Plot Gallery

FigRecipe supports all standard matplotlib plot types. Below are examples of each plot type with the declarative specification format used by the MCP server.

Basic Plots

Line Plot

Line plot with multiple series.

import figrecipe as fr
import numpy as np

fig, ax = fr.subplots()
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label="sin(x)", id="sine")
ax.plot(x, np.cos(x), label="cos(x)", id="cosine")
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.legend()
fr.save(fig, "line_plot.png")

MCP Specification:

figure:
  figsize: [6, 4]
plots:
  - type: line
    x: [0, 1, 2, 3, 4, 5]
    y: [0, 1, 4, 9, 16, 25]
    label: "quadratic"
xlabel: "X"
ylabel: "Y"
title: "Line Plot"

Scatter Plot

Scatter plot with color mapping.

import figrecipe as fr
import numpy as np

fig, ax = fr.subplots()
np.random.seed(42)
x = np.random.randn(50)
y = x + np.random.randn(50) * 0.5
ax.scatter(x, y, c=x, cmap="viridis", s=50, id="data")
ax.set_xlabel("X")
ax.set_ylabel("Y")
fr.save(fig, "scatter_plot.png")

MCP Specification:

plots:
  - type: scatter
    x: [1, 2, 3, 4, 5]
    y: [2, 4, 1, 5, 3]
    c: [0.1, 0.3, 0.5, 0.7, 0.9]
    cmap: viridis
    s: 50
xlabel: "X"
ylabel: "Y"

Bar Plot

Vertical bar plot.

import figrecipe as fr

fig, ax = fr.subplots()
categories = ["A", "B", "C", "D", "E"]
values = [23, 45, 12, 67, 34]
ax.bar(categories, values, color="steelblue", id="bars")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fr.save(fig, "bar_plot.png")

MCP Specification:

plots:
  - type: bar
    x: ["A", "B", "C", "D"]
    height: [10, 20, 15, 25]
    color: steelblue
xlabel: "Category"
ylabel: "Value"

Statistical Plots

Histogram

Histogram showing data distribution.

import figrecipe as fr
import numpy as np

fig, ax = fr.subplots()
np.random.seed(42)
data = np.random.randn(1000)
ax.hist(data, bins=30, alpha=0.7, color="steelblue", edgecolor="white", id="hist")
ax.set_xlabel("Value")
ax.set_ylabel("Frequency")
fr.save(fig, "histogram.png")

Box Plot

Box plot comparing distributions.

import figrecipe as fr
import numpy as np

fig, ax = fr.subplots()
np.random.seed(42)
data = [np.random.randn(100) + i for i in range(4)]
ax.boxplot(data, labels=["A", "B", "C", "D"], id="boxplot")
ax.set_xlabel("Group")
ax.set_ylabel("Value")
fr.save(fig, "boxplot.png")

Violin Plot

Violin plot showing distribution shape.

import figrecipe as fr
import numpy as np

fig, ax = fr.subplots()
np.random.seed(42)
data = [np.random.randn(100) + i for i in range(4)]
ax.violinplot(data, positions=[1, 2, 3, 4], id="violin")
ax.set_xticks([1, 2, 3, 4])
ax.set_xticklabels(["A", "B", "C", "D"])
fr.save(fig, "violinplot.png")

2D Plots

Heatmap / Imshow

Heatmap visualization of 2D data.

import figrecipe as fr
import numpy as np

fig, ax = fr.subplots()
np.random.seed(42)
data = np.random.randn(10, 10)
im = ax.imshow(data, cmap="coolwarm", id="heatmap")
fig.fig.colorbar(im, ax=ax._ax)
fr.save(fig, "heatmap.png")

Contour Plot

Filled contour plot.

import figrecipe as fr
import numpy as np

fig, ax = fr.subplots()
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)
cs = ax.contourf(X, Y, Z, levels=20, cmap="viridis", id="contour")
fig.fig.colorbar(cs, ax=ax._ax)
fr.save(fig, "contour.png")

Supported Plot Types (47)

FigRecipe supports all standard matplotlib axes methods:

Line & Curve: plot, step, fill, fill_between, fill_betweenx, errorbar, stackplot, stairs

Scatter & Points: scatter

Bar & Categorical: bar, barh

Distribution: hist, hist2d, boxplot, violinplot, ecdf

2D Image & Matrix: imshow, matshow, pcolor, pcolormesh, hexbin, spy

Contour & Surface: contour, contourf, tricontour, tricontourf, tripcolor, triplot

Spectral & Signal: specgram, psd, csd, cohere, angle_spectrum, magnitude_spectrum, phase_spectrum, acorr, xcorr

Vector & Flow: quiver, barbs, streamplot

Special: pie, stem, eventplot, loglog, semilogx, semilogy, graph

CSV Column Workflow

FigRecipe can read data directly from CSV files:

plots:
  - type: scatter
    data_file: "experiment_data.csv"
    x_column: "time_seconds"
    y_column: "signal_amplitude"
    label: "Experiment 1"

xlabel: "Time (s)"
ylabel: "Amplitude"

Statistical Annotations

Add significance brackets with p-values:

stat_annotations:
  - group1_idx: 0
    group2_idx: 1
    p_value: 0.003
    test: "t-test"
    stars: true