1005 lines
35 KiB
Python
1005 lines
35 KiB
Python
"""
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This module contains pdsolve() and different helper functions that it
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uses. It is heavily inspired by the ode module and hence the basic
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infrastructure remains the same.
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**Functions in this module**
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These are the user functions in this module:
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- pdsolve() - Solves PDE's
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- classify_pde() - Classifies PDEs into possible hints for dsolve().
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- pde_separate() - Separate variables in partial differential equation either by
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additive or multiplicative separation approach.
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These are the helper functions in this module:
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- pde_separate_add() - Helper function for searching additive separable solutions.
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- pde_separate_mul() - Helper function for searching multiplicative
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separable solutions.
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**Currently implemented solver methods**
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The following methods are implemented for solving partial differential
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equations. See the docstrings of the various pde_hint() functions for
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more information on each (run help(pde)):
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- 1st order linear homogeneous partial differential equations
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with constant coefficients.
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- 1st order linear general partial differential equations
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with constant coefficients.
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- 1st order linear partial differential equations with
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variable coefficients.
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"""
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from functools import reduce
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from itertools import combinations_with_replacement
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from sympy.simplify import simplify # type: ignore
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from sympy.core import Add, S
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from sympy.core.function import Function, expand, AppliedUndef, Subs
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from sympy.core.relational import Equality, Eq
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from sympy.core.symbol import Symbol, Wild, symbols
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from sympy.functions import exp
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from sympy.integrals.integrals import Integral, integrate
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from sympy.utilities.iterables import has_dups, is_sequence
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from sympy.utilities.misc import filldedent
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from sympy.solvers.deutils import _preprocess, ode_order, _desolve
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from sympy.solvers.solvers import solve
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from sympy.simplify.radsimp import collect
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import operator
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allhints = (
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"1st_linear_constant_coeff_homogeneous",
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"1st_linear_constant_coeff",
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"1st_linear_constant_coeff_Integral",
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"1st_linear_variable_coeff"
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)
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def pdsolve(eq, func=None, hint='default', dict=False, solvefun=None, **kwargs):
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"""
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Solves any (supported) kind of partial differential equation.
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**Usage**
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pdsolve(eq, f(x,y), hint) -> Solve partial differential equation
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eq for function f(x,y), using method hint.
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**Details**
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``eq`` can be any supported partial differential equation (see
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the pde docstring for supported methods). This can either
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be an Equality, or an expression, which is assumed to be
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equal to 0.
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``f(x,y)`` is a function of two variables whose derivatives in that
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variable make up the partial differential equation. In many
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cases it is not necessary to provide this; it will be autodetected
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(and an error raised if it could not be detected).
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``hint`` is the solving method that you want pdsolve to use. Use
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classify_pde(eq, f(x,y)) to get all of the possible hints for
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a PDE. The default hint, 'default', will use whatever hint
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is returned first by classify_pde(). See Hints below for
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more options that you can use for hint.
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``solvefun`` is the convention used for arbitrary functions returned
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by the PDE solver. If not set by the user, it is set by default
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to be F.
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**Hints**
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Aside from the various solving methods, there are also some
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meta-hints that you can pass to pdsolve():
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"default":
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This uses whatever hint is returned first by
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classify_pde(). This is the default argument to
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pdsolve().
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"all":
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To make pdsolve apply all relevant classification hints,
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use pdsolve(PDE, func, hint="all"). This will return a
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dictionary of hint:solution terms. If a hint causes
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pdsolve to raise the NotImplementedError, value of that
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hint's key will be the exception object raised. The
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dictionary will also include some special keys:
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- order: The order of the PDE. See also ode_order() in
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deutils.py
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- default: The solution that would be returned by
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default. This is the one produced by the hint that
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appears first in the tuple returned by classify_pde().
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"all_Integral":
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This is the same as "all", except if a hint also has a
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corresponding "_Integral" hint, it only returns the
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"_Integral" hint. This is useful if "all" causes
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pdsolve() to hang because of a difficult or impossible
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integral. This meta-hint will also be much faster than
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"all", because integrate() is an expensive routine.
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See also the classify_pde() docstring for more info on hints,
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and the pde docstring for a list of all supported hints.
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**Tips**
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- You can declare the derivative of an unknown function this way:
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>>> from sympy import Function, Derivative
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>>> from sympy.abc import x, y # x and y are the independent variables
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>>> f = Function("f")(x, y) # f is a function of x and y
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>>> # fx will be the partial derivative of f with respect to x
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>>> fx = Derivative(f, x)
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>>> # fy will be the partial derivative of f with respect to y
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>>> fy = Derivative(f, y)
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- See test_pde.py for many tests, which serves also as a set of
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examples for how to use pdsolve().
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- pdsolve always returns an Equality class (except for the case
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when the hint is "all" or "all_Integral"). Note that it is not possible
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to get an explicit solution for f(x, y) as in the case of ODE's
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- Do help(pde.pde_hintname) to get help more information on a
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specific hint
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Examples
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========
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>>> from sympy.solvers.pde import pdsolve
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>>> from sympy import Function, Eq
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>>> from sympy.abc import x, y
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>>> f = Function('f')
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>>> u = f(x, y)
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>>> ux = u.diff(x)
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>>> uy = u.diff(y)
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>>> eq = Eq(1 + (2*(ux/u)) + (3*(uy/u)), 0)
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>>> pdsolve(eq)
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Eq(f(x, y), F(3*x - 2*y)*exp(-2*x/13 - 3*y/13))
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"""
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if not solvefun:
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solvefun = Function('F')
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# See the docstring of _desolve for more details.
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hints = _desolve(eq, func=func, hint=hint, simplify=True,
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type='pde', **kwargs)
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eq = hints.pop('eq', False)
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all_ = hints.pop('all', False)
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if all_:
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# TODO : 'best' hint should be implemented when adequate
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# number of hints are added.
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pdedict = {}
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failed_hints = {}
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gethints = classify_pde(eq, dict=True)
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pdedict.update({'order': gethints['order'],
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'default': gethints['default']})
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for hint in hints:
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try:
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rv = _helper_simplify(eq, hint, hints[hint]['func'],
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hints[hint]['order'], hints[hint][hint], solvefun)
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except NotImplementedError as detail:
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failed_hints[hint] = detail
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else:
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pdedict[hint] = rv
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pdedict.update(failed_hints)
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return pdedict
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else:
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return _helper_simplify(eq, hints['hint'], hints['func'],
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hints['order'], hints[hints['hint']], solvefun)
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def _helper_simplify(eq, hint, func, order, match, solvefun):
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"""Helper function of pdsolve that calls the respective
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pde functions to solve for the partial differential
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equations. This minimizes the computation in
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calling _desolve multiple times.
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"""
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if hint.endswith("_Integral"):
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solvefunc = globals()[
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"pde_" + hint[:-len("_Integral")]]
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else:
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solvefunc = globals()["pde_" + hint]
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return _handle_Integral(solvefunc(eq, func, order,
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match, solvefun), func, order, hint)
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def _handle_Integral(expr, func, order, hint):
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r"""
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Converts a solution with integrals in it into an actual solution.
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Simplifies the integral mainly using doit()
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"""
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if hint.endswith("_Integral"):
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return expr
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elif hint == "1st_linear_constant_coeff":
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return simplify(expr.doit())
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else:
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return expr
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def classify_pde(eq, func=None, dict=False, *, prep=True, **kwargs):
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"""
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Returns a tuple of possible pdsolve() classifications for a PDE.
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The tuple is ordered so that first item is the classification that
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pdsolve() uses to solve the PDE by default. In general,
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classifications near the beginning of the list will produce
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better solutions faster than those near the end, though there are
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always exceptions. To make pdsolve use a different classification,
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use pdsolve(PDE, func, hint=<classification>). See also the pdsolve()
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docstring for different meta-hints you can use.
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If ``dict`` is true, classify_pde() will return a dictionary of
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hint:match expression terms. This is intended for internal use by
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pdsolve(). Note that because dictionaries are ordered arbitrarily,
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this will most likely not be in the same order as the tuple.
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You can get help on different hints by doing help(pde.pde_hintname),
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where hintname is the name of the hint without "_Integral".
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See sympy.pde.allhints or the sympy.pde docstring for a list of all
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supported hints that can be returned from classify_pde.
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Examples
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========
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>>> from sympy.solvers.pde import classify_pde
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>>> from sympy import Function, Eq
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>>> from sympy.abc import x, y
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>>> f = Function('f')
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>>> u = f(x, y)
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>>> ux = u.diff(x)
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>>> uy = u.diff(y)
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>>> eq = Eq(1 + (2*(ux/u)) + (3*(uy/u)), 0)
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>>> classify_pde(eq)
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('1st_linear_constant_coeff_homogeneous',)
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"""
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if func and len(func.args) != 2:
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raise NotImplementedError("Right now only partial "
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"differential equations of two variables are supported")
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if prep or func is None:
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prep, func_ = _preprocess(eq, func)
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if func is None:
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func = func_
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if isinstance(eq, Equality):
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if eq.rhs != 0:
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return classify_pde(eq.lhs - eq.rhs, func)
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eq = eq.lhs
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f = func.func
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x = func.args[0]
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y = func.args[1]
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fx = f(x,y).diff(x)
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fy = f(x,y).diff(y)
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# TODO : For now pde.py uses support offered by the ode_order function
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# to find the order with respect to a multi-variable function. An
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# improvement could be to classify the order of the PDE on the basis of
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# individual variables.
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order = ode_order(eq, f(x,y))
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# hint:matchdict or hint:(tuple of matchdicts)
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# Also will contain "default":<default hint> and "order":order items.
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matching_hints = {'order': order}
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if not order:
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if dict:
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matching_hints["default"] = None
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return matching_hints
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else:
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return ()
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eq = expand(eq)
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a = Wild('a', exclude = [f(x,y)])
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b = Wild('b', exclude = [f(x,y), fx, fy, x, y])
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c = Wild('c', exclude = [f(x,y), fx, fy, x, y])
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d = Wild('d', exclude = [f(x,y), fx, fy, x, y])
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e = Wild('e', exclude = [f(x,y), fx, fy])
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n = Wild('n', exclude = [x, y])
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# Try removing the smallest power of f(x,y)
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# from the highest partial derivatives of f(x,y)
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reduced_eq = None
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if eq.is_Add:
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var = set(combinations_with_replacement((x,y), order))
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dummyvar = var.copy()
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power = None
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for i in var:
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coeff = eq.coeff(f(x,y).diff(*i))
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if coeff != 1:
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match = coeff.match(a*f(x,y)**n)
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if match and match[a]:
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power = match[n]
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dummyvar.remove(i)
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break
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dummyvar.remove(i)
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for i in dummyvar:
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coeff = eq.coeff(f(x,y).diff(*i))
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if coeff != 1:
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match = coeff.match(a*f(x,y)**n)
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if match and match[a] and match[n] < power:
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power = match[n]
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if power:
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den = f(x,y)**power
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reduced_eq = Add(*[arg/den for arg in eq.args])
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if not reduced_eq:
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reduced_eq = eq
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if order == 1:
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reduced_eq = collect(reduced_eq, f(x, y))
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r = reduced_eq.match(b*fx + c*fy + d*f(x,y) + e)
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if r:
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if not r[e]:
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## Linear first-order homogeneous partial-differential
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## equation with constant coefficients
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r.update({'b': b, 'c': c, 'd': d})
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matching_hints["1st_linear_constant_coeff_homogeneous"] = r
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else:
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if r[b]**2 + r[c]**2 != 0:
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## Linear first-order general partial-differential
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## equation with constant coefficients
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r.update({'b': b, 'c': c, 'd': d, 'e': e})
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matching_hints["1st_linear_constant_coeff"] = r
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matching_hints[
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"1st_linear_constant_coeff_Integral"] = r
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else:
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b = Wild('b', exclude=[f(x, y), fx, fy])
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c = Wild('c', exclude=[f(x, y), fx, fy])
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d = Wild('d', exclude=[f(x, y), fx, fy])
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r = reduced_eq.match(b*fx + c*fy + d*f(x,y) + e)
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if r:
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r.update({'b': b, 'c': c, 'd': d, 'e': e})
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matching_hints["1st_linear_variable_coeff"] = r
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# Order keys based on allhints.
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retlist = [i for i in allhints if i in matching_hints]
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if dict:
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# Dictionaries are ordered arbitrarily, so make note of which
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# hint would come first for pdsolve(). Use an ordered dict in Py 3.
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matching_hints["default"] = None
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matching_hints["ordered_hints"] = tuple(retlist)
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for i in allhints:
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if i in matching_hints:
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matching_hints["default"] = i
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break
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return matching_hints
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else:
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return tuple(retlist)
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def checkpdesol(pde, sol, func=None, solve_for_func=True):
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"""
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Checks if the given solution satisfies the partial differential
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equation.
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pde is the partial differential equation which can be given in the
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form of an equation or an expression. sol is the solution for which
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the pde is to be checked. This can also be given in an equation or
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an expression form. If the function is not provided, the helper
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function _preprocess from deutils is used to identify the function.
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If a sequence of solutions is passed, the same sort of container will be
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used to return the result for each solution.
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The following methods are currently being implemented to check if the
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solution satisfies the PDE:
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1. Directly substitute the solution in the PDE and check. If the
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solution has not been solved for f, then it will solve for f
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provided solve_for_func has not been set to False.
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If the solution satisfies the PDE, then a tuple (True, 0) is returned.
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Otherwise a tuple (False, expr) where expr is the value obtained
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after substituting the solution in the PDE. However if a known solution
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returns False, it may be due to the inability of doit() to simplify it to zero.
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Examples
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========
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>>> from sympy import Function, symbols
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>>> from sympy.solvers.pde import checkpdesol, pdsolve
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>>> x, y = symbols('x y')
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>>> f = Function('f')
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>>> eq = 2*f(x,y) + 3*f(x,y).diff(x) + 4*f(x,y).diff(y)
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>>> sol = pdsolve(eq)
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>>> assert checkpdesol(eq, sol)[0]
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>>> eq = x*f(x,y) + f(x,y).diff(x)
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>>> checkpdesol(eq, sol)
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(False, (x*F(4*x - 3*y) - 6*F(4*x - 3*y)/25 + 4*Subs(Derivative(F(_xi_1), _xi_1), _xi_1, 4*x - 3*y))*exp(-6*x/25 - 8*y/25))
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"""
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# Converting the pde into an equation
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if not isinstance(pde, Equality):
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pde = Eq(pde, 0)
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# If no function is given, try finding the function present.
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if func is None:
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try:
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_, func = _preprocess(pde.lhs)
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except ValueError:
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funcs = [s.atoms(AppliedUndef) for s in (
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sol if is_sequence(sol, set) else [sol])]
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funcs = set().union(funcs)
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if len(funcs) != 1:
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raise ValueError(
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'must pass func arg to checkpdesol for this case.')
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func = funcs.pop()
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# If the given solution is in the form of a list or a set
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# then return a list or set of tuples.
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if is_sequence(sol, set):
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return type(sol)([checkpdesol(
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pde, i, func=func,
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solve_for_func=solve_for_func) for i in sol])
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# Convert solution into an equation
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if not isinstance(sol, Equality):
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sol = Eq(func, sol)
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elif sol.rhs == func:
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sol = sol.reversed
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# Try solving for the function
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solved = sol.lhs == func and not sol.rhs.has(func)
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if solve_for_func and not solved:
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solved = solve(sol, func)
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if solved:
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if len(solved) == 1:
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return checkpdesol(pde, Eq(func, solved[0]),
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func=func, solve_for_func=False)
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else:
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return checkpdesol(pde, [Eq(func, t) for t in solved],
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func=func, solve_for_func=False)
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# try direct substitution of the solution into the PDE and simplify
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if sol.lhs == func:
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pde = pde.lhs - pde.rhs
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s = simplify(pde.subs(func, sol.rhs).doit())
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return s is S.Zero, s
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raise NotImplementedError(filldedent('''
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Unable to test if %s is a solution to %s.''' % (sol, pde)))
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def pde_1st_linear_constant_coeff_homogeneous(eq, func, order, match, solvefun):
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r"""
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Solves a first order linear homogeneous
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partial differential equation with constant coefficients.
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The general form of this partial differential equation is
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.. math:: a \frac{\partial f(x,y)}{\partial x}
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+ b \frac{\partial f(x,y)}{\partial y} + c f(x,y) = 0
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|
|
where `a`, `b` and `c` are constants.
|
|
|
|
The general solution is of the form:
|
|
|
|
.. math::
|
|
f(x, y) = F(- a y + b x ) e^{- \frac{c (a x + b y)}{a^2 + b^2}}
|
|
|
|
and can be found in SymPy with ``pdsolve``::
|
|
|
|
>>> from sympy.solvers import pdsolve
|
|
>>> from sympy.abc import x, y, a, b, c
|
|
>>> from sympy import Function, pprint
|
|
>>> f = Function('f')
|
|
>>> u = f(x,y)
|
|
>>> ux = u.diff(x)
|
|
>>> uy = u.diff(y)
|
|
>>> genform = a*ux + b*uy + c*u
|
|
>>> pprint(genform)
|
|
d d
|
|
a*--(f(x, y)) + b*--(f(x, y)) + c*f(x, y)
|
|
dx dy
|
|
|
|
>>> pprint(pdsolve(genform))
|
|
-c*(a*x + b*y)
|
|
---------------
|
|
2 2
|
|
a + b
|
|
f(x, y) = F(-a*y + b*x)*e
|
|
|
|
Examples
|
|
========
|
|
|
|
>>> from sympy import pdsolve
|
|
>>> from sympy import Function, pprint
|
|
>>> from sympy.abc import x,y
|
|
>>> f = Function('f')
|
|
>>> pdsolve(f(x,y) + f(x,y).diff(x) + f(x,y).diff(y))
|
|
Eq(f(x, y), F(x - y)*exp(-x/2 - y/2))
|
|
>>> pprint(pdsolve(f(x,y) + f(x,y).diff(x) + f(x,y).diff(y)))
|
|
x y
|
|
- - - -
|
|
2 2
|
|
f(x, y) = F(x - y)*e
|
|
|
|
References
|
|
==========
|
|
|
|
- Viktor Grigoryan, "Partial Differential Equations"
|
|
Math 124A - Fall 2010, pp.7
|
|
|
|
"""
|
|
# TODO : For now homogeneous first order linear PDE's having
|
|
# two variables are implemented. Once there is support for
|
|
# solving systems of ODE's, this can be extended to n variables.
|
|
|
|
f = func.func
|
|
x = func.args[0]
|
|
y = func.args[1]
|
|
b = match[match['b']]
|
|
c = match[match['c']]
|
|
d = match[match['d']]
|
|
return Eq(f(x,y), exp(-S(d)/(b**2 + c**2)*(b*x + c*y))*solvefun(c*x - b*y))
|
|
|
|
|
|
def pde_1st_linear_constant_coeff(eq, func, order, match, solvefun):
|
|
r"""
|
|
Solves a first order linear partial differential equation
|
|
with constant coefficients.
|
|
|
|
The general form of this partial differential equation is
|
|
|
|
.. math:: a \frac{\partial f(x,y)}{\partial x}
|
|
+ b \frac{\partial f(x,y)}{\partial y}
|
|
+ c f(x,y) = G(x,y)
|
|
|
|
where `a`, `b` and `c` are constants and `G(x, y)` can be an arbitrary
|
|
function in `x` and `y`.
|
|
|
|
The general solution of the PDE is:
|
|
|
|
.. math::
|
|
f(x, y) = \left. \left[F(\eta) + \frac{1}{a^2 + b^2}
|
|
\int\limits^{a x + b y} G\left(\frac{a \xi + b \eta}{a^2 + b^2},
|
|
\frac{- a \eta + b \xi}{a^2 + b^2} \right)
|
|
e^{\frac{c \xi}{a^2 + b^2}}\, d\xi\right]
|
|
e^{- \frac{c \xi}{a^2 + b^2}}
|
|
\right|_{\substack{\eta=- a y + b x\\ \xi=a x + b y }}\, ,
|
|
|
|
where `F(\eta)` is an arbitrary single-valued function. The solution
|
|
can be found in SymPy with ``pdsolve``::
|
|
|
|
>>> from sympy.solvers import pdsolve
|
|
>>> from sympy.abc import x, y, a, b, c
|
|
>>> from sympy import Function, pprint
|
|
>>> f = Function('f')
|
|
>>> G = Function('G')
|
|
>>> u = f(x,y)
|
|
>>> ux = u.diff(x)
|
|
>>> uy = u.diff(y)
|
|
>>> genform = a*ux + b*uy + c*u - G(x,y)
|
|
>>> pprint(genform)
|
|
d d
|
|
a*--(f(x, y)) + b*--(f(x, y)) + c*f(x, y) - G(x, y)
|
|
dx dy
|
|
>>> pprint(pdsolve(genform, hint='1st_linear_constant_coeff_Integral'))
|
|
// a*x + b*y \
|
|
|| / |
|
|
|| | |
|
|
|| | c*xi |
|
|
|| | ------- |
|
|
|| | 2 2 |
|
|
|| | /a*xi + b*eta -a*eta + b*xi\ a + b |
|
|
|| | G|------------, -------------|*e d(xi)|
|
|
|| | | 2 2 2 2 | |
|
|
|| | \ a + b a + b / |
|
|
|| | |
|
|
|| / |
|
|
|| |
|
|
f(x, y) = ||F(eta) + -------------------------------------------------------|*
|
|
|| 2 2 |
|
|
\\ a + b /
|
|
<BLANKLINE>
|
|
\|
|
|
||
|
|
||
|
|
||
|
|
||
|
|
||
|
|
||
|
|
||
|
|
||
|
|
-c*xi ||
|
|
-------||
|
|
2 2||
|
|
a + b ||
|
|
e ||
|
|
||
|
|
/|eta=-a*y + b*x, xi=a*x + b*y
|
|
|
|
|
|
Examples
|
|
========
|
|
|
|
>>> from sympy.solvers.pde import pdsolve
|
|
>>> from sympy import Function, pprint, exp
|
|
>>> from sympy.abc import x,y
|
|
>>> f = Function('f')
|
|
>>> eq = -2*f(x,y).diff(x) + 4*f(x,y).diff(y) + 5*f(x,y) - exp(x + 3*y)
|
|
>>> pdsolve(eq)
|
|
Eq(f(x, y), (F(4*x + 2*y)*exp(x/2) + exp(x + 4*y)/15)*exp(-y))
|
|
|
|
References
|
|
==========
|
|
|
|
- Viktor Grigoryan, "Partial Differential Equations"
|
|
Math 124A - Fall 2010, pp.7
|
|
|
|
"""
|
|
|
|
# TODO : For now homogeneous first order linear PDE's having
|
|
# two variables are implemented. Once there is support for
|
|
# solving systems of ODE's, this can be extended to n variables.
|
|
xi, eta = symbols("xi eta")
|
|
f = func.func
|
|
x = func.args[0]
|
|
y = func.args[1]
|
|
b = match[match['b']]
|
|
c = match[match['c']]
|
|
d = match[match['d']]
|
|
e = -match[match['e']]
|
|
expterm = exp(-S(d)/(b**2 + c**2)*xi)
|
|
functerm = solvefun(eta)
|
|
solvedict = solve((b*x + c*y - xi, c*x - b*y - eta), x, y)
|
|
# Integral should remain as it is in terms of xi,
|
|
# doit() should be done in _handle_Integral.
|
|
genterm = (1/S(b**2 + c**2))*Integral(
|
|
(1/expterm*e).subs(solvedict), (xi, b*x + c*y))
|
|
return Eq(f(x,y), Subs(expterm*(functerm + genterm),
|
|
(eta, xi), (c*x - b*y, b*x + c*y)))
|
|
|
|
|
|
def pde_1st_linear_variable_coeff(eq, func, order, match, solvefun):
|
|
r"""
|
|
Solves a first order linear partial differential equation
|
|
with variable coefficients. The general form of this partial
|
|
differential equation is
|
|
|
|
.. math:: a(x, y) \frac{\partial f(x, y)}{\partial x}
|
|
+ b(x, y) \frac{\partial f(x, y)}{\partial y}
|
|
+ c(x, y) f(x, y) = G(x, y)
|
|
|
|
where `a(x, y)`, `b(x, y)`, `c(x, y)` and `G(x, y)` are arbitrary
|
|
functions in `x` and `y`. This PDE is converted into an ODE by
|
|
making the following transformation:
|
|
|
|
1. `\xi` as `x`
|
|
|
|
2. `\eta` as the constant in the solution to the differential
|
|
equation `\frac{dy}{dx} = -\frac{b}{a}`
|
|
|
|
Making the previous substitutions reduces it to the linear ODE
|
|
|
|
.. math:: a(\xi, \eta)\frac{du}{d\xi} + c(\xi, \eta)u - G(\xi, \eta) = 0
|
|
|
|
which can be solved using ``dsolve``.
|
|
|
|
>>> from sympy.abc import x, y
|
|
>>> from sympy import Function, pprint
|
|
>>> a, b, c, G, f= [Function(i) for i in ['a', 'b', 'c', 'G', 'f']]
|
|
>>> u = f(x,y)
|
|
>>> ux = u.diff(x)
|
|
>>> uy = u.diff(y)
|
|
>>> genform = a(x, y)*u + b(x, y)*ux + c(x, y)*uy - G(x,y)
|
|
>>> pprint(genform)
|
|
d d
|
|
-G(x, y) + a(x, y)*f(x, y) + b(x, y)*--(f(x, y)) + c(x, y)*--(f(x, y))
|
|
dx dy
|
|
|
|
|
|
Examples
|
|
========
|
|
|
|
>>> from sympy.solvers.pde import pdsolve
|
|
>>> from sympy import Function, pprint
|
|
>>> from sympy.abc import x,y
|
|
>>> f = Function('f')
|
|
>>> eq = x*(u.diff(x)) - y*(u.diff(y)) + y**2*u - y**2
|
|
>>> pdsolve(eq)
|
|
Eq(f(x, y), F(x*y)*exp(y**2/2) + 1)
|
|
|
|
References
|
|
==========
|
|
|
|
- Viktor Grigoryan, "Partial Differential Equations"
|
|
Math 124A - Fall 2010, pp.7
|
|
|
|
"""
|
|
from sympy.solvers.ode import dsolve
|
|
|
|
xi, eta = symbols("xi eta")
|
|
f = func.func
|
|
x = func.args[0]
|
|
y = func.args[1]
|
|
b = match[match['b']]
|
|
c = match[match['c']]
|
|
d = match[match['d']]
|
|
e = -match[match['e']]
|
|
|
|
|
|
if not d:
|
|
# To deal with cases like b*ux = e or c*uy = e
|
|
if not (b and c):
|
|
if c:
|
|
try:
|
|
tsol = integrate(e/c, y)
|
|
except NotImplementedError:
|
|
raise NotImplementedError("Unable to find a solution"
|
|
" due to inability of integrate")
|
|
else:
|
|
return Eq(f(x,y), solvefun(x) + tsol)
|
|
if b:
|
|
try:
|
|
tsol = integrate(e/b, x)
|
|
except NotImplementedError:
|
|
raise NotImplementedError("Unable to find a solution"
|
|
" due to inability of integrate")
|
|
else:
|
|
return Eq(f(x,y), solvefun(y) + tsol)
|
|
|
|
if not c:
|
|
# To deal with cases when c is 0, a simpler method is used.
|
|
# The PDE reduces to b*(u.diff(x)) + d*u = e, which is a linear ODE in x
|
|
plode = f(x).diff(x)*b + d*f(x) - e
|
|
sol = dsolve(plode, f(x))
|
|
syms = sol.free_symbols - plode.free_symbols - {x, y}
|
|
rhs = _simplify_variable_coeff(sol.rhs, syms, solvefun, y)
|
|
return Eq(f(x, y), rhs)
|
|
|
|
if not b:
|
|
# To deal with cases when b is 0, a simpler method is used.
|
|
# The PDE reduces to c*(u.diff(y)) + d*u = e, which is a linear ODE in y
|
|
plode = f(y).diff(y)*c + d*f(y) - e
|
|
sol = dsolve(plode, f(y))
|
|
syms = sol.free_symbols - plode.free_symbols - {x, y}
|
|
rhs = _simplify_variable_coeff(sol.rhs, syms, solvefun, x)
|
|
return Eq(f(x, y), rhs)
|
|
|
|
dummy = Function('d')
|
|
h = (c/b).subs(y, dummy(x))
|
|
sol = dsolve(dummy(x).diff(x) - h, dummy(x))
|
|
if isinstance(sol, list):
|
|
sol = sol[0]
|
|
solsym = sol.free_symbols - h.free_symbols - {x, y}
|
|
if len(solsym) == 1:
|
|
solsym = solsym.pop()
|
|
etat = (solve(sol, solsym)[0]).subs(dummy(x), y)
|
|
ysub = solve(eta - etat, y)[0]
|
|
deq = (b*(f(x).diff(x)) + d*f(x) - e).subs(y, ysub)
|
|
final = (dsolve(deq, f(x), hint='1st_linear')).rhs
|
|
if isinstance(final, list):
|
|
final = final[0]
|
|
finsyms = final.free_symbols - deq.free_symbols - {x, y}
|
|
rhs = _simplify_variable_coeff(final, finsyms, solvefun, etat)
|
|
return Eq(f(x, y), rhs)
|
|
|
|
else:
|
|
raise NotImplementedError("Cannot solve the partial differential equation due"
|
|
" to inability of constantsimp")
|
|
|
|
|
|
def _simplify_variable_coeff(sol, syms, func, funcarg):
|
|
r"""
|
|
Helper function to replace constants by functions in 1st_linear_variable_coeff
|
|
"""
|
|
eta = Symbol("eta")
|
|
if len(syms) == 1:
|
|
sym = syms.pop()
|
|
final = sol.subs(sym, func(funcarg))
|
|
|
|
else:
|
|
for key, sym in enumerate(syms):
|
|
final = sol.subs(sym, func(funcarg))
|
|
|
|
return simplify(final.subs(eta, funcarg))
|
|
|
|
|
|
def pde_separate(eq, fun, sep, strategy='mul'):
|
|
"""Separate variables in partial differential equation either by additive
|
|
or multiplicative separation approach. It tries to rewrite an equation so
|
|
that one of the specified variables occurs on a different side of the
|
|
equation than the others.
|
|
|
|
:param eq: Partial differential equation
|
|
|
|
:param fun: Original function F(x, y, z)
|
|
|
|
:param sep: List of separated functions [X(x), u(y, z)]
|
|
|
|
:param strategy: Separation strategy. You can choose between additive
|
|
separation ('add') and multiplicative separation ('mul') which is
|
|
default.
|
|
|
|
Examples
|
|
========
|
|
|
|
>>> from sympy import E, Eq, Function, pde_separate, Derivative as D
|
|
>>> from sympy.abc import x, t
|
|
>>> u, X, T = map(Function, 'uXT')
|
|
|
|
>>> eq = Eq(D(u(x, t), x), E**(u(x, t))*D(u(x, t), t))
|
|
>>> pde_separate(eq, u(x, t), [X(x), T(t)], strategy='add')
|
|
[exp(-X(x))*Derivative(X(x), x), exp(T(t))*Derivative(T(t), t)]
|
|
|
|
>>> eq = Eq(D(u(x, t), x, 2), D(u(x, t), t, 2))
|
|
>>> pde_separate(eq, u(x, t), [X(x), T(t)], strategy='mul')
|
|
[Derivative(X(x), (x, 2))/X(x), Derivative(T(t), (t, 2))/T(t)]
|
|
|
|
See Also
|
|
========
|
|
pde_separate_add, pde_separate_mul
|
|
"""
|
|
|
|
do_add = False
|
|
if strategy == 'add':
|
|
do_add = True
|
|
elif strategy == 'mul':
|
|
do_add = False
|
|
else:
|
|
raise ValueError('Unknown strategy: %s' % strategy)
|
|
|
|
if isinstance(eq, Equality):
|
|
if eq.rhs != 0:
|
|
return pde_separate(Eq(eq.lhs - eq.rhs, 0), fun, sep, strategy)
|
|
else:
|
|
return pde_separate(Eq(eq, 0), fun, sep, strategy)
|
|
|
|
if eq.rhs != 0:
|
|
raise ValueError("Value should be 0")
|
|
|
|
# Handle arguments
|
|
orig_args = list(fun.args)
|
|
subs_args = [arg for s in sep for arg in s.args]
|
|
|
|
if do_add:
|
|
functions = reduce(operator.add, sep)
|
|
else:
|
|
functions = reduce(operator.mul, sep)
|
|
|
|
# Check whether variables match
|
|
if len(subs_args) != len(orig_args):
|
|
raise ValueError("Variable counts do not match")
|
|
# Check for duplicate arguments like [X(x), u(x, y)]
|
|
if has_dups(subs_args):
|
|
raise ValueError("Duplicate substitution arguments detected")
|
|
# Check whether the variables match
|
|
if set(orig_args) != set(subs_args):
|
|
raise ValueError("Arguments do not match")
|
|
|
|
# Substitute original function with separated...
|
|
result = eq.lhs.subs(fun, functions).doit()
|
|
|
|
# Divide by terms when doing multiplicative separation
|
|
if not do_add:
|
|
eq = 0
|
|
for i in result.args:
|
|
eq += i/functions
|
|
result = eq
|
|
|
|
svar = subs_args[0]
|
|
dvar = subs_args[1:]
|
|
return _separate(result, svar, dvar)
|
|
|
|
|
|
def pde_separate_add(eq, fun, sep):
|
|
"""
|
|
Helper function for searching additive separable solutions.
|
|
|
|
Consider an equation of two independent variables x, y and a dependent
|
|
variable w, we look for the product of two functions depending on different
|
|
arguments:
|
|
|
|
`w(x, y, z) = X(x) + y(y, z)`
|
|
|
|
Examples
|
|
========
|
|
|
|
>>> from sympy import E, Eq, Function, pde_separate_add, Derivative as D
|
|
>>> from sympy.abc import x, t
|
|
>>> u, X, T = map(Function, 'uXT')
|
|
|
|
>>> eq = Eq(D(u(x, t), x), E**(u(x, t))*D(u(x, t), t))
|
|
>>> pde_separate_add(eq, u(x, t), [X(x), T(t)])
|
|
[exp(-X(x))*Derivative(X(x), x), exp(T(t))*Derivative(T(t), t)]
|
|
|
|
"""
|
|
return pde_separate(eq, fun, sep, strategy='add')
|
|
|
|
|
|
def pde_separate_mul(eq, fun, sep):
|
|
"""
|
|
Helper function for searching multiplicative separable solutions.
|
|
|
|
Consider an equation of two independent variables x, y and a dependent
|
|
variable w, we look for the product of two functions depending on different
|
|
arguments:
|
|
|
|
`w(x, y, z) = X(x)*u(y, z)`
|
|
|
|
Examples
|
|
========
|
|
|
|
>>> from sympy import Function, Eq, pde_separate_mul, Derivative as D
|
|
>>> from sympy.abc import x, y
|
|
>>> u, X, Y = map(Function, 'uXY')
|
|
|
|
>>> eq = Eq(D(u(x, y), x, 2), D(u(x, y), y, 2))
|
|
>>> pde_separate_mul(eq, u(x, y), [X(x), Y(y)])
|
|
[Derivative(X(x), (x, 2))/X(x), Derivative(Y(y), (y, 2))/Y(y)]
|
|
|
|
"""
|
|
return pde_separate(eq, fun, sep, strategy='mul')
|
|
|
|
|
|
def _separate(eq, dep, others):
|
|
"""Separate expression into two parts based on dependencies of variables."""
|
|
|
|
# FIRST PASS
|
|
# Extract derivatives depending our separable variable...
|
|
terms = set()
|
|
for term in eq.args:
|
|
if term.is_Mul:
|
|
for i in term.args:
|
|
if i.is_Derivative and not i.has(*others):
|
|
terms.add(term)
|
|
continue
|
|
elif term.is_Derivative and not term.has(*others):
|
|
terms.add(term)
|
|
# Find the factor that we need to divide by
|
|
div = set()
|
|
for term in terms:
|
|
ext, sep = term.expand().as_independent(dep)
|
|
# Failed?
|
|
if sep.has(*others):
|
|
return None
|
|
div.add(ext)
|
|
# FIXME: Find lcm() of all the divisors and divide with it, instead of
|
|
# current hack :(
|
|
# https://github.com/sympy/sympy/issues/4597
|
|
if len(div) > 0:
|
|
# double sum required or some tests will fail
|
|
eq = Add(*[simplify(Add(*[term/i for i in div])) for term in eq.args])
|
|
# SECOND PASS - separate the derivatives
|
|
div = set()
|
|
lhs = rhs = 0
|
|
for term in eq.args:
|
|
# Check, whether we have already term with independent variable...
|
|
if not term.has(*others):
|
|
lhs += term
|
|
continue
|
|
# ...otherwise, try to separate
|
|
temp, sep = term.expand().as_independent(dep)
|
|
# Failed?
|
|
if sep.has(*others):
|
|
return None
|
|
# Extract the divisors
|
|
div.add(sep)
|
|
rhs -= term.expand()
|
|
# Do the division
|
|
fulldiv = reduce(operator.add, div)
|
|
lhs = simplify(lhs/fulldiv).expand()
|
|
rhs = simplify(rhs/fulldiv).expand()
|
|
# ...and check whether we were successful :)
|
|
if lhs.has(*others) or rhs.has(dep):
|
|
return None
|
|
return [lhs, rhs]
|