flasc.utilities.utilities

Utility functions for the FLASC module.

Functions

estimate_dt	Automatically estimate timestep in a time_array.
get_num_turbines	Get the number of turbines in a dataframe.
interp_with_max_gap	Interpolate data linearly or using nearest-neighbor with maximum gap.

flasc.utilities.utilities.estimate_dt(time_array)

Automatically estimate timestep in a time_array.

Parameters:

time_array (list) -- List or dataframe with time entries

Returns:

Timestep in dt.timedelta format

Return type:

datetime.timedelta

flasc.utilities.utilities.get_num_turbines(df)

Get the number of turbines in a dataframe.

Parameters:

df (pd.DataFrame) -- Dataframe with turbine data

Returns:

Number of turbines in the dataframe

Return type:

int

flasc.utilities.utilities.interp_with_max_gap(x, xp, fp, max_gap, kind, wrap_around_360=False)

Interpolate data linearly or using nearest-neighbor with maximum gap.

If there is larger gap in data than max_gap, the gap will be filled with np.nan.

Parameters:

• x (np.array) -- The output x-data; the data points in x-axis that you want the interpolation results from.

• xp (np.array) -- The input x-data.

• fp (np.array) -- The input y-data.

• max_gap (float) -- The maximum allowable distance between x and xp for which interpolation is still performed. Gaps larger than this will be filled with np.nan in the output target_y.

• kind (str) -- The interpolation method to use. Can be 'linear' or 'nearest'.

• wrap_around_360 (bool) -- If True, the interpolation will be done in a circular fashion, i.e., the interpolation will wrap around 360 degrees.

Returns:

The interpolation results.

Return type:

np.array

flasc.utilities.utilities._interpolate_with_max_gap(x, xp, fp, max_gap, assume_sorted=False, kind='linear', extrapolate=True)

Interpolate data linearly or using nearest-neighbor with maximum gap.

If there is larger gap in data than max_gap, the gap will be filled with np.nan.

The input values should not contain NaNs.

Parameters:

• x (np.array) -- The output x-data; the data points in x-axis that you want the interpolation results from.

• xp (np.array) -- The input x-data.

• fp (np.array) -- The input y-data.

• max_gap (float) -- The maximum allowable distance between x and xp for which interpolation is still performed. Gaps larger than this will be filled with np.nan in the output target_y.

• assume_sorted (bool) -- If True, assume that xp is sorted in ascending order. If False, sort xp and fp to be monotonous.

• kind (str) -- The interpolation method to use. Can be 'linear' or 'nearest'.

• extrapolate (bool) -- If True, extrapolate the data points on the boundaries

Returns:

The interpolation results.

Return type:

np.array