Installation

Requirements

Continuo targets Python 3.13 and above. Its runtime dependencies are:

  • CasADi ≥ 3.6 (symbolic engine and AD),

  • NumPy ≥ 1.26,

  • SciPy ≥ 1.11 (sparse linear algebra),

  • Lark ≥ 1.1 (parser generator).

From a clone

The recommended way during development is an editable install from the repository root:

$ git clone https://github.com/stepan-a/continuo.git
$ cd continuo
$ pip install -e .

That installs the continuo Python package and the continuo command-line entry point.

Optional extras

The Solution object can be converted to a pandas DataFrame or an xarray Dataset; those conversions require the optional extras:

$ pip install -e ".[pandas,xarray]"

Linear-solver backends

The Newton solve runs on a pluggable linear backend (see Linear solvers). SuperLU (via SciPy) is always available, so nothing extra is needed. The faster default for one-step schemes, KLU, needs the SuiteSparse system library libklu.so — it has no pip package; install it from your distribution:

$ sudo apt install libsuitesparse-dev      # Debian / Ubuntu
# or:  conda install -c conda-forge suitesparse

When libklu.so is absent, continuo simply falls back to SuperLU. The two further optional backends are PyPI packages, installed as extras:

$ pip install -e ".[umfpack]"      # SuiteSparse UMFPACK
$ pip install -e ".[pardiso]"      # Intel MKL PARDISO (pulls in MKL)
$ pip install -e ".[solvers]"      # both

To build this manual locally:

$ pip install sphinx furo sphinx-copybutton
$ cd doc/manual && make html
$ xdg-open _build/html/index.html

Verifying the install

A working install runs the test suite cleanly:

$ pip install -e ".[dev]"
$ pytest -q

and the CLI executes the smallest model:

$ cat > saddle.mod <<'EOF'
var(state) x;
var(jump) y;
model;
  diff(x) = -x;
  diff(y) = y;
end;
initval; x = 1; end;
simulate(T=5, N=20);
EOF
$ continuo saddle.mod
continuo: wrote 21 rows to saddle.csv