id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
551344538,MDU6SXNzdWU1NTEzNDQ1Mzg=,3701,Error using reduce(): percentile() got an unexpected keyword argument 'axis',57364981,closed,0,,,12,2020-01-17T10:51:49Z,2020-01-17T18:18:08Z,2020-01-17T15:24:48Z,NONE,,,,"#### MCVE Code Sample
```python
import os
import numpy as np
import pandas as pd
import xarray as xr
import gcsfs
# Search for available CMIP6 data:
df = pd.read_csv('https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv')
# and daily 'tas' data:
df_daily_tas = df[(df.table_id == 'day') & (df.variable_id == 'tas')]
# get data for a single model and experiment:
source_id = 'GFDL-CM4'
expt_id = 'historical'
# define a func to load 'tas' data
def load_tas_data(source_id, expt_id):
""""""
Load daily tas data for given source and expt ids
""""""
uri = df_daily_tas[(df_daily_tas.source_id == source_id) &
(df_daily_tas.experiment_id == expt_id)].zstore.values[0]
gcs = gcsfs.GCSFileSystem(token='anon')
ds = xr.open_zarr(gcs.get_mapper(uri), consolidated=True)
return ds
# Specify the percentile to look at:
percentile = 99
# Load the data:
ds = load_tas_data(source_id, expt_id).sel(time=slice('2000', '2000'))
# Get the percentiles at each lat/lon:
percentiles = ds.reduce(np.percentile, q=percentile, dim='time')
```
Code above gives following error:
`TypeError: percentile() got an unexpected keyword argument 'axis'`
#### Expected Output
Expected code to return percentiles of daily temperature for each latitude and longitude.
#### Problem Description
Code works fine on a different system with the following modules installed:
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 12:04:33)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.1
libnetcdf: 4.4.1.1
xarray: 0.14.1
pandas: 0.25.3
numpy: 1.16.2
scipy: 1.2.1
netCDF4: 1.3.1
pydap: installed
h5netcdf: None
h5py: 2.7.1
Nio: None
zarr: 2.3.2
cftime: 1.0.4.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 0.16.1
distributed: 1.20.2
matplotlib: 2.2.2
cartopy: 0.16.0
seaborn: 0.8.1
numbagg: None
setuptools: 38.4.0
pip: 9.0.1
conda: 4.4.10
pytest: 3.3.2
IPython: 6.2.1
sphinx: 1.6.6
#### Output of ``xr.show_versions()``
Issue occurs on system with the following installed modules:
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.6 | packaged by conda-forge | (default, Jan 7 2020, 22:33:48)
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 4.19.76+
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.5
libnetcdf: 4.6.2
xarray: 0.14.1
pandas: 0.25.3
numpy: 1.17.3
scipy: 1.3.2
netCDF4: 1.5.1.2
pydap: installed
h5netcdf: 0.7.4
h5py: 2.10.0
Nio: None
zarr: 2.3.2
cftime: 1.0.4.2
nc_time_axis: 1.2.0
PseudoNetCDF: None
rasterio: 1.0.25
cfgrib: None
iris: 2.2.0
bottleneck: 1.3.1
dask: 2.9.0
distributed: 2.9.0
matplotlib: 3.1.2
cartopy: 0.17.0
seaborn: 0.9.0
numbagg: None
setuptools: 44.0.0.post20200102
pip: 19.3.1
conda: None
pytest: 5.3.1
IPython: 7.11.1
sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3701/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
538380868,MDU6SXNzdWU1MzgzODA4Njg=,3627,Error time slicing for some CMIP6 models,57364981,closed,0,,,10,2019-12-16T12:25:42Z,2019-12-16T15:42:56Z,2019-12-16T15:42:56Z,NONE,,,,"Hi there,
I'm using xarray's `open_zarr()` function to analyse CMIP6 data:
```python
# Function to load data: df_data has the catalogue of the variable of interest
def load_data(df_data, source_id, expt_id):
""""""
Load data for given variable, source and expt ids.
""""""
uri = df_data[(df_data.source_id == source_id) &
(df_data.experiment_id == expt_id)].zstore.values[0]
gcs = gcsfs.GCSFileSystem(token='anon')
ds = xr.open_zarr(gcs.get_mapper(uri), consolidated=True)
return ds
# Just test with 1 model for now:
source_ids_tmp = ['CESM2']
for model_name in source_ids_tmp:
print('\n\nStarting ' + model_name +'\n')
ds_hist = load_data(df_mon_tas, model_name, experiment_ids[0]).sel(time=slice('1976', '2005'))
```
#### Problem Description
However, the time slicing fails with the following error:
> TypeError: cannot compare netcdftime._netcdftime.DatetimeNoLeap(1932, 7, 15, 12, 0, 0, 0, 1, 196) and '1976'
Looking at the Dataset metedata, the `time` variable is described as follows:
> * time (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
time_bnds (time, nbnd) object dask.array
For another CMIP6 model (MIROC6), the time slicing works fine. That model has the following metadat for `time`:
> * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00
time_bnds (time, bnds) datetime64[ns] dask.array
When the `time` variable is converted to a `datetime[ns]` object, time slicing seems to work. Any idea what the problem is?
Thanks,
Mike
#### Output of ``xr.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
libhdf5: 1.10.1
libnetcdf: 4.4.1.1
xarray: 0.14.0
pandas: 0.25.1
numpy: 1.16.2
scipy: 1.2.1
netCDF4: 1.3.1
pydap: installed
h5netcdf: 0.5.0
h5py: 2.7.0
Nio: None
zarr: 2.3.2
cftime: None
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 0.15.3
distributed: 1.19.1
matplotlib: 3.0.0
cartopy: 0.16.0
seaborn: 0.9.0
numbagg: None
setuptools: 36.5.0.post20170921
pip: 19.0.3
conda: 4.4.6
pytest: 3.3.0
IPython: 6.1.0
sphinx: 1.6.3
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