Package: terraref-terrautils
Python library for interfacing TERRA-REF science algorithms with the
TERRA REF databases and workflow.This package provides standard functions for interacting with TERRA-REF
services and data.Two primary use cases: * interactive exploration of the TERRA REF
database and file system. * development of the TERRA REF pipeline.InstallationThe easiest way install terrautils is using pip and pulling from PyPI.:pip install terrautilsBecause this system is still under rapid development, you may need a
specific branch from the terrautils repository. You can either clone the
repository from GitHub and install locally with following commands:git clone https://github.com/terraref/terrautils
git checkout <branch>
cd terrautils
pip install .Or you can install directly from GitHub with a single command:.. code:: shpip installhttps://github.com/terraref/terrautils/archive/<branch>.zipNote:the terrautils package depends on the GDAL library and tools.
Installing GDAL can most easily be accomplished at the operating system
level using packages designed for your distribution. If you see errors
about failing to import osgeo you need to make ensure GDAL and its
python wrappers are installed. Terrautils will work with both GDAL v1.11
and v2.2 so whichever is available for your system should be fine.Functionsbetydb.pyfunctions for interacting with BETYdb, the trait and
agronomic metadata databaseget_sites() – Gets list of stations from BETYdb, filtered by city or
sitename prefix if provided.submit_traits() – Submit a CSV containing traits to the BETYdb API.get_sitename_boundary() – Retrieve the clip boundary dynamically from
betyDB API given sitename and turns the obtained json data into a
geojson polygon.extractors.pyutilities for interacting with the TERRA REF pipelinebuild_metadata() – Construct extractor metadata object ready for
submission to a Clowder file/dataset.get_output_directory() – Determine output directory path given root
path and dataset name.get_output_filename() – Determine output filename given input
information.is_latest_file() – Check whether the extractor-triggering file is the
latest file in the dataset.load_json_file() – Load contents of a .json file on disk into a JSON
object.error_notification() – Send an error message notification, e.g. to
Slack.log_to_influxdb() – Send extractor job detail summary to InfluxDB
instance.trigger_file_extractions_by_dataset() – Manually trigger an
extraction on all files in a dataset.trigger_dataset_extractions_by_collection() – Manually trigger an
extraction on all datasets in a collection._search_for_key() – Check for presence of any key variants in
metadata. Does basic capitalization check.formats.pyutilities for converting numpy arrays into consistently formatted raster images and data productscreate_geotiff() – Generate output GeoTIFF file given a numpy pixel
array and GPS boundary.create_netcdf() – Generate output netCDF file given an input numpy
pixel array.create_image() – Generate output JPG/PNG file given an input numpy
pixel array.gdal.pygis utilities for raster datasetsarray_to_image() – Converts a gdalnumeric array to a Python Imaging
Library (PIL) Image.image_to_array() – Converts a Python Imaging Library (PIL) array to a
gdalnumeric image.world_to_pixel(geo_matrix, x, y) – Uses a gdal geomatrix
(gdal.GetGeoTransform()) to calculate the pixel location of a
geospatial coordinate.clip_raster(rast_path, features_path, gt=None, nodata=-9999) – Clip a
raster and return the clipped result in form of numpy array.get_raster_extents(fname) – Calculates the extent and the center of
the given raster.metadata.pyutilities for querying and processing sensor and image
metadataclean_metadata() – Returns a standarized metadata object.get_terraref_metadata() – Combines cleaned metadata with fixed
metadata.get_extractor_metadata() – Returns Clowder extractor metadata.get_sensor_fixed_metadata() – Returns fixed metadata from Clowder.calculate_scan_time() –sensors.pyget_sensors(station) – Get all sensors for a given station.get_sensor_filename(station, sensor, date, mode=“full”) – Gets the
filename for the image for the given date, sensor and station from
the database. If the mode is full, choose the full resolution image,
otherwise the reduced resolution version.get_sitename(station, date, range_=None, column=None) – Returns a
full sitename for the plot (or fullfield image) corresponding to the
given station, date, range and column.check_site(station) – Checks for valid station given the station
name, and return its path in the file system.check_sensor(station, sensor, date=None) – Checks for valid sensor
with optional date, and return its path in the file system.get_sensor_product(site, sensor) – Returns the downloadable product
for each site-sensor pair.get_attachment_name(site, sensor, date, product) – Encodes site,
sensor, and date to create a unique attachment name.plot_attachment_name(sitename, sensor, date, product) – Encodes
sitename, sensor, and date to create a unqiue attachment name.spatial.pygis helper functionscalculate_bounding_box() – Given a set of GPS boundaries, return
array of 4 vertices representing the polygon.calculate_centroid() – Given a set of GPS boundaries, return lat/lon
of centroid.calculate_gps_bounds() – Extract bounding box geometry, depending on
sensor type.geom_from_metadata() – Parse location elements from metadata._get_bounding_box_with_formula() – Convert scannerbox center
position & sensor field-of-view to actual bound