From 1188906691545f62d422e3a07d3e0c0a63e11736 Mon Sep 17 00:00:00 2001 From: hpuhr Date: Fri, 21 Aug 2020 08:41:13 +0200 Subject: [PATCH] Delete reconstructor.py --- analyze/reconst_util/reconstructor.py | 237 -------------------------- 1 file changed, 237 deletions(-) delete mode 100644 analyze/reconst_util/reconstructor.py diff --git a/analyze/reconst_util/reconstructor.py b/analyze/reconst_util/reconstructor.py deleted file mode 100644 index cb47b2a..0000000 --- a/analyze/reconst_util/reconstructor.py +++ /dev/null @@ -1,237 +0,0 @@ -from compasslib.data.datasourcesensor import DataSourceSensor -from compasslib.data.datasourcetracker import DataSourceTracker -from compasslib.data.datasourcetype import DataSourceType -from compasslib.data.datasource import DataSource, status_row_count -from compasslib.common.utils import * -from compasslib.track.track import Track -from compasslib.track.trackclassification import TrackClassification -from compasslib.report.report import * -from compasslib.data.datastore import DataStore -import compasslib.config.config_global as config_global -from compasslib.reconstruct.umkalman2d import UMKalmanFilter2D -from compasslib.reconstruct.umkalman3d import UMKalmanFilter3D -from compasslib.reconstruct.exkalman3d import ExtendedKalmanFilter3D -from compasslib.reconstruct.unscentedkalman3d import UnscentedKalmanFilter3D -from compasslib.reconstruct.imm import IMMFilter -from compasslib.data.targetreportcollection import TargetReportCollection - -from operator import itemgetter - -import numpy as np - -import array -import math -import datetime -import random - - -class Reconstructor: - """Calculates reference trajectories/tracks using nifty math""" - - def __init__(self, datastore): - - print ('reconstructor: setting up') - self._datastore = datastore - - self._excluded_sources = {} - - if config_global.reconstruction_exclude_sources: - sensors = self._datastore.sensors() - - found = False - for src_name in config_global.reconstruction_exclude_sources: - for sensor_id, sensor in sensors.iteritems(): - if src_name == sensor.name or src_name == sensor.name: - if sensor.sensor_type not in self._excluded_sources: - self._excluded_sources [sensor.sensor_type] = [] - self._excluded_sources [sensor.sensor_type].append(str(sensor_id)) - found = True - - if not found: - print ('WARN: reconstructor excluded source {} not found'.format(src_name)) - - def work(self): - print ('reconstructor: working') - - key_lambda = lambda x: x.target_address - subkey_lambda = lambda x: x.time_detection - - np.set_printoptions(suppress=True, precision=2, linewidth=200) - - # load data from sources - src_collection = self.collectData(key_lambda, subkey_lambda) - - # get new sensor id - ref_src = self._datastore.data_sources[DataSourceType.REFTRAJ] - #ref_src = self._datastore.data_sources[DataSourceType.TRACK] - assert ref_src - ref_sensor_id = ref_src.getOrAddSensorId(config_global.reconstruction_run_name) - - # filter collected chains - no_modes_chain, filtered_collection = self.filterChains(src_collection, key_lambda, subkey_lambda, ref_sensor_id) - - if config_global.reconstruction_associate_no_mode_s: - # associate non-mode-s target reports - unassociated_target_reports = self.associateNoModeSTargetReports (no_modes_chain, filtered_collection, - original_collection=src_collection) - # remove the none chain from original data - src_collection.removeChain (None) - - # re-filter original data - no_modes_chain, filtered_collection = self.filterChains(src_collection, key_lambda, subkey_lambda, - ref_sensor_id) - - #extrapolate if required - if config_global.reconstruction_extrapolate: - print ('reconstructor: extrapolating to full seconds') - for key, filtered_chain in filtered_collection.data.iteritems(): - filtered_chain.extrapolateToFullSeconds() - - data = filtered_collection.getDataDicts() - print ('reconstructor: got {} filtered target reports'.format(len(data))) - - for key, xlim, ylim in config_global.reconstruction_plot_keys: - test_chain = filtered_collection.getChain (key) # 5022335, 3772899 - if test_chain is not None: - print ('reconstructor: plotting chain {}'.format(key)) - test_chain.plot(xlim, ylim) - - sorted_data = sorted(data, key=itemgetter('time_detection')) - - assert len(sorted_data) == len(data) - - # inserted as track, the speed must be multiplied by 100, since stupidly stored as int cm/s - #for key, filtered_chain in filtered_collection.data.iteritems(): - # for target_report in filtered_chain.getChain(): - # target_report.groundspeed_x *= 100 - # target_report.groundspeed_y *= 100 - - if len(sorted_data) > 0 and config_global.reconstruction_write_db: - print ('reconstructor: inserting data') - for target_report_dict in sorted_data: - #print target_report_dict - ref_src.insertData(target_report_dict) - print ('reconstructor: post-processing reference') - ref_src.updateCount() - ref_src.postProcess() - #ref_src.postProcess(self._datastore.data_sources, False) - #ref_src.findTrackReferenceCorrelations(self._datastore.data_sources[DataSourceType.REFTRAJ]) - - def collectData (self, key_lambda, subkey_lambda): - src_collection = None - - print ('reconstructor: collecting sensor information') - - variables = ['rec_num', 'sensor_id', 'time_detection', 'position_x', 'position_y', 'flight_level', - 'target_address', 'date', 'position_x_variance', 'position_y_variance', 'position_xy_covariance'] - - for src_type, source in self._datastore.data_sources.iteritems(): - if source.hasData(): - - filter_str = None - - if src_type in self._excluded_sources: - filter_str = 'sensor_id not in ('+",".join(self._excluded_sources [src_type])+')' - - print ('reconstructor: collecting target reports from {}, filter {}'.format(source.name, filter_str)) - - if src_collection is None: - src_collection = source.getTargetReportCollection(variables, key_lambda, subkey_lambda, filter_str) - else: - src_collection.addData(source, source.getTargetReports(variables, filter_str)) - - return src_collection - - def filterChains (self, src_collection, key_lambda, subkey_lambda, ref_sensor_id): - filtered_collection = TargetReportCollection(key_lambda, subkey_lambda) - - no_modes_chain = None - - print ('reconstructor: doing chaining based on Mode S') - - for key, target_chain in src_collection.data.iteritems(): - - if key is None: - assert no_modes_chain is None - no_modes_chain = target_chain.getChain() - continue - - - if len(target_chain) < config_global.reconstruction_min_num_updates: - print ('reconstructor: skipping short {}'.format(target_chain)) - continue - - if key in config_global.reconstruction_exclude_mode_s \ - or (config_global.reconstruction_limit_mode_s_to is not None - and key not in config_global.reconstruction_limit_mode_s_to): #exlucde certain addresses - print ('reconstructor: skipping excluded {}'.format(target_chain)) - continue - - #print ('filtering {}'.format(target_chain)) - - kalman_filter = UMKalmanFilter2D ('UMKalman2D') - kalman_filter = UMKalmanFilter3D('UMKalman3D') - #kalman_filter = ExtendedKalmanFilter3D ('ExKalman3D') - kalman_filter = UnscentedKalmanFilter3D ('FragrantKalman3D') - kalman_filter = IMMFilter ('IMM3D') - - filtered_chain = kalman_filter.filter(chain=target_chain, new_sensor_id=ref_sensor_id, smooth=False) - - if filtered_chain: - filtered_collection.addChain(key, filtered_chain) - - return no_modes_chain, filtered_collection - - - def associateNoModeSTargetReports (self, no_modes_chain, filtered_collection, original_collection): - - print ('reconstructor: processing non-Mode S data, len {}'.format(len(no_modes_chain))) - - no_modes_unassociated_chain = [] - - cnt=0 - matches_cnt = 0 - - for target_report in no_modes_chain: - matches = {} # score -> filtered chain - - if cnt % 2000 == 0: - print ('processed {} of {} ({}%)'.format(cnt, len(no_modes_chain), int(100*cnt/len(no_modes_chain)))) - - for key, filtered_chain in filtered_collection.data.iteritems(): - if not filtered_chain.existsAtTime (target_report.time_detection): - continue - - score = filtered_chain.getPositionMatchScore (target_report) - - if score is not None: - matches[score] = filtered_chain - - if len(matches) > 0: - #for score_cnt in range(0, 3): - # min_key = min(matches.keys()) - # print ('score #{}: {} to {}'.format(score_cnt, min_key, matches[min_key])) - # del matches[min_key] - - min_key = min(matches.keys()) - - if min_key <= config_global.reconstruction_primary_association_score_threshold: - chain_key = matches[min_key].key - - associated_chain = original_collection.getChain(chain_key) - assert associated_chain is not None - - associated_chain.addData (target_report) - - matches_cnt += 1 - else: - no_modes_unassociated_chain.append(target_report) - else: - no_modes_unassociated_chain.append(target_report) - - cnt += 1 - - print ('reconstructor: chaining non-Mode S done, found {} matches ({}%), rest {}'.format( - matches_cnt, int(100*matches_cnt/len(no_modes_chain)), len(no_modes_unassociated_chain))) - - return no_modes_unassociated_chain