The numeric module provides methods that are lacking from the numpy module.

nutils.numeric.overlapping(arr, axis=- 1, n=2)

reinterpret data with overlaps

nutils.numeric.full(shape, fill_value, dtype)

read-only equivalent to numpy.full()

nutils.numeric.normdim(ndim, n)

check bounds and make positive

nutils.numeric.get(arr, axis, item)

take single item from array axis

nutils.numeric.contract(A, B, axis=- 1), B, axis=- 1)

Transform axis of A by contraction with first axis of B and inserting remaining axes. Note: with default axis=-1 this leads to multiplication of vectors and matrices following linear algebra conventions.

nutils.numeric.meshgrid(*args, dtype=None)

Multi-dimensional meshgrid generalisation.

Meshgrid stacks n arbitry-dimensional arrays into an array that is one dimension higher than all dimensions combined, such that retval[i] equals args[i] broadcasted to consecutive dimension slices. For two vector arguments this is almost equal to numpy.meshgrid(), with the main difference that dimensions are not swapped in the return values. The other difference is that the return value is a single array, but since the stacked axis is the first dimension the result can always be tuple unpacked.

  • args (sequence of numpy.ndarray objects or equivalent) – The arrays that are to be grid-stacked.

  • dtype (type of output array) – If unspecified the dtype is determined automatically from the input arrays using numpy.result_type().

Return type


nutils.numeric.simplex_grid(shape, spacing)

Multi-dimensional generator for equilateral simplex grids.

Simplex_grid generates a point cloud within an n-dimensional orthotope, which ranges from zero to a specified shape. The point coordinates are spaced in such a way that the nearest neighbours are at distance spacing, thus forming vertices of regular simplices. The returned array is two-dimensional, with the first axis being the spatial dimension (matching shape) and the second a stacking of the generated points.

  • shape (tuple) – list or tuple of dimensions of the orthotope to be filled.

  • spacing (float) – minimum spacing in the generated point cloud.

Return type


nutils.numeric.normalize(A, axis=- 1)

devide by normal

nutils.numeric.diagonalize(arg, axis=- 1, newaxis=- 1)

insert newaxis, place axis on diagonal of axis and newaxis


Matrix inverse.

Fully equivalent to numpy.linalg.inv(), with the exception that upon singular systems inv() does not raise a LinAlgError, but rather issues a RuntimeWarning and returns NaN (not a number) values. For arguments of dimension >2 the return array contains NaN values only for those entries that correspond to singular matrices.


version of numpy.ix_() that allows for scalars


Exterior For array of shape (n,n-1) return n-vector ex such that ex.array = 0 and det(arr;ex) = ex.ex

nutils.numeric.unpack(n, atol, rtol)

Convert packed representation to floating point data.

The packed binary form is a floating point interpretation of signed integer data, such that any integer n maps onto float a as follows:

a = nan                       if n = -N-1
a = -inf                      if n = -N
a = sinh(n*rtol)*atol/rtol    if -N < n < N
a = +inf                      if n = N,

where N = 2**(nbits-1)-1 is the largest representable signed integer.

Note that packing is both order and zero preserving. The transformation is designed such that the spacing around zero equals atol, while the relative spacing for most of the data range is approximately constant at rtol. Precisely, the spacing between a value a and the adjacent value is sqrt(atol**2 + (a*rtol)**2). Note that the truncation error equals half the spacing.

The representable data range depends on the values of atol and rtol and the bitsize of n. Useful values for different data types are:

















  • n (int array) – Integer data.

  • atol (float) – Absolute tolerance.

  • rtol (float) – Relative tolerance.

Return type

float array

nutils.numeric.pack(a, atol, rtol, dtype)

Lossy compression of floating point data.

See unpack() for the definition of the packed binary form. The converse transformation uses rounding in packed domain to determine the closest matching value. In particular this may lead to values falling outside the representable data range to be clipped to infinity. Some examples of packed truncation:

>>> def truncate(a, dtype, **tol):
...   return unpack(pack(a, dtype=dtype, **tol), **tol)
>>> truncate(0.5, dtype='int16', atol=2e-15, rtol=2e-3)
>>> truncate(1, dtype='int16', atol=2e-15, rtol=2e-3)
>>> truncate(2, dtype='int16', atol=2e-15, rtol=2e-3)
>>> truncate(2, dtype='int16', atol=2e-15, rtol=2e-4)
>>> truncate(2, dtype='int32', atol=2e-15, rtol=2e-4)
  • a (float array) – Input data.

  • atol (float) – Absolute tolerance.

  • rtol (float) – Relative tolerance.

  • dtype (str or numpy dtype) – Target dtype for packed data.

Return type

int array.

nutils.numeric.accumulate(data, index, shape)

accumulate scattered data in dense array.

Accumulates values from data in an array of shape shape at positions index, equivalent with:

>>> def accumulate(data, index, shape):
...   array = numpy.zeros(shape, data.dtype)
...   for v, *ij in zip(data, *index):
...     array[ij] += v
...   return array
nutils.numeric.asboolean(array, size, ordered=True)

convert index array to boolean.

A boolean array is returned as-is after confirming that the length is correct.

>>> asboolean([True, False], size=2)
array([ True, False], dtype=bool)

A strictly increasing integer array is converted to the equivalent boolean array such that asboolean(array, n).nonzero()[0] == array.

>>> asboolean([1,3], size=4)
array([False,  True, False,  True], dtype=bool)

In case the order of integers is not important this must be explicitly specified using the ordered argument.

>>> asboolean([3,1,1], size=4, ordered=False)
array([False,  True, False,  True], dtype=bool)
  • array (int or bool array_like or None) – Integer or boolean index data.

  • size (int) – Target array length.

  • ordered (bool) – Assert that integers are strictly increasing.

nutils.numeric.invmap(indices, length, missing=- 1)

Create inverse index array.

Create the index array inverse with the given length such that inverse[indices[i]] == i and inverse[j] == missing for all j not in indices. It is an error to pass an indices array with repeated indices, in which case the result is undefined.

>>> m = invmap([3,1], length=5)
>>> m[3]
>>> m[1]
  • indices (int array_like) – Integer or index data.

  • length (int) – Target array length; must be larger than max(indices).

  • missing (int (default: -1)) – Value to insert for missing indices.

Return type


nutils.numeric.levicivita(n, dtype=<class 'float'>)

n-dimensional Levi-Civita symbol.