def __init__(self, data, Neff, correlation, do_correlation=True, fallback_t=None):
size = data.shape[0]
if size != data.shape[1]:
raise ValueError('KernelOptimizer2D only handles square arrays currently')
self.a2 = dct2d(data / np.sum(data))[1:, 1:] ** 2
self.I = np.arange(1, size, dtype=np.float64) ** 2
self.logI = np.log(self.I)
self.do_correlation = do_correlation
if do_correlation:
self.aFFT = np.fft.fft2(data[:, :] / np.sum(data))
self.aFFT *= np.conj(self.aFFT)
self.N = Neff
self.corr = correlation
try:
# t is the bandwidth squared (used for estimating moments), calculated using fixed point
self.t_star = brentq(self._bandwidth_fixed_point_2D, 0, 0.1, xtol=0.001 ** 2)
# noinspection PyTypeChecker
if fallback_t and self.t_star > 0.01 and self.t_star > 2 * fallback_t:
# For 2D distributions with boundaries, fixed point can overestimate significantly
logging.debug('KernelOptimizer2D Using fallback (t* > 2*t_gallback)')
self.t_star = fallback_t
except Exception:
if fallback_t is not None:
# Note the fallback result actually appears to be better in some cases,
# e.g. Gaussian with four cuts
logging.debug('2D kernel density optimizer using fallback plugin width %s' % (np.sqrt(fallback_t)))
self.t_star = fallback_t
else:
raise
def __init__(self, data, Neff, correlation, do_correlation=True):
size = data.shape[0]
if size != data.shape[1]:
raise ValueError('KernelOptimizer2D only handles square arrays currently')
self.a2 = dct2d(data / np.sum(data))[1:, 1:] ** 2
self.I = np.arange(1, size, dtype=np.float64) ** 2
self.logI = np.log(self.I)
self.do_correlation = do_correlation
if do_correlation:
self.aFFT = np.fft.fft2(data[:, :] / np.sum(data))
self.aFFT *= np.conj(self.aFFT)
self.N = Neff
self.corr = correlation
self.t_star = brentq(self._bandwidth_fixed_point_2D, 0, 0.1, xtol=0.001 ** 2)
def __init__(self, data, Neff, correlation, do_correlation=True):
size = data.shape[0]
if size != data.shape[1]:
raise ValueError(
'KernelOptimizer2D only handles square arrays currently')
self.a2 = dct2d(data / np.sum(data))[1:, 1:]**2
self.I = np.arange(1, size, dtype=np.float64)**2
self.logI = np.log(self.I)
self.do_correlation = do_correlation
if do_correlation:
self.aFFT = np.fft.fft2(data[:, :] / np.sum(data))
self.aFFT *= np.conj(self.aFFT)
self.N = Neff
self.corr = correlation
self.t_star = brentq(self._bandwidth_fixed_point_2D,
0,
0.1,
xtol=0.001**2)