-
Notifications
You must be signed in to change notification settings - Fork 1
/
test_core.py
288 lines (243 loc) · 14.7 KB
/
test_core.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
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
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
import numpy as np
import measurements as m
from core import Experiment, Rat, TrialEpoch
epoch_expt = Experiment(
name="test",
plot_key="",
cache_key="",
trial_epochs=[
TrialEpoch("mags", start_idx=1, stop_idx=2),
TrialEpoch("light1", start_idx=4, stop_idx=5),
TrialEpoch("light2", start_idx=6, stop_idx=7),
TrialEpoch("sound1", start_idx=8, stop_idx=9),
TrialEpoch("sound2", start_idx=10, stop_idx=11),
TrialEpoch("trial1", start_idx=12, stop_idx=13),
TrialEpoch("trial2", start_idx=14, stop_idx=15),
TrialEpoch("trial3", start_idx=16, stop_idx=17),
TrialEpoch("trial4", start_idx=18, stop_idx=19),
TrialEpoch("baseline", start_idx=4, duration=-10),
TrialEpoch("baseline", start_idx=6, duration=-10),
],
measurements=[m.Duration(), m.Count(), m.Latency(), m.AtLeastOne()],
rats=[
Rat('1', group="1"),
Rat('2', group="2"),
Rat('3', group="1"),
Rat('4', group="2"),
Rat('5', group="1"),
Rat('6', group="2"),
Rat('7', group="1"),
Rat('8', group="2"),
],
ignore_sessions='',
sessionfiles=['!roborats']
)
def add_datapoints(session, data, rat):
def add_data(cue, trial=None):
if trial is not None:
meta = {
"cue_type": cue[:5],
"trial_type": trial[-1],
"rewarded": "rewarded" if trial == "trial2" else "unrewarded",
"cue": cue,
}
trial = data[trial]
cue = data[cue]
session.add_epoch_data(rat.rat_id, trial.intersect(cue), meta)
else:
meta = {
"cue_type": cue,
"trial_type": "",
"rewarded": "",
"cue": cue,
}
session.add_epoch_data(rat.rat_id, data[cue], meta)
add_data("light1", "trial1")
add_data("sound1", "trial1")
add_data("light2", "trial2")
add_data("sound1", "trial2")
add_data("light1", "trial3")
add_data("sound2", "trial3")
add_data("light2", "trial4")
add_data("sound2", "trial4")
add_data("baseline")
epoch_expt.add_datapoints = add_datapoints
df = epoch_expt.analyze()
def test_no_mags():
rat = '1'
for cue in ['light', 'sound']:
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
def test_all_mags():
rat = '2'
for cue in ['light', 'sound']:
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
def test_sound_only():
rat = '3'
cue = 'sound'
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
cue = 'light'
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
def test_light_only():
rat = '4'
cue = 'sound'
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
cue = 'light'
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
def test_rewarded_sound():
rat = '5'
cue = 'light'
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
cue = 'sound'
for trial in [2, 3]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
for trial in [1, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
def test_iti_only():
rat = '6'
for cue in ['light', 'sound']:
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
def test_half_light():
rat = '7'
cue = 'sound'
for trial in [1, 2, 3, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
cue = 'light'
for trial in [1, 4]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 5.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 5.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
for trial in [2, 3]:
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 5.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
def test_complex():
rat = '8'
trial = 1
cue = 'light'
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 9.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
cue = 'sound'
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 1.98))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
trial = 2
cue = 'light'
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
cue = 'sound'
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
trial = 3
cue = 'light'
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 2.5))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 2.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
cue = 'sound'
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 1.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 1.0))
trial = 4
cue = 'light'
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))
cue = 'sound'
this_df = (df.groupby(['rat']).get_group(rat).groupby(['cue_type']).get_group(cue).
groupby(['trial_type']).get_group(str(trial))[['measure', 'value']])
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Duration']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Count']['value']), 0.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'Latency']['value']), 10.0))
assert (np.allclose(np.mean(this_df[this_df['measure'] == 'AtLeastOne']['value']), 0.0))