└── temp-predict
├── .idea
├── encodings.xml
├── misc.xml
├── modules.xml
├── temp-predict.iml
└── workspace.xml
├── code
├── predict.py
├── readdata.py
├── tree.dot
└── treeparam.py
├── data
├── data.txt
├── stopwords.txt
├── temps.csv
└── temps_extended.csv
└── news
└── newsbayes.py
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/temp-predict/code/predict.py:
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1 | # -*- encoding: utf-8 -*-
2 | """
3 | @File : predict.py
4 | @Time : 2020/5/24 9:29
5 | @Author : 王一宁
6 | @Email : wyn_365@163.com
7 | 庞大数据集的预测
8 | """
9 |
10 | # 1.数据读取
11 | import pandas as pd
12 |
13 | features = pd.read_csv('D:\\APP\\PythonCharm2018\\workspace\\temp-predict\data\\temps_extended.csv')
14 |
15 | print('数据规模',features.shape)
16 |
17 | # 统计指标
18 | round(features.describe(), 2)
19 |
20 | # 2.转换成标准格式
21 | import datetime
22 |
23 | # 得到各种日期数据
24 | years = features['year']
25 | months = features['month']
26 | days = features['day']
27 |
28 | # 格式转换
29 | dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
30 | dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
31 |
32 | # 绘图
33 | import matplotlib.pyplot as plt
34 |
35 | # 风格设置
36 | plt.style.use('fivethirtyeight')
37 |
38 | # Set up the plotting layout
39 | fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (15,10))
40 | fig.autofmt_xdate(rotation = 45)
41 |
42 | # Actual max temperature measurement
43 | ax1.plot(dates, features['actual'])
44 | ax1.set_xlabel(''); ax1.set_ylabel('Temperature (F)'); ax1.set_title('Max Temp')
45 |
46 | # Temperature from 1 day ago
47 | ax2.plot(dates, features['temp_1'])
48 | ax2.set_xlabel(''); ax2.set_ylabel('Temperature (F)'); ax2.set_title('Prior Max Temp')
49 |
50 | # Temperature from 2 days ago
51 | ax3.plot(dates, features['temp_2'])
52 | ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature (F)'); ax3.set_title('Two Days Prior Max Temp')
53 |
54 | # Friend Estimate
55 | ax4.plot(dates, features['friend'])
56 | ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature (F)'); ax4.set_title('Friend Estimate')
57 |
58 | plt.tight_layout(pad=2)
59 |
60 | plt.show()
61 |
62 | # 设置整体布局
63 | fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (15,10))
64 | fig.autofmt_xdate(rotation = 45)
65 |
66 | # 平均最高气温
67 | ax1.plot(dates, features['average'])
68 | ax1.set_xlabel(''); ax1.set_ylabel('Temperature (F)'); ax1.set_title('Historical Avg Max Temp')
69 |
70 | # 风速
71 | ax2.plot(dates, features['ws_1'], 'r-')
72 | ax2.set_xlabel(''); ax2.set_ylabel('Wind Speed (mph)'); ax2.set_title('Prior Wind Speed')
73 |
74 | # 降水
75 | ax3.plot(dates, features['prcp_1'], 'r-')
76 | ax3.set_xlabel('Date'); ax3.set_ylabel('Precipitation (in)'); ax3.set_title('Prior Precipitation')
77 |
78 | # 积雪
79 | ax4.plot(dates, features['snwd_1'], 'ro')
80 | ax4.set_xlabel('Date'); ax4.set_ylabel('Snow Depth (in)'); ax4.set_title('Prior Snow Depth')
81 |
82 | plt.tight_layout(pad=2)
83 |
84 | plt.show()
85 |
86 | # 3.Pairplots画图好看的图
87 | # 创建一个季节变量
88 | seasons = []
89 |
90 | for month in features['month']:
91 | if month in [1, 2, 12]:
92 | seasons.append('winter')
93 | elif month in [3, 4, 5]:
94 | seasons.append('spring')
95 | elif month in [6, 7, 8]:
96 | seasons.append('summer')
97 | elif month in [9, 10, 11]:
98 | seasons.append('fall')
99 |
100 | # 有了季节我们就可以分析更多东西了
101 | reduced_features = features[['temp_1', 'prcp_1', 'average', 'actual']]
102 | reduced_features['season'] = seasons
103 |
104 | # 导入seaborn工具包
105 | import seaborn as sns
106 | sns.set(style="ticks", color_codes=True);
107 |
108 | # 选择你喜欢的颜色模板
109 | palette = sns.xkcd_palette(['dark blue', 'dark green', 'gold', 'orange'])
110 |
111 | # 绘制pairplot
112 | sns.pairplot(reduced_features, hue = 'season', diag_kind = 'kde', palette= palette, plot_kws=dict(alpha = 0.7),diag_kws=dict(shade=True));
113 |
114 | # 4.数据预处理
115 | # 独热编码
116 | features = pd.get_dummies(features)
117 |
118 | # 提取特征和标签
119 | labels = features['actual']
120 | features = features.drop('actual', axis = 1)
121 |
122 | # 特征名字留着备用
123 | feature_list = list(features.columns)
124 |
125 | # 转换成所需格式
126 | import numpy as np
127 |
128 | features = np.array(features)
129 | labels = np.array(labels)
130 |
131 | # 数据集切分
132 | from sklearn.model_selection import train_test_split
133 |
134 | train_features, test_features, train_labels, test_labels = train_test_split(features, labels,
135 | test_size = 0.25, random_state = 0)
136 | print('Training Features Shape:', train_features.shape)
137 | print('Training Labels Shape:', train_labels.shape)
138 | print('Testing Features Shape:', test_features.shape)
139 | print('Testing Labels Shape:', test_labels.shape)
140 |
141 | # 5.老数据的结果
142 | # 工具包导入
143 | import pandas as pd
144 |
145 | # 为了剔除特征个数对结果的影响，这里特征统一只有老数据集中特征
146 | original_feature_indices = [feature_list.index(feature) for feature in
147 | feature_list if feature not in
148 | ['ws_1', 'prcp_1', 'snwd_1']]
149 |
150 | # 读取老数据集
151 | original_features = pd.read_csv('D:\\APP\\PythonCharm2018\\workspace\\temp-predict\\data\\temps.csv')
152 |
153 | original_features = pd.get_dummies(original_features)
154 |
155 | import numpy as np
156 |
157 | # 数据和标签转换
158 | original_labels = np.array(original_features['actual'])
159 |
160 | original_features= original_features.drop('actual', axis = 1)
161 |
162 | original_feature_list = list(original_features.columns)
163 |
164 | original_features = np.array(original_features)
165 |
166 | # 数据集切分
167 | from sklearn.model_selection import train_test_split
168 |
169 | original_train_features, original_test_features, original_train_labels, original_test_labels = train_test_split(original_features, original_labels, test_size = 0.25, random_state = 42)
170 |
171 | # 同样的树模型进行建模
172 | from sklearn.ensemble import RandomForestRegressor
173 |
174 | # 同样的参数与随机种子
175 | rf = RandomForestRegressor(n_estimators= 100, random_state=0)
176 |
177 | # 这里的训练集使用的是老数据集的
178 | rf.fit(original_train_features, original_train_labels);
179 |
180 | # 为了测试效果能够公平，统一使用一致的测试集，这里选择了刚刚我切分过的新数据集的测试集
181 | predictions = rf.predict(test_features[:,original_feature_indices])
182 |
183 | # 先计算温度平均误差
184 | errors = abs(predictions - test_labels)
185 |
186 | print('平均温度误差:', round(np.mean(errors), 2), 'degrees.')
187 |
188 | # MAPE
189 | mape = 100 * (errors / test_labels)
190 |
191 | # 这里的Accuracy为了方便观察，我们就用100减去误差了，希望这个值能够越大越好
192 | # 当我们把数据量增大之后，效果发生了一些提升，这也符合实际情况，
193 | # 在机器学习任务中，我们都是希望数据量能够越大越好，这样可利用的信息就更多了。
194 | # 下面我们要再对比一下特征数量对结果的影响，之前这两次比较还没有加入新的特征，
195 |
196 | accuracy = 100 - np.mean(mape)
197 | print('Accuracy:', round(accuracy, 2), '%.')
198 |
199 | # 6.新数据集
200 | # 这回我们把降水，风速，积雪3特征加入训练集中，看看效果又会怎样：
201 | from sklearn.ensemble import RandomForestRegressor
202 |
203 | # 剔除掉新的特征，保证数据特征是一致的
204 | original_train_features = train_features[:,original_feature_indices]
205 |
206 | original_test_features = test_features[:, original_feature_indices]
207 |
208 | rf = RandomForestRegressor(n_estimators= 100 ,random_state=0)
209 |
210 | rf.fit(original_train_features, train_labels);
211 |
212 | # 预测
213 | baseline_predictions = rf.predict(original_test_features)
214 |
215 | # 结果
216 | baseline_errors = abs(baseline_predictions - test_labels)
217 |
218 | print('平均温度误差:', round(np.mean(baseline_errors), 2), 'degrees.')
219 |
220 | # (MAPE)
221 | baseline_mape = 100 * np.mean((baseline_errors / test_labels))
222 |
223 | # accuracy
224 | baseline_accuracy = 100 - baseline_mape
225 | print('Accuracy:', round(baseline_accuracy, 2), '%.')
226 |
227 | # 7.加入新特征
228 | # 准备加入新的特征
229 | from sklearn.ensemble import RandomForestRegressor
230 |
231 | rf_exp = RandomForestRegressor(n_estimators= 100, random_state=0)
232 | rf_exp.fit(train_features, train_labels)
233 |
234 | # 同样的测试集
235 | predictions = rf_exp.predict(test_features)
236 |
237 | # 评估
238 | errors = abs(predictions - test_labels)
239 |
240 | print('平均温度误差:', round(np.mean(errors), 2), 'degrees.')
241 |
242 | # (MAPE)
243 | mape = np.mean(100 * (errors / test_labels))
244 |
245 | # 看一下提升了多少
246 | improvement_baseline = 100 * abs(mape - baseline_mape) / baseline_mape
247 | print('特征增多后模型效果提升:', round(improvement_baseline, 2), '%.')
248 |
249 | # accuracy
250 | accuracy = 100 - mape
251 | print('Accuracy:', round(accuracy, 2), '%.')
252 |
253 | # 8.特征重要性
254 | # 特征名字
255 | importances = list(rf_exp.feature_importances_)
256 |
257 | # 名字，数值组合在一起
258 | feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]
259 |
260 | # 排序
261 | feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)
262 |
263 | # 打印出来
264 | [print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];
265 |
266 | # 指定风格
267 | plt.style.use('fivethirtyeight')
268 |
269 | # 指定位置
270 | x_values = list(range(len(importances)))
271 |
272 | # 绘图
273 | plt.bar(x_values, importances, orientation = 'vertical', color = 'r', edgecolor = 'k', linewidth = 1.2)
274 |
275 | # x轴名字得竖着写
276 | plt.xticks(x_values, feature_list, rotation='vertical')
277 |
278 | # 图名
279 | plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');
280 | plt.show()
281 |
282 | # 对特征进行排序 0.95那 累加特征重要性
283 | sorted_importances = [importance[1] for importance in feature_importances]
284 | sorted_features = [importance[0] for importance in feature_importances]
285 |
286 | # 累计重要性
287 | cumulative_importances = np.cumsum(sorted_importances)
288 |
289 | # 绘制折线图
290 | plt.plot(x_values, cumulative_importances, 'g-')
291 |
292 | # 画一条红色虚线，0.95那 累加特征重要性
293 | plt.hlines(y = 0.95, xmin=0, xmax=len(sorted_importances), color = 'r', linestyles = 'dashed')
294 |
295 | # X轴
296 | plt.xticks(x_values, sorted_features, rotation = 'vertical')
297 |
298 | # Y轴和名字
299 | plt.xlabel('Variable'); plt.ylabel('Cumulative Importance'); plt.title('Cumulative Importances');
300 | plt.show()
301 |
302 | # 看看有几个特征
303 | print('Number of features for 95% importance:', np.where(cumulative_importances > 0.95)[0][0] + 1)
304 |
305 | # 9.效率对比分析
306 | # 选择这些特征
307 | important_feature_names = [feature[0] for feature in feature_importances[0:5]]
308 | # 找到它们的名字
309 | important_indices = [feature_list.index(feature) for feature in important_feature_names]
310 |
311 | # 重新创建训练集
312 | important_train_features = train_features[:, important_indices]
313 | important_test_features = test_features[:, important_indices]
314 |
315 | # 数据维度
316 | print('Important train features shape:', important_train_features.shape)
317 | print('Important test features shape:', important_test_features.shape)
318 |
319 | # 再训练模型
320 | rf_exp.fit(important_train_features, train_labels);
321 |
322 | # 同样的测试集
323 | predictions = rf_exp.predict(important_test_features)
324 |
325 | # 评估结果
326 | errors = abs(predictions - test_labels)
327 |
328 | print('平均温度误差:', round(np.mean(errors), 2), 'degrees.')
329 |
330 | mape = 100 * (errors / test_labels)
331 |
332 | # accuracy
333 | accuracy = 100 - np.mean(mape)
334 | print('Accuracy:', round(accuracy, 2), '%.')
335 |
336 | # 10.看起来奇迹并没有出现，本以为效果反而会更好，其实还有一点点下降，
337 | # 这里可能由于是树模型本身具有特征选择的被动技能了。虽然模型没有提升，
338 | # 我们还可以再看看在时间效率的层面上有没有进步呢：
339 | # 要计算时间了
340 | import time
341 |
342 | # 10.1这次是用所有特征
343 | all_features_time = []
344 |
345 | # 算一次可能不太准，来10次取个平均
346 | for _ in range(10):
347 | start_time = time.time()
348 | rf_exp.fit(train_features, train_labels)
349 | all_features_predictions = rf_exp.predict(test_features)
350 | end_time = time.time()
351 | all_features_time.append(end_time - start_time)
352 |
353 | all_features_time = np.mean(all_features_time)
354 | print('使用所有特征时建模与测试的平均时间消耗:', round(all_features_time, 2), '秒.')
355 |
356 | # 10.2 这次是用部分5个重要的特征
357 | reduced_features_time = []
358 |
359 | # 算一次可能不太准，来10次取个平均
360 | for _ in range(10):
361 | start_time = time.time()
362 | rf_exp.fit(important_train_features, train_labels)
363 | reduced_features_predictions = rf_exp.predict(important_test_features)
364 | end_time = time.time()
365 | reduced_features_time.append(end_time - start_time)
366 |
367 | reduced_features_time = np.mean(reduced_features_time)
368 | print('使用部分5个特征时建模与测试的平均时间消耗:', round(reduced_features_time, 2), '秒.')
369 |
370 | # 11.用分别的预测值来计算评估结果
371 | all_accuracy = 100 * (1- np.mean(abs(all_features_predictions - test_labels) / test_labels))
372 | reduced_accuracy = 100 * (1- np.mean(abs(reduced_features_predictions - test_labels) / test_labels))
373 |
374 | #创建一个df来保存结果
375 | comparison = pd.DataFrame({'features': ['all (17)', 'reduced (5)'],
376 | 'run_time': [round(all_features_time, 2), round(reduced_features_time, 2)],
377 | 'accuracy': [round(all_accuracy, 2), round(reduced_accuracy, 2)]})
378 |
379 | comparison[['features', 'accuracy', 'run_time']]
380 |
381 | relative_accuracy_decrease = 100 * (all_accuracy - reduced_accuracy) / all_accuracy
382 | print('相对accuracy下降:', round(relative_accuracy_decrease, 3), '%.')
383 |
384 | relative_runtime_decrease = 100 * (all_features_time - reduced_features_time) / all_features_time
385 | print('相对时间效率提升:', round(relative_runtime_decrease, 3), '%.')
386 |
387 | # 12. 通常我们买东西都会考虑性价比，这里同样也是这个问题，时间效率的提升相对更大一些，
388 | # 而且基本保证了模型效果是差不多的。
389 | # 最后让我们把所有的实验结果汇总到一起来进行对比吧：
390 | # Pandas is used for data manipulation
391 | import pandas as pd
392 |
393 | # Read in data as pandas dataframe and display first 5 rows
394 | original_features = pd.read_csv('D:\\APP\\PythonCharm2018\\workspace\\temp-predict\\data\\temps.csv')
395 | original_features = pd.get_dummies(original_features)
396 |
397 | # Use numpy to convert to arrays
398 | import numpy as np
399 |
400 | # Labels are the values we want to predict
401 | original_labels = np.array(original_features['actual'])
402 |
403 | # Remove the labels from the features
404 | # axis 1 refers to the columns
405 | original_features= original_features.drop('actual', axis = 1)
406 |
407 | # Saving feature names for later use
408 | original_feature_list = list(original_features.columns)
409 |
410 | # Convert to numpy array
411 | original_features = np.array(original_features)
412 |
413 | # Using Skicit-learn to split data into training and testing sets
414 | from sklearn.model_selection import train_test_split
415 |
416 | # Split the data into training and testing sets
417 | original_train_features, original_test_features, original_train_labels, original_test_labels = train_test_split(original_features, original_labels, test_size = 0.25, random_state = 42)
418 |
419 | # Find the original feature indices
420 | original_feature_indices = [feature_list.index(feature) for feature in
421 | feature_list if feature not in
422 | ['ws_1', 'prcp_1', 'snwd_1']]
423 |
424 | # Create a test set of the original features
425 | original_test_features = test_features[:, original_feature_indices]
426 |
427 | # Time to train on original data set (1 year)
428 | original_features_time = []
429 |
430 | # Do 10 iterations and take average for all features
431 | for _ in range(10):
432 | start_time = time.time()
433 | rf.fit(original_train_features, original_train_labels)
434 | original_features_predictions = rf.predict(original_test_features)
435 | end_time = time.time()
436 | original_features_time.append(end_time - start_time)
437 |
438 | original_features_time = np.mean(original_features_time)
439 |
440 | # Calculate mean absolute error for each model
441 | original_mae = np.mean(abs(original_features_predictions - test_labels))
442 | exp_all_mae = np.mean(abs(all_features_predictions - test_labels))
443 | exp_reduced_mae = np.mean(abs(reduced_features_predictions - test_labels))
444 |
445 | # Calculate accuracy for model trained on 1 year of data
446 | original_accuracy = 100 * (1 - np.mean(abs(original_features_predictions - test_labels) / test_labels))
447 |
448 | # Create a dataframe for comparison
449 | model_comparison = pd.DataFrame({'model': ['original', 'exp_all', 'exp_reduced'],
450 | 'error (degrees)': [original_mae, exp_all_mae, exp_reduced_mae],
451 | 'accuracy': [original_accuracy, all_accuracy, reduced_accuracy],
452 | 'run_time (s)': [original_features_time, all_features_time, reduced_features_time]})
453 |
454 | # Order the dataframe
455 | model_comparison = model_comparison[['model', 'error (degrees)', 'accuracy', 'run_time (s)']]
456 |
457 | model_comparison
458 |
459 | # 绘图来总结把
460 | # 设置总体布局，还是一整行看起来好一些
461 | fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize = (16,5), sharex = True)
462 |
463 | # X轴
464 | x_values = [0, 1, 2]
465 | labels = list(model_comparison['model'])
466 | plt.xticks(x_values, labels)
467 |
468 | # 字体大小
469 | fontdict = {'fontsize': 18}
470 | fontdict_yaxis = {'fontsize': 14}
471 |
472 | # 预测温度和真实温度差异对比
473 | ax1.bar(x_values, model_comparison['error (degrees)'], color = ['b', 'r', 'g'], edgecolor = 'k', linewidth = 1.5)
474 | ax1.set_ylim(bottom = 3.5, top = 4.5)
475 | ax1.set_ylabel('Error (degrees) (F)', fontdict = fontdict_yaxis);
476 | ax1.set_title('Model Error Comparison', fontdict= fontdict)
477 |
478 | # Accuracy 对比
479 | ax2.bar(x_values, model_comparison['accuracy'], color = ['b', 'r', 'g'], edgecolor = 'k', linewidth = 1.5)
480 | ax2.set_ylim(bottom = 92, top = 94)
481 | ax2.set_ylabel('Accuracy (%)', fontdict = fontdict_yaxis);
482 | ax2.set_title('Model Accuracy Comparison', fontdict= fontdict)
483 |
484 | # 时间效率对比
485 | ax3.bar(x_values, model_comparison['run_time (s)'], color = ['b', 'r', 'g'], edgecolor = 'k', linewidth = 1.5)
486 | ax3.set_ylim(bottom = 0, top = 1)
487 | ax3.set_ylabel('Run Time (sec)', fontdict = fontdict_yaxis);
488 | ax3.set_title('Model Run-Time Comparison', fontdict= fontdict);
489 | plt.show()
490 | print("original代表是我们的老数据，也就是量少特征少的那份；exp_all代表我们的完整新数据；exp_reduced代表我们按照95%阈值选择的部分重要特征数据集。结果也是很明显的，数据量和特征越多，效果会提升一些，但是时间效率也会有所下降。")
491 |
492 |
493 |
494 |
495 |
496 |
497 |
498 |
499 |
500 |
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/temp-predict/code/readdata.py:
--------------------------------------------------------------------------------
1 | # -*- encoding: utf-8 -*-
2 | """
3 | @File : readdata.py
4 | @Time : 2020/5/23 18:36
5 | @Author : 王一宁
6 | @Email : wyn_365@163.com
7 | 小数据集预测
8 | """
9 | # 数据读取
10 | import pandas as pd
11 | # 处理时间数据
12 | import datetime
13 | # 准备画图
14 | import matplotlib.pyplot as plt
15 |
16 | import pydot
17 | # 指定默认风格
18 | # 数据与标签
19 | import numpy as np
20 | # 数据集切分
21 | from sklearn.model_selection import train_test_split
22 |
23 | plt.style.use('fivethirtyeight')
24 |
25 | # 1.读取数据
26 | features = pd.read_csv('D:\\APP\\PythonCharm2018\\workspace\\temp-predict\\data\\temps.csv')
27 | features.head(5)
28 | print('一共有348条记录，每个样本有9个特征:', features.shape)
29 |
30 | # 2.统计指标
31 | features.describe()
32 |
33 | # 分别得到年，月，日
34 | years = features['year']
35 | months = features['month']
36 | days = features['day']
37 | # datetime格式
38 | dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
39 | dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
40 | dates[:5]
41 |
42 | # 设置布局
43 | fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))
44 | fig.autofmt_xdate(rotation = 45)
45 |
46 | # 标签值
47 | ax1.plot(dates, features['actual'])
48 | ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp')
49 |
50 | # 昨天
51 | ax2.plot(dates, features['temp_1'])
52 | ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp')
53 |
54 | # 前天
55 | ax3.plot(dates, features['temp_2'])
56 | ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp')
57 |
58 | # 我的朋友
59 | ax4.plot(dates, features['friend'])
60 | ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')
61 |
62 | plt.tight_layout(pad=2)
63 |
64 | # 3.独热编码
65 | features = pd.get_dummies(features)
66 | features.head(5)
67 |
68 | # print (help(pd.get_dummies))
69 |
70 | print('处理完成日期后当前特征的个数:', features.shape)
71 |
72 |
73 | # 4.标签与数据格式的转换 Y
74 | labels = np.array(features['actual'])
75 |
76 | # 在特征中去掉标签 横轴X没有Y X
77 | features= features.drop('actual', axis = 1)
78 |
79 | # 名字单独保存一下，以备后患
80 | feature_list = list(features.columns)
81 |
82 | # 5.转换成合适的格式
83 | features = np.array(features)
84 |
85 | # 6.训练集与测试集 x y
86 | train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25,
87 | random_state = 42)
88 | print('训练集特征:', train_features.shape)
89 | print('训练集标签:', train_labels.shape)
90 | print('测试集特征:', test_features.shape)
91 | print('测试集标签:', test_labels.shape)
92 |
93 | # 7.导入随机森林算法 回归的随机森林
94 | from sklearn.ensemble import RandomForestRegressor
95 |
96 | # 8.建模 1000个树，并行训练
97 | rf = RandomForestRegressor(n_estimators= 1000, random_state=42)
98 |
99 | # 9.训练执行
100 | rf.fit(train_features, train_labels)
101 |
102 | # 10.预测结果
103 | predictions = rf.predict(test_features)
104 |
105 | # 11.计算误差
106 | errors = abs(predictions - test_labels)
107 |
108 | # mean absolute percentage error (MAPE)
109 | mape = 100 * (errors / test_labels)
110 |
111 | print ('MAPE:',np.mean(mape))
112 |
113 | # 12.得到特征重要性
114 | importances = list(rf.feature_importances_)
115 |
116 | # 转换格式
117 | feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]
118 |
119 | # 排序
120 | feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)
121 |
122 | # 对应进行打印
123 | [print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances]
124 |
125 | # 画图展示
126 | # 转换成list格式
127 | x_values = list(range(len(importances)))
128 |
129 | # 绘图
130 | plt.bar(x_values, importances, orientation = 'vertical')
131 |
132 | # x轴名字
133 | plt.xticks(x_values, feature_list, rotation='vertical')
134 |
135 | # 图名
136 | plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');
137 |
138 | plt.show()
139 |
140 |
141 |
142 | # 预测值与真实值之间的差异
143 | # 日期数据
144 | months = features[:, feature_list.index('month')]
145 | days = features[:, feature_list.index('day')]
146 | years = features[:, feature_list.index('year')]
147 |
148 | # 转换日期格式
149 | dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
150 | dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
151 |
152 | # 创建一个表格来存日期和其对应的标签数值
153 | true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})
154 |
155 | # 同理，再创建一个来存日期和其对应的模型预测值
156 | months = test_features[:, feature_list.index('month')]
157 | days = test_features[:, feature_list.index('day')]
158 | years = test_features[:, feature_list.index('year')]
159 |
160 | test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
161 |
162 | test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]
163 |
164 | predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions})
165 | # 真实值
166 | plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')
167 |
168 | # 预测值
169 | plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')
170 | plt.xticks(rotation = '60');
171 | plt.legend()
172 |
173 | # 图名
174 | plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values');
175 | plt.show()
--------------------------------------------------------------------------------
/temp-predict/code/tree.dot:
--------------------------------------------------------------------------------
1 | digraph Tree {
2 | node [shape=box, style="rounded", color="black", fontname=helvetica] ;
3 | edge [fontname=helvetica] ;
4 | 0 [label="temp_1 <= 59.5\nmse = 145.7\nsamples = 162\nvalue = 62.7"] ;
5 | 1 [label="average <= 46.9\nmse = 42.6\nsamples = 63\nvalue = 51.2"] ;
6 | 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
7 | 2 [label="temp_1 <= 44.5\nmse = 17.0\nsamples = 17\nvalue = 42.9"] ;
8 | 1 -> 2 ;
9 | 3 [label="week_Mon <= 0.5\nmse = 4.4\nsamples = 8\nvalue = 41.0"] ;
10 | 2 -> 3 ;
11 | 4 [label="friend <= 35.5\nmse = 2.7\nsamples = 7\nvalue = 40.6"] ;
12 | 3 -> 4 ;
13 | 5 [label="mse = 0.0\nsamples = 1\nvalue = 44.0"] ;
14 | 4 -> 5 ;
15 | 6 [label="temp_2 <= 44.5\nmse = 0.7\nsamples = 6\nvalue = 40.0"] ;
16 | 4 -> 6 ;
17 | 7 [label="average <= 45.5\nmse = 0.5\nsamples = 4\nvalue = 39.6"] ;
18 | 6 -> 7 ;
19 | 8 [label="mse = 0.0\nsamples = 2\nvalue = 39.0"] ;
20 | 7 -> 8 ;
21 | 9 [label="day <= 10.5\nmse = 0.2\nsamples = 2\nvalue = 40.2"] ;
22 | 7 -> 9 ;
23 | 10 [label="mse = 0.0\nsamples = 1\nvalue = 41.0"] ;
24 | 9 -> 10 ;
25 | 11 [label="mse = 0.0\nsamples = 1\nvalue = 40.0"] ;
26 | 9 -> 11 ;
27 | 12 [label="mse = 0.0\nsamples = 2\nvalue = 41.0"] ;
28 | 6 -> 12 ;
29 | 13 [label="mse = 0.0\nsamples = 1\nvalue = 46.0"] ;
30 | 3 -> 13 ;
31 | 14 [label="average <= 45.2\nmse = 22.2\nsamples = 9\nvalue = 45.0"] ;
32 | 2 -> 14 ;
33 | 15 [label="mse = 0.0\nsamples = 1\nvalue = 40.0"] ;
34 | 14 -> 15 ;
35 | 16 [label="average <= 45.5\nmse = 20.8\nsamples = 8\nvalue = 45.9"] ;
36 | 14 -> 16 ;
37 | 17 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ;
38 | 16 -> 17 ;
39 | 18 [label="average <= 45.5\nmse = 9.4\nsamples = 7\nvalue = 44.8"] ;
40 | 16 -> 18 ;
41 | 19 [label="mse = 0.0\nsamples = 1\nvalue = 40.0"] ;
42 | 18 -> 19 ;
43 | 20 [label="average <= 46.1\nmse = 4.5\nsamples = 6\nvalue = 46.0"] ;
44 | 18 -> 20 ;
45 | 21 [label="day <= 1.5\nmse = 0.2\nsamples = 2\nvalue = 44.3"] ;
46 | 20 -> 21 ;
47 | 22 [label="mse = 0.0\nsamples = 1\nvalue = 45.0"] ;
48 | 21 -> 22 ;
49 | 23 [label="mse = 0.0\nsamples = 1\nvalue = 44.0"] ;
50 | 21 -> 23 ;
51 | 24 [label="temp_1 <= 48.5\nmse = 4.4\nsamples = 4\nvalue = 47.0"] ;
52 | 20 -> 24 ;
53 | 25 [label="average <= 46.6\nmse = 0.2\nsamples = 2\nvalue = 49.5"] ;
54 | 24 -> 25 ;
55 | 26 [label="mse = 0.0\nsamples = 1\nvalue = 50.0"] ;
56 | 25 -> 26 ;
57 | 27 [label="mse = 0.0\nsamples = 1\nvalue = 49.0"] ;
58 | 25 -> 27 ;
59 | 28 [label="week_Sun <= 0.5\nmse = 0.2\nsamples = 2\nvalue = 45.3"] ;
60 | 24 -> 28 ;
61 | 29 [label="mse = 0.0\nsamples = 1\nvalue = 45.0"] ;
62 | 28 -> 29 ;
63 | 30 [label="mse = 0.0\nsamples = 1\nvalue = 46.0"] ;
64 | 28 -> 30 ;
65 | 31 [label="temp_1 <= 55.5\nmse = 19.5\nsamples = 46\nvalue = 54.1"] ;
66 | 1 -> 31 ;
67 | 32 [label="day <= 4.0\nmse = 7.7\nsamples = 29\nvalue = 51.9"] ;
68 | 31 -> 32 ;
69 | 33 [label="week_Sat <= 0.5\nmse = 1.7\nsamples = 3\nvalue = 46.8"] ;
70 | 32 -> 33 ;
71 | 34 [label="mse = 0.0\nsamples = 2\nvalue = 46.0"] ;
72 | 33 -> 34 ;
73 | 35 [label="mse = 0.0\nsamples = 1\nvalue = 49.0"] ;
74 | 33 -> 35 ;
75 | 36 [label="friend <= 43.5\nmse = 5.8\nsamples = 26\nvalue = 52.3"] ;
76 | 32 -> 36 ;
77 | 37 [label="friend <= 41.5\nmse = 7.4\nsamples = 7\nvalue = 53.5"] ;
78 | 36 -> 37 ;
79 | 38 [label="average <= 50.5\nmse = 3.4\nsamples = 5\nvalue = 52.7"] ;
80 | 37 -> 38 ;
81 | 39 [label="day <= 21.5\nmse = 1.4\nsamples = 4\nvalue = 53.6"] ;
82 | 38 -> 39 ;
83 | 40 [label="friend <= 30.5\nmse = 0.2\nsamples = 3\nvalue = 54.3"] ;
84 | 39 -> 40 ;
85 | 41 [label="mse = 0.0\nsamples = 1\nvalue = 54.0"] ;
86 | 40 -> 41 ;
87 | 42 [label="mse = 0.0\nsamples = 2\nvalue = 55.0"] ;
88 | 40 -> 42 ;
89 | 43 [label="mse = 0.0\nsamples = 1\nvalue = 52.0"] ;
90 | 39 -> 43 ;
91 | 44 [label="mse = 0.0\nsamples = 1\nvalue = 50.0"] ;
92 | 38 -> 44 ;
93 | 45 [label="average <= 51.0\nmse = 2.2\nsamples = 2\nvalue = 58.5"] ;
94 | 37 -> 45 ;
95 | 46 [label="mse = 0.0\nsamples = 1\nvalue = 60.0"] ;
96 | 45 -> 46 ;
97 | 47 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ;
98 | 45 -> 47 ;
99 | 48 [label="average <= 47.3\nmse = 4.3\nsamples = 19\nvalue = 51.8"] ;
100 | 36 -> 48 ;
101 | 49 [label="mse = 0.0\nsamples = 1\nvalue = 48.0"] ;
102 | 48 -> 49 ;
103 | 50 [label="friend <= 53.5\nmse = 3.6\nsamples = 18\nvalue = 52.1"] ;
104 | 48 -> 50 ;
105 | 51 [label="temp_2 <= 49.5\nmse = 3.1\nsamples = 5\nvalue = 50.7"] ;
106 | 50 -> 51 ;
107 | 52 [label="mse = 0.0\nsamples = 1\nvalue = 53.0"] ;
108 | 51 -> 52 ;
109 | 53 [label="week_Sun <= 0.5\nmse = 1.7\nsamples = 4\nvalue = 49.9"] ;
110 | 51 -> 53 ;
111 | 54 [label="day <= 16.5\nmse = 0.2\nsamples = 3\nvalue = 48.5"] ;
112 | 53 -> 54 ;
113 | 55 [label="mse = 0.0\nsamples = 1\nvalue = 49.0"] ;
114 | 54 -> 55 ;
115 | 56 [label="mse = 0.0\nsamples = 2\nvalue = 48.0"] ;
116 | 54 -> 56 ;
117 | 57 [label="mse = 0.0\nsamples = 1\nvalue = 51.0"] ;
118 | 53 -> 57 ;
119 | 58 [label="temp_1 <= 49.5\nmse = 1.8\nsamples = 13\nvalue = 53.0"] ;
120 | 50 -> 58 ;
121 | 59 [label="friend <= 62.0\nmse = 0.2\nsamples = 3\nvalue = 51.5"] ;
122 | 58 -> 59 ;
123 | 60 [label="mse = 0.0\nsamples = 1\nvalue = 52.0"] ;
124 | 59 -> 60 ;
125 | 61 [label="mse = 0.0\nsamples = 2\nvalue = 51.0"] ;
126 | 59 -> 61 ;
127 | 62 [label="month <= 2.5\nmse = 1.4\nsamples = 10\nvalue = 53.4"] ;
128 | 58 -> 62 ;
129 | 63 [label="month <= 1.5\nmse = 1.9\nsamples = 4\nvalue = 52.7"] ;
130 | 62 -> 63 ;
131 | 64 [label="temp_1 <= 51.5\nmse = 0.8\nsamples = 3\nvalue = 53.5"] ;
132 | 63 -> 64 ;
133 | 65 [label="mse = 0.0\nsamples = 2\nvalue = 54.0"] ;
134 | 64 -> 65 ;
135 | 66 [label="mse = 0.0\nsamples = 1\nvalue = 52.0"] ;
136 | 64 -> 66 ;
137 | 67 [label="mse = 0.0\nsamples = 1\nvalue = 51.0"] ;
138 | 63 -> 67 ;
139 | 68 [label="week_Sat <= 0.5\nmse = 0.5\nsamples = 6\nvalue = 53.9"] ;
140 | 62 -> 68 ;
141 | 69 [label="day <= 11.0\nmse = 0.1\nsamples = 5\nvalue = 54.1"] ;
142 | 68 -> 69 ;
143 | 70 [label="mse = 0.0\nsamples = 1\nvalue = 55.0"] ;
144 | 69 -> 70 ;
145 | 71 [label="mse = 0.0\nsamples = 4\nvalue = 54.0"] ;
146 | 69 -> 71 ;
147 | 72 [label="mse = 0.0\nsamples = 1\nvalue = 52.0"] ;
148 | 68 -> 72 ;
149 | 73 [label="week_Wed <= 0.5\nmse = 15.6\nsamples = 17\nvalue = 58.2"] ;
150 | 31 -> 73 ;
151 | 74 [label="average <= 51.8\nmse = 13.5\nsamples = 14\nvalue = 59.7"] ;
152 | 73 -> 74 ;
153 | 75 [label="mse = 0.0\nsamples = 4\nvalue = 55.0"] ;
154 | 74 -> 75 ;
155 | 76 [label="day <= 5.5\nmse = 7.7\nsamples = 10\nvalue = 61.4"] ;
156 | 74 -> 76 ;
157 | 77 [label="average <= 53.0\nmse = 0.2\nsamples = 2\nvalue = 64.7"] ;
158 | 76 -> 77 ;
159 | 78 [label="mse = 0.0\nsamples = 1\nvalue = 64.0"] ;
160 | 77 -> 78 ;
161 | 79 [label="mse = 0.0\nsamples = 1\nvalue = 65.0"] ;
162 | 77 -> 79 ;
163 | 80 [label="day <= 17.0\nmse = 5.9\nsamples = 8\nvalue = 60.5"] ;
164 | 76 -> 80 ;
165 | 81 [label="temp_1 <= 56.5\nmse = 3.1\nsamples = 5\nvalue = 59.0"] ;
166 | 80 -> 81 ;
167 | 82 [label="mse = 0.0\nsamples = 1\nvalue = 55.0"] ;
168 | 81 -> 82 ;
169 | 83 [label="week_Thurs <= 0.5\nmse = 0.6\nsamples = 4\nvalue = 59.7"] ;
170 | 81 -> 83 ;
171 | 84 [label="week_Sun <= 0.5\nmse = 0.2\nsamples = 3\nvalue = 60.3"] ;
172 | 83 -> 84 ;
173 | 85 [label="mse = 0.0\nsamples = 2\nvalue = 60.0"] ;
174 | 84 -> 85 ;
175 | 86 [label="mse = 0.0\nsamples = 1\nvalue = 61.0"] ;
176 | 84 -> 86 ;
177 | 87 [label="mse = 0.0\nsamples = 1\nvalue = 59.0"] ;
178 | 83 -> 87 ;
179 | 88 [label="month <= 6.5\nmse = 0.5\nsamples = 3\nvalue = 63.0"] ;
180 | 80 -> 88 ;
181 | 89 [label="temp_1 <= 57.0\nmse = 0.2\nsamples = 2\nvalue = 63.3"] ;
182 | 88 -> 89 ;
183 | 90 [label="mse = 0.0\nsamples = 1\nvalue = 64.0"] ;
184 | 89 -> 90 ;
185 | 91 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ;
186 | 89 -> 91 ;
187 | 92 [label="mse = 0.0\nsamples = 1\nvalue = 62.0"] ;
188 | 88 -> 92 ;
189 | 93 [label="day <= 12.5\nmse = 3.4\nsamples = 3\nvalue = 54.8"] ;
190 | 73 -> 93 ;
191 | 94 [label="temp_2 <= 56.5\nmse = 0.8\nsamples = 2\nvalue = 56.5"] ;
192 | 93 -> 94 ;
193 | 95 [label="mse = 0.0\nsamples = 1\nvalue = 55.0"] ;
194 | 94 -> 95 ;
195 | 96 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ;
196 | 94 -> 96 ;
197 | 97 [label="mse = 0.0\nsamples = 1\nvalue = 53.0"] ;
198 | 93 -> 97 ;
199 | 98 [label="temp_1 <= 67.5\nmse = 66.8\nsamples = 99\nvalue = 70.4"] ;
200 | 0 -> 98 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
201 | 99 [label="average <= 60.8\nmse = 23.5\nsamples = 42\nvalue = 63.9"] ;
202 | 98 -> 99 ;
203 | 100 [label="day <= 27.5\nmse = 13.7\nsamples = 19\nvalue = 60.7"] ;
204 | 99 -> 100 ;
205 | 101 [label="temp_2 <= 59.5\nmse = 8.2\nsamples = 17\nvalue = 59.8"] ;
206 | 100 -> 101 ;
207 | 102 [label="week_Sat <= 0.5\nmse = 7.7\nsamples = 7\nvalue = 62.4"] ;
208 | 101 -> 102 ;
209 | 103 [label="month <= 6.5\nmse = 3.7\nsamples = 6\nvalue = 61.6"] ;
210 | 102 -> 103 ;
211 | 104 [label="average <= 50.9\nmse = 0.2\nsamples = 3\nvalue = 59.7"] ;
212 | 103 -> 104 ;
213 | 105 [label="mse = 0.0\nsamples = 1\nvalue = 59.0"] ;
214 | 104 -> 105 ;
215 | 106 [label="mse = 0.0\nsamples = 2\nvalue = 60.0"] ;
216 | 104 -> 106 ;
217 | 107 [label="week_Fri <= 0.5\nmse = 1.5\nsamples = 3\nvalue = 63.0"] ;
218 | 103 -> 107 ;
219 | 108 [label="temp_1 <= 61.5\nmse = 0.2\nsamples = 2\nvalue = 62.3"] ;
220 | 107 -> 108 ;
221 | 109 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ;
222 | 108 -> 109 ;
223 | 110 [label="mse = 0.0\nsamples = 1\nvalue = 62.0"] ;
224 | 108 -> 110 ;
225 | 111 [label="mse = 0.0\nsamples = 1\nvalue = 65.0"] ;
226 | 107 -> 111 ;
227 | 112 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ;
228 | 102 -> 112 ;
229 | 113 [label="friend <= 47.0\nmse = 3.8\nsamples = 10\nvalue = 58.6"] ;
230 | 101 -> 113 ;
231 | 114 [label="average <= 59.2\nmse = 2.2\nsamples = 3\nvalue = 56.2"] ;
232 | 113 -> 114 ;
233 | 115 [label="mse = 0.0\nsamples = 2\nvalue = 58.0"] ;
234 | 114 -> 115 ;
235 | 116 [label="mse = 0.0\nsamples = 1\nvalue = 55.0"] ;
236 | 114 -> 116 ;
237 | 117 [label="day <= 12.5\nmse = 1.1\nsamples = 7\nvalue = 59.6"] ;
238 | 113 -> 117 ;
239 | 118 [label="temp_1 <= 61.5\nmse = 0.6\nsamples = 3\nvalue = 58.6"] ;
240 | 117 -> 118 ;
241 | 119 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ;
242 | 118 -> 119 ;
243 | 120 [label="mse = 0.0\nsamples = 2\nvalue = 59.0"] ;
244 | 118 -> 120 ;
245 | 121 [label="average <= 59.5\nmse = 0.2\nsamples = 4\nvalue = 60.3"] ;
246 | 117 -> 121 ;
247 | 122 [label="mse = 0.0\nsamples = 2\nvalue = 61.0"] ;
248 | 121 -> 122 ;
249 | 123 [label="mse = 0.0\nsamples = 2\nvalue = 60.0"] ;
250 | 121 -> 123 ;
251 | 124 [label="mse = 0.0\nsamples = 2\nvalue = 68.0"] ;
252 | 100 -> 124 ;
253 | 125 [label="temp_2 <= 78.0\nmse = 17.3\nsamples = 23\nvalue = 66.3"] ;
254 | 99 -> 125 ;
255 | 126 [label="friend <= 54.5\nmse = 13.4\nsamples = 22\nvalue = 66.8"] ;
256 | 125 -> 126 ;
257 | 127 [label="month <= 7.5\nmse = 3.6\nsamples = 2\nvalue = 60.3"] ;
258 | 126 -> 127 ;
259 | 128 [label="mse = 0.0\nsamples = 1\nvalue = 59.0"] ;
260 | 127 -> 128 ;
261 | 129 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ;
262 | 127 -> 129 ;
263 | 130 [label="temp_2 <= 64.5\nmse = 10.2\nsamples = 20\nvalue = 67.4"] ;
264 | 126 -> 130 ;
265 | 131 [label="friend <= 71.0\nmse = 9.0\nsamples = 8\nvalue = 68.9"] ;
266 | 130 -> 131 ;
267 | 132 [label="temp_1 <= 65.5\nmse = 2.0\nsamples = 3\nvalue = 71.6"] ;
268 | 131 -> 132 ;
269 | 133 [label="mse = 0.0\nsamples = 2\nvalue = 71.0"] ;
270 | 132 -> 133 ;
271 | 134 [label="mse = 0.0\nsamples = 1\nvalue = 75.0"] ;
272 | 132 -> 134 ;
273 | 135 [label="friend <= 77.0\nmse = 4.4\nsamples = 5\nvalue = 66.8"] ;
274 | 131 -> 135 ;
275 | 136 [label="day <= 24.0\nmse = 1.0\nsamples = 3\nvalue = 65.2"] ;
276 | 135 -> 136 ;
277 | 137 [label="mse = 0.0\nsamples = 1\nvalue = 66.0"] ;
278 | 136 -> 137 ;
279 | 138 [label="mse = 0.0\nsamples = 2\nvalue = 64.0"] ;
280 | 136 -> 138 ;
281 | 139 [label="week_Thurs <= 0.5\nmse = 1.7\nsamples = 2\nvalue = 68.8"] ;
282 | 135 -> 139 ;
283 | 140 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ;
284 | 139 -> 140 ;
285 | 141 [label="mse = 0.0\nsamples = 1\nvalue = 71.0"] ;
286 | 139 -> 141 ;
287 | 142 [label="average <= 71.2\nmse = 7.4\nsamples = 12\nvalue = 66.0"] ;
288 | 130 -> 142 ;
289 | 143 [label="average <= 69.2\nmse = 5.1\nsamples = 10\nvalue = 64.9"] ;
290 | 142 -> 143 ;
291 | 144 [label="average <= 67.3\nmse = 3.4\nsamples = 9\nvalue = 65.3"] ;
292 | 143 -> 144 ;
293 | 145 [label="temp_2 <= 72.5\nmse = 3.0\nsamples = 6\nvalue = 64.6"] ;
294 | 144 -> 145 ;
295 | 146 [label="temp_1 <= 64.5\nmse = 1.6\nsamples = 5\nvalue = 64.1"] ;
296 | 145 -> 146 ;
297 | 147 [label="average <= 63.5\nmse = 0.2\nsamples = 3\nvalue = 63.2"] ;
298 | 146 -> 147 ;
299 | 148 [label="mse = 0.0\nsamples = 1\nvalue = 64.0"] ;
300 | 147 -> 148 ;
301 | 149 [label="mse = 0.0\nsamples = 2\nvalue = 63.0"] ;
302 | 147 -> 149 ;
303 | 150 [label="average <= 66.4\nmse = 0.9\nsamples = 2\nvalue = 65.3"] ;
304 | 146 -> 150 ;
305 | 151 [label="mse = 0.0\nsamples = 1\nvalue = 66.0"] ;
306 | 150 -> 151 ;
307 | 152 [label="mse = 0.0\nsamples = 1\nvalue = 64.0"] ;
308 | 150 -> 152 ;
309 | 153 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ;
310 | 145 -> 153 ;
311 | 154 [label="day <= 16.0\nmse = 1.2\nsamples = 3\nvalue = 66.8"] ;
312 | 144 -> 154 ;
313 | 155 [label="mse = 0.0\nsamples = 1\nvalue = 65.0"] ;
314 | 154 -> 155 ;
315 | 156 [label="week_Sat <= 0.5\nmse = 0.2\nsamples = 2\nvalue = 67.3"] ;
316 | 154 -> 156 ;
317 | 157 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ;
318 | 156 -> 157 ;
319 | 158 [label="mse = 0.0\nsamples = 1\nvalue = 67.0"] ;
320 | 156 -> 158 ;
321 | 159 [label="mse = 0.0\nsamples = 1\nvalue = 60.0"] ;
322 | 143 -> 159 ;
323 | 160 [label="average <= 73.2\nmse = 2.6\nsamples = 2\nvalue = 68.8"] ;
324 | 142 -> 160 ;
325 | 161 [label="mse = 0.0\nsamples = 1\nvalue = 72.0"] ;
326 | 160 -> 161 ;
327 | 162 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ;
328 | 160 -> 162 ;
329 | 163 [label="mse = 0.0\nsamples = 1\nvalue = 57.0"] ;
330 | 125 -> 163 ;
331 | 164 [label="average <= 75.6\nmse = 44.2\nsamples = 57\nvalue = 75.3"] ;
332 | 98 -> 164 ;
333 | 165 [label="temp_1 <= 88.0\nmse = 34.3\nsamples = 42\nvalue = 73.0"] ;
334 | 164 -> 165 ;
335 | 166 [label="week_Sat <= 0.5\nmse = 28.9\nsamples = 40\nvalue = 72.4"] ;
336 | 165 -> 166 ;
337 | 167 [label="week_Mon <= 0.5\nmse = 27.0\nsamples = 34\nvalue = 73.5"] ;
338 | 166 -> 167 ;
339 | 168 [label="month <= 7.5\nmse = 22.7\nsamples = 30\nvalue = 74.3"] ;
340 | 167 -> 168 ;
341 | 169 [label="temp_1 <= 81.5\nmse = 22.1\nsamples = 22\nvalue = 75.1"] ;
342 | 168 -> 169 ;
343 | 170 [label="week_Thurs <= 0.5\nmse = 18.6\nsamples = 20\nvalue = 75.5"] ;
344 | 169 -> 170 ;
345 | 171 [label="day <= 10.5\nmse = 18.2\nsamples = 17\nvalue = 76.1"] ;
346 | 170 -> 171 ;
347 | 172 [label="temp_2 <= 81.5\nmse = 24.1\nsamples = 8\nvalue = 74.2"] ;
348 | 171 -> 172 ;
349 | 173 [label="average <= 61.7\nmse = 6.4\nsamples = 7\nvalue = 75.5"] ;
350 | 172 -> 173 ;
351 | 174 [label="mse = 0.0\nsamples = 1\nvalue = 71.0"] ;
352 | 173 -> 174 ;
353 | 175 [label="friend <= 56.5\nmse = 4.9\nsamples = 6\nvalue = 75.9"] ;
354 | 173 -> 175 ;
355 | 176 [label="mse = 0.0\nsamples = 1\nvalue = 80.0"] ;
356 | 175 -> 176 ;
357 | 177 [label="average <= 74.4\nmse = 0.9\nsamples = 5\nvalue = 74.9"] ;
358 | 175 -> 177 ;
359 | 178 [label="temp_1 <= 76.0\nmse = 0.2\nsamples = 3\nvalue = 75.8"] ;
360 | 177 -> 178 ;
361 | 179 [label="mse = 0.0\nsamples = 2\nvalue = 76.0"] ;
362 | 178 -> 179 ;
363 | 180 [label="mse = 0.0\nsamples = 1\nvalue = 75.0"] ;
364 | 178 -> 180 ;
365 | 181 [label="mse = 0.0\nsamples = 2\nvalue = 74.0"] ;
366 | 177 -> 181 ;
367 | 182 [label="mse = 0.0\nsamples = 1\nvalue = 60.0"] ;
368 | 172 -> 182 ;
369 | 183 [label="average <= 69.1\nmse = 8.2\nsamples = 9\nvalue = 77.6"] ;
370 | 171 -> 183 ;
371 | 184 [label="temp_2 <= 65.5\nmse = 2.4\nsamples = 5\nvalue = 79.9"] ;
372 | 183 -> 184 ;
373 | 185 [label="temp_2 <= 62.0\nmse = 0.8\nsamples = 2\nvalue = 78.5"] ;
374 | 184 -> 185 ;
375 | 186 [label="mse = 0.0\nsamples = 1\nvalue = 77.0"] ;
376 | 185 -> 186 ;
377 | 187 [label="mse = 0.0\nsamples = 1\nvalue = 79.0"] ;
378 | 185 -> 187 ;
379 | 188 [label="week_Fri <= 0.5\nmse = 0.2\nsamples = 3\nvalue = 81.2"] ;
380 | 184 -> 188 ;
381 | 189 [label="mse = 0.0\nsamples = 2\nvalue = 81.0"] ;
382 | 188 -> 189 ;
383 | 190 [label="mse = 0.0\nsamples = 1\nvalue = 82.0"] ;
384 | 188 -> 190 ;
385 | 191 [label="average <= 70.9\nmse = 2.3\nsamples = 4\nvalue = 75.0"] ;
386 | 183 -> 191 ;
387 | 192 [label="mse = 0.0\nsamples = 1\nvalue = 73.0"] ;
388 | 191 -> 192 ;
389 | 193 [label="week_Tues <= 0.5\nmse = 1.0\nsamples = 3\nvalue = 75.8"] ;
390 | 191 -> 193 ;
391 | 194 [label="mse = 0.0\nsamples = 2\nvalue = 75.0"] ;
392 | 193 -> 194 ;
393 | 195 [label="mse = 0.0\nsamples = 1\nvalue = 77.0"] ;
394 | 193 -> 195 ;
395 | 196 [label="day <= 26.5\nmse = 4.7\nsamples = 3\nvalue = 71.8"] ;
396 | 170 -> 196 ;
397 | 197 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ;
398 | 196 -> 197 ;
399 | 198 [label="mse = 0.0\nsamples = 2\nvalue = 73.0"] ;
400 | 196 -> 198 ;
401 | 199 [label="week_Sun <= 0.5\nmse = 30.2\nsamples = 2\nvalue = 68.5"] ;
402 | 169 -> 199 ;
403 | 200 [label="mse = 0.0\nsamples = 1\nvalue = 74.0"] ;
404 | 199 -> 200 ;
405 | 201 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ;
406 | 199 -> 201 ;
407 | 202 [label="temp_1 <= 71.5\nmse = 14.6\nsamples = 8\nvalue = 71.5"] ;
408 | 168 -> 202 ;
409 | 203 [label="temp_1 <= 69.5\nmse = 0.2\nsamples = 2\nvalue = 67.8"] ;
410 | 202 -> 203 ;
411 | 204 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ;
412 | 203 -> 204 ;
413 | 205 [label="mse = 0.0\nsamples = 1\nvalue = 67.0"] ;
414 | 203 -> 205 ;
415 | 206 [label="friend <= 70.5\nmse = 8.7\nsamples = 6\nvalue = 74.0"] ;
416 | 202 -> 206 ;
417 | 207 [label="temp_1 <= 77.0\nmse = 0.2\nsamples = 2\nvalue = 70.5"] ;
418 | 206 -> 207 ;
419 | 208 [label="mse = 0.0\nsamples = 1\nvalue = 70.0"] ;
420 | 207 -> 208 ;
421 | 209 [label="mse = 0.0\nsamples = 1\nvalue = 71.0"] ;
422 | 207 -> 209 ;
423 | 210 [label="friend <= 77.5\nmse = 3.7\nsamples = 4\nvalue = 75.8"] ;
424 | 206 -> 210 ;
425 | 211 [label="mse = 0.0\nsamples = 1\nvalue = 79.0"] ;
426 | 210 -> 211 ;
427 | 212 [label="temp_2 <= 68.5\nmse = 0.2\nsamples = 3\nvalue = 74.7"] ;
428 | 210 -> 212 ;
429 | 213 [label="mse = 0.0\nsamples = 1\nvalue = 74.0"] ;
430 | 212 -> 213 ;
431 | 214 [label="mse = 0.0\nsamples = 2\nvalue = 75.0"] ;
432 | 212 -> 214 ;
433 | 215 [label="temp_2 <= 64.0\nmse = 20.2\nsamples = 4\nvalue = 67.2"] ;
434 | 167 -> 215 ;
435 | 216 [label="mse = 0.0\nsamples = 1\nvalue = 60.0"] ;
436 | 215 -> 216 ;
437 | 217 [label="average <= 74.6\nmse = 9.0\nsamples = 3\nvalue = 69.0"] ;
438 | 215 -> 217 ;
439 | 218 [label="day <= 8.5\nmse = 0.9\nsamples = 2\nvalue = 67.3"] ;
440 | 217 -> 218 ;
441 | 219 [label="mse = 0.0\nsamples = 1\nvalue = 68.0"] ;
442 | 218 -> 219 ;
443 | 220 [label="mse = 0.0\nsamples = 1\nvalue = 66.0"] ;
444 | 218 -> 220 ;
445 | 221 [label="mse = 0.0\nsamples = 1\nvalue = 74.0"] ;
446 | 217 -> 221 ;
447 | 222 [label="temp_2 <= 70.5\nmse = 7.9\nsamples = 6\nvalue = 67.5"] ;
448 | 166 -> 222 ;
449 | 223 [label="mse = 0.0\nsamples = 2\nvalue = 71.0"] ;
450 | 222 -> 223 ;
451 | 224 [label="friend <= 66.5\nmse = 1.7\nsamples = 4\nvalue = 65.6"] ;
452 | 222 -> 224 ;
453 | 225 [label="day <= 8.0\nmse = 0.9\nsamples = 2\nvalue = 64.3"] ;
454 | 224 -> 225 ;
455 | 226 [label="mse = 0.0\nsamples = 1\nvalue = 63.0"] ;
456 | 225 -> 226 ;
457 | 227 [label="mse = 0.0\nsamples = 1\nvalue = 65.0"] ;
458 | 225 -> 227 ;
459 | 228 [label="temp_1 <= 73.0\nmse = 0.2\nsamples = 2\nvalue = 66.5"] ;
460 | 224 -> 228 ;
461 | 229 [label="mse = 0.0\nsamples = 1\nvalue = 67.0"] ;
462 | 228 -> 229 ;
463 | 230 [label="mse = 0.0\nsamples = 1\nvalue = 66.0"] ;
464 | 228 -> 230 ;
465 | 231 [label="temp_2 <= 79.0\nmse = 5.6\nsamples = 2\nvalue = 84.3"] ;
466 | 165 -> 231 ;
467 | 232 [label="mse = 0.0\nsamples = 1\nvalue = 81.0"] ;
468 | 231 -> 232 ;
469 | 233 [label="mse = 0.0\nsamples = 1\nvalue = 86.0"] ;
470 | 231 -> 233 ;
471 | 234 [label="average <= 77.0\nmse = 27.1\nsamples = 15\nvalue = 80.6"] ;
472 | 164 -> 234 ;
473 | 235 [label="average <= 76.8\nmse = 22.5\nsamples = 6\nvalue = 83.7"] ;
474 | 234 -> 235 ;
475 | 236 [label="week_Mon <= 0.5\nmse = 8.8\nsamples = 4\nvalue = 79.6"] ;
476 | 235 -> 236 ;
477 | 237 [label="friend <= 77.0\nmse = 1.9\nsamples = 3\nvalue = 80.7"] ;
478 | 236 -> 237 ;
479 | 238 [label="mse = 0.0\nsamples = 1\nvalue = 82.0"] ;
480 | 237 -> 238 ;
481 | 239 [label="day <= 20.0\nmse = 0.2\nsamples = 2\nvalue = 79.3"] ;
482 | 237 -> 239 ;
483 | 240 [label="mse = 0.0\nsamples = 1\nvalue = 80.0"] ;
484 | 239 -> 240 ;
485 | 241 [label="mse = 0.0\nsamples = 1\nvalue = 79.0"] ;
486 | 239 -> 241 ;
487 | 242 [label="mse = 0.0\nsamples = 1\nvalue = 73.0"] ;
488 | 236 -> 242 ;
489 | 243 [label="temp_1 <= 83.5\nmse = 1.8\nsamples = 2\nvalue = 87.9"] ;
490 | 235 -> 243 ;
491 | 244 [label="mse = 0.0\nsamples = 1\nvalue = 87.0"] ;
492 | 243 -> 244 ;
493 | 245 [label="mse = 0.0\nsamples = 1\nvalue = 90.0"] ;
494 | 243 -> 245 ;
495 | 246 [label="temp_1 <= 77.5\nmse = 9.8\nsamples = 9\nvalue = 77.2"] ;
496 | 234 -> 246 ;
497 | 247 [label="temp_2 <= 71.5\nmse = 6.3\nsamples = 6\nvalue = 75.0"] ;
498 | 246 -> 247 ;
499 | 248 [label="mse = 0.0\nsamples = 1\nvalue = 80.0"] ;
500 | 247 -> 248 ;
501 | 249 [label="average <= 77.0\nmse = 2.5\nsamples = 5\nvalue = 74.2"] ;
502 | 247 -> 249 ;
503 | 250 [label="temp_1 <= 71.5\nmse = 0.2\nsamples = 2\nvalue = 75.7"] ;
504 | 249 -> 250 ;
505 | 251 [label="mse = 0.0\nsamples = 1\nvalue = 75.0"] ;
506 | 250 -> 251 ;
507 | 252 [label="mse = 0.0\nsamples = 1\nvalue = 76.0"] ;
508 | 250 -> 252 ;
509 | 253 [label="week_Tues <= 0.5\nmse = 0.2\nsamples = 3\nvalue = 72.7"] ;
510 | 249 -> 253 ;
511 | 254 [label="mse = 0.0\nsamples = 2\nvalue = 73.0"] ;
512 | 253 -> 254 ;
513 | 255 [label="mse = 0.0\nsamples = 1\nvalue = 72.0"] ;
514 | 253 -> 255 ;
515 | 256 [label="temp_1 <= 79.5\nmse = 2.2\nsamples = 3\nvalue = 79.7"] ;
516 | 246 -> 256 ;
517 | 257 [label="mse = 0.0\nsamples = 1\nvalue = 83.0"] ;
518 | 256 -> 257 ;
519 | 258 [label="mse = 0.0\nsamples = 2\nvalue = 79.0"] ;
520 | 256 -> 258 ;
521 | }
--------------------------------------------------------------------------------
/temp-predict/code/treeparam.py:
--------------------------------------------------------------------------------
1 | # -*- encoding: utf-8 -*-
2 | """
3 | @File : treeparam.py
4 | @Time : 2020/5/24 10:46
5 | @Author : 王一宁
6 | @Email : wyn_365@163.com
7 | 参数调优
8 | """
9 | import pandas as pd
10 |
11 | # Read in data as a dataframe
12 | features = pd.read_csv('D:\\APP\\PythonCharm2018\\workspace\\temp-predict\data\\temps_extended.csv')
13 |
14 | # 编码
15 | # One Hot Encoding
16 | features = pd.get_dummies(features)
17 |
18 | # Extract features and labels
19 | labels = features['actual']
20 | features = features.drop('actual', axis = 1)
21 |
22 | # List of features for later use
23 | feature_list = list(features.columns)
24 |
25 | # Convert to numpy arrays
26 | import numpy as np
27 |
28 | features = np.array(features)
29 | labels = np.array(labels)
30 |
31 | # Training and Testing Sets
32 | from sklearn.model_selection import train_test_split
33 |
34 | train_features, test_features, train_labels, test_labels = train_test_split(features, labels,
35 | test_size = 0.25, random_state = 42)
36 |
37 | print('Training Features Shape:', train_features.shape)
38 | print('Training Labels Shape:', train_labels.shape)
39 | print('Testing Features Shape:', test_features.shape)
40 | print('Testing Labels Shape:', test_labels.shape)
41 |
42 | print('{:0.1f} years of data in the training set'.format(train_features.shape[0] / 365.))
43 | print('{:0.1f} years of data in the test set'.format(test_features.shape[0] / 365.))
44 |
45 | # 选取特征 95% of total importance
46 | important_feature_names = ['temp_1', 'average', 'ws_1', 'temp_2', 'friend', 'year']
47 |
48 | # Find the columns of the most important features
49 | important_indices = [feature_list.index(feature) for feature in important_feature_names]
50 |
51 | # Create training and testing sets with only the important features
52 | important_train_features = train_features[:, important_indices]
53 | important_test_features = test_features[:, important_indices]
54 |
55 | # Sanity check on operations
56 | print('Important train features shape:', important_train_features.shape)
57 | print('Important test features shape:', important_test_features.shape)
58 |
59 | # Use only the most important features
60 | train_features = important_train_features[:]
61 | test_features = important_test_features[:]
62 |
63 | # Update feature list for visualizations
64 | feature_list = important_feature_names[:]
65 |
66 |
67 | # 开始调参
68 | from sklearn.ensemble import RandomForestRegressor
69 |
70 | rf = RandomForestRegressor(random_state = 42)
71 |
72 | from pprint import pprint
73 |
74 | # 打印所有参数
75 | pprint(rf.get_params())
76 |
77 |
78 | # 随机查找
79 | from sklearn.model_selection import RandomizedSearchCV
80 |
81 | # 建立树的个数 200 210 220
82 | n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
83 | # 最大特征的选择方式
84 | max_features = ['auto', 'sqrt']
85 | # 树的最大深度
86 | max_depth = [int(x) for x in np.linspace(10, 20, num = 2)]
87 | max_depth.append(None)
88 | # 节点最小分裂所需样本个数
89 | min_samples_split = [2, 5, 10]
90 | # 叶子节点最小样本数，任何分裂不能让其子节点样本数少于此值
91 | min_samples_leaf = [1, 2, 4]
92 | # 样本采样方法
93 | bootstrap = [True, False]
94 |
95 | # 参数空间 决定结果的好坏
96 | random_grid = {'n_estimators': n_estimators,
97 | 'max_features': max_features,
98 | 'max_depth': max_depth,
99 | 'min_samples_split': min_samples_split,
100 | 'min_samples_leaf': min_samples_leaf,
101 | 'bootstrap': bootstrap}
102 | # 随机选择最合适的参数组合 回归算法
103 | rf = RandomForestRegressor()
104 |
105 | # 随机100次 cv交叉验证 评估方法 打印指令 随机数字 所有CPU执行
106 | rf_random = RandomizedSearchCV(estimator=rf, param_distributions=random_grid,
107 | n_iter = 100, scoring='neg_mean_absolute_error',
108 | cv = 3, verbose=2, random_state=42, n_jobs=-1)
109 |
110 | # 执行寻找操作 建立100个模型
111 | rf_random.fit(train_features, train_labels)
112 |
113 | # 最好的参数 输出！
114 | rf_random.best_params_
115 |
116 |
117 | print("========================================================")
118 |
119 | # 参数评估
120 | def evaluate(model, test_features, test_labels):
121 | predictions = model.predict(test_features)
122 | errors = abs(predictions - test_labels)
123 | mape = 100 * np.mean(errors / test_labels)
124 | accuracy = 100 - mape
125 |
126 | print('平均气温误差.',np.mean(errors))
127 | print('Accuracy = {:0.2f}%.'.format(accuracy))
128 |
129 | # 老模型 默认参数
130 | base_model = RandomForestRegressor( random_state = 42)
131 | base_model.fit(train_features, train_labels)
132 | evaluate(base_model, test_features, test_labels)
133 |
134 | # 最优的参数
135 | best_random = rf_random.best_estimator_
136 | evaluate(best_random, test_features, test_labels)
--------------------------------------------------------------------------------
/temp-predict/data/stopwords.txt:
--------------------------------------------------------------------------------
1 | !
2 | "
3 | #
4 | $
5 | %
6 | &
7 | '
8 | (
9 | )
10 | *
11 | +
12 | ,
13 | -
14 | --
15 | .
16 | ..
17 | ...
18 | ......
19 | ...................
20 | ./
21 | .一
22 | 记者
23 | 数
24 | 年
25 | 月
26 | 日
27 | 时
28 | 分
29 | 秒
30 | /
31 | //
32 | 0
33 | 1
34 | 2
35 | 3
36 | 4
37 | 5
38 | 6
39 | 7
40 | 8
41 | 9
42 | :
43 | ://
44 | ::
45 | ;
46 | <
47 | =
48 | >
49 | >>
50 | ?
51 | @
52 | A
53 | Lex
54 | [
55 | \
56 | ]
57 | 【
58 | 】
59 | ^
60 | _
61 | `
62 | exp
63 | sub
64 | sup
65 | |
66 | }
67 | ~
68 | ~~~~
69 | ·
70 | ×
71 | ×××
72 | Δ
73 | Ψ
74 | γ
75 | μ
76 | φ
77 | φ．
78 | В
79 | —
80 | ——
81 | ———
82 | ‘
83 | ’
84 | ’‘
85 | “
86 | ”
87 | ”，
88 | …
89 | ……
90 | …………………………………………………③
91 | ′∈
92 | ′｜
93 | ℃
94 | Ⅲ
95 | ↑
96 | →
97 | ∈［
98 | ∪φ∈
99 | ≈
100 | ①
101 | ②
102 | ②ｃ
103 | ③
104 | ③］
105 | ④
106 | ⑤
107 | ⑥
108 | ⑦
109 | ⑧
110 | ⑨
111 | ⑩
112 | ──
113 | ■
114 | ▲
115 |
116 | 、
117 | 。
118 | 〈
119 | 〉
120 | 《
121 | 》
122 | 》），
123 | 」
124 | 『
125 | 』
126 | 〔
127 | 〕
128 | 〕〔
129 | ㈧
130 | 一
131 | 一.
132 | 一一
133 | 一下
134 | 一个
135 | 一些
136 | 一何
137 | 一切
138 | 一则
139 | 一则通过
140 | 一天
141 | 一定
142 | 一方面
143 | 一旦
144 | 一时
145 | 一来
146 | 一样
147 | 一次
148 | 一片
149 | 一番
150 | 一直
151 | 一致
152 | 一般
153 | 一起
154 | 一转眼
155 | 一边
156 | 一面
157 | 男子
158 | 女子
159 | 七
160 | 万一
161 | 三
162 | 三天两头
163 | 三番两次
164 | 三番五次
165 | 上
166 | 上下
167 | 上升
168 | 上去
169 | 上来
170 | 上述
171 | 上面
172 | 下
173 | 下列
174 | 下去
175 | 下来
176 | 下面
177 | 不
178 | 不一
179 | 不下
180 | 不久
181 | 不了
182 | 不亦乐乎
183 | 不仅
184 | 不仅...而且
185 | 不仅仅
186 | 不仅仅是
187 | 不会
188 | 不但
189 | 不但...而且
190 | 不光
191 | 不免
192 | 不再
193 | 不力
194 | 不单
195 | 不变
196 | 不只
197 | 不可
198 | 不可开交
199 | 不可抗拒
200 | 不同
201 | 不外
202 | 不外乎
203 | 不够
204 | 不大
205 | 不如
206 | 不妨
207 | 不定
208 | 不对
209 | 不少
210 | 不尽
211 | 不尽然
212 | 不巧
213 | 不已
214 | 不常
215 | 不得
216 | 不得不
217 | 不得了
218 | 不得已
219 | 不必
220 | 不怎么
221 | 不怕
222 | 不惟
223 | 不成
224 | 不拘
225 | 不择手段
226 | 不敢
227 | 不料
228 | 不断
229 | 不日
230 | 不时
231 | 不是
232 | 不曾
233 | 不止
234 | 不止一次
235 | 不比
236 | 不消
237 | 不满
238 | 不然
239 | 不然的话
240 | 不特
241 | 不独
242 | 不由得
243 | 不知不觉
244 | 不管
245 | 不管怎样
246 | 不经意
247 | 不胜
248 | 不能
249 | 不能不
250 | 不至于
251 | 不若
252 | 不要
253 | 不论
254 | 不起
255 | 不足
256 | 不过
257 | 不迭
258 | 不问
259 | 不限
260 | 与
261 | 与其
262 | 与其说
263 | 与否
264 | 与此同时
265 | 专门
266 | 且
267 | 且不说
268 | 且说
269 | 两者
270 | 严格
271 | 严重
272 | 个
273 | 个人
274 | 个别
275 | 中小
276 | 中间
277 | 丰富
278 | 串行
279 | 临
280 | 临到
281 | 为
282 | 为主
283 | 为了
284 | 为什么
285 | 为什麽
286 | 为何
287 | 为止
288 | 为此
289 | 为着
290 | 主张
291 | 主要
292 | 举凡
293 | 举行
294 | 乃
295 | 乃至
296 | 乃至于
297 | 么
298 | 之
299 | 之一
300 | 之前
301 | 之后
302 | 之後
303 | 之所以
304 | 之类
305 | 乌乎
306 | 乎
307 | 乒
308 | 乘
309 | 乘势
310 | 乘机
311 | 乘胜
312 | 乘虚
313 | 乘隙
314 | 九
315 | 也
316 | 也好
317 | 也就是说
318 | 也是
319 | 也罢
320 | 了
321 | 了解
322 | 争取
323 | 二
324 | 二来
325 | 二话不说
326 | 二话没说
327 | 于
328 | 于是
329 | 于是乎
330 | 云云
331 | 云尔
332 | 互
333 | 互相
334 | 五
335 | 些
336 | 交口
337 | 亦
338 | 产生
339 | 亲口
340 | 亲手
341 | 亲眼
342 | 亲自
343 | 亲身
344 | 人
345 | 人人
346 | 人们
347 | 人家
348 | 人民
349 | 什么
350 | 什么样
351 | 什麽
352 | 仅
353 | 仅仅
354 | 今
355 | 今后
356 | 今天
357 | 今年
358 | 今後
359 | 介于
360 | 仍
361 | 仍旧
362 | 仍然
363 | 从
364 | 从不
365 | 从严
366 | 从中
367 | 从事
368 | 从今以后
369 | 从优
370 | 从古到今
371 | 从古至今
372 | 从头
373 | 从宽
374 | 从小
375 | 从新
376 | 从无到有
377 | 从早到晚
378 | 从未
379 | 从来
380 | 从此
381 | 从此以后
382 | 从而
383 | 从轻
384 | 从速
385 | 从重
386 | 他
387 | 他人
388 | 他们
389 | 他是
390 | 他的
391 | 代替
392 | 以
393 | 以上
394 | 以下
395 | 以为
396 | 以便
397 | 以免
398 | 以前
399 | 以及
400 | 以后
401 | 以外
402 | 以後
403 | 以故
404 | 以期
405 | 以来
406 | 以至
407 | 以至于
408 | 以致
409 | 们
410 | 任
411 | 任何
412 | 任凭
413 | 任务
414 | 企图
415 | 伙同
416 | 会
417 | 伟大
418 | 传
419 | 传说
420 | 传闻
421 | 似乎
422 | 似的
423 | 但
424 | 但凡
425 | 但愿
426 | 但是
427 | 何
428 | 何乐而不为
429 | 何以
430 | 何况
431 | 何处
432 | 何妨
433 | 何尝
434 | 何必
435 | 何时
436 | 何止
437 | 何苦
438 | 何须
439 | 余外
440 | 作为
441 | 你
442 | 你们
443 | 你是
444 | 你的
445 | 使
446 | 使得
447 | 使用
448 | 例如
449 | 依
450 | 依据
451 | 依照
452 | 依靠
453 | 便
454 | 便于
455 | 促进
456 | 保持
457 | 保管
458 | 保险
459 | 俺
460 | 俺们
461 | 倍加
462 | 倍感
463 | 倒不如
464 | 倒不如说
465 | 倒是
466 | 倘
467 | 倘使
468 | 倘或
469 | 倘然
470 | 倘若
471 | 借
472 | 借以
473 | 借此
474 | 假使
475 | 假如
476 | 假若
477 | 偏偏
478 | 做到
479 | 偶尔
480 | 偶而
481 | 傥然
482 | 像
483 | 儿
484 | 允许
485 | 元／吨
486 | 充其极
487 | 充其量
488 | 充分
489 | 先不先
490 | 先后
491 | 先後
492 | 先生
493 | 光
494 | 光是
495 | 全体
496 | 全力
497 | 全年
498 | 全然
499 | 全身心
500 | 全部
501 | 全都
502 | 全面
503 | 八
504 | 八成
505 | 公然
506 | 六
507 | 兮
508 | 共
509 | 共同
510 | 共总
511 | 关于
512 | 其
513 | 其一
514 | 其中
515 | 其二
516 | 其他
517 | 其余
518 | 其后
519 | 其它
520 | 其实
521 | 其次
522 | 具体
523 | 具体地说
524 | 具体来说
525 | 具体说来
526 | 具有
527 | 兼之
528 | 内
529 | 再
530 | 再其次
531 | 再则
532 | 再有
533 | 再次
534 | 再者
535 | 再者说
536 | 再说
537 | 冒
538 | 冲
539 | 决不
540 | 决定
541 | 决非
542 | 况且
543 | 准备
544 | 凑巧
545 | 凝神
546 | 几
547 | 几乎
548 | 几度
549 | 几时
550 | 几番
551 | 几经
552 | 凡
553 | 凡是
554 | 凭
555 | 凭借
556 | 出
557 | 出于
558 | 出去
559 | 出来
560 | 出现
561 | 分别
562 | 分头
563 | 分期
564 | 分期分批
565 | 切
566 | 切不可
567 | 切切
568 | 切勿
569 | 切莫
570 | 则
571 | 则甚
572 | 刚
573 | 刚好
574 | 刚巧
575 | 刚才
576 | 初
577 | 别
578 | 别人
579 | 别处
580 | 别是
581 | 别的
582 | 别管
583 | 别说
584 | 到
585 | 到了儿
586 | 到处
587 | 到头
588 | 到头来
589 | 到底
590 | 到目前为止
591 | 前后
592 | 前此
593 | 前者
594 | 前进
595 | 前面
596 | 加上
597 | 加之
598 | 加以
599 | 加入
600 | 加强
601 | 动不动
602 | 动辄
603 | 勃然
604 | 匆匆
605 | 十分
606 | 千
607 | 千万
608 | 千万千万
609 | 半
610 | 单
611 | 单单
612 | 单纯
613 | 即
614 | 即令
615 | 即使
616 | 即便
617 | 即刻
618 | 即如
619 | 即将
620 | 即或
621 | 即是说
622 | 即若
623 | 却
624 | 却不
625 | 历
626 | 原来
627 | 去
628 | 又
629 | 又及
630 | 及
631 | 及其
632 | 及时
633 | 及至
634 | 双方
635 | 反之
636 | 反之亦然
637 | 反之则
638 | 反倒
639 | 反倒是
640 | 反应
641 | 反手
642 | 反映
643 | 反而
644 | 反过来
645 | 反过来说
646 | 取得
647 | 取道
648 | 受到
649 | 变成
650 | 古来
651 | 另
652 | 另一个
653 | 另一方面
654 | 另外
655 | 另悉
656 | 另方面
657 | 另行
658 | 只
659 | 只当
660 | 只怕
661 | 只是
662 | 只有
663 | 只消
664 | 只要
665 | 只限
666 | 叫
667 | 叫做
668 | 召开
669 | 叮咚
670 | 叮当
671 | 可
672 | 可以
673 | 可好
674 | 可是
675 | 可能
676 | 可见
677 | 各
678 | 各个
679 | 各人
680 | 各位
681 | 各地
682 | 各式
683 | 各种
684 | 各级
685 | 各自
686 | 合理
687 | 同
688 | 同一
689 | 同时
690 | 同样
691 | 后
692 | 后来
693 | 后者
694 | 后面
695 | 向
696 | 向使
697 | 向着
698 | 吓
699 | 吗
700 | 否则
701 | 吧
702 | 吧哒
703 | 吱
704 | 呀
705 | 呃
706 | 呆呆地
707 | 呐
708 | 呕
709 | 呗
710 | 呜
711 | 呜呼
712 | 呢
713 | 周围
714 | 呵
715 | 呵呵
716 | 呸
717 | 呼哧
718 | 呼啦
719 | 咋
720 | 和
721 | 咚
722 | 咦
723 | 咧
724 | 咱
725 | 咱们
726 | 咳
727 | 哇
728 | 哈
729 | 哈哈
730 | 哉
731 | 哎
732 | 哎呀
733 | 哎哟
734 | 哗
735 | 哗啦
736 | 哟
737 | 哦
738 | 哩
739 | 哪
740 | 哪个
741 | 哪些
742 | 哪儿
743 | 哪天
744 | 哪年
745 | 哪怕
746 | 哪样
747 | 哪边
748 | 哪里
749 | 哼
750 | 哼唷
751 | 唉
752 | 唯有
753 | 啊
754 | 啊呀
755 | 啊哈
756 | 啊哟
757 | 啐
758 | 啥
759 | 啦
760 | 啪达
761 | 啷当
762 | 喀
763 | 喂
764 | 喏
765 | 喔唷
766 | 喽
767 | 嗡
768 | 嗡嗡
769 | 嗬
770 | 嗯
771 | 嗳
772 | 嘎
773 | 嘎嘎
774 | 嘎登
775 | 嘘
776 | 嘛
777 | 嘻
778 | 嘿
779 | 嘿嘿
780 | 四
781 | 因
782 | 因为
783 | 因了
784 | 因此
785 | 因着
786 | 因而
787 | 固
788 | 固然
789 | 在
790 | 在下
791 | 在于
792 | 地
793 | 均
794 | 坚决
795 | 坚持
796 | 基于
797 | 基本
798 | 基本上
799 | 处在
800 | 处处
801 | 处理
802 | 复杂
803 | 多
804 | 多么
805 | 多亏
806 | 多多
807 | 多多少少
808 | 多多益善
809 | 多少
810 | 多年前
811 | 多年来
812 | 多数
813 | 多次
814 | 够瞧的
815 | 大
816 | 大不了
817 | 大举
818 | 大事
819 | 大体
820 | 大体上
821 | 大凡
822 | 大力
823 | 大多
824 | 大多数
825 | 大大
826 | 大家
827 | 大张旗鼓
828 | 大批
829 | 大抵
830 | 大概
831 | 大略
832 | 大约
833 | 大致
834 | 大都
835 | 大量
836 | 大面儿上
837 | 失去
838 | 奇
839 | 奈
840 | 奋勇
841 | 她
842 | 她们
843 | 她是
844 | 她的
845 | 好
846 | 好在
847 | 好的
848 | 好象
849 | 如
850 | 如上
851 | 如上所述
852 | 如下
853 | 如今
854 | 如何
855 | 如其
856 | 如前所述
857 | 如同
858 | 如常
859 | 如是
860 | 如期
861 | 如果
862 | 如次
863 | 如此
864 | 如此等等
865 | 如若
866 | 始而
867 | 姑且
868 | 存在
869 | 存心
870 | 孰料
871 | 孰知
872 | 宁
873 | 宁可
874 | 宁愿
875 | 宁肯
876 | 它
877 | 它们
878 | 它们的
879 | 它是
880 | 它的
881 | 安全
882 | 完全
883 | 完成
884 | 定
885 | 实现
886 | 实际
887 | 宣布
888 | 容易
889 | 密切
890 | 对
891 | 对于
892 | 对应
893 | 对待
894 | 对方
895 | 对比
896 | 将
897 | 将才
898 | 将要
899 | 将近
900 | 小
901 | 少数
902 | 尔
903 | 尔后
904 | 尔尔
905 | 尔等
906 | 尚且
907 | 尤其
908 | 就
909 | 就地
910 | 就是
911 | 就是了
912 | 就是说
913 | 就此
914 | 就算
915 | 就要
916 | 尽
917 | 尽可能
918 | 尽如人意
919 | 尽心尽力
920 | 尽心竭力
921 | 尽快
922 | 尽早
923 | 尽然
924 | 尽管
925 | 尽管如此
926 | 尽量
927 | 局外
928 | 居然
929 | 届时
930 | 属于
931 | 屡
932 | 屡屡
933 | 屡次
934 | 屡次三番
935 | 岂
936 | 岂但
937 | 岂止
938 | 岂非
939 | 川流不息
940 | 左右
941 | 巨大
942 | 巩固
943 | 差一点
944 | 差不多
945 | 己
946 | 已
947 | 已矣
948 | 已经
949 | 巴
950 | 巴巴
951 | 带
952 | 帮助
953 | 常
954 | 常常
955 | 常言说
956 | 常言说得好
957 | 常言道
958 | 平素
959 | 年复一年
960 | 并
961 | 并不
962 | 并不是
963 | 并且
964 | 并排
965 | 并无
966 | 并没
967 | 并没有
968 | 并肩
969 | 并非
970 | 广大
971 | 广泛
972 | 应当
973 | 应用
974 | 应该
975 | 庶乎
976 | 庶几
977 | 开外
978 | 开始
979 | 开展
980 | 引起
981 | 弗
982 | 弹指之间
983 | 强烈
984 | 强调
985 | 归
986 | 归根到底
987 | 归根结底
988 | 归齐
989 | 当
990 | 当下
991 | 当中
992 | 当儿
993 | 当前
994 | 当即
995 | 当口儿
996 | 当地
997 | 当场
998 | 当头
999 | 当庭
1000 | 当时
1001 | 当然
1002 | 当真
1003 | 当着
1004 | 形成
1005 | 彻夜
1006 | 彻底
1007 | 彼
1008 | 彼时
1009 | 彼此
1010 | 往
1011 | 往往
1012 | 待
1013 | 待到
1014 | 很
1015 | 很多
1016 | 很少
1017 | 後来
1018 | 後面
1019 | 得
1020 | 得了
1021 | 得出
1022 | 得到
1023 | 得天独厚
1024 | 得起
1025 | 心里
1026 | 必
1027 | 必定
1028 | 必将
1029 | 必然
1030 | 必要
1031 | 必须
1032 | 快
1033 | 快要
1034 | 忽地
1035 | 忽然
1036 | 怎
1037 | 怎么
1038 | 怎么办
1039 | 怎么样
1040 | 怎奈
1041 | 怎样
1042 | 怎麽
1043 | 怕
1044 | 急匆匆
1045 | 怪
1046 | 怪不得
1047 | 总之
1048 | 总是
1049 | 总的来看
1050 | 总的来说
1051 | 总的说来
1052 | 总结
1053 | 总而言之
1054 | 恍然
1055 | 恐怕
1056 | 恰似
1057 | 恰好
1058 | 恰如
1059 | 恰巧
1060 | 恰恰
1061 | 恰恰相反
1062 | 恰逢
1063 | 您
1064 | 您们
1065 | 您是
1066 | 惟其
1067 | 惯常
1068 | 意思
1069 | 愤然
1070 | 愿意
1071 | 慢说
1072 | 成为
1073 | 成年
1074 | 成年累月
1075 | 成心
1076 | 我
1077 | 我们
1078 | 我是
1079 | 我的
1080 | 或
1081 | 或则
1082 | 或多或少
1083 | 或是
1084 | 或曰
1085 | 或者
1086 | 或许
1087 | 战斗
1088 | 截然
1089 | 截至
1090 | 所
1091 | 所以
1092 | 所在
1093 | 所幸
1094 | 所有
1095 | 所谓
1096 | 才
1097 | 才能
1098 | 扑通
1099 | 打
1100 | 打从
1101 | 打开天窗说亮话
1102 | 扩大
1103 | 把
1104 | 抑或
1105 | 抽冷子
1106 | 拦腰
1107 | 拿
1108 | 按
1109 | 按时
1110 | 按期
1111 | 按照
1112 | 按理
1113 | 按说
1114 | 挨个
1115 | 挨家挨户
1116 | 挨次
1117 | 挨着
1118 | 挨门挨户
1119 | 挨门逐户
1120 | 换句话说
1121 | 换言之
1122 | 据
1123 | 据实
1124 | 据悉
1125 | 据我所知
1126 | 据此
1127 | 据称
1128 | 据说
1129 | 掌握
1130 | 接下来
1131 | 接着
1132 | 接著
1133 | 接连不断
1134 | 放量
1135 | 故
1136 | 故意
1137 | 故此
1138 | 故而
1139 | 敞开儿
1140 | 敢
1141 | 敢于
1142 | 敢情
1143 | 数/
1144 | 整个
1145 | 断然
1146 | 方
1147 | 方便
1148 | 方才
1149 | 方能
1150 | 方面
1151 | 旁人
1152 | 无
1153 | 无宁
1154 | 无法
1155 | 无论
1156 | 既
1157 | 既...又
1158 | 既往
1159 | 既是
1160 | 既然
1161 | 日复一日
1162 | 日渐
1163 | 日益
1164 | 日臻
1165 | 日见
1166 | 时候
1167 | 昂然
1168 | 明显
1169 | 明确
1170 | 是
1171 | 是不是
1172 | 是以
1173 | 是否
1174 | 是的
1175 | 显然
1176 | 显著
1177 | 普通
1178 | 普遍
1179 | 暗中
1180 | 暗地里
1181 | 暗自
1182 | 更
1183 | 更为
1184 | 更加
1185 | 更进一步
1186 | 曾
1187 | 曾经
1188 | 替
1189 | 替代
1190 | 最
1191 | 最后
1192 | 最大
1193 | 最好
1194 | 最後
1195 | 最近
1196 | 最高
1197 | 有
1198 | 有些
1199 | 有关
1200 | 有利
1201 | 有力
1202 | 有及
1203 | 有所
1204 | 有效
1205 | 有时
1206 | 有点
1207 | 有的
1208 | 有的是
1209 | 有着
1210 | 有著
1211 | 望
1212 | 朝
1213 | 朝着
1214 | 末##末
1215 | 本
1216 | 本人
1217 | 本地
1218 | 本着
1219 | 本身
1220 | 权时
1221 | 来
1222 | 来不及
1223 | 来得及
1224 | 来看
1225 | 来着
1226 | 来自
1227 | 来讲
1228 | 来说
1229 | 极
1230 | 极为
1231 | 极了
1232 | 极其
1233 | 极力
1234 | 极大
1235 | 极度
1236 | 极端
1237 | 构成
1238 | 果然
1239 | 果真
1240 | 某
1241 | 某个
1242 | 某些
1243 | 某某
1244 | 根据
1245 | 根本
1246 | 格外
1247 | 梆
1248 | 概
1249 | 次第
1250 | 欢迎
1251 | 欤
1252 | 正值
1253 | 正在
1254 | 正如
1255 | 正巧
1256 | 正常
1257 | 正是
1258 | 此
1259 | 此中
1260 | 此后
1261 | 此地
1262 | 此处
1263 | 此外
1264 | 此时
1265 | 此次
1266 | 此间
1267 | 殆
1268 | 毋宁
1269 | 每
1270 | 每个
1271 | 每天
1272 | 每年
1273 | 每当
1274 | 每时每刻
1275 | 每每
1276 | 每逢
1277 | 比
1278 | 比及
1279 | 比如
1280 | 比如说
1281 | 比方
1282 | 比照
1283 | 比起
1284 | 比较
1285 | 毕竟
1286 | 毫不
1287 | 毫无
1288 | 毫无例外
1289 | 毫无保留地
1290 | 汝
1291 | 沙沙
1292 | 没
1293 | 没奈何
1294 | 没有
1295 | 沿
1296 | 沿着
1297 | 注意
1298 | 活
1299 | 深入
1300 | 清楚
1301 | 满
1302 | 满足
1303 | 漫说
1304 | 焉
1305 | 然
1306 | 然则
1307 | 然后
1308 | 然後
1309 | 然而
1310 | 照
1311 | 照着
1312 | 牢牢
1313 | 特别是
1314 | 特殊
1315 | 特点
1316 | 犹且
1317 | 犹自
1318 | 独
1319 | 独自
1320 | 猛然
1321 | 猛然间
1322 | 率尔
1323 | 率然
1324 | 现代
1325 | 现在
1326 | 理应
1327 | 理当
1328 | 理该
1329 | 瑟瑟
1330 | 甚且
1331 | 甚么
1332 | 甚或
1333 | 甚而
1334 | 甚至
1335 | 甚至于
1336 | 用
1337 | 用来
1338 | 甫
1339 | 甭
1340 | 由
1341 | 由于
1342 | 由是
1343 | 由此
1344 | 由此可见
1345 | 略
1346 | 略为
1347 | 略加
1348 | 略微
1349 | 白
1350 | 白白
1351 | 的
1352 | 的确
1353 | 的话
1354 | 皆可
1355 | 目前
1356 | 直到
1357 | 直接
1358 | 相似
1359 | 相信
1360 | 相反
1361 | 相同
1362 | 相对
1363 | 相对而言
1364 | 相应
1365 | 相当
1366 | 相等
1367 | 省得
1368 | 看
1369 | 看上去
1370 | 看出
1371 | 看到
1372 | 看来
1373 | 看样子
1374 | 看看
1375 | 看见
1376 | 看起来
1377 | 真是
1378 | 真正
1379 | 眨眼
1380 | 着
1381 | 着呢
1382 | 矣
1383 | 矣乎
1384 | 矣哉
1385 | 知道
1386 | 砰
1387 | 确定
1388 | 碰巧
1389 | 社会主义
1390 | 离
1391 | 种
1392 | 积极
1393 | 移动
1394 | 究竟
1395 | 穷年累月
1396 | 突出
1397 | 突然
1398 | 窃
1399 | 立
1400 | 立刻
1401 | 立即
1402 | 立地
1403 | 立时
1404 | 立马
1405 | 竟
1406 | 竟然
1407 | 竟而
1408 | 第
1409 | 第二
1410 | 等
1411 | 等到
1412 | 等等
1413 | 策略地
1414 | 简直
1415 | 简而言之
1416 | 简言之
1417 | 管
1418 | 类如
1419 | 粗
1420 | 精光
1421 | 紧接着
1422 | 累年
1423 | 累次
1424 | 纯
1425 | 纯粹
1426 | 纵
1427 | 纵令
1428 | 纵使
1429 | 纵然
1430 | 练习
1431 | 组成
1432 | 经
1433 | 经常
1434 | 经过
1435 | 结合
1436 | 结果
1437 | 给
1438 | 绝
1439 | 绝不
1440 | 绝对
1441 | 绝非
1442 | 绝顶
1443 | 继之
1444 | 继后
1445 | 继续
1446 | 继而
1447 | 维持
1448 | 综上所述
1449 | 缕缕
1450 | 罢了
1451 | 老
1452 | 老大
1453 | 老是
1454 | 老老实实
1455 | 考虑
1456 | 者
1457 | 而
1458 | 而且
1459 | 而况
1460 | 而又
1461 | 而后
1462 | 而外
1463 | 而已
1464 | 而是
1465 | 而言
1466 | 而论
1467 | 联系
1468 | 联袂
1469 | 背地里
1470 | 背靠背
1471 | 能
1472 | 能否
1473 | 能够
1474 | 腾
1475 | 自
1476 | 自个儿
1477 | 自从
1478 | 自各儿
1479 | 自后
1480 | 自家
1481 | 自己
1482 | 自打
1483 | 自身
1484 | 臭
1485 | 至
1486 | 至于
1487 | 至今
1488 | 至若
1489 | 致
1490 | 般的
1491 | 良好
1492 | 若
1493 | 若夫
1494 | 若是
1495 | 若果
1496 | 若非
1497 | 范围
1498 | 莫
1499 | 莫不
1500 | 莫不然
1501 | 莫如
1502 | 莫若
1503 | 莫非
1504 | 获得
1505 | 藉以
1506 | 虽
1507 | 虽则
1508 | 虽然
1509 | 虽说
1510 | 蛮
1511 | 行为
1512 | 行动
1513 | 表明
1514 | 表示
1515 | 被
1516 | 要
1517 | 要不
1518 | 要不是
1519 | 要不然
1520 | 要么
1521 | 要是
1522 | 要求
1523 | 见
1524 | 规定
1525 | 觉得
1526 | 譬喻
1527 | 譬如
1528 | 认为
1529 | 认真
1530 | 认识
1531 | 让
1532 | 许多
1533 | 论
1534 | 论说
1535 | 设使
1536 | 设或
1537 | 设若
1538 | 诚如
1539 | 诚然
1540 | 话说
1541 | 该
1542 | 该当
1543 | 说明
1544 | 说来
1545 | 说说
1546 | 请勿
1547 | 诸
1548 | 诸位
1549 | 诸如
1550 | 谁
1551 | 谁人
1552 | 谁料
1553 | 谁知
1554 | 谨
1555 | 豁然
1556 | 贼死
1557 | 赖以
1558 | 赶
1559 | 赶快
1560 | 赶早不赶晚
1561 | 起
1562 | 起先
1563 | 起初
1564 | 起头
1565 | 起来
1566 | 起见
1567 | 起首
1568 | 趁
1569 | 趁便
1570 | 趁势
1571 | 趁早
1572 | 趁机
1573 | 趁热
1574 | 趁着
1575 | 越是
1576 | 距
1577 | 跟
1578 | 路经
1579 | 转动
1580 | 转变
1581 | 转贴
1582 | 轰然
1583 | 较
1584 | 较为
1585 | 较之
1586 | 较比
1587 | 边
1588 | 达到
1589 | 达旦
1590 | 迄
1591 | 迅速
1592 | 过
1593 | 过于
1594 | 过去
1595 | 过来
1596 | 运用
1597 | 近
1598 | 近几年来
1599 | 近年来
1600 | 近来
1601 | 还
1602 | 还是
1603 | 还有
1604 | 还要
1605 | 这
1606 | 这一来
1607 | 这个
1608 | 这么
1609 | 这么些
1610 | 这么样
1611 | 这么点儿
1612 | 这些
1613 | 这会儿
1614 | 这儿
1615 | 这就是说
1616 | 这时
1617 | 这样
1618 | 这次
1619 | 这点
1620 | 这种
1621 | 这般
1622 | 这边
1623 | 这里
1624 | 这麽
1625 | 进入
1626 | 进去
1627 | 进来
1628 | 进步
1629 | 进而
1630 | 进行
1631 | 连
1632 | 连同
1633 | 连声
1634 | 连日
1635 | 连日来
1636 | 连袂
1637 | 连连
1638 | 迟早
1639 | 迫于
1640 | 适应
1641 | 适当
1642 | 适用
1643 | 逐步
1644 | 逐渐
1645 | 通常
1646 | 通过
1647 | 造成
1648 | 逢
1649 | 遇到
1650 | 遭到
1651 | 遵循
1652 | 遵照
1653 | 避免
1654 | 那
1655 | 那个
1656 | 那么
1657 | 那么些
1658 | 那么样
1659 | 那些
1660 | 那会儿
1661 | 那儿
1662 | 那时
1663 | 那末
1664 | 那样
1665 | 那般
1666 | 那边
1667 | 那里
1668 | 那麽
1669 | 部分
1670 | 都
1671 | 鄙人
1672 | 采取
1673 | 里面
1674 | 重大
1675 | 重新
1676 | 重要
1677 | 鉴于
1678 | 针对
1679 | 长期以来
1680 | 长此下去
1681 | 长线
1682 | 长话短说
1683 | 问题
1684 | 间或
1685 | 防止
1686 | 阿
1687 | 附近
1688 | 陈年
1689 | 限制
1690 | 陡然
1691 | 除
1692 | 除了
1693 | 除却
1694 | 除去
1695 | 除外
1696 | 除开
1697 | 除此
1698 | 除此之外
1699 | 除此以外
1700 | 除此而外
1701 | 除非
1702 | 随
1703 | 随后
1704 | 随时
1705 | 随着
1706 | 随著
1707 | 隔夜
1708 | 隔日
1709 | 难得
1710 | 难怪
1711 | 难说
1712 | 难道
1713 | 难道说
1714 | 集中
1715 | 零
1716 | 需要
1717 | 非但
1718 | 非常
1719 | 非徒
1720 | 非得
1721 | 非特
1722 | 非独
1723 | 靠
1724 | 顶多
1725 | 顷
1726 | 顷刻
1727 | 顷刻之间
1728 | 顷刻间
1729 | 顺
1730 | 顺着
1731 | 顿时
1732 | 颇
1733 | 风雨无阻
1734 | 饱
1735 | 首先
1736 | 马上
1737 | 高低
1738 | 高兴
1739 | 默然
1740 | 默默地
1741 | 齐
1742 | ︿
1743 | ！
1744 | ＃
1745 | ＄
1746 | ％
1747 | ＆
1748 | ＇
1749 | （
1750 | ）
1751 | ）÷（１－
1752 | ）、
1753 | ＊
1754 | ＋
1755 | ＋ξ
1756 | ＋＋
1757 | ，
1758 | ，也
1759 | －
1760 | －β
1761 | －－
1762 | －［＊］－
1763 | ．
1764 | ／
1765 | ０
1766 | ０：２
1767 | １
1768 | １．
1769 | １２％
1770 | ２
1771 | ２．３％
1772 | ３
1773 | ４
1774 | ５
1775 | ５：０
1776 | ６
1777 | ７
1778 | ８
1779 | ９
1780 | ：
1781 | ；
1782 | ＜
1783 | ＜±
1784 | ＜Δ
1785 | ＜λ
1786 | ＜φ
1787 | ＜＜
1788 | ＝
1789 | ＝″
1790 | ＝☆
1791 | ＝（
1792 | ＝－
1793 | ＝［
1794 | ＝｛
1795 | ＞
1796 | ＞λ
1797 | ？
1798 | ＠
1799 | Ａ
1800 | ＬＩ
1801 | Ｒ．Ｌ．
1802 | ＺＸＦＩＴＬ
1803 |
1804 | ［＊］
1805 | ［－
1806 | ［］
1807 | ］
1808 | ］∧′＝［
1809 | ］［
1810 | ＿
1811 | ａ］
1812 | ｂ］
1813 | ｃ］
1814 | ｅ］
1815 | ｆ］
1816 | ｎｇ昉
1817 | ｛
1818 | ｛－
1819 | ｜
1820 | ｝
1821 | ｝＞
1822 | ～
1823 | ～±
1824 | ～＋
1825 | ￥
1826 | secondly
1827 | all
1828 | whose
1829 | under
1830 | sorry
1831 | four
1832 | we'll
1833 | somewhere
1834 | likely
1835 | even
1836 | above
1837 | ever
1838 | never
1839 | ZZ
1840 | hers
1841 | i'd
1842 | howbeit
1843 | i'm
1844 | theres
1845 | changes
1846 | anyhow
1847 | would
1848 | therefore
1849 | is
1850 | hereby
1851 | must
1852 | me
1853 | my
1854 | indicated
1855 | indicates
1856 | keep
1857 | far
1858 | after
1859 | hereupon
1860 | keeps
1861 | every
1862 | over
1863 | before
1864 | better
1865 | then
1866 | them
1867 | they
1868 | reasonably
1869 | each
1870 | went
1871 | mean
1872 | we'd
1873 | rd
1874 | re
1875 | got
1876 | forth
1877 | you're
1878 | little
1879 | whereupon
1880 | uses
1881 | already
1882 | another
1883 | took
1884 | second
1885 | seen
1886 | seem
1887 | relatively
1888 | thoroughly
1889 | latter
1890 | that
1891 | thorough
1892 | nobody
1893 | definitely
1894 | came
1895 | saying
1896 | specify
1897 | do
1898 | next
1899 | despite
1900 | unfortunately
1901 | twice
1902 | best
1903 | said
1904 | away
1905 | there's
1906 | unto
1907 | hopefully
1908 | seven
1909 | we
1910 | ltd
1911 | here
1912 | against
1913 | com
1914 | ZT
1915 | aren't
1916 | been
1917 | much
1918 | concerning
1919 | wish
1920 | say
1921 | near
1922 | unlikely
1923 | cant
1924 | in
1925 | ie
1926 | if
1927 | containing
1928 | beside
1929 | several
1930 | kept
1931 | whereby
1932 | whoever
1933 | the
1934 | yours
1935 | just
1936 | yes
1937 | yet
1938 | had
1939 | has
1940 | t's
1941 | possible
1942 | apart
1943 | right
1944 | old
1945 | somehow
1946 | for
1947 | everything
1948 | asking
1949 | who
1950 | of
1951 | theirs
1952 | plus
1953 | formerly
1954 | down
1955 | c's
1956 | accordingly
1957 | way
1958 | was
1959 | becoming
1960 | tell
1961 | sometime
1962 | no
1963 | whereas
1964 | nd
1965 | welcome
1966 | let's
1967 | certainly
1968 | a's
1969 | did
1970 | it'll
1971 | says
1972 | appear
1973 | alone
1974 | wherever
1975 | example
1976 | usually
1977 | nowhere
1978 | hither
1979 | regardless
1980 | everybody
1981 | thru
1982 | everywhere
1983 | can
1984 | following
1985 | want
1986 | didn't
1987 | may
1988 | such
1989 | whenever
1990 | maybe
1991 | ones
1992 | so
1993 | seeing
1994 | indeed
1995 | course
1996 | still
1997 | thank
1998 | he's
1999 | selves
2000 | ours
2001 | outside
2002 | non
2003 | within
2004 | thereby
2005 | not
2006 | now
2007 | nor
2008 | entirely
2009 | eg
2010 | ex
2011 | et
2012 | hadn't
2013 | furthermore
2014 | looking
2015 | seriously
2016 | shouldn't
2017 | she
2018 | quite
2019 | besides
2020 | think
2021 | first
2022 | ignored
2023 | awfully
2024 | given
2025 | anyone
2026 | indicate
2027 | gives
2028 | mostly
2029 | than
2030 | here's
2031 | were
2032 | and
2033 | appreciate
2034 | himself
2035 | saw
2036 | any
2037 | downwards
2038 | take
2039 | sure
2040 | especially
2041 | later
2042 | that's
2043 | fifth
2044 | don't
2045 | aside
2046 | only
2047 | going
2048 | get
2049 | truly
2050 | cannot
2051 | nearly
2052 | regarding
2053 | us
2054 | where
2055 | up
2056 | namely
2057 | anyways
2058 | wonder
2059 | behind
2060 | between
2061 | it
2062 | across
2063 | come
2064 | many
2065 | whereafter
2066 | according
2067 | comes
2068 | afterwards
2069 | couldn't
2070 | moreover
2071 | considering
2072 | sensible
2073 | hardly
2074 | wants
2075 | former
2076 | those
2077 | these
2078 | [
2079 | somebody
2080 | different
2081 | etc
2082 | insofar
2083 | same
2084 | without
2085 | can't
2086 | very
2087 | you've
2088 | among
2089 | being
2090 | we've
2091 | seems
2092 | around
2093 | using
2094 | specified
2095 | on
2096 | ok
2097 | oh
2098 | whence
2099 | it's
2100 | or
2101 | everyone
2102 | your
2103 | her
2104 | there
2105 | amongst
2106 | trying
2107 | with
2108 | they're
2109 | wasn't
2110 | gone
2111 | certain
2112 | am
2113 | an
2114 | as
2115 | at
2116 | again
2117 | serious
2118 | hello
2119 | since
2120 | consider
2121 | causes
2122 | to
2123 | th
2124 | myself
2125 | i'll
2126 | zero
2127 | further
2128 | what
2129 | brief
2130 | seemed
2131 | c'mon
2132 | allows
2133 | followed
2134 | ask
2135 | viz
2136 | contains
2137 | two
2138 | taken
2139 | more
2140 | knows
2141 | ain't
2142 | particular
2143 | known
2144 | none
2145 | nine
2146 | needs
2147 | rather
2148 | ［
2149 | okay
2150 | tried
2151 | tries
2152 | onto
2153 | perhaps
2154 | specifying
2155 | ]
2156 | help
2157 | soon
2158 | through
2159 | its
2160 | seeming
2161 | inward
2162 | actually
2163 | might
2164 | haven't
2165 | someone
2166 | hereafter
2167 | always
2168 | isn't
2169 | beyond
2170 | really
2171 | they'll
2172 | enough
2173 | thereafter
2174 | done
2175 | together
2176 | least
2177 | too
2178 | immediate
2179 | believe
2180 | gotten
2181 | toward
2182 | self
2183 | also
2184 | towards
2185 | most
2186 | nothing
2187 | they'd
2188 | sometimes
2189 | lest
2190 | particularly
2191 | somewhat
2192 | his
2193 | goes
2194 | meanwhile
2195 | during
2196 | him
2197 | greetings
2198 | see
2199 | are
2200 | currently
2201 | please
2202 | various
2203 | probably
2204 | available
2205 | both
2206 | last
2207 | wouldn't
2208 | became
2209 | whole
2210 | liked
2211 | whatever
2212 | except
2213 | throughout
2214 | along
2215 | described
2216 | though
2217 | whom
2218 | beforehand
2219 | what's
2220 | new
2221 | else
2222 | look
2223 | while
2224 | herein
2225 | itself
2226 | wherein
2227 | used
2228 | anybody
2229 | obviously
2230 | thats
2231 | from
2232 | useful
2233 | merely
2234 | follows
2235 | often
2236 | some
2237 | ourselves
2238 | shall
2239 | per
2240 | tends
2241 | either
2242 | be
2243 | by
2244 | anything
2245 | consequently
2246 | into
2247 | appropriate
2248 | we're
2249 | elsewhere
2250 | hasn't
2251 | un
2252 | noone
2253 | associated
2254 | thanks
2255 | having
2256 | once
2257 | edu
2258 | go
2259 | sent
2260 | provides
2261 | yourselves
2262 | they've
2263 | try
2264 | this
2265 | you'd
2266 | yourself
2267 | zz
2268 | zt
2269 | respectively
2270 | let
2271 | others
2272 | until
2273 | weren't
2274 | use
2275 | few
2276 | themselves
2277 | becomes
2278 | anywhere
2279 | something
2280 | six
2281 | allow
2282 | won't
2283 | thence
2284 | willing
2285 | instead
2286 | whither
2287 | doing
2288 | how
2289 | cause
2290 | thereupon
2291 | que
2292 | via
2293 | could
2294 | hence
2295 | third
2296 | doesn't
2297 | their
2298 | exactly
2299 | regards
2300 | herself
2301 | have
2302 | need
2303 | clearly
2304 | i've
2305 | able
2306 | which
2307 | unless
2308 | where's
2309 | eight
2310 | why
2311 | you'll
2312 | normally
2313 | anyway
2314 | one
2315 | should
2316 | mainly
2317 | overall
2318 | qv
2319 | contain
2320 | looks
2321 | neither
2322 | however
2323 | otherwise
2324 | co
2325 | it'd
2326 | corresponding
2327 | thanx
2328 | novel
2329 | value
2330 | will
2331 | almost
2332 | thus
2333 | vs
2334 | when
2335 | gets
2336 | upon
2337 | off
2338 | nevertheless
2339 | well
2340 | less
2341 | presumably
2342 | ought
2343 | who's
2344 | five
2345 | know
2346 | you
2347 | name
2348 | necessary
2349 | like
2350 | become
2351 | therein
2352 | because
2353 | happens
2354 | does
2355 | although
2356 | about
2357 | getting
2358 | own
2359 | three
2360 | inasmuch
2361 | inner
2362 | but
2363 | hi
2364 | he
2365 | whether
2366 | placed
2367 | below
2368 | our
2369 | 上去--
2370 | inc
2371 | lately
2372 | other
2373 | latterly
2374 | out
2375 | 是什么
2376 | 什么时候
2377 | 是什么意思
2378 | 什么意思
2379 | 多少钱
2380 | 有没有
2381 | 更有趣
2382 | 更有甚者
2383 | 更有效
2384 | 更有意义
2385 | 更远的
2386 | 更重要的是
2387 | 正确
2388 | 错误
2389 | 第二把
2390 | 第二波
2391 | 第二大节
2392 | 第二单元
2393 | 第二关
2394 | 第二行
2395 | 第二集
2396 | 第二讲
2397 | 第二款
2398 | 第二类
2399 | 第二盘
2400 | 第二任
2401 | 第二声
2402 | 第二十
2403 | 第二首
2404 | 第二项
2405 | 第三遍
2406 | 第三册
2407 | 第三层
2408 | 第三产业
2409 | 第三大
2410 | 第三单元
2411 | 第三行
2412 | 第三回
2413 | 第三集
2414 | 第三件
2415 | 第三句
2416 | 第三卷
2417 | 第三课
2418 | 第三类
2419 | 第三篇
2420 | 第三期
2421 | 第三日
2422 | 第三声
2423 | 地三鲜
2424 | 第三项
2425 | 第三站
2426 | 第三张
2427 | 第十八
2428 | 第十次
2429 | 第十二
2430 | 的士高
2431 | 第十集
2432 | 第十届
2433 | 第十九
2434 | 第十六
2435 | 第十名
2436 | 第十三
2437 | 第十四
2438 | 第十天
2439 | 第十一
2440 | 第十一个
2441 | 第四版
2442 | 第四册
2443 | 第四场
2444 | 第四代
2445 | 第四单元
2446 | 第四集
2447 | 第四届
2448 | 第四年
2449 | 第四期
2450 | 第四声
2451 | 第四套
2452 | 第四位
2453 | 第四张
2454 | 第四者
2455 | 第四种
2456 | 第五部
2457 | 第五大道
2458 | 第五单元
2459 | 第五集
2460 | 第五卷
2461 | 第五课
2462 | 第五年
2463 | 第五期
2464 | 第五位
2465 | 第五元素
2466 | 第五组
2467 | 召唤
2468 | 最后一班
2469 | 最后一遍
2470 | 最后一关
2471 | 最后一集
2472 | 最后一科
2473 | 最后一颗子弹
2474 | 最后一派
2475 | 最后一题
2476 | 最后一眼
2477 | 最后一页
2478 | 10
2479 | 11
2480 | 12
2481 | 35
2482 | 25
2483 | 2016
2484 | 2015
2485 | 2014
2486 | 又为什么
2487 | 有问题吗
2488 | 有问题么
2489 | 又喜欢
2490 | 有喜欢
2491 | 又小
2492 | 又笑
2493 | 有笑
2494 | 有效地
2495 | 有一百
2496 | 又一遍
2497 | 有一部
2498 | 又一城
2499 | 又一村
2500 | 有一道
2501 | 有意的
2502 | 有一堆
2503 | 有一对
2504 | 有一方
2505 | 有一根
2506 | 有一会了
2507 | 有一批
2508 | 有一片
2509 | 有一期
2510 | 有一起
2511 | 有一群
2512 | 又又
2513 | 由由
2514 | 财新网
2515 | 上午
2516 | 下午
2517 | NULL
2518 | 新华社
2519 | 消息
2520 | 13
2521 | 14
2522 | 15
2523 | 16
2524 | 17
2525 | 18
2526 | 19
2527 | 20
2528 | 21
2529 | 22
2530 | 23
2531 | 24
2532 | 26
2533 | 27
2534 | 28
2535 | 29
2536 | 30
2537 | 31
2538 | 32
2539 | 33
2540 | 34
2541 | 36
2542 | 37
2543 | 38
2544 | 39
2545 | 40
2546 | 41
2547 | 42
2548 | 43
2549 | 44
2550 | 45
2551 | 46
2552 | 47
2553 | 48
2554 | 49
2555 | 50
2556 | 51
2557 | 52
2558 | 53
2559 | 54
2560 | 55
2561 | 56
2562 | 57
2563 | 58
2564 | 59
2565 | 60
2566 | 61
2567 | 62
2568 | 63
2569 | 64
2570 | 65
2571 | 66
2572 | 67
2573 | 68
2574 | 69
2575 | 70
2576 | 71
2577 | 72
2578 | 73
2579 | 74
2580 | 75
2581 | 76
2582 | 77
2583 | 78
2584 | 79
2585 | 80
2586 | 81
2587 | 82
2588 | 83
2589 | 84
2590 | 85
2591 | 86
2592 | 87
2593 | 88
2594 | 89
2595 | 90
2596 | 91
2597 | 92
2598 | 93
2599 | 94
2600 | 95
2601 | 96
2602 | 97
2603 | 98
2604 | 99
2605 | 100
2606 | 01
2607 | 02
2608 | 03
2609 | 04
2610 | 05
2611 | 06
2612 | 07
2613 | 08
2614 | 09
2615 |
--------------------------------------------------------------------------------
/temp-predict/data/temps.csv:
--------------------------------------------------------------------------------
1 | year,month,day,week,temp_2,temp_1,average,actual,friend
2 | 2016,1,1,Fri,45,45,45.6,45,29
3 | 2016,1,2,Sat,44,45,45.7,44,61
4 | 2016,1,3,Sun,45,44,45.8,41,56
5 | 2016,1,4,Mon,44,41,45.9,40,53
6 | 2016,1,5,Tues,41,40,46,44,41
7 | 2016,1,6,Wed,40,44,46.1,51,40
8 | 2016,1,7,Thurs,44,51,46.2,45,38
9 | 2016,1,8,Fri,51,45,46.3,48,34
10 | 2016,1,9,Sat,45,48,46.4,50,47
11 | 2016,1,10,Sun,48,50,46.5,52,49
12 | 2016,1,11,Mon,50,52,46.7,45,39
13 | 2016,1,12,Tues,52,45,46.8,49,61
14 | 2016,1,13,Wed,45,49,46.9,55,33
15 | 2016,1,14,Thurs,49,55,47,49,58
16 | 2016,1,15,Fri,55,49,47.1,48,65
17 | 2016,1,16,Sat,49,48,47.3,54,28
18 | 2016,1,17,Sun,48,54,47.4,50,47
19 | 2016,1,18,Mon,54,50,47.5,54,58
20 | 2016,1,19,Tues,50,54,47.6,48,53
21 | 2016,1,20,Wed,54,48,47.7,52,61
22 | 2016,1,21,Thurs,48,52,47.8,52,57
23 | 2016,1,22,Fri,52,52,47.9,57,60
24 | 2016,1,23,Sat,52,57,48,48,37
25 | 2016,1,24,Sun,57,48,48.1,51,54
26 | 2016,1,25,Mon,48,51,48.2,54,63
27 | 2016,1,26,Tues,51,54,48.3,56,61
28 | 2016,1,27,Wed,54,56,48.4,57,54
29 | 2016,1,28,Thurs,56,57,48.4,56,34
30 | 2016,1,29,Fri,57,56,48.5,52,49
31 | 2016,1,30,Sat,56,52,48.6,48,47
32 | 2016,1,31,Sun,52,48,48.7,47,61
33 | 2016,2,1,Mon,48,47,48.8,46,51
34 | 2016,2,2,Tues,47,46,48.8,51,56
35 | 2016,2,3,Wed,46,51,48.9,49,40
36 | 2016,2,4,Thurs,51,49,49,49,44
37 | 2016,2,5,Fri,49,49,49.1,53,45
38 | 2016,2,6,Sat,49,53,49.1,49,56
39 | 2016,2,7,Sun,53,49,49.2,51,63
40 | 2016,2,8,Mon,49,51,49.3,57,34
41 | 2016,2,9,Tues,51,57,49.4,62,57
42 | 2016,2,10,Wed,57,62,49.4,56,30
43 | 2016,2,11,Thurs,62,56,49.5,55,37
44 | 2016,2,12,Fri,56,55,49.6,58,33
45 | 2016,2,15,Mon,55,58,49.9,55,53
46 | 2016,2,16,Tues,58,55,49.9,56,55
47 | 2016,2,17,Wed,55,56,50,57,46
48 | 2016,2,18,Thurs,56,57,50.1,53,34
49 | 2016,2,19,Fri,57,53,50.2,51,42
50 | 2016,2,20,Sat,53,51,50.4,53,43
51 | 2016,2,21,Sun,51,53,50.5,51,46
52 | 2016,2,22,Mon,53,51,50.6,51,59
53 | 2016,2,23,Tues,51,51,50.7,60,43
54 | 2016,2,24,Wed,51,60,50.8,59,46
55 | 2016,2,25,Thurs,60,59,50.9,61,35
56 | 2016,2,26,Fri,59,61,51.1,60,65
57 | 2016,2,27,Sat,61,60,51.2,57,61
58 | 2016,2,28,Sun,60,57,51.3,53,66
59 | 2016,3,1,Tues,53,54,51.5,58,53
60 | 2016,3,2,Wed,54,58,51.6,55,37
61 | 2016,3,3,Thurs,58,55,51.8,59,71
62 | 2016,3,4,Fri,55,59,51.9,57,45
63 | 2016,3,5,Sat,59,57,52.1,64,46
64 | 2016,3,6,Sun,57,64,52.2,60,49
65 | 2016,3,7,Mon,64,60,52.4,53,71
66 | 2016,3,8,Tues,60,53,52.5,54,70
67 | 2016,3,9,Wed,53,54,52.7,55,57
68 | 2016,3,10,Thurs,54,55,52.8,56,50
69 | 2016,3,11,Fri,55,56,53,55,36
70 | 2016,3,12,Sat,56,55,53.1,52,65
71 | 2016,3,13,Sun,55,52,53.3,54,54
72 | 2016,3,14,Mon,52,54,53.4,49,44
73 | 2016,3,15,Tues,54,49,53.6,51,70
74 | 2016,3,16,Wed,49,51,53.7,53,65
75 | 2016,3,17,Thurs,51,53,53.9,58,62
76 | 2016,3,18,Fri,53,58,54,63,56
77 | 2016,3,19,Sat,58,63,54.2,61,62
78 | 2016,3,20,Sun,63,61,54.3,55,50
79 | 2016,3,21,Mon,61,55,54.5,56,52
80 | 2016,3,22,Tues,55,56,54.6,57,64
81 | 2016,3,23,Wed,56,57,54.7,53,70
82 | 2016,3,24,Thurs,57,53,54.9,54,72
83 | 2016,3,25,Fri,53,54,55,57,42
84 | 2016,3,26,Sat,54,57,55.2,59,54
85 | 2016,3,27,Sun,57,59,55.3,51,39
86 | 2016,3,28,Mon,59,51,55.5,56,47
87 | 2016,3,29,Tues,51,56,55.6,64,45
88 | 2016,3,30,Wed,56,64,55.7,68,57
89 | 2016,3,31,Thurs,64,68,55.9,73,56
90 | 2016,4,1,Fri,68,73,56,71,41
91 | 2016,4,2,Sat,73,71,56.2,63,45
92 | 2016,4,3,Sun,71,63,56.3,69,64
93 | 2016,4,4,Mon,63,69,56.5,60,45
94 | 2016,4,5,Tues,69,60,56.6,57,72
95 | 2016,4,6,Wed,60,57,56.8,68,64
96 | 2016,4,7,Thurs,57,68,56.9,77,38
97 | 2016,4,8,Fri,68,77,57.1,76,41
98 | 2016,4,9,Sat,77,76,57.2,66,74
99 | 2016,4,10,Sun,76,66,57.4,59,60
100 | 2016,4,11,Mon,66,59,57.6,58,40
101 | 2016,4,12,Tues,59,58,57.7,60,61
102 | 2016,4,13,Wed,58,60,57.9,59,77
103 | 2016,4,14,Thurs,60,59,58.1,59,66
104 | 2016,4,15,Fri,59,59,58.3,60,40
105 | 2016,4,16,Sat,59,60,58.5,68,59
106 | 2016,4,17,Sun,60,68,58.6,77,54
107 | 2016,4,18,Mon,68,77,58.8,89,39
108 | 2016,4,19,Tues,77,89,59,81,61
109 | 2016,4,20,Wed,89,81,59.2,81,66
110 | 2016,4,21,Thurs,81,81,59.4,73,55
111 | 2016,4,22,Fri,81,73,59.7,64,59
112 | 2016,4,23,Sat,73,64,59.9,65,57
113 | 2016,4,24,Sun,64,65,60.1,55,41
114 | 2016,4,25,Mon,65,55,60.3,59,77
115 | 2016,4,26,Tues,55,59,60.5,60,75
116 | 2016,4,27,Wed,59,60,60.7,61,50
117 | 2016,4,28,Thurs,60,61,61,64,73
118 | 2016,4,29,Fri,61,64,61.2,61,49
119 | 2016,4,30,Sat,64,61,61.4,68,78
120 | 2016,5,1,Sun,61,68,61.6,77,75
121 | 2016,5,2,Mon,68,77,61.9,87,59
122 | 2016,5,3,Tues,77,87,62.1,74,69
123 | 2016,5,4,Wed,87,74,62.3,60,61
124 | 2016,5,5,Thurs,74,60,62.5,68,56
125 | 2016,5,6,Fri,60,68,62.8,77,64
126 | 2016,5,7,Sat,68,77,63,82,83
127 | 2016,5,8,Sun,77,82,63.2,63,83
128 | 2016,5,9,Mon,82,63,63.4,67,64
129 | 2016,5,10,Tues,63,67,63.6,75,68
130 | 2016,5,11,Wed,67,75,63.8,81,60
131 | 2016,5,12,Thurs,75,81,64.1,77,81
132 | 2016,5,13,Fri,81,77,64.3,82,67
133 | 2016,5,14,Sat,77,82,64.5,65,65
134 | 2016,5,15,Sun,82,65,64.7,57,58
135 | 2016,5,16,Mon,65,57,64.8,60,53
136 | 2016,5,17,Tues,57,60,65,71,55
137 | 2016,5,18,Wed,60,71,65.2,64,56
138 | 2016,5,19,Thurs,71,64,65.4,63,56
139 | 2016,5,20,Fri,64,63,65.6,66,73
140 | 2016,5,21,Sat,63,66,65.7,59,49
141 | 2016,5,22,Sun,66,59,65.9,66,80
142 | 2016,5,23,Mon,59,66,66.1,65,66
143 | 2016,5,24,Tues,66,65,66.2,66,67
144 | 2016,5,25,Wed,65,66,66.4,66,60
145 | 2016,5,26,Thurs,66,66,66.5,65,85
146 | 2016,5,27,Fri,66,65,66.7,64,73
147 | 2016,5,28,Sat,65,64,66.8,64,64
148 | 2016,5,29,Sun,64,64,67,64,76
149 | 2016,5,30,Mon,64,64,67.1,71,69
150 | 2016,5,31,Tues,64,71,67.3,79,85
151 | 2016,6,1,Wed,71,79,67.4,75,58
152 | 2016,6,2,Thurs,79,75,67.6,71,77
153 | 2016,6,3,Fri,75,71,67.7,80,55
154 | 2016,6,4,Sat,71,80,67.9,81,76
155 | 2016,6,5,Sun,80,81,68,92,54
156 | 2016,6,6,Mon,81,92,68.2,86,71
157 | 2016,6,7,Tues,92,86,68.3,85,58
158 | 2016,6,8,Wed,86,85,68.5,67,81
159 | 2016,6,9,Thurs,85,67,68.6,65,80
160 | 2016,6,10,Fri,67,65,68.8,67,73
161 | 2016,6,11,Sat,65,67,69,65,87
162 | 2016,6,12,Sun,67,65,69.1,70,83
163 | 2016,6,13,Mon,65,70,69.3,66,79
164 | 2016,6,14,Tues,70,66,69.5,60,85
165 | 2016,6,15,Wed,66,60,69.7,67,69
166 | 2016,6,16,Thurs,60,67,69.8,71,87
167 | 2016,6,17,Fri,67,71,70,67,54
168 | 2016,6,18,Sat,71,67,70.2,65,77
169 | 2016,6,19,Sun,67,65,70.4,70,58
170 | 2016,6,20,Mon,65,70,70.6,76,79
171 | 2016,6,21,Tues,70,76,70.8,73,57
172 | 2016,6,22,Wed,76,73,71,75,78
173 | 2016,6,23,Thurs,73,75,71.3,68,56
174 | 2016,6,24,Fri,75,68,71.5,69,65
175 | 2016,6,25,Sat,68,69,71.7,71,89
176 | 2016,6,26,Sun,69,71,71.9,78,70
177 | 2016,6,27,Mon,71,78,72.2,85,84
178 | 2016,6,28,Tues,78,85,72.4,79,67
179 | 2016,6,29,Wed,85,79,72.6,74,81
180 | 2016,6,30,Thurs,79,74,72.8,73,87
181 | 2016,7,1,Fri,74,73,73.1,76,93
182 | 2016,7,2,Sat,73,76,73.3,76,84
183 | 2016,7,3,Sun,76,76,73.5,71,85
184 | 2016,7,4,Mon,76,71,73.8,68,86
185 | 2016,7,5,Tues,71,68,74,69,62
186 | 2016,7,6,Wed,68,69,74.2,76,86
187 | 2016,7,7,Thurs,69,76,74.4,68,72
188 | 2016,7,8,Fri,76,68,74.6,74,77
189 | 2016,7,9,Sat,68,74,74.9,71,60
190 | 2016,7,10,Sun,74,71,75.1,74,95
191 | 2016,7,11,Mon,71,74,75.3,74,71
192 | 2016,7,12,Tues,74,74,75.4,77,71
193 | 2016,7,13,Wed,74,77,75.6,75,56
194 | 2016,7,14,Thurs,77,75,75.8,77,77
195 | 2016,7,15,Fri,75,77,76,76,75
196 | 2016,7,16,Sat,77,76,76.1,72,61
197 | 2016,7,17,Sun,76,72,76.3,80,88
198 | 2016,7,18,Mon,72,80,76.4,73,66
199 | 2016,7,19,Tues,80,73,76.6,78,90
200 | 2016,7,20,Wed,73,78,76.7,82,66
201 | 2016,7,21,Thurs,78,82,76.8,81,84
202 | 2016,7,22,Fri,82,81,76.9,71,70
203 | 2016,7,23,Sat,81,71,77,75,86
204 | 2016,7,24,Sun,71,75,77.1,80,75
205 | 2016,7,25,Mon,75,80,77.1,85,81
206 | 2016,7,26,Tues,80,85,77.2,79,74
207 | 2016,7,27,Wed,85,79,77.3,83,79
208 | 2016,7,28,Thurs,79,83,77.3,85,76
209 | 2016,7,29,Fri,83,85,77.3,88,77
210 | 2016,7,30,Sat,85,88,77.3,76,70
211 | 2016,7,31,Sun,88,76,77.4,73,95
212 | 2016,8,1,Mon,76,73,77.4,77,65
213 | 2016,8,2,Tues,73,77,77.4,73,62
214 | 2016,8,3,Wed,77,73,77.3,75,93
215 | 2016,8,4,Thurs,73,75,77.3,80,66
216 | 2016,8,5,Fri,75,80,77.3,79,71
217 | 2016,8,6,Sat,80,79,77.2,72,60
218 | 2016,8,7,Sun,79,72,77.2,72,95
219 | 2016,8,8,Mon,72,72,77.1,73,65
220 | 2016,8,9,Tues,72,73,77.1,72,94
221 | 2016,8,10,Wed,73,72,77,76,68
222 | 2016,8,11,Thurs,72,76,76.9,80,80
223 | 2016,8,12,Fri,76,80,76.9,87,81
224 | 2016,8,13,Sat,80,87,76.8,90,73
225 | 2016,8,14,Sun,87,90,76.7,83,65
226 | 2016,8,15,Mon,90,83,76.6,84,70
227 | 2016,8,16,Tues,83,84,76.5,81,90
228 | 2016,8,23,Tues,84,81,75.7,79,89
229 | 2016,8,28,Sun,81,79,75,75,85
230 | 2016,8,30,Tues,79,75,74.6,70,63
231 | 2016,9,3,Sat,75,70,73.9,67,68
232 | 2016,9,4,Sun,70,67,73.7,68,64
233 | 2016,9,5,Mon,67,68,73.5,68,54
234 | 2016,9,6,Tues,68,68,73.3,68,79
235 | 2016,9,7,Wed,68,68,73,67,70
236 | 2016,9,8,Thurs,68,67,72.8,72,56
237 | 2016,9,9,Fri,67,72,72.6,74,78
238 | 2016,9,10,Sat,72,74,72.3,77,91
239 | 2016,9,11,Sun,74,77,72.1,70,70
240 | 2016,9,12,Mon,77,70,71.8,74,90
241 | 2016,9,13,Tues,70,74,71.5,75,82
242 | 2016,9,14,Wed,74,75,71.2,79,77
243 | 2016,9,15,Thurs,75,79,71,71,64
244 | 2016,9,16,Fri,79,71,70.7,75,52
245 | 2016,9,17,Sat,71,75,70.3,68,84
246 | 2016,9,18,Sun,75,68,70,69,90
247 | 2016,9,19,Mon,68,69,69.7,71,88
248 | 2016,9,20,Tues,69,71,69.4,67,81
249 | 2016,9,21,Wed,71,67,69,68,76
250 | 2016,9,22,Thurs,67,68,68.7,67,56
251 | 2016,9,23,Fri,68,67,68.3,64,61
252 | 2016,9,24,Sat,67,64,68,67,64
253 | 2016,9,25,Sun,64,67,67.6,76,62
254 | 2016,9,26,Mon,67,76,67.2,77,74
255 | 2016,9,27,Tues,76,77,66.8,69,64
256 | 2016,9,28,Wed,77,69,66.5,68,62
257 | 2016,9,29,Thurs,69,68,66.1,66,57
258 | 2016,9,30,Fri,68,66,65.7,67,74
259 | 2016,10,1,Sat,66,67,65.3,63,54
260 | 2016,10,2,Sun,67,63,64.9,65,82
261 | 2016,10,3,Mon,63,65,64.5,61,49
262 | 2016,10,4,Tues,65,61,64.1,63,60
263 | 2016,10,5,Wed,61,63,63.7,66,48
264 | 2016,10,6,Thurs,63,66,63.3,63,55
265 | 2016,10,7,Fri,66,63,62.9,64,78
266 | 2016,10,8,Sat,63,64,62.5,68,73
267 | 2016,10,9,Sun,64,68,62.1,57,55
268 | 2016,10,10,Mon,68,57,61.8,60,62
269 | 2016,10,11,Tues,57,60,61.4,62,58
270 | 2016,10,12,Wed,60,62,61,66,52
271 | 2016,10,13,Thurs,62,66,60.6,60,57
272 | 2016,10,14,Fri,66,60,60.2,60,78
273 | 2016,10,15,Sat,60,60,59.9,62,46
274 | 2016,10,16,Sun,60,62,59.5,60,40
275 | 2016,10,17,Mon,62,60,59.1,60,62
276 | 2016,10,18,Tues,60,60,58.8,61,53
277 | 2016,10,19,Wed,60,61,58.4,58,41
278 | 2016,10,20,Thurs,61,58,58.1,62,43
279 | 2016,10,21,Fri,58,62,57.8,59,44
280 | 2016,10,22,Sat,62,59,57.4,62,44
281 | 2016,10,23,Sun,59,62,57.1,62,67
282 | 2016,10,24,Mon,62,62,56.8,61,70
283 | 2016,10,25,Tues,62,61,56.5,65,70
284 | 2016,10,26,Wed,61,65,56.2,58,41
285 | 2016,10,27,Thurs,65,58,55.9,60,39
286 | 2016,10,28,Fri,58,60,55.6,65,52
287 | 2016,10,29,Sat,60,65,55.3,68,65
288 | 2016,10,31,Mon,65,68,54.8,59,62
289 | 2016,11,1,Tues,68,59,54.5,57,61
290 | 2016,11,2,Wed,59,57,54.2,57,70
291 | 2016,11,3,Thurs,57,57,53.9,65,35
292 | 2016,11,4,Fri,57,65,53.7,65,38
293 | 2016,11,5,Sat,65,65,53.4,58,41
294 | 2016,11,6,Sun,65,58,53.2,61,71
295 | 2016,11,7,Mon,58,61,52.9,63,35
296 | 2016,11,8,Tues,61,63,52.7,71,49
297 | 2016,11,9,Wed,63,71,52.4,65,42
298 | 2016,11,10,Thurs,71,65,52.2,64,38
299 | 2016,11,11,Fri,65,64,51.9,63,55
300 | 2016,11,12,Sat,64,63,51.7,59,63
301 | 2016,11,13,Sun,63,59,51.4,55,64
302 | 2016,11,14,Mon,59,55,51.2,57,42
303 | 2016,11,15,Tues,55,57,51,55,46
304 | 2016,11,16,Wed,57,55,50.7,50,34
305 | 2016,11,17,Thurs,55,50,50.5,52,57
306 | 2016,11,18,Fri,50,52,50.3,55,35
307 | 2016,11,19,Sat,52,55,50,57,56
308 | 2016,11,20,Sun,55,57,49.8,55,30
309 | 2016,11,21,Mon,57,55,49.5,54,67
310 | 2016,11,22,Tues,55,54,49.3,54,58
311 | 2016,11,23,Wed,54,54,49.1,49,38
312 | 2016,11,24,Thurs,54,49,48.9,52,29
313 | 2016,11,25,Fri,49,52,48.6,52,41
314 | 2016,11,26,Sat,52,52,48.4,53,58
315 | 2016,11,27,Sun,52,53,48.2,48,53
316 | 2016,11,28,Mon,53,48,48,52,44
317 | 2016,11,29,Tues,48,52,47.8,52,50
318 | 2016,11,30,Wed,52,52,47.6,52,44
319 | 2016,12,1,Thurs,52,52,47.4,46,39
320 | 2016,12,2,Fri,52,46,47.2,50,41
321 | 2016,12,3,Sat,46,50,47,49,58
322 | 2016,12,4,Sun,50,49,46.8,46,53
323 | 2016,12,5,Mon,49,46,46.6,40,65
324 | 2016,12,6,Tues,46,40,46.4,42,56
325 | 2016,12,7,Wed,40,42,46.3,40,62
326 | 2016,12,8,Thurs,42,40,46.1,41,36
327 | 2016,12,9,Fri,40,41,46,36,54
328 | 2016,12,10,Sat,41,36,45.9,44,65
329 | 2016,12,11,Sun,36,44,45.7,44,35
330 | 2016,12,12,Mon,44,44,45.6,43,42
331 | 2016,12,13,Tues,44,43,45.5,40,46
332 | 2016,12,14,Wed,43,40,45.4,39,49
333 | 2016,12,15,Thurs,40,39,45.3,39,46
334 | 2016,12,16,Fri,39,39,45.3,35,39
335 | 2016,12,17,Sat,39,35,45.2,35,38
336 | 2016,12,18,Sun,35,35,45.2,39,36
337 | 2016,12,19,Mon,35,39,45.1,46,51
338 | 2016,12,20,Tues,39,46,45.1,51,62
339 | 2016,12,21,Wed,46,51,45.1,49,39
340 | 2016,12,22,Thurs,51,49,45.1,45,38
341 | 2016,12,23,Fri,49,45,45.1,40,35
342 | 2016,12,24,Sat,45,40,45.1,41,39
343 | 2016,12,25,Sun,40,41,45.1,42,31
344 | 2016,12,26,Mon,41,42,45.2,42,58
345 | 2016,12,27,Tues,42,42,45.2,47,47
346 | 2016,12,28,Wed,42,47,45.3,48,58
347 | 2016,12,29,Thurs,47,48,45.3,48,65
348 | 2016,12,30,Fri,48,48,45.4,57,42
349 | 2016,12,31,Sat,48,57,45.5,40,57
--------------------------------------------------------------------------------
/temp-predict/data/temps_extended.csv:
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1 | year,month,day,weekday,ws_1,prcp_1,snwd_1,temp_2,temp_1,average,actual,friend
2 | 2011,1,1,Sat,4.92,0,0,36,37,45.6,40,40
3 | 2011,1,2,Sun,5.37,0,0,37,40,45.7,39,50
4 | 2011,1,3,Mon,6.26,0,0,40,39,45.8,42,42
5 | 2011,1,4,Tues,5.59,0,0,39,42,45.9,38,59
6 | 2011,1,5,Wed,3.8,0.03,0,42,38,46,45,39
7 | 2011,1,6,Thurs,4.92,0.12,0,38,45,46.1,49,52
8 | 2011,1,7,Fri,8.72,0.17,0,45,49,46.2,48,62
9 | 2011,1,8,Sat,10.96,0.37,0,49,48,46.3,40,65
10 | 2011,1,9,Sun,5.82,0.03,0,48,40,46.4,39,53
11 | 2011,1,10,Mon,5.59,0.02,0,40,39,46.5,38,40
12 | 2011,1,11,Tues,8.72,0,0,39,38,46.7,39,59
13 | 2011,1,12,Wed,11.18,0.3,0,38,39,46.8,47,47
14 | 2011,1,13,Thurs,10.29,0.81,0,39,47,46.9,53,59
15 | 2011,1,14,Fri,11.86,0.82,0,47,53,47,54,39
16 | 2011,1,15,Sat,13.42,0.29,0,53,54,47.1,52,38
17 | 2011,1,16,Sun,8.05,0.45,0,54,52,47.3,55,45
18 | 2011,1,17,Mon,17,0.27,0,52,55,47.4,50,49
19 | 2011,1,18,Tues,15.88,0.04,0,55,50,47.5,42,43
20 | 2011,1,19,Wed,7.83,0.08,0,50,42,47.6,45,60
21 | 2011,1,20,Thurs,3.13,0,0,42,45,47.7,44,52
22 | 2011,1,21,Fri,6.93,0.05,0,45,44,47.8,49,64
23 | 2011,1,22,Sat,11.63,0.51,0,44,49,47.9,49,32
24 | 2011,1,23,Sun,4.47,0,0,49,49,48,46,40
25 | 2011,1,24,Mon,3.58,0.04,0,49,46,48.1,48,48
26 | 2011,1,25,Tues,7.16,0.21,0,46,48,48.2,50,41
27 | 2011,1,26,Wed,5.37,0,0,48,50,48.3,54,31
28 | 2011,1,27,Thurs,2.01,0,0,50,54,48.4,50,34
29 | 2011,1,28,Fri,2.24,0,0,54,50,48.4,52,29
30 | 2011,1,29,Sat,9.17,0.14,0,50,52,48.5,48,34
31 | 2011,1,30,Sun,7.61,0.24,0,52,48,48.6,44,35
32 | 2011,1,31,Mon,10.74,0,0,48,44,48.7,44,53
33 | 2011,2,1,Tues,8.28,0,0,44,44,48.8,46,59
34 | 2011,2,2,Wed,7.16,0,0,44,46,48.8,51,36
35 | 2011,2,3,Thurs,2.91,0,0,46,51,48.9,49,38
36 | 2011,2,4,Fri,10.51,0.01,0,51,49,49,50,36
37 | 2011,2,5,Sat,14.76,0.07,0,49,50,49.1,47,38
38 | 2011,2,6,Sun,3.36,0.08,0,50,47,49.1,50,63
39 | 2011,2,7,Mon,12.08,0.25,0,47,50,49.2,48,58
40 | 2011,2,8,Tues,6.71,0.05,0,50,48,49.3,46,48
41 | 2011,2,9,Wed,5.82,0,0,48,46,49.4,45,66
42 | 2011,2,10,Thurs,3.13,0,0,46,45,49.4,47,58
43 | 2011,2,11,Fri,2.91,0,0,45,47,49.5,47,69
44 | 2011,2,12,Sat,3.8,0,0,47,47,49.6,54,48
45 | 2011,2,13,Sun,14.54,0.37,0,47,54,49.7,52,61
46 | 2011,2,14,Mon,7.38,0.24,0,54,52,49.8,50,32
47 | 2011,2,15,Tues,9.84,0.54,0,52,50,49.9,46,39
48 | 2011,2,16,Wed,7.61,0.09,0,50,46,49.9,44,38
49 | 2011,2,17,Thurs,10.07,0.04,0,46,44,50,44,41
50 | 2011,2,18,Fri,7.61,0.11,0,44,44,50.1,49,60
51 | 2011,2,19,Sat,6.04,0,0,44,49,50.2,43,32
52 | 2011,2,20,Sun,14.32,0,0,49,43,50.4,45,42
53 | 2011,2,21,Mon,5.37,0,0,43,45,50.5,45,46
54 | 2011,2,22,Tues,7.38,0.03,0,45,45,50.6,44,70
55 | 2011,2,23,Wed,7.83,0.04,0,45,44,50.7,37,71
56 | 2011,2,24,Thurs,12.08,0.24,0,44,37,50.8,35,58
57 | 2011,2,25,Fri,9.84,0,3,37,35,50.9,31,57
58 | 2011,2,26,Sat,10.29,0,1,35,31,51.1,34,45
59 | 2011,2,27,Sun,9.62,0,0,31,34,51.2,42,59
60 | 2011,2,28,Mon,18.57,0.47,0,34,42,51.3,40,49
61 | 2011,3,1,Tues,12.3,0.42,0,42,40,51.5,44,45
62 | 2011,3,2,Wed,6.93,0.24,0,40,44,51.6,50,51
63 | 2011,3,3,Thurs,12.3,0.07,0,44,50,51.8,48,46
64 | 2011,3,4,Fri,10.96,0.22,0,50,48,51.9,49,52
65 | 2011,3,5,Sat,10.07,0.12,0,48,49,52.1,50,67
66 | 2011,3,6,Sun,6.93,0.02,0,49,50,52.2,47,52
67 | 2011,3,7,Mon,5.82,0,0,50,47,52.4,48,46
68 | 2011,3,8,Tues,4.03,0,0,47,48,52.5,52,42
69 | 2011,3,9,Wed,10.07,0.16,0,48,52,52.7,52,50
70 | 2011,3,10,Thurs,11.63,1.47,0,52,52,52.8,53,41
71 | 2011,3,11,Fri,20.8,0.55,0,52,53,53,53,58
72 | 2011,3,12,Sat,10.74,0,0,53,53,53.1,48,41
73 | 2011,3,13,Sun,5.82,0.41,0,53,48,53.3,53,33
74 | 2011,3,14,Mon,12.3,0.33,0,48,53,53.4,53,36
75 | 2011,3,15,Tues,12.97,0.12,0,53,53,53.6,55,35
76 | 2011,3,16,Wed,11.63,0.37,0,53,55,53.7,48,65
77 | 2011,3,17,Thurs,12.97,0.09,0,55,48,53.9,51,74
78 | 2011,3,18,Fri,5.37,0,0,48,51,54,50,50
79 | 2011,3,19,Sat,8.95,0.27,0,51,50,54.2,54,58
80 | 2011,3,20,Sun,4.47,0,0,50,54,54.3,50,66
81 | 2011,3,21,Mon,13.87,0.01,0,54,50,54.5,50,45
82 | 2011,3,22,Tues,8.72,0.01,0,50,50,54.6,51,71
83 | 2011,3,23,Wed,6.93,0,0,50,51,54.7,62,72
84 | 2011,3,24,Thurs,11.86,0,0,51,62,54.9,61,59
85 | 2011,3,25,Fri,8.72,0.23,0,62,61,55,55,72
86 | 2011,3,26,Sat,8.05,0.33,0,61,55,55.2,50,39
87 | 2011,3,27,Sun,5.82,0.15,0,55,50,55.3,51,51
88 | 2011,3,28,Mon,7.61,0.28,0,50,51,55.5,51,56
89 | 2011,3,29,Tues,9.62,0.14,0,51,51,55.6,49,50
90 | 2011,3,30,Wed,8.5,0.12,0,51,49,55.7,51,54
91 | 2011,3,31,Thurs,15.43,0.26,0,49,51,55.9,54,38
92 | 2011,4,1,Fri,14.54,0.32,0,51,54,56,48,58
93 | 2011,4,2,Sat,5.82,1.13,0,54,48,56.2,48,47
94 | 2011,4,3,Sun,8.72,0.37,0,48,48,56.3,49,47
95 | 2011,4,4,Mon,7.38,0.06,0,48,49,56.5,47,43
96 | 2011,4,5,Tues,10.29,0.24,0,49,47,56.6,46,54
97 | 2011,4,6,Wed,15.88,0.23,0,47,46,56.8,48,60
98 | 2011,4,7,Thurs,11.86,0.12,0,46,48,56.9,49,57
99 | 2011,4,8,Fri,4.92,0.08,0,48,49,57.1,54,43
100 | 2011,4,9,Sat,3.13,0,0,49,54,57.2,55,41
101 | 2011,4,10,Sun,8.5,0.01,0,54,55,57.4,55,45
102 | 2011,4,11,Mon,11.86,0.16,0,55,55,57.6,51,70
103 | 2011,4,12,Tues,13.42,0,0,55,51,57.7,53,60
104 | 2011,4,13,Wed,4.47,0,0,51,53,57.9,48,43
105 | 2011,4,14,Thurs,9.4,0.17,0,53,48,58.1,47,47
106 | 2011,4,15,Fri,10.51,0.4,0,48,47,58.3,53,46
107 | 2011,4,16,Sat,3.8,0.09,0,47,53,58.5,53,56
108 | 2011,4,17,Sun,10.51,0.06,0,53,53,58.6,52,46
109 | 2011,4,18,Mon,5.82,0.01,0,53,52,58.8,51,57
110 | 2011,4,19,Tues,5.82,0.01,0,52,51,59,53,71
111 | 2011,4,20,Wed,6.04,0,0,51,53,59.2,54,46
112 | 2011,4,21,Thurs,9.62,0,0,53,54,59.4,53,73
113 | 2011,4,22,Fri,8.05,0,0,54,53,59.7,57,41
114 | 2011,4,23,Sat,7.38,0,0,53,57,59.9,66,75
115 | 2011,4,24,Sun,5.82,0,0,57,66,60.1,55,49
116 | 2011,4,25,Mon,8.72,0.18,0,66,55,60.3,52,64
117 | 2011,4,26,Tues,10.51,0.52,0,55,52,60.5,57,72
118 | 2011,4,27,Wed,9.4,0.01,0,52,57,60.7,52,53
119 | 2011,4,28,Thurs,12.53,0.45,0,57,52,61,51,59
120 | 2011,4,29,Fri,12.08,0.04,0,52,51,61.2,54,48
121 | 2011,4,30,Sat,4.92,0,0,51,54,61.4,56,57
122 | 2011,5,1,Sun,5.14,0.13,0,54,56,61.6,62,51
123 | 2011,5,2,Mon,3.58,0,0,56,62,61.9,53,79
124 | 2011,5,3,Tues,9.62,0.18,0,62,53,62.1,58,52
125 | 2011,5,4,Wed,8.72,0,0,53,58,62.3,62,82
126 | 2011,5,5,Thurs,6.93,0,0,58,62,62.5,57,55
127 | 2011,5,6,Fri,8.5,0.03,0,62,57,62.8,54,52
128 | 2011,5,7,Sat,8.5,0.13,0,57,54,63,56,75
129 | 2011,5,8,Sun,9.17,0.04,0,54,56,63.2,58,66
130 | 2011,5,9,Mon,4.92,0.02,0,56,58,63.4,57,53
131 | 2011,5,10,Tues,3.36,0,0,58,57,63.6,59,48
132 | 2011,5,11,Wed,5.59,0,0,57,59,63.8,51,47
133 | 2011,5,12,Thurs,7.83,0.42,0,59,51,64.1,60,74
134 | 2011,5,13,Fri,5.59,0,0,51,60,64.3,64,45
135 | 2011,5,14,Sat,10.51,0,0,60,64,64.5,66,57
136 | 2011,5,15,Sun,8.5,0.97,0,64,66,64.7,51,58
137 | 2011,5,16,Mon,9.62,0.81,0,66,51,64.8,56,71
138 | 2011,5,17,Tues,12.08,0,0,51,56,65,64,56
139 | 2011,5,18,Wed,7.38,0,0,56,64,65.2,66,47
140 | 2011,5,19,Thurs,9.62,0,0,64,66,65.4,68,62
141 | 2011,5,20,Fri,5.37,0,0,66,68,65.6,70,56
142 | 2011,5,21,Sat,4.25,0,0,68,70,65.7,54,75
143 | 2011,5,22,Sun,3.58,0.04,0,70,54,65.9,60,85
144 | 2011,5,23,Mon,6.71,0,0,54,60,66.1,63,59
145 | 2011,5,24,Tues,2.68,0,0,60,63,66.2,65,81
146 | 2011,5,25,Wed,6.71,0,0,63,65,66.4,56,76
147 | 2011,5,26,Thurs,7.16,0.41,0,65,56,66.5,61,86
148 | 2011,5,27,Fri,10.51,0.01,0,56,61,66.7,58,77
149 | 2011,5,28,Sat,10.51,0.02,0,61,58,66.8,62,56
150 | 2011,5,29,Sun,7.16,0.01,0,58,62,67,59,67
151 | 2011,5,30,Mon,6.71,0,0,62,59,67.1,61,75
152 | 2011,5,31,Tues,6.71,0,0,59,61,67.3,61,74
153 | 2011,6,1,Wed,5.82,0.11,0,61,61,67.4,59,53
154 | 2011,6,2,Thurs,5.82,0.17,0,61,59,67.6,61,59
155 | 2011,6,3,Fri,10.74,0.08,0,59,61,67.7,68,74
156 | 2011,6,4,Sat,8.72,0,0,61,68,67.9,78,60
157 | 2011,6,5,Sun,9.62,0,0,68,78,68,71,86
158 | 2011,6,6,Mon,3.8,0,0,78,71,68.2,72,49
159 | 2011,6,7,Tues,8.28,0,0,71,72,68.3,64,72
160 | 2011,6,8,Wed,12.97,0.21,0,72,64,68.5,62,88
161 | 2011,6,9,Thurs,7.38,0,0,64,62,68.6,69,54
162 | 2011,6,10,Fri,4.7,0,0,62,69,68.8,65,61
163 | 2011,6,11,Sat,5.59,0,0,69,65,69,66,60
164 | 2011,6,12,Sun,3.58,0,0,65,66,69.1,70,71
165 | 2011,6,13,Mon,5.59,0.01,0,66,70,69.3,68,64
166 | 2011,6,14,Tues,12.08,0.11,0,70,68,69.5,63,85
167 | 2011,6,15,Wed,8.5,0,0,68,63,69.7,65,74
168 | 2011,6,16,Thurs,4.7,0.06,0,63,65,69.8,70,74
169 | 2011,6,17,Fri,5.59,0,0,65,70,70,71,54
170 | 2011,6,18,Sat,3.36,0,0,70,71,70.2,58,62
171 | 2011,6,19,Sun,5.82,0.41,0,71,58,70.4,63,84
172 | 2011,6,20,Mon,6.93,0.03,0,58,63,70.6,70,76
173 | 2011,6,21,Tues,3.13,0,0,63,70,70.8,78,77
174 | 2011,6,22,Wed,3.58,0,0,70,78,71,67,81
175 | 2011,6,23,Thurs,6.93,0,0,78,67,71.3,66,82
176 | 2011,6,24,Fri,8.05,0.07,0,67,66,71.5,67,88
177 | 2011,6,25,Sat,5.37,0.26,0,66,67,71.7,68,62
178 | 2011,6,26,Sun,6.04,0.01,0,67,68,71.9,72,53
179 | 2011,6,27,Mon,5.14,0,0,68,72,72.2,69,91
180 | 2011,6,28,Tues,2.24,0,0,72,69,72.4,74,66
181 | 2011,6,29,Wed,6.93,0,0,69,74,72.6,67,86
182 | 2011,6,30,Thurs,14.76,0,0,74,67,72.8,67,92
183 | 2011,7,1,Fri,10.07,0,0,67,67,73.1,72,72
184 | 2011,7,2,Sat,5.82,0,0,67,72,73.3,82,91
185 | 2011,7,3,Sun,6.26,0,0,72,82,73.5,72,85
186 | 2011,7,4,Mon,9.84,0,0,82,72,73.8,78,78
187 | 2011,7,5,Tues,8.28,0,0,72,78,74,81,55
188 | 2011,7,6,Wed,9.4,0,0,78,81,74.2,84,62
189 | 2011,7,7,Thurs,7.83,0,0,81,84,74.4,67,65
190 | 2011,7,8,Fri,9.62,0.03,0,84,67,74.6,71,75
191 | 2011,7,9,Sat,8.95,0,0,67,71,74.9,74,58
192 | 2011,7,10,Sun,8.05,0,0,71,74,75.1,76,87
193 | 2011,7,11,Mon,5.14,0,0,74,76,75.3,75,88
194 | 2011,7,12,Tues,5.37,0,0,76,75,75.4,70,57
195 | 2011,7,13,Wed,4.92,0.08,0,75,70,75.6,73,83
196 | 2011,7,14,Thurs,8.28,0,0,70,73,75.8,62,63
197 | 2011,7,15,Fri,6.93,0.02,0,73,62,76,76,60
198 | 2011,7,16,Sat,4.47,0.02,0,62,76,76.1,74,62
199 | 2011,7,17,Sun,5.59,0.35,0,76,74,76.3,63,86
200 | 2011,7,18,Mon,4.25,0.07,0,74,63,76.4,74,74
201 | 2011,7,19,Tues,5.14,0,0,63,74,76.6,70,82
202 | 2011,7,20,Wed,9.17,0,0,74,70,76.7,74,80
203 | 2011,7,21,Thurs,8.28,0,0,70,74,76.8,70,60
204 | 2011,7,22,Fri,12.53,0,0,74,70,76.9,72,59
205 | 2011,7,23,Sat,6.49,0,0,70,72,77,79,73
206 | 2011,7,24,Sun,9.62,0,0,72,79,77.1,83,82
207 | 2011,7,25,Mon,7.38,0,0,79,83,77.1,63,95
208 | 2011,7,26,Tues,6.04,0.13,0,83,63,77.2,70,85
209 | 2011,7,27,Wed,7.61,0,0,63,70,77.3,72,67
210 | 2011,7,28,Thurs,6.93,0,0,70,72,77.3,78,63
211 | 2011,7,29,Fri,8.5,0,0,72,78,77.3,76,86
212 | 2011,7,30,Sat,9.62,0,0,78,76,77.3,79,71
213 | 2011,7,31,Sun,7.38,0,0,76,79,77.4,71,89
214 | 2011,8,1,Mon,9.4,0,0,79,71,77.4,77,85
215 | 2011,8,2,Tues,7.61,0,0,71,77,77.4,77,81
216 | 2011,8,3,Wed,4.25,0,0,77,77,77.3,79,68
217 | 2011,8,4,Thurs,5.14,0,0,77,79,77.3,81,91
218 | 2011,8,5,Fri,6.71,0,0,79,81,77.3,66,77
219 | 2011,8,6,Sat,4.92,0,0,81,66,77.2,73,78
220 | 2011,8,7,Sun,3.13,0,0,66,73,77.2,74,78
221 | 2011,8,8,Mon,3.8,0,0,73,74,77.1,70,85
222 | 2011,8,9,Tues,6.71,0,0,74,70,77.1,70,84
223 | 2011,8,10,Wed,5.82,0,0,70,70,77,71,92
224 | 2011,8,11,Thurs,4.92,0,0,70,71,76.9,74,65
225 | 2011,8,12,Fri,3.58,0,0,71,74,76.9,76,74
226 | 2011,8,13,Sat,4.03,0,0,74,76,76.8,70,60
227 | 2011,8,14,Sun,5.82,0,0,76,70,76.7,71,94
228 | 2011,8,15,Mon,6.04,0,0,70,71,76.6,74,94
229 | 2011,8,16,Tues,5.37,0,0,71,74,76.5,78,60
230 | 2011,8,17,Wed,8.5,0,0,74,78,76.4,77,69
231 | 2011,8,18,Thurs,6.26,0,0,78,77,76.3,73,81
232 | 2011,8,19,Fri,6.49,0,0,77,73,76.2,78,63
233 | 2011,8,20,Sat,9.17,0,0,73,78,76.1,85,72
234 | 2011,8,21,Sun,6.04,0,0,78,85,75.9,87,62
235 | 2011,8,22,Mon,4.92,0,0,85,87,75.8,71,90
236 | 2011,8,23,Tues,9.62,0.12,0,87,71,75.7,77,59
237 | 2011,8,24,Wed,5.14,0,0,71,77,75.5,84,89
238 | 2011,8,25,Thurs,5.82,0,0,77,84,75.4,82,74
239 | 2011,8,26,Fri,6.26,0,0,84,82,75.3,84,67
240 | 2011,8,27,Sat,6.71,0,0,82,84,75.1,84,65
241 | 2011,8,28,Sun,6.49,0,0,84,84,75,80,66
242 | 2011,8,29,Mon,4.92,0,0,84,80,74.8,70,84
243 | 2011,8,30,Tues,6.93,0,0,80,70,74.6,69,90
244 | 2011,8,31,Wed,4.92,0.01,0,70,69,74.4,69,57
245 | 2011,9,1,Thurs,4.92,0,0,69,69,74.3,73,57
246 | 2011,9,2,Fri,6.04,0,0,69,73,74.1,71,92
247 | 2011,9,3,Sat,6.93,0,0,73,71,73.9,82,80
248 | 2011,9,4,Sun,10.96,0,0,71,82,73.7,83,63
249 | 2011,9,5,Mon,3.8,0,0,82,83,73.5,82,84
250 | 2011,9,6,Tues,3.8,0,0,83,82,73.3,84,63
251 | 2011,9,7,Wed,5.37,0,0,82,84,73,83,67
252 | 2011,9,8,Thurs,7.61,0,0,84,83,72.8,85,60
253 | 2011,9,9,Fri,2.91,0,0,83,85,72.6,85,67
254 | 2011,9,10,Sat,4.92,0,0,85,85,72.3,84,61
255 | 2011,9,11,Sun,6.26,0,0,85,84,72.1,85,81
256 | 2011,9,12,Mon,5.14,0,0,84,85,71.8,68,89
257 | 2011,9,13,Tues,6.93,0,0,85,68,71.5,64,62
258 | 2011,9,14,Wed,3.8,0,0,68,64,71.2,67,71
259 | 2011,9,15,Thurs,2.68,0,0,64,67,71,65,58
260 | 2011,9,16,Fri,8.28,0,0,67,65,70.7,64,54
261 | 2011,9,17,Sat,4.25,0,0,65,64,70.3,62,65
262 | 2011,9,18,Sun,8.5,0.2,0,64,62,70,65,60
263 | 2011,9,19,Mon,5.82,0.28,0,62,65,69.7,69,82
264 | 2011,9,20,Tues,7.16,0.01,0,65,69,69.4,77,68
265 | 2011,9,21,Wed,3.36,0,0,69,77,69,76,84
266 | 2011,9,22,Thurs,5.59,0,0,77,76,68.7,76,66
267 | 2011,9,23,Fri,5.82,0,0,76,76,68.3,80,62
268 | 2011,9,24,Sat,6.49,0,0,76,80,68,75,87
269 | 2011,9,25,Sun,5.14,0,0,80,75,67.6,67,82
270 | 2011,9,26,Mon,11.41,0.19,0,75,67,67.2,61,59
271 | 2011,9,27,Tues,9.62,0.59,0,67,61,66.8,65,81
272 | 2011,9,28,Wed,9.17,0.01,0,61,65,66.5,65,74
273 | 2011,9,29,Thurs,10.51,0,0,65,65,66.1,77,82
274 | 2011,9,30,Fri,4.25,0,0,65,77,65.7,65,80
275 | 2011,10,1,Sat,6.93,0.01,0,77,65,65.3,59,78
276 | 2011,10,2,Sun,3.8,0.02,0,65,59,64.9,64,80
277 | 2011,10,3,Mon,4.47,0.37,0,59,64,64.5,62,60
278 | 2011,10,4,Tues,6.04,0.1,0,64,62,64.1,63,70
279 | 2011,10,5,Wed,6.26,0.05,0,62,63,63.7,55,74
280 | 2011,10,6,Thurs,3.8,0.09,0,63,55,63.3,58,53
281 | 2011,10,7,Fri,3.58,0.11,0,55,58,62.9,55,81
282 | 2011,10,8,Sat,5.37,0.06,0,58,55,62.5,65,79
283 | 2011,10,9,Sun,4.92,0.02,0,55,65,62.1,60,77
284 | 2011,10,10,Mon,4.7,0.1,0,65,60,61.8,59,67
285 | 2011,10,11,Tues,6.71,0.25,0,60,59,61.4,59,72
286 | 2011,10,12,Wed,13.42,0.89,0,59,59,61,59,41
287 | 2011,10,13,Thurs,8.95,0,0,59,59,60.6,59,46
288 | 2011,10,14,Fri,7.61,0,0,59,59,60.2,58,59
289 | 2011,10,15,Sat,8.05,0.01,0,59,58,59.9,57,66
290 | 2011,10,16,Sun,6.71,0,0,58,57,59.5,50,45
291 | 2011,10,17,Mon,3.58,0,0,57,50,59.1,61,76
292 | 2011,10,18,Tues,5.82,0,0,50,61,58.8,67,54
293 | 2011,10,19,Wed,7.61,0,0,61,67,58.4,59,75
294 | 2011,10,20,Thurs,7.16,0,0,67,59,58.1,59,60
295 | 2011,10,21,Fri,4.47,0,0,59,59,57.8,58,70
296 | 2011,10,22,Sat,8.95,0.12,0,59,58,57.4,60,66
297 | 2011,10,23,Sun,10.29,0.58,0,58,60,57.1,57,59
298 | 2011,10,24,Mon,3.8,0,0,60,57,56.8,57,59
299 | 2011,10,25,Tues,3.8,0,0,57,57,56.5,54,74
300 | 2011,10,26,Wed,4.92,0,0,57,54,56.2,50,53
301 | 2011,10,27,Thurs,5.82,0.02,0,54,50,55.9,54,47
302 | 2011,10,28,Fri,5.82,0,0,50,54,55.6,54,70
303 | 2011,10,29,Sat,6.71,0.5,0,54,54,55.3,56,53
304 | 2011,10,30,Sun,4.7,0,0,54,56,55,57,51
305 | 2011,10,31,Mon,9.17,0.16,0,56,57,54.8,53,47
306 | 2011,11,1,Tues,5.14,0,0,57,53,54.5,52,48
307 | 2011,11,2,Wed,2.24,0,0,53,52,54.2,52,45
308 | 2011,11,3,Thurs,7.83,0.35,0,52,52,53.9,52,62
309 | 2011,11,4,Fri,6.93,0,0,52,52,53.7,44,40
310 | 2011,11,5,Sat,5.82,0.05,0,52,44,53.4,50,66
311 | 2011,11,6,Sun,5.14,0,0,44,50,53.2,49,43
312 | 2011,11,7,Mon,3.36,0,0,50,49,52.9,49,45
313 | 2011,11,8,Tues,7.83,0.01,0,49,49,52.7,55,66
314 | 2011,11,9,Wed,4.47,0,0,49,55,52.4,62,42
315 | 2011,11,10,Thurs,8.05,0,0,55,62,52.2,59,71
316 | 2011,11,11,Fri,5.82,0,0,62,59,51.9,51,45
317 | 2011,11,12,Sat,9.4,0.23,0,59,51,51.7,49,56
318 | 2011,11,13,Sun,10.51,0.25,0,51,49,51.4,49,62
319 | 2011,11,14,Mon,14.99,0.02,0,49,49,51.2,47,43
320 | 2011,11,15,Tues,8.72,0,0,49,47,51,48,49
321 | 2011,11,16,Wed,5.14,0,0,47,48,50.7,52,40
322 | 2011,11,17,Thurs,10.51,0.49,0,48,52,50.5,48,66
323 | 2011,11,18,Fri,14.99,0.28,0,52,48,50.3,39,50
324 | 2011,11,19,Sat,5.37,0.08,0,48,39,50,41,38
325 | 2011,11,20,Sun,6.04,0,0,39,41,49.8,39,51
326 | 2011,11,21,Mon,3.36,0,0,41,39,49.5,50,48
327 | 2011,11,22,Tues,11.63,0.3,0,39,50,49.3,52,37
328 | 2011,11,23,Wed,14.54,1.76,0,50,52,49.1,53,58
329 | 2011,11,24,Thurs,10.51,0.55,0,52,53,48.9,45,37
330 | 2011,11,25,Fri,12.75,0.26,0,53,45,48.6,48,54
331 | 2011,11,26,Sat,7.16,0,0,45,48,48.4,51,59
332 | 2011,11,27,Sun,2.68,0.02,0,48,51,48.2,56,41
333 | 2011,11,28,Mon,12.3,0.42,0,51,56,48,48,32
334 | 2011,11,29,Tues,4.03,0,0,56,48,47.8,44,57
335 | 2011,11,30,Wed,7.61,0.09,0,48,44,47.6,45,41
336 | 2011,12,1,Thurs,7.16,0,0,44,45,47.4,41,43
337 | 2011,12,2,Fri,2.91,0,0,45,41,47.2,44,37
338 | 2011,12,3,Sat,3.58,0.01,0,41,44,47,42,54
339 | 2011,12,4,Sun,2.24,0,0,44,42,46.8,43,60
340 | 2011,12,5,Mon,6.49,0,0,42,43,46.6,42,66
341 | 2011,12,6,Tues,4.03,0,0,43,42,46.4,38,41
342 | 2011,12,7,Wed,3.8,0,0,42,38,46.3,41,32
343 | 2011,12,8,Thurs,5.14,0,0,38,41,46.1,40,34
344 | 2011,12,9,Fri,7.38,0,0,41,40,46,40,49
345 | 2011,12,10,Sat,2.68,0,0,40,40,45.9,40,53
346 | 2011,12,11,Sun,4.25,0,0,40,40,45.7,40,62
347 | 2011,12,12,Mon,5.82,0.02,0,40,40,45.6,39,43
348 | 2011,12,13,Tues,7.61,0,0,40,39,45.5,41,41
349 | 2011,12,14,Wed,3.8,0,0,39,41,45.4,40,56
350 | 2011,12,15,Thurs,4.25,0,0,41,40,45.3,44,39
351 | 2011,12,16,Fri,4.03,0.03,0,40,44,45.3,49,36
352 | 2011,12,17,Sat,4.47,0,0,44,49,45.2,50,29
353 | 2011,12,18,Sun,5.14,0,0,49,50,45.2,46,58
354 | 2011,12,19,Mon,4.25,0.19,0,50,46,45.1,46,43
355 | 2011,12,20,Tues,2.91,0,0,46,46,45.1,44,51
356 | 2011,12,21,Wed,5.59,0,0,46,44,45.1,45,51
357 | 2011,12,22,Thurs,3.8,0,0,44,45,45.1,38,25
358 | 2011,12,23,Fri,4.03,0,0,45,38,45.1,49,39
359 | 2011,12,24,Sat,5.82,0.02,0,38,49,45.1,52,30
360 | 2011,12,25,Sun,4.03,0,0,49,52,45.1,50,37
361 | 2011,12,26,Mon,13.2,0.03,0,52,50,45.2,42,49
362 | 2011,12,27,Tues,6.93,0.05,0,50,42,45.2,51,52
363 | 2011,12,28,Wed,7.61,0.9,0,42,51,45.3,53,42
364 | 2011,12,29,Thurs,16.33,0.63,0,51,53,45.3,51,63
365 | 2011,12,30,Fri,11.41,0.29,0,53,51,45.4,49,59
366 | 2011,12,31,Sat,12.75,0.07,0,51,49,45.5,43,47
367 | 2012,1,1,Sun,6.26,0,0,49,43,45.6,55,64
368 | 2012,1,2,Mon,10.51,0,0,43,55,45.7,51,46
369 | 2012,1,3,Tues,10.07,0.43,0,55,51,45.8,53,42
370 | 2012,1,4,Wed,5.14,0.03,0,51,53,45.9,54,42
371 | 2012,1,5,Thurs,10.51,0.8,0,53,54,46,48,44
372 | 2012,1,6,Fri,13.65,0.05,0,54,48,46.1,40,55
373 | 2012,1,7,Sat,4.92,0.1,0,48,40,46.2,45,66
374 | 2012,1,8,Sun,5.14,0,0,40,45,46.3,50,31
375 | 2012,1,9,Mon,4.47,0,0,45,50,46.4,49,57
376 | 2012,1,10,Tues,7.61,0.17,0,50,49,46.5,43,48
377 | 2012,1,11,Wed,7.61,0.04,0,49,43,46.7,43,53
378 | 2012,1,12,Thurs,11.41,0,0,43,43,46.8,43,34
379 | 2012,1,13,Fri,4.25,0,0,43,43,46.9,41,31
380 | 2012,1,14,Sat,2.91,0,0,43,41,47,40,33
381 | 2012,1,15,Sun,11.86,0.16,0,41,40,47.1,34,48
382 | 2012,1,16,Mon,7.16,0.21,2,40,34,47.3,35,66
383 | 2012,1,17,Tues,11.18,0.1,2,34,35,47.4,38,44
384 | 2012,1,18,Wed,12.53,0.32,0,35,38,47.5,32,61
385 | 2012,1,19,Thurs,11.18,0.78,1,38,32,47.6,30,34
386 | 2012,1,20,Fri,3.58,0.6,3,32,30,47.7,45,32
387 | 2012,1,21,Sat,5.14,0.53,3,30,45,47.8,47,63
388 | 2012,1,22,Sun,18.34,0.12,2,45,47,47.9,44,67
389 | 2012,1,23,Mon,10.74,0.24,0,47,44,48,47,38
390 | 2012,1,24,Tues,8.05,0,0,44,47,48.1,50,36
391 | 2012,1,25,Wed,11.41,0.34,0,47,50,48.2,48,64
392 | 2012,1,26,Thurs,12.08,0.32,0,50,48,48.3,48,42
393 | 2012,1,27,Fri,10.74,0.19,0,48,48,48.4,44,48
394 | 2012,1,28,Sat,3.13,0,0,48,44,48.4,44,56
395 | 2012,1,29,Sun,4.92,0,0,44,44,48.5,49,38
396 | 2012,1,30,Mon,10.07,1.09,0,44,49,48.6,47,55
397 | 2012,1,31,Tues,11.41,0.14,0,49,47,48.7,49,39
398 | 2012,2,1,Wed,8.72,0.07,0,47,49,48.8,48,44
399 | 2012,2,2,Thurs,6.04,0.53,0,49,48,48.8,47,45
400 | 2012,2,3,Fri,5.82,0,0,48,47,48.9,58,31
401 | 2012,2,4,Sat,11.86,0,0,47,58,49,60,69
402 | 2012,2,5,Sun,9.62,0,0,58,60,49.1,57,40
403 | 2012,2,6,Mon,6.49,0,0,60,57,49.1,61,55
404 | 2012,2,7,Tues,11.18,0,0,57,61,49.2,60,42
405 | 2012,2,8,Wed,11.86,0.01,0,61,60,49.3,50,31
406 | 2012,2,9,Thurs,6.04,0.11,0,60,50,49.4,52,32
407 | 2012,2,10,Fri,5.37,0.1,0,50,52,49.4,55,42
408 | 2012,2,11,Sat,6.71,0.1,0,52,55,49.5,48,53
409 | 2012,2,12,Sun,7.61,0.03,0,55,48,49.6,47,32
410 | 2012,2,13,Mon,2.91,0.04,0,48,47,49.7,45,37
411 | 2012,2,14,Tues,3.13,0.45,0,47,45,49.8,44,32
412 | 2012,2,15,Wed,6.93,0.1,0,45,44,49.9,45,44
413 | 2012,2,16,Thurs,4.03,0,0,44,45,49.9,45,30
414 | 2012,2,17,Fri,4.7,0.07,0,45,45,50,50,49
415 | 2012,2,18,Sat,7.61,0.68,0,45,50,50.1,44,39
416 | 2012,2,19,Sun,18.12,0.25,0,50,44,50.2,44,67
417 | 2012,2,20,Mon,10.51,0,0,44,44,50.4,46,54
418 | 2012,2,21,Tues,6.49,0.12,0,44,46,50.5,50,68
419 | 2012,2,22,Wed,16.78,0.03,0,46,50,50.6,50,52
420 | 2012,2,23,Thurs,13.2,0.34,0,50,50,50.7,47,34
421 | 2012,2,24,Fri,8.72,0,0,50,47,50.8,44,32
422 | 2012,2,25,Sat,7.83,0.45,0,47,44,50.9,45,64
423 | 2012,2,26,Sun,14.32,0,0,44,45,51.1,41,51
424 | 2012,2,27,Mon,7.61,0.05,0,45,41,51.2,44,34
425 | 2012,2,28,Tues,6.71,0,0,41,44,51.3,44,53
426 | 2012,3,1,Thurs,15.66,0.03,0,44,41,51.5,43,66
427 | 2012,3,2,Fri,6.93,0,0,41,43,51.6,44,32
428 | 2012,3,3,Sat,11.41,0.08,0,43,44,51.8,54,39
429 | 2012,3,4,Sun,15.66,0,0,44,54,51.9,51,56
430 | 2012,3,5,Mon,12.53,0,0,54,51,52.1,46,70
431 | 2012,3,6,Tues,13.87,0.27,0,51,46,52.2,44,36
432 | 2012,3,7,Wed,6.04,0.02,0,46,44,52.4,48,49
433 | 2012,3,8,Thurs,6.04,0,0,44,48,52.5,60,72
434 | 2012,3,9,Fri,5.59,0,0,48,60,52.7,49,39
435 | 2012,3,10,Sat,6.26,0.14,0,60,49,52.8,45,44
436 | 2012,3,11,Sun,7.61,0.41,0,49,45,53,44,54
437 | 2012,3,12,Mon,12.97,0.54,0,45,44,53.1,47,46
438 | 2012,3,13,Tues,13.87,0.76,0,44,47,53.3,42,48
439 | 2012,3,14,Wed,11.86,0.37,0,47,42,53.4,46,49
440 | 2012,3,15,Thurs,10.51,0.34,0,42,46,53.6,52,51
441 | 2012,3,16,Fri,12.97,0.94,0,46,52,53.7,48,72
442 | 2012,3,17,Sat,11.41,0.33,0,52,48,53.9,50,67
443 | 2012,3,18,Sun,8.5,0.37,0,48,50,54,41,56
444 | 2012,3,19,Mon,6.04,0.14,0,50,41,54.2,45,67
445 | 2012,3,20,Tues,6.71,0.08,0,41,45,54.3,46,74
446 | 2012,3,21,Wed,14.32,0.14,0,45,46,54.5,48,61
447 | 2012,3,22,Thurs,5.59,0.05,0,46,48,54.6,50,61
448 | 2012,3,23,Fri,4.7,0.16,0,48,50,54.7,54,50
449 | 2012,3,24,Sat,6.26,0,0,50,54,54.9,59,61
450 | 2012,3,25,Sun,11.63,0,0,54,59,55,56,63
451 | 2012,3,26,Mon,6.04,0,0,59,56,55.2,55,74
452 | 2012,3,27,Tues,9.62,0,0,56,55,55.3,58,73
453 | 2012,3,28,Wed,8.5,0.19,0,55,58,55.5,51,75
454 | 2012,3,29,Thurs,13.2,0.05,0,58,51,55.6,50,70
455 | 2012,3,30,Fri,9.84,1.08,0,51,50,55.7,49,51
456 | 2012,3,31,Sat,10.51,0.22,0,50,49,55.9,50,71
457 | 2012,4,1,Sun,7.61,0.52,0,49,50,56,48,42
458 | 2012,4,2,Mon,15.21,0.06,0,50,48,56.2,62,41
459 | 2012,4,3,Tues,6.93,0,0,48,62,56.3,53,60
460 | 2012,4,4,Wed,6.93,0.06,0,62,53,56.5,51,68
461 | 2012,4,5,Thurs,4.7,0,0,53,51,56.6,49,47
462 | 2012,4,6,Fri,4.03,0.18,0,51,49,56.8,52,38
463 | 2012,4,7,Sat,5.82,0.01,0,49,52,56.9,61,68
464 | 2012,4,8,Sun,9.62,0,0,52,61,57.1,70,43
465 | 2012,4,9,Mon,9.17,0,0,61,70,57.2,68,40
466 | 2012,4,10,Tues,4.7,0,0,70,68,57.4,64,75
467 | 2012,4,11,Wed,7.16,0,0,68,64,57.6,52,60
468 | 2012,4,12,Thurs,5.82,0.09,0,64,52,57.7,57,60
469 | 2012,4,13,Fri,5.82,0.02,0,52,57,57.9,59,74
470 | 2012,4,14,Sat,8.95,0,0,57,59,58.1,60,46
471 | 2012,4,15,Sun,6.71,0,0,59,60,58.3,61,68
472 | 2012,4,16,Mon,6.49,0,0,60,61,58.5,56,46
473 | 2012,4,17,Tues,12.97,0.32,0,61,56,58.6,50,56
474 | 2012,4,18,Wed,4.47,0.07,0,56,50,58.8,56,45
475 | 2012,4,19,Thurs,8.72,0.07,0,50,56,59,57,78
476 | 2012,4,20,Fri,5.82,0.43,0,56,57,59.2,56,66
477 | 2012,4,21,Sat,6.04,0.26,0,57,56,59.4,68,64
478 | 2012,4,22,Sun,5.14,0,0,56,68,59.7,74,62
479 | 2012,4,23,Mon,5.82,0,0,68,74,59.9,71,56
480 | 2012,4,24,Tues,7.83,0,0,74,71,60.1,57,70
481 | 2012,4,25,Wed,6.26,0.17,0,71,57,60.3,62,73
482 | 2012,4,26,Thurs,5.82,0.42,0,57,62,60.5,57,72
483 | 2012,4,27,Fri,11.63,0.15,0,62,57,60.7,56,69
484 | 2012,4,28,Sat,10.74,0.03,0,57,56,61,61,77
485 | 2012,4,29,Sun,5.59,0,0,56,61,61.2,60,46
486 | 2012,4,30,Mon,3.58,0.17,0,61,60,61.4,55,59
487 | 2012,5,1,Tues,17.9,0.17,0,60,55,61.6,53,43
488 | 2012,5,2,Wed,14.32,0.02,0,55,53,61.9,56,48
489 | 2012,5,3,Thurs,5.59,0.02,0,53,56,62.1,52,67
490 | 2012,5,4,Fri,7.61,0.73,0,56,52,62.3,54,75
491 | 2012,5,5,Sat,10.29,0.07,0,52,54,62.5,56,80
492 | 2012,5,6,Sun,5.14,0,0,54,56,62.8,64,55
493 | 2012,5,7,Mon,5.37,0,0,56,64,63,75,76
494 | 2012,5,8,Tues,4.92,0,0,64,75,63.2,65,52
495 | 2012,5,9,Wed,6.71,0,0,75,65,63.4,56,60
496 | 2012,5,10,Thurs,8.72,0,0,65,56,63.6,58,47
497 | 2012,5,11,Fri,6.71,0,0,56,58,63.8,65,59
498 | 2012,5,12,Sat,9.62,0,0,58,65,64.1,76,48
499 | 2012,5,13,Sun,7.61,0,0,65,76,64.3,78,61
500 | 2012,5,14,Mon,9.4,0,0,76,78,64.5,80,58
501 | 2012,5,15,Tues,8.5,0,0,78,80,64.7,76,78
502 | 2012,5,16,Wed,9.17,0,0,80,76,64.8,67,83
503 | 2012,5,17,Thurs,7.83,0,0,76,67,65,64,71
504 | 2012,5,18,Fri,6.49,0,0,67,64,65.2,60,81
505 | 2012,5,19,Sat,6.93,0,0,64,60,65.4,67,64
506 | 2012,5,20,Sun,3.36,0,0,60,67,65.6,58,61
507 | 2012,5,21,Mon,2.91,0.25,0,67,58,65.7,62,54
508 | 2012,5,22,Tues,8.95,0.55,0,58,62,65.9,55,56
509 | 2012,5,23,Wed,10.74,0.24,0,62,55,66.1,58,70
510 | 2012,5,24,Thurs,14.09,0.01,0,55,58,66.2,63,64
511 | 2012,5,25,Fri,7.38,0,0,58,63,66.4,72,71
512 | 2012,5,26,Sat,6.93,0,0,63,72,66.5,72,59
513 | 2012,5,27,Sun,8.05,0,0,72,72,66.7,63,70
514 | 2012,5,28,Mon,8.28,0,0,72,63,66.8,62,72
515 | 2012,5,29,Tues,7.61,0,0,63,62,67,61,71
516 | 2012,5,30,Wed,4.03,0,0,62,61,67.1,66,82
517 | 2012,5,31,Thurs,3.36,0.01,0,61,66,67.3,64,74
518 | 2012,6,1,Fri,6.04,0.15,0,66,64,67.4,68,65
519 | 2012,6,2,Sat,8.28,0.26,0,64,68,67.6,66,83
520 | 2012,6,3,Sun,8.28,0.01,0,68,66,67.7,63,81
521 | 2012,6,4,Mon,6.49,0,0,66,63,67.9,55,85
522 | 2012,6,5,Tues,6.93,0.05,0,63,55,68,56,67
523 | 2012,6,6,Wed,7.38,0.63,0,55,56,68.2,61,49
524 | 2012,6,7,Thurs,7.61,0,0,56,61,68.3,61,80
525 | 2012,6,8,Fri,7.83,0.65,0,61,61,68.5,59,77
526 | 2012,6,9,Sat,6.71,0.06,0,61,59,68.6,63,52
527 | 2012,6,10,Sun,10.51,0,0,59,63,68.8,66,51
528 | 2012,6,11,Mon,6.49,0,0,63,66,69,74,54
529 | 2012,6,12,Tues,4.03,0,0,66,74,69.1,65,83
530 | 2012,6,13,Wed,8.72,0.03,0,74,65,69.3,61,64
531 | 2012,6,14,Thurs,9.62,0,0,65,61,69.5,63,62
532 | 2012,6,15,Fri,6.04,0,0,61,63,69.7,72,81
533 | 2012,6,16,Sat,3.8,0,0,63,72,69.8,70,62
534 | 2012,6,17,Sun,9.17,0,0,72,70,70,66,65
535 | 2012,6,18,Mon,14.32,0,0,70,66,70.2,63,89
536 | 2012,6,19,Tues,8.5,0.12,0,66,63,70.4,67,70
537 | 2012,6,20,Wed,6.71,0.04,0,63,67,70.6,76,80
538 | 2012,6,21,Thurs,6.71,0,0,67,76,70.8,75,71
539 | 2012,6,22,Fri,4.7,0,0,76,75,71,57,80
540 | 2012,6,23,Sat,4.25,0.62,0,75,57,71.3,60,68
541 | 2012,6,24,Sun,5.59,0.34,0,57,60,71.5,67,67
542 | 2012,6,25,Mon,4.47,0,0,60,67,71.7,67,78
543 | 2012,6,26,Tues,6.93,0.02,0,67,67,71.9,65,78
544 | 2012,6,27,Wed,7.61,0,0,67,65,72.2,73,91
545 | 2012,6,28,Thurs,4.03,0,0,65,73,72.4,72,83
546 | 2012,6,29,Fri,5.59,0,0,73,72,72.6,71,66
547 | 2012,6,30,Sat,4.25,0.01,0,72,71,72.8,68,66
548 | 2012,7,1,Sun,5.37,0.12,0,71,68,73.1,68,69
549 | 2012,7,2,Mon,5.14,0,0,68,68,73.3,66,62
550 | 2012,7,3,Tues,4.7,0.08,0,68,66,73.5,65,91
551 | 2012,7,4,Wed,13.42,0.23,0,66,65,73.8,69,63
552 | 2012,7,5,Thurs,8.5,0,0,65,69,74,76,57
553 | 2012,7,6,Fri,6.93,0,0,69,76,74.2,77,91
554 | 2012,7,7,Sat,4.7,0,0,76,77,74.4,80,67
555 | 2012,7,8,Sun,8.5,0,0,77,80,74.6,83,84
556 | 2012,7,9,Mon,6.26,0,0,80,83,74.9,77,81
557 | 2012,7,10,Tues,4.47,0.06,0,83,77,75.1,75,87
558 | 2012,7,11,Wed,5.14,0,0,77,75,75.3,82,74
559 | 2012,7,12,Thurs,6.49,0,0,75,82,75.4,78,68
560 | 2012,7,13,Fri,6.04,0,0,82,78,75.6,74,89
561 | 2012,7,14,Sat,4.92,0.02,0,78,74,75.8,77,82
562 | 2012,7,15,Sun,4.92,0,0,74,77,76,66,85
563 | 2012,7,16,Mon,8.5,0,0,77,66,76.1,79,90
564 | 2012,7,17,Tues,5.59,0.01,0,66,79,76.3,71,71
565 | 2012,7,18,Wed,5.82,0,0,79,71,76.4,70,85
566 | 2012,7,19,Thurs,6.49,0,0,71,70,76.6,77,68
567 | 2012,7,20,Fri,4.92,0,0,70,77,76.7,67,79
568 | 2012,7,21,Sat,8.95,0.6,0,77,67,76.8,75,85
569 | 2012,7,22,Sun,5.14,0,0,67,75,76.9,69,76
570 | 2012,7,23,Mon,8.72,0.04,0,75,69,77,66,72
571 | 2012,7,24,Tues,7.38,0,0,69,66,77.1,74,80
572 | 2012,7,25,Wed,9.62,0,0,66,74,77.1,80,85
573 | 2012,7,26,Thurs,5.82,0,0,74,80,77.2,78,88
574 | 2012,7,27,Fri,4.92,0,0,80,78,77.3,66,70
575 | 2012,7,28,Sat,6.26,0,0,78,66,77.3,72,84
576 | 2012,7,29,Sun,3.8,0,0,66,72,77.3,73,88
577 | 2012,7,30,Mon,4.47,0,0,72,73,77.3,67,66
578 | 2012,7,31,Tues,6.71,0,0,73,67,77.4,73,62
579 | 2012,8,1,Wed,6.26,0,0,67,73,77.4,75,57
580 | 2012,8,2,Thurs,4.92,0,0,73,75,77.4,74,96
581 | 2012,8,3,Fri,5.59,0,0,75,74,77.3,81,84
582 | 2012,8,4,Sat,8.72,0,0,74,81,77.3,93,80
583 | 2012,8,5,Sun,8.28,0,0,81,93,77.3,93,63
584 | 2012,8,6,Mon,4.25,0,0,93,93,77.2,83,96
585 | 2012,8,7,Tues,5.59,0,0,93,83,77.2,70,74
586 | 2012,8,8,Wed,5.82,0,0,83,70,77.1,72,61
587 | 2012,8,9,Thurs,6.93,0,0,70,72,77.1,76,85
588 | 2012,8,10,Fri,8.5,0,0,72,76,77,78,96
589 | 2012,8,11,Sat,5.14,0,0,76,78,76.9,83,87
590 | 2012,8,12,Sun,5.59,0,0,78,83,76.9,87,70
591 | 2012,8,13,Mon,6.71,0,0,83,87,76.8,87,71
592 | 2012,8,14,Tues,6.26,0,0,87,87,76.7,84,93
593 | 2012,8,15,Wed,6.26,0,0,87,84,76.6,88,95
594 | 2012,8,16,Thurs,10.51,0,0,84,88,76.5,94,79
595 | 2012,8,17,Fri,6.26,0,0,88,94,76.4,91,89
596 | 2012,8,18,Sat,4.03,0,0,94,91,76.3,71,61
597 | 2012,8,19,Sun,6.71,0,0,91,71,76.2,74,81
598 | 2012,8,20,Mon,6.04,0,0,71,74,76.1,78,89
599 | 2012,8,21,Tues,4.25,0,0,74,78,75.9,74,61
600 | 2012,8,22,Wed,6.71,0,0,78,74,75.8,72,79
601 | 2012,8,23,Thurs,5.14,0,0,74,72,75.7,70,70
602 | 2012,8,24,Fri,8.5,0,0,72,70,75.5,72,88
603 | 2012,8,25,Sat,7.38,0,0,70,72,75.4,79,65
604 | 2012,8,26,Sun,7.16,0,0,72,79,75.3,70,59
605 | 2012,8,27,Mon,7.61,0,0,79,70,75.1,75,55
606 | 2012,8,28,Tues,4.03,0,0,70,75,75,73,91
607 | 2012,8,29,Wed,7.16,0,0,75,73,74.8,73,71
608 | 2012,8,30,Thurs,5.37,0,0,73,73,74.6,73,68
609 | 2012,8,31,Fri,4.25,0,0,73,73,74.4,72,68
610 | 2012,9,1,Sat,6.49,0,0,73,72,74.3,71,84
611 | 2012,9,2,Sun,4.7,0,0,72,71,74.1,70,84
612 | 2012,9,3,Mon,4.47,0,0,71,70,73.9,73,78
613 | 2012,9,4,Tues,7.38,0,0,70,73,73.7,76,83
614 | 2012,9,5,Wed,6.93,0,0,73,76,73.5,79,61
615 | 2012,9,6,Thurs,5.82,0,0,76,79,73.3,83,58
616 | 2012,9,7,Fri,9.4,0,0,79,83,73,90,62
617 | 2012,9,8,Sat,6.93,0,0,83,90,72.8,77,55
618 | 2012,9,9,Sun,6.71,0,0,90,77,72.6,66,62
619 | 2012,9,10,Mon,11.18,0.01,0,77,66,72.3,68,89
620 | 2012,9,11,Tues,8.72,0.01,0,66,68,72.1,68,82
621 | 2012,9,12,Wed,9.4,0,0,68,68,71.8,72,54
622 | 2012,9,13,Thurs,12.53,0,0,68,72,71.5,82,70
623 | 2012,9,14,Fri,8.05,0,0,72,82,71.2,79,81
624 | 2012,9,15,Sat,3.36,0,0,82,79,71,72,83
625 | 2012,9,16,Sun,4.25,0,0,79,72,70.7,76,63
626 | 2012,9,17,Mon,5.14,0,0,72,76,70.3,82,87
627 | 2012,9,18,Tues,4.92,0,0,76,82,70,82,89
628 | 2012,9,19,Wed,3.13,0,0,82,82,69.7,75,73
629 | 2012,9,20,Thurs,4.25,0,0,82,75,69.4,67,70
630 | 2012,9,21,Fri,5.59,0,0,75,67,69,61,55
631 | 2012,9,22,Sat,4.7,0,0,67,61,68.7,67,80
632 | 2012,9,23,Sun,2.46,0.01,0,61,67,68.3,67,84
633 | 2012,9,24,Mon,3.13,0,0,67,67,68,70,68
634 | 2012,9,25,Tues,4.03,0,0,67,70,67.6,67,60
635 | 2012,9,26,Wed,3.8,0,0,70,67,67.2,67,71
636 | 2012,9,27,Thurs,3.8,0,0,67,67,66.8,73,63
637 | 2012,9,28,Fri,3.8,0,0,67,73,66.5,77,69
638 | 2012,9,29,Sat,2.46,0,0,73,77,66.1,69,64
639 | 2012,9,30,Sun,9.62,0,0,77,69,65.7,70,67
640 | 2012,10,1,Mon,6.93,0,0,69,70,65.3,74,53
641 | 2012,10,2,Tues,6.71,0,0,70,74,64.9,64,58
642 | 2012,10,3,Wed,9.17,0,0,74,64,64.5,66,50
643 | 2012,10,4,Thurs,16.33,0,0,64,66,64.1,66,67
644 | 2012,10,5,Fri,14.54,0,0,66,66,63.7,71,49
645 | 2012,10,6,Sat,12.75,0,0,66,71,63.3,75,78
646 | 2012,10,7,Sun,11.41,0,0,71,75,62.9,75,59
647 | 2012,10,8,Mon,2.91,0,0,75,75,62.5,70,76
648 | 2012,10,9,Tues,4.25,0,0,75,70,62.1,61,72
649 | 2012,10,10,Wed,3.58,0,0,70,61,61.8,54,61
650 | 2012,10,11,Thurs,3.13,0,0,61,54,61.4,57,71
651 | 2012,10,12,Fri,2.91,0,0,54,57,61,57,54
652 | 2012,10,13,Sat,10.29,0.08,0,57,57,60.6,60,74
653 | 2012,10,14,Sun,8.72,0.19,0,57,60,60.2,64,76
654 | 2012,10,15,Mon,7.61,0.65,0,60,64,59.9,63,64
655 | 2012,10,16,Tues,10.29,0.31,0,64,63,59.5,61,53
656 | 2012,10,17,Wed,12.3,0,0,63,61,59.1,58,46
657 | 2012,10,18,Thurs,3.58,0,0,61,58,58.8,64,72
658 | 2012,10,19,Fri,4.47,0.82,0,58,64,58.4,59,52
659 | 2012,10,20,Sat,11.86,0.19,0,64,59,58.1,52,51
660 | 2012,10,21,Sun,12.75,0.02,0,59,52,57.8,53,62
661 | 2012,10,22,Mon,6.04,0.25,0,52,53,57.4,46,68
662 | 2012,10,23,Tues,5.82,0.35,0,53,46,57.1,52,43
663 | 2012,10,24,Wed,6.71,0,0,46,52,56.8,53,45
664 | 2012,10,25,Thurs,4.7,0.28,0,52,53,56.5,53,74
665 | 2012,10,26,Fri,3.36,0,0,53,53,56.2,52,62
666 | 2012,10,27,Sat,5.59,0.06,0,53,52,55.9,58,72
667 | 2012,10,28,Sun,11.41,0.91,0,52,58,55.6,58,45
668 | 2012,10,29,Mon,8.5,0.24,0,58,58,55.3,60,47
669 | 2012,10,30,Tues,10.96,0.43,0,58,60,55,59,48
670 | 2012,10,31,Wed,6.26,1.36,0,60,59,54.8,60,67
671 | 2012,11,1,Thurs,6.04,0.57,0,59,60,54.5,59,54
672 | 2012,11,2,Fri,6.71,0.38,0,60,59,54.2,59,46
673 | 2012,11,3,Sat,2.24,0.22,0,59,59,53.9,60,72
674 | 2012,11,4,Sun,8.05,0.02,0,59,60,53.7,64,38
675 | 2012,11,5,Mon,8.5,0.32,0,60,64,53.4,59,53
676 | 2012,11,6,Tues,8.95,0.03,0,64,59,53.2,55,44
677 | 2012,11,7,Wed,7.83,0.01,0,59,55,52.9,54,58
678 | 2012,11,8,Thurs,7.61,0,0,55,54,52.7,50,53
679 | 2012,11,9,Fri,7.61,0,0,54,50,52.4,48,36
680 | 2012,11,10,Sat,4.47,0,0,50,48,52.2,46,64
681 | 2012,11,11,Sun,4.92,0,0,48,46,51.9,48,61
682 | 2012,11,12,Mon,6.71,0.6,0,46,48,51.7,55,51
683 | 2012,11,13,Tues,6.71,0.14,0,48,55,51.4,52,43
684 | 2012,11,14,Wed,5.59,0.21,0,55,52,51.2,52,68
685 | 2012,11,15,Thurs,5.82,0.03,0,52,52,51,49,32
686 | 2012,11,16,Fri,5.37,0,0,52,49,50.7,49,50
687 | 2012,11,17,Sat,3.58,0.22,0,49,49,50.5,54,33
688 | 2012,11,18,Sun,11.86,0.24,0,49,54,50.3,50,58
689 | 2012,11,19,Mon,10.96,0.31,0,54,50,50,56,59
690 | 2012,11,20,Tues,13.42,2.13,0,50,56,49.8,52,43
691 | 2012,11,21,Wed,9.4,0.15,0,56,52,49.5,47,53
692 | 2012,11,22,Thurs,12.3,0.44,0,52,47,49.3,48,33
693 | 2012,11,23,Fri,3.36,0,0,47,48,49.1,49,62
694 | 2012,11,24,Sat,5.37,1.26,0,48,49,48.9,48,51
695 | 2012,11,25,Sun,2.68,0,0,49,48,48.6,47,57
696 | 2012,11,26,Mon,8.05,0,0,48,47,48.4,49,40
697 | 2012,11,27,Tues,8.5,0,0,47,49,48.2,50,38
698 | 2012,11,28,Wed,3.36,0,0,49,50,48,49,68
699 | 2012,11,29,Thurs,6.49,0.11,0,50,49,47.8,55,63
700 | 2012,11,30,Fri,9.4,0.06,0,49,55,47.6,59,47
701 | 2012,12,1,Sat,10.29,1.4,0,55,59,47.4,56,42
702 | 2012,12,2,Sun,12.3,0.16,0,59,56,47.2,47,29
703 | 2012,12,3,Mon,13.87,0.77,0,56,47,47,49,39
704 | 2012,12,4,Tues,9.84,0.51,0,47,49,46.8,53,37
705 | 2012,12,5,Wed,13.87,0.56,0,49,53,46.6,48,27
706 | 2012,12,6,Thurs,11.18,0.06,0,53,48,46.4,45,65
707 | 2012,12,7,Fri,11.41,0.06,0,48,45,46.3,46,61
708 | 2012,12,8,Sat,10.29,0.04,0,45,46,46.1,44,47
709 | 2012,12,9,Sun,4.47,0,0,46,44,46,44,59
710 | 2012,12,10,Mon,4.7,0.06,0,44,44,45.9,45,40
711 | 2012,12,11,Tues,4.03,0.02,0,44,45,45.7,46,33
712 | 2012,12,12,Wed,10.07,0.12,0,45,46,45.6,44,49
713 | 2012,12,13,Thurs,4.47,0.32,0,46,44,45.5,45,63
714 | 2012,12,14,Fri,6.26,0.09,0,44,45,45.4,43,52
715 | 2012,12,15,Sat,3.8,0.31,0,45,43,45.3,40,64
716 | 2012,12,16,Sun,11.41,0.21,0,43,40,45.3,44,55
717 | 2012,12,17,Mon,12.3,0.89,0,40,44,45.2,47,26
718 | 2012,12,18,Tues,21.25,0.08,0,44,47,45.2,39,54
719 | 2012,12,19,Wed,11.86,0.13,1,47,39,45.1,47,33
720 | 2012,12,20,Thurs,12.97,0.54,0,39,47,45.1,45,61
721 | 2012,12,21,Fri,8.28,0.52,0,47,45,45.1,47,62
722 | 2012,12,22,Sat,3.8,0.07,0,45,47,45.1,47,56
723 | 2012,12,23,Sun,7.83,0.13,0,47,47,45.1,45,34
724 | 2012,12,24,Mon,5.59,0.26,0,47,45,45.1,42,50
725 | 2012,12,25,Tues,6.26,0.01,0,45,42,45.1,42,33
726 | 2012,12,26,Wed,9.4,0.53,0,42,42,45.2,44,31
727 | 2012,12,27,Thurs,10.96,0.18,0,42,44,45.2,46,58
728 | 2012,12,28,Fri,7.16,0.16,0,44,46,45.3,47,54
729 | 2012,12,29,Sat,3.8,0,0,46,47,45.3,41,44
730 | 2012,12,30,Sun,3.8,0.06,0,47,41,45.4,40,48
731 | 2012,12,31,Mon,4.03,0,0,41,40,45.5,38,53
732 | 2013,1,1,Tues,4.47,0,0,40,38,45.6,41,46
733 | 2013,1,2,Wed,6.04,0,0,38,41,45.7,43,42
734 | 2013,1,3,Thurs,7.16,0,0,41,43,45.8,44,41
735 | 2013,1,4,Fri,6.71,0.16,0,43,44,45.9,50,34
736 | 2013,1,5,Sat,6.26,0.1,0,44,50,46,44,26
737 | 2013,1,6,Sun,6.93,0.12,0,50,44,46.1,45,36
738 | 2013,1,7,Mon,6.71,0.08,0,44,45,46.2,50,43
739 | 2013,1,8,Tues,16.33,0.09,0,45,50,46.3,53,29
740 | 2013,1,9,Wed,14.09,0.64,0,50,53,46.4,50,48
741 | 2013,1,10,Thurs,11.41,1.51,0,53,50,46.5,38,46
742 | 2013,1,11,Fri,4.7,0.01,0,50,38,46.7,37,47
743 | 2013,1,12,Sat,4.25,0,0,38,37,46.8,37,51
744 | 2013,1,13,Sun,4.47,0,0,37,37,46.9,36,45
745 | 2013,1,14,Mon,3.36,0,0,37,36,47,38,60
746 | 2013,1,15,Tues,2.91,0,0,36,38,47.1,44,31
747 | 2013,1,16,Wed,5.14,0,0,38,44,47.3,43,31
748 | 2013,1,17,Thurs,4.03,0,0,44,43,47.4,39,29
749 | 2013,1,18,Fri,2.24,0,0,43,39,47.5,38,44
750 | 2013,1,19,Sat,2.91,0,0,39,38,47.6,34,53
751 | 2013,1,20,Sun,4.25,0,0,38,34,47.7,38,52
752 | 2013,1,21,Mon,4.7,0,0,34,38,47.8,36,43
753 | 2013,1,22,Tues,2.46,0,0,38,36,47.9,38,52
754 | 2013,1,23,Wed,1.34,0,0,36,38,48,45,43
755 | 2013,1,24,Thurs,6.93,0.2,0,38,45,48.1,45,37
756 | 2013,1,25,Fri,5.82,0.23,0,45,45,48.2,51,68
757 | 2013,1,26,Sat,4.7,0.12,0,45,51,48.3,47,35
758 | 2013,1,27,Sun,10.07,0.09,0,51,47,48.4,42,51
759 | 2013,1,28,Mon,10.07,0.07,0,47,42,48.4,43,36
760 | 2013,1,29,Tues,7.16,0.31,0,42,43,48.5,47,62
761 | 2013,1,30,Wed,8.72,0.17,0,43,47,48.6,48,67
762 | 2013,1,31,Thurs,8.72,0.14,0,47,48,48.7,49,58
763 | 2013,2,1,Fri,8.95,0.12,0,48,49,48.8,53,37
764 | 2013,2,2,Sat,6.49,0.01,0,49,53,48.8,43,45
765 | 2013,2,3,Sun,4.47,0,0,53,43,48.9,48,57
766 | 2013,2,4,Mon,6.49,0.09,0,43,48,49,51,43
767 | 2013,2,5,Tues,5.82,0,0,48,51,49.1,50,35
768 | 2013,2,6,Wed,11.41,0.13,0,51,50,49.1,51,62
769 | 2013,2,7,Thurs,10.07,0.04,0,50,51,49.2,49,58
770 | 2013,2,8,Fri,9.17,0.05,0,51,49,49.3,46,40
771 | 2013,2,9,Sat,2.91,0,0,49,46,49.4,47,38
772 | 2013,2,10,Sun,2.91,0.01,0,46,47,49.4,48,54
773 | 2013,2,11,Mon,4.47,0,0,47,48,49.5,47,64
774 | 2013,2,12,Tues,3.13,0.01,0,48,47,49.6,52,46
775 | 2013,2,13,Wed,12.53,0.04,0,47,52,49.7,49,35
776 | 2013,2,14,Thurs,9.17,0.09,0,52,49,49.8,49,62
777 | 2013,2,15,Fri,4.92,0.04,0,49,49,49.9,56,42
778 | 2013,2,16,Sat,5.37,0,0,49,56,49.9,52,36
779 | 2013,2,17,Sun,12.53,0,0,56,52,50,49,61
780 | 2013,2,18,Mon,7.61,0,0,52,49,50.1,46,35
781 | 2013,2,19,Tues,4.25,0,0,49,46,50.2,51,65
782 | 2013,2,20,Wed,7.61,0,0,46,51,50.4,46,37
783 | 2013,2,21,Thurs,4.7,0.06,0,51,46,50.5,44,31
784 | 2013,2,22,Fri,13.87,0.02,0,46,44,50.6,46,38
785 | 2013,2,23,Sat,18.12,0.37,0,44,46,50.7,50,38
786 | 2013,2,24,Sun,10.29,0.01,0,46,50,50.8,48,53
787 | 2013,2,25,Mon,12.3,0,0,50,48,50.9,51,44
788 | 2013,2,26,Tues,15.88,0.09,0,48,51,51.1,48,61
789 | 2013,2,27,Wed,8.5,0.02,0,51,48,51.2,50,67
790 | 2013,2,28,Thurs,4.03,0.18,0,48,50,51.3,53,46
791 | 2013,3,1,Fri,8.5,0.32,0,50,53,51.5,59,33
792 | 2013,3,2,Sat,12.08,0.16,0,53,59,51.6,57,40
793 | 2013,3,3,Sun,10.07,0.03,0,59,57,51.8,52,50
794 | 2013,3,4,Mon,6.26,0,0,57,52,51.9,56,44
795 | 2013,3,5,Tues,8.72,0,0,52,56,52.1,49,43
796 | 2013,3,6,Wed,5.37,0,0,56,49,52.2,45,33
797 | 2013,3,7,Thurs,9.17,0.47,0,49,45,52.4,54,58
798 | 2013,3,8,Fri,5.59,0.29,0,45,54,52.5,53,53
799 | 2013,3,9,Sat,5.82,0,0,54,53,52.7,55,43
800 | 2013,3,10,Sun,2.91,0,0,53,55,52.8,46,54
801 | 2013,3,11,Mon,3.58,0.03,0,55,46,53,51,44
802 | 2013,3,12,Tues,2.46,0.05,0,46,51,53.1,55,66
803 | 2013,3,13,Wed,12.75,0.08,0,51,55,53.3,53,53
804 | 2013,3,14,Thurs,8.28,0.09,0,55,53,53.4,53,64
805 | 2013,3,15,Fri,6.71,0.11,0,53,53,53.6,58,57
806 | 2013,3,16,Sat,9.62,0,0,53,58,53.7,51,58
807 | 2013,3,17,Sun,14.32,0.17,0,58,51,53.9,48,59
808 | 2013,3,18,Mon,13.65,0,0,51,48,54,53,50
809 | 2013,3,19,Tues,13.2,0,0,48,53,54.2,55,61
810 | 2013,3,20,Wed,7.61,0.46,0,53,55,54.3,52,58
811 | 2013,3,21,Thurs,17,0.39,0,55,52,54.5,50,48
812 | 2013,3,22,Fri,10.96,0.32,0,52,50,54.6,49,53
813 | 2013,3,23,Sat,4.92,0,0,50,49,54.7,50,50
814 | 2013,3,24,Sun,5.82,0,0,49,50,54.9,54,56
815 | 2013,3,25,Mon,4.7,0,0,50,54,55,62,73
816 | 2013,3,26,Tues,6.26,0,0,54,62,55.2,62,44
817 | 2013,3,27,Wed,3.8,0,0,62,62,55.3,56,70
818 | 2013,3,28,Thurs,3.58,0.01,0,62,56,55.5,61,42
819 | 2013,3,29,Fri,2.91,0.08,0,56,61,55.6,65,49
820 | 2013,3,30,Sat,5.59,0,0,61,65,55.7,68,43
821 | 2013,3,31,Sun,9.84,0,0,65,68,55.9,69,39
822 | 2013,4,1,Mon,6.49,0,0,68,69,56,63,72
823 | 2013,4,2,Tues,8.05,0,0,69,63,56.2,57,51
824 | 2013,4,3,Wed,4.92,0,0,63,57,56.3,62,74
825 | 2013,4,4,Thurs,3.58,0,0,57,62,56.5,58,65
826 | 2013,4,5,Fri,6.71,0.33,0,62,58,56.6,57,52
827 | 2013,4,6,Sat,12.53,0.73,0,58,57,56.8,54,42
828 | 2013,4,7,Sun,11.18,0.5,0,57,54,56.9,47,54
829 | 2013,4,8,Mon,8.72,1.54,0,54,47,57.1,56,73
830 | 2013,4,9,Tues,6.93,0.03,0,47,56,57.2,54,44
831 | 2013,4,10,Wed,5.37,0,0,56,54,57.4,59,65
832 | 2013,4,11,Thurs,14.32,0.37,0,54,59,57.6,54,71
833 | 2013,4,12,Fri,8.5,0.06,0,59,54,57.7,46,44
834 | 2013,4,13,Sat,10.29,0.38,0,54,46,57.9,51,41
835 | 2013,4,14,Sun,12.75,0.37,0,46,51,58.1,55,47
836 | 2013,4,15,Mon,5.14,0.23,0,51,55,58.3,57,73
837 | 2013,4,16,Tues,5.37,0,0,55,57,58.5,57,66
838 | 2013,4,17,Wed,5.82,0.01,0,57,57,58.6,59,75
839 | 2013,4,18,Thurs,7.38,0,0,57,59,58.8,53,60
840 | 2013,4,19,Fri,8.95,0.21,0,59,53,59,56,55
841 | 2013,4,20,Sat,10.96,0.81,0,53,56,59.2,57,55
842 | 2013,4,21,Sun,12.97,0,0,56,57,59.4,54,51
843 | 2013,4,22,Mon,9.17,0.13,0,57,54,59.7,61,47
844 | 2013,4,23,Tues,9.62,0,0,54,61,59.9,64,50
845 | 2013,4,24,Wed,6.26,0,0,61,64,60.1,70,58
846 | 2013,4,25,Thurs,6.71,0,0,64,70,60.3,71,71
847 | 2013,4,26,Fri,2.46,0,0,70,71,60.5,69,75
848 | 2013,4,27,Sat,4.92,0,0,71,69,60.7,57,69
849 | 2013,4,28,Sun,13.2,0,0,69,57,61,59,73
850 | 2013,4,29,Mon,11.63,0.04,0,57,59,61.2,57,54
851 | 2013,4,30,Tues,9.4,0.15,0,59,57,61.4,55,69
852 | 2013,5,1,Wed,5.37,0,0,57,55,61.6,65,44
853 | 2013,5,2,Thurs,6.93,0,0,55,65,61.9,69,43
854 | 2013,5,3,Fri,8.95,0,0,65,69,62.1,71,73
855 | 2013,5,4,Sat,10.96,0,0,69,71,62.3,77,80
856 | 2013,5,5,Sun,14.54,0,0,71,77,62.5,84,77
857 | 2013,5,6,Mon,11.86,0,0,77,84,62.8,87,78
858 | 2013,5,7,Tues,4.47,0,0,84,87,63,69,45
859 | 2013,5,8,Wed,7.38,0,0,87,69,63.2,67,68
860 | 2013,5,9,Thurs,4.25,0,0,69,67,63.4,73,79
861 | 2013,5,10,Fri,2.91,0,0,67,73,63.6,79,55
862 | 2013,5,11,Sat,2.24,0,0,73,79,63.8,81,57
863 | 2013,5,12,Sun,5.82,0,0,79,81,64.1,71,71
864 | 2013,5,13,Mon,8.72,0.26,0,81,71,64.3,66,84
865 | 2013,5,14,Tues,11.18,0.13,0,71,66,64.5,65,69
866 | 2013,5,15,Wed,5.37,0,0,66,65,64.7,63,82
867 | 2013,5,16,Thurs,5.14,0.04,0,65,63,64.8,71,49
868 | 2013,5,17,Fri,6.04,0,0,63,71,65,63,59
869 | 2013,5,18,Sat,8.28,0.02,0,71,63,65.2,62,66
870 | 2013,5,19,Sun,6.49,0,0,63,62,65.4,65,54
871 | 2013,5,20,Mon,5.14,0,0,62,65,65.6,67,66
872 | 2013,5,21,Tues,4.03,0,0,65,67,65.7,60,51
873 | 2013,5,22,Wed,10.74,0.54,0,67,60,65.9,52,56
874 | 2013,5,23,Thurs,6.71,0.54,0,60,52,66.1,54,84
875 | 2013,5,24,Fri,4.25,0.16,0,52,54,66.2,62,70
876 | 2013,5,25,Sat,6.04,0.01,0,54,62,66.4,64,55
877 | 2013,5,26,Sun,6.04,0,0,62,64,66.5,65,81
878 | 2013,5,27,Mon,4.92,0.06,0,64,65,66.7,62,67
879 | 2013,5,28,Tues,6.93,0.38,0,65,62,66.8,63,53
880 | 2013,5,29,Wed,6.26,0.02,0,62,63,67,61,54
881 | 2013,5,30,Thurs,8.95,0.22,0,63,61,67.1,62,69
882 | 2013,5,31,Fri,11.86,0,0,61,62,67.3,67,74
883 | 2013,6,1,Sat,5.59,0,0,62,67,67.4,73,52
884 | 2013,6,2,Sun,5.59,0,0,67,73,67.6,69,63
885 | 2013,6,3,Mon,6.93,0.04,0,73,69,67.7,72,54
886 | 2013,6,4,Tues,6.49,0,0,69,72,67.9,79,76
887 | 2013,6,5,Wed,7.61,0,0,72,79,68,80,85
888 | 2013,6,6,Thurs,6.93,0,0,79,80,68.2,80,62
889 | 2013,6,7,Fri,5.59,0,0,80,80,68.3,71,66
890 | 2013,6,8,Sat,7.16,0,0,80,71,68.5,69,80
891 | 2013,6,9,Sun,6.93,0,0,71,69,68.6,69,77
892 | 2013,6,10,Mon,8.28,0,0,69,69,68.8,71,70
893 | 2013,6,11,Tues,7.16,0,0,69,71,69,68,81
894 | 2013,6,12,Wed,12.75,0,0,71,68,69.1,69,51
895 | 2013,6,13,Thurs,9.4,0.01,0,68,69,69.3,70,74
896 | 2013,6,14,Fri,5.82,0,0,69,70,69.5,68,60
897 | 2013,6,15,Sat,8.28,0,0,70,68,69.7,78,77
898 | 2013,6,16,Sun,6.49,0,0,68,78,69.8,75,86
899 | 2013,6,17,Mon,7.61,0,0,78,75,70,78,71
900 | 2013,6,18,Tues,6.71,0,0,75,78,70.2,74,70
901 | 2013,6,19,Wed,7.61,0.01,0,78,74,70.4,68,67
902 | 2013,6,20,Thurs,8.28,0,0,74,68,70.6,63,84
903 | 2013,6,21,Fri,11.18,0.12,0,68,63,70.8,69,78
904 | 2013,6,22,Sat,3.36,0.01,0,63,69,71,78,53
905 | 2013,6,23,Sun,3.8,0,0,69,78,71.3,72,65
906 | 2013,6,24,Mon,4.7,0.31,0,78,72,71.5,70,71
907 | 2013,6,25,Tues,8.28,0.19,0,72,70,71.7,74,71
908 | 2013,6,26,Wed,6.26,0.39,0,70,74,71.9,72,61
909 | 2013,6,27,Thurs,5.14,0.08,0,74,72,72.2,70,66
910 | 2013,6,28,Fri,2.91,0.14,0,72,70,72.4,87,72
911 | 2013,6,29,Sat,4.92,0,0,70,87,72.6,86,53
912 | 2013,6,30,Sun,3.8,0,0,87,86,72.8,93,71
913 | 2013,7,1,Mon,5.59,0,0,86,93,73.1,89,56
914 | 2013,7,2,Tues,5.14,0,0,93,89,73.3,83,81
915 | 2013,7,3,Wed,6.71,0,0,89,83,73.5,79,82
916 | 2013,7,4,Thurs,7.16,0,0,83,79,73.8,71,88
917 | 2013,7,5,Fri,4.92,0,0,79,71,74,74,55
918 | 2013,7,6,Sat,5.82,0,0,71,74,74.2,79,77
919 | 2013,7,7,Sun,4.92,0,0,74,79,74.4,75,82
920 | 2013,7,8,Mon,6.49,0,0,79,75,74.6,80,63
921 | 2013,7,9,Tues,6.26,0,0,75,80,74.9,86,87
922 | 2013,7,10,Wed,5.59,0,0,80,86,75.1,72,67
923 | 2013,7,11,Thurs,5.82,0,0,86,72,75.3,73,77
924 | 2013,7,12,Fri,6.71,0,0,72,73,75.4,67,60
925 | 2013,7,13,Sat,4.92,0,0,73,67,75.6,79,63
926 | 2013,7,14,Sun,6.93,0,0,67,79,75.8,82,93
927 | 2013,7,15,Mon,6.71,0,0,79,82,76,82,77
928 | 2013,7,16,Tues,10.29,0,0,82,82,76.1,88,95
929 | 2013,7,17,Wed,9.17,0,0,82,88,76.3,72,95
930 | 2013,7,18,Thurs,8.28,0,0,88,72,76.4,79,67
931 | 2013,7,19,Fri,4.47,0,0,72,79,76.6,82,94
932 | 2013,7,20,Sat,4.25,0,0,79,82,76.7,77,89
933 | 2013,7,21,Sun,4.47,0,0,82,77,76.8,75,70
934 | 2013,7,22,Mon,5.14,0,0,77,75,76.9,79,66
935 | 2013,7,23,Tues,5.37,0,0,75,79,77,88,65
936 | 2013,7,24,Wed,6.71,0,0,79,88,77.1,88,92
937 | 2013,7,25,Thurs,5.59,0,0,88,88,77.1,88,67
938 | 2013,7,26,Fri,5.14,0,0,88,88,77.2,88,85
939 | 2013,7,27,Sat,6.49,0,0,88,88,77.3,78,90
940 | 2013,7,28,Sun,5.82,0,0,88,78,77.3,70,77
941 | 2013,7,29,Mon,7.61,0,0,78,70,77.3,77,87
942 | 2013,7,30,Tues,3.13,0,0,70,77,77.3,77,81
943 | 2013,7,31,Wed,6.26,0,0,77,77,77.4,71,95
944 | 2013,8,1,Thurs,4.03,0,0,77,71,77.4,69,92
945 | 2013,8,2,Fri,8.72,0,0,71,69,77.4,63,72
946 | 2013,8,3,Sat,4.47,0.08,0,69,63,77.3,77,58
947 | 2013,8,4,Sun,5.37,0,0,63,77,77.3,84,84
948 | 2013,8,5,Mon,7.61,0,0,77,84,77.3,86,67
949 | 2013,8,6,Tues,4.7,0,0,84,86,77.2,87,84
950 | 2013,8,7,Wed,3.13,0,0,86,87,77.2,88,62
951 | 2013,8,8,Thurs,4.25,0,0,87,88,77.1,83,87
952 | 2013,8,9,Fri,5.59,0,0,88,83,77.1,83,96
953 | 2013,8,10,Sat,4.7,0,0,83,83,77,78,80
954 | 2013,8,11,Sun,6.49,0.09,0,83,78,76.9,77,88
955 | 2013,8,12,Mon,6.49,0,0,78,77,76.9,78,92
956 | 2013,8,13,Tues,4.25,0,0,77,78,76.8,82,76
957 | 2013,8,14,Wed,4.03,0,0,78,82,76.7,81,84
958 | 2013,8,15,Thurs,4.47,0.03,0,82,81,76.6,70,83
959 | 2013,8,16,Fri,2.24,0.07,0,81,70,76.5,84,66
960 | 2013,8,17,Sat,4.92,0,0,70,84,76.4,78,91
961 | 2013,8,18,Sun,6.71,0,0,84,78,76.3,79,82
962 | 2013,8,19,Mon,6.93,0,0,78,79,76.2,80,57
963 | 2013,8,20,Tues,6.71,0,0,79,80,76.1,78,89
964 | 2013,8,21,Wed,10.29,0,0,80,78,75.9,82,70
965 | 2013,8,22,Thurs,9.62,0,0,78,82,75.8,84,96
966 | 2013,8,23,Fri,4.25,0,0,82,84,75.7,77,69
967 | 2013,8,24,Sat,9.17,0,0,84,77,75.5,77,77
968 | 2013,8,25,Sun,6.04,0,0,77,77,75.4,72,87
969 | 2013,8,26,Mon,5.82,0.01,0,77,72,75.3,76,63
970 | 2013,8,27,Tues,4.25,0.04,0,72,76,75.1,80,80
971 | 2013,8,28,Wed,3.13,0.05,0,76,80,75,80,69
972 | 2013,8,29,Thurs,2.91,0.22,0,80,80,74.8,75,75
973 | 2013,8,30,Fri,6.71,0.76,0,80,75,74.6,79,93
974 | 2013,8,31,Sat,6.49,0,0,75,79,74.4,82,82
975 | 2013,9,1,Sun,5.82,0,0,79,82,74.3,82,68
976 | 2013,9,2,Mon,5.59,0,0,82,82,74.1,82,90
977 | 2013,9,3,Tues,4.7,0,0,82,82,73.9,77,82
978 | 2013,9,4,Wed,3.8,0.09,0,82,77,73.7,73,69
979 | 2013,9,5,Thurs,5.37,0.01,0,77,73,73.5,68,75
980 | 2013,9,6,Fri,5.59,1.09,0,73,68,73.3,71,88
981 | 2013,9,7,Sat,5.82,0.84,0,68,71,73,74,85
982 | 2013,9,8,Sun,4.47,0,0,71,74,72.8,80,78
983 | 2013,9,9,Mon,3.36,0,0,74,80,72.6,79,55
984 | 2013,9,10,Tues,4.7,0,0,80,79,72.3,80,83
985 | 2013,9,11,Wed,8.28,0,0,79,80,72.1,93,74
986 | 2013,9,12,Thurs,5.37,0,0,80,93,71.8,78,77
987 | 2013,9,13,Fri,3.8,0,0,93,78,71.5,66,60
988 | 2013,9,14,Sat,4.47,0,0,78,66,71.2,71,59
989 | 2013,9,15,Sun,3.13,0,0,66,71,71,66,53
990 | 2013,9,16,Mon,4.92,0.13,0,71,66,70.7,71,52
991 | 2013,9,17,Tues,9.62,0.01,0,66,71,70.3,64,78
992 | 2013,9,18,Wed,5.14,0,0,71,64,70,70,54
993 | 2013,9,19,Thurs,5.59,0,0,64,70,69.7,78,75
994 | 2013,9,20,Fri,3.36,0,0,70,78,69.4,74,82
995 | 2013,9,21,Sat,6.71,0.14,0,78,74,69,70,54
996 | 2013,9,22,Sun,5.59,0,0,74,70,68.7,63,78
997 | 2013,9,23,Mon,12.3,0.53,0,70,63,68.3,61,68
998 | 2013,9,24,Tues,10.07,0.11,0,63,61,68,64,71
999 | 2013,9,25,Wed,5.82,0,0,61,64,67.6,61,81
1000 | 2013,9,26,Thurs,6.71,0.08,0,64,61,67.2,63,71
1001 | 2013,9,27,Fri,4.92,0,0,61,63,66.8,57,74
1002 | 2013,9,28,Sat,9.62,0.04,0,63,57,66.5,62,85
1003 | 2013,9,29,Sun,13.42,1.71,0,57,62,66.1,58,67
1004 | 2013,9,30,Mon,15.88,0.66,0,62,58,65.7,57,60
1005 | 2013,10,1,Tues,14.09,0.73,0,58,57,65.3,58,75
1006 | 2013,10,2,Wed,10.51,0.31,0,57,58,64.9,55,59
1007 | 2013,10,3,Thurs,5.37,0.21,0,58,55,64.5,58,79
1008 | 2013,10,4,Fri,2.01,0.03,0,55,58,64.1,64,76
1009 | 2013,10,5,Sat,2.46,0,0,58,64,63.7,68,45
1010 | 2013,10,6,Sun,3.58,0,0,64,68,63.3,73,50
1011 | 2013,10,7,Mon,5.82,0.16,0,68,73,62.9,61,65
1012 | 2013,10,8,Tues,14.09,0.02,0,73,61,62.5,57,69
1013 | 2013,10,9,Wed,6.71,0.27,0,61,57,62.1,59,52
1014 | 2013,10,10,Thurs,3.58,0,0,57,59,61.8,58,51
1015 | 2013,10,11,Fri,3.8,0.04,0,59,58,61.4,57,56
1016 | 2013,10,12,Sat,2.24,0.36,0,58,57,61,58,70
1017 | 2013,10,13,Sun,4.92,0.04,0,57,58,60.6,59,71
1018 | 2013,10,14,Mon,4.03,0,0,58,59,60.2,60,47
1019 | 2013,10,15,Tues,3.58,0,0,59,60,59.9,60,66
1020 | 2013,10,16,Wed,2.01,0,0,60,60,59.5,55,59
1021 | 2013,10,17,Thurs,6.04,0,0,60,55,59.1,58,77
1022 | 2013,10,18,Fri,3.8,0,0,55,58,58.8,55,42
1023 | 2013,10,19,Sat,2.68,0,0,58,55,58.4,51,55
1024 | 2013,10,20,Sun,3.13,0,0,55,51,58.1,51,68
1025 | 2013,10,21,Mon,5.37,0,0,51,51,57.8,53,42
1026 | 2013,10,22,Tues,5.59,0,0,51,53,57.4,58,76
1027 | 2013,10,23,Wed,4.25,0,0,53,58,57.1,55,58
1028 | 2013,10,24,Thurs,0.89,0,0,58,55,56.8,50,57
1029 | 2013,10,25,Fri,1.34,0,0,55,50,56.5,54,72
1030 | 2013,10,26,Sat,4.03,0,0,50,54,56.2,53,59
1031 | 2013,10,27,Sun,6.04,0,0,54,53,55.9,57,58
1032 | 2013,10,28,Mon,9.84,0.07,0,53,57,55.6,58,52
1033 | 2013,10,29,Tues,11.41,0,0,57,58,55.3,56,49
1034 | 2013,10,30,Wed,4.92,0,0,58,56,55,59,66
1035 | 2013,10,31,Thurs,8.72,0.02,0,56,59,54.8,58,41
1036 | 2013,11,1,Fri,4.92,0.01,0,59,58,54.5,64,52
1037 | 2013,11,2,Sat,3.13,0.05,0,58,64,54.2,58,40
1038 | 2013,11,3,Sun,17.67,0.5,0,64,58,53.9,54,62
1039 | 2013,11,4,Mon,5.37,0.02,0,58,54,53.7,51,40
1040 | 2013,11,5,Tues,3.58,0,0,54,51,53.4,56,73
1041 | 2013,11,6,Wed,6.93,0.1,0,51,56,53.2,55,45
1042 | 2013,11,7,Thurs,3.8,0.15,0,56,55,52.9,52,67
1043 | 2013,11,8,Fri,16.11,1.18,0,55,52,52.7,56,51
1044 | 2013,11,9,Sat,9.17,0,0,52,56,52.4,52,58
1045 | 2013,11,10,Sun,3.13,0.07,0,56,52,52.2,52,67
1046 | 2013,11,11,Mon,9.84,0,0,52,52,51.9,61,43
1047 | 2013,11,12,Tues,5.82,0,0,52,61,51.7,60,67
1048 | 2013,11,13,Wed,4.92,0.16,0,61,60,51.4,57,39
1049 | 2013,11,14,Thurs,8.5,0,0,60,57,51.2,52,48
1050 | 2013,11,15,Fri,2.46,0.05,0,57,52,51,51,70
1051 | 2013,11,16,Sat,13.42,0.12,0,52,51,50.7,50,33
1052 | 2013,11,17,Sun,10.29,0,0,51,50,50.5,53,57
1053 | 2013,11,18,Mon,12.08,0.21,0,50,53,50.3,55,35
1054 | 2013,11,19,Tues,8.72,1.03,0,53,55,50,56,62
1055 | 2013,11,20,Wed,11.41,0.04,0,55,56,49.8,46,53
1056 | 2013,11,21,Thurs,9.62,0,0,56,46,49.5,46,41
1057 | 2013,11,22,Fri,8.05,0,0,46,46,49.3,49,50
1058 | 2013,11,23,Sat,10.29,0,0,46,49,49.1,52,43
1059 | 2013,11,24,Sun,5.82,0,0,49,52,48.9,53,30
1060 | 2013,11,25,Mon,2.01,0,0,52,53,48.6,54,64
1061 | 2013,11,26,Tues,1.12,0,0,53,54,48.4,54,65
1062 | 2013,11,27,Wed,2.24,0,0,54,54,48.2,58,29
1063 | 2013,11,28,Thurs,2.91,0,0,54,58,48,53,47
1064 | 2013,11,29,Fri,1.57,0,0,58,53,47.8,49,57
1065 | 2013,11,30,Sat,4.7,0.02,0,53,49,47.6,52,63
1066 | 2013,12,1,Sun,8.72,0.09,0,49,52,47.4,56,64
1067 | 2013,12,2,Mon,19.69,0.12,0,52,56,47.2,46,61
1068 | 2013,12,3,Tues,7.83,0.18,0,56,46,47,41,35
1069 | 2013,12,4,Wed,12.53,0,0,46,41,46.8,40,40
1070 | 2013,12,5,Thurs,3.58,0,0,41,40,46.6,34,64
1071 | 2013,12,6,Fri,5.82,0,0,40,34,46.4,34,45
1072 | 2013,12,7,Sat,10.51,0,0,34,34,46.3,32,54
1073 | 2013,12,8,Sun,6.93,0,0,34,32,46.1,36,56
1074 | 2013,12,9,Mon,4.92,0,0,32,36,46,34,33
1075 | 2013,12,10,Tues,2.91,0,0,36,34,45.9,42,57
1076 | 2013,12,11,Wed,3.36,0,0,34,42,45.7,41,37
1077 | 2013,12,12,Thurs,1.79,0,0,42,41,45.6,42,47
1078 | 2013,12,13,Fri,5.14,0.27,0,41,42,45.5,49,39
1079 | 2013,12,14,Sat,6.49,0.02,0,42,49,45.4,49,55
1080 | 2013,12,15,Sun,8.28,0,0,49,49,45.3,53,32
1081 | 2013,12,16,Mon,8.72,0.05,0,49,53,45.3,50,37
1082 | 2013,12,17,Tues,2.24,0.01,0,53,50,45.2,47,56
1083 | 2013,12,18,Wed,6.04,0,0,50,47,45.2,46,57
1084 | 2013,12,19,Thurs,6.26,0.05,0,47,46,45.1,41,26
1085 | 2013,12,20,Fri,4.7,0,0,46,41,45.1,47,40
1086 | 2013,12,21,Sat,8.28,0.22,0,41,47,45.1,48,51
1087 | 2013,12,22,Sun,5.14,0.22,0,47,48,45.1,51,44
1088 | 2013,12,23,Mon,8.95,0.42,0,48,51,45.1,53,28
1089 | 2013,12,24,Tues,13.2,0.06,0,51,53,45.1,47,31
1090 | 2013,12,25,Wed,3.8,0,0,53,47,45.1,44,53
1091 | 2013,12,26,Thurs,1.79,0,0,47,44,45.2,44,36
1092 | 2013,12,27,Fri,1.12,0,0,44,44,45.2,48,33
1093 | 2013,12,28,Sat,4.7,0.01,0,44,48,45.3,49,43
1094 | 2013,12,29,Sun,2.91,0,0,48,49,45.3,45,46
1095 | 2013,12,30,Mon,2.46,0,0,49,45,45.4,48,49
1096 | 2013,12,31,Tues,5.82,0.01,0,45,48,45.5,47,56
1097 | 2014,1,1,Wed,3.8,0.02,0,48,47,45.6,45,50
1098 | 2014,1,2,Thurs,2.68,0,0,47,45,45.7,51,33
1099 | 2014,1,3,Fri,7.16,0.16,0,45,51,45.8,48,65
1100 | 2014,1,4,Sat,5.82,0.06,0,51,48,45.9,46,53
1101 | 2014,1,5,Sun,6.04,0,0,48,46,46,47,39
1102 | 2014,1,6,Mon,8.28,0,0,46,47,46.1,46,28
1103 | 2014,1,7,Tues,5.82,0.01,0,47,46,46.2,47,58
1104 | 2014,1,8,Wed,3.58,0.48,0,46,47,46.3,50,43
1105 | 2014,1,9,Thurs,10.29,0.38,0,47,50,46.4,49,61
1106 | 2014,1,10,Fri,14.09,0.23,0,50,49,46.5,55,49
1107 | 2014,1,11,Sat,15.66,0.17,0,49,55,46.7,58,36
1108 | 2014,1,12,Sun,19.69,0.84,0,55,58,46.8,52,38
1109 | 2014,1,13,Mon,18.12,0.06,0,58,52,46.9,51,39
1110 | 2014,1,14,Tues,15.88,0,0,52,51,47,52,36
1111 | 2014,1,15,Wed,2.91,0,0,51,52,47.1,52,54
1112 | 2014,1,16,Thurs,5.59,0,0,52,52,47.3,44,43
1113 | 2014,1,17,Fri,6.04,0,0,52,44,47.4,42,35
1114 | 2014,1,18,Sat,5.14,0,0,44,42,47.5,49,48
1115 | 2014,1,19,Sun,4.92,0,0,42,49,47.6,43,56
1116 | 2014,1,20,Mon,5.59,0,0,49,43,47.7,50,38
1117 | 2014,1,21,Tues,4.92,0,0,43,50,47.8,50,68
1118 | 2014,1,22,Wed,3.36,0,0,50,50,47.9,49,43
1119 | 2014,1,23,Thurs,5.82,0.02,0,50,49,48,50,31
1120 | 2014,1,24,Fri,11.63,0,0,49,50,48.1,55,30
1121 | 2014,1,25,Sat,4.25,0,0,50,55,48.2,54,62
1122 | 2014,1,26,Sun,1.79,0,0,55,54,48.3,47,29
1123 | 2014,1,27,Mon,2.91,0,0,54,47,48.4,49,31
1124 | 2014,1,28,Tues,2.91,0,0,47,49,48.4,52,30
1125 | 2014,1,29,Wed,3.58,0.35,0,49,52,48.5,52,48
1126 | 2014,1,30,Thurs,7.61,0.85,0,52,52,48.6,47,30
1127 | 2014,1,31,Fri,14.32,0,0,52,47,48.7,46,29
1128 | 2014,2,1,Sat,5.82,0.09,0,47,46,48.8,46,61
1129 | 2014,2,2,Sun,1.79,0.08,0,46,46,48.8,48,54
1130 | 2014,2,3,Mon,5.59,0,0,46,48,48.9,41,38
1131 | 2014,2,4,Tues,9.62,0,0,48,41,49,37,69
1132 | 2014,2,5,Wed,10.51,0,0,41,37,49.1,31,45
1133 | 2014,2,6,Thurs,14.76,0,0,37,31,49.1,29,61
1134 | 2014,2,7,Fri,10.07,0,0,31,29,49.2,38,67
1135 | 2014,2,8,Sat,9.4,0,0,29,38,49.3,42,46
1136 | 2014,2,9,Sun,10.29,0.2,0,38,42,49.4,39,65
1137 | 2014,2,10,Mon,5.37,0.02,3,42,39,49.4,50,31
1138 | 2014,2,11,Tues,10.51,0.72,0,39,50,49.5,54,32
1139 | 2014,2,12,Wed,8.5,0.67,0,50,54,49.6,54,55
1140 | 2014,2,13,Thurs,14.32,0.18,0,54,54,49.7,55,67
1141 | 2014,2,14,Fri,14.09,0.07,0,54,55,49.8,53,41
1142 | 2014,2,15,Sat,14.32,0.37,0,55,53,49.9,52,54
1143 | 2014,2,16,Sun,11.41,0.46,0,53,52,49.9,49,62
1144 | 2014,2,17,Mon,17.67,1.04,0,52,49,50,47,34
1145 | 2014,2,18,Tues,12.3,0.57,0,49,47,50.1,48,62
1146 | 2014,2,19,Wed,13.87,0.6,0,47,48,50.2,47,60
1147 | 2014,2,20,Thurs,13.42,0.04,0,48,47,50.4,50,55
1148 | 2014,2,21,Fri,15.43,0.12,0,47,50,50.5,44,43
1149 | 2014,2,22,Sat,6.49,0.11,0,50,44,50.6,42,70
1150 | 2014,2,23,Sun,6.93,0.1,0,44,42,50.7,45,39
1151 | 2014,2,24,Mon,5.82,0.24,0,42,45,50.8,44,38
1152 | 2014,2,25,Tues,7.16,0.51,0,45,44,50.9,54,59
1153 | 2014,2,26,Wed,10.07,0.01,0,44,54,51.1,57,55
1154 | 2014,2,27,Thurs,5.59,0,0,54,57,51.2,55,52
1155 | 2014,2,28,Fri,5.14,0,0,57,55,51.3,58,63
1156 | 2014,3,1,Sat,13.2,0,0,55,58,51.5,45,49
1157 | 2014,3,2,Sun,10.51,0.02,0,58,45,51.6,52,70
1158 | 2014,3,3,Mon,12.75,0.75,0,45,52,51.8,58,62
1159 | 2014,3,4,Tues,11.41,0.42,0,52,58,51.9,57,48
1160 | 2014,3,5,Wed,8.72,0.65,0,58,57,52.1,60,32
1161 | 2014,3,6,Thurs,8.72,1.84,0,57,60,52.2,56,43
1162 | 2014,3,7,Fri,13.87,0.12,0,60,56,52.4,60,62
1163 | 2014,3,8,Sat,9.4,0,0,56,60,52.5,55,62
1164 | 2014,3,9,Sun,6.04,1.27,0,60,55,52.7,59,46
1165 | 2014,3,10,Mon,9.62,0.17,0,55,59,52.8,54,69
1166 | 2014,3,11,Tues,4.92,0.74,0,59,54,53,58,71
1167 | 2014,3,12,Wed,5.14,0,0,54,58,53.1,61,34
1168 | 2014,3,13,Thurs,4.25,0,0,58,61,53.3,57,72
1169 | 2014,3,14,Fri,5.59,0.02,0,61,57,53.4,58,55
1170 | 2014,3,15,Sat,13.65,0.27,0,57,58,53.6,62,54
1171 | 2014,3,16,Sun,6.71,0.32,0,58,62,53.7,51,48
1172 | 2014,3,17,Mon,8.5,1.09,0,62,51,53.9,50,69
1173 | 2014,3,18,Tues,7.16,0.01,0,51,50,54,50,35
1174 | 2014,3,19,Wed,3.58,0,0,50,50,54.2,52,37
1175 | 2014,3,20,Thurs,11.41,0.02,0,50,52,54.3,52,58
1176 | 2014,3,21,Fri,6.71,0,0,52,52,54.5,51,49
1177 | 2014,3,22,Sat,8.5,0,0,52,51,54.6,52,67
1178 | 2014,3,23,Sun,4.03,0,0,51,52,54.7,55,51
1179 | 2014,3,24,Mon,7.38,0,0,52,55,54.9,66,41
1180 | 2014,3,25,Tues,4.92,0,0,55,66,55,57,68
1181 | 2014,3,26,Wed,9.84,0.16,0,66,57,55.2,52,64
1182 | 2014,3,27,Thurs,5.37,0.14,0,57,52,55.3,54,73
1183 | 2014,3,28,Fri,6.26,0.01,0,52,54,55.5,53,49
1184 | 2014,3,29,Sat,8.72,0.87,0,54,53,55.6,53,68
1185 | 2014,3,30,Sun,11.41,0.55,0,53,53,55.7,52,71
1186 | 2014,3,31,Mon,11.41,0,0,53,52,55.9,60,73
1187 | 2014,4,1,Tues,8.5,0,0,52,60,56,58,55
1188 | 2014,4,2,Wed,6.26,0,0,60,58,56.2,58,53
1189 | 2014,4,3,Thurs,9.4,0,0,58,58,56.3,56,57
1190 | 2014,4,4,Fri,8.72,0.1,0,58,56,56.5,55,62
1191 | 2014,4,5,Sat,10.51,0,0,56,55,56.6,53,56
1192 | 2014,4,6,Sun,9.62,0.18,0,55,53,56.8,57,46
1193 | 2014,4,7,Mon,5.82,0,0,53,57,56.9,70,46
1194 | 2014,4,8,Tues,5.59,0,0,57,70,57.1,60,55
1195 | 2014,4,9,Wed,9.4,0.18,0,70,60,57.2,58,54
1196 | 2014,4,10,Thurs,6.49,0,0,60,58,57.4,59,54
1197 | 2014,4,11,Fri,8.05,0,0,58,59,57.6,63,46
1198 | 2014,4,12,Sat,6.26,0,0,59,63,57.7,61,53
1199 | 2014,4,13,Sun,9.84,0,0,63,61,57.9,69,69
1200 | 2014,4,14,Mon,6.93,0,0,61,69,58.1,68,60
1201 | 2014,4,15,Tues,5.82,0,0,69,68,58.3,58,44
1202 | 2014,4,16,Wed,8.95,0.02,0,68,58,58.5,52,76
1203 | 2014,4,17,Thurs,10.29,0.43,0,58,52,58.6,53,69
1204 | 2014,4,18,Fri,10.51,0.73,0,52,53,58.8,58,62
1205 | 2014,4,19,Sat,8.5,0,0,53,58,59,53,43
1206 | 2014,4,20,Sun,10.51,0.54,0,58,53,59.2,60,63
1207 | 2014,4,21,Mon,6.04,0,0,53,60,59.4,63,42
1208 | 2014,4,22,Tues,5.59,0.2,0,60,63,59.7,54,54
1209 | 2014,4,23,Wed,9.4,0.56,0,63,54,59.9,53,47
1210 | 2014,4,24,Thurs,11.18,0.35,0,54,53,60.1,57,59
1211 | 2014,4,25,Fri,11.86,0.49,0,53,57,60.3,58,41
1212 | 2014,4,26,Sat,5.14,0,0,57,58,60.5,59,69
1213 | 2014,4,27,Sun,8.72,0.13,0,58,59,60.7,52,41
1214 | 2014,4,28,Mon,12.97,0.27,0,59,52,61,61,46
1215 | 2014,4,29,Tues,5.82,0,0,52,61,61.2,77,70
1216 | 2014,4,30,Wed,5.14,0,0,61,77,61.4,82,56
1217 | 2014,5,1,Thurs,8.72,0,0,77,82,61.6,85,61
1218 | 2014,5,2,Fri,6.71,0,0,82,85,61.9,65,68
1219 | 2014,5,3,Sat,10.51,0,0,85,65,62.1,59,55
1220 | 2014,5,4,Sun,7.61,1.31,0,65,59,62.3,58,56
1221 | 2014,5,5,Mon,9.4,0.63,0,59,58,62.5,60,44
1222 | 2014,5,6,Tues,8.5,0.2,0,58,60,62.8,62,44
1223 | 2014,5,7,Wed,5.82,0,0,60,62,63,65,80
1224 | 2014,5,8,Thurs,3.8,0,0,62,65,63.2,57,53
1225 | 2014,5,9,Fri,7.61,0.54,0,65,57,63.4,56,76
1226 | 2014,5,10,Sat,12.53,0.08,0,57,56,63.6,60,58
1227 | 2014,5,11,Sun,4.7,0.02,0,56,60,63.8,66,76
1228 | 2014,5,12,Mon,3.8,0,0,60,66,64.1,76,64
1229 | 2014,5,13,Tues,6.04,0,0,66,76,64.3,80,82
1230 | 2014,5,14,Wed,8.5,0,0,76,80,64.5,82,80
1231 | 2014,5,15,Thurs,7.38,0,0,80,82,64.7,80,55
1232 | 2014,5,16,Fri,6.71,0,0,82,80,64.8,68,65
1233 | 2014,5,17,Sat,9.17,0,0,80,68,65,68,77
1234 | 2014,5,18,Sun,7.16,0,0,68,68,65.2,68,85
1235 | 2014,5,19,Mon,7.16,0,0,68,68,65.4,70,78
1236 | 2014,5,20,Tues,4.92,0,0,68,70,65.6,72,56
1237 | 2014,5,21,Wed,6.04,0,0,70,72,65.7,68,46
1238 | 2014,5,22,Thurs,3.8,0,0,72,68,65.9,76,67
1239 | 2014,5,23,Fri,5.59,0,0,68,76,66.1,68,70
1240 | 2014,5,24,Sat,8.95,0.15,0,76,68,66.2,65,61
1241 | 2014,5,25,Sun,5.37,0,0,68,65,66.4,59,63
1242 | 2014,5,26,Mon,3.13,0.22,0,65,59,66.5,65,65
1243 | 2014,5,27,Tues,10.07,0,0,59,65,66.7,68,56
1244 | 2014,5,28,Wed,5.59,0,0,65,68,66.8,66,54
1245 | 2014,5,29,Thurs,7.61,0,0,68,66,67,66,48
1246 | 2014,5,30,Fri,9.62,0,0,66,66,67.1,69,52
1247 | 2014,5,31,Sat,10.07,0,0,66,69,67.3,74,53
1248 | 2014,6,1,Sun,4.92,0,0,69,74,67.4,72,50
1249 | 2014,6,2,Mon,5.14,0,0,74,72,67.6,74,68
1250 | 2014,6,3,Tues,5.37,0,0,72,74,67.7,65,75
1251 | 2014,6,4,Wed,7.16,0,0,74,65,67.9,67,75
1252 | 2014,6,5,Thurs,5.82,0,0,65,67,68,72,85
1253 | 2014,6,6,Fri,5.37,0,0,67,72,68.2,77,60
1254 | 2014,6,7,Sat,7.16,0,0,72,77,68.3,76,59
1255 | 2014,6,8,Sun,6.93,0,0,77,76,68.5,74,53
1256 | 2014,6,9,Mon,4.7,0,0,76,74,68.6,70,88
1257 | 2014,6,10,Tues,8.05,0,0,74,70,68.8,68,53
1258 | 2014,6,11,Wed,6.49,0,0,70,68,69,75,63
1259 | 2014,6,12,Thurs,6.04,0,0,68,75,69.1,71,58
1260 | 2014,6,13,Fri,8.95,0.07,0,75,71,69.3,60,69
1261 | 2014,6,14,Sat,11.18,0.25,0,71,60,69.5,64,57
1262 | 2014,6,15,Sun,7.16,0,0,60,64,69.7,65,74
1263 | 2014,6,16,Mon,8.05,0.02,0,64,65,69.8,64,76
1264 | 2014,6,17,Tues,5.37,0.14,0,65,64,70,64,58
1265 | 2014,6,18,Wed,6.71,0.05,0,64,64,70.2,66,86
1266 | 2014,6,19,Thurs,6.04,0,0,64,66,70.4,78,81
1267 | 2014,6,20,Fri,8.28,0.03,0,66,78,70.6,68,75
1268 | 2014,6,21,Sat,7.61,0.01,0,78,68,70.8,72,58
1269 | 2014,6,22,Sun,8.05,0,0,68,72,71,77,68
1270 | 2014,6,23,Mon,6.04,0,0,72,77,71.3,77,82
1271 | 2014,6,24,Tues,5.59,0,0,77,77,71.5,76,79
1272 | 2014,6,25,Wed,5.59,0,0,77,76,71.7,79,64
1273 | 2014,6,26,Thurs,5.37,0,0,76,79,71.9,70,63
1274 | 2014,6,27,Fri,9.17,0,0,79,70,72.2,70,76
1275 | 2014,6,28,Sat,10.07,0.07,0,70,70,72.4,68,61
1276 | 2014,6,29,Sun,9.62,0.09,0,70,68,72.6,69,90
1277 | 2014,6,30,Mon,7.16,0,0,68,69,72.8,78,93
1278 | 2014,7,1,Tues,9.84,0,0,69,78,73.1,94,65
1279 | 2014,7,2,Wed,7.83,0,0,78,94,73.3,81,68
1280 | 2014,7,3,Thurs,8.05,0,0,94,81,73.5,71,70
1281 | 2014,7,4,Fri,6.93,0,0,81,71,73.8,75,90
1282 | 2014,7,5,Sat,8.05,0,0,71,75,74,76,71
1283 | 2014,7,6,Sun,4.92,0,0,75,76,74.2,84,90
1284 | 2014,7,7,Mon,6.71,0,0,76,84,74.4,81,79
1285 | 2014,7,8,Tues,9.17,0,0,84,81,74.6,86,69
1286 | 2014,7,9,Wed,7.83,0,0,81,86,74.9,80,86
1287 | 2014,7,10,Thurs,5.14,0,0,86,80,75.1,84,86
1288 | 2014,7,11,Fri,4.92,0,0,80,84,75.3,88,74
1289 | 2014,7,12,Sat,4.92,0,0,84,88,75.4,90,74
1290 | 2014,7,13,Sun,4.92,0,0,88,90,75.6,85,89
1291 | 2014,7,14,Mon,5.82,0,0,90,85,75.8,82,78
1292 | 2014,7,15,Tues,6.26,0,0,85,82,76,88,63
1293 | 2014,7,16,Wed,5.14,0,0,82,88,76.1,88,84
1294 | 2014,7,17,Thurs,5.37,0,0,88,88,76.3,80,89
1295 | 2014,7,18,Fri,8.28,0,0,88,80,76.4,75,70
1296 | 2014,7,19,Sat,6.26,0,0,80,75,76.6,78,84
1297 | 2014,7,20,Sun,12.08,0,0,75,78,76.7,67,80
1298 | 2014,7,21,Mon,6.26,0,0,78,67,76.8,75,77
1299 | 2014,7,22,Tues,4.92,0,0,67,75,76.9,70,93
1300 | 2014,7,23,Wed,2.46,0.01,0,75,70,77,66,64
1301 | 2014,7,24,Thurs,7.38,0.76,0,70,66,77.1,69,73
1302 | 2014,7,25,Fri,10.51,0,0,66,69,77.1,73,93
1303 | 2014,7,26,Sat,6.04,0,0,69,73,77.2,79,90
1304 | 2014,7,27,Sun,8.05,0,0,73,79,77.3,83,64
1305 | 2014,7,28,Mon,9.17,0,0,79,83,77.3,87,61
1306 | 2014,7,29,Tues,8.28,0,0,83,87,77.3,86,82
1307 | 2014,7,30,Wed,6.26,0,0,87,86,77.3,85,64
1308 | 2014,7,31,Thurs,7.61,0,0,86,85,77.4,87,60
1309 | 2014,8,1,Fri,9.17,0,0,85,87,77.4,84,64
1310 | 2014,8,2,Sat,7.38,0,0,87,84,77.4,85,63
1311 | 2014,8,3,Sun,3.8,0.02,0,84,85,77.3,89,92
1312 | 2014,8,4,Mon,5.82,0,0,85,89,77.3,91,64
1313 | 2014,8,5,Tues,5.82,0,0,89,91,77.3,77,81
1314 | 2014,8,6,Wed,6.04,0,0,91,77,77.2,79,90
1315 | 2014,8,7,Thurs,4.92,0,0,77,79,77.2,78,72
1316 | 2014,8,8,Fri,5.37,0,0,79,78,77.1,78,59
1317 | 2014,8,9,Sat,6.49,0,0,78,78,77.1,81,73
1318 | 2014,8,10,Sun,9.17,0,0,78,81,77,87,85
1319 | 2014,8,11,Mon,7.61,0,0,81,87,76.9,96,68
1320 | 2014,8,12,Tues,5.82,0.02,0,87,96,76.9,81,86
1321 | 2014,8,13,Wed,6.93,0.5,0,96,81,76.8,74,80
1322 | 2014,8,14,Thurs,6.04,0.85,0,81,74,76.7,70,88
1323 | 2014,8,15,Fri,1.34,0,0,74,70,76.6,76,89
1324 | 2014,8,16,Sat,3.36,0.04,0,70,76,76.5,78,71
1325 | 2014,8,17,Sun,4.92,0,0,76,78,76.4,82,89
1326 | 2014,8,18,Mon,6.26,0,0,78,82,76.3,85,56
1327 | 2014,8,19,Tues,7.38,0,0,82,85,76.2,81,86
1328 | 2014,8,20,Wed,5.37,0,0,85,81,76.1,71,84
1329 | 2014,8,21,Thurs,8.05,0,0,81,71,75.9,70,73
1330 | 2014,8,22,Fri,3.8,0,0,71,70,75.8,75,71
1331 | 2014,8,23,Sat,6.49,0,0,70,75,75.7,82,61
1332 | 2014,8,24,Sun,4.47,0,0,75,82,75.5,77,64
1333 | 2014,8,25,Mon,5.14,0,0,82,77,75.4,84,83
1334 | 2014,8,26,Tues,4.47,0,0,77,84,75.3,88,71
1335 | 2014,8,27,Wed,4.03,0,0,84,88,75.1,84,57
1336 | 2014,8,28,Thurs,3.58,0,0,88,84,75,74,63
1337 | 2014,8,29,Fri,5.14,0,0,84,74,74.8,73,78
1338 | 2014,8,30,Sat,7.61,0,0,74,73,74.6,64,79
1339 | 2014,8,31,Sun,4.92,0.33,0,73,64,74.4,70,83
1340 | 2014,9,1,Mon,4.25,0.05,0,64,70,74.3,74,94
1341 | 2014,9,2,Tues,5.59,0,0,70,74,74.1,68,75
1342 | 2014,9,3,Wed,9.62,0.12,0,74,68,73.9,69,82
1343 | 2014,9,4,Thurs,6.04,0,0,68,69,73.7,75,72
1344 | 2014,9,5,Fri,6.93,0,0,69,75,73.5,82,92
1345 | 2014,9,6,Sat,14.54,0,0,75,82,73.3,90,90
1346 | 2014,9,7,Sun,6.49,0,0,82,90,73,83,88
1347 | 2014,9,8,Mon,4.7,0,0,90,83,72.8,70,71
1348 | 2014,9,9,Tues,6.26,0,0,83,70,72.6,71,65
1349 | 2014,9,10,Wed,5.14,0,0,70,71,72.3,72,71
1350 | 2014,9,11,Thurs,8.72,0,0,71,72,72.1,76,72
1351 | 2014,9,12,Fri,11.86,0,0,72,76,71.8,76,59
1352 | 2014,9,13,Sat,13.2,0,0,76,76,71.5,83,91
1353 | 2014,9,14,Sun,9.4,0,0,76,83,71.2,86,59
1354 | 2014,9,15,Mon,4.03,0,0,83,86,71,87,80
1355 | 2014,9,16,Tues,2.68,0,0,86,87,70.7,72,58
1356 | 2014,9,17,Wed,6.26,0,0,87,72,70.3,73,86
1357 | 2014,9,18,Thurs,5.14,0.02,0,72,73,70,67,57
1358 | 2014,9,19,Fri,6.93,0.01,0,73,67,69.7,75,54
1359 | 2014,9,20,Sat,6.26,0,0,67,75,69.4,76,84
1360 | 2014,9,21,Sun,9.84,0,0,75,76,69,79,86
1361 | 2014,9,22,Mon,6.71,0,0,76,79,68.7,72,76
1362 | 2014,9,23,Tues,4.7,0.01,0,79,72,68.3,66,71
1363 | 2014,9,24,Wed,5.59,0.72,0,72,66,68,66,54
1364 | 2014,9,25,Thurs,6.04,0.8,0,66,66,67.6,71,80
1365 | 2014,9,26,Fri,5.59,0.17,0,66,71,67.2,68,84
1366 | 2014,9,27,Sat,7.38,0.35,0,71,68,66.8,69,53
1367 | 2014,9,28,Sun,7.16,0,0,68,69,66.5,66,52
1368 | 2014,9,29,Mon,4.47,0,0,69,66,66.1,62,77
1369 | 2014,9,30,Tues,7.83,0.03,0,66,62,65.7,67,74
1370 | 2014,10,1,Wed,5.82,0,0,62,67,65.3,65,61
1371 | 2014,10,2,Thurs,4.7,0,0,67,65,64.9,67,84
1372 | 2014,10,3,Fri,4.47,0,0,65,67,64.5,72,73
1373 | 2014,10,4,Sat,2.24,0,0,67,72,64.1,71,63
1374 | 2014,10,5,Sun,2.68,0,0,72,71,63.7,75,81
1375 | 2014,10,6,Mon,3.13,0,0,71,75,63.3,78,75
1376 | 2014,10,7,Tues,5.59,0,0,75,78,62.9,66,61
1377 | 2014,10,8,Wed,2.24,0,0,78,66,62.5,69,80
1378 | 2014,10,9,Thurs,4.03,0,0,66,69,62.1,63,75
1379 | 2014,10,10,Fri,2.24,0,0,69,63,61.8,65,58
1380 | 2014,10,11,Sat,8.5,0.01,0,63,65,61.4,65,68
1381 | 2014,10,12,Sun,7.83,0.29,0,65,65,61,64,61
1382 | 2014,10,13,Mon,4.7,0,0,65,64,60.6,70,77
1383 | 2014,10,14,Tues,6.93,0.3,0,64,70,60.2,62,74
1384 | 2014,10,15,Wed,4.92,0.28,0,70,62,59.9,61,58
1385 | 2014,10,16,Thurs,10.51,0.34,0,62,61,59.5,69,67
1386 | 2014,10,17,Fri,7.38,0,0,61,69,59.1,62,40
1387 | 2014,10,18,Sat,6.71,0.13,0,69,62,58.8,67,75
1388 | 2014,10,19,Sun,4.25,0.59,0,62,67,58.4,72,52
1389 | 2014,10,20,Mon,7.16,0,0,67,72,58.1,61,62
1390 | 2014,10,21,Tues,6.93,0.46,0,72,61,57.8,61,54
1391 | 2014,10,22,Wed,10.51,0.04,0,61,61,57.4,60,43
1392 | 2014,10,23,Thurs,11.18,1.26,0,61,60,57.1,58,75
1393 | 2014,10,24,Fri,10.29,0.37,0,60,58,56.8,58,46
1394 | 2014,10,25,Sat,7.16,0.16,0,58,58,56.5,62,51
1395 | 2014,10,26,Sun,12.08,0.24,0,58,62,56.2,55,52
1396 | 2014,10,27,Mon,11.18,0.06,0,62,55,55.9,60,44
1397 | 2014,10,28,Tues,5.37,0.03,0,55,60,55.6,59,49
1398 | 2014,10,29,Wed,8.72,0.5,0,60,59,55.3,62,52
1399 | 2014,10,30,Thurs,6.93,0.02,0,59,62,55,60,62
1400 | 2014,10,31,Fri,7.16,1,0,62,60,54.8,55,69
1401 | 2014,11,1,Sat,4.47,0.67,0,60,55,54.5,52,64
1402 | 2014,11,2,Sun,2.68,0,0,55,52,54.2,56,69
1403 | 2014,11,3,Mon,6.49,0.07,0,52,56,53.9,57,44
1404 | 2014,11,4,Tues,10.74,0.43,0,56,57,53.7,58,34
1405 | 2014,11,5,Wed,7.38,0.16,0,57,58,53.4,59,34
1406 | 2014,11,6,Thurs,4.7,0.19,0,58,59,53.2,62,70
1407 | 2014,11,7,Fri,14.99,0.16,0,59,62,52.9,58,39
1408 | 2014,11,8,Sat,5.14,0,0,62,58,52.7,55,67
1409 | 2014,11,9,Sun,1.79,0,0,58,55,52.4,56,35
1410 | 2014,11,10,Mon,6.71,0.2,0,55,56,52.2,52,39
1411 | 2014,11,11,Tues,8.72,0,0,56,52,51.9,46,32
1412 | 2014,11,12,Wed,17.22,0,0,52,46,51.7,44,35
1413 | 2014,11,13,Thurs,17,0,0,46,44,51.4,45,45
1414 | 2014,11,14,Fri,10.51,0,0,44,45,51.2,45,63
1415 | 2014,11,15,Sat,10.07,0,0,45,45,51,47,63
1416 | 2014,11,16,Sun,9.4,0,0,45,47,50.7,49,53
1417 | 2014,11,17,Mon,9.4,0,0,47,49,50.5,51,50
1418 | 2014,11,18,Tues,4.25,0,0,49,51,50.3,45,33
1419 | 2014,11,19,Wed,2.01,0,0,51,45,50,52,47
1420 | 2014,11,20,Thurs,4.25,0,0,45,52,49.8,52,61
1421 | 2014,11,21,Fri,4.7,0.14,0,52,52,49.5,52,67
1422 | 2014,11,22,Sat,10.51,0.6,0,52,52,49.3,49,38
1423 | 2014,11,23,Sun,10.51,0.02,0,52,49,49.1,55,58
1424 | 2014,11,24,Mon,11.41,0.47,0,49,55,48.9,53,68
1425 | 2014,11,25,Tues,8.5,0.05,0,55,53,48.6,57,54
1426 | 2014,11,26,Wed,10.07,0.72,0,53,57,48.4,59,58
1427 | 2014,11,27,Thurs,8.72,0.01,0,57,59,48.2,58,51
1428 | 2014,11,28,Fri,14.76,0.13,0,59,58,48,55,38
1429 | 2014,11,29,Sat,12.97,1.35,0,58,55,47.8,40,32
1430 | 2014,11,30,Sun,11.86,0.14,0,55,40,47.6,37,44
1431 | 2014,12,1,Mon,9.84,0,0,40,37,47.4,40,43
1432 | 2014,12,2,Tues,4.92,0,0,37,40,47.2,42,37
1433 | 2014,12,3,Wed,12.75,0,0,40,42,47,50,30
1434 | 2014,12,4,Thurs,8.05,0,0,42,50,46.8,47,56
1435 | 2014,12,5,Fri,2.46,0.03,0,50,47,46.6,55,48
1436 | 2014,12,6,Sat,6.93,0.12,0,47,55,46.4,53,27
1437 | 2014,12,7,Sun,8.05,0.29,0,55,53,46.3,58,35
1438 | 2014,12,8,Mon,6.26,0,0,53,58,46.1,58,60
1439 | 2014,12,9,Tues,9.4,0.36,0,58,58,46,61,56
1440 | 2014,12,10,Wed,11.41,0.39,0,58,61,45.9,66,61
1441 | 2014,12,11,Thurs,14.99,0.51,0,61,66,45.7,58,65
1442 | 2014,12,12,Fri,14.32,0.27,0,66,58,45.6,52,32
1443 | 2014,12,13,Sat,8.28,0,0,58,52,45.5,50,33
1444 | 2014,12,14,Sun,2.46,0,0,52,50,45.4,55,49
1445 | 2014,12,15,Mon,7.83,0,0,50,55,45.3,54,50
1446 | 2014,12,16,Tues,13.2,0,0,55,54,45.3,50,30
1447 | 2014,12,17,Wed,8.95,0,0,54,50,45.2,48,31
1448 | 2014,12,18,Thurs,3.58,0.11,0,50,48,45.2,49,58
1449 | 2014,12,19,Fri,6.93,0.51,0,48,49,45.1,52,60
1450 | 2014,12,20,Sat,9.62,0.12,0,49,52,45.1,55,32
1451 | 2014,12,21,Sun,12.3,0.77,0,52,55,45.1,55,31
1452 | 2014,12,22,Mon,11.63,0,0,55,55,45.1,51,61
1453 | 2014,12,23,Tues,3.36,0,0,55,51,45.1,54,32
1454 | 2014,12,24,Wed,8.5,0.81,0,51,54,45.1,45,38
1455 | 2014,12,25,Thurs,4.03,0.21,0,54,45,45.1,46,41
1456 | 2014,12,26,Fri,4.92,0,0,45,46,45.2,42,54
1457 | 2014,12,27,Sat,2.68,0,0,46,42,45.2,49,59
1458 | 2014,12,28,Sun,10.96,0.13,0,42,49,45.3,44,41
1459 | 2014,12,29,Mon,4.03,0.16,0,49,44,45.3,43,49
1460 | 2014,12,30,Tues,9.62,0,0,44,43,45.4,38,28
1461 | 2014,12,31,Wed,8.05,0,0,43,38,45.5,38,59
1462 | 2015,1,1,Thurs,6.71,0,0,38,38,45.6,42,49
1463 | 2015,1,2,Fri,2.68,0,0,38,42,45.7,42,33
1464 | 2015,1,3,Sat,5.14,0.06,0,42,42,45.8,41,57
1465 | 2015,1,4,Sun,3.8,0,0,42,41,45.9,51,54
1466 | 2015,1,5,Mon,10.07,0.4,0,41,51,46,54,44
1467 | 2015,1,6,Tues,14.32,0.32,0,51,54,46.1,54,55
1468 | 2015,1,7,Wed,2.91,0,0,54,54,46.2,46,40
1469 | 2015,1,8,Thurs,3.58,0,0,54,46,46.3,46,31
1470 | 2015,1,9,Fri,5.82,0,0,46,46,46.4,50,62
1471 | 2015,1,10,Sat,1.34,0.01,0,46,50,46.5,46,62
1472 | 2015,1,11,Sun,1.12,0.23,0,50,46,46.7,49,54
1473 | 2015,1,12,Mon,2.46,0.06,0,46,49,46.8,52,43
1474 | 2015,1,13,Tues,3.58,0,0,49,52,46.9,49,66
1475 | 2015,1,14,Wed,6.04,0,0,52,49,47,43,31
1476 | 2015,1,15,Thurs,6.26,0,0,49,43,47.1,46,36
1477 | 2015,1,16,Fri,7.16,0.38,0,43,46,47.3,53,35
1478 | 2015,1,17,Sat,10.07,0,0,46,53,47.4,56,54
1479 | 2015,1,18,Sun,6.26,1.03,0,53,56,47.5,57,31
1480 | 2015,1,19,Mon,14.76,0.84,0,56,57,47.6,50,39
1481 | 2015,1,20,Tues,6.26,0.02,0,57,50,47.7,50,51
1482 | 2015,1,21,Wed,6.71,0,0,50,50,47.8,45,66
1483 | 2015,1,22,Thurs,2.91,0,0,50,45,47.9,49,59
1484 | 2015,1,23,Fri,2.91,0.03,0,45,49,48,54,54
1485 | 2015,1,24,Sat,5.82,0.23,0,49,54,48.1,58,66
1486 | 2015,1,25,Sun,7.38,0.02,0,54,58,48.2,63,66
1487 | 2015,1,26,Mon,3.13,0,0,58,63,48.3,61,48
1488 | 2015,1,27,Tues,4.92,0,0,63,61,48.4,52,48
1489 | 2015,1,28,Wed,4.47,0.03,0,61,52,48.4,54,49
1490 | 2015,1,29,Thurs,4.03,0,0,52,54,48.5,54,63
1491 | 2015,1,30,Fri,6.49,0,0,54,54,48.6,47,51
1492 | 2015,1,31,Sat,1.79,0,0,54,47,48.7,45,66
1493 | 2015,2,1,Sun,4.25,0,0,47,45,48.8,49,35
1494 | 2015,2,2,Mon,5.82,0.06,0,45,49,48.8,52,37
1495 | 2015,2,3,Tues,8.95,0.29,0,49,52,48.9,50,67
1496 | 2015,2,4,Wed,4.25,0.05,0,52,50,49,51,67
1497 | 2015,2,5,Thurs,3.8,0.33,0,50,51,49.1,56,50
1498 | 2015,2,6,Fri,10.29,1.03,0,51,56,49.1,58,56
1499 | 2015,2,7,Sat,10.07,0.68,0,56,58,49.2,54,35
1500 | 2015,2,8,Sun,10.29,0.93,0,58,54,49.3,59,46
1501 | 2015,2,9,Mon,8.72,0.14,0,54,59,49.4,56,34
1502 | 2015,2,10,Tues,5.59,0.24,0,59,56,49.4,55,47
1503 | 2015,2,11,Wed,8.95,0.01,0,56,55,49.5,55,61
1504 | 2015,2,12,Thurs,2.24,0,0,55,55,49.6,62,48
1505 | 2015,2,13,Fri,4.7,0.04,0,55,62,49.7,60,59
1506 | 2015,2,14,Sat,3.8,0,0,62,60,49.8,58,49
1507 | 2015,2,15,Sun,6.49,0.01,0,60,58,49.9,54,32
1508 | 2015,2,16,Mon,10.74,0,0,58,54,49.9,59,63
1509 | 2015,2,17,Tues,14.76,0,0,54,59,50,61,30
1510 | 2015,2,18,Wed,8.95,0,0,59,61,50.1,54,56
1511 | 2015,2,19,Thurs,5.82,0,0,61,54,50.2,51,60
1512 | 2015,2,20,Fri,4.92,0.18,0,54,51,50.4,52,69
1513 | 2015,2,21,Sat,2.01,0.03,0,51,52,50.5,54,63
1514 | 2015,2,22,Sun,10.07,0,0,52,54,50.6,53,62
1515 | 2015,2,23,Mon,9.4,0,0,54,53,50.7,55,42
1516 | 2015,2,24,Tues,3.13,0,0,53,55,50.8,52,37
1517 | 2015,2,25,Wed,3.36,0,0,55,52,50.9,50,61
1518 | 2015,2,26,Thurs,2.24,0.16,0,52,50,51.1,53,69
1519 | 2015,2,27,Fri,3.13,0.37,0,50,53,51.2,50,57
1520 | 2015,2,28,Sat,8.95,0.72,0,53,50,51.3,54,49
1521 | 2015,3,1,Sun,11.41,0,0,50,54,51.5,52,63
1522 | 2015,3,2,Mon,4.92,0,0,54,52,51.6,52,33
1523 | 2015,3,3,Tues,10.74,0,0,52,52,51.8,51,65
1524 | 2015,3,4,Wed,4.7,0,0,52,51,51.9,55,64
1525 | 2015,3,5,Thurs,4.03,0,0,51,55,52.1,56,38
1526 | 2015,3,6,Fri,2.91,0,0,55,56,52.2,59,60
1527 | 2015,3,7,Sat,3.13,0,0,56,59,52.4,62,54
1528 | 2015,3,8,Sun,6.04,0,0,59,62,52.5,63,63
1529 | 2015,3,9,Mon,3.8,0,0,62,63,52.7,58,59
1530 | 2015,3,10,Tues,4.03,0,0,63,58,52.8,56,35
1531 | 2015,3,11,Wed,5.82,0.03,0,58,56,53,58,45
1532 | 2015,3,12,Thurs,6.93,0.1,0,56,58,53.1,64,43
1533 | 2015,3,13,Fri,7.16,0,0,58,64,53.3,63,39
1534 | 2015,3,14,Sat,4.92,0.08,0,64,63,53.4,57,44
1535 | 2015,3,15,Sun,8.5,0.67,0,63,57,53.6,51,47
1536 | 2015,3,16,Mon,9.4,2.2,0,57,51,53.7,57,70
1537 | 2015,3,17,Tues,6.71,0.04,0,51,57,53.9,56,57
1538 | 2015,3,18,Wed,5.82,0.03,0,57,56,54,60,50
1539 | 2015,3,19,Thurs,5.59,0,0,56,60,54.2,60,71
1540 | 2015,3,20,Fri,4.25,0,0,60,60,54.3,57,53
1541 | 2015,3,21,Sat,4.25,0.16,0,60,57,54.5,56,68
1542 | 2015,3,22,Sun,10.51,0.15,0,57,56,54.6,53,44
1543 | 2015,3,23,Mon,5.14,0.04,0,56,53,54.7,52,41
1544 | 2015,3,24,Tues,6.26,0.32,0,53,52,54.9,55,45
1545 | 2015,3,25,Wed,8.72,0.3,0,52,55,55,58,39
1546 | 2015,3,26,Thurs,9.84,0.2,0,55,58,55.2,69,67
1547 | 2015,3,27,Fri,4.92,0,0,58,69,55.3,65,46
1548 | 2015,3,28,Sat,8.95,0.04,0,69,65,55.5,60,69
1549 | 2015,3,29,Sun,12.75,0,0,65,60,55.6,60,56
1550 | 2015,3,30,Mon,6.71,0,0,60,60,55.7,64,57
1551 | 2015,3,31,Tues,6.49,0.07,0,60,64,55.9,55,71
1552 | 2015,4,1,Wed,9.4,0.04,0,64,55,56,55,53
1553 | 2015,4,2,Thurs,7.16,0.2,0,55,55,56.2,56,40
1554 | 2015,4,3,Fri,5.37,0,0,55,56,56.3,52,49
1555 | 2015,4,4,Sat,8.05,0.06,0,56,52,56.5,55,40
1556 | 2015,4,5,Sun,3.8,0,0,52,55,56.6,62,49
1557 | 2015,4,6,Mon,5.37,0,0,55,62,56.8,57,40
1558 | 2015,4,7,Tues,7.83,0.04,0,62,57,56.9,58,51
1559 | 2015,4,8,Wed,8.72,0.02,0,57,58,57.1,63,62
1560 | 2015,4,9,Thurs,3.8,0,0,58,63,57.2,63,47
1561 | 2015,4,10,Fri,5.14,0,0,63,63,57.4,57,75
1562 | 2015,4,11,Sat,10.29,0.43,0,63,57,57.6,53,50
1563 | 2015,4,12,Sun,14.54,0,0,57,53,57.7,56,50
1564 | 2015,4,13,Mon,8.05,0,0,53,56,57.9,53,78
1565 | 2015,4,14,Tues,8.05,0.55,0,56,53,58.1,53,75
1566 | 2015,4,15,Wed,7.38,0.13,0,53,53,58.3,57,74
1567 | 2015,4,16,Thurs,5.37,0,0,53,57,58.5,64,76
1568 | 2015,4,17,Fri,6.93,0,0,57,64,58.6,66,75
1569 | 2015,4,18,Sat,8.05,0,0,64,66,58.8,66,61
1570 | 2015,4,19,Sun,8.72,0,0,66,66,59,70,41
1571 | 2015,4,20,Mon,8.05,0,0,66,70,59.2,73,63
1572 | 2015,4,21,Tues,5.82,0,0,70,73,59.4,63,77
1573 | 2015,4,22,Wed,7.61,0.22,0,73,63,59.7,60,64
1574 | 2015,4,23,Thurs,5.14,0,0,63,60,59.9,54,59
1575 | 2015,4,24,Fri,9.17,0.12,0,60,54,60.1,54,70
1576 | 2015,4,25,Sat,11.18,0.13,0,54,54,60.3,56,50
1577 | 2015,4,26,Sun,6.71,0.05,0,54,56,60.5,60,65
1578 | 2015,4,27,Mon,6.04,0,0,56,60,60.7,77,74
1579 | 2015,4,28,Tues,5.14,0.01,0,60,77,61,60,73
1580 | 2015,4,29,Wed,9.62,0.07,0,77,60,61.2,61,77
1581 | 2015,4,30,Thurs,10.51,0,0,60,61,61.4,63,74
1582 | 2015,5,1,Fri,4.7,0,0,61,63,61.6,65,70
1583 | 2015,5,2,Sat,8.28,0,0,63,65,61.9,65,55
1584 | 2015,5,3,Sun,8.28,0,0,65,65,62.1,69,64
1585 | 2015,5,4,Mon,5.82,0,0,65,69,62.3,63,53
1586 | 2015,5,5,Tues,11.63,0,0,69,63,62.5,58,63
1587 | 2015,5,6,Wed,11.41,0.24,0,63,58,62.8,62,63
1588 | 2015,5,7,Thurs,5.82,0,0,58,62,63,69,50
1589 | 2015,5,8,Fri,6.71,0,0,62,69,63.2,75,52
1590 | 2015,5,9,Sat,6.71,0,0,69,75,63.4,80,53
1591 | 2015,5,10,Sun,5.82,0,0,75,80,63.6,67,66
1592 | 2015,5,11,Mon,6.26,0,0,80,67,63.8,57,71
1593 | 2015,5,12,Tues,5.59,0,0,67,57,64.1,60,78
1594 | 2015,5,13,Wed,7.38,0.17,0,57,60,64.3,54,48
1595 | 2015,5,14,Thurs,6.26,0.16,0,60,54,64.5,64,75
1596 | 2015,5,15,Fri,4.47,0.01,0,54,64,64.7,68,74
1597 | 2015,5,16,Sat,6.26,0,0,64,68,64.8,60,59
1598 | 2015,5,17,Sun,6.71,0,0,68,60,65,67,47
1599 | 2015,5,18,Mon,4.7,0,0,60,67,65.2,78,79
1600 | 2015,5,19,Tues,6.71,0,0,67,78,65.4,71,49
1601 | 2015,5,20,Wed,5.82,0,0,78,71,65.6,74,67
1602 | 2015,5,21,Thurs,4.03,0,0,71,74,65.7,78,63
1603 | 2015,5,22,Fri,4.7,0,0,74,78,65.9,62,50
1604 | 2015,5,23,Sat,8.28,0,0,78,62,66.1,61,62
1605 | 2015,5,24,Sun,5.82,0,0,62,61,66.2,64,69
1606 | 2015,5,25,Mon,6.04,0,0,61,64,66.4,60,51
1607 | 2015,5,26,Tues,6.04,0,0,64,60,66.5,71,79
1608 | 2015,5,27,Wed,4.7,0,0,60,71,66.7,76,71
1609 | 2015,5,28,Thurs,4.03,0,0,71,76,66.8,82,52
1610 | 2015,5,29,Fri,4.7,0,0,76,82,67,79,83
1611 | 2015,5,30,Sat,5.59,0,0,82,79,67.1,73,59
1612 | 2015,5,31,Sun,5.59,0,0,79,73,67.3,77,80
1613 | 2015,6,1,Mon,4.92,0,0,73,77,67.4,61,66
1614 | 2015,6,2,Tues,7.61,0.18,0,77,61,67.6,64,54
1615 | 2015,6,3,Wed,11.18,0.02,0,61,64,67.7,68,86
1616 | 2015,6,4,Thurs,6.71,0,0,64,68,67.9,73,54
1617 | 2015,6,5,Fri,8.72,0,0,68,73,68,80,54
1618 | 2015,6,6,Sat,9.62,0,0,73,80,68.2,85,74
1619 | 2015,6,7,Sun,5.82,0,0,80,85,68.3,88,50
1620 | 2015,6,8,Mon,7.16,0,0,85,88,68.5,87,56
1621 | 2015,6,9,Tues,7.83,0,0,88,87,68.6,84,79
1622 | 2015,6,10,Wed,6.04,0,0,87,84,68.8,78,58
1623 | 2015,6,11,Thurs,6.71,0,0,84,78,69,76,62
1624 | 2015,6,12,Fri,7.83,0,0,78,76,69.1,68,88
1625 | 2015,6,13,Sat,5.14,0,0,76,68,69.3,75,55
1626 | 2015,6,14,Sun,5.82,0,0,68,75,69.5,82,72
1627 | 2015,6,15,Mon,8.28,0,0,75,82,69.7,86,51
1628 | 2015,6,16,Tues,7.83,0,0,82,86,69.8,73,56
1629 | 2015,6,17,Wed,6.71,0,0,86,73,70,77,71
1630 | 2015,6,18,Thurs,6.93,0,0,73,77,70.2,76,73
1631 | 2015,6,19,Fri,6.71,0,0,77,76,70.4,75,76
1632 | 2015,6,20,Sat,7.16,0.02,0,76,75,70.6,77,79
1633 | 2015,6,21,Sun,9.62,0,0,75,77,70.8,78,51
1634 | 2015,6,22,Mon,7.61,0,0,77,78,71,77,71
1635 | 2015,6,23,Tues,5.37,0,0,78,77,71.3,79,78
1636 | 2015,6,24,Wed,5.37,0,0,77,79,71.5,78,58
1637 | 2015,6,25,Thurs,5.82,0,0,79,78,71.7,87,79
1638 | 2015,6,26,Fri,6.71,0,0,78,87,71.9,89,64
1639 | 2015,6,27,Sat,10.51,0,0,87,89,72.2,92,71
1640 | 2015,6,28,Sun,8.72,0,0,89,92,72.4,83,72
1641 | 2015,6,29,Mon,4.7,0.01,0,92,83,72.6,84,83
1642 | 2015,6,30,Tues,6.04,0,0,83,84,72.8,87,90
1643 | 2015,7,1,Wed,7.61,0,0,84,87,73.1,90,72
1644 | 2015,7,2,Thurs,9.62,0,0,87,90,73.3,93,77
1645 | 2015,7,3,Fri,7.61,0,0,90,93,73.5,92,77
1646 | 2015,7,4,Sat,5.82,0,0,93,92,73.8,92,58
1647 | 2015,7,5,Sun,6.49,0,0,92,92,74,91,73
1648 | 2015,7,6,Mon,4.7,0,0,92,91,74.2,85,94
1649 | 2015,7,7,Tues,7.16,0,0,91,85,74.4,81,64
1650 | 2015,7,8,Wed,5.37,0,0,85,81,74.6,86,90
1651 | 2015,7,9,Thurs,4.25,0,0,81,86,74.9,84,93
1652 | 2015,7,10,Fri,7.61,0,0,86,84,75.1,70,91
1653 | 2015,7,11,Sat,8.28,0,0,84,70,75.3,72,95
1654 | 2015,7,12,Sun,6.71,0,0,70,72,75.4,79,59
1655 | 2015,7,13,Mon,4.92,0,0,72,79,75.6,78,65
1656 | 2015,7,14,Tues,6.93,0,0,79,78,75.8,82,81
1657 | 2015,7,15,Wed,7.38,0,0,78,82,76,79,90
1658 | 2015,7,16,Thurs,7.16,0,0,82,79,76.1,79,58
1659 | 2015,7,17,Fri,6.26,0,0,79,79,76.3,82,65
1660 | 2015,7,18,Sat,7.38,0,0,79,82,76.4,92,63
1661 | 2015,7,19,Sun,7.61,0,0,82,92,76.6,95,88
1662 | 2015,7,20,Mon,7.38,0,0,92,95,76.7,80,84
1663 | 2015,7,21,Tues,8.72,0,0,95,80,76.8,75,90
1664 | 2015,7,22,Wed,5.37,0,0,80,75,76.9,75,74
1665 | 2015,7,23,Thurs,6.26,0,0,75,75,77,79,64
1666 | 2015,7,24,Fri,4.25,0,0,75,79,77.1,73,65
1667 | 2015,7,25,Sat,8.5,0.01,0,79,73,77.1,70,80
1668 | 2015,7,26,Sun,5.37,0,0,73,70,77.2,72,85
1669 | 2015,7,27,Mon,5.82,0.08,0,70,72,77.3,74,92
1670 | 2015,7,28,Tues,4.25,0,0,72,74,77.3,82,71
1671 | 2015,7,29,Wed,7.61,0,0,74,82,77.3,90,90
1672 | 2015,7,30,Thurs,8.5,0,0,82,90,77.3,94,71
1673 | 2015,7,31,Fri,7.83,0,0,90,94,77.4,94,69
1674 | 2015,8,1,Sat,5.82,0,0,94,94,77.4,92,64
1675 | 2015,8,2,Sun,6.93,0,0,94,92,77.4,87,65
1676 | 2015,8,3,Mon,4.47,0,0,92,87,77.3,83,67
1677 | 2015,8,4,Tues,5.14,0,0,87,83,77.3,79,72
1678 | 2015,8,5,Wed,5.82,0,0,83,79,77.3,74,97
1679 | 2015,8,6,Thurs,7.83,0,0,79,74,77.2,77,68
1680 | 2015,8,7,Fri,6.49,0,0,74,77,77.2,83,85
1681 | 2015,8,8,Sat,8.28,0,0,77,83,77.1,77,69
1682 | 2015,8,9,Sun,8.05,0,0,83,77,77.1,83,80
1683 | 2015,8,10,Mon,4.92,0,0,77,83,77,84,88
1684 | 2015,8,11,Tues,5.37,0,0,83,84,76.9,86,66
1685 | 2015,8,12,Wed,9.84,0,0,84,86,76.9,83,60
1686 | 2015,8,13,Thurs,6.04,0.3,0,86,83,76.8,83,67
1687 | 2015,8,14,Fri,4.92,0,0,83,83,76.7,65,65
1688 | 2015,8,15,Sat,11.63,1.2,0,83,65,76.6,71,78
1689 | 2015,8,16,Sun,8.28,0,0,65,71,76.5,77,69
1690 | 2015,8,17,Mon,8.28,0,0,71,77,76.4,81,91
1691 | 2015,8,18,Tues,6.71,0,0,77,81,76.3,86,90
1692 | 2015,8,19,Wed,6.71,0,0,81,86,76.2,89,60
1693 | 2015,8,20,Thurs,4.7,0,0,86,89,76.1,73,57
1694 | 2015,8,21,Fri,9.4,0.08,0,89,73,75.9,72,74
1695 | 2015,8,22,Sat,5.82,0,0,73,72,75.8,80,67
1696 | 2015,8,23,Sun,5.59,0,0,72,80,75.7,82,77
1697 | 2015,8,24,Mon,4.03,0,0,80,82,75.5,75,72
1698 | 2015,8,25,Tues,5.14,0,0,82,75,75.4,78,72
1699 | 2015,8,26,Wed,7.61,0,0,75,78,75.3,83,59
1700 | 2015,8,27,Thurs,3.8,0,0,78,83,75.1,85,73
1701 | 2015,8,28,Fri,4.7,0,0,83,85,75,74,58
1702 | 2015,8,29,Sat,5.82,0.02,0,85,74,74.8,72,60
1703 | 2015,8,30,Sun,12.97,1.28,0,74,72,74.6,68,93
1704 | 2015,8,31,Mon,10.51,0.4,0,72,68,74.4,66,80
1705 | 2015,9,1,Tues,12.97,0,0,68,66,74.3,67,92
1706 | 2015,9,2,Wed,11.18,0.23,0,66,67,74.1,67,91
1707 | 2015,9,3,Thurs,8.5,0,0,67,67,73.9,65,86
1708 | 2015,9,4,Fri,6.49,0,0,67,65,73.7,65,89
1709 | 2015,9,5,Sat,6.49,0,0,65,65,73.5,69,61
1710 | 2015,9,6,Sun,7.83,0.01,0,65,69,73.3,61,59
1711 | 2015,9,7,Mon,5.37,0.21,0,69,61,73,70,62
1712 | 2015,9,8,Tues,3.36,0.01,0,61,70,72.8,73,60
1713 | 2015,9,9,Wed,5.37,0,0,70,73,72.6,76,58
1714 | 2015,9,10,Thurs,7.38,0,0,73,76,72.3,77,71
1715 | 2015,9,11,Fri,8.05,0,0,76,77,72.1,81,64
1716 | 2015,9,12,Sat,6.93,0,0,77,81,71.8,80,54
1717 | 2015,9,13,Sun,4.7,0,0,81,80,71.5,69,91
1718 | 2015,9,14,Mon,6.71,0.02,0,80,69,71.2,62,84
1719 | 2015,9,15,Tues,7.61,0,0,69,62,71,64,53
1720 | 2015,9,16,Wed,6.26,0,0,62,64,70.7,68,85
1721 | 2015,9,17,Thurs,4.25,0.04,0,64,68,70.3,65,78
1722 | 2015,9,18,Fri,8.5,0.07,0,68,65,70,67,52
1723 | 2015,9,19,Sat,5.82,0,0,65,67,69.7,70,75
1724 | 2015,9,20,Sun,9.62,0,0,67,70,69.4,73,56
1725 | 2015,9,21,Mon,15.21,0.16,0,70,73,69,65,64
1726 | 2015,9,22,Tues,6.04,0,0,73,65,68.7,66,70
1727 | 2015,9,23,Wed,4.47,0,0,65,66,68.3,69,68
1728 | 2015,9,24,Thurs,4.03,0,0,66,69,68,72,49
1729 | 2015,9,25,Fri,5.59,0,0,69,72,67.6,60,64
1730 | 2015,9,26,Sat,5.82,0.08,0,72,60,67.2,65,64
1731 | 2015,9,27,Sun,6.04,0,0,60,65,66.8,64,80
1732 | 2015,9,28,Mon,8.5,0,0,65,64,66.5,70,60
1733 | 2015,9,29,Tues,11.41,0,0,64,70,66.1,71,81
1734 | 2015,9,30,Wed,4.25,0,0,70,71,65.7,65,71
1735 | 2015,10,1,Thurs,2.91,0,0,71,65,65.3,70,83
1736 | 2015,10,2,Fri,2.91,0,0,65,70,64.9,60,57
1737 | 2015,10,3,Sat,6.49,0,0,70,60,64.5,67,46
1738 | 2015,10,4,Sun,10.74,0,0,60,67,64.1,73,71
1739 | 2015,10,5,Mon,8.28,0,0,67,73,63.7,74,72
1740 | 2015,10,6,Tues,3.58,0,0,73,74,63.3,65,66
1741 | 2015,10,7,Wed,5.82,0,0,74,65,62.9,61,73
1742 | 2015,10,8,Thurs,4.92,0.39,0,65,61,62.5,66,81
1743 | 2015,10,9,Fri,2.46,0,0,61,66,62.1,67,66
1744 | 2015,10,10,Sat,5.82,0.01,0,66,67,61.8,70,69
1745 | 2015,10,11,Sun,10.51,1.13,0,67,70,61.4,64,53
1746 | 2015,10,12,Mon,5.82,0,0,70,64,61,65,55
1747 | 2015,10,13,Tues,6.26,0.18,0,64,65,60.6,62,46
1748 | 2015,10,14,Wed,7.16,0.05,0,65,62,60.2,59,69
1749 | 2015,10,15,Thurs,11.18,0,0,62,59,59.9,70,53
1750 | 2015,10,16,Fri,7.61,0,0,59,70,59.5,68,49
1751 | 2015,10,17,Sat,2.91,0,0,70,68,59.1,67,60
1752 | 2015,10,18,Sun,2.91,0.01,0,68,67,58.8,59,41
1753 | 2015,10,19,Mon,4.47,0.15,0,67,59,58.4,63,51
1754 | 2015,10,20,Tues,5.82,0.01,0,59,63,58.1,64,56
1755 | 2015,10,21,Wed,4.03,0,0,63,64,57.8,61,66
1756 | 2015,10,22,Thurs,2.91,0,0,64,61,57.4,61,37
1757 | 2015,10,23,Fri,6.04,0,0,61,61,57.1,55,55
1758 | 2015,10,24,Sat,5.82,0,0,61,55,56.8,59,68
1759 | 2015,10,25,Sun,6.49,0,0,55,59,56.5,67,57
1760 | 2015,10,26,Mon,7.61,0.35,0,59,67,56.2,54,74
1761 | 2015,10,27,Tues,10.29,0.27,0,67,54,55.9,61,49
1762 | 2015,10,28,Wed,3.8,0,0,54,61,55.6,57,40
1763 | 2015,10,29,Thurs,6.26,0.13,0,61,57,55.3,59,55
1764 | 2015,10,30,Fri,10.51,0.07,0,57,59,55,63,65
1765 | 2015,10,31,Sat,14.99,0.76,0,59,63,54.8,60,49
1766 | 2015,11,1,Sun,16.11,1.3,0,63,60,54.5,54,70
1767 | 2015,11,2,Mon,13.42,1.03,0,60,54,54.2,52,58
1768 | 2015,11,3,Tues,6.26,0.01,0,54,52,53.9,51,62
1769 | 2015,11,4,Wed,3.13,0.03,0,52,51,53.7,50,55
1770 | 2015,11,5,Thurs,4.92,0,0,51,50,53.4,53,66
1771 | 2015,11,6,Fri,5.14,0.05,0,50,53,53.2,60,67
1772 | 2015,11,7,Sat,6.04,0,0,53,60,52.9,54,52
1773 | 2015,11,8,Sun,6.71,0.5,0,60,54,52.7,52,56
1774 | 2015,11,9,Mon,4.03,0.26,0,54,52,52.4,50,44
1775 | 2015,11,10,Tues,2.91,0.13,0,52,50,52.2,52,51
1776 | 2015,11,11,Wed,8.72,0.05,0,50,52,51.9,52,63
1777 | 2015,11,12,Thurs,10.29,0.06,0,52,52,51.7,52,46
1778 | 2015,11,13,Fri,11.41,0.39,0,52,52,51.4,56,62
1779 | 2015,11,14,Sat,14.54,1.32,0,52,56,51.2,49,54
1780 | 2015,11,15,Sun,10.07,1.86,0,56,49,51,48,37
1781 | 2015,11,16,Mon,9.17,0.88,0,49,48,50.7,48,32
1782 | 2015,11,17,Tues,8.95,0.08,0,48,48,50.5,56,68
1783 | 2015,11,18,Wed,17.9,1.16,0,48,56,50.3,48,35
1784 | 2015,11,19,Thurs,8.5,0.06,0,56,48,50,48,70
1785 | 2015,11,20,Fri,9.4,0.08,0,48,48,49.8,47,41
1786 | 2015,11,21,Sat,8.95,0,0,48,47,49.5,48,31
1787 | 2015,11,22,Sun,10.51,0,0,47,48,49.3,50,53
1788 | 2015,11,23,Mon,6.93,0,0,48,50,49.1,44,53
1789 | 2015,11,24,Tues,2.91,0.12,0,50,44,48.9,44,44
1790 | 2015,11,25,Wed,10.07,0.28,0,44,44,48.6,45,34
1791 | 2015,11,26,Thurs,12.75,0,0,44,45,48.4,49,42
1792 | 2015,11,27,Fri,9.62,0,0,45,49,48.2,49,33
1793 | 2015,11,28,Sat,6.71,0,0,49,49,48,45,59
1794 | 2015,11,29,Sun,2.24,0,0,49,45,47.8,35,63
1795 | 2015,11,30,Mon,2.01,0,0,45,35,47.6,42,32
1796 | 2015,12,1,Tues,3.8,0.02,0,35,42,47.4,50,61
1797 | 2015,12,2,Wed,7.83,0.48,0,42,50,47.2,51,65
1798 | 2015,12,3,Thurs,11.18,0.1,0,50,51,47,60,67
1799 | 2015,12,4,Fri,13.2,0.5,0,51,60,46.8,51,53
1800 | 2015,12,5,Sat,10.51,0.08,0,60,51,46.6,50,34
1801 | 2015,12,6,Sun,8.95,0.62,0,51,50,46.4,55,33
1802 | 2015,12,7,Mon,13.2,0.44,0,50,55,46.3,52,64
1803 | 2015,12,8,Tues,7.61,1.08,0,55,52,46.1,60,63
1804 | 2015,12,9,Wed,13.87,2.13,0,52,60,46,54,54
1805 | 2015,12,10,Thurs,14.09,0.53,0,60,54,45.9,53,36
1806 | 2015,12,11,Fri,16.78,0.37,0,54,53,45.7,49,63
1807 | 2015,12,12,Sat,6.26,0.01,0,53,49,45.6,48,65
1808 | 2015,12,13,Sun,12.53,0.63,0,49,48,45.5,46,31
1809 | 2015,12,14,Mon,13.65,0.05,0,48,46,45.4,46,38
1810 | 2015,12,15,Tues,3.8,0,0,46,46,45.3,44,53
1811 | 2015,12,16,Wed,6.49,0.06,0,46,44,45.3,43,47
1812 | 2015,12,17,Thurs,5.14,0.14,0,44,43,45.2,44,60
1813 | 2015,12,18,Fri,13.42,0.86,0,43,44,45.2,48,55
1814 | 2015,12,19,Sat,11.41,0.73,0,44,48,45.1,47,26
1815 | 2015,12,20,Sun,9.17,0,0,48,47,45.1,46,52
1816 | 2015,12,21,Mon,14.99,0.17,0,47,46,45.1,42,34
1817 | 2015,12,22,Tues,9.62,1.08,0,46,42,45.1,46,55
1818 | 2015,12,23,Wed,11.18,0.18,0,42,46,45.1,41,57
1819 | 2015,12,24,Thurs,17,0.24,0,46,41,45.1,42,50
1820 | 2015,12,25,Fri,9.62,0.1,0,41,42,45.1,41,41
1821 | 2015,12,26,Sat,3.36,0.23,0,42,41,45.2,40,47
1822 | 2015,12,27,Sun,5.59,0,0,41,40,45.2,40,59
1823 | 2015,12,28,Mon,6.49,0.34,0,40,40,45.3,41,29
1824 | 2015,12,29,Tues,2.91,0.06,0,40,41,45.3,45,43
1825 | 2015,12,30,Wed,5.82,0,0,41,45,45.4,42,50
1826 | 2015,12,31,Thurs,7.61,0,0,45,42,45.5,42,57
1827 | 2016,1,1,Fri,7.83,0,0,42,42,45.6,46,53
1828 | 2016,1,2,Sat,4.92,0,0,42,46,45.7,42,33
1829 | 2016,1,3,Sun,5.59,0,0,46,42,45.8,40,52
1830 | 2016,1,4,Mon,9.17,0.02,0,42,40,45.9,38,53
1831 | 2016,1,5,Tues,7.61,0.15,0,40,38,46,46,60
1832 | 2016,1,6,Wed,5.37,0.11,0,38,46,46.1,53,29
1833 | 2016,1,7,Thurs,6.49,0,0,46,53,46.2,44,48
1834 | 2016,1,8,Fri,2.91,0,0,53,44,46.3,48,46
1835 | 2016,1,9,Sat,1.57,0,0,44,48,46.4,49,47
1836 | 2016,1,10,Sun,2.91,0,0,48,49,46.5,52,58
1837 | 2016,1,11,Mon,8.05,0,0,49,52,46.7,47,58
1838 | 2016,1,12,Tues,6.93,0.16,0,52,47,46.8,48,40
1839 | 2016,1,13,Wed,11.63,0.51,0,47,48,46.9,54,56
1840 | 2016,1,14,Thurs,7.83,0.75,0,48,54,47,48,56
1841 | 2016,1,15,Fri,6.93,0,0,54,48,47.1,47,51
1842 | 2016,1,16,Sat,6.04,0.05,0,48,47,47.3,52,35
1843 | 2016,1,17,Sun,9.17,0.4,0,47,52,47.4,49,38
1844 | 2016,1,18,Mon,7.61,0.33,0,52,49,47.5,52,62
1845 | 2016,1,19,Tues,7.61,0.06,0,49,52,47.6,47,60
1846 | 2016,1,20,Wed,4.47,0.49,0,52,47,47.7,50,38
1847 | 2016,1,21,Thurs,6.26,0.27,0,47,50,47.8,53,52
1848 | 2016,1,22,Fri,7.61,1.15,0,50,53,47.9,55,36
1849 | 2016,1,23,Sat,9.4,0.26,0,53,55,48,46,44
1850 | 2016,1,24,Sun,4.7,0.77,0,55,46,48.1,50,47
1851 | 2016,1,25,Mon,5.14,0,0,46,50,48.2,55,30
1852 | 2016,1,26,Tues,4.7,0,0,50,55,48.3,56,39
1853 | 2016,1,27,Wed,6.04,0.3,0,55,56,48.4,58,64
1854 | 2016,1,28,Thurs,5.37,0.82,0,56,58,48.4,56,49
1855 | 2016,1,29,Fri,10.74,0.61,0,58,56,48.5,49,64
1856 | 2016,1,30,Sat,10.51,0.19,0,56,49,48.6,46,44
1857 | 2016,1,31,Sun,11.41,0.05,0,49,46,48.7,45,54
1858 | 2016,2,1,Mon,9.62,0,0,46,45,48.8,45,35
1859 | 2016,2,2,Tues,4.47,0.01,0,45,45,48.8,50,47
1860 | 2016,2,3,Wed,3.58,0.01,0,45,50,48.9,47,67
1861 | 2016,2,4,Thurs,9.84,0.53,0,50,47,49,49,48
1862 | 2016,2,5,Fri,9.84,0.33,0,47,49,49.1,52,68
1863 | 2016,2,6,Sat,8.72,0.14,0,49,52,49.1,48,40
1864 | 2016,2,7,Sun,8.28,0,0,52,48,49.2,52,39
1865 | 2016,2,8,Mon,5.37,0,0,48,52,49.3,59,55
1866 | 2016,2,9,Tues,8.5,0,0,52,59,49.4,63,34
1867 | 2016,2,10,Wed,5.59,0,0,59,63,49.4,56,39
1868 | 2016,2,11,Thurs,5.82,0.13,0,63,56,49.5,55,62
1869 | 2016,2,12,Fri,5.37,0.48,0,56,55,49.6,57,61
1870 | 2016,2,13,Sat,9.17,0.8,0,55,57,49.7,50,35
1871 | 2016,2,14,Sun,11.63,0.49,0,57,50,49.8,53,40
1872 | 2016,2,15,Mon,13.65,0.15,0,50,53,49.9,54,43
1873 | 2016,2,16,Tues,14.32,0.46,0,53,54,49.9,54,31
1874 | 2016,2,17,Wed,7.61,0,0,54,54,50,63,51
1875 | 2016,2,18,Thurs,6.04,0.47,0,54,63,50.1,51,41
1876 | 2016,2,19,Fri,10.51,0.12,0,63,51,50.2,50,42
1877 | 2016,2,20,Sat,11.63,0.4,0,51,50,50.4,51,65
1878 | 2016,2,21,Sun,8.05,0,0,50,51,50.5,50,59
1879 | 2016,2,22,Mon,7.61,0.05,0,51,50,50.6,51,57
1880 | 2016,2,23,Tues,6.71,0.17,0,50,51,50.7,58,65
1881 | 2016,2,24,Wed,9.62,0,0,51,58,50.8,57,68
1882 | 2016,2,25,Thurs,2.91,0.02,0,58,57,50.9,62,40
1883 | 2016,2,26,Fri,2.91,0,0,57,62,51.1,57,40
1884 | 2016,2,27,Sat,3.58,0.17,0,62,57,51.2,56,37
1885 | 2016,2,28,Sun,5.59,0.13,0,57,56,51.3,50,48
1886 | 2016,3,1,Tues,5.14,0.09,0,50,53,51.5,57,55
1887 | 2016,3,2,Wed,13.42,0.81,0,53,57,51.6,54,65
1888 | 2016,3,3,Thurs,10.07,0.28,0,57,54,51.8,58,52
1889 | 2016,3,4,Fri,10.29,0.03,0,54,58,51.9,55,55
1890 | 2016,3,5,Sat,5.37,0.16,0,58,55,52.1,64,54
1891 | 2016,3,6,Sun,9.84,0.19,0,55,64,52.2,59,60
1892 | 2016,3,7,Mon,10.29,0.16,0,64,59,52.4,51,67
1893 | 2016,3,8,Tues,12.53,0.2,0,59,51,52.5,53,47
1894 | 2016,3,9,Wed,6.71,0.14,0,51,53,52.7,55,33
1895 | 2016,3,10,Thurs,12.08,0.93,0,53,55,52.8,55,72
1896 | 2016,3,11,Fri,15.43,0.23,0,55,55,53,60,68
1897 | 2016,3,12,Sat,6.04,0.32,0,55,60,53.1,50,65
1898 | 2016,3,13,Sun,12.08,0.15,0,60,50,53.3,54,68
1899 | 2016,3,14,Mon,14.99,0.62,0,50,54,53.4,47,34
1900 | 2016,3,15,Tues,15.43,0.25,0,54,47,53.6,50,56
1901 | 2016,3,16,Wed,10.51,0,0,47,50,53.7,53,52
1902 | 2016,3,17,Thurs,6.04,0,0,50,53,53.9,57,51
1903 | 2016,3,18,Fri,11.63,0,0,53,57,54,63,60
1904 | 2016,3,19,Sat,14.54,0,0,57,63,54.2,61,51
1905 | 2016,3,20,Sun,4.47,0,0,63,61,54.3,54,53
1906 | 2016,3,21,Mon,4.7,0.17,0,61,54,54.5,55,58
1907 | 2016,3,22,Tues,7.38,0.34,0,54,55,54.6,55,37
1908 | 2016,3,23,Wed,12.08,0,0,55,55,54.7,51,36
1909 | 2016,3,24,Thurs,12.3,0.23,0,55,51,54.9,52,37
1910 | 2016,3,25,Fri,14.99,0,0,51,52,55,56,72
1911 | 2016,3,26,Sat,6.26,0,0,52,56,55.2,58,61
1912 | 2016,3,27,Sun,4.7,0.13,0,56,58,55.3,55,40
1913 | 2016,3,28,Mon,14.54,0.18,0,58,55,55.5,55,61
1914 | 2016,3,29,Tues,5.59,0,0,55,55,55.6,63,40
1915 | 2016,3,30,Wed,9.62,0,0,55,63,55.7,68,69
1916 | 2016,3,31,Thurs,4.47,0,0,63,68,55.9,71,44
1917 | 2016,4,1,Fri,3.13,0,0,68,71,56,71,40
1918 | 2016,4,2,Sat,4.25,0,0,71,71,56.2,62,42
1919 | 2016,4,3,Sun,5.82,0,0,71,62,56.3,68,65
1920 | 2016,4,4,Mon,5.82,0.16,0,62,68,56.5,58,69
1921 | 2016,4,5,Tues,15.66,0.03,0,68,58,56.6,56,52
1922 | 2016,4,6,Wed,8.5,0,0,58,56,56.8,69,61
1923 | 2016,4,7,Thurs,8.72,0,0,56,69,56.9,78,56
1924 | 2016,4,8,Fri,9.4,0,0,69,78,57.1,76,67
1925 | 2016,4,9,Sat,7.61,0,0,78,76,57.2,64,41
1926 | 2016,4,10,Sun,4.92,0,0,76,64,57.4,58,42
1927 | 2016,4,11,Mon,7.61,0,0,64,58,57.6,58,54
1928 | 2016,4,12,Tues,7.38,0,0,58,58,57.7,57,62
1929 | 2016,4,13,Wed,9.84,0.32,0,58,57,57.9,58,49
1930 | 2016,4,14,Thurs,6.04,0.1,0,57,58,58.1,59,47
1931 | 2016,4,15,Fri,6.93,0.15,0,58,59,58.3,59,48
1932 | 2016,4,16,Sat,7.16,0,0,59,59,58.5,67,46
1933 | 2016,4,17,Sun,5.37,0,0,59,67,58.6,80,72
1934 | 2016,4,18,Mon,8.72,0,0,67,80,58.8,89,62
1935 | 2016,4,19,Tues,5.14,0,0,80,89,59,84,55
1936 | 2016,4,20,Wed,4.92,0,0,89,84,59.2,81,54
1937 | 2016,4,21,Thurs,5.37,0,0,84,81,59.4,72,43
1938 | 2016,4,22,Fri,7.83,0,0,81,72,59.7,62,47
1939 | 2016,4,23,Sat,9.62,0,0,72,62,59.9,64,54
1940 | 2016,4,24,Sun,11.41,0.01,0,62,64,60.1,55,62
1941 | 2016,4,25,Mon,9.17,0.3,0,64,55,60.3,59,50
1942 | 2016,4,26,Tues,5.37,0.11,0,55,59,60.5,59,74
1943 | 2016,4,27,Wed,4.92,0,0,59,59,60.7,63,62
1944 | 2016,4,28,Thurs,4.7,0,0,59,63,61,63,48
1945 | 2016,4,29,Fri,5.82,0,0,63,63,61.2,61,69
1946 | 2016,4,30,Sat,4.47,0.01,0,63,61,61.4,67,62
1947 | 2016,5,1,Sun,6.71,0,0,61,67,61.6,79,56
1948 | 2016,5,2,Mon,9.84,0,0,67,79,61.9,87,66
1949 | 2016,5,3,Tues,4.92,0,0,79,87,62.1,72,81
1950 | 2016,5,4,Wed,6.49,0,0,87,72,62.3,60,45
1951 | 2016,5,5,Thurs,8.95,0,0,72,60,62.5,68,71
1952 | 2016,5,6,Fri,8.95,0,0,60,68,62.8,77,69
1953 | 2016,5,7,Sat,10.51,0,0,68,77,63,82,46
1954 | 2016,5,8,Sun,6.71,0,0,77,82,63.2,66,73
1955 | 2016,5,9,Mon,9.84,0.03,0,82,66,63.4,67,55
1956 | 2016,5,10,Tues,5.82,0,0,66,67,63.6,75,63
1957 | 2016,5,11,Wed,6.71,0,0,67,75,63.8,80,54
1958 | 2016,5,12,Thurs,5.59,0,0,75,80,64.1,76,74
1959 | 2016,5,13,Fri,4.92,0,0,80,76,64.3,84,75
1960 | 2016,5,14,Sat,9.17,0,0,76,84,64.5,58,80
1961 | 2016,5,15,Sun,6.93,0.02,0,84,58,64.7,56,45
1962 | 2016,5,16,Mon,6.04,0.02,0,58,56,64.8,59,47
1963 | 2016,5,17,Tues,5.14,0.06,0,56,59,65,69,65
1964 | 2016,5,18,Wed,7.83,0,0,59,69,65.2,65,67
1965 | 2016,5,19,Thurs,6.71,0,0,69,65,65.4,63,73
1966 | 2016,5,20,Fri,9.4,0.5,0,65,63,65.6,66,49
1967 | 2016,5,21,Sat,7.38,0,0,63,66,65.7,58,71
1968 | 2016,5,22,Sun,9.62,0.21,0,66,58,65.9,66,50
1969 | 2016,5,23,Mon,12.3,0,0,58,66,66.1,66,72
1970 | 2016,5,24,Tues,6.71,0,0,66,66,66.2,66,75
1971 | 2016,5,25,Wed,4.47,0,0,66,66,66.4,65,66
1972 | 2016,5,26,Thurs,8.95,0,0,66,65,66.5,62,52
1973 | 2016,5,27,Fri,10.51,0,0,65,62,66.7,64,47
1974 | 2016,5,28,Sat,9.62,0,0,62,64,66.8,62,59
1975 | 2016,5,29,Sun,10.07,0.1,0,64,62,67,64,56
1976 | 2016,5,30,Mon,10.74,0,0,62,64,67.1,72,62
1977 | 2016,5,31,Tues,9.62,0,0,64,72,67.3,80,74
1978 | 2016,6,1,Wed,6.49,0,0,72,80,67.4,74,68
1979 | 2016,6,2,Thurs,7.83,0.04,0,80,74,67.6,70,65
1980 | 2016,6,3,Fri,10.74,0.01,0,74,70,67.7,82,84
1981 | 2016,6,4,Sat,8.05,0,0,70,82,67.9,85,77
1982 | 2016,6,5,Sun,12.3,0,0,82,85,68,93,75
1983 | 2016,6,6,Mon,7.61,0,0,85,93,68.2,88,75
1984 | 2016,6,7,Tues,7.61,0,0,93,88,68.3,84,69
1985 | 2016,6,8,Wed,6.93,0,0,88,84,68.5,67,65
1986 | 2016,6,9,Thurs,10.74,0.01,0,84,67,68.6,64,84
1987 | 2016,6,10,Fri,7.16,0.13,0,67,64,68.8,65,78
1988 | 2016,6,11,Sat,9.84,0.18,0,64,65,69,67,66
1989 | 2016,6,12,Sun,7.16,0,0,65,67,69.1,70,88
1990 | 2016,6,13,Mon,7.16,0,0,67,70,69.3,65,58
1991 | 2016,6,14,Tues,12.3,0,0,70,65,69.5,60,71
1992 | 2016,6,15,Wed,13.65,0.02,0,65,60,69.7,66,59
1993 | 2016,6,16,Thurs,7.16,0,0,60,66,69.8,70,77
1994 | 2016,6,17,Fri,7.61,0,0,66,70,70,69,88
1995 | 2016,6,18,Sat,7.61,0.14,0,70,69,70.2,66,83
1996 | 2016,6,19,Sun,9.4,0,0,69,66,70.4,72,71
1997 | 2016,6,20,Mon,10.74,0,0,66,72,70.6,75,69
1998 | 2016,6,21,Tues,8.5,0.55,0,72,75,70.8,71,79
1999 | 2016,6,22,Wed,9.62,0,0,75,71,71,75,80
2000 | 2016,6,23,Thurs,4.92,0,0,71,75,71.3,68,80
2001 | 2016,6,24,Fri,8.72,0.35,0,75,68,71.5,69,78
2002 | 2016,6,25,Sat,9.62,0.34,0,68,69,71.7,72,60
2003 | 2016,6,26,Sun,8.28,0,0,69,72,71.9,80,76
2004 | 2016,6,27,Mon,10.96,0,0,72,80,72.2,85,60
2005 | 2016,6,28,Tues,5.82,0,0,80,85,72.4,76,78
2006 | 2016,6,29,Wed,6.71,0,0,85,76,72.6,72,92
2007 | 2016,6,30,Thurs,6.04,0,0,76,72,72.8,73,63
2008 | 2016,7,1,Fri,4.7,0,0,72,73,73.1,75,58
2009 | 2016,7,2,Sat,6.04,0,0,73,75,73.3,75,60
2010 | 2016,7,3,Sun,7.16,0,0,75,75,73.5,71,76
2011 | 2016,7,4,Mon,5.82,0,0,75,71,73.8,69,55
2012 | 2016,7,5,Tues,7.61,0,0,71,69,74,67,93
2013 | 2016,7,6,Wed,4.7,0,0,69,67,74.2,76,81
2014 | 2016,7,7,Thurs,5.82,0.01,0,67,76,74.4,68,63
2015 | 2016,7,8,Fri,6.71,0.12,0,76,68,74.6,72,86
2016 | 2016,7,9,Sat,8.05,0.24,0,68,72,74.9,71,79
2017 | 2016,7,10,Sun,6.93,0.01,0,72,71,75.1,73,83
2018 | 2016,7,11,Mon,5.59,0,0,71,73,75.3,73,94
2019 | 2016,7,12,Tues,8.05,0,0,73,73,75.4,76,62
2020 | 2016,7,13,Wed,7.61,0,0,73,76,75.6,74,83
2021 | 2016,7,14,Thurs,6.93,0,0,76,74,75.8,76,75
2022 | 2016,7,15,Fri,8.05,0,0,74,76,76,76,57
2023 | 2016,7,16,Sat,7.38,0,0,76,76,76.1,73,59
2024 | 2016,7,17,Sun,6.49,0,0,76,73,76.3,79,70
2025 | 2016,7,18,Mon,4.7,0,0,73,79,76.4,71,76
2026 | 2016,7,19,Tues,6.71,0.01,0,79,71,76.6,77,66
2027 | 2016,7,20,Wed,6.26,0,0,71,77,76.7,80,60
2028 | 2016,7,21,Thurs,5.82,0,0,77,80,76.8,84,59
2029 | 2016,7,22,Fri,9.4,0,0,80,84,76.9,73,91
2030 | 2016,7,23,Sat,8.28,0.33,0,84,73,77,73,72
2031 | 2016,7,24,Sun,5.14,0,0,73,73,77.1,81,91
2032 | 2016,7,25,Mon,9.62,0,0,73,81,77.1,86,80
2033 | 2016,7,26,Tues,6.26,0,0,81,86,77.2,78,78
2034 | 2016,7,27,Wed,4.92,0,0,86,78,77.3,84,67
2035 | 2016,7,28,Thurs,7.16,0,0,78,84,77.3,88,83
2036 | 2016,7,29,Fri,8.28,0,0,84,88,77.3,89,93
2037 | 2016,7,30,Sat,8.05,0,0,88,89,77.3,75,95
2038 | 2016,7,31,Sun,9.17,0,0,89,75,77.4,72,68
2039 | 2016,8,1,Mon,7.61,0,0,75,72,77.4,77,88
2040 | 2016,8,2,Tues,5.82,0,0,72,77,77.4,71,83
2041 | 2016,8,3,Wed,8.05,0.13,0,77,71,77.3,76,70
2042 | 2016,8,4,Thurs,6.93,0,0,71,76,77.3,82,78
2043 | 2016,8,5,Fri,10.07,0,0,76,82,77.3,79,64
2044 | 2016,8,6,Sat,6.49,0,0,82,79,77.2,71,69
2045 | 2016,8,7,Sun,5.82,0,0,79,71,77.2,69,78
2046 | 2016,8,8,Mon,7.16,0.03,0,71,69,77.1,73,87
2047 | 2016,8,9,Tues,6.49,0,0,69,73,77.1,71,70
2048 | 2016,8,10,Wed,6.26,0,0,73,71,77,76,73
2049 | 2016,8,11,Thurs,6.04,0,0,71,76,76.9,81,84
2050 | 2016,8,12,Fri,8.5,0,0,76,81,76.9,90,75
2051 | 2016,8,13,Sat,7.38,0,0,81,90,76.8,91,92
2052 | 2016,8,14,Sun,5.82,0,0,90,91,76.7,85,61
2053 | 2016,8,15,Mon,6.71,0,0,91,85,76.6,83,76
2054 | 2016,8,16,Tues,5.37,0,0,85,83,76.5,83,67
2055 | 2016,8,17,Wed,6.04,0,0,83,83,76.4,80,95
2056 | 2016,8,18,Thurs,7.38,0,0,83,80,76.3,87,76
2057 | 2016,8,19,Fri,9.62,0,0,80,87,76.2,95,91
2058 | 2016,8,20,Sat,11.63,0,0,87,95,76.1,91,88
2059 | 2016,8,21,Sun,4.25,0,0,95,91,75.9,73,69
2060 | 2016,8,22,Mon,10.74,0,0,91,73,75.8,72,67
2061 | 2016,8,23,Tues,6.26,0,0,73,72,75.7,80,73
2062 | 2016,8,24,Wed,7.16,0,0,72,80,75.5,85,88
2063 | 2016,8,25,Thurs,7.61,0,0,80,85,75.4,91,83
2064 | 2016,8,26,Fri,7.16,0,0,85,91,75.3,92,84
2065 | 2016,8,27,Sat,4.25,0,0,91,92,75.1,73,63
2066 | 2016,8,28,Sun,11.41,0,0,92,73,75,70,69
2067 | 2016,8,29,Mon,5.14,0,0,73,70,74.8,79,60
2068 | 2016,8,30,Tues,3.8,0,0,70,79,74.6,71,61
2069 | 2016,8,31,Wed,8.05,0,0,79,71,74.4,69,63
2070 | 2016,9,1,Thurs,8.05,0.01,0,71,69,74.3,66,72
2071 | 2016,9,2,Fri,8.05,0.17,0,69,66,74.1,69,68
2072 | 2016,9,3,Sat,11.18,0.05,0,66,69,73.9,69,58
2073 | 2016,9,4,Sun,7.38,0,0,69,69,73.7,70,93
2074 | 2016,9,5,Mon,4.7,0,0,69,70,73.5,64,70
2075 | 2016,9,6,Tues,7.38,0.01,0,70,64,73.3,65,57
2076 | 2016,9,7,Wed,3.8,0.42,0,64,65,73,66,72
2077 | 2016,9,8,Thurs,8.28,0.04,0,65,66,72.8,70,69
2078 | 2016,9,9,Fri,7.38,0.03,0,66,70,72.6,73,68
2079 | 2016,9,10,Sat,5.59,0,0,70,73,72.3,77,71
2080 | 2016,9,11,Sun,4.25,0,0,73,77,72.1,70,86
2081 | 2016,9,12,Mon,6.71,0,0,77,70,71.8,75,62
2082 | 2016,9,13,Tues,11.63,0,0,70,75,71.5,77,54
2083 | 2016,9,14,Wed,7.16,0,0,75,77,71.2,78,86
2084 | 2016,9,15,Thurs,5.82,0,0,77,78,71,72,89
2085 | 2016,9,16,Fri,4.25,0,0,78,72,70.7,74,72
2086 | 2016,9,17,Sat,4.7,0,0,72,74,70.3,67,79
2087 | 2016,9,18,Sun,11.86,0.22,0,74,67,70,68,81
2088 | 2016,9,19,Mon,7.83,0,0,67,68,69.7,68,77
2089 | 2016,9,20,Tues,11.41,0.08,0,68,68,69.4,67,62
2090 | 2016,9,21,Wed,6.71,0,0,68,67,69,68,77
2091 | 2016,9,22,Thurs,8.5,0,0,67,68,68.7,65,83
2092 | 2016,9,23,Fri,7.83,0,0,68,65,68.3,63,82
2093 | 2016,9,24,Sat,10.51,0.01,0,65,63,68,66,67
2094 | 2016,9,25,Sun,4.47,0,0,63,66,67.6,76,80
2095 | 2016,9,26,Mon,5.82,0,0,66,76,67.2,77,71
2096 | 2016,9,27,Tues,5.82,0,0,76,77,66.8,69,52
2097 | 2016,9,28,Wed,10.51,0.02,0,77,69,66.5,67,51
2098 | 2016,9,29,Thurs,7.83,0,0,69,67,66.1,66,58
2099 | 2016,9,30,Fri,5.14,0,0,67,66,65.7,65,65
2100 | 2016,10,1,Sat,5.37,0,0,66,65,65.3,63,46
2101 | 2016,10,2,Sun,7.38,0.05,0,65,63,64.9,63,49
2102 | 2016,10,3,Mon,4.03,0,0,63,63,64.5,60,73
2103 | 2016,10,4,Tues,6.93,0,0,63,60,64.1,60,77
2104 | 2016,10,5,Wed,9.62,0.13,0,60,60,63.7,65,49
2105 | 2016,10,6,Thurs,5.59,0.04,0,60,65,63.3,62,44
2106 | 2016,10,7,Fri,9.62,0.31,0,65,62,62.9,63,60
2107 | 2016,10,8,Sat,11.41,0.11,0,62,63,62.5,65,71
2108 | 2016,10,9,Sun,11.63,0.61,0,63,65,62.1,56,57
2109 | 2016,10,10,Mon,6.04,0.06,0,65,56,61.8,60,44
2110 | 2016,10,11,Tues,5.59,0,0,56,60,61.4,62,81
2111 | 2016,10,12,Wed,8.05,0,0,60,62,61,66,48
2112 | 2016,10,13,Thurs,3.8,0.01,0,62,66,60.6,59,46
2113 | 2016,10,14,Fri,10.74,1.75,0,66,59,60.2,57,75
2114 | 2016,10,15,Sat,15.88,1.36,0,59,57,59.9,62,55
2115 | 2016,10,16,Sun,14.32,0.73,0,57,62,59.5,60,77
2116 | 2016,10,17,Mon,8.28,0.56,0,62,60,59.1,59,52
2117 | 2016,10,18,Tues,9.84,0.17,0,60,59,58.8,58,66
2118 | 2016,10,19,Wed,10.74,0.08,0,59,58,58.4,58,44
2119 | 2016,10,20,Thurs,4.92,0.22,0,58,58,58.1,60,45
2120 | 2016,10,21,Fri,8.5,1.19,0,58,60,57.8,57,51
2121 | 2016,10,22,Sat,7.38,0.02,0,60,57,57.4,62,58
2122 | 2016,10,23,Sun,3.36,0.05,0,57,62,57.1,62,40
2123 | 2016,10,24,Mon,6.26,0.12,0,62,62,56.8,63,50
2124 | 2016,10,25,Tues,7.61,0.1,0,62,63,56.5,63,41
2125 | 2016,10,26,Wed,11.18,0,0,63,63,56.2,58,70
2126 | 2016,10,27,Thurs,8.05,1.23,0,63,58,55.9,58,41
2127 | 2016,10,28,Fri,7.16,0.04,0,58,58,55.6,67,50
2128 | 2016,10,29,Sat,7.61,0,0,58,67,55.3,60,57
2129 | 2016,10,30,Sun,4.92,0.19,0,67,60,55,52,73
2130 | 2016,10,31,Mon,6.93,0.26,0,60,52,54.8,57,57
2131 | 2016,11,1,Tues,10.07,0.66,0,52,57,54.5,56,72
2132 | 2016,11,2,Wed,9.17,0.22,0,57,56,54.2,56,69
2133 | 2016,11,3,Thurs,7.38,0.46,0,56,56,53.9,65,36
2134 | 2016,11,4,Fri,4.47,0,0,56,65,53.7,63,34
2135 | 2016,11,5,Sat,4.03,0,0,65,63,53.4,60,58
2136 | 2016,11,6,Sun,6.93,1.31,0,63,60,53.2,60,34
2137 | 2016,11,7,Mon,7.16,0.12,0,60,60,52.9,66,65
2138 | 2016,11,8,Tues,4.92,0.04,0,60,66,52.7,70,65
2139 | 2016,11,9,Wed,5.59,0,0,66,70,52.4,64,65
2140 | 2016,11,10,Thurs,6.71,0.24,0,70,64,52.2,62,36
2141 | 2016,11,11,Fri,3.8,0,0,64,62,51.9,61,50
2142 | 2016,11,12,Sat,2.46,0,0,62,61,51.7,58,62
2143 | 2016,11,13,Sun,12.97,0.1,0,61,58,51.4,54,39
2144 | 2016,11,14,Mon,8.05,0.24,0,58,54,51.2,57,63
2145 | 2016,11,15,Tues,5.82,0.31,0,54,57,51,54,48
2146 | 2016,11,16,Wed,10.96,1.08,0,57,54,50.7,50,35
2147 | 2016,11,17,Thurs,6.49,0.2,0,54,50,50.5,51,37
2148 | 2016,11,18,Fri,4.25,0,0,50,51,50.3,54,65
2149 | 2016,11,19,Sat,12.3,0,0,51,54,50,57,38
2150 | 2016,11,20,Sun,10.51,0.04,0,54,57,49.8,57,39
2151 | 2016,11,21,Mon,10.07,0.01,0,57,57,49.5,53,53
2152 | 2016,11,22,Tues,10.07,0.02,0,57,53,49.3,53,60
2153 | 2016,11,23,Wed,8.5,0.45,0,53,53,49.1,48,62
2154 | 2016,11,24,Thurs,11.18,0.13,0,53,48,48.9,51,57
2155 | 2016,11,25,Fri,12.53,0.66,0,48,51,48.6,52,36
2156 | 2016,11,26,Sat,5.82,0.01,0,51,52,48.4,51,64
2157 | 2016,11,27,Sun,6.71,0.38,0,52,51,48.2,47,68
2158 | 2016,11,28,Mon,10.07,0.32,0,51,47,48,50,61
2159 | 2016,11,29,Tues,6.26,0,0,47,50,47.8,51,67
2160 | 2016,11,30,Wed,6.26,0.04,0,50,51,47.6,50,44
2161 | 2016,12,1,Thurs,14.09,0.1,0,51,50,47.4,46,40
2162 | 2016,12,2,Fri,6.93,0.01,0,50,46,47.2,49,49
2163 | 2016,12,3,Sat,11.41,0.27,0,46,49,47,48,43
2164 | 2016,12,4,Sun,13.87,0.18,0,49,48,46.8,45,42
2165 | 2016,12,5,Mon,8.72,0.17,0,48,45,46.6,39,51
2166 | 2016,12,6,Tues,8.05,0.24,0,45,39,46.4,40,66
2167 | 2016,12,7,Wed,7.61,0,0,39,40,46.3,38,52
2168 | 2016,12,8,Thurs,11.18,0,0,40,38,46.1,38,52
2169 | 2016,12,9,Fri,14.32,0.06,0,38,38,46,36,34
2170 | 2016,12,10,Sat,12.75,0.34,1,38,36,45.9,44,55
2171 | 2016,12,11,Sun,12.3,0.18,0,36,44,45.7,42,60
2172 | 2016,12,12,Mon,13.87,0.09,0,44,42,45.6,42,26
2173 | 2016,12,13,Tues,8.95,0.09,0,42,42,45.5,38,60
2174 | 2016,12,14,Wed,10.51,0,0,42,38,45.4,39,53
2175 | 2016,12,15,Thurs,13.87,0,0,38,39,45.3,38,64
2176 | 2016,12,16,Fri,6.93,0,0,39,38,45.3,34,35
2177 | 2016,12,17,Sat,8.5,0,0,38,34,45.2,33,61
2178 | 2016,12,18,Sun,5.37,0,0,34,33,45.2,39,32
2179 | 2016,12,19,Mon,5.82,0,0,33,39,45.1,45,43
2180 | 2016,12,20,Tues,10.51,0.81,0,39,45,45.1,50,56
2181 | 2016,12,21,Wed,14.32,0.01,0,45,50,45.1,47,27
2182 | 2016,12,22,Thurs,4.03,0,0,50,47,45.1,44,41
2183 | 2016,12,23,Fri,6.71,0.3,0,47,44,45.1,39,35
2184 | 2016,12,24,Sat,5.37,0.58,0,44,39,45.1,40,54
2185 | 2016,12,25,Sun,2.01,0.1,0,39,40,45.1,42,34
2186 | 2016,12,26,Mon,4.25,0,0,40,42,45.2,42,30
2187 | 2016,12,27,Tues,8.5,0.29,0,42,42,45.2,44,27
2188 | 2016,12,28,Wed,15.21,0.05,0,42,44,45.3,47,30
2189 | 2016,12,29,Thurs,8.72,0,0,44,47,45.3,48,63
2190 | 2016,12,30,Fri,8.5,0.05,0,47,48,45.4,45,57
2191 | 2016,12,31,Sat,6.93,0.02,0,48,45,45.5,38,56
2192 | 2017,1,1,Sun,8.05,0.03,0,45,38,45.6,37,27
--------------------------------------------------------------------------------
/temp-predict/news/newsbayes.py:
--------------------------------------------------------------------------------
1 | # -*- encoding: utf-8 -*-
2 | """
3 | @File : newsbayes.py
4 | @Time : 2020/5/25 15:51
5 | @Author : 王一宁
6 | @Email : wyn_365@163.com
7 | """
8 | import pandas as pd
9 | import jieba
10 |
11 | # 数据 指定列名字
12 | df_news = pd.read_table('D:\\APP\\PythonCharm2018\\workspace\\temp-predict\\data\\data.txt',names=['category','theme','URL','content'],encoding='utf-8')
13 | df_news = df_news.dropna()
14 | df_news.tail()
15 |
16 | df_news.shape
17 |
18 | # 分词
19 | content = df_news.content.values.tolist() #将每一篇文章转换成一个list
20 | print (content[1000]) #随便选择其中一个看看
21 |
22 | content_S = []
23 | for line in content:
24 | current_segment = jieba.lcut(line) #对每一篇文章进行分词
25 | if len(current_segment) > 1 and current_segment != '\r\n': #换行符
26 | content_S.append(current_segment) #保存分词的结果
27 |
28 | # 查看第1000个分词结果
29 | content_S[1000]
30 |
31 | # 专门展示分词后的结果
32 | df_content=pd.DataFrame({'content_S':content_S})
33 | df_content.head()
34 |
35 | stopwords=pd.read_csv("D:\\APP\\PythonCharm2018\\workspace\\temp-predict\\data\\stopwords.txt",index_col=False,sep="\t",quoting=3,names=['stopword'], encoding='utf-8')
36 | stopwords.head(20)
37 |
38 | # 筛选 停用词 没用的词
39 | def drop_stopwords(contents, stopwords):
40 | contents_clean = []
41 | all_words = []
42 | for line in contents:
43 | line_clean = []
44 | for word in line:
45 | if word in stopwords:
46 | continue
47 | line_clean.append(word) # 保存
48 | all_words.append(str(word))
49 | contents_clean.append(line_clean)
50 | return contents_clean, all_words
51 |
52 |
53 | contents = df_content.content_S.values.tolist()
54 | stopwords = stopwords.stopword.values.tolist()
55 | contents_clean, all_words = drop_stopwords(contents, stopwords)
56 |
57 | df_content=pd.DataFrame({'contents_clean':contents_clean})
58 | df_content.head()
59 |
60 | df_all_words=pd.DataFrame({'all_words':all_words})
61 | df_all_words.head()
62 |
63 | print("=================================================")
64 |
65 | # 词云展示
66 | import numpy
67 | words_count = df_all_words.groupby(by=['all_words'])['all_words'].agg({"count":numpy.size})
68 | words_count = words_count.reset_index().sort_values(by=["count"],ascending=False)
69 | words_count.head()
70 |
71 | from wordcloud import WordCloud
72 | import matplotlib.pyplot as plt
73 | import matplotlib
74 | matplotlib.rcParams['figure.figsize'] = (10.0, 5.0)
75 |
76 | wordcloud = WordCloud(font_path="",background_color="while",max_font_size=80)
77 | word_frequence = {x[0]:x[1] for x in words_count.head(100).values}
78 | wordcloud=wordcloud.fit_words(word_frequence)
79 | plt.imshow(wordcloud)
80 | plt.show()
81 |
82 |
83 | # TF-IDF 提取关键词
84 | import jieba.analyse #工具包
85 | index = 2400 #随便找一篇文章就行
86 | content_S_str = "".join(content_S[index]) #把分词的结果组合在一起，形成一个句子
87 | print (content_S_str) #打印这个句子
88 | print (" ".join(jieba.analyse.extract_tags(content_S_str, topK=5, withWeight=False)))#选出来5个核心词
89 |
90 | df_train=pd.DataFrame({'contents_clean':contents_clean,'label':df_news['category']})
91 | df_train.tail()
92 |
93 | # 制作数据集标签
94 | df_train.label.unique()
95 |
96 | label_mapping = {"汽车": 1, "财经": 2, "科技": 3, "健康": 4, "体育":5, "教育": 6,"文化": 7,"军事": 8,"娱乐": 9,"时尚": 0}
97 | df_train['label'] = df_train['label'].map(label_mapping) #构建一个映射方法
98 | df_train.head()
99 |
100 | from sklearn.model_selection import train_test_split
101 |
102 | x_train, x_test, y_train, y_test = train_test_split(df_train['contents_clean'].values, df_train['label'].values, random_state=1)
103 |
104 | #x_train = x_train.flatten()
105 | x_train[0][1]
106 |
107 | words = []
108 | for line_index in range(len(x_train)):
109 | try:
110 | #x_train[line_index][word_index] = str(x_train[line_index][word_index])
111 | words.append(' '.join(x_train[line_index]))
112 | except:
113 | print (line_index,word_index)
114 | words[0]
115 |
116 | print (len(words))
117 |
118 | # 制作词袋模型特征
119 | from sklearn.feature_extraction.text import CountVectorizer
120 | texts=["dog cat fish","dog cat cat","fish bird", 'bird'] #为了简单期间，这里4句话我们就当做4篇文章了
121 | cv = CountVectorizer() #词频统计
122 | cv_fit=cv.fit_transform(texts) #转换数据
123 |
124 | print(cv.get_feature_names())
125 | print(cv_fit.toarray())
126 |
127 |
128 | print(cv_fit.toarray().sum(axis=0))
129 |
130 | from sklearn.feature_extraction.text import CountVectorizer
131 | texts=["dog cat fish","dog cat cat","fish bird", 'bird']
132 | cv = CountVectorizer(ngram_range=(1,4)) #设置ngram参数，让结果不光包含一个词，还有2个，3个的组合
133 | cv_fit=cv.fit_transform(texts)
134 |
135 | print(cv.get_feature_names())
136 | print(cv_fit.toarray())
137 |
138 |
139 | print(cv_fit.toarray().sum(axis=0))
140 |
141 | from sklearn.feature_extraction.text import CountVectorizer
142 |
143 | vec = CountVectorizer(analyzer='word',lowercase = False)
144 | feature = vec.fit_transform(words)
145 | feature.shape
146 |
147 | from sklearn.feature_extraction.text import CountVectorizer
148 |
149 | vec = CountVectorizer(analyzer='word', max_features=4000, lowercase = False)
150 | feature = vec.fit_transform(words)
151 |
152 | feature.shape
153 |
154 | # 磁带特征建模
155 | from sklearn.naive_bayes import MultinomialNB #贝叶斯模型
156 | classifier = MultinomialNB()
157 | classifier.fit(feature, y_train)
158 |
159 | test_words = []
160 | for line_index in range(len(x_test)):
161 | try:
162 | #
163 | test_words.append(' '.join(x_test[line_index]))
164 | except:
165 | print (line_index,word_index)
166 | test_words[0]
167 |
168 | classifier.score(vec.transform(test_words), y_test)
169 |
170 | # 制作TF-IDF特征
171 | from sklearn.feature_extraction.text import TfidfVectorizer
172 |
173 | X_test = ['卡尔 敌法师 蓝胖子 小小','卡尔 敌法师 蓝胖子 痛苦女王']
174 |
175 | tfidf=TfidfVectorizer()
176 | weight=tfidf.fit_transform(X_test).toarray()
177 | word=tfidf.get_feature_names()
178 | print (weight)
179 | for i in range(len(weight)):
180 | print (u"第", i, u"篇文章的tf-idf权重特征")
181 | for j in range(len(word)):
182 | print (word[j], weight[i][j])
183 |
184 | # 使用TF-IDF特征建模来观察结果
185 | from sklearn.feature_extraction.text import TfidfVectorizer
186 |
187 | vectorizer = TfidfVectorizer(analyzer='word', max_features=4000, lowercase = False)
188 | vectorizer.fit(words)
189 |
190 | from sklearn.naive_bayes import MultinomialNB
191 | classifier = MultinomialNB()
192 | classifier.fit(vectorizer.transform(words), y_train)
193 |
194 | classifier.score(vectorizer.transform(test_words), y_test)
195 |
196 |
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