Simple Plots

Static Plot

We start by making a static plot. The syntax is similar to matplotlib:

import squap
import numpy as np

x = np.linspace(0, 2*np.pi, 50)
squap.plot(x, np.sin(x))
squap.scatter(x, np.cos(x), color="red")

squap.set_xlim(0, 2*np.pi)
squap.set_ylim(-1, 1)

squap.show()

We use np.linspace to initialise x as 50 evenly spaced points between 0 and \(2\pi\).

Time Dependent Plot

To make a plot that updates it is useful to make use of the dictionary like object Internal Variables. Using var.t we create a new globally accessible variable which we use to keep track of the time. The following example draws a cosine with time dependence with a static plot in the background:

import squap
from squap import var
import numpy as np


def update():
    curve.set_data(x, np.cos(x + var.t))
    var.t += dt


var.t = 0
dt = 0.005
x = np.linspace(0, 2*np.pi, 50)
squap.plot(x, np.sin(x))
curve = squap.scatter(x, np.cos(x), color="red")

squap.set_xlim(0, 2 * np.pi)
squap.set_ylim(-1, 1)
squap.on_refresh(update)
squap.show()

First we define an update function that should run on each refresh, and then we use squap.on_refresh() to make sure this function is called on every screen refresh.

Tip

Try out adding squap.display_fps() before squap.show() to see how fast the plot is refreshing.

Adding A Slider

Now we add a slider for the sine function:

import squap
from squap import var
import numpy as np


def update():
    curve_1.set_data(x, np.cos(x + var.t))
    curve_2.set_data(x, np.sin(x + var.a))
    var.t += dt


var.t = 0
dt = 0.005
x = np.linspace(0, 2*np.pi, 50)
squap.add_slider("a", 0, 0, 2*np.pi)

curve_1 = squap.scatter(x, np.cos(x), color="red")
curve_2 = squap.plot(x, np.sin(x))

squap.set_xlim(0, 2 * np.pi)
squap.set_ylim(-1, 1)
squap.on_refresh(update)
squap.show()

The slider is added using squap.add_slider(), where we specify the name is a, the value sarts off at 0, and can be varied between 0 and \(2\pi\). For other input methods see this page.