def MapSynapsesAndSurfaces(input_meta_filename, output_meta_filename): # read in the meta data files input_data = ReadMetaData(input_meta_filename) output_data = ReadMetaData(output_meta_filename) input_synapse_directory = input_data.SynapseDirectory() output_synapse_directory = output_data.SynapseDirectory() # create the output synapse directory if it does not exist if not os.path.exists(output_synapse_directory): os.makedirs(output_synapse_directory, exist_ok=True) # read in all of the input syanpses input_synapses = {} for iz in range(input_data.StartZ(), input_data.EndZ()): for iy in range(input_data.StartY(), input_data.EndY()): for ix in range(input_data.StartX(), input_data.EndX()): input_synapse_filename = '{}/{:04d}z-{:04d}y-{:04d}x.pts'.format(input_synapse_directory, iz, iy, ix) # ignore the local points global_pts, _ = ReadPtsFile(input_data, input_synapse_filename) for label in global_pts: if not label in input_synapses: input_synapses[label] = [] # add the array from this block to the input synapses input_synapses[label] += global_pts[label] # create an output dictionary of synapses per block output_synapses_per_block = {} # iterate over all output blocks for iz in range(output_data.StartZ(), output_data.EndZ()): for iy in range(output_data.StartY(), output_data.EndY()): for ix in range(output_data.StartX(), output_data.EndX()): # create empty synapses_per_block dictionaries whose keys will be labels output_synapses_per_block[(iz, iy, ix)] = {} # for every label, go through the discovered synapses from the input data for label in input_synapses.keys(): input_synapses_per_label = input_synapses[label] for input_global_index in input_synapses_per_label: # the global iz, iy, ix coordinates remain the same across blocks global_iz, global_iy, global_ix = input_data.GlobalIndexToIndices(input_global_index) # get the new block from the global coordinates output_block_iz = global_iz // output_data.BlockZLength() output_block_iy = global_iy // output_data.BlockYLength() output_block_ix = global_ix // output_data.BlockXLength() if not label in output_synapses_per_block[(output_block_iz, output_block_iy, output_block_ix)]: output_synapses_per_block[(output_block_iz, output_block_iy, output_block_ix)][label] = [] # get the new global index output_global_index = output_data.GlobalIndicesToIndex(global_iz, global_iy, global_ix) output_synapses_per_block[(output_block_iz, output_block_iy, output_block_ix)][label].append(output_global_index) # write all of the synapse block files output_synapses_directory = output_data.SynapseDirectory() for iz in range(output_data.StartZ(), output_data.EndZ()): for iy in range(output_data.StartY(), output_data.EndY()): for ix in range(output_data.StartX(), output_data.EndX()): output_synapse_filename = '{}/{:04d}z-{:04d}y-{:04d}x.pts'.format(output_synapse_directory, iz, iy, ix) # write the pts file (use global indices) WritePtsFile(output_data, output_synapse_filename, output_synapses_per_block[(iz, iy, ix)], input_local_indices = False) # get the input/output directories for the surfaces input_surfaces_directory = input_data.SurfacesDirectory() output_surfaces_directory = output_data.SurfacesDirectory() # create the output synapse directory if it does not exist if not os.path.exists(output_surfaces_directory): os.makedirs(output_surfaces_directory, exist_ok=True) # iterate over all labels for label in range(1, input_data.NLabels()): start_time = time.time() # skip over labels that do not exist input_surface_filename = '{}/{:016d}.pts'.format(input_surfaces_directory, label) if not os.path.exists(input_surface_filename): continue # read in the input global points input_global_points, _ = ReadPtsFile(input_data, input_surface_filename) # create an empty dictionary for the output points output_global_points = {} output_global_points[label] = [] for input_global_index in input_global_points[label]: # the global iz, iy, ix coordinates remain the same across blocks global_iz, global_iy, global_ix = input_data.GlobalIndexToIndices(input_global_index) # get the new global index output_global_index = output_data.GlobalIndicesToIndex(global_iz, global_iy, global_ix) output_global_points[label].append(output_global_index) # write the new surface filename output_surface_filename = '{}/{:016d}.pts'.format(output_surfaces_directory, label) WritePtsFile(output_data, output_surface_filename, output_global_points, input_local_indices = False) print ('Completed label {} in {:0.2f} seconds'.format(label, time.time() - start_time))
def ConvertSynapsesAndProject(meta_filename, input_synapse_directory, xyz, conversion_rate): data = ReadMetaData(meta_filename) resolution = data.Resolution() # create an empty set of synapses synapses = {} # iterate over all labels for label in range(1, data.NLabels()): # read the surfaces for this label surface_filename = '{}/{:016d}.pts'.format(data.SurfacesDirectory(), label) # some surfaces (i.e., labels) will not exist in the volume if not os.path.exists(surface_filename): continue # read in the surface points, ignore the local coordinates surfaces, _ = ReadPtsFile(data, surface_filename) surface = surfaces[label] npts = len(surface) surface_point_cloud = np.zeros((npts, 3), dtype=np.int32) for index, iv in enumerate(surface): iz, iy, ix = data.GlobalIndexToIndices(iv) surface_point_cloud[index, :] = (iz * resolution[OR_Z], iy * resolution[OR_Y], ix * resolution[OR_X]) # create an empty array for the synapses synapses[label] = [] projected = 0 missed = 0 # read in the original synapses input_synapse_filename = '{}/syn_{:04}.txt'.format( input_synapse_directory, label) if os.path.exists(input_synapse_filename): with open(input_synapse_filename, 'r') as fd: for line in fd: # separate the line into coordinates coordinates = line.strip().split() if xyz: ix = round(int(coordinates[0]) / conversion_rate[OR_X]) iy = round(int(coordinates[1]) / conversion_rate[OR_Y]) iz = round(int(coordinates[2]) / conversion_rate[OR_Z]) else: iz = round(int(coordinates[0]) / conversion_rate[OR_Z]) iy = round(int(coordinates[1]) / conversion_rate[OR_Y]) ix = round(int(coordinates[2]) / conversion_rate[OR_X]) # create a 2D vector for this point vec = np.zeros((1, 3), dtype=np.int32) vec[0, :] = (iz * resolution[OR_Z], iy * resolution[OR_Y], ix * resolution[OR_X]) closest_point = surface[scipy.spatial.distance.cdist( surface_point_cloud, vec).argmin()] closest_iz, closest_iy, closest_ix = data.GlobalIndexToIndices( closest_point) deltaz = resolution[OR_Z] * (iz - closest_iz) deltay = resolution[OR_Y] * (iy - closest_iy) deltax = resolution[OR_X] * (ix - closest_ix) distance = math.sqrt(deltaz * deltaz + deltay * deltay + deltax * deltax) # skip distances that are clearly off (over 200 nanometers) max_deviation = 800 if distance < max_deviation: # add to the list of valid synapses synapses[label].append(closest_point) projected += 1 else: missed += 1 print('Synapses within {} nanometers from surface: {}'.format( max_deviation, projected)) print('Synapses over {} nanometers from surface: {}'.format( max_deviation, missed)) # divide all synapses into blocks synapses_per_block = {} # iterate over all blocks for iz in range(data.StartZ(), data.EndZ()): for iy in range(data.StartY(), data.EndY()): for ix in range(data.StartX(), data.EndX()): # create empty synapses_per_block dictionaries whose keys will be labels synapses_per_block[(iz, iy, ix)] = {} # for every label, iterate over the discovered synapses for label in synapses.keys(): synapses_per_label = synapses[label] # iterate over all of the projected synapses for global_index in synapses_per_label: global_iz, global_iy, global_ix = data.GlobalIndexToIndices( global_index) block_iz = global_iz // data.BlockZLength() block_iy = global_iy // data.BlockYLength() block_ix = global_ix // data.BlockXLength() # create the array for this label per block if it does not exist if not label in synapses_per_block[(block_iz, block_iy, block_ix)]: synapses_per_block[(block_iz, block_iy, block_ix)][label] = [] synapses_per_block[(block_iz, block_iy, block_ix)][label].append(global_index) # write all of the synapse block files synapse_directory = data.SynapseDirectory() if not os.path.exists(synapse_directory): os.makedirs(synapse_directory, exist_ok=True) for iz in range(data.StartZ(), data.EndZ()): for iy in range(data.StartY(), data.EndY()): for ix in range(data.StartX(), data.EndX()): synapse_filename = '{}/{:04d}z-{:04d}y-{:04d}x.pts'.format( synapse_directory, iz, iy, ix) # write the pts file (use global indices) WritePtsFile(data, synapse_filename, synapses_per_block[(iz, iy, ix)], input_local_indices=False)