issues

What happened?

I was trying to read multiple byte netcdf (requires h5netcdf engine) file with xr.open_mfdataset with parallel=True to leverage dask.delayed capabilities (parallel=False works though) but it failed.

The netcdf files were noaa-goes16 satellite images, but I can't tell if it matters.

What did you expect to happen?

It should have loaded all the netcdf files into a xarray.DataSet object

Minimal Complete Verifiable Example

```python import fsspec import xarray as xr

paths = [ 's3://noaa-goes16/ABI-L2-LSTC/2022/185/03/OR_ABI-L2-LSTC-M6_G16_s20221850301180_e20221850303553_c20221850305091.nc', 's3://noaa-goes16/ABI-L2-LSTC/2022/185/02/OR_ABI-L2-LSTC-M6_G16_s20221850201180_e20221850203553_c20221850205142.nc' ]

fs = fsspec.filesystem('s3')

xr.open_mfdataset( [fs.open(path, mode="rb") for path in paths], engine="h5netcdf", combine="nested", concat_dim="t", parallel=True )

```

MVCE confirmation

• [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [ ] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

```Python

KeyError Traceback (most recent call last) File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/xarray/backends/file_manager.py:210, in CachingFileManager._acquire_with_cache_info(self, needs_lock) 209 try: --> 210 file = self._cache[self._key] 211 except KeyError:

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/xarray/backends/lru_cache.py:56, in LRUCache.getitem(self, key) 55 with self._lock: ---> 56 value = self._cache[key] 57 self._cache.move_to_end(key)

?[0;31mKeyError?[0m: [<class 'h5netcdf.core.File'>, ((b'\x89HDF\r\n', b'\x1a\n', b'\x02\x08\x08\x00\x00\x00 ... EXTREMELY STRING ... 00\x00\x00\x00\x00\x00\x0ef']

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last) Cell In[9], line 11 4 paths = [ 5 's3://noaa-goes16/ABI-L2-LSTC/2022/185/03/OR_ABI-L2-LSTC-M6_G16_s20221850301180_e20221850303553_c20221850305091.nc', 6 's3://noaa-goes16/ABI-L2-LSTC/2022/185/02/OR_ABI-L2-LSTC-M6_G16_s20221850201180_e20221850203553_c20221850205142.nc' 7 ] 9 fs = fsspec.filesystem('s3') ---> 11 xr.open_mfdataset( 12 [fs.open(path, mode="rb") for path in paths], 13 engine="h5netcdf", 14 combine="nested", 15 concat_dim="t", 16 parallel=True 17 ).LST

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/xarray/backends/api.py:991, in open_mfdataset(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs) 986 datasets = [preprocess(ds) for ds in datasets] 988 if parallel: 989 # calling compute here will return the datasets/file_objs lists, 990 # the underlying datasets will still be stored as dask arrays --> 991 datasets, closers = dask.compute(datasets, closers) 993 # Combine all datasets, closing them in case of a ValueError 994 try:

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/dask/base.py:599, in compute(traverse, optimize_graph, scheduler, get, args, kwargs) 596 keys.append(x.dask_keys()) 597 postcomputes.append(x.dask_postcompute()) --> 599 results = schedule(dsk, keys, kwargs) 600 return repack([f(r, a) for r, (f, a) in zip(results, postcomputes)])

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/dask/threaded.py:89, in get(dsk, keys, cache, num_workers, pool, kwargs) 86 elif isinstance(pool, multiprocessing.pool.Pool): 87 pool = MultiprocessingPoolExecutor(pool) ---> 89 results = get_async( 90 pool.submit, 91 pool._max_workers, 92 dsk, 93 keys, 94 cache=cache, 95 get_id=_thread_get_id, 96 pack_exception=pack_exception, 97 kwargs, 98 ) 100 # Cleanup pools associated to dead threads 101 with pools_lock:

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/dask/local.py:511, in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs) 509 _execute_task(task, data) # Re-execute locally 510 else: --> 511 raise_exception(exc, tb) 512 res, worker_id = loads(res_info) 513 state["cache"][key] = res

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/dask/local.py:319, in reraise(exc, tb) 317 if exc.traceback is not tb: 318 raise exc.with_traceback(tb) --> 319 raise exc

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/dask/local.py:224, in execute_task(key, task_info, dumps, loads, get_id, pack_exception) 222 try: 223 task, data = loads(task_info) --> 224 result = _execute_task(task, data) 225 id = get_id() 226 result = dumps((result, id))

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk) 115 func, args = arg[0], arg[1:] 116 # Note: Don't assign the subtask results to a variable. numpy detects 117 # temporaries by their reference count and can execute certain 118 # operations in-place. --> 119 return func(*(_execute_task(a, cache) for a in args)) 120 elif not ishashable(arg): 121 return arg

File ~/miniconda3/envs/rxr/lib/python3.11/site-packages/dask/utils.py:73, in apply(func, args, kwargs) 42 """Apply a function given its positional and keyword arguments. 43 44 Equivalent to func(*args, **kwargs) (...) ... ---> 19 filename = fspath(filename) 20 if sys.platform == "win32": 21 if isinstance(filename, str):

TypeError: expected str, bytes or os.PathLike object, not tuple ```

Anything else we need to know?

No response

Environment

What is your issue?

climateModels['CSIRO-QCCCE-CSIRO-Mk3-6']['RCP 45'][2]['tasmax'].sel(lon = 74, lat= 31, time = '2041-06-16', method='nearest').data[0]

`\anaconda3\lib\site-packages\xarray\core\indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

I don't know much about how to solve this issue, can anyone help me out please?

What is your issue?

This code I use was working about a year ago but today gives me an error:

import xarray as xr url = 'http://psl.noaa.gov/thredds/dodsC/Datasets/NARR/monolevel/uwnd.10m.2000.nc' ds = xr. open_dataset(url) The Traceback includes the following: File "C:\Users\Codiga_D\AppData\Local\Continuum\miniconda3\envs\EQ\lib\site-packages\xarray\backends\lru_cache.py", line 53, in __getitem__ value = self._cache[key] and ``` OSError: [Errno -68] NetCDF: I/O failure: b'http://psl.noaa.gov/thredds/dodsC/Datasets/NARR/monolevel/uwnd.10m.2000.nc'

Note:Caching=1 Error:curl error: SSL connect error curl error details: Warning:oc_open: Could not read url ``` I have confirmed that the file I am trying to read is on the server and the server is not requiring a password (nothing I am aware of, about the server, has changed since my code used to work successfully).

I am on Windows using a conda virtual env (no pip). My xarray is 0.20.2 and my netCDF4 is 1.6.0-- these are almost certainly more recent than the ones I was using when my code used to succeed, but I didn't record which version(s) used to work.

It was suggested that I pin netcdf4 to 1.5.8, so I tried this but got the same error.

Recently I had to update security certificates locally here, and this could be related, but I'm not sure.

Any suggestions for how I should troubleshoot this?

Also, should I post an issue at https://github.com/Unidata/netcdf4-python instead of, and/or in addition to, this one?

I found these issues, which seem possibly related, but don't seem to be resolved well yet: https://github.com/Unidata/netcdf4-python/issues/755 https://github.com/pydata/xarray/issues/4925

(I also opened 'discussion' #6742 but so far there has been little response there.)

Could you please change xarray.open_rasterio from experimental to stable with more faster capability of reading geotiff files (if possible)? For original array indexing capabilities, I would like to stick in xarray than rioxarray. With much respected. Thank you.

When displaying a DatasetGroupBy in an interactive Python session, the first line of output contains a trailing whitespace. The first example in the documentation demonstrate this:

```pycon

import xarray as xr, numpy as np ds = xr.Dataset( ... {"foo": (("x", "y"), np.random.rand(4, 3))}, ... coords={"x": [10, 20, 30, 40], "letters": ("x", list("abba"))}, ... ) ds.groupby("letters") DatasetGroupBy, grouped over 'letters' 2 groups with labels 'a', 'b'. ```

There is a trailing whitespace in the first line of output which is "DatasetGroupBy, grouped over 'letters' ". This can be seen more clearly by converting the object to a string (note the whitespace before \n):

```pycon

str(ds.groupby("letters")) "DatasetGroupBy, grouped over 'letters' \n2 groups with labels 'a', 'b'." ```

While this isn't a problem in itself, it causes an issue for us because we use flake8 in continuous integration to verify that our code is correctly formatted and we also have doctests that rely on DatasetGroupBy textual representation. Flake8 reports a violation on the trailing whitespaces in our docstrings. If we remove the trailing whitespaces, our doctests fail because the expected output doesn't match the actual output. So we have conflicting constraints coming from our tools which both seem reasonable. Trailing whitespaces are forbidden by flake8 because, among other reasons, they lead to noisy git diffs. Doctest want the expected output to be exactly the same as the actual output and considers a trailing whitespace to be a significant difference. We could configure flake8 to ignore this particular violation for the files in which we have these doctests, but this may cause other trailing whitespaces to creep in our code, which we don't want. Unfortunately it's not possible to just add # NoQA comments to get flake8 to ignore the violation only for specific lines because that creates a difference between expected and actual output from doctest point of view. Flake8 doesn't allow to disable checks for blocks of code either.

Is there a reason for having this trailing whitespace in DatasetGroupBy representation? Whould it be OK to remove it? If so please let me know and I can make a pull request.

I have a billion rows of data, but really it's just two categorical variables, time, lat, lon and some data variables.

Thinking it would somehow help me get the data into xarray, I created a five level pandas MultiIndex array out of the data, but thus far this has not been successful. xarray tries to create a product and that's just not going to work..

Trying to write a NetCDF file has presented its own issues, and I'm left wondering if there isn't a much simpler way to go about this?

Code Sample, a copy-pastable example if possible

```python wind_surface hurs bui fwi lat lon time
34.511383 16.467664 1971-01-10 12:00:00 29.658546 70.481293 ... 8.134300 7.409146 34.515558 16.723973 1971-01-10 12:00:00 30.896049 71.356644 ... 8.874528 8.399877 34.517359 16.852138 1971-01-10 12:00:00 31.514799 71.708603 ... 8.789351 8.763743 34.518970 16.980310 1971-01-10 12:00:00 32.105423 72.023773 ... 8.962551 9.125644 34.520391 17.108487 1971-01-10 12:00:00 32.724174 72.106110 ... 8.725038 9.249104

[5 rows x 10 columns]

In [81]: df.to_xarray()
Out[81]: <xarray.Dataset> Dimensions: (lat: 5, lon: 5, time: 1) Coordinates: * lat (lat) float64 34.51 34.52 34.52 34.52 34.52 * lon (lon) float64 16.47 16.72 16.85 16.98 17.11 * time (time) object '1971-01-10 12:00:00' Data variables: wind_surface (lat, lon, time) float64 29.658546 nan nan ... nan 32.724174 hurs (lat, lon, time) float64 70.48129 nan nan ... nan nan 72.10611 precip (lat, lon, time) float64 0.0 nan nan nan ... nan nan nan 0.0 tmax (lat, lon, time) float64 16.060822 nan nan ... nan 16.185822 ffmc (lat, lon, time) float64 83.58528 nan nan ... nan nan 84.05673 isi (lat, lon, time) float64 7.7641253 nan nan ... nan nan 9.64494 dmc (lat, lon, time) float64 6.797345 nan nan ... nan nan 7.90833 dc (lat, lon, time) float64 25.314878 nan nan ... nan 24.324644 bui (lat, lon, time) float64 8.1343 nan nan ... nan nan 8.725038 fwi (lat, lon, time) float64 7.409146 nan nan ... nan 9.2491045 ```

Problem description

Hi, I get those nan values for variables when I try to convert from a pandas.DataFrame with MultiIndex to a xarray.DataArray. The same happend if I try to build a xarray.Dataset and then unstack the multiindex as shown below:

python ds = xr.Dataset(df) ds.unstack('dim_0') <xarray.Dataset> Dimensions: (lat: 5, lon: 5, time: 1) Coordinates: * lat (lat) float64 34.51 34.52 34.52 34.52 34.52 * lon (lon) float64 16.47 16.72 16.85 16.98 17.11 * time (time) object '1971-01-10 12:00:00' Data variables: wind_surface (lat, lon, time) float32 29.658546 nan nan ... nan 32.724174 hurs (lat, lon, time) float32 70.48129 nan nan ... nan nan 72.10611 precip (lat, lon, time) float32 0.0 nan nan nan ... nan nan nan 0.0 Maybe it's not an issue. I don't know. I'm lost. Any help is welcome.

Regards

Output of xr.show_versions()

Code Sample, a copy-pastable example if possible

python import xarray as xa from pydap.cas.urs import setup_session url = 'https://goldsmr4.gesdisc.eosdis.nasa.gov/dods/M2T1NXFLX' session = setup_session(username='****', password='****', check_url=url) store = xa.backends.PydapDataStore.open(url, session=session) ds = xa.open_dataset(store) tlml = ds['tlml'] print(tlml[0,0,0])

Problem description

I was trying to connect to NASA MERRA-2 data through OPeNDAP, following the documentation here: http://xarray.pydata.org/en/stable/io.html#OPeNDAP. Opening the dataset works fine, but trying to access a data variable throws a strange IndexError. Traceback below: ``` Traceback (most recent call last):

File "C:\Anaconda3\envs\jaws\lib\site-packages\IPython\core\formatters.py", line 702, in call printer.pretty(obj) File "C:\Anaconda3\envs\jaws\lib\site-packages\IPython\lib\pretty.py", line 395, in pretty return default_pprint(obj, self, cycle) File "C:\Anaconda3\envs\jaws\lib\site-packages\IPython\lib\pretty.py", line 510, in _default_pprint _repr_pprint(obj, p, cycle) File "C:\Anaconda3\envs\jaws\lib\site-packages\IPython\lib\pretty.py", line 701, in _repr_pprint output = repr(obj) File "c:\src\xarray\xarray\core\common.py", line 100, in __repr__ return formatting.array_repr(self) File "c:\src\xarray\xarray\core\formatting.py", line 393, in array_repr summary.append(short_array_repr(arr.values)) File "c:\src\xarray\xarray\core\dataarray.py", line 411, in values return self.variable.values File "c:\src\xarray\xarray\core\variable.py", line 392, in values return _as_array_or_item(self._data) File "c:\src\xarray\xarray\core\variable.py", line 216, in _as_array_or_item data = np.asarray(data) File "C:\Anaconda3\envs\jaws\lib\site-packages\numpy\core\numeric.py", line 492, in asarray return array(a, dtype, copy=False, order=order) File "c:\src\xarray\xarray\core\indexing.py", line 572, in __array__ self._ensure_cached() File "c:\src\xarray\xarray\core\indexing.py", line 569, in _ensure_cached self.array = NumpyIndexingAdapter(np.asarray(self.array)) File "C:\Anaconda3\envs\jaws\lib\site-packages\numpy\core\numeric.py", line 492, in asarray return array(a, dtype, copy=False, order=order) File "c:\src\xarray\xarray\core\indexing.py", line 553, in __array__ return np.asarray(self.array, dtype=dtype) File "C:\Anaconda3\envs\jaws\lib\site-packages\numpy\core\numeric.py", line 492, in asarray return array(a, dtype, copy=False, order=order) File "c:\src\xarray\xarray\core\indexing.py", line 520, in __array__ return np.asarray(array[self.key], dtype=None) File "c:\src\xarray\xarray\conventions.py", line 134, in __getitem__ return np.asarray(self.array[key], dtype=self.dtype) File "c:\src\xarray\xarray\coding\variables.py", line 71, in __getitem__ return self.func(self.array[key]) File "c:\src\xarray\xarray\coding\variables.py", line 140, in _apply_mask data = np.asarray(data, dtype=dtype) File "C:\Anaconda3\envs\jaws\lib\site-packages\numpy\core\numeric.py", line 492, in asarray return array(a, dtype, copy=False, order=order) File "c:\src\xarray\xarray\core\indexing.py", line 520, in __array__ return np.asarray(array[self.key], dtype=None) File "c:\src\xarray\xarray\backends\pydap.py", line 33, in getitem result = robust_getitem(array, key, catch=ValueError) File "c:\src\xarray\xarray\backends\common.py", line 67, in robust_getitem return array[key] File "C:\src\pydap\src\pydap\model.py", line 320, in getitem out.data = self._get_data_index(index) File "C:\src\pydap\src\pydap\model.py", line 350, in _get_data_index return self._data[index] File "C:\src\pydap\src\pydap\handlers\dap.py", line 149, in getitem return dataset[self.id].data File "C:\src\pydap\src\pydap\model.py", line 426, in getitem return self._getitem_string(key) File "C:\src\pydap\src\pydap\model.py", line 410, in _getitem_string return self[splitted[0]]['.'.join(splitted[1:])] File "C:\src\pydap\src\pydap\model.py", line 320, in getitem out.data = self._get_data_index(index) File "C:\src\pydap\src\pydap\model.py", line 350, in _get_data_index return self._data[index] IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices ```

Expected Output

Expecting to see the value of variable 'tlml' at these coordinates.

Output of xr.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

xarray: 0.10.0+dev44.g0a0593d
pandas: 0.22.0
numpy: 1.14.0
scipy: 1.0.0
netCDF4: 1.3.1
h5netcdf: 0.5.0
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.16.1
distributed: 1.20.2
matplotlib: None
cartopy: None
seaborn: None
setuptools: 38.4.0
pip: 9.0.1
conda: None
pytest: None
IPython: 6.2.1
sphinx: 1.6.6

Code Sample, a copy-pastable example if possible

python import xarray as xa from pydap.cas.urs import setup_session url = 'https://goldsmr4.gesdisc.eosdis.nasa.gov/dods/M2T1NXFLX' session = setup_session(username='****', password='****', check_url=url) store = xa.backends.PydapDataStore.open(url, session=session) ds = xa.open_dataset(store)

Problem description

I was trying to connect to NASA MERRA-2 data through OPeNDAP, following the documentation here: http://xarray.pydata.org/en/stable/io.html#OPeNDAP. I was able to get through a previous bug (#1775) by installing the latest master version of xarray.

Expected Output

Expecting the collection (M2T1NXFLX) content to show up as an xarray dataset.

Output of xr.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

xarray: 0.10.0+dev44.g0a0593d
pandas: 0.22.0
numpy: 1.14.0
scipy: 1.0.0
netCDF4: None
h5netcdf: None
Nio: None
zarr: None
bottleneck: None
cyordereddict: None
dask: None
distributed: None
matplotlib: None
cartopy: None
seaborn: None
setuptools: 38.4.0
pip: 9.0.1
conda: None
pytest: None
IPython: None
sphinx: None

I am trying to figure out how boolean indexing works in xarray. I have a couple of data arrays below: X_day[latitude,longitude,time] X_night[latitude,longitude,time] Rule[latitude,longitude]

I want to merge X_day and X_night as a new X based on Rule. First I make a copy of X_day to be X: X = X_day Then I tried: X[Rule==True, :] = X_night and this: X.values[Rule==True, :] = X_night.values and also this: X.where(Rule==True) = X_night None of the above assignment worked. Please help.

Hi I am new to xarray and need some advice on one task. I need to do a cross product calculation using variables from a netcdf file and write the output to a new netcdf file. I feel this could be done using netcdf-python and pandas, but I hope to use xarray to simplify the task. My code will be something like this: ds = xr.open_dataset(NC_FILE) var1 = ds['VAR1'] var2 = ds['VAR2'] var3 = ds['VAR3'] var4 = ds['VAR4']

var1-4 above will have dimensions [latitude, longitude]. I will use var1-4 to generate a matrix of dimensions [Nlat, Nlon, M], something like: [var1, var1-var2, var1-var3, (var1-var2)*np.cos(var4)] (here, M=4).

My question here is, how do I build this matrix in xarray Dataset? Since this matrix will be eventually used to cross product with another matrix (pd.DataFrame) of dimensions [M, K], is it better to convert var1-4 to pd.DataFrame first?

Following code will be like this: matrix = [var1, var1-var2, var1-var3, (var1-var2)*np.cos(var4)] # Nlat x Nlon x M factor = pd.read_csv(somefile) # M x K result = pd.DataFrame.dot(matrix,factor) # Nlat x Nlon x K result2 = xr.Dataset(result) result2.to_netcdf(outfile)

Can someone show me the correct code to build the Nlat x Nlon x M matrix? Can the cross product be done in xr.Dataset to avoid conversion to and from pd.DataFrame?

Thank you, Xin

If a variable has an unsigned integer type (uint16, uint32, etc.), resampling time will drop that variable. Does not occur with signed integer types (int16, etc.).

``` import numpy as np import pandas as pd import xray

numbers = np.arange(1, 6).astype('uint32') ds = xray.Dataset( {'numbers': ('time', numbers)}, coords = {'time': pd.date_range('2000-01-01', periods=5)}) resampled = ds.resample('24H', dim='time') assert 'numbers' not in resampled ```

To "numpify" a function, usually asarray is used:

def db2w(arr): return 10 ** (np.asarray(arr) / 20.0)

Now you could replace the divide with np.divide, but it seems much simpler to use np.asarray. Unfortunately, if you use any function that has been "vectorized," it will only return the values of the DataArray as an ndarray. This strips the object of any "xray" meta-data and severely limits the use of this class. It requires that any function that wants to work seamlessly with a DataArray explicitly check that it's an instance of DataArray

This seems counter-intuitive to the numpy framework where any function, once properly vectorized, can work with python scalars (int, float) or list types (tuple, list) as well as the actual ndarray class. It would be awesome if code that worked for these cases could just work for DataArrays.