def read_data(include_nonconnected_cells=False, neuron_connect=False):
# reading the NeuronConnect.xls file if neuron_connect = True
if neuron_connect:
conns = []
cells = []
filename = "%sNeuronConnectFormatted.xlsx"%spreadsheet_location
rb = open_workbook(filename)
print_("Opened Excel file: " + filename)
for row in range(1,rb.sheet_by_index(0).nrows):
pre = str(rb.sheet_by_index(0).cell(row,0).value)
post = str(rb.sheet_by_index(0).cell(row,1).value)
syntype = rb.sheet_by_index(0).cell(row,2).value
num = int(rb.sheet_by_index(0).cell(row,3).value)
synclass = 'Generic_GJ' if 'EJ' in syntype else 'Chemical_Synapse'
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
if pre not in cells:
cells.append(pre)
if post not in cells:
cells.append(post)
return cells, conns
else:
conns = []
cells = []
filename = "%sCElegansNeuronTables.xls"%spreadsheet_location
rb = open_workbook(filename)
print_("Opened Excel file: " + filename)
known_nonconnected_cells = ['CANL', 'CANR', 'VC6']
for row in range(1,rb.sheet_by_index(0).nrows):
pre = str(rb.sheet_by_index(0).cell(row,0).value)
post = str(rb.sheet_by_index(0).cell(row,1).value)
syntype = rb.sheet_by_index(0).cell(row,2).value
num = int(rb.sheet_by_index(0).cell(row,3).value)
synclass = rb.sheet_by_index(0).cell(row,4).value
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
if pre not in cells:
cells.append(pre)
if post not in cells:
cells.append(post)
if include_nonconnected_cells:
for c in known_nonconnected_cells: cells.append(c)
return cells, conns
def read_muscle_data():
conns = []
neurons = []
muscles = []
filename = "%sCElegansNeuronTables.xls"%spreadsheet_location
rb = open_workbook(filename)
print_("Opened Excel file: "+ filename)
sheet = rb.sheet_by_index(1)
for row in range(1,sheet.nrows):
pre = str(sheet.cell(row,0).value)
post = str(sheet.cell(row,1).value)
syntype = 'Send'
num = int(sheet.cell(row,2).value)
synclass = sheet.cell(row,3).value.replace(',', 'plus').replace(' ', '_')
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
if pre not in neurons:
neurons.append(pre)
if post not in muscles:
muscles.append(post)
return neurons, muscles, conns
def read_data(include_nonconnected_cells=False):
"""
Args:
include_nonconnected_cells (bool): Also append neurons without known connections to other neurons to the 'cells' list. True if they should get appended, False otherwise.
Returns:
cells (:obj:`list` of :obj:`str`): List of neurons
conns (:obj:`list` of :obj:`ConnectionInfo`): List of connections from neuron to neuron
"""
conns = []
cells = []
with open(filename, 'r') as f:
reader = csv.DictReader(f)
print_("Opened file: " + filename)
known_nonconnected_cells = ['CANL', 'CANR']
for row in reader:
pre, post, num, syntype, synclass = parse_row(row)
if not is_neuron(pre) or not is_neuron(post):
continue # pre or post is not a neuron
pre = remove_leading_index_zero(pre)
post = remove_leading_index_zero(post)
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
#print ConnectionInfo(pre, post, num, syntype, synclass)
if pre not in cells:
cells.append(pre)
if post not in cells:
cells.append(post)
if include_nonconnected_cells:
for c in known_nonconnected_cells:
if c not in cells:
cells.append(c)
return cells, conns
def read_muscle_data():
"""
Returns:
neurons (:obj:`list` of :obj:`str`): List of motor neurons. Each neuron has at least one connection with a post-synaptic muscle cell.
muscles (:obj:`list` of :obj:`str`): List of muscle cells.
conns (:obj:`list` of :obj:`ConnectionInfo`): List of neuron-muscle connections.
"""
neurons = []
muscles = []
conns = []
with open(filename, 'r') as f:
reader = csv.DictReader(f)
print_("Opened file: " + filename)
for row in reader:
pre, post, num, syntype, synclass = parse_row(row)
if (not is_neuron(pre) and not is_body_wall_muscle(pre)
) or not is_body_wall_muscle(post):
# Don't add connections unless pre=neuron and post=body_wall_muscle
continue
if is_neuron(pre):
pre = remove_leading_index_zero(pre)
else:
pre = get_old_muscle_name(pre)
post = get_old_muscle_name(post)
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
#print ConnectionInfo(pre, post, num, syntype, synclass)
if is_neuron(pre) and pre not in neurons:
neurons.append(pre)
elif is_body_wall_muscle(pre) and pre not in muscles:
muscles.append(pre)
if post not in muscles:
muscles.append(post)
return neurons, muscles, conns
def read_data(include_nonconnected_cells=False):
print_("Initialising OpenWormReader")
with pyopenworm_connect() as conn:
ctx = Context(ident="http://openworm.org/data", conf=conn.conf).stored
#Extract the network object from the worm object.
net = ctx(Worm)().neuron_network()
all_connections = net.synapses()
conns = []
cells = []
cell_names = get_cells_in_model(net)
for s in all_connections:
pre = str(s.pre_cell().name())
post = str(s.post_cell().name())
if isinstance(s.post_cell(),
Neuron) and pre in cell_names and post in cell_names:
syntype = str(s.syntype())
syntype = syntype[0].upper() + syntype[1:]
num = int(s.number())
synclass = str(s.synclass())
ci = ConnectionInfo(pre, post, num, syntype, synclass)
conns.append(ci)
if pre not in cells:
cells.append(pre)
if post not in cells:
cells.append(post)
print_("Total cells %i (%i with connections)" %
(len(cell_names), len(cells)))
print_("Total connections found %i " % len(conns))
if include_nonconnected_cells:
return cell_names, conns
else:
return cells, conns
def _read_connections(self, termination=None):
if not self.cached:
with Bundle('openworm/owmeta-data', version=6) as bnd:
ctx = bnd(Context)(ident="http://openworm.org/data").stored
# Extract the network object from the worm object.
net = ctx(Worm).query().neuron_network()
syn = net.synapse.expr
pre = syn.pre_cell
post = syn.post_cell
(pre | post).rdf_type(multiple=True)
(pre | post).name()
pre()
post()
syn.syntype()
syn.synclass()
syn.number()
self.connlist = syn.to_objects()
self.cell_names = self.get_cells_in_model(net)
self.cached = True
if termination == 'neuron':
term_type = set([Neuron.rdf_type])
elif termination == 'muscle':
term_type = set([BodyWallMuscle.rdf_type])
else:
term_type = set([Neuron.rdf_type, BodyWallMuscle.rdf_type])
conns = []
pre_cell_names = set()
post_cell_names = set()
for conn in self.connlist:
if (Neuron.rdf_type in conn.pre_cell.rdf_type and
term_type & set(conn.post_cell.rdf_type)):
num = conn.number
syntype = conn.syntype or ''
synclass = conn.synclass or ''
pre_name = conn.pre_cell.name
post_name = conn.post_cell.name
if BodyWallMuscle.rdf_type in conn.post_cell.rdf_type:
post_name = format_muscle_name(post_name)
if not synclass:
# Hack/guess
if syntype and syntype.lower() == "gapjunction":
synclass = "Generic_GJ"
else:
if pre_name.startswith("DD") or pre_name.startswith("VD"):
synclass = "GABA"
synclass = "Acetylcholine"
conns.append(ConnectionInfo(pre_name, post_name, num, syntype, synclass))
pre_cell_names.add(pre_name)
post_cell_names.add(post_name)
print_("Total cells %i (%i with connections)" % (
len(self.cell_names | pre_cell_names | post_cell_names),
len(pre_cell_names | post_cell_names)))
print_("Total connections found %i " % len(conns))
return list(self.cell_names), pre_cell_names, post_cell_names, conns