Contour plots#

class sage.plot.contour_plot.ContourPlot(xy_data_array, xrange, yrange, options)#

Bases: GraphicPrimitive

Primitive class for the contour plot graphics type.

See contour_plot? for help actually doing contour plots.

INPUT:

  • xy_data_array - list of lists giving evaluated values of the function on the grid

  • xrange - tuple of 2 floats indicating range for horizontal direction

  • yrange - tuple of 2 floats indicating range for vertical direction

  • options - dict of valid plot options to pass to constructor

EXAMPLES:

Note this should normally be used indirectly via contour_plot:

sage: from sage.plot.contour_plot import ContourPlot
sage: C = ContourPlot([[1,3],[2,4]], (1,2), (2,3), options={})
sage: C
ContourPlot defined by a 2 x 2 data grid
sage: C.xrange
(1, 2)
get_minmax_data()#

Return a dictionary with the bounding box data.

EXAMPLES:

sage: x,y = var('x,y')
sage: f(x,y) = x^2 + y^2
sage: d = contour_plot(f, (3,6), (3,6))[0].get_minmax_data()
sage: d['xmin']
3.0
sage: d['ymin']
3.0
sage.plot.contour_plot.contour_plot(f, xrange, yrange, plot_points=100, fill=True, contours=None, linewidths=None, linestyles=None, labels=False, frame=True, axes=False, colorbar=False, legend_label=None, aspect_ratio=1, region=None, label_fontsize=9, label_colors='blue', label_inline=None, label_inline_spacing=3, label_fmt='%1.2f', colorbar_orientation='vertical', colorbar_format=None, colorbar_spacing='uniform', **options)#

contour_plot takes a function of two variables, \(f(x,y)\) and plots contour lines of the function over the specified xrange and yrange as demonstrated below.

contour_plot(f, (xmin,xmax), (ymin,ymax), ...)

INPUT:

  • f – a function of two variables

  • (xmin,xmax) – 2-tuple, the range of x values OR 3-tuple (x,xmin,xmax)

  • (ymin,ymax) – 2-tuple, the range of y values OR 3-tuple (y,ymin,ymax)

The following inputs must all be passed in as named parameters:

  • plot_points – integer (default: 100); number of points to plot in each direction of the grid. For old computers, 25 is fine, but should not be used to verify specific intersection points.

  • fill – bool (default: True), whether to color in the area between contour lines

  • cmap – a colormap (default: 'gray'), the name of a predefined colormap, a list of colors or an instance of a matplotlib Colormap. Type: import matplotlib.cm; matplotlib.cm.datad.keys() for available colormap names.

  • contours – integer or list of numbers (default: None): If a list of numbers is given, then this specifies the contour levels to use. If an integer is given, then this many contour lines are used, but the exact levels are determined automatically. If None is passed (or the option is not given), then the number of contour lines is determined automatically, and is usually about 5.

  • linewidths – integer or list of integer (default: None), if a single integer all levels will be of the width given, otherwise the levels will be plotted with the width in the order given. If the list is shorter than the number of contours, then the widths will be repeated cyclically.

  • linestyles – string or list of strings (default: None), the style of the lines to be plotted, one of: "solid", "dashed", "dashdot", "dotted", respectively "-", "--", "-.", ":". If the list is shorter than the number of contours, then the styles will be repeated cyclically.

  • labels – boolean (default: False) Show level labels or not.

    The following options are to adjust the style and placement of labels, they have no effect if no labels are shown.

    • label_fontsize – integer (default: 9), the font size of the labels.

    • label_colors – string or sequence of colors (default: None) If a string, gives the name of a single color with which to draw all labels. If a sequence, gives the colors of the labels. A color is a string giving the name of one or a 3-tuple of floats.

    • label_inline – boolean (default: False if fill is True, otherwise True), controls whether the underlying contour is removed or not.

    • label_inline_spacing – integer (default: 3), When inline, this is the amount of contour that is removed from each side, in pixels.

    • label_fmt – a format string (default: “%1.2f”), this is used to get the label text from the level. This can also be a dictionary with the contour levels as keys and corresponding text string labels as values. It can also be any callable which returns a string when called with a numeric contour level.

  • colorbar – boolean (default: False) Show a colorbar or not.

    The following options are to adjust the style and placement of colorbars. They have no effect if a colorbar is not shown.

    • colorbar_orientation – string (default: ‘vertical’), controls placement of the colorbar, can be either ‘vertical’ or ‘horizontal’

    • colorbar_format – a format string, this is used to format the colorbar labels.

    • colorbar_spacing – string (default: ‘proportional’). If ‘proportional’, make the contour divisions proportional to values. If ‘uniform’, space the colorbar divisions uniformly, without regard for numeric values.

  • legend_label – the label for this item in the legend

  • region - (default: None) If region is given, it must be a function

    of two variables. Only segments of the surface where region(x,y) returns a number >0 will be included in the plot.

Warning

Due to an implementation detail in matplotlib, single-contour plots whose data all lie on one side of the sole contour may not be plotted correctly. We attempt to detect this situation and to produce something better than an empty plot when it happens; a UserWarning is emitted in that case.

EXAMPLES:

Here we plot a simple function of two variables. Note that since the input function is an expression, we need to explicitly declare the variables in 3-tuples for the range:

sage: x,y = var('x,y')
sage: contour_plot(cos(x^2 + y^2), (x,-4,4), (y,-4,4))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-1.svg

Here we change the ranges and add some options:

sage: x,y = var('x,y')
sage: contour_plot((x^2) * cos(x*y), (x,-10,5), (y,-5,5), fill=False, plot_points=150)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-2.svg

An even more complicated plot:

sage: x,y = var('x,y')
sage: contour_plot(sin(x^2+y^2) * cos(x) * sin(y), (x,-4,4), (y,-4,4), plot_points=150)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-3.svg

Some elliptic curves, but with symbolic endpoints. In the first example, the plot is rotated 90 degrees because we switch the variables \(x\), \(y\):

sage: x,y = var('x,y')
sage: contour_plot(y^2 + 1 - x^3 - x, (y,-pi,pi), (x,-pi,pi))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-4.svg
sage: contour_plot(y^2 + 1 - x^3 - x, (x,-pi,pi), (y,-pi,pi))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-5.svg

We can play with the contour levels:

sage: x,y = var('x,y')
sage: f(x,y) = x^2 + y^2
sage: contour_plot(f, (-2,2), (-2,2))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-6.svg
sage: contour_plot(f, (-2,2), (-2,2), contours=2, cmap=[(1,0,0), (0,1,0), (0,0,1)])
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-7.svg
sage: contour_plot(f, (-2,2), (-2,2),
....:              contours=(0.1,1.0,1.2,1.4), cmap='hsv')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-8.svg
sage: contour_plot(f, (-2,2), (-2,2), contours=(1.0,), fill=False)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-9.svg
sage: contour_plot(x - y^2, (x,-5,5), (y,-3,3), contours=[-4,0,1])
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-10.svg

We can change the style of the lines:

sage: contour_plot(f, (-2,2), (-2,2), fill=False, linewidths=10)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-11.svg
sage: contour_plot(f, (-2,2), (-2,2), fill=False, linestyles='dashdot')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-12.svg
sage: P = contour_plot(x^2 - y^2, (x,-3,3), (y,-3,3),
....:                  contours=[0,1,2,3,4], linewidths=[1,5],
....:                  linestyles=['solid','dashed'], fill=False)
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-13.svg
sage: P = contour_plot(x^2 - y^2, (x,-3,3), (y,-3,3),
....:                  contours=[0,1,2,3,4], linewidths=[1,5],
....:                  linestyles=['solid','dashed'])
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-14.svg
sage: P = contour_plot(x^2 - y^2, (x,-3,3), (y,-3,3),
....:                  contours=[0,1,2,3,4], linewidths=[1,5],
....:                  linestyles=['-',':'])
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-15.svg

We can add labels and play with them:

sage: contour_plot(y^2 + 1 - x^3 - x, (x,-pi,pi), (y,-pi,pi),
....:              fill=False, cmap='hsv', labels=True)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-16.svg
sage: P=contour_plot(y^2 + 1 - x^3 - x, (x,-pi,pi), (y,-pi,pi),
....:                fill=False, cmap='hsv',
....:                labels=True, label_fmt="%1.0f",
....:                label_colors='black')
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-17.svg
sage: P = contour_plot(y^2 + 1 - x^3 - x, (x,-pi,pi), (y,-pi,pi),
....:                  fill=False, cmap='hsv', labels=True,
....:                  contours=[-4,0,4],
....:                  label_fmt={-4:"low", 0:"medium", 4: "hi"},
....:                  label_colors='black')
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-18.svg
sage: P = contour_plot(y^2 + 1 - x^3 - x, (x,-pi,pi), (y,-pi,pi),
....:                fill=False, cmap='hsv', labels=True,
....:                contours=[-4,0,4], label_fmt=lambda x: "$z=%s$"%x,
....:                label_colors='black', label_inline=True,
....:                label_fontsize=12)
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-19.svg
sage: P = contour_plot(y^2 + 1 - x^3 - x, (x,-pi,pi), (y,-pi,pi),
....:                  fill=False, cmap='hsv', labels=True,
....:                  label_fontsize=18)
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-20.svg
sage: P = contour_plot(y^2 + 1 - x^3 - x, (x,-pi,pi), (y,-pi,pi),
....:                fill=False, cmap='hsv', labels=True,
....:                label_inline_spacing=1)
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-21.svg
sage: P = contour_plot(y^2 + 1 - x^3 - x, (x,-pi,pi), (y,-pi,pi),
....:                  fill=False, cmap='hsv', labels=True,
....:                  label_inline=False)
sage: P
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-22.svg

We can change the color of the labels if so desired:

sage: contour_plot(f, (-2,2), (-2,2), labels=True, label_colors='red')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-23.svg

We can add a colorbar as well:

sage: f(x, y) = x^2 + y^2
sage: contour_plot(f, (x,-3,3), (y,-3,3), colorbar=True)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-24.svg
sage: contour_plot(f, (x,-3,3), (y,-3,3), colorbar=True, colorbar_orientation='horizontal')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-25.svg
sage: contour_plot(f, (x,-3,3), (y,-3,3), contours=[-2,-1,4], colorbar=True)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-26.svg
sage: contour_plot(f, (x,-3,3), (y,-3,3), contours=[-2,-1,4],
....:              colorbar=True, colorbar_spacing='uniform')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-27.svg
sage: contour_plot(f, (x,-3,3), (y,-3,3), contours=[0,2,3,6],
....:              colorbar=True, colorbar_format='%.3f')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-28.svg
sage: contour_plot(f, (x,-3,3), (y,-3,3), labels=True,
....:              label_colors='red', contours=[0,2,3,6],
....:              colorbar=True)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-29.svg
sage: contour_plot(f, (x,-3,3), (y,-3,3), cmap='winter',
....:              contours=20, fill=False, colorbar=True)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-30.svg

This should plot concentric circles centered at the origin:

sage: x,y = var('x,y')
sage: contour_plot(x^2 + y^2-2,(x,-1,1), (y,-1,1))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-31.svg

Extra options will get passed on to show(), as long as they are valid:

sage: f(x,y) = cos(x) + sin(y)
sage: contour_plot(f, (0,pi), (0,pi), axes=True)
Graphics object consisting of 1 graphics primitive

sage: contour_plot(f, (0,pi), (0,pi)).show(axes=True) # These are equivalent
../../_images/contour_plot-32.svg

One can also plot over a reduced region:

sage: contour_plot(x**2 - y**2, (x,-2,2), (y,-2,2), region=x - y, plot_points=300)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-33.svg

Note that with fill=False and grayscale contours, there is the possibility of confusion between the contours and the axes, so use fill=False together with axes=True with caution:

sage: contour_plot(f, (-pi,pi), (-pi,pi), fill=False, axes=True)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-34.svg

If you are plotting a sole contour and if all of your data lie on one side of it, then (as part of trac ticket #21042) a heuristic may be used to improve the result; in that case, a warning is emitted:

sage: contour_plot(lambda x,y: abs(x^2-y^2), (-1,1), (-1,1),
....:              contours=[0], fill=False, cmap=['blue'])
...
UserWarning: pathological contour plot of a function whose values
all lie on one side of the sole contour; we are adding more plot
points and perturbing your function values.
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-35.svg

Constant functions (with a single contour) can be plotted as well; this was not possible before trac ticket #21042:

sage: contour_plot(lambda x,y: 0, (-1,1), (-1,1),
....:              contours=[0], fill=False, cmap=['blue'])
...
UserWarning: No contour levels were found within the data range.
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-36.svg
sage.plot.contour_plot.equify(f)#

Return the equation rewritten as a symbolic function to give negative values when True, positive when False.

EXAMPLES:

sage: from sage.plot.contour_plot import equify
sage: var('x, y')
(x, y)
sage: equify(x^2 < 2)
x^2 - 2
sage: equify(x^2 > 2)
-x^2 + 2
sage: equify(x*y > 1)
-x*y + 1
sage: equify(y > 0)
-y
sage: f = equify(lambda x, y: x > y)
sage: f(1, 2)
1
sage: f(2, 1)
-1
sage.plot.contour_plot.implicit_plot(f, xrange, yrange, plot_points=150, contours=(0,), fill=False, cmap=['blue'], **options)#

implicit_plot takes a function of two variables, \(f(x, y)\) and plots the curve \(f(x,y) = 0\) over the specified xrange and yrange as demonstrated below.

implicit_plot(f, (xmin,xmax), (ymin,ymax), ...)

implicit_plot(f, (x,xmin,xmax), (y,ymin,ymax), ...)

INPUT:

  • f – a function of two variables or equation in two variables

  • (xmin,xmax) – 2-tuple, the range of x values or (x,xmin,xmax)

  • (ymin,ymax) – 2-tuple, the range of y values or (y,ymin,ymax)

The following inputs must all be passed in as named parameters:

  • plot_points – integer (default: 150); number of points to plot in each direction of the grid

  • fill – boolean (default: False); if True, fill the region \(f(x, y) < 0\).

  • fillcolor – string (default: 'blue'), the color of the region where \(f(x,y) < 0\) if fill = True. Colors are defined in sage.plot.colors; try colors? to see them all.

  • linewidth – integer (default: None), if a single integer all levels will be of the width given, otherwise the levels will be plotted with the widths in the order given.

  • linestyle – string (default: None), the style of the line to be plotted, one of: "solid", "dashed", "dashdot" or "dotted", respectively "-", "--", "-.", or ":".

  • color – string (default: 'blue'), the color of the plot. Colors are defined in sage.plot.colors; try colors? to see them all. If fill = True, then this sets only the color of the border of the plot. See fillcolor for setting the color of the fill region.

  • legend_label – the label for this item in the legend

  • base – (default: 10) the base of the logarithm if a logarithmic scale is set. This must be greater than 1. The base can be also given as a list or tuple (basex, basey). basex sets the base of the logarithm along the horizontal axis and basey sets the base along the vertical axis.

  • scale – (default: "linear") string. The scale of the axes. Possible values are "linear", "loglog", "semilogx", "semilogy".

    The scale can be also be given as single argument that is a list or tuple (scale, base) or (scale, basex, basey).

    The "loglog" scale sets both the horizontal and vertical axes to logarithmic scale. The "semilogx" scale sets the horizontal axis to logarithmic scale. The "semilogy" scale sets the vertical axis to logarithmic scale. The "linear" scale is the default value when Graphics is initialized.

Warning

Due to an implementation detail in matplotlib, implicit plots whose data are all nonpositive or nonnegative may not be plotted correctly. We attempt to detect this situation and to produce something better than an empty plot when it happens; a UserWarning is emitted in that case.

EXAMPLES:

A simple circle with a radius of 2. Note that since the input function is an expression, we need to explicitly declare the variables in 3-tuples for the range:

sage: var("x y")
(x, y)
sage: implicit_plot(x^2 + y^2 - 2, (x,-3,3), (y,-3,3))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-37.svg

We can do the same thing, but using a callable function so we do not need to explicitly define the variables in the ranges. We also fill the inside:

sage: f(x,y) = x^2 + y^2 - 2
sage: implicit_plot(f, (-3,3), (-3,3), fill=True, plot_points=500) # long time
Graphics object consisting of 2 graphics primitives
../../_images/contour_plot-38.svg

The same circle but with a different line width:

sage: implicit_plot(f, (-3,3), (-3,3), linewidth=6)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-39.svg

Again the same circle but this time with a dashdot border:

sage: implicit_plot(f, (-3,3), (-3,3), linestyle='dashdot')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-40.svg

The same circle with different line and fill colors:

sage: implicit_plot(f, (-3,3), (-3,3), color='red', fill=True, fillcolor='green',
....:                                  plot_points=500) # long time
Graphics object consisting of 2 graphics primitives
../../_images/contour_plot-41.svg

You can also plot an equation:

sage: var("x y")
(x, y)
sage: implicit_plot(x^2 + y^2 == 2, (x,-3,3), (y,-3,3))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-42.svg

You can even change the color of the plot:

sage: implicit_plot(x^2 + y^2 == 2, (x,-3,3), (y,-3,3), color="red")
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-43.svg

The color of the fill region can be changed:

sage: implicit_plot(x**2 + y**2 == 2, (x,-3,3), (y,-3,3), fill=True, fillcolor='red')
Graphics object consisting of 2 graphics primitives
../../_images/contour_plot-44.svg

Here is a beautiful (and long) example which also tests that all colors work with this:

sage: G = Graphics()
sage: counter = 0
sage: for col in colors.keys():  # long time
....:     G += implicit_plot(x^2 + y^2 == 1 + counter*.1, (x,-4,4),(y,-4,4), color=col)
....:     counter += 1
sage: G  # long time
Graphics object consisting of 148 graphics primitives
../../_images/contour_plot-45.svg

We can define a level-\(n\) approximation of the boundary of the Mandelbrot set:

sage: def mandel(n):
....:     c = polygen(CDF, 'c')
....:     z = 0
....:     for i in range(n):
....:         z = z*z + c
....:     def f(x,y):
....:         val = z(CDF(x, y))
....:         return val.norm() - 4
....:     return f

The first-level approximation is just a circle:

sage: implicit_plot(mandel(1), (-3,3), (-3,3))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-46.svg

A third-level approximation starts to get interesting:

sage: implicit_plot(mandel(3), (-2,1), (-1.5,1.5))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-47.svg

The seventh-level approximation is a degree 64 polynomial, and implicit_plot does a pretty good job on this part of the curve. (plot_points=200 looks even better, but it takes over a second.)

sage: implicit_plot(mandel(7), (-0.3, 0.05), (-1.15, -0.9), plot_points=50)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-48.svg

When making a filled implicit plot using a python function rather than a symbolic expression the user should increase the number of plot points to avoid artifacts:

sage: implicit_plot(lambda x, y: x^2 + y^2 - 2, (x,-3,3), (y,-3,3),
....:               fill=True, plot_points=500) # long time
Graphics object consisting of 2 graphics primitives
../../_images/contour_plot-49.svg

An example of an implicit plot on ‘loglog’ scale:

sage: implicit_plot(x^2 + y^2 == 200, (x,1,200), (y,1,200), scale='loglog')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-50.svg
region_plot(f, xrange, yrange, plot_points, incol, outcol, bordercol, borderstyle, borderwidth, alpha, plot_points=100, incol='blue', outcol=None, bordercol=None, borderstyle=None, borderwidth=None, frame=False, axes=True, legend_label=None, aspect_ratio=1, alpha=1, **options)

region_plot takes a boolean function of two variables, \(f(x, y)\) and plots the region where f is True over the specified xrange and yrange as demonstrated below.

region_plot(f, (xmin,xmax), (ymin,ymax), ...)

INPUT:

  • f – a boolean function or a list of boolean functions of two variables

  • (xmin,xmax) – 2-tuple, the range of x values OR 3-tuple (x,xmin,xmax)

  • (ymin,ymax) – 2-tuple, the range of y values OR 3-tuple (y,ymin,ymax)

  • plot_points – integer (default: 100); number of points to plot in each direction of the grid

  • incol – a color (default: 'blue'), the color inside the region

  • outcol – a color (default: None), the color of the outside of the region

If any of these options are specified, the border will be shown as indicated, otherwise it is only implicit (with color incol) as the border of the inside of the region.

  • bordercol – a color (default: None), the color of the border

    ('black' if borderwidth or borderstyle is specified but not bordercol)

  • borderstyle – string (default: 'solid'), one of 'solid', 'dashed', 'dotted', 'dashdot', respectively '-', '--', ':', '-.'.

  • borderwidth – integer (default: None), the width of the border in pixels

  • alpha – (default: 1) how transparent the fill is; a number between 0 and 1

  • legend_label – the label for this item in the legend

  • base - (default: 10) the base of the logarithm if a logarithmic scale is set. This must be greater than 1. The base can be also given as a list or tuple (basex, basey). basex sets the base of the logarithm along the horizontal axis and basey sets the base along the vertical axis.

  • scale – (default: "linear") string. The scale of the axes. Possible values are "linear", "loglog", "semilogx", "semilogy".

    The scale can be also be given as single argument that is a list or tuple (scale, base) or (scale, basex, basey).

    The "loglog" scale sets both the horizontal and vertical axes to logarithmic scale. The "semilogx" scale sets the horizontal axis to logarithmic scale. The "semilogy" scale sets the vertical axis to logarithmic scale. The "linear" scale is the default value when Graphics is initialized.

EXAMPLES:

Here we plot a simple function of two variables:

sage: x,y = var('x,y')
sage: region_plot(cos(x^2 + y^2) <= 0, (x,-3,3), (y,-3,3))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-51.svg

Here we play with the colors:

sage: region_plot(x^2 + y^3 < 2, (x,-2,2), (y,-2,2), incol='lightblue', bordercol='gray')
Graphics object consisting of 2 graphics primitives
../../_images/contour_plot-52.svg

An even more complicated plot, with dashed borders:

sage: region_plot(sin(x) * sin(y) >= 1/4, (x,-10,10), (y,-10,10),
....:             incol='yellow', bordercol='black',
....:             borderstyle='dashed', plot_points=250)
Graphics object consisting of 2 graphics primitives
../../_images/contour_plot-53.svg

A disk centered at the origin:

sage: region_plot(x^2 + y^2 < 1, (x,-1,1), (y,-1,1))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-54.svg

A plot with more than one condition (all conditions must be true for the statement to be true):

sage: region_plot([x^2 + y^2 < 1, x < y], (x,-2,2), (y,-2,2))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-55.svg

Since it does not look very good, let us increase plot_points:

sage: region_plot([x^2 + y^2 < 1, x< y], (x,-2,2), (y,-2,2), plot_points=400)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-56.svg

To get plots where only one condition needs to be true, use a function. Using lambda functions, we definitely need the extra plot_points:

sage: region_plot(lambda x, y: x^2 + y^2 < 1 or x < y, (x,-2,2), (y,-2,2), plot_points=400)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-57.svg

The first quadrant of the unit circle:

sage: region_plot([y > 0, x > 0, x^2 + y^2 < 1], (x,-1.1,1.1), (y,-1.1,1.1), plot_points=400)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-58.svg

Here is another plot, with a huge border:

sage: region_plot(x*(x-1)*(x+1) + y^2 < 0, (x,-3,2), (y,-3,3),
....:             incol='lightblue', bordercol='gray', borderwidth=10,
....:             plot_points=50)
Graphics object consisting of 2 graphics primitives
../../_images/contour_plot-59.svg

If we want to keep only the region where x is positive:

sage: region_plot([x*(x-1)*(x+1) + y^2 < 0, x > -1], (x,-3,2), (y,-3,3),
....:             incol='lightblue', plot_points=50)
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-60.svg

Here we have a cut circle:

sage: region_plot([x^2 + y^2 < 4, x > -1], (x,-2,2), (y,-2,2),
....:             incol='lightblue', bordercol='gray', plot_points=200)
Graphics object consisting of 2 graphics primitives
../../_images/contour_plot-61.svg

The first variable range corresponds to the horizontal axis and the second variable range corresponds to the vertical axis:

sage: s, t = var('s, t')
sage: region_plot(s > 0, (t,-2,2), (s,-2,2))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-62.svg
sage: region_plot(s>0,(s,-2,2),(t,-2,2))
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-63.svg

An example of a region plot in ‘loglog’ scale:

sage: region_plot(x^2 + y^2 < 100, (x,1,10), (y,1,10), scale='loglog')
Graphics object consisting of 1 graphics primitive
../../_images/contour_plot-64.svg