Mercurial > hg > GlobalNeighbors
annotate globalneighbors/neighbors.py @ 24:40a9850076a4
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author | Jeff Hammel <k0scist@gmail.com> |
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date | Mon, 04 Sep 2017 14:54:38 -0700 |
parents | 6891c5523b69 |
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display country + population in autocomplete drop down
Jeff Hammel <k0scist@gmail.com>
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1 """ |
2fef925fbf37
display country + population in autocomplete drop down
Jeff Hammel <k0scist@gmail.com>
parents:
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2 read neighbors file; |
2fef925fbf37
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Jeff Hammel <k0scist@gmail.com>
parents:
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3 this should be in the form of: |
2fef925fbf37
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Jeff Hammel <k0scist@gmail.com>
parents:
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4 |
22 | 5 `geoid [(geoid_closest_neighbor, distance), (geoid_2nd_closest_neighbor, distance), ...]` |
6 | |
7 *PER LINE* this format was chosen because it is easier to | |
8 iteratively read and write vs JSON. | |
9 | |
10 While CSV could be made to fit this model, because | |
11 there are both distances and geo IDs as pairs, it is not | |
12 the most natural fit. So we'll settle for our own data model. | |
13 No, it's not the best, but so be it (for now). | |
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Jeff Hammel <k0scist@gmail.com>
parents:
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14 """ |
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Jeff Hammel <k0scist@gmail.com>
parents:
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15 |
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Jeff Hammel <k0scist@gmail.com>
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16 import json |
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Jeff Hammel <k0scist@gmail.com>
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17 import os |
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Jeff Hammel <k0scist@gmail.com>
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18 |
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19 string = (str, basestring) # python2 |
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Jeff Hammel <k0scist@gmail.com>
parents:
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20 |
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Jeff Hammel <k0scist@gmail.com>
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21 def read_neighbors_file(f): |
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Jeff Hammel <k0scist@gmail.com>
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22 |
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23 if isinstance(f, string): |
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24 with open(f) as _f: |
23 | 25 return read_neighbors_file(_f) |
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Jeff Hammel <k0scist@gmail.com>
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26 |
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Jeff Hammel <k0scist@gmail.com>
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27 retval = {} |
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Jeff Hammel <k0scist@gmail.com>
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28 for line in f: |
22 | 29 key, value = line.split(None, 1) |
30 key = int(key) | |
31 data = json.loads(value) | |
32 data = [tuple(item) for item in data] | |
33 retval[key] = data | |
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Jeff Hammel <k0scist@gmail.com>
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34 return retval |