statanim 0.1.0

A Manim extension for animated statistical visualisations — distributions, regression, probability, inference and more.

A Manim extension for animated statistical visualisations. Distributions, inference, regression, probability theory, and physical props — all built on Manim Community.

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Overview

statanim adds domain-specific tools to Manim that standard Manim does not provide: statistically-aware objects that understand distributions, parameters, and sampling; preset animation sequences for common demonstrations; 3D charts with correct depth perception; and physical probability props (dice, coins, cards, urns) that carry their own probability logic.

The library is structured around the same workflow as Manim: you create objects, add them to a scene, and call self.play(). Everything that is specific to statistics lives in statanim. Everything else remains standard Manim.

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Demo

Sample Space	Classical Probability

Conditional Probability	Hypergeometric Distribution

Birthday Paradox

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Installation

Prerequisites

pip install manim

LaTeX is required for formula rendering:

# macOS
brew install mactex-no-gui

# Ubuntu / Debian
sudo apt-get install texlive-full

# Windows: install MiKTeX from https://miktex.org/

Install statanim

pip install statanim

Install from source

git clone https://github.com/rishabhbhartiya/statanim.git
cd statanim
pip install -e .

Verify

from manim import *
from statanim.props.card import Card3D
from statanim.distributions.normal3d import NormalCurve3D
from statanim.core.colors import DARK_THEME

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Quick Start

Normal distribution with shaded region

from manim import *
from statanim.distributions.normal3d import NormalCurve3D

class NormalDemo(Scene):
    def construct(self):
        axes = Axes(x_range=[-4, 4, 1], y_range=[0, 0.45, 0.1],
                    x_length=8, y_length=4)
        curve = NormalCurve3D(mu=0, sigma=1, axes=axes)

        self.play(Create(axes))
        self.play(Create(curve))
        self.play(curve.shade_region(x_min=-1, x_max=1))
        self.wait(2)

Central Limit Theorem

from manim import *
from statanim.animations.clt_demo import CLTDemo

class CLTScene(ThreeDScene):
    def construct(self):
        self.set_camera_orientation(phi=70*DEGREES, theta=-45*DEGREES)
        demo = CLTDemo(source="uniform", params={"a": 0, "b": 1}, n_samples=500)
        demo.run(self)

Probability with playing cards

from manim import *
from statanim.props.card import Deck3D, CardFacing, standard_deck

class CardDraw(Scene):
    def construct(self):
        deck = Deck3D(cards=standard_deck(shuffle=True, seed=0),
                      initial_facing=CardFacing.FACE_DOWN)
        deck.move_to(LEFT * 3)
        self.play(FadeIn(deck))
        self.play(deck.deal_one(target=RIGHT * 2, flip=True))
        self.wait(2)

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Module Structure

statanim/
├── animations/
│   ├── clt_demo.py          CLTDemo, PopulationDistribution3D, SampleMeanHistogram3D
│   ├── flip_roll.py         Coin flip and die roll animations
│   ├── sampling.py          SimpleRandomSampling3D, StratifiedSampling3D,
│   │                        ClusterSampling3D, BootstrapSampling3D
│   └── transitions.py       DistMorph3D, HistMorph3D, CDFBuild3D,
│                             ParameterSweep3D, ScatterToRegression3D
│
├── axes/
│   ├── axes3d.py            Enhanced ThreeDAxes with statistical tick formatting
│   ├── grid3d.py            FullGrid3D, statistical grid overlays
│   └── number_plane3d.py    3D number planes for bivariate data
│
├── charts/
│   ├── bar_chart3d.py       BarChart3D, grouped and stacked variants
│   ├── box_plot3d.py        BoxPlot3D with whiskers and outlier markers
│   ├── heat_map3d.py        HeatMap3D for correlation and confusion matrices
│   ├── histogram3d.py       Histogram3D with configurable binning
│   ├── line_plot3d.py       LinePlot3D, MultiLine3D, CDFLine3D, ParametricCurve3D
│   ├── scatter_plot3d.py    ScatterPlot3D with regression overlay
│   └── violin_plot3d.py     ViolinPlot3D for distribution shape comparison
│
├── core/
│   ├── base.py              StatsObject3D base class
│   ├── colors.py            StatColor, ColorFamily, StatsTheme, 6 built-in themes
│   ├── math_utils.py        PDF, CDF, PMF computation helpers
│   └── tex_utils.py         TexFormula, 50+ formula builders, formula registry
│
├── distributions/
│   ├── base_dist.py         BaseDistribution3D
│   ├── continuous_dists.py  Exponential, Gamma, Beta, Chi-squared, Student-t, F
│   ├── discrete_dists.py    Binomial, Poisson, Geometric, Hypergeometric
│   ├── normal3d.py          NormalCurve3D, BivariateNormal3D
│   ├── pdf_viz.py           PDFVisualizer3D with shaded regions
│   ├── pmf_viz.py           PMFVisualizer3D
│   └── cdf_viz.py           CDFVisualizer3D
│
├── inference/
│   ├── hypothesis.py        HypothesisRegion3D, rejection zones
│   ├── confidence_interval.py  ConfidenceInterval3D
│   ├── sampling_dist.py     SamplingDistribution3D, CLT demonstration
│   └── error_types.py       Type I / Type II error visualisation
│
├── probability/
│   ├── bayes.py             BayesBox3D, prior/posterior update
│   ├── prob_tree.py         ProbabilityTree3D
│   ├── sample_space.py      SampleSpace3D, EventRegion3D
│   └── venn3d.py            VennDiagram3D (2-set and 3-set)
│
├── props/
│   ├── card.py              Card3D, Deck3D, CardFace, deal/shuffle/flip animations
│   ├── coin.py              Coin3D with flip animation
│   ├── die.py               Die3D (D4, D6, D8, D12, D20) with roll animation
│   ├── spinner.py           Spinner3D with sector probabilities
│   └── urn.py               Urn3D and Ball3D for sampling models
│
├── regression/
│   ├── correlation.py       pearson, spearman, kendall, OLS fit,
│   │                        CorrelationEllipse3D, CorrelationMatrix3D
│   ├── regression_plane.py  RegressionPlane3D, ScatterCloud3D,
│   │                        PlaneResiduals3D, CIShell3D, PIShell3D
│   └── residuals.py         ResidualVsFittedPlot, QQPlot3D, ScaleLocationPlot,
│                             InfluencePlot3D, DiagnosticPanel
│
├── scenes/
│   ├── demo_bayes.py        Bayesian inference demonstration
│   ├── demo_clt.py          Central Limit Theorem demonstration
│   ├── demo_distributions.py  Distribution showcase
│   └── demo_hypothesis.py   Hypothesis testing demonstration
│
└── ui/
    ├── labels.py            StatLabel3D, AnnotationArrow3D
    ├── panels.py            FormulaPanel3D, LegendPanel3D
    ├── table3d.py           DataTable3D floating data grid
    └── ticker.py            Ticker3D, PValueTicker3D, TickerGroup3D

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Key Classes

Distributions

Class	Module	Parameters
NormalCurve3D	distributions.normal3d	mu, sigma, axes, color
BivariateNormal3D	distributions.normal3d	mu1, mu2, sigma1, sigma2, rho
PDFVisualizer3D	distributions.pdf_viz	pdf_func, x_range, axes
PMFVisualizer3D	distributions.pmf_viz	pmf_dict, axes
CDFVisualizer3D	distributions.cdf_viz	cdf_func, x_range, axes

Props

Class	Module	Key animations
Card3D	props.card	flip(), deal_anim(), reveal_anim()
Deck3D	props.card	deal_one(), deal_n(), shuffle_anim(), fan_out()
Coin3D	props.coin	flip_anim()
Die3D	props.die	roll_anim()
Urn3D	props.urn	draw_anim(), replace_anim()

Regression

Class	Module	Description
RegressionPlane3D	regression.regression_plane	Fitted plane for two-predictor OLS
ScatterCloud3D	regression.regression_plane	3D scatter coloured by residual or leverage
PlaneResiduals3D	regression.regression_plane	Vertical residual lines to the fitted plane
CIShell3D	regression.regression_plane	95% confidence surface around the plane
DiagnosticPanel	regression.residuals	2×2 grid: residual plot, Q-Q, scale-location, influence

UI

Class	Module	Description
Ticker3D	ui.ticker	Animated statistical value badge with count_to(), odometer_to()
PValueTicker3D	ui.ticker	P-value ticker with significance stars and threshold colouring
TickerGroup3D	ui.ticker	Dashboard of multiple tickers in row / column / grid layout
FormulaPanel3D	ui.panels	Floating LaTeX formula overlay panel
DataTable3D	ui.table3d	Floating data table in 3D space

Color system

from statanim.core.colors import DARK_THEME, LIGHT_THEME, PAPER_THEME
from statanim.core.colors import NORMAL_FAMILY, REGRESSION_FAMILY, INFERENCE_FAMILY

# Apply a theme to a scene
DARK_THEME.apply(self)

# Use a family colour
curve.set_color(ManimColor(NORMAL_FAMILY.base.hex))

# Diverging colormap for a heatmap
from statanim.core.colors import diverging_map, PURPLE_700, CORAL_700
cmap = diverging_map(PURPLE_700, CORAL_700, n=256)

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Camera Orientation Reference

All ThreeDScene subclasses need an explicit camera orientation at the top of construct(). Without it everything appears flat on the floor.

def construct(self):
    self.set_camera_orientation(phi=70*DEGREES, theta=-45*DEGREES)
    # ... rest of scene

Recommended values by scene type:

Scene type	phi	theta
3D bar chart	65°	-55°
Regression plane	70°	-45°
Scatter cloud	70°	-60°
Card grid (table-top)	60°	-45°
Bivariate normal surface	68°	-45°

Use Scene (not ThreeDScene) for all 2D content: PDF curves, PMF bars, Venn diagrams, histograms, box plots, probability trees, and hypothesis test plots.

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Examples

The examples/ directory contains six working scenes:

File	Scene	Concepts
examples/card_probability_scene.py	CardProbabilityScene	Sample space, classical probability
examples/classical_probability.py	ClassicalProbabilityScene	P(A), P(B), P(A∩B), addition rule
examples/conditional_probability.py	ConditionalProbabilityScene	P(A|B), independence
examples/hypergeometricscene.py	HypergeometricScene	Hypergeometric PMF, sampling without replacement
examples/birthdayparadox_scene.py	BirthdayParadoxScene	Birthday problem, collision probability
examples/card_probability.py	CardProbabilityScene	Full card probability demonstration

Run any example:

manim -pql examples/classical_probability.py ClassicalProbabilityScene
manim -pqh examples/classical_probability.py ClassicalProbabilityScene  # high quality

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API Reference

Full class, function, and parameter documentation is in API_REFERENCE.md.

The reference covers all 57 files across 13 modules. REFERENCE.md contains narrative documentation for each module.

To regenerate the reference files after making changes:

python reference.py

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Requirements

Package	Minimum version
Python	3.10
manim	0.18.0
numpy	1.24
scipy	1.10

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Development Setup

git clone https://github.com/rishabhbhartiya/statanim.git
cd statanim
python -m venv .venv
source .venv/bin/activate       # Windows: .venv\Scripts\activate
pip install -e ".[dev]"

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Contributing

Issues and pull requests are welcome at github.com/rishabhbhartiya/statanim.

When adding a new distribution, inherit from BaseDistribution3D, implement pdf/pmf, cdf, mean, variance, and add a scene in scenes/demo_distributions.py.

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Acknowledgements

Built on Manim Community. Statistical algorithms from SciPy and NumPy. Inspired by the mathematical animation work of 3Blue1Brown.

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License

MIT License. See LICENSE for details.

Author: Rishabh Bhartiya — [e-mail]