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Animation engine for explanatory math videos

Project description


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pip install manim_express

Run in pycharm

(Find action 'Registry' in PyCharm) named 'run.processes.with.pty' that allows to run Python processes with tty

Quick start

  • Render an animation: 3b1b:SquareToCircle

    from manimlib import *
    from manim_express import GlEagerScene
    scene = GlEagerScene()
    circle = Circle()
    circle.set_fill(BLUE, opacity=0.5)
    circle.set_stroke(BLUE_E, width=4)
    square = Square()
    scene.show_creation(square), circle))

    Operating graphics:

    • hold down the d key or mouse left on the keyboard and move the mouse to change the three-dimensional perspective.
    • hold down the s key or mouse right on the keyboard and move the mouse to pan the screen
    • hold down the z or ctrl on the keyboard while scrolling the middle mouse button to zoom the screen
    • scroll the middle mouse button to move the screen up and down
    • reset camera view by pressing r
    • close the window and exit the program by pressing q or tab
    • pause the animation by pressing space or ctrl or alt
    • previews animation clip by pressing LEFT
    • next animation clip: RIGHT
    • replay current animation clip: DOWN
  • manim_express vs Matplotlib:
    Eager mode usage:

    from manimlib import *
    from manim_express import GlEagerScene
    CONFIG.use_online_tex = True # If you don't have installed latex locally.
    theta = np.linspace(0, 2*np.pi, 200)
    x = np.cos(theta)
    y = np.sin(theta)
    scene = GlEagerScene()
    scene.plot(x, y, color=GREEN, width=2, scale_ratio=1)

    Object oriented usage:

    from manimlib import *
    from manim_express import GlEagerScene
    from sklearn.datasets import make_multilabel_classification 
    class ScatterExample(GlEagerScene):
        def clip_1(self):
            X1, y1 =make_multilabel_classification(n_samples=200, n_classes=4, n_features=2)
            X2, y2 =make_multilabel_classification(n_samples=200, n_classes=4, n_features=2)
            self.scatter2d(X1[:, 0], X1[:, 1], size=.05, color=BLUE)
            self.scatter2d(X2[:, 0], X2[:, 1], size=.05, color=YELLOW)
    It should be noted that manim is not suitable for drawing patterns that need to be accurately realized!



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