# PACKAGES

import os

import numpy as np

from modules.reaction_models import Model

from modules.arrhenius import rateConstant, conv2mass

import pandas as pd

import random

# convFlag = True if you are about to generate conversion data

# = False if not

convFlag = False

# DIRECTORIES

MAIN_DIR = os.getcwd() # current working directory

DATA = os.path.join(MAIN_DIR,'data') # data directory

# create DATA directory

if not os.path.exists(DATA):

os.makedirs(DATA)

# models names supported in this software

modelNames = ["A2","A3","A4","D1","D2","D3","D4","F0","F1","F2","F3","P2","P3","P4","R2","R3"]

# pick up a model

model = Model('A2')

# physical properties

enthalpy = 147.8e+3 # J mol-1

frequency = 1.75e+22

temperature = np.linspace(320.0,330.0,3)

# random number seed for generating data noise

random.seed(10)

# generate a kinetics (m,t) or (conversion,t) data set for each temperature

for T in temperature:

# determine an Arrhenius rate constant

k = rateConstant(frequency,enthalpy,T)

print(k)

# calculate the start end end time for the sample data

alpha = np.linspace(0.05,0.95,100)

startTime = model.g(alpha[0])/k

endTime = model.g(alpha[-1])/k

time = np.linspace(startTime,endTime,100)

# calculate the conversion based on the chosen model

# conversion = np.array([ model.alpha(t,k)+random.uniform(0.01, 0.03) for t in time])

conversion = np.array([ model.alpha(t,k) for t in time])

# assume initial and final mass

m0, minf = 10.0, 8.2

# convert converstion fraction to mass

mass = np.array([conv2mass(m0,minf,alpha) for alpha in conversion])

# data to dictionary

if convFlag:

data = {'mass': mass, 'mass units': 'mg', 'conversion': conversion, 'time': time, 'time units': 'min', 'temperature': T, 'temperature units' : 'Kelvin'}

else:

data = {'mass': mass, 'mass units': 'mg', 'time': time, 'time units': 'min', 'temperature': T, 'temperature units' : 'Kelvin'}

# dictionary to dataframe

df = pd.DataFrame(data)

# CSV filename

Csv = os.path.join(DATA,str(T)+'_Kelvin.csv')

# dataframe to csv

df.to_csv(Csv,index=False)