BSMPT v3.0.8 pull request

include/BSMPT/models/ClassPotentialOrigin.h

@@ -1148,14 +1148,6 @@ class Class_Potential_Origin
   virtual void Debugging(const std::vector<double> &input,
                          std::vector<double> &output) const = 0;
 
-  /**
-   * Checks if the tensors are correctly implemented. For this the fermion,
-   * quark and gauge boson masses are calculated and printed next to the values
-   * defined in SMparah.h
-   */
-  void CheckImplementation(
-      const int &WhichMinimizer = Minimizer::WhichMinimizerDefault) const;
-
   /**
    * Find all possible sign combinations of the vevs under which the potential
    * is invariant

include/BSMPT/models/ModelTestfunctions.h → include/BSMPT/models/modeltests/ModelTestfunctions.h

@@ -8,6 +8,7 @@
 #include <string>
 #include <vector>
 
+#include <BSMPT/minimizer/Minimizer.h>
 #include <BSMPT/models/SMparam.h>
 
 namespace BSMPT
@@ -63,5 +64,14 @@ TestResults CheckSymmetricTensorQuarks(
 TestResults CheckSymmetricTensorGauge(
     const std::vector<std::vector<std::vector<std::vector<double>>>> &Tensor);
 
+/**
+ * Checks if the tensors are correctly implemented. For this the fermion,
+ * quark and gauge boson masses are calculated and printed next to the values
+ * defined in SMparah.h
+ */
+void CheckImplementation(
+    const Class_Potential_Origin &point,
+    const int &WhichMinimizer = Minimizer::WhichMinimizerDefault);
+
 } // namespace ModelTests
 } // namespace BSMPT

include/BSMPT/transition_tracer/transition_tracer.h

@@ -181,14 +181,24 @@ class TransitionTracer
 
   /**
    * @brief Store the list of bounce solutions
-   *
    */
   std::vector<BounceSolution> ListBounceSolution;
 
   /**
    * @brief output data storage
    */
   output output_store;
+
+  /**
+   * @brief CheckMassRatio
+   * @param input
+   * @param vev
+   * @param temp
+   * @return maximal ratio
+   */
+  double CheckMassRatio(const user_input &input,
+                        const std::vector<double> &vec,
+                        const double &temp) const;
 };
 
-} // namespace BSMPT
+} // namespace BSMPT

sh/prepareData_C2HDM.py

@@ -6,13 +6,22 @@
 import pandas as pd
 import sys
 
-####### 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 
+####### 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
 ####### is using (e.g. , \t or space). Your InputFILE will then be saved to OutputFILE.
 
-def convert(InputFile, OutputFile):
-    print(f'Reading {InputFile}.')
-    print(f'Output is 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"
 
     HasIndexCol=False
     Separator='\t'
@@ -26,17 +35,39 @@ def convert(InputFile, OutputFile):
     tanbeta='tbeta'
     re_m12squared='re_m12sq'
 
-    with open(InputFile, 'r') as file:
-        df = pd.read_csv(file,index_col=HasIndexCol,sep=Separator)
+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)
 
-    frontcol=[Type,Lambda1,Lambda2,Lambda3,Lambda4,re_Lambda5,im_Lambda5,re_m12squared,tanbeta]
+    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]
 
-    with open(OutputFile, 'w') as file:
-        df.to_csv(file, 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",
+)
 
 if __name__ == "__main__":
-    convert(sys.argv[1], sys.argv[2])
+    args = parser.parse_args()
+    convert(args.indexcol)

sh/prepareData_N2HDM.py

@@ -5,43 +5,62 @@
 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

@@ -6,35 +6,53 @@
 import pandas as pd
 import sys
 
-####### 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
 
 def convert(InputFile, OutputFile):
     print(f'Reading {InputFile}.')
     print(f'Output is saved to {OutputFile}.')
 
-    HasIndexCol=False
-    Separator='\t'
-    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"
 
     with open(InputFile, 'r') as file:
         df = pd.read_csv(file,index_col=HasIndexCol,sep=Separator)
 
-    frontcol=[Type,Lambda1,Lambda2,Lambda3,Lambda4,Lambda5,m12squared,tanbeta]
+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)
+
+    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]
 
-    with open(OutputFile, 'w') as file:
-        df.to_csv(file, 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",
+)
 
 if __name__ == "__main__":
-    convert(sys.argv[1], sys.argv[2])
+    args = parser.parse_args()
+    convert(args.indexcol)

src/CMakeLists.txt

@@ -5,6 +5,7 @@
 
 add_subdirectory(minimizer)
 add_subdirectory(models)
+add_subdirectory(models/modeltests)
 if(BSMPTCompileBaryo)
   add_subdirectory(baryo_calculation)
   add_subdirectory(WallThickness)
@@ -46,7 +47,7 @@ target_link_libraries(TripleHiggsCouplingsNLO Models Utility)
 target_compile_features(TripleHiggsCouplingsNLO PUBLIC cxx_std_17)
 
 add_executable(Test prog/Test.cpp)
-target_link_libraries(Test Minimizer Models Utility)
+target_link_libraries(Test Minimizer Models ModelTests Utility)
 target_compile_features(Test PUBLIC cxx_std_17)
 
 add_executable(CalcGW prog/CalcGW.cpp)