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N-D labeled arrays and datasets in Python
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"write to read-only" Error in xarray.dataset_to_netcdf() #4157

Closed EliT1626 closed 4 years ago

EliT1626 commented 4 years ago

MCVE Code Sample

# Your code here

Expected Output

Problem Description

Versions

Output of xr.show_versions()
EliT1626 commented 4 years ago

Code Sample

xr.set_options(file_cache_maxsize=10)

# Assumes daily increments
def list_dates(start, end):
    num_days = (end - start).days
    return [start + dt.timedelta(days=x) for x in range(num_days)]

def list_dates1(start, end):
    num_days = (end - start).days
    dates = [start + dt.timedelta(days=x) for x in range(num_days)]
    sorted_dates = sorted(dates, key=lambda date: (date.month, date.day))
    grouped_dates = [list(g) for _, g in groupby(sorted_dates, key=lambda date: (date.month, date.day))]
    return grouped_dates

start_date = dt.date(2010, 1, 1)
end_date = dt.date(2019, 12, 31)
date_list = list_dates1(start_date, end_date)
window1 = dt.timedelta(days=5)
window2 = dt.timedelta(days=6)

url = 'https://www.ncei.noaa.gov/thredds/dodsC/OisstBase/NetCDF/V2.1/AVHRR/{0:%Y%m}/oisst-avhrr-v02r01.{0:%Y%m%d}.nc'
end_date2 = dt.date(2010, 1, 2)

sst_mean=[]
cur_date = start_date

for cur_date in date_list:
    sst_mean_calc = []
    for i in cur_date:
        date_window=list_dates(i - window1, i + window2)
        url_list_window = [url.format(x) for x in date_window]
        window_data=xr.open_mfdataset(url_list_window).sst
        sst_mean_calc.append(window_data.mean('time'))  
    sst_mean.append(xr.concat(sst_mean_calc, dim='time').mean('time'))
    cur_date+=cur_date
    if cur_date[0] >= end_date2:
        break
    else:
        continue  

sst_mean_climo_test=xr.concat(sst_mean, dim='time')
#sst_std=xr.concat(sst_std_calc, dim=pd.DatetimeIndex(date_list, name='time'))
#sst_min = xr.concat(sst_min_calc, dim=pd.DatetimeIndex(date_list, name='time'))
#sst_max = xr.concat(sst_max_calc, dim=pd.DatetimeIndex(date_list, name='time'))

sst_mean_climo_test.to_netcdf(path='E:/Riskpulse_HD/SST_stuff/sst_mean_climo_test')

Explanation of Code This code (climatology for SSTs) creates a list of dates between the specified start and end dates that contains the same day number for every month through the year span. For example, date_list[0] contains 10 datetime dates that start with 1-1-2010, 1-1-2011...1-1-2019. I then request OISST data from an opendap server and take a centered mean of the date in question (this case I did it for the first and second of January). In other words, I am opening the files for Dec 27-Jan 6 and averaging all of them together. The final xarray dataset then contains two 'times', which is 10 years worth of data for Jan 1 and Jan 2. I want to then send this to a netcdf file such that I can save it on my local machine and use to create plots down the road. Hope this makes sense.

Error Messages

KeyError                                  Traceback (most recent call last)
~\Anaconda3\lib\site-packages\xarray\backends\file_manager.py in _acquire_with_cache_info(self, needs_lock)
    197             try:
--> 198                 file = self._cache[self._key]
    199             except KeyError:

~\Anaconda3\lib\site-packages\xarray\backends\lru_cache.py in __getitem__(self, key)
     52         with self._lock:
---> 53             value = self._cache[key]
     54             self._cache.move_to_end(key)

KeyError: [<class 'netCDF4._netCDF4.Dataset'>, ('https://www.ncei.noaa.gov/thredds/dodsC/OisstBase/NetCDF/V2.1/AVHRR/201801/oisst-avhrr-v02r01.20180106.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False))]

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
<ipython-input-3-f8395dcffb5e> in <module>
      1 #xr.set_options(file_cache_maxsize=500)
----> 2 sst_mean_climo_test.to_netcdf(path='E:/Riskpulse_HD/SST_stuff/sst_mean_climo_test')

~\Anaconda3\lib\site-packages\xarray\core\dataarray.py in to_netcdf(self, *args, **kwargs)
   2356             dataset = self.to_dataset()
   2357 
-> 2358         return dataset.to_netcdf(*args, **kwargs)
   2359 
   2360     def to_dict(self, data: bool = True) -> dict:

~\Anaconda3\lib\site-packages\xarray\core\dataset.py in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)
   1552             unlimited_dims=unlimited_dims,
   1553             compute=compute,
-> 1554             invalid_netcdf=invalid_netcdf,
   1555         )
   1556 

~\Anaconda3\lib\site-packages\xarray\backends\api.py in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)
   1095             return writer, store
   1096 
-> 1097         writes = writer.sync(compute=compute)
   1098 
   1099         if path_or_file is None:

~\Anaconda3\lib\site-packages\xarray\backends\common.py in sync(self, compute)
    202                 compute=compute,
    203                 flush=True,
--> 204                 regions=self.regions,
    205             )
    206             self.sources = []

~\Anaconda3\lib\site-packages\dask\array\core.py in store(sources, targets, lock, regions, compute, return_stored, **kwargs)
    943 
    944         if compute:
--> 945             result.compute(**kwargs)
    946             return None
    947         else:

~\Anaconda3\lib\site-packages\dask\base.py in compute(self, **kwargs)
    164         dask.base.compute
    165         """
--> 166         (result,) = compute(self, traverse=False, **kwargs)
    167         return result
    168 

~\Anaconda3\lib\site-packages\dask\base.py in compute(*args, **kwargs)
    442         postcomputes.append(x.__dask_postcompute__())
    443 
--> 444     results = schedule(dsk, keys, **kwargs)
    445     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    446 

~\Anaconda3\lib\site-packages\dask\threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs)
     82         get_id=_thread_get_id,
     83         pack_exception=pack_exception,
---> 84         **kwargs
     85     )
     86 

~\Anaconda3\lib\site-packages\dask\local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
    484                         _execute_task(task, data)  # Re-execute locally
    485                     else:
--> 486                         raise_exception(exc, tb)
    487                 res, worker_id = loads(res_info)
    488                 state["cache"][key] = res

~\Anaconda3\lib\site-packages\dask\local.py in reraise(exc, tb)
    314     if exc.__traceback__ is not tb:
    315         raise exc.with_traceback(tb)
--> 316     raise exc
    317 
    318 

~\Anaconda3\lib\site-packages\dask\local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    220     try:
    221         task, data = loads(task_info)
--> 222         result = _execute_task(task, data)
    223         id = get_id()
    224         result = dumps((result, id))

~\Anaconda3\lib\site-packages\dask\core.py in _execute_task(arg, cache, dsk)
    119         # temporaries by their reference count and can execute certain
    120         # operations in-place.
--> 121         return func(*(_execute_task(a, cache) for a in args))
    122     elif not ishashable(arg):
    123         return arg

~\Anaconda3\lib\site-packages\dask\array\core.py in getter(a, b, asarray, lock)
     98         c = a[b]
     99         if asarray:
--> 100             c = np.asarray(c)
    101     finally:
    102         if lock:

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in __array__(self, dtype)
    489 
    490     def __array__(self, dtype=None):
--> 491         return np.asarray(self.array, dtype=dtype)
    492 
    493     def __getitem__(self, key):

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in __array__(self, dtype)
    651 
    652     def __array__(self, dtype=None):
--> 653         return np.asarray(self.array, dtype=dtype)
    654 
    655     def __getitem__(self, key):

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in __array__(self, dtype)
    555     def __array__(self, dtype=None):
    556         array = as_indexable(self.array)
--> 557         return np.asarray(array[self.key], dtype=None)
    558 
    559     def transpose(self, order):

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\coding\variables.py in __array__(self, dtype)
     70 
     71     def __array__(self, dtype=None):
---> 72         return self.func(self.array)
     73 
     74     def __repr__(self):

~\Anaconda3\lib\site-packages\xarray\coding\variables.py in _scale_offset_decoding(data, scale_factor, add_offset, dtype)
    216 
    217 def _scale_offset_decoding(data, scale_factor, add_offset, dtype):
--> 218     data = np.array(data, dtype=dtype, copy=True)
    219     if scale_factor is not None:
    220         data *= scale_factor

~\Anaconda3\lib\site-packages\xarray\coding\variables.py in __array__(self, dtype)
     70 
     71     def __array__(self, dtype=None):
---> 72         return self.func(self.array)
     73 
     74     def __repr__(self):

~\Anaconda3\lib\site-packages\xarray\coding\variables.py in _apply_mask(data, encoded_fill_values, decoded_fill_value, dtype)
    136 ) -> np.ndarray:
    137     """Mask all matching values in a NumPy arrays."""
--> 138     data = np.asarray(data, dtype=dtype)
    139     condition = False
    140     for fv in encoded_fill_values:

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in __array__(self, dtype)
    555     def __array__(self, dtype=None):
    556         array = as_indexable(self.array)
--> 557         return np.asarray(array[self.key], dtype=None)
    558 
    559     def transpose(self, order):

~\Anaconda3\lib\site-packages\xarray\backends\netCDF4_.py in __getitem__(self, key)
     71     def __getitem__(self, key):
     72         return indexing.explicit_indexing_adapter(
---> 73             key, self.shape, indexing.IndexingSupport.OUTER, self._getitem
     74         )
     75 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in explicit_indexing_adapter(key, shape, indexing_support, raw_indexing_method)
    835     """
    836     raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support)
--> 837     result = raw_indexing_method(raw_key.tuple)
    838     if numpy_indices.tuple:
    839         # index the loaded np.ndarray

~\Anaconda3\lib\site-packages\xarray\backends\netCDF4_.py in _getitem(self, key)
     82         try:
     83             with self.datastore.lock:
---> 84                 original_array = self.get_array(needs_lock=False)
     85                 array = getitem(original_array, key)
     86         except IndexError:

~\Anaconda3\lib\site-packages\xarray\backends\netCDF4_.py in get_array(self, needs_lock)
     61 
     62     def get_array(self, needs_lock=True):
---> 63         ds = self.datastore._acquire(needs_lock)
     64         variable = ds.variables[self.variable_name]
     65         variable.set_auto_maskandscale(False)

~\Anaconda3\lib\site-packages\xarray\backends\netCDF4_.py in _acquire(self, needs_lock)
    359 
    360     def _acquire(self, needs_lock=True):
--> 361         with self._manager.acquire_context(needs_lock) as root:
    362             ds = _nc4_require_group(root, self._group, self._mode)
    363         return ds

~\Anaconda3\lib\contextlib.py in __enter__(self)
    110         del self.args, self.kwds, self.func
    111         try:
--> 112             return next(self.gen)
    113         except StopIteration:
    114             raise RuntimeError("generator didn't yield") from None

~\Anaconda3\lib\site-packages\xarray\backends\file_manager.py in acquire_context(self, needs_lock)
    184     def acquire_context(self, needs_lock=True):
    185         """Context manager for acquiring a file."""
--> 186         file, cached = self._acquire_with_cache_info(needs_lock)
    187         try:
    188             yield file

~\Anaconda3\lib\site-packages\xarray\backends\file_manager.py in _acquire_with_cache_info(self, needs_lock)
    206                     # ensure file doesn't get overriden when opened again
    207                     self._mode = "a"
--> 208                 self._cache[self._key] = file
    209                 return file, False
    210             else:

~\Anaconda3\lib\site-packages\xarray\backends\lru_cache.py in __setitem__(self, key, value)
     71             elif self._maxsize:
     72                 # make room if necessary
---> 73                 self._enforce_size_limit(self._maxsize - 1)
     74                 self._cache[key] = value
     75             elif self._on_evict is not None:

~\Anaconda3\lib\site-packages\xarray\backends\lru_cache.py in _enforce_size_limit(self, capacity)
     61             key, value = self._cache.popitem(last=False)
     62             if self._on_evict is not None:
---> 63                 self._on_evict(key, value)
     64 
     65     def __setitem__(self, key: K, value: V) -> None:

~\Anaconda3\lib\site-packages\xarray\backends\file_manager.py in <lambda>(k, v)
     12 # Global cache for storing open files.
     13 FILE_CACHE: LRUCache[str, io.IOBase] = LRUCache(
---> 14     maxsize=cast(int, OPTIONS["file_cache_maxsize"]), on_evict=lambda k, v: v.close()
     15 )
     16 assert FILE_CACHE.maxsize, "file cache must be at least size one"

netCDF4\_netCDF4.pyx in netCDF4._netCDF4.Dataset.close()

netCDF4\_netCDF4.pyx in netCDF4._netCDF4.Dataset._close()

netCDF4\_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()

RuntimeError: NetCDF: HDF error

I also tried changing setting xr.set_options(file_cache_maxsize=500) outside of the loop before trying to create the netcdf file and received this error:

KeyError                                  Traceback (most recent call last)
~\Anaconda3\lib\site-packages\xarray\backends\file_manager.py in _acquire_with_cache_info(self, needs_lock)
    197             try:
--> 198                 file = self._cache[self._key]
    199             except KeyError:

~\Anaconda3\lib\site-packages\xarray\backends\lru_cache.py in __getitem__(self, key)
     52         with self._lock:
---> 53             value = self._cache[key]
     54             self._cache.move_to_end(key)

KeyError: [<class 'netCDF4._netCDF4.Dataset'>, ('https://www.ncei.noaa.gov/thredds/dodsC/OisstBase/NetCDF/V2.1/AVHRR/201512/oisst-avhrr-v02r01.20151231.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False))]

During handling of the above exception, another exception occurred:

OSError                                   Traceback (most recent call last)
<ipython-input-4-474cdce51e60> in <module>
      1 xr.set_options(file_cache_maxsize=500)
----> 2 sst_mean_climo_test.to_netcdf(path='E:/Riskpulse_HD/SST_stuff/sst_mean_climo_test')

~\Anaconda3\lib\site-packages\xarray\core\dataarray.py in to_netcdf(self, *args, **kwargs)
   2356             dataset = self.to_dataset()
   2357 
-> 2358         return dataset.to_netcdf(*args, **kwargs)
   2359 
   2360     def to_dict(self, data: bool = True) -> dict:

~\Anaconda3\lib\site-packages\xarray\core\dataset.py in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)
   1552             unlimited_dims=unlimited_dims,
   1553             compute=compute,
-> 1554             invalid_netcdf=invalid_netcdf,
   1555         )
   1556 

~\Anaconda3\lib\site-packages\xarray\backends\api.py in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)
   1095             return writer, store
   1096 
-> 1097         writes = writer.sync(compute=compute)
   1098 
   1099         if path_or_file is None:

~\Anaconda3\lib\site-packages\xarray\backends\common.py in sync(self, compute)
    202                 compute=compute,
    203                 flush=True,
--> 204                 regions=self.regions,
    205             )
    206             self.sources = []

~\Anaconda3\lib\site-packages\dask\array\core.py in store(sources, targets, lock, regions, compute, return_stored, **kwargs)
    943 
    944         if compute:
--> 945             result.compute(**kwargs)
    946             return None
    947         else:

~\Anaconda3\lib\site-packages\dask\base.py in compute(self, **kwargs)
    164         dask.base.compute
    165         """
--> 166         (result,) = compute(self, traverse=False, **kwargs)
    167         return result
    168 

~\Anaconda3\lib\site-packages\dask\base.py in compute(*args, **kwargs)
    442         postcomputes.append(x.__dask_postcompute__())
    443 
--> 444     results = schedule(dsk, keys, **kwargs)
    445     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    446 

~\Anaconda3\lib\site-packages\dask\threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs)
     82         get_id=_thread_get_id,
     83         pack_exception=pack_exception,
---> 84         **kwargs
     85     )
     86 

~\Anaconda3\lib\site-packages\dask\local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
    484                         _execute_task(task, data)  # Re-execute locally
    485                     else:
--> 486                         raise_exception(exc, tb)
    487                 res, worker_id = loads(res_info)
    488                 state["cache"][key] = res

~\Anaconda3\lib\site-packages\dask\local.py in reraise(exc, tb)
    314     if exc.__traceback__ is not tb:
    315         raise exc.with_traceback(tb)
--> 316     raise exc
    317 
    318 

~\Anaconda3\lib\site-packages\dask\local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    220     try:
    221         task, data = loads(task_info)
--> 222         result = _execute_task(task, data)
    223         id = get_id()
    224         result = dumps((result, id))

~\Anaconda3\lib\site-packages\dask\core.py in _execute_task(arg, cache, dsk)
    119         # temporaries by their reference count and can execute certain
    120         # operations in-place.
--> 121         return func(*(_execute_task(a, cache) for a in args))
    122     elif not ishashable(arg):
    123         return arg

~\Anaconda3\lib\site-packages\dask\array\core.py in getter(a, b, asarray, lock)
     98         c = a[b]
     99         if asarray:
--> 100             c = np.asarray(c)
    101     finally:
    102         if lock:

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in __array__(self, dtype)
    489 
    490     def __array__(self, dtype=None):
--> 491         return np.asarray(self.array, dtype=dtype)
    492 
    493     def __getitem__(self, key):

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in __array__(self, dtype)
    651 
    652     def __array__(self, dtype=None):
--> 653         return np.asarray(self.array, dtype=dtype)
    654 
    655     def __getitem__(self, key):

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in __array__(self, dtype)
    555     def __array__(self, dtype=None):
    556         array = as_indexable(self.array)
--> 557         return np.asarray(array[self.key], dtype=None)
    558 
    559     def transpose(self, order):

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\coding\variables.py in __array__(self, dtype)
     70 
     71     def __array__(self, dtype=None):
---> 72         return self.func(self.array)
     73 
     74     def __repr__(self):

~\Anaconda3\lib\site-packages\xarray\coding\variables.py in _scale_offset_decoding(data, scale_factor, add_offset, dtype)
    216 
    217 def _scale_offset_decoding(data, scale_factor, add_offset, dtype):
--> 218     data = np.array(data, dtype=dtype, copy=True)
    219     if scale_factor is not None:
    220         data *= scale_factor

~\Anaconda3\lib\site-packages\xarray\coding\variables.py in __array__(self, dtype)
     70 
     71     def __array__(self, dtype=None):
---> 72         return self.func(self.array)
     73 
     74     def __repr__(self):

~\Anaconda3\lib\site-packages\xarray\coding\variables.py in _apply_mask(data, encoded_fill_values, decoded_fill_value, dtype)
    136 ) -> np.ndarray:
    137     """Mask all matching values in a NumPy arrays."""
--> 138     data = np.asarray(data, dtype=dtype)
    139     condition = False
    140     for fv in encoded_fill_values:

~\Anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in __array__(self, dtype)
    555     def __array__(self, dtype=None):
    556         array = as_indexable(self.array)
--> 557         return np.asarray(array[self.key], dtype=None)
    558 
    559     def transpose(self, order):

~\Anaconda3\lib\site-packages\xarray\backends\netCDF4_.py in __getitem__(self, key)
     71     def __getitem__(self, key):
     72         return indexing.explicit_indexing_adapter(
---> 73             key, self.shape, indexing.IndexingSupport.OUTER, self._getitem
     74         )
     75 

~\Anaconda3\lib\site-packages\xarray\core\indexing.py in explicit_indexing_adapter(key, shape, indexing_support, raw_indexing_method)
    835     """
    836     raw_key, numpy_indices = decompose_indexer(key, shape, indexing_support)
--> 837     result = raw_indexing_method(raw_key.tuple)
    838     if numpy_indices.tuple:
    839         # index the loaded np.ndarray

~\Anaconda3\lib\site-packages\xarray\backends\netCDF4_.py in _getitem(self, key)
     82         try:
     83             with self.datastore.lock:
---> 84                 original_array = self.get_array(needs_lock=False)
     85                 array = getitem(original_array, key)
     86         except IndexError:

~\Anaconda3\lib\site-packages\xarray\backends\netCDF4_.py in get_array(self, needs_lock)
     61 
     62     def get_array(self, needs_lock=True):
---> 63         ds = self.datastore._acquire(needs_lock)
     64         variable = ds.variables[self.variable_name]
     65         variable.set_auto_maskandscale(False)

~\Anaconda3\lib\site-packages\xarray\backends\netCDF4_.py in _acquire(self, needs_lock)
    359 
    360     def _acquire(self, needs_lock=True):
--> 361         with self._manager.acquire_context(needs_lock) as root:
    362             ds = _nc4_require_group(root, self._group, self._mode)
    363         return ds

~\Anaconda3\lib\contextlib.py in __enter__(self)
    110         del self.args, self.kwds, self.func
    111         try:
--> 112             return next(self.gen)
    113         except StopIteration:
    114             raise RuntimeError("generator didn't yield") from None

~\Anaconda3\lib\site-packages\xarray\backends\file_manager.py in acquire_context(self, needs_lock)
    184     def acquire_context(self, needs_lock=True):
    185         """Context manager for acquiring a file."""
--> 186         file, cached = self._acquire_with_cache_info(needs_lock)
    187         try:
    188             yield file

~\Anaconda3\lib\site-packages\xarray\backends\file_manager.py in _acquire_with_cache_info(self, needs_lock)
    202                     kwargs = kwargs.copy()
    203                     kwargs["mode"] = self._mode
--> 204                 file = self._opener(*self._args, **kwargs)
    205                 if self._mode == "w":
    206                     # ensure file doesn't get overriden when opened again

netCDF4\_netCDF4.pyx in netCDF4._netCDF4.Dataset.__init__()

netCDF4\_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()

OSError: [Errno -37] NetCDF: Write to read only: b'https://www.ncei.noaa.gov/thredds/dodsC/OisstBase/NetCDF/V2.1/AVHRR/201512/oisst-avhrr-v02r01.20151231.nc'

I believe these errors have something to do with a post that I created a couple weeks ago (https://github.com/pydata/xarray/issues/4082).

I'm not sure if you can @ users on here, but @rsignell-usgs found out something about the caching before hand. It seems that this is some sort of Windows issue.

Versions python: 3.7.4 xarray: 0.15.1 pandas: 1.0.3 numpy: 1.18.1 scipy: 1.4.1 netcdf4: 1.4.2