Creating a template dataset

With the variables dictionary prepared, only two more specifications are required to build a template dataset. First a dictionary that defines the sizes of all the dimensions used in the variables dictionary, e.g.:

dim_size_dict = {"x": 1000, "y": 2000}

Secondly, a dictionary of dataset global metadata, e.g.:

metadata = {"dataset_name": "my cool image"}

Combining the above together a template dataset can be created as follows:

ds = dsbuilder.create_template_dataset(
    variables_dict,
    dim_sizes_dict,
    metadata
)

Where ds is an empty xarray dataset with variables defined by the template definition. Fill values for the empty arrays are chosen using the cf convention values.

Populating and writing the dataset

Populating and writing the dataset can be achieved using xarray’s builtin functionality. Here’s a dummy example:

ds["band_red"] = ... # populate variable with red image array
ds["band_green"] = ... # populate variable with green image array
ds["band_blue"] = ... # populate variable with blue image array

ds.to_netcdf("path/to/file.nc")

import numpy as np

# define ds variables
variables_dict = {
    "temperature": {
        "dtype": np.float32,
        "dim": ["x", "y", "time"],
        "attrs": {
            "units": "K",
            "u_components": ["u_temperature"]
        }
    },
    "u_temperature": {
        "dtype": np.int16,
        "dim": ["x", "y", "time"],
        "attrs": {"units": "%"},
        "err_corr": [
            {
                "dim": "x",
                "form": "systematic",
                "params": [],
                "units": []
            }
        ]
    },
    "quality_flag_time": {
        "dtype": "flag",
        "dim": ["time"],
        "flag_meanings": ["bad", "dubious"]
    },
}

# define dim_size_dict to specify size of arrays
dim_sizes_dict = {
    "x": 500,
    "y": 600,
    "time": 6
}

# create dataset
ds = create_template_dataset(variables_dict, dim_sizes_dict)