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- Append along an unlimited dimension to an existing netCDF file · 3 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 735428830 | https://github.com/pydata/xarray/issues/1672#issuecomment-735428830 | https://api.github.com/repos/pydata/xarray/issues/1672 | MDEyOklzc3VlQ29tbWVudDczNTQyODgzMA== | hmaarrfk 90008 | 2020-11-29T17:34:44Z | 2020-11-29T17:35:04Z | CONTRIBUTOR | It isn't really part of any library. I don't really have plans of making it into a public library. I think the discussion is really around the xarray API, and what functions to implement at first. Then somebody can take the code and integrate it into the decided upon API. |
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Append along an unlimited dimension to an existing netCDF file 269700511 | |
| 685222909 | https://github.com/pydata/xarray/issues/1672#issuecomment-685222909 | https://api.github.com/repos/pydata/xarray/issues/1672 | MDEyOklzc3VlQ29tbWVudDY4NTIyMjkwOQ== | hmaarrfk 90008 | 2020-09-02T01:17:05Z | 2020-09-02T01:17:05Z | CONTRIBUTOR | Small prototype, but maybe it can help boost the development.
```python
import netCDF4
def _expand_variable(nc_variable, data, expanding_dim, nc_shape, added_size):
# For time deltas, we must ensure that we use the same encoding as
# what was previously stored.
# We likely need to do this as well for variables that had custom
# econdings too
if hasattr(nc_variable, 'calendar'):
data.encoding = {
'units': nc_variable.units,
'calendar': nc_variable.calendar,
}
data_encoded = xr.conventions.encode_cf_variable(data) # , name=name)
left_slices = data.dims.index(expanding_dim)
right_slices = data.ndim - left_slices - 1
nc_slice = (slice(None),) * left_slices + (slice(nc_shape, nc_shape + added_size),) + (slice(None),) * (right_slices)
nc_variable[nc_slice] = data_encoded.data
def append_to_netcdf(filename, ds_to_append, unlimited_dims):
if isinstance(unlimited_dims, str):
unlimited_dims = [unlimited_dims]
if len(unlimited_dims) != 1:
# TODO: change this so it can support multiple expanding dims
raise ValueError(
"We only support one unlimited dim for now, "
f"got {len(unlimited_dims)}.")
unlimited_dims = list(set(unlimited_dims))
expanding_dim = unlimited_dims[0]
with netCDF4.Dataset(filename, mode='a') as nc:
nc_dims = set(nc.dimensions.keys())
nc_coord = nc[expanding_dim]
nc_shape = len(nc_coord)
added_size = len(ds_to_append[expanding_dim])
variables, attrs = xr.conventions.encode_dataset_coordinates(ds_to_append)
for name, data in variables.items():
if expanding_dim not in data.dims:
# Nothing to do, data assumed to the identical
continue
nc_variable = nc[name]
_expand_variable(nc_variable, data, expanding_dim, nc_shape, added_size)
from xarray.tests.test_dataset import create_append_test_data
from xarray.testing import assert_equal
ds, ds_to_append, ds_with_new_var = create_append_test_data()
filename = 'test_dataset.nc'
ds.to_netcdf(filename, mode='w', unlimited_dims=['time'])
append_to_netcdf('test_dataset.nc', ds_to_append, unlimited_dims='time')
loaded = xr.load_dataset('test_dataset.nc')
assert_equal(xr.concat([ds, ds_to_append], dim="time"), loaded)
```
|
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Append along an unlimited dimension to an existing netCDF file 269700511 | |
| 684833575 | https://github.com/pydata/xarray/issues/1672#issuecomment-684833575 | https://api.github.com/repos/pydata/xarray/issues/1672 | MDEyOklzc3VlQ29tbWVudDY4NDgzMzU3NQ== | hmaarrfk 90008 | 2020-09-01T12:58:52Z | 2020-09-01T12:58:52Z | CONTRIBUTOR | I think I got a basic prototype working. That said, I think a real challenge lies in supporting the numerous backends and lazy arrays. For example, I was only able to add data in peculiar fashions using the netcdf4 library which may trigger complex computations many times. Is this a use case that we must optimize for now? |
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Append along an unlimited dimension to an existing netCDF file 269700511 |
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