# remove csv headers
reader.pop(0)
for row_idx, row in enumerate(reader):
# discards rows with the wrong number of columns
if len(row) != len(headers):
reader.remove(row_idx)
# convert each column to float
for element_idx, element in enumerate(row):
reader[row_idx][element_idx] = float(element)
# execute zaccel std filter
filtered_points = filters.general_std_peaks(reader, 5)
# determine severity
#
mean, std = filters.running_window(reader,5)
for filtered_point_idx, filtered_point in enumerate(filtered_points):
filtered_points[filtered_point_idx].append(abs(filtered_point[5]-mean)*10)
# convert filtered points list to json object
json_object = '{"pothole": ['
for filtered_point in filtered_points:
json_object += '{"longitude": "'+str(filtered_point[c.longitude])+'", "latitude": "'+str(filtered_point[c.latitude])+'", "severity":"'+str(filtered_point[16])+'"}'
if filtered_point != filtered_points[-1]:
json_object += ','
json_object += ']}'
def get(self):
# csv_file_name = self.request.get('csv_file_name')
# self.response.out.write(csv_file_name)
csv_row = db.GqlQuery('SELECT * FROM csv_files WHERE csv_name = :csv_file_name LIMIT 1', csv_file_name = self.request.get('csv_file_name')).get()
if csv_row:
f = StringIO.StringIO(csv_row.csv_contents)
# reader = csv.reader(f, delimiter=',')
reader = list(csv.reader(f))
headers = reader[0]
# move past headers
reader.pop(0)
# remove last couple points as tend to be empty
reader.pop()
reader.pop()
sorted(reader, key=lambda reader_item: reader_item[0])
filtered_points = []
for row_idx, row in enumerate(reader):
# discards rows with the wrong number of columns
if len(row) != len(headers):
reader.remove(row_idx)
# convert each column to float
for element_idx, element in enumerate(row):
reader[row_idx][element_idx] = float(element)
filtered_points = reader
if self.request.get('z_accel_enabled'):
# implementation of zaccel filter
filtered_points = filters.general_std_peaks(reader, 5, float(self.request.get('threshold')), 'both')
if self.request.get('z_x_ratio_enabled'):
# implementation of z/x ratio
filtered_points = filters.ratio(filtered_points, c.accelz,c.accelx, float(self.request.get('z_x_ratio_threshold')))
if self.request.get('min_speed_enabled'):
# implementation of min speed filter
filtered_points = filters.min_speed(filtered_points, float(self.request.get('min_speed_threshold_value')))
if self.request.get('z_vs_speed_enabled'):
# implementation of min speed filter
filtered_points = filters.ratio(filtered_points, c.accelz, c.speed, float(self.request.get('z_vs_speed_threshold_value')))
# determine severity
# get filtered set of points on lowered std spike threshold. this will give us more points clustered around our estimated locations. the greater the number of spikes around our location will help score the severity
# zaccel_spike_points = filters.general_std_peaks(reader, 5, 1, 'both')
mean, std = filters.running_window(reader,5)
for filtered_point_idx, filtered_point in enumerate(filtered_points):
std_diffs = []
point_score = 0
'''
filtered_point_reader_index = reader.index( filtered_point )
for point_around_filterered_point in reader[filtered_point_reader_index-5:filtered_point_reader_index+5]:
std_diffs.append(abs(point_around_filterered_point[5]-std))
if len(std_diffs) > 0:
std_diff_mean = filters.mean(std_diffs)
filtered_points[filtered_point_idx].append(std_diff_mean)
else:
filtered_points[filtered_point_idx].append(0)
#### filtered_points[filtered_point_idx].append(abs(filtered_point[5]-mean))
#filtered_points[filtered_point_idx].append(std_diff_mean)
'''
#filtered_points[filtered_point_idx].append(abs(filtered_point[5])-abs(mean)*10)
#filtered_points[filtered_point_idx].append(abs((filtered_point[5]-mean)/std)*10)
filtered_points[filtered_point_idx].append(abs(filtered_point[5]-mean)*10)
#mean - (devs * std)
#filtered_point[5]*devs = std = mean - (x * std)
# convert filtered points list to json object
json_object = '{"pothole": ['
for filtered_point in filtered_points:
json_object += '{"longitude": "'+str(filtered_point[c.longitude])+'", "latitude": "'+str(filtered_point[c.latitude])+'", "severity":"'+str(filtered_point[16])+'"}'
if filtered_point != filtered_points[-1]:
json_object += ','
json_object += ']}'
self.response.out.write( json_object )
else:
# that particular one not found, return 404
self.error(404)
headers = reader[0]
# remove csv headers
reader.pop(0)
for row_idx, row in enumerate(reader):
# discards rows with the wrong number of columns
if len(row) != len(headers):
reader.remove(row_idx)
# convert each column to float
for element_idx, element in enumerate(row):
reader[row_idx][element_idx] = float(element)
# execute zaccel std filter
filtered_points = filters.general_std_peaks(reader, 5)
# determine severity
#
mean, std = filters.running_window(reader, 5)
for filtered_point_idx, filtered_point in enumerate(filtered_points):
filtered_points[filtered_point_idx].append(
abs(filtered_point[5] - mean) * 10)
# convert filtered points list to json object
json_object = '{"pothole": ['
for filtered_point in filtered_points:
json_object += '{"longitude": "' + str(
filtered_point[c.longitude]) + '", "latitude": "' + str(
filtered_point[c.latitude]) + '", "severity":"' + str(
filtered_point[16]) + '"}'
def get(self):
# csv_file_name = self.request.get('csv_file_name')
# self.response.out.write(csv_file_name)
csv_row = db.GqlQuery(
'SELECT * FROM csv_files WHERE csv_name = :csv_file_name LIMIT 1',
csv_file_name=self.request.get('csv_file_name')).get()
if csv_row:
f = StringIO.StringIO(csv_row.csv_contents)
# reader = csv.reader(f, delimiter=',')
reader = list(csv.reader(f))
headers = reader[0]
# move past headers
reader.pop(0)
# remove last couple points as tend to be empty
reader.pop()
reader.pop()
sorted(reader, key=lambda reader_item: reader_item[0])
filtered_points = []
for row_idx, row in enumerate(reader):
# discards rows with the wrong number of columns
if len(row) != len(headers):
reader.remove(row_idx)
# convert each column to float
for element_idx, element in enumerate(row):
reader[row_idx][element_idx] = float(element)
filtered_points = reader
if self.request.get('z_accel_enabled'):
# implementation of zaccel filter
filtered_points = filters.general_std_peaks(
reader, 5, float(self.request.get('threshold')), 'both')
if self.request.get('z_x_ratio_enabled'):
# implementation of z/x ratio
filtered_points = filters.ratio(
filtered_points, c.accelz, c.accelx,
float(self.request.get('z_x_ratio_threshold')))
if self.request.get('min_speed_enabled'):
# implementation of min speed filter
filtered_points = filters.min_speed(
filtered_points,
float(self.request.get('min_speed_threshold_value')))
if self.request.get('z_vs_speed_enabled'):
# implementation of min speed filter
filtered_points = filters.ratio(
filtered_points, c.accelz, c.speed,
float(self.request.get('z_vs_speed_threshold_value')))
# determine severity
# get filtered set of points on lowered std spike threshold. this will give us more points clustered around our estimated locations. the greater the number of spikes around our location will help score the severity
# zaccel_spike_points = filters.general_std_peaks(reader, 5, 1, 'both')
mean, std = filters.running_window(reader, 5)
for filtered_point_idx, filtered_point in enumerate(
filtered_points):
std_diffs = []
point_score = 0
'''
filtered_point_reader_index = reader.index( filtered_point )
for point_around_filterered_point in reader[filtered_point_reader_index-5:filtered_point_reader_index+5]:
std_diffs.append(abs(point_around_filterered_point[5]-std))
if len(std_diffs) > 0:
std_diff_mean = filters.mean(std_diffs)
filtered_points[filtered_point_idx].append(std_diff_mean)
else:
filtered_points[filtered_point_idx].append(0)
#### filtered_points[filtered_point_idx].append(abs(filtered_point[5]-mean))
#filtered_points[filtered_point_idx].append(std_diff_mean)
'''
#filtered_points[filtered_point_idx].append(abs(filtered_point[5])-abs(mean)*10)
#filtered_points[filtered_point_idx].append(abs((filtered_point[5]-mean)/std)*10)
filtered_points[filtered_point_idx].append(
abs(filtered_point[5] - mean) * 10)
#mean - (devs * std)
#filtered_point[5]*devs = std = mean - (x * std)
# convert filtered points list to json object
json_object = '{"pothole": ['
for filtered_point in filtered_points:
json_object += '{"longitude": "' + str(
filtered_point[c.longitude]) + '", "latitude": "' + str(
filtered_point[c.latitude]) + '", "severity":"' + str(
filtered_point[16]) + '"}'
if filtered_point != filtered_points[-1]:
json_object += ','
json_object += ']}'
self.response.out.write(json_object)
else:
# that particular one not found, return 404
self.error(404)
