def markers():
logging.debug('getting markers')
kwargs = get_kwargs()
logging.debug('querying markers in bounding box: %s' % kwargs)
is_thin = (kwargs['zoom'] < CONST.MINIMAL_ZOOM)
accidents = Marker.bounding_box_query(is_thin,
yield_per=50,
involved_and_vehicles=False,
**kwargs)
discussion_args = ('ne_lat', 'ne_lng', 'sw_lat', 'sw_lng',
'show_discussions')
discussions = DiscussionMarker.bounding_box_query(
**{arg: kwargs[arg]
for arg in discussion_args})
if request.values.get('format') == 'csv':
date_format = '%Y-%m-%d'
return Response(
generate_csv(accidents),
headers={
"Content-Type":
"text/csv",
"Content-Disposition":
'attachment; '
'filename="Anyway-accidents-from-{0}-to-{1}.csv"'.format(
kwargs["start_date"].strftime(date_format),
kwargs["end_date"].strftime(date_format))
})
else: # defaults to json
return generate_json(accidents, discussions, is_thin)
def retrieve_clusters(ne_lat, ne_lng, sw_lat, sw_lng, start_date, end_date, fatal, severe, light, inaccurate, zoom):
marker_boxes = divide_to_boxes(ne_lat, ne_lng, sw_lat, sw_lng)
result_futures = []
logging.info("number of cores: " + str(multiprocessing.cpu_count()))
with concurrent.futures.ThreadPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
for marker_box in marker_boxes:
markers_in_box = Marker.bounding_box_query(
marker_box[0],
marker_box[1],
marker_box[2],
marker_box[3],
start_date,
end_date,
fatal,
severe,
light,
inaccurate,
).all()
result_futures.append(executor.submit(calculate_clusters, markers_in_box, zoom))
completed_futures = concurrent.futures.wait(result_futures)
result = []
for future in completed_futures.done:
result.extend(future.result())
return result
def charts_data():
logging.debug('getting charts data')
kwargs = get_kwargs()
accidents, vehicles, involved = Marker.bounding_box_query(is_thin=False, yield_per=50, involved_and_vehicles=True, **kwargs)
accidents_list = [acc.serialize() for acc in accidents]
vehicles_list = [vehicles_data_refinement(veh.serialize()) for veh in vehicles]
involved_list = [involved_data_refinement(inv.serialize()) for inv in involved]
return Response(json.dumps({'accidents': accidents_list, 'vehicles': vehicles_list, 'involved': involved_list}), mimetype="application/json")
def setUp(self):
kwargs = {'approx': True, 'show_day': 7, 'show_discussions': True, 'accurate': True, 'surface': 0, 'weather': 0,
'district': 0, 'show_markers': True, 'show_fatal': True, 'show_time': 24, 'show_intersection': 3,
'show_light': True, 'sw_lat': 32.06711066128336, 'controlmeasure': 0, 'ne_lng': 34.799307929669226,
'show_severe': True, 'start_time': 25, 'acctype': 0, 'separation': 0, 'show_urban': 3, 'show_lane': 3,
'sw_lng': 34.78879367033085, 'zoom': 17, 'show_holiday': 0, 'end_time': 25, 'road': 0,
'ne_lat': 32.07254745790576, 'start_date': "01/01/2014", 'end_date': "01/01/2015"}
self.query = Marker.bounding_box_query(yield_per=50, **kwargs)
print self.query
def setUp(self):
kwargs = {'approx': True, 'show_day': 7, 'show_discussions': True, 'accurate': True, 'surface': 0,
'weather': 0, 'district': 0, 'show_markers': True, 'show_fatal': True, 'show_time': 24,
'show_intersection': 3, 'show_light': True, 'sw_lat': 32.067363446951944, 'controlmeasure': 0,
'start_date': datetime.date(2014, 1, 1), 'ne_lng': 34.79928962966915, 'show_severe': True,
'end_date': datetime.date(2016, 1, 1), 'start_time': 25, 'acctype': 0, 'separation': 0,
'show_urban': 3, 'show_lane': 3, 'sw_lng': 34.78877537033077, 'zoom': 17, 'show_holiday': 0,
'end_time': 25, 'road': 0, 'ne_lat': 32.072427482938345}
self.query_args = kwargs
self.query = Marker.bounding_box_query(yield_per=50, **kwargs)
def setUp(self):
self.query = Marker.bounding_box_query(ne_lat=32.36,
ne_lng=35.088,
sw_lat=32.292,
sw_lng=34.884,
start_date=start_date,
end_date=end_date,
fatal=False,
severe=True,
light=True,
inaccurate=False,
is_thin=False,
yield_per=None)
def retrieve_clusters(**kwargs):
marker_boxes = divide_to_boxes(kwargs["ne_lat"], kwargs["ne_lng"], kwargs["sw_lat"], kwargs["sw_lng"])
result_futures = []
logging.info("number of cores: " + str(multiprocessing.cpu_count()))
with concurrent.futures.ThreadPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
for marker_box in marker_boxes:
kwargs.update(marker_box)
markers_in_box = Marker.bounding_box_query(**kwargs).all()
result_futures.append(executor.submit(calculate_clusters, markers_in_box, kwargs["zoom"]))
completed_futures = concurrent.futures.wait(result_futures)
result = []
for future in completed_futures.done:
result.extend(future.result())
return result
def retrieve_clusters(**kwargs):
marker_boxes = divide_to_boxes(kwargs['ne_lat'], kwargs['ne_lng'], kwargs['sw_lat'], kwargs['sw_lng'])
result_futures = []
logging.info('number of cores: ' + str(multiprocessing.cpu_count()))
with concurrent.futures.ThreadPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
for marker_box in marker_boxes:
kwargs.update(marker_box)
markers_in_box = Marker.bounding_box_query(**kwargs).all()
result_futures.append(executor.submit(calculate_clusters, markers_in_box, kwargs['zoom']))
completed_futures = concurrent.futures.wait(result_futures)
result = []
for future in completed_futures.done:
result.extend(future.result())
return result
def charts_data():
logging.debug('getting charts data')
kwargs = get_kwargs()
accidents, vehicles, involved = Marker.bounding_box_query(
is_thin=False, yield_per=50, involved_and_vehicles=True, **kwargs)
accidents_list = [acc.serialize() for acc in accidents]
vehicles_list = [
vehicles_data_refinement(veh.serialize()) for veh in vehicles
]
involved_list = [
involved_data_refinement(inv.serialize()) for inv in involved
]
return Response(json.dumps({
'accidents': accidents_list,
'vehicles': vehicles_list,
'involved': involved_list
}),
mimetype="application/json")
def retrieve_clusters(ne_lat, ne_lng, sw_lat, sw_lng, start_date, end_date,
fatal, severe, light, inaccurate, zoom):
marker_boxes = divide_to_boxes(ne_lat, ne_lng, sw_lat, sw_lng)
result_futures = []
logging.info('number of cores: ' + str(multiprocessing.cpu_count()))
with concurrent.futures.ThreadPoolExecutor(
max_workers=multiprocessing.cpu_count()) as executor:
for marker_box in marker_boxes:
markers_in_box = Marker.bounding_box_query(
marker_box[0], marker_box[1], marker_box[2], marker_box[3],
start_date, end_date, fatal, severe, light, inaccurate).all()
result_futures.append(
executor.submit(calculate_clusters, markers_in_box, zoom))
completed_futures = concurrent.futures.wait(result_futures)
result = []
for future in completed_futures.done:
result.extend(future.result())
return result
def markers():
logging.debug('getting markers')
kwargs = get_kwargs()
logging.debug('querying markers in bounding box: %s' % kwargs)
is_thin = (kwargs['zoom'] < CONST.MINIMAL_ZOOM)
accidents = Marker.bounding_box_query(is_thin, yield_per=50, involved_and_vehicles=False, **kwargs)
discussion_args = ('ne_lat', 'ne_lng', 'sw_lat', 'sw_lng', 'show_discussions')
discussions = DiscussionMarker.bounding_box_query(**{arg: kwargs[arg] for arg in discussion_args})
if request.values.get('format') == 'csv':
date_format = '%Y-%m-%d'
return Response(generate_csv(accidents), headers={
"Content-Type": "text/csv",
"Content-Disposition": 'attachment; '
'filename="Anyway-accidents-from-{0}-to-{1}.csv"'
.format(kwargs["start_date"].strftime(date_format), kwargs["end_date"].strftime(date_format))
})
else: # defaults to json
return generate_json(accidents, discussions, is_thin)
def setUp(self):
self.query = Marker.bounding_box_query(ne_lat=32.36, ne_lng=35.088, sw_lat=32.292, sw_lng=34.884,
start_date=start_date, end_date=end_date,
fatal=False, severe=True, light=True, inaccurate=False,
is_thin=False, yield_per=None)
def retrieve_clusters(ne_lat, ne_lng, sw_lat, sw_lng, start_date, end_date, fatal, severe, light, inaccurate, zoom):
start_time = time.time()
filtered_markers = Marker.bounding_box_query(ne_lat, ne_lng, sw_lat, sw_lng, start_date, end_date, fatal,
severe, light, inaccurate).all()
print('bounding_box_query took ' + str(time.time() - start_time))
return generate_clusters_json(filtered_markers, zoom)
def test_light_severity_filter(self):
kwargs = self.query_args.copy()
kwargs['show_light'] = False
markers = Marker.bounding_box_query(yield_per=50, **kwargs)
for marker in markers:
self.assertTrue(marker.severity != 3)
def test_approx_filter(self):
kwargs = self.query_args.copy()
kwargs['accurate'] = False
markers = Marker.bounding_box_query(yield_per=50, **kwargs)
for marker in markers:
self.assertTrue(marker.locationAccuracy != 1)
