pyair/stats.py
def fullStats(a, b):
    """Performs several stats on a against b, typically a is the predictions
    array, and b the observations array

    Returns:
        A dataFrame of stat name, stat description, result
    """

    stats = [
        ['bias', 'Bias', bias(a, b)],
        ['stderr', 'Standard Deviation Error', stderr(a, b)],
        ['mae', 'Mean Absolute Error', mae(a, b)],
        ['rmse', 'Root Mean Square Error', rmse(a, b)],
        ['nmse', 'Normalized Mean Square Error', nmse(a, b)],
        ['mfbe', 'Mean Fractionalized bias Error', mfbe(a, b)],
        ['fa2', 'Factor of Two', fa(a, b, 2)],
        ['foex', 'Factor of Exceedance', foex(a, b)],
        ['correlation', 'Correlation R', correlation(a, b)],
        ['determination', 'Coefficient of Determination r2', determination(a, b)],
        ['gmb', 'Geometric Mean Bias', gmb(a, b)],
        ['gmv', 'Geometric Mean Variance', gmv(a, b)],
        ['fmt', 'Figure of Merit in Time', fmt(a, b)]
    ]
    rec = np.rec.fromrecords(stats, names=('stat', 'description', 'result'))
    df = pd.DataFrame.from_records(rec, index='stat')
    return df
def fullStats(a, b):
    """Performs several stats on a against b, typically a is the predictions
    array, and b the observations array

    Returns:
        A dataFrame of stat name, stat description, result
    """

    stats = [
        ['bias', 'Bias', bias(a, b)],
        ['stderr', 'Standard Deviation Error', stderr(a, b)],
        ['mae', 'Mean Absolute Error', mae(a, b)],
        ['rmse', 'Root Mean Square Error', rmse(a, b)],
        ['nmse', 'Normalized Mean Square Error', nmse(a, b)],
        ['mfbe', 'Mean Fractionalized bias Error', mfbe(a, b)],
        ['fa2', 'Factor of Two', fa(a, b, 2)],
        ['foex', 'Factor of Exceedance', foex(a, b)],
        ['correlation', 'Correlation R', correlation(a, b)],
        ['determination', 'Coefficient of Determination r2', determination(a, b)],
        ['gmb', 'Geometric Mean Bias', gmb(a, b)],
        ['gmv', 'Geometric Mean Variance', gmv(a, b)],
        ['fmt', 'Figure of Merit in Time', fmt(a, b)]
    ]
    rec = np.rec.fromrecords(stats, names=('stat', 'description', 'result'))
    df = pd.DataFrame.from_records(rec, index='stat')
    return df
def fullStats ( a , b ) : stats = [ [ 'bias' , 'Bias' , bias ( a , b ) ] , [ 'stderr' , 'Standard Deviation Error' , stderr ( a , b ) ] , [ 'mae' , 'Mean Absolute Error' , mae ( a , b ) ] , [ 'rmse' , 'Root Mean Square Error' , rmse ( a , b ) ] , [ 'nmse' , 'Normalized Mean Square Error' , nmse ( a , b ) ] , [ 'mfbe' , 'Mean Fractionalized bias Error' , mfbe ( a , b ) ] , [ 'fa2' , 'Factor of Two' , fa ( a , b , 2 ) ] , [ 'foex' , 'Factor of Exceedance' , foex ( a , b ) ] , [ 'correlation' , 'Correlation R' , correlation ( a , b ) ] , [ 'determination' , 'Coefficient of Determination r2' , determination ( a , b ) ] , [ 'gmb' , 'Geometric Mean Bias' , gmb ( a , b ) ] , [ 'gmv' , 'Geometric Mean Variance' , gmv ( a , b ) ] , [ 'fmt' , 'Figure of Merit in Time' , fmt ( a , b ) ] ] rec = np . rec . fromrecords ( stats , names = ( 'stat' , 'description' , 'result' ) ) df = pd . DataFrame . from_records ( rec , index = 'stat' ) return df
Performs several stats on a against b, typically a is the predictions
    array, and b the observations array

    Returns:
        A dataFrame of stat name, stat description, result
Performs several stats on a against b typically a is the predictions array and b the observations array