years,
smooth,
datatype='mortality',
param='b')
b_params = a_MLE, b_MLE, c_MLE, d_MLE, e_MLE
print(b_params)
#########################################
#Fit c_list to logistic function
L_0 = max(c_list)
k_0 = 1e-5 #1.5#0.2#1e-50
x_0 = 1985 #1995
L_MLE_c, k_MLE_c, x_MLE_c = util.logistic_est(c_list,
L_0,
k_0,
x_0,
years,
smooth,
datatype='mortality',
param='c')
c_params = L_MLE_c, k_MLE_c, x_MLE_c, np.min(c_list)
ages = np.linspace(0, 99, 100)
#Transition graphs
util.plot_data_transition_exp_estimates(a_params,
b_params,
c_params,
start,
end,
ages,
smooth,
ms.append(m)
scales.append(scale)
alphas = np.array(alphas)
betas = np.array(betas)
ms = np.array(ms)
scales = np.array(scales)
util.plot_params(start, end, smooth, alphas, betas, ms, scales, datatype='fertility')
#########################################
#Fit betas to logistic function
L_0 = 0.55
k_0 = 1.5
x_0 = 1995
L_MLE_beta, k_MLE_beta, x_MLE_beta = util.logistic_est(betas, L_0, k_0, x_0, years, smooth, datatype='fertility', param='Beta')
beta_params = L_MLE_beta, k_MLE_beta, x_MLE_beta, np.min(betas)
#########################################
#Fit alphas to logistic function
L_0 = max(alphas)
k_0 = 1.5
x_0 = 1995
L_MLE_alpha, k_MLE_alpha, x_MLE_alpha = util.logistic_est(alphas, L_0, k_0, x_0, years, smooth, datatype='fertility', param='Alpha', flip=True)
alpha_params = L_MLE_alpha, k_MLE_alpha, x_MLE_alpha, np.min(alphas)
#########################################
#Fit ms to logistic function
L_0 = 5#max(ms)
k_0 = 0.2#1e-50
x_0 = 1995
plt.plot(imm_yr)
plt.savefig('graphs/' + datatype + '/smooth_' + str(smooth) + '/' + section[0] + str(year))
plt.close()
####################
##### Fit Data #####
####################
years = np.linspace(1948, 2015, 2015 - 1948 + 1)
#########################################
#Fit imm_birth to logistic function
L_0 = max(imm_birth)
k_0 = 1.5
x_0 = 1995
L_MLE, k_MLE, x_MLE = util.logistic_est(imm_birth, L_0, k_0, x_0, years, smooth, datatype='immigration', param='birth', flip=True)
birth_params = L_MLE, k_MLE, x_MLE, np.min(imm_birth)
#########################################
#Fit imm_birth_1 to logistic function
L_0 = max(imm_birth_1)
k_0 = 1
x_0 = 1995
L_MLE, k_MLE, x_MLE = util.logistic_est(imm_birth_1, L_0, k_0, x_0, years, smooth, datatype='immigration', param='birth_1', flip=True)
birth_1_params = L_MLE, k_MLE, x_MLE, np.min(imm_birth_1)
#########################################
#Fit imm_birth_2 and imm_birth_3 to mean
birth_2_params = np.mean(imm_birth_2)
birth_3_params = np.mean(imm_birth_3)
ms.append(m)
scales.append(scale)
alphas = np.array(alphas)
betas = np.array(betas)
ms = np.array(ms)
scales = np.array(scales)
util.plot_params(start, end, smooth, alphas, betas, ms, scales, datatype='population')
#########################################
#Fit betas to logistic function
L_0 = 0.55
k_0 = 1.5
x_0 = 1995
L_MLE_beta, k_MLE_beta, x_MLE_beta = util.logistic_est(betas, L_0, k_0, x_0, years, smooth, datatype='population', param='Beta')
beta_params = L_MLE_beta, k_MLE_beta, x_MLE_beta, np.min(betas)
#########################################
#Fit alphas to logistic function
L_0 = max(alphas)
k_0 = 1.5
x_0 = 1995
L_MLE_alpha, k_MLE_alpha, x_MLE_alpha = util.logistic_est(alphas, L_0, k_0, x_0, years, smooth, datatype='population', param='Alpha')
alpha_params = L_MLE_alpha, k_MLE_alpha, x_MLE_alpha, np.min(alphas)
#########################################
#Fit ms to logistic function
L_0 = 5#max(ms)
k_0 = 0.2#1e-50
x_0 = 1995
params_list = [('a', a_list), ('b', b_list), ('p', p_list), ('q', q_list),
('Scale', scales)]
util.plot_params(start, end, smooth, params_list, datatype='population')
#########################################
#Fit a_list to logistic function
L_0 = max(a_list)
k_0 = 1.5
x_0 = 1995
L_MLE_a, k_MLE_a, x_MLE_a = util.logistic_est(a_list,
L_0,
k_0,
x_0,
years,
smooth,
datatype='population',
param='a',
flip=True)
a_params = L_MLE_a, k_MLE_a, x_MLE_a, np.min(a_list)
#########################################
#Fit b_list to logistic function
L_0 = 0.55
k_0 = 1.5
x_0 = 1995
L_MLE_b, k_MLE_b, x_MLE_b = util.logistic_est(b_list,
L_0,
k_0,
x_0,