mdtraj.utils.ensure_type

mdtraj.utils.ensure_type(val, dtype, ndim, name, length=None, can_be_none=False, shape=None, warn_on_cast=True, add_newaxis_on_deficient_ndim=False)

Typecheck the size, shape and dtype of a numpy array, with optional casting.

Parameters:

val : {np.ndaraay, None}

The array to check

dtype : {nd.dtype, str}

The dtype you’d like the array to have

ndim : int

The number of dimensions you’d like the array to have

name : str

name of the array. This is used when throwing exceptions, so that we can describe to the user which array is messed up.

length : int, optional

How long should the array be?

can_be_none : bool

Is val == None acceptable?

shape : tuple, optional

What should be shape of the array be? If the provided tuple has Nones in it, those will be semantically interpreted as matching any length in that dimension. So, for example, using the shape spec (None, None, 3) will ensure that the last dimension is of length three without constraining the first two dimensions

warn_on_cast : bool, default=True

Raise a warning when the dtypes don’t match and a cast is done.

add_newaxis_on_deficient_ndim : bool, default=True

Add a new axis to the beginining of the array if the number of dimensions is deficient by one compared to your specification. For instance, if you’re trying to get out an array of ndim == 3, but the user provides an array of shape == (10, 10), a new axis will be created with length 1 in front, so that the return value is of shape (1, 10, 10).

Returns:

typechecked_val : np.ndarray, None

If val=None and can_be_none=True, then this will return None. Otherwise, it will return val (or a copy of val). If the dtype wasn’t right, it’ll be casted to the right shape. If the array was not C-contiguous, it’ll be copied as well.

Notes

The returned value will always be C-contiguous.