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diff --git a/src/sage/all.py b/src/sage/all.py
index 300f519..19bc8ec 100644
--- a/src/sage/all.py
+++ b/src/sage/all.py
@@ -313,8 +313,6 @@ warnings.filters.remove(('ignore', None, DeprecationWarning, None, 0))
# Ignore all deprecations from IPython etc.
warnings.filterwarnings('ignore',
module='.*(IPython|ipykernel|jupyter_client|jupyter_core|nbformat|notebook|ipywidgets|storemagic)')
-# Ignore warnings due to matplotlib-1.5 together with numpy-1.13
-warnings.filterwarnings('ignore', module='matplotlib[.]contour|numpy[.]ma[.]core')
# However, be sure to keep OUR deprecation warnings
warnings.filterwarnings('default',
'[\s\S]*See http://trac.sagemath.org/[0-9]* for details.')
diff --git a/src/sage/plot/arrow.py b/src/sage/plot/arrow.py
index 98d6224..fbce029 100644
--- a/src/sage/plot/arrow.py
+++ b/src/sage/plot/arrow.py
@@ -147,8 +147,8 @@ class CurveArrow(GraphicPrimitive):
bpath = Path(self.vertices, self.codes)
p = FancyArrowPatch(path=bpath,
lw=width, arrowstyle='%s,head_width=%s,head_length=%s' % (style, head_width, head_length),
- fc=color, ec=color)
- p.set_linestyle(get_matplotlib_linestyle(options['linestyle'], return_type='long'))
+ fc=color, ec=color,
+ linestyle=get_matplotlib_linestyle(options['linestyle'], return_type='long'))
p.set_zorder(options['zorder'])
p.set_label(options['legend_label'])
subplot.add_patch(p)
@@ -336,7 +336,7 @@ class Arrow(GraphicPrimitive):
this into account. See :trac:`12836`::
sage: fig = Graphics().matplotlib()
- sage: sp = fig.add_subplot(1,1,1)
+ sage: sp = fig.add_subplot(1,1,1, label='axis1')
sage: a = arrow((0,0), (1,1))
sage: b = arrow((0,0), (1,1), width=20)
sage: p1 = a[0]._render_on_subplot(sp)
@@ -358,7 +358,7 @@ class Arrow(GraphicPrimitive):
sage: a.save(filename=filename)
sage: with open(filename, 'r') as f:
....: contents = f.read().replace('\n', ' ')
- sage: two_stroke_pattern = r'setdash.*stroke.*stroke.*setdash'
+ sage: two_stroke_pattern = r'setdash.*setdash.*stroke.*stroke.*setdash'
sage: import re
sage: two_stroke_re = re.compile(two_stroke_pattern)
sage: two_stroke_re.search(contents) is None
@@ -383,8 +383,8 @@ class Arrow(GraphicPrimitive):
lw=width,
arrowstyle='%s,head_width=%s,head_length=%s' % (style, head_width, head_length),
shrinkA=arrowshorten_end, shrinkB=arrowshorten_end,
- fc=color, ec=color)
- p.set_linestyle(get_matplotlib_linestyle(options['linestyle'], return_type='long'))
+ fc=color, ec=color,
+ linestyle=get_matplotlib_linestyle(options['linestyle'], return_type='long'))
p.set_zorder(options['zorder'])
p.set_label(options['legend_label'])
@@ -437,7 +437,7 @@ class Arrow(GraphicPrimitive):
pe1.draw_path(renderer, gc, tpath, affine, rgbFace)
pe1 = ConditionalStroke(CheckNthSubPath(p, 0), [pe.Stroke()])
- pe2 = ConditionalStroke(CheckNthSubPath(p, 1), [pe.Stroke(linestyle="solid")])
+ pe2 = ConditionalStroke(CheckNthSubPath(p, 1), [pe.Stroke(dashes={'dash_offset': 0, 'dash_list': None})])
p.set_path_effects([pe1, pe2])
subplot.add_patch(p)
diff --git a/src/sage/plot/contour_plot.py b/src/sage/plot/contour_plot.py
index 76077e3..efc1f2b 100644
--- a/src/sage/plot/contour_plot.py
+++ b/src/sage/plot/contour_plot.py
@@ -178,12 +178,10 @@ class ContourPlot(GraphicPrimitive):
if fill:
if contours is None:
CSF = subplot.contourf(self.xy_data_array, cmap=cmap,
- extent=(x0, x1, y0, y1),
- label=options['legend_label'])
+ extent=(x0, x1, y0, y1))
else:
CSF = subplot.contourf(self.xy_data_array, contours, cmap=cmap,
- extent=(x0, x1, y0, y1), extend='both',
- label=options['legend_label'])
+ extent=(x0, x1, y0, y1), extend='both')
linewidths = options.get('linewidths', None)
if isinstance(linewidths, (int, Integer)):
@@ -200,13 +198,11 @@ class ContourPlot(GraphicPrimitive):
if contours is None:
CS = subplot.contour(self.xy_data_array, cmap=cmap,
extent=(x0, x1, y0, y1),
- linewidths=linewidths, linestyles=linestyles,
- label=options['legend_label'])
+ linewidths=linewidths, linestyles=linestyles)
else:
CS = subplot.contour(self.xy_data_array, contours, cmap=cmap,
extent=(x0, x1, y0, y1),
- linewidths=linewidths, linestyles=linestyles,
- label=options['legend_label'])
+ linewidths=linewidths, linestyles=linestyles)
if options.get('labels', False):
label_options = options['label_options']
label_options['fontsize'] = int(label_options['fontsize'])
diff --git a/src/sage/plot/plot.py b/src/sage/plot/plot.py
index aa82eda..03a0f3a 100644
--- a/src/sage/plot/plot.py
+++ b/src/sage/plot/plot.py
@@ -478,7 +478,7 @@ We test that ``imshow`` works as well, verifying that
::
- sage: plt.imshow([[(0,0,0)]])
+ sage: plt.imshow([[(0.0,0.0,0.0)]])
<matplotlib.image.AxesImage object at ...>
sage: plt.savefig(os.path.join(SAGE_TMP, 'foo.png'))
@@ -2908,11 +2908,14 @@ def list_plot(data, plotjoined=False, **kwargs):
If ``plotjoined`` is ``False`` then the axis that is in log scale
must have all points strictly positive. For instance, the following
plot will show no points in the figure since the points in the
- horizontal axis starts from `(0,1)`.
+ horizontal axis starts from `(0,1)`. Further, matplotlib will display
+ a user warning.
::
sage: list_plot(yl, scale='loglog') # both axes are log
+ doctest:warning
+ ...
Graphics object consisting of 1 graphics primitive
Instead this will work. We drop the point `(0,1)`.::
@@ -3288,12 +3291,15 @@ def list_plot_semilogy(data, plotjoined=False, **kwds):
If ``plotjoined`` is ``False`` then the vertical axis must have all
points strictly positive. Otherwise the plot will come up empty.
- For instance the following plot contains a point at `(1,0)`.
+ For instance the following plot contains a point at `(1,0)`. Further,
+ matplotlib will display a user warning.
::
sage: xl = [2**k for k in range(12)]; yl = range(len(xl))
sage: list_plot_semilogy(list(zip(xl,yl))) # plot empty due to (1,0)
+ doctest:warning
+ ...
Graphics object consisting of 1 graphics primitive
We remove `(1,0)` to fix this.::
diff --git a/src/sage/plot/plot3d/list_plot3d.py b/src/sage/plot/plot3d/list_plot3d.py
index c9f22dd..be87ff2 100644
--- a/src/sage/plot/plot3d/list_plot3d.py
+++ b/src/sage/plot/plot3d/list_plot3d.py
@@ -29,21 +29,20 @@ def list_plot3d(v, interpolation_type='default', texture="automatic", point_list
OPTIONAL KEYWORDS:
- - ``interpolation_type`` - 'linear', 'nn' (natural neighbor), 'spline'
+ - ``interpolation_type`` - 'linear', 'clough' (CloughTocher2D), 'spline'
'linear' will perform linear interpolation
- The option 'nn' An interpolation method for multivariate data in a
- Delaunay triangulation. The value for an interpolation point is
- estimated using weighted values of the closest surrounding points in
- the triangulation. These points, the natural neighbors, are the ones
- the interpolation point would connect to if inserted into the
- triangulation.
+ The option 'clough' will interpolate by using a piecewise cubic interpolating
+ Bezier polynomial on each triangle, using a Clough-Tocher scheme.
+ The interpolant is guaranteed to be continuously differentiable.
+ The gradients of the interpolant are chosen so that the curvature of the
+ interpolating surface is approximatively minimized.
The option 'spline' interpolates using a bivariate B-spline.
When v is a matrix the default is to use linear interpolation, when
- v is a list of points the default is nearest neighbor.
+ v is a list of points the default is 'clough'.
- ``degree`` - an integer between 1 and 5, controls the degree of spline
used for spline interpolation. For data that is highly oscillatory
@@ -320,18 +319,18 @@ def list_plot3d_tuples(v, interpolation_type, texture, **kwds):
OPTIONAL KEYWORDS:
- - ``interpolation_type`` - 'linear', 'nn' (natural neighbor), 'spline'
+ - ``interpolation_type`` - 'linear', 'clough' (CloughTocher2D), 'spline'
'linear' will perform linear interpolation
- The option 'nn' will interpolate by using natural neighbors. The
- value for an interpolation point is estimated using weighted values
- of the closest surrounding points in the triangulation.
+ The option 'clough' will interpolate by using a piecewise cubic interpolating
+ Bezier polynomial on each triangle, using a Clough-Tocher scheme.
+ The interpolant is guaranteed to be continuously differentiable.
The option 'spline' interpolates using a bivariate B-spline.
When v is a matrix the default is to use linear interpolation, when
- v is a list of points the default is nearest neighbor.
+ v is a list of points the default is 'clough'.
- ``degree`` - an integer between 1 and 5, controls the degree of spline
used for spline interpolation. For data that is highly oscillatory
@@ -373,7 +372,7 @@ def list_plot3d_tuples(v, interpolation_type, texture, **kwds):
sage: list_plot3d([(1, 2, 3), (0, 1, 3), (2, 1, 4), (1, 0, -2)], texture='yellow', num_points=50)
Graphics3d Object
"""
- from matplotlib import tri, delaunay
+ from matplotlib import tri
import numpy
import scipy
from random import random
@@ -439,19 +438,15 @@ def list_plot3d_tuples(v, interpolation_type, texture, **kwds):
G._set_extra_kwds(kwds)
return G
- if interpolation_type == 'nn' or interpolation_type =='default':
+ if interpolation_type == 'clough' or interpolation_type =='default':
- T=delaunay.Triangulation(x,y)
- f=T.nn_interpolator(z)
- f.default_value=0.0
- j=numpy.complex(0,1)
- vals=f[ymin:ymax:j*num_points,xmin:xmax:j*num_points]
+ points=[[x[i],y[i]] for i in range(len(x))]
+ j = numpy.complex(0, 1)
+ f = interpolate.CloughTocher2DInterpolator(points,z)
from .parametric_surface import ParametricSurface
- def g(x,y):
- i=round( (x-xmin)/(xmax-xmin)*(num_points-1) )
- j=round( (y-ymin)/(ymax-ymin)*(num_points-1) )
- z=vals[int(j),int(i)]
- return (x,y,z)
+ def g(x, y):
+ z = f([x, y])
+ return (x, y, z)
G = ParametricSurface(g, (list(numpy.r_[xmin:xmax:num_points*j]), list(numpy.r_[ymin:ymax:num_points*j])), texture=texture, **kwds)
G._set_extra_kwds(kwds)
return G
diff --git a/src/sage/probability/probability_distribution.pyx b/src/sage/probability/probability_distribution.pyx
index 3ce4642..f66cd89 100644
--- a/src/sage/probability/probability_distribution.pyx
+++ b/src/sage/probability/probability_distribution.pyx
@@ -118,7 +118,7 @@ cdef class ProbabilityDistribution:
sage: P = [0.3, 0.4, 0.3]
sage: X = GeneralDiscreteDistribution(P)
sage: h, b = X.generate_histogram_data(bins = 10)
- sage: h
+ sage: h # rel tol 1e-08
[1.6299999999999999,
0.0,
0.0,
|