问题
Grouping by a single dimension works fine for xarray DataArrays:
d = xr.DataArray([1, 2, 3], coords={'a': ['x', 'x', 'y']}, dims=['a'])
d.groupby('a').mean()) # -> DataArray (a: 2) array([1.5, 3. ])`
However, this is only supported for a single dimension, grouping by multiple dimensions does thus not work:
d = DataAssembly([[1, 2, 3], [4, 5, 6]],
coords={'a': ('multi_dim', ['a', 'b']), 'c': ('multi_dim', ['c', 'c']), 'b': ['x', 'y', 'z']},
dims=['multi_dim', 'b'])
d.groupby(['a', 'b']) # TypeError: `group` must be an xarray.DataArray or the name of an xarray variable or dimension
I only have an inefficient solution which does the for loops manually:
a, b = np.unique(d['a'].values), np.unique(d['b'].values)
result = xr.DataArray(np.zeros([len(a), len(b)]), coords={'a': a, 'b': b}, dims=['a', 'b'])
for a, b in itertools.product(a, b):
cells = d.sel(a=a, b=b)
merge = cells.mean()
result.loc[{'a': a, 'b': b}] = merge
# result = DataArray (a: 2, b: 2)> array([[2., 3.], [5., 6.]])
# Coordinates:
# * a (a) <U1 'x' 'y'
# * b (b) int64 0 1
This is however horribly slow for larger arrays. Is there a more efficient / straight-forward work-around?
回答1:
I built a manual solution. To make it efficient, I discard all of xarray and rebuild indices and values by hand. Any change to use more xarray (e.g. using sel, re-packaging cells into a DataArray; also see https://github.com/pydata/xarray/issues/2452) led to serious losses in speed.
import itertools
from collections import defaultdict
import numpy as np
import xarray as xr
from xarray import DataArray
class DataAssembly(DataArray):
def multi_dim_groupby(self, groups, apply):
# align
groups = sorted(groups, key=lambda group: self.dims.index(self[group].dims[0]))
# build indices
groups = {group: np.unique(self[group]) for group in groups}
group_dims = {self[group].dims: group for group in groups}
indices = defaultdict(lambda: defaultdict(list))
result_indices = defaultdict(dict)
for group in groups:
for index, value in enumerate(self[group].values):
indices[group][value].append(index)
if value not in result_indices[group]: # if captured once, it will be "grouped away"
index = max(result_indices[group].values()) + 1 if len(result_indices[group]) > 0 else 0
result_indices[group][value] = index
coords = {coord: (dims, value) for coord, dims, value in walk_coords(self)}
def simplify(value):
return value.item() if value.size == 1 else value
def indexify(dict_indices):
return [(i,) if isinstance(i, int) else tuple(i) for i in dict_indices.values()]
# group and apply
# making this a DataArray right away and then inserting through .loc would slow things down
result = np.zeros([len(indices) for indices in result_indices.values()])
result_coords = {coord: (dims, [None] * len(result_indices[group_dims[dims]]))
for coord, (dims, value) in coords.items()}
for values in itertools.product(*groups.values()):
group_values = dict(zip(groups.keys(), values))
self_indices = {group: indices[group][value] for group, value in group_values.items()}
values_indices = indexify(self_indices)
cells = self.values[values_indices] # using DataArray would slow things down. thus we pass coords as kwargs
cells = simplify(cells)
cell_coords = {coord: (dims, value[self_indices[group_dims[dims]]])
for coord, (dims, value) in coords.items()}
cell_coords = {coord: (dims, simplify(np.unique(value))) for coord, (dims, value) in cell_coords.items()}
# ignore dims when passing to function
passed_coords = {coord: value for coord, (dims, value) in cell_coords.items()}
merge = apply(cells, **passed_coords)
result_idx = {group: result_indices[group][value] for group, value in group_values.items()}
result[indexify(result_idx)] = merge
for coord, (dims, value) in cell_coords.items():
if isinstance(value, np.ndarray): # multiple values for coord -> ignore
if coord in result_coords: # delete from result coords if not yet deleted
del result_coords[coord]
continue
assert dims == result_coords[coord][0]
coord_index = result_idx[group_dims[dims]]
result_coords[coord][1][coord_index] = value
# re-package
result = type(self)(result, coords=result_coords, dims=list(itertools.chain(*group_dims.keys())))
return result
def walk_coords(assembly):
"""
walks through coords and all levels, just like the `__repr__` function, yielding `(name, dims, values)`.
"""
coords = {}
for name, values in assembly.coords.items():
# partly borrowed from xarray.core.formatting#summarize_coord
is_index = name in assembly.dims
if is_index and values.variable.level_names:
for level in values.variable.level_names:
level_values = assembly.coords[level]
yield level, level_values.dims, level_values.values
else:
yield name, values.dims, values.values
return coords
The method multi_dim_groupby performs grouping and apply in one step.
The passed apply method can accept group coords via parameters named after the coords (or ignore the coords by putting **_ in the function header).
It's not particularly pretty and does not cover all possible cases but at least covers the following test cases:
import DataAssembly
class TestMultiDimGroupby:
def test_unique_values(self):
d = DataAssembly([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
coords={'a': ['a', 'b', 'c', 'd'],
'b': ['x', 'y', 'z']},
dims=['a', 'b'])
g = d.multi_dim_groupby(['a', 'b'], lambda x, **_: x)
assert g.equals(d)
def test_nonunique_singledim(self):
d = DataAssembly([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
coords={'a': ['a', 'a', 'b', 'b'],
'b': ['x', 'y', 'z']},
dims=['a', 'b'])
g = d.multi_dim_groupby(['a', 'b'], lambda x, **_: x.mean())
assert g.equals(DataAssembly([[2.5, 3.5, 4.5], [8.5, 9.5, 10.5]],
coords={'a': ['a', 'b'], 'b': ['x', 'y', 'z']},
dims=['a', 'b']))
def test_nonunique_adjacentcoord(self):
d = DataAssembly([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
coords={'a': ('adim', ['a', 'a', 'b', 'b']),
'aa': ('adim', ['a', 'b', 'a', 'b']),
'b': ['x', 'y', 'z']},
dims=['adim', 'b'])
g = d.multi_dim_groupby(['a', 'b'], lambda x, **_: x.mean())
assert g.equals(DataAssembly([[2.5, 3.5, 4.5], [8.5, 9.5, 10.5]],
coords={'adim': ['a', 'b'], 'b': ['x', 'y', 'z']},
dims=['adim', 'b'])), \
"adjacent coord aa should be discarded due to non-mappability"
def test_unique_values_swappeddims(self):
d = DataAssembly([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
coords={'a': ['a', 'b', 'c', 'd'],
'b': ['x', 'y', 'z']},
dims=['a', 'b'])
g = d.multi_dim_groupby(['b', 'a'], lambda x, **_: x)
assert g.equals(d)
来源：https://stackoverflow.com/questions/52453426/grouping-by-multiple-dimensions