"Orion_Cal_R_0117": {"night": 20150117, "numbers": (465, 488)},
"Orion_Cal_V_0117": {"night": 20150117, "numbers": (489, 512)},
"Taurus_Cal_V_0118": {"night": 20150118, "numbers": (277, 300)},
"Taurus_Cal_R_0118": {"night": 20150118, "numbers": (301, 324)},
"Orion_Cal_V_0119": {"night": 20150119, "numbers": (1, 24)},
"Orion_Cal_R_0119": {"night": 20150119, "numbers": (25, 48)},
}
pyBDP_reduced = os.path.join(pyBDP_data, "pyBDP_reduced_images")
for group in groupDict.keys():
print("Processing group {0}".format(group))
night = "{0:d}".format(groupDict[group]["night"])
startNum, stopNum = groupDict[group]["numbers"]
# Loop through all the file numbers (be sure to include the LAST file!)
for fileNum in range(startNum, stopNum + 1):
# Construct the filename for this file
strNum = "{0:03d}".format(fileNum)
fileName = ".".join([str(night), strNum, "fits"])
thisFile = os.path.join(pyBDP_reduced, fileName)
if os.path.isfile(thisFile):
# Read in the file
thisImg = AstroImage(thisFile)
# Modify the header to match the group name and re-write
thisImg.header["OBJECT"] = group
thisImg.write()
print("Done!")
effective_gain = effective_gain,
read_noise = read_noise)
# It is only possible to "redo the astrometry" if the file on the disk
# First make a copy of the average image
tmpImg = avgImg.copy()
# Replace NaNs with something finite and name the file "tmp.fits"
tmpImg.arr = np.nan_to_num(tmpImg.arr)
tmpImg.filename = 'tmp.fits'
# Delete the sigma attribute
if hasattr(tmpImg, 'sigma'):
del tmpImg.sigma
# Record the temporary file to disk for performing astrometry
tmpImg.write()
# Solve the stacked image astrometry
avgImg1, success = image_tools.astrometry(tmpImg, override = True)
# With successful astrometry, save result to disk
if success:
print('astrometry succeded')
# Clean up temporary files
# TODO update to by system independent
# (use subprocess module and "del" command for Windows)
# See AstroImage.astrometry for example
del tmpImg
def on_key(event):
global fileList, targetList, fig, imgNum, brushSize
global maskDir, maskImg
global prevImg, thisImg, nextImg
global prevAxImg, thisAxImg, nextAxImg
global prevTarget, thisTarget, nextTarget
global prevMin, thisMin, nextMin
global prevMax, thisMax, nextMax
global prevLabel, thisLabel, nextLabel
# Handle brush sizing
if event.key == '1':
brushSize = 1
elif event.key == '2':
brushSize = 2
elif event.key == '3':
brushSize = 3
elif event.key == '4':
brushSize = 4
elif event.key == '5':
brushSize = 5
elif event.key == '6':
brushSize = 6
# Increment the image number
if event.key == 'right' or event.key == 'left':
if event.key == 'right':
#Advance to the next image
imgNum += 1
# Read in the new files
prevImg = thisImg
thisImg = nextImg
nextImg = AstroImage(fileList[(imgNum + 1) % len(fileList)])
# Update target info
prevTarget = thisTarget
thisTarget = nextTarget
nextTarget = targetList[(imgNum + 1) % len(fileList)]
# Compute new image display minima
prevMin = thisMin
thisMin = nextMin
nextMin = np.median(nextImg.arr) - 0.25*np.std(nextImg.arr)
# Compute new image display maxima
prevMax = thisMax
thisMax = nextMax
nextMax = np.median(nextImg.arr) + 2*np.std(nextImg.arr)
if event.key == 'left':
#Move back to the previous image
imgNum -= 1
# Read in the new files
nextImg = thisImg
thisImg = prevImg
prevImg = AstroImage(fileList[(imgNum - 1) % len(fileList)])
# Update target info
nextTarget = thisTarget
thisTarget = prevTarget
prevTarget = targetList[(imgNum - 1) % len(fileList)]
# Compute new image display minima
nextMin = thisMin
thisMin = prevMin
prevMin = np.median(prevImg.arr) - 0.25*np.std(prevImg.arr)
# Compute new image display maxima
nextMax = thisMax
thisMax = prevMax
prevMax = np.median(prevImg.arr) + 2*np.std(prevImg.arr)
#*******************************
# Update the displayed mask
#*******************************
# Check which mask files might be usable...
prevMaskFile = os.path.join(maskDir,
os.path.basename(prevImg.filename))
thisMaskFile = os.path.join(maskDir,
os.path.basename(thisImg.filename))
nextMaskFile = os.path.join(maskDir,
os.path.basename(nextImg.filename))
if os.path.isfile(thisMaskFile):
# If the mask for this file exists, use it
print('using this mask: ',os.path.basename(thisMaskFile))
maskImg = AstroImage(thisMaskFile)
elif os.path.isfile(prevMaskFile) and (prevTarget == thisTarget):
# Otherwise check for the mask for the previous file
print('using previous mask: ',os.path.basename(prevMaskFile))
maskImg = AstroImage(prevMaskFile)
elif os.path.isfile(nextMaskFile) and (nextTarget == thisTarget):
# Then check for the mask of the next file
print('using next mask: ',os.path.basename(nextMaskFile))
maskImg = AstroImage(nextMaskFile)
else:
# If none of those files exist, build a blank slate
# Build a mask template (0 = not masked, 1 = masked)
maskImg = thisImg.copy()
maskImg.filename = thisMaskFile
maskImg.arr = maskImg.arr.astype(np.int16) * np.int16(0)
maskImg.dtype = np.byte
maskImg.header['BITPIX'] = 16
# Update contour plot (clear old lines redo contouring)
axarr[1].collections = []
axarr[1].contour(xx, yy, maskImg.arr, levels=[0.5], colors='white', alpha = 0.2)
# Reassign image display limits
prevAxImg.set_clim(vmin = prevMin, vmax = prevMax)
thisAxImg.set_clim(vmin = thisMin, vmax = thisMax)
nextAxImg.set_clim(vmin = nextMin, vmax = nextMax)
# Display the new images
prevAxImg.set_data(prevImg.arr)
thisAxImg.set_data(thisImg.arr)
nextAxImg.set_data(nextImg.arr)
# Update the annotation
axList = fig.get_axes()
axList[1].set_title(os.path.basename(thisImg.filename))
prevStr = (str(prevImg.header['OBJECT']) + '\n' +
str(prevImg.header['FILTNME3'] + '\n' +
str(prevImg.header['POLPOS'])))
thisStr = (str(thisImg.header['OBJECT']) + '\n' +
str(thisImg.header['FILTNME3'] + '\n' +
str(thisImg.header['POLPOS'])))
nextStr = (str(nextImg.header['OBJECT']) + '\n' +
str(nextImg.header['FILTNME3'] + '\n' +
str(nextImg.header['POLPOS'])))
prevLabel.set_text(prevStr)
thisLabel.set_text(thisStr)
nextLabel.set_text(nextStr)
# Update the display
fig.canvas.draw()
# Save the generated mask
if event.key == 'enter':
# Make sure the header has the right values
maskImg.header = thisImg.header
# Write the mask to disk
print('Writing mask for file ', os.path.basename(maskImg.filename))
maskImg.write()
# Clear out the mask values
if event.key == 'backspace':
# Clear out the mask array
maskImg.arr = maskImg.arr * np.byte(0)
# Update contour plot (clear old lines redo contouring)
axarr[1].collections = []
axarr[1].contour(xx, yy, maskImg.arr, levels=[0.5], colors='white', alpha = 0.2)
# Update the display
fig.canvas.draw()
