def stack_ims(path=None,band='gp',source='NGC188',bias=None,dark=None,flat=None):
if not path:
path = set_path()
if length(bias) == 1:
bias = make_bias(path=path)
if length(dark) == 1:
dark = make_dark(path=path)
if length(flat) == 1:
flat = make_flat(path=path,band=band,bias=bias,dark=dark)
files,sz = tp.get_files(dir=path, tag=source+'.'+band)
# Designate reference file
reffile = files[sz/2]
image0, header0 = qi.readimage(reffile)
ysz, xsz = np.shape(image0)
refim = h.pyfits.open(reffile)
refh = h.pyfits.getheader(reffile)
stack = np.zeros((xsz,ysz,sz))
for i in range(sz):
im = h.pyfits.open(files[i])
newim = h.hcongrid((im[0].data-dark-bias)/flat, im[0].header,refh)
stack[:,:,i] = newim
final = np.median(stack, axis=2)
return final,refh
def finish():
files, fct = tp.get_files(dir='/home/student/nedwards/2017A*/',
suffix='solved.fits')
data = reduc(files, dir='/home/student/nedwards/independent/')
standard = standards(data)
tabby = source(stuff, standard)
save(tabby)
return
def headerplot(fluxdata,ykeyword='airmass',badimindices = [],band = 'gp',titleextra = '',path = None, source = 'BD710031', bias = None, dark=None, flat=None):
if not path:
path = set_path()
if length(bias) == 1:
bias = make_bias(path=path)
if length(dark) == 1:
dark = make_dark(path=path)
if length(flat) == 1:
flat = make_flat(path=path,band=band,bias=bias,dark=dark)
files,osz = tp.get_files(dir=path, tag=source+'.'+band)
#remove faulty images
sz = osz
if len(badimindices) != 0:
for g in range(osz):
if g in badimindices:
del files[g]
sz=sz-1
keyworddata = []
headers = []
#extract headers from each image
for file in files:
file,header = cal_image(file,plot=False,path=path,band=band,source=source,bias=bias,dark=dark,flat=flat)
headers.append(header)
#extract keyword data from each header
for j in range(sz):
header = headers[j]
keyworddata.append(header[ykeyword])
#determine peak flux value, index, and its header for annotation
peakflux = max(fluxdata)
peakfluxindex = fluxdata.argmax()
keyworddataofpeakflux = keyworddata[peakfluxindex]
#normalize flux data
nfluxdata = fluxdata
for q in range(len(fluxdata)):
nfluxdata[q] = nfluxdata[q]/peakflux
#display plot; set y axis
plt.scatter(keyworddata,nfluxdata)
axes = plt.gca()
axes.set_ylim([0,1])
#labels
plt.title(ykeyword.capitalize()+' v. Flux (Band: ' + band + ')'+'\n'+'('+titleextra+')')
plt.xlabel(ykeyword.capitalize())
plt.ylabel('Flux')
xcoord = keyworddataofpeakflux
ycoord = 1
plt.annotate('Peak Flux = '+str(peakflux), xy=(xcoord,ycoord), xytext=(xcoord ,ycoord - .05))
def display_raws(path=None,band='gp',source='BD710031'):
if not path:
path = set_path()
files,sz = tp.get_files(dir=path, tag=source+'.'+band)
for i in range(len(files)):
image = fits.getdata(files[i],0)
qi.display_image(image,fignum=i)
def make_bias(path=None):
"""
Make master bias from raw data files
"""
if not path:
path = set_path()
biases,bct = tp.get_files(dir=path, tag='Bias')
masterbias = tp.master_bias(biases,outdir='./')
return masterbias
def make_dark(path=None, bias=None):
"""
Make master dark from raw data files
"""
if not path:
path = set_path()
if length(bias) == 1:
bias = make_bias(path=path)
darks,dct = tp.get_files(dir = path,tag='Dark')
masterdark = tp.master_dark(darks,bias=bias,outdir='./')
return masterdark
def make_flat(path=None,band='gp',bias=None,dark=None):
"""
Make master flat from raw data files in specified band
"""
if not path:
path = set_path()
if length(bias) == 1:
bias = make_bias(path=path)
if length(dark) == 1:
dark = make_dark(path=path, bias=bias)
flats,ct = tp.get_files(dir=path, tag='SkyFlat.'+band)
masterflat = tp.master_flat(flats, bias=bias, dark=dark, outdir='./', suffix=band)
return masterflat
def flux_all(path=None,band='gp',extraprecision=True,extraprecisionrange=100,source='BD710031',badimindices = [],bias=None,dark=None,flat=None):
if not path:
path = set_path()
if length(bias) == 1:
bias = make_bias(path=path)
if length(dark) == 1:
dark = make_dark(path=path)
if length(flat) == 1:
flat = make_flat(path=path,band=band,bias=bias,dark=dark)
files,osz = tp.get_files(dir=path, tag=source+'.'+band)
sz = osz
#remove faulty images
if len(badimindices) != 0:
for g in range(osz):
if g in badimindices:
del files[g]
sz=sz-1
flux = []
for file in files:
image,header = cal_image(file,plot=False,path=path,band=band,source=source,bias=bias,dark=dark,flat=flat)
w = wcs.WCS(header)
x,y = w.all_world2pix(float(header['TARGRA']), float(header['TARGDEC']), 1)
position = (float(x),float(y))
if extraprecision == True:
for k in range(extraprecisionrange):
if check_radii(image,header,position,rin=k+10,rout=k+15) == True:
op_ap,xval,yval,fwhm,aspect,snrmax,totflux,totap,chisq = \
tp.optimal_aperture(float(x),float(y),image,[k+10,k+15],use_old=True)
break
else:
op_ap,xval,yval,fwhm,aspect,snrmax,totflux,totap,chisq = \
tp.optimal_aperture(float(x),float(y),image,[10,15],use_old=True)
#print "Final aperture: " + str(op_ap)
phot = do_phot(image,position,radius=op_ap)
flux = np.append(flux,phot['aperture_sum_bkgsub'])
return flux
image,header = qi.readimage(files[50],plot=True,siglo=2,sighi=2)
w = wcs.WCS(header)
target = SkyCoord('19 51 39.82 +48 19 55.4', unit=(u.hourangle, u.deg))
world0 = np.array([[target.ra.degree, target.dec.degree]])
pix0 = w.wcs_world2pix(world0,1)
x0 = pix0[0,0]
y0 = pix0[0,1]
plt.figure(1)
plt.plot([x0],[y0],'y+',markersize=30)
plt.xlim(0,2048)
plt.ylim(0,2048)
darks,dct = tp.get_files(tag='Dark')
biases,bct = tp.get_files(tag='Bias')
flats,fct = tp.get_files(tag='SkyFlat')
targetfiles,tct = tp.get_files(tag='KIC10935310')
bias = tp.master_bias(biases,outdir='./Photometry/',readnoise=False)
dark = tp.master_dark(darks,bias=bias,outdir='./Photometry/')
flat = tp.master_flat(flats,bias=bias,dark=dark,outdir='./Photometry/')
cal = (image - dark - bias)/flat
qi.display_image(cal,siglo=2,sighi=2)
plt.plot([x0],[y0],'y+',markersize=30,linewidth=1e6)
plt.xlim(0,2048)
plt.ylim(0,2048)
import numpy as np
import matplotlib.pyplot as plt
import thacherphot as tp
import matplotlib as mpl
from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange
import matplotlib.gridspec as gridspec
import datetime
import jdutil
from astropysics.coords import AngularCoordinate as angcor
path = '/Users/jonswift/Astronomy/ThacherObservatory/data/22Dec2016/'
#path = '/Users/ONeill/astronomy/Data/04Dec2016/'
files,fct = tp.get_files(dir=path, prefix='WASP-50b',suffix='solved.fits')
bfiles,bct = tp.get_files(dir=path,prefix='WASP-50b',suffix='bias.fit')
master_bias = tp.master_bias(bfiles)
data = tp.batch_total_flux(files,object='WASP-50b',mag=11.6,SpT='g8v',bias=master_bias,
nearest=True,camera='iKON-L',filter='V',gain=3.0,network='swift')
#tau = data['tau']
#secz = data['secz']
#plt.clf()
#plt.ion()
#plt.figure()
#plt.plot(secz,tau,'o')
#plt.ylabel('tau')
#plt.xlabel('secz')
