def merge_short_lines_into_groups(events, chapter_times, max_ts_duration, max_ts_distance):
search_groups = []
chapter_times = iter(chapter_times[1:] + [100000000])
next_chapter = next(chapter_times)
events = ensure_static_collection(events)
processed = set()
for idx, event in enumerate(events):
if idx in processed:
continue
while event.end > next_chapter:
next_chapter = next(chapter_times)
if event.duration > max_ts_duration:
search_groups.append([event])
processed.add(idx)
else:
group = [event]
group_end = event.end
i = idx+1
while i < len(events) and abs(group_end - events[i].start) < max_ts_distance:
if events[i].end < next_chapter and events[i].duration <= max_ts_duration:
processed.add(i)
group.append(events[i])
group_end = max(group_end, events[i].end)
i += 1
search_groups.append(group)
return search_groups
def merge_short_lines_into_groups(events, chapter_times, max_ts_duration,
max_ts_distance):
search_groups = []
chapter_times = iter(chapter_times[1:] + [100000000])
next_chapter = next(chapter_times)
events = ensure_static_collection(events)
processed = set()
for idx, event in enumerate(events):
if idx in processed:
continue
while event.end > next_chapter:
next_chapter = next(chapter_times)
if event.duration > max_ts_duration:
search_groups.append([event])
processed.add(idx)
else:
group = [event]
group_end = event.end
i = idx + 1
while i < len(events) and abs(group_end -
events[i].start) < max_ts_distance:
if events[i].end < next_chapter and events[
i].duration <= max_ts_duration:
processed.add(i)
group.append(events[i])
group_end = max(group_end, events[i].end)
i += 1
search_groups.append(group)
return search_groups
def interpolate_nones(data, points):
data = ensure_static_collection(data)
data_idx = [point for point, value in izip(points, data) if value is not None]
if not data_idx:
return []
zero_idx = [point for point, value in izip(points, data) if value is None]
if not zero_idx:
return data
data_values = [value for value in data if value is not None]
out = iter(np.interp(zero_idx, data_idx, data_values))
for point, value in enumerate(data):
if value is None:
data[point] = next(out, None)
return data
def interpolate_nones(data, points):
data = ensure_static_collection(data)
values_lookup = {p: v for p, v in izip(points, data) if v is not None}
if not values_lookup:
return []
zero_points = {p for p, v in izip(points, data) if v is None}
if not zero_points:
return data
data_list = sorted(values_lookup.iteritems())
zero_points = sorted(x for x in zero_points if x not in values_lookup)
out = np.interp(x=zero_points,
xp=map(operator.itemgetter(0), data_list),
fp=map(operator.itemgetter(1), data_list))
values_lookup.update(izip(zero_points, out))
return [
values_lookup[point] if value is None else value
for point, value in izip(points, data)
]
def interpolate_nones(data, points):
data = ensure_static_collection(data)
values_lookup = {p: v for p, v in izip(points, data) if v is not None}
if not values_lookup:
return []
zero_points = {p for p, v in izip(points, data) if v is None}
if not zero_points:
return data
data_list = sorted(values_lookup.iteritems())
zero_points = sorted(x for x in zero_points if x not in values_lookup)
out = np.interp(x=zero_points,
xp=map(operator.itemgetter(0), data_list),
fp=map(operator.itemgetter(1), data_list))
values_lookup.update(izip(zero_points, out))
return [
values_lookup[point] if value is None else value
for point, value in izip(points, data)
]
