Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It provides MATLAB-like plotting while giving you complete control over every element of your figures.

Why Matplotlib?

• Publication Ready: High-quality figures suitable for papers
• Complete Control: Fine-tune every aspect of your plots
• Versatile: Line plots, scatter plots, bar charts, heatmaps, 3D plots, and more
• Integration: Works seamlessly with NumPy and Pandas
• Standard: The foundation for many other plotting libraries

Key Features

Basic Plotting

import matplotlib.pyplot as plt
import numpy as np

# Simple line plot
x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(10, 4))
plt.plot(x, y)
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.title('Sine Wave')
plt.show()

Multiple Subplots

fig, axes = plt.subplots(2, 2, figsize=(10, 8))

axes[0, 0].plot(x, np.sin(x))
axes[0, 1].plot(x, np.cos(x))
axes[1, 0].hist(np.random.randn(1000))
axes[1, 1].scatter(x, np.random.randn(100))

Common Plot Types

• Line plots: Time series, signals
• Scatter plots: Correlations, relationships
• Histograms: Distributions
• Bar charts: Comparisons across conditions
• Heatmaps: Matrix data, correlation matrices
• Raster plots: Spike trains
• Error bars: Show variability

Common Use Cases in Neuroscience

• Electrophysiology: Plot LFPs, spike rasters, PSTHs
• Calcium Imaging: Visualize fluorescence traces
• Behavioral Analysis: Show reaction times, accuracy across conditions
• Spectrograms: Time-frequency representations
• Connectivity: Visualize correlation matrices

Getting Started

Install Matplotlib:

conda install matplotlib
# or
pip install matplotlib

Basic example:

import matplotlib.pyplot as plt
import numpy as np

# Generate sample neural data
time = np.linspace(0, 2, 1000)
signal = np.sin(2 * np.pi * 10 * time) + 0.5 * np.random.randn(1000)

# Create figure
plt.figure(figsize=(12, 4))
plt.plot(time, signal, linewidth=0.5)
plt.xlabel('Time (s)')
plt.ylabel('Voltage (mV)')
plt.title('Simulated LFP Signal')
plt.grid(alpha=0.3)
plt.tight_layout()
plt.savefig('lfp_signal.png', dpi=300)
plt.show()

Tips

• Use plt.style.use('seaborn') for nicer default styles
• Always label axes and add titles for clarity
• Use plt.tight_layout() to prevent overlapping labels
• Save high-resolution figures with plt.savefig('figure.png', dpi=300)
• Create reusable figure templates for consistency
• Learn to use fig and ax objects for more control
• Use colormaps thoughtfully (consider colorblind-friendly options)