Support alignment/broadcasting with unlabeled dimensions of size 1

[shoyer]

Sometimes, it's convenient to include placeholder dimensions of size 1, which allows for removing any ambiguity related to the order of output dimensions.

Currently, this is not supported with xarray:

>>> xr.DataArray([1], dims='x') + xr.DataArray([1, 2, 3], dims='x')
ValueError: arguments without labels along dimension 'x' cannot be aligned because they have different dimension sizes: {1, 3}

>>> xr.Variable(('x',), [1]) + xr.Variable(('x',), [1, 2, 3])
ValueError: operands cannot be broadcast together with mismatched lengths for dimension 'x': (1, 3)

However, these operations aren't really ambiguous. With size 1 dimensions, we could logically do broadcasting like NumPy arrays, e.g.,

>>> np.array([1]) + np.array([1, 2, 3])
array([2, 3, 4])

This would be particularly convenient if we add keepdims=True to xarray operations (#2170).

[ambrosejcarr]

We'd really like to be able to do this for broadcastable matrix multiplication. See e.g. spacetx/starfish#983

Do you have a sense of how this might be implemented, or a suggested work around in the mean time?

[shoyer]

I think there are three places where you would need to fix this:

Index based alignment in align():

xarray/xarray/core/alignment.py

Line 143 in 6d20766

'arguments without labels along dimension %r cannot be '

Unified dimensions for broadcasting variables, which is defined in two places:

xarray/xarray/core/variable.py

Line 1978 in 6d20766

def _unified_dims(variables):

xarray/xarray/core/computation.py

Line 441 in 6d20766

def unified_dim_sizes(

The easiest work around is probably to call .squeeze() on any objects with size 1 dimensions -- then xarray's broadcasting/alignment should all work fine.

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[gagebeni]

i am not yet experimenting enough on python. My problem is that I wrote some code to draw a line using a colon at the beginning and at the end of my line I used numpy exclusively and since it is an xrarray array it takes me a long time. My data is all of dimensions lat, lon, SST, (1501,2001), and here is my code:

import xarray as xr
import glob
from numpy import *
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import math
import pandas as pd
import numpy as np

filepaths_TEMP = "./ 2014 / MUR-SST_2014__Day_027_27-Jan-2014.nc"

data_sst = xr.open_dataset (filepaths_TEMP)
lon_sst = data_sst.variables ['lon'] [:]
lat_sst = data_sst.variables ['lat'] [:] #
sst = data_sst.variables ['SST'] [:]
#% to draw the line
latlim = [50, 50.5];
lonlim = [-10.2];
n1 = 1200; #% number of point along profile
delta_lon = -10 - (2);
delta_lat = 50 - 50.5;

#cd (input_path1)
#matfiles = dir (fullfile ('*. nc'));
# %%
Flat = latlim [1] + np.arange (0, n1) * delta_lat / n1
Plon = lonlim [1] + np.arange (0, n1) * delta_lon / n1
transect = np.zeros ((1200,1))
for ii in range (0, len (Flat)):
      
    lat_S1 = Flat [ii];
    lon_S1 = Plon [ii];
    a_1 = abs (lat_S1-lat_sst);
    a_2 = abs (lon_S1-lon_sst);
    a_3 = (a_1 + a_2);
    m_3 = a_3 [:]. min ()
    [px, py] = np.where (m_3 == a_3);
    px = px [0];
    py = py [0]; # clear a_1 a_2 a_3 m_3
    transect [ii] = sst [px, py];

My question is, isn't there a way to make it simpler and faster, especially thank you very much in advance.

[shoyer]

@gagebeni please open a new discussion for your issue: https://github.com/pydata/xarray/discussions