def test_sector_ratio():
data = bw.load_csv(bw.datasets.shell_flats_80m_csv)
sec_rat_data = bw.sector_ratio(data['WS70mA100NW_Avg'], data['WS70mA100SE_Avg'], data['WD50mW200PNW_VAvg'],
sectors=72, boom_dir_1=315, boom_dir_2=135, return_data=True)[1]
data = bw.load_csv(bw.datasets.demo_data)
bw.sector_ratio(data[['Spd40mN']], data.Spd60mN, wdir=data[['Dir38mS']])
bw.sector_ratio(data.Spd40mN, data.Spd60mN, wdir=data.Dir38mS,
direction_bin_array=[0, 45, 135, 180, 220, 360], boom_dir_1=160, boom_dir_2=340)
assert True
def test_monthly_means():
# Load data
bw.monthly_means(bw.load_csv(bw.datasets.shell_flats_80m_csv))
bw.monthly_means(bw.load_csv(bw.datasets.shell_flats_80m_csv)[['WS70mA100NW_Avg']])
bw.monthly_means(bw.load_csv(bw.datasets.shell_flats_80m_csv)[['WS70mA100NW_Avg','WS70mA100SE_Avg',
'WS50mA100NW_Avg','WS50mA100SE_Avg',
'WS20mA100CB1_Avg','WS20mA100CB2_Avg']],
return_data=True)
bw.monthly_means(bw.load_csv(bw.datasets.shell_flats_80m_csv).WS80mWS425NW_Avg, return_data=True)
assert True
def test_load_csv():
data = bw.load_csv('../datasets/demo/demo_data.csv')
data2 = bw.load_csv('../datasets/demo/demo_data2.csv', dayfirst=True)
data3 = bw.load_csv('../datasets/demo/demo_data3.csv', dayfirst=True)
data4 = bw.load_csv('../datasets/demo/demo_data4.csv', dayfirst=True)
assert (data['2016-01-09 15:30:00':'2016-01-10 23:50:00'].fillna(-999) ==
data2['2016-01-09 15:30:00':'2016-01-10 23:50:00'].fillna(-999)
).all().all()
assert (data['2016-01-09 15:30:00':'2016-01-10 23:50:00'].fillna(-999) ==
data3['2016-01-09 15:30:00':'2016-01-10 23:50:00'].fillna(-999)
).all().all()
assert (data['2016-01-09 15:30:00':'2016-01-10 23:50:00'].fillna(-999) ==
data4['2016-01-09 15:30:00':'2016-01-10 23:50:00'].fillna(-999)
).all().all()
def test_plot_timeseries():
data = bw.load_csv(bw.datasets.demo_data)
bw.plot_timeseries(data[['Spd40mN', 'Spd60mS', 'T2m']])
bw.plot_timeseries(data.Spd40mN, date_from='2017-09-01')
bw.plot_timeseries(data.Spd40mN, date_to='2017-10-01')
bw.plot_timeseries(data.Spd40mN,
date_from='2017-09-01',
date_to='2017-10-01')
bw.plot_timeseries(data.Spd40mN,
date_from='2017-09-01',
date_to='2017-10-01',
y_limits=(0, None))
bw.plot_timeseries(data.Spd40mN,
date_from='2017-09-01',
date_to='2017-10-01',
y_limits=None)
bw.plot_timeseries(data.Spd40mN,
date_from='2017-09-01',
date_to='2017-10-01',
y_limits=(0, 25))
bw.plot_timeseries(data.Spd40mN,
date_from='2017-09-01',
date_to='2017-10-01',
y_limits=(None, 25))
assert True
def test_TimeOfDay():
# Load data
# load data as dataframe
data = bw.load_csv(bw.datasets.demo_data)
# Specify columns in data which contain the anemometer measurements from which to calculate shear
anemometers = data[['Spd80mN', 'Spd60mN', 'Spd40mN']]
# Specify the heights of these anemometers
heights = [80, 60, 40]
# Test initialisation
shear_by_tod_power_law = bw.Shear.TimeOfDay(anemometers, heights)
shear_by_tod_power_law = bw.Shear.TimeOfDay(anemometers,
heights,
by_month=False)
shear_by_tod_log_law = bw.Shear.TimeOfDay(anemometers,
heights,
calc_method='log_law')
shear_by_tod_log_law = bw.Shear.TimeOfDay(anemometers,
heights,
by_month=False,
calc_method='log_law')
# Test attributes
shear_by_tod_power_law.alpha
shear_by_tod_log_law.roughness
# Test apply
shear_by_tod_power_law.apply(data['Spd80mN'], 40, 60)
shear_by_tod_log_law.apply(data['Spd80mN'], 40, 60)
assert True
def test_BySector():
# load data as dataframe
data = bw.load_csv(bw.datasets.demo_data)
# Specify columns in data which contain the anemometer measurements from which to calculate shear
anemometers = data[['Spd80mN', 'Spd60mN', 'Spd40mN']]
# Specify the heights of these anemometers
heights = [80, 60, 40]
# Specify directions
directions = data['Dir78mS']
# custom bins
custom_bins = [0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360]
# Test initialisation
shear_by_sector_power_law = bw.Shear.BySector(anemometers, heights,
directions)
shear_by_sector_log_law = bw.Shear.BySector(anemometers,
heights,
directions,
calc_method='log_law')
shear_by_sector_custom_bins = bw.Shear.BySector(
anemometers, heights, directions, direction_bin_array=custom_bins)
# test attributes
shear_by_sector_power_law.plot
shear_by_sector_power_law.alpha
shear_by_sector_custom_bins.plot
shear_by_sector_custom_bins.alpha
# Test apply
shear_by_sector_power_law.apply(data['Spd80mN'], directions, 40, 60)
shear_by_sector_log_law.apply(data['Spd80mN'], directions, 40, 60)
shear_by_sector_custom_bins.apply(data['Spd80mN'], directions, 40, 60)
assert True
def test_basic_stats():
data = bw.load_csv(bw.datasets.shell_flats_80m_csv)
bw.basic_stats(data)
bs1 = bw.basic_stats(data[['WS70mA100NW_Avg']])
bs2 = bw.basic_stats(data['WS70mA100NW_Avg'])
assert (bs2['count'] == 58874.0).bool() and((bs2['mean']-9.169382) < 1e-6).bool() and \
((bs2['std']-4.932851) < 1e-6).bool() and (bs2['max'] == 27.66).bool() and (bs2['min'] == 0.0).bool()
def test_freq_distribution():
df = bw.load_csv(bw.datasets.demo_data)
spd_dist = bw.freq_distribution(df.Spd80mN,
max_speed=30,
max_y_value=10,
return_data=False)
assert True
def test_dist_of_wind_speed():
df = bw.load_csv(bw.datasets.demo_data)
spd_dist = bw.dist_of_wind_speed(df.Spd80mN,
max_speed=30,
max_y_value=10,
return_data=False)
assert True
def test_time_continuity_gaps():
import pandas as pd
data = bw.load_csv(bw.datasets.shell_flats_80m_csv)
gaps = bw.time_continuity_gaps(data['WS70mA100NW_Avg'][:400])
assert gaps.iloc[0, 0] == pd.Timestamp('2011-07-16 17:50:00')
assert gaps.iloc[0, 1] == pd.Timestamp('2011-07-16 18:10:00')
assert gaps.iloc[1, 0] == pd.Timestamp('2011-07-16 23:00:00')
assert gaps.iloc[1, 1] == pd.Timestamp('2011-07-16 23:20:00')
assert abs(gaps.iloc[0, 2] - 0.01388) < 1e-5
assert abs(gaps.iloc[1, 2] - 0.01388) < 1e-5
def test_scale():
# load data as dataframe
data = bw.load_csv(bw.datasets.demo_data)
# Specify columns in data which contain the anemometer measurements from which to calculate shear
bw.Shear.scale(data['Spd40mN'], 40, 60, alpha=.2)
bw.Shear.scale(data['Spd40mN'],
40,
60,
calc_method='log_law',
roughness=.03)
assert True
def test_plot_scatter():
data = bw.load_csv(bw.datasets.demo_data)
graph = bw.plot_scatter(data.Spd80mN, data.Spd80mS)
graph = bw.plot_scatter(data.Spd80mN, data[['Spd80mS']])
bw.plot_scatter(data.Dir78mS, data.Dir58mS, x_axis_title='Dir78mS', y_axis_title='Dir58mS',
x_limits=(50, 300), y_limits=(250, 300))
bw.plot_scatter_wdir(data.Dir78mS, data.Dir58mS, x_axis_title='Reference', y_axis_title='Target',
x_limits=(50, 300), y_limits=(250, 300))
bw.plot_scatter_wspd(data.Spd80mN, data.Spd80mS, x_axis_title='Speed at 80m North',
y_axis_title='Speed at 80m South', x_limits=(0, 25), y_limits=(0, 25))
assert True
def test_dist_12x24():
df = bw.load_csv(bw.datasets.demo_data)
graph, table12x24 = bw.dist_12x24(df[['Spd40mN']], return_data=True)
graph, table12x24 = bw.dist_12x24(df.Spd40mN, var_name_label='wind speed', return_data=True)
graph = bw.dist_12x24(df.PrcpTot, aggregation_method='sum')
def custom_agg(x):
return x.mean() + (2 * x.std())
graph, table12x24 = bw.dist_12x24(df.PrcpTot, aggregation_method=custom_agg, return_data=True)
assert True
def test_sector_ratio_by_sector():
data = bw.load_csv(bw.datasets.shell_flats_80m_csv)
bw.SectorRatio.by_sector(data['WS70mA100NW_Avg'],
data['WS70mA100SE_Avg'],
data['WD50mW200PNW_VAvg'],
sectors=72,
boom_dir_1=315,
boom_dir_2=135,
return_data=True)[1]
data = bw.load_campbell_scientific(bw.datasets.demo_site_data)
bw.SectorRatio.by_sector(data.Spd40mN,
data.Spd60mN,
wdir=data.Dir38mS,
direction_bin_array=[0, 45, 135, 180, 220, 360],
boom_dir_1=160,
boom_dir_2=340)
assert True
def test_Average():
# Load data
# load data as dataframe
data = bw.load_csv(bw.datasets.demo_data)
# Specify columns in data which contain the anemometer measurements from which to calculate shear
anemometers = data[['Spd80mN', 'Spd60mN', 'Spd40mN']]
# Specify the heights of these anemometers
heights = [80, 60, 40]
# Test initialisation
shear_avg_power_law = bw.Shear.Average(anemometers, heights)
shear_avg_log_law = bw.Shear.Average(anemometers,
heights,
calc_method='log_law')
# Test attributes
shear_avg_power_law.alpha
shear_avg_log_law.roughness
# Test apply
shear_avg_power_law.apply(data['Spd80mN'], 40, 60)
shear_avg_log_law.apply(data['Spd80mN'], 40, 60)
assert True
# Test specific values
wspds = [7.74, 8.2, 8.57]
heights = [60, 80, 100]
specific_test = bw.Shear.Average(wspds, heights)
assert round(specific_test.alpha, 9) == 0.199474297
wspds = [8, 8.365116]
heights = [80, 100]
specific_test = bw.Shear.Average(wspds, heights)
assert round(specific_test.alpha, 1) == 0.2
specific_test_log = bw.Shear.Average(wspds, heights, calc_method='log_law')
assert round(specific_test_log.roughness, 9) == 0.602156994
