def logarithmic_theta_from_mean(mean):
'''Return a theta that leads to a given logarithmic mean.'''
# set lower and upper bounds to limit the search process
lower = 0.01
upper = 0.99999
if mean < logser.mean(lower):
return lower
elif mean > logser.mean(upper):
return upper
else:
return bisect(lambda nu: logser.mean(nu) - mean, lower, upper)
def logarithmic_theta_from_mean(mean):
'''Return a theta that leads to a given logarithmic mean.'''
# set lower and upper bounds to limit the search process
lower = 0.01
upper = 0.99999
if mean<logser.mean(lower):
return lower
elif mean>logser.mean(upper):
return upper
else:
return bisect(lambda nu: logser.mean(nu)-mean,lower,upper)
decade_mean = np.mat(
np.kron(np.eye((T2 - T1 + 1) / 10 * dt),
np.ones((10 / dt, 10 / dt)) / 10 * dt))
# walk through each alternative
for value in ALTERNATIVES[PARAMETER]:
# use default parameter values from settings.py
parameters = DEFAULT.copy()
# except for PARAMETER, for which we are doing the loop
parameters[PARAMETER] = value
# two percent expected growth rate
parameters['Lambda'] = parameters['gamma'] + 0.02
# USA variance should match data
theta_USA = logarithmic_theta_from_concentration(USA_VAR /
parameters['gamma'])
nbar_USA = logser.mean(theta_USA)
print 'USA has theta=%f and n=%f' % (theta_USA, nbar_USA)
parameters['epsilonpibar'] = USA_GDP / nbar_USA
parameters['Ncap'] = Ncap
# test assumptions
assert 1 / (parameters['epsilon'] -
1) + parameters['theta_N'] <= 1 + parameters['theta_m0']
# create a scenario to be simulated with the parameters
scenario = Scenario(GDP_list,
population_list,
dt=dt,
how_long=142,
T1=T1,
# annual simulation
dt = 1
# helper matrix to calcuate decade means
decade_mean = np.mat(np.kron(np.eye((T2-T1+1)/10*dt),np.ones((10/dt,10/dt))/10*dt))
# walk through each alternative
for value in ALTERNATIVES[PARAMETER]:
# use default parameter values from settings.py
parameters = DEFAULT.copy()
# except for PARAMETER, for which we are doing the loop
parameters[PARAMETER] = value
# two percent expected growth rate
parameters['Lambda'] = parameters['gamma']+0.02
# USA variance should match data
theta_USA = logarithmic_theta_from_concentration(USA_VAR/parameters['gamma'])
nbar_USA = logser.mean(theta_USA)
print 'USA has theta=%f and n=%f' % (theta_USA, nbar_USA)
parameters['epsilonpibar'] = USA_GDP/nbar_USA
parameters['Ncap'] = Ncap
# test assumptions
assert 1/(parameters['epsilon']-1) + parameters['theta_N'] <= 1+parameters['theta_m0']
# create a scenario to be simulated with the parameters
scenario = Scenario(
GDP_list,population_list,dt=dt,how_long=142,
T1=T1,T2=T2,**parameters)
# size of scenario object. crucial if sending to picloud over network
print len(pickle.dumps(scenario))
