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iss #317 fix bug for ti_by_speed and ti_by_sector and fix doc string
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@BiancaMorandi
BiancaMorandi committed Sep 22, 2022
1 parent f9e424a commit 4c20311
Showing 1 changed file with 68 additions and 19 deletions.
87 changes: 68 additions & 19 deletions brightwind/analyse/analyse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1051,8 +1051,8 @@ def by_speed(wspd, wspd_std, speed_bin_array=np.arange(-0.5, 41, 1), speed_bin_l
percentile=90, IEC_class=None, return_data=False):
"""
Accepts a wind speed series and its standard deviation, calculates turbulence intensity (TI) and returns a
scatter plot of TI versus speed and the distribution of TI by speed bins. Note that speed values lower than
3 m/s are filtered out when deriving the TI values.
scatter plot of TI versus speed and the distribution of TI by speed bins if return_data is set to True.
Note that speed values lower than 3 m/s are filtered out when deriving the TI values.
:param wspd: Wind speed data series
:type wspd: pandas.Series
Expand Down Expand Up @@ -1099,12 +1099,17 @@ class pass a DataFrame. Note: we have removed option to include IEC Class 1999 a
display(fig_ti_dist)
# Plot TI distribution by speed bins giving as input speed_bin_array and speed_bin_labels and return TI
# distribution table
# distribution by speed table
fig_ti_dist, ti_dist = bw.TI.by_speed(data.Spd80mN, data.Spd80mNStd, speed_bin_array=[1,3,6,9,15],
speed_bin_labels=[1.5,4.5,7.5,12], return_data=True)
display(fig_ti_dist)
display(ti_dist)
fig_ti_dist, ti_dist = bw.TI.by_speed(data.Spd80mN, data.Spd80mNStd, speed_bin_array=[0, 10, 14, 51],
speed_bin_labels=['low', 'mid', 'high'], return_data=True)
display(fig_ti_dist)
display(ti_dist)
# Plot TI distribution by speed bins considering a 60 percentile representative of TI and return TI
# distribution table
fig_ti_dist, ti_dist = bw.TI.by_speed(data.Spd80mN, data.Spd80mNStd, percentile=60, return_data=True)
Expand All @@ -1131,41 +1136,48 @@ class pass a DataFrame. Note: we have removed option to include IEC Class 1999 a
ti['Turbulence_Intensity'] = TI.calc(ti['wspd'], ti['wspd_std'])
ti_dist = pd.concat([
dist(var_to_bin=ti['Turbulence_Intensity'], var_to_bin_against=ti['wspd'],
bins=speed_bin_array, bin_labels=speed_bin_labels,
bins=speed_bin_array, bin_labels=None,
aggregation_method='mean', return_data=True)[-1].rename("Mean_TI"),
dist(var_to_bin=ti['Turbulence_Intensity'],
var_to_bin_against=ti['wspd'],
bins=speed_bin_array,
bin_labels=speed_bin_labels,
bin_labels=None,
aggregation_method='count', return_data=True)[-1].rename("TI_Count"),
dist(var_to_bin=ti['Turbulence_Intensity'],
var_to_bin_against=ti['wspd'],
bins=speed_bin_array,
bin_labels=speed_bin_labels,
bin_labels=None,
aggregation_method=lambda x: np.percentile(x, q=percentile),
return_data=True)[-1].rename("Rep_TI"),
dist(var_to_bin=ti['Turbulence_Intensity'],
var_to_bin_against=ti['wspd'],
bins=speed_bin_array,
bin_labels=speed_bin_labels,
bin_labels=None,
aggregation_method='std', return_data=True)[-1].rename("TI_2Sigma")], axis=1, join='inner')
categ_index = dist(var_to_bin=ti['Turbulence_Intensity'], var_to_bin_against=ti['wspd'],
bins=speed_bin_array, aggregation_method='mean', return_data=True)[-1].index
num_index = [i.mid for i in categ_index]
ti_dist.loc[:, 'Char_TI'] = ti_dist.loc[:, 'Mean_TI'] + (ti_dist.loc[:, 'TI_2Sigma'] / num_index)
# ti_dist.loc[0, 'Char_TI'] = 0

ti_dist.index.rename('Speed Bin', inplace=True)
# return ti_dist
ti_dist.index = [i.mid for i in ti_dist.index]
graph_ti_dist_by_speed = bw_plt.plot_TI_by_speed(wspd, wspd_std, ti_dist, IEC_class=IEC_class)

# replace index of ti_dist with input speed_bin_labels only after generating the plot
if speed_bin_labels:
ti_dist.index = speed_bin_labels

if return_data:
return bw_plt.plot_TI_by_speed(wspd, wspd_std, ti_dist, IEC_class=IEC_class), ti_dist.dropna(how='any')
return bw_plt.plot_TI_by_speed(wspd, wspd_std, ti_dist, IEC_class=IEC_class)
return graph_ti_dist_by_speed, ti_dist.dropna(how='any')
return graph_ti_dist_by_speed

@staticmethod
def by_sector(wspd, wspd_std, wdir, min_speed=0, sectors=12, direction_bin_array=None,
direction_bin_labels=None, return_data=False):
"""
Accepts a wind speed series, its standard deviation and a direction series, calculates turbulence intensity (TI)
and returns the distribution of TI by sector
Accepts a wind speed series, its standard deviation and a direction series. Calculates turbulence intensity (TI)
and returns a plot of TI by sector and the distribution of TI by sector if return_data is set to True.
Note that speed values lower than 3 m/s are filtered out when deriving the TI values.
:param wspd: Wind speed data series
:type wspd: pandas.Series
Expand All @@ -1178,8 +1190,8 @@ def by_sector(wspd, wspd_std, wdir, min_speed=0, sectors=12, direction_bin_array
:param sectors: Set the number of direction sectors. Usually 12, 16, 24, 36 or 72.
:type sectors: int
:param direction_bin_array: (Optional) Array of wind speeds where adjacent elements of array form a bin.
To change default behaviour of first sector centered at 0 assign an
array of bins to this
This overwrites the sectors. To change default behaviour of first sector
centered at 0 assign an array of bins to this
:type direction_bin_array: numpy.array or list
:param direction_bin_labels: (Optional) you can specify an array of labels to be used for the bins.
Default is using string labels of format '30-90'
Expand All @@ -1194,7 +1206,38 @@ def by_sector(wspd, wspd_std, wdir, min_speed=0, sectors=12, direction_bin_array
:rtype: matplotlib.figure.Figure, pandas.DataFrame
**Example usage**
::
import brightwind as bw
data = bw.load_csv(bw.demo_datasets.demo_data)
# Plot TI distribution by sector using default inputs
fig_ti_dist = bw.TI.by_sector(data[['Spd80mN']], data[['Spd80mNStd']], data[['Dir78mS']])
display(fig_ti_dist)
# Plot TI distribution by sector giving as input min_speed and sectors and return TI
# distribution by sector table
fig_ti_dist, ti_dist = bw.TI.by_sector(data.Spd80mN, data.Spd80mNStd, data.Dir78mS, min_speed=4, sectors=6,
return_data=True)
display(fig_ti_dist)
display(ti_dist)
# Plot TI distribution by sector giving as input direction_bin_array and direction_bin_labels. Return TI
# distribution by sector table
fig_ti_dist, ti_dist = bw.TI.by_sector(data.Spd80mN, data.Spd80mNStd, data.Dir78mS,
direction_bin_array=[0,90,130,200,360],
direction_bin_labels=['northerly','easterly','southerly','westerly'],
return_data=True)
display(fig_ti_dist)
display(ti_dist)
"""

wspd = _convert_df_to_series(wspd)
wspd_std = _convert_df_to_series(wspd_std)
wdir = _convert_df_to_series(wdir)

ti = pd.concat([wspd.rename('wspd'), wspd_std.rename('wspd_std'), wdir.rename('wdir')], axis=1,
join='inner')
ti = ti[ti['wspd'] >= min_speed]
Expand All @@ -1203,20 +1246,26 @@ def by_sector(wspd, wspd_std, wdir, min_speed=0, sectors=12, direction_bin_array
dist_by_dir_sector(var_series=ti['Turbulence_Intensity'],
direction_series=ti['wdir'],
sectors=sectors, direction_bin_array=direction_bin_array,
direction_bin_labels=direction_bin_labels,
direction_bin_labels=None,
aggregation_method='mean', return_data=True)[-1].rename("Mean_TI"),
dist_by_dir_sector(var_series=ti['Turbulence_Intensity'],
direction_series=ti['wdir'],
sectors=sectors, direction_bin_array=direction_bin_array,
direction_bin_labels=direction_bin_labels,
direction_bin_labels=None,
aggregation_method='count', return_data=True)[-1].rename("TI_Count")
], axis=1, join='outer')

ti_dist.index.rename('Direction Bin', inplace=True)
graph_ti_dist_by_sector = bw_plt.plot_TI_by_sector(ti['Turbulence_Intensity'], ti['wdir'], ti_dist)

# replace index of ti_dist with input direction_bin_labels only after generating the plot
if direction_bin_labels:
ti_dist.index = direction_bin_labels

if return_data:
return bw_plt.plot_TI_by_sector(ti['Turbulence_Intensity'], ti['wdir'], ti_dist), ti_dist.dropna(how='all')
return graph_ti_dist_by_sector, ti_dist.dropna(how='all')
else:
return bw_plt.plot_TI_by_sector(ti['Turbulence_Intensity'], ti['wdir'], ti_dist)
return graph_ti_dist_by_sector

@staticmethod
def twelve_by_24(wspd, wspd_std, return_data=False, var_name_label='Turbulence Intensity'):
Expand Down

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