def closest_by_code(klass, search_string, n=100):
d = []
for obj in klass.objects.all():
args = [str(obj.code), search_string]
alt_args = [str(obj.code) + str(obj.satellite_number), search_string]
args_dists = utils.calc_dists(*args)
alt_dists = utils.calc_dists(*alt_args)
# use the shortest of these distances
d.append((search_string, obj,\
min([args_dists[0], alt_dists[0]]),\
min([args_dists[1], alt_dists[1]]),\
max([args_dists[2], alt_dists[2]])))
return utils.closest_matches(d, n)
def closest_by_spelling(klass, search_string, n=100):
d = []
for obj in klass.objects.all():
args = [str(obj.name).upper(), search_string.upper()]
# also calculate distance between search_string and
# the best guess of a surname (longest word in name)
name_list = str(obj.name).upper().replace('.', ' ').split()
surname_guess = max(name_list, key=len)
alt_args = [surname_guess, search_string.upper()]
args_dists = utils.calc_dists(*args)
alt_dists = utils.calc_dists(*alt_args)
# use the shortest of these distances
d.append((search_string, obj,\
min([args_dists[0], alt_dists[0]]),\
min([args_dists[1], alt_dists[1]]),\
max([args_dists[2], alt_dists[2]])))
return utils.closest_matches(d, n)
def closest_by_spelling(klass, search_string, n=100):
d = []
for obj in klass.objects.all():
args = [str(obj.name).upper(), search_string.upper()]
args_dists = utils.calc_dists(*args)
# use the shortest of these distances
d.append((search_string, obj,\
args_dists[0], args_dists[1],\
args_dists[2]))
return utils.closest_matches(d, n)
sigma=None,
preproc=combo)
else:
# TODO: The route object can not be passed intothe test_nav function here only the starting coordinates
# are needed by the test_nav function
traj, nav = agent.test_nav(route, nav, t=t, r=r, preproc=combo)
toc = time.perf_counter()
time_compl = toc - tic
# Get the errors and the minimum distant index of the route memory
errors, min_dist_index = route.calc_errors(traj)
# Difference between matched index and minimum distance index and distance between points
matched_index = nav.get_index_log()
abs_index_diffs = np.absolute(
np.subtract(nav.get_index_log(), min_dist_index))
dist_diff = calc_dists(route.get_xycoords(), min_dist_index,
matched_index)
mean_route_error = np.mean(errors)
log['route_id'].extend([route.get_route_id()])
log['blur'].extend([combo.get('blur')])
log['edge'].extend([combo.get('edge_range')])
log['res'].append(combo.get('shape'))
log['window'].extend([window])
log['matcher'].extend([matcher])
log['mean_error'].append(mean_route_error)
log['seconds'].append(time_compl)
log['window_log'].append(window_log)
log['tx'].append(traj['x'].tolist())
log['ty'].append(traj['y'].tolist())
log['th'].append(traj['heading'].tolist())
log['matched_index'].append(matched_index)
log['abs_index_diff'].append(abs_index_diffs.tolist())
def bench(params, routes_path, route_ids):
log = {'route_id': [], 'blur': [], 'edge': [], 'res': [], 'window': [],
'matcher': [], 'mean_error': [], 'seconds': [], 'errors': [],
'dist_diff': [], 'abs_index_diff': [], 'window_log': [],
'tx': [], 'ty': [], 'th': []}
agent = aw.Agent()
grid = get_grid_dict(params)
total_jobs = len(grid) * len(route_ids)
jobs = 0
# Go though all combinations in the chunk
for combo in grid:
matcher = combo['matcher']
window = combo['window']
t = combo['t']
r = combo['r']
segment_length = combo.get('segment_l')
window_log = None
for route_id in route_ids: # for every route
route_path = routes_path + '/route' + str(route_id) + '/'
route = Route(route_path, route_id)
tic = time.perf_counter()
# Preprocess images
route_imgs = pre_process(route.get_imgs(), combo)
# Run navigation algorithm
if window:
nav = spm.SequentialPerfectMemory(route_imgs, matcher, deg_range=(-180, 180), window=window)
else:
nav = pm.PerfectMemory(route_imgs, matcher, deg_range=(-180, 180))
if segment_length:
traj, nav = agent.segment_test(route, nav, segment_length=segment_length, t=t, r=r, sigma=None, preproc=combo)
else:
start = route.get_starting_coords()
traj, nav = agent.test_nav(start, nav, t=t, r=r, preproc=combo)
# agent.run_agent(route, nav, t=t, r=r, preproc=combo)
toc = time.perf_counter()
time_compl = toc - tic
# Get the errors and the minimum distant index of the route memory
errors, min_dist_index = route.calc_errors(traj)
# Difference between matched index and minimum distance index and distance between points
matched_index = nav.get_index_log()
abs_index_diffs = np.absolute(np.subtract(nav.get_index_log(), min_dist_index))
dist_diff = calc_dists(route.get_xycoords(), min_dist_index, matched_index)
mean_route_error = np.mean(errors)
log['route_id'].extend([route_id])
log['blur'].extend([combo.get('blur')])
log['edge'].extend([combo.get('edge_range')])
log['res'].append(combo.get('shape'))
log['window'].extend([window])
log['matcher'].extend([matcher])
log['mean_error'].append(mean_route_error)
log['seconds'].append(time_compl)
log['window_log'].append(window_log)
log['tx'].append(traj['x'].tolist())
log['ty'].append(traj['y'].tolist())
log['th'].append(traj['heading'].tolist())
log['abs_index_diff'].append(abs_index_diffs.tolist())
log['dist_diff'].append(dist_diff.tolist())
log['errors'].append(errors)
# Increment the complete jobs shared variable
jobs += 1
print('jobs completed: {}/{}'.format(jobs, total_jobs))
return log
