def cal_image(filepath,plot = True,path=None,band='gp',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)
image0, header0 = qi.readimage(filepath)
refh = h.pyfits.getheader(filepath)
im = h.pyfits.open(filepath)
newim = h.hcongrid((im[0].data-dark-bias)/flat, im[0].header,refh)
if plot:
qi.display_image(newim)
return newim,header0
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 get_fiber_flux(file,fwhm=20,threshold=100):
image,header = qi.readimage(file,plot=False)
mean, median, std = sigma_clipped_stats(image, sigma=3.0)
source = daofind(image - median, fwhm=fwhm, threshold=threshold*std)
xcen = np.int(np.round(source['xcentroid'][0]))
ycen = np.int(np.round(source['ycentroid'][0]))
plt.plot([xcen],[ycen],'rx',markersize=20)
params = fitgaussian(image)
y,x = params[1],params[2]
apdict = tp.optimal_aperture(x,y,image,[150,160])
return apdict
# 3) Post cable repair, determine if repair improves fluxcable:fluxbase
# KO/JS 6/14/16: Drew from fiberphot.py and thacherphot.py, modified for purpose
import thacherphot as tp
import numpy as np
import matplotlib.pyplot as plt
from length import length
import quick_image as qi
import glob
from fitgaussian import *
from astropy.stats import sigma_clipped_stats
path = '/Users/ONeill/Dropbox/Observatory/Data/minerva/FiberTests/'
files = glob.glob(path+"lamp2*fit")
image,header = qi.readimage(files[0],plot=True,siglo=2,sighi=2)
params = fitgaussian(image)
#using params make model: amplitude, x center, y center, x width, y width, angle rotated
#using thacherphot to overplot contours of gaussian
#for every x y pixel have data value and model value, so subtract from each other and find residuals - should just look like noise
fit = gaussian(*params)
level = (np.max(image) - np.median(image))*np.array([0.95,0.5,0.1])
plt.figure(1)
plt.contour(fit(*indices(image.shape)),level,colors='blue')
def find_flux(image):
model = fit(*indices(image.shape))
qi.display_image(model)
