def method_new(name="Untitled Q-Method", owner="Your Name", email="email", phone='phone', notes=''):
#look for existing methods:
options = os.listdir(data_path)
new_option = ""
#make sure that:
#a) we have a new id and
#b) the new id has not already been used
while (not new_option) or (new_option in options):
new_option = generate_id()
#make new directory in method_path
method_path = os.path.join(data_path, new_option)
if not os.path.exists(method_path):
os.makedirs(method_path)
else:
#This should never happen with above while loop, but just in case...
raise ValueError, "Path exists, but it shouldn't: %s" % method_path
#make an empty configuration file
config = os.path.join(method_path, "config.json")
result = load_json(config, create=True)
result['name'] = name
result['owner'] = owner
result['email'] = email
result['phone'] = phone
result['notes'] = notes
result['statements'] = """1. First sample statement
2. Second sample statement"""
result['columns'] = '2 3 5 6 8 6 5 3 2'
save_json(config, result)
#redirect to the new method's page:
redirect("/method/" + new_option + "/bookmark/")
def skf_cross_validate(model, X, y):
"""
wrapper function to do sklearn style stratified k fold cross validation
on a keras model. Some code borrowed from:
https://medium.com/@literallywords/stratified-k-fold-with-keras-e57c487b1416
input - X, training data
y, training data labels
"""
print("Stratified K Fold cross Validating")
skf = StratifiedKFold(n_splits=5, random_state=42, shuffle=True)
for index, (train_index, test_index) in enumerate(skf.split(X, y)):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# Convert class labels to categorical data/one-hot encoding
y_test = to_categorical(y_test)
y_train = to_categorical(y_train)
print ('Training '+'lenet-cv-'+str(index))
# Train model and validate
results = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=64)
training_eval(results, 'lenet-cv-'+str(index))
save_json(model, 'lenet-cv-'+str(index))
model.save_weights('models/lenet-cv-'+str(index)+'_weights.h5')
def update_json(source, city_tag):
cache_file = "%s.json" % city_tag
cache_destination = os.path.join(os.path.dirname(source), cache_file)
local_cache = load_json(cache_destination, create=True)
assert local_cache.has_key('buildings')
assert local_cache.has_key('parcels')
locations = {}
for key, value in local_cache['buildings'].items():
location = Location(value)
for source in location.sources:
if hasattr(location, source):
result = getattr(location, source)
#convert from old dict format here
if isinstance(result, dict):
print "Found dictionary in: %s for: %s" % (source, location.address)
result = [ result ]
setattr(location, source, result)
locations[key] = location
#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 locations.items():
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
def update_json(source, city_tag):
cache_file = "%s.json" % city_tag
cache_destination = os.path.join(os.path.dirname(source), cache_file)
local_cache = load_json(cache_destination, create=True)
assert local_cache.has_key('buildings')
assert local_cache.has_key('parcels')
locations = {}
for key, value in local_cache['buildings'].items():
location = Location(value)
for source in location.sources:
if hasattr(location, source):
result = getattr(location, source)
#convert from old dict format here
if isinstance(result, dict):
print "Found dictionary in: %s for: %s" % (
source, location.address)
result = [result]
setattr(location, source, result)
locations[key] = location
#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 locations.items():
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
def subject_new(key):
"""
create a new subject for the Q-Method specified by key
"""
method_path = os.path.join(data_path, key)
if not os.path.exists(method_path):
return template('404', key=key, item="method")
else:
#look for existing subjects:
options = os.listdir(method_path)
new_option = ""
#make sure that:
#a) we have a new id and
#b) the new id has not already been used
while (not new_option) or (new_option in options):
new_option = generate_id()
#make new directory in method_path
subject_path = os.path.join(method_path, new_option)
if not os.path.exists(subject_path):
os.makedirs(subject_path)
else:
#This should not ever happen with above check, but just in case...
raise ValueError, "Subject path exists, but it shouldn't: %s" % subject_path
#make an empty configuration file
config = os.path.join(subject_path, "subject_config.json")
result = load_json(config, create=True)
#once the subject starts sorting, we will cache this locally
#based on the current state of the method configuration
#result['statements'] = ""
result['columns'] = u""
result['json'] = u""
result['started'] = u""
#a textual representation of where each statement is
result['state'] = u""
result['history'] = u""
#is it finished? complete? this will prevent further changes:
result['locked'] = False
#now:
now = datetime.now()
result['created'] = now.strftime("%Y.%m.%d %H:%M:%S")
# after first movement
result['started'] = u""
result['last_update'] = u""
save_json(config, result)
#redirect to the new method's page:
redirect("/method/" + key + "/")
def post_subject_json(mkey=None, skey=None):
method_path = os.path.join(data_path, mkey)
if not os.path.exists(method_path):
return template('404', key=mkey, item="method")
else:
subject_path = os.path.join(method_path, skey)
if not os.path.exists(subject_path):
return template('404', key=skey, item="subject")
else:
method_config = os.path.join(method_path, "config.json")
#result = load_json(config)
method_json_file = codecs.open(method_config, 'r', encoding='utf-8', errors='ignore')
method_json = method_json_file.read()
method_details = json.loads(method_json)
subject_config = os.path.join(subject_path, "subject_config.json")
subject_json_file = codecs.open(subject_config, 'r', encoding='utf-8', errors='ignore')
subject_json = subject_json_file.read()
subject_details = json.loads(subject_json)
#subject_data = load_json(subject_config)
#changed = False
now = datetime.now()
if not subject_details['columns']:
subject_details['columns'] = method_details['columns']
#changed = True
#if request.forms.get('json') != subject_details['json']:
#this includes the whole tree from javascript:
# available, placed, and soon responses
subject_details['json'] = request.forms.get('json')
#changed = True
if not subject_details['started']:
subject_details['started'] = now.strftime("%Y.%m.%d %H:%M:%S")
subject_details['last_update'] = now.strftime("%Y.%m.%d %H:%M:%S")
#subject_details['history'] += u"%s,%s\n" % (now.strftime("%Y.%m.%d %H:%M:%S"), request.forms.get('action'))
#subject_details['history'] += unicode(now.strftime("%Y.%m.%d %H:%M:%S")) + u"," + unicode(request.forms.get('action'))
#this avoids:
# UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 99: ordinal not in range(128)
subject_details['history'] += unicode(now.strftime("%Y.%m.%d %H:%M:%S")) + u"," + request.forms.get('action').decode("utf-8") + u"\n"
save_json(subject_config, subject_details)
return template('success')
def save_results(cache_destination, local_cache):
#destination = '%s.tsv' % city_tag
#save_results(locations, destination)
#convert all results to json serializable
for_saving = {}
for key in local_cache.keys():
current = local_cache[key]
results = current['results']
dupe = copy.copy(current)
dupe['results'] = results.to_dict()
for_saving[key] = dupe
save_json(cache_destination, for_saving)
def extract(self):
"""Actually run the extract process."""
data = OrderedDict()
for game_type in VALID_GAME_TYPES:
click.echo(game_type)
data[game_type] = OrderedDict()
self._extract_radio_calls(data[game_type], game_type)
self._extract_throwables(data[game_type], game_type)
self._extract_weapons(data[game_type], game_type)
self._extract_equipment(data[game_type], game_type)
data[game_type] = OrderedDict(sorted(data[game_type].items(), key=lambda k: k[0]))
helpers.save_json(app.config['UNLOCKABLES_DATA_FILE'], data)
def extract(self):
"""Actually run the extract process."""
maps_paths = []
maps_paths.extend(glob(os.path.join(self.packages_dir, '*', 'maps', '*', 'objects.svg'))) # Maps in RWR game directory
maps_paths.extend(glob(os.path.join(self.workshop_dir, '*', 'media', 'packages', '*', 'maps', '*', 'objects.svg'))) # Maps in RWR workshop directory
data = OrderedDict()
for map_path in maps_paths:
server_type, map_id = utils.parse_map_path(map_path.replace('\\', '/').replace('/objects.svg', ''))
if not map_id or map_id in INVALID_MAPS or server_type in INVALID_GAME_TYPES:
click.secho('Invalid map ID ({}) or server type ({})'.format(map_id, server_type), fg='yellow')
continue
map_xml = etree.parse(map_path)
map_infos = map_xml.findtext('//svg:rect[@inkscape:label=\'#general\']/svg:desc', namespaces={'svg': 'http://www.w3.org/2000/svg', 'inkscape': 'http://www.inkscape.org/namespaces/inkscape'})
if not map_infos:
click.secho('No general map info found', fg='yellow')
continue
map_infos = self._parse_map_data(map_infos)
if 'name' not in map_infos:
click.secho('Map name not found', fg='yellow')
continue
click.echo(server_type + ':' + map_id)
if server_type not in data:
data[server_type] = OrderedDict()
data[server_type][map_id] = OrderedDict([
('name', map_infos['name'].replace('Pacific: ', '').title()),
('has_minimap', os.path.isfile(os.path.join(app.config['MINIMAPS_IMAGES_DIR'], server_type, map_id + '.png'))),
('has_preview', os.path.isfile(os.path.join(app.config['MAPS_PREVIEW_IMAGES_DIR'], server_type, map_id + '.png')))
])
helpers.save_json(app.config['MAPS_DATA_FILE'], data)
def post_method_json(key=None):
#print dir(request.forms)
#print request.forms.keys()
method_path = os.path.join(data_path, key)
if not os.path.exists(method_path):
return template('404', key=key, item="method")
else:
config = os.path.join(method_path, "config.json")
result = load_json(config)
changed = False
for key in request.forms.keys():
#special case for 'statements' key...
#want to get rid of any extra newline characters
#this will help calculate the number of statements more accurately
#(rather than stripping newlines everywhere we look at statements)
#
#this works here, but it will make it difficult to provide
#feedback to the user about how many statements there are
#compared to how many spaces there are available in columns
#adding a similar check in method.js
if key == "statements":
text = request.forms.get(key)
lines = text.splitlines()
new_lines = []
for line in lines:
if line:
new_lines.append(line)
value = '\n'.join(new_lines)
else:
value = request.forms.get(key)
if value != result[key]:
#print "%s (original) != %s (new)" % (result[key], request.forms.get(key))
result[key] = value
changed = True
if changed:
#print "METHOD CONFIG CHANGED!!!! (saving)"
save_json(config, result)
return template('success')
def extract(self):
"""Actually run the extract process."""
# Only handle official ranks
ranks_files_paths = [
{ # In Vanilla, ranks from all factions are the same, inspired from the US Army
'country': 'us',
'path': os.path.join(self.packages_dir, 'vanilla', 'factions', 'brown.xml'),
'game_type': 'vanilla'
},
{ # In Pacific, US factions are the same as the Vanilla ones, so only parse IJA ranks
'country': 'jp',
'path': os.path.join(self.packages_dir, 'pacific', 'factions', 'ija.xml'),
'game_type': 'pacific'
}
]
data = OrderedDict()
for ranks_file_path in ranks_files_paths:
click.echo(ranks_file_path['country'])
data[ranks_file_path['country']] = OrderedDict()
faction_xml = etree.parse(ranks_file_path['path'])
faction_xml_root = faction_xml.getroot()
i = 0
for rank_node in faction_xml_root.iterchildren('rank'):
rank_name = rank_node.get('name')
click.echo(rank_name)
data[ranks_file_path['country']][i] = OrderedDict([
('name', rank_name),
('xp', int(float(rank_node.get('xp')) * 10000))
])
self._extract_images(i, ranks_file_path['game_type'], ranks_file_path['country'], rank_node.find('hud_icon').get('filename'))
i += 1
helpers.save_json(app.config['RANKS_DATA_FILE'], data)
def read_csv(source):
#for reading unicode
#f = codecs.open(source, 'r', encoding='utf-8')
city_options = City.objects.filter(tag="ann_arbor")
print len(city_options)
if not len(city_options):
city = City()
city.name = "Ann Arbor"
city.tag = to_tag(city.name)
city.save()
else:
city = city_options[0]
print city
#TODO:
#setup FeedInfo item
#and also create a Source item
permit_sub_types = []
status_types = []
building_nums = []
applicants = []
managers = []
cache_file = "%s.json" % city.tag
cache_destination = os.path.join(os.path.dirname(source), 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'] = {}
if not local_cache.has_key('parcels'):
local_cache['parcels'] = {}
locations = {}
for key, value in local_cache['buildings'].items():
locations[key] = Location(value)
#geocoder helper:
geo = Geo()
#with open('eggs.csv', 'rb') as csvfile:
with codecs.open(source, 'rb', encoding='utf-8') as csvfile:
#reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
reader = csv.reader(csvfile)
#just print the first row:
print '>, <'.join(reader.next())
count = 0
for row in reader:
count += 1
#could exit out early here, if needed
if count > 10:
pass
print row
#type of building (eg: sf attached, duplex, etc)
permit_id = row[0]
#should always be "RENTAL" (don't need to track this one)
permit_type = row[1]
if not permit_type == "RENTAL" and not permit_type == "MECHANICAL":
raise ValueError, "Unexpected permit type: %s in row: %s" % (
permit_type, row)
sub_type = row[2]
#can use this to filter out non-rental or obsolete entries
#don't need to track otherwise:
status = row[3]
parcel_id = row[4]
address = row[5]
#should be fixed per source:
city = row[6]
if not ((city.lower() == 'ann arbor') or (city == '')):
raise ValueError, "Unexpected city: %s" % (city)
sqft = row[7]
number_of_buildings = row[8]
applicant_name = row[9]
number_of_stories = row[10]
number_of_units = row[11]
if (not status in ['EXPIRED', 'CLOSED']) and (permit_type
in ['RENTAL']):
#check if we've started processing any results for this row
#if local_cache['buildings'].has_key(address.upper()):
# local_cache_cur = local_cache['buildings'][address.upper()]
#else:
# local_cache_cur = {}
if locations.has_key(address.upper()):
location = locations[address.upper()]
else:
location = Location()
#do some geocoding, as needed:
search = "%s, Ann Arbor MI" % address.upper()
for source in location.sources:
geo.lookup(search, source, location)
location.address_alt = search
locations[address.upper()] = location
#local_cache['buildings'][address.upper()] = local_cache_cur
#and check if a previous building object in the db exists
#CREATE A NEW BUILDING OBJECT HERE
#cur_building = Building()
bldg = Building()
bldg.type = sub_type
#back it up for later
local_cache['buildings'] = {}
for key, value in locations.items():
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
#exit()
#THE FOLLOWING ARE FOR INFORMATIONAL PURPOSES ONLY
#(to see what data is available)
if not status in status_types:
#print "adding: %s" % sub_type
status_types.append(status)
if not sub_type in permit_sub_types:
#print "adding: %s" % sub_type
permit_sub_types.append(sub_type)
building_num = row[8]
if not building_num in building_nums:
#print "adding: %s" % sub_type
building_nums.append(building_num)
applicant = row[9]
if (re.search('MGMT', applicant) or re.search('REALTY', applicant)
or re.search('PROPERTIES', applicant)
or re.search('MANAGEMENT', applicant)
or re.search('GROUP', applicant)
or re.search('LLC', applicant)
or re.search('L.L.C.', applicant)
or re.search('INC', applicant)):
if not applicant in managers:
managers.append(applicant)
else:
if not applicant in applicants:
applicants.append(applicant)
#print ', '.join(row)
#print
## print permit_sub_types
print status_types
print building_nums
save_results(locations)
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 = "2013-07-31"
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.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'] = {}
if not local_cache.has_key('parcels'):
local_cache['parcels'] = {}
locations = {}
for key, value in local_cache['buildings'].items():
locations[key] = Location(value)
#geocoder helper:
geo = Geo()
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())
count = 0
for row in reader:
count += 1
print "Looking at row: %s" % count
#could exit out early here, if needed
if count > 1000:
#exit()
pass
address = row[0]
#need to fix the number being at the end of the address
parts = address.split(',')
anumber = parts[-1]
parts = parts[:-1]
street = ",".join(parts)
address = "%s %s" % (anumber, street)
invoice_number = row[1]
bldg_id = row[1]
print bldg_id
#this is where owner is stored
invoice_note = row[6]
print invoice_note
if re.match('Sent to:', invoice_note):
print "changing invoice note from: %s" % invoice_note
invoice_note = invoice_note[8:]
print "to: %s" % invoice_note
else:
#raise ValueError, "invoice note does not start with Sent to"
print "!!!!!invoice note does not start with Sent to!!!!!"
print ""
print ""
no_units = row[12]
## #should always be "RENTAL" (don't need to track this one)
## permit_type = row[1]
## if not permit_type == "RENTAL" and not permit_type == "MECHANICAL":
## raise ValueError, "Unexpected permit type: %s in row: %s" % (
## permit_type, row)
## bldg_type = row[2]
## #can use this to filter out non-rental or obsolete entries
## #don't need to track otherwise:
## status = row[3]
## parcel_id = row[4]
## #should be fixed per source:
## ss_city = row[6]
## bldg_sf = row[7]
## no_bldgs = row[8]
## applicant_name = row[9]
## no_stories = row[10]
## no_units = row[11]
## if not ( (ss_city.lower() == city_name.lower()) or (ss_city == '') ):
## raise ValueError, "Unexpected city: %s" % (ss_city)
## sqft = row[7]
## number_of_buildings = row[8]
## applicant_name = row[9]
## number_of_stories = row[10]
## number_of_units = row[11]
#check if this is one we want to skip
if conversions.has_key(address.upper()):
address = conversions[address.upper()]
## if (not status in ['EXPIRED', 'CLOSED']) and (permit_type in ['RENTAL']):
#make sure it's not one we're skipping:
if not address:
print "SKIPPING ITEM: %s" % row[1]
skips += 1
else:
#check if we've started processing any results for this row
if locations.has_key(address.upper()):
location = locations[address.upper()]
else:
location = Location()
#temporarily just want to look at google again
#location.sources = ["google"]
#location.sources = ["google", "bing", "usgeo", "geonames", "openmq"]
location.sources = ["google", "bing"]
#do some geocoding, as needed:
search = "%s, %s, %s" % (address.upper(), city_name, city.state)
any_updated = False
for geo_source in location.sources:
update = geo.lookup(search, geo_source, location, force=True)
#update = geo.lookup(search, geo_source, location, force=False)
if update:
any_updated = True
location.sources = ["google", "bing", "usgeo", "geonames", "openmq", "mq"]
#this is the case for brand new searches
#(which are updated in a different sense)
if not hasattr(location, "address_alt") or not location.address_alt:
any_updated = True
location.address_alt = search
#location.bldg_units = bldg_units
#location.units_bdrms = units_bdrms
locations[address.upper()] = location
#handle the database storage
bldg = make_building(location, bldg_id, city, feed_source, no_units=no_units)
if invoice_note:
(person, bldg_person) = make_person(invoice_note, bldg, "Permit Applicant")
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 locations.items():
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
print
destination = '%s.tsv' % city_tag
save_results(locations, destination)
def read_csv(source):
#for reading unicode
#f = codecs.open(source, 'r', encoding='utf-8')
city_options = City.objects.filter(tag="ann_arbor")
print len(city_options)
if not len(city_options):
city = City()
city.name = "Ann Arbor"
city.tag = to_tag(city.name)
city.save()
else:
city = city_options[0]
print city
#TODO:
#setup FeedInfo item
#and also create a Source item
permit_sub_types = []
status_types = []
building_nums = []
applicants = []
managers = []
cache_file = "%s.json" % city.tag
cache_destination = os.path.join(os.path.dirname(source), 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'] = {}
if not local_cache.has_key('parcels'):
local_cache['parcels'] = {}
locations = {}
for key, value in local_cache['buildings'].items():
locations[key] = Location(value)
#geocoder helper:
geo = Geo()
#with open('eggs.csv', 'rb') as csvfile:
with codecs.open(source, 'rb', encoding='utf-8') as csvfile:
#reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
reader = csv.reader(csvfile)
#just print the first row:
print '>, <'.join(reader.next())
count = 0
for row in reader:
count += 1
#could exit out early here, if needed
if count > 10:
pass
print row
#type of building (eg: sf attached, duplex, etc)
permit_id = row[0]
#should always be "RENTAL" (don't need to track this one)
permit_type = row[1]
if not permit_type == "RENTAL" and not permit_type == "MECHANICAL":
raise ValueError, "Unexpected permit type: %s in row: %s" % (
permit_type, row)
sub_type = row[2]
#can use this to filter out non-rental or obsolete entries
#don't need to track otherwise:
status = row[3]
parcel_id = row[4]
address = row[5]
#should be fixed per source:
city = row[6]
if not ( (city.lower() == 'ann arbor') or (city == '') ):
raise ValueError, "Unexpected city: %s" % (city)
sqft = row[7]
number_of_buildings = row[8]
applicant_name = row[9]
number_of_stories = row[10]
number_of_units = row[11]
if (not status in ['EXPIRED', 'CLOSED']) and (permit_type in ['RENTAL']):
#check if we've started processing any results for this row
#if local_cache['buildings'].has_key(address.upper()):
# local_cache_cur = local_cache['buildings'][address.upper()]
#else:
# local_cache_cur = {}
if locations.has_key(address.upper()):
location = locations[address.upper()]
else:
location = Location()
#do some geocoding, as needed:
search = "%s, Ann Arbor MI" % address.upper()
for source in location.sources:
geo.lookup(search, source, location)
location.address_alt = search
locations[address.upper()] = location
#local_cache['buildings'][address.upper()] = local_cache_cur
#and check if a previous building object in the db exists
#CREATE A NEW BUILDING OBJECT HERE
#cur_building = Building()
bldg = Building()
bldg.type = sub_type
#back it up for later
local_cache['buildings'] = {}
for key, value in locations.items():
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
#exit()
#THE FOLLOWING ARE FOR INFORMATIONAL PURPOSES ONLY
#(to see what data is available)
if not status in status_types:
#print "adding: %s" % sub_type
status_types.append(status)
if not sub_type in permit_sub_types:
#print "adding: %s" % sub_type
permit_sub_types.append(sub_type)
building_num = row[8]
if not building_num in building_nums:
#print "adding: %s" % sub_type
building_nums.append(building_num)
applicant = row[9]
if ( re.search('MGMT', applicant) or
re.search('REALTY', applicant) or
re.search('PROPERTIES', applicant) or
re.search('MANAGEMENT', applicant) or
re.search('GROUP', applicant) or
re.search('LLC', applicant) or
re.search('L.L.C.', applicant) or
re.search('INC', applicant)
):
if not applicant in managers:
managers.append(applicant)
else:
if not applicant in applicants:
applicants.append(applicant)
#print ', '.join(row)
#print
## print permit_sub_types
print status_types
print building_nums
save_results(locations)
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_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 = "2013-07-31"
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.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'] = {}
if not local_cache.has_key('parcels'):
local_cache['parcels'] = {}
locations = {}
for key, value in local_cache['buildings'].items():
locations[key] = Location(value)
#geocoder helper:
geo = Geo()
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())
count = 0
for row in reader:
count += 1
print "Looking at row: %s" % count
#could exit out early here, if needed
if count > 1000:
#exit()
pass
address = row[0]
#need to fix the number being at the end of the address
parts = address.split(',')
anumber = parts[-1]
parts = parts[:-1]
street = ",".join(parts)
address = "%s %s" % (anumber, street)
invoice_number = row[1]
bldg_id = row[1]
print bldg_id
#this is where owner is stored
invoice_note = row[6]
print invoice_note
if re.match('Sent to:', invoice_note):
print "changing invoice note from: %s" % invoice_note
invoice_note = invoice_note[8:]
print "to: %s" % invoice_note
else:
#raise ValueError, "invoice note does not start with Sent to"
print "!!!!!invoice note does not start with Sent to!!!!!"
print ""
print ""
no_units = row[12]
## #should always be "RENTAL" (don't need to track this one)
## permit_type = row[1]
## if not permit_type == "RENTAL" and not permit_type == "MECHANICAL":
## raise ValueError, "Unexpected permit type: %s in row: %s" % (
## permit_type, row)
## bldg_type = row[2]
## #can use this to filter out non-rental or obsolete entries
## #don't need to track otherwise:
## status = row[3]
## parcel_id = row[4]
## #should be fixed per source:
## ss_city = row[6]
## bldg_sf = row[7]
## no_bldgs = row[8]
## applicant_name = row[9]
## no_stories = row[10]
## no_units = row[11]
## if not ( (ss_city.lower() == city_name.lower()) or (ss_city == '') ):
## raise ValueError, "Unexpected city: %s" % (ss_city)
## sqft = row[7]
## number_of_buildings = row[8]
## applicant_name = row[9]
## number_of_stories = row[10]
## number_of_units = row[11]
#check if this is one we want to skip
if conversions.has_key(address.upper()):
address = conversions[address.upper()]
## if (not status in ['EXPIRED', 'CLOSED']) and (permit_type in ['RENTAL']):
#make sure it's not one we're skipping:
if not address:
print "SKIPPING ITEM: %s" % row[1]
skips += 1
else:
#check if we've started processing any results for this row
if locations.has_key(address.upper()):
location = locations[address.upper()]
else:
location = Location()
#temporarily just want to look at google again
#location.sources = ["google"]
#location.sources = ["google", "bing", "usgeo", "geonames", "openmq"]
location.sources = ["google", "bing"]
#do some geocoding, as needed:
search = "%s, %s, %s" % (address.upper(), city_name, city.state)
any_updated = False
for geo_source in location.sources:
update = geo.lookup(search, geo_source, location, force=True)
#update = geo.lookup(search, geo_source, location, force=False)
if update:
any_updated = True
location.sources = [
"google", "bing", "usgeo", "geonames", "openmq", "mq"
]
#this is the case for brand new searches
#(which are updated in a different sense)
if not hasattr(location,
"address_alt") or not location.address_alt:
any_updated = True
location.address_alt = search
#location.bldg_units = bldg_units
#location.units_bdrms = units_bdrms
locations[address.upper()] = location
#handle the database storage
bldg = make_building(location,
bldg_id,
city,
feed_source,
no_units=no_units)
if invoice_note:
(person, bldg_person) = make_person(invoice_note, bldg,
"Permit Applicant")
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 locations.items():
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
print
destination = '%s.tsv' % city_tag
save_results(locations, destination)
#do some geocoding, as needed:
search = "%s %s" % (city_name, city_state)
any_updated = False
for geo_source in location.sources:
update = geo.lookup(search, geo_source, location, force=True)
if update:
any_updated = True
result = location.get_source(geo_source)
print len(result)
print result
city.latitude = result[0]['lat']
city.longitude = result[0]['lng']
location.sources = [
"google", "bing", "usgeo", "geonames", "openmq", "mq"
]
saved_cities[city_tag] = {
"name": city.name,
"state": city.state,
"tag": city.tag,
"lat": city.latitude,
"lng": city.longitude
}
save_json(cache_destination, saved_cities)
city.save()
y_train = to_categorical(y_train)
# Reshape data for input to Dense layer
X_train = X_train.reshape(-1, 28 * 28)
X_test = X_test.reshape(-1, 28 * 28)
# Create sequential 2-layer model
model = Sequential()
model.add(Dense(400, input_dim=28 * 28, activation='sigmoid'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(),
metrics=['accuracy'])
# Write the summary to file
save_summary(model, 'simple')
# Train model and evaluate training
results = model.fit(X_train.reshape(-1, 28 * 28),
y_train,
epochs=10,
batch_size=64,
validation_split=1 / 12)
training_eval(results, 'simple')
# Predict and evaluate performance
y_fit = model.predict(X_test, batch_size=128)
performance_eval('simple', y_fit.argmax(axis=1), y_test.argmax(axis=1))
save_json(model, 'simple')
model.save_weights('models/simple_weights.h5')
def read_csv(source_csv):
city_options = City.objects.filter(tag="bloomington_in")
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 = "Bloomington"
## city.tag = to_tag(city.name)
## city.save()
else:
city = city_options[0]
print city
feed_date = "2013-08-29"
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
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
cache_file = "%s.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"] = {}
if not local_cache.has_key("parcels"):
local_cache["parcels"] = {}
locations = {}
for key, value in local_cache["buildings"].items():
locations[key] = Location(value)
# geocoder helper:
geo = Geo()
skips = 0
with codecs.open(source_csv, "rb", encoding="utf-8") as csvfile:
# reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
reader = csv.reader(csvfile)
# just print the first row:
print ">, <".join(reader.next())
count = 0
for row in reader:
count += 1
print "Looking at row: %s" % count
# could exit out early here, if needed
if count > 1000:
# exit()
pass
bldg_id = row[0]
print bldg_id
address = row[1]
print address
owner = row[2]
# skip this:
ownder_contact = row[3]
agent = row[4]
bldg_units = row[9]
print bldg_units
units_bdrms = row[10]
print units_bdrms
# check if this is one we want to skip
if conversions.has_key(address.upper()):
address = conversions[address.upper()]
# make sure it's not one we're skipping:
if not address:
print "SKIPPING ITEM: %s" % row[1]
skips += 1
else:
if locations.has_key(address.upper()):
location = locations[address.upper()]
else:
location = Location()
# temporarily just want to look at google again
location.sources = ["google"]
# do some geocoding, as needed:
search = "%s, Bloomington IN" % address.upper()
any_updated = False
for geo_source in location.sources:
update = geo.lookup(search, geo_source, location, force=True)
if update:
any_updated = True
location.sources = ["google", "bing", "usgeo", "geonames", "openmq", "mq"]
if not hasattr(location, "address_alt") or not location.address_alt:
any_updated = True
location.address_alt = search
location.bldg_units = bldg_units
location.units_bdrms = units_bdrms
locations[address.upper()] = location
# handle the database storage
bldg = make_building(location, bldg_id, city, feed_source)
# owner_details = parse_person(owner)
if owner:
result = special_cases(owner)
if result:
(owner_name, owner_address) = result
else:
(owner_name, owner_address, owner_phone, remainder) = parse_person(owner)
## print "owner name: %s" % owner_name
## print "owner address: %s" % owner_address
## print ""
if owner_name:
(person, bldg_person) = make_person(owner_name, bldg, "Owner", address=owner_address)
if agent and agent != "No Agent":
# agent_details = parse_person(agent)
(agent_name, agent_address, agent_phone, remainder) = parse_person(agent)
## print "agent name: %s" % agent_name
## print "agent address: %s" % agent_address
## print ""
if agent_name:
(person, bldg_person) = make_person(agent_name, bldg, "Agent", address=agent_address, city=city)
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 locations.items():
local_cache["buildings"][key] = value.to_dict()
save_json(cache_destination, local_cache)
print
save_results(locations, "bloomington-filtered.tsv")
# FC6 Fully connected layer
model.add(Dense(84, activation='tanh'))
# Output layer
model.add(Dense(10, activation='softmax'))
# compile model
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(),
metrics=['accuracy'])
# Write the summary to file
save_summary(model, 'lenet')
# Train model and evaluate training
results = model.fit(X_train,
y_train,
epochs=10,
batch_size=64,
validation_split=1 / 12)
training_eval(results, 'lenet')
# Predict and evaluate performance
y_fit = model.predict(X_test, batch_size=128)
performance_eval('lenet', y_fit.argmax(axis=1), y_test.argmax(axis=1))
save_json(model, 'lenet')
model.save_weights('models/lenet_weights.h5')
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.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'] = {}
if not local_cache.has_key('parcels'):
local_cache['parcels'] = {}
locations = {}
for key, value in local_cache['buildings'].items():
locations[key] = Location(value)
#geocoder helper:
geo = Geo()
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())
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
#could exit out early here, if needed
if count > 10:
#exit()
pass
print row
address = 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])
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(" ", " ")
apt_main = " ".join([qualifier_post, apt_num])
apt_main = apt_main.strip()
address = address_main
print address
owner_address = ", ".join([
owner_address1, owner_address2, owner_city, owner_state,
owner_zip
])
## #should always be "RENTAL" (don't need to track this one)
## permit_type = row[1]
## if not permit_type == "RENTAL" and not permit_type == "MECHANICAL":
## raise ValueError, "Unexpected permit type: %s in row: %s" % (
## permit_type, row)
## bldg_type = row[2]
## #can use this to filter out non-rental or obsolete entries
## #don't need to track otherwise:
## status = row[3]
## parcel_id = row[4]
## #should be fixed per source:
## ss_city = row[6]
## bldg_sf = row[7]
## no_bldgs = row[8]
## applicant_name = row[9]
## no_stories = row[10]
## no_units = row[11]
## sqft = row[7]
## number_of_buildings = row[8]
## applicant_name = row[9]
## number_of_stories = row[10]
## number_of_units = row[11]
#check if this is one we want to skip
if conversions.has_key(address.upper()):
address = conversions[address.upper()]
## if (not status in ['EXPIRED', 'CLOSED']) and (permit_type in ['RENTAL']):
#make sure it's not one we're skipping:
if not address:
print "SKIPPING ITEM: %s" % row[1]
skips += 1
else:
#check if we've started processing any results for this row
if locations.has_key(address.upper()):
location = locations[address.upper()]
else:
location = Location()
#temporarily just want to look at google again
#location.sources = ["google"]
#location.sources = ["google", "bing"]
#location.sources = ["google", "bing", "usgeo", "geonames", "openmq"]
#skip geocoding for columbia
location.sources = []
#do some geocoding, as needed:
search = "%s, %s, %s" % (address.upper(), city_name, city.state)
any_updated = False
for geo_source in location.sources:
update = geo.lookup(search, geo_source, location, force=True)
#update = geo.lookup(search, geo_source, location, force=False)
if update:
any_updated = True
location.sources = [
'csv', "google", "bing", "usgeo", "geonames", "openmq", "mq"
]
#manually add data from csv here:
result = []
result.append({'place': address, 'lat': lat, 'lng': lng})
setattr(location, 'csv', result)
#this is the case for brand new searches
#(which are updated in a different sense)
if not hasattr(location,
"address_alt") or not location.address_alt:
any_updated = True
location.address_alt = search
#location.bldg_units = bldg_units
#location.units_bdrms = units_bdrms
locations[address.upper()] = location
#handle the database storage
bldg = make_building(location,
bldg_id,
city,
feed_source,
no_units=no_units,
bldg_type=bldg_type)
if apt_main:
unit = make_unit(apt_main, bldg)
(person, bldg_person) = make_person(owner_name,
bldg,
"Agent",
address=owner_address)
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 locations.items():
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
print
#exit()
destination = '%s.tsv' % city_tag
save_results(locations, destination)
# Convert class labels to categorical data/one-hot encoding
y_test = to_categorical(y_test)
y_train = to_categorical(y_train)
model = create_lenet()
# Train model and evaluate training
results = model.fit(X_train, y_train, epochs=10, batch_size=64)
#training_eval(results, 'final')
# Predict and evaluate performance
y_fit = model.predict(X_test, batch_size=128)
performance_eval('final', y_fit.argmax(axis=1), y_test.argmax(axis=1))
save_json(model, 'final')
model.save_weights('models/final_weights.h5')
# Plot the problems
mis_index = np.where(y_fit.argmax(axis=1) != y_test.argmax(axis=1))
misclassifieds = X_test[mis_index]
predicted_labels = y_fit.argmax(axis=1)[mis_index]
target_labels = y_test.argmax(axis=1)[mis_index]
print('MNIST misclassifieds - predicted labels')
print(np.resize(predicted_labels, 10 * 10).reshape((10, 10)))
print('\nMNIST misclassifieds - target labels')
print(np.resize(target_labels, 10 * 10).reshape((10, 10)))
plot_samples(misclassifieds.reshape(np.shape(mis_index)[1], 28, 28),
title='MNIST_misclassifieds',
width=10,
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
else:
location = Location()
#temporarily just want to look at google again
location.sources = ["google"]
#do some geocoding, as needed:
search = "%s %s" % (city_name, city_state)
any_updated = False
for geo_source in location.sources:
update = geo.lookup(search, geo_source, location, force=True)
if update:
any_updated = True
result = location.get_source(geo_source)
print len(result)
print result
city.latitude = result[0]['lat']
city.longitude = result[0]['lng']
location.sources = ["google", "bing", "usgeo", "geonames", "openmq", "mq"]
saved_cities[city_tag] = {"name":city.name, "state":city.state, "tag":city.tag, "lat":city.latitude, "lng":city.longitude}
save_json(cache_destination, saved_cities)
city.save()
for building in buildings:
if not building.tag in previous:
previous[building.tag] = [ building.id ]
else:
print "%s previous buildings found with tag: %s" % (len(previous[building.tag]), building.tag)
first_building = Building.objects.get(id=previous[building.tag][0])
#print "%s units associated" % (dir(building.units))
print "%s units associated" % (len(building.units.all()))
for unit in building.units.all():
print unit.tag
unit.building = first_building
unit.save()
previous[building.tag].append(building.id)
#TODO
#delete this building now
building.delete()
count += 1
## keys = range(1496, 1512)
## for key in keys:
## b = Building.objects.get(id=key)
## print b
#print json.dumps(previous)
save_json("building_dupes.json", previous)
print "saving to: building_dupes.json"
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.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'] = {}
if not local_cache.has_key('parcels'):
local_cache['parcels'] = {}
locations = {}
for key, value in local_cache['buildings'].items():
locations[key] = Location(value)
#geocoder helper:
geo = Geo()
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())
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
#could exit out early here, if needed
if count > 10:
#exit()
pass
print row
address = 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])
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(" ", " ")
apt_main = " ".join([qualifier_post, apt_num])
apt_main = apt_main.strip()
address = address_main
print address
owner_address = ", ".join([owner_address1, owner_address2, owner_city, owner_state, owner_zip])
## #should always be "RENTAL" (don't need to track this one)
## permit_type = row[1]
## if not permit_type == "RENTAL" and not permit_type == "MECHANICAL":
## raise ValueError, "Unexpected permit type: %s in row: %s" % (
## permit_type, row)
## bldg_type = row[2]
## #can use this to filter out non-rental or obsolete entries
## #don't need to track otherwise:
## status = row[3]
## parcel_id = row[4]
## #should be fixed per source:
## ss_city = row[6]
## bldg_sf = row[7]
## no_bldgs = row[8]
## applicant_name = row[9]
## no_stories = row[10]
## no_units = row[11]
## sqft = row[7]
## number_of_buildings = row[8]
## applicant_name = row[9]
## number_of_stories = row[10]
## number_of_units = row[11]
#check if this is one we want to skip
if conversions.has_key(address.upper()):
address = conversions[address.upper()]
## if (not status in ['EXPIRED', 'CLOSED']) and (permit_type in ['RENTAL']):
#make sure it's not one we're skipping:
if not address:
print "SKIPPING ITEM: %s" % row[1]
skips += 1
else:
#check if we've started processing any results for this row
if locations.has_key(address.upper()):
location = locations[address.upper()]
else:
location = Location()
#temporarily just want to look at google again
#location.sources = ["google"]
#location.sources = ["google", "bing"]
#location.sources = ["google", "bing", "usgeo", "geonames", "openmq"]
#skip geocoding for columbia
location.sources = []
#do some geocoding, as needed:
search = "%s, %s, %s" % (address.upper(), city_name, city.state)
any_updated = False
for geo_source in location.sources:
update = geo.lookup(search, geo_source, location, force=True)
#update = geo.lookup(search, geo_source, location, force=False)
if update:
any_updated = True
location.sources = ['csv', "google", "bing", "usgeo", "geonames", "openmq", "mq"]
#manually add data from csv here:
result = []
result.append({'place': address, 'lat': lat, 'lng': lng})
setattr(location, 'csv', result)
#this is the case for brand new searches
#(which are updated in a different sense)
if not hasattr(location, "address_alt") or not location.address_alt:
any_updated = True
location.address_alt = search
#location.bldg_units = bldg_units
#location.units_bdrms = units_bdrms
locations[address.upper()] = location
#handle the database storage
bldg = make_building(location, bldg_id, city, feed_source, no_units=no_units, bldg_type=bldg_type)
if apt_main:
unit = make_unit(apt_main, bldg)
(person, bldg_person) = make_person(owner_name, bldg, "Agent", address=owner_address)
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 locations.items():
local_cache['buildings'][key] = value.to_dict()
save_json(cache_destination, local_cache)
print
#exit()
destination = '%s.tsv' % city_tag
save_results(locations, destination)