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+# Publication-Ready Matplotlib Examples
+
+## Overview
+
+This reference provides practical code examples for creating publication-ready scientific figures using Matplotlib, Seaborn, and Plotly. All examples follow best practices from `publication_guidelines.md` and use colorblind-friendly palettes from `color_palettes.md`.
+
+## Setup and Configuration
+
+### Publication-Quality Matplotlib Configuration
+
+```python
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import numpy as np
+
+# Set publication quality parameters
+mpl.rcParams['figure.dpi'] = 300
+mpl.rcParams['savefig.dpi'] = 300
+mpl.rcParams['font.size'] = 8
+mpl.rcParams['font.family'] = 'sans-serif'
+mpl.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
+mpl.rcParams['axes.labelsize'] = 9
+mpl.rcParams['axes.titlesize'] = 9
+mpl.rcParams['xtick.labelsize'] = 7
+mpl.rcParams['ytick.labelsize'] = 7
+mpl.rcParams['legend.fontsize'] = 7
+mpl.rcParams['axes.linewidth'] = 0.5
+mpl.rcParams['xtick.major.width'] = 0.5
+mpl.rcParams['ytick.major.width'] = 0.5
+mpl.rcParams['lines.linewidth'] = 1.5
+
+# Use colorblind-friendly colors (Okabe-Ito palette)
+okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
+             '#0072B2', '#D55E00', '#CC79A7', '#000000']
+mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=okabe_ito)
+
+# Use perceptually uniform colormap
+mpl.rcParams['image.cmap'] = 'viridis'
+```
+
+### Helper Function for Saving
+
+```python
+def save_publication_figure(fig, filename, formats=['pdf', 'png'], dpi=300):
+    """
+    Save figure in multiple formats for publication.
+
+    Parameters:
+    -----------
+    fig : matplotlib.figure.Figure
+        Figure to save
+    filename : str
+        Base filename (without extension)
+    formats : list
+        List of file formats to save ['pdf', 'png', 'eps', 'svg']
+    dpi : int
+        Resolution for raster formats
+    """
+    for fmt in formats:
+        output_file = f"{filename}.{fmt}"
+        fig.savefig(output_file, dpi=dpi, bbox_inches='tight',
+                   facecolor='white', edgecolor='none',
+                   transparent=False, format=fmt)
+        print(f"Saved: {output_file}")
+```
+
+## Example 1: Line Plot with Error Bars
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Generate sample data
+x = np.linspace(0, 10, 50)
+y1 = 2 * x + 1 + np.random.normal(0, 1, 50)
+y2 = 1.5 * x + 2 + np.random.normal(0, 1.2, 50)
+
+# Calculate means and standard errors for binned data
+bins = np.linspace(0, 10, 11)
+y1_mean = [y1[(x >= bins[i]) & (x < bins[i+1])].mean() for i in range(len(bins)-1)]
+y1_sem = [y1[(x >= bins[i]) & (x < bins[i+1])].std() /
+          np.sqrt(len(y1[(x >= bins[i]) & (x < bins[i+1])]))
+          for i in range(len(bins)-1)]
+x_binned = (bins[:-1] + bins[1:]) / 2
+
+# Create figure with appropriate size (single column width = 3.5 inches)
+fig, ax = plt.subplots(figsize=(3.5, 2.5))
+
+# Plot with error bars
+ax.errorbar(x_binned, y1_mean, yerr=y1_sem,
+            marker='o', markersize=4, capsize=3, capthick=0.5,
+            label='Condition A', linewidth=1.5)
+
+# Add labels with units
+ax.set_xlabel('Time (hours)')
+ax.set_ylabel('Fluorescence intensity (a.u.)')
+
+# Add legend
+ax.legend(frameon=False, loc='upper left')
+
+# Remove top and right spines
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+
+# Tight layout
+fig.tight_layout()
+
+# Save
+save_publication_figure(fig, 'line_plot_with_errors')
+plt.show()
+```
+
+## Example 2: Multi-Panel Figure
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+from string import ascii_uppercase
+
+# Create figure with multiple panels (double column width = 7 inches)
+fig = plt.figure(figsize=(7, 4))
+
+# Define grid for panels
+gs = fig.add_gridspec(2, 3, hspace=0.4, wspace=0.4,
+                      left=0.08, right=0.98, top=0.95, bottom=0.08)
+
+# Panel A: Line plot
+ax_a = fig.add_subplot(gs[0, :2])
+x = np.linspace(0, 10, 100)
+for i, offset in enumerate([0, 0.5, 1.0]):
+    ax_a.plot(x, np.sin(x) + offset, label=f'Dataset {i+1}')
+ax_a.set_xlabel('Time (s)')
+ax_a.set_ylabel('Amplitude (V)')
+ax_a.legend(frameon=False, fontsize=6)
+ax_a.spines['top'].set_visible(False)
+ax_a.spines['right'].set_visible(False)
+
+# Panel B: Bar plot
+ax_b = fig.add_subplot(gs[0, 2])
+categories = ['Control', 'Treatment\nA', 'Treatment\nB']
+values = [100, 125, 140]
+errors = [5, 8, 6]
+ax_b.bar(categories, values, yerr=errors, capsize=3,
+         color=['#0072B2', '#E69F00', '#009E73'], alpha=0.8)
+ax_b.set_ylabel('Response (%)')
+ax_b.spines['top'].set_visible(False)
+ax_b.spines['right'].set_visible(False)
+ax_b.set_ylim(0, 160)
+
+# Panel C: Scatter plot
+ax_c = fig.add_subplot(gs[1, 0])
+x = np.random.randn(100)
+y = 2*x + np.random.randn(100)
+ax_c.scatter(x, y, s=10, alpha=0.6, color='#0072B2')
+ax_c.set_xlabel('Variable X')
+ax_c.set_ylabel('Variable Y')
+ax_c.spines['top'].set_visible(False)
+ax_c.spines['right'].set_visible(False)
+
+# Panel D: Heatmap
+ax_d = fig.add_subplot(gs[1, 1:])
+data = np.random.randn(10, 20)
+im = ax_d.imshow(data, cmap='viridis', aspect='auto')
+ax_d.set_xlabel('Sample number')
+ax_d.set_ylabel('Feature')
+cbar = plt.colorbar(im, ax=ax_d, fraction=0.046, pad=0.04)
+cbar.set_label('Intensity (a.u.)', rotation=270, labelpad=12)
+
+# Add panel labels
+panels = [ax_a, ax_b, ax_c, ax_d]
+for i, ax in enumerate(panels):
+    ax.text(-0.15, 1.05, ascii_uppercase[i], transform=ax.transAxes,
+            fontsize=10, fontweight='bold', va='top')
+
+save_publication_figure(fig, 'multi_panel_figure')
+plt.show()
+```
+
+## Example 3: Box Plot with Individual Points
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Generate sample data
+np.random.seed(42)
+data = [np.random.normal(100, 15, 30),
+        np.random.normal(120, 20, 30),
+        np.random.normal(140, 18, 30),
+        np.random.normal(110, 22, 30)]
+
+fig, ax = plt.subplots(figsize=(3.5, 3))
+
+# Create box plot
+bp = ax.boxplot(data, widths=0.5, patch_artist=True,
+                showfliers=False,  # We'll add points manually
+                boxprops=dict(facecolor='lightgray', edgecolor='black', linewidth=0.8),
+                medianprops=dict(color='black', linewidth=1.5),
+                whiskerprops=dict(linewidth=0.8),
+                capprops=dict(linewidth=0.8))
+
+# Overlay individual points
+colors = ['#0072B2', '#E69F00', '#009E73', '#D55E00']
+for i, (d, color) in enumerate(zip(data, colors)):
+    # Add jitter to x positions
+    x = np.random.normal(i+1, 0.04, size=len(d))
+    ax.scatter(x, d, alpha=0.4, s=8, color=color)
+
+# Customize
+ax.set_xticklabels(['Control', 'Treatment A', 'Treatment B', 'Treatment C'])
+ax.set_ylabel('Cell count')
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.set_ylim(50, 200)
+
+fig.tight_layout()
+save_publication_figure(fig, 'boxplot_with_points')
+plt.show()
+```
+
+## Example 4: Heatmap with Colorbar
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Generate correlation matrix
+np.random.seed(42)
+n = 10
+A = np.random.randn(n, n)
+corr_matrix = np.corrcoef(A)
+
+# Create figure
+fig, ax = plt.subplots(figsize=(4, 3.5))
+
+# Plot heatmap
+im = ax.imshow(corr_matrix, cmap='RdBu_r', vmin=-1, vmax=1, aspect='auto')
+
+# Add colorbar
+cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+cbar.set_label('Correlation coefficient', rotation=270, labelpad=15)
+
+# Set ticks and labels
+gene_names = [f'Gene{i+1}' for i in range(n)]
+ax.set_xticks(np.arange(n))
+ax.set_yticks(np.arange(n))
+ax.set_xticklabels(gene_names, rotation=45, ha='right')
+ax.set_yticklabels(gene_names)
+
+# Add grid
+ax.set_xticks(np.arange(n)-.5, minor=True)
+ax.set_yticks(np.arange(n)-.5, minor=True)
+ax.grid(which='minor', color='white', linestyle='-', linewidth=0.5)
+
+fig.tight_layout()
+save_publication_figure(fig, 'correlation_heatmap')
+plt.show()
+```
+
+## Example 5: Seaborn Violin Plot
+
+```python
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+import numpy as np
+
+# Generate sample data
+np.random.seed(42)
+data = pd.DataFrame({
+    'condition': np.repeat(['Control', 'Drug A', 'Drug B'], 50),
+    'value': np.concatenate([
+        np.random.normal(100, 15, 50),
+        np.random.normal(120, 20, 50),
+        np.random.normal(140, 18, 50)
+    ])
+})
+
+# Set style
+sns.set_style('ticks')
+sns.set_palette(['#0072B2', '#E69F00', '#009E73'])
+
+fig, ax = plt.subplots(figsize=(3.5, 3))
+
+# Create violin plot
+sns.violinplot(data=data, x='condition', y='value', ax=ax,
+               inner='box', linewidth=0.8)
+
+# Add strip plot
+sns.stripplot(data=data, x='condition', y='value', ax=ax,
+              size=2, alpha=0.3, color='black')
+
+# Customize
+ax.set_xlabel('')
+ax.set_ylabel('Expression level (AU)')
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+
+fig.tight_layout()
+save_publication_figure(fig, 'violin_plot')
+plt.show()
+```
+
+## Example 6: Scientific Scatter with Regression
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy import stats
+
+# Generate data with correlation
+np.random.seed(42)
+x = np.random.randn(100)
+y = 2.5 * x + np.random.randn(100) * 0.8
+
+# Calculate regression
+slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
+
+# Create figure
+fig, ax = plt.subplots(figsize=(3.5, 3.5))
+
+# Scatter plot
+ax.scatter(x, y, s=15, alpha=0.6, color='#0072B2', edgecolors='none')
+
+# Regression line
+x_line = np.array([x.min(), x.max()])
+y_line = slope * x_line + intercept
+ax.plot(x_line, y_line, 'r-', linewidth=1.5, label=f'y = {slope:.2f}x + {intercept:.2f}')
+
+# Add statistics text
+stats_text = f'$R^2$ = {r_value**2:.3f}\n$p$ < 0.001' if p_value < 0.001 else f'$R^2$ = {r_value**2:.3f}\n$p$ = {p_value:.3f}'
+ax.text(0.05, 0.95, stats_text, transform=ax.transAxes,
+        verticalalignment='top', fontsize=7,
+        bbox=dict(boxstyle='round', facecolor='white', alpha=0.8, edgecolor='gray', linewidth=0.5))
+
+# Customize
+ax.set_xlabel('Predictor variable')
+ax.set_ylabel('Response variable')
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+
+fig.tight_layout()
+save_publication_figure(fig, 'scatter_regression')
+plt.show()
+```
+
+## Example 7: Time Series with Shaded Error
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Generate time series data
+np.random.seed(42)
+time = np.linspace(0, 24, 100)
+n_replicates = 5
+
+# Simulate multiple replicates
+data = np.array([10 * np.exp(-time/10) + np.random.normal(0, 0.5, 100)
+                 for _ in range(n_replicates)])
+
+# Calculate mean and SEM
+mean = data.mean(axis=0)
+sem = data.std(axis=0) / np.sqrt(n_replicates)
+
+# Create figure
+fig, ax = plt.subplots(figsize=(4, 2.5))
+
+# Plot mean line
+ax.plot(time, mean, linewidth=1.5, color='#0072B2', label='Mean ± SEM')
+
+# Add shaded error region
+ax.fill_between(time, mean - sem, mean + sem,
+                alpha=0.3, color='#0072B2', linewidth=0)
+
+# Customize
+ax.set_xlabel('Time (hours)')
+ax.set_ylabel('Concentration (μM)')
+ax.legend(frameon=False, loc='upper right')
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.set_xlim(0, 24)
+ax.set_ylim(0, 12)
+
+fig.tight_layout()
+save_publication_figure(fig, 'timeseries_shaded')
+plt.show()
+```
+
+## Example 8: Plotly Interactive Figure
+
+```python
+import plotly.graph_objects as go
+import numpy as np
+
+# Generate data
+np.random.seed(42)
+x = np.random.randn(100)
+y = 2*x + np.random.randn(100)
+colors = np.random.choice(['Group A', 'Group B'], 100)
+
+# Okabe-Ito colors for Plotly
+okabe_ito_plotly = ['#E69F00', '#56B4E9']
+
+# Create figure
+fig = go.Figure()
+
+for group, color in zip(['Group A', 'Group B'], okabe_ito_plotly):
+    mask = colors == group
+    fig.add_trace(go.Scatter(
+        x=x[mask], y=y[mask],
+        mode='markers',
+        name=group,
+        marker=dict(size=6, color=color, opacity=0.6)
+    ))
+
+# Update layout for publication quality
+fig.update_layout(
+    width=500,
+    height=400,
+    font=dict(family='Arial, sans-serif', size=10),
+    plot_bgcolor='white',
+    xaxis=dict(
+        title='Variable X',
+        showgrid=False,
+        showline=True,
+        linewidth=1,
+        linecolor='black',
+        mirror=False
+    ),
+    yaxis=dict(
+        title='Variable Y',
+        showgrid=False,
+        showline=True,
+        linewidth=1,
+        linecolor='black',
+        mirror=False
+    ),
+    legend=dict(
+        x=0.02,
+        y=0.98,
+        bgcolor='rgba(255,255,255,0.8)',
+        bordercolor='gray',
+        borderwidth=0.5
+    )
+)
+
+# Save as static image (requires kaleido)
+fig.write_image('plotly_scatter.png', width=500, height=400, scale=3)  # scale=3 gives ~300 DPI
+fig.write_html('plotly_scatter.html')  # Interactive version
+
+fig.show()
+```
+
+## Example 9: Grouped Bar Plot with Significance
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Data
+categories = ['WT', 'Mutant A', 'Mutant B']
+control_means = [100, 85, 70]
+control_sem = [5, 6, 5]
+treatment_means = [100, 120, 140]
+treatment_sem = [6, 8, 9]
+
+x = np.arange(len(categories))
+width = 0.35
+
+fig, ax = plt.subplots(figsize=(3.5, 3))
+
+# Create bars
+bars1 = ax.bar(x - width/2, control_means, width, yerr=control_sem,
+               capsize=3, label='Control', color='#0072B2', alpha=0.8)
+bars2 = ax.bar(x + width/2, treatment_means, width, yerr=treatment_sem,
+               capsize=3, label='Treatment', color='#E69F00', alpha=0.8)
+
+# Add significance markers
+def add_significance_bar(ax, x1, x2, y, h, text):
+    """Add significance bar between two bars"""
+    ax.plot([x1, x1, x2, x2], [y, y+h, y+h, y], linewidth=0.8, c='black')
+    ax.text((x1+x2)/2, y+h, text, ha='center', va='bottom', fontsize=7)
+
+# Mark significant differences
+add_significance_bar(ax, x[1]-width/2, x[1]+width/2, 135, 3, '***')
+add_significance_bar(ax, x[2]-width/2, x[2]+width/2, 155, 3, '***')
+
+# Customize
+ax.set_ylabel('Activity (% of WT control)')
+ax.set_xticks(x)
+ax.set_xticklabels(categories)
+ax.legend(frameon=False, loc='upper left')
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.set_ylim(0, 180)
+
+# Add note about significance
+ax.text(0.98, 0.02, '*** p < 0.001', transform=ax.transAxes,
+        ha='right', va='bottom', fontsize=6)
+
+fig.tight_layout()
+save_publication_figure(fig, 'grouped_bar_significance')
+plt.show()
+```
+
+## Example 10: Publication-Ready Figure for Nature
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+from string import ascii_lowercase
+
+# Nature specifications: 89mm single column
+inch_per_mm = 0.0393701
+width_mm = 89
+height_mm = 110
+figsize = (width_mm * inch_per_mm, height_mm * inch_per_mm)
+
+fig = plt.figure(figsize=figsize)
+gs = fig.add_gridspec(3, 2, hspace=0.5, wspace=0.4,
+                      left=0.12, right=0.95, top=0.96, bottom=0.08)
+
+# Panel a: Time course
+ax_a = fig.add_subplot(gs[0, :])
+time = np.linspace(0, 48, 100)
+for i, label in enumerate(['Control', 'Treatment']):
+    y = (1 + i*0.5) * np.exp(-time/20) * (1 + 0.3*np.sin(time/5))
+    ax_a.plot(time, y, linewidth=1.2, label=label)
+ax_a.set_xlabel('Time (h)', fontsize=7)
+ax_a.set_ylabel('Growth (OD$_{600}$)', fontsize=7)
+ax_a.legend(frameon=False, fontsize=6)
+ax_a.tick_params(labelsize=6)
+ax_a.spines['top'].set_visible(False)
+ax_a.spines['right'].set_visible(False)
+
+# Panel b: Bar plot
+ax_b = fig.add_subplot(gs[1, 0])
+categories = ['A', 'B', 'C']
+values = [1.0, 1.5, 2.2]
+errors = [0.1, 0.15, 0.2]
+ax_b.bar(categories, values, yerr=errors, capsize=2, width=0.6,
+         color='#0072B2', alpha=0.8)
+ax_b.set_ylabel('Fold change', fontsize=7)
+ax_b.tick_params(labelsize=6)
+ax_b.spines['top'].set_visible(False)
+ax_b.spines['right'].set_visible(False)
+
+# Panel c: Heatmap
+ax_c = fig.add_subplot(gs[1, 1])
+data = np.random.randn(8, 6)
+im = ax_c.imshow(data, cmap='viridis', aspect='auto')
+ax_c.set_xlabel('Sample', fontsize=7)
+ax_c.set_ylabel('Gene', fontsize=7)
+ax_c.tick_params(labelsize=6)
+
+# Panel d: Scatter
+ax_d = fig.add_subplot(gs[2, :])
+x = np.random.randn(50)
+y = 2*x + np.random.randn(50)*0.5
+ax_d.scatter(x, y, s=8, alpha=0.6, color='#E69F00')
+ax_d.set_xlabel('Expression gene X', fontsize=7)
+ax_d.set_ylabel('Expression gene Y', fontsize=7)
+ax_d.tick_params(labelsize=6)
+ax_d.spines['top'].set_visible(False)
+ax_d.spines['right'].set_visible(False)
+
+# Add lowercase panel labels (Nature style)
+for i, ax in enumerate([ax_a, ax_b, ax_c, ax_d]):
+    ax.text(-0.2, 1.1, f'{ascii_lowercase[i]}', transform=ax.transAxes,
+            fontsize=9, fontweight='bold', va='top')
+
+# Save in Nature-preferred format
+fig.savefig('nature_figure.pdf', dpi=1000, bbox_inches='tight',
+           facecolor='white', edgecolor='none')
+fig.savefig('nature_figure.png', dpi=300, bbox_inches='tight',
+           facecolor='white', edgecolor='none')
+
+plt.show()
+```
+
+## Tips for Each Library
+
+### Matplotlib
+- Use `fig.tight_layout()` or `constrained_layout=True` to prevent overlapping
+- Set DPI to 300-600 for publication
+- Use vector formats (PDF, EPS) for line plots
+- Embed fonts in PDF/EPS files
+
+### Seaborn
+- Built on matplotlib, so all matplotlib customizations work
+- Use `sns.set_style('ticks')` or `'whitegrid'` for clean looks
+- `sns.despine()` removes top and right spines
+- Set custom palette with `sns.set_palette()`
+
+### Plotly
+- Great for interactive exploratory analysis
+- Export static images with `fig.write_image()` (requires kaleido package)
+- Use `scale` parameter to control DPI (scale=3 ≈ 300 DPI)
+- Update layout extensively for publication quality
+
+## Common Workflow
+
+1. **Explore with default settings**
+2. **Apply publication configuration** (see Setup section)
+3. **Create plot with appropriate size** (check journal requirements)
+4. **Customize colors** (use colorblind-friendly palettes)
+5. **Adjust fonts and line widths** (readable at final size)
+6. **Remove chart junk** (top/right spines, excessive grid)
+7. **Add clear labels with units**
+8. **Test in grayscale**
+9. **Save in multiple formats** (PDF for vector, PNG for raster)
+10. **Verify in final context** (import into manuscript to check size)
+
+## Resources
+
+- Matplotlib documentation: https://matplotlib.org/
+- Seaborn gallery: https://seaborn.pydata.org/examples/index.html
+- Plotly documentation: https://plotly.com/python/
+- Nature Methods Points of View: Data visualization column archive