plotext 2.1.0

plotext plots data directly on terminal

Plotting directly on terminal.

Scatter Plot

Use plotext to plot data directly on terminal: the syntax is very similar to matplotlib. Here is an example:

import plotext as plt
import numpy as np

l = 100
x = np.arange(0, l + 1)
f = 2 * np.pi / l
y = np.sin(2 * f * x)

plt.scatter(y)
plt.show()

which outputs this plot on terminal: Each data point is represented by the character • and the plot automatically extends to fill the entire terminal.

Line Plot

For a line plot use the the plot function instead:

import plotext as plt
import numpy as np

l = 1000
x = np.arange(0, l + 1)
f = 2 * np.pi / l
y = np.sin(2 * f * x)

plt.plot(x, y)
plt.show()

Incidentally here we have shown that both the x and y coordinates could be inputted in either the plot or scatter function (or y alone as in the first example).

Multiple Data

Multiple data sets could be plotted using consecutive scatter or plot functions:

import plotext as plt
import numpy as np

l = 1000
x = np.arange(0, l + 1)
f = 2 * np.pi / l
y1 = np.sin(2 * f * x)
y2 = y1 * np.exp(-0.25 * f * x)
plt.plot(x, y1, label = "periodic signal")
plt.scatter(x, y2, label = "decaying signal")
plt.show()

where using the label parameter, a legend is automatically added in the upper left corner of the plot.

Plot Aspect

You can personalize the plot aspect in many ways. Here is an example:

import plotext as plt
import numpy as np

l = 1000
x = np.arange(0, l + 1)
n = 2
f = n * np.pi / l
y1 = np.sin(n * f * x)
y2 = y1 * np.exp(-0.25 * f * x)

plt.plot(x, y1, label="periodic signal", line_color="tomato")
plt.scatter(x, y2, label="decaying signal", point_color="blue")
plt.canvas_size(150, 40)
plt.grid(1)
plt.title("plotext demonstrative plot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.canvas_color("white")
plt.facecolor("cloud")
plt.ticks_color("iron")
plt.show()

Here we have changed the color of each line plotted, changed the figure dimensions, added a title on the top and the axes labels on the bottom, changed the background color of the canvas (canvas_color), the color of the surrounding elements (facecolor) and the ticks full-ground color (ticks_color).

Data Ticks

You can change the numerical ticks on both axes:

import plotext as plt
import numpy as np

l = 1000
x = np.arange(0, l + 1)
n = 2
f = n * np.pi / l
y1 = np.sin(n * f * x)
y2 = y1 * np.exp(-0.25 * f * x)
xticks = np.arange(0, l + l / (2 * n), l / (2 * n))
xlabels = [str(i) + "𝜋" for i in range(2*n+1)]

plt.plot(x, y1, label="periodic signal")
plt.scatter(x, y2, label="decaying signal")
plt.ticks(7)
plt.xticks(xticks, xlabels) 
plt.show()

where we have first set 7 ticks on both axes (using plt.ticks()) and then we have directly changed only the x axis ticks coordinates and corresponding labels (with plt.xticks()).

Streaming Data

You can use plotext to plot a continuous flow of data:

import plotext as plt
import numpy as np

n = 2
frames = 18
l = 30 * frames
f = 2 * np.pi / l
x = np.arange(0, l)
xticks = np.arange(0, l + l / (2 * n), l / (2 * n))
xlabels = [str(i) + "π" for i in range(2*(n+1))]
for i in range(frames):
    y = np.sin(n * f * x + 2 * np.pi / frames * i)
    plt.clear_terminal()
    plt.clear_plot()
    plt.scatter(x, y, point_color = "blue")
    plt.ylim(-1, 1)
    plt.canvas_size(150, 40)
    plt.title("plotting streaming data using plotext")
    plt.ticks_color("blue")
    plt.xticks(xticks, xlabels)
    plt.sleep(0.001)
    plt.show()

where before each plot we have used the function clear_terminal (or in short clt) to clear the terminal, and the function clear_plot (or short clp) to resets all the plot parameters to their default value, including the data coordinates. Without it, the previous code would add the trailing data to the same plot. Also note that we have set the plot limits, on the y axis, using ylim. Finally, in order to reduce a possible screen flickering, we have used the sleep function: an input of, for example, 0.001 to it would add approximately 0.001 secs to the computation; this time parameters will depend on your processor speed and it needs some manual tweaking. Plotting the same data using matplotlib was roughly 15 to 50 times slower on my Linux-based machine (depending on the colors and data size).

Other Functions

• fill is a parameter (of the scatter or plot function) which allows to fill the area between the data and the y=0 level:

import plotext as plt
import numpy as np

l = 1000
x = np.arange(0, l + 1)
f = 2 * np.pi / l
y = np.sin(2 * f * x)
plt.scatter(x, y, point_color="blue", fill=True)
plt.ylim(-1.2 , 1.2)
plt.canvas_size(150, 40)
plt.title("plotext demonstrative plot using fill")
plt.canvas_color("cloud")
plt.facecolor("iron")
plt.ticks_color("gold")
plt.show()

• set_legend provides and alternative way to set the labels of each plot (as a list of strings) to be printed as a legend. If all labels are an empty string, no legend will be printed. Here is the idea:

plt.scatter(y1)
plt.plot(y2)
plt.set_legend(["signal 1", "signal2"])
plt.show()

• save_fig saves your plot as a text file. Here is the idea:

plt.scatter(y)
plt.show()
plt.save_fig(path)

where path is the file address where the data will be written. Note that (for now), this function doesn't preserve the plot colors.

• colors You can uce plt.colors() to see the available full-ground and background color codes. Here is the output for simplicity: where using flash will result in an actually flashing character.

• version In order to check the installed plotext version use plt.version()

Other Documentation

Other relevant documentation could be accessed using the following commands:

print(plx.scatter.__doc__)
print(plx.plot.__doc__)
print(plx.set_xticks.__doc__)
print(plx.set_yticks.__doc__)
print(plx.show.__doc__)
print(plx.clear_terminal.__doc__)
print(plx.clear_plot.__doc__)
print(plx.sleep.__doc__)
print(plx.save_fig.__doc__)

Installation

Use pip install plotext --upgrade

Main Updates:

• the plot now shows the actual data ticks using a simpler algorithm (no necessity for ticks_length).
• ticks_number is now simply ticks
• set functions like plt.set_title have reduced to plt.title()
• an optional grid can be added
• fill option added
• axes_color is now facecolor to adapt to matplotlib standards
• new legend positioning
• new color codes
• code restructured and revised.

Future Plans:

Any help on the following or new ideas is more then welcomed.

• creation of an histogram plot.
• creation of logarithmic plots
• data ticks for time based data.
• color and terminal size support for IDLE python editor and compiler.
• same as previous point but for Spider.
• saving text files with color.