Easing Animations with Python

This package is meant to make animations easy in python. There are a number of methods built around an "easing" class.

Quick-start guide

Full walk-through available in this notebook

begin by installing via pip

pip install easing

from easing import easing

In order to render any of these animation, you'll need to have the following installed

• FFMPEG for .mp4 renders (brew install ffmpeg on mac)
• Imagekick for .gif renders(brew install imagemagick)

This code takes either numpy arrays or pandas dataframes and turns them into animations. You can make scatter plots..

data=np.random.random((10,2))
easing.Eased(data).scatter_animation2d(n=3,speed=0.5,destination='media/singlepoint.gif')

size=100
u=np.random.multivariate_normal([1,1],[[1, 0.5], [0.5, 1]],size=size).reshape(1,-1)
v=np.random.multivariate_normal([1,1],[[2, 1], [1, 2]],size=size).reshape(1,-1)
w=np.random.multivariate_normal([1,1],[[4, 2], [2, 4]],size=size).reshape(1,-1)

data=pd.DataFrame(np.vstack([u,v,w]),index=['small','medium','large'])
easing.Eased(data).scatter_animation2d(speed=0.5,label=True,plot_kws={'alpha':0.5},destination='media/multipoint.gif')

Animated barcharts...

data=pd.DataFrame(abs(np.random.random((3, 5))),
                index=['Jurassic', 'Cretaceous', 'Precambrian'],
                columns=['Tyrannosaurus','Pterodactyl','Stegosaurus','Raptor','Megaloadon'])

easing.Eased(data).barchart_animation(plot_kws={'ylim':[0,1],'xlim':[-2,5]},smoothness=40,speed=0.5,label=True,destination='media/animatedbar.gif')

Or time series plots...

data = np.random.normal(scale=0.5,size=400)
easing.Eased(data).timeseries_animation(starting_pos = 25,
                                      speed=25,
                                      plot_kws={'ylim':[-3,3],'bins':np.linspace(-3,3,50)},destination='media/normdist.gif')

Further Explanations

Ingredients necessary to make the animations:

• an intitial time vector
• a dependent data matrix - each row will be a different variable, the columns correspond to the initial time vector
• an output time vecotr upon which to be interpolated

Interpolation class

Before generating the easing, you must create the object of class easing

    ease = Eased(data, input_time_vector, output_time_vector)

All subsequent functions will be called on this object.

Power Interpolation

The primary form of interpolative easing (or tweening) is based on powers (e.g. linear, quadratic, cubic etc.) The power easing function takes one variable - the exponent integer (n). Increasing the interger increases the sharpness of the transition.

   out_data = ease.power_ease(n)

No interpolation

If you simply want to extend the data to have the same number of points as an interpolated set without actually interpolating, simply call the No_interp() function

    out_data = ease.No_interp()