reserr[key] = [[] for i in range(NM0,nm)]
#use only valid inputs
delkeys=[]
for k in bss[0].iterkeys():
if k=='flag' or k=='galnum' or bss[0][k][1]==float('nan') :#or bss[1][k][1]==bss[0][k][1]:
delkeys.append(k)
for k in delkeys:
del bss[0][k]
del bss[1][k]
import metscales as ms
nps = min (mpc.cpu_count()-1 or 1, MAXPROCESSES)
if multiproc and nps>1:
scales=[ms.diagnostics(newnsample, logf,nps) for i in range(nm)]
print >>logf, "\n\n\nrunning on %d threads\n\n\n"%nps
second_args=[sample,flux,err,nm,bss,mds,VERBOSE, dust_corr,VERBOSE,res,scales,nps, logf]
pool = mpc.Pool(processes=nps) # depends on available cores
rr = pool.map(calc, itertools.izip(range(NM0,nm), itertools.repeat(second_args))) # for i in range(nm): result[i] = f(i, second_args)
for ri,r in enumerate(rr):
for kk in r.iterkeys(): res[kk][ri]=r[kk][ri]
for ri,r in enumerate(rr):
for kk in r.iterkeys(): res[kk][ri]=r[kk][ri]
pool.close() # not optimal! but easy
pool.join()
for key in scales[i].mds.iterkeys():
if key in Zs:
res[key] = np.array(res[key]).T
for key in Zserr:
reserr[key] = [[] for i in range(NM0, nm)]
#use only valid inputs
delkeys = []
for k in bss[0].iterkeys():
if k == 'flag' or k == 'galnum' or bss[0][k][1] == float('nan'): # or bss[1][k][1]==bss[0][k][1]:
delkeys.append(k)
for k in delkeys:
del bss[0][k]
del bss[1][k]
import metscales as ms
nps = min(mpc.cpu_count() - 1 or 1, MAXPROCESSES)
if multiproc and nps > 1:
scales = [ms.diagnostics(newnsample, logf, nps) for i in range(nm)]
logf.write( "\n\n\nrunning on %d threads\n\n\n" % nps)
#print >> logf, "\n\n\nrunning on %d threads\n\n\n" % nps
second_args = [sample, flux, err, nm, bss, mds, VERBOSE, dust_corr, VERBOSE, res, scales, nps, logf]
pool = mpc.Pool(processes=nps) # depends on available cores
rr = pool.map(calc, itertools.izip(range(NM0, nm), itertools.repeat(second_args))) # for i in range(nm): result[i] = f(i, second_args)
for ri, r in enumerate(rr):
for kk in r.iterkeys():
res[kk][ri] = r[kk][ri]
pool.close() # not optimal! but easy
pool.join()
for key in scales[i].mds.iterkeys():
def sample(self, nsample=1000, from_estimate=False, test=False):
if (nsample == 1) and (from_estimate == False):
raise ValueError("for nsample = 1, use .estimate() method!")
elif (1 < nsample < 100) and (test == False):
raise ValueError("need at least 100 samples!")
elif (1 < nsample < 100) and (test == True):
print "not enough samples, remember to only " + "use this as a test-bed!"
# increasing sample by 10% to ensure
# robustness against rejected samples
nsample = int(nsample)
# set up a dictionary to store tables of relevant data for each spaxel
res_d = {}
tfcnames = [k for k in self.flux.colnames if len(self.flux[k]) > np.isnan(self.flux[k]).sum()]
self.tfcnames = tfcnames
# looping over nm measurements
pbar = ProgressBar(widgets=[Percentage(), Bar(), ETA()], maxval=self.nm).start()
for i in range(self.NM0, self.nm):
blockPrint()
galnum = self.flux["galnum"][i]
fr = self.flux[i]
er = self.err[i]
fluxi = {
k: np.random.normal(fr[k], er[k], nsample)
for k in fr.colnames
if ((k != "galnum") and (~np.isnan(fr[k])))
}
# set up a table for a given galnum
res_d[galnum] = t.Table()
# add a column for flux information
for n in tfcnames:
if n != "galnum":
if np.isnan(self.flux[n]).sum() != len(self.flux[n]):
res_d[galnum][n] = fluxi[n]
res_d[galnum][n].unit = u.Unit("1e-17 erg cm^-2 s^-1")
scales = ms.diagnostics(nsample, None, self.nps)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
success = metallicity.calculation(
scales,
fluxi,
self.nm,
"all",
1,
self.logf,
disp=self.verbose,
dust_corr=self.dust_corr,
verbose=self.verbose,
)
if success == -1:
raise ValueError(
"MINIMUM REQUIRED LINES: [OII]3727 "
+ "& [OIII] + 5007, or [NII]6584, and Ha & Hb if "
+ "you want dereddening"
)
for k, v in scales.mds.iteritems():
if type(v) == np.ndarray:
if np.isnan(v).sum() != len(v):
res_d[galnum][k] = v
enablePrint()
pbar.update(i)
pbar.finish()
self.res_d = res_d
self.nsample = nsample
self.Zdiags = res_d[galnum].colnames
def sample(self, nsample=1000, from_estimate=False, test=False):
if (nsample == 1) and (from_estimate == False):
raise ValueError('for nsample = 1, use .estimate() method!')
elif (1 < nsample < 100) and (test == False):
raise ValueError('need at least 100 samples!')
elif (1 < nsample < 100) and (test == True):
print 'not enough samples, remember to only ' + \
'use this as a test-bed!'
# increasing sample by 10% to ensure
# robustness against rejected samples
nsample = int(nsample)
# set up a dictionary to store tables of relevant data for each spaxel
res_d = {}
tfcnames = [
k for k in self.flux.colnames
if len(self.flux[k]) > np.isnan(self.flux[k]).sum()
]
self.tfcnames = tfcnames
#looping over nm measurements
pbar = ProgressBar(widgets=[Percentage(), Bar(),
ETA()],
maxval=self.nm).start()
for i in range(self.NM0, self.nm):
blockPrint()
galnum = self.flux['galnum'][i]
fr = self.flux[i]
er = self.err[i]
fluxi = {
k: np.random.normal(fr[k], er[k], nsample)
for k in fr.colnames
if ((k != 'galnum') and (~np.isnan(fr[k])))
}
# set up a table for a given galnum
res_d[galnum] = t.Table()
# add a column for flux information
for n in tfcnames:
if (n != 'galnum'):
if (np.isnan(self.flux[n]).sum() != len(self.flux[n])):
res_d[galnum][n] = fluxi[n]
res_d[galnum][n].unit = u.Unit('1e-17 erg cm^-2 s^-1')
scales = ms.diagnostics(nsample, None, self.nps)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
success = metallicity.calculation(scales,
fluxi,
self.nm,
'all',
1,
self.logf,
disp=self.verbose,
dust_corr=self.dust_corr,
verbose=self.verbose)
if success == -1:
raise ValueError('MINIMUM REQUIRED LINES: [OII]3727 ' + \
'& [OIII] + 5007, or [NII]6584, and Ha & Hb if ' + \
'you want dereddening')
for k, v in scales.mds.iteritems():
if type(v) == np.ndarray:
if np.isnan(v).sum() != len(v):
res_d[galnum][k] = v
enablePrint()
pbar.update(i)
pbar.finish()
self.res_d = res_d
self.nsample = nsample
self.Zdiags = res_d[galnum].colnames
for key in Zs:
res[key]=[[] for i in range(NM0,nm)]
#use only valid inputs
delkeys=[]
for k in bss[0].iterkeys():
if k=='flag' or k=='galnum' or bss[0][k][1]==0 :#or bss[1][k][1]==bss[0][k][1]:
delkeys.append(k)
for k in delkeys:
del bss[0][k]
del bss[1][k]
import metscales as ms
nps = min (mpc.cpu_count()-1 or 1, MAXPROCESSES)
scales=ms.diagnostics(newnsample, logf,nps)
if multiproc and nps>1:
print >>logf, "\n\n\nrunning on %d threads\n\n\n"%nps
second_args=[sample,flux,err,nm,bss,mds,VERBOSE, dust_corr,VERBOSE,res,scales,nps, logf]
pool = mpc.Pool(processes=nps) # depends on available cores
rr = pool.map(calc, itertools.izip(range(NM0,nm), itertools.repeat(second_args))) # for i in range(nm): result[i] = f(i, second_args)
for ri,r in enumerate(rr):
for kk in r.iterkeys(): res[kk][ri]=r[kk][ri]
for ri,r in enumerate(rr):
for kk in r.iterkeys(): res[kk][ri]=r[kk][ri]
pool.close() # not optimal! but easy
pool.join()
else:
#looping over nm spectra
