├── MANIFEST.in
├── README.md
├── setup.py
└── tsbitmaps
    ├── __init__.py
    ├── bitmapviz.py
    ├── test
        ├── __init__.py
        ├── data
        │   ├── DNA.zip
        │   ├── ECG.txt
        │   ├── chfdbchf15.txt
        │   ├── ecg_anom.txt
        │   ├── ecg_normal.txt
        │   ├── mitdbx_108.txt
        │   ├── pattern_anom.txt
        │   └── pattern_normal.txt
        └── test_tsbitmaps.py
    └── tsbitmapper.py


/MANIFEST.in:
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1 | include README.md
2 | include test/data/


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/README.md:
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1 | Implementation of the Time-series Bitmap model for anomaly detection (both unsupervised and supervised)
2 | 
3 | Based on the papers "Time-series Bitmaps: A Practical Visualization Tool for working with Large Time Series Databases"
4 | and "Assumption-Free Anomaly Detection in Time Series"
5 | 
6 | Test data and parameter settings taken from http://alumni.cs.ucr.edu/~wli/SSDBM05/
7 | 


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/setup.py:
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 1 | from setuptools import setup
 2 | 
 3 | 
 4 | def readme():
 5 |     with open('README.md') as f:
 6 |         return f.read()
 7 | 
 8 | 
 9 | setup(name='tsbitmaps',
10 |       version='0.1',
11 |       description='Time-series Bitmap implementation for anomaly detection on time series data',
12 |       long_description=readme(),
13 |       url='http://github.com/binhmop/tsbitmaps',
14 |       author='Binh Han',
15 |       author_email='binhhan.gt@gmail.com',
16 |       license='Apache 2.0',
17 |       packages=['tsbitmaps'],
18 |       install_requires=['numpy','pandas'],
19 |       test_suite='nose.collector',
20 |       tests_require=['nose'],
21 |       include_package_data=True,
22 |       zip_safe=False)


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/tsbitmaps/__init__.py:
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https://raw.githubusercontent.com/binhmop/tsbitmaps/ddfee0125c17f18c1e7c988c0ede57bc2c53ce03/tsbitmaps/__init__.py


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/tsbitmaps/bitmapviz.py:
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 1 | import numpy as np
 2 | import math
 3 | 
 4 | 
 5 | def create_bitmap_grid(bitmap, n, num_bins, level_size):
 6 |     """
 7 |     Arranges a time-series bitmap into a 2-D grid for heatmap visualization
 8 |     """
 9 |     assert num_bins % n == 0, 'num_bins has to be a multiple of n'
10 |     m = num_bins // n
11 | 
12 |     row_count = int(math.pow(m, level_size))
13 |     col_count = int(math.pow(n, level_size))
14 | 
15 |     grid = np.full((row_count, col_count), 0.0)
16 | 
17 |     for feat, count in bitmap.items():
18 |         i, j = symbols2index(m, n, feat)
19 |         grid[i, j] = count
20 |     return grid
21 | 
22 | 
23 | def create_unit_grid(m, n):
24 |     """
25 |     
26 |     :param m: row count
27 |     :param n: column count
28 |     :return unit_grid: array of shape `(m,n)` contains all integers in `[0, m*n - 1]`
29 |     """
30 |     unit_grid = np.ndarray(shape=(m, n), dtype=int)
31 |     for i in range(0, m):
32 |         for j in range(0, n):
33 |             unit_grid[i, j] = i * n + j
34 | 
35 |     return unit_grid
36 | 
37 | 
38 | def num2index(n, s):
39 |     i = s // n
40 |     j = s % n
41 |     return i, j
42 | 
43 | 
44 | def symbols2index(m, n, feat):
45 |     level_size = len(feat)
46 |     i = 0
47 |     j = 0
48 |     for k in range(0, level_size):
49 |         cur_i, cur_j = num2index(n, int(feat[k]))
50 |         i += int(cur_i * math.pow(m, level_size - k - 1))
51 |         j += int(cur_j * math.pow(n, level_size - k - 1))
52 |     return i, j
53 | 
54 | 
55 | def index2symbos(m, n, i, j, level_size):
56 |     feat_arr = np.ndarray(level_size, dtype=int)
57 |     for k in range(0, level_size):
58 |         cur_i = int(i // math.pow(m, level_size - k - 1))
59 |         cur_j = int(j // math.pow(n, level_size - k - 1))
60 | 
61 |         feat_arr[k] = cur_i * m + cur_j
62 |         i = i % math.pow(m, level_size - k - 1)
63 |         j = j % math.pow(n, level_size - k - 1)
64 |     feat = tuple(str(symbol) for symbol in feat_arr)
65 |     return feat
66 | 


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/tsbitmaps/test/__init__.py:
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https://raw.githubusercontent.com/binhmop/tsbitmaps/ddfee0125c17f18c1e7c988c0ede57bc2c53ce03/tsbitmaps/test/__init__.py


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/tsbitmaps/test/data/DNA.zip:
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https://raw.githubusercontent.com/binhmop/tsbitmaps/ddfee0125c17f18c1e7c988c0ede57bc2c53ce03/tsbitmaps/test/data/DNA.zip


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/tsbitmaps/test/data/ECG.txt:
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1961 |   4.8510000e+000
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1963 |   4.9120000e+000
1964 |   4.8780000e+000
1965 |   4.8500000e+000
1966 |   4.8050000e+000
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1970 |   4.8940000e+000
1971 |   4.8400000e+000
1972 |   4.8330000e+000
1973 |   4.8410000e+000
1974 |   4.8910000e+000
1975 |   4.9580000e+000
1976 |   4.9030000e+000
1977 |   4.9210000e+000
1978 |   4.9100000e+000
1979 |   4.9200000e+000
1980 |   4.8980000e+000
1981 |   4.8720000e+000
1982 |   4.8800000e+000
1983 |   4.9640000e+000
1984 |   4.9590000e+000
1985 |   4.9120000e+000
1986 |   4.9020000e+000
1987 |   4.9070000e+000
1988 |   4.9160000e+000
1989 |   4.9300000e+000
1990 |   4.9160000e+000
1991 |   4.9070000e+000
1992 |   4.9060000e+000
1993 |   4.9270000e+000
1994 |   4.9310000e+000
1995 |   4.9240000e+000
1996 |   4.9160000e+000
1997 |   4.9100000e+000
1998 |   4.9000000e+000
1999 |   4.9100000e+000
2000 |   4.9480000e+000
2001 | 


--------------------------------------------------------------------------------
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1399 | -0.080000
1400 | -0.095000
1401 | 0.155000
1402 | 0.180000
1403 | 0.130000
1404 | 0.105000
1405 | 0.155000
1406 | 0.405000
1407 | 0.560000
1408 | 0.225000
1409 | 0.345000
1410 | 2.810000
1411 | -0.165000
1412 | 0.080000
1413 | 0.135000
1414 | 0.145000
1415 | 0.075000
1416 | 0.275000
1417 | 0.385000
1418 | 0.355000
1419 | 0.315000
1420 | 0.345000
1421 | 0.490000
1422 | 0.775000
1423 | 0.630000
1424 | 0.455000
1425 | 1.795000
1426 | 1.350000
1427 | 0.235000
1428 | 0.285000
1429 | 0.330000
1430 | 0.285000
1431 | 0.270000
1432 | 0.545000
1433 | 0.525000
1434 | 0.460000
1435 | 0.420000
1436 | 0.445000
1437 | 0.675000
1438 | 0.800000
1439 | 0.490000
1440 | 0.460000
1441 | 3.320000
1442 | 0.035000
1443 | 0.210000
1444 | 0.270000
1445 | 0.230000
1446 | 0.130000
1447 | 0.320000
1448 | 0.460000
1449 | 0.385000
1450 | 0.330000
1451 | 0.335000
1452 | 0.340000
1453 | 0.640000
1454 | 0.575000
1455 | 0.265000
1456 | 1.280000
1457 | 1.940000
1458 | -0.110000
1459 | -0.040000
1460 | -0.010000
1461 | -0.045000
1462 | -0.130000
1463 | 0.130000
1464 | 0.135000
1465 | 0.105000
1466 | 0.035000
1467 | 0.055000
1468 | 0.225000
1469 | 0.420000
1470 | 0.095000
1471 | -0.050000
1472 | 2.680000
1473 | 0.130000
1474 | -0.195000
1475 | -0.175000
1476 | -0.180000
1477 | -0.250000
1478 | -0.235000
1479 | -0.030000
1480 | -0.095000
1481 | -0.160000
1482 | -0.205000
1483 | -0.225000
1484 | -0.025000
1485 | 0.045000
1486 | -0.380000
1487 | -0.220000
1488 | 2.190000
1489 | -0.960000
1490 | -0.800000
1491 | -0.810000
1492 | -0.885000
1493 | -1.010000
1494 | -0.880000
1495 | -0.840000
1496 | -0.945000
1497 | -1.055000
1498 | -1.055000
1499 | -1.020000
1500 | -0.785000
1501 | 


--------------------------------------------------------------------------------
/tsbitmaps/test/data/ecg_anom.txt:
--------------------------------------------------------------------------------
   1 | 4.7275
   2 | 4.7083
   3 | 4.6700
   4 | 4.6600
   5 | 4.6617
   6 | 4.6517
   7 | 4.6500
   8 | 4.6500
   9 | 4.6917
  10 | 4.7533
  11 | 4.8233
  12 | 4.8700
  13 | 4.8783
  14 | 4.8700
  15 | 4.8500
  16 | 4.8433
  17 | 4.8383
  18 | 4.8400
  19 | 4.8433
  20 | 4.8333
  21 | 4.8300
  22 | 4.8133
  23 | 4.8100
  24 | 4.8000
  25 | 4.7983
  26 | 4.7967
  27 | 4.7950
  28 | 4.7917
  29 | 4.8000
  30 | 4.8233
  31 | 4.8783
  32 | 5.0000
  33 | 5.1467
  34 | 5.2883
  35 | 5.2050
  36 | 5.0367
  37 | 4.8083
  38 | 4.7017
  39 | 4.6483
  40 | 4.6367
  41 | 4.6550
  42 | 4.6733
  43 | 4.6933
  44 | 4.7267
  45 | 4.7700
  46 | 4.8317
  47 | 4.8633
  48 | 4.8767
  49 | 4.8550
  50 | 4.8350
  51 | 4.8267
  52 | 4.8117
  53 | 4.8150
  54 | 4.8000
  55 | 4.8100
  56 | 4.7933
  57 | 4.7900
  58 | 4.7717
  59 | 4.7717
  60 | 4.7717
  61 | 4.7667
  62 | 4.7667
  63 | 4.7500
  64 | 4.7567
  65 | 4.7550
  66 | 4.7933
  67 | 5.1417
  68 | 5.2900
  69 | 5.4067
  70 | 5.0850
  71 | 4.9200
  72 | 4.7300
  73 | 4.6700
  74 | 4.6567
  75 | 4.6783
  76 | 4.6983
  77 | 4.7133
  78 | 4.7367
  79 | 4.7517
  80 | 4.7933
  81 | 4.8317
  82 | 4.8733
  83 | 4.8800
  84 | 4.8683
  85 | 4.8450
  86 | 4.8267
  87 | 4.8167
  88 | 4.8067
  89 | 4.8050
  90 | 4.7967
  91 | 4.7867
  92 | 4.7667
  93 | 4.7500
  94 | 4.7400
  95 | 4.7300
  96 | 4.7267
  97 | 4.7117
  98 | 4.7167
  99 | 4.7167
 100 | 4.7333
 101 | 4.7483
 102 | 5.0750
 103 | 5.2283
 104 | 5.3600
 105 | 5.0817
 106 | 4.8817
 107 | 4.6967
 108 | 4.6283
 109 | 4.6367
 110 | 4.6400
 111 | 4.6117
 112 | 4.5933
 113 | 4.5683
 114 | 4.5783
 115 | 4.6083
 116 | 4.6833
 117 | 4.7667
 118 | 4.8400
 119 | 4.8700
 120 | 4.8500
 121 | 4.8233
 122 | 4.8000
 123 | 4.8000
 124 | 4.7917
 125 | 4.7850
 126 | 4.7767
 127 | 4.7683
 128 | 4.7633
 129 | 4.7517
 130 | 4.7517
 131 | 4.7500
 132 | 4.7550
 133 | 4.7550
 134 | 4.7550
 135 | 4.7617
 136 | 4.7833
 137 | 4.9750
 138 | 5.1150
 139 | 5.2317
 140 | 5.0267
 141 | 4.8550
 142 | 4.7167
 143 | 4.7017
 144 | 4.7200
 145 | 4.7167
 146 | 4.7383
 147 | 4.7167
 148 | 4.6867
 149 | 4.6667
 150 | 4.6917
 151 | 4.7533
 152 | 4.8200
 153 | 4.8700
 154 | 4.8683
 155 | 4.8550
 156 | 4.8250
 157 | 4.8217
 158 | 4.8100
 159 | 4.8100
 160 | 4.8017
 161 | 4.7900
 162 | 4.7883
 163 | 4.7833
 164 | 4.7917
 165 | 4.7883
 166 | 4.7817
 167 | 4.7783
 168 | 4.7800
 169 | 4.7883
 170 | 4.8067
 171 | 5.1517
 172 | 5.2950
 173 | 5.4117
 174 | 5.0633
 175 | 4.8517
 176 | 4.6933
 177 | 4.6800
 178 | 4.7467
 179 | 4.7717
 180 | 4.7950
 181 | 4.8050
 182 | 4.8250
 183 | 4.8500
 184 | 4.8683
 185 | 4.8933
 186 | 4.9267
 187 | 4.9500
 188 | 4.9383
 189 | 4.9083
 190 | 4.8817
 191 | 4.8683
 192 | 4.8500
 193 | 4.8400
 194 | 4.8317
 195 | 4.8183
 196 | 4.8117
 197 | 4.8033
 198 | 4.8017
 199 | 4.7917
 200 | 4.7883
 201 | 4.7883
 202 | 4.7917
 203 | 4.8017
 204 | 4.8267
 205 | 4.9567
 206 | 5.1183
 207 | 5.2350
 208 | 5.0600
 209 | 4.8383
 210 | 4.6717
 211 | 4.6800
 212 | 4.7133
 213 | 4.7083
 214 | 4.7200
 215 | 4.7100
 216 | 4.7233
 217 | 4.7583
 218 | 4.8367
 219 | 4.9233
 220 | 4.9683
 221 | 4.9633
 222 | 4.9183
 223 | 4.8850
 224 | 4.8617
 225 | 4.8483
 226 | 4.8467
 227 | 4.8417
 228 | 4.8450
 229 | 4.8350
 230 | 4.8350
 231 | 4.8233
 232 | 4.8233
 233 | 4.8117
 234 | 4.8200
 235 | 4.8233
 236 | 4.8283
 237 | 4.8450
 238 | 5.0967
 239 | 5.2517
 240 | 5.3717
 241 | 5.1067
 242 | 4.9250
 243 | 4.7867
 244 | 4.7750
 245 | 4.7850
 246 | 4.7683
 247 | 4.7717
 248 | 4.7783
 249 | 4.7883
 250 | 4.8217
 251 | 4.8783
 252 | 4.9450
 253 | 4.9933
 254 | 4.9933
 255 | 4.9583
 256 | 4.9150
 257 | 4.8933
 258 | 4.8817
 259 | 4.8883
 260 | 4.8950
 261 | 4.9050
 262 | 4.8883
 263 | 4.8567
 264 | 4.8300
 265 | 4.8133
 266 | 4.8100
 267 | 4.8067
 268 | 4.8133
 269 | 4.8233
 270 | 4.8317
 271 | 4.8517
 272 | 5.2367
 273 | 5.3917
 274 | 5.5250
 275 | 5.2767
 276 | 5.0933
 277 | 4.9100
 278 | 4.7467
 279 | 4.7317
 280 | 4.7417
 281 | 4.7383
 282 | 4.7567
 283 | 4.7600
 284 | 4.7750
 285 | 4.8033
 286 | 4.8550
 287 | 4.9150
 288 | 4.9667
 289 | 4.9883
 290 | 4.9833
 291 | 4.9683
 292 | 4.9533
 293 | 4.9400
 294 | 4.9317
 295 | 4.9183
 296 | 4.9133
 297 | 4.9017
 298 | 4.8933
 299 | 4.8767
 300 | 4.8667
 301 | 4.8583
 302 | 4.8567
 303 | 4.8483
 304 | 4.8450
 305 | 4.8617
 306 | 4.8783
 307 | 4.8850
 308 | 5.0633
 309 | 5.1983
 310 | 5.3817
 311 | 5.4000
 312 | 5.2867
 313 | 5.0800
 314 | 4.8267
 315 | 4.7200
 316 | 4.6833
 317 | 4.6683
 318 | 4.6850
 319 | 4.6917
 320 | 4.7000
 321 | 4.7300
 322 | 4.7683
 323 | 4.8333
 324 | 4.8767
 325 | 4.9067
 326 | 4.9000
 327 | 4.8867
 328 | 4.8750
 329 | 4.8667
 330 | 4.8667
 331 | 4.8600
 332 | 4.8567
 333 | 4.8533
 334 | 4.8417
 335 | 4.8333
 336 | 4.8167
 337 | 4.8117
 338 | 4.8000
 339 | 4.8050
 340 | 4.8033
 341 | 4.8100
 342 | 4.8100
 343 | 4.8533
 344 | 4.9900
 345 | 5.1500
 346 | 5.2583
 347 | 5.0950
 348 | 4.9117
 349 | 4.7400
 350 | 4.7233
 351 | 4.7033
 352 | 4.6833
 353 | 4.6800
 354 | 4.6717
 355 | 4.6800
 356 | 4.7133
 357 | 4.7800
 358 | 4.8600
 359 | 4.9267
 360 | 4.9617
 361 | 4.9550
 362 | 4.9317
 363 | 4.9000
 364 | 4.8867
 365 | 4.8783
 366 | 4.8767
 367 | 4.8783
 368 | 4.8817
 369 | 4.8783
 370 | 4.8667
 371 | 4.8633
 372 | 4.8550
 373 | 4.8617
 374 | 4.8617
 375 | 4.8717
 376 | 4.8750
 377 | 4.8883
 378 | 5.2383
 379 | 5.3900
 380 | 5.5450
 381 | 5.2733
 382 | 5.0983
 383 | 4.8867
 384 | 4.7800
 385 | 4.7717
 386 | 4.8033
 387 | 4.8400
 388 | 4.8567
 389 | 4.8417
 390 | 4.8183
 391 | 4.8250
 392 | 4.8717
 393 | 4.9467
 394 | 5.0133
 395 | 5.0467
 396 | 5.0317
 397 | 4.9967
 398 | 4.9650
 399 | 4.9517
 400 | 4.9500
 401 | 4.9517
 402 | 4.9417
 403 | 4.9333
 404 | 4.9250
 405 | 4.9233
 406 | 4.9200
 407 | 4.9217
 408 | 4.9200
 409 | 4.9233
 410 | 4.9250
 411 | 4.9433
 412 | 5.1933
 413 | 5.3617
 414 | 5.5100
 415 | 5.2783
 416 | 5.0800
 417 | 4.8817
 418 | 4.8183
 419 | 4.8150
 420 | 4.8200
 421 | 4.8367
 422 | 4.8317
 423 | 4.8350
 424 | 4.8650
 425 | 4.9283
 426 | 4.9900
 427 | 5.0350
 428 | 5.0483
 429 | 5.0333
 430 | 5.0017
 431 | 4.9700
 432 | 4.9583
 433 | 4.9483
 434 | 4.9417
 435 | 4.9250
 436 | 4.9117
 437 | 4.9033
 438 | 4.8983
 439 | 4.8983
 440 | 4.8950
 441 | 4.8983
 442 | 4.9000
 443 | 4.9067
 444 | 4.9167
 445 | 4.9533
 446 | 5.0967
 447 | 5.2367
 448 | 5.3250
 449 | 5.1367
 450 | 4.9600
 451 | 4.8400
 452 | 4.8633
 453 | 4.8867
 454 | 4.8733
 455 | 4.8867
 456 | 4.8817
 457 | 4.8917
 458 | 4.9100
 459 | 4.9567
 460 | 5.0133
 461 | 5.0683
 462 | 5.1017
 463 | 5.1017
 464 | 5.0833
 465 | 5.0367
 466 | 4.9917
 467 | 4.9567
 468 | 4.9433
 469 | 4.9350
 470 | 4.9150
 471 | 4.9100
 472 | 4.8867
 473 | 4.8967
 474 | 4.8883
 475 | 4.9067
 476 | 4.9133
 477 | 4.9283
 478 | 4.9450
 479 | 4.9950
 480 | 5.1400
 481 | 5.2867
 482 | 5.3967
 483 | 5.2233
 484 | 5.0283
 485 | 4.8300
 486 | 4.8117
 487 | 4.8167
 488 | 4.8267
 489 | 4.8467
 490 | 4.8550
 491 | 4.8683
 492 | 4.9000
 493 | 4.9583
 494 | 5.0200
 495 | 5.0817
 496 | 5.1083
 497 | 5.0967
 498 | 5.0600
 499 | 5.0183
 500 | 4.9900
 501 | 4.9650
 502 | 4.9517
 503 | 4.9450
 504 | 4.9267
 505 | 4.9150
 506 | 4.9083
 507 | 4.9167
 508 | 4.9200
 509 | 4.9150
 510 | 4.9117
 511 | 4.9133
 512 | 4.9283
 513 | 5.2517
 514 | 5.4183
 515 | 5.5650
 516 | 5.3083
 517 | 5.0617
 518 | 4.8267
 519 | 4.7200
 520 | 4.7567
 521 | 4.8017
 522 | 4.8000
 523 | 4.7967
 524 | 4.7800
 525 | 4.7817
 526 | 4.8117
 527 | 4.8717
 528 | 4.9500
 529 | 5.0017
 530 | 5.0250
 531 | 5.0083
 532 | 4.9900
 533 | 4.9650
 534 | 4.9417
 535 | 4.9183
 536 | 4.9000
 537 | 4.8900
 538 | 4.8700
 539 | 4.8550
 540 | 4.8383
 541 | 4.8383
 542 | 4.8350
 543 | 4.8333
 544 | 4.8350
 545 | 4.8333
 546 | 4.8417
 547 | 4.8433
 548 | 4.8850
 549 | 5.0417
 550 | 5.2067
 551 | 5.3383
 552 | 5.2733
 553 | 5.0933
 554 | 4.8883
 555 | 4.7700
 556 | 4.7367
 557 | 4.7450
 558 | 4.7417
 559 | 4.7567
 560 | 4.7500
 561 | 4.7467
 562 | 4.7617
 563 | 4.8050
 564 | 4.8700
 565 | 4.9383
 566 | 4.9900
 567 | 5.0050
 568 | 4.9917
 569 | 4.9617
 570 | 4.9400
 571 | 4.9250
 572 | 4.9167
 573 | 4.9100
 574 | 4.8917
 575 | 4.8850
 576 | 4.8867
 577 | 4.9000
 578 | 4.8800
 579 | 4.8533
 580 | 4.8317
 581 | 4.8283
 582 | 4.8317
 583 | 4.8383
 584 | 4.8567
 585 | 5.0717
 586 | 5.2267
 587 | 5.3900
 588 | 5.3433
 589 | 5.1750
 590 | 4.9767
 591 | 4.7750
 592 | 4.7450
 593 | 4.7317
 594 | 4.7433
 595 | 4.7517
 596 | 4.7583
 597 | 4.7600
 598 | 4.7983
 599 | 4.8550
 600 | 4.9233
 601 | 4.9733
 602 | 4.9917
 603 | 4.9850
 604 | 4.9517
 605 | 4.9417
 606 | 4.9167
 607 | 4.9183
 608 | 4.9033
 609 | 4.9150
 610 | 4.9000
 611 | 4.8933
 612 | 4.8733
 613 | 4.8650
 614 | 4.8667
 615 | 4.8633
 616 | 4.8767
 617 | 4.8850
 618 | 4.9033
 619 | 4.9083
 620 | 5.1000
 621 | 5.2517
 622 | 5.4333
 623 | 5.4200
 624 | 5.2317
 625 | 4.9883
 626 | 4.7500
 627 | 4.7100
 628 | 4.7100
 629 | 4.7017
 630 | 4.7100
 631 | 4.7000
 632 | 4.7100
 633 | 4.7417
 634 | 4.8050
 635 | 4.8617
 636 | 4.8967
 637 | 4.8967
 638 | 4.8783
 639 | 4.8600
 640 | 4.8433
 641 | 4.8417
 642 | 4.8383
 643 | 4.8417
 644 | 4.8350
 645 | 4.8233
 646 | 4.8117
 647 | 4.7983
 648 | 4.7933
 649 | 4.7817
 650 | 4.7783
 651 | 4.7750
 652 | 4.7783
 653 | 4.7883
 654 | 5.1250
 655 | 5.2917
 656 | 5.4600
 657 | 5.2350
 658 | 5.0350
 659 | 4.8300
 660 | 4.6850
 661 | 4.6650
 662 | 4.6417
 663 | 4.6200
 664 | 4.6233
 665 | 4.6233
 666 | 4.6433
 667 | 4.6833
 668 | 4.7517
 669 | 4.8217
 670 | 4.8600
 671 | 4.8700
 672 | 4.8433
 673 | 4.8283
 674 | 4.8100
 675 | 4.8117
 676 | 4.8017
 677 | 4.7933
 678 | 4.7850
 679 | 4.7817
 680 | 4.7750
 681 | 4.7567
 682 | 4.7567
 683 | 4.7550
 684 | 4.7650
 685 | 4.7583
 686 | 4.7700
 687 | 4.8717
 688 | 5.0233
 689 | 5.1750
 690 | 5.2033
 691 | 4.9917
 692 | 4.7700
 693 | 4.6183
 694 | 4.6467
 695 | 4.6633
 696 | 4.6567
 697 | 4.6350
 698 | 4.6450
 699 | 4.6433
 700 | 4.6950
 701 | 4.7633
 702 | 4.8500
 703 | 4.9017
 704 | 4.9033
 705 | 4.8717
 706 | 4.8333
 707 | 4.8133
 708 | 4.7583
 709 | 4.6750
 710 | 4.6583
 711 | 4.8750
 712 | 4.7467
 713 | 4.6217
 714 | 4.3983
 715 | 4.5683
 716 | 4.6733
 717 | 4.7017
 718 | 4.7367
 719 | 4.7667
 720 | 4.7750
 721 | 4.7733
 722 | 4.7733
 723 | 4.7833
 724 | 4.7817
 725 | 4.7750
 726 | 4.7583
 727 | 4.7500
 728 | 4.7317
 729 | 4.7333
 730 | 4.7467
 731 | 4.7567
 732 | 4.7533
 733 | 4.7300
 734 | 4.7250
 735 | 4.7200
 736 | 4.7267
 737 | 4.7233
 738 | 4.7217
 739 | 4.7200
 740 | 4.7133
 741 | 4.7183
 742 | 4.7200
 743 | 4.7367
 744 | 4.7467
 745 | 4.7917
 746 | 4.9450
 747 | 5.1200
 748 | 5.2617
 749 | 5.1800
 750 | 5.0433
 751 | 4.8433
 752 | 4.7300
 753 | 4.6517
 754 | 4.6367
 755 | 4.6500
 756 | 4.6550
 757 | 4.6767
 758 | 4.7050
 759 | 4.7667
 760 | 4.8283
 761 | 4.8933
 762 | 4.9317
 763 | 4.9367
 764 | 4.9267
 765 | 4.8983
 766 | 4.8867
 767 | 4.8600
 768 | 4.8483
 769 | 4.8417
 770 | 4.8400
 771 | 4.8267
 772 | 4.7983
 773 | 4.7733
 774 | 4.7583
 775 | 4.7567
 776 | 4.7583
 777 | 4.7650
 778 | 4.7717
 779 | 4.7933
 780 | 5.0650
 781 | 5.2300
 782 | 5.3800
 783 | 5.1717
 784 | 4.9950
 785 | 4.7883
 786 | 4.7000
 787 | 4.6850
 788 | 4.7067
 789 | 4.7117
 790 | 4.7183
 791 | 4.7400
 792 | 4.7700
 793 | 4.8383
 794 | 4.9117
 795 | 4.9750
 796 | 4.9983
 797 | 4.9800
 798 | 4.9650
 799 | 4.9450
 800 | 4.9417
 801 | 4.9383
 802 | 4.9400
 803 | 4.9300
 804 | 4.9167
 805 | 4.9100
 806 | 4.8950
 807 | 4.9050
 808 | 4.8917
 809 | 4.9000
 810 | 4.9033
 811 | 4.9333
 812 | 4.9483
 813 | 4.9367
 814 | 4.9267
 815 | 5.2433
 816 | 5.4117
 817 | 5.6067
 818 | 5.5050
 819 | 5.3683
 820 | 5.1367
 821 | 4.8700
 822 | 4.7883
 823 | 4.7717
 824 | 4.7767
 825 | 4.7883
 826 | 4.7850
 827 | 4.7783
 828 | 4.7933
 829 | 4.8283
 830 | 4.8933
 831 | 4.9450
 832 | 4.9867
 833 | 4.9817
 834 | 4.9633
 835 | 4.9400
 836 | 4.9300
 837 | 4.9267
 838 | 4.9200
 839 | 4.9200
 840 | 4.9167
 841 | 4.9150
 842 | 4.9017
 843 | 4.8950
 844 | 4.8850
 845 | 4.8833
 846 | 4.8800
 847 | 4.8750
 848 | 4.8717
 849 | 4.8700
 850 | 4.8750
 851 | 4.9067
 852 | 5.0600
 853 | 5.2383
 854 | 5.3650
 855 | 5.2233
 856 | 5.0417
 857 | 4.8667
 858 | 4.8050
 859 | 4.7567
 860 | 4.7267
 861 | 4.7300
 862 | 4.7367
 863 | 4.7417
 864 | 4.7667
 865 | 4.8083
 866 | 4.8767
 867 | 4.9350
 868 | 4.9717
 869 | 4.9733
 870 | 4.9517
 871 | 4.9300
 872 | 4.9150
 873 | 4.9150
 874 | 4.9117
 875 | 4.9067
 876 | 4.8917
 877 | 4.8883
 878 | 4.8800
 879 | 4.8733
 880 | 4.8700
 881 | 4.8667
 882 | 4.8733
 883 | 4.8600
 884 | 4.8633
 885 | 4.8600
 886 | 4.9433
 887 | 5.0933
 888 | 5.2450
 889 | 5.2683
 890 | 5.0850
 891 | 4.8967
 892 | 4.7683
 893 | 4.7550
 894 | 4.7550
 895 | 4.7300
 896 | 4.7250
 897 | 4.7250
 898 | 4.7650
 899 | 4.8283
 900 | 4.9100
 901 | 4.9733
 902 | 4.9983
 903 | 4.9783
 904 | 4.9433
 905 | 4.9133
 906 | 4.9050
 907 | 4.8983
 908 | 4.8933
 909 | 4.8783
 910 | 4.8650
 911 | 4.8600
 912 | 4.8517
 913 | 4.8467
 914 | 4.8500
 915 | 4.8467
 916 | 4.8483
 917 | 4.8450
 918 | 4.8500
 919 | 4.9100
 920 | 5.0417
 921 | 5.2183
 922 | 5.2917
 923 | 5.0867
 924 | 4.8267
 925 | 4.6650
 926 | 4.7133
 927 | 4.7767
 928 | 4.7883
 929 | 4.7733
 930 | 4.7517
 931 | 4.7317
 932 | 4.7583
 933 | 4.8183
 934 | 4.9067
 935 | 4.9700
 936 | 4.9867
 937 | 4.9550
 938 | 4.9117
 939 | 4.8900
 940 | 4.8783
 941 | 4.8733
 942 | 4.8600
 943 | 4.8550
 944 | 4.8417
 945 | 4.8300
 946 | 4.8217
 947 | 4.8200
 948 | 4.8233
 949 | 4.8200
 950 | 4.8217
 951 | 4.8250
 952 | 4.8500
 953 | 5.0567
 954 | 5.2250
 955 | 5.3700
 956 | 5.1800
 957 | 4.9817
 958 | 4.7867
 959 | 4.7450
 960 | 4.7500
 961 | 4.7483
 962 | 4.7317
 963 | 4.6933
 964 | 4.6683
 965 | 4.6667
 966 | 4.7217
 967 | 4.8083
 968 | 4.9050
 969 | 4.9583
 970 | 4.9583
 971 | 4.9283
 972 | 4.8967
 973 | 4.8867
 974 | 4.8750
 975 | 4.8733
 976 | 4.8583
 977 | 4.8550
 978 | 4.8417
 979 | 4.8367
 980 | 4.8267
 981 | 4.8217
 982 | 4.8283
 983 | 4.8300
 984 | 4.8400
 985 | 4.8367
 986 | 4.8700
 987 | 5.0517
 988 | 5.2033
 989 | 5.3100
 990 | 5.1200
 991 | 4.9383
 992 | 4.7883
 993 | 4.7700
 994 | 4.7817
 995 | 4.7783
 996 | 4.7833
 997 | 4.7800
 998 | 4.7967
 999 | 4.8333
1000 | 4.8625


--------------------------------------------------------------------------------
/tsbitmaps/test/data/ecg_normal.txt:
--------------------------------------------------------------------------------
   1 | 4.9525
   2 | 4.9683
   3 | 4.9817
   4 | 4.9717
   5 | 4.9367
   6 | 4.9017
   7 | 4.8933
   8 | 4.8867
   9 | 4.8883
  10 | 4.8767
  11 | 4.8700
  12 | 4.8500
  13 | 4.8450
  14 | 4.8417
  15 | 4.8550
  16 | 4.8567
  17 | 4.8567
  18 | 4.8567
  19 | 4.8617
  20 | 4.8733
  21 | 4.9567
  22 | 5.1200
  23 | 5.2700
  24 | 5.2783
  25 | 5.0167
  26 | 4.7900
  27 | 4.6783
  28 | 4.7533
  29 | 4.8233
  30 | 4.8517
  31 | 4.8733
  32 | 4.8767
  33 | 4.8867
  34 | 4.9267
  35 | 4.9750
  36 | 5.0383
  37 | 5.0917
  38 | 5.1100
  39 | 5.0767
  40 | 5.0250
  41 | 4.9933
  42 | 4.9833
  43 | 4.9883
  44 | 4.9850
  45 | 4.9650
  46 | 4.9350
  47 | 4.9033
  48 | 4.8833
  49 | 4.8583
  50 | 4.8517
  51 | 4.8450
  52 | 4.8467
  53 | 4.8417
  54 | 4.8367
  55 | 4.8367
  56 | 4.8767
  57 | 5.0333
  58 | 5.2100
  59 | 5.3433
  60 | 5.2300
  61 | 5.0300
  62 | 4.8283
  63 | 4.7417
  64 | 4.7167
  65 | 4.6983
  66 | 4.6967
  67 | 4.6850
  68 | 4.6800
  69 | 4.6900
  70 | 4.7383
  71 | 4.8167
  72 | 4.8933
  73 | 4.9483
  74 | 4.9533
  75 | 4.9417
  76 | 4.9167
  77 | 4.9050
  78 | 4.8983
  79 | 4.8967
  80 | 4.8983
  81 | 4.9017
  82 | 4.8983
  83 | 4.8783
  84 | 4.8533
  85 | 4.8317
  86 | 4.8217
  87 | 4.8150
  88 | 4.8117
  89 | 4.8167
  90 | 4.8167
  91 | 4.8350
  92 | 5.0200
  93 | 5.1817
  94 | 5.3400
  95 | 5.3133
  96 | 5.1717
  97 | 4.9883
  98 | 4.8017
  99 | 4.7500
 100 | 4.7350
 101 | 4.7317
 102 | 4.7400
 103 | 4.7350
 104 | 4.7350
 105 | 4.7517
 106 | 4.7967
 107 | 4.8667
 108 | 4.9267
 109 | 4.9667
 110 | 4.9733
 111 | 4.9600
 112 | 4.9433
 113 | 4.9333
 114 | 4.9283
 115 | 4.9217
 116 | 4.9167
 117 | 4.9067
 118 | 4.9033
 119 | 4.8900
 120 | 4.8733
 121 | 4.8567
 122 | 4.8467
 123 | 4.8650
 124 | 4.8550
 125 | 4.8633
 126 | 4.8667
 127 | 4.9000
 128 | 5.2000
 129 | 5.3417
 130 | 5.5233
 131 | 5.4400
 132 | 5.3133
 133 | 5.0633
 134 | 4.8000
 135 | 4.7133
 136 | 4.7217
 137 | 4.7450
 138 | 4.7783
 139 | 4.7967
 140 | 4.8133
 141 | 4.8650
 142 | 4.9283
 143 | 4.9783
 144 | 4.9817
 145 | 4.9633
 146 | 4.9500
 147 | 4.9283
 148 | 4.9267
 149 | 4.9050
 150 | 4.9183
 151 | 4.9067
 152 | 4.9117
 153 | 4.9000
 154 | 4.8917
 155 | 4.8867
 156 | 4.8783
 157 | 4.8733
 158 | 4.8567
 159 | 4.8517
 160 | 4.8517
 161 | 4.8600
 162 | 5.1117
 163 | 5.2650
 164 | 5.4167
 165 | 5.1850
 166 | 4.9550
 167 | 4.7400
 168 | 4.6467
 169 | 4.6483
 170 | 4.6300
 171 | 4.6267
 172 | 4.6167
 173 | 4.6250
 174 | 4.6517
 175 | 4.7167
 176 | 4.7900
 177 | 4.8517
 178 | 4.8767
 179 | 4.8600
 180 | 4.8400
 181 | 4.8183
 182 | 4.8183
 183 | 4.8100
 184 | 4.8117
 185 | 4.7967
 186 | 4.7850
 187 | 4.7700
 188 | 4.7633
 189 | 4.7567
 190 | 4.7483
 191 | 4.7483
 192 | 4.7433
 193 | 4.7533
 194 | 4.7700
 195 | 5.0117
 196 | 5.1733
 197 | 5.3100
 198 | 5.0950
 199 | 4.8867
 200 | 4.7067
 201 | 4.6567
 202 | 4.6750
 203 | 4.6683
 204 | 4.6483
 205 | 4.6200
 206 | 4.5983
 207 | 4.6217
 208 | 4.6750
 209 | 4.7700
 210 | 4.8517
 211 | 4.8967
 212 | 4.8867
 213 | 4.8500
 214 | 4.8267
 215 | 4.8133
 216 | 4.8100
 217 | 4.8050
 218 | 4.8067
 219 | 4.8033
 220 | 4.7967
 221 | 4.7850
 222 | 4.7850
 223 | 4.7867
 224 | 4.7900
 225 | 4.7933
 226 | 4.8000
 227 | 4.8150
 228 | 5.1000
 229 | 5.2467
 230 | 5.3767
 231 | 5.0600
 232 | 4.8500
 233 | 4.6917
 234 | 4.7017
 235 | 4.7650
 236 | 4.7900
 237 | 4.8050
 238 | 4.7900
 239 | 4.7817
 240 | 4.7900
 241 | 4.8450
 242 | 4.8883
 243 | 4.9683
 244 | 4.9983
 245 | 5.0167
 246 | 4.9833
 247 | 4.9533
 248 | 4.9317
 249 | 4.9283
 250 | 4.9283
 251 | 4.9283
 252 | 4.9250
 253 | 4.9167
 254 | 4.9117
 255 | 4.9033
 256 | 4.8917
 257 | 4.8883
 258 | 4.8817
 259 | 4.8817
 260 | 4.8717
 261 | 4.8700
 262 | 4.9617
 263 | 5.1217
 264 | 5.2900
 265 | 5.3000
 266 | 5.0633
 267 | 4.7850
 268 | 4.6183
 269 | 4.6417
 270 | 4.7017
 271 | 4.7200
 272 | 4.7100
 273 | 4.6800
 274 | 4.6283
 275 | 4.6100
 276 | 4.6300
 277 | 4.7117
 278 | 4.8183
 279 | 4.9067
 280 | 4.9433
 281 | 4.9267
 282 | 4.9017
 283 | 4.8783
 284 | 4.8750
 285 | 4.8667
 286 | 4.8667
 287 | 4.8550
 288 | 4.8450
 289 | 4.8400
 290 | 4.8383
 291 | 4.8433
 292 | 4.8267
 293 | 4.8217
 294 | 4.8150
 295 | 4.8250
 296 | 4.8333
 297 | 4.8833
 298 | 5.0383
 299 | 5.2133
 300 | 5.3400
 301 | 5.2183
 302 | 5.0283
 303 | 4.8233
 304 | 4.7733
 305 | 4.7683
 306 | 4.7617
 307 | 4.7367
 308 | 4.6917
 309 | 4.6717
 310 | 4.6700
 311 | 4.7150
 312 | 4.7917
 313 | 4.8817
 314 | 4.9533
 315 | 4.9717
 316 | 4.9567
 317 | 4.9250
 318 | 4.9133
 319 | 4.9133
 320 | 4.9033
 321 | 4.8983
 322 | 4.8767
 323 | 4.8767
 324 | 4.8617
 325 | 4.8717
 326 | 4.8600
 327 | 4.8550
 328 | 4.8467
 329 | 4.8533
 330 | 4.8583
 331 | 4.8567
 332 | 4.8917
 333 | 5.1433
 334 | 5.3100
 335 | 5.4350
 336 | 5.2367
 337 | 5.0350
 338 | 4.8300
 339 | 4.7633
 340 | 4.7683
 341 | 4.7883
 342 | 4.7683
 343 | 4.7750
 344 | 4.7733
 345 | 4.8050
 346 | 4.8683
 347 | 4.9550
 348 | 5.0400
 349 | 5.0733
 350 | 5.0733
 351 | 5.0517
 352 | 5.0300
 353 | 5.0183
 354 | 5.0033
 355 | 4.9900
 356 | 4.9750
 357 | 4.9683
 358 | 4.9583
 359 | 4.9550
 360 | 4.9350
 361 | 4.9200
 362 | 4.9150
 363 | 4.9117
 364 | 4.9167
 365 | 4.9017
 366 | 4.9050
 367 | 4.8967
 368 | 5.0200
 369 | 5.1617
 370 | 5.3317
 371 | 5.3467
 372 | 5.1667
 373 | 4.9400
 374 | 4.7500
 375 | 4.7317
 376 | 4.7200
 377 | 4.7067
 378 | 4.6783
 379 | 4.6583
 380 | 4.6350
 381 | 4.6350
 382 | 4.6800
 383 | 4.7633
 384 | 4.8717
 385 | 4.9517
 386 | 4.9833
 387 | 4.9683
 388 | 4.9417
 389 | 4.9200
 390 | 4.9067
 391 | 4.9033
 392 | 4.8950
 393 | 4.8883
 394 | 4.8733
 395 | 4.8667
 396 | 4.8567
 397 | 4.8450
 398 | 4.8350
 399 | 4.8283
 400 | 4.8267
 401 | 4.8250
 402 | 4.8283
 403 | 4.8700
 404 | 5.0167
 405 | 5.1983
 406 | 5.3333
 407 | 5.2017
 408 | 4.9967
 409 | 4.7883
 410 | 4.7133
 411 | 4.6767
 412 | 4.6450
 413 | 4.6383
 414 | 4.6317
 415 | 4.6400
 416 | 4.6817
 417 | 4.7450
 418 | 4.8300
 419 | 4.8900
 420 | 4.9200
 421 | 4.9067
 422 | 4.8817
 423 | 4.8600
 424 | 4.8417
 425 | 4.8383
 426 | 4.8300
 427 | 4.8350
 428 | 4.8250
 429 | 4.8167
 430 | 4.8033
 431 | 4.7950
 432 | 4.7983
 433 | 4.8017
 434 | 4.8100
 435 | 4.8033
 436 | 4.8167
 437 | 5.1917
 438 | 5.3817
 439 | 5.5500
 440 | 5.2017
 441 | 5.0000
 442 | 4.7750
 443 | 4.6883
 444 | 4.6633
 445 | 4.6650
 446 | 4.6883
 447 | 4.7017
 448 | 4.7500
 449 | 4.7783
 450 | 4.8517
 451 | 4.8933
 452 | 4.9400
 453 | 4.9183
 454 | 4.9017
 455 | 4.8767
 456 | 4.8833
 457 | 4.8683
 458 | 4.8700
 459 | 4.8717
 460 | 4.8633
 461 | 4.8700
 462 | 4.8533
 463 | 4.8517
 464 | 4.8450
 465 | 4.8417
 466 | 4.8517
 467 | 4.8433
 468 | 4.8500
 469 | 4.8633
 470 | 5.0950
 471 | 5.2683
 472 | 5.4033
 473 | 5.1883
 474 | 4.9417
 475 | 4.7017
 476 | 4.6083
 477 | 4.6017
 478 | 4.6200
 479 | 4.6283
 480 | 4.6317
 481 | 4.6367
 482 | 4.6617
 483 | 4.7133
 484 | 4.7933
 485 | 4.8533
 486 | 4.8867
 487 | 4.8683
 488 | 4.8483
 489 | 4.8317
 490 | 4.8300
 491 | 4.8350
 492 | 4.8350
 493 | 4.8333
 494 | 4.8233
 495 | 4.8100
 496 | 4.7967
 497 | 4.7867
 498 | 4.7783
 499 | 4.7683
 500 | 4.7650
 501 | 4.7633
 502 | 4.7800
 503 | 4.8600
 504 | 5.0250
 505 | 5.1967
 506 | 5.3083
 507 | 5.2067
 508 | 5.0383
 509 | 4.8150
 510 | 4.7000
 511 | 4.6350
 512 | 4.6233
 513 | 4.6350
 514 | 4.6500
 515 | 4.6650
 516 | 4.6967
 517 | 4.7400
 518 | 4.8033
 519 | 4.8533
 520 | 4.8767
 521 | 4.8617
 522 | 4.8367
 523 | 4.8200
 524 | 4.8100
 525 | 4.8117
 526 | 4.8067
 527 | 4.8100
 528 | 4.7967
 529 | 4.7883
 530 | 4.7817
 531 | 4.7750
 532 | 4.7717
 533 | 4.7567
 534 | 4.7550
 535 | 4.7483
 536 | 4.7550
 537 | 4.7550
 538 | 4.7683
 539 | 5.1333
 540 | 5.2833
 541 | 5.4433
 542 | 5.2233
 543 | 5.0650
 544 | 4.8733
 545 | 4.6917
 546 | 4.6517
 547 | 4.6233
 548 | 4.6050
 549 | 4.5933
 550 | 4.5833
 551 | 4.5700
 552 | 4.6000
 553 | 4.6567
 554 | 4.7533
 555 | 4.8300
 556 | 4.8883
 557 | 4.9017
 558 | 4.8767
 559 | 4.8483
 560 | 4.8233
 561 | 4.8200
 562 | 4.8133
 563 | 4.8017
 564 | 4.8000
 565 | 4.7850
 566 | 4.7883
 567 | 4.7717
 568 | 4.7750
 569 | 4.7650
 570 | 4.7617
 571 | 4.7617
 572 | 4.7667
 573 | 4.7833
 574 | 4.8117
 575 | 5.2150
 576 | 5.3817
 577 | 5.5267
 578 | 5.1717
 579 | 4.9267
 580 | 4.6617
 581 | 4.5717
 582 | 4.6233
 583 | 4.7033
 584 | 4.7300
 585 | 4.7217
 586 | 4.6983
 587 | 4.6933
 588 | 4.7333
 589 | 4.8133
 590 | 4.8983
 591 | 4.9533
 592 | 4.9583
 593 | 4.9367
 594 | 4.9017
 595 | 4.8817
 596 | 4.8633
 597 | 4.8567
 598 | 4.8433
 599 | 4.8300
 600 | 4.8183
 601 | 4.8033
 602 | 4.7950
 603 | 4.7783
 604 | 4.7750
 605 | 4.7700
 606 | 4.7700
 607 | 4.7750
 608 | 4.7767
 609 | 4.8200
 610 | 4.9733
 611 | 5.1617
 612 | 5.2983
 613 | 5.1967
 614 | 4.9833
 615 | 4.7583
 616 | 4.6567
 617 | 4.6367
 618 | 4.6517
 619 | 4.6517
 620 | 4.6617
 621 | 4.6617
 622 | 4.6767
 623 | 4.6950
 624 | 4.7383
 625 | 4.8000
 626 | 4.8433
 627 | 4.8517
 628 | 4.8300
 629 | 4.8133
 630 | 4.8000
 631 | 4.7933
 632 | 4.7867
 633 | 4.7767
 634 | 4.7717
 635 | 4.7567
 636 | 4.7533
 637 | 4.7417
 638 | 4.7400
 639 | 4.7367
 640 | 4.7350
 641 | 4.7417
 642 | 4.7383
 643 | 4.7483
 644 | 4.7583
 645 | 5.1000
 646 | 5.2417
 647 | 5.3850
 648 | 5.1033
 649 | 4.9033
 650 | 4.7033
 651 | 4.6267
 652 | 4.6667
 653 | 4.6983
 654 | 4.7083
 655 | 4.7083
 656 | 4.7117
 657 | 4.7333
 658 | 4.7700
 659 | 4.8250
 660 | 4.8583
 661 | 4.8817
 662 | 4.8700
 663 | 4.8533
 664 | 4.8283
 665 | 4.8083
 666 | 4.7950
 667 | 4.7767
 668 | 4.7750
 669 | 4.7700
 670 | 4.7700
 671 | 4.7600
 672 | 4.7617
 673 | 4.7617
 674 | 4.7550
 675 | 4.7567
 676 | 4.7667
 677 | 4.7783
 678 | 4.7950
 679 | 5.1717
 680 | 5.3250
 681 | 5.4550
 682 | 5.0900
 683 | 4.8767
 684 | 4.6883
 685 | 4.6450
 686 | 4.7017
 687 | 4.7433
 688 | 4.7717
 689 | 4.7750
 690 | 4.7733
 691 | 4.8000
 692 | 4.8500
 693 | 4.9200
 694 | 4.9533
 695 | 4.9650
 696 | 4.9283
 697 | 4.8967
 698 | 4.8833
 699 | 4.8833
 700 | 4.8983
 701 | 4.8700
 702 | 4.8633
 703 | 4.8300
 704 | 4.8200
 705 | 4.8100
 706 | 4.8183
 707 | 4.8233
 708 | 4.8150
 709 | 4.8150
 710 | 4.8233
 711 | 4.8467
 712 | 5.0600
 713 | 5.2250
 714 | 5.3483
 715 | 5.1083
 716 | 4.8667
 717 | 4.6617
 718 | 4.6250
 719 | 4.6467
 720 | 4.6667
 721 | 4.6800
 722 | 4.6800
 723 | 4.6867
 724 | 4.7183
 725 | 4.7833
 726 | 4.8517
 727 | 4.8967
 728 | 4.8967
 729 | 4.8783
 730 | 4.8550
 731 | 4.8433
 732 | 4.8400
 733 | 4.8317
 734 | 4.8333
 735 | 4.8217
 736 | 4.8183
 737 | 4.7950
 738 | 4.7950
 739 | 4.8050
 740 | 4.8150
 741 | 4.8050
 742 | 4.7783
 743 | 4.7700
 744 | 4.7767
 745 | 5.1617
 746 | 5.3350
 747 | 5.5417
 748 | 5.3433
 749 | 5.1817
 750 | 4.9483
 751 | 4.7217
 752 | 4.6600
 753 | 4.6417
 754 | 4.6533
 755 | 4.6633
 756 | 4.6600
 757 | 4.6717
 758 | 4.7067
 759 | 4.7733
 760 | 4.8267
 761 | 4.8667
 762 | 4.8617
 763 | 4.8500
 764 | 4.8333
 765 | 4.8283
 766 | 4.8333
 767 | 4.8317
 768 | 4.8333
 769 | 4.8200
 770 | 4.8133
 771 | 4.8017
 772 | 4.7983
 773 | 4.7933
 774 | 4.7950
 775 | 4.8017
 776 | 4.8017
 777 | 4.8117
 778 | 4.8317
 779 | 5.1283
 780 | 5.2750
 781 | 5.4200
 782 | 5.3167
 783 | 5.2017
 784 | 5.0450
 785 | 4.8150
 786 | 4.7283
 787 | 4.6700
 788 | 4.6700
 789 | 4.6817
 790 | 4.7000
 791 | 4.7267
 792 | 4.7600
 793 | 4.8133
 794 | 4.8700
 795 | 4.9183
 796 | 4.9267
 797 | 4.9100
 798 | 4.8867
 799 | 4.8667
 800 | 4.8683
 801 | 4.8650
 802 | 4.8767
 803 | 4.8633
 804 | 4.8550
 805 | 4.8400
 806 | 4.8367
 807 | 4.8417
 808 | 4.8383
 809 | 4.8317
 810 | 4.8183
 811 | 4.8233
 812 | 4.8183
 813 | 4.8217
 814 | 4.8217
 815 | 4.9933
 816 | 5.1517
 817 | 5.3033
 818 | 5.2817
 819 | 5.1083
 820 | 4.8850
 821 | 4.6850
 822 | 4.6500
 823 | 4.6817
 824 | 4.7000
 825 | 4.7100
 826 | 4.7017
 827 | 4.7100
 828 | 4.7233
 829 | 4.7700
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 866 | 4.7533
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 896 | 4.7333
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 899 | 4.8233
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 902 | 4.8950
 903 | 4.8800
 904 | 4.8650
 905 | 4.8533
 906 | 4.8433
 907 | 4.8433
 908 | 4.8333
 909 | 4.8200
 910 | 4.8083
 911 | 4.8067
 912 | 4.8117
 913 | 4.8167
 914 | 4.8167
 915 | 4.8133
 916 | 4.8033
 917 | 4.8050
 918 | 4.8117
 919 | 4.8250
 920 | 5.1683
 921 | 5.3083
 922 | 5.4350
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 924 | 4.9083
 925 | 4.7217
 926 | 4.6767
 927 | 4.7450
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 933 | 4.9167
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 939 | 4.9217
 940 | 4.8950
 941 | 4.9000
 942 | 4.8817
 943 | 4.8850
 944 | 4.8667
 945 | 4.8633
 946 | 4.8550
 947 | 4.8550
 948 | 4.8550
 949 | 4.8550
 950 | 4.8617
 951 | 4.8667
 952 | 4.8833
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 954 | 5.1033
 955 | 5.2633
 956 | 5.3883
 957 | 5.1583
 958 | 4.8833
 959 | 4.6700
 960 | 4.6383
 961 | 4.7000
 962 | 4.7350
 963 | 4.7533
 964 | 4.7517
 965 | 4.7500
 966 | 4.7700
 967 | 4.8217
 968 | 4.9000
 969 | 4.9567
 970 | 4.9800
 971 | 4.9500
 972 | 4.9183
 973 | 4.8883
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 975 | 4.8733
 976 | 4.8650
 977 | 4.8567
 978 | 4.8367
 979 | 4.8233
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 981 | 4.8150
 982 | 4.8233
 983 | 4.8233
 984 | 4.8333
 985 | 4.8367
 986 | 4.8650
 987 | 5.0800
 988 | 5.2450
 989 | 5.3967
 990 | 5.2350
 991 | 5.0583
 992 | 4.8450
 993 | 4.7583
 994 | 4.7333
 995 | 4.7550
 996 | 4.7583
 997 | 4.7650
 998 | 4.7583
 999 | 4.7733
1000 | 4.7825
1001 | 


--------------------------------------------------------------------------------
/tsbitmaps/test/data/mitdbx_108.txt:
--------------------------------------------------------------------------------
   1 | 4.995000
   2 | 4.995000
   3 | 4.780000
   4 | 4.715000
   5 | 4.725000
   6 | 4.685000
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   9 | 4.435000
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 778 | 5.100000
 779 | 5.035000
 780 | 4.985000
 781 | 4.935000
 782 | 4.900000
 783 | 4.885000
 784 | 4.860000
 785 | 4.890000
 786 | 4.830000
 787 | 4.835000
 788 | 4.830000
 789 | 4.810000
 790 | 4.840000
 791 | 4.860000
 792 | 4.845000
 793 | 4.875000
 794 | 4.870000
 795 | 4.855000
 796 | 4.880000
 797 | 4.900000
 798 | 4.925000
 799 | 5.075000
 800 | 5.015000
 801 | 4.810000
 802 | 4.840000
 803 | 4.835000
 804 | 4.835000
 805 | 4.805000
 806 | 4.485000
 807 | 4.350000
 808 | 4.845000
 809 | 4.895000
 810 | 4.870000
 811 | 4.945000
 812 | 4.970000
 813 | 4.980000
 814 | 5.035000
 815 | 5.045000
 816 | 5.010000
 817 | 4.975000
 818 | 4.900000
 819 | 4.890000
 820 | 4.900000
 821 | 4.880000
 822 | 4.795000
 823 | 4.835000
 824 | 4.800000
 825 | 4.825000
 826 | 4.830000
 827 | 4.830000
 828 | 4.845000
 829 | 4.865000
 830 | 4.855000
 831 | 4.850000
 832 | 4.865000
 833 | 4.875000
 834 | 4.825000
 835 | 4.945000
 836 | 5.040000
 837 | 5.050000
 838 | 4.815000
 839 | 4.825000
 840 | 4.800000
 841 | 4.835000
 842 | 4.840000
 843 | 4.675000
 844 | 4.345000
 845 | 4.625000
 846 | 4.875000
 847 | 4.915000
 848 | 4.900000
 849 | 4.945000
 850 | 4.950000
 851 | 5.020000
 852 | 5.025000
 853 | 5.050000
 854 | 5.010000
 855 | 4.955000
 856 | 4.950000
 857 | 4.945000
 858 | 4.915000
 859 | 4.900000
 860 | 4.905000
 861 | 4.865000
 862 | 4.865000
 863 | 4.900000
 864 | 4.875000
 865 | 4.895000
 866 | 4.910000
 867 | 4.890000
 868 | 4.910000
 869 | 4.920000
 870 | 4.910000
 871 | 4.935000
 872 | 4.980000
 873 | 5.060000
 874 | 5.160000
 875 | 4.895000
 876 | 4.865000
 877 | 4.880000
 878 | 4.870000
 879 | 4.805000
 880 | 4.715000
 881 | 4.395000
 882 | 4.505000
 883 | 4.925000
 884 | 4.950000
 885 | 4.945000
 886 | 4.990000
 887 | 4.990000
 888 | 5.035000
 889 | 5.100000
 890 | 5.090000
 891 | 5.045000
 892 | 4.970000
 893 | 4.930000
 894 | 4.910000
 895 | 4.920000
 896 | 4.880000
 897 | 4.845000
 898 | 4.875000
 899 | 4.845000
 900 | 4.825000
 901 | 4.835000
 902 | 4.870000
 903 | 4.860000
 904 | 4.865000
 905 | 4.850000
 906 | 4.865000
 907 | 4.875000
 908 | 4.885000
 909 | 4.890000
 910 | 4.875000
 911 | 4.960000
 912 | 5.075000
 913 | 5.080000
 914 | 4.875000
 915 | 4.820000
 916 | 4.855000
 917 | 4.835000
 918 | 4.795000
 919 | 4.710000
 920 | 4.490000
 921 | 4.340000
 922 | 4.865000
 923 | 4.920000
 924 | 4.915000
 925 | 4.955000
 926 | 4.975000
 927 | 5.010000
 928 | 5.025000
 929 | 5.070000
 930 | 5.055000
 931 | 5.005000
 932 | 5.000000
 933 | 4.915000
 934 | 4.920000
 935 | 4.930000
 936 | 4.900000
 937 | 4.915000
 938 | 4.875000
 939 | 4.810000
 940 | 4.860000
 941 | 4.850000
 942 | 4.845000
 943 | 4.850000
 944 | 4.895000
 945 | 4.865000
 946 | 4.890000
 947 | 4.875000
 948 | 4.860000
 949 | 4.900000
 950 | 4.930000
 951 | 5.075000
 952 | 4.990000
 953 | 4.860000
 954 | 4.830000
 955 | 4.835000
 956 | 4.860000
 957 | 4.820000
 958 | 4.645000
 959 | 4.380000
 960 | 4.360000
 961 | 4.840000
 962 | 4.905000
 963 | 4.940000
 964 | 4.940000
 965 | 4.970000
 966 | 5.025000
 967 | 5.035000
 968 | 5.085000
 969 | 5.005000
 970 | 4.965000
 971 | 4.930000
 972 | 4.890000
 973 | 4.845000
 974 | 4.910000
 975 | 4.855000
 976 | 4.830000
 977 | 4.805000
 978 | 4.780000
 979 | 4.810000
 980 | 4.790000
 981 | 4.825000
 982 | 4.800000
 983 | 4.815000
 984 | 4.770000
 985 | 4.815000
 986 | 4.830000
 987 | 4.810000
 988 | 4.870000
 989 | 4.975000
 990 | 5.025000
 991 | 4.765000
 992 | 4.790000
 993 | 4.770000
 994 | 4.765000
 995 | 4.700000
 996 | 4.540000
 997 | 4.240000
 998 | 4.485000
 999 | 4.800000
1000 | 4.885000
1001 | 4.885000
1002 | 4.890000
1003 | 4.860000
1004 | 5.055000
1005 | 4.840000
1006 | 4.605000
1007 | 4.865000
1008 | 4.725000
1009 | 4.795000
1010 | 4.800000
1011 | 5.180000
1012 | 4.520000
1013 | 4.955000
1014 | 4.720000
1015 | 4.795000
1016 | 4.575000
1017 | 4.860000
1018 | 4.820000
1019 | 5.160000
1020 | 4.850000
1021 | 4.835000
1022 | 4.950000
1023 | 4.900000
1024 | 4.960000
1025 | 4.735000
1026 | 4.995000
1027 | 4.920000
1028 | 4.815000
1029 | 4.805000
1030 | 4.920000
1031 | 4.760000
1032 | 4.785000
1033 | 4.545000
1034 | 4.525000
1035 | 4.895000
1036 | 4.925000
1037 | 4.865000
1038 | 4.990000
1039 | 4.980000
1040 | 5.045000
1041 | 5.045000
1042 | 5.040000
1043 | 4.985000
1044 | 4.915000
1045 | 4.930000
1046 | 4.880000
1047 | 4.870000
1048 | 4.860000
1049 | 4.815000
1050 | 4.795000
1051 | 4.770000
1052 | 4.785000
1053 | 4.750000
1054 | 4.790000
1055 | 4.825000
1056 | 4.755000
1057 | 4.800000
1058 | 4.830000
1059 | 4.805000
1060 | 4.790000
1061 | 4.890000
1062 | 4.930000
1063 | 5.030000
1064 | 4.785000
1065 | 4.700000
1066 | 4.760000
1067 | 4.770000
1068 | 4.755000
1069 | 4.635000
1070 | 4.415000
1071 | 4.295000
1072 | 4.795000
1073 | 4.845000
1074 | 4.835000
1075 | 4.860000
1076 | 4.955000
1077 | 4.930000
1078 | 4.995000
1079 | 4.995000
1080 | 4.985000
1081 | 4.970000
1082 | 4.920000
1083 | 4.885000
1084 | 4.860000
1085 | 4.870000
1086 | 4.805000
1087 | 4.895000
1088 | 5.645000
1089 | 5.985000
1090 | 4.905000
1091 | 4.500000
1092 | 4.565000
1093 | 4.510000
1094 | 4.460000
1095 | 4.385000
1096 | 4.365000
1097 | 4.410000
1098 | 4.475000
1099 | 4.615000
1100 | 4.840000
1101 | 4.955000
1102 | 4.820000
1103 | 4.775000
1104 | 4.790000
1105 | 4.820000
1106 | 4.870000
1107 | 4.860000
1108 | 4.880000
1109 | 4.900000
1110 | 4.920000
1111 | 4.905000
1112 | 4.905000
1113 | 4.925000
1114 | 4.915000
1115 | 4.935000
1116 | 4.930000
1117 | 4.905000
1118 | 4.945000
1119 | 4.915000
1120 | 4.930000
1121 | 4.940000
1122 | 4.910000
1123 | 4.955000
1124 | 4.950000
1125 | 4.930000
1126 | 4.945000
1127 | 4.960000
1128 | 4.905000
1129 | 4.910000
1130 | 4.935000
1131 | 4.915000
1132 | 4.930000
1133 | 4.935000
1134 | 4.900000
1135 | 4.920000
1136 | 4.940000
1137 | 4.980000
1138 | 5.125000
1139 | 5.175000
1140 | 4.870000
1141 | 4.870000
1142 | 4.895000
1143 | 4.880000
1144 | 4.930000
1145 | 4.720000
1146 | 4.390000
1147 | 4.540000
1148 | 4.960000
1149 | 4.970000
1150 | 4.995000
1151 | 4.985000
1152 | 5.005000
1153 | 5.030000
1154 | 5.045000
1155 | 5.060000
1156 | 5.025000
1157 | 5.030000
1158 | 4.950000
1159 | 4.960000
1160 | 4.940000
1161 | 4.935000
1162 | 4.940000
1163 | 4.915000
1164 | 4.890000
1165 | 4.860000
1166 | 4.880000
1167 | 4.900000
1168 | 4.895000
1169 | 4.935000
1170 | 4.905000
1171 | 4.930000
1172 | 4.925000
1173 | 4.935000
1174 | 5.020000
1175 | 5.145000
1176 | 5.135000
1177 | 4.890000
1178 | 4.885000
1179 | 4.865000
1180 | 4.900000
1181 | 4.905000
1182 | 4.510000
1183 | 4.225000
1184 | 4.775000
1185 | 4.945000
1186 | 4.980000
1187 | 5.035000
1188 | 5.000000
1189 | 5.075000
1190 | 5.110000
1191 | 5.075000
1192 | 5.115000
1193 | 5.070000
1194 | 4.995000
1195 | 4.980000
1196 | 4.975000
1197 | 4.940000
1198 | 4.945000
1199 | 4.920000
1200 | 4.915000
1201 | 4.905000
1202 | 4.885000
1203 | 4.900000
1204 | 4.910000
1205 | 4.885000
1206 | 4.885000
1207 | 4.900000
1208 | 4.935000
1209 | 4.960000
1210 | 5.105000
1211 | 5.210000
1212 | 4.895000
1213 | 4.905000
1214 | 4.925000
1215 | 4.895000
1216 | 4.895000
1217 | 4.730000
1218 | 4.410000
1219 | 4.515000
1220 | 4.970000
1221 | 5.025000
1222 | 5.015000
1223 | 5.045000
1224 | 5.055000
1225 | 5.100000
1226 | 5.160000
1227 | 5.115000
1228 | 5.115000
1229 | 5.080000
1230 | 5.005000
1231 | 5.000000
1232 | 5.005000
1233 | 4.980000
1234 | 4.945000
1235 | 4.940000
1236 | 4.875000
1237 | 4.900000
1238 | 4.925000
1239 | 4.930000
1240 | 4.925000
1241 | 4.945000
1242 | 4.930000
1243 | 5.005000
1244 | 5.125000
1245 | 5.150000
1246 | 4.900000
1247 | 4.885000
1248 | 4.885000
1249 | 4.900000
1250 | 4.905000
1251 | 4.555000
1252 | 4.225000
1253 | 4.725000
1254 | 4.910000
1255 | 4.960000
1256 | 4.985000
1257 | 4.925000
1258 | 5.020000
1259 | 5.070000
1260 | 5.075000
1261 | 5.100000
1262 | 5.015000
1263 | 4.955000
1264 | 4.945000
1265 | 4.960000
1266 | 4.915000
1267 | 4.895000
1268 | 4.905000
1269 | 4.830000
1270 | 4.830000
1271 | 4.855000
1272 | 4.845000
1273 | 4.890000
1274 | 4.885000
1275 | 4.850000
1276 | 4.880000
1277 | 4.910000
1278 | 4.970000
1279 | 5.120000
1280 | 4.900000
1281 | 4.830000
1282 | 4.850000
1283 | 4.840000
1284 | 4.845000
1285 | 4.730000
1286 | 4.425000
1287 | 4.315000
1288 | 4.865000
1289 | 4.935000
1290 | 4.960000
1291 | 4.985000
1292 | 5.000000
1293 | 5.005000
1294 | 5.020000
1295 | 5.060000
1296 | 5.010000
1297 | 4.970000
1298 | 4.930000
1299 | 4.890000
1300 | 4.880000
1301 | 4.905000
1302 | 4.870000
1303 | 4.830000
1304 | 4.805000
1305 | 4.775000
1306 | 4.775000
1307 | 4.800000
1308 | 4.780000
1309 | 4.805000
1310 | 4.805000
1311 | 4.795000
1312 | 4.795000
1313 | 4.825000
1314 | 4.795000
1315 | 4.925000
1316 | 5.025000
1317 | 4.935000
1318 | 4.745000
1319 | 4.775000
1320 | 4.755000
1321 | 4.785000
1322 | 4.735000
1323 | 4.535000
1324 | 4.270000
1325 | 4.270000
1326 | 4.730000
1327 | 4.845000
1328 | 4.865000
1329 | 4.890000
1330 | 4.890000
1331 | 4.950000
1332 | 4.965000
1333 | 4.975000
1334 | 4.995000
1335 | 4.890000
1336 | 4.855000
1337 | 4.840000
1338 | 4.800000
1339 | 4.800000
1340 | 4.800000
1341 | 4.755000
1342 | 4.745000
1343 | 4.735000
1344 | 4.755000
1345 | 4.765000
1346 | 4.775000
1347 | 4.745000
1348 | 4.775000
1349 | 4.755000
1350 | 4.800000
1351 | 4.770000
1352 | 4.805000
1353 | 4.780000
1354 | 4.775000
1355 | 4.795000
1356 | 4.820000
1357 | 4.945000
1358 | 5.020000
1359 | 4.775000
1360 | 4.720000
1361 | 4.725000
1362 | 4.700000
1363 | 4.700000
1364 | 4.535000
1365 | 4.200000
1366 | 4.410000
1367 | 4.785000
1368 | 4.780000
1369 | 4.810000
1370 | 4.860000
1371 | 4.865000
1372 | 4.915000
1373 | 4.925000
1374 | 4.920000
1375 | 4.880000
1376 | 4.815000
1377 | 4.780000
1378 | 4.785000
1379 | 4.815000
1380 | 4.790000
1381 | 4.800000
1382 | 4.780000
1383 | 4.725000
1384 | 4.750000
1385 | 4.750000
1386 | 4.735000
1387 | 4.760000
1388 | 4.795000
1389 | 4.760000
1390 | 4.785000
1391 | 4.795000
1392 | 4.795000
1393 | 4.790000
1394 | 4.800000
1395 | 4.810000
1396 | 4.810000
1397 | 4.845000
1398 | 4.800000
1399 | 4.850000
1400 | 4.995000
1401 | 5.070000
1402 | 4.755000
1403 | 4.790000
1404 | 4.765000
1405 | 4.800000
1406 | 4.740000
1407 | 4.575000
1408 | 4.235000
1409 | 4.425000
1410 | 4.845000
1411 | 4.880000
1412 | 4.925000
1413 | 4.910000
1414 | 4.975000
1415 | 4.985000
1416 | 5.010000
1417 | 5.035000
1418 | 4.975000
1419 | 4.930000
1420 | 4.905000
1421 | 4.895000
1422 | 4.875000
1423 | 4.875000
1424 | 4.880000
1425 | 4.815000
1426 | 4.830000
1427 | 4.815000
1428 | 4.825000
1429 | 4.815000
1430 | 4.865000
1431 | 4.845000
1432 | 4.855000
1433 | 4.865000
1434 | 4.845000
1435 | 4.845000
1436 | 4.860000
1437 | 4.850000
1438 | 4.870000
1439 | 4.945000
1440 | 5.065000
1441 | 5.015000
1442 | 4.815000
1443 | 4.810000
1444 | 4.835000
1445 | 4.835000
1446 | 4.875000
1447 | 4.500000
1448 | 4.305000
1449 | 4.775000
1450 | 4.915000
1451 | 4.960000
1452 | 4.960000
1453 | 4.980000
1454 | 5.040000
1455 | 5.070000
1456 | 5.075000
1457 | 5.070000
1458 | 5.010000
1459 | 4.965000
1460 | 4.960000
1461 | 4.950000
1462 | 4.930000
1463 | 4.920000
1464 | 4.880000
1465 | 4.880000
1466 | 4.875000
1467 | 4.825000
1468 | 4.865000
1469 | 4.875000
1470 | 4.860000
1471 | 4.860000
1472 | 4.880000
1473 | 4.870000
1474 | 4.870000
1475 | 4.850000
1476 | 4.910000
1477 | 4.970000
1478 | 5.100000
1479 | 5.050000
1480 | 4.830000
1481 | 4.840000
1482 | 4.835000
1483 | 4.855000
1484 | 4.835000
1485 | 4.415000
1486 | 4.315000
1487 | 4.830000
1488 | 4.915000
1489 | 4.945000
1490 | 4.940000
1491 | 5.000000
1492 | 5.020000
1493 | 5.080000
1494 | 5.105000
1495 | 5.035000
1496 | 4.990000
1497 | 4.925000
1498 | 4.940000
1499 | 4.930000
1500 | 4.915000
1501 | 4.895000
1502 | 4.910000
1503 | 4.840000
1504 | 4.835000
1505 | 4.875000
1506 | 4.870000
1507 | 4.885000
1508 | 4.880000
1509 | 4.865000
1510 | 4.885000
1511 | 4.870000
1512 | 4.885000
1513 | 4.875000
1514 | 4.930000
1515 | 5.055000
1516 | 5.085000
1517 | 4.875000
1518 | 4.825000
1519 | 4.830000
1520 | 4.855000
1521 | 4.890000
1522 | 4.610000
1523 | 4.265000
1524 | 4.615000
1525 | 4.885000
1526 | 4.915000
1527 | 4.935000
1528 | 4.990000
1529 | 4.995000
1530 | 5.030000
1531 | 5.080000
1532 | 5.095000
1533 | 5.040000
1534 | 4.945000
1535 | 4.935000
1536 | 4.925000
1537 | 4.935000
1538 | 4.925000
1539 | 4.910000
1540 | 4.855000
1541 | 4.860000
1542 | 4.820000
1543 | 4.830000
1544 | 4.835000
1545 | 4.830000
1546 | 4.855000
1547 | 4.860000
1548 | 4.855000
1549 | 4.870000
1550 | 4.845000
1551 | 4.850000
1552 | 4.870000
1553 | 4.935000
1554 | 5.000000
1555 | 5.105000
1556 | 4.815000
1557 | 4.815000
1558 | 4.840000
1559 | 4.815000
1560 | 4.820000
1561 | 4.835000
1562 | 4.520000
1563 | 4.195000
1564 | 4.645000
1565 | 4.900000
1566 | 4.920000
1567 | 4.905000
1568 | 4.960000
1569 | 4.965000
1570 | 5.035000
1571 | 5.075000
1572 | 5.045000
1573 | 5.060000
1574 | 4.975000
1575 | 4.930000
1576 | 4.925000
1577 | 4.925000
1578 | 4.890000
1579 | 4.870000
1580 | 4.865000
1581 | 4.845000
1582 | 4.850000
1583 | 4.855000
1584 | 4.855000
1585 | 4.865000
1586 | 4.870000
1587 | 4.885000
1588 | 4.875000
1589 | 4.900000
1590 | 4.885000
1591 | 4.900000
1592 | 4.900000
1593 | 4.915000
1594 | 5.055000
1595 | 5.160000
1596 | 4.990000
1597 | 4.875000
1598 | 4.880000
1599 | 4.860000
1600 | 4.845000
1601 | 4.815000
1602 | 4.450000
1603 | 4.275000
1604 | 4.850000
1605 | 4.935000
1606 | 4.955000
1607 | 5.000000
1608 | 5.025000
1609 | 5.045000
1610 | 5.110000
1611 | 5.130000
1612 | 5.070000
1613 | 5.025000
1614 | 4.970000
1615 | 4.955000
1616 | 4.945000
1617 | 4.915000
1618 | 4.895000
1619 | 4.875000
1620 | 4.850000
1621 | 4.850000
1622 | 4.840000
1623 | 4.850000
1624 | 4.855000
1625 | 4.885000
1626 | 4.880000
1627 | 4.905000
1628 | 4.885000
1629 | 4.875000
1630 | 4.860000
1631 | 4.900000
1632 | 4.900000
1633 | 5.005000
1634 | 5.100000
1635 | 4.835000
1636 | 4.830000
1637 | 4.830000
1638 | 4.810000
1639 | 4.775000
1640 | 4.615000
1641 | 4.245000
1642 | 4.480000
1643 | 4.875000
1644 | 4.915000
1645 | 4.925000
1646 | 4.950000
1647 | 4.955000
1648 | 5.020000
1649 | 5.070000
1650 | 5.060000
1651 | 5.015000
1652 | 4.985000
1653 | 4.930000
1654 | 4.925000
1655 | 4.920000
1656 | 4.925000
1657 | 4.885000
1658 | 4.855000
1659 | 4.845000
1660 | 4.840000
1661 | 4.905000
1662 | 4.880000
1663 | 4.880000
1664 | 4.925000
1665 | 4.905000
1666 | 4.920000
1667 | 4.925000
1668 | 4.920000
1669 | 4.905000
1670 | 4.945000
1671 | 5.020000
1672 | 5.115000
1673 | 5.010000
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1685 | 5.055000
1686 | 5.060000
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1688 | 5.140000
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1704 | 4.950000
1705 | 4.950000
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1906 | 4.565000
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1911 | 4.820000
1912 | 4.840000
1913 | 4.920000
1914 | 4.915000
1915 | 4.910000
1916 | 4.930000
1917 | 4.870000
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1919 | 4.915000
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1921 | 4.780000
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1924 | 4.770000
1925 | 4.815000
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1929 | 4.840000
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2100 | 5.175000
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2103 | 5.285000
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2113 | 5.075000
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2116 | 5.100000
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2122 | 5.085000
2123 | 5.345000
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2159 | 5.240000
2160 | 5.305000
2161 | 


--------------------------------------------------------------------------------
/tsbitmaps/test/data/pattern_anom.txt:
--------------------------------------------------------------------------------
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1003 | 


--------------------------------------------------------------------------------
/tsbitmaps/test/data/pattern_normal.txt:
--------------------------------------------------------------------------------
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 960 | 1.019529
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 997 | -0.795703
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 999 | -0.498914
1000 | -0.557184
1001 | -0.434769
1002 | 


--------------------------------------------------------------------------------
/tsbitmaps/test/test_tsbitmaps.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import pandas as pd
 3 | import unittest
 4 | 
 5 | from tsbitmaps.tsbitmapper import TSBitMapper
 6 | from tsbitmaps.bitmapviz import create_bitmap_grid
 7 | 
 8 | 
 9 | class TestBitmapAlgorithm(unittest.TestCase):
10 |     def test_bitmap(self):
11 |         bmp = TSBitMapper(feature_window_size=5, bins=8, level_size=2, lag_window_size=10, lead_window_size=10,
12 |                           q=95)
13 |         x = np.random.rand(500)
14 |         binned_x = bmp.discretize(x)
15 | 
16 |         self.assertEqual(len(binned_x), len(x))
17 |         self.assertTrue(set(binned_x) == set('01234567'))
18 | 
19 |         symbol_seq = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '1', '2', '3')  # '01234567890123'
20 |         sample_bitmap = bmp.get_bitmap(symbol_seq)
21 |         self.assertEqual(len(sample_bitmap), 10)
22 |         self.assertTrue(('4', '5') in sample_bitmap.keys())
23 |         self.assertTrue(('9', '0') in sample_bitmap.keys())
24 |         self.assertEqual(sample_bitmap['0', '1'], 1)
25 | 
26 |         sample_bitmap_w = bmp.get_bitmap_with_feat_window(symbol_seq)
27 |         self.assertEqual(len(sample_bitmap_w), 8)
28 |         self.assertTrue(('4', '5') not in sample_bitmap_w.keys())
29 |         self.assertTrue(('9', '0') not in sample_bitmap_w.keys())
30 |         self.assertEqual(sample_bitmap_w[('0', '1')], 1)
31 | 
32 |         ypred = bmp.fit_predict(x)
33 |         scores = bmp.get_ref_bitmap_scores()
34 |         self.assertTrue((scores[0:bmp._lag_window_size] == 0.0).all())
35 |         self.assertTrue((scores[bmp._lag_window_size:-bmp._lead_window_size] >= 0).all())
36 |         self.assertTrue(0 < (ypred == -1).sum() <= 25)
37 | 
38 |     def test_anomaly_detection_ecg(self):
39 |         ecg_norm = np.loadtxt('data/ecg_normal.txt')
40 |         ecg_anom = np.loadtxt('data/ecg_anom.txt')
41 | 
42 |         bmp = TSBitMapper(feature_window_size=20, bins=5, level_size=3, lag_window_size=200, lead_window_size=40)
43 |         ypred_unsupervised = bmp.fit_predict(ecg_anom)
44 |         self.assertTrue(0 < (ypred_unsupervised == -1).sum() <= 3)
45 | 
46 |         bmp.fit(ecg_norm)
47 |         ypred_supervised = bmp.predict(ecg_anom)
48 |         self.assertTrue(0 < (ypred_supervised == -1).sum() <= 3)
49 | 
50 |     def test_anomaly_detection_pattern(self):
51 |         pattern_norm = np.loadtxt('data/pattern_normal.txt')
52 |         pattern_anom = pd.read_csv('data/pattern_anom.txt').iloc[:, 0]
53 | 
54 |         bmp = TSBitMapper(feature_window_size=50, bins=5, level_size=2, lag_window_size=200, lead_window_size=100)
55 |         ypred_unsupervised = bmp.fit_predict(pattern_anom)
56 |         self.assertTrue(0 < (ypred_unsupervised == -1).sum() <= 3)
57 | 
58 |         bmp.fit(pattern_norm)
59 |         ypred_supervised = bmp.predict(pattern_anom)
60 |         self.assertTrue(0 < (ypred_supervised == -1).sum() <= 3)
61 | 
62 |     # @unittest.skip("tmp")
63 |     def test_bitmapviz(self):
64 |         bmp = TSBitMapper(feature_window_size=20, bins=12, level_size=3, lag_window_size=200, lead_window_size=40)
65 |         ecg_anom = np.loadtxt('data/ecg_anom.txt')
66 |         ecg_bitmap = bmp.get_tsbitmap(ecg_anom)
67 |         bmp_grid = create_bitmap_grid(ecg_bitmap, n=4, num_bins=12, level_size=3)
68 |         self.assertEqual((bmp_grid > 0).sum(), len(ecg_bitmap))
69 |         self.assertEqual(bmp_grid.shape, (27, 64))
70 | 
71 | 
72 | if __name__ == '__main__':
73 |     unittest.main()
74 | 


--------------------------------------------------------------------------------
/tsbitmaps/tsbitmapper.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | from collections import defaultdict
  3 | 
  4 | 
  5 | class TSBitMapper:
  6 |     """
  7 |     
  8 |     Implements Time-series Bitmap model for anomaly detection
  9 |     
 10 |     Based on the papers "Time-series Bitmaps: A Practical Visualization Tool for working with Large Time Series Databases"
 11 |     and "Assumption-Free Anomaly Detection in Time Series"
 12 |     
 13 |     Test data and parameter settings taken from http://alumni.cs.ucr.edu/~wli/SSDBM05/
 14 |     """
 15 | 
 16 |     def __init__(self, feature_window_size=None, bins=5, level_size=3, lag_window_size=None, lead_window_size=None,
 17 |                  q=99.7):
 18 | 
 19 |         """
 20 |         
 21 |         :param int feature_window_size: should be about the size at which events happen
 22 |         :param int or array-like bins: a scalar number of equal-width bins or a 1-D and monotonic array of bins.
 23 |         :param int level_size: desired level of recursion of the bitmap
 24 |         :param int lag_window_size: how far to look back, None for supervised learning
 25 |         :param int lead_window_size: how far to look ahead
 26 |         :param float in range of [0,100] q: the qth percentile as the threshold for anomalies
 27 |         """
 28 | 
 29 |         assert feature_window_size > 0, 'feature_window_size must be a positive integer'
 30 |         assert lead_window_size >= feature_window_size, 'lead_window_size must be >= feature_window_size'
 31 | 
 32 |         # bitmap parameters
 33 |         self._feature_window_size = feature_window_size
 34 | 
 35 |         self._level_size = level_size
 36 | 
 37 |         self._lag_window_size = lag_window_size
 38 | 
 39 |         self._lead_window_size = lead_window_size
 40 | 
 41 |         self._bins = bins
 42 |         self._num_bins = self._get_num_bins(bins)
 43 |         self._q = q
 44 |         self._bitmap_scores = None
 45 | 
 46 |     def _get_num_bins(self, bins):
 47 |         if np.isscalar(bins):
 48 |             num_bins = bins
 49 |         else:
 50 |             num_bins = len(bins)  # bins is an array of bins
 51 |         return num_bins
 52 | 
 53 |     def discretize(self, ts, bins=None, global_min=None, global_max=None):
 54 |         if bins is None:
 55 |             bins = self._bins
 56 | 
 57 |         if np.isscalar(bins):
 58 |             num_bins = bins
 59 | 
 60 |             min_value = ts.min()
 61 |             max_value = ts.max()
 62 |             if min_value == max_value:
 63 |                 min_value = global_min
 64 |                 max_value = global_max
 65 |             step = (max_value - min_value) / num_bins
 66 |             ts_bins = np.arange(min_value, max_value, step)
 67 |         else:
 68 |             ts_bins = bins
 69 | 
 70 |         inds = np.digitize(ts, ts_bins)
 71 |         binned_ts = tuple(str(i - 1) for i in inds)
 72 |         return binned_ts
 73 | 
 74 |     def discretize_by_feat_window(self, ts, bins=None, feature_window_size=None):
 75 |         if bins is None:
 76 |             bins = self._bins
 77 | 
 78 |         if feature_window_size is None:
 79 |             feature_window_size = self._feature_window_size
 80 | 
 81 |         n = len(ts)
 82 |         windows = ()
 83 |         global_min = ts.min()
 84 |         global_max = ts.max()
 85 | 
 86 |         for i in range(0, n - n % feature_window_size, feature_window_size):
 87 |             binned_fw = self.discretize(ts[i: i + feature_window_size], bins, global_min, global_max)
 88 |             windows += binned_fw
 89 |         if n % feature_window_size > 0:
 90 |             last_binned_fw = self.discretize(ts[- (n % feature_window_size):], bins, global_min, global_max)
 91 |             windows += last_binned_fw
 92 | 
 93 |         return windows
 94 | 
 95 |     def get_tsbitmap(self, ts, with_feat_window=True, level_size=None, step=None):
 96 |         binned_ts = self.discretize(ts)
 97 |         tsbitmap = {}
 98 |         if with_feat_window:
 99 |             tsbitmap = self.get_bitmap_with_feat_window(binned_ts)
100 |         else:
101 |             tsbitmap = self.get_bitmap(binned_ts)
102 |         return tsbitmap
103 | 
104 |     def get_bitmap(self, chunk, level_size=None):
105 |         """
106 |         
107 |         :param str chunk: symbol sequence representation of a univariate time series
108 |         :param int level_size: desired level of recursion of the bitmap
109 |         :return : bitmap representation of `chunk`
110 |         """
111 |         bitmap = defaultdict(int)
112 |         n = len(chunk)
113 |         if level_size is None:
114 |             level_size = self._level_size
115 |         for i in range(n):
116 |             if i <= n - level_size:
117 |                 feat = chunk[i: i + level_size]
118 |                 bitmap[feat] += 1  # frequency count
119 |         max_freq = max(bitmap.values())
120 |         for feat in bitmap.keys():
121 |             bitmap[feat] = bitmap[feat] / max_freq
122 |         return bitmap
123 | 
124 |     def get_bitmap_with_feat_window(self, chunk, level_size=None, step=None):
125 |         """
126 |         
127 |         :param str chunk: symbol sequence representation of a univariate time series
128 |         :param int level_size: desired level of recursion of the bitmap
129 |         :param int step: length of the feature window
130 |         :return : bitmap representation of `chunk`
131 |         """
132 |         if step is None:
133 |             step = self._feature_window_size
134 |         if level_size is None:
135 |             level_size = self._level_size
136 | 
137 |         bitmap = defaultdict(int)
138 |         n = len(chunk)
139 | 
140 |         for i in range(0, n - n % step, step):
141 | 
142 |             for j in range(step - level_size + 1):
143 |                 feat = chunk[i + j: i + j + level_size]
144 |                 bitmap[feat] += 1  # frequency count
145 | 
146 |         if n % step > 0:
147 |             for i in range(n - n % step, n - level_size + 1):
148 |                 feat = chunk[i: i + level_size]
149 |                 bitmap[feat] += 1
150 | 
151 |         max_freq = max(bitmap.values())
152 | 
153 |         for feat in bitmap.keys():
154 |             bitmap[feat] = bitmap[feat] / max_freq
155 |         return bitmap
156 | 
157 |     def _slide_lead_chunks(self, ts):
158 |         """
159 |         In supervised training, the entire training time series as the reference data can be used as the lag window.
160 | 
161 |         """
162 | 
163 |         binned_ts = self.discretize_by_feat_window(ts)
164 |         ts_len = len(binned_ts)
165 | 
166 |         scores = np.zeros(len(ts))
167 | 
168 |         leadws = self._lead_window_size
169 |         featws = self._level_size
170 | 
171 |         lead_bitmap = self.get_bitmap_with_feat_window(binned_ts[0: leadws])
172 |         egress_lead_feat = binned_ts[0: featws]
173 | 
174 |         for i in range(1, ts_len - leadws + 1):
175 |             lead_chunk = binned_ts[i: i + leadws]
176 |             ingress_lead_feat = lead_chunk[-featws:]
177 | 
178 |             lead_bitmap[ingress_lead_feat] += 1
179 |             lead_bitmap[egress_lead_feat] -= 1
180 | 
181 |             scores[i] = self.bitmap_distance(self._ref_ts_bitmap, lead_bitmap)
182 | 
183 |             egress_lead_feat = lead_chunk[0: featws]
184 | 
185 |         return scores
186 | 
187 |     def _slide_chunks(self, ts):
188 |         lag_bitmap = {}
189 |         lead_bitmap = {}
190 |         scores = np.zeros(len(ts))
191 | 
192 |         egress_lag_feat = ()
193 |         egress_lead_feat = ()
194 | 
195 |         binned_ts = self.discretize_by_feat_window(ts)
196 |         ts_len = len(binned_ts)
197 | 
198 |         lagws = self._lag_window_size
199 |         leadws = self._lead_window_size
200 |         featws = self._level_size
201 | 
202 |         for i in range(lagws, ts_len - leadws + 1):
203 | 
204 |             if i == lagws:
205 |                 lag_chunk = binned_ts[i - lagws: i]
206 |                 lead_chunk = binned_ts[i: i + leadws]
207 | 
208 |                 lag_bitmap = self.get_bitmap_with_feat_window(lag_chunk)
209 |                 lead_bitmap = self.get_bitmap_with_feat_window(lead_chunk)
210 | 
211 |                 scores[i] = self.bitmap_distance(lag_bitmap, lead_bitmap)
212 | 
213 |                 egress_lag_feat = lag_chunk[0: featws]
214 |                 egress_lead_feat = lead_chunk[0: featws]
215 | 
216 |             else:
217 | 
218 |                 lag_chunk = binned_ts[i - lagws: i]
219 |                 lead_chunk = binned_ts[i: i + leadws]
220 | 
221 |                 ingress_lag_feat = lag_chunk[-featws:]
222 |                 ingress_lead_feat = lead_chunk[-featws:]
223 | 
224 |                 lag_bitmap[ingress_lag_feat] += 1
225 |                 lag_bitmap[egress_lag_feat] -= 1
226 | 
227 |                 lead_bitmap[ingress_lead_feat] += 1
228 |                 lead_bitmap[egress_lead_feat] -= 1
229 | 
230 |                 scores[i] = self.bitmap_distance(lag_bitmap, lead_bitmap)
231 | 
232 |                 egress_lag_feat = lag_chunk[0: featws]
233 |                 egress_lead_feat = lead_chunk[0: featws]
234 | 
235 |         return scores
236 | 
237 |     def bitmap_distance(self, lag_bitmap, lead_bitmap):
238 |         """
239 |         Computes the dissimilarity of two bitmaps.
240 |         """
241 |         dist = 0
242 |         lag_feats = set(lag_bitmap.keys())
243 |         lead_feats = set(lead_bitmap.keys())
244 |         shared_feats = lag_feats & lead_feats
245 | 
246 |         for feat in shared_feats:
247 |             dist += (lead_bitmap[feat] - lag_bitmap[feat]) ** 2
248 | 
249 |         for feat in lag_feats - shared_feats:
250 |             dist += lag_bitmap[feat] ** 2
251 | 
252 |         for feat in lead_feats - shared_feats:
253 |             dist += lead_bitmap[feat] ** 2
254 | 
255 |         return dist
256 | 
257 |     def fit(self, ts):
258 |         """
259 |         Computes the reference bitmaps of a univariate time series `ts`.
260 |         
261 |         :param ts: 1-D numpy array or pandas.Series 
262 |         
263 |         """
264 |         if self._lag_window_size is None:
265 |             assert len(ts) >= self._lead_window_size, 'sequence length must be >= than lead_window_size'
266 |         else:
267 |             assert len(
268 |                 ts) >= self._lag_window_size + self._lead_window_size, 'sequence length must be >= (lag_window_size + lead_window_size)'
269 | 
270 |         self._ref_ts = ts
271 |         self._ref_ts_bitmap = self.get_bitmap_with_feat_window(self.discretize(ts))
272 | 
273 |     def fit_predict(self, ts):
274 |         """
275 |         Unsupervised training of TSBitMaps.
276 |         
277 |         :param ts: 1-D numpy array or pandas.Series 
278 |         :return labels: `+1` for normal observations and `-1` for abnormal observations
279 |         """
280 |         assert self._lag_window_size > self._feature_window_size, 'lag_window_size must be >= feature_window_size'
281 | 
282 |         self._ref_ts = ts
283 |         scores = self._slide_chunks(ts)
284 |         self._ref_bitmap_scores = scores
285 | 
286 |         thres = np.percentile(scores[self._lag_window_size: -self._lead_window_size + 1], self._q)
287 | 
288 |         labels = np.full(len(ts), 1)
289 |         for idx, score in enumerate(scores):
290 |             if score > thres:
291 |                 labels[idx] = -1
292 | 
293 |         return labels
294 | 
295 |     def predict(self, ts):
296 |         """
297 |         Predict if a time series contains outliers or not.
298 |         
299 |         :param ts: 1-D numpy array or pandas.Series 
300 |         :return: labels: `+1` for normal observations and `-1` for abnormal observations
301 |         """
302 |         cur_scores = None
303 |         labels = np.full(len(ts), 1)
304 | 
305 |         if (len(self._ref_ts) != len(ts)):
306 |             cur_scores = self._slide_lead_chunks(ts)
307 |         elif (np.allclose(self._ref_ts, ts)):
308 |             labels = self.fit_predict(ts)
309 |         else:
310 |             cur_scores = self._slide_lead_chunks(ts)
311 | 
312 |         if cur_scores is not None:
313 |             self._bitmap_scores = cur_scores
314 | 
315 |             thres = np.percentile(cur_scores[0: -self._lead_window_size + 1], self._q)
316 | 
317 |             for idx, score in enumerate(cur_scores):
318 |                 if score > thres:
319 |                     labels[idx] = -1
320 | 
321 |         return labels
322 | 
323 |     def get_bitmap_scores(self):
324 |         if self._bitmap_scores is None:
325 |             return self._ref_bitmap_scores
326 |         else:
327 |             return self._bitmap_scores
328 | 
329 |     def get_ref_bitmap_scores(self):
330 |         return self._ref_bitmap_scores
331 | 


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