Creating template datasets

Dataset definition structures

obsarray can create xarray.Dataset’s to a particular templates, defined as a dict’s (referred to hereafter as template dictionaries), which can range from very simple to more complex. Every key in the template dictionary is the name of a variable, with the corresponding entry a further variable specification dictionary (referred to hereafter as variable dictionaries).

So a template dictionary may look something like this:

template = {
    "temperature": temperature_variable,
    "u_temperature": u_temperature_variable

Each variable dictionary defines the following entries:

  • dim - list of variable dimension names.

  • dtype - variable data type, generally a numpy.dtype, though for some special variables particular values may be required.

  • attributes - dictionary of variable metadata, for some special variables particular entries may be required.

  • encoding - (optional) variable encoding.

So for the previous example we may define:

import numpy as np

temperature_variable = {
    "dim": ["lon", "lat", "time"]
    "dtype": np.float32,
    "attributes": {"units": "K", "unc_comps": ["u_temperature"]}

u_temperature_variable = {
    "dim": ["lon", "lat", "time"]
    "dtype": np.float16,
    "attributes": {"units": "%"}

The following section details the special variable types that can be defined with obsarray.

Special variable types

obsarray’s special variables allow the quick definition of a set of standardised variable formats. The following special variable types are available.


Recent work in the Earth Observation metrology domain is working towards the standardisation of the representation of measurement uncertainty information in data, with a particular focus on capturing the error-covariance associated with the uncertainty. Although it is typically the case that for large measurement datasets storing full error-covariance matrices is impractical, often the error-covariance between measurements may be efficiently parameterised. Work to standardise such parameterisations is on-going (see for example the EU H2020 FIDUCEO project defintions list in Appendix A of this project report).

dsbuilder enables the specification of such error-correlation parameterisations for uncertainty variables through the variable attributes. This is achieved by including an "err_corr" list entry in a variable’s variable_spec dictionary. Each element of err_corr is a dictionary defining the error-correlation along one or more dimensions, which should include the following entries:

  • dim (str/list) - name of the dimension(s) as a str or list of str’s (i.e. from dim_names)

  • form (str) - error-correlation form, defines functional form of error-correlation structure along dimension. Suggested error-correlation forms are defined in a table below.

  • params (list) - (optional) parameters of the error-correlation structure defining function for dimension if required. The number of parameters required depends on the particular form.

  • units (list) - (optional) units of the error-correlation function parameters for dimension (ordered as the parameters)

Measurement variables with uncertainties should include a list of unc_comps in their attributes, as in the above example.

An example err_corr dictionary may therefore look like:

err_corr = {
        "dim": "x"
        "form": "rectangular_absolute",
        "params": [val1, val2],
        "units": ["m", "m"]
        "dim": "y"
        "form": "random",
        "params": [],
        "units": []

If the error-correlation structure is not defined along a particular dimension (i.e. it is not included in err_corr), the error-correlation is assumed random. Variable attributes are populated to the effect of this assumption.

Suggested error correlation parameterisations

Form Name




None required

Errors uncorrelated along dimension(s)


None required

Errors fully correlated along dimension(s)


Error-correlation matrix variable name

Error-correlation for dimension(s) not parameterised, defined as a full matrix in another named variable in dataset.


Setting the "flag" dtype builds a variable in the cf conventions flag format. Each datum bit corresponds to boolean condition flag with a given meaning.

The variable must be defined with an attribute that lists the per bit flag meanings as follows:

variables = {
    "quality_flag": {
        "dim": ["x", "y"],
        "dtype": "flag"
        "attributes": {
            "flag_meanings": ["good_data", "bad_data"]

The smallest necessary integer is used as the flag variable numpy.dtype, given the number of flag meanings defined (i.e. 7 flag meanings results in an 8 bit integer variable).

Creating a template dataset

With the template 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 template dictionary, e.g.:

dim_size= {"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 = obsarray.create_ds(

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

import numpy as np

# define ds variables
template = {
    "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 = {
    "x": 500,
    "y": 600,
    "time": 6

# create dataset
ds = obsarray.create_ds(template, dim_sizes)