Poster basically done, finished organizing directory structure

src/]

@@ -1,59 +0,0 @@
-import json
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib import cm
-import copy
-
-
-
-def main():
-    data = {}
-    with open("results/cifar10_fgsm.json", "r") as infile:
-        data = json.load(infile)
-
-    attack = data["attack"]
-    epsilons = data["epsilons"]
-    filters = data["filters"]
-    dataset = data["dataset"]
-    strength_count = len(filters[list(filters.keys())[0]][0])
-
-    strengths = np.arange(strength_count)
-    epsilons = np.array(epsilons)
-
-    # Assume constant step of strength and epsilon
-    dstrength = strengths[1] - strengths[0]
-    depsilon = epsilons[1] - epsilons[0]
-
-    # Make a grid from strengths and epsilons
-    strengths, epsilons = np.meshgrid(strengths,epsilons)
-    #strengths, epsilons = strengths.ravel(), epsilons.ravel()
-
-    colors = ('blue', 'orange', 'red', 'purple', 'green', 'yellow', 'brown')
-    
-    z = np.zeros_like(strengths)
-    best_performance = np.zeros_like(strengths)
-    bottoms = np.zeros_like(strengths)
-
-    for i, filter in enumerate(filters):
-        performance = np.array(filters[filter])
-        best_performance = np.where(performance > best_performance, i, best_performance)
-        z = np.where(performance > z, performance, z)
-        print(best_performance)
-
-    for i, filter in enumerate(filters): 
-        fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
-        tops = np.where(best_performance == i, best_performance, 0)
-        print(strengths.shape, epsilons.shape, bottoms.shape, dstrength.shape, depsilon.shape, tops.shape)
-        ax.bar3d(strengths, epsilons, bottoms, dstrength, depsilon, tops, color=colors[i])
-        plt.show()
-
-    ax.view_init(90, -90, 0)
-
-    plt.legend()
-    plt.title(f"{filter} Performance")
-    plt.xlabel(f"{attack} Attack Strength ($\\epsilon$)")
-    plt.ylabel(f"{dataset} Classification Accuracy")
-    plt.show()
-
-if __name__ == "__main__":
-    main()