visualization_utils/plotting-ying.py [15:339]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def get_column_name_number(dir_addr, mode): column_name_number_dic = {} try: if mode == "all": file_name = "result_summary/FARSI_simple_run_0_1_all_reults.csv" else: file_name = "result_summary/FARSI_simple_run_0_1.csv" file_full_addr = os.path.join(dir_addr, file_name) with open(file_full_addr) as f: resultReader = csv.reader(f, delimiter=',', quotechar='|') for row in resultReader: for idx, el_name in enumerate(row): column_name_number_dic[el_name] = idx break return column_name_number_dic except Exception as e: raise e # # the function to get the column information of the given category def columnNum(dirName, fileName, cate, result): if result == "all": with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') for i, row in enumerate(resultReader): if i == 0: for j in range(0, len(row)): if row[j] == cate: return j raise Exception("No such category in the list! Check the name: " + cate) break elif result == "simple": with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') for i, row in enumerate(resultReader): if i == 0: for j in range(0, len(row)): if row[j] == cate: return j raise Exception("No such category in the list! Check the name: " + cate) break else: raise Exception("No such result file! Check the result type! It should be either \"all\" or \"simple\"") # the function to plot the frequency of all comm_comp in the pie chart def plotCommCompAll(dirName, fileName, all_res_column_name_number): colNum = all_res_column_name_number["comm_comp"] truNum = all_res_column_name_number["move validity"] with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') commNum = 0 compNum = 0 for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: if row[colNum] == "comm": commNum += 1 elif row[colNum] == "comp": compNum += 1 else: raise Exception("comm_comp is not giving comm or comp! The new type: " + row[colNum]) plt.figure() plt.pie([commNum, compNum], labels = ["comm", "comp"]) plt.title("comm_comp: Frequency") plt.savefig(dirName + fileName + "/comm-compFreq-" + fileName + ".png") # plt.show() plt.close('all') # the function to plot the frequency of all high level optimizations in the pie chart def plothighLevelOptAll(dirName, fileName, all_res_column_name_number): colNum = all_res_column_name_number["high level optimization name"] truNum = all_res_column_name_number["move validity"] with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') topoNum = 0 tunNum = 0 mapNum = 0 idenOptNum = 0 for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: if row[colNum] == "topology": topoNum += 1 elif row[colNum] == "customization": tunNum += 1 elif row[colNum] == "mapping": mapNum += 1 elif row[colNum] == "identity": idenOptNum += 1 else: raise Exception("high level optimization name is not giving topology or customization or mapping or identity! The new type: " + row[colNum]) plt.figure() plt.pie([topoNum, tunNum, mapNum, idenOptNum], labels = ["topology", "customization", "mapping", "identity"]) plt.title("High Level Optimization: Frequency") plt.savefig(dirName + fileName + "/highLevelOpt-" + fileName + ".png") # plt.show() plt.close('all') # the function to plot the frequency of all architectural variables to improve in the pie chart def plotArchVarImpAll(dirName, fileName, colNum, trueNum): with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') parazNum = 0 custNum = 0 localNum = 0 idenImpNum = 0 for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: if row[colNum] == "parallelization": parazNum += 1 elif row[colNum] == "customization": custNum += 1 elif row[colNum] == "locality": localNum += 1 elif row[colNum] == "identity": idenImpNum += 1 else: raise Exception("architectural principle is not parallelization or customization or locality or identity! The new type: " + row[colNum]) plt.figure() plt.pie([parazNum, custNum, localNum, idenImpNum], labels = ["parallelization", "customization", "locality", "identity"]) plt.title("Architectural Principle: Frequency") plt.savefig(dirName + fileName + "/archVarImp-" + fileName + ".png") # plt.show() plt.close('all') # the function to plot simulation time vs. system block count def plotSimTimeVSblk(dirName, fileName, blkColNum, simColNum, trueNum): with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') sysBlkCount = [] simTime = [] for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: sysBlkCount.append(int(row[blkColNum])) simTime.append(float(row[simColNum])) plt.figure() plt.plot(sysBlkCount, simTime) plt.xlabel("System Block Count") plt.ylabel("Simulation Time") plt.title("Simulation Time vs. Sytem Block Count") plt.savefig(dirName + fileName + "/simTimeVSblk-" + fileName + ".png") # plt.show() plt.close('all') # the function to plot move generation time vs. system block count def plotMoveGenTimeVSblk(dirName, fileName, blkColNum, movColNum, trueNum): with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') sysBlkCount = [] moveGenTime = [] for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: sysBlkCount.append(int(row[blkColNum])) moveGenTime.append(float(row[movColNum])) plt.figure() plt.plot(sysBlkCount, moveGenTime) plt.xlabel("System Block Count") plt.ylabel("Move Generation Time") plt.title("Move Generation Time vs. System Block Count") plt.savefig(dirName + fileName + "/moveGenTimeVSblk-" + fileName + ".png") # plt.show() plt.close('all') def get_experiments_workload(all_res_column_name): latency_budget = all_res_column_name_number["latency budget"][:-1] workload_latency = latency_budget.split(";") workloads = [] for workload_latency in workload_latency: workloads.append(workload_latency.split("=")[0]) return workloads def get_experiments_name(file_full_addr, all_res_column_name_number): with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') row1 = next(resultReader) row2 = next(resultReader) latency_budget = row2[all_res_column_name_number["latency_budget"]] power_budget = row2[all_res_column_name_number["power_budget"]] area_budget = row2[all_res_column_name_number["area_budget"]] try: transformation_selection_mode = row2[all_res_column_name_number["transformation_selection_mode"]] except: transformation_selection_mode = "" workload_latency = latency_budget[:-1].split(';') latency_budget_refined ="" for workload_latency in workload_latency: latency_budget_refined +="_" + (workload_latency.split("=")[0][0]+workload_latency.split("=")[1]) return latency_budget_refined[1:]+"_" + power_budget + "_" + area_budget+"_"+transformation_selection_mode def get_all_col_values_of_a_file(file_full_addr, all_res_column_name_number, column_name): column_number = all_res_column_name_number[column_name] all_values = [] with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') experiment_name = get_experiments_name(file_full_addr, all_res_column_name_number) for i, row in enumerate(resultReader): if i > 1: if not row[column_number] == '': value =row[column_number] values = value.split(";") # if mutiple values for val in values: if "=" in val: val_splitted = val.split("=") all_values.append(val_splitted[0]) else: all_values.append(val) return all_values def get_all_col_values_of_a_folders(input_dir_names, input_all_res_column_name_number, column_name): all_values = [] for dir_name in input_dir_names: file_full_addr = os.path.join(dir_name, "result_summary/FARSI_simple_run_0_1_all_reults.csv") all_values.extend(get_all_col_values_of_a_file(file_full_addr, input_all_res_column_name_number, column_name)) # get rid of duplicates all_values_rid_of_duplicates = list(set(all_values)) return all_values_rid_of_duplicates def extract_latency_values(values_): print("") def plot_codesign_rate_efficacy_cross_workloads_updated(input_dir_names, res_column_name_number): #itrColNum = all_res_column_name_number["iteration cnt"] #distColNum = all_res_column_name_number["dist_to_goal_non_cost"] trueNum = all_res_column_name_number["move validity"] move_name_number = all_res_column_name_number["move name"] # experiment_names file_full_addr_list = [] for dir_name in input_dir_names: file_full_addr = os.path.join(dir_name, "result_summary/FARSI_simple_run_0_1_all_reults.csv") file_full_addr_list.append(file_full_addr) axis_font = {'fontname': 'Arial', 'size': '4'} x_column_name = "iteration cnt" #y_column_name_list = ["high level optimization name", "exact optimization name", "architectural principle", "comm_comp"] y_column_name_list = ["exact optimization name", "architectural principle", "comm_comp", "workload"] #y_column_name_list = ["high level optimization name", "exact optimization name", "architectural principle", "comm_comp"] column_co_design_cnt = {} column_non_co_design_cnt = {} column_co_design_rate = {} column_non_co_design_rate = {} column_co_design_efficacy_avg = {} column_non_co_design_efficacy_rate = {} column_non_co_design_efficacy = {} column_co_design_dist= {} column_co_design_dist_avg= {} column_co_design_improvement = {} experiment_name_list = [] last_col_val = "" for file_full_addr in file_full_addr_list: experiment_name = get_experiments_name(file_full_addr, res_column_name_number) experiment_name_list.append(experiment_name) column_co_design_dist_avg[experiment_name] = {} column_co_design_efficacy_avg[experiment_name] = {} column_co_design_cnt = {} for y_column_name in y_column_name_list: y_column_number = res_column_name_number[y_column_name] x_column_number = res_column_name_number[x_column_name] dis_to_goal_column_number = res_column_name_number["dist_to_goal_non_cost"] ref_des_dis_to_goal_column_number = res_column_name_number["ref_des_dist_to_goal_non_cost"] column_co_design_cnt[y_column_name] = [] column_non_co_design_cnt[y_column_name] = [] column_non_co_design_efficacy[y_column_name] = [] column_co_design_dist[y_column_name] = [] column_co_design_improvement[y_column_name] = [] column_co_design_rate[y_column_name] = [] all_values = get_all_col_values_of_a_folders(input_dir_names, all_res_column_name_number, y_column_name) last_row_change = "" with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') rows = list(resultReader) for i, row in enumerate(rows): - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - visualization_utils/plotting.py [17:341]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def get_column_name_number(dir_addr, mode): column_name_number_dic = {} try: if mode == "all": file_name = "result_summary/FARSI_simple_run_0_1_all_reults.csv" else: file_name = "result_summary/FARSI_simple_run_0_1.csv" file_full_addr = os.path.join(dir_addr, file_name) with open(file_full_addr) as f: resultReader = csv.reader(f, delimiter=',', quotechar='|') for row in resultReader: for idx, el_name in enumerate(row): column_name_number_dic[el_name] = idx break return column_name_number_dic except Exception as e: raise e # # the function to get the column information of the given category def columnNum(dirName, fileName, cate, result): if result == "all": with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') for i, row in enumerate(resultReader): if i == 0: for j in range(0, len(row)): if row[j] == cate: return j raise Exception("No such category in the list! Check the name: " + cate) break elif result == "simple": with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') for i, row in enumerate(resultReader): if i == 0: for j in range(0, len(row)): if row[j] == cate: return j raise Exception("No such category in the list! Check the name: " + cate) break else: raise Exception("No such result file! Check the result type! It should be either \"all\" or \"simple\"") # the function to plot the frequency of all comm_comp in the pie chart def plotCommCompAll(dirName, fileName, all_res_column_name_number): colNum = all_res_column_name_number["comm_comp"] truNum = all_res_column_name_number["move validity"] with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') commNum = 0 compNum = 0 for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: if row[colNum] == "comm": commNum += 1 elif row[colNum] == "comp": compNum += 1 else: raise Exception("comm_comp is not giving comm or comp! The new type: " + row[colNum]) plt.figure() plt.pie([commNum, compNum], labels = ["comm", "comp"]) plt.title("comm_comp: Frequency") plt.savefig(dirName + fileName + "/comm-compFreq-" + fileName + ".png") # plt.show() plt.close('all') # the function to plot the frequency of all high level optimizations in the pie chart def plothighLevelOptAll(dirName, fileName, all_res_column_name_number): colNum = all_res_column_name_number["high level optimization name"] truNum = all_res_column_name_number["move validity"] with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') topoNum = 0 tunNum = 0 mapNum = 0 idenOptNum = 0 for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: if row[colNum] == "topology": topoNum += 1 elif row[colNum] == "customization": tunNum += 1 elif row[colNum] == "mapping": mapNum += 1 elif row[colNum] == "identity": idenOptNum += 1 else: raise Exception("high level optimization name is not giving topology or customization or mapping or identity! The new type: " + row[colNum]) plt.figure() plt.pie([topoNum, tunNum, mapNum, idenOptNum], labels = ["topology", "customization", "mapping", "identity"]) plt.title("High Level Optimization: Frequency") plt.savefig(dirName + fileName + "/highLevelOpt-" + fileName + ".png") # plt.show() plt.close('all') # the function to plot the frequency of all architectural variables to improve in the pie chart def plotArchVarImpAll(dirName, fileName, colNum, trueNum): with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') parazNum = 0 custNum = 0 localNum = 0 idenImpNum = 0 for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: if row[colNum] == "parallelization": parazNum += 1 elif row[colNum] == "customization": custNum += 1 elif row[colNum] == "locality": localNum += 1 elif row[colNum] == "identity": idenImpNum += 1 else: raise Exception("architectural principle is not parallelization or customization or locality or identity! The new type: " + row[colNum]) plt.figure() plt.pie([parazNum, custNum, localNum, idenImpNum], labels = ["parallelization", "customization", "locality", "identity"]) plt.title("Architectural Principle: Frequency") plt.savefig(dirName + fileName + "/archVarImp-" + fileName + ".png") # plt.show() plt.close('all') # the function to plot simulation time vs. system block count def plotSimTimeVSblk(dirName, fileName, blkColNum, simColNum, trueNum): with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') sysBlkCount = [] simTime = [] for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: sysBlkCount.append(int(row[blkColNum])) simTime.append(float(row[simColNum])) plt.figure() plt.plot(sysBlkCount, simTime) plt.xlabel("System Block Count") plt.ylabel("Simulation Time") plt.title("Simulation Time vs. Sytem Block Count") plt.savefig(dirName + fileName + "/simTimeVSblk-" + fileName + ".png") # plt.show() plt.close('all') # the function to plot move generation time vs. system block count def plotMoveGenTimeVSblk(dirName, fileName, blkColNum, movColNum, trueNum): with open(dirName + fileName + "/result_summary/FARSI_simple_run_0_1_all_reults.csv", newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') sysBlkCount = [] moveGenTime = [] for i, row in enumerate(resultReader): if row[trueNum] != "True": continue if i > 1: sysBlkCount.append(int(row[blkColNum])) moveGenTime.append(float(row[movColNum])) plt.figure() plt.plot(sysBlkCount, moveGenTime) plt.xlabel("System Block Count") plt.ylabel("Move Generation Time") plt.title("Move Generation Time vs. System Block Count") plt.savefig(dirName + fileName + "/moveGenTimeVSblk-" + fileName + ".png") # plt.show() plt.close('all') def get_experiments_workload(all_res_column_name): latency_budget = all_res_column_name_number["latency budget"][:-1] workload_latency = latency_budget.split(";") workloads = [] for workload_latency in workload_latency: workloads.append(workload_latency.split("=")[0]) return workloads def get_experiments_name(file_full_addr, all_res_column_name_number): with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') row1 = next(resultReader) row2 = next(resultReader) latency_budget = row2[all_res_column_name_number["latency_budget"]] power_budget = row2[all_res_column_name_number["power_budget"]] area_budget = row2[all_res_column_name_number["area_budget"]] try: transformation_selection_mode = row2[all_res_column_name_number["transformation_selection_mode"]] except: transformation_selection_mode = "" workload_latency = latency_budget[:-1].split(';') latency_budget_refined ="" for workload_latency in workload_latency: latency_budget_refined +="_" + (workload_latency.split("=")[0][0]+workload_latency.split("=")[1]) return latency_budget_refined[1:]+"_" + power_budget + "_" + area_budget+"_"+transformation_selection_mode def get_all_col_values_of_a_file(file_full_addr, all_res_column_name_number, column_name): column_number = all_res_column_name_number[column_name] all_values = [] with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') experiment_name = get_experiments_name(file_full_addr, all_res_column_name_number) for i, row in enumerate(resultReader): if i > 1: if not row[column_number] == '': value =row[column_number] values = value.split(";") # if mutiple values for val in values: if "=" in val: val_splitted = val.split("=") all_values.append(val_splitted[0]) else: all_values.append(val) return all_values def get_all_col_values_of_a_folders(input_dir_names, input_all_res_column_name_number, column_name): all_values = [] for dir_name in input_dir_names: file_full_addr = os.path.join(dir_name, "result_summary/FARSI_simple_run_0_1_all_reults.csv") all_values.extend(get_all_col_values_of_a_file(file_full_addr, input_all_res_column_name_number, column_name)) # get rid of duplicates all_values_rid_of_duplicates = list(set(all_values)) return all_values_rid_of_duplicates def extract_latency_values(values_): print("") def plot_codesign_rate_efficacy_cross_workloads_updated(input_dir_names, res_column_name_number): #itrColNum = all_res_column_name_number["iteration cnt"] #distColNum = all_res_column_name_number["dist_to_goal_non_cost"] trueNum = all_res_column_name_number["move validity"] move_name_number = all_res_column_name_number["move name"] # experiment_names file_full_addr_list = [] for dir_name in input_dir_names: file_full_addr = os.path.join(dir_name, "result_summary/FARSI_simple_run_0_1_all_reults.csv") file_full_addr_list.append(file_full_addr) axis_font = {'fontname': 'Arial', 'size': '4'} x_column_name = "iteration cnt" #y_column_name_list = ["high level optimization name", "exact optimization name", "architectural principle", "comm_comp"] y_column_name_list = ["exact optimization name", "architectural principle", "comm_comp", "workload"] #y_column_name_list = ["high level optimization name", "exact optimization name", "architectural principle", "comm_comp"] column_co_design_cnt = {} column_non_co_design_cnt = {} column_co_design_rate = {} column_non_co_design_rate = {} column_co_design_efficacy_avg = {} column_non_co_design_efficacy_rate = {} column_non_co_design_efficacy = {} column_co_design_dist= {} column_co_design_dist_avg= {} column_co_design_improvement = {} experiment_name_list = [] last_col_val = "" for file_full_addr in file_full_addr_list: experiment_name = get_experiments_name(file_full_addr, res_column_name_number) experiment_name_list.append(experiment_name) column_co_design_dist_avg[experiment_name] = {} column_co_design_efficacy_avg[experiment_name] = {} column_co_design_cnt = {} for y_column_name in y_column_name_list: y_column_number = res_column_name_number[y_column_name] x_column_number = res_column_name_number[x_column_name] dis_to_goal_column_number = res_column_name_number["dist_to_goal_non_cost"] ref_des_dis_to_goal_column_number = res_column_name_number["ref_des_dist_to_goal_non_cost"] column_co_design_cnt[y_column_name] = [] column_non_co_design_cnt[y_column_name] = [] column_non_co_design_efficacy[y_column_name] = [] column_co_design_dist[y_column_name] = [] column_co_design_improvement[y_column_name] = [] column_co_design_rate[y_column_name] = [] all_values = get_all_col_values_of_a_folders(input_dir_names, all_res_column_name_number, y_column_name) last_row_change = "" with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') rows = list(resultReader) for i, row in enumerate(rows): - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -