flasc.utilities.utilities#
Utility functions for the FLASC module.
Functions
Automatically estimate timestep in a time_array. |
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Get the number of turbines in a dataframe. |
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Interpolate data linearly or using nearest-neighbor with maximum gap. |
- flasc.utilities.utilities.estimate_dt(time_array)[source]#
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)[source]#
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)[source]#
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)[source]#
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