From 05e0080b9105a406c07175b7f9b896c67d1b7c4f Mon Sep 17 00:00:00 2001
From: FlorisCalkoen <FlorisCalkoen@users.noreply.github.com>
Date: Wed, 28 Feb 2024 22:40:19 +0000
Subject: [PATCH] deploy: 954730dd671d32347ebec1d80cc4e03aec615243

---
 _images/02_tidal_env.png                      | Bin 0 -> 664792 bytes
 ...8e8328850fc6fa1e8d1215cef8adb01977167f.png | Bin 0 -> 200061 bytes
 .../notebooks/3a_tidal_environments.ipynb     | 409 ++++++++
 notebooks/1_coastal_classification.html       |  52 +-
 notebooks/3a_tidal_environments.html          | 966 ++++++++++++++++++
 .../initialize/2a_pre_knowledge_waves.html    |   8 +-
 .../2b_wave_dispersion_and_grouping.html      |   8 +-
 notebooks/initialize/init_2a.html             |   8 +-
 notebooks/initialize/init_2b.html             |   8 +-
 objects.inv                                   | Bin 706 -> 725 bytes
 searchindex.js                                |   2 +-
 11 files changed, 1418 insertions(+), 43 deletions(-)
 create mode 100644 _images/02_tidal_env.png
 create mode 100644 _images/ec3cd879fb578e890d9b5246668e8328850fc6fa1e8d1215cef8adb01977167f.png
 create mode 100644 _sources/notebooks/3a_tidal_environments.ipynb
 create mode 100644 notebooks/3a_tidal_environments.html

diff --git a/_images/02_tidal_env.png b/_images/02_tidal_env.png
new file mode 100644
index 0000000000000000000000000000000000000000..134b5120ee332cedb314f2f30aed5f5522962e83
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diff --git a/_sources/notebooks/3a_tidal_environments.ipynb b/_sources/notebooks/3a_tidal_environments.ipynb
new file mode 100644
index 0000000..45f7ab2
--- /dev/null
+++ b/_sources/notebooks/3a_tidal_environments.ipynb
@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "070a3d1e-3e53-4d9d-adf0-1100343150ea",
+   "metadata": {},
+   "source": [
+    "# Tidal Environments\n",
+    "\n",
+    "In chapter 4 of the textbook, we looked into the global variation in the main processes that shape the coast: wind, waves, and tides. In this notebook, we will focus on the large-scale variation in tides. \n",
+    "The two main variables on the basis of which tidal environments can be classified are:\n",
+    "- **Magnitude of the tide**, characterised by the tidal range;\n",
+    "- **Tidal character**, determined by the importance of diurnal vs. semi-diurnal components\n",
+    "\n",
+    "In this notebook, we will explore both classifications using python code. Therefore, please import the libraries that we need for the analysis from the cell below.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b2ce2ff-523a-4801-9dce-e70af8c20a05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "from pathlib import Path\n",
+    "import sys\n",
+    "from warnings import filterwarnings\n",
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import interact\n",
+    "from matplotlib.patches import Patch\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import cartopy.crs as ccrs\n",
+    "import xarray as xr\n",
+    "from datetime import datetime, timedelta\n",
+    "from IPython.display import display, Image\n",
+    "import math\n",
+    "import pandas as pd\n",
+    "import pooch\n",
+    "import pickle\n",
+    "\n",
+    "F_data_fp = pooch.retrieve(\n",
+    "    \"https://coclico.blob.core.windows.net/coastal-dynamics/02-tide/02_F_data.pkl\",\n",
+    "    known_hash=\"eae7be0e7b44ed5b211e931bd6e5948e0aa8db067403956fe2d486f69e49c769\",\n",
+    ")\n",
+    "F_data = pd.read_pickle(F_data_fp)\n",
+    "\n",
+    "cwd = pathlib.Path().resolve()\n",
+    "proj_dir = cwd.parent.parent.parent  # this is the root of the CoastalCodeBook\n",
+    "sys.path.append(str(proj_dir))\n",
+    "\n",
+    "from initialize.Tide_Initialize import questions_3a1, questions_3a2, plot_4timeseries_with_interactive_controls\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "30da269b-6d4f-4bcc-87ac-9ae5ed3050d2",
+   "metadata": {},
+   "source": [
+    "<br><br>\n",
+    "## 1. Tidal Environments\n",
+    "\n",
+    "The tidal wave is distorted by local differences in water depth and by the location and shape of land masses and large embayments. This results in a global variation in tidal range controlled by the large-scale coastal configuration. The tidal character expressed through mean spring tidal range:\n",
+    "\n",
+    "\n",
+    "|Category| Mean spring tidal range |\n",
+    "|----------------|:--:|\n",
+    "| Micro-tidal | < 2m | \n",
+    "| Meso-tidal | 2m - 4m | \n",
+    "| Macro-tidal | > 4m | \n",
+    "\n",
+    "<br>The tidal character, on the other hand, is defined by the form factor F: <center>*F = (K1 + O1)/(M2 + S2)*,</center>\n",
+    "where *K1*, *O1*, *M2*, and *S2* are the amplitudes of the corresponding tidal constituents. <br><br>\n",
+    "\n",
+    "|Category| Value of F |\n",
+    "|----------------|:--:|\n",
+    "| Semi-diurnal | 0 - 0.25 | \n",
+    "| Mixed, mainly semi-diurnal | 0.25 - 1.5 | \n",
+    "| Mixed, mainly diurnal | 1.5 - 3 | \n",
+    "| Diurnal | > 3 | \n",
+    "\n",
+    "<br> In the figure below, you can see the world distribution of mean spring tidal range (*left*) and tidal characters (*right*). The attribution of the tidal types follows J.L. Davies and Clayton (1980) (Figures 4.10 and 4.12 from the textbook).\n",
+    "- Look into the semi-enclosed seas vs. open coasts; do you notice anything? Why?\n",
+    "- Compare the two: do you notice any repetitive patterns? Hint: Look at the tidal range for specific tidal characters.\n",
+    "\n",
+    "![image](../notebooks/figures/02_tidal_env.png)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aabecdb1-75a9-438d-88a1-e268af67134b",
+   "metadata": {},
+   "source": [
+    "<br><br><br>\n",
+    "## 2. Tidal Character at Specific Locations\n",
+    "\n",
+    "Let's now categorize the tide at four specific locations. We will once again use the [FES2014 Global Tide data](https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes/description-fes2014.html), which provides amplitude and phase information for 34 tidal constituents, distributed on 1/16˚ grids.\n",
+    "\n",
+    "For each location, the amplitudes of the constituents that are needed to compute the form factor are shown in the table below. \n",
+    "\n",
+    "|Tidal amplitudes [cm]| M2 | S2 | K1 | O1 |\n",
+    "|----------------|:--:|:--:|:--:|----|\n",
+    "| Scheveningen (Netherlands)  | 75.78 | 17.74 | 8.39  |  11.85 | \n",
+    "| Galveston (US Gulf of Mexico)  | 13.08| 3.97  |  16.17 | 15.89 | \n",
+    "| Jakarta (Indonesia)| 4.58 |  5.18 |  25.75 | 13.46 |\n",
+    "| Valparaiso (Chile) | 42.91 | 14.40 | 15.29  |  10.22 | \n",
+    "\n",
+    "\n",
+    "<br> In cell below, write your own code to calculate the form factor *F* at each location."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bfc5f12-c0bb-4d61-beb9-3828c28e61d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write your code here.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecc81791-aa21-4c9b-9be0-1d8c1dea6070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run to get questions\n",
+    "\n",
+    "print(\"\\n What is the category of:\")\n",
+    "questions_3a1()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd1da051-26d3-4bbc-8cfa-c7859bb48e81",
+   "metadata": {},
+   "source": [
+    "<br><br>\n",
+    "\n",
+    "Let's now plot the tidal characters across the globe obtained from the FES2014 dataset and mark the four locations from above. Do your answers match the locations on the map?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c25ed39-a85d-4ce3-aea9-6425dd90a698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## FES2014 Tidal Characters\n",
+    "# the code is a bit slow, it is doing a global contour!\n",
+    "\n",
+    "# Define the categories and corresponding colors\n",
+    "categories = {\n",
+    "    'Diurnal': {'min': 3, 'max': float('inf'), 'color': '#ad4123'},\n",
+    "    'Mixed, mainly diurnal': {'min': 1.5, 'max': 2.9, 'color': '#e7541e'},\n",
+    "    'Mixed, mainly semidiurnal': {'min': 0.25, 'max': 1.49, 'color': '#f4a030'},\n",
+    "    'Semidiurnal': {'min': 0, 'max': 0.249, 'color': '#f8ce97'}\n",
+    "}\n",
+    "\n",
+    "# Create a figure and axis\n",
+    "fig, ax = plt.subplots(figsize=(15, 13), subplot_kw={'projection': ccrs.Robinson(central_longitude=0.0)})\n",
+    "ax.set_global()\n",
+    "\n",
+    "# Plot the scatter points with specific colors for each category\n",
+    "legend_patches = []\n",
+    "for category, values in categories.items():\n",
+    "    subset_data = F_data[(F_data['F'] >= values['min']) & (F_data['F'] <= values['max'])]\n",
+    "    if not subset_data.empty:\n",
+    "        scatter = ax.scatter(subset_data.index.get_level_values('lon').values, \n",
+    "                             subset_data.index.get_level_values('lat').values, s=1, color=values['color'], \n",
+    "                             label=category, transform=ccrs.PlateCarree())\n",
+    "        legend_patches.append(Patch(color=scatter.get_facecolor()[0], label=category))\n",
+    "\n",
+    "# Add markers for specific locations - here you can edit the code if you are wondering for a specific location\n",
+    "locs = {\n",
+    "    'Scheveningen': [4.25, 52.125],  # lon, lat\n",
+    "    'Galveston': [-94.6875, 29.25],\n",
+    "    'Valparaiso': [-71.625, -33],\n",
+    "    'Jakarta': [106.8125, -6.0625],\n",
+    "}\n",
+    "\n",
+    "for loc, coordinates in locs.items():\n",
+    "    lon, lat = coordinates\n",
+    "    ax.scatter(lon, lat, color='black', s=10, transform=ccrs.PlateCarree(), zorder=4)\n",
+    "    ax.text(lon - 25, lat+3, loc, color='black', fontsize=12, fontweight='bold', transform=ccrs.PlateCarree())\n",
+    "\n",
+    "ax.legend(handles=legend_patches, loc='lower center', bbox_to_anchor=(0.5, -0.2), ncol=2, fontsize=12)\n",
+    "ax.coastlines(resolution='110m', color='black', linewidth=0.5)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e294d16-733c-4ece-a443-b2554d706cc7",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "Compare this figure to the tidal characters illustration mentioned earlier (Figure 4.12 in the textbook). What could be the reasons behind the observed differences?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86b2c7d2-275c-492b-97df-ec6b62639a79",
+   "metadata": {},
+   "source": [
+    "<br><br><br>\n",
+    "## 3. Variation of Tidal Constituents\n",
+    "\n",
+    "\n",
+    "Now that we've identified the tidal characteristics of each location, we can visualize their tidal constituents and address various questions.\n",
+    "\n",
+    "Execute the block below to generate an interactive figure. The figure displays the individual tidal components (upper plot) and their combined tidal signal (second plot) for four locations,  each characterized by distinct tidal behaviours.\n",
+    "\n",
+    "You can adjust the plotted time range using the slider (from 1 day to 1 year) and select which tidal constituents to display with tick boxes. This allows you to experiment with different constituents, observe the resulting signals, and compare the locations.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "092a4ba2-d56a-4f6b-bf85-f24bd933be04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Choose tidal constituents\n",
+    "comps = ['M2', 'S2', 'N2', 'K2',  #semi-diurnal\n",
+    "         'K1', 'O1', 'P1', 'Q1',  #diurnal\n",
+    "         'MM', 'MF', 'SSA',       #long period\n",
+    "         'M4', 'M6', 'S4', 'MN4'] #short period (overtides)\n",
+    "\n",
+    "# We choose one year to plot, 2000-2001 same as in the previous Notebook\n",
+    "dates = np.array([\n",
+    "    datetime(2000, 1, 1, 0, 0, 0) + timedelta(seconds=item * 3600)\n",
+    "    for item in range(24*365) #1 year\n",
+    "])\n",
+    "\n",
+    "# download and load tidal signals\n",
+    "scheveningen_fp = pooch.retrieve(\n",
+    "    \"https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_scheveningen.p\",\n",
+    "    known_hash=\"4ebac210fc0893e52655cbc3c9501a6c805e3537f327fed7edb9e7dbfe7fa06a\",\n",
+    ")\n",
+    "galveston_fp = pooch.retrieve(\n",
+    "    \"https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_galveston.p\",\n",
+    "    known_hash=\"26af20b240804a18a939d477632b4507d44ebdc98510deb07e601732c0846224\",\n",
+    ")\n",
+    "jakarta_fp = pooch.retrieve(\n",
+    "    \"https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_jakarta.p\",\n",
+    "    known_hash=\"7950246c47e757d9dd427063d7c809fda4267ed119efd18de43237aa9f98c9c6\",\n",
+    ")\n",
+    "valparaiso_fp = pooch.retrieve(\n",
+    "    \"https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_valparaiso.p\",\n",
+    "    known_hash=\"a19a51e3607822bc72ab902f83990d2b318a08d1982c25461f8ffd6e5caae35f\",\n",
+    ")\n",
+    "with open(scheveningen_fp, 'rb') as pickle_file:\n",
+    "    scheveningen = pickle.load(pickle_file)\n",
+    "with open(galveston_fp, 'rb') as pickle_file:\n",
+    "    galveston = pickle.load(pickle_file)\n",
+    "with open(jakarta_fp, 'rb') as pickle_file:\n",
+    "    jakarta = pickle.load(pickle_file)\n",
+    "with open(valparaiso_fp, 'rb') as pickle_file:\n",
+    "    valparaiso = pickle.load(pickle_file)\n",
+    "\n",
+    "tide = {\n",
+    "    'Scheveningen': scheveningen,\n",
+    "    'Valparaiso': valparaiso,\n",
+    "    'Jakarta': jakarta,\n",
+    "    'Galveston': galveston,\n",
+    "    }\n",
+    "\n",
+    "plot_4timeseries_with_interactive_controls(comps, dates, tide)\n",
+    "# We start with the main constituents preselected"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0edb1bfe-ddd5-43e4-b20f-b0e448984cc7",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "\n",
+    "Set the time range to around 30 days and \\\n",
+    "&emsp;a) select only the main semi-diurnal components \\\n",
+    "&emsp;b) select only the main diurnal components \\\n",
+    "&emsp;c) select only one semi-diurnal (M2) and one diurnal (K1) component \\\n",
+    "&emsp;*What kind of signal do you see for each location? Why are there differences between locations? Don't forget to look at the y-axis range.*\n",
+    "\n",
+    "We also know that there are much longer variations than the ones above. Set the time range to the whole year and \\\n",
+    "&emsp;a) select only S2 and K2 \\\n",
+    "&emsp;b) select only K1 and P1 \\\n",
+    "&emsp;*What kind of combined signal can you see now?*\n",
+    "\n",
+    "Finally activate all components, including some short period constituents that we will further explore next week. Analyse the combined signals. What are the dominant components at each location?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d1e8d06-dd02-414f-b940-a6e16c2b39f5",
+   "metadata": {},
+   "source": [
+    "<br><br>\n",
+    "# 4. Tidal Beating\n",
+    "Using the knowledge gained from Chapter 3 of the textbook and the interactive figure above, try to answer the questions below. You can use the next cell as a calculator. Give your numeric answer with at least three significant figures.\n",
+    "\n",
+    "|Tidal constituents | Name | Equil. <br> Amplitude [m] | Period [h] |\n",
+    "|-|-|-|-|\n",
+    "| **Semi-diurnal** |\n",
+    "| Principal lunar | M2 | 0.24 | 12.4206012 |\n",
+    "| Principal solar | S2 | 0.11 | 12 |\n",
+    "| Lunar elliptical | N2 | 0.046 | 12.65834751 |\n",
+    "| Lunar-solar declinational | K2 | 0.031 | 11.96723606 |\n",
+    "| **Diurnal** |\n",
+    "| Lunar-solar declinational | K1 | 0.14 | 23.93447213 |\n",
+    "| Principal lunar | O1 | 0.10 | 25.81933871 | \n",
+    "| Principal solar | P1 | 0.047 | 24.06588766 | \n",
+    "| Lunar elliptical | Q1 | 0.019 | 26.868350 |\n",
+    "| **Long period** |\n",
+    "| Fortnightly | Mf | 0.042 | 327.8599387 |\n",
+    "| Monthly | Mm | 0.022 | 661.3111655 | \n",
+    "| Semi-annual | Ssa | 0.019 | 4383.076325 | "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5f37d5d-396b-4f23-8588-df2673b94b54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write your code here to get answers to questions 1 and 2. Use the values from the table above \n",
+    "# and use at least 3 significant figures.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d026cfe-833b-459b-960a-12a74535111a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run to get questions. \n",
+    "# The following answers are interchangeable, so do not put the same answer twice - this will result in a false correct answer.\n",
+    "# TC1, TC2\n",
+    "# TC3, TC4\n",
+    "# Month1, Month2\n",
+    "# TC5, TC6\n",
+    "# Month3, Month4\n",
+    "# TC7, TC8\n",
+    "\n",
+    "questions_3a2()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "964b2f25-496f-474b-8261-89ef1c35cdbc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7be80944-3487-4a90-88bb-ca7dc531051e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65966d17-99cf-49a2-9cf7-c2042eb6ceb1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:coastal]",
+   "language": "python",
+   "name": "conda-env-coastal-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/1_coastal_classification.html b/notebooks/1_coastal_classification.html
index 37de0ff..c6b34c3 100644
--- a/notebooks/1_coastal_classification.html
+++ b/notebooks/1_coastal_classification.html
@@ -1743,7 +1743,7 @@ <h3>Now plot the earthquake data<a class="headerlink" href="#now-plot-the-earthq
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Launching server at http://localhost:35159
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Launching server at http://localhost:38193
 </pre></div>
 </div>
 </div>
@@ -1765,12 +1765,12 @@ <h3>Plate tectonics<a class="headerlink" href="#plate-tectonics" title="Link to
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_html"><div id='4cfdd2dc-896b-4622-b1d9-3262204339cc'>
-  <div id="d7146759-9d2d-4154-89ec-79b80100e004" data-root-id="4cfdd2dc-896b-4622-b1d9-3262204339cc" style="display: contents;"></div>
+<div class="output text_html"><div id='2f3d8872-8a48-49ba-84a8-ce3e82de7f5b'>
+  <div id="de47f622-1741-4427-be01-c556e3e3d934" data-root-id="2f3d8872-8a48-49ba-84a8-ce3e82de7f5b" style="display: contents;"></div>
 </div>
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@@ -2196,7 +2196,7 @@ <h3>Plot coastal systems<a class="headerlink" href="#plot-coastal-systems" title
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Select the best fitting reasons for this."}},{"type":"object","name":"CheckboxGroup","id":"95c0199b-f697-4980-b011-6f97e2aa4bbf","attributes":{"stylesheets":["\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\n  background-image: url(\"data:image/svg+xml;base64,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\");\n  background-size: auto calc(min(50%, 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muddy sediment supply can be seen from (you can select multiple answers):"}},{"type":"object","name":"CheckboxGroup","id":"4bb9e2e3-5ee9-41b5-9f45-fe6ca64271cb","attributes":{"stylesheets":["\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\n  background-image: url(\"data:image/svg+xml;base64,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\");\n  background-size: auto calc(min(50%, 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Select the best fitting reasons for this."}},{"type":"object","name":"CheckboxGroup","id":"4b7ef317-6ac2-4409-8b36-355e282542c7","attributes":{"stylesheets":["\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\n  background-image: url(\"data:image/svg+xml;base64,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\");\n  background-size: auto calc(min(50%, 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"}}], 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+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 1</span></p>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="1_coastal_classification.html">Characterization of Coastal Systems</a></li>
+</ul>
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Week 2</span></p>
+<ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="2c_theory_equilibrium_tide.html">Theory Equilibrium tide</a></li>
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+    <h1>Tidal Environments</h1>
+    <!-- Table of contents -->
+    <div id="print-main-content">
+        <div id="jb-print-toc">
+            
+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Tidal Environments</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id1">1. Tidal Environments</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#tidal-character-at-specific-locations">2. Tidal Character at Specific Locations</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#variation-of-tidal-constituents">3. Variation of Tidal Constituents</a></li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#tidal-beating">4. Tidal Beating</a></li>
+</ul>
+
+            </nav>
+        </div>
+    </div>
+</div>
+
+              
+                
+<div id="searchbox"></div>
+                <article class="bd-article">
+                  
+  <section class="tex2jax_ignore mathjax_ignore" id="tidal-environments">
+<h1>Tidal Environments<a class="headerlink" href="#tidal-environments" title="Link to this heading">#</a></h1>
+<p>In chapter 4 of the textbook, we looked into the global variation in the main processes that shape the coast: wind, waves, and tides. In this notebook, we will focus on the large-scale variation in tides.
+The two main variables on the basis of which tidal environments can be classified are:</p>
+<ul class="simple">
+<li><p><strong>Magnitude of the tide</strong>, characterised by the tidal range;</p></li>
+<li><p><strong>Tidal character</strong>, determined by the importance of diurnal vs. semi-diurnal components</p></li>
+</ul>
+<p>In this notebook, we will explore both classifications using python code. Therefore, please import the libraries that we need for the analysis from the cell below.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pathlib</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">filterwarnings</span>
+<span class="kn">import</span> <span class="nn">ipywidgets</span> <span class="k">as</span> <span class="nn">widgets</span>
+<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interact</span>
+<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Patch</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Image</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">pooch</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+
+<span class="n">F_data_fp</span> <span class="o">=</span> <span class="n">pooch</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span>
+    <span class="s2">&quot;https://coclico.blob.core.windows.net/coastal-dynamics/02-tide/02_F_data.pkl&quot;</span><span class="p">,</span>
+    <span class="n">known_hash</span><span class="o">=</span><span class="s2">&quot;eae7be0e7b44ed5b211e931bd6e5948e0aa8db067403956fe2d486f69e49c769&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">F_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_pickle</span><span class="p">(</span><span class="n">F_data_fp</span><span class="p">)</span>
+
+<span class="n">cwd</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>
+<span class="n">proj_dir</span> <span class="o">=</span> <span class="n">cwd</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span>  <span class="c1"># this is the root of the CoastalCodeBook</span>
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">proj_dir</span><span class="p">))</span>
+
+<span class="kn">from</span> <span class="nn">initialize.Tide_Initialize</span> <span class="kn">import</span> <span class="n">questions_3a1</span><span class="p">,</span> <span class="n">questions_3a2</span><span class="p">,</span> <span class="n">plot_4timeseries_with_interactive_controls</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Downloading data from &#39;https://coclico.blob.core.windows.net/coastal-dynamics/02-tide/02_F_data.pkl&#39; to file &#39;/home/runner/.cache/pooch/918dec1d06d7bc0691082dd30d324d60-02_F_data.pkl&#39;.
+</pre></div>
+</div>
+</div>
+</div>
+<p><br><br></p>
+<section id="id1">
+<h2>1. Tidal Environments<a class="headerlink" href="#id1" title="Link to this heading">#</a></h2>
+<p>The tidal wave is distorted by local differences in water depth and by the location and shape of land masses and large embayments. This results in a global variation in tidal range controlled by the large-scale coastal configuration. The tidal character expressed through mean spring tidal range:</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Category</p></th>
+<th class="head text-center"><p>Mean spring tidal range</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Micro-tidal</p></td>
+<td class="text-center"><p>&lt; 2m</p></td>
+</tr>
+<tr class="row-odd"><td><p>Meso-tidal</p></td>
+<td class="text-center"><p>2m - 4m</p></td>
+</tr>
+<tr class="row-even"><td><p>Macro-tidal</p></td>
+<td class="text-center"><p>&gt; 4m</p></td>
+</tr>
+</tbody>
+</table>
+<p><br>The tidal character, on the other hand, is defined by the form factor F: <center><em>F = (K1 + O1)/(M2 + S2)</em>,</center>
+where <em>K1</em>, <em>O1</em>, <em>M2</em>, and <em>S2</em> are the amplitudes of the corresponding tidal constituents. <br><br></p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Category</p></th>
+<th class="head text-center"><p>Value of F</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Semi-diurnal</p></td>
+<td class="text-center"><p>0 - 0.25</p></td>
+</tr>
+<tr class="row-odd"><td><p>Mixed, mainly semi-diurnal</p></td>
+<td class="text-center"><p>0.25 - 1.5</p></td>
+</tr>
+<tr class="row-even"><td><p>Mixed, mainly diurnal</p></td>
+<td class="text-center"><p>1.5 - 3</p></td>
+</tr>
+<tr class="row-odd"><td><p>Diurnal</p></td>
+<td class="text-center"><p>&gt; 3</p></td>
+</tr>
+</tbody>
+</table>
+<p><br> In the figure below, you can see the world distribution of mean spring tidal range (<em>left</em>) and tidal characters (<em>right</em>). The attribution of the tidal types follows J.L. Davies and Clayton (1980) (Figures 4.10 and 4.12 from the textbook).</p>
+<ul class="simple">
+<li><p>Look into the semi-enclosed seas vs. open coasts; do you notice anything? Why?</p></li>
+<li><p>Compare the two: do you notice any repetitive patterns? Hint: Look at the tidal range for specific tidal characters.</p></li>
+</ul>
+<p><img alt="image" src="../_images/02_tidal_env.png" /></p>
+<p><br><br><br></p>
+</section>
+<section id="tidal-character-at-specific-locations">
+<h2>2. Tidal Character at Specific Locations<a class="headerlink" href="#tidal-character-at-specific-locations" title="Link to this heading">#</a></h2>
+<p>Let’s now categorize the tide at four specific locations. We will once again use the <a class="reference external" href="https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes/description-fes2014.html">FES2014 Global Tide data</a>, which provides amplitude and phase information for 34 tidal constituents, distributed on 1/16˚ grids.</p>
+<p>For each location, the amplitudes of the constituents that are needed to compute the form factor are shown in the table below.</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Tidal amplitudes [cm]</p></th>
+<th class="head text-center"><p>M2</p></th>
+<th class="head text-center"><p>S2</p></th>
+<th class="head text-center"><p>K1</p></th>
+<th class="head"><p>O1</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Scheveningen (Netherlands)</p></td>
+<td class="text-center"><p>75.78</p></td>
+<td class="text-center"><p>17.74</p></td>
+<td class="text-center"><p>8.39</p></td>
+<td><p>11.85</p></td>
+</tr>
+<tr class="row-odd"><td><p>Galveston (US Gulf of Mexico)</p></td>
+<td class="text-center"><p>13.08</p></td>
+<td class="text-center"><p>3.97</p></td>
+<td class="text-center"><p>16.17</p></td>
+<td><p>15.89</p></td>
+</tr>
+<tr class="row-even"><td><p>Jakarta (Indonesia)</p></td>
+<td class="text-center"><p>4.58</p></td>
+<td class="text-center"><p>5.18</p></td>
+<td class="text-center"><p>25.75</p></td>
+<td><p>13.46</p></td>
+</tr>
+<tr class="row-odd"><td><p>Valparaiso (Chile)</p></td>
+<td class="text-center"><p>42.91</p></td>
+<td class="text-center"><p>14.40</p></td>
+<td class="text-center"><p>15.29</p></td>
+<td><p>10.22</p></td>
+</tr>
+</tbody>
+</table>
+<p><br> In cell below, write your own code to calculate the form factor <em>F</em> at each location.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Write your code here.</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Run to get questions</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> What is the category of:&quot;</span><span class="p">)</span>
+<span class="n">questions_3a1</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> What is the category of:
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "199acd7a52424cb9b2c8eabc32acda35"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "9919d5373f5c402ebce10536480b9094"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "3eb5320d44a245d09de6771f40ccc940"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "d8b26c5d04364e3cab427e7c2b836ecc"}</script></div>
+</div>
+<p><br><br></p>
+<p>Let’s now plot the tidal characters across the globe obtained from the FES2014 dataset and mark the four locations from above. Do your answers match the locations on the map?</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">## FES2014 Tidal Characters</span>
+<span class="c1"># the code is a bit slow, it is doing a global contour!</span>
+
+<span class="c1"># Define the categories and corresponding colors</span>
+<span class="n">categories</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;Diurnal&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;min&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;inf&#39;</span><span class="p">),</span> <span class="s1">&#39;color&#39;</span><span class="p">:</span> <span class="s1">&#39;#ad4123&#39;</span><span class="p">},</span>
+    <span class="s1">&#39;Mixed, mainly diurnal&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;min&#39;</span><span class="p">:</span> <span class="mf">1.5</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">:</span> <span class="mf">2.9</span><span class="p">,</span> <span class="s1">&#39;color&#39;</span><span class="p">:</span> <span class="s1">&#39;#e7541e&#39;</span><span class="p">},</span>
+    <span class="s1">&#39;Mixed, mainly semidiurnal&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;min&#39;</span><span class="p">:</span> <span class="mf">0.25</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">:</span> <span class="mf">1.49</span><span class="p">,</span> <span class="s1">&#39;color&#39;</span><span class="p">:</span> <span class="s1">&#39;#f4a030&#39;</span><span class="p">},</span>
+    <span class="s1">&#39;Semidiurnal&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;min&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">:</span> <span class="mf">0.249</span><span class="p">,</span> <span class="s1">&#39;color&#39;</span><span class="p">:</span> <span class="s1">&#39;#f8ce97&#39;</span><span class="p">}</span>
+<span class="p">}</span>
+
+<span class="c1"># Create a figure and axis</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">13</span><span class="p">),</span> <span class="n">subplot_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;projection&#39;</span><span class="p">:</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">Robinson</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)})</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_global</span><span class="p">()</span>
+
+<span class="c1"># Plot the scatter points with specific colors for each category</span>
+<span class="n">legend_patches</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">category</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="n">categories</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">subset_data</span> <span class="o">=</span> <span class="n">F_data</span><span class="p">[(</span><span class="n">F_data</span><span class="p">[</span><span class="s1">&#39;F&#39;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">values</span><span class="p">[</span><span class="s1">&#39;min&#39;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">F_data</span><span class="p">[</span><span class="s1">&#39;F&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">values</span><span class="p">[</span><span class="s1">&#39;max&#39;</span><span class="p">])]</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">subset_data</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
+        <span class="n">scatter</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">subset_data</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="s1">&#39;lon&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> 
+                             <span class="n">subset_data</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="s1">&#39;lat&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">values</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">],</span> 
+                             <span class="n">label</span><span class="o">=</span><span class="n">category</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+        <span class="n">legend_patches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">scatter</span><span class="o">.</span><span class="n">get_facecolor</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">category</span><span class="p">))</span>
+
+<span class="c1"># Add markers for specific locations - here you can edit the code if you are wondering for a specific location</span>
+<span class="n">locs</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;Scheveningen&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">4.25</span><span class="p">,</span> <span class="mf">52.125</span><span class="p">],</span>  <span class="c1"># lon, lat</span>
+    <span class="s1">&#39;Galveston&#39;</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mf">94.6875</span><span class="p">,</span> <span class="mf">29.25</span><span class="p">],</span>
+    <span class="s1">&#39;Valparaiso&#39;</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mf">71.625</span><span class="p">,</span> <span class="o">-</span><span class="mi">33</span><span class="p">],</span>
+    <span class="s1">&#39;Jakarta&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">106.8125</span><span class="p">,</span> <span class="o">-</span><span class="mf">6.0625</span><span class="p">],</span>
+<span class="p">}</span>
+
+<span class="k">for</span> <span class="n">loc</span><span class="p">,</span> <span class="n">coordinates</span> <span class="ow">in</span> <span class="n">locs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">lon</span><span class="p">,</span> <span class="n">lat</span> <span class="o">=</span> <span class="n">coordinates</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">lon</span> <span class="o">-</span> <span class="mi">25</span><span class="p">,</span> <span class="n">lat</span><span class="o">+</span><span class="mi">3</span><span class="p">,</span> <span class="n">loc</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">&#39;bold&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">legend_patches</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;lower center&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">),</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="n">resolution</span><span class="o">=</span><span class="s1">&#39;110m&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/micromamba/envs/coastal/lib/python3.11/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
+  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
+</pre></div>
+</div>
+<img alt="../_images/ec3cd879fb578e890d9b5246668e8328850fc6fa1e8d1215cef8adb01977167f.png" src="../_images/ec3cd879fb578e890d9b5246668e8328850fc6fa1e8d1215cef8adb01977167f.png" />
+</div>
+</div>
+<br>
+Compare this figure to the tidal characters illustration mentioned earlier (Figure 4.12 in the textbook). What could be the reasons behind the observed differences?<p><br><br><br></p>
+</section>
+<section id="variation-of-tidal-constituents">
+<h2>3. Variation of Tidal Constituents<a class="headerlink" href="#variation-of-tidal-constituents" title="Link to this heading">#</a></h2>
+<p>Now that we’ve identified the tidal characteristics of each location, we can visualize their tidal constituents and address various questions.</p>
+<p>Execute the block below to generate an interactive figure. The figure displays the individual tidal components (upper plot) and their combined tidal signal (second plot) for four locations,  each characterized by distinct tidal behaviours.</p>
+<p>You can adjust the plotted time range using the slider (from 1 day to 1 year) and select which tidal constituents to display with tick boxes. This allows you to experiment with different constituents, observe the resulting signals, and compare the locations.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Choose tidal constituents</span>
+<span class="n">comps</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;M2&#39;</span><span class="p">,</span> <span class="s1">&#39;S2&#39;</span><span class="p">,</span> <span class="s1">&#39;N2&#39;</span><span class="p">,</span> <span class="s1">&#39;K2&#39;</span><span class="p">,</span>  <span class="c1">#semi-diurnal</span>
+         <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;O1&#39;</span><span class="p">,</span> <span class="s1">&#39;P1&#39;</span><span class="p">,</span> <span class="s1">&#39;Q1&#39;</span><span class="p">,</span>  <span class="c1">#diurnal</span>
+         <span class="s1">&#39;MM&#39;</span><span class="p">,</span> <span class="s1">&#39;MF&#39;</span><span class="p">,</span> <span class="s1">&#39;SSA&#39;</span><span class="p">,</span>       <span class="c1">#long period</span>
+         <span class="s1">&#39;M4&#39;</span><span class="p">,</span> <span class="s1">&#39;M6&#39;</span><span class="p">,</span> <span class="s1">&#39;S4&#39;</span><span class="p">,</span> <span class="s1">&#39;MN4&#39;</span><span class="p">]</span> <span class="c1">#short period (overtides)</span>
+
+<span class="c1"># We choose one year to plot, 2000-2001 same as in the previous Notebook</span>
+<span class="n">dates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
+    <span class="n">datetime</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="n">item</span> <span class="o">*</span> <span class="mi">3600</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">24</span><span class="o">*</span><span class="mi">365</span><span class="p">)</span> <span class="c1">#1 year</span>
+<span class="p">])</span>
+
+<span class="c1"># download and load tidal signals</span>
+<span class="n">scheveningen_fp</span> <span class="o">=</span> <span class="n">pooch</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span>
+    <span class="s2">&quot;https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_scheveningen.p&quot;</span><span class="p">,</span>
+    <span class="n">known_hash</span><span class="o">=</span><span class="s2">&quot;4ebac210fc0893e52655cbc3c9501a6c805e3537f327fed7edb9e7dbfe7fa06a&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">galveston_fp</span> <span class="o">=</span> <span class="n">pooch</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span>
+    <span class="s2">&quot;https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_galveston.p&quot;</span><span class="p">,</span>
+    <span class="n">known_hash</span><span class="o">=</span><span class="s2">&quot;26af20b240804a18a939d477632b4507d44ebdc98510deb07e601732c0846224&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">jakarta_fp</span> <span class="o">=</span> <span class="n">pooch</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span>
+    <span class="s2">&quot;https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_jakarta.p&quot;</span><span class="p">,</span>
+    <span class="n">known_hash</span><span class="o">=</span><span class="s2">&quot;7950246c47e757d9dd427063d7c809fda4267ed119efd18de43237aa9f98c9c6&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">valparaiso_fp</span> <span class="o">=</span> <span class="n">pooch</span><span class="o">.</span><span class="n">retrieve</span><span class="p">(</span>
+    <span class="s2">&quot;https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_valparaiso.p&quot;</span><span class="p">,</span>
+    <span class="n">known_hash</span><span class="o">=</span><span class="s2">&quot;a19a51e3607822bc72ab902f83990d2b318a08d1982c25461f8ffd6e5caae35f&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">scheveningen_fp</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pickle_file</span><span class="p">:</span>
+    <span class="n">scheveningen</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">)</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">galveston_fp</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pickle_file</span><span class="p">:</span>
+    <span class="n">galveston</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">)</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">jakarta_fp</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pickle_file</span><span class="p">:</span>
+    <span class="n">jakarta</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">)</span>
+<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">valparaiso_fp</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pickle_file</span><span class="p">:</span>
+    <span class="n">valparaiso</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">)</span>
+
+<span class="n">tide</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;Scheveningen&#39;</span><span class="p">:</span> <span class="n">scheveningen</span><span class="p">,</span>
+    <span class="s1">&#39;Valparaiso&#39;</span><span class="p">:</span> <span class="n">valparaiso</span><span class="p">,</span>
+    <span class="s1">&#39;Jakarta&#39;</span><span class="p">:</span> <span class="n">jakarta</span><span class="p">,</span>
+    <span class="s1">&#39;Galveston&#39;</span><span class="p">:</span> <span class="n">galveston</span><span class="p">,</span>
+    <span class="p">}</span>
+
+<span class="n">plot_4timeseries_with_interactive_controls</span><span class="p">(</span><span class="n">comps</span><span class="p">,</span> <span class="n">dates</span><span class="p">,</span> <span class="n">tide</span><span class="p">)</span>
+<span class="c1"># We start with the main constituents preselected</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Downloading data from &#39;https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_scheveningen.p&#39; to file &#39;/home/runner/.cache/pooch/8b8a6001da356b17145494f50655b9b7-tide_scheveningen.p&#39;.
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Downloading data from &#39;https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_galveston.p&#39; to file &#39;/home/runner/.cache/pooch/6416667b65e990929e59772433e8f314-tide_galveston.p&#39;.
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Downloading data from &#39;https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_jakarta.p&#39; to file &#39;/home/runner/.cache/pooch/55213b0e5d355ac5908a79c2debefd22-tide_jakarta.p&#39;.
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Downloading data from &#39;https://coclico.blob.core.windows.net/coastal-dynamics/2_wind_waves_tides/tide_valparaiso.p&#39; to file &#39;/home/runner/.cache/pooch/61d712935b118bac0aca0c119882a2b6-tide_valparaiso.p&#39;.
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "40a0fb70c8734840bb58517cf3c44407"}</script></div>
+</div>
+<br>
+<p>Set the time range to around 30 days and <br />
+ a) select only the main semi-diurnal components <br />
+ b) select only the main diurnal components <br />
+ c) select only one semi-diurnal (M2) and one diurnal (K1) component <br />
+ <em>What kind of signal do you see for each location? Why are there differences between locations? Don’t forget to look at the y-axis range.</em></p>
+<p>We also know that there are much longer variations than the ones above. Set the time range to the whole year and <br />
+ a) select only S2 and K2 <br />
+ b) select only K1 and P1 <br />
+ <em>What kind of combined signal can you see now?</em></p>
+<p>Finally activate all components, including some short period constituents that we will further explore next week. Analyse the combined signals. What are the dominant components at each location?</p>
+<p><br><br></p>
+</section>
+</section>
+<section class="tex2jax_ignore mathjax_ignore" id="tidal-beating">
+<h1>4. Tidal Beating<a class="headerlink" href="#tidal-beating" title="Link to this heading">#</a></h1>
+<p>Using the knowledge gained from Chapter 3 of the textbook and the interactive figure above, try to answer the questions below. You can use the next cell as a calculator. Give your numeric answer with at least three significant figures.</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Tidal constituents</p></th>
+<th class="head"><p>Name</p></th>
+<th class="head"><p>Equil. <br> Amplitude [m]</p></th>
+<th class="head"><p>Period [h]</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><strong>Semi-diurnal</strong></p></td>
+<td><p></p></td>
+<td><p></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p>Principal lunar</p></td>
+<td><p>M2</p></td>
+<td><p>0.24</p></td>
+<td><p>12.4206012</p></td>
+</tr>
+<tr class="row-even"><td><p>Principal solar</p></td>
+<td><p>S2</p></td>
+<td><p>0.11</p></td>
+<td><p>12</p></td>
+</tr>
+<tr class="row-odd"><td><p>Lunar elliptical</p></td>
+<td><p>N2</p></td>
+<td><p>0.046</p></td>
+<td><p>12.65834751</p></td>
+</tr>
+<tr class="row-even"><td><p>Lunar-solar declinational</p></td>
+<td><p>K2</p></td>
+<td><p>0.031</p></td>
+<td><p>11.96723606</p></td>
+</tr>
+<tr class="row-odd"><td><p><strong>Diurnal</strong></p></td>
+<td><p></p></td>
+<td><p></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p>Lunar-solar declinational</p></td>
+<td><p>K1</p></td>
+<td><p>0.14</p></td>
+<td><p>23.93447213</p></td>
+</tr>
+<tr class="row-odd"><td><p>Principal lunar</p></td>
+<td><p>O1</p></td>
+<td><p>0.10</p></td>
+<td><p>25.81933871</p></td>
+</tr>
+<tr class="row-even"><td><p>Principal solar</p></td>
+<td><p>P1</p></td>
+<td><p>0.047</p></td>
+<td><p>24.06588766</p></td>
+</tr>
+<tr class="row-odd"><td><p>Lunar elliptical</p></td>
+<td><p>Q1</p></td>
+<td><p>0.019</p></td>
+<td><p>26.868350</p></td>
+</tr>
+<tr class="row-even"><td><p><strong>Long period</strong></p></td>
+<td><p></p></td>
+<td><p></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p>Fortnightly</p></td>
+<td><p>Mf</p></td>
+<td><p>0.042</p></td>
+<td><p>327.8599387</p></td>
+</tr>
+<tr class="row-even"><td><p>Monthly</p></td>
+<td><p>Mm</p></td>
+<td><p>0.022</p></td>
+<td><p>661.3111655</p></td>
+</tr>
+<tr class="row-odd"><td><p>Semi-annual</p></td>
+<td><p>Ssa</p></td>
+<td><p>0.019</p></td>
+<td><p>4383.076325</p></td>
+</tr>
+</tbody>
+</table>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Write your code here to get answers to questions 1 and 2. Use the values from the table above </span>
+<span class="c1"># and use at least 3 significant figures.</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Run to get questions. </span>
+<span class="c1"># The following answers are interchangeable, so do not put the same answer twice - this will result in a false correct answer.</span>
+<span class="c1"># TC1, TC2</span>
+<span class="c1"># TC3, TC4</span>
+<span class="c1"># Month1, Month2</span>
+<span class="c1"># TC5, TC6</span>
+<span class="c1"># Month3, Month4</span>
+<span class="c1"># TC7, TC8</span>
+
+<span class="n">questions_3a2</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><span class=" -Color -Color-Bold">1. In a semi-diurnal environment, spring tide occurs for tidal constituents TC1 and TC2 every D1 days. Set the time range to around 30 days, which phenomenon can you detect when looking at the combined signal of these two constituents?</span>
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "9dc285924c9f45be8fe46faf114731f9"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "f276851a53ea47feab3f84fb95516a4d"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "58dbf5b3b8f34a79bff80c7c296c998c"}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><span class=" -Color -Color-Bold">2. In a diurnal environment, spring tide occurs for tidal constituents TC3 and TC4 every D2 days. What is the main difference to the signal from question 1?</span>
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "9152ff5ed3064b1c8e8f1560f7b6f36f"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "c3569d1f949f4555b4bca8b2e8d204ed"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "b1b03946483f43688bb5997a8babf300"}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><span class=" -Color -Color-Bold">3. Strongest semi-diurnal tides are in the months Month1 and Month2, as can be seen from adding constituents TC5 and TC6.</span>
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "1cc8c29da5c64382928ca741ff95d9c3"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "0b47da4d824b45dfb0e6a3388d0a375c"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "e521977e6aac4965b8162b4ce6af26a4"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "5b2dcc8ba3c24070817ac6353a03bc44"}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><span class=" -Color -Color-Bold">4. Strongest diurnal tides are in the months Month3 and Month4, as can be seen from adding constituents TC7 and TC8.</span>
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "24b32a6bee024ae5aa45b80f443717eb"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "7feb765504784d1ba6e75993758bb404"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "80cc81278beb4d74b05a859476ce0b31"}</script><script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "9e28d13ae6ce4ecd860a54012c1d027b"}</script></div>
+</div>
+</section>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
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+                </article>
+              
+
+              
+              
+              
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+                <footer class="prev-next-footer">
+                  
+<div class="prev-next-area">
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+                </footer>
+              
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+            
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+
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+  <div class="sidebar-secondary-item">
+  <div class="page-toc tocsection onthispage">
+    <i class="fa-solid fa-list"></i> Contents
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Tidal Environments</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id1">1. Tidal Environments</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#tidal-character-at-specific-locations">2. Tidal Character at Specific Locations</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#variation-of-tidal-constituents">3. Variation of Tidal Constituents</a></li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#tidal-beating">4. Tidal Beating</a></li>
+</ul>
+
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+    
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assignment schedule": [[2, "weekly-assignment-schedule"]], "Notebook overview": [[2, "notebook-overview"]], "Welcome to Coastal Codebook": [[3, "welcome-to-coastal-codebook"]], "About this book": [[3, null]], "Usage": [[3, null]], "Week 1": [[3, null]], "Week 2": [[3, null]], "Characterization of Coastal Systems": [[4, "characterization-of-coastal-systems"]], "Import libraries that we use for our analysis": [[4, "import-libraries-that-we-use-for-our-analysis"]], "Part 1: Tectonic classification": [[4, "part-1-tectonic-classification"]], "Load the earthquake data": [[4, "load-the-earthquake-data"]], "Isobaths": [[4, "isobaths"]], "Plot function for earthquake data": [[4, "plot-function-for-earthquake-data"]], "Now plot the earthquake data": [[4, "now-plot-the-earthquake-data"]], "Plate tectonics": [[4, "plate-tectonics"]], "Australian Gold Coast": [[4, "australian-gold-coast"]], "Tectonic influence on coasts": [[4, "tectonic-influence-on-coasts"]], "The influence of sediment supply": 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"Brightspace": [[2, "brightspace"]], "Weekly assignment schedule": [[2, "weekly-assignment-schedule"]], "Notebook overview": [[2, "notebook-overview"]], "Welcome to Coastal Codebook": [[3, "welcome-to-coastal-codebook"]], "About this book": [[3, null]], "Usage": [[3, null]], "Week 1": [[3, null]], "Week 2": [[3, null]], "Characterization of Coastal Systems": [[4, "characterization-of-coastal-systems"]], "Import libraries that we use for our analysis": [[4, "import-libraries-that-we-use-for-our-analysis"]], "Part 1: Tectonic classification": [[4, "part-1-tectonic-classification"]], "Load the earthquake data": [[4, "load-the-earthquake-data"]], "Isobaths": [[4, "isobaths"]], "Plot function for earthquake data": [[4, "plot-function-for-earthquake-data"]], "Now plot the earthquake data": [[4, "now-plot-the-earthquake-data"]], "Plate tectonics": [[4, "plate-tectonics"]], "Australian Gold Coast": [[4, "australian-gold-coast"]], "Tectonic influence on coasts": [[4, "tectonic-influence-on-coasts"]], "The influence of sediment supply": [[4, "the-influence-of-sediment-supply"]], "Part 2: Process-based coastal classification": [[4, "part-2-process-based-coastal-classification"]], "Load coastal systems data and define a plot function": [[4, "load-coastal-systems-data-and-define-a-plot-function"]], "Plot coastal systems": [[4, "plot-coastal-systems"]], "Part 2a: Characterizing Individual Coastal Systems": [[4, "part-2a-characterizing-individual-coastal-systems"]], "Part 2b: application!": [[4, "part-2b-application"]], "Pre-knowledge waves": [[5, "pre-knowledge-waves"]], "1) Dispersion relation": [[5, "dispersion-relation"]], "1.1) Iterative approach": [[5, "iterative-approach"]], "1.2) Wavelength through table": [[5, "wavelength-through-table"]], "1.3) Explicit formula": [[5, "explicit-formula"]], "2.1) The dispersion relation": [[5, "the-dispersion-relation"]], "2.2) Shallow, intermediate, and deep water": [[5, "shallow-intermediate-and-deep-water"]], "Wave dispersion and grouping": [[6, "wave-dispersion-and-grouping"]], "1) Dispersion relation for tsunami waves": [[6, "dispersion-relation-for-tsunami-waves"]], "2) Dispersion relation in the cross-shore direction": [[6, "dispersion-relation-in-the-cross-shore-direction"]], "3) Wave groups": [[6, "wave-groups"]], "3.1) Waves with same wave amplitude": [[6, "waves-with-same-wave-amplitude"]], "3.1.1) Time series of water elevation": [[6, "time-series-of-water-elevation"]], "3.1.2) Spatial distribution of the water elevation": [[6, "spatial-distribution-of-the-water-elevation"]], "3.1.3) Theory question": [[6, "theory-question"]], "3.2) Waves with diffent wave amplitude": [[6, "waves-with-diffent-wave-amplitude"]], "Theory Equilibrium tide": [[7, "theory-equilibrium-tide"]], "Tidal Constituents": [[8, "tidal-constituents"]], "Visualisation of Astronomical Constituents": [[8, "visualisation-of-astronomical-constituents"]], "Tidal Amplitudes": [[8, "tidal-amplitudes"]], "Tidal Propagation": [[8, "tidal-propagation"]], "Tidal Environments": [[9, "tidal-environments"]], "1. Tidal Environments": [[9, "id1"]], "2. Tidal Character at Specific Locations": [[9, "tidal-character-at-specific-locations"]], "3. Variation of Tidal Constituents": [[9, "variation-of-tidal-constituents"]], "4. Tidal Beating": [[9, "tidal-beating"]], "Comment Floris:": [[10, "comment-floris"], [10, "id1"], [10, "id2"], [10, "id3"]], "Import Packages": [[10, "import-packages"], [13, "import-packages"], [14, "import-packages"]], "Directories": [[10, "directories"], [13, "directories"], [14, "directories"]], "Initialize": [[10, "initialize"], [13, "initialize"], [14, "initialize"]], "Question functions": [[10, "question-functions"]], "Section 1": [[10, "section-1"], [13, "section-1"], [14, "section-1"]], "Section 2": [[10, "section-2"], [13, "section-2"], [14, "section-2"]], "BELOW OLD QUESTION AND CODE": [[10, "below-old-question-and-code"]], "Explore the coastal systems": [[10, "explore-the-coastal-systems"]], "Start": [[11, "start"], [15, "start"], [16, "start"]], "Functions from cookbook": [[11, "functions-from-cookbook"], [12, "functions-from-cookbook"], [15, "functions-from-cookbook"], [16, "functions-from-cookbook"]], "Commonly used functions": [[11, "commonly-used-functions"], [12, "commonly-used-functions"], [15, "commonly-used-functions"], [16, "commonly-used-functions"]], "Student calculators": [[11, "student-calculators"]], "Questions": [[11, "questions"], [15, "questions"], [16, "questions"]], "Q1": [[11, "q1"], [15, "q1"]], "Q2": [[11, "q2"], [15, "q2"]], "Q3": [[11, "q3"], [15, "q3"]], "Q4": [[11, "q4"], [15, "q4"]], "Q5": [[11, "q5"], [15, "q5"]], "Q6": [[11, "q6"], [16, "q6"]], "Q7": [[11, "q7"], [16, "q7"]], "Q8": [[11, "q8"], [16, "q8"]], "Wave groups": [[11, "wave-groups"], [16, "wave-groups"]], "Q9 Coding": [[11, "q9-coding"], [16, "q9-coding"]], "Q10": [[11, "q10"]], "Q11 Animation (upcoming)": [[11, "q11-animation-upcoming"]], "new functions for cookbook": [[12, "new-functions-for-cookbook"]], "Part 1B": [[12, "part-1b"]], "Part 2": [[12, "part-2"]], "Normal incident waves": [[12, "normal-incident-waves"]], "Oblique waves": [[12, "oblique-waves"]], "Wave height": [[12, "wave-height"]], "Radiation stress": [[12, "radiation-stress"]], "Setup": [[12, "setup"]], "Nearshore force (Fx)": [[12, "nearshore-force-fx"]], "Syx": [[12, "syx"]], "Fy": [[12, "fy"]], "Longshore current V(x)": [[12, "longshore-current-v-x"]], "Wave orbital velocity near bed": [[12, "wave-orbital-velocity-near-bed"]], "Current underneath breaking waves": [[12, "current-underneath-breaking-waves"]], "Include wave setup": [[12, "include-wave-setup"]], "Question types": [[13, "question-types"], [14, "question-types"]], "Question data & functions": [[13, "question-data-functions"], [14, "question-data-functions"]], "Section 3": [[14, "section-3"]], "Section  4": [[14, "section-4"]], "Q10 Multiple Choice": [[16, "q10-multiple-choice"]], "Widget": [[16, "widget"]], "Q11) Theory questions - Not equal amplitudes": [[16, "q11-theory-questions-not-equal-amplitudes"]], "Q12) Coding question": [[16, "q12-coding-question"]], "W2_Wave_animation": [[16, "w2-wave-animation"]], "Contributing (Advanced)": [[18, "contributing-advanced"]], "Building the book": [[18, "building-the-book"]], "Propose changes": [[18, "propose-changes"]], "Getting Started": [[19, "getting-started"]], "Launching the Notebooks": [[19, "launching-the-notebooks"]], "New to JupyterLab?": [[19, "new-to-jupyterlab"]], "Installation Guide": [[20, "installation-guide"]], "1. Setting up Git": [[20, "setting-up-git"]], "2. Installing Mamba Package Manager": [[20, "installing-mamba-package-manager"]], "3. Creating the software environment": [[20, "creating-the-software-environment"]], "4. Running the notebooks": [[20, "running-the-notebooks"]], "Troubleshooting": [[21, "troubleshooting"]], "Why Open an Issue?": [[21, "why-open-an-issue"]], "How to Open an Issue": [[21, "how-to-open-an-issue"]]}, "indexentries": {}})
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