diff --git a/turbox/__init__.py b/turbox/__init__.py
index fdd7cf8..ea45dc6 100644
--- a/turbox/__init__.py
+++ b/turbox/__init__.py
@@ -1,7 +1,7 @@
 from .turbox import (
     get_pspec,
     get_pspec_slope,
-    build_suite,
+    build_turbox_data,
     apply_rule_pdf,
     span_resolution,
 )
diff --git a/turbox/turbox.py b/turbox/turbox.py
index 3d31d56..ac8632a 100644
--- a/turbox/turbox.py
+++ b/turbox/turbox.py
@@ -91,7 +91,7 @@ def get_pspec_slope(pp, field: str, resol: int, plotdebug: bool = False):
     return results.slope, results.intercept, results.rvalue**2
 
 
-def build_suite(pl, redo=False, cs0=0.28834810480560674):
+def build_turbox_data(pl, redo=False, cs0=0.28834810480560674):
     """Compute an array of parameter for each run in the Plotter pl
 
     Parameters
@@ -131,36 +131,34 @@ def build_suite(pl, redo=False, cs0=0.28834810480560674):
         pl.study.avg_time_sigma("y", overwrite_dep=False)
         pl.study.avg_time_sigma("z", overwrite_dep=False)
         pl.study.time(overwrite=True)
+    
+    df["time"] = np.empty(len(pl.runs), dtype=list)
+    time = pl.study.get_value("/series/time", unit=U.Myr)
+    for run in pl.runs:
+        time[run] = list(time[run].T[0])
+        df["time"][run] = time[run]
+        print(run, time[run])
 
+    sigma1d = []
     for ax in ["x", "y", "z"]:
-        df[f"sigma_{ax}"] = np.array(
-            list(
-                map(
-                    lambda x: x.T[0],
-                    [
-                        pl.study.get_value(f"/series/time_sigma_{ax}", unit=U.km_s)[run]
-                        for run in pl.runs
-                    ],
-                )
-            )
-        )
+        sigma_ax = pl.study.get_value(f"/series/time_sigma_{ax}", unit=U.km_s)
+        for run in sigma_ax:
+            sigma_ax[run] = sigma_ax[run].T[0]
+        sigma1d.append(sigma_ax)
+    
+    sigma3d = {}
+    for run in pl.runs:
+        sigma3d[run] = np.sum([sigma1d_i[run]**2  for sigma1d_i in sigma1d], axis=0)
+        sigma3d[run] = list(map(np.sqrt, sigma3d[run]))
 
-    df["sigma_all"] = df["sigma_x"] ** 2 + df["sigma_y"] ** 2 + df["sigma_z"] ** 2
-    df["sigma_all"] = list(map(np.sqrt, df["sigma_all"].values))
-    df["Mach_all"] = list(map(lambda v: v / cs0, df["sigma_all"].values))
-    df["time"] = list(
-        map(
-            lambda x: x.T[0],
-            [pl.study.get_value("/series/time", unit=U.Myr)[run] for run in pl.runs],
-        )
-    )
-
-    df["sigma"] = list(map(lambda sig_list: np.mean(sig_list), df["sigma_all"].values))
+    df["sigma"] = np.zeros(len(pl.runs))
+    for run in pl.runs:
+        df["sigma"][run] = np.mean(sigma3d[run])
     df["Mach"] = df["sigma"] / cs0
     df["turnover"] = (df["L"] * U.pc.express(U.km) / (2 * df["sigma"])) * U.s.express(
         U.Myr
     )
-
+    pl.turbox = df
     return df