brightwind.analyse.shear.Shear.TimeSeries

class brightwind.analyse.shear.Shear.TimeSeries(wspds, heights, min_speed=3, calc_method='power_law', max_plot_height=None, maximise_data=False)

Calculates alpha, using the power law, or the roughness coefficient, using the log law, for each timestamp of a wind series.

Parameters

  • wspds (pandas.DataFrame, list of pandas.Series or list.) – pandas DataFrame, list of pandas.Series or list of wind speeds to be used for calculating shear.

  • heights (list) – List of anemometer heights.

  • min_speed (float) – Only speeds higher than this would be considered for calculating shear, default is 3.

  • calc_method (str) – method to use for calculation, either ‘power_law’ (returns alpha) or ‘log_law’ (returns the roughness coefficient).

  • max_plot_height (float) – height to which the wind profile plot is extended.

  • maximise_data (Boolean) – If maximise_data is True, calculations will be carried out on all data where two or more anemometers readings exist for a timestamp. If False, calculations will only be carried out on timestamps where readings exist for all anemometers.

:return TimeSeries object containing calculated alpha/roughness coefficient values, a plot

and other data.

:rtype TimeSeries object

Example usage

import brightwind as bw
import pprint

# Load anemometer data to calculate exponents
data = bw.load_csv(C:\Users\Stephen\Documents\Analysis\demo_data)
anemometers = data[['Spd80mS', 'Spd60mS','Spd40mS']]
heights = [80, 60, 40]

# Using with a DataFrame of wind speeds
timeseries_power_law = bw.Shear.TimeSeries(anemometers, heights, maximise_data=True)
timeseries_log_law = bw.Shear.TimeSeries(anemometers, heights, calc_method='log_law',
                                         max_plot_height=120)

# Get the alpha or roughness values calculated
timeseries_power_law.alpha
timeseries_log_law.roughness

# View plot
timeseries_power_law.plot
timeseries_log_law.plot

# View input anemometer data
timeseries_power_law.wspds
timeseries_log_law.wspds

# View other information
pprint.pprint(timeseries_power_law.info)
pprint.pprint(timeseries_log_law.info)
__init__(wspds, heights, min_speed=3, calc_method='power_law', max_plot_height=None, maximise_data=False)

Calculates alpha, using the power law, or the roughness coefficient, using the log law, for each timestamp of a wind series.

Parameters

  • wspds (pandas.DataFrame, list of pandas.Series or list.) – pandas DataFrame, list of pandas.Series or list of wind speeds to be used for calculating shear.

  • heights (list) – List of anemometer heights.

  • min_speed (float) – Only speeds higher than this would be considered for calculating shear, default is 3.

  • calc_method (str) – method to use for calculation, either ‘power_law’ (returns alpha) or ‘log_law’ (returns the roughness coefficient).

  • max_plot_height (float) – height to which the wind profile plot is extended.

  • maximise_data (Boolean) – If maximise_data is True, calculations will be carried out on all data where two or more anemometers readings exist for a timestamp. If False, calculations will only be carried out on timestamps where readings exist for all anemometers.

:return TimeSeries object containing calculated alpha/roughness coefficient values, a plot

and other data.

:rtype TimeSeries object

Example usage

import brightwind as bw
import pprint

# Load anemometer data to calculate exponents
data = bw.load_csv(C:\Users\Stephen\Documents\Analysis\demo_data)
anemometers = data[['Spd80mS', 'Spd60mS','Spd40mS']]
heights = [80, 60, 40]

# Using with a DataFrame of wind speeds
timeseries_power_law = bw.Shear.TimeSeries(anemometers, heights, maximise_data=True)
timeseries_log_law = bw.Shear.TimeSeries(anemometers, heights, calc_method='log_law',
                                         max_plot_height=120)

# Get the alpha or roughness values calculated
timeseries_power_law.alpha
timeseries_log_law.roughness

# View plot
timeseries_power_law.plot
timeseries_log_law.plot

# View input anemometer data
timeseries_power_law.wspds
timeseries_log_law.wspds

# View other information
pprint.pprint(timeseries_power_law.info)
pprint.pprint(timeseries_log_law.info)

Methods

__init__(wspds, heights[, min_speed, …])

Calculates alpha, using the power law, or the roughness coefficient, using the log law, for each timestamp

apply(wspds, height, shear_to)

”

Attributes

alpha

roughness