arviz.kde#
- arviz.kde(x, circular=False, **kwargs)[source]#
One dimensional density estimation.
It is a wrapper around
kde_linear()
andkde_circular()
.- Parameters
- x: 1D numpy array
Data used to calculate the density estimation.
- circular: bool, optional
Whether
x
is a circular variable or not. Defaults to False.- **kwargs
Arguments passed to
kde_linear()
andkde_circular()
. See their documentation for more info.
- Returns
- grid: Gridded numpy array for the x values.
- pdf: Numpy array for the density estimates.
- bw: optional, the estimated bandwidth.
See also
plot_kde
Compute and plot a kernel density estimate.
Examples
Default density estimation for linear data
>>> import numpy as np >>> import matplotlib.pyplot as plt >>> from arviz import kde >>> >>> rvs = np.random.gamma(shape=1.8, size=1000) >>> grid, pdf = kde(rvs) >>> plt.plot(grid, pdf) >>> plt.show()
Density estimation for linear data with Silverman’s rule bandwidth
>>> grid, pdf = kde(rvs, bw="silverman") >>> plt.plot(grid, pdf) >>> plt.show()
Density estimation for linear data with scaled bandwidth
>>> # bw_fct > 1 means more smoothness. >>> grid, pdf = kde(rvs, bw_fct=2.5) >>> plt.plot(grid, pdf) >>> plt.show()
Default density estimation for linear data with extended limits
>>> grid, pdf = kde(rvs, bound_correction=False, extend=True, extend_fct=0.5) >>> plt.plot(grid, pdf) >>> plt.show()
Default density estimation for linear data with custom limits
>>> # It accepts tuples and lists of length 2. >>> grid, pdf = kde(rvs, bound_correction=False, custom_lims=(0, 10)) >>> plt.plot(grid, pdf) >>> plt.show()
Default density estimation for circular data
>>> rvs = np.random.vonmises(mu=np.pi, kappa=1, size=500) >>> grid, pdf = kde(rvs, circular=True) >>> plt.plot(grid, pdf) >>> plt.show()
Density estimation for circular data with scaled bandwidth
>>> rvs = np.random.vonmises(mu=np.pi, kappa=1, size=500) >>> # bw_fct > 1 means less smoothness. >>> grid, pdf = kde(rvs, circular=True, bw_fct=3) >>> plt.plot(grid, pdf) >>> plt.show()
Density estimation for circular data with custom limits
>>> # This is still experimental, does not always work. >>> rvs = np.random.vonmises(mu=0, kappa=30, size=500) >>> grid, pdf = kde(rvs, circular=True, custom_lims=(-1, 1)) >>> plt.plot(grid, pdf) >>> plt.show()