Symbolic computer algebra systems like SymPy facilitate the construction and manipulation of mathematical expressions. Unfortunately when it comes time to evaluate these expressions on numerical data, symbolic systems often have poor performance.

Fortunately SymPy offers a number of easy-to-use hooks into other numeric
systems, allowing you to create mathematical expressions in SymPy and then
ship them off to the numeric system of your choice. This page documents many
of the options available including the `math` library, the popular array
computing package `numpy`, code generation in `Fortran` or `C`, and the
use of the array compiler `Theano`.

Subs is the slowest but simplest option. It runs at SymPy speeds.
The `.subs(...).evalf()` method can substitute a numeric value
for a symbolic one and then evaluate the result within SymPy.

```
>>> from sympy import *
>>> from sympy.abc import x
>>> expr = sin(x)/x
>>> expr.evalf(subs={x: 3.14})
0.000507214304613640
```

This method is slow. You should use this method production only if performance
is not an issue. You can expect `.subs` to take tens of microseconds. It
can be useful while prototyping or if you just want to see a value once.

The `lambdify` function translates SymPy expressions into Python functions,
leveraging a variety of numerical libraries. It is used as follows:

```
>>> from sympy import *
>>> from sympy.abc import x
>>> expr = sin(x)/x
>>> f = lambdify(x, expr)
>>> f(3.14)
0.000507214304614
```

Here lambdify makes a function that computes `f(x) = sin(x)/x`. By default
lambdify relies on implementations in the `math` standard library. This
numerical evaluation takes on the order of hundreds of nanoseconds, roughly two
orders of magnitude faster than the `.subs` method. This is the speed
difference between SymPy and raw Python.

Lambdify can leverage a variety of numerical backends. By default it uses the
`math` library. However it also supports `mpmath` and most notably,
`numpy`. Using the `numpy` library gives the generated function access to
powerful vectorized ufuncs that are backed by compiled C code.

```
>>> from sympy import *
>>> from sympy.abc import x
>>> expr = sin(x)/x
>>> f = lambdify(x, expr, "numpy")
```

```
>>> import numpy
>>> data = numpy.linspace(1, 10, 10000)
>>> f(data)
[ 0.84147098 0.84119981 0.84092844 ..., -0.05426074 -0.05433146
-0.05440211]
```

If you have array-based data this can confer a considerable speedup, on the order of 10 nano-seconds per element. Unfortunately numpy incurs some start-up time and introduces an overhead of a few microseconds.

While NumPy operations are very efficient for vectorized data they sometimes incur unnecessary costs when chained together. Consider the following operation

```
>>> x = get_numpy_array(...)
>>> y = sin(x) / x
```

The operators `sin` and `/` call routines that execute tight for loops in
`C`. The resulting computation looks something like this

```
for(int i = 0; i < n; i++)
{
temp[i] = sin(x[i]);
}
for(int i = i; i < n; i++)
{
y[i] = temp[i] / x[i];
}
```

This is slightly sub-optimal because

- We allocate an extra
`temp`array - We walk over
`x`memory twice when once would have been sufficient

A better solution would fuse both element-wise operations into a single for loop

```
for(int i = i; i < n; i++)
{
y[i] = sin(x[i]) / x[i];
}
```

Statically compiled projects like NumPy are unable to take advantage of such
optimizations. Fortunately, SymPy is able to generate efficient low-level C
or Fortran code. It can then depend on projects like `Cython` or `f2py` to
compile and reconnect that code back up to Python. Fortunately this process is
well automated and a SymPy user wishing to make use of this code generation
should call the `ufuncify` function

```
>>> from sympy import *
>>> from sympy.abc import x
>>> expr = sin(x)/x
```

```
>>> from sympy.utilities.autowrap import ufuncify
>>> f = ufuncify([x], expr)
```

This function `f` consumes and returns a NumPy array. Generally `ufuncify`
performs at least as well as `lambdify`. If the expression is complicated
then `ufuncify` often significantly outperforms the NumPy backed solution.
Jensen has a good blogpost on this topic
http://ojensen.wordpress.com/2010/08/10/fast-ufunc-ish-hydrogen-solutions/

SymPy has a strong connection with [Theano](http://deeplearning.net/software/theano/), a mathematical array compiler. SymPy expressions can be easily translated to Theano graphs and then compiled using the Theano compiler chain.

```
>>> from sympy import *
>>> from sympy.abc import x
>>> expr = sin(x)/x
```

```
>>> from sympy.printing.theanocode import theano_function
>>> f = theano_function([x], [expr])
```

If array broadcasting or types are desired then Theano requires this extra information

```
>>> f = theano_function([x], [expr], dims={x: 1}, dtypes={x: 'float64'})
```

Theano has a more sophisticated code generation system than SymPy’s C/Fortran code printers. Among other things it handles common sub-expressions and compilation onto the GPU. Theano also supports SymPy Matrix and Matrix Expression objects.

The options here were listed in order from slowest and least dependencies to
fastest and most dependencies. For example, if you have Theano installed then
that will often be the best choice. If you don’t have Theano but do have
`f2py` then you should use `ufuncify`.

Tool | Speed | Qualities | Dependencies |
---|---|---|---|

subs/evalf | 50us | Simple | None |

lambdify | 1us | Scalar functions | math |

lambdify-numpy | 10ns | Vector functions | numpy |

ufuncify | 10ns | Complex vector expressions | f2py, Cython |

Theano | 10ns | Many outputs, CSE, GPUs | Theano |