def lpp_averageIndivTransit(tcedata, mapInfo):
    '\n    \n    Create the loop over individual transits and return \n    array normalized lpp values, mean and std.\n    Input TCE object and mapInfo object.\n    \n    It is unclear that this individual transit approach\n    separates out several new false positives.\n    It probably would require retuning for low SNR signals.\n    \n    '
    length = (tcedata.time[(- 1)] - tcedata.time[0])
    ntransits = int(np.floor((length / tcedata.period)))
    lppNorms = np.ones(ntransits)
    lppRaws = np.ones(ntransits)
    nExpCad = ((tcedata.time[(- 1)] - tcedata.time[0]) / tcedata.period)
    for i in range(ntransits):
        (lppNorms[i], lppRaws[i]) = lpp_onetransit(tcedata, mapInfo, i)
    lppMed = np.nanmedian(lppNorms)
    lppStd = np.nanstd(lppNorms)
    return (lppNorms, lppMed, lppStd, ntransits)
Create the loop over individual transits and return 
array normalized lpp values, mean and std.
Input TCE object and mapInfo object.

It is unclear that this individual transit approach
separates out several new false positives.
It probably would require retuning for low SNR signals.
def lpp_averageIndivTransit(tcedata, mapInfo):
    '\n    \n    Create the loop over individual transits and return \n    array normalized lpp values, mean and std.\n    Input TCE object and mapInfo object.\n    \n    It is unclear that this individual transit approach\n    separates out several new false positives.\n    It probably would require retuning for low SNR signals.\n    \n    '
    length = (tcedata.time[(- 1)] - tcedata.time[0])
    ntransits = int(np.floor((length / tcedata.period)))
    lppNorms = np.ones(ntransits)
    lppRaws = np.ones(ntransits)
    nExpCad = ((tcedata.time[(- 1)] - tcedata.time[0]) / tcedata.period)
    for i in range(ntransits):
        (lppNorms[i], lppRaws[i]) = lpp_onetransit(tcedata, mapInfo, i)
    lppMed = np.nanmedian(lppNorms)
    lppStd = np.nanstd(lppNorms)
    return (lppNorms, lppMed, lppStd, ntransits)