visualization_utils/plotting-ying.py [590:775]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - plt.close('all') def plot_codesign_progression_per_workloads(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"] # experiment_names 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) experiment_name = get_experiments_name(file_full_addr, res_column_name_number) experiment_names.append(experiment_name) axis_font = {'size': '20'} x_column_name = "iteration cnt" y_column_name_list = ["high level optimization name", "exact optimization name", "architectural principle", "comm_comp"] experiment_column_value = {} for file_full_addr in file_full_addr_list: experiment_name = get_experiments_name(file_full_addr, res_column_name_number) 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] experiment_column_value[experiment_name] = [] all_values = get_all_col_values_of_a_folders(input_dir_names, all_res_column_name_number, y_column_name) all_values_encoding = {} for idx, val in enumerate(all_values): all_values_encoding[val] = idx with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') rows = list(resultReader) for i, row in enumerate(rows): #if row[trueNum] != "True": # continue if i >= 1: if row[y_column_number] not in all_values: continue col_value = row[y_column_number] col_values = col_value.split(";") for idx, col_val in enumerate(col_values): last_row = rows[i-1] delta_x_column = (float(row[x_column_number]) - float(last_row[x_column_number]))/len(col_values) value_to_add = (float(last_row[x_column_number])+ idx*delta_x_column, col_val) experiment_column_value[experiment_name].append(value_to_add) # prepare for plotting and plot axis_font = {'size': '20'} fontSize = 20 fig = plt.figure(figsize=(12, 8)) plt.rc('font', **axis_font) ax = fig.add_subplot(111) x_values = [el[0] for el in experiment_column_value[experiment_name]] #y_values = [all_values_encoding[el[1]] for el in experiment_column_value[experiment_name]] y_values = [el[1] for el in experiment_column_value[experiment_name]] #ax.set_title("experiment vs system implicaction") ax.tick_params(axis='both', which='major', labelsize=fontSize, rotation=60) ax.set_xlabel(x_column_name, fontsize=20) ax.set_ylabel(y_column_name, fontsize=20) ax.plot(x_values, y_values, label=y_column_name, linewidth=2) ax.legend(bbox_to_anchor=(1, 1), loc='upper left', fontsize=fontSize) # dump in the top folder output_base_dir = '/'.join(input_dir_names[0].split("/")[:-2]) output_dir = os.path.join(output_base_dir, "single_workload/progression") if not os.path.exists(output_dir): os.makedirs(output_dir) plt.tight_layout() fig.savefig(os.path.join(output_dir,experiment_name+"_progression_"+'_'.join(y_column_name_list)+".png")) # plt.show() plt.close('all') fig = plt.figure(figsize=(12, 8)) plt.rc('font', **axis_font) ax = fig.add_subplot(111) x_values = [el[0] for el in experiment_column_value[experiment_name]] # y_values = [all_values_encoding[el[1]] for el in experiment_column_value[experiment_name]] y_values = [el[1] for el in experiment_column_value[experiment_name]] # ax.set_title("experiment vs system implicaction") ax.tick_params(axis='both', which='major', labelsize=fontSize, rotation=60) ax.set_xlabel(x_column_name, fontsize=20) ax.set_ylabel(y_column_name, fontsize=20) ax.plot(x_values, y_values, label=y_column_name, linewidth=2) ax.legend(bbox_to_anchor=(1, 1), loc='upper left', fontsize=fontSize) # dump in the top folder output_base_dir = '/'.join(input_dir_names[0].split("/")[:-2]) output_dir = os.path.join(output_base_dir, "single_workload/progression") if not os.path.exists(output_dir): os.makedirs(output_dir) plt.tight_layout() fig.savefig(os.path.join(output_dir, experiment_name + "_progression_" + y_column_name + ".png")) # plt.show() plt.close('all') def plot_3d(input_dir_names, res_column_name_number): # experiment_names 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.csv") file_full_addr_list.append(file_full_addr) experiment_name = get_experiments_name(file_full_addr, res_column_name_number) experiment_names.append(experiment_name) axis_font = {'size': '10'} fontSize = 10 column_value = {} # initialize the dictionary column_name_list = ["budget_scaling_power", "budget_scaling_area","budget_scaling_latency"] under_study_vars =["iteration cnt", "local_bus_avg_theoretical_bandwidth", "local_bus_max_actual_bandwidth", "local_bus_avg_actual_bandwidth", "system_bus_avg_theoretical_bandwidth", "system_bus_max_actual_bandwidth", "system_bus_avg_actual_bandwidth", "global_total_traffic", "local_total_traffic", "global_memory_total_area", "local_memory_total_area", "ips_total_area", "gpps_total_area","ip_cnt", "max_accel_parallelism", "avg_accel_parallelism", "gpp_cnt", "max_gpp_parallelism", "avg_gpp_parallelism"] # get all the data for file_full_addr in file_full_addr_list: with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') experiment_name = get_experiments_name( file_full_addr, res_column_name_number) for i, row in enumerate(resultReader): #if row[trueNum] != "True": # continue if i >= 1: for column_name in column_name_list + under_study_vars: if column_name not in column_value.keys() : column_value[column_name] = [] column_number = res_column_name_number[column_name] col_value = row[column_number] col_values = col_value.split(";") if "=" in col_values[0]: column_value[column_name].append(float((col_values[0]).split("=")[1])) else: column_value[column_name].append(float(col_values[0])) for idx,under_study_var in enumerate(under_study_vars): fig_budget_blkcnt = plt.figure(figsize=(12, 12)) plt.rc('font', **axis_font) ax_blkcnt = fig_budget_blkcnt.add_subplot(projection='3d') img = ax_blkcnt.scatter3D(column_value["budget_scaling_power"], column_value["budget_scaling_area"], column_value["budget_scaling_latency"], c=column_value[under_study_var], cmap="bwr", s=80, label="System Block Count") for idx,_ in enumerate(column_value[under_study_var]): coordinate = column_value[under_study_var][idx] coord_in_scientific_notatio = "{:.2e}".format(coordinate) ax_blkcnt.text(column_value["budget_scaling_power"][idx], column_value["budget_scaling_area"][idx], column_value["budget_scaling_latency"][idx], '%s' % coord_in_scientific_notatio, size=fontSize) ax_blkcnt.set_xlabel("Power Budget", fontsize=fontSize) ax_blkcnt.set_ylabel("Area Budget", fontsize=fontSize) ax_blkcnt.set_zlabel("Latency Budget", fontsize=fontSize) ax_blkcnt.legend() cbar = fig_budget_blkcnt.colorbar(img, aspect=40) cbar.set_label("System Block Count", rotation=270) #plt.title("{Power Budget, Area Budget, Latency Budget} VS System Block Count: " + subDirName) plt.tight_layout() output_base_dir = '/'.join(input_dir_names[0].split("/")[:-2]) output_dir = os.path.join(output_base_dir, "3D/case_studies") if not os.path.exists(output_dir): os.makedirs(output_dir) plt.savefig(os.path.join(output_dir, under_study_var+ ".png")) # plt.show() plt.close('all') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - visualization_utils/plotting.py [620:965]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - plt.close('all') """ def plot_codesign_rate_efficacy_per_workloads(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_rate = {} column_non_co_design_efficacy_rate = {} column_non_co_design_efficacy = {} column_co_design_efficacy= {} last_col_val = "" for file_full_addr in file_full_addr_list: experiment_name = get_experiments_name(file_full_addr, res_column_name_number) 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_efficacy[y_column_name] = [] all_values = get_all_col_values_of_a_folders(input_dir_names, all_res_column_name_number, y_column_name) with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') rows = list(resultReader) for i, row in enumerate(rows): if i >= 1: last_row = rows[i - 1] if row[y_column_number] not in all_values or row[trueNum] == "False" or row[move_name_number]=="identity": continue col_value = row[y_column_number] col_values = col_value.split(";") for idx, col_val in enumerate(col_values): delta_x_column = (float(row[x_column_number]) - float(last_row[x_column_number]))/len(col_values) value_to_add_1 = (float(last_row[x_column_number]) + idx * delta_x_column, 1) value_to_add_0 = (float(last_row[x_column_number]) + idx * delta_x_column, 0) # only for improvement if float(row[ref_des_dis_to_goal_column_number]) - float(row[dis_to_goal_column_number]) < 0: continue if not col_val == last_col_val: column_co_design_cnt[y_column_name].append(value_to_add_1) column_non_co_design_cnt[y_column_name].append(value_to_add_0) column_co_design_efficacy[y_column_name].append((float(row[ref_des_dis_to_goal_column_number]) - float(row[dis_to_goal_column_number]))/float(row[ref_des_dis_to_goal_column_number])) column_non_co_design_efficacy[y_column_name].append(0) else: column_co_design_cnt[y_column_name].append(value_to_add_0) column_non_co_design_cnt[y_column_name].append(value_to_add_1) column_co_design_efficacy[y_column_name].append(0) column_non_co_design_efficacy[y_column_name].append((float(row[ref_des_dis_to_goal_column_number]) - float(row[dis_to_goal_column_number]))/float(row[ref_des_dis_to_goal_column_number])) last_col_val = col_val # co_des cnt x_values_co_design_cnt = [el[0] for el in column_co_design_cnt[y_column_name]] y_values_co_design_cnt = [el[1] for el in column_co_design_cnt[y_column_name]] y_values_co_design_cnt_total =sum(y_values_co_design_cnt) total_iter = x_values_co_design_cnt[-1] # non co_des cnt x_values_non_co_design_cnt = [el[0] for el in column_non_co_design_cnt[y_column_name]] y_values_non_co_design_cnt = [el[1] for el in column_non_co_design_cnt[y_column_name]] y_values_non_co_design_cnt_total =sum(y_values_non_co_design_cnt) column_co_design_rate[y_column_name] = y_values_co_design_cnt_total/total_iter column_non_co_design_rate[y_column_name] = y_values_non_co_design_cnt_total/total_iter # co_des efficacy y_values_co_design_efficacy = column_co_design_efficacy[y_column_name] y_values_co_design_efficacy_total =sum(y_values_co_design_efficacy) # non co_des efficacy y_values_non_co_design_efficacy = column_non_co_design_efficacy[y_column_name] y_values_non_co_design_efficacy_total =sum(y_values_non_co_design_efficacy) column_co_design_efficacy_rate[y_column_name] = y_values_co_design_efficacy_total/(y_values_non_co_design_efficacy_total + y_values_co_design_efficacy_total) column_non_co_design_efficacy_rate[y_column_name] = y_values_non_co_design_efficacy_total/(y_values_non_co_design_efficacy_total + y_values_co_design_efficacy_total) result = {"rate":{}, "efficacy":{}} rate_column_co_design = {} result["rate"] = {"co_design":column_co_design_rate, "non_co_design": column_non_co_design_rate} result["efficacy_rate"] = {"co_design":column_co_design_efficacy_rate, "non_co_design": column_non_co_design_efficacy_rate} # prepare for plotting and plot plt.figure() plotdata = pd.DataFrame(result["rate"], index=y_column_name_list) fontSize = 10 plotdata.plot(kind='bar', fontsize=fontSize, stacked=True) plt.xticks(fontsize=fontSize, rotation=6) plt.yticks(fontsize=fontSize) plt.xlabel("co design parameter", fontsize=fontSize) plt.ylabel("co design rate", fontsize=fontSize) plt.title("co desgin rate of different parameters", fontsize=fontSize) # dump in the top folder output_base_dir = '/'.join(input_dir_names[0].split("/")[:-2]) output_dir = os.path.join(output_base_dir, "single_workload/co_design_rate") if not os.path.exists(output_dir): os.makedirs(output_dir) plt.savefig(os.path.join(output_dir,experiment_name +"_"+"co_design_rate_"+'_'.join(y_column_name_list)+".png")) plt.close('all') plt.figure() plotdata = pd.DataFrame(result["efficacy_rate"], index=y_column_name_list) fontSize = 10 plotdata.plot(kind='bar', fontsize=fontSize, stacked=True) plt.xticks(fontsize=fontSize, rotation=6) plt.yticks(fontsize=fontSize) plt.xlabel("co design parameter", fontsize=fontSize) plt.ylabel("co design efficacy rate", fontsize=fontSize) plt.title("co design efficacy rate of different parameters", fontsize=fontSize) # dump in the top folder output_base_dir = '/'.join(input_dir_names[0].split("/")[:-2]) output_dir = os.path.join(output_base_dir, "single_workload/co_design_rate") if not os.path.exists(output_dir): os.makedirs(output_dir) plt.savefig(os.path.join(output_dir,experiment_name+"_"+"co_design_efficacy_rate_"+'_'.join(y_column_name_list)+".png")) plt.close('all') """ def plot_codesign_progression_per_workloads(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"] # experiment_names 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) experiment_name = get_experiments_name(file_full_addr, res_column_name_number) experiment_names.append(experiment_name) axis_font = {'size': '20'} x_column_name = "iteration cnt" y_column_name_list = ["high level optimization name", "exact optimization name", "architectural principle", "comm_comp"] experiment_column_value = {} for file_full_addr in file_full_addr_list: experiment_name = get_experiments_name(file_full_addr, res_column_name_number) 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] experiment_column_value[experiment_name] = [] all_values = get_all_col_values_of_a_folders(input_dir_names, all_res_column_name_number, y_column_name) all_values_encoding = {} for idx, val in enumerate(all_values): all_values_encoding[val] = idx with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') rows = list(resultReader) for i, row in enumerate(rows): #if row[trueNum] != "True": # continue if i >= 1: if row[y_column_number] not in all_values: continue col_value = row[y_column_number] col_values = col_value.split(";") for idx, col_val in enumerate(col_values): last_row = rows[i-1] delta_x_column = (float(row[x_column_number]) - float(last_row[x_column_number]))/len(col_values) value_to_add = (float(last_row[x_column_number])+ idx*delta_x_column, col_val) experiment_column_value[experiment_name].append(value_to_add) # prepare for plotting and plot axis_font = {'size': '20'} fontSize = 20 fig = plt.figure(figsize=(12, 8)) plt.rc('font', **axis_font) ax = fig.add_subplot(111) x_values = [el[0] for el in experiment_column_value[experiment_name]] #y_values = [all_values_encoding[el[1]] for el in experiment_column_value[experiment_name]] y_values = [el[1] for el in experiment_column_value[experiment_name]] #ax.set_title("experiment vs system implicaction") ax.tick_params(axis='both', which='major', labelsize=fontSize, rotation=60) ax.set_xlabel(x_column_name, fontsize=20) ax.set_ylabel(y_column_name, fontsize=20) ax.plot(x_values, y_values, label=y_column_name, linewidth=2) ax.legend(bbox_to_anchor=(1, 1), loc='upper left', fontsize=fontSize) # dump in the top folder output_base_dir = '/'.join(input_dir_names[0].split("/")[:-2]) output_dir = os.path.join(output_base_dir, "single_workload/progression") if not os.path.exists(output_dir): os.makedirs(output_dir) plt.tight_layout() fig.savefig(os.path.join(output_dir,experiment_name+"_progression_"+'_'.join(y_column_name_list)+".png")) # plt.show() plt.close('all') fig = plt.figure(figsize=(12, 8)) plt.rc('font', **axis_font) ax = fig.add_subplot(111) x_values = [el[0] for el in experiment_column_value[experiment_name]] # y_values = [all_values_encoding[el[1]] for el in experiment_column_value[experiment_name]] y_values = [el[1] for el in experiment_column_value[experiment_name]] # ax.set_title("experiment vs system implicaction") ax.tick_params(axis='both', which='major', labelsize=fontSize, rotation=60) ax.set_xlabel(x_column_name, fontsize=20) ax.set_ylabel(y_column_name, fontsize=20) ax.plot(x_values, y_values, label=y_column_name, linewidth=2) ax.legend(bbox_to_anchor=(1, 1), loc='upper left', fontsize=fontSize) # dump in the top folder output_base_dir = '/'.join(input_dir_names[0].split("/")[:-2]) output_dir = os.path.join(output_base_dir, "single_workload/progression") if not os.path.exists(output_dir): os.makedirs(output_dir) plt.tight_layout() fig.savefig(os.path.join(output_dir, experiment_name + "_progression_" + y_column_name + ".png")) # plt.show() plt.close('all') def plot_3d(input_dir_names, res_column_name_number): # experiment_names 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.csv") file_full_addr_list.append(file_full_addr) experiment_name = get_experiments_name(file_full_addr, res_column_name_number) experiment_names.append(experiment_name) axis_font = {'size': '10'} fontSize = 10 column_value = {} # initialize the dictionary column_name_list = ["budget_scaling_power", "budget_scaling_area","budget_scaling_latency"] under_study_vars =["iteration cnt", "local_bus_avg_theoretical_bandwidth", "local_bus_max_actual_bandwidth", "local_bus_avg_actual_bandwidth", "system_bus_avg_theoretical_bandwidth", "system_bus_max_actual_bandwidth", "system_bus_avg_actual_bandwidth", "global_total_traffic", "local_total_traffic", "global_memory_total_area", "local_memory_total_area", "ips_total_area", "gpps_total_area","ip_cnt", "max_accel_parallelism", "avg_accel_parallelism", "gpp_cnt", "max_gpp_parallelism", "avg_gpp_parallelism"] # get all the data for file_full_addr in file_full_addr_list: with open(file_full_addr, newline='') as csvfile: resultReader = csv.reader(csvfile, delimiter=',', quotechar='|') experiment_name = get_experiments_name( file_full_addr, res_column_name_number) for i, row in enumerate(resultReader): #if row[trueNum] != "True": # continue if i >= 1: for column_name in column_name_list + under_study_vars: if column_name not in column_value.keys() : column_value[column_name] = [] column_number = res_column_name_number[column_name] col_value = row[column_number] col_values = col_value.split(";") if "=" in col_values[0]: column_value[column_name].append(float((col_values[0]).split("=")[1])) else: column_value[column_name].append(float(col_values[0])) for idx,under_study_var in enumerate(under_study_vars): fig_budget_blkcnt = plt.figure(figsize=(12, 12)) plt.rc('font', **axis_font) ax_blkcnt = fig_budget_blkcnt.add_subplot(projection='3d') img = ax_blkcnt.scatter3D(column_value["budget_scaling_power"], column_value["budget_scaling_area"], column_value["budget_scaling_latency"], c=column_value[under_study_var], cmap="bwr", s=80, label="System Block Count") for idx,_ in enumerate(column_value[under_study_var]): coordinate = column_value[under_study_var][idx] coord_in_scientific_notatio = "{:.2e}".format(coordinate) ax_blkcnt.text(column_value["budget_scaling_power"][idx], column_value["budget_scaling_area"][idx], column_value["budget_scaling_latency"][idx], '%s' % coord_in_scientific_notatio, size=fontSize) ax_blkcnt.set_xlabel("Power Budget", fontsize=fontSize) ax_blkcnt.set_ylabel("Area Budget", fontsize=fontSize) ax_blkcnt.set_zlabel("Latency Budget", fontsize=fontSize) ax_blkcnt.legend() cbar = fig_budget_blkcnt.colorbar(img, aspect=40) cbar.set_label("System Block Count", rotation=270) #plt.title("{Power Budget, Area Budget, Latency Budget} VS System Block Count: " + subDirName) plt.tight_layout() output_base_dir = '/'.join(input_dir_names[0].split("/")[:-2]) output_dir = os.path.join(output_base_dir, "3D/case_studies") if not os.path.exists(output_dir): os.makedirs(output_dir) plt.savefig(os.path.join(output_dir, under_study_var+ ".png")) # plt.show() plt.close('all') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -