def lnprob(x, C0, P, D, t_steps, pops):
xmin = zeros((len(x))) # set boundaries of prior (non-zero prior prob for parameters)
iswithinbounds = x > xmin
if iswithinbounds.all():
P['mortality']['0-1']['non-stunted'] = x[0]
P['mortality']['0-1']['stunted'] = x[1]
P['mortality']['1-6']['non-stunted'] = x[2]
P['mortality']['1-6']['stunted'] = x[3]
P['mortality']['6-12']['non-stunted'] = x[4]
P['mortality']['6-12']['stunted'] = x[5]
P['mortality']['12-24']['non-stunted']= x[6]
P['mortality']['12-24']['stunted'] = x[7]
P['mortality']['24-72']['non-stunted'] = x[8]
P['mortality']['24-72']['stunted'] = x[9]
return lnlike(C0, P, D, t_steps, pops)
else:
return -inf
def lnprob(x, C0, P, D, t_steps, pops):
xmin = zeros(
(len(x)
)) # set boundaries of prior (non-zero prior prob for parameters)
iswithinbounds = x > xmin
if iswithinbounds.all():
P['mortality']['0-1']['non-stunted'] = x[0]
P['mortality']['0-1']['stunted'] = x[1]
P['mortality']['1-6']['non-stunted'] = x[2]
P['mortality']['1-6']['stunted'] = x[3]
P['mortality']['6-12']['non-stunted'] = x[4]
P['mortality']['6-12']['stunted'] = x[5]
P['mortality']['12-24']['non-stunted'] = x[6]
P['mortality']['12-24']['stunted'] = x[7]
P['mortality']['24-72']['non-stunted'] = x[8]
P['mortality']['24-72']['stunted'] = x[9]
return lnlike(C0, P, D, t_steps, pops)
else:
return -inf
def gridPlot2D(numpts, data_mc, data_dirt, a0s):
# make empty grid
a1svec = np.linspace(-8, 5, numpts)
a2svec = np.linspace(-50, 50, numpts)
a1s, a2s = np.meshgrid(a1svec, a2svec)
lnlikes = np.zeros((len(a1s), len(a2s)))
# make lnlikelihood matrix
for i in tqdm(range(len(a1s))):
for j in range(len(a2s)):
theta = a0s, a1s[i][j], a2s[i][j]
lnlikes[i][j] = lk.lnlike(theta, np.zeros(6), data_mc, data_dirt)
'''
# convert to delta s and MA s
a3s = -20.*a0s - 10.*a1s - 4.*a2s
a4s = 45*a0s + 20.*a1s + 6.*a2s
a5s = -36.*a0s - 15.*a1s - 4.*a2s
a6s = 10.*a0s + 4.*a1s + a2s
t_cut = 9.*0.13957**2
t0 = -0.28
z0 = (np.sqrt(t_cut) - np.sqrt(t_cut-t0))/(np.sqrt(t_cut) + np.sqrt(t_cut - t0))
GaS = a0s + a1s*z0 + a2s*z0**2 + a3s*z0**3 + a4s*z0**4 + a5s*z0**5 + a6s*z0**6
MA = np.sqrt(np.abs(2.*GaS/(a1s + 2.*a2s*z0 + 3.*a3s*z0**2 + 4.*a4s*z0**3 + 5.*a5s*z0**4 + 6.*a6s*z0**5)))
'''
plt.figure(figsize=(5, 3.5))
plt.pcolor(a2s, a1s, np.exp(lnlikes), cmap=cmocean.cm.tempo)
plt.xlabel(r'$a_2^s$', fontsize=16)
plt.ylabel(r'$a_1^s$', fontsize=16)
plt.tick_params(axis='both', which='major', labelsize=14)
#plt.xlim(MA.min(),MA.max());
#plt.ylim(GaS.min(),GaS.max());
plt.colorbar()
from model import steps
from likelihood import lnlike, model_outcomes
from inputs import get_data, get_params, get_initial_conditions
from mcmc import mcmc
pops = ['fertile_women', '0-1', '1-6', '6-12', '12-24', '24-72']
D = get_data()
P = get_params()
C0 = get_initial_conditions()
t_steps = 1000 # model run time (months)
# Run the model
y = steps(P, C0, t_steps)
LL = lnlike(C0, P, D, t_steps, pops)
# Parameters to calibrate
meta_prefit = dict()
meta_prefit['mortality, 0-1, non-stunted'] = P['mortality']['0-1'][
'non-stunted']
meta_prefit['mortality, 0-1, stunted'] = P['mortality']['0-1']['stunted']
meta_prefit['mortality, 1-6, non-stunted'] = P['mortality']['1-6'][
'non-stunted']
meta_prefit['mortality, 1-6, stunted'] = P['mortality']['1-6']['stunted']
meta_prefit['mortality, 6-12, non-stunted'] = P['mortality']['6-12'][
'non-stunted']
meta_prefit['mortality, 6-12, stunted'] = P['mortality']['6-12']['stunted']
meta_prefit['mortality, 12-24, non-stunted'] = P['mortality']['12-24'][
'non-stunted']
meta_prefit['mortality, 12-24, stunted'] = P['mortality']['12-24']['stunted']
from model import steps from likelihood import lnlike, model_outcomes from inputs import get_data, get_params, get_initial_conditions from mcmc import mcmc pops = ['fertile_women', '0-1', '1-6', '6-12', '12-24', '24-72'] D = get_data() P = get_params() C0 = get_initial_conditions() t_steps = 1000 # model run time (months) # Run the model y = steps(P, C0, t_steps) LL = lnlike(C0, P, D, t_steps, pops) # Parameters to calibrate meta_prefit = dict() meta_prefit['mortality, 0-1, non-stunted'] = P['mortality']['0-1']['non-stunted'] meta_prefit['mortality, 0-1, stunted'] = P['mortality']['0-1']['stunted'] meta_prefit['mortality, 1-6, non-stunted'] = P['mortality']['1-6']['non-stunted'] meta_prefit['mortality, 1-6, stunted'] = P['mortality']['1-6']['stunted'] meta_prefit['mortality, 6-12, non-stunted'] = P['mortality']['6-12']['non-stunted'] meta_prefit['mortality, 6-12, stunted'] = P['mortality']['6-12']['stunted'] meta_prefit['mortality, 12-24, non-stunted'] = P['mortality']['12-24']['non-stunted'] meta_prefit['mortality, 12-24, stunted'] = P['mortality']['12-24']['stunted'] meta_prefit['mortality, 24-72, non-stunted'] = P['mortality']['24-72']['non-stunted'] meta_prefit['mortality, 24-72, stunted'] = P['mortality']['24-72']['stunted']
