FlowPlotPy 0.9.10

Python bindings for the FlowPlot plotting library

FlowPlot

FlowPlot is a prerelease plotting library for C++ and Python. It renders plots from reusable JSON templates and runtime data, with a small chaining API for changing template values, binding data, and writing images.

The project is currently focused on scatter and histogram-style plots, template-driven layout, reusable visual defaults, and a GUI workflow for authoring templates.

What Is FlowPlot?

FlowPlot is a plotting system built from a few programmatic parts rather than a single monolithic plotting call.

At the core is a compiler pipeline. It takes a plot template plus runtime data, normalizes the template with defaults, binds the supplied data fields, resolves layout and axes, and outputs renderer-independent drawing commands. Those commands can be consumed directly by an application, or passed to FlowPlot's built-in CPU renderer to produce a final image such as a PNG.

For C++, FlowPlot is header-only. You can include the modular headers or use one of the generated single-header FlowPlot_Mega*.hpp variants. For Python, FlowPlot is packaged as a native extension module built from the same C++ core.

FlowPlot also comes with FlowPlotGUI, a desktop app for visually authoring templates. The GUI is intended to make templates reusable and composable: instead of hard-coding every plot from scratch, users can build pluggable template blocks and feed them new data from C++ or Python. As a prerelease, customization is still limited in places, but the scaffolding for that template-first workflow is already in the project.

Quick Start

Python

Install the Python package:

pip install FlowPlotPy

Create or export a template, then bind data and write a PNG:

import flowplot

plot = flowplot.plot("./ScatterTemplate")
plot.with_data("main.x", [1, 2, 3, 4, 5])
plot.with_data("main.y", [3, 4, 1, 4, 6])
plot.write_png("pythonPlot.png")

The Python package includes a default font, so basic PNG rendering works without manually registering one. NumPy arrays are recommended for larger data, but normal Python lists are supported.

C++

Use the full single-header build when you want the simplest include path:

#define FLOW_PLOT_RENDERER
#define FLOW_PLOT_IMPLEMENTATION
#define FLOW_PLOT_DEFAULT_FONT_PATH "./Inter.ttf"
#include "FlowPlot_Mega.hpp"

#include <vector>

int main()
{
    std::vector<int> x{1, 2, 3, 4, 5};
    std::vector<int> y{3, 4, 1, 4, 6};

    FlowPlot::plot("./ScatterTemplate")
        .withData("main.x", x)
        .withData("main.y", y)
        .writePng("./CppPlot.png");
}

Compile:

g++ -std=c++20 -I. main.cpp -o plot

For modular headers, include FlowPlot/FlowPlot.hpp and add the needed include paths for bundled dependencies.

Tech Stack

FlowPlot is written in modern C++ and uses:

• RapidJSON for template parsing and normalization.
• stb libraries for the built-in CPU image/font path.
• pybind11, scikit-build-core, and CMake for the Python extension build.
• FlowUi, Vulkan, GLFW, and Native File Dialog for FlowPlotGUI.
• Doxygen for generated C++ API documentation.

The C++ library can be used as modular headers or as generated amalgamated headers. Regenerate the mega headers with:

python3 tools/generate_flowplot_mega.py

Available generated headers:

• FlowPlot_Mega_Core.hpp: FlowPlot headers only.
• FlowPlot_Mega_Stb.hpp: FlowPlot plus stb headers.
• FlowPlot_Mega_Json.hpp: FlowPlot plus the used RapidJSON subset.
• FlowPlot_Mega.hpp: FlowPlot plus RapidJSON subset plus stb, intended as the most self-contained option.

Template Overview

FlowPlot templates are JSON documents that describe the static structure of a plot. Runtime code supplies data and may override selected template properties before rendering.

A typical template contains:

• version: template format version.
• fonts: optional font manifest for C++ rendering.
• figure: image size, background, padding, title, and figure-level layout.
• datasets: named dataset schemas.
• panels: plot panels with axes, titles, styles, and layers.
• layers: visual layers such as scatter or histogram mappings.

Runtime data is bound by dataset and field name:

plot.withData("main.x", xValues);
plot.withData("main.y", yValues);

plot.with_data("main.x", x_values)
plot.with_data("main.y", y_values)

Templates are meant to be reusable. You can author a template once, then use it with different data or small property overrides:

FlowPlot::plot("./ScatterTemplate")
    .set("figure.title.text", "Experiment 12")
    .withData("main.x", x)
    .withData("main.y", y)
    .writePng("experiment-12.png");

Detailed API, schema, packaging, and GUI documentation will live in separate docs as the prerelease stabilizes.