Python 从列表/XY散点中删除异常值
我有两个包含心跳间隔（Y轴，单位为ms；下面是IBIs）及其绝对时间点（X轴，单位为ms；下面是RR_时间）的列表。有一些误读，例如第一个列表包含需要删除的异常值，第二个列表包含相应的时间点。如果对第一个列表中的异常值进行统计，使记录的总时间保持不变，这将是最佳选择Python 从列表/XY散点中删除异常值,python,numpy,scipy,Python,Numpy,Scipy,我有两个包含心跳间隔（Y轴，单位为ms；下面是IBIs）及其绝对时间点（X轴，单位为ms；下面是RR_时间）的列表。有一些误读，例如第一个列表包含需要删除的异常值，第二个列表包含相应的时间点。如果对第一个列表中的异常值进行统计，使记录的总时间保持不变，这将是最佳选择 RR_times = [411, 827, 1241, 1653, 2066, 2481, 2894, 3308, 3714, 4126, 4532, 4938, 5343, 5751
RR_times = [411, 827, 1241, 1653, 2066, 2481, 2894, 3308,
3714, 4126, 4532, 4938, 5343, 5751, 6156, 6552,
6951, 7346, 7749, 8149, 8546, 8944, 9338, 9735,
10123, 10511, 10905, 11290, 11675, 12060, 12441, 12825,
13205, 13581, 13960, 14342, 14717, 15087, 15462, 15829,
16204, 16531, 16902, 17304, 17670, 18040, 18398, 18762,
19127, 19465, 19823, 20196, 20554, 20906, 21256, 21609,
21959, 22264, 22637, 22995, 23308, 23649, 24012, 24352,
24687, 25026, 25390, 25681, 26014, 26347, 26680, 27330,
27985, 28628, 28951, 29596, 29915, 30238, 30562, 31191,
31826, 32141, 32461, 32775, 33095, 33382, 33695, 34029,
34341, 34654, 34967, 35281, 35595, 36220, 36530, 36844,
37150, 37462, 37775, 38084, 38395, 38703, 39014, 39324,
39632, 39937, 40246, 40554, 40862, 41169, 41479, 41787,
42095, 42406, 42714, 43019, 43330, 43642, 43945, 44254,
44563, 44871, 45183, 45491, 45796, 46101, 46410, 46713,
47327, 47632, 47937, 48244, 48555, 48867, 49177, 49488,
49792, 50094, 50398, 50707, 50993, 51324, 51626, 51931,
52239, 52550, 52857, 53161, 53773, 54080, 54387, 54693,
54998, 55311, 55617, 55924, 56235, 56547, 56852, 57159,
57470, 57781, 58091, 58400, 58709, 59020, 59331, 59644,
59955, 60265, 60579, 60890, 61206, 61521, 61833, 62149,
62463, 62772, 63088, 63403, 63716, 64034, 64352, 64665,
64984, 65624, 65940, 66262, 66578, 66900, 67221, 67543,
67861, 68179, 68504, 68819, 69145, 69459, 69782, 70111,
70428, 70747, 71070, 71389, 71710, 72036, 72358, 72680,
73003, 73326, 73648, 73973, 74296, 74620, 74944, 75269,
75592, 75916, 76241, 76566, 76889, 77216, 77543, 77869,
78191, 78518, 78843, 79165, 79496, 79823, 80148, 80479,
80803, 81128, 81459, 81783, 82110, 82439, 82771, 83095,
83426, 83757, 84086, 84416, 84741, 85074, 85400, 85729,
86060, 86390, 86719, 87051, 87380, 87711, 88041, 88373,
88705, 89029, 89365, 89698, 90023, 90356, 90690, 91019,
91352, 91684, 92014, 92347, 92681, 93014, 93349, 93678,
94011, 94344, 94675, 95009, 95339, 95673, 96007, 96341,
96668, 97002, 97337, 97665, 98003, 98335, 98668, 99003,
99339, 99673, 100007, 100346, 100684, 101017, 101357, 101693,
102028, 102368, 102705, 103043, 103380, 103718, 104061, 104403,
104736, 105077, 105421, 105756, 106096, 106437, 106777, 107118,
107461, 107800, 108141, 108485, 108822, 109167, 109507, 109848,
110196, 110538, 110884, 111230, 111571, 111918, 112263, 112606,
112952, 113639, 113987, 114336, 114680, 115025, 115372, 115722,
116068, 116418, 116766, 117114, 117464, 117811, 118158, 118511,
118858, 119208, 119557, 119904, 120257, 120606, 120952, 121303,
121655, 122003, 122354, 122707, 123057, 123408, 123760, 124114,
124466, 124815, 125172, 125523, 125879, 126231, 126586, 126946,
127298, 127653, 128014, 128369, 128724, 129084, 129441, 129794,
130150, 130504, 130863, 131219, 131576, 131937, 132297, 132653,
133012, 133375, 133731, 134091, 134455, 134813, 135174, 135534,
135897, 136258, 136621, 136986, 137349, 137711, 138073, 138439,
138799, 139164, 139526, 139887, 140253, 140617, 140977, 141344,
141706, 142071, 142438, 142803, 143170, 143537, 143904, 144274,
144641, 145011, 145382, 145749, 146124, 146493, 146864, 147235,
147605, 147977, 148346, 148718, 149085, 149455, 149826, 150195,
150566, 150936, 151310, 151676, 152048, 152423, 152795, 153167,
153539, 153916, 154290, 154661, 155036, 155408, 155782, 156159,
156530, 156905, 157280, 157655, 158029, 158404, 158783, 159157,
159532, 159910, 160290, 160660, 161037, 161415, 161786, 162161,
162538, 162913, 163289, 163665, 164040, 164415, 164789, 165164,
165539, 165911, 166286, 166661, 167040, 167418, 167791, 168169,
168545, 168922, 169300, 169676, 170053, 170429, 170811, 171195,
171571, 171952, 172335, 172717, 173098, 173484, 173869, 174254,
174637, 175020, 175403, 175785, 176167, 176552, 176933, 177316,
177698, 178080, 178463, 178840, 179224, 179603, 179979, 180360,
180739, 181114, 181492, 181870, 182248, 182626, 183001, 183378,
183752, 184128, 184503, 184876, 185252, 185629, 186003, 186384,
186760, 187134, 187515, 187900, 188281, 188656, 189031, 189415,
189798, 190176, 190555, 190936, 191313, 191692, 192069, 192448,
192824, 193203, 193578, 193953, 194330, 194707]
IBIs = [411,416,414,412,413,415, 413, 414, 406, 412, 406, 406, 405,
408, 405, 396, 399, 395, 403, 400, 397, 398, 394, 397, 388, 388,
394, 385, 385, 385, 381, 384, 380, 376, 379, 382, 375, 370, 375,
367, 375, 327, 371, 402, 366, 370, 358, 364, 365, 338, 358, 373,
358, 352, 350, 353, 350, 305, 373, 358, 313, 341, 363, 340, 335,
339, 364, 291, 333, 333, 333, 650, 655, 643, 323, 645, 319, 323,
324, 629, 635, 315, 320, 314, 320, 287, 313, 334, 312, 313, 313,
314, 314, 625, 310, 314, 306, 312, 313, 309, 311, 308, 311, 310,
308, 305, 309, 308, 308, 307, 310, 308, 308, 311, 308, 305, 311,
312, 303, 309, 309, 308, 312, 308, 305, 305, 309, 303, 614, 305,
305, 307, 311, 312, 310, 311, 304, 302, 304, 309, 286, 331, 302,
305, 308, 311, 307, 304, 612, 307, 307, 306, 305, 313, 306, 307,
311, 312, 305, 307, 311, 311, 310, 309, 309, 311, 311, 313, 311,
310, 314, 311, 316, 315, 312, 316, 314, 309, 316, 315, 313, 318,
318, 313, 319, 640, 316, 322, 316, 322, 321, 322, 318, 318, 325,
315, 326, 314, 323, 329, 317, 319, 323, 319, 321, 326, 322, 322,
323, 323, 322, 325, 323, 324, 324, 325, 323, 324, 325, 325, 323,
327, 327, 326, 322, 327, 325, 322, 331, 327, 325, 331, 324, 325,
331, 324, 327, 329, 332, 324, 331, 331, 329, 330, 325, 333, 326,
329, 331, 330, 329, 332, 329, 331, 330, 332, 332, 324, 336, 333,
325, 333, 334, 329, 333, 332, 330, 333, 334, 333, 335, 329, 333,
333, 331, 334, 330, 334, 334, 334, 327, 334, 335, 328, 338, 332,
333, 335, 336, 334, 334, 339, 338, 333, 340, 336, 335, 340, 337,
338, 337, 338, 343, 342, 333, 341, 344, 335, 340, 341, 340, 341,
343, 339, 341, 344, 337, 345, 340, 341, 348, 342, 346, 346, 341,
347, 345, 343, 346, 687, 348, 349, 344, 345, 347, 350, 346, 350,
348, 348, 350, 347, 347, 353, 347, 350, 349, 347, 353, 349, 346,
351, 352, 348, 351, 353, 350, 351, 352, 354, 352, 349, 357, 351,
356, 352, 355, 360, 352, 355, 361, 355, 355, 360, 357, 353, 356,
354, 359, 356, 357, 361, 360, 356, 359, 363, 356, 360, 364, 358,
361, 360, 363, 361, 363, 365, 363, 362, 362, 366, 360, 365, 362,
361, 366, 364, 360, 367, 362, 365, 367, 365, 367, 367, 367, 370,
367, 370, 371, 367, 375, 369, 371, 371, 370, 372, 369, 372, 367,
370, 371, 369, 371, 370, 374, 366, 372, 375, 372, 372, 372, 377,
374, 371, 375, 372, 374, 377, 371, 375, 375, 375, 374, 375, 379,
374, 375, 378, 380, 370, 377, 378, 371, 375, 377, 375, 376, 376,
375, 375, 374, 375, 375, 372, 375, 375, 379, 378, 373, 378, 376,
377, 378, 376, 377, 376, 382, 384, 376, 381, 383, 382, 381, 386,
385, 385, 383, 383, 383, 382, 382, 385, 381, 383, 382, 382, 383,
377, 384, 379, 376, 381, 379, 375, 378, 378, 378, 378, 375, 377,
374, 376, 375, 373, 376, 377, 374, 381, 376, 374, 381, 385, 381,
375, 375, 384, 383, 378, 379, 381, 377, 379, 377, 379, 376, 379,
375, 375, 377, 377]
在这种情况下，去除异常值的最佳方法是什么？如何实施？移动平均法、基于标准差分的排除法、中值滤波法……？这里有一种丑陋的方法似乎有效：
import numpy as np
RR_times = np.array([411, 827, 1241, ...])
IBIs = np.array([411, 416, 414, ...])
diffs = [np.abs(IBIs[i]-IBIs[i+1]) for i in range(len(IBIs)-1)]
IBIs_cleaned = np.full(IBIs.shape, np.nan) # create a array full of NaNs
IBIs_cleaned[0] = IBIs[0] # The first value isn't a outlier
for i in range(1, len(IBIs)):
if np.abs(IBIs[i]-IBIs[i-1]) < np.mean(diffs) and IBIs[i] < 1.6 * np.mean(IBIs):
IBIs_cleaned[i] = IBIs[i]
将numpy导入为np
RR_times=np.array（[4118271241，…）
IBIs=np.数组（[411416414，…）
范围（len（IBIs）-1）内i的差值=[np.abs（IBIs[i]-IBIs[i+1]）
IBIs_=np.full（IBIs.shape，np.nan）#创建一个充满nan的数组
IBIs_[0]=IBIs[0]#第一个值不是异常值
对于范围（1，len（IBIs））中的i：
如果np.abs（IBIs[i]-IBIs[i-1]）