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