Wrong variable in compute_mean_std

cnv_sv_caller_gpu.py

@@ -445,7 +445,7 @@ def dico_mappabilite(mappability_file):
     
     end_time_3 = time.time()
     elapsed_time_3 = end_time_3 - start_time_3
-    logging.info(f"In dico_mappability : Ending merge intervals with the same score (Time taken: {elapsed_time_2:.4f} seconds)")
+    logging.info(f"In dico_mappability : Ending merge intervals with the same score (Time taken: {elapsed_time_3:.4f} seconds)")
 
     end_time = time.time()
     elapsed_time = end_time - start_time
@@ -680,8 +680,7 @@ def calcul_med_same_gc(gc_results, depth_correction_results, chr):
     """
     Calculate the median depth correction for each unique GC content value.
 
-    This function computes the median depth correction values for each unique GC content value in `gc_results`, 
-    filtering out zero values from `depth_correction_results`.
+    This function computes the median depth correction values for each unique GC content value, filtering out zero values.
 
     Parameters
     ----------
@@ -689,8 +688,6 @@ def calcul_med_same_gc(gc_results, depth_correction_results, chr):
         A list or array of GC content values.
     depth_correction_results : list or numpy.ndarray
         A list or array of depth correction values.
-    chr : str
-        The chromosome identifier for which the medians are calculated.
 
     Returns
     -------
@@ -735,15 +732,12 @@ def calcul_moy_totale(normalize_depth_results, chr):
     """
     Calculate the mean of non-zero normalized depth results.
 
-    This function filters out zero values from the normalized depth results and computes the mean 
-    of the remaining non-zero values.
+    This function filters out zero values from the normalized depth results and computes the mean of the remaining values.
 
     Parameters
     ----------
     normalize_depth_results : list or numpy.ndarray
         A list or array of normalized depth values.
-    chr : str
-        The chromosome identifier for which the mean is calculated.
 
     Returns
     -------
@@ -757,15 +751,15 @@ def calcul_moy_totale(normalize_depth_results, chr):
     # Filter results to remove zero values
     non_zero_results = normalize_depth_results[normalize_depth_results != 0]
     # Calculate the mean of non-zero results
-    mean_chr = np.mean(non_zero_results) if non_zero_results.size > 0 else 0
+    mean_chr_norm = np.mean(non_zero_results) if non_zero_results.size > 0 else 0
 
-    sys.stderr.write("Chromosome : %s, mean_chr : %s\n" % (chr, mean_chr))
+    logging.info(f"Mean chr_norm_no_zero = {mean_chr_norm}")
 
     end_time = time.time()
     elapsed_time = end_time - start_time
     logging.info(f"Leaving calcul_moy_totale for {chr} (Time taken: {elapsed_time:.4f} seconds)")    
     
-    return mean_chr
+    return mean_chr_norm
 
 def calcul_std(normalize_depth_results, chr):
     """
@@ -805,7 +799,7 @@ def calcul_std(normalize_depth_results, chr):
     return std_chr
 
 
-def compute_mean_std_med(ratio_par_window_results, chr):
+def compute_mean_std_med(ratio_par_window_norm_results, chr, normalize_depth_results):
     """
     Compute the mean, standard deviation, and median of non-zero ratio results per window.
 
@@ -814,7 +808,7 @@ def compute_mean_std_med(ratio_par_window_results, chr):
 
     Parameters
     ----------
-    ratio_par_window_results : list or numpy.ndarray
+    ratio_par_window_norm_results : list or numpy.ndarray
         A list or array of ratio values per window.
     chr : str
         The chromosome identifier for which the statistics are calculated.
@@ -829,8 +823,9 @@ def compute_mean_std_med(ratio_par_window_results, chr):
     start_time = time.time()
 
     # Filter results to remove zero and -1 values
-    ratio_par_window_results = np.array(ratio_par_window_results)
-    non_zero_results = ratio_par_window_results[ratio_par_window_results != 0]
+    ratio_par_window_norm_results = np.array(ratio_par_window_norm_results)
+    non_zero_results = ratio_par_window_norm_results[ratio_par_window_norm_results != 0]
+    non_zero_results = non_zero_results[np.isfinite(non_zero_results)]
 
     # Initialize list for stats computation
     table = []