Mercurial Hosting > traffic-intelligence
view scripts/process.py @ 1216:8356e15fd691
fix for result files
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Sun, 07 May 2023 16:24:27 -0400 |
parents | b968c33f8c2f |
children |
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#! /usr/bin/env python3 import sys, argparse from pathlib import Path from multiprocessing.pool import Pool #import matplotlib #atplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import pandas as pd from trafficintelligence import storage, events, prediction, cvutils, utils, moving, processing, ml from trafficintelligence.metadata import * parser = argparse.ArgumentParser(description='This program manages the processing of several files based on a description of the sites and video data in an SQLite database following the metadata module.') # input parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True) parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*') parser.add_argument('--sites', dest = 'siteIds', help = 'indices of the video sequences', nargs = '*') # main function parser.add_argument('--delete', dest = 'delete', help = 'data to delete', choices = ['feature', 'object', 'classification', 'interaction']) parser.add_argument('--process', dest = 'process', help = 'data to process', choices = ['feature', 'object', 'classification', 'prototype', 'interaction']) parser.add_argument('--display', dest = 'display', help = 'data to display (replay over video)', choices = ['feature', 'object', 'classification', 'interaction']) parser.add_argument('--progress', dest = 'progress', help = 'information about the progress of processing', action = 'store_true') parser.add_argument('--analyze', dest = 'analyze', help = 'data to analyze (results)', choices = ['feature', 'object', 'classification', 'interaction', 'collision-map', 'event-speed', 'event-interaction']) # common options parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file') parser.add_argument('-n', dest = 'nObjects', help = 'number of objects/interactions to process', type = int) parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories', choices = ['feature', 'object'], default = 'feature') parser.add_argument('--dry', dest = 'dryRun', help = 'dry run of processing', action = 'store_true') parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1) parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int) ### process options # motion pattern learning and assignment parser.add_argument('--prototype-filename', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes', default = 'prototypes.sqlite') #parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with') parser.add_argument('--nobjects-mp', dest = 'nMPObjects', help = 'number of objects/interactions to process', type = int) parser.add_argument('--nfeatures-per-object', dest = 'nLongestFeaturesPerObject', help = 'maximum number of features per object to load', type = int) parser.add_argument('--epsilon', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float) parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance parser.add_argument('--minsimil', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float) parser.add_argument('--min-cluster-size', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0) #parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true') parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true') parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true') #parser.add_argument('--similarities-filename', dest = 'similaritiesFilename', help = 'filename of the similarities') parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true') parser.add_argument('--save-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects to the prototypes', action = 'store_true') parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true') # safety analysis parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp']) parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true') # override other tracking config, erase sqlite? # analysis options parser.add_argument('--output', dest = 'output', help = 'kind of output to produce (interval means)', choices = ['figure', 'interval', 'event']) parser.add_argument('--min-duration', dest = 'minDuration', help = 'mininum duration we have to see the user or interaction to take into account in the analysis (s)', type = float) parser.add_argument('--max-time-indicator-value', dest = 'maxTimeIndicatorValue', help = 'maximum indicator value for time indicators like PET and TTC (s)', type = float) parser.add_argument('--max-speed-indicator-value', dest = 'maxSpeedIndicatorValue', help = 'maximum indicator value for speed indicators like individual speed statistics and speed differential (km/h)', type = float) parser.add_argument('--interval-duration', dest = 'intervalDuration', help = 'length of time interval to aggregate data (min)', type = int, default = 15) parser.add_argument('--aggregation', dest = 'aggMethods', help = 'aggregation method per user/interaction and per interval', choices = ['mean', 'median', 'centile'], nargs = '*', default = ['median']) parser.add_argument('--aggregation-centiles', dest = 'aggCentiles', help = 'centile(s) to compute from the observations', nargs = '*', type = int) parser.add_argument('--event-thresholds', dest = 'eventThresholds', help = 'threshold to count severe situations', nargs = '*', type = float) parser.add_argument('--event-filename', dest = 'eventFilename', help = 'filename of the event data') dpi = 150 # unit of analysis: site - camera-view # need way of selecting sites as similar as possible to sql alchemy syntax # override tracking.cfg from db # manage cfg files, overwrite them (or a subset of parameters) # delete sqlite files # info of metadata args = parser.parse_args() ################################# # Data preparation ################################# session = connectDatabase(args.metadataFilename) parentPath = Path(args.metadataFilename).parent # files are relative to metadata location videoSequences = [] sites = [] if args.videoIds is not None: for videoId in args.videoIds: if '-' in videoId: videoSequences.extend([session.query(VideoSequence).get(i) for i in moving.TimeInterval.parse(videoId)]) else: videoSequences.append(session.query(VideoSequence).get(int(videoId))) videoSequences = [vs for vs in videoSequences if vs is not None] sites = set([vs.cameraView.site for vs in videoSequences]) elif args.siteIds is not None: for siteId in args.siteIds: if '-' in siteId: sites.extend([session.query(Site).get(i) for i in moving.TimeInterval.parse(siteId)]) else: sites.append(session.query(Site).get(int(siteId))) sites = [s for s in sites if s is not None] for site in sites: videoSequences.extend(getSiteVideoSequences(site)) else: print('No video/site to process') if args.nProcesses > 1: pool = Pool(args.nProcesses) ################################# # Report progress in the processing ################################# if args.progress: # TODO find video sequences that have null camera view, to work with them print('Providing information on progress of data processing') headers = ['site', 'vs', 'features', 'objects', 'interactions'] # todo add prototypes and object classification data = [] for site in sites: unprocessedVideoSequences = [] for vs in getSiteVideoSequences(site): if (parentPath/vs.getDatabaseFilename()).is_file(): # TODO check time of file? tableNames = storage.tableNames(str(parentPath.absolute()/vs.getDatabaseFilename())) data.append([site.name, vs.idx, 'positions' in tableNames, 'objects' in tableNames, 'interactions' in tableNames]) else: unprocessedVideoSequences.append(vs) data.append([site.name, vs.idx, False, False, False]) #if len(unprocessedVideoSequences): # print('Site {} ({}) has {} completely unprocessed video sequences'.format (site.name, site.idx, len(unprocessedVideoSequences))) videoSequences = session.query(VideoSequence).filter(VideoSequence.cameraViewIdx.is_(None)).all() data = pd.DataFrame(data, columns = headers) print('-'*80) print('\t'+' '.join(headers[2:])) print('-'*80) for name, group in data.groupby(['site']): #.agg({'vs': 'count'})) n = group.vs.count() print('{}: {} % / {} % / {} % ({})'.format(name, 100*group.features.sum()/float(n), 100*group.objects.sum()/float(n), 100*group.interactions.sum()/float(n), n)) print('-'*80) if len(videoSequences) > 0: print('{} video sequences without a camera view:'.format(len(videoSequences))) print([vs.idx for vs in videoSequences]) print('-'*80) with pd.option_context('display.max_rows', None, 'display.max_columns', None): print(data) ################################# # Delete ################################# if args.delete is not None: if args.delete == 'feature': response = input('Are you sure you want to delete the tracking results (SQLite files) of all these sites (y/n)?') if response == 'y': for vs in videoSequences: p = parentPath.absolute()/vs.getDatabaseFilename() p.unlink() elif args.delete in ['object', 'interaction']: #parser.add_argument('-t', dest = 'dataType', help = 'type of the data to remove', required = True, choices = ['object','interaction', 'bb', 'pois', 'prototype']) for vs in videoSequences: storage.deleteFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.delete) ################################# # Process ################################# if args.process in ['feature', 'object']: # tracking if args.nProcesses == 1: for vs in videoSequences: if not (parentPath/vs.getDatabaseFilename()).is_file() or args.process == 'object': if args.configFilename is None: configFilename = str(parentPath/vs.cameraView.getTrackingConfigurationFilename()) else: configFilename = args.configFilename if vs.cameraView.cameraType is None: cvutils.tracking(configFilename, args.process == 'object', str(parentPath.absolute()/vs.getVideoSequenceFilename()), str(parentPath.absolute()/vs.getDatabaseFilename()), str(parentPath.absolute()/vs.cameraView.getHomographyFilename()), str(parentPath.absolute()/vs.cameraView.getMaskFilename()), False, None, None, args.dryRun) else: #caution: cameratype can be not none, but without parameters for undistortion cvutils.tracking(configFilename, args.process == 'object', str(parentPath.absolute()/vs.getVideoSequenceFilename()), str(parentPath.absolute()/vs.getDatabaseFilename()), str(parentPath.absolute()/vs.cameraView.getHomographyFilename()), str(parentPath.absolute()/vs.cameraView.getMaskFilename()), True, vs.cameraView.cameraType.intrinsicCameraMatrix, vs.cameraView.cameraType.distortionCoefficients, args.dryRun) else: print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename())) else: for vs in videoSequences: if not (parentPath/vs.getDatabaseFilename()).is_file() or args.process == 'object': if args.configFilename is None: configFilename = str(parentPath/vs.cameraView.getTrackingConfigurationFilename()) else: configFilename = args.configFilename if vs.cameraView.cameraType is None: pool.apply_async(cvutils.tracking, args = (configFilename, args.process == 'object', str(parentPath.absolute()/vs.getVideoSequenceFilename()), str(parentPath.absolute()/vs.getDatabaseFilename()), str(parentPath.absolute()/vs.cameraView.getHomographyFilename()), str(parentPath.absolute()/vs.cameraView.getMaskFilename()), False, None, None, args.dryRun)) else: pool.apply_async(cvutils.tracking, args = (configFilename, args.process == 'object', str(parentPath.absolute()/vs.getVideoSequenceFilename()), str(parentPath.absolute()/vs.getDatabaseFilename()), str(parentPath.absolute()/vs.cameraView.getHomographyFilename()), str(parentPath.absolute()/vs.cameraView.getMaskFilename()), True, vs.cameraView.cameraType.intrinsicCameraMatrix, vs.cameraView.cameraType.distortionCoefficients, args.dryRun)) else: print('SQLite already exists: {}'.format(parentPath/vs.getDatabaseFilename())) pool.close() pool.join() elif args.process == 'prototype': # motion pattern learning # learn by site by default -> group videos by camera view TODO # by default, load all objects, learn and then assign (BUT not save the assignments) for site in sites: print('Learning motion patterns for site {} ({})'.format(site.idx, site.name)) objects = {} object2VideoSequences = {} for cv in site.cameraViews: for vs in cv.videoSequences: print('Loading '+vs.getDatabaseFilename()) objects[vs.idx] = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), args.trajectoryType, args.nObjects, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject) if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None: objectsWithFeatures = objects[vs.idx] objects[vs.idx] = [f for o in objectsWithFeatures for f in o.getFeatures()] prototypeType = 'feature' else: prototypeType = args.trajectoryType for obj in objects[vs.idx]: object2VideoSequences[obj] = vs lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) similarityFunc = lambda x,y : lcss.computeNormalized(x, y) trainingObjects = [o for tmpobjects in objects.values() for o in tmpobjects] if args.nMPObjects is not None and args.nMPObjects < len(trainingObjects): m = int(np.floor(float(len(trainingObjects))/args.nMPObjects)) trainingObjects = trainingObjects[::m] similarities = -np.ones((len(trainingObjects), len(trainingObjects))) prototypeIndices, labels = processing.learnAssignMotionPatterns(True, True, trainingObjects, similarities, args.minSimilarity, similarityFunc, args.minClusterSize, args.optimizeCentroid, args.randomInitialization, True, []) if args.outputPrototypeDatabaseFilename is None: outputPrototypeDatabaseFilename = args.databaseFilename else: outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1) storage.savePrototypesToSqlite(str(parentPath/site.getPath()/outputPrototypeDatabaseFilename), [moving.Prototype(object2VideoSequences[trainingObjects[i]].getDatabaseFilename(False), trainingObjects[i].getNum(), prototypeType, clusterSizes[i]) for i in prototypeIndices]) elif args.process == 'interaction': # safety analysis TODO make function in safety analysis script if args.predictionMethod == 'cvd': predictionParameters = prediction.CVDirectPredictionParameters() elif args.predictionMethod == 'cve': predictionParameters = prediction.CVExactPredictionParameters() for vs in videoSequences: print('Processing '+vs.getDatabaseFilename()) if args.configFilename is None: params = storage.ProcessParameters(str(parentPath/vs.cameraView.getTrackingConfigurationFilename())) else: params = storage.ProcessParameters(args.configFilename) objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp')) interactions = events.createInteractions(objects) if args.nProcesses == 1: #print(len(interactions), args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones) processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, False, False, None) # params.crossingZones else: #pool = Pool(processes = args.nProcesses) nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses))) jobs = [pool.apply_async(events.computeIndicators, args = (interactions[i*nInteractionPerProcess:(i+1)*nInteractionPerProcess], True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, False, False, None)) for i in range(args.nProcesses)] # params.crossingZones processed = [] for job in jobs: processed += job.get() #pool.close() storage.saveIndicatorsToSqlite(str(parentPath/vs.getDatabaseFilename()), processed) ################################# # Analyze ################################# if args.analyze == 'object': # user speeds, accelerations # aggregation per site if args.eventFilename is None: print('Missing output filename (event-filename). Exiting') sys.exit(0) data = [] # list of observation per site-user with time headers = ['site', 'date', 'time', 'user_type'] aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles) headers.extend(tmpheaders) if args.nProcesses == 1: for vs in videoSequences: data.extend(processing.extractVideoSequenceSpeeds(str(parentPath/vs.getDatabaseFilename()), vs.cameraView.site.name, args.nObjects, vs.startTime, vs.cameraView.cameraType.frameRate, vs.cameraView.cameraType.frameRate*args.minDuration, args.aggMethods, args.aggCentiles)) else: jobs = [pool.apply_async(processing.extractVideoSequenceSpeeds, args = (str(parentPath/vs.getDatabaseFilename()), vs.cameraView.site.name, args.nObjects, vs.startTime, vs.cameraView.cameraType.frameRate, vs.cameraView.cameraType.frameRate*args.minDuration, args.aggMethods, args.aggCentiles)) for vs in videoSequences] for job in jobs: data.extend(job.get()) pool.close() data = pd.DataFrame(data, columns = headers) if args.output == 'figure': for name in headers[4:]: plt.ioff() plt.figure() plt.boxplot([data.loc[data['site']==site.name, name] for site in sites], labels = [site.name for site in sites]) plt.ylabel(name+' Speeds (km/h)') plt.savefig(name.lower()+'-speeds.png', dpi=dpi) plt.close() elif args.output == 'event': data.to_csv(args.eventFilename, index = False) if args.analyze == 'interaction': # redo as for object, export in dataframe all interaction data indicatorIds = [2,5,7,10] #maxIndicatorValue = {2: float('inf'), 5: float('inf'), 7:10., 10:10.} data = [] # list of observation per site-user with time headers = ['site', 'date', 'time', events.Interaction.indicatorNames[10].replace(' ','-')] # user types? aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles) nAggFunctions = len(tmpheaders) indicatorUnits = [events.Interaction.indicatorUnits[10]] # for PET above for i in indicatorIds[:3]: for h in tmpheaders: headers.append(events.Interaction.indicatorNames[i].replace(' ','-')+'-'+h) indicatorUnits.append(events.Interaction.indicatorUnits[i]) for vs in videoSequences: print('Extracting SMoS from '+vs.getDatabaseFilename()) interactions = storage.loadInteractionsFromSqlite(str(parentPath/vs.getDatabaseFilename())) minDuration = vs.cameraView.cameraType.frameRate*args.minDuration conversionFactors = {2: 1., 5: 3.6*vs.cameraView.cameraType.frameRate, 7:1./vs.cameraView.cameraType.frameRate, 10:1./vs.cameraView.cameraType.frameRate} for inter in interactions: if inter.length() > minDuration: d = vs.startTime.date() t = vs.startTime.time() row = [vs.cameraView.site.name, d, utils.framesToTime(inter.getFirstInstant(), vs.cameraView.cameraType.frameRate, t)] pet = inter.getIndicator(events.Interaction.indicatorNames[10]) if pet is None: row.append(None) else: row.append(conversionFactors[10]*pet.getValues()[0]) for i in indicatorIds[:3]: indic = inter.getIndicator(events.Interaction.indicatorNames[i]) if indic is not None: #v = indic.getMostSevereValue()* tmp = list(indic.values.values()) for method,func in aggFunctions.items(): agg = conversionFactors[i]*func(tmp) if method == 'centile': row.extend(agg.tolist()) else: row.append(agg) else: row.extend([None]*nAggFunctions) data.append(row) data = pd.DataFrame(data, columns = headers) if args.output == 'figure': plt.ioff() for i, indic in enumerate(headers[3:]): if 'Time' in indic and args.maxTimeIndicatorValue is not None: tmp = data.loc[data[indic] < args.maxTimeIndicatorValue, ['site', indic]] elif 'Speed' in indic and args.maxSpeedIndicatorValue is not None: tmp = data.loc[data[indic] < args.maxSpeedIndicatorValue, ['site', indic]] else: tmp = data[['site', indic]] plt.figure() tmp.boxplot(indic, 'site') # plt.boxplot(tmp, labels = [session.query(Site).get(siteId).name for siteId in indicators]) plt.ylabel(indic+' ('+indicatorUnits[i]+')') plt.savefig('boxplot-sites-'+indic+'.pdf')#, dpi=150) plt.close() plt.figure() for site in sorted(tmp.site.unique()): x = sorted(tmp.loc[tmp.site == site, indic]) plt.plot(x, np.arange(1,len(x)+1)/len(x), label=site) plt.legend() plt.title('Cumulative Distribution Function by Site') plt.xlabel(indic+' ('+indicatorUnits[i]+')') plt.savefig('cdf-sites-'+indic+'.pdf') plt.close() elif args.output == 'event': data.to_csv(args.eventFilename, index = False) if args.analyze == 'collision-map': predictionParameters = prediction.CVExactPredictionParameters() data = [] for vs in videoSequences: print('Extracting potential collision points from '+vs.getDatabaseFilename()) interactions = storage.loadInteractionsFromSqlite(str(parentPath/vs.getDatabaseFilename())) objects = storage.loadTrajectoriesFromSqlite(str(parentPath/vs.getDatabaseFilename()), 'object') params = storage.ProcessParameters(str(parentPath/vs.cameraView.getTrackingConfigurationFilename())) minDuration = vs.cameraView.cameraType.frameRate*args.minDuration maxTimeIndicatorValue = vs.cameraView.cameraType.frameRate*args.maxTimeIndicatorValue for inter in interactions: if inter.length() > minDuration: ttc = inter.getIndicator(events.Interaction.indicatorNames[7]) if ttc is not None: t = min(ttc.values, key = ttc.values.get) if args.maxTimeIndicatorValue is None or ttc.values[t] < maxTimeIndicatorValue: inter.setRoadUsers(objects) cps, _ = predictionParameters.computeCrossingsCollisionsAtInstant(t, inter.roadUser1, inter.roadUser2, params.collisionDistance, params.predictionTimeHorizon) data.append([vs.cameraView.site.name, cps[0].x, cps[0].y, cps[0].indicator]) data = pd.DataFrame(data, columns = ['site', 'x', 'y', 'ttc']) margin = 0.1 for site in data.site.unique(): s = session.query(Site).filter(Site.name.like('%'+site+'%')).first() img = plt.imread(str(parentPath/s.getMapImageFilename())) tmp = data[data.site == site].copy() tmp.x = tmp.x/s.nUnitsPerPixel tmp.y = tmp.y/s.nUnitsPerPixel h, w, _ = img.shape tmp = tmp[(tmp.x>-margin*w) & (tmp.x < (1+margin)*w) & (tmp.y > -margin*h) & (tmp.y < (1+margin)*h)] plt.figure() plt.imshow(img) plt.hexbin(tmp.x, tmp.y, alpha = 0.5, edgecolors = 'face', mincnt=1, gridsize=50) plt.title('Density of Potential Collision Points at Site '+site) plt.colorbar() plt.axis('equal') plt.savefig('collision-map-'+site+'.pdf') #plt.close() if args.analyze == 'event-speed': # aggregate event data by 15 min interval (args.intervalDuration), count events with thresholds data = pd.read_csv(args.eventFilename, parse_dates = [2]) #data = pd.read_csv('./speeds.csv', converters = {'time': lambda s: datetime.datetime.strptime(s, "%H:%M:%S").time()}, nrows = 5000) # create time for end of each 15 min, then group by, using the agg method for each data column headers = ['site', 'date', 'intervalend15', 'duration', 'count'] aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles) dataColumns = list(data.columns[4:]) print(dataColumns) for h in dataColumns: for h2 in tmpheaders: headers.append(h+'-'+h2) if args.eventThresholds is not None: for h in dataColumns: for t in args.eventThresholds: headers.append('n-{}-{}'.format(h, t)) data['intervalend15'] = data.time.apply(lambda t: (pd.Timestamp(year = t.year, month = t.month, day = t.day,hour = t.hour, minute = (t.minute // args.intervalDuration)*args.intervalDuration)+pd.Timedelta(minutes = 15)).time()) outputData = [] for name, group in data.groupby(['site', 'date', 'intervalend15']): row = [] row.extend(name) groupStartTime = group.time.min() groupEndTime = group.time.max() row.append((groupEndTime.minute+1-groupStartTime.minute) % 60)#(name[2].minute*60+name[2].second-groupStartTime.minute*60+groupStartTime.second) % 3600) row.append(len(group)) for h in dataColumns: for method,func in aggFunctions.items(): aggregated = func(group[h]) if method == 'centile': row.extend(aggregated) else: row.append(aggregated) if args.eventThresholds is not None: for h in dataColumns: for t in args.eventThresholds: row.append((group[h] > t).sum()) outputData.append(row) pd.DataFrame(outputData, columns = headers).to_csv(utils.removeExtension(args.eventFilename)+'-aggregated.csv', index = False) elif args.analyze == 'event-interaction': # aggregate event data by 15 min interval (args.intervalDuration), count events with thresholds data = pd.read_csv(args.eventFilename, parse_dates = [2]) headers = ['site', 'date', 'intervalend15', 'duration', 'count'] aggFunctions, tmpheaders = utils.aggregationMethods(args.aggMethods, args.aggCentiles) nAggFunctions = len(tmpheaders) dataColumns = list(data.columns[3:]) for h in dataColumns: if not 'speed' in h.lower(): # proximity indicators are reversed, taking 85th centile of this column will yield the 15th centile (which we have to take the opposite again) data[h] = -data[h] for h in dataColumns: for h2 in tmpheaders: headers.append(h+'-'+h2) for h,t in zip(dataColumns, args.eventThresholds): # each threshold in this case applies to one indicator headers.append('n-{}-{}'.format(h, t)) data['intervalend15'] = data.time.apply(lambda t: (pd.Timestamp(year = t.year, month = t.month, day = t.day,hour = t.hour, minute = (t.minute // args.intervalDuration)*args.intervalDuration)+pd.Timedelta(minutes = 15)).time()) outputData = [] for name, group in data.groupby(['site', 'date', 'intervalend15']): row = [] row.extend(name) groupStartTime = group.time.min() groupEndTime = group.time.max() row.append((groupEndTime.minute+1-groupStartTime.minute) % 60)#(name[2].minute*60+name[2].second-groupStartTime.minute*60+groupStartTime.second) % 3600) row.append(len(group)) for h in dataColumns: for method,func in aggFunctions.items(): tmp = group.loc[~group[h].isna(), h] if len(tmp)>0: aggregated = func(tmp) # todo invert if the resulting stat is negative if method == 'centile': row.extend(np.abs(aggregated)) else: row.append(np.abs(aggregated)) else: row.extend([None]*nAggFunctions) for h,t in zip(dataColumns, args.eventThresholds): # each threshold in this case applies to one indicator if 'speed' in h.lower(): row.append((group[h] > t).sum()) else: row.append((group[h] > -t).sum()) # take larger than than negative threshold for proximity indicators outputData.append(row) pd.DataFrame(outputData, columns = headers).to_csv(utils.removeExtension(args.eventFilename)+'-aggregated.csv', index = False)