11407201950

Committed 18 Oct 2024 04:12PM UTC coverage: 47.898% (+1.1%) from 46.79%

Build # 11407201950

Build Type

Pull #730

github

Specific Base 86ab26

Committed by

web-flow

Commit Message

Merge 9801a3e94 into 0f4a57326

Pull Request Pull Request #730: 8.24.4

Run Details

47 of 68 new or added lines in 8 files covered. (69.12%)

1013 existing lines in 22 files now uncovered.

4170 of 8706 relevant lines covered (47.9%)

0.96 hits per line

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/iblrig_tasks/_iblrig_tasks_neuroModulatorChoiceWorld/task_validation.py

# first read jsonable file containing the trials in record oriented way
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from iblutil.io import jsonable

task_data = jsonable.read('/Users/olivier/Downloads/D6/_iblrig_taskData.raw.jsonable')

# pop-out the bpod data from the table
bpod_data = []
for td in task_data:
    bpod_data.append(td.pop('behavior_data'))

# and convert the remaining dictionary in a proper table format
task_data = pd.DataFrame(task_data)

## %%
# from the bpod data, we create columns that contain the time elapsed during reward and delays
task_data['bpod_valve_time'] = 0
task_data['bpod_delay'] = 0
for i, bp in enumerate(bpod_data):
    sts = bp['States timestamps']
    task_data.at[i, 'bpod_valve_time'] = np.diff(sts.get('reward', np.nan))
    task_data.at[i, 'bpod_delay'] = np.nansum(
        np.r_[
            np.diff(sts['delay_reward'])[0] if 'delay_reward' in sts else 0,
            np.diff(sts['delay_error'])[0] if 'delay_error' in sts else 0,
            np.diff(sts['delay_nogo'])[0] if 'delay_nogo' in sts else 0,
        ]
    )

## %%
# if all checks out the valve time should almost proportional to the reward amound
r = np.corrcoef(
    task_data['reward_amount'][~np.isnan(task_data['valve_time'])], task_data['valve_time'][~np.isnan(task_data['valve_time'])]
)
assert r[0, 1] >= 0.9999

# the other test is to check that we have delays - this fails on data collected 27/01/2023
# here the rewarded trials have no delay. Oups...
assert np.all(task_data['bpod_delay'] >= 1.5)

## %%
plt.figure()
plt.plot(task_data['reward_amount'].values, task_data['valve_time'].values, '.')
plt.figure()
plt.plot(task_data['bpod_delay'])
plt.show()

1	# first read jsonable file containing the trials in record oriented way
2	import matplotlib.pyplot as plt	×
3	import numpy as np	×
4	import pandas as pd	×
5
6	from iblutil.io import jsonable	×
7
8	task_data = jsonable.read('/Users/olivier/Downloads/D6/_iblrig_taskData.raw.jsonable')	×
9
10	# pop-out the bpod data from the table
11	bpod_data = []	×
12	for td in task_data:	×
13	bpod_data.append(td.pop('behavior_data'))	×
14
15	# and convert the remaining dictionary in a proper table format
16	task_data = pd.DataFrame(task_data)	×
17
18	## %%
19	# from the bpod data, we create columns that contain the time elapsed during reward and delays
20	task_data['bpod_valve_time'] = 0	×
21	task_data['bpod_delay'] = 0	×
22	for i, bp in enumerate(bpod_data):	×
23	sts = bp['States timestamps']	×
NEW 24	task_data.at[i, 'bpod_valve_time'] = np.diff(sts.get('reward', np.nan))	×
25	task_data.at[i, 'bpod_delay'] = np.nansum(	×
26	np.r_[
27	np.diff(sts['delay_reward'])[0] if 'delay_reward' in sts else 0,
28	np.diff(sts['delay_error'])[0] if 'delay_error' in sts else 0,
29	np.diff(sts['delay_nogo'])[0] if 'delay_nogo' in sts else 0,
30	]
31	)
32
33	## %%
34	# if all checks out the valve time should almost proportional to the reward amound
35	r = np.corrcoef(	×
36	task_data['reward_amount'][~np.isnan(task_data['valve_time'])], task_data['valve_time'][~np.isnan(task_data['valve_time'])]
37	)
38	assert r[0, 1] >= 0.9999	×
39
40	# the other test is to check that we have delays - this fails on data collected 27/01/2023
41	# here the rewarded trials have no delay. Oups...
42	assert np.all(task_data['bpod_delay'] >= 1.5)	×
43
44	## %%
45	plt.figure()	×
46	plt.plot(task_data['reward_amount'].values, task_data['valve_time'].values, '.')	×
47	plt.figure()	×
48	plt.plot(task_data['bpod_delay'])	×
49	plt.show()	×

int-brain-lab / iblrig / 11407201950

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