Uniplot

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

When working with production data science code it can be handy to have a plotting tool that does not rely on graphics dependencies or works only in a Jupyter notebook.

There are two main use cases:

1. ML / data science CI/CD pipelines — when something goes wrong, you get not only the error and backtrace but also plots that show what the problem was.
2. AI coding agents like Claude Code — uniplot output is plain terminal text, which works in both directions: the agent can render plots back to you as visual feedback inside its response (e.g. summarising a parameter scan across pipeline runs, or showing how a return variable changes), and it can also read its own plots to reason about shapes, trends and outliers. With uv no install step is needed:

   uv run --with uniplot python -c "from uniplot import plot; ys = [1, 4, 16]; plot(ys)"
   

Features

• Unicode drawing, so 4x the resolution (pixels) of usual ASCII plots, or even 8x when using Braille characters
• Super simple API
• Interactive mode (pass interactive=True)
• Color mode (pass color=True) useful in particular when plotting multiple series
• Works directly with the data ecosystem you already use: NumPy, pandas and Polars
• Integrates with Rich: embed plots in panels, columns and live-updating dashboards (see below)
• It's fast: Plotting 1M data points takes 26ms thanks to NumPy magic

Please note that Unicode drawing will work correctly only when using a font that fully supports the Block Elements character set or the Braille character set. Please refer to this page for a (incomplete) list of supported fonts and the options below to select the character set.

Simple example

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

For more examples, please see the examples/ folder.

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 or None 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.

Options

In alphabetical order:

Basic options

• color - Draw series in color. Defaults to False when plotting a single series, and to True when plotting multiple. Also accepts a list of colors, identified by strings like "red" for simple ANSI colors, tuples of RGB values like (255,0,0), or hexadecimal RGB colors like "#B4FBB8". Alternaively, you can specify a color theme as a string, as defined in uniplot/color_themes.py. Note that for RGB colors you need to use a terminal that supports them.
• 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 Boolean values for plotting multiple series. Defaults to False.
• 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 (see "Advanced options" below) is used and there is not enough space, the actual width may be smaller.
• 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_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 "".

Advanced options

• character_set - Which Unicode character set to use. Use "block" for the Block Elements character set with 4x resolution, or "braille" for the Braille character set with 8x resolution, "ascii" to use ASCII characters only. Braille has the highest resolution, and a lighter look overall. Defaults to "block".
• force_ascii_characters - List of characters to use when using the ASCII character set. Defaults to ["+", "x", "o", "*", "~", "."].
• legend_placement - Arrangement of the legend labels. "auto" attempts to place them one or more rows next to each other, while "vertical" is a block with one label per row. Defaults to "auto".
• 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.
• rounded_corners - Draw bounding box with round corners. Defaults to `False.
• 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_gridlines_color - A boolean or a list of colors for the vertical gridlines, as specified above for the color option. Defaults to False.
• x_labels - Enable axis labels for the x axis. Defaults to True.
• x_unit_scaling - Scale the x axis labels, assuming x_unit is the base unit. Supported values are "" (no scaling) and "si" (SI prefixes k, M, G, … and m, µ, n, …). For example, with x_unit=" m" a label of 1000 is shown as 1 km and 0.003 as 3 mm. The prefix is inserted before the first non-whitespace character of the unit (so a leading space is preserved), and a blank unit still gets a prefix (e.g. 200k). 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_gridlines_color - A boolean or a list of colors for the horizontal gridlines, as specified above for the color option. Defaults to False.
• y_labels - Enable axis labels for the y axis. Defaults to True.
• y_unit_scaling - Scale the y axis labels, assuming y_unit is the base unit. See x_unit_scaling. 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:

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:

import numpy as np

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

histogram(x)

Result:

┌────────────────────────────────────────────────────────────┐
│   ▛▀▀▌                       │                   ▐▀▀▜      │ 5
│   ▌  ▌                       │                   ▐  ▐      │
│   ▌  ▌                       │                   ▐  ▐      │
│   ▌  ▀▀▀▌                    │                ▐▀▀▀  ▝▀▀▜   │
│   ▌     ▌                    │                ▐        ▐   │
│   ▌     ▌                    │                ▐        ▐   │
│   ▌     ▙▄▄▄▄▄▖              │          ▗▄▄▄  ▐        ▐   │
│   ▌           ▌              │          ▐  ▐  ▐        ▐   │
│   ▌           ▌              │          ▐  ▐  ▐        ▐   │
│   ▌           ▌              │          ▐  ▐  ▐        ▐   │
│   ▌           ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▜  ▐▀▀▀  ▝▀▀▀        ▐   │
│   ▌                          │    ▐  ▐                 ▐   │
│   ▌                          │    ▐  ▐                 ▐   │
│   ▌                          │    ▐▄▄▟                 ▐   │
│   ▌                          │                         ▐   │
│   ▌                          │                         ▐   │
│▄▄▄▌▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁│▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▐▄▄▄│ 0
└────────────────────────────────────────────────────────────┘
     -1                        0                       1

Arrow keys and FPS-style keys

In interactive mode, we now also support wasd or FPS-style keyboard layout and the arrow keys. Arrows should work on most platforms like Mac, Linux or Windows. We might make the keyboard layout fully configurable, to change the current Vim-inspired one, at a later date. For now, you have 3 ways to move the view:

• Vim-style: h left, j down, k up, l right, u zoom in, n zoom out.
• FPS-style: a left, s down, w up, d right, ] zoom in, [ zoom out.
• Arrow keys: movement obvious, ] zoom in, [ zoom out.

Streaming

There is support for streaming using the plot_gen class. It wraps the plot function and holds the plotting state, so you can update it with new data and have it re-drawn in place.

Example, assuming we had a function called get_new_data to get new data from some source:

from uniplot import plot_gen

plt = plot_gen()
ys = []

while True:
    ys.append(get_new_data())
    plt.update(ys=ys, title=f"Streaming: {len(ys)} data point(s) ...")

Each update() re-draws from the current state. Any bound you set explicitly (e.g. y_min=-1, y_max=1) — or via interactive pan/zoom — is pinned and kept across updates, while all other bounds keep auto-ranging to fit the new data. Other options (such as title, color, lines) persist across updates too, so you only need to pass them once.

update() returns the rendered string as well. If you only want the string (no printing), use plt.to_string() or the plot_to_string() function.

See examples/5-streaming.py for a more complete example.

Rich integration

uniplot plots are Rich renderables. Pass a plot_gen object straight to a Rich Console, or embed it in any Rich container such as Panel, Group or Columns. Colors are preserved and the plot fits the width Rich allocates, so it sits cleanly inside layouts and dashboards.

from rich.console import Console
from rich.panel import Panel
from uniplot import plot_gen

console = Console()

# Print a plot directly ...
console.print(plot_gen(ys=[1, 2, 4, 3], title="My plot"))

# ... or embed it in any Rich container.
console.print(Panel(plot_gen(ys=[1, 2, 4, 3]), title="Wrapped in Rich"))

This makes uniplot a natural fit for Rich-based CLIs, monitoring dashboards and agent tooling. Rich is an optional dependency — install it with:

pip install uniplot[rich]

See examples/10-rich_integration.py for a fuller demo including colored multi-series plots and a multi-column layout.

Live updates

For a smoothly updating display, drive a plot with rich.live.Live. Feed new data with the thread-safe set_data() (which updates state without printing) and let Rich re-render on its own schedule. A fast producer and a slower refresh rate decouple cleanly — updates between two refreshes simply coalesce into one render. set_data() snapshots the data and options you pass, so it is safe to hand over a live, still-growing list even while Rich renders on another thread (pass copy=False to skip the snapshot if you already supply fresh objects each call):

import math, time
from rich.live import Live
from uniplot import plot_gen

plt = plot_gen(lines=True, y_min=-1.2, y_max=1.2, title="Live sine")
ys = []
with Live(plt, refresh_per_second=10) as live:
    for i in range(400):
        ys.append(math.sin(i / 10))
        plt.set_data(ys=ys[-100:])  # high-rate updates; no printing
        time.sleep(0.005)

See examples/11-rich_live.py.

Installation

Install via pip using:

pip install uniplot

For faster line rendering with very large datasets (1M+ points), install the optional Numba dependency:

pip install uniplot[fast]

Note: the first plot after installation (or after a Numba/Python upgrade) will take a few extra seconds to compile — subsequent calls reuse a disk cache and are fast.

For the Rich integration, install the optional Rich dependency:

pip install uniplot[rich]

Contributing

Clone this repository, and make sure you have uv installed.

On most Linux-like systems like MacOS you can run:

make

See the Makefile for details and more granular commands.

Alternatively, on systems like Windows that do not have make installed, you can run:

uv run scripts/run_tests.sh

Then proceed with issues, PRs etc. the usual way.

Projects that use uniplot

• The Photovoltaic Geographic Information System (PVGIS) uses uniplot to generate horizon plots.
• The [FlexMeasures] CLI uses uniplot to plot beliefs in sensors for smart power control.

You can find many more in the Dependency Graph.