### Install uv for Development

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/installation.md

Install the uv tool, a fast Python package installer and resolver, which is recommended for development installations. Choose the script appropriate for your operating system.

```bash
# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
```

```powershell
# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```

--------------------------------

### Install Build Essentials (Linux)

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/installation.md

On Linux systems, if compilation fails, install the necessary build tools with this command.

```bash
sudo apt-get install build-essential
```

--------------------------------

### Configuration & Setup

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides functions for configuring matplotlib, scanpy, axes, and ggplot styles.

```APIDOC
## Configuration & Setup

### Description
Provides functions for configuring plotting environments.

### Setup Functions
- `setup_matplotlib`
- `setup_scanpy`
- `setup_ax`
- `setup_ggplot`
```

--------------------------------

### Clone Repository and Install for Development

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/installation.md

Clone the cnsplots repository and use the provided Makefile with uv to install the package in editable mode, including development dependencies.

```bash
git clone https://github.com/faridrashidi/cnsplots
cd cnsplots
make install
```

--------------------------------

### Install Xcode Command Line Tools (macOS)

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/installation.md

If you encounter compiler errors on macOS, install the Xcode Command Line Tools using this command.

```bash
xcode-select --install
```

--------------------------------

### Install cnsplots using pip

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/installation.md

Use this command to install the cnsplots package from PyPI. Ensure you have Python 3.10+ and pip installed.

```bash
pip install cnsplots
```

--------------------------------

### Quick Start with cnsplots

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/index.md

Demonstrates basic usage of cnsplots for creating a boxplot. Imports necessary libraries, sets figure dimensions, generates the plot, and saves it to a file.

```python
import cnsplots as cns
import seaborn as sns

df = sns.load_dataset("tips")
cns.figure(150, 100)  # Height x Width in pixels
cns.boxplot(data=df, x="day", y="total_bill")
cns.savefig("figure.svg")
```

--------------------------------

### Verify cnsplots Installation

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/installation.md

Run this Python code after installation to confirm that cnsplots is accessible and to check its version.

```python
import cnsplots

print(cnsplots.__version__)
```

--------------------------------

### Quick Start: Create and Save a Boxplot

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/getting_started.md

This snippet shows the basic workflow: loading data, creating a figure with specific pixel dimensions, generating a boxplot, and saving the figure to an SVG file. It requires importing cnsplots, seaborn, and matplotlib.

```python
import cnsplots as cns
import seaborn as sns
import matplotlib.pyplot as plt

# Load example data
df = sns.load_dataset("tips")

# Create a figure with specific dimensions (height x width in pixels)
cns.figure(150, 100)

# Create a boxplot
cns.boxplot(data=df, x="day", y="total_bill")

# Save the figure
cns.savefig("my_figure.svg")
```

--------------------------------

### Apply Temporary Settings Overrides

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/settings.md

Use the `context()` method as a context manager to apply temporary overrides to specific settings. Changes are reverted upon exiting the context.

```python
with cns.settings.context(
    palette_qual="Dark2",
    figure_width=200,
    figure_height=120,
):
    ...
```

--------------------------------

### Inspect Current Settings

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/settings.md

Print the current global settings object to review all active defaults.

```python
import cnsplots as cns

print(cns.settings)
```

--------------------------------

### Settings Management

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Allows inspection, modification, and temporary overriding of package-wide defaults for styling and behavior.

```APIDOC
## Settings

### Description
Manages package-wide defaults for styling and behavior.

### Usage
- Inspect defaults: `print(cns.settings)`
- Set global styling: `cns.settings.title_fontsize = 10`
- Apply temporary overrides: `with cns.settings.context(palette_qual="Dark2", figure_width=200): ...`
- Restore defaults: `cns.settings.reset()`
```

--------------------------------

### Save Figures in Different Formats

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/getting_started.md

Demonstrates how to save figures in various formats suitable for publication and presentations: SVG for editing, PDF for vector quality, and PNG for raster workflows.

```python
# SVG - editable in Adobe Illustrator
cns.savefig("figure.svg")

# PDF - maintains vector quality
cns.savefig("figure.pdf")

# PNG for presentations or raster workflows
cns.savefig("figure.png")
```

--------------------------------

### Use Sequential Color Maps

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Apply sequential color maps for continuous data. 'gnuplot' is the default sequential option.

```python
cns.figure(color_map="parula")  # MATLAB-style
```

```python
cns.figure(color_map="gnuplot")  # Default sequential
```

--------------------------------

### Use Qualitative Color Palettes

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Access curated qualitative color palettes suitable for categorical data. The default 'Ecotyper1' is optimized for journals.

```python
cns.figure(color_cycle="Ecotyper1")  # Default, optimized for journals
```

```python
cns.figure(color_cycle="Cell")  # Custom Cell-inspired journal palette
```

```python
cns.figure(color_cycle="Nature")  # Nature-inspired journal palette
```

```python
cns.figure(color_cycle="Science")  # Science-inspired journal palette
```

```python
cns.figure(color_cycle="Set1")  # ColorBrewer Set1
```

--------------------------------

### Reset Settings to Package Defaults

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/settings.md

Call the `reset()` method on the settings object to revert all attributes back to their original package defaults.

```python
cns.settings.reset()
```

--------------------------------

### Figure & Layout Utilities

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides utilities for figure and layout management, including figure creation, saving, multipanel layouts, and label/legend manipulation.

```APIDOC
## Figure & Layout Utilities

### Description
Provides utilities for figure and layout management.

### Utilities
- `figure`
- `savefig`
- `multipanel`
- `add_panel_label`
- `take_legend_out`
```

--------------------------------

### Inspect and Modify cnsplots Settings

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Demonstrates how to inspect, modify, and temporarily override cnsplots package-wide settings. Use `cns.settings.reset()` to restore defaults.

```python
print(cns.settings)
cns.settings.title_fontsize = 10

with cns.settings.context(palette_qual="Dark2", figure_width=200):
    ...
```

```python
cns.settings.reset()
```

--------------------------------

### Basic Boxplot with CNSPlots

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Create a publication-ready boxplot using CNSPlots. Load data with seaborn, set figure dimensions, and then generate the plot.

```python
import cnsplots as cns
import seaborn as sns

# Load example data
df = sns.load_dataset("tips")

# Create a figure (height, width in pixels)
cns.figure(height=150, width=100)

# Create a publication-ready boxplot
cns.boxplot(data=df, x="day", y="total_bill")

# Save as vector graphic
cns.savefig("figure.svg")
```

--------------------------------

### Create a Barplot

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/getting_started.md

Generates a barplot with specified dimensions, data, and mapping of columns to x, y axes, and hue. The figure size is set in pixels.

```python
cns.figure(100, 80)
cns.barplot(data=df, x="day", y="total_bill", hue="sex")
cns.savefig("barplot.svg")
```

--------------------------------

### Import cnsplots Library

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Import the cnsplots library for use in your Python scripts.

```python
import cnsplots as cns
```

--------------------------------

### Create a Boxplot

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/getting_started.md

Generates a boxplot with specified dimensions, data, and mapping of columns to x, y axes, and hue. The figure size is set in pixels.

```python
cns.figure(100, 80)
cns.boxplot(data=df, x="day", y="total_bill", hue="sex")
cns.savefig("boxplot.svg")
```

--------------------------------

### Export Figure as PDF

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Save the current figure as a PDF file, which allows for editable text elements.

```python
# PDF with editable text
cns.savefig("figure.pdf")
```

--------------------------------

### Export Figure as PNG

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Save the current figure as a high-resolution PNG image file.

```python
# High-resolution PNG
cns.savefig("figure.png")
```

--------------------------------

### Statistical Models

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to statistical modeling functionalities including CoxModel, LogisticModel, and prerank.

```APIDOC
## Statistical Models

### Description
Provides access to statistical modeling functionalities.

### Models
- `CoxModel`
- `LogisticModel`
- `prerank`
```

--------------------------------

### Perform Statistical Tests in Stackplots

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Generate a stackplot and conduct Fisher's exact test between specified categories.

```python
# Stackplot with Fisher's exact test
cns.stackplot(data=df, x="group", y="category", pairs=[("A", "B")])
```

--------------------------------

### Access Individual Colors

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Retrieve predefined individual colors like red and blue for custom use.

```python
red = cns.RED
blue = cns.BLUE
```

--------------------------------

### Define Figure Dimensions in Pixels

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/getting_started.md

Demonstrates how to set the figure's height and width in pixels using cns.figure(). Use this to control the exact canvas size before plotting.

```python
# Small figure: 100 px tall and 80 px wide
cns.figure(100, 80)

# Larger figure: 200 px tall and 150 px wide
cns.figure(200, 150)
```

--------------------------------

### Color Palettes

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to color palettes and functions to retrieve hex colors from palettes.

```APIDOC
## Color Palettes

### Description
Provides access to color palettes and related utilities.

### Functions
- `palettes`
- `get_hexcolors_from_apalette`
```

--------------------------------

### Create a Scatter Plot

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/getting_started.md

Generates a scatter plot with specified dimensions, data, and mapping of columns to x, y axes, and hue. The figure size is set in pixels.

```python
cns.figure(100, 80)
cns.scatterplot(data=df, x="total_bill", y="tip", hue="day")
cns.savefig("scatter.svg")
```

--------------------------------

### Plotting Functions: Heatmaps & Matrix Plots

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to heatmap and matrix plotting functions such as heatmapplot, dotplot, and confusionplot.

```APIDOC
## Plotting Functions: Heatmaps & Matrix Plots

### Description
Provides access to heatmap and matrix plotting functions.

### Functions
- `heatmapplot`
- `dotplot`
- `confusionplot`
```

--------------------------------

### Perform Statistical Tests in Barplots

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Create a barplot and include Welch's t-test for a specific pair of groups.

```python
# Barplot with Welch's t-test
cns.barplot(data=df, x="group", y="value", pairs=[("A", "B")])
```

--------------------------------

### Plotting Functions: Distribution & Comparison Plots

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to various distribution and comparison plotting functions such as boxplot, violinplot, stripplot, barplot, lollipopplot, histplot, distplot, kdeplot, and ridgeplot.

```APIDOC
## Plotting Functions: Distribution & Comparison Plots

### Description
Provides access to various distribution and comparison plotting functions.

### Functions
- `boxplot`
- `violinplot`
- `stripplot`
- `barplot`
- `lollipopplot`
- `histplot`
- `distplot`
- `kdeplot`
- `ridgeplot`
```

--------------------------------

### Violinplot with Custom Colors

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Create a violin plot with a custom color palette. Specify the color cycle using the 'color_cycle' argument in cns.figure.

```python
# Use custom color palette
cns.figure(150, 200, color_cycle="Ecotyper1")
cns.violinplot(data=df, x="day", y="total_bill", hue="sex")
```

--------------------------------

### Plotting Functions: Genomics & Statistical Plots

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to genomics and statistical plotting functions including volcanoplot, gseaplot, rocplot, and qqplot.

```APIDOC
## Plotting Functions: Genomics & Statistical Plots

### Description
Provides access to genomics and statistical plotting functions.

### Functions
- `volcanoplot`
- `gseaplot`
- `rocplot`
- `qqplot`
```

--------------------------------

### Plotting Functions: Categorical & Proportion Plots

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to categorical and proportion plotting functions including stackplot, pieplot, donutplot, vennplot, upsetplot, and sankeyplot.

```APIDOC
## Plotting Functions: Categorical & Proportion Plots

### Description
Provides access to categorical and proportion plotting functions.

### Functions
- `stackplot`
- `pieplot`
- `donutplot`
- `vennplot`
- `upsetplot`
- `sankeyplot`
```

--------------------------------

### Create Multi-Panel Figures

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Use the multipanel function to create a figure with multiple panels. Each panel can be defined with specific dimensions and then populated with different plot types. Automatic panel labeling (A, B, C...) is supported.

```python
import cnsplots as cns

mp = cns.multipanel(max_width=540)

# Panel A
mp.panel("A", height=150, width=150)
cns.boxplot(data=df1, x="group", y="value")

# Panel B
mp.panel("B", height=150, width=150)
cns.scatterplot(data=df2, x="x", y="y")

# Continues...
```

--------------------------------

### Create Subplots with Boxplot and Barplot

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/getting_started.md

Sets up matplotlib for subplots and creates a figure containing a boxplot and a barplot side-by-side. It configures subplot dimensions and titles, then saves the combined figure.

```python
cns.setup_matplotlib()
fig, axes = plt.subplots(1, 2, figsize=(170 / 72, 80 / 72), dpi=144)

cns.boxplot(data=df, x="day", y="total_bill", ax=axes[0])
axes[0].set_title("Box Plot")

cns.barplot(data=df, x="day", y="total_bill", ax=axes[1])
axes[1].set_title("Bar Plot")

plt.tight_layout()
cns.savefig("subplots.svg")
```

--------------------------------

### Export Figure as SVG

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Save the current figure as an SVG file, which is recommended for vector graphics and publication. This uses a standard matplotlib SVG backend.

```python
# SVG for vector graphics (recommended)
cns.savefig("figure.svg")
```

--------------------------------

### Plotting Functions: Scatter & Regression Plots

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to scatter and regression plotting functions including scatterplot, regplot, lineplot, and slopeplot.

```APIDOC
## Plotting Functions: Scatter & Regression Plots

### Description
Provides access to scatter and regression plotting functions.

### Functions
- `scatterplot`
- `regplot`
- `lineplot`
- `slopeplot`
```

--------------------------------

### Boxplot with Statistical Comparisons

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Generate a boxplot with statistical significance annotations. Use the 'pairs' argument to specify which groups to compare using the Mann-Whitney U test.

```python
# Add statistical significance annotations
cns.figure(150, 150)
cns.boxplot(
    data=df,
    x="day",
    y="total_bill",
    pairs=[("Thur", "Fri"), ("Sat", "Sun")],  # Compare these pairs
)
# Prints: P-values were determined by two-sided Mann-Whitney U test.
```

--------------------------------

### Set Figure Dimensions in Pixels

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Specify exact pixel dimensions for the figure's canvas. This ensures precise control over the final output size.

```python
cns.figure(height=150, width=100)  # Final canvas size is 150px × 100px
```

--------------------------------

### Commit Message Format

Source: https://github.com/faridrashidi/cnsplots/blob/main/AGENTS.md

The standard format for commit messages in this repository includes an emoji, a subject line, and an optional body.

```git
📝 Clarify pull request label rules
```

```git
🐛 Prevent crash when input data is empty
```

```git
♻️ Simplify plot configuration parsing
```

```git
💥 Remove legacy export format

Clients must switch to the new export path before upgrading.
```

--------------------------------

### Color Constants

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Defines a set of predefined color constants for use in plots.

```APIDOC
## Constants

### Description
Predefined color constants.

### Colors
- `RED`
- `BLUE`
- `GREEN`
- `PURPLE`
- `ORANGE`
- `YELLOW`
- `BROWN`
- `PINK`
- `GRAY`
- `VIOLET`
- `CHOCOLATE`
```

--------------------------------

### Perform Statistical Tests in Boxplots

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Generate a boxplot and automatically perform the Mann-Whitney U test for all specified pairs.

```python
# Boxplot with Mann-Whitney U test
cns.boxplot(data=df, x="group", y="value", pairs="all")
```

--------------------------------

### Plotting Functions: Survival Analysis Plots

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to survival analysis plotting functions such as survivalplot, cumulativeincidenceplot, and forestplot.

```APIDOC
## Plotting Functions: Survival Analysis Plots

### Description
Provides access to survival analysis plotting functions.

### Functions
- `survivalplot`
- `cumulativeincidenceplot`
- `forestplot`
```

--------------------------------

### Assign New Global Default Setting

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/settings.md

Update specific attributes of the global settings object to change defaults for subsequent plotting calls.

```python
cns.settings.title_fontsize = 10
cns.settings.palette_qual = "Set2"
```

--------------------------------

### Plotting Functions: Specialized Plots

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/api.md

Provides access to specialized plotting functions such as phyloplot and placeholderplot.

```APIDOC
## Plotting Functions: Specialized Plots

### Description
Provides access to specialized plotting functions.

### Functions
- `phyloplot`
- `placeholderplot`
```

--------------------------------

### BibTeX Citation for cnsplots

Source: https://github.com/faridrashidi/cnsplots/blob/main/README.md

Use this BibTeX entry to cite the cnsplots library in your research publications.

```bibtex
@software{cnsplots,
  author = {Rashidi, Farid},
  title = {cnsplots: Publication-Ready Scientific Plots},
  year = {2026},
  url = {https://github.com/faridrashidi/cnsplots}
}
```

--------------------------------

### Add Labels and Title to a Boxplot

Source: https://github.com/faridrashidi/cnsplots/blob/main/docs/getting_started.md

Customizes a boxplot by adding custom labels to the x and y axes and setting a title for the plot. The plot is generated with specified pixel dimensions.

```python
cns.figure(100, 80)
ax = cns.boxplot(data=df, x="day", y="total_bill")
ax.set_xlabel("Day of Week")
ax.set_ylabel("Total Bill ($)")
ax.set_title("Tips by Day")
cns.savefig("labeled_plot.svg")
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.