https://github.com/nasaharvest/mlhub

A python library for interacting with https://mlhub.earth
https://github.com/nasaharvest/mlhub

Last synced: 2 months ago
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A python library for interacting with https://mlhub.earth

• Host: GitHub
• URL: https://github.com/nasaharvest/mlhub
• Owner: nasaharvest
• Created: 2020-03-22T08:30:42.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-03-31T03:20:11.000Z (over 4 years ago)
• Last Synced: 2024-06-11T17:40:42.206Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 423 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# `mlhub`

A python library for downloading data from https://www.mlhub.earth/#datasets

A lot of this code is copied from `https://github.com/radiantearth/mlhub-tutorials`, with a few additional functions

for data exploration.

### Quickstart

Install by downloading this repository, and (from the repository root) running

```bash

pip install -e .

```

To start, you will need to add your API token to interact with the mlhub API. In addition, if you want to download source images from AWS, you will need to add your AWS credentials. The buckets are [Requester Pays](https://docs.aws.amazon.com/AmazonS3/latest/dev/RequesterPaysBuckets.html) buckets, which means you will get charged for each download.

```python

import mlhub

mlhub.set_token(

    "token123"

)

mlhub.set_aws_credentials(

    aws_access_key="access123",

    aws_secret_key="secret123",

)

```

You can then explore which collections are available:

```python

collections = mlhub.get_collections()

```

And download a collection

```python

from pathlib import Path

download_location = Path("../data")

mlhub.download_collection(

    collection_id="ref_african_crops_tanzania_01",

    download_path=download_location,

    ignore_source_images=False

)

```

If `ignore_source_images=True`, then no source images are downloaded, only the labels and the documentation. If you are not downloading the source images, there is no need for any AWS credentials.

In addition, you can explore different collections, for instance by seeing the availability of source images across features and timesteps:

```python

assets = mlhub.get_all_assets("ref_african_crops_uganda_01")

assets_df = mlhub.features_to_df(assets)

dates_df = mlhub.check_dates_across_features(assets_df)

mlhub.plot_range(dates_df)

```

Would yield the following plot:



More of these plots can be found in the [exploration notebook](notebooks/Exploration.ipynb)