increase speed, decrease memory usage using dataarray selection #18
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basininflow/inflow.py(2 hunks)
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basininflow/inflow.py (2)
217-223: Verify handling of 3-dimensional datasets
In the if ds.ndim == 3: block, the code currently has a pass statement, which means no action is taken for 3D datasets. Ensure that this is the intended behavior and that inflow_df is correctly processed for 3D datasets without additional modifications.
268-268: Confirm NetCDF default format compatibility
By not specifying the format parameter when creating the NetCDF dataset, the code defaults to the NetCDF4 format. Verify that the downstream applications and users can work with this format. If compatibility with NetCDF classic format (NETCDF3_CLASSIC) is required, consider specifying the format explicitly.
basininflow/inflow.py
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| elif ds.ndim == 4: | ||
| inflow_df = ds.values[:, :, lat_indices, lon_indices] | ||
| inflow_df = np.where(np.isnan(inflow_df[:, 0, :]), inflow_df[:, 1, :], inflow_df[:, 0, :]), |
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Remove unintended trailing comma to fix variable assignment
The trailing comma at the end of line 221 results in inflow_df being assigned as a tuple containing the array, rather than the array itself. This can cause issues when inflow_df is used later in the code.
Apply this diff to fix the issue:
-inflow_df = np.where(np.isnan(inflow_df[:, 0, :]), inflow_df[:, 1, :], inflow_df[:, 0, :]),
+inflow_df = np.where(np.isnan(inflow_df[:, 0, :]), inflow_df[:, 1, :], inflow_df[:, 0, :])📝 Committable suggestion
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| inflow_df = np.where(np.isnan(inflow_df[:, 0, :]), inflow_df[:, 1, :], inflow_df[:, 0, :]), | |
| inflow_df = np.where(np.isnan(inflow_df[:, 0, :]), inflow_df[:, 1, :], inflow_df[:, 0, :]) |
Significantly increased the speed with which this script runs by using fancy xarray indexing to avoid loading unnecessary values. Also removed netcdf classic since we'll be using river route from here on out
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New Features
Bug Fixes
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