summary_multi_anon.py

import pickle as pkl
import os
import pandas as pd
import numpy as np
import json
import copy
from math import isnan
import matplotlib as mlp
import shutil
import argparse

import matplotlib as mlp
mlp.use("agg")
import matplotlib.pyplot as plt

#font = {'size': 24}
#mlp.rc('font', **font)

NAN_LABEL = -1


def parse_args():
    parser = argparse.ArgumentParser()
    # parser.add_argument("--memo", type=str, default='initial')
    parser.add_argument("--memo", type=str, default='0515_afternoon_Colight_6_6_bi')
    parser.add_argument("-b", action="store_true",default=False, help="run baseline analysis") ##
    return parser.parse_args()


def get_traffic_volume(file_name, run_cnt):
    scale = run_cnt / 3600  # run_cnt > traffic_time, no exact scale
    if "synthetic" in file_name:
        sta = file_name.rfind("-") + 1
        print(file_name, int(int(file_name[sta:-4]) * scale))
        return int(int(file_name[sta:-4]) * scale)
    elif "cross" in file_name:
        sta = file_name.find("equal_") + len("equal_")
        end = file_name.find(".xml")
        return int(int(file_name[sta:end]) * scale * 4)  # lane_num = 4


def get_metrics(duration_list, queue_length_list, min_duration, min_duration_id, min_queue_length, min_queue_length_id,
                traffic_name, total_summary, mode_name, save_path, num_rounds, min_duration2=None):
    validation_duration_length = 10
    minimum_round = 50 if num_rounds > 50 else 0
    duration_list = np.array(duration_list)
    queue_length_list = np.array(queue_length_list)

    # min_duration, min_duration_id = np.min(duration_list), np.argmin(duration_list)
    # min_queue_length, min_queue_length_id = np.min(queue_length_list), np.argmin(queue_length_list)

    nan_count = len(np.where(duration_list == NAN_LABEL)[0])
    validation_duration = duration_list[-validation_duration_length:]
    final_duration = np.round(np.mean(validation_duration[validation_duration > 0]), decimals=2)
    final_duration_std = np.round(np.std(validation_duration[validation_duration > 0]), decimals=2)

    if nan_count == 0:
        convergence = {1.2: len(duration_list) - 1, 1.1: len(duration_list) - 1}
        for j in range(minimum_round, len(duration_list)):
            for level in [1.2, 1.1]:
                if max(duration_list[j:]) <= level * final_duration:
                    if convergence[level] > j:
                        convergence[level] = j
        conv_12 = convergence[1.2]
        conv_11 = convergence[1.1]
    else:
        conv_12, conv_11 = 0, 0

    # simple plot for each training instance
    f, ax = plt.subplots(1, 1, figsize=(12, 9), dpi=100)
    ax.plot(duration_list, linewidth=2, color='k')
    ax.plot([0, len(duration_list)], [final_duration, final_duration], linewidth=2, color="g")
    ax.plot([conv_12, conv_12], [duration_list[conv_12], duration_list[conv_12] * 3], linewidth=2, color="b")
    ax.plot([conv_11, conv_11], [duration_list[conv_11], duration_list[conv_11] * 3], linewidth=2, color="b")
    ax.plot([0, len(duration_list)], [min_duration, min_duration], linewidth=2, color="r")
    ax.plot([min_duration_id, min_duration_id], [min_duration, min_duration * 3], linewidth=2, color="r")
    ax.set_title(traffic_name + "-" + str(final_duration))
    plt.savefig(save_path + "/" + traffic_name + "-" + mode_name + ".png")
    plt.close()

    inter_num = traffic_name.split('_')[0]
    vol = traffic_name.split('_')[3]
    ratio = traffic_name.split('_')[4]


    total_summary['inter_num'].append(inter_num)
    total_summary['traffic_volume'].append(vol)
    total_summary['ratio'].append(ratio)

    if ".xml" in traffic_name:
        total_summary["traffic"].append(traffic_name.split(".xml"))
    elif ".json" in traffic_name:
        total_summary["traffic"].append(traffic_name.split(".json"))

    total_summary["min_queue_length"].append(min_queue_length)
    total_summary["min_queue_length_round"].append(min_queue_length_id)
    total_summary["min_duration"].append(min_duration)
    total_summary["min_duration_round"].append(min_duration_id)
    total_summary["final_duration"].append(final_duration)
    total_summary["final_duration_std"].append(final_duration_std)
    total_summary["convergence_1.2"].append(conv_12)
    total_summary["convergence_1.1"].append(conv_11)
    total_summary["nan_count"].append(nan_count)
    total_summary["min_duration2"].append(min_duration2)

    return total_summary


def summary_plot(traffic_performance, figure_dir, mode_name, num_rounds):
    minimum_round = 50 if num_rounds > 50 else 0
    validation_duration_length = 10
    anomaly_threshold = 1.3

    for traffic_name in traffic_performance:
        f, ax = plt.subplots(2, 1, figsize=(12, 9), dpi=100)
        performance_tmp = []
        check_list = []
        for ti in range(len(traffic_performance[traffic_name])):
            ax[0].plot(traffic_performance[traffic_name][ti][0], linewidth=2)
            validation_duration = traffic_performance[traffic_name][ti][0][-validation_duration_length:]
            final_duration = np.round(np.mean(validation_duration), decimals=2)
            if len(np.where(traffic_performance[traffic_name][ti][0] == NAN_LABEL)[0]) == 0:
                # and len(traffic_performance[traffic_name][ti][0]) == num_rounds:
                tmp = traffic_performance[traffic_name][ti][0]
                if len(tmp) < num_rounds:
                    tmp.extend([float("nan")] * (num_rounds - len(traffic_performance[traffic_name][ti][0])))
                performance_tmp.append(tmp)
                check_list.append(final_duration)
            else:
                print("the length of traffic {} is shorter than {}".format(traffic_name, num_rounds))
        check_list = np.array(check_list)
        for ci in np.where(check_list > anomaly_threshold * np.mean(check_list))[0][::-1]:
            del performance_tmp[ci]
            print("anomaly traffic_name:{} id:{} err:{}".format(traffic_name, ci, check_list[ci] - np.mean(check_list)))
        if len(performance_tmp) == 0:
            print("The result of {} is not enough for analysis.".format(traffic_name))
            continue
        try:
            performance_summary = np.array(performance_tmp)
            print(traffic_name, performance_summary.shape)
            ax[1].errorbar(x=range(len(traffic_performance[traffic_name][0][0])),
                           y=np.mean(performance_summary, axis=0),
                           yerr=np.std(performance_summary, axis=0))

            psm = np.mean(performance_summary, axis=0)
            validation_duration = psm[-validation_duration_length:]
            final_duration = np.round(np.mean(validation_duration), decimals=2)

            convergence = {1.2: len(psm) - 1, 1.1: len(psm) - 1}
            for j in range(minimum_round, len(psm)):
                for level in [1.2, 1.1]:
                    if max(psm[j:]) <= level * final_duration:
                        if convergence[level] > j:
                            convergence[level] = j
            ax[1].plot([0, len(psm)], [final_duration, final_duration], linewidth=2, color="g")
            ax[1].text(len(psm), final_duration * 2, "final-" + str(final_duration))
            ax[1].plot([convergence[1.2], convergence[1.2]], [psm[convergence[1.2]], psm[convergence[1.2]] * 3],
                       linewidth=2, color="b")
            ax[1].text(convergence[1.2], psm[convergence[1.2]] * 2, "conv 1.2-" + str(convergence[1.2]))
            ax[1].plot([convergence[1.1], convergence[1.1]], [psm[convergence[1.1]], psm[convergence[1.1]] * 3],
                       linewidth=2, color="b")
            ax[1].text(convergence[1.1], psm[convergence[1.1]] * 2, "conv 1.1-" + str(convergence[1.1]))
            ax[1].set_title(traffic_name)
            plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.3)
            plt.savefig(figure_dir + "/" + traffic_name + "-" + mode_name + ".png")
            plt.close()
        except:
            print("plot error")


def plot_segment_duration(round_summary, path, mode_name):
    save_path = os.path.join(path, "segments")
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    for key in round_summary.keys():
        if "duration" in key:
            f, ax = plt.subplots(1, 1, figsize=(12, 9), dpi=100)
            ax.plot(round_summary[key], linewidth=2, color='k')
            ax.set_title(key)
            plt.savefig(save_path + "/" + key + "-" + mode_name + ".png")
            plt.close()


def padding_duration(performance_duration):
    for traffic_name in performance_duration.keys():
        max_duration_length = max([len(x[0]) for x in performance_duration[traffic_name]])
        for i, ti in enumerate(performance_duration[traffic_name]):
            performance_duration[traffic_name][i][0].extend((max_duration_length - len(ti[0]))*[ti[0][-1]])

    return performance_duration


def performance_at_min_duration_round_plot(performance_at_min_duration_round, figure_dir, mode_name):
    for traffic_name in performance_at_min_duration_round:
        f, ax = plt.subplots(1, 1, figsize=(12, 9), dpi=100)
        for ti in range(len(performance_at_min_duration_round[traffic_name])):
            ax.plot(performance_at_min_duration_round[traffic_name][ti][0], linewidth=2)
        plt.savefig(figure_dir + "/" + "min_duration_round" + "-" + mode_name + ".png")
        plt.close()


def summary_detail_test(memo, total_summary):
    # each_round_train_duration

    performance_duration = {}
    performance_at_min_duration_round = {}

    records_dir = os.path.join("records", memo)

    for traffic_file in os.listdir(records_dir):
        if ".xml" not in traffic_file and ".json" not in traffic_file:
            continue

        #if "cross.2phases_rou01_equal_700.xml_12_11_08_16_00" != traffic_file:
        #    continue
        print(traffic_file)

        min_queue_length = min_duration = min_duration2 = float('inf')
        min_queue_length_id = min_duration_ind = 0

        # get run_counts to calculate the queue_length each second
        exp_conf = open(os.path.join(records_dir, traffic_file, "exp.conf"), 'r')
        dic_exp_conf = json.load(exp_conf)
        print(dic_exp_conf)


        traffic_env_conf = open(os.path.join(records_dir, traffic_file, "traffic_env.conf"), 'r')
        dic_traffic_env_conf = json.load(traffic_env_conf)
        run_counts = dic_exp_conf["RUN_COUNTS"]
        num_rounds = dic_exp_conf["NUM_ROUNDS"]
        time_interval = 120
        import math
        num_seg = math.ceil(run_counts/time_interval)
        # num_seg = run_counts//time_interval
        num_intersection = dic_traffic_env_conf['NUM_INTERSECTIONS']


        traffic_vol = get_traffic_volume(dic_exp_conf["TRAFFIC_FILE"][0], run_counts)
        nan_thres = 120

        duration_each_round_list = []
        duration_each_round_list2 = []
        queue_length_each_round_list = []
        num_of_vehicle_in = []
        num_of_vehicle_out = []

        train_round_dir = os.path.join(records_dir, traffic_file, "test_round")
        try:
            round_files = os.listdir(train_round_dir)
        except:
            print("no test round in {}".format(traffic_file))
            continue
        round_files = [f for f in round_files if "round" in f]
        round_files.sort(key=lambda x: int(x[6:]))

        round_summary = {}
        for round in round_files:
            print("===={0}".format(round))

            df_vehicle_all = []
            queue_length_each_round = []

            list_duration_seg = [float('inf')] * num_seg
            list_queue_length_seg = [float('inf')] * num_seg
            list_queue_length_id_seg = [0] * num_seg
            list_duration_id_seg = [0] * num_seg
            for inter_index in range(num_intersection):

                try:

                    round_dir = os.path.join(train_round_dir, round)

                    # summary items (queue_length) from pickle
                    f = open(os.path.join(round_dir, "inter_{0}.pkl".format(inter_index)), "rb")
                    samples = pkl.load(f)
                    queue_length_each_inter_each_round = 0
                    for sample in samples:
                        queue_length_each_inter_each_round += sum(sample['state']['lane_num_vehicle_been_stopped_thres1'])
                    queue_length_each_inter_each_round = queue_length_each_inter_each_round//len(samples)
                    f.close()

                    # summary items (duration) from csv
                    df_vehicle_inter = pd.read_csv(os.path.join(round_dir, "vehicle_inter_{0}.csv".format(inter_index)),
                                                     sep=',', header=0, dtype={0: str, 1: float, 2: float},
                                                     names=["vehicle_id", "enter_time", "leave_time"])
                    df_vehicle_inter['leave_time_origin'] = df_vehicle_inter['leave_time']
                    df_vehicle_inter['leave_time'].fillna(run_counts,inplace=True)
                    df_vehicle_inter['duration'] = df_vehicle_inter["leave_time"].values - df_vehicle_inter["enter_time"].values
                    ave_duration = df_vehicle_inter['duration'].mean(skipna=True)
                    print("------------- inter_index: {0}\tave_duration: {1}\tave_queue_length:{2}"
                          .format(inter_index, ave_duration, queue_length_each_inter_each_round))

                    # if "peak" in traffic_file:
                    #     did1 = df_vehicle_inter_0["enter_time"].values <= run_counts / 2
                    #     duration = df_vehicle_inter_0["leave_time"][did1].values - df_vehicle_inter_0["enter_time"][
                    #         did1].values
                    #     ave_duration = np.mean([time for time in duration if not isnan(time)])
                    #
                    #     did2 = df_vehicle_inter_0["enter_time"].values > run_counts / 2
                    #     duration2 = df_vehicle_inter_0["leave_time"][did2].values - df_vehicle_inter_0["enter_time"][
                    #         did2].values
                    #     ave_duration2 = np.mean([time for time in duration2 if not isnan(time)])
                    #     duration_each_round_list2.append(ave_duration2)
                    #
                    #     real_traffic_vol2 = 0
                    #     nan_num2 = 0
                    #     for time in duration2:
                    #         if not isnan(time):
                    #             real_traffic_vol2 += 1
                    #         else:
                    #             nan_num2 += 1
                    #
                    #     if nan_num2 < nan_thres:
                    #         if min_duration2 > ave_duration2 and ave_duration2 > 24:
                    #             min_duration2 = ave_duration2
                    #             min_duration_ind2 = int(round[6:])
                    df_vehicle_all.append(df_vehicle_inter)
                    queue_length_each_round.append(queue_length_each_inter_each_round)

                except:
                    queue_length_each_round.append(NAN_LABEL)
                    # num_of_vehicle_in.append(NAN_LABEL)
                    # num_of_vehicle_out.append(NAN_LABEL)

            if len(df_vehicle_all)==0:
                print("====================================EMPTY")
                continue

            df_vehicle_all = pd.concat(df_vehicle_all)
            # vehicle_duration = df_vehicle_all.groupby(by=['vehicle_id'])['duration'].sum()
            # ave_duration = vehicle_duration.mean()
            # --- my modification ---
            # vehicle_duration = []
            # for vehicle_id, group in df_vehicle_all.sort_values(by='enter_time').groupby(by='vehicle_id'):
            #     true_duration = 0
            #     beg, end = -1, -1
            #     for i, row in group.iterrows():
            #         print(vehicle_id, row['enter_time'], row['leave_time'])
            #         if row['enter_time'] < end:
            #             end = max(end, row['leave_time'])
            #         else:
            #             true_duration += end - beg
            #             beg, end = row['enter_time'], row['leave_time']
            #     true_duration += end - beg
            #     vehicle_duration.append(true_duration)
            df_vehicle_all_group = df_vehicle_all.groupby(by=['vehicle_id'])
            vehicle_duration = pd.DataFrame((df_vehicle_all_group['leave_time'].max() - df_vehicle_all_group['enter_time'].min()),
                columns=['duration'])
            ave_duration = vehicle_duration['duration'].mean()

            ave_queue_length = np.mean(queue_length_each_round)

            duration_each_round_list.append(ave_duration)
            queue_length_each_round_list.append(ave_queue_length)


            num_of_vehicle_in.append(len(df_vehicle_all['vehicle_id'].unique()))
            num_of_vehicle_out.append(len(df_vehicle_all.dropna()['vehicle_id'].unique()))

            print("==== round: {0}\tave_duration: {1}\tave_queue_length_per_intersection:{2}\t"
                  "num_of_vehicle_in:{3}\tnum_of_vehicle_out:{4}"
                  .format(round, ave_duration,ave_queue_length,num_of_vehicle_in[-1],num_of_vehicle_out[-1]))

            duration_flow = vehicle_duration.reset_index()

            duration_flow['direction'] = duration_flow['vehicle_id'].apply(lambda x:x.split('_')[1])
            duration_flow_ave = duration_flow.groupby(by=['direction'])['duration'].mean()
            print(duration_flow_ave)

            # print(real_traffic_vol, traffic_vol, traffic_vol - real_traffic_vol, nan_num)
            if min_queue_length > ave_queue_length:
                min_queue_length = np.mean(queue_length_each_round)
                min_queue_length_id = int(round[6:])
            #
            # valid_flag = json.load(open(os.path.join(round_dir, "valid_flag.json")))
            # if valid_flag['0']:  # temporary for one intersection
            #     if min_duration > ave_duration and ave_duration > 24:
            #         min_duration = ave_duration
            #         min_duration_ind = int(round[6:])


            #### This is for long time

            if num_seg > 1:
                for i, interval in enumerate(range(0, run_counts, time_interval)):
                    did = df_vehicle_all[(df_vehicle_all["enter_time"]< interval+time_interval) &
                                         (df_vehicle_all["enter_time"].values > interval)]
                    #vehicle_in_seg = sum([int(x) for x in (df_vehicle_inter_0["enter_time"][did].values > 0)])
                    #vehicle_out_seg = sum([int(x) for x in (df_vehicle_inter_0["leave_time"][did].values > 0)])

                    vehicle_duration_seg = did.groupby(by=['vehicle_id'])['duration'].sum()
                    ave_duration_seg = vehicle_duration_seg[vehicle_duration_seg>10].mean()
                    # print(traffic_file, round, i, ave_duration)
                    # real_traffic_vol_seg = 0
                    # nan_num_seg = 0
                    # for time in duration_seg:
                    #     if not isnan(time):
                    #         real_traffic_vol_seg += 1
                    #     else:
                    #         nan_num_seg += 1

                    # print(real_traffic_vol, traffic_vol, traffic_vol - real_traffic_vol, nan_num)
                    nan_num_seg = did['leave_time_origin'].isna().sum()

                    if nan_num_seg < nan_thres:
                        list_duration_seg[i] = ave_duration_seg
                        list_duration_id_seg[i] = int(round[6:])

                    #round_summary = {}
                for j in range(num_seg):
                    key = "min_duration-" + str(j)

                    if key not in round_summary.keys():
                        round_summary[key] = [list_duration_seg[j]]
                    else:
                        round_summary[key].append(list_duration_seg[j])
                #round_result_dir = os.path.join("summary", memo, traffic_file)
                #if not os.path.exists(round_result_dir):
                #    os.makedirs(round_result_dir)


        # result_dir = os.path.join(records_dir, traffic_file)
        result_dir = os.path.join("summary", memo, traffic_file)
        if not os.path.exists(result_dir):
            os.makedirs(result_dir)
        _res = {
            "duration": duration_each_round_list,
            "queue_length": queue_length_each_round_list,
            "vehicle_in": num_of_vehicle_in,
            "vehicle_out": num_of_vehicle_out
        }
        result = pd.DataFrame(_res)
        result.to_csv(os.path.join(result_dir, "test_results.csv"))

        if ".xml" in traffic_file:
            traffic_name, traffic_time = traffic_file.split(".xml")
        elif ".json" in traffic_file:
            traffic_name, traffic_time = traffic_file.split(".json")

        if num_seg > 1:
            round_result = pd.DataFrame(round_summary)
            round_result.to_csv(os.path.join(result_dir, "test_seg_results.csv"), index=False)
            plot_segment_duration(round_summary, result_dir, mode_name="test")
            duration_each_segment_list = round_result.iloc[min_duration_ind][1:].values

            if traffic_name not in performance_at_min_duration_round:
                performance_at_min_duration_round[traffic_name] = [(duration_each_segment_list, traffic_time)]
            else:
                performance_at_min_duration_round[traffic_name].append((duration_each_segment_list, traffic_time))


        # print(os.path.join(result_dir, "test_results.csv"))

        # total_summary
        total_summary = get_metrics(duration_each_round_list, queue_length_each_round_list,
                                    min_duration, min_duration_ind, min_queue_length, min_queue_length_id,
                                    traffic_file, total_summary,
                                    mode_name="test", save_path=result_dir, num_rounds=num_rounds,
                                    min_duration2=None if "peak" not in traffic_file else min_duration2)

        if traffic_name not in performance_duration:
            performance_duration[traffic_name] = [(duration_each_round_list, traffic_time)]
        else:
            performance_duration[traffic_name].append((duration_each_round_list, traffic_time))


        total_result = pd.DataFrame(total_summary)
        total_result.to_csv(os.path.join("summary", memo, "total_test_results.csv"))


    figure_dir = os.path.join("summary", memo, "figures")
    if not os.path.exists(figure_dir):
        os.makedirs(figure_dir)
    #if dic_exp_conf["EARLY_STOP"]:
    #    performance_duration = padding_duration(performance_duration)
    summary_plot(performance_duration, figure_dir, mode_name="test", num_rounds=94)
    performance_at_min_duration_round_plot(performance_at_min_duration_round, figure_dir, mode_name="test")


##TODO multi-intersection
def summary_detail_baseline(memo):

    DETAIL_ARTERIAL = True
    total_summary = []

    records_dir = os.path.join("records", memo)
    for traffic_file in os.listdir(records_dir):
        ANON_ENV = False


        if ".xml" not in traffic_file and "anon" not in traffic_file:
            continue
        if "anon" in traffic_file:
            ANON_ENV = True

        exp_conf = open(os.path.join(records_dir, traffic_file, "exp.conf"), 'r')
        dic_exp_conf = json.load(exp_conf)
        run_counts = dic_exp_conf["RUN_COUNTS"]

        avg_pressure = 0

        print(traffic_file)

        train_dir = os.path.join(records_dir, traffic_file)

        if os.path.getsize(os.path.join(train_dir, "inter_0.pkl")) > 0:
            with open(os.path.join(records_dir, traffic_file, 'agent.conf'), 'r') as agent_conf:
                dic_agent_conf = json.load(agent_conf)

            df_vehicle = []
            NUM_OF_INTERSECTIONS = int(traffic_file.split('_')[1])*int(traffic_file.split('_')[2])

            list_f = ["inter_%d.pkl" % i for i in range(int(NUM_OF_INTERSECTIONS))]

            for f in list_f:
                pressure_each_inter = 0

                node_index = f.split('inter_')[1].split('.pkl')[0]
                print("node",node_index)
                f = open(os.path.join(train_dir, f), "rb")
                samples = pkl.load(f)
                for sample in samples:
                    pressure_each_inter += sum((sample['state']['lane_num_vehicle_been_stopped_thres1']))
                f.close()

                pressure_each_inter = pressure_each_inter/len(samples)
                avg_pressure += pressure_each_inter

                vehicle_csv = "vehicle_inter_{0}.csv".format(node_index)

                df_vehicle_inter_0 = pd.read_csv(os.path.join(train_dir, vehicle_csv),
                                                 sep=',', header=0, dtype={0: str, 1: float, 2: float},
                                                 names=["vehicle_id", "enter_time", "leave_time"])

                # print(df_vehicle_inter_0)

                if ANON_ENV:
                    flow_car = pd.DataFrame(df_vehicle_inter_0['vehicle_id'].str.split('_', -1).tolist(), columns=['flow','flow_id', 'car_id'])
                else:
                    flow_car = pd.DataFrame(df_vehicle_inter_0['vehicle_id'].str.split('.', 1).tolist(), columns=['flow_id', 'car_id'])
                df_vehicle_inter_0 = pd.concat([flow_car, df_vehicle_inter_0], axis=1)
                df_vehicle_inter_0.fillna(run_counts,inplace=True)
                df_vehicle_inter_0['duration'] = df_vehicle_inter_0["leave_time"] - df_vehicle_inter_0["enter_time"]


                df_vehicle.append(df_vehicle_inter_0)
                print(df_vehicle_inter_0.groupby(['flow_id'])['duration'].mean()) # mean for every intersection

            df_vehicle = pd.concat(df_vehicle,axis=0)

            flow_df = df_vehicle.groupby(['flow_id', 'car_id']).sum()
            arterial_duration = 0
            side_street_duration = 0
            if DETAIL_ARTERIAL:
                detail_arterial = flow_df.groupby('flow_id').mean()
                save_path = os.path.join('records',memo, traffic_file).replace("records","summary")
                if not os.path.exists(save_path):
                    os.makedirs(save_path)
                detail_arterial.to_csv(os.path.join(save_path, 'flow.csv'))
                arterial_duration = np.average(detail_arterial[:2])
                side_street_duration = np.average(detail_arterial[3:])
                avg_pressure = avg_pressure/NUM_OF_INTERSECTIONS


            car_num_out_df = df_vehicle.groupby(by=['flow_id', 'car_id'])['leave_time'].apply(lambda x: x.shape[0] != x.count())
            car_num_out = car_num_out_df[car_num_out_df].count()

            ave_duration_all = flow_df['duration'].mean()
            total_summary.append([traffic_file,ave_duration_all, avg_pressure,flow_df.shape[0],car_num_out,dic_agent_conf["FIXED_TIME"],arterial_duration,side_street_duration])
        else:
            shutil.rmtree(train_dir)

    total_summary = pd.DataFrame(total_summary)
    total_summary.sort_values([0], ascending=[True], inplace=True)
    total_summary.columns = ['TRAFFIC','DURATION','PRESSURE','CAR_NUMBER_IN','CAR_NUMBER_OUT','CONFIG','ARTERIAL','SIDE_STREET']
    total_summary.to_csv(os.path.join("records", memo, "total_baseline_results.txt").replace("records", "summary"),sep='\t',index=False)



# def main(memo=None):
#     total_summary = {
#         "traffic": [],
#         "traffic_file": [],
#         "min_queue_length": [],
#         "min_queue_length_round": [],
#         "min_duration": [],
#         "min_duration_round": [],
#         "final_duration": [],
#         "final_duration_std": [],
#         "convergence_1.2": [],
#         "convergence_1.1": [],
#         "nan_count": [],
#         "min_duration2": []
#     }
#     if not memo:
#         memo = "pipeline_500"
#     #summary_detail_train(memo, copy.deepcopy(total_summary))
#     summary_detail_test(memo, copy.deepcopy(total_summary))
#     # summary_detail_test_segments(memo, copy.deepcopy(total_summary))

def print_samples(memo):
    records_dir = os.path.join("records", memo)

    for traffic_file in os.listdir(records_dir):
        if ".xml" not in traffic_file:
            continue
        print(traffic_file)
        exp_conf = open(os.path.join(records_dir, traffic_file, "exp.conf"), 'r')
        dic_exp_conf = json.load(exp_conf)
        run_counts = dic_exp_conf["RUN_COUNTS"]
        num_rounds = dic_exp_conf["NUM_ROUNDS"]
        num_seg = run_counts // 3600
        train_round_dir = os.path.join(records_dir, traffic_file, "train_round")
        round_files = os.listdir(train_round_dir)
        round_files = [f for f in round_files if "round" in f]
        round_files.sort(key=lambda x: int(x[6:]))
        for round in round_files:
            print(round)
            try:
                round_dir = os.path.join(train_round_dir, round)
                for gen in os.listdir(round_dir):
                    if "generator" not in gen:
                        continue

                    gen_dir = os.path.join(records_dir, traffic_file, "train_round", round, gen)
                    f = open(os.path.join(gen_dir, "inter_0.pkl"), "rb")
                    samples = pkl.load(f)

                    for sample in samples:
                        print("action", sample['action'], end='')
                        print("time", sample['time'], end='')
                        for key in sample['state'].keys():
                            if sample['state'][key] != None:
                                print(key, sample['state'][key], end='')

                        print('\n')
                    f.close()
            except:
                print("NO SAMPLES! round", round)


def print_samples():
    # gen_dir="/Users/chenchacha/RLSignal/records/test_pressure/2_intersections_uniform_300_0.3_uni.xml_12_20_03_14_11/train_round/round_7/generator_0"
    #
    #
    # f = open(os.path.join(gen_dir, "inter_0.pkl"), "rb")
    # samples = pkl.load(f)
    #
    # for sample in samples:
    #     print("action", sample['action'], end='\t')
    #     print("time", sample['time'], end='\t')
    #     for key in sample['state'].keys():
    #         if sample['state'][key] != None:
    #             print(key, sample['state'][key], end='')
    #
    #     print('\n')
    # f.close()

    f=open("/Users/chenchacha/RLSignal/records/test_pressure/2_intersections_uniform_300_0.3_uni.xml_12_20_03_14_11/train_round/total_samples_inter_0.pkl","rb")
    samples = []
    try:
        while 1:
            samples.extend(pkl.load(f))
    except:
        pass
    # samples = (pkl.load(f))
    print(len(samples))
    count = 0
    for round in samples:
        print(count)
        count +=1
        for sample in round:
            print(sample)
    #for sample in samples:
    #    print(sample)
    f.close()


if __name__ == "__main__":
    total_summary = {
        "traffic": [],
        "inter_num":[],
        "traffic_volume":[],
        "ratio":[],
        "min_queue_length": [],
        "min_queue_length_round": [],
        "min_duration": [],
        "min_duration_round": [],
        "final_duration": [],
        "final_duration_std": [],
        "convergence_1.2": [],
        "convergence_1.1": [],
        "nan_count": [],
        "min_duration2": []
    }




    memo = "multi_phase/multi_phase_12_12_600_700_layer_10"

    args = parse_args()

    # print_samples()
    if args.b:
        summary_detail_baseline(args.memo)
    else:
        summary_detail_test(args.memo, copy.deepcopy(total_summary))