Matplotlib

Matplotlib is the standard Python library for creating scientific visualizations.

Basic Plotting

import matplotlib.pyplot as plt
import numpy as np

# Create figure and axis
fig, ax = plt.subplots(figsize=(10, 6))

# Line plot
x = np.linspace(0, 10, 100)
y = np.sin(x)
ax.plot(x, y, label='sin(x)')

# Scatter plot with markers
ax.scatter(x[::10], y[::10], marker='o', s=50)

# Labels and title
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_title('Sample Plot')
ax.legend()

# Save figure
plt.savefig('/app/output/plot.png', dpi=150, bbox_inches='tight')
plt.close()

Multi-Series Arrhenius Plot

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 8))

colors = plt.cm.tab10.colors
markers = ['o', 's', '^', 'D', 'v', '<', '>', 'p']

for i, (name, group) in enumerate(df.groupby('electrolyte_id')):
    # Data points
    inv_t = 1000 / (group['temperature_c'] + 273.15)
    ln_sigma = np.log(group['conductivity_scm'])

    # Scatter plot
    ax.scatter(inv_t, ln_sigma,
               color=colors[i % len(colors)],
               marker=markers[i % len(markers)],
               s=80, label=name)

    # Fit line
    slope, intercept = np.polyfit(inv_t, ln_sigma, 1)
    x_fit = np.linspace(inv_t.min(), inv_t.max(), 100)
    y_fit = slope * x_fit + intercept
    ax.plot(x_fit, y_fit, color=colors[i % len(colors)],
            linestyle='--', alpha=0.7)

ax.set_xlabel('1000/T (K$^{-1}$)', fontsize=12)
ax.set_ylabel('ln($\\sigma$) (S/cm)', fontsize=12)
ax.set_title('Arrhenius Plot: Electrolyte Conductivity', fontsize=14)
ax.legend(loc='best')
ax.grid(True, alpha=0.3)

plt.savefig('/app/output/arrhenius_plot.png', dpi=150, bbox_inches='tight')
plt.close()

Multiple Subplots

fig, axes = plt.subplots(2, 2, figsize=(12, 10))

# Access individual subplots
axes[0, 0].plot(x, y1)
axes[0, 0].set_title('Plot 1')

axes[0, 1].scatter(x, y2)
axes[0, 1].set_title('Plot 2')

axes[1, 0].bar(categories, values)
axes[1, 1].hist(data, bins=20)

plt.tight_layout()
plt.savefig('/app/output/multi_plot.png', dpi=150)