64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194 | class Yin2024DataReader(ConnectomeDataset):
"""
Reader of data from Yin et al. 2024 - Dauer connectome
"""
verbose = False
def __init__(self):
ConnectomeDataset.__init__(self)
wb = load_workbook(filename)
print_("Opened the Excel file: " + filename)
self.pre_cells = {}
self.post_cells = {}
self.conn_nums = {}
sheet = wb.get_sheet_by_name(DAUER_NORM)
print_("Looking at sheet: %s" % DAUER_NORM)
conn_type = DAUER_NORM
self.pre_cells[conn_type] = []
self.post_cells[conn_type] = []
for i in pre_range[conn_type]:
self.pre_cells[conn_type].append(sheet["A%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=1, 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=float,
)
for i in range(len(self.pre_cells[conn_type])):
for j in range(len(self.post_cells[conn_type])):
row = 3 + i
col = 3 + 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] = 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_data(self):
return self._read_data()
def read_muscle_data(self):
return self._read_muscle_data()
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 = []
conn_type = DAUER_NORM
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"
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
|