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.. _numpy:
Numeric arrays in Python
========================
Links to NumPy's webpage:
* `Numpy and Scipy Documentation`_
* `Numpy user guide <https://docs.scipy.org/doc/numpy/user/index.html>`_
.. _Numpy and Scipy Documentation: https://docs.scipy.org/doc/
ASE makes heavy use of an extension to Python called NumPy. The
NumPy module defines an ``ndarray`` type that can hold large arrays of
uniform multidimensional numeric data. An array is similar to a
``list`` or a ``tuple``, but it is a lot more powerful and efficient.
XXX More examples from everyday ASE-life here ...
>>> import numpy as np
>>> a = np.zeros((3, 2))
>>> a[:, 1] = 1.0
>>> a[1] = 2.0
>>> a
array([[ 0., 1.],
[ 2., 2.],
[ 0., 1.]])
>>> a.shape
(3, 2)
>>> a.ndim
2
The conventions of numpy's linear algebra package:
>>> import numpy as np
>>>
>>> # Make a random hermitian matrix, H
>>> H = np.random.rand(6, 6) + 1.j * np.random.rand(6, 6)
>>> H = H + H.T.conj()
>>>
>>> # Determine eigenvalues and rotation matrix
>>> eps, U = np.linalg.eigh(H)
>>>
>>> # Sort eigenvalues
>>> sorted_indices = eps.real.argsort()
>>> eps = eps[sorted_indices]
>>> U = U[:, sorted_indices]
>>>
>>> # Check that U diagonalizes H:
>>> print(np.dot(np.dot(U.T.conj(), H), U) - np.diag(eps))
>>> print(np.allclose(np.dot(np.dot(U.T.conj(), H), U), np.diag(eps)))
>>>
>>> # The eigenvectors of H are the *coloumns* of U:
>>> np.allclose(np.dot(H, U[:, 3]), eps[3] * U[:, 3])
>>> np.allclose(np.dot(H, U), eps * U)
The rules for multiplying 1D arrays with 2D arrays:
* 1D arrays and treated like shape (1, N) arrays (row vectors).
* left and right multiplications are treated identically.
* A length `m` *row* vector can be multiplied with an `n \times m`
matrix, producing the same result as if replaced by a matrix with
`n` copies of the vector as rows.
* A length `n` *column* vector can be multiplied with an `n \times m`
matrix, producing the same result as if replaced by a matrix with
`m` copies of the vector as columns.
Thus, for the arrays below:
>>> M = np.arange(5 * 6).reshape(5, 6) # A matrix af shape (5, 6)
>>> v5 = np.arange(5) + 10 # A vector of length 5
>>> v51 = v5[:, None] # A length 5 column vector
>>> v6 = np.arange(6) - 12 # A vector of length 6
>>> v16 = v6[None, :] # A length 6 row vector
The following identities hold::
v6 * M == v16 * M == M * v6 == M * v16 == M * v16.repeat(5, 0)
v51 * M == M * v51 == M * v51.repeat(6, 1)
The same rules apply for adding and subtracting 1D arrays to /
from 2D arrays.
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