def __init__(self, splitter, channel_meta=None):
"""Calculate a standard set of metrics."""
self.register_event_metric('median_current', apply_to_field(np.median, 'mean'))
self.register_event_metric('median_sd', apply_to_field(np.median, 'stdv'))
self.register_event_metric('median_dwell', apply_to_field(np.median, 'length'))
self.register_event_metric('num_events', len)
self.register_event_metric('start_time', read_start_time)
self.register_event_metric('duration', duration)
self.register_event_metric('range_current', apply_to_field(range_data, 'mean'))
super(StandardMetrics, self).__init__(splitter, channel_meta=channel_meta)
def __init__(self, splitter, channel_meta=None):
"""Calculate metrics of interest for solid state runs (but potentially useful elsewhere"""
self.register_event_metric('max_current', apply_to_field(np.max, 'mean')) # as estimate of abasic peak
self.register_event_metric('stdev_current', apply_to_field(np.std, 'mean'))
# copy read meta from AdaptiveSplitter as metrics
copy_meta = ('pore_level', 'capture_level', 'threshold')
for name in copy_meta:
self.register_read_meta_metric(name)
#TODO: add metric for strand level (excluding abasic), for whole read, and up to start of abasic peak.
super(SolidStateMetrics, self).__init__(splitter, channel_meta=channel_meta)
def __init__(self,
splitter,
channel_meta=None,
window_bounds=(0, 1, 3, 500, 900)):
"""Calculate a standard set of metrics including Minion state data."""
copy_meta = (
'start_event',
'end_event',
'initial_classification',
'initial_n_reads',
)
for name in copy_meta:
self.register_read_meta_metric(name)
self.register_event_metric('entropy_current',
apply_to_field(entropy, 'mean'))
self.register_event_multi_metric(
('stall_duration', 'stall_median_current', 'stall_range'),
locate_stall)
# register mean current calculated in several windows
for start, end in window(window_bounds, 2):
self.register_event_metric(
'median_current_{}_to_{}_s'.format(start, end),
partial(windowed_metric,
feature='mean',
window_start=start,
window_length=end - start,
func_to_apply=np.median))
super(BlockingMetrics, self).__init__(splitter,
channel_meta=channel_meta)
def __init__(self, splitter):
"""Example of registering functions to calculate metrics."""
self.register_event_metric('mean', apply_to_field(np.mean, 'mean'))
self.register_event_metric('stdv', apply_to_field(np.std, 'mean'))
super(SimpleMetrics, self).__init__(splitter)