diff --git a/impute_jass/impute_jass/imputation_launcher.py b/impute_jass/impute_jass/imputation_launcher.py
index d1759d99d0787a5d1406fa727bc71691a3961eaf..85a250bb394bb2bab3e39db37c7553807b435078 100644
--- a/impute_jass/impute_jass/imputation_launcher.py
+++ b/impute_jass/impute_jass/imputation_launcher.py
@@ -1,5 +1,5 @@
 """
-Function set to launch imputation on a complete chromosome or
+Function set to launch SNP imputation on a complete chromosome or
 on the genome
 """
 import glob
@@ -13,17 +13,14 @@ class ImputationLauncher(object):
     Class perform imputation of snp from summary statistic
 
     """
-    def __init__(self, window_size=10000, imputation_style="batch", buf=2500,
+    def __init__(self, window_size=10000, buf=2500,
                  lamb= 0.01, pinv_rcond = 0.01 ):
         """
         Initialise the imputation object. Fix the windows size, the buffer size
-         and the king of imputation employed
+        and the king of imputation employed
+
         Args:
             window_size (int): size of the imputation window in bp
-            imputation_style (str): define if the windows while span the genome
-                                    in a non overlapping fashion ("batch") or
-                                    by being centered on each snp to impute
-                                    ('online')
             buffer (int): the size of the padding around the windows of
                             imputation (relevant only for batch imputation)
             lamb (float): size of the increment added to snp correlation
@@ -32,7 +29,7 @@ class ImputationLauncher(object):
             The scipy.linalg.pinv is used to invert
              the correlation matrices
         """
-        self.imputation_style = imputation_style
+
         self.window_size = window_size
         self.buffer = buf
         self.lamb = lamb
@@ -55,16 +52,11 @@ class ImputationLauncher(object):
         pattern = "{0}/{1}_*.ld".format(ld_folder, chrom)
         zscore = prepare_zscore_for_imputation(ref_panel, zscore)
         zscore_results = zscore.copy(deep=True)
-        if self.imputation_style == "online":
-            def imputer(ld_file):
-                return ld_region_centered_window_imputation(ld_file, ref_panel,
-                                                            zscore,
-                                                            self.window_size)
-        elif self.imputation_style == "batch":
-            def imputer(ld_file):
-                return impg_like_imputation(ld_file, ref_panel, zscore,
-                                            self.window_size, self.buffer,
-                                             self.lamb, self.rcond)
+
+        def imputer(ld_file):
+            return impg_like_imputation(ld_file, ref_panel, zscore,
+                                        self.window_size, self.buffer,
+                                         self.lamb, self.rcond)
 
         for ld_file in glob.glob(pattern):
             print("processing Region: {0}".format(ld_file))
diff --git a/impute_jass/impute_jass/windows.py b/impute_jass/impute_jass/windows.py
index 3f30ae026d6821900efa4fb1f56b4d0447da864e..8368711bc98fa6d8a3716fe95ca301fe9618ea4e 100644
--- a/impute_jass/impute_jass/windows.py
+++ b/impute_jass/impute_jass/windows.py
@@ -127,7 +127,7 @@ def impg_like_imputation(ld_file, ref_panel, zscore, window_size, buffer, lamb,
         Args:
             ld_file (str): Linkage desiquilibrium matrix files
             ref_panel (pd.dataframe): the dataframe containing reference panel
-            snps 
+            snps
     """
     (chrom, start_ld_block, end_ld_block) = parse_region_position(ld_file)
     LD_mat = generate_sparse_matrix(ld_file, ref_panel)
@@ -174,42 +174,3 @@ def impg_like_imputation(ld_file, ref_panel, zscore, window_size, buffer, lamb,
         print_progression(i, Nwindows)
 
     return zscore_results.sort_values(by="pos")
-
-
-def ld_region_centered_window_imputation(ld_file, ref_panel, zscore, window_size, unknowns=pd.Series([])):
-    """
-        Each missing Snp is imputed by known snp found in a window centered on the SNP to impute
-        Argument
-    """
-    (chrom, start_ld_block, end_ld_block) = parse_region_position(ld_file)
-
-    LD_mat = generate_sparse_matrix(ld_file, ref_panel)
-    zscore = prepare_zscore_for_imputation(ref_panel, zscore)
-
-    # Find Snp to impute
-    if len(unknowns) == 0:
-        unknowns = LD_mat.index.difference(zscore.index)
-
-    N_snp = len(unknowns)
-    print("### Imputation of {0} snps ###".format(len(unknowns)))
-
-    for i,snp_unknown in enumerate(unknowns):
-        # Boundary of the centered_window
-        start_pos = max((ref_panel.loc[snp_unknown,'pos'] - window_size), float(start_ld_block))
-        end_pos = min(ref_panel.loc[snp_unknown,'pos'] + window_size, float(end_ld_block))
-
-        in_LD_reg_n_window =  in_region(zscore.pos, start_pos, end_pos)
-
-        known = zscore.loc[in_LD_reg_n_window].index
-        sig_t = LD_mat.loc[known, known]
-        sig_i_t = LD_mat.loc[snp_unknown, known]
-        zt = zscore.loc[known,'Z']
-
-        if(len(known) > 0):
-            imp = impg_model(zt, sig_t, sig_i_t, batch=False)
-            zscore.loc[snp_unknown] = [ref_panel.loc[snp_unknown, 'pos'], ref_panel.loc[snp_unknown, "Ref_all"],  ref_panel.loc[snp_unknown, "alt_all"], imp['mu'], imp['var'], len(known)]
-
-        if i%300 == 0:
-            print("{0}\%".format(np.round(i/N_snp,4)))
-
-    return zscore.sort_values(by="pos")