Fixed certain model predictions not importing into dynamic_tga.py, Be…

…gan formatting scripts to plot A vs RMSE, and E vs RMSE

Verification/Scripts/Master.py

@@ -6,6 +6,9 @@
 
 import subprocess
 import argparse
+import json
+import numpy as np
+import matplotlib.pyplot as     plt
 
 # create the parser
 parser = argparse.ArgumentParser()
@@ -18,9 +21,7 @@
 # parse arguments
 args = parser.parse_args()
 
-if args.compare_all is True or args.generate_all_fds is True:
-    error_list = []
-    matl_set_list = [
+matl_set_list = [
         "MaCFP_PMMA_NIST",
         "MaCFP_PMMA_Aalto_I",
         "MaCFP_PMMA_BUW-FZJ_A",
@@ -46,11 +47,13 @@
         "MaCFP_PMMA_NIST - StMU",
         "MaCFP_PMMA_UMET",
         ]
+
+if args.compare_all is True or args.generate_all_fds is True:
+    error_list = []
     if args.compare_all is True:
         for matl_set in matl_set_list:
             try:
                 subprocess.run(["python", "dynamic_tga" + ".py", str(matl_set), str(2021),"-co"])
-                subprocess.run(["python", "dynamic_tga" + ".py", str(matl_set), str(2023),"-co"])
             except:
                 pass
     if args.generate_all_fds is True:
@@ -59,9 +62,29 @@
                 subprocess.run(["python","testing"+".py",str(matl_set)])
             except:
                 pass
-      #  try:
-# running exact solution of scenario
-       #     subprocess.run(["python", dynamic_tga + ".py", matl_set, 2021])
-       # except:
-        #    meaningless = 0
+
+with open('comparison_data.json','r') as file:
+            data = json.load(file)
+#print(data)
+#RMSE = data["A"]
+#A = data["A"]
+#E = data["E"]
+
+A = np.zeros(len(data))
+E = np.zeros(len(data))
+RMSE = np.zeros(len(data))
+
+#for i in range(0,len(matl_set_list)):
+#    try:
+#        matl = matl_set_list[i]
+#        A[i] = data[matl]['A']
+#        E[i] = data[matl]['E']
+#        RMSE[i] = data[matl]['RMSE']
+#        print(A[i],RMSE[i])
+#    except:
+#        pass
+#plt.plot(A,E)
+#plt.scatter(E,RMSE, label        = 'Model Predictions', color = 'red', marker = '.')
+#plt.plot(E, RMSE, label        = 'Model Predictions', color = 'blue', marker = '.')
+#plt.show()
 

Verification/Scripts/dynamic_tga.py

@@ -32,14 +32,22 @@ def run(matl_set, matl_set_year, compare_all_boolean):
     json_file_path         = '../../PMMA/Material_Properties/' + str(matl_set_year) + '/' + args.matl_set + '.json'
     with open(json_file_path, 'r') as file:
         json_data          = json.load(file)
-
-    n_reactions            = json_data['Kinetics']['Number of Reactions']
+    try:
+        n_reactions            = json_data['Kinetics']['Number of Reactions']
+    except:
+        n_reactions            = json_data['Kinetics']['Reactants']
     A                      = json_data['Kinetics']['Pre-exponential']
     E                      = json_data['Kinetics']['Activation Energy']
     N_S                    = json_data['Kinetics']['Reaction Order']
     NU_MATL                = json_data['Kinetics']["Solid Yield"]
-    print(json_data['Kinetics']["Initial Mass Fraction"])
-    Initial_Mass_Fraction  = json_data['Kinetics']["Initial Mass Fraction"]
+    try:
+        Initial_Mass_Fraction  = json_data['Kinetics']["Initial Mass Fraction"]
+    except:
+        try:
+            Initial_Mass_Fraction  = json_data['Composition']["Initial Mass Fraction"]
+        except:
+            print("Error:  No Initial Mass Fraction in csv, ['kinetics']", matl_set)
+            return
 
     # place kinetic values within dictionaries as they are retrieved from JSON as lists
     fetched_kinetic_values = {"A" : A, "E" : E, "n_reactions" : n_reactions, "N_S" : N_S, "NU_MATL" : NU_MATL, "Initial_Mass_Fraction" : Initial_Mass_Fraction}
@@ -100,7 +108,6 @@ def run(matl_set, matl_set_year, compare_all_boolean):
             n_i                             = (fetched_kinetic_values["N_S"])[i]
 
         except(TypeError):
-            print("hi")
             A                               = (fetched_kinetic_values["A"])
             E                               = (fetched_kinetic_values["E"])
             NU_MATL                         = (fetched_kinetic_values["NU_MATL"])
@@ -142,7 +149,6 @@ def equation(x):
                             initial_guess  = alpha[i2 - 1]
                         # find root of equation as solution for alpha
                         root               = findroot(equation, initial_guess)
-                        print(root)
                         alpha[i2]          = root
         # convert alpha to masses
         v             = NU_MATL
@@ -151,10 +157,10 @@ def equation(x):
         m_e           = m_i_0 - (m_i_0 - m_i_f) * alpha
         total_mass    = total_mass + m_e
 
-    rms_err           = plot_and_rms(total_mass, n_reactions,T_m, m_m, N, compare)
+    rms_err           = plot_and_rms(total_mass, n_reactions,T_m, m_m, N, compare,A,E)
     return rms_err
 
-def plot_and_rms(total_mass, n_reactions, T_m, m_m, N, compare):
+def plot_and_rms(total_mass, n_reactions, T_m, m_m, N, compare,A,E):
 
     # root mean square error is calculated
     rms_err                         = np.sqrt(np.sum( (m_m - total_mass) ** 2) / N)
@@ -174,7 +180,18 @@ def plot_and_rms(total_mass, n_reactions, T_m, m_m, N, compare):
         plt.savefig("../Plot Results/" + args.matl_set + "_" + "dynamic_TGA_10K" + "_" + "FDS" + "plot.pdf")
         plt.show()
 
-        return rms_err
+# update comparison_data.json with RMSE and kinetics for creating graph of A vs RMSE
+#    dictionary_1 = {}
+#    results_dictionaries = {matl_set : {"A":A,"E":E, "RMSE":rms_err }}
+#        with open('comparison_data.json','r') as file:
+#            data = json.load(file)
+#            data.update(results_dictionaries)
+#    except:
+#        print("error first try statement")
+#        json.dump(results_dictionaries, file, indent = 4)
+#    with open('comparison_data.json','w') as file:
+#         json.dump(data, file, indent = 4)
+    return rms_err
 
 #output formatting tool            
 def f(string, length_of_space):
@@ -184,12 +201,16 @@ def f(string, length_of_space):
          spaces_string += " "
     return string + spaces_string
 
+exceptions = 0
 if compare is False:
     run(matl_set, matl_set_year, compare)
 if compare is True:
     try:
         run(matl_set, matl_set_year,compare)
     except:
+        if exceptions < 2:    
+            print("error running", matl_set)
+            exceptions = exceptions + 1
         pass
 
  # save as CSV