def dropout_rate_per_period(data, rule, window_start=None, window_end=None):
"""
Parameters
----------
data : pandas.DataFrame or Series or DatetimeIndex
rule : pandas Offset string (or what ever the `rule` parameter in
pd.Series.resample accepts)
window_start, window_end : pd.Timestamp
The start and end of the window of interest. If this window
is larger than the duration of `data` then gaps will be
appended to the front / back as necessary. If this window
is shorter than the duration of `data` data will be cropped.
Returns
-------
pd.Series
Index is a regular DatetimeIndex with freq=rule and
timezone=data.index.tzinfo
Values are the number of dropped in that time period.
"""
# TODO: this might be a rather nasty hack to fix the circular dependency
from nilmtk.preprocessing.electricity.single import reframe_index
try:
data = data.dropna()
except AttributeError:
# if data is DatetimeIndex then it has no `dropna()` method
pass
sample_period_secs = get_sample_period(data)
n_expected_samples_per_period = (secs_per_period_alias(rule) /
sample_period_secs)
if n_expected_samples_per_period < 1.0:
raise ValueError('Date period specified by rule is shorter than'
' sample period!')
index = _get_index(data)
index = reframe_index(index, window_start, window_end)
n_samples_per_period = (pd.Series(1, index=index)
.resample(rule=rule, how='sum')
.fillna(0))
dropout_rate_per_period_ = 1 - (n_samples_per_period /
n_expected_samples_per_period)
return dropout_rate_per_period_
def get_gap_starts_and_gap_ends(data, max_sample_period,
window_start=None, window_end=None):
"""
Parameters
---------
data : pandas.DataFrame or Series or DatetimeIndex
max_sample_period : int or float
Maximum allowed sample period in seconds. This defines what
counts as a 'gap'.
window_start, window_end : pd.Timestamp
The start and end of the window of interest. If this window
is larger than the duration of `data` then gaps will be
appended to the front / back as necessary. If this window
is shorter than the duration of `data` data will be cropped.
Returns
-------
gap_starts, gap_ends: DatetimeIndex
"""
# TODO: this might be a rather nasty hack to fix the circular dependency
from nilmtk.preprocessing.electricity.single import reframe_index
try:
data = data.dropna()
except AttributeError:
# if data is DatetimeIndex then it has no `dropna()` method
pass
index = _get_index(data)
index = reframe_index(index, window_start, window_end)
timedeltas_sec = timedelta64_to_secs(np.diff(index.values))
overlong_timedeltas = timedeltas_sec > max_sample_period
gap_starts = index[:-1][overlong_timedeltas]
gap_ends = index[1:][overlong_timedeltas]
return gap_starts, gap_ends
