9032957364

Committed 10 May 2024 01:25PM UTC coverage: 48.538% (+1.7%) from 46.79%

Build # 9032957364

Build Type

Pull #643

github

Specific Base 74d2ec

Committed by

web-flow

Commit Message

Merge aebf2c9af into ec2d8e4fe

Pull Request Pull Request #643: 8.19.0

Run Details

377 of 1074 new or added lines in 38 files covered. (35.1%)

977 existing lines in 19 files now uncovered.

3253 of 6702 relevant lines covered (48.54%)

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97.92

/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'],
        **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
        # 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'] = 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 ' f'(deg/mm, default: {DEFAULTS["STIM_GAIN"]})',
        )
        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	**kwargs,
34	):
35	super().__init__(args, *kwargs)	2✔
36	nc = len(contrast_set)	2✔
37	assert len(probability_set) in [nc, 1], 'probability_set must be a scalar or have the same length as contrast_set'	2✔
38	assert len(reward_set_ul) in [nc, 1], 'reward_set_ul must be a scalar or have the same length as contrast_set'	2✔
39	assert len(position_set) == nc, 'position_set must have the same length as contrast_set'	2✔
40	self.task_params['CONTRAST_SET'] = contrast_set	2✔
41	self.task_params['PROBABILITY_SET'] = probability_set	2✔
42	self.task_params['REWARD_SET_UL'] = reward_set_ul	2✔
43	self.task_params['POSITION_SET'] = position_set	2✔
44	self.task_params['STIM_GAIN'] = stim_gain	2✔
45	# it is easier to work with parameters as a dataframe
46	self.df_contingencies = pd.DataFrame(columns=['contrast', 'probability', 'reward_amount_ul', 'position'])	2✔
47	self.df_contingencies['contrast'] = contrast_set	2✔
48	self.df_contingencies['probability'] = probability_set if len(probability_set) == nc else probability_set[0]	2✔
49	self.df_contingencies['reward_amount_ul'] = reward_set_ul if len(reward_set_ul) == nc else reward_set_ul[0]	2✔
50	self.df_contingencies['position'] = position_set	2✔
51	# normalize the probabilities
52	self.df_contingencies.loc[:, 'probability'] = self.df_contingencies.loc[:, 'probability'] / np.sum(	2✔
53	self.df_contingencies.loc[:, 'probability']
54	)
55	# update the PROBABILITY LEFT field to reflect the probabilities in the parameters above
56	self.task_params['PROBABILITY_LEFT'] = np.sum(	2✔
57	self.df_contingencies['probability'] * (self.df_contingencies['position'] < 0)
58	)
59	self.trials_table['debias_trial'] = np.zeros(NTRIALS_INIT, dtype=bool)	2✔
60
61	def draw_next_trial_info(self, **kwargs):	2✔
62	nc = self.df_contingencies.shape[0]	2✔
63	ic = np.random.choice(np.arange(nc), p=self.df_contingencies['probability'])	2✔
64	# now calling the super class with the proper parameters
65	super().draw_next_trial_info(	2✔
66	pleft=self.task_params.PROBABILITY_LEFT,
67	contrast=self.df_contingencies.at[ic, 'contrast'],
68	position=self.df_contingencies.at[ic, 'position'],
69	reward_amount=self.df_contingencies.at[ic, 'reward_amount_ul'],
70	)
71
72	@property	2✔
73	def reward_amount(self):	2✔
NEW 74	return self.task_params.REWARD_AMOUNTS_UL[0]	×
75
76	@staticmethod	2✔
77	def extra_parser():	2✔
78	""":return: argparse.parser()"""
79	parser = super(Session, Session).extra_parser()	2✔
80	parser.add_argument(	2✔
81	'--contrast_set',
82	option_strings=['--contrast_set'],
83	dest='contrast_set',
84	default=DEFAULTS['CONTRAST_SET'],
85	nargs='+',
86	type=float,
87	help='Set of contrasts to present',
88	)
89	parser.add_argument(	2✔
90	'--probability_set',
91	option_strings=['--probability_set'],
92	dest='probability_set',
93	default=DEFAULTS['PROBABILITY_SET'],
94	nargs='+',
95	type=float,
96	help='Probabilities of each contrast in contrast_set. If scalar all contrasts are equiprobable',
97	)
98	parser.add_argument(	2✔
99	'--reward_set_ul',
100	option_strings=['--reward_set_ul'],
101	dest='reward_set_ul',
102	default=DEFAULTS['REWARD_SET_UL'],
103	nargs='+',
104	type=float,
105	help='Reward for contrast in contrast set.',
106	)
107	parser.add_argument(	2✔
108	'--position_set',
109	option_strings=['--position_set'],
110	dest='position_set',
111	default=DEFAULTS['POSITION_SET'],
112	nargs='+',
113	type=float,
114	help='Position for each contrast in contrast set.',
115	)
116	parser.add_argument(	2✔
117	'--stim_gain',
118	option_strings=['--stim_gain'],
119	dest='stim_gain',
120	default=DEFAULTS['STIM_GAIN'],
121	type=float,
122	help=f'Visual angle/wheel displacement ' f'(deg/mm, default: {DEFAULTS["STIM_GAIN"]})',
123	)
124	return parser	2✔
125
126	def next_trial(self):	2✔
127	# update counters
128	self.trial_num += 1	2✔
129	# save and send trial info to bonsai
130	self.draw_next_trial_info(pleft=self.task_params.PROBABILITY_LEFT)	2✔
131
132
133	if __name__ == '__main__': # pragma: no cover
134	kwargs = iblrig.misc.get_task_arguments(parents=[Session.extra_parser()])
135	sess = Session(**kwargs)
136	sess.run()

int-brain-lab / iblrig / 9032957364

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