SymPy
functionality into Julia
via PyCall
SymPy Package to bring Python's SymPy (http://sympy.org/
) is a Python library for symbolic mathematics.
With the excellent PyCall
package of julia
, one has access to the
many features of the SymPy library from within a Julia
session.
This SymPy
package provides a light interface for the
features of the SymPy library that makes working with SymPy objects a bit
easier.
The documentation inludes an introduction document and a version of the SymPy tutorial translated from the Python syntax into Julia.
Installation
To use this package, both Python and its SymPy library must be
installed on your system. If PyCall
is installed using Conda
(which is the default if no system python
is found), then the
underlying SymPy library will be installed via Conda
when the
package is first loaded. Otherwise, installing both Python and the
SymPy library (which also requires mpmath) can be done by other means.
In this case, the Anaconda
distribution is suggested, as it provides a single
installation of Python that includes SymPy and many other
scientific libraries that can be profitably accessed within Julia
via PyCall
. (Otherwise, install Python then download the SymPy
library from https://github.com/sympy/sympy/releases and install.)
To upgrade the underlying sympy
library, which has new releases at a
rate similar to Julia
, when installed with Conda
, the following commands
are available:
using Pkg
Pkg.add("Conda") # if needed
using Conda
Conda.update()
PyCall
interface to SymPy
The The only point to this package is that using PyCall
to access
SymPy is somewhat cumbersome. The following is how one would define
a symbolic value x
, take its sine, then evaluate the symboic
expression for x
equal pi
, say:
using PyCall
sympy = pyimport("sympy") #
x = sympy.Symbol("x") # PyObject x
y = sympy.sin(x) # PyObject sin(x)
z = y.subs(x, sympy.pi) # PyObject 0
convert(Float64, z) # 0.0
The sympy
object imported on the second line provides the access to
much of SymPy's functionality, allowing access to functions
(sympy.sin
), properties, modules (sympy
), and classes
(sympy.Symbol
, sympy.Pi
). The Symbol
and sin
operations are found
within the imported sympy
module and, as seen, are referenced with
Python
's dot call syntax, as implemented in PyCall
through a
specialized getproperty
method.
SymPy's functionality is also found through methods bound to
an object of a certain class. The subs
method of the y
object is an
example. Such methods are also accessed with Python's dotcall
syntax. The call above substitutes a value of sympy.pi
for the
symbolic variable x
. This leaves the object as a PyObject
storing
a number which can be brought back into julia
through conversion, in
this case through an explicit convert
call.
Alternatively, PyCall
now has a *
method, so the above could also be done with:
x = sympy.Symbol("x")
y = sympy.pi * x
z = sympy.sin(y)
convert(Float64, z.subs(x, 1))
With the SymPy
package this gets replaced by a more julia
n syntax:
using SymPy
x = symbols("x") # or @syms x
y = sin(pi*x)
y(1) # Does y.subs(x, 1). Use y(x=>1) to be specific as to which symbol to substitute
The object x
we create is of type Sym
, a simple proxy for the
underlying PyObject
. The package overloads the familiar math functions so
that working with symbolic expressions can use natural julia
idioms. The final result here is a symbolic value of 0
, which
prints as 0
and not PyObject 0
. To convert it into a numeric value
within Julia
, the N
function may be used, which acts like the
float conversion, only there is an attempt to preserve the variable type.
(There is a subtlety, the value of pi
here (an Irrational
in
Julia
) is converted to the symbolic PI
, but in general won't be if
the math constant is coerced to a floating point value before it
encounters a symbolic object. It is better to just use the symbolic
value PI
, an alias for sympy.pi
used above.)
SymPy has a mix of function calls (as in sin(x)
) and method calls
(as in y.subs(x,1)
). The function calls are from objects in the base
sympy
module. When the SymPy
package is loaded, in addition to
specialized methods for many generic Julia
functions, such as sin
,
a priviledged set of the function calls in sympy
are imported as
generic functions narrowed on their first argument being a symbolic
object, as constructed by @syms
, Sym
, or symbols
. (Calling
import_from(sympy)
will import all the function calls.)
The basic usage follows these points:

generic methods from
Julia
and imported functions in thesympy
namespace are called throughfn(object)

SymPy methods are called through Python's dotcall syntax:
object.fn(...)

Contructors, like
sympy.Symbol
, and other nonfunction calls fromsympy
are qualified withsympy.Constructor(...)
. Such qualified calls are also useful when the first argument is not symbolic.
So, these three calls are different,
sin(1), sin(Sym(1)), sympy.sin(1)
The first involves no symbolic values. The second and third are
related and return a symbolic value for sin(1)
. The second
dispatches on the symbolic argument Sym(1)
, the third has no
dispatch, but refers to a SymPy function from the sympy
object. Its
argument, 1
, is converted by PyCall
into a Python object for the
function to process.
In the initial example, slightly rewritten, we could have issued:
x = symbols("x")
y = sin(pi*x)
y.subs(x, 1)
The first line calls a provided alias for sympy.symbols
which is
defined to allow a string (or a symbol) as an argument. The second,
dispatches to sympy.sin
, as pi*x
is symbolic x
is, and
multiplication promotes to a symbolic value. The third line uses the
dotcall syntax of PyCall
to call the subs
method of the symbolic
y
object.
Not illustrated above, but classes and other objects from SymPy are
not brought in by default, and can be accessed using qualification, as
in sympy.Function
(used, as is @syms
, to define symbolic functions).