Predictor search is a small tool for exploring statistical relations between two datasets. For example, precipitation and sea surface temperature. First, Predictor search calculates correlations between all possible pairs of points (grid nodes) from the datasets and store the results into the db. Second, the Predictor search provides the tools to query this database and explore the relationships.

Install

Clone repository to your computer. No other actions is needed.

Dependencies

This programms have been tested to work with Python 3.5

• sqlalchemy 1.1.5
• numpy 1.11
• netCDF4 1.2.7, h5py 2.6 (used to read input data)
• scipy 0.18.1 (only scipy.stats.spearmanr)

Tutorial

1. Open settings.py and following the example provided set the x_config and y_config to point to your datafiles.

2. Set the database connection in settings.py if needed. By default the sqlite will be used. Default db location is ./db/main.sqlite

3. Run create_db.py to execute

>>> create_db()

>>> add_meta()

This will create the db, tables and fill the list of points existing in the datafile

4. To add data run add_data(yMin, yMax, month) from create_db.py. This can take a long time depending on a size of your dataset.

5. Use functions from queries.py to extract results from db