def get_trial_reward_time(rebased_reward_times, start_time, stop_time):
'''extract reward times in time range'''
reward_times = rebased_reward_times[np.where(np.logical_and(
rebased_reward_times >= start_time,
rebased_reward_times <= stop_time
))]
return float('nan') if len(reward_times) == 0 else one(reward_times)
def get_dprime(hit_rate, fa_rate, sliding_window=SLIDING_WINDOW):
""" calculates the d-prime for a given hit rate and false alarm rate
https://en.wikipedia.org/wiki/Sensitivity_index
Parameters
----------
hit_rate : float
rate of hits in the True class
fa_rate : float
rate of false alarms in the False class
limits : tuple, optional
limits on extreme values, which distort. default: (0.01,0.99)
Returns
-------
d_prime
"""
limits = (1/SLIDING_WINDOW, 1 - 1/SLIDING_WINDOW)
assert limits[0] > 0.0, 'limits[0] must be greater than 0.0'
assert limits[1] < 1.0, 'limits[1] must be less than 1.0'
Z = norm.ppf
# Limit values in order to avoid d' infinity
hit_rate = np.clip(hit_rate, limits[0], limits[1])
fa_rate = np.clip(fa_rate, limits[0], limits[1])
d_prime = Z(pd.Series(hit_rate)) - Z(pd.Series(fa_rate))
return one(d_prime)
def _get_reward_time(rebased_reward_times,
start_time,
stop_time) -> float:
"""extract reward times in time range"""
reward_times = rebased_reward_times[np.where(np.logical_and(
rebased_reward_times >= start_time,
rebased_reward_times <= stop_time
))]
return float('nan') if len(reward_times) == 0 else one(
reward_times)
def find_licks(reward_times, licks, window=3.5):
if len(reward_times) == 0:
return []
else:
reward_time = one(reward_times)
reward_lick_mask = ((licks['time'] > reward_time) & (licks['time'] < (reward_time + window)))
tr_licks = licks[reward_lick_mask].copy()
tr_licks['time'] -= reward_time
return tr_licks['time'].values
def fetchall(self, query, strict=True):
response = self.select(query)
return [one(x) for x in response.values.flat]
def fetchone(self, query, strict=True):
response = one(list(self.select(query).to_dict().values()))
if strict is True and (len(response) != 1 or response[0] is None):
raise OneResultExpectedError
return response[0]
def get_image_index_names(self):
image_index_names = self.get_stimulus_presentations().groupby(
'image_index').apply(
lambda group: one(group['image_name'].unique()))
return image_index_names
