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,
datatype='mortality')
util.plot_data_transition(mort_data,
start,
end,
ages,
smooth,
datatype='mortality')
util.overlay_estimates_mort(mort_data,
a_params,
b_params,
c_params,
start,
end,
ages,
smooth,
datatype='mortality')
#Graph comparison between 2014 and 2100
util.plot_2100_mort(a_params,
b_params,
fert_alt, mort_alt, imm_alt = prep_demog_alternate(fert_data, mort_data,
imm_data)
datatype = 'population_forecasts'
start = 1971
end = 2017
ages = np.linspace(0, 99, 100).astype(int)
birth_ages = np.linspace(14, 50, 37)
years = np.linspace(start, end, end - start + 1).astype(int)
# Plot true data transition
util.plot_data_transition(pop_data,
ages,
1970,
2014,
smooth=0,
datatype='population')
# Start forecasts
prev_pop = pop_data[start - 1]
for year in years:
pred_fert = util.forecast(fert_params,
year - 1,
birth_ages,
datatype='fertility',
options=False)
pred_mort = util.forecast(mort_params,
year - 1,
c_0 = -0.5
a_MLE, b_MLE, c_MLE = util.exponential_est(imm_yr, year, a_0, b_0, c_0, imm_yr.index, smooth, datatype='immigration', param='imm_fourth_' + str(year))
imm_fourth_as.append(a_MLE)
imm_fourth_bs.append(b_MLE)
imm_fourth_cs.append(c_MLE)
#########################################
#Fit imm_fourth_a, imm_fourth_b, and imm_fourth_c to mean
imm_fourth_a_params = np.mean(imm_fourth_as[:25]), np.mean(imm_fourth_as[25:])
imm_fourth_b_params = np.mean(imm_fourth_bs)
imm_fourth_c_params = np.mean(imm_fourth_cs)
plt.plot(range(start, end + 1), imm_fourth_as)
a_vals = [imm_fourth_a_params[0]] * 25 + [imm_fourth_a_params[1]] * (len(imm_fourth_as) - 25)
plt.plot(range(start, end + 1), a_vals)
plt.savefig('graphs/' + datatype + '/smooth_' + str(smooth) + '/_imm_fourth_as_predicted')
plt.close()
plt.plot(range(start, end + 1), imm_fourth_bs)
plt.plot(range(start, end + 1), [imm_fourth_b_params] * len(imm_fourth_bs))
plt.savefig('graphs/' + datatype + '/smooth_' + str(smooth) + '/_imm_fourth_bs_predicted')
plt.close()
plt.plot(range(start, end + 1), imm_fourth_cs)
plt.plot(range(start, end + 1), [imm_fourth_c_params] * len(imm_fourth_cs))
plt.savefig('graphs/' + datatype + '/smooth_' + str(smooth) + '/_imm_fourth_cs_predicted')
plt.close()
util.plot_data_transition(imm_rate, start, end, ages, smooth, datatype='immigration')
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 L_MLE_m, k_MLE_m, x_MLE_m = util.logistic_est(ms, L_0, k_0, x_0, years, smooth, datatype='population', param='M') m_params = L_MLE_m, k_MLE_m, x_MLE_m, np.min(ms) ######################################### #Fit scales to log function a_0 = 2e6 b_0 = np.min(years) - 1 c_0 = 1.9 d_0 = np.min(scales) e_0 = 1 a_MLE_scale, b_MLE_scale, c_MLE_scale, d_MLE_scale, e_MLE_scale = util.poly_est(scales, a_0, b_0, c_0, d_0, e_0, years, smooth, datatype='population', param='Scale', print_params=True) scale_params = a_MLE_scale, b_MLE_scale, c_MLE_scale, d_MLE_scale, e_MLE_scale #Transition graphs util.plot_data_transition_gen_gamma_estimates(beta_params, alpha_params, m_params, scale_params, start, end, ages, smooth, datatype='population') util.plot_data_transition(pop_data, start, end, ages, smooth, datatype='population') util.plot_data_transition_gen_gamma_overlay_estimates(pop_data, beta_params, alpha_params, m_params, scale_params, start, end, ages, smooth, datatype='population') #Graph comparison between 2014 and 2100 util.plot_2100(beta_params, alpha_params, m_params, scale_params, ages, smooth, datatype='population')
p_params = L_MLE_p, k_MLE_p, x_MLE_p, np.min(p_list)
#########################################
#Fit q_list to logistic function
L_0 = 0.55
k_0 = 0.31
x_0 = 1995
L_MLE_q, k_MLE_q, x_MLE_q = util.logistic_est(q_list, L_0, k_0, x_0, years, smooth, datatype='mortality', param='q', flip=True)
q_params = L_MLE_q, k_MLE_q, x_MLE_q, np.min(q_list)
#########################################
#Fit scales to logistic function
L_0 = max(scales)
k_0 = 1.5
x_0 = 1995
L_MLE_scale, k_MLE_scale, x_MLE_scale = util.logistic_est(scales, L_0, k_0, x_0, years, smooth, datatype='mortality', param='Scale')
scale_params = L_MLE_scale, k_MLE_scale, x_MLE_scale, np.min(scales)
non_infant_params = a_params, b_params, p_params, q_params, scale_params
ages = np.linspace(0, 99, 100)
#Transition graphs
util.plot_forecast_transition( (non_infant_params, infant_params), ages, start, end, smooth, datatype='mortality')
util.plot_data_transition(non_infant_mort, ages, start, end, smooth, datatype='mortality')
util.overlay_estimates(non_infant_mort, (non_infant_params, infant_params), ages, start, end, smooth, datatype='mortality')
#Graph comparison between 2014 and 2100
util.plot_2100( (non_infant_params, infant_params), ages, smooth, datatype='mortality')
pickle.dump( (non_infant_params, infant_params), open('data/demographic/parameters/mort.p', 'wb') )