except (OverflowError,AttributeError,ValueError):
if VERBOSE: print data
print name, 'had infinities (or something in plotting went wrong)'
return "-1, -1,-1",[],None
def calc((i, (sample, flux, err, nm, bss, mds, disp, dust_corr, verbose, res, scales, nps, logf))):
logf=sys.stdout
print >>logf,"\n\nreading in measurements ",i+1
fluxi={}#np.zeros((len(bss[0]),nm),float)
for k in bss[0].iterkeys():
print >>logf,'{0:15} '.format(k),
print >>logf,'{0:0.2} +/- {1:0.2}'.format(flux[k][i],err[k][i])
fluxi[k]=flux[k][i]*np.ones(len(sample[i]))+err[k][i]*sample[i]
warnings.filterwarnings("ignore")
success=metallicity.calculation(scales[i],fluxi,nm,mds,nps,logf,disp=disp, dust_corr=dust_corr,verbose=verbose)
if success==-1:
print >>logf, "MINIMUM REQUIRED LINES: '[OII]3727','[OIII]5007','[NII]6584','[SII]6717'"
for key in scales[i].mds.iterkeys():
if key in res.keys():
res[key][i] = scales[i].mds[key]
if res[key][i] is None:
res[key][i] = [float('NaN')]*len(sample)
return res
##############################################################################
## The main function. takes the flux and its error as input.
## filename - a string 'filename' common to the three flux files
## flux - np array of the fluxes
## err - the flux errors, must be the same dimension as flux
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
return "-1\t -1\t -1", [], None
def calc((i, (sample, flux, err, nm, bss, mds, disp, dust_corr,
verbose, res, scales, nps, logf))):
logf = sys.stdout
logf.write("\n\nreading in measurements %d\n"%(i + 1))
fluxi = {} # np.zeros((len(bss[0]),nm),float)
for k in bss[0].iterkeys():
logf.write('{0:15} '.format(k))
logf.write('{0:0.2} +/- {1:0.2}\n'.format(flux[k][i], err[k][i]))
#print >> logf, '{0:15} '.format(k),
#print >> logf, '{0:0.2} +/- {1:0.2}'.format(flux[k][i], err[k][i])
fluxi[k] = flux[k][i] * np.ones(len(sample[i])) + err[k][i] * sample[i]
warnings.filterwarnings("ignore")
success = metallicity.calculation(scales[i], fluxi, nm, mds, nps, logf, disp=disp, dust_corr=dust_corr, verbose=verbose)
if success == -1:
logf.write("MINIMUM REQUIRED LINES: '[OII]3727','[OIII]5007','[NII]6584','[SII]6717'\n")
#print >> logf, "MINIMUM REQUIRED LINES: '[OII]3727','[OIII]5007','[NII]6584','[SII]6717'"
for key in scales[i].mds.iterkeys():
if key in res.keys():
res[key][i] = scales[i].mds[key]
if res[key][i] is None:
res[key][i] = [float('NaN')] * len(sample)
return res
##############################################################################
## The main function. takes the flux and its error as input.
## filename - a string 'filename' common to the three flux files
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