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src/a/s/astroML-0.2/book_figures/chapter3/fig_robust_pca.py   astroML(Download)
from matplotlib import pyplot as plt
from matplotlib.patches import Ellipse
from astroML.stats import fit_bivariate_normal
from astroML.stats.random import bivariate_normal
 
    # compute the non-robust statistics
    (mu_nr, sigma1_nr,
     sigma2_nr, alpha_nr) = fit_bivariate_normal(x, y, robust=False)
 
    # compute the robust statistics
    (mu_r, sigma1_r,
     sigma2_r, alpha_r) = fit_bivariate_normal(x, y, robust=True)

src/a/s/astroML-HEAD/book_figures/chapter3/fig_robust_pca.py   astroML(Download)
from matplotlib import pyplot as plt
from matplotlib.patches import Ellipse
from astroML.stats import fit_bivariate_normal
from astroML.stats.random import bivariate_normal
 
    # compute the non-robust statistics
    (mu_nr, sigma1_nr,
     sigma2_nr, alpha_nr) = fit_bivariate_normal(x, y, robust=False)
 
    # compute the robust statistics
    (mu_r, sigma1_r,
     sigma2_r, alpha_r) = fit_bivariate_normal(x, y, robust=True)

src/a/s/astroML-0.2/astroML/stats/tests/test_stats.py   astroML(Download)
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
                           assert_equal, assert_allclose)
from astroML.stats import (mean_sigma, median_sigmaG, sigmaG,
                           fit_bivariate_normal)
 
    x, y = bivariate_normal(mu, sigma1, sigma2, alpha, N).T
    mu_fit, sigma1_fit, sigma2_fit, alpha_fit = fit_bivariate_normal(x, y)
 
    if alpha_fit > np.pi / 2:

src/a/s/astroML-HEAD/astroML/stats/tests/test_stats.py   astroML(Download)
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
                           assert_equal, assert_allclose)
from astroML.stats import (mean_sigma, median_sigmaG, sigmaG,
                           fit_bivariate_normal)
 
    x, y = bivariate_normal(mu, sigma1, sigma2, alpha, N).T
    mu_fit, sigma1_fit, sigma2_fit, alpha_fit = fit_bivariate_normal(x, y)
 
    if alpha_fit > np.pi / 2: