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name: C:\Users\z5189959\Desktop\nsw-2016-storm-impact\.venv
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name: C:\Users\z5189959\Desktop\nsw-2016-storm-impact\.venv
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channels:
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channels:
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- plotly
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- defaults
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- defaults
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- conda-forge
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- conda-forge
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dependencies:
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dependencies:
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- appdirs=1.4.3=py_1
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- boost=1.66.0=py36_vc14_1
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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||||||
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|
||||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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- xz=5.2.4=h2fa13f4_4
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|
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- pip:
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- pip:
|
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- blackcellmagic==0.0.1
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|
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|
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prefix: C:\Users\z5189959\Desktop\nsw-2016-storm-impact\.venv
|
prefix: C:\Users\z5189959\Desktop\nsw-2016-storm-impact\.venv
|
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|
|||||||
File diff suppressed because one or more lines are too long
@ -1,627 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Investigate \"collision protection volume\" concept"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T02:45:14.908283Z",
|
|
||||||
"start_time": "2018-12-05T02:45:14.556163Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"%matplotlib inline\n",
|
|
||||||
"%reload_ext autoreload\n",
|
|
||||||
"%autoreload"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T02:45:34.323928Z",
|
|
||||||
"start_time": "2018-12-05T02:45:14.911088Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from IPython.core.debugger import set_trace\n",
|
|
||||||
"\n",
|
|
||||||
"import pandas as pd\n",
|
|
||||||
"import numpy as np\n",
|
|
||||||
"import os\n",
|
|
||||||
"\n",
|
|
||||||
"import plotly\n",
|
|
||||||
"import plotly.graph_objs as go\n",
|
|
||||||
"import plotly.plotly as py\n",
|
|
||||||
"import plotly.tools as tls\n",
|
|
||||||
"import plotly.figure_factory as ff\n",
|
|
||||||
"import plotly.io as pio"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### Load data\n",
|
|
||||||
"Load data from the `./data/interim/` folder and parse into `pandas` dataframes."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T02:45:53.010702Z",
|
|
||||||
"start_time": "2018-12-05T02:45:34.324930Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Importing profiles.csv\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"C:\\Users\\z5189959\\Desktop\\nsw-2016-storm-impact\\.venv\\lib\\site-packages\\numpy\\lib\\arraysetops.py:522: FutureWarning:\n",
|
|
||||||
"\n",
|
|
||||||
"elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Importing profile_features.csv\n",
|
|
||||||
"Importing impacts_forecasted_foreshore_slope_sto06.csv\n",
|
|
||||||
"Importing impacts_forecasted_mean_slope_sto06.csv\n",
|
|
||||||
"Importing impacts_observed.csv\n",
|
|
||||||
"Importing twl_foreshore_slope_sto06.csv\n",
|
|
||||||
"Importing twl_mean_slope_sto06.csv\n",
|
|
||||||
"Done!\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
|
|
||||||
" print('Importing {}'.format(csv))\n",
|
|
||||||
" return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
|
|
||||||
"\n",
|
|
||||||
"df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
|
|
||||||
"df_profile_features = df_from_csv('profile_features.csv', index_col=[0])\n",
|
|
||||||
"\n",
|
|
||||||
"impacts = {\n",
|
|
||||||
" 'forecasted': {\n",
|
|
||||||
" 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
|
|
||||||
" 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
|
|
||||||
" },\n",
|
|
||||||
" 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
"twls = {\n",
|
|
||||||
" 'forecasted': {\n",
|
|
||||||
" 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
|
|
||||||
" 'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
|
|
||||||
" }\n",
|
|
||||||
"}\n",
|
|
||||||
"\n",
|
|
||||||
"print('Done!')"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"Lets define a function to calculate the \"collision protection volume\" based on prestorm profiles."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### Get berm feature functions\n",
|
|
||||||
"Define a couple of functions which are going to help us get features of our berms."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 27,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T03:01:56.646213Z",
|
|
||||||
"start_time": "2018-12-05T03:01:56.366466Z"
|
|
||||||
},
|
|
||||||
"code_folding": []
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from shapely.geometry import Point, LineString, Polygon\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def collision_protection_vol(x, z, d_low_x, d_low_z, lower_z, angle):\n",
|
|
||||||
" # First, get the bounding line strings of our protection volume\n",
|
|
||||||
" lower_line = LineString([Point(min(x), lower_z), Point(max(x), lower_z)])\n",
|
|
||||||
" profile_line = LineString([Point(x_coord, z_coord) for x_coord, z_coord in zip(x, z)\n",
|
|
||||||
" if all([not np.isnan(x_coord), not np.isnan(z_coord)])])\n",
|
|
||||||
" slope_line = LineString([Point(d_low_x, d_low_z),\n",
|
|
||||||
" Point(max(x), d_low_z - max(x) * np.sin(np.deg2rad(angle)))])\n",
|
|
||||||
"\n",
|
|
||||||
" # Work out where our lower line and slope line intersect\n",
|
|
||||||
" lower_profile_intersection = lower_line.intersection(profile_line)\n",
|
|
||||||
" d_protected_intersection = lower_line.intersection(slope_line)\n",
|
|
||||||
"\n",
|
|
||||||
" # Define the perimeter of the protection area\n",
|
|
||||||
" profile_protected = LineString([Point(x_coord, z_coord) for x_coord, z_coord\n",
|
|
||||||
" in zip(profile_line.xy[0], profile_line.xy[1])\n",
|
|
||||||
" if d_low_x < x_coord < lower_profile_intersection.xy[0][0]]\n",
|
|
||||||
" + [lower_profile_intersection]\n",
|
|
||||||
" + [d_protected_intersection]\n",
|
|
||||||
" + [Point(d_low_x, d_low_z)])\n",
|
|
||||||
"\n",
|
|
||||||
" # Convert to polygon and return the area (m3/m)\n",
|
|
||||||
" protection_area_poly = Polygon(profile_protected)\n",
|
|
||||||
" protection_area_vol = protection_area_poly.area\n",
|
|
||||||
" return protection_area_vol\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def get_berm_width(z, d_low_x):\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" Returns the width of the berm, defined by the distance between dune toe to z=0\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" x_seaward_limit = z.dropna().tail(1).reset_index().x[0]\n",
|
|
||||||
" return x_seaward_limit - d_low_x\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"site_id = 'NARRA0018'\n",
|
|
||||||
"profile_type = 'prestorm'\n",
|
|
||||||
"query = \"site_id == '{}' and profile_type == '{}'\".format(\n",
|
|
||||||
" site_id, profile_type)\n",
|
|
||||||
"prestorm_profile = df_profiles.query(query)\n",
|
|
||||||
"profile_features = df_profile_features.query(query)\n",
|
|
||||||
"\n",
|
|
||||||
"x = prestorm_profile.index.get_level_values('x')\n",
|
|
||||||
"z = prestorm_profile.z\n",
|
|
||||||
"d_low_x = profile_features.dune_toe_x.tolist()[0]\n",
|
|
||||||
"d_low_z = profile_features.dune_toe_z.tolist()[0]\n",
|
|
||||||
"angle = 60 # degrees from the horizontal\n",
|
|
||||||
"lower_z = 0.5 # from mhw\n",
|
|
||||||
"\n",
|
|
||||||
"vol = collision_protection_vol(x, z, d_low_x, d_low_z, lower_z, angle)\n",
|
|
||||||
"berm_width = get_berm_width(z, d_low_x)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 5,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T02:45:54.224110Z",
|
|
||||||
"start_time": "2018-12-05T02:45:54.030142Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from datetime import timedelta\n",
|
|
||||||
"\n",
|
|
||||||
"def wl_time(t, wl, z_lower, z_upper):\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" Returns the amount of time the water level is between two elevations.\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" df_wl = pd.DataFrame.from_records([(t_val, R2_val) for t_val, R2_val in zip(t,R2)], columns=['datetime','wl'])\n",
|
|
||||||
" df_wl.set_index(pd.DatetimeIndex(df_wl['datetime']),inplace=True)\n",
|
|
||||||
" df_wl.drop(columns=['datetime'], inplace=True)\n",
|
|
||||||
" \n",
|
|
||||||
" # Assumes that each record is one hour... probably need to check this\n",
|
|
||||||
" hours = len(df_wl.query('{} < wl < {}'.format(z_lower, z_upper)))\n",
|
|
||||||
" return timedelta(hours=hours)\n",
|
|
||||||
"\n",
|
|
||||||
"def wave_power(t, wl, z_lower, z_upper, Hs0, Tp):\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" Returns the cumulative wave power when the water level is between two elevations.\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" df_wl = pd.DataFrame.from_records([(t_val, R2_val,Hs0_val,Tp_val) for t_val, R2_val,Hs0_val,Tp_val in zip(t,R2,Hs0,Tp)], columns=['datetime','wl', 'Hs0','Tp'])\n",
|
|
||||||
" df_wl.set_index(pd.DatetimeIndex(df_wl['datetime']),inplace=True)\n",
|
|
||||||
" df_wl.drop(columns=['datetime'], inplace=True)\n",
|
|
||||||
" \n",
|
|
||||||
" # Assumes that each record is one hour... probably need to check this\n",
|
|
||||||
" rho = 1025 # kg/m3\n",
|
|
||||||
" g = 9.8 # m/s2\n",
|
|
||||||
" df_wl_times = df_wl.query('{} < wl < {}'.format(z_lower, z_upper))\n",
|
|
||||||
" power = rho * g ** 2 / 64 / np.pi * df_wl_times.Hs0 ** 2 * df_wl_times.Tp\n",
|
|
||||||
" return power.sum()\n",
|
|
||||||
"\n",
|
|
||||||
"df_twl = twls['forecasted']['mean_slope_sto06']\n",
|
|
||||||
"df_twl_site = df_twl.query(\"site_id == '{}'\".format(site_id))\n",
|
|
||||||
"\n",
|
|
||||||
"R2 = df_twl_site.R2.tolist()\n",
|
|
||||||
"t = df_twl_site.index.get_level_values('datetime')\n",
|
|
||||||
"z_lower = 0.5\n",
|
|
||||||
"z_upper = d_low_z\n",
|
|
||||||
"\n",
|
|
||||||
"exposed_time = wl_time(t, R2, z_lower,z_upper)\n",
|
|
||||||
"\n",
|
|
||||||
"Hs0 = df_twl.Hs0.tolist()\n",
|
|
||||||
"Tp = df_twl.Tp.tolist()\n",
|
|
||||||
"wave_p = wave_power(t, R2, z_lower,z_upper,Hs0, Tp)\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T02:45:54.231129Z",
|
|
||||||
"start_time": "2018-12-05T02:45:54.225660Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 57,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T03:37:45.472885Z",
|
|
||||||
"start_time": "2018-12-05T03:37:45.462857Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"0.96"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 57,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"def dune_toe_elevation_change(site_id, df_profile_features):\n",
|
|
||||||
" query = \"site_id == '{}'\".format(site_id)\n",
|
|
||||||
" profile_features = df_profile_features.query(query)\n",
|
|
||||||
" prestorm_dune_toe_z = profile_features.query(\"profile_type=='prestorm'\").dune_toe_z.tolist()[0]\n",
|
|
||||||
" poststorm_dune_toe_z = profile_features.query(\"profile_type=='poststorm'\").dune_toe_z.tolist()[0]\n",
|
|
||||||
" return prestorm_dune_toe_z - poststorm_dune_toe_z\n",
|
|
||||||
"\n",
|
|
||||||
"toe_ele_change = dune_toe_elevation_change(\"MANNING0081\", df_profile_features)\n",
|
|
||||||
"toe_ele_change"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 62,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T03:45:45.203827Z",
|
|
||||||
"start_time": "2018-12-05T03:45:13.608478Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"0 of 816\n",
|
|
||||||
"20 of 816\n",
|
|
||||||
"40 of 816\n",
|
|
||||||
"60 of 816\n",
|
|
||||||
"80 of 816\n",
|
|
||||||
"100 of 816\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vols = []\n",
|
|
||||||
"exposed_times = []\n",
|
|
||||||
"toe_ele_changes = []\n",
|
|
||||||
"wave_powers = []\n",
|
|
||||||
"berm_widths = []\n",
|
|
||||||
"swash_vol_changes = []\n",
|
|
||||||
"dune_face_vol_changes = []\n",
|
|
||||||
"site_ids_to_plot = []\n",
|
|
||||||
"\n",
|
|
||||||
"# Get site ids where we observed collision\n",
|
|
||||||
"observed_site_ids = impacts['observed'].query(\"storm_regime=='collision'\").index.get_level_values('site_id').unique()\n",
|
|
||||||
"\n",
|
|
||||||
"# # Get site ids where we forecast swash\n",
|
|
||||||
"# forecasted_site_ids = impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime=='swash'\").index.get_level_values('site_id').unique()\n",
|
|
||||||
"\n",
|
|
||||||
"# site_ids = set(observed_site_ids).intersection(set(forecasted_site_ids))\n",
|
|
||||||
"\n",
|
|
||||||
"site_ids = observed_site_ids\n",
|
|
||||||
"\n",
|
|
||||||
"# Calculate for each site\n",
|
|
||||||
"\n",
|
|
||||||
"for n, site_id in enumerate(site_ids):\n",
|
|
||||||
" \n",
|
|
||||||
" if n%20 ==0:\n",
|
|
||||||
" print('{} of {}'.format(n, len(site_ids)))\n",
|
|
||||||
" \n",
|
|
||||||
" try:\n",
|
|
||||||
" query = \"site_id == '{}' and profile_type == '{}'\".format(site_id, 'prestorm')\n",
|
|
||||||
" prestorm_profile = df_profiles.query(query)\n",
|
|
||||||
" profile_features = df_profile_features.query(query)\n",
|
|
||||||
"\n",
|
|
||||||
" vol = collision_protection_vol(x = prestorm_profile.index.get_level_values('x'),\n",
|
|
||||||
" z = prestorm_profile.z,\n",
|
|
||||||
" d_low_x = profile_features.dune_toe_x.tolist()[0],\n",
|
|
||||||
" d_low_z = profile_features.dune_toe_z.tolist()[0],\n",
|
|
||||||
" lower_z = profile_features.dune_toe_z.tolist()[0] - 2, # from mhw\n",
|
|
||||||
" angle = 60, # degrees from the horizontal\n",
|
|
||||||
" )\n",
|
|
||||||
" \n",
|
|
||||||
" df_twl = twls['forecasted']['mean_slope_sto06']\n",
|
|
||||||
" df_twl_site = df_twl.query(\"site_id == '{}'\".format(site_id))\n",
|
|
||||||
" \n",
|
|
||||||
" berm_width = get_berm_width(z = prestorm_profile.z,\n",
|
|
||||||
" d_low_x = profile_features.dune_toe_x.tolist()[0]) \n",
|
|
||||||
" \n",
|
|
||||||
" exposed_time = wl_time(t = df_twl_site.index.get_level_values('datetime'),\n",
|
|
||||||
" wl = df_twl_site.R2.tolist(),\n",
|
|
||||||
" z_lower = profile_features.dune_toe_z.tolist()[0] -2,\n",
|
|
||||||
" z_upper = profile_features.dune_toe_z.tolist()[0],\n",
|
|
||||||
" )\n",
|
|
||||||
" swash_vol_change = impacts['observed'].query(\"site_id == '{}'\".format(site_id)).swash_vol_change.tolist()[0]\n",
|
|
||||||
" dune_face_vol_change = impacts['observed'].query(\"site_id == '{}'\".format(site_id)).dune_face_vol_change.tolist()[0]\n",
|
|
||||||
" \n",
|
|
||||||
" power = wave_power(t = df_twl_site.index.get_level_values('datetime'),\n",
|
|
||||||
" wl = df_twl_site.R2.tolist(),\n",
|
|
||||||
" z_lower = profile_features.dune_toe_z.tolist()[0] -2,\n",
|
|
||||||
" z_upper = profile_features.dune_toe_z.tolist()[0],\n",
|
|
||||||
" Hs0=df_twl_site.Hs0.tolist(),\n",
|
|
||||||
" Tp=df_twl_site.Tp.tolist())\n",
|
|
||||||
" \n",
|
|
||||||
" toe_ele_change = dune_toe_elevation_change(site_id, df_profile_features)\n",
|
|
||||||
" except:\n",
|
|
||||||
" continue\n",
|
|
||||||
" \n",
|
|
||||||
"# print(site_id, toe_ele_change)\n",
|
|
||||||
" vols.append(vol)\n",
|
|
||||||
" exposed_times.append(exposed_time)\n",
|
|
||||||
" toe_ele_changes.append(toe_ele_change)\n",
|
|
||||||
" wave_powers.append(power)\n",
|
|
||||||
" berm_widths.append(berm_width)\n",
|
|
||||||
" swash_vol_changes.append(swash_vol_change)\n",
|
|
||||||
" dune_face_vol_changes.append(dune_face_vol_change)\n",
|
|
||||||
" site_ids_to_plot.append(site_id)\n",
|
|
||||||
" \n",
|
|
||||||
" if n>100:\n",
|
|
||||||
" break\n",
|
|
||||||
"\n",
|
|
||||||
" \n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-03T03:12:11.598150Z",
|
|
||||||
"start_time": "2018-12-03T03:12:11.590128Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 72,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T05:03:39.147413Z",
|
|
||||||
"start_time": "2018-12-05T05:03:39.070207Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "225855bac0d0464d9be74917812c19ac",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"FigureWidget({\n",
|
|
||||||
" 'data': [{'marker': {'size': 4},\n",
|
|
||||||
" 'mode': 'markers',\n",
|
|
||||||
" 'text': [-0…"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"trace1 = go.Scatter(\n",
|
|
||||||
" x=berm_widths,\n",
|
|
||||||
" y=dune_face_vol_changes,\n",
|
|
||||||
" text = ['{}<br>{}'.format(ele, site_id) for ele,site_id in zip(toe_ele_changes,site_ids_to_plot)],\n",
|
|
||||||
" mode='markers',\n",
|
|
||||||
" marker=dict(\n",
|
|
||||||
" size=4,\n",
|
|
||||||
"# color = [-1 if x<0 else 1 for x in toe_ele_changes],\n",
|
|
||||||
"# color = toe_ele_changes,\n",
|
|
||||||
"# color = dune_face_vol_changes,\n",
|
|
||||||
"# color = [x.total_seconds() / 60 / 60 for x in exposed_times],\n",
|
|
||||||
"# colorscale='Viridis',\n",
|
|
||||||
"# showscale=True\n",
|
|
||||||
" ))\n",
|
|
||||||
"\n",
|
|
||||||
"layout = go.Layout(\n",
|
|
||||||
" title='Dune Collision Protection',\n",
|
|
||||||
"# height=300,\n",
|
|
||||||
"# legend=dict(font={'size': 10}),\n",
|
|
||||||
"# margin=dict(t=50, b=50, l=50, r=20),\n",
|
|
||||||
" xaxis=dict(\n",
|
|
||||||
" title='Berm width',\n",
|
|
||||||
" autorange=True,\n",
|
|
||||||
" showgrid=True,\n",
|
|
||||||
" zeroline=True,\n",
|
|
||||||
" showline=True,\n",
|
|
||||||
" ),\n",
|
|
||||||
" yaxis=dict(\n",
|
|
||||||
" title='Dune face vol change',\n",
|
|
||||||
" autorange=True,\n",
|
|
||||||
" showgrid=True,\n",
|
|
||||||
" zeroline=True,\n",
|
|
||||||
" showline=True,\n",
|
|
||||||
" ))\n",
|
|
||||||
"\n",
|
|
||||||
"g_plot = go.FigureWidget(data=[trace1], layout=layout)\n",
|
|
||||||
"g_plot"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 51,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T03:15:46.517975Z",
|
|
||||||
"start_time": "2018-12-05T03:15:46.512936Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"[64.5799,\n",
|
|
||||||
" 21.0163,\n",
|
|
||||||
" 38.106,\n",
|
|
||||||
" 28.101,\n",
|
|
||||||
" 58.7247,\n",
|
|
||||||
" 33.5534,\n",
|
|
||||||
" 71.1675,\n",
|
|
||||||
" 52.6043,\n",
|
|
||||||
" 50.5765,\n",
|
|
||||||
" 39.9074,\n",
|
|
||||||
" 67.8385,\n",
|
|
||||||
" 43.9043,\n",
|
|
||||||
" 39.8181,\n",
|
|
||||||
" 37.7153,\n",
|
|
||||||
" 20.4454,\n",
|
|
||||||
" 39.7757,\n",
|
|
||||||
" 42.1843,\n",
|
|
||||||
" 33.6152,\n",
|
|
||||||
" 42.9587,\n",
|
|
||||||
" 39.9773,\n",
|
|
||||||
" 35.7835,\n",
|
|
||||||
" 31.2884,\n",
|
|
||||||
" -0.4618,\n",
|
|
||||||
" 31.0094,\n",
|
|
||||||
" 33.3479,\n",
|
|
||||||
" 47.8394,\n",
|
|
||||||
" 32.3566,\n",
|
|
||||||
" 36.5205,\n",
|
|
||||||
" 45.7109,\n",
|
|
||||||
" 16.0687,\n",
|
|
||||||
" 35.4375,\n",
|
|
||||||
" 43.327,\n",
|
|
||||||
" 53.5016,\n",
|
|
||||||
" 31.0357,\n",
|
|
||||||
" 47.6528,\n",
|
|
||||||
" 25.5658,\n",
|
|
||||||
" 41.0514,\n",
|
|
||||||
" 28.1645,\n",
|
|
||||||
" 44.5443,\n",
|
|
||||||
" 42.925,\n",
|
|
||||||
" 33.9535,\n",
|
|
||||||
" 36.2626,\n",
|
|
||||||
" 35.2536]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 51,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"# impacts['observed']\n",
|
|
||||||
"swash_vol_changes"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"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"
|
|
||||||
},
|
|
||||||
"toc": {
|
|
||||||
"base_numbering": 1,
|
|
||||||
"nav_menu": {},
|
|
||||||
"number_sections": true,
|
|
||||||
"sideBar": true,
|
|
||||||
"skip_h1_title": false,
|
|
||||||
"title_cell": "Table of Contents",
|
|
||||||
"title_sidebar": "Contents",
|
|
||||||
"toc_cell": false,
|
|
||||||
"toc_position": {},
|
|
||||||
"toc_section_display": true,
|
|
||||||
"toc_window_display": true
|
|
||||||
},
|
|
||||||
"varInspector": {
|
|
||||||
"cols": {
|
|
||||||
"lenName": 16,
|
|
||||||
"lenType": 16,
|
|
||||||
"lenVar": 40
|
|
||||||
},
|
|
||||||
"kernels_config": {
|
|
||||||
"python": {
|
|
||||||
"delete_cmd_postfix": "",
|
|
||||||
"delete_cmd_prefix": "del ",
|
|
||||||
"library": "var_list.py",
|
|
||||||
"varRefreshCmd": "print(var_dic_list())"
|
|
||||||
},
|
|
||||||
"r": {
|
|
||||||
"delete_cmd_postfix": ") ",
|
|
||||||
"delete_cmd_prefix": "rm(",
|
|
||||||
"library": "var_list.r",
|
|
||||||
"varRefreshCmd": "cat(var_dic_list()) "
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"types_to_exclude": [
|
|
||||||
"module",
|
|
||||||
"function",
|
|
||||||
"builtin_function_or_method",
|
|
||||||
"instance",
|
|
||||||
"_Feature"
|
|
||||||
],
|
|
||||||
"window_display": false
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 2
|
|
||||||
}
|
|
||||||
@ -1,782 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Investigate how dune toe compares to R_high"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-03T23:04:57.331037Z",
|
|
||||||
"start_time": "2018-12-03T23:04:57.006071Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"%matplotlib inline\n",
|
|
||||||
"%reload_ext autoreload\n",
|
|
||||||
"%autoreload"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-03T23:04:58.749827Z",
|
|
||||||
"start_time": "2018-12-03T23:04:57.333943Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from IPython.core.debugger import set_trace\n",
|
|
||||||
"\n",
|
|
||||||
"import pandas as pd\n",
|
|
||||||
"import numpy as np\n",
|
|
||||||
"import os\n",
|
|
||||||
"\n",
|
|
||||||
"import plotly\n",
|
|
||||||
"import plotly.graph_objs as go\n",
|
|
||||||
"import plotly.plotly as py\n",
|
|
||||||
"import plotly.tools as tls\n",
|
|
||||||
"import plotly.figure_factory as ff\n",
|
|
||||||
"import plotly.io as pio"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### Load data\n",
|
|
||||||
"Load data from the `./data/interim/` folder and parse into `pandas` dataframes."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-03T23:05:05.800496Z",
|
|
||||||
"start_time": "2018-12-03T23:04:58.751721Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Importing profiles.csv\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"C:\\Users\\z5189959\\Desktop\\nsw-2016-storm-impact\\.venv\\lib\\site-packages\\numpy\\lib\\arraysetops.py:522: FutureWarning:\n",
|
|
||||||
"\n",
|
|
||||||
"elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Importing profile_features.csv\n",
|
|
||||||
"Importing impacts_forecasted_foreshore_slope_sto06.csv\n",
|
|
||||||
"Importing impacts_forecasted_mean_slope_sto06.csv\n",
|
|
||||||
"Importing impacts_observed.csv\n",
|
|
||||||
"Importing twl_foreshore_slope_sto06.csv\n",
|
|
||||||
"Importing twl_mean_slope_sto06.csv\n",
|
|
||||||
"Done!\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
|
|
||||||
" print('Importing {}'.format(csv))\n",
|
|
||||||
" return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
|
|
||||||
"\n",
|
|
||||||
"df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
|
|
||||||
"df_profile_features = df_from_csv('profile_features.csv', index_col=[0])\n",
|
|
||||||
"\n",
|
|
||||||
"impacts = {\n",
|
|
||||||
" 'forecasted': {\n",
|
|
||||||
" 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
|
|
||||||
" 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
|
|
||||||
" },\n",
|
|
||||||
" 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
|
|
||||||
" }\n",
|
|
||||||
"\n",
|
|
||||||
"twls = {\n",
|
|
||||||
" 'forecasted': {\n",
|
|
||||||
" 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
|
|
||||||
" 'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
|
|
||||||
" }\n",
|
|
||||||
"}\n",
|
|
||||||
"\n",
|
|
||||||
"print('Done!')"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### Compare predicted R_high with D_low\n",
|
|
||||||
"Let's see what the distribution of R_high is compared with D_low. How far off are the predicted water levels compared with the dune toes?"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 29,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-04T02:20:58.446500Z",
|
|
||||||
"start_time": "2018-12-04T02:20:58.439480Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"def get_site_ids(df_forecasted, df_observed, forecasted_regime, observed_regime):\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" Returns list of site_ids which match the given forecasted and observed regime\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" set1 = set(df_forecasted.query(\"storm_regime == '{}'\".format(\n",
|
|
||||||
" forecasted_regime)).index.get_level_values('site_id'))\n",
|
|
||||||
" set2 = set(df_observed.query(\"storm_regime == '{}'\".format(\n",
|
|
||||||
" observed_regime)).index.get_level_values('site_id'))\n",
|
|
||||||
" return sorted(list(set1.intersection(set2)))\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"def get_R_high_D_low_diff(site_ids, df_profile_features, df_twls):\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" Returns a dataframe of the difference between the R_high and D_low differences. \n",
|
|
||||||
" Positive values indicate R_high is larger than D_low.\n",
|
|
||||||
" \"\"\"\n",
|
|
||||||
" # Get dune toes at these sites and predicted max R_high\n",
|
|
||||||
" df_toes = df_profile_features.loc[site_ids].query(\n",
|
|
||||||
" 'profile_type==\"prestorm\"').dune_toe_z\n",
|
|
||||||
" df_R_highs = df_twls.loc[site_ids].groupby('site_id')['R_high'].max()\n",
|
|
||||||
"\n",
|
|
||||||
" # Join into one dataframe\n",
|
|
||||||
" df_twl_toes = pd.concat([df_toes, df_R_highs], axis=1, sort=True)\n",
|
|
||||||
" df_twl_toes['diff'] = df_twl_toes['R_high'] - df_twl_toes['dune_toe_z']\n",
|
|
||||||
" return df_twl_toes['diff']\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 53,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-04T03:55:51.858020Z",
|
|
||||||
"start_time": "2018-12-04T03:55:50.879155Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "94883b85733444528fe8a73379ce4611",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"FigureWidget({\n",
|
|
||||||
" 'data': [{'marker': {'color': '#ef8a62'},\n",
|
|
||||||
" 'name': 'Overpredicted',\n",
|
|
||||||
" …"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"swash_overpredicted_site_ids = get_site_ids(df_forecasted=impacts['forecasted']['mean_slope_sto06'],\n",
|
|
||||||
" df_observed=impacts['observed'],\n",
|
|
||||||
" forecasted_regime='collision',\n",
|
|
||||||
" observed_regime='swash')\n",
|
|
||||||
"swash_overpredicted_diffs = get_R_high_D_low_diff(site_ids=swash_overpredicted_site_ids,\n",
|
|
||||||
" df_profile_features=df_profile_features,\n",
|
|
||||||
" df_twls=twls['forecasted']['mean_slope_sto06'])\n",
|
|
||||||
"\n",
|
|
||||||
"swash_correct_site_ids = get_site_ids(df_forecasted=impacts['forecasted']['mean_slope_sto06'],\n",
|
|
||||||
" df_observed=impacts['observed'],\n",
|
|
||||||
" forecasted_regime='swash',\n",
|
|
||||||
" observed_regime='swash')\n",
|
|
||||||
"swash_correct_diffs = get_R_high_D_low_diff(site_ids=swash_correct_site_ids,\n",
|
|
||||||
" df_profile_features=df_profile_features,\n",
|
|
||||||
" df_twls=twls['forecasted']['mean_slope_sto06'])\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"trace1 = go.Histogram(y=swash_correct_diffs.tolist(),\n",
|
|
||||||
" opacity=0.75,\n",
|
|
||||||
" name='Correctly predicted',\n",
|
|
||||||
" marker=dict(\n",
|
|
||||||
" color='#67a9cf',\n",
|
|
||||||
" ),\n",
|
|
||||||
" ybins=dict(\n",
|
|
||||||
" size=0.1\n",
|
|
||||||
"),)\n",
|
|
||||||
"trace2 = go.Histogram(y=swash_overpredicted_diffs.tolist(),\n",
|
|
||||||
" opacity=0.75,\n",
|
|
||||||
" name='Overpredicted',\n",
|
|
||||||
" marker=dict(\n",
|
|
||||||
" color='#ef8a62',\n",
|
|
||||||
"),\n",
|
|
||||||
" ybins=dict(\n",
|
|
||||||
" size=0.1\n",
|
|
||||||
"),)\n",
|
|
||||||
"\n",
|
|
||||||
"layout = go.Layout(\n",
|
|
||||||
" title='R_high - D_low<br>Swash Regime',\n",
|
|
||||||
" barmode='overlay',\n",
|
|
||||||
" yaxis=dict(\n",
|
|
||||||
" title='z (m AHD)'\n",
|
|
||||||
" ),\n",
|
|
||||||
" xaxis=dict(\n",
|
|
||||||
" title='Count'\n",
|
|
||||||
" ),\n",
|
|
||||||
" bargap=0.2,\n",
|
|
||||||
" bargroupgap=0.1,\n",
|
|
||||||
" legend=dict(x=.6, y=1)\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"g_plot_swash = go.FigureWidget(data=[trace2, trace1], layout=layout)\n",
|
|
||||||
"\n",
|
|
||||||
"# To output to file\n",
|
|
||||||
"img_bytes = pio.write_image(g_plot_swash, 'g_plot_swash.png',format='png', width=600, height=400, scale=5)\n",
|
|
||||||
"\n",
|
|
||||||
"g_plot_swash\n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 54,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-04T04:10:47.339268Z",
|
|
||||||
"start_time": "2018-12-04T04:10:45.796887Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "3933da9295fe446f9413bca8842100c2",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"FigureWidget({\n",
|
|
||||||
" 'data': [{'marker': {'color': '#ef8a62'},\n",
|
|
||||||
" 'name': 'Underpredicted',\n",
|
|
||||||
" …"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"collision_underpredicted_site_ids = get_site_ids(df_forecasted=impacts['forecasted']['mean_slope_sto06'],\n",
|
|
||||||
" df_observed=impacts['observed'],\n",
|
|
||||||
" forecasted_regime='swash',\n",
|
|
||||||
" observed_regime='collision')\n",
|
|
||||||
"collision_underpredicted_diffs = get_R_high_D_low_diff(site_ids=collision_underpredicted_site_ids,\n",
|
|
||||||
" df_profile_features=df_profile_features,\n",
|
|
||||||
" df_twls=twls['forecasted']['mean_slope_sto06'])\n",
|
|
||||||
"\n",
|
|
||||||
"collision_correct_site_ids = get_site_ids(df_forecasted=impacts['forecasted']['mean_slope_sto06'],\n",
|
|
||||||
" df_observed=impacts['observed'],\n",
|
|
||||||
" forecasted_regime='collision',\n",
|
|
||||||
" observed_regime='collision')\n",
|
|
||||||
"collision_correct_diffs = get_R_high_D_low_diff(site_ids=collision_correct_site_ids,\n",
|
|
||||||
" df_profile_features=df_profile_features,\n",
|
|
||||||
" df_twls=twls['forecasted']['mean_slope_sto06'])\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"trace1 = go.Histogram(y=collision_correct_diffs.tolist(),\n",
|
|
||||||
" opacity=0.75,\n",
|
|
||||||
" name='Correctly predicted',\n",
|
|
||||||
" marker=dict(\n",
|
|
||||||
" color='#67a9cf',\n",
|
|
||||||
" ),\n",
|
|
||||||
" ybins=dict(\n",
|
|
||||||
" size=0.1\n",
|
|
||||||
"),)\n",
|
|
||||||
"trace2 = go.Histogram(y=collision_underpredicted_diffs.tolist(),\n",
|
|
||||||
" opacity=0.75,\n",
|
|
||||||
" name='Underpredicted',\n",
|
|
||||||
" marker=dict(\n",
|
|
||||||
" color='#ef8a62',\n",
|
|
||||||
" ),\n",
|
|
||||||
" ybins=dict(\n",
|
|
||||||
" size=0.1\n",
|
|
||||||
"),)\n",
|
|
||||||
"\n",
|
|
||||||
"layout = go.Layout(\n",
|
|
||||||
" title='R_high - D_low<br>Collision Regime',\n",
|
|
||||||
" barmode='overlay',\n",
|
|
||||||
" yaxis=dict(\n",
|
|
||||||
" title='z (m AHD)'\n",
|
|
||||||
" ),\n",
|
|
||||||
" xaxis=dict(\n",
|
|
||||||
" title='Count'\n",
|
|
||||||
" ),\n",
|
|
||||||
" bargap=0.2,\n",
|
|
||||||
" bargroupgap=0.1,\n",
|
|
||||||
" legend=dict(x=.6, y=1)\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"g_plot_collision = go.FigureWidget(data=[trace2, trace1], layout=layout)\n",
|
|
||||||
"\n",
|
|
||||||
"# To output to file\n",
|
|
||||||
"img_bytes = pio.write_image(g_plot_collision, 'g_plot_collision.png',format='png', width=600, height=400, scale=5)\n",
|
|
||||||
"\n",
|
|
||||||
"g_plot_collision"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### Does dune toe lower?\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {
|
|
||||||
"heading_collapsed": true
|
|
||||||
},
|
|
||||||
"source": [
|
|
||||||
"### What do over predicted and underpredicted profiles look like?"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {
|
|
||||||
"hidden": true
|
|
||||||
},
|
|
||||||
"source": [
|
|
||||||
"Define a function for getting the average beach profile for a number of given site_ids:"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 156,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-04T23:11:08.853877Z",
|
|
||||||
"start_time": "2018-12-04T23:11:08.846876Z"
|
|
||||||
},
|
|
||||||
"hidden": true
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"def get_avg_profile(site_ids, debug=False):\n",
|
|
||||||
" rows = []\n",
|
|
||||||
" for n,site_id in enumerate(site_ids):\n",
|
|
||||||
" profile = df_profiles.query(\"site_id == '{}' and profile_type == 'prestorm'\".format(site_id))\n",
|
|
||||||
" profile_z = np.array(profile.z.tolist())\n",
|
|
||||||
" profile_x = np.array(profile.index.get_level_values('x').tolist())\n",
|
|
||||||
" \n",
|
|
||||||
" # Let's center the profile based on the z=0 location\n",
|
|
||||||
" idx_last_z_val = max(np.argwhere(~np.isnan(profile_z)==True))[0]\n",
|
|
||||||
" x_last_val = profile_x[idx_last_z_val]\n",
|
|
||||||
" profile_x = [x - x_last_val for x in profile_x]\n",
|
|
||||||
" \n",
|
|
||||||
" # Put values into a dictionary\n",
|
|
||||||
" for x,z in zip(profile_x, profile_z):\n",
|
|
||||||
" rows.append({'x':x, 'z': z})\n",
|
|
||||||
"\n",
|
|
||||||
" # Return early for debugging\n",
|
|
||||||
" if debug and n>3:\n",
|
|
||||||
" break\n",
|
|
||||||
" \n",
|
|
||||||
" # Create dataframe from rows\n",
|
|
||||||
" df = pd.DataFrame(rows)\n",
|
|
||||||
" avg_profile = df.groupby('x').agg({'z': [np.nanmean, np.nanstd]}).reset_index()\n",
|
|
||||||
"\n",
|
|
||||||
" return {\n",
|
|
||||||
" 'x': avg_profile.x.tolist(),\n",
|
|
||||||
" 'z': avg_profile.z.nanmean.tolist(),\n",
|
|
||||||
" 'std': avg_profile.z.nanstd.tolist(),\n",
|
|
||||||
" 'n': n+1 # number of profiles\n",
|
|
||||||
" }"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {
|
|
||||||
"hidden": true
|
|
||||||
},
|
|
||||||
"source": [
|
|
||||||
"Now, let's look at whether there is a difference between the average beach profile of correctly forecasted site_ids and incorrectly forecasted site_ids. First, looking at sites where we observed swash regime."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 161,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T02:00:36.853374Z",
|
|
||||||
"start_time": "2018-12-05T01:58:21.839366Z"
|
|
||||||
},
|
|
||||||
"code_folding": [],
|
|
||||||
"hidden": true
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "03f2e99d20a347f3922a0e6a36f99ccd",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"FigureWidget({\n",
|
|
||||||
" 'data': [{'line': {'color': 'rgb(205, 0, 0)', 'width': 2},\n",
|
|
||||||
" 'mode': 'lines',\n",
|
|
||||||
" …"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"overpredicted = get_avg_profile(swash_overpredicted_site_ids)\n",
|
|
||||||
"correct = get_avg_profile(swash_correct_site_ids)\n",
|
|
||||||
"\n",
|
|
||||||
"# Add mean profile\n",
|
|
||||||
"trace_overpredicted_mean = go.Scatter(\n",
|
|
||||||
" x=overpredicted['x'],\n",
|
|
||||||
" y=overpredicted['z'],\n",
|
|
||||||
" opacity=1,\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" name='Mean overpredicted profile (n={})'.format(overpredicted['n']),\n",
|
|
||||||
" line=dict(\n",
|
|
||||||
" color=('rgb(205, 0, 0)'),\n",
|
|
||||||
" width=2)\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"trace_overpredited_std_top = go.Scatter(\n",
|
|
||||||
" x=overpredicted['x'],\n",
|
|
||||||
" y=np.add(overpredicted['z'], overpredicted['std']),\n",
|
|
||||||
" opacity=1,\n",
|
|
||||||
" hoverinfo='none',\n",
|
|
||||||
" showlegend=False,\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" line=dict(\n",
|
|
||||||
" color=('rgb(205, 0, 0)'),\n",
|
|
||||||
" width=0.5,\n",
|
|
||||||
" dash='dash')\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"trace_overpredited_std_btm = go.Scatter(\n",
|
|
||||||
" x=overpredicted['x'],\n",
|
|
||||||
" y=np.subtract(overpredicted['z'], overpredicted['std']),\n",
|
|
||||||
" opacity=1,\n",
|
|
||||||
" hoverinfo='none',\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" showlegend=False,\n",
|
|
||||||
" line=dict(\n",
|
|
||||||
" color=('rgb(205, 0, 0)'),\n",
|
|
||||||
" width=0.5,\n",
|
|
||||||
" dash='dash')\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"trace_correct_mean = go.Scatter(\n",
|
|
||||||
" x=avg_correct_x,\n",
|
|
||||||
" y=avg_correct_z,\n",
|
|
||||||
" opacity=1,\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" name='Mean correct profile (n={})'.format(correct['n']),\n",
|
|
||||||
" line=dict(\n",
|
|
||||||
" color=('rgb(0, 205, 0)'),\n",
|
|
||||||
" width=2)\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"trace_correct_std_top = go.Scatter(\n",
|
|
||||||
" x=avg_correct_x,\n",
|
|
||||||
" y=np.add(avg_correct_z, avg_correct_std),\n",
|
|
||||||
" opacity=1,\n",
|
|
||||||
" hoverinfo='none',\n",
|
|
||||||
" showlegend=False,\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" line=dict(\n",
|
|
||||||
" color=('rgb(0, 205, 0)'),\n",
|
|
||||||
" width=0.5,\n",
|
|
||||||
" dash='dash')\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"trace_correct_std_btm = go.Scatter(\n",
|
|
||||||
" x=avg_correct_x,\n",
|
|
||||||
" y=np.subtract(avg_correct_z, avg_correct_std),\n",
|
|
||||||
" opacity=1,\n",
|
|
||||||
" hoverinfo='none',\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" showlegend=False,\n",
|
|
||||||
" line=dict(\n",
|
|
||||||
" color=('rgb(0, 205, 0)'),\n",
|
|
||||||
" width=0.5,\n",
|
|
||||||
" dash='dash')\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"layout = dict(showlegend=True,\n",
|
|
||||||
" title='Observed Swash Impact Regime',\n",
|
|
||||||
" legend=dict(x=.6, y=1),\n",
|
|
||||||
" xaxis=dict(\n",
|
|
||||||
" range=[-150, 0]),\n",
|
|
||||||
" yaxis=dict(\n",
|
|
||||||
" range=[0, 10]))\n",
|
|
||||||
"\n",
|
|
||||||
"fig = go.FigureWidget(data=[trace_overpredicted_mean,\n",
|
|
||||||
" trace_overpredited_std_top,\n",
|
|
||||||
" trace_overpredited_std_btm,\n",
|
|
||||||
" trace_correct_mean,\n",
|
|
||||||
" trace_correct_std_top,\n",
|
|
||||||
" trace_correct_std_btm],\n",
|
|
||||||
" layout=layout)\n",
|
|
||||||
"\n",
|
|
||||||
"# To output to file\n",
|
|
||||||
"img_bytes = pio.write_image(\n",
|
|
||||||
" fig, 'mean_profiles_swash.png', format='png', width=600, height=600, scale=5)\n",
|
|
||||||
"\n",
|
|
||||||
"fig"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {
|
|
||||||
"hidden": true
|
|
||||||
},
|
|
||||||
"source": [
|
|
||||||
"We can see that the difference is pretty minimal. For cases where we predicted collision, but observed swash (overprediction), we see that overpredicted profiles are slightly more concave than correctly predicted sites."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 162,
|
|
||||||
"metadata": {
|
|
||||||
"ExecuteTime": {
|
|
||||||
"end_time": "2018-12-05T02:03:38.394415Z",
|
|
||||||
"start_time": "2018-12-05T02:00:37.335377Z"
|
|
||||||
},
|
|
||||||
"hidden": true
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "1255bccc024e4690b4b8ff4ccc8e9e35",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"FigureWidget({\n",
|
|
||||||
" 'data': [{'line': {'color': 'rgb(205, 0, 0)', 'width': 2},\n",
|
|
||||||
" 'mode': 'lines',\n",
|
|
||||||
" …"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"underpredicted = get_avg_profile(collision_underpredicted_site_ids)\n",
|
|
||||||
"correct = get_avg_profile(collision_correct_site_ids)\n",
|
|
||||||
"\n",
|
|
||||||
"# Add mean profile\n",
|
|
||||||
"trace_underpredicted_mean = go.Scatter(\n",
|
|
||||||
" x = underpredicted['x'],\n",
|
|
||||||
" y= underpredicted['z'],\n",
|
|
||||||
" opacity = 1,\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" name='Mean underpredicted profile (n={})'.format(underpredicted['n']),\n",
|
|
||||||
" line = dict(\n",
|
|
||||||
" color = ('rgb(205, 0, 0)'),\n",
|
|
||||||
" width = 2)\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"trace_underpredicted_std_top = go.Scatter(\n",
|
|
||||||
" x = underpredicted['x'],\n",
|
|
||||||
" y= np.add(underpredicted['z'],underpredicted['std']),\n",
|
|
||||||
" opacity = 1,\n",
|
|
||||||
" hoverinfo='none',\n",
|
|
||||||
" showlegend=False,\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" line = dict(\n",
|
|
||||||
" color = ('rgb(205, 0, 0)'),\n",
|
|
||||||
" width = 0.5,\n",
|
|
||||||
" dash = 'dash')\n",
|
|
||||||
") \n",
|
|
||||||
"\n",
|
|
||||||
"trace_underpredicted_std_btm = go.Scatter(\n",
|
|
||||||
" x = underpredicted['x'],\n",
|
|
||||||
" y= np.subtract(underpredicted['z'],underpredicted['std']),\n",
|
|
||||||
" opacity = 1,\n",
|
|
||||||
" hoverinfo='none',\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" showlegend=False,\n",
|
|
||||||
" line = dict(\n",
|
|
||||||
" color = ('rgb(205, 0, 0)'),\n",
|
|
||||||
" width = 0.5,\n",
|
|
||||||
" dash = 'dash')\n",
|
|
||||||
") \n",
|
|
||||||
"\n",
|
|
||||||
"trace_correct_mean = go.Scatter(\n",
|
|
||||||
" x = avg_correct_x,\n",
|
|
||||||
" y= avg_correct_z,\n",
|
|
||||||
" opacity = 1,\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" name='Mean correct profile (n={})'.format(correct['n']),\n",
|
|
||||||
" line = dict(\n",
|
|
||||||
" color = ('rgb(0, 205, 0)'),\n",
|
|
||||||
" width = 2)\n",
|
|
||||||
")\n",
|
|
||||||
"\n",
|
|
||||||
"trace_correct_std_top = go.Scatter(\n",
|
|
||||||
" x = avg_correct_x,\n",
|
|
||||||
" y= np.add(avg_correct_z, avg_correct_std),\n",
|
|
||||||
" opacity = 1,\n",
|
|
||||||
" hoverinfo='none',\n",
|
|
||||||
" showlegend=False,\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" line = dict(\n",
|
|
||||||
" color = ('rgb(0, 205, 0)'),\n",
|
|
||||||
" width = 0.5,\n",
|
|
||||||
" dash = 'dash')\n",
|
|
||||||
") \n",
|
|
||||||
"\n",
|
|
||||||
"trace_correct_std_btm = go.Scatter(\n",
|
|
||||||
" x = avg_correct_x,\n",
|
|
||||||
" y= np.subtract(avg_correct_z, avg_correct_std),\n",
|
|
||||||
" opacity = 1,\n",
|
|
||||||
" hoverinfo='none',\n",
|
|
||||||
" mode='lines',\n",
|
|
||||||
" showlegend=False,\n",
|
|
||||||
" line = dict(\n",
|
|
||||||
" color = ('rgb(0, 205, 0)'),\n",
|
|
||||||
" width = 0.5,\n",
|
|
||||||
" dash = 'dash')\n",
|
|
||||||
") \n",
|
|
||||||
" \n",
|
|
||||||
"layout = dict(showlegend=True,\n",
|
|
||||||
" title='Observed Collision Impact Regime',\n",
|
|
||||||
" legend=dict(x=.6, y=1),\n",
|
|
||||||
" xaxis=dict(\n",
|
|
||||||
" range=[-150,0]),\n",
|
|
||||||
" yaxis=dict(\n",
|
|
||||||
" range=[0,10]))\n",
|
|
||||||
" \n",
|
|
||||||
"fig=go.FigureWidget(data=[trace_underpredicted_mean, \n",
|
|
||||||
" trace_underpredicted_std_top,\n",
|
|
||||||
" trace_underpredicted_std_btm, \n",
|
|
||||||
" trace_correct_mean, \n",
|
|
||||||
" trace_correct_std_top, \n",
|
|
||||||
" trace_correct_std_btm], \n",
|
|
||||||
" layout=layout)\n",
|
|
||||||
"\n",
|
|
||||||
"# To output to file\n",
|
|
||||||
"img_bytes = pio.write_image(fig, 'mean_profiles_collision.png',format='png', width=600, height=600, scale=5)\n",
|
|
||||||
"\n",
|
|
||||||
"fig\n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"metadata": {
|
|
||||||
"hidden": true
|
|
||||||
},
|
|
||||||
"source": [
|
|
||||||
"This plot is a bit more interesting. It shows that we are correctly forecasting collision when the profile is more accreted/convex, but when the profile is more eroded/concave, the water level is underpredicted. Why is this? "
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"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"
|
|
||||||
},
|
|
||||||
"toc": {
|
|
||||||
"base_numbering": 1,
|
|
||||||
"nav_menu": {},
|
|
||||||
"number_sections": true,
|
|
||||||
"sideBar": true,
|
|
||||||
"skip_h1_title": false,
|
|
||||||
"title_cell": "Table of Contents",
|
|
||||||
"title_sidebar": "Contents",
|
|
||||||
"toc_cell": false,
|
|
||||||
"toc_position": {
|
|
||||||
"height": "calc(100% - 180px)",
|
|
||||||
"left": "10px",
|
|
||||||
"top": "150px",
|
|
||||||
"width": "232.391px"
|
|
||||||
},
|
|
||||||
"toc_section_display": true,
|
|
||||||
"toc_window_display": true
|
|
||||||
},
|
|
||||||
"varInspector": {
|
|
||||||
"cols": {
|
|
||||||
"lenName": 16,
|
|
||||||
"lenType": 16,
|
|
||||||
"lenVar": 40
|
|
||||||
},
|
|
||||||
"kernels_config": {
|
|
||||||
"python": {
|
|
||||||
"delete_cmd_postfix": "",
|
|
||||||
"delete_cmd_prefix": "del ",
|
|
||||||
"library": "var_list.py",
|
|
||||||
"varRefreshCmd": "print(var_dic_list())"
|
|
||||||
},
|
|
||||||
"r": {
|
|
||||||
"delete_cmd_postfix": ") ",
|
|
||||||
"delete_cmd_prefix": "rm(",
|
|
||||||
"library": "var_list.r",
|
|
||||||
"varRefreshCmd": "cat(var_dic_list()) "
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"types_to_exclude": [
|
|
||||||
"module",
|
|
||||||
"function",
|
|
||||||
"builtin_function_or_method",
|
|
||||||
"instance",
|
|
||||||
"_Feature"
|
|
||||||
],
|
|
||||||
"window_display": false
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 2
|
|
||||||
}
|
|
||||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue