def fudge_data(time_list_2): deviation_pos_list = [] deviation_neg_list = [] variance_list = [] mean_list = [] time_list = [] int_list = [] deviation_amount = [] n = 0 init_data_list = [ 1675, 3223, 2017, 1501, 2067, 2500, 1000, 2972, 1975, 2104, 2100, 2973, 1980 ] deviation = CommonMath.map_deviation(init_data_list) deviation_amount = [0 for i in range(len(init_data_list))] mean_list = init_data_list int_list = range(len(init_data_list)) time_list = time_list_2[:len(init_data_list)] time_list_2 = time_list_2[:len(init_data_list)] variance_list = [0 for i in range(len(init_data_list))] deviation_neg_list = [0 for i in range(len(init_data_list))] deviation_pos_list = [0 for i in range(len(init_data_list))] return deviation_amount, mean_list, int_list, time_list, time_list_2, variance_list, deviation_neg_list, deviation_pos_list
def post_processing(self, axol_task_value): clusters = {'api': [], 'web': []} clusters = CommonMath.derive_clusters(clusters=clusters, map_value='current_usage', axol_task_value=axol_task_value) for cluster in clusters: clusters[cluster] = CommonMath.map_deviation( integer_list=clusters[cluster]) clusters[cluster]['source'] = self.source clusters[cluster]['name'] = cluster self.query([ insert(data_object=clusters, table_space='axol_metrics.clusters') ]) return axol_task_value
def post_processing(self, axol_task_value): clusters = {'api': [], 'web': []} clusters = CommonMath.derive_clusters( clusters=clusters, map_value='current_usage', axol_task_value=axol_task_value ) for cluster in clusters: clusters[cluster] = CommonMath.map_deviation( integer_list=clusters[cluster] ) clusters[cluster]['source'] = self.source clusters[cluster]['name'] = cluster self.query([ insert( data_object=clusters, table_space='axol_metrics.clusters' )] ) return axol_task_value
def fudge_data(time_list_2): deviation_pos_list = [] deviation_neg_list = [] variance_list = [] mean_list = [] time_list = [] int_list = [] deviation_amount = [] n = 0 init_data_list = [ 1675, 3223, 2017, 1501, 2067, 2500, 1000, 2972, 1975, 2104, 2100, 2973, 1980 ] deviation = CommonMath.map_deviation(init_data_list) deviation_amount = [0 for i in range(len(init_data_list))] mean_list = init_data_list int_list = range(len(init_data_list)) time_list = time_list_2[:len(init_data_list)] time_list_2 = time_list_2[:len(init_data_list)] variance_list = [0 for i in range(len(init_data_list))] deviation_neg_list = [0 for i in range(len(init_data_list))] deviation_pos_list = [0 for i in range(len(init_data_list))] return deviation_amount, mean_list, int_list, time_list, time_list_2, variance_list, deviation_neg_list, deviation_pos_list
return response_object def post_processing(self, axol_task_value): clusters = {'api': [], 'web': []} try: clusters = CommonMath.derive_clusters( clusters=clusters, map_value='current_usage', axol_task_value=axol_task_value ) except Exception, e: print 'ERROR POST-PROC: %s' % e try: for cluster in clusters: clusters[cluster] = CommonMath.map_deviation( integer_list=clusters[cluster] ) clusters[cluster]['source'] = self.source clusters[cluster]['name'] = cluster except Exception, e: print 'ERROR POST-PROC 2: %s' % e try: self.query([ insert( data_object=clusters, table_space='axol_metrics.clusters' )] ) except Exception, e: print 'ERROR POST-PROC 3: %s' % e return axol_task_value