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sh/prepareData_C2HDM.py

@@ -6,40 +6,56 @@
 import argparse
 
 ####### The parameters Type,Lambda1 to Lambda4,re_Lambda5,im_Lambda5, tanbeta and re_m12squared should have the label
-####### of the corresponding parameter. With Seperator you have to tell which seperator your data file 
+####### of the corresponding parameter. With Seperator you have to tell which seperator your data file
 ####### is using (e.g. , \t or space). Your InputFILE will then be saved to OutputFILE.
 
-Seperator='\t'
-InputFILE='../example/C2HDM_Input.dat'
-OutputFILE='C2HDM_Ordered.dat'
-Type='Type'
-Lambda1='L1'
-Lambda2='L2'
-Lambda3='L3'
-Lambda4='L4'
-re_Lambda5='re_L5'
-im_Lambda5='im_L5'
-tanbeta='tbeta'
-re_m12squared='re_m12sq'
+Seperator = "\t"
+InputFILE = "../example/C2HDM_Input.dat"
+OutputFILE = "C2HDM_Ordered.dat"
+Type = "Type"
+Lambda1 = "L1"
+Lambda2 = "L2"
+Lambda3 = "L3"
+Lambda4 = "L4"
+re_Lambda5 = "re_L5"
+im_Lambda5 = "im_L5"
+tanbeta = "tbeta"
+re_m12squared = "re_m12sq"
 
 
 def convert(IndexCol):
-	df=pd.DataFrame()
-	if IndexCol == 'False':
-		df=pd.read_table(InputFILE,index_col=False,sep=Seperator)	
-	else:
-		df=pd.read_table(InputFILE,index_col=int(IndexCol),sep=Seperator)
+    df = pd.DataFrame()
+    if IndexCol == "False":
+        df = pd.read_table(InputFILE, index_col=False, sep=Seperator)
+    else:
+        df = pd.read_table(InputFILE, index_col=int(IndexCol), sep=Seperator)
+
+    frontcol = [
+        Type,
+        Lambda1,
+        Lambda2,
+        Lambda3,
+        Lambda4,
+        re_Lambda5,
+        im_Lambda5,
+        re_m12squared,
+        tanbeta,
+    ]
+
+    Col = [c for c in frontcol if c in df] + [c for c in df if c not in frontcol]
+    df = df[Col]
+
+    df.to_csv(OutputFILE, index=False, sep="\t")
 
-	frontcol=[Type,Lambda1,Lambda2,Lambda3,Lambda4,re_Lambda5,im_Lambda5,re_m12squared,tanbeta]
-
-	Col = [c for c in frontcol if c in df] + [c for c in df if c not in frontcol]
-	df=df[Col]
-
-	df.to_csv(OutputFILE,index=False,sep='\t')
 
 parser = argparse.ArgumentParser()
-parser.add_argument('-i','--indexcol',help='Column which stores the index of your data', default='False')
+parser.add_argument(
+    "-i",
+    "--indexcol",
+    help="Column which stores the index of your data",
+    default="False",
+)
 
 if __name__ == "__main__":
-	args = parser.parse_args()
-	convert(args.indexcol)
+    args = parser.parse_args()
+    convert(args.indexcol)

sh/prepareData_N2HDM.py

@@ -5,45 +5,63 @@
 import pandas as pd
 import argparse
 
-####### The parameters Type,Lambda1 to Lambda8, tanbeta, m12squared and v_s should have the label of the 
+####### The parameters Type,Lambda1 to Lambda8, tanbeta, m12squared and v_s should have the label of the
 ####### corresponding parameter. With Seperator you have to tell which seperator your data file
 ####### is using (e.g. , \t or space). Your InputFILE will then be saved to OutputFILE.
 
-Seperator='\t'
-InputFILE='../example/N2HDM_Input.dat'
-OutputFILE='N2HDM_Ordered.dat'
-Type='p_THDMtype'
-Lambda1='p_L1'
-Lambda2='p_L2'
-Lambda3='p_L3'
-Lambda4='p_L4'
-Lambda5='p_L5'
-Lambda6='p_L6'
-Lambda7='p_L7'
-Lambda8='p_L8'
-tanbeta='p_tbeta'
-m12squared='p_m12sq'
-vs='p_vs'
+Seperator = "\t"
+InputFILE = "../example/N2HDM_Input.dat"
+OutputFILE = "N2HDM_Ordered.dat"
+Type = "p_THDMtype"
+Lambda1 = "p_L1"
+Lambda2 = "p_L2"
+Lambda3 = "p_L3"
+Lambda4 = "p_L4"
+Lambda5 = "p_L5"
+Lambda6 = "p_L6"
+Lambda7 = "p_L7"
+Lambda8 = "p_L8"
+tanbeta = "p_tbeta"
+m12squared = "p_m12sq"
+vs = "p_vs"
 
 
 def convert(IndexCol):
-	df=pd.DataFrame()
-	if IndexCol == 'False':
-		df=pd.read_table(InputFILE,index_col=False,sep=Seperator)	
-	else:
-		df=pd.read_table(InputFILE,index_col=int(IndexCol),sep=Seperator)
+    df = pd.DataFrame()
+    if IndexCol == "False":
+        df = pd.read_table(InputFILE, index_col=False, sep=Seperator)
+    else:
+        df = pd.read_table(InputFILE, index_col=int(IndexCol), sep=Seperator)
 
-	frontcol=[Type,Lambda1,Lambda2,Lambda3,Lambda4,Lambda5,Lambda6,Lambda7,Lambda8,vs,tanbeta,m12squared]
+    frontcol = [
+        Type,
+        Lambda1,
+        Lambda2,
+        Lambda3,
+        Lambda4,
+        Lambda5,
+        Lambda6,
+        Lambda7,
+        Lambda8,
+        vs,
+        tanbeta,
+        m12squared,
+    ]
 
-	Col = [c for c in frontcol if c in df] + [c for c in df if c not in frontcol]
-	df=df[Col]
+    Col = [c for c in frontcol if c in df] + [c for c in df if c not in frontcol]
+    df = df[Col]
+
+    df.to_csv(OutputFILE, index=False, sep="\t")
 
-	df.to_csv(OutputFILE,index=False,sep='\t')
 
 parser = argparse.ArgumentParser()
-parser.add_argument('-i','--indexcol',help='Column which stores the index of your data', default='False')
+parser.add_argument(
+    "-i",
+    "--indexcol",
+    help="Column which stores the index of your data",
+    default="False",
+)
 
 if __name__ == "__main__":
-	args = parser.parse_args()
-	convert(args.indexcol)
-
+    args = parser.parse_args()
+    convert(args.indexcol)

sh/prepareData_R2HDM.py

@@ -5,43 +5,48 @@
 import pandas as pd
 import argparse
 
-####### The parameters Type,Lambda1 to Lambda5, tanbeta and m12squared should have the label of the corresponding 
+####### The parameters Type,Lambda1 to Lambda5, tanbeta and m12squared should have the label of the corresponding
 ####### parameter. With Seperator you have to tell which seperator your data file
 #######  is using (e.g. , \t or space). Your InputFILE will then be saved to OutputFILE
 
 
-Seperator='\t'
-InputFILE='../example/R2HDM_Input.dat'
-InputFILE='wI.csv'
-OutputFILE='R2HDM_Ordered.dat'
-Type='yuktype'
-Lambda1='L1'
-Lambda2='L2'
-Lambda3='L3'
-Lambda4='L4'
-Lambda5='L5'
-tanbeta='tbeta'
-m12squared='m12sq'
+Seperator = "\t"
+InputFILE = "../example/R2HDM_Input.dat"
+InputFILE = "wI.csv"
+OutputFILE = "R2HDM_Ordered.dat"
+Type = "yuktype"
+Lambda1 = "L1"
+Lambda2 = "L2"
+Lambda3 = "L3"
+Lambda4 = "L4"
+Lambda5 = "L5"
+tanbeta = "tbeta"
+m12squared = "m12sq"
 
 
 def convert(IndexCol):
-	df=pd.DataFrame()
-	if IndexCol == 'False':
-		df=pd.read_table(InputFILE,index_col=False,sep=Seperator)	
-	else:
-		df=pd.read_table(InputFILE,index_col=int(IndexCol),sep=Seperator)
+    df = pd.DataFrame()
+    if IndexCol == "False":
+        df = pd.read_table(InputFILE, index_col=False, sep=Seperator)
+    else:
+        df = pd.read_table(InputFILE, index_col=int(IndexCol), sep=Seperator)
 
-	frontcol=[Type,Lambda1,Lambda2,Lambda3,Lambda4,Lambda5,m12squared,tanbeta]
+    frontcol = [Type, Lambda1, Lambda2, Lambda3, Lambda4, Lambda5, m12squared, tanbeta]
 
-	Col = [c for c in frontcol if c in df] + [c for c in df if c not in frontcol]
-	df=df[Col]
+    Col = [c for c in frontcol if c in df] + [c for c in df if c not in frontcol]
+    df = df[Col]
+
+    df.to_csv(OutputFILE, index=False, sep="\t")
 
-	df.to_csv(OutputFILE,index=False,sep='\t')
 
 parser = argparse.ArgumentParser()
-parser.add_argument('-i','--indexcol',help='Column which stores the index of your data', default='False')
+parser.add_argument(
+    "-i",
+    "--indexcol",
+    help="Column which stores the index of your data",
+    default="False",
+)
 
 if __name__ == "__main__":
-	args = parser.parse_args()
-	convert(args.indexcol)
-
+    args = parser.parse_args()
+    convert(args.indexcol)