Hi there

I am in a bit of a struggle. And I hope my problem has a solution.

It's all about to avoid memory errors, like: MemoryError: Unable to allocate 1.52 GIB for an array with shape (41760,75,65) and data type float64.
I am seeking a solution where I can append my data stored in my memory into an existing Dfs2 file, without loading the existing data into memory. Like a "load and dump" solution.

The example above with the MemoryError, the first "dimension" is the time as you might know, which is 41760 timesteps with timestep of 60 seconds. This means that it tried to load 29 days before it "crashed"... and I would like to have a year.

When loading Dfs2 from the mikeio, I am able to see some details about the file in-memory. Is it possible to store data in the existing DataArray (not Dataset) in this mode?

from mikeio import Dfs2
import numpy as np

dfs2 = Dfs2('data.dfs2')   # Assuming this is the existing Dfs2 file.
test_data = np.zeros(dfs2.shape)
# ds = dfs2.read()            # I would like to avoid this...
dfs2.write(to_existing=DataArray,data=test_data)    # Of course, 'to_existing', is a made up argument, but something like this.

Is there any solution to my memory problem?

Thanks in advance!