Math Visualizer
by rohitg00
|
Skill Details
Repository Files
14 files in this skill directory
name: math-visualizer version: "1.0.0" description: | Mathematical visualization skill for equations, proofs, and geometric concepts.
Triggers when:
- User mentions equations, formulas, or mathematical expressions
- Request involves mathematical proofs or derivations
- Content includes geometric relationships
- User mentions LaTeX, calculus, algebra, geometry, trigonometry
- Patterns: "equation", "formula", "prove", "derive", "graph", "plot"
Capabilities:
- LaTeX equation rendering with color-coded components
- Function graphing and transformations
- Geometric constructions and proofs
- 3D mathematical surfaces
- Step-by-step derivations with highlights author: manim-video-generator license: MIT
Math Visualizer Skill
The Math Visualizer brings mathematical concepts to life through precise, beautiful animations that reveal the structure and relationships within mathematics.
Mathematical Domains
Supported Areas
- Algebra: Equations, inequalities, polynomials
- Calculus: Derivatives, integrals, limits, series
- Geometry: Shapes, transformations, proofs
- Trigonometry: Functions, identities, unit circle
- Linear Algebra: Vectors, matrices, transformations
- Complex Analysis: Complex numbers, transformations
- Number Theory: Primes, sequences, patterns
Rules
rules/equation-presentation.md
How to present equations with proper pacing and emphasis.
rules/color-coding-math.md
Consistent color schemes for mathematical elements.
rules/graphing-best-practices.md
Creating clear, informative function graphs.
rules/proof-visualization.md
Step-by-step proof animations that build understanding.
Color Coding Standard
| Element | Color | Hex |
|---|---|---|
| Variables (x, y) | BLUE | #58C4DD |
| Constants | YELLOW | #FFFF00 |
| Operators | WHITE | #FFFFFF |
| Key Terms | GREEN | #83C167 |
| Equals/Results | GOLD | #FFD700 |
| Negative/Subtract | RED | #FC6255 |
Templates
Equation Derivation
from manim import *
class EquationDerivation(Scene):
def construct(self):
# Initial equation
eq1 = MathTex(r"x^2 + 2x + 1 = 0")
self.play(Write(eq1))
self.wait()
# Transform step by step
eq2 = MathTex(r"(x + 1)^2 = 0")
eq3 = MathTex(r"x + 1 = 0")
eq4 = MathTex(r"x = -1")
# Show each transformation
for new_eq in [eq2, eq3, eq4]:
self.play(TransformMatchingTex(eq1, new_eq))
self.wait()
eq1 = new_eq
# Highlight final answer
box = SurroundingRectangle(eq4, color=GREEN, buff=0.2)
self.play(Create(box))
Color-Coded Equation
from manim import *
class ColorCodedEquation(Scene):
def construct(self):
# Equation with color-coded parts
equation = MathTex(
r"f(", r"x", r") = ", r"a", r"x^2", r" + ", r"b", r"x", r" + ", r"c"
)
# Color code
equation[1].set_color(BLUE) # x
equation[3].set_color(YELLOW) # a
equation[4].set_color(BLUE) # x^2
equation[6].set_color(YELLOW) # b
equation[7].set_color(BLUE) # x
equation[9].set_color(YELLOW) # c
self.play(Write(equation))
# Explain each part
labels = [
(equation[3], "coefficient"),
(equation[1], "variable"),
(equation[9], "constant")
]
for part, label_text in labels:
self.play(Indicate(part))
label = Text(label_text, font_size=24).next_to(part, DOWN)
self.play(Write(label))
self.wait()
self.play(FadeOut(label))
Function Graph with Animation
from manim import *
class FunctionGraph(Scene):
def construct(self):
# Create axes
axes = Axes(
x_range=[-4, 4, 1],
y_range=[-2, 8, 1],
x_length=8,
y_length=5,
axis_config={"include_tip": True}
)
labels = axes.get_axis_labels(x_label="x", y_label="y")
self.play(Create(axes), Write(labels))
# Function
func = axes.plot(lambda x: x**2, color=BLUE)
func_label = MathTex(r"f(x) = x^2", color=BLUE).to_corner(UR)
self.play(Create(func), Write(func_label))
# Show derivative
deriv = axes.plot(lambda x: 2*x, color=GREEN)
deriv_label = MathTex(r"f'(x) = 2x", color=GREEN).next_to(func_label, DOWN)
self.play(Create(deriv), Write(deriv_label))
# Tangent line demonstration
x_tracker = ValueTracker(-2)
tangent = always_redraw(lambda: axes.get_secant_slope_group(
x=x_tracker.get_value(),
graph=func,
dx=0.01,
secant_line_color=YELLOW,
secant_line_length=4
))
dot = always_redraw(lambda: Dot(
axes.c2p(x_tracker.get_value(), x_tracker.get_value()**2),
color=RED
))
self.play(Create(tangent), Create(dot))
self.play(x_tracker.animate.set_value(2), run_time=4)
3D Mathematical Surface
from manim import *
class Surface3D(ThreeDScene):
def construct(self):
# Set up camera
self.set_camera_orientation(phi=75 * DEGREES, theta=-45 * DEGREES)
# Create axes
axes = ThreeDAxes(
x_range=[-3, 3, 1],
y_range=[-3, 3, 1],
z_range=[-2, 2, 1]
)
# Create surface
surface = Surface(
lambda u, v: axes.c2p(u, v, np.sin(u) * np.cos(v)),
u_range=[-PI, PI],
v_range=[-PI, PI],
resolution=(30, 30),
fill_opacity=0.7
)
surface.set_fill_by_value(
axes=axes,
colorscale=[(RED, -1), (YELLOW, 0), (GREEN, 1)]
)
# Animate
self.play(Create(axes))
self.play(Create(surface), run_time=3)
self.begin_ambient_camera_rotation(rate=0.2)
self.wait(5)
Geometric Proof
from manim import *
class PythagoreanProof(Scene):
def construct(self):
# Create right triangle
triangle = Polygon(
ORIGIN, RIGHT * 3, RIGHT * 3 + UP * 4,
color=WHITE, fill_opacity=0.3
)
# Labels
a_label = MathTex("a").next_to(triangle, DOWN)
b_label = MathTex("b").next_to(triangle, RIGHT)
c_label = MathTex("c").move_to(
(ORIGIN + RIGHT * 3 + UP * 4) / 2 + LEFT * 0.5 + UP * 0.3
)
self.play(Create(triangle))
self.play(Write(a_label), Write(b_label), Write(c_label))
# Show squares on each side
sq_a = Square(side_length=3, color=BLUE, fill_opacity=0.5)
sq_a.next_to(triangle, DOWN, buff=0)
sq_b = Square(side_length=4, color=GREEN, fill_opacity=0.5)
sq_b.next_to(triangle, RIGHT, buff=0)
self.play(Create(sq_a), Create(sq_b))
# Area labels
area_a = MathTex(r"a^2", color=BLUE).move_to(sq_a)
area_b = MathTex(r"b^2", color=GREEN).move_to(sq_b)
self.play(Write(area_a), Write(area_b))
# Conclusion
theorem = MathTex(r"a^2 + b^2 = c^2").to_edge(UP)
box = SurroundingRectangle(theorem, color=GOLD)
self.play(Write(theorem), Create(box))
LaTeX Quick Reference
Common Expressions
% Fractions
\frac{a}{b}
% Square root
\sqrt{x} \sqrt[n]{x}
% Summation
\sum_{i=1}^{n} x_i
% Integral
\int_{a}^{b} f(x) \, dx
% Limit
\lim_{x \to \infty} f(x)
% Matrix
\begin{pmatrix} a & b \\ c & d \end{pmatrix}
% Partial derivative
\frac{\partial f}{\partial x}
Greek Letters
\alpha \beta \gamma \delta \epsilon
\theta \lambda \mu \pi \sigma \omega
\Gamma \Delta \Theta \Lambda \Sigma \Omega
Best Practices
- Reveal equations gradually - Build up complex equations piece by piece
- Use consistent notation - Same symbol = same meaning throughout
- Annotate meaningfully - Labels should clarify, not clutter
- Show, don't just state - Animate the mathematical relationships
- Connect to intuition - Bridge abstract math to visual understanding
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
