brightwind.analyse.shear.Shear.Average¶

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

Calculates alpha, using the power law, or the roughness coefficient, using the log law, based on the average wind speeds of each supplied time 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.

Returns

Average object containing calculated alpha/roughness coefficient values, a plot and other data.

Return type

Average 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
average_power_law = bw.Shear.Average(anemometers, heights)
average_log_law = bw.Shear.Average(anemometers, heights, calc_method='log_law', max_plot_height=120)

# Get the alpha or roughness values calculated
average_power_law.alpha
average_log_law.roughness

# View plot
average_power_law.plot
average_log_law.plot

# View input data
average_power_law.wspds
average_log_law.wspds

# View other information
pprint.pprint(average_power_law.info)
pprint.pprint(average_log_law.info)

__init__(wspds, heights, min_speed=3, calc_method='power_law', plot_both=False, max_plot_height=None)¶