def params(self):
" Runs through each spectrum in the sp array to calculate the parameters"
useezmap = False
from Sp_parameters import param
import warnings
print('Running Parameters')
if useezmap:
import ezmap
from ezmap import map as zmap
warnings.filterwarnings("ignore",category=RuntimeWarning,module="param")
warnings.filterwarnings("ignore",category=RuntimeWarning,module="Sp_parameters.normsp")
sp = self.sp
wvl = self.wvl
if sp.ndim == 5:
w,r,t = np.mgrid[0:2,0:len(self.ref),0:len(self.tau)]
if useezmap:
gx = lambda a:param(sp[a[0],:,0,a[1],a[2]],wvl)
args = ((aw,ar,at) for aw in w.ravel() for ar in r.ravel() for at in t.ravel())
partemp = zmap(gx,args,progress=True,ncpu=2)
else:
applypar = lambda w,r,t:param(sp[w,:,0,r,t],wvl)
partemp = map(applypar,w.ravel(),r.ravel(),t.ravel())
par = np.reshape(partemp,[2,len(self.ref),len(self.tau),-1])
self.npar = par.shape[3]
elif sp.ndim == 2:
applypartime = lambda tt:param(sp[tt,:],wvl)
if useezmap:
par = np.array(zmap(applypartime,xrange(len(self.utc)),ncpu=2,progress=True))
else:
part = map(applypartime,xrange(len(self.utc)))
par = np.array(part)
self.npar = par.shape[1]
else: raise LookupError
self.par = par
return
def build_std(self):
"""
Function that creates a set of uncertainty for each parameter.
Currently just uses white noise (gaussian)
"""
from Sp_parameters import param
meansp = self.mean()
stdsp = self.std()
num_noise = 200
noise = np.random.normal(1,0.005,(num_noise,self.wvl.size)) # add 0.5% variance to signal at all wavelengths
# should be at every sp in utc, but for now, use mean sp
sp_arr = meansp*noise
#import code; code.interact(local=locals())
par_noisy = np.array(map(lambda tt:param(sp_arr[tt,:],self.wvl),xrange(num_noise)))
notaxis = tuple(np.where(par_noisy.shape != self.npar)[0])
stdpar = np.nanstd(par_noisy,axis=notaxis)
self.stdpar = stdpar
return stdpar
