chartexa 1.0.1

High-performance charting engine for Python — render publication-quality charts via .NET interop

Chartexa — High-Performance Charting for Python

Render publication-quality charts in Python using the Chartexa .NET engine — 40+ series types, real-time streaming, Jupyter integration, and GPU-accelerated rendering.

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Installation

pip install chartexa

Prerequisites

• Python 3.9+
• .NET 10 Runtime installed on your system

Production-Ready Capabilities

1. 40+ series types (2D, 3D, financial, scientific, instrumentation)
2. High-volume rendering with downsampling and batched updates
3. Native NumPy/Pandas interoperability
4. Real-time streaming and dashboard layout primitives
5. Notebook-first experience across Jupyter/VS Code/Colab

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Quick Start

One-liner

import chartexa as cx

cx.line([10, 20, 15, 30, 25]).save("chart.png")

Fluent API

import chartexa as cx

(cx.Chart(800, 600)
    .line([1, 2, 3, 4], [10, 20, 15, 30], stroke="#4FC3F7", label="Revenue")
    .scatter([1, 2, 3, 4], [12, 18, 17, 28], fill="red", label="Targets")
    .save("multi_series.png"))

Financial Chart

import chartexa as cx
import pandas as pd

df = pd.read_csv("stock_data.csv")
cx.candlestick(df).save("candlestick.png")

Multi-Panel Dashboard

import chartexa as cx

fig, axes = cx.subplots(2, 2)
axes[0, 0].line([1, 2, 3], [10, 20, 15])
axes[0, 1].scatter([1, 2, 3], [30, 10, 25])
axes[1, 0].bar(["A", "B", "C"], [40, 60, 30])
axes[1, 1].mountain([1, 2, 3], [5, 15, 10])
fig.save("dashboard.png")

Real-Time Streaming

import chartexa as cx

chart = cx.Chart()
with chart.begin_update():
    chart.line(sensor_x, sensor_y, label="Temperature")
    chart.line(sensor_x, humidity_y, label="Humidity")
chart.save("sensors.png")

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Features

40+ Series Types

Category	Series
Basic	Line, Scatter, Column, Bar, Mountain, Band, Bubble
Financial	Candlestick, OHLC
Statistical	BoxPlot, ErrorBar, Fan, Heatmap, Pie, Donut
Stacked	StackedBar, StackedColumn, StackedMountain
Polar	Polar, Radar
Scientific	Oscilloscope, SpectrumAnalyzer, Waterfall, Lissajous, BodePlot, NyquistPlot
Instruments	Gauges (Angular, Radial, Linear), Thermometer, Compass, VuMeter
Digital	DigitalLine, DigitalBand, LogicAnalyzer, EyeDiagram, ConstellationDiagram
Geospatial	GpsTrack, SkyView, RadarSweep
Widgets	Alarm, Battery, PowerMeter, StatusLamp
3D	XYZSurface

Interactive Modifiers

Zoom/pan, tooltips, crosshair, cursor, rubber-band zoom, data point selection, and series selection.

Theming & Styling

Built-in themes, custom colors (hex, RGB, CSS names), dash patterns, markers, and gradient palettes.

Axes

Numeric, DateTime, Logarithmic, and Category axes with full customization.

Data Sources & Streaming

Connect to MQTT, WebSocket, REST, gRPC, Serial (Arduino/ESP32), GPS, Audio, System Metrics, Docker, Market Data, OBD-II, CAN Bus, SDR, Flight Sims, Racing Sims, and more.

Data Transforms

Built-in moving_average, exponential_smoothing, derivative, integral, low_pass_filter, high_pass_filter, bollinger_bands, and more.

Performance & Large Data

• LTTB downsampling — visually lossless reduction for million-point datasets
• Min/Max downsampling — preserves extrema for financial/sensor data
• FIFO ring buffer — fixed-capacity streaming with automatic eviction
• Batch updates — chart.begin_update() suspends rendering for bulk operations
• NumPy/Pandas integration — pass arrays directly, zero-copy where possible

Layout & Dashboards

• subplots(rows, cols) — matplotlib-style grid layouts
• Figure / Dashboard — named panels with row/col spans
• Constraint-based sizing (fixed + proportional)

Jupyter & Notebook Integration

• Auto-detected in Jupyter, VS Code, Colab, Kaggle, and more
• display_chart() renders inline
• ChartWidget for interactive ipywidgets
• %cx / %%cx magic commands
• Colab GPU/CPU/TPU runtime detection

Export Formats

• PNG — chart.save("chart.png") or chart.to_bytes()
• HTML — chart.to_html() / chart.save_html("chart.html")
• Inline — chart.show() (opens in browser or displays in notebook)

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How It Works

Chartexa for Python uses pythonnet to call into the Chartexa .NET charting engine. The Python package bundles the compiled .NET assemblies and provides a Pythonic API with fluent method chaining, NumPy/Pandas interop, and Jupyter integration.

Python API → pythonnet bridge → Chartexa .NET Engine → SkiaSharp renderer → PNG/HTML

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Links

• GitHub Repository
• NuGet Packages (C# libraries)
• Changelog
• Publishing Guide

PyPI Publishing (Maintainers)

Use the full instructions in ../docs/PUBLISHING.md. Summary:

python -m build
twine check dist/*
twine upload dist/*

For test deployments:

twine upload --repository testpypi dist/*

License

MIT — see LICENSE for details.