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Home Page: https://medaprep.readthedocs.io/en/latest/
License: MIT License
medaprep is a data preparation and feature engineering toolkit for geospatial applications.
Home Page: https://medaprep.readthedocs.io/en/latest/
License: MIT License
Load xarray dataset based on parameters instead of loading catalog
Visualize geographical coverage of a dataset
Given a STAC catalog, expose the locations of each sample within the dataset.
Design:
A function within the summarize
module that returns the bounding box extents of all the samples within the dataset.
Usage:
import geolt
cat = geolt.load_catalog(parameters)
gdf = geolt.summarize.locations(catalog=cat, metadata=True, bbox=[x1,y1,x2,y2])
returns a GeoDataFrame
with bounding boxes of all the locations covered within the catalog as well as basic attributes (e.g., data type, source, etc.). This GeoDataFrame
can be visualized easily by calling gdf.explore()
or output to .shp
file. This can be used to help with making sure a specific area of interest is covered in the dataset.
Given a STAC catalog, we should be able to summarize all data within the catalog.
This module will hold functions to list metadata, plot distributions of various features, detect anomalies within the datasets, produce visualizations of features, return bounding boxes of data coverage (see #1)
Design:
A new module summarize
which houses many functions that operate directly from the STAC catalog.
Usage:
import geolt
cat = geolt.load_catalog(parameters)
gdf_summary = geolt.summarize(catalog=cat, distributions=True, patches=True, bbox=[x1,y1,x2,y2])
based on the bbox
, return:
bbox
GeoDataFrame
Author of Proposal:
Setting up pre-commit would be nice to help keep everyone accountable for best practices
Setup a pre commit config for running style checks on merges
We need to generate rasters on the fly for testing.
create_test_raster
will be a function to instantiate a test raster given desired parameters.
Design:
Within the tests
module, a create_test_raster
function will create an xarray DataArray
given specified input parameters. Like in xarray-spatial
Usage:
test_raster_parameters = ()
data = create_test_raster(test_raster_parameters)
Given xarray Dataset
geolt.skim
function returns a pandas DataFrame
containing:
We need to be able to load a STAC catalog given a set of parameters.
load_catalog
will be used to instantiate catalog(s) for downstream use.
Design:
A new module load_catalog
, which houses functions to load a STAC catalog based on input parameters.
Usage:
first version:
cat = geolt.load_catalog(address, bbox)
simple use:
import geolt
cat = geolt.load_catalog(address, bbox, sensor_type, labeled)
advanced case:
import geolt
cat = geolt.load_catalog(address=list(addresses), bbox=list(bboxes), sensor_type=list(sensor_types), labeled=list(labeled))
returns STAC catalog(s) based on input arguments.
Author of Proposal:
We need to setup packaging for geolt
Setup pyproject and setup files
This is just a test of the GH - ClickUp integration and can be closed/deleted!
Author of Proposal:
Describe what the need for this new feature is or what problem this new feature will address.
Description of the new feature, how it will be used, what it will fix, etc.
Design:
Include description of this feature's design with enough detail for those who are familiar enough with this project to understand the feature and how it could be implmented. This section should get into specifics of how the feature will be designed and implemented.
Usage:
Detailed instructions for this feature's use.
Value: What value does the implementation of this new feature bring to geolt.
Who are the stakeholders in this update? Will you be implementing this new feature or will someone else? What is the potential impact of implementing this new feature? Specifically, what are some other components would be impacted?
What are potential reasons why this feature should not be implemented?
Describe other solutions or features you have considered when coming up with this proposal.
What are parts of this feature's design that are undecided.
Anything elses that is important to know for the implementation of this new feature.
Shapely is a dependency for visualize.py
. It should be added to requirements.txt.
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