matplotlib-visualization

# Matplotlib Visualization for Engineering Create professional engineering plots including pump performance curves, CFD results visualization, velocity contours, and publication-quality figures. ## Installation and Setup ```bash pip install matplotlib numpy scipy ``` Basic import pattern: ```python import matplotlib.pyplot as plt import numpy as np from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D # Set style for professional figures plt.style.use('seaborn-v0_8-darkgrid') # or 'classic', 'bmh', 'ggplot' ``` ## Common Plot Types for Engineering ### Pump Performance Curves (H-Q, P-Q, η-Q) Standard pump characteristic curves showing head, power, and efficiency vs flow rate: ```python import matplotlib.pyplot as plt import numpy as np # Sample pump data Q = np.linspace(0, 120, 50) # Flow rate [m³/h] H = 80 - 0.005 * Q**2 # Head [m] P = 5 + 0.08 * Q + 0.0005 * Q**2 # Power [kW] eta = (Q * H) / (367 * P) * 100 # Efficiency [%] # Create figure with three subplots fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 10), sharex=True) # Head-Flow curve ax1.plot(Q, H, 'b-', linewidth=2, label='Head curve') ax1.set_ylabel('Head [m]', fontsize=12) ax1.set_title('Pump Performance Curves', fontsize=14, fontweight='bold') ax1.grid(True, alpha=0.3) ax1.legend() # Power-Flow curve ax2.plot(Q, P, 'r-', linewidth=2, label='Power curve') ax2.set_ylabel('Power [kW]', fontsize=12) ax2.grid(True, alpha=0.3) ax2.legend() # Efficiency-Flow curve ax3.plot(Q, eta, 'g-', linewidth=2, label='Efficiency curve') ax3.set_xlabel('Flow Rate [m³/h]', fontsize=12) ax3.set_ylabel('Efficiency [%]', fontsize=12) ax3.grid(True, alpha=0.3) ax3.legend() plt.tight_layout() plt.savefig('pump_curves.png', dpi=300, bbox_inches='tight') plt.show() ``` ### System Curves Plot pump curve with system curve overlay to find operating point: ```python # Pump curve Q = np.linspace(0, 120, 50) H_pump = 80 - 0.005 * Q**2 # System curve: H = H_static + k*Q² H_static = 20 # Static head [m] k = 0.003 # System resistance coefficient H_system = H_static + k * Q**2 fig, ax = plt.subplots(figsize=(10, 6)) ax.plot(Q, H_pump, 'b-', linewidth=2, label='Pump curve') ax.plot(Q, H_system, 'r--', linewidth=2, label='System curve') # Find and mark operating point idx = np.argmin(np.abs(H_pump - H_system)) ax.plot(Q[idx], H_pump[idx], 'ko', markersize=10, label=f'Operating point\n({Q[idx]:.1f} m³/h, {H_pump[idx]:.1f} m)') ax.set_xlabel('Flow Rate [m³/h]', fontsize=12) ax.set_ylabel('Head [m]', fontsize=12) ax.set_title('Pump and System Curves', fontsize=14, fontweight='bold') ax.grid(True, alpha=0.3) ax.legend(fontsize=10) plt.tight_layout() plt.savefig('system_curve.png', dpi=300, bbox_inches='tight') plt.show() ``` ### Velocity/Pressure Contours Visualize CFD results with contour plots: ```python import matplotlib.pyplot as plt import numpy as np # Create mesh grid x = np.linspace(0, 10, 100) y = np.linspace(0, 5, 50) X, Y = np.meshgrid(x, y) # Example velocity field (flow around obstacle) U = 2.0 - 1.5 * np.exp(-((X-3)**2 + (Y-2.5)**2) / 2) V = 0.5 * np.sin(np.pi * X / 5) * np.exp(-((X-3)**2 + (Y-2.5)**2) / 4) velocity = np.sqrt(U**2 + V**2) fig, ax = plt.subplots(figsize=(12, 6)) # Filled contours levels = np.linspace(velocity.min(), velocity.max(), 20) contourf = ax.contourf(X, Y, velocity, levels=levels, cmap='jet') cbar = plt.colorbar(contourf, ax=ax, label='Velocity [m/s]') # Optional: Add contour lines contour = ax.contour(X, Y, velocity, levels=10, colors='k', linewidths=0.5, alpha=0.4) ax.clabel(contour, inline=True, fontsize=8, fmt='%.2f') ax.set_xlabel('x [m]', fontsize=12) ax.set_ylabel('y [m]', fontsize=12) ax.set_title('Velocity Contour Plot', fontsize=14, fontweight='bold') ax.set_aspect('equal') plt.tight_layout() plt.savefig('velocity_contour.png', dpi=300, bbox_inches='tight') plt.show() ``` ### Vector Fields Display flow direction and magnitude: ```python import matplotlib.pyplot as plt import numpy as np # Create coarser grid for vectors x = np.linspace(0, 10, 20) y = np.linspace(0, 5, 10) X, Y = np.meshgrid(x, y) # Velocity components U = 2.0 - 1.5 * np.exp(-((X-3)**2 + (Y-2.5)**2) / 2) V = 0.5 * np.sin(np.pi * X / 5) * np.exp(-((X-3)**2 + (Y-2.5)**2) / 4) velocity = np.sqrt(U**2 + V**2) fig, ax = plt.subplots(figsize=(12, 6)) # Quiver plot with color by magnitude quiver = ax.quiver(X, Y, U, V, velocity, cmap='viridis', scale=30, width=0.003) cbar = plt.colorbar(quiver, ax=ax, label='Velocity magnitude [m/s]') ax.set_xlabel('x [m]', fontsize=12) ax.set_ylabel('y [m]', fontsize=12) ax.set_title('Velocity Vector Field', fontsize=14, fontweight='bold') ax.set_aspect('equal') plt.tight_layout() plt.savefig('velocity_vectors.png', dpi=300, bbox_inches='tight') plt.show() ``` ### 3D Surface Plots Visualize pressure or temperature distributions: ```python import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.mplot3d import Axes3D # Create mesh x = np.linspace(-5, 5, 50) y = np.linspace(-5, 5, 50) X, Y = np.meshgrid(x, y) # Example: Pressure distribution Z = 100 + 10 * np.exp(-0.1 * (X**2 + Y**2)) * np.cos(0.5 * np.sqrt(X**2 + Y**2)) fig = plt.figure(figsize=(12, 8)) ax = fig.add_subplot(111, projection='3d') # Surface plot surf = ax.plot_surface(X, Y, Z, cmap='coolwarm', linewidth=0, antialiased=True, alpha=0.9) # Customize ax.set_xlabel('x [m]', fontsize=11) ax.set_ylabel('y [m]', fontsize=11) ax.set_zlabel('Pressure [kPa]', fontsize=11) ax.set_title('Pressure Distribution', fontsize=14, fontweight='bold', pad=20) # Add colorbar cbar = fig.colorbar(surf, ax=ax, shrink=0.5, aspect=5, label='Pressure [kPa]') # Set viewing angle ax.view_init(elev=25, azim=45) plt.tight_layout() plt.savefig('pressure_3d.png', dpi=300, bbox_inches='tight') plt.show() ``` ### Subplots and Multi-Panel Figures Complex layouts for comprehensive analysis: ```python import matplotlib.pyplot as plt import numpy as np fig = plt.figure(figsize=(14, 10)) # Create 2x2 grid gs = fig.add_gridspec(2, 2, hspace=0.3, wspace=0.3) # Subplot 1: Line plot ax1 = fig.add_subplot(gs[0, 0]) x = np.linspace(0, 100, 100) y1 = 80 - 0.005 * x**2 ax1.plot(x, y1, 'b-', linewidth=2) ax1.set_xlabel('Flow Rate [m³/h]') ax1.set_ylabel('Head [m]') ax1.set_title('(a) Pump Curve') ax1.grid(True, alpha=0.3) # Subplot 2: Contour ax2 = fig.add_subplot(gs[0, 1]) x = np.linspace(0, 10, 50) y = np.linspace(0, 5, 25) X, Y = np.meshgrid(x, y) Z = np.sin(X) * np.cos(Y) contourf = ax2.contourf(X, Y, Z, levels=15, cmap='RdBu_r') plt.colorbar(contourf, ax=ax2, label='Value') ax2.set_xlabel('x [m]') ax2.set_ylabel('y [m]') ax2.set_title('(b) Contour Plot') # Subplot 3: Bar chart ax3 = fig.add_subplot(gs[1, 0]) categories = ['Case 1', 'Case 2', 'Case 3', 'Case 4'] values = [75, 82, 68, 91] bars = ax3.bar(categories, values, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']) ax3.set_ylabel('Efficiency [%]') ax3.set_title('(c) Performance Comparison') ax3.grid(True, axis='y', alpha=0.3) # Subplot 4: Scatter with trend ax4 = fig.add_subplot(gs[1, 1]) x_data = np.random.rand(50) * 100 y_data = 30 + 0.5 * x_data + np.random.randn(50) * 5 ax4.scatter(x_data, y_data, alpha=0.6, s=50) # Add trend line z = np.polyfit(x_data, y_data, 1) p = np.poly1d(z) ax4.plot(x_data, p(x_data), 'r--', linewidth=2, label=f'y={z[0]:.2f}x+{z[1]:.2f}') ax4.set_xlabel('Parameter X') ax4.set_ylabel('Parameter Y') ax4.set_title('(d) Correlation Analysis') ax4.legend() ax4.grid(True, alpha=0.3) plt.suptitle('Multi-Panel Engineering Analysis', fontsize=16, fontweight='bold', y=0.995) plt.savefig('multipanel.png', dpi=300, bbox_inches='tight') plt.show() ``` ## Styling for Professional Figures ### Using Style Sheets ```python # Available styles import matplotlib.pyplot as plt print(plt.style.available) # Apply a style plt.style.use('seaborn-v0_8-whitegrid') # Or create custom style plt.rcParams['font.family'] = 'serif' plt.rcParams['font.size'] = 11 plt.rcParams['axes.linewidth'] = 1.5 plt.rcParams['lines.linewidth'] = 2 plt.rcParams['grid.alpha'] = 0.3 ``` ### Custom Colors and Styling ```python # Professional color schemes colors_engineering = { 'blue': '#0072BD', 'red': '#D95319', 'yellow': '#EDB120', 'purple': '#7E2F8E', 'green': '#77AC30', 'cyan': '#4DBEEE', 'maroon': '#A2142F' } # Apply to plots fig, ax = plt.subplots(figsize=(10, 6)) ax.plot(x, y1, color=colors_engineering['blue'], linewidth=2.5, label='Series 1') ax.plot(x, y2, color=colors_engineering['red'], linewidth=2.5, label='Series 2') ``` ### LaTeX in Labels Enable LaTeX rendering for mathematical expressions: