14196118657

Committed 01 Apr 2025 12:52PM UTC coverage: 47.634% (+0.8%) from 46.79%

Build # 14196118657

Build Type

Pull #795

github

Specific Base cfb5bd

Committed by

web-flow

Commit Message

Merge 5ba5d5f25 into 58cf64236

Pull Request Pull Request #795: fixes for habituation CW

Run Details

11 of 12 new or added lines in 1 file covered. (91.67%)

1083 existing lines in 22 files now uncovered.

4288 of 9002 relevant lines covered (47.63%)

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98.0

/iblrig_tasks/_iblrig_tasks_advancedChoiceWorld/task.py

from pathlib import Path

import numpy as np
import pandas as pd
import yaml

import iblrig.misc
from iblrig.base_choice_world import NTRIALS_INIT, ActiveChoiceWorldSession

# read defaults from task_parameters.yaml
with open(Path(__file__).parent.joinpath('task_parameters.yaml')) as f:
    DEFAULTS = yaml.safe_load(f)


class Session(ActiveChoiceWorldSession):
    """
    Advanced Choice World is the ChoiceWorld task using fixed 50/50 probability for the side
    and contrasts defined in the parameters.
    It differs from TrainingChoiceWorld in that it does not implement adaptive contrasts or debiasing,
    and it differs from BiasedChoiceWorld in that it does not implement biased blocks.
    """

    protocol_name = '_iblrig_tasks_advancedChoiceWorld'

    def __init__(
        self,
        *args,
        contrast_set: list[float] = DEFAULTS['CONTRAST_SET'],
        probability_set: list[float] = DEFAULTS['PROBABILITY_SET'],
        reward_set_ul: list[float] = DEFAULTS['REWARD_SET_UL'],
        position_set: list[float] = DEFAULTS['POSITION_SET'],
        stim_gain: float = DEFAULTS['STIM_GAIN'],
        stim_reverse: float = DEFAULTS['STIM_REVERSE'],
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        nc = len(contrast_set)
        assert len(probability_set) in [nc, 1], 'probability_set must be a scalar or have the same length as contrast_set'
        assert len(reward_set_ul) in [nc, 1], 'reward_set_ul must be a scalar or have the same length as contrast_set'
        assert len(position_set) == nc, 'position_set must have the same length as contrast_set'
        self.task_params['CONTRAST_SET'] = contrast_set
        self.task_params['PROBABILITY_SET'] = probability_set
        self.task_params['REWARD_SET_UL'] = reward_set_ul
        self.task_params['POSITION_SET'] = position_set
        self.task_params['STIM_GAIN'] = stim_gain
        self.task_params['STIM_REVERSE'] = stim_reverse
        # it is easier to work with parameters as a dataframe
        self.df_contingencies = pd.DataFrame(columns=['contrast', 'probability', 'reward_amount_ul', 'position'])
        self.df_contingencies['contrast'] = contrast_set
        self.df_contingencies['probability'] = np.float64(probability_set if len(probability_set) == nc else probability_set[0])
        self.df_contingencies['reward_amount_ul'] = reward_set_ul if len(reward_set_ul) == nc else reward_set_ul[0]
        self.df_contingencies['position'] = position_set
        # normalize the probabilities
        self.df_contingencies.loc[:, 'probability'] = self.df_contingencies.loc[:, 'probability'] / np.sum(
            self.df_contingencies.loc[:, 'probability']
        )
        # update the PROBABILITY LEFT field to reflect the probabilities in the parameters above
        self.task_params['PROBABILITY_LEFT'] = np.sum(
            self.df_contingencies['probability'] * (self.df_contingencies['position'] < 0)
        )
        self.trials_table['debias_trial'] = np.zeros(NTRIALS_INIT, dtype=bool)

    def draw_next_trial_info(self, **kwargs):
        nc = self.df_contingencies.shape[0]
        ic = np.random.choice(np.arange(nc), p=self.df_contingencies['probability'])
        # now calling the super class with the proper parameters
        super().draw_next_trial_info(
            pleft=self.task_params.PROBABILITY_LEFT,
            contrast=self.df_contingencies.at[ic, 'contrast'],
            position=self.df_contingencies.at[ic, 'position'],
            reward_amount=self.df_contingencies.at[ic, 'reward_amount_ul'],
        )

    @property
    def reward_amount(self):
        return self.task_params.REWARD_AMOUNTS_UL[0]

    @staticmethod
    def extra_parser():
        """:return: argparse.parser()"""
        parser = super(Session, Session).extra_parser()
        parser.add_argument(
            '--contrast_set',
            option_strings=['--contrast_set'],
            dest='contrast_set',
            default=DEFAULTS['CONTRAST_SET'],
            nargs='+',
            type=float,
            help='Set of contrasts to present',
        )
        parser.add_argument(
            '--probability_set',
            option_strings=['--probability_set'],
            dest='probability_set',
            default=DEFAULTS['PROBABILITY_SET'],
            nargs='+',
            type=float,
            help='Probabilities of each contrast in contrast_set. If scalar all contrasts are equiprobable',
        )
        parser.add_argument(
            '--reward_set_ul',
            option_strings=['--reward_set_ul'],
            dest='reward_set_ul',
            default=DEFAULTS['REWARD_SET_UL'],
            nargs='+',
            type=float,
            help='Reward for contrast in contrast set.',
        )
        parser.add_argument(
            '--position_set',
            option_strings=['--position_set'],
            dest='position_set',
            default=DEFAULTS['POSITION_SET'],
            nargs='+',
            type=float,
            help='Position for each contrast in contrast set.',
        )
        parser.add_argument(
            '--stim_gain',
            option_strings=['--stim_gain'],
            dest='stim_gain',
            default=DEFAULTS['STIM_GAIN'],
            type=float,
            help=f'Visual angle/wheel displacement (deg/mm, default: {DEFAULTS["STIM_GAIN"]})',
        )
        parser.add_argument(
            '--stim_reverse',
            option_strings=['--stim_reverse'],
            action='store_true',
            dest='stim_reverse',
            help='Inverse relationship of wheel to stimulus movement',
        )
        return parser

    def next_trial(self):
        # update counters
        self.trial_num += 1
        # save and send trial info to bonsai
        self.draw_next_trial_info(pleft=self.task_params.PROBABILITY_LEFT)


if __name__ == '__main__':  # pragma: no cover
    kwargs = iblrig.misc.get_task_arguments(parents=[Session.extra_parser()])
    sess = Session(**kwargs)
    sess.run()

1	from pathlib import Path	2✔
2
3	import numpy as np	2✔
4	import pandas as pd	2✔
5	import yaml	2✔
6
7	import iblrig.misc	2✔
8	from iblrig.base_choice_world import NTRIALS_INIT, ActiveChoiceWorldSession	2✔
9
10	# read defaults from task_parameters.yaml
11	with open(Path(__file__).parent.joinpath('task_parameters.yaml')) as f:	2✔
12	DEFAULTS = yaml.safe_load(f)	2✔
13
14
15	class Session(ActiveChoiceWorldSession):	2✔
16	"""
17	Advanced Choice World is the ChoiceWorld task using fixed 50/50 probability for the side
18	and contrasts defined in the parameters.
19	It differs from TrainingChoiceWorld in that it does not implement adaptive contrasts or debiasing,
20	and it differs from BiasedChoiceWorld in that it does not implement biased blocks.
21	"""
22
23	protocol_name = '_iblrig_tasks_advancedChoiceWorld'	2✔
24
25	def __init__(	2✔
26	self,
27	*args,
28	contrast_set: list[float] = DEFAULTS['CONTRAST_SET'],
29	probability_set: list[float] = DEFAULTS['PROBABILITY_SET'],
30	reward_set_ul: list[float] = DEFAULTS['REWARD_SET_UL'],
31	position_set: list[float] = DEFAULTS['POSITION_SET'],
32	stim_gain: float = DEFAULTS['STIM_GAIN'],
33	stim_reverse: float = DEFAULTS['STIM_REVERSE'],
34	**kwargs,
35	):
36	super().__init__(args, *kwargs)	2✔
37	nc = len(contrast_set)	2✔
38	assert len(probability_set) in [nc, 1], 'probability_set must be a scalar or have the same length as contrast_set'	2✔
39	assert len(reward_set_ul) in [nc, 1], 'reward_set_ul must be a scalar or have the same length as contrast_set'	2✔
40	assert len(position_set) == nc, 'position_set must have the same length as contrast_set'	2✔
41	self.task_params['CONTRAST_SET'] = contrast_set	2✔
42	self.task_params['PROBABILITY_SET'] = probability_set	2✔
43	self.task_params['REWARD_SET_UL'] = reward_set_ul	2✔
44	self.task_params['POSITION_SET'] = position_set	2✔
45	self.task_params['STIM_GAIN'] = stim_gain	2✔
46	self.task_params['STIM_REVERSE'] = stim_reverse	2✔
47	# it is easier to work with parameters as a dataframe
48	self.df_contingencies = pd.DataFrame(columns=['contrast', 'probability', 'reward_amount_ul', 'position'])	2✔
49	self.df_contingencies['contrast'] = contrast_set	2✔
50	self.df_contingencies['probability'] = np.float64(probability_set if len(probability_set) == nc else probability_set[0])	2✔
51	self.df_contingencies['reward_amount_ul'] = reward_set_ul if len(reward_set_ul) == nc else reward_set_ul[0]	2✔
52	self.df_contingencies['position'] = position_set	2✔
53	# normalize the probabilities
54	self.df_contingencies.loc[:, 'probability'] = self.df_contingencies.loc[:, 'probability'] / np.sum(	2✔
55	self.df_contingencies.loc[:, 'probability']
56	)
57	# update the PROBABILITY LEFT field to reflect the probabilities in the parameters above
58	self.task_params['PROBABILITY_LEFT'] = np.sum(	2✔
59	self.df_contingencies['probability'] * (self.df_contingencies['position'] < 0)
60	)
61	self.trials_table['debias_trial'] = np.zeros(NTRIALS_INIT, dtype=bool)	2✔
62
63	def draw_next_trial_info(self, **kwargs):	2✔
64	nc = self.df_contingencies.shape[0]	2✔
65	ic = np.random.choice(np.arange(nc), p=self.df_contingencies['probability'])	2✔
66	# now calling the super class with the proper parameters
67	super().draw_next_trial_info(	2✔
68	pleft=self.task_params.PROBABILITY_LEFT,
69	contrast=self.df_contingencies.at[ic, 'contrast'],
70	position=self.df_contingencies.at[ic, 'position'],
71	reward_amount=self.df_contingencies.at[ic, 'reward_amount_ul'],
72	)
73
74	@property	2✔
75	def reward_amount(self):	2✔
UNCOV 76	return self.task_params.REWARD_AMOUNTS_UL[0]	×
77
78	@staticmethod	2✔
79	def extra_parser():	2✔
80	""":return: argparse.parser()"""
81	parser = super(Session, Session).extra_parser()	2✔
82	parser.add_argument(	2✔
83	'--contrast_set',
84	option_strings=['--contrast_set'],
85	dest='contrast_set',
86	default=DEFAULTS['CONTRAST_SET'],
87	nargs='+',
88	type=float,
89	help='Set of contrasts to present',
90	)
91	parser.add_argument(	2✔
92	'--probability_set',
93	option_strings=['--probability_set'],
94	dest='probability_set',
95	default=DEFAULTS['PROBABILITY_SET'],
96	nargs='+',
97	type=float,
98	help='Probabilities of each contrast in contrast_set. If scalar all contrasts are equiprobable',
99	)
100	parser.add_argument(	2✔
101	'--reward_set_ul',
102	option_strings=['--reward_set_ul'],
103	dest='reward_set_ul',
104	default=DEFAULTS['REWARD_SET_UL'],
105	nargs='+',
106	type=float,
107	help='Reward for contrast in contrast set.',
108	)
109	parser.add_argument(	2✔
110	'--position_set',
111	option_strings=['--position_set'],
112	dest='position_set',
113	default=DEFAULTS['POSITION_SET'],
114	nargs='+',
115	type=float,
116	help='Position for each contrast in contrast set.',
117	)
118	parser.add_argument(	2✔
119	'--stim_gain',
120	option_strings=['--stim_gain'],
121	dest='stim_gain',
122	default=DEFAULTS['STIM_GAIN'],
123	type=float,
124	help=f'Visual angle/wheel displacement (deg/mm, default: {DEFAULTS["STIM_GAIN"]})',
125	)
126	parser.add_argument(	2✔
127	'--stim_reverse',
128	option_strings=['--stim_reverse'],
129	action='store_true',
130	dest='stim_reverse',
131	help='Inverse relationship of wheel to stimulus movement',
132	)
133	return parser	2✔
134
135	def next_trial(self):	2✔
136	# update counters
137	self.trial_num += 1	2✔
138	# save and send trial info to bonsai
139	self.draw_next_trial_info(pleft=self.task_params.PROBABILITY_LEFT)	2✔
140
141
142	if __name__ == '__main__': # pragma: no cover
143	kwargs = iblrig.misc.get_task_arguments(parents=[Session.extra_parser()])
144	sess = Session(**kwargs)
145	sess.run()

int-brain-lab / iblrig / 14196118657

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