From 0ffce2f17a9405d069789f23b5176039d31d0b74 Mon Sep 17 00:00:00 2001
From: robrobo <robin.horstmann@gmx.net>
Date: Thu, 17 Jul 2025 18:45:11 +0200
Subject: [PATCH] added full_output option to non gaussian parameter so that
 correct statistics can be done afterwards

---
 src/mdevaluate/correlation.py | 36 +++++++++++++++++++++++------------
 1 file changed, 24 insertions(+), 12 deletions(-)

diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py
index 11be7da..07d667c 100644
--- a/src/mdevaluate/correlation.py
+++ b/src/mdevaluate/correlation.py
@@ -18,7 +18,7 @@ def log_indices(first: int, last: int, num: int = 100) -> np.ndarray:
     return np.unique(np.int_(ls) - 1 + first)
 
 
-@autosave_data(2)
+@autosave_data(nargs=2, kwargs_keys=('selector', 'segments', 'skip', 'window', 'average', 'points',), version=1.0)
 def shifted_correlation(
     function: Callable,
     frames: Coordinates,
@@ -460,6 +460,7 @@ def non_gaussian_parameter(
     end_frame: CoordinateFrame,
     trajectory: Coordinates = None,
     axis: str = "all",
+    full_output = False,
 ) -> float:
     r"""
     Calculate the non-Gaussian parameter.
@@ -472,30 +473,41 @@ def non_gaussian_parameter(
     else:
         vectors = displacements_without_drift(start_frame, end_frame, trajectory)
     if axis == "all":
-        r = (vectors**2).sum(axis=1)
+        r2 = (vectors**2).sum(axis=1)
         dimensions = 3
     elif axis == "xy" or axis == "yx":
-        r = (vectors[:, [0, 1]]**2).sum(axis=1)
+        r2 = (vectors[:, [0, 1]]**2).sum(axis=1)
         dimensions = 2
     elif axis == "xz" or axis == "zx":
-        r = (vectors[:, [0, 2]]**2).sum(axis=1)
+        r2 = (vectors[:, [0, 2]]**2).sum(axis=1)
         dimensions = 2
     elif axis == "yz" or axis == "zy":
-        r = (vectors[:, [1, 2]]**2).sum(axis=1)
+        r2 = (vectors[:, [1, 2]]**2).sum(axis=1)
         dimensions = 2
     elif axis == "x":
-        r = vectors[:, 0] ** 2
+        r2 = vectors[:, 0] ** 2
         dimensions = 1
     elif axis == "y":
-        r = vectors[:, 1] ** 2
+        r2 = vectors[:, 1] ** 2
         dimensions = 1
     elif axis == "z":
-        r = vectors[:, 2] ** 2
+        r2 = vectors[:, 2] ** 2
         dimensions = 1
     else:
         raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!')
     
-    mean_r = np.mean(r)
-    if mean_r == 0.0:
-        return 0.0
-    return (np.mean(r**2) / ((1 + 2 / dimensions) * (mean_r ** 2))) - 1
+    m2 = np.mean(r2)
+    m4 = np.mean(r2**2)
+    if m2 == 0.0:
+        if full_output:
+            return 0.0, 0.0, 0.0
+        else:
+            return 0.0
+
+    alpha_2 = (m4 / ((1 + 2 / dimensions) * m2**2)) - 1
+    if full_output:
+        return alpha_2, m2, m4
+    else:
+        return alpha_2
+
+