def export_aggregated_events(pj: dict, parameters: dict, obsId: str):
"""
export aggregated events
Args:
pj (dict): BORIS project
parameters (dict): subjects, behaviors
obsId (str): observation id
Returns:
tablib.Dataset:
"""
logging.debug(f"function: export aggregated events {parameters} {obsId}")
interval = parameters["time"]
start_time = parameters[START_TIME]
end_time = parameters[END_TIME]
data = tablib.Dataset()
observation = pj[OBSERVATIONS][obsId]
# obs description
obs_description = observation["description"]
duration1 = [] # in seconds
if observation[TYPE] in [MEDIA]:
try:
for mediaFile in observation[FILE][PLAYER1]:
if MEDIA_INFO in observation:
duration1.append(observation[MEDIA_INFO]["length"][mediaFile])
except Exception:
duration1 = []
obs_length = project_functions.observation_total_length(pj[OBSERVATIONS][obsId])
if obs_length == Decimal("-1"): # media length not available
interval = TIME_EVENTS
logging.debug(f"obs_length: {obs_length}")
ok, msg, connector = db_functions.load_aggregated_events_in_db(pj,
parameters[SELECTED_SUBJECTS],
[obsId],
parameters[SELECTED_BEHAVIORS])
if connector is None:
logging.critical(f"error when loading aggregated events in DB")
return data
# time
cursor = connector.cursor()
if interval == TIME_FULL_OBS:
min_time = float(0)
max_time = float(obs_length)
if interval == TIME_EVENTS:
try:
min_time = float(pj[OBSERVATIONS][obsId][EVENTS][0][0])
except Exception:
min_time = float(0)
try:
max_time = float(pj[OBSERVATIONS][obsId][EVENTS][-1][0])
except Exception:
max_time = float(obs_length)
if interval == TIME_ARBITRARY_INTERVAL:
min_time = float(start_time)
max_time = float(end_time)
# adapt start and stop to the selected time interval
cursor.execute("UPDATE aggregated_events SET start = ? WHERE observation = ? AND start < ? AND stop BETWEEN ? AND ?",
(min_time, obsId, min_time, min_time, max_time, ))
cursor.execute("UPDATE aggregated_events SET stop = ? WHERE observation = ? AND stop > ? AND start BETWEEN ? AND ?",
(max_time, obsId, max_time, min_time, max_time, ))
cursor.execute("UPDATE aggregated_events SET start = ?, stop = ? WHERE observation = ? AND start < ? AND stop > ?",
(min_time, max_time, obsId, min_time, max_time, ))
cursor.execute("DELETE FROM aggregated_events WHERE observation = ? AND (start < ? AND stop < ?) OR (start > ? AND stop > ?)",
(obsId, min_time, min_time, max_time, max_time, ))
behavioral_category = project_functions.behavior_category(pj[ETHOGRAM])
for subject in parameters[SELECTED_SUBJECTS]:
for behavior in parameters[SELECTED_BEHAVIORS]:
cursor.execute("SELECT distinct modifiers FROM aggregated_events where subject=? AND behavior=? order by modifiers",
(subject, behavior,))
rows_distinct_modifiers = list(x[0] for x in cursor.fetchall())
for distinct_modifiers in rows_distinct_modifiers:
cursor.execute(("SELECT start, stop, type, modifiers, comment, comment_stop FROM aggregated_events "
"WHERE subject = ? AND behavior = ? AND modifiers = ? ORDER by start"),
(subject, behavior, distinct_modifiers))
rows = list(cursor.fetchall())
for row in rows:
if observation[TYPE] in [MEDIA]:
if duration1:
mediaFileIdx = [idx1 for idx1, x in enumerate(duration1) if row["start"] >= sum(duration1[0:idx1])][-1]
mediaFileString = observation[FILE][PLAYER1][mediaFileIdx]
try:
fpsString = observation[MEDIA_INFO]["fps"][observation[FILE][PLAYER1][mediaFileIdx]]
except Exception:
fpsString = "NA"
else:
try:
if len(observation[FILE][PLAYER1]) == 1:
mediaFileString = observation[FILE][PLAYER1][0]
else:
mediaFileString = "NA"
except Exception:
mediaFileString = "NA"
fpsString = "NA"
if observation[TYPE] in [LIVE]:
mediaFileString = "LIVE"
fpsString = "NA"
if row["type"] == POINT:
row_data = []
row_data.extend([obsId,
observation["date"].replace("T", " "),
obs_description,
mediaFileString,
f"{obs_length:.3f}" if obs_length != Decimal("-1") else "NA",
fpsString])
# independent variables
if INDEPENDENT_VARIABLES in pj:
for idx_var in utilities.sorted_keys(pj[INDEPENDENT_VARIABLES]):
if pj[INDEPENDENT_VARIABLES][idx_var]["label"] in observation[INDEPENDENT_VARIABLES]:
row_data.append(observation[INDEPENDENT_VARIABLES][pj[INDEPENDENT_VARIABLES][idx_var]["label"]])
else:
row_data.append("")
row_data.extend([subject,
behavior,
behavioral_category[behavior],
row["modifiers"],
POINT,
f"{row['start']:.3f}", # start
f"{row['stop']:.3f}", # stop
"NA", # duration
row["comment"],
""
])
data.append(row_data)
if row["type"] == STATE:
if idx % 2 == 0:
row_data = []
row_data.extend([obsId,
observation["date"].replace("T", " "),
obs_description,
mediaFileString,
f"{obs_length:.3f}" if obs_length != Decimal("-1") else "NA",
fpsString])
# independent variables
if INDEPENDENT_VARIABLES in pj:
for idx_var in utilities.sorted_keys(pj[INDEPENDENT_VARIABLES]):
if pj[INDEPENDENT_VARIABLES][idx_var]["label"] in observation[INDEPENDENT_VARIABLES]:
row_data.append(observation[INDEPENDENT_VARIABLES][pj[INDEPENDENT_VARIABLES][idx_var]["label"]])
else:
row_data.append("")
row_data.extend([subject,
behavior,
behavioral_category[behavior],
row["modifiers"],
STATE,
f"{row['start']:.3f}",
f"{row['stop']:.3f}",
f"{row['stop'] - row['start']:.3f}",
row["comment"],
row["comment_stop"]
])
data.append(row_data)
return data
def create_events_plot(pj,
selected_observations,
parameters,
plot_colors=BEHAVIORS_PLOT_COLORS,
plot_directory="",
file_format="png"):
"""
create a time diagram plot (sort of gantt chart)
with matplotlib barh function (https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.barh.html)
"""
selected_subjects = parameters[SELECTED_SUBJECTS]
selected_behaviors = parameters[SELECTED_BEHAVIORS]
include_modifiers = parameters[INCLUDE_MODIFIERS]
interval = parameters[TIME_INTERVAL]
start_time = parameters[START_TIME]
end_time = parameters[END_TIME]
ok, msg, db_connector = db_functions.load_aggregated_events_in_db(pj,
selected_subjects,
selected_observations,
selected_behaviors)
if not ok:
return False, msg, None
cursor = db_connector.cursor()
# if modifiers not to be included set modifiers to ""
if not include_modifiers:
cursor.execute("UPDATE aggregated_events SET modifiers = ''")
cursor.execute("SELECT distinct behavior, modifiers FROM aggregated_events")
distinct_behav_modif = [[rows["behavior"], rows["modifiers"]] for rows in cursor.fetchall()]
# add selected behaviors that are not observed
for behav in selected_behaviors:
if [x for x in distinct_behav_modif if x[0] == behav] == []:
distinct_behav_modif.append([behav, "-"])
distinct_behav_modif = sorted(distinct_behav_modif)
max_len = len(distinct_behav_modif)
all_behaviors = [pj[ETHOGRAM][x][BEHAVIOR_CODE] for x in utilities.sorted_keys(pj[ETHOGRAM])]
par1 = 1
bar_height = 0.5
init = dt.datetime(2017, 1, 1)
for obs_id in selected_observations:
if len(selected_subjects) > 1:
fig, axs = plt.subplots(figsize=(20, 8), nrows=len(selected_subjects), ncols=1, sharex=True)
else:
fig, ax = plt.subplots(figsize=(20, 8), nrows=len(selected_subjects), ncols=1, sharex=True)
axs = np.ndarray(shape=(1), dtype=type(ax))
axs[0] = ax
ok, msg, db_connector = db_functions.load_aggregated_events_in_db(
pj,
selected_subjects,
[obs_id],
selected_behaviors)
cursor = db_connector.cursor()
# if modifiers not to be included set modifiers to ""
if not include_modifiers:
cursor.execute("UPDATE aggregated_events SET modifiers = ''")
cursor = db_connector.cursor()
cursor.execute("SELECT distinct behavior, modifiers FROM aggregated_events")
distinct_behav_modif = [[rows["behavior"], rows["modifiers"]] for rows in cursor.fetchall()]
# add selected behaviors that are not observed
if not parameters["exclude behaviors"]:
for behav in selected_behaviors:
if [x for x in distinct_behav_modif if x[0] == behav] == []:
distinct_behav_modif.append([behav, "-"])
distinct_behav_modif = sorted(distinct_behav_modif)
max_len = len(distinct_behav_modif)
# time
obs_length = project_functions.observation_total_length(pj[OBSERVATIONS][obs_id])
if obs_length == -1: # media length not available
interval = TIME_EVENTS
if interval == TIME_FULL_OBS:
min_time = float(0)
max_time = float(obs_length)
if interval == TIME_EVENTS:
try:
min_time = float(pj[OBSERVATIONS][obs_id][EVENTS][0][0])
except Exception:
min_time = float(0)
try:
max_time = float(pj[OBSERVATIONS][obs_id][EVENTS][-1][0])
except Exception:
max_time = float(obs_length)
if interval == TIME_ARBITRARY_INTERVAL:
min_time = float(start_time)
max_time = float(end_time)
cursor.execute("UPDATE aggregated_events SET start = ? WHERE observation = ? AND start < ? AND stop BETWEEN ? AND ?",
(min_time, obs_id, min_time, min_time, max_time, ))
cursor.execute("UPDATE aggregated_events SET stop = ? WHERE observation = ? AND stop > ? AND start BETWEEN ? AND ?",
(max_time, obs_id, max_time, min_time, max_time, ))
cursor.execute("UPDATE aggregated_events SET start = ?, stop = ? WHERE observation = ? AND start < ? AND stop > ?",
(min_time, max_time, obs_id, min_time, max_time, ))
ylabels = [" ".join(x) for x in distinct_behav_modif]
for ax_idx, subject in enumerate(selected_subjects):
if parameters["exclude behaviors"]:
cursor.execute("SELECT distinct behavior, modifiers FROM aggregated_events WHERE subject = ?", (subject, ))
distinct_behav_modif = [[rows["behavior"], rows["modifiers"]] for rows in cursor.fetchall()]
# add selected behaviors that are not observed
if not parameters["exclude behaviors"]:
for behav in selected_behaviors:
if [x for x in distinct_behav_modif if x[0] == behav] == []:
distinct_behav_modif.append([behav, "-"])
distinct_behav_modif = sorted(distinct_behav_modif)
max_len = len(distinct_behav_modif)
ylabels = [" ".join(x) for x in distinct_behav_modif]
if not ax_idx:
axs[ax_idx].set_title(f"Observation {obs_id}\n{subject}", fontsize=14)
else:
axs[ax_idx].set_title(subject, fontsize=14)
bars = {}
i = 0
for behavior_modifiers in distinct_behav_modif:
behavior, modifiers = behavior_modifiers
behavior_modifiers_str = "|".join(behavior_modifiers) if modifiers else behavior
bars[behavior_modifiers_str] = []
# total duration
cursor.execute(("SELECT start, stop FROM aggregated_events "
"WHERE observation = ? AND subject = ? AND behavior = ? AND modifiers = ?"),
(obs_id, subject, behavior, modifiers,))
for row in cursor.fetchall():
bars[behavior_modifiers_str].append((row["start"], row["stop"]))
start_date = matplotlib.dates.date2num(init + dt.timedelta(seconds=row["start"]))
end_date = matplotlib.dates.date2num(
init + dt.timedelta(seconds=row["stop"] + POINT_EVENT_PLOT_DURATION * (row["stop"] == row["start"])))
try:
bar_color = utilities.behavior_color(plot_colors, all_behaviors.index(behavior))
except Exception:
bar_color = "darkgray"
bar_color = POINT_EVENT_PLOT_COLOR if row["stop"] == row["start"] else bar_color
# sage colors removed from matplotlib colors list
if bar_color in ["sage", "darksage", "lightsage"]:
bar_color = {"darksage": "#598556", "lightsage": "#bcecac", "sage": "#87ae73"}[bar_color]
try:
axs[ax_idx].barh((i * par1) + par1, end_date - start_date, left=start_date, height=bar_height,
align="center", edgecolor=bar_color, color=bar_color, alpha=1)
except Exception:
axs[ax_idx].barh((i * par1) + par1, end_date - start_date, left=start_date, height=bar_height,
align="center", edgecolor="darkgray", color="darkgray", alpha=1)
i += 1
axs[ax_idx].set_ylim(bottom=0, top=(max_len * par1) + par1)
pos = np.arange(par1, max_len * par1 + par1 + 1, par1)
axs[ax_idx].set_yticks(pos[:len(ylabels)])
axs[ax_idx].set_yticklabels(ylabels, fontdict={"fontsize": 10})
axs[ax_idx].set_ylabel("Behaviors" + " (modifiers)" * include_modifiers, fontdict={"fontsize": 10})
axs[ax_idx].set_xlim(left=matplotlib.dates.date2num(init + dt.timedelta(seconds=min_time)),
right=matplotlib.dates.date2num(init + dt.timedelta(seconds=max_time + 1)))
axs[ax_idx].grid(color="g", linestyle=":")
axs[ax_idx].xaxis_date()
axs[ax_idx].xaxis.set_major_formatter(DateFormatter("%H:%M:%S"))
axs[ax_idx].set_xlabel("Time (HH:MM:SS)", fontdict={"fontsize": 12})
axs[ax_idx].invert_yaxis()
fig.autofmt_xdate()
plt.tight_layout()
if len(selected_observations) > 1:
plt.savefig(f"{pathlib.Path(plot_directory) / utilities.safeFileName(obs_id)}.{file_format}")
else:
plt.show()
def synthetic_time_budget(pj: dict, selected_observations: list,
parameters_obs: dict):
"""
create a synthetic time budget
Args:
pj (dict): project dictionary
selected_observations (list): list of observations to include in time budget
parameters_obs (dict):
Returns:
bool: True if everything OK
str: message
tablib.Dataset: dataset containing synthetic time budget data
"""
try:
selected_subjects = parameters_obs[SELECTED_SUBJECTS]
selected_behaviors = parameters_obs[SELECTED_BEHAVIORS]
include_modifiers = parameters_obs[INCLUDE_MODIFIERS]
interval = parameters_obs["time"]
start_time = parameters_obs["start time"]
end_time = parameters_obs["end time"]
parameters = [
["duration", "Total duration"],
["number", "Number of occurrences"],
["duration mean", "Duration mean"],
["duration stdev", "Duration std dev"],
["proportion of time", "Proportion of time"],
]
data_report = tablib.Dataset()
data_report.title = "Synthetic time budget"
ok, msg, db_connector = db_functions.load_aggregated_events_in_db(
pj, selected_subjects, selected_observations, selected_behaviors)
if not ok:
return False, msg, None
db_connector.create_aggregate("stdev", 1, StdevFunc)
cursor = db_connector.cursor()
# modifiers
if include_modifiers:
cursor.execute(
"SELECT distinct behavior, modifiers FROM aggregated_events")
distinct_behav_modif = [[rows["behavior"], rows["modifiers"]]
for rows in cursor.fetchall()]
else:
cursor.execute("SELECT distinct behavior FROM aggregated_events")
distinct_behav_modif = [[rows["behavior"], ""]
for rows in cursor.fetchall()]
# add selected behaviors that are not observed
for behav in selected_behaviors:
if [x for x in distinct_behav_modif if x[0] == behav] == []:
distinct_behav_modif.append([behav, ""])
behaviors = init_behav_modif(pj[ETHOGRAM], selected_subjects,
distinct_behav_modif, include_modifiers,
parameters)
param_header = ["", "Total length (s)"]
subj_header, behav_header, modif_header = [""] * len(param_header), [
""
] * len(param_header), [""] * len(param_header)
for subj in selected_subjects:
for behavior_modifiers in distinct_behav_modif:
behavior, modifiers = behavior_modifiers
behavior_modifiers_str = "|".join(
behavior_modifiers) if modifiers else behavior
for param in parameters:
subj_header.append(subj)
behav_header.append(behavior)
modif_header.append(modifiers)
param_header.append(param[1])
'''
if parameters_obs["group observations"]:
cursor.execute("UPDATE aggregated_events SET observation = 'all' " )
#selected_observations = ["all"]
'''
data_report.append(subj_header)
data_report.append(behav_header)
if include_modifiers:
data_report.append(modif_header)
data_report.append(param_header)
# select time interval
for obs_id in selected_observations:
ok, msg, db_connector = db_functions.load_aggregated_events_in_db(
pj, selected_subjects, [obs_id], selected_behaviors)
if not ok:
return False, msg, None
db_connector.create_aggregate("stdev", 1, StdevFunc)
cursor = db_connector.cursor()
# if modifiers not to be included set modifiers to ""
if not include_modifiers:
cursor.execute("UPDATE aggregated_events SET modifiers = ''")
# time
obs_length = project_functions.observation_total_length(
pj[OBSERVATIONS][obs_id])
if obs_length == -1:
obs_length = 0
if interval == TIME_FULL_OBS:
min_time = float(0)
max_time = float(obs_length)
if interval == TIME_EVENTS:
try:
min_time = float(pj[OBSERVATIONS][obs_id][EVENTS][0][0])
except Exception:
min_time = float(0)
try:
max_time = float(pj[OBSERVATIONS][obs_id][EVENTS][-1][0])
except Exception:
max_time = float(obs_length)
if interval == TIME_ARBITRARY_INTERVAL:
min_time = float(start_time)
max_time = float(end_time)
# adapt start and stop to the selected time interval
cursor.execute(
"UPDATE aggregated_events SET start = ? WHERE observation = ? AND start < ? AND stop BETWEEN ? AND ?",
(
min_time,
obs_id,
min_time,
min_time,
max_time,
))
cursor.execute(
"UPDATE aggregated_events SET stop = ? WHERE observation = ? AND stop > ? AND start BETWEEN ? AND ?",
(
max_time,
obs_id,
max_time,
min_time,
max_time,
))
cursor.execute(
"UPDATE aggregated_events SET start = ?, stop = ? WHERE observation = ? AND start < ? AND stop > ?",
(
min_time,
max_time,
obs_id,
min_time,
max_time,
))
cursor.execute(
"DELETE FROM aggregated_events WHERE observation = ? AND (start < ? AND stop < ?) OR (start > ? AND stop > ?)",
(
obs_id,
min_time,
min_time,
max_time,
max_time,
))
for subject in selected_subjects:
# check if behaviors are to exclude from total time
time_to_subtract = 0
if EXCLUDED_BEHAVIORS in parameters_obs:
for excluded_behav in parameters_obs[EXCLUDED_BEHAVIORS]:
cursor.execute((
"SELECT SUM(stop-start) "
"FROM aggregated_events "
"WHERE observation = ? AND subject = ? AND behavior = ? "
), (
obs_id,
subject,
excluded_behav,
))
for row in cursor.fetchall():
if row[0] is not None:
time_to_subtract += row[0]
for behavior_modifiers in distinct_behav_modif:
behavior, modifiers = behavior_modifiers
behavior_modifiers_str = "|".join(
behavior_modifiers) if modifiers else behavior
cursor.execute((
"SELECT SUM(stop-start), COUNT(*), AVG(stop-start), stdev(stop-start) "
"FROM aggregated_events "
"WHERE observation = ? AND subject = ? AND behavior = ? AND modifiers = ? "
), (
obs_id,
subject,
behavior,
modifiers,
))
for row in cursor.fetchall():
behaviors[subject][behavior_modifiers_str][
"duration"] = (0 if row[0] is None else
f"{row[0]:.3f}")
behaviors[subject][behavior_modifiers_str][
"number"] = 0 if row[1] is None else row[1]
behaviors[subject][behavior_modifiers_str][
"duration mean"] = (0 if row[2] is None else
f"{row[2]:.3f}")
behaviors[subject][behavior_modifiers_str][
"duration stdev"] = (0 if row[3] is None else
f"{row[3]:.3f}")
if behavior not in parameters_obs[EXCLUDED_BEHAVIORS]:
try:
behaviors[subject][behavior_modifiers_str][
"proportion of time"] = (
0 if row[0] is None else
f"{row[0] / ((max_time - min_time) - time_to_subtract):.3f}"
)
except ZeroDivisionError:
behaviors[subject][behavior_modifiers_str][
"proportion of time"] = "-"
else:
# behavior subtracted
behaviors[subject][behavior_modifiers_str][
"proportion of time"] = (
0 if row[0] is None else
f"{row[0] / (max_time - min_time):.3f}")
columns = [obs_id, f"{max_time - min_time:0.3f}"]
for subj in selected_subjects:
for behavior_modifiers in distinct_behav_modif:
behavior, modifiers = behavior_modifiers
behavior_modifiers_str = "|".join(
behavior_modifiers) if modifiers else behavior
for param in parameters:
columns.append(
behaviors[subj][behavior_modifiers_str][param[0]])
data_report.append(columns)
except Exception:
error_type, error_file_name, error_lineno = utilities.error_info(
sys.exc_info())
logging.critical(
f"Error in edit_event function: {error_type} {error_file_name} {error_lineno}"
)
msg = f"Error type: {error_type}\nError file name: {error_file_name}\nError line number: {error_lineno}"
logging.critical(msg)
return (False, msg, tablib.Dataset())
return True, msg, data_report
def create_behaviors_bar_plot(pj: dict,
selected_observations: list,
param: dict,
plot_directory: str,
output_format: str,
plot_colors:list=BEHAVIORS_PLOT_COLORS):
"""
time budget bar plot
Args:
pj (dict): project
param (dict): parameters
plot_directory (str): path of directory
output_format (str): image format
Returns:
dict:
"""
selected_subjects = param[SELECTED_SUBJECTS]
selected_behaviors = param[SELECTED_BEHAVIORS]
time_interval = param["time"]
start_time = param[START_TIME]
end_time = param[END_TIME]
parameters = ["duration", "number of occurences"]
ok, msg, db_connector = db_functions.load_aggregated_events_in_db(pj,
selected_subjects,
selected_observations,
selected_behaviors)
if not ok:
return {"error": True, "message": msg}
try:
# extract all behaviors from ethogram for colors in plot
all_behaviors = [pj[ETHOGRAM][x][BEHAVIOR_CODE] for x in utilities.sorted_keys(pj[ETHOGRAM])]
for obs_id in selected_observations:
cursor = db_connector.cursor()
# distinct behaviors
cursor.execute("SELECT distinct behavior FROM aggregated_events WHERE observation = ?",
(obs_id,))
distinct_behav = [rows["behavior"] for rows in cursor.fetchall()]
# add selected behaviors that are not observed
'''
if not param[EXCLUDE_BEHAVIORS]:
for behavior in selected_behaviors:
if [x for x in distinct_behav if x == behavior] == []:
distinct_behav.append(behavior)
'''
# distinct subjects
cursor.execute("SELECT distinct subject FROM aggregated_events WHERE observation = ?",
(obs_id,))
distinct_subjects = [rows["subject"] for rows in cursor.fetchall()]
behaviors = init_behav(pj[ETHOGRAM],
distinct_subjects,
distinct_behav,
parameters)
# plot creation
if len(distinct_subjects) > 1:
fig, axs = plt.subplots(nrows=1, ncols=len(distinct_subjects), sharey=True)
fig2, axs2 = plt.subplots(nrows=1, ncols=len(distinct_subjects), sharey=True)
else:
fig, ax = plt.subplots(nrows=1, ncols=len(distinct_subjects), sharey=True)
axs = np.ndarray(shape=(1), dtype=type(ax))
axs[0] = ax
fig2, ax2 = plt.subplots(nrows=1, ncols=len(distinct_subjects), sharey=True)
axs2 = np.ndarray(shape=(1), dtype=type(ax2))
axs2[0] = ax2
fig.suptitle("Durations of behaviors")
fig2.suptitle("Number of occurences of behaviors")
# if modifiers not to be included set modifiers to ""
cursor.execute("UPDATE aggregated_events SET modifiers = ''")
# time
obs_length = project_functions.observation_total_length(pj[OBSERVATIONS][obs_id])
if obs_length == -1:
obs_length = 0
if param["time"] == TIME_FULL_OBS:
min_time = float(0)
max_time = float(obs_length)
if param["time"] == TIME_EVENTS:
try:
min_time = float(pj[OBSERVATIONS][obs_id][EVENTS][0][0])
except Exception:
min_time = float(0)
try:
max_time = float(pj[OBSERVATIONS][obs_id][EVENTS][-1][0])
except Exception:
max_time = float(obs_length)
if param["time"] == TIME_ARBITRARY_INTERVAL:
min_time = float(start_time)
max_time = float(end_time)
cursor.execute("UPDATE aggregated_events SET start = ? WHERE observation = ? AND start < ? AND stop BETWEEN ? AND ?",
(min_time, obs_id, min_time, min_time, max_time, ))
cursor.execute("UPDATE aggregated_events SET stop = ? WHERE observation = ? AND stop > ? AND start BETWEEN ? AND ?",
(max_time, obs_id, max_time, min_time, max_time, ))
cursor.execute("UPDATE aggregated_events SET start = ?, stop = ? WHERE observation = ? AND start < ? AND stop > ?",
(min_time, max_time, obs_id, min_time, max_time, ))
for ax_idx, subject in enumerate(sorted(distinct_subjects)):
for behavior in distinct_behav:
# number of occurences
cursor.execute(("SELECT COUNT(*) AS count FROM aggregated_events "
"WHERE observation = ? AND subject = ? AND behavior = ?"),
(obs_id, subject, behavior, ))
for row in cursor.fetchall():
behaviors[subject][behavior]["number of occurences"] = 0 if row["count"] is None else row["count"]
# total duration
if STATE in project_functions.event_type(behavior, pj[ETHOGRAM]):
cursor.execute(("SELECT SUM(stop - start) AS duration FROM aggregated_events "
"WHERE observation = ? AND subject = ? AND behavior = ?"),
(obs_id, subject, behavior, ))
for row in cursor.fetchall():
behaviors[subject][behavior]["duration"] = 0 if row["duration"] is None else row["duration"]
durations, n_occurences, colors, x_labels, colors_duration, x_labels_duration = [], [], [], [], [], []
for behavior in sorted(distinct_behav):
if param[EXCLUDE_BEHAVIORS] and behaviors[subject][behavior]["number of occurences"] == 0:
continue
n_occurences.append(behaviors[subject][behavior]["number of occurences"])
x_labels.append(behavior)
try:
colors.append(utilities.behavior_color(plot_colors, all_behaviors.index(behavior)))
except Exception:
colors.append("darkgray")
if STATE in project_functions.event_type(behavior, pj[ETHOGRAM]):
durations.append(behaviors[subject][behavior]["duration"])
x_labels_duration.append(behavior)
try:
colors_duration.append(utilities.behavior_color(plot_colors, all_behaviors.index(behavior)))
except Exception:
colors_duration.append("darkgray")
#width = 0.35 # the width of the bars: can also be len(x) sequence
axs2[ax_idx].bar(np.arange(len(n_occurences)),
n_occurences,
#width,
color=colors
)
axs[ax_idx].bar(np.arange(len(durations)),
durations,
#width,
color=colors_duration
)
if ax_idx == 0:
axs[ax_idx].set_ylabel("Duration (s)")
axs[ax_idx].set_xlabel("Behaviors")
axs[ax_idx].set_title(f"{subject}")
axs[ax_idx].set_xticks(np.arange(len(durations)))
axs[ax_idx].set_xticklabels(x_labels_duration, rotation='vertical', fontsize=8)
if ax_idx == 0:
axs2[ax_idx].set_ylabel("Number of occurences")
axs2[ax_idx].set_xlabel("Behaviors")
axs2[ax_idx].set_title(f"{subject}")
axs2[ax_idx].set_xticks(np.arange(len(n_occurences)))
axs2[ax_idx].set_xticklabels(x_labels, rotation='vertical', fontsize=8)
fig.align_labels()
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
fig2.align_labels()
fig2.tight_layout(rect=[0, 0.03, 1, 0.95])
if plot_directory:
# output_file_name = f"{pathlib.Path(plot_directory) / utilities.safeFileName(obs_id)}.{output_format}"
fig.savefig(f"{pathlib.Path(plot_directory) / utilities.safeFileName(obs_id)}.duration.{output_format}")
fig2.savefig(f"{pathlib.Path(plot_directory) / utilities.safeFileName(obs_id)}.number_of_occurences.{output_format}")
plt.close()
else:
fig.show()
fig2.show()
return {}
except Exception:
error_type, error_file_name, error_lineno = utilities.error_info(sys.exc_info())
logging.critical(f"Error in time budget bar plot: {error_type} {error_file_name} {error_lineno}")
return {"error": True, "exception": sys.exc_info()}
def synthetic_time_budget_bin(pj: dict, selected_observations: list,
parameters_obs: dict):
"""
create a synthetic time budget divised in time bin
Args:
pj (dict): project dictionary
selected_observations (list): list of observations to include in time budget
parameters_obs (dict):
Returns:
bool: True if everything OK
str: message
tablib.Dataset: dataset containing synthetic time budget data
"""
def interval_len(interval):
if interval.empty:
return dec(0)
else:
return sum([x.upper - x.lower for x in interval])
def interval_number(interval):
if interval.empty:
return dec(0)
else:
return len(interval)
def interval_mean(interval):
if interval.empty:
return dec(0)
else:
return sum([x.upper - x.lower for x in interval]) / len(interval)
def interval_std_dev(interval):
if interval.empty:
return "NA"
else:
try:
return round(
statistics.stdev([x.upper - x.lower for x in interval]), 3)
except:
return "NA"
try:
selected_subjects = parameters_obs[SELECTED_SUBJECTS]
selected_behaviors = parameters_obs[SELECTED_BEHAVIORS]
include_modifiers = parameters_obs[INCLUDE_MODIFIERS]
time_interval = parameters_obs["time"]
start_time = parameters_obs[START_TIME]
end_time = parameters_obs[END_TIME]
time_bin_size = dec(parameters_obs[TIME_BIN_SIZE])
parameters = [
["duration", "Total duration"],
["number", "Number of occurrences"],
["duration mean", "Duration mean"],
["duration stdev", "Duration std dev"],
["proportion of time", "Proportion of time"],
]
data_report = tablib.Dataset()
data_report.title = "Synthetic time budget with time bin"
distinct_behav_modif = []
for obs_id in selected_observations:
for event in pj[OBSERVATIONS][obs_id][EVENTS]:
if include_modifiers:
if (event[EVENT_BEHAVIOR_FIELD_IDX],
event[EVENT_MODIFIER_FIELD_IDX]
) not in distinct_behav_modif:
distinct_behav_modif.append(
(event[EVENT_BEHAVIOR_FIELD_IDX],
event[EVENT_MODIFIER_FIELD_IDX]))
else:
if (event[EVENT_BEHAVIOR_FIELD_IDX],
"") not in distinct_behav_modif:
distinct_behav_modif.append(
(event[EVENT_BEHAVIOR_FIELD_IDX], ""))
distinct_behav_modif.sort()
'''
print("distinct_behav_modif", distinct_behav_modif)
'''
# add selected behaviors that are not observed
for behav in selected_behaviors:
if [x for x in distinct_behav_modif if x[0] == behav] == []:
distinct_behav_modif.append([behav, ""])
'''
print("distinct_behav_modif with not observed behav", distinct_behav_modif)
'''
behaviors = init_behav_modif_bin(pj[ETHOGRAM], selected_subjects,
distinct_behav_modif,
include_modifiers, parameters)
'''
print("init behaviors", behaviors)
print(f"selected subjects: {selected_subjects}")
'''
param_header = [
"Observations id", "Total length (s)", "Time interval (s)"
]
subj_header, behav_header, modif_header = [""] * len(param_header), [
""
] * len(param_header), [""] * len(param_header)
subj_header[1] = "Subjects:"
behav_header[1] = "Behaviors:"
modif_header[1] = "Modifiers:"
for subj in selected_subjects:
for behavior_modifiers in distinct_behav_modif:
behavior, modifiers = behavior_modifiers
behavior_modifiers_str = "|".join(
behavior_modifiers) if modifiers else behavior
for param in parameters:
subj_header.append(subj)
behav_header.append(behavior)
modif_header.append(modifiers)
param_header.append(param[1])
data_report.append(subj_header)
data_report.append(behav_header)
if include_modifiers:
data_report.append(modif_header)
data_report.append(param_header)
state_events_list = [
pj[ETHOGRAM][x][BEHAVIOR_CODE] for x in pj[ETHOGRAM]
if STATE in pj[ETHOGRAM][x][TYPE].upper()
]
# select time interval
for obs_id in selected_observations:
obs_length = project_functions.observation_total_length(
pj[OBSERVATIONS][obs_id])
if obs_length == -1:
obs_length = 0
if time_interval == TIME_FULL_OBS:
min_time = dec(0)
max_time = dec(obs_length)
if time_interval == TIME_EVENTS:
try:
min_time = dec(pj[OBSERVATIONS][obs_id][EVENTS][0][0])
except Exception:
min_time = dec(0)
try:
max_time = dec(pj[OBSERVATIONS][obs_id][EVENTS][-1][0])
except Exception:
max_time = dec(obs_length)
if time_interval == TIME_ARBITRARY_INTERVAL:
min_time = dec(start_time)
max_time = dec(end_time)
#print("observation:", obs_id)
events_interval = {}
mem_events_interval = {}
for event in pj[OBSERVATIONS][obs_id][EVENTS]:
if event[EVENT_SUBJECT_FIELD_IDX] == "":
current_subject = NO_FOCAL_SUBJECT
else:
current_subject = event[EVENT_SUBJECT_FIELD_IDX]
if current_subject not in selected_subjects:
continue
if current_subject not in events_interval:
events_interval[current_subject] = {}
mem_events_interval[current_subject] = {}
if include_modifiers:
modif = event[EVENT_MODIFIER_FIELD_IDX]
else:
modif = ""
if (event[EVENT_BEHAVIOR_FIELD_IDX],
modif) not in distinct_behav_modif:
continue
if (event[EVENT_BEHAVIOR_FIELD_IDX],
modif) not in events_interval[current_subject]:
events_interval[current_subject][(
event[EVENT_BEHAVIOR_FIELD_IDX], modif)] = I.empty()
mem_events_interval[current_subject][(
event[EVENT_BEHAVIOR_FIELD_IDX], modif)] = []
if event[EVENT_BEHAVIOR_FIELD_IDX] in state_events_list:
mem_events_interval[current_subject][(
event[EVENT_BEHAVIOR_FIELD_IDX],
modif)].append(event[EVENT_TIME_FIELD_IDX])
if len(mem_events_interval[current_subject][(
event[EVENT_BEHAVIOR_FIELD_IDX], modif)]) == 2:
events_interval[current_subject][(event[EVENT_BEHAVIOR_FIELD_IDX], modif)] |= \
I.closedopen(mem_events_interval[current_subject][(event[EVENT_BEHAVIOR_FIELD_IDX], modif)][0],
mem_events_interval[current_subject][(event[EVENT_BEHAVIOR_FIELD_IDX], modif)][1])
mem_events_interval[current_subject][(
event[EVENT_BEHAVIOR_FIELD_IDX], modif)] = []
else:
events_interval[current_subject][(
event[EVENT_BEHAVIOR_FIELD_IDX],
modif)] |= I.singleton(event[EVENT_TIME_FIELD_IDX])
'''
print("\n\n events interval", events_interval)
'''
time_bin_start = min_time
if time_bin_size:
time_bin_end = time_bin_start + time_bin_size
if time_bin_end > max_time:
time_bin_end = max_time
else:
time_bin_end = max_time
'''
print("time_bin_start type", type(time_bin_start))
'''
while True:
for subject in events_interval:
# check behavior to exclude from total time
time_to_subtract = 0
if EXCLUDED_BEHAVIORS in parameters_obs:
for behav in events_interval[subject]:
if behav[0] in parameters_obs.get(
EXCLUDED_BEHAVIORS, []):
interval_intersec = events_interval[
subject][behav] & I.closed(
time_bin_start, time_bin_end)
time_to_subtract += interval_len(
interval_intersec)
for behav in events_interval[subject]:
interval_intersec = events_interval[subject][
behav] & I.closed(time_bin_start, time_bin_end)
dur = interval_len(interval_intersec)
nocc = interval_number(interval_intersec)
mean = interval_mean(interval_intersec)
'''
print(interval_intersec)
print(subject, behav)
print("duration", dur)
print("n. occ", nocc)
print("mean", mean)
print("std dev", interval_std_dev(interval_intersec))
print(time_bin_start)
print(time_bin_end)
print(time_to_subtract)
'''
if behav[0] in parameters_obs.get(
EXCLUDED_BEHAVIORS, []):
proportion = dur / (
(time_bin_end - time_bin_start))
else:
proportion = dur / (
(time_bin_end - time_bin_start) -
time_to_subtract)
behaviors[subject][behav]["duration"] = dur
behaviors[subject][behav]["number"] = nocc
behaviors[subject][behav]["duration mean"] = mean
behaviors[subject][behav][
"duration stdev"] = interval_std_dev(
interval_intersec)
behaviors[subject][behav][
"proportion of time"] = f"{proportion:.3f}"
columns = [
obs_id, f"{max_time - min_time:0.3f}",
f"{time_bin_start:.3f}-{time_bin_end:.3f}"
]
for subject in selected_subjects:
for behavior_modifiers in distinct_behav_modif:
behavior, modifiers = behavior_modifiers
#behavior_modifiers_str = "|".join(behavior_modifiers) if modifiers else behavior
behavior_modifiers_str = behavior_modifiers
for param in parameters:
columns.append(behaviors[subject]
[behavior_modifiers_str][param[0]])
#columns.append(behaviors[subject][behavior][param[0]])
data_report.append(columns)
time_bin_start = time_bin_end
time_bin_end = time_bin_start + time_bin_size
if time_bin_end > max_time:
time_bin_end = max_time
#print(f"start: {time_bin_start} end: {time_bin_end} max time: {max_time}")
if time_bin_start == time_bin_end:
break
except Exception:
dialog.error_message("synthetic_time_budget_bin", sys.exc_info())
return (False, tablib.Dataset())
return True, data_report
