def __init__(self, results=None, meta=None, computeOptions=None):
NexusResults.__init__(self,
results=results,
meta=meta,
stats=None,
computeOptions=computeOptions)
def calc(self, compute_options, **args):
"""
:param compute_options: StatsComputeOptions
:param args: dict
:return:
"""
ds, bbox, start_time, end_time, nparts_requested = self.parse_arguments(
compute_options)
self._setQueryParams(ds, (float(bbox.bounds[1]), float(
bbox.bounds[3]), float(bbox.bounds[0]), float(bbox.bounds[2])),
start_time, end_time)
nexus_tiles = self._find_global_tile_set()
if len(nexus_tiles) == 0:
raise NoDataException(
reason="No data found for selected timeframe")
self.log.debug('Found {0} tiles'.format(len(nexus_tiles)))
print(('Found {} tiles'.format(len(nexus_tiles))))
daysinrange = self._get_tile_service().find_days_in_range_asc(
bbox.bounds[1], bbox.bounds[3], bbox.bounds[0], bbox.bounds[2], ds,
start_time, end_time)
ndays = len(daysinrange)
if ndays == 0:
raise NoDataException(
reason="No data found for selected timeframe")
self.log.debug('Found {0} days in range'.format(ndays))
for i, d in enumerate(daysinrange):
self.log.debug('{0}, {1}'.format(i, datetime.utcfromtimestamp(d)))
self.log.debug(
'Using Native resolution: lat_res={0}, lon_res={1}'.format(
self._latRes, self._lonRes))
self.log.debug('nlats={0}, nlons={1}'.format(self._nlats, self._nlons))
self.log.debug('center lat range = {0} to {1}'.format(
self._minLatCent, self._maxLatCent))
self.log.debug('center lon range = {0} to {1}'.format(
self._minLonCent, self._maxLonCent))
# Create array of tuples to pass to Spark map function
nexus_tiles_spark = [[
self._find_tile_bounds(t), self._startTime, self._endTime, self._ds
] for t in nexus_tiles]
# Remove empty tiles (should have bounds set to None)
bad_tile_inds = np.where([t[0] is None for t in nexus_tiles_spark])[0]
for i in np.flipud(bad_tile_inds):
del nexus_tiles_spark[i]
# Expand Spark map tuple array by duplicating each entry N times,
# where N is the number of ways we want the time dimension carved up.
# Set the time boundaries for each of the Spark map tuples so that
# every Nth element in the array gets the same time bounds.
max_time_parts = 72
num_time_parts = min(max_time_parts, ndays)
spark_part_time_ranges = np.tile(
np.array([
a[[0, -1]]
for a in np.array_split(np.array(daysinrange), num_time_parts)
]), (len(nexus_tiles_spark), 1))
nexus_tiles_spark = np.repeat(nexus_tiles_spark,
num_time_parts,
axis=0)
nexus_tiles_spark[:, 1:3] = spark_part_time_ranges
# Launch Spark computations to calculate x_bar
spark_nparts = self._spark_nparts(nparts_requested)
self.log.info('Using {} partitions'.format(spark_nparts))
rdd = self._sc.parallelize(nexus_tiles_spark, spark_nparts)
sum_count_part = rdd.map(partial(self._map,
self._tile_service_factory))
sum_count = \
sum_count_part.combineByKey(lambda val: val,
lambda x, val: (x[0] + val[0],
x[1] + val[1]),
lambda x, y: (x[0] + y[0], x[1] + y[1]))
fill = self._fill
avg_tiles = \
sum_count.map(lambda bounds_sum_tile_cnt_tile:
(bounds_sum_tile_cnt_tile[0], [[(bounds_sum_tile_cnt_tile[1][0][y, x] / bounds_sum_tile_cnt_tile[1][1][y, x])
if (bounds_sum_tile_cnt_tile[1][1][y, x] > 0)
else fill
for x in
range(bounds_sum_tile_cnt_tile[1][0].shape[1])]
for y in
range(bounds_sum_tile_cnt_tile[1][0].shape[0])])).collect()
#
# Launch a second parallel computation to calculate variance from x_bar
#
# Create array of tuples to pass to Spark map function - first param are the tile bounds that were in the
# results and the last param is the data for the results (x bar)
nexus_tiles_spark = [[
t[0], self._startTime, self._endTime, self._ds, t[1]
] for t in avg_tiles]
self.log.info('Using {} partitions'.format(spark_nparts))
rdd = self._sc.parallelize(nexus_tiles_spark, spark_nparts)
anomaly_squared_part = rdd.map(
partial(self._calc_variance, self._tile_service_factory))
anomaly_squared = \
anomaly_squared_part.combineByKey(lambda val: val,
lambda x, val: (x[0] + val[0],
x[1] + val[1]),
lambda x, y: (x[0] + y[0], x[1] + y[1]))
variance_tiles = \
anomaly_squared.map(lambda bounds_anomaly_squared_tile_cnt_tile:
(bounds_anomaly_squared_tile_cnt_tile[0], [[{'variance': (bounds_anomaly_squared_tile_cnt_tile[1][0][y, x] / bounds_anomaly_squared_tile_cnt_tile[1][1][y, x])
if (bounds_anomaly_squared_tile_cnt_tile[1][1][y, x] > 0)
else fill,
'cnt': bounds_anomaly_squared_tile_cnt_tile[1][1][y, x]}
for x in range(bounds_anomaly_squared_tile_cnt_tile[1][0].shape[1])]
for y in range(bounds_anomaly_squared_tile_cnt_tile[1][0].shape[0])])).collect()
# Combine subset results to produce global map.
#
# The tiles below are NOT Nexus objects. They are tuples
# with the time avg map data and lat-lon bounding box.
a = np.zeros((self._nlats, self._nlons), dtype=np.float64, order='C')
n = np.zeros((self._nlats, self._nlons), dtype=np.uint32, order='C')
for tile in variance_tiles:
if tile is not None:
((tile_min_lat, tile_max_lat, tile_min_lon, tile_max_lon),
tile_stats) = tile
tile_data = np.ma.array([[
tile_stats[y][x]['variance']
for x in range(len(tile_stats[0]))
] for y in range(len(tile_stats))])
tile_cnt = np.array([[
tile_stats[y][x]['cnt'] for x in range(len(tile_stats[0]))
] for y in range(len(tile_stats))])
tile_data.mask = ~(tile_cnt.astype(bool))
y0 = self._lat2ind(tile_min_lat)
y1 = y0 + tile_data.shape[0] - 1
x0 = self._lon2ind(tile_min_lon)
x1 = x0 + tile_data.shape[1] - 1
if np.any(np.logical_not(tile_data.mask)):
self.log.debug(
'writing tile lat {0}-{1}, lon {2}-{3}, map y {4}-{5}, map x {6}-{7}'
.format(tile_min_lat, tile_max_lat, tile_min_lon,
tile_max_lon, y0, y1, x0, x1))
a[y0:y1 + 1, x0:x1 + 1] = tile_data
n[y0:y1 + 1, x0:x1 + 1] = tile_cnt
else:
self.log.debug(
'All pixels masked in tile lat {0}-{1}, lon {2}-{3}, map y {4}-{5}, map x {6}-{7}'
.format(tile_min_lat, tile_max_lat, tile_min_lon,
tile_max_lon, y0, y1, x0, x1))
# Store global map in a NetCDF file.
self._create_nc_file(a, 'tam.nc', 'val', fill=self._fill)
# Create dict for JSON response
results = [[{
'variance': a[y, x],
'cnt': int(n[y, x]),
'lat': self._ind2lat(y),
'lon': self._ind2lon(x)
} for x in range(a.shape[1])] for y in range(a.shape[0])]
return NexusResults(results=results,
meta={},
stats=None,
computeOptions=None,
minLat=bbox.bounds[1],
maxLat=bbox.bounds[3],
minLon=bbox.bounds[0],
maxLon=bbox.bounds[2],
ds=ds,
startTime=start_time,
endTime=end_time)
def __init__(self, results=None, meta=None, stats=None, compute_options=None, **args):
NexusResults.__init__(self, results=results, meta=meta, stats=stats, compute_options=compute_options)
self.__type = args['type']
def __init__(self, capabilities):
NexusResults.__init__(self)
self.__capabilities = capabilities