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/7096#issuecomment-1264643360,https://api.github.com/repos/pydata/xarray/issues/7096,1264643360,IC_kwDOAMm_X85LYO0g,1006477,2022-10-02T13:23:06Z,2022-10-02T13:23:06Z,NONE,"Hi, we are getting similar failures when building xarray for Arch Linux riscv64.

> I'm not sure what that has to do with xarray though? Does this give the same result?
> 
> ```
> import numpy as np
> import pandas as pd
> num_dates = np.asarray([0., np.nan])
> flat_num_dates = num_dates.ravel()
> flat_num_dates_ns_int = (flat_num_dates * (int(1e9) * 60 * 60 * 24)).astype(np.int64)
> flat_num_dates_ns_int
> array([ 0, 9223372036854775807])
> ```

I got the same result in riscv64. One thing I could guess is that the sign bit of NaN is not kept during conversions. Some more details could be found at: https://sourceware.org/pipermail/libc-alpha/2022-September/142011.html

Repeating the same steps result in `array([0, -9223372036854775808])` in x86_64 and `array([0, 0])` in aarch64.

> > Please could you answer the question on whether pandas tests pass?

I have tried pandas' tests and got many failures like:

```
E       AssertionError: Attributes of DataFrame.iloc[:, 4] (column name=""date"") are different                                                                                                                                                                                      
E                                                                                                                                                                                                                                                                                  
E       Attribute ""dtype"" are different                                                                                                                                                                                                                                            
E       [left]:  float64                                                                                                                                                                                                                                                           
E       [right]: datetime64[ns]    
```
or
```
E           AssertionError: numpy array are different               
E                                                                   
E           numpy array values are different (50.0 %)               
E           [index]: [0, 1]                                         
E           [left]:  [1036713600000, -9223372036854775808]          
E           [right]: [1036713600000000000, -9223372036854775808]    
```

Quite some of the tests are having NaN in the context as well. So you are probably right that pandas or numpy may be where the problem lies.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1389019400