def run_tlinear(loc, region, scol=True):
"""Runs ``IRAF/TLINEAR`` (linear fit) on data in file ``tcreate.tab``.
Parameters:
scol : {True, False}
True by default. Set False if don't want to weight the fit
by the deltaflux error.
Returns:
nothing
Outputs:
text file. The linear fit stored in
``<filtername>_<amp><outfilemark>_tlinear.dat``.
text file. The parameters of the fit stored in
``<filtername>_<amp><outfilemark>_tlinear.hdr.dat``
"""
if scol:
iraf.tlinear(loc+'tcreate.tab', loc+'tlinear.tab', \
xcol='x', ycol='y', scol='yerror' )
else:
iraf.tlinear(loc+'tcreate.tab', loc+'tlinear.tab', \
xcol='x', ycol='y')
# Convert IRAF table to ascii file.
iraf.tdump(loc+'tlinear.tab', \
datafile = loc + region + '_tlinear.dat', \
pfile = loc + region + '_tlinear.hdr.dat')
# Delete the IRAF tables.
os.remove(loc + 'tcreate.tab')
os.remove(loc + 'tlinear.tab')
def getBgrd(tablename):
iraf.tables()
dumpfile = tablename + '_dump.txt'
try:
os.remove(dumpfile)
except OSError:
pass
iraf.tdump(table=tablename, columns='INTENS', datafile=dumpfile)
file = open(dumpfile, "r")
lineList = file.readlines()
lastLine = lineList[-1]
lastLine = lastLine.replace('\t', '')
lastLine = lastLine.replace('\s', '')
lastLine = lastLine.replace('\n', '')
bg = float(lastLine)
file.close()
os.remove(dumpfile)
print(bg)
iraf.ellipse.geompar.step = 0.01 iraf.ellipse.geompar.linear = False iraf.ellipse.geompar.recenter = True # iraf.ellipse.geompar.saveParList(filename="ellipse.par") # samplepar iraf.ellipse.samplepar.nclip = 1 iraf.ellipse.samplepar.sdevice = "stdgraph" # iraf.ellipse.samplepar.saveParList(filename="ellipse.par") # magpar : zero point photometry iraf.ellipse.magpar.mag0 = 20.3 # ellipse parameters iraf.ellipse(input=input_filename, output=output_bin) iraf.tdump(table=output_bin, datafile=output_table, cdfile=output_cdf) # model image construction iraf.bmodel.unlearn() iraf.bmodel(table=output_bin, output=model_image) # subtraction image = fits.open(input_filename)[0].data model = fits.open(model_image)[0].data fn = lambda x: np.nanmean((image - x[0] * model - np.nanmedian(image))**2) res = minimize(fn, (0.1, ), method="Nelder-Mead") iraf.imarith.unlearn() iraf.imarith.operand1 = model_image
def getMeaningfulColumns(self, input, columns, output):
'''
Uses IRAF task tdump for parsing columns from a table.
'''
I.tdump(input, colum=columns, datafil=output)
elif pa > 90:
pa = pa - 180.0
# iraf.ellipse.controlpar.minit = 10
# iraf.ellipse.controlpar.maxit = 100
# iraf.ellipse.controlpar.olthresh = 0.0
# iraf.ellipse(input=cur_dir+'tmp_V.fits',output=cur_dir+'tmp_V.tab',inter='no',x0=i,y0=j,ellip0=ellip,pa0=pa,sma0=thumb/10.0, \
# minsma=1,maxsma=thumb,linear='no',recenter='no',hcenter='yes',hellip='yes',hpa='yes',integrmode='median',refer=1.0, \
# zerolevel=0.0,mag0=22.5,nclip=0,step=0.3)
iraf.ellipse(input=cur_dir+'tmp_V.fits',output=cur_dir+'tmp_V.tab',inter='no',x0=i,y0=j,ellip0=ellip,pa0=pa,sma0=thumb/10.0, \
minsma=1,maxsma=thumb,linear='no',recenter='yes',hcenter='no',hellip='no',hpa='no',integrmode='median',refer=1.0, \
zerolevel=0.0,mag0=22.5,nclip=0,step=0.3)
iraf.tdump(table=cur_dir + 'tmp_V.tab',
datafile='/Users/dhk/work/data/NGC_IC/ellipse/pa_ellip/' +
name + '.txt',
columns='@colnames.lis')
copyfile(cur_dir + 'tmp_V.tab',
'/Users/dhk/work/data/NGC_IC/ellipse/pa_ellip/' + name + '.tab')
iraf.ellipse(input=work_dir + 'SHA/SHA_NGC_IC_LONG_mean/' + name + '.fits',
output=cur_dir + 'tmp_I1.tab',
inter='no',
inellip=cur_dir + 'tmp_V.tab',
mag0=21.5814)
iraf.tdump(table=cur_dir + 'tmp_I1.tab',
datafile='/Users/dhk/work/data/NGC_IC/ellipse/pa_ellip/' +
name + '_I1.txt',
columns='@colnames.lis')
copyfile(
cur_dir + 'tmp_I1.tab',
iraf.ellipse.controlpar.maxit = 100 # 9. Threshold for the object locator algorithm # By lowering this value, the locator become less strict. iraf.ellipse.controlpar.olthresh = 1.00000 # 10. Make sure the Interactive Mode is turned off iraf.ellipse.controlpar.interactive = "no" # Check and remove outputs from the previous Ellipse run, or Ellipse will report # error (Quite stupid!) if os.path.exists(outBin): os.remove(outBin) if os.path.exists(outTab): os.remove(outTab) if os.path.exists(outCdf): os.remove(outCdf) # Start the fitting iraf.ellipse(input=inputImg, output=outTab) # TODO: Demonstrate the direct photometry mode using input catalog # inBin = input_bin_file ## The inBin is a Binary result from previous Ellipse run, and the isophote ## stored in it will overwrite all the above settings. Ellipse will simply ## extract surface brightness profile using these isophote instead of doing any ## fitting # iraf.ellipse(input=inputImg, output=outBin, inellip=inBin) # The Ellipse output is a binary table file, which is very hard to deal with # "Dump" it into a nice ASCII table iraf.tdump(table=outBin, datafile=outTab, cdfile=outCdf) os.remove(outCdf)
if os.path.exists('tmp7.dqf'):
os.remove('tmp7.dqf')
hdu7 = fits.PrimaryHDU(betav_seg)
hdu7.writeto('tmp7.fits')
mask = betav_seg
mask[np.isnan(mask)] = int(32)
mask[np.where(mask != 32)] = int(0)
hdu7pl = fits.PrimaryHDU(mask)
hdu7pl.writeto('tmp7.dqf')
#iraf.ellipse(input='tmp7.fits',output='tmp7.tab',inter='no',inellip=work_dir+'ellipse/pa_ellip/'+name+'_I1.tab',dqf='.c1h')
iraf.ellipse(input='tmp7.fits',output='tmp7.tab',inter='no',x0=i,y0=j,dqf='.dqf')
iraf.tdump(table='tmp7.tab',datafile=work_dir+'ellipse/pa_ellip/'+name+'_bv_pl9.txt',columns='@colnames.lis')
if AV_fit:
if os.path.exists('tmp.fits'):
os.remove('tmp.fits')
if os.path.exists('tmp1.fits'):
os.remove('tmp1.fits')
if os.path.exists('tmp2.fits'):
os.remove('tmp2.fits')
if os.path.exists('tmp3.fits'):
os.remove('tmp3.fits')
if os.path.exists('tmp4.fits'):
os.remove('tmp4.fits')
if os.path.exists('tmp5.fits'):
os.remove('tmp5.fits')
## ====>>>> Constroi um modelo baseado no ellipse
iraf.bmodel(table=tab, output=model)
iraf.imarith(operand1=fich_comb, op="-", operand2=model, result=resid)
## ====>>>> Faz o grafico mag X R^(1/4) (perfil de de Vaucouleurs)
# iraf.isoplot(input=tab, xaxis="RSMA", yaxis="INTENS")
### ------ Saida de uma tabela ascii (.txt) para ajuste em outro programa
colunas = "SMA,RSMA,INTENS,INT_ERR,RMS,MAG,MAG_LERR,MAG_UERR"
colunas_2 = [
'SMA', 'RSMA', 'INTENS', 'INT_ERR', 'RMS', 'MAG', 'MAG_LERR', 'MAG_UERR'
]
iraf.tdump(table=tab, datafile=fich4, columns=colunas)
#ACABA AQUI
data = pd.read_csv(fich4, sep="\s+", header=None)
data.columns = colunas_2
data.to_csv(tabcsv)
### Apaga arquivos temporarios
# os.remove(fich2)
os.remove(fich4)
os.remove(tab)
print('Terminou. Veja os arquivos ', nome, ' Model e Resid.fits')
def getMeaningfulColumns(self, input, columns, output): ''' Uses IRAF task tdump for parsing columns from a table. ''' I.tdump(input, colum=columns, datafil=output)