def _cache_hit_or_run(md5hash, func, crime_rows, column_names, num_clusters=None):
convex_hulls_file_path = CACHE_DIR\
+ 'convex_hulls__' + md5hash.hexdigest() + '.cache'
cluster_points_file_path = CACHE_DIR\
+ 'cluster_points__' + md5hash.hexdigest() + '.cache'
if USE_CACHING:
for r in crime_rows:
formatted_r = list(r)
formatted_r[4] = str(r[4])
md5hash.update(json.dumps(formatted_r, sort_keys=True).encode('utf-8'))
md5hash.update(func.__name__.encode('utf-8'))
cached_file = None
# Check for cached version
print("Files in Cache: ")
print("current hash: " + md5hash.hexdigest())
for f in os.listdir(CACHE_DIR):
print(f)
if f == (md5hash.hexdigest() + '.cache'):
cached_file = f
break
# Check for cached version or compute and cache
if not USE_CACHING or cached_file is None:
start_time = time.perf_counter()
if num_clusters is not None:
cluster_convex_hulls, cluster_points = func(crime_rows, column_names, num_clusters)
else:
cluster_convex_hulls, cluster_points = func(crime_rows, column_names)
if cluster_convex_hulls is None or cluster_points is None:
return None
end_time = time.perf_counter()
print("Clustering ran in time: " + str(end_time - start_time))
if USE_CACHING:
with open(convex_hulls_file_path, 'w') as new_cache_file:
json.dump(cluster_convex_hulls, new_cache_file, cls=DatetimeEncoder)
with open(cluster_points_file_path, 'w') as new_cache_file:
json.dump(cluster_points, new_cache_file, cls=DatetimeEncoder)
else:
print("RETRIEVED CACHED VERSION!")
with open(convex_hulls_file_path, 'r') as cache_file:
cluster_convex_hulls = json.load(cache_file, cls=DatetimeEncoder)
with open(cluster_points_file_path, 'r') as cache_file:
cluster_points = json.load(cache_file, cls=DatetimeEncoder)
statistics = {}
for i in range(len(cluster_points)):
cluster_statistics = {}
cluster_statistics['top_5_crimes'] = crime_statistics.top_n_crimes(
cluster_points[i], column_names, n=5)
"""
cluster_statistics['crime_per_year'] = crime_statistics.crimes_per_year(
cluster_points[i])
"""
statistics[i] = cluster_statistics
return_dict = {}
return_dict['area_outline'] = cluster_convex_hulls
return_dict['statistics'] = statistics
return return_dict
def get_dc_outline():
print("Get DC Outline request!")
print(request)
print(request.args)
request_args = request.args
db_session = scoped_session(sessionmaker(bind=engine))
dc_row = db_session.query(StateOutlines)\
.filter(StateOutlines.name == 'District of Columbia')
# Parse out the coordinates
outline_points = json.loads(db_session.scalar(dc_row[0].geom.ST_AsGeoJSON()))
# For some reason there's two absolutely pointless lists containing the
# content. Like, all they do is enclose another list....wtf mate
dc_outline_points = list()
for i in range(len(outline_points['coordinates'][0][0])):
dc_point_dict = dict()
# longitude
dc_point_dict['x_cord'] = outline_points['coordinates'][0][0][i][0]
# latitude
dc_point_dict['y_cord'] = outline_points['coordinates'][0][0][i][1]
dc_outline_points.append(dc_point_dict)
# Crimes for Statistics
dc_crime_rows = db_session.query(DCCrime.id, DCCrime.report_date,\
DCCrime.offense,\
DCCrime.address, DCCrime.ward,\
DCCrime.x_cord, DCCrime.y_cord)\
.order_by(DCCrime.id)
formatted_dc_crime_rows = [dict(zip(dc_column_names, r)) for r in dc_crime_rows]
crime_type_tuples = crime_statistics.top_n_crimes(
formatted_dc_crime_rows,
column_names=dc_column_names,
offense_column_name='offense')
db_session.close()
return jsonify({'area_outline': {'District of Columbia': dc_outline_points},
'area_statistics': {'District of Columbia':
{'top_5_crimes': crime_type_tuples}
}})
def get_nova_county_outlines():
"""
SELECT c.gid,
s.name AS state_name,
s.statefp AS geom,
c.name AS county_name,
c.geom AS geom
FROM state_outlines s,
county_outlines c
WHERE s.name::text = 'Virginia'::text;
"""
print("Get Nova County Outlines request!")
print(request)
print(request.args)
request_args = request.args
db_session = scoped_session(sessionmaker(bind=engine))
nova_county_rows = db_session.query(StateOutlines, CountyOutlines)\
.join(CountyOutlines, CountyOutlines.statefp == StateOutlines.statefp)\
.filter(StateOutlines.name == 'Virginia')\
.filter(or_(CountyOutlines.name == county_name for county_name in county_names))
county_outlines = {}
county_statistics = {}
# Parse out the coordinates
for state, county in nova_county_rows:
outline_points = json.loads(db_session.scalar(county.geom.ST_AsGeoJSON()))
# For some reason there's two absolutely pointless lists containing the
# content. Like, all they do is enclose another list....wtf mate
county_outline_points = list()
for i in range(len(outline_points['coordinates'][0][0])):
county_point_dict = dict()
# longitude
county_point_dict['x_cord'] = outline_points['coordinates'][0][0][i][0]
# latitude
county_point_dict['y_cord'] = outline_points['coordinates'][0][0][i][1]
county_outline_points.append(county_point_dict)
if county.name in county_outlines:
county_outlines[county.name + '_2'] = county_outline_points
else:
county_outlines[county.name] = county_outline_points
# Crimes for Statistics
nova_crime_rows = db_session.query(NovaCrime.id, NovaCrime.report_date,\
NovaCrime.offense_specific, NovaCrime.offense_general,\
NovaCrime.address, NovaCrime.city,\
NovaCrime.county, NovaCrime.zip_code,\
NovaCrime.x_cord, NovaCrime.y_cord)\
.filter(NovaCrime.county == county.name)\
.order_by(NovaCrime.id)
formatted_nova_crime_rows = [dict(zip(nova_column_names, r)) for r in nova_crime_rows]
crime_type_tuples = crime_statistics.top_n_crimes(
formatted_nova_crime_rows,
column_names=nova_column_names,
offense_column_name='offense_specific')
if county.name in county_statistics:
county_statistics[county.name + '_2'] = {'top_5_crimes': crime_type_tuples}
else:
county_statistics[county.name] = {'top_5_crimes': crime_type_tuples}
db_session.close()
print("Returned: " + str(len(county_outlines)) + " county outlines")
return jsonify({'area_outline': county_outlines,
'area_statistics': county_statistics})
