Header datatypes

There are 10 possible datatypes that a value can have in the header. Below are the valid datatypes along with the datatype they are parsed into in Python and examples of the datatypes.

int

NRRD Syntax:
NRRD Example: 12
Python Datatype: int
Python Example: 12

double

NRRD Syntax: <d>
NRRD Example: 3.14
Python Datatype: float
Python Example: 3.14

string

NRRD Syntax: <s>
NRRD Example: test
Python Datatype: str
Python Example: ‘test’

int list

NRRD Syntax: …
NRRD Example: 1 2 3 4
Python Datatype: numpy.ndarray (1D, dtype=int)
Python Example: np.array([1, 2, 3, 4])

double list

NRRD Syntax: <d> <d> … <d>
NRRD Example: 1.2 2.3 3.4 4.5
Python Datatype: numpy.ndarray (1D, dtype=float)
Python Example: np.array([1.2, 2.3, 3.4, 4.5])

string list

NRRD Syntax: <s> <s> … <s>
NRRD Example: this is space separated
Python Datatype: list of str
Python Example: [‘this’, ‘is’, ‘space’, ‘separated’]

quoted string list

NRRD Syntax: “<s>” “<s>” … “<s>”
NRRD Example: “one item” “two items” “three” “four”
Python Datatype: list of str
Python Example: [‘one item’, ‘two items’, ‘three’, ‘four’]

int vector

NRRD Syntax: (,,…,)
NRRD Example: (1,2,3,4)
Python Datatype: (N,) numpy.ndarray of int
Python Example: np.array([1, 2, 3, 4])

pynrrd will correctly handle vectors with or without spaces between the comma-delimiter. Saving the NRRD file back will remove all spaces between the comma-delimiters.

double vector

NRRD Syntax: (<d>,<d>,…,<d>)
NRRD Example: (1.2,2.3,3.4,4.5)
Python Datatype: (N,) numpy.ndarray of float
Python Example: np.array([1.2, 2.3, 3.4, 4.5])

pynrrd will correctly handle vectors with or without spaces between the comma-delimiter. Saving the NRRD file back will remove all spaces between the comma-delimiters.

int matrix

NRRD Syntax: (,,…,) (,,…,) … (,,…,)
NRRD Example: (1,0,0) (0,1,0) (0,0,1)
Python Datatype: (M,N) numpy.ndarray of int
Python Example: np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])

All rows of the matrix are required, unlike that of the double matrix. If some of the rows need to be ‘none’, then use a double matrix instead. The reason is that empty rows (i.e. containing ‘none’) are represented as a row of NaN’s, and NaN’s are only available for floating point numbers.

double matrix

NRRD Syntax: (<d>,<d>,…,<d>) (<d>,<d>,…,<d>) … (<d>,<d>,…,<d>)
NRRD Example: (2.54, 1.3, 0.0) (3.14, 0.3, 3.3) none (0.05, -12.3, -3.3)
Python Datatype: (M,N) numpy.ndarray of float
Python Example: np.array([[2.54, 1.3, 0.0], [3.14, 0.3, 3.3], [np.nan, np.nan, np.nan], [0.0, -12.3, -3.3]])

This datatype has the added feature where rows can be defined as empty by setting the vector as none. In the NRRD specification, instead of the row, the none keyword is used in it’s place. This is represented in the Python NumPy array as a row of all NaN’s. An example use case for this optional row matrix is for the ‘space directions’ field where one row may be empty because it is not a domain type.

int vector list

NRRD Syntax: (,,…,) (,,…,) … (,,…,)
NRRD Example: (1,0,0) (0,1,0) none (0,0,1)
Python Datatype: list of (N,) numpy.ndarray of int
Python Example: [np.array([1, 0, 0]), np.array([0, 1, 0]), None, np.array([0, 0, 1])]

This datatype is similar to int matrix except instead of returning a (M,N) numpy.ndarray, it returns a list of (N,) numpy.ndarray. Each row is optional and designated by none in the NRRD specification and represented as None in this library.

double vector list

NRRD Syntax: (<d>,<d>,…,<d>) (<d>,<d>,…,<d>) … (<d>,<d>,…,<d>)
NRRD Example: (2.54, 1.3, 0.0) (3.14, 0.3, 3.3) none (0.05, -12.3, -3.3)
Python Datatype: list of (N,) numpy.ndarray of float
Python Example: [np.array([2.54, 1.3, 0.0]), np.array([3.14, 0.3, 3.3]), None, np.array([0.0, -12.3, -3.3])]

This datatype is similar to double matrix except instead of returning a (M,N) numpy.ndarray, it returns a list of (N,) numpy.ndarray. Each row is optional and designated by none in the NRRD specification and represented as None in this library.