def clipped_area(ts, thresh=0, integrator=integrate.trapz):
    """Total value * time above the starting value within a TimeSeries

    Arguments:
      ts (pandas.Series): Time series to be integrated.
      thresh (float): Value to clip the tops off at (crossings will be interpolated)

    References:
      http://nbviewer.ipython.org/gist/kermit666/5720498

    >>> t = ['2014-12-09T00:00', '2014-12-09T00:15', '2014-12-09T00:30', '2014-12-09T00:45',
    ...      '2014-12-09T01:00', '2014-12-09T01:15', '2014-12-09T01:30', '2014-12-09T01:45']
    >>> import pandas as pd
    >>> ts = pd.Series([217, 234, 235, 231, 219, 219, 231, 232], index=pd.to_datetime(t))
    >>> clipped_area(ts, thresh=230)  # doctest: +ELLIPSIS
    8598.52941...
    >>> clipped_area(ts, thresh=234)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    562.5
    >>> clipped_area(pd.Series(ts.values, index=ts.index.values.astype(pd.np.int64)),
    ...              thresh=234)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    562.5
    """
    integrator = get_integrator(integrator or 0)
    ts = insert_crossings(ts, thresh) - thresh
    ts = ts[ts >= 0]
    # timestamp is in nanoseconds (since 1/1/1970) but this converts it to seconds (SI units)
    return integrator(ts, ts.index.astype(np.int64)) / 1.0e9
def clipped_area ( ts , thresh = 0 , integrator = integrate . trapz ) : integrator = get_integrator ( integrator or 0 ) ts = insert_crossings ( ts , thresh ) - thresh ts = ts [ ts >= 0 ] return integrator ( ts , ts . index . astype ( np . int64 ) ) / 1.0e9
Total value * time above the starting value within a TimeSeries

    Arguments:
      ts (pandas.Series): Time series to be integrated.
      thresh (float): Value to clip the tops off at (crossings will be interpolated)

    References:
      http://nbviewer.ipython.org/gist/kermit666/5720498

    >>> t = ['2014-12-09T00:00', '2014-12-09T00:15', '2014-12-09T00:30', '2014-12-09T00:45',
    ...      '2014-12-09T01:00', '2014-12-09T01:15', '2014-12-09T01:30', '2014-12-09T01:45']
    >>> import pandas as pd
    >>> ts = pd.Series([217, 234, 235, 231, 219, 219, 231, 232], index=pd.to_datetime(t))
    >>> clipped_area(ts, thresh=230)  # doctest: +ELLIPSIS
    8598.52941...
    >>> clipped_area(ts, thresh=234)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    562.5
    >>> clipped_area(pd.Series(ts.values, index=ts.index.values.astype(pd.np.int64)),
    ...              thresh=234)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    562.5