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')