angle_aggregation.py

import numpy as np

## angle format: 0/360 to -180/180
def angles_format(angle_0_360:np.array)->np.array:
    return np.array([v-360 if v>=180 else v for v in angle_0_360])

## aggregation for angular data
def angles_agg(angle_0_360:np.array, func_agg) -> float:
    """
    Calculate wind direction average.
    angle_0_360 -- array of angles to be averaged.
    func_agg -- aggregation fucntion.
    return -- aggregated value.
    """
    # validation
    if len(angle_0_360) == 0:
        return np.nan
    # format
    D0 = angles_format(angle_0_360)
    # degree to rad
    ph = D0/180*np.pi
    # angular to cartesian components
    Ds = np.sin(ph)
    Dc = np.cos(ph)
    # aggregation, cartesian to angular and rad to angular 
    wd0 = 180/np.pi*np.arctan2(func_agg(Ds), func_agg(Dc))
    # format -180/180
    if wd0 > 180:
        wd0 -= 360
    agg_wd = wd0
    # return
    return agg_wd  

angles_mean.py

## aggregation for angular data: sum
def angles_sum(angles:np.array)->float:
    return angles_agg(angles, func_agg=np.nanmean)

angles_sum.py

## aggregation for angular data: sum
def angles_sum(angles:np.array)->float:
    return angles_agg(angles, func_agg=np.nansum)

angles_variance.py

## aggregation for angular data: variance
def angles_var(angles:np.array)->float:
    return np.sum((angles - angles_agg(angles, func_agg=np.nanmean))**2) / (len(angles)-1)