city-data/classify.py at master · bripc/city-data · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
# lib imports
import csv

# local imports
import config
import util


# loop through all of the Tree objects to calculate statistics about native vs. non-native trees
# write this information to a CSV for easy portability in future projects
def classify_as_native(urban_forest, ca_native_flora):
    total_native = 0
    total_nonnative = 0
    total_unclassified = 0
    with open(config.LOCAL_PATH + '/output/classified_trees.csv', 'w') as csvfile:
        filewriter = csv.writer(csvfile, delimiter=',',
                                quotechar="'", quoting=csv.QUOTE_MINIMAL)
        filewriter.writerow(['common_name', 'scientific_name', 'flora_matched', 'address', 'is_native'])
        for tree in urban_forest:
            # there is some bogus data from the API that has no common or scientific name, ignoring these
            if tree.scientific_name == '':
                total_unclassified += 1
                continue

            native_flora_found = util.binary_search(ca_native_flora, tree.get_scientific_name())
            if native_flora_found:
                tree.set_native(True)
                total_native += 1
            else:
                tree.set_native(False)
                total_nonnative += 1
            filewriter.writerow([tree.common_name, tree.scientific_name, native_flora_found, tree.address, tree.is_native])

    total_trees = len(urban_forest) - total_unclassified

    print('-'*80)
    print('Native:', total_native)
    print('Non-native:', total_nonnative)
    print('Total classified trees:', total_trees)
    print('Unclassified trees:', total_unclassified)
    percNative = float("{0:.2f}".format((total_native / total_trees) * 100))
    print('% total that are native:', percNative, '%')