def load_data(self, ch=None):
if ch is None: ch = self.ch
#outmeta = {'ch':ch}
outmeta = dict(ch=ch, timestamp=self.timestamps[0])
outmeta['axes'] = lib.alist_to_scale([(self.dz, 'um'),
(self.dx, 'um')])
var_names = self.img_names
nchannels = self.nchannels
if ch is None or ch == 'all':
## TODO: use smth like np.concatenate(map(lambda m:\
## m.reshape(m.shape+(1,)), (x1,x2,x3)), 3)
sh = tuple(self.base_shape) + (max(3, nchannels),)
#print 'Shape', sh, self.base_shape, nchannels
recs = self._get_recs(var_names)
stream = lib.clip_and_rescale(np.array([recs[n] for n in var_names]))
data = np.zeros(sh)
framecount = 0
#print 'Shape2:', stream[0].shape
for k in xrange(0, self.nframes//nchannels,nchannels):
for j in xrange(nchannels):
data[framecount,...,j] = stream[k+j]
framecount += 1
else:
if isinstance(ch,int) :
var_names = var_names[ch::nchannels]
elif isinstance(ch,basestring) :
var_names = [n for n,c in zip(var_names,self.channels) if ch.lower() in c]
recs = self._get_recs(var_names)
data = np.array([recs[n] for n in var_names])
return data, outmeta
def load_data(self,ch=None):
if ch == None: ch = self.ch
outmeta = dict(ch=ch, timestamp=self.timestamps[0])
outmeta['axes'] = lib.alist_to_scale([(self.dt,'s'), (self.dx, 'um')])
nframes, nlines, line_len = self.nframes, self.nlines, self.line_length
base_shape = (nframes-1, nlines, line_len)
streams = self._load_streams(ch=ch)
if len(streams) == 1:
data = np.zeros(base_shape, dtype=streams[0].dtype)
for k,f in enumerate(self._reshape_frames(streams[0])):
data[k] = f
else:
#streams = [lib.clip_and_rescale(s) for s in streams]
reshape_iter = itt.izip(*map(self._reshape_frames, streams))
sh = base_shape + (max(3, len(streams)),)
data = np.zeros(sh, dtype=streams[0].dtype)
for k, a in enumerate(reshape_iter):
for j, f in enumerate(a):
data[k,...,j] = f
self.data = data
self.outmeta = outmeta
return data, outmeta
