def test_load_save_JSON(self):
ride = GpxParser(
'tests/data/sample_with_stop.gpx').get_ride_from_track()
p1 = ride.points[42]
filename = 'tests/data/test.json'
utils.save_json(filename, p1.to_JSON())
ok_(os.path.exists(filename))
js = utils.load_json(filename, GeoPoint)
eq_(js.lat, p1.lat)
os.remove(filename)
ok_(not os.path.exists(filename))
def detectstops(self):
# Getting all gpx files in specified folder
gpx_files = []
for f in os.listdir(self.gpx_folder):
if f.endswith('.gpx'):
gpx_files.append(os.path.join(self.gpx_folder, f))
self.l.info("There's %d gpx files to be proccessed. Starting now..." %
len(gpx_files))
self.csv.info(
"\n--CSV-Ride-data--\nride_file, distance_meters, duration_seconds, moving_time, stop_time, stop_count, stop_duration_list"
)
# Detecting Stops and storing Points of Interest
total = 0
stop_durations = []
for gpx in gpx_files:
ride = GpxParser(gpx).get_ride_from_track(
self.config.region_ignores)
stops, ignored_time = detect_stops(
ride, self.config.stops_cap_durations_at)
for s in stops:
utils.save_json(
os.path.join(self.workspace_folder, "poi_%s.json" % s.id),
s.to_JSON())
ride.duration -= ignored_time
stop_duration_list = [s.duration for s in stops]
stop_durations += stop_duration_list
stop_time = sum(stop_duration_list)
stop_count = len(stops)
moving_time = ride.duration - stop_time
total += stop_count
self.csv.info("%s,%8.2f,%5d,%5d,%5d,%3d,%s" %
(os.path.basename(gpx), ride.distance, ride.duration,
moving_time, stop_time, stop_count, " ".join(
map(str, stop_duration_list))))
self.csv.info("--CSV--\n")
self.csv.info("\n--CSV-stop-durations--")
self.csv.info(",".join(map(str, stop_durations)))
self.csv.info("--CSV--\n")
self.l.info(
"Done! There was %d stops detected\nThe data is available at %s" %
(total, self.workspace_folder))
def generatemetrics(self):
# Loading ROIs
json_files = []
for f in os.listdir(self.workspace_folder):
if os.path.basename(f).startswith(
"roi_" + self.roi_version) and f.endswith('.json'):
json_files.append(os.path.join(self.workspace_folder, f))
ROIs = {}
pattern = re.compile("roi_(\d+_\d+_\d+)\.")
for jsf in json_files:
roi = utils.load_json(jsf, RegionOfInterest)
ROIs[pattern.search(jsf).group(1)] = roi
self.l.info("Loaded %d ROIs." % len(json_files))
self.l.info("Generating metrics...")
obj = Metrics(ROIs, self.workspace_folder).generate()
output = os.path.join(self.workspace_folder, "metrics.json")
utils.save_json(os.path.join(output), json.dumps(obj, indent=4))
self.l.info("Done! The metrics are available at %s" % output)
def detectpasses(self):
# Getting all gpx files in specified folder
gpx_files = []
for f in os.listdir(self.gpx_folder):
if f.endswith('.gpx'):
gpx_files.append(os.path.join(self.gpx_folder, f))
self.l.info("There's %d gpx files to be proccessed." % len(gpx_files))
# Loading ROIs
json_files = []
for f in os.listdir(self.workspace_folder):
if os.path.basename(f).startswith("roi_") and f.endswith('.json'):
json_files.append(os.path.join(self.workspace_folder, f))
ROIs = []
for jsf in json_files:
roi = utils.load_json(jsf, RegionOfInterest)
ROIs.append(roi)
self.l.info("Loaded %d ROIs." % len(json_files))
# Detecting Passes and storing Points of Interest
total = 0
total_stats = {
"# ROIs entered": 0,
"# ROIs stop POI": 0,
"# ROIs stop without POI": 0,
"# ROIs pass": 0,
"# ROIs pass but no cluster": 0,
"pass_speed_list": []
}
self.csv.info(
"\n--CSV-passes-data--\nride_file, ROI_in, ROI_stop_POI, ROI_stop_no_POI, ROI_pass_outside_cluster, ROI_pass, pass_speed_list"
)
for gpx in gpx_files:
ride = GpxParser(gpx).get_ride_from_track(
self.config.region_ignores)
passes, stats = detect_passes(ride, ROIs,
self.config.dbscan_eps_in_meters,
self.config.dbscan_min_samples,
self.workspace_folder)
for k in stats.keys():
total_stats[k] += stats[k]
passes_count = len(passes)
total += passes_count
self.csv.info(
"%s,%3d,%3d,%3d,%3d,%3d,%s" %
(os.path.basename(gpx), stats["# ROIs entered"],
stats["# ROIs stop POI"], stats["# ROIs stop without POI"],
stats["# ROIs pass but no cluster"], passes_count, " ".join(
map(str, stats['pass_speed_list']))))
for p in passes:
utils.save_json(
os.path.join(self.workspace_folder, "poi_%s.json" % p.id),
p.to_JSON())
self.csv.info("--CSV--\n")
self.csv.info("\n--CSV-pass-speeds--")
self.csv.info(",".join(map(str, total_stats['pass_speed_list'])))
self.csv.info("--CSV--\n")
self.l.info(
"Done! There was %d passes detected\nThe data is available at %s" %
(total, self.workspace_folder))
def detect_passes(ride, ROIs, eps_in_meters, min_samples, workspace_folder):
import numpy
import commutemate.clustering as clustering
stops = []
on_a_stop = False
stop_buffer = None
previous_stop = None
passes = []
on_a_roi = False
pass_buffer = []
current_roi = None
previous_pass = None
stats = {
"# ROIs entered": 0,
"# ROIs stop POI": 0,
"# ROIs stop without POI": 0,
"# ROIs pass": 0,
"# ROIs pass but no cluster": 0,
"pass_speed_list": []
}
for p in ride.points[1:]:
if not current_roi:
roi = __inside_a_ROI(p, ROIs)
elif current_roi and utils.is_inside_range(current_roi.center_range,
p):
roi = current_roi
else:
roi = None
if roi:
on_a_roi = True
if not current_roi:
stats["# ROIs entered"] += 1
current_roi = roi
if p.speed < STOPPED_SPEED_KMH_THRESHOLD:
on_a_stop = True
stop_buffer = p
else:
pass_buffer.append(p)
else:
if on_a_stop:
poi = PointOfInterest(stop_buffer, PointOfInterest.TYPE_STOP,
ride.origin, ride.destination)
if current_roi.is_poi_included(poi.id):
# Updating POI with preivous ROIs info
poi = utils.load_json(
os.path.join(workspace_folder, "poi_%s.json" % poi.id),
PointOfInterest)
poi.previous_stop_ROI = previous_stop.id if previous_stop else None
poi.previous_pass_ROI = previous_pass.id if previous_pass else None
utils.save_json(
os.path.join(workspace_folder, "poi_%s.json" % poi.id),
poi.to_JSON())
previous_stop = poi
stats["# ROIs stop POI"] += 1
else:
stats["# ROIs stop without POI"] += 1
on_a_stop = False
stop_buffer = None
on_a_roi = False
pass_buffer = []
current_roi = None
elif on_a_roi:
pass_in_cluster = []
# check from all the points inside a ROI which ones are inside the original cluster
for ppass in pass_buffer:
poi = PointOfInterest(ppass, PointOfInterest.TYPE_PASS,
ride.origin, ride.destination)
poi.set_duration(0)
poi.set_previous_stop(previous_stop)
poi.previous_stop_ROI = previous_stop.id if previous_stop else None
poi.previous_pass_ROI = previous_pass.id if previous_pass else None
# need to hydrate ROI to have POIs bearing info
RegionOfInterest.hydrate_POIs(current_roi,
workspace_folder)
current_roi.set_poi_list([poi], PointOfInterest.TYPE_PASS)
POIs = numpy.array(current_roi.get_all_pois())
X = numpy.array(current_roi.get_all_poi_coords())
# If pass point is not part of stop cluster, this means that the pass is in another direction
db = clustering.cluster_with_bearing_weight(
POIs, X, eps_in_meters, min_samples)
n_clusters_ = len(set(db))
if n_clusters_ == 1:
pass_in_cluster.append(poi)
current_roi.set_poi_list([], PointOfInterest.TYPE_PASS)
if len(pass_in_cluster) > 0:
# we have officially a pass, the crowd goes crazy
stats["# ROIs pass"] += 1
poi = pass_in_cluster[
len(pass_in_cluster) /
2] # get buffer mid point as point for POI
stats["pass_speed_list"].append(poi.point.speed)
previous_pass = poi
passes.append(poi)
else:
stats["# ROIs pass but no cluster"] += 1
on_a_stop = False
stop_buffer = None
on_a_roi = False
pass_buffer = []
current_roi = None
return passes, stats