dhubax

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Oct 26th, 2020
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  1. def find_confidence_interval(x, pdf, confidence_level):
  2.     return pdf[pdf > x].sum() - confidence_level
  3.  
  4. def density_contour(xdata, ydata, binsx, binsy, ax=None, levels_confidence=[0.68, 0.95, 0.99], range=None, **contour_kwargs):
  5.     """ Create a density contour plot.
  6.    Parameters
  7.    ----------
  8.    xdata : numpy.ndarray
  9.    ydata : numpy.ndarray
  10.    binsx : int
  11.        Number of bins along x dimension
  12.    binsy : int
  13.        Number of bins along y dimension
  14.    ax : matplotlib.Axes (optional)
  15.        If supplied, plot the contour to this axis. Otherwise, open a new figure
  16.    contour_kwargs : dict
  17.        kwargs to be passed to pyplot.contour()
  18.    """
  19.     import scipy.optimize as so
  20.     # nbins_x = len(binsx) - 1
  21.     # nbins_y = len(binsy) - 1
  22.     H, xedges, yedges = np.histogram2d(xdata, ydata, bins=[binsx, binsy], range=range, normed=True)
  23.     x_bin_sizes = (xedges[1:] - xedges[:-1])
  24.     y_bin_sizes = (yedges[1:] - yedges[:-1])
  25.     pdf = (H * (x_bin_sizes * y_bin_sizes))
  26.     levels = [so.brentq(find_confidence_interval, 0., 1., args=(pdf, lvl)) for lvl in levels_confidence]
  27.     # one_sigma = so.brentq(find_confidence_interval, 0., 1., args = (pdf, 0.68))
  28.     # two_sigma = so.brentq(find_confidence_interval, 0., 1., args = (pdf, 0.95))
  29.     # three_sigma = so.brentq(find_confidence_interval, 0., 1., args = (pdf, 0.99))
  30.     # levels = [one_sigma, two_sigma, three_sigma]
  31.     X, Y = 0.5 * (xedges[1:] + xedges[:-1]), 0.5 * (yedges[1:] + yedges[:-1])
  32.     Z = pdf.T
  33.     if ax is None:
  34.         contour = plt.tricontour(X, Y, Z, levels=levels[::-1], origin="lower", **contour_kwargs)
  35.     else:
  36.         # contour = ax.contour(X, Y, Z, levels=levels, origin="lower", **contour_kwargs)
  37.         contour = ax.contour(X, Y, Z, levels=levels[::-1], origin="lower", **contour_kwargs)
  38.     return contour
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