# Setting up mpmath

Mpmath requires at least Python 3.9. It has been tested with CPython 3.9 through 3.14 and for PyPy 3.11.

## Download and installation

### Using pip

Releases are registered on PyPI, so you can install latest release of the Mpmath with pip:

    pip install mpmath

or some specific version with:

    pip install mpmath==1.3.0

You can install also extra dependencies, e.g. gmpy2 support:

    pip install mpmath[gmpy2]

Tip

Use `venv` to create isolated Python environment first, instead of installing everything system-wide.

### Debian/Ubuntu

Debian and Ubuntu users can install mpmath with:

    sudo apt install python3-mpmath

See debian and ubuntu package information; please verify that you are getting the latest version.

### Current development version

If you are a developer or like to get the latest updates as they come, be sure to install from git:

    git clone git://github.com/mpmath/mpmath.git
    cd mpmath
    pip install -e .[develop,docs]

### Checking that it works

After the setup has completed, you should be able to fire up the interactive Python interpreter and do the following:

    >>> from mpmath import mp, mpf, pi
    >>> mp.dps = 50
    >>> print(mpf(2) ** mpf('0.5'))
    1.4142135623730950488016887242096980785696718753769
    >>> print(2*pi)
    6.2831853071795864769252867665590057683943387987502

Tip

Run mpmath as a module for interactive work:

    python -m mpmath

## Using gmpy2 (optional)

If gmpy2 version 2.3.0 or later is installed on your system, mpmath will automatically detect it and transparently use gmpy2 integers instead of Python integers. This makes mpmath much faster, especially at high precision (approximately above 100 digits).

To verify that mpmath uses gmpy2, check the internal variable `BACKEND` is equal to ‘gmpy’.

Using the gmpy2 backend can be disabled by setting the `MPMATH_NOGMPY` environment variable. Note that the mode cannot be switched during runtime; mpmath must be re-imported for this change to take effect.

Alternatively, you can use python-gmp extension. The `BACKEND` value will be equal to ‘gmp’ in this case. If both extensions are installed on your system, the gmpy2 will be preferred.

## Running tests

It is recommended that you run mpmath’s full set of unit tests to make sure everything works. The pytest is a required dependence for testing. The tests are located in the `tests` subdirectory of the mpmath source tree. They can be run using:

    pytest --pyargs mpmath

Developers may run tests from the source tree with:

    pytest

If any test fails, please send a detailed bug report to the mpmath issue tracker.

## Compiling the documentation

If you downloaded the source package, the text source for these documentation pages is included in the `docs` directory. The documentation can be compiled to pretty HTML using Sphinx:

    sphinx-build --color -W --keep-going -b html docs build/sphinx/html

The create a PDF:

    sphinx-build --color -W --keep-going -b latex docs build/sphinx/latex
    make -C build/sphinx/latex all-pdf

Some additional demo scripts are available in the `demo` directory included in the source package.

## Mpmath under Sage

Mpmath is a standard package in Sage, in version 4.1 or later of Sage. Mpmath is preinstalled a regular Python module, and can be imported as usual within Sage:

    ----------------------------------------------------------------------
    | Sage Version 4.1, Release Date: 2009-07-09                         |
    | Type notebook() for the GUI, and license() for information.        |
    ----------------------------------------------------------------------
    sage: import mpmath
    sage: mpmath.mp.dps = 50
    sage: print(mpmath.mpf(2) ** 0.5)
    1.4142135623730950488016887242096980785696718753769

In Sage, mpmath can alternatively be imported via the interface library `sage.libs.mpmath.all`. For example:

    sage: import sage.libs.mpmath.all as mpmath

This module provides a few extra conversion functions, including `mpmath.call()` which permits calling any mpmath function with Sage numbers as input, and getting Sage `RealNumber` or `ComplexNumber` instances with the appropriate precision back:

    sage: w = mpmath.call(mpmath.erf, 2+3*I, prec=100)
    sage: w
    -20.829461427614568389103088452 + 8.6873182714701631444280787545*I
    sage: type(w)
    <type 'sage.rings.complex_number.ComplexNumber'>
    sage: w.prec()
    100

See the help for `sage.libs.mpmath.all` for further information.