def main():
token = 'collman15v2'
channel = 'annotation'
metadata = readMetadata('cleft_class.csv')
nd = ND(hostname='synaptomes.neurodata.io/nd/')
for ridx in metadata.keys():
robj = createRamonSynapse(ridx, metadata[ridx], 0, confidence=1, author='Forrest Collman')
nd.post_ramon(token, channel, robj)
print "Successfully posted ramon obj {}".format(ridx)
def create_ramon_volume(self, token, channel, annofile, ramonobj,
conncomp=0, remote='neurodata'):
"""
Use ndio to put annotated nifti volume to a remote
This first prototype only uploads annotation labels
TODO: Extend to parse upload params from filename
Arguments:
data: nifti volume to convert
Returns:
Success or failure
"""
import ndio.convert.nifti as ndnifti
if remote is 'neurodata':
import ndio.remote.neurodata as ND
nd = ND()
else:
raise ValueError("remote option not implemented.")
anno = ndnifti.import_nifti(annofile)
anno = np.int32(anno)
if conncomp is 1:
anno = mahotas.labeled.label(anno, Bc=np.ones([3, 3, 3]))[0]
# relabel ids from 1
print('relabeling IDs...')
anno, n_label = mahotas.labeled.relabel(anno)
print('reserving IDs...')
n_label = int(n_label)
ids = nd.reserve_ids(token, channel, n_label)
# ids is 0 indexed
# anno begins at 1
# TODO: guarantee that these are contiguous
anno[anno > 0] += ids[0] - 1
ramon_list = []
for x in range(0, n_label):
r = ramonobj
r.id = ids[x]
ramon_list.append(r)
return anno, ramon_list
def main():
token = 'collman15v2'
channel = 'annotation'
metadata = readMetadata('cleft_class.csv')
nd = ND(hostname='synaptomes.neurodata.io/nd/')
for ridx in metadata.keys():
robj = createRamonSynapse(ridx,
metadata[ridx],
0,
confidence=1,
author='Forrest Collman')
nd.post_ramon(token, channel, robj)
print "Successfully posted ramon obj {}".format(ridx)
def get_mana_volume(self, token, channel, x_start, x_stop, y_start,
y_stop, z_start, z_stop, resolution,
server='openconnecto.me', remote='neurodata',
outdir='.'):
"""
Use ndio to get volume from a remote and convert to NIFTI.
We recommend users use ITK-Snap for annotations.
Arguments:
token (str): Token to identify data to download
channel (str): Channel
resolution (int): Resolution level
Q_start (int): The lower bound of dimension 'Q'
Q_stop (int): The upper bound of dimension 'Q'
server: default server for remote
remote: name for remote to use
outdir: location for nifti file
Returns:
Downloaded data.
"""
import ndio.convert.nifti as ndnifti
import os.path
if remote is 'neurodata':
import ndio.remote.neurodata as ND
nd = ND(server)
else:
raise ValueError("remote option not implemented.")
image = nd.get_cutout(token, channel, x_start, x_stop, y_start,
y_stop, z_start, z_stop, resolution=resolution)
fileout = '{}_{}_x{}-{}_y{}-{}_z{}-{}_r{}' \
'.nii'.format(token, channel, x_start, x_stop,
y_start, y_stop, z_start, z_stop,
resolution)
print(fileout)
ndnifti.export_nifti(os.path.abspath(os.path.join(
outdir, fileout)), image)
return fileout
import ndio.convert.nifti as ndnii #import nifti workflow
print 'imported nifti convert'
data = ndnii.load("Fear199_400_600_1850_2050_450_550_res2_anno.nii") #numpy array from my file
print 'made numpy array'
print data.shape #check array shape
import ndio.remote.neurodata as ND #import neurodata
print 'imported neurodata remote'
oo = ND() #instantiate neurodata
print 'instance of neurodata created'
#oo.delete_channel("Fear199", "GLMannotation")
#print 'deleted f****d up channel'
oo.create_channel('Fear199', 'GLMAnnotationFixed', oo.ANNOTATION, 'uint32', False) #make channel to add annotation
print 'created new channel'
oo.post_cutout('Fear199', 'GLMAnnotationFixed', 0, 0, 0, data, 2) #token, channel, z, x, y, data, resolution
print 'uploaded data to channel'
print 'done'
def gen_ramon_graph(token_synapse, channel_synapse,
token_neurons, channel_neurons,
resolution, is_directed=False,
save_graphml=None):
# TODO support segment graphs
# TODO support filter synapses
# TODO support multiple servers
# TODO support other graph output options
# TODO support enriched graph
import numpy as np
import ndio.remote.neurodata as ND
import ndio.ramon as ramon
import networkx as nx
nd = ND()
id_synapse = nd.get_ramon_ids(token_synapse, channel_synapse,
ramon_type=ramon.RAMONSynapse)
print('There are: {} synapses'.format(len(id_synapse)))
# Instantiate graph instance
if is_directed is False: # undirected case
G = nx.Graph()
else:
G = nx.DiGraph()
# for each synapse
for x in range(np.shape(id_synapse)[0]):
#print(str(x).zfill(4),end=" ")
s = nd.get_ramon_metadata(token_synapse, channel_synapse,
[id_synapse[x]])[0]
# for each segment
segments = s.segments[:, 0]
direction = s.segments[:, 1] # 1: axon/pre, 2: dendrite/post
# print direction
if len(segments) != 2:
print('multi-way synapses not implemented!')
raise
s1 = nd.get_ramon_metadata(token_neurons, channel_neurons,
[segments[0]])[0]
n1 = s1.neuron
s2 = nd.get_ramon_metadata(token_neurons, channel_neurons,
[segments[1]])[0]
n2 = s2.neuron
if is_directed is False or (direction[0] == 1 and direction[1] == 2):
if G.has_edge(n1, n2): # edge already exists, increase weight
G[n1][n2]['weight'] += 1
else:
# new edge. add with weight=1
G.add_edge(n1, n2, weight=1)
elif direction[0] == 2 and direction[1] == 1:
if G.has_edge(n2, n1): # edge already exists, increase weight
G[n2][n1]['weight'] += 1
else:
# new edge. add with weight=1
G.add_edge(n2, n1, weight=1)
else:
print('1 pre and 1 post synaptic partner are'
' required for directed graph estimation.')
raise
if save_file is not None:
# Save graphml graph
nx.write_graphml(G, save_graphml)
