def test_simplenew(self):
# okay we have all the data, time to open us up an ndf
indf = ndf.open(self.testndf,'WRITE','NEW')
newindf = indf.new('_REAL',2,
numpy.array([0,0]),numpy.array([4,4]))
# map primary data to make sure NDF does not complain
ptr,el = newindf.map('DATA','_REAL','WRITE')
# make sure we got a file
self.assertTrue( os.path.exists( self.testndf ), "Test existence of NDF file" )
def test_newwithwrite(self):
# okay we have all the data, time to open us up an ndf
indf = ndf.open(self.testndf,'WRITE','NEW')
newindf = indf.new('_REAL',2,
numpy.array([0,0]),numpy.array([4,4]))
# create PAMELA extension
loc = newindf.xnew('PAMELA','STRUCT')
hdsloc = hds._transfer(loc)
name = hdsloc.name()
self.assertEqual( name, "PAMELA" )
ccd = numpy.zeros([5,5])
# map primary data
ptr,el = newindf.map('DATA','_REAL','WRITE')
ndf.ndf_numpytoptr(ccd,ptr,el,'_REAL')
# shut down ndf system
newindf.annul()
# make sure we got a file
self.assertTrue( os.path.exists( self.testndf ), "Test existence of NDF file" )
def test_filnf(self):
with self.assertRaises(IOError):
indf = ndf.open('shouldnotbepresent','READ','OLD')
def test_badstate(self):
with self.assertRaises(ValueError):
indf = ndf.open('badmode', 'READ', 'VERYOLD')
def test_badmode(self):
with self.assertRaises(ValueError):
indf = ndf.open('badmode', 'UNKNOWN', 'OLD')
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.ndf.Ndf('image')
subim = image.data[0:5,0:4,0:3]
The following attributes are created:
data -- the data array, a numpy N-d array
bound -- pixel limits of data array. 2xndim array of lower and upper bounds
var -- variances, a numpy N-d array
axes -- Axis components
label -- label string
title -- title string
units -- data unit string
head -- header/extensions, a dictionary
"""
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)
self.data = indf.read('Dat')
self.bound = indf.bound()
self.var = indf.read('Var')
self.label = indf.cget('Label')
self.title = indf.cget('Title')
self.units = indf.cget('Units')
# Read the axes
self.axes = []
for nax in range(self.data.ndim):
self.axes.append(Axis(indf, nax))
# Read the extensions
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
