bmi-topography is a Python library for fetching and caching land elevation data using the OpenTopography REST API.

The bmi-topography library provides access to the following global raster datasets:

• SRTMGL3 (SRTM GL3 90m)
• SRTMGL1 (SRTM GL1 30m)
• SRTMGL1_E (SRTM GL1 Ellipsoidal 30m)
• AW3D30 (ALOS World 3D 30m)
• AW3D30_E (ALOS World 3D Ellipsoidal, 30m)
• SRTM15Plus (Global Bathymetry SRTM15+ V2.1)
• NASADEM (NASADEM Global DEM)
• COP30 (Copernicus Global DSM 30m)
• COP90 (Copernicus Global DSM 90m)

The library includes an API and a CLI that accept the dataset type, a latitude-longitude bounding box, and the output file format. Data are downloaded from OpenTopography and cached locally. The cache is checked before downloading new data. Data from a cached file can optionally be loaded into an xarray DataArray through rioxarray.

The bmi-topography API is wrapped with a Basic Model Interface (BMI), which provides a standard set of functions for coupling with data or models that also expose a BMI. More information on the BMI can found in its documentation.

Install the latest stable release of bmi-topography with pip:

pip install bmi-topography

or with conda:

conda install -c conda-forge bmi-topography

The bmi-topography library can also be built and installed from source. The library uses several other open source libraries, so a convenient way of building and installing it is within a conda environment. After cloning or downloading the bmi-topography repository, change into the repository directory and set up a conda environment with the included environment file:

conda env create --file=environment.yml

Then build and install bmi-topography from source with

pip install -e .

To better understand usage, OpenTopography requires an API key to access datasets they host. Getting an API key is easy, and it's free: just follow the instructions in the link above.

Once you have an API key, there are three ways to use it with bmi-topography:

1. parameter: Pass the API key as a string through the api_key parameter.
2. environment variable: In the shell, set the OPENTOPOGRAPHY_API_KEY environment variable to the API key value.
3. dot file: Put the API key in the file .opentopography.txt in the current directory or in your home directory.

If you attempt to use bmi-topography to access an OpenTopography dataset without an API key, you'll get a error like this:

requests.exceptions.HTTPError: 401 Client Error: This dataset requires an API Key for access.

A brief example of using the bmi-topography API is given in the following steps.

Start a Python session and import the Topography class:

>>> from bmi_topography import Topography

For convenience, a set of default parameter values for Topography are included in the class definition. Copy these and modify them with custom values:

>>> params = Topography.DEFAULT.copy()
>>> params["south"] = 39.93
>>> params["north"] = 40.00
>>> params["west"] = -105.33
>>> params["east"] = -105.26
>>> params
{'dem_type': 'SRTMGL3',
 'south': 39.93,
 'north': 40.0,
 'west': -105.33,
 'east': -105.26,
 'output_format': 'GTiff',
 'cache_dir': '~/.bmi_topography'}

These coordinate values represent an area around Boulder, Colorado.

Make a instance of Topography with these parameters:

>>> boulder = Topography(**params)

then fetch the data from OpenTopography:

>>> boulder.fetch()
PosixPath('/Users/mpiper/.bmi_topography/SRTMGL3_39.93_-105.33_40.0_-105.26.tif')

This step might take a few moments, and it will increase for requests of larger areas. Note that the file has been saved to a local cache directory.

Load the data into an xarray DataArray for further work:

>>> boulder.load()
<xarray.DataArray 'SRTMGL3' (band: 1, y: 84, x: 84)>
array([[[2052, 2035, ..., 1645, 1643],
        [2084, 2059, ..., 1643, 1642],
        ...,
        [2181, 2170, ..., 1764, 1763],
        [2184, 2179, ..., 1773, 1769]]], dtype=int16)
Coordinates:
  * band         (band) int64 1
  * x            (x) float64 -105.3 -105.3 -105.3 ... -105.3 -105.3 -105.3
  * y            (y) float64 40.0 40.0 40.0 40.0 ... 39.93 39.93 39.93 39.93
    spatial_ref  int64 0
Attributes:
    _FillValue:    0.0
    scale_factor:  1.0
    add_offset:    0.0
    units:         meters
    location:      node

Note that coordinate reference system information is stored in the spatial_ref non-dimension coordinate:

>>> boulder.da.spatial_ref
<xarray.DataArray 'spatial_ref' ()>
array(0)
Coordinates:
    spatial_ref  int64 0
Attributes:
    crs_wkt:                      GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["...
    semi_major_axis:              6378137.0
    semi_minor_axis:              6356752.314245179
    inverse_flattening:           298.257223563
    reference_ellipsoid_name:     WGS 84
    longitude_of_prime_meridian:  0.0
    prime_meridian_name:          Greenwich
    geographic_crs_name:          WGS 84
    grid_mapping_name:            latitude_longitude
    spatial_ref:                  GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["...
    GeoTransform:                 -105.33041666668363 0.000833333333333144 0....

Display the elevations with the default xarray DataArray plot method.

>>> import matplotlib.pyplot as plt
>>> boulder.da.plot()
>>> plt.show()

For examples with more detail, see the two Jupyter Notebooks, Python script, and shell script included in the examples directory of the bmi-topography repository.

User and developer documentation for bmi-topography is available at https://bmi-topography.readthedocs.io.