├── CPSC_combine_leads.py ├── CPSC_config.py ├── CPSC_extract_features.py ├── CPSC_hybrid.py ├── CPSC_model.py ├── CPSC_train_multi_leads.py ├── CPSC_train_single_lead.py ├── CPSC_utils.py ├── DataSet_250Hz.zip ├── LICENSE ├── Man_features.zip ├── Net_models.zip ├── README.md ├── Record_Label.npy ├── info_age_gen.csv └── model_t.zip /CPSC_combine_leads.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Dec 26 14:57:27 2018 4 | 5 | @author: Winham 6 | 7 | # CPSC_combine_leads.py: 组合各导联神经网络的输出概率 8 | 9 | """ 10 | 11 | import os 12 | import warnings 13 | import numpy as np 14 | import tensorflow as tf 15 | from keras import backend as bk 16 | from keras.models import load_model 17 | from keras.utils import to_categorical 18 | from sklearn.preprocessing import scale 19 | from sklearn.model_selection import train_test_split 20 | from CPSC_config import Config 21 | import CPSC_utils as utils 22 | 23 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2' 24 | warnings.filterwarnings("ignore") 25 | config = Config() 26 | config.MODEL_PATH = 'E:/CPSC_Scheme/Net_models/' 27 | 28 | records_name = np.array(os.listdir(config.DATA_PATH)) 29 | records_label = np.load(config.REVISED_LABEL) - 1 30 | class_num = len(np.unique(records_label)) 31 | 32 | train_val_records, test_records, train_val_labels, test_labels = train_test_split( 33 | records_name, records_label, test_size=0.2, random_state=config.RANDOM_STATE) 34 | 35 | train_records, val_records, train_labels, val_labels = train_test_split( 36 | train_val_records, train_val_labels, test_size=0.2, random_state=config.RANDOM_STATE) 37 | 38 | train_records, train_labels = utils.oversample_balance(train_records, train_labels, config.RANDOM_STATE) 39 | val_records, val_labels = utils.oversample_balance(val_records, val_labels, config.RANDOM_STATE) 40 | 41 | for i in range(config.LEAD_NUM): # 分别载入各个导联对应的模型并进行概率预测，并拼接 42 | TARGET_LEAD = i 43 | train_x = utils.Fetch_Pats_Lbs_sLead(train_records, Path=config.DATA_PATH, 44 | target_lead=TARGET_LEAD, seg_num=config.SEG_NUM, 45 | seg_length=config.SEG_LENGTH) 46 | train_y = to_categorical(train_labels, num_classes=class_num) 47 | val_x = utils.Fetch_Pats_Lbs_sLead(val_records, Path=config.DATA_PATH, 48 | target_lead=TARGET_LEAD, seg_num=config.SEG_NUM, 49 | seg_length=config.SEG_LENGTH) 50 | val_y = to_categorical(val_labels, num_classes=class_num) 51 | for j in range(train_x.shape[0]): 52 | train_x[j, :, :] = scale(train_x[j, :, :], axis=0) 53 | for j in range(val_x.shape[0]): 54 | val_x[j, :, :] = scale(val_x[j, :, :], axis=0) 55 | model_name = 'net_lead_' + str(TARGET_LEAD) + '.hdf5' 56 | model = load_model(config.MODEL_PATH + model_name) 57 | pred_nnet_rt = model.predict(train_x, batch_size=64, verbose=1) 58 | del train_x 59 | pred_nnet_vt = model.predict(val_x, batch_size=64, verbose=1) 60 | del val_x 61 | 62 | test_x = utils.Fetch_Pats_Lbs_sLead(test_records, Path=config.DATA_PATH, 63 | target_lead=TARGET_LEAD, seg_num=config.SEG_NUM, 64 | seg_length=config.SEG_LENGTH) 65 | test_y = to_categorical(test_labels, num_classes=class_num) 66 | for j in range(test_x.shape[0]): 67 | test_x[j, :, :] = scale(test_x[j, :, :], axis=0) 68 | 69 | pred_nnet_tt = model.predict(test_x, batch_size=64, verbose=1) 70 | del test_x 71 | 72 | if i == 0: 73 | pred_nnet_r = pred_nnet_rt[:, 1:] 74 | pred_nnet_v = pred_nnet_vt[:, 1:] 75 | pred_nnet_t = pred_nnet_tt[:, 1:] 76 | else: 77 | pred_nnet_r = np.concatenate((pred_nnet_r, pred_nnet_rt[:, 1:]), axis=1) 78 | pred_nnet_v = np.concatenate((pred_nnet_v, pred_nnet_vt[:, 1:]), axis=1) 79 | pred_nnet_t = np.concatenate((pred_nnet_t, pred_nnet_tt[:, 1:]), axis=1) 80 | del model 81 | bk.clear_session() 82 | tf.reset_default_graph() 83 | 84 | np.save(config.MODEL_PATH + 'pred_nnet_r.npy', pred_nnet_r) 85 | np.save(config.MODEL_PATH + 'pred_nnet_v.npy', pred_nnet_v) 86 | np.save(config.MODEL_PATH + 'pred_nnet_t.npy', pred_nnet_t) 87 | -------------------------------------------------------------------------------- /CPSC_config.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Dec 26 13:51:10 2018 4 | 5 | @author: Winham 6 | 7 | # CPSC_config.py: 相关参数配置 8 | 9 | """ 10 | 11 | 12 | class Config(object): 13 | def __init__(self): 14 | # 随机数种子 15 | self.RANDOM_STATE = 42 16 | 17 | # 导联数目 18 | self.LEAD_NUM = 12 19 | 20 | # 各类名称 21 | self.CLASS_NAME = ['N', 'AF', 'AVB', 'LBBB', 22 | 'RBBB', 'PAC', 'PVC', 'STD', 'STE'] 23 | 24 | # 数据存放路径 25 | self.DATA_PATH = 'E:/CPSC_Scheme/DataSet_250Hz/' 26 | 27 | # 标签存放路径 28 | self.REVISED_LABEL = 'E:/CPSC_Scheme/Record_Label.npy' 29 | 30 | # keras深度模型存放路径 31 | self.MODEL_PATH = 'E:/CPSC_Scheme/model_t/' 32 | 33 | # 人工特征存放路径 34 | self.MAN_FEATURE_PATH = 'E:/CPSC_Scheme/Man_features/' 35 | 36 | # 个体年龄性别信息存放路径 37 | self.AGE_GEN_INFO = 'E:/CPSC_Scheme/info_age_gen.csv' 38 | 39 | # 信号采样率 40 | self.Fs = 250 41 | 42 | # 信号切片数目 43 | self.SEG_NUM = 24 44 | 45 | # 信号切片时间长度 46 | self.SEG_TIME_LENGTH = 6.0 47 | 48 | # 信号采样点长度 49 | self.SEG_LENGTH = int(self.Fs * self.SEG_TIME_LENGTH) 50 | 51 | @staticmethod 52 | def lr_schedule(epoch): 53 | # 训练网络时学习率衰减方案 54 | 55 | lr = 0.1 56 | if epoch >= 20 and epoch < 60: 57 | lr = 0.01 58 | if epoch >= 60: 59 | lr = 0.001 60 | print('Learning rate: ', lr) 61 | return lr 62 | -------------------------------------------------------------------------------- /CPSC_extract_features.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Dec 26 15:08:56 2018 4 | 5 | @author: Winham 6 | 7 | # CPSC_extract_features.py: 对记录提取HRV特征以及年龄性别信息 8 | 9 | """ 10 | 11 | import os 12 | import warnings 13 | import numpy as np 14 | import pandas as pd 15 | from sklearn.model_selection import train_test_split 16 | from CPSC_model import ManFeat_HRV 17 | from CPSC_config import Config 18 | import CPSC_utils as utils 19 | 20 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2' 21 | warnings.filterwarnings("ignore") 22 | config = Config() 23 | 24 | records_name = np.array(os.listdir(config.DATA_PATH)) 25 | records_label = np.load(config.REVISED_LABEL) - 1 26 | class_num = len(np.unique(records_label)) 27 | age_gen = pd.read_csv(config.AGE_GEN_INFO) 28 | AGE_GEN_DIMENSION = age_gen.shape[0] 29 | 30 | train_val_records, test_records, train_val_labels, test_labels = train_test_split( 31 | records_name, records_label, test_size=0.2, random_state=config.RANDOM_STATE) 32 | train_records, val_records, train_labels, val_labels = train_test_split( 33 | train_val_records, train_val_labels, test_size=0.2, random_state=config.RANDOM_STATE) 34 | 35 | train_records, train_labels = utils.oversample_balance(train_records, train_labels, config.RANDOM_STATE) 36 | val_records, val_labels = utils.oversample_balance(val_records, val_labels, config.RANDOM_STATE) 37 | 38 | # 对训练集提取特征 ----------------------------------------------------------------------------------------------------- 39 | man_features_r = np.zeros([len(train_records), ManFeat_HRV.FEAT_DIMENSION + AGE_GEN_DIMENSION]) 40 | for i in range(len(train_records)): 41 | print('Process train No.' + str(i+1) + '/' + str(len(train_records))) 42 | sig = np.load(config.DATA_PATH + train_records[i])[1, :] 43 | Feat_HRV = ManFeat_HRV(sig, config.Fs) 44 | feat_hrv = Feat_HRV.extract_features() 45 | feat_a_g = np.array(age_gen[train_records[i]]) 46 | man_features_rt = np.concatenate((feat_hrv, feat_a_g)) 47 | man_features_r[i] = man_features_rt 48 | del sig, Feat_HRV, feat_hrv, feat_a_g, man_features_rt 49 | 50 | # 对验证集提取特征 ----------------------------------------------------------------------------------------------------- 51 | man_features_v = np.zeros([len(val_records), ManFeat_HRV.FEAT_DIMENSION + AGE_GEN_DIMENSION]) 52 | for i in range(len(val_records)): 53 | print('Process val No.' + str(i+1) + '/' + str(len(val_records))) 54 | sig = np.load(config.DATA_PATH + val_records[i])[1, :] 55 | Feat_HRV = ManFeat_HRV(sig, config.Fs) 56 | feat_hrv = Feat_HRV.extract_features() 57 | feat_a_g = np.array(age_gen[val_records[i]]) 58 | man_features_vt = np.concatenate((feat_hrv, feat_a_g)) 59 | man_features_v[i] = man_features_vt 60 | del sig, Feat_HRV, feat_hrv, feat_a_g, man_features_vt 61 | 62 | # 对测试集提取特征 ----------------------------------------------------------------------------------------------------- 63 | man_features_t = np.zeros([len(test_records), ManFeat_HRV.FEAT_DIMENSION + AGE_GEN_DIMENSION]) 64 | for i in range(len(test_records)): 65 | print('Process test No.' + str(i+1) + '/' + str(len(test_records))) 66 | sig = np.load(config.DATA_PATH + test_records[i])[1, :] 67 | Feat_HRV = ManFeat_HRV(sig, config.Fs) 68 | feat_hrv = Feat_HRV.extract_features() 69 | feat_a_g = np.array(age_gen[test_records[i]]) 70 | man_features_tt = np.concatenate((feat_hrv, feat_a_g)) 71 | man_features_t[i] = man_features_tt 72 | del sig, Feat_HRV, feat_hrv, feat_a_g, man_features_tt 73 | 74 | # 保存特征集 ----------------------------------------------------------------------------------------------------------- 75 | np.save(config.MAN_FEATURE_PATH + 'man_features_r.npy', man_features_r) 76 | np.save(config.MAN_FEATURE_PATH + 'man_features_v.npy', man_features_v) 77 | np.save(config.MAN_FEATURE_PATH + 'man_features_t.npy', man_features_t) 78 | -------------------------------------------------------------------------------- /CPSC_hybrid.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Dec 26 15:43:18 2018 4 | 5 | @author: Winham 6 | 7 | # CPSC_hybrid.py: 使用xgboost混合深度学习网络和人工特征，得到最后结果 8 | 9 | """ 10 | 11 | import os 12 | import numpy as np 13 | import xgboost as xgb 14 | from sklearn.model_selection import train_test_split 15 | from sklearn.metrics import confusion_matrix 16 | from CPSC_config import Config 17 | import CPSC_utils as utils 18 | 19 | os.environ["CUDA_VISIBLE_DEVICES"] = "-1" 20 | config = Config() 21 | config.MODEL_PATH = 'E:/CPSC_Scheme/Net_models/' 22 | config.MAN_FEATURE_PATH = 'E:/CPSC_Scheme/Man_features/' 23 | 24 | records_name = np.array(os.listdir(config.DATA_PATH)) 25 | records_label = np.load(config.REVISED_LABEL) - 1 26 | class_num = len(np.unique(records_label)) 27 | 28 | train_val_records, _, train_val_labels, test_labels = train_test_split( 29 | records_name, records_label, test_size=0.2, random_state=config.RANDOM_STATE) 30 | 31 | train_records, val_records, train_labels, val_labels = train_test_split( 32 | train_val_records, train_val_labels, test_size=0.2, random_state=config.RANDOM_STATE) 33 | 34 | _, train_labels = utils.oversample_balance(train_records, train_labels, config.RANDOM_STATE) 35 | _, val_labels = utils.oversample_balance(val_records, val_labels, config.RANDOM_STATE) 36 | 37 | # 载入之前保存的网络输出概率以及人工特征 ------------------------------------------------------------------------------- 38 | pred_nnet_r = np.load(config.MODEL_PATH + 'pred_nnet_r.npy') 39 | pred_nnet_v = np.load(config.MODEL_PATH + 'pred_nnet_v.npy') 40 | pred_nnet_t = np.load(config.MODEL_PATH + 'pred_nnet_t.npy') 41 | 42 | man_features_r = np.load(config.MAN_FEATURE_PATH + 'man_features_r.npy') 43 | man_features_v = np.load(config.MAN_FEATURE_PATH + 'man_features_v.npy') 44 | man_features_t = np.load(config.MAN_FEATURE_PATH + 'man_features_t.npy') 45 | 46 | pred_r = np.concatenate((pred_nnet_r, man_features_r), axis=1) 47 | pred_v = np.concatenate((pred_nnet_v, man_features_v), axis=1) 48 | pred_t = np.concatenate((pred_nnet_t, man_features_t), axis=1) 49 | 50 | lb_r = train_labels 51 | lb_v = val_labels 52 | lb_t = test_labels 53 | 54 | # 训练xgboost （仅根据验证集表现调参）---------------------------------------------------------------------------------- 55 | dtrain = xgb.DMatrix(pred_r, label=lb_r) 56 | dval = xgb.DMatrix(pred_v, label=lb_v) 57 | dtest = xgb.DMatrix(pred_t, label=lb_t) 58 | 59 | param = [('max_depth', 10), ('objective', 'multi:softmax'), 60 | ('eval_metric', 'merror'), ('subsample', 0.5), 61 | ('eta', 0.01), ('num_class', 9), ('min_child_weight', 1.0), 62 | ('gamma', 0.2), ('colsample_bytree', 0.7), 63 | ('lambda', 20), ('max_delta_step', 7) 64 | ] 65 | 66 | watchlist = [(dtrain, 'train'), (dval, 'test')] 67 | num_round = 25 68 | bst = xgb.train(param, dtrain, num_round, watchlist) 69 | 70 | # 评估在过采样验证集，原始验证集，以及测试集上的性能 ------------------------------------------------------------------- 71 | pred = bst.predict(dval) 72 | Conf_Matv = confusion_matrix(lb_v, pred) 73 | 74 | print('\nResult for oversampling val_set:--------------------\n') 75 | F1s_val = [] 76 | for j in range(Conf_Matv.shape[0]): 77 | f1vt = 2*Conf_Matv[j][j]/(np.sum(Conf_Matv[j, :])+np.sum(Conf_Matv[:, j])) 78 | print('| F1-'+config.CLASS_NAME[j]+':'+str(f1vt)+' |') 79 | F1s_val.append(f1vt) 80 | print('\nF1-mean: ' + str(np.mean(F1s_val))) 81 | 82 | val_records_raw, val_records_ind = np.unique(val_records, return_index=True) 83 | lb_v_raw = lb_v[val_records_ind] 84 | pred_raw = pred[val_records_ind] 85 | 86 | Conf_Matv_raw = confusion_matrix(lb_v_raw,pred_raw) 87 | print('\nResult for raw val_set:--------------------\n') 88 | F1s_val_raw = [] 89 | for j in range(Conf_Matv_raw.shape[0]): 90 | f1vt = 2*Conf_Matv_raw[j][j]/(np.sum(Conf_Matv_raw[j, :])+np.sum(Conf_Matv_raw[:, j])) 91 | print('| F1-'+config.CLASS_NAME[j]+':'+str(f1vt)+' |') 92 | F1s_val_raw.append(f1vt) 93 | print('\nF1-mean: ' + str(np.mean(F1s_val_raw))) 94 | 95 | pred = bst.predict(dtest) 96 | Conf_Matt = confusion_matrix(lb_t, pred) 97 | 98 | print('\nResult for test_set:--------------------\n') 99 | F1s_test = [] 100 | for j in range(Conf_Matt.shape[0]): 101 | f1tt = 2*Conf_Matt[j][j]/(np.sum(Conf_Matt[j, :])+np.sum(Conf_Matt[:, j])) 102 | print('| F1-'+config.CLASS_NAME[j]+':'+str(f1tt)+' |') 103 | F1s_test.append(f1tt) 104 | print('\nF1-mean: ' + str(np.mean(F1s_test))) 105 | -------------------------------------------------------------------------------- /CPSC_model.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Dec 26 13:40:31 2018 4 | 5 | @author: Winham 6 | 7 | # CPSC_model.py:深度学习网络模型和人工HRV特征提取 8 | 9 | """ 10 | 11 | import warnings 12 | import numpy as np 13 | from keras.layers import Conv1D, BatchNormalization, Activation, AveragePooling1D, Dense 14 | from keras.layers import Dropout, Concatenate, Flatten, Lambda 15 | from keras import regularizers 16 | from keras.layers import Reshape, CuDNNLSTM, Bidirectional 17 | from biosppy.signals import ecg 18 | from pyentrp import entropy as ent 19 | import CPSC_utils as utils 20 | 21 | warnings.filterwarnings("ignore") 22 | 23 | 24 | class Net(object): 25 | """ 26 | 结合CNN和RNN（双向LSTM）的深度学习网络模型 27 | """ 28 | def __init__(self): 29 | pass 30 | 31 | @staticmethod 32 | def __slice(x, index): 33 | return x[:, :, index] 34 | 35 | @staticmethod 36 | def __backbone(inp, C=0.001, initial='he_normal'): 37 | """ 38 | # 用于信号片段特征学习的卷积层组合 39 | :param inp: keras tensor, 单个信号切片输入 40 | :param C: double, 正则化系数， 默认0.001 41 | :param initial: str, 初始化方式， 默认he_normal 42 | :return: keras tensor, 单个信号切片经过卷积层后的输出 43 | """ 44 | net = Conv1D(4, 31, padding='same', kernel_initializer=initial, kernel_regularizer=regularizers.l2(C))(inp) 45 | net = BatchNormalization()(net) 46 | net = Activation('relu')(net) 47 | net = AveragePooling1D(5, 5)(net) 48 | 49 | net = Conv1D(8, 11, padding='same', kernel_initializer=initial, kernel_regularizer=regularizers.l2(C))(net) 50 | net = BatchNormalization()(net) 51 | net = Activation('relu')(net) 52 | net = AveragePooling1D(5, 5)(net) 53 | 54 | net = Conv1D(8, 7, padding='same', kernel_initializer=initial, kernel_regularizer=regularizers.l2(C))(net) 55 | net = BatchNormalization()(net) 56 | net = Activation('relu')(net) 57 | net = AveragePooling1D(5, 5)(net) 58 | 59 | net = Conv1D(16, 5, padding='same', kernel_initializer=initial, kernel_regularizer=regularizers.l2(C))(net) 60 | net = BatchNormalization()(net) 61 | net = Activation('relu')(net) 62 | net = AveragePooling1D(int(net.shape[1]), int(net.shape[1]))(net) 63 | 64 | return net 65 | 66 | @staticmethod 67 | def nnet(inputs, keep_prob, num_classes): 68 | """ 69 | # 适用于单导联的深度网络模型 70 | :param inputs: keras tensor, 切片并堆叠后的单导联信号. 71 | :param keep_prob: float, dropout-随机片段屏蔽概率. 72 | :param num_classes: int, 目标类别数. 73 | :return: keras tensor， 各类概率及全连接层前自动提取的特征. 74 | """ 75 | branches = [] 76 | for i in range(int(inputs.shape[-1])): 77 | ld = Lambda(Net.__slice, output_shape=(int(inputs.shape[1]), 1), arguments={'index': i})(inputs) 78 | ld = Reshape((int(inputs.shape[1]), 1))(ld) 79 | bch = Net.__backbone(ld) 80 | branches.append(bch) 81 | features = Concatenate(axis=1)(branches) 82 | features = Dropout(keep_prob, [1, int(inputs.shape[-1]), 1])(features) 83 | features = Bidirectional(CuDNNLSTM(1, return_sequences=True), merge_mode='concat')(features) 84 | features = Flatten()(features) 85 | net = Dense(units=num_classes, activation='softmax')(features) 86 | return net, features 87 | 88 | 89 | class ManFeat_HRV(object): 90 | """ 91 | 针对一条记录的HRV特征提取， 以II导联为基准 92 | """ 93 | FEAT_DIMENSION = 9 94 | 95 | def __init__(self, sig, fs=250.0): 96 | assert len(sig.shape) == 1, 'The signal must be 1-dimension.' 97 | assert sig.shape[0] >= fs * 6, 'The signal must >= 6 seconds.' 98 | self.sig = utils.WTfilt_1d(sig) 99 | self.fs = fs 100 | self.rpeaks, = ecg.hamilton_segmenter(signal=self.sig, sampling_rate=self.fs) 101 | self.rpeaks, = ecg.correct_rpeaks(signal=self.sig, rpeaks=self.rpeaks, 102 | sampling_rate=self.fs) 103 | self.RR_intervals = np.diff(self.rpeaks) 104 | self.dRR = np.diff(self.RR_intervals) 105 | 106 | def __get_sdnn(self): # 计算RR间期标准差 107 | return np.array([np.std(self.RR_intervals)]) 108 | 109 | def __get_maxRR(self): # 计算最大RR间期 110 | return np.array([np.max(self.RR_intervals)]) 111 | 112 | def __get_minRR(self): # 计算最小RR间期 113 | return np.array([np.min(self.RR_intervals)]) 114 | 115 | def __get_meanRR(self): # 计算平均RR间期 116 | return np.array([np.mean(self.RR_intervals)]) 117 | 118 | def __get_Rdensity(self): # 计算R波密度 119 | return np.array([(self.RR_intervals.shape[0] + 1) 120 | / self.sig.shape[0] * self.fs]) 121 | 122 | def __get_pNN50(self): # 计算pNN50 123 | return np.array([self.dRR[self.dRR >= self.fs*0.05].shape[0] 124 | / self.RR_intervals.shape[0]]) 125 | 126 | def __get_RMSSD(self): # 计算RMSSD 127 | return np.array([np.sqrt(np.mean(self.dRR*self.dRR))]) 128 | 129 | def __get_SampEn(self): # 计算RR间期采样熵 130 | sampEn = ent.sample_entropy(self.RR_intervals, 131 | 2, 0.2 * np.std(self.RR_intervals)) 132 | for i in range(len(sampEn)): 133 | if np.isnan(sampEn[i]): 134 | sampEn[i] = -2 135 | if np.isinf(sampEn[i]): 136 | sampEn[i] = -1 137 | return sampEn 138 | 139 | def extract_features(self): # 提取HRV所有特征 140 | features = np.concatenate((self.__get_sdnn(), 141 | self.__get_maxRR(), 142 | self.__get_minRR(), 143 | self.__get_meanRR(), 144 | self.__get_Rdensity(), 145 | self.__get_pNN50(), 146 | self.__get_RMSSD(), 147 | self.__get_SampEn(), 148 | )) 149 | assert features.shape[0] == ManFeat_HRV.FEAT_DIMENSION 150 | return features 151 | 152 | -------------------------------------------------------------------------------- /CPSC_train_multi_leads.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Dec 26 14:41:17 2018 4 | 5 | @author: Winham 6 | 7 | # CPSC_train_multi_leads.py: 针对每个导联训练网络并保存模型，主体与CPSC_train_single_lead.py基本一致 8 | 9 | """ 10 | 11 | import os 12 | import warnings 13 | import numpy as np 14 | import tensorflow as tf 15 | from keras import backend as bk 16 | from keras import optimizers 17 | from keras.layers import Input 18 | from keras.models import Model, load_model 19 | from keras.utils import to_categorical 20 | from keras.callbacks import ModelCheckpoint, LearningRateScheduler 21 | from sklearn.preprocessing import scale 22 | from sklearn.metrics import confusion_matrix 23 | from sklearn.model_selection import train_test_split 24 | from CPSC_model import Net 25 | from CPSC_config import Config 26 | import CPSC_utils as utils 27 | 28 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2' 29 | warnings.filterwarnings("ignore") 30 | config = Config() 31 | 32 | records_name = np.array(os.listdir(config.DATA_PATH)) 33 | records_label = np.load(config.REVISED_LABEL) - 1 34 | class_num = len(np.unique(records_label)) 35 | 36 | train_val_records, test_records, train_val_labels, test_labels = train_test_split( 37 | records_name, records_label, test_size=0.2, random_state=config.RANDOM_STATE) 38 | del test_records, test_labels 39 | 40 | train_records, val_records, train_labels, val_labels = train_test_split( 41 | train_val_records, train_val_labels, test_size=0.2, random_state=config.RANDOM_STATE) 42 | 43 | train_records, train_labels = utils.oversample_balance(train_records, train_labels, config.RANDOM_STATE) 44 | val_records, val_labels = utils.oversample_balance(val_records, val_labels, config.RANDOM_STATE) 45 | 46 | for i in range(config.LEAD_NUM): 47 | TARGET_LEAD = i 48 | print('Fetching data for Lead ' + str(TARGET_LEAD) + ' ...-----------------\n') 49 | train_x = utils.Fetch_Pats_Lbs_sLead(train_records, Path=config.DATA_PATH, 50 | target_lead=TARGET_LEAD, seg_num=config.SEG_NUM, 51 | seg_length=config.SEG_LENGTH) 52 | train_y = to_categorical(train_labels, num_classes=class_num) 53 | val_x = utils.Fetch_Pats_Lbs_sLead(val_records, Path=config.DATA_PATH, 54 | target_lead=TARGET_LEAD, seg_num=config.SEG_NUM, 55 | seg_length=config.SEG_LENGTH) 56 | val_y = to_categorical(val_labels, num_classes=class_num) 57 | 58 | model_name = 'net_lead_' + str(TARGET_LEAD) + '.hdf5' 59 | 60 | print('Scaling data ...-----------------\n') 61 | for j in range(train_x.shape[0]): 62 | train_x[j, :, :] = scale(train_x[j, :, :], axis=0) 63 | for j in range(val_x.shape[0]): 64 | val_x[j, :, :] = scale(val_x[j, :, :], axis=0) 65 | 66 | batch_size = 64 67 | epochs = 100 68 | momentum = 0.9 69 | keep_prob = 0.5 70 | 71 | bk.clear_session() 72 | tf.reset_default_graph() 73 | 74 | inputs = Input(shape=(config.SEG_LENGTH, config.SEG_NUM)) 75 | net = Net() 76 | outputs, _ = net.nnet(inputs, keep_prob, num_classes=class_num) 77 | model = Model(inputs=inputs, outputs=outputs) 78 | 79 | opt = optimizers.SGD(lr=config.lr_schedule(0), momentum=momentum) 80 | model.compile(optimizer=opt, loss='categorical_crossentropy', 81 | metrics=['categorical_accuracy']) 82 | 83 | checkpoint = ModelCheckpoint(filepath=config.MODEL_PATH+model_name, 84 | monitor='val_categorical_accuracy', mode='max', 85 | save_best_only='True') 86 | lr_scheduler = LearningRateScheduler(config.lr_schedule) 87 | callback_lists = [checkpoint, lr_scheduler] 88 | model.fit(x=train_x, y=train_y, batch_size=batch_size, epochs=epochs, verbose=2, 89 | validation_data=(val_x, val_y), callbacks=callback_lists) 90 | 91 | del train_x, train_y 92 | 93 | model = load_model(config.MODEL_PATH + model_name) 94 | 95 | pred_vt = model.predict(val_x, batch_size=batch_size, verbose=1) 96 | pred_v = np.argmax(pred_vt, axis=1) 97 | true_v = np.argmax(val_y, axis=1) 98 | del val_x, val_y 99 | 100 | Conf_Mat_val = confusion_matrix(true_v, pred_v) 101 | print('\nResult for Lead ' + str(TARGET_LEAD) + '-----------------------------\n') 102 | print(Conf_Mat_val) 103 | F1s_val = [] 104 | for j in range(class_num): 105 | f1t = 2 * Conf_Mat_val[j][j] / (np.sum(Conf_Mat_val[j, :]) + np.sum(Conf_Mat_val[:, j])) 106 | print('| F1-' + config.CLASS_NAME[j] + ':' + str(f1t) + ' |') 107 | F1s_val.append(f1t) 108 | print('F1-mean: ' + str(np.mean(F1s_val))) 109 | -------------------------------------------------------------------------------- /CPSC_train_single_lead.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Dec 26 14:21:06 2018 4 | 5 | @author: Winham 6 | 7 | # CPSC_train_single_lead.py: 训练针对指定导联的网络模型 8 | 9 | """ 10 | 11 | import os 12 | import warnings 13 | import numpy as np 14 | from keras import optimizers 15 | from keras.layers import Input 16 | from keras.models import Model, load_model 17 | from keras.utils import to_categorical 18 | from keras.callbacks import ModelCheckpoint, LearningRateScheduler 19 | from sklearn.preprocessing import scale 20 | from sklearn.metrics import confusion_matrix 21 | from sklearn.model_selection import train_test_split 22 | from CPSC_model import Net 23 | from CPSC_config import Config 24 | import CPSC_utils as utils 25 | 26 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2' 27 | warnings.filterwarnings("ignore") 28 | config = Config() 29 | 30 | records_name = np.array(os.listdir(config.DATA_PATH)) 31 | records_label = np.load(config.REVISED_LABEL) - 1 32 | class_num = len(np.unique(records_label)) 33 | 34 | # 划分训练，验证与测试集 ----------------------------------------------------------------------------------------------- 35 | train_val_records, test_records, train_val_labels, test_labels = train_test_split( 36 | records_name, records_label, test_size=0.2, random_state=config.RANDOM_STATE) 37 | del test_records, test_labels 38 | 39 | train_records, val_records, train_labels, val_labels = train_test_split( 40 | train_val_records, train_val_labels, test_size=0.2, random_state=config.RANDOM_STATE) 41 | 42 | # 过采样使训练和验证集样本分布平衡 ------------------------------------------------------------------------------------- 43 | train_records, train_labels = utils.oversample_balance(train_records, train_labels, config.RANDOM_STATE) 44 | val_records, val_labels = utils.oversample_balance(val_records, val_labels, config.RANDOM_STATE) 45 | 46 | # 取出训练集和测试集病人对应导联信号，并进行切片和z-score标准化 -------------------------------------------------------- 47 | print('Fetching data ...-----------------\n') 48 | TARGET_LEAD = 1 49 | train_x = utils.Fetch_Pats_Lbs_sLead(train_records, Path=config.DATA_PATH, 50 | target_lead=TARGET_LEAD, seg_num=config.SEG_NUM, 51 | seg_length=config.SEG_LENGTH) 52 | train_y = to_categorical(train_labels, num_classes=class_num) 53 | val_x = utils.Fetch_Pats_Lbs_sLead(val_records, Path=config.DATA_PATH, 54 | target_lead=TARGET_LEAD, seg_num=config.SEG_NUM, 55 | seg_length=config.SEG_LENGTH) 56 | val_y = to_categorical(val_labels, num_classes=class_num) 57 | 58 | model_name = 'net_lead_' + str(TARGET_LEAD) + '.hdf5' 59 | 60 | print('Scaling data ...-----------------\n') 61 | for j in range(train_x.shape[0]): 62 | train_x[j, :, :] = scale(train_x[j, :, :], axis=0) 63 | for j in range(val_x.shape[0]): 64 | val_x[j, :, :] = scale(val_x[j, :, :], axis=0) 65 | 66 | # 设定训练参数，搭建模型进行训练 （仅根据验证集调参，以及保存性能最好的模型）------------------------------------------- 67 | batch_size = 64 68 | epochs = 100 69 | momentum = 0.9 70 | keep_prob = 0.5 71 | 72 | inputs = Input(shape=(config.SEG_LENGTH, config.SEG_NUM)) 73 | net = Net() 74 | outputs, _ = net.nnet(inputs, keep_prob, num_classes=class_num) 75 | model = Model(inputs=inputs, outputs=outputs) 76 | 77 | opt = optimizers.SGD(lr=config.lr_schedule(0), momentum=momentum) 78 | model.compile(optimizer=opt, loss='categorical_crossentropy', 79 | metrics=['categorical_accuracy']) 80 | 81 | checkpoint = ModelCheckpoint(filepath=config.MODEL_PATH+model_name, 82 | monitor='val_categorical_accuracy', mode='max', 83 | save_best_only='True') 84 | lr_scheduler = LearningRateScheduler(config.lr_schedule) 85 | callback_lists = [checkpoint, lr_scheduler] 86 | model.fit(x=train_x, y=train_y, batch_size=batch_size, epochs=epochs, verbose=1, 87 | validation_data=(val_x, val_y), callbacks=callback_lists) 88 | 89 | del train_x, train_y 90 | 91 | model = load_model(config.MODEL_PATH + model_name) 92 | 93 | pred_vt = model.predict(val_x, batch_size=batch_size, verbose=1) 94 | pred_v = np.argmax(pred_vt, axis=1) 95 | true_v = np.argmax(val_y, axis=1) 96 | del val_x, val_y 97 | 98 | # 评估模型在验证集上的性能 --------------------------------------------------------------------------------------------- 99 | Conf_Mat_val = confusion_matrix(true_v, pred_v) 100 | print('Result-----------------------------\n') 101 | print(Conf_Mat_val) 102 | F1s_val = [] 103 | for j in range(class_num): 104 | f1t = 2 * Conf_Mat_val[j][j] / (np.sum(Conf_Mat_val[j, :]) + np.sum(Conf_Mat_val[:, j])) 105 | print('| F1-' + config.CLASS_NAME[j] + ':' + str(f1t) + ' |') 106 | F1s_val.append(f1t) 107 | 108 | print('F1-mean: ' + str(np.mean(F1s_val))) 109 | -------------------------------------------------------------------------------- /CPSC_utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Dec 5 20:26:27 2018 4 | 5 | @author: Winham 6 | 7 | # CPSC_utils.py: 辅助函数模块 8 | 9 | """ 10 | 11 | import numpy as np 12 | import pywt 13 | 14 | 15 | def WTfilt_1d(sig): 16 | """ 17 | # 使用小波变换对单导联ECG滤波 18 | # 参考：Martis R J, Acharya U R, Min L C. ECG beat classification using PCA, LDA, ICA and discrete 19 | wavelet transform[J].Biomedical Signal Processing and Control, 2013, 8(5): 437-448. 20 | :param sig: 1-D numpy Array，单导联ECG 21 | :return: 1-D numpy Array，滤波后信号 22 | """ 23 | coeffs = pywt.wavedec(sig, 'db6', level=9) 24 | coeffs[-1] = np.zeros(len(coeffs[-1])) 25 | coeffs[-2] = np.zeros(len(coeffs[-2])) 26 | coeffs[0] = np.zeros(len(coeffs[0])) 27 | sig_filt = pywt.waverec(coeffs, 'db6') 28 | return sig_filt 29 | 30 | 31 | def SegSig_1d(sig, seg_length=1500, overlap_length=0, 32 | full_seg=True, stt=0): 33 | """ 34 | # 按指定参数对单导联ECG进行切片 35 | :param sig: 1-D numpy Array，单导联ECG 36 | :param seg_length: int，切片的采样点长度 37 | :param overlap_length: int, 切片之间相互覆盖的采样点长度，默认为0 38 | :param full_seg: bool， 是否对信号末尾不足seg_length的片段进行延拓并切片，默认True 39 | :param stt: int, 开始进行切片的位置， 默认从头开始（0） 40 | :return: 2-D numpy Array, 切片个数 * 切片长度 41 | """ 42 | length = len(sig) 43 | SEGs = np.zeros([1, seg_length]) 44 | start = stt 45 | while start+seg_length <= length: 46 | tmp = sig[start:start+seg_length].reshape([1, seg_length]) 47 | SEGs = np.concatenate((SEGs, tmp)) 48 | start += seg_length 49 | start -= overlap_length 50 | if full_seg: 51 | if start < length: 52 | pad_length = seg_length-(length-start) 53 | tmp = np.concatenate((sig[start:length].reshape([1, length-start]), 54 | sig[:pad_length].reshape([1, pad_length])), axis=1) 55 | SEGs = np.concatenate((SEGs, tmp)) 56 | SEGs = SEGs[1:] 57 | return SEGs 58 | 59 | 60 | def Pad_1d(sig, target_length): 61 | """ 62 | # 对小于target_length的信号进行补零 63 | :param sig: 1-D numpy Array，输入信号 64 | :param target_length: int，目标长度 65 | :return: 1-D numpy Array，输出补零后的信号 66 | """ 67 | pad_length = target_length - sig.shape[0] 68 | if pad_length > 0: 69 | sig = np.concatenate((sig, np.zeros(int(pad_length)))) 70 | return sig 71 | 72 | 73 | def Stack_Segs_generate(sig, seg_num=24, seg_length=1500, full_seg=True, stt=0): 74 | """ 75 | # 对单导联信号滤波，按照指定切片数目和长度进行切片，并堆叠为矩阵 76 | :param sig: 1-D numpy Array, 输入单导联信号 77 | :param seg_num: int，指定切片个数 78 | :param seg_length: int，指定切片采样点长度 79 | :param full_seg: bool，是否对信号末尾不足seg_length的片段进行延拓并切片，默认True 80 | :param stt: int, 开始进行切片的位置， 默认从头开始（0） 81 | :return: 3-D numpy Array, 1 * 切片长度 * 切片个数 82 | """ 83 | sig = WTfilt_1d(sig) 84 | if len(sig) < seg_length+seg_num: 85 | sig = Pad_1d(sig, target_length=(seg_length+seg_num-1)) 86 | 87 | overlap_length = int(seg_length-(len(sig) - seg_length)/(seg_num-1)) 88 | 89 | if (len(sig) - seg_length) % (seg_num-1) == 0: 90 | full_seg = False 91 | 92 | SEGs = SegSig_1d(sig, seg_length=seg_length, 93 | overlap_length=overlap_length, full_seg=full_seg, stt=stt) 94 | del sig 95 | SEGs = SEGs.transpose() 96 | SEGs = SEGs.reshape([1, SEGs.shape[0], SEGs.shape[1]]) 97 | return SEGs 98 | 99 | 100 | def Fetch_Pats_Lbs_sLead(Pat_files, Path, target_lead=1, seg_num=24, 101 | seg_length=1500, full_seg=True, stt=0, buf_size=100): 102 | """ 103 | # 对指定病人的单导联信号进行滤波，按照指定切片数目和长度进行切片，并堆叠为矩阵 104 | :param Pat_files: list or 1-D numpy Array, 指定病人文件 105 | :param Path: str，数据存放路径 106 | :param target_lead: int，指定单导联，例如1指II导联 107 | :param seg_num: int，指定切片个数 108 | :param seg_length: int，指定切片采样点长度 109 | :param full_seg: bool，是否对信号末尾不足seg_length的片段进行延拓并切片，默认True 110 | :param stt: int, 开始进行切片的位置， 默认从头开始（0） 111 | :param buf_size: 用于加速过程的缓存Array大小，默认为100 112 | :return: 113 | """ 114 | seg_length = int(seg_length) 115 | SEG_buf = np.zeros([1, seg_length, seg_num]) 116 | SEGs = np.zeros([1, seg_length, seg_num]) 117 | for i in range(len(Pat_files)): 118 | sig = np.load(Path+Pat_files[i])[target_lead, :] 119 | SEGt = Stack_Segs_generate(sig, seg_num=seg_num, 120 | seg_length=seg_length, full_seg=full_seg, stt=stt) 121 | SEG_buf = np.concatenate((SEG_buf, SEGt)) 122 | del SEGt 123 | if SEG_buf.shape[0] >= buf_size: 124 | SEGs = np.concatenate((SEGs, SEG_buf[1:])) 125 | del SEG_buf 126 | SEG_buf = np.zeros([1, seg_length, seg_num]) 127 | if SEG_buf.shape[0] > 1: 128 | SEGs = np.concatenate((SEGs, SEG_buf[1:])) 129 | del SEG_buf 130 | return SEGs[1:] 131 | 132 | 133 | def oversample_balance(records, labels, rand_seed): 134 | """ 135 | # 通过随机过采样使各类样本数目平衡 136 | :param records: 1-D numpy Array，不平衡样本记录名集合 137 | :param labels: 1-D numpy Array，对应标签 138 | :param rand_seed：int, 随机数种子 139 | :return: 平衡后的记录名集合和对应标签 140 | """ 141 | class_num = len(np.unique(labels)) 142 | num_records = len(records) 143 | num_categories = [] 144 | for i in range(class_num): 145 | num_categories.append(len(labels[labels == i])) 146 | upsample_rate = max(num_categories)/np.array(num_categories)-1 147 | for i in range(class_num): 148 | rate = upsample_rate[i] 149 | if rate < 1 and rate > 0: 150 | records_this_class = records[labels == i] 151 | oversample_size = int(np.ceil(num_categories[i]*rate)) 152 | np.random.seed(rand_seed) 153 | rand_sample = np.random.choice(records_this_class, 154 | size=oversample_size, 155 | replace=False) 156 | records = np.concatenate((records, rand_sample)) 157 | labels = np.concatenate((labels, np.ones(oversample_size)*i)) 158 | over_sample_records = [] 159 | over_sample_labels = [] 160 | for i in range(num_records): 161 | rate = upsample_rate[int(labels[i])] 162 | if rate >= 1: 163 | over_sample_records = over_sample_records + [records[i]] * int(round(rate)) 164 | over_sample_labels = over_sample_labels + [labels[i]] * int(round(rate)) 165 | 166 | records = np.concatenate((records, np.array(over_sample_records))) 167 | labels = np.concatenate((labels, np.array(over_sample_labels))) 168 | return records, labels 169 | -------------------------------------------------------------------------------- /DataSet_250Hz.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/CPSC_Scheme/f7a593502833bc6a494179dada19c2e3c5d40fe2/DataSet_250Hz.zip -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Aiwiscal 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Man_features.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/CPSC_Scheme/f7a593502833bc6a494179dada19c2e3c5d40fe2/Man_features.zip -------------------------------------------------------------------------------- /Net_models.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/CPSC_Scheme/f7a593502833bc6a494179dada19c2e3c5d40fe2/Net_models.zip -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CPSC_Scheme 2 | 3 | ## Blog: 4 | https://blog.csdn.net/qq_15746879 5 | 一个不成熟的开源小方案：关于2018中国生理信号挑战赛（CPSC-2018）(1)-(4) 6 | 7 | ## Environment： 8 | biosppy==0.6.1 9 | h5py==2.6.0 10 | keras==2.2.4 11 | numpy==1.15.4 12 | pandas==0.19.2 13 | pyentrp==0.5.0 14 | PyWavelets==1.0.1 15 | scikit-learn==0.18.1 16 | tensorflow-gpu==1.9.0 17 | xgboost==0.81 18 | 19 | ## warning: 20 | A CUDA device is required if you want to use the models shared in this repo. 21 | If you don't have a CUDA device, change the "CuDNNLSTM" in CPSC_model.py to "LSTM" and train it using CPUs.But it may be very slow. 22 | -------------------------------------------------------------------------------- /Record_Label.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/CPSC_Scheme/f7a593502833bc6a494179dada19c2e3c5d40fe2/Record_Label.npy -------------------------------------------------------------------------------- /info_age_gen.csv: -------------------------------------------------------------------------------- 1 | ,A0001.npy,A0002.npy,A0003.npy,A0004.npy,A0005.npy,A0006.npy,A0007.npy,A0008.npy,A0009.npy,A0010.npy,A0011.npy,A0012.npy,A0013.npy,A0014.npy,A0015.npy,A0016.npy,A0017.npy,A0018.npy,A0019.npy,A0020.npy,A0021.npy,A0022.npy,A0023.npy,A0024.npy,A0025.npy,A0026.npy,A0027.npy,A0028.npy,A0029.npy,A0030.npy,A0031.npy,A0032.npy,A0033.npy,A0034.npy,A0035.npy,A0036.npy,A0037.npy,A0038.npy,A0039.npy,A0040.npy,A0041.npy,A0042.npy,A0043.npy,A0044.npy,A0045.npy,A0046.npy,A0047.npy,A0048.npy,A0049.npy,A0050.npy,A0051.npy,A0052.npy,A0053.npy,A0054.npy,A0055.npy,A0056.npy,A0057.npy,A0058.npy,A0059.npy,A0060.npy,A0061.npy,A0062.npy,A0063.npy,A0064.npy,A0065.npy,A0066.npy,A0067.npy,A0068.npy,A0069.npy,A0070.npy,A0071.npy,A0072.npy,A0073.npy,A0074.npy,A0075.npy,A0076.npy,A0077.npy,A0078.npy,A0079.npy,A0080.npy,A0081.npy,A0082.npy,A0083.npy,A0084.npy,A0085.npy,A0086.npy,A0087.npy,A0088.npy,A0089.npy,A0090.npy,A0091.npy,A0092.npy,A0093.npy,A0094.npy,A0095.npy,A0096.npy,A0097.npy,A0098.npy,A0099.npy,A0100.npy,A0101.npy,A0102.npy,A0103.npy,A0104.npy,A0105.npy,A0106.npy,A0107.npy,A0108.npy,A0109.npy,A0110.npy,A0111.npy,A0112.npy,A0113.npy,A0114.npy,A0115.npy,A0116.npy,A0117.npy,A0118.npy,A0119.npy,A0120.npy,A0121.npy,A0122.npy,A0123.npy,A0124.npy,A0125.npy,A0126.npy,A0127.npy,A0128.npy,A0129.npy,A0130.npy,A0131.npy,A0132.npy,A0133.npy,A0134.npy,A0135.npy,A0136.npy,A0137.npy,A0138.npy,A0139.npy,A0140.npy,A0141.npy,A0142.npy,A0143.npy,A0144.npy,A0145.npy,A0146.npy,A0147.npy,A0148.npy,A0149.npy,A0150.npy,A0151.npy,A0152.npy,A0153.npy,A0154.npy,A0155.npy,A0156.npy,A0157.npy,A0158.npy,A0159.npy,A0160.npy,A0161.npy,A0162.npy,A0163.npy,A0164.npy,A0165.npy,A0166.npy,A0167.npy,A0168.npy,A0169.npy,A0170.npy,A0171.npy,A0172.npy,A0173.npy,A0174.npy,A0175.npy,A0176.npy,A0177.npy,A0178.npy,A0179.npy,A0180.npy,A0181.npy,A0182.npy,A0183.npy,A0184.npy,A0185.npy,A0186.npy,A0187.npy,A0188.npy,A0189.npy,A0190.npy,A0191.npy,A0192.npy,A0193.npy,A0194.npy,A0195.npy,A0196.npy,A0197.npy,A0198.npy,A0199.npy,A0200.npy,A0201.npy,A0202.npy,A0203.npy,A0204.npy,A0205.npy,A0206.npy,A0207.npy,A0208.npy,A0209.npy,A0210.npy,A0211.npy,A0212.npy,A0213.npy,A0214.npy,A0215.npy,A0216.npy,A0217.npy,A0218.npy,A0219.npy,A0220.npy,A0221.npy,A0222.npy,A0223.npy,A0224.npy,A0225.npy,A0226.npy,A0227.npy,A0228.npy,A0229.npy,A0230.npy,A0231.npy,A0232.npy,A0233.npy,A0234.npy,A0235.npy,A0236.npy,A0237.npy,A0238.npy,A0239.npy,A0240.npy,A0241.npy,A0242.npy,A0243.npy,A0244.npy,A0245.npy,A0246.npy,A0247.npy,A0248.npy,A0249.npy,A0250.npy,A0251.npy,A0252.npy,A0253.npy,A0254.npy,A0255.npy,A0256.npy,A0257.npy,A0258.npy,A0259.npy,A0260.npy,A0261.npy,A0262.npy,A0263.npy,A0264.npy,A0265.npy,A0266.npy,A0267.npy,A0268.npy,A0269.npy,A0270.npy,A0271.npy,A0272.npy,A0273.npy,A0274.npy,A0275.npy,A0276.npy,A0277.npy,A0278.npy,A0279.npy,A0280.npy,A0281.npy,A0282.npy,A0283.npy,A0284.npy,A0285.npy,A0286.npy,A0287.npy,A0288.npy,A0289.npy,A0290.npy,A0291.npy,A0292.npy,A0293.npy,A0294.npy,A0295.npy,A0296.npy,A0297.npy,A0298.npy,A0299.npy,A0300.npy,A0301.npy,A0302.npy,A0303.npy,A0304.npy,A0305.npy,A0306.npy,A0307.npy,A0308.npy,A0309.npy,A0310.npy,A0311.npy,A0312.npy,A0313.npy,A0314.npy,A0315.npy,A0316.npy,A0317.npy,A0318.npy,A0319.npy,A0320.npy,A0321.npy,A0322.npy,A0323.npy,A0324.npy,A0325.npy,A0326.npy,A0327.npy,A0328.npy,A0329.npy,A0330.npy,A0331.npy,A0332.npy,A0333.npy,A0334.npy,A0335.npy,A0336.npy,A0337.npy,A0338.npy,A0339.npy,A0340.npy,A0341.npy,A0342.npy,A0343.npy,A0344.npy,A0345.npy,A0346.npy,A0347.npy,A0348.npy,A0349.npy,A0350.npy,A0351.npy,A0352.npy,A0353.npy,A0354.npy,A0355.npy,A0356.npy,A0357.npy,A0358.npy,A0359.npy,A0360.npy,A0361.npy,A0362.npy,A0363.npy,A0364.npy,A0365.npy,A0366.npy,A0367.npy,A0368.npy,A0369.npy,A0370.npy,A0371.npy,A0372.npy,A0373.npy,A0374.npy,A0375.npy,A0376.npy,A0377.npy,A0378.npy,A0379.npy,A0380.npy,A0381.npy,A0382.npy,A0383.npy,A0384.npy,A0385.npy,A0386.npy,A0387.npy,A0388.npy,A0389.npy,A0390.npy,A0391.npy,A0392.npy,A0393.npy,A0394.npy,A0395.npy,A0396.npy,A0397.npy,A0398.npy,A0399.npy,A0400.npy,A0401.npy,A0402.npy,A0403.npy,A0404.npy,A0405.npy,A0406.npy,A0407.npy,A0408.npy,A0409.npy,A0410.npy,A0411.npy,A0412.npy,A0413.npy,A0414.npy,A0415.npy,A0416.npy,A0417.npy,A0418.npy,A0419.npy,A0420.npy,A0421.npy,A0422.npy,A0423.npy,A0424.npy,A0425.npy,A0426.npy,A0427.npy,A0428.npy,A0429.npy,A0430.npy,A0431.npy,A0432.npy,A0433.npy,A0434.npy,A0435.npy,A0436.npy,A0437.npy,A0438.npy,A0439.npy,A0440.npy,A0441.npy,A0442.npy,A0443.npy,A0444.npy,A0445.npy,A0446.npy,A0447.npy,A0448.npy,A0449.npy,A0450.npy,A0451.npy,A0452.npy,A0453.npy,A0454.npy,A0455.npy,A0456.npy,A0457.npy,A0458.npy,A0459.npy,A0460.npy,A0461.npy,A0462.npy,A0463.npy,A0464.npy,A0465.npy,A0466.npy,A0467.npy,A0468.npy,A0469.npy,A0470.npy,A0471.npy,A0472.npy,A0473.npy,A0474.npy,A0475.npy,A0476.npy,A0477.npy,A0478.npy,A0479.npy,A0480.npy,A0481.npy,A0482.npy,A0483.npy,A0484.npy,A0485.npy,A0486.npy,A0487.npy,A0488.npy,A0489.npy,A0490.npy,A0491.npy,A0492.npy,A0493.npy,A0494.npy,A0495.npy,A0496.npy,A0497.npy,A0498.npy,A0499.npy,A0500.npy,A0501.npy,A0502.npy,A0503.npy,A0504.npy,A0505.npy,A0506.npy,A0507.npy,A0508.npy,A0509.npy,A0510.npy,A0511.npy,A0512.npy,A0513.npy,A0514.npy,A0515.npy,A0516.npy,A0517.npy,A0518.npy,A0519.npy,A0520.npy,A0521.npy,A0522.npy,A0523.npy,A0524.npy,A0525.npy,A0526.npy,A0527.npy,A0528.npy,A0529.npy,A0530.npy,A0531.npy,A0532.npy,A0533.npy,A0534.npy,A0535.npy,A0536.npy,A0537.npy,A0538.npy,A0539.npy,A0540.npy,A0541.npy,A0542.npy,A0543.npy,A0544.npy,A0545.npy,A0546.npy,A0547.npy,A0548.npy,A0549.npy,A0550.npy,A0551.npy,A0552.npy,A0553.npy,A0554.npy,A0555.npy,A0556.npy,A0557.npy,A0558.npy,A0559.npy,A0560.npy,A0561.npy,A0562.npy,A0563.npy,A0564.npy,A0565.npy,A0566.npy,A0567.npy,A0568.npy,A0569.npy,A0570.npy,A0571.npy,A0572.npy,A0573.npy,A0574.npy,A0575.npy,A0576.npy,A0577.npy,A0578.npy,A0579.npy,A0580.npy,A0581.npy,A0582.npy,A0583.npy,A0584.npy,A0585.npy,A0586.npy,A0587.npy,A0588.npy,A0589.npy,A0590.npy,A0591.npy,A0592.npy,A0593.npy,A0594.npy,A0595.npy,A0596.npy,A0597.npy,A0598.npy,A0599.npy,A0600.npy,A0601.npy,A0602.npy,A0603.npy,A0604.npy,A0605.npy,A0606.npy,A0607.npy,A0608.npy,A0609.npy,A0610.npy,A0611.npy,A0612.npy,A0613.npy,A0614.npy,A0615.npy,A0616.npy,A0617.npy,A0618.npy,A0619.npy,A0620.npy,A0621.npy,A0622.npy,A0623.npy,A0624.npy,A0625.npy,A0626.npy,A0627.npy,A0628.npy,A0629.npy,A0630.npy,A0631.npy,A0632.npy,A0633.npy,A0634.npy,A0635.npy,A0636.npy,A0637.npy,A0638.npy,A0639.npy,A0640.npy,A0641.npy,A0642.npy,A0643.npy,A0644.npy,A0645.npy,A0646.npy,A0647.npy,A0648.npy,A0649.npy,A0650.npy,A0651.npy,A0652.npy,A0653.npy,A0654.npy,A0655.npy,A0656.npy,A0657.npy,A0658.npy,A0659.npy,A0660.npy,A0661.npy,A0662.npy,A0663.npy,A0664.npy,A0665.npy,A0666.npy,A0667.npy,A0668.npy,A0669.npy,A0670.npy,A0671.npy,A0672.npy,A0673.npy,A0674.npy,A0675.npy,A0676.npy,A0677.npy,A0678.npy,A0679.npy,A0680.npy,A0681.npy,A0682.npy,A0683.npy,A0684.npy,A0685.npy,A0686.npy,A0687.npy,A0688.npy,A0689.npy,A0690.npy,A0691.npy,A0692.npy,A0693.npy,A0694.npy,A0695.npy,A0696.npy,A0697.npy,A0698.npy,A0699.npy,A0700.npy,A0701.npy,A0702.npy,A0703.npy,A0704.npy,A0705.npy,A0706.npy,A0707.npy,A0708.npy,A0709.npy,A0710.npy,A0711.npy,A0712.npy,A0713.npy,A0714.npy,A0715.npy,A0716.npy,A0717.npy,A0718.npy,A0719.npy,A0720.npy,A0721.npy,A0722.npy,A0723.npy,A0724.npy,A0725.npy,A0726.npy,A0727.npy,A0728.npy,A0729.npy,A0730.npy,A0731.npy,A0732.npy,A0733.npy,A0734.npy,A0735.npy,A0736.npy,A0737.npy,A0738.npy,A0739.npy,A0740.npy,A0741.npy,A0742.npy,A0743.npy,A0744.npy,A0745.npy,A0746.npy,A0747.npy,A0748.npy,A0749.npy,A0750.npy,A0751.npy,A0752.npy,A0753.npy,A0754.npy,A0755.npy,A0756.npy,A0757.npy,A0758.npy,A0759.npy,A0760.npy,A0761.npy,A0762.npy,A0763.npy,A0764.npy,A0765.npy,A0766.npy,A0767.npy,A0768.npy,A0769.npy,A0770.npy,A0771.npy,A0772.npy,A0773.npy,A0774.npy,A0775.npy,A0776.npy,A0777.npy,A0778.npy,A0779.npy,A0780.npy,A0781.npy,A0782.npy,A0783.npy,A0784.npy,A0785.npy,A0786.npy,A0787.npy,A0788.npy,A0789.npy,A0790.npy,A0791.npy,A0792.npy,A0793.npy,A0794.npy,A0795.npy,A0796.npy,A0797.npy,A0798.npy,A0799.npy,A0800.npy,A0801.npy,A0802.npy,A0803.npy,A0804.npy,A0805.npy,A0806.npy,A0807.npy,A0808.npy,A0809.npy,A0810.npy,A0811.npy,A0812.npy,A0813.npy,A0814.npy,A0815.npy,A0816.npy,A0817.npy,A0818.npy,A0819.npy,A0820.npy,A0821.npy,A0822.npy,A0823.npy,A0824.npy,A0825.npy,A0826.npy,A0827.npy,A0828.npy,A0829.npy,A0830.npy,A0831.npy,A0832.npy,A0833.npy,A0834.npy,A0835.npy,A0836.npy,A0837.npy,A0838.npy,A0839.npy,A0840.npy,A0841.npy,A0842.npy,A0843.npy,A0844.npy,A0845.npy,A0846.npy,A0847.npy,A0848.npy,A0849.npy,A0850.npy,A0851.npy,A0852.npy,A0853.npy,A0854.npy,A0855.npy,A0856.npy,A0857.npy,A0858.npy,A0859.npy,A0860.npy,A0861.npy,A0862.npy,A0863.npy,A0864.npy,A0865.npy,A0866.npy,A0867.npy,A0868.npy,A0869.npy,A0870.npy,A0871.npy,A0872.npy,A0873.npy,A0874.npy,A0875.npy,A0876.npy,A0877.npy,A0878.npy,A0879.npy,A0880.npy,A0881.npy,A0882.npy,A0883.npy,A0884.npy,A0885.npy,A0886.npy,A0887.npy,A0888.npy,A0889.npy,A0890.npy,A0891.npy,A0892.npy,A0893.npy,A0894.npy,A0895.npy,A0896.npy,A0897.npy,A0898.npy,A0899.npy,A0900.npy,A0901.npy,A0902.npy,A0903.npy,A0904.npy,A0905.npy,A0906.npy,A0907.npy,A0908.npy,A0909.npy,A0910.npy,A0911.npy,A0912.npy,A0913.npy,A0914.npy,A0915.npy,A0916.npy,A0917.npy,A0918.npy,A0919.npy,A0920.npy,A0921.npy,A0922.npy,A0923.npy,A0924.npy,A0925.npy,A0926.npy,A0927.npy,A0928.npy,A0929.npy,A0930.npy,A0931.npy,A0932.npy,A0933.npy,A0934.npy,A0935.npy,A0936.npy,A0937.npy,A0938.npy,A0939.npy,A0940.npy,A0941.npy,A0942.npy,A0943.npy,A0944.npy,A0945.npy,A0946.npy,A0947.npy,A0948.npy,A0949.npy,A0950.npy,A0951.npy,A0952.npy,A0953.npy,A0954.npy,A0955.npy,A0956.npy,A0957.npy,A0958.npy,A0959.npy,A0960.npy,A0961.npy,A0962.npy,A0963.npy,A0964.npy,A0965.npy,A0966.npy,A0967.npy,A0968.npy,A0969.npy,A0970.npy,A0971.npy,A0972.npy,A0973.npy,A0974.npy,A0975.npy,A0976.npy,A0977.npy,A0978.npy,A0979.npy,A0980.npy,A0981.npy,A0982.npy,A0983.npy,A0984.npy,A0985.npy,A0986.npy,A0987.npy,A0988.npy,A0989.npy,A0990.npy,A0991.npy,A0992.npy,A0993.npy,A0994.npy,A0995.npy,A0996.npy,A0997.npy,A0998.npy,A0999.npy,A1000.npy,A1001.npy,A1002.npy,A1003.npy,A1004.npy,A1005.npy,A1006.npy,A1007.npy,A1008.npy,A1009.npy,A1010.npy,A1011.npy,A1012.npy,A1013.npy,A1014.npy,A1015.npy,A1016.npy,A1017.npy,A1018.npy,A1019.npy,A1020.npy,A1021.npy,A1022.npy,A1023.npy,A1024.npy,A1025.npy,A1026.npy,A1027.npy,A1028.npy,A1029.npy,A1030.npy,A1031.npy,A1032.npy,A1033.npy,A1034.npy,A1035.npy,A1036.npy,A1037.npy,A1038.npy,A1039.npy,A1040.npy,A1041.npy,A1042.npy,A1043.npy,A1044.npy,A1045.npy,A1046.npy,A1047.npy,A1048.npy,A1049.npy,A1050.npy,A1051.npy,A1052.npy,A1053.npy,A1054.npy,A1055.npy,A1056.npy,A1057.npy,A1058.npy,A1059.npy,A1060.npy,A1061.npy,A1062.npy,A1063.npy,A1064.npy,A1065.npy,A1066.npy,A1067.npy,A1068.npy,A1069.npy,A1070.npy,A1071.npy,A1072.npy,A1073.npy,A1074.npy,A1075.npy,A1076.npy,A1077.npy,A1078.npy,A1079.npy,A1080.npy,A1081.npy,A1082.npy,A1083.npy,A1084.npy,A1085.npy,A1086.npy,A1087.npy,A1088.npy,A1089.npy,A1090.npy,A1091.npy,A1092.npy,A1093.npy,A1094.npy,A1095.npy,A1096.npy,A1097.npy,A1098.npy,A1099.npy,A1100.npy,A1101.npy,A1102.npy,A1103.npy,A1104.npy,A1105.npy,A1106.npy,A1107.npy,A1108.npy,A1109.npy,A1110.npy,A1111.npy,A1112.npy,A1113.npy,A1114.npy,A1115.npy,A1116.npy,A1117.npy,A1118.npy,A1119.npy,A1120.npy,A1121.npy,A1122.npy,A1123.npy,A1124.npy,A1125.npy,A1126.npy,A1127.npy,A1128.npy,A1129.npy,A1130.npy,A1131.npy,A1132.npy,A1133.npy,A1134.npy,A1135.npy,A1136.npy,A1137.npy,A1138.npy,A1139.npy,A1140.npy,A1141.npy,A1142.npy,A1143.npy,A1144.npy,A1145.npy,A1146.npy,A1147.npy,A1148.npy,A1149.npy,A1150.npy,A1151.npy,A1152.npy,A1153.npy,A1154.npy,A1155.npy,A1156.npy,A1157.npy,A1158.npy,A1159.npy,A1160.npy,A1161.npy,A1162.npy,A1163.npy,A1164.npy,A1165.npy,A1166.npy,A1167.npy,A1168.npy,A1169.npy,A1170.npy,A1171.npy,A1172.npy,A1173.npy,A1174.npy,A1175.npy,A1176.npy,A1177.npy,A1178.npy,A1179.npy,A1180.npy,A1181.npy,A1182.npy,A1183.npy,A1184.npy,A1185.npy,A1186.npy,A1187.npy,A1188.npy,A1189.npy,A1190.npy,A1191.npy,A1192.npy,A1193.npy,A1194.npy,A1195.npy,A1196.npy,A1197.npy,A1198.npy,A1199.npy,A1200.npy,A1201.npy,A1202.npy,A1203.npy,A1204.npy,A1205.npy,A1206.npy,A1207.npy,A1208.npy,A1209.npy,A1210.npy,A1211.npy,A1212.npy,A1213.npy,A1214.npy,A1215.npy,A1216.npy,A1217.npy,A1218.npy,A1219.npy,A1220.npy,A1221.npy,A1222.npy,A1223.npy,A1224.npy,A1225.npy,A1226.npy,A1227.npy,A1228.npy,A1229.npy,A1230.npy,A1231.npy,A1232.npy,A1233.npy,A1234.npy,A1235.npy,A1236.npy,A1237.npy,A1238.npy,A1239.npy,A1240.npy,A1241.npy,A1242.npy,A1243.npy,A1244.npy,A1245.npy,A1246.npy,A1247.npy,A1248.npy,A1249.npy,A1250.npy,A1251.npy,A1252.npy,A1253.npy,A1254.npy,A1255.npy,A1256.npy,A1257.npy,A1258.npy,A1259.npy,A1260.npy,A1261.npy,A1262.npy,A1263.npy,A1264.npy,A1265.npy,A1266.npy,A1267.npy,A1268.npy,A1269.npy,A1270.npy,A1271.npy,A1272.npy,A1273.npy,A1274.npy,A1275.npy,A1276.npy,A1277.npy,A1278.npy,A1279.npy,A1280.npy,A1281.npy,A1282.npy,A1283.npy,A1284.npy,A1285.npy,A1286.npy,A1287.npy,A1288.npy,A1289.npy,A1290.npy,A1291.npy,A1292.npy,A1293.npy,A1294.npy,A1295.npy,A1296.npy,A1297.npy,A1298.npy,A1299.npy,A1300.npy,A1301.npy,A1302.npy,A1303.npy,A1304.npy,A1305.npy,A1306.npy,A1307.npy,A1308.npy,A1309.npy,A1310.npy,A1311.npy,A1312.npy,A1313.npy,A1314.npy,A1315.npy,A1316.npy,A1317.npy,A1318.npy,A1319.npy,A1320.npy,A1321.npy,A1322.npy,A1323.npy,A1324.npy,A1325.npy,A1326.npy,A1327.npy,A1328.npy,A1329.npy,A1330.npy,A1331.npy,A1332.npy,A1333.npy,A1334.npy,A1335.npy,A1336.npy,A1337.npy,A1338.npy,A1339.npy,A1340.npy,A1341.npy,A1342.npy,A1343.npy,A1344.npy,A1345.npy,A1346.npy,A1347.npy,A1348.npy,A1349.npy,A1350.npy,A1351.npy,A1352.npy,A1353.npy,A1354.npy,A1355.npy,A1356.npy,A1357.npy,A1358.npy,A1359.npy,A1360.npy,A1361.npy,A1362.npy,A1363.npy,A1364.npy,A1365.npy,A1366.npy,A1367.npy,A1368.npy,A1369.npy,A1370.npy,A1371.npy,A1372.npy,A1373.npy,A1374.npy,A1375.npy,A1376.npy,A1377.npy,A1378.npy,A1379.npy,A1380.npy,A1381.npy,A1382.npy,A1383.npy,A1384.npy,A1385.npy,A1386.npy,A1387.npy,A1388.npy,A1389.npy,A1390.npy,A1391.npy,A1392.npy,A1393.npy,A1394.npy,A1395.npy,A1396.npy,A1397.npy,A1398.npy,A1399.npy,A1400.npy,A1401.npy,A1402.npy,A1403.npy,A1404.npy,A1405.npy,A1406.npy,A1407.npy,A1408.npy,A1409.npy,A1410.npy,A1411.npy,A1412.npy,A1413.npy,A1414.npy,A1415.npy,A1416.npy,A1417.npy,A1418.npy,A1419.npy,A1420.npy,A1421.npy,A1422.npy,A1423.npy,A1424.npy,A1425.npy,A1426.npy,A1427.npy,A1428.npy,A1429.npy,A1430.npy,A1431.npy,A1432.npy,A1433.npy,A1434.npy,A1435.npy,A1436.npy,A1437.npy,A1438.npy,A1439.npy,A1440.npy,A1441.npy,A1442.npy,A1443.npy,A1444.npy,A1445.npy,A1446.npy,A1447.npy,A1448.npy,A1449.npy,A1450.npy,A1451.npy,A1452.npy,A1453.npy,A1454.npy,A1455.npy,A1456.npy,A1457.npy,A1458.npy,A1459.npy,A1460.npy,A1461.npy,A1462.npy,A1463.npy,A1464.npy,A1465.npy,A1466.npy,A1467.npy,A1468.npy,A1469.npy,A1470.npy,A1471.npy,A1472.npy,A1473.npy,A1474.npy,A1475.npy,A1476.npy,A1477.npy,A1478.npy,A1479.npy,A1480.npy,A1481.npy,A1482.npy,A1483.npy,A1484.npy,A1485.npy,A1486.npy,A1487.npy,A1488.npy,A1489.npy,A1490.npy,A1491.npy,A1492.npy,A1493.npy,A1494.npy,A1495.npy,A1496.npy,A1497.npy,A1498.npy,A1499.npy,A1500.npy,A1501.npy,A1502.npy,A1503.npy,A1504.npy,A1505.npy,A1506.npy,A1507.npy,A1508.npy,A1509.npy,A1510.npy,A1511.npy,A1512.npy,A1513.npy,A1514.npy,A1515.npy,A1516.npy,A1517.npy,A1518.npy,A1519.npy,A1520.npy,A1521.npy,A1522.npy,A1523.npy,A1524.npy,A1525.npy,A1526.npy,A1527.npy,A1528.npy,A1529.npy,A1530.npy,A1531.npy,A1532.npy,A1533.npy,A1534.npy,A1535.npy,A1536.npy,A1537.npy,A1538.npy,A1539.npy,A1540.npy,A1541.npy,A1542.npy,A1543.npy,A1544.npy,A1545.npy,A1546.npy,A1547.npy,A1548.npy,A1549.npy,A1550.npy,A1551.npy,A1552.npy,A1553.npy,A1554.npy,A1555.npy,A1556.npy,A1557.npy,A1558.npy,A1559.npy,A1560.npy,A1561.npy,A1562.npy,A1563.npy,A1564.npy,A1565.npy,A1566.npy,A1567.npy,A1568.npy,A1569.npy,A1570.npy,A1571.npy,A1572.npy,A1573.npy,A1574.npy,A1575.npy,A1576.npy,A1577.npy,A1578.npy,A1579.npy,A1580.npy,A1581.npy,A1582.npy,A1583.npy,A1584.npy,A1585.npy,A1586.npy,A1587.npy,A1588.npy,A1589.npy,A1590.npy,A1591.npy,A1592.npy,A1593.npy,A1594.npy,A1595.npy,A1596.npy,A1597.npy,A1598.npy,A1599.npy,A1600.npy,A1601.npy,A1602.npy,A1603.npy,A1604.npy,A1605.npy,A1606.npy,A1607.npy,A1608.npy,A1609.npy,A1610.npy,A1611.npy,A1612.npy,A1613.npy,A1614.npy,A1615.npy,A1616.npy,A1617.npy,A1618.npy,A1619.npy,A1620.npy,A1621.npy,A1622.npy,A1623.npy,A1624.npy,A1625.npy,A1626.npy,A1627.npy,A1628.npy,A1629.npy,A1630.npy,A1631.npy,A1632.npy,A1633.npy,A1634.npy,A1635.npy,A1636.npy,A1637.npy,A1638.npy,A1639.npy,A1640.npy,A1641.npy,A1642.npy,A1643.npy,A1644.npy,A1645.npy,A1646.npy,A1647.npy,A1648.npy,A1649.npy,A1650.npy,A1651.npy,A1652.npy,A1653.npy,A1654.npy,A1655.npy,A1656.npy,A1657.npy,A1658.npy,A1659.npy,A1660.npy,A1661.npy,A1662.npy,A1663.npy,A1664.npy,A1665.npy,A1666.npy,A1667.npy,A1668.npy,A1669.npy,A1670.npy,A1671.npy,A1672.npy,A1673.npy,A1674.npy,A1675.npy,A1676.npy,A1677.npy,A1678.npy,A1679.npy,A1680.npy,A1681.npy,A1682.npy,A1683.npy,A1684.npy,A1685.npy,A1686.npy,A1687.npy,A1688.npy,A1689.npy,A1690.npy,A1691.npy,A1692.npy,A1693.npy,A1694.npy,A1695.npy,A1696.npy,A1697.npy,A1698.npy,A1699.npy,A1700.npy,A1701.npy,A1702.npy,A1703.npy,A1704.npy,A1705.npy,A1706.npy,A1707.npy,A1708.npy,A1709.npy,A1710.npy,A1711.npy,A1712.npy,A1713.npy,A1714.npy,A1715.npy,A1716.npy,A1717.npy,A1718.npy,A1719.npy,A1720.npy,A1721.npy,A1722.npy,A1723.npy,A1724.npy,A1725.npy,A1726.npy,A1727.npy,A1728.npy,A1729.npy,A1730.npy,A1731.npy,A1732.npy,A1733.npy,A1734.npy,A1735.npy,A1736.npy,A1737.npy,A1738.npy,A1739.npy,A1740.npy,A1741.npy,A1742.npy,A1743.npy,A1744.npy,A1745.npy,A1746.npy,A1747.npy,A1748.npy,A1749.npy,A1750.npy,A1751.npy,A1752.npy,A1753.npy,A1754.npy,A1755.npy,A1756.npy,A1757.npy,A1758.npy,A1759.npy,A1760.npy,A1761.npy,A1762.npy,A1763.npy,A1764.npy,A1765.npy,A1766.npy,A1767.npy,A1768.npy,A1769.npy,A1770.npy,A1771.npy,A1772.npy,A1773.npy,A1774.npy,A1775.npy,A1776.npy,A1777.npy,A1778.npy,A1779.npy,A1780.npy,A1781.npy,A1782.npy,A1783.npy,A1784.npy,A1785.npy,A1786.npy,A1787.npy,A1788.npy,A1789.npy,A1790.npy,A1791.npy,A1792.npy,A1793.npy,A1794.npy,A1795.npy,A1796.npy,A1797.npy,A1798.npy,A1799.npy,A1800.npy,A1801.npy,A1802.npy,A1803.npy,A1804.npy,A1805.npy,A1806.npy,A1807.npy,A1808.npy,A1809.npy,A1810.npy,A1811.npy,A1812.npy,A1813.npy,A1814.npy,A1815.npy,A1816.npy,A1817.npy,A1818.npy,A1819.npy,A1820.npy,A1821.npy,A1822.npy,A1823.npy,A1824.npy,A1825.npy,A1826.npy,A1827.npy,A1828.npy,A1829.npy,A1830.npy,A1831.npy,A1832.npy,A1833.npy,A1834.npy,A1835.npy,A1836.npy,A1837.npy,A1838.npy,A1839.npy,A1840.npy,A1841.npy,A1842.npy,A1843.npy,A1844.npy,A1845.npy,A1846.npy,A1847.npy,A1848.npy,A1849.npy,A1850.npy,A1851.npy,A1852.npy,A1853.npy,A1854.npy,A1855.npy,A1856.npy,A1857.npy,A1858.npy,A1859.npy,A1860.npy,A1861.npy,A1862.npy,A1863.npy,A1864.npy,A1865.npy,A1866.npy,A1867.npy,A1868.npy,A1869.npy,A1870.npy,A1871.npy,A1872.npy,A1873.npy,A1874.npy,A1875.npy,A1876.npy,A1877.npy,A1878.npy,A1879.npy,A1880.npy,A1881.npy,A1882.npy,A1883.npy,A1884.npy,A1885.npy,A1886.npy,A1887.npy,A1888.npy,A1889.npy,A1890.npy,A1891.npy,A1892.npy,A1893.npy,A1894.npy,A1895.npy,A1896.npy,A1897.npy,A1898.npy,A1899.npy,A1900.npy,A1901.npy,A1902.npy,A1903.npy,A1904.npy,A1905.npy,A1906.npy,A1907.npy,A1908.npy,A1909.npy,A1910.npy,A1911.npy,A1912.npy,A1913.npy,A1914.npy,A1915.npy,A1916.npy,A1917.npy,A1918.npy,A1919.npy,A1920.npy,A1921.npy,A1922.npy,A1923.npy,A1924.npy,A1925.npy,A1926.npy,A1927.npy,A1928.npy,A1929.npy,A1930.npy,A1931.npy,A1932.npy,A1933.npy,A1934.npy,A1935.npy,A1936.npy,A1937.npy,A1938.npy,A1939.npy,A1940.npy,A1941.npy,A1942.npy,A1943.npy,A1944.npy,A1945.npy,A1946.npy,A1947.npy,A1948.npy,A1949.npy,A1950.npy,A1951.npy,A1952.npy,A1953.npy,A1954.npy,A1955.npy,A1956.npy,A1957.npy,A1958.npy,A1959.npy,A1960.npy,A1961.npy,A1962.npy,A1963.npy,A1964.npy,A1965.npy,A1966.npy,A1967.npy,A1968.npy,A1969.npy,A1970.npy,A1971.npy,A1972.npy,A1973.npy,A1974.npy,A1975.npy,A1976.npy,A1977.npy,A1978.npy,A1979.npy,A1980.npy,A1981.npy,A1982.npy,A1983.npy,A1984.npy,A1985.npy,A1986.npy,A1987.npy,A1988.npy,A1989.npy,A1990.npy,A1991.npy,A1992.npy,A1993.npy,A1994.npy,A1995.npy,A1996.npy,A1997.npy,A1998.npy,A1999.npy,A2000.npy,A2001.npy,A2002.npy,A2003.npy,A2004.npy,A2005.npy,A2006.npy,A2007.npy,A2008.npy,A2009.npy,A2010.npy,A2011.npy,A2012.npy,A2013.npy,A2014.npy,A2015.npy,A2016.npy,A2017.npy,A2018.npy,A2019.npy,A2020.npy,A2021.npy,A2022.npy,A2023.npy,A2024.npy,A2025.npy,A2026.npy,A2027.npy,A2028.npy,A2029.npy,A2030.npy,A2031.npy,A2032.npy,A2033.npy,A2034.npy,A2035.npy,A2036.npy,A2037.npy,A2038.npy,A2039.npy,A2040.npy,A2041.npy,A2042.npy,A2043.npy,A2044.npy,A2045.npy,A2046.npy,A2047.npy,A2048.npy,A2049.npy,A2050.npy,A2051.npy,A2052.npy,A2053.npy,A2054.npy,A2055.npy,A2056.npy,A2057.npy,A2058.npy,A2059.npy,A2060.npy,A2061.npy,A2062.npy,A2063.npy,A2064.npy,A2065.npy,A2066.npy,A2067.npy,A2068.npy,A2069.npy,A2070.npy,A2071.npy,A2072.npy,A2073.npy,A2074.npy,A2075.npy,A2076.npy,A2077.npy,A2078.npy,A2079.npy,A2080.npy,A2081.npy,A2082.npy,A2083.npy,A2084.npy,A2085.npy,A2086.npy,A2087.npy,A2088.npy,A2089.npy,A2090.npy,A2091.npy,A2092.npy,A2093.npy,A2094.npy,A2095.npy,A2096.npy,A2097.npy,A2098.npy,A2099.npy,A2100.npy,A2101.npy,A2102.npy,A2103.npy,A2104.npy,A2105.npy,A2106.npy,A2107.npy,A2108.npy,A2109.npy,A2110.npy,A2111.npy,A2112.npy,A2113.npy,A2114.npy,A2115.npy,A2116.npy,A2117.npy,A2118.npy,A2119.npy,A2120.npy,A2121.npy,A2122.npy,A2123.npy,A2124.npy,A2125.npy,A2126.npy,A2127.npy,A2128.npy,A2129.npy,A2130.npy,A2131.npy,A2132.npy,A2133.npy,A2134.npy,A2135.npy,A2136.npy,A2137.npy,A2138.npy,A2139.npy,A2140.npy,A2141.npy,A2142.npy,A2143.npy,A2144.npy,A2145.npy,A2146.npy,A2147.npy,A2148.npy,A2149.npy,A2150.npy,A2151.npy,A2152.npy,A2153.npy,A2154.npy,A2155.npy,A2156.npy,A2157.npy,A2158.npy,A2159.npy,A2160.npy,A2161.npy,A2162.npy,A2163.npy,A2164.npy,A2165.npy,A2166.npy,A2167.npy,A2168.npy,A2169.npy,A2170.npy,A2171.npy,A2172.npy,A2173.npy,A2174.npy,A2175.npy,A2176.npy,A2177.npy,A2178.npy,A2179.npy,A2180.npy,A2181.npy,A2182.npy,A2183.npy,A2184.npy,A2185.npy,A2186.npy,A2187.npy,A2188.npy,A2189.npy,A2190.npy,A2191.npy,A2192.npy,A2193.npy,A2194.npy,A2195.npy,A2196.npy,A2197.npy,A2198.npy,A2199.npy,A2200.npy,A2201.npy,A2202.npy,A2203.npy,A2204.npy,A2205.npy,A2206.npy,A2207.npy,A2208.npy,A2209.npy,A2210.npy,A2211.npy,A2212.npy,A2213.npy,A2214.npy,A2215.npy,A2216.npy,A2217.npy,A2218.npy,A2219.npy,A2220.npy,A2221.npy,A2222.npy,A2223.npy,A2224.npy,A2225.npy,A2226.npy,A2227.npy,A2228.npy,A2229.npy,A2230.npy,A2231.npy,A2232.npy,A2233.npy,A2234.npy,A2235.npy,A2236.npy,A2237.npy,A2238.npy,A2239.npy,A2240.npy,A2241.npy,A2242.npy,A2243.npy,A2244.npy,A2245.npy,A2246.npy,A2247.npy,A2248.npy,A2249.npy,A2250.npy,A2251.npy,A2252.npy,A2253.npy,A2254.npy,A2255.npy,A2256.npy,A2257.npy,A2258.npy,A2259.npy,A2260.npy,A2261.npy,A2262.npy,A2263.npy,A2264.npy,A2265.npy,A2266.npy,A2267.npy,A2268.npy,A2269.npy,A2270.npy,A2271.npy,A2272.npy,A2273.npy,A2274.npy,A2275.npy,A2276.npy,A2277.npy,A2278.npy,A2279.npy,A2280.npy,A2281.npy,A2282.npy,A2283.npy,A2284.npy,A2285.npy,A2286.npy,A2287.npy,A2288.npy,A2289.npy,A2290.npy,A2291.npy,A2292.npy,A2293.npy,A2294.npy,A2295.npy,A2296.npy,A2297.npy,A2298.npy,A2299.npy,A2300.npy,A2301.npy,A2302.npy,A2303.npy,A2304.npy,A2305.npy,A2306.npy,A2307.npy,A2308.npy,A2309.npy,A2310.npy,A2311.npy,A2312.npy,A2313.npy,A2314.npy,A2315.npy,A2316.npy,A2317.npy,A2318.npy,A2319.npy,A2320.npy,A2321.npy,A2322.npy,A2323.npy,A2324.npy,A2325.npy,A2326.npy,A2327.npy,A2328.npy,A2329.npy,A2330.npy,A2331.npy,A2332.npy,A2333.npy,A2334.npy,A2335.npy,A2336.npy,A2337.npy,A2338.npy,A2339.npy,A2340.npy,A2341.npy,A2342.npy,A2343.npy,A2344.npy,A2345.npy,A2346.npy,A2347.npy,A2348.npy,A2349.npy,A2350.npy,A2351.npy,A2352.npy,A2353.npy,A2354.npy,A2355.npy,A2356.npy,A2357.npy,A2358.npy,A2359.npy,A2360.npy,A2361.npy,A2362.npy,A2363.npy,A2364.npy,A2365.npy,A2366.npy,A2367.npy,A2368.npy,A2369.npy,A2370.npy,A2371.npy,A2372.npy,A2373.npy,A2374.npy,A2375.npy,A2376.npy,A2377.npy,A2378.npy,A2379.npy,A2380.npy,A2381.npy,A2382.npy,A2383.npy,A2384.npy,A2385.npy,A2386.npy,A2387.npy,A2388.npy,A2389.npy,A2390.npy,A2391.npy,A2392.npy,A2393.npy,A2394.npy,A2395.npy,A2396.npy,A2397.npy,A2398.npy,A2399.npy,A2400.npy,A2401.npy,A2402.npy,A2403.npy,A2404.npy,A2405.npy,A2406.npy,A2407.npy,A2408.npy,A2409.npy,A2410.npy,A2411.npy,A2412.npy,A2413.npy,A2414.npy,A2415.npy,A2416.npy,A2417.npy,A2418.npy,A2419.npy,A2420.npy,A2421.npy,A2422.npy,A2423.npy,A2424.npy,A2425.npy,A2426.npy,A2427.npy,A2428.npy,A2429.npy,A2430.npy,A2431.npy,A2432.npy,A2433.npy,A2434.npy,A2435.npy,A2436.npy,A2437.npy,A2438.npy,A2439.npy,A2440.npy,A2441.npy,A2442.npy,A2443.npy,A2444.npy,A2445.npy,A2446.npy,A2447.npy,A2448.npy,A2449.npy,A2450.npy,A2451.npy,A2452.npy,A2453.npy,A2454.npy,A2455.npy,A2456.npy,A2457.npy,A2458.npy,A2459.npy,A2460.npy,A2461.npy,A2462.npy,A2463.npy,A2464.npy,A2465.npy,A2466.npy,A2467.npy,A2468.npy,A2469.npy,A2470.npy,A2471.npy,A2472.npy,A2473.npy,A2474.npy,A2475.npy,A2476.npy,A2477.npy,A2478.npy,A2479.npy,A2480.npy,A2481.npy,A2482.npy,A2483.npy,A2484.npy,A2485.npy,A2486.npy,A2487.npy,A2488.npy,A2489.npy,A2490.npy,A2491.npy,A2492.npy,A2493.npy,A2494.npy,A2495.npy,A2496.npy,A2497.npy,A2498.npy,A2499.npy,A2500.npy,A2501.npy,A2502.npy,A2503.npy,A2504.npy,A2505.npy,A2506.npy,A2507.npy,A2508.npy,A2509.npy,A2510.npy,A2511.npy,A2512.npy,A2513.npy,A2514.npy,A2515.npy,A2516.npy,A2517.npy,A2518.npy,A2519.npy,A2520.npy,A2521.npy,A2522.npy,A2523.npy,A2524.npy,A2525.npy,A2526.npy,A2527.npy,A2528.npy,A2529.npy,A2530.npy,A2531.npy,A2532.npy,A2533.npy,A2534.npy,A2535.npy,A2536.npy,A2537.npy,A2538.npy,A2539.npy,A2540.npy,A2541.npy,A2542.npy,A2543.npy,A2544.npy,A2545.npy,A2546.npy,A2547.npy,A2548.npy,A2549.npy,A2550.npy,A2551.npy,A2552.npy,A2553.npy,A2554.npy,A2555.npy,A2556.npy,A2557.npy,A2558.npy,A2559.npy,A2560.npy,A2561.npy,A2562.npy,A2563.npy,A2564.npy,A2565.npy,A2566.npy,A2567.npy,A2568.npy,A2569.npy,A2570.npy,A2571.npy,A2572.npy,A2573.npy,A2574.npy,A2575.npy,A2576.npy,A2577.npy,A2578.npy,A2579.npy,A2580.npy,A2581.npy,A2582.npy,A2583.npy,A2584.npy,A2585.npy,A2586.npy,A2587.npy,A2588.npy,A2589.npy,A2590.npy,A2591.npy,A2592.npy,A2593.npy,A2594.npy,A2595.npy,A2596.npy,A2597.npy,A2598.npy,A2599.npy,A2600.npy,A2601.npy,A2602.npy,A2603.npy,A2604.npy,A2605.npy,A2606.npy,A2607.npy,A2608.npy,A2609.npy,A2610.npy,A2611.npy,A2612.npy,A2613.npy,A2614.npy,A2615.npy,A2616.npy,A2617.npy,A2618.npy,A2619.npy,A2620.npy,A2621.npy,A2622.npy,A2623.npy,A2624.npy,A2625.npy,A2626.npy,A2627.npy,A2628.npy,A2629.npy,A2630.npy,A2631.npy,A2632.npy,A2633.npy,A2634.npy,A2635.npy,A2636.npy,A2637.npy,A2638.npy,A2639.npy,A2640.npy,A2641.npy,A2642.npy,A2643.npy,A2644.npy,A2645.npy,A2646.npy,A2647.npy,A2648.npy,A2649.npy,A2650.npy,A2651.npy,A2652.npy,A2653.npy,A2654.npy,A2655.npy,A2656.npy,A2657.npy,A2658.npy,A2659.npy,A2660.npy,A2661.npy,A2662.npy,A2663.npy,A2664.npy,A2665.npy,A2666.npy,A2667.npy,A2668.npy,A2669.npy,A2670.npy,A2671.npy,A2672.npy,A2673.npy,A2674.npy,A2675.npy,A2676.npy,A2677.npy,A2678.npy,A2679.npy,A2680.npy,A2681.npy,A2682.npy,A2683.npy,A2684.npy,A2685.npy,A2686.npy,A2687.npy,A2688.npy,A2689.npy,A2690.npy,A2691.npy,A2692.npy,A2693.npy,A2694.npy,A2695.npy,A2696.npy,A2697.npy,A2698.npy,A2699.npy,A2700.npy,A2701.npy,A2702.npy,A2703.npy,A2704.npy,A2705.npy,A2706.npy,A2707.npy,A2708.npy,A2709.npy,A2710.npy,A2711.npy,A2712.npy,A2713.npy,A2714.npy,A2715.npy,A2716.npy,A2717.npy,A2718.npy,A2719.npy,A2720.npy,A2721.npy,A2722.npy,A2723.npy,A2724.npy,A2725.npy,A2726.npy,A2727.npy,A2728.npy,A2729.npy,A2730.npy,A2731.npy,A2732.npy,A2733.npy,A2734.npy,A2735.npy,A2736.npy,A2737.npy,A2738.npy,A2739.npy,A2740.npy,A2741.npy,A2742.npy,A2743.npy,A2744.npy,A2745.npy,A2746.npy,A2747.npy,A2748.npy,A2749.npy,A2750.npy,A2751.npy,A2752.npy,A2753.npy,A2754.npy,A2755.npy,A2756.npy,A2757.npy,A2758.npy,A2759.npy,A2760.npy,A2761.npy,A2762.npy,A2763.npy,A2764.npy,A2765.npy,A2766.npy,A2767.npy,A2768.npy,A2769.npy,A2770.npy,A2771.npy,A2772.npy,A2773.npy,A2774.npy,A2775.npy,A2776.npy,A2777.npy,A2778.npy,A2779.npy,A2780.npy,A2781.npy,A2782.npy,A2783.npy,A2784.npy,A2785.npy,A2786.npy,A2787.npy,A2788.npy,A2789.npy,A2790.npy,A2791.npy,A2792.npy,A2793.npy,A2794.npy,A2795.npy,A2796.npy,A2797.npy,A2798.npy,A2799.npy,A2800.npy,A2801.npy,A2802.npy,A2803.npy,A2804.npy,A2805.npy,A2806.npy,A2807.npy,A2808.npy,A2809.npy,A2810.npy,A2811.npy,A2812.npy,A2813.npy,A2814.npy,A2815.npy,A2816.npy,A2817.npy,A2818.npy,A2819.npy,A2820.npy,A2821.npy,A2822.npy,A2823.npy,A2824.npy,A2825.npy,A2826.npy,A2827.npy,A2828.npy,A2829.npy,A2830.npy,A2831.npy,A2832.npy,A2833.npy,A2834.npy,A2835.npy,A2836.npy,A2837.npy,A2838.npy,A2839.npy,A2840.npy,A2841.npy,A2842.npy,A2843.npy,A2844.npy,A2845.npy,A2846.npy,A2847.npy,A2848.npy,A2849.npy,A2850.npy,A2851.npy,A2852.npy,A2853.npy,A2854.npy,A2855.npy,A2856.npy,A2857.npy,A2858.npy,A2859.npy,A2860.npy,A2861.npy,A2862.npy,A2863.npy,A2864.npy,A2865.npy,A2866.npy,A2867.npy,A2868.npy,A2869.npy,A2870.npy,A2871.npy,A2872.npy,A2873.npy,A2874.npy,A2875.npy,A2876.npy,A2877.npy,A2878.npy,A2879.npy,A2880.npy,A2881.npy,A2882.npy,A2883.npy,A2884.npy,A2885.npy,A2886.npy,A2887.npy,A2888.npy,A2889.npy,A2890.npy,A2891.npy,A2892.npy,A2893.npy,A2894.npy,A2895.npy,A2896.npy,A2897.npy,A2898.npy,A2899.npy,A2900.npy,A2901.npy,A2902.npy,A2903.npy,A2904.npy,A2905.npy,A2906.npy,A2907.npy,A2908.npy,A2909.npy,A2910.npy,A2911.npy,A2912.npy,A2913.npy,A2914.npy,A2915.npy,A2916.npy,A2917.npy,A2918.npy,A2919.npy,A2920.npy,A2921.npy,A2922.npy,A2923.npy,A2924.npy,A2925.npy,A2926.npy,A2927.npy,A2928.npy,A2929.npy,A2930.npy,A2931.npy,A2932.npy,A2933.npy,A2934.npy,A2935.npy,A2936.npy,A2937.npy,A2938.npy,A2939.npy,A2940.npy,A2941.npy,A2942.npy,A2943.npy,A2944.npy,A2945.npy,A2946.npy,A2947.npy,A2948.npy,A2949.npy,A2950.npy,A2951.npy,A2952.npy,A2953.npy,A2954.npy,A2955.npy,A2956.npy,A2957.npy,A2958.npy,A2959.npy,A2960.npy,A2961.npy,A2962.npy,A2963.npy,A2964.npy,A2965.npy,A2966.npy,A2967.npy,A2968.npy,A2969.npy,A2970.npy,A2971.npy,A2972.npy,A2973.npy,A2974.npy,A2975.npy,A2976.npy,A2977.npy,A2978.npy,A2979.npy,A2980.npy,A2981.npy,A2982.npy,A2983.npy,A2984.npy,A2985.npy,A2986.npy,A2987.npy,A2988.npy,A2989.npy,A2990.npy,A2991.npy,A2992.npy,A2993.npy,A2994.npy,A2995.npy,A2996.npy,A2997.npy,A2998.npy,A2999.npy,A3000.npy,A3001.npy,A3002.npy,A3003.npy,A3004.npy,A3005.npy,A3006.npy,A3007.npy,A3008.npy,A3009.npy,A3010.npy,A3011.npy,A3012.npy,A3013.npy,A3014.npy,A3015.npy,A3016.npy,A3017.npy,A3018.npy,A3019.npy,A3020.npy,A3021.npy,A3022.npy,A3023.npy,A3024.npy,A3025.npy,A3026.npy,A3027.npy,A3028.npy,A3029.npy,A3030.npy,A3031.npy,A3032.npy,A3033.npy,A3034.npy,A3035.npy,A3036.npy,A3037.npy,A3038.npy,A3039.npy,A3040.npy,A3041.npy,A3042.npy,A3043.npy,A3044.npy,A3045.npy,A3046.npy,A3047.npy,A3048.npy,A3049.npy,A3050.npy,A3051.npy,A3052.npy,A3053.npy,A3054.npy,A3055.npy,A3056.npy,A3057.npy,A3058.npy,A3059.npy,A3060.npy,A3061.npy,A3062.npy,A3063.npy,A3064.npy,A3065.npy,A3066.npy,A3067.npy,A3068.npy,A3069.npy,A3070.npy,A3071.npy,A3072.npy,A3073.npy,A3074.npy,A3075.npy,A3076.npy,A3077.npy,A3078.npy,A3079.npy,A3080.npy,A3081.npy,A3082.npy,A3083.npy,A3084.npy,A3085.npy,A3086.npy,A3087.npy,A3088.npy,A3089.npy,A3090.npy,A3091.npy,A3092.npy,A3093.npy,A3094.npy,A3095.npy,A3096.npy,A3097.npy,A3098.npy,A3099.npy,A3100.npy,A3101.npy,A3102.npy,A3103.npy,A3104.npy,A3105.npy,A3106.npy,A3107.npy,A3108.npy,A3109.npy,A3110.npy,A3111.npy,A3112.npy,A3113.npy,A3114.npy,A3115.npy,A3116.npy,A3117.npy,A3118.npy,A3119.npy,A3120.npy,A3121.npy,A3122.npy,A3123.npy,A3124.npy,A3125.npy,A3126.npy,A3127.npy,A3128.npy,A3129.npy,A3130.npy,A3131.npy,A3132.npy,A3133.npy,A3134.npy,A3135.npy,A3136.npy,A3137.npy,A3138.npy,A3139.npy,A3140.npy,A3141.npy,A3142.npy,A3143.npy,A3144.npy,A3145.npy,A3146.npy,A3147.npy,A3148.npy,A3149.npy,A3150.npy,A3151.npy,A3152.npy,A3153.npy,A3154.npy,A3155.npy,A3156.npy,A3157.npy,A3158.npy,A3159.npy,A3160.npy,A3161.npy,A3162.npy,A3163.npy,A3164.npy,A3165.npy,A3166.npy,A3167.npy,A3168.npy,A3169.npy,A3170.npy,A3171.npy,A3172.npy,A3173.npy,A3174.npy,A3175.npy,A3176.npy,A3177.npy,A3178.npy,A3179.npy,A3180.npy,A3181.npy,A3182.npy,A3183.npy,A3184.npy,A3185.npy,A3186.npy,A3187.npy,A3188.npy,A3189.npy,A3190.npy,A3191.npy,A3192.npy,A3193.npy,A3194.npy,A3195.npy,A3196.npy,A3197.npy,A3198.npy,A3199.npy,A3200.npy,A3201.npy,A3202.npy,A3203.npy,A3204.npy,A3205.npy,A3206.npy,A3207.npy,A3208.npy,A3209.npy,A3210.npy,A3211.npy,A3212.npy,A3213.npy,A3214.npy,A3215.npy,A3216.npy,A3217.npy,A3218.npy,A3219.npy,A3220.npy,A3221.npy,A3222.npy,A3223.npy,A3224.npy,A3225.npy,A3226.npy,A3227.npy,A3228.npy,A3229.npy,A3230.npy,A3231.npy,A3232.npy,A3233.npy,A3234.npy,A3235.npy,A3236.npy,A3237.npy,A3238.npy,A3239.npy,A3240.npy,A3241.npy,A3242.npy,A3243.npy,A3244.npy,A3245.npy,A3246.npy,A3247.npy,A3248.npy,A3249.npy,A3250.npy,A3251.npy,A3252.npy,A3253.npy,A3254.npy,A3255.npy,A3256.npy,A3257.npy,A3258.npy,A3259.npy,A3260.npy,A3261.npy,A3262.npy,A3263.npy,A3264.npy,A3265.npy,A3266.npy,A3267.npy,A3268.npy,A3269.npy,A3270.npy,A3271.npy,A3272.npy,A3273.npy,A3274.npy,A3275.npy,A3276.npy,A3277.npy,A3278.npy,A3279.npy,A3280.npy,A3281.npy,A3282.npy,A3283.npy,A3284.npy,A3285.npy,A3286.npy,A3287.npy,A3288.npy,A3289.npy,A3290.npy,A3291.npy,A3292.npy,A3293.npy,A3294.npy,A3295.npy,A3296.npy,A3297.npy,A3298.npy,A3299.npy,A3300.npy,A3301.npy,A3302.npy,A3303.npy,A3304.npy,A3305.npy,A3306.npy,A3307.npy,A3308.npy,A3309.npy,A3310.npy,A3311.npy,A3312.npy,A3313.npy,A3314.npy,A3315.npy,A3316.npy,A3317.npy,A3318.npy,A3319.npy,A3320.npy,A3321.npy,A3322.npy,A3323.npy,A3324.npy,A3325.npy,A3326.npy,A3327.npy,A3328.npy,A3329.npy,A3330.npy,A3331.npy,A3332.npy,A3333.npy,A3334.npy,A3335.npy,A3336.npy,A3337.npy,A3338.npy,A3339.npy,A3340.npy,A3341.npy,A3342.npy,A3343.npy,A3344.npy,A3345.npy,A3346.npy,A3347.npy,A3348.npy,A3349.npy,A3350.npy,A3351.npy,A3352.npy,A3353.npy,A3354.npy,A3355.npy,A3356.npy,A3357.npy,A3358.npy,A3359.npy,A3360.npy,A3361.npy,A3362.npy,A3363.npy,A3364.npy,A3365.npy,A3366.npy,A3367.npy,A3368.npy,A3369.npy,A3370.npy,A3371.npy,A3372.npy,A3373.npy,A3374.npy,A3375.npy,A3376.npy,A3377.npy,A3378.npy,A3379.npy,A3380.npy,A3381.npy,A3382.npy,A3383.npy,A3384.npy,A3385.npy,A3386.npy,A3387.npy,A3388.npy,A3389.npy,A3390.npy,A3391.npy,A3392.npy,A3393.npy,A3394.npy,A3395.npy,A3396.npy,A3397.npy,A3398.npy,A3399.npy,A3400.npy,A3401.npy,A3402.npy,A3403.npy,A3404.npy,A3405.npy,A3406.npy,A3407.npy,A3408.npy,A3409.npy,A3410.npy,A3411.npy,A3412.npy,A3413.npy,A3414.npy,A3415.npy,A3416.npy,A3417.npy,A3418.npy,A3419.npy,A3420.npy,A3421.npy,A3422.npy,A3423.npy,A3424.npy,A3425.npy,A3426.npy,A3427.npy,A3428.npy,A3429.npy,A3430.npy,A3431.npy,A3432.npy,A3433.npy,A3434.npy,A3435.npy,A3436.npy,A3437.npy,A3438.npy,A3439.npy,A3440.npy,A3441.npy,A3442.npy,A3443.npy,A3444.npy,A3445.npy,A3446.npy,A3447.npy,A3448.npy,A3449.npy,A3450.npy,A3451.npy,A3452.npy,A3453.npy,A3454.npy,A3455.npy,A3456.npy,A3457.npy,A3458.npy,A3459.npy,A3460.npy,A3461.npy,A3462.npy,A3463.npy,A3464.npy,A3465.npy,A3466.npy,A3467.npy,A3468.npy,A3469.npy,A3470.npy,A3471.npy,A3472.npy,A3473.npy,A3474.npy,A3475.npy,A3476.npy,A3477.npy,A3478.npy,A3479.npy,A3480.npy,A3481.npy,A3482.npy,A3483.npy,A3484.npy,A3485.npy,A3486.npy,A3487.npy,A3488.npy,A3489.npy,A3490.npy,A3491.npy,A3492.npy,A3493.npy,A3494.npy,A3495.npy,A3496.npy,A3497.npy,A3498.npy,A3499.npy,A3500.npy,A3501.npy,A3502.npy,A3503.npy,A3504.npy,A3505.npy,A3506.npy,A3507.npy,A3508.npy,A3509.npy,A3510.npy,A3511.npy,A3512.npy,A3513.npy,A3514.npy,A3515.npy,A3516.npy,A3517.npy,A3518.npy,A3519.npy,A3520.npy,A3521.npy,A3522.npy,A3523.npy,A3524.npy,A3525.npy,A3526.npy,A3527.npy,A3528.npy,A3529.npy,A3530.npy,A3531.npy,A3532.npy,A3533.npy,A3534.npy,A3535.npy,A3536.npy,A3537.npy,A3538.npy,A3539.npy,A3540.npy,A3541.npy,A3542.npy,A3543.npy,A3544.npy,A3545.npy,A3546.npy,A3547.npy,A3548.npy,A3549.npy,A3550.npy,A3551.npy,A3552.npy,A3553.npy,A3554.npy,A3555.npy,A3556.npy,A3557.npy,A3558.npy,A3559.npy,A3560.npy,A3561.npy,A3562.npy,A3563.npy,A3564.npy,A3565.npy,A3566.npy,A3567.npy,A3568.npy,A3569.npy,A3570.npy,A3571.npy,A3572.npy,A3573.npy,A3574.npy,A3575.npy,A3576.npy,A3577.npy,A3578.npy,A3579.npy,A3580.npy,A3581.npy,A3582.npy,A3583.npy,A3584.npy,A3585.npy,A3586.npy,A3587.npy,A3588.npy,A3589.npy,A3590.npy,A3591.npy,A3592.npy,A3593.npy,A3594.npy,A3595.npy,A3596.npy,A3597.npy,A3598.npy,A3599.npy,A3600.npy,A3601.npy,A3602.npy,A3603.npy,A3604.npy,A3605.npy,A3606.npy,A3607.npy,A3608.npy,A3609.npy,A3610.npy,A3611.npy,A3612.npy,A3613.npy,A3614.npy,A3615.npy,A3616.npy,A3617.npy,A3618.npy,A3619.npy,A3620.npy,A3621.npy,A3622.npy,A3623.npy,A3624.npy,A3625.npy,A3626.npy,A3627.npy,A3628.npy,A3629.npy,A3630.npy,A3631.npy,A3632.npy,A3633.npy,A3634.npy,A3635.npy,A3636.npy,A3637.npy,A3638.npy,A3639.npy,A3640.npy,A3641.npy,A3642.npy,A3643.npy,A3644.npy,A3645.npy,A3646.npy,A3647.npy,A3648.npy,A3649.npy,A3650.npy,A3651.npy,A3652.npy,A3653.npy,A3654.npy,A3655.npy,A3656.npy,A3657.npy,A3658.npy,A3659.npy,A3660.npy,A3661.npy,A3662.npy,A3663.npy,A3664.npy,A3665.npy,A3666.npy,A3667.npy,A3668.npy,A3669.npy,A3670.npy,A3671.npy,A3672.npy,A3673.npy,A3674.npy,A3675.npy,A3676.npy,A3677.npy,A3678.npy,A3679.npy,A3680.npy,A3681.npy,A3682.npy,A3683.npy,A3684.npy,A3685.npy,A3686.npy,A3687.npy,A3688.npy,A3689.npy,A3690.npy,A3691.npy,A3692.npy,A3693.npy,A3694.npy,A3695.npy,A3696.npy,A3697.npy,A3698.npy,A3699.npy,A3700.npy,A3701.npy,A3702.npy,A3703.npy,A3704.npy,A3705.npy,A3706.npy,A3707.npy,A3708.npy,A3709.npy,A3710.npy,A3711.npy,A3712.npy,A3713.npy,A3714.npy,A3715.npy,A3716.npy,A3717.npy,A3718.npy,A3719.npy,A3720.npy,A3721.npy,A3722.npy,A3723.npy,A3724.npy,A3725.npy,A3726.npy,A3727.npy,A3728.npy,A3729.npy,A3730.npy,A3731.npy,A3732.npy,A3733.npy,A3734.npy,A3735.npy,A3736.npy,A3737.npy,A3738.npy,A3739.npy,A3740.npy,A3741.npy,A3742.npy,A3743.npy,A3744.npy,A3745.npy,A3746.npy,A3747.npy,A3748.npy,A3749.npy,A3750.npy,A3751.npy,A3752.npy,A3753.npy,A3754.npy,A3755.npy,A3756.npy,A3757.npy,A3758.npy,A3759.npy,A3760.npy,A3761.npy,A3762.npy,A3763.npy,A3764.npy,A3765.npy,A3766.npy,A3767.npy,A3768.npy,A3769.npy,A3770.npy,A3771.npy,A3772.npy,A3773.npy,A3774.npy,A3775.npy,A3776.npy,A3777.npy,A3778.npy,A3779.npy,A3780.npy,A3781.npy,A3782.npy,A3783.npy,A3784.npy,A3785.npy,A3786.npy,A3787.npy,A3788.npy,A3789.npy,A3790.npy,A3791.npy,A3792.npy,A3793.npy,A3794.npy,A3795.npy,A3796.npy,A3797.npy,A3798.npy,A3799.npy,A3800.npy,A3801.npy,A3802.npy,A3803.npy,A3804.npy,A3805.npy,A3806.npy,A3807.npy,A3808.npy,A3809.npy,A3810.npy,A3811.npy,A3812.npy,A3813.npy,A3814.npy,A3815.npy,A3816.npy,A3817.npy,A3818.npy,A3819.npy,A3820.npy,A3821.npy,A3822.npy,A3823.npy,A3824.npy,A3825.npy,A3826.npy,A3827.npy,A3828.npy,A3829.npy,A3830.npy,A3831.npy,A3832.npy,A3833.npy,A3834.npy,A3835.npy,A3836.npy,A3837.npy,A3838.npy,A3839.npy,A3840.npy,A3841.npy,A3842.npy,A3843.npy,A3844.npy,A3845.npy,A3846.npy,A3847.npy,A3848.npy,A3849.npy,A3850.npy,A3851.npy,A3852.npy,A3853.npy,A3854.npy,A3855.npy,A3856.npy,A3857.npy,A3858.npy,A3859.npy,A3860.npy,A3861.npy,A3862.npy,A3863.npy,A3864.npy,A3865.npy,A3866.npy,A3867.npy,A3868.npy,A3869.npy,A3870.npy,A3871.npy,A3872.npy,A3873.npy,A3874.npy,A3875.npy,A3876.npy,A3877.npy,A3878.npy,A3879.npy,A3880.npy,A3881.npy,A3882.npy,A3883.npy,A3884.npy,A3885.npy,A3886.npy,A3887.npy,A3888.npy,A3889.npy,A3890.npy,A3891.npy,A3892.npy,A3893.npy,A3894.npy,A3895.npy,A3896.npy,A3897.npy,A3898.npy,A3899.npy,A3900.npy,A3901.npy,A3902.npy,A3903.npy,A3904.npy,A3905.npy,A3906.npy,A3907.npy,A3908.npy,A3909.npy,A3910.npy,A3911.npy,A3912.npy,A3913.npy,A3914.npy,A3915.npy,A3916.npy,A3917.npy,A3918.npy,A3919.npy,A3920.npy,A3921.npy,A3922.npy,A3923.npy,A3924.npy,A3925.npy,A3926.npy,A3927.npy,A3928.npy,A3929.npy,A3930.npy,A3931.npy,A3932.npy,A3933.npy,A3934.npy,A3935.npy,A3936.npy,A3937.npy,A3938.npy,A3939.npy,A3940.npy,A3941.npy,A3942.npy,A3943.npy,A3944.npy,A3945.npy,A3946.npy,A3947.npy,A3948.npy,A3949.npy,A3950.npy,A3951.npy,A3952.npy,A3953.npy,A3954.npy,A3955.npy,A3956.npy,A3957.npy,A3958.npy,A3959.npy,A3960.npy,A3961.npy,A3962.npy,A3963.npy,A3964.npy,A3965.npy,A3966.npy,A3967.npy,A3968.npy,A3969.npy,A3970.npy,A3971.npy,A3972.npy,A3973.npy,A3974.npy,A3975.npy,A3976.npy,A3977.npy,A3978.npy,A3979.npy,A3980.npy,A3981.npy,A3982.npy,A3983.npy,A3984.npy,A3985.npy,A3986.npy,A3987.npy,A3988.npy,A3989.npy,A3990.npy,A3991.npy,A3992.npy,A3993.npy,A3994.npy,A3995.npy,A3996.npy,A3997.npy,A3998.npy,A3999.npy,A4000.npy,A4001.npy,A4002.npy,A4003.npy,A4004.npy,A4005.npy,A4006.npy,A4007.npy,A4008.npy,A4009.npy,A4010.npy,A4011.npy,A4012.npy,A4013.npy,A4014.npy,A4015.npy,A4016.npy,A4017.npy,A4018.npy,A4019.npy,A4020.npy,A4021.npy,A4022.npy,A4023.npy,A4024.npy,A4025.npy,A4026.npy,A4027.npy,A4028.npy,A4029.npy,A4030.npy,A4031.npy,A4032.npy,A4033.npy,A4034.npy,A4035.npy,A4036.npy,A4037.npy,A4038.npy,A4039.npy,A4040.npy,A4041.npy,A4042.npy,A4043.npy,A4044.npy,A4045.npy,A4046.npy,A4047.npy,A4048.npy,A4049.npy,A4050.npy,A4051.npy,A4052.npy,A4053.npy,A4054.npy,A4055.npy,A4056.npy,A4057.npy,A4058.npy,A4059.npy,A4060.npy,A4061.npy,A4062.npy,A4063.npy,A4064.npy,A4065.npy,A4066.npy,A4067.npy,A4068.npy,A4069.npy,A4070.npy,A4071.npy,A4072.npy,A4073.npy,A4074.npy,A4075.npy,A4076.npy,A4077.npy,A4078.npy,A4079.npy,A4080.npy,A4081.npy,A4082.npy,A4083.npy,A4084.npy,A4085.npy,A4086.npy,A4087.npy,A4088.npy,A4089.npy,A4090.npy,A4091.npy,A4092.npy,A4093.npy,A4094.npy,A4095.npy,A4096.npy,A4097.npy,A4098.npy,A4099.npy,A4100.npy,A4101.npy,A4102.npy,A4103.npy,A4104.npy,A4105.npy,A4106.npy,A4107.npy,A4108.npy,A4109.npy,A4110.npy,A4111.npy,A4112.npy,A4113.npy,A4114.npy,A4115.npy,A4116.npy,A4117.npy,A4118.npy,A4119.npy,A4120.npy,A4121.npy,A4122.npy,A4123.npy,A4124.npy,A4125.npy,A4126.npy,A4127.npy,A4128.npy,A4129.npy,A4130.npy,A4131.npy,A4132.npy,A4133.npy,A4134.npy,A4135.npy,A4136.npy,A4137.npy,A4138.npy,A4139.npy,A4140.npy,A4141.npy,A4142.npy,A4143.npy,A4144.npy,A4145.npy,A4146.npy,A4147.npy,A4148.npy,A4149.npy,A4150.npy,A4151.npy,A4152.npy,A4153.npy,A4154.npy,A4155.npy,A4156.npy,A4157.npy,A4158.npy,A4159.npy,A4160.npy,A4161.npy,A4162.npy,A4163.npy,A4164.npy,A4165.npy,A4166.npy,A4167.npy,A4168.npy,A4169.npy,A4170.npy,A4171.npy,A4172.npy,A4173.npy,A4174.npy,A4175.npy,A4176.npy,A4177.npy,A4178.npy,A4179.npy,A4180.npy,A4181.npy,A4182.npy,A4183.npy,A4184.npy,A4185.npy,A4186.npy,A4187.npy,A4188.npy,A4189.npy,A4190.npy,A4191.npy,A4192.npy,A4193.npy,A4194.npy,A4195.npy,A4196.npy,A4197.npy,A4198.npy,A4199.npy,A4200.npy,A4201.npy,A4202.npy,A4203.npy,A4204.npy,A4205.npy,A4206.npy,A4207.npy,A4208.npy,A4209.npy,A4210.npy,A4211.npy,A4212.npy,A4213.npy,A4214.npy,A4215.npy,A4216.npy,A4217.npy,A4218.npy,A4219.npy,A4220.npy,A4221.npy,A4222.npy,A4223.npy,A4224.npy,A4225.npy,A4226.npy,A4227.npy,A4228.npy,A4229.npy,A4230.npy,A4231.npy,A4232.npy,A4233.npy,A4234.npy,A4235.npy,A4236.npy,A4237.npy,A4238.npy,A4239.npy,A4240.npy,A4241.npy,A4242.npy,A4243.npy,A4244.npy,A4245.npy,A4246.npy,A4247.npy,A4248.npy,A4249.npy,A4250.npy,A4251.npy,A4252.npy,A4253.npy,A4254.npy,A4255.npy,A4256.npy,A4257.npy,A4258.npy,A4259.npy,A4260.npy,A4261.npy,A4262.npy,A4263.npy,A4264.npy,A4265.npy,A4266.npy,A4267.npy,A4268.npy,A4269.npy,A4270.npy,A4271.npy,A4272.npy,A4273.npy,A4274.npy,A4275.npy,A4276.npy,A4277.npy,A4278.npy,A4279.npy,A4280.npy,A4281.npy,A4282.npy,A4283.npy,A4284.npy,A4285.npy,A4286.npy,A4287.npy,A4288.npy,A4289.npy,A4290.npy,A4291.npy,A4292.npy,A4293.npy,A4294.npy,A4295.npy,A4296.npy,A4297.npy,A4298.npy,A4299.npy,A4300.npy,A4301.npy,A4302.npy,A4303.npy,A4304.npy,A4305.npy,A4306.npy,A4307.npy,A4308.npy,A4309.npy,A4310.npy,A4311.npy,A4312.npy,A4313.npy,A4314.npy,A4315.npy,A4316.npy,A4317.npy,A4318.npy,A4319.npy,A4320.npy,A4321.npy,A4322.npy,A4323.npy,A4324.npy,A4325.npy,A4326.npy,A4327.npy,A4328.npy,A4329.npy,A4330.npy,A4331.npy,A4332.npy,A4333.npy,A4334.npy,A4335.npy,A4336.npy,A4337.npy,A4338.npy,A4339.npy,A4340.npy,A4341.npy,A4342.npy,A4343.npy,A4344.npy,A4345.npy,A4346.npy,A4347.npy,A4348.npy,A4349.npy,A4350.npy,A4351.npy,A4352.npy,A4353.npy,A4354.npy,A4355.npy,A4356.npy,A4357.npy,A4358.npy,A4359.npy,A4360.npy,A4361.npy,A4362.npy,A4363.npy,A4364.npy,A4365.npy,A4366.npy,A4367.npy,A4368.npy,A4369.npy,A4370.npy,A4371.npy,A4372.npy,A4373.npy,A4374.npy,A4375.npy,A4376.npy,A4377.npy,A4378.npy,A4379.npy,A4380.npy,A4381.npy,A4382.npy,A4383.npy,A4384.npy,A4385.npy,A4386.npy,A4387.npy,A4388.npy,A4389.npy,A4390.npy,A4391.npy,A4392.npy,A4393.npy,A4394.npy,A4395.npy,A4396.npy,A4397.npy,A4398.npy,A4399.npy,A4400.npy,A4401.npy,A4402.npy,A4403.npy,A4404.npy,A4405.npy,A4406.npy,A4407.npy,A4408.npy,A4409.npy,A4410.npy,A4411.npy,A4412.npy,A4413.npy,A4414.npy,A4415.npy,A4416.npy,A4417.npy,A4418.npy,A4419.npy,A4420.npy,A4421.npy,A4422.npy,A4423.npy,A4424.npy,A4425.npy,A4426.npy,A4427.npy,A4428.npy,A4429.npy,A4430.npy,A4431.npy,A4432.npy,A4433.npy,A4434.npy,A4435.npy,A4436.npy,A4437.npy,A4438.npy,A4439.npy,A4440.npy,A4441.npy,A4442.npy,A4443.npy,A4444.npy,A4445.npy,A4446.npy,A4447.npy,A4448.npy,A4449.npy,A4450.npy,A4451.npy,A4452.npy,A4453.npy,A4454.npy,A4455.npy,A4456.npy,A4457.npy,A4458.npy,A4459.npy,A4460.npy,A4461.npy,A4462.npy,A4463.npy,A4464.npy,A4465.npy,A4466.npy,A4467.npy,A4468.npy,A4469.npy,A4470.npy,A4471.npy,A4472.npy,A4473.npy,A4474.npy,A4475.npy,A4476.npy,A4477.npy,A4478.npy,A4479.npy,A4480.npy,A4481.npy,A4482.npy,A4483.npy,A4484.npy,A4485.npy,A4486.npy,A4487.npy,A4488.npy,A4489.npy,A4490.npy,A4491.npy,A4492.npy,A4493.npy,A4494.npy,A4495.npy,A4496.npy,A4497.npy,A4498.npy,A4499.npy,A4500.npy,A4501.npy,A4502.npy,A4503.npy,A4504.npy,A4505.npy,A4506.npy,A4507.npy,A4508.npy,A4509.npy,A4510.npy,A4511.npy,A4512.npy,A4513.npy,A4514.npy,A4515.npy,A4516.npy,A4517.npy,A4518.npy,A4519.npy,A4520.npy,A4521.npy,A4522.npy,A4523.npy,A4524.npy,A4525.npy,A4526.npy,A4527.npy,A4528.npy,A4529.npy,A4530.npy,A4531.npy,A4532.npy,A4533.npy,A4534.npy,A4535.npy,A4536.npy,A4537.npy,A4538.npy,A4539.npy,A4540.npy,A4541.npy,A4542.npy,A4543.npy,A4544.npy,A4545.npy,A4546.npy,A4547.npy,A4548.npy,A4549.npy,A4550.npy,A4551.npy,A4552.npy,A4553.npy,A4554.npy,A4555.npy,A4556.npy,A4557.npy,A4558.npy,A4559.npy,A4560.npy,A4561.npy,A4562.npy,A4563.npy,A4564.npy,A4565.npy,A4566.npy,A4567.npy,A4568.npy,A4569.npy,A4570.npy,A4571.npy,A4572.npy,A4573.npy,A4574.npy,A4575.npy,A4576.npy,A4577.npy,A4578.npy,A4579.npy,A4580.npy,A4581.npy,A4582.npy,A4583.npy,A4584.npy,A4585.npy,A4586.npy,A4587.npy,A4588.npy,A4589.npy,A4590.npy,A4591.npy,A4592.npy,A4593.npy,A4594.npy,A4595.npy,A4596.npy,A4597.npy,A4598.npy,A4599.npy,A4600.npy,A4601.npy,A4602.npy,A4603.npy,A4604.npy,A4605.npy,A4606.npy,A4607.npy,A4608.npy,A4609.npy,A4610.npy,A4611.npy,A4612.npy,A4613.npy,A4614.npy,A4615.npy,A4616.npy,A4617.npy,A4618.npy,A4619.npy,A4620.npy,A4621.npy,A4622.npy,A4623.npy,A4624.npy,A4625.npy,A4626.npy,A4627.npy,A4628.npy,A4629.npy,A4630.npy,A4631.npy,A4632.npy,A4633.npy,A4634.npy,A4635.npy,A4636.npy,A4637.npy,A4638.npy,A4639.npy,A4640.npy,A4641.npy,A4642.npy,A4643.npy,A4644.npy,A4645.npy,A4646.npy,A4647.npy,A4648.npy,A4649.npy,A4650.npy,A4651.npy,A4652.npy,A4653.npy,A4654.npy,A4655.npy,A4656.npy,A4657.npy,A4658.npy,A4659.npy,A4660.npy,A4661.npy,A4662.npy,A4663.npy,A4664.npy,A4665.npy,A4666.npy,A4667.npy,A4668.npy,A4669.npy,A4670.npy,A4671.npy,A4672.npy,A4673.npy,A4674.npy,A4675.npy,A4676.npy,A4677.npy,A4678.npy,A4679.npy,A4680.npy,A4681.npy,A4682.npy,A4683.npy,A4684.npy,A4685.npy,A4686.npy,A4687.npy,A4688.npy,A4689.npy,A4690.npy,A4691.npy,A4692.npy,A4693.npy,A4694.npy,A4695.npy,A4696.npy,A4697.npy,A4698.npy,A4699.npy,A4700.npy,A4701.npy,A4702.npy,A4703.npy,A4704.npy,A4705.npy,A4706.npy,A4707.npy,A4708.npy,A4709.npy,A4710.npy,A4711.npy,A4712.npy,A4713.npy,A4714.npy,A4715.npy,A4716.npy,A4717.npy,A4718.npy,A4719.npy,A4720.npy,A4721.npy,A4722.npy,A4723.npy,A4724.npy,A4725.npy,A4726.npy,A4727.npy,A4728.npy,A4729.npy,A4730.npy,A4731.npy,A4732.npy,A4733.npy,A4734.npy,A4735.npy,A4736.npy,A4737.npy,A4738.npy,A4739.npy,A4740.npy,A4741.npy,A4742.npy,A4743.npy,A4744.npy,A4745.npy,A4746.npy,A4747.npy,A4748.npy,A4749.npy,A4750.npy,A4751.npy,A4752.npy,A4753.npy,A4754.npy,A4755.npy,A4756.npy,A4757.npy,A4758.npy,A4759.npy,A4760.npy,A4761.npy,A4762.npy,A4763.npy,A4764.npy,A4765.npy,A4766.npy,A4767.npy,A4768.npy,A4769.npy,A4770.npy,A4771.npy,A4772.npy,A4773.npy,A4774.npy,A4775.npy,A4776.npy,A4777.npy,A4778.npy,A4779.npy,A4780.npy,A4781.npy,A4782.npy,A4783.npy,A4784.npy,A4785.npy,A4786.npy,A4787.npy,A4788.npy,A4789.npy,A4790.npy,A4791.npy,A4792.npy,A4793.npy,A4794.npy,A4795.npy,A4796.npy,A4797.npy,A4798.npy,A4799.npy,A4800.npy,A4801.npy,A4802.npy,A4803.npy,A4804.npy,A4805.npy,A4806.npy,A4807.npy,A4808.npy,A4809.npy,A4810.npy,A4811.npy,A4812.npy,A4813.npy,A4814.npy,A4815.npy,A4816.npy,A4817.npy,A4818.npy,A4819.npy,A4820.npy,A4821.npy,A4822.npy,A4823.npy,A4824.npy,A4825.npy,A4826.npy,A4827.npy,A4828.npy,A4829.npy,A4830.npy,A4831.npy,A4832.npy,A4833.npy,A4834.npy,A4835.npy,A4836.npy,A4837.npy,A4838.npy,A4839.npy,A4840.npy,A4841.npy,A4842.npy,A4843.npy,A4844.npy,A4845.npy,A4846.npy,A4847.npy,A4848.npy,A4849.npy,A4850.npy,A4851.npy,A4852.npy,A4853.npy,A4854.npy,A4855.npy,A4856.npy,A4857.npy,A4858.npy,A4859.npy,A4860.npy,A4861.npy,A4862.npy,A4863.npy,A4864.npy,A4865.npy,A4866.npy,A4867.npy,A4868.npy,A4869.npy,A4870.npy,A4871.npy,A4872.npy,A4873.npy,A4874.npy,A4875.npy,A4876.npy,A4877.npy,A4878.npy,A4879.npy,A4880.npy,A4881.npy,A4882.npy,A4883.npy,A4884.npy,A4885.npy,A4886.npy,A4887.npy,A4888.npy,A4889.npy,A4890.npy,A4891.npy,A4892.npy,A4893.npy,A4894.npy,A4895.npy,A4896.npy,A4897.npy,A4898.npy,A4899.npy,A4900.npy,A4901.npy,A4902.npy,A4903.npy,A4904.npy,A4905.npy,A4906.npy,A4907.npy,A4908.npy,A4909.npy,A4910.npy,A4911.npy,A4912.npy,A4913.npy,A4914.npy,A4915.npy,A4916.npy,A4917.npy,A4918.npy,A4919.npy,A4920.npy,A4921.npy,A4922.npy,A4923.npy,A4924.npy,A4925.npy,A4926.npy,A4927.npy,A4928.npy,A4929.npy,A4930.npy,A4931.npy,A4932.npy,A4933.npy,A4934.npy,A4935.npy,A4936.npy,A4937.npy,A4938.npy,A4939.npy,A4940.npy,A4941.npy,A4942.npy,A4943.npy,A4944.npy,A4945.npy,A4946.npy,A4947.npy,A4948.npy,A4949.npy,A4950.npy,A4951.npy,A4952.npy,A4953.npy,A4954.npy,A4955.npy,A4956.npy,A4957.npy,A4958.npy,A4959.npy,A4960.npy,A4961.npy,A4962.npy,A4963.npy,A4964.npy,A4965.npy,A4966.npy,A4967.npy,A4968.npy,A4969.npy,A4970.npy,A4971.npy,A4972.npy,A4973.npy,A4974.npy,A4975.npy,A4976.npy,A4977.npy,A4978.npy,A4979.npy,A4980.npy,A4981.npy,A4982.npy,A4983.npy,A4984.npy,A4985.npy,A4986.npy,A4987.npy,A4988.npy,A4989.npy,A4990.npy,A4991.npy,A4992.npy,A4993.npy,A4994.npy,A4995.npy,A4996.npy,A4997.npy,A4998.npy,A4999.npy,A5000.npy,A5001.npy,A5002.npy,A5003.npy,A5004.npy,A5005.npy,A5006.npy,A5007.npy,A5008.npy,A5009.npy,A5010.npy,A5011.npy,A5012.npy,A5013.npy,A5014.npy,A5015.npy,A5016.npy,A5017.npy,A5018.npy,A5019.npy,A5020.npy,A5021.npy,A5022.npy,A5023.npy,A5024.npy,A5025.npy,A5026.npy,A5027.npy,A5028.npy,A5029.npy,A5030.npy,A5031.npy,A5032.npy,A5033.npy,A5034.npy,A5035.npy,A5036.npy,A5037.npy,A5038.npy,A5039.npy,A5040.npy,A5041.npy,A5042.npy,A5043.npy,A5044.npy,A5045.npy,A5046.npy,A5047.npy,A5048.npy,A5049.npy,A5050.npy,A5051.npy,A5052.npy,A5053.npy,A5054.npy,A5055.npy,A5056.npy,A5057.npy,A5058.npy,A5059.npy,A5060.npy,A5061.npy,A5062.npy,A5063.npy,A5064.npy,A5065.npy,A5066.npy,A5067.npy,A5068.npy,A5069.npy,A5070.npy,A5071.npy,A5072.npy,A5073.npy,A5074.npy,A5075.npy,A5076.npy,A5077.npy,A5078.npy,A5079.npy,A5080.npy,A5081.npy,A5082.npy,A5083.npy,A5084.npy,A5085.npy,A5086.npy,A5087.npy,A5088.npy,A5089.npy,A5090.npy,A5091.npy,A5092.npy,A5093.npy,A5094.npy,A5095.npy,A5096.npy,A5097.npy,A5098.npy,A5099.npy,A5100.npy,A5101.npy,A5102.npy,A5103.npy,A5104.npy,A5105.npy,A5106.npy,A5107.npy,A5108.npy,A5109.npy,A5110.npy,A5111.npy,A5112.npy,A5113.npy,A5114.npy,A5115.npy,A5116.npy,A5117.npy,A5118.npy,A5119.npy,A5120.npy,A5121.npy,A5122.npy,A5123.npy,A5124.npy,A5125.npy,A5126.npy,A5127.npy,A5128.npy,A5129.npy,A5130.npy,A5131.npy,A5132.npy,A5133.npy,A5134.npy,A5135.npy,A5136.npy,A5137.npy,A5138.npy,A5139.npy,A5140.npy,A5141.npy,A5142.npy,A5143.npy,A5144.npy,A5145.npy,A5146.npy,A5147.npy,A5148.npy,A5149.npy,A5150.npy,A5151.npy,A5152.npy,A5153.npy,A5154.npy,A5155.npy,A5156.npy,A5157.npy,A5158.npy,A5159.npy,A5160.npy,A5161.npy,A5162.npy,A5163.npy,A5164.npy,A5165.npy,A5166.npy,A5167.npy,A5168.npy,A5169.npy,A5170.npy,A5171.npy,A5172.npy,A5173.npy,A5174.npy,A5175.npy,A5176.npy,A5177.npy,A5178.npy,A5179.npy,A5180.npy,A5181.npy,A5182.npy,A5183.npy,A5184.npy,A5185.npy,A5186.npy,A5187.npy,A5188.npy,A5189.npy,A5190.npy,A5191.npy,A5192.npy,A5193.npy,A5194.npy,A5195.npy,A5196.npy,A5197.npy,A5198.npy,A5199.npy,A5200.npy,A5201.npy,A5202.npy,A5203.npy,A5204.npy,A5205.npy,A5206.npy,A5207.npy,A5208.npy,A5209.npy,A5210.npy,A5211.npy,A5212.npy,A5213.npy,A5214.npy,A5215.npy,A5216.npy,A5217.npy,A5218.npy,A5219.npy,A5220.npy,A5221.npy,A5222.npy,A5223.npy,A5224.npy,A5225.npy,A5226.npy,A5227.npy,A5228.npy,A5229.npy,A5230.npy,A5231.npy,A5232.npy,A5233.npy,A5234.npy,A5235.npy,A5236.npy,A5237.npy,A5238.npy,A5239.npy,A5240.npy,A5241.npy,A5242.npy,A5243.npy,A5244.npy,A5245.npy,A5246.npy,A5247.npy,A5248.npy,A5249.npy,A5250.npy,A5251.npy,A5252.npy,A5253.npy,A5254.npy,A5255.npy,A5256.npy,A5257.npy,A5258.npy,A5259.npy,A5260.npy,A5261.npy,A5262.npy,A5263.npy,A5264.npy,A5265.npy,A5266.npy,A5267.npy,A5268.npy,A5269.npy,A5270.npy,A5271.npy,A5272.npy,A5273.npy,A5274.npy,A5275.npy,A5276.npy,A5277.npy,A5278.npy,A5279.npy,A5280.npy,A5281.npy,A5282.npy,A5283.npy,A5284.npy,A5285.npy,A5286.npy,A5287.npy,A5288.npy,A5289.npy,A5290.npy,A5291.npy,A5292.npy,A5293.npy,A5294.npy,A5295.npy,A5296.npy,A5297.npy,A5298.npy,A5299.npy,A5300.npy,A5301.npy,A5302.npy,A5303.npy,A5304.npy,A5305.npy,A5306.npy,A5307.npy,A5308.npy,A5309.npy,A5310.npy,A5311.npy,A5312.npy,A5313.npy,A5314.npy,A5315.npy,A5316.npy,A5317.npy,A5318.npy,A5319.npy,A5320.npy,A5321.npy,A5322.npy,A5323.npy,A5324.npy,A5325.npy,A5326.npy,A5327.npy,A5328.npy,A5329.npy,A5330.npy,A5331.npy,A5332.npy,A5333.npy,A5334.npy,A5335.npy,A5336.npy,A5337.npy,A5338.npy,A5339.npy,A5340.npy,A5341.npy,A5342.npy,A5343.npy,A5344.npy,A5345.npy,A5346.npy,A5347.npy,A5348.npy,A5349.npy,A5350.npy,A5351.npy,A5352.npy,A5353.npy,A5354.npy,A5355.npy,A5356.npy,A5357.npy,A5358.npy,A5359.npy,A5360.npy,A5361.npy,A5362.npy,A5363.npy,A5364.npy,A5365.npy,A5366.npy,A5367.npy,A5368.npy,A5369.npy,A5370.npy,A5371.npy,A5372.npy,A5373.npy,A5374.npy,A5375.npy,A5376.npy,A5377.npy,A5378.npy,A5379.npy,A5380.npy,A5381.npy,A5382.npy,A5383.npy,A5384.npy,A5385.npy,A5386.npy,A5387.npy,A5388.npy,A5389.npy,A5390.npy,A5391.npy,A5392.npy,A5393.npy,A5394.npy,A5395.npy,A5396.npy,A5397.npy,A5398.npy,A5399.npy,A5400.npy,A5401.npy,A5402.npy,A5403.npy,A5404.npy,A5405.npy,A5406.npy,A5407.npy,A5408.npy,A5409.npy,A5410.npy,A5411.npy,A5412.npy,A5413.npy,A5414.npy,A5415.npy,A5416.npy,A5417.npy,A5418.npy,A5419.npy,A5420.npy,A5421.npy,A5422.npy,A5423.npy,A5424.npy,A5425.npy,A5426.npy,A5427.npy,A5428.npy,A5429.npy,A5430.npy,A5431.npy,A5432.npy,A5433.npy,A5434.npy,A5435.npy,A5436.npy,A5437.npy,A5438.npy,A5439.npy,A5440.npy,A5441.npy,A5442.npy,A5443.npy,A5444.npy,A5445.npy,A5446.npy,A5447.npy,A5448.npy,A5449.npy,A5450.npy,A5451.npy,A5452.npy,A5453.npy,A5454.npy,A5455.npy,A5456.npy,A5457.npy,A5458.npy,A5459.npy,A5460.npy,A5461.npy,A5462.npy,A5463.npy,A5464.npy,A5465.npy,A5466.npy,A5467.npy,A5468.npy,A5469.npy,A5470.npy,A5471.npy,A5472.npy,A5473.npy,A5474.npy,A5475.npy,A5476.npy,A5477.npy,A5478.npy,A5479.npy,A5480.npy,A5481.npy,A5482.npy,A5483.npy,A5484.npy,A5485.npy,A5486.npy,A5487.npy,A5488.npy,A5489.npy,A5490.npy,A5491.npy,A5492.npy,A5493.npy,A5494.npy,A5495.npy,A5496.npy,A5497.npy,A5498.npy,A5499.npy,A5500.npy,A5501.npy,A5502.npy,A5503.npy,A5504.npy,A5505.npy,A5506.npy,A5507.npy,A5508.npy,A5509.npy,A5510.npy,A5511.npy,A5512.npy,A5513.npy,A5514.npy,A5515.npy,A5516.npy,A5517.npy,A5518.npy,A5519.npy,A5520.npy,A5521.npy,A5522.npy,A5523.npy,A5524.npy,A5525.npy,A5526.npy,A5527.npy,A5528.npy,A5529.npy,A5530.npy,A5531.npy,A5532.npy,A5533.npy,A5534.npy,A5535.npy,A5536.npy,A5537.npy,A5538.npy,A5539.npy,A5540.npy,A5541.npy,A5542.npy,A5543.npy,A5544.npy,A5545.npy,A5546.npy,A5547.npy,A5548.npy,A5549.npy,A5550.npy,A5551.npy,A5552.npy,A5553.npy,A5554.npy,A5555.npy,A5556.npy,A5557.npy,A5558.npy,A5559.npy,A5560.npy,A5561.npy,A5562.npy,A5563.npy,A5564.npy,A5565.npy,A5566.npy,A5567.npy,A5568.npy,A5569.npy,A5570.npy,A5571.npy,A5572.npy,A5573.npy,A5574.npy,A5575.npy,A5576.npy,A5577.npy,A5578.npy,A5579.npy,A5580.npy,A5581.npy,A5582.npy,A5583.npy,A5584.npy,A5585.npy,A5586.npy,A5587.npy,A5588.npy,A5589.npy,A5590.npy,A5591.npy,A5592.npy,A5593.npy,A5594.npy,A5595.npy,A5596.npy,A5597.npy,A5598.npy,A5599.npy,A5600.npy,A5601.npy,A5602.npy,A5603.npy,A5604.npy,A5605.npy,A5606.npy,A5607.npy,A5608.npy,A5609.npy,A5610.npy,A5611.npy,A5612.npy,A5613.npy,A5614.npy,A5615.npy,A5616.npy,A5617.npy,A5618.npy,A5619.npy,A5620.npy,A5621.npy,A5622.npy,A5623.npy,A5624.npy,A5625.npy,A5626.npy,A5627.npy,A5628.npy,A5629.npy,A5630.npy,A5631.npy,A5632.npy,A5633.npy,A5634.npy,A5635.npy,A5636.npy,A5637.npy,A5638.npy,A5639.npy,A5640.npy,A5641.npy,A5642.npy,A5643.npy,A5644.npy,A5645.npy,A5646.npy,A5647.npy,A5648.npy,A5649.npy,A5650.npy,A5651.npy,A5652.npy,A5653.npy,A5654.npy,A5655.npy,A5656.npy,A5657.npy,A5658.npy,A5659.npy,A5660.npy,A5661.npy,A5662.npy,A5663.npy,A5664.npy,A5665.npy,A5666.npy,A5667.npy,A5668.npy,A5669.npy,A5670.npy,A5671.npy,A5672.npy,A5673.npy,A5674.npy,A5675.npy,A5676.npy,A5677.npy,A5678.npy,A5679.npy,A5680.npy,A5681.npy,A5682.npy,A5683.npy,A5684.npy,A5685.npy,A5686.npy,A5687.npy,A5688.npy,A5689.npy,A5690.npy,A5691.npy,A5692.npy,A5693.npy,A5694.npy,A5695.npy,A5696.npy,A5697.npy,A5698.npy,A5699.npy,A5700.npy,A5701.npy,A5702.npy,A5703.npy,A5704.npy,A5705.npy,A5706.npy,A5707.npy,A5708.npy,A5709.npy,A5710.npy,A5711.npy,A5712.npy,A5713.npy,A5714.npy,A5715.npy,A5716.npy,A5717.npy,A5718.npy,A5719.npy,A5720.npy,A5721.npy,A5722.npy,A5723.npy,A5724.npy,A5725.npy,A5726.npy,A5727.npy,A5728.npy,A5729.npy,A5730.npy,A5731.npy,A5732.npy,A5733.npy,A5734.npy,A5735.npy,A5736.npy,A5737.npy,A5738.npy,A5739.npy,A5740.npy,A5741.npy,A5742.npy,A5743.npy,A5744.npy,A5745.npy,A5746.npy,A5747.npy,A5748.npy,A5749.npy,A5750.npy,A5751.npy,A5752.npy,A5753.npy,A5754.npy,A5755.npy,A5756.npy,A5757.npy,A5758.npy,A5759.npy,A5760.npy,A5761.npy,A5762.npy,A5763.npy,A5764.npy,A5765.npy,A5766.npy,A5767.npy,A5768.npy,A5769.npy,A5770.npy,A5771.npy,A5772.npy,A5773.npy,A5774.npy,A5775.npy,A5776.npy,A5777.npy,A5778.npy,A5779.npy,A5780.npy,A5781.npy,A5782.npy,A5783.npy,A5784.npy,A5785.npy,A5786.npy,A5787.npy,A5788.npy,A5789.npy,A5790.npy,A5791.npy,A5792.npy,A5793.npy,A5794.npy,A5795.npy,A5796.npy,A5797.npy,A5798.npy,A5799.npy,A5800.npy,A5801.npy,A5802.npy,A5803.npy,A5804.npy,A5805.npy,A5806.npy,A5807.npy,A5808.npy,A5809.npy,A5810.npy,A5811.npy,A5812.npy,A5813.npy,A5814.npy,A5815.npy,A5816.npy,A5817.npy,A5818.npy,A5819.npy,A5820.npy,A5821.npy,A5822.npy,A5823.npy,A5824.npy,A5825.npy,A5826.npy,A5827.npy,A5828.npy,A5829.npy,A5830.npy,A5831.npy,A5832.npy,A5833.npy,A5834.npy,A5835.npy,A5836.npy,A5837.npy,A5838.npy,A5839.npy,A5840.npy,A5841.npy,A5842.npy,A5843.npy,A5844.npy,A5845.npy,A5846.npy,A5847.npy,A5848.npy,A5849.npy,A5850.npy,A5851.npy,A5852.npy,A5853.npy,A5854.npy,A5855.npy,A5856.npy,A5857.npy,A5858.npy,A5859.npy,A5860.npy,A5861.npy,A5862.npy,A5863.npy,A5864.npy,A5865.npy,A5866.npy,A5867.npy,A5868.npy,A5869.npy,A5870.npy,A5871.npy,A5872.npy,A5873.npy,A5874.npy,A5875.npy,A5876.npy,A5877.npy,A5878.npy,A5879.npy,A5880.npy,A5881.npy,A5882.npy,A5883.npy,A5884.npy,A5885.npy,A5886.npy,A5887.npy,A5888.npy,A5889.npy,A5890.npy,A5891.npy,A5892.npy,A5893.npy,A5894.npy,A5895.npy,A5896.npy,A5897.npy,A5898.npy,A5899.npy,A5900.npy,A5901.npy,A5902.npy,A5903.npy,A5904.npy,A5905.npy,A5906.npy,A5907.npy,A5908.npy,A5909.npy,A5910.npy,A5911.npy,A5912.npy,A5913.npy,A5914.npy,A5915.npy,A5916.npy,A5917.npy,A5918.npy,A5919.npy,A5920.npy,A5921.npy,A5922.npy,A5923.npy,A5924.npy,A5925.npy,A5926.npy,A5927.npy,A5928.npy,A5929.npy,A5930.npy,A5931.npy,A5932.npy,A5933.npy,A5934.npy,A5935.npy,A5936.npy,A5937.npy,A5938.npy,A5939.npy,A5940.npy,A5941.npy,A5942.npy,A5943.npy,A5944.npy,A5945.npy,A5946.npy,A5947.npy,A5948.npy,A5949.npy,A5950.npy,A5951.npy,A5952.npy,A5953.npy,A5954.npy,A5955.npy,A5956.npy,A5957.npy,A5958.npy,A5959.npy,A5960.npy,A5961.npy,A5962.npy,A5963.npy,A5964.npy,A5965.npy,A5966.npy,A5967.npy,A5968.npy,A5969.npy,A5970.npy,A5971.npy,A5972.npy,A5973.npy,A5974.npy,A5975.npy,A5976.npy,A5977.npy,A5978.npy,A5979.npy,A5980.npy,A5981.npy,A5982.npy,A5983.npy,A5984.npy,A5985.npy,A5986.npy,A5987.npy,A5988.npy,A5989.npy,A5990.npy,A5991.npy,A5992.npy,A5993.npy,A5994.npy,A5995.npy,A5996.npy,A5997.npy,A5998.npy,A5999.npy,A6000.npy,A6001.npy,A6002.npy,A6003.npy,A6004.npy,A6005.npy,A6006.npy,A6007.npy,A6008.npy,A6009.npy,A6010.npy,A6011.npy,A6012.npy,A6013.npy,A6014.npy,A6015.npy,A6016.npy,A6017.npy,A6018.npy,A6019.npy,A6020.npy,A6021.npy,A6022.npy,A6023.npy,A6024.npy,A6025.npy,A6026.npy,A6027.npy,A6028.npy,A6029.npy,A6030.npy,A6031.npy,A6032.npy,A6033.npy,A6034.npy,A6035.npy,A6036.npy,A6037.npy,A6038.npy,A6039.npy,A6040.npy,A6041.npy,A6042.npy,A6043.npy,A6044.npy,A6045.npy,A6046.npy,A6047.npy,A6048.npy,A6049.npy,A6050.npy,A6051.npy,A6052.npy,A6053.npy,A6054.npy,A6055.npy,A6056.npy,A6057.npy,A6058.npy,A6059.npy,A6060.npy,A6061.npy,A6062.npy,A6063.npy,A6064.npy,A6065.npy,A6066.npy,A6067.npy,A6068.npy,A6069.npy,A6070.npy,A6071.npy,A6072.npy,A6073.npy,A6074.npy,A6075.npy,A6076.npy,A6077.npy,A6078.npy,A6079.npy,A6080.npy,A6081.npy,A6082.npy,A6083.npy,A6084.npy,A6085.npy,A6086.npy,A6087.npy,A6088.npy,A6089.npy,A6090.npy,A6091.npy,A6092.npy,A6093.npy,A6094.npy,A6095.npy,A6096.npy,A6097.npy,A6098.npy,A6099.npy,A6100.npy,A6101.npy,A6102.npy,A6103.npy,A6104.npy,A6105.npy,A6106.npy,A6107.npy,A6108.npy,A6109.npy,A6110.npy,A6111.npy,A6112.npy,A6113.npy,A6114.npy,A6115.npy,A6116.npy,A6117.npy,A6118.npy,A6119.npy,A6120.npy,A6121.npy,A6122.npy,A6123.npy,A6124.npy,A6125.npy,A6126.npy,A6127.npy,A6128.npy,A6129.npy,A6130.npy,A6131.npy,A6132.npy,A6133.npy,A6134.npy,A6135.npy,A6136.npy,A6137.npy,A6138.npy,A6139.npy,A6140.npy,A6141.npy,A6142.npy,A6143.npy,A6144.npy,A6145.npy,A6146.npy,A6147.npy,A6148.npy,A6149.npy,A6150.npy,A6151.npy,A6152.npy,A6153.npy,A6154.npy,A6155.npy,A6156.npy,A6157.npy,A6158.npy,A6159.npy,A6160.npy,A6161.npy,A6162.npy,A6163.npy,A6164.npy,A6165.npy,A6166.npy,A6167.npy,A6168.npy,A6169.npy,A6170.npy,A6171.npy,A6172.npy,A6173.npy,A6174.npy,A6175.npy,A6176.npy,A6177.npy,A6178.npy,A6179.npy,A6180.npy,A6181.npy,A6182.npy,A6183.npy,A6184.npy,A6185.npy,A6186.npy,A6187.npy,A6188.npy,A6189.npy,A6190.npy,A6191.npy,A6192.npy,A6193.npy,A6194.npy,A6195.npy,A6196.npy,A6197.npy,A6198.npy,A6199.npy,A6200.npy,A6201.npy,A6202.npy,A6203.npy,A6204.npy,A6205.npy,A6206.npy,A6207.npy,A6208.npy,A6209.npy,A6210.npy,A6211.npy,A6212.npy,A6213.npy,A6214.npy,A6215.npy,A6216.npy,A6217.npy,A6218.npy,A6219.npy,A6220.npy,A6221.npy,A6222.npy,A6223.npy,A6224.npy,A6225.npy,A6226.npy,A6227.npy,A6228.npy,A6229.npy,A6230.npy,A6231.npy,A6232.npy,A6233.npy,A6234.npy,A6235.npy,A6236.npy,A6237.npy,A6238.npy,A6239.npy,A6240.npy,A6241.npy,A6242.npy,A6243.npy,A6244.npy,A6245.npy,A6246.npy,A6247.npy,A6248.npy,A6249.npy,A6250.npy,A6251.npy,A6252.npy,A6253.npy,A6254.npy,A6255.npy,A6256.npy,A6257.npy,A6258.npy,A6259.npy,A6260.npy,A6261.npy,A6262.npy,A6263.npy,A6264.npy,A6265.npy,A6266.npy,A6267.npy,A6268.npy,A6269.npy,A6270.npy,A6271.npy,A6272.npy,A6273.npy,A6274.npy,A6275.npy,A6276.npy,A6277.npy,A6278.npy,A6279.npy,A6280.npy,A6281.npy,A6282.npy,A6283.npy,A6284.npy,A6285.npy,A6286.npy,A6287.npy,A6288.npy,A6289.npy,A6290.npy,A6291.npy,A6292.npy,A6293.npy,A6294.npy,A6295.npy,A6296.npy,A6297.npy,A6298.npy,A6299.npy,A6300.npy,A6301.npy,A6302.npy,A6303.npy,A6304.npy,A6305.npy,A6306.npy,A6307.npy,A6308.npy,A6309.npy,A6310.npy,A6311.npy,A6312.npy,A6313.npy,A6314.npy,A6315.npy,A6316.npy,A6317.npy,A6318.npy,A6319.npy,A6320.npy,A6321.npy,A6322.npy,A6323.npy,A6324.npy,A6325.npy,A6326.npy,A6327.npy,A6328.npy,A6329.npy,A6330.npy,A6331.npy,A6332.npy,A6333.npy,A6334.npy,A6335.npy,A6336.npy,A6337.npy,A6338.npy,A6339.npy,A6340.npy,A6341.npy,A6342.npy,A6343.npy,A6344.npy,A6345.npy,A6346.npy,A6347.npy,A6348.npy,A6349.npy,A6350.npy,A6351.npy,A6352.npy,A6353.npy,A6354.npy,A6355.npy,A6356.npy,A6357.npy,A6358.npy,A6359.npy,A6360.npy,A6361.npy,A6362.npy,A6363.npy,A6364.npy,A6365.npy,A6366.npy,A6367.npy,A6368.npy,A6369.npy,A6370.npy,A6371.npy,A6372.npy,A6373.npy,A6374.npy,A6375.npy,A6376.npy,A6377.npy,A6378.npy,A6379.npy,A6380.npy,A6381.npy,A6382.npy,A6383.npy,A6384.npy,A6385.npy,A6386.npy,A6387.npy,A6388.npy,A6389.npy,A6390.npy,A6391.npy,A6392.npy,A6393.npy,A6394.npy,A6395.npy,A6396.npy,A6397.npy,A6398.npy,A6399.npy,A6400.npy,A6401.npy,A6402.npy,A6403.npy,A6404.npy,A6405.npy,A6406.npy,A6407.npy,A6408.npy,A6409.npy,A6410.npy,A6411.npy,A6412.npy,A6413.npy,A6414.npy,A6415.npy,A6416.npy,A6417.npy,A6418.npy,A6419.npy,A6420.npy,A6421.npy,A6422.npy,A6423.npy,A6424.npy,A6425.npy,A6426.npy,A6427.npy,A6428.npy,A6429.npy,A6430.npy,A6431.npy,A6432.npy,A6433.npy,A6434.npy,A6435.npy,A6436.npy,A6437.npy,A6438.npy,A6439.npy,A6440.npy,A6441.npy,A6442.npy,A6443.npy,A6444.npy,A6445.npy,A6446.npy,A6447.npy,A6448.npy,A6449.npy,A6450.npy,A6451.npy,A6452.npy,A6453.npy,A6454.npy,A6455.npy,A6456.npy,A6457.npy,A6458.npy,A6459.npy,A6460.npy,A6461.npy,A6462.npy,A6463.npy,A6464.npy,A6465.npy,A6466.npy,A6467.npy,A6468.npy,A6469.npy,A6470.npy,A6471.npy,A6472.npy,A6473.npy,A6474.npy,A6475.npy,A6476.npy,A6477.npy,A6478.npy,A6479.npy,A6480.npy,A6481.npy,A6482.npy,A6483.npy,A6484.npy,A6485.npy,A6486.npy,A6487.npy,A6488.npy,A6489.npy,A6490.npy,A6491.npy,A6492.npy,A6493.npy,A6494.npy,A6495.npy,A6496.npy,A6497.npy,A6498.npy,A6499.npy,A6500.npy,A6501.npy,A6502.npy,A6503.npy,A6504.npy,A6505.npy,A6506.npy,A6507.npy,A6508.npy,A6509.npy,A6510.npy,A6511.npy,A6512.npy,A6513.npy,A6514.npy,A6515.npy,A6516.npy,A6517.npy,A6518.npy,A6519.npy,A6520.npy,A6521.npy,A6522.npy,A6523.npy,A6524.npy,A6525.npy,A6526.npy,A6527.npy,A6528.npy,A6529.npy,A6530.npy,A6531.npy,A6532.npy,A6533.npy,A6534.npy,A6535.npy,A6536.npy,A6537.npy,A6538.npy,A6539.npy,A6540.npy,A6541.npy,A6542.npy,A6543.npy,A6544.npy,A6545.npy,A6546.npy,A6547.npy,A6548.npy,A6549.npy,A6550.npy,A6551.npy,A6552.npy,A6553.npy,A6554.npy,A6555.npy,A6556.npy,A6557.npy,A6558.npy,A6559.npy,A6560.npy,A6561.npy,A6562.npy,A6563.npy,A6564.npy,A6565.npy,A6566.npy,A6567.npy,A6568.npy,A6569.npy,A6570.npy,A6571.npy,A6572.npy,A6573.npy,A6574.npy,A6575.npy,A6576.npy,A6577.npy,A6578.npy,A6579.npy,A6580.npy,A6581.npy,A6582.npy,A6583.npy,A6584.npy,A6585.npy,A6586.npy,A6587.npy,A6588.npy,A6589.npy,A6590.npy,A6591.npy,A6592.npy,A6593.npy,A6594.npy,A6595.npy,A6596.npy,A6597.npy,A6598.npy,A6599.npy,A6600.npy,A6601.npy,A6602.npy,A6603.npy,A6604.npy,A6605.npy,A6606.npy,A6607.npy,A6608.npy,A6609.npy,A6610.npy,A6611.npy,A6612.npy,A6613.npy,A6614.npy,A6615.npy,A6616.npy,A6617.npy,A6618.npy,A6619.npy,A6620.npy,A6621.npy,A6622.npy,A6623.npy,A6624.npy,A6625.npy,A6626.npy,A6627.npy,A6628.npy,A6629.npy,A6630.npy,A6631.npy,A6632.npy,A6633.npy,A6634.npy,A6635.npy,A6636.npy,A6637.npy,A6638.npy,A6639.npy,A6640.npy,A6641.npy,A6642.npy,A6643.npy,A6644.npy,A6645.npy,A6646.npy,A6647.npy,A6648.npy,A6649.npy,A6650.npy,A6651.npy,A6652.npy,A6653.npy,A6654.npy,A6655.npy,A6656.npy,A6657.npy,A6658.npy,A6659.npy,A6660.npy,A6661.npy,A6662.npy,A6663.npy,A6664.npy,A6665.npy,A6666.npy,A6667.npy,A6668.npy,A6669.npy,A6670.npy,A6671.npy,A6672.npy,A6673.npy,A6674.npy,A6675.npy,A6676.npy,A6677.npy,A6678.npy,A6679.npy,A6680.npy,A6681.npy,A6682.npy,A6683.npy,A6684.npy,A6685.npy,A6686.npy,A6687.npy,A6688.npy,A6689.npy,A6690.npy,A6691.npy,A6692.npy,A6693.npy,A6694.npy,A6695.npy,A6696.npy,A6697.npy,A6698.npy,A6699.npy,A6700.npy,A6701.npy,A6702.npy,A6703.npy,A6704.npy,A6705.npy,A6706.npy,A6707.npy,A6708.npy,A6709.npy,A6710.npy,A6711.npy,A6712.npy,A6713.npy,A6714.npy,A6715.npy,A6716.npy,A6717.npy,A6718.npy,A6719.npy,A6720.npy,A6721.npy,A6722.npy,A6723.npy,A6724.npy,A6725.npy,A6726.npy,A6727.npy,A6728.npy,A6729.npy,A6730.npy,A6731.npy,A6732.npy,A6733.npy,A6734.npy,A6735.npy,A6736.npy,A6737.npy,A6738.npy,A6739.npy,A6740.npy,A6741.npy,A6742.npy,A6743.npy,A6744.npy,A6745.npy,A6746.npy,A6747.npy,A6748.npy,A6749.npy,A6750.npy,A6751.npy,A6752.npy,A6753.npy,A6754.npy,A6755.npy,A6756.npy,A6757.npy,A6758.npy,A6759.npy,A6760.npy,A6761.npy,A6762.npy,A6763.npy,A6764.npy,A6765.npy,A6766.npy,A6767.npy,A6768.npy,A6769.npy,A6770.npy,A6771.npy,A6772.npy,A6773.npy,A6774.npy,A6775.npy,A6776.npy,A6777.npy,A6778.npy,A6779.npy,A6780.npy,A6781.npy,A6782.npy,A6783.npy,A6784.npy,A6785.npy,A6786.npy,A6787.npy,A6788.npy,A6789.npy,A6790.npy,A6791.npy,A6792.npy,A6793.npy,A6794.npy,A6795.npy,A6796.npy,A6797.npy,A6798.npy,A6799.npy,A6800.npy,A6801.npy,A6802.npy,A6803.npy,A6804.npy,A6805.npy,A6806.npy,A6807.npy,A6808.npy,A6809.npy,A6810.npy,A6811.npy,A6812.npy,A6813.npy,A6814.npy,A6815.npy,A6816.npy,A6817.npy,A6818.npy,A6819.npy,A6820.npy,A6821.npy,A6822.npy,A6823.npy,A6824.npy,A6825.npy,A6826.npy,A6827.npy,A6828.npy,A6829.npy,A6830.npy,A6831.npy,A6832.npy,A6833.npy,A6834.npy,A6835.npy,A6836.npy,A6837.npy,A6838.npy,A6839.npy,A6840.npy,A6841.npy,A6842.npy,A6843.npy,A6844.npy,A6845.npy,A6846.npy,A6847.npy,A6848.npy,A6849.npy,A6850.npy,A6851.npy,A6852.npy,A6853.npy,A6854.npy,A6855.npy,A6856.npy,A6857.npy,A6858.npy,A6859.npy,A6860.npy,A6861.npy,A6862.npy,A6863.npy,A6864.npy,A6865.npy,A6866.npy,A6867.npy,A6868.npy,A6869.npy,A6870.npy,A6871.npy,A6872.npy,A6873.npy,A6874.npy,A6875.npy,A6876.npy,A6877.npy 2 | 0,74,49,81,45,53,29,74,80,81,82,87,74,57,76,41,14,60,92,74,27,64,27,67,64,74,74,55,58,35,46,62,30,34,61,52,62,42,55,17,28,40,88,85,84,67,58,31,54,85,67,72,80,76,46,79,51,85,78,28,64,82,66,50,83,66,63,26,55,11,68,82,14,14,80,48,55,50,64,77,74,39,48,62,72,50,65,65,37,49,51,78,54,49,29,53,45,17,31,48,75,79,80,58,48,65,89,26,83,75,33,40,88,34,40,78,73,70,55,64,82,81,74,83,33,25,87,64,73,53,68,65,72,26,66,56,61,66,50,62,79,34,73,36,48,45,72,52,49,28,85,60,63,52,81,66,55,14,63,32,34,68,49,52,47,57,50,75,66,52,22,78,55,34,65,47,49,23,70,29,57,70,76,85,52,47,65,68,46,53,36,60,33,31,58,71,65,82,90,78,48,22,83,77,48,79,62,72,74,35,42,57,16,61,86,63,65,68,71,29,79,35,70,67,92,19,80,72,94,80,42,65,37,26,28,49,78,22,70,76,80,83,70,76,62,70,52,90,83,23,63,73,32,80,42,82,46,88,60,58,83,81,42,45,70,75,39,52,64,64,85,92,87,59,69,84,54,71,61,51,71,47,75,54,71,26,62,18,53,71,69,72,63,79,70,64,76,35,65,14,94,38,60,85,83,12,74,85,85,78,50,84,55,65,79,72,62,86,41,64,27,72,67,28,56,81,49,64,78,53,77,60,36,66,75,22,70,67,64,32,23,35,67,70,65,77,60,63,40,62,63,84,64,71,70,78,78,13,80,51,66,52,55,75,72,15,73,76,49,59,74,29,39,75,88,58,80,49,54,46,73,63,84,65,68,85,64,27,60,86,78,71,74,34,80,81,74,44,51,90,62,75,60,74,50,59,83,68,32,84,28,63,78,74,87,68,87,78,71,48,63,39,74,73,61,62,18,85,79,46,45,58,78,17,66,86,86,61,83,28,59,56,72,62,26,59,87,70,51,45,33,67,32,12,71,65,83,25,40,65,48,58,42,87,79,80,30,65,53,60,80,62,53,64,72,62,46,33,91,84,65,39,48,72,61,35,77,79,47,31,55,66,64,27,81,73,71,54,72,48,54,47,91,78,79,7,54,66,46,54,38,69,19,66,79,20,50,71,89,81,54,66,22,51,37,47,78,43,26,80,83,62,53,39,12,59,73,61,81,65,27,56,71,78,53,84,74,80,76,49,55,87,48,27,64,60,84,82,78,68,95,65,70,88,79,72,62,30,72,27,37,73,48,72,76,77,51,28,83,49,51,50,78,62,82,37,61,53,55,50,77,75,76,86,75,74,79,30,68,29,67,55,55,76,57,79,69,65,0,92,25,85,82,82,51,51,52,29,40,81,61,65,59,53,65,31,51,56,32,88,89,60,14,89,72,80,46,79,21,82,29,73,65,83,46,55,81,75,73,76,34,58,57,15,48,55,65,66,85,65,86,76,80,81,86,58,62,76,36,64,82,74,71,55,30,91,80,71,85,49,65,39,66,39,44,90,68,64,46,80,23,58,65,22,79,85,53,69,46,75,75,74,32,56,59,92,63,71,73,21,71,37,82,85,34,80,76,63,85,73,74,62,61,78,52,70,39,66,61,56,83,50,31,55,74,47,74,75,34,82,63,76,64,11,89,75,41,87,61,47,62,69,89,45,53,62,76,20,22,40,73,87,30,87,70,70,89,83,75,64,39,73,79,68,30,62,75,77,76,60,74,28,73,84,61,67,52,82,66,53,72,81,91,24,66,93,64,51,80,80,54,71,77,51,76,60,24,67,52,17,70,65,87,89,51,66,53,66,70,69,64,82,67,29,22,70,35,28,68,80,54,85,47,78,90,92,37,50,37,64,60,30,68,14,58,25,43,76,90,64,59,79,55,75,59,41,71,61,31,30,39,71,66,69,44,64,56,83,46,76,79,61,48,28,61,28,54,83,69,61,82,56,32,60,78,80,78,70,43,43,66,65,53,78,72,80,63,24,71,24,54,55,80,65,35,83,68,78,79,72,91,27,67,37,41,70,62,54,12,90,65,61,51,54,30,88,23,42,59,83,41,71,49,62,37,63,77,67,60,51,62,75,27,80,61,44,84,36,75,60,39,33,12,78,54,40,67,64,75,44,38,63,39,65,56,74,37,47,68,84,74,89,68,56,48,24,80,84,63,71,85,93,71,65,28,29,67,28,27,91,61,58,70,93,67,78,59,59,53,46,44,41,66,76,27,67,46,73,82,52,55,55,33,64,53,29,56,75,55,49,61,60,45,79,60,85,30,76,48,75,68,67,75,67,76,49,89,69,74,59,54,66,77,47,90,57,50,67,58,56,14,25,27,80,78,84,35,54,60,22,43,50,55,65,69,78,64,86,58,62,87,84,87,86,83,30,72,13,68,73,32,54,64,11,66,104,77,57,67,24,62,61,68,68,88,67,32,64,76,50,82,90,68,73,65,66,23,48,76,59,61,30,60,55,55,26,84,80,79,85,34,25,29,52,88,73,48,97,60,27,59,76,39,84,83,31,88,29,59,62,79,12,82,54,38,65,68,73,83,73,48,68,66,66,75,64,89,79,79,61,54,30,55,55,63,75,75,68,47,21,96,61,60,47,72,43,70,22,65,62,72,48,63,84,74,47,63,67,64,48,70,76,87,88,74,72,77,38,78,69,60,79,29,85,47,32,34,62,43,50,73,29,71,65,15,85,37,37,68,74,68,65,48,53,77,80,29,45,77,64,76,39,79,66,50,83,61,90,77,24,53,37,78,75,73,52,24,37,76,67,47,37,81,54,70,51,5,46,67,55,91,62,74,56,24,64,66,79,18,42,30,68,64,65,66,70,52,54,62,42,21,10,62,57,79,82,70,63,49,47,45,82,93,68,35,62,51,72,58,85,89,30,86,54,68,87,61,77,56,65,73,59,34,64,51,84,85,80,64,68,55,63,70,80,70,84,65,27,59,66,62,74,59,81,57,69,73,84,62,28,89,27,87,55,76,86,50,86,61,26,64,68,70,46,87,58,65,65,30,73,75,78,61,42,49,83,70,27,77,68,54,70,68,59,88,67,27,61,73,37,85,21,73,28,33,64,70,57,68,53,79,79,88,76,14,85,65,70,63,62,76,55,71,52,56,69,45,54,83,81,75,73,77,38,70,65,55,78,78,44,61,73,64,75,61,72,59,82,90,71,66,68,85,74,65,82,32,76,79,71,81,70,60,66,72,58,32,46,84,86,32,82,77,68,87,51,24,72,81,59,51,63,84,27,89,46,61,63,79,71,36,46,75,64,83,52,81,74,46,46,50,57,50,69,52,77,29,77,77,56,58,49,64,63,57,86,76,45,45,74,64,23,70,79,84,72,32,52,31,73,33,55,20,55,77,27,82,66,37,77,87,80,82,46,67,55,87,61,84,61,69,26,94,43,75,83,32,48,60,52,46,55,81,30,59,74,48,85,65,0,53,66,56,72,77,68,69,16,31,62,60,15,61,39,82,58,66,54,29,63,58,89,81,66,45,61,49,60,74,41,64,50,25,79,49,76,50,58,84,79,68,50,49,75,25,64,27,33,58,39,54,73,48,77,83,70,75,80,68,71,22,52,67,27,60,28,27,44,46,60,78,77,73,85,60,69,65,41,28,62,28,70,35,70,21,79,55,77,75,25,57,88,52,52,57,81,36,62,66,65,90,30,65,92,80,46,80,89,68,69,39,85,55,74,81,77,61,15,30,67,84,80,25,24,14,67,68,78,84,59,52,74,74,62,80,14,51,56,35,74,72,79,14,32,53,26,56,28,70,34,52,58,46,71,89,49,79,49,52,67,64,77,72,90,69,71,52,65,73,46,68,92,44,67,78,71,19,45,60,72,65,84,94,46,65,69,41,85,60,76,60,40,56,77,58,48,88,76,63,74,35,69,59,71,75,82,52,69,68,52,85,87,83,71,73,80,60,72,90,64,83,49,54,61,42,52,67,66,31,80,88,76,76,57,44,22,69,84,76,63,51,53,76,83,61,69,82,85,2,61,47,52,75,84,92,30,44,82,42,68,93,63,35,63,74,40,73,94,41,27,82,82,46,53,74,46,73,64,67,87,54,49,85,68,67,80,66,76,57,74,75,69,85,46,84,67,68,55,55,49,52,30,71,86,64,75,72,28,51,75,52,59,38,47,12,65,76,68,75,23,73,82,81,45,91,84,0,81,57,51,69,45,79,62,62,71,68,67,29,53,83,59,43,54,74,89,72,30,79,49,27,54,74,72,39,59,70,45,75,51,29,59,48,68,50,77,81,77,60,38,30,13,27,42,52,86,66,82,84,57,79,75,77,54,87,76,69,78,27,75,88,93,68,26,61,53,39,32,81,68,53,80,72,63,65,78,47,82,72,71,43,36,84,76,13,64,73,57,74,50,52,28,82,79,23,43,65,70,88,25,52,48,89,49,23,26,53,59,49,34,72,92,64,7,50,78,48,67,66,66,61,68,69,64,62,90,88,69,29,82,76,80,23,90,77,68,71,34,78,59,80,62,38,65,33,33,55,50,77,54,28,87,48,84,42,81,28,86,67,69,46,42,61,48,44,50,66,71,24,67,60,80,77,62,69,29,52,79,87,65,25,78,54,45,69,63,82,26,67,92,14,47,31,51,77,36,59,56,59,68,74,80,66,62,78,63,25,70,55,33,61,79,64,64,49,20,84,29,66,75,77,72,77,61,74,83,90,78,65,73,28,60,55,65,85,51,52,67,68,29,70,49,25,41,62,35,41,29,33,35,62,14,41,60,70,79,36,80,69,55,22,78,40,67,50,17,22,52,72,50,60,61,38,47,54,81,79,81,84,79,66,71,15,65,62,59,65,65,14,68,14,74,40,59,70,78,66,67,50,82,80,71,48,61,58,60,64,42,44,77,62,78,72,66,71,73,75,26,49,52,44,60,81,64,34,45,37,34,78,65,62,76,45,77,82,77,41,24,49,29,78,82,66,72,62,60,67,82,25,54,46,64,79,69,28,63,80,81,76,62,55,67,80,27,69,63,61,54,71,60,77,81,77,63,47,48,87,14,80,47,28,23,28,70,27,92,13,37,30,27,28,77,43,49,63,27,36,94,79,53,51,42,53,53,80,50,53,77,87,54,82,85,71,52,89,32,87,76,58,0,62,24,59,86,66,81,44,30,50,71,77,48,22,54,41,50,19,63,51,53,59,66,59,68,67,50,67,86,47,71,55,61,81,41,60,65,67,21,30,73,59,60,70,74,62,72,62,64,87,90,39,72,44,67,20,61,67,61,53,82,55,48,24,84,53,70,75,59,67,62,38,13,35,74,84,83,55,32,81,61,65,72,79,44,86,75,82,86,54,53,47,60,82,69,35,39,82,8,67,63,60,80,68,24,62,65,74,77,76,81,60,53,31,27,55,51,69,72,33,23,96,79,73,49,83,73,48,43,18,59,81,60,47,67,80,68,70,27,72,68,60,33,50,61,46,77,85,78,84,52,74,69,50,56,29,80,81,81,34,79,64,74,55,60,54,57,32,65,66,65,47,34,62,31,58,30,50,90,70,83,75,84,69,61,31,61,72,86,74,80,57,48,70,36,70,27,70,18,51,76,69,27,61,55,90,13,56,31,81,62,52,83,54,83,63,83,75,67,62,23,61,33,69,27,34,52,68,67,71,86,70,76,78,30,75,73,78,83,73,80,77,49,68,23,45,71,70,36,82,30,63,73,52,76,77,80,79,35,62,66,4,40,74,74,87,77,64,22,51,33,6,28,94,68,75,31,61,78,81,82,46,87,25,73,22,60,84,75,26,80,85,36,37,77,73,75,34,62,67,28,32,49,59,77,77,43,61,73,47,58,46,80,55,77,78,23,87,72,87,67,81,62,76,85,83,53,13,83,66,69,63,36,70,62,67,51,75,78,52,65,79,81,92,56,75,80,76,61,82,27,55,24,54,71,51,40,83,82,69,82,26,28,64,76,58,52,89,58,63,62,68,62,84,79,54,20,80,43,67,66,61,79,67,29,59,55,29,12,37,65,82,83,76,27,80,75,80,68,40,79,61,35,53,69,74,90,68,37,46,36,65,39,55,74,80,68,56,68,67,48,79,62,40,18,76,80,53,54,69,61,3,25,89,64,69,22,81,51,61,38,74,27,77,46,39,57,56,69,62,64,70,30,24,71,67,70,84,60,58,78,37,55,77,69,70,62,27,67,28,67,43,46,66,39,72,94,43,81,36,66,30,50,81,72,75,51,45,86,54,56,88,88,33,66,65,77,66,25,76,83,62,70,68,33,62,36,53,82,27,28,57,35,64,59,65,72,41,21,57,88,59,75,66,37,39,61,52,64,61,85,51,83,81,77,72,84,71,57,36,78,70,68,38,50,85,64,67,50,81,61,57,63,61,75,71,57,52,76,66,67,36,29,29,74,75,65,46,84,74,58,28,60,85,73,24,30,69,54,82,33,85,63,62,55,11,93,50,64,65,40,35,16,74,92,46,14,69,65,77,42,8,84,69,82,67,70,56,71,67,49,73,23,79,60,36,40,85,55,65,29,79,76,82,70,38,52,34,65,87,70,18,67,34,96,76,76,85,73,63,77,83,76,90,29,32,62,31,66,45,61,27,84,68,68,71,24,15,73,75,57,88,31,68,59,74,65,73,82,68,30,83,65,68,62,78,50,12,66,49,59,85,41,28,63,77,64,14,70,60,87,52,64,65,77,74,62,76,21,60,54,58,83,82,29,60,64,83,59,60,59,70,13,45,43,76,67,72,80,71,59,29,70,73,37,65,72,75,85,48,60,69,76,60,55,57,20,70,62,12,92,53,81,15,79,33,45,87,71,66,59,82,62,79,71,89,70,48,61,49,63,69,63,82,90,90,53,82,26,14,79,57,32,63,54,51,82,77,71,82,76,-1,45,84,65,74,60,55,65,65,65,88,75,22,52,78,83,78,60,54,66,27,86,33,51,73,30,69,60,65,12,49,57,78,37,81,77,55,32,48,77,59,64,69,54,52,82,80,41,66,61,28,62,67,38,64,58,55,15,64,41,66,76,54,70,82,49,83,84,81,72,80,75,52,73,65,35,32,31,84,86,75,81,51,76,73,66,33,82,66,79,74,70,80,70,64,43,79,43,83,25,75,67,78,83,53,71,8,53,86,67,34,63,55,66,54,41,81,90,69,76,62,73,59,77,67,66,79,54,26,62,23,50,79,55,68,34,33,42,13,66,39,48,73,80,77,75,78,15,35,79,83,62,52,50,60,60,29,29,58,71,79,23,85,59,15,53,14,74,27,49,64,35,31,67,91,23,26,31,26,87,66,76,72,65,73,37,52,31,74,30,67,78,30,62,76,71,29,80,50,83,87,78,62,45,71,67,36,63,33,65,68,63,79,70,73,62,71,80,55,85,77,51,62,79,12,70,80,25,78,77,30,67,84,71,23,63,76,70,81,69,46,49,53,82,82,73,55,82,74,83,81,87,41,83,47,65,52,54,57,83,42,25,76,92,67,80,83,62,27,79,49,34,46,81,48,52,87,20,26,72,56,30,66,31,41,67,69,75,66,47,38,72,82,60,55,69,68,30,73,71,66,69,58,49,59,39,42,83,52,28,29,76,38,56,41,77,54,83,60,27,71,86,65,85,71,88,25,55,15,50,25,36,74,62,49,54,86,83,70,65,81,71,48,17,10,67,84,49,65,43,69,36,71,49,86,32,60,64,57,79,79,48,55,46,61,53,84,75,62,58,50,64,86,27,21,65,61,39,79,72,64,69,66,59,70,24,43,26,50,83,49,47,87,17,65,17,91,55,29,58,90,48,78,75,78,51,65,67,56,69,65,90,42,90,56,88,75,82,53,74,26,28,48,63,82,49,82,60,89,87,72,41,90,52,76,66,48,85,90,74,77,70,67,71,76,61,46,61,65,40,46,70,73,83,76,41,70,75,78,40,56,65,62,76,81,79,27,90,36,30,42,49,55,58,32,53,60,27,50,75,70,72,54,75,70,63,42,47,82,20,82,67,31,36,89,21,68,65,86,51,66,67,14,64,86,80,16,48,33,59,40,69,46,69,53,61,56,44,72,67,34,72,65,53,58,86,73,27,55,78,52,65,58,42,81,68,67,29,63,43,52,39,82,50,77,37,82,85,65,27,79,30,51,51,24,32,63,53,85,68,63,76,51,32,90,76,14,76,38,64,31,59,70,70,26,32,60,18,37,80,73,57,49,51,83,24,60,72,71,65,27,44,67,79,60,68,86,66,71,41,30,80,53,51,85,83,75,69,62,50,58,76,76,97,81,24,64,34,28,72,82,56,61,74,75,47,59,41,74,87,58,57,65,10,66,71,77,25,56,25,85,67,90,53,68,59,86,66,23,31,64,76,44,32,80,54,70,74,57,23,70,65,80,67,75,49,63,29,80,39,64,31,46,73,61,59,61,45,31,64,73,76,51,68,68,72,26,40,78,34,80,65,72,72,30,54,14,91,79,83,78,31,48,46,62,36,76,77,63,75,65,52,53,46,64,51,47,63,68,20,62,40,62,65,62,56,53,21,87,31,83,68,75,49,30,62,49,55,60,74,63,62,32,78,93,71,51,67,24,29,85,85,59,49,57,80,44,52,42,74,80,60,80,71,70,86,85,74,55,65,50,71,61,82,81,75,68,86,24,67,80,81,77,55,94,75,64,30,83,14,37,74,84,29,75,79,84,38,64,52,69,73,46,16,52,60,29,81,76,51,34,62,85,63,42,63,63,86,65,65,26,81,71,24,58,46,64,72,63,59,68,81,15,47,36,58,62,81,30,64,49,63,46,31,44,53,73,88,78,64,24,83,28,65,63,77,88,70,64,68,55,55,53,80,31,61,83,42,23,85,30,83,44,30,81,87,53,71,74,26,49,62,75,83,43,88,68,43,40,92,61,18,62,49,45,26,76,51,68,81,83,68,58,40,63,81,71,46,56,24,77,80,54,45,37,81,58,77,87,59,81,60,9,47,86,83,65,12,71,84,78,23,73,70,76,22,80,55,63,43,62,44,51,48,14,74,27,59,61,62,31,72,20,27,74,64,64,64,36,77,30,36,71,82,60,45,59,84,68,60,53,52,54,71,45,62,59,54,79,18,77,24,70,67,35,24,43,59,82,76,66,71,75,76,30,51,58,44,76,67,76,79,32,75,80,73,28,55,29,54,55,76,61,18,71,47,39,57,25,63,67,42,40,63,38,79,50,64,76,75,82,70,64,34,48,32,85,64,75,84,86,62,77,77,61,80,57,76,74,72,53,47,81,84,47,64,78,51,75,83,53,74,75,47,74,49,52,14,65,69,30,83,60,55,15,43,77,75,70,93,14,64,64,61,55,31,77,36,82,17,26,84,78,69,71,75,31,63,61,28,57,69,70,79,53,60,84,49,26,39,54,61,46,84,79,68,31,80,89,65,87,80,76,61,37,69,46,69,70,29,72,68,67,52,62,42,84,68,32,52,27,55,36,77,42,90,84,74,68,48,52,70,31,90,53,53,42,46,81,67,26,66,51,70,82,63,72,39,88,48,65,12,74,62,53,85,55,45,26,55,42,62,33,38,62,74,79,83,90,87,49,22,80,70,52,30,75,37,67,54,59,80,60,66,60,77,32,61,81,55,59,55,44,32,66,55,22,23,43,45,70,82,35,85,75,65,77,77,58,55,51,64,81,85,75,51,71,84,63,60,78,39,84,65,33,64,42,32,87,79,58,73,24,52,55,58,60,51,61,55,53,88,75,49,65,84,46,46,76,71,79,35,78,52,69,36,82,59,67,82,55,67,60,45,46,83,23,74,48,54,41,38,92,55,51,36,84,49,13,50,55,46,47,63,68,62,70,31,56,54,39,60,55,67,80,84,52,62,49,84,73,43,84,78,85,53,52,42,83,85,60,58,89,62,61,70,63,80,75,66,76,60,30,62,46,66,81,20,46,31,79,84,52,62,55,70,21,26,68,84,69,68,50,26,42,74,54,62,29,10,21,29,81,35,37,63,43,71,87,42,74,83,38,27,44,21,28,60,25,66,40,86,35,70,46,62,61,20,63,31,74,72,51,34,86,31,30,68,77,48,76,73,49,61,63,65,82,69,64,71,84,77,79,45,12,78,67,69,28,29,78,81,31,68,54,71,71,78,77,12,54,37,76,31,71,82,13,93,44,69,63,85,66,13,52,45,75,81,62,77,74,53,55,49,62,80,1,41,50,-1,62,78,25,22,66,26,65,53,80,80,86,77,74,44,74,80,69,86,63,25,57,29,68,79,75,74,78,25,45,49,69,63,71,67,67,53,71,86,69,30,81,85,82,22,60,81,25,24,75,69,73,26,45,63,81,49,35,47,69,76,78,75,65,41,82,70,74,79,63,94,38,82,62,43,83,65,53,28,61,89,69,62,78,68,72,65,61,63,73,71,43,67,62,80,43,49,71,42,73,31,40,66,53,73,85,79,27,41,75,40,27,63,39,37,85,76,79,79,68,37,69,27,71,92,67,75,63,63,28,90,80,85,77,66,73,7,14,27,29,63,77,81,64,24,60,74,83,31,64,79,25,73,28,43,35,60,49,53,54,67,68,65,83,66,30,75,73,73,75,51,64,17,49,28,63,84,31,66,39,79,72,74,29,69,74,64,79,28,49,60,81,66,72,13,52,68,82,73,21,50,69,65,79,68,36,19,90,52,33,59,42,49,27,80,60,47,75,63,67,70,76,40,36,74,48,78,77,76,51,63,53,54,51,61,67,45,55,58,80,40,61,18,32,16,52,64,73,51,49,74,60,23,60,89,89,65,62,81,40,65,83,35,59,37,76,86,72,78,75,30,88,80,27,67,78,83,39,30,11,76,90,77,-1,65,81,60,41,75,78,70,61,73,43,61,44,27,84,67,55,54,47,51,68,58,75,77,35,77,62,60,54,74,36,78,82,61,39,61,72,76,71,67,72,52,80,55,42,87,69,48,45,40,40,52,73,56,27,72,22,70,78,68,57,87,83,51,60,78,69,78,67,69,61,70,64,75,66,64,67,62,26,22,51,49,30,64,74,75,75,68,26,74,82,57,56,71,57,65,49,86,53,33,65,77,54,22,85,57,70,84,13,33,46,76,26,85,14,49,62,65,80,61,34,65,44,64,42,65,24,53,42,60,20,64,76,19,71,24,82,56,62,86,62,9,89,74,35,33,66,71,59,75,65,76,26,86,88,88,78,44,52,87,66,73,54,59,39,74,60,61,67,31,72,66,72,73,54,43,78,31,74,35,27,63,58,77,64,60,47,78,81,37,38,86,29,56,32,68,60,51,58,50,72,73,76,20,73,82,73,53,81,98,70,62,58,48,37,71,52,68,54,28,69,56,62,66,73,55,45,67,52,67,59,27,60,22,62,84,52,70,43,60,19,83,54,64,41,27,64,36,51,65,68,69,71,65,70,72,23,47,74,46,49,80,80,69,75,67,78,65,81,84,42,48,78,62,71,67,58,49,72,66,85,91,52,66,59,59,68,63,72,21,62,54,62,60,79,86,53,52,52,64,70,54,27,79,42,27,60,73,62,49,59,44,84,76,74,72,31,26,28,94,78,33,58,76,73,51,72,80,60,63,63,30,55,70,57,27,38,75,49,78,73,91,27,38,60,81,71,78,28,62,76,52,70,45,71,77,79,61,87,70,68,73,52,52,82,70,58,67,49,28,56,65,79,72,79,54,79,45,86,91,48,81,73,87,78,53,74,75,70,83,41,43,67,56,65,72,75,66,70,76,71,75,61,78,66,45,85,25,81,76,60,32,30,65,72,64,29,65,55,59,54,50,70,89,58,64,66,85,60,60,46,74,70,70,70,75,44,79,79,48,62,82,42,72,47,29,59,44,24,27,56,61,46,76,26,73,86,35,75,80,78,49,58,76,61,54,37,78,66,57,53,85,48,29,68,74,79,84,50,83,81,82,15,83,59,60,54,60,69,83,62,85,71,55,56,28,85,50,63,79,51,57,91,81,42,68,40,79,51,81,63,42,29,28,78,45,76,71,58,27,58,65,25,76,61,31,29,72,68,61,71,80,25,79,63,66,54,46,84,65,65,65,73,54,75,72,42,38,57,64,70,55,56,60,45,56,65,41,62,53,83,66,38,28,81,65,76,74,48,62,59,64,79,65,83,59,78,70,48,41,87,45,55,64,74,79,47,45,79,51,51,75,56,76,78,33,49,70,63,79,38,84,80,61,45,49,61,60,55,54,55,42,65,61,21,37,32,40,57,89,53,39,74,62,76,70,86,53,55,17,60,35,63,69,51,75,47,86,46,76,28,60,87,57,68,66,54,67,47,24,59,29,83,73,65,70,71,66,61,50,73,40,84,77,74,88,67,69,60,50,46,67,49,42,20,36,76,34,78,86,75,46,68,78,56,91,67,74,80,79,81,78,62,59,70,49,78,38,29,53,61,62,80,84,15,81,76,70,34,74,25,76,38,79,25,66,79,22,67,42,47,82,30,50,84,82,37,75,85,87,62,61,67,56,61,55,82,71,89,68,12,94,70,54,62,39,62,49,70,75,30,52,77,54,81,75,68,61,47,26,78,64,35,61,83,63,66,80,76,77,34,49,62,90,88,67,59,35,30,61,46,72,70,17,36,35,83,63,68,64,68,44,67,64,90,65,47,77,61,27,68,62,88,81,14,60,36,66,76,49,80,85,71,67,66,49,88,74,87,65,67,81,49,77,64,72,64,61,91,25,61,83,29,66,64,12,90,68,58,18,60,76,54,47,63,76,40,52,78,89,75,65,87,76,35,23,30,26,80,50,61,39,47,49,78,66,17,76,78,83,84,55,51,24,79,81,40,87,59,50,29,26,74,44,65,88,84,62,60,70,73,85,76,29,81,32,71,27,63,43,30,68,65,71,74,85,47,44,73,55,45,64,54,65,67,65,58,65,55,49,62,80,57,66,62,57,32,35,55,70,91,46,86,63,73,40,49,70,80,88,76,64,78,78,62,62,73,20,68,43,88,65,79,14,61,87,64,41,60,55,31,40,60,61,71,80,86,76,61,77,73,61,73,67,93,61,55,32,61,69,75,88,76,77,66,71,78,44,54,76,65,73,35,82,52,26,81,60,88,29,66,75,58,50,79,70,27,53,68,73,73,80,84,50,81,37,21,78,33,74,32,48,45,38,73,77,56,30,25,51,60,54,61,32,53,72,79,67,91,27,56,71,77,25,76,68,58,83,68,86,44,53,78,83,81,71,79,70,73,78,64,84,67,70,52,65,84,35,87,50,57,49,45,66,71,59,55,75,20,51,67,64,63,81,41,69,57,66,46,65,74,50,62,28,34,33,60,75,68,73,74,20,27,79,60,35,82,67,30,22,55,56,39,50,81,68,71,51,51,66,60,26,67,43,66,32,83,45,53,50,69,67,77,79,75,66,65,85,42,77,87,65,66,61,17,42,70,83,30,65,27,81,25,61,51,75,70,63,0,57,34,76,57,45,90,86,31,64,86,42,72,80,42,63,72,57,49,44,75,43,37,75,84,85,74,25,69,60,22,40,60,27,71,63,33,52,74,87,33,72,84,66,33,43,85,54,85,80,75,46,48,48,64,70,29,70,77,57,26,65,75,67,33,26,58,27,51,58,71,84,67,74,63,54,20,77,26,91,27,69,18,76,51,65,48,39,27,22,85,70,59,59,45,70,72,70,64,62,71,71,67,26,51,40,61,60,61,86,72,49,66,65,57,63,52,73,61,51,62,50,69,84,76,67,73,68,72,72,70,44,40,56,48,59,62,70,75,54,46,45,80,66,53,74,52,85,82,67,48,80,37,74,46,61,60,83,71,75,52,29,82,72,45,72,54,36,69,62,65,59,54,34,68,83,65,37,62,27,38,28,77,83,88,66,79,84,27,77,78,80,25,30,62,74,82,52,34,82,77,56,27,61,83,50,13,50,30,63,78,48,69,46,74,72,73,74,38,60,73,51,56,72,77,85,68,50,73,82,66,83,52,74,77,55,63,76,68,54,95,54,63,74,90,50,55,78,75,15,59,52,54,54,71,75,82,81,73,26,66,81,53,42,78,49,82,53,14,53,86,65,72,66,91,74,73,69,72,45,86,79,62,59,29,12,60,85,32,44,55,79,70,46,28,78,47,54,79,63,83,58,66,75,81,68,92,19,20,76,80,62,80,76,35,68,26,24,85,32,78,41,83,71,57,74,69,53,51,68,87,51,76,28,63,52,84,68,77,72,42,58,90,53,77,76,46,80,61,85,74,38,58,37,50,76,65,29,68,47,64,43,56,70,82,40,41,63,66,77,78,78,63,47,45,63,53,51,80,73,61,38,75,68,51,36,40,67,65,53,65,97,84,80,64,35,81,62,74,53,73,71,72,72,63,34,77,78,76,75,46,31,77,83,62,75,79,28,28,78,78,48,36,59,29,47,87,29,71,70,79,81,68,61,58,49,73,45,87,76,69,62,81,49,14,78,66,31,76,12,24,88,62,49,70,61,58,14,80,32,55,80,89,76,22,71,65,63,62,81,74,72,60,83,25,54,28,13,45,55,65,76,38,84,67,55,47,79,30,73,80,50,90,82,23,46,66,77,51,67,93,34,50,53,30,70,80,74,63,29,58,29,48,83,53,26,62,13,84,81,64,57,49,56,79,71,69,64,68,64,54,52,37,74,47,48,75,75,68,67,88,85,65,87,46,27,67,65,79,68,56,33,75,79,81,66,79,63,74,43,64,84,73,75,90,64,69,34,42,31,39,61,29,77,74,13,80,65,54,83,62,55,54,14,77,24,48,75,64,40,26,72,54,74,68,50,85,69,50,67,38,43,88,39,80,37,47,73,64,56,52,68,77,59,29,65,47,53,80,59,64,76,50,35,72,72,28,12,49,61,83,33,75,31,25,61,54,31,61,64,31,79,63,71,48,50,79,71,79,76,74,73,61,42,49,75,76,73,77,65,51,33,70,43,64,76,82,16,60,64,66,36,83,75,78,62,60,50,31,87,36,77,82,73,90,76,66,48,54,84,67,65,79,80,74,87,31,25,46,72,69,76,31,27,63,81,79,13,56,13,71,79,48,32,54,76,68,80,58,54,87,53,55,72,60,50,54,53,81,65,56,85,68,47,79,58,82,61,77,61,82,51,77,61,26,13,69,45,75,52,67,68,76,77,72,74,55,50,65,47,53,59,6,43,64,85,36,84,65,29,53,92,62,84,72,76,74,19,64,59,79,70,52,67,64,37,51,44,85,71,60,64,79,64,53,90,80,74,75,26,69,73,60,65,88,49,73,59,82,69,72,30,68,76,62,46,55,52,66,32,70,66,50,60,63,68,76,59,48,48,70,49,81,71,59,81,32,54,68,70,68,70,12,78,65,79,38,67,64,89,64,29,28,35,52,14,65,63,63,78,69,55,18,52,56,54,80,43,14,70,43,86,55,84,67,80,76,46,73,82,52,57,67,80,62,78,-1,71 3 | 1,1,0,0,1,1,1,1,0,1,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,0,1,1,1,0,0,0,1,0,0,1,1,0,0,1,1,0,1,0,1,1,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,1,0,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,1,1,0,1,0,1,0,1,1,0,1,0,0,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,0,1,1,0,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,0,1,0,0,0,0,1,1,0,1,1,0,0,1,1,1,0,1,1,1,0,0,1,0,1,1,1,1,0,0,1,0,1,1,0,0,1,1,1,0,0,1,0,1,0,0,0,1,0,0,1,1,0,1,1,1,0,1,0,0,0,1,1,1,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,1,0,1,1,0,1,1,1,0,0,1,1,1,1,0,0,1,1,1,1,0,1,0,0,1,0,1,1,1,1,1,0,0,0,0,0,1,0,1,1,1,0,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,1,1,0,0,0,1,1,0,1,0,0,1,0,1,1,0,0,1,0,0,1,1,1,0,1,1,0,1,1,1,1,1,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,1,0,1,1,0,0,0,1,0,1,1,0,1,1,1,0,1,1,0,1,0,0,0,0,0,1,0,0,1,0,1,1,0,0,1,1,0,0,1,1,1,0,0,1,1,0,0,1,1,1,0,1,1,1,1,0,0,1,1,0,0,0,0,1,0,0,1,0,0,1,0,1,1,0,0,0,0,1,1,1,0,1,0,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,0,0,1,0,0,1,0,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,1,1,0,0,1,1,0,0,1,0,1,1,1,1,1,0,0,1,1,1,1,1,0,1,1,1,0,0,0,0,0,1,0,1,0,0,0,1,1,0,1,0,0,1,0,1,1,0,0,1,1,1,1,0,1,0,1,1,0,1,1,1,1,0,0,1,1,0,1,0,1,1,1,0,1,0,1,0,0,0,1,0,1,0,0,1,1,1,0,1,1,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,1,1,1,1,1,1,0,1,0,1,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,0,0,1,1,1,1,0,0,0,1,1,0,1,1,0,1,1,0,1,0,1,1,0,1,0,0,1,1,0,0,0,0,1,1,1,0,1,0,1,1,0,0,0,1,0,1,1,1,1,1,0,0,1,0,1,1,0,1,1,0,1,0,1,1,0,1,1,1,0,1,1,1,0,1,1,1,0,1,1,1,1,0,0,1,1,0,1,0,1,0,1,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,1,1,1,1,0,1,0,0,1,1,0,1,0,1,0,0,0,0,1,0,1,1,1,0,1,1,0,0,1,1,1,1,0,0,0,0,1,1,0,1,0,1,1,1,1,0,1,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,1,0,1,0,1,1,1,1,0,1,0,1,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,1,0,1,1,0,0,0,1,0,1,1,1,0,1,0,1,0,1,1,0,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,1,0,1,1,0,1,1,1,1,1,1,1,0,1,1,0,1,0,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,0,0,0,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,1,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,1,0,1,1,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,1,0,1,0,1,1,1,0,1,0,0,1,0,1,1,1,1,0,1,1,1,0,1,1,0,0,1,1,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,0,1,1,1,1,1,0,1,0,0,1,1,1,1,1,0,0,0,0,1,0,0,1,1,1,0,1,0,1,1,0,0,0,0,1,0,0,0,1,0,0,1,1,1,0,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,0,1,1,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0,1,1,1,0,1,0,0,1,0,1,0,1,1,0,0,1,1,1,1,0,1,0,1,0,1,1,1,1,0,1,1,1,1,0,1,1,0,1,1,0,1,1,0,0,1,0,1,1,1,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,1,0,1,0,1,0,0,1,0,1,1,1,1,1,1,0,0,0,0,1,1,1,0,1,0,1,0,1,1,0,1,1,1,1,0,1,0,0,0,0,0,1,1,1,0,0,0,1,1,0,1,0,1,1,1,0,1,0,1,0,1,0,0,1,1,0,0,1,0,1,1,1,1,0,0,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,1,1,1,1,0,1,0,1,0,1,0,1,0,1,1,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,0,1,0,0,0,1,1,1,1,1,0,0,1,1,1,0,0,1,1,1,0,1,0,1,0,1,0,1,0,0,1,1,1,1,0,0,1,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,0,0,0,0,1,1,1,1,0,1,0,0,0,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,0,0,1,1,1,1,0,0,1,0,1,1,1,1,0,1,0,0,0,0,1,1,1,0,0,1,0,1,1,1,1,1,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,1,0,1,0,0,1,1,0,1,1,1,1,0,1,1,1,1,1,1,0,0,0,1,1,0,1,0,1,0,1,0,1,1,1,0,0,1,1,1,1,1,1,1,0,1,0,1,0,0,1,0,1,1,0,0,1,0,0,1,1,0,0,0,1,1,1,1,0,1,0,0,1,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,1,1,0,1,1,1,0,0,0,0,0,0,1,1,0,0,1,0,1,1,1,0,0,1,1,0,1,0,1,0,1,1,1,1,1,0,0,1,0,0,0,0,0,0,1,1,1,0,1,1,0,0,1,0,1,1,0,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,1,1,1,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,1,0,1,0,1,0,1,0,1,1,1,0,1,0,1,0,0,1,1,1,1,0,1,1,1,0,1,1,1,0,0,1,1,0,1,1,1,0,1,1,0,0,0,0,0,1,0,1,0,1,0,1,1,0,0,0,1,0,0,0,1,0,1,1,0,1,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,1,1,1,0,1,0,1,1,1,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,1,0,1,0,1,0,1,1,1,0,1,1,1,1,0,1,1,1,0,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,0,0,1,0,0,1,0,0,0,1,1,1,0,0,1,1,1,0,0,1,1,0,1,1,0,1,1,1,0,1,1,0,0,1,0,1,1,1,0,1,1,0,0,0,0,0,0,1,1,1,0,0,0,1,1,0,1,1,1,0,0,1,0,0,1,1,1,0,1,1,0,0,1,0,1,0,1,0,0,1,1,1,0,0,1,1,0,0,1,0,1,1,0,1,0,0,0,1,1,1,1,0,0,0,1,0,1,1,0,0,0,1,0,0,0,1,1,1,1,1,0,0,1,1,0,1,1,0,1,1,0,1,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,1,1,0,1,1,1,0,0,1,1,0,1,1,1,1,1,1,0,1,0,1,0,1,0,0,1,0,1,1,1,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,0,1,0,0,0,0,1,1,1,1,0,0,1,0,1,0,1,1,1,0,0,1,1,0,1,1,1,1,1,1,0,1,0,0,1,1,0,0,0,0,0,0,1,1,1,0,1,1,1,1,0,1,1,0,0,0,1,1,1,1,1,1,0,1,0,0,1,0,0,1,1,0,1,1,0,1,0,1,1,1,0,0,1,1,0,1,0,0,1,1,0,1,1,0,1,0,0,0,1,0,0,1,1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,0,0,1,0,1,0,0,1,1,1,0,1,1,1,0,0,1,1,1,0,1,1,0,0,0,1,0,1,1,1,0,1,1,1,1,1,1,0,0,0,1,0,1,1,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,1,1,1,1,0,0,0,1,1,0,0,0,1,1,1,1,1,0,0,1,1,0,1,0,1,1,0,0,0,1,1,0,0,0,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0,0,1,0,1,1,1,1,0,0,0,0,1,0,0,1,1,0,1,0,1,1,1,1,1,0,0,0,1,0,0,1,1,0,1,1,1,1,0,0,1,0,1,0,1,0,0,0,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,1,0,0,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,1,0,1,1,0,1,1,1,1,0,0,0,0,1,1,0,0,1,1,0,0,0,1,1,1,0,1,0,0,0,0,1,0,1,1,1,0,1,1,0,1,1,1,0,1,1,0,1,1,0,1,1,0,1,1,1,0,0,1,0,0,1,0,0,1,0,1,0,1,1,1,0,1,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,1,1,0,0,1,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,1,1,1,0,0,1,0,1,1,0,1,1,1,1,0,0,0,0,1,0,0,1,1,1,1,0,0,1,1,0,1,1,1,0,0,1,1,1,0,0,1,0,1,1,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,1,1,0,1,1,0,0,0,1,1,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,1,0,0,0,1,1,1,0,1,0,0,0,1,0,1,1,0,1,0,0,1,1,0,0,1,0,1,1,1,1,0,0,1,1,0,0,1,1,1,1,1,1,1,0,0,1,1,1,0,0,0,0,0,0,1,0,1,1,1,1,1,1,1,1,0,0,0,0,1,1,0,1,0,0,1,1,0,0,0,1,1,0,1,0,1,0,0,1,1,0,1,1,1,0,1,1,0,1,1,0,1,1,1,0,0,1,1,1,0,1,0,1,0,0,1,0,0,1,0,1,1,0,0,1,1,0,0,1,1,1,0,1,0,1,0,1,0,1,1,1,0,1,0,1,0,0,1,0,0,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,0,1,1,0,1,1,0,0,0,1,1,0,0,0,1,0,1,1,0,1,0,1,1,0,0,0,0,0,1,0,1,1,0,0,1,1,1,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,0,1,1,0,1,1,0,1,1,1,0,0,0,1,0,0,1,1,1,0,1,1,1,1,0,0,0,0,1,1,0,1,0,1,1,0,1,0,1,0,0,0,0,1,0,0,1,0,1,1,0,1,0,1,1,0,1,0,0,1,0,1,0,0,1,0,1,0,0,1,1,0,1,0,1,1,0,1,1,1,1,1,0,1,1,0,0,1,1,1,1,1,1,0,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,0,0,0,0,0,0,1,0,1,0,0,1,0,1,1,1,0,1,0,0,0,0,1,1,1,1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,0,1,1,1,0,1,1,1,1,1,1,0,1,1,1,1,0,1,1,0,1,0,1,1,1,0,0,1,1,1,1,1,1,1,0,0,1,0,0,1,1,0,0,1,1,0,1,1,1,1,1,1,0,1,0,1,0,0,1,0,1,1,1,0,1,0,1,0,1,1,0,0,1,1,1,0,1,0,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,1,1,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,1,1,1,1,1,1,0,0,1,0,1,1,0,0,0,1,1,1,0,1,1,1,0,1,1,1,0,1,1,1,1,0,0,0,1,1,0,0,0,1,0,1,1,1,0,0,1,0,1,1,0,1,1,0,0,0,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,0,0,0,1,1,0,0,1,1,1,1,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,1,1,1,1,0,1,1,1,1,0,1,0,1,1,0,0,1,0,1,1,1,0,0,1,0,0,0,1,1,1,1,1,1,0,1,0,1,0,1,1,0,0,0,1,0,0,1,0,0,1,0,1,1,1,1,0,0,1,1,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,1,1,1,0,1,1,1,1,1,0,0,0,1,0,1,0,1,1,1,1,1,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,1,1,1,1,1,0,0,1,0,1,0,1,1,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,1,1,1,0,0,1,1,1,0,1,1,0,1,0,1,1,1,1,0,0,0,1,1,1,1,0,1,1,1,1,0,1,1,1,0,0,1,1,0,1,0,1,0,1,1,0,1,0,0,1,0,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,1,0,1,0,1,1,1,1,0,0,0,0,0,1,1,0,1,1,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,1,0,1,0,0,1,0,0,1,1,1,1,0,1,0,0,1,1,1,0,0,0,0,0,0,1,0,1,1,1,0,1,0,1,1,0,1,1,0,0,1,1,1,0,1,0,1,0,0,1,0,0,1,1,0,1,1,0,0,0,1,1,0,1,1,1,1,0,0,0,0,0,1,0,0,1,1,1,1,0,1,0,1,1,1,0,1,0,0,1,0,0,1,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,1,1,1,1,0,0,1,0,0,0,1,1,1,1,0,0,0,1,1,1,0,1,1,0,1,0,0,0,1,0,0,0,1,1,0,1,0,1,0,1,0,0,1,1,0,0,0,0,0,0,1,1,1,0,1,0,0,1,1,0,1,0,0,0,1,0,0,1,1,1,0,1,0,1,0,1,0,1,0,1,1,1,1,0,1,1,0,1,0,1,0,1,0,1,1,1,0,1,1,1,0,0,0,0,1,0,0,1,0,0,1,1,0,1,1,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0,0,1,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,0,1,1,0,1,1,1,0,1,1,0,0,1,1,1,0,1,0,1,0,1,1,1,1,1,1,0,1,1,0,1,0,1,1,0,1,0,1,0,1,0,0,0,1,0,1,0,1,1,0,1,0,0,1,0,1,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,0,1,1,0,1,1,1,0,0,1,1,0,1,1,0,1,0,1,1,0,1,0,1,1,1,1,0,0,1,0,0,1,1,0,1,1,1,0,1,0,0,0,0,1,1,1,0,1,1,1,1,1,0,0,0,1,0,0,0,1,0,1,0,0,1,1,1,0,0,1,1,0,0,0,1,1,1,1,0,0,0,1,1,1,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,1,1,0,1,1,1,0,0,1,0,1,0,0,1,1,1,1,0,1,0,1,0,1,1,0,0,1,1,1,0,1,1,0,1,1,1,0,0,1,0,1,1,0,0,1,1,0,0,0,0,1,0,1,0,1,1,0,1,0,1,0,1,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,1,0,0,0,0,0,1,1,1,0,1,1,0,0,0,0,1,1,1,1,1,0,1,1,1,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,1,1,1,0,1,1,0,1,1,0,1,0,1,1,0,0,1,0,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,0,0,0,1,1,0,1,0,0,1,1,1,0,0,0,0,0,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,1,1,0,1,0,1,0,1,0,0,0,1,1,1,0,0,1,1,1,0,0,1,1,0,1,1,1,1,1,1,0,1,0,1,1,0,0,1,1,1,0,1,1,1,0,0,1,0,0,0,0,1,0,1,1,0,1,1,0,0,0,1,1,0,1,1,0,1,1,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,1,0,0,1,0,0,1,1,1,0,1,1,1,0,1,0,1,1,1,1,1,0,1,1,0,1,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,0,1,1,1,1,0,1,1,0,0,0,1,1,1,0,0,0,1,0,0,1,1,0,1,0,1,1,0,0,1,1,0,1,1,1,0,0,1,0,0,1,0,0,1,1,0,1,1,0,1,0,0,1,1,0,0,1,1,1,0,1,0,0,1,1,0,0,0,0,0,0,0,1,1,0,1,1,1,0,1,1,1,1,1,0,0,1,0,1,1,0,1,0,1,0,0,1,1,1,1,0,1,0,0,1,0,1,1,1,1,1,0,1,1,0,1,0,1,1,1,1,1,1,0,0,1,1,1,0,1,1,0,1,1,0,1,1,1,1,0,0,0,1,1,1,1,0,1,1,1,1,0,0,0,0,0,0,1,1,0,1,1,1,1,1,0,0,0,0,1,1,0,0,1,1,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,1,0,1,1,0,1,0,0,1,0,0,0,1,1,0,1,0,1,1,0,1,0,1,1,1,1,1,0,0,0,0,0,1,0,0,1,0,1,0,1,0,1,1,0,0,1,0,0,1,1,0,1,0,1,1,1,0,1,1,1,1,1,0,0,1,0,0,0,0,1,1,1,1,1,0,1,0,0,1,1,1,1,1,0,1,1,0,0,1,0,1,1,1,1,0,1,0,1,0,0,1,0,1,1,0,0,1,1,0,1,1,1,1,1,0,0,1,0,0,1,0,0,0,0,1,1,1,1,0,1,1,0,0,1,1,0,1,0,1,1,0,0,1,0,1,0,0,0,1,0,0,1,1,0,1,0,1,0,0,0,1,1,0,1,1,0,1,0,0,1,1,0,1,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,0,0,1,1,1,0,0,0,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,0,0,1,1,1,0,0,0,1,1,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,1,0,1,1,0,1,1,1,1,1,0,0,1,1,0,0,0,1,1,1,0,1,0,0,1,0,0,0,0,0,1,1,1,1,1,0,1,1,0,1,0,0,1,0,1,1,0,1,0,0,1,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,1,1,0,0,1,1,0,1,1,1,1,1,0,1,1,0,1,0,1,0,0,0,1,1,1,0,0,0,1,1,1,0,1,0,1,0,0,1,1,1,1,1,0,1,1,0,0,1,1,1,1,1,1,0,1,0,1,1,1,0,0,1,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,1,0,1,1,0,1,1,1,1,1,0,0,0,1,1,1,1,0,1,1,0,1,0,1,1,0,0,1,1,0,0,0,1,0,1,0,1,0,0,0,1,1,0,1,1,0,0,0,1,1,1,1,0,1,0,0,1,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,1,0,1,0,0,1,1,0,0,0,0,0,1,0,1,1,1,1,0,1,0,0,1,1,0,1,0,1,1,0,1,1,0,0,1,0,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,1,1,0,0,0,0,1,1,1,0,0,1,1,0,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1,1,1,0,1,0,1,0,0,1,0,1,1,0,0,1,0,1,1,1,1,1,1,1,1,0,0,0,1,0,1,0,1,0,0,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,1,0,0,1,1,1,1,0,1,0,1,1,1,1,0,1,0,0,1,0,0,0,1,1,1,0,0,1,0,1,1,0,1,1,1,1,0,0,1,1,0,0,1,0,1,1,0,1,1,0,1,0,1,0,1,1,0,0,1,1,0,0,0,0,0,0,1,0,1,1,1,1,0,1,0,1,1,1,1,0,0,0,1,1,0,1,0,1,1,1,1,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,1,0,0,1,1,0,1,0,1,1,0,0,1,0,1,0,0,1,0,0,1,1,0,1,1,1,1,1,1,1,0,1,0,1,1,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,1,1,1,0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,0,1,1,0,1,1,1,0,0,0,0,1,1,1,1,0,1,0,1,1,0,1,1,0,1,0,1,0,0,1,1,1,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,0,1,0,1,0,0,0,0,1,1,0,1,1,1,0,1,1,1,1,0,1,1,0,0,1,1,0,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0,0,0,0,0,0,1,0,1,1,0,0,1,0,0,1,1,0,0,1,1,1,1,0,0,0,0,1,0,1,0,0,0,1,1,1,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,1,0,1,1,1,1,0,1,0,0,1,1,0,0,1,0,0,0,1,1,0,1,1,1,0,0,0,0,0,1,1,0,0,0,1,1,1,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,1,0,1,0,0,1,1,0,1,0,1,1,0,1,0,1,1,1,1,0,0,1,0,0,0,0,0,1,1,0,0,1,1,1,0,1,1,1,0,0,0,1,0,0,1,1,0,0,1,0,1,0,1,1,1,0,1,1,1,0,0,0,0,1,1,1,0,0,0,1,0,1,0,0,1,1,1,0,1,1,0,1,0,0,0,0,1,1,1,1,0,0,1,1,0,1,0,0,0,1,0,0,1,1,1,0,0,1,1,0,0,1,1,0,0,0,1,0,1,1,0,1,0,0,1,1,1,1,1,1,0,1,1,1,1,0,0,0,1,1,0,1,1,1,0,0,1,0,1,1,1,1,1,1,1,0,0,1,0,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0,1,0,1,1,1,0,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1,0,1,0,1,0,1,0,0,1,1,0,0,0,1,0,0,1,1,1,1,1,1,1,0,1,0,0,1,1,1,0,1,1,0,0,1,1,1,1,1,1,1,0,1,1,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,0,1,1,0,1,0,0,1,0,1,1,0,1,1,0,1,1,0,1,0,0,1,0,0,1,1,0,0,1,0,1,1,0,0,1,1,1,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,1,0,1,0,0,0,1,1,1,0,1,0,1,0,0,0,1,1,1,0,1,1,1,0,1,1,1,1,0,0,0,0,1,1,0,0,0,0,0,1,0,1,1,0,1,0,1,0,1,0,1,0,0,1,1,1,0,0,1,1,0,0,1,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,1,1,1,1,1,0,0,1,0,0,1,1,1,1,0,0,1,0,1,1,1,0,1,1,0,1,0,0,1,1,1,1,0,0,0,0,1,0,1,1,1,0,0,1,1,1,1,0,1,1,1,1,0,1,0,0,1,1,0,1,1,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,1,1,0,0,0,1,1,1,0,0,0,1,1,1,1,0,1,1,0,0,0,1,1,1,0,1,1,1,0,1,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0,1,1,1,1,0,0,1,1,0,0,1,0,1,1,0,1,1,0,0,0,0,1,0,1,1,0,1,1,0,1,0,0,1,0,1,0,0,1,0,0,1,1,0,1,1,0,0,1,1,1,1,1,0,1,0,0,1,1,1,1,0,1,1,1,0,0,1,0,1,0,1,1,1,0,1,1,0,0,0,1,0,1,0,1,1,0,0,1,1,1,0,1,0,0,1,1,0,0,1,1,1,0,0,1,1,1,0,0,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,0,1,1,0,1,1,1,1,1,0,0,0,0,0,1,0,1,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,1,1,0,0,1,1,0,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0,1,0,1,0,1,0,1,0,0,0,1,0,0,1,0,1,1,0,1,0,1,0,0,0,0,1,1,0,0,1,1,1,0,1,0,1,0,0,1,1,0,1,0,1,0,0,1,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,1,1,1,0,1,1,1,1,1,1,0,0,1,0,0,0,1,0,0,1,1,1,1,1,0,0,1,0,0,1,0,0,0,1,1,0,1,0,0,1,0,1,1,0,0,0,1,0,0,1,0,1,0,1,1,1,0,1,1,0,1,0,1,0,0,1,0,1,1,0,1,0,0,1,0,1,1,0,1,1,1,0,1,0,1,1,1,1,0,0,0,1,0,1,0,0,1,0,0,1,1,0,0,1,1,0,0,0,0,0,1,1,0,1,1,1,0,0,1,1,1,1,0,1,0,1,0,0,1,0,1,0,1,1,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,0,0,0,0,1,1,0,0,0,1,1,1,0,1,1,1,1,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,1,1,1,0,0,1,1,0,1,0,0,1,1,1,1,0,0,0,0,1,1,0,0,0,1,1,1,1,0,0,0,1,0,1,1,1,1,1,1,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,1,1,1,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,0,1,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,1,1,1,1,1,0,0,0,0,1,0,1,0,1,1,1,1,0,0,0,0,0,1,1,1,0,1,0,0,0,1,0,1,1,1,1,0,1,0,1,0,0,0,0,0,0,1,1,1,1,0,1,1,1,1,0,1,1,0,1,1,1,1,0,1,0,1,0,1,1,1,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,1,1,0,0,1,1,1,1,1,1,0,0,1,1,1,0,0,1,0,1,1,1,0,0,0,0,1,0,0,1,1,1,0,1,0,1,0,1,1,1,1,1,1,0,1,0,0,0,0,1,0,1,1,1,1,1,1,1,0,0,1,0,1,0,1,0,1,0,1,0,1,0,0,0,1,1,0,1,1,1,1,1,1,1,0,1,0,1,1,1,1,0,1,1,1,0,1,0,1,0,1,0,1,0,0 4 | -------------------------------------------------------------------------------- /model_t.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Aiwiscal/CPSC_Scheme/f7a593502833bc6a494179dada19c2e3c5d40fe2/model_t.zip --------------------------------------------------------------------------------