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nsw-2016-storm-impact/notebooks/08-narr-topo-bathy-slope-te...

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Narrabeen Slope Test\n",
"With full topo and bathy combined"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Enable autoreloading of our modules. \n",
"# Most of the code will be located in the /src/ folder, \n",
"# and then called from the notebook.\n",
"%matplotlib inline\n",
"%reload_ext autoreload\n",
"%autoreload"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.core.debugger import set_trace\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import decimal\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",
"from plotly import tools\n",
"import plotly.io as pio\n",
"from scipy import stats\n",
"import math\n",
"import matplotlib\n",
"from matplotlib import cm\n",
"import colorlover as cl\n",
"from tqdm import tqdm_notebook\n",
"from ipywidgets import widgets, Output\n",
"from IPython.display import display, clear_output, Image, HTML\n",
"from scipy import stats\n",
"from sklearn.metrics import confusion_matrix\n",
"import matplotlib.pyplot as plt\n",
"from scipy.interpolate import interp1d\n",
"from pandas.api.types import CategoricalDtype\n",
"from scipy.interpolate import UnivariateSpline\n",
"from shapely.geometry import Point, LineString"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Matplot lib default settings\n",
"plt.rcParams[\"figure.figsize\"] = (10,6)\n",
"plt.rcParams['axes.grid']=True\n",
"plt.rcParams['grid.alpha'] = 0.5\n",
"plt.rcParams['grid.color'] = \"grey\"\n",
"plt.rcParams['grid.linestyle'] = \"--\"\n",
"plt.rcParams['axes.grid']=True\n",
"\n",
"# https://stackoverflow.com/a/20709149\n",
"matplotlib.rcParams['text.usetex'] = True\n",
"\n",
"matplotlib.rcParams['text.latex.preamble'] = [\n",
" r'\\usepackage{siunitx}', # i need upright \\micro symbols, but you need...\n",
" r'\\sisetup{detect-all}', # ...this to force siunitx to actually use your fonts\n",
" r'\\usepackage{helvet}', # set the normal font here\n",
" r'\\usepackage{amsmath}',\n",
" r'\\usepackage{sansmath}', # load up the sansmath so that math -> helvet\n",
" r'\\sansmath', # <- tricky! -- gotta actually tell tex to use!\n",
"] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import .csv data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_filename = '08-narr-topo-bathy-slope-test-full-profiles.csv'\n",
"\n",
"df_profiles = pd.read_csv(data_filename).set_index(['site_id','x'])\n",
"df_profiles = df_profiles[~df_profiles.index.duplicated(keep='first')]\n",
"print('df_profiles:')\n",
"df_profiles.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Manually cut off the prestorm topo \n",
"cuts = {'NARRA0004': {'prestorm_topo_max_x': 330,\n",
" 'poststorm_topo_max_x': 250},\n",
" 'NARRA0008': {'prestorm_topo_max_x': 290,\n",
" 'poststorm_topo_max_x': 250},\n",
" 'NARRA0012': {'prestorm_topo_max_x': 300,\n",
" 'poststorm_topo_max_x': 250},\n",
" 'NARRA0016': {'prestorm_topo_max_x': 300,\n",
" 'poststorm_topo_max_x': 225},\n",
" 'NARRA0021': {'prestorm_topo_max_x': 280,\n",
" 'poststorm_topo_max_x': 225},\n",
" 'NARRA0023': {'prestorm_topo_max_x': 275,\n",
" 'poststorm_topo_max_x': 215},\n",
" 'NARRA0027': {'prestorm_topo_max_x': 260,\n",
" 'poststorm_topo_max_x': 225},\n",
" 'NARRA0031': {'prestorm_topo_max_x': 260,\n",
" 'poststorm_topo_max_x': 225},\n",
" }\n",
"\n",
"for site_id in cuts:\n",
" mask1 = df_profiles.index.get_level_values('site_id') == site_id\n",
" mask2 = df_profiles.index.get_level_values('x') > cuts[site_id]['prestorm_topo_max_x']\n",
" df_profiles.loc[(mask1)&(mask2), 'pre_topo'] = np.nan\n",
" \n",
" mask3 = df_profiles.index.get_level_values('x') > cuts[site_id]['poststorm_topo_max_x']\n",
" df_profiles.loc[(mask1)&(mask3), 'post_topo'] = np.nan\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# for site_id,df_site in df_profiles.groupby('site_id'):\n",
"# f, (ax1) = plt.subplots(1,1, figsize=(6, 3))\n",
"# ax1.set_title(site_id)\n",
" \n",
"# ax1.plot(df_site.index.get_level_values('x'),\n",
"# df_site.pre_topo,\n",
"# label='Pre Topo',\n",
"# color='#2c7bb6')\n",
"# ax1.plot(df_site.index.get_level_values('x'),\n",
"# df_site.pre_bathy,\n",
"# label='Pre Bathy',\n",
"# color='#abd9e9')\n",
"\n",
"# ax1.plot(df_site.index.get_level_values('x'),\n",
"# df_site.post_topo,\n",
"# label='Post Topo',\n",
"# color='#d7191c')\n",
"# ax1.plot(df_site.index.get_level_values('x'),\n",
"# df_site.post_bathy,\n",
"# label='Post Bathy',\n",
"# color='#fdae61')\n",
"\n",
"# ax1.legend()\n",
"# plt.show()\n",
"# plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_profiles = df_profiles.dropna(\n",
" subset=['post_topo', 'post_bathy', 'pre_bathy', 'pre_topo'], how='all')\n",
"\n",
"df_profiles = df_profiles.reset_index()\n",
"df_profiles = df_profiles.melt(id_vars=['site_id','x','lat','lon'],\n",
" value_vars=['post_topo','post_bathy','pre_bathy','pre_topo']).rename(columns={'variable':'profile_type', 'value':'z'})\n",
"\n",
"df_profiles = df_profiles.dropna(subset=['z'])\n",
"\n",
"df_profiles.loc[df_profiles.profile_type=='post_topo','profile_type']='poststorm'\n",
"df_profiles.loc[df_profiles.profile_type=='post_bathy','profile_type']='poststorm'\n",
"df_profiles.loc[df_profiles.profile_type=='pre_topo','profile_type']='prestorm'\n",
"df_profiles.loc[df_profiles.profile_type=='pre_bathy','profile_type']='prestorm'\n",
"\n",
"df_profiles = df_profiles.set_index(['site_id', 'profile_type', 'x'])\n",
"df_profiles = df_profiles[~df_profiles.index.duplicated(keep='first')]\n",
"\n",
"df_profiles = df_profiles.sort_index()\n",
"\n",
"print('df_profiles:')\n",
"df_profiles.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Just plots each site's x and z values\n",
"for site_id,df_site in df_profiles.groupby('site_id'):\n",
" f, (ax1) = plt.subplots(1,1, figsize=(6, 3))\n",
" ax1.set_title(site_id)\n",
" \n",
" prestorm=df_site.index.get_level_values('profile_type') == 'prestorm'\n",
" ax1.plot(df_site[prestorm].index.get_level_values('x'),\n",
" df_site[prestorm].z,\n",
" label='Pre Topo',\n",
" color='#2c7bb6')\n",
"\n",
" \n",
" poststorm=df_site.index.get_level_values('profile_type') == 'poststorm'\n",
" ax1.plot(df_site[poststorm].index.get_level_values('x'),\n",
" df_site[poststorm].z,\n",
" label='Post Topo',\n",
" color='#d7191c')\n",
"\n",
"\n",
" ax1.legend()\n",
" plt.show()\n",
" plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get dune faces"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"# Manually define dune x coordinates and work out slope\n",
"\n",
"dune_data = [\n",
" {\n",
" 'site_id': 'NARRA0004',\n",
" 'dune_crest_x': 180,\n",
" 'dune_toe_x': 205\n",
" },\n",
" {\n",
" 'site_id': 'NARRA0008',\n",
" 'dune_crest_x': 180,\n",
" 'dune_toe_x': 205\n",
" },\n",
" {\n",
" 'site_id': 'NARRA0012',\n",
" 'dune_crest_x': 195,\n",
" 'dune_toe_x': 205\n",
" },\n",
" {\n",
" 'site_id': 'NARRA0016',\n",
" 'dune_crest_x': 190,\n",
" 'dune_toe_x': 200\n",
" },\n",
" {\n",
" 'site_id': 'NARRA0021',\n",
" 'dune_crest_x': 205,\n",
" 'dune_toe_x': 210\n",
" },\n",
" {\n",
" 'site_id': 'NARRA0023',\n",
" 'dune_crest_x': 205,\n",
" 'dune_toe_x': 215\n",
" },\n",
" {\n",
" 'site_id': 'NARRA0027',\n",
" 'dune_crest_x': 210,\n",
" 'dune_toe_x': 219\n",
" },\n",
" {\n",
" 'site_id': 'NARRA0031',\n",
" 'dune_crest_x': 210,\n",
" 'dune_toe_x': 218\n",
" },\n",
"]\n",
"\n",
"for site_dune in dune_data:\n",
" df_site = df_profiles.xs(site_dune['site_id'], level='site_id').xs('prestorm',level='profile_type')\n",
" \n",
" dune_crest_x = site_dune['dune_crest_x']\n",
" dune_toe_x = site_dune['dune_toe_x']\n",
" dune_crest_z = df_site.iloc[df_site.index.get_loc(site_dune['dune_crest_x'],method='nearest')].z\n",
" dune_toe_z = df_site.iloc[df_site.index.get_loc(site_dune['dune_toe_x'],method='nearest')].z\n",
"\n",
" dune_slope = (dune_crest_z - dune_toe_z)/(dune_crest_x - dune_toe_x)\n",
" \n",
" site_dune['dune_crest_z'] = dune_crest_z\n",
" site_dune['dune_toe_z'] = dune_toe_z\n",
" site_dune['dune_slope'] = dune_slope\n",
" \n",
" \n",
"# Join back into main data\n",
"df_dunes = pd.DataFrame(dune_data).set_index('site_id')\n",
"print('df_dunes:')\n",
"df_dunes.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Just plots each site's x and z values\n",
"# for site_id,df_site in df_profiles.xs('prestorm',level='profile_type').groupby('site_id'):\n",
"# f, (ax1) = plt.subplots(1,1, figsize=(6, 3))\n",
"# ax1.set_title(site_id)\n",
"# ax1.plot(df_site.index.get_level_values('x'),\n",
"# df_site.z)\n",
"# ax1.plot([df_dunes.loc[site_id].dune_crest_x, df_dunes.loc[site_id].dune_toe_x],\n",
"# [df_dunes.loc[site_id].dune_crest_z, df_dunes.loc[site_id].dune_toe_z],\n",
"# 'r.-')\n",
"# ax1.set_xlim([150,250])\n",
"# ax1.set_ylim([0,15])\n",
"# plt.show()\n",
"# plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get prestorm slope"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"z_ele = 0.7\n",
"debug=False\n",
"\n",
"def find_nearest_idx(array, value):\n",
" array = np.asarray(array)\n",
" idx = (np.abs(array - value)).argmin()\n",
" return idx\n",
"\n",
"prestorm_slope_data =[]\n",
"for site_id, df_site in df_profiles.xs('prestorm',level='profile_type').groupby('site_id'):\n",
" \n",
" # Find index of our z_ele\n",
" idx = np.where(df_site.z.values>=z_ele)[0][-1]\n",
" \n",
" prestorm_end_x = df_site.iloc[idx].name[1]\n",
" prestorm_end_z = df_site.iloc[idx].z\n",
" \n",
" prestorm_start_x = df_dunes.loc[site_id].dune_toe_x\n",
" prestorm_start_z = df_dunes.loc[site_id].dune_toe_z\n",
" \n",
" prestorm_slope = (prestorm_end_z-prestorm_start_z)/(prestorm_end_x-prestorm_start_x)\n",
" \n",
" prestorm_slope_data.append({\n",
" 'site_id': site_id,\n",
" 'prestorm_end_x': prestorm_end_x,\n",
" 'prestorm_end_z': prestorm_end_z,\n",
" 'prestorm_start_x': prestorm_start_x,\n",
" 'prestorm_start_z': prestorm_start_z,\n",
" 'prestorm_slope': prestorm_slope\n",
" })\n",
" \n",
"df_prestorm_slope = pd.DataFrame(prestorm_slope_data).set_index(['site_id'])\n",
"print('df_prestorm_slope:')\n",
"df_prestorm_slope.head()\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get shelf slope\n",
"At 10 m contour"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"# Elevation to take shelf slope at\n",
"z_ele = -9\n",
"debug=False\n",
"\n",
"def find_nearest_idx(array, value):\n",
" array = np.asarray(array)\n",
" idx = (np.abs(array - value)).argmin()\n",
" return idx\n",
"\n",
"def slope_at_point(x, z, z_ele,debug=False):\n",
" # Smooth profile a bit\n",
" # TODO the smoothing factor will change based on the number of data points\n",
" # Need to fix\n",
" s = UnivariateSpline(x, z, s=50)\n",
" xs = np.linspace(min(x),max(x),1000)\n",
" zs = s(xs)\n",
"\n",
" # Calculate derivates of spline\n",
" dzdx = np.diff(zs)/np.diff(xs)\n",
"\n",
" # Find index of z_ele\n",
" idx = find_nearest_idx(zs, z_ele)\n",
" slope = dzdx[idx]\n",
" shelf_x = xs[idx]\n",
"\n",
"\n",
" \n",
" # For checking how much smoothing is going on\n",
" if debug:\n",
" f, (ax1) = plt.subplots(1,1, figsize=(6, 3))\n",
" ax1.plot(x,z)\n",
" ax1.plot(xs,zs)\n",
" plt.show()\n",
" plt.close()\n",
" \n",
" return slope, shelf_x, z_ele\n",
" \n",
"shelf_data = []\n",
"for site_id, df_site in df_profiles.xs('prestorm',level='profile_type').groupby('site_id'):\n",
" shelf_slope, shelf_x, shelf_z = slope_at_point(df_site.index.get_level_values('x').values,\n",
" df_site.z, \n",
" z_ele, debug=debug)\n",
" shelf_data.append({\n",
" 'site_id': site_id,\n",
" 'shelf_slope': shelf_slope,\n",
" 'shelf_x': shelf_x,\n",
" 'shelf_z': shelf_z\n",
" })\n",
" \n",
"df_shelf = pd.DataFrame(shelf_data).set_index(['site_id'])\n",
"\n",
"df_shelf.loc['NARRA0004','shelf_slope'] = -0.02\n",
"\n",
"print('df_shelf:')\n",
"df_shelf.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Do geometry\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"df_site"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for site_id, df_site in df_profiles.groupby('site_id'):\n",
"\n",
" # Project the dune face outwards\n",
" dune_face_toe = Point(df_dunes.loc[site_id].dune_toe_x,\n",
" df_dunes.loc[site_id].dune_toe_z)\n",
" dune_face_sea = Point(\n",
" df_dunes.loc[site_id].dune_toe_x + 1000,\n",
" # df_dunes.loc[site_id].dune_toe_z +1000 * -1\n",
" df_dunes.loc[site_id].dune_toe_z +\n",
" 1000 * df_dunes.loc[site_id].dune_slope)\n",
" dune_line = LineString([dune_face_toe, dune_face_sea])\n",
"\n",
" # Project the shelf slope landwards\n",
" shelf_point = Point(df_shelf.loc[site_id].shelf_x,\n",
" df_shelf.loc[site_id].shelf_z)\n",
" shelf_land = Point(\n",
" df_shelf.loc[site_id].shelf_x - 1000, df_shelf.loc[site_id].shelf_z -\n",
" 1000 * df_shelf.loc[site_id].shelf_slope)\n",
" shelf_sea = Point(\n",
" df_shelf.loc[site_id].shelf_x + 1000, df_shelf.loc[site_id].shelf_z +\n",
" 1000 * df_shelf.loc[site_id].shelf_slope)\n",
" shelf_line = LineString([shelf_land, shelf_point, shelf_sea])\n",
"\n",
" # Find intersection between to lines\n",
" dune_shelf_int = dune_line.intersection(shelf_line)\n",
" dist_toe_to_int = dune_face_toe.distance(dune_shelf_int)\n",
"\n",
" # Plots\n",
" f, (ax1) = plt.subplots(1, 1, figsize=(12, 4))\n",
"\n",
" # Raw profile prestorm\n",
" ax1.plot(\n",
" df_site.xs('prestorm',\n",
" level='profile_type').index.get_level_values('x'),\n",
" df_site.xs('prestorm', level='profile_type').z,\n",
" label='Prestorm profile')\n",
"\n",
" # Raw profile poststorm\n",
" ax1.plot(\n",
" df_site.xs('poststorm',\n",
" level='profile_type').index.get_level_values('x'),\n",
" df_site.xs('poststorm', level='profile_type').z,\n",
" label='Poststorm profile')\n",
"\n",
" # Dune face\n",
" ax1.plot(\n",
" [df_dunes.loc[site_id].dune_crest_x, df_dunes.loc[site_id].dune_toe_x],\n",
" [df_dunes.loc[site_id].dune_crest_z, df_dunes.loc[site_id].dune_toe_z],\n",
" linestyle=':',\n",
" color='#999999',\n",
" label='Dune face ({:.2f})'.format(-df_dunes.loc[site_id].dune_slope))\n",
"\n",
" # Projected dune face\n",
" ax1.plot(\n",
" dune_line.xy[0],\n",
" dune_line.xy[1],\n",
" linestyle='--',\n",
" color='#999999',\n",
" label='Dune face (projected)')\n",
"\n",
" # Projected shelf slope\n",
" ax1.plot(\n",
" shelf_line.xy[0],\n",
" shelf_line.xy[1],\n",
" linestyle='--',\n",
" color='#999999',\n",
" label='Shelf slope (projected)')\n",
"\n",
" # Intersection\n",
" ax1.scatter(\n",
" dune_shelf_int.xy[0],\n",
" dune_shelf_int.xy[1],\n",
" marker='x',\n",
" color='#999999',\n",
" label='Dune/shelf projected intersection')\n",
"\n",
" # Prestorm slope\n",
" ax1.plot([\n",
" df_prestorm_slope.loc[site_id].prestorm_start_x,\n",
" df_prestorm_slope.loc[site_id].prestorm_end_x\n",
" ], [\n",
" df_prestorm_slope.loc[site_id].prestorm_start_z,\n",
" df_prestorm_slope.loc[site_id].prestorm_end_z\n",
" ],\n",
" color='violet',\n",
" label='Prestorm slope ({:.2f})'.format(\n",
" -df_prestorm_slope.loc[site_id].prestorm_slope))\n",
"\n",
" # # Find best slope based on distance form toe to intersection?\n",
" # best_slope_toe = shelf_line.interpolate(\n",
" # shelf_line.project(intersection) - 4 * dist_toe_to_int)\n",
" # best_slope = (dune_face_toe.xy[1][0] - best_slope_toe.xy[1][0]) / (\n",
" # dune_face_toe.xy[0][0] - best_slope_toe.xy[0][0])\n",
"\n",
" # # Best slope toe\n",
" # ax1.scatter(\n",
" # best_slope_toe.xy[0], best_slope_toe.xy[1], marker='o', color='g')\n",
"\n",
" # # Best slope\n",
" # ax1.plot([dune_face_toe.xy[0], best_slope_toe.xy[0]],\n",
" # [dune_face_toe.xy[1], best_slope_toe.xy[1]],\n",
" # color='g',\n",
" # label='Best slope ({:.3f})'.format(-best_slope))\n",
"\n",
" # Find best slope based on intersection of prestorm slope and surf zone slope\n",
" prestorm_slope_line = LineString([\n",
" Point(\n",
" df_prestorm_slope.loc[site_id].prestorm_start_x,\n",
" df_prestorm_slope.loc[site_id].prestorm_start_z,\n",
" ),\n",
" Point(\n",
" df_prestorm_slope.loc[site_id].prestorm_start_x + 10000,\n",
" df_prestorm_slope.loc[site_id].prestorm_start_z +\n",
" 10000 * df_prestorm_slope.loc[site_id].prestorm_slope)\n",
" ])\n",
"\n",
" # Where prestorm slope projection intersects shelf line\n",
" prestorm_slope_shelf_int = prestorm_slope_line.intersection(shelf_line)\n",
"\n",
" # Distance between dune/shelf intersection and prestorm/shelf intersection\n",
" dist_shelf_prestorm_ints = prestorm_slope_shelf_int.distance(\n",
" dune_shelf_int)\n",
"\n",
" best_slope_pt = shelf_line.interpolate(\n",
" shelf_line.project(dune_shelf_int) + 0.3 * (shelf_line.project(prestorm_slope_shelf_int) -\n",
" shelf_line.project(dune_shelf_int)))\n",
" \n",
" best_slope =(df_prestorm_slope.loc[site_id].prestorm_start_z-best_slope_pt.xy[1][0])/(df_prestorm_slope.loc[site_id].prestorm_start_x-best_slope_pt.xy[0][0])\n",
" \n",
" if not prestorm_slope_shelf_int.is_empty:\n",
" ax1.plot(\n",
" prestorm_slope_shelf_int.xy[0],\n",
" prestorm_slope_shelf_int.xy[1],\n",
" marker='x',\n",
" color='#999999',\n",
" label='Prestorm slope/shelf\\nprojected intersection')\n",
" ax1.plot(\n",
" prestorm_slope_line.xy[0],\n",
" prestorm_slope_line.xy[1],\n",
" color='#999999',\n",
" linestyle='--',\n",
" label='Prestorm slope projected line')\n",
" ax1.plot(\n",
" [df_prestorm_slope.loc[site_id].prestorm_start_x,\n",
" best_slope_pt.xy[0][0]],\n",
" [df_prestorm_slope.loc[site_id].prestorm_start_z,\n",
" best_slope_pt.xy[1][0]],\n",
" color='red',\n",
" linestyle='--',\n",
" label='Best slope ({:.3f})'.format(-best_slope))\n",
" \n",
" # TEMP Target slopes\n",
" target_slopes = {\n",
" 'NARRA0004': 0.076,\n",
" 'NARRA0008': 0.093,\n",
" 'NARRA0012': 0.060,\n",
" 'NARRA0016': 0.11,\n",
" 'NARRA0021': 0.063,\n",
" 'NARRA0023': 0.061,\n",
" 'NARRA0027': 0.060,\n",
" 'NARRA0031': 0.057,\n",
" }\n",
"\n",
" target_direction = {\n",
" 'NARRA0004': \"flatter\",\n",
" 'NARRA0008': \"steeper\",\n",
" 'NARRA0012': \"flatter\",\n",
" 'NARRA0016': \"flatter\",\n",
" 'NARRA0021': \"steeper\",\n",
" 'NARRA0023': \"steeper\",\n",
" 'NARRA0027': \"steeper\",\n",
" 'NARRA0031': \"steeper\",\n",
" }\n",
" ax1.plot([dune_face_toe.xy[0][0], dune_face_toe.xy[0][0] + 1000], [\n",
" dune_face_toe.xy[1][0],\n",
" dune_face_toe.xy[1][0] - 1000 * target_slopes[site_id]\n",
" ],\n",
" color='red',\n",
" label='Target slope\\n({} than {:.3f})'.format(\n",
" target_direction[site_id], target_slopes[site_id]))\n",
"\n",
" ax1.set_xlim([100, 800])\n",
" ax1.set_ylim([-15, 12])\n",
"# ax1.set_xlim([100, 600])\n",
"# ax1.set_ylim([-10, 12])\n",
"\n",
" # ax1.set_xlim([df_dunes.loc[site_id].dune_crest_x - 50,\n",
" # intersection.xy[0][0] + 50])\n",
" # ax1.set_ylim([intersection.xy[1][0] -3,\n",
" # df_dunes.loc[site_id].dune_crest_z + 3])\n",
"\n",
" ax1.set_title(site_id)\n",
" ax1.legend(loc='upper right', prop={'size': 10})\n",
" f.savefig('08-{}.png'.format(site_id), dpi=600)\n",
" plt.show()\n",
" plt.close()"
]
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