Skip to main content
100%

Matplotlib subplots with TeX rendering: good figure baseline for publications

✓ Published0🌍 Public
Aabehmiel
Last edited Mar 31, 2017
Created on Mar 31, 2017

This example creates a publication-quality figure with two stacked Matplotlib subplots, each displaying four labeled data series with distinct marker styles. The code enables TeX rendering for bold mathematical text throughout the figure, including axis labels, titles, and annotations, while a shared y-axis label spans both plots. It uses the `subplot` API to add axes, `plot` with custom markers, and `set_position` to adjust subplot extents. The script saves the result as a PNG file using `savefig` and applies a serif font family for a polished academic appearance.

AI-generated description

Similar vizzes

Loading thumbnail…

Create beautiful square figures with big labels and the correct number of ticks

This example demonstrates how to create square matplotlib figures with large axis labels and precisely controlled tick counts. The `create_figure` function builds a figure with a fixed square size and uses `MaxNLocator` to set the number of x-axis ticks, while `format_axes` applies custom numeric formatting via `FormatStrFormatter` and independently controls tick counts and label sizes for both axes. The code relies on matplotlib’s `figure`, `add_subplot`, and `tick_params` APIs to produce clean, publication-ready plots.

Aabehmiel
64% match
Loading thumbnail…

Theme for publication quality graphs. Modified from https://rpubs.com/Koundy/71792!

This example demonstrates a publication-quality ggplot2 theme with custom fill and color palettes. It shows how to apply a consistent, clean aesthetic—featuring bold axis titles, light gray gridlines, and a horizontal legend—to any plot. The code defines a `theme_Publication_custom` function using `theme_foundation` from `ggthemes` and custom `scale_fill_Publication` and `scale_colour_Publication` functions built on `discrete_scale` and `manual_pal` from `scales`. The theme and palettes are intended for reproducible, polished figures from data frames.

FFloWuenne
61% match
Loading thumbnail…

Gist f052b3898971d32fb695

This example uses Python’s matplotlib within a Jupyter notebook to create subplots, leveraging NumPy and matplotlib’s interactive mode. The visualization demonstrates the construction of multiple panels in a single figure, with code likely generating sample plots for comparison or exploration. The notebook runs `%pylab inline` to enable inline plotting and produce rendered subplot images as output.

JJonasHeylen
60% match
Loading thumbnail…

Quick 3d plot snippet for 4 series on the same plot

This example shows four distinct data series plotted together in a single three-dimensional scatterplot, with each series assigned a different color (black, red, blue, and green) and a corresponding legend label. The plot uses Matplotlib’s `add_subplot` with the `projection='3d'` option to create the axes, then calls `ax.scatter` four times for the x, y, and z coordinates of each dataset. It sets axis labels via `xlabel`, `ylabel`, and `zlabel`, adds a title, and saves the figure as a PNG using `savefig`. The code comments note that 3D plots are generally discouraged for transferability but useful for exploratory analysis.

Aabehmiel
59% match
Loading thumbnail…

Gist 9ff6a92be90289b542cecf8360828ac7

This visualization compiles a scientific paper layout from a plain-text order file, rendering a table of HTML figure names and paths. The notebook reads the layout with pandas, parses a BibTeX bibliography using bibtexparser with unicode conversion, and prints extracted DOI identifiers from cited references. It organizes the paper’s components—abstract, figures, and HTML sections—into a structured sequence for further assembly. The code relies on pandas for tabular data handling and IPython’s HTML display to preview the ordering.

Aakeshavan
57% match
Loading thumbnail…

Plots in Tables

This example demonstrates how to embed small, arrow-based line charts directly within the cells of a LaTeX table, creating an integrated display of data and its visual summary. The R code uses `ggplot2` to generate four separate horizontal arrow segments (each representing a different x-range on a common scale), saving each as a PNG file with `ggsave`. The `dplyr` and `kableExtra` libraries then build a table where the second column contains LaTeX `\includegraphics` commands, referencing those saved images. The `kbl` function formats the table with `escape = F` to allow the raw LaTeX code, and `column_spec` sets the plot column's width. Overall, it shows a workflow for combining statistical graphics and tabular text in a single rendered document.

JJustinCally
57% match
Loading thumbnail…

A Pinterest-like dynamic image layout for TeX

This example demonstrates a Pinterest-like image grid that arranges up to eight photos automatically into two columns, with optional slots left blank when fewer images are supplied. The layout is fully dynamic, requiring only file paths as input. The code defines a custom LaTeX command using TikZ nodes and includegraphics from the graphicx package, positioning each image sequentially within its column. The xparse package enables the command’s optional arguments, while example images come from Unsplash. The design shows how vanilla TeX tooling in texlive can produce responsive visual layouts without external dependencies.

BBastiTee
57% match
Loading thumbnail…

Gist ff898d829474754ee41930e78894fa7e

This example demonstrates a Markdown editor’s multi-format documentation capabilities, showcasing syntax for LaTeX equations, highlighted Python code, flowcharts, sequence diagrams, Gantt charts, and tables. The document mixes inline math with a codecogs image URL for a formula, while leveraging fenced code blocks to invoke specific rendering engines like flow, seq, and gantt. It also includes a Todo list and a Bash command snippet, illustrating how structured text can be transformed into interactive diagrams and formatted output.

AAlsaxian
57% match