diff --git a/.gitignore b/.gitignore index 97e20c6..1f48f60 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,10 @@ .ipynb_checkpoints/ /notebooks/*.png /notebooks/*.csv - +/notebooks/14_nearshore_waves/ +/notebooks/15_nearshore_waves/ +/notebooks/*.pkl +/notebooks/15_data/ # exclude data from source control by default /data/ diff --git a/environment.yml b/environment.yml index 8196edd..21ed866 100644 --- a/environment.yml +++ b/environment.yml @@ -5,6 +5,7 @@ dependencies: - python=3.6 - attrs - pre_commit + - beautifulsoup4 - autopep8 - black - cartopy @@ -23,6 +24,7 @@ dependencies: - notebook - numpy - pandas + - geopandas - pandoc - pip - plotly @@ -43,6 +45,7 @@ dependencies: - tqdm - yaml - yapf + - jupyterlab - pip: - blackcellmagic - mat4py diff --git a/notebooks/01_exploration.ipynb b/notebooks/01_exploration.ipynb index aa84d10..80918c9 100644 --- a/notebooks/01_exploration.ipynb +++ b/notebooks/01_exploration.ipynb @@ -604,6 +604,13 @@ "])" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -899,7 +906,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.7" }, "toc": { "base_numbering": 1, diff --git a/notebooks/07_evaluate_model_performance.ipynb b/notebooks/07_evaluate_model_performance.ipynb index b15db14..e80e135 100644 --- a/notebooks/07_evaluate_model_performance.ipynb +++ b/notebooks/07_evaluate_model_performance.ipynb @@ -655,10 +655,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "code_folding": [ - 23, - 223 - ] + "code_folding": [] }, "outputs": [], "source": [ @@ -921,6 +918,19 @@ "fig.savefig('07_c&p_confusion_matrix',dpi=600,bbox_inches = \"tight\")\n", "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": { @@ -940,7 +950,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.7" }, "toc": { "base_numbering": 1, diff --git a/notebooks/11_investigate.ipynb b/notebooks/11_investigate.ipynb index 9b3e211..b66dd77 100644 --- a/notebooks/11_investigate.ipynb +++ b/notebooks/11_investigate.ipynb @@ -399,6 +399,46 @@ "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", "metadata": {}, @@ -824,7 +864,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.7" }, "toc": { "base_numbering": 1, diff --git a/notebooks/12_method_plots.ipynb b/notebooks/12_method_plots.ipynb index 9c40c5f..4d85160 100644 --- a/notebooks/12_method_plots.ipynb +++ b/notebooks/12_method_plots.ipynb @@ -1108,7 +1108,7 @@ "# Add title\n", "fig.text(\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", " verticalalignment='top',\n", " size=8)\n", @@ -1592,6 +1592,18 @@ "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" + }, "toc": { "base_numbering": 1, "nav_menu": {}, diff --git a/notebooks/13_locality_plot.ipynb b/notebooks/13_locality_plot.ipynb index f9f0f17..df4fcc3 100644 --- a/notebooks/13_locality_plot.ipynb +++ b/notebooks/13_locality_plot.ipynb @@ -459,7 +459,7 @@ " label='Beaches included')\n", "\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", "# create legend\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(150.891708-0.1, -34.433129, r'\\textsc{Wollongong}', transform=ccrs.Geodetic(),ha='right',zorder=4)\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", "\n", "# # Add inset for Narrabeen\n", @@ -492,19 +499,19 @@ "metadata": {}, "outputs": [], "source": [ - "# Try using overpass api\n", - "# Hard because coastline is given as line string, not shapefile.\n", + "# # Try using overpass api\n", + "# # Hard because coastline is given as line string, not shapefile.\n", "\n", - "import overpass\n", - "api = overpass.API()\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", + "# import overpass\n", + "# api = overpass.API()\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", "\n", "\n", - "for line in coords:\n", - " lats = [x[1] for x in line]\n", - " lons = [x[0] for x in line]\n", - " ax3.plot(lons,lats, transform=ccrs.Geodetic())" + "# for line in coords:\n", + "# lats = [x[1] for x in line]\n", + "# lons = [x[0] for x in line]\n", + "# ax3.plot(lons,lats, transform=ccrs.Geodetic())" ] }, { diff --git a/notebooks/14_beaches_Australia.geojson b/notebooks/14_beaches_Australia.geojson new file mode 100644 index 0000000..5cd6818 --- /dev/null +++ b/notebooks/14_beaches_Australia.geojson @@ -0,0 +1,497 @@ +{ +"type": "FeatureCollection", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, +"features": [ +{ "type": "Feature", "properties": { "name": "Freshwater Beach", "id": "way4042219" }, "geometry": { "type": "LineString", "coordinates": [ [ 151.293218912307509, -33.781066634474719 ], [ 151.293007100000011, -33.7810247 ], [ 151.292589600000014, -33.7810377 ], [ 151.29227, -33.7810559 ], [ 151.292243500000012, -33.7810606 ], [ 151.2917787, -33.7811402 ], [ 151.291275399999989, 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152.243454296747956, -32.582770954550604 ] ] ] } }, +{ "type": "Feature", "properties": { "area": 3.6 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 151.377071861239386, -33.523596463255998 ], [ 151.353052179498889, -33.529137911615919 ], [ 151.356141273190929, -33.538442265783701 ], [ 151.38016095493137, -33.53290141373239 ], [ 151.377071861239386, -33.523596463255998 ] ] ] } } +] +} diff --git a/notebooks/14_shoreline_position_vs_wave_energy.ipynb b/notebooks/14_shoreline_position_vs_wave_energy.ipynb new file mode 100644 index 0000000..568baae --- /dev/null +++ b/notebooks/14_shoreline_position_vs_wave_energy.ipynb @@ -0,0 +1,817 @@ +{ + "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)" + ] + } + ], + "metadata": { + "hide_input": false, + 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"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 ] ] } } +] +} diff --git a/notebooks/15_shoreline_positions_vs_wave_energy_v2.ipynb b/notebooks/15_shoreline_positions_vs_wave_energy_v2.ipynb new file mode 100644 index 0000000..e56cdf7 --- /dev/null +++ b/notebooks/15_shoreline_positions_vs_wave_energy_v2.ipynb @@ -0,0 +1,656 @@ +{ + "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 +} diff --git a/src/analysis/observed_storm_impacts.py b/src/analysis/observed_storm_impacts.py index 66f681f..321bd68 100644 --- a/src/analysis/observed_storm_impacts.py +++ b/src/analysis/observed_storm_impacts.py @@ -172,7 +172,12 @@ def volume_change(df_profiles, df_profile_features): elif zone == "dune": d["x_min"] = df_profile_feature.dune_crest_x - d["x_max"] = df_profile_feature.dune_toe_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 # 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 # 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( x=z_diff.index.values, z=z_diff.values, @@ -195,6 +204,9 @@ def volume_change(df_profiles, df_profile_features): 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]["vol_change"] / params[zone]["prestorm"]["subaerial_vol"] )