pyinterp.reduced_gaussian.column_kernel

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pyinterp.reduced_gaussian.column_kernel#

pyinterp.reduced_gaussian.column_kernel(n_out=1)[source]#

Compile a Python function into a native column kernel with numba.

The function is compiled in nopython mode and called once per point from the threads of apply(), without the GIL: use it for computations that loop over the levels and do not vectorize over points. It receives numpy views and returns 0, or a non-zero code to report a failure (the point is then NaN). A kernel cannot raise: an exception is printed and lost, so report failures through the return code. The function, and any function it calls, is compiled with numba’s error_model="numpy": a division by zero gives inf or NaN as in C; decorate the helpers it calls with numba.njit(error_model="numpy") too:

@column_kernel(n_out=1)
def kernel(columns, widths, a, b, point, out):
    # columns: the fields concatenated, widths[k] values for field k
    # a, b: hybrid coefficients (empty if not given)
    # point: lon, lat, [time,] then the extra args, in order
    # out: the n_out values to compute
    out[0] = columns[0] * point[1]
    return 0
Parameters:

n_out (int) – Number of values the kernel computes per point.

Returns:

A decorator turning the function into a ColumnKernel.

Raises:

ImportError – If numba is not installed.

Return type:

Callable[[Callable[…, int]], ColumnKernel]

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