def latitude_time_hofmoeller_stats(tile_in_spark):
(tile_id, index, min_lat, max_lat, min_lon, max_lon) = tile_in_spark
tile_service = NexusTileService()
try:
# Load the dataset tile
tile = tile_service.find_tile_by_id(tile_id)[0]
# Mask it to the search domain
tile = tile_service.mask_tiles_to_bbox(min_lat, max_lat, min_lon,
max_lon, [tile])[0]
except IndexError:
return None
stat = {'sequence': index, 'time': np.ma.min(tile.times), 'lats': []}
points = list(tile.nexus_point_generator())
data = sorted(points, key=lambda p: p.latitude)
points_by_lat = itertools.groupby(data, key=lambda p: p.latitude)
for lat, points_at_lat in points_by_lat:
values_at_lat = np.array(
[point.data_val for point in points_at_lat])
stat['lats'].append({
'latitude': float(lat),
'cnt': len(values_at_lat),
'avg': np.mean(values_at_lat).item(),
'max': np.max(values_at_lat).item(),
'min': np.min(values_at_lat).item(),
'std': np.std(values_at_lat).item()
})
return stat
def hofmoeller_stats(tile_in_spark):
(latlon, tile_id, index,
min_lat, max_lat, min_lon, max_lon) = tile_in_spark
tile_service = NexusTileService()
try:
# Load the dataset tile
tile = tile_service.find_tile_by_id(tile_id)[0]
# Mask it to the search domain
tile = tile_service.mask_tiles_to_bbox(min_lat, max_lat,
min_lon, max_lon, [tile])[0]
except IndexError:
# return None
return []
t = np.ma.min(tile.times)
stats = []
points = list(tile.nexus_point_generator())
if latlon == 0:
# Latitude-Time Map (Average over longitudes)
data = sorted(points, key=lambda p: p.latitude)
points_by_coord = itertools.groupby(data, key=lambda p: p.latitude)
else:
# Longitude-Time Map (Average over latitudes)
data = sorted(points, key=lambda p: p.longitude)
points_by_coord = itertools.groupby(data, key=lambda p: p.longitude)
for coord, points_at_coord in points_by_coord:
values_at_coord = np.array([[p.data_val,
np.cos(np.radians(p.latitude))]
for p in points_at_coord])
vals = np.nan_to_num(values_at_coord[:, 0])
weights = values_at_coord[:, 1]
coord_cnt = len(values_at_coord)
if latlon == 0:
# Latitude-Time Map (Average over longitudes)
# In this case there is no weighting by cos(lat)
weighted_sum = np.sum(vals).item()
sum_of_weights = coord_cnt
else:
# Longitude-Time Map (Average over latitudes)
# In this case we need to weight by cos(lat)
weighted_sum = np.dot(vals, weights)
sum_of_weights = np.sum(weights).item()
stats.append(((t, float(coord)), (t, index, float(coord),
coord_cnt,
weighted_sum,
sum_of_weights,
np.max(vals).item(),
np.min(vals).item(),
np.var(vals).item())))
return stats
