added comments

impute_jass/impute_jass/imputation_launcher.py

@@ -3,11 +3,27 @@ Function set to launch imputation on a complete chromosome or
 on the genome
 """
 import glob
+import pandas as pd
 from .windows import ld_region_centered_window_imputation, impg_like_imputation, realigned_zfiles_on_panel
 
 class ImputationLauncher(object):
+    """
+    Class perform imputation of snp from summary statistic
 
+    """
     def __init__(self, window_size=10000, imputation_style="online", buf=2500, lamb= 0.01, pinv_rcond = 0.01 ):
+        """
+
+        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 matrices to make it less singular
+            pinv_rcond (float): the rcond scipy.linalg.pinv function argument. The scipy.linalg.pinv is used to invert
+             the correlationmatrices
+
+        """
         self.imputation_style = imputation_style
         self.window_size = window_size
         self.buffer = buf
@@ -54,11 +70,11 @@ class ImputationLauncher(object):
             ref_panel = pd.read_csv(ref_panel_file, sep="\t", names=['chr', "nothing", 'pos', 'Ref_all', 'alt_all'], index_col = 1)
 
 
-            known_zscore_file = zscore_folder + "/z_" + tag + "chr" + str(i) + ".txt"
+            known_zscore_file = zscore_folder + "/z_" + gwas_tag + "_chr" + str(i) + ".txt"
             known_zscore = pd.read_csv(known_zscore_file, index_col=0, sep="\t")
 
             chrom = "chr"+str(i)
             z_imp = self.chromosome_imputation(chrom, known_zscore, ref_panel, ld_folder)
 
-            imputed_zscore = folder_output + "/z_" + tag + "chr" + str(i) + ".txt"
+            imputed_zscore = folder_output + "/z_" + gwas_tag + "_chr" + str(i) + ".txt"
             z_imp.to_csv(imputed_zscore, sep="\t")

impute_jass/impute_jass/ld_matrix.py

@@ -44,9 +44,7 @@ def generate_sparse_matrix(plink_ld, ref_chr_df):
     mat_ld = mat_ld.reindex(index=un_index, columns=un_index)
     mat_ld.fillna(0, inplace=True)
 
-
     sym = np.maximum(mat_ld.values,mat_ld.values.transpose())
-    np.fill_diagonal(sym, 1.01)
 
     mat_ld = pd.DataFrame(sym, index=mat_ld.index, columns=mat_ld.columns)
     re_index = ref_chr_df.loc[mat_ld.index].sort_values(by="pos").index

impute_jass/impute_jass/stat_models.py

@@ -26,10 +26,9 @@ def impg_model(zt, sig_t, sig_i_t, lamb=0.01, rcond=0.01,  batch=True):
     Argument:
         zt : (vector) the vector of known Z scores
     """
-    snps = sig_t.columns
     sig_t = sig_t.values
     np.fill_diagonal(sig_t, (1+lamb))
-    sig_t_inv = sc.linalg.pinv(sig_t, rcond=rcond)
+    sig_t_inv = sc.linalg.pinv(sig_t)#, rcond=rcond)
 
     if batch:
         condition_number = np.array([np.linalg.cond(sig_t)]*sig_i_t.shape[0])
@@ -38,10 +37,9 @@ def impg_model(zt, sig_t, sig_i_t, lamb=0.01, rcond=0.01,  batch=True):
         condition_number = np.linalg.cond(sig_t)
         correct_inversion = check_inversion(sig_t, sig_t_inv)
 
-
     var, ld_score = compute_var(sig_i_t, sig_t_inv, lamb, batch)
     mu = compute_mu(sig_i_t, sig_t_inv, zt)
-    if np.any(mu > 50):
+    if np.any(mu > 30):
         print("ABERANT SNP SNiP ")
     #mu = mu / (((1+lamb)-var)**0.5)
-    return({"var":var, "mu":mu, "ld_score": ld_score, "condition_number":condition_number, "correct_inversion":correct_inversion })
+    return({"var" : var, "mu" : mu, "ld_score" : ld_score, "condition_number" : condition_number, "correct_inversion":correct_inversion })

impute_jass/setup.py

@@ -10,4 +10,13 @@ setup(name='impute_jass',
       #package_dir = {'': 'jass_preprocessing'},
       packages= ['impute_jass'],
       package_data = {'impute_jass':'./data/*.csv'},
-      zip_safe=False)
+      zip_safe=False
+
+      entry_points={
+          'console_scripts' : [
+            'impute_chromosome = '
+
+          ]                              
+      }
+
+      )