bmi-geotiff¶
Access data (and metadata) from a GeoTIFF file through an API or a BMI.
The bmi-geotiff library accepts a filepath or an URL to a GeoTIFF file. Data are loaded into an xarray DataArray using the rioxarray open_rasterio method. The 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.
Installation¶
Install the latest stable release of bmi-geotiff with pip
:
pip install bmi-geotiff
or with conda
:
conda install -c conda-forge bmi-geotiff
Alternately, the bmi-geotiff library can 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-geotiff 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-geotiff from source with
pip install -e .
Examples¶
A brief example of using the bmi-geotiff API is given in the following steps. The example is derived from a similar example in the xarray documentation.
Start a Python session and import the GeoTiff
class:
>>> from bmi_geotiff import GeoTiff
For convenience, let’s use a test image from the rasterio project:
>>> url = "https://github.com/rasterio/rasterio/raw/main/tests/data/RGB.byte.tif"
Make an instance of GeoTiff
with this URL:
>>> g = GeoTiff(url)
This step might take a few moments as the data are pulled from GitHub.
The data have been loaded into an xarray DataArray
, which can be
accessed through the da
property:
>>> g.da
<xarray.DataArray (band: 3, y: 718, x: 791)>
[1703814 values with dtype=uint8]
Coordinates:
* band (band) int64 1 2 3
* x (x) float64 1.021e+05 1.024e+05 ... 3.389e+05 3.392e+05
* y (y) float64 2.827e+06 2.826e+06 ... 2.612e+06 2.612e+06
spatial_ref int64 0
Attributes:
STATISTICS_MAXIMUM: 255
STATISTICS_MEAN: 29.947726688477
STATISTICS_MINIMUM: 0
STATISTICS_STDDEV: 52.340921626611
_FillValue: 0.0
scale_factor: 1.0
add_offset: 0.0
units: metre
Note that coordinate reference system information is stored in the
spatial_ref
non-dimensional coordinate:
>>> g.da.spatial_ref
<xarray.DataArray 'spatial_ref' ()>
array(0)
Coordinates:
spatial_ref int64 0
Attributes:
crs_wkt: PROJCS["WGS 84 / UTM zone 18N",GEOGCS[...
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
horizontal_datum_name: World Geodetic System 1984
projected_crs_name: WGS 84 / UTM zone 18N
grid_mapping_name: transverse_mercator
latitude_of_projection_origin: 0.0
longitude_of_central_meridian: -75.0
false_easting: 500000.0
false_northing: 0.0
scale_factor_at_central_meridian: 0.9996
spatial_ref: PROJCS["WGS 84 / UTM zone 18N",GEOGCS[...
GeoTransform: 101985.0 300.0379266750948 0.0 2826915...
Display the image with the xarray.plot.imshow method.
>>> import matplotlib.pyplot as plt
>>> g.da.plot.imshow()
>>> plt.show()
For examples with more detail, see the Jupyter Notebooks and Python scripts included in the examples directory of the bmi-geotiff repository.
Documentation for bmi-geotiff is available at https://bmi-geotiff.readthedocs.io.
Additional Information¶
API Reference¶
Looking for information on a particular function, class, or method? This part of the documentation is for you.
Changelog¶
Project documents¶
Indices and tables¶
Help¶
Depending on your need, CSDMS can provide advice or consulting services. Feel free to contact us through the CSDMS Help Desk.
Acknowledgments¶
This work is supported by the National Science Foundation under Award No. 1831623, Community Facility Support: The Community Surface Dynamics Modeling System (CSDMS).