import sys
from ExtractDataCsv import extract_data_csv
from TableDataGenerator import write_table_data
import copy
timestamp = sys.argv[1]
data_type = sys.argv[2]
test_type = "database_string_10"
deployments = ["classic", "docker", "orchestration"]
save_filename = f"{timestamp}__{test_type}_rps"
average_data = {}
# Get data from csv
data = extract_data_csv(deployments,
timestamp,
test_type,
x_position=0,
y_position=4,
calc_average=False)
data_table_data = extract_data_csv(deployments,
timestamp,
test_type,
x_position=1,
y_position=4,
calc_average=False)
cut_front = 11
cut_back = 4
write_table_data(data_table_data, save_filename, cut_front, cut_back)
# delete entries that are too early:
import matplotlib.pyplot as plt
import sys
from ExtractDataCsv import extract_data_csv
from TableDataGenerator import write_table_data
timestamp = sys.argv[1]
data_type = sys.argv[2]
test_type = "database_int_step350"
deployments = ["classic", "docker", "orchestration"]
save_filename = f"{timestamp}__{test_type}_rps"
average_data = {}
# Get data from csv
data = extract_data_csv(deployments,
timestamp,
test_type,
x_position=1,
y_position=4)
cut_front = 0
cut_back = 0
write_table_data(data, save_filename, cut_front, cut_back)
# delete entries that are too early:
for deployment, deployment_data in data.items():
for i in range(0, cut_front):
del (deployment_data["x"][0])
del (deployment_data["y"][0])
# delete entries that are too late: