# Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from datetime import datetime from functools import partial import numpy as np import shapely.geometry from pytz import timezone from webservice.NexusHandler import nexus_handler from webservice.algorithms_spark.NexusCalcSparkHandler import NexusCalcSparkHandler from webservice.webmodel import NexusResults, NexusProcessingException, NoDataException EPOCH = timezone('UTC').localize(datetime(1970, 1, 1)) ISO_8601 = '%Y-%m-%dT%H:%M:%S%z' @nexus_handler class TimeAvgMapNexusSparkHandlerImpl(NexusCalcSparkHandler): # __singleton_lock = threading.Lock() # __singleton_instance = None name = "Time Average Map Spark" path = "/timeAvgMapSpark" description = "Computes a Latitude/Longitude Time Average plot given an arbitrary geographical area and time range" params = { "ds": { "name": "Dataset", "type": "String", "description": "The dataset used to generate the map. Required" }, "startTime": { "name": "Start Time", "type": "string", "description": "Starting time in format YYYY-MM-DDTHH:mm:ssZ or seconds since EPOCH. Required" }, "endTime": { "name": "End Time", "type": "string", "description": "Ending time in format YYYY-MM-DDTHH:mm:ssZ or seconds since EPOCH. Required" }, "b": { "name": "Bounding box", "type": "comma-delimited float", "description": "Minimum (Western) Longitude, Minimum (Southern) Latitude, " "Maximum (Eastern) Longitude, Maximum (Northern) Latitude. Required" }, "spark": { "name": "Spark Configuration", "type": "comma-delimited value", "description": "Configuration used to launch in the Spark cluster. Value should be 3 elements separated by " "commas. 1) Spark Master 2) Number of Spark Executors 3) Number of Spark Partitions. Only " "Number of Spark Partitions is used by this function. Optional (Default: local,1,1)" } } singleton = True # @classmethod # def instance(cls, algorithm_config=None, sc=None): # with cls.__singleton_lock: # if not cls.__singleton_instance: # try: # singleton_instance = cls() # singleton_instance.set_config(algorithm_config) # singleton_instance.set_spark_context(sc) # cls.__singleton_instance = singleton_instance # except AttributeError: # pass # return cls.__singleton_instance def parse_arguments(self, request): # Parse input arguments self.log.debug("Parsing arguments") try: ds = request.get_dataset() if type(ds) == list or type(ds) == tuple: ds = next(iter(ds)) except: raise NexusProcessingException( reason="'ds' argument is required. Must be a string", code=400) # Do not allow time series on Climatology if next(iter([clim for clim in ds if 'CLIM' in clim]), False): raise NexusProcessingException( reason="Cannot compute Latitude/Longitude Time Average plot on a climatology", code=400) west, south, east, north = request.get_bounding_box() bounding_polygon = shapely.geometry.Polygon( [(west, south), (east, south), (east, north), (west, north), (west, south)]) start_time = request.get_start_datetime() end_time = request.get_end_datetime() if start_time > end_time: raise NexusProcessingException( reason="The starting time must be before the ending time. Received startTime: %s, endTime: %s" % ( request.get_start_datetime().strftime(ISO_8601), request.get_end_datetime().strftime(ISO_8601)), code=400) nparts_requested = request.get_nparts() start_seconds_from_epoch = int((start_time - EPOCH).total_seconds()) end_seconds_from_epoch = int((end_time - EPOCH).total_seconds()) min_elevation, max_elevation = request.get_elevation_args() if (min_elevation and max_elevation) and min_elevation > max_elevation: raise NexusProcessingException( reason='Min elevation must be less than or equal to max elevation', code=400 ) return ds, bounding_polygon, start_seconds_from_epoch, end_seconds_from_epoch, nparts_requested, min_elevation, max_elevation def calc(self, compute_options, **args): """ :param compute_options: StatsComputeOptions :param args: dict :return: """ request_start_time = datetime.now() metrics_record = self._create_metrics_record() ds, bbox, start_time, end_time, nparts_requested, min_elevation, max_elevation = 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(metrics_callback=metrics_record.record_metrics) 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, metrics_callback=metrics_record.record_metrics) 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 = np.array([[self._find_tile_bounds(t), self._startTime, self._endTime, self._ds] for t in nexus_tiles], dtype='object') # 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 spark_nparts = self._spark_nparts(nparts_requested) self.log.info('Using {} partitions'.format(spark_nparts)) rdd = self._sc.parallelize(nexus_tiles_spark, spark_nparts) metrics_record.record_metrics(partitions=rdd.getNumPartitions()) sum_count_part = rdd.map(partial(self._map, self._tile_service_factory, metrics_record.record_metrics, min_elevation, max_elevation)) reduce_duration = 0 reduce_start = datetime.now() 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])) reduce_duration += (datetime.now() - reduce_start).total_seconds() avg_tiles = sum_count.map(partial(calculate_means, metrics_record.record_metrics, self._fill)).collect() reduce_start = datetime.now() # 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 avg_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]['avg'] 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 for debugging purpose # if activated this line is not thread safe and might cause error when concurrent access occurs # self._create_nc_file(a, 'tam.nc', 'val', fill=self._fill) # Create dict for JSON response results = [[{'mean': 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])] total_duration = (datetime.now() - request_start_time).total_seconds() metrics_record.record_metrics(actual_time=total_duration, reduce=reduce_duration) metrics_record.print_metrics(self.log) 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) @staticmethod def _map(tile_service_factory, metrics_callback, min_elevation, max_elevation, tile_in_spark): tile_bounds = tile_in_spark[0] (min_lat, max_lat, min_lon, max_lon, min_y, max_y, min_x, max_x) = tile_bounds startTime = tile_in_spark[1] endTime = tile_in_spark[2] ds = tile_in_spark[3] tile_service = tile_service_factory() tile_inbounds_shape = (max_y - min_y + 1, max_x - min_x + 1) days_at_a_time = 30 t_incr = 86400 * days_at_a_time sum_tile = np.array(np.zeros(tile_inbounds_shape, dtype=np.float64)) cnt_tile = np.array(np.zeros(tile_inbounds_shape, dtype=np.uint32)) t_start = startTime calculation_duration = 0.0 while t_start <= endTime: t_end = min(t_start + t_incr, endTime) nexus_tiles = tile_service.get_tiles_bounded_by_box(min_lat, max_lat, min_lon, max_lon, ds=ds, start_time=t_start, end_time=t_end, min_elevation=min_elevation, max_elevation=max_elevation, metrics_callback=metrics_callback) calculation_start = datetime.now() for tile in nexus_tiles: tile.data.data[:, :] = np.nan_to_num(tile.data.data) sum_tile += tile.data.data[0, min_y:max_y + 1, min_x:max_x + 1] cnt_tile += (~tile.data.mask[0, min_y:max_y + 1, min_x:max_x + 1]).astype(np.uint8) t_start = t_end + 1 calculation_duration += (datetime.now() - calculation_start).total_seconds() metrics_callback(calculation=calculation_duration) return (min_lat, max_lat, min_lon, max_lon), (sum_tile, cnt_tile) def calculate_means(metrics_callback, fill, xxx_todo_changeme): (bounds, (sum_tile, cnt_tile)) = xxx_todo_changeme start_time = datetime.now() outer = [] for y in range(sum_tile.shape[0]): inner = [] for x in range(sum_tile.shape[1]): value = { 'avg': (sum_tile[y, x] / cnt_tile[y, x]) if (cnt_tile[y, x] > 0) else fill, 'cnt': cnt_tile[y, x] } inner.append(value) outer.append(inner) duration = (datetime.now() - start_time).total_seconds() metrics_callback(calculation=duration) return bounds, outer