--- a
+++ b/scripts/plot_complexity.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+
+"""Code to generate plots for Extended Data Fig. 4."""
+
+import os
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+
+import echonet
+
+
+def main(root=os.path.join("timing", "video"),
+         fig_root=os.path.join("figure", "complexity"),
+         FRAMES=(1, 8, 16, 32, 64, 96),
+         pretrained=True):
+    """Generate plots for Extended Data Fig. 4."""
+
+    echonet.utils.latexify()
+
+    os.makedirs(fig_root, exist_ok=True)
+    fig = plt.figure(figsize=(6.50, 2.50))
+    gs = matplotlib.gridspec.GridSpec(1, 3, width_ratios=[2.5, 2.5, 1.50])
+    ax = (plt.subplot(gs[0]), plt.subplot(gs[1]), plt.subplot(gs[2]))
+
+    # Create legend
+    for (model, color) in zip(["EchoNet-Dynamic (EF)", "R3D", "MC3"], matplotlib.colors.TABLEAU_COLORS):
+        ax[2].plot([float("nan")], [float("nan")], "-", color=color, label=model)
+    ax[2].set_title("")
+    ax[2].axis("off")
+    ax[2].legend(loc="center")
+
+    for (model, color) in zip(["r2plus1d_18", "r3d_18", "mc3_18"], matplotlib.colors.TABLEAU_COLORS):
+        for split in ["val"]:  # ["val", "train"]:
+            print(model, split)
+            data = [load(root, model, frames, 1, pretrained, split) for frames in FRAMES]
+            time = np.array(list(map(lambda x: x[0], data)))
+            n = np.array(list(map(lambda x: x[1], data)))
+            mem_allocated = np.array(list(map(lambda x: x[2], data)))
+            # mem_cached = np.array(list(map(lambda x: x[3], data)))
+            batch_size = np.array(list(map(lambda x: x[4], data)))
+
+            # Plot Time (panel a)
+            ax[0].plot(FRAMES, time / n, "-" if pretrained else "--", marker=".", color=color, linewidth=(1 if split == "train" else None))
+            print("Time:\n" + "\n".join(map(lambda x: "{:8d}: {:f}".format(*x), zip(FRAMES, time / n))))
+
+            # Plot Memory (panel b)
+            ax[1].plot(FRAMES, mem_allocated / batch_size / 1e9, "-" if pretrained else "--", marker=".", color=color, linewidth=(1 if split == "train" else None))
+            print("Memory:\n" + "\n".join(map(lambda x: "{:8d}: {:f}".format(*x), zip(FRAMES, mem_allocated / batch_size / 1e9))))
+            print()
+
+    # Labels for panel a
+    ax[0].set_xticks(FRAMES)
+    ax[0].text(-0.05, 1.10, "(a)", transform=ax[0].transAxes)
+    ax[0].set_xlabel("Clip length (frames)")
+    ax[0].set_ylabel("Time Per Clip (seconds)")
+
+    # Labels for panel b
+    ax[1].set_xticks(FRAMES)
+    ax[1].text(-0.05, 1.10, "(b)", transform=ax[1].transAxes)
+    ax[1].set_xlabel("Clip length (frames)")
+    ax[1].set_ylabel("Memory Per Clip (GB)")
+
+    # Save figure
+    plt.tight_layout()
+    plt.savefig(os.path.join(fig_root, "complexity.pdf"))
+    plt.savefig(os.path.join(fig_root, "complexity.eps"))
+    plt.close(fig)
+
+
+def load(root, model, frames, period, pretrained, split):
+    """Loads runtime and memory usage for specified hyperparameter choice."""
+    with open(os.path.join(root, "{}_{}_{}_{}".format(model, frames, period, "pretrained" if pretrained else "random"), "log.csv"), "r") as f:
+        for line in f:
+            line = line.split(",")
+            if len(line) < 4:
+                # Skip lines that are not csv (these lines log information)
+                continue
+            if line[1] == split:
+                *_, time, n, mem_allocated, mem_cached, batch_size = line
+                time = float(time)
+                n = int(n)
+                mem_allocated = int(mem_allocated)
+                mem_cached = int(mem_cached)
+                batch_size = int(batch_size)
+                return time, n, mem_allocated, mem_cached, batch_size
+    raise ValueError("File missing information.")
+
+
+if __name__ == "__main__":
+    main()