def test_calculate_signal_spectrum(self):
signal_1 = [1, 1, 1, 1, 1, 1]
signal_2 = [1, 3, 1, 3, 3, 1]
spectrums = DWS()._calculate_signal_spectrum([signal_1, signal_2])
np.testing.assert_approx_equal(spectrums[0], 0.0)
np.testing.assert_approx_equal(spectrums[1], 1.0)
def test_get_approximations(self):
y = np.array([1, 2, 3, 4, 5, 6, 7, 8])
approximations = DWS(wavelet='db1')._get_approximations(y, 2)
first_level = [2.12132034, 4.94974747, 7.77817459, 10.60660172]
second_level = [5., 13.]
np.testing.assert_almost_equal(approximations[0], first_level)
np.testing.assert_almost_equal(approximations[1], second_level)
def test_detect_base_case(self):
data_points = 1000
x = np.arange(0, data_points)
noise = np.random.normal(size=data_points)
dws = DWS()
trend = dws.estimate_trend(x, noise)
assert trend is None
def test_detect_trend(self):
data_points = 1000
x = np.arange(0, data_points)
y = 1 / 1000 * x**2
noise = np.random.normal(loc=0, scale=200, size=data_points)
signal = y + noise
dws = DWS()
trend = dws.estimate_trend(x, signal)
assert trend is not None
assert trend[0] < trend[-1]
def test_detect_no_trend(self):
data_points = 100
x = np.arange(0, data_points)
y = x / 10
season = np.sin(x + np.pi / 2)
noise = np.random.normal(size=data_points)
signal = y + season + noise
dws = DWS()
trend = dws.estimate_trend(x, signal)
assert trend is not None
assert trend[0] < trend[-1]
# %%
import matplotlib.pyplot as plt
from src.compare_functions import trend_estimation_comparison
from src.data_handler import data_handler
from src.methods.dws import DWS
from src.methods.emd import EmpiricalModeDecomposition
from src.methods.lowess import Lowess
from src.methods.hp_filter import HPfilter
from src.methods.ita import ITA
from src.methods.mk import MannKendall
from src.methods.regression import Regression
from src.methods.splines import Splines
from src.methods.theil import Theil
from src.methods.regression import Regression
if __name__ == '__main__':
methods_detection = [ITA(), MannKendall(), Regression(), Theil()]
methods_estimation = [DWS(), HPfilter(), Splines(), Theil(), Lowess(), EmpiricalModeDecomposition(), Regression()]
x, y, trend, seasonality, noise = data_handler.generate_synthetic_data('data', 'monthly.ini')
plt.plot(x, y)
plt.plot(x, trend)
plt.show()
trend_estimation_comparison(methods_estimation, 'monthly')
file_results.append(distance)
# plt.plot(x, trend, label='True trend', linewidth=3.0, color='k', linestyle=':')
# plt.legend()
# plt.savefig(f'{PLOTS_DIR}/{name}_{timestamp}.png')
# plt.close()
results[name] = file_results
table = pd.DataFrame(results, columns)
table.to_csv(
f'{RESULTS_DIR}/trend_estimation_{file_prefix}_{timestamp}.csv')
return table
if __name__ == '__main__':
# methods_detection = [MannKendall(), ITA(), Theil(), DWS(), EmpiricalModeDecomposition(),
# HPfilter(), Splines()]
#
# trend_detection_comparison(methods_detection, 'func')
methods_estimation = [
Theil(),
DWS(),
EmpiricalModeDecomposition(),
HPfilter(),
Splines()
]
trend_estimation_comparison(methods_estimation, 'func')
from src.utils import generate_timestamp
def show_estimation(methods: List[Method], time_series_x: np.ndarray,
time_series_y: np.ndarray) -> None:
for method in methods:
trend_estimation = method.estimate_trend(time_series_x, time_series_y)
plt.figure(figsize=(12, 8))
plt.plot(time_series_x, time_series_y)
plt.plot(time_series_x, trend_estimation, label='Estimated trend')
plt.title(f'Trend estimation of {method.name}')
plt.xlabel('Days from January 1962 to July 2012')
plt.ylabel('Difference between LOD and 86400 seconds (ms)')
plt.savefig(f'{PLOTS_DIR}/case_study_{generate_timestamp()}.png')
plt.show()
plt.close()
if __name__ == '__main__':
methods_detection = [MannKendall(), ITA(plot=True)]
methods_estimation = [Regression(), Lowess(), Splines(), HPfilter(), DWS()]
data = loadmat(f'{DATA_DIR}/case_study.mat')
y = data['LOD'].squeeze()
x = np.arange(0, y.shape[0])
#print(method_detection.detect_trend(x, y))
show_estimation(methods_estimation, x, y)
f'snr: {noise_title}')
for method in methods_list:
estimation = method.estimate_trend(x, y)
plt.plot(x, estimation, label=method.name)
distance = np.linalg.norm((estimation - trend))
file_results.append(distance)
plt.plot(x, trend, label='True trend', linewidth=3.0, color='k', linestyle=':')
plt.legend()
plt.savefig(f'{PLOTS_DIR}/{name}_{timestamp}.png')
plt.close()
results[name] = file_results
table = pd.DataFrame(results, columns)
table.to_csv(f'{RESULTS_DIR}/trend_estimation_{file_prefix}_{timestamp}.csv')
return table
if __name__ == '__main__':
# methods_detection = [MannKendall(), ITA(), Theil(), DWS(), EmpiricalModeDecomposition(),
# HPfilter(), Splines()]
#
# trend_detection_comparison(methods_detection, 'func')
methods_estimation = [Theil(), DWS(), EmpiricalModeDecomposition(), HPfilter(), Splines()]
trend_estimation_comparison(methods_estimation, 'func')