def read(self, fname, frame=None):
"""
Read in and unpack the pixels (including overflow table
"""
infile = self._open(fname, "rb")
try:
self._readheader(infile)
except:
raise
rows = self.dim1
cols = self.dim2
try:
# you had to read the Bruker docs to know this!
npixelb = int(self.header['NPIXELB'])
except:
errmsg = "length " + str(len(self.header['NPIXELB'])) + "\n"
for byt in self.header['NPIXELB']:
errmsg += "char: " + str(byt) + " " + str(ord(byt)) + "\n"
logger.warning(errmsg)
raise
self.data = readbytestream(infile,
infile.tell(),
rows,
cols,
npixelb,
datatype="int",
signed='n',
swap='n')
#handle overflows
nov = int(self.header['NOVERFL'])
if nov > 0: # Read in the overflows
# need at least int32 sized data I guess - can reach 2^21
self.data = self.data.astype(numpy.uint32)
# 16 character overflows:
# 9 characters of intensity
# 7 character position
for i in range(nov):
ovfl = infile.read(16)
intensity = int(ovfl[0:9])
position = int(ovfl[9:16])
# relies on python style modulo being always +
row = position % rows
# relies on truncation down
col = position / rows
#print "Overflow ", r, c, intensity, position,\
# self.data[r,c],self.data[c,r]
self.data[col, row] = intensity
infile.close()
self.resetvals()
self.pilimage = None
return self
def read(self, fname):
"""
Read in and unpack the pixels (including overflow table
"""
infile = self._open(fname, "rb")
try:
self._readheader(infile)
except:
raise
rows = self.dim1
cols = self.dim2
try:
# you had to read the Bruker docs to know this!
npixelb = int(self.header['NPIXELB'])
except:
errmsg = "length " + str(len(self.header['NPIXELB'])) + "\n"
for byt in self.header['NPIXELB']:
errmsg += "char: " + str(byt) + " " + str(ord(byt)) + "\n"
logging.warning(errmsg)
raise
self.data = readbytestream(infile, infile.tell(),
rows, cols, npixelb,
datatype="int",
signed='n',
swap='n')
#handle overflows
nov = int(self.header['NOVERFL'])
if nov > 0: # Read in the overflows
# need at least int32 sized data I guess - can reach 2^21
self.data = self.data.astype(N.uint32)
# 16 character overflows:
# 9 characters of intensity
# 7 character position
for i in range(nov):
ovfl = infile.read(16)
intensity = int(ovfl[0: 9])
position = int(ovfl[9: 16])
# relies on python style modulo being always +
row = position % rows
# relies on truncation down
col = position / rows
#print "Overflow ", r, c, intensity, position,\
# self.data[r,c],self.data[c,r]
self.data[col, row] = intensity
infile.close()
self.resetvals()
self.pilimage = None
return self
def read(self, fname, frame=None):
f = open(fname, "rb")
try:
self._readheader(f)
except:
raise
rows = int(self.header['NROWS'])
cols = int(self.header['NCOLS'])
npixelb = int(self.header['NPIXELB'][0])
# you had to read the Bruker docs to know this!
# We are now at the start of the image - assuming
# readbrukerheader worked
# size = rows * cols * npixelb
self.data = readbytestream(f, f.tell(), rows, cols, npixelb,
datatype="int", signed='n', swap='n')
noverfl = self.header['NOVERFL'].split() # now process the overflows
#read the set of "underflow pixels" - these will be completely
# disregarded for now
data = self.data
k = 0
while k < 2:#for the time being things - are done in 16 bits
datatype = {'1' : numpy.uint8,
'2' : numpy.uint16,
'4' : numpy.uint32 }[("%d" % 2 ** k)]
ar = numpy.array(numpy.fromstring(f.read(int(noverfl[k]) * (2 ** k)),
datatype), numpy.uint16)
#insert the the overflow pixels in the image array:
#this is probably a memory intensive way of doing this -
# might be done in a more clever way
lim = 2 ** (8 * k) - 1
#generate an array comprising of the indices into data.ravel()
# where its value equals lim.
M = numpy.compress(numpy.equal(data.ravel(), lim), numpy.arange(rows * cols))
#now put values from ar into those indices
numpy.put(data.ravel(), M, ar)
padding = 16 * int(math.ceil(int(noverfl[k]) * (2 ** k) / 16.)) - \
int(noverfl[k]) * (2 ** k)
f.seek(padding, 1)
print noverfl[k] + " bytes read + %d bytes padding" % padding
k = k + 1
f.close()
(self.dim1, self.dim2) = (rows, cols)
print self.dim1, self.dim2
self.resetvals()
return self
def read(self, fname, frame=None):
f = open(fname, "rb")
try:
self._readheader(f)
except:
raise
rows = int(self.header['NROWS'])
cols = int(self.header['NCOLS'])
npixelb = int(self.header['NPIXELB'][0])
# you had to read the Bruker docs to know this!
# We are now at the start of the image - assuming
# readbrukerheader worked
# size = rows * cols * npixelb
self.data = readbytestream(f,
f.tell(),
rows,
cols,
npixelb,
datatype="int",
signed='n',
swap='n')
noverfl = self.header['NOVERFL'].split() # now process the overflows
#read the set of "underflow pixels" - these will be completely
# disregarded for now
data = self.data
k = 0
while k < 2: #for the time being things - are done in 16 bits
datatype = {
'1': numpy.uint8,
'2': numpy.uint16,
'4': numpy.uint32
}[("%d" % 2**k)]
ar = numpy.array(
numpy.fromstring(f.read(int(noverfl[k]) * (2**k)), datatype),
numpy.uint16)
#insert the the overflow pixels in the image array:
#this is probably a memory intensive way of doing this -
# might be done in a more clever way
lim = 2**(8 * k) - 1
#generate an array comprising of the indices into data.ravel()
# where its value equals lim.
M = numpy.compress(numpy.equal(data.ravel(), lim),
numpy.arange(rows * cols))
#now put values from ar into those indices
numpy.put(data.ravel(), M, ar)
padding = 16 * int(math.ceil(int(noverfl[k]) * (2 ** k) / 16.)) - \
int(noverfl[k]) * (2 ** k)
f.seek(padding, 1)
print noverfl[k] + " bytes read + %d bytes padding" % padding
k = k + 1
f.close()
(self.dim1, self.dim2) = (rows, cols)
print self.dim1, self.dim2
self.resetvals()
return self