noise_generator = ng.Gaussian() trend_generator = tg.TrendGenerator() season = season_generator.gen_season() length = len(season[0]) noise = [ noise_generator.gen(0, sigma, length) for sigma in np.linspace(0.5, 2, 100) ] trend = trend_generator.gen(0, 0, length) # assembler = assem.AssemblerWithAdditiveAnomalyInjector_v1(season,noise,trend,'noise',10e-7,0.2,a_type='spike') assembler = assem.AssemblerWithAdditiveAnomalyInjector_v1(season, noise, trend, 'noise', 10e-7, 0.2, a_type='beat') # assembler = assem.AssemblerWithAdditiveAnomalyInjector_v1(season,noise,trend,'noise',10e-7,0.2,a_type='type1') # assembler = assem.AssemblerWithAdditiveAnomalyInjector_v1(season,noise,trend,'noise',10e-7,0.2,a_type='type2') assembler.assemble() # assembler.save(path='output/TSABen/group_2/spike') # assembler.save(path='output/TSABen/group_1/beat_noise') # assembler.save(path='output/TSABen/group_1/type1_noise') # assembler.save(path='output/TSABen/group_1/type2_noise') #==================================================================================== # Group2 # spike, beat, type1, type2
import matplotlib.pyplot as plt import generator.noise_generator as ng import generator.season_generator as sg import matplotlib.pyplot as plt # season_generator = sg.SeasonGeneratorWithShapeDeformation(10,10,200,drift_a=0,drift_f=0,forking_depth=7) season_generator = sg.NormalSeasonGenerator(10, 10, 200, drift_a=0, drift_f=0, forking_depth=7) noise_generator = ng.Gaussian() # noise_generator = ng.GaussianWithChangePoints() trend_generator = tg.TrendGenerator() season = [season_generator.gen_season() for x in range(1)] length = len(season[0][0]) noise = noise_generator.gen(0, 0.5, length) trend = trend_generator.gen(15, 0, length) # assembler = assem.AbstractAssembler(season,noise,trend,'season') # assembler = assem.AbstractAssembler(season,noise,trend,'season') assembler = assem.AssemblerWithAdditiveAnomalyInjector_v1(season, noise, trend, 'season', q=10e-7, a_type='type2') assembler.assemble() assembler.save(path='output/TSACorr')