Academic Plotting

# Data Visualization Patterns for ML Papers

Complete pattern library for generating polished, distinctive figures.

## Setup and Imports

```python
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import seaborn as sns
from matplotlib.ticker import MaxNLocator, FuncFormatter

# --- Publication defaults (polished, not generic) ---
plt.rcParams.update({
    "font.family": "serif",
    "font.serif": ["Times New Roman", "DejaVu Serif"],
    "font.size": 10,
    "axes.titlesize": 11,
    "axes.titleweight": "bold",
    "axes.labelsize": 10,
    "axes.labelweight": "medium",
    "xtick.labelsize": 8.5,
    "ytick.labelsize": 8.5,
    "legend.fontsize": 8.5,
    "legend.frameon": False,
    "figure.dpi": 300,
    "savefig.dpi": 300,
    "savefig.bbox": "tight",
    "savefig.pad_inches": 0.08,
    "axes.spines.top": False,
    "axes.spines.right": False,
    "axes.linewidth": 0.8,
    "xtick.major.width": 0.8,
    "ytick.major.width": 0.8,
    "axes.grid": True,
    "grid.alpha": 0.15,        # Very subtle — guides the eye without competing
    "grid.linewidth": 0.6,
    "grid.linestyle": "-",      # Solid but faint, not dashed (less visual noise)
    "lines.linewidth": 1.8,
    "lines.markersize": 5,
    "patch.edgecolor": "white",  # White borders between bars (cleaner look)
    "patch.linewidth": 0.5,
})
```

## Color Palettes

### "Ocean Dusk" (default — professional, distinctive)

```python
COLORS = {
    "teal":    "#264653",   # deep, authoritative
    "cyan":    "#2A9D8F",   # fresh, modern
    "gold":    "#E9C46A",   # warm accent
    "orange":  "#F4A261",   # energetic
    "coral":   "#E76F51",   # standout (use for "our method")
    "blue":    "#0072B2",   # Okabe-Ito accessible blue
    "sky":     "#56B4E9",   # Okabe-Ito accessible sky
    "gray":    "#8C8C8C",   # neutral baseline
}
COLOR_LIST = list(COLORS.values())

# Semantic colors for highlighting
OUR_COLOR = "#E76F51"       # coral — warm, draws attention
BASELINE_COLOR = "#B0BEC5"  # cool gray — recedes
BEST_BASELINE = "#264653"   # deep teal — strongest competitor
```

### "Okabe-Ito" (maximum colorblind safety)

```python
OKABE_ITO = ["#E69F00", "#56B4E9", "#009E73", "#F0E442",
             "#0072B2", "#D55E00", "#CC79A7", "#000000"]
```

### Sequential Palettes (for heatmaps)

```python
# Warm sequential (more interesting than plain Blues)
cmap_warm = sns.color_palette("YlOrRd", as_cmap=True)

# Cool sequential (clean, professional)
cmap_cool = sns.light_palette("#264653", as_cmap=True)

# Diverging (for correlation/difference, centered at 0)
cmap_div = sns.color_palette("RdBu_r", as_cmap=True)

# Perceptually uniform (for continuous scientific data)
cmap_viridis = plt.cm.viridis
```

### Making Charts Visually Distinctive

Common mistakes that make charts look "boring" and their fixes:

| Boring Default | Better Version |
|---------------|---------------|
| Black lines, no markers | Colored lines + distinct markers per method |
| No shading around lines | Confidence bands with `fill_between(alpha=0.12)` |
| Generic blue bars | "Ocean Dusk" palette + white edge between bars |
| All same color baselines | Gray baselines + coral highlight for "ours" |
| Dashed grid lines | Very faint solid grid (`alpha=0.15`) |
| Default tight spacing | `pad_inches=0.08`, generous axis margins |
| No value labels on bars | Small value text above each bar |
| Box legend with frame | Frameless legend, positioned inside plot area |

## Figure Sizes by Venue

```python
# NeurIPS / ICLR (single column, 5.5in text width)
FIG_NEURIPS_SINGLE = (5.5, 3.5)
FIG_NEURIPS_HALF = (2.65, 2.5)

# ICML (two column, 6.75in text width)
FIG_ICML_SINGLE = (3.25, 2.5)
FIG_ICML_FULL = (6.75, 2.5)

# ACL (two column, 6.8in text width)
FIG_ACL_SINGLE = (3.3, 2.5)
FIG_ACL_FULL = (6.8, 3.0)

# General safe default
FIG_DEFAULT = (5, 3.5)
```

## Chart Type 1: Training Curves (Line Plot)

The most common figure in ML papers. Shows loss/accuracy over training steps.

```python
def p