--- a
+++ b/experiments/analysis.py
@@ -0,0 +1,42 @@
+from os import listdir
+from os.path import join
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+def get_predictorwise_distribution(experiment_path):
+    mean_ious = []
+    for experiment in listdir(experiment_path):
+        # mean_ious.append(np.load(join(experiment_path, experiment))) #cool
+        mean_ious.append(np.mean(np.load(join(experiment_path, experiment))))
+    plt.hist(mean_ious, bins=np.linspace(min(mean_ious), max(mean_ious), 25))
+    plt.show()
+
+
+def get_datawise_distribution(experiment_path):
+    mean_ious = []
+    for experiment in listdir(experiment_path):
+        # mean_ious.append(np.load(join(experiment_path, experiment))) #cool
+        mean_ious = np.concatenate((mean_ious, np.mean(np.load(join(experiment_path, experiment)))))
+        plt.hist(mean_ious, bins=np.linspace(min(mean_ious), max(mean_ious), 25))
+        plt.show()
+
+
+def plot_training_progression(csv_name):
+    df = pd.read_csv(csv_name)
+    plt.plot(df["epoch"], df["iid_test_iou"], label="IID")
+    plt.plot(df["epoch"], df["ood_iou"], label="OOD")
+    plt.plot(df["epoch"], df["iid_test_iou"] - df["ood_iou"], label="Diff")
+    plt.legend()
+    plt.ylim((0, 1))
+    plt.xlim((0, 250))
+    plt.show()
+
+
+if __name__ == '__main__':
+    plot_training_progression("logs/consistency/DeepLab/0.csv")
+    plot_training_progression("logs/consistency/DeepLab/1.csv")
+    # df_iid = df["Augmented" not in df["name"]]
+    # get_predictorwise_distribution("experiments/Data/Normal-Pipelines/DeepLab")