mirror of
https://github.com/helblazer811/ManimML.git
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101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
from manim import *
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import numpy as np
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import math
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class GaussianDistribution(VGroup):
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"""Object for drawing a Gaussian distribution"""
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def __init__(self, axes, mean=None, cov=None, dist_theme="gaussian", **kwargs):
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super(VGroup, self).__init__(**kwargs)
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self.axes = axes
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self.mean = mean
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self.cov = cov
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self.dist_theme = dist_theme
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if mean is None:
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self.mean = np.array([0.0, 0.0])
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if cov is None:
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self.cov = np.array([[1, 0], [0, 1]])
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# Make the Gaussian
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if self.dist_theme is "gaussian":
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self.ellipses = self.construct_gaussian_distribution(self.mean, self.cov)
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elif self.dist_theme is "ellipse":
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self.ellipses = self.construct_simple_gaussian_ellipse(self.mean, self.cov)
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else:
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raise Exception(f"Uncrecognized distribution theme: {self.dist_theme}")
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@override_animation(Create)
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def _create_gaussian_distribution(self):
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return Create(self.ellipses)
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def compute_covariance_rotation_and_scale(self, covariance):
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# Get the eigenvectors and eigenvalues
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eigenvalues, eigenvectors = np.linalg.eig(covariance)
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y, x = eigenvectors[0, 1], eigenvectors[0, 0]
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center_location = np.array([y, x, 0])
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center_location = self.axes.coords_to_point(*center_location)
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angle = math.atan(x / y) # x over y to denote the angle between y axis and vector
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# Calculate the width and height
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height = np.abs(eigenvalues[0])
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width = np.abs(eigenvalues[1])
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shape_coord = np.array([width, height, 0])
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shape_coord = self.axes.coords_to_point(*shape_coord)
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width = shape_coord[0]
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height = shape_coord[1]
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return angle, width, height
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def construct_gaussian_distribution(self, mean, covariance, color=ORANGE,
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num_ellipses=4):
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"""Returns a 2d Gaussian distribution object with given mean and covariance"""
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# map mean and covariance to frame coordinates
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mean = self.axes.coords_to_point(*mean)
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# Figure out the scale and angle of rotation
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rotation, width, height = self.compute_covariance_rotation_and_scale(covariance)
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# Make covariance ellipses
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opacity = 0.0
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ellipses = VGroup()
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for ellipse_number in range(num_ellipses):
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opacity += 1.0 / num_ellipses
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ellipse_width = width * (1 - opacity)
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ellipse_height = height * (1 - opacity)
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ellipse = Ellipse(
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width=ellipse_width,
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height=ellipse_height,
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color=color,
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fill_opacity=opacity,
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stroke_width=0.0
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)
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ellipse.move_to(mean)
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ellipse.rotate(rotation)
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ellipses.add(ellipse)
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return ellipses
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def construct_simple_gaussian_ellipse(self, mean, covariance, color=ORANGE):
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"""Returns a 2d Gaussian distribution object with given mean and covariance"""
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# map mean and covariance to frame coordinates
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mean = self.axes.coords_to_point(*mean)
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# Figure out the scale and angle of rotation
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# TODO fix this
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# rotation, width, height = self.compute_covariance_rotation_and_scale(covariance)
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mean = np.array([0, 0, 0])
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mean = self.axes.coords_to_point(*mean)
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rotation = 0.0
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# Make covariance ellipses
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opacity = 0.0
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ellipses = VGroup()
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opacity = 0.2
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ellipse = Ellipse(
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width=0.6,
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height=0.6,
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color=color,
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fill_opacity=opacity,
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stroke_width=2.0
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)
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ellipse.move_to(mean)
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ellipse.rotate(rotation)
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ellipses.add(ellipse)
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return ellipses
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