[yt-users] Derived fields for generic array data

[Nathan Goldbaum]

Hi Will,

There isn't a way to do what you're describing in yt right now because
fields are defined locally in terms of the values of other fields. We have
long-term plans to make it possible to define a field in terms of women
non-local property of other fields (e.g. a field defined in terms of
streamlines) but this functionality does not exist yet.

That said, if you could make a 3D numpy array of the same shape as your
other fields, you could load *another* dataset using load_uniform_grid and
pass it the original fields along with the new interpolated density field.

Hope that's helpful,

 Nathan Goldbaum

On Saturday, October 15, 2016, Will Barnes <Will_Barnes at alumni.baylor.edu>
wrote:

> Hi yt users,
>
> I have a question about derived fields in a dataset loaded from generic
> array data. I’ve created a dataset from a three dimensional Numpy array and
> traced some streamlines (say N of them) through the volume (this is a 3D
> magnetic field) such that I now have a set of N fieldlines. External to the
> dataset, I then have N Numpy arrays corresponding to each of the N
> streamlines, each the same length of the corresponding streamline, that
> represent the density as a function of the position along the streamline.
>
> What I’d like to do is create a new derived field (“density”) where the
> values from each of my external numpy arrays are mapped to the
> corresponding coordinate (as determined by the streamlines) and then
> “density” is zero everywhere else. I’ve read the documentation about
> creating derived fields and adding fields to a dataset, but it is not clear
> to me how to use external information (e.g. my set of N arrays) to create a
> new or derived field as all the examples show only how to use already
> existing fields in the dataset to create a new field.
>
> This seems like a relatively simple thing so perhaps I am just missing
> something or not quite understanding how a new field is added to a dataset?
> Since my explanation above is a bit confusing, I’ve included a code snippet
> below that hopefully illustrates a bit better what I’d like to do.
>
> Thanks!
>
> Will Barnes
>
> ```
> # create 3d numpy array
> arr = np.random.random(size=(64,64,64))
>
> # load generic data into a dataset
> data = dict(density = (arr, "g/cm**3"))
> bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [-1.5, 1.5]])
> ds = yt.load_uniform_grid(data, arr.shape, length_unit="Mpc", bbox=bbox,
> nprocs=64)
>
> # set seed points
> c = ds.domain_center
> N = 100
> scale = ds.domain_width[0]
> pos_dx = np.random.random((N,3))*scale-scale/2.
> pos = c+pos_dx
>
> # Create streamlines
> streamlines = Streamlines(ds, pos, 'velocity_x', 'velocity_y',
> 'velocity_z',length=1.0*Mpc, get_magnitude=True)
> streamlines.integrate_through_volume()
>
> # create a density field in ds initialized as 0 everywhere
> # for each streamline in streamlines, find coordinates from streamline and
> add the appropriate density value to the coordinates
> ```
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