-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtabla.py
319 lines (307 loc) · 14.6 KB
/
tabla.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
import pandas as pd
tabla_peso_edad_boys = pd.DataFrame([
[2.8, 3.2, 3.6, 4.1, 4.9, 5.6, 6.6],
[3.5, 4.1, 4.5, 5.2, 5.9, 6.6, 7.7],
[4.2, 4.8, 5.3, 6.0, 6.7, 7.4, 8.5],
[4.8, 5.4, 6.0, 6.6, 7.4, 8.2, 9.2],
[5.3, 6.0, 6.6, 7.2, 8.0, 8.8, 9.8],
[5.7, 6.4, 7.1, 7.7, 8.5, 9.2, 10.3],
[6.1, 6.8, 7.5, 8.2, 8.9, 9.9, 10.8],
[6.4, 7.1, 7.8, 8.6, 9.3, 10.2, 11.2],
[6.7, 7.4, 8.1, 8.9, 9.7, 10.5, 11.6],
[7.0, 7.7, 8.4, 9.2, 10.0, 10.8, 11.9],
[7.3, 8.0, 8.7, 9.5, 10.3, 11.1, 12.2],
[7.6, 8.3, 9.0, 9.8, 10.5, 11.3, 12.5],
[7.9, 8.5, 9.2, 10.0, 10.7, 11.5, 12.8],
[8.1, 8.7, 9.5, 10.2, 10.9, 11.7, 13.0],
[8.3, 8.9, 9.7, 10.4, 11.1, 12.0, 13.2],
[8.5, 9.1, 9.9, 10.6, 11.3, 12.2, 13.4],
[8.7, 9.3, 10.1, 10.8, 11.6, 12.5, 13.6],
[8.8, 9.4, 10.2, 11.0, 11.8, 12.7, 13.8],
[8.9, 9.6, 10.4, 11.1, 12.0, 12.9, 14.0],
[9.0, 9.7, 10.5, 11.3, 12.2, 13.1, 14.2],
[9.1, 9.8, 10.7, 11.4, 12.4, 13.3, 14.4],
[9.2, 9.9, 10.8, 11.6, 12.6, 13.5, 14.6],
[9.3, 10.0, 10.9, 11.8, 12.7, 13.7, 14.8],
[9.4, 10.1, 11.1, 11.9, 12.9, 13.9, 15.0]], columns=[3, 10, 25, 50, 75, 90, 97])
tabla_peso_edad_boys_years = pd.DataFrame([
[7.7, 8.3, 9.1, 9.8, 10.6, 11.4, 12.5],
[9.4, 10.3, 11.2, 12.1, 12.9, 13.8, 15.0],
[10.8, 11.8, 12.8, 13.8, 14.9, 16.0, 17.5],
[12.2, 13.2, 14.3, 15.4, 16.8, 18.3, 19.9],
[13.6, 14.7, 15.8, 17.0, 18.7, 20.6, 22.6],
[15.0, 16.1, 17.3, 18.7, 20.7, 22.8, 25.5],
[16.3, 17.6, 19.0, 20.7, 22.9, 25.5, 28.9],
[17.8, 19.3, 20.8, 22.7, 25.1, 28.0, 32.4],
[19.4, 20.9, 22.8, 24.9, 27.7, 31.0, 36.6],
[20.9, 22.9, 24.9, 27.2, 30.4, 34.4, 41.0],
[22.5, 24.7, 27.0, 29.7, 33.2, 39.9, 46.0],
[24.4, 26.8, 29.3, 32.7, 37.0, 43.9, 51.5],
[26.4, 29.4, 32.0, 36.3, 42.0, 49.3, 57.0],
[29.0, 32.5, 36.0, 41.3, 47.2, 54.7, 63.0],
[32.0, 36.2, 41.7, 47.0, 53.6, 59.5, 67.9],
[36.9, 41.9, 47.0, 51.7, 58.0, 63.4, 70.9],
[41.4, 46.2, 50.4, 55.0, 60.4, 66.1, 72.4],
[44.3, 48.4, 52.6, 56.8, 61.8, 67.5, 73.1],
[45.6, 49.4, 53.3, 57.7, 62.5, 68.0, 73.6]], columns=[3, 10, 25, 50, 75, 90, 97])
tabla_peso_edad_girls_years = pd.DataFrame([
[7.0, 7.7, 8.4, 9.0, 9.9, 10.8, 11.9],
[9.0, 9.7, 10.5, 11.4, 12.3, 13.4, 14.8],
[10.4, 11.2, 12.2, 13.4, 14.5, 15.8, 17.6],
[11.7, 12.6, 13.7, 15.1, 16.6, 18.1, 20.2],
[12.9, 14.1, 15.3, 16.8, 18.6, 20.4, 23.1],
[14.3, 15.5, 17.0, 18.7, 20.8, 23.2, 26.8],
[15.6, 17.0, 18.4, 20.2, 22.9, 25.9, 30.0],
[17.1, 18.4, 20.1, 22.4, 25.3, 29.5, 34.3],
[18.7, 20.1, 22.2, 24.8, 28.1, 33.7, 39.6],
[20.4, 22.1, 24.5, 27.3, 31.7, 37.9, 45.0],
[22.4, 24.8, 27.1, 30.8, 35.7, 42.8, 51.5],
[24.8, 27.7, 30.3, 35.0, 40.3, 48.3, 57.1],
[27.7, 31.1, 34.7, 40.0, 45.3, 53.1, 62.0],
[31.1, 35.0, 38.9, 44.0, 49.6, 56.8, 65.0],
[34.7, 38.4, 42.1, 47.0, 52.4, 59.0, 66.7],
[37.0, 40.7, 44.3, 48.9, 54.0, 60.3, 67.3],
[38.0, 41.5, 45.2, 49.7, 55.1, 61.0, 67.8],
[38.3, 41.8, 45.4, 50.0, 55.7, 61.5, 67.8],
[38.3, 41.8, 45.4, 50.0, 55.7, 61.5, 67.8]], columns=[3, 10, 25, 50, 75, 90, 97])
tabla_peso_edad_girls = pd.DataFrame([
[2.8, 3.2, 3.6, 4.1, 4.7, 5.2, 6.1],
[3.4, 3.9, 4.3, 4.9, 5.4, 6.0, 6.9],
[3.9, 4.5, 5.0, 5.7, 6.2, 6.7, 7.6],
[4.4, 5.0, 5.6, 6.3, 6.8, 7.4, 8.3],
[4.8, 5.5, 6.1, 6.8, 7.4, 8.1, 9.0],
[5.2, 5.9, 6.5, 7.3, 7.9, 8.7, 9.7],
[5.6, 6.3, 6.9, 7.7, 8.4, 9.2, 10.2],
[5.9, 6.7, 7.4, 8.0, 8.8, 9.6, 10.6],
[6.2, 7.0, 7.7, 8.3, 9.1, 9.9, 11.0],
[6.5, 7.3, 7.9, 8.6, 9.4, 10.2, 11.4],
[6.7, 7.5, 8.1, 8.8, 9.7, 10.5, 11.7],
[7.0, 7.7, 8.3, 9.0, 9.9, 10.8, 11.9],
[7.2, 7.9, 8.5, 9.3, 10.1, 11.1, 12.1],
[7.4, 8.1, 8.7, 9.5, 10.3, 11.3, 12.3],
[7.7, 8.4, 9.0, 9.7, 10.6, 11.5, 12.5],
[7.9, 8.6, 9.2, 9.9, 10.8, 11.7, 12.7],
[8.1, 8.8, 9.4, 10.1, 11.0, 11.9, 12.9],
[8.2, 9.0, 9.6, 10.3, 11.2, 12.1, 13.1],
[8.3, 9.2, 9.8, 10.5, 11.4, 12.3, 13.4],
[8.4, 9.3, 9.9, 10.7, 11.6, 12.5, 13.6],
[8.5, 9.4, 10.0, 10.9, 11.8, 12.7, 13.9],
[8.6, 9.5, 10.1, 11.1, 12.0, 12.9, 14.1],
[8.7, 9.6, 10.3, 11.2, 12.2, 13.1, 14.3],
[8.8, 9.7, 10.4, 11.3, 12.3, 13.3, 14.5]], columns = [3, 10, 25, 50, 75, 90, 97])
tabla_talla_edad_boys = pd.DataFrame([
[48.2, 49.8, 51.5, 53.3, 55.1, 56.8, 58.4],
[52.1, 53.8, 55.4, 57.3, 59.2, 60.8, 62.5],
[54.7, 56.4, 58.1, 60.0, 61.9, 63.6, 65.3],
[57.0, 58.7, 60.5, 62.4, 64.3, 66.1, 67.8],
[59.2, 60.9, 62.7, 64.6, 66.5, 68.3, 70.0],
[67.0, 62.7, 64.5, 66.5, 68.5, 70.3, 72.0],
[62.5, 64.3, 66.1, 68.1, 70.1, 71.9, 73.7],
[63.7, 65.5, 67.4, 69.4, 71.4, 73.3, 75.1],
[64.9, 66.8, 68.6, 70.7, 72.8, 74.6, 76.5],
[66.0, 67.9, 69.8, 71.9, 74.0, 75.9, 77.8],
[67.1, 69.0, 71.0, 73.1, 75.2, 77.2, 79.7],
[68.2, 70.1, 72.0, 74.2, 76.4, 78.3, 80.2],
[69.1, 71.1, 73.1, 75.3, 77.5, 79.5, 81.5],
[70.1, 72.0, 74.1, 76.3, 78.5, 80.6, 82.5],
[71.0, 73.0, 75.0, 77.3, 79.6, 81.6, 83.6],
[71.9, 73.9, 76.0, 78.3, 80.6, 82.7, 84.7],
[72.7, 74.8, 76.9, 79.2, 81.5, 83.6, 85.7],
[73.6, 75.7, 77.8, 80.2, 82.6, 84.7, 86.8],
[74.5, 76.6, 78.0, 81.2, 83.6, 85.8, 87.9],
[75.4, 77.5, 79.7, 82.2, 84.7, 86.9, 89.0],
[76.2, 78.5, 80.7, 83.2, 85.7, 87.9, 90.2],
[76.9, 79.2, 81.5, 84.0, 86.5, 88.8, 91.1],
[77.6, 79.9, 82.2, 84.8, 87.4, 89.7, 92.0],
[78.1, 80.4, 82.7, 85.3, 87.9, 90.2, 92.5]], columns = [3, 10, 25, 50, 75, 90, 97])
tabla_talla_edad_boys_years = pd.DataFrame([
[68.5, 70.4, 72.3, 74.5, 76.7, 78.6, 80.5],
[77.7, 80.0, 82.3, 84.9, 87.5, 89.8, 92.1],
[85.0, 87.6, 90.1, 93.0, 95.9, 98.4, 101.0],
[91.8, 94.5, 97.4, 100.5, 103.6, 106.5, 109.2],
[97.7, 100.7, 103.8, 107.2, 110.6, 113.7, 116.7],
[102.9, 106.2, 109.5, 113.2, 116.9, 120.3, 123.6],
[108.0, 111.5, 115.1, 119.1, 123.1, 126.7, 130.2],
[113.0, 116.7, 120.4, 124.5, 128.6, 132.3, 136.0],
[117.3, 121.1, 125.0, 129.3, 133.6, 137.5, 141.3],
[121.6, 125.5, 129.5, 134.0, 138.5, 142.5, 146.4],
[125.5, 129.7, 133.8, 138.5, 143.2, 147.3, 151.5],
[129.8, 134.2, 138.8, 143.8, 148.8, 153.4, 157.8],
[133.2, 138.6, 144.0, 150.0, 156.0, 161.4, 166.8],
[139.0, 144.4, 149.9, 156.0, 162.1, 167.6, 173.0],
[144.9, 150.2, 155.6, 161.6, 167.6, 173.0, 178.3],
[151.1, 155.8, 160.6, 165.9, 171.2, 176.0, 180.7],
[154.8, 159.0, 163.2, 168.0, 172.8, 177.0, 181.2],
[156.1, 160.1, 164.2, 168.7, 173.2, 177.3, 181.3],
[157.0, 160.9, 164.8, 169.2, 173.6, 177.5, 181.4]], columns = [3, 10, 25, 50, 75, 90, 97])
tabla_talla_edad_girls = pd.DataFrame([
[47.1, 48.7, 50.3, 52.1, 53.9, 55.5, 57.1],
[50.8, 52.4, 54.1, 55.9, 57.7, 59.4, 61.0],
[53.2, 54.9, 56.6, 58.4, 60.2, 61.9, 63.6],
[55.5, 57.1, 58.8, 60.7, 62.6, 64.3, 65.9],
[57.5, 59.2, 60.9, 62.8, 64.7, 66.4, 68.1],
[59.5, 61.2, 63.0, 64.9, 66.8, 68.6, 70.3],
[61.0, 62.7, 64.5, 66.4, 68.3, 70.1, 71.8],
[62.3, 64.1, 65.8, 67.8, 69.8, 71.5, 73.3],
[63.6, 65.4, 67.2, 69.2, 71.2, 73.0, 74.8],
[64.9, 66.7, 68.5, 70.5, 72.5, 74.3, 76.1],
[66.0, 67.8, 69.7, 71.7, 73.7, 75.6, 77.4],
[67.1, 69.0, 70.8, 72.9, 75.0, 76.8, 78.7],
[68.2, 70.0, 71.9, 74.0, 76.1, 78.0, 79.8],
[69.2, 71.1, 73.0, 75.1, 77.2, 79.1, 81.0],
[70.2, 72.1, 74.0, 76.2, 78.4, 80.3, 82.2],
[71.1, 73.1, 75.0, 77.2, 79.4, 81.3, 83.3],
[72.0, 73.9, 75.9, 78.1, 80.3, 82.3, 84.2],
[72.8, 74.8, 76.8, 79.0, 81.2, 83.2, 85.2],
[73.6, 75.6, 77.6, 79.9, 82.2, 84.2, 86.2],
[74.5, 76.5, 78.5, 80.8, 83.1, 85.1, 87.1],
[75.2, 77.2, 79.3, 81.6, 83.9, 86.0, 88.0],
[75.9, 78.0, 80.1, 82.4, 84.7, 86.8, 88.9],
[76.6, 78.6, 80.8, 83.1, 85.4, 87.6, 89.6],
[77.1, 79.2, 81.3, 83.7, 86.1, 88.2]], columns = [3, 10, 25, 50, 75, 90, 97])
tabla_talla_edad_girls_years =pd.DataFrame([
[67.2, 69.2, 71.3, 73.5, 75.7, 77.8, 79.8],
[76.3, 78.5, 80.8, 83.4, 86.0, 88.3, 90.5],
[84.2, 86.7, 89.3, 92.1, 94.9, 97.5, 100.0],
[91.5, 94.3, 97.1, 100.2, 103.3, 106.1, 108.9],
[97.3, 100.4, 103.5, 106.9, 110.3, 113.4, 116.4],
[102.8, 106.0, 109.3, 113.0, 116.7, 120.0, 123.2],
[108.1, 111.6, 115.1, 119.0, 122.9, 126.4, 129.9],
[112.5, 116.2, 119.9, 124.1, 128.3, 132.0, 135.7],
[117.5, 121.4, 125.3, 129.7, 134.1, 138.0, 141.9],
[121.7, 125.9, 130.2, 135.0, 139.8, 144.1, 148.3],
[126.4, 131.0, 135.6, 140.8, 146.0, 150.6, 155.2],
[131.4, 136.2, 141.1, 146.5, 151.9, 156.8, 161.6],
[137.3, 141.6, 146.1, 151.0, 155.9, 160.4, 164.7],
[142.1, 145.9, 149.8, 154.1, 158.4, 162.3, 166.1],
[144.8, 148.4, 152.0, 156.0, 160.0, 163.6, 167.2],
[146.0, 149.5, 153.1, 157.0, 160.9, 164.5, 168.0],
[146.0, 149.5, 153.1, 157.0, 160.9, 164.5, 168.0],
[146.0, 149.5, 153.1, 157.0, 160.9, 164.5, 168.0],
[146.0, 149.5, 153.1, 157.0, 160.9, 164.5, 168.0]], columns = [3, 10, 25, 50, 75, 90, 97])
tabla_talla_peso_boys = pd.DataFrame([
[2.9, 3.2, 3.4, 3.7, 4.1, 4.6, 5.2],
[3.3, 3.6, 3.8, 4.1, 4.5, 5.1, 5.8],
[3.6, 4.0, 4.3, 4.6, 5.1, 5.6, 6.4],
[4.0, 4.4, 4.8, 5.2, 5.7, 6.2, 7.0],
[4.4, 4.9, 5.3, 5.8, 6.3, 6.9, 7.6],
[4.9, 5.4, 5.8, 6.4, 6.9, 7.4, 8.3],
[5.4, 6.0, 6.4, 7.0, 7.5, 8.0, 8.9],
[6.0, 6.6, 7.0, 7.5, 8.1, 8.6, 9.5],
[6.5, 7.1, 7.5, 8.1, 8.6, 9.2, 10.1],
[7.1, 7.7, 8.1, 8.7, 9.2, 9.8, 10.8],
[7.6, 8.2, 8.6, 9.2, 9.7, 10.3, 11.3],
[8.1, 8.7, 9.1, 9.6, 10.2, 10.8, 11.8],
[8.6, 9.1, 9.5, 10.1, 10.7, 11.2, 12.2],
[9.0, 9.5, 9.9, 10.6, 11.1, 11.7, 12.6],
[9.3, 9.8, 10.3, 11.0, 11.6, 12.1, 13.0],
[9.7, 10.2, 10.7, 11.3, 12.0, 12.5, 13.5],
[10.0, 10.5, 11.0, 11.6, 12.3, 12.9, 14.0],
[10.3, 10.9, 11.4, 12.0, 12.7, 13.3, 14.5],
[10.7, 11.3, 11.8, 12.4, 13.1, 13.7, 15.0],
[11.1, 11.7, 12.3, 12.8, 13.5, 14.3, 15.8],
[11.6, 12.2, 12.8, 13.4, 14.1, 14.9, 16.5],
[12.1, 12.7, 13.3, 14.0, 14.7, 15.7, 17.2],
[12.6, 13.2, 13.8, 14.7, 15.7, 16.8, 18.3],
[12.8, 13.4, 14.0, 14.7, 15.5, 16.4, 17.5],
[13.4, 13.9, 14.6, 15.4, 16.0, 16.9, 17.9],
[13.8, 14.5, 15.0, 15.7, 16.5, 17.3, 18.3],
[14.2, 14.8, 15.3, 16.2, 17.0, 17.8, 18.9],
[14.6, 15.3, 15.9, 16.8, 17.5, 18.3, 19.5],
[15.1, 15.7, 16.4, 17.3, 18.2, 19.0, 20.3],
[15.5, 16.3, 17.0, 17.7, 18.7, 19.6, 21.2],
[16.1, 16.9, 17.6, 18.4, 19.5, 20.5, 22.0],
[16.7, 17.5, 18.2, 19.0, 20.2, 21.2, 23.0],
[17.4, 18.1, 19.0, 19.9, 21.0, 22.0, 23.9],
[18.0, 18.8, 19.5, 20.5, 21.7, 23.0, 24.8],
[18.6, 19.5, 20.3, 21.3, 22.4, 23.8, 25.8],
[19.3, 20.2, 21.1, 22.1, 23.4, 24.9, 26.9],
[20.0, 20.9, 22.0, 23.0, 24.3, 25.7, 28.0],
[20.7, 21.5, 22.7, 23.8, 25.3, 26.7, 29.2],
[21.5, 22.5, 23.5, 24.7, 26.2, 27.7, 30.5],
[22.2, 23.3, 24.5, 25.6, 27.2, 29.0, 32.0],
[22.9, 24.0, 25.3, 26.6, 28.2, 30.0, 33.5],
[23.7, 25.0, 26.1, 27.6, 29.2, 31.3, 35.0],
[24.5, 25.7, 27.1, 28.7, 30.2, 32.5, 36.6],
[25.4, 26.7, 28.3, 29.7, 31.4, 33.8, 38.2],
[26.5, 27.7, 29.2, 30.9, 32.5, 35.3, 39.9],
[27.4, 28.5, 30.3, 32.0, 34.0, 36.7, 41.5],
[28.4, 29.7, 32.0, 33.4, 36.8, 38.5, 43.5],
[29.4, 30.8, 32.6, 34.5, 37.1, 40.3, 45.7],
[30.5, 32.0, 34.0, 36.0, 38.8, 42.2, 47.7],
[31.7, 33.5, 35.4, 37.5, 40.6, 44.2, 49.9],
[33.0, 34.5, 36.5, 39.0, 42.2, 46.0, 52.7],
[34.2, 36.1, 38.0, 40.8, 43.9, 48.1, 54.0],
[35.6, 37.7, 39.8, 42.7, 46.4, 50.5, 56.0],
[37.3, 39.5, 51.4, 44.5, 48.2, 53.0, 58.5],
[38.9, 41.0, 53.2, 47.0, 50.5, 55.3, 61.0],
[40.5, 43.0, 45.3, 49.0, 52.5, 57.9, 63.5],
[41.9, 44.7, 47.3, 51.0, 54.5, 60.0, 66.0],
[42.7, 46.4, 49.0, 52.8, 56.5, 62.0, 68.0],
[44.5, 47.9, 51.3, 54.5, 58.9, 64.0, 70.8],
[46.0, 49.3, 52.7, 56.3, 61.0, 66.0, 73.0],
[47.5, 50.7, 54.2, 58.0, 62.5, 68.0, 74.7],
[48.6, 52.0, 55.6, 59.5, 64.0, 66.7, 76.8],
[50.0, 53.4, 57.0, 60.7, 65.6, 71.8, 78.7],
[51.0, 54.5, 58.4, 62.1, 67.0, 73.3, 80.5],
[52.3, 56.3, 60.0, 63.2, 68.5, 75.0, 83.0]], columns = [3, 10, 25, 50, 75, 90, 97])
tabla_talla_peso_girls = pd.DataFrame([
[2.7, 3.1, 3.6, 3.7, 4.3, 4.9, 5.5],
[3.1, 3.5, 3.9, 4.2, 4.7, 5.8, 6.0],
[3.5, 3.9, 4.4, 4.7, 5.2, 5.8, 6.4],
[4.0, 4.4, 4.9, 5.2, 5.8, 6.3, 6.8],
[4.5, 4.9, 5.4, 5.8, 6.3, 6.8, 7.2],
[5.0, 5.4, 5.9, 6.4, 6.9, 7.4, 7.9],
[5.5, 5.9, 6.4, 6.9, 7.5, 8.0, 8.5],
[6.0, 6.4, 6.9, 7.4, 8.0, 8.6, 9.2],
[6.5, 7.0, 7.4, 8.0, 8.5, 9.1, 9.8],
[7.0, 7.5, 7.9, 8.5, 9.0, 9.6, 10.4],
[7.5, 7.9, 8.3, 8.9, 9.5, 10.1, 11.0],
[7.9, 8.4, 8.8, 9.3, 9.9, 10.5, 11.5],
[8.3, 8.8, 9.2, 9.7, 10.3, 10.9, 12.0],
[8.6, 9.2, 9.6, 10.1, 10.7, 11.4, 12.5],
[8.9, 9.5, 9.9, 10.5, 11.1, 11.8, 12.9],
[9.2, 9.8, 10.2, 10.8, 11.5, 12.2, 13.3],
[9.6, 10.2, 10.6, 11.2, 11.9, 12.6, 13.7],
[10.0, 10.6, 11.0, 11.6, 12.4, 13.1, 14.0],
[10.4, 11.0, 11.5, 12.1, 12.8, 13.5, 14.4],
[10.8, 11.4, 12.0, 12.6, 13.2, 14.0, 14.9],
[11.2, 11.8, 12.5, 13.2, 13.8, 14.7, 15.7],
[11.6, 12.3, 13.0, 13.9, 14.5, 15.6, 16.6],
[12.1, 12.9, 13.6, 14.6, 15.5, 16.6, 17.6],
[12.4, 13.2, 13.7, 14.5, 15.3, 16.5, 17.8],
[13.0, 13.7, 14.2, 15.1, 15.9, 17.2, 18.6],
[13.5, 14.1, 14.8, 15.6, 16.5, 17.7, 19.4],
[14.0, 14.7, 15.2, 16.0, 17.0, 18.2, 20.0],
[14.4, 15.0, 15.7, 16.5, 17.4, 18.5, 20.6],
[14.7, 15.5, 16.2, 17.0, 18.0, 19.0, 21.3],
[15.1, 16.0, 16.8, 17.7, 18.7, 19.9, 22.1],
[15.6, 16.5, 17.4, 18.4, 19.5, 20.8, 23.0],
[16.2, 17.0, 18.0, 19.0, 20.3, 21.5, 24.0],
[16.8, 17.7, 18.5, 19.8, 21.0, 22.5, 25.0],
[17.4, 18.4, 19.2, 20.5, 21.8, 23.4, 26.2],
[18.0, 19.1, 20.0, 21.2, 22.6, 24.2, 27.3],
[18.8, 19.8, 20.8, 22.0, 23.5, 25.2, 28.4],
[19.5, 20.5, 21.5, 22.7, 24.5, 26.2, 29.5],
[20.2, 21.2, 22.4, 23.5, 25.4, 27.5, 30.7],
[20.9, 22.0, 23.3, 24.5, 26.4, 28.8, 32.2],
[21.6, 22.7, 24.0, 25.4, 27.4, 30.2, 34.0],
[22.3, 23.5, 25.0, 26.5, 28.5, 32.0, 36.0],
[23.0, 24.5, 26.0, 27.6, 30.0, 33.5, 38.0],
[24.0, 25.3, 27.0, 28.8, 31.4, 35.2, 40.0],
[25.0, 26.2, 28.0, 30.0, 32.7, 37.0, 42.0],
[26.0, 27.3, 29.0, 31.3, 34.2, 39.0, 44.5],
[27.0, 28.5, 30.1, 32.8, 36.0, 41.4, 47.0],
[28.1, 29.7, 31.5, 34.5, 38.2, 43.6, 49.5],
[29.4, 31.0, 33.2, 36.5, 40.7, 46.2, 52.0],
[30.7, 32.6, 35.2, 38.8, 43.4, 48.8, 54.8],
[32.0, 34.5, 37.5, 41.0, 45.8, 51.1, 57.6],
[33.5, 36.2, 39.0, 43.3, 48.0, 53.3, 60.0],
[35.0, 38.0, 41.1, 45.2, 50.0, 55.4, 62.4],
[36.4, 39.5, 43.0, 47.2, 51.8, 57.5, 64.5],
[38.0, 41.0, 44.5, 48.8, 53.5, 59.2, 66.5],
[39.5, 42.7, 46.0, 50.4, 55.0, 61.2, 68.5],
[40.8, 44.0, 47.4, 52.0, 57.0, 63.0, 70.5],
[42.2, 45.3, 48.7, 53.4, 58.5, 65.0, 72.5],
[43.2, 46.4, 50.0, 54.7, 60.2, 66.8, 74.5],
[44.0, 47.5, 51.0, 56.4, 62.0, 68.5, 76.3]], columns=[3, 10, 25, 50, 75, 90, 97])