Add gallery of examples

.gitignore

@@ -65,6 +65,7 @@ instance/
 
 # Sphinx documentation
 docs/_build/
+docs/auto_examples/
 
 # PyBuilder
 target/

docs/Makefile

@@ -11,6 +11,11 @@ BUILDDIR = _build
 help:
  @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 
+# Need to remove auto generated examples as well
+clean:
+ rm -rf $(BUILDDIR)/*
+ rm -rf auto_examples/
+
 .PHONY: help Makefile
 
 # Catch-all target: route all unknown targets to Sphinx using the new

docs/conf.py

@@ -35,6 +35,7 @@
  "sphinx.ext.mathjax",
  "sphinx.ext.napoleon",
  "sphinx.ext.autosummary",
+ "sphinx_gallery.gen_gallery",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -48,6 +49,12 @@
 # The master toctree document.
 master_doc = "index"
 
+sphinx_gallery_conf = {
+ "examples_dirs": "../examples", # path to your example scripts
+ "gallery_dirs": "auto_examples", # path to where to save gallery generated output
+ "download_all_examples": False,
+}
+
 source_suffix = ".rst"
 autoclass_content = "both"
 
@@ -66,10 +73,6 @@
 
 pygments_style = "sphinx"
 
-html_sidebars = {
- "**": ["globaltoc.html", "relations.html", "sourcelink.html", "searchbox.html"]
-}
-
 html_sidebars = {
  "**": ["about.html", "navigation.html", "relations.html", "searchbox.html"]
 }

docs/datasets.rst

@@ -0,0 +1,10 @@
+Datasets
+=========
+
+This module also includes a number of wave runup datasets which can be used to test or create your own wave runup model.
+
+------------
+
+.. automodule:: datasets
+ :members:
+ :undoc-members:

docs/index.rst

@@ -38,6 +38,6 @@ Contents
  installation
  usage
  models
-
-..
+ datasets
+ auto_examples/index
 

examples/README.rst

@@ -0,0 +1,4 @@
+Examples
+~~~~~~~~
+
+Here we show examples of how to apply py-wave-runup in practice:

examples/plot_stockdon.py

@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+"""
+Assessing Stockdon et al (2006) runup model
+===========================================
+
+
+In this example, we will evaluate the accuracy of the Stockdon et al (2006) runup
+model, using the Power et al (2018) dataset.
+
+The Stockdon et al (2006) model looks like:
+
+For dissipative beaches (i.e. :math:`\\zeta < 0.3`) Eqn (18) from the paper is used:
+
+.. math:: R_{2} = 0.043(H_{s}L_{p})^{0.5}
+
+For intermediate and reflective beaches (i.e. :math:`\\zeta > 0.3`), the function
+returns the result from Eqn (19):
+
+.. math::
+
+ R_{2} = 1.1 \\left( 0.35 \\beta (H_{s}L_{p})^{0.5} + \\frac{H_{s}L_{p}(
+ 0.563 \\beta^{2} +0.004)^{0.5}}{2} \\right)
+
+First, let's import the Power et al (2018) runup data, which is included in this
+package.
+"""
+import py_wave_runup
+
+df = py_wave_runup.datasets.load_power18()
+print(df.head())
+
+#############################################
+# We can see that this dataset gives us :math:`H_{s}` (significant wave height),
+# :math:`T_{p}` (peak wave period), :math:`\tan \beta` (beach slope). Let's import
+# the Stockdon runup model and calculate the estimated :math:`R_{2}` runup value for
+# each row in this dataset.
+
+# Initalize the Stockdon 2006 model with values from the dataset
+sto06 = py_wave_runup.models.Stockdon2006(Hs=df.hs, Tp=df.tp, beta=df.beta)
+
+# Append a new column at the end of our dataset with Stockdon 2006 R2 estimations
+df["sto06_r2"] = sto06.R2
+
+# Check the first few rows of observed vs. modelled R2
+print(df[["r2", "sto06_r2"]].head())
+
+#############################################
+# Let's now make a plot of observed vs. modelled R2 to assess performance
+import matplotlib.pyplot as plt
+
+fig, ax1 = plt.subplots(1, 1, figsize=(4, 4), dpi=300)
+ax1.plot(df.r2, df.sto06_r2, "b.", markersize=2, linewidth=0.5)
+
+# Add 1:1 line to indicate perfect fit
+ax1.plot([0, 12], [0, 12], "k-")
+
+# Add axis labels
+ax1.set_xlabel("Observed R2 (m)")
+ax1.set_ylabel("Modelled R2 (m)")
+ax1.set_title("Stockdon et al. (2006) Runup Model")
+
+plt.tight_layout()
+
+#############################################
+# Let's also check RMSE and coefficient of determination values:
+
+import numpy as np
+from sklearn.metrics import r2_score, mean_squared_error
+
+print(f"R2 Score: {r2_score(df.r2, df.sto06_r2):.2f}")
+print(f"RMSE: {np.sqrt(mean_squared_error(df.r2, df.sto06_r2)):.2f} m")