def analyze_behavior(data_path, condition):
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
analysis.add_time(res)
lick_res = convert(res, condition)
days_per_mouse = _get_days_per_condition(data_path, condition)
last_day_per_mouse = np.array([x[-1] for x in days_per_mouse])
plot_res = agglomerate_days(lick_res, condition,
condition.training_start_day,
last_day_per_mouse)
add_odor_value(plot_res, condition)
add_behavior_stats(plot_res, condition)
return plot_res
def get_licks_per_day(data_path, condition, return_raw=False):
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
analysis.add_time(res)
lick_res = convert(res, condition)
lick_res['lick_boolean'] = np.array([y > 0 for y in lick_res['lick']])
out = reduce.new_filter_reduce(lick_res, ['odor', 'day', 'mouse'],
'lick_boolean')
if return_raw:
add_odor_value(lick_res, condition)
return lick_res
else:
return out
def custom_analyze_behavior(data_path, condition, start_time, end_time):
import analysis
from behavior import behavior_analysis
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
analysis.add_time(res)
lick_res = custom_convert(res, condition, start_time, end_time)
days_per_mouse = behavior_analysis._get_days_per_condition(
data_path, condition)
last_day_per_mouse = np.array([x[-1] for x in days_per_mouse])
plot_res = behavior_analysis.agglomerate_days(lick_res, condition,
condition.training_start_day,
last_day_per_mouse)
analysis.add_odor_value(plot_res, condition)
behavior_analysis.add_behavior_stats(plot_res, condition)
return plot_res
def _get_days_per_condition(data_path, condition, odor_valence=None):
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
lick_res = convert(res, condition)
if odor_valence is not None:
add_odor_value(lick_res, condition)
lick_res = filter.filter(lick_res, {'odor_valence': odor_valence})
days = []
list_of_dates = lick_res['day']
list_of_mice = lick_res['mouse']
_, mouse_ixs = np.unique(list_of_mice, return_inverse=True)
for mouse_ix in np.unique(mouse_ixs):
mouse_dates = list_of_dates[mouse_ixs == mouse_ix]
counts = np.unique(mouse_dates)
days.append(counts)
return days
def get_days_per_mouse(data_path, condition, odor_valence='CS+'):
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
days_per_mouse = np.array(
_get_days_per_condition(data_path, condition, odor_valence))
first_day_per_mouse = np.array([x[0] for x in days_per_mouse])
last_day_per_mouse = np.array([x[-1] for x in days_per_mouse])
if hasattr(condition, 'csp') or hasattr(condition, 'pt_csp'):
res_behavior = analyze_behavior(data_path, condition)
res_behavior_csp = filter.filter(res_behavior,
{'odor_valence': odor_valence})
res_behavior_summary = reduce.filter_reduce(res_behavior_csp,
filter_keys='mouse',
reduce_key='learned_day')
mice, ix = np.unique(res_behavior_summary['mouse'],
return_inverse=True)
temp = res_behavior_summary['learned_day'][ix]
temp[temp == None] = last_day_per_mouse[temp == None]
temp[np.isnan(temp)] = last_day_per_mouse[np.isnan(temp)]
learned_days_per_mouse = np.ceil(temp)
else:
learned_days_per_mouse = last_day_per_mouse
return learned_days_per_mouse, last_day_per_mouse
# select_dict={'mouse':mouse, 'odor_valence':valence},
# error_key= error_key,
# plot_function=plt.fill_between,
# colors = 'black',
# plot_args=fill_args,
# ax_args = ax_args,
# path=save_path)
condition = experimental_conditions.BEHAVIOR_MPFC_YFP_DISCRIMINATION
data_path = os.path.join(Config.LOCAL_DATA_PATH,
Config.LOCAL_DATA_BEHAVIOR_FOLDER, condition.name)
save_path = os.path.join(Config.LOCAL_DATA_PATH, Config.LOCAL_FIGURE_PATH,
'MISC', 'ball_movement', condition.name)
res = analysis.load_all_cons(data_path)
analysis.add_indices(res)
res = _convert(res, condition)
res = _filter(res)
behavior.behavior_analysis.add_odor_value(res, condition)
print(np.unique(res['mouse']))
_angle(res)
# _average_velocity(res, save_path)
# _hist(res, save_path)
_example_velocity(res, save_path)
# print(res.keys())
# print(res['mouse'])
# print(res['day'])