add examples/standard_studies/std201/ directory

examples/standard_studies/std2017/Standards2017_TNC.txt

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+# Table 11. Thermal Neutron Constants (cross sections and average total neutron yield) for fissile actinides for neutrons with energy 0.0253 eV. 
+# The 0.4 % unrecognized systematic uncertainty is included in the uncertainties of the total neutron yields (nu-bar).  
+#
+Quantity            U3      U3-UNC  U5     U5-UNC  PU9      PU9-UNC   PU1     PU1-UNC
+SF                  533.0   2.2     587.3  1.4     752.4    2.2       1023.6  10.8
+SG                  44.9    0.9     99.5   1.3     269.8    2.5       362.3   6.1
+SS                  12.2    0.7     14.09  0.22    7.8      1.0       11.9    2.6
+# Nu-bar, Total average
+# neutron yield 
+# per 1 fission        2.487+-0.011 2.425+-0.011 2.878+-0.013 2.940+-0.013
+# Total average neutron yield per 1 fission in Cf-252(sf) 3.764+-0.016 

examples/standard_studies/std2017/helperfuns.py

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+import pandas as pd
+import numpy as np
+
+
+def translate_to_absreacs(reacs):
+    absreacs = []
+    for reac in reacs:
+        if reac.startswith('MT:2-'):
+            absreac = 'MT:1-' + reac[5:]
+        elif reac.startswith('MT:4-'):
+            absreac = 'MT:3-' + reac[5:]
+        elif reac.startswith('MT:8-'):
+            absreac = 'MT:5-' + reac[5:]
+        elif reac.startswith('MT:9-'):
+            absreac = 'MT:7-' + reac[5:]
+        else:
+            absreac = reac
+        absreacs.append(absreac)
+    return np.array(absreacs)
+
+
+def renormalize_data(priortable, exptable, priorvals, expvals):
+    renorm_vals = np.array(expvals) 
+    expids = exptable.loc[exptable.REAC.str.match('MT:[2489]-'), 'NODE'].unique()
+    for expid in expids:
+        norm_node = 'norm_' + expid[4:]
+        norm_index = priortable.index[priortable.NODE == norm_node]
+        if len(norm_index) != 1:
+            raise IndexError(
+                f'exactly one normalization error must be present for {expid}'
+            )
+        norm_index = norm_index[0]
+        norm_fact = priorvals[norm_index]
+        exp_idcs = exptable.index[exptable.NODE == expid]
+        renorm_vals[exp_idcs] /= norm_fact
+    return renorm_vals
+
+
+import sys
+sys.path.append('../../..')
+from gmapy.data_management.object_utils import load_objects
+
+priortable, exptable, is_adj = load_objects('../../tensorflow/example-005/output/01_model_preparation_output.pkl', 'priortable', 'exptable', 'is_adj')
+optres, = load_objects('../../tensorflow/example-005/output/02_parameter_optimization_output.pkl', 'optres')
+red_priortable = priortable[is_adj].reset_index(drop=True)
+
+renorm_data = renormalize_data(red_priortable, exptable, optres.position.numpy(), exptable.DATA)
+absreacs = translate_to_absreacs(exptable.REAC)
+
+exptable['ABSREAC'] = absreacs
+exptable['RENORM_DATA'] = renorm_data
+
+
+
+
+
+
+
+
+
+
+
+