functions2.py

import scipy as sp

def exp_func1(x, alpha, beta, gamma):
    return alpha * sp.exp(-(x-beta)/gamma)
def exp_func2(x, alpha1, beta, gamma1, alpha2, gamma2):
    return alpha1 * sp.exp(-(x-beta)/gamma1)  +  alpha2 * sp.exp(-(x-beta)/gamma2)
def exp_func3(x, alpha1, beta, gamma1, a, b):
    return alpha1 * sp.exp(-(x-beta)/gamma1)  +  a*x+b

def cost_fn1(param):
    return sum((exp_func1(x, *param)-y)**2 for x,y in zip(X,Y))
def cost_fn2(param):
    return sum((exp_func2(x, *param)-y)**2 for x,y in zip(X,Y))
def cost_fn3(param):
    return sum((exp_func3(x, *param)-y)**2 for x,y in zip(X,Y))
def residue(param, y, x):
    return y-expFunc3(x,*param)

def gamma(x, k, theta, alpha, beta):
    gamma = alpha*pow(x-beta, k-1) * sp.exp(-(x-beta)/theta)/(pow(theta,k) * math.gamma(k))
    return gamma
def cost_gamma(g_para):
    return sum((gamma(x,*g_para)-y)**2 for x, y in zip(hx[1:40],hy[1:]))

def gaussian(x, sigma, mu,alpha):
    return alpha/(sigma*np.sqrt(np.pi*2))*sp.exp(-pow((x-mu),2)/(2*pow(sigma,2)))
def cost_gaussian(gaus_para):
    return sum((gaussian(x, *gaus_para)-y)**2 for x, y in zip(GX, GY))

def Michaelis_Menten_1(c,K,Tau_max):
    n = 1.0
    r = Tau_max/(1+np.power(K/c,n))
    return r

def Michaelis_Menten_2(c,parameter):
    K = parameter[0]
    Tau_max = parameter[1]
    n = parameter[2]
    r = Tau_max/(1+np.power(K/c,n))
    return r

def fit_func(parameter,x,y):
    residual = y-Michaelis_Menten_2(x,parameter)
    return residual