def execute_scheme(self):
#model = TimeSeriesClassificationNeuralNet(self.settings)
#model = TimeSeriesPredictionNeuralNet(self.settings)
connection = SQLAConnection()
query_generator = QueryGenerator(
self.settings.sensors,
self.settings.start_date,
self.settings.end_date
)
report_generator = ReportGenerator(self.settings)
link_generator = LinkGenerator(self.settings)
#data = RegularityData(link_generator,connection)
data = AnomalyData(link_generator,connection)
#data.generate_metadata_report(ReportGenerator(self.settings))
#data.make_df()
#data.save_df(name=self.settings.dataset_name)
#data.find_discontinuities()
#data.split_at_discontinuities()
#data.plot_data()
#data.add_temp()
#data.save_dfs(name=self.settings.dataset_name)
#data.load_dfs(date='2020-11-01')
#data.load_extend_dfs(date='2020-11-13')
startdate = datetime.strptime('2020-11-01',config.dateformat)
data.load_dfs(date=datetime.strftime(startdate,config.dateformat))
dates_ahead = 4
mode = 'while'
if mode == 'for':
for i in range(dates_ahead):
data.load_extend_dfs(date=datetime.strftime(startdate+timedelta(days=i), config.dateformat))
elif mode == 'while':
tdate = startdate
while tdate.date() != date.today():
try:
data.load_extend_dfs(date=datetime.strftime(tdate, config.dateformat))
except FileNotFoundError:
pass
tdate = tdate+timedelta(days=1)
data.purge_empty_dfs()
data.preprocess()
data.merge_dfs()
#data.plot_data()
#data.find_correlation()
anomaly_settings = AnomalySettings()
kmeans_settings = KMeansSettings()
start_hour = '00:00:00'
end_hour = '23:59:59'
data.filter_hours(start_hour,end_hour)
data.purge_empty_time_filtered_dfs()
#data.plot_filtered_hours(plot_objects=False)
data.set_object_settings(anomaly_settings)
anomaly_name = f"{startdate}_{mode}_{start_hour}_{end_hour}_{anomaly_settings.anomaly_sensor}_anomaly"
print(os.listdir(config.anomaly_path))
print(anomaly_name)
if f"{anomaly_name}.json" in os.listdir(config.anomaly_path):
data.load_objects(name=f"{anomaly_name}.json")
print(f"{anomaly_name} loaded")
else:
for feature in anomaly_settings.anomaly_sensor:
#data.locate_anomalies_filtered_dfs(feature)
data.locate_objects_dfs(feature)
#data.save_plots(feature)
#data.plot_filtered_hours(foi = feature)
data.save_objects(name=anomaly_name)
kmeans = KMeansClustering(data.objects,kmeans_settings)
kmeans.fit_Kmeans()
#sensor_prediction = SensorPrediction(data.anomalies,self.settings)
data.plot_filtered_hours(foi = 'acc1_ch_x')#,project_anomalies = 'acc1_ch_z')
pca = PCAAnomalies(data.objects,self.settings)
pca.fit_PCA()
pca.save_pca(f'{anomaly_name}_pca')
pca.set_labels(kmeans.send_labels())
#pca.get_cov()
#anomaly_key, df_number = pca.get_argmax(col='sigma')
#data.plot_regularities()
pca.plot_components_labels(n_categories = kmeans_settings.n_clusters)
pca.scree_plot()
pca.plot_hist_pca()
#pca.plot_components_3d()
pca.plot_components(features = ['Duration','frequency'])
import EpipolarGeometry import ImageFeaturesHomography import KMeansClustering # start task 1 task1 = ImageFeaturesHomography.ImageFeaturesHomography() task1.start() # start task 2 task2 = EpipolarGeometry.EpipolarGeometry() task2.start() # start task 3 task3 = KMeansClustering.KMeansClustering() task3.start()
