# HG changeset patch
# User francesco_lapi
# Date 1736937129 0
# Node ID c6d78b0d324db6c4432583e5e9cb5e702c3ca53f
# Parent  049aa0f4844f121610fc7b9995297089053136ae
Uploaded

diff -r 049aa0f4844f -r c6d78b0d324d COBRAxy/flux_to_map.py
--- a/COBRAxy/flux_to_map.py	Mon Jan 13 15:16:18 2025 +0000
+++ b/COBRAxy/flux_to_map.py	Wed Jan 15 10:32:09 2025 +0000
@@ -698,8 +698,8 @@
     ks_statistic, p_value = st.ks_2samp(dataset1Data, dataset2Data)
     
     # Calculate means and standard deviations
-    mean1 = np.mean(dataset1Data)
-    mean2 = np.mean(dataset2Data)
+    mean1 = np.nanmean(dataset1Data)
+    mean2 = np.nanmean(dataset2Data)
     std1 = np.std(dataset1Data, ddof=1)
     std2 = np.std(dataset2Data, ddof=1)
     
@@ -958,8 +958,8 @@
     metabMap_median = copy.deepcopy(metabMap)
 
     # Compute medians and means
-    medians = {key: np.round(np.median(np.array(value), axis=1), 6) for key, value in class_pat.items()}
-    means = {key: np.round(np.mean(np.array(value), axis=1),6) for key, value in class_pat.items()}
+    medians = {key: np.round(np.nanmedian(np.array(value), axis=1), 6) for key, value in class_pat.items()}
+    means = {key: np.round(np.nanmean(np.array(value), axis=1),6) for key, value in class_pat.items()}
 
     # Normalize medians and means
     max_flux_medians = max(np.max(np.abs(arr)) for arr in medians.values())