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195 | class Cook2019DataReader(ConnectomeDataset):
"""
Reader of data from Cook et al. 2019 - Whole-animal connectomes of both Caeonrhabditis elegans sexes
"""
spreadsheet_location = os.path.dirname(os.path.abspath(__file__)) + "/data/"
filename = "%sSI 5 Connectome adjacency matrices.xlsx" % spreadsheet_location
verbose = False
def __init__(self, sex):
ConnectomeDataset.__init__(self)
self.sex = sex
wb = load_workbook(self.filename)
print_("Opened the Excel file: " + self.filename)
self.pre_cells = {}
self.post_cells = {}
self.conn_nums = {}
for conn_type in SEX_SPECIFIC_SHEETS[self.sex]:
sheet = wb.get_sheet_by_name(conn_type)
print_("Looking at sheet: %s" % conn_type)
self.pre_cells[conn_type] = []
self.post_cells[conn_type] = []
for i in pre_range[conn_type]:
self.pre_cells[conn_type].append(sheet["C%i" % i].value)
if self.verbose:
print_(
" - Pre cells for %s (%i):\n%s"
% (
conn_type,
len(self.pre_cells[conn_type]),
self.pre_cells[conn_type],
)
)
for i in post_range[conn_type]:
self.post_cells[conn_type].append(sheet.cell(row=3, column=i).value)
if self.verbose:
print_(
" - Post cells for %s (%i):\n%s"
% (
conn_type,
len(self.post_cells[conn_type]),
self.post_cells[conn_type],
)
)
self.conn_nums[conn_type] = np.zeros(
[len(self.pre_cells[conn_type]), len(self.post_cells[conn_type])],
dtype=int,
)
for i in range(len(self.pre_cells[conn_type])):
for j in range(len(self.post_cells[conn_type])):
row = 4 + i
col = 4 + j
val = sheet.cell(row=row, column=col).value
# print("Cell (%i,%i) [row %i, col %i] = %s" % (i, j, row, col, val))
if val is not None:
self.conn_nums[conn_type][i, j] = int(val)
if self.verbose:
print_(
" - Conns for %s (%s):\n%s"
% (
conn_type,
self.conn_nums[conn_type].shape,
self.conn_nums[conn_type],
)
)
neurons, muscles, other_cells, conns = self.read_all_data()
for conn in conns:
self.add_connection_info(conn)
def read_all_data(self):
"""
Returns:
Tuple[list, list, list, list]: List of neurons, muscles), other cells and connections which have been read in
"""
neurons = set([])
muscles = set([])
other_cells = set([])
conns = []
for conn_type in SEX_SPECIFIC_SHEETS[self.sex]:
for pre_index in range(len(self.pre_cells[conn_type])):
for post_index in range(len(self.post_cells[conn_type])):
num = self.conn_nums[conn_type][pre_index, post_index]
pre = remove_leading_index_zero(
self.pre_cells[conn_type][pre_index]
)
post = remove_leading_index_zero(
self.post_cells[conn_type][post_index]
)
if self.verbose and num > 0:
print_("Conn %s -> %s #%i" % (pre, post, num))
if is_potential_muscle(pre):
pre = convert_to_preferred_muscle_name(pre)
if is_potential_muscle(post):
post = convert_to_preferred_muscle_name(post)
if num > 0:
syntype = "Send" if "chemical" in conn_type else "GapJunction"
synclass = get_synclass(pre, syntype)
ci = ConnectionInfo(pre, post, num, syntype, synclass)
if self.verbose:
print_("Conn: %s" % (ci))
conns.append(ci)
for p in [pre, post]:
if is_any_neuron(p):
neurons.add(pre)
elif is_known_muscle(p):
muscles.add(pre)
else:
other_cells.add(p)
return list(neurons), list(muscles), list(other_cells), conns
|