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https://github.com/csdms/bmi-geotiff
Access data (and metadata) from a GeoTIFF file through a BMI
https://github.com/csdms/bmi-geotiff
bmi csdms geotiff python
Last synced: 3 days ago
JSON representation
Access data (and metadata) from a GeoTIFF file through a BMI
- Host: GitHub
- URL: https://github.com/csdms/bmi-geotiff
- Owner: csdms
- License: mit
- Created: 2021-03-30T20:20:18.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-11-12T21:40:27.000Z (about 2 months ago)
- Last Synced: 2024-12-13T02:09:10.343Z (23 days ago)
- Topics: bmi, csdms, geotiff, python
- Language: Python
- Homepage: https://bmi-geotiff.csdms.io
- Size: 2.13 MB
- Stars: 7
- Watchers: 7
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE-OF-CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
[](https://doi.org/10.5281/zenodo.4721474)
[](https://pypi.org/project/bmi-geotiff)
[](https://anaconda.org/conda-forge/bmi-geotiff)
[](https://github.com/csdms/bmi-geotiff/actions/workflows/test.yml)
[](https://bmi-geotiff.readthedocs.io/en/latest/?badge=latest)
# 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](http://xarray.pydata.org/en/stable/)
[DataArray](http://xarray.pydata.org/en/stable/api.html#dataarray)
using the [rioxarray](https://corteva.github.io/rioxarray/stable/index.html)
[open_rasterio](https://corteva.github.io/rioxarray/stable/rioxarray.html#rioxarray.open_rasterio) method.
The API is wrapped with a
[Basic Model Interface](https://bmi.readthedocs.io) (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](https://bmi.readthedocs.io).
## 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](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).
After cloning or downloading the *bmi-geotiff*
[repository](https://github.com/csdms/bmi-geotiff),
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](http://xarray.pydata.org/en/stable/examples/visualization_gallery.html#imshow()-and-rasterio-map-projections) in the xarray documentation.
Start a Python session and import the `GeoTiff` class:
```python
>>> from bmi_geotiff import GeoTiff
```
For convenience,
let's use a test image from the [rasterio](https://rasterio.readthedocs.io) project:
```python
>>> url = "https://github.com/rasterio/rasterio/raw/main/tests/data/RGB.byte.tif"
```
Make an instance of `GeoTiff` with this URL:
```python
>>> 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:
```python
>>> g.da
[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:
```python
>>> g.da.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](http://xarray.pydata.org/en/stable/generated/xarray.plot.imshow.html) method.
```python
>>> 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](https://github.com/csdms/bmi-geotiff/tree/main/examples) directory
of the *bmi-geotiff* repository.
Documentation for *bmi-geotiff*
is available at https://bmi-geotiff.readthedocs.io.