52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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sns.set_theme()
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plt.rcParams['text.usetex'] = True
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def plot_transformations(save: bool=False) -> None:
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# Load data
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tridiag = pd.read_csv("../latex/output/transform_tridiag.csv", header=0)
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dense = pd.read_csv("../latex/output/transform_dense.csv", header=0)
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fig, ax = plt.subplots()
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ax.loglog(dense['N'], dense['T'], '--', label='Dense')
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ax.loglog(tridiag['N'], tridiag['T'], label='Tridiagonal')
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ax.set_xlabel('N')
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ax.set_ylabel('Similarity transformations')
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ax.legend()
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# Save to file
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if save is True:
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fig.savefig("../latex/images/transform.pdf")
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def plot_eigenvectors(N: int, save: bool=False) -> None:
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# Load data based on matrix size
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path = f"../latex/output/eigenvector_{N}.csv"
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eigvec = pd.read_csv(path, header=0)
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fig, ax = plt.subplots()
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ax.plot(eigvec['x'], eigvec['Vector 1'], label=r'$\vec{v}_{1}$')
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ax.plot(eigvec['x'], eigvec['Vector 2'], label=r'$\vec{v}_{2}$')
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ax.plot(eigvec['x'], eigvec['Vector 3'], label=r'$\vec{v}_{3}$')
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ax.plot(eigvec['x'], eigvec['Analytic 1'], '--', label=r'$\vec{v}^{(1)}$')
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ax.plot(eigvec['x'], eigvec['Analytic 2'], '--', label=r'$\vec{v}^{(2)}$')
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ax.plot(eigvec['x'], eigvec['Analytic 3'], '--', label=r'$\vec{v}^{(3)}$')
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ax.set_xlabel(r'Element $\hat{x}_{i}$')
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ax.set_ylabel(r'Element $v_{i}$')
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ax.legend(loc='upper left')
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# Save to file
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if save is True:
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fig.savefig(f"../latex/images/eigenvector_{N}.pdf")
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if __name__ == '__main__':
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plot_transformations(True)
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plot_eigenvectors(10, True)
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plot_eigenvectors(100, True)
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