This notebook is designed to provide examples of different types of outputs that can be used to test the JupyterLab frontend and other Jupyter frontends.
In [27]:
from IPython.display import display
from IPython.display import (
HTML, Image, Latex, Math, Markdown, SVG
)
Plain text:
In [28]:
text = """Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam urna
libero, dictum a egestas non, placerat vel neque. In imperdiet iaculis fermentum.
Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia
Curae; Cras augue tortor, tristique vitae varius nec, dictum eu lectus. Pellentesque
id eleifend eros. In non odio in lorem iaculis sollicitudin. In faucibus ante ut
arcu fringilla interdum. Maecenas elit nulla, imperdiet nec blandit et, consequat
ut elit."""
print(text)
Text as output:
In [29]:
text
Out[29]:
Standard error:
In [30]:
import sys; print('this is stderr', file=sys.stderr)
In [31]:
div = HTML('<div style="width:100px;height:100px;background:grey;" />')
div
Out[31]:
In [32]:
for i in range(3):
print(10**10)
display(div)
In [33]:
md = Markdown("""
### Subtitle
This is some *markdown* text with math $F=ma$.
""")
md
Out[33]:
In [34]:
display(md)
Examples LaTeX in a markdown cell:
\begin{align} \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\ \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\ \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\ \nabla \cdot \vec{\mathbf{B}} & = 0 \end{align}
In [35]:
math = Latex("$F=ma$")
math
Out[35]:
In [36]:
maxwells = Latex(r"""
\begin{align}
\nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\ \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
\nabla \cdot \vec{\mathbf{B}} & = 0
\end{align}
""")
maxwells
Out[36]:
In [37]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf')
In [38]:
plt.scatter(np.random.rand(20), np.random.rand(20), c=np.random.rand(20))
Out[38]:
In [39]:
img = Image("https://apod.nasa.gov/apod/image/1707/GreatWallMilkyWay_Yu_1686.jpg")
img
Out[39]:
Set the image metadata:
In [40]:
img2 = Image(
"https://apod.nasa.gov/apod/image/1707/GreatWallMilkyWay_Yu_1686.jpg",
width=100,
height=200
)
img2
Out[40]:
In [41]:
svg_source = """
<svg width="400" height="110">
<rect width="300" height="100" style="fill:#E0E0E0;" />
</svg>
"""
svg = SVG(svg_source)
svg
Out[41]:
In [42]:
for i in range(3):
print(10**10)
display(svg)
In [47]:
from vega_datasets import data
In [50]:
df = data.cars()
In [51]:
df.head()
Out[51]:
Compared to a table in a Markdown cell:
| Firstname | Lastname | Age |
|---|---|---|
| Jill | Smith | 50 |
| Eve | Jackson | 94 |