First commit for cross section extraction.

src/DataLib/fendl32B_retrofit/process_fendl3.2.py

@@ -20,6 +20,10 @@ def main():
     groupr_tools.cleanup_njoy_files()
 
     pKZA = tp.extract_gendf_pkza(gendf_path)
+    # Extract MT values again from GENDF file as there may be some difference
+    # from the original MT values in the ENDF/PENDF files
+    endftk_file_obj, MTs = tp.extract_endf_specs(gendf_path, 'GENDF')
+    gendf_data = tp.iterate_MTs(MTs, endftk_file_obj, mt_dict, pKZA)
 
 if __name__ == '__main__':
     main()

src/DataLib/fendl32B_retrofit/tendl_processing.py

@@ -1,16 +1,22 @@
 # Import packages
 import ENDFtk
+import pandas as pd
 
-def extract_endf_specs(path):
+def extract_endf_specs(path, filetype = 'ENDF'):
     """
     Extract the material ID and MT numbers from an ENDF file.
 
     Arguments:
         path (str): File path to the selected ENDF file.
+        filetype (str, optional): Either ENDF or GENDF (case insensitive).
+            Defaults to 'ENDF'.
     
     Returns:
         matb (int): Unique material ID extracted from the file.
         MTs (list): List of reaction types (MT's) present in the file.
+        file (ENDFtk.tree.File or None): ENDFtk file object containing the
+            contents for a specific material's cross-section data.
+            Only returns the file for GENDF filetypes.
     """
 
     tape = ENDFtk.tree.Tape.from_file(path)
@@ -21,7 +27,12 @@ def extract_endf_specs(path):
     # Extract the MT numbers that are present in the file
     MTs = [MT.MT for MT in file.sections.to_list()]
 
-    return matb, MTs
+    if filetype.lower() == 'gendf':
+        return file, MTs
+    else:
+        return matb, MTs
+
+    return (endf_specs)
 
 def extract_gendf_pkza(gendf_path):
     """
@@ -49,4 +60,81 @@ def extract_gendf_pkza(gendf_path):
     M = 1 if 'm' in A.lower() else 0
     A = int(A.lower().split(' ')[0].split('m')[0])
     pKZA = (Z * 1000 + A) * 10 + M
-    return pKZA
+    return pKZA
+
+def extract_cross_sections(file, MT):
+    """
+    Parse through the contents of a GENDF file section to extract the
+        cross-section data for a specific reaction type (MT).
+    
+    Arguments:
+        file (ENDFtk.tree.File): ENDFtk file object containing a specific
+            material's cross-section data.
+        MT (int): Numerical identifier for the reaction type corresponding to
+            the file's sectional organization.
+    
+    Returns:
+        sigma_list (list): All of the cross-sections for a given reaction type
+            and material, listed as floating point numbers. If the run fails,
+            the function will just return an empty list.
+    """
+
+    section = file.section(MT).content
+
+    # Only every 2nd line starting at the 3rd line has cross-section data.
+    lines = section.split('\n')[2:-2:2]
+
+    # Extract the 3rd token and convert to more conventional string
+    # representation of a float
+    sigma_list = [
+        float(line.split(' ')[2].replace('+','E+').replace('-','E-'))
+        for line in lines
+    ]
+
+    return sigma_list
+
+def iterate_MTs(MTs, file_obj, mt_dict, pKZA):
+    """
+    Iterate through all of the MTs present in a given GENDF file to extract
+        the necessary data to be able to run ALARA.
+    
+    Arguments:
+        MTs (list of int): List of reaction types present in the GENDF file.
+        file_obj (ENDFtk.tree.File): ENDFtk file object containing the
+            contents for a specific material's cross-section data.
+        mt_dict (dict): Dictionary formatted data structure for mt_table.csv
+        pKZA (int): Parent KZA identifier.
+
+    Returns:
+        gendf_data (pandas.core.frame.DataFrame): Pandas DataFrame containing
+            parent KZA values, daughter KZA values, emitted particles,
+            counts of the number of non-zero groups in the Vitamin-J groupwise
+            structure, and the cross-section values for those groups.
+    """
+
+    cross_sections_by_MT    =    []
+    emitted_particles_list  =    []
+    dKZAs                   =    []
+    groups                  =    []
+
+    for MT in MTs:
+        sigma_list = extract_cross_sections(file_obj, MT)
+        if mt_dict[MT]['delKZA'] == 'N/A':
+            dKZA = 'N/A'
+        else:
+            dKZA = pKZA - mt_dict[MT]['delKZA']
+        emitted_particles = mt_dict[MT]['Emitted Particles']
+        cross_sections_by_MT.append(sigma_list)
+        dKZAs.append(dKZA)
+        emitted_particles_list.append(emitted_particles)
+        groups.append(len(sigma_list))
+
+    gendf_data = pd.DataFrame({
+        'Parent KZA'            :       [pKZA] * len(dKZAs),
+        'Daughter KZA'          :       dKZAs,
+        'Emitted Particles'     :       emitted_particles_list,
+        'Non-Zero Groups'       :       groups,
+        'Cross Sections'        :       cross_sections_by_MT
+    })
+
+    return gendf_data