def append_png_assertion(self, badge):
""" Append the assertion to a PNG file """
badge.signed = _signature
chunks = list()
png = Reader(bytes=badge.source.image)
for chunk in png.chunks():
chunks.append(chunk)
itxt_data = b'openbadges' + pack(
'BBBBB', 0, 0, 0, 0, 0) + badge.get_assertion().encode('utf-8')
itxt = ('iTXt', itxt_data)
chunks.insert(len(chunks) - 1, itxt)
text_data = 'Comment Signed with OpenBadgesLib %s' % __version__
text = ('tEXt', text_data.encode('utf-8'))
chunks.insert(len(chunks) - 1, text)
for tag, data in chunks:
badge.signed = badge.signed + pack("!I", len(data))
tag = tag.encode('iso8859-1')
badge.signed = badge.signed + tag
badge.signed = badge.signed + data
checksum = crc32(tag)
checksum = crc32(data, checksum)
checksum &= 2**32 - 1
badge.signed = badge.signed + pack("!I", checksum)
def extract_png_assertion(file_data):
png = Reader(bytes=file_data)
for tag, data in png.chunks():
if tag == 'iTXt':
fmt_len = len(data) - 15 # 15=len('openbadges'+pack('BBBBB'))
fmt = '<10s5B%ds' % fmt_len
return Assertion.decode(unpack(fmt, data)[6])
def has_png_assertion(self, badge):
png = Reader(bytes=badge.image)
for tag, data in png.chunks():
if tag == 'iTXt':
if data.startswith(b'openbadges'):
return True
return False
def to_fits(self, out_file):
"""
Converts a PNG to file a FITS file.
:param out_file: User specified filename for the FITS
"""
if not self.png:
raise ValueError, "AstroPNG was not initialized with a PNG image"
# Reading the PNG takes two passes (for now)
r = Reader(self.png)
width, height, imgdata, metadata = r.read()
fluxes = numpy.vstack( itertools.imap(numpy.uint16, imgdata) )
r = Reader(self.png)
chunks = r.chunks()
has_fits = False
has_quantization = False
has_nans = False
# Read the custom astro chunks
while True:
try:
chunk_name, data = chunks.next()
except:
break
if chunk_name == 'fITS':
header = self.__read_fits_header(data)
has_fits = True
elif chunk_name == 'qANT':
zzero, zscale = self.__read_quantization_parameters(data, height)
has_quantization = True
elif chunk_name == 'nANS':
y_nans, x_nans = self.__read_nan_locations(data)
has_nans = True
elif chunk_name == 'iEND':
break
if has_quantization:
random_numbers = self.__random_number_generator(N = width * height).reshape( (height, width) )
fluxes = (fluxes - random_numbers + 0.5) * numpy.vstack(zscale) + numpy.vstack(zzero)
if has_nans:
if y_nans.size > 0:
fluxes[y_nans, x_nans] = numpy.nan
fluxes = numpy.flipud(fluxes)
hdu = pyfits.PrimaryHDU(fluxes, header)
hdu.verify()
hdu.writeto(out_file, output_verify='ignore')
