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
