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3c3ec60dea
* Update setup.py remove special handling of scipy dependencies for old python versions * Update __init__.py replace numpy type by native type * replace np.bool by bool
208 lines
6.4 KiB
Python
208 lines
6.4 KiB
Python
# #Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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from matplotlib import pyplot as plt
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import numpy as np
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from .util import align_subplot_array, align_subplots
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def ax_default(fignum, ax):
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if ax is None:
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fig = plt.figure(fignum)
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ax = fig.add_subplot(111)
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else:
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fig = ax.figure
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return fig, ax
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def meanplot(x, mu, color='#3300FF', ax=None, fignum=None, linewidth=2,**kw):
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_, axes = ax_default(fignum, ax)
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return axes.plot(x,mu,color=color,linewidth=linewidth,**kw)
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def gpplot(x, mu, lower, upper, edgecol='#3300FF', fillcol='#33CCFF', ax=None, fignum=None, **kwargs):
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_, axes = ax_default(fignum, ax)
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mu = mu.flatten()
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x = x.flatten()
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lower = lower.flatten()
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upper = upper.flatten()
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plots = []
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#here's the mean
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plots.append(meanplot(x, mu, edgecol, axes))
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#here's the box
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kwargs['linewidth']=0.5
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if not 'alpha' in kwargs.keys():
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kwargs['alpha'] = 0.3
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plots.append(axes.fill(np.hstack((x,x[::-1])),np.hstack((upper,lower[::-1])),color=fillcol,**kwargs))
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#this is the edge:
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plots.append(meanplot(x, upper,color=edgecol, linewidth=0.2, ax=axes))
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plots.append(meanplot(x, lower,color=edgecol, linewidth=0.2, ax=axes))
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return plots
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def gradient_fill(x, percentiles, ax=None, fignum=None, **kwargs):
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_, ax = ax_default(fignum, ax)
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plots = []
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#here's the box
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if 'linewidth' not in kwargs:
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kwargs['linewidth'] = 0.5
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if not 'alpha' in kwargs.keys():
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kwargs['alpha'] = 1./(len(percentiles))
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# pop where from kwargs
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where = kwargs.pop('where') if 'where' in kwargs else None
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# pop interpolate, which we actually do not do here!
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if 'interpolate' in kwargs: kwargs.pop('interpolate')
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def pairwise(inlist):
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l = len(inlist)
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for i in range(int(np.ceil(l/2.))):
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yield inlist[:][i], inlist[:][(l-1)-i]
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polycol = []
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for y1, y2 in pairwise(percentiles):
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import matplotlib.mlab as mlab
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# Handle united data, such as dates
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ax._process_unit_info(xdata=x, ydata=y1)
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ax._process_unit_info(ydata=y2)
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# Convert the arrays so we can work with them
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from numpy import ma
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x = ma.masked_invalid(ax.convert_xunits(x))
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y1 = ma.masked_invalid(ax.convert_yunits(y1))
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y2 = ma.masked_invalid(ax.convert_yunits(y2))
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if y1.ndim == 0:
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y1 = np.ones_like(x) * y1
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if y2.ndim == 0:
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y2 = np.ones_like(x) * y2
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if where is None:
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where = np.ones(len(x), bool)
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else:
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where = np.asarray(where, bool)
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if not (x.shape == y1.shape == y2.shape == where.shape):
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raise ValueError("Argument dimensions are incompatible")
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mask = reduce(ma.mask_or, [ma.getmask(a) for a in (x, y1, y2)])
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if mask is not ma.nomask:
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where &= ~mask
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polys = []
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for ind0, ind1 in mlab.contiguous_regions(where):
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xslice = x[ind0:ind1]
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y1slice = y1[ind0:ind1]
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y2slice = y2[ind0:ind1]
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if not len(xslice):
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continue
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N = len(xslice)
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X = np.zeros((2 * N + 2, 2), np.float)
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# the purpose of the next two lines is for when y2 is a
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# scalar like 0 and we want the fill to go all the way
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# down to 0 even if none of the y1 sample points do
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start = xslice[0], y2slice[0]
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end = xslice[-1], y2slice[-1]
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X[0] = start
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X[N + 1] = end
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X[1:N + 1, 0] = xslice
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X[1:N + 1, 1] = y1slice
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X[N + 2:, 0] = xslice[::-1]
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X[N + 2:, 1] = y2slice[::-1]
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polys.append(X)
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polycol.extend(polys)
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from matplotlib.collections import PolyCollection
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plots.append(PolyCollection(polycol, **kwargs))
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ax.add_collection(plots[-1], autolim=True)
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ax.autoscale_view()
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return plots
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def gperrors(x, mu, lower, upper, edgecol=None, ax=None, fignum=None, **kwargs):
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_, axes = ax_default(fignum, ax)
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mu = mu.flatten()
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x = x.flatten()
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lower = lower.flatten()
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upper = upper.flatten()
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plots = []
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if edgecol is None:
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edgecol='#3300FF'
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if not 'alpha' in kwargs.keys():
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kwargs['alpha'] = 1.
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if not 'lw' in kwargs.keys():
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kwargs['lw'] = 1.
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plots.append(axes.errorbar(x,mu,yerr=np.vstack([mu-lower,upper-mu]),color=edgecol,**kwargs))
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plots[-1][0].remove()
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return plots
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def removeRightTicks(ax=None):
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ax = ax or plt.gca()
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for i, line in enumerate(ax.get_yticklines()):
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if i%2 == 1: # odd indices
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line.set_visible(False)
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def removeUpperTicks(ax=None):
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ax = ax or plt.gca()
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for i, line in enumerate(ax.get_xticklines()):
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if i%2 == 1: # odd indices
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line.set_visible(False)
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def fewerXticks(ax=None,divideby=2):
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ax = ax or plt.gca()
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ax.set_xticks(ax.get_xticks()[::divideby])
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def x_frame1D(X,plot_limits=None,resolution=None):
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"""
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Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
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"""
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assert X.shape[1] ==1, "x_frame1D is defined for one-dimensional inputs"
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if plot_limits is None:
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from ...core.parameterization.variational import VariationalPosterior
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if isinstance(X, VariationalPosterior):
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xmin,xmax = X.mean.min(0),X.mean.max(0)
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else:
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xmin,xmax = X.min(0),X.max(0)
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xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
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elif len(plot_limits)==2:
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xmin, xmax = plot_limits
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else:
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raise ValueError("Bad limits for plotting")
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Xnew = np.linspace(xmin,xmax,resolution or 200)[:,None]
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return Xnew, xmin, xmax
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def x_frame2D(X,plot_limits=None,resolution=None):
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"""
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Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
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"""
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assert X.shape[1] ==2, "x_frame2D is defined for two-dimensional inputs"
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if plot_limits is None:
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xmin,xmax = X.min(0),X.max(0)
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xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
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elif len(plot_limits)==2:
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xmin, xmax = plot_limits
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else:
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raise ValueError("Bad limits for plotting")
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resolution = resolution or 50
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xx,yy = np.mgrid[xmin[0]:xmax[0]:1j*resolution,xmin[1]:xmax[1]:1j*resolution]
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Xnew = np.vstack((xx.flatten(),yy.flatten())).T
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return Xnew, xx, yy, xmin, xmax
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