def readDataFromSpreadsheet(dir="../../",
include_nonconnected_cells=False,
neuron_connect=False):
# reading the NeuronConnect.xls file if neuron_connect = True
if neuron_connect:
conns = []
cells = []
filename = dir + "NeuronConnectFormatted.xlsx"
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 = dir + "CElegansNeuronTables.xls"
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 readMuscleDataFromSpreadsheet():
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 readDataFromSpreadsheet(dir="../../", include_nonconnected_cells=False):
conns = []
cells = []
filename = dir + "CElegansNeuronTables.xls"
rb = open_workbook(filename)
print "Opened Excel file: " + filename
known_nonconnected_cells = ['CANL', 'CANR', 'VC6']
sheet = rb.sheet_by_index(0)
for row in range(1, sheet.nrows):
pre = str(sheet.cell(row, 0).value)
post = str(sheet.cell(row, 1).value)
syntype = sheet.cell(row, 2).value
num = int(sheet.cell(row, 3).value)
synclass = sheet.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(self):
conns = []
cells = []
rb = open_workbook(self.filename)
print "Opened Excel file: "+ self.filename
for row in range(2,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)
# print "------------------------------------------\nConnection %i has %i from %s to %s (type: %s, synapse: %s)" %(row, num, pre, post, syntype, synclass)
# print "----------------------------------------------------------------------"
return cells, conns
def read(self):
conns = []
cells = []
cell_names = owr.get_cells_in_model()
for s in self.all_connections:
pre = str(s.pre_cell().name())
post = str(s.post_cell().name())
if isinstance(
s.post_cell(),
P.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)
logger.info("Total cells read " + str(len(cells)))
logger.info("Total connections read " + str(len(conns)))
P.disconnect()
return cells, conns
def readDataFromSpreadsheet(dir="../../"):
conns = []
cells = []
filename = dir + "CElegansNeuronTables.xls"
rb = open_workbook(filename)
print "Opened Excel file: " + filename
for row in range(2, 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)
# print "------------------------------------------\nConnection %i has %i from %s to %s (type: %s, synapse: %s)" %(row, num, pre, post, syntype, synclass)
# print "----------------------------------------------------------------------"
return cells, conns
def readMuscleDataFromSpreadsheet():
"""
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, 'rb') 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) or not is_body_wall_muscle(post):
# Don't add connections unless pre=neuron and post=body_wall_muscle
continue
pre = remove_leading_index_zero(pre)
post = get_old_muscle_name(post)
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
#print 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 readMuscleData(self):
conns = []
neurons = []
muscles = []
for s in self.muscle_connections:
pre = str(s.pre_cell().name())
post = str(s.post_cell().name())
syntype = str(s.syntype())
num = int(s.number())
synclass = str(s.synclass())
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
if isinstance(s.post_cell(), P.Muscle) and post not in muscles:
muscles.append(post)
elif post not in neurons:
neurons.append(post)
if isinstance(s.pre_cell(), P.Muscle) and pre not in muscles:
muscles.append(pre)
elif post not in neurons:
neurons.append(pre)
logger.info("Total neurons connected to muscles read " + str(len(neurons)))
logger.info("Total muscles connected to neurons read " + str(len(muscles)))
logger.info("Total connections between neurons and muscles read " + str(len(conns)))
return neurons, muscles, conns
def read(self, include_nonconnected_cells=False):
conns = []
cells = []
for s in self.neuron_connections:
pre = str(s.pre_cell().name())
post = str(s.post_cell().name())
syntype = str(s.syntype())
num = int(s.number())
synclass = str(s.synclass())
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
if pre not in cells:
cells.append(pre)
if post not in cells:
cells.append(post)
known_nonconnected_cells = ['CANL', 'CANR', 'VC6']
# ignore the cells unconnected if neuron_connect = True
if(include_nonconnected_cells):
for c in known_nonconnected_cells:
# to avoid an exception: nonconnected cell becomes connected.
if c not in cells:
cells.append(c)
logger.info("Total cells read " + str(len(cells)))
logger.info("Total connections read " + str(len(conns)))
return cells, conns
def read(self):
conns = []
cells = []
for s in self.net.synapses():
pre = str(s[0])
post = str(s[1])
syntype = str(s[2][0])
num = int(s[2][2])
synclass = str(s[2][2])
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
def read(self):
conns = []
cells = []
for s in self.all_connections:
pre = str(s.pre_cell())
post = str(s.post_cell())
syntype = str(s.syntype())
num = int(s.number())
synclass = str(s.synclass())
conns.append(ConnectionInfo(pre, post, num, syntype, synclass))
if pre not in cells:
cells.append(pre)
if post not in cells:
cells.append(post)
logger.info("Total cells read " + str(len(cells)))
logger.info("Total connections read " + str(len(conns)))
P.disconnect()
return cells, conns
def readDataFromSpreadsheet(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_data(include_nonconnected_cells=False):
print_("Initialising OpenWormReader")
P.connect()
net = P.Worm().get_neuron_network()
all_connections = net.synapses()
print_("Finished initialising OpenWormReader")
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(),
P.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))
P.disconnect()
if include_nonconnected_cells:
return cell_names, conns
else:
return cells, conns