html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/3484#issuecomment-627316607,https://api.github.com/repos/pydata/xarray/issues/3484,627316607,MDEyOklzc3VlQ29tbWVudDYyNzMxNjYwNw==,23187108,2020-05-12T12:39:07Z,2020-05-12T12:39:07Z,NONE,"> da.copy(data=da.data.todense()).plot() should work.

It works indeed, thank you!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,517338735
https://github.com/pydata/xarray/issues/3484#issuecomment-626793618,https://api.github.com/repos/pydata/xarray/issues/3484,626793618,MDEyOklzc3VlQ29tbWVudDYyNjc5MzYxOA==,2448579,2020-05-11T15:59:41Z,2020-05-11T15:59:41Z,MEMBER,"`da.copy(data=da.data.todense()).plot()` should work. 

We should add `as_sparse` and `to_dense` methods. See discussion here: https://github.com/pydata/xarray/issues/3245. A PR would be very welcome if you have the time.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,517338735
https://github.com/pydata/xarray/issues/3484#issuecomment-626787590,https://api.github.com/repos/pydata/xarray/issues/3484,626787590,MDEyOklzc3VlQ29tbWVudDYyNjc4NzU5MA==,23187108,2020-05-11T15:49:24Z,2020-05-11T15:49:24Z,NONE,"Hello, do you have any documentation on how to plot data in a sparse array using `xarray.plot` accessor? 
I get this error, but if I convert to numpy/scipy  with `todense()` method I will likely lose the convenient plot method from xarray... Thank you for your help
```python
-------------------------------------------------------------------------
RuntimeError                            Traceback (most recent call last)
<ipython-input-331-9d69abf57c11> in <module>
----> 1 slice_res_ds['value'].plot()

~/.pyenv/versions/emi/lib/python3.6/site-packages/xarray/plot/plot.py in __call__(self, **kwargs)
    463 
    464     def __call__(self, **kwargs):
--> 465         return plot(self._da, **kwargs)
    466 
    467     @functools.wraps(hist)

~/.pyenv/versions/emi/lib/python3.6/site-packages/xarray/plot/plot.py in plot(darray, row, col, col_wrap, ax, hue, rtol, subplot_kws, **kwargs)
    200     kwargs[""ax""] = ax
    201 
--> 202     return plotfunc(darray, **kwargs)
    203 
    204 

~/.pyenv/versions/emi/lib/python3.6/site-packages/xarray/plot/plot.py in newplotfunc(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)
    692 
    693         # Pass the data as a masked ndarray too
--> 694         zval = darray.to_masked_array(copy=False)
    695 
    696         # Replace pd.Intervals if contained in xval or yval.

~/.pyenv/versions/emi/lib/python3.6/site-packages/xarray/core/dataarray.py in to_masked_array(self, copy)
   2301             Masked where invalid values (nan or inf) occur.
   2302         """"""
-> 2303         values = self.values  # only compute lazy arrays once
   2304         isnull = pd.isnull(values)
   2305         return np.ma.MaskedArray(data=values, mask=isnull, copy=copy)

~/.pyenv/versions/emi/lib/python3.6/site-packages/xarray/core/dataarray.py in values(self)
    565     def values(self) -> np.ndarray:
    566         """"""The array's data as a numpy.ndarray""""""
--> 567         return self.variable.values
    568 
    569     @values.setter

~/.pyenv/versions/emi/lib/python3.6/site-packages/xarray/core/variable.py in values(self)
    446     def values(self):
    447         """"""The variable's data as a numpy.ndarray""""""
--> 448         return _as_array_or_item(self._data)
    449 
    450     @values.setter

~/.pyenv/versions/emi/lib/python3.6/site-packages/xarray/core/variable.py in _as_array_or_item(data)
    252     TODO: remove this (replace with np.asarray) once these issues are fixed
    253     """"""
--> 254     data = np.asarray(data)
    255     if data.ndim == 0:
    256         if data.dtype.kind == ""M"":

~/.pyenv/versions/emi/lib/python3.6/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     83 
     84     """"""
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~/.pyenv/versions/emi/lib/python3.6/site-packages/sparse/_sparse_array.py in __array__(self, **kwargs)
    221         if not AUTO_DENSIFY:
    222             raise RuntimeError(
--> 223                 ""Cannot convert a sparse array to dense automatically. ""
    224                 ""To manually densify, use the todense method.""
    225             )

RuntimeError: Cannot convert a sparse array to dense automatically. To manually densify, use the todense method.
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,517338735
https://github.com/pydata/xarray/issues/3484#issuecomment-549627590,https://api.github.com/repos/pydata/xarray/issues/3484,549627590,MDEyOklzc3VlQ29tbWVudDU0OTYyNzU5MA==,10554254,2019-11-05T01:50:29Z,2020-02-12T02:51:51Z,NONE,"After reading through the issue tracker and PRs, it looks like sparse arrays can safely be wrapped with xarray, thanks to the work done in [PR#3117](https://github.com/pydata/xarray/pull/3117), but built-in functions are still under development (e.g. [PR#3542](https://github.com/pydata/xarray/pull/3542)). As a user, here is what I am seeing when test driving sparse:

Sparse gives me a smaller in-memory array

```python
In [1]: import xarray as xr, sparse, sys, numpy as np, dask.array as da

In [2]: x = np.random.random((100, 100, 100))

In [3]: x[x < 0.9] = np.nan

In [4]: s = sparse.COO.from_numpy(x, fill_value=np.nan)

In [5]: sys.getsizeof(s)
Out[5]: 3189592

In [6]: sys.getsizeof(x)
Out[6]: 8000128
```
Which I can wrap with dask and xarray

```python
In [7]: x = da.from_array(x)

In [8]: s = da.from_array(s)

In [9]: ds_dense = xr.DataArray(x).to_dataset(name='data_variable')

In [10]: ds_sparse = xr.DataArray(s).to_dataset(name='data_variable')

In [11]: ds_dense
Out[11]:
<xarray.Dataset>
Dimensions:        (dim_0: 100, dim_1: 100, dim_2: 100)
Dimensions without coordinates: dim_0, dim_1, dim_2
Data variables:
    data_variable  (dim_0, dim_1, dim_2) float64 dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

In [12]: ds_sparse
Out[12]:
<xarray.Dataset>
Dimensions:        (dim_0: 100, dim_1: 100, dim_2: 100)
Dimensions without coordinates: dim_0, dim_1, dim_2
Data variables:
    data_variable  (dim_0, dim_1, dim_2) float64 dask.array<chunksize=(100, 100, 100), meta=sparse.COO>
```
However, computation on a sparse array takes longer than running compute on a dense array (which I think is expected...?)

```python
In [13]: %%time
    ...: ds_sparse.mean().compute()
CPU times: user 487 ms, sys: 22.9 ms, total: 510 ms
Wall time: 518 ms
Out[13]:
<xarray.Dataset>
Dimensions:        ()
Data variables:
    data_variable  float64 0.9501

In [14]: %%time
    ...: ds_dense.mean().compute()
CPU times: user 10.9 ms, sys: 3.91 ms, total: 14.8 ms
Wall time: 13.8 ms
Out[14]:
<xarray.Dataset>
Dimensions:        ()
Data variables:
    data_variable  float64 0.9501
```

And writing to netcdf, to take advantage of the smaller data size, doesn't work out of the box (yet)

```python
In [15]: ds_sparse.to_netcdf('ds_sparse.nc')
Out[15]: ...
RuntimeError: Cannot convert a sparse array to dense automatically. To manually densify, use the todense method.
```

Additional discussion happening at #3213 

@dcherian @shoyer Am I missing any built-in methods that are working and ready for public release? Happy to send in a PR, if any of what is provided here should go into a basic example for the docs. 

At this stage, I am not using sparse arrays for my own research just yet, but when I get to that anticipated phase I can dig in more on this and hopefully send in some useful PRs for improved documentation and fixes/features. 
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,517338735
https://github.com/pydata/xarray/issues/3484#issuecomment-550471849,https://api.github.com/repos/pydata/xarray/issues/3484,550471849,MDEyOklzc3VlQ29tbWVudDU1MDQ3MTg0OQ==,4605410,2019-11-06T19:48:17Z,2019-11-06T19:48:17Z,NONE,"@friedrichknuth One of my motivations behind exploring sparse DataArray backends is in reducing the memory footprint during merge operations. Consider the following:

<a href=""https://www.codecogs.com/eqnedit.php?latex=\begin{Vmatrix}&space;X_{11}(t)&space;&&space;X_{12}(t))\\&space;\end{Vmatrix}&space;\textbf{merge&space;with}&space;\begin{Vmatrix}&space;X_{22}(t)&space;&&space;X_{23}(t))\\&space;\end{Vmatrix}&space;=&space;\begin{Vmatrix}&space;X_{11}(t)&space;&&space;X_{12}(t)&space;&&space;\textbf{NaN/0}\\&space;\textbf{NaN/0}&space;&&space;X_{22}(t)&space;&&space;X_{23}(t)&space;\end{Vmatrix}"" target=""_blank""><img src=""https://latex.codecogs.com/gif.latex?\begin{Vmatrix}&space;X_{11}(t)&space;&&space;X_{12}(t))\\&space;\end{Vmatrix}&space;\textbf{merge&space;with}&space;\begin{Vmatrix}&space;X_{22}(t)&space;&&space;X_{23}(t))\\&space;\end{Vmatrix}&space;=&space;\begin{Vmatrix}&space;X_{11}(t)&space;&&space;X_{12}(t)&space;&&space;\textbf{NaN/0}\\&space;\textbf{NaN/0}&space;&&space;X_{22}(t)&space;&&space;X_{23}(t)&space;\end{Vmatrix}"" title=""\begin{Vmatrix} X_{11}(t) & X_{12}(t))\\ \end{Vmatrix} \textbf{merge with} \begin{Vmatrix} X_{22}(t) & X_{23}(t))\\ \end{Vmatrix} = \begin{Vmatrix} X_{11}(t) & X_{12}(t) & \textbf{NaN/0}\\ \textbf{NaN/0} & X_{22}(t) & X_{23}(t) \end{Vmatrix}"" /></a>

One can imagine many such merge operations producing a lot of effectively empty indices. While sparse backed arrays might have the ability to condense these empty indices in memory, it seems like [xarray sparse merging isnt quite compatible yet.](https://github.com/pydata/xarray/issues/3445#issue-512205079)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,517338735
https://github.com/pydata/xarray/issues/3484#issuecomment-549559629,https://api.github.com/repos/pydata/xarray/issues/3484,549559629,MDEyOklzc3VlQ29tbWVudDU0OTU1OTYyOQ==,2448579,2019-11-04T21:40:16Z,2019-11-04T21:40:16Z,MEMBER,maybe @friedrichknuth can contribute some :),"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,517338735