uniplot 0.12.3

Lightweight plotting to the terminal. 4x resolution via Unicode.

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. Please refer to this page for a (incomplete) list of supported fonts.

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

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.

First we load the data, rename a column and and filter the data:

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:

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.

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.
• 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.
• 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:

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.

When calling the histogram function, the lines option is True by default.

Example:

import numpy as np
x = np.sin(np.linspace(1, 1000))
from uniplot import histogram
histogram(x)

Result:

┌────────────────────────────────────────────────────────────┐
│   ▛▀▀▌                       │                   ▐▀▀▜      │ 5
│   ▌  ▌                       │                   ▐  ▐      │
│   ▌  ▌                       │                   ▐  ▐      │
│   ▌  ▀▀▀▌                    │                ▐▀▀▀  ▝▀▀▜   │ 4
│   ▌     ▌                    │                ▐        ▐   │
│   ▌     ▌                    │                ▐        ▐   │
│   ▌     ▙▄▄▄▄▄▖              │          ▗▄▄▄  ▐        ▐   │ 3
│   ▌           ▌              │          ▐  ▐  ▐        ▐   │
│   ▌           ▌              │          ▐  ▐  ▐        ▐   │
│   ▌           ▌              │          ▐  ▐  ▐        ▐   │
│   ▌           ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▜  ▐▀▀▀  ▝▀▀▀        ▐   │ 2
│   ▌                          │    ▐  ▐                 ▐   │
│   ▌                          │    ▐  ▐                 ▐   │
│   ▌                          │    ▐▄▄▟                 ▐   │ 1
│   ▌                          │                         ▐   │
│   ▌                          │                         ▐   │
│▄▄▄▌▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁│▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▐▄▄▄│ 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:

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
└────────────────────────────────────────────────────────────┘
 12:24:51  12:54:54  13:24:58  13:55:01  14:25:05   14:55:09

Installation

Install via pip using:

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.