Description
What happened?
I am trying to open netcdf files or even zarr files but with the latest version of numpy==2.0.0, the values for the time dimensions are wrong (by some seconds).
It happens when I open directly netcdf files but also some zarr files.
What did you expect to happen?
I am expecting the datetime for the time dimension to work as it was with numpy<2.0.0
Minimal Complete Verifiable Example
# download any of the files here: https://data.marine.copernicus.eu/product/IBI_MULTIYEAR_PHY_005_002/files?subdataset=cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012&path=IBI_MULTIYEAR_PHY_005_002%2Fcmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012%2F2021%2F
import xarray
ds = xr.open_dataset(
"CMEMS_v5r1_IBI_PHY_MY_PdE_01mav_20211001_20211031_R20230101_RE01.nc"
)
print(ds)
# with numpy<2.0.0: (time) datetime64[ns] 2021-10-16T12:00:00
# with numpy==2.0.0: (time) datetime64[ns] 8B 2021-10-16T11:58:44.279541760MVCE confirmation
- Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- Complete example — the example is self-contained, including all data and the text of any traceback.
- Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
- New issue — a search of GitHub Issues suggests this is not a duplicate.
- Recent environment — the issue occurs with the latest version of xarray and its dependencies.
Relevant log output
<xarray.Dataset> Size: 85MB
Dimensions: (time: 1, longitude: 289, latitude: 361, depth: 50)
Coordinates:
* time (time) datetime64[ns] 8B 2021-10-16T11:58:44.279541760
* longitude (longitude) float32 1kB -19.0 -18.92 -18.83 ... 4.833 4.917 5.0
* latitude (latitude) float32 1kB 26.0 26.08 26.17 ... 55.83 55.92 56.0
* depth (depth) float32 200B 0.5058 1.556 2.668 ... 5.292e+03 5.698e+03
Data variables:
thetao (time, depth, latitude, longitude) float32 21MB ...
so (time, depth, latitude, longitude) float32 21MB ...
uo (time, depth, latitude, longitude) float32 21MB ...
vo (time, depth, latitude, longitude) float32 21MB ...
zos (time, latitude, longitude) float32 417kB ...
bottomT (time, latitude, longitude) float32 417kB ...
mlotst (time, latitude, longitude) float32 417kB ...Anything else we need to know?
I tried to do something like this:
import numpy as np
import pandas as pd
import xarray as xr
np.random.seed(0)
temperature = 15 + 8 * np.random.randn(2, 3, 4)
precipitation = 10 * np.random.rand(2, 3, 4)
lon = [-99.83, -99.32]
lat = [42.25, 42.21]
instruments = ["manufac1", "manufac2", "manufac3"]
time = pd.date_range("2014-09-06", periods=4)
reference_time = pd.Timestamp("2014-09-05")
ds = xr.Dataset(
data_vars=dict(
temperature=(["loc", "instrument", "time"], temperature),
precipitation=(["loc", "instrument", "time"], precipitation),
),
coords=dict(
lon=("loc", lon),
lat=("loc", lat),
instrument=instruments,
time=time,
reference_time=reference_time,
),
attrs=dict(description="Weather related data."),
)
print(ds)
# save to netcdf
ds.to_netcdf("lololol.nc")
# open the netcdf file
ds = xr.open_dataset("lololol.nc")
# print the dataset
print(ds)
But it seemed to work as expected so maybe the example is too simple. However, anything else I tried had the error.
Environment
xarray: 2024.6.0
pandas: 2.2.2
numpy: 2.0.0
scipy: None
netCDF4: 1.7.1.post1
pydap: None
h5netcdf: None
h5py: None
zarr: None
cftime: 1.6.4
nc_time_axis: None
iris: None
bottleneck: None
dask: None
distributed: None
matplotlib: None
cartopy: None
seaborn: None
numbagg: None
fsspec: None
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 69.5.1
pip: 24.0
conda: None
pytest: None
mypy: None
IPython: None
sphinx: None