[Shear.TimeSeries] Function has option of setting maximise_data as True but results are same as when this is set as False #494

data = bw.load_csv(bw.demo_datasets.demo_data)
# add nan to one speed
df = data[['Spd80mN', 'Spd60mN', 'Spd40mN']]['2016-01-10 00:00':'2016-02-20 02:00'].copy()
df['Spd80mN']['2016-01-10 00:20': '2016-01-10 00:50'] = np.nan
heights_shear = [80, 60, 40]
df

# calculate concurrent period coverage if using all speeds (e.g maximise_data = False)
print(bw.coverage(df[df>3].dropna(), period='1AS'))

# calculate concurrent period coverage if using all data where two or more anemometers readings exist for a 
# timestamp (e.g maximise_data = True)
count = df[df>3].count(axis=1)
# count
count = count[count >=2]
count.rename('count', inplace=True)
bw.coverage(count, period='1AS')

# results obtained with maximise_data = False
obj_shear1 = bw.Shear.TimeSeries(df, heights_shear,calc_method='power_law', maximise_data=False)
print(obj_shear1.info['output data']['concurrent_period'])
obj_shear1.alpha.dropna()

# results obtained with maximise_data = True
obj_shear2 = bw.Shear.TimeSeries(df, heights_shear,calc_method='power_law', maximise_data=True)
print(obj_shear2.info['output data']['concurrent_period'])
obj_shear2.alpha.dropna()