Peroxide_Gallery/Numeric/newmark_beta/pq_plot.py at master · Axect/Peroxide_Gallery · GitHub

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import matplotlib.pyplot as plt
import pandas as pd

# Use latex
plt.rc('text', usetex=True)
plt.rc('font', family='serif')

# Import netCDF file
df = pd.read_parquet("./newmark_beta.parquet")

# Prepare Data to Plot
t = df['t'][:]
x = df['x'][:]
v = df['v'][:]
a = df['a'][:]

# Prepare Plot
plt.figure(figsize=(10,6), dpi=300)
plt.title(r"Newmark Beta method for Damped SHO", fontsize=16)
plt.xlabel(r'$t$', fontsize=14)
plt.ylabel(r'$x$', fontsize=14)

# Plot with Legends
plt.plot(t, x, label=r'Position')
#plt.plot(t, v, label=r'Velocity')
#plt.plot(t, a, label=r'Acceleration')

# Other options
plt.legend(fontsize=12)
plt.grid()
plt.savefig("position.png", dpi=300)

# Prepare Plot
plt.figure(figsize=(10,6), dpi=300)
plt.title(r"Newmark Beta method for Damped SHO", fontsize=16)
plt.xlabel(r'$t$', fontsize=14)
plt.ylabel(r'$v$', fontsize=14)

# Plot with Legends
#plt.plot(t, x, label=r'Position')
plt.plot(t, v, label=r'Velocity')
#plt.plot(t, a, label=r'Acceleration')

# Other options
plt.legend(fontsize=12)
plt.grid()
plt.savefig("velocity.png", dpi=300)

# Prepare Plot
plt.figure(figsize=(10,6), dpi=300)
plt.title(r"Newmark Beta method for Damped SHO", fontsize=16)
plt.xlabel(r'$t$', fontsize=14)
plt.ylabel(r'$a$', fontsize=14)

# Plot with Legends
#plt.plot(t, x, label=r'Position')
#plt.plot(t, v, label=r'Velocity')
plt.plot(t, a, label=r'Acceleration')

# Other options
plt.legend(fontsize=12)
plt.grid()
plt.savefig("acceleration.png", dpi=300)

# Prepare Plot
plt.figure(figsize=(10,6), dpi=300)
plt.title(r"Newmark Beta method for Damped SHO", fontsize=16)
plt.xlabel(r'$t$', fontsize=14)
plt.ylabel(r'$E$', fontsize=14)

# Plot with Legends
#plt.plot(t, x, label=r'Position')
#plt.plot(t, v, label=r'Velocity')
#plt.plot(t, a, label=r'Acceleration')
plt.plot(t, 0.5*(v**2) + 0.5*200*x**2, label=r'Energy')

# Other options
plt.legend(fontsize=12)
plt.grid()
plt.savefig("energy.png", dpi=300)