DataArray and Dataset meta data handling

This section describes details about how coords (and masks) of datasets and data arrays behave when slicing, combining, or inserting.

import numpy as np
import scipp as sc

x = sc.Variable(dims=['x'], values=[1,2,3,4])
da = sc.DataArray(data=x,
                  masks={'x':sc.less(x, 2 *})
ds = sc.Dataset(data={'a':da})

Consider a data array da and a dataset ds with an aligned coord and an aligned mask. The following conditions must hold:

assert 'x' in da['x', 0:1].coords # range slice preserves coord
assert 'x' in da['x', 0:1].masks # range slice preserves coord
assert 'x' in da['x', 0].attrs # point slice converts coord to attr
assert 'x' not in da['x', 0].coords
assert 'x' in da['x', 0].attrs
assert 'x' in da['x', 0].masks # point slice preserves masks as aligned
assert sc.identical(ds['a']['x', 0:1], ds['x', 0:1]['a'])
assert sc.identical(ds['a']['x', 0], ds['x', 0]['a'])
assert 'x' in ds['a'].coords
assert 'x' in ds['x', 0:1].coords
assert 'x' not in ds['x', 0].coords # cannot have attr (unaligned coord) in dataset
assert 'x' in ds['x', 0:1]['a'].coords
assert 'x' in ds['a']['x', 0].attrs
assert 'x' in ds['x', 0]['a'].attrs

assert 'x' in ds['a'].masks
assert 'x' in ds['x', 0:1]['a'].masks
assert 'x' in ds['a']['x', 0].masks
assert 'x' in ds['x', 0]['a'].masks

In operations, coords are compared:

    ok = da['x', 0:1] + da['x', 1:2]
    ok = False
assert not ok

Mismatching attrs (“unaligned coords”) are dropped:

assert sc.identical(da + da['x', 1], da + da['x', 1].data)

Masks are ORed, there is no concept of “unaligned masks”:

assert not sc.identical(da + da['x', 0], da + da['x', 0].data)

A missing attr is interpreted as mismatch to ensure that:

a = da['x', 0]
b = da['x', 1]
c = da['x', 2]
assert sc.identical(a + (b + c), (a + b) + c)

Insertion order does not matter for attrs:

a = da.copy()
a.attrs['attr'] = 1.0 * sc.units.m
b = da.copy()
b.attrs['attr'] = 2.0 * sc.units.m
ds1 = sc.Dataset()
ds2 = sc.Dataset()
ds1['a'] = a
ds1['b'] = b
ds2['b'] = b
ds2['a'] = a
assert sc.identical(ds1, ds2)

Insert into dataset with mismatching attrs drops attr:

ds = sc.Dataset()
ds.coords['x'] = x['x', 0]
ds['a'] = da['x', 1] # Drops 'x' from 'a'
assert sc.identical(ds.coords['x'], ds['a'].coords['x']) # shadowing is NOT supported

Masks of dataset items are independent:

ds = sc.Dataset()
masked1 = da.copy()
masked1.masks['x'] = sc.less(x, 1 *
masked2 = da.copy()
masked2.masks['x'] = sc.less(x, 2 *
assert not sc.identical(masked1, masked2)
ds['a'] = masked1
ds['b'] = masked2
assert not sc.identical(ds['a'].masks['x'], ds['b'].masks['x'])

If there is no coord it is preserved for all items. Adding a coord later makes the meta property invalid because of ambiguous name shadowing:

ds = sc.Dataset()
ds['a'] = da['x', 0]
ds['b'] = da['x', 1]
assert 'x' not in ds.coords
assert 'x' in ds['a'].attrs
assert 'x' in ds['b'].attrs
ds.coords['x'] = x['x', 0] # introduce shadowing
    ds['a'].meta # raises because of shadowing
    ok = True
    ok = False
assert ok
del ds.coords['x']
edges = sc.Variable(dims=['x'], values=[1,2,3,4,5])
da.coords['x'] = edges
assert sc.identical(sc.concat([da['x', :2], da['x', 2:]], 'x'), da)
assert sc.identical(sc.concat([da['x', 0], da['x', 1]], 'x'), da['x', 0:2])
assert sc.identical(sc.concat([da['x', :-1], da['x', -1]], 'x'), da)
da_yx = sc.concat([da['x', :2], da['x', 2:]], 'y') # create 2-D coord
assert sc.identical(da_yx.coords['x'], sc.concat([da.coords['x']['x', :3], da.coords['x']['x', 2:]], 'y'))

2-D coords for a dimension prevent operations between slices that are not along that dimension:

da_2d = sc.DataArray(
    data=sc.zeros(dims=['y', 'x'], shape=[2, 2]),
        'x':sc.Variable(dims=['y', 'x'], values=np.array([[1, 2], [3, 4]])),
        'y':sc.Variable(dims=['y'], values=[3, 4])})

da_2d['x', 0] + da_2d['x', 1] # Same as with 1-D coord: x-coord differs but not aligned due to slice.
    # 'y' sliced, so 'x' coord is aligned and yields different values from slices of 2-D coord.
    da_2d['y', 0] + da_2d['y', 1]
except RuntimeError:
    ok = False
    ok = True
assert not ok

coords always refers to (aligned) coords in dataset, cannot add or erase via item since a new coord dict is created when getting a dataset item:

    ds['a'].coords['fail'] = 1.0 * sc.units.m
except sc.DataArrayError:
    ok = False
    ok = True
assert not ok
assert 'fail' not in ds.coords
ds.coords['xx'] = 1.0 * sc.units.m
assert 'xx' in ds['a'].coords
    del ds['a'].coords['xx']
except sc.DataArrayError:
    ok = False
    ok = True
assert not ok
assert 'xx' in ds.coords

The same mechanism applies for coords, masks, and attrs of slices:

    da['x', 0].coords['fail'] = 1.0 * sc.units.m
except sc.DataArrayError:
    ok = False
    ok = True
assert not ok
assert 'fail' not in da.coords

meta contains dataset coordinates as well as item attributes, cannot add or erase, since ambiguous:

    ds['a'].meta['fail'] = 1.0 * sc.units.m
except sc.DataArrayError:
    ok = False
    ok = True
assert not ok
assert 'fail' not in ds['a'].meta
ds['a'].attrs['attr'] = 1.0 * sc.units.m
assert 'attr' in ds['a'].meta
    del ds['a'].meta['attr']
except sc.DataArrayError:
    ok = False
    ok = True
assert not ok
assert 'attr' in ds['a'].meta

Attributes are independent for each item, and show up in meta of the items:

ds['a'].attrs['attr'] = 1.0 * sc.units.m
ds['b'].attrs['attr'] = 2.0 * sc.units.m
assert 'attr' in ds['a'].meta
assert 'attr' in ds['b'].meta
assert 'attr' not in ds.meta
assert not sc.identical(ds['a'].attrs['attr'], ds['b'].attrs['attr'])
del ds['a'].attrs['attr']
del ds['b'].attrs['attr']