def __init__(self, fname):
"""
Initialise an NDF from a file.
This slurps the whole thing in, including all extensions, axes etc. This could
cause memory problems on large files. You can use either standard format
NDF sections in such case or Pythonic ones. e.g. Given an NDF 'image' listed by
hdstrace to have a data array DATA(3,4,5), the entire image can be specified
using any of 'image', 'image(1:3,1:4,1:5)' or 'image[0:5,0:4,0:3]' where as usual
with the index ranges in Python, the last index is NOT included and indices start
at 0. While those used to dealing with NDFs may find the first with () more
familiar, the second may be simpler if you deal with the outout from a python script
since it is identically ordered and more consistent with taking sub-sections using
Python as in::
ndf = starlink.ndfpack.Ndf('image')
subim = image.data[0:5,0:4,0:3]
"""
object.__init__(self)
# Next section changes from a pseudo-Pythonic version of an NDF section
# to a Fortran-like one i.e. reverse the indices, add 1 to the first of a pair
# or to the sole index
reg = re.compile(r'([^\[\]]*)\[([^\[\]]*)\]')
m = reg.match(fname)
if m != None:
tup = m.group(2).split(',')
nname = m.group(1) + '('
for st in tup[-1:0:-1]:
subt = st.split(':')
if len(subt) == 1:
add = str(int(subt[0])+1)
elif len(subt) == 2:
add = str(int(subt[0])+1) + ':' + str(int(subt[1]))
else:
raise Exception('Could not understand ' + fname)
nname += add + ','
subt = tup[0].split(':')
if len(subt) == 1:
add = str(int(subt[0])+1)
elif len(subt) == 2:
add = str(int(subt[0])+1) + ':' + str(int(subt[1]))
else:
raise Exception('Could not understand ' + nname)
nname += add + ')'
fname = nname
# OK, get on with NDF stuff
ndf.init()
ndf.begin()
try:
indf = ndf.open(fname)
#: the data array, a numpy N-d array
self.data = indf.read('Dat')
#: pixel limits of data array.
#: 2xndim array of lower and upper bounds
self.bound = indf.bound()
#: variances, a numpy N-d array
self.var = indf.read('Var')
#: label string associated with the data
self.label = indf.label
#: title string associated with the data
self.title = indf.title
#: data unit string
self.units = indf.units
try:
#: WCS information, a PyAST FrameSet
self.wcs = indf.gtwcs()
except NotImplementedError:
self.wcs = None
# Read the axes
#: a list of Axis objects, one for each dimension of data
self.axes = []
for nax in range(self.data.ndim):
self.axes.append(Axis(indf, nax))
# Read the extensions
#: header/extensions, a dictionary
self.head = {}
nextn = indf.xnumb
for nex in range(nextn):
xname = indf.xname(nex)
loc1 = indf.xloc(xname, 'READ')
hdsloc = hds._transfer(loc1)
_read_hds(hdsloc, self.head)
hdsloc.annul()
ndf.end()
except:
ndf.end()
raise
def setUp(self):
ndf.begin()
self.testndf = 'testme.sdf'
def setUp(self):
ndf.begin()
self.testndf = 'testme.sdf'
def __init__(self, fname):
"""
Initialise an NDF from a file.
This slurps the whole thing in, including all extensions, axes etc. This could
cause memory problems on large files. You can use either standard format
NDF sections in such case or Pythonic ones. e.g. Given an NDF 'image' listed by
hdstrace to have a data array DATA(3,4,5), the entire image can be specified
using any of 'image', 'image(1:3,1:4,1:5)' or 'image[0:5,0:4,0:3]' where as usual
with the index ranges in Python, the last index is NOT included and indices start
at 0. While those used to dealing with NDFs may find the first with () more
familiar, the second may be simpler if you deal with the outout from a python script
since it is identically ordered and more consistent with taking sub-sections using
Python as in::
ndf = starlink.ndfpack.Ndf('image')
subim = image.data[0:5,0:4,0:3]
"""
object.__init__(self)
# Next section changes from a pseudo-Pythonic version of an NDF section
# to a Fortran-like one i.e. reverse the indices, add 1 to the first of a pair
# or to the sole index
reg = re.compile(r'([^\[\]]*)\[([^\[\]]*)\]')
m = reg.match(fname)
if m != None:
tup = m.group(2).split(',')
nname = m.group(1) + '('
for st in tup[-1:0:-1]:
subt = st.split(':')
if len(subt) == 1:
add = str(int(subt[0])+1)
elif len(subt) == 2:
add = str(int(subt[0])+1) + ':' + str(int(subt[1]))
else:
raise Exception('Could not understand ' + fname)
nname += add + ','
subt = tup[0].split(':')
if len(subt) == 1:
add = str(int(subt[0])+1)
elif len(subt) == 2:
add = str(int(subt[0])+1) + ':' + str(int(subt[1]))
else:
raise Exception('Could not understand ' + nname)
nname += add + ')'
fname = nname
# OK, get on with NDF stuff
ndf.init()
ndf.begin()
try:
indf = ndf.open(fname)
#: the data array, a numpy N-d array
self.data = indf.read('Dat')
#: pixel limits of data array.
#: 2xndim array of lower and upper bounds
self.bound = indf.bound()
#: variances, a numpy N-d array
self.var = indf.read('Var')
#: label string associated with the data
self.label = indf.label
#: title string associated with the data
self.title = indf.title
#: data unit string
self.units = indf.units
try:
#: WCS information, a PyAST FrameSet
self.wcs = indf.gtwcs()
except NotImplementedError:
self.wcs = None
# Read the axes
#: a list of Axis objects, one for each dimension of data
self.axes = []
for nax in range(self.data.ndim):
self.axes.append(Axis(indf, nax))
# Read the extensions
#: header/extensions, a dictionary
self.head = {}
nextn = indf.xnumb
for nex in range(nextn):
xname = indf.xname(nex)
loc1 = indf.xloc(xname, 'READ')
hdsloc = hds._transfer(loc1)
_read_hds(hdsloc, self.head)
hdsloc.annul()
ndf.end()
except:
ndf.end()
raise