# #Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from matplotlib import pyplot as plt
import numpy as np
from .util import align_subplot_array, align_subplots
[docs]def ax_default(fignum, ax):
if ax is None:
fig = plt.figure(fignum)
ax = fig.add_subplot(111)
else:
fig = ax.figure
return fig, ax
[docs]def meanplot(x, mu, color='#3300FF', ax=None, fignum=None, linewidth=2,**kw):
_, axes = ax_default(fignum, ax)
return axes.plot(x,mu,color=color,linewidth=linewidth,**kw)
[docs]def gpplot(x, mu, lower, upper, edgecol='#3300FF', fillcol='#33CCFF', ax=None, fignum=None, **kwargs):
_, axes = ax_default(fignum, ax)
mu = mu.flatten()
x = x.flatten()
lower = lower.flatten()
upper = upper.flatten()
plots = []
#here's the mean
plots.append(meanplot(x, mu, edgecol, axes))
#here's the box
kwargs['linewidth']=0.5
if not 'alpha' in kwargs.keys():
kwargs['alpha'] = 0.3
plots.append(axes.fill(np.hstack((x,x[::-1])),np.hstack((upper,lower[::-1])),color=fillcol,**kwargs))
#this is the edge:
plots.append(meanplot(x, upper,color=edgecol, linewidth=0.2, ax=axes))
plots.append(meanplot(x, lower,color=edgecol, linewidth=0.2, ax=axes))
return plots
[docs]def gradient_fill(x, percentiles, ax=None, fignum=None, **kwargs):
_, ax = ax_default(fignum, ax)
plots = []
#here's the box
if 'linewidth' not in kwargs:
kwargs['linewidth'] = 0.5
if not 'alpha' in kwargs.keys():
kwargs['alpha'] = 1./(len(percentiles))
# pop where from kwargs
where = kwargs.pop('where') if 'where' in kwargs else None
# pop interpolate, which we actually do not do here!
if 'interpolate' in kwargs: kwargs.pop('interpolate')
def pairwise(inlist):
l = len(inlist)
for i in range(int(np.ceil(l/2.))):
yield inlist[:][i], inlist[:][(l-1)-i]
polycol = []
for y1, y2 in pairwise(percentiles):
import matplotlib.mlab as mlab
# Handle united data, such as dates
ax._process_unit_info(xdata=x, ydata=y1)
ax._process_unit_info(ydata=y2)
# Convert the arrays so we can work with them
from numpy import ma
x = ma.masked_invalid(ax.convert_xunits(x))
y1 = ma.masked_invalid(ax.convert_yunits(y1))
y2 = ma.masked_invalid(ax.convert_yunits(y2))
if y1.ndim == 0:
y1 = np.ones_like(x) * y1
if y2.ndim == 0:
y2 = np.ones_like(x) * y2
if where is None:
where = np.ones(len(x), bool)
else:
where = np.asarray(where, bool)
if not (x.shape == y1.shape == y2.shape == where.shape):
raise ValueError("Argument dimensions are incompatible")
mask = reduce(ma.mask_or, [ma.getmask(a) for a in (x, y1, y2)])
if mask is not ma.nomask:
where &= ~mask
polys = []
for ind0, ind1 in mlab.contiguous_regions(where):
xslice = x[ind0:ind1]
y1slice = y1[ind0:ind1]
y2slice = y2[ind0:ind1]
if not len(xslice):
continue
N = len(xslice)
X = np.zeros((2 * N + 2, 2), np.float)
# the purpose of the next two lines is for when y2 is a
# scalar like 0 and we want the fill to go all the way
# down to 0 even if none of the y1 sample points do
start = xslice[0], y2slice[0]
end = xslice[-1], y2slice[-1]
X[0] = start
X[N + 1] = end
X[1:N + 1, 0] = xslice
X[1:N + 1, 1] = y1slice
X[N + 2:, 0] = xslice[::-1]
X[N + 2:, 1] = y2slice[::-1]
polys.append(X)
polycol.extend(polys)
from matplotlib.collections import PolyCollection
plots.append(PolyCollection(polycol, **kwargs))
ax.add_collection(plots[-1], autolim=True)
ax.autoscale_view()
return plots
[docs]def gperrors(x, mu, lower, upper, edgecol=None, ax=None, fignum=None, **kwargs):
_, axes = ax_default(fignum, ax)
mu = mu.flatten()
x = x.flatten()
lower = lower.flatten()
upper = upper.flatten()
plots = []
if edgecol is None:
edgecol='#3300FF'
if not 'alpha' in kwargs.keys():
kwargs['alpha'] = 1.
if not 'lw' in kwargs.keys():
kwargs['lw'] = 1.
plots.append(axes.errorbar(x,mu,yerr=np.vstack([mu-lower,upper-mu]),color=edgecol,**kwargs))
plots[-1][0].remove()
return plots
[docs]def removeRightTicks(ax=None):
ax = ax or plt.gca()
for i, line in enumerate(ax.get_yticklines()):
if i%2 == 1: # odd indices
line.set_visible(False)
[docs]def removeUpperTicks(ax=None):
ax = ax or plt.gca()
for i, line in enumerate(ax.get_xticklines()):
if i%2 == 1: # odd indices
line.set_visible(False)
[docs]def fewerXticks(ax=None,divideby=2):
ax = ax or plt.gca()
ax.set_xticks(ax.get_xticks()[::divideby])
[docs]def x_frame1D(X,plot_limits=None,resolution=None):
"""
Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
"""
assert X.shape[1] ==1, "x_frame1D is defined for one-dimensional inputs"
if plot_limits is None:
from ...core.parameterization.variational import VariationalPosterior
if isinstance(X, VariationalPosterior):
xmin,xmax = X.mean.min(0),X.mean.max(0)
else:
xmin,xmax = X.min(0),X.max(0)
xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
elif len(plot_limits)==2:
xmin, xmax = plot_limits
else:
raise ValueError("Bad limits for plotting")
Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
return Xnew, xmin, xmax
[docs]def x_frame2D(X,plot_limits=None,resolution=None):
"""
Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
"""
assert X.shape[1] ==2, "x_frame2D is defined for two-dimensional inputs"
if plot_limits is None:
xmin,xmax = X.min(0),X.max(0)
xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
elif len(plot_limits)==2:
xmin, xmax = plot_limits
else:
raise ValueError("Bad limits for plotting")
resolution = resolution or 50
xx,yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
Xnew = np.vstack((xx.flatten(),yy.flatten())).T
return Xnew, xx, yy, xmin, xmax