4.6.8. pde.tools.numba module

Helper functions for just-in-time compilation with numba

class Counter(value: int = 0)[source]

Bases: object

helper class for implementing JIT_COUNT

We cannot use a simple integer for this, since integers are immutable, so if one imports JIT_COUNT from this module it would always stay at the fixed value it had when it was first imported. The workaround would be to import the symbol every time the counter is read, but this is error-prone. Instead, we implement a thin wrapper class around an int, which only supports reading and incrementing the value. Since this object is now mutable it can be used easily. A disadvantage is that the object needs to be converted to int before it can be used in most expressions.


helper function that turns 0d-arrays into scalars

This helps to avoid the bug discussed in https://github.com/numba/numba/issues/6000

flat_idx(arr, i)[source]

helper function allowing indexing of scalars as if they arrays


returns a numba numerical type in which all arrays can be represented


*args – All items to be tested

Returns: numba.complex128 if any entry is complex, otherwise numba.double

jit(function: TFunc, signature=None, parallel: bool = False, **kwargs) TFunc[source]

apply nb.jit with predefined arguments

  • function – The function which is jitted

  • signature – Signature of the function to compile

  • parallel (bool) – Allow parallel compilation of the function

  • **kwargs – Additional arguments to nb.jit


Function that will be compiled using numba

jit_allocate_out(func: Callable, parallel: bool = False, out_shape: Optional[Tuple[int, ...]] = None, num_args: int = 1, **kwargs) Callable[source]

Decorator that compiles a function with allocating an output array.

This decorator compiles a function that takes the arguments arr and out. The point of this decorator is to make the out array optional by supplying an empty array of the same shape as arr if necessary. This is implemented efficiently by using numba.generated_jit().

  • func – The function to be compiled

  • parallel (bool) – Determines whether the function is jitted with parallel=True.

  • out_shape (tuple) – Determines the shape of the out array. If omitted, the same shape as the input array is used.

  • num_args (int, optional) – Determines the number of input arguments of the function.

  • **kwargs – Additional arguments used in numba.jit()


The decorated function

make_array_constructor(arr: ndarray) Callable[[], ndarray][source]

returns an array within a jitted function using basic information


arr (ndarray) – The array that should be accessible within jit


A reference to the array needs to be retained outside the numba code to prevent garbage collection from removing the array

numba_dict(data: Optional[Dict[str, Any]] = None) Optional[Dict][source]

converts a python dictionary to a numba typed dictionary

numba_environment() Dict[str, Any][source]

return information about the numba setup used


(dict) information about the numba setup