将连续数据更改为离散数据以在 ggplot 中使用

Change continious data to discrete ones to be used in ggplot

我有这个数据

return_cluster
    year variable      value
1   1988       V1   1.721150
2   1989       V1   5.288607
3   1990       V1  63.355563
4   1991       V1  47.516021
5   1992       V1  48.766120
6   1993       V1  20.463455
7   1994       V1   1.116961
8   1995       V1   1.804225
9   1996       V1   5.159672
10  1997       V1   2.342435
11  1998       V1   1.831826
12  1999       V1   2.916135
13  2000       V1   2.096115
14  2001       V1   2.205473
15  2002       V1  97.766868
16  2003       V1   1.395028
17  2004       V1  14.778782
18  2005       V1  21.626368
19  2006       V1 335.569275
20  2007       V1   2.577229
21  2008       V1  46.794667
22  2009       V1  18.456694
23  2010       V1  28.347202
24  2011       V1   1.754569
25  2012       V1   1.967250
26  2013       V1   1.355391
27  2014       V1   2.310848
28  2015       V1   1.236857
29  2016       V1  72.404651
30  2017       V1 327.830361
31  1988       V2   1.602976
32  1989       V2 144.354881
33  1990       V2  98.927558
34  1991       V2  47.287984
35  1992       V2  47.254139
36  1993       V2  50.085161
37  1994       V2   1.519317
38  1995       V2   1.290500
39  1996       V2  27.165630
40  1997       V2   3.708591
41  1998       V2   2.900900
42  1999       V2  32.971143
43  2000       V2   1.783584
44  2001       V2   1.556819
45  2002       V2   2.397101
46  2003       V2   2.100313
47  2004       V2   1.878331
48  2005       V2  59.814862
49  2006       V2 561.607269
50  2007       V2  18.060577
51  2008       V2  24.921608
52  2009       V2  25.684559
53  2010       V2  30.211938
54  2011       V2  14.210188
55  2012       V2  19.619167
56  2013       V2   1.614514
57  2014       V2   9.915848
58  2015       V2  46.625465
59  2016       V2   2.088176
60  2017       V2  27.044079
61  1988       V3   3.491779
62  1989       V3   2.808945
63  1990       V3  91.647147
64  1991       V3   3.398350
65  1992       V3  50.143573
66  1993       V3 183.457528
67  1994       V3   1.281821
68  1995       V3   1.875897
69  1996       V3 371.005217
70  1997       V3   2.425475
71  1998       V3  18.015665
72  1999       V3   1.419050
73  2000       V3  47.724070
74  2001       V3   1.143962
75  2002       V3   1.951659
76  2003       V3   2.676810
77  2004       V3   2.597871
78  2005       V3  69.679082
79  2006       V3   7.110426
80  2007       V3  47.511509
81  2008       V3   1.880928
82  2009       V3   1.988084
83  2010       V3  83.713620
84  2011       V3  31.541859
85  2012       V3   1.897654
86  2013       V3   2.341847
87  2014       V3   3.733591
88  2015       V3  23.842150
89  2016       V3  13.638751
90  2017       V3   1.712088
91  1988       V4   1.486628
92  1989       V4   1.407240
93  1990       V4  26.211749
94  1991       V4  67.534555
95  1992       V4  92.307045
96  1993       V4   3.528735
97  1994       V4  17.328366
98  1995       V4   4.674465
99  1996       V4  88.268235
100 1997       V4 132.922890
101 1998       V4   4.439413
102 1999       V4   1.401003
103 2000       V4   2.812608
104 2001       V4   1.526576
105 2002       V4   4.921232
106 2003       V4  51.542854
107 2004       V4   3.860508
108 2005       V4   2.243960
109 2006       V4   2.507551
110 2007       V4  18.467204
111 2008       V4   4.301163
112 2009       V4   1.188909
113 2010       V4 186.013334
114 2011       V4 260.879110
115 2012       V4   1.833610
116 2013       V4  69.432973
117 2014       V4   9.092139
118 2015       V4   1.710509
119 2016       V4   1.442288
120 2017       V4  21.648317
121 1988       V5   1.124042
122 1989       V5   5.834290
123 1990       V5  18.423492
124 1991       V5  71.584537
125 1992       V5  48.946872
126 1993       V5   3.994614
127 1994       V5   1.375991
128 1995       V5   1.389525
129 1996       V5   4.434723
130 1997       V5   3.192515
131 1998       V5  57.507683
132 1999       V5   1.107362
133 2000       V5   1.440586
134 2001       V5   1.216205
135 2002       V5   1.601559
136 2003       V5   4.691851
137 2004       V5   2.350900
138 2005       V5   4.148232
139 2006       V5   2.560688
140 2007       V5 161.408504
141 2008       V5   1.526090
142 2009       V5   2.304330
143 2010       V5   3.424447
144 2011       V5 251.419437
145 2012       V5   1.477274
146 2013       V5   1.403403
147 2014       V5   2.473882
148 2015       V5   1.708639
149 2016       V5   2.773274
150 2017       V5   6.344827
151 1988       V6   1.518616
152 1989       V6  58.778503
153 1990       V6   2.912868
154 1991       V6   2.684705
155 1992       V6  24.784557
156 1993       V6  47.099474
157 1994       V6   1.901775
158 1995       V6  16.253749
159 1996       V6  11.504747
160 1997       V6   2.819182
161 1998       V6  19.980632
162 1999       V6   1.390191
163 2000       V6   4.207280
164 2001       V6   1.332001
165 2002       V6   3.676687
166 2003       V6 208.344203
167 2004       V6   4.790638
168 2005       V6  68.214507
169 2006       V6 244.268239
170 2007       V6   8.262237
171 2008       V6   1.743053
172 2009       V6  19.925573
173 2010       V6   2.313235
174 2011       V6 265.988933
175 2012       V6   1.549337
176 2013       V6   1.484683
177 2014       V6 135.733452
178 2015       V6   2.846374
179 2016       V6   1.094535
180 2017       V6 148.108424
181 1988       V7   1.686723
182 1989       V7   7.227748
183 1990       V7  35.340633
184 1991       V7  10.835810
185 1992       V7   2.138298
186 1993       V7   1.575984
187 1994       V7  10.129774
188 1995       V7  50.201159
189 1996       V7  13.209625
190 1997       V7   2.184044
191 1998       V7  16.584965
192 1999       V7   2.480640
193 2000       V7   5.299119
194 2001       V7   1.319700
195 2002       V7   2.265038
196 2003       V7 355.111101
197 2004       V7  21.919797
198 2005       V7 104.912027
199 2006       V7 254.276631
200 2007       V7  49.192583
201 2008       V7  24.358314
202 2009       V7  11.218015
203 2010       V7  14.356157
204 2011       V7  48.386913
205 2012       V7   7.390883
206 2013       V7   4.089435
207 2014       V7   1.693219
208 2015       V7 297.646638
209 2016       V7   1.670901
210 2017       V7  29.193866
211 1988       V8   4.291061
212 1989       V8  10.147915
213 1990       V8  45.486480
214 1991       V8  68.942930
215 1992       V8  16.576701
216 1993       V8  11.493921
217 1994       V8   8.940650
218 1995       V8  47.943372
219 1996       V8   7.088878
220 1997       V8  11.432932
221 1998       V8  76.057542
222 1999       V8   1.912122
223 2000       V8   2.881820
224 2001       V8  17.467523
225 2002       V8   1.830679
226 2003       V8 353.781607
227 2004       V8   3.107457
228 2005       V8  35.021645
229 2006       V8  64.324620
230 2007       V8  59.491304
231 2008       V8  24.755776
232 2009       V8   2.546088
233 2010       V8   1.617441
234 2011       V8   3.438010
235 2012       V8  12.320183
236 2013       V8  13.426661
237 2014       V8   1.687504
238 2015       V8 109.999377
239 2016       V8   5.236363
240 2017       V8  14.251569
241 1988       V9  11.798883
242 1989       V9  38.970656
243 1990       V9  95.412293
244 1991       V9 162.053168
245 1992       V9  10.486107
246 1993       V9  62.022276
247 1994       V9   1.305770
248 1995       V9  46.413613
249 1996       V9  10.403840
250 1997       V9  68.976595
251 1998       V9  10.699454
252 1999       V9   2.762492
253 2000       V9  11.052923
254 2001       V9   2.285492
255 2002       V9   2.210362
256 2003       V9 363.602498
257 2004       V9  49.067139
258 2005       V9  21.810117
259 2006       V9  10.043886
260 2007       V9  23.549936
261 2008       V9  24.546635
262 2009       V9 157.500968
263 2010       V9   2.152319
264 2011       V9   2.684216
265 2012       V9   9.913968
266 2013       V9  10.366976
267 2014       V9   3.802875
268 2015       V9  62.889429
269 2016       V9  36.761245
270 2017       V9  27.634371
271 1988      V10  24.666011
272 1989      V10 187.050844
273 1990      V10  61.212670
274 1991      V10  23.364418
275 1992      V10  18.781973
276 1993      V10   1.101492
277 1994      V10   4.368106
278 1995      V10  78.504693
279 1996      V10   2.042534
280 1997      V10 208.185547
281 1998      V10   9.015323
282 1999      V10   1.846591
283 2000      V10   1.535912
284 2001      V10  24.826558
285 2002      V10  10.650948
286 2003      V10 207.507455
287 2004      V10  20.041347
288 2005      V10 101.049600
289 2006      V10  50.859651
290 2007      V10  41.764536
291 2008      V10  18.482884
292 2009      V10 344.989241
293 2010      V10  36.112390
294 2011      V10  78.213088
295 2012      V10   1.848186
296 2013      V10   2.696125
297 2014      V10  56.530760
298 2015      V10  48.419746
299 2016      V10  87.035051
300 2017      V10  26.644928
301 1988      V11  70.310367
302 1989      V11 174.213909
303 1990      V11  15.481841
304 1991      V11  15.031165
305 1992      V11  23.790369
306 1993      V11   1.751277
307 1994      V11   3.166003
308 1995      V11  57.540285
309 1996      V11   2.287525
310 1997      V11  20.564880
311 1998      V11  17.683897
312 1999      V11   1.706362
313 2000      V11   1.411072
314 2001      V11   9.545351
315 2002      V11   5.165300
316 2003      V11  12.511814
317 2004      V11  54.369030
318 2005      V11 324.799491
319 2006      V11  18.882354
320 2007      V11 111.124844
321 2008      V11   1.890256
322 2009      V11  18.330233
323 2010      V11   1.681225
324 2011      V11 268.381319
325 2012      V11   1.590532
326 2013      V11   1.217613
327 2014      V11   1.221466
328 2015      V11 172.429858
329 2016      V11  18.171812
330 2017      V11  13.392772
331 1988      V12  13.568733
332 1989      V12 283.648545
333 1990      V12  28.669739
334 1991      V12  54.023489
335 1992      V12  12.933720
336 1993      V12   5.902544
337 1994      V12  12.104002
338 1995      V12  53.059435
339 1996      V12   1.565862
340 1997      V12  23.069261
341 1998      V12  34.233273
342 1999      V12   6.442077
343 2000      V12   7.617126
344 2001      V12  66.787312
345 2002      V12   1.324712
346 2003      V12  27.446611
347 2004      V12  82.908726
348 2005      V12 364.147874
349 2006      V12  39.927251
350 2007      V12  54.989735
351 2008      V12   2.448610
352 2009      V12   5.168975
353 2010      V12   1.674610
354 2011      V12  11.135477
355 2012      V12   3.013439
356 2013      V12   5.467428
357 2014      V12   2.414924
358 2015      V12 101.976833
359 2016      V12 209.803373
360 2017      V12  18.037623

这给了我这个 ggplot:

ggplot(return_cluster, mapping = aes(x = variable, y = year, fill = value)) +
  geom_raster() +
  scale_y_continuous(trans = "reverse") +
  scale_fill_gradientn(colors = c("blue4", "dodgerblue1", "green3", "yellow", "red"), limits = c(0,600), name = "value")

一切正常,但我想做一个小改动。我想要一个具有用户定义的比例范围和颜色的离散色标,而不是连续的色标。因此,例如 0-10 应该是蓝色,10-20 应该是绿色,依此类推……最后一个是 100-500 的一种颜色。这种离散表示具有优势,即使在低值区域(低于 100)也可以看到一些差异。在上面的图(连续比例)中,几乎所有的瓷砖都是蓝色的,因为有一些非常高的值“拉伸”了比例。或者有人知道更好的表示法,不仅可以看到 100 和 500 之间的差异,还可以看到 10 和 20 之间的差异吗?

乍一看,您只需在 scale_fill_gradientn() 调用中使用 breaks 即可获得所需内容。但这给了你一个没有吸引力的图例:

所以,您需要做一些数据操作来创建一个因子,然后使用 scale_fill_manual()

return_cluster %>% 
  mutate(ValueCut=cut(value, breaks=c(0, 10, 20, 30, 100, 600))) %>% 
  ggplot(mapping = aes(x = variable, y = year, fill = ValueCut)) +
  geom_raster() +
  scale_y_continuous(trans = "reverse") +
  scale_fill_manual(values = c("blue4", "dodgerblue1", "green3", "yellow", "red"))

然后您可以根据需要自定义图例,使用 scale_fill_manualcut 的其他参数。

下次,请post以更方便的形式提供您的数据。 [dput() 将是完美的。]。获取您的数据比找到解决方案花费的时间更长。

我喜欢 Limey 的方法。这是另一种在 scale_fill_gradientn():

中对 value 进行对数转换的方法

对数变换的结果图

代码

# to factor and tweak for correct order in x axis
return_cluster$variable <- factor(return_cluster$variable, 
                                  levels=unique(as.character(return_cluster$variable)))
# desired breaks
my_breaks <- c(10, 20, 100, 500)
# labels for legend
my_label <- c(10, 20, 100, 500)
 
# plot
p <- ggplot(return_cluster, mapping = aes(x = variable, y = year, fill = value)) +
  geom_raster() +
  scale_y_continuous(trans = "reverse") 

p + scale_fill_gradientn(colors = c("blue4", "dodgerblue1", "green3", "yellow", "red"), 
                         trans = "log", 
                         breaks = my_breaks, 
                         label = my_label)

说明

  • 首先定义你的休息和标签,并调整变量 variable:
# to factor and tweak for right order in x axis
return_cluster$variable <- factor(return_cluster$variable, 
                                  levels=unique(as.character(return_cluster$variable)))
# desired breaks
my_breaks <- c(10, 20, 100, 500)
# labels for legend
my_label <- c(10, 20, 100, 500)
  • 您的数据是右偏的:histogram(return_cluster$value)

  • 我建议对您的数据进行日志转换:histogram(log(return_cluster$value))

  • 这可以通过 scale_fill_gradientn()

    中的 trans = "log" 来实现
  • 然后用ggplot

    绘制你的情节

数据

return_cluster <- structure(list(year = c(1988, 1989, 1990, 1991, 1992, 1993, 1994, 
                        1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 
                        2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 
                        2017, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 
                        1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 
                        2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 1988, 1989, 
                        1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 
                        2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 
                        2012, 2013, 2014, 2015, 2016, 2017, 1988, 1989, 1990, 1991, 1992, 
                        1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 
                        2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 
                        2015, 2016, 2017, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 
                        1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 
                        2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 
                        1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 
                        1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 
                        2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 1988, 1989, 1990, 
                        1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 
                        2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 
                        2013, 2014, 2015, 2016, 2017, 1988, 1989, 1990, 1991, 1992, 1993, 
                        1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 
                        2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 
                        2016, 2017, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 
                        1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 
                        2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 1988, 
                        1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 
                        2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 
                        2011, 2012, 2013, 2014, 2015, 2016, 2017, 1988, 1989, 1990, 1991, 
                        1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 
                        2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 
                        2014, 2015, 2016, 2017, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 
                        1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 
                        2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 
                        2017), variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                      1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                      1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
                                                      5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
                                                      5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
                                                      6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
                                                      6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
                                                      7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
                                                      8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
                                                      8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 
                                                      9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
                                                      9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 
                                                      10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
                                                      10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
                                                      10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
                                                      11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
                                                      11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
                                                      12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
                                                      12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 2L, 2L, 2L, 2L, 
                                                      2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                      2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                                      3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                                      3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
                                                      4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
                                                      4L, 4L, 4L, 4L, 4L, 4L), .Label = c("V1", "V10", "V11", "V12", 
                                                                                          "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9"), class = "factor"), 
               value = c(1.72115, 5.288607, 63.355563, 47.516021, 48.76612, 
                         20.463455, 1.116961, 1.804225, 5.159672, 2.342435, 1.831826, 
                         2.916135, 2.096115, 2.205473, 97.766868, 1.395028, 14.778782, 
                         21.626368, 335.569275, 2.577229, 46.794667, 18.456694, 28.347202, 
                         1.754569, 1.96725, 1.355391, 2.310848, 1.236857, 72.404651, 
                         327.830361, 1.602976, 144.354881, 98.927558, 47.287984, 47.254139, 
                         50.085161, 1.519317, 1.2905, 27.16563, 3.708591, 2.9009, 
                         32.971143, 1.783584, 1.556819, 2.397101, 2.100313, 1.878331, 
                         59.814862, 561.607269, 18.060577, 24.921608, 25.684559, 30.211938, 
                         14.210188, 19.619167, 1.614514, 9.915848, 46.625465, 2.088176, 
                         27.044079, 3.491779, 2.808945, 91.647147, 3.39835, 50.143573, 
                         183.457528, 1.281821, 1.875897, 371.005217, 2.425475, 18.015665, 
                         1.41905, 47.72407, 1.143962, 1.951659, 2.67681, 2.597871, 
                         69.679082, 7.110426, 47.511509, 1.880928, 1.988084, 83.71362, 
                         31.541859, 1.897654, 2.341847, 3.733591, 23.84215, 13.638751, 
                         1.712088, 1.486628, 1.40724, 26.211749, 67.534555, 92.307045, 
                         3.528735, 17.328366, 4.674465, 88.268235, 132.92289, 4.439413, 
                         1.401003, 2.812608, 1.526576, 4.921232, 51.542854, 3.860508, 
                         2.24396, 2.507551, 18.467204, 4.301163, 1.188909, 186.013334, 
                         260.87911, 1.83361, 69.432973, 9.092139, 1.710509, 1.442288, 
                         21.648317, 1.124042, 5.83429, 18.423492, 71.584537, 48.946872, 
                         3.994614, 1.375991, 1.389525, 4.434723, 3.192515, 57.507683, 
                         1.107362, 1.440586, 1.216205, 1.601559, 4.691851, 2.3509, 
                         4.148232, 2.560688, 161.408504, 1.52609, 2.30433, 3.424447, 
                         251.419437, 1.477274, 1.403403, 2.473882, 1.708639, 2.773274, 
                         6.344827, 1.518616, 58.778503, 2.912868, 2.684705, 24.784557, 
                         47.099474, 1.901775, 16.253749, 11.504747, 2.819182, 19.980632, 
                         1.390191, 4.20728, 1.332001, 3.676687, 208.344203, 4.790638, 
                         68.214507, 244.268239, 8.262237, 1.743053, 19.925573, 2.313235, 
                         265.988933, 1.549337, 1.484683, 135.733452, 2.846374, 1.094535, 
                         148.108424, 1.686723, 7.227748, 35.340633, 10.83581, 2.138298, 
                         1.575984, 10.129774, 50.201159, 13.209625, 2.184044, 16.584965, 
                         2.48064, 5.299119, 1.3197, 2.265038, 355.111101, 21.919797, 
                         104.912027, 254.276631, 49.192583, 24.358314, 11.218015, 
                         14.356157, 48.386913, 7.390883, 4.089435, 1.693219, 297.646638, 
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                         16.576701, 11.493921, 8.94065, 47.943372, 7.088878, 11.432932, 
                         76.057542, 1.912122, 2.88182, 17.467523, 1.830679, 353.781607, 
                         3.107457, 35.021645, 64.32462, 59.491304, 24.755776, 2.546088, 
                         1.617441, 3.43801, 12.320183, 13.426661, 1.687504, 109.999377, 
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                         10.486107, 62.022276, 1.30577, 46.413613, 10.40384, 68.976595, 
                         10.699454, 2.762492, 11.052923, 2.285492, 2.210362, 363.602498, 
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