#!/usr/bin/env python
# coding: utf-8

# In[20]:


import numpy as np

a = np.array([1, 11, 23, 13, 200, 10, 11])

# find max of a

# alogorithm mymax = a[0], 
# loop through array and if a[i] > a[0], then mymax = a[i]

mymax = a[0]
N = len(a)
for i in range(N):
#     print(i)
    if a[i] > mymax:
        mymax = a[i]
print(mymax)

np.max(a)


# In[9]:


import numpy as np #for arrays
import matplotlib.pyplot as plt #for plotting
from scipy.integrate import odeint
get_ipython().run_line_magic('matplotlib', 'inline')
plt.style.use('seaborn')

# ODE
# Batch reactor
# -ra = k CA^2

# dCA/dt = -(-ra) = -k CA^2

# step 0 - modules

# step 1 : define a function that needs to be solved
# dy/dt = f(y,t)

def f(y,t):
    return -k*y**2

# step 2: define arrays
# 

time_init = 0
time_final = 10 
N_points = 50
time_array = np.linspace(time_init,time_final, N_points)

k = 0.001
C_A0 = 10.


#step 3: solving the stuff
# odeint(function, initial value, time array)
C_A_calc = odeint(f, C_A0, time_array)

#step 4: plotting
# plt.plot(x, y, 'go-')
plt.plot(time_array, C_A_calc, 'ro--')
plt.xlabel('t, s')
plt.ylabel('$C_{A}$, moles')
plt.show()




# In[10]:


# REGRESSION 
import numpy as np

time_array = np.array([   0.,  125.,  250.,  375.,  500.,  625.,  750.,  875., 1000.,
       1125., 1250., 1375., 1500., 1625., 1750., 1875., 2000., 2125.,
       2250., 2375., 2500., 2625., 2750., 2875.])

C_A_array = np.array([10.28148623,  8.92676003,  7.47370739,  7.20498621,  6.01448975,
        4.85174028,  4.68868558,  4.01410753,  3.94055544,  3.04186757,
        2.40544184,  2.62290428,  2.18704979,  1.31502537,  1.03730156,
        2.28837979,  0.45062549,  1.43886825,  1.26808295,  1.24125421,
        0.28353151,  0.90117414,  0.74132173,  0.73710597])


# In[11]:


#step 1 : plot the data:

plt.plot(time_array, C_A_array, 'o')


# In[13]:


# step 2: define the function (that we think will fit the data)
# and define parameters

def f(x, k, C_A0):
    return C_A0*np.exp(-k*x)

k_test_1 = 0.01
C_A0 = 10.

#step 3: plotting stuff

plt.plot(time_array, C_A_array, 'o')

plt.plot(time_array, f(time_array, k_test_1, C_A0), 'r-')


# In[14]:


# step 2: define the function (that we think will fit the data)
# and define parameters

def f(x, k, C_A0):
    return C_A0*np.exp(-k*x)

k_test_1 = 0.001
C_A0 = 10.

#step 3: plotting stuff

plt.plot(time_array, C_A_array, 'o')

plt.plot(time_array, f(time_array, k_test_1, C_A0), 'r-')


# In[19]:


import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit



time_array = np.array([   0.,  125.,  250.,  375.,  500.,  625.,  750.,  875., 1000.,
       1125., 1250., 1375., 1500., 1625., 1750., 1875., 2000., 2125.,
       2250., 2375., 2500., 2625., 2750., 2875.])

C_A_array = np.array([10.28148623,  8.92676003,  7.47370739,  7.20498621,  6.01448975,
        4.85174028,  4.68868558,  4.01410753,  3.94055544,  3.04186757,
        2.40544184,  2.62290428,  2.18704979,  1.31502537,  1.03730156,
        2.28837979,  0.45062549,  1.43886825,  1.26808295,  1.24125421,
        0.28353151,  0.90117414,  0.74132173,  0.73710597])

#LEts start fitting the data

# Step 1: function


def f(x, k, C_A0):
    return C_A0*np.exp(-k*x)

#Step 2: init parameters:

k_guess = 0.005
C_A0_guess = 11.

#step 3: fitting the data:
# curve_fit(function, time_array, C_A_array initial parameters)
popt, pcov = curve_fit(f, time_array,C_A_array, p0=(k_guess, C_A0_guess))

print(popt)

print("k = {0}".format(popt[0]))
# print(popt[0])
print("C_A0 = {0}".format(popt[1]))
# print(popt[1])

C_A_final = f(time_array, popt[0], popt[1])


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