possible use case with prism

[sdtaylor]

It would be good if given a csv with lats and longs to be able to automagically extract various prism variables. For example, given a simple csv like

site_info.head()

    site_id   latitude   longitude
0       258  39.184269 -106.854614
1       414  44.277962  -70.315315
2       475  47.027077 -114.049248
3       637  44.340950  -72.461220
4       681  41.296783 -105.574600
5       703  37.576965 -106.100365
6       744  37.282257 -107.857346
7       844  39.864285 -105.100296
8      1020  39.699631 -105.263504
9      1524  45.339485  -92.913094
10     1526  48.065807 -122.144325
11     1597  48.054951  -92.765915
12     1598  48.051636  -92.766304
13     1657  39.603176 -105.071312

Then a possible invocation could be:

retriever.retrieve(locations = site_info, dataset='prism_daily_tmean', start_date='2015-07-01', end_date='2017-07-30')

and it returns a pandas dataframe like

    site_id   latitude   longitude  prism_daily_tmean
0       258  39.184269 -106.854614         6.28
1       414  44.277962  -70.315315         8.12
2       475  47.027077 -114.049248         5.30
3       637  44.340950  -72.461220         8.30
4       681  41.296783 -105.574600         9.85
5       703  37.576965 -106.100365         6.01
6       744  37.282257 -107.857346        10.43
7       844  39.864285 -105.100296        15.10
8      1020  39.699631 -105.263504        12.92
9      1524  45.339485  -92.913094        10.83
10     1526  48.065807 -122.144325         6.84
11     1597  48.054951  -92.765915         5.82
12     1598  48.051636  -92.766304         5.82
13     1657  39.603176 -105.071312        13.61

(Note I just made this up, I don't know what is setup already)

[ethanwhite]

👍 👍