def spatial_scan(tag):
"""The main method loads the data from the disk (or compute them) and
calls appropriate methods to find top discrepancy regions for `tag`."""
print(tag)
grid_size = GRID_SIZE
background_name = u'mfreq/freq_{}_{}.mat'.format(grid_size, '_background')
measured_name = u'mfreq/freq_{}_{}.mat'.format(grid_size, tag)
res = []
for tag, filename in [(None, background_name), (tag, measured_name)]:
try:
res.append(sio.loadmat(filename)['c'])
except IOError:
conn, client = get_connection()
compute_frequency(conn,
tag,
BBOX,
FIRST_TIME,
LAST_TIME,
grid_size,
plot=False)
client.close()
res.append(sio.loadmat(filename)['c'])
background, measured = res
total_b = np.sum(background)
total_m = np.sum(measured)
if not total_m > 0: # photos taken before 2008
return
if 0 < total_m <= 500:
support = 20
if 500 < total_m <= 2000:
support = 40
if 2000 < total_m:
support = MAX_SUPPORT
discrepancy = get_discrepancy_function(total_m, total_b, support)
grid_dim = (grid_size, grid_size)
top_loc = exact_grid(np.reshape(measured, grid_dim),
np.reshape(background, grid_dim), discrepancy, TOP_K,
GRID_SIZE / MAX_SIZE)
info = u'\n{}: g={}, s={}, k={}, w={}, h={}, max={}'
print(
info.format(tag, GRID_SIZE, support, TOP_K, MIN_WIDTH, MIN_HEIGHT,
MAX_SIZE))
# persistent.save_var(u'disc/top_{}'.format(tag), top_loc)
# print('\n')
# for v in top_loc:
# print('{:.4f}'.format(v[0]))
# print(top_loc[0][1].intersects(v[1]))
plot_regions(merge_regions(top_loc), BBOX, tag)
def spatial_scan(tag):
"""The main method loads the data from the disk (or compute them) and
calls appropriate methods to find top discrepancy regions for `tag`."""
print(tag)
grid_size = GRID_SIZE
background_name = u'mfreq/freq_{}_{}.mat'.format(grid_size, '_background')
measured_name = u'mfreq/freq_{}_{}.mat'.format(grid_size, tag)
res = []
for tag, filename in [(None, background_name), (tag, measured_name)]:
try:
res.append(sio.loadmat(filename)['c'])
except IOError:
conn, client = get_connection()
compute_frequency(conn, tag, BBOX, FIRST_TIME,
LAST_TIME, grid_size, plot=False)
client.close()
res.append(sio.loadmat(filename)['c'])
background, measured = res
total_b = np.sum(background)
total_m = np.sum(measured)
if not total_m > 0: # photos taken before 2008
return
if 0 < total_m <= 500:
support = 20
if 500 < total_m <= 2000:
support = 40
if 2000 < total_m:
support = MAX_SUPPORT
discrepancy = get_discrepancy_function(total_m, total_b, support)
grid_dim = (grid_size, grid_size)
top_loc = exact_grid(np.reshape(measured, grid_dim),
np.reshape(background, grid_dim),
discrepancy, TOP_K, GRID_SIZE/MAX_SIZE)
info = u'\n{}: g={}, s={}, k={}, w={}, h={}, max={}'
print(info.format(tag, GRID_SIZE, support, TOP_K, MIN_WIDTH, MIN_HEIGHT,
MAX_SIZE))
# persistent.save_var(u'disc/top_{}'.format(tag), top_loc)
# print('\n')
# for v in top_loc:
# print('{:.4f}'.format(v[0]))
# print(top_loc[0][1].intersects(v[1]))
plot_regions(merge_regions(top_loc), BBOX, tag)
def get_freq(tag):
print(tag)
mq.compute_frequency(DB.photos, tag, mq.SF_BBOX, sot, now, plot=False)
def get_freq(tag):
print(tag)
mq.compute_frequency(DB.photos, tag, mq.SF_BBOX, sot, now, plot=False)