Plots, scatter plots, histograms and heatmaps in the terminal using braille dots, and foreground and background colors - with no dependancies. Make complex figures using the Figure class or make fast and simple plots using graphing function - similar to a very small sibling to matplotlib. Or use the canvas to plot dots, lines and images yourself.
Install:
pip install plotilleSimilar to other libraries:
- like drawille, but focused on graphing – plus X/Y-axis.
- like termplot, but with braille (finer dots), left to right histogram and linear interpolation for plotting function.
- like termgraph (not on pypi), but very different style.
- like terminalplot, but with braille, X/Y-axis, histogram, linear interpolation.
Basic support for timeseries plotting is provided with release 3.2: for any X or Y values you can also add datetime.datetime, pendulum.datetime or numpy.datetime64 values. Labels are generated respecting the difference of x_limits and y_limits.
Support for heatmaps using background colors for figures and displaying images binary with braille, or in color with background colors using the canvas - provided with release 4.0
If you are still using python 2.7, please use plotille v4 or before. With v5 I am dropping support for python 2.7, as the effort to maintain the discontinued version is too much.
In [1]: import plotille
In [2]: import numpy as np
In [3]: X = np.sort(np.random.normal(size=1000))To construct plots the recomended way is to use a Figure:
In [12]: plotille.Figure?
Init signature: plotille.Figure()
Docstring:
Figure class to compose multiple plots.
Within a Figure you can easily compose many plots, assign labels to plots
and define the properties of the underlying Canvas. Possible properties that
can be defined are:
width, height: int Define the number of characters in X / Y direction
which are used for plotting.
x_limits: float Define the X limits of the reference coordinate system,
that will be plottered.
y_limits: float Define the Y limits of the reference coordinate system,
that will be plottered.
color_mode: str Define the used color mode. See `plotille.color()`.
with_colors: bool Define, whether to use colors at all.
background: multiple Define the background color.
x_label, y_label: str Define the X / Y axis label.Basically, you create a Figure, define the properties and add your plots. Using the show() function, the Figure generates the plot using a new canvas:
In [13] fig = plotille.Figure()
In [14] fig.width = 60
In [15] fig.height = 30
In [16] fig.set_x_limits(min_=-3, max_=3)
In [17] fig.set_y_limits(min_=-1, max_=1)
In [18] fig.color_mode = 'byte'
In [19] fig.plot([-0.5, 1], [-1, 1], lc=25, label='First line')
In [20] fig.scatter(X, np.sin(X), lc=100, label='sin')
In [21] fig.plot(X, (X+2)**2 , lc=200, label='square')
In [22] print(fig.show(legend=True))
The available plotting functions are:
# create a plot with linear interpolation between points
Figure.plot(self, X, Y, lc=None, interp='linear', label=None, marker=None)
# create a scatter plot with no interpolation between points
Figure.scatter(self, X, Y, lc=None, label=None, marker=None)
# create a histogram over X
Figure.histogram(self, X, bins=160, lc=None)
# print texts at coordinates X, Y
Figure.text(self, X, Y, texts, lc=None)
# The following functions use relative coordinates on the canvas
# i.e. all coordinates are \in [0, 1]
# plot a vertical line at x
Figure.axvline(self, x, ymin=0, ymax=1, lc=None)
# plot a vertical rectangle from (xmin,ymin) to (xmax, ymax).
Figure.axvspan(self, xmin, xmax, ymin=0, ymax=1, lc=None)
# plot a horizontal line at y
Figure.axhline(self, y, xmin=0, xmax=1, lc=None)
# plot a horizontal rectangle from (xmin,ymin) to (xmax, ymax).
Figure.axhspan(self, ymin, ymax, xmin=0, xmax=1, lc=None)
# Display data as an image, i.e. on a 2D regular raster.
Figure.imgshow(self, X, cmap=None)Other interesting functions are:
# remove all plots, texts, spans and images from the figure
Figure.clear(self)
# Create a canvas, plot the registered plots and return the string for displaying the plot
Figure.show(self, legend=False)Please have a look at the examples/ folder.
There are some utility functions for fast graphing of single plots.
In [4]: plotille.plot?
Signature:
plotille.plot(
X,
Y,
width=80,
height=40,
X_label='X',
Y_label='Y',
linesep=os.linesep,
interp='linear',
x_min=None,
x_max=None,
y_min=None,
y_max=None,
lc=None,
bg=None,
color_mode='names',
origin=True,
marker=None,
)
Docstring:
Create plot with X , Y values and linear interpolation between points
Parameters:
X: List[float] X values.
Y: List[float] Y values. X and Y must have the same number of entries.
width: int The number of characters for the width (columns) of the canvas.
hight: int The number of characters for the hight (rows) of the canvas.
X_label: str Label for X-axis.
Y_label: str Label for Y-axis. max 8 characters.
linesep: str The requested line seperator. default: os.linesep
interp: Optional[str] Specify interpolation; values None, 'linear'
x_min, x_max: float Limits for the displayed X values.
y_min, y_max: float Limits for the displayed Y values.
lc: multiple Give the line color.
bg: multiple Give the background color.
color_mode: str Specify color input mode; 'names' (default), 'byte' or 'rgb'
see plotille.color.__docs__
origin: bool Whether to print the origin. default: True
marker: str Instead of braille dots set a marker char for actual values.
Returns:
str: plot over `X`, `Y`.
In [5]: print(plotille.plot(X, np.sin(X), height=30, width=60))
In [6]: plotille.scatter?
Signature:
plotille.scatter(
X,
Y,
width=80,
height=40,
X_label='X',
Y_label='Y',
linesep='\n',
x_min=None,
x_max=None,
y_min=None,
y_max=None,
lc=None,
bg=None,
color_mode='names',
origin=True,
marker=None,
)
Docstring:
Create scatter plot with X , Y values
Basically plotting without interpolation:
`plot(X, Y, ... , interp=None)`
Parameters:
X: List[float] X values.
Y: List[float] Y values. X and Y must have the same number of entries.
width: int The number of characters for the width (columns) of the canvas.
hight: int The number of characters for the hight (rows) of the canvas.
X_label: str Label for X-axis.
Y_label: str Label for Y-axis. max 8 characters.
linesep: str The requested line seperator. default: os.linesep
x_min, x_max: float Limits for the displayed X values.
y_min, y_max: float Limits for the displayed Y values.
lc: multiple Give the line color.
bg: multiple Give the background color.
color_mode: str Specify color input mode; 'names' (default), 'byte' or 'rgb'
see plotille.color.__docs__
origin: bool Whether to print the origin. default: True
marker: str Instead of braille dots set a marker char.
Returns:
str: scatter plot over `X`, `Y`.
In [7]: print(plotille.scatter(X, np.sin(X), height=30, width=60))
Inspired by crappyhist (link is gone, but I made a gist).
In [8]: plotille.hist?
Signature:
plotille.hist(
X,
bins=40,
width=80,
log_scale=False,
linesep='\n',
lc=None,
bg=None,
color_mode='names',
)
Docstring:
Create histogram over `X` from left to right
The values on the left are the center of the bucket, i.e. `(bin[i] + bin[i+1]) / 2`.
The values on the right are the total counts of this bucket.
Parameters:
X: List[float] The items to count over.
bins: int The number of bins to put X entries in (rows).
width: int The number of characters for the width (columns).
log_scale: bool Scale the histogram with `log` function.
linesep: str The requested line seperator. default: os.linesep
lc: multiple Give the line color.
bg: multiple Give the background color.
color_mode: str Specify color input mode; 'names' (default), 'byte' or 'rgb'
see plotille.color.__docs__
Returns:
str: histogram over `X` from left to right.
In [9]: print(plotille.hist(np.random.normal(size=10000)))
This function allows you to create a histogram when your data is already aggregated (aka you don't have access to raw values, but you have access to bins and counts for each bin).
This comes handy when working with APIs such as OpenTelemetry Metrics API where views such as ExplicitBucketHistogramAggregation only expose access to aggregated values (counts for each bin / bucket).
In [8]: plotille.hist_aggregated?
Signature:
plotille.hist_aggregated(
counts,
bins,
width=80,
log_scale=False,
linesep='\n',
lc=None,
bg=None,
color_mode='names',
)
Docstring:
Create histogram for aggregated data.
Parameters:
counts: List[int] Counts for each bucket.
bins: List[float] Limits for the bins for the provided counts: limits for
bin `i` are `[bins[i], bins[i+1])`.
Hence, `len(bins) == len(counts) + 1`.
width: int The number of characters for the width (columns).
log_scale: bool Scale the histogram with `log` function.
linesep: str The requested line seperator. default: os.linesep
lc: multiple Give the line color.
bg: multiple Give the background color.
color_mode: str Specify color input mode; 'names' (default), 'byte' or 'rgb'
see plotille.color.__docs__
Returns:
str: histogram over `X` from left to right.
In [9]: counts = [1945, 0, 0, 0, 0, 0, 10555, 798, 0, 28351, 0]
In [10]: bins = [float('-inf'), 10, 50, 100, 200, 300, 500, 800, 1000, 2000, 10000, float('+inf')]
In [11]: print(plotille.hist_aggregated(counts, bins))Keep in mind that there must always be n+1 bins (n is a total number of count values, 11 in the example above).
In this example the first bin is from [-inf, 10) with a count of 1945 and the last bin is from [10000, +inf] with a count of 0.
There is also another more 'usual' histogram function available:
In [10]: plotille.histogram?
Signature:
plotille.histogram(
X,
bins=160,
width=80,
height=40,
X_label='X',
Y_label='Counts',
linesep='\n',
x_min=None,
x_max=None,
y_min=None,
y_max=None,
lc=None,
bg=None,
color_mode='names',
)
Docstring:
Create histogram over `X`
In contrast to `hist`, this is the more `usual` histogram from bottom
to up. The X-axis represents the values in `X` and the Y-axis is the
corresponding frequency.
Parameters:
X: List[float] The items to count over.
bins: int The number of bins to put X entries in (columns).
height: int The number of characters for the height (rows).
X_label: str Label for X-axis.
Y_label: str Label for Y-axis. max 8 characters.
linesep: str The requested line seperator. default: os.linesep
x_min, x_max: float Limits for the displayed X values.
y_min, y_max: float Limits for the displayed Y values.
lc: multiple Give the line color.
bg: multiple Give the background color.
color_mode: str Specify color input mode; 'names' (default), 'byte' or 'rgb'
see plotille.color.__docs__
Returns:
str: histogram over `X`.
In [11]: print(plotille.histogram(np.random.normal(size=10000)))
The underlying plotting area is modeled as the Canvas class:
In [12]: plotille.Canvas?
Init signature:
plotille.Canvas(
width,
height,
xmin=0,
ymin=0,
xmax=1,
ymax=1,
background=None,
**color_kwargs,
)
Docstring:
A canvas object for plotting braille dots
A Canvas object has a `width` x `height` characters large canvas, in which it
can plot indivitual braille point, lines out of braille points, rectangles,...
Since a full braille character has 2 x 4 dots (⣿), the canvas has `width` * 2, `height` * 4
dots to plot into in total.
It maintains two coordinate systems: a reference system with the limits (xmin, ymin)
in the lower left corner to (xmax, ymax) in the upper right corner is transformed
into the canvas discrete, i.e. dots, coordinate system (0, 0) to (`width` * 2, `height` * 4).
It does so transparently to clients of the Canvas, i.e. all plotting functions
only accept coordinates in the reference system. If the coordinates are outside
the reference system, they are not plotted.
Init docstring:
Initiate a Canvas object
Parameters:
width: int The number of characters for the width (columns) of the canvas.
hight: int The number of characters for the hight (rows) of the canvas.
xmin, ymin: float Lower left corner of reference system.
xmax, ymax: float Upper right corner of reference system.
background: multiple Background color of the canvas.
**color_kwargs: More arguments to the color-function. See `plotille.color()`.
Returns:
Canvas objectThe most interesting functions are:
point:
In [11]: plotille.Canvas.point?
Signature: plotille.Canvas.point(self, x, y, set_=True, color=None, marker=None)
Docstring:
Put a point into the canvas at (x, y) [reference coordinate system]
Parameters:
x: float x-coordinate on reference system.
y: float y-coordinate on reference system.
set_: bool Whether to plot or remove the point.
color: multiple Color of the point.
marker: str Instead of braille dots set a marker char.line:
In [14]: plotille.Canvas.line?
Signature: plotille.Canvas.line(self, x0, y0, x1, y1, set_=True, color=None)
Docstring:
Plot line between point (x0, y0) and (x1, y1) [reference coordinate system].
Parameters:
x0, y0: float Point 0
x1, y1: float Point 1
set_: bool Whether to plot or remove the line.
color: multiple Color of the line.rect:
In [15]: plotille.Canvas.rect?
Signature: plotille.Canvas.rect(self, xmin, ymin, xmax, ymax, set_=True, color=None)
Docstring:
Plot rectangle with bbox (xmin, ymin) and (xmax, ymax) [reference coordinate system].
Parameters:
xmin, ymin: float Lower left corner of rectangle.
xmax, ymax: float Upper right corner of rectangle.
set_: bool Whether to plot or remove the rect.
color: multiple Color of the rect.text:
In [16]: plotille.Canvas.text?
Signature: plotille.Canvas.text(self, x, y, text, set_=True, color=None)
Docstring:
Put some text into the canvas at (x, y) [reference coordinate system]
Parameters:
x: float x-coordinate on reference system.
y: float y-coordinate on reference system.
set_: bool Whether to set the text or clear the characters.
text: str The text to add.
color: multiple Color of the point.braille_image:
In [17]: plotille.Canvas.braille_image?
Signature:
plotille.Canvas.braille_image(
self,
pixels,
threshold=127,
inverse=False,
set_=True,
)
Docstring:
Print an image using braille dots into the canvas.
The pixels and braille dots in the canvas are a 1-to-1 mapping, hence
a 80 x 80 pixel image will need a 40 x 20 canvas.
Example:
from PIL import Image
import plotille as plt
img = Image.open("/path/to/image")
img = img.convert('L')
img = img.resize((80, 80))
cvs = plt.Canvas(40, 20)
cvs.braille_image(img.getdata(), 125)
print(cvs.plot())
Parameters:
pixels: list[number] All pixels of the image in one list.
threshold: float All pixels above this threshold will be
drawn.
inverse: bool Whether to invert the image.
set_: bool Whether to plot or remove the dots.image:
In [18]: plotille.Canvas.image?
Signature: plotille.Canvas.image(self, pixels, set_=True)
Docstring:
Print an image using background colors into the canvas.
The pixels of the image and the characters in the canvas are a
1-to-1 mapping, hence a 80 x 80 image will need a 80 x 80 canvas.
Example:
from PIL import Image
import plotille as plt
img = Image.open("/path/to/image")
img = img.convert('RGB')
img = img.resize((40, 40))
cvs = plt.Canvas(40, 40, mode='rgb')
cvs.image(img.getdata())
print(cvs.plot())
Parameters:
pixels: list[(R,G,B)] All pixels of the image in one list.
set_: bool Whether to plot or remove the background
colors.plot:
In [16]: plotille.Canvas.plot?
Signature: plotille.Canvas.plot(self, linesep='\n')
Docstring:
Transform canvas into `print`-able string
Parameters:
linesep: str The requested line seperator. default: os.linesep
Returns:
unicode: The canvas as a string.You can use it for example to plot a house in the terminal:
In [17]: c = Canvas(width=40, height=20)
In [18]: c.rect(0.1, 0.1, 0.6, 0.6)
In [19]: c.line(0.1, 0.1, 0.6, 0.6)
In [20]: c.line(0.1, 0.6, 0.6, 0.1)
In [21]: c.line(0.1, 0.6, 0.35, 0.8)
In [22]: c.line(0.35, 0.8, 0.6, 0.6)
In [23]: print(c.plot())
Or you could render images with braille dots:
In [24]: img = Image.open('https://github.com/tammoippen/plotille/raw/master/imgs/ich.jpg')
In [25]: img = img.convert('L')
In [26]: img = img.resize((80, 80))
In [27]: cvs = Canvas(40, 20)
In [28]: cvs.braille_image(img.getdata())
In [29]: print(cvs.plot())
Or you could render images with the background color of characters:
In [24]: img = Image.open('https://github.com/tammoippen/plotille/raw/master/imgs/ich.jpg')
In [25]: img = img.convert('RGB')
In [25]: img = img.resize((80, 40))
In [27]: cvs = Canvas(80, 40, mode="rgb")
In [28]: cvs.image(img.getdata())
In [29]: print(cvs.plot())
