Version 1.0.5

LDpred.py

@@ -6,31 +6,46 @@
 from ldpred import LDpred_inf
 from ldpred import LD_pruning_thres
 from ldpred import validate
+from ldpred import reporting
+
 import sys
 import textwrap
 
-__version__ = '1.0.4'
+__version__ = '1.0.5'
+__date__ = '15 March 2019'
 
-
-parser = argparse.ArgumentParser(prog='LDpred',
-                                 formatter_class=argparse.RawDescriptionHelpFormatter,
-                                 description=textwrap.dedent("""\
-                                    \033[96mLDpred v. 1.0.2\033[0m
---------------------------------------------------------------------------------------
-                            Thank you for using\033[1m LDpred\033[0m!
-
+title = '\033[96mLDpred v. %s\033[0m'%__version__
+title_len= len(title)-9
+num_dashes = reporting.window_length-title_len-2
+left_dashes = '='*int(num_dashes/2)
+right_dashes = '='*int(num_dashes/2+num_dashes%2)
+title_string = '\n'+left_dashes  + ' '+title+' '+right_dashes +'\n'
+description_string = """
 Typicial workflow:
-    1. Use\033[1m LDpred coord\033[0m to parse summary statistics and genotypes and coordinate data.
-       See LDpred coord --help for further usage description and options.
+    1. Use\033[1m LDpred coord\033[0m to parse summary statistics and genotypes 
+       and coordinate data. See LDpred coord --help for further usage 
+       description and options.
        
-    2. Use\033[1m LDpred gibbs|inf|p+t\033[0m to obtain SNP weights for polygenic scoring using one or 
-       more methods. See LDpred gibbs|inf|p+t --help for further usage description and options.
+    2. Use\033[1m LDpred gibbs|inf|p+t\033[0m to obtain SNP weights for polygenic
+       scoring using one or more methods. See LDpred gibbs|inf|p+t --help for 
+       further usage description and options.
     
-    3. Use\033[1m LDpred score\033[0m to calculate polygenic scores using SNP weights from previous 
-       step. See LDpred score --help for further usage description and options.
+    3. Use\033[1m LDpred score\033[0m to calculate polygenic scores using SNP
+       weights from previous step. See LDpred score --help for further usage 
+       description and options.
          
-2019 (c) Bjarni J Vilhjalmsson: [email protected]
-"""))
+Thank you for using\033[1m LDpred\033[0m!
+
+%s 
+(c) Bjarni J Vilhjalmsson: [email protected]
+
+"""%(__date__)
+description_string += '='*reporting.window_length
+
+parser = argparse.ArgumentParser(prog='LDpred',
+                                 formatter_class=argparse.RawDescriptionHelpFormatter,
+                                 description=textwrap.dedent(description_string))
+
 
 subparsers = parser.add_subparsers(help='Select ldpred action among the following options: ', dest='ldpred_action')
 parser_coord = subparsers.add_parser('coord', help='Parse and coordinate summary statistics and genotypes.')
@@ -246,8 +261,8 @@
 
 
 def main():
+    print(title_string)
     if len(sys.argv)==1:
-        print ('ERROR: No options provided.\n')
         parser.print_help(sys.stderr)
         sys.exit(1)
     parameters = parser.parse_args()

README.md

@@ -6,12 +6,12 @@ LDpred is a Python based software package that adjusts GWAS summary statistics
 for the effects of linkage disequilibrium (LD).  The details of the method is
 described in Vilhjalmsson et al. (AJHG 2015) [http://www.cell.com/ajhg/abstract/S0002-9297(15)00365-1]
 
-* The current version is 1.0.4
+* The current version is 1.0.5
 
 ## Getting Started ##
 ### News ###
 
-Mar 14th, 2019: Version 1.0.4 released and is available on pip using 
+Mar 15th, 2019: Version 1.0.5 released and is available on pip using 
 
 `pip install ldpred`
 

ldpred/reporting.py

@@ -1,34 +1,37 @@
 """
 Bunch of functions for beautifying output.
 """
+window_length = 80
+name_length = 50
 
 def print_summary(summary_dict, title, log_file=None):
     print('')
     title_len= len(title)
-    num_dashes = 80-title_len-2
+    num_dashes = window_length-title_len-2
     left_dashes = '='*int(num_dashes/2)
     right_dashes = '='*int(num_dashes/2+num_dashes%2)
+    val_length=window_length-name_length
 
     print( left_dashes  + ' '+title+' '+right_dashes)
     for k in sorted(summary_dict.keys()):
         sd = summary_dict[k]
         if sd['name']=='dash':
             if 'value' not in sd:
-                print ('-' * 80)
+                print ('-' * window_length)
             else:
                 val_str= str(sd['value'])
                 str_len= len(val_str)
-                num_dashes = 80-str_len-2
+                num_dashes = window_length-str_len-2
                 left_dashes = '-'*int(num_dashes/2)
                 right_dashes = '-'*int(num_dashes/2+num_dashes%2)
                 print( left_dashes  + ' '+val_str+' '+right_dashes)
         else:
             val_str= str(sd['value'])
-            if len(val_str)>29:
-                print ('{:<50}'.format(sd['name']))
-                print ('{:>80}'.format(val_str))
+            if len(val_str)>val_length-1:
+                print (('{:<%d}'%name_length).format(sd['name']))
+                print (('{:>%d}'%window_length).format(val_str))
             else:
-                print ('{:<50}{:>30}'.format(sd['name'],str(sd['value'])))
-    print ('=' * 80)
+                print (('{:<%d}{:>%d}'%(name_length,val_length)).format(sd['name'],str(sd['value'])))
+    print ('=' * window_length)
     print('')