def gridFn():
mask = (self.i3data['c'] == channel)
self.i3data = i3dtype.maskData(self.i3data, mask)
return self.i3To2DGridAllChannelsMerged(zmin = zmin,
zmax = zmax,
uncorrected = uncorrected,
verbose = verbose)
def gridFn():
mask = (self.i3data['c'] == channel)
self.i3data = i3dtype.maskData(self.i3data, mask)
return self.i3To2DGridAllChannelsMerged(zmin=zmin,
zmax=zmax,
uncorrected=uncorrected,
verbose=verbose)
def getMoleculesInFrameRange(self, start, stop, good_only = True):
start_mol_num = self.findFrame(start)
stop_mol_num = self.findFrame(stop)
if (type(self.localizations) == type(numpy.array([]))):
data = self.localizations[start_mol_num:stop_mol_num]
else:
cur = self.fp.tell()
self.fp.seek(16 + start_mol_num*self.record_size)
size = stop_mol_num - start_mol_num
data = numpy.fromfile(self.fp,
dtype=i3dtype.i3DataType(),
count=size)
self.fp.seek(cur)
if good_only:
return i3dtype.maskData(data, (data['c'] != 9))
else:
return data
def getMoleculesInFrameRange(self, start, stop, good_only = True):
start_mol_num = self.findFrame(start)
stop_mol_num = self.findFrame(stop)
if (type(self.localizations) == type(numpy.array([]))):
data = self.localizations[start_mol_num:stop_mol_num]
else:
cur = self.fp.tell()
self.fp.seek(16 + start_mol_num*self.record_size)
size = stop_mol_num - start_mol_num
data = numpy.fromfile(self.fp,
dtype=i3dtype.i3DataType(),
count=size)
self.fp.seek(cur)
if good_only:
return i3dtype.maskData(data, (data['c'] != 9))
else:
return data
def nextBlock(self, block_size=400000, good_only=True):
# check if we have read all the molecules.
if (self.cur_molecule >= self.molecules):
return False
size = block_size
# adjust size if we'll read past the end of the file.
if ((self.cur_molecule + size) > self.molecules):
size = self.molecules - self.cur_molecule
self.cur_molecule += size
# read the data
data = numpy.fromfile(self.fp, dtype=i3dtype.i3DataType(), count=size)
if good_only:
return i3dtype.maskData(data, (data['c'] != 9))
else:
return data
def nextBlock(self, block_size = 400000, good_only = True):
# check if we have read all the molecules.
if(self.cur_molecule>=self.molecules):
return False
size = block_size
# adjust size if we'll read past the end of the file.
if((self.cur_molecule+size)>self.molecules):
size = self.molecules - self.cur_molecule
self.cur_molecule += size
# read the data
data = numpy.fromfile(self.fp,
dtype=i3dtype.i3DataType(),
count=size)
if good_only:
return i3dtype.maskData(data, (data['c'] != 9))
else:
return data
def loadI3NumpyGoodOnly(filename, verbose = 1):
data = loadI3FileNumpy(filename, verbose = verbose)
return i3dtype.maskData(data, (data['c'] != 9))
def loadI3NumpyGoodOnly(filename, verbose = 1):
data = loadI3FileNumpy(filename, verbose = verbose)
return i3dtype.maskData(data, (data['c'] != 9))
def demodulate_individual_localizations(self,
chunk_size=1000,
f_type='single',
block_size=None,
lasers=None):
daxIn = DAX.DAX()
daxIn.open(self.nobg_DAX_name)
_num_frames = daxIn.numFrames
_im_size = daxIn.height
if block_size == None:
block_size = self.block_size
if lasers == None:
lasers = self.lasers
_chunk_size = chunk_size*block_size
_num_chunks = _num_frames//(_chunk_size)
_extra_frames = _num_frames%(_chunk_size)
print(_chunk_size,_num_chunks,_extra_frames,_chunk_size*_num_chunks+_extra_frames)
i3in = readinsight3.I3Reader(self.i3TypeIndivBinNames[f_type])
locs = i3in.getMoleculesInFrameRange(0,_num_frames+1)
demod_locs = np.zeros([locs.shape[0],4+np.sum(lasers)])
idx_dm = 0
data = np.zeros([_chunk_size+block_size, _im_size, _im_size]).astype(np.uint16)
for i in range(_num_chunks):
t_locs = i3dtype.maskData(locs,locs['fr']>=_chunk_size*i+1)
i_locs = i3dtype.maskData(t_locs,t_locs['fr']<_chunk_size*(i+1)+1)
start_time = timer()
for j in range(_chunk_size+block_size):
data[j] = daxIn.read(_chunk_size*i+j)
curr_locs = idx_dm
for frame in range(_chunk_size):
_f = frame+_chunk_size*i
l_locs = i3dtype.maskData(i_locs,i_locs['fr']==_f+1)
l_data = data[frame:frame+block_size]
temp_locs = local_dft.calc_indiv_demods_new(l_locs,l_data,lasers,_im_size)
demod_locs[idx_dm:idx_dm+temp_locs.shape[0]] = temp_locs
idx_dm += temp_locs.shape[0]
stop_time = timer()
new_locs = idx_dm - curr_locs
print('Chunk %d of %d complete in %d seconds (%d localizations added).' % (i+1, _num_chunks, stop_time-start_time, new_locs))
start_time = timer()
for j in range(_extra_frames):
data[j] = daxIn.read(_chunk_size*_num_chunks+j)
curr_locs = idx_dm
i_locs = i3dtype.maskData(locs,locs['fr']>=_chunk_size*_num_chunks+1)
for frame in range(_extra_frames-block_size):
_f = frame+_num_chunks*_chunk_size
l_locs = i3dtype.maskData(i_locs,i_locs['fr']==_f+1)
l_data = data[frame:frame+block_size]
temp_locs = local_dft.calc_indiv_demods_new(l_locs,l_data,lasers,_im_size)
demod_locs[idx_dm:idx_dm+temp_locs.shape[0]] = temp_locs
idx_dm += temp_locs.shape[0]
stop_time = timer()
new_locs = idx_dm - curr_locs
print('Extra frames complete in %d seconds (%d localizations added).' % (stop_time-start_time, new_locs))
i3in.close()
daxIn.close()
i3out = writeinsight3.I3Writer(self.i3CatIndivBinNames[f_type],_num_frames-1)
i3out.addMoleculesWithXYZF(demod_locs[:,0],demod_locs[:,1],demod_locs[:,2],demod_locs[:,3])
i3out.close()
self.write_to_indiv_ints(demod_locs, f_type)
return True