# Image index ordering¶

## Background¶

In general, images - and in particular NIfTI format images, are ordered in memory with the X dimension changing fastest, and the Z dimension changing slowest.

Numpy has two different ways of indexing arrays in memory, C and fortran. With C index ordering, the first index into an array indexes the slowest changing dimension, and the last indexes the fastest changing dimension. With fortran ordering, the first index refers to the fastest changing dimension - X in the case of the image mentioned above.

C is the default index ordering for arrays in Numpy.

For example, let’s imagine that we have a binary block of 3D image data, in standard NIfTI / Analyze format, with the X dimension changing fastest, called my.img, containing Float32 data. Then we memory map it:

```
img_arr = memmap('my.img', dtype=float32)
```

When we index this new array, the first index indexes the Z dimension, and the third indexes X. For example, if I want a voxel X=3, Y=10, Z=20 (zero-based), I have to get this from the array with:

```
img_arr[20, 10, 3]
```

## The problem¶

Most potential users of NiPy are likely to have experience of using image arrays in Matlab and SPM. Matlab uses Fortran index ordering. For fortran, the first index is the fastest changing, and the last is the slowest-changing. For example, here is how to get voxel X=3, Y=10, Z=20 (zero-based) using SPM in Matlab:

```
img_arr = spm_read_vols(spm_vol('my.img'));
img_arr(4, 11, 21) % matlab indexing is one-based
```

This ordering fits better with the way that we talk about coordinates in functional imaging, as we invariably use XYZ ordered coordinates in papers. It is possible to do the same in numpy, by specifying that the image should have fortran index ordering:

```
img_arr = memmap('my.img', dtype=float32, order='F')
img_arr[3, 10, 20]
```

## Native fortran or C indexing for images¶

We could change the default ordering of image arrays to fortran, in order to allow XYZ index ordering. So, change the access to the image array in the image class so that, to get the voxel at X=3, Y=10, Z=20 (zero-based):

```
img = load_image('my.img')
img[3, 10, 20]
```

instead of the current situation, which requires:

```
img = load_image('my.img')
img[20, 10, 3]
```

### For and against fortran ordering¶

For:

Fortran index ordering is more intuitive for functional imaging because of conventional XYZ ordering of spatial coordinates, and Fortran index ordering in packages such as Matlab

Indexing into a raw array is fast, and common in lower-level applications, so it would be useful to implement the more intuitive XYZ ordering at this level rather than via interpolators (see below)

Standardizing to one index ordering (XYZ) would mean users would not have to think about the arrangement of the image in memory

Against:

C index ordering is more familiar to C users

C index ordering is the default in numpy

XYZ ordering can be implemented by wrapping by an interpolator

Note that there is no performance penalty for either array ordering, as this is dealt with internally by NumPy. For example, imagine the following:

```
arr = np.empty((100,50)) # Indexing is C by default
arr2 = arr.transpose() # Now it is fortran
# There should be no effective difference in speed for the next two lines
b = arr[0] # get first row of data - most discontiguous memory
c = arr2[:,0] # gets same data, again most discontiguous memory
```

### Potential problems for fortran ordering¶

#### Clash between default ordering of numpy arrays and nipy images¶

C index ordering is the default in numpy, and using fortran ordering for images might be confusing in some circumstances. Consider for example:

```
img_obj = load_image('my.img') # Where the Image class has been changed to implement Fortran ordering
first_z_slice = img_obj[...,0] # returns a Z slice
img_arr = memmap('my.img', dtype=float32) # C ordering, the numpy default
img_obj = Image.from_array(img_arr) # this call may not be correct
first_z_slice = img_obj[...,0] # in fact returns an X slice
```

I suppose that we could check that arrays are fortran index ordered in the Image __init__ routine.

## An alternative proposal - XYZ ordering of output coordinates¶

JT: Another thought, that is a compromise between the XYZ coordinates and Fortran ordering.

To me, having worked mostly with C-type arrays, when I index an array I think in C terms. But, the Image objects have the “warp” attached to them, which describes the output coordinates. We could insist that the output coordinates are XYZT (or make this an option). So, for instance, if the 4x4 transform was the identity, the following two calls would give something like:

```
>>> interp = interpolator(img)
>>> img[3,4,5] == interp(5,4,3)
True
```

This way, users would be sure in the interpolator of the order of the coordinates, but users who want access to the array would know that they would be using the array order on disk…

I see that a lot of users will want to think of the first coordinate as “x”, but depending on the sampling the [0] slice of img may be the leftmost or the rightmost. To find out which is which, users will have to look at the 4x4 transform (or equivalently the start and the step). So just knowing the first array coordinate is the “x” coordinate still misses some information, all of which is contained in the transform.

MB replied:

I agree that the output coordinates are very important - and I think we all agree that this should be XYZ(T)?

For the raw array indices - it is very common for people to want to do things to the raw image array - the quickstart examples containing a few - and you usually don’t care about which end of X is left in that situation, only which spatial etc dimension the index refers to.