def centroidFunc():
# Display the Object Image
"Open the object image and display using DS9"
numdisplay.display(objectImage)
normalizedObject = modify(flatImage)
image = noise(normalizedObject)
# Bright Pixel
"Get bright pixel location"
xPos = input("Please enter the x value for the location of the bright pixel: ")
yPos = input("Please enter the y value for the location of the bright pixel: ")
print "The radius corresponds to the numer of points to be used in the calculation i.e. 1->9 and 3->49"
radius = input("Please enter the radius for the location of the bright pixel: ")
brightLoc = image[yPos - radius - 1 : yPos + radius, xPos - radius - 1 : xPos + radius]
# Find Centroid
"Call Centroid Function to locate the given centroid"
centroidArray = calcCentroid(brightLoc)
centroid = (xPos + centroidArray[0], yPos + centroidArray[1])
uncertainty = centroidArray[2]
# Output
print "The centroid of the object is: " + str(centroid)
print "The uncertainty of the calculation is: " + str(uncertainty)
"Give choice for a visual"
choice = raw_input("Would you like to see a visual (Y/N)? ")
if choice == "y" or choice == "Y" or choice == "Yes" or choice == "yes":
visualFunc(brightLoc, centroidArray[0], centroidArray[1], uncertainty)
else:
print "Goodbye!"
def centroidFunc():
# -- Display the Object Image -- #
#Open the object image and display using DS9
numdisplay.display(objectImage)
image = noise(objectImage)
choice = raw_input(
"Would you like to create an output centroid file (Y/N)? ")
outputChoice = False
fileOut = None
if choice == "y" or choice == "Y" or choice == "Yes" or choice == "yes":
fileName3 = raw_input("Please enter file name of centroid outputs: ")
fileOut = open(fileName3 + ".txt", "w")
fileOut.write("Centroid Report\n\n")
outputChoice = True
choice2 = raw_input(
"Would you like to import search data from a file (Y/N)? ")
if choice2 == "y" or choice2 == "Y" or choice2 == "Yes" or choice2 == "yes":
centroidProcess2(image, outputChoice, fileOut)
else:
centroidProcess1(image, outputChoice, fileOut, True)
if outputChoice == True:
fileOut.close()
def centroidFunc( ):
# -- Display the Object Image -- #
#Open the object image and display using DS9
numdisplay.display(objectImage)
image = noise( objectImage )
choice = raw_input("Would you like to create an output centroid file (Y/N)? ")
outputChoice = False
fileOut = None
if choice == "y" or choice == "Y" or choice == "Yes" or choice == "yes":
fileName3 = raw_input("Please enter file name of centroid outputs: ")
fileOut = open(fileName3 + ".txt", "w" )
fileOut.write( "Centroid Report\n\n" )
outputChoice = True
choice2 = raw_input("Would you like to import search data from a file (Y/N)? ")
if choice2 == "y" or choice2 == "Y" or choice2 == "Yes" or choice2 == "yes":
centroidProcess2( image, outputChoice, fileOut )
else:
centroidProcess1( image, outputChoice, fileOut, True )
if outputChoice == True:
fileOut.close()
def array2ds9(array, frame=1, name=None, zscale=False):
ds9frame = frame % 16
try:
numdisplay.display(array.transpose(),
z1=array.min(),
z2=array.min(),
name=name,
frame=ds9frame,
zscale=zscale,
quiet=True)
except IOError:
import os, time
os.system('ds9 &')
while True:
try:
numdisplay.display(array.transpose(),
z1=array.min(),
z2=array.min(),
name=name,
frame=ds9frame,
zscale=zscale,
quiet=True)
except IOError:
time.sleep(0.5)
else:
break
def open_in_ds9(self, field, take_log=True):
"""
This will open a given field in DS9. This requires the *numdisplay*
package, which is a simple download from STSci. Furthermore, it
presupposed that it can connect to DS9 -- that is, that DS9 is already open.
"""
import numdisplay
numdisplay.open()
if take_log: data = na.log10(self[field])
else: data = self[field]
numdisplay.display(data)
def process(obj,flat):
#Normalize with respect to flat file
obj_img=pyfits.getdata(obj)
numdisplay.display(obj_img,z1=2000,z2=40000)
flat_img=pyfits.getdata(flat)
flat_img=flat_img/flat_img.mean()
obj_img=obj_img/flat_img
x_c=input("Enter x coordinate of asteroid: ")
y_c=input("Enter y coordinate of asteroid: ")
#Subtract background noise
obj_img=obj_img-background_noise(obj_img,x_c,y_c)
obj_img[where(obj_img<0)]=0
numdisplay.display(obj_img,z1=100,z2=400)
#Computes centroid
n=input("Radius of asteroid-field: ")
center=centroid(obj_img[y_c-1-n:y_c-1+n+1,x_c-1-n:x_c-1+n+1])
obj_img*=0.1
x=round(x_c+center[0][0])
y=round(y_c+center[1][0])
obj_img[y-3:y+3,x-3:x+3]=20000
numdisplay.display(obj_img,z1=100,z2=10000)
return [[x_c+center[0][0],center[0][1]],[y_c+center[1][0],center[1][1]]]
import pyfits import numdisplay from numpy import * filename = "file" xcentre = 500 ycentre = 500 radlimit = 50 imdata = pyfits.getdata(filename) y, x = indices(imdata.shape, dtype=float32) x = x - xcentre y = y - ycentre radius = sqrt(x**2 + y**2) mask = radius < radlimit flux = (mask*im).sum() numdisplay.display(mask*im)
if not os.path.isfile(file):
print file,' is not a file.'
exit(1)
file = file if file.endswith('.ucm') else file + '.ucm'
ucm = ucm.rucm(file)
print 'read ucm file'
if len(sys.argv) < 3 and len(ucm.data) > 1:
nccd = int(raw_input('CCD number to display: '))
elif len(ucm.data) > 1:
nccd = int(sys.argv[2])
else:
nccd = 1
xbin = ucm.xbin
ybin = ucm.ybin
image = npy.zeros((ucm.nxtot//xbin,ucm.nytot//ybin))
for i in range(len(ucm.data[nccd-1])):
win = ucm.data[nccd-1][i]
(llx,lly) = ucm.off[nccd-1][i]
x1 = (llx-1)/xbin+1
y1 = (lly-1)/ybin+1
image[y1:y1+win.shape[0],x1:x1+win.shape[1]] = win
ndpy.display(image)
#!/usr/bin/env python
import numdisplay, numpy
import astropy.io.fits as pyfits
fitsobj=pyfits.open('ned_test.fits',"readonly")
pix=fitsobj[0].data
numdisplay.display(pix,z1=5.,z2=25.)
def flatten(obj_img,flat_img):
numdisplay.display(obj_img,z1=0,z2=400)
flat_img=flat_img/flat_img.mean()
obj_img=obj_img/flat_img
return obj_img
def display(*args, **kwargs):
numdisplay.display(*args, **kwargs)
