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363 lines
11 KiB
Plaintext
363 lines
11 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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"# TWL Exceedance"
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]
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},
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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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"## Setup notebook"
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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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"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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"%matplotlib inline\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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"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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"import decimal\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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"import plotly.figure_factory as ff\n",
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"from plotly import tools\n",
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"import plotly.io as pio\n",
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"from scipy import stats\n",
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"import math\n",
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"import matplotlib\n",
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"from matplotlib import cm\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\n",
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"\n",
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"from sklearn.metrics import confusion_matrix"
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]
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},
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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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"## Import data"
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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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"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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" print('Importing {}'.format(csv))\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_crest_toes = df_from_csv('profile_features_crest_toes.csv', index_col=[0,1])\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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"}\n",
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"print('Done!')"
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]
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},
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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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"## Calculate vertical distribution of wave count\n",
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"For each site, calculate how many waves reached a certain elevation (store as a binned histogram)."
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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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"outputs": [],
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"source": [
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"# Helper functions\n",
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"def find_nearest(array, value):\n",
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" array = np.asarray(array)\n",
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" idx = np.nanargmin((np.abs(array - value)))\n",
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" return array[idx], idx"
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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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"outputs": [],
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"source": [
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"df_profile_features_crest_toes.loc[(site_id,'prestorm'),'dune_toe_z']"
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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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"outputs": [],
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"source": [
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"data = []\n",
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"for site_id, df_site_twl in twls['forecasted']['mean_slope_sto06'].groupby('site_id'):\n",
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" \n",
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" twl_eles_per_wave = []\n",
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" \n",
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" # Iterate through each timestamp and calculate the number of waves at each interavl.\n",
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" # THIS LOOP IS SLOW\n",
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" for row in df_site_twl.itertuples():\n",
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" \n",
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" distribution = stats.norm(loc=row.tide+row.setup, scale=row.S_total/4) # CHECK\n",
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"\n",
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" # Total number of waves we expect in this period\n",
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" n_waves = int(3600 / row.Tp) # Check that we have 1 hour\n",
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" \n",
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" # Get z elevation of each wave twl in this hour and append to list\n",
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" twl_eles_per_wave.extend([distribution.ppf(1-x/n_waves) for x in range(1,n_waves+1)])\n",
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" \n",
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" # Remove nans and infs # CHECK WHY INF\n",
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" twl_eles_per_wave = list(np.asarray(twl_eles_per_wave)[np.isfinite(twl_eles_per_wave)])\n",
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" \n",
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" # Sort wave twl z elevations in descending list\n",
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" twl_eles_per_wave.sort(reverse=True) \n",
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" \n",
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" # Get index of closest value of dune toe. This is the number of waves that exceeded the the dune toe\n",
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" try:\n",
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" _, idx = find_nearest(twl_eles_per_wave, dune_toe_z)\n",
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" except:\n",
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" continue\n",
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" \n",
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" # Get forecasted and observed impacts\n",
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" forecasted_regime = impacts['forecasted']['mean_slope_sto06'].loc[site_id,'storm_regime']\n",
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" observed_regime = impacts['observed'].loc[site_id,'storm_regime']\n",
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" \n",
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" counts, bin_edges = np.histogram(twl_eles_per_wave, bins=100) \n",
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" \n",
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" data.append({\n",
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" 'site_id': site_id,\n",
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" 'forecasted_regime': forecasted_regime,\n",
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" 'observed_regime': observed_regime,\n",
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" 'n_waves_exceeding_dune_toe': idx,\n",
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" 'n_waves': [x for x in range(0,500,1)],\n",
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" 'truncated_twl_levels': [twl_eles_per_wave[x] for x in range(0,500,1)],\n",
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" 'truncated_dune_toe_z': df_profile_features_crest_toes.loc[(site_id,'prestorm'),'dune_toe_z'],\n",
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" 'full_counts': counts,\n",
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" 'full_bin_edges': bin_edges,\n",
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" })\n",
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" \n",
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" print('Done {}'.format(site_id))\n",
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"\n",
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"data_twl = data\n",
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"# df = pd.DataFrame(data)\n",
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"# df = df.set_index('site_id')"
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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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"outputs": [],
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"source": [
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"counts, bin_edges = np.histogram (data_twl[0]['twl_levels'], bins=50) "
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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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"outputs": [],
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"source": [
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"list(np.asarray(twl_eles_per_wave)[~np.isfinite(twl_eles_per_wave)])"
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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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"outputs": [],
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"source": [
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"fig = tools.make_subplots(\n",
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" rows=2,\n",
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" cols=2,\n",
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" specs=[[{}, {}], [{}, {}]],\n",
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" subplot_titles=('Swash/Swash', 'Swash/Collision', \n",
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" 'Collision/Swash', 'Collision/Collision'),\n",
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" shared_xaxes=True, shared_yaxes=True,)\n",
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"\n",
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"data = []\n",
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"for site in data_twl:\n",
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" if site['forecasted_regime'] == 'swash' and site[\n",
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" 'observed_regime'] == 'swash':\n",
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" x_col = 1\n",
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" y_col = 1\n",
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" elif site['forecasted_regime'] == 'collision' and site[\n",
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" 'observed_regime'] == 'collision':\n",
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" x_col = 2\n",
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" y_col = 2\n",
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" elif site['forecasted_regime'] == 'swash' and site[\n",
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" 'observed_regime'] == 'collision':\n",
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" x_col = 2\n",
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" y_col = 1\n",
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" elif site['forecasted_regime'] == 'collision' and site[\n",
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" 'observed_regime'] == 'swash':\n",
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" x_col = 1\n",
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" y_col = 2\n",
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" else:\n",
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" continue\n",
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"\n",
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" fig.append_trace(\n",
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" go.Scattergl(\n",
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" x=[x - site['dune_toe_z'] for x in site['twl_levels']],\n",
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" y=site['n_waves'],\n",
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" name=site['site_id'],\n",
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" line = dict(\n",
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" color = ('rgba(22, 22, 22, 0.2)'),\n",
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" width = 0.5,)),\n",
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" x_col,\n",
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" y_col)\n",
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"\n",
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"# layout = go.Layout(\n",
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"# xaxis=dict(domain=[0, 0.45]),\n",
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"# yaxis=dict(\n",
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"# domain=[0, 0.45],\n",
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"# type='log',\n",
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"# ),\n",
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"# xaxis2=dict(domain=[0.55, 1]),\n",
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"# xaxis4=dict(domain=[0.55, 1], anchor='y4'),\n",
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"# yaxis3=dict(\n",
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"# domain=[0.55, 1],\n",
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"# type='log',\n",
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"# ),\n",
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"# yaxis4=dict(\n",
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"# domain=[0.55, 1],\n",
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"# anchor='x4',\n",
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"# type='log',\n",
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"# ))\n",
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"\n",
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"fig['layout'].update(showlegend=False, title='Specs with Subplot Title',height=800,)\n",
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"\n",
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"for ax in ['yaxis','yaxis2']:\n",
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"# fig['layout'][ax]['type']='log'\n",
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" fig['layout'][ax]['range']= [0,100]\n",
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"\n",
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"for ax in ['xaxis', 'xaxis2']:\n",
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" fig['layout'][ax]['range']= [-1,1]\n",
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"\n",
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"go.FigureWidget(fig)"
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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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"outputs": [],
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"source": [
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"fig['layout']['yaxis']"
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]
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}
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],
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"metadata": {
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"hide_input": false,
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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"varInspector": {
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"cols": {
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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},
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"types_to_exclude": [
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"module",
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"function",
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"instance",
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"_Feature"
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