Multiple time "Init failure" error witth attribute error "__dict__" - python-2.7

Question

I have a bunch of code, Program is written in python2 and used old version of pymc. probably version2.x .
When i run
python run.py
the error i am facing
Init failure
Init failure
Init failure
Init failure
Init failure
Init failure
Init failure
Init failure
No previous MCMC data found.
Traceback (most recent call last):
File "run.py", line 106, in <module>
M=run_MCMC(ms)
File "run.py", line 94, in run_MCMC
mcmc = pm.MCMC(model, db=db, name=name)
File "/home/divyadeep/miniconda3/envs/detrital/lib/python2.7/site-packages/pymc/MCMC.py", line 90, in init
**kwds)
File "/home/divyadeep/miniconda3/envs/detrital/lib/python2.7/site-packages/pymc/Model.py", line 191, in init
Model.init(self, input, name, verbose)
File "/home/divyadeep/miniconda3/envs/detrital/lib/python2.7/site-packages/pymc/Model.py", line 92, in init
ObjectContainer.init(self, input)
File "/home/divyadeep/miniconda3/envs/detrital/lib/python2.7/site-packages/pymc/Container.py", line 605, in init
input_to_file = input.dict
AttributeError: 'NoneType' object has no attribute 'dict'`
I have tried to comment out some of 'init' in the program. but still not able to run.
the run.py is as
def InitExhumation(settings):
"""Initialize piece-wise linear exhumation model"""
#Check that erosion and age break priors are meaningful
if (settings.erate_prior[0] >= settings.erate_prior[1]):
print "\nInvalid range for erate_prior."
sys.exit()
if (settings.abr_prior[0] >= settings.abr_prior[1]):
print "\nInvalid range for abr_prior."
sys.exit()
#Create erosion rate parameters (e1, e2, ...)
e = []
for i in range(1,settings.breaks+2):
e.append(pm.Uniform("e%i" % i, settings.erate_prior[0], settings.erate_prior[1]))
#Create age break parameters (abr1, ...)
abr_i = settings.abr_prior[0]
abr = []
for i in range(1,settings.breaks+1):
abr_i = pm.Uniform("abr%i" % i, abr_i, settings.abr_prior[1])
abr.append(abr_i)
return e, abr
def ExhumationModel(settings):
"""Set up the exhumation model"""
#Check that error rate priors are meaningful
if (settings.error_prior[0] >= settings.error_prior[1]):
print "\nInvalid range for error_prior."
sys.exit()
err = pm.Uniform('RelErr',settings.error_prior[0],settings.error_prior[1])
#Closure elevation priors
hc_parms={'AFT':[3.7, 0.8, 6.0, 2.9], 'AHe':[2.2, 0.5, 3.7, 1.6]}
e, abr = InitExhumation(settings)
nodes = [err, e, abr]
hc = {}
for sample in settings.samples:
parms = e[:]
h_mu = np.mean(sample.catchment.z)
if sample.tc_type not in hc.keys():
hc[sample.tc_type] = pm.TruncatedNormal("hc_%s"%sample.tc_type, h_mu-hc_parms[sample.tc_type][0],
1/hc_parms[sample.tc_type][1]**2,
h_mu-hc_parms[sample.tc_type][2],
h_mu-hc_parms[sample.tc_type][3])
nodes.append(hc[sample.tc_type])
parms.append(hc[sample.tc_type])
parms.extend(abr)
if isinstance(sample, DetritalSample):
idx_i = pm.Categorical("Index_" + sample.sample_name, p = sample.catchment.bins['w'], size=len(sample.dt_ages))
nodes.extend([idx_i])
exp_i = pm.Lambda("ExpAge_" + sample.sample_name, lambda parm=parms, idx=idx_i: ba.h2a(sample.catchment.bins['h'][idx],parm))
value = sample.dt_ages
else:
idx_i = None
exp_i = pm.Lambda("ExpAge_" + sample.sample_name, lambda parm=parms: ba.h2a(sample.br_elevation,parm), plot=False)
value = sample.br_ages
obs_i = pm.Normal("ObsAge_" + sample.sample_name, mu = exp_i, tau = 1./(err*exp_i)**2, value = value, observed=True)
sim_i = pm.Lambda("SimAge_" + sample.sample_name, lambda ta=exp_i, err=err: pm.rnormal(mu = ta, tau = 1./(err*ta)**2))
nodes.extend([exp_i, obs_i, sim_i])
return nodes
def run_MCMC(settings):
"""Run MCMC algorithm"""
burn = settings.iterations/2
thin = (settings.iterations-burn) / settings.finalChainSize
name = "%s" % settings.model_name + "_%ibrk" % settings.breaks
attempt = 0
model=None
while attempt<5000:
try:
model = ExhumationModel(settings)
break
except pm.ZeroProbability, ValueError:
attempt+=1
#print "Init failure %i" % attemp
print "Init failure "
try:
#The following creates text files for the chains rather than hdf5
db = pm.database.txt.load(name + '.txt')
#db = pm.database.hdf5.load(name + '.hdf5')
print "\nExisting MCMC data loaded.\n"
except AttributeError:
print "\nNo previous MCMC data found.\n"
db='txt'
mcmc = pm.MCMC(model, db=db, name=name)
#mcmc.use_step_method(pm.AdaptiveMetropolis, M.parm)
if settings.iterations > 1:
mcmc.sample(settings.iterations,burn=burn,thin=thin)
return mcmc
if __name__ == '__main__':
sys.path[0:0] = './' # Puts current directory at the start of path
import model_setup as ms
if len(sys.argv)>1: ms.iterations = int(sys.argv[1])
M=run_MCMC(ms)
#import pdb; pdb.set_trace()
#Output and diagnostics
try:
ba.statistics(M, ms.samples)
except TypeError:
print "\nCannot compute stats without resampling (PyMC bug?).\n"
ps.chains(M, ms.finalChainSize, ms.iterations, ms.samples, ms.output_format)
ps.summary(M, ms.samples, ms.output_format)
ps.ks_gof(M, ms.samples, ms.output_format)
ps.histograms(ms.samples, ms.show_histogram, ms.output_format)
ps.discrepancy(M, ms.samples, ms.output_format)
## ps.unorthodox_ks(M, ms.output_format)
## try:
## ps.catchment(M.catchment_dem, format=ms.output_format)
## except KeyError:
## print "\nUnable to generate catchment plot."
M.db.close()
`