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: