| # 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()) |
| |
| return ds, bounding_polygon, start_seconds_from_epoch, end_seconds_from_epoch, nparts_requested |
| |
| 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 = 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 = [[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 |
| 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)) |
| 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, 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, |
| 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 |
