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import xml.etree.cElementTree as ET

from collections import defaultdict

import re

import pprint

'''

Wrangling Open Street Map Data for -

Vancouver, Canada

In This Script to get sense of Data ->

1. Count the number of tags for each type of Tag

3. Create csv from the XML

'''

#OSM File Name and path

OSM_PATH = 'vancouver_canada.osm'

SAMPLE = 'sample_vc.osm'

lower = re.compile(r'^([a-z]|_)*$')

lower_colon = re.compile(r'^([a-z]|_)*:([a-z]|_)*$')

problemchars = re.compile(r'[=\+/&<>;\'"\?%#$@\,\. \t\r\n]')

#Regex for different cases

#Finding counts for different tag categories

def get_element(osm_file, tags=('node', 'way', 'relation')):

context = ET.iterparse(osm_file, events=('start', 'end'))

_, root = next(context)

for event, elem in context:

if event == 'end':

yield elem

root.clear()

def key_type(element, keys):

'''

Count of validated strings in different tags

'''

if element.tag == "tag":

for tag in element.iter('tag'):

k = tag.get('k')

if lower.search(k):

keys['lower'] += 1

elif lower_colon.search(k):

keys['lower_colon'] += 1

elif problemchars.search(k):

keys['problemchars'] += 1

else:

keys['other'] += 1

return keys

def process_map(filename):

'''

Function for Distributing in different categories and finding tag count

'''

keys = {"lower": 0, "lower_colon": 0, "problemchars": 0, "other": 0}

for _, element in ET.iterparse(filename):

keys = key_type(element, keys)

return keys

def count_tags(filename):

'''

Initial Function to get a sense of data, how is the data structured

.

'''

tags = {}

for element in get_element(filename):

if element.tag not in tags.keys():

tags[element.tag] = 1

else:

tags[element.tag] += 1

return tags

def test(filename):

keys = process_map(filename)

tags = count_tags(filename)

pprint.pprint(keys)

pprint.pprint(tags)

if __name__ == "__main__":

#test(OSM_PATH)

test(SAMPLE)