I can reproduce it with calling bn.nanmax directly, but I can not reproduce it without the xarray.transpose() function.

• If I call nanmax on the internal data of xarray then nanmax fails with a segfault:

python np_data = xdata['Spec name'].data bn.nanmax(np_data) # Segfault - But if I create a copy of that data and then call nanmax then it works fine.

python np_data = xdata['Spec name'].data new_data = np_data.copy() bn.nanmax(new_data) # works

I suspect that the xarray.transpose function does something with the data-structure (lazy reshuffling of dimensions?) that triggers the fault in bottleneck.

Full code:

```python from collections import OrderedDict import numpy as np import xarray as xr

xr.show_versions()

n_time = 1 # 1 : Fails, 2 : everything is fine

from xarray.core.options import OPTIONS OPTIONS["use_bottleneck"] = True # Set to False for work-around

Build some dataset

dirs = np.linspace(0,360, num=121) freqs = np.linspace(0,4,num=192) spec_data = np.random.random(size=(n_time,192,121))

dims = ('time', 'freq', 'dir') coords = OrderedDict() coords['time'] = range(n_time) coords['freq'] = freqs coords['dir'] = dirs

xdata = xr.DataArray( data=spec_data, coords=coords, dims=dims, name='Spec name', ).to_dataset()

xdata = xdata.transpose(..., "freq")

import bottleneck as bn np_data = xdata['Spec name'].data

new_data = np_data.copy() bn.nanmax(new_data) # works

bn.nanmax(np_data) # Segfault print('direct bn call done') ```