solvien-graph 0.1.4

Professional data visualization library - Optimized for bioinformatics and scientific analyses (D3.js/Vega-Lite/Altair based)

Solvien Graph

Professional data visualization library - Optimized for bioinformatics and scientific analyses, designed to create D3.js/Vega-Lite based Altair charts.

Features

• Professional Design: Minimalist, corporate appearance
• Fully Interactive: D3.js/Vega-Lite based, zoom, pan, hover, export features
• Bioinformatics Focused: Gene expression, differential analysis and genomic data visualization
• Comprehensive Chart Types: Bar, Line, Scatter, Pie, Histogram, Heatmap, Volcano, MA Plot, Violin, Box Plot
• Professional Themes: Scientific, Publication, Bioinformatics and more
• Publication Quality: High resolution, optimized for scientific publications
• Scientific Color Palettes: Viridis, Plasma, Inferno, Magma, Cividis, Coolwarm, RdBu
• D3.js Technology: Altair with D3.js/Vega-Lite based, web standards compliant, professional charts

Installation

pip install -e .

or

pip install solvien-graph

Note: Since it's based on Altair (D3.js/Vega-Lite), all charts are automatically interactive. Opens in Jupyter Notebook or web browser. Produces output in JSON format, web standards compliant.

Quick Start

Bar Chart (Interactive)

import solvien_graph as sg

data = {
    "Python": 45,
    "JavaScript": 30,
    "Java": 20,
    "C++": 15
}

# Interactive chart - with hover, zoom, pan features (D3.js based)
chart = sg.quick_bar(data, title="Programming Languages Popularity")

# Save as HTML
chart.save("chart.html")

Line Chart (Interactive)

import numpy as np
import solvien_graph as sg

x = np.linspace(0, 10, 100)
y = np.sin(x)

# Interactive line chart - you can see values with hover (D3.js based)
chart = sg.quick_line(x, y, title="Sine Function")

# Save as HTML
chart.save("chart.html")

# Save as PNG/SVG (requires vega-cli)
chart.save("chart.png")

Scatter Plot

import numpy as np
import solvien_graph as sg

x = np.random.randn(100)
y = np.random.randn(100)

sg.quick_scatter(x, y, title="Random Distribution")

Pie Chart

import solvien_graph as sg

data = {
    "Mobile": 45,
    "Web": 30,
    "Desktop": 15,
    "Other": 10
}

sg.quick_pie(data, title="Platform Distribution")

Histogram

import numpy as np
import solvien_graph as sg

data = np.random.normal(100, 15, 1000)

sg.quick_hist(data, title="Normal Distribution", bins=30)

Themes

Solvien Graph comes with professional themes:

• scientific: Theme optimized for scientific publications
• publication: For high quality black and white publications
• bioinformatics: Color palette optimized for bioinformatics analyses
• And more...

sg.quick_bar(data)

Biograph Module - Specialized Bioinformatics Toolkit

The biograph module provides a dedicated namespace for bioinformatics visualizations. This is the recommended way to use bioinformatics functions:

Import Style

# Recommended: Import biograph as a separate module
import solvien_graph.biograph as bg

# Alternative style
from solvien_graph import biograph as bg

# Direct function import
from solvien_graph.biograph import quick_heatmap, quick_volcano

# Backward compatibility (still works)
from solvien_graph import quick_heatmap, quick_volcano

Why use biograph?

• Clear namespace: Separates bioinformatics functions from general plotting
• Professional workflow: import solvien_graph.biograph as bg is concise and clear
• Specialized toolkit: All bioinformatics-specific visualizations in one place
• Easy to remember: bg.quick_heatmap(), bg.quick_volcano(), etc.

Bioinformatics Charts

Heatmap (Gene Expression)

import numpy as np
import solvien_graph.biograph as bg

# Gene expression data
gene_data = np.random.randn(20, 10) * 2 + 5
gene_names = [f"Gene_{i+1}" for i in range(20)]
sample_names = [f"Sample_{i+1}" for i in range(10)]

bg.quick_heatmap(gene_data, 
                title="Gene Expression Heatmap",
                cmap="viridis",
                xticklabels=sample_names,
                yticklabels=gene_names,
                cbar_label="Expression Level")

Volcano Plot (Differential Expression)

import numpy as np
import solvien_graph.biograph as bg

log2fc = np.random.randn(1000) * 2
pvalues = np.random.exponential(0.1, 1000)
pvalues = np.clip(pvalues, 0, 1)

bg.quick_volcano(log2fc, pvalues,
                title="Differential Gene Expression",
                fc_threshold=1.0,
                pvalue_threshold=0.05)

MA Plot

mean_expr = np.random.lognormal(5, 1, 1000)
log2fc = np.random.randn(1000) * 1.5

bg.quick_ma_plot(mean_expr, log2fc,
                title="MA Plot - Differential Expression")

Violin Plot & Box Plot

conditions = {
    "Control": np.random.normal(5, 1, 100),
    "Treatment A": np.random.normal(6.5, 1.2, 100),
    "Treatment B": np.random.normal(4.5, 0.8, 100)
}

bg.quick_violin(conditions, title="Gene Expression - Conditions")
bg.quick_boxplot(conditions, title="Gene Expression - Box Plot")

Examples

The examples/ directory contains organized, ready-to-run examples:

File	Description
01_basic_charts.py	Bar, Line, Scatter, Pie, and Histogram charts
02_bioinformatics.py	Heatmap, Volcano, MA Plot, Violin, and Box Plot
03_advanced_analysis.py	PCA analysis and dimensionality reduction
04_biograph_demo.py	Complete RNA-Seq analysis workflow demo

Running Examples

cd examples

# Basic charts (getting started)
python 01_basic_charts.py

# Bioinformatics charts
python 02_bioinformatics.py

# Advanced PCA analysis
python 03_advanced_analysis.py

# Full biograph demo (recommended for bioinformatics users)
python 04_biograph_demo.py

solvien_graph vs solvien_graph.biograph

Feature	solvien_graph	solvien_graph.biograph
Purpose	General-purpose plotting	Bioinformatics-specific
Chart Types	Bar, Line, Scatter, Pie, Histogram	Heatmap, Volcano, MA, Violin, Boxplot
Target Users	General data visualization	Researchers, bioinformaticians
Output Focus	Interactive web charts	Publication-quality exports
Annotations	Basic tooltips	Statistical annotations (counts, medians)
Color Palettes	Standard	Scientific (Viridis, Coolwarm)
Clustering	Not available	Hierarchical clustering for heatmaps

When to use which:

# General data visualization
import solvien_graph as sg
sg.quick_bar(data, title="Sales by Region")
sg.quick_line(x, y, title="Time Series")

# Bioinformatics / Scientific work
import solvien_graph.biograph as bg
bg.quick_volcano(log2fc, pvalues, title="Differential Expression")
bg.quick_heatmap(gene_data, title="Gene Expression", cluster_rows=True)

Advanced Usage

Creating Chart Without Displaying

# Create chart but don't display
chart = sg.quick_bar(data, show=False)

# You can perform additional operations on Altair chart
chart = chart.properties(title="New Title")
chart = chart.encode(y=alt.Y('Value:Q', title="New Y Label"))

# Then display
chart.show()

Custom Colors

sg.quick_bar(data, color="#FF5733")
# or
sg.quick_bar(data, color=["#FF5733", "#33FF57", "#3357FF"])

Custom Sizes

sg.quick_bar(data, figsize=(12, 8))

Interactive Features

All charts are automatically interactive:

• Hover: Hover to see values
• Zoom: Zoom in/out on chart
• Pan: Pan the chart
• Export: Save as PNG, SVG, HTML
• Legend: Click legend to show/hide series

# Create chart (D3.js/Vega-Lite based)
chart = sg.quick_bar(data, title="Analysis")

# Save as HTML
chart.save("analysis.html")

# Save as PNG/SVG (requires vega-cli)
chart.save("analysis.png")

# Save as JSON (Vega-Lite spec)
chart.save("analysis.json")

# Publication quality mode
chart = sg.quick_bar(data, publication_quality=True)

# Clone the repository
git clone https://github.com/Solvien-Open-Source/solvien-graph
cd solvien-graph

# Install in development mode
pip install -e .

# Run tests
python -m pytest tests/

License

MIT License

Contributing

We welcome your contributions! Please open an issue before sending a pull request.

Contact

You can open an issue for questions.