colour.algebra.sdiv#

colour.algebra.sdiv(a: FloatingOrArrayLike, b: FloatingOrArrayLike) FloatingOrNDArray[source]#

Divide given array \(b\) with array \(b\) while handling zero-division.

This definition avoids NaNs and +/- infs generation when array \(b\) is equal to zero. This behaviour can be controlled with the colour.algebra.set_sdiv_mode() definition or with the sdiv_mode() context manager. The following modes are available:

  • Numpy: The current Numpy zero-division handling occurs.

  • Ignore: Zero-division occurs silently.

  • Warning: Zero-division occurs with a warning.

  • Ignore Zero Conversion: Zero-division occurs silently and NaNs or +/- infs values are converted to zeros. See numpy.nan_to_num() definition for more details.

  • Warning Zero Conversion: Zero-division occurs with a warning and NaNs or +/- infs values are converted to zeros. See numpy.nan_to_num() definition for more details.

  • Ignore Limit Conversion: Zero-division occurs silently and NaNs or +/- infs values are converted to zeros or the largest +/- finite floating point values representable by the division result numpy.dtype. See numpy.nan_to_num() definition for more details.

  • Warning Limit Conversion: Zero-division occurs with a warning and NaNs or +/- infs values are converted to zeros or the largest +/- finite floating point values representable by the division result numpy.dtype.

Parameters:
  • a (FloatingOrArrayLike) – Numerator array \(a\).

  • b (FloatingOrArrayLike) – Denominator array \(b\).

Returns:

Array \(b\) safely divided by \(a\).

Return type:

np.floating or numpy.ndarray

Examples

>>> a = np.array([0, 1, 2])
>>> b = np.array([2, 1, 0])
>>> sdiv(a, b)
array([ 0.,  1.,  0.])
>>> try:
...     with sdiv_mode("Raise"):
...         sdiv(a, b)
... except Exception as error:
...     error
FloatingPointError('divide by zero encountered in true_divide')
>>> with sdiv_mode("Ignore Zero Conversion"):
...     sdiv(a, b)
array([ 0.,  1.,  0.])
>>> with sdiv_mode("Warning Zero Conversion"):
...     sdiv(a, b)
array([ 0.,  1.,  0.])
>>> with sdiv_mode("Ignore Limit Conversion"):
...     sdiv(a, b)  
array([  0.00000000e+000,   1.00000000e+000,   1.79769313e+308])
>>> with sdiv_mode("Warning Limit Conversion"):
...     sdiv(a, b)  
array([  0.00000000e+000,   1.00000000e+000,   1.79769313e+308])