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691 lines
23 KiB
Plaintext
691 lines
23 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data exploration\n",
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"This notebook provides an example how the data has been loaded and accessed for further analysis."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-11-22T22:48:17.973982Z",
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"start_time": "2018-11-22T22:48:17.825797Z"
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}
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},
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"outputs": [],
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"source": [
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"# Enable autoreloading of our modules. \n",
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"# Most of the code will be located in the /src/ folder, \n",
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"# and then called from the notebook.\n",
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"\n",
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"%reload_ext autoreload\n",
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"%autoreload"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2018-11-22T22:48:17.826Z"
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},
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"from IPython.core.debugger import set_trace\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"\n",
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"import plotly\n",
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"import plotly.graph_objs as go\n",
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"import plotly.plotly as py\n",
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"import plotly.tools as tls\n",
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"\n",
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"import colorlover as cl\n",
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"\n",
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"from ipywidgets import widgets, Output\n",
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"from IPython.display import display, clear_output, Image, HTML"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2018-11-22T22:48:17.829Z"
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},
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"pixiedust": {
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"displayParams": {}
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}
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},
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"outputs": [],
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"source": [
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"def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
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" return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
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"\n",
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"df_waves = df_from_csv('waves.csv', index_col=[0, 1])\n",
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"df_tides = df_from_csv('tides.csv', index_col=[0, 1])\n",
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"df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
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"df_sites = df_from_csv('sites.csv', index_col=[0])\n",
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"df_profile_features = df_from_csv('profile_features.csv', index_col=[0])\n",
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"\n",
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"# Note that the forecasted data sets should be in the same order for impacts and twls\n",
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"impacts = {\n",
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" 'forecasted': {\n",
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" 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
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" 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
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" },\n",
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" 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
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" }\n",
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"\n",
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"\n",
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"twls = {\n",
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" 'forecasted': {\n",
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" 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
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" 'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
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" }\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2018-11-22T22:48:17.832Z"
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}
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},
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"outputs": [],
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"source": [
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"tables = [Output() for x in range(len(impacts['forecasted']) + 1)]\n",
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"tables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2018-11-22T22:48:17.835Z"
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},
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"code_folding": [
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408
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],
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"# Create widgets for filtering by observed and forecasted impacts\n",
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"filter_title = widgets.HTML(\n",
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" value=\"<b>Filter by observed and predicted impacts:</b>\",\n",
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")\n",
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"\n",
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"titles = ['Observed Impacts']\n",
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"selectboxes = [widgets.SelectMultiple(\n",
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" options=impacts['observed'].storm_regime.dropna().unique().tolist(),\n",
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" value=impacts['observed'].storm_regime.dropna().unique().tolist(),\n",
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" disabled=False)]\n",
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"\n",
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"# Iterate through each of our forecasted impacts\n",
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"for forecast in impacts['forecasted']:\n",
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" selectboxes.append(\n",
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" widgets.SelectMultiple(\n",
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" options=impacts['forecasted'][forecast].storm_regime.dropna(\n",
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" ).unique().tolist(),\n",
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" value=impacts['forecasted'][forecast].storm_regime.dropna(\n",
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" ).unique().tolist(),\n",
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" disabled=False))\n",
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" titles.append('Forecasted: {}'.format(forecast))\n",
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"\n",
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"titles = [widgets.HTML(value=title) for title in titles]\n",
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"\n",
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"children = widgets.HBox(children=[widgets.VBox(\n",
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" children=[title, box]) for title, box in zip(titles, selectboxes)])\n",
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"filter_container = widgets.VBox(children=[filter_title, children])\n",
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"\n",
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"\n",
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"# Create widgets for selecting site_id\n",
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"site_id_title = widgets.HTML(\n",
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" value=\"<b>Filter by site_id:</b>\",\n",
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")\n",
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"\n",
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"site_id_select = widgets.Dropdown(\n",
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" description='site_id: ',\n",
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" value='NARRA0001',\n",
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" options=df_profiles.index.get_level_values(\n",
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" 'site_id').unique().sort_values().tolist()\n",
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")\n",
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"\n",
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"site_id_impacts = widgets.HTML(\n",
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" value=\"\",\n",
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")\n",
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"\n",
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"site_id_container = widgets.HBox(children=[\n",
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" widgets.VBox(children=[site_id_title, widgets.HBox(children=[site_id_select])]),\n",
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" site_id_impacts\n",
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"])\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"# Build colors for each of our forecasts\n",
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"colors = list(reversed(cl.scales[str(max(len(impacts['forecasted']), 3))]['seq']['YlGnBu']))\n",
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"\n",
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"# Add panel for pre/post storm profiles\n",
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"trace1 = go.Scatter(\n",
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" x=[0],\n",
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" y=[0],\n",
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" name='Pre Storm Profile',\n",
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" line=dict(\n",
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" color=('rgb(51,160,44)'),\n",
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" width=2)\n",
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")\n",
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"trace2 = go.Scatter(\n",
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" x=[0],\n",
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" y=[0],\n",
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" name='Post Storm Profile',\n",
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" line=dict(\n",
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" color=('rgb(255,127,0)'),\n",
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" width=2)\n",
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")\n",
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"trace3 = go.Scatter(\n",
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" x=[0],\n",
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" y=[0],\n",
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" name='Pre-storm dune crest',\n",
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" mode='markers',\n",
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" marker=dict(\n",
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" color='rgba(255,255,255,0)',\n",
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" size=10,\n",
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" line=dict(\n",
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" color='rgba(106,61,154, 1)',\n",
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" width=2\n",
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" )\n",
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" ),\n",
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")\n",
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"trace4 = go.Scatter(\n",
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" x=[0],\n",
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" y=[0],\n",
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" name='Pre-storm dune toe',\n",
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" mode='markers',\n",
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" marker=dict(\n",
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" color='rgba(255,255,255,0)',\n",
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" size=10,\n",
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" line=dict(\n",
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" color='rgba(202,178,214,1)',\n",
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" width=2\n",
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" )\n",
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" ),\n",
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")\n",
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"\n",
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"forecast_traces = []\n",
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"for forecast, color in zip(impacts['forecasted'], colors):\n",
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" forecast_traces.append(go.Scatter(\n",
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" x=[0],\n",
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" y=[0],\n",
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" name='Peak R_high: {}'.format(forecast),\n",
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" mode='lines',\n",
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" line=dict(\n",
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" color=color,\n",
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" width=4,)\n",
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"\n",
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" ))\n",
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"\n",
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"layout = go.Layout(\n",
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" title='Bed Profiles',\n",
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" height=300,\n",
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" legend=dict(font={'size': 10}),\n",
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" margin=dict(t=50, b=50, l=50, r=20),\n",
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" xaxis=dict(\n",
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" title='x (m)',\n",
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" autorange=True,\n",
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" showgrid=True,\n",
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" zeroline=True,\n",
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" showline=True,\n",
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" range=[0, 200]\n",
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" ),\n",
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" yaxis=dict(\n",
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" title='z (m)',\n",
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" autorange=False,\n",
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" showgrid=True,\n",
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" zeroline=True,\n",
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" showline=True,\n",
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" range=[-1, 20]\n",
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" )\n",
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")\n",
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"\n",
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"g_profiles = go.FigureWidget(data=[trace1, trace2, trace3, trace4]+forecast_traces,\n",
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" layout=layout)\n",
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"\n",
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"\n",
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"# Add panel for google maps\n",
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"mapbox_access_token = 'pk.eyJ1IjoiY2hyaXNsZWFtYW4iLCJhIjoiY2pvNTY1MzZpMDc2OTN2bmw5MGsycHp5bCJ9.U2dwFg2c7RFjUNSayERUiw'\n",
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"\n",
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"data = [\n",
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" go.Scattermapbox(\n",
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" lat=df_sites['lat'],\n",
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" lon=df_sites['lon'],\n",
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" mode='markers',\n",
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" marker=dict(\n",
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" size=10\n",
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" ),\n",
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" text=df_sites.index.get_level_values('site_id'),\n",
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" ),\n",
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" go.Scattermapbox(\n",
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" lat=[0],\n",
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" lon=[0],\n",
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" mode='markers',\n",
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" marker=dict(\n",
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" size=20,\n",
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" color='rgb(255, 0, 0)',\n",
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" opacity=0.5,\n",
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" ),\n",
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" text=df_sites.index.get_level_values('site_id'),\n",
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" ),\n",
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"]\n",
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"\n",
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"layout = go.Layout(\n",
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" autosize=True,\n",
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" height=300,\n",
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" hovermode='closest',\n",
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" showlegend=False,\n",
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" margin=dict(t=50, b=50, l=20, r=20),\n",
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" mapbox=dict(\n",
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" accesstoken=mapbox_access_token,\n",
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" bearing=0,\n",
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" center=dict(\n",
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" lat=-33.7,\n",
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" lon=151.3\n",
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" ),\n",
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" pitch=0,\n",
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" zoom=12,\n",
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" style='satellite-streets'\n",
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" ),\n",
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")\n",
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"\n",
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"fig = dict(data=data, layout=layout)\n",
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"g_map = go.FigureWidget(data=data, layout=layout)\n",
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"\n",
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"\n",
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"subplot = tls.make_subplots(3, 1, print_grid=False, shared_xaxes=True)\n",
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"g_timeseries = go.FigureWidget(subplot)\n",
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"\n",
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"# Add trace for Hs0\n",
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"g_timeseries.add_trace(\n",
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" go.Scatter(\n",
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" x=[0, 1],\n",
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" y=[0, 1],\n",
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" name='Hs0',\n",
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" ),\n",
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" row=3,\n",
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" col=1,\n",
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")\n",
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"\n",
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"# Add trace for Tp\n",
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"g_timeseries.add_trace(\n",
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" go.Scatter(\n",
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" x=[0, 1],\n",
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" y=[0, 1],\n",
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" name='Tp',\n",
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" ),\n",
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" row=3,\n",
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" col=1,\n",
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")\n",
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"\n",
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"# Add water levels\n",
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"g_timeseries.add_trace(\n",
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" go.Scatter(\n",
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" x=[0, 3],\n",
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" y=[0, 3],\n",
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" name='Dune Crest',\n",
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" mode='lines',\n",
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" line=dict(\n",
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" color=('rgb(214, 117, 14)'),\n",
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" width=2,\n",
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" dash='dot')\n",
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" ), row=1, col=1)\n",
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"\n",
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"\n",
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"g_timeseries.add_trace(\n",
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" go.Scatter(\n",
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" x=[0, 3],\n",
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" y=[0, 3],\n",
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" name='Dune Toe',\n",
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" mode='lines',\n",
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" line=dict(\n",
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" color=('rgb(142, 77, 8)'),\n",
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" width=2,\n",
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" dash='dash')\n",
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" ), row=1, col=1)\n",
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"\n",
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"g_timeseries.add_trace(\n",
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" go.Scatter(\n",
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" x=[0, 3],\n",
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" y=[0, 3],\n",
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" name='Tide+Surge WL',\n",
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" line=dict(\n",
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" color=('rgb(8,51,137)'),\n",
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" width=2,\n",
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" dash='dot')\n",
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" ), row=1, col=1)\n",
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"\n",
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"for forecast, color in zip(twls['forecasted'], colors):\n",
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" g_timeseries.add_trace(\n",
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" go.Scatter(\n",
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" x=[0],\n",
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" y=[0],\n",
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" name='R_high: {}'.format(forecast),\n",
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" line=dict(\n",
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" color=color,\n",
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" width=3)), row=1, col=1)\n",
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"\n",
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"\n",
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"# Add trace for each forecasted beta term\n",
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"for forecast, color in zip(impacts['forecasted'],colors):\n",
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" g_timeseries.add_trace(\n",
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" go.Scatter(\n",
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" x=[0, 1],\n",
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" y=[0, 1],\n",
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" name='Beta: {}'.format(forecast),\n",
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" line=dict(\n",
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" color=color,\n",
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" width=3)),\n",
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" row=2,\n",
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" col=1,\n",
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" )\n",
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"\n",
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"\n",
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"# Create axis for Tp on same plot as Hs\n",
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"g_timeseries['layout']['yaxis4'] = {'overlaying': 'y3', 'side': 'right'}\n",
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"g_timeseries.data[1]['yaxis'] = 'y4'\n",
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"\n",
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"# Add labels to each axis\n",
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"g_timeseries.layout['xaxis']['title'] = 'datetime'\n",
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"g_timeseries.layout['yaxis1']['title'] = 'z (mAHD)'\n",
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"g_timeseries.layout['yaxis2']['title'] = 'beta (-)'\n",
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"g_timeseries.layout['yaxis3']['title'] = 'Hs0 (m)'\n",
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"g_timeseries.layout['yaxis4']['title'] = 'Tp (s)'\n",
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"\n",
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"# Update figure size\n",
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"g_timeseries['layout'].update(height=400, legend=dict(font={'size': 10}))\n",
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"g_timeseries['layout'].update(margin=dict(t=20, l=50, r=20, b=100))\n",
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"\n",
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"\n",
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"\n",
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"# Add panel for some tables\n",
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"titles = ['observed'] + [forecast for forecast in impacts['forecasted']]\n",
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"titles = [widgets.HTML(value=\"{}\".format(title)) for title in titles]\n",
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"\n",
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"def get_observed_impacts_table(site_id):\n",
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" display(impacts['observed'].query(\"site_id=='{}'\".format(site_id)).T)\n",
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"\n",
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"\n",
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"def get_forecasted_impacts_table(site_id,forecast):\n",
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" display(impacts['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id)).T)\n",
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"\n",
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" \n",
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"impacts_table_observed = widgets.interactive_output(get_observed_impacts_table, {'site_id': site_id_select})\n",
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"forecasted_impacts_tables = []\n",
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"for forecast, title in zip(impacts['forecasted'], titles[1:]):\n",
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" forecasted_impacts_tables.append(widgets.interactive_output(get_forecasted_impacts_table, {'site_id': site_id_select, 'forecast':title}))\n",
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"\n",
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"tables = [impacts_table_observed] + forecasted_impacts_tables\n",
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"\n",
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"title_tables=[widgets.VBox(children=[title,table]) for title,table in zip(titles,tables)]\n",
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" \n",
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"tables_container= widgets.HBox(children=[*title_tables])\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
|
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"def update_profile(change):\n",
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"\n",
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" site_id = site_id_select.value\n",
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"\n",
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" if site_id is None:\n",
|
|
" return\n",
|
|
"\n",
|
|
" site_profile = df_profiles.query('site_id == \"{}\"'.format(site_id))\n",
|
|
" prestorm_profile = site_profile.query('profile_type == \"prestorm\"')\n",
|
|
" poststorm_profile = site_profile.query('profile_type == \"poststorm\"')\n",
|
|
"\n",
|
|
" poststorm_x = poststorm_profile.index.get_level_values('x').tolist()\n",
|
|
" poststorm_z = poststorm_profile.z.tolist()\n",
|
|
"\n",
|
|
" prestorm_x = prestorm_profile.index.get_level_values('x').tolist()\n",
|
|
" prestorm_z = prestorm_profile.z.tolist()\n",
|
|
"\n",
|
|
" site_features = df_profile_features.query(\n",
|
|
" 'site_id == \"{}\"'.format(site_id))\n",
|
|
" dune_crest_x = site_features.dune_crest_x\n",
|
|
" dune_crest_z = site_features.dune_crest_z\n",
|
|
" dune_toe_x = site_features.dune_toe_x\n",
|
|
" dune_toe_z = site_features.dune_toe_z\n",
|
|
"\n",
|
|
" # Update beach profile section plots\n",
|
|
" with g_profiles.batch_update():\n",
|
|
" g_profiles.data[0].x = prestorm_x\n",
|
|
" g_profiles.data[0].y = prestorm_z\n",
|
|
" g_profiles.data[1].x = poststorm_x\n",
|
|
" g_profiles.data[1].y = poststorm_z\n",
|
|
" g_profiles.data[2].x = dune_crest_x\n",
|
|
" g_profiles.data[2].y = dune_crest_z\n",
|
|
" g_profiles.data[3].x = dune_toe_x\n",
|
|
" g_profiles.data[3].y = dune_toe_z\n",
|
|
"\n",
|
|
" for n, forecast in enumerate(impacts['forecasted']):\n",
|
|
" R_high = max(impacts['forecasted'][forecast].query(\n",
|
|
" \"site_id=='{}'\".format(site_id)).R_high)\n",
|
|
" g_profiles.data[4+n].x = [200, 400]\n",
|
|
" g_profiles.data[4+n].y = [R_high, R_high]\n",
|
|
"\n",
|
|
" # Relocate plan of satellite imagery\n",
|
|
" site_coords = df_sites.query('site_id == \"{}\"'.format(site_id))\n",
|
|
" with g_map.batch_update():\n",
|
|
" g_map.layout.mapbox['center'] = {\n",
|
|
" 'lat': site_coords['lat'].values[0],\n",
|
|
" 'lon': site_coords['lon'].values[0]\n",
|
|
" }\n",
|
|
" g_map.layout.mapbox['zoom'] = 15\n",
|
|
" g_map.data[1].lat = [site_coords['lat'].values[0]]\n",
|
|
" g_map.data[1].lon = [site_coords['lon'].values[0]]\n",
|
|
" g_map.data[1].text = site_coords['lon'].index.get_level_values(\n",
|
|
" 'site_id').tolist()\n",
|
|
"\n",
|
|
" # Update time series plots\n",
|
|
" df_waves_site = df_waves.query(\"site_id=='{}'\".format(site_id))\n",
|
|
" times = df_waves_site.index.get_level_values('datetime').tolist()\n",
|
|
" Hs0s = df_waves_site.Hs0.tolist()\n",
|
|
" Tps = df_waves_site.Tp.tolist()\n",
|
|
" with g_timeseries.batch_update():\n",
|
|
" g_timeseries.data[0].x = times\n",
|
|
" g_timeseries.data[0].y = Hs0s\n",
|
|
" g_timeseries.data[1].x = times\n",
|
|
" g_timeseries.data[1].y = Tps\n",
|
|
"\n",
|
|
" # Update beta values\n",
|
|
" idx_betas = [n for n, x in enumerate(\n",
|
|
" g_timeseries.data) if 'Beta' in x.name]\n",
|
|
" for i, forecast in zip(idx_betas, twls['forecasted']):\n",
|
|
" df_twl = twls['forecasted'][forecast].query(\n",
|
|
" \"site_id=='{}'\".format(site_id))\n",
|
|
" times = df_twl.index.get_level_values('datetime').tolist()\n",
|
|
" beta = df_twl.beta.tolist()\n",
|
|
" g_timeseries.data[i].x = times\n",
|
|
" g_timeseries.data[i].y = beta\n",
|
|
"\n",
|
|
" # Update water levels plot\n",
|
|
" df_tide_site = df_tides.query(\"site_id=='{}'\".format(site_id))\n",
|
|
" mask = (df_tide_site.index.get_level_values('datetime') >= min(times)) & (\n",
|
|
" df_tide_site.index.get_level_values('datetime') <= max(times))\n",
|
|
" df_tide_site = df_tide_site.loc[mask]\n",
|
|
"\n",
|
|
" with g_timeseries.batch_update():\n",
|
|
" g_timeseries.data[2].x = [min(times), max(times)]\n",
|
|
" g_timeseries.data[3].x = [min(times), max(times)]\n",
|
|
" g_timeseries.data[4].x = df_tide_site.index.get_level_values(\n",
|
|
" 'datetime')\n",
|
|
" g_timeseries.data[2].y = dune_crest_z.tolist()[0], dune_crest_z.tolist()[\n",
|
|
" 0],\n",
|
|
" g_timeseries.data[3].y = dune_toe_z.tolist()[0], dune_toe_z.tolist()[\n",
|
|
" 0],\n",
|
|
" g_timeseries.data[4].y = df_tide_site.tide.tolist()\n",
|
|
"\n",
|
|
" # Update rhigh values\n",
|
|
" idx_betas = [n for n, x in enumerate(\n",
|
|
" g_timeseries.data) if 'R_high' in x.name]\n",
|
|
" for i, forecast in zip(idx_betas, twls['forecasted']):\n",
|
|
" df_twl = twls['forecasted'][forecast].query(\n",
|
|
" \"site_id=='{}'\".format(site_id))\n",
|
|
" times = df_twl.index.get_level_values('datetime').tolist()\n",
|
|
" R_high = df_twl.R_high.tolist()\n",
|
|
" g_timeseries.data[i].x = times\n",
|
|
" g_timeseries.data[i].y = R_high\n",
|
|
"\n",
|
|
" # Update site id impacts\n",
|
|
" observed_regime = impacts['observed'].query(\"site_id=='{}'\".format(site_id)).storm_regime.values[0]\n",
|
|
" site_id_impacts.value = \"Observed: <b>{}</b><br>\".format(observed_regime)\n",
|
|
" \n",
|
|
" for forecast in impacts['forecasted']:\n",
|
|
" regime = impacts['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id)).storm_regime.values[0]\n",
|
|
" site_id_impacts.value += '{}: <b>{}</b><br>'.format(forecast, regime)\n",
|
|
" \n",
|
|
" # Update our tables\n",
|
|
" \n",
|
|
"site_id_select.observe(update_profile, names=\"value\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def update_filter(change):\n",
|
|
"\n",
|
|
" # Iterate through each box, only keeping site_ids which are not filtered out by each box\n",
|
|
" valid_site_ids = impacts['observed'].index.tolist()\n",
|
|
" dfs = [impacts['observed']] + [impacts['forecasted'][key]\n",
|
|
" for key in impacts['forecasted']]\n",
|
|
"\n",
|
|
" for box, df in zip(selectboxes, dfs):\n",
|
|
" valid_site_ids = list(set(valid_site_ids).intersection(\n",
|
|
" set(df[df.storm_regime.isin(box.value)].index.tolist())))\n",
|
|
" site_id_select.options = sorted(valid_site_ids)\n",
|
|
"\n",
|
|
" # TODO Update options in selectboxes with number of observations?\n",
|
|
"\n",
|
|
"\n",
|
|
"# Update the filter if any of the boxes changes\n",
|
|
"for box in selectboxes:\n",
|
|
" box.observe(update_filter, names=\"value\")\n",
|
|
"\n",
|
|
" \n",
|
|
"\n",
|
|
"\n",
|
|
"# Display our widgets!\n",
|
|
"widgets.VBox([filter_container, site_id_container,\n",
|
|
" widgets.HBox([g_profiles, g_map]), g_timeseries, tables_container])\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"start_time": "2018-11-22T22:48:17.837Z"
|
|
},
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"titles[0].observe"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"start_time": "2018-11-22T22:48:17.840Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# impacts['observed'].query(\"site_id=='{}'\".format(\"NARRA0018\")).T\n",
|
|
"impacts['forecasted']['foreshore_slope_sto06'].query(\"site_id=='{}'\".format(\"NARRA0018\")).T\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.6.4"
|
|
},
|
|
"toc": {
|
|
"base_numbering": 1,
|
|
"nav_menu": {
|
|
"height": "47px",
|
|
"width": "262px"
|
|
},
|
|
"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": false
|
|
},
|
|
"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
|
|
}
|