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"""
Data from Hogg et al 2010; useful for testing robust regression methods
"""
import numpy as np
def fetch_hogg2010test(structured=False):
"""Fetch the Hogg et al 2010 test data
"""
data = np.array([[1, 201, 592, 61, 9, -0.84],
[2, 244, 401, 25, 4, 0.31],
[3, 47, 583, 38, 11, 0.64],
[4, 287, 402, 15, 7, -0.27],
[5, 203, 495, 21, 5, -0.33],
[6, 58, 173, 15, 9, 0.67],
[7, 210, 479, 27, 4, -0.02],
[8, 202, 504, 14, 4, -0.05],
[9, 198, 510, 30, 11, -0.84],
[10, 158, 416, 16, 7, -0.69],
[11, 165, 393, 14, 5, 0.30],
[12, 201, 442, 25, 5, -0.46],
[13, 157, 317, 52, 5, -0.03],
[14, 131, 311, 16, 6, 0.50],
[15, 166, 400, 34, 6, 0.73],
[16, 160, 337, 31, 5, -0.52],
[17, 186, 423, 42, 9, 0.90],
[18, 125, 334, 26, 8, 0.40],
[19, 218, 533, 16, 6, -0.78],
[20, 146, 344, 22, 5, -0.56]])
dtype = [("ID", np.int32),
("x", np.float64),
("y", np.float64),
("sigma_x", np.float64),
("sigma_y", np.float64),
("rho_xy", np.float64)]
recarray = np.empty(data.shape[0], dtype=dtype)
recarray['ID'] = data[:, 0]
recarray['x'] = data[:, 1]
recarray['y'] = data[:, 2]
recarray['sigma_x'] = data[:, 4]
recarray['sigma_y'] = data[:, 3]
recarray['rho_xy'] = data[:, 5]
return recarray