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_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