Add program for plotting problem 5 and 6

src/plot.py

@@ -4,42 +4,50 @@ import pandas as pd
 import seaborn as sns
 
 sns.set_theme()
-sns.dark_palette("seagreen")
-
-"""Write to file (for dense matrix): 
-Line give size of matrix (column give number of transformations until convergence)
-    N   1   (2   3   4   5)
-    3
-    6
-    9
-    12
-    15
-"""
-
-def read_data(filename: str) -> tuple:
-    N = []
-    T = []
-    with open(filename, "r") as f:
-        lines = f.readlines()
-        for line in lines:
-            n_i, t_i = line.strip().split(",")
-            N.append(float(n_i))
-            T.append(float(t_i))
-    return (N, T)
+plt.rcParams['text.usetex'] = True
 
 
+def plot_transformations(save: bool=False) -> None:
+    # Load data
+    tridiag = pd.read_csv("../latex/output/transform_tridiag.csv", header=0)
+    dense = pd.read_csv("../latex/output/transform_dense.csv", header=0)
 
-def plot_similarity_transformations(N: np.ndarray, T: np.ndarray) -> None:
     fig, ax = plt.subplots()
-    ax.plot(N, T, label='Transformations')
+    ax.loglog(dense['N'], dense['T'], '--', label='Dense')
+    ax.loglog(tridiag['N'], tridiag['T'], label='Tridiagonal')
     ax.set_xlabel('N')
     ax.set_ylabel('Similarity transformations')
-    ax.set_xlim(xmin=N[0], xmax=N[-1]) 
-    fig.savefig("similarity_transformation.pdf")
+    ax.legend()
 
-def plot_eigenvectors():
-    pass 
+    # Save to file
+    if save is True:
+        fig.savefig("../latex/images/transform.pdf")
+
+
+
+def plot_eigenvectors(N: int, save: bool=False) -> None:
+    # Load data based on matrix size
+    name = "eigenvector_" + str(N)
+    path = "../latex/output/" + name + ".csv"
+    eigvec = pd.read_csv(path, header=0)
+
+    fig, ax = plt.subplots()
+    ax.plot(eigvec['x'], eigvec['Vector 1'], label='Vector 1')
+    ax.plot(eigvec['x'], eigvec['Vector 2'], label='Vector 2')
+    ax.plot(eigvec['x'], eigvec['Vector 3'], label='Vector 3')
+    ax.set_xlabel(r'Element $\hat{x}_{i}$')
+    ax.set_ylabel(r'Value of element $v_{i}$')
+    ax.legend()
+
+    # Save to file
+    if save is True:
+        fig.savefig("../latex/images/" + name + ".pdf")
+
+     
 
 
 if __name__ == '__main__':
-    pass
+    plot_transformations(True)
+    plot_eigenvectors(6, True)
+    plot_eigenvectors(100, True)
+    # plt.show()