├── GUI_Model.py ├── ML_Solar_model.py ├── PNN_Solar_model.py ├── README.md ├── Solar_categorical.csv ├── dimensional_reduction.py ├── fault_model.model └── icon.ico /GUI_Model.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | import numpy as np 4 | import pandas as pd 5 | from sklearn.preprocessing import LabelEncoder 6 | from keras.utils import to_categorical 7 | from sklearn.model_selection import train_test_split 8 | from sklearn.preprocessing import StandardScaler 9 | from keras.models import load_model 10 | 11 | dataset = pd.read_csv('Solar_categorical.csv') 12 | X = dataset.iloc[:3000, 0:7].values 13 | y = dataset.iloc[:3000, 7].values 14 | encoder= LabelEncoder() 15 | X[:,6] = encoder.fit_transform(X[:, 6]) 16 | y = encoder.fit_transform(y) 17 | y = to_categorical(y) 18 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0) 19 | sc = StandardScaler() 20 | X_train = sc.fit_transform(X_train) 21 | X_test = sc.transform(X_test) 22 | 23 | new_model = load_model('fault_model.model') 24 | 25 | 26 | ##### Making Graphical User Interface (GUI) #### 27 | from tkinter import * 28 | 29 | root = Tk() 30 | root.title("Fault Detection Model") 31 | root.geometry("320x510+0+0") 32 | root.wm_iconbitmap('icon.ico') 33 | 34 | def infoMsg(): 35 | messagebox.askokcancel(title="Help", message="This application was developed by Barun Basnet using tkinter.") 36 | 37 | my_menu = Menu(root) 38 | 39 | file_menu = Menu(my_menu, tearoff=0) 40 | file_menu.add_command(label="Exit", command=root.destroy) 41 | my_menu.add_cascade(label="File", menu=file_menu) 42 | 43 | info_menu = Menu(my_menu, tearoff=0) 44 | info_menu.add_command(label="Info", command=infoMsg) 45 | my_menu.add_cascade(label="About", menu=info_menu) 46 | 47 | root.config(menu=my_menu) 48 | 49 | 50 | heading = Label(root, text="1.8KW Grid-type PV System", font=("arial", 10,"bold"), fg="black").pack() 51 | 52 | label1 = Label(root, text="Sensor1 (Amps):", font=("arial", 10,"bold"), fg="green").place(x =10, y=40) 53 | name1 = DoubleVar() 54 | entry_box1 = Entry(root, textvariable=name1).place(x=160, y=40) 55 | 56 | label2 = Label(root, text="Sensor2 (Amps):", font=("arial", 10,"bold"), fg="green").place(x =10, y=80) 57 | name2 = DoubleVar() 58 | entry_box2 = Entry(root, textvariable=name2).place(x=160, y=80) 59 | 60 | label3 = Label(root, text="Sensor3 (Volts):", font=("arial", 10,"bold"), fg="green").place(x =10, y=120) 61 | name3 = DoubleVar() 62 | entry_box3 = Entry(root, textvariable=name3).place(x=160, y=120) 63 | 64 | label4 = Label(root, text="Sensor4 (Volts):", font=("arial", 10,"bold"), fg="green").place(x =10, y=160) 65 | name4 = DoubleVar() 66 | entry_box4 = Entry(root, textvariable=name4).place(x=160, y=160) 67 | 68 | label5 = Label(root, text="Irradiance (Klux):", font=("arial", 10,"bold"), fg="green").place(x =10, y=200) 69 | name5 = DoubleVar() 70 | entry_box5 = Entry(root, textvariable=name5).place(x=160, y=200) 71 | 72 | label6 = Label(root, text="Temperature (degC):", font=("arial", 10,"bold"), fg="green").place(x =10, y=240) 73 | name6 = DoubleVar() 74 | entry_box6 = Entry(root, textvariable=name6).place(x=160, y=240) 75 | 76 | label7 = Label(root, text="Sunny (yes:'0' no:'1'):", font=("arial", 10,"bold"), fg="green").place(x =10, y=280) 77 | name7 = IntVar() 78 | entry_box6 = Entry(root, textvariable=name7).place(x=160, y=280) 79 | 80 | def fault_diagnosis(): 81 | ResultBox.delete(0.0, 'end') 82 | new_mod_test = new_model.predict(sc.transform(np.array([[name1.get(), name2.get(), name3.get(), 83 | name4.get(), name5.get(), name6.get(), name7.get()]]))) 84 | new_mod_test_original = encoder.inverse_transform([np.argmax(new_mod_test)]) 85 | ResultBox.insert(INSERT, new_mod_test_original) 86 | 87 | work = Button(root, text="Diagnose", width=20, height=2, bg="lightblue", command=fault_diagnosis).place(x=60, y=340) 88 | 89 | ResultBox = Text(root, width=35, height=5) 90 | ResultBox.place(x=10, y=390) 91 | 92 | 93 | root.mainloop() 94 | -------------------------------------------------------------------------------- /ML_Solar_model.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | import pandas as pd 4 | import matplotlib.pyplot as plt 5 | import seaborn as sb 6 | 7 | dataset = pd.read_csv('Solar_categorical.csv') 8 | X = dataset.iloc[:3000, 0:7].values 9 | y = dataset.iloc[:3000, 7].values 10 | print(y) 11 | 12 | 13 | ###########################VISUALIZATION################################################################# 14 | ########################################################################### 15 | from sklearn.model_selection import train_test_split 16 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0) 17 | 18 | #The ratio of train and validation set 19 | allLabels = np.concatenate((y_train, y_test)) 20 | cat1 = np.repeat('training',len(y_train)) 21 | cat2 = np.repeat('validation',len(y_test)) 22 | cat = np.concatenate((cat1,cat2)) 23 | hist_df = pd.DataFrame(({'labels':allLabels, 'datatype':cat})) 24 | p = sb.countplot(data=hist_df,x='labels',hue='datatype',saturation=1,palette=['c', 'm']) 25 | leg = p.get_legend() 26 | leg.set_title("") 27 | labs = leg.texts 28 | labs[0].set_text("Training") 29 | labs[1].set_text("Validation") 30 | plt.xlabel('labels', fontsize=20) 31 | plt.ylabel('count', fontsize=20) 32 | ##################################################################################### 33 | 34 | 35 | from sklearn.preprocessing import LabelEncoder 36 | from keras.utils import to_categorical 37 | encoder= LabelEncoder() 38 | X[:,6] = encoder.fit_transform(X[:, 6]) 39 | #y = encoder.fit_transform(y) 40 | #y_original = encoder.inverse_transform(y_encoded) 41 | #y = to_categorical(y) 42 | 43 | from sklearn.model_selection import train_test_split 44 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0) 45 | 46 | from sklearn.preprocessing import LabelEncoder 47 | encoder = LabelEncoder() 48 | y_train = encoder.fit_transform(y_train) 49 | y_test = encoder.transform(y_test) 50 | #y_original = encoder.inverse_transform(y_encoded) 51 | ######################################################################################### 52 | from sklearn.svm import SVC 53 | svc_clf = SVC(kernel="linear", probability=True) 54 | svc_clf.fit(X_train, y_train) 55 | 56 | y_pred = svc_clf.predict(X_test) 57 | y_pred_label = encoder.inverse_transform(y_pred) 58 | print(y_pred_label) 59 | 60 | y_test_label = encoder.inverse_transform(y_test) 61 | print(y_test_label) 62 | 63 | from sklearn.metrics import confusion_matrix 64 | cm = confusion_matrix(y_test_label, y_pred_label) 65 | 66 | cm_fig = pd.DataFrame(cm, columns=np.unique(y_test_label), index=np.unique(y_test_label)) 67 | sb.set(font_scale=1.4) 68 | sb.heatmap(cm_fig, cmap="RdBu_r", annot=True, annot_kws={"size":20}) 69 | plt.xlabel('Predicted label') 70 | plt.ylabel('True label') 71 | plt.title('Confusion Matrix') 72 | 73 | #new_pred1 = svc_clf.predict(np.array([[2.1, 3.1, 1.1, 43, 99]])) 74 | #pred1_label = encoder.inverse_transform(new_pred1) 75 | #print(pred1_label) 76 | # 77 | #new_pred2 = svc_clf.predict(np.array([[4.1, 3.5, 4.6, 45, 100]])) 78 | #pred2_label = encoder.inverse_transform(new_pred2) 79 | #print(pred2_label) 80 | # 81 | #new_pred3 = svc_clf.predict(np.array([[0, 4, 0.4, 15, 64]])) 82 | #pred3_label = encoder.inverse_transform(new_pred3) 83 | #print(pred3_label) 84 | ################################################################################### 85 | ######################################Evaluation################################### 86 | from sklearn.metrics import f1_score 87 | 88 | def evaluate(labelsTrue, predictions): 89 | if len(predictions)>0: 90 | f1 = f1_score(labelsTrue,predictions, average="weighted") 91 | print("F1 score: ",f1) 92 | 93 | pred_svc = svc_clf.predict(X_test) 94 | evaluate(y_test, pred_svc) 95 | ############################################################################################### 96 | ############################################################################################### 97 | ################################Compare several classifiers#################################### 98 | from sklearn.neighbors import KNeighborsClassifier 99 | knn_clf = KNeighborsClassifier() 100 | knn_clf.fit(X_train, y_train) 101 | 102 | pred_knn = knn_clf.predict(X_test) 103 | evaluate(y_test, pred_knn) 104 | ############################################################# 105 | from sklearn.ensemble import RandomForestClassifier 106 | forest_clf = RandomForestClassifier(n_estimators=50,n_jobs=-1) 107 | forest_clf.fit(X_train, y_train) 108 | 109 | pred_forest = forest_clf.predict(X_test) 110 | evaluate(y_test, pred_forest) 111 | ######################################################### 112 | from sklearn.ensemble import ExtraTreesClassifier 113 | trees_clf = ExtraTreesClassifier(n_estimators=50, n_jobs=-1) 114 | trees_clf.fit(X_train, y_train) 115 | 116 | pred_trees = trees_clf.predict(X_test) 117 | evaluate(y_test, pred_trees) 118 | ########################################################## 119 | from sklearn.ensemble import AdaBoostClassifier 120 | ada_clf = AdaBoostClassifier() 121 | ada_clf.fit(X_train, y_train) 122 | 123 | pred_ada = ada_clf.predict(X_test) 124 | #evaluate(y_test, pred_ada) 125 | f1_score(y_test, pred_ada, average='weighted', labels=np.unique(pred_ada)) 126 | ######################################################### 127 | from sklearn.naive_bayes import GaussianNB 128 | bayes_clf = GaussianNB() 129 | bayes_clf.fit(X_train, y_train) 130 | 131 | pred_bayes = bayes_clf.predict(X_test) 132 | evaluate(y_test, pred_bayes) 133 | ###################################################################################################### 134 | ###########################################Correlations between predicted classes##################### 135 | predictions = pd.DataFrame( {'Rand_For': pred_forest,'KNear_Neigh': pred_knn,'Sup_Vec_Mac': pred_svc, 136 | 'ExtraTrees': pred_trees, 'AdaBoost': pred_ada,'NaiveBayes': pred_bayes}) 137 | 138 | sb.heatmap(predictions.corr(), linewidths=0.5, vmax=1.0, square=True, cmap='jet', linecolor='white', annot=True) 139 | ######################################################################################################## 140 | 141 | 142 | -------------------------------------------------------------------------------- /PNN_Solar_model.py: -------------------------------------------------------------------------------- 1 | 2 | ### 3 Faults selection ### 3 | ## Solar_data_Experiment 4 | 5 | ########### Part1 (DATA PRE-PROCESSING) ############### 6 | import numpy as np 7 | import pandas as pd 8 | import matplotlib.pyplot as plt 9 | import seaborn as sb 10 | 11 | dataset = pd.read_csv('Solar_categorical.csv') 12 | X = dataset.iloc[:3000, 0:7].values 13 | y = dataset.iloc[:3000, 7].values 14 | #print(y) 15 | 16 | """ 17 | ###### Input data Data Visualization ##### 18 | 19 | plt.xlabel('Sample size', fontsize = 10) 20 | plt.ylabel('Current (A)', fontsize = 10) 21 | plt.title('Summer features', fontsize = 12) 22 | plt.plot(X[:250-1, 0], label='Normal Sunny') # Summer normal sunny 23 | plt.plot(X[251:501-1, 0], label='Normal Cloudy') # Summer normal cloudy 24 | plt.plot(X[1002:1249-1, 1], label='Line-line Sunny') # Summer line-line cloudy 25 | plt.plot(X[1251:1501-1, 0], label='Line-line Cloudy') # Summer line-line cloudy 26 | plt.legend(fancybox=True, shadow=True) 27 | 28 | plt.xlabel('Sample size', fontsize = 10) 29 | plt.ylabel('Current (A)', fontsize = 10) 30 | plt.title('Winter features', fontsize = 12) 31 | plt.plot(X[1502:1751-1, 0], label='Normal Sunny') # Winter normal sunny 32 | plt.plot(X[1752:2001, 0], label='Normal Cloudy') # Winter normal cloudy 33 | plt.plot(X[2502:2750, 1], label='Line-line Sunny') # Winter line-line sunny 34 | plt.plot(X[2751:3001, 1], label='Line-line Cloudy') # Winter normal cloudy (temp 4 deg to -5,) 35 | plt.legend(fancybox=True, shadow=True) 36 | 37 | #Plotting in subplots 38 | fig, (ax1, ax2) = plt.subplots(1,2) 39 | fig.suptitle( "Summer and winter features") 40 | ax1.plot(X[:250-1, 0], label='Normal Sunny') 41 | ax1.plot(X[251:501-1, 0], label='Normal Cloudy') # Summer normal cloudy 42 | ax1.plot(X[1002:1249-1, 1], label='Line-line Sunny') # Summer line-line cloudy 43 | ax1.plot(X[1251:1501-1, 0], label='Line-line Cloudy') # Summer line-line cloudy 44 | ax1.legend() 45 | 46 | ax2.plot(X[1502:1751-1, 0], label='Normal Sunny') # Winter normal sunny 47 | ax2.plot(X[1752:2001, 0], label='Normal Cloudy') # Winter normal cloudy 48 | ax2.plot(X[2502:2750, 1], label='Line-line Sunny') # Winter line-line sunny 49 | ax2.plot(X[2751:3001, 1], label='Line-line Cloudy') # Winter normal cloudy (temp 4 deg to -5,) 50 | ax2.legend() 51 | """ 52 | 53 | ########## Label Encoding categorical data ########### 54 | from sklearn.preprocessing import LabelEncoder 55 | from keras.utils import to_categorical 56 | encoder= LabelEncoder() 57 | X[:,6] = encoder.fit_transform(X[:, 6]) 58 | y = encoder.fit_transform(y) 59 | #y_original = encoder.inverse_transform(y_encoded) 60 | y = to_categorical(y) 61 | 62 | ########################################################################## 63 | # Splitting the dataset into the Training set and Test set 64 | from sklearn.model_selection import train_test_split 65 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0) 66 | ##################################################################################### 67 | 68 | # Feature Scaling (To scale all variables to similar scale) 69 | from sklearn.preprocessing import StandardScaler 70 | sc = StandardScaler() 71 | X_train = sc.fit_transform(X_train) 72 | X_test = sc.transform(X_test) 73 | 74 | 75 | ############################################################################ 76 | ########### Part2 (DEEP NEURAL NETWORK)/Multiple Layer Perceptron(MLP)######################### 77 | from keras.models import Sequential ##module to initialize ANN 78 | from keras.layers import Dense #module required to build layers 79 | 80 | DNN_model = Sequential() 81 | DNN_model.add(Dense(input_dim = 7, units = 8, kernel_initializer = 'uniform', activation = 'relu')) 82 | DNN_model.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu')) 83 | DNN_model.add(Dense(units = 3, kernel_initializer = 'uniform', activation = 'softmax')) 84 | DNN_model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy']) 85 | print(DNN_model.summary()) 86 | 87 | history = DNN_model.fit(X_train, y_train, batch_size = 5, epochs = 200, 88 | validation_data=(X_test, y_test), shuffle=True) 89 | print(history.history.keys()) 90 | 91 | ######Visualizing model: train & test accuracy and loss ############ 92 | """ 93 | plt.plot(history.history['accuracy']) 94 | plt.plot(history.history['val_accuracy']) 95 | plt.title('model accuracy') 96 | plt.ylabel('accuracy') 97 | plt.xlabel('epoch') 98 | plt.legend(['train', 'test']) 99 | 100 | plt.plot(history.history['loss']) # summarize history for loss 101 | plt.plot(history.history['val_loss']) 102 | plt.title('model loss') 103 | plt.ylabel('loss') 104 | plt.xlabel('epoch') 105 | plt.legend(['train', 'test'], loc='upper right') 106 | """ 107 | ################### Part3 (Making predictions and evaluating the model)################# 108 | y_pred = DNN_model.predict(X_test) # Predicting the Test set results 109 | y_pred = (y_pred > 0.95) 110 | y_pred_label = encoder.inverse_transform(np.argmax(y_pred, axis=1)) 111 | print(y_pred_label) 112 | 113 | y_test_label = encoder.inverse_transform(np.argmax(y_test, axis=1)) 114 | print(y_test_label) 115 | 116 | #Making confusion matrix that checks accuracy of the model 117 | from sklearn.metrics import confusion_matrix 118 | cm = confusion_matrix(y_test_label, y_pred_label) 119 | 120 | #### Visualizing Confusion Matrix ######## 121 | cm_fig = pd.DataFrame(cm, columns=np.unique(y_test_label), index=np.unique(y_test_label)) 122 | sb.set(font_scale=1.3) 123 | sb.heatmap(cm_fig, cmap="RdBu_r", annot=True, annot_kws={"size":15}, fmt='.2f') 124 | plt.xlabel('Predicted label') 125 | plt.ylabel('True label') 126 | plt.title('Confusion Matrix') 127 | ##################################################################### 128 | ######### Predicting with new data ########## 129 | new_pred1 = DNN_model.predict(sc.transform(np.array([[5, 6.2, 92, 93, 85, 16, 1]]))) 130 | new_pred1_original = encoder.inverse_transform([np.argmax(new_pred1)]) 131 | print(new_pred1_original) 132 | 133 | new_pred2 = DNN_model.predict(sc.transform(np.array([[0, 3.5, 0, 95, 96, 12, 1]]))) 134 | new_pred2_original = encoder.inverse_transform([np.argmax(new_pred2)]) 135 | print(new_pred2_original) 136 | 137 | new_pred3 = DNN_model.predict(sc.transform(np.array([[3.7, 0.2, 85, 65, 78, 7, 0]]))) 138 | new_pred3_original = encoder.inverse_transform([np.argmax(new_pred3)]) 139 | print(new_pred3_original) 140 | ################################################################################# 141 | 142 | ###Saving the model 143 | DNN_model.save('fault_model.model') 144 | 145 | ### Opening the saved model 146 | from keras.models import load_model 147 | new_model = load_model('fault_model.model') 148 | new_model.summary() 149 | 150 | ###### Predictiong with new data 151 | new_mod_test = new_model.predict(sc.transform(np.array([[2.1, 3.1, 90, 83, 88, 15, 1]]))) 152 | new_mod_test_original = encoder.inverse_transform([np.argmax(new_mod_test)]) 153 | print(new_mod_test_original) 154 | ##################################################### 155 | 156 | #Evaluating, Improving and Tuning the ANN 157 | 158 | # Evaluating the ANN 159 | from keras.wrappers.scikit_learn import KerasClassifier 160 | from sklearn.model_selection import cross_val_score 161 | from keras.models import Sequential 162 | from keras.layers import Dense 163 | 164 | # to check bias-variance tradeoff 165 | 166 | def build_classifier(): 167 | model = Sequential() 168 | model.add(Dense(input_dim = 7, units = 8, kernel_initializer = 'uniform', activation = 'relu')) 169 | model.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu')) 170 | model.add(Dense(units = 3, kernel_initializer = 'uniform', activation = 'softmax')) 171 | model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy']) 172 | return model 173 | 174 | model = KerasClassifier(build_fn = build_classifier, batch_size = 5, epochs = 200) 175 | accuracies = cross_val_score(estimator = model, X = X_train, y = y_train, cv = 5) #CV = K-fold cross val. splits 176 | mean = accuracies.mean() 177 | variance = accuracies.std() 178 | print("Average accuracy:", mean, "Variance:", variance) 179 | 180 | # Improving the ANN 181 | # Dropout Regularization to reduce overfitting if needed 182 | from keras.layers import Dropout 183 | 184 | model = Sequential() 185 | model.add(Dense(input_dim = 7, units = 8, kernel_initializer = 'uniform', activation = 'relu')) 186 | model.add(Dropout(rate = 0.1)) 187 | 188 | model.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu')) 189 | model.add(Dropout(rate = 0.2)) 190 | 191 | model.add(Dense(units = 3, kernel_initializer = 'uniform', activation = 'softmax')) 192 | 193 | model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy']) 194 | model.fit(X_train, y_train, batch_size = 5, epochs = 200) 195 | 196 | y_pred_drop = model.predict(X_test) 197 | y_pred_drop_encoded = np.argmax(y_pred_drop, axis=1) 198 | y_pred_drop_original = encoder.inverse_transform(y_pred_drop_encoded) 199 | print(y_pred_drop_original) 200 | 201 | # Tuning the ANN 202 | from sklearn.model_selection import GridSearchCV 203 | from keras.wrappers.scikit_learn import KerasClassifier 204 | 205 | def build_network(optimizer): 206 | network = Sequential() 207 | network.add(Dense(input_dim = 5, units = 4, kernel_initializer = 'uniform', activation = 'relu')) 208 | network.add(Dense(units = 4, kernel_initializer = 'uniform', activation = 'relu')) 209 | network.add(Dense(units = 3, kernel_initializer = 'uniform', activation = 'softmax')) 210 | network.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy']) 211 | return network 212 | 213 | network = KerasClassifier(build_fn = build_network, class_weight='balanced') 214 | parameters = {'batch_size': [1, 2, 3], 'epochs': [100, 200, 300], 215 | 'optimizer': ['sgd', 'rmsprop', 'adam', 'adagrad']} 216 | grid_search = GridSearchCV(estimator = model, param_grid = parameters, scoring = 'accuracy', cv = 5) 217 | grid_result = grid_search.fit(X_train, np.argmax(y_train, axis=1)) 218 | best_parameters = grid_result.best_params_ 219 | best_accuracy = grid_search.best_score_ 220 | ################## END ###################### 221 | 222 | 223 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PV_fault_Python 2 | This repository contains a ML model trained to predict faults for PV systems. 3 | 4 | Python libraries required to run the code 5 | - keras, tensorflow 6 | 7 | GUI application developed using tkinter library 8 | -------------------------------------------------------------------------------- /Solar_categorical.csv: -------------------------------------------------------------------------------- 1 | S1(Amp),S2(Amp),S1(Volt),S2(Volt),Light(kiloLux),Temp(degC),Weather,State 2 | 6.4,6.8,108,109,107,34,Sunny,Normal 3 | 7.1,6.5,110,107,108,38,Sunny,Normal 4 | 6.2,6.5,106,107,107,36,Sunny,Normal 5 | 7.4,6.1,108,109,104,35,Sunny,Normal 6 | 6.5,6.9,109,109,109,38,Sunny,Normal 7 | 7.1,7.4,107,109,104,36,Sunny,Normal 8 | 7.1,7.4,109,107,108,34,Sunny,Normal 9 | 6.1,6.4,108,109,105,33,Sunny,Normal 10 | 7.2,6.6,105,107,103,32,Sunny,Normal 11 | 6,7,107,109,104,34,Sunny,Normal 12 | 6.3,6.3,107,109,102,38,Sunny,Normal 13 | 6.6,6.9,107,109,107,32,Sunny,Normal 14 | 6.1,6.9,108,108,108,39,Sunny,Normal 15 | 6.6,6.9,107,106,105,34,Sunny,Normal 16 | 7,6.1,106,106,103,33,Sunny,Normal 17 | 7,6.3,106,109,103,33,Sunny,Normal 18 | 7.6,7.5,105,108,104,34,Sunny,Normal 19 | 7.5,7.1,108,108,108,31,Sunny,Normal 20 | 6.6,6.5,106,109,104,38,Sunny,Normal 21 | 7.4,7.3,107,109,110,38,Sunny,Normal 22 | 6.9,6.7,106,109,103,32,Sunny,Normal 23 | 6.8,6.7,106,110,106,31,Sunny,Normal 24 | 6.8,7,105,107,109,39,Sunny,Normal 25 | 7.3,6.9,105,108,109,40,Sunny,Normal 26 | 6.1,6.3,108,108,108,33,Sunny,Normal 27 | 7.6,6.2,110,107,104,30,Sunny,Normal 28 | 6.9,6.2,107,108,109,39,Sunny,Normal 29 | 6.2,6.3,109,110,102,40,Sunny,Normal 30 | 6.2,6.3,108,106,104,32,Sunny,Normal 31 | 6.3,7.5,109,106,109,33,Sunny,Normal 32 | 6.7,7.5,108,107,102,38,Sunny,Normal 33 | 6.4,6.1,106,109,106,39,Sunny,Normal 34 | 6.4,6.7,107,106,106,39,Sunny,Normal 35 | 6.6,6.8,109,107,103,34,Sunny,Normal 36 | 6.9,6.7,110,107,102,36,Sunny,Normal 37 | 6.2,7.4,110,109,104,36,Sunny,Normal 38 | 6.3,6.4,108,106,105,38,Sunny,Normal 39 | 7.4,6.6,106,107,103,40,Sunny,Normal 40 | 6.3,6.3,110,106,104,40,Sunny,Normal 41 | 7.2,6.7,109,108,110,31,Sunny,Normal 42 | 7.3,7.2,107,107,105,34,Sunny,Normal 43 | 7.4,6.3,107,107,108,33,Sunny,Normal 44 | 6.9,6.4,109,107,108,39,Sunny,Normal 45 | 6.9,7.6,105,109,103,40,Sunny,Normal 46 | 6.5,6.8,109,106,108,35,Sunny,Normal 47 | 7.4,7,108,106,108,33,Sunny,Normal 48 | 6.5,6,106,110,107,34,Sunny,Normal 49 | 7.4,6.2,109,107,103,34,Sunny,Normal 50 | 6.9,6.2,109,109,106,38,Sunny,Normal 51 | 6.3,6.5,110,108,106,33,Sunny,Normal 52 | 6.9,6.8,108,110,105,39,Sunny,Normal 53 | 7.3,6.3,109,106,107,32,Sunny,Normal 54 | 7.2,6.6,108,107,104,34,Sunny,Normal 55 | 6.2,6.2,110,110,106,38,Sunny,Normal 56 | 7.1,7.2,107,108,103,39,Sunny,Normal 57 | 7.4,7.6,109,108,105,39,Sunny,Normal 58 | 7.5,6.3,106,106,104,32,Sunny,Normal 59 | 6.9,7.1,108,108,107,37,Sunny,Normal 60 | 7.2,7.3,109,107,107,32,Sunny,Normal 61 | 7,7,105,106,110,33,Sunny,Normal 62 | 7,6.4,106,109,105,40,Sunny,Normal 63 | 7.1,7.1,106,109,105,30,Sunny,Normal 64 | 6.2,6.2,110,109,107,30,Sunny,Normal 65 | 6.1,7.5,109,107,109,39,Sunny,Normal 66 | 7,6.6,107,108,105,38,Sunny,Normal 67 | 6,6.3,106,105,103,37,Sunny,Normal 68 | 6.2,6.8,106,106,109,31,Sunny,Normal 69 | 6.4,7.6,106,108,106,35,Sunny,Normal 70 | 6.1,6.8,109,109,109,32,Sunny,Normal 71 | 6.7,7.5,109,105,106,36,Sunny,Normal 72 | 7,7.5,106,106,109,35,Sunny,Normal 73 | 6.1,6.9,110,105,106,36,Sunny,Normal 74 | 6.9,6.7,107,109,109,32,Sunny,Normal 75 | 7.3,7.4,106,108,106,33,Sunny,Normal 76 | 6.6,7.4,106,105,102,37,Sunny,Normal 77 | 7.6,7.6,110,108,110,31,Sunny,Normal 78 | 6.3,7,110,106,109,36,Sunny,Normal 79 | 7,7.5,106,110,106,38,Sunny,Normal 80 | 7.2,6.3,108,108,106,32,Sunny,Normal 81 | 6.7,6.9,108,107,109,34,Sunny,Normal 82 | 6.6,7.6,105,107,104,33,Sunny,Normal 83 | 6.5,7.2,108,110,104,32,Sunny,Normal 84 | 7.4,6,110,106,107,36,Sunny,Normal 85 | 7.1,7.4,109,109,104,40,Sunny,Normal 86 | 7.4,7.2,105,107,104,33,Sunny,Normal 87 | 7,7.3,107,109,107,30,Sunny,Normal 88 | 7.2,6.8,107,107,108,35,Sunny,Normal 89 | 7.3,7.4,109,107,106,30,Sunny,Normal 90 | 6.8,6.6,106,109,106,37,Sunny,Normal 91 | 7.4,6.3,106,109,110,38,Sunny,Normal 92 | 6.1,6,110,108,107,36,Sunny,Normal 93 | 7.3,6.8,108,109,106,39,Sunny,Normal 94 | 6,7.4,108,109,107,32,Sunny,Normal 95 | 7.2,7.1,105,108,108,32,Sunny,Normal 96 | 6.9,6.8,110,107,105,38,Sunny,Normal 97 | 6.5,6.2,106,106,104,35,Sunny,Normal 98 | 6.8,7.1,105,108,108,36,Sunny,Normal 99 | 6.5,7.5,110,109,106,35,Sunny,Normal 100 | 6.2,7,108,108,110,38,Sunny,Normal 101 | 6,6.2,105,105,109,31,Sunny,Normal 102 | 6.5,6.9,110,107,105,36,Sunny,Normal 103 | 7.5,7.4,110,108,109,34,Sunny,Normal 104 | 6.8,6.1,108,107,103,32,Sunny,Normal 105 | 6.8,6.6,110,110,110,34,Sunny,Normal 106 | 6.7,7.5,109,106,105,38,Sunny,Normal 107 | 6.3,7.1,108,109,102,39,Sunny,Normal 108 | 7.3,6.2,105,110,104,37,Sunny,Normal 109 | 6.6,7.4,108,109,105,35,Sunny,Normal 110 | 6.7,6,108,109,105,34,Sunny,Normal 111 | 6.2,6.7,107,109,105,30,Sunny,Normal 112 | 7.5,7.1,108,109,106,35,Sunny,Normal 113 | 6.8,6.1,108,107,109,34,Sunny,Normal 114 | 6.3,7.1,105,108,105,39,Sunny,Normal 115 | 6.1,7.5,107,110,108,38,Sunny,Normal 116 | 7.6,6.7,107,106,108,35,Sunny,Normal 117 | 6.4,6.9,110,109,106,34,Sunny,Normal 118 | 6.2,7.5,107,108,109,36,Sunny,Normal 119 | 6.5,6.8,109,109,110,34,Sunny,Normal 120 | 6.7,7.4,106,109,103,30,Sunny,Normal 121 | 6.2,7,109,106,110,37,Sunny,Normal 122 | 6.2,6.7,108,105,106,33,Sunny,Normal 123 | 7.1,6.8,108,110,102,31,Sunny,Normal 124 | 6.5,6.2,108,109,103,35,Sunny,Normal 125 | 6.3,7.3,108,109,106,33,Sunny,Normal 126 | 7.3,7.5,110,106,104,40,Sunny,Normal 127 | 6.6,7,110,107,106,34,Sunny,Normal 128 | 6.3,6.7,107,108,105,33,Sunny,Normal 129 | 7.5,7.1,110,109,105,34,Sunny,Normal 130 | 7.6,6.8,110,106,102,36,Sunny,Normal 131 | 7.4,6.8,109,109,109,40,Sunny,Normal 132 | 6.7,7.2,107,108,104,35,Sunny,Normal 133 | 6.5,7.4,108,108,107,33,Sunny,Normal 134 | 6.7,7.2,109,110,106,30,Sunny,Normal 135 | 7.4,7.5,106,106,103,31,Sunny,Normal 136 | 6.6,6.8,107,106,106,38,Sunny,Normal 137 | 7.1,6,109,106,104,31,Sunny,Normal 138 | 7.2,6.3,107,108,110,36,Sunny,Normal 139 | 6.4,6.5,106,105,105,37,Sunny,Normal 140 | 6.8,6.7,109,106,105,30,Sunny,Normal 141 | 7.3,7.4,108,107,102,37,Sunny,Normal 142 | 6.2,7.3,107,106,109,33,Sunny,Normal 143 | 7.5,7,109,107,104,33,Sunny,Normal 144 | 6.3,7.1,107,106,105,38,Sunny,Normal 145 | 6.8,7,110,107,105,31,Sunny,Normal 146 | 6.2,6.4,105,108,109,38,Sunny,Normal 147 | 6.6,6.9,105,108,109,38,Sunny,Normal 148 | 6.7,6.7,107,108,103,38,Sunny,Normal 149 | 6.6,7.3,108,108,107,38,Sunny,Normal 150 | 6.6,7.2,105,106,110,33,Sunny,Normal 151 | 6.5,7.5,107,105,109,33,Sunny,Normal 152 | 6.2,7.4,107,108,107,32,Sunny,Normal 153 | 6.2,7.1,108,109,108,37,Sunny,Normal 154 | 7.1,6.8,110,106,106,36,Sunny,Normal 155 | 6.3,7,107,110,106,37,Sunny,Normal 156 | 7.6,6.5,107,106,108,34,Sunny,Normal 157 | 6.7,7.4,109,109,102,33,Sunny,Normal 158 | 7,7.2,110,108,108,38,Sunny,Normal 159 | 6.4,6.7,105,107,103,40,Sunny,Normal 160 | 6.6,7.5,110,106,105,32,Sunny,Normal 161 | 6.8,6.5,109,109,108,33,Sunny,Normal 162 | 6.4,7.5,108,106,105,39,Sunny,Normal 163 | 7,6.3,109,109,104,40,Sunny,Normal 164 | 7,6.3,107,110,104,40,Sunny,Normal 165 | 7.3,7.4,107,108,108,30,Sunny,Normal 166 | 6.2,7,108,107,103,36,Sunny,Normal 167 | 6.2,6.2,108,108,107,36,Sunny,Normal 168 | 6.8,6.9,107,110,109,40,Sunny,Normal 169 | 6.2,6,107,106,104,35,Sunny,Normal 170 | 7,6.2,106,107,105,35,Sunny,Normal 171 | 6.1,7.2,106,109,106,33,Sunny,Normal 172 | 6.8,6.2,109,109,107,38,Sunny,Normal 173 | 6.3,6.7,108,108,104,38,Sunny,Normal 174 | 7.2,6.9,109,110,107,30,Sunny,Normal 175 | 7.4,6.4,105,105,104,40,Sunny,Normal 176 | 6.2,6.6,109,105,105,39,Sunny,Normal 177 | 6.5,7.3,107,105,107,34,Sunny,Normal 178 | 7.1,7.4,108,106,108,36,Sunny,Normal 179 | 7.2,7.4,107,108,103,35,Sunny,Normal 180 | 7,6.2,105,110,102,32,Sunny,Normal 181 | 7.2,6.4,109,108,104,33,Sunny,Normal 182 | 6.4,7.2,105,108,107,36,Sunny,Normal 183 | 7,6.4,106,107,108,32,Sunny,Normal 184 | 6.8,6.6,109,110,110,33,Sunny,Normal 185 | 6,6.1,106,107,106,36,Sunny,Normal 186 | 6.2,6.6,110,108,104,32,Sunny,Normal 187 | 7.2,6.9,108,107,107,35,Sunny,Normal 188 | 6.2,6.9,108,109,103,34,Sunny,Normal 189 | 6.4,7.5,106,110,108,38,Sunny,Normal 190 | 6.5,6.5,110,109,103,36,Sunny,Normal 191 | 6,6.9,105,106,105,33,Sunny,Normal 192 | 7.4,6.5,108,107,105,33,Sunny,Normal 193 | 6.2,6.4,110,106,107,38,Sunny,Normal 194 | 6.5,7.5,106,109,108,34,Sunny,Normal 195 | 7.2,6.1,109,107,103,34,Sunny,Normal 196 | 6.5,7,107,108,109,36,Sunny,Normal 197 | 6.9,7.2,107,107,107,40,Sunny,Normal 198 | 6.1,7.5,107,106,103,40,Sunny,Normal 199 | 6.7,6.2,110,106,109,40,Sunny,Normal 200 | 6.9,7.5,110,107,107,38,Sunny,Normal 201 | 6.9,6.1,110,108,103,31,Sunny,Normal 202 | 6.7,6.5,109,106,105,34,Sunny,Normal 203 | 7.2,6.2,108,108,104,34,Sunny,Normal 204 | 7.5,6.6,107,110,104,34,Sunny,Normal 205 | 7.1,6.6,109,107,108,34,Sunny,Normal 206 | 6.7,6.8,105,106,104,33,Sunny,Normal 207 | 6.9,6.7,108,108,109,31,Sunny,Normal 208 | 6.2,6.8,110,106,103,31,Sunny,Normal 209 | 7.1,6.2,106,105,104,36,Sunny,Normal 210 | 7.4,6.5,110,110,104,33,Sunny,Normal 211 | 6.9,6,106,109,104,33,Sunny,Normal 212 | 6.3,7,107,108,102,31,Sunny,Normal 213 | 6.4,6.2,109,109,106,33,Sunny,Normal 214 | 7,7,106,108,109,38,Sunny,Normal 215 | 7.2,7.3,108,106,105,34,Sunny,Normal 216 | 7.3,6.3,107,110,103,36,Sunny,Normal 217 | 7.3,7.5,108,109,109,33,Sunny,Normal 218 | 6.5,6.6,105,107,107,32,Sunny,Normal 219 | 6.4,6.4,108,108,104,36,Sunny,Normal 220 | 6.4,7.4,110,108,106,32,Sunny,Normal 221 | 6.3,6.6,108,107,106,33,Sunny,Normal 222 | 6.6,6.4,109,106,106,31,Sunny,Normal 223 | 7.3,7.2,108,109,108,39,Sunny,Normal 224 | 6.1,6.3,108,107,103,32,Sunny,Normal 225 | 6.5,6.1,109,107,109,36,Sunny,Normal 226 | 6.5,6,110,108,106,39,Sunny,Normal 227 | 7.1,6.6,110,109,107,35,Sunny,Normal 228 | 7.4,6.7,108,107,103,38,Sunny,Normal 229 | 6.4,6.1,107,106,103,33,Sunny,Normal 230 | 7.5,6.7,110,107,103,39,Sunny,Normal 231 | 6.1,6.5,109,106,108,39,Sunny,Normal 232 | 6,7.2,109,109,103,39,Sunny,Normal 233 | 7.3,6.9,106,107,108,34,Sunny,Normal 234 | 7.3,6.3,105,109,103,34,Sunny,Normal 235 | 6.4,6,107,109,105,30,Sunny,Normal 236 | 6.1,6.8,109,107,103,30,Sunny,Normal 237 | 6.7,6.7,105,109,108,31,Sunny,Normal 238 | 6.5,6.2,110,105,109,39,Sunny,Normal 239 | 7.4,6.1,109,106,105,34,Sunny,Normal 240 | 6.8,7.2,106,107,103,38,Sunny,Normal 241 | 6.3,6.6,106,109,103,40,Sunny,Normal 242 | 6.9,7.4,107,106,107,37,Sunny,Normal 243 | 6.9,6.7,109,105,102,40,Sunny,Normal 244 | 7,6.8,106,108,102,35,Sunny,Normal 245 | 6.4,7.6,108,108,110,38,Sunny,Normal 246 | 7.4,6,107,109,103,32,Sunny,Normal 247 | 7.6,6.2,106,108,107,31,Sunny,Normal 248 | 6.4,6.7,108,106,108,36,Sunny,Normal 249 | 6.4,6.3,107,109,103,30,Sunny,Normal 250 | 6.2,7.2,109,107,106,37,Sunny,Normal 251 | 5.2,5.6,106,103,91,29,Sunny,Normal 252 | 5.8,5.9,102,100,92,28,Cloudy,Normal 253 | 5.9,5.2,106,107,94,23,Cloudy,Normal 254 | 5.3,5.3,109,107,98,26,Cloudy,Normal 255 | 5.6,5.6,108,102,93,28,Cloudy,Normal 256 | 5.1,5.1,101,103,98,28,Cloudy,Normal 257 | 5.4,5.6,102,109,96,23,Cloudy,Normal 258 | 5.6,5.3,110,104,92,22,Cloudy,Normal 259 | 5.4,5.1,103,105,94,22,Cloudy,Normal 260 | 5.6,5.7,104,103,98,24,Cloudy,Normal 261 | 5.7,5.4,102,105,95,30,Cloudy,Normal 262 | 5.6,5.4,104,105,94,22,Cloudy,Normal 263 | 5.3,5.6,101,110,92,22,Cloudy,Normal 264 | 5.8,5.7,103,108,99,22,Cloudy,Normal 265 | 5.6,5.7,101,100,94,30,Cloudy,Normal 266 | 5.7,5.7,102,103,96,22,Cloudy,Normal 267 | 5.8,5.2,104,105,95,27,Cloudy,Normal 268 | 5.7,5.4,107,105,94,21,Cloudy,Normal 269 | 5.8,5.5,108,100,93,28,Cloudy,Normal 270 | 5.8,5.5,110,105,95,25,Cloudy,Normal 271 | 5.3,5.6,108,107,92,28,Cloudy,Normal 272 | 5.2,5.6,106,101,94,29,Cloudy,Normal 273 | 5.3,5.2,110,108,96,21,Cloudy,Normal 274 | 5.7,5.7,110,109,92,26,Cloudy,Normal 275 | 5.2,5.7,106,102,95,25,Cloudy,Normal 276 | 5.7,5.9,103,106,94,27,Cloudy,Normal 277 | 5.2,5.9,106,104,94,22,Cloudy,Normal 278 | 5.6,5.4,102,106,96,28,Cloudy,Normal 279 | 5.2,5.7,107,104,98,27,Cloudy,Normal 280 | 5.4,5.8,101,109,96,22,Cloudy,Normal 281 | 5.4,5.7,102,104,93,29,Cloudy,Normal 282 | 5.5,5.6,104,105,92,28,Cloudy,Normal 283 | 5.2,5.4,104,107,97,27,Cloudy,Normal 284 | 5.9,5.5,109,100,93,28,Cloudy,Normal 285 | 5.8,5.6,100,101,95,23,Cloudy,Normal 286 | 5.2,5.6,102,109,91,23,Cloudy,Normal 287 | 5.8,5.5,101,103,93,27,Cloudy,Normal 288 | 5.3,5.7,108,108,98,27,Cloudy,Normal 289 | 5.3,5.3,102,107,98,23,Cloudy,Normal 290 | 5.8,5.1,107,109,93,24,Cloudy,Normal 291 | 5.1,5.4,107,107,94,27,Cloudy,Normal 292 | 5.4,5.9,109,106,91,29,Cloudy,Normal 293 | 5.3,5.6,101,106,92,28,Cloudy,Normal 294 | 5.3,5.6,101,108,97,23,Cloudy,Normal 295 | 5.2,5.3,102,104,98,24,Cloudy,Normal 296 | 5.8,5.6,108,109,93,28,Cloudy,Normal 297 | 5.4,5.3,107,102,94,29,Cloudy,Normal 298 | 5.9,5.5,107,101,97,30,Cloudy,Normal 299 | 5.5,5.4,110,103,91,22,Cloudy,Normal 300 | 5.5,5.4,109,104,91,21,Cloudy,Normal 301 | 5.8,5.8,101,100,95,29,Cloudy,Normal 302 | 5.6,5.4,104,101,96,28,Cloudy,Normal 303 | 5.3,5.5,107,104,94,27,Cloudy,Normal 304 | 5.5,5.1,107,108,93,20,Cloudy,Normal 305 | 5.1,5.3,106,102,91,22,Cloudy,Normal 306 | 5.6,5.7,104,109,98,20,Cloudy,Normal 307 | 5.7,5.2,106,101,92,29,Cloudy,Normal 308 | 5.5,5.3,105,104,99,29,Cloudy,Normal 309 | 5.8,5.6,108,106,92,29,Cloudy,Normal 310 | 5.3,5.9,105,106,91,23,Cloudy,Normal 311 | 5.6,5.5,106,103,97,26,Cloudy,Normal 312 | 5.7,5.5,108,106,93,25,Cloudy,Normal 313 | 5.4,5.4,101,103,92,28,Cloudy,Normal 314 | 5.2,5.1,107,102,93,25,Cloudy,Normal 315 | 5.2,5.7,108,103,99,21,Cloudy,Normal 316 | 5.4,5.2,102,103,93,24,Cloudy,Normal 317 | 5.8,5.8,103,103,96,21,Cloudy,Normal 318 | 5.8,5.9,105,107,97,28,Cloudy,Normal 319 | 5.3,5.6,104,103,94,23,Cloudy,Normal 320 | 5.4,5.4,107,110,92,29,Cloudy,Normal 321 | 5.4,5.1,104,104,95,23,Cloudy,Normal 322 | 5.7,5.6,101,106,93,21,Cloudy,Normal 323 | 5.8,5.3,100,102,99,22,Cloudy,Normal 324 | 5.2,5.6,106,105,94,28,Cloudy,Normal 325 | 5.6,5.8,103,107,98,27,Cloudy,Normal 326 | 5.1,5.3,108,105,93,29,Cloudy,Normal 327 | 5.8,5.6,103,110,95,26,Cloudy,Normal 328 | 5.4,5.9,108,105,95,21,Cloudy,Normal 329 | 5.8,5.1,109,104,97,26,Cloudy,Normal 330 | 5.9,5.8,109,104,92,24,Cloudy,Normal 331 | 5.4,5.6,105,102,94,21,Cloudy,Normal 332 | 5.4,5.1,100,108,99,22,Cloudy,Normal 333 | 5.4,5.5,105,101,96,30,Cloudy,Normal 334 | 5.1,5.5,109,108,97,29,Cloudy,Normal 335 | 5.4,5.2,105,107,91,25,Cloudy,Normal 336 | 5.7,5.6,100,102,94,28,Cloudy,Normal 337 | 5.7,5.5,103,102,91,22,Cloudy,Normal 338 | 5.4,5.6,105,104,92,21,Cloudy,Normal 339 | 5.2,5.3,100,104,97,22,Cloudy,Normal 340 | 5.5,5.3,109,108,91,24,Cloudy,Normal 341 | 5.2,5.8,106,108,92,22,Cloudy,Normal 342 | 5.9,5.4,107,109,99,28,Cloudy,Normal 343 | 5.9,5.5,110,102,95,22,Cloudy,Normal 344 | 5.6,5.9,105,110,94,26,Cloudy,Normal 345 | 5.7,5.5,109,110,95,29,Cloudy,Normal 346 | 5.5,5.5,103,105,99,29,Cloudy,Normal 347 | 5.7,5.7,105,104,98,22,Cloudy,Normal 348 | 5.6,5.4,107,106,98,22,Cloudy,Normal 349 | 5.6,5.8,107,107,99,26,Cloudy,Normal 350 | 5.3,5.2,100,109,97,29,Cloudy,Normal 351 | 5.6,5.7,107,109,92,24,Cloudy,Normal 352 | 5.6,5.1,101,101,98,27,Cloudy,Normal 353 | 5.9,5.2,105,109,94,28,Cloudy,Normal 354 | 5.7,5.5,102,105,94,26,Cloudy,Normal 355 | 5.5,5.1,100,106,94,25,Cloudy,Normal 356 | 5.3,5.1,108,109,95,28,Cloudy,Normal 357 | 5.2,5.7,105,107,92,24,Cloudy,Normal 358 | 5.5,5.3,102,106,93,24,Cloudy,Normal 359 | 5.2,5.2,104,108,95,29,Cloudy,Normal 360 | 5.6,5.4,101,109,92,22,Cloudy,Normal 361 | 5.3,5.5,109,101,97,28,Cloudy,Normal 362 | 5.5,5.5,109,106,97,21,Cloudy,Normal 363 | 5.8,5.2,110,105,96,29,Cloudy,Normal 364 | 5.8,5.9,107,109,98,29,Cloudy,Normal 365 | 5.4,5.5,103,100,96,26,Cloudy,Normal 366 | 5.8,5.2,109,108,96,21,Cloudy,Normal 367 | 5.2,5.3,109,106,98,24,Cloudy,Normal 368 | 5.2,5.4,108,100,93,23,Cloudy,Normal 369 | 5.1,5.8,105,101,93,28,Cloudy,Normal 370 | 5.4,5.8,100,106,92,21,Cloudy,Normal 371 | 5.8,5.3,110,101,94,27,Cloudy,Normal 372 | 5.7,5.7,102,106,95,23,Cloudy,Normal 373 | 5.6,5.8,107,110,93,23,Cloudy,Normal 374 | 5.1,5.3,104,109,93,25,Cloudy,Normal 375 | 5.5,5.4,108,102,96,28,Cloudy,Normal 376 | 5.5,5.7,101,104,95,28,Cloudy,Normal 377 | 5.9,5.5,102,103,95,24,Cloudy,Normal 378 | 5.6,5.6,106,102,96,26,Cloudy,Normal 379 | 5.7,5.3,107,110,94,21,Cloudy,Normal 380 | 5.2,5.9,108,102,93,24,Cloudy,Normal 381 | 5.6,5.4,103,107,91,24,Cloudy,Normal 382 | 5.4,5.3,104,100,98,29,Cloudy,Normal 383 | 5.4,5.3,110,108,99,30,Cloudy,Normal 384 | 5.2,5.6,103,101,97,30,Cloudy,Normal 385 | 5.3,5.8,101,101,91,28,Cloudy,Normal 386 | 5.3,5.6,106,107,92,21,Cloudy,Normal 387 | 5.7,5.5,106,102,99,24,Cloudy,Normal 388 | 5.4,5.6,103,102,91,27,Cloudy,Normal 389 | 5.3,5.2,106,102,94,29,Cloudy,Normal 390 | 5.6,5.1,105,100,99,25,Cloudy,Normal 391 | 5.6,5.7,106,104,91,21,Cloudy,Normal 392 | 5.1,5.5,107,107,93,26,Cloudy,Normal 393 | 5.5,5.7,108,105,94,29,Cloudy,Normal 394 | 5.6,5.3,101,107,97,29,Cloudy,Normal 395 | 5.1,5.1,107,107,98,24,Cloudy,Normal 396 | 5.9,5.6,101,106,97,28,Cloudy,Normal 397 | 5.3,5.3,106,103,95,25,Cloudy,Normal 398 | 5.4,5.8,109,109,94,24,Cloudy,Normal 399 | 5.2,5.5,101,103,95,30,Cloudy,Normal 400 | 5.3,5.1,104,106,92,27,Cloudy,Normal 401 | 5.5,5.5,104,107,92,30,Cloudy,Normal 402 | 5.7,5.6,105,103,96,23,Cloudy,Normal 403 | 5.8,5.4,108,109,92,27,Cloudy,Normal 404 | 5.8,5.5,105,102,93,21,Cloudy,Normal 405 | 5.5,5.5,101,105,97,30,Cloudy,Normal 406 | 5.5,5.3,105,106,97,22,Cloudy,Normal 407 | 5.4,5.9,104,109,94,28,Cloudy,Normal 408 | 5.4,5.5,103,106,96,30,Cloudy,Normal 409 | 5.8,5.2,105,108,96,28,Cloudy,Normal 410 | 5.9,5.6,103,102,97,27,Cloudy,Normal 411 | 5.2,5.2,102,105,93,29,Cloudy,Normal 412 | 5.4,5.6,109,109,99,29,Cloudy,Normal 413 | 5.4,5.4,110,104,98,29,Cloudy,Normal 414 | 5.8,5.7,102,100,97,23,Cloudy,Normal 415 | 5.2,5.1,106,102,99,30,Cloudy,Normal 416 | 5.2,5.3,107,108,92,30,Cloudy,Normal 417 | 5.4,5.5,101,108,99,24,Cloudy,Normal 418 | 5.8,5.5,109,109,93,22,Cloudy,Normal 419 | 5.2,5.5,107,103,99,27,Cloudy,Normal 420 | 5.5,5.7,109,103,93,26,Cloudy,Normal 421 | 5.4,5.4,101,102,95,21,Cloudy,Normal 422 | 5.4,5.2,109,105,92,23,Cloudy,Normal 423 | 5.3,5.7,110,106,96,25,Cloudy,Normal 424 | 5.9,5.4,107,105,98,28,Cloudy,Normal 425 | 5.8,5.1,107,101,96,26,Cloudy,Normal 426 | 5.7,5.5,110,104,96,28,Cloudy,Normal 427 | 5.3,5.4,104,104,94,21,Cloudy,Normal 428 | 5.5,5.7,108,104,94,24,Cloudy,Normal 429 | 5.4,5.1,108,101,92,30,Cloudy,Normal 430 | 5.8,5.1,107,107,94,23,Cloudy,Normal 431 | 5.6,5.8,108,109,93,27,Cloudy,Normal 432 | 5.5,5.3,103,101,98,27,Cloudy,Normal 433 | 5.8,5.1,104,104,97,28,Cloudy,Normal 434 | 5.9,5.1,109,110,91,30,Cloudy,Normal 435 | 5.8,5.4,103,106,96,21,Cloudy,Normal 436 | 5.8,5.1,100,110,94,21,Cloudy,Normal 437 | 5.5,5.6,100,100,98,29,Cloudy,Normal 438 | 5.2,5.4,108,109,94,27,Cloudy,Normal 439 | 5.7,5.2,103,105,94,25,Cloudy,Normal 440 | 5.4,5.3,108,103,93,28,Cloudy,Normal 441 | 5.3,5.5,104,107,97,20,Cloudy,Normal 442 | 5.5,5.1,104,102,94,28,Cloudy,Normal 443 | 5.8,5.8,102,101,97,25,Cloudy,Normal 444 | 5.6,5.6,104,107,97,22,Cloudy,Normal 445 | 5.5,5.4,102,101,96,30,Cloudy,Normal 446 | 5.7,5.3,102,107,96,25,Cloudy,Normal 447 | 5.4,5.2,105,103,97,27,Cloudy,Normal 448 | 5.7,5.1,110,102,96,29,Cloudy,Normal 449 | 5.8,5.8,100,100,93,30,Cloudy,Normal 450 | 5.3,5.4,109,100,98,27,Cloudy,Normal 451 | 5.3,5.2,107,108,93,27,Cloudy,Normal 452 | 5.8,5.3,109,109,98,29,Cloudy,Normal 453 | 5.8,5.2,101,103,98,26,Cloudy,Normal 454 | 5.2,5.2,109,103,93,24,Cloudy,Normal 455 | 5.9,5.5,109,109,97,24,Cloudy,Normal 456 | 5.6,5.3,104,100,95,29,Cloudy,Normal 457 | 5.4,5.2,100,108,93,30,Cloudy,Normal 458 | 5.7,5.5,100,108,92,25,Cloudy,Normal 459 | 5.2,5.3,108,100,98,26,Cloudy,Normal 460 | 5.4,5.1,101,103,92,27,Cloudy,Normal 461 | 5.1,5.4,102,109,93,27,Cloudy,Normal 462 | 5.5,5.1,107,107,97,29,Cloudy,Normal 463 | 5.8,5.7,103,103,97,24,Cloudy,Normal 464 | 5.1,5.5,104,108,97,24,Cloudy,Normal 465 | 5.8,5.3,109,107,95,28,Cloudy,Normal 466 | 5.8,5.4,106,107,94,30,Cloudy,Normal 467 | 5.6,5.8,100,103,97,21,Cloudy,Normal 468 | 5.4,5.2,103,106,97,26,Cloudy,Normal 469 | 5.4,5.2,106,108,95,25,Cloudy,Normal 470 | 5.7,5.2,107,105,95,23,Cloudy,Normal 471 | 5.3,5.3,105,109,92,24,Cloudy,Normal 472 | 5.4,5.7,107,103,91,28,Cloudy,Normal 473 | 5.2,5.6,109,102,98,29,Cloudy,Normal 474 | 5.8,5.6,106,101,99,29,Cloudy,Normal 475 | 5.6,5.3,107,104,95,26,Cloudy,Normal 476 | 5.7,5.8,102,109,93,27,Cloudy,Normal 477 | 5.6,5.3,105,101,93,29,Cloudy,Normal 478 | 5.3,5.7,108,102,97,30,Cloudy,Normal 479 | 5.9,5.8,104,105,92,26,Cloudy,Normal 480 | 5.2,5.8,105,104,98,23,Cloudy,Normal 481 | 5.6,5.6,105,106,96,23,Cloudy,Normal 482 | 5.3,5.6,102,106,93,23,Cloudy,Normal 483 | 5.2,5.7,104,102,93,29,Cloudy,Normal 484 | 5.4,5.4,107,106,94,28,Cloudy,Normal 485 | 5.5,5.2,102,106,92,30,Cloudy,Normal 486 | 5.6,5.5,105,103,96,22,Cloudy,Normal 487 | 5.4,5.5,109,104,96,29,Cloudy,Normal 488 | 5.4,5.3,104,103,93,24,Cloudy,Normal 489 | 5.6,5.8,108,108,97,27,Cloudy,Normal 490 | 5.8,5.2,109,103,98,21,Cloudy,Normal 491 | 5.5,5.2,105,105,97,26,Cloudy,Normal 492 | 5.9,5.3,109,100,93,26,Cloudy,Normal 493 | 5.5,5.2,109,103,96,20,Cloudy,Normal 494 | 5.3,5.5,101,109,93,21,Cloudy,Normal 495 | 5.3,5.8,102,105,96,25,Cloudy,Normal 496 | 5.3,5.2,107,101,91,28,Cloudy,Normal 497 | 5.4,5.3,108,108,93,22,Cloudy,Normal 498 | 5.4,5.1,106,101,97,26,Cloudy,Normal 499 | 5.2,5.7,102,110,92,29,Cloudy,Normal 500 | 5.6,5.3,105,104,93,21,Cloudy,Normal 501 | 5.6,5.5,106,103,94,22,Cloudy,Normal 502 | 0,5.3,0,108,97,28,Sunny,Open 503 | 0,5.2,0,102,97,20,Sunny,Open 504 | 0,5.2,0,105,96,25,Sunny,Open 505 | 0,5.8,0,103,98,22,Sunny,Open 506 | 0,5.4,0,106,92,22,Sunny,Open 507 | 0,5.5,0,109,98,26,Sunny,Open 508 | 0,5.7,0,105,99,20,Sunny,Open 509 | 0,5.2,0,110,93,27,Sunny,Open 510 | 0,5.5,0,100,93,27,Sunny,Open 511 | 0,5.4,0,107,96,26,Sunny,Open 512 | 0,5.3,0,101,98,24,Sunny,Open 513 | 0,5.3,0,110,96,20,Sunny,Open 514 | 0,5.1,0,105,92,24,Sunny,Open 515 | 0,5.5,0,101,96,23,Sunny,Open 516 | 0,5.1,0,105,94,24,Sunny,Open 517 | 0,5.6,0,106,95,23,Sunny,Open 518 | 0,5.2,0,104,98,28,Sunny,Open 519 | 0,5.9,0,109,99,28,Sunny,Open 520 | 0,5.9,0,106,98,28,Sunny,Open 521 | 0,5.3,0,103,94,24,Sunny,Open 522 | 0,5.6,0,107,98,21,Sunny,Open 523 | 0,5.4,0,100,97,29,Sunny,Open 524 | 0,5.5,0,106,96,27,Sunny,Open 525 | 0,5.4,0,106,97,27,Sunny,Open 526 | 0,5.4,0,101,92,28,Sunny,Open 527 | 0,5.1,0,109,93,24,Sunny,Open 528 | 0,5.1,0,108,97,23,Sunny,Open 529 | 0,5.5,0,102,98,26,Sunny,Open 530 | 0,5.9,0,107,97,29,Sunny,Open 531 | 0,5.9,0,109,94,24,Sunny,Open 532 | 0,5.1,0,104,97,27,Sunny,Open 533 | 0,5.7,0,108,94,26,Sunny,Open 534 | 0,5.6,0,102,97,22,Sunny,Open 535 | 0,5.8,0,106,93,29,Sunny,Open 536 | 0,5.4,0,102,96,27,Sunny,Open 537 | 0,5.8,0,105,97,24,Sunny,Open 538 | 0,5.5,0,101,97,21,Sunny,Open 539 | 0,5.8,0,110,95,24,Sunny,Open 540 | 0,5.7,0,101,93,23,Sunny,Open 541 | 0,5.7,0,105,94,24,Sunny,Open 542 | 0,5.9,0,102,92,25,Sunny,Open 543 | 0,5.8,0,101,94,22,Sunny,Open 544 | 0,5.2,0,108,92,28,Sunny,Open 545 | 0,5.2,0,107,92,26,Sunny,Open 546 | 0,5.7,0,109,96,28,Sunny,Open 547 | 0,5.1,0,107,98,29,Sunny,Open 548 | 0,5.2,0,102,94,25,Sunny,Open 549 | 0,5.9,0,103,93,24,Sunny,Open 550 | 0,5.9,0,102,94,22,Sunny,Open 551 | 0,5.4,0,105,93,20,Sunny,Open 552 | 0,5.8,0,103,95,22,Sunny,Open 553 | 0,5.8,0,108,92,27,Sunny,Open 554 | 0,5.4,0,102,94,21,Sunny,Open 555 | 0,5.9,0,101,92,29,Sunny,Open 556 | 0,5.3,0,104,96,27,Sunny,Open 557 | 0,5.4,0,108,95,26,Sunny,Open 558 | 0,5.2,0,101,94,26,Sunny,Open 559 | 0,5.4,0,101,91,26,Sunny,Open 560 | 0,5.9,0,109,96,21,Sunny,Open 561 | 0,5.3,0,109,95,24,Sunny,Open 562 | 0,5.1,0,101,94,29,Sunny,Open 563 | 0,5.2,0,110,91,21,Sunny,Open 564 | 0,5.5,0,104,93,29,Sunny,Open 565 | 0,5.3,0,100,93,22,Sunny,Open 566 | 0,5.7,0,104,92,21,Sunny,Open 567 | 0,5.2,0,101,97,23,Sunny,Open 568 | 0,5.2,0,109,97,28,Sunny,Open 569 | 0,5.3,0,110,95,24,Sunny,Open 570 | 0,5.7,0,109,98,22,Sunny,Open 571 | 0,5.7,0,102,94,27,Sunny,Open 572 | 0,5.8,0,110,97,24,Sunny,Open 573 | 0,5.7,0,107,95,27,Sunny,Open 574 | 0,5.5,0,109,91,29,Sunny,Open 575 | 0,5.1,0,106,96,24,Sunny,Open 576 | 0,5.9,0,104,97,25,Sunny,Open 577 | 0,5.6,0,103,93,21,Sunny,Open 578 | 0,5.7,0,104,97,24,Sunny,Open 579 | 0,5.8,0,101,95,26,Sunny,Open 580 | 0,5.2,0,107,99,28,Sunny,Open 581 | 0,5.3,0,104,97,29,Sunny,Open 582 | 0,5.1,0,106,95,25,Sunny,Open 583 | 0,5.8,0,108,97,23,Sunny,Open 584 | 0,5.2,0,109,92,21,Sunny,Open 585 | 0,5.8,0,110,92,26,Sunny,Open 586 | 0,5.8,0,107,96,22,Sunny,Open 587 | 0,5.5,0,108,94,28,Sunny,Open 588 | 0,5.6,0,102,94,20,Sunny,Open 589 | 0,5.5,0,107,98,23,Sunny,Open 590 | 0,5.2,0,101,99,29,Sunny,Open 591 | 0,5.8,0,110,93,23,Sunny,Open 592 | 0,5.4,0,104,93,26,Sunny,Open 593 | 0,5.5,0,104,91,28,Sunny,Open 594 | 0,5.5,0,101,99,27,Sunny,Open 595 | 0,5.5,0,103,91,28,Sunny,Open 596 | 0,5.8,0,101,94,28,Sunny,Open 597 | 0,5.7,0,100,96,27,Sunny,Open 598 | 0,5.2,0,108,95,28,Sunny,Open 599 | 0,5.5,0,100,98,20,Sunny,Open 600 | 0,5.2,0,103,99,23,Sunny,Open 601 | 0,5.3,0,106,96,21,Sunny,Open 602 | 0,5.8,0,103,96,30,Sunny,Open 603 | 0,5.8,0,108,95,24,Sunny,Open 604 | 0,5.5,0,102,94,27,Sunny,Open 605 | 0,5.1,0,106,91,23,Sunny,Open 606 | 0,5.5,0,109,92,24,Sunny,Open 607 | 0,5.8,0,107,94,25,Sunny,Open 608 | 0,5.8,0,101,93,27,Sunny,Open 609 | 0,5.7,0,109,94,25,Sunny,Open 610 | 0,5.2,0,107,95,26,Sunny,Open 611 | 0,5.4,0,106,94,21,Sunny,Open 612 | 0,5.6,0,103,96,28,Sunny,Open 613 | 0,5.9,0,109,97,29,Sunny,Open 614 | 0,5.2,0,110,95,29,Sunny,Open 615 | 0,5.3,0,105,95,29,Sunny,Open 616 | 0,5.5,0,110,98,21,Sunny,Open 617 | 0,5.7,0,110,92,28,Sunny,Open 618 | 0,5.8,0,107,97,25,Sunny,Open 619 | 0,5.7,0,106,92,21,Sunny,Open 620 | 0,5.2,0,109,91,24,Sunny,Open 621 | 0,5.2,0,104,94,22,Sunny,Open 622 | 0,5.7,0,101,96,21,Sunny,Open 623 | 0,5.6,0,101,93,20,Sunny,Open 624 | 0,5.4,0,106,91,26,Sunny,Open 625 | 0,5.7,0,101,93,26,Sunny,Open 626 | 0,5.6,0,102,98,25,Sunny,Open 627 | 0,5.3,0,102,95,29,Sunny,Open 628 | 0,5.2,0,106,96,21,Sunny,Open 629 | 0,5.5,0,107,95,26,Sunny,Open 630 | 0,5.2,0,106,98,25,Sunny,Open 631 | 0,5.7,0,107,98,20,Sunny,Open 632 | 0,5.2,0,110,91,21,Sunny,Open 633 | 0,5.9,0,106,95,22,Sunny,Open 634 | 0,5.3,0,109,98,29,Sunny,Open 635 | 0,5.7,0,106,97,21,Sunny,Open 636 | 0,5.8,0,103,95,27,Sunny,Open 637 | 0,5.5,0,103,92,20,Sunny,Open 638 | 0,5.2,0,107,97,25,Sunny,Open 639 | 0,5.7,0,106,94,26,Sunny,Open 640 | 0,5.3,0,103,98,22,Sunny,Open 641 | 0,5.3,0,105,97,30,Sunny,Open 642 | 0,5.8,0,105,99,26,Sunny,Open 643 | 0,5.3,0,110,92,24,Sunny,Open 644 | 0,5.2,0,105,97,26,Sunny,Open 645 | 0,5.2,0,105,96,21,Sunny,Open 646 | 0,5.5,0,106,98,28,Sunny,Open 647 | 0,5.8,0,103,92,23,Sunny,Open 648 | 0,5.8,0,106,95,22,Sunny,Open 649 | 0,5.7,0,102,94,26,Sunny,Open 650 | 0,5.2,0,109,98,26,Sunny,Open 651 | 0,5.3,0,109,97,21,Sunny,Open 652 | 0,5.4,0,103,95,25,Sunny,Open 653 | 0,5.4,0,106,94,29,Sunny,Open 654 | 0,5.3,0,104,97,29,Sunny,Open 655 | 0,5.9,0,103,93,29,Sunny,Open 656 | 0,5.5,0,107,94,25,Sunny,Open 657 | 0,5.6,0,107,91,21,Sunny,Open 658 | 0,5.3,0,105,93,21,Sunny,Open 659 | 0,5.8,0,103,98,27,Sunny,Open 660 | 0,5.1,0,108,91,26,Sunny,Open 661 | 0,5.2,0,108,98,23,Sunny,Open 662 | 0,5.3,0,109,93,27,Sunny,Open 663 | 0,5.7,0,104,92,29,Sunny,Open 664 | 0,5.9,0,100,95,24,Sunny,Open 665 | 0,5.7,0,107,94,23,Sunny,Open 666 | 0,5.6,0,108,97,25,Sunny,Open 667 | 0,5.6,0,104,96,28,Sunny,Open 668 | 0,5.5,0,110,93,21,Sunny,Open 669 | 0,5.6,0,108,94,28,Sunny,Open 670 | 0,5.7,0,100,98,29,Sunny,Open 671 | 0,5.8,0,110,92,25,Sunny,Open 672 | 0,5.1,0,106,98,23,Sunny,Open 673 | 0,5.6,0,101,94,21,Sunny,Open 674 | 0,5.7,0,107,92,25,Sunny,Open 675 | 0,5.3,0,104,95,29,Sunny,Open 676 | 0,5.6,0,101,94,26,Sunny,Open 677 | 0,5.6,0,106,94,28,Sunny,Open 678 | 0,5.4,0,110,94,29,Sunny,Open 679 | 0,5.1,0,106,97,30,Sunny,Open 680 | 0,5.5,0,108,92,22,Sunny,Open 681 | 0,5.5,0,108,95,28,Sunny,Open 682 | 0,5.8,0,102,97,28,Sunny,Open 683 | 0,5.9,0,102,96,21,Sunny,Open 684 | 0,5.7,0,108,92,22,Sunny,Open 685 | 0,5.6,0,102,97,22,Sunny,Open 686 | 0,5.3,0,100,93,26,Sunny,Open 687 | 0,5.8,0,105,96,22,Sunny,Open 688 | 0,5.2,0,106,93,27,Sunny,Open 689 | 0,5.2,0,105,95,29,Sunny,Open 690 | 0,5.6,0,104,96,29,Sunny,Open 691 | 0,5.4,0,106,94,22,Sunny,Open 692 | 0,5.4,0,104,97,28,Sunny,Open 693 | 0,5.4,0,104,98,27,Sunny,Open 694 | 0,5.8,0,107,98,27,Sunny,Open 695 | 0,5.7,0,108,96,21,Sunny,Open 696 | 0,5.1,0,108,92,25,Sunny,Open 697 | 0,5.1,0,100,99,25,Sunny,Open 698 | 0,5.2,0,107,98,21,Sunny,Open 699 | 0,5.4,0,107,99,20,Sunny,Open 700 | 0,5.7,0,102,95,24,Sunny,Open 701 | 0,5.4,0,100,98,21,Sunny,Open 702 | 0,5.8,0,106,95,24,Sunny,Open 703 | 0,5.4,0,108,98,27,Sunny,Open 704 | 0,5.4,0,102,97,23,Sunny,Open 705 | 0,5.4,0,110,96,23,Sunny,Open 706 | 0,5.9,0,105,96,21,Sunny,Open 707 | 0,5.8,0,101,95,29,Sunny,Open 708 | 0,5.5,0,104,97,23,Sunny,Open 709 | 0,5.2,0,103,92,24,Sunny,Open 710 | 0,5.1,0,104,97,27,Sunny,Open 711 | 0,5.9,0,107,95,27,Sunny,Open 712 | 0,5.2,0,102,95,25,Sunny,Open 713 | 0,5.6,0,104,97,21,Sunny,Open 714 | 0,5.2,0,101,99,25,Sunny,Open 715 | 0,5.8,0,107,94,22,Sunny,Open 716 | 0,5.7,0,105,93,22,Sunny,Open 717 | 0,5.5,0,101,92,21,Sunny,Open 718 | 0,5.7,0,110,92,24,Sunny,Open 719 | 0,5.6,0,106,98,27,Sunny,Open 720 | 0,5.8,0,107,94,22,Sunny,Open 721 | 0,5.3,0,106,92,21,Sunny,Open 722 | 0,5.6,0,105,96,30,Sunny,Open 723 | 0,5.3,0,108,92,27,Sunny,Open 724 | 0,5.2,0,109,97,21,Sunny,Open 725 | 0,5.8,0,108,92,22,Sunny,Open 726 | 0,5.6,0,108,98,24,Sunny,Open 727 | 0,5.1,0,104,92,27,Sunny,Open 728 | 0,5.5,0,106,95,22,Sunny,Open 729 | 0,5.3,0,104,98,22,Sunny,Open 730 | 0,5.5,0,107,97,30,Sunny,Open 731 | 0,5.1,0,104,91,24,Sunny,Open 732 | 0,5.3,0,108,92,26,Sunny,Open 733 | 0,5.3,0,106,99,21,Sunny,Open 734 | 0,5.7,0,107,94,30,Sunny,Open 735 | 0,5.2,0,106,94,28,Sunny,Open 736 | 0,5.7,0,107,95,26,Sunny,Open 737 | 0,5.8,0,104,92,29,Sunny,Open 738 | 0,5.9,0,104,98,29,Sunny,Open 739 | 0,5.2,0,105,92,25,Sunny,Open 740 | 0,5.6,0,106,99,27,Sunny,Open 741 | 0,5.4,0,101,93,27,Sunny,Open 742 | 0,5.3,0,100,97,22,Sunny,Open 743 | 0,5.5,0,107,91,27,Sunny,Open 744 | 0,5.7,0,107,95,29,Sunny,Open 745 | 0,5.9,0,102,97,24,Sunny,Open 746 | 0,5.3,0,103,95,25,Sunny,Open 747 | 0,5.8,0,109,96,25,Sunny,Open 748 | 0,5.5,0,102,97,28,Sunny,Open 749 | 0,5.6,0,102,93,20,Sunny,Open 750 | 0,5.4,0,103,98,27,Sunny,Open 751 | 6.4,0,106,0,104,34,Cloudy,Open 752 | 7.5,0,109,0,106,31,Cloudy,Open 753 | 6.2,0,105,0,104,35,Cloudy,Open 754 | 7.5,0,108,0,110,31,Cloudy,Open 755 | 6.5,0,107,0,108,35,Cloudy,Open 756 | 6.2,0,106,0,105,34,Cloudy,Open 757 | 7.6,0,105,0,107,33,Cloudy,Open 758 | 6.9,0,106,0,106,32,Cloudy,Open 759 | 6.2,0,109,0,106,34,Cloudy,Open 760 | 6.5,0,108,0,104,39,Cloudy,Open 761 | 6.3,0,108,0,107,37,Cloudy,Open 762 | 6.8,0,110,0,107,31,Cloudy,Open 763 | 6.5,0,106,0,103,34,Cloudy,Open 764 | 7.4,0,110,0,105,38,Cloudy,Open 765 | 7.1,0,108,0,108,37,Cloudy,Open 766 | 7.3,0,106,0,107,36,Cloudy,Open 767 | 6.2,0,107,0,106,39,Cloudy,Open 768 | 6.8,0,106,0,104,35,Cloudy,Open 769 | 6.6,0,109,0,107,37,Cloudy,Open 770 | 6.1,0,106,0,102,39,Cloudy,Open 771 | 7.1,0,107,0,107,32,Cloudy,Open 772 | 6.2,0,106,0,104,31,Cloudy,Open 773 | 7.3,0,108,0,108,30,Cloudy,Open 774 | 6.1,0,107,0,105,39,Cloudy,Open 775 | 6.8,0,108,0,103,39,Cloudy,Open 776 | 7.1,0,110,0,105,37,Cloudy,Open 777 | 6.8,0,107,0,108,39,Cloudy,Open 778 | 7.2,0,107,0,103,39,Cloudy,Open 779 | 7.3,0,106,0,105,39,Cloudy,Open 780 | 6.8,0,110,0,107,39,Cloudy,Open 781 | 6.4,0,107,0,107,33,Cloudy,Open 782 | 6.1,0,107,0,106,36,Cloudy,Open 783 | 6.1,0,108,0,106,33,Cloudy,Open 784 | 7.6,0,109,0,106,33,Cloudy,Open 785 | 6.6,0,109,0,110,40,Cloudy,Open 786 | 6.8,0,109,0,105,39,Cloudy,Open 787 | 6.4,0,108,0,103,39,Cloudy,Open 788 | 6.2,0,105,0,107,31,Cloudy,Open 789 | 7.2,0,108,0,103,40,Cloudy,Open 790 | 6.8,0,109,0,105,37,Cloudy,Open 791 | 6,0,109,0,108,39,Cloudy,Open 792 | 7.6,0,107,0,106,36,Cloudy,Open 793 | 7,0,105,0,108,35,Cloudy,Open 794 | 6.7,0,109,0,103,38,Cloudy,Open 795 | 7.4,0,109,0,104,36,Cloudy,Open 796 | 6.6,0,106,0,104,34,Cloudy,Open 797 | 7.3,0,108,0,107,33,Cloudy,Open 798 | 6.7,0,109,0,103,38,Cloudy,Open 799 | 7,0,108,0,108,31,Cloudy,Open 800 | 6.1,0,110,0,103,32,Cloudy,Open 801 | 7.1,0,108,0,108,39,Cloudy,Open 802 | 7.2,0,109,0,107,34,Cloudy,Open 803 | 7.5,0,106,0,104,32,Cloudy,Open 804 | 6.5,0,108,0,109,33,Cloudy,Open 805 | 6.5,0,110,0,105,32,Cloudy,Open 806 | 6.2,0,108,0,108,36,Cloudy,Open 807 | 7.3,0,108,0,103,34,Cloudy,Open 808 | 7.1,0,109,0,103,31,Cloudy,Open 809 | 6.3,0,106,0,102,38,Cloudy,Open 810 | 6.7,0,108,0,107,33,Cloudy,Open 811 | 6.1,0,108,0,108,39,Cloudy,Open 812 | 6.7,0,106,0,107,33,Cloudy,Open 813 | 6.9,0,110,0,103,36,Cloudy,Open 814 | 6.4,0,110,0,107,36,Cloudy,Open 815 | 7.5,0,108,0,106,39,Cloudy,Open 816 | 6.7,0,109,0,104,36,Cloudy,Open 817 | 6.8,0,109,0,109,32,Cloudy,Open 818 | 7.3,0,108,0,109,38,Cloudy,Open 819 | 6.6,0,108,0,107,40,Cloudy,Open 820 | 6.6,0,106,0,108,35,Cloudy,Open 821 | 6.2,0,109,0,106,33,Cloudy,Open 822 | 7.1,0,109,0,110,30,Cloudy,Open 823 | 6.6,0,109,0,109,39,Cloudy,Open 824 | 7.5,0,108,0,103,34,Cloudy,Open 825 | 7.4,0,109,0,103,31,Cloudy,Open 826 | 6.4,0,108,0,103,37,Cloudy,Open 827 | 6.7,0,108,0,109,33,Cloudy,Open 828 | 6.9,0,108,0,106,35,Cloudy,Open 829 | 7.3,0,105,0,103,38,Cloudy,Open 830 | 7.4,0,107,0,107,39,Cloudy,Open 831 | 6.4,0,109,0,105,31,Cloudy,Open 832 | 7.2,0,109,0,107,39,Cloudy,Open 833 | 6.7,0,106,0,110,36,Cloudy,Open 834 | 6.1,0,110,0,103,33,Cloudy,Open 835 | 7.1,0,107,0,105,32,Cloudy,Open 836 | 6.5,0,107,0,107,31,Cloudy,Open 837 | 6.4,0,106,0,108,33,Cloudy,Open 838 | 6.1,0,109,0,103,31,Cloudy,Open 839 | 7.2,0,106,0,105,32,Cloudy,Open 840 | 6.5,0,106,0,105,34,Cloudy,Open 841 | 6.8,0,110,0,105,34,Cloudy,Open 842 | 6.7,0,109,0,107,33,Cloudy,Open 843 | 6.8,0,106,0,102,36,Cloudy,Open 844 | 6.7,0,109,0,108,39,Cloudy,Open 845 | 7.1,0,109,0,103,32,Cloudy,Open 846 | 6.5,0,107,0,104,35,Cloudy,Open 847 | 6.6,0,105,0,106,37,Cloudy,Open 848 | 7,0,109,0,103,36,Cloudy,Open 849 | 6.3,0,109,0,107,36,Cloudy,Open 850 | 6.8,0,106,0,110,30,Cloudy,Open 851 | 6.4,0,109,0,107,37,Cloudy,Open 852 | 7.1,0,106,0,109,36,Cloudy,Open 853 | 7.3,0,109,0,109,35,Cloudy,Open 854 | 6,0,107,0,103,31,Cloudy,Open 855 | 6.5,0,107,0,104,33,Cloudy,Open 856 | 6.3,0,106,0,105,35,Cloudy,Open 857 | 7.3,0,109,0,102,37,Cloudy,Open 858 | 6.9,0,107,0,106,33,Cloudy,Open 859 | 7,0,105,0,104,39,Cloudy,Open 860 | 6.7,0,110,0,108,36,Cloudy,Open 861 | 6.3,0,108,0,109,36,Cloudy,Open 862 | 6.1,0,109,0,108,34,Cloudy,Open 863 | 6.9,0,109,0,103,34,Cloudy,Open 864 | 6.7,0,107,0,109,35,Cloudy,Open 865 | 6.1,0,109,0,109,32,Cloudy,Open 866 | 6.4,0,106,0,107,34,Cloudy,Open 867 | 7.4,0,109,0,107,34,Cloudy,Open 868 | 7.1,0,108,0,105,35,Cloudy,Open 869 | 7.5,0,107,0,103,33,Cloudy,Open 870 | 6.2,0,108,0,106,36,Cloudy,Open 871 | 6.8,0,107,0,106,35,Cloudy,Open 872 | 6.9,0,108,0,104,32,Cloudy,Open 873 | 7.2,0,108,0,106,33,Cloudy,Open 874 | 7,0,108,0,105,34,Cloudy,Open 875 | 7.4,0,110,0,105,32,Cloudy,Open 876 | 6.8,0,107,0,104,32,Cloudy,Open 877 | 7.3,0,109,0,107,33,Cloudy,Open 878 | 6.9,0,108,0,106,40,Cloudy,Open 879 | 6.6,0,110,0,106,34,Cloudy,Open 880 | 6.5,0,108,0,108,38,Cloudy,Open 881 | 7.4,0,105,0,103,31,Cloudy,Open 882 | 7.3,0,108,0,105,31,Cloudy,Open 883 | 6.7,0,109,0,109,33,Cloudy,Open 884 | 7,0,106,0,106,34,Cloudy,Open 885 | 6,0,110,0,104,37,Cloudy,Open 886 | 6.7,0,105,0,107,34,Cloudy,Open 887 | 6.1,0,110,0,104,32,Cloudy,Open 888 | 7.2,0,108,0,107,38,Cloudy,Open 889 | 7.1,0,110,0,110,38,Cloudy,Open 890 | 6.6,0,109,0,106,30,Cloudy,Open 891 | 6.1,0,109,0,106,38,Cloudy,Open 892 | 6.9,0,109,0,107,34,Cloudy,Open 893 | 7.1,0,108,0,106,35,Cloudy,Open 894 | 6.4,0,110,0,109,32,Cloudy,Open 895 | 7,0,109,0,106,36,Cloudy,Open 896 | 7.1,0,107,0,105,35,Cloudy,Open 897 | 7.5,0,105,0,108,37,Cloudy,Open 898 | 7.1,0,108,0,105,39,Cloudy,Open 899 | 6.2,0,107,0,109,30,Cloudy,Open 900 | 6.7,0,106,0,104,39,Cloudy,Open 901 | 7.2,0,107,0,107,36,Cloudy,Open 902 | 6.7,0,107,0,107,31,Cloudy,Open 903 | 7.6,0,106,0,107,38,Cloudy,Open 904 | 6.9,0,106,0,103,37,Cloudy,Open 905 | 7.2,0,107,0,105,36,Cloudy,Open 906 | 7.4,0,107,0,102,35,Cloudy,Open 907 | 7,0,107,0,107,39,Cloudy,Open 908 | 6.6,0,109,0,109,37,Cloudy,Open 909 | 7.4,0,105,0,109,35,Cloudy,Open 910 | 6.1,0,106,0,103,35,Cloudy,Open 911 | 7.6,0,109,0,108,38,Cloudy,Open 912 | 6.5,0,108,0,103,34,Cloudy,Open 913 | 6.2,0,108,0,107,38,Cloudy,Open 914 | 6.1,0,108,0,108,37,Cloudy,Open 915 | 7.1,0,110,0,105,35,Cloudy,Open 916 | 6.4,0,106,0,108,36,Cloudy,Open 917 | 6.6,0,108,0,107,35,Cloudy,Open 918 | 7,0,106,0,107,37,Cloudy,Open 919 | 6.2,0,107,0,110,35,Cloudy,Open 920 | 7.5,0,108,0,107,39,Cloudy,Open 921 | 6.6,0,110,0,110,39,Cloudy,Open 922 | 6.8,0,107,0,110,39,Cloudy,Open 923 | 7.5,0,109,0,104,31,Cloudy,Open 924 | 6.4,0,107,0,107,37,Cloudy,Open 925 | 7.5,0,107,0,110,34,Cloudy,Open 926 | 6.6,0,109,0,110,33,Cloudy,Open 927 | 6.4,0,109,0,106,38,Cloudy,Open 928 | 6.2,0,110,0,103,31,Cloudy,Open 929 | 6.3,0,110,0,105,31,Cloudy,Open 930 | 7,0,109,0,106,37,Cloudy,Open 931 | 6.9,0,108,0,102,37,Cloudy,Open 932 | 7.4,0,110,0,104,30,Cloudy,Open 933 | 7.1,0,109,0,104,39,Cloudy,Open 934 | 6.5,0,108,0,107,37,Cloudy,Open 935 | 7.1,0,110,0,108,33,Cloudy,Open 936 | 7,0,108,0,106,33,Cloudy,Open 937 | 6.3,0,109,0,103,36,Cloudy,Open 938 | 6.9,0,107,0,102,35,Cloudy,Open 939 | 6.7,0,107,0,106,35,Cloudy,Open 940 | 7,0,106,0,105,31,Cloudy,Open 941 | 6.9,0,110,0,108,36,Cloudy,Open 942 | 6.3,0,107,0,109,32,Cloudy,Open 943 | 6.1,0,107,0,109,31,Cloudy,Open 944 | 6.3,0,106,0,104,32,Cloudy,Open 945 | 6.5,0,107,0,104,39,Cloudy,Open 946 | 6.5,0,105,0,103,40,Cloudy,Open 947 | 6.1,0,108,0,102,35,Cloudy,Open 948 | 6.2,0,106,0,107,36,Cloudy,Open 949 | 7.3,0,107,0,103,37,Cloudy,Open 950 | 7.2,0,106,0,110,35,Cloudy,Open 951 | 7.3,0,110,0,110,33,Cloudy,Open 952 | 7,0,109,0,106,36,Cloudy,Open 953 | 7,0,108,0,104,34,Cloudy,Open 954 | 6.5,0,107,0,105,31,Cloudy,Open 955 | 7.5,0,105,0,102,33,Cloudy,Open 956 | 7,0,108,0,104,31,Cloudy,Open 957 | 6.1,0,107,0,106,39,Cloudy,Open 958 | 7.4,0,107,0,108,37,Cloudy,Open 959 | 6.5,0,110,0,106,34,Cloudy,Open 960 | 6.8,0,107,0,105,36,Cloudy,Open 961 | 7.1,0,109,0,106,31,Cloudy,Open 962 | 6.3,0,106,0,106,35,Cloudy,Open 963 | 7.3,0,108,0,102,32,Cloudy,Open 964 | 6.9,0,110,0,105,31,Cloudy,Open 965 | 7.4,0,107,0,104,39,Cloudy,Open 966 | 6.6,0,107,0,108,33,Cloudy,Open 967 | 6.3,0,108,0,108,37,Cloudy,Open 968 | 7.6,0,110,0,109,37,Cloudy,Open 969 | 6.3,0,107,0,105,37,Cloudy,Open 970 | 6.4,0,109,0,107,36,Cloudy,Open 971 | 6,0,109,0,107,31,Cloudy,Open 972 | 7.5,0,107,0,108,39,Cloudy,Open 973 | 6.7,0,108,0,109,35,Cloudy,Open 974 | 7.6,0,106,0,107,39,Cloudy,Open 975 | 6.3,0,107,0,110,35,Cloudy,Open 976 | 6.2,0,109,0,108,37,Cloudy,Open 977 | 6.2,0,107,0,109,35,Cloudy,Open 978 | 6.5,0,107,0,109,31,Cloudy,Open 979 | 6.9,0,106,0,108,32,Cloudy,Open 980 | 6.7,0,108,0,107,40,Cloudy,Open 981 | 6.8,0,109,0,105,31,Cloudy,Open 982 | 6.7,0,108,0,109,38,Cloudy,Open 983 | 7.5,0,108,0,106,33,Cloudy,Open 984 | 6.3,0,107,0,109,32,Cloudy,Open 985 | 7.6,0,109,0,109,35,Cloudy,Open 986 | 7.1,0,110,0,108,33,Cloudy,Open 987 | 6.7,0,106,0,103,32,Cloudy,Open 988 | 6.1,0,107,0,104,32,Cloudy,Open 989 | 7.5,0,109,0,104,34,Cloudy,Open 990 | 7.3,0,106,0,104,32,Cloudy,Open 991 | 6.3,0,107,0,103,36,Cloudy,Open 992 | 6.8,0,109,0,109,34,Cloudy,Open 993 | 6.9,0,106,0,107,36,Cloudy,Open 994 | 6.5,0,108,0,103,35,Cloudy,Open 995 | 6.8,0,106,0,108,31,Cloudy,Open 996 | 6,0,106,0,107,34,Cloudy,Open 997 | 7.3,0,105,0,103,37,Cloudy,Open 998 | 6.8,0,108,0,109,37,Cloudy,Open 999 | 7.5,0,108,0,110,33,Cloudy,Open 1000 | 6.5,0,106,0,103,31,Cloudy,Open 1001 | 7.1,0,107,0,106,30,Cloudy,Open 1002 | 6.4,3.6,107,81,107,30,Sunny,Line-line 1003 | 6.5,3.8,108,81,105,35,Sunny,Line-line 1004 | 6,3.8,106,81,108,34,Sunny,Line-line 1005 | 6.5,4,109,75,108,36,Sunny,Line-line 1006 | 6.7,3.4,108,80,108,39,Sunny,Line-line 1007 | 6.9,2.2,106,81,104,38,Sunny,Line-line 1008 | 7.2,3.2,108,80,107,32,Sunny,Line-line 1009 | 6.9,2.9,106,82,108,37,Sunny,Line-line 1010 | 7.4,3.1,110,83,107,32,Sunny,Line-line 1011 | 6.5,3.3,105,78,105,38,Sunny,Line-line 1012 | 6.5,3.2,109,77,105,34,Sunny,Line-line 1013 | 6.8,3.1,106,79,110,37,Sunny,Line-line 1014 | 6.6,3.1,109,79,110,39,Sunny,Line-line 1015 | 6.8,3.3,107,75,103,35,Sunny,Line-line 1016 | 6.1,3.8,107,76,104,32,Sunny,Line-line 1017 | 7.4,3.8,106,82,108,38,Sunny,Line-line 1018 | 7.2,3.7,108,76,110,40,Sunny,Line-line 1019 | 7.5,2.6,107,81,105,34,Sunny,Line-line 1020 | 6.9,3.2,106,75,107,33,Sunny,Line-line 1021 | 6.3,2.2,106,84,107,40,Sunny,Line-line 1022 | 7.2,3,109,81,108,30,Sunny,Line-line 1023 | 6.8,2.9,110,77,106,31,Sunny,Line-line 1024 | 6.4,4,108,81,107,39,Sunny,Line-line 1025 | 6.9,3.4,109,79,105,40,Sunny,Line-line 1026 | 7.4,2.4,108,78,110,34,Sunny,Line-line 1027 | 7.3,3.4,109,80,108,32,Sunny,Line-line 1028 | 7.1,3.3,109,82,109,32,Sunny,Line-line 1029 | 7.2,2.9,109,83,108,33,Sunny,Line-line 1030 | 6.6,3,109,79,108,39,Sunny,Line-line 1031 | 7,2.4,107,76,107,37,Sunny,Line-line 1032 | 7.2,2.9,109,79,108,38,Sunny,Line-line 1033 | 7.6,3.5,109,84,104,31,Sunny,Line-line 1034 | 6.2,3.7,105,76,110,30,Sunny,Line-line 1035 | 7,2.6,107,84,108,38,Sunny,Line-line 1036 | 6.4,3.6,108,81,109,33,Sunny,Line-line 1037 | 7.5,2.6,109,81,105,32,Sunny,Line-line 1038 | 7,3.7,107,84,102,36,Sunny,Line-line 1039 | 6.1,3.1,106,76,102,35,Sunny,Line-line 1040 | 6.3,3.7,107,77,103,32,Sunny,Line-line 1041 | 7.1,2.7,109,84,106,37,Sunny,Line-line 1042 | 6.7,2.5,109,79,105,39,Sunny,Line-line 1043 | 6.8,3.3,106,82,103,37,Sunny,Line-line 1044 | 6.7,3.6,106,81,105,35,Sunny,Line-line 1045 | 7.3,2.9,106,84,106,30,Sunny,Line-line 1046 | 7,3.4,108,78,110,40,Sunny,Line-line 1047 | 7.3,3.9,107,81,109,37,Sunny,Line-line 1048 | 6.4,2.3,109,80,110,35,Sunny,Line-line 1049 | 6.4,2.2,107,76,105,38,Sunny,Line-line 1050 | 6.2,3.4,109,84,102,31,Sunny,Line-line 1051 | 6.8,2.9,107,78,108,39,Sunny,Line-line 1052 | 6.8,3.4,108,81,108,39,Sunny,Line-line 1053 | 6.5,3.5,109,82,107,33,Sunny,Line-line 1054 | 6.5,2.2,108,76,107,36,Sunny,Line-line 1055 | 6.1,3,109,82,104,37,Sunny,Line-line 1056 | 6.3,3.6,110,82,106,31,Sunny,Line-line 1057 | 7.2,3.9,107,81,103,31,Sunny,Line-line 1058 | 6.5,2.4,106,83,109,32,Sunny,Line-line 1059 | 6.4,2.6,108,80,106,33,Sunny,Line-line 1060 | 7.6,3.4,107,79,104,39,Sunny,Line-line 1061 | 6.7,2.5,108,76,103,33,Sunny,Line-line 1062 | 7.1,2.9,108,84,104,33,Sunny,Line-line 1063 | 7.3,3.5,106,77,108,30,Sunny,Line-line 1064 | 7.4,3.5,106,80,104,35,Sunny,Line-line 1065 | 6.5,2.5,108,78,107,39,Sunny,Line-line 1066 | 6.2,3.3,107,81,104,32,Sunny,Line-line 1067 | 6.9,2.8,105,77,110,35,Sunny,Line-line 1068 | 7.2,2.9,109,78,108,37,Sunny,Line-line 1069 | 7.1,3.9,107,80,110,33,Sunny,Line-line 1070 | 7.4,3.3,109,77,110,35,Sunny,Line-line 1071 | 7.4,2.1,109,84,102,35,Sunny,Line-line 1072 | 6.4,2.1,105,84,106,32,Sunny,Line-line 1073 | 6.9,4,108,78,107,37,Sunny,Line-line 1074 | 7.4,3.1,105,78,104,39,Sunny,Line-line 1075 | 6.1,3.6,106,75,108,39,Sunny,Line-line 1076 | 6.5,3.3,109,78,105,32,Sunny,Line-line 1077 | 6.7,2.3,109,82,107,35,Sunny,Line-line 1078 | 7.1,3.6,108,77,102,39,Sunny,Line-line 1079 | 7.5,3,109,78,103,31,Sunny,Line-line 1080 | 7.3,2,106,78,108,37,Sunny,Line-line 1081 | 6.9,3.6,105,81,103,33,Sunny,Line-line 1082 | 6.8,2.5,109,76,106,31,Sunny,Line-line 1083 | 6.4,2.4,106,77,109,39,Sunny,Line-line 1084 | 6.7,3,110,78,108,35,Sunny,Line-line 1085 | 7,4,108,77,109,35,Sunny,Line-line 1086 | 6.8,2.1,107,81,109,36,Sunny,Line-line 1087 | 6.8,2.1,109,82,104,34,Sunny,Line-line 1088 | 6.6,3.1,107,84,102,32,Sunny,Line-line 1089 | 6.7,2.1,109,84,105,34,Sunny,Line-line 1090 | 6.7,3.2,105,78,107,36,Sunny,Line-line 1091 | 6.1,3.9,108,82,107,33,Sunny,Line-line 1092 | 6.6,2.7,105,77,110,34,Sunny,Line-line 1093 | 6.4,3.8,108,81,105,39,Sunny,Line-line 1094 | 7.6,2.2,109,85,102,39,Sunny,Line-line 1095 | 6.6,2.9,108,80,106,31,Sunny,Line-line 1096 | 7.2,3.7,108,81,106,38,Sunny,Line-line 1097 | 7,2.1,108,76,109,31,Sunny,Line-line 1098 | 7.4,2.8,110,83,104,39,Sunny,Line-line 1099 | 6.5,3.1,109,78,104,32,Sunny,Line-line 1100 | 7.4,2.3,110,76,108,37,Sunny,Line-line 1101 | 7.1,3.5,109,81,104,30,Sunny,Line-line 1102 | 6.2,2.1,108,80,107,32,Sunny,Line-line 1103 | 6.6,2.7,110,75,105,33,Sunny,Line-line 1104 | 7,2.8,106,80,108,32,Sunny,Line-line 1105 | 6.9,2.9,108,77,105,31,Sunny,Line-line 1106 | 6.8,3.9,109,83,106,36,Sunny,Line-line 1107 | 6.2,2.1,107,76,105,35,Sunny,Line-line 1108 | 6.3,3.4,108,84,106,33,Sunny,Line-line 1109 | 7.6,3.2,109,78,102,36,Sunny,Line-line 1110 | 7.2,2.4,110,77,108,37,Sunny,Line-line 1111 | 7.4,3.4,109,82,103,32,Sunny,Line-line 1112 | 6.8,2.3,109,83,108,32,Sunny,Line-line 1113 | 6.7,3.1,106,80,109,38,Sunny,Line-line 1114 | 6.6,4,105,79,104,38,Sunny,Line-line 1115 | 6.6,2.6,106,80,108,32,Sunny,Line-line 1116 | 6.6,2.8,105,80,105,32,Sunny,Line-line 1117 | 7.4,2.1,108,80,106,33,Sunny,Line-line 1118 | 7.4,3.8,107,80,106,39,Sunny,Line-line 1119 | 7.1,2.5,108,84,105,34,Sunny,Line-line 1120 | 6.1,2.6,106,82,108,33,Sunny,Line-line 1121 | 6.6,2.4,110,79,109,36,Sunny,Line-line 1122 | 7,2.4,108,79,102,35,Sunny,Line-line 1123 | 7.6,3.3,105,84,108,38,Sunny,Line-line 1124 | 6.6,2.3,109,76,108,38,Sunny,Line-line 1125 | 7.2,2.3,107,76,110,30,Sunny,Line-line 1126 | 7,2.9,108,82,105,32,Sunny,Line-line 1127 | 6,2.9,107,77,108,38,Sunny,Line-line 1128 | 7.3,3.4,109,82,105,34,Sunny,Line-line 1129 | 7.1,3.5,107,81,103,31,Sunny,Line-line 1130 | 7.4,3.1,108,77,107,31,Sunny,Line-line 1131 | 7.6,3.5,107,77,106,40,Sunny,Line-line 1132 | 7.4,3.8,105,79,106,35,Sunny,Line-line 1133 | 7.6,2.9,108,78,107,33,Sunny,Line-line 1134 | 6.9,3.6,108,83,105,38,Sunny,Line-line 1135 | 6.5,3.6,110,85,104,30,Sunny,Line-line 1136 | 7.5,2.9,110,85,108,38,Sunny,Line-line 1137 | 7.3,2.8,107,78,103,36,Sunny,Line-line 1138 | 7.4,2.8,109,83,106,36,Sunny,Line-line 1139 | 7.2,2.9,106,75,110,30,Sunny,Line-line 1140 | 6.9,2.3,108,79,104,38,Sunny,Line-line 1141 | 7,3.2,108,76,102,39,Sunny,Line-line 1142 | 7.4,3.8,107,77,108,31,Sunny,Line-line 1143 | 7.6,3.4,107,82,104,35,Sunny,Line-line 1144 | 7.1,3.4,108,76,105,33,Sunny,Line-line 1145 | 6.7,3.9,109,77,109,36,Sunny,Line-line 1146 | 7.1,2.2,106,84,106,37,Sunny,Line-line 1147 | 6.4,3.9,106,75,105,30,Sunny,Line-line 1148 | 7.2,2.1,107,76,108,36,Sunny,Line-line 1149 | 6.8,3.1,108,82,107,34,Sunny,Line-line 1150 | 6.4,2.7,109,80,102,34,Sunny,Line-line 1151 | 7.3,3.8,109,82,106,39,Sunny,Line-line 1152 | 6.5,2.7,108,82,106,33,Sunny,Line-line 1153 | 6.6,3.1,107,84,104,37,Sunny,Line-line 1154 | 7.1,3.4,106,77,104,39,Sunny,Line-line 1155 | 6.6,3.6,107,81,109,32,Sunny,Line-line 1156 | 6.6,2.5,108,78,104,33,Sunny,Line-line 1157 | 6.5,3.6,107,83,110,40,Sunny,Line-line 1158 | 6.5,3.8,108,76,109,36,Sunny,Line-line 1159 | 6.5,3.4,110,83,102,33,Sunny,Line-line 1160 | 6.3,3.2,107,83,104,38,Sunny,Line-line 1161 | 6,3.8,107,81,104,33,Sunny,Line-line 1162 | 6.5,4,109,79,104,40,Sunny,Line-line 1163 | 7.2,2.9,108,80,103,36,Sunny,Line-line 1164 | 6.5,2.2,107,80,104,38,Sunny,Line-line 1165 | 6.4,3.6,107,78,108,39,Sunny,Line-line 1166 | 6.6,3.2,105,75,107,37,Sunny,Line-line 1167 | 6.2,2.6,107,81,109,37,Sunny,Line-line 1168 | 7.3,2.8,105,80,106,40,Sunny,Line-line 1169 | 7.3,2.9,107,83,107,37,Sunny,Line-line 1170 | 6.7,3.3,108,78,104,36,Sunny,Line-line 1171 | 7,2.9,106,77,105,33,Sunny,Line-line 1172 | 6.6,3.2,105,78,107,34,Sunny,Line-line 1173 | 7.2,2.5,108,80,110,34,Sunny,Line-line 1174 | 6.4,3.9,107,78,107,35,Sunny,Line-line 1175 | 7.1,2.5,106,82,109,39,Sunny,Line-line 1176 | 7,2.6,107,83,105,32,Sunny,Line-line 1177 | 7.5,3.5,107,75,104,39,Sunny,Line-line 1178 | 6.1,2.7,109,82,105,38,Sunny,Line-line 1179 | 7.1,2.4,108,80,110,39,Sunny,Line-line 1180 | 7.2,3.6,108,84,102,34,Sunny,Line-line 1181 | 7.2,2.5,108,82,104,35,Sunny,Line-line 1182 | 6.6,2.2,105,84,106,35,Sunny,Line-line 1183 | 6.8,2.7,105,75,106,31,Sunny,Line-line 1184 | 7.1,2.8,105,83,103,32,Sunny,Line-line 1185 | 6.5,2.6,109,84,103,34,Sunny,Line-line 1186 | 7.5,2.1,109,80,105,39,Sunny,Line-line 1187 | 6.9,3,106,83,108,39,Sunny,Line-line 1188 | 6.1,3.1,107,82,102,31,Sunny,Line-line 1189 | 7.4,2.7,109,82,108,37,Sunny,Line-line 1190 | 6,2.1,108,79,109,38,Sunny,Line-line 1191 | 7,3.9,109,85,109,31,Sunny,Line-line 1192 | 7.4,2.9,108,82,106,35,Sunny,Line-line 1193 | 6.8,4,109,77,108,31,Sunny,Line-line 1194 | 6.2,3.6,106,78,106,37,Sunny,Line-line 1195 | 6.8,2.1,106,84,102,32,Sunny,Line-line 1196 | 6.1,3.3,110,81,109,38,Sunny,Line-line 1197 | 7.5,3.8,107,85,104,36,Sunny,Line-line 1198 | 6,4,109,77,109,30,Sunny,Line-line 1199 | 6.5,3.2,108,82,110,34,Sunny,Line-line 1200 | 7.2,2.4,108,81,104,37,Sunny,Line-line 1201 | 7.4,2.3,106,79,104,36,Sunny,Line-line 1202 | 6.4,2.2,106,81,106,39,Sunny,Line-line 1203 | 7,3,107,80,104,34,Sunny,Line-line 1204 | 7.2,2.8,106,82,109,37,Sunny,Line-line 1205 | 6.1,3,108,77,105,38,Sunny,Line-line 1206 | 6.6,2.6,109,76,108,34,Sunny,Line-line 1207 | 6.5,2.7,105,80,105,36,Sunny,Line-line 1208 | 6.6,3.5,110,83,105,31,Sunny,Line-line 1209 | 7.4,3.7,109,83,109,33,Sunny,Line-line 1210 | 6.4,2.6,110,82,108,35,Sunny,Line-line 1211 | 7.5,3.9,110,78,109,33,Sunny,Line-line 1212 | 7,3.5,107,83,102,39,Sunny,Line-line 1213 | 6.5,2.1,106,78,103,37,Sunny,Line-line 1214 | 7.3,2.2,108,82,106,36,Sunny,Line-line 1215 | 6.3,3.3,108,83,108,31,Sunny,Line-line 1216 | 6.7,3.8,106,83,107,38,Sunny,Line-line 1217 | 6.6,3.9,106,79,108,36,Sunny,Line-line 1218 | 6.3,3,108,76,104,39,Sunny,Line-line 1219 | 6.3,3.6,110,79,107,30,Sunny,Line-line 1220 | 7.3,3.5,109,77,108,39,Sunny,Line-line 1221 | 6.8,3.8,108,77,106,32,Sunny,Line-line 1222 | 7.4,2.4,108,82,107,38,Sunny,Line-line 1223 | 6.7,3.8,108,80,108,37,Sunny,Line-line 1224 | 6.9,2.2,107,84,105,38,Sunny,Line-line 1225 | 7.2,3.5,108,80,106,30,Sunny,Line-line 1226 | 6,2.1,106,80,105,32,Sunny,Line-line 1227 | 6.9,2.3,106,81,108,31,Sunny,Line-line 1228 | 6.4,2.9,108,85,102,32,Sunny,Line-line 1229 | 7.5,2.4,109,83,106,39,Sunny,Line-line 1230 | 7.1,3.3,108,78,109,36,Sunny,Line-line 1231 | 7.3,3.9,110,77,102,39,Sunny,Line-line 1232 | 6.3,2.4,105,81,104,37,Sunny,Line-line 1233 | 7,3.5,106,78,105,36,Sunny,Line-line 1234 | 7.1,3.1,107,84,110,33,Sunny,Line-line 1235 | 6.9,3.2,109,78,102,38,Sunny,Line-line 1236 | 6.6,3.1,105,81,107,31,Sunny,Line-line 1237 | 6.2,3.8,106,78,106,37,Sunny,Line-line 1238 | 7.5,2.3,106,81,105,32,Sunny,Line-line 1239 | 6.5,3.2,107,80,103,34,Sunny,Line-line 1240 | 6.2,3.9,108,81,103,36,Sunny,Line-line 1241 | 7.4,2.5,110,79,105,36,Sunny,Line-line 1242 | 6.2,2.6,106,85,105,38,Sunny,Line-line 1243 | 6.1,2.9,109,79,110,36,Sunny,Line-line 1244 | 6.8,2.8,107,80,105,35,Sunny,Line-line 1245 | 7.5,3.8,109,81,102,34,Sunny,Line-line 1246 | 7.2,3.3,109,75,103,35,Sunny,Line-line 1247 | 7.2,2.9,108,79,106,31,Sunny,Line-line 1248 | 7.5,3.1,106,77,110,35,Sunny,Line-line 1249 | 6.5,3.4,107,82,106,35,Sunny,Line-line 1250 | 2.1,5.1,73,102,107,37,Sunny,Line-line 1251 | 1.9,5.4,75,100,104,37,Cloudy,Line-line 1252 | 2.4,5.2,74,108,106,33,Cloudy,Line-line 1253 | 1.3,5.2,76,104,104,37,Cloudy,Line-line 1254 | 1.7,5.1,73,101,105,36,Cloudy,Line-line 1255 | 2.1,5.4,74,109,104,37,Cloudy,Line-line 1256 | 1,5.3,71,105,105,31,Cloudy,Line-line 1257 | 2.7,5.2,73,105,105,39,Cloudy,Line-line 1258 | 1.3,5.6,75,101,106,37,Cloudy,Line-line 1259 | 2.2,5.6,75,101,110,32,Cloudy,Line-line 1260 | 2.3,5.6,74,107,104,38,Cloudy,Line-line 1261 | 2.2,5.6,74,103,107,39,Cloudy,Line-line 1262 | 2.9,5.6,75,107,109,37,Cloudy,Line-line 1263 | 1.5,5.4,73,107,104,40,Cloudy,Line-line 1264 | 1.9,5.6,75,101,105,31,Cloudy,Line-line 1265 | 2.5,5.4,74,102,103,32,Cloudy,Line-line 1266 | 2.8,5.2,75,101,109,38,Cloudy,Line-line 1267 | 0.9,5.5,72,107,104,40,Cloudy,Line-line 1268 | 2.2,5.8,72,109,106,33,Cloudy,Line-line 1269 | 1,5.5,73,101,102,36,Cloudy,Line-line 1270 | 0.9,5.4,72,103,104,30,Cloudy,Line-line 1271 | 1.7,5.8,76,109,107,37,Cloudy,Line-line 1272 | 2.7,5.6,71,102,102,38,Cloudy,Line-line 1273 | 1.3,5.8,71,106,107,34,Cloudy,Line-line 1274 | 2.4,5.7,74,102,108,37,Cloudy,Line-line 1275 | 1.2,5.2,75,101,102,37,Cloudy,Line-line 1276 | 2.7,5.3,71,101,108,35,Cloudy,Line-line 1277 | 1.1,5.6,74,107,103,33,Cloudy,Line-line 1278 | 1.7,5.8,74,101,106,33,Cloudy,Line-line 1279 | 2.8,5.7,73,105,110,38,Cloudy,Line-line 1280 | 2.6,5.3,71,102,107,35,Cloudy,Line-line 1281 | 1.2,5.8,76,110,110,36,Cloudy,Line-line 1282 | 1.4,5.4,72,108,109,33,Cloudy,Line-line 1283 | 1.2,5.2,74,108,108,38,Cloudy,Line-line 1284 | 1.3,5.6,73,103,102,33,Cloudy,Line-line 1285 | 2.3,5.2,72,101,107,33,Cloudy,Line-line 1286 | 1.3,5.8,73,103,108,30,Cloudy,Line-line 1287 | 2.5,5.2,73,106,104,32,Cloudy,Line-line 1288 | 2.6,5.2,73,109,106,34,Cloudy,Line-line 1289 | 2.5,5.9,75,101,108,32,Cloudy,Line-line 1290 | 1.4,5.8,73,110,105,31,Cloudy,Line-line 1291 | 2.1,5.4,72,105,107,30,Cloudy,Line-line 1292 | 2.5,5.6,74,103,110,33,Cloudy,Line-line 1293 | 2.6,5.5,75,109,104,39,Cloudy,Line-line 1294 | 1.2,5.7,76,107,103,35,Cloudy,Line-line 1295 | 1.4,5.3,71,106,103,37,Cloudy,Line-line 1296 | 1.4,5.7,71,109,103,33,Cloudy,Line-line 1297 | 1.3,5.5,73,102,103,37,Cloudy,Line-line 1298 | 1.1,5.5,74,100,104,39,Cloudy,Line-line 1299 | 1.2,5.5,75,110,107,32,Cloudy,Line-line 1300 | 1.1,5.6,76,109,108,40,Cloudy,Line-line 1301 | 2.7,5.3,75,101,110,33,Cloudy,Line-line 1302 | 1.2,5.9,75,107,108,33,Cloudy,Line-line 1303 | 1,5.7,75,106,108,39,Cloudy,Line-line 1304 | 2.4,5.2,76,103,107,33,Cloudy,Line-line 1305 | 2.6,5.5,71,107,108,40,Cloudy,Line-line 1306 | 1.2,5.8,73,106,105,32,Cloudy,Line-line 1307 | 1.1,5.2,76,107,109,39,Cloudy,Line-line 1308 | 1.2,5.9,74,108,108,36,Cloudy,Line-line 1309 | 1.6,5.8,72,108,104,34,Cloudy,Line-line 1310 | 1,5.2,72,110,103,40,Cloudy,Line-line 1311 | 2.6,5.7,76,110,104,39,Cloudy,Line-line 1312 | 1.8,5.2,72,110,102,31,Cloudy,Line-line 1313 | 1.1,5.4,75,106,105,35,Cloudy,Line-line 1314 | 1.2,5.2,74,107,106,31,Cloudy,Line-line 1315 | 2.4,5.6,73,103,107,36,Cloudy,Line-line 1316 | 2.8,5.5,72,104,109,40,Cloudy,Line-line 1317 | 2.4,5.6,73,100,107,34,Cloudy,Line-line 1318 | 1.2,5.2,74,104,107,34,Cloudy,Line-line 1319 | 1.5,5.4,73,108,105,38,Cloudy,Line-line 1320 | 1.1,5.3,72,101,109,37,Cloudy,Line-line 1321 | 2.7,5.4,71,101,105,32,Cloudy,Line-line 1322 | 2,5.3,74,102,103,32,Cloudy,Line-line 1323 | 1.7,5.2,72,109,108,33,Cloudy,Line-line 1324 | 1.1,5.4,72,103,108,35,Cloudy,Line-line 1325 | 1.6,5.6,75,104,108,35,Cloudy,Line-line 1326 | 2.3,5.4,75,101,106,36,Cloudy,Line-line 1327 | 2.5,5.5,76,100,103,31,Cloudy,Line-line 1328 | 1.1,5.6,73,106,103,34,Cloudy,Line-line 1329 | 1.7,5.9,75,101,106,39,Cloudy,Line-line 1330 | 1.4,5.2,71,108,105,34,Cloudy,Line-line 1331 | 2,5.5,74,108,109,38,Cloudy,Line-line 1332 | 1.7,5.4,72,109,108,38,Cloudy,Line-line 1333 | 1.8,5.6,75,103,106,35,Cloudy,Line-line 1334 | 2.5,5.7,76,108,106,30,Cloudy,Line-line 1335 | 2.8,5.4,74,106,105,39,Cloudy,Line-line 1336 | 2.6,5.8,75,105,107,35,Cloudy,Line-line 1337 | 1.8,5.3,75,101,105,34,Cloudy,Line-line 1338 | 1.2,5.7,72,101,105,33,Cloudy,Line-line 1339 | 1.3,5.8,72,110,107,36,Cloudy,Line-line 1340 | 2.6,5.6,74,103,103,32,Cloudy,Line-line 1341 | 1.7,5.6,75,100,106,33,Cloudy,Line-line 1342 | 2.9,5.3,72,109,105,36,Cloudy,Line-line 1343 | 2.4,5.5,74,109,106,38,Cloudy,Line-line 1344 | 1,5.6,73,101,104,36,Cloudy,Line-line 1345 | 2,5.9,72,109,108,32,Cloudy,Line-line 1346 | 1.3,5.7,73,105,103,36,Cloudy,Line-line 1347 | 0.9,5.6,71,107,109,35,Cloudy,Line-line 1348 | 1.4,5.7,71,104,107,33,Cloudy,Line-line 1349 | 2.4,5.3,74,101,103,35,Cloudy,Line-line 1350 | 2.4,5.3,75,101,104,33,Cloudy,Line-line 1351 | 2.1,5.6,74,100,108,30,Cloudy,Line-line 1352 | 1.4,5.2,72,103,103,38,Cloudy,Line-line 1353 | 1.2,5.4,76,101,105,35,Cloudy,Line-line 1354 | 2.3,5.9,73,110,103,30,Cloudy,Line-line 1355 | 1.3,5.4,76,106,104,38,Cloudy,Line-line 1356 | 2.6,5.3,74,104,108,39,Cloudy,Line-line 1357 | 2.8,5.7,72,109,105,34,Cloudy,Line-line 1358 | 1.4,5.6,75,104,106,32,Cloudy,Line-line 1359 | 1.8,5.5,74,104,110,32,Cloudy,Line-line 1360 | 1.8,5.6,71,107,105,37,Cloudy,Line-line 1361 | 2.1,5.5,75,108,106,31,Cloudy,Line-line 1362 | 2.7,5.2,75,101,104,36,Cloudy,Line-line 1363 | 1.6,5.3,72,100,109,39,Cloudy,Line-line 1364 | 1.2,5.5,71,100,109,33,Cloudy,Line-line 1365 | 1.5,5.6,72,109,110,36,Cloudy,Line-line 1366 | 1.1,5.2,75,107,108,39,Cloudy,Line-line 1367 | 1.9,5.2,73,107,103,31,Cloudy,Line-line 1368 | 2.8,5.5,71,108,109,32,Cloudy,Line-line 1369 | 1.3,5.3,71,107,110,30,Cloudy,Line-line 1370 | 1.2,5.7,75,108,110,37,Cloudy,Line-line 1371 | 2.5,5.2,72,103,105,36,Cloudy,Line-line 1372 | 1.9,5.3,72,107,105,38,Cloudy,Line-line 1373 | 2.3,5.9,71,108,105,30,Cloudy,Line-line 1374 | 1.6,5.7,74,110,109,37,Cloudy,Line-line 1375 | 2.7,5.5,72,107,109,38,Cloudy,Line-line 1376 | 2.8,5.3,76,105,108,32,Cloudy,Line-line 1377 | 1,5.7,72,107,106,35,Cloudy,Line-line 1378 | 2.2,5.9,73,108,103,35,Cloudy,Line-line 1379 | 2.2,5.3,74,103,108,35,Cloudy,Line-line 1380 | 1.9,5.5,73,108,109,36,Cloudy,Line-line 1381 | 1.7,5.5,73,109,109,32,Cloudy,Line-line 1382 | 0.9,5.6,72,104,102,34,Cloudy,Line-line 1383 | 2.6,5.3,73,106,105,33,Cloudy,Line-line 1384 | 1.1,5.6,71,108,105,39,Cloudy,Line-line 1385 | 2.3,5.4,72,107,108,39,Cloudy,Line-line 1386 | 1,5.6,76,106,108,38,Cloudy,Line-line 1387 | 1.8,5.5,72,105,105,31,Cloudy,Line-line 1388 | 0.9,5.6,71,108,108,37,Cloudy,Line-line 1389 | 2.4,5.8,72,105,104,36,Cloudy,Line-line 1390 | 1.5,5.8,74,107,105,39,Cloudy,Line-line 1391 | 2.7,5.5,72,104,108,31,Cloudy,Line-line 1392 | 2.5,5.8,73,105,103,40,Cloudy,Line-line 1393 | 2.7,5.6,71,106,103,36,Cloudy,Line-line 1394 | 1.5,5.4,76,108,110,35,Cloudy,Line-line 1395 | 1,5.4,74,108,102,37,Cloudy,Line-line 1396 | 1.8,5.8,74,107,107,34,Cloudy,Line-line 1397 | 0.9,5.1,76,104,103,36,Cloudy,Line-line 1398 | 1.3,5.5,73,105,105,37,Cloudy,Line-line 1399 | 1.7,5.6,75,108,106,39,Cloudy,Line-line 1400 | 1.8,5.2,75,104,107,33,Cloudy,Line-line 1401 | 1.9,5.9,76,108,110,36,Cloudy,Line-line 1402 | 2.1,5.4,73,108,103,34,Cloudy,Line-line 1403 | 2.7,5.2,76,101,106,38,Cloudy,Line-line 1404 | 2.8,5.2,76,108,108,33,Cloudy,Line-line 1405 | 1,5.5,75,103,106,38,Cloudy,Line-line 1406 | 1.2,5.6,76,109,103,37,Cloudy,Line-line 1407 | 1.8,5.4,72,107,108,36,Cloudy,Line-line 1408 | 1.5,5.9,72,107,110,32,Cloudy,Line-line 1409 | 1.9,5.3,73,107,106,39,Cloudy,Line-line 1410 | 1.8,5.2,76,101,105,38,Cloudy,Line-line 1411 | 2.1,5.7,75,108,109,35,Cloudy,Line-line 1412 | 0.9,5.5,76,104,105,40,Cloudy,Line-line 1413 | 1.3,5.5,74,101,102,35,Cloudy,Line-line 1414 | 2.9,5.4,74,110,108,34,Cloudy,Line-line 1415 | 1.2,5.3,71,107,104,35,Cloudy,Line-line 1416 | 2.7,5.3,72,107,109,36,Cloudy,Line-line 1417 | 2.6,5.7,74,104,108,36,Cloudy,Line-line 1418 | 2,5.3,73,103,106,36,Cloudy,Line-line 1419 | 2.5,5.3,74,105,110,39,Cloudy,Line-line 1420 | 1.8,5.1,74,107,104,39,Cloudy,Line-line 1421 | 2.3,5.1,72,106,109,37,Cloudy,Line-line 1422 | 1.4,5.5,73,103,106,33,Cloudy,Line-line 1423 | 1.3,5.2,72,106,105,30,Cloudy,Line-line 1424 | 2.9,5.4,76,107,106,35,Cloudy,Line-line 1425 | 1.6,5.2,75,109,106,32,Cloudy,Line-line 1426 | 2.1,5.8,75,108,108,32,Cloudy,Line-line 1427 | 1.3,5.7,74,107,103,31,Cloudy,Line-line 1428 | 2.8,5.2,72,103,103,34,Cloudy,Line-line 1429 | 2.7,5.4,75,106,105,33,Cloudy,Line-line 1430 | 1.2,5.4,74,108,109,38,Cloudy,Line-line 1431 | 1.8,5.3,71,109,105,34,Cloudy,Line-line 1432 | 2.2,5.1,73,103,105,34,Cloudy,Line-line 1433 | 2.8,5.7,74,105,103,39,Cloudy,Line-line 1434 | 1.2,5.1,76,107,105,33,Cloudy,Line-line 1435 | 1.2,5.5,72,108,106,36,Cloudy,Line-line 1436 | 2,5.4,76,107,109,36,Cloudy,Line-line 1437 | 1.4,5.9,75,104,102,36,Cloudy,Line-line 1438 | 2.2,5.6,73,108,102,32,Cloudy,Line-line 1439 | 0.9,5.2,74,109,109,34,Cloudy,Line-line 1440 | 1.5,5.2,74,102,104,40,Cloudy,Line-line 1441 | 1.8,5.4,74,108,109,38,Cloudy,Line-line 1442 | 1.1,5.3,74,110,108,38,Cloudy,Line-line 1443 | 1.2,5.2,74,100,105,35,Cloudy,Line-line 1444 | 2.5,5.5,75,101,104,36,Cloudy,Line-line 1445 | 2.7,5.2,73,108,103,30,Cloudy,Line-line 1446 | 2,5.5,73,109,103,36,Cloudy,Line-line 1447 | 1.8,5.7,74,110,106,35,Cloudy,Line-line 1448 | 1.8,5.7,72,110,103,34,Cloudy,Line-line 1449 | 2.3,5.3,75,108,107,35,Cloudy,Line-line 1450 | 2.8,5.3,74,106,104,34,Cloudy,Line-line 1451 | 2.2,5.3,74,102,104,36,Cloudy,Line-line 1452 | 2.8,5.5,75,104,107,33,Cloudy,Line-line 1453 | 2.6,5.4,75,103,106,37,Cloudy,Line-line 1454 | 2.2,5.4,71,105,106,36,Cloudy,Line-line 1455 | 2.2,5.4,75,106,107,33,Cloudy,Line-line 1456 | 1.9,5.4,72,109,109,39,Cloudy,Line-line 1457 | 1.4,5.5,75,101,108,38,Cloudy,Line-line 1458 | 1.9,5.4,75,105,106,36,Cloudy,Line-line 1459 | 1,5.4,75,106,108,32,Cloudy,Line-line 1460 | 2,5.1,72,105,103,35,Cloudy,Line-line 1461 | 1,5.4,71,106,107,35,Cloudy,Line-line 1462 | 1.9,5.8,73,105,103,34,Cloudy,Line-line 1463 | 2,5.5,74,110,105,37,Cloudy,Line-line 1464 | 1.6,5.2,74,108,109,38,Cloudy,Line-line 1465 | 2.6,5.6,72,107,103,36,Cloudy,Line-line 1466 | 1.1,5.4,72,101,109,32,Cloudy,Line-line 1467 | 1.7,5.7,72,106,104,38,Cloudy,Line-line 1468 | 1.9,5.1,75,109,106,30,Cloudy,Line-line 1469 | 1,5.4,73,102,105,39,Cloudy,Line-line 1470 | 2.3,5.2,74,101,107,39,Cloudy,Line-line 1471 | 2.4,5.5,73,108,108,32,Cloudy,Line-line 1472 | 2.2,5.7,75,103,103,38,Cloudy,Line-line 1473 | 2.5,5.6,73,102,102,36,Cloudy,Line-line 1474 | 2.5,5.4,72,104,104,36,Cloudy,Line-line 1475 | 1.1,5.5,74,105,108,38,Cloudy,Line-line 1476 | 2,5.6,73,108,108,34,Cloudy,Line-line 1477 | 2.2,5.2,75,107,109,32,Cloudy,Line-line 1478 | 1.6,5.8,72,103,106,39,Cloudy,Line-line 1479 | 1.3,5.9,73,103,104,33,Cloudy,Line-line 1480 | 2.1,5.5,75,108,107,38,Cloudy,Line-line 1481 | 1.1,5.8,74,104,104,34,Cloudy,Line-line 1482 | 2.3,5.9,75,110,106,36,Cloudy,Line-line 1483 | 2.5,5.4,73,105,102,40,Cloudy,Line-line 1484 | 1.1,5.5,75,105,108,40,Cloudy,Line-line 1485 | 1.3,5.2,75,101,110,39,Cloudy,Line-line 1486 | 2.5,5.2,72,101,104,40,Cloudy,Line-line 1487 | 2.8,5.8,75,109,103,30,Cloudy,Line-line 1488 | 2.1,5.9,71,105,105,30,Cloudy,Line-line 1489 | 1.5,5.7,76,100,106,34,Cloudy,Line-line 1490 | 1.2,5.8,73,103,103,34,Cloudy,Line-line 1491 | 2,5.6,75,106,103,35,Cloudy,Line-line 1492 | 1.8,5.5,72,110,108,33,Cloudy,Line-line 1493 | 1.6,5.3,74,103,106,32,Cloudy,Line-line 1494 | 1.9,5.3,73,110,107,36,Cloudy,Line-line 1495 | 1.5,5.7,73,103,105,35,Cloudy,Line-line 1496 | 2,5.6,71,101,106,40,Cloudy,Line-line 1497 | 2.6,5.8,74,103,109,31,Cloudy,Line-line 1498 | 1.2,5.2,71,103,110,31,Cloudy,Line-line 1499 | 2.4,5.3,72,106,103,37,Cloudy,Line-line 1500 | 1.8,5.5,71,103,108,35,Cloudy,Line-line 1501 | 1.8,5.2,72,105,106,38,Cloudy,Line-line 1502 | 5.4,4.6,100,101,97,15,Sunny,Normal 1503 | 4.7,5.1,104,104,72,16,Sunny,Normal 1504 | 3.2,3.7,81,81,88,16,Sunny,Normal 1505 | 3.6,3.8,85,87,92,14,Sunny,Normal 1506 | 5.4,5.4,102,95,82,12,Sunny,Normal 1507 | 5.1,3.8,100,105,100,16,Sunny,Normal 1508 | 3.4,4,80,83,98,14,Sunny,Normal 1509 | 4.7,4.1,91,87,93,16,Sunny,Normal 1510 | 4.3,4.9,93,86,95,12,Sunny,Normal 1511 | 3.6,4.2,102,92,87,12,Sunny,Normal 1512 | 3.4,4.4,85,90,93,16,Sunny,Normal 1513 | 3.9,5.2,91,80,89,14,Sunny,Normal 1514 | 4.1,3.7,80,106,90,12,Sunny,Normal 1515 | 4.2,5.7,98,96,81,12,Sunny,Normal 1516 | 5.5,3.8,94,92,92,12,Sunny,Normal 1517 | 5.1,4.2,96,81,99,15,Sunny,Normal 1518 | 5.3,3.8,93,98,99,14,Sunny,Normal 1519 | 3,4.9,88,81,97,15,Sunny,Normal 1520 | 3.5,3.3,93,101,86,15,Sunny,Normal 1521 | 3.2,4.9,102,105,99,16,Sunny,Normal 1522 | 4.4,4.1,93,101,98,12,Sunny,Normal 1523 | 5.1,5.6,103,92,81,13,Sunny,Normal 1524 | 5.1,4.3,99,81,86,15,Sunny,Normal 1525 | 5,4.4,86,95,86,15,Sunny,Normal 1526 | 3.6,5.1,82,89,80,13,Sunny,Normal 1527 | 5.5,4.6,99,85,92,16,Sunny,Normal 1528 | 5.3,5,84,97,86,16,Sunny,Normal 1529 | 4.6,4.8,92,86,99,12,Sunny,Normal 1530 | 4.6,3.5,80,100,98,12,Sunny,Normal 1531 | 4.6,3.5,100,85,95,13,Sunny,Normal 1532 | 3.4,5,83,92,96,16,Sunny,Normal 1533 | 3.9,4.1,87,84,87,15,Sunny,Normal 1534 | 4.3,5.2,86,96,90,12,Sunny,Normal 1535 | 4.4,4.5,97,89,93,12,Sunny,Normal 1536 | 5.5,3.3,87,101,84,15,Sunny,Normal 1537 | 4.9,4.2,83,86,85,16,Sunny,Normal 1538 | 5.4,5.4,100,101,90,13,Sunny,Normal 1539 | 4.5,5.4,100,92,94,14,Sunny,Normal 1540 | 3.4,5,88,96,90,13,Sunny,Normal 1541 | 3.8,5.5,90,81,86,12,Sunny,Normal 1542 | 3.6,3.8,103,103,80,14,Sunny,Normal 1543 | 3.5,5.3,90,103,85,16,Sunny,Normal 1544 | 4.4,4.8,102,104,85,13,Sunny,Normal 1545 | 5.7,3.9,105,91,88,15,Sunny,Normal 1546 | 3,5.7,96,92,80,13,Sunny,Normal 1547 | 5.5,4.8,102,105,87,14,Sunny,Normal 1548 | 4.4,4.7,100,95,95,15,Sunny,Normal 1549 | 5,3.4,84,81,83,14,Sunny,Normal 1550 | 5.2,3,103,83,100,15,Sunny,Normal 1551 | 4.9,5.6,88,98,81,16,Sunny,Normal 1552 | 4.5,5.5,105,98,89,16,Sunny,Normal 1553 | 4,5.5,82,106,85,16,Sunny,Normal 1554 | 3.6,4.1,80,95,92,12,Sunny,Normal 1555 | 3,5.3,99,80,92,14,Sunny,Normal 1556 | 3.2,3.3,83,97,100,13,Sunny,Normal 1557 | 4,3.1,80,86,97,13,Sunny,Normal 1558 | 5.6,3.7,95,85,83,12,Sunny,Normal 1559 | 3.4,5.1,101,83,86,13,Sunny,Normal 1560 | 3.7,5.2,81,88,98,13,Sunny,Normal 1561 | 5.3,4.2,94,104,96,14,Sunny,Normal 1562 | 5.5,4.5,103,89,80,13,Sunny,Normal 1563 | 4.1,4.7,86,80,83,16,Sunny,Normal 1564 | 4.1,3.1,104,102,84,16,Sunny,Normal 1565 | 3,4.8,104,83,93,15,Sunny,Normal 1566 | 5.2,5.2,101,94,89,16,Sunny,Normal 1567 | 5.3,3.2,100,85,99,14,Sunny,Normal 1568 | 5,3.2,103,93,100,16,Sunny,Normal 1569 | 5.7,4.4,86,98,80,14,Sunny,Normal 1570 | 4,3.9,95,102,90,12,Sunny,Normal 1571 | 4.1,5,82,89,96,13,Sunny,Normal 1572 | 3.1,4.6,100,88,95,15,Sunny,Normal 1573 | 4,5.6,82,84,88,14,Sunny,Normal 1574 | 4.4,5.3,103,82,92,12,Sunny,Normal 1575 | 4.5,4.6,101,87,85,15,Sunny,Normal 1576 | 3.9,4.1,101,97,99,12,Sunny,Normal 1577 | 3.1,4.8,99,84,85,14,Sunny,Normal 1578 | 3.7,4.7,105,106,96,12,Sunny,Normal 1579 | 5,3.5,94,80,92,16,Sunny,Normal 1580 | 5.1,4.3,88,90,83,15,Sunny,Normal 1581 | 3.7,3.8,101,86,90,12,Sunny,Normal 1582 | 4,4,100,88,96,15,Sunny,Normal 1583 | 4.5,5.6,87,95,98,12,Sunny,Normal 1584 | 5.4,3.2,94,100,80,16,Sunny,Normal 1585 | 4.8,3.6,101,104,93,14,Sunny,Normal 1586 | 3.5,4.6,97,101,83,16,Sunny,Normal 1587 | 5.4,5.1,100,84,93,12,Sunny,Normal 1588 | 4.4,3.4,106,82,83,16,Sunny,Normal 1589 | 4.2,3.8,86,104,80,13,Sunny,Normal 1590 | 4,4.8,104,97,99,13,Sunny,Normal 1591 | 3.7,5.4,85,104,99,12,Sunny,Normal 1592 | 4.2,4,89,105,94,16,Sunny,Normal 1593 | 5.2,4.2,106,105,89,12,Sunny,Normal 1594 | 3.3,3.5,97,100,98,16,Sunny,Normal 1595 | 4.7,5.1,104,93,86,16,Sunny,Normal 1596 | 4,4.5,104,104,80,15,Sunny,Normal 1597 | 5.3,4.9,90,89,81,12,Sunny,Normal 1598 | 4.6,3.7,90,85,93,13,Sunny,Normal 1599 | 3.9,4,89,100,88,14,Sunny,Normal 1600 | 4,4.2,101,106,98,13,Sunny,Normal 1601 | 5,5.7,94,88,89,15,Sunny,Normal 1602 | 4.8,4.7,83,106,86,13,Sunny,Normal 1603 | 5.2,3.1,87,80,98,12,Sunny,Normal 1604 | 3.3,5.3,89,84,93,16,Sunny,Normal 1605 | 5.2,3.8,99,94,93,12,Sunny,Normal 1606 | 5.1,3.8,87,102,82,16,Sunny,Normal 1607 | 5.3,4.8,97,98,83,13,Sunny,Normal 1608 | 3.4,4.1,91,100,100,16,Sunny,Normal 1609 | 4.2,5.4,104,92,86,13,Sunny,Normal 1610 | 4.4,4.1,91,104,82,13,Sunny,Normal 1611 | 5.5,4.2,82,88,89,15,Sunny,Normal 1612 | 3.7,5.4,83,102,81,12,Sunny,Normal 1613 | 3,4.7,83,92,85,16,Sunny,Normal 1614 | 5.3,3.7,83,81,80,12,Sunny,Normal 1615 | 4.8,5.3,81,90,80,12,Sunny,Normal 1616 | 3.6,4,99,85,100,14,Sunny,Normal 1617 | 4.8,3.8,91,89,92,13,Sunny,Normal 1618 | 4.2,3.5,93,102,93,15,Sunny,Normal 1619 | 5.4,4.9,105,98,88,12,Sunny,Normal 1620 | 5.1,3,83,89,100,16,Sunny,Normal 1621 | 5,5.1,87,89,95,14,Sunny,Normal 1622 | 3.6,3.4,88,97,97,14,Sunny,Normal 1623 | 3.5,3.7,87,82,96,16,Sunny,Normal 1624 | 3.3,5.3,104,95,99,12,Sunny,Normal 1625 | 5.1,3.6,103,102,85,14,Sunny,Normal 1626 | 5.6,3.4,81,88,94,14,Sunny,Normal 1627 | 5.1,3.6,85,97,85,12,Sunny,Normal 1628 | 3.5,5.3,105,85,80,16,Sunny,Normal 1629 | 3.1,5.7,102,100,84,16,Sunny,Normal 1630 | 4.7,4.5,97,99,92,14,Sunny,Normal 1631 | 3.8,5.6,82,96,99,16,Sunny,Normal 1632 | 5.2,3.8,84,89,91,13,Sunny,Normal 1633 | 4.3,3.9,97,81,94,12,Sunny,Normal 1634 | 3.8,3.6,94,104,81,12,Sunny,Normal 1635 | 4.3,3.1,82,92,97,12,Sunny,Normal 1636 | 5.4,3.9,83,105,92,13,Sunny,Normal 1637 | 5.2,3.1,99,97,89,15,Sunny,Normal 1638 | 3.7,5,101,92,82,12,Sunny,Normal 1639 | 3.7,4.6,88,94,94,14,Sunny,Normal 1640 | 4.7,3.2,81,94,97,16,Sunny,Normal 1641 | 4.7,4.8,92,89,98,14,Sunny,Normal 1642 | 3.9,3.7,83,104,89,14,Sunny,Normal 1643 | 4.7,4.3,101,93,87,14,Sunny,Normal 1644 | 4.3,4.4,96,105,99,15,Sunny,Normal 1645 | 5.1,3.3,104,91,89,13,Sunny,Normal 1646 | 4.6,3.4,96,90,97,12,Sunny,Normal 1647 | 4,3.3,101,84,97,14,Sunny,Normal 1648 | 4.2,3.2,82,82,91,13,Sunny,Normal 1649 | 4,3.5,90,93,98,13,Sunny,Normal 1650 | 4.4,3.7,106,96,81,13,Sunny,Normal 1651 | 5,4.4,80,98,95,13,Sunny,Normal 1652 | 3.2,3.3,92,90,95,14,Sunny,Normal 1653 | 3.7,5,85,84,98,13,Sunny,Normal 1654 | 3.2,3.4,87,95,80,13,Sunny,Normal 1655 | 4.9,3.1,96,106,92,16,Sunny,Normal 1656 | 4,4.6,81,91,95,16,Sunny,Normal 1657 | 3.2,4.1,85,86,92,13,Sunny,Normal 1658 | 4.4,5.1,80,91,91,12,Sunny,Normal 1659 | 5.2,4.2,94,92,90,14,Sunny,Normal 1660 | 4.9,4.9,81,99,94,13,Sunny,Normal 1661 | 5.6,5.5,81,90,97,16,Sunny,Normal 1662 | 3.8,4.8,103,99,88,12,Sunny,Normal 1663 | 5.2,5.3,87,95,97,16,Sunny,Normal 1664 | 4.4,5.5,90,81,83,15,Sunny,Normal 1665 | 5.1,3,101,86,99,14,Sunny,Normal 1666 | 4.4,4.3,104,106,93,14,Sunny,Normal 1667 | 3.6,5.6,101,102,89,14,Sunny,Normal 1668 | 3.6,5.4,81,90,81,13,Sunny,Normal 1669 | 3.5,4.1,103,94,90,14,Sunny,Normal 1670 | 4.1,3.7,83,89,85,13,Sunny,Normal 1671 | 3.1,4,84,104,89,13,Sunny,Normal 1672 | 3,3.3,81,91,98,12,Sunny,Normal 1673 | 4.3,3.1,80,106,94,13,Sunny,Normal 1674 | 3.9,4.2,87,91,97,16,Sunny,Normal 1675 | 5.1,4,94,97,91,13,Sunny,Normal 1676 | 3.1,3.8,92,82,81,16,Sunny,Normal 1677 | 3.8,5.5,80,104,82,12,Sunny,Normal 1678 | 4.6,5.7,97,89,80,16,Sunny,Normal 1679 | 4.6,4,91,88,100,14,Sunny,Normal 1680 | 4.6,4,99,90,100,15,Sunny,Normal 1681 | 4.6,4.7,84,82,98,14,Sunny,Normal 1682 | 5.1,5.1,106,97,84,15,Sunny,Normal 1683 | 5.1,3.3,82,103,91,14,Sunny,Normal 1684 | 4.7,5.2,97,104,90,16,Sunny,Normal 1685 | 4.1,4.3,85,86,91,14,Sunny,Normal 1686 | 4.5,4.6,85,96,84,14,Sunny,Normal 1687 | 5,4.6,92,83,88,15,Sunny,Normal 1688 | 4,3.5,91,83,99,12,Sunny,Normal 1689 | 4.2,4.4,96,93,82,13,Sunny,Normal 1690 | 4.5,5.1,95,93,98,15,Sunny,Normal 1691 | 4.9,3.9,98,86,98,15,Sunny,Normal 1692 | 4.6,4.7,100,97,99,14,Sunny,Normal 1693 | 3.3,3.2,83,80,87,13,Sunny,Normal 1694 | 5.4,3.5,105,83,86,12,Sunny,Normal 1695 | 4.4,4.7,91,92,82,15,Sunny,Normal 1696 | 4.4,4.8,89,92,96,15,Sunny,Normal 1697 | 4.6,3.3,94,87,90,13,Sunny,Normal 1698 | 4.1,4.3,82,105,86,12,Sunny,Normal 1699 | 3.1,5.2,80,102,90,12,Sunny,Normal 1700 | 5.2,5.5,86,94,90,16,Sunny,Normal 1701 | 4.1,4.1,83,96,89,13,Sunny,Normal 1702 | 3.4,5.1,92,81,93,12,Sunny,Normal 1703 | 5.4,3.3,85,89,89,16,Sunny,Normal 1704 | 4.3,3.9,93,102,99,14,Sunny,Normal 1705 | 4.3,5.3,105,100,92,14,Sunny,Normal 1706 | 4.9,4.4,97,96,88,15,Sunny,Normal 1707 | 4.6,3.6,92,105,81,14,Sunny,Normal 1708 | 5.7,4,101,90,83,16,Sunny,Normal 1709 | 4,5,93,96,94,15,Sunny,Normal 1710 | 5.5,3.4,97,91,82,13,Sunny,Normal 1711 | 4.6,3.1,89,92,88,14,Sunny,Normal 1712 | 4.5,3.1,91,102,80,14,Sunny,Normal 1713 | 5.2,4.1,81,92,89,13,Sunny,Normal 1714 | 3.8,4.3,86,82,81,12,Sunny,Normal 1715 | 5.6,4.1,91,97,84,12,Sunny,Normal 1716 | 3.2,4,87,88,85,16,Sunny,Normal 1717 | 5.2,3.5,98,91,80,12,Sunny,Normal 1718 | 5.6,3.9,105,82,91,14,Sunny,Normal 1719 | 3.4,4,88,99,96,16,Sunny,Normal 1720 | 5.7,4.9,86,93,98,12,Sunny,Normal 1721 | 4.3,3.7,86,94,96,16,Sunny,Normal 1722 | 3.9,4.4,93,88,84,13,Sunny,Normal 1723 | 4.7,3.1,85,86,95,14,Sunny,Normal 1724 | 4.1,3.7,84,90,86,15,Sunny,Normal 1725 | 3.2,3.9,106,87,95,15,Sunny,Normal 1726 | 4.9,5.3,102,88,97,16,Sunny,Normal 1727 | 5.6,3,102,92,99,12,Sunny,Normal 1728 | 5.3,4.6,102,96,91,16,Sunny,Normal 1729 | 3.4,3.8,85,81,89,15,Sunny,Normal 1730 | 5.3,4.7,99,85,99,16,Sunny,Normal 1731 | 5.2,4.8,81,98,82,14,Sunny,Normal 1732 | 5.5,4.2,93,97,93,12,Sunny,Normal 1733 | 3.3,3.7,84,103,91,14,Sunny,Normal 1734 | 4.5,5.6,96,95,80,14,Sunny,Normal 1735 | 3.4,3.2,103,105,95,16,Sunny,Normal 1736 | 3.9,5.1,92,81,86,15,Sunny,Normal 1737 | 5.3,4.2,85,105,91,13,Sunny,Normal 1738 | 5.1,4.6,101,83,81,16,Sunny,Normal 1739 | 3.2,5.4,104,97,92,13,Sunny,Normal 1740 | 3.7,3,93,98,84,13,Sunny,Normal 1741 | 3.9,5,87,90,88,15,Sunny,Normal 1742 | 4.9,5.6,105,88,97,14,Sunny,Normal 1743 | 3.7,4.8,98,85,90,12,Sunny,Normal 1744 | 3.1,5.6,80,81,89,13,Sunny,Normal 1745 | 4.6,3.7,81,88,93,15,Sunny,Normal 1746 | 5.1,4.4,80,95,88,15,Sunny,Normal 1747 | 3,5.6,80,101,91,14,Sunny,Normal 1748 | 4.9,5.5,81,90,93,12,Sunny,Normal 1749 | 5.4,5.2,91,102,88,14,Sunny,Normal 1750 | 3.2,3.2,105,83,84,14,Sunny,Normal 1751 | 5.3,3.7,93,99,91,12,Sunny,Normal 1752 | 0.5,0.5,93,90,9,4,Cloudy,Normal 1753 | 0.4,0.5,93,92,9.2,4,Cloudy,Normal 1754 | 0.6,0.6,97,99,12,4,Cloudy,Normal 1755 | 0.7,0.8,82,81,11,2,Cloudy,Normal 1756 | 0.8,0.6,93,88,11,2,Cloudy,Normal 1757 | 0.9,0.8,90,91,11,4,Cloudy,Normal 1758 | 0.8,0.6,98,91,12,5,Cloudy,Normal 1759 | 0.4,0.5,82,95,12,3,Cloudy,Normal 1760 | 0.6,0.8,97,94,12,3,Cloudy,Normal 1761 | 0.4,0.8,94,81,9,2,Cloudy,Normal 1762 | 0.8,0.5,97,84,9,5,Cloudy,Normal 1763 | 0.6,0.7,85,98,13,4,Cloudy,Normal 1764 | 0.5,0.4,93,80,11,2,Cloudy,Normal 1765 | 0.7,0.6,80,95,11,4,Cloudy,Normal 1766 | 0.9,0.5,88,95,10,6,Cloudy,Normal 1767 | 0.6,0.9,91,82,13,3,Cloudy,Normal 1768 | 0.8,0.4,98,80,10,5,Cloudy,Normal 1769 | 0.5,0.7,86,93,10,5,Cloudy,Normal 1770 | 0.7,0.7,92,98,13,4,Cloudy,Normal 1771 | 0.7,0.8,88,91,13,6,Cloudy,Normal 1772 | 0.4,0.7,99,80,13,5,Cloudy,Normal 1773 | 0.5,0.5,87,81,14,6,Cloudy,Normal 1774 | 0.4,0.6,81,87,13,2,Cloudy,Normal 1775 | 0.7,0.6,90,85,10,6,Cloudy,Normal 1776 | 0.6,0.8,99,82,9,4,Cloudy,Normal 1777 | 0.4,0.5,92,80,12,2,Cloudy,Normal 1778 | 0.8,0.7,93,86,10,4,Cloudy,Normal 1779 | 0.4,0.7,89,80,12,5,Cloudy,Normal 1780 | 0.7,0.6,93,90,12,2,Cloudy,Normal 1781 | 0.8,0.6,88,93,9,3,Cloudy,Normal 1782 | 0.8,0.5,84,95,14,4,Cloudy,Normal 1783 | 0.8,0.4,96,93,10,2,Cloudy,Normal 1784 | 0.5,0.5,93,83,12,6,Cloudy,Normal 1785 | 0.9,0.6,80,85,12,4,Cloudy,Normal 1786 | 0.7,0.7,94,80,11,4,Cloudy,Normal 1787 | 0.7,0.6,89,98,12,6,Cloudy,Normal 1788 | 0.6,0.8,92,96,12,6,Cloudy,Normal 1789 | 0.8,0.9,92,91,12,5,Cloudy,Normal 1790 | 0.6,0.6,85,92,13,3,Cloudy,Normal 1791 | 0.8,0.7,98,93,11,3,Cloudy,Normal 1792 | 0.6,0.4,88,80,11,6,Cloudy,Normal 1793 | 0.6,0.7,92,84,13,4,Cloudy,Normal 1794 | 0.8,0.7,93,96,9,2,Cloudy,Normal 1795 | 0.5,0.6,91,86,11,5,Cloudy,Normal 1796 | 0.8,0.5,97,80,9,3,Cloudy,Normal 1797 | 0.8,0.6,93,80,13,4,Cloudy,Normal 1798 | 0.5,0.9,81,99,14,2,Cloudy,Normal 1799 | 0.5,0.5,84,86,14,5,Cloudy,Normal 1800 | 0.8,0.5,86,80,9,4,Cloudy,Normal 1801 | 0.5,0.6,81,97,12,5,Cloudy,Normal 1802 | 1.1,0.8,80,99,9,3,Cloudy,Normal 1803 | 0.9,0.4,98,85,9,5,Cloudy,Normal 1804 | 0.6,0.7,85,93,12,2,Cloudy,Normal 1805 | 0.9,0.7,88,98,13,5,Cloudy,Normal 1806 | 0.7,0.4,81,91,11,4,Cloudy,Normal 1807 | 0.6,0.5,86,91,14,3,Cloudy,Normal 1808 | 0.9,0.9,85,81,13,5,Cloudy,Normal 1809 | 0.6,0.6,91,90,14,3,Cloudy,Normal 1810 | 0.6,0.5,88,85,9,2,Cloudy,Normal 1811 | 0.6,0.4,97,83,12,3,Cloudy,Normal 1812 | 0.6,0.7,94,94,10,5,Cloudy,Normal 1813 | 0.4,0.8,85,86,14,2,Cloudy,Normal 1814 | 0.6,0.9,84,94,13,3,Cloudy,Normal 1815 | 0.6,0.5,98,97,12,6,Cloudy,Normal 1816 | 0.8,0.6,85,99,9,3,Cloudy,Normal 1817 | 0.6,0.6,82,89,9,5,Cloudy,Normal 1818 | 0.8,0.8,83,89,10,3,Cloudy,Normal 1819 | 0.8,0.6,82,85,9,5,Cloudy,Normal 1820 | 0.7,0.5,97,96,14,5,Cloudy,Normal 1821 | 0.8,0.7,95,80,14,2,Cloudy,Normal 1822 | 0.4,0.6,83,90,10,2,Cloudy,Normal 1823 | 0.5,0.6,88,80,11,6,Cloudy,Normal 1824 | 0.5,0.6,80,97,14,2,Cloudy,Normal 1825 | 0.6,0.5,98,91,9,4,Cloudy,Normal 1826 | 0.7,0.4,98,95,9,5,Cloudy,Normal 1827 | 0.7,0.9,84,91,10,2,Cloudy,Normal 1828 | 0.9,0.5,88,83,11,2,Cloudy,Normal 1829 | 0.8,0.5,93,93,9,4,Cloudy,Normal 1830 | 0.6,0.4,95,80,12,6,Cloudy,Normal 1831 | 0.7,0.6,98,80,12,4,Cloudy,Normal 1832 | 0.9,0.9,95,98,9,3,Cloudy,Normal 1833 | 0.7,0.7,97,87,11,5,Cloudy,Normal 1834 | 0.9,0.5,90,83,14,6,Cloudy,Normal 1835 | 0.7,0.6,81,84,14,2,Cloudy,Normal 1836 | 0.7,0.4,83,84,13,5,Cloudy,Normal 1837 | 0.4,0.9,83,87,10,5,Cloudy,Normal 1838 | 0.9,0.8,96,99,13,3,Cloudy,Normal 1839 | 0.6,0.5,88,87,11,5,Cloudy,Normal 1840 | 1,0.7,99,86,13,2,Cloudy,Normal 1841 | 0.6,0.8,83,98,13,5,Cloudy,Normal 1842 | 0.7,0.6,97,99,13,6,Cloudy,Normal 1843 | 0.4,0.7,86,85,14,4,Cloudy,Normal 1844 | 0.8,0.7,97,84,11,2,Cloudy,Normal 1845 | 0.5,0.6,98,93,13,6,Cloudy,Normal 1846 | 0.4,0.5,95,87,11,5,Cloudy,Normal 1847 | 0.4,0.7,83,95,13,5,Cloudy,Normal 1848 | 0.6,0.7,92,84,13,4,Cloudy,Normal 1849 | 0.7,0.6,91,83,10,4,Cloudy,Normal 1850 | 0.7,0.7,88,96,14,5,Cloudy,Normal 1851 | 0.6,0.9,94,81,10,6,Cloudy,Normal 1852 | 0.8,0.6,92,89,12,6,Cloudy,Normal 1853 | 0.9,0.6,99,82,12,5,Cloudy,Normal 1854 | 0.7,0.7,94,97,13,3,Cloudy,Normal 1855 | 0.8,0.7,80,80,12,2,Cloudy,Normal 1856 | 0.9,0.7,82,87,12,2,Cloudy,Normal 1857 | 0.6,0.8,84,80,11,2,Cloudy,Normal 1858 | 0.8,0.7,92,94,9,4,Cloudy,Normal 1859 | 0.5,0.9,83,93,12,2,Cloudy,Normal 1860 | 1,0.7,95,98,12,4,Cloudy,Normal 1861 | 0.7,0.5,94,90,14,4,Cloudy,Normal 1862 | 0.5,0.8,89,80,10,5,Cloudy,Normal 1863 | 0.5,0.9,95,96,9,5,Cloudy,Normal 1864 | 0.9,0.6,90,84,9,2,Cloudy,Normal 1865 | 0.9,0.6,81,87,9,4,Cloudy,Normal 1866 | 0.9,0.8,97,94,13,5,Cloudy,Normal 1867 | 0.6,0.7,84,96,11,5,Cloudy,Normal 1868 | 0.5,0.6,99,86,10,2,Cloudy,Normal 1869 | 0.7,0.5,98,91,12,3,Cloudy,Normal 1870 | 0.8,0.6,93,91,10,6,Cloudy,Normal 1871 | 0.4,0.8,99,81,9,4,Cloudy,Normal 1872 | 0.6,0.7,99,86,13,2,Cloudy,Normal 1873 | 0.5,0.8,82,83,11,3,Cloudy,Normal 1874 | 0.6,0.7,98,90,12,5,Cloudy,Normal 1875 | 0.5,0.7,92,97,13,5,Cloudy,Normal 1876 | 0.8,0.8,92,98,14,2,Cloudy,Normal 1877 | 0.8,0.7,81,81,11,6,Cloudy,Normal 1878 | 0.9,0.8,80,97,13,4,Cloudy,Normal 1879 | 0.5,0.5,89,82,10,6,Cloudy,Normal 1880 | 0.9,0.5,86,90,11,5,Cloudy,Normal 1881 | 0.4,0.7,80,88,11,2,Cloudy,Normal 1882 | 0.6,0.6,82,91,14,4,Cloudy,Normal 1883 | 0.7,0.7,99,90,9,4,Cloudy,Normal 1884 | 0.6,0.5,95,81,13,5,Cloudy,Normal 1885 | 1.1,0.5,87,97,12,2,Cloudy,Normal 1886 | 0.8,0.6,83,83,14,6,Cloudy,Normal 1887 | 0.6,0.8,99,97,10,4,Cloudy,Normal 1888 | 0.7,0.5,87,96,10,2,Cloudy,Normal 1889 | 0.5,0.9,83,95,9,4,Cloudy,Normal 1890 | 0.4,0.5,90,80,11,6,Cloudy,Normal 1891 | 0.6,0.5,99,90,9,2,Cloudy,Normal 1892 | 0.5,0.8,83,92,12,5,Cloudy,Normal 1893 | 0.9,0.7,92,90,12,5,Cloudy,Normal 1894 | 0.6,0.6,87,88,13,4,Cloudy,Normal 1895 | 0.7,0.5,80,96,10,2,Cloudy,Normal 1896 | 0.5,0.6,96,85,13,2,Cloudy,Normal 1897 | 0.5,0.9,93,86,9,4,Cloudy,Normal 1898 | 0.6,0.7,82,82,11,4,Cloudy,Normal 1899 | 0.4,0.8,97,93,9,4,Cloudy,Normal 1900 | 0.6,0.6,83,90,12,4,Cloudy,Normal 1901 | 0.9,0.7,91,86,10,3,Cloudy,Normal 1902 | 0.5,0.8,81,94,11,3,Cloudy,Normal 1903 | 0.8,0.9,94,89,10,2,Cloudy,Normal 1904 | 0.7,0.9,90,88,14,3,Cloudy,Normal 1905 | 0.7,0.5,91,91,13,6,Cloudy,Normal 1906 | 0.5,0.9,80,94,9,2,Cloudy,Normal 1907 | 0.7,0.5,87,81,14,5,Cloudy,Normal 1908 | 0.5,0.8,84,96,10,2,Cloudy,Normal 1909 | 0.4,0.5,99,80,11,5,Cloudy,Normal 1910 | 1,0.9,96,96,14,6,Cloudy,Normal 1911 | 0.9,0.8,86,82,13,5,Cloudy,Normal 1912 | 0.7,0.7,97,96,9,3,Cloudy,Normal 1913 | 0.6,0.9,85,86,10,6,Cloudy,Normal 1914 | 0.6,0.7,94,88,9,6,Cloudy,Normal 1915 | 0.8,0.6,82,90,12,5,Cloudy,Normal 1916 | 0.4,0.9,98,88,14,3,Cloudy,Normal 1917 | 0.4,0.4,86,81,13,6,Cloudy,Normal 1918 | 0.8,0.9,90,88,12,6,Cloudy,Normal 1919 | 0.6,0.4,81,86,9,4,Cloudy,Normal 1920 | 0.7,0.8,94,96,14,4,Cloudy,Normal 1921 | 0.6,0.7,84,85,12,6,Cloudy,Normal 1922 | 0.5,0.4,97,90,10,5,Cloudy,Normal 1923 | 0.5,0.7,96,95,12,3,Cloudy,Normal 1924 | 0.7,0.8,97,84,12,5,Cloudy,Normal 1925 | 0.4,0.6,86,98,10,3,Cloudy,Normal 1926 | 0.7,0.5,86,97,10,3,Cloudy,Normal 1927 | 0.4,0.7,80,89,10,4,Cloudy,Normal 1928 | 0.7,0.6,86,85,14,4,Cloudy,Normal 1929 | 0.7,0.7,96,90,14,6,Cloudy,Normal 1930 | 0.9,0.6,93,83,10,3,Cloudy,Normal 1931 | 0.6,0.4,94,90,11,4,Cloudy,Normal 1932 | 1,0.8,82,87,14,5,Cloudy,Normal 1933 | 0.8,0.6,97,83,11,2,Cloudy,Normal 1934 | 0.4,0.7,91,88,13,5,Cloudy,Normal 1935 | 0.4,0.8,87,85,11,2,Cloudy,Normal 1936 | 0.5,0.5,82,96,13,6,Cloudy,Normal 1937 | 0.5,0.8,97,99,12,2,Cloudy,Normal 1938 | 0.7,0.6,97,85,9,4,Cloudy,Normal 1939 | 0.6,0.8,84,99,14,5,Cloudy,Normal 1940 | 0.5,0.4,80,87,13,6,Cloudy,Normal 1941 | 0.4,0.8,94,93,14,5,Cloudy,Normal 1942 | 0.4,0.8,90,98,9,2,Cloudy,Normal 1943 | 0.4,0.9,96,89,10,5,Cloudy,Normal 1944 | 0.6,0.7,94,88,14,4,Cloudy,Normal 1945 | 0.9,0.7,80,81,13,3,Cloudy,Normal 1946 | 0.6,0.8,96,97,12,6,Cloudy,Normal 1947 | 0.9,0.6,93,85,9,6,Cloudy,Normal 1948 | 0.4,0.6,80,98,13,2,Cloudy,Normal 1949 | 0.8,0.9,97,81,12,4,Cloudy,Normal 1950 | 0.5,0.5,88,90,11,5,Cloudy,Normal 1951 | 0.5,0.7,93,90,13,6,Cloudy,Normal 1952 | 0.6,0.7,96,98,14,2,Cloudy,Normal 1953 | 0.5,0.7,96,81,10,6,Cloudy,Normal 1954 | 0.9,0.6,96,80,14,4,Cloudy,Normal 1955 | 0.5,0.7,82,80,13,5,Cloudy,Normal 1956 | 0.6,0.5,86,93,14,5,Cloudy,Normal 1957 | 0.8,0.8,84,82,14,3,Cloudy,Normal 1958 | 0.7,0.9,80,82,11,3,Cloudy,Normal 1959 | 0.9,0.9,96,82,9,4,Cloudy,Normal 1960 | 0.4,0.6,91,92,9,6,Cloudy,Normal 1961 | 0.5,0.5,89,91,11,5,Cloudy,Normal 1962 | 0.8,0.7,80,89,9,4,Cloudy,Normal 1963 | 0.8,0.7,99,81,10,3,Cloudy,Normal 1964 | 0.6,0.6,87,85,12,6,Cloudy,Normal 1965 | 0.7,0.6,99,90,14,3,Cloudy,Normal 1966 | 0.5,0.5,81,83,11,3,Cloudy,Normal 1967 | 0.8,0.7,81,90,11,5,Cloudy,Normal 1968 | 0.7,0.5,84,83,11,3,Cloudy,Normal 1969 | 0.7,0.7,98,92,9,4,Cloudy,Normal 1970 | 0.4,0.7,83,96,9,4,Cloudy,Normal 1971 | 0.8,0.5,95,85,13,3,Cloudy,Normal 1972 | 0.8,0.8,86,81,9,5,Cloudy,Normal 1973 | 0.4,0.7,97,90,13,2,Cloudy,Normal 1974 | 0.5,0.6,93,86,14,6,Cloudy,Normal 1975 | 0.6,0.4,84,91,13,3,Cloudy,Normal 1976 | 0.8,0.8,86,99,10,6,Cloudy,Normal 1977 | 0.7,0.5,86,99,9,2,Cloudy,Normal 1978 | 0.8,0.6,92,84,11,5,Cloudy,Normal 1979 | 0.7,0.7,88,81,11,2,Cloudy,Normal 1980 | 0.7,0.4,87,92,11,5,Cloudy,Normal 1981 | 0.7,0.6,80,96,12,4,Cloudy,Normal 1982 | 0.4,0.7,97,86,13,4,Cloudy,Normal 1983 | 0.6,0.7,86,96,12,2,Cloudy,Normal 1984 | 0.7,0.9,86,97,14,2,Cloudy,Normal 1985 | 0.6,0.7,86,92,14,4,Cloudy,Normal 1986 | 0.4,0.6,83,93,12,3,Cloudy,Normal 1987 | 0.5,0.7,95,92,10,5,Cloudy,Normal 1988 | 0.6,0.7,93,93,11,4,Cloudy,Normal 1989 | 0.7,0.7,97,91,9,6,Cloudy,Normal 1990 | 0.7,0.7,90,92,11,6,Cloudy,Normal 1991 | 0.5,0.7,83,81,11,5,Cloudy,Normal 1992 | 0.6,0.6,94,88,9,3,Cloudy,Normal 1993 | 0.8,0.4,81,97,13,5,Cloudy,Normal 1994 | 0.6,0.8,86,89,9,3,Cloudy,Normal 1995 | 0.8,0.9,84,80,13,2,Cloudy,Normal 1996 | 0.5,0.4,84,87,13,4,Cloudy,Normal 1997 | 0.5,0.5,84,88,12,4,Cloudy,Normal 1998 | 0.8,0.9,88,90,12,6,Cloudy,Normal 1999 | 1,0.5,84,82,14,3,Cloudy,Normal 2000 | 0.6,0.7,97,83,10,6,Cloudy,Normal 2001 | 0.5,0.6,93,97,10,5,Cloudy,Normal 2002 | 0,4.7,0,101,98,14,Sunny,Open 2003 | 0,4.6,0,101,97,15,Sunny,Open 2004 | 0,5.1,0,104,72,16,Sunny,Open 2005 | 0,3.7,0,81,88,16,Sunny,Open 2006 | 0,4.1,0,80,94,13,Sunny,Open 2007 | 0,4.1,0,81,100,16,Sunny,Open 2008 | 0,3.6,0,95,100,15,Sunny,Open 2009 | 0,5.7,0,103,89,16,Sunny,Open 2010 | 0,3.4,0,95,94,15,Sunny,Open 2011 | 0,3.3,0,99,93,16,Sunny,Open 2012 | 0,4.3,0,102,88,15,Sunny,Open 2013 | 0,4.3,0,86,86,13,Sunny,Open 2014 | 0,3.8,0,93,81,15,Sunny,Open 2015 | 0,3.9,0,94,82,16,Sunny,Open 2016 | 0,4,0,92,95,14,Sunny,Open 2017 | 0,5.2,0,82,88,15,Sunny,Open 2018 | 0,5.4,0,85,95,15,Sunny,Open 2019 | 0,3.7,0,99,89,16,Sunny,Open 2020 | 0,4.2,0,101,89,14,Sunny,Open 2021 | 0,4.5,0,104,93,15,Sunny,Open 2022 | 0,5,0,83,83,15,Sunny,Open 2023 | 0,3.5,0,91,99,15,Sunny,Open 2024 | 0,3.8,0,86,96,13,Sunny,Open 2025 | 0,5.2,0,104,87,16,Sunny,Open 2026 | 0,3.3,0,84,94,15,Sunny,Open 2027 | 0,4.6,0,99,81,12,Sunny,Open 2028 | 0,3.3,0,98,96,12,Sunny,Open 2029 | 0,4.5,0,81,84,13,Sunny,Open 2030 | 0,5.7,0,96,93,16,Sunny,Open 2031 | 0,4.9,0,94,100,16,Sunny,Open 2032 | 0,5.1,0,104,95,13,Sunny,Open 2033 | 0,3.5,0,82,90,16,Sunny,Open 2034 | 0,3.6,0,98,95,14,Sunny,Open 2035 | 0,4.9,0,104,88,16,Sunny,Open 2036 | 0,3.9,0,88,91,16,Sunny,Open 2037 | 0,4.2,0,101,87,13,Sunny,Open 2038 | 0,4.7,0,91,86,15,Sunny,Open 2039 | 0,3.8,0,93,89,16,Sunny,Open 2040 | 0,5.6,0,91,89,14,Sunny,Open 2041 | 0,4.4,0,100,84,12,Sunny,Open 2042 | 0,3.6,0,105,88,14,Sunny,Open 2043 | 0,5.2,0,102,95,13,Sunny,Open 2044 | 0,5.2,0,104,81,15,Sunny,Open 2045 | 0,3.3,0,102,80,15,Sunny,Open 2046 | 0,3.7,0,91,92,16,Sunny,Open 2047 | 0,4.7,0,85,80,13,Sunny,Open 2048 | 0,3.7,0,99,88,15,Sunny,Open 2049 | 0,3.2,0,99,94,15,Sunny,Open 2050 | 0,4.5,0,91,86,15,Sunny,Open 2051 | 0,5.2,0,88,99,16,Sunny,Open 2052 | 0,5.1,0,105,88,13,Sunny,Open 2053 | 0,4.8,0,101,87,12,Sunny,Open 2054 | 0,3.1,0,81,96,15,Sunny,Open 2055 | 0,3.9,0,100,80,13,Sunny,Open 2056 | 0,5.6,0,100,98,15,Sunny,Open 2057 | 0,3.1,0,85,98,12,Sunny,Open 2058 | 0,4,0,87,83,12,Sunny,Open 2059 | 0,3,0,92,82,16,Sunny,Open 2060 | 0,3.1,0,103,93,15,Sunny,Open 2061 | 0,5.5,0,99,83,12,Sunny,Open 2062 | 0,5.5,0,83,87,12,Sunny,Open 2063 | 0,4.9,0,98,98,14,Sunny,Open 2064 | 0,4.1,0,91,95,14,Sunny,Open 2065 | 0,4.4,0,80,86,16,Sunny,Open 2066 | 0,3.9,0,96,94,14,Sunny,Open 2067 | 0,3.1,0,84,86,14,Sunny,Open 2068 | 0,4,0,85,99,16,Sunny,Open 2069 | 0,4.6,0,97,86,15,Sunny,Open 2070 | 0,3,0,103,97,14,Sunny,Open 2071 | 0,4.2,0,105,85,14,Sunny,Open 2072 | 0,4,0,98,94,13,Sunny,Open 2073 | 0,5.5,0,103,92,13,Sunny,Open 2074 | 0,3.2,0,97,90,12,Sunny,Open 2075 | 0,3.6,0,85,85,16,Sunny,Open 2076 | 0,4.4,0,92,91,12,Sunny,Open 2077 | 0,3.1,0,102,88,16,Sunny,Open 2078 | 0,4.1,0,88,93,15,Sunny,Open 2079 | 0,3.6,0,92,98,15,Sunny,Open 2080 | 0,5.5,0,106,82,14,Sunny,Open 2081 | 0,3.4,0,102,93,16,Sunny,Open 2082 | 0,4.6,0,105,99,15,Sunny,Open 2083 | 0,5.3,0,94,87,14,Sunny,Open 2084 | 0,3,0,88,87,15,Sunny,Open 2085 | 0,3,0,105,80,12,Sunny,Open 2086 | 0,4.3,0,85,92,13,Sunny,Open 2087 | 0,4.6,0,90,84,15,Sunny,Open 2088 | 0,4,0,102,90,14,Sunny,Open 2089 | 0,4.9,0,80,94,15,Sunny,Open 2090 | 0,3,0,104,91,14,Sunny,Open 2091 | 0,5,0,87,90,14,Sunny,Open 2092 | 0,4.2,0,98,87,12,Sunny,Open 2093 | 0,5.4,0,84,95,12,Sunny,Open 2094 | 0,3.6,0,105,89,14,Sunny,Open 2095 | 0,4.6,0,99,83,13,Sunny,Open 2096 | 0,4.7,0,99,99,14,Sunny,Open 2097 | 0,4.1,0,93,89,16,Sunny,Open 2098 | 0,5.2,0,96,82,13,Sunny,Open 2099 | 0,4.2,0,89,95,16,Sunny,Open 2100 | 0,4.6,0,90,82,14,Sunny,Open 2101 | 0,4.6,0,99,84,16,Sunny,Open 2102 | 0,5.2,0,87,97,14,Sunny,Open 2103 | 0,3.5,0,88,82,15,Sunny,Open 2104 | 0,5.2,0,99,99,13,Sunny,Open 2105 | 0,4.2,0,99,83,13,Sunny,Open 2106 | 0,4.2,0,98,83,12,Sunny,Open 2107 | 0,5.1,0,106,94,13,Sunny,Open 2108 | 0,3.6,0,91,93,13,Sunny,Open 2109 | 0,3,0,92,83,13,Sunny,Open 2110 | 0,3.2,0,94,80,13,Sunny,Open 2111 | 0,3.1,0,94,89,15,Sunny,Open 2112 | 0,3.3,0,104,95,16,Sunny,Open 2113 | 0,3.6,0,90,92,14,Sunny,Open 2114 | 0,5.1,0,89,90,13,Sunny,Open 2115 | 0,3.7,0,95,84,13,Sunny,Open 2116 | 0,3.5,0,88,99,13,Sunny,Open 2117 | 0,4.8,0,102,83,12,Sunny,Open 2118 | 0,3.7,0,103,90,16,Sunny,Open 2119 | 0,3.2,0,94,85,14,Sunny,Open 2120 | 0,4.8,0,85,84,15,Sunny,Open 2121 | 0,4.9,0,103,89,15,Sunny,Open 2122 | 0,3.6,0,81,94,14,Sunny,Open 2123 | 0,3.6,0,84,100,16,Sunny,Open 2124 | 0,3.4,0,104,86,15,Sunny,Open 2125 | 0,4.9,0,83,94,14,Sunny,Open 2126 | 0,4.1,0,96,83,15,Sunny,Open 2127 | 0,3,0,98,96,15,Sunny,Open 2128 | 0,4.9,0,80,92,13,Sunny,Open 2129 | 0,4.6,0,83,99,13,Sunny,Open 2130 | 0,5.6,0,103,99,13,Sunny,Open 2131 | 4.1,0,82,0,96,12,Sunny,Open 2132 | 3.7,0,103,0,98,13,Sunny,Open 2133 | 5.7,0,84,0,95,16,Sunny,Open 2134 | 3.6,0,91,0,98,12,Sunny,Open 2135 | 4.3,0,98,0,95,14,Sunny,Open 2136 | 5.4,0,89,0,81,16,Sunny,Open 2137 | 3.2,0,99,0,85,13,Sunny,Open 2138 | 4.9,0,94,0,100,16,Sunny,Open 2139 | 3.7,0,103,0,82,16,Sunny,Open 2140 | 3.8,0,94,0,93,13,Sunny,Open 2141 | 4.5,0,93,0,98,12,Sunny,Open 2142 | 5.5,0,103,0,94,14,Sunny,Open 2143 | 4.1,0,87,0,99,12,Sunny,Open 2144 | 5.6,0,106,0,96,16,Sunny,Open 2145 | 4.5,0,92,0,87,13,Sunny,Open 2146 | 3.2,0,95,0,86,15,Sunny,Open 2147 | 5.3,0,99,0,91,13,Sunny,Open 2148 | 3.9,0,94,0,91,14,Sunny,Open 2149 | 4.1,0,105,0,95,15,Sunny,Open 2150 | 5.6,0,106,0,89,14,Sunny,Open 2151 | 4.7,0,106,0,81,15,Sunny,Open 2152 | 4.2,0,106,0,98,12,Sunny,Open 2153 | 4.3,0,101,0,87,12,Sunny,Open 2154 | 4.1,0,94,0,84,12,Sunny,Open 2155 | 3,0,85,0,94,15,Sunny,Open 2156 | 4.4,0,100,0,82,13,Sunny,Open 2157 | 3,0,89,0,86,15,Sunny,Open 2158 | 4.8,0,94,0,91,15,Sunny,Open 2159 | 3.9,0,95,0,90,14,Sunny,Open 2160 | 5.2,0,87,0,84,15,Sunny,Open 2161 | 3.1,0,93,0,93,15,Sunny,Open 2162 | 4.1,0,104,0,81,15,Sunny,Open 2163 | 5.6,0,92,0,81,15,Sunny,Open 2164 | 5.5,0,104,0,83,13,Sunny,Open 2165 | 5.1,0,91,0,91,15,Sunny,Open 2166 | 4.4,0,82,0,91,16,Sunny,Open 2167 | 3.6,0,83,0,92,13,Sunny,Open 2168 | 3.4,0,92,0,90,12,Sunny,Open 2169 | 3.8,0,82,0,87,13,Sunny,Open 2170 | 3,0,92,0,81,14,Sunny,Open 2171 | 4.6,0,104,0,81,14,Sunny,Open 2172 | 4,0,81,0,96,15,Sunny,Open 2173 | 5.5,0,88,0,87,14,Sunny,Open 2174 | 4,0,92,0,92,14,Sunny,Open 2175 | 5.2,0,86,0,91,13,Sunny,Open 2176 | 4.7,0,101,0,80,14,Sunny,Open 2177 | 5.4,0,98,0,86,15,Sunny,Open 2178 | 5.3,0,85,0,91,13,Sunny,Open 2179 | 3.5,0,90,0,95,14,Sunny,Open 2180 | 4.7,0,91,0,85,12,Sunny,Open 2181 | 4.5,0,105,0,88,15,Sunny,Open 2182 | 3.9,0,96,0,86,12,Sunny,Open 2183 | 4.1,0,82,0,84,14,Sunny,Open 2184 | 3,0,81,0,91,16,Sunny,Open 2185 | 4.1,0,91,0,80,16,Sunny,Open 2186 | 5.2,0,89,0,96,15,Sunny,Open 2187 | 4.8,0,96,0,92,12,Sunny,Open 2188 | 4.2,0,99,0,86,14,Sunny,Open 2189 | 3.8,0,87,0,89,13,Sunny,Open 2190 | 3.5,0,87,0,97,12,Sunny,Open 2191 | 3.4,0,81,0,92,12,Sunny,Open 2192 | 4.9,0,83,0,81,13,Sunny,Open 2193 | 3.7,0,101,0,92,14,Sunny,Open 2194 | 3.4,0,106,0,100,13,Sunny,Open 2195 | 5.4,0,82,0,87,16,Sunny,Open 2196 | 5.3,0,97,0,85,16,Sunny,Open 2197 | 5.2,0,80,0,81,14,Sunny,Open 2198 | 4.9,0,87,0,99,14,Sunny,Open 2199 | 5.4,0,97,0,82,16,Sunny,Open 2200 | 3.3,0,86,0,82,12,Sunny,Open 2201 | 5.1,0,103,0,88,15,Sunny,Open 2202 | 3.4,0,85,0,98,12,Sunny,Open 2203 | 3.7,0,92,0,80,15,Sunny,Open 2204 | 5.1,0,100,0,87,13,Sunny,Open 2205 | 5.2,0,80,0,81,15,Sunny,Open 2206 | 3.9,0,102,0,94,15,Sunny,Open 2207 | 3,0,99,0,95,14,Sunny,Open 2208 | 4.6,0,90,0,99,16,Sunny,Open 2209 | 3.5,0,92,0,80,13,Sunny,Open 2210 | 4.4,0,100,0,91,14,Sunny,Open 2211 | 5.5,0,98,0,80,15,Sunny,Open 2212 | 3.3,0,91,0,88,16,Sunny,Open 2213 | 4.3,0,97,0,82,16,Sunny,Open 2214 | 4.3,0,105,0,98,14,Sunny,Open 2215 | 3.1,0,83,0,96,13,Sunny,Open 2216 | 3.4,0,100,0,86,13,Sunny,Open 2217 | 5,0,97,0,93,14,Sunny,Open 2218 | 5.2,0,85,0,96,12,Sunny,Open 2219 | 4.8,0,106,0,95,16,Sunny,Open 2220 | 4.3,0,91,0,91,14,Sunny,Open 2221 | 4.7,0,83,0,92,16,Sunny,Open 2222 | 4.9,0,102,0,91,14,Sunny,Open 2223 | 5.6,0,90,0,87,14,Sunny,Open 2224 | 4.8,0,103,0,95,14,Sunny,Open 2225 | 3.3,0,81,0,87,16,Sunny,Open 2226 | 3.1,0,101,0,86,15,Sunny,Open 2227 | 3.3,0,88,0,100,14,Sunny,Open 2228 | 5,0,91,0,86,14,Sunny,Open 2229 | 3.9,0,90,0,92,12,Sunny,Open 2230 | 4.5,0,81,0,86,15,Sunny,Open 2231 | 4.8,0,95,0,98,14,Sunny,Open 2232 | 3.4,0,87,0,98,13,Sunny,Open 2233 | 4.2,0,104,0,99,16,Sunny,Open 2234 | 3.2,0,88,0,84,15,Sunny,Open 2235 | 5.7,0,98,0,88,16,Sunny,Open 2236 | 3.4,0,93,0,91,12,Sunny,Open 2237 | 5.5,0,87,0,80,14,Sunny,Open 2238 | 3.7,0,100,0,92,12,Sunny,Open 2239 | 5.4,0,97,0,85,16,Sunny,Open 2240 | 5.6,0,104,0,90,14,Sunny,Open 2241 | 5.3,0,97,0,99,16,Sunny,Open 2242 | 5.6,0,106,0,98,16,Sunny,Open 2243 | 3.5,0,99,0,92,14,Sunny,Open 2244 | 5.1,0,100,0,84,15,Sunny,Open 2245 | 4.9,0,86,0,88,14,Sunny,Open 2246 | 4.7,0,93,0,89,12,Sunny,Open 2247 | 4.4,0,103,0,98,13,Sunny,Open 2248 | 3.3,0,95,0,100,16,Sunny,Open 2249 | 4.9,0,106,0,92,15,Sunny,Open 2250 | 3.3,0,95,0,95,13,Sunny,Open 2251 | 0,0.5,0,90,9,4,Cloudy,Open 2252 | 0,0.8,0,87,14,6,Cloudy,Open 2253 | 0,0.8,0,84,11,5,Cloudy,Open 2254 | 0,0.5,0,98,9,5,Cloudy,Open 2255 | 0,0.7,0,85,13,3,Cloudy,Open 2256 | 0,0.6,0,86,9,3,Cloudy,Open 2257 | 0,0.4,0,84,10,6,Cloudy,Open 2258 | 0,0.8,0,90,10,6,Cloudy,Open 2259 | 0,0.4,0,97,12,6,Cloudy,Open 2260 | 0,0.8,0,83,12,6,Cloudy,Open 2261 | 0,0.5,0,87,12,3,Cloudy,Open 2262 | 0,0.7,0,89,9,2,Cloudy,Open 2263 | 0,0.6,0,80,14,6,Cloudy,Open 2264 | 0,0.9,0,80,11,2,Cloudy,Open 2265 | 0,0.5,0,97,11,6,Cloudy,Open 2266 | 0,0.6,0,96,14,3,Cloudy,Open 2267 | 0,0.9,0,83,11,3,Cloudy,Open 2268 | 0,0.7,0,98,10,2,Cloudy,Open 2269 | 0,0.6,0,99,14,5,Cloudy,Open 2270 | 0,0.8,0,96,11,3,Cloudy,Open 2271 | 0,0.9,0,87,11,3,Cloudy,Open 2272 | 0,0.9,0,95,12,5,Cloudy,Open 2273 | 0,0.8,0,83,14,3,Cloudy,Open 2274 | 0,0.7,0,84,13,5,Cloudy,Open 2275 | 0,0.6,0,90,12,3,Cloudy,Open 2276 | 0,0.6,0,84,10,4,Cloudy,Open 2277 | 0,0.6,0,95,13,3,Cloudy,Open 2278 | 0,0.8,0,96,14,3,Cloudy,Open 2279 | 0,0.5,0,90,11,6,Cloudy,Open 2280 | 0,0.4,0,98,13,6,Cloudy,Open 2281 | 0,0.5,0,84,10,2,Cloudy,Open 2282 | 0,0.5,0,85,9,3,Cloudy,Open 2283 | 0,0.6,0,80,10,5,Cloudy,Open 2284 | 0,0.7,0,97,9,3,Cloudy,Open 2285 | 0,0.6,0,99,12,4,Cloudy,Open 2286 | 0,0.7,0,99,13,2,Cloudy,Open 2287 | 0,0.6,0,96,14,2,Cloudy,Open 2288 | 0,0.6,0,95,10,5,Cloudy,Open 2289 | 0,0.5,0,86,9,3,Cloudy,Open 2290 | 0,0.5,0,90,11,2,Cloudy,Open 2291 | 0,0.5,0,95,11,5,Cloudy,Open 2292 | 0,0.5,0,83,13,4,Cloudy,Open 2293 | 0,0.6,0,98,14,2,Cloudy,Open 2294 | 0,0.8,0,94,13,6,Cloudy,Open 2295 | 0,0.6,0,89,9,4,Cloudy,Open 2296 | 0,0.4,0,87,14,6,Cloudy,Open 2297 | 0,0.8,0,85,10,5,Cloudy,Open 2298 | 0,0.5,0,84,10,3,Cloudy,Open 2299 | 0,0.5,0,86,13,3,Cloudy,Open 2300 | 0,0.5,0,81,10,4,Cloudy,Open 2301 | 0,0.7,0,89,10,4,Cloudy,Open 2302 | 0,0.5,0,95,13,4,Cloudy,Open 2303 | 0,0.7,0,99,11,2,Cloudy,Open 2304 | 0,0.9,0,89,9,3,Cloudy,Open 2305 | 0,0.6,0,94,12,3,Cloudy,Open 2306 | 0,0.4,0,88,11,5,Cloudy,Open 2307 | 0,0.6,0,98,11,6,Cloudy,Open 2308 | 0,0.7,0,94,12,3,Cloudy,Open 2309 | 0,0.8,0,81,9,3,Cloudy,Open 2310 | 0,0.7,0,86,9,2,Cloudy,Open 2311 | 0,0.6,0,81,14,5,Cloudy,Open 2312 | 0,0.8,0,85,12,2,Cloudy,Open 2313 | 0,0.4,0,88,13,4,Cloudy,Open 2314 | 0,0.8,0,89,10,6,Cloudy,Open 2315 | 0,0.8,0,98,13,6,Cloudy,Open 2316 | 0.7,0,90,0,11,6,Cloudy,Open 2317 | 0.7,0,96,0,10,6,Cloudy,Open 2318 | 0.9,0,93,0,12,3,Cloudy,Open 2319 | 0.5,0,88,0,14,5,Cloudy,Open 2320 | 0.4,0,86,0,11,4,Cloudy,Open 2321 | 0.4,0,82,0,12,3,Cloudy,Open 2322 | 0.8,0,82,0,14,3,Cloudy,Open 2323 | 0.9,0,86,0,14,4,Cloudy,Open 2324 | 0.4,0,84,0,13,2,Cloudy,Open 2325 | 0.9,0,83,0,10,3,Cloudy,Open 2326 | 0.8,0,83,0,12,4,Cloudy,Open 2327 | 0.5,0,85,0,14,2,Cloudy,Open 2328 | 0.5,0,97,0,12,3,Cloudy,Open 2329 | 0.5,0,99,0,12,3,Cloudy,Open 2330 | 0.7,0,90,0,14,6,Cloudy,Open 2331 | 0.6,0,98,0,10,3,Cloudy,Open 2332 | 0.8,0,89,0,13,3,Cloudy,Open 2333 | 0.6,0,86,0,11,3,Cloudy,Open 2334 | 0.7,0,89,0,12,3,Cloudy,Open 2335 | 0.9,0,96,0,12,3,Cloudy,Open 2336 | 0.5,0,99,0,14,4,Cloudy,Open 2337 | 0.7,0,82,0,14,3,Cloudy,Open 2338 | 0.8,0,85,0,9,3,Cloudy,Open 2339 | 0.7,0,88,0,12,5,Cloudy,Open 2340 | 0.6,0,82,0,9,4,Cloudy,Open 2341 | 0.6,0,91,0,12,3,Cloudy,Open 2342 | 0.7,0,88,0,11,6,Cloudy,Open 2343 | 0.4,0,95,0,12,3,Cloudy,Open 2344 | 0.5,0,97,0,14,2,Cloudy,Open 2345 | 0.7,0,87,0,9,2,Cloudy,Open 2346 | 0.8,0,97,0,13,4,Cloudy,Open 2347 | 0.6,0,86,0,13,4,Cloudy,Open 2348 | 0.7,0,88,0,9,6,Cloudy,Open 2349 | 0.9,0,90,0,12,2,Cloudy,Open 2350 | 0.6,0,89,0,11,6,Cloudy,Open 2351 | 0.5,0,95,0,11,3,Cloudy,Open 2352 | 0.4,0,95,0,13,3,Cloudy,Open 2353 | 0.4,0,95,0,13,2,Cloudy,Open 2354 | 0.8,0,85,0,11,6,Cloudy,Open 2355 | 0.6,0,89,0,12,4,Cloudy,Open 2356 | 0.5,0,90,0,9,5,Cloudy,Open 2357 | 0.5,0,82,0,11,3,Cloudy,Open 2358 | 0.4,0,87,0,10,2,Cloudy,Open 2359 | 0.5,0,93,0,9,6,Cloudy,Open 2360 | 0.6,0,84,0,12,6,Cloudy,Open 2361 | 0.6,0,81,0,10,4,Cloudy,Open 2362 | 0.6,0,96,0,11,2,Cloudy,Open 2363 | 0.9,0,84,0,9,6,Cloudy,Open 2364 | 0.5,0,99,0,13,4,Cloudy,Open 2365 | 0.5,0,98,0,9,3,Cloudy,Open 2366 | 0.7,0,88,0,14,3,Cloudy,Open 2367 | 0.4,0,82,0,11,5,Cloudy,Open 2368 | 0.6,0,83,0,12,2,Cloudy,Open 2369 | 0.7,0,86,0,13,2,Cloudy,Open 2370 | 0.8,0,87,0,11,4,Cloudy,Open 2371 | 0.5,0,95,0,12,3,Cloudy,Open 2372 | 0.9,0,94,0,14,3,Cloudy,Open 2373 | 0.5,0,91,0,13,3,Cloudy,Open 2374 | 0.7,0,88,0,13,4,Cloudy,Open 2375 | 0.6,0,89,0,9,6,Cloudy,Open 2376 | 0.8,0,82,0,13,5,Cloudy,Open 2377 | 0.9,0,97,0,14,3,Cloudy,Open 2378 | 0.8,0,86,0,11,4,Cloudy,Open 2379 | 0.7,0,97,0,13,3,Cloudy,Open 2380 | 0.7,0,81,0,14,3,Cloudy,Open 2381 | 0.8,0,96,0,13,2,Cloudy,Open 2382 | 0.6,0,95,0,10,2,Cloudy,Open 2383 | 0.9,0,95,0,9,6,Cloudy,Open 2384 | 0.6,0,81,0,10,3,Cloudy,Open 2385 | 0.5,0,92,0,13,3,Cloudy,Open 2386 | 0.9,0,89,0,11,5,Cloudy,Open 2387 | 0.4,0,95,0,11,2,Cloudy,Open 2388 | 0.7,0,84,0,14,2,Cloudy,Open 2389 | 0.8,0,82,0,10,3,Cloudy,Open 2390 | 0.8,0,97,0,9,2,Cloudy,Open 2391 | 0.4,0,86,0,13,3,Cloudy,Open 2392 | 0.9,0,83,0,12,5,Cloudy,Open 2393 | 0.9,0,99,0,14,5,Cloudy,Open 2394 | 0.5,0,82,0,14,4,Cloudy,Open 2395 | 0.8,0,91,0,9,5,Cloudy,Open 2396 | 0.7,0,87,0,14,6,Cloudy,Open 2397 | 0.7,0,90,0,14,3,Cloudy,Open 2398 | 0.6,0,86,0,11,3,Cloudy,Open 2399 | 0.6,0,84,0,12,3,Cloudy,Open 2400 | 0.8,0,92,0,14,4,Cloudy,Open 2401 | 0.7,0,86,0,11,3,Cloudy,Open 2402 | 0.6,0,82,0,13,6,Cloudy,Open 2403 | 0.4,0,80,0,13,3,Cloudy,Open 2404 | 0.7,0,84,0,13,6,Cloudy,Open 2405 | 0.4,0,85,0,11,2,Cloudy,Open 2406 | 0.6,0,86,0,12,3,Cloudy,Open 2407 | 0.6,0,95,0,14,5,Cloudy,Open 2408 | 0.4,0,82,0,11,5,Cloudy,Open 2409 | 0.5,0,99,0,9,6,Cloudy,Open 2410 | 0.7,0,88,0,14,2,Cloudy,Open 2411 | 0.6,0,84,0,13,2,Cloudy,Open 2412 | 0.7,0,82,0,12,5,Cloudy,Open 2413 | 0.5,0,85,0,13,2,Cloudy,Open 2414 | 0.7,0,97,0,13,3,Cloudy,Open 2415 | 0.4,0,89,0,11,2,Cloudy,Open 2416 | 0.8,0,85,0,10,4,Cloudy,Open 2417 | 0.7,0,89,0,9,2,Cloudy,Open 2418 | 0.5,0,80,0,10,5,Cloudy,Open 2419 | 0.6,0,86,0,12,3,Cloudy,Open 2420 | 0.5,0,88,0,12,4,Cloudy,Open 2421 | 0.6,0,84,0,14,3,Cloudy,Open 2422 | 0.5,0,94,0,12,3,Cloudy,Open 2423 | 0.9,0,87,0,10,3,Cloudy,Open 2424 | 0.8,0,81,0,13,5,Cloudy,Open 2425 | 0.8,0,82,0,12,6,Cloudy,Open 2426 | 0.8,0,81,0,9,5,Cloudy,Open 2427 | 0.6,0,95,0,10,5,Cloudy,Open 2428 | 0.7,0,82,0,10,5,Cloudy,Open 2429 | 0.5,0,80,0,9,4,Cloudy,Open 2430 | 0.8,0,95,0,12,2,Cloudy,Open 2431 | 0.8,0,94,0,12,3,Cloudy,Open 2432 | 0.8,0,80,0,11,5,Cloudy,Open 2433 | 0.6,0,91,0,12,5,Cloudy,Open 2434 | 0.4,0,87,0,13,6,Cloudy,Open 2435 | 0.8,0,86,0,13,5,Cloudy,Open 2436 | 0.4,0,91,0,14,2,Cloudy,Open 2437 | 0.8,0,96,0,11,6,Cloudy,Open 2438 | 0.4,0,85,0,13,3,Cloudy,Open 2439 | 0.8,0,96,0,12,5,Cloudy,Open 2440 | 0.6,0,93,0,9,2,Cloudy,Open 2441 | 0.9,0,86,0,14,4,Cloudy,Open 2442 | 0.7,0,92,0,10,4,Cloudy,Open 2443 | 0.6,0,88,0,11,3,Cloudy,Open 2444 | 0.5,0,88,0,12,2,Cloudy,Open 2445 | 0.4,0,92,0,11,5,Cloudy,Open 2446 | 0.8,0,86,0,13,6,Cloudy,Open 2447 | 0.6,0,86,0,11,4,Cloudy,Open 2448 | 0.9,0,82,0,14,6,Cloudy,Open 2449 | 0.7,0,86,0,12,2,Cloudy,Open 2450 | 0.8,0,84,0,10,6,Cloudy,Open 2451 | 0.9,0,87,0,11,5,Cloudy,Open 2452 | 0.7,0,84,0,12,5,Cloudy,Open 2453 | 0.7,0,87,0,10,2,Cloudy,Open 2454 | 0.5,0,86,0,9,4,Cloudy,Open 2455 | 0.6,0,82,0,12,6,Cloudy,Open 2456 | 0.6,0,88,0,10,5,Cloudy,Open 2457 | 0.8,0,83,0,10,5,Cloudy,Open 2458 | 0.5,0,99,0,11,4,Cloudy,Open 2459 | 0.6,0,89,0,14,5,Cloudy,Open 2460 | 0.9,0,87,0,14,6,Cloudy,Open 2461 | 0.4,0,80,0,11,4,Cloudy,Open 2462 | 0.4,0,89,0,11,4,Cloudy,Open 2463 | 0.6,0,82,0,12,4,Cloudy,Open 2464 | 0.6,0,90,0,14,5,Cloudy,Open 2465 | 0.6,0,99,0,13,4,Cloudy,Open 2466 | 0.7,0,85,0,14,4,Cloudy,Open 2467 | 0.8,0,92,0,11,2,Cloudy,Open 2468 | 0.5,0,85,0,10,6,Cloudy,Open 2469 | 0.7,0,86,0,11,4,Cloudy,Open 2470 | 0.5,0,89,0,12,2,Cloudy,Open 2471 | 0.9,0,82,0,12,4,Cloudy,Open 2472 | 0.8,0,80,0,14,2,Cloudy,Open 2473 | 0.5,0,84,0,11,6,Cloudy,Open 2474 | 0.7,0,97,0,13,6,Cloudy,Open 2475 | 0.8,0,82,0,13,5,Cloudy,Open 2476 | 0.4,0,91,0,11,4,Cloudy,Open 2477 | 0.9,0,83,0,12,6,Cloudy,Open 2478 | 0.9,0,97,0,9,5,Cloudy,Open 2479 | 0.6,0,87,0,14,5,Cloudy,Open 2480 | 0.7,0,98,0,11,3,Cloudy,Open 2481 | 0.4,0,93,0,11,5,Cloudy,Open 2482 | 0.7,0,98,0,9,5,Cloudy,Open 2483 | 0.6,0,95,0,13,2,Cloudy,Open 2484 | 0.5,0,90,0,12,2,Cloudy,Open 2485 | 0.5,0,97,0,13,2,Cloudy,Open 2486 | 0.5,0,95,0,9,4,Cloudy,Open 2487 | 0.5,0,86,0,11,5,Cloudy,Open 2488 | 0.6,0,93,0,14,6,Cloudy,Open 2489 | 0.4,0,90,0,13,5,Cloudy,Open 2490 | 0.4,0,95,0,9,2,Cloudy,Open 2491 | 0.6,0,80,0,9,5,Cloudy,Open 2492 | 0.5,0,93,0,10,4,Cloudy,Open 2493 | 0.5,0,81,0,14,5,Cloudy,Open 2494 | 0.9,0,91,0,11,3,Cloudy,Open 2495 | 0.4,0,89,0,11,2,Cloudy,Open 2496 | 0.5,0,94,0,12,3,Cloudy,Open 2497 | 0.6,0,88,0,14,4,Cloudy,Open 2498 | 0.8,0,85,0,14,4,Cloudy,Open 2499 | 0.7,0,89,0,12,6,Cloudy,Open 2500 | 0.4,0,93,0,10,2,Cloudy,Open 2501 | 0.5,0,83,0,11,3,Cloudy,Open 2502 | 5,1,90,75,97,15,Sunny,Line-line 2503 | 4.2,1,85,72,75,16,Sunny,Line-line 2504 | 2.8,0.9,76,70,80,16,Sunny,Line-line 2505 | 4.7,1,83,75,82,13,Sunny,Line-line 2506 | 3.9,1,83,73,98,16,Sunny,Line-line 2507 | 5,1.1,85,75,89,15,Sunny,Line-line 2508 | 4.5,0.9,81,70,93,15,Sunny,Line-line 2509 | 3.9,0.9,90,70,91,14,Sunny,Line-line 2510 | 4.2,1.1,87,75,84,13,Sunny,Line-line 2511 | 3.8,1,88,71,80,13,Sunny,Line-line 2512 | 4,1,84,70,91,16,Sunny,Line-line 2513 | 3.6,0.7,87,75,80,13,Sunny,Line-line 2514 | 4.3,0.6,87,70,97,16,Sunny,Line-line 2515 | 3.9,0.6,86,75,92,12,Sunny,Line-line 2516 | 3.2,1,82,73,97,15,Sunny,Line-line 2517 | 4.2,0.7,88,70,80,15,Sunny,Line-line 2518 | 3.8,0.9,82,74,99,12,Sunny,Line-line 2519 | 4.5,0.7,90,75,99,12,Sunny,Line-line 2520 | 5,0.6,85,71,81,15,Sunny,Line-line 2521 | 3.4,1.1,82,72,96,15,Sunny,Line-line 2522 | 4.6,0.7,80,71,91,13,Sunny,Line-line 2523 | 3,1,86,70,92,12,Sunny,Line-line 2524 | 3.2,0.7,89,73,100,15,Sunny,Line-line 2525 | 3.3,0.6,89,70,91,12,Sunny,Line-line 2526 | 4,0.7,83,70,98,13,Sunny,Line-line 2527 | 4.6,1,80,71,93,15,Sunny,Line-line 2528 | 3.7,1,80,72,97,16,Sunny,Line-line 2529 | 3.2,0.9,89,75,97,14,Sunny,Line-line 2530 | 2.9,0.8,80,75,86,14,Sunny,Line-line 2531 | 3.3,0.7,88,73,100,15,Sunny,Line-line 2532 | 3.4,0.8,87,70,99,12,Sunny,Line-line 2533 | 3,0.9,89,74,80,15,Sunny,Line-line 2534 | 4.1,1,83,73,85,13,Sunny,Line-line 2535 | 4.8,0.7,89,71,92,13,Sunny,Line-line 2536 | 3.3,1.1,89,73,80,12,Sunny,Line-line 2537 | 3.4,0.6,82,73,96,15,Sunny,Line-line 2538 | 3.2,1,86,71,80,12,Sunny,Line-line 2539 | 3.4,1,84,71,96,15,Sunny,Line-line 2540 | 4,1,84,70,97,12,Sunny,Line-line 2541 | 3.2,0.9,86,74,95,14,Sunny,Line-line 2542 | 3.8,0.9,83,72,95,13,Sunny,Line-line 2543 | 3.1,0.7,80,70,85,16,Sunny,Line-line 2544 | 3.6,0.7,82,73,84,15,Sunny,Line-line 2545 | 4,0.7,83,74,97,16,Sunny,Line-line 2546 | 3.7,0.9,90,70,97,15,Sunny,Line-line 2547 | 4.6,0.7,83,73,96,14,Sunny,Line-line 2548 | 3,0.6,87,74,93,14,Sunny,Line-line 2549 | 4.1,0.9,86,70,92,15,Sunny,Line-line 2550 | 4.9,0.6,90,74,87,13,Sunny,Line-line 2551 | 3.7,0.7,87,72,92,15,Sunny,Line-line 2552 | 2.8,1.1,85,73,85,14,Sunny,Line-line 2553 | 3.7,0.7,90,71,100,15,Sunny,Line-line 2554 | 4,0.7,80,70,81,13,Sunny,Line-line 2555 | 4.8,0.7,88,71,90,16,Sunny,Line-line 2556 | 3.4,0.9,86,74,97,13,Sunny,Line-line 2557 | 4.7,0.9,85,74,96,15,Sunny,Line-line 2558 | 4.1,0.9,85,74,90,15,Sunny,Line-line 2559 | 4.5,1,83,74,81,12,Sunny,Line-line 2560 | 3.1,0.9,85,70,93,14,Sunny,Line-line 2561 | 2.9,0.8,85,74,98,14,Sunny,Line-line 2562 | 2.9,0.7,85,71,96,13,Sunny,Line-line 2563 | 4,0.8,82,72,95,14,Sunny,Line-line 2564 | 3.6,0.7,80,75,90,15,Sunny,Line-line 2565 | 2.9,1,88,73,93,13,Sunny,Line-line 2566 | 2.9,1,81,70,93,15,Sunny,Line-line 2567 | 3,0.9,89,73,88,14,Sunny,Line-line 2568 | 3.3,0.9,87,75,81,12,Sunny,Line-line 2569 | 3.1,1,85,72,80,16,Sunny,Line-line 2570 | 3.1,0.7,87,70,96,14,Sunny,Line-line 2571 | 4.8,0.8,87,70,88,13,Sunny,Line-line 2572 | 3.6,0.8,80,71,82,12,Sunny,Line-line 2573 | 3.1,0.6,86,70,83,12,Sunny,Line-line 2574 | 3,0.7,81,75,98,12,Sunny,Line-line 2575 | 4.2,0.8,89,74,85,16,Sunny,Line-line 2576 | 4.6,0.7,88,75,89,12,Sunny,Line-line 2577 | 3.2,1,89,74,96,14,Sunny,Line-line 2578 | 3.4,0.9,87,74,92,13,Sunny,Line-line 2579 | 3.8,0.9,89,70,87,14,Sunny,Line-line 2580 | 4.3,0.8,86,71,81,13,Sunny,Line-line 2581 | 4.1,0.8,87,74,100,15,Sunny,Line-line 2582 | 4.5,1,87,74,93,16,Sunny,Line-line 2583 | 4.8,0.6,89,74,89,15,Sunny,Line-line 2584 | 3,0.8,81,74,88,15,Sunny,Line-line 2585 | 3.7,0.9,84,74,98,12,Sunny,Line-line 2586 | 3.8,1,81,75,87,12,Sunny,Line-line 2587 | 3.9,0.9,86,73,85,12,Sunny,Line-line 2588 | 4.9,0.8,80,75,83,14,Sunny,Line-line 2589 | 3.5,1,90,75,87,13,Sunny,Line-line 2590 | 3.1,0.9,81,74,80,16,Sunny,Line-line 2591 | 4.8,0.6,88,72,89,16,Sunny,Line-line 2592 | 4.4,0.7,89,71,86,16,Sunny,Line-line 2593 | 3.7,0.8,87,70,90,15,Sunny,Line-line 2594 | 4.5,0.9,87,72,84,14,Sunny,Line-line 2595 | 3,0.8,90,71,94,16,Sunny,Line-line 2596 | 4.4,0.7,87,73,98,14,Sunny,Line-line 2597 | 4.6,0.7,89,71,97,12,Sunny,Line-line 2598 | 3.2,0.8,84,73,93,14,Sunny,Line-line 2599 | 4.3,1.1,83,72,83,16,Sunny,Line-line 2600 | 4.6,0.6,82,75,88,15,Sunny,Line-line 2601 | 4.1,0.9,81,70,96,16,Sunny,Line-line 2602 | 3.8,0.9,82,70,100,14,Sunny,Line-line 2603 | 5,1.1,90,71,92,15,Sunny,Line-line 2604 | 3.2,0.6,84,74,92,14,Sunny,Line-line 2605 | 3.6,1,88,73,84,16,Sunny,Line-line 2606 | 3.7,0.7,89,75,93,16,Sunny,Line-line 2607 | 3.7,1,85,75,92,12,Sunny,Line-line 2608 | 2.9,0.8,80,70,94,14,Sunny,Line-line 2609 | 3.6,0.9,83,73,85,15,Sunny,Line-line 2610 | 4.8,0.8,80,70,93,15,Sunny,Line-line 2611 | 4.2,0.6,80,75,97,14,Sunny,Line-line 2612 | 4.6,0.9,80,70,98,12,Sunny,Line-line 2613 | 4.8,1.1,88,73,98,16,Sunny,Line-line 2614 | 4.4,0.8,80,73,99,16,Sunny,Line-line 2615 | 3.1,0.7,83,72,99,14,Sunny,Line-line 2616 | 4.9,0.9,89,74,95,16,Sunny,Line-line 2617 | 4.1,1.1,86,73,92,13,Sunny,Line-line 2618 | 4.1,1,83,72,100,15,Sunny,Line-line 2619 | 4.3,0.8,90,74,80,12,Sunny,Line-line 2620 | 4.6,1.1,85,70,100,12,Sunny,Line-line 2621 | 3.1,0.8,90,72,90,16,Sunny,Line-line 2622 | 4.8,0.9,81,73,85,16,Sunny,Line-line 2623 | 4.3,1,88,70,84,13,Sunny,Line-line 2624 | 4.3,0.7,90,71,92,12,Sunny,Line-line 2625 | 4,0.9,83,74,88,14,Sunny,Line-line 2626 | 3.6,0.6,86,75,96,14,Sunny,Line-line 2627 | 4.5,1,90,70,100,15,Sunny,Line-line 2628 | 4.9,0.9,82,71,96,16,Sunny,Line-line 2629 | 3.5,1,81,73,94,13,Sunny,Line-line 2630 | 4.8,1,84,74,95,15,Sunny,Line-line 2631 | 3.6,0.9,80,70,92,16,Sunny,Line-line 2632 | 4.2,0.9,81,72,81,15,Sunny,Line-line 2633 | 4,0.8,83,71,82,12,Sunny,Line-line 2634 | 3.1,0.9,82,71,89,14,Sunny,Line-line 2635 | 4,0.6,82,72,80,14,Sunny,Line-line 2636 | 3.9,0.9,81,70,97,15,Sunny,Line-line 2637 | 2.8,0.6,85,74,89,13,Sunny,Line-line 2638 | 3.3,0.9,83,71,80,13,Sunny,Line-line 2639 | 4.5,0.9,80,70,95,14,Sunny,Line-line 2640 | 3.9,0.8,84,73,99,14,Sunny,Line-line 2641 | 3.5,0.8,82,72,92,13,Sunny,Line-line 2642 | 2.8,0.7,80,74,92,16,Sunny,Line-line 2643 | 3.4,0.7,84,71,91,15,Sunny,Line-line 2644 | 3.6,1,90,75,92,16,Sunny,Line-line 2645 | 5,0.6,83,72,82,16,Sunny,Line-line 2646 | 4.7,0.7,89,73,90,14,Sunny,Line-line 2647 | 4.3,0.7,85,75,96,15,Sunny,Line-line 2648 | 4.5,0.8,80,71,94,15,Sunny,Line-line 2649 | 3.9,0.7,90,74,87,15,Sunny,Line-line 2650 | 4.1,0.7,85,75,92,12,Sunny,Line-line 2651 | 4.8,0.6,81,70,99,15,Sunny,Line-line 2652 | 3.5,0.7,85,74,88,13,Sunny,Line-line 2653 | 3.9,0.7,88,70,93,12,Sunny,Line-line 2654 | 3.1,0.6,87,74,99,13,Sunny,Line-line 2655 | 3.8,0.8,89,72,97,15,Sunny,Line-line 2656 | 4.1,0.9,85,72,96,15,Sunny,Line-line 2657 | 4.8,0.9,80,73,95,12,Sunny,Line-line 2658 | 3.6,0.9,84,71,81,15,Sunny,Line-line 2659 | 4.1,1.1,85,72,95,12,Sunny,Line-line 2660 | 2.9,1,85,73,93,16,Sunny,Line-line 2661 | 3.5,1,84,70,86,13,Sunny,Line-line 2662 | 4.4,0.8,84,74,99,14,Sunny,Line-line 2663 | 4,1,88,73,93,16,Sunny,Line-line 2664 | 3.4,0.7,84,74,84,16,Sunny,Line-line 2665 | 3.7,1,80,72,91,14,Sunny,Line-line 2666 | 3,0.6,90,70,96,16,Sunny,Line-line 2667 | 3.8,0.8,88,73,81,15,Sunny,Line-line 2668 | 4.4,0.8,89,73,92,13,Sunny,Line-line 2669 | 4.3,0.9,87,73,81,12,Sunny,Line-line 2670 | 3.9,0.8,83,73,97,13,Sunny,Line-line 2671 | 4.1,0.9,90,74,84,12,Sunny,Line-line 2672 | 3.5,1,86,71,83,14,Sunny,Line-line 2673 | 3.3,0.8,88,70,80,14,Sunny,Line-line 2674 | 4.2,1,85,70,80,15,Sunny,Line-line 2675 | 3.2,0.9,82,75,84,14,Sunny,Line-line 2676 | 3.1,1.1,82,71,90,14,Sunny,Line-line 2677 | 4.4,1,87,70,86,12,Sunny,Line-line 2678 | 4.3,0.7,85,74,96,13,Sunny,Line-line 2679 | 4.7,1,85,72,88,13,Sunny,Line-line 2680 | 3.5,1,80,73,97,12,Sunny,Line-line 2681 | 4.5,0.8,90,75,99,16,Sunny,Line-line 2682 | 3.4,0.8,90,74,98,14,Sunny,Line-line 2683 | 4.7,1.1,83,72,92,14,Sunny,Line-line 2684 | 2.8,0.7,80,75,85,14,Sunny,Line-line 2685 | 2.9,0.9,88,72,93,14,Sunny,Line-line 2686 | 5,1,82,71,98,16,Sunny,Line-line 2687 | 3.9,0.9,88,70,98,13,Sunny,Line-line 2688 | 4.7,0.7,83,75,87,15,Sunny,Line-line 2689 | 3.8,1,89,70,93,15,Sunny,Line-line 2690 | 4.2,0.9,85,72,92,16,Sunny,Line-line 2691 | 3.8,0.9,85,75,91,12,Sunny,Line-line 2692 | 3.7,1,80,73,85,15,Sunny,Line-line 2693 | 4.1,1.1,86,73,90,13,Sunny,Line-line 2694 | 4.7,0.9,81,73,90,12,Sunny,Line-line 2695 | 4.6,0.8,82,70,92,13,Sunny,Line-line 2696 | 4.5,0.7,88,75,95,15,Sunny,Line-line 2697 | 4,0.9,82,72,90,13,Sunny,Line-line 2698 | 3.1,1,81,73,93,13,Sunny,Line-line 2699 | 4.6,0.9,83,71,98,16,Sunny,Line-line 2700 | 3,0.6,80,70,89,14,Sunny,Line-line 2701 | 4.4,0.8,88,71,95,15,Sunny,Line-line 2702 | 3.5,0.7,82,72,89,15,Sunny,Line-line 2703 | 3.4,0.6,85,75,94,12,Sunny,Line-line 2704 | 2.9,1,88,70,98,12,Sunny,Line-line 2705 | 4.5,0.7,86,71,91,15,Sunny,Line-line 2706 | 4.9,1,81,73,86,12,Sunny,Line-line 2707 | 4.5,0.7,80,72,97,16,Sunny,Line-line 2708 | 3.8,1.1,87,72,95,15,Sunny,Line-line 2709 | 3.8,0.9,81,73,83,14,Sunny,Line-line 2710 | 3,1.1,81,74,80,13,Sunny,Line-line 2711 | 3.1,1.1,88,73,95,16,Sunny,Line-line 2712 | 3.4,1,82,72,84,15,Sunny,Line-line 2713 | 3,0.7,89,75,96,13,Sunny,Line-line 2714 | 4.4,0.9,84,71,82,15,Sunny,Line-line 2715 | 3.4,1.1,81,71,96,12,Sunny,Line-line 2716 | 3.8,0.9,88,75,87,13,Sunny,Line-line 2717 | 3,0.7,86,71,92,16,Sunny,Line-line 2718 | 3.4,0.7,83,71,80,13,Sunny,Line-line 2719 | 3.6,1.1,90,73,91,15,Sunny,Line-line 2720 | 2.9,0.9,82,75,89,16,Sunny,Line-line 2721 | 3.3,0.9,90,73,92,13,Sunny,Line-line 2722 | 4.2,0.8,90,75,92,14,Sunny,Line-line 2723 | 3.1,0.9,90,75,95,13,Sunny,Line-line 2724 | 3.6,0.7,86,70,93,13,Sunny,Line-line 2725 | 3.5,0.6,87,74,84,12,Sunny,Line-line 2726 | 4.9,1,87,73,94,14,Sunny,Line-line 2727 | 4.5,0.8,80,75,83,14,Sunny,Line-line 2728 | 3.7,1.1,82,74,99,13,Sunny,Line-line 2729 | 2.8,0.9,80,75,94,13,Sunny,Line-line 2730 | 4.4,1,85,75,92,16,Sunny,Line-line 2731 | 3,0.8,88,75,83,14,Sunny,Line-line 2732 | 4.9,1,84,74,83,13,Sunny,Line-line 2733 | 4.2,0.7,80,75,92,15,Sunny,Line-line 2734 | 4.9,0.7,90,71,88,15,Sunny,Line-line 2735 | 3.5,0.9,81,74,100,14,Sunny,Line-line 2736 | 4.4,0.7,90,72,84,12,Sunny,Line-line 2737 | 4.5,0.8,85,74,82,15,Sunny,Line-line 2738 | 4,0.7,87,70,81,16,Sunny,Line-line 2739 | 3.8,0.7,81,72,90,16,Sunny,Line-line 2740 | 3.2,1,80,71,83,14,Sunny,Line-line 2741 | 4.9,1,89,71,100,14,Sunny,Line-line 2742 | 3.2,1,83,71,85,13,Sunny,Line-line 2743 | 4.1,1,88,73,85,14,Sunny,Line-line 2744 | 4.5,0.8,85,75,81,16,Sunny,Line-line 2745 | 3.4,0.8,87,73,85,16,Sunny,Line-line 2746 | 4.5,0.7,87,73,98,13,Sunny,Line-line 2747 | 2.9,0.6,83,75,98,15,Sunny,Line-line 2748 | 5,1,87,73,98,12,Sunny,Line-line 2749 | 3.3,0.8,88,74,85,12,Sunny,Line-line 2750 | 4.8,0.9,88,72,89,16,Sunny,Line-line 2751 | 0.1,0.4,72,90,12,4,Cloudy,Line-line 2752 | 0.2,0.5,75,82,12,4,Cloudy,Line-line 2753 | 0.2,0.5,69,89,13,5,Cloudy,Line-line 2754 | 0.1,0.4,72,82,11,4,Cloudy,Line-line 2755 | 0.2,0.3,69,82,12,1,Cloudy,Line-line 2756 | 0.1,0.4,75,90,12,5,Cloudy,Line-line 2757 | 0.1,0.5,75,90,12,4,Cloudy,Line-line 2758 | 0.2,0.3,67,83,11,3,Cloudy,Line-line 2759 | 0.2,0.5,74,84,13,3,Cloudy,Line-line 2760 | 0.1,0.5,65,90,12,5,Cloudy,Line-line 2761 | 0.1,0.4,73,86,13,4,Cloudy,Line-line 2762 | 0.1,0.6,66,83,10,5,Cloudy,Line-line 2763 | 0.2,0.5,66,87,9,3,Cloudy,Line-line 2764 | 0.1,0.4,75,80,9,4,Cloudy,Line-line 2765 | 0.2,0.6,75,82,9,5,Cloudy,Line-line 2766 | 0.2,0.6,73,86,10,3,Cloudy,Line-line 2767 | 0.1,0.4,70,88,12,3,Cloudy,Line-line 2768 | 0.2,0.5,70,83,10,2,Cloudy,Line-line 2769 | 0.1,0.6,75,90,10,3,Cloudy,Line-line 2770 | 0.1,0.3,72,80,12,1,Cloudy,Line-line 2771 | 0.2,0.5,65,86,12,3,Cloudy,Line-line 2772 | 0.1,0.5,71,83,12,5,Cloudy,Line-line 2773 | 0.1,0.4,72,80,12,1,Cloudy,Line-line 2774 | 0.2,0.5,71,88,13,1,Cloudy,Line-line 2775 | 0.1,0.3,65,85,14,2,Cloudy,Line-line 2776 | 0.2,0.4,72,84,13,5,Cloudy,Line-line 2777 | 0.1,0.4,69,85,10,2,Cloudy,Line-line 2778 | 0.2,0.6,66,90,11,4,Cloudy,Line-line 2779 | 0.2,0.6,67,84,12,1,Cloudy,Line-line 2780 | 0.1,0.3,73,86,10,5,Cloudy,Line-line 2781 | 0.2,0.4,73,86,9,2,Cloudy,Line-line 2782 | 0.1,0.5,73,84,9,3,Cloudy,Line-line 2783 | 0.2,0.4,68,86,12,5,Cloudy,Line-line 2784 | 0.2,0.5,68,80,14,1,Cloudy,Line-line 2785 | 0.1,0.4,69,82,14,4,Cloudy,Line-line 2786 | 0.1,0.3,74,85,12,1,Cloudy,Line-line 2787 | 0.2,0.4,75,86,13,2,Cloudy,Line-line 2788 | 0.2,0.3,74,87,14,4,Cloudy,Line-line 2789 | 0.2,0.3,75,80,14,4,Cloudy,Line-line 2790 | 0.1,0.5,70,88,11,3,Cloudy,Line-line 2791 | 0.1,0.5,67,87,14,5,Cloudy,Line-line 2792 | 0.1,0.4,68,89,9,3,Cloudy,Line-line 2793 | 0.2,0.4,74,87,14,4,Cloudy,Line-line 2794 | 0.2,0.6,68,80,9,4,Cloudy,Line-line 2795 | 0.1,0.4,67,80,10,1,Cloudy,Line-line 2796 | 0.2,0.6,72,85,11,5,Cloudy,Line-line 2797 | 0.2,0.4,72,80,10,4,Cloudy,Line-line 2798 | 0.1,0.6,66,83,13,5,Cloudy,Line-line 2799 | 0.1,0.5,75,81,10,5,Cloudy,Line-line 2800 | 0.1,0.5,69,84,11,1,Cloudy,Line-line 2801 | 0.1,0.3,74,89,11,2,Cloudy,Line-line 2802 | 0.2,0.4,66,82,10,3,Cloudy,Line-line 2803 | 0.2,0.5,69,90,12,4,Cloudy,Line-line 2804 | 0.1,0.6,65,85,14,5,Cloudy,Line-line 2805 | 0.1,0.4,74,89,9,3,Cloudy,Line-line 2806 | 0.1,0.4,70,85,10,5,Cloudy,Line-line 2807 | 0.2,0.3,74,86,9,1,Cloudy,Line-line 2808 | 0.2,0.5,75,86,13,5,Cloudy,Line-line 2809 | 0.2,0.5,73,80,12,1,Cloudy,Line-line 2810 | 0.2,0.5,75,88,12,5,Cloudy,Line-line 2811 | 0.2,0.6,72,83,12,1,Cloudy,Line-line 2812 | 0.2,0.4,71,87,11,1,Cloudy,Line-line 2813 | 0.2,0.4,67,90,9,1,Cloudy,Line-line 2814 | 0.1,0.5,68,86,11,1,Cloudy,Line-line 2815 | 0.1,0.4,72,84,14,2,Cloudy,Line-line 2816 | 0.2,0.4,65,85,12,4,Cloudy,Line-line 2817 | 0.1,0.4,68,86,10,2,Cloudy,Line-line 2818 | 0.1,0.3,67,82,14,4,Cloudy,Line-line 2819 | 0.1,0.4,70,90,11,3,Cloudy,Line-line 2820 | 0.2,0.3,70,85,10,5,Cloudy,Line-line 2821 | 0.1,0.5,69,85,14,3,Cloudy,Line-line 2822 | 0.2,0.4,70,83,10,1,Cloudy,Line-line 2823 | 0.1,0.3,72,89,14,2,Cloudy,Line-line 2824 | 0.1,0.3,70,80,11,2,Cloudy,Line-line 2825 | 0.2,0.5,72,81,12,3,Cloudy,Line-line 2826 | 0.1,0.3,69,86,10,1,Cloudy,Line-line 2827 | 0.2,0.6,68,90,10,5,Cloudy,Line-line 2828 | 0.2,0.6,73,82,10,5,Cloudy,Line-line 2829 | 0.2,0.6,72,89,13,4,Cloudy,Line-line 2830 | 0.1,0.4,67,82,13,3,Cloudy,Line-line 2831 | 0.1,0.6,69,86,14,4,Cloudy,Line-line 2832 | 0.2,0.4,75,80,12,2,Cloudy,Line-line 2833 | 0.1,0.6,74,81,14,3,Cloudy,Line-line 2834 | 0.2,0.5,66,89,11,5,Cloudy,Line-line 2835 | 0.1,0.5,71,90,13,2,Cloudy,Line-line 2836 | 0.2,0.4,66,82,12,3,Cloudy,Line-line 2837 | 0.1,0.5,72,88,13,2,Cloudy,Line-line 2838 | 0.2,0.4,67,84,9,5,Cloudy,Line-line 2839 | 0.2,0.6,66,83,12,1,Cloudy,Line-line 2840 | 0.2,0.6,67,81,12,1,Cloudy,Line-line 2841 | 0.1,0.5,67,85,10,5,Cloudy,Line-line 2842 | 0.2,0.4,66,85,12,3,Cloudy,Line-line 2843 | 0.2,0.3,75,90,10,5,Cloudy,Line-line 2844 | 0.1,0.3,75,85,10,3,Cloudy,Line-line 2845 | 0.1,0.5,68,83,14,3,Cloudy,Line-line 2846 | 0.1,0.6,74,85,12,3,Cloudy,Line-line 2847 | 0.1,0.4,72,89,11,2,Cloudy,Line-line 2848 | 0.1,0.4,66,83,14,2,Cloudy,Line-line 2849 | 0.2,0.5,68,81,13,5,Cloudy,Line-line 2850 | 0.2,0.5,70,86,14,1,Cloudy,Line-line 2851 | 0.1,0.6,66,86,11,4,Cloudy,Line-line 2852 | 0.2,0.4,75,89,10,5,Cloudy,Line-line 2853 | 0.2,0.6,72,85,12,2,Cloudy,Line-line 2854 | 0.2,0.6,70,87,13,1,Cloudy,Line-line 2855 | 0.1,0.6,73,86,11,3,Cloudy,Line-line 2856 | 0.1,0.5,66,88,13,3,Cloudy,Line-line 2857 | 0.2,0.3,66,81,13,1,Cloudy,Line-line 2858 | 0.1,0.5,72,84,11,2,Cloudy,Line-line 2859 | 0.1,0.6,74,83,10,5,Cloudy,Line-line 2860 | 0.2,0.4,73,90,11,1,Cloudy,Line-line 2861 | 0.2,0.4,68,89,11,4,Cloudy,Line-line 2862 | 0.1,0.5,72,80,11,1,Cloudy,Line-line 2863 | 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0.2,0.4,71,87,14,3,Cloudy,Line-line 2887 | 0.1,0.6,67,89,11,4,Cloudy,Line-line 2888 | 0.2,0.5,72,87,12,1,Cloudy,Line-line 2889 | 0.2,0.5,65,85,14,2,Cloudy,Line-line 2890 | 0.2,0.5,71,88,13,3,Cloudy,Line-line 2891 | 0.2,0.3,71,85,13,1,Cloudy,Line-line 2892 | 0.1,0.6,67,85,13,2,Cloudy,Line-line 2893 | 0.2,0.4,66,83,13,5,Cloudy,Line-line 2894 | 0.2,0.5,74,82,12,4,Cloudy,Line-line 2895 | 0.2,0.5,65,81,13,2,Cloudy,Line-line 2896 | 0.1,0.4,72,80,14,4,Cloudy,Line-line 2897 | 0.2,0.4,69,81,9,2,Cloudy,Line-line 2898 | 0.1,0.4,71,88,13,2,Cloudy,Line-line 2899 | 0.2,0.4,75,89,10,2,Cloudy,Line-line 2900 | 0.1,0.5,69,88,9,1,Cloudy,Line-line 2901 | 0.2,0.3,68,84,10,3,Cloudy,Line-line 2902 | 0.1,0.4,71,81,11,3,Cloudy,Line-line 2903 | 0.1,0.3,67,83,10,4,Cloudy,Line-line 2904 | 0.2,0.6,70,90,12,3,Cloudy,Line-line 2905 | 0.1,0.3,71,86,12,1,Cloudy,Line-line 2906 | 0.1,0.4,69,88,13,1,Cloudy,Line-line 2907 | 0.2,0.3,65,89,10,2,Cloudy,Line-line 2908 | 0.1,0.5,75,86,12,5,Cloudy,Line-line 2909 | 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0.2,0.5,68,86,9,2,Cloudy,Line-line 3002 | -------------------------------------------------------------------------------- /dimensional_reduction.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | import pandas as pd 4 | import matplotlib.pyplot as plt 5 | import seaborn as sb 6 | 7 | 8 | dataset = pd.read_csv('Solar_categorical.csv') 9 | X = dataset.iloc[:3000, 0:7].values 10 | y = dataset.iloc[:3000, 7].values 11 | 12 | from sklearn.preprocessing import LabelEncoder 13 | from keras.utils import to_categorical 14 | encoder= LabelEncoder() 15 | X[:,6] = encoder.fit_transform(X[:, 6]) 16 | 17 | ################################################################################### 18 | ####### PCA (Unsupervised)############################################ 19 | from sklearn.preprocessing import StandardScaler 20 | X_scaled = StandardScaler().fit_transform(X) 21 | 22 | from sklearn.decomposition import PCA 23 | pca = PCA(n_components=2) 24 | p_components = pca.fit_transform(X_scaled) 25 | var_ratio = pca.explained_variance_ratio_ 26 | 27 | principalDF = pd.DataFrame(data = p_components, columns = ['principal component 1', 'principal component 2']) 28 | principalDF = pd.concat([principalDF, dataset[['State']]], axis=1) 29 | 30 | type_faults = ['Normal', 'Open', 'Line-line'] 31 | colors = ['r', 'g', 'b'] 32 | 33 | fig = plt.figure() 34 | ax = fig.add_subplot(1,1,1) 35 | ax.set_xlabel('Principal Component 1', fontsize = 10) 36 | ax.set_ylabel('Principal Component 2', fontsize = 10) 37 | ax.set_title('2 Component PCA', fontsize = 12) 38 | for Fault, color in zip(type_faults, colors): 39 | indicesToKeep = principalDF['State'] == Fault 40 | ax.scatter(principalDF.loc[indicesToKeep, 'principal component 1'], 41 | principalDF.loc[indicesToKeep, 'principal component 2'] , 42 | c = color, s = 12) 43 | ax.legend(type_faults) 44 | ################################################################################### 45 | 46 | # Splitting the dataset into the Training set and Test set 47 | from sklearn.model_selection import train_test_split 48 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) 49 | 50 | # Feature Scaling 51 | from sklearn.preprocessing import StandardScaler 52 | sc = StandardScaler() 53 | X_train = sc.fit_transform(X_train) 54 | X_test = sc.transform(X_test) 55 | 56 | from sklearn.preprocessing import LabelEncoder 57 | encoder = LabelEncoder() 58 | y_train = encoder.fit_transform(y_train) 59 | y_test = encoder.transform(y_test) 60 | 61 | # Applying PCA 62 | from sklearn.decomposition import PCA 63 | pca = PCA(n_components = 2) 64 | X_train = pca.fit_transform(X_train) 65 | X_test = pca.transform(X_test) 66 | explained_variance = pca.explained_variance_ratio_ 67 | 68 | from sklearn.svm import SVC 69 | svc_clf = SVC(kernel="linear", probability=True) 70 | svc_clf.fit(X_train, y_train) 71 | 72 | y_pred = svc_clf.predict(X_test) 73 | y_pred_label = encoder.inverse_transform(y_pred) 74 | print(y_pred_label) 75 | 76 | y_test_label = encoder.inverse_transform(y_test) 77 | print(y_test_label) 78 | 79 | # Making the Confusion Matrix 80 | from sklearn.metrics import confusion_matrix 81 | cm = confusion_matrix(y_test_label, y_pred_label) 82 | 83 | 84 | # Visualising the Training set results 85 | from matplotlib.colors import ListedColormap 86 | X_set, y_set = X_train, y_train 87 | X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01), 88 | np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) 89 | plt.contourf(X1, X2, svc_clf.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), 90 | alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue'))) 91 | plt.xlim(X1.min(), X1.max()) 92 | plt.ylim(X2.min(), X2.max()) 93 | for i, j in enumerate(np.unique(y_set)): 94 | plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], 95 | c = ListedColormap(('red', 'green', 'blue'))(i), label = j) 96 | plt.title('Support Vector Machine (Training set)') 97 | plt.xlabel('PC1') 98 | plt.ylabel('PC2') 99 | plt.legend() 100 | plt.show() 101 | 102 | # Visualising the Test set results 103 | from matplotlib.colors import ListedColormap 104 | X_set, y_set = X_test, y_test 105 | X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01), 106 | np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) 107 | plt.contourf(X1, X2, svc_clf.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), 108 | alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue'))) 109 | plt.xlim(X1.min(), X1.max()) 110 | plt.ylim(X2.min(), X2.max()) 111 | for i, j in enumerate(np.unique(y_set)): 112 | plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], 113 | c = ListedColormap(('red', 'green', 'blue'))(i), label = j) 114 | plt.title('Support Vector Machine (Test set)') 115 | plt.xlabel('PC1') 116 | plt.ylabel('PC2') 117 | plt.legend() 118 | plt.show() 119 | 120 | ############################################################################################## 121 | ######################## LDA (Supervised) ########################################### 122 | 123 | from sklearn.preprocessing import StandardScaler 124 | X_scaled = StandardScaler().fit_transform(X) 125 | 126 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA 127 | lda = LDA(n_components=2) 128 | p_components = lda.fit_transform(X_scaled, y) 129 | var_ratio = lda.explained_variance_ratio_ 130 | 131 | principalDF = pd.DataFrame(data = p_components, columns = ['principal component 1', 'principal component 2']) 132 | principalDF = pd.concat([principalDF, dataset[['Fault']]], axis=1) 133 | 134 | type_faults = ['Normal', 'Open', 'Short'] 135 | colors = ['r', 'g', 'b'] 136 | 137 | fig = plt.figure() 138 | ax = fig.add_subplot(1,1,1) 139 | ax.set_xlabel('Principal Component 1', fontsize = 15) 140 | ax.set_ylabel('Principal Component 2', fontsize = 15) 141 | ax.set_title('2 Component LDA', fontsize = 20) 142 | for Fault, color in zip(type_faults, colors): 143 | indicesToKeep = principalDF['Fault'] == Fault 144 | ax.scatter(principalDF.loc[indicesToKeep, 'principal component 1'], 145 | principalDF.loc[indicesToKeep, 'principal component 2'] , 146 | c = color, s = 50) 147 | ax.legend(type_faults) 148 | 149 | # Splitting the dataset into the Training set and Test set 150 | from sklearn.model_selection import train_test_split 151 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) 152 | 153 | # Feature Scaling 154 | from sklearn.preprocessing import StandardScaler 155 | sc = StandardScaler() 156 | X_train = sc.fit_transform(X_train) 157 | X_test = sc.transform(X_test) 158 | 159 | from sklearn.preprocessing import LabelEncoder 160 | encoder = LabelEncoder() 161 | y_train = encoder.fit_transform(y_train) 162 | y_test = encoder.transform(y_test) 163 | 164 | 165 | # Applying LDA 166 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA 167 | lda = LDA(n_components = 2) 168 | X_train = lda.fit_transform(X_train, y_train) 169 | X_test = lda.transform(X_test) 170 | explained_variance = lda.explained_variance_ratio_ 171 | 172 | from sklearn.svm import SVC 173 | svc_clf = SVC(kernel="linear", probability=True) 174 | svc_clf.fit(X_train, y_train) 175 | 176 | y_pred = svc_clf.predict(X_test) 177 | y_pred_label = encoder.inverse_transform(y_pred) 178 | print(y_pred_label) 179 | 180 | y_test_label = encoder.inverse_transform(y_test) 181 | print(y_test_label) 182 | 183 | ## Predicting with new data ##### 184 | new_pred1 = svc_clf.predict(lda.transform(sc.transform(np.array([[2.1, 3.1, 1.2, 33, 92]])))) 185 | new_pred1_original = encoder.inverse_transform(new_pred1) 186 | print(new_pred1_original) 187 | 188 | new_pred2 = svc_clf.predict(lda.transform(sc.transform(np.array([[4.1, 3.5, 4.6, 45, 100]])))) 189 | new_pred2_original = encoder.inverse_transform(new_pred2) 190 | print(new_pred2_original) 191 | 192 | new_pred3 = svc_clf.predict(lda.transform(sc.transform(np.array([[0, 4, 0.4, 15, 64]])))) 193 | new_pred3_original = encoder.inverse_transform(new_pred3) 194 | print(new_pred3_original) 195 | 196 | # Making the Confusion Matrix 197 | from sklearn.metrics import confusion_matrix 198 | cm = confusion_matrix(y_test_label, y_pred_label) 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | -------------------------------------------------------------------------------- /fault_model.model: -------------------------------------------------------------------------------- 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