├── README.md ├── Python_project.py └── concrete_data.csv /README.md: -------------------------------------------------------------------------------- 1 | # Concrete-Compressive-Strength-Prediction 2 | Description and Project Overview 3 | This project will be based on a dataset obtained from the UCI Repository. The dataset consists of 1030 observations under 9 attributes. 4 | The attributes consist of 8 quantitative inputs and 1 quantitative output. 5 | The dataset does not contain any missing values. The dataset is focused on the compressive strength of a concrete. 6 | The attributes include factors that affect concrete strength such as cement, water, aggregate (coarse and fine), and fly ash etc… 7 | The objective of this project is trying to predict the concrete compressive strength based important predictors. 8 | The study will consist of evaluating the impact of different factors such as cement, water, age, fly ash, and or additives. 9 | We will evaluate the components that are highly correlated with concrete compressive strength and other components that are less influential 10 | and can be neglected through visualization or correlation matrix. 11 | In this study, we will use different machine learning techniques to predict the concrete compressive strength. 12 | Different modeling techniques will be used for the prediction. 13 | The modeling technique will include multiple linear regression, decision tree, and random forest, etc. 14 | A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy. 15 | The best model will be helpful for civil engineers in choosing the appropriate concrete for bridges, houses construction. 16 | -------------------------------------------------------------------------------- /Python_project.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # In[1]: 5 | 6 | 7 | 8 | # Concrete compressive strength prediction in civil engineering 9 | 10 | 11 | # In[2]: 12 | 13 | 14 | # Important libraries 15 | import pandas as pd 16 | import matplotlib.pyplot as plt 17 | import matplotlib.cm as cm 18 | import seaborn as sns 19 | import numpy as np 20 | import itertools as it 21 | from sklearn.linear_model import LinearRegression # Linear regression 22 | from sklearn.metrics import mean_squared_error # Compute mean square error 23 | from sklearn.model_selection import train_test_split # Splitting dataset into training and test data 24 | from sklearn.linear_model import Lasso #Lasso Regression 25 | from sklearn.neighbors import KNeighborsRegressor #KNN Neighbor 26 | from sklearn.svm import SVR # SVM 27 | from sklearn import metrics 28 | get_ipython().run_line_magic('matplotlib', 'inline') 29 | from sklearn.preprocessing import PolynomialFeatures 30 | from sklearn.preprocessing import PolynomialFeatures 31 | import statsmodels.api as sm 32 | 33 | 34 | # In[4]: 35 | 36 | 37 | # Loading of dataset 38 | df=pd.read_csv('concrete_data.csv', sep=',') # Create a dataframe 39 | df.head(5) #Reading of first 5 rows 40 | 41 | 42 | # In[5]: 43 | 44 | 45 | # Data Structuring 46 | print('Number of rows',df.shape[0]) 47 | print('Number of columns',df.shape[1]) 48 | print(df.info()) 49 | 50 | 51 | # In[6]: 52 | 53 | 54 | # Missing Values 55 | print('Number of missing values', df.isnull().sum()) 56 | 'The dataset contains no missing values' 57 | 58 | 59 | # In[7]: 60 | 61 | 62 | # Data visualization 63 | #1 Correlation Matrix 64 | sns.heatmap(df.corr(), annot=True, linewidth=2) 65 | plt.title("Correlation between variables") 66 | plt.show() 67 | 68 | #2 Pair plot 69 | sns.pairplot(df,markers="h") 70 | plt.show() 71 | 72 | #3 Distribution plot 73 | sns.distplot(df['concrete_compressive_strength'], bins=10, color='b') 74 | plt.ylabel("Frequency") 75 | plt.title('Distribution of concrete strength') 76 | 77 | 78 | # In[8]: 79 | 80 | 81 | # Distribution of components of concrete 82 | cols = [i for i in df.columns if i not in 'compressive_strength'] 83 | length = len(cols) 84 | cs = ["b","r","g","c","m","k","lime","c"] 85 | fig = plt.figure(figsize=(13,25)) 86 | 87 | for i,j,k in it.zip_longest(cols,range(length),cs): 88 | plt.subplot(4,2,j+1) 89 | ax = sns.distplot(df[i],color=k,rug=True) 90 | ax.set_facecolor("w") 91 | plt.axvline(df[i].mean(),linestyle="dashed",label="mean",color="k") 92 | plt.legend(loc="best") 93 | plt.title(i,color="navy") 94 | plt.xlabel("") 95 | 96 | 97 | # In[9]: 98 | 99 | 100 | # Scatterplot between components 101 | fig = plt.figure(figsize=(13,8)) 102 | ax = fig.add_subplot(111) 103 | plt.scatter(df["water"],df["cement"], 104 | c=df["concrete_compressive_strength"],s=df["concrete_compressive_strength"]*3, 105 | linewidth=1,edgecolor="k",cmap="viridis") 106 | ax.set_facecolor("w") 107 | ax.set_xlabel("water") 108 | ax.set_ylabel("cement") 109 | lab = plt.colorbar() 110 | lab.set_label("concrete_compressive_strength") 111 | plt.title("cement vs water") 112 | plt.show() 113 | 114 | 115 | # In[10]: 116 | 117 | 118 | # Data Splitting 119 | # The dataset is divided into a 70 to 30 splitting between training data and test data 120 | 121 | train,test = train_test_split(df,test_size =.3,random_state = 0) 122 | train_X = train[[x for x in train.columns if x not in ["concrete_compressive_strength"] + ["age_months"]]] 123 | train_Y = train["concrete_compressive_strength"] 124 | test_X = test[[x for x in test.columns if x not in ["concrete_compressive_strength"] + ["age_months"]]] 125 | test_Y = test["concrete_compressive_strength"] 126 | 127 | 128 | # In[206]: 129 | 130 | 131 | #Model 1= Multiple linear regression 132 | # fit a model 133 | lm = LinearRegression() 134 | model = lm.fit(train_X, train_Y) 135 | predictions = lm.predict(test_X) 136 | m1=model.score(test_X, test_Y) 137 | RMSE1=np.sqrt(metrics.mean_squared_error(test_Y, predictions)) 138 | print('Accuracy of model is', model.score(test_X, test_Y)) 139 | print('Mean Absolute Error:', metrics.mean_absolute_error(test_Y, predictions)) 140 | print('Mean Squared Error:', metrics.mean_squared_error(test_Y, predictions)) 141 | print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(test_Y, predictions))) 142 | 143 | 144 | # In[177]: 145 | 146 | 147 | # Features Importance 148 | coef=pd.DataFrame(lm.coef_.ravel()) 149 | coef['feat']=train_X.columns 150 | c=coef['feat'].rows=['C','Bfs','Fa','W','Sp','Ca','Fa','a'] 151 | num_colors = 8; 152 | colors = cm.rainbow(np.linspace(0,1,num_colors)) 153 | plt.bar(c, coef[0], color=colors) 154 | plt.show() 155 | 'where C:Cement, Bfs: Blast_furnace_slag, Fa: Fly_ash, W:water, Sp:Superlasticizer, Ca:Coarse_aggregate, Fa: Fine_aggregate, a: Age' 156 | 157 | 158 | # In[178]: 159 | 160 | 161 | #Plot of true value vs. predicted values 162 | dat = pd.DataFrame({'Actual': test_Y, 'Predicted': predictions}) 163 | dat1=dat.head(25) #just a sample which shows top 25 columns 164 | dat1.plot(kind='bar',figsize=(7,7)) 165 | plt.grid(which='major', linestyle='-', linewidth='0.5', color='green') 166 | plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') 167 | plt.show() 168 | 169 | 170 | # In[205]: 171 | 172 | 173 | # Model 2: LASSO Model 174 | las = Lasso(alpha=0.1) 175 | model2 = las.fit(train_X, train_Y) 176 | predictions2 = las.predict(test_X) 177 | m12=model2.score(test_X, test_Y) 178 | RMSE12=np.sqrt(metrics.mean_squared_error(test_Y, predictions2)) 179 | print('Accuracy of model is', model2.score(test_X, test_Y)) 180 | print('Mean Absolute Error:', metrics.mean_absolute_error(test_Y, predictions2)) 181 | print('Mean Squared Error:', metrics.mean_squared_error(test_Y, predictions2)) 182 | print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(test_Y, predictions2))) 183 | 184 | 185 | # In[180]: 186 | 187 | 188 | # Feature selection 189 | coef1=pd.DataFrame(las.coef_.ravel()) 190 | coef1['feat']=train_X.columns 191 | c1=coef['feat'].rows=['C','Bfs','Fa','W','Sp','Ca','Fa','a'] 192 | num_colors = 8; 193 | colors = cm.rainbow(np.linspace(0,1,num_colors)) 194 | plt.bar(c1, coef[0], color=colors) 195 | plt.show() 196 | 197 | 198 | # In[181]: 199 | 200 | 201 | #Plot of true value vs. predicted values 202 | dat = pd.DataFrame({'Actual': test_Y, 'Predicted': predictions2}) 203 | dat1=dat.head(25) #just a sample which shows top 25 columns 204 | dat1.plot(kind='bar',figsize=(7,7)) 205 | plt.grid(which='major', linestyle='-', linewidth='0.5', color='green') 206 | plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') 207 | plt.show() 208 | 209 | 210 | # In[204]: 211 | 212 | 213 | # Model 3: KNN Neighbor 214 | knn = KNeighborsRegressor() 215 | model3=knn.fit(train_X,train_Y) 216 | predictions3 = knn.predict(test_X) 217 | m13=model3.score(test_X, test_Y) 218 | RMSE13=np.sqrt(metrics.mean_squared_error(test_Y, predictions3)) 219 | print('Accuracy of model is', model3.score(test_X, test_Y)) 220 | print('Mean Absolute Error:', metrics.mean_absolute_error(test_Y, predictions3)) 221 | print('Mean Squared Error:', metrics.mean_squared_error(test_Y, predictions3)) 222 | print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(test_Y, predictions3))) 223 | 224 | 225 | # In[183]: 226 | 227 | 228 | dat = pd.DataFrame({'Actual': test_Y, 'Predicted': predictions3}) 229 | dat1=dat.head(25) #just a sample which shows top 25 columns 230 | dat1.plot(kind='bar',figsize=(7,7)) 231 | plt.grid(which='major', linestyle='-', linewidth='0.5', color='green') 232 | plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') 233 | plt.show() 234 | 235 | 236 | # In[ ]: 237 | 238 | 239 | svm= SVR(kernel='linear') 240 | model4=svm.fit(train_X, train_Y) 241 | predictions4 = svm.predict(test_X) 242 | m4=model4.score(test_X, test_Y) 243 | RMSE4=np.sqrt(metrics.mean_squared_error(test_Y, predictions4)) 244 | print('Accuracy of model is', model4.score(test_X, test_Y)) 245 | print('Mean Absolute Error:', metrics.mean_absolute_error(test_Y, predictions4)) 246 | print('Mean Squared Error:', metrics.mean_squared_error(test_Y, predictions4)) 247 | print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(test_Y, predictions4))) 248 | 249 | 250 | # In[185]: 251 | 252 | 253 | dat = pd.DataFrame({'Actual': test_Y, 'Predicted': predictions4}) 254 | dat1=dat.head(25) #just a sample which shows top 25 columns 255 | dat1.plot(kind='bar',figsize=(7,7)) 256 | plt.grid(which='major', linestyle='-', linewidth='0.5', color='green') 257 | plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') 258 | plt.show() 259 | 260 | 261 | # In[202]: 262 | 263 | 264 | # Polynomial degree 2 265 | 266 | train_X1 = PolynomialFeatures(degree=2, include_bias=False).fit_transform(train_X) 267 | test_X1 = PolynomialFeatures(degree=2, include_bias=False).fit_transform(test_X) 268 | regressor = LinearRegression() 269 | m1=regressor.fit(train_X1, train_Y) 270 | y_pred = regressor.predict(test_X1) 271 | 272 | df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) 273 | df1 = df.head(25) #just a sample which shows top 25 columns 274 | df1.plot(kind='bar',figsize=(7,7)) 275 | plt.grid(which='major', linestyle='-', linewidth='0.5', color='green') 276 | plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') 277 | plt.show() 278 | 279 | # Model Performance 280 | p2=m1.score(test_X1, test_Y) 281 | RMSE2=np.sqrt(metrics.mean_squared_error(y_test, y_pred)) 282 | 283 | print('Accuracy of model is', m1.score(test_X1, test_Y)) 284 | print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) 285 | print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) 286 | print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) 287 | 288 | 289 | # In[225]: 290 | 291 | 292 | # Polynomial degree 3 293 | 294 | train_X1 = PolynomialFeatures(degree=3, include_bias=False).fit_transform(train_X) 295 | test_X1 = PolynomialFeatures(degree=3, include_bias=False).fit_transform(test_X) 296 | regressor = LinearRegression() 297 | m1=regressor.fit(train_X1, train_Y) 298 | y_pred = regressor.predict(test_X1) 299 | 300 | df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) 301 | df1 = df.head(25) #just a sample which shows top 25 columns 302 | df1.plot(kind='bar',figsize=(7,7)) 303 | plt.grid(which='major', linestyle='-', linewidth='0.5', color='green') 304 | plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') 305 | plt.show() 306 | 307 | # Model Performance 308 | p3=m1.score(test_X1, test_Y) 309 | RMSE3=np.sqrt(metrics.mean_squared_error(y_test, y_pred)) 310 | 311 | print('Accuracy of model is', m1.score(test_X1, test_Y)) 312 | print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) 313 | print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) 314 | print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) 315 | 316 | 317 | # In[223]: 318 | 319 | 320 | # Model Comparison 321 | height = [m1, p2, p3, m12, m13,m4] 322 | bars = ('Linear', 'Poly n=2', 'Poly n=3', 'LASSO', 'KNN','SVM') 323 | y_pos = np.arange(len(bars)) 324 | plt.bar(bars, height, color=colors) 325 | plt.xlabel('Models') 326 | plt.ylabel('Accuracy(%)') 327 | plt.show() 328 | 329 | 330 | # In[226]: 331 | 332 | 333 | # Model Comparison 334 | height = [RMSE1, RMSE2, RMSE3, RMSE12, RMSE13,RMSE4] 335 | bars = ('Linear', 'Poly n=2', 'Poly n=3', 'LASSO', 'KNN','SVM') 336 | y_pos = np.arange(len(bars)) 337 | plt.bar(bars, height, color=colors) 338 | plt.xlabel('Models') 339 | plt.ylabel('RMSE') 340 | plt.show() 341 | 342 | -------------------------------------------------------------------------------- /concrete_data.csv: -------------------------------------------------------------------------------- 1 | cement,blast_furnace_slag,fly_ash,water,superplasticizer,coarse_aggregate,fine_aggregate,age,concrete_compressive_strength 2 | 540,0,0,162,2.5,1040,676,28,79.99 3 | 540,0,0,162,2.5,1055,676,28,61.89 4 | 332.5,142.5,0,228,0,932,594,270,40.27 5 | 332.5,142.5,0,228,0,932,594,365,41.05 6 | 198.6,132.4,0,192,0,978.4,825.5,360,44.3 7 | 266,114,0,228,0,932,670,90,47.03 8 | 380,95,0,228,0,932,594,365,43.7 9 | 380,95,0,228,0,932,594,28,36.45 10 | 266,114,0,228,0,932,670,28,45.85 11 | 475,0,0,228,0,932,594,28,39.29 12 | 198.6,132.4,0,192,0,978.4,825.5,90,38.07 13 | 198.6,132.4,0,192,0,978.4,825.5,28,28.02 14 | 427.5,47.5,0,228,0,932,594,270,43.01 15 | 190,190,0,228,0,932,670,90,42.33 16 | 304,76,0,228,0,932,670,28,47.81 17 | 380,0,0,228,0,932,670,90,52.91 18 | 139.6,209.4,0,192,0,1047,806.9,90,39.36 19 | 342,38,0,228,0,932,670,365,56.14 20 | 380,95,0,228,0,932,594,90,40.56 21 | 475,0,0,228,0,932,594,180,42.62 22 | 427.5,47.5,0,228,0,932,594,180,41.84 23 | 139.6,209.4,0,192,0,1047,806.9,28,28.24 24 | 139.6,209.4,0,192,0,1047,806.9,3,8.06 25 | 139.6,209.4,0,192,0,1047,806.9,180,44.21 26 | 380,0,0,228,0,932,670,365,52.52 27 | 380,0,0,228,0,932,670,270,53.3 28 | 380,95,0,228,0,932,594,270,41.15 29 | 342,38,0,228,0,932,670,180,52.12 30 | 427.5,47.5,0,228,0,932,594,28,37.43 31 | 475,0,0,228,0,932,594,7,38.6 32 | 304,76,0,228,0,932,670,365,55.26 33 | 266,114,0,228,0,932,670,365,52.91 34 | 198.6,132.4,0,192,0,978.4,825.5,180,41.72 35 | 475,0,0,228,0,932,594,270,42.13 36 | 190,190,0,228,0,932,670,365,53.69 37 | 237.5,237.5,0,228,0,932,594,270,38.41 38 | 237.5,237.5,0,228,0,932,594,28,30.08 39 | 332.5,142.5,0,228,0,932,594,90,37.72 40 | 475,0,0,228,0,932,594,90,42.23 41 | 237.5,237.5,0,228,0,932,594,180,36.25 42 | 342,38,0,228,0,932,670,90,50.46 43 | 427.5,47.5,0,228,0,932,594,365,43.7 44 | 237.5,237.5,0,228,0,932,594,365,39 45 | 380,0,0,228,0,932,670,180,53.1 46 | 427.5,47.5,0,228,0,932,594,90,41.54 47 | 427.5,47.5,0,228,0,932,594,7,35.08 48 | 349,0,0,192,0,1047,806.9,3,15.05 49 | 380,95,0,228,0,932,594,180,40.76 50 | 237.5,237.5,0,228,0,932,594,7,26.26 51 | 380,95,0,228,0,932,594,7,32.82 52 | 332.5,142.5,0,228,0,932,594,180,39.78 53 | 190,190,0,228,0,932,670,180,46.93 54 | 237.5,237.5,0,228,0,932,594,90,33.12 55 | 304,76,0,228,0,932,670,90,49.19 56 | 139.6,209.4,0,192,0,1047,806.9,7,14.59 57 | 198.6,132.4,0,192,0,978.4,825.5,7,14.64 58 | 475,0,0,228,0,932,594,365,41.93 59 | 198.6,132.4,0,192,0,978.4,825.5,3,9.13 60 | 304,76,0,228,0,932,670,180,50.95 61 | 332.5,142.5,0,228,0,932,594,28,33.02 62 | 304,76,0,228,0,932,670,270,54.38 63 | 266,114,0,228,0,932,670,270,51.73 64 | 310,0,0,192,0,971,850.6,3,9.87 65 | 190,190,0,228,0,932,670,270,50.66 66 | 266,114,0,228,0,932,670,180,48.7 67 | 342,38,0,228,0,932,670,270,55.06 68 | 139.6,209.4,0,192,0,1047,806.9,360,44.7 69 | 332.5,142.5,0,228,0,932,594,7,30.28 70 | 190,190,0,228,0,932,670,28,40.86 71 | 485,0,0,146,0,1120,800,28,71.99 72 | 374,189.2,0,170.1,10.1,926.1,756.7,3,34.4 73 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,3,28.8 74 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 75 | 425,106.3,0,151.4,18.6,936,803.7,3,36.3 76 | 375,93.8,0,126.6,23.4,852.1,992.6,3,29 77 | 475,118.8,0,181.1,8.9,852.1,781.5,3,37.8 78 | 469,117.2,0,137.8,32.2,852.1,840.5,3,40.2 79 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 80 | 388.6,97.1,0,157.9,12.1,852.1,925.7,3,28.1 81 | 531.3,0,0,141.8,28.2,852.1,893.7,3,41.3 82 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 83 | 318.8,212.5,0,155.7,14.3,852.1,880.4,3,25.2 84 | 401.8,94.7,0,147.4,11.4,946.8,852.1,3,41.1 85 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 86 | 323.7,282.8,0,183.8,10.3,942.7,659.9,3,28.3 87 | 379.5,151.2,0,153.9,15.9,1134.3,605,3,28.6 88 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 89 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,3,24.4 90 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 91 | 439,177,0,186,11.1,884.9,707.9,3,39.3 92 | 389.9,189,0,145.9,22,944.7,755.8,3,40.6 93 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 94 | 337.9,189,0,174.9,9.5,944.7,755.8,3,24.1 95 | 374,189.2,0,170.1,10.1,926.1,756.7,7,46.2 96 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,7,42.8 97 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 98 | 425,106.3,0,151.4,18.6,936,803.7,7,46.8 99 | 375,93.8,0,126.6,23.4,852.1,992.6,7,45.7 100 | 475,118.8,0,181.1,8.9,852.1,781.5,7,55.6 101 | 469,117.2,0,137.8,32.2,852.1,840.5,7,54.9 102 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 103 | 388.6,97.1,0,157.9,12.1,852.1,925.7,7,34.9 104 | 531.3,0,0,141.8,28.2,852.1,893.7,7,46.9 105 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 106 | 318.8,212.5,0,155.7,14.3,852.1,880.4,7,33.4 107 | 401.8,94.7,0,147.4,11.4,946.8,852.1,7,54.1 108 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 109 | 323.7,282.8,0,183.8,10.3,942.7,659.9,7,49.8 110 | 379.5,151.2,0,153.9,15.9,1134.3,605,7,47.1 111 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 112 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,7,38 113 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 114 | 439,177,0,186,11.1,884.9,707.9,7,56.1 115 | 389.9,189,0,145.9,22,944.7,755.8,7,59.09 116 | 362.6,189,0,164.9,11.6,944.7,755.8,7,22.9 117 | 337.9,189,0,174.9,9.5,944.7,755.8,7,35.1 118 | 374,189.2,0,170.1,10.1,926.1,756.7,28,61.09 119 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,28,59.8 120 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 121 | 425,106.3,0,151.4,18.6,936,803.7,28,61.8 122 | 375,93.8,0,126.6,23.4,852.1,992.6,28,56.7 123 | 475,118.8,0,181.1,8.9,852.1,781.5,28,68.3 124 | 469,117.2,0,137.8,32.2,852.1,840.5,28,66.9 125 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 126 | 388.6,97.1,0,157.9,12.1,852.1,925.7,28,50.7 127 | 531.3,0,0,141.8,28.2,852.1,893.7,28,56.4 128 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 129 | 318.8,212.5,0,155.7,14.3,852.1,880.4,28,55.5 130 | 401.8,94.7,0,147.4,11.4,946.8,852.1,28,68.5 131 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 132 | 323.7,282.8,0,183.8,10.3,942.7,659.9,28,74.7 133 | 379.5,151.2,0,153.9,15.9,1134.3,605,28,52.2 134 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 135 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,28,67.7 136 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 137 | 439,177,0,186,11.1,884.9,707.9,28,66 138 | 389.9,189,0,145.9,22,944.7,755.8,28,74.5 139 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 140 | 337.9,189,0,174.9,9.5,944.7,755.8,28,49.9 141 | 374,189.2,0,170.1,10.1,926.1,756.7,56,63.4 142 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,56,64.9 143 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 144 | 425,106.3,0,151.4,18.6,936,803.7,56,64.9 145 | 375,93.8,0,126.6,23.4,852.1,992.6,56,60.2 146 | 475,118.8,0,181.1,8.9,852.1,781.5,56,72.3 147 | 469,117.2,0,137.8,32.2,852.1,840.5,56,69.3 148 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 149 | 388.6,97.1,0,157.9,12.1,852.1,925.7,56,55.2 150 | 531.3,0,0,141.8,28.2,852.1,893.7,56,58.8 151 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 152 | 318.8,212.5,0,155.7,14.3,852.1,880.4,56,66.1 153 | 401.8,94.7,0,147.4,11.4,946.8,852.1,56,73.7 154 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 155 | 323.7,282.8,0,183.8,10.3,942.7,659.9,56,80.2 156 | 379.5,151.2,0,153.9,15.9,1134.3,605,56,54.9 157 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 158 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,56,72.99 159 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 160 | 439,177,0,186,11.1,884.9,707.9,56,71.7 161 | 389.9,189,0,145.9,22,944.7,755.8,56,79.4 162 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 163 | 337.9,189,0,174.9,9.5,944.7,755.8,56,59.89 164 | 374,189.2,0,170.1,10.1,926.1,756.7,91,64.9 165 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,91,66.6 166 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 167 | 425,106.3,0,151.4,18.6,936,803.7,91,66.7 168 | 375,93.8,0,126.6,23.4,852.1,992.6,91,62.5 169 | 475,118.8,0,181.1,8.9,852.1,781.5,91,74.19 170 | 469,117.2,0,137.8,32.2,852.1,840.5,91,70.7 171 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 172 | 388.6,97.1,0,157.9,12.1,852.1,925.7,91,57.6 173 | 531.3,0,0,141.8,28.2,852.1,893.7,91,59.2 174 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 175 | 318.8,212.5,0,155.7,14.3,852.1,880.4,91,68.1 176 | 401.8,94.7,0,147.4,11.4,946.8,852.1,91,75.5 177 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 178 | 379.5,151.2,0,153.9,15.9,1134.3,605,91,56.5 179 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 180 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,91,76.8 181 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 182 | 439,177,0,186,11.1,884.9,707.9,91,73.3 183 | 389.9,189,0,145.9,22,944.7,755.8,91,82.6 184 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 185 | 337.9,189,0,174.9,9.5,944.7,755.8,91,67.8 186 | 222.4,0,96.7,189.3,4.5,967.1,870.3,3,11.58 187 | 222.4,0,96.7,189.3,4.5,967.1,870.3,14,24.45 188 | 222.4,0,96.7,189.3,4.5,967.1,870.3,28,24.89 189 | 222.4,0,96.7,189.3,4.5,967.1,870.3,56,29.45 190 | 222.4,0,96.7,189.3,4.5,967.1,870.3,100,40.71 191 | 233.8,0,94.6,197.9,4.6,947,852.2,3,10.38 192 | 233.8,0,94.6,197.9,4.6,947,852.2,14,22.14 193 | 233.8,0,94.6,197.9,4.6,947,852.2,28,22.84 194 | 233.8,0,94.6,197.9,4.6,947,852.2,56,27.66 195 | 233.8,0,94.6,197.9,4.6,947,852.2,100,34.56 196 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,3,12.45 197 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,14,24.99 198 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,28,25.72 199 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,56,33.96 200 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,100,37.34 201 | 190.7,0,125.4,162.1,7.8,1090,804,3,15.04 202 | 190.7,0,125.4,162.1,7.8,1090,804,14,21.06 203 | 190.7,0,125.4,162.1,7.8,1090,804,28,26.4 204 | 190.7,0,125.4,162.1,7.8,1090,804,56,35.34 205 | 190.7,0,125.4,162.1,7.8,1090,804,100,40.57 206 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,3,12.47 207 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,14,20.92 208 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,28,24.9 209 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,56,34.2 210 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,100,39.61 211 | 230,0,118.3,195.5,4.6,1029.4,758.6,3,10.03 212 | 230,0,118.3,195.5,4.6,1029.4,758.6,14,20.08 213 | 230,0,118.3,195.5,4.6,1029.4,758.6,28,24.48 214 | 230,0,118.3,195.5,4.6,1029.4,758.6,56,31.54 215 | 230,0,118.3,195.5,4.6,1029.4,758.6,100,35.34 216 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,3,9.45 217 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,14,22.72 218 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,28,28.47 219 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,56,38.56 220 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,100,40.39 221 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,3,10.76 222 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,14,25.48 223 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,28,21.54 224 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,56,28.63 225 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,100,33.54 226 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,3,7.75 227 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,14,17.82 228 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,28,24.24 229 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,56,32.85 230 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,100,39.23 231 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,3,18 232 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,14,30.39 233 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,28,45.71 234 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,56,50.77 235 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,100,53.9 236 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,3,13.18 237 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,14,17.84 238 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,28,40.23 239 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,56,47.13 240 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,100,49.97 241 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,3,13.36 242 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,14,22.32 243 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,28,24.54 244 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,56,31.35 245 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,100,40.86 246 | 238.1,0,94.1,186.7,7,949.9,847,3,19.93 247 | 238.1,0,94.1,186.7,7,949.9,847,14,25.69 248 | 238.1,0,94.1,186.7,7,949.9,847,28,30.23 249 | 238.1,0,94.1,186.7,7,949.9,847,56,39.59 250 | 238.1,0,94.1,186.7,7,949.9,847,100,44.3 251 | 250,0,95.7,187.4,5.5,956.9,861.2,3,13.82 252 | 250,0,95.7,187.4,5.5,956.9,861.2,14,24.92 253 | 250,0,95.7,187.4,5.5,956.9,861.2,28,29.22 254 | 250,0,95.7,187.4,5.5,956.9,861.2,56,38.33 255 | 250,0,95.7,187.4,5.5,956.9,861.2,100,42.35 256 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,3,13.54 257 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,14,26.31 258 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,28,31.64 259 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,56,42.55 260 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,100,42.92 261 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,3,13.33 262 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,14,25.37 263 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,28,37.4 264 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,56,44.4 265 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,100,47.74 266 | 212,0,124.8,159,7.8,1085.4,799.5,3,19.52 267 | 212,0,124.8,159,7.8,1085.4,799.5,14,31.35 268 | 212,0,124.8,159,7.8,1085.4,799.5,28,38.5 269 | 212,0,124.8,159,7.8,1085.4,799.5,56,45.08 270 | 212,0,124.8,159,7.8,1085.4,799.5,100,47.82 271 | 231.8,0,121.6,174,6.7,1056.4,778.5,3,15.44 272 | 231.8,0,121.6,174,6.7,1056.4,778.5,14,26.77 273 | 231.8,0,121.6,174,6.7,1056.4,778.5,28,33.73 274 | 231.8,0,121.6,174,6.7,1056.4,778.5,56,42.7 275 | 231.8,0,121.6,174,6.7,1056.4,778.5,100,45.84 276 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,3,17.22 277 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,14,29.93 278 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,28,29.65 279 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,56,36.97 280 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,100,43.58 281 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,3,13.12 282 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,14,24.43 283 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,28,32.66 284 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,56,36.64 285 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,100,44.21 286 | 181.4,0,167,169.6,7.6,1055.6,777.8,3,13.62 287 | 181.4,0,167,169.6,7.6,1055.6,777.8,14,21.6 288 | 181.4,0,167,169.6,7.6,1055.6,777.8,28,27.77 289 | 181.4,0,167,169.6,7.6,1055.6,777.8,56,35.57 290 | 181.4,0,167,169.6,7.6,1055.6,777.8,100,45.37 291 | 182,45.2,122,170.2,8.2,1059.4,780.7,3,7.32 292 | 182,45.2,122,170.2,8.2,1059.4,780.7,14,21.5 293 | 182,45.2,122,170.2,8.2,1059.4,780.7,28,31.27 294 | 182,45.2,122,170.2,8.2,1059.4,780.7,56,43.5 295 | 182,45.2,122,170.2,8.2,1059.4,780.7,100,48.67 296 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,3,7.4 297 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,14,23.51 298 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,28,31.12 299 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,56,39.15 300 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,100,48.15 301 | 290.4,0,96.2,168.1,9.4,961.2,865,3,22.5 302 | 290.4,0,96.2,168.1,9.4,961.2,865,14,34.67 303 | 290.4,0,96.2,168.1,9.4,961.2,865,28,34.74 304 | 290.4,0,96.2,168.1,9.4,961.2,865,56,45.08 305 | 290.4,0,96.2,168.1,9.4,961.2,865,100,48.97 306 | 277.1,0,97.4,160.6,11.8,973.9,875.6,3,23.14 307 | 277.1,0,97.4,160.6,11.8,973.9,875.6,14,41.89 308 | 277.1,0,97.4,160.6,11.8,973.9,875.6,28,48.28 309 | 277.1,0,97.4,160.6,11.8,973.9,875.6,56,51.04 310 | 277.1,0,97.4,160.6,11.8,973.9,875.6,100,55.64 311 | 295.7,0,95.6,171.5,8.9,955.1,859.2,3,22.95 312 | 295.7,0,95.6,171.5,8.9,955.1,859.2,14,35.23 313 | 295.7,0,95.6,171.5,8.9,955.1,859.2,28,39.94 314 | 295.7,0,95.6,171.5,8.9,955.1,859.2,56,48.72 315 | 295.7,0,95.6,171.5,8.9,955.1,859.2,100,52.04 316 | 251.8,0,99.9,146.1,12.4,1006,899.8,3,21.02 317 | 251.8,0,99.9,146.1,12.4,1006,899.8,14,33.36 318 | 251.8,0,99.9,146.1,12.4,1006,899.8,28,33.94 319 | 251.8,0,99.9,146.1,12.4,1006,899.8,56,44.14 320 | 251.8,0,99.9,146.1,12.4,1006,899.8,100,45.37 321 | 249.1,0,98.8,158.1,12.8,987.8,889,3,15.36 322 | 249.1,0,98.8,158.1,12.8,987.8,889,14,28.68 323 | 249.1,0,98.8,158.1,12.8,987.8,889,28,30.85 324 | 249.1,0,98.8,158.1,12.8,987.8,889,56,42.03 325 | 249.1,0,98.8,158.1,12.8,987.8,889,100,51.06 326 | 252.3,0,98.8,146.3,14.2,987.8,889,3,21.78 327 | 252.3,0,98.8,146.3,14.2,987.8,889,14,42.29 328 | 252.3,0,98.8,146.3,14.2,987.8,889,28,50.6 329 | 252.3,0,98.8,146.3,14.2,987.8,889,56,55.83 330 | 252.3,0,98.8,146.3,14.2,987.8,889,100,60.95 331 | 246.8,0,125.1,143.3,12,1086.8,800.9,3,23.52 332 | 246.8,0,125.1,143.3,12,1086.8,800.9,14,42.22 333 | 246.8,0,125.1,143.3,12,1086.8,800.9,28,52.5 334 | 246.8,0,125.1,143.3,12,1086.8,800.9,56,60.32 335 | 246.8,0,125.1,143.3,12,1086.8,800.9,100,66.42 336 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,3,23.8 337 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,14,38.77 338 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,28,51.33 339 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,56,56.85 340 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,100,58.61 341 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,3,21.91 342 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,14,36.99 343 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,28,47.4 344 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,56,51.96 345 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,100,56.74 346 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,3,17.57 347 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,14,33.73 348 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,28,40.15 349 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,56,46.64 350 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,100,50.08 351 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,3,17.37 352 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,14,33.7 353 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,28,45.94 354 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,56,51.43 355 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,100,59.3 356 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,3,30.45 357 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,14,47.71 358 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,28,63.14 359 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,56,66.82 360 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,100,66.95 361 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,3,27.42 362 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,14,35.96 363 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,28,55.51 364 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,56,61.99 365 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,100,63.53 366 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,3,18.02 367 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,14,38.6 368 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,28,52.2 369 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,56,53.96 370 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,100,56.63 371 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,3,15.34 372 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,14,26.05 373 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,28,30.22 374 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,56,37.27 375 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,100,46.23 376 | 376,0,0,214.6,0,1003.5,762.4,3,16.28 377 | 376,0,0,214.6,0,1003.5,762.4,14,25.62 378 | 376,0,0,214.6,0,1003.5,762.4,28,31.97 379 | 376,0,0,214.6,0,1003.5,762.4,56,36.3 380 | 376,0,0,214.6,0,1003.5,762.4,100,43.06 381 | 500,0,0,140,4,966,853,28,67.57 382 | 475,0,59,142,1.9,1098,641,28,57.23 383 | 315,137,0,145,5.9,1130,745,28,81.75 384 | 505,0,60,195,0,1030,630,28,64.02 385 | 451,0,0,165,11.3,1030,745,28,78.8 386 | 516,0,0,162,8.2,801,802,28,41.37 387 | 520,0,0,170,5.2,855,855,28,60.28 388 | 528,0,0,185,6.9,920,720,28,56.83 389 | 520,0,0,175,5.2,870,805,28,51.02 390 | 385,0,136,158,20,903,768,28,55.55 391 | 500.1,0,0,200,3,1124.4,613.2,28,44.13 392 | 450.1,50,0,200,3,1124.4,613.2,28,39.38 393 | 397,17.2,158,167,20.8,967,633,28,55.65 394 | 333,17.5,163,167,17.9,996,652,28,47.28 395 | 334,17.6,158,189,15.3,967,633,28,44.33 396 | 405,0,0,175,0,1120,695,28,52.3 397 | 200,200,0,190,0,1145,660,28,49.25 398 | 516,0,0,162,8.3,801,802,28,41.37 399 | 145,116,119,184,5.7,833,880,28,29.16 400 | 160,128,122,182,6.4,824,879,28,39.4 401 | 234,156,0,189,5.9,981,760,28,39.3 402 | 250,180,95,159,9.5,860,800,28,67.87 403 | 475,0,0,162,9.5,1044,662,28,58.52 404 | 285,190,0,163,7.6,1031,685,28,53.58 405 | 356,119,0,160,9,1061,657,28,59 406 | 275,180,120,162,10.4,830,765,28,76.24 407 | 500,0,0,151,9,1033,655,28,69.84 408 | 165,0,143.6,163.8,0,1005.6,900.9,3,14.4 409 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,3,19.42 410 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,3,20.73 411 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,3,14.94 412 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,3,21.29 413 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,3,23.08 414 | 167,75.4,167,164,7.9,1007.3,770.1,3,15.52 415 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,3,15.82 416 | 190.3,0,125.2,166.6,9.9,1079,798.9,3,12.55 417 | 250,0,95.7,191.8,5.3,948.9,857.2,3,8.49 418 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,3,15.61 419 | 194.7,0,100.5,170.2,7.5,998,901.8,3,12.18 420 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,3,11.98 421 | 165,0,143.6,163.8,0,1005.6,900.9,14,16.88 422 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,14,33.09 423 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,14,34.24 424 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,14,31.81 425 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,14,29.75 426 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,14,33.01 427 | 167,75.4,167,164,7.9,1007.3,770.1,14,32.9 428 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,14,29.55 429 | 190.3,0,125.2,166.6,9.9,1079,798.9,14,19.42 430 | 250,0,95.7,191.8,5.3,948.9,857.2,14,24.66 431 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,14,29.59 432 | 194.7,0,100.5,170.2,7.5,998,901.8,14,24.28 433 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,14,20.73 434 | 165,0,143.6,163.8,0,1005.6,900.9,28,26.2 435 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,28,46.39 436 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,28,39.16 437 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,28,41.2 438 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,28,33.69 439 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,28,38.2 440 | 167,75.4,167,164,7.9,1007.3,770.1,28,41.41 441 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,28,37.81 442 | 190.3,0,125.2,166.6,9.9,1079,798.9,28,24.85 443 | 250,0,95.7,191.8,5.3,948.9,857.2,28,27.22 444 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,28,44.64 445 | 194.7,0,100.5,170.2,7.5,998,901.8,28,37.27 446 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,28,33.27 447 | 165,0,143.6,163.8,0,1005.6,900.9,56,36.56 448 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,56,53.72 449 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,56,48.59 450 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,56,51.72 451 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,56,35.85 452 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,56,53.77 453 | 167,75.4,167,164,7.9,1007.3,770.1,56,53.46 454 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,56,48.99 455 | 190.3,0,125.2,166.6,9.9,1079,798.9,56,31.72 456 | 250,0,95.7,191.8,5.3,948.9,857.2,56,39.64 457 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,56,51.26 458 | 194.7,0,100.5,170.2,7.5,998,901.8,56,43.39 459 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,56,39.27 460 | 165,0,143.6,163.8,0,1005.6,900.9,100,37.96 461 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,100,55.02 462 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,100,49.99 463 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,100,53.66 464 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,100,37.68 465 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,100,56.06 466 | 167,75.4,167,164,7.9,1007.3,770.1,100,56.81 467 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,100,50.94 468 | 190.3,0,125.2,166.6,9.9,1079,798.9,100,33.56 469 | 250,0,95.7,191.8,5.3,948.9,857.2,100,41.16 470 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,100,52.96 471 | 194.7,0,100.5,170.2,7.5,998,901.8,100,44.28 472 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,100,40.15 473 | 446,24,79,162,11.6,967,712,28,57.03 474 | 446,24,79,162,11.6,967,712,28,44.42 475 | 446,24,79,162,11.6,967,712,28,51.02 476 | 446,24,79,162,10.3,967,712,28,53.39 477 | 446,24,79,162,11.6,967,712,3,35.36 478 | 446,24,79,162,11.6,967,712,3,25.02 479 | 446,24,79,162,11.6,967,712,3,23.35 480 | 446,24,79,162,11.6,967,712,7,52.01 481 | 446,24,79,162,11.6,967,712,7,38.02 482 | 446,24,79,162,11.6,967,712,7,39.3 483 | 446,24,79,162,11.6,967,712,56,61.07 484 | 446,24,79,162,11.6,967,712,56,56.14 485 | 446,24,79,162,11.6,967,712,56,55.25 486 | 446,24,79,162,10.3,967,712,56,54.77 487 | 387,20,94,157,14.3,938,845,28,50.24 488 | 387,20,94,157,13.9,938,845,28,46.68 489 | 387,20,94,157,11.6,938,845,28,46.68 490 | 387,20,94,157,14.3,938,845,3,22.75 491 | 387,20,94,157,13.9,938,845,3,25.51 492 | 387,20,94,157,11.6,938,845,3,34.77 493 | 387,20,94,157,14.3,938,845,7,36.84 494 | 387,20,94,157,13.9,938,845,7,45.9 495 | 387,20,94,157,11.6,938,845,7,41.67 496 | 387,20,94,157,14.3,938,845,56,56.34 497 | 387,20,94,157,13.9,938,845,56,47.97 498 | 387,20,94,157,11.6,938,845,56,61.46 499 | 355,19,97,145,13.1,967,871,28,44.03 500 | 355,19,97,145,12.3,967,871,28,55.45 501 | 491,26,123,210,3.9,882,699,28,55.55 502 | 491,26,123,201,3.9,822,699,28,57.92 503 | 491,26,123,210,3.9,882,699,3,25.61 504 | 491,26,123,210,3.9,882,699,7,33.49 505 | 491,26,123,210,3.9,882,699,56,59.59 506 | 491,26,123,201,3.9,822,699,3,29.55 507 | 491,26,123,201,3.9,822,699,7,37.92 508 | 491,26,123,201,3.9,822,699,56,61.86 509 | 424,22,132,178,8.5,822,750,28,62.05 510 | 424,22,132,178,8.5,882,750,3,32.01 511 | 424,22,132,168,8.9,822,750,28,72.1 512 | 424,22,132,178,8.5,822,750,7,39 513 | 424,22,132,178,8.5,822,750,56,65.7 514 | 424,22,132,168,8.9,822,750,3,32.11 515 | 424,22,132,168,8.9,822,750,7,40.29 516 | 424,22,132,168,8.9,822,750,56,74.36 517 | 202,11,141,206,1.7,942,801,28,21.97 518 | 202,11,141,206,1.7,942,801,3,9.85 519 | 202,11,141,206,1.7,942,801,7,15.07 520 | 202,11,141,206,1.7,942,801,56,23.25 521 | 284,15,141,179,5.5,842,801,28,43.73 522 | 284,15,141,179,5.5,842,801,3,13.4 523 | 284,15,141,179,5.5,842,801,7,24.13 524 | 284,15,141,179,5.5,842,801,56,44.52 525 | 359,19,141,154,10.9,942,801,28,62.94 526 | 359,19,141,154,10.9,942,801,28,59.49 527 | 359,19,141,154,10.9,942,801,3,25.12 528 | 359,19,141,154,10.9,942,801,3,23.64 529 | 359,19,141,154,10.9,942,801,7,35.75 530 | 359,19,141,154,10.9,942,801,7,38.61 531 | 359,19,141,154,10.9,942,801,56,68.75 532 | 359,19,141,154,10.9,942,801,56,66.78 533 | 436,0,0,218,0,838.4,719.7,28,23.85 534 | 289,0,0,192,0,913.2,895.3,90,32.07 535 | 289,0,0,192,0,913.2,895.3,3,11.65 536 | 393,0,0,192,0,940.6,785.6,3,19.2 537 | 393,0,0,192,0,940.6,785.6,90,48.85 538 | 393,0,0,192,0,940.6,785.6,28,39.6 539 | 480,0,0,192,0,936.2,712.2,28,43.94 540 | 480,0,0,192,0,936.2,712.2,7,34.57 541 | 480,0,0,192,0,936.2,712.2,90,54.32 542 | 480,0,0,192,0,936.2,712.2,3,24.4 543 | 333,0,0,192,0,931.2,842.6,3,15.62 544 | 255,0,0,192,0,889.8,945,90,21.86 545 | 255,0,0,192,0,889.8,945,7,10.22 546 | 289,0,0,192,0,913.2,895.3,7,14.6 547 | 255,0,0,192,0,889.8,945,28,18.75 548 | 333,0,0,192,0,931.2,842.6,28,31.97 549 | 333,0,0,192,0,931.2,842.6,7,23.4 550 | 289,0,0,192,0,913.2,895.3,28,25.57 551 | 333,0,0,192,0,931.2,842.6,90,41.68 552 | 393,0,0,192,0,940.6,785.6,7,27.74 553 | 255,0,0,192,0,889.8,945,3,8.2 554 | 158.8,238.2,0,185.7,0,1040.6,734.3,7,9.62 555 | 239.6,359.4,0,185.7,0,941.6,664.3,7,25.42 556 | 238.2,158.8,0,185.7,0,1040.6,734.3,7,15.69 557 | 181.9,272.8,0,185.7,0,1012.4,714.3,28,27.94 558 | 193.5,290.2,0,185.7,0,998.2,704.3,28,32.63 559 | 255.5,170.3,0,185.7,0,1026.6,724.3,7,17.24 560 | 272.8,181.9,0,185.7,0,1012.4,714.3,7,19.77 561 | 239.6,359.4,0,185.7,0,941.6,664.3,28,39.44 562 | 220.8,147.2,0,185.7,0,1055,744.3,28,25.75 563 | 397,0,0,185.7,0,1040.6,734.3,28,33.08 564 | 382.5,0,0,185.7,0,1047.8,739.3,7,24.07 565 | 210.7,316.1,0,185.7,0,977,689.3,7,21.82 566 | 158.8,238.2,0,185.7,0,1040.6,734.3,28,21.07 567 | 295.8,0,0,185.7,0,1091.4,769.3,7,14.84 568 | 255.5,170.3,0,185.7,0,1026.6,724.3,28,32.05 569 | 203.5,135.7,0,185.7,0,1076.2,759.3,7,11.96 570 | 397,0,0,185.7,0,1040.6,734.3,7,25.45 571 | 381.4,0,0,185.7,0,1104.6,784.3,28,22.49 572 | 295.8,0,0,185.7,0,1091.4,769.3,28,25.22 573 | 228,342.1,0,185.7,0,955.8,674.3,28,39.7 574 | 220.8,147.2,0,185.7,0,1055,744.3,7,13.09 575 | 316.1,210.7,0,185.7,0,977,689.3,28,38.7 576 | 135.7,203.5,0,185.7,0,1076.2,759.3,7,7.51 577 | 238.1,0,0,185.7,0,1118.8,789.3,28,17.58 578 | 339.2,0,0,185.7,0,1069.2,754.3,7,21.18 579 | 135.7,203.5,0,185.7,0,1076.2,759.3,28,18.2 580 | 193.5,290.2,0,185.7,0,998.2,704.3,7,17.2 581 | 203.5,135.7,0,185.7,0,1076.2,759.3,28,22.63 582 | 290.2,193.5,0,185.7,0,998.2,704.3,7,21.86 583 | 181.9,272.8,0,185.7,0,1012.4,714.3,7,12.37 584 | 170.3,155.5,0,185.7,0,1026.6,724.3,28,25.73 585 | 210.7,316.1,0,185.7,0,977,689.3,28,37.81 586 | 228,342.1,0,185.7,0,955.8,674.3,7,21.92 587 | 290.2,193.5,0,185.7,0,998.2,704.3,28,33.04 588 | 381.4,0,0,185.7,0,1104.6,784.3,7,14.54 589 | 238.2,158.8,0,185.7,0,1040.6,734.3,28,26.91 590 | 186.2,124.1,0,185.7,0,1083.4,764.3,7,8 591 | 339.2,0,0,185.7,0,1069.2,754.3,28,31.9 592 | 238.1,0,0,185.7,0,1118.8,789.3,7,10.34 593 | 252.5,0,0,185.7,0,1111.6,784.3,28,19.77 594 | 382.5,0,0,185.7,0,1047.8,739.3,28,37.44 595 | 252.5,0,0,185.7,0,1111.6,784.3,7,11.48 596 | 316.1,210.7,0,185.7,0,977,689.3,7,24.44 597 | 186.2,124.1,0,185.7,0,1083.4,764.3,28,17.6 598 | 170.3,155.5,0,185.7,0,1026.6,724.3,7,10.73 599 | 272.8,181.9,0,185.7,0,1012.4,714.3,28,31.38 600 | 339,0,0,197,0,968,781,3,13.22 601 | 339,0,0,197,0,968,781,7,20.97 602 | 339,0,0,197,0,968,781,14,27.04 603 | 339,0,0,197,0,968,781,28,32.04 604 | 339,0,0,197,0,968,781,90,35.17 605 | 339,0,0,197,0,968,781,180,36.45 606 | 339,0,0,197,0,968,781,365,38.89 607 | 236,0,0,194,0,968,885,3,6.47 608 | 236,0,0,194,0,968,885,14,12.84 609 | 236,0,0,194,0,968,885,28,18.42 610 | 236,0,0,194,0,968,885,90,21.95 611 | 236,0,0,193,0,968,885,180,24.1 612 | 236,0,0,193,0,968,885,365,25.08 613 | 277,0,0,191,0,968,856,14,21.26 614 | 277,0,0,191,0,968,856,28,25.97 615 | 277,0,0,191,0,968,856,3,11.36 616 | 277,0,0,191,0,968,856,90,31.25 617 | 277,0,0,191,0,968,856,180,32.33 618 | 277,0,0,191,0,968,856,360,33.7 619 | 254,0,0,198,0,968,863,3,9.31 620 | 254,0,0,198,0,968,863,90,26.94 621 | 254,0,0,198,0,968,863,180,27.63 622 | 254,0,0,198,0,968,863,365,29.79 623 | 307,0,0,193,0,968,812,180,34.49 624 | 307,0,0,193,0,968,812,365,36.15 625 | 307,0,0,193,0,968,812,3,12.54 626 | 307,0,0,193,0,968,812,28,27.53 627 | 307,0,0,193,0,968,812,90,32.92 628 | 236,0,0,193,0,968,885,7,9.99 629 | 200,0,0,180,0,1125,845,7,7.84 630 | 200,0,0,180,0,1125,845,28,12.25 631 | 225,0,0,181,0,1113,833,7,11.17 632 | 225,0,0,181,0,1113,833,28,17.34 633 | 325,0,0,184,0,1063,783,7,17.54 634 | 325,0,0,184,0,1063,783,28,30.57 635 | 275,0,0,183,0,1088,808,7,14.2 636 | 275,0,0,183,0,1088,808,28,24.5 637 | 300,0,0,184,0,1075,795,7,15.58 638 | 300,0,0,184,0,1075,795,28,26.85 639 | 375,0,0,186,0,1038,758,7,26.06 640 | 375,0,0,186,0,1038,758,28,38.21 641 | 400,0,0,187,0,1025,745,28,43.7 642 | 400,0,0,187,0,1025,745,7,30.14 643 | 250,0,0,182,0,1100,820,7,12.73 644 | 250,0,0,182,0,1100,820,28,20.87 645 | 350,0,0,186,0,1050,770,7,20.28 646 | 350,0,0,186,0,1050,770,28,34.29 647 | 203.5,305.3,0,203.5,0,963.4,630,7,19.54 648 | 250.2,166.8,0,203.5,0,977.6,694.1,90,47.71 649 | 157,236,0,192,0,935.4,781.2,90,43.38 650 | 141.3,212,0,203.5,0,971.8,748.5,28,29.89 651 | 166.8,250.2,0,203.5,0,975.6,692.6,3,6.9 652 | 122.6,183.9,0,203.5,0,958.2,800.1,90,33.19 653 | 183.9,122.6,0,203.5,0,959.2,800,3,4.9 654 | 102,153,0,192,0,887,942,3,4.57 655 | 102,153,0,192,0,887,942,90,25.46 656 | 122.6,183.9,0,203.5,0,958.2,800.1,28,24.29 657 | 166.8,250.2,0,203.5,0,975.6,692.6,28,33.95 658 | 200,133,0,192,0,965.4,806.2,3,11.41 659 | 108.3,162.4,0,203.5,0,938.2,849,28,20.59 660 | 305.3,203.5,0,203.5,0,965.4,631,7,25.89 661 | 108.3,162.4,0,203.5,0,938.2,849,90,29.23 662 | 116,173,0,192,0,909.8,891.9,90,31.02 663 | 141.3,212,0,203.5,0,971.8,748.5,7,10.39 664 | 157,236,0,192,0,935.4,781.2,28,33.66 665 | 133,200,0,192,0,927.4,839.2,28,27.87 666 | 250.2,166.8,0,203.5,0,977.6,694.1,7,19.35 667 | 173,116,0,192,0,946.8,856.8,7,11.39 668 | 192,288,0,192,0,929.8,716.1,3,12.79 669 | 192,288,0,192,0,929.8,716.1,28,39.32 670 | 153,102,0,192,0,888,943.1,3,4.78 671 | 288,192,0,192,0,932,717.8,3,16.11 672 | 305.3,203.5,0,203.5,0,965.4,631,28,43.38 673 | 236,157,0,192,0,972.6,749.1,7,20.42 674 | 173,116,0,192,0,946.8,856.8,3,6.94 675 | 212,141.3,0,203.5,0,973.4,750,7,15.03 676 | 236,157,0,192,0,972.6,749.1,3,13.57 677 | 183.9,122.6,0,203.5,0,959.2,800,90,32.53 678 | 166.8,250.2,0,203.5,0,975.6,692.6,7,15.75 679 | 102,153,0,192,0,887,942,7,7.68 680 | 288,192,0,192,0,932,717.8,28,38.8 681 | 212,141.3,0,203.5,0,973.4,750,28,33 682 | 102,153,0,192,0,887,942,28,17.28 683 | 173,116,0,192,0,946.8,856.8,28,24.28 684 | 183.9,122.6,0,203.5,0,959.2,800,28,24.05 685 | 133,200,0,192,0,927.4,839.2,90,36.59 686 | 192,288,0,192,0,929.8,716.1,90,50.73 687 | 133,200,0,192,0,927.4,839.2,7,13.66 688 | 305.3,203.5,0,203.5,0,965.4,631,3,14.14 689 | 236,157,0,192,0,972.6,749.1,90,47.78 690 | 108.3,162.4,0,203.5,0,938.2,849,3,2.33 691 | 157,236,0,192,0,935.4,781.2,7,16.89 692 | 288,192,0,192,0,932,717.8,7,23.52 693 | 212,141.3,0,203.5,0,973.4,750,3,6.81 694 | 212,141.3,0,203.5,0,973.4,750,90,39.7 695 | 153,102,0,192,0,888,943.1,28,17.96 696 | 236,157,0,192,0,972.6,749.1,28,32.88 697 | 116,173,0,192,0,909.8,891.9,28,22.35 698 | 183.9,122.6,0,203.5,0,959.2,800,7,10.79 699 | 108.3,162.4,0,203.5,0,938.2,849,7,7.72 700 | 203.5,305.3,0,203.5,0,963.4,630,28,41.68 701 | 203.5,305.3,0,203.5,0,963.4,630,3,9.56 702 | 133,200,0,192,0,927.4,839.2,3,6.88 703 | 288,192,0,192,0,932,717.8,90,50.53 704 | 200,133,0,192,0,965.4,806.2,7,17.17 705 | 200,133,0,192,0,965.4,806.2,28,30.44 706 | 250.2,166.8,0,203.5,0,977.6,694.1,3,9.73 707 | 122.6,183.9,0,203.5,0,958.2,800.1,3,3.32 708 | 153,102,0,192,0,888,943.1,90,26.32 709 | 200,133,0,192,0,965.4,806.2,90,43.25 710 | 116,173,0,192,0,909.8,891.9,3,6.28 711 | 173,116,0,192,0,946.8,856.8,90,32.1 712 | 250.2,166.8,0,203.5,0,977.6,694.1,28,36.96 713 | 305.3,203.5,0,203.5,0,965.4,631,90,54.6 714 | 192,288,0,192,0,929.8,716.1,7,21.48 715 | 157,236,0,192,0,935.4,781.2,3,9.69 716 | 153,102,0,192,0,888,943.1,7,8.37 717 | 141.3,212,0,203.5,0,971.8,748.5,90,39.66 718 | 116,173,0,192,0,909.8,891.9,7,10.09 719 | 141.3,212,0,203.5,0,971.8,748.5,3,4.83 720 | 122.6,183.9,0,203.5,0,958.2,800.1,7,10.35 721 | 166.8,250.2,0,203.5,0,975.6,692.6,90,43.57 722 | 203.5,305.3,0,203.5,0,963.4,630,90,51.86 723 | 310,0,0,192,0,1012,830,3,11.85 724 | 310,0,0,192,0,1012,830,7,17.24 725 | 310,0,0,192,0,1012,830,28,27.83 726 | 310,0,0,192,0,1012,830,90,35.76 727 | 310,0,0,192,0,1012,830,120,38.7 728 | 331,0,0,192,0,1025,821,3,14.31 729 | 331,0,0,192,0,1025,821,7,17.44 730 | 331,0,0,192,0,1025,821,28,31.74 731 | 331,0,0,192,0,1025,821,90,37.91 732 | 331,0,0,192,0,1025,821,120,39.38 733 | 349,0,0,192,0,1056,809,3,15.87 734 | 349,0,0,192,0,1056,809,7,9.01 735 | 349,0,0,192,0,1056,809,28,33.61 736 | 349,0,0,192,0,1056,809,90,40.66 737 | 349,0,0,192,0,1056,809,120,40.86 738 | 238,0,0,186,0,1119,789,7,12.05 739 | 238,0,0,186,0,1119,789,28,17.54 740 | 296,0,0,186,0,1090,769,7,18.91 741 | 296,0,0,186,0,1090,769,28,25.18 742 | 297,0,0,186,0,1040,734,7,30.96 743 | 480,0,0,192,0,936,721,28,43.89 744 | 480,0,0,192,0,936,721,90,54.28 745 | 397,0,0,186,0,1040,734,28,36.94 746 | 281,0,0,186,0,1104,774,7,14.5 747 | 281,0,0,185,0,1104,774,28,22.44 748 | 500,0,0,200,0,1125,613,1,12.64 749 | 500,0,0,200,0,1125,613,3,26.06 750 | 500,0,0,200,0,1125,613,7,33.21 751 | 500,0,0,200,0,1125,613,14,36.94 752 | 500,0,0,200,0,1125,613,28,44.09 753 | 540,0,0,173,0,1125,613,7,52.61 754 | 540,0,0,173,0,1125,613,14,59.76 755 | 540,0,0,173,0,1125,613,28,67.31 756 | 540,0,0,173,0,1125,613,90,69.66 757 | 540,0,0,173,0,1125,613,180,71.62 758 | 540,0,0,173,0,1125,613,270,74.17 759 | 350,0,0,203,0,974,775,7,18.13 760 | 350,0,0,203,0,974,775,14,22.53 761 | 350,0,0,203,0,974,775,28,27.34 762 | 350,0,0,203,0,974,775,56,29.98 763 | 350,0,0,203,0,974,775,90,31.35 764 | 350,0,0,203,0,974,775,180,32.72 765 | 385,0,0,186,0,966,763,1,6.27 766 | 385,0,0,186,0,966,763,3,14.7 767 | 385,0,0,186,0,966,763,7,23.22 768 | 385,0,0,186,0,966,763,14,27.92 769 | 385,0,0,186,0,966,763,28,31.35 770 | 331,0,0,192,0,978,825,180,39 771 | 331,0,0,192,0,978,825,360,41.24 772 | 349,0,0,192,0,1047,806,3,14.99 773 | 331,0,0,192,0,978,825,3,13.52 774 | 382,0,0,186,0,1047,739,7,24 775 | 382,0,0,186,0,1047,739,28,37.42 776 | 382,0,0,186,0,1111,784,7,11.47 777 | 281,0,0,186,0,1104,774,28,22.44 778 | 339,0,0,185,0,1069,754,7,21.16 779 | 339,0,0,185,0,1069,754,28,31.84 780 | 295,0,0,185,0,1069,769,7,14.8 781 | 295,0,0,185,0,1069,769,28,25.18 782 | 238,0,0,185,0,1118,789,28,17.54 783 | 296,0,0,192,0,1085,765,7,14.2 784 | 296,0,0,192,0,1085,765,28,21.65 785 | 296,0,0,192,0,1085,765,90,29.39 786 | 331,0,0,192,0,879,825,3,13.52 787 | 331,0,0,192,0,978,825,7,16.26 788 | 331,0,0,192,0,978,825,28,31.45 789 | 331,0,0,192,0,978,825,90,37.23 790 | 349,0,0,192,0,1047,806,7,18.13 791 | 349,0,0,192,0,1047,806,28,32.72 792 | 349,0,0,192,0,1047,806,90,39.49 793 | 349,0,0,192,0,1047,806,180,41.05 794 | 349,0,0,192,0,1047,806,360,42.13 795 | 302,0,0,203,0,974,817,14,18.13 796 | 302,0,0,203,0,974,817,180,26.74 797 | 525,0,0,189,0,1125,613,180,61.92 798 | 500,0,0,200,0,1125,613,90,47.22 799 | 500,0,0,200,0,1125,613,180,51.04 800 | 500,0,0,200,0,1125,613,270,55.16 801 | 540,0,0,173,0,1125,613,3,41.64 802 | 252,0,0,185,0,1111,784,7,13.71 803 | 252,0,0,185,0,1111,784,28,19.69 804 | 339,0,0,185,0,1060,754,28,31.65 805 | 393,0,0,192,0,940,758,3,19.11 806 | 393,0,0,192,0,940,758,28,39.58 807 | 393,0,0,192,0,940,758,90,48.79 808 | 382,0,0,185,0,1047,739,7,24 809 | 382,0,0,185,0,1047,739,28,37.42 810 | 252,0,0,186,0,1111,784,7,11.47 811 | 252,0,0,185,0,1111,784,28,19.69 812 | 310,0,0,192,0,970,850,7,14.99 813 | 310,0,0,192,0,970,850,28,27.92 814 | 310,0,0,192,0,970,850,90,34.68 815 | 310,0,0,192,0,970,850,180,37.33 816 | 310,0,0,192,0,970,850,360,38.11 817 | 525,0,0,189,0,1125,613,3,33.8 818 | 525,0,0,189,0,1125,613,7,42.42 819 | 525,0,0,189,0,1125,613,14,48.4 820 | 525,0,0,189,0,1125,613,28,55.94 821 | 525,0,0,189,0,1125,613,90,58.78 822 | 525,0,0,189,0,1125,613,270,67.11 823 | 322,0,0,203,0,974,800,14,20.77 824 | 322,0,0,203,0,974,800,28,25.18 825 | 322,0,0,203,0,974,800,180,29.59 826 | 302,0,0,203,0,974,817,28,21.75 827 | 397,0,0,185,0,1040,734,28,39.09 828 | 480,0,0,192,0,936,721,3,24.39 829 | 522,0,0,146,0,896,896,7,50.51 830 | 522,0,0,146,0,896,896,28,74.99 831 | 273,105,82,210,9,904,680,28,37.17 832 | 162,190,148,179,19,838,741,28,33.76 833 | 154,144,112,220,10,923,658,28,16.5 834 | 147,115,89,202,9,860,829,28,19.99 835 | 152,178,139,168,18,944,695,28,36.35 836 | 310,143,111,168,22,914,651,28,33.69 837 | 144,0,175,158,18,943,844,28,15.42 838 | 304,140,0,214,6,895,722,28,33.42 839 | 374,0,0,190,7,1013,730,28,39.05 840 | 159,149,116,175,15,953,720,28,27.68 841 | 153,239,0,200,6,1002,684,28,26.86 842 | 310,143,0,168,10,914,804,28,45.3 843 | 305,0,100,196,10,959,705,28,30.12 844 | 151,0,184,167,12,991,772,28,15.57 845 | 142,167,130,174,11,883,785,28,44.61 846 | 298,137,107,201,6,878,655,28,53.52 847 | 321,164,0,190,5,870,774,28,57.21 848 | 366,187,0,191,7,824,757,28,65.91 849 | 280,129,100,172,9,825,805,28,52.82 850 | 252,97,76,194,8,835,821,28,33.4 851 | 165,0,150,182,12,1023,729,28,18.03 852 | 156,243,0,180,11,1022,698,28,37.36 853 | 160,188,146,203,11,829,710,28,32.84 854 | 298,0,107,186,6,879,815,28,42.64 855 | 318,0,126,210,6,861,737,28,40.06 856 | 287,121,94,188,9,904,696,28,41.94 857 | 326,166,0,174,9,882,790,28,61.23 858 | 356,0,142,193,11,801,778,28,40.87 859 | 132,207,161,179,5,867,736,28,33.3 860 | 322,149,0,186,8,951,709,28,52.42 861 | 164,0,200,181,13,849,846,28,15.09 862 | 314,0,113,170,10,925,783,28,38.46 863 | 321,0,128,182,11,870,780,28,37.26 864 | 140,164,128,237,6,869,656,28,35.23 865 | 288,121,0,177,7,908,829,28,42.13 866 | 298,0,107,210,11,880,744,28,31.87 867 | 265,111,86,195,6,833,790,28,41.54 868 | 160,250,0,168,12,1049,688,28,39.45 869 | 166,260,0,183,13,859,827,28,37.91 870 | 276,116,90,180,9,870,768,28,44.28 871 | 322,0,116,196,10,818,813,28,31.18 872 | 149,139,109,193,6,892,780,28,23.69 873 | 159,187,0,176,11,990,789,28,32.76 874 | 261,100,78,201,9,864,761,28,32.4 875 | 237,92,71,247,6,853,695,28,28.63 876 | 313,0,113,178,8,1002,689,28,36.8 877 | 155,183,0,193,9,1047,697,28,18.28 878 | 146,230,0,202,3,827,872,28,33.06 879 | 296,0,107,221,11,819,778,28,31.42 880 | 133,210,0,196,3,949,795,28,31.03 881 | 313,145,0,178,8,867,824,28,44.39 882 | 152,0,112,184,8,992,816,28,12.18 883 | 153,145,113,178,8,1002,689,28,25.56 884 | 140,133,103,200,7,916,753,28,36.44 885 | 149,236,0,176,13,847,893,28,32.96 886 | 300,0,120,212,10,878,728,28,23.84 887 | 153,145,113,178,8,867,824,28,26.23 888 | 148,0,137,158,16,1002,830,28,17.95 889 | 326,0,138,199,11,801,792,28,40.68 890 | 153,145,0,178,8,1000,822,28,19.01 891 | 262,111,86,195,5,895,733,28,33.72 892 | 158,0,195,220,11,898,713,28,8.54 893 | 151,0,185,167,16,1074,678,28,13.46 894 | 273,0,90,199,11,931,762,28,32.24 895 | 149,118,92,183,7,953,780,28,23.52 896 | 143,169,143,191,8,967,643,28,29.72 897 | 260,101,78,171,10,936,763,28,49.77 898 | 313,161,0,178,10,917,759,28,52.44 899 | 284,120,0,168,7,970,794,28,40.93 900 | 336,0,0,182,3,986,817,28,44.86 901 | 145,0,134,181,11,979,812,28,13.2 902 | 150,237,0,174,12,1069,675,28,37.43 903 | 144,170,133,192,8,814,805,28,29.87 904 | 331,170,0,195,8,811,802,28,56.61 905 | 155,0,143,193,9,1047,697,28,12.46 906 | 155,183,0,193,9,877,868,28,23.79 907 | 135,0,166,180,10,961,805,28,13.29 908 | 266,112,87,178,10,910,745,28,39.42 909 | 314,145,113,179,8,869,690,28,46.23 910 | 313,145,0,127,8,1000,822,28,44.52 911 | 146,173,0,182,3,986,817,28,23.74 912 | 144,136,106,178,7,941,774,28,26.14 913 | 148,0,182,181,15,839,884,28,15.52 914 | 277,117,91,191,7,946,666,28,43.57 915 | 298,0,107,164,13,953,784,28,35.86 916 | 313,145,0,178,8,1002,689,28,41.05 917 | 155,184,143,194,9,880,699,28,28.99 918 | 289,134,0,195,6,924,760,28,46.24 919 | 148,175,0,171,2,1000,828,28,26.92 920 | 145,0,179,202,8,824,869,28,10.54 921 | 313,0,0,178,8,1000,822,28,25.1 922 | 136,162,126,172,10,923,764,28,29.07 923 | 155,0,143,193,9,877,868,28,9.74 924 | 255,99,77,189,6,919,749,28,33.8 925 | 162,207,172,216,10,822,638,28,39.84 926 | 136,196,98,199,6,847,783,28,26.97 927 | 164,163,128,197,8,961,641,28,27.23 928 | 162,214,164,202,10,820,680,28,30.65 929 | 157,214,152,200,9,819,704,28,33.05 930 | 149,153,194,192,8,935,623,28,24.58 931 | 135,105,193,196,6,965,643,28,21.91 932 | 159,209,161,201,7,848,669,28,30.88 933 | 144,15,195,176,6,1021,709,28,15.34 934 | 154,174,185,228,7,845,612,28,24.34 935 | 167,187,195,185,7,898,636,28,23.89 936 | 184,86,190,213,6,923,623,28,22.93 937 | 156,178,187,221,7,854,614,28,29.41 938 | 236.9,91.7,71.5,246.9,6,852.9,695.4,28,28.63 939 | 313.3,0,113,178.5,8,1001.9,688.7,28,36.8 940 | 154.8,183.4,0,193.3,9.1,1047.4,696.7,28,18.29 941 | 145.9,230.5,0,202.5,3.4,827,871.8,28,32.72 942 | 296,0,106.7,221.4,10.5,819.2,778.4,28,31.42 943 | 133.1,210.2,0,195.7,3.1,949.4,795.3,28,28.94 944 | 313.3,145,0,178.5,8,867.2,824,28,40.93 945 | 151.6,0,111.9,184.4,7.9,992,815.9,28,12.18 946 | 153.1,145,113,178.5,8,1001.9,688.7,28,25.56 947 | 139.9,132.6,103.3,200.3,7.4,916,753.4,28,36.44 948 | 149.5,236,0,175.8,12.6,846.8,892.7,28,32.96 949 | 299.8,0,119.8,211.5,9.9,878.2,727.6,28,23.84 950 | 153.1,145,113,178.5,8,867.2,824,28,26.23 951 | 148.1,0,136.6,158.1,16.1,1001.8,830.1,28,17.96 952 | 326.5,0,137.9,199,10.8,801.1,792.5,28,38.63 953 | 152.7,144.7,0,178.1,8,999.7,822.2,28,19.01 954 | 261.9,110.5,86.1,195.4,5,895.2,732.6,28,33.72 955 | 158.4,0,194.9,219.7,11,897.7,712.9,28,8.54 956 | 150.7,0,185.3,166.7,15.6,1074.5,678,28,13.46 957 | 272.6,0,89.6,198.7,10.6,931.3,762.2,28,32.25 958 | 149,117.6,91.7,182.9,7.1,953.4,780.3,28,23.52 959 | 143,169.4,142.7,190.7,8.4,967.4,643.5,28,29.73 960 | 259.9,100.6,78.4,170.6,10.4,935.7,762.9,28,49.77 961 | 312.9,160.5,0,177.6,9.6,916.6,759.5,28,52.45 962 | 284,119.7,0,168.3,7.2,970.4,794.2,28,40.93 963 | 336.5,0,0,181.9,3.4,985.8,816.8,28,44.87 964 | 144.8,0,133.6,180.8,11.1,979.5,811.5,28,13.2 965 | 150,236.8,0,173.8,11.9,1069.3,674.8,28,37.43 966 | 143.7,170.2,132.6,191.6,8.5,814.1,805.3,28,29.87 967 | 330.5,169.6,0,194.9,8.1,811,802.3,28,56.62 968 | 154.8,0,142.8,193.3,9.1,1047.4,696.7,28,12.46 969 | 154.8,183.4,0,193.3,9.1,877.2,867.7,28,23.79 970 | 134.7,0,165.7,180.2,10,961,804.9,28,13.29 971 | 266.2,112.3,87.5,177.9,10.4,909.7,744.5,28,39.42 972 | 314,145.3,113.2,178.9,8,869.1,690.2,28,46.23 973 | 312.7,144.7,0,127.3,8,999.7,822.2,28,44.52 974 | 145.7,172.6,0,181.9,3.4,985.8,816.8,28,23.74 975 | 143.8,136.3,106.2,178.1,7.5,941.5,774.3,28,26.15 976 | 148.1,0,182.1,181.4,15,838.9,884.3,28,15.53 977 | 277,116.8,91,190.6,7,946.5,665.6,28,43.58 978 | 298.1,0,107.5,163.6,12.8,953.2,784,28,35.87 979 | 313.3,145,0,178.5,8,1001.9,688.7,28,41.05 980 | 155.2,183.9,143.2,193.8,9.2,879.6,698.5,28,28.99 981 | 289,133.7,0,194.9,5.5,924.1,760.1,28,46.25 982 | 147.8,175.1,0,171.2,2.2,1000,828.5,28,26.92 983 | 145.4,0,178.9,201.7,7.8,824,868.7,28,10.54 984 | 312.7,0,0,178.1,8,999.7,822.2,28,25.1 985 | 136.4,161.6,125.8,171.6,10.4,922.6,764.4,28,29.07 986 | 154.8,0,142.8,193.3,9.1,877.2,867.7,28,9.74 987 | 255.3,98.8,77,188.6,6.5,919,749.3,28,33.8 988 | 272.8,105.1,81.8,209.7,9,904,679.7,28,37.17 989 | 162,190.1,148.1,178.8,18.8,838.1,741.4,28,33.76 990 | 153.6,144.2,112.3,220.1,10.1,923.2,657.9,28,16.5 991 | 146.5,114.6,89.3,201.9,8.8,860,829.5,28,19.99 992 | 151.8,178.1,138.7,167.5,18.3,944,694.6,28,36.35 993 | 309.9,142.8,111.2,167.8,22.1,913.9,651.2,28,38.22 994 | 143.6,0,174.9,158.4,17.9,942.7,844.5,28,15.42 995 | 303.6,139.9,0,213.5,6.2,895.5,722.5,28,33.42 996 | 374.3,0,0,190.2,6.7,1013.2,730.4,28,39.06 997 | 158.6,148.9,116,175.1,15,953.3,719.7,28,27.68 998 | 152.6,238.7,0,200,6.3,1001.8,683.9,28,26.86 999 | 310,142.8,0,167.9,10,914.3,804,28,45.3 1000 | 304.8,0,99.6,196,9.8,959.4,705.2,28,30.12 1001 | 150.9,0,183.9,166.6,11.6,991.2,772.2,28,15.57 1002 | 141.9,166.6,129.7,173.5,10.9,882.6,785.3,28,44.61 1003 | 297.8,137.2,106.9,201.3,6,878.4,655.3,28,53.52 1004 | 321.3,164.2,0,190.5,4.6,870,774,28,57.22 1005 | 366,187,0,191.3,6.6,824.3,756.9,28,65.91 1006 | 279.8,128.9,100.4,172.4,9.5,825.1,804.9,28,52.83 1007 | 252.1,97.1,75.6,193.8,8.3,835.5,821.4,28,33.4 1008 | 164.6,0,150.4,181.6,11.7,1023.3,728.9,28,18.03 1009 | 155.6,243.5,0,180.3,10.7,1022,697.7,28,37.36 1010 | 160.2,188,146.4,203.2,11.3,828.7,709.7,28,35.31 1011 | 298.1,0,107,186.4,6.1,879,815.2,28,42.64 1012 | 317.9,0,126.5,209.7,5.7,860.5,736.6,28,40.06 1013 | 287.3,120.5,93.9,187.6,9.2,904.4,695.9,28,43.8 1014 | 325.6,166.4,0,174,8.9,881.6,790,28,61.24 1015 | 355.9,0,141.6,193.3,11,801.4,778.4,28,40.87 1016 | 132,206.5,160.9,178.9,5.5,866.9,735.6,28,33.31 1017 | 322.5,148.6,0,185.8,8.5,951,709.5,28,52.43 1018 | 164.2,0,200.1,181.2,12.6,849.3,846,28,15.09 1019 | 313.8,0,112.6,169.9,10.1,925.3,782.9,28,38.46 1020 | 321.4,0,127.9,182.5,11.5,870.1,779.7,28,37.27 1021 | 139.7,163.9,127.7,236.7,5.8,868.6,655.6,28,35.23 1022 | 288.4,121,0,177.4,7,907.9,829.5,28,42.14 1023 | 298.2,0,107,209.7,11.1,879.6,744.2,28,31.88 1024 | 264.5,111,86.5,195.5,5.9,832.6,790.4,28,41.54 1025 | 159.8,250,0,168.4,12.2,1049.3,688.2,28,39.46 1026 | 166,259.7,0,183.2,12.7,858.8,826.8,28,37.92 1027 | 276.4,116,90.3,179.6,8.9,870.1,768.3,28,44.28 1028 | 322.2,0,115.6,196,10.4,817.9,813.4,28,31.18 1029 | 148.5,139.4,108.6,192.7,6.1,892.4,780,28,23.7 1030 | 159.1,186.7,0,175.6,11.3,989.6,788.9,28,32.77 1031 | 260.9,100.5,78.3,200.6,8.6,864.5,761.5,28,32.4 1032 | --------------------------------------------------------------------------------