',\n",
" 'site_id': 'MACM0009',\n",
" 'nsw_nearshore_id': 1007354, \n",
"},{\n",
" 'id': 'way54609773',\n",
" 'site_id': 'WAMBE0005',\n",
" 'nsw_nearshore_id': 1007264, \n",
"},{\n",
" 'id': 'way54667480',\n",
" 'site_id': 'AVOCAn0005',\n",
" 'nsw_nearshore_id': 1007306, \n",
"},{\n",
" 'id': 'way54669965',\n",
" 'site_id': 'AVOCAs0004',\n",
" 'nsw_nearshore_id': 1007312, \n",
"},{\n",
" 'id': 'way134627391',\n",
" 'site_id': 'ONEMILE0007',\n",
" 'nsw_nearshore_id': 1005098, \n",
"},{\n",
" 'id': 'way159040990',\n",
" 'site_id': 'LHOUSE0004',\n",
" 'nsw_nearshore_id': 1005448, \n",
"},{\n",
" 'id': 'way173070325',\n",
" 'site_id': 'LHOUSEn0077',\n",
" 'nsw_nearshore_id': 1004186, \n",
"},{\n",
" 'id': 'way182614828',\n",
" 'site_id': 'TREACH0009',\n",
" 'nsw_nearshore_id': 1005472, \n",
"},{\n",
" 'id': 'way189407637',\n",
" 'site_id': 'NSHORE_n0063',\n",
" 'nsw_nearshore_id': 1003994, \n",
"},{\n",
" 'id': 'way190929758',\n",
" 'site_id': 'CRESn0069',\n",
" 'nsw_nearshore_id': 1003708, \n",
"},{\n",
" 'id': 'way222144734',\n",
" 'site_id': 'BLUEYS0002',\n",
" 'nsw_nearshore_id': 1005316, \n",
"},{\n",
" 'id': 'way222145626',\n",
" 'site_id': 'BOOM0008',\n",
" 'nsw_nearshore_id': 1005298, \n",
"},{\n",
" 'id': 'way224198013',\n",
" 'site_id': 'MANNING0048',\n",
" 'nsw_nearshore_id': 1004712, \n",
"},{\n",
" 'id': 'way450323845',\n",
" 'site_id': 'NAMB0033',\n",
" 'nsw_nearshore_id': np.nan, \n",
"},{\n",
" 'id': 'relation2303044',\n",
" 'site_id': 'ENTRA0041',\n",
" 'nsw_nearshore_id': 1007110, \n",
"},{\n",
" 'id': 'relation2723197',\n",
" 'site_id': 'GRANTSn0022',\n",
" 'nsw_nearshore_id': 1004296, \n",
"}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def nearshore_wave_csv_url(id,start_date,end_date):\n",
" URL = 'http://www.nswaves.com.au/transform.php'\n",
" payload = {\n",
" 'init': '1',\n",
" 'type': 'Transform-Full',\n",
" 'startsite': '{}'.format(id),\n",
" 'endsite': '{}'.format(id),\n",
" 'timestep': 'null',\n",
" 'startdate': start_date.strftime('%Y-%m-%d'),\n",
" 'starthour': '00',\n",
" 'enddate': end_date.strftime('%Y-%m-%d'),\n",
" 'endhour': '00',\n",
" 'sitestep': '1',\n",
" 'method': 'Parametric',\n",
" 'source': 'Waverider',\n",
" 'filename': 'ckl',\n",
" 'format': 'csv',\n",
" }\n",
"\n",
" session = requests.session()\n",
" r = requests.post(URL, data=payload)\n",
" \n",
" soup = BeautifulSoup(r.text)\n",
" \n",
" # Check if data extraction was successful\n",
" if soup.findAll(text=\"OK : Data Extraction Successful - Click filename/s to download data file\"):\n",
"\n",
" # Find all links\n",
" for link in soup.find_all('a'):\n",
"\n",
" href = link.get('href')\n",
" if '/data/full' not in href:\n",
" continue\n",
"\n",
" # Convert to absolute convert to absolute url\n",
" csv_url = urllib.parse.urljoin(URL, href)\n",
"\n",
" return csv_url\n",
" else:\n",
" return None\n",
"\n",
" \n",
"def download_csv(url, file_path):\n",
" urllib.request.urlretrieve(url,file_path)\n",
" print('Downloaded {}'.format(file_path))\n",
" \n",
" \n",
"def daterange(start_date, end_date,delta):\n",
" while start_date < end_date:\n",
" yield start_date\n",
" start_date += delta\n",
" \n",
"def download_nearshore_csv(id, site_id, nsw_nearshore_id, start_date, end_date,output_folder='./14_nearshore_waves/'):\n",
" \n",
" # Create output folder if doesn't already exists\n",
" os.makedirs(output_folder, exist_ok=True)\n",
"\n",
" # Output filename\n",
" output_filename = '{}_{}_{}_{}_{}.csv'.format(\n",
" id,\n",
" site_id,\n",
" nsw_nearshore_id,\n",
" start_date.strftime('%Y%m%d'),\n",
" end_date.strftime('%Y%m%d'),\n",
" )\n",
" output_filepath = os.path.join(output_folder,output_filename)\n",
"\n",
" # Don't download if file already exists\n",
" if os.path.isfile(output_filepath):\n",
" return\n",
"\n",
" csv_url = nearshore_wave_csv_url(nsw_nearshore_id,start_date,end_date)\n",
"\n",
" if csv_url:\n",
" download_csv(csv_url, output_filepath)\n",
" else:\n",
" print('No url found')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# start_year = 2005\n",
"# end_year = 2015\n",
"# output_folder = './14_nearshore_waves/'\n",
"\n",
"# # Create list of start end dates we want to request\n",
"# date_ranges = [(datetime.datetime(x, 1, 1), datetime.datetime(x, 12, 31))\n",
"# for x in range(start_year, end_year + 1)]\n",
"\n",
"# inputs = list(itertools.product(sites, date_ranges))\n",
"\n",
"# for inpt in inputs:\n",
"# download_nearshore_csv(inpt[0]['id'], inpt[0]['site_id'], inpt[0]['nsw_nearshore_id'], inpt[1][0], inpt[1][1])\n",
"# break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Use a queue to get data\n",
"\n",
"# from queue import Queue\n",
"# from threading import Thread\n",
"# q = Queue(maxsize=0)\n",
"# num_theads = 4\n",
"\n",
"# start_year = 2005\n",
"# end_year = 2015\n",
"# date_ranges = [(datetime.datetime(x, 1, 1), datetime.datetime(x, 12, 31))\n",
"# for x in range(start_year, end_year + 1)]\n",
"\n",
"# inputs = [x for x in list(itertools.product(sites, date_ranges))]\n",
"\n",
"# #Populating Queue with tasks\n",
"# results = [{} for x in inputs]\n",
"\n",
"# #load up the queue with the urls to fetch and the index for each job (as a tuple):\n",
"# for i, inpt in enumerate(inputs):\n",
"# q.put((i, inpt))\n",
"\n",
"\n",
"# # Threaded function for queue processing.\n",
"# def crawl(q, result):\n",
"# while not q.empty():\n",
"# work = q.get() #fetch new work from the Queue\n",
"# print(work)\n",
"# download_nearshore_csv(work[1][0]['id'], work[1][0]['site_id'],\n",
"# work[1][0]['nsw_nearshore_id'], work[1][1][0],\n",
"# work[1][1][1])\n",
"# #signal to the queue that task has been processed\n",
"# q.task_done()\n",
"# return True\n",
"\n",
"\n",
"# #Starting worker threads on queue processing\n",
"# for i in range(num_theads):\n",
"# print('Starting thread {}'.format(i))\n",
"# worker = Thread(target=crawl, args=(q, results))\n",
"# worker.setDaemon(True) #setting threads as \"daemon\" allows main program to\n",
"# #exit eventually even if these dont finish\n",
"# #correctly.\n",
"# worker.start()\n",
"\n",
"# #now we wait until the queue has been processed\n",
"# q.join()\n",
"# print('All tasks completed.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# For each site, get\n",
"for site in sites:\n",
"\n",
" # print(site)\n",
" df_sites\n",
"\n",
" # Get shoreline orientation\n",
" orientation = df_sites.loc[[site['site_id']]].orientation.iloc[0]\n",
"\n",
" # Get peak hour wave energy from June 2016 storm\n",
" max_hrly_wave_power = df_waves.loc[[site['site_id']]].Pxs.max()\n",
"\n",
" # Load nearshore wave csv files into one dataframe\n",
" site_nearshore_wave_files = glob.glob('./14_nearshore_waves/*{}*'.format(\n",
" site['site_id']))\n",
"\n",
" if len(site_nearshore_wave_files) == 0:\n",
" continue\n",
"\n",
" df_hist_waves = pd.concat((pd.read_csv(f,\n",
" skiprows=8,\n",
" index_col=0,\n",
" names=['Hs', 'Tp', 'dir'],\n",
" na_values=' NaN')\n",
" for f in site_nearshore_wave_files))\n",
" df_hist_waves.index = pd.to_datetime(df_hist_waves.index)\n",
"\n",
" # At each row, calculate crossshore component of nearshore wave energy\n",
" df_hist_waves['d'] = 10\n",
" df_hist_waves['L'] = 9.81 * df_hist_waves.Tp**2 / 2 / np.pi\n",
" df_hist_waves['n'] = 0.5 * (\n",
" 1 + (4 * np.pi * df_hist_waves.d / df_hist_waves.L) /\n",
" (np.sinh(4 * np.pi * df_hist_waves.d / df_hist_waves.L)))\n",
" df_hist_waves['E'] = 1 / 16 * 1025 * 9.81 * df_hist_waves.Hs**2\n",
" df_hist_waves['C'] = 9.81 * df_hist_waves.Tp / 2 / np.pi * np.tanh(\n",
" 2 * np.pi * df_hist_waves.d / df_hist_waves.L)\n",
" df_hist_waves['shoreline_tn_angle'] = 270 - orientation\n",
" df_hist_waves.loc[\n",
" df_hist_waves.shoreline_tn_angle > 360,\n",
" 'shoreline_tn_angle'] = df_hist_waves.shoreline_tn_angle - 360\n",
" df_hist_waves[\n",
" 'alpha'] = df_hist_waves.shoreline_tn_angle - df_hist_waves.dir\n",
" df_hist_waves[\n",
" 'Px'] = df_hist_waves.n * df_hist_waves.E * df_hist_waves.C * np.cos(\n",
" np.deg2rad(df_hist_waves.alpha))\n",
"\n",
" # Apply percentileofscore for June 2016 wave energy\n",
" storm_Px_hrly_pctile = percentileofscore(df_hist_waves.Px.dropna().values,\n",
" max_hrly_wave_power,\n",
" kind='mean')\n",
"\n",
" # Calculate cumulate wave energy from storm\n",
" idx = ((df_waves.index.get_level_values('datetime') > '2016-06-04') &\n",
" (df_waves.index.get_level_values('datetime') < '2016-06-07') &\n",
" (df_waves.index.get_level_values('site_id') == site['site_id']))\n",
" hrs = len(df_waves[idx])\n",
" Pxscum_storm = df_waves[idx].Pxs.sum()\n",
" \n",
" # Calculate cumulate wave energy of mean wave conditions over length of storm\n",
" Pxscum_mean = df_hist_waves['Px'].mean() * hrs\n",
" Pxscum_storm_mean_ratio = Pxscum_storm / Pxscum_mean\n",
"\n",
" # Add to gdf_transects dataframe\n",
" idx = gdf_transects[gdf_transects.chris_site_id == site['site_id']].index\n",
" gdf_transects.loc[idx, 'storm_Px_hrly_pctile'] = storm_Px_hrly_pctile\n",
" gdf_transects.loc[idx, 'Pxscum_storm'] = Pxscum_storm\n",
" gdf_transects.loc[idx, 'Pxscum_mean'] = Pxscum_mean\n",
" gdf_transects.loc[idx, 'Pxscum_storm_mean_ratio'] = Pxscum_storm_mean_ratio\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gdf_transects.sort_values(by='Pxscum_storm_mean_ratio',ascending=False).head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gdf_transects.sort_values(by='rel_change_shoreline_pctile').head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Drop nans\n",
"gdf_transects = gdf_transects.dropna(axis='index',\n",
" subset=[\n",
" 'Pxscum_storm_mean_ratio',\n",
" 'prestorm_shoreline_pctile',\n",
" 'change_shoreline_pctile',\n",
" 'rel_change_shoreline_pctile'\n",
" ],\n",
" how='any')\n",
"\n",
"# Grid results\n",
"grid_x, grid_y = np.mgrid[0:2:100j, 0:100:100j]\n",
"\n",
"x_vals = gdf_transects.Pxscum_storm_mean_ratio.values\n",
"y_vals = gdf_transects.prestorm_shoreline_pctile.values\n",
"z_vals = gdf_transects.rel_change_shoreline_pctile.values\n",
"\n",
"points = [[x, y] for x, y in zip(\n",
" x_vals,\n",
" y_vals,\n",
")]\n",
"\n",
"grid = griddata((x_vals,y_vals), z_vals, (grid_x, grid_y), method='cubic')\n",
"\n",
"# Smooth data\n",
"# https://stackoverflow.com/a/34370291\n",
"# grid = gaussian_filter(grid, sigma=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def round_down(num, divisor):\n",
" return num - (num%divisor)\n",
"\n",
"def round_up(x, divisor): \n",
" return (x + divisor - 1) // divisor * divisor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gdf_transects[gdf_transects.prestorm_shoreline_pctile<40].sort_values(by='change_shoreline_pctile',ascending=True).head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot peak wave energy pctile vs prestorm shoreline percentile vs change in shoreline percentile\n",
"\n",
"x_col = 'Pxscum_storm_mean_ratio'\n",
"y_col = 'prestorm_shoreline_pctile'\n",
"# z_col = 'rel_change_shoreline_pctile'\n",
"z_col = 'change_shoreline_pctile'\n",
"\n",
"# Drop nans\n",
"gdf_transects = gdf_transects.dropna(axis='index',\n",
" subset=[x_col, y_col,z_col\n",
" ],\n",
" how='any')\n",
"\n",
"# Grid results\n",
"grid_x, grid_y = np.mgrid[0:25:100j, 0:100:100j]\n",
"\n",
"x_vals = gdf_transects[x_col].values\n",
"y_vals = gdf_transects[y_col].values\n",
"z_vals = gdf_transects[z_col].values\n",
"\n",
"grid = griddata((x_vals,y_vals), z_vals, (grid_x, grid_y), method='linear',rescale=True)\n",
"\n",
"# Smooth data\n",
"# https://stackoverflow.com/a/34370291\n",
"# grid = gaussian_filter(grid, sigma=0.5)\n",
"\n",
"\n",
"# # 2D Spline interpolation\n",
"# s = SmoothBivariateSpline(x_vals, y_vals,z_vals)\n",
"# spline_x = np.arange(1,25,0.1)\n",
"# spline_y = np.arange(0,100,0.5)\n",
"# spline_z = s(spline_x, spline_y,grid=True)\n",
"# spline_grid_x, spline_grid_y = np.meshgrid(spline_x, spline_y)\n",
"\n",
"\n",
"# Create figure\n",
"fig = plt.figure(figsize=(3, 3), dpi=150, facecolor='w', edgecolor='k')\n",
"ax = fig.add_subplot(111)\n",
"\n",
"# Define colors\n",
"cmap_interval = 25\n",
"cmap = cc.cm.fire\n",
"vmin = round_down(np.min(z_vals), cmap_interval)\n",
"vmax = round_up(np.max(z_vals), cmap_interval)\n",
"levels = [x*cmap_interval for x in range(-4,2)]\n",
"\n",
"\n",
"# Plot SPLINE grid surface\n",
"# cf = ax.contourf(spline_grid_x, spline_grid_y, spline_z.T,levels=levels, cmap=cmap,vmin=vmin,vmax=vmax)\n",
"\n",
"# Plot SPLINE contours\n",
"# cs = plt.contour(grid_x, grid_y,grid,levels=levels,linewidths=0.5,colors='white', vmin=vmin,vmax=vmax)\n",
"# ax.clabel(cs, inline=1, fontsize=4, fmt='%1.0f%%')\n",
"\n",
"\n",
"# Plot CUBIC FIT grid surface\n",
"cf = plt.contourf(grid_x, grid_y,grid,levels=levels, cmap=cmap,vmin=vmin,vmax=vmax)\n",
"\n",
"# Plot CUBIC FIT contours\n",
"cs = plt.contour(grid_x, grid_y,grid,levels=levels,linewidths=0.5,colors='white', vmin=vmin,vmax=vmax)\n",
"ax.clabel(cs, inline=1, fontsize=4, fmt='%1.0f%%')\n",
"\n",
"\n",
"scatter = ax.scatter(\n",
" x=x_vals,\n",
" y=y_vals,\n",
" c=z_vals,\n",
" s=1,\n",
" cmap=cmap,vmin=vmin,vmax=vmax\n",
")\n",
"\n",
"ax.set_xlim([1,25])\n",
"\n",
"ax.set_xlabel(x_col)\n",
"ax.set_ylabel(y_col)\n",
"\n",
"cbar = plt.colorbar(cf)\n",
"cbar.set_label(z_col)\n",
"\n",
"ax.grid(True, linestyle=\"--\", alpha=0.2, color='grey', linewidth=1)\n",
"\n",
"plt.show()\n",
"\n",
"fig.savefig('14_beach_state_vs_wave_energy_{}'.format(z_col),dpi=600,bbox_inches = \"tight\", pad_inches=0.01)"
]
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176
notebooks/14_transects_Australia.geojson
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{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "name": "Dee Why Beach", "id": "way4042355", "orientation": 117.8, "beach_length": 1367.6787658900539 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.297426209301022, -33.749879728713736 ], [ 151.303385953876585, -33.752492350405944 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "way5757445", "orientation": 143.1, "beach_length": 7266.5228394129863 }, "geometry": { "type": "LineString", "coordinates": [ [ 150.688273909621756, -35.014570881874867 ], [ 150.692319159611543, -35.018983377256809 ] ] } },
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{ "type": "Feature", "properties": { "name": "noname", "id": "way664606765", "orientation": 122.6, "beach_length": 3772.0435881595317 }, "geometry": { "type": "LineString", "coordinates": [ [ 150.135725762944929, -36.275995490791587 ], [ 150.141401671927724, -36.278921771233478 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "way664713177", "orientation": 69.5, "beach_length": 1088.4824703652382 }, "geometry": { "type": "LineString", "coordinates": [ [ 150.132623945658793, -36.091488510007352 ], [ 150.138934647737358, -36.089581848595152 ] ] } },
{ "type": "Feature", "properties": { "name": "Maroubra Beach", "id": "relation2251446", "orientation": 92.3, "beach_length": 1304.7786949436613 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.255225107515685, -33.949647248967025 ], [ 151.261957044492732, -33.949871538726846 ] ] } },
{ "type": "Feature", "properties": { "name": "North Entrance Beach", "id": "relation2303044", "orientation": 137.0, "beach_length": 9900.7078833312644 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.524384869207324, -33.313435454839677 ], [ 151.528979740836718, -33.31755307743564 ] ] } },
{ "type": "Feature", "properties": { "name": "Seven Mile Beach", "id": "relation2580527", "orientation": 101.9, "beach_length": 9795.7333395711594 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.597650977776965, -28.763556961476439 ], [ 153.604243549604206, -28.764774808848394 ] ] } },
{ "type": "Feature", "properties": { "name": "Seventy Five Mile Beach", "id": "relation2611157", "orientation": 109.8, "beach_length": 104340.30115250713 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.175701667463983, -25.394744299356859 ], [ 153.182040724278636, -25.396805964137293 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "relation2723197", "orientation": 99.4, "beach_length": 3817.6368266284194 }, "geometry": { "type": "LineString", "coordinates": [ [ 152.833201610246448, -31.621366879809862 ], [ 152.839848506633871, -31.622303888877077 ] ] } },
{ "type": "Feature", "properties": { "name": "Burwood Beach", "id": "relation2874470", "orientation": 132.6, "beach_length": 1740.965507343255 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.739813879778922, -32.959293379850727 ], [ 151.744773234258588, -32.963119700548077 ] ] } },
{ "type": "Feature", "properties": { "name": "Palm Beach", "id": "relation2978551", "orientation": 98.7, "beach_length": 2771.560782507047 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.323728294761452, -33.590944774061647 ], [ 151.330388138512319, -33.591793688458139 ] ] } },
{ "type": "Feature", "properties": { "name": "Fishermans Beach", "id": "relation3359340", "orientation": 54.7, "beach_length": 1102.4177924785465 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.304926647932973, -33.738182783520628 ], [ 151.310425264466318, -33.734945167696978 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "relation3429622", "orientation": 100.0, "beach_length": 1491.3424905260258 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.499150446483497, -33.357989048245138 ], [ 151.505785455406851, -33.358966229185413 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "relation3433510", "orientation": 112.4, "beach_length": 11802.823651619099 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.206015285788453, -30.020201713319093 ], [ 153.212244289534908, -30.022424677992785 ] ] } },
{ "type": "Feature", "properties": { "name": "Safety Beach", "id": "relation3903790", "orientation": 92.9, "beach_length": 1337.5116588251049 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.198295596874715, -30.089175916493701 ], [ 153.205024333348007, -30.089470846525359 ] ] } },
{ "type": "Feature", "properties": { "name": "Woolgoolga Beach", "id": "relation3906007", "orientation": 63.7, "beach_length": 1582.8092428572195 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.198088813490756, -30.106416729330963 ], [ 153.20412876945889, -30.10383427192788 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "relation5141614", "orientation": 100.3, "beach_length": 19300.646943814729 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.040794241999237, -30.988061743377703 ], [ 153.047423034254706, -30.989094458534261 ] ] } },
{ "type": "Feature", "properties": { "name": "Boambee Beach", "id": "relation5206367", "orientation": 124.8, "beach_length": 6957.6457413516446 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.118093971968221, -30.33072027520112 ], [ 153.123626353606511, -30.334039025235214 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "relation5374363", "orientation": 120.2, "beach_length": 35839.673826800223 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.485185922621667, -28.984732247121318 ], [ 153.491008857103736, -28.987696753726748 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "relation5403455", "orientation": 107.4, "beach_length": 3467.7786659012691 }, "geometry": { "type": "LineString", "coordinates": [ [ 153.571344835025997, -28.374362035021626 ], [ 153.577773900064699, -28.37613471802031 ] ] } },
{ "type": "Feature", "properties": { "name": "Birdie Beach", "id": "relation5610370", "orientation": 141.0, "beach_length": 1786.9807326381458 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.605942741343654, -33.204047496782778 ], [ 151.610182702287801, -33.2084284120345 ] ] } },
{ "type": "Feature", "properties": { "name": "Birdie Beach", "id": "relation5610374", "orientation": 122.0, "beach_length": 9329.2708899211502 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.571638528455793, -33.233911939456952 ], [ 151.577352137704139, -33.236898196437302 ] ] } },
{ "type": "Feature", "properties": { "name": "Moonee Beach", "id": "relation5614833", "orientation": 85.2, "beach_length": 1414.8674396814627 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.629334061854081, -33.170133845309714 ], [ 151.636047797598934, -33.16966194212808 ] ] } },
{ "type": "Feature", "properties": { "name": "Catherine Hill Bay Beach", "id": "relation5614835", "orientation": 114.4, "beach_length": 2223.576899805525 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.629636403814999, -33.150707397544664 ], [ 151.635772011701221, -33.153037588724935 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "relation5622499", "orientation": 126.3, "beach_length": 3544.1909415687978 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.652586518417479, -33.099743272054873 ], [ 151.658016351121375, -33.103084550421912 ] ] } },
{ "type": "Feature", "properties": { "name": "Stockton Beach", "id": "relation5632945", "orientation": 154.4, "beach_length": 39038.19328012778 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.914889658623906, -32.82458287310245 ], [ 151.917800777865409, -32.829688574995842 ] ] } },
{ "type": "Feature", "properties": { "name": "Bennetts Beach", "id": "relation6158652", "orientation": 125.3, "beach_length": 17070.736099033849 }, "geometry": { "type": "LineString", "coordinates": [ [ 152.204507339562895, -32.634999855045457 ], [ 152.210005956096239, -32.638278380319974 ] ] } },
{ "type": "Feature", "properties": { "name": "Soldiers Beach", "id": "relation7491177", "orientation": 128.9, "beach_length": 1341.891275117024 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.562510072353433, -33.290767981140093 ], [ 151.567753380216516, -33.294304430933003 ] ] } },
{ "type": "Feature", "properties": { "name": "Putty Beach", "id": "relation7966651", "orientation": 162.2, "beach_length": 2032.480907416342 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.365952074280472, -33.528593310798257 ], [ 151.368011655015948, -33.533940624398603 ] ] } },
{ "type": "Feature", "properties": { "name": "noname", "id": "relation8302312", "orientation": 129.6, "beach_length": 12508.032082382493 }, "geometry": { "type": "LineString", "coordinates": [ [ 151.67716704962902, -33.041776618031669 ], [ 151.682358278298523, -33.045376557328964 ] ] } }
]
}

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notebooks/15_shoreline_positions_vs_wave_energy_v2.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import datetime\n",
"import pickle\n",
"import fiona\n",
"import shapely\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import geopandas\n",
"from scipy.stats import percentileofscore\n",
"from shapely.geometry import Point, MultiPoint\n",
"import numpy as np\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"import urllib.parse\n",
"import itertools\n",
"from tqdm import tqdm\n",
"import glob\n",
"from scipy.interpolate import griddata, SmoothBivariateSpline\n",
"from scipy.ndimage.filters import gaussian_filter\n",
"import colorcet as cc\n",
"import pytz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import Chris' 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_sites = df_from_csv('sites.csv', index_col=[0])\n",
"df_obs_impacts = df_from_csv('impacts_observed.csv', index_col=[0])\n",
"df_waves = df_from_csv('waves.csv', index_col=[0,1])\n",
"df_waves.index = df_waves.index.set_levels([df_waves.index.levels[0], pd.to_datetime(df_waves.index.levels[1])])\n",
"df_profiles = df_from_csv('profiles.csv', index_col=[0,1,2])\n",
"\n",
"# Load shoreline transects from coastsat\n",
"shoreline_transect_files = glob.glob('./15_data/df_transect_shorelines_*.csv')\n",
"df_shoreline_transects = pd.concat((pd.read_csv(f,\n",
" skiprows=0,\n",
" index_col=[0,1])\n",
" for f in shoreline_transect_files))\n",
"\n",
"# Convert index to datetime\n",
"df_shoreline_transects.index = df_shoreline_transects.index.set_levels(\n",
" [df_shoreline_transects.index.levels[0], pd.to_datetime(df_shoreline_transects.index.levels[1])])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('df_shoreline_transects:')\n",
"df_shoreline_transects.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate shoreline percentiles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = []\n",
"\n",
"for site_id, df in df_shoreline_transects.groupby(['site_id']):\n",
" \n",
" # Find last date before June 2016 storm\n",
" mask = pd.Series(df.index.get_level_values('time') < datetime.datetime(2016,6,3).replace(tzinfo=pytz.utc))\n",
" i_last_obs = mask[::-1].idxmax()\n",
" prestorm_observation = df.iloc[i_last_obs]\n",
"\n",
" # Get prestorm and post storm shoreline locations\n",
" # This is a shortcut, because the last x value of our profiles are at the shoreline\n",
" # If we wanted another elevation, this code needs to change\n",
" prestorm_shoreline_x = df_profiles.loc[(site_id,'prestorm',)].dropna(subset=['z']).iloc[-1].name\n",
" poststorm_shoreline_x = df_profiles.loc[(site_id,'poststorm',)].dropna(subset=['z']).iloc[-1].name\n",
"\n",
" # Find the corresponding percentile of prestorm and poststorm shorelines\n",
" prestorm_shoreline_pct = percentileofscore(df.shoreline_chainage_w_tide_correction.dropna(), prestorm_shoreline_x)\n",
" poststorm_shoreline_pct = percentileofscore(df.shoreline_chainage_w_tide_correction.dropna(), poststorm_shoreline_x)\n",
" change_shoreline_pct = poststorm_shoreline_pct - prestorm_shoreline_pct\n",
"\n",
" data.append({\n",
" 'site_id': site_id,\n",
" 'prestorm_shoreline_x': prestorm_shoreline_x,\n",
" 'poststorm_shoreline_x': poststorm_shoreline_x,\n",
" 'prestorm_shoreline_pct': prestorm_shoreline_pct,\n",
" 'poststorm_shoreline_pct': poststorm_shoreline_pct,\n",
" 'change_shoreline_pct': change_shoreline_pct,\n",
" })\n",
"\n",
"df_shorelines_pct = pd.DataFrame(data).set_index('site_id')\n",
"\n",
"print('df_shorelines_pct:')\n",
"df_shorelines_pct.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get wave data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Find closest nsw nearshore transformation output point to each site_id\n",
"\n",
"# Import nearshore wave site_id data\n",
"df_nearshore_ids = df_from_csv('nsw_nearshore_tool_site_locations.csv', index_col=[0],data_folder='./15_data/')\n",
"df_nearshore_ids.lat = pd.to_numeric(df_nearshore_ids.lat,errors='coerce')\n",
"df_nearshore_ids.lon = pd.to_numeric(df_nearshore_ids.lon,errors='coerce')\n",
"df_nearshore_ids.depth = pd.to_numeric(df_nearshore_ids.depth,errors='coerce')\n",
"gdf_nearshore_ids = geopandas.GeoDataFrame(\n",
" df_nearshore_ids, geometry=geopandas.points_from_xy(df_nearshore_ids.lon, df_nearshore_ids.lat))\n",
"gdf_nearshore_ids.crs = {'init' :'epsg:4283'}\n",
"gdf_nearshore_ids = gdf_nearshore_ids.to_crs(epsg=28356)\n",
"\n",
"gdf_nearshore_ids.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Convert sites to geopandas dataframe\n",
"\n",
"gdf_sites = geopandas.GeoDataFrame(\n",
" df_sites, geometry=geopandas.points_from_xy(df_sites.lon, df_sites.lat))\n",
"gdf_sites.crs = {'init' :'epsg:4283'}\n",
"gdf_sites = gdf_sites.to_crs(epsg=28356)\n",
"gdf_sites.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Warning, this cell takes around 9 mins to compute\n",
"# Need to do some optimizations\n",
"\n",
"# Calculate closest nearshore wave id for each site\n",
"from shapely.ops import nearest_points\n",
"\n",
"def near(point, df2=gdf_nearshore_ids):\n",
" # find the nearest point and return the corresponding Place value\n",
" nearest = df2.geometry == nearest_points(point, df2.geometry.unary_union)[1]\n",
" return df_nearshore_ids[nearest].index[0]\n",
"\n",
"start_time = datetime.datetime.now()\n",
"gdf_sites.loc[:,'nearshore_wave_id'] = gdf_sites.apply(lambda row: near(row.geometry), axis=1)\n",
"end_time = datetime.datetime.now()\n",
"print(\"Executed in: {}\".format(end_time - start_time))\n",
"gdf_sites.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download wave data using the NSW nearshore transformation website, for sites where we have shoreline measurements"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def nearshore_wave_csv_url(id,start_date,end_date):\n",
" URL = 'http://www.nswaves.com.au/transform.php'\n",
" payload = {\n",
" 'init': '1',\n",
" 'type': 'Transform-Full',\n",
" 'startsite': '{}'.format(id),\n",
" 'endsite': '{}'.format(id),\n",
" 'timestep': 'null',\n",
" 'startdate': start_date.strftime('%Y-%m-%d'),\n",
" 'starthour': '00',\n",
" 'enddate': end_date.strftime('%Y-%m-%d'),\n",
" 'endhour': '00',\n",
" 'sitestep': '1',\n",
" 'method': 'Parametric',\n",
" 'source': 'Waverider',\n",
" 'filename': 'ckl',\n",
" 'format': 'csv',\n",
" }\n",
"\n",
" session = requests.session()\n",
" r = requests.post(URL, data=payload)\n",
" \n",
" soup = BeautifulSoup(r.text)\n",
" \n",
" # Check if data extraction was successful\n",
" if soup.findAll(text=\"OK : Data Extraction Successful - Click filename/s to download data file\"):\n",
"\n",
" # Find all links\n",
" for link in soup.find_all('a'):\n",
"\n",
" href = link.get('href')\n",
" if '/data/full' not in href:\n",
" continue\n",
"\n",
" # Convert to absolute convert to absolute url\n",
" csv_url = urllib.parse.urljoin(URL, href)\n",
"\n",
" return csv_url\n",
" else:\n",
" return None\n",
"\n",
" \n",
"def download_csv(url, file_path):\n",
" urllib.request.urlretrieve(url,file_path)\n",
" print('Downloaded {}'.format(file_path))\n",
" \n",
" \n",
"def daterange(start_date, end_date,delta):\n",
" while start_date < end_date:\n",
" yield start_date\n",
" start_date += delta\n",
" \n",
"def download_nearshore_csv(site_id, nsw_nearshore_id, start_date, end_date,output_folder='./15_nearshore_waves/'):\n",
" \n",
" # Create output folder if doesn't already exists\n",
" os.makedirs(output_folder, exist_ok=True)\n",
"\n",
" # Output filename\n",
" output_filename = '{}_{}_{}_{}.csv'.format(\n",
" site_id,\n",
" nsw_nearshore_id,\n",
" start_date.strftime('%Y%m%d'),\n",
" end_date.strftime('%Y%m%d'),\n",
" )\n",
" output_filepath = os.path.join(output_folder,output_filename)\n",
"\n",
" # Don't download if file already exists\n",
" if os.path.isfile(output_filepath):\n",
" return\n",
"\n",
" csv_url = nearshore_wave_csv_url(nsw_nearshore_id,start_date,end_date)\n",
"\n",
" if csv_url:\n",
" download_csv(csv_url, output_filepath)\n",
" else:\n",
" print('No url found')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_sites[df_sites.beach.isin(['DIAMONDn','HARR','OLDBAR','DEEWHYn'])].index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# beach = 'DIAMONDn'\n",
"# beach = 'HARR'\n",
"# beach = 'OLDBAR'\n",
"# beach = 'DEEWHYn'\n",
"\n",
"site_ids_to_get = df_sites[df_sites.beach.isin(['DIAMONDn','HARR','OLDBAR','DEEWHYn'])].index\n",
"# site_ids_to_get = df_shorelines_pct.index\n",
"\n",
"# Define what start and end year we want to get\n",
"start_year=2005\n",
"end_year=2014\n",
"\n",
"# Construct a list of start and end times we can query\n",
"date_ranges = [(datetime.datetime(x, 1, 1), datetime.datetime(x, 12, 31))\n",
" for x in range(start_year, end_year + 1)]\n",
"\n",
"# Creates a list of inputs...\n",
"# [('NARRA0001',\n",
"# (datetime.datetime(2005, 1, 1, 0, 0),\n",
"# datetime.datetime(2005, 12, 31, 0, 0))),\n",
"# ('NARRA0001',\n",
"# (datetime.datetime(2006, 1, 1, 0, 0),\n",
"# datetime.datetime(2006, 12, 31, 0, 0))), ...\n",
"inputs = [x for x in list(itertools.product(site_ids_to_get, date_ranges))]\n",
"\n",
"for input_params in inputs:\n",
" site_id, (start_date, end_date) = input_params\n",
" download_nearshore_csv(site_id=site_id,\n",
" nsw_nearshore_id = gdf_sites.loc[site_id].nearshore_wave_id,\n",
" start_date=start_date,\n",
" end_date=end_date)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate mean wave conditions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"from tqdm import tqdm\n",
"wave_power_data = []\n",
"\n",
"# For each site, calculate mean wave power and storm wave power\n",
"site_ids = df_shorelines_pct.index\n",
"\n",
"for site_id in tqdm(site_ids):\n",
" \n",
" # Get shoreline orientation\n",
" orientation = df_sites.loc[site_id].orientation\n",
"\n",
"# # Get peak hour wave energy from June 2016 storm\n",
"# max_hrly_wave_power = df_waves.loc[[site['site_id']]].Pxs.max()\n",
"\n",
" # Load nearshore wave csv files into one dataframe\n",
" site_nearshore_wave_files = glob.glob('./15_nearshore_waves/*{}*'.format(site_id))\n",
"\n",
" if len(site_nearshore_wave_files) == 0:\n",
" continue\n",
"\n",
" df_hist_waves = pd.concat((pd.read_csv(f,\n",
" skiprows=8,\n",
" index_col=0,\n",
" names=['Hs', 'Tp', 'dir'],\n",
" na_values=' NaN')\n",
" for f in site_nearshore_wave_files))\n",
" df_hist_waves.index = pd.to_datetime(df_hist_waves.index)\n",
"\n",
" # At each row, calculate crossshore component of nearshore wave energy\n",
" df_hist_waves['d'] = 10\n",
" df_hist_waves['L'] = 9.81 * df_hist_waves.Tp**2 / 2 / np.pi\n",
" df_hist_waves['n'] = 0.5 * (\n",
" 1 + (4 * np.pi * df_hist_waves.d / df_hist_waves.L) /\n",
" (np.sinh(4 * np.pi * df_hist_waves.d / df_hist_waves.L)))\n",
" df_hist_waves['E'] = 1 / 16 * 1025 * 9.81 * df_hist_waves.Hs**2\n",
" df_hist_waves['C'] = 9.81 * df_hist_waves.Tp / 2 / np.pi * np.tanh(\n",
" 2 * np.pi * df_hist_waves.d / df_hist_waves.L)\n",
" df_hist_waves['shoreline_tn_angle'] = 270 - orientation\n",
" df_hist_waves.loc[\n",
" df_hist_waves.shoreline_tn_angle > 360,\n",
" 'shoreline_tn_angle'] = df_hist_waves.shoreline_tn_angle - 360\n",
" df_hist_waves[\n",
" 'alpha'] = df_hist_waves.shoreline_tn_angle - df_hist_waves.dir\n",
" df_hist_waves[\n",
" 'Px'] = df_hist_waves.n * df_hist_waves.E * df_hist_waves.C * np.cos(\n",
" np.deg2rad(df_hist_waves.alpha))\n",
"\n",
"# # Apply percentileofscore for June 2016 wave energy\n",
"# storm_Px_hrly_pctile = percentileofscore(df_hist_waves.Px.dropna().values,\n",
"# max_hrly_wave_power,\n",
"# kind='mean')\n",
"\n",
" # Calculate cumulate wave energy from storm\n",
" idx = ((df_waves.index.get_level_values('datetime') > '2016-06-04') &\n",
" (df_waves.index.get_level_values('datetime') < '2016-06-07') &\n",
" (df_waves.index.get_level_values('site_id') == site_id))\n",
" hrs = len(df_waves[idx])\n",
" Pxscum_storm = df_waves[idx].Pxs.sum()\n",
" \n",
" # Calculate cumulate wave energy of mean wave conditions over length of storm\n",
" Pxscum_mean = df_hist_waves['Px'].mean() * hrs\n",
" Pxscum_storm_mean_ratio = Pxscum_storm / Pxscum_mean\n",
"\n",
" wave_power_data.append({\n",
" 'site_id': site_id,\n",
" 'Pxscum_mean': Pxscum_mean,\n",
" 'Pxscum_storm': Pxscum_storm,\n",
" 'Pxscum_storm_mean_ratio': Pxscum_storm_mean_ratio\n",
" })\n",
"\n",
"df_wave_power = pd.DataFrame(wave_power_data).set_index('site_id')\n",
"df_wave_power.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Combine data into one data frame\n",
"df_plot = pd.concat([df_wave_power,df_shorelines_pct],axis=1)\n",
"df_plot.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define some helper functions\n",
"def round_down(num, divisor):\n",
" return num - (num%divisor)\n",
"\n",
"def round_up(x, divisor): \n",
" return (x + divisor - 1) // divisor * divisor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create interpolated grid to plot\n",
"x_col = 'Pxscum_storm_mean_ratio'\n",
"y_col = 'prestorm_shoreline_pct'\n",
"z_col = 'change_shoreline_pct'\n",
"\n",
"# Grid data\n",
"x_grid_max = round_up(max(df_plot[x_col]), 2)\n",
"y_grid_max = 100\n",
"\n",
"grid_x, grid_y = np.mgrid[0:x_grid_max:100j, 0:y_grid_max:100j]\n",
"\n",
"x_vals = df_plot[x_col].values\n",
"y_vals = df_plot[y_col].values\n",
"z_vals = df_plot[z_col].values\n",
"\n",
"points = [[x, y] for x, y in zip(\n",
" x_vals,\n",
" y_vals,\n",
")]\n",
"\n",
"grid = griddata((x_vals,y_vals), z_vals, (grid_x, grid_y), method='linear',rescale=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#https://stackoverflow.com/a/47500863\n",
"import scipy as sp\n",
"import scipy.interpolate\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"spline = sp.interpolate.Rbf(x_vals,y_vals,z_vals,function='linear',smooth=30)\n",
"\n",
"x_grid = np.linspace(0, max(x_vals), 100)\n",
"y_grid = np.linspace(0, max(y_vals), 100)\n",
"B1, B2 = np.meshgrid(x_grid, y_grid, indexing='xy')\n",
"Z = np.zeros((x_vals.size, z_vals.size))\n",
"\n",
"Z = spline(B1,B2)\n",
"fig = plt.figure(figsize=(10,6))\n",
"ax = axes3d.Axes3D(fig)\n",
"ax.view_init(elev=10., azim=230)\n",
"ax.plot_wireframe(B1, B2, Z,alpha=0.1)\n",
"ax.plot_surface(B1, B2, Z,alpha=0.1)\n",
"ax.scatter3D(x_vals,y_vals,z_vals, c='r')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create figure\n",
"fig = plt.figure(figsize=(3, 3), dpi=150, facecolor='w', edgecolor='k')\n",
"ax = fig.add_subplot(111)\n",
"\n",
"# Define colors\n",
"cmap_interval = 25\n",
"cmap = cc.cm.fire\n",
"vmin = round_down(np.min(z_vals), cmap_interval)\n",
"vmax = round_up(np.max(z_vals), cmap_interval)\n",
"levels = [x*cmap_interval for x in range(-4,2)]\n",
"\n",
"# Plot grid surface\n",
"cf = plt.contourf(grid_x, grid_y,grid,levels=levels, cmap=cmap,vmin=vmin,vmax=vmax)\n",
"\n",
"# Plot contours\n",
"cs = plt.contour(grid_x, grid_y,grid,levels=levels,linewidths=0.5,colors='white', vmin=vmin,vmax=vmax)\n",
"ax.clabel(cs, inline=1, fontsize=4, fmt='%1.0f%%')\n",
"\n",
"scatter = ax.scatter(\n",
" x=x_vals,\n",
" y=y_vals,\n",
" c=z_vals,\n",
" s=10,\n",
" linewidth=0.8,\n",
" edgecolor='k',\n",
" cmap=cmap,vmin=vmin,vmax=vmax\n",
")\n",
"\n",
"ax.set_xlim([round_down(min(x_vals), 1),round_up(max(x_vals), 1)])\n",
"\n",
"ax.set_xlabel(x_col)\n",
"ax.set_ylabel(y_col)\n",
"\n",
"cbar = plt.colorbar(cf)\n",
"cbar.set_label(z_col)\n",
"\n",
"ax.grid(True, linestyle=\"--\", alpha=0.2, color='grey', linewidth=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Find which beaches we should extract next\n",
"\n",
"d = pd.concat([df_sites, df_obs_impacts], axis=1)\n",
"for site_id, df in d.groupby(['beach']):\n",
" break\n",
" \n",
"d_msl_change= d.groupby(['beach']).width_msl_change_m.mean()\n",
"d_site_count = d.groupby(['beach']).lat.count()\n",
"d_site_count_msl_change = pd.concat([d_msl_change,d_site_count],axis=1).sort_values(by='width_msl_change_m')\n",
"l = len(d_site_count_msl_change)\n",
"print(d_site_count_msl_change[:10])\n",
"print('...')\n",
"print(d_site_count_msl_change[int(l/2-5):int(l/2+5)])\n",
"print('...')\n",
"print(d_site_count_msl_change[-10:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get polygons for coastsat\n",
"\n",
"# beach = 'DIAMONDn'\n",
"# beach = 'HARR'\n",
"# beach = 'OLDBAR'\n",
"beach = 'DEEWHYn'\n",
"buffer = 0.3 #percent\n",
"\n",
"points = MultiPoint([Point(r[1].lon, r[1].lat) for r in df_sites[df_sites.beach==beach].iterrows()])\n",
"# bounds = points.envelope.bounds\n",
"bounds = points.minimum_rotated_rectangle.bounds\n",
"\n",
"lon_range = bounds[2] - bounds[0]\n",
"lat_range = bounds[3] - bounds[1]\n",
"\n",
"x1 = bounds[0] - lon_range * buffer\n",
"x2 = bounds[2] + lon_range* buffer\n",
"y1 = bounds[1] - lat_range * buffer\n",
"y2 = bounds[3] + lat_range * buffer\n",
"\n",
"# Create our polygon\n",
"polygon = [[[x1, y1],\n",
" [x1, y2],\n",
" [x2, y2],\n",
" [x2, y1],\n",
" [x1, y1]]] \n",
"\n",
"print(beach)\n",
"polygon"
]
}
],
"metadata": {
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

12
src/analysis/observed_storm_impacts.py
Unescape Escape View File

@ -172,6 +172,11 @@ def volume_change(df_profiles, df_profile_features):
elif zone == "dune": elif zone == "dune":
d["x_min"] = df_profile_feature.dune_crest_x d["x_min"] = df_profile_feature.dune_crest_x
if np.isnan(df_profile_feature.dune_toe_x):
# If there's no dune toe, take most seaward value
d["x_max"] = max(df_profile.index.get_level_values("x"))
else:
d["x_max"] = df_profile_feature.dune_toe_x d["x_max"] = df_profile_feature.dune_toe_x
# For profiles with no Dlow value, the dune is undefined and we cannot calculate a dune volume. # For profiles with no Dlow value, the dune is undefined and we cannot calculate a dune volume.
@ -188,6 +193,10 @@ def volume_change(df_profiles, df_profile_features):
# Calculate change in volumes. Use the z_diff array which has been # Calculate change in volumes. Use the z_diff array which has been
# zero'ed out landward of the x_change_point # zero'ed out landward of the x_change_point
# Zero out nans so we can calculate change in beach volume.
z_diff.loc[np.isnan(z_diff)] = 0
params[zone]["vol_change"] = beach_volume( params[zone]["vol_change"] = beach_volume(
x=z_diff.index.values, x=z_diff.index.values,
z=z_diff.values, z=z_diff.values,
@ -195,6 +204,9 @@ def volume_change(df_profiles, df_profile_features):
x_max=params[zone]["prestorm"]["x_max"], x_max=params[zone]["prestorm"]["x_max"],
) )
if (zone == 'dune') & (np.isnan(params[zone]['vol_change'])):
print(site_id)
params[zone]["pct_change"] = ( params[zone]["pct_change"] = (
params[zone]["vol_change"] / params[zone]["prestorm"]["subaerial_vol"] params[zone]["vol_change"] / params[zone]["prestorm"]["subaerial_vol"]
) )

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