from xmip.postprocessing: concat_experiments, merge_variables are not working on CESM2

Thanks for using xMIP <a class="user-mention notranslate" data-hovercard-type="user" d

<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clip

I was just able to run this without error on the LEAP-Pange hub (<code class="notransl

CESM2 Members Not Combining Along 'time' about xmip HOT 5 OPEN

AbbySh commented on July 22, 2024

CESM2 Members Not Combining Along 'time'

from xmip.

Comments (5)

jbusecke commented on July 22, 2024

Thanks for using xMIP @AbbySh. Do you have a code snippet that I can use to produce this error? That will make it much easier to look into this.

from xmip.

AbbySh commented on July 22, 2024

from xmip.utils import google_cmip_col
from xmip.preprocessing import combined_preprocessing
from xmip.postprocessing import concat_experiments, merge_variables

cat = col.search(
    variable_id=['tos', 'sos', 'chl', 'mlotst', 'spco2', 'dpco2'],
    table_id='Omon', # monthly ocean output only
    experiment_id=['historical','ssp245'],
    require_all_on=['source_id', 'member_id', 'grid_label'] # this ensures that results will have all variables and experiments available
)

ddict = cat.to_dataset_dict(
    preprocess=combined_preprocessing,
    xarray_open_kwargs=dict(use_cftime=True),
    aggregate=False
)

ds = merge_variables(ddict)
ds = concat_experiments(ds)

This should reproduce it, let me know!

from xmip.

jbusecke commented on July 22, 2024

Taking a look now.

from xmip.

jbusecke commented on July 22, 2024

I was just able to run this without error on the LEAP-Pange hub ('pangeo/pangeo-notebook:2023.08.29'):

from xmip.utils import google_cmip_col
from xmip.preprocessing import combined_preprocessing
from xmip.postprocessing import concat_experiments, merge_variables

col = google_cmip_col()

cat = col.search(
    variable_id=['tos', 'sos', 'chl', 'mlotst', 'spco2', 'dpco2'],
    table_id='Omon', # monthly ocean output only
    experiment_id=['historical','ssp245'],
    require_all_on=['source_id', 'member_id', 'grid_label'] # this ensures that results will have all variables and experiments available
)

ddict = cat.to_dataset_dict(
    preprocess=combined_preprocessing,
    xarray_open_kwargs=dict(use_cftime=True),
    aggregate=False
)

ds = merge_variables(ddict)
ds = concat_experiments(ds)