def __init__(self, meta):
self.meta = meta
self.vals = self.meta.ivals
self.sampler = self.meta.sampler
self.burns = 0
self.runs = 0
with open(self.meta.calctime,'w') as calctimer:
calctimer.write(str(timeit.default_timer())+' icalc \n')
calctimer.close()
# what outputs of emcee will we be saving?
self.stats = [ stats.stat_chains(self.meta),
stats.stat_probs(self.meta),
stats.stat_fracs(self.meta),
stats.stat_times(self.meta) ]
def __init__(self,input_address):
self.key = key.key(t=input_address)
# read input parameters
self.testdir = os.path.join('..','tests')
with open(os.path.join(self.testdir,input_address)) as infile:
lines = (line.split(None) for line in infile)
indict = {defn[0]:defn[1] for defn in lines}
# load data
with open(os.path.join(self.testdir,'topdirs.p')) as topdirs:
self.topdir = cpkl.load(topdirs)[input_address]
self.datadir = os.path.join(self.topdir,'data')
self.topdir = os.path.join(self.topdir,'mcmc')
if os.path.exists(self.topdir):
shutil.rmtree(self.topdir)
os.makedirs(self.topdir)
with open(os.path.join(self.datadir,'logdata.csv'),'rb') as csvfile:
tuples = (line.split(None) for line in csvfile)
alldata = [[float(pair[k]) for k in range(0,len(pair))] for pair in tuples]
self.binends = np.array(alldata[0])
self.nbins = len(self.binends)-1
self.binlos = self.binends[:-1]
self.binhis = self.binends[1:]
self.bindifs = self.binhis-self.binlos
self.bindif = sum(self.bindifs)/self.nbins
self.binmids = (self.binlos+self.binhis)/2.
self.logpobs = np.array(alldata[1:])
self.pobs = np.exp(np.array(alldata[1:]))
self.ngals = len(self.logpobs)
self.flatNz = np.array([float(self.ngals)/float(self.nbins)/self.bindif]*self.nbins)
self.logflatNz = np.log(self.flatNz)
self.trueZs = None
self.trueNz = None
self.logtrueNz = None
if os.path.exists(os.path.join(self.datadir,'logtrue.csv')):
with open(os.path.join(self.datadir,'logtrue.csv'),'rb') as csvfile:
tuples = (line.split(None) for line in csvfile)
trudata = [[float(pair[k]) for k in range(0,len(pair))] for pair in tuples]
self.trueZs = np.array(trudata[1:])
trueNz = [sys.float_info.epsilon]*self.nbins
for z in self.trueZs:
for k in xrange(self.nbins):
if z[0] > self.binlos[k] and z[0] < self.binhis[k]:
trueNz[k] += 1./self.bindif
self.trueNz = np.array(trueNz)
self.logtrueNz = np.log(self.trueNz)
# generate full Sheldon, et al. 2011 "posterior"
stackprep = np.sum(np.array(self.pobs),axis=0)
self.stack = np.array([max(sys.float_info.epsilon,stackprep[k]) for k in xrange(self.nbins)])
self.logstack = np.log(self.stack)
# generate MAP N(z)
self.mapNz = [sys.float_info.epsilon]*self.nbins
mappreps = [np.argmax(l) for l in self.logpobs]
for m in mappreps:
self.mapNz[m] += 1./self.bindifs[m]
self.logmapNz = np.log(self.mapNz)
# generate expected value N(z)
expprep = [sum(z) for z in self.binmids*self.pobs*self.bindifs]
self.expNz = [sys.float_info.epsilon]*self.nbins
for z in expprep:
for k in xrange(self.nbins):
if z > self.binlos[k] and z < self.binhis[k]:
self.expNz[k] += 1./self.bindifs[k]
self.logexpNz = np.log(self.expNz)
#print('logexpNz='+str(self.logexpNz))
# how many walkers
self.nwalkers = 2*self.nbins
#self.walknos = xrange(self.nwalkers)
# prior specification
if 'prior' in indict:
mean = indict['priormean']
self.mean = np.array([float(mean[i]) for i in range(0,self.nbins)])
covmat = indict['priorcov']
# if self.meta.random[self.n]:
# q = 1.#0.5
# e = 0.15/self.meta.zdif**2
# tiny = q*1e-6
# self.covmat = np.array([[q*m.exp(-0.5*e*(self.binmids[a]-self.binmids[b])**2.) for a in xrange(0,self.nbins)] for b in xrange(0,self.nbins)])+tiny*np.identity(self.nbins)
# else:
self.covmat = np.reshape(np.array([float(covmat[i]) for i in range(0,self.nbins**2)]),(self.nbins,self.nbins))
else:
self.mean = self.logflatNz
self.covmat = np.identity(self.nbins)
self.priordist = mvn(self.mean,self.covmat)
# posterior specification
self.postdist = post(self.priordist, self.binends, self.logpobs)
# sampler specification
self.sampler = emcee.EnsembleSampler(self.nwalkers, self.nbins, self.postdist.lnprob)
# initialization schemes
if 'inits' in indict:
self.inits = indict['init']
else:
self.inits = 'gs'#corresponding to 'ps', 'gm'
#generate initial values for walkers
if self.inits == 'ps':
self.ivals,self.mean = self.priordist.sample_ps(self.nwalkers)
self.init_names = 'Prior Samples'
elif self.inits == 'gm':
self.ivals,self.mean = self.priordist.sample_gm(self.nwalkers)
self.init_names = 'Gaussian Ball Around Mean'
elif self.inits == 'gs':
self.ivals,self.mean = self.priordist.sample_gs(self.nwalkers)
self.init_names = 'Gaussian Ball Around Prior Sample'
self.ivals_dir = os.path.join(self.topdir,'ivals.p')
with open(self.ivals_dir,'wb') as ival_file:
cpkl.dump(self.ivals,ival_file)
# parameters for MCMC
if 'miniters' in indict:
self.miniters = int(indict['miniters'])
else:
self.miniters = int(1e3)
if 'thinto' in indict:
self.thinto = int(indict['thinto'])
else:
self.thinto = 1
assert(self.miniters%self.thinto==0)
self.ntimes = self.miniters / self.thinto
assert(self.ntimes > self.nwalkers)
# # what outputs of emcee will we be saving?
self.stats = [ stats.stat_chains(self),
stats.stat_probs(self),
stats.stat_fracs(self),
stats.stat_times(self) ]
# colors for plots
self.colors='rgbymc'
outdict = {
'topdir': self.topdir,
'binends': self.binends,
'logpobs': self.logpobs,
'inits': self.inits,
'miniters': self.miniters,
'thinto': self.thinto,
'ivals': self.ivals,
'mean': self.mean,
'covmat': self.covmat
}
with open(os.path.join(self.topdir,'README.md'), 'a') as readme:
readme.write('\n')
readme.write(repr(outdict))
self.calctime = os.path.join(self.testdir, 'calctimer.txt')
if os.path.exists(self.calctime):
os.remove(self.calctime)
self.plottime = os.path.join(self.testdir, 'plottimer.txt')
if os.path.exists(self.plottime):
os.remove(self.plottime)
# self.iotime = os.path.join(self.testdir, 'iotimer.txt')
# if os.path.exists(self.iotime):
# os.remove(self.iotime)
print('ingested inputs and initialized sampling')