A terminal viewer for Jupyter notebooks. It’s like cat for ipynb files.
% nbpreview --theme material --image-drawing braille notebook.ipynb
nbpreview
─────────────────────────────────────────────────────────────────────────
╭────────────────────────────────────────────────────────────────────────╮
[1]: │ from typing import Optional │
│ │
│ import arviz as az │
│ import matplotlib.pyplot as plt │
│ import pandas as pd │
│ import pymc as pm │
│ from arviz import InferenceData │
│ from matplotlib.axes import Axes │
│ from matplotlib.axes._subplots import Subplot │
│ from pandas import DataFrame │
│ │
│ import plots │
╰────────────────────────────────────────────────────────────────────────╯
Thanks for checking out nbpreview. This example notebook is inspired by
an example in Bayesian Analysis with Python by Osvaldo Martin. A more
detailed breakdown of how nbpreview renders notebooks, examples, and
command-line options may be found in the documentation.
## Load data
─────────────────────────────────────────────────────────────────────────
This dataset contains the heights (Length) and age (Month) of newborn
girls.
╭────────────────────────────────────────────────────────────────────────╮
[2]: │ babies_data = pd.read_csv( │
│ "https://github.com/aloctavodia/BAP/blob/master/code/data/babies.… │
│ ).rename(columns={"Lenght": "Length"}) │
│ months_of_interest = list(range(0, 13, 4)) │
│ ( │
│ babies_data.groupby("Month") │
│ .agg( │
│ mean_length=("Length", "mean"), │
│ median_length=("Length", "median"), │
│ mean_std=("Length", "std"), │
│ measurement_count=("Month", "count"), │
│ ) │
│ .loc[months_of_interest] │
│ ) │
╰────────────────────────────────────────────────────────────────────────╯
[2]: 🌐 Click to view HTML
[2]: mean_length median_length mean_std measurement_count
Month
────────────────────────────────────────────────────────────────────
0 49.458333 49.25 1.824285 48
4 62.060606 62.50 2.548767 33
8 68.809524 69.00 2.677714 42
12 74.456522 74.50 2.549122 23
╭────────────────────────────────────────────────────────────────────────╮
[3]: │ babies_data.plot.scatter( │
│ x="Month", │
│ y="Length", │
│ figsize=(30, 7), │
│ s=500, │
│ xticks=[], │
│ yticks=[], │
│ ); │
╰────────────────────────────────────────────────────────────────────────╯
🖼 Click to view Image
⣿⡏⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⢉⡉⠉⣉⠉⠉⣯⠉⢩⡍⠉⢉⡉⠉⣿⠉⠉⢹
⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠀⠀⡀⠀⢠⡄⠀⢀⠀⠀⣦⠀⢸⡇⠀⣾⠀⠀⣿⠀⢸⡇⠀⢸⡇⠀⣿⠀⠀⢸
⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡀⠀⠀⠀⠀⣀⠀⢀⣄⠀⢸⡇⠀⣿⠀⠀⣿⠀⢸⡇⠀⣿⠀⠀⡿⠀⢸⡇⠀⣻⠂⠀⣿⠀⢸⡇⠀⠸⠇⠀⠿⠀⠀⢸
⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡀⠀⣠⡄⠀⣶⠀⢸⡇⠀⢸⡇⠀⣿⠀⠸⡇⠀⢸⡇⠀⣿⠀⠀⡿⠀⢸⡇⠀⠈⠀⠀⠉⠀⠘⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⡀⠀⢀⡀⠀⣲⠀⠀⣷⠀⢸⡇⠀⣿⡇⠀⣿⠀⠸⡇⠀⠘⠃⠀⠛⠀⠐⠆⠀⠈⠁⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⣿⡇⠀⠀⠀⠀⠀⢶⠀⠀⣿⠀⢸⡇⠀⣿⠀⠀⠛⠀⠘⠃⠀⠸⠅⠀⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⣿⡇⠀⠀⢰⡆⠀⣿⠀⠀⠿⠀⠈⠁⠀⠙⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⣿⡇⠀⠀⢸⡇⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⣿⣷⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣾
## Make model
─────────────────────────────────────────────────────────────────────────
We'll model Length as a linear function of the square root of Month.
Length ∼𝒩(μ= α+ β√(Month), ϵ)
Where the variance itself is also a linear function of Month.
ϵ∼γ+ σMonth
╭────────────────────────────────────────────────────────────────────────╮
[4]: │ with pm.Model(coords={"time_idx": babies_data.index}) as babies_model: │
│ # Priors │
│ alpha = pm.Normal("alpha", sigma=10) │
│ beta = pm.Normal("beta", sigma=10) │
│ gamma = pm.HalfNormal("gamma", sigma=10) │
│ sigma = pm.HalfNormal("sigma", sigma=10) │
│ │
│ month = pm.MutableData( │
│ "month", │
│ value=babies_data["Month"].astype(float), │
│ ) │
│ │
│ mu = pm.Deterministic( │
│ "mu", │
│ alpha + beta * month ** 0.5, │
│ dims="time_idx", │
│ ) │
│ epsilon = pm.Deterministic( │
│ "epsilon", │
│ gamma + sigma * month, │
│ dims="time_idx", │
│ ) │
│ pm.Normal( │
│ "length", │
│ mu=mu, │
│ sigma=epsilon, │
│ observed=babies_data["Length"], │
│ dims="time_idx", │
│ ) │
│ │
│ # Sample model │
│ babies_idata = pm.sample(tune=2_000, return_inferencedata=True) │
│ babies_idata.extend(pm.sample_posterior_predictive(babies_idata)) │
╰────────────────────────────────────────────────────────────────────────╯
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [alpha, beta, gamma, sigma]
🌐 Click to view HTML
100.00% [12000/12000 00:07<00:00 Sampling 4 chains, 0 divergences]
Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 +
4_000 draws total) took 20 seconds.
🌐 Click to view HTML
100.00% [4000/4000 00:00<00:00]
╭────────────────────────────────────────────────────────────────────────╮
[5]: │ epsilon │
╰────────────────────────────────────────────────────────────────────────╯
[5]: epsilon∼Deterministic(f(gamma, sigma))
## Plots
─────────────────────────────────────────────────────────────────────────
Let's plot the fit as a band where we expect 95% of the data to be
contained in.
╭────────────────────────────────────────────────────────────────────────╮
[6]: │ _, hdi_ax = plt.subplots(figsize=(25, 5)) │
│ az.plot_hdi( │
│ x=babies_data["Month"], │
│ y=babies_idata["posterior_predictive"]["length"], │
│ hdi_prob=0.95, │
│ fill_kwargs={"alpha": 1.0}, │
│ ax=hdi_ax, │
│ ).set(xticks=[], yticks=[]); │
╰────────────────────────────────────────────────────────────────────────╯
🖼 Click to view Image
⡏⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⢉⣉⣉⣉⣉⣉⣩⣭⣭⣭⡍⠉⠉⢹
⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⣀⣀⣤⣤⣤⣤⣴⣶⣶⣶⣶⣶⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡇⠀⠀⢸
⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣀⣀⣠⣤⣤⣤⣶⣶⣶⣶⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠿⠿⠿⠿⠿⠟⠛⠛⠛⠛⠛⠛⠛⠉⠉⠉⠁⠀⠀⢸
⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⣤⣤⣴⣶⣶⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⠿⠿⠿⠿⠛⠛⠛⠛⠛⠉⠉⠉⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⡇⠀⠀⠀⠀⠀⠀⣀⣠⣤⣶⣾⣿⣿⣿⣿⣿⣿⠿⠿⠿⠛⠛⠛⠋⠉⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⡇⠀⠀⢀⣤⣶⣿⣿⣿⠿⠟⠛⠋⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⡇⠀⠀⢸⠿⠛⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
Let's directly compare the distributions of lengths for newborns that are
0, 4, 8, and 12 months old.
╭────────────────────────────────────────────────────────────────────────╮
[7]: │ _, dist_ax = plt.subplots(figsize=(25, 5)) │
│ │
│ │
│ def plot_length_dist( │
│ babies_idata: InferenceData, │
│ babies_data: DataFrame, │
│ month: int, │
│ ax: Optional[Axes] = None, │
│ color: Optional[str] = None, │
│ ) -> Subplot: │
│ """Plot the length distribution given an age in months.""" │
│ if ax is None: │
│ ax = plt.gca() │
│ │
│ length_data = babies_idata.sel( │
│ time_idx=babies_data.loc[lambda df: df["Month"] == month].ind… │
│ )["posterior_predictive"].stack( │
│ dim=[ │
│ "chain", │
│ "draw", │
│ "time_idx", │
│ ] │
│ )[ │
│ "length" │
│ ] │
│ plot = az.plot_dist( │
│ length_data, │
│ fill_kwargs={"alpha": 1}, │
│ ax=ax, │
│ color=color, │
│ ) │
│ return plot │
│ │
│ │
│ for idx, month in enumerate(months_of_interest): │
│ color = f"C{idx}" │
│ plot_length_dist( │
│ babies_idata, │
│ babies_data=babies_data, │
│ month=month, │
│ color=color, │
│ ax=dist_ax, │
│ ).set(xticks=[], yticks=[]); │
╰────────────────────────────────────────────────────────────────────────╯
🖼 Click to view Image
⡏⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⣩⣭⡉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⢉⣉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⢹
⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣿⣿⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣿⣷⡀⠀⠀⠀⠀⣰⣾⣶⡄⠀⠀⠀⣠⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⣿⣿⣿⣿⣿⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣿⣿⣿⣷⡀⠀⠀⣼⣿⣿⣿⣿⣄⢀⣾⣿⣿⣿⣷⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣿⣿⣿⣿⣿⣿⣷⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣿⣿⣿⣿⣿⣧⠀⣼⣿⣿⣿⣿⣿⣿⣾⣿⣿⣿⣿⣿⣷⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⣿⣿⣿⣿⣿⣿⣿⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣧⡀⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣦⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸
⡇⠀⠀⠀⠀⠀⠀⢀⣀⣠⣴⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣦⣀⣀⠀⣀⣀⣤⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣦⣄⣀⣀⠀⠀⠀⠀⠀⠀⠀⢸
Installation#
nbpreview can be installed through pipx or pip from PyPI.
pipx provides an easy way to install Python applications in isolated environments. See the documentation for how to install pipx.
% pipx install nbpreview
If pipx is not installed, nbpreview may also be installed via pip:
% python -m pip install nbpreview
Usage#
nbpreview has only one required argument—FILE
—which
expects a Jupyter notebook (.ipynb
) file path.
FILE
is a flexible argument.
It can take:
A Jupyter notebook (
ipynb
) file pathMultiple notebook paths
Take in input from stdin
For more details, see features.
nbpreview also comes with a convenient alias—nbp
.
Invoke either nbpreview
% nbpreview notebook.ipynb
or nbp
% nbp notebook.ipynb
on the command-line to run the program.
- --help#
To read the documentation on all options, their effects, values, and environmental variables, run
% nbpreview --help
nbpreview#
Render a Jupyter Notebook in the terminal.
nbpreview [OPTIONS] [FILE]...
Options
- -t, --theme <theme>#
The theme to use for syntax highlighting. Call
--list-themes
to preview all available themes.- Default
dark
- Options
default | emacs | friendly | friendly_grayscale | colorful | autumn | murphy | manni | material | monokai | perldoc | pastie | borland | trac | native | fruity | bw | vim | vs | tango | rrt | xcode | igor | paraiso-light | paraiso-dark | lovelace | algol | algol_nu | arduino | rainbow_dash | abap | solarized-dark | solarized-light | sas | stata | stata-light | stata-dark | inkpot | zenburn | gruvbox-dark | gruvbox-light | dracula | one-dark | lilypond | light | dark | ansi_light | ansi_dark
- --list-themes, --lt#
Display a preview of all available themes.
- -p, --plain, -d, --decorated#
Whether to render in a plain style with no boxes, execution counts, or spacing. By default detected depending on usage context.
- -u, --unicode, -x, --no-unicode#
Force the display or replacement of Unicode characters instead of determining automatically.
- -h, --hide-output#
Whether to hide the notebook outputs.
- Default
False
- -n, --nerd-font#
Whether to use Nerd Font icons.
- Default
False
- -l, --no-files#
Do not write temporary files for previews.
- Default
False
- -s, --positive-space#
Draw character images in positive space. Generally, negative space works best on charts or images with light backgrounds, while positive space will look best on dark background images. Only affects character drawings. By default set to negative space.
- Default
False
- -k, --hyperlinks, -r, --no-hyperlinks#
Whether to use terminal hyperlinks when rendering content. By default autodetects.
- -y, --hide-hyperlink-hints#
Hide text hints that hyperlinks are clickable.
- Default
False
- -i, --images, -e, --no-images#
Whether to render images. By default will autodetect. May significantly affect performance.
- --image-drawing, --id <image_drawing>#
The type of image drawing. Accepted values are ‘block’, ‘character’, or ‘braille’.
- Options
block | character | braille
- -c, --color, -o, --no-color#
Whether to render with color. By default will autodetect. Additionally respects NO_COLOR, NBPREVIEW_NO_COLOR, and TERM=’dumb’.
- --color-system, --cs <color_system>#
The type of color system to use.
- Options
standard | 256 | truecolor | windows | none | auto
- -w, --width <width>#
Explicitly set the width of the render instead of determining automatically.
- -V, --version#
Display the version and exit.
- -m, --line-numbers#
Show line numbers for code in cells.
- Default
False
- -q, --code-wrap#
Wrap code onto the next line if it does not fit in width. May be used with
--line-numbers
for clarity.- Default
False
- -g, --paging, -f, --no-paging#
Whether to display the output in a pager. By default autodetects.
- --install-completion <install_completion>#
Install completion for the specified shell.
- Options
bash | zsh | fish | powershell | pwsh
- --show-completion <show_completion>#
Show completion for the specified shell, to copy it or customize the installation.
- Options
bash | zsh | fish | powershell | pwsh
Arguments
- FILE#
Optional argument(s)
Environment variables
- NBPREVIEW_THEME
Provide a default for
--theme
- NBPREVIEW_PLAIN
Provide a default for
--plain
- NBPREVIEW_UNICODE
Provide a default for
--unicode
- NBPREVIEW_HIDE_OUTPUT
Provide a default for
--hide-output
- NBPREVIEW_NERD_FONT
Provide a default for
--nerd-font
- NBPREVIEW_NO_FILES
Provide a default for
--no-files
- NBPREVIEW_POSITIVE_SPACE
Provide a default for
--positive-space
- NBPREVIEW_HYPERLINKS
Provide a default for
--hyperlinks
- NBPREVIEW_HIDE_HYPERLINK_HINTS
Provide a default for
--hide-hyperlink-hints
- NBPREVIEW_IMAGES
Provide a default for
--images
- NBPREVIEW_IMAGE_DRAWING
Provide a default for
--image-drawing
- NBPREVIEW_COLOR
Provide a default for
--color
- NBPREVIEW_COLOR_SYSTEM
Provide a default for
--color-system
- NBPREVIEW_WIDTH
Provide a default for
--width
- NBPREVIEW_LINE_NUMBERS
Provide a default for
--line-numbers
- NBPREVIEW_CODE_WRAP
Provide a default for
--code-wrap
- NBPREVIEW_PAGING
Provide a default for
--paging
Features#
Flexible FILE
argument#
nbpreview has only one required argument—FILE
—which
expects a Jupyter notebook (.ipynb
) file path.
% nbpreview notebook.ipynb
FILE
is a flexible argument.
It can take in multiple files
and render them all at once.
nbpreview will accept multiple file paths manually listed out,
% nbpreview notebook1.ipynb notebook2.ipynb
or a glob that expands to one or more notebook files.
% nbpreview notebooks/*.ipynb
FILE
also accepts text from stdin
and treats it as the contents of a notebook file.
This can be used to easily view notebooks from the web1 using curl2.
% curl https://raw.githubusercontent.com/paw-lu/nbpreview/main/tests/unit/assets/notebook.ipynb | nbpreview
This can even be used to filter cells before rendering them. For example, jq3 can be used to select only the markdown cells from a notebook. These cells are then passed on to nbpreview to render.
% jq 'with_entries(if .key == "cells" then .value |= map(select(.cell_type == "markdown")) else . end)' tests/unit/assets/notebook.ipynb | nbp
Smart output#
Automatic plain output#
nbpreview is smart about its output.
By default it will strip out decorations—such
as boxes, execution counts, and extra spacing—when
its output is piped to stdout.
This makes nbpreview usable as a preprocessor for other command-line tools.
For example,
if fgrep4 is used to search a notebook file for the string 'parietal'
,
the output can be difficult to parse.
% fgrep parietal notebook.ipynb
" <td>parietal</td>\n",
" <td>parietal</td>\n",
" <td>parietal</td>\n",
" <td>parietal</td>\n",
" <td>parietal</td>\n",
"0 s13 18 stim parietal -0.017552\n",
"1 s5 14 stim parietal -0.080883\n",
"2 s12 18 stim parietal -0.081033\n",
"3 s11 18 stim parietal -0.046134\n",
"4 s10 18 stim parietal -0.037970"
Instead, if the notebook is run through nbpreview first, it will process the file before passing it onto fgrep, creating a more human-readable output.
% nbpreview notebook.ipynb | fgrep parietal
0 s13 18 stim parietal -0.017552
1 s5 14 stim parietal -0.080883
2 s12 18 stim parietal -0.081033
3 s11 18 stim parietal -0.046134
4 s10 18 stim parietal -0.037970
Plain rendering can be manually forced
by using the --plain
(or -p
)
option,
% nbpreview --plain notebook.ipynb
or completely disabled
by using the --decorated
(or -d
)
option.
% nbpreview --decorated notebook.ipynb
This can be configured by setting the NBPREVIEW_PLAIN environmental variable. For example, to set the default rendering to be plain, run:
% export NBPREVIEW_PLAIN=1
Automatic paging#
nbpreview will automatically view the output in a pager if the output is longer than the terminal—which is often. Similar to the automatic plain output, this will be automatically disabled when piping to other commands.
Thanks to Click
,
nbpreview attempts to choose a pager that renders the notebook in color.
If the PAGER
environmental variable is set,
nbpreview will use the value as the pager command.
To disable the automatic paging,
use the --no-paging
(or -f
) option.
% nbpreview --no-paging notebook.ipynb
Conversely,
to manually force paging,
use the --paging
(or -g
)
option.
This can be configured by setting
the NBPREVIEW_PAGING environmental variable.
Syntax highlighting#
Themes#
Thanks to Pygments and Rich
,
nbpreview comes with many different syntax highlighting themes.
They can be applied using the --theme
(or -t
)
option.
Some themes may clash with the terminal theme,
but 'dark'
—the
default theme—and
'light'
will match the terminal’s colors.
% nbpreview --theme material notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[ ]: │ from typing import Iterator │
│ │
│ │
│ class Math: │
│ """An example class.""" │
│ │
│ @staticmethod │
│ def fib(n: int) -> Iterator[int]: │
│ """Fibonacci series up to n.""" │
│ a, b = 0, 1 # Manually set first two terms │
│ while a < n: │
│ yield a │
│ a, b = b, a + b │
│ │
│ │
│ result = sum(Math.fib(42)) │
│ print(f"The answer is {result}") │
╰────────────────────────────────────────────────────────────────────────╯
% nbpreview --theme dracula notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[ ]: │ from typing import Iterator │
│ │
│ │
│ class Math: │
│ """An example class.""" │
│ │
│ @staticmethod │
│ def fib(n: int) -> Iterator[int]: │
│ """Fibonacci series up to n.""" │
│ a, b = 0, 1 # Manually set first two terms │
│ while a < n: │
│ yield a │
│ a, b = b, a + b │
│ │
│ │
│ result = sum(Math.fib(42)) │
│ print(f"The answer is {result}") │
╰────────────────────────────────────────────────────────────────────────╯
% nbpreview --theme one-dark notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[ ]: │ from typing import Iterator │
│ │
│ │
│ class Math: │
│ """An example class.""" │
│ │
│ @staticmethod │
│ def fib(n: int) -> Iterator[int]: │
│ """Fibonacci series up to n.""" │
│ a, b = 0, 1 # Manually set first two terms │
│ while a < n: │
│ yield a │
│ a, b = b, a + b │
│ │
│ │
│ result = sum(Math.fib(42)) │
│ print(f"The answer is {result}") │
╰────────────────────────────────────────────────────────────────────────╯
% nbpreview --theme monokai notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[ ]: │ from typing import Iterator │
│ │
│ │
│ class Math: │
│ """An example class.""" │
│ │
│ @staticmethod │
│ def fib(n: int) -> Iterator[int]: │
│ """Fibonacci series up to n.""" │
│ a, b = 0, 1 # Manually set first two terms │
│ while a < n: │
│ yield a │
│ a, b = b, a + b │
│ │
│ │
│ result = sum(Math.fib(42)) │
│ print(f"The answer is {result}") │
╰────────────────────────────────────────────────────────────────────────╯
% nbpreview --theme paraiso-light notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[ ]: │ from typing import Iterator │
│ │
│ │
│ class Math: │
│ """An example class.""" │
│ │
│ @staticmethod │
│ def fib(n: int) -> Iterator[int]: │
│ """Fibonacci series up to n.""" │
│ a, b = 0, 1 # Manually set first two terms │
│ while a < n: │
│ yield a │
│ a, b = b, a + b │
│ │
│ │
│ result = sum(Math.fib(42)) │
│ print(f"The answer is {result}") │
╰────────────────────────────────────────────────────────────────────────╯
% nbpreview --theme rainbow_dash notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[ ]: │ from typing import Iterator │
│ │
│ │
│ class Math: │
│ """An example class.""" │
│ │
│ @staticmethod │
│ def fib(n: int) -> Iterator[int]: │
│ """Fibonacci series up to n.""" │
│ a, b = 0, 1 # Manually set first two terms │
│ while a < n: │
│ yield a │
│ a, b = b, a + b │
│ │
│ │
│ result = sum(Math.fib(42)) │
│ print(f"The answer is {result}") │
╰────────────────────────────────────────────────────────────────────────╯
For a list of all available themes
along with a preview of how they look on the terminal
use the --list-themes
option.
% nbpreview --list-themes
Cell magic#
Certain cell magics may be used to run other languages in a Jupyter Notebook cell.
nbpreview detects the use of these magic commands
and adjusts its syntax highlighting to match it.
For example,
here it switches to bash syntax highlighting when the %%bash
cell magic is used.
% nbpreview --theme material notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[ ]: │ %%bash │
│ for file in *.csv; do │
│ echo "$file" │
│ awk -F ',' '{print $5}' "$file" | sort | uniq -c │
│ done │
╰────────────────────────────────────────────────────────────────────────╯
Multi-language support#
Jupyter Notebooks are not Python exclusive. nbpreview will detect the usage of other languages—such as Julia.
% nbpreview --theme material notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[2]: │ function printx(x) │
│ println("x = $x") │
│ return nothing │
│ end │
╰────────────────────────────────────────────────────────────────────────╯
Wrapping and line numbers#
Depending on your terminal size,
code cell contents might be too long to fit on the terminal.
By default,
nbpreview truncates the long code.
But if --code-wrap
(or -q
)
is used,
nbpreview will wrap the code around so that it’s all visible.
It’s usually best to use this will --line-numbers
(or -m
)
to enable line numbers—so
that wrapping is clearly distinguished from a line break.
% nbpreview --theme material --code-wrap --line-numbers notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[5]: │ 1 ( │
│ 2 df.loc[lambda _df: (_df["sepal.length"] < 6.0) & │
│ (_df["petal.length"] < 3.5)] │
│ 3 .groupby("variety")["petal.width"] │
│ 4 .mean() │
│ 5 ) │
╰────────────────────────────────────────────────────────────────────────╯
Markdown rendering#
Thanks to Rich
,
markdown-it-py,
and pylatexenc,
nbpreview renders markdown content with some extensions.
In addition to typical CommonMark,
nbpreview will also render markdown tables,
create clickable hyperlinks
(if it’s supported by the terminal),
syntax highlight code blocks
(which respect --theme
),
and render block math equations.
It will even render images—which
respect --image-drawing
.
For example,
# Lorem ipsum
Lorem ipsum dolor sit amet,
consectetur **adipiscing** elit,
sed do eiusmod tempor incididunt
ut labore et dolore magna [aliqua](https://github.com/paw-lu/nbpreview).
$$
\alpha \sim \text{Normal(0, 1)}
$$
_Ut enim ad minim veniam_,
quis nostrud exercitation ullamco
Excepteur sint occaecat `cupidatat` non proident,
sunt in culpa qui.
![Turtle](emoji_u1f422.png)
## At ultrices
```python
def add(x: float, y: float) -> float:
"""Add two numbers."""
return x + y
```
| Lorep | ipsum | doret |
| ----- | ----- | ----- |
| 1 | 2 | 3 |
| 4 | 5 | 6 |
renders as
% nbpreview --theme material --image-drawing character notebook.ipynb
Lorem ipsum
─────────────────────────────────────────────────────────────────────────────
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod
tempor incididunt ut labore et dolore magna aliqua.
α∼Normal(0, 1)
Ut enim ad minim veniam, quis nostrud exercitation ullamco Excepteur sint
occaecat cupidatat non proident, sunt in culpa qui.
🖼 Click to view Turtle
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P??????????????????????????PPP???
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???PPPPPPPPPPPP??????PPPPPPPP????PPP?????
????PPP?????PPPPPPPPPPPPPP??????????PPPP?????
::!??PPPP??????????????PPPP??????????????PPP?????
!!!!!!!!!!!!!!!!???????????????PPP???????????????PPPP???P!
!!!!!!!!!!!!!!!!!!!!!????????????PPPP????????????????PPPP?PP !!
!!!!!!!!!!!!!!!!!!!!!!!!!???????????PPP?????????????????PPPPPP?!!!!!
:!!!!GGGG!!!!!!!PGG!!!!!!!!!??????????PPP??????????????????PPPP??!!!!
:!!!!GGGGGG!!!!!GGGGGG!!!!!!!!??????????PPP?????????????????PPP???!!:
!!!!!!GGGG!!!!!!!GGGGG!!!!!!!!!?????????PPP???????????????PPPP???!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!??????????PPP???????????PPPPP????!:
!!!!!!!!GGG!!!!!GG!!!!!!!!!!!!!!?????????PPP??????PPPPPPP??????!!
!!!!!!!!!GGGGGG!!!!!!!!!!!!!!!!?PPPPPPPPPPPPPPPPPPPPP???????!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!???PPPPPPP??????????????!!!!!!!!
:!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!???????????????????!!!!!!!!!!!!:
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! :!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! :::
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!! !!!!!!!!!!!!:
!!!!!!!!!!!! !!!!!!!!!!!!!
!!!!!!!!!!! !!!!!!!!!!!!!
!!!!!!!!!!!!
!!!!!!!
## At ultrices
─────────────────────────────────────────────────────────────────────────────
def add(x: float, y: float) -> float:
"""Add two numbers."""
return x + y
Lorep ipsum doret
─────────────────────────────────────────────────────────────────────────────
1 2 3
4 5 6
Images#
Thanks to Picharsso
and term-image
,
nbpreview renders images.
Drawing types#
The --image-drawing
(or --id
)
option can be used to control the method nbpreview uses to draw images.
% nbpreview --theme material --image-drawing block notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[4]: │ ( │
│ sns.load_dataset("penguins").pipe( │
│ (sns.kdeplot, "data"), │
│ x="flipper_length_mm", │
│ hue="species", │
│ multiple="stack", │
│ ) │
│ ) │
╰────────────────────────────────────────────────────────────────────────╯
[4]: <AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>
🖼 Click to view Image
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▀▀▀▀▀ ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
▄▄▄ ▄▄▄ ▄▄▄ ▄▄▄ ▄▄▄
▀▀▀ ▀▀▀ ▀▀▀ ▀▀▀ ▀▀▀
▄▄▄▄▄▄▄▄▄▄▄▄ ▄▄▄▄
▀▀▀▀ ▀▀ ▀▀ ▀
% nbpreview --theme material --image-drawing character notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[4]: │ ( │
│ sns.load_dataset("penguins").pipe( │
│ (sns.kdeplot, "data"), │
│ x="flipper_length_mm", │
│ hue="species", │
│ multiple="stack", │
│ ) │
│ ) │
╰────────────────────────────────────────────────────────────────────────╯
[4]: <AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>
🖼 Click to view Image
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PPP
?! PPPPP
:PPPPP G!!!! : !! :!
PPPPPPP
PPPPPPP
PPPPPPPPP
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? ? PPPPPPPPPPP PP!!!!!!
PPPPPPPPPPPPP !!!!!!!!!
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PPPPPPP!!!!!!!!!!?!!!!!!!!!!!!!G
?: PPPPPP?!!!!!!!!!!!!!!!!!!!!!!!!!!P
PPPPPG!!!!!!!!!!!!!!!!!!!!!!!!!!!!!G
!?: PPPPP!!!!!!!!!!!!!!P!!!!!!!!!!!!!!!!!!!!!G
:: :: : :: ??
:
% nbpreview --theme material --image-drawing braille notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[4]: │ ( │
│ sns.load_dataset("penguins").pipe( │
│ (sns.kdeplot, "data"), │
│ x="flipper_length_mm", │
│ hue="species", │
│ multiple="stack", │
│ ) │
│ ) │
╰────────────────────────────────────────────────────────────────────────╯
[4]: <AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>
🖼 Click to view Image
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣟⣛⣛⣛⣻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⠿⠿⠿⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣍⡻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⣷⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡽⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⢿⣿⣿⣿⣯⣿⣿⣿⣿⣿⣿⣿⣽⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣽⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣼⣿⣿⣿⣿⣿⣿⣿⣷⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⣿⣟⣿⣿⣿⣿⣿⣯⣿⣿⣿⣿⣿⣿⣿⣿⣿⣟⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣯⣾⣿⣿⣿⣿⣿⣿⣿⡾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣞⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⢟⣿⣿⣿⣿⣿⣿⣿⣿⣿⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣾⣯⡿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣞⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⢟⣽⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡽⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⣿⣿⣿⢿⣿⣿⣽⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⣟⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣝⢿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿
Negative and positive space#
By default,
nbpreview draws figures in negative space—meaning
characters are used to draw the dark portions of the image.
This works well as a default since most charts have a light background by default.
However, when working with darker images—like
if a dark theme is being used on a plot—the
drawing can be switched to positive space
using the --positive-space
(or -s
)
option.
Attention
--positive-space
only works on --image-drawing='character'
.
--image-drawing='braille'
only draws in positive space.
% nbpreview --theme material --image-drawing character --positive-space
notebook.ipynb
╭───────────────────────────────────────────────────────────────────────╮
[17]: │ _, ax = plt.subplots(facecolor="#1C1B1F") │
│ ( │
│ sns.load_dataset("fmri").pipe( │
│ (sns.lineplot, "data"), │
│ x="timepoint", │
│ y="signal", │
│ hue="region", │
│ alpha=1, │
│ ax=ax, │
│ ) │
│ ) │
╰───────────────────────────────────────────────────────────────────────╯
[17]: <AxesSubplot:xlabel='timepoint', ylabel='signal'>
🖼 Click to view Image
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
:::::::::::::::::::::::::::::::!::!:::::::::::::::::::::::::::::::::::::
:::::::::::?:::::::::::::::::::::::!::::::::::::::::::::::::::::::::::::
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
::::::::G::G::::::::::::::::::G:::G::!:::::::::::::::::::????:::G?:!::::
:::::::::::::::::::::::::::::G!!!!!G::::::::::::::::::::::::::::::::::::
:::::::::::::::::::::::::::!G!!::!!!G:::::::::::::::::::::::::::::::::::
:::::::::::?:::::::::::::::!!!P::?:!!G:!::::::::::::::::::::::::::::::::
::!::::::::::::::::::::::::!P::::::?!!P:::::::::::::::::::::::::::::::::
::!::::::::::::::::::::::!GP::::::::P!::::::::::::::::::::::::::::::::::
:::::::::::::::::::::::::GP::::::::!:P!:::::::::::::::::::::::::::::::::
::::::::::::::::::::::::G!::::::::::::P!::!:::::::::::::::::::::::::::::
::::::::::G!::::::!::!:P!::::::::::::::P!!!!::::::::::::::::::!!!:::::::
:::::::::::::::::!!!!!!!:::::::::::::::::!!::::::::::::!P::!!!!!!!!!::::
:::::::::::::::::::!!:::::::::::::::::::::P!G!!!:!P:!!!!:?P:::::::::::::
::::::::::::::::::::::::::::::::::::::::::!:!!??!!!:PP::::::::::::::::::
:::::::::::::::::::::::::::::::::::::::::::::::::::::!::::::::::::::::::
:::::::G:::?::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
:::::::::::::::::!:::::!:G::::?:!:::::::::::!?P::::G::P::::::P::::P:!:::
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
::::::::::::::::::::::::::::::::::::::::::!:::::::::::::::::::::::::::::
% nbpreview --theme material --image-drawing character notebook.ipynb
╭───────────────────────────────────────────────────────────────────────╮
[17]: │ _, ax = plt.subplots(facecolor="#1C1B1F") │
│ ( │
│ sns.load_dataset("fmri").pipe( │
│ (sns.lineplot, "data"), │
│ x="timepoint", │
│ y="signal", │
│ hue="region", │
│ alpha=1, │
│ ax=ax, │
│ ) │
│ ) │
╰───────────────────────────────────────────────────────────────────────╯
[17]: <AxesSubplot:xlabel='timepoint', ylabel='signal'>
🖼 Click to view Image
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGPGGPGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGG?GGGGGGGGGGGGGGGGGGGGGGGPGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGG:GG:GGGGGGGGGGGGGGGGGG:GGG:GGPGGGGGGGGGGGGGGGGGGG????GGG:?GPGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGG:PPPPP:GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGP:PPGGPPP:GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGG?GGGGGGGGGGGGGGGPPP!GG?GPP:GPGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGPGGGGGGGGGGGGGGGGGGGGGGGGP!GGGGGG?PP!GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGPGGGGGGGGGGGGGGGGGGGGGGP:!GGGGGGGG!PGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGG:!GGGGGGGGPG!PGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGG:PGGGGGGGGGGGG!PGGPGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGG:PGGGGGGPGGPG!PGGGGGGGGGGGGGG!PPPPGGGGGGGGGGGGGGGGGGPPPGGGGGGG
GGGGGGGGGGGGGGGGGPPPPPPPGGGGGGGGGGGGGGGGGPPGGGGGGGGGGGGP!GGPPPPPPPPPGGGG
GGGGGGGGGGGGGGGGGGGPPGGGGGGGGGGGGGGGGGGGGG!P:PPPGP!GPPPPG?!GGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGPGPP??PPPG!!GGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGPGGGGGGGGGGGGGGGGGG
GGGGGGG:GGG?GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGPGGGGGPG:GGGG?GPGGGGGGGGGGGP?!GGGG:GG!GGGGGG!GGGG!GPGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGPGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
% nbpreview --theme material --image-drawing braille notebook.ipynb
╭───────────────────────────────────────────────────────────────────────╮
[17]: │ _, ax = plt.subplots(facecolor="#1C1B1F") │
│ ( │
│ sns.load_dataset("fmri").pipe( │
│ (sns.lineplot, "data"), │
│ x="timepoint", │
│ y="signal", │
│ hue="region", │
│ alpha=1, │
│ ax=ax, │
│ ) │
│ ) │
╰───────────────────────────────────────────────────────────────────────╯
[17]: <AxesSubplot:xlabel='timepoint', ylabel='signal'>
🖼 Click to view Image
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡀⠀⠀⢀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠯⠈⠳⠁⠀⠀⠐⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⡒⠒⠒⠒⠒⢒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠚⠛⠛⠚⠒⠒⠒⠒⢒⠒⠒⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠠⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠐⠒⠒⠒⠒⠀⢸⠽⠿⠑⠏⠽⠿⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⣀⢀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠠⠁⣰⠋⠙⠒⢆⠀⠀⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⣀⣀⠀⢠⡀⢀⢀⡄⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠓⠀⠓⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠎⣹⣫⣿⣿⣯⡍⢏⠉⠉⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠁⠈⠈⠉⠁⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠐⣰⣷⣿⠿⢿⣿⣿⡘⢇⠀⠠⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⡤⠠⣤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣣⣿⣿⣫⠶⢤⣈⢻⣷⣌⣆⣀⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠉⠀⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡈⣾⣟⠝⠁⠀⠀⠈⢳⣻⣿⡼⡄⠐⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⢨⣉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣹⣿⠋⠀⡠⠀⠄⠀⠀⢳⣿⣷⣹⡀⢡⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⢸⣿⠃⠀⠀⠀⠀⣖⢰⡂⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢦⣿⠇⡠⠊⠀⠀⠀⠀⠠⠀⠹⣿⣷⢧⠤⠆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣨⣿⡏⠄⠀⠀⠀⠀⠀⠀⠀⠁⠀⠹⣿⣯⢇⠈⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⡟⠂⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠄⠹⣿⣯⣆⠠⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠯⠼⠝⠄⠀⠀⠀⠀⠀⠤⣀⣂⣐⣠⣭⣿⠏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠀⠹⣿⣿⣏⠑⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠀⠠⠀⣀⣀⣒⣂⣀⣠⣤⠬⠭⠅⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠺⢷⣒⣷⠿⠋⠍⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢂⠘⢿⣿⣦⣌⡄⠀⢀⡀⠠⠐⠀⢉⣀⠤⢔⣒⣯⡭⠷⠶⢒⣒⣒⣒⣒⣒⣒⡂⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⢄⢠⡄⡄⠀⠀⢀⣀⣀⣀⣁⣂⣅⣂⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣻⣿⣿⣿⣷⣦⣠⣴⣒⣯⣥⣶⣾⣛⣩⣴⣒⣋⣉⣉⣠⣤⣤⣄⣀⣀⣀⣀⣀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠈⠈⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠠⠈⡻⣿⣿⣿⣿⣿⣿⡯⠵⠒⠋⠁⡀⠄⠂⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠐⠀⣀⣀⣀⡀⠠⠄⠂⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⢰⠐⢰⠂⠀⠀⢠⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣖⢰⡂⠀⠀⠀⠀⡲⢰⡄⠀⠀⠀⠀⠀⠠⠀⠀⠀⠀⠀⢀⠂⣦⠀⠀⠀⠀⠂⢰⢰⡆⠀⠀⠀⠂⠐⢰⡄⠀⠀⠀⢲⣦⠀⠠⠀⠀⠀⢰⠠⠀⢠⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠰⠀⠶⢶⠶⠀⠀⠠⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
% nbpreview --theme material --image-drawing block notebook.ipynb
╭───────────────────────────────────────────────────────────────────────╮
[17]: │ _, ax = plt.subplots(facecolor="#1C1B1F") │
│ ( │
│ sns.load_dataset("fmri").pipe( │
│ (sns.lineplot, "data"), │
│ x="timepoint", │
│ y="signal", │
│ hue="region", │
│ alpha=1, │
│ ax=ax, │
│ ) │
│ ) │
╰───────────────────────────────────────────────────────────────────────╯
[17]: <AxesSubplot:xlabel='timepoint', ylabel='signal'>
🖼 Click to view Image
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Enabling and disabling image rendering#
By default,
nbpreview will attempt to detect
if images can be viewed on the terminal.
This can be manually controlled via the
--images
or --no-images
options.
Caution
Rendering images can impact nbpreview’s performance—especially
if the notebook contains many images.
The drawing type selected
via --image-drawing
can play a role in how severe the performance impact is.
DataFrame rendering#
Thanks to Rich
and lxml
,
nbpreview renders Pandas DataFrame as a table.
% nbpreview --theme material notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[2]: │ pd.DataFrame( │
│ [ │
│ [38.0, 2.0, 18.0, 22.0], │
│ [19, 439, 6, 452], │
│ ], │
│ index=pd.Index( │
│ ["Tumour (Positive)", "Non-Tumour (Negative)"], │
│ name="Actual Label:", │
│ ), │
│ columns=pd.MultiIndex.from_product( │
│ [["Decision Tree", "Regression"], ["Tumour", "Non-Tumour"]], │
│ names=["Model:", "Predicted:"], │
│ ), │
│ ) │
╰────────────────────────────────────────────────────────────────────────╯
[2]: 🌐 Click to view HTML
[2]: Model: Decision Tree Regression
Predicted: Tumour Non-Tumour Tumour Non-Tumour
Actual Label:
──────────────────────────────────────────────────────────────────────
Tumour (Positive) 38.0 2.0 18.0 22.0
Non-Tumour (Negative) 19.0 439.0 6.0 452.0
Vega and VegaLite charts#
nbpreview will renderer static previews of Vega and VegaLite charts along with a link to an interactive version (thanks to justcharts).
% nbpreview --theme material --image-drawing character notebook.ipynb
╭───────────────────────────────────────────────────────────────────────╮
[12]: │ VegaLite( │
│ { │
│ "$schema": "https://vega.github.io/schema/vega-lite/v5.json", │
│ "description": "Google's stock price over time.", │
│ "data": {"url": "https://raw.githubusercontent.com/vega/vega… │
│ "transform": [{"filter": "datum.symbol==='GOOG'"}], │
│ "mark": { │
│ "type": "area", │
│ "line": {"color": "darkgreen"}, │
│ "color": { │
│ "x1": 1, │
│ "y1": 1, │
│ "x2": 1, │
│ "y2": 0, │
│ "gradient": "linear", │
│ "stops": [ │
│ {"offset": 0, "color": "white"}, │
│ {"offset": 1, "color": "darkgreen"}, │
│ ], │
│ }, │
│ }, │
│ "encoding": { │
│ "x": {"field": "date", "type": "temporal"}, │
│ "y": {"field": "price", "type": "quantitative"}, │
│ }, │
│ } │
│ ) │
╰───────────────────────────────────────────────────────────────────────╯
📊 Click to view Vega chart
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? ?!!G:P!!!!!!!! !!!P P!!!!!
G ? : !!!!!!!!!!!!!!!!!!!P :!!!!!!!
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! ? G!!G!!!!?!!!!!!!!!!!!!!!!!!!!!! P!!!!!!!!!
? ?!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!G G!!!!!!!!!!
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? G:::::::::::::::::::::::::::::::::::::::::::::::::
? !:::::::::::::::::::::::::::::::::::::::::::::::::
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?G:::::::::::::::::::::::::::::::::::::::::::::::::::::::
G
:
:
:
P
?@ : P! : :?G: ! ?@ !G ?@ @? :! :
G!GP !
\(\LaTeX\)#
Thanks to pylatexenc
,
nbpreview can render \(\LaTeX\) as unicode characters.
% nbpreview --theme material notebook.ipynb
╭───────────────────────────────────────────────────────────────────────╮
[22]: │ with pm.Model() as model: │
│ alpha = pm.Normal("alpha", mu=0, sd=10) │
│ beta = pm.Normal("beta", mu=0, sd=1) │
│ epsilon = pm.HalfCauchy("epsilon", beta=5) │
│ │
│ mu = pm.Deterministic("mu", var=alpha + beta * x) │
│ y_pred = pm.Normal("y_pred", mu=mu, sd=epsilon, observed=y) │
│ y_pred │
╰───────────────────────────────────────────────────────────────────────╯
[22]: y_pred∼Normal(𝑚𝑢=mu, 𝑠𝑖𝑔𝑚𝑎=f(epsilon))
HTML#
Thanks to html2text, nbpreview renders basic HTML. It will also generate a link to the output so it can be easily previewed in the browser.
% nbpreview --theme material notebook.ipynb
╭───────────────────────────────────────────────────────────────────────╮
[15]: │ %%html │
│ <p> │
│ Lorem <em>ipsum</em> dolor sit amet, │
│ consectetur adipiscing elit, │
│ sed do eiusmod tempor │
│ <q>incididunt ut labore et dolore magna aliqua.</q> │
│ </p> │
│ <p> │
│ Sit amet consectetur <b>adipiscing</b> elit pellentesque habitan… │
│ </p> │
╰───────────────────────────────────────────────────────────────────────╯
🌐 Click to view HTML
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod
tempor "incididunt ut labore et dolore magna aliqua."
Sit amet consectetur adipiscing elit pellentesque habitant.
Hyperlinks#
With certain complex content—such as images and HTML—nbpreview will display hyperlinks to them in the render.
% nbpreview --theme material --image-drawing block notebook.ipynb
╭───────────────────────────────────────────────────────────────────────╮
[20]: │ df.head() │
╰───────────────────────────────────────────────────────────────────────╯
[20]: 🌐 Click to view HTML
[20]: sepalLength sepalWidth petalLength petalWidth species
───────────────────────────────────────────────────────────────────
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
╭───────────────────────────────────────────────────────────────────────╮
[21]: │ df.pipe( │
│ (sns.kdeplot, "data"), │
│ x="petalLength", │
│ hue="species", │
│ fill=True, │
│ alpha=1, │
│ ) │
╰───────────────────────────────────────────────────────────────────────╯
[21]: <AxesSubplot:xlabel='petalLength', ylabel='Density'>
🖼 Click to view Image
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╭───────────────────────────────────────────────────────────────────────╮
[6]: │ VegaLite( │
│ { │
│ "$schema": "https://vega.github.io/schema/vega-lite/v5.json", │
│ "width": 300, │
│ "height": 200, │
│ "data": { │
│ "url": "https://raw.githubusercontent.com/vega/vega-data… │
│ }, │
│ "mark": "area", │
│ "encoding": { │
│ "x": {"timeUnit": "yearmonth", "field": "date", "axis": … │
│ "y": {"aggregate": "sum", "field": "count"}, │
│ "color": {"field": "series", "scale": {"scheme": "catego… │
│ }, │
│ } │
│ ) │
╰───────────────────────────────────────────────────────────────────────╯
📊 Click to view Vega chart
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The hyperlinks will only work if supported by the terminal.
nbpreview attempts to detect this,
but it can be manually controlled
through the --hyperlinks
or --no-hyperlinks
options.
If hyperlinks are not enabled,
the link address will instead be directly printed to the terminal
so that it’s easy to click or copy.
By default,
nbpreview displays a hint message
that prompts the user to click on the link.
These hints may be removed
by using the --hide-hyperlink-hints
(or -y
)
option.
To create previews,
nbpreview will write the content to temporary files
as the notebook is rendered.
To prevent nbpreview from writing files to your machine,
use the --no-files
(or -l
)
option.
Nerd Fonts#
By default,
nbpreview uses emoji to highlight certain content
(like clickable links).
Instead of using emoji,
nbpreview also supports using icons from Nerd Fonts5.
Simply use the --nerd-font
option
to enable them.
Attention
You’ll need to have a Nerd Font installed and applied
to your terminal to view the Nerd Font icons—or
else you’ll get tofu (
) characters where the icons should be.
Stderr#
Similar to Jupyter Notebooks, stderr text is highlighted in a bright red box.
% nbpreview --theme material notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[5]: │ with pm.Model() as model: │
│ pm.Normal("normal", mu=0, sd=1) │
│ trace = pm.sample(return_inferencedata=True) │
╰────────────────────────────────────────────────────────────────────────╯
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [normal]
🌐 Click to view HTML
100.00% [8000/8000 00:01<00:00 Sampling 4 chains, 0 divergences]
Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 +
4_000 draws total) took 12 seconds.
Tracebacks#
Tracebacks are rendered with syntax highlighting.
% nbpreview --theme material notebook.ipynb
╭────────────────────────────────────────────────────────────────────────╮
[1]: │ 1 / 0 │
╰────────────────────────────────────────────────────────────────────────╯
------------------------------------------------------------------------…
ZeroDivisionError Traceback (most recent call
last)
<ipython-input-1-bc757c3fda29> in <module>
----> 1 1 / 0
ZeroDivisionError: division by zero
- 1
Like always, do not view notebooks from untrusted sources.
- 2
curl is a command-line tool to transfer data from servers. In this example it was used to download the file contents from an address.
- 3
jq is a command-line JSON processor. Since Jupyter notebook (
ipynb
) files are in a JSON format, it can be used to filter and transform cells.- 4
fgrep is equivalent to running
grep -F
—which searches an input file for the literal text given.- 5
Nerd Fonts are fonts patched with support for extra icons.
Configure#
Every option in nbpreview has an associated environmental variable
that can be set to provide a default value.
For example,
to set the theme to 'material'
,
run:
% nbpreview --theme='material' notebook.ipynb
To apply the 'material'
theme
without having to specify it in the --theme
option,
set the environmental variable associated with the command-line option.
The environmental variables for each option
are explicitly listed at the end of the command-line usage.
They may also be found in the --help
message under env var:
.
% nbpreview --help
⋮
-t, --theme
The theme to use for syntax highlighting.
Call '--list-themes' to preview all
available themes. [env var:
NBPREVIEW_THEME; default: dark]
In the case of --theme
,
the environmental variable is NBPREVIEW_THEME.
Set it by running
% export NBPREVIEW_THEME='material'
Now, whenever nbpreview is run,
it will automatically set the --theme
value to 'material'
.
To set this permanently,
set the environmental variable in the shell’s startup file—such as
~/.zshrc
, ~/.zshenv
, ~/.bashrc
, ~/.bash_profile
, etc.
Environmental variables set the new default for nbpreview,
but they can still be overridden anytime by manually the relevant command-line option.
Reference#
- class nbpreview.notebook.Notebook(notebook_node, theme='ansi_dark', plain=None, unicode=None, hide_output=False, nerd_font=False, files=True, negative_space=True, hyperlinks=None, hide_hyperlink_hints=False, images=None, image_drawing=None, color=None, relative_dir=None, line_numbers=False, code_wrap=False)#
Construct a Notebook object to render Jupyter Notebooks.
- Parameters
notebook_node (NotebookNode) – A NotebookNode of the notebook to render.
theme (str) – The theme to use for syntax highlighting. May be
'ansi_light'
,'ansi_dark'
, or any Pygments theme. By default'ansi_dark'
.plain (Optional[bool]) – Only show plain style. No decorations such as boxes or execution counts. If set to
None
will autodetect. By defaultNone
.unicode (Optional[bool]) – Whether to use unicode characters to render the notebook. If set to
None
will autodetect. By defaultNone
.hide_output (bool) – Do not render the notebook outputs. By default
False
.nerd_font (bool) – Use nerd fonts when appropriate. By default
False
.files (bool) – Create files when needed to render HTML content. By default
True
.negative_space (bool) – Whether render character images in negative space. By default
True
hyperlinks (Optional[bool]) – Whether to use hyperlinks. If
False
will explicitly print out path. If set toNone
will autodetect. By defaultNone
.hide_hyperlink_hints (bool) – Hide text hints of when content is clickable. By default
False
.images (Optional[bool]) – Whether to render images. If set to
None
will autodetect. By defaultNone
.image_drawing (Optional[Union[ImageDrawingEnum, Literal['block', 'character', 'braille']]]) – The characters used to render images. Options are
'block'
,'character'
,'braille'
orNone
. If set toNone
will autodetect. By defaultNone
.color (Optional[bool]) – Whether to use color. If set to
None
will autodetect. By defaultNone
.relative_dir (dataclasses.InitVar[Optional[pathlib.Path]]) – The directory to prefix relative paths to convert them to absolute. If
None
will assume current directory is relative prefix. By defaultNone
.line_numbers (bool) – Whether to render line numbers in code cells. By default
False
.code_wrap (bool) – Whether to wrap code if it does not fit. By default
False
.
- classmethod from_file(file, theme='ansi_dark', plain=None, unicode=None, hide_output=False, nerd_font=False, files=True, negative_space=True, hyperlinks=None, hide_hyperlink_hints=False, images=None, image_drawing=None, color=None, line_numbers=False, code_wrap=False)#
Create a Notebook from notebook file.
- Parameters
file (
Union
[Path
,IO
[AnyStr
],KeepOpenFile
]) – A path to a Jupyter Notebook file.theme (
str
) – The theme to use for syntax highlighting. May be'ansi_light'
,'ansi_dark'
, or any Pygments theme. By default'ansi_dark'
.plain (
Optional
[bool
]) – Only show plain style. No decorations such as boxes or execution counts. If set toNone
will autodetect. By defaultNone
.unicode (
Optional
[bool
]) – Whether to use unicode characters to render the notebook. If set toNone
will autodetect. By defaultNone
.hide_output (
bool
) – Do not render the notebook outputs. By defaultFalse
.nerd_font (
bool
) – Use nerd fonts when appropriate. By defaultFalse
.files (
bool
) – Create files when needed to render HTML content. By defaultTrue
.negative_space (
bool
) – Whether render character images in negative space. By defaultTrue
.hyperlinks (
Optional
[bool
]) – Whether to use hyperlinks. IfFalse
will explicitly print out path. If set toNone
will autodetect. By defaultNone
.hide_hyperlink_hints (
bool
) – Hide text hints of when content is clickable. By defaultFalse
.images (
Optional
[bool
]) – Whether to render images. If set toNone
will autodetect. By defaultNone
.image_drawing (
Union
[ImageDrawingEnum
,Literal
[‘block’, ‘character’, ‘braille’],None
]) – The characters used to render images. Options are'block'
,'character'
,'braille'
orNone
. If set toNone
will autodetect. By defaultNone
.color (
Optional
[bool
]) – Whether to use color. If set toNone
will autodetect. By defaultNone
.line_numbers (
bool
) – Whether to render line numbers in code cells. By defaultFalse
.code_wrap (
bool
) – Whether to wrap code if it does not fit. By defaultFalse
.
- Returns
A Notebook object created from the file.
- Return type
- Raises
InvalidNotebookError – If the file is not a valid Jupyter notebook.
Contributor guide#
Thank you for your interest in improving this project. This project is open-source under the MIT license and welcomes contributions in the form of bug reports, feature requests, and pull requests.
Here is a list of important resources for contributors:
How to report a bug#
Report bugs on the Issue Tracker.
When filing an issue, make sure to answer these questions:
Which operating system and Python version are you using?
Which version of this project are you using?
What did you do?
What did you expect to see?
What did you see instead?
The best way to get your bug fixed is to provide a test case, and/or steps to reproduce the issue.
How to request a feature#
Request features on the Issue Tracker.
How to set up your development environment#
You need Python 3.8+ and the following tools:
Install the package with development requirements:
$ poetry install
You can now run an interactive Python session, or the command-line interface:
$ poetry run python
$ poetry run nbpreview
How to test the project#
Run the full test suite:
$ nox
List the available Nox sessions:
$ nox --list-sessions
You can also run a specific Nox session. For example, invoke the unit test suite like this:
$ nox --session=tests
Unit tests are located in the tests
directory,
and are written using the pytest testing framework.
How to submit changes#
Open a pull request to submit changes to this project.
Your pull request needs to meet the following guidelines for acceptance:
The Nox test suite must pass without errors and warnings.
Include unit tests. This project maintains 100% code coverage.
If your changes add functionality, update the documentation accordingly.
Feel free to submit early, though—we can always iterate on this.
To run linting and code formatting checks before committing your change, you can install pre-commit as a Git hook by running the following command:
$ nox --session=pre-commit -- install
It is recommended to open an issue before starting work on anything. This will allow a chance to talk it over with the owners and validate your approach.
Prior art#
Similar tools#
Thanks to @joouha for maintaining a list of these tools. Many of the projects here were found directly on their page.
Complimentary tools#
If you’re interested in complimentary tools that help improve the terminal experience for notebooks, there are many amazing projects out there.
bat is not a tool for notebooks specifically. But similar to nbpreview, it provides a rich output for many types of files on the terminal, and is the primary inspiration for nbpreview.
euporie is a really exciting project that allows you to edit and run Jupyter notebooks on the terminal.
nbclient is a library for executing notebooks from the command line.
nbpreview is another project that coincidentally shares a name with this one. It allows for Jupyter notebooks to be rendered without running a notebook server.
nbqa allows the use of linters and formatters on notebooks. It’s also used by this project.
jpterm is and up-and-coming successor to nbterm which will be accompanied by a web client. Looking forward to seeing this develop.
nbterm lets you edit and execute Jupyter Notebooks on the terminal.
papermill allows the parameterization and execution of Jupyter Notebooks.
Credits#
nbpreview relies on a lot of fantastic projects. Check out the dependencies for a complete list of libraries that are leveraged.
Besides the direct dependencies, there are some other projects that directly enabled the development of nbpreview.
bat is not explicitly used in this project, but served as the primary inspiration. This projects strives to be bat—but for notebooks. Many of nbpreview’s features and command-line options are directly adopted from bat.
Hypermodern Python Cookiecutter is the template this project was generated on. It is a fantastic project that integrates Poetry, Nox, and pre-commit. It’s responsible for most of this project’s CI.
justcharts is directly used by this project to generate the Vega and Vega-Lite charts.
Dependencies#
[tool.poetry.dependencies]
python = "^3.8.0"
rich = ">=12.4.1"
typer = ">=0.4.1,<0.6.0"
nbformat = { extras = ["fast"], version = ">=5.2.0" }
Pygments = ">=2.10.0"
ipython = ">=7.27,<9.0"
lxml = ">=4.6.3"
pylatexenc = ">=2.10"
httpx = ">=0.19,<0.24"
Jinja2 = ">=3.0.1"
html2text = ">=2020.1.16"
types-click = ">=7.1.5"
Pillow = ">=8.3.1,<10.0.0"
picharsso = ">=2.0.1"
validators = ">=0.18.2,<0.21.0"
yarl = ">=1.6.3"
markdown-it-py = ">=1.1,<3.0"
mdit-py-plugins = ">=0.3.0"
click-help-colors = ">=0.9.1"
term-image = ">=0.3.0"
[tool.poetry.dev-dependencies]
pytest = ">=7.1.2"
coverage = { extras = ["toml"], version = ">=6.4" }
safety = ">=2.0.0"
mypy = ">=0.961"
typeguard = ">=2.13.3"
xdoctest = { extras = ["colors"], version = ">=1.0.0" }
sphinx = ">=5.0.2"
sphinx-autobuild = ">=2021.3.14"
pre-commit = ">=2.19.0"
flake8 = ">=4.0.1"
black = { extras = ["jupyter"], version = ">=21.12b0" }
flake8-bandit = ">=3.0.0"
flake8-bugbear = ">=22.6.22"
flake8-docstrings = ">=1.5.0"
flake8-rst-docstrings = ">=0.2.6"
pep8-naming = ">=0.13.0"
darglint = ">=1.8.1"
pre-commit-hooks = ">=4.3.0"
sphinx-click = ">=4.2.0"
Pygments = ">=2.10.0"
pyupgrade = ">=2.34.0"
furo = ">=2021.11.12"
pdbpp = ">=0.10.3"
ipykernel = ">=6.15.0"
pytest-mock = ">=3.8.1"
interrogate = ">=1.5.0"
isort = ">=5.10.1"
nbqa = ">=1.3.1"
click = ">=8.1.3"
autoflake = ">=1.4"
myst-parser = ">=0.18.0"
sphinxext-opengraph = ">=0.6.3"
sphinx-copybutton = ">=0.5.0"
sphinx-design = ">=0.2.0"
sphinx-autodoc-typehints = ">=1.18.3"
tomli = ">=2.0.1"
sphinx-favicon = ">=0.2"
Contributor Covenant Code of Conduct#
Our Pledge#
We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
Our Standards#
Examples of behavior that contributes to a positive environment for our community include:
Demonstrating empathy and kindness toward other people
Being respectful of differing opinions, viewpoints, and experiences
Giving and gracefully accepting constructive feedback
Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
Focusing on what is best not just for us as individuals, but for the overall community
Examples of unacceptable behavior include:
The use of sexualized language or imagery, and sexual attention or advances of any kind
Trolling, insulting or derogatory comments, and personal or political attacks
Public or private harassment
Publishing others’ private information, such as a physical or email address, without their explicit permission
Other conduct which could reasonably be considered inappropriate in a professional setting
Enforcement Responsibilities#
Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.
Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.
Scope#
This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.
Enforcement#
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at mailto:Paulo.S.Costa5@gmail.com. All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the reporter of any incident.
Enforcement Guidelines#
Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:
1. Correction#
Community Impact: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.
Consequence: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.
2. Warning#
Community Impact: A violation through a single incident or series of actions.
Consequence: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.
3. Temporary Ban#
Community Impact: A serious violation of community standards, including sustained inappropriate behavior.
Consequence: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
4. Permanent Ban#
Community Impact: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
Consequence: A permanent ban from any sort of public interaction within the community.
Attribution#
This Code of Conduct is adapted from the Contributor Covenant, version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by Mozilla’s code of conduct enforcement ladder.
For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.
License#
MIT License
Copyright (c) 2022 Paulo S. Costa
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.