{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data exploration\n", "This notebook provides an example how the data has been loaded and accessed for further analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-11-19T00:22:35.172482Z", "start_time": "2018-11-19T00:22:35.000206Z" } }, "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", "\n", "%reload_ext autoreload\n", "%autoreload" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-11-19T00:22:50.594936Z", "start_time": "2018-11-19T00:22:35.173486Z" }, "scrolled": true }, "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", "\n", "from ipywidgets import widgets\n", "from IPython.display import display, clear_output, Image" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-11-19T00:51:58.002082Z", "start_time": "2018-11-19T00:51:45.127794Z" }, "pixiedust": { "displayParams": {} } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:472: FutureWarning:\n", "\n", "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", "\n" ] } ], "source": [ "data_folder = '../data/interim'\n", "df_waves = pd.read_csv(os.path.join(data_folder, 'waves.csv'), index_col=[0,1])\n", "df_tides = pd.read_csv(os.path.join(data_folder, 'tides.csv'), index_col=[0,1])\n", "df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0,1,2])\n", "df_sites = pd.read_csv(os.path.join(data_folder, 'sites.csv'),index_col=[0])\n", "df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'),index_col=[0])\n", "df_impacts_compared = pd.read_csv(os.path.join(data_folder,'impacts_observed_vs_forecasted_mean_slope_sto06.csv'),index_col=[0])\n", "df_twl = pd.read_csv(os.path.join(data_folder,'twl_mean_slope_sto06.csv'),index_col=[0,1])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2018-11-19T01:46:34.068613Z", "start_time": "2018-11-19T01:46:34.021932Z" } }, "outputs": [ { "data": { "text/plain": [ "[1.2632991506198927,\n", " 1.393768803168096,\n", " 1.4898137015209056,\n", " 1.4536075884721669,\n", " 1.4108238472203196,\n", " 1.3456902382958191,\n", " 1.3190526770579034,\n", " 1.291134623539095,\n", " 1.2716049325008096,\n", " 1.234754724771738,\n", " 1.1825435278076464,\n", " 1.2252064606390358,\n", " 1.2640989420800277,\n", " 1.2757496999030895,\n", " 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Failed to display Jupyter Widget of type VBox.

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\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "

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\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "

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'NINEMs0020', 'NINEMs0021', 'NINEMs0022', 'NINEMs0023', 'NINEMs0024', 'NINEMs0025', 'NINEMs0026', 'NINEMs0027', 'NINEMs0028', 'NINEMs0029', 'NINEMs0030', 'NINEMs0031', 'NINEMs0032', 'NINEMs0033', 'NINEMs0034', 'NINEMs0035', 'NINEMs0036', 'NINEMs0037', 'NINEMs0038', 'NINEMs0039', 'NINEMs0040', 'NINEMs0041', 'NINEMs0042', 'NINEMs0043', 'NINEMs0044', 'NINEMs0045', 'NINEMs0046', 'NINEMs0047', 'NINEMs0048', 'NINEMs0049', 'NINEMs0050', 'NINEMs0051', 'NINEMs0052', 'NINEMs0053', 'NINEMs0054', 'NINEMs0055', 'NINEMs0056', 'NINEMs0057', 'NINEMs0058', 'NINEMs0059', 'NINEMs0060', 'NSHORE_n0001', 'NSHORE_n0002', 'NSHORE_n0003', 'NSHORE_n0004', 'NSHORE_n0005', 'NSHORE_n0006', 'NSHORE_n0007', 'NSHORE_n0008', 'NSHORE_n0009', 'NSHORE_n0010', 'NSHORE_n0011', 'NSHORE_n0012', 'NSHORE_n0013', 'NSHORE_n0014', 'NSHORE_n0015', 'NSHORE_n0016', 'NSHORE_n0017', 'NSHORE_n0018', 'NSHORE_n0019', 'NSHORE_n0020', 'NSHORE_n0021', 'NSHORE_n0022', 'NSHORE_n0023', 'NSHORE_n0024', 'NSHORE_n0025', 'NSHORE_n0026', 'NSHORE_n0027', 'NSHORE_n0028', 'NSHORE_n0029', 'NSHORE_n0030', 'NSHORE_n0031', 'NSHORE_n0032', 'NSHORE_n0033', 'NSHORE_n0034', 'NSHORE_n0035', 'NSHORE_n0036', 'NSHORE_n0037', 'NSHORE_n0038', 'NSHORE_n0039', 'NSHORE_n0040', 'NSHORE_n0041', 'NSHORE_n0042', 'NSHORE_n0043', 'NSHORE_n0044', 'NSHORE_n0045', 'NSHORE_n0046', 'NSHORE_n0047', 'NSHORE_n0048', 'NSHORE_n0049', 'NSHORE_n0050', 'NSHORE_n0051', 'NSHORE_n0052', 'NSHORE_n0053', 'NSHORE_n0054', 'NSHORE_n0055', 'NSHORE_n0056', 'NSHORE_n0057', 'NSHORE_n0058', 'NSHORE_n0059', 'NSHORE_n0060', 'NSHORE_n0061', 'NSHORE_n0062', 'NSHORE_n0063', 'NSHORE_n0064', 'NSHORE_n0065', 'NSHORE_n0066', 'NSHORE_n0067', 'NSHORE_n0068', 'NSHORE_n0069', 'NSHORE_n0070', 'NSHORE_n0071', 'NSHORE_n0072', 'NSHORE_n0073', 'NSHORE_n0074', 'NSHORE_n0075', 'NSHORE_n0076', 'NSHORE_n0077', 'NSHORE_n0078', 'NSHORE_n0079', 'NSHORE_n0080', 'NSHORE_n0081', 'NSHORE_n0082', 'NSHORE_s0001', 'NSHORE_s0002', 'NSHORE_s0003', 'NSHORE_s0004', 'NSHORE_s0005', 'NSHORE_s0006', 'NSHORE_s0007', 'NSHORE_s0008', 'NSHORE_s0009', 'NSHORE_s0010', 'NSHORE_s0011', 'NSHORE_s0012', 'NSHORE_s0013', 'NSHORE_s0014', 'NSHORE_s0015', 'NSHORE_s0016', 'NSHORE_s0017', 'NSHORE_s0018', 'NSHORE_s0019', 'NSHORE_s0020', 'NSHORE_s0021', 'NSHORE_s0022', 'NSHORE_s0023', 'NSHORE_s0024', 'NSHORE_s0025', 'NSHORE_s0026', 'NSHORE_s0027', 'NSHORE_s0028', 'NSHORE_s0029', 'NSHORE_s0030', 'NSHORE_s0031', 'NSHORE_s0032', 'NSHORE_s0033', 'NSHORE_s0034', 'NSHORE_s0035', 'NSHORE_s0036', 'NSHORE_s0037', 'NSHORE_s0038', 'NSHORE_s0039', 'NSHORE_s0040', 'NSHORE_s0041', 'NSHORE_s0042', 'NSHORE_s0043', 'NSHORE_s0044', 'OLDBAR0001', 'OLDBAR0002', 'OLDBAR0003', 'OLDBAR0004', 'OLDBAR0005', 'OLDBAR0006', 'OLDBAR0007', 'OLDBAR0008', 'OLDBAR0009', 'OLDBAR0010', 'OLDBAR0011', 'OLDBAR0012', 'OLDBAR0013', 'OLDBAR0014', 'OLDBAR0015', 'OLDBAR0016', 'OLDBAR0017', 'OLDBAR0018', 'OLDBAR0019', 'OLDBAR0020', 'OLDBAR0021', 'OLDBAR0022', 'OLDBAR0023', 'OLDBAR0024', 'OLDBAR0025', 'OLDBAR0026', 'OLDBAR0027', 'OLDBAR0028', 'OLDBAR0029', 'OLDBAR0030', 'OLDBAR0031', 'OLDBAR0032', 'OLDBAR0033', 'OLDBAR0034', 'OLDBAR0035', 'OLDBAR0036', 'ONEMILE0001', 'ONEMILE0002', 'ONEMILE0003', 'ONEMILE0004', 'ONEMILE0005', 'ONEMILE0006', 'ONEMILE0007', 'ONEMILE0008', 'ONEMILE0009', 'ONEMILE0010', 'ONEMILE0011', 'ONEMILE0012', 'ONEMILE0013', 'PEARLn0001', 'PEARLn0002', 'PEARLn0003', 'PEARLn0004', 'PEARLn0005', 'PEARLs0001', 'PEARLs0002', 'PEARLs0003', 'PEARLs0004', 'PEARLs0005', 'SCOT0001', 'SCOT0002', 'SCOT0003', 'SCOT0004', 'SCOT0005', 'SCOT0006', 'SCOT0007', 'SCOT0008', 'SCOT0009', 'SCOT0010', 'SCOT0011', 'SCOT0012', 'STOCNn0001', 'STOCNn0002', 'STOCNn0003', 'STOCNn0004', 'STOCNn0005', 'STOCNn0006', 'STOCNn0007', 'STOCNn0008', 'STOCNn0009', 'STOCNn0010', 'STOCNn0011', 'STOCNn0012', 'STOCNn0013', 'STOCNn0014', 'STOCNn0015', 'STOCNn0016', 'STOCNn0017', 'STOCNn0018', 'STOCNn0019', 'STOCNn0020', 'STOCNn0021', 'STOCNn0022', 'STOCNn0023', 'STOCNn0024', 'STOCNn0025', 'STOCNn0026', 'STOCNn0027', 'STOCNn0028', 'STOCNn0029', 'STOCNn0030', 'STOCNn0031', 'STOCNn0032', 'STOCNn0033', 'STOCNn0034', 'STOCNn0035', 'STOCNn0036', 'STOCNn0037', 'STOCNn0038', 'STOCNn0039', 'STOCNn0040', 'STOCNn0041', 'STOCNn0042', 'STOCNn0043', 'STOCNn0044', 'STOCNn0045', 'STOCNn0046', 'STOCNn0047', 'STOCNn0048', 'STOCNn0049', 'STOCNn0050', 'STOCNn0051', 'STOCNn0052', 'STOCNn0053', 'STOCNn0054', 'STOCNn0055', 'STOCNn0056', 'STOCNn0057', 'STOCNn0058', 'STOCNn0059', 'STOCNn0060', 'STOCNn0061', 'STOCNn0062', 'STOCNn0063', 'STOCNn0064', 'STOCNn0065', 'STOCNs0001', 'STOCNs0002', 'STOCNs0003', 'STOCNs0004', 'STOCNs0005', 'STOCNs0006', 'STOCNs0007', 'STOCNs0008', 'STOCNs0009', 'STOCNs0010', 'STOCNs0011', 'STOCNs0012', 'STOCNs0013', 'STOCNs0014', 'STOCNs0015', 'STOCNs0016', 'STOCNs0017', 'STOCNs0018', 'STOCNs0019', 'STOCNs0020', 'STOCNs0021', 'STOCNs0022', 'STOCNs0023', 'STOCNs0024', 'STOCNs0025', 'STOCNs0026', 'STOCNs0027', 'STOCNs0028', 'STOCNs0029', 'STOCNs0030', 'STOCNs0031', 'STOCNs0032', 'STOCNs0033', 'STOCNs0034', 'STOCNs0035', 'STOCNs0036', 'STOCNs0037', 'STOCNs0038', 'STOCNs0039', 'STOCNs0040', 'STOCNs0041', 'STOCNs0042', 'STOCNs0043', 'STOCNs0044', 'STOCNs0045', 'STOCNs0046', 'STOCNs0047', 'STOCNs0048', 'STOCNs0049', 'STOCNs0050', 'STOCNs0051', 'STOCNs0052', 'STOCNs0053', 'STOCNs0054', 'STOCNs0055', 'STOCNs0056', 'STOCNs0057', 'STOCNs0058', 'STOCNs0059', 'STOCNs0060', 'STOCNs0061', 'STOCNs0062', 'STOCNs0063', 'STOCNs0064', 'STOCNs0065', 'STOCNs0066', 'STOCNs0067', 'STOCNs0068', 'STOCNs0069', 'STOCNs0070', 'STOCNs0071', 'STOCNs0072', 'STOCNs0073', 'STOCNs0074', 'STOCNs0075', 'STOCNs0076', 'STOCNs0077', 'STOCNs0078', 'STOCNs0079', 'STOCNs0080', 'STOCNs0081', 'STOCNs0082', 'STOCNs0083', 'STOCNs0084', 'STOCNs0085', 'STOCNs0086', 'STOCNs0087', 'STOCNs0088', 'STOCNs0089', 'STOCNs0090', 'STOCNs0091', 'STOCNs0092', 'STOCNs0093', 'STOCNs0094', 'STOCNs0095', 'STOCNs0096', 'STOCNs0097', 'STOCNs0098', 'STOCNs0099', 'STOCNs0100', 'STOCNs0101', 'STOCNs0102', 'STOCNs0103', 'STOCNs0104', 'STOCNs0105', 'STOCNs0106', 'STOCNs0107', 'STOCNs0108', 'STOCNs0109', 'STOCNs0110', 'STOCNs0111', 'STOCNs0112', 'STOCNs0113', 'STOCNs0114', 'STOCNs0115', 'STOCNs0116', 'STOCNs0117', 'STOCNs0118', 'STOCNs0119', 'STOCNs0120', 'STOCNs0121', 'STOCNs0122', 'STOCNs0123', 'STOCNs0124', 'STOCNs0125', 'STOCNs0126', 'STOCNs0127', 'STOCNs0128', 'STOCNs0129', 'STOCNs0130', 'STOCNs0131', 'STOCNs0132', 'STOCNs0133', 'STOCNs0134', 'STOCNs0135', 'STOCNs0136', 'STOCNs0137', 'STOCNs0138', 'STOCNs0139', 'STOCNs0140', 'STOCNs0141', 'STOCNs0142', 'STOCNs0143', 'STOCNs0144', 'STOCNs0145', 'STOCNs0146', 'STOCNs0147', 'STOCNs0148', 'STOCNs0149', 'STOCNs0150', 'STOCNs0151', 'STOCNs0152', 'STOCNs0153', 'STOCNs0154', 'STOCNs0155', 'STOCNs0156', 'STOCNs0157', 'STOCNs0158', 'STOCNs0159', 'STOCNs0160', 'STOCNs0161', 'STOCNs0162', 'STOCNs0163', 'STOCNs0164', 'STOCNs0165', 'STOCNs0166', 'STOCNs0167', 'STOCNs0168', 'STOCNs0169', 'STOCNs0170', 'STOCNs0171', 'STOCNs0172', 'STOCNs0173', 'STOCNs0174', 'STOCNs0175', 'STOCNs0176', 'STOCNs0177', 'STOCNs0178', 'STOCNs0179', 'STOCNs0180', 'STOCNs0181', 'STOCNs0182', 'STOCNs0183', 'STOCNs0184', 'STOCNs0185', 'STOCNs0186', 'STOCNs0187', 'STOCNs0188', 'STOCNs0189', 'STOCNs0190', 'STOCNs0191', 'STOCNs0192', 'STOCNs0193', 'STOCNs0194', 'STOCNs0195', 'STOCNs0196', 'STOCNs0197', 'STOCNs0198', 'STOCNs0199', 'STOCNs0200', 'STOCNs0201', 'STOCNs0202', 'STOCNs0203', 'STOCNs0204', 'STOCNs0205', 'STOCNs0206', 'STOCNs0207', 'STOCNs0208', 'STOCNs0209', 'STOCS0001', 'STOCS0002', 'STOCS0003', 'STOCS0004', 'STOCS0005', 'STOCS0006', 'STOCS0007', 'STOCS0008', 'STOCS0009', 'STOCS0010', 'STOCS0011', 'STOCS0012', 'STOCS0013', 'STOCS0014', 'STOCS0015', 'STOCS0016', 'STOCS0017', 'STOCS0018', 'STOCS0019', 'STOCS0020', 'STOCS0021', 'STOCS0022', 'STOCS0023', 'STOCS0024', 'STOCS0025', 'STOCS0026', 'STOCS0027', 'STOCS0028', 'STOCS0029', 'STOCS0030', 'STOCS0031', 'STOCS0032', 'STOCS0033', 'STOCS0034', 'STOCS0035', 'STOCS0036', 'STOCS0037', 'STOCS0038', 'STOCS0039', 'STOCS0040', 'STOCS0041', 'STOCS0042', 'STOCS0043', 'STOCS0044', 'STOCS0045', 'STOCS0046', 'STUART0001', 'STUART0002', 'STUART0003', 'STUART0004', 'STUART0005', 'STUART0006', 'STUART0007', 'STUART0008', 'STUART0009', 'STUART0010', 'STUART0011', 'STUART0012', 'STUART0013', 'STUART0014', 'STUART0015', 'STUART0016', 'STUART0017', 'STUART0018', 'STUART0019', 'STUART0020', 'STUART0021', 'STUART0022', 'STUART0023', 'STUART0024', 'STUART0025', 'STUART0026', 'STUART0027', 'STUART0028', 'STUART0029', 'STUART0030', 'STUART0031', 'STUART0032', 'STUART0033', 'STUART0034', 'STUART0035', 'STUART0036', 'STUART0037', 'STUART0038', 'STUART0039', 'STUART0040', 'STUART0041', 'STUART0042', 'STUART0043', 'STUART0044', 'STUART0045', 'STUART0046', 'STUART0047', 'STUART0048', 'STUART0049', 'STUART0050', 'STUART0051', 'STUART0052', 'STUART0053', 'STUART0054', 'STUART0055', 'STUART0056', 'STUART0057', 'STUART0058', 'STUART0059', 'STUART0060', 'STUART0061', 'STUART0062', 'STUART0063', 'STUART0064', 'STUART0065', 'STUART0066', 'STUART0067', 'STUART0068', 'STUART0069', 'STUART0070', 'STUART0071', 'STUART0072', 'STUART0073', 'STUART0074', 'STUART0075', 'STUART0076', 'STUART0077', 'STUART0078', 'STUART0079', 'STUART0080', 'STUART0081', 'STUART0082', 'STUART0083', 'STUART0084', 'STUART0085', 'STUART0086', 'STUART0087', 'STUART0088', 'STUART0089', 'SWRO0001', 'SWRO0002', 'SWRO0003', 'SWRO0004', 'SWRO0005', 'SWRO0006', 'SWRO0007', 'SWRO0008', 'SWRO0009', 'SWRO0010', 'SWRO0011', 'SWRO0012', 'SWRO0013', 'SWRO0014', 'SWRO0015', 'SWRO0016', 'SWRO0017', 'SWRO0018', 'SWRO0019', 'SWRO0020', 'SWRO0021', 'SWRO0022', 'SWRO0023', 'SWRO0024', 'SWRO0025', 'SWRO0026', 'TREACH0001', 'TREACH0002', 'TREACH0003', 'TREACH0004', 'TREACH0005', 'TREACH0006', 'TREACH0007', 'TREACH0008', 'TREACH0009', 'TREACH0010', 'TREACH0011', 'TREACH0012', 'TREACH0013', 'TREACH0014', 'TREACH0015', 'TREACH0016', 'WAMBE0001', 'WAMBE0002', 'WAMBE0003', 'WAMBE0004', 'WAMBE0005', 'WAMBE0006', 'WAMBE0007', 'WAMBE0008', 'WAMBE0009', 'WAMBE0010', 'WAMBE0011', 'WAMBE0012', 'WAMBE0013', 'WAMBE0014', 'WAMBE0015', 'WAMBE0016', 'WAMBE0017', 'WAMBE0018', 'WAMBE0019', 'WAMBE0020', 'WAMBE0021', 'WAMBE0022', 'WAMBE0023', 'WAMBE0024', 'WAMBE0025', 'WAMBE0026', 'WAMBE0027'), value='NARRA0001'),)))), HBox(children=(FigureWidget({\n", " 'data': [{'name': 'Pre Storm Profile',\n", " 'type': 'scatter',\n", " 'uid': 'a7ef1527-c36f-4f59-9d62-64928b7b924f',\n", " 'x': [0],\n", " 'y': [0]},\n", " {'name': 'Post Storm Profile',\n", " 'type': 'scatter',\n", " 'uid': '8f93d4ab-7ef5-4798-b76d-ea742faf88a5',\n", " 'x': [0],\n", " 'y': [0]},\n", " {'marker': {'color': 'rgb(17, 157, 255)', 'size': 20},\n", " 'mode': 'markers',\n", " 'name': 'Pre-storm dune crest',\n", " 'type': 'scatter',\n", " 'uid': 'fd8574f3-f280-4ef6-9792-d1c14df1eb55',\n", " 'x': [0],\n", " 'y': [0]},\n", " {'marker': {'color': 'rgb(231, 99, 250)', 'size': 20},\n", " 'mode': 'markers',\n", " 'name': 'Pre-storm dune toe',\n", " 'type': 'scatter',\n", " 'uid': '4e6d2022-7ff2-42e6-9334-d76411906808',\n", " 'x': [0],\n", " 'y': [0]}],\n", " 'layout': {'height': 300,\n", " 'legend': {'x': 0.5, 'y': 1},\n", " 'margin': {'b': 50, 'l': 20, 'r': 20, 't': 50},\n", " 'title': 'Bed Profiles',\n", " 'xaxis': {'autorange': True,\n", " 'range': [0, 200],\n", " 'showgrid': True,\n", " 'showline': True,\n", " 'title': 'x (m)',\n", " 'zeroline': True},\n", " 'yaxis': {'autorange': False,\n", " 'range': [-1, 20],\n", " 'showgrid': True,\n", " 'showline': True,\n", " 'title': 'z (m)',\n", " 'zeroline': True}}\n", "}), FigureWidget({\n", " 'data': [{'lat': array([-33.46381539, -33.46301835, -33.46221051, ..., -33.4279646 ,\n", " -33.42732743, -33.42671036]),\n", " 'lon': array([151.43639576, 151.43690633, 151.43738179, ..., 151.4501613 ,\n", " 151.45092222, 151.45170635]),\n", " 'marker': {'size': 10},\n", " 'mode': 'markers',\n", " 'text': array(['AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', ..., 'WAMBE0025', 'WAMBE0026',\n", " 'WAMBE0027'], dtype='Filter by observed and predicted impacts:\",\n", ")\n", "\n", "observed_impact_select = widgets.SelectMultiple(\n", " options=df_impacts_compared.storm_regime_observed.dropna().unique(),\n", " value=df_impacts_compared.storm_regime_observed.dropna().unique().tolist(),\n", " description='Observed Impacts',\n", " disabled=False\n", ")\n", "\n", "forecasted_impact_select = widgets.SelectMultiple(\n", " options=df_impacts_compared.storm_regime_forecasted.dropna().unique(),\n", " value=df_impacts_compared.storm_regime_forecasted.dropna().unique().tolist(),\n", " description='Forecasted Impacts',\n", " disabled=False\n", ")\n", "\n", "filter_container = widgets.VBox(children=[filter_title,widgets.HBox(children=[forecasted_impact_select,observed_impact_select])])\n", "\n", "\n", "# Create widgets for selecting site_id\n", "\n", "site_id_title = widgets.HTML(\n", " value=\"Filter by site_id:\",\n", ")\n", "\n", "site_id_select = widgets.Dropdown(\n", " description='site_id: ',\n", " value='NARRA0001',\n", " options=df_profiles.index.get_level_values('site_id').unique().sort_values().tolist()\n", ")\n", "site_id_container = widgets.VBox(children=[site_id_title,widgets.HBox(children=[site_id_select])])\n", "\n", "\n", "\n", "# Add panel for pre/post storm profiles\n", "trace1 = go.Scatter(\n", " x = [0],\n", " y = [0],\n", " name='Pre Storm Profile'\n", ")\n", "trace2 = go.Scatter(\n", " x = [0],\n", " y = [0],\n", " name='Post Storm Profile'\n", ")\n", "trace3 = go.Scatter(\n", " x = [0],\n", " y = [0],\n", " name='Pre-storm dune crest',\n", " mode = 'markers',\n", " marker = dict(\n", " color = 'rgb(17, 157, 255)',\n", " size = 20,\n", " ),\n", ")\n", "trace4 = go.Scatter(\n", " x = [0],\n", " y = [0],\n", " name='Pre-storm dune toe',\n", " mode = 'markers',\n", " marker = dict(\n", " color = 'rgb(231, 99, 250)',\n", " size = 20,\n", " ),\n", ")\n", "\n", "layout = go.Layout(\n", " title = 'Bed Profiles',\n", " height=300,\n", " legend=dict(x=0.5, y=1),\n", " margin=dict(t=50,b=50,l=20,r=20),\n", " xaxis=dict(\n", " title = 'x (m)',\n", " autorange=True,\n", " showgrid=True,\n", " zeroline=True,\n", " showline=True,\n", " range=[0, 200]\n", " ),\n", " yaxis=dict(\n", " title = 'z (m)',\n", " autorange=False,\n", " showgrid=True,\n", " zeroline=True,\n", " showline=True,\n", " range=[-1, 20]\n", " )\n", ")\n", "\n", "g1 = go.FigureWidget(data=[trace1, trace2, trace3, trace4],\n", " layout=layout)\n", "\n", "\n", "# Add panel for google maps\n", "mapbox_access_token = 'pk.eyJ1IjoiY2hyaXNsZWFtYW4iLCJhIjoiY2pvNTY1MzZpMDc2OTN2bmw5MGsycHp5bCJ9.U2dwFg2c7RFjUNSayERUiw'\n", "\n", "data = [\n", " go.Scattermapbox(\n", " lat=df_sites['lat'],\n", " lon=df_sites['lon'],\n", " mode='markers',\n", " marker=dict(\n", " size=10\n", " ),\n", " text=df_sites.index.get_level_values('site_id'),\n", " ),\n", " go.Scattermapbox(\n", " lat=[0],\n", " lon=[0],\n", " mode='markers',\n", " marker=dict(\n", " size=20,\n", " color='rgb(255, 0, 0)',\n", " opacity = 0.5,\n", " ),\n", " text=df_sites.index.get_level_values('site_id'),\n", " ),\n", "]\n", "\n", "layout = go.Layout(\n", " autosize=True,\n", " height=300,\n", " hovermode='closest',\n", " showlegend=False,\n", " margin=dict(t=50,b=50,l=20,r=20),\n", " mapbox=dict(\n", " accesstoken=mapbox_access_token,\n", " bearing=0,\n", " center=dict(\n", " lat=-33.7,\n", " lon=151.3\n", " ),\n", " pitch=0,\n", " zoom=12,\n", " style='satellite-streets'\n", " ),\n", ")\n", "\n", "fig = dict(data=data, layout=layout)\n", "g2 = go.FigureWidget(data=data,layout=layout)\n", "\n", "\n", "# Add panel for time series\n", "\n", "trace_Hs0 = go.Scatter(\n", " x = [0,1],\n", " y = [0,1],\n", " name='Hs0'\n", ")\n", "trace_Tp = go.Scatter(\n", " x = [0,2],\n", " y = [0,2],\n", " name='Tp',\n", " yaxis='y2'\n", ")\n", "trace_beta = go.Scatter(\n", " x = [0,3],\n", " y = [0,3],\n", " name='beta',\n", " yaxis='y3'\n", ")\n", "data=[trace_Hs0, trace_Tp, trace_beta]\n", "\n", "layout = go.Layout(\n", " title = 'Hydro/Morpho Parameters',\n", " height=200,\n", " margin=dict(t=50,b=50,l=50,r=50),\n", " xaxis=dict(\n", " title='time',\n", " domain=[0.0, 0.9],\n", " zeroline=False,\n", " ),\n", " yaxis=dict(\n", " title = 'Hs0 (m)',\n", " ),\n", " yaxis2=dict(\n", " title='Tp (s)',\n", " overlaying='y',\n", " side='right'\n", " ),\n", " yaxis3=dict(\n", " title='beta (-)',\n", " overlaying='y',\n", " side='right',\n", " position=0.97\n", " )\n", ")\n", "\n", "g3 = go.FigureWidget(data=data, layout=layout)\n", "\n", "\n", "# Add panel for water level\n", "\n", "trace_R_high = go.Scatter(\n", " x = [0,1],\n", " y = [0,1],\n", " name='R High',\n", " line = dict(\n", " color = ('rgb(91,220,229)'),\n", " width = 2)\n", ")\n", "trace_R_low = go.Scatter(\n", " x = [0,2],\n", " y = [0,2],\n", " name='R Low',\n", " line = dict(\n", " color = ('rgb(13,174,186)'),\n", " width = 2)\n", ")\n", "trace_dune_crest = go.Scatter(\n", " x = [0,3],\n", " y = [0,3],\n", " name='Dune Crest',\n", " line = dict(\n", " color = ('rgb(214, 117, 14)'),\n", " width = 2,\n", " dash = 'dot')\n", ")\n", "trace_dune_toe = go.Scatter(\n", " x = [0,3],\n", " y = [0,3],\n", " name='Dune Toe',\n", " line = dict(\n", " color = ('rgb(142, 77, 8)'),\n", " width = 2,\n", " dash = 'dash')\n", ")\n", "trace_tide = go.Scatter(\n", " x = [0,4],\n", " y = [0,4],\n", " name='Tide+Surge WL',\n", " line = dict(\n", " color = ('rgb(8,51,137)'),\n", " width = 2,\n", " dash = 'dot')\n", ")\n", "\n", "data=[trace_R_high, trace_R_low, trace_dune_crest, trace_dune_toe,trace_tide]\n", "\n", "layout = go.Layout(\n", " title = 'Water Level & Dune Toe/Crest',\n", " height=200,\n", " margin=dict(t=50,b=50,l=50,r=50),\n", " xaxis=dict(\n", " title='time',\n", " domain=[0.0, 0.95],\n", " zeroline=False,\n", " ),\n", " yaxis=dict(\n", " title = 'Water Level (m)',\n", " ),\n", ")\n", "\n", "g4 = go.FigureWidget(data=data, layout=layout)\n", "\n", "\n", "def update_profile(change):\n", " \n", " site_id = site_id_select.value\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('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 g1.batch_update():\n", " g1.data[0].x = prestorm_x\n", " g1.data[0].y = prestorm_z\n", " g1.data[1].x = poststorm_x\n", " g1.data[1].y = poststorm_z\n", " g1.data[2].x = dune_crest_x\n", " g1.data[2].y = dune_crest_z\n", " g1.data[3].x = dune_toe_x\n", " g1.data[3].y = dune_toe_z\n", " \n", " # Relocate plan of satellite imagery\n", " site_coords = df_sites.query('site_id == \"{}\"'.format(site_id))\n", " with g2.batch_update():\n", " g2.layout.mapbox['center'] = {\n", " 'lat': site_coords['lat'].values[0],\n", " 'lon': site_coords['lon'].values[0]\n", " }\n", " g2.layout.mapbox['zoom'] = 15\n", " g2.data[1].lat = [site_coords['lat'].values[0]]\n", " g2.data[1].lon = [site_coords['lon'].values[0]]\n", " g2.data[1].text = site_coords['lon'].index.get_level_values('site_id').tolist()\n", "\n", " # Update time series plots \n", " df_timeseries = df_twl.query(\"site_id=='{}'\".format(site_id))\n", " times = df_timeseries.index.get_level_values('datetime').tolist()\n", " with g3.batch_update():\n", " g3.data[0].x = times\n", " g3.data[1].x = times\n", " g3.data[2].x = times\n", " g3.data[0].y = df_timeseries.Hs0.tolist()\n", " g3.data[1].y = df_timeseries.Tp.tolist()\n", " g3.data[2].y = df_timeseries.beta.tolist()\n", " \n", " # Update water levels plot\n", " df_timeseries = df_twl.query(\"site_id=='{}'\".format(site_id))\n", " with g4.batch_update():\n", " g4.data[0].x = times\n", " g4.data[1].x = times\n", " g4.data[2].x = [min(times), max(times)]\n", " g4.data[3].x = [min(times), max(times)]\n", " g4.data[4].x = times\n", " g4.data[0].y = df_timeseries.R_high.tolist()\n", " g4.data[1].y = df_timeseries.R_low.tolist()\n", " g4.data[2].y = dune_crest_z.tolist()[0], dune_crest_z.tolist()[0],\n", " g4.data[3].y = dune_toe_z.tolist()[0], dune_toe_z.tolist()[0],\n", " g4.data[4].y = df_timeseries.tide.tolist()\n", " \n", " \n", "def update_filter(change):\n", " \n", " # Get filtered impacts\n", " observed_impacts = observed_impact_select.value\n", " forecasted_impacts = forecasted_impact_select.value\n", " \n", " # Get sites with these impacts \n", " site_id_select.options = df_impacts_compared.loc[df_impacts_compared.storm_regime_forecasted.isin(forecasted_impacts) & \n", " df_impacts_compared.storm_regime_observed.isin(observed_impacts),].index.tolist()\n", " \n", " \n", "site_id_select.observe(update_profile, names=\"value\")\n", "observed_impact_select.observe(update_filter, names=\"value\")\n", "forecasted_impact_select.observe(update_filter, names=\"value\")\n", "\n", "widgets.VBox([filter_container,site_id_container,widgets.HBox([g1,g2]),g3,g4])" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "ExecuteTime": { "end_time": "2018-11-19T03:23:37.582663Z", "start_time": "2018-11-19T03:23:37.577662Z" } }, "outputs": [ { "data": { "text/plain": [ "([3.111490610630103, 3.111490610630103],)" ] }, "execution_count": 178, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g4.data[2].y" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { 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