def read_csv(source_csv, city_name, city_tag, driver):
city_options = City.objects.filter(tag=city_tag)
print "Number of cities available: %s" % len(city_options)
if not len(city_options):
raise ValueError, "CITY NOT FOUND! run make_cities.py first"
## city = City()
## city.name = city_name
## city.tag = to_tag(city.name)
## city.save()
else:
city = city_options[0]
print city
position_file = "position.json"
position = load_json(position_file, create=True)
if not position:
position = 0
cache_file = "%s-20150525.json.bkup" % city.tag
cache_destination = os.path.join(os.path.dirname(source_csv), cache_file)
#keep a local copy of data we've processed...
#this should help with subsequent calls
#to make sure we don't need to duplicate calls to remote geolocation APIs:
local_cache = load_json(cache_destination, create=True)
if not local_cache.has_key('buildings'):
local_cache['buildings'] = {}
search_results = {}
for key, value in local_cache['buildings'].items():
#search_results[key] = Location(value)
sr = SearchResults()
sr.from_dict(value)
#print
#print sr
#print
search_results[key] = sr
#geocoder helper:
#geo = Geo()
provider = ''
provider_options = ServiceProvider.objects.filter(name='City of Columbia')
if len(provider_options):
provider = provider_options[0]
else:
raise ValueError, "error finding utility_provider: %s matches" % len(provider_options)
skips = 0
with open(source_csv) as csvfile:
reader = unicode_csv_reader(csvfile)
#just print the first row:
print '>, <'.join(reader.next())
count = 0
#want to randomize the order... distribute options more evenly
#print len(reader)
#exit()
#in order to randomize, should randomize the order in the csv
for row in reader:
count += 1
print "Looking at row: %s, position: %s" % (count, position)
start = datetime.now()
print "Started: ", start
any_updated = False
#could exit out early here, if needed
if count > 10:
#exit()
pass
#if you want to skip ahead more quickly:
#if count < 0:
if count < position:
pass
else:
#print row
objectid = row[0]
## no_units = row[12]
#can pass this in as bldg_id to make_building
#that gets used for parcel too
parcel_id = row[1]
bldg_id = parcel_id
street_num = row[2]
street_dir = row[3]
street_name = row[4]
street_sfx = row[5]
#eg building number
qualifier_pre = row[6]
#eg "UNIT" or "APT"
qualifier_post = row[7]
apt_num = row[8]
#skip row9 (in/out... whatever that means)
zip_code = row[10]
#skip row11, assessor id
#skip row12, address num
#skip row13, x
#skip row14, y
#xcoord == lng
lng = row[15]
lat = row[16]
#entry floor number: (named 'z' in sheet)
floor = row[17]
#skip row18, strcid... not sure
#skip row19, parent
#skip row20, app_
#skip row21, hteloc
zone = row[22]
bldg_type = row[23]
#number of buildings
bldg_num = row[24]
no_units = row[25]
#skip row[26], inspection type
#skip row27, app number
#skip row28, date received
#skip row29, application type
#skip row30, ownerid
#skip row31, operator id
#skip row32, agent_id
#skip row33, mail to
central_heat = row[34]
if central_heat == 'Y':
central_heat = True
else:
central_heat = False
#heat mechanism? heat mechanic??? not sure
heat_mech = row[35]
#skip row36, agent id (2)
#skip row37, agent last name
#skip row38 agent first name
#skip row39 agent middle initial
#skip row40, agent title
#skip row41, business name
#could be owner, could be agent
## owner_name = row[42]
## owner_address1 = row[43]
## owner_address2 = row[44]
## owner_city = row[45]
## owner_state = row[46]
## owner_zip = row[47]
#address = " ".join([street_num, street_dir, street_name, street_sfx, qualifier_pre, qualifier_post, apt_num])
#this is causing problems with lookups in google
if qualifier_pre == "DUP" or qualifier_pre == "DUPE" or qualifier_pre == "2-Jan" or qualifier_pre == "HM" or qualifier_pre == "DWN":
qualifier_pre = ''
address_main = " ".join([street_num, street_dir, street_name, street_sfx, qualifier_pre])
address_main = address_main.strip()
#get rid of any double spaces
address_main = address_main.replace(" ", " ")
#similar to conversions,
#but there are too many of these to list there
if re.search('HOLLY RIDGE LN', address_main):
address_main = address_main.replace('HOLLY RIDGE LN', 'HOLLYRIDGE LN')
if re.search('BERKSHIRE CT', address_main):
address_main = address_main.replace('BERKSHIRE CT', 'BERKSHIRE')
#address_main = ''
if re.search('CAMERON CT', address_main):
address_main = address_main.replace('CAMERON CT', 'CAMERON')
#address_main = ''
if re.search('ATHENS CT', address_main):
address_main = address_main.replace('ATHENS CT', 'ATHENS')
#address_main = ''
if re.search('LAMAR CT', address_main):
address_main = address_main.replace('LAMAR CT', 'LAMAR')
#address_main = ''
if re.search('MONITEAU CT', address_main):
address_main = address_main.replace('MONITEAU CT', 'MONITEAU')
#address_main = ''
if re.search('IMPERIAL CT', address_main):
address_main = ''
if re.search('PERKINS DR', address_main):
address_main = ''
if re.search('GRANITE OAKS CT', address_main):
address_main = ''
#sometimes the 'BLDG' data is added in the wrong place
#then it gets treated as a unit item
#(but it's not *always* a unit item, so can't generalize it that way)
if qualifier_post == "BLDG" or qualifier_post == "LOT":
address_main = " ".join([address_main, qualifier_post, apt_main])
address_main = address_main.strip()
apt_main = ''
else:
apt_main = " ".join([qualifier_post, apt_num])
apt_main = apt_main.strip()
#check if this is one we want to skip
if conversions.has_key(address_main.upper()):
address_main = conversions[address_main.upper()]
if address_main:
print "APT_MAIN: ", apt_main
address = ", ".join( [address_main, apt_main] )
## if (not status in ['EXPIRED', 'CLOSED']) and (permit_type in ['RENTAL']):
print "Parcel ID:", parcel_id
print address
results = None
#make sure it's not one we're skipping:
if not address:
print "SKIPPING ITEM: %s" % row[1]
skips += 1
## skips = codecs.open("skips.txt", 'a', encoding='utf-8')
## original = " ".join([street_num, street_dir, street_name, street_sfx, qualifier_pre])
## skips.write(original)
## skips.write('\n')
## skips.close()
#check if we've started processing any results for this row
elif not search_results.has_key(address.upper()):
print "No saved search results for address: %s" % address
print "Skipping."
print
#raise ValueError, "No results found for %s" % address
else:
print "Already had building: %s" % address
results = search_results[address.upper()]
assert results
#print results
lookup_building_with_geo(results, make=True, parcel_id=parcel_id)
#print results
#current['results'] = results
#print results
if results.errors:
print results
raise ValueError, results.errors
else:
bldg = results.building
assert bldg
unit = results.unit
#at this point there should be at least one unit
#and we will want to associate results with that unit
#assert unit
# can just pass this up in this case
if not unit:
print "Skipping address... no matching Unit!"
else:
#now that we have a building
#look up energy data on the remote website
#result = urllib2.urlopen("http://example.com/foo/bar")
#print result.read()
## base = "http://www.gocolumbiamo.com/cfforms/ub/rental.html"
## driver.get(base)
## search = driver.find_element_by_css_selector('#address')
## search.send_keys(address)
## button = driver.find_element_by_css_selector('.ui-bar > a:nth-child(2)')
## #button = driver.find_element_by_css_selector('#PrimaryCenterColumn > div > div.ui-bar-b.ui-header > div > a.ui-btn.ui-btn-corner-all.ui-shadow.ui-btn-down-b.ui-btn-up-b')
## #button = driver.find_element_by_css_selector('#PrimaryCenterColumn > div > div.ui-bar-b.ui-header > div > a.ui-btn.ui-btn-corner-all.ui-shadow.ui-btn-down-b.ui-btn-up-b > span > span')
## button.click()
## time.sleep(4)
## #results = driver.find_element_by_css_selector('.dojoxGridMasterView')
## results = driver.find_element_by_css_selector('.dojoxGridContent > div:nth-child(1)')
## print results.get_attribute('innerHTML')
## print parcel_id
## options = results.find_elements_by_tag_name('div')
## #options = results.find_elements_by_link_text(parcel_id)
## print options
## #something didn't work with this:
## #look_for = '<td tabindex="-1" role="gridcell" colspan="1" class="dojoxGridCell" idx="0" style="width:90px;">%s</td>' % parcel_id
## look_for = '>%s<' % parcel_id
## matches = []
## for option in options:
## markup = option.get_attribute('innerHTML')
## #print markup
## if re.search(look_for, markup):
## matches.append(option)
## #print "MATCH!"
## if len(matches) > 1:
## print matches
## raise ValueError, "Too many matches!"
## else:
## matches[0].click()
#just realized that this form uses the property_id
#which we already have...
#can skip the steps above that are trying to make this link:
base = "http://www.gocolumbiamo.com/cfforms/ub/ubdata.cfm?LOCID=%s&AppNum=79" % parcel_id
driver.get(base)
try:
heat_source = driver.find_element_by_css_selector('#PrimaryCenterColumn > table:nth-child(1) > tbody:nth-child(1) > tr:nth-child(3) > td:nth-child(1) > strong:nth-child(1) > font:nth-child(1)')
if heat_source.text.strip() == "Heating Source: Gas Heat":
bldg.heat_source_details = 'gas'
bldg.save()
else:
print heat_source.text
exit()
#TODO:
bldg.heat_source_details = 'electric'
bldg.who_pays_gas = 'not_available'
except:
print "heat source not found... skipping"
try:
selector = driver.find_element_by_css_selector('#el_table_length > label:nth-child(1) > select:nth-child(1) > option:nth-child(3)')
selector.click()
except:
print "No Water data available... skipping"
else:
body = driver.find_element_by_css_selector('#el_table > tbody:nth-child(3)')
rows = body.find_elements_by_tag_name('tr')
#row = rows[0]
query = bldg.utilitysummary_set.filter(type='electricity')
for row in rows:
#print row.get_attribute('innerHTML')
cols = row.find_elements_by_tag_name('td')
date = cols[0].text + '-01'
cost = cols[1].text.replace('$', '').strip()
amount = cols[2].text
amount = amount.replace(' KWH', '')
update_summary(query, date, cost, amount, bldg, unit, provider, 'electricity', 'kwh')
#update_summary(query, date, cost, amount)
#for item in cols:
# print item.text
#print dir(bldg)
#print bldg.utilitysummary_set
#query = bldg.utilitysummary_set.filter(type=utility_type[0])
#could look up type from UTILITY_TYPES...
#but in this case we know what they should be
#query = bldg.utilitysummary_set.filter(type='water')
#if len(query):
try:
water = driver.find_element_by_css_selector('#ext-gen23')
water.click()
selector = driver.find_element_by_css_selector('#wr_table_length > label:nth-child(1) > select:nth-child(1) > option:nth-child(3)')
selector.click()
except:
print "No Water data available... skipping"
else:
body = driver.find_element_by_css_selector('#wr_table > tbody:nth-child(3)')
rows = body.find_elements_by_tag_name('tr')
#row = rows[0]
query = bldg.utilitysummary_set.filter(type='water')
for row in rows:
#print row.get_attribute('innerHTML')
cols = row.find_elements_by_tag_name('td')
date = cols[0].text + '-01'
cost = cols[1].text.replace('$', '').strip()
amount = cols[2].text
amount = amount.replace(' CCF', '')
update_summary(query, date, cost, amount, bldg, unit, provider, 'water', 'ccf')
#update_summary(query, date, cost, amount)
#for item in cols:
# print item.text
unit.update_averages()
#see if we have enough info now to make a score:
unit.update_energy_score()
#now that we've saved the unit,
#update the averages for the whole building:
unit.building.update_utility_averages()
unit.building.update_rent_details()
position += 1
save_json(position_file, position)
if any_updated:
#back it up for later
#enable this when downloading GPS coordinates...
#the rest of the time it slows things down
local_cache['buildings'] = {}
for key, value in search_results.items():
#search_results[key] = SearchResults().from_dict(value)
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
position = count
save_json(position_file, position)
exit()
end = datetime.now()
print "finished: ", end
total_time = end - start
print total_time
print
def read_csv(source_csv, city_tag, feed_date):
#could also use city.models.find_by_city_state
city_options = City.objects.filter(tag=city_tag)
#print "Number of cities available: %s" % len(city_options)
if not len(city_options):
raise ValueError, "CITY NOT FOUND! run make_cities.py first"
else:
city = city_options[0]
print city
feeds = FeedInfo.objects.filter(city=city).filter(added=feed_date)
if feeds.exists():
feed = feeds[0]
print "Already had feed: %s, %s" % (feed.city, feed.added)
else:
feed = FeedInfo()
feed.city = city
feed.added = feed_date
feed.version = "0.1"
feed.save()
print "Created new feed: %s" % feed.city.name
people = Person.objects.filter(name="Blank")
if people.exists():
person = people[0]
print "Already had person: %s" % (person.name)
else:
person = Person()
person.name = "Blank"
person.save()
print "Created new person: %s" % person.name
sources = Source.objects.filter(feed=feed)
if sources.exists():
feed_source = sources[0]
print "Already had source: %s, %s" % (feed_source.feed.city, feed_source.feed.added)
else:
feed_source = Source()
feed_source.feed = feed
feed_source.person = person
feed_source.save()
print "Created new source: %s" % feed_source.feed.city.name
# ideally, should be able to use the database itself as the cache,
# instead of using a local file
# but it's also good to not have to repeat geo queries if going in bulk
# the site code *will* make geo queries
# so it's still a good idea to cache the coded address locally
# even if using the site code for everything else.
cache_file = "%s.json" % city.tag
#print cache_file
cache_destination = os.path.join(os.path.dirname(source_csv), cache_file)
print cache_destination
#keep a local copy of data we've processed...
#this should help with subsequent calls
#to make sure we don't need to duplicate calls to remote geolocation APIs:
loaded_cache = load_json(cache_destination, create=True)
#need to go through and load SearchResults separately
local_cache = {}
for key in loaded_cache.keys():
#this is useful if there is a cached value
#that was not parsed correctly... this will remove it:
#if key.strip() == "314 North Washington Street Apt. C":
if key.strip() == "some address with bad cached data":
print "not adding: ", key
#exit()
pass
else:
current = loaded_cache[key]
results = current['results']
#print results
sr = SearchResults()
#sr.from_dict(results, debug=True)
sr.from_dict(results, debug=False)
#print sr
current['results'] = sr
#print current['results']
local_cache[key] = current
#use street address as the key
#for each address, store SearchResults object
#reset skips for every run:
skips = codecs.open("skips.txt", 'w', encoding='utf-8')
skips.close()
skips = 0
#with codecs.open(source_csv, 'rb', encoding='utf-8') as csvfile:
with open(source_csv) as csvfile:
#reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
#reader = csv.reader(csvfile)
#reader = unicodecsv.UnicodeReader(csvfile, encoding='utf-8')
reader = unicode_csv_reader(csvfile)
#just print the first row:
print '>, <'.join(reader.next())
print
keys = []
for item in reader.next():
key = item.lower().strip()
key = key.replace('(', '')
key = key.replace(')', '')
key = key.replace('-', '_')
key = key.replace('.', '')
key = key.replace('/ ', '')
key = key.replace('/', '_')
key = key.replace('"', '')
key = key.replace('#', 'num')
key = key.replace(' ', '_')
keys.append(key)
#*and* the second row in this case
print '>, <'.join(keys)
#currently:
#<street_address>, <unit_if_applicable>, <unit_type>, <rent>, <security_deposit>, <sq_feet_per_unit>, <num_bedrooms>, <num_bathrooms>, <maximum_occupancy_per_unit>, <lease_period>, <availability>, <laundry>, <parking>, <air_conditioning>, <pets>, <gym_fitness_center>, <game_room_rec_center_community_center>, <pool>, <other_amenities>, <bike_friendly>, <recycling>, <composting>, <gardening>, <public_transit>, <walk_friendly>, <other_smartliving_features>, <who_pays_for_electricity>, <who_pays_for_natural_gas>, <who_pays_for_water>, <who_pays_for_trash_recycling_pickup>, <who_pays_for_telephone_land_line>, <who_pays_for_cable>, <who_pays_for_internet>, <electricity_provider>, <electric_utility_cost_average_per_mo>, <electric_utility_cost_low>, <electric_utility_cost_high>, <natural_gas_provider>, <natural_gas_utility_cost_average_per_mo>, <natural_gas_utility_cost_low>, <natural_gas_utility_cost_high>, <energy_saving_features>, <utility_info_source>, <agent_property_manager>, <property_website_url>, <agent_property_manager_address>, <agent_property_manager_phone>, <owner>, <comments>
#exit()
count = 0
#start = 6439
start = 0
#if you want to randomize the order... to distribute options more evenly
#just do this in the original spreadsheet.
#in order to randomize, should randomize the order in the csv
for row in reader:
current = {}
count += 1
print "Looking at row: %s" % count
#could exit out early here, if needed (for testing)
if count > 7220:
#all_done(cache_destination, local_cache)
pass
if count >= start:
address = process_row(current, row, keys, local_cache, city, feed_source, count)
print
local_cache[address] = current
#save every time...
#never know when a crash will happen:
#however, this does make things run considerably slower
#especially once the cached file size grows.
#save_results(cache_destination, local_cache)
#exit()
all_done(cache_destination, local_cache)
def read_csv(source_csv, city_name, city_tag):
city_options = City.objects.filter(tag=city_tag)
print "Number of cities available: %s" % len(city_options)
if not len(city_options):
raise ValueError, "CITY NOT FOUND! run make_cities.py first"
## city = City()
## city.name = city_name
## city.tag = to_tag(city.name)
## city.save()
else:
city = city_options[0]
print city
feed_date = "2013-10-16"
feeds = FeedInfo.objects.filter(city=city).filter(added=feed_date)
if feeds.exists():
feed = feeds[0]
print "Already had feed: %s, %s" % (feed.city, feed.added)
else:
feed = FeedInfo()
feed.city = city
feed.added = feed_date
feed.version = "0.1"
feed.save()
print "Created new feed: %s" % feed.city.name
people = Person.objects.filter(name="Blank")
if people.exists():
person = people[0]
print "Already had person: %s" % (person.name)
else:
person = Person()
person.name = "Blank"
person.save()
print "Created new person: %s" % person.name
sources = Source.objects.filter(feed=feed)
if sources.exists():
feed_source = sources[0]
print "Already had source: %s, %s" % (feed_source.feed.city, feed_source.feed.added)
else:
feed_source = Source()
feed_source.feed = feed
feed_source.person = person
feed_source.save()
print "Created new source: %s" % feed_source.feed.city.name
cache_file = "%s-20150525.json" % city.tag
cache_destination = os.path.join(os.path.dirname(source_csv), cache_file)
# keep a local copy of data we've processed...
# this should help with subsequent calls
# to make sure we don't need to duplicate calls to remote geolocation APIs:
local_cache = load_json(cache_destination, create=True)
if not local_cache.has_key("buildings"):
local_cache["buildings"] = {}
search_results = {}
for key, value in local_cache["buildings"].items():
# search_results[key] = Location(value)
sr = SearchResults()
sr.from_dict(value)
# print
# print sr
# print
search_results[key] = sr
# geocoder helper:
# geo = Geo()
skips = 0
with open(source_csv) as csvfile:
reader = unicode_csv_reader(csvfile)
# just print the first row:
print ">, <".join(reader.next())
count = 0
# want to randomize the order... distribute options more evenly
# print len(reader)
# exit()
# in order to randomize, should randomize the order in the csv
for row in reader:
count += 1
print "Looking at row: %s" % count
any_updated = False
# could exit out early here, if needed
if count > 10:
# exit()
pass
# if you want to skip ahead more quickly:
if count < 27187:
pass
else:
# print row
objectid = row[0]
## no_units = row[12]
# can pass this in as bldg_id to make_building
# that gets used for parcel too
parcel_id = row[1]
bldg_id = parcel_id
street_num = row[2]
street_dir = row[3]
street_name = row[4]
street_sfx = row[5]
# eg building number
qualifier_pre = row[6]
# eg "UNIT" or "APT"
qualifier_post = row[7]
apt_num = row[8]
# skip row9 (in/out... whatever that means)
zip_code = row[10]
# skip row11, assessor id
# skip row12, address num
# skip row13, x
# skip row14, y
# xcoord == lng
lng = row[15]
lat = row[16]
# entry floor number: (named 'z' in sheet)
floor = row[17]
# skip row18, strcid... not sure
# skip row19, parent
# skip row20, app_
# skip row21, hteloc
zone = row[22]
bldg_type = row[23]
# number of buildings
bldg_num = row[24]
no_units = row[25]
# skip row[26], inspection type
# skip row27, app number
# skip row28, date received
# skip row29, application type
# skip row30, ownerid
# skip row31, operator id
# skip row32, agent_id
# skip row33, mail to
central_heat = row[34]
if central_heat == "Y":
central_heat = True
else:
central_heat = False
# heat mechanism? heat mechanic??? not sure
heat_mech = row[35]
# skip row36, agent id (2)
# skip row37, agent last name
# skip row38 agent first name
# skip row39 agent middle initial
# skip row40, agent title
# skip row41, business name
# could be owner, could be agent
owner_name = row[42]
owner_address1 = row[43]
owner_address2 = row[44]
owner_city = row[45]
owner_state = row[46]
owner_zip = row[47]
# address = " ".join([street_num, street_dir, street_name, street_sfx, qualifier_pre, qualifier_post, apt_num])
# this is causing problems with lookups in google
if (
qualifier_pre == "DUP"
or qualifier_pre == "DUPE"
or qualifier_pre == "2-Jan"
or qualifier_pre == "HM"
or qualifier_pre == "DWN"
):
qualifier_pre = ""
address_main = " ".join([street_num, street_dir, street_name, street_sfx, qualifier_pre])
address_main = address_main.strip()
# get rid of any double spaces
address_main = address_main.replace(" ", " ")
# similar to conversions,
# but there are too many of these to list there
if re.search("HOLLY RIDGE LN", address_main):
address_main = address_main.replace("HOLLY RIDGE LN", "HOLLYRIDGE LN")
if re.search("BERKSHIRE CT", address_main):
address_main = address_main.replace("BERKSHIRE CT", "BERKSHIRE")
# address_main = ''
if re.search("CAMERON CT", address_main):
address_main = address_main.replace("CAMERON CT", "CAMERON")
# address_main = ''
if re.search("ATHENS CT", address_main):
address_main = address_main.replace("ATHENS CT", "ATHENS")
# address_main = ''
if re.search("LAMAR CT", address_main):
address_main = address_main.replace("LAMAR CT", "LAMAR")
# address_main = ''
if re.search("MONITEAU CT", address_main):
address_main = address_main.replace("MONITEAU CT", "MONITEAU")
# address_main = ''
if re.search("IMPERIAL CT", address_main):
address_main = ""
if re.search("PERKINS DR", address_main):
address_main = ""
if re.search("GRANITE OAKS CT", address_main):
address_main = ""
# sometimes the 'BLDG' data is added in the wrong place
# then it gets treated as a unit item
# (but it's not *always* a unit item, so can't generalize it that way)
if qualifier_post == "BLDG" or qualifier_post == "LOT":
address_main = " ".join([address_main, qualifier_post, apt_main])
address_main = address_main.strip()
apt_main = ""
else:
apt_main = " ".join([qualifier_post, apt_num])
apt_main = apt_main.strip()
# check if this is one we want to skip
if conversions.has_key(address_main.upper()):
address_main = conversions[address_main.upper()]
if address_main:
print "APT_MAIN: ", apt_main
address = ", ".join([address_main, apt_main])
else:
address = ""
owner_address = ", ".join([owner_address1, owner_address2, owner_city, owner_state, owner_zip])
## if (not status in ['EXPIRED', 'CLOSED']) and (permit_type in ['RENTAL']):
print "Parcel ID:", parcel_id
print address
results = None
# make sure it's not one we're skipping:
if not address:
print "SKIPPING ITEM: %s" % row[1]
skips += 1
skipf = codecs.open("skips.txt", "a", encoding="utf-8")
original = " ".join([street_num, street_dir, street_name, street_sfx, qualifier_pre])
skipf.write(original)
skipf.write("\n")
skipf.close()
else:
# check if we've started processing any results for this row
if search_results.has_key(address.upper()):
print "Already had building: %s" % address
results = search_results[address.upper()]
# print results
else:
addy = ", ".join([address_main, city.name, city.state])
addy += " " + zip_code
# addy += ", USA"
print addy
# toggle betweeen an actual google query
results = address_search(addy, apt_main)
# print dir(results)
if len(results.matches) > 1:
print results
for option in results.matches:
print "%s: %s, %s" % (option["place"], option["lat"], option["lng"])
print
print "Source Lat: %s, Lng: %s" % (lat, lng)
src_lat = int(float(lat) * 100)
src_lng = int(float(lng) * 100)
matched = False
for current in results.matches:
# current = results.matches[0]
print current["lat"]
print current["lng"]
# only want to look at the first 2 decimal places:
comp_lat = int(float(current["lat"]) * 100)
comp_lng = int(float(current["lng"]) * 100)
print comp_lat
print comp_lng
if (src_lat == comp_lat) and (src_lng == comp_lng):
# results.matches = results.matches[:1]
results.matches = [current]
matched = True
if not matched:
print "DIDN'T MATCH!"
exit()
any_updated = True
# or just using results as specified in csv
# (THIS DOES NOT NORMALIZE THE ADDRESS VIA GOOGLE)
# results = SearchResults()
# results.unit_text = apt_main
# handle_place(results, addy, lat, lng, apt_main)
assert results
# print results
lookup_building_with_geo(results, make=True, parcel_id=parcel_id)
# print results
# current['results'] = results
# print results
if results.errors:
print results
raise ValueError, results.errors
else:
search_results[address.upper()] = results
bldg = results.building
assert bldg
unit = results.unit
# may be a case where the unit is blank
# and another unit with an number/letter was created earlier
# in that case, we won't be creating one here
# and the building will already exist...
# not necessarily an error though
# just redundant data
# assert unit
(person, bldg_person) = make_person(owner_name, bldg, "Agent", address=owner_address)
# time.sleep(1)
if any_updated:
# back it up for later
# enable this when downloading GPS coordinates...
# the rest of the time it slows things down
local_cache["buildings"] = {}
for key, value in search_results.items():
# search_results[key] = SearchResults().from_dict(value)
local_cache["buildings"][key] = value.to_dict()
save_json(cache_destination, local_cache)
print