treetime/utils.py
def median_interp(interp_object):
    """
    Find the median of the function represented as an interpolation object.
    """
    new_grid = np.sort(np.concatenate([interp_object.x[:-1] + 0.1*ii*np.diff(interp_object.x)
                                       for ii in range(10)]).flatten())

    tmp_prop = np.exp(-(interp_object(new_grid)-interp_object.y.min()))
    tmp_cumsum = np.cumsum(0.5*(tmp_prop[1:]+tmp_prop[:-1])*np.diff(new_grid))
    median_index = min(len(tmp_cumsum)-3, max(2,np.searchsorted(tmp_cumsum, tmp_cumsum[-1]*0.5)+1))
    return new_grid[median_index]
def median_interp(interp_object):
    """
    Find the median of the function represented as an interpolation object.
    """
    new_grid = np.sort(np.concatenate([interp_object.x[:-1] + 0.1*ii*np.diff(interp_object.x)
                                       for ii in range(10)]).flatten())

    tmp_prop = np.exp(-(interp_object(new_grid)-interp_object.y.min()))
    tmp_cumsum = np.cumsum(0.5*(tmp_prop[1:]+tmp_prop[:-1])*np.diff(new_grid))
    median_index = min(len(tmp_cumsum)-3, max(2,np.searchsorted(tmp_cumsum, tmp_cumsum[-1]*0.5)+1))
    return new_grid[median_index]
def median_interp ( interp_object ) : new_grid = np . sort ( np . concatenate ( [ interp_object . x [ : - 1 ] + 0.1 * ii * np . diff ( interp_object . x ) for ii in range ( 10 ) ] ) . flatten ( ) ) tmp_prop = np . exp ( - ( interp_object ( new_grid ) - interp_object . y . min ( ) ) ) tmp_cumsum = np . cumsum ( 0.5 * ( tmp_prop [ 1 : ] + tmp_prop [ : - 1 ] ) * np . diff ( new_grid ) ) median_index = min ( len ( tmp_cumsum ) - 3 , max ( 2 , np . searchsorted ( tmp_cumsum , tmp_cumsum [ - 1 ] * 0.5 ) + 1 ) ) return new_grid [ median_index ]
Find the median of the function represented as an interpolation object.
Find the median of the function represented as an interpolation object .