| # 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. |
| |
| |
| import math |
| import logging |
| from calendar import timegm, monthrange |
| from datetime import datetime |
| |
| import numpy as np |
| from nexustiles.nexustiles import NexusTileService |
| |
| from webservice.NexusHandler import nexus_handler, DEFAULT_PARAMETERS_SPEC |
| from webservice.algorithms_spark.NexusCalcSparkHandler import NexusCalcSparkHandler |
| from webservice.webmodel import NexusResults, NexusProcessingException, NoDataException |
| from functools import partial |
| |
| @nexus_handler |
| class ClimMapNexusSparkHandlerImpl(NexusCalcSparkHandler): |
| name = "Climatology Map Spark" |
| path = "/climMapSpark" |
| description = "Computes a Latitude/Longitude Time Average map for a given month given an arbitrary geographical area and year range" |
| params = DEFAULT_PARAMETERS_SPEC |
| |
| @staticmethod |
| def _map(tile_service_factory, 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() |
| # print 'Started tile', tile_bounds |
| # sys.stdout.flush() |
| tile_inbounds_shape = (max_y - min_y + 1, max_x - min_x + 1) |
| days_at_a_time = 90 |
| # days_at_a_time = 30 |
| # days_at_a_time = 7 |
| # days_at_a_time = 1 |
| # print 'days_at_a_time = ', days_at_a_time |
| 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 |
| while t_start <= endTime: |
| t_end = min(t_start + t_incr, endTime) |
| # t1 = time() |
| # print 'nexus call start at time %f' % t1 |
| # sys.stdout.flush() |
| nexus_tiles = \ |
| ClimMapNexusSparkHandlerImpl.query_by_parts(tile_service, |
| min_lat, max_lat, |
| min_lon, max_lon, |
| ds, |
| t_start, |
| t_end, |
| part_dim=2) |
| # 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) |
| # t2 = time() |
| # print 'nexus call end at time %f' % t2 |
| # print 'secs in nexus call: ', t2-t1 |
| # sys.stdout.flush() |
| # print 't %d to %d - Got %d tiles' % (t_start, t_end, |
| # len(nexus_tiles)) |
| # for nt in nexus_tiles: |
| # print nt.granule |
| # print nt.section_spec |
| # print 'lat min/max:', np.ma.min(nt.latitudes), np.ma.max(nt.latitudes) |
| # print 'lon min/max:', np.ma.min(nt.longitudes), np.ma.max(nt.longitudes) |
| # sys.stdout.flush() |
| |
| 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 |
| |
| # print 'cnt_tile = ', cnt_tile |
| # cnt_tile.mask = ~(cnt_tile.data.astype(bool)) |
| # sum_tile.mask = cnt_tile.mask |
| # avg_tile = sum_tile / cnt_tile |
| # stats_tile = [[{'avg': avg_tile.data[y,x], 'cnt': cnt_tile.data[y,x]} for x in range(tile_inbounds_shape[1])] for y in range(tile_inbounds_shape[0])] |
| # print 'Finished tile', tile_bounds |
| # print 'Tile avg = ', avg_tile |
| # sys.stdout.flush() |
| return ((min_lat, max_lat, min_lon, max_lon), (sum_tile, cnt_tile)) |
| |
| def _month_from_timestamp(self, t): |
| return datetime.utcfromtimestamp(t).month |
| |
| def calc(self, computeOptions, **args): |
| """ |
| |
| :param computeOptions: StatsComputeOptions |
| :param args: dict |
| :return: |
| """ |
| |
| self._setQueryParams(computeOptions.get_dataset()[0], |
| (float(computeOptions.get_min_lat()), |
| float(computeOptions.get_max_lat()), |
| float(computeOptions.get_min_lon()), |
| float(computeOptions.get_max_lon())), |
| start_year=computeOptions.get_start_year(), |
| end_year=computeOptions.get_end_year(), |
| clim_month=computeOptions.get_clim_month()) |
| self._startTime = timegm((self._startYear, 1, 1, 0, 0, 0)) |
| self._endTime = timegm((self._endYear, 12, 31, 23, 59, 59)) |
| |
| if 'CLIM' in self._ds: |
| raise NexusProcessingException(reason="Cannot compute Latitude/Longitude Time Average map on a climatology", |
| code=400) |
| |
| nparts_requested = computeOptions.get_nparts() |
| |
| nexus_tiles = self._find_global_tile_set() |
| # print 'tiles:' |
| # for tile in nexus_tiles: |
| # print tile.granule |
| # print tile.section_spec |
| # print 'lat:', tile.latitudes |
| # print 'lon:', tile.longitudes |
| |
| # nexus_tiles) |
| if len(nexus_tiles) == 0: |
| raise NoDataException(reason="No data found for selected timeframe") |
| |
| self.log.debug('Found {0} tiles'.format(len(nexus_tiles))) |
| # for tile in nexus_tiles: |
| # print 'lats: ', tile.latitudes.compressed() |
| # print 'lons: ', tile.longitudes.compressed() |
| 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] |
| # print 'nexus_tiles_spark = ', nexus_tiles_spark |
| # 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] |
| num_nexus_tiles_spark = len(nexus_tiles_spark) |
| self.log.debug('Created {0} spark tiles'.format(num_nexus_tiles_spark)) |
| |
| # 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. |
| # (one partition per year in this case). |
| num_years = self._endYear - self._startYear + 1 |
| nexus_tiles_spark = np.repeat(nexus_tiles_spark, num_years, axis=0) |
| self.log.debug('repeated len(nexus_tiles_spark) = {0}'.format(len(nexus_tiles_spark))) |
| |
| # Set the time boundaries for each of the Spark map tuples. |
| # Every Nth element in the array gets the same time bounds. |
| spark_part_time_ranges = \ |
| np.repeat(np.array([[timegm((y, self._climMonth, 1, 0, 0, 0)), |
| timegm((y, self._climMonth, |
| monthrange(y, self._climMonth)[1], |
| 23, 59, 59))] |
| for y in range(self._startYear, |
| self._endYear + 1)]), |
| num_nexus_tiles_spark, |
| axis=0).reshape((len(nexus_tiles_spark), 2)) |
| self.log.debug('spark_part_time_ranges={0}'.format(spark_part_time_ranges)) |
| nexus_tiles_spark[:, 1:3] = spark_part_time_ranges |
| # print 'nexus_tiles_spark final = ' |
| # for i in range(len(nexus_tiles_spark)): |
| # print nexus_tiles_spark[i] |
| |
| # 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) |
| 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])) |
| avg_tiles = \ |
| sum_count.map(lambda (bounds, (sum_tile, cnt_tile)): |
| (bounds, [[{'avg': (sum_tile[y, x] / cnt_tile[y, x]) |
| if (cnt_tile[y, x] > 0) else 0., |
| 'cnt': cnt_tile[y, x]} |
| for x in |
| range(sum_tile.shape[1])] |
| for y in |
| range(sum_tile.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 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. |
| self._create_nc_file(a, 'clmap.nc', 'val') |
| |
| # Create dict for JSON response |
| results = [[{'avg': 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 ClimMapSparkResults(results=results, meta={}, computeOptions=computeOptions) |
| |
| |
| class ClimMapSparkResults(NexusResults): |
| def __init__(self, results=None, meta=None, computeOptions=None): |
| NexusResults.__init__(self, results=results, meta=meta, stats=None, computeOptions=computeOptions) |
