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plot_implicit.py
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plot_implicit.py
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"""Implicit plotting module for Diofant
The module implements a data series called ImplicitSeries which is used by
``Plot`` class to plot implicit plots for different backends.
See Also
========
diofant.plotting.plot
"""
from ..core import Dummy, Eq, Symbol, Tuple, sympify
from ..core.relational import Equality, GreaterThan, LessThan, Relational
from ..external import import_module
from ..logic.boolalg import BooleanFunction
from ..polys.polyutils import _sort_gens
from ..utilities import flatten, lambdify
from ..utilities.decorator import doctest_depends_on
from .plot import BaseSeries, Plot
class ImplicitSeries(BaseSeries):
"""Representation for Implicit plot."""
is_implicit = True
def __init__(self, expr, var_start_end_x, var_start_end_y,
has_equality, use_interval_math, depth, nb_of_points,
line_color):
super().__init__()
self.expr = sympify(expr)
self.var_x = sympify(var_start_end_x[0])
self.start_x = float(var_start_end_x[1])
self.end_x = float(var_start_end_x[2])
self.var_y = sympify(var_start_end_y[0])
self.start_y = float(var_start_end_y[1])
self.end_y = float(var_start_end_y[2])
self.get_points = self.get_raster
# If the expression has equality, i.e. Eq, Greaterthan, LessThan.
self.has_equality = has_equality
self.nb_of_points = nb_of_points
self.use_interval_math = use_interval_math
self.depth = 4 + depth
self.line_color = line_color
def get_raster(self):
return self._get_meshes_grid()
def _get_meshes_grid(self):
"""Generates the mesh for generating a contour.
In the case of equality, ``contour`` function of matplotlib can
be used. In other cases, matplotlib's ``contourf`` is used.
"""
equal = False
if isinstance(self.expr, Equality):
expr = self.expr.lhs - self.expr.rhs
equal = True
elif self.expr.has(Equality): # pragma: no cover
raise NotImplementedError('The expression is not supported for '
'plotting in uniform meshed plot.')
else:
expr = self.expr
np = import_module('numpy')
xarray = np.linspace(self.start_x, self.end_x, self.nb_of_points)
yarray = np.linspace(self.start_y, self.end_y, self.nb_of_points)
x_grid, y_grid = np.meshgrid(xarray, yarray)
func = lambdify((self.var_x, self.var_y), expr, 'numpy')
z_grid = func(x_grid, y_grid)
z_grid[np.ma.where(z_grid < 0)] = -1
z_grid[np.ma.where(z_grid > 0)] = 1
if equal:
return xarray, yarray, z_grid, 'contour'
else:
return xarray, yarray, z_grid, 'contourf'
@doctest_depends_on(modules=('matplotlib',))
def plot_implicit(expr, x_var=None, y_var=None, **kwargs):
"""A plot function to plot implicit equations / inequalities.
Parameters
==========
``expr`` : Expr
The equation / inequality that is to be plotted.
``x_var`` : symbol or tuple, optional
symbol to plot on x-axis or tuple giving symbol and range
as ``(symbol, xmin, xmax)``
``y_var`` : symbol or tuple, optional
symbol to plot on y-axis or tuple giving symbol and range
as ``(symbol, ymin, ymax)``
If neither ``x_var`` nor ``y_var`` are given then the free symbols in the
expression will be assigned in the order they are sorted.
The following keyword arguments can also be used:
``adaptive`` : Boolean, optional
The default value is set to True. It has to be
set to False if you want to use a mesh grid.
``depth`` : integer
The depth of recursion for adaptive mesh grid.
Default value is 0. Takes value in the range (0, 4).
``points`` : integer
The number of points if adaptive mesh grid is not
used. Default value is 200.
``title`` : str
The title for the plot.
``xlabel`` : str
The label for the x-axis
``ylabel`` : string
The label for the y-axis
Aesthetics options:
``line_color`` : float or str
Specifies the color for the plot. See ``Plot`` to see how to
set color for the plots.
plot_implicit, by default, uses interval arithmetic to plot functions. If
the expression cannot be plotted using interval arithmetic, it defaults to
a generating a contour using a mesh grid of fixed number of points. By
setting adaptive to False, you can force plot_implicit to use the mesh
grid. The mesh grid method can be effective when adaptive plotting using
interval arithmetic, fails to plot with small line width.
Examples
========
Plot expressions:
Without any ranges for the symbols in the expression
>>> p1 = plot_implicit(Eq(x**2 + y**2, 5))
With the range for the symbols
>>> p2 = plot_implicit(Eq(x**2 + y**2, 3),
... (x, -3, 3), (y, -3, 3))
With depth of recursion as argument.
>>> p3 = plot_implicit(Eq(x**2 + y**2, 5),
... (x, -4, 4), (y, -4, 4), depth=2)
Using mesh grid and not using adaptive meshing.
>>> p4 = plot_implicit(Eq(x**2 + y**2, 5),
... (x, -5, 5), (y, -2, 2), adaptive=False)
Using mesh grid with number of points as input.
>>> p5 = plot_implicit(Eq(x**2 + y**2, 5),
... (x, -5, 5), (y, -2, 2),
... adaptive=False, points=400)
Plotting regions.
>>> p6 = plot_implicit(y > x**2)
Plotting Using boolean conjunctions.
>>> p7 = plot_implicit(And(y > x, y > -x))
When plotting an expression with a single variable (y - 1, for example),
specify the x or the y variable explicitly:
>>> p8 = plot_implicit(y - 1, y_var=y)
>>> p9 = plot_implicit(x - 1, x_var=x)
"""
# Represents whether the expression contains an Equality,
# GreaterThan or LessThan
has_equality = False
def arg_expand(bool_expr):
"""Recursively expands the arguments of an Boolean Function."""
for arg in bool_expr.args:
if isinstance(arg, BooleanFunction):
arg_expand(arg)
elif isinstance(arg, Relational):
arg_list.append(arg)
arg_list = []
if isinstance(expr, BooleanFunction):
arg_expand(expr)
# Check whether there is an equality in the expression provided.
if any(isinstance(e, (Equality, GreaterThan, LessThan))
for e in arg_list):
has_equality = True
elif not isinstance(expr, Relational):
expr = Eq(expr, 0)
has_equality = True
elif isinstance(expr, (Equality, GreaterThan, LessThan)):
has_equality = True
xyvar = [i for i in (x_var, y_var) if i is not None]
free_symbols = expr.free_symbols
range_symbols = Tuple(*flatten(xyvar)).free_symbols
undeclared = free_symbols - range_symbols
if len(free_symbols & range_symbols) > 2: # pragma: no cover
raise NotImplementedError('Implicit plotting is not implemented for '
'more than 2 variables')
# Create default ranges if the range is not provided.
default_range = Tuple(-5, 5)
def _range_tuple(s):
if isinstance(s, (Dummy, Symbol)):
return Tuple(s) + default_range
if len(s) == 3:
return Tuple(*s)
raise ValueError(f'symbol or `(symbol, min, max)` expected but got {s!s}')
if len(xyvar) == 0:
xyvar = list(_sort_gens(free_symbols))
var_start_end_x = _range_tuple(xyvar[0])
x = var_start_end_x[0]
if len(xyvar) != 2:
if x in undeclared or not undeclared:
xyvar.append(Dummy(f'f({x.name})'))
else:
xyvar.append(undeclared.pop())
var_start_end_y = _range_tuple(xyvar[1])
use_interval = kwargs.pop('adaptive', False)
nb_of_points = kwargs.pop('points', 300)
depth = kwargs.pop('depth', 0)
line_color = kwargs.pop('line_color', 'blue')
# Check whether the depth is greater than 4 or less than 0.
if depth > 4:
depth = 4
elif depth < 0:
depth = 0
series_argument = ImplicitSeries(expr, var_start_end_x, var_start_end_y,
has_equality, use_interval, depth,
nb_of_points, line_color)
show = kwargs.pop('show', True)
# set the x and y limits
kwargs['xlim'] = tuple(float(x) for x in var_start_end_x[1:])
kwargs['ylim'] = tuple(float(y) for y in var_start_end_y[1:])
# set the x and y labels
kwargs.setdefault('xlabel', var_start_end_x[0].name)
kwargs.setdefault('ylabel', var_start_end_y[0].name)
p = Plot(series_argument, **kwargs)
if show:
p.show()
return p