hmbp 0.3.0
Simple matplotlib and PGFPlots plotting with consistent, publication-ready styling
License: MIT
Requires Python: >=3.10
Keywords: plotting, matplotlib, pgfplots, latex, visualization, data-science

# hmbp

A simple matplotlib wrapper with consistent, publication-ready styling.

## Installation

```bash
pip install hmbp
```

## Style

- Font: Helvetica
- Colormap: RdPu (primary), PiYG (secondary/diverging)
- 400 DPI output
- Consistent sizing: title 15pt, labels 14pt, ticks 13pt, legend 12pt

## Usage

```python
import hmbp

fig, ax = hmbp.new_figure()
hmbp.line_plot(y_values, x_values, label="Model A")
hmbp.set_labels("Title", "X Label", "Y Label")
hmbp.save("output.png")
```

## Available Functions

| Function | Description |
|----------|-------------|
| `line_plot` | Line with optional fill and smoothing |
| `multi_line_plot` | Multiple lines on same axes with smoothing |
| `scatter_plot` | Scatter with optional color mapping |
| `histogram` | Color-mapped histogram |
| `histogram_overlay` | Overlaid histograms for comparison |
| `bar_plot` | Vertical/horizontal bars |
| `box_plot` | Box plot distributions |
| `violin_plot` | Violin plot distributions |
| `heatmap` | 2D heatmap with colorbar |
| `line_plot_with_error` | Line with shaded error region |
| `confusion_matrix` | Annotated confusion matrix |
| `roc_curve` | ROC curve with AUC |
| `precision_recall_curve` | PR curve with AP |
| `residual_plot` | Regression residuals |
| `learning_curve` | Train/val learning curves |
| `metric_comparison` | Horizontal bar comparison |
| `volcano_plot` | Volcano plot for differential analysis |

## Smoothing

Line plots support EMA smoothing for noisy data (e.g., training curves):

```python
# smooth=0.9 means 90% weight on previous value (heavy smoothing)
hmbp.quick.line(noisy_loss, smooth=0.9, path="smoothed.png")
hmbp.quick.lines([y1, y2], labels=["A", "B"], smooth=0.8, path="comparison.png")
```

## Helpers

- `new_figure(figsize)` - Create figure and axes
- `set_labels(title, xlabel, ylabel)` - Apply labels
- `save(path, fig, close)` - Save with auto-legend

## Quick API

Single-call functions that create, label, and save in one step:

```python
import hmbp

hmbp.quick.histogram(data, title="Scores", xlabel="Value", path="hist.png")
hmbp.quick.bar(values, labels, title="Comparison", ylabel="F1", path="bars.png")
hmbp.quick.confusion_matrix(cm, class_names=["A", "B"], path="cm.png")
```

Available: `hmbp.quick.line`, `hmbp.quick.lines`, `hmbp.quick.scatter`, `hmbp.quick.histogram`, `hmbp.quick.histogram_overlay`, `hmbp.quick.bar`, `hmbp.quick.heatmap`, `hmbp.quick.confusion_matrix`, `hmbp.quick.roc`, `hmbp.quick.volcano`, `hmbp.quick.violin`

## PGFPlots Output

Generate LaTeX figures using PGFPlots for direct inclusion in papers:

```python
import hmbp.pgfplots as pgf

# Step-by-step
pgf.new_figure()
pgf.line_plot(y, x=x, label="Data", marker="*")
pgf.set_labels(title="Results", xlabel="Time", ylabel="Value")
pgf.save("figure.tex")  # Creates .tex and .pdf

# Quick API
pgf.quick_bar(values, labels, title="Comparison", path="bars.tex")
pgf.quick_histogram(data, title="Distribution", path="hist.tex")
```

Features:
- Outputs both `.tex` and compiled `.pdf` files
- Helvetica font, clean axis styling, y-major grids
- Supports `line_plot`, `multi_line_plot`, `bar_plot`, `histogram`
- Log scale via `new_figure(ymode='log')`

Requires a LaTeX distribution with `lualatex` or `pdflatex`.