sp-fd figures

notebooks/sp-fd-rust-vs-c.py

@@ -0,0 +1,71 @@
+import numpy as np
+from matplotlib import pyplot as plt
+
+from notebooks.lib import read_sp
+
+
+def process_sp(sp):
+    by_run = sp.groupby(['probability', 'N'])
+
+    by_probability = by_run.agg(
+        overall_fd=('fd', lambda fd: np.mean(fd[-100:])),
+        overall_fd_std=('fd', 'std')
+    ).reset_index().groupby('probability')
+
+    ggg = by_probability.agg(
+        fd=('overall_fd', 'mean'),
+        # TODO Check stats, do we need to do /sqrt(n) here?
+        fd_std=('overall_fd_std', lambda std: np.sqrt(np.mean(np.square(std))))
+    )
+
+    return ggg
+
+
+c_sp = read_sp("../data/stick-probability")
+rust_sp = read_sp("../data/rust-sticking-probability")
+
+# %%
+c_data = process_sp(c_sp)
+rust_data = process_sp(rust_sp)
+
+# %%
+
+# plt.fill_between(
+#     c_data.index,
+#     c_data.fd - c_data.fd_std,
+#     c_data.fd + c_data.fd_std,
+#     alpha=0.2,
+#     color='tab:blue',
+#     label=f"IPC + PS, Standard error band"
+# )
+
+plt.plot(
+    c_data.index,
+    c_data.fd,
+    color='tab:blue',
+    label='IPC + PS, fd mean, seeds = 100'
+)
+
+# plt.fill_between(
+#     rust_data.index,
+#     rust_data.fd - rust_data.fd_std,
+#     rust_data.fd + rust_data.fd_std,
+#     alpha=0.2,
+#     color='tab:orange',
+#     label=f"NF, Standard error band"
+# )
+
+plt.plot(
+    rust_data.index,
+    rust_data.fd,
+    color='tab:orange',
+    label='NF, fd mean, seeds = 100'
+)
+
+plt.xlabel("$p_{stick}$")
+plt.ylabel("$fd$")
+plt.legend()
+
+plt.savefig('../figures/sp-fd-rust-vs-c.svg')
+plt.savefig('../figures/sp-fd-rust-vs-c.png')
+plt.show()

notebooks/sp-fd.py

@@ -17,9 +17,9 @@ ggg = by_probability.agg(
     fd_std=('overall_fd_std', lambda std: np.sqrt(np.mean(np.square(std))))
 )
 
+# %%
 plt.fill_between(ggg.index, ggg.fd - ggg.fd_std, ggg.fd + ggg.fd_std, alpha=0.2, label=f"Standard error band")
 plt.plot(ggg.index, ggg.fd, color='tab:blue', label='fd mean, seeds = 100')
-plt.plot(ggg.index, ggg.fd)
 
 plt.xlabel("$p_{stick}$")
 plt.ylabel("$fd$")