def test_no_events(self):
pj = json.loads(open("files/test.boris").read())
r = utilities.get_current_points_by_subject(point_behaviors_codes=["p"],
events=pj["observations"]["observation #1"]["events"],
subjects=pj["subjects_conf"],
time=Decimal("3"),
tolerance=Decimal("3"))
assert r == {'0': [], '1': []}
def test_no_events(self):
pj = json.loads(open("files/test.boris").read())
r = utilities.get_current_points_by_subject(point_behaviors_codes=["p"],
events=pj[config.OBSERVATIONS]["observation #1"]["events"],
subjects=pj["subjects_conf"],
time=Decimal("3"),
tolerance=Decimal("3"))
assert r == {'0': [], '1': []}
def test_events(self):
pj_float = json.loads(open("files/test.boris").read())
pj = utilities.convert_time_to_decimal(pj_float)
r = utilities.get_current_points_by_subject(point_behaviors_codes=["p"],
events=pj["observations"]["offset positif"]["events"],
subjects={"0":{"key":"", "name": "", "description":"no focal subject"}},
time=Decimal("22.6"),
tolerance=Decimal("1"))
assert r == {'0': [['p', '']]}
def test_events(self):
pj_float = json.loads(open("files/test.boris").read())
pj = utilities.convert_time_to_decimal(pj_float)
r = utilities.get_current_points_by_subject(point_behaviors_codes=["p"],
events=pj["observations"]["offset positif"]["events"],
subjects={"0":{"key":"", "name": "", "description":"no focal subject"}},
time=Decimal("22.6"),
tolerance=Decimal("1"))
assert r == {'0': [['p', '']]}
def test_events_without_modifiers2(self):
pj_float = json.loads(open("files/test.boris").read())
pj = utilities.convert_time_to_decimal(pj_float)
r = utilities.get_current_points_by_subject(point_behaviors_codes=["p"],
events=pj[config.OBSERVATIONS]["modifiers"]["events"],
subjects={"0":{"key":"", "name": "", "description":"no focal subject"}},
time=Decimal("8.000"),
tolerance=Decimal("5"),
include_modifiers=True)
assert r == {'0': []}
def test_events_with_modifiers3(self):
# no events should correspond to selected behavior
pj_float = json.loads(open("files/test.boris").read())
pj = utilities.convert_time_to_decimal(pj_float)
r = utilities.get_current_points_by_subject(point_behaviors_codes=["q"],
events=pj[config.OBSERVATIONS]["modifiers"][config.EVENTS],
subjects={"0":{"key":"", "name": "", "description":"no focal subject"}},
time=Decimal("8.000"),
tolerance=Decimal("5"),
include_modifiers=True)
assert r == {'0': [('q', 'm1'), ('q', 'm2')]}
def create_behavior_binary_table(pj: dict, selected_observations: list,
parameters_obs: dict,
time_interval: float) -> dict:
"""
create behavior binary table
Args:
pj (dict): project dictionary
selected_observations (list): list of selected observations
parameters_obs (dict): dcit of parameters
time_interval (float): time interval (in seconds)
Returns:
dict: dictionary of tablib dataset
"""
results_df = {}
state_behavior_codes = [
x for x in utilities.state_behavior_codes(pj[ETHOGRAM])
if x in parameters_obs[SELECTED_BEHAVIORS]
]
point_behavior_codes = [
x for x in utilities.point_behavior_codes(pj[ETHOGRAM])
if x in parameters_obs[SELECTED_BEHAVIORS]
]
if not state_behavior_codes and not point_behavior_codes:
return {"error": True, "msg": "No state events selected"}
for obs_id in selected_observations:
if obs_id not in results_df:
results_df[obs_id] = {}
for subject in parameters_obs[SELECTED_SUBJECTS]:
# extract tuple (behavior, modifier)
behav_modif_list = [
(idx[2], idx[3]) for idx in pj[OBSERVATIONS][obs_id][EVENTS]
if idx[1] == (subject if subject != NO_FOCAL_SUBJECT else "")
and idx[2] in parameters_obs[SELECTED_BEHAVIORS]
]
# extract observed subjects NOT USED at the moment
observed_subjects = [
event[EVENT_SUBJECT_FIELD_IDX]
for event in pj[OBSERVATIONS][obs_id][EVENTS]
]
# add selected behavior if not found in (behavior, modifier)
if not parameters_obs[EXCLUDE_BEHAVIORS]:
#for behav in state_behavior_codes:
for behav in parameters_obs[SELECTED_BEHAVIORS]:
if behav not in [x[0] for x in behav_modif_list]:
behav_modif_list.append((behav, ""))
behav_modif_set = set(behav_modif_list)
if parameters_obs[INCLUDE_MODIFIERS]:
results_df[obs_id][subject] = tablib.Dataset(
headers=["time"] + [
f"{x[0]}" + f" ({x[1]})" * (x[1] != "")
for x in sorted(behav_modif_set)
])
else:
results_df[obs_id][subject] = tablib.Dataset(
headers=["time"] + [x[0] for x in sorted(behav_modif_set)])
if subject == NO_FOCAL_SUBJECT:
sel_subject_dict = {"": {SUBJECT_NAME: ""}}
else:
sel_subject_dict = dict([
(idx, pj[SUBJECTS][idx]) for idx in pj[SUBJECTS]
if pj[SUBJECTS][idx][SUBJECT_NAME] == subject
])
row_idx = 0
t = parameters_obs[START_TIME]
while t < parameters_obs[END_TIME]:
# state events
current_states = utilities.get_current_states_modifiers_by_subject(
state_behavior_codes,
pj[OBSERVATIONS][obs_id][EVENTS],
sel_subject_dict,
t,
include_modifiers=parameters_obs[INCLUDE_MODIFIERS])
# point events
current_point = utilities.get_current_points_by_subject(
point_behavior_codes,
pj[OBSERVATIONS][obs_id][EVENTS],
sel_subject_dict,
t,
time_interval,
include_modifiers=parameters_obs[INCLUDE_MODIFIERS])
cols = [float(t)] # time
for behav in results_df[obs_id][subject].headers[
1:]: # skip time
if behav in state_behavior_codes:
cols.append(
int(behav in current_states[list(
current_states.keys())[0]]))
if behav in point_behavior_codes:
cols.append(current_point[list(
current_point.keys())[0]].count(behav))
#int(behav in current_point[list(current_point.keys())[0]]))
results_df[obs_id][subject].append(cols)
t += time_interval
row_idx += 1
return results_df