lamm-mit/datavis

Scientific Data Visualization

Create publication-quality scientific plots and visualizations using matplotlib and seaborn.

Overview

This skill provides data visualization capabilities for scientific data:

• Line plots, scatter plots, bar charts
• Heatmaps and clustermaps
• Box plots and violin plots
• Histograms and density plots
• Sequence logos (for bioinformatics)
• Multiple subplot layouts

Usage

Create a line plot from CSV:

python3 {baseDir}/scripts/plot_data.py line --data data.csv --x time --y value --output plot.png

Create a scatter plot:

python3 {baseDir}/scripts/plot_data.py scatter --data data.csv --x x_col --y y_col --hue group

Create a heatmap:

python3 {baseDir}/scripts/plot_data.py heatmap --data matrix.csv --output heatmap.png

Create a bar chart:

python3 {baseDir}/scripts/plot_data.py bar --data data.csv --x category --y value

Plot from JSON data:

python3 {baseDir}/scripts/plot_data.py line --json '{"x": [1,2,3], "y": [4,5,6]}'

Plot Types

line

Line plot for continuous data.

| Parameter | Description | Default | |-----------|-------------|---------| | --data | CSV file path | - | | --json | JSON data string | - | | --x | X-axis column | Required | | --y | Y-axis column(s), comma-separated | Required | | --hue | Color grouping column | - | | --style | Line style column | - | | --markers | Add markers | False |

scatter

Scatter plot for showing relationships.

| Parameter | Description | Default | |-----------|-------------|---------| | --data | CSV file path | - | | --x | X-axis column | Required | | --y | Y-axis column | Required | | --hue | Color grouping column | - | | --size | Size column | - | | --alpha | Point transparency | 0.7 |

bar

Bar chart for categorical data.

| Parameter | Description | Default | |-----------|-------------|---------| | --data | CSV file path | - | | --x | Category column | Required | | --y | Value column | Required | | --hue | Color grouping column | - | | --horizontal | Horizontal bars | False | | --error | Error bar column | - |

heatmap

Heatmap for matrix data.

| Parameter | Description | Default | |-----------|-------------|---------| | --data | CSV file path | Required | | --cmap | Color map | viridis | | --annotate | Show values | False | | --cluster | Cluster rows/columns | False |

box

Box plot for distributions.

| Parameter | Description | Default | |-----------|-------------|---------| | --data | CSV file path | - | | --x | Grouping column | - | | --y | Value column | Required | | --hue | Color grouping column | - |

violin

Violin plot for distributions.

| Parameter | Description | Default | |-----------|-------------|---------| | --data | CSV file path | - | | --x | Grouping column | - | | --y | Value column | Required | | --hue | Color grouping column | - | | --split | Split violins by hue | False |

histogram

Histogram for distributions.

| Parameter | Description | Default | |-----------|-------------|---------| | --data | CSV file path | - | | --x | Value column | Required | | --bins | Number of bins | auto | | --kde | Add KDE line | False | | --hue | Color grouping column | - |

Common Options

| Option | Description | Default | |--------|-------------|---------| | --output | Output file path | plot.png | | --format | Output format: png, svg, pdf | png | | --title | Plot title | - | | --xlabel | X-axis label | column name | | --ylabel | Y-axis label | column name | | --figsize | Figure size (width,height) | 10,6 | | --style | Seaborn style | whitegrid | | --palette | Color palette | deep | | --dpi | Output resolution | 150 | | --legend | Legend position | auto | | --logx | Log scale X-axis | False | | --logy | Log scale Y-axis | False |

Examples

Multi-line plot with legend:

python3 {baseDir}/scripts/plot_data.py line --data timeseries.csv --x date --y "temp,humidity" --title "Weather Data" --output weather.png

Scatter plot with regression line:

python3 {baseDir}/scripts/plot_data.py scatter --data experiment.csv --x dose --y response --hue treatment --title "Dose Response" --output dose_response.png

Clustered heatmap:

python3 {baseDir}/scripts/plot_data.py heatmap --data expression.csv --cluster --cmap RdBu_r --title "Gene Expression" --output heatmap.svg --format svg

Box plot with multiple groups:

python3 {baseDir}/scripts/plot_data.py box --data measurements.csv --x condition --y value --hue treatment --title "Treatment Effects"

Histogram with KDE:

python3 {baseDir}/scripts/plot_data.py histogram --data samples.csv --x measurement --bins 30 --kde --title "Distribution"

Publication-quality figure:

python3 {baseDir}/scripts/plot_data.py scatter --data results.csv --x x --y y --figsize 8,6 --dpi 300 --format svg --style white --output figure1.svg

Color Palettes

• deep: Default seaborn palette
• muted: Muted colors
• bright: Bright colors
• pastel: Pastel colors
• dark: Dark colors
• colorblind: Colorblind-friendly
• viridis: Perceptually uniform
• plasma: Perceptually uniform
• RdBu: Red-Blue diverging
• coolwarm: Cool-Warm diverging

Notes

• Data can be provided as CSV files or JSON strings
• SVG output is recommended for publications
• Use --dpi 300 for high-resolution figures
• Column names with spaces should be quoted