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starprocessor.py
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starprocessor.py
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import numpy as np
import pyfits # PyFITS at https://pythonhosted.org/pyfits
import matplotlib.pyplot as plt
# Easier to hardcode the file, since we're working with a single file for the moment
filename = 'mosaic.fits'
maskthreshold = 35000
class StarProcessor:
def count_to_flux_error(self,inputcount,inputerror):
return 2.5*(inputcount**-1)*(np.log(10)**-1)*inputerror
def __init__(self):
self.OpenFile()
self.MaskAboveThreshold()
#self.ConvertFlux() #work with counts to start with not flux
#self.PreMask()
#self.RemoveBackground()
def ConvertFlux(self):
#converts whole image to flux
print self.header['MAGZPT']
print self.header['MAGZRR']
self.img = -2.5*np.log10(self.img)
self.img += self.header['MAGZPT']
self.RecalculateMasked()
def count_to_flux(self, inputcount):
return self.header['MAGZPT'] - 2.5*np.log10(inputcount)
def flux(self, coords):
#returns flux at given coordinates, converting the count reading into flux using the predefined MAGZPT value.
return self.header['MAGZPT'] - 2.5*np.log10(self.img[coords])
def PreMask(self):
#specfies any intial areas to be masked out and maskes them
#code to be written
self.RecalculateMasked()
def RemoveBackground(self):
#removes background
self.img = self.img - np.median(self.img)
self.img.clip(min=0)
self.RecalculateMasked()
def OpenFile(self):
self.hdudata = pyfits.open(filename) # we hardcode the input data file
self.img = self.hdudata[0].data # img is a NumPy array with the data
self.img = self.img[113:4512, 95:2470] #cuts off the noisy edges
self.mask = np.ones(self.img.shape, dtype='bool') # create mask with the same dimensions. We set the bit low to mask it
self.header = pyfits.getheader('mosaic.fits')
self.RecalculateMasked()
def FindBrightest(self):
# Finds the brightest unmasked pixel in an image and returns a tuple with its coordinates.
return np.unravel_index(np.argmax(self.masked), self.img.shape)
def MaskAboveThreshold(self):
self.mask[self.img > maskthreshold] = False
self.RecalculateMasked()
def MaskStar(self, coords, radius=700, threshperc = 0.10): # percentage of local maximum star intensity until we consider it no longer a star
# Masks star based on given pixel value, and returns a mask
newstar = True
localmask = np.ones(self.img.shape, dtype='bool')
#threshold = self.img[coords] + threshval # threshold value (edge of star)
threshold = self.img[coords]*threshperc
print threshold
print coords
# look directly up first until below threshold
for x in range(coords[0], coords[0] - radius, -1):
print "up: x=", x
if x < 0:
print "break point 1"
break
if self.img[x, coords[1]] < threshold: # not a star any more
break
else:
for y in range(coords[1], coords[1] - radius, -1): # left
if y < 0:
print "break point 2"
break
if self.img[x, y]==0:
newstar = false
print "not new star"
if self.img[x, y] < threshold: # not a star any more
break
else:
localmask[x, y] = 0
for y in range(coords[1], coords[1] + radius): # right
if y >= self.img.shape[1]:
print "break point 3"
break
if self.img[x, y] == 0:
newstar = false
print "not new star"
if self.img[x, y] < threshold: # not a star any more
break
else:
localmask[x, y] = 0
#now look down
for x in range(coords[0], coords[0] + radius):
print "down: x=", x
if x >= self.img.shape[0]:
#print "break point 4"
break
if self.img[x, coords[1]] < threshold: # not a star any more
break
else:
for y in range(coords[1], coords[1] - radius , -1): # left
if y < 0:
print "break point 5"
break
if self.img[x, y] == 0:
newstar = false
print "not new star"
if self.img[x, y] < threshold: # not a star any more
break
else:
localmask[x, y] = 0
for y in range(coords[1], coords[1] + radius): # right
if y >= self.img.shape[1]:
print "break point 6"
break
if self.img[x, y] == 0:
newstar = false
print "not new star"
if self.img[x, y] < threshold: # not a star any more
break
else:
localmask[x, y] = 0
self.mask = np.logical_and(self.mask, localmask)
self.RecalculateMasked()
return newstar
def MaskGalaxy(self, coords, Gradius = 12, inner_Bradius = 20, Bradius = 50):
#algorithm to caluclate average count of a galaxy
a, b = coords
y,x = np.ogrid[-a:self.img.shape[0]-a, -b:self.img.shape[1]-b]
gal = (x*x + y*y <= Gradius*Gradius)
annulus = (x*x + y*y <= Bradius*Bradius) & (x*x + y*y >= inner_Bradius*inner_Bradius)
#plt.clf()
#plt.imshow(localmask)
#plt.show()
localbck = np.median(self.img[np.logical_and(self.mask, annulus)])
localbckerror = np.std(self.img[np.logical_and(self.mask, annulus)])
#print 'backerro:',localbckerror
#print "localbck", localbck
avecount = np.mean(self.img[gal])
counterror = np.std(self.img[gal])
#print 'count error:',counterror
#print "average count", avecount
avecount = avecount-localbck
localmask = np.logical_not(gal)
#print "true <count>", avecount
self.mask = np.logical_and(self.mask, localmask)
self.RecalculateMasked()
totalerror = (localbckerror**2+counterror**2)**0.5
#prtin 'total error',
return avecount, totalerror
def RecalculateMasked(self):
self.masked = self.img*self.mask
def FindGalaxyRadius(self,coords,threshold=1.1, Gradius=12, Bradius=50, maxradius=100):
# Adaptively tries to find the radius of the galaxy by estimating a local background
a, b = coords
y,x = np.ogrid[-a:self.img.shape[0]-a, -b:self.img.shape[1]-b]
annulus = (x*x + y*y <= Bradius*Bradius) & (x*x + y*y >= Gradius*Gradius)
localbck = np.median(self.img[annulus])
thresh = localbck * threshold
for r in range(1, 100):
testring = (x*x + y*y == r*r)
if np.mean(self.img[testring]) < thresh: break
return r
def MaskCircle(self, coords, radius=12):
for y in range(-radius+1,radius):
for x in range(-radius+1,+radius):
if ((coords[0]+x)<self.img.shape[0]) and ((coords[1]+y)<self.img.shape[1]):
if((coords[0]+x)>0) and ((coords[1]+y)>0):
if (x**2+y**2)<radius**2:
if self.mask[coords[0]+x,coords[1]+y]:
self.mask[coords[0]+x,coords[1]+y] = 0
else:
newstar = False
self.RecalculateMasked()
return newstar