[plotting] library is unfolding and should be working tonight

GPy/plotting/matplot_dep/plot_definitions.py

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+#===============================================================================
+# Copyright (c) 2015, Max Zwiessele
+# All rights reserved.
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+# 
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+# 
+# * Neither the name of GPy.plotting.matplot_dep.plot_definitions nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+# 
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#===============================================================================
+import numpy as np
+from matplotlib import pyplot as plt
+from ..abstract_plotting_library import AbstractPlottingLibrary
+from . import defaults
+
+class MatplotlibPlots(AbstractPlottingLibrary):
+    def __init__(self):
+        super(MatplotlibPlots, self).__init__()
+        self._defaults = defaults.__dict__
+    
+    def get_new_canvas(self, kwargs):
+        if 'ax' in kwargs:
+            ax = kwargs.pop('ax')
+        elif 'num' in kwargs and 'figsize' in kwargs:
+            ax = plt.figure(num=kwargs.pop('num'), figsize=kwargs.pop('figsize')).add_subplot(111) 
+        elif 'num' in kwargs:
+            ax = plt.figure(num=kwargs.pop('num')).add_subplot(111)
+        elif 'figsize' in kwargs:
+            ax = plt.figure(figsize=kwargs.pop('figsize')).add_subplot(111)
+        else:
+            ax = plt.figure().add_subplot(111)
+        # Add ax to kwargs to add all subsequent plots to this axis:
+        #kwargs['ax'] = ax
+        return ax, kwargs
+    
+    def show_canvas(self, ax, plots):
+        try:
+            ax.autoscale_view()
+            ax.figure.canvas.draw()
+            ax.figure.tight_layout()
+        except:
+            pass
+        return ax
+    
+    def scatter(self, ax, X, Y, **kwargs):
+        return ax.scatter(X, Y, **kwargs)
+    
+    def plot(self, ax, X, Y, **kwargs):
+        return ax.plot(X, Y, **kwargs)
+    
+    def xerrorbar(self, ax, X, Y, error, **kwargs):
+        if not('linestyle' in kwargs or 'ls' in kwargs):
+            kwargs['ls'] = 'none' 
+        return ax.errorbar(X, Y, xerr=error, **kwargs)
+    
+    def yerrorbar(self, ax, X, Y, error, **kwargs):
+        if not('linestyle' in kwargs or 'ls' in kwargs):
+            kwargs['ls'] = 'none' 
+        return ax.errorbar(X, Y, yerr=error, **kwargs)
+    
+    def imshow(self, ax, X, **kwargs):
+        return ax.imshow(**kwargs)
+    
+    def contour(self, ax, X, Y, C, levels=20, **kwargs):
+        return ax.contour(X, Y, C, levels=np.linspace(C.min(), C.max(), levels), **kwargs)
+
+    def fill_between(self, ax, X, lower, upper, **kwargs):
+        return ax.fill_between(X.flatten(), lower.flatten(), upper.flatten(), **kwargs)
+
+    def fill_gradient(self, canvas, X, percentiles, **kwargs):
+        ax = canvas
+        plots = []
+        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), np.bool)
+            else:
+                where = np.asarray(where, np.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