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  1. .gitattributes +3 -0
  2. .gitignore +11 -0
  3. README.md +29 -0
  4. neo_1month/data/AOD/2000-03.npz +3 -0
  5. neo_1month/data/AOD/2000-04.npz +3 -0
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.gitattributes ADDED
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.npz filter=lfs diff=lfs merge=lfs -text
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+ *.ipynb filter=lfs diff=lfs merge=lfs -text
.gitignore ADDED
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+ *.PNG
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+ *.png
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+ *.csv
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+ archives/
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+ neo_1month/
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+ *.npz
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+ *.npy
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+ __pycache__
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+ *.pyc
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+ dataset/
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+
README.md ADDED
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+ <b><h1>NEO dataset</h1></b>
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+
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+ Preprocessing and loading scripts for NEO datasets: 1 week and 1 month variants. Go in either [1 week](neo_1week) or
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+ [1 month](neo_1month) directories.
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+
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+ Steps:
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+ 1. **Download raw data**
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+
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+ Use the `./download_raw_data.py -o raw_data` script.
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+
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+ 1.1 **For weekly data only**. Use the `preprocess_raw_1week_data.py raw_data -o raw_data_fixed` script.
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+
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+ - NDVI is only sampled at 16 days, we make an average of two consecutive samples to create an 8 day average.
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+
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+ - We rename the data from YYYY-MM-DD to YYYY-MM-E (E=1,2,3,4 depending on which week bucket). This is needed because
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+ different sensors sample data at different weeks
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+
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+
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+ 2. **Convert from PNG to NPY files**
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+ - Converts from raw PNG to [0:1] normalied, 540x1080 npy files, and adds NaNs to invalid regions in the NEO data.
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+
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+ `./convert_png_to_npy.py raw_data -o npy_data [--resolution 540 1080]`
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+
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+ - If `--resolution` is not set, it will default to `540 x 1080`.
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+
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+ 3. Run the [neo_viewer](neo_viewer.ipynb) and [data analysis](neo_data_analysis.ipynb) notebook
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+ - Set the path to `neo_1week/npy_data` or `neo_1month/npy_data`.
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+
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+ 4. Use in ML python scripts via the [neo reader](neo_reader/neo_reader.py). You can also run it with the `npy_data`.
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