lamm-mit 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 300for high-resolution figures - Column names with spaces should be quoted