Note
Go to the end to download the full example code.
Sphinx-Gallery#
This example shows how to add a new example when using Sphinx-Gallery.
To use Sphinx-Gallery, first install the package with this command:
pip install sphinx-gallery
Then, add the package to the extensions variable in your Sphinx conf.py file:
extensions = [
"sphinx_gallery.gen_gallery",
]
Plot a simple sphere using PyVista#
This code plots a simple sphere using PyVista.
import pyvista as pv
pv.set_jupyter_backend("html")
sphere = pv.Sphere()
sphere.plot()
Plot a simple sphere using PyVista with a plotter
plotter = pv.Plotter(notebook=True)
plotter.add_mesh(sphere, color="white", show_edges=True)
plotter.title = "3D Sphere Visualization"
plotter.show()
EmbeddableWidget(value='<iframe srcdoc="<!DOCTYPE html>\n<html lang="en-US" dir="ltr">\n <head>\n <meta charset="utf-8">\n <meta name="viewport" content="width=device-width,initial-scale=1">\n <title>404 | VTK.js </title>\n <meta name="description" content="Not Found">\n <meta name="generator" content="VitePress v1.6.4">\n <link rel="preload stylesheet" href="/vtk-js/assets/style.BfuHIIC4.css" as="style">\n <link rel="preload stylesheet" href="/vtk-js/vp-icons.css" as="style">\n \n <script type="module" src="/vtk-js/assets/app.DfjizcSH.js"></script>\n <link rel="preload" href="/vtk-js/assets/inter-roman-latin.Di8DUHzh.woff2" as="font" type="font/woff2" crossorigin="">\n <script id="check-dark-mode">(()=>{const e=localStorage.getItem("vitepress-theme-appearance")||"auto",a=window.matchMedia("(prefers-color-scheme: dark)").matches;(!e||e==="auto"?a:e==="dark")&&document.documentElement.classList.add("dark")})();</script>\n <script 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{ base64Str });\n</script>\n </body>\n</html>" class="pyvista" style="width: 100%; height: 600px; border: 1px solid rgb(221,221,221);"></iframe>')
Figures with Matplotlib#
This example shows how to render a figure using Matplotlib.
import matplotlib.pyplot as plt
import numpy as np
time = np.linspace(0, 2 * np.pi, 100)
fig, ax = plt.subplots()
ax.plot(time, np.cos(time), color="blue", label=r"$\cos{(t)}$")
ax.plot(time, np.sin(time), color="red", label=r"$\sin{(t)}$")
ax.set_xlabel("Time [time units]")
ax.set_ylabel("Amplitude [distance units]")
ax.set_title("Trigonometric functions")
plt.show()
Figures with Plotly#
This example shows how to render a figure using Plotly.
import plotly.graph_objs as go
# More info: https://plotly.com/python/renderers/
time = np.linspace(0, 2 * np.pi, 100)
cos_trace = go.Scatter(x=time, y=np.cos(time), mode="lines", name="cos(t)")
sin_trace = go.Scatter(x=time, y=np.sin(time), mode="lines", name="sin(t)")
fig = go.Figure(data=[cos_trace, sin_trace])
fig
import numpy as np
import plotly.express as px
df = px.data.tips()
fig = px.bar(
df,
x="sex",
y="total_bill",
facet_col="day",
color="smoker",
barmode="group",
template="presentation+plotly",
)
fig.update_layout(height=400)
fig
Render equations using IPython math#
This example shows how to render equations using the IPython math module.
<IPython.core.display.Math object>
from IPython.display import Latex
Latex(r"This is a \LaTeX{} equation: $a^2 + b^2 = c^2$")
<IPython.core.display.Latex object>
Render a table#
This is an example to render a table with Sphinx-Gallery.
A |
B |
A and B |
|---|---|---|
False |
False |
False |
True |
False |
False |
False |
True |
False |
True |
True |
True |
Render a table using pandas
Total running time of the script: (0 minutes 3.965 seconds)