3D graphs

notebooks/lib.py

@@ -28,6 +28,24 @@ def read_xyz(path: str):
     return df
 
 
+def read_xyz_alt(path: str):
+    df = pd.read_csv(path)
+    df['N'] = df.index + 1
+
+    # Find the outermost corner of this object
+    df['r'] = (
+                      (df.x.abs() + np.sqrt(0.5)) ** 2 +
+                      (df.y.abs() + np.sqrt(0.5)) ** 2 +
+                      (df.z.abs() + np.sqrt(0.5)) ** 2
+              ) ** 0.5
+
+    df['cr'] = df.r.cummax()
+    df['fd'] = np.log(df.N) / np.log(df.cr)
+    df['run'] = os.path.splitext(Path(path).name)[0]
+
+    return df
+
+
 def read_load(load_dir: str, reader=read_xy):
     paths = glob(f'{load_dir}/*.csv')
     return pd.concat([reader(path) for path in paths])
@@ -78,3 +96,16 @@ def fd_stats(dfs):
     fds = [mean_of_tail(df.fd, 0.1) for df in dfs]
     fds_clean = [f for f in fds if f < np.inf]
     return np.mean(fds_clean), np.mean(fds_clean) / np.sqrt(fds_clean.length())
+
+
+def linear(x, a, b):
+    return x * a + b
+
+
+def mean_across(df):
+    runs = df.run.unique().size
+    data = df.groupby('N').agg({'fd': ['mean', 'std', ['stderr', lambda fd: np.std(fd) / np.sqrt(runs)]]}) \
+        .reset_index() \
+        .replace([np.inf, -np.inf], np.nan)
+
+    return data