def kde_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d marginalised distribution.
This functions as a wrapper around matplotlib.axes.Axes.plot, with a kernel
density estimation computation provided by scipy.stats.gaussian_kde in
between. All remaining keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on.
data: numpy.array
Samples to generate kernel density estimator.
weights: numpy.array, optional
Sample weights.
ncompress: int, optional
Degree of compression. Default 1000
xmin, xmax: float
lower/upper prior bound.
optional, default None
Returns
-------
lines: matplotlib.lines.Line2D
A list of line objects representing the plotted data (same as
matplotlib matplotlib.axes.Axes.plot command)
"""
if len(data) == 0:
return numpy.zeros(0), numpy.zeros(0)
if data.max() - data.min() <= 0:
return
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
weights = kwargs.pop('weights', None)
ncompress = kwargs.pop('ncompress', 1000)
x, w = sample_compression_1d(data, weights, ncompress)
kde = gaussian_kde(x, weights=w)
p = kde(x)
p /= p.max()
i = ((x < quantile(x, 0.999, w)) & (x > quantile(x, 0.001, w))) | (p > 0.1)
if xmin is not None:
i = i & (x > xmin)
if xmax is not None:
i = i & (x < xmax)
sigma = numpy.sqrt(kde.covariance[0, 0])
pp = cut_and_normalise_gaussian(x[i], p[i], sigma, xmin, xmax)
pp /= pp.max()
ans = ax.plot(x[i], pp, *args, **kwargs)
ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
return ans
def test_sample_compression_1d():
np.random.seed(0)
N = 10000
x_ = np.random.rand(N)
w_ = np.random.rand(N)
n = 1000
x, w = sample_compression_1d(x_, w_, n)
assert len(x) == n
assert len(w) == n
assert np.isclose(w.sum(), w_.sum())
def kde_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d marginalised distribution.
This functions as a wrapper around matplotlib.axes.Axes.plot, with a kernel
density estimation computation provided by scipy.stats.gaussian_kde in
between. All remaining keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on.
data: np.array
Samples to generate kernel density estimator.
weights: np.array, optional
Sample weights.
ncompress: int, optional
Degree of compression. Default 1000
xmin, xmax: float
lower/upper prior bound.
optional, default None
levels: list
values at which to draw iso-probability lines.
optional, default [0.95, 0.68]
facecolor: bool or string
If set to True then the 1d plot will be shaded with the value of the
``color`` kwarg. Set to a string such as 'blue', 'k', 'r', 'C1' ect.
to define the color of the shading directly.
optional, default False
Returns
-------
lines: matplotlib.lines.Line2D
A list of line objects representing the plotted data (same as
matplotlib matplotlib.axes.Axes.plot command)
"""
if len(data) == 0:
return np.zeros(0), np.zeros(0)
if data.max()-data.min() <= 0:
return
kwargs = normalize_kwargs(
kwargs,
dict(linewidth=['lw'], linestyle=['ls'], color=['c'],
facecolor=['fc'], edgecolor=['ec']))
levels = kwargs.pop('levels', [0.95, 0.68])
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
weights = kwargs.pop('weights', None)
ncompress = kwargs.pop('ncompress', 1000)
density = kwargs.pop('density', False)
cmap = kwargs.pop('cmap', None)
color = kwargs.pop('color', (next(ax._get_lines.prop_cycler)['color']
if cmap is None else cmap(0.68)))
facecolor = kwargs.pop('facecolor', False)
if 'edgecolor' in kwargs:
edgecolor = kwargs.pop('edgecolor')
if edgecolor:
color = edgecolor
else:
edgecolor = color
q = kwargs.pop('q', '5sigma')
q = quantile_plot_interval(q=q)
if weights is not None:
data = data[weights != 0]
weights = weights[weights != 0]
x, w = sample_compression_1d(data, weights, ncompress)
kde = gaussian_kde(x, weights=w)
p = kde(x)
p /= p.max()
i = ((x > quantile(x, q[0], w)) & (x < quantile(x, q[1], w)))
if xmin is not None:
i = i & (x > xmin)
if xmax is not None:
i = i & (x < xmax)
sigma = np.sqrt(kde.covariance[0, 0])
pp = cut_and_normalise_gaussian(x[i], p[i], sigma, xmin, xmax)
pp /= pp.max()
area = np.trapz(x=x[i], y=pp) if density else 1
ans = ax.plot(x[i], pp/area, color=color, *args, **kwargs)
ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
if facecolor and facecolor not in [None, 'None', 'none']:
if facecolor is True:
facecolor = color
c = iso_probability_contours_from_samples(pp, contours=levels,
weights=w)
cmap = basic_cmap(facecolor)
fill = []
for j in range(len(c)-1):
fill.append(ax.fill_between(x[i], pp, where=pp >= c[j],
color=cmap(c[j]), edgecolor=edgecolor))
return ans, fill
return ans
def kde_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d marginalised distribution.
This functions as a wrapper around matplotlib.axes.Axes.plot, with a kernel
density estimation computation provided by scipy.stats.gaussian_kde in
between. All remaining keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on.
data: np.array
Samples to generate kernel density estimator.
weights: np.array, optional
Sample weights.
ncompress: int, optional
Degree of compression. Default 1000
xmin, xmax: float
lower/upper prior bound.
optional, default None
Returns
-------
lines: matplotlib.lines.Line2D
A list of line objects representing the plotted data (same as
matplotlib matplotlib.axes.Axes.plot command)
"""
if len(data) == 0:
return np.zeros(0), np.zeros(0)
if data.max() - data.min() <= 0:
return
kwargs = normalize_kwargs(kwargs,
dict(linewidth=['lw'],
linestyle=['ls'],
color=['c']),
drop=['fc', 'ec'])
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
weights = kwargs.pop('weights', None)
ncompress = kwargs.pop('ncompress', 1000)
cmap = kwargs.pop('cmap', None)
color = kwargs.pop('color', (next(ax._get_lines.prop_cycler)['color']
if cmap is None else cmap(0.68)))
q = kwargs.pop('q', '5sigma')
q = quantile_plot_interval(q=q)
if weights is not None:
data = data[weights != 0]
weights = weights[weights != 0]
x, w = sample_compression_1d(data, weights, ncompress)
kde = gaussian_kde(x, weights=w)
p = kde(x)
p /= p.max()
i = ((x > quantile(x, q[0], w)) & (x < quantile(x, q[1], w)))
if xmin is not None:
i = i & (x > xmin)
if xmax is not None:
i = i & (x < xmax)
sigma = np.sqrt(kde.covariance[0, 0])
pp = cut_and_normalise_gaussian(x[i], p[i], sigma, xmin, xmax)
pp /= pp.max()
ans = ax.plot(x[i], pp, color=color, *args, **kwargs)
ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
return ans