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https://github.com/olavolav/uniplot
Lightweight plotting to the terminal. 4x resolution via Unicode.
https://github.com/olavolav/uniplot
data-analysis data-science plot python
Last synced: 3 months ago
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Lightweight plotting to the terminal. 4x resolution via Unicode.
- Host: GitHub
- URL: https://github.com/olavolav/uniplot
- Owner: olavolav
- License: mit
- Created: 2020-08-15T12:21:10.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-08-24T11:38:56.000Z (5 months ago)
- Last Synced: 2024-10-08T00:29:08.507Z (4 months ago)
- Topics: data-analysis, data-science, plot, python
- Language: Python
- Homepage:
- Size: 51.6 MB
- Stars: 356
- Watchers: 4
- Forks: 16
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# Uniplot
[](https://github.com/olavolav/uniplot/actions?query=workflow%3A"Unit+Tests")
[](https://pypi.org/project/uniplot/)
[](https://pepy.tech/project/uniplot)
Lightweight plotting to the terminal. 4x resolution via Unicode.
When working with production data science code it can be handy to have plotting
tool that does not rely on graphics dependencies or works only in a Jupyter
notebook.
The **use case** that this was built for is to have plots as part of your data
science / machine learning CI/CD pipeline - that way whenever something goes
wrong, you get not only the error and backtrace but also plots that show what
the problem was.
## Features
* Unicode drawing, so 4x the resolution (pixels) of usual ASCII plots
* Super simple API
* Interactive mode (pass `interactive=True`)
* Color mode (pass `color=True`) useful in particular when plotting multiple series
* It's fast: Plotting 1M data points takes 100ms thanks to NumPy magic
* Only one dependency: NumPy (but you have that anyway don't you)
Please note that Unicode drawing will work correctly only when using a font
that fully supports the [Box-drawing character
set](https://en.wikipedia.org/wiki/Box-drawing_character). Please refer to
[this page for a (incomplete) list of supported
fonts](https://www.fileformat.info/info/unicode/block/block_elements/fontsupport.htm).
## Examples
Note that all the examples are without color and plotting only a single series
of data. For using color see the GIF example above.
### Plot sine wave
```python
import math
x = [math.sin(i/20)+i/300 for i in range(600)]
from uniplot import plot
plot(x, title="Sine wave")
```
Result:
```
Sine wave
┌────────────────────────────────────────────────────────────┐
│ ▟▀▚ │
│ ▗▘ ▝▌ │
│ ▗▛▜▖ ▞ ▐ │
│ ▞ ▜ ▗▌ ▌ │ 2
│ ▟▀▙ ▗▘ ▝▌ ▐ ▜ │
│ ▐▘ ▝▖ ▞ ▜ ▌ ▝▌ │
│ ▗▛▜▖ ▛ ▜ ▗▌ ▝▌ ▐▘ ▜ │
│ ▛ ▙ ▗▘ ▝▖ ▐ ▚ ▞ ▝▌ │
│ ▟▀▖ ▐▘ ▝▖ ▟ ▚ ▌ ▝▖ ▗▌ ▜▄│ 1
│ ▐▘ ▐▖ ▛ ▙ ▌ ▐▖ ▗▘ ▚ ▞ │
│ ▛ ▙ ▗▘ ▐▖ ▐ ▙ ▞ ▝▙▟▘ │
│▐▘ ▐▖ ▐ ▌ ▛ ▐▖ ▗▘ │
│▞ ▌ ▌ ▐ ▗▘ ▜▄▛ │
│▌─────▐────▐▘───────▙──▞────────────────────────────────────│ 0
│ ▌ ▛ ▝▙▟▘ │
│ ▜ ▐▘ │
│ ▙▄▛ │
└────────────────────────────────────────────────────────────┘
100 200 300 400 500 600
```
### Plot global temperature data
Here we are using Pandas to load and prepare global temperature data from the
[Our World in Data GitHub repository](https://github.com/owid/owid-datasets).
First we load the data, rename a column and and filter the data:
```python
import pandas as pd
uri = "https://github.com/owid/owid-datasets/raw/master/datasets/Global%20average%20temperature%20anomaly%20-%20Hadley%20Centre/Global%20average%20temperature%20anomaly%20-%20Hadley%20Centre.csv"
data = pd.read_csv(uri)
data = data.rename(columns={"Global average temperature anomaly (Hadley Centre)": "Global"})
data = data[data.Entity == "median"]
```
Then we can plot it:
```python
from uniplot import plot
plot(xs=data.Year, ys=data.Global, lines=True, title="Global normalized land-sea temperature anomaly", y_unit=" °C")
```
Result:
```
Global normalized land-sea temperature anomaly
┌────────────────────────────────────────────────────────────┐
│ ▞▀│
│ ▐ │
│ ▐ │
│ ▗ ▌ │ 0.6 °C
│ ▙ ▗▄ ▛▄▖▗▘▌ ▞ │
│ ▗▜ ▌ ▜ ▚▞ ▚▞ │
│ ▐▝▖▐ ▘ │
│ ▗ ▗ ▌ ▙▌ │ 0.3 °C
│ ▛▖ ▞▙▘ ▘ │
│ ▖ ▗▄▗▘▐ ▐▘▜ │
│ ▟ █ ▞ ▜ ▝▄▘ │
│ ▗▚ ▗ ▖ ▗ ▖▗▞ █▐ ▌ ▘ │
│▁▁▁▞▐▁▁▗▘▜▗▀▀▌▁▁▁▁▙▁▁▟▁▁▁▙▐▁▁▜▁▌▞▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁│ 0 °C
│▚ ▐ ▝▖ ▐ ▛ ▌ ▗▄▐ ▌▗▘▌ ▐▝▌ ▝▘ │
│ ▌▌ ▌ ▞ ▐▗▘ ▛ ▐▞ ▌ ▐ │
│ ▝ ▝▖▌ ▐▞ ▝▌ ▚▜▐ │
│ ▗▌ ▝ ▝ ▌ │
└────────────────────────────────────────────────────────────┘
1,950 1,960 1,970 1,980 1,990 2,000 2,010
```
## Parameters
The `plot` function accepts a number of parameters, all listed below. Note that
only `ys` is required, all others are optional.
There is also a `plot_to_string` function with the same signature, if you want
the result as a list of strings, to include the output elsewhere. The only
difference is that `plot_to_string` does not support interactive mode.
### Data
* `xs` - The x coordinates of the points to plot. Can either be `None`, or a
list or NumPy array for plotting a single series, or a list of those for
plotting multiple series. Defaults to `None`, meaning that the x axis will be
just the sample index of `ys`.
* `ys` - The y coordinates of the points to plot. Can either be a list or NumPy
array for plotting a single series, or a list of those for plotting multiple
series.
In both cases, NaN values are ignored.
Note that since v0.12.0 you can also pass a list or an NumPy array of
timestamps, and the axis labels should be formatted correctly. Limitations see
below.
### Options
In alphabetical order:
* `color` - Draw series in color. Defaults to `False` when plotting a single
series, and to `True` when plotting multiple. Also accepts a list of strings,
to modify the default order of
`["blue", "magenta", "green", "yellow", "cyan", "red"]`.
* `force_ascii` - Force ASCII characters for plotting only. This can be useful
for compatibility, for example when using uniplot inside of CI/CD systems
that do not support Unicode. Defaults to `False`.
* `force_ascii_characters` - List of characters to use when plotting in
`force_ascii` mode. Default to `["+", "x", "o", "*", "~", "."]`.
* `height` - The height of the plotting region, in characters. Default is `17`.
* `interactive` - Enable interactive mode. Defaults to `False`.
* `legend_labels` - Labels for the series. Can be `None` or a list of strings.
Defaults to `None`.
* `lines` - Enable lines between points. Can either be `True` or `False`, or a
list of those values for plotting multiple series. Defaults to `False`.
* `line_length_hard_cap` - Enforce a hard limit on the number of characters per
line of the plot area. This may override the `width` option if there is not
enough space. Defaults to `None`.
* `title` - The title of the plot. Defaults to `None`.
* `width` - The width of the plotting region, in characters. Default is `60`.
Note that if the `line_length_hard_cap` option is used and there is not
enough space, the actual width may be smaller.
* `x_as_log` - Plot the x axis as logarithmic scale. Defaults to `False`.
* `x_gridlines` - A list of x values that have a vertical line for better
orientation. Defaults to `[0]`, or to `[]` if `x_as_log` is enabled.
* `x_max` - Maximum x value of the view. Defaults to a value that shows all
data points.
* `x_min` - Minimum x value of the view. Defaults to a value that shows all
data points.
* `x_unit` - Unit of the x axis. This is a string that is appended to the axis
labels. Defaults to `""`.
* `y_as_log` - Plot the y axis as logarithmic scale. Defaults to `False`.
* `y_gridlines` - A list of y values that have a horizontal line for better
orientation. Defaults to `[0]`, or to `[]` if `y_as_log` is enabled.
* `y_max` - Maximum y value of the view. Defaults to a value that shows all
data points.
* `y_min` - Minimum y value of the view. Defaults to a value that shows all
data points.
* `y_unit` - Unit of the y axis. This is a string that is appended to the axis
labels. Defaults to `""`.
### Changing default parameters
uniplot does not store a state of the configuration parameters. However, you
can define a new plot funtion with new defaults by defining a `partial`. See
the following example:
```python
from functools import partial
from uniplot import plot as default_plot
plot = partial(default_plot, height=25, width=80)
```
This defines a new `plot` function that is identical to the original, except
the default values for `width` and `height` are now different.
## Experimental features
### Plotting histograms
For convenience there is also a `histogram` function that accepts one or more
series and plots bar-chart like histograms. It will automatically discretize
the series into a number of bins given by the `bins` option and display the
result.
Additional options, in alphabetical order:
* `bins` - Number of bins to use. Defaults to `20`.
* `bins_min` - Lower limit of the first bin. Defaults to the minimum of the
series.
* `bins_max` - Upper limit of the last bin. Defaults to the maximum of the
series.
When calling the `histogram` function, the `lines` option is `True` by default.
Example:
```python
import numpy as np
x = np.sin(np.linspace(1, 1000))
from uniplot import histogram
histogram(x)
```
Result:
```
┌────────────────────────────────────────────────────────────┐
│ ▛▀▀▌ │ ▐▀▀▜ │ 5
│ ▌ ▌ │ ▐ ▐ │
│ ▌ ▌ │ ▐ ▐ │
│ ▌ ▀▀▀▌ │ ▐▀▀▀ ▝▀▀▜ │
│ ▌ ▌ │ ▐ ▐ │
│ ▌ ▌ │ ▐ ▐ │
│ ▌ ▙▄▄▄▄▄▖ │ ▗▄▄▄ ▐ ▐ │
│ ▌ ▌ │ ▐ ▐ ▐ ▐ │
│ ▌ ▌ │ ▐ ▐ ▐ ▐ │
│ ▌ ▌ │ ▐ ▐ ▐ ▐ │
│ ▌ ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▜ ▐▀▀▀ ▝▀▀▀ ▐ │
│ ▌ │ ▐ ▐ ▐ │
│ ▌ │ ▐ ▐ ▐ │
│ ▌ │ ▐▄▄▟ ▐ │
│ ▌ │ ▐ │
│ ▌ │ ▐ │
│▄▄▄▌▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁│▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▐▄▄▄│ 0
└────────────────────────────────────────────────────────────┘
-1 0 1
```
### Plotting time series
There is inital support for using timestamps for the axis labels. It should
work with most formats.
Missing so far are nicer axis labels for time stamps, as well as timezone
support.
Example:
```python
import numpy as np
dates = np.arange('2024-02-17T12:10', 4*60, 60, dtype='M8[m]')
from uniplot import plot
plot(xs=dates, ys=[1,2,3,2])
```
Result:
```
┌────────────────────────────────────────────────────────────┐
│ ▝ │ 3
│ │
│ │
│ │
│ │
│ │
│ │
│ │
│ ▘ ▝│ 2
│ │
│ │
│ │
│ │
│ │
│ │
│ │
│▖ │ 1
└────────────────────────────────────────────────────────────┘
13:00 14:00 15:00
```
## Installation
Install via pip using:
```sh
pip install uniplot
```
## Contributing
Clone this repository, and install dependecies via `poetry install`.
You can run the tests via `poetry run ./run_tests` to make sure your setup is
good. Then proceed with issues, PRs etc. the usual way.