def genkml(fitsfile=None,
imagefile=None,
input_image_origin_is_lower_left=False):
max_side_length = 3000
# Check for an input image.
if not imagefile:
print "Error: no input imagefile was given"
print "Type '%s --help' for more information" % argv[0]
sys.exit(2)
elif not os.path.exists(imagefile):
print "Error: file '%s' doesn't exist" % imagefile
sys.exit(2)
# Read the WCS.
sys.stdout.write("Reading WCS from %s... " % fitsfile)
sys.stdout.flush()
fitslib.fits_simple_verify(fitsfile)
fits = pyfits.open(fitsfile)
try:
header = fits[0].header
wcs = wcslib.WcsProjection(header)
finally:
fits.close()
sys.stdout.write("done\n")
# Set the various warping options and warp the input image.
image = Image.open(imagefile)
projection = SkyProjection(image, wcs)
projection.backgroundColor = (0, 0, 0, 0)
projection.maxSideLength = max_side_length
if input_image_origin_is_lower_left:
projection.inputImageOrigin = ImageOrigin.LOWER_LEFT
else:
projection.inputImageOrigin = ImageOrigin.UPPER_LEFT
sys.stdout.write("Warping image... ")
sys.stdout.flush()
start = time.time()
projected_image = projection.warpImage()
stop = time.time()
sys.stdout.write("done in %f sec\n" % (stop - start))
# Write warped image to file.
name, ext = os.path.splitext(imagefile)
projected_imagefile = "%s_warped.png" % name
sys.stdout.write("Writing warped image to %s... " % projected_imagefile)
sys.stdout.flush()
projected_image.save(projected_imagefile)
sys.stdout.write("done\n")
# Write bounding box to KML file.
kmlname = fitsfile[0:-4] + 'kml'
bounding_box = projection.boundingBox
f = open(kmlname, "w")
try:
f.write(bounding_box.toKml(projected_imagefile))
finally:
f.close()
sys.stdout.write("done\n")
def genkml(fitsfile=None,imagefile=None,input_image_origin_is_lower_left = False):
max_side_length = 3000
# Check for an input image.
if not imagefile:
print "Error: no input imagefile was given"
print "Type '%s --help' for more information" % argv[0]
sys.exit(2)
elif not os.path.exists(imagefile):
print "Error: file '%s' doesn't exist" % imagefile
sys.exit(2)
# Read the WCS.
sys.stdout.write("Reading WCS from %s... " % fitsfile)
sys.stdout.flush()
fitslib.fits_simple_verify(fitsfile)
fits = pyfits.open(fitsfile)
try:
header = fits[0].header
wcs = wcslib.WcsProjection(header)
finally:
fits.close()
sys.stdout.write("done\n")
# Set the various warping options and warp the input image.
image = Image.open(imagefile)
projection = SkyProjection(image, wcs)
projection.backgroundColor = (0, 0, 0, 0)
projection.maxSideLength = max_side_length
if input_image_origin_is_lower_left:
projection.inputImageOrigin = ImageOrigin.LOWER_LEFT
else:
projection.inputImageOrigin = ImageOrigin.UPPER_LEFT
sys.stdout.write("Warping image... ")
sys.stdout.flush()
start = time.time()
projected_image = projection.warpImage()
stop = time.time()
sys.stdout.write("done in %f sec\n" % (stop - start))
# Write warped image to file.
name, ext = os.path.splitext(imagefile)
projected_imagefile = "%s_warped.png" % name
sys.stdout.write("Writing warped image to %s... " % projected_imagefile)
sys.stdout.flush()
projected_image.save(projected_imagefile)
sys.stdout.write("done\n")
# Write bounding box to KML file.
kmlname = fitsfile[0:-4]+'kml'
bounding_box = projection.boundingBox
f = open(kmlname, "w")
try:
f.write(bounding_box.toKml(projected_imagefile))
finally:
f.close()
sys.stdout.write("done\n")
def main(argv):
if len(argv) != 2:
print "Usage: %s <FITS file>" % os.path.basename(argv[0])
sys.exit(2)
fitsfile = argv[1]
if not os.path.exists(fitsfile):
print "Error: file '%s' doesn't exist" % fitsfile
sys.exit(2)
name, ext = os.path.splitext(fitsfile)
print "Extacting extensions from '%s'... " % fitsfile
fitslib.fits_simple_verify(fitsfile)
fits = pyfits.open(fitsfile)
for i, hdu in enumerate(fits):
new_fitsfile = "%s_ext_%d.fits" % (name, i)
# Write out the new file
pyfits.writeto(new_fitsfile, hdu.data, hdu.header)
print "Wrote extension %d to %s" % (i, new_fitsfile)
fits.close()
def main(argv):
if len(argv) != 2:
print "Usage: %s <FITS file>" % os.path.basename(argv[0])
sys.exit(2)
fitsfile = argv[1]
if not os.path.exists(fitsfile):
print "Error: file '%s' doesn't exist" % fitsfile
sys.exit(2)
name, ext = os.path.splitext(fitsfile)
print "Extacting extensions from '%s'... " % fitsfile
fitslib.fits_simple_verify(fitsfile)
fits = pyfits.open(fitsfile)
for i, hdu in enumerate(fits):
new_fitsfile = "%s_ext_%d.fits" % (name, i)
# Write out the new file
pyfits.writeto(new_fitsfile, hdu.data, hdu.header)
print "Wrote extension %d to %s" % (i, new_fitsfile)
fits.close()
print "Error: invalid value '%s' for %s" % (arg, opt)
sys.exit(2)
# Check for an input image.
if not imagefile:
print "Error: no input imagefile was given"
print "Type '%s --help' for more information" % argv[0]
sys.exit(2)
elif not os.path.exists(imagefile):
print "Error: file '%s' doesn't exist" % imagefile
sys.exit(2)
# Determine if the input image is a FITS file.
is_fits_file = True
try:
fitslib.fits_simple_verify(imagefile)
except (IOError, ValueError, EOFError):
is_fits_file = False
if not fitsfile:
print "Error: no FITS file input for --fitsfile"
sys.exit(2)
# Convert input FITS images to raster format if the --fitsfile option has
# not been given.
if is_fits_file and not fitsfile:
sys.stdout.write("Converting input FITS image to raster format... ")
sys.stdout.flush()
image = fitsimage.FitsImage(imagefile)
sys.stdout.write("done\n")
fitsfile = imagefile
# This should always be True for fitsimage conversions.
def FITSParse(fitsFile, orderbyField='', hdu=1, surveyName=''):
"""
Parse a FITS file into a list of FITSPoint instances that contain
placemark data for each object.
"""
fitslib.fits_simple_verify(fitsFile)
hduList = pyfits.open(fitsFile)
if len(surveyName) == 0:
surveyName = os.path.basename(fitsFile).split('.')[0]
cols = hduList[hdu].columns
if not cols:
return
raTag, raIndex, decTag, decIndex = parseFitsColumns(cols)
if raIndex != -1 and decIndex != -1:
print 'Found possibilities for RA and DEC in header: %s, %s' % (raTag,
decTag)
else:
print "Didn't find either both RA and DEC equivalent in header."
return
tbData = hduList[1].data
if len(orderbyField) > 0:
print "Re-sorting FITS data using '" + orderbyField.upper(
) + "' field."
tmpCol = tbData.field(orderbyField)
idx = tmpCol.argsort()
tbData = tbData[idx]
fitsObjectList = []
objCounter = 0
for objData in tbData:
fitsObject = SimplePlacemark()
ra = objData.field(raIndex)
dec = objData.field(decIndex)
raGeo = ra - 180.0
decGeo = dec
fitsObject.SetLongitude(raGeo)
fitsObject.SetLatitude(decGeo)
(ra_str, dec_str, iauName) = objIAUName(surveyName, ra, dec)
fitsObject.SetName(iauName)
description = []
description.append(
"<table width='300' cellspacing='0' cellpadding='0'>")
idx = 0
for colNames in cols.names:
if idx == raIndex:
description.append("<tr><td align='center'>" + colNames +
"</td><td align='center'>" + ra_str +
"</td></tr>\n")
elif idx == decIndex:
description.append("<tr><td align='center'>" + colNames +
"</td><td align='center'>" + dec_str +
"</td></tr>\n")
else:
description.append("<tr><td align='center'>" + colNames +
"</td><td align='center'>" +
str(objData.field(colNames)) +
"</td></tr>\n")
idx = idx + 1
description.append('</table>\n')
description = ''.join(description)
fitsObject.SetDescription(description)
fitsObject.SetStyleUrl('#FitsPoint')
objCounter = objCounter + 1
if objCounter % 10000 == 0:
print '%i/%i objects...' % (objCounter, len(tbData))
fitsObjectList.append(fitsObject)
return fitsObjectList
def FitsImage(fitsfile, contrast="zscale", contrast_opts={}, scale="linear",
scale_opts={}):
"""
Constructor-like function that returns a Python Imaging Library (PIL)
Image object. This allows extremely easy and powerful manipulation of
FITs files as images. The contrast is automatically adjusted using the
zscale algorithm (see zscale_range() above).
Input: fitsfile -- a FITS image filename
contrast -- the algorithm for determining the min/max
values in the FITS pixel data to use when
compressing the dynamic range of the FITS
data to something visible by the eye, either
"zscale" or "percentile"
contrast_opts -- options for the contrast algorithm, see
the optional args of [contrast]_range()
for what to name the keys
scale -- how to scale the pixel values between the
min/max values from the contrast
algorithm when converting to a a raster
format, either "linear" or "arcsinh"
scale_opts -- options for the scaling algorithm, currently
only "nonlinearity" is supported for arcsinh,
which has a default value of 3
"""
if contrast not in ("zscale", "percentile"):
raise ValueError("invalid contrast algorithm '%s'" % contrast)
if scale not in ("linear", "arcsinh"):
raise ValueError("invalid scale value '%s'" % scale)
# open the fits file and read the image data and size
fitslib.fits_simple_verify(fitsfile)
fits = pyfits.open(fitsfile)
try:
hdr = fits[0].header
xsize = hdr["NAXIS1"]
ysize = hdr["NAXIS2"]
fits_data = fits[0].data
finally:
fits.close()
# compute the proper scaling for the image
if contrast == "zscale":
contrast_value = contrast_opts.get("contrast", 0.25)
num_points = contrast_opts.get("num_points", 600)
num_per_row = contrast_opts.get("num_per_row", 120)
zmin, zmax = zscale_range(fits_data, contrast=contrast_value,
num_points=num_points,
num_per_row=num_per_row)
elif contrast == "percentile":
min_percent = contrast_opts.get("min_percent", 3.0)
max_percent = contrast_opts.get("max_percent", 99.0)
num_points = contrast_opts.get("num_points", 5000)
num_per_row = contrast_opts.get("num_per_row", 250)
zmin, zmax = percentile_range(fits_data, min_percent=min_percent,
max_percent=max_percent,
num_points=num_points,
num_per_row=num_per_row)
# set all points less than zmin to zmin and points greater than
# zmax to zmax
fits_data = numpy.where(fits_data > zmin, fits_data, zmin)
fits_data = numpy.where(fits_data < zmax, fits_data, zmax)
if scale == "linear":
scaled_data = (fits_data - zmin) * (255.0 / (zmax - zmin)) + 0.5
elif scale == "arcsinh":
# nonlinearity sets the range over which we sample values of the
# asinh function; values near 0 are linear and values near infinity
# are logarithmic
nonlinearity = scale_opts.get("nonlinearity", 3.0)
nonlinearity = max(nonlinearity, 0.001)
max_asinh = cmath.asinh(nonlinearity).real
scaled_data = (255.0 / max_asinh) * \
(numpy.arcsinh((fits_data - zmin) * \
(nonlinearity / (zmax - zmin))))
# convert to 8 bit unsigned int ("b" in numpy)
#scaled_data = scaled_data.astype("b")
# create the image
print 'hi'
image = Image.frombuffer("L", (xsize, ysize), scaled_data, "raw", "L", 0, 0)
return image
print "Error: invalid value '%s' for %s" % (arg, opt)
sys.exit(2)
# Check for an input image.
if not imagefile:
print "Error: no input imagefile was given"
print "Type '%s --help' for more information" % argv[0]
sys.exit(2)
elif not os.path.exists(imagefile):
print "Error: file '%s' doesn't exist" % imagefile
sys.exit(2)
# Determine if the input image is a FITS file.
is_fits_file = True
try:
fitslib.fits_simple_verify(imagefile)
except (IOError, ValueError, EOFError):
is_fits_file = False
if not fitsfile:
print "Error: no FITS file input for --fitsfile"
sys.exit(2)
# Convert input FITS images to raster format if the --fitsfile option has
# not been given.
if is_fits_file and not fitsfile:
sys.stdout.write("Converting input FITS image to raster format... ")
sys.stdout.flush()
image = fitsimage.FitsImage(imagefile)
sys.stdout.write("done\n")
fitsfile = imagefile
# This should always be True for fitsimage conversions.
