/usr/share/doc/python-tables-doc/html/_modules/tables/index.html is in python-tables-doc 3.1.1-0ubuntu1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 | <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>tables.index — PyTables 3.1.1 documentation</title>
<link rel="stylesheet" href="../../_static/cloud.css" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../../" type="text/css" />
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
VERSION: '3.1.1',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true
};
</script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
<script type="text/javascript" src="../../_static/underscore.js"></script>
<script type="text/javascript" src="../../_static/doctools.js"></script>
<script type="text/javascript" src="../../_static/jquery.cookie.js"></script>
<script type="text/javascript" src="../../_static/toggle_sections.js"></script>
<script type="text/javascript" src="../../_static/toggle_sidebar.js"></script>
<link rel="shortcut icon" href="../../_static/favicon.ico"/>
<link rel="top" title="PyTables 3.1.1 documentation" href="../../index.html" />
<link rel="up" title="tables" href="../tables.html" />
</head>
<body>
<div class="relbar-top">
<div class="related">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="../../genindex.html" title="General Index"
accesskey="I">index</a></li>
<li class="right" >
<a href="../../py-modindex.html" title="Python Module Index"
>modules</a> </li>
<li class="right" >
<a href="../../np-modindex.html" title="Python Module Index"
>modules</a> </li>
<li><a href="../../index.html">PyTables 3.1.1 documentation</a> »</li>
<li><a href="../index.html" >Module code</a> »</li>
<li><a href="../tables.html" accesskey="U">tables</a> »</li>
</ul>
</div>
</div>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body">
<h1>Source code for tables.index</h1><div class="highlight"><pre>
<span class="c"># -*- coding: utf-8 -*-</span>
<span class="c">#######################################################################</span>
<span class="c">#</span>
<span class="c"># License: BSD</span>
<span class="c"># Created: June 08, 2004</span>
<span class="c"># Author: Francesc Alted - faltet@pytables.com</span>
<span class="c">#</span>
<span class="c"># $Id$</span>
<span class="c">#</span>
<span class="c">########################################################################</span>
<span class="sd">"""Here is defined the Index class."""</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">bisect</span> <span class="kn">import</span> <span class="n">bisect_left</span><span class="p">,</span> <span class="n">bisect_right</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span><span class="p">,</span> <span class="n">clock</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">os.path</span>
<span class="kn">import</span> <span class="nn">tempfile</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">import</span> <span class="nn">numpy</span>
<span class="kn">from</span> <span class="nn">tables.idxutils</span> <span class="kn">import</span> <span class="p">(</span><span class="n">calc_chunksize</span><span class="p">,</span> <span class="n">calcoptlevels</span><span class="p">,</span>
<span class="n">get_reduction_level</span><span class="p">,</span> <span class="n">nextafter</span><span class="p">,</span> <span class="n">inftype</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">tables</span> <span class="kn">import</span> <span class="n">indexesextension</span>
<span class="kn">from</span> <span class="nn">tables.node</span> <span class="kn">import</span> <span class="n">NotLoggedMixin</span>
<span class="kn">from</span> <span class="nn">tables.atom</span> <span class="kn">import</span> <span class="n">UIntAtom</span><span class="p">,</span> <span class="n">Atom</span>
<span class="kn">from</span> <span class="nn">tables.earray</span> <span class="kn">import</span> <span class="n">EArray</span>
<span class="kn">from</span> <span class="nn">tables.carray</span> <span class="kn">import</span> <span class="n">CArray</span>
<span class="kn">from</span> <span class="nn">tables.leaf</span> <span class="kn">import</span> <span class="n">Filters</span>
<span class="kn">from</span> <span class="nn">tables.indexes</span> <span class="kn">import</span> <span class="n">CacheArray</span><span class="p">,</span> <span class="n">LastRowArray</span><span class="p">,</span> <span class="n">IndexArray</span>
<span class="kn">from</span> <span class="nn">tables.group</span> <span class="kn">import</span> <span class="n">Group</span>
<span class="kn">from</span> <span class="nn">tables.path</span> <span class="kn">import</span> <span class="n">join_path</span>
<span class="kn">from</span> <span class="nn">tables.exceptions</span> <span class="kn">import</span> <span class="n">PerformanceWarning</span>
<span class="kn">from</span> <span class="nn">tables.utils</span> <span class="kn">import</span> <span class="n">is_idx</span><span class="p">,</span> <span class="n">idx2long</span><span class="p">,</span> <span class="n">lazyattr</span>
<span class="kn">from</span> <span class="nn">tables.lrucacheextension</span> <span class="kn">import</span> <span class="n">ObjectCache</span>
<span class="kn">from</span> <span class="nn">tables._past</span> <span class="kn">import</span> <span class="n">previous_api</span><span class="p">,</span> <span class="n">previous_api_property</span>
<span class="c"># default version for INDEX objects</span>
<span class="c"># obversion = "1.0" # Version of indexes in PyTables 1.x series</span>
<span class="c"># obversion = "2.0" # Version of indexes in PyTables Pro 2.0 series</span>
<span class="n">obversion</span> <span class="o">=</span> <span class="s">"2.1"</span> <span class="c"># Version of indexes in PyTables Pro 2.1 and up series,</span>
<span class="c"># including the join 2.3 Std + Pro version</span>
<span class="n">debug</span> <span class="o">=</span> <span class="bp">False</span>
<span class="c"># debug = True # Uncomment this for printing sizes purposes</span>
<span class="n">profile</span> <span class="o">=</span> <span class="bp">False</span>
<span class="c"># profile = True # Uncomment for profiling</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">tables.utils</span> <span class="kn">import</span> <span class="n">show_stats</span>
<span class="c"># The default method for sorting</span>
<span class="n">defsort</span> <span class="o">=</span> <span class="s">"quicksort"</span>
<span class="c"># defsort = "mergesort"</span>
<span class="c"># Default policy for automatically updating indexes after a table</span>
<span class="c"># append operation, or automatically reindexing after an</span>
<span class="c"># index-invalidating operation like removing or modifying table rows.</span>
<span class="n">default_auto_index</span> <span class="o">=</span> <span class="bp">True</span>
<span class="c"># Keep in sync with ``Table.autoindex`` docstring.</span>
<span class="c"># Default filters used to compress indexes. This is quite fast and</span>
<span class="c"># compression is pretty good.</span>
<span class="c"># Remember to keep these defaults in sync with the docstrings and UG.</span>
<span class="n">default_index_filters</span> <span class="o">=</span> <span class="n">Filters</span><span class="p">(</span><span class="n">complevel</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">complib</span><span class="o">=</span><span class="s">'zlib'</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">fletcher32</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="c"># Deprecated API</span>
<span class="n">defaultAutoIndex</span> <span class="o">=</span> <span class="n">default_auto_index</span>
<span class="n">defaultIndexFilters</span> <span class="o">=</span> <span class="n">default_index_filters</span>
<span class="c"># The list of types for which an optimised search in cython and C has</span>
<span class="c"># been implemented. Always add here the name of a new optimised type.</span>
<span class="n">opt_search_types</span> <span class="o">=</span> <span class="p">(</span><span class="s">"int8"</span><span class="p">,</span> <span class="s">"int16"</span><span class="p">,</span> <span class="s">"int32"</span><span class="p">,</span> <span class="s">"int64"</span><span class="p">,</span>
<span class="s">"uint8"</span><span class="p">,</span> <span class="s">"uint16"</span><span class="p">,</span> <span class="s">"uint32"</span><span class="p">,</span> <span class="s">"uint64"</span><span class="p">,</span>
<span class="s">"float32"</span><span class="p">,</span> <span class="s">"float64"</span><span class="p">)</span>
<span class="c"># The upper limit for uint32 ints</span>
<span class="n">max32</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="mi">32</span>
<span class="k">def</span> <span class="nf">_table_column_pathname_of_index</span><span class="p">(</span><span class="n">indexpathname</span><span class="p">):</span>
<span class="n">names</span> <span class="o">=</span> <span class="n">indexpathname</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">"/"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">names</span><span class="p">):</span>
<span class="k">if</span> <span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s">'_i_'</span><span class="p">):</span>
<span class="k">break</span>
<span class="n">tablepathname</span> <span class="o">=</span> <span class="s">"/"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">names</span><span class="p">[:</span><span class="n">i</span><span class="p">])</span> <span class="o">+</span> <span class="s">"/"</span> <span class="o">+</span> <span class="n">name</span><span class="p">[</span><span class="mi">3</span><span class="p">:]</span>
<span class="n">colpathname</span> <span class="o">=</span> <span class="s">"/"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:])</span>
<span class="k">return</span> <span class="p">(</span><span class="n">tablepathname</span><span class="p">,</span> <span class="n">colpathname</span><span class="p">)</span>
<span class="n">_tableColumnPathnameOfIndex</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">_table_column_pathname_of_index</span><span class="p">)</span>
<div class="viewcode-block" id="Index"><a class="viewcode-back" href="../../usersguide/libref/helper_classes.html#tables.index.Index">[docs]</a><span class="k">class</span> <span class="nc">Index</span><span class="p">(</span><span class="n">NotLoggedMixin</span><span class="p">,</span> <span class="n">indexesextension</span><span class="o">.</span><span class="n">Index</span><span class="p">,</span> <span class="n">Group</span><span class="p">):</span>
<span class="sd">"""Represents the index of a column in a table.</span>
<span class="sd"> This class is used to keep the indexing information for columns in a Table</span>
<span class="sd"> dataset (see :ref:`TableClassDescr`). It is actually a descendant of the</span>
<span class="sd"> Group class (see :ref:`GroupClassDescr`), with some added functionality. An</span>
<span class="sd"> Index is always associated with one and only one column in the table.</span>
<span class="sd"> .. note::</span>
<span class="sd"> This class is mainly intended for internal use, but some of its</span>
<span class="sd"> documented attributes and methods may be interesting for the</span>
<span class="sd"> programmer.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> parentnode</span>
<span class="sd"> The parent :class:`Group` object.</span>
<span class="sd"> .. versionchanged:: 3.0</span>
<span class="sd"> Renamed from *parentNode* to *parentnode*.</span>
<span class="sd"> name : str</span>
<span class="sd"> The name of this node in its parent group.</span>
<span class="sd"> atom : Atom</span>
<span class="sd"> An Atom object representing the shape and type of the atomic objects to</span>
<span class="sd"> be saved. Only scalar atoms are supported.</span>
<span class="sd"> title</span>
<span class="sd"> Sets a TITLE attribute of the Index entity.</span>
<span class="sd"> kind</span>
<span class="sd"> The desired kind for this index. The 'full' kind specifies a complete</span>
<span class="sd"> track of the row position (64-bit), while the 'medium', 'light' or</span>
<span class="sd"> 'ultralight' kinds only specify in which chunk the row is (using</span>
<span class="sd"> 32-bit, 16-bit and 8-bit respectively).</span>
<span class="sd"> optlevel</span>
<span class="sd"> The desired optimization level for this index.</span>
<span class="sd"> filters : Filters</span>
<span class="sd"> An instance of the Filters class that provides information about the</span>
<span class="sd"> desired I/O filters to be applied during the life of this object.</span>
<span class="sd"> tmp_dir</span>
<span class="sd"> The directory for the temporary files.</span>
<span class="sd"> expectedrows</span>
<span class="sd"> Represents an user estimate about the number of row slices that will be</span>
<span class="sd"> added to the growable dimension in the IndexArray object.</span>
<span class="sd"> byteorder</span>
<span class="sd"> The byteorder of the index datasets *on-disk*.</span>
<span class="sd"> blocksizes</span>
<span class="sd"> The four main sizes of the compound blocks in index datasets (a low</span>
<span class="sd"> level parameter).</span>
<span class="sd"> """</span>
<span class="n">_c_classid</span> <span class="o">=</span> <span class="s">'INDEX'</span>
<span class="n">_c_classId</span> <span class="o">=</span> <span class="n">previous_api_property</span><span class="p">(</span><span class="s">'_c_classid'</span><span class="p">)</span>
<span class="c"># <properties></span>
<span class="n">kind</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="s">'ultralight'</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="s">'light'</span><span class="p">,</span>
<span class="mi">4</span><span class="p">:</span> <span class="s">'medium'</span><span class="p">,</span> <span class="mi">8</span><span class="p">:</span> <span class="s">'full'</span><span class="p">}[</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">],</span>
<span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The kind of this index."</span><span class="p">)</span>
<span class="n">filters</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_filters</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="sd">"""Filter properties for this index - see Filters in</span>
<span class="sd"> :ref:`FiltersClassDescr`."""</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_getdirty</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="s">'DIRTY'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="p">:</span>
<span class="c"># If there is no ``DIRTY`` attribute, index should be clean.</span>
<span class="k">return</span> <span class="bp">False</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">DIRTY</span>
<span class="k">def</span> <span class="nf">_setdirty</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dirty</span><span class="p">):</span>
<span class="n">wasdirty</span><span class="p">,</span> <span class="n">isdirty</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dirty</span><span class="p">,</span> <span class="nb">bool</span><span class="p">(</span><span class="n">dirty</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">DIRTY</span> <span class="o">=</span> <span class="n">dirty</span>
<span class="c"># If an *actual* change in dirtiness happens,</span>
<span class="c"># notify the condition cache by setting or removing a nail.</span>
<span class="n">conditioncache</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="o">.</span><span class="n">_condition_cache</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">wasdirty</span> <span class="ow">and</span> <span class="n">isdirty</span><span class="p">:</span>
<span class="n">conditioncache</span><span class="o">.</span><span class="n">nail</span><span class="p">()</span>
<span class="k">if</span> <span class="n">wasdirty</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">isdirty</span><span class="p">:</span>
<span class="n">conditioncache</span><span class="o">.</span><span class="n">unnail</span><span class="p">()</span>
<span class="n">dirty</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="n">_getdirty</span><span class="p">,</span> <span class="n">_setdirty</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="sd">"""Whether the index is dirty or not.</span>
<span class="sd"> Dirty indexes are out of sync with column data, so they exist but they</span>
<span class="sd"> are not usable.</span>
<span class="sd"> """</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_getcolumn</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">tablepath</span><span class="p">,</span> <span class="n">columnpath</span> <span class="o">=</span> <span class="n">_table_column_pathname_of_index</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_pathname</span><span class="p">)</span>
<span class="n">table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_file</span><span class="o">.</span><span class="n">_get_node</span><span class="p">(</span><span class="n">tablepath</span><span class="p">)</span>
<span class="n">column</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">cols</span><span class="o">.</span><span class="n">_g_col</span><span class="p">(</span><span class="n">columnpath</span><span class="p">)</span>
<span class="k">return</span> <span class="n">column</span>
<span class="n">column</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_getcolumn</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="sd">"""The Column (see :ref:`ColumnClassDescr`) instance for the indexed</span>
<span class="sd"> column."""</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_gettable</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">tablepath</span><span class="p">,</span> <span class="n">columnpath</span> <span class="o">=</span> <span class="n">_table_column_pathname_of_index</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_pathname</span><span class="p">)</span>
<span class="n">table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_file</span><span class="o">.</span><span class="n">_get_node</span><span class="p">(</span><span class="n">tablepath</span><span class="p">)</span>
<span class="k">return</span> <span class="n">table</span>
<span class="n">table</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_gettable</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"Accessor for the `Table` object of this index."</span><span class="p">)</span>
<span class="n">nblockssuperblock</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The number of blocks in a superblock."</span><span class="p">)</span>
<span class="n">nslicesblock</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The number of slices in a block."</span><span class="p">)</span>
<span class="n">nchunkslice</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The number of chunks in a slice."</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_g_nsuperblocks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c"># Last row should not be considered as a superblock</span>
<span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="n">nblocks</span> <span class="o">=</span> <span class="n">nelements</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span>
<span class="k">if</span> <span class="n">nelements</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">nblocks</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">nblocks</span>
<span class="n">nsuperblocks</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_g_nsuperblocks</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The total number of superblocks in index."</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_g_nblocks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c"># Last row should not be considered as a block</span>
<span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="n">nblocks</span> <span class="o">=</span> <span class="n">nelements</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span>
<span class="k">if</span> <span class="n">nelements</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">nblocks</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">nblocks</span>
<span class="n">nblocks</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_g_nblocks</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The total number of blocks in index."</span><span class="p">)</span>
<span class="n">nslices</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The number of complete slices in index."</span><span class="p">)</span>
<span class="n">nchunks</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The number of complete chunks in index."</span><span class="p">)</span>
<span class="n">shape</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nrows</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">),</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"The shape of this index (in slices and elements)."</span><span class="p">)</span>
<span class="n">temp_required</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">and</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span>
<span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="o">.</span><span class="n">nrows</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">),</span>
<span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"Whether a temporary file for indexes is required or not."</span><span class="p">)</span>
<span class="n">want_complete_sort</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="k">lambda</span> <span class="bp">self</span><span class="p">:</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">8</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span> <span class="o">==</span> <span class="mi">9</span><span class="p">),</span>
<span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"Whether we should try to build a completely sorted index or not."</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_is_csi</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="c"># An index with 0 indexed elements is not a CSI one (by definition)</span>
<span class="k">return</span> <span class="bp">False</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o"><</span> <span class="mi">8</span><span class="p">:</span>
<span class="c"># An index that is not full cannot be completely sorted</span>
<span class="k">return</span> <span class="bp">False</span>
<span class="c"># Try with the 'is_csi' attribute</span>
<span class="k">if</span> <span class="s">'is_csi'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">is_csi</span>
<span class="c"># If not, then compute the overlaps manually</span>
<span class="c"># (the attribute 'is_csi' will be set there)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">compute_overlaps</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">False</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">noverlaps</span> <span class="o">==</span> <span class="mi">0</span>
<span class="n">_is_CSI</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">_is_csi</span><span class="p">)</span>
<span class="n">is_csi</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_is_csi</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="sd">"""Whether the index is completely sorted or not.</span>
<span class="sd"> .. versionchanged:: 3.0</span>
<span class="sd"> The *is_CSI* property has been renamed into *is_csi*.</span>
<span class="sd"> """</span><span class="p">)</span>
<span class="n">is_CSI</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">is_csi</span><span class="p">)</span>
<span class="nd">@lazyattr</span>
<span class="k">def</span> <span class="nf">nrowsinchunk</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""The number of rows that fits in a *table* chunk."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="o">.</span><span class="n">chunkshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="nd">@lazyattr</span>
<span class="k">def</span> <span class="nf">lbucket</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Return the length of a bucket based index type."""</span>
<span class="c"># Avoid to set a too large lbucket size (mainly useful for tests)</span>
<span class="n">lbucket</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nrowsinchunk</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="c"># For ultra-light, we will never have to keep track of a</span>
<span class="c"># bucket outside of a slice.</span>
<span class="n">maxnb</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="mi">8</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">></span> <span class="n">maxnb</span> <span class="o">*</span> <span class="n">lbucket</span><span class="p">:</span>
<span class="n">lbucket</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">)</span> <span class="o">/</span> <span class="n">maxnb</span><span class="p">))</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="c"># For light, we will never have to keep track of a</span>
<span class="c"># bucket outside of a block.</span>
<span class="n">maxnb</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="mi">16</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span> <span class="o">></span> <span class="n">maxnb</span> <span class="o">*</span> <span class="n">lbucket</span><span class="p">:</span>
<span class="n">lbucket</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span><span class="p">)</span> <span class="o">/</span> <span class="n">maxnb</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="c"># For medium and full indexes there should not be a need to</span>
<span class="c"># increase lbucket</span>
<span class="k">pass</span>
<span class="k">return</span> <span class="n">lbucket</span>
<span class="c"># </properties></span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parentnode</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span>
<span class="n">atom</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s">""</span><span class="p">,</span>
<span class="n">kind</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="n">optlevel</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="n">filters</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="n">tmp_dir</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="n">expectedrows</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">byteorder</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="n">blocksizes</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="n">new</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_version</span> <span class="o">=</span> <span class="bp">None</span>
<span class="sd">"""The object version of this index."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span> <span class="o">=</span> <span class="n">optlevel</span>
<span class="sd">"""The optimization level for this index."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmp_dir</span> <span class="o">=</span> <span class="n">tmp_dir</span>
<span class="sd">"""The directory for the temporary files."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">expectedrows</span> <span class="o">=</span> <span class="n">expectedrows</span>
<span class="sd">"""The expected number of items of index arrays."""</span>
<span class="k">if</span> <span class="n">byteorder</span> <span class="ow">in</span> <span class="p">[</span><span class="s">"little"</span><span class="p">,</span> <span class="s">"big"</span><span class="p">]:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span> <span class="o">=</span> <span class="n">byteorder</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">byteorder</span>
<span class="sd">"""The byteorder of the index datasets."""</span>
<span class="k">if</span> <span class="n">atom</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">atom</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="n">atom</span><span class="o">.</span><span class="n">type</span>
<span class="sd">"""The datatypes to be stored by the sorted index array."""</span>
<span class="c">############### Important note ###########################</span>
<span class="c"># The datatypes saved as index values are NumPy native</span>
<span class="c"># types, so we get rid of type metainfo like Time* or Enum*</span>
<span class="c"># that belongs to HDF5 types (actually, this metainfo is</span>
<span class="c"># not needed for sorting and looking-up purposes).</span>
<span class="c">##########################################################</span>
<span class="n">indsize</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'ultralight'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s">'light'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s">'medium'</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s">'full'</span><span class="p">:</span> <span class="mi">8</span><span class="p">}[</span><span class="n">kind</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">indsize</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="s">"indsize should be 1, 2, 4 or 8!"</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">=</span> <span class="n">indsize</span>
<span class="sd">"""The itemsize for the indices part of the index."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nrows</span> <span class="o">=</span> <span class="bp">None</span>
<span class="sd">"""The total number of slices in the index."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="bp">None</span>
<span class="sd">"""The number of currently indexed rows for this column."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">blocksizes</span> <span class="o">=</span> <span class="n">blocksizes</span>
<span class="sd">"""The four main sizes of the compound blocks (if specified)."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dirtycache</span> <span class="o">=</span> <span class="bp">True</span>
<span class="sd">"""Dirty cache (for ranges, bounds & sorted) flag."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span> <span class="o">=</span> <span class="bp">None</span>
<span class="sd">"""Size of the superblock for this index."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span> <span class="o">=</span> <span class="bp">None</span>
<span class="sd">"""Size of the block for this index."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">=</span> <span class="bp">None</span>
<span class="sd">"""Size of the slice for this index."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">=</span> <span class="bp">None</span>
<span class="sd">"""Size of the chunk for this index."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmpfilename</span> <span class="o">=</span> <span class="bp">None</span>
<span class="sd">"""Filename for temporary bounds."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">opt_search_types</span> <span class="o">=</span> <span class="n">opt_search_types</span>
<span class="sd">"""The types for which and optimized search has been implemented."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">noverlaps</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="sd">"""The number of overlaps in an index. 0 means a completely</span>
<span class="sd"> sorted index. -1 means that this number is not computed yet."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tprof</span> <span class="o">=</span> <span class="mi">0</span>
<span class="sd">"""Time counter for benchmarking purposes."""</span>
<span class="kn">from</span> <span class="nn">tables.file</span> <span class="kn">import</span> <span class="n">open_file</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_openFile</span> <span class="o">=</span> <span class="n">open_file</span>
<span class="sd">"""The `open_file()` function, to avoid a circular import."""</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Index</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="n">parentnode</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">new</span><span class="p">,</span> <span class="n">filters</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_g_post_init_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_new</span><span class="p">:</span>
<span class="c"># The version for newly created indexes</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_version</span> <span class="o">=</span> <span class="n">obversion</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Index</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_g_post_init_hook</span><span class="p">()</span>
<span class="c"># Index arrays must only be created for new indexes</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_new</span><span class="p">:</span>
<span class="n">idxversion</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_version</span>
<span class="c"># Set-up some variables from info on disk and return</span>
<span class="n">attrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span>
<span class="c"># Coerce NumPy scalars to Python scalars in order</span>
<span class="c"># to avoid undesired upcasting operations.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span> <span class="o">=</span> <span class="nb">long</span><span class="p">(</span><span class="n">attrs</span><span class="o">.</span><span class="n">superblocksize</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span> <span class="o">=</span> <span class="nb">long</span><span class="p">(</span><span class="n">attrs</span><span class="o">.</span><span class="n">blocksize</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">attrs</span><span class="o">.</span><span class="n">slicesize</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">attrs</span><span class="o">.</span><span class="n">chunksize</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">blocksizes</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">attrs</span><span class="o">.</span><span class="n">optlevel</span><span class="p">)</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">atom</span><span class="o">.</span><span class="n">dtype</span>
<span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">atom</span><span class="o">.</span><span class="n">type</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">=</span> <span class="n">indices</span><span class="o">.</span><span class="n">atom</span><span class="o">.</span><span class="n">itemsize</span>
<span class="c"># Some sanity checks for slicesize, chunksize and indsize</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">==</span> <span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s">"Wrong slicesize"</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">==</span> <span class="n">indices</span><span class="o">.</span><span class="n">_v_chunkshape</span><span class="p">[</span>
<span class="mi">1</span><span class="p">],</span> <span class="s">"Wrong chunksize"</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="s">"Wrong indices itemsize"</span>
<span class="k">if</span> <span class="n">idxversion</span> <span class="o">></span> <span class="s">"2.0"</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">attrs</span><span class="o">.</span><span class="n">reduction</span><span class="p">)</span>
<span class="n">nelementsSLR</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span><span class="p">)</span>
<span class="n">nelementsILR</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indicesLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">nelementsILR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indicesLR</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">nelementsSLR</span> <span class="o">=</span> <span class="n">nelementsILR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nrows</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">nrows</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nrows</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">+</span> <span class="n">nelementsILR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span> <span class="o">=</span> <span class="n">nelementsSLR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span> <span class="o">=</span> <span class="n">nelementsILR</span>
<span class="k">if</span> <span class="n">nelementsILR</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nrows</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c"># Get the bounds as a cache (this has to remain here!)</span>
<span class="n">rchunksize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="n">nboundsLR</span> <span class="o">=</span> <span class="p">(</span><span class="n">nelementsSLR</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">rchunksize</span>
<span class="k">if</span> <span class="n">nboundsLR</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">nboundsLR</span> <span class="o">=</span> <span class="mi">0</span> <span class="c"># correction for -1 bounds</span>
<span class="n">nboundsLR</span> <span class="o">+=</span> <span class="mi">2</span> <span class="c"># bounds + begin + end</span>
<span class="c"># All bounds values (+begin + end) are at the end of sortedLR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span><span class="p">[</span>
<span class="n">nelementsSLR</span><span class="p">:</span><span class="n">nelementsSLR</span> <span class="o">+</span> <span class="n">nboundsLR</span><span class="p">]</span>
<span class="k">return</span>
<span class="c"># The index is new. Initialize the values</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nrows</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c"># The atom</span>
<span class="n">atom</span> <span class="o">=</span> <span class="n">Atom</span><span class="o">.</span><span class="n">from_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="c"># The filters</span>
<span class="n">filters</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">filters</span>
<span class="c"># Compute the superblocksize, blocksize, slicesize and chunksize values</span>
<span class="c"># (in case these parameters haven't been passed to the constructor)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksizes</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">blocksizes</span> <span class="o">=</span> <span class="n">calc_chunksize</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">expectedrows</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">)</span>
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksizes</span>
<span class="k">if</span> <span class="n">debug</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">"blocksizes:"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksizes</span><span class="p">)</span>
<span class="c"># Compute the reduction level</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">get_reduction_level</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">)</span>
<span class="n">rchunksize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="n">rslicesize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="c"># Save them on disk as attributes</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">superblocksize</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">blocksize</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">uint32</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">uint32</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">)</span>
<span class="c"># Save the optlevel as well</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">optlevel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span>
<span class="c"># Save the reduction level</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="c"># Create the IndexArray for sorted values</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="n">IndexArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'sorted'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="s">"Sorted Values"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">)</span>
<span class="c"># Create the IndexArray for index values</span>
<span class="n">IndexArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'indices'</span><span class="p">,</span> <span class="n">UIntAtom</span><span class="p">(</span><span class="n">itemsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">),</span>
<span class="s">"Number of chunk in table"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">)</span>
<span class="c"># Create the cache for range values (1st order cache)</span>
<span class="n">CacheArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'ranges'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="s">"Range Values"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">expectedrows</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,</span>
<span class="n">byteorder</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">)</span>
<span class="c"># median ranges</span>
<span class="n">EArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'mranges'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,),</span> <span class="s">"Median ranges"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span>
<span class="n">byteorder</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">,</span> <span class="n">_log</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="c"># Create the cache for boundary values (2nd order cache)</span>
<span class="n">nbounds_inslice</span> <span class="o">=</span> <span class="p">(</span><span class="n">rslicesize</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">rchunksize</span>
<span class="n">CacheArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'bounds'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">nbounds_inslice</span><span class="p">),</span>
<span class="s">"Boundary Values"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nchunks</span><span class="p">,</span>
<span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nbounds_inslice</span><span class="p">),</span> <span class="n">byteorder</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">)</span>
<span class="c"># begin, end & median bounds (only for numerical types)</span>
<span class="n">EArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'abounds'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,),</span> <span class="s">"Start bounds"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span>
<span class="n">byteorder</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">,</span> <span class="n">_log</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">EArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'zbounds'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,),</span> <span class="s">"End bounds"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span>
<span class="n">byteorder</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">,</span> <span class="n">_log</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">EArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'mbounds'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,),</span> <span class="s">"Median bounds"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span>
<span class="n">byteorder</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">,</span> <span class="n">_log</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="c"># Create the Array for last (sorted) row values + bounds</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">rslicesize</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">nbounds_inslice</span><span class="p">,)</span>
<span class="n">sortedLR</span> <span class="o">=</span> <span class="n">LastRowArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'sortedLR'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span>
<span class="s">"Last Row sorted values + bounds"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="p">(</span><span class="n">rchunksize</span><span class="p">,),</span>
<span class="n">byteorder</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">)</span>
<span class="c"># Create the Array for the number of chunk in last row</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,)</span> <span class="c"># enough for indexes and length</span>
<span class="n">indicesLR</span> <span class="o">=</span> <span class="n">LastRowArray</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">'indicesLR'</span><span class="p">,</span>
<span class="n">UIntAtom</span><span class="p">(</span><span class="n">itemsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">),</span>
<span class="n">shape</span><span class="p">,</span> <span class="s">"Last Row indices"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">,),</span>
<span class="n">byteorder</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">)</span>
<span class="c"># The number of elements in LR will be initialized here</span>
<span class="n">sortedLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">indicesLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c"># All bounds values (+begin + end) are uninitialized in creation time</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span> <span class="o">=</span> <span class="bp">None</span>
<span class="c"># The starts and lengths initialization</span>
<span class="bp">self</span><span class="o">.</span><span class="n">starts</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nrows</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="sd">"""Where the values fulfiling conditions starts for every slice."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lengths</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nrows</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="sd">"""Lengths of the values fulfilling conditions for every slice."""</span>
<span class="c"># Finally, create a temporary file for indexes if needed</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">temp_required</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">create_temp</span><span class="p">()</span>
<span class="n">_g_postInitHook</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">_g_post_init_hook</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">initial_append</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xarr</span><span class="p">,</span> <span class="n">nrow</span><span class="p">,</span> <span class="n">reduction</span><span class="p">):</span>
<span class="sd">"""Compute an initial indices arrays for data to be indexed."""</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">tref</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Entering initial_append"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="n">arr</span> <span class="o">=</span> <span class="n">xarr</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
<span class="n">indsize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span>
<span class="n">slicesize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">nelementsILR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Before creating idx"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">if</span> <span class="n">indsize</span> <span class="o">==</span> <span class="mi">8</span><span class="p">:</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s">"uint64"</span><span class="p">)</span> <span class="o">+</span> <span class="n">nrow</span> <span class="o">*</span> <span class="n">slicesize</span>
<span class="k">elif</span> <span class="n">indsize</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="c"># For medium (32-bit) all the rows in tables should be</span>
<span class="c"># directly reachable. But as len(arr) < 2**31, we can</span>
<span class="c"># choose uint32 for representing indices. In this way, we</span>
<span class="c"># consume far less memory during the keysort process. The</span>
<span class="c"># offset will be added in self.final_idx32() later on.</span>
<span class="c">#</span>
<span class="c"># This optimization also prevents the values in LR to</span>
<span class="c"># participate in the ``swap_chunks`` process, and this is</span>
<span class="c"># the main reason to not allow the medium indexes to create</span>
<span class="c"># completely sorted indexes. However, I don't find this to</span>
<span class="c"># be a big limitation, as probably fully indexes are much</span>
<span class="c"># more suitable for producing completely sorted indexes</span>
<span class="c"># because in this case the indices part is usable for</span>
<span class="c"># getting the reverse indices of the index, and I forsee</span>
<span class="c"># this to be a common requirement in many operations (for</span>
<span class="c"># example, in table sorts).</span>
<span class="c">#</span>
<span class="c"># F. Alted 2008-09-15</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s">"uint32"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">),</span> <span class="s">"uint</span><span class="si">%d</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">indsize</span> <span class="o">*</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">lbucket</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lbucket</span>
<span class="c"># Fill the idx with the bucket indices</span>
<span class="n">offset</span> <span class="o">=</span> <span class="n">lbucket</span> <span class="o">-</span> <span class="p">((</span><span class="n">nrow</span> <span class="o">*</span> <span class="p">(</span><span class="n">slicesize</span> <span class="o">%</span> <span class="n">lbucket</span><span class="p">))</span> <span class="o">%</span> <span class="n">lbucket</span><span class="p">)</span>
<span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">offset</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="n">slicesize</span><span class="p">,</span> <span class="n">lbucket</span><span class="p">):</span>
<span class="n">idx</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span> <span class="o">+</span> <span class="n">lbucket</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="n">lbucket</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">lbucket</span>
<span class="k">if</span> <span class="n">indsize</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="c"># Add a second offset in this case</span>
<span class="c"># First normalize the number of rows</span>
<span class="n">offset2</span> <span class="o">=</span> <span class="p">(</span><span class="n">nrow</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslicesblock</span><span class="p">)</span> <span class="o">*</span> <span class="n">slicesize</span> <span class="o">//</span> <span class="n">lbucket</span>
<span class="n">idx</span> <span class="o">+=</span> <span class="n">offset2</span>
<span class="c"># Add the last row at the beginning of arr & idx (if needed)</span>
<span class="k">if</span> <span class="p">(</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">8</span> <span class="ow">and</span> <span class="n">nelementsILR</span> <span class="o">></span> <span class="mi">0</span><span class="p">):</span>
<span class="c"># It is possible that the values in LR are already sorted.</span>
<span class="c"># Fetch them and override existing values in arr and idx.</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> <span class="o">></span> <span class="n">nelementsILR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice_lr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span><span class="p">,</span> <span class="n">arr</span><span class="p">[:</span><span class="n">nelementsILR</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice_lr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indicesLR</span><span class="p">,</span> <span class="n">idx</span><span class="p">[:</span><span class="n">nelementsILR</span><span class="p">])</span>
<span class="c"># In-place sorting</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Before keysort"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="n">indexesextension</span><span class="o">.</span><span class="n">keysort</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">idx</span><span class="p">)</span>
<span class="n">larr</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="n">reduction</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
<span class="c"># It's important to do a copy() here in order to ensure that</span>
<span class="c"># sorted._append() will receive a contiguous array.</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Before reduction"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="n">reduc</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[::</span><span class="n">reduction</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"After reduction"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="n">arr</span> <span class="o">=</span> <span class="n">reduc</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"After arr <-- reduc"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="c"># A completely sorted index is not longer possible after an</span>
<span class="c"># append of an index with already one slice.</span>
<span class="k">if</span> <span class="n">nrow</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">is_csi</span> <span class="o">=</span> <span class="bp">False</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Exiting initial_append"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">return</span> <span class="n">larr</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">idx</span>
<span class="k">def</span> <span class="nf">final_idx32</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">offset</span><span class="p">):</span>
<span class="sd">"""Perform final operations in 32-bit indices."""</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">tref</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Entering final_idx32"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="c"># Do an upcast first in order to add the offset.</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">'uint64'</span><span class="p">)</span>
<span class="n">idx</span> <span class="o">+=</span> <span class="n">offset</span>
<span class="c"># The next partition is valid up to table sizes of</span>
<span class="c"># 2**30 * 2**18 = 2**48 bytes, that is, 256 Tera-elements,</span>
<span class="c"># which should be a safe figure, at least for a while.</span>
<span class="n">idx</span> <span class="o">//=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lbucket</span>
<span class="c"># After the division, we can downsize the indexes to 'uint32'</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">'uint32'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Exiting final_idx32"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">return</span> <span class="n">idx</span>
<span class="k">def</span> <span class="nf">append</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xarr</span><span class="p">,</span> <span class="n">update</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Append the array to the index objects."""</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">tref</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Entering append"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">update</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">temp_required</span><span class="p">:</span>
<span class="n">where</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="c"># The reduction will take place *after* the optimization process</span>
<span class="n">reduction</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">where</span> <span class="o">=</span> <span class="bp">self</span>
<span class="n">reduction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">indices</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">ranges</span>
<span class="n">mranges</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">mranges</span>
<span class="n">bounds</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">bounds</span>
<span class="n">mbounds</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">mbounds</span>
<span class="n">abounds</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">abounds</span>
<span class="n">zbounds</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">zbounds</span>
<span class="n">sortedLR</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">indicesLR</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">indicesLR</span>
<span class="n">nrows</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">nrows</span> <span class="c"># before sorted.append()</span>
<span class="n">larr</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">initial_append</span><span class="p">(</span><span class="n">xarr</span><span class="p">,</span> <span class="n">nrows</span><span class="p">,</span> <span class="n">reduction</span><span class="p">)</span>
<span class="c"># Save the sorted array</span>
<span class="nb">sorted</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">//</span> <span class="n">reduction</span>
<span class="n">ncs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nchunkslice</span>
<span class="c"># Save ranges & bounds</span>
<span class="n">ranges</span><span class="o">.</span><span class="n">append</span><span class="p">([[</span><span class="n">arr</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">larr</span><span class="p">]])</span>
<span class="n">bounds</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">arr</span><span class="p">[</span><span class="n">cs</span><span class="p">::</span><span class="n">cs</span><span class="p">]])</span>
<span class="n">abounds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="mi">0</span><span class="p">::</span><span class="n">cs</span><span class="p">])</span>
<span class="n">zbounds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arr</span><span class="p">[</span><span class="n">cs</span> <span class="o">-</span> <span class="mi">1</span><span class="p">::</span><span class="n">cs</span><span class="p">])</span>
<span class="c"># Compute the medians</span>
<span class="n">smedian</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[</span><span class="n">cs</span> <span class="o">//</span> <span class="mi">2</span><span class="p">::</span><span class="n">cs</span><span class="p">]</span>
<span class="n">mbounds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">smedian</span><span class="p">)</span>
<span class="n">mranges</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">smedian</span><span class="p">[</span><span class="n">ncs</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]])</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Before deleting arr & smedian"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">del</span> <span class="n">arr</span><span class="p">,</span> <span class="n">smedian</span> <span class="c"># delete references</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"After deleting arr & smedian"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="c"># Now that arr is gone, we can upcast the indices and add the offset</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_idx32</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">nrows</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">)</span>
<span class="n">indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">idx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">idx</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Before deleting idx"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">del</span> <span class="n">idx</span>
<span class="c"># Update counters after a successful append</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nrows</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">+</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nrows</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span> <span class="o">=</span> <span class="mi">0</span> <span class="c"># reset the counter of the last row index to 0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span> <span class="o">=</span> <span class="mi">0</span> <span class="c"># reset the counter of the last row index to 0</span>
<span class="c"># The number of elements will be saved as an attribute.</span>
<span class="c"># This is necessary in case the LR arrays can remember its values</span>
<span class="c"># after a possible node preemtion/reload.</span>
<span class="n">sortedLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span>
<span class="n">indicesLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dirtycache</span> <span class="o">=</span> <span class="bp">True</span> <span class="c"># the cache is dirty now</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Exiting append"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">append_last_row</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">xarr</span><span class="p">,</span> <span class="n">update</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Append the array to the last row index objects."""</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">tref</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Entering appendLR"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="c"># compute the elements in the last row sorted & bounds array</span>
<span class="n">nrows</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">update</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">temp_required</span><span class="p">:</span>
<span class="n">where</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="c"># The reduction will take place *after* the optimization process</span>
<span class="n">reduction</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">where</span> <span class="o">=</span> <span class="bp">self</span>
<span class="n">reduction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="n">indicesLR</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">indicesLR</span>
<span class="n">sortedLR</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">larr</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">initial_append</span><span class="p">(</span><span class="n">xarr</span><span class="p">,</span> <span class="n">nrows</span><span class="p">,</span> <span class="n">reduction</span><span class="p">)</span>
<span class="n">nelementsSLR</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
<span class="n">nelementsILR</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
<span class="c"># Build the cache of bounds</span>
<span class="n">rchunksize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">//</span> <span class="n">reduction</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">arr</span><span class="p">[::</span><span class="n">rchunksize</span><span class="p">],</span> <span class="p">[</span><span class="n">larr</span><span class="p">]))</span>
<span class="c"># The number of elements will be saved as an attribute</span>
<span class="n">sortedLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="n">nelementsSLR</span>
<span class="n">indicesLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="n">nelementsILR</span>
<span class="c"># Save the number of elements, bounds and sorted values</span>
<span class="c"># at the end of the sorted array</span>
<span class="n">offset2</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">)</span>
<span class="n">sortedLR</span><span class="p">[</span><span class="n">nelementsSLR</span><span class="p">:</span><span class="n">nelementsSLR</span> <span class="o">+</span> <span class="n">offset2</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span>
<span class="n">sortedLR</span><span class="p">[:</span><span class="n">nelementsSLR</span><span class="p">]</span> <span class="o">=</span> <span class="n">arr</span>
<span class="k">del</span> <span class="n">arr</span>
<span class="c"># Now that arr is gone, we can upcast the indices and add the offset</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_idx32</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">nrows</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">)</span>
<span class="c"># Save the reverse index array</span>
<span class="n">indicesLR</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">)]</span> <span class="o">=</span> <span class="n">idx</span>
<span class="k">del</span> <span class="n">idx</span>
<span class="c"># Update counters after a successful append</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nrows</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">+</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span> <span class="o">+</span> <span class="n">nelementsILR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span> <span class="o">=</span> <span class="n">nelementsILR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span> <span class="o">=</span> <span class="n">nelementsSLR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dirtycache</span> <span class="o">=</span> <span class="bp">True</span> <span class="c"># the cache is dirty now</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Exiting appendLR"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="n">appendLastRow</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">append_last_row</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">optimize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Optimize an index so as to allow faster searches.</span>
<span class="sd"> verbose</span>
<span class="sd"> If True, messages about the progress of the</span>
<span class="sd"> optimization process are printed out.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">temp_required</span><span class="p">:</span>
<span class="k">return</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="bp">True</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">debug</span>
<span class="c"># Initialize last_tover and last_nover</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_tover</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_nover</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c"># Compute the correct optimizations for current optim level</span>
<span class="n">opts</span> <span class="o">=</span> <span class="n">calcoptlevels</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nblocks</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">)</span>
<span class="n">optmedian</span><span class="p">,</span> <span class="n">optstarts</span><span class="p">,</span> <span class="n">optstops</span><span class="p">,</span> <span class="n">optfull</span> <span class="o">=</span> <span class="n">opts</span>
<span class="k">if</span> <span class="n">debug</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">"optvalues:"</span><span class="p">,</span> <span class="n">opts</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">create_temp2</span><span class="p">()</span>
<span class="c"># Start the optimization process</span>
<span class="k">while</span> <span class="bp">True</span><span class="p">:</span>
<span class="k">if</span> <span class="n">optfull</span><span class="p">:</span>
<span class="k">for</span> <span class="n">niter</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">optfull</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">swap</span><span class="p">(</span><span class="s">'chunks'</span><span class="p">,</span> <span class="s">'median'</span><span class="p">):</span>
<span class="k">break</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nblocks</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
<span class="c"># Swap slices only in the case that we have</span>
<span class="c"># several blocks</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">swap</span><span class="p">(</span><span class="s">'slices'</span><span class="p">,</span> <span class="s">'median'</span><span class="p">):</span>
<span class="k">break</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">swap</span><span class="p">(</span><span class="s">'chunks'</span><span class="p">,</span> <span class="s">'median'</span><span class="p">):</span>
<span class="k">break</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">swap</span><span class="p">(</span><span class="s">'chunks'</span><span class="p">,</span> <span class="s">'start'</span><span class="p">):</span>
<span class="k">break</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">swap</span><span class="p">(</span><span class="s">'chunks'</span><span class="p">,</span> <span class="s">'stop'</span><span class="p">):</span>
<span class="k">break</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">optmedian</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">swap</span><span class="p">(</span><span class="s">'chunks'</span><span class="p">,</span> <span class="s">'median'</span><span class="p">):</span>
<span class="k">break</span>
<span class="k">if</span> <span class="n">optstarts</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">swap</span><span class="p">(</span><span class="s">'chunks'</span><span class="p">,</span> <span class="s">'start'</span><span class="p">):</span>
<span class="k">break</span>
<span class="k">if</span> <span class="n">optstops</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">swap</span><span class="p">(</span><span class="s">'chunks'</span><span class="p">,</span> <span class="s">'stop'</span><span class="p">):</span>
<span class="k">break</span>
<span class="k">break</span> <span class="c"># If we reach this, exit the loop</span>
<span class="c"># Check if we require a complete sort. Important: this step</span>
<span class="c"># should be carried out *after* the optimization process has</span>
<span class="c"># been completed (this is to guarantee that the complete sort</span>
<span class="c"># does not take too much memory).</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">want_complete_sort</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">noverlaps</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">do_complete_sort</span><span class="p">()</span>
<span class="c"># Check that we have effectively achieved the complete sort</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">noverlaps</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s">"OPSI was not able to achieve a completely sorted index."</span>
<span class="s">" Please report this to the authors."</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
<span class="c"># Close and delete the temporal optimization index file</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cleanup_temp</span><span class="p">()</span>
<span class="k">return</span>
<span class="k">def</span> <span class="nf">do_complete_sort</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Bring an already optimized index into a complete sorted state."""</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">c1</span> <span class="o">=</span> <span class="n">clock</span><span class="p">()</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">ranges</span><span class="p">[:]</span>
<span class="n">nslices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span>
<span class="n">nelementsLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="k">if</span> <span class="n">nelementsLR</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="c"># Add the ranges corresponding to the last row</span>
<span class="n">rangeslr</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]])</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">ranges</span><span class="p">,</span> <span class="p">[</span><span class="n">rangeslr</span><span class="p">]))</span>
<span class="n">nslices</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices</span>
<span class="n">sortedLR</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">indicesLR</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indicesLR</span>
<span class="n">sremain</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">iremain</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="s">'u</span><span class="si">%d</span><span class="s">'</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">)</span>
<span class="n">starts</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">nslices</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int_</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nslices</span><span class="p">):</span>
<span class="c"># Find the overlapping elements for slice i</span>
<span class="n">sover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">iover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="s">'u</span><span class="si">%d</span><span class="s">'</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">)</span>
<span class="n">prev_end</span> <span class="o">=</span> <span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nslices</span><span class="p">):</span>
<span class="n">stj</span> <span class="o">=</span> <span class="n">starts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="k">if</span> <span class="p">((</span><span class="n">j</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span> <span class="ow">and</span> <span class="n">stj</span> <span class="o">==</span> <span class="n">ss</span><span class="p">)</span> <span class="ow">or</span>
<span class="p">(</span><span class="n">j</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span> <span class="ow">and</span> <span class="n">stj</span> <span class="o">==</span> <span class="n">nelementsLR</span><span class="p">)):</span>
<span class="c"># This slice has been already dealt with</span>
<span class="k">continue</span>
<span class="k">if</span> <span class="n">j</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">stj</span> <span class="o"><</span> <span class="n">ss</span><span class="p">,</span> \
<span class="s">"Two slices cannot overlap completely at this stage!"</span>
<span class="n">next_beg</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">stj</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">stj</span> <span class="o"><</span> <span class="n">nelementsLR</span><span class="p">,</span> \
<span class="s">"Two slices cannot overlap completely at this stage!"</span>
<span class="n">next_beg</span> <span class="o">=</span> <span class="n">sortedLR</span><span class="p">[</span><span class="n">stj</span><span class="p">]</span>
<span class="n">next_end</span> <span class="o">=</span> <span class="n">ranges</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="n">prev_end</span> <span class="o">></span> <span class="n">next_end</span><span class="p">:</span>
<span class="c"># Complete overlapping case</span>
<span class="k">if</span> <span class="n">j</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="n">sover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sover</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">stj</span><span class="p">:]))</span>
<span class="n">iover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">iover</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">stj</span><span class="p">:]))</span>
<span class="n">starts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">ss</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">nelementsLR</span>
<span class="n">sover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sover</span><span class="p">,</span> <span class="n">sortedLR</span><span class="p">[</span><span class="n">stj</span><span class="p">:</span><span class="n">n</span><span class="p">]))</span>
<span class="n">iover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">iover</span><span class="p">,</span> <span class="n">indicesLR</span><span class="p">[</span><span class="n">stj</span><span class="p">:</span><span class="n">n</span><span class="p">]))</span>
<span class="n">starts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">nelementsLR</span>
<span class="k">elif</span> <span class="n">prev_end</span> <span class="o">></span> <span class="n">next_beg</span><span class="p">:</span>
<span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">search_item_lt</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">prev_end</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">ranges</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">stj</span><span class="p">)</span>
<span class="k">if</span> <span class="n">j</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="n">sover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sover</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">stj</span><span class="p">:</span><span class="n">idx</span><span class="p">]))</span>
<span class="n">iover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">iover</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">stj</span><span class="p">:</span><span class="n">idx</span><span class="p">]))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">sover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sover</span><span class="p">,</span> <span class="n">sortedLR</span><span class="p">[</span><span class="n">stj</span><span class="p">:</span><span class="n">idx</span><span class="p">]))</span>
<span class="n">iover</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">iover</span><span class="p">,</span> <span class="n">indicesLR</span><span class="p">[</span><span class="n">stj</span><span class="p">:</span><span class="n">idx</span><span class="p">]))</span>
<span class="n">starts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">idx</span>
<span class="c"># Build the extended slices to sort out</span>
<span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="n">ssorted</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
<span class="p">(</span><span class="n">sremain</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">starts</span><span class="p">[</span><span class="n">i</span><span class="p">]:],</span> <span class="n">sover</span><span class="p">))</span>
<span class="n">sindices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
<span class="p">(</span><span class="n">iremain</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">starts</span><span class="p">[</span><span class="n">i</span><span class="p">]:],</span> <span class="n">iover</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ssorted</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
<span class="p">(</span><span class="n">sremain</span><span class="p">,</span> <span class="n">sortedLR</span><span class="p">[</span><span class="n">starts</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">nelementsLR</span><span class="p">],</span> <span class="n">sover</span><span class="p">))</span>
<span class="n">sindices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
<span class="p">(</span><span class="n">iremain</span><span class="p">,</span> <span class="n">indicesLR</span><span class="p">[</span><span class="n">starts</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">nelementsLR</span><span class="p">],</span> <span class="n">iover</span><span class="p">))</span>
<span class="c"># Sort the extended slices</span>
<span class="n">indexesextension</span><span class="o">.</span><span class="n">keysort</span><span class="p">(</span><span class="n">ssorted</span><span class="p">,</span> <span class="n">sindices</span><span class="p">)</span>
<span class="c"># Save the first elements of extended slices in the slice i</span>
<span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="nb">sorted</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">]</span>
<span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">sindices</span><span class="p">[:</span><span class="n">ss</span><span class="p">]</span>
<span class="c"># Update caches for this slice</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_caches</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="c"># Save the remaining values in a separate array</span>
<span class="n">send</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sover</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">sremain</span><span class="p">)</span>
<span class="n">sremain</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[</span><span class="n">ss</span><span class="p">:</span><span class="n">ss</span> <span class="o">+</span> <span class="n">send</span><span class="p">]</span>
<span class="n">iremain</span> <span class="o">=</span> <span class="n">sindices</span><span class="p">[</span><span class="n">ss</span><span class="p">:</span><span class="n">ss</span> <span class="o">+</span> <span class="n">send</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="c"># Still some elements remain for the last row</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ssorted</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">n</span> <span class="o">==</span> <span class="n">nelementsLR</span>
<span class="n">send</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">sortedLR</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">ssorted</span>
<span class="n">indicesLR</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">sindices</span>
<span class="c"># Update the caches for last row</span>
<span class="n">sortedlr</span> <span class="o">=</span> <span class="n">sortedLR</span><span class="p">[:</span><span class="n">nelementsLR</span><span class="p">]</span>
<span class="n">bebounds</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
<span class="p">(</span><span class="n">sortedlr</span><span class="p">[::</span><span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">],</span> <span class="p">[</span><span class="n">sortedlr</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]))</span>
<span class="n">sortedLR</span><span class="p">[</span><span class="n">nelementsLR</span><span class="p">:</span><span class="n">nelementsLR</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">bebounds</span><span class="p">)]</span> <span class="o">=</span> <span class="n">bebounds</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span> <span class="o">=</span> <span class="n">bebounds</span>
<span class="c"># Verify that we have dealt with all the remaining values</span>
<span class="k">assert</span> <span class="n">send</span> <span class="o">==</span> <span class="mi">0</span>
<span class="c"># Compute the overlaps in order to verify that we have achieved</span>
<span class="c"># a complete sort. This has to be executed always (and not only</span>
<span class="c"># in verbose mode!).</span>
<span class="bp">self</span><span class="o">.</span><span class="n">compute_overlaps</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp</span><span class="p">,</span> <span class="s">"do_complete_sort()"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="n">t</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">clock</span><span class="p">()</span> <span class="o">-</span> <span class="n">c1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"time: </span><span class="si">%s</span><span class="s">. clock: </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">c</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">swap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">what</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Swap chunks or slices using a certain bounds reference."""</span>
<span class="c"># Thresholds for avoiding continuing the optimization</span>
<span class="c"># thnover = 4 * self.slicesize # minimum number of overlapping</span>
<span class="c"># # elements</span>
<span class="n">thnover</span> <span class="o">=</span> <span class="mi">40</span>
<span class="n">thmult</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="c"># minimum ratio of multiplicity (a 10%)</span>
<span class="n">thtover</span> <span class="o">=</span> <span class="mf">0.01</span> <span class="c"># minimum overlaping index for slices (a 1%)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">c1</span> <span class="o">=</span> <span class="n">clock</span><span class="p">()</span>
<span class="k">if</span> <span class="n">what</span> <span class="o">==</span> <span class="s">"chunks"</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">swap_chunks</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">what</span> <span class="o">==</span> <span class="s">"slices"</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">swap_slices</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span>
<span class="k">if</span> <span class="n">mode</span><span class="p">:</span>
<span class="n">message</span> <span class="o">=</span> <span class="s">"swap_</span><span class="si">%s</span><span class="s">(</span><span class="si">%s</span><span class="s">)"</span> <span class="o">%</span> <span class="p">(</span><span class="n">what</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">message</span> <span class="o">=</span> <span class="s">"swap_</span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">what</span><span class="p">,)</span>
<span class="p">(</span><span class="n">nover</span><span class="p">,</span> <span class="n">mult</span><span class="p">,</span> <span class="n">tover</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_overlaps</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmp</span><span class="p">,</span> <span class="n">message</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">)</span>
<span class="n">rmult</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mult</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mult</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="n">t</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">clock</span><span class="p">()</span> <span class="o">-</span> <span class="n">c1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"time: </span><span class="si">%s</span><span class="s">. clock: </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">c</span><span class="p">))</span>
<span class="c"># Check that entropy is actually decreasing</span>
<span class="k">if</span> <span class="n">what</span> <span class="o">==</span> <span class="s">"chunks"</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_tover</span> <span class="o">></span> <span class="mf">0.</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_nover</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">tover_var</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">last_tover</span> <span class="o">-</span> <span class="n">tover</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_tover</span>
<span class="n">nover_var</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">last_nover</span> <span class="o">-</span> <span class="n">nover</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_nover</span>
<span class="k">if</span> <span class="n">tover_var</span> <span class="o"><</span> <span class="mf">0.05</span> <span class="ow">and</span> <span class="n">nover_var</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
<span class="c"># Less than a 5% of improvement is too few</span>
<span class="k">return</span> <span class="bp">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_tover</span> <span class="o">=</span> <span class="n">tover</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_nover</span> <span class="o">=</span> <span class="n">nover</span>
<span class="c"># Check if some threshold has met</span>
<span class="k">if</span> <span class="n">nover</span> <span class="o"><</span> <span class="n">thnover</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">True</span>
<span class="k">if</span> <span class="n">rmult</span> <span class="o"><</span> <span class="n">thmult</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">True</span>
<span class="c"># Additional check for the overlap ratio</span>
<span class="k">if</span> <span class="n">tover</span> <span class="o">>=</span> <span class="mf">0.</span> <span class="ow">and</span> <span class="n">tover</span> <span class="o"><</span> <span class="n">thtover</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">True</span>
<span class="k">return</span> <span class="bp">False</span>
<span class="k">def</span> <span class="nf">create_temp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Create some temporary objects for slice sorting purposes."""</span>
<span class="c"># The index will be dirty during the index optimization process</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dirty</span> <span class="o">=</span> <span class="bp">True</span>
<span class="c"># Build the name of the temporary file</span>
<span class="n">fd</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmpfilename</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">mkstemp</span><span class="p">(</span>
<span class="s">".tmp"</span><span class="p">,</span> <span class="s">"pytables-"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_dir</span><span class="p">)</span>
<span class="c"># Close the file descriptor so as to avoid leaks</span>
<span class="n">os</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fd</span><span class="p">)</span>
<span class="c"># Create the proper PyTables file</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmpfile</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_openFile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpfilename</span><span class="p">,</span> <span class="s">"w"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmpfile</span><span class="o">.</span><span class="n">root</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">filters</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">filters</span>
<span class="c"># temporary sorted & indices arrays</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ss</span><span class="p">)</span>
<span class="n">atom</span> <span class="o">=</span> <span class="n">Atom</span><span class="o">.</span><span class="n">from_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">EArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'sorted'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span>
<span class="s">"Temporary sorted"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">cs</span><span class="p">))</span>
<span class="n">EArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'indices'</span><span class="p">,</span> <span class="n">UIntAtom</span><span class="p">(</span><span class="n">itemsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">),</span> <span class="n">shape</span><span class="p">,</span>
<span class="s">"Temporary indices"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">cs</span><span class="p">))</span>
<span class="c"># temporary bounds</span>
<span class="n">nbounds_inslice</span> <span class="o">=</span> <span class="p">(</span><span class="n">ss</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">cs</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">nbounds_inslice</span><span class="p">)</span>
<span class="n">EArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'bounds'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="s">"Temp chunk bounds"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">nbounds_inslice</span><span class="p">))</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,)</span>
<span class="n">EArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'abounds'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="s">"Temp start bounds"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="n">EArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'zbounds'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="s">"Temp end bounds"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="n">EArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'mbounds'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="s">"Median bounds"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="c"># temporary ranges</span>
<span class="n">EArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'ranges'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
<span class="s">"Temporary range values"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">EArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'mranges'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,),</span>
<span class="s">"Median ranges"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="c"># temporary last row (sorted)</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">ss</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">nbounds_inslice</span><span class="p">,)</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'sortedLR'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span>
<span class="s">"Temp Last Row sorted values + bounds"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="c"># temporary last row (indices)</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">ss</span><span class="p">,)</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'indicesLR'</span><span class="p">,</span>
<span class="n">UIntAtom</span><span class="p">(</span><span class="n">itemsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">),</span>
<span class="n">shape</span><span class="p">,</span> <span class="s">"Temp Last Row indices"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="k">def</span> <span class="nf">create_temp2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Create some temporary objects for slice sorting purposes."""</span>
<span class="c"># The algorithms for doing the swap can be optimized so that</span>
<span class="c"># one should be necessary to create temporaries for keeping just</span>
<span class="c"># the contents of a single superblock.</span>
<span class="c"># F. Alted 2007-01-03</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">filters</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">filters</span>
<span class="c"># temporary sorted & indices arrays</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">,</span> <span class="n">ss</span><span class="p">)</span>
<span class="n">atom</span> <span class="o">=</span> <span class="n">Atom</span><span class="o">.</span><span class="n">from_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'sorted2'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span>
<span class="s">"Temporary sorted 2"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">cs</span><span class="p">))</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'indices2'</span><span class="p">,</span> <span class="n">UIntAtom</span><span class="p">(</span><span class="n">itemsize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">),</span> <span class="n">shape</span><span class="p">,</span>
<span class="s">"Temporary indices 2"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">cs</span><span class="p">))</span>
<span class="c"># temporary bounds</span>
<span class="n">nbounds_inslice</span> <span class="o">=</span> <span class="p">(</span><span class="n">ss</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">cs</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">,</span> <span class="n">nbounds_inslice</span><span class="p">)</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'bounds2'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="s">"Temp chunk bounds 2"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">nbounds_inslice</span><span class="p">))</span>
<span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nchunks</span><span class="p">,)</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'abounds2'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="s">"Temp start bounds 2"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'zbounds2'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="s">"Temp end bounds 2"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'mbounds2'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="s">"Median bounds 2"</span><span class="p">,</span>
<span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="c"># temporary ranges</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'ranges2'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
<span class="s">"Temporary range values 2"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">CArray</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="s">'mranges2'</span><span class="p">,</span> <span class="n">atom</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">,),</span>
<span class="s">"Median ranges 2"</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">chunkshape</span><span class="o">=</span><span class="p">(</span><span class="n">cs</span><span class="p">,))</span>
<span class="k">def</span> <span class="nf">cleanup_temp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Copy the data and delete the temporaries for sorting purposes."""</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Copying temporary data..."</span><span class="p">)</span>
<span class="c"># tmp -> index</span>
<span class="n">reduction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">//</span> <span class="n">reduction</span>
<span class="n">ncs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nchunkslice</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">):</span>
<span class="c"># Copy sorted & indices slices</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted</span><span class="p">[</span><span class="n">i</span><span class="p">][::</span><span class="n">reduction</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sorted</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sorted</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
<span class="c"># Compute ranges</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="o">.</span><span class="n">append</span><span class="p">([[</span><span class="nb">sorted</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">sorted</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]])</span>
<span class="c"># Compute chunk bounds</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bounds</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">sorted</span><span class="p">[</span><span class="n">cs</span><span class="p">::</span><span class="n">cs</span><span class="p">]])</span>
<span class="c"># Compute start, stop & median bounds and ranges</span>
<span class="bp">self</span><span class="o">.</span><span class="n">abounds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sorted</span><span class="p">[</span><span class="mi">0</span><span class="p">::</span><span class="n">cs</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">zbounds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sorted</span><span class="p">[</span><span class="n">cs</span> <span class="o">-</span> <span class="mi">1</span><span class="p">::</span><span class="n">cs</span><span class="p">])</span>
<span class="n">smedian</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">[</span><span class="n">cs</span> <span class="o">//</span> <span class="mi">2</span><span class="p">::</span><span class="n">cs</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mbounds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">smedian</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mranges</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">smedian</span><span class="p">[</span><span class="n">ncs</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]])</span>
<span class="k">del</span> <span class="nb">sorted</span><span class="p">,</span> <span class="n">smedian</span> <span class="c"># delete references</span>
<span class="c"># Now that sorted is gone, we can copy the indices</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">indices</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">indices</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
<span class="c"># Now it is the last row turn (if needed)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="c"># First, the sorted values</span>
<span class="n">sortedLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">indicesLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indicesLR</span>
<span class="n">nelementsLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="n">sortedlr</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sortedLR</span><span class="p">[:</span><span class="n">nelementsLR</span><span class="p">][::</span><span class="n">reduction</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">nelementsSLR</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sortedlr</span><span class="p">)</span>
<span class="n">sortedLR</span><span class="p">[:</span><span class="n">nelementsSLR</span><span class="p">]</span> <span class="o">=</span> <span class="n">sortedlr</span>
<span class="c"># Now, the bounds</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sortedlr</span><span class="p">[::</span><span class="n">cs</span><span class="p">],</span> <span class="p">[</span><span class="n">sortedlr</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]))</span>
<span class="n">offset2</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">)</span>
<span class="n">sortedLR</span><span class="p">[</span><span class="n">nelementsSLR</span><span class="p">:</span><span class="n">nelementsSLR</span> <span class="o">+</span> <span class="n">offset2</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span>
<span class="c"># Finally, the indices</span>
<span class="n">indicesLR</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indicesLR</span><span class="p">[:]</span>
<span class="c"># Update the number of (reduced) sorted elements</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span> <span class="o">=</span> <span class="n">nelementsSLR</span>
<span class="c"># The number of elements will be saved as an attribute</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indicesLR</span><span class="o">.</span><span class="n">attrs</span><span class="o">.</span><span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Deleting temporaries..."</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmp</span> <span class="o">=</span> <span class="bp">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmpfile</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpfilename</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmpfilename</span> <span class="o">=</span> <span class="bp">None</span>
<span class="c"># The optimization process has finished, and the index is ok now</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dirty</span> <span class="o">=</span> <span class="bp">False</span>
<span class="c"># ...but the memory data cache is dirty now</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dirtycache</span> <span class="o">=</span> <span class="bp">True</span>
<span class="k">def</span> <span class="nf">get_neworder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neworder</span><span class="p">,</span> <span class="n">src_disk</span><span class="p">,</span> <span class="n">tmp_disk</span><span class="p">,</span>
<span class="n">lastrow</span><span class="p">,</span> <span class="n">nslices</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
<span class="sd">"""Get sorted & indices values in new order."""</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span>
<span class="n">ncs</span> <span class="o">=</span> <span class="n">ncs2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nchunkslice</span>
<span class="n">self_nslices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nslices</span><span class="p">):</span>
<span class="n">ns</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">+</span> <span class="n">i</span>
<span class="k">if</span> <span class="n">ns</span> <span class="o">==</span> <span class="n">self_nslices</span><span class="p">:</span>
<span class="c"># The number of complete chunks in the last row</span>
<span class="n">ncs2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span> <span class="o">//</span> <span class="n">cs</span>
<span class="c"># Get slices in new order</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">ncs2</span><span class="p">):</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">neworder</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="n">ncs</span> <span class="o">+</span> <span class="n">j</span><span class="p">]</span>
<span class="n">ins</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">//</span> <span class="n">ncs</span>
<span class="n">inc</span> <span class="o">=</span> <span class="p">(</span><span class="n">idx</span> <span class="o">-</span> <span class="n">ins</span> <span class="o">*</span> <span class="n">ncs</span><span class="p">)</span> <span class="o">*</span> <span class="n">cs</span>
<span class="n">ins</span> <span class="o">+=</span> <span class="n">offset</span>
<span class="n">nc</span> <span class="o">=</span> <span class="n">j</span> <span class="o">*</span> <span class="n">cs</span>
<span class="k">if</span> <span class="n">ins</span> <span class="o">==</span> <span class="n">self_nslices</span><span class="p">:</span>
<span class="n">tmp</span><span class="p">[</span><span class="n">nc</span><span class="p">:</span><span class="n">nc</span> <span class="o">+</span> <span class="n">cs</span><span class="p">]</span> <span class="o">=</span> <span class="n">lastrow</span><span class="p">[</span><span class="n">inc</span><span class="p">:</span><span class="n">inc</span> <span class="o">+</span> <span class="n">cs</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tmp</span><span class="p">[</span><span class="n">nc</span><span class="p">:</span><span class="n">nc</span> <span class="o">+</span> <span class="n">cs</span><span class="p">]</span> <span class="o">=</span> <span class="n">src_disk</span><span class="p">[</span><span class="n">ins</span><span class="p">,</span> <span class="n">inc</span><span class="p">:</span><span class="n">inc</span> <span class="o">+</span> <span class="n">cs</span><span class="p">]</span>
<span class="k">if</span> <span class="n">ns</span> <span class="o">==</span> <span class="n">self_nslices</span><span class="p">:</span>
<span class="c"># The number of complete chunks in the last row</span>
<span class="n">lastrow</span><span class="p">[:</span><span class="n">ncs2</span> <span class="o">*</span> <span class="n">cs</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[:</span><span class="n">ncs2</span> <span class="o">*</span> <span class="n">cs</span><span class="p">]</span>
<span class="c"># The elements in the last chunk of the last row will</span>
<span class="c"># participate in the global reordering later on, during</span>
<span class="c"># the phase of sorting of *two* slices at a time</span>
<span class="c"># (including the last row slice, see</span>
<span class="c"># self.reorder_slices()). The caches for last row will</span>
<span class="c"># be updated in self.reorder_slices() too.</span>
<span class="c"># F. Altet 2008-08-25</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tmp_disk</span><span class="p">[</span><span class="n">ns</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span>
<span class="k">def</span> <span class="nf">swap_chunks</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s">"median"</span><span class="p">):</span>
<span class="sd">"""Swap & reorder the different chunks in a block."""</span>
<span class="n">boundsnames</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'start'</span><span class="p">:</span> <span class="s">'abounds'</span><span class="p">,</span> <span class="s">'stop'</span><span class="p">:</span> <span class="s">'zbounds'</span><span class="p">,</span> <span class="s">'median'</span><span class="p">:</span> <span class="s">'mbounds'</span><span class="p">}</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices</span>
<span class="n">tmp_sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted2</span>
<span class="n">tmp_indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices2</span>
<span class="n">sortedLR</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">indicesLR</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indicesLR</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span>
<span class="n">ncs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nchunkslice</span>
<span class="n">nsb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslicesblock</span>
<span class="n">ncb</span> <span class="o">=</span> <span class="n">ncs</span> <span class="o">*</span> <span class="n">nsb</span>
<span class="n">ncb2</span> <span class="o">=</span> <span class="n">ncb</span>
<span class="n">boundsobj</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">_f_get_child</span><span class="p">(</span><span class="n">boundsnames</span><span class="p">[</span><span class="n">mode</span><span class="p">])</span>
<span class="n">can_cross_bbounds</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">8</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span>
<span class="k">for</span> <span class="n">nblock</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nblocks</span><span class="p">):</span>
<span class="c"># Protection for last block having less chunks than ncb</span>
<span class="n">remainingchunks</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nchunks</span> <span class="o">-</span> <span class="n">nblock</span> <span class="o">*</span> <span class="n">ncb</span>
<span class="k">if</span> <span class="n">remainingchunks</span> <span class="o"><</span> <span class="n">ncb</span><span class="p">:</span>
<span class="n">ncb2</span> <span class="o">=</span> <span class="n">remainingchunks</span>
<span class="k">if</span> <span class="n">ncb2</span> <span class="o"><=</span> <span class="mi">1</span><span class="p">:</span>
<span class="c"># if only zero or one chunks remains we are done</span>
<span class="k">break</span>
<span class="n">nslices</span> <span class="o">=</span> <span class="n">ncb2</span> <span class="o">//</span> <span class="n">ncs</span>
<span class="n">bounds</span> <span class="o">=</span> <span class="n">boundsobj</span><span class="p">[</span><span class="n">nblock</span> <span class="o">*</span> <span class="n">ncb</span><span class="p">:</span><span class="n">nblock</span> <span class="o">*</span> <span class="n">ncb</span> <span class="o">+</span> <span class="n">ncb2</span><span class="p">]</span>
<span class="c"># Do this only if lastrow elements can cross block boundaries</span>
<span class="k">if</span> <span class="p">(</span><span class="n">nblock</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nblocks</span> <span class="o">-</span> <span class="mi">1</span> <span class="ow">and</span> <span class="c"># last block</span>
<span class="n">can_cross_bbounds</span><span class="p">):</span>
<span class="n">nslices</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">ul</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span> <span class="o">//</span> <span class="n">cs</span>
<span class="n">bounds</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">bounds</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">[:</span><span class="n">ul</span><span class="p">]))</span>
<span class="n">sbounds_idx</span> <span class="o">=</span> <span class="n">bounds</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="n">defsort</span><span class="p">)</span>
<span class="n">offset</span> <span class="o">=</span> <span class="n">nblock</span> <span class="o">*</span> <span class="n">nsb</span>
<span class="c"># Swap sorted and indices following the new order</span>
<span class="bp">self</span><span class="o">.</span><span class="n">get_neworder</span><span class="p">(</span><span class="n">sbounds_idx</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">,</span> <span class="n">tmp_sorted</span><span class="p">,</span> <span class="n">sortedLR</span><span class="p">,</span>
<span class="n">nslices</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">get_neworder</span><span class="p">(</span><span class="n">sbounds_idx</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">tmp_indices</span><span class="p">,</span> <span class="n">indicesLR</span><span class="p">,</span>
<span class="n">nslices</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="s">'u</span><span class="si">%d</span><span class="s">'</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">)</span>
<span class="c"># Reorder completely the index at slice level</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reorder_slices</span><span class="p">(</span><span class="n">tmp</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">read_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">where</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="nb">buffer</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Read a slice from the `where` dataset and put it in `buffer`."""</span>
<span class="c"># Create the buffers for specifying the coordinates</span>
<span class="bp">self</span><span class="o">.</span><span class="n">startl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">nslice</span><span class="p">,</span> <span class="n">start</span><span class="p">],</span> <span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stopl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">nslice</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">start</span> <span class="o">+</span> <span class="nb">buffer</span><span class="o">.</span><span class="n">size</span><span class="p">],</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stepl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="n">where</span><span class="o">.</span><span class="n">_g_read_slice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">startl</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stopl</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stepl</span><span class="p">,</span> <span class="nb">buffer</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">write_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">where</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="nb">buffer</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Write a `slice` to the `where` dataset with the `buffer` data."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">startl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">nslice</span><span class="p">,</span> <span class="n">start</span><span class="p">],</span> <span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stopl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">nslice</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">start</span> <span class="o">+</span> <span class="nb">buffer</span><span class="o">.</span><span class="n">size</span><span class="p">],</span>
<span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stepl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="n">countl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stopl</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">startl</span> <span class="c"># (1, self.slicesize)</span>
<span class="n">where</span><span class="o">.</span><span class="n">_g_write_slice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">startl</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stepl</span><span class="p">,</span> <span class="n">countl</span><span class="p">,</span> <span class="nb">buffer</span><span class="p">)</span>
<span class="c"># Read version for LastRow</span>
<span class="k">def</span> <span class="nf">read_slice_lr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">where</span><span class="p">,</span> <span class="nb">buffer</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Read a slice from the `where` dataset and put it in `buffer`."""</span>
<span class="n">startl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">start</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="n">stopl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">start</span> <span class="o">+</span> <span class="nb">buffer</span><span class="o">.</span><span class="n">size</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="n">stepl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="n">where</span><span class="o">.</span><span class="n">_g_read_slice</span><span class="p">(</span><span class="n">startl</span><span class="p">,</span> <span class="n">stopl</span><span class="p">,</span> <span class="n">stepl</span><span class="p">,</span> <span class="nb">buffer</span><span class="p">)</span>
<span class="c"># Write version for LastRow</span>
<span class="k">def</span> <span class="nf">write_sliceLR</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">where</span><span class="p">,</span> <span class="nb">buffer</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Write a slice from the `where` dataset with the `buffer` data."""</span>
<span class="n">startl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">start</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="n">countl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">start</span> <span class="o">+</span> <span class="nb">buffer</span><span class="o">.</span><span class="n">size</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="n">stepl</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">uint64</span><span class="p">)</span>
<span class="n">where</span><span class="o">.</span><span class="n">_g_write_slice</span><span class="p">(</span><span class="n">startl</span><span class="p">,</span> <span class="n">stepl</span><span class="p">,</span> <span class="n">countl</span><span class="p">,</span> <span class="nb">buffer</span><span class="p">)</span>
<span class="n">read_sliceLR</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">read_slice_lr</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">reorder_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">,</span> <span class="n">sindices</span><span class="p">,</span>
<span class="n">tmp_sorted</span><span class="p">,</span> <span class="n">tmp_indices</span><span class="p">):</span>
<span class="sd">"""Copy & reorder the slice in source to final destination."""</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="c"># Load the second part in buffers</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice</span><span class="p">(</span><span class="n">tmp_sorted</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[</span><span class="n">ss</span><span class="p">:])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice</span><span class="p">(</span><span class="n">tmp_indices</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="n">sindices</span><span class="p">[</span><span class="n">ss</span><span class="p">:])</span>
<span class="n">indexesextension</span><span class="o">.</span><span class="n">keysort</span><span class="p">(</span><span class="n">ssorted</span><span class="p">,</span> <span class="n">sindices</span><span class="p">)</span>
<span class="c"># Write the first part of the buffers to the regular leaves</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write_slice</span><span class="p">(</span><span class="nb">sorted</span><span class="p">,</span> <span class="n">nslice</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write_slice</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">nslice</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sindices</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="c"># Update caches</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_caches</span><span class="p">(</span><span class="n">nslice</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="c"># Shift the slice in the end to the beginning</span>
<span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">]</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[</span><span class="n">ss</span><span class="p">:]</span>
<span class="n">sindices</span><span class="p">[:</span><span class="n">ss</span><span class="p">]</span> <span class="o">=</span> <span class="n">sindices</span><span class="p">[</span><span class="n">ss</span><span class="p">:]</span>
<span class="k">def</span> <span class="nf">update_caches</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">):</span>
<span class="sd">"""Update the caches for faster lookups."""</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span>
<span class="n">ncs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nchunkslice</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="c"># update first & second cache bounds (ranges & bounds)</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">ranges</span><span class="p">[</span><span class="n">nslice</span><span class="p">]</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">bounds</span><span class="p">[</span><span class="n">nslice</span><span class="p">]</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[</span><span class="n">cs</span><span class="p">::</span><span class="n">cs</span><span class="p">]</span>
<span class="c"># update start & stop bounds</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">abounds</span><span class="p">[</span><span class="n">nslice</span> <span class="o">*</span> <span class="n">ncs</span><span class="p">:(</span><span class="n">nslice</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncs</span><span class="p">]</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[</span><span class="mi">0</span><span class="p">::</span><span class="n">cs</span><span class="p">]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">zbounds</span><span class="p">[</span><span class="n">nslice</span> <span class="o">*</span> <span class="n">ncs</span><span class="p">:(</span><span class="n">nslice</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncs</span><span class="p">]</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[</span><span class="n">cs</span> <span class="o">-</span> <span class="mi">1</span><span class="p">::</span><span class="n">cs</span><span class="p">]</span>
<span class="c"># update median bounds</span>
<span class="n">smedian</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[</span><span class="n">cs</span> <span class="o">//</span> <span class="mi">2</span><span class="p">::</span><span class="n">cs</span><span class="p">]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">mbounds</span><span class="p">[</span><span class="n">nslice</span> <span class="o">*</span> <span class="n">ncs</span><span class="p">:(</span><span class="n">nslice</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncs</span><span class="p">]</span> <span class="o">=</span> <span class="n">smedian</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">mranges</span><span class="p">[</span><span class="n">nslice</span><span class="p">]</span> <span class="o">=</span> <span class="n">smedian</span><span class="p">[</span><span class="n">ncs</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">reorder_slices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tmp</span><span class="p">):</span>
<span class="sd">"""Reorder completely the index at slice level.</span>
<span class="sd"> This method has to maintain the locality of elements in the</span>
<span class="sd"> ambit of ``blocks``, i.e. an element of a ``block`` cannot be</span>
<span class="sd"> sent to another ``block`` during this reordering. This is</span>
<span class="sd"> *critical* for ``light`` indexes to be able to use this.</span>
<span class="sd"> This version of reorder_slices is optimized in that *two*</span>
<span class="sd"> complete slices are taken at a time (including the last row</span>
<span class="sd"> slice) so as to sort them. Then, each new slice that is read is</span>
<span class="sd"> put at the end of this two-slice buffer, while the previous one</span>
<span class="sd"> is moved to the beginning of the buffer. This is in order to</span>
<span class="sd"> better reduce the entropy of the regular part (i.e. all except</span>
<span class="sd"> the last row) of the index.</span>
<span class="sd"> A secondary effect of this is that it takes at least *twice* of</span>
<span class="sd"> memory than a previous version of reorder_slices() that only</span>
<span class="sd"> reorders on a slice-by-slice basis. However, as this is more</span>
<span class="sd"> efficient than the old version, one can configure the slicesize</span>
<span class="sd"> to be smaller, so the memory consumption is barely similar.</span>
<span class="sd"> """</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices</span>
<span class="k">if</span> <span class="n">tmp</span><span class="p">:</span>
<span class="n">tmp_sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted2</span>
<span class="n">tmp_indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices2</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tmp_sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">tmp_indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">nsb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">nslices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span>
<span class="n">nblocks</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nblocks</span>
<span class="n">nelementsLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="c"># Create the buffer for reordering 2 slices at a time</span>
<span class="n">ssorted</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">ss</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">sindices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">ss</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="s">'u</span><span class="si">%d</span><span class="s">'</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">8</span><span class="p">:</span>
<span class="c"># Bootstrap the process for reordering</span>
<span class="c"># Read the first slice in buffers</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice</span><span class="p">(</span><span class="n">tmp_sorted</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice</span><span class="p">(</span><span class="n">tmp_indices</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">sindices</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="n">nslice</span> <span class="o">=</span> <span class="mi">0</span> <span class="c"># Just in case the loop behind executes nothing</span>
<span class="c"># Loop over the remainding slices in block</span>
<span class="k">for</span> <span class="n">nslice</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">nrows</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reorder_slice</span><span class="p">(</span><span class="n">nslice</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span>
<span class="n">ssorted</span><span class="p">,</span> <span class="n">sindices</span><span class="p">,</span>
<span class="n">tmp_sorted</span><span class="p">,</span> <span class="n">tmp_indices</span><span class="p">)</span>
<span class="c"># End the process (enrolling the lastrow if necessary)</span>
<span class="k">if</span> <span class="n">nelementsLR</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">sortedLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">indicesLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span><span class="o">.</span><span class="n">indicesLR</span>
<span class="c"># Shrink the ssorted and sindices arrays to the minimum</span>
<span class="n">ssorted2</span> <span class="o">=</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span> <span class="o">+</span> <span class="n">nelementsLR</span><span class="p">]</span>
<span class="n">sortedlr</span> <span class="o">=</span> <span class="n">ssorted2</span><span class="p">[</span><span class="n">ss</span><span class="p">:]</span>
<span class="n">sindices2</span> <span class="o">=</span> <span class="n">sindices</span><span class="p">[:</span><span class="n">ss</span> <span class="o">+</span> <span class="n">nelementsLR</span><span class="p">]</span>
<span class="n">indiceslr</span> <span class="o">=</span> <span class="n">sindices2</span><span class="p">[</span><span class="n">ss</span><span class="p">:]</span>
<span class="c"># Read the last row info in the second part of the buffer</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice_lr</span><span class="p">(</span><span class="n">sortedLR</span><span class="p">,</span> <span class="n">sortedlr</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice_lr</span><span class="p">(</span><span class="n">indicesLR</span><span class="p">,</span> <span class="n">indiceslr</span><span class="p">)</span>
<span class="n">indexesextension</span><span class="o">.</span><span class="n">keysort</span><span class="p">(</span><span class="n">ssorted2</span><span class="p">,</span> <span class="n">sindices2</span><span class="p">)</span>
<span class="c"># Write the second part of the buffers to the lastrow indices</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write_sliceLR</span><span class="p">(</span><span class="n">sortedLR</span><span class="p">,</span> <span class="n">sortedlr</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write_sliceLR</span><span class="p">(</span><span class="n">indicesLR</span><span class="p">,</span> <span class="n">indiceslr</span><span class="p">)</span>
<span class="c"># Update the caches for last row</span>
<span class="n">bebounds</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sortedlr</span><span class="p">[::</span><span class="n">cs</span><span class="p">],</span> <span class="p">[</span><span class="n">sortedlr</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]))</span>
<span class="n">sortedLR</span><span class="p">[</span><span class="n">nelementsLR</span><span class="p">:</span><span class="n">nelementsLR</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">bebounds</span><span class="p">)]</span> <span class="o">=</span> <span class="n">bebounds</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span> <span class="o">=</span> <span class="n">bebounds</span>
<span class="c"># Write the first part of the buffers to the regular leaves</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write_slice</span><span class="p">(</span><span class="nb">sorted</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write_slice</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="n">sindices</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="c"># Update caches for this slice</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_caches</span><span class="p">(</span><span class="n">nslice</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="c"># Iterate over each block. No data should cross block</span>
<span class="c"># boundaries to avoid adressing problems with short indices.</span>
<span class="k">for</span> <span class="n">nb</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nblocks</span><span class="p">):</span>
<span class="c"># Bootstrap the process for reordering</span>
<span class="c"># Read the first slice in buffers</span>
<span class="n">nrow</span> <span class="o">=</span> <span class="n">nb</span> <span class="o">*</span> <span class="n">nsb</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice</span><span class="p">(</span><span class="n">tmp_sorted</span><span class="p">,</span> <span class="n">nrow</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice</span><span class="p">(</span><span class="n">tmp_indices</span><span class="p">,</span> <span class="n">nrow</span><span class="p">,</span> <span class="n">sindices</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="c"># Loop over the remainding slices in block</span>
<span class="n">lrb</span> <span class="o">=</span> <span class="n">nrow</span> <span class="o">+</span> <span class="n">nsb</span>
<span class="k">if</span> <span class="n">lrb</span> <span class="o">></span> <span class="n">nslices</span><span class="p">:</span>
<span class="n">lrb</span> <span class="o">=</span> <span class="n">nslices</span>
<span class="n">nslice</span> <span class="o">=</span> <span class="n">nrow</span> <span class="c"># Just in case the loop behind executes nothing</span>
<span class="k">for</span> <span class="n">nslice</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nrow</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">lrb</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reorder_slice</span><span class="p">(</span><span class="n">nslice</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span>
<span class="n">ssorted</span><span class="p">,</span> <span class="n">sindices</span><span class="p">,</span>
<span class="n">tmp_sorted</span><span class="p">,</span> <span class="n">tmp_indices</span><span class="p">)</span>
<span class="c"># Write the first part of the buffers to the regular leaves</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write_slice</span><span class="p">(</span><span class="nb">sorted</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">write_slice</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="n">sindices</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="c"># Update caches for this slice</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update_caches</span><span class="p">(</span><span class="n">nslice</span><span class="p">,</span> <span class="n">ssorted</span><span class="p">[:</span><span class="n">ss</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">swap_slices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s">"median"</span><span class="p">):</span>
<span class="sd">"""Swap slices in a superblock."""</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices</span>
<span class="n">tmp_sorted</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sorted2</span>
<span class="n">tmp_indices</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">indices2</span>
<span class="n">ncs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nchunkslice</span>
<span class="n">nss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">nss2</span> <span class="o">=</span> <span class="n">nss</span>
<span class="k">for</span> <span class="n">sblock</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nsuperblocks</span><span class="p">):</span>
<span class="c"># Protection for last superblock having less slices than nss</span>
<span class="n">remainingslices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span> <span class="o">-</span> <span class="n">sblock</span> <span class="o">*</span> <span class="n">nss</span>
<span class="k">if</span> <span class="n">remainingslices</span> <span class="o"><</span> <span class="n">nss</span><span class="p">:</span>
<span class="n">nss2</span> <span class="o">=</span> <span class="n">remainingslices</span>
<span class="k">if</span> <span class="n">nss2</span> <span class="o"><=</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">break</span>
<span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s">"start"</span><span class="p">:</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">ranges</span><span class="p">[</span><span class="n">sblock</span> <span class="o">*</span> <span class="n">nss</span><span class="p">:</span><span class="n">sblock</span> <span class="o">*</span> <span class="n">nss</span> <span class="o">+</span> <span class="n">nss2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s">"stop"</span><span class="p">:</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">ranges</span><span class="p">[</span><span class="n">sblock</span> <span class="o">*</span> <span class="n">nss</span><span class="p">:</span><span class="n">sblock</span> <span class="o">*</span> <span class="n">nss</span> <span class="o">+</span> <span class="n">nss2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s">"median"</span><span class="p">:</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">mranges</span><span class="p">[</span><span class="n">sblock</span> <span class="o">*</span> <span class="n">nss</span><span class="p">:</span><span class="n">sblock</span> <span class="o">*</span> <span class="n">nss</span> <span class="o">+</span> <span class="n">nss2</span><span class="p">]</span>
<span class="n">sranges_idx</span> <span class="o">=</span> <span class="n">ranges</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="n">defsort</span><span class="p">)</span>
<span class="c"># Don't swap the superblock at all if one doesn't need to</span>
<span class="n">ndiff</span> <span class="o">=</span> <span class="p">(</span><span class="n">sranges_idx</span> <span class="o">!=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">nss2</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="mi">2</span>
<span class="k">if</span> <span class="n">ndiff</span> <span class="o">*</span> <span class="mi">50</span> <span class="o"><</span> <span class="n">nss2</span><span class="p">:</span>
<span class="c"># The number of slices to rearrange is less than 2.5%,</span>
<span class="c"># so skip the reordering of this superblock</span>
<span class="c"># (too expensive for such a little improvement)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">"skipping reordering of superblock ->"</span><span class="p">,</span> <span class="n">sblock</span><span class="p">)</span>
<span class="k">continue</span>
<span class="n">ns</span> <span class="o">=</span> <span class="n">sblock</span> <span class="o">*</span> <span class="n">nss2</span>
<span class="c"># Swap sorted and indices slices following the new order</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nss2</span><span class="p">):</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">sranges_idx</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="c"># Swap sorted & indices slices</span>
<span class="n">oi</span> <span class="o">=</span> <span class="n">ns</span> <span class="o">+</span> <span class="n">i</span>
<span class="n">oidx</span> <span class="o">=</span> <span class="n">ns</span> <span class="o">+</span> <span class="n">idx</span>
<span class="n">tmp_sorted</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">[</span><span class="n">oidx</span><span class="p">]</span>
<span class="n">tmp_indices</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">oidx</span><span class="p">]</span>
<span class="c"># Swap start, stop & median ranges</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">ranges2</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">ranges</span><span class="p">[</span><span class="n">oidx</span><span class="p">]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">mranges2</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">mranges</span><span class="p">[</span><span class="n">oidx</span><span class="p">]</span>
<span class="c"># Swap chunk bounds</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">bounds2</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">bounds</span><span class="p">[</span><span class="n">oidx</span><span class="p">]</span>
<span class="c"># Swap start, stop & median bounds</span>
<span class="n">j</span> <span class="o">=</span> <span class="n">oi</span> <span class="o">*</span> <span class="n">ncs</span>
<span class="n">jn</span> <span class="o">=</span> <span class="p">(</span><span class="n">oi</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncs</span>
<span class="n">xj</span> <span class="o">=</span> <span class="n">oidx</span> <span class="o">*</span> <span class="n">ncs</span>
<span class="n">xjn</span> <span class="o">=</span> <span class="p">(</span><span class="n">oidx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncs</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">abounds2</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">abounds</span><span class="p">[</span><span class="n">xj</span><span class="p">:</span><span class="n">xjn</span><span class="p">]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">zbounds2</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">zbounds</span><span class="p">[</span><span class="n">xj</span><span class="p">:</span><span class="n">xjn</span><span class="p">]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">mbounds2</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">mbounds</span><span class="p">[</span><span class="n">xj</span><span class="p">:</span><span class="n">xjn</span><span class="p">]</span>
<span class="c"># tmp -> originals</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nss2</span><span class="p">):</span>
<span class="c"># Copy sorted & indices slices</span>
<span class="n">oi</span> <span class="o">=</span> <span class="n">ns</span> <span class="o">+</span> <span class="n">i</span>
<span class="nb">sorted</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_sorted</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span>
<span class="n">indices</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_indices</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span>
<span class="c"># Copy start, stop & median ranges</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">ranges</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">ranges2</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">mranges</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">mranges2</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span>
<span class="c"># Copy chunk bounds</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">bounds</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">bounds2</span><span class="p">[</span><span class="n">oi</span><span class="p">]</span>
<span class="c"># Copy start, stop & median bounds</span>
<span class="n">j</span> <span class="o">=</span> <span class="n">oi</span> <span class="o">*</span> <span class="n">ncs</span>
<span class="n">jn</span> <span class="o">=</span> <span class="p">(</span><span class="n">oi</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncs</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">abounds</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">abounds2</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">zbounds</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">zbounds2</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">mbounds</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">mbounds2</span><span class="p">[</span><span class="n">j</span><span class="p">:</span><span class="n">jn</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">search_item_lt</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">where</span><span class="p">,</span> <span class="n">item</span><span class="p">,</span> <span class="n">nslice</span><span class="p">,</span> <span class="n">limits</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""Search a single item in a specific sorted slice."""</span>
<span class="c"># This method will only works under the assumtion that item</span>
<span class="c"># *is to be found* in the nslice.</span>
<span class="k">assert</span> <span class="n">limits</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><</span> <span class="n">item</span> <span class="o"><=</span> <span class="n">limits</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">nelementsLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="n">bstart</span> <span class="o">=</span> <span class="n">start</span> <span class="o">//</span> <span class="n">cs</span>
<span class="c"># Find the chunk</span>
<span class="k">if</span> <span class="n">nslice</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="n">nchunk</span> <span class="o">=</span> <span class="n">bisect_left</span><span class="p">(</span><span class="n">where</span><span class="o">.</span><span class="n">bounds</span><span class="p">[</span><span class="n">nslice</span><span class="p">],</span> <span class="n">item</span><span class="p">,</span> <span class="n">bstart</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c"># We need to subtract 1 chunk here because bebounds</span>
<span class="c"># has a leading value</span>
<span class="n">nchunk</span> <span class="o">=</span> <span class="n">bisect_left</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">,</span> <span class="n">item</span><span class="p">,</span> <span class="n">bstart</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
<span class="k">assert</span> <span class="n">nchunk</span> <span class="o">>=</span> <span class="mi">0</span>
<span class="c"># Find the element in chunk</span>
<span class="n">pos</span> <span class="o">=</span> <span class="n">nchunk</span> <span class="o">*</span> <span class="n">cs</span>
<span class="k">if</span> <span class="n">nslice</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="n">pos</span> <span class="o">+=</span> <span class="n">bisect_left</span><span class="p">(</span><span class="n">where</span><span class="o">.</span><span class="n">sorted</span><span class="p">[</span><span class="n">nslice</span><span class="p">,</span> <span class="n">pos</span><span class="p">:</span><span class="n">pos</span> <span class="o">+</span> <span class="n">cs</span><span class="p">],</span> <span class="n">item</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">pos</span> <span class="o"><=</span> <span class="n">ss</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">pos</span> <span class="o">+</span> <span class="n">cs</span>
<span class="k">if</span> <span class="n">end</span> <span class="o">></span> <span class="n">nelementsLR</span><span class="p">:</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">nelementsLR</span>
<span class="n">pos</span> <span class="o">+=</span> <span class="n">bisect_left</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span><span class="p">[</span><span class="n">pos</span><span class="p">:</span><span class="n">end</span><span class="p">],</span> <span class="n">item</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">pos</span> <span class="o"><=</span> <span class="n">nelementsLR</span>
<span class="k">assert</span> <span class="n">pos</span> <span class="o">></span> <span class="mi">0</span>
<span class="k">return</span> <span class="n">pos</span>
<span class="k">def</span> <span class="nf">compute_overlaps_finegrain</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">where</span><span class="p">,</span> <span class="n">message</span><span class="p">,</span> <span class="n">verbose</span><span class="p">):</span>
<span class="sd">"""Compute some statistics about overlaping of slices in index.</span>
<span class="sd"> It returns the following info:</span>
<span class="sd"> noverlaps : int</span>
<span class="sd"> The total number of elements that overlaps in index.</span>
<span class="sd"> multiplicity : array of int</span>
<span class="sd"> The number of times that a concrete slice overlaps with any other.</span>
<span class="sd"> toverlap : float</span>
<span class="sd"> An ovelap index: the sum of the values in segment slices that</span>
<span class="sd"> overlaps divided by the entire range of values. This index is only</span>
<span class="sd"> computed for numerical types.</span>
<span class="sd"> """</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">ranges</span><span class="p">[:]</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">sortedLR</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">nslices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span>
<span class="n">nelementsLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span>
<span class="k">if</span> <span class="n">nelementsLR</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="c"># Add the ranges corresponding to the last row</span>
<span class="n">rangeslr</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]])</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">ranges</span><span class="p">,</span> <span class="p">[</span><span class="n">rangeslr</span><span class="p">]))</span>
<span class="n">nslices</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">soverlap</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">toverlap</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.</span>
<span class="n">multiplicity</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">nslices</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">"int_"</span><span class="p">)</span>
<span class="n">overlaps</span> <span class="o">=</span> <span class="n">multiplicity</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">starts</span> <span class="o">=</span> <span class="n">multiplicity</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nslices</span><span class="p">):</span>
<span class="n">prev_end</span> <span class="o">=</span> <span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nslices</span><span class="p">):</span>
<span class="n">stj</span> <span class="o">=</span> <span class="n">starts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">stj</span> <span class="o"><=</span> <span class="n">ss</span>
<span class="k">if</span> <span class="n">stj</span> <span class="o">==</span> <span class="n">ss</span><span class="p">:</span>
<span class="c"># This slice has already been counted</span>
<span class="k">continue</span>
<span class="k">if</span> <span class="n">j</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="n">next_beg</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">stj</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">next_beg</span> <span class="o">=</span> <span class="n">sortedLR</span><span class="p">[</span><span class="n">stj</span><span class="p">]</span>
<span class="n">next_end</span> <span class="o">=</span> <span class="n">ranges</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="n">prev_end</span> <span class="o">></span> <span class="n">next_end</span><span class="p">:</span>
<span class="c"># Complete overlapping case</span>
<span class="n">multiplicity</span><span class="p">[</span><span class="n">j</span> <span class="o">-</span> <span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">j</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="n">overlaps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">ss</span> <span class="o">-</span> <span class="n">stj</span>
<span class="n">starts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">ss</span> <span class="c"># a sentinel</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">overlaps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">nelementsLR</span> <span class="o">-</span> <span class="n">stj</span>
<span class="n">starts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">nelementsLR</span> <span class="c"># a sentinel</span>
<span class="k">elif</span> <span class="n">prev_end</span> <span class="o">></span> <span class="n">next_beg</span><span class="p">:</span>
<span class="n">multiplicity</span><span class="p">[</span><span class="n">j</span> <span class="o">-</span> <span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">search_item_lt</span><span class="p">(</span>
<span class="n">where</span><span class="p">,</span> <span class="n">prev_end</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">ranges</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">stj</span><span class="p">)</span>
<span class="n">nelem</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">-</span> <span class="n">stj</span>
<span class="n">overlaps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">nelem</span>
<span class="n">starts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">idx</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="s">"string"</span><span class="p">:</span>
<span class="c"># Convert ranges into floats in order to allow</span>
<span class="c"># doing operations with them without overflows</span>
<span class="n">soverlap</span> <span class="o">+=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="n">ranges</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="c"># Return the overlap as the ratio between overlaps and entire range</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="s">"string"</span><span class="p">:</span>
<span class="n">erange</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ranges</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="n">ranges</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="c"># Check that there is an effective range of values</span>
<span class="c"># Beware, erange can be negative in situations where</span>
<span class="c"># the values are suffering overflow. This can happen</span>
<span class="c"># specially on big signed integer values (on overflows,</span>
<span class="c"># the end value will become negative!).</span>
<span class="c"># Also, there is no way to compute overlap ratios for</span>
<span class="c"># non-numerical types. So, be careful and always check</span>
<span class="c"># that toverlap has a positive value (it must have been</span>
<span class="c"># initialized to -1. before) before using it.</span>
<span class="c"># F. Alted 2007-01-19</span>
<span class="k">if</span> <span class="n">erange</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">toverlap</span> <span class="o">=</span> <span class="n">soverlap</span> <span class="o">/</span> <span class="n">erange</span>
<span class="k">if</span> <span class="n">verbose</span> <span class="ow">and</span> <span class="n">message</span> <span class="o">!=</span> <span class="s">"init"</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">"toverlap (</span><span class="si">%s</span><span class="s">):"</span> <span class="o">%</span> <span class="n">message</span><span class="p">,</span> <span class="n">toverlap</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s">"multiplicity:</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span> <span class="n">multiplicity</span><span class="p">,</span> <span class="n">multiplicity</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
<span class="k">print</span><span class="p">(</span><span class="s">"overlaps:</span><span class="se">\n</span><span class="s">"</span><span class="p">,</span> <span class="n">overlaps</span><span class="p">,</span> <span class="n">overlaps</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
<span class="n">noverlaps</span> <span class="o">=</span> <span class="n">overlaps</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="c"># For full indexes, set the 'is_csi' flag</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">8</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_file</span><span class="o">.</span><span class="n">_iswritable</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">is_csi</span> <span class="o">=</span> <span class="p">(</span><span class="n">noverlaps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="c"># Save the number of overlaps for future references</span>
<span class="bp">self</span><span class="o">.</span><span class="n">noverlaps</span> <span class="o">=</span> <span class="n">noverlaps</span>
<span class="k">return</span> <span class="p">(</span><span class="n">noverlaps</span><span class="p">,</span> <span class="n">multiplicity</span><span class="p">,</span> <span class="n">toverlap</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">compute_overlaps</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">where</span><span class="p">,</span> <span class="n">message</span><span class="p">,</span> <span class="n">verbose</span><span class="p">):</span>
<span class="sd">"""Compute some statistics about overlaping of slices in index.</span>
<span class="sd"> It returns the following info:</span>
<span class="sd"> noverlaps : int</span>
<span class="sd"> The total number of slices that overlaps in index.</span>
<span class="sd"> multiplicity : array of int</span>
<span class="sd"> The number of times that a concrete slice overlaps with any other.</span>
<span class="sd"> toverlap : float</span>
<span class="sd"> An ovelap index: the sum of the values in segment slices that</span>
<span class="sd"> overlaps divided by the entire range of values. This index is only</span>
<span class="sd"> computed for numerical types.</span>
<span class="sd"> """</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">where</span><span class="o">.</span><span class="n">ranges</span><span class="p">[:]</span>
<span class="n">nslices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsILR</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="c"># Add the ranges corresponding to the last row</span>
<span class="n">rangeslr</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]])</span>
<span class="n">ranges</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">ranges</span><span class="p">,</span> <span class="p">[</span><span class="n">rangeslr</span><span class="p">]))</span>
<span class="n">nslices</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">noverlaps</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">soverlap</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">toverlap</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.</span>
<span class="n">multiplicity</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">nslices</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">"int_"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nslices</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nslices</span><span class="p">):</span>
<span class="k">if</span> <span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">></span> <span class="n">ranges</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]:</span>
<span class="n">noverlaps</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">multiplicity</span><span class="p">[</span><span class="n">j</span> <span class="o">-</span> <span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="s">"string"</span><span class="p">:</span>
<span class="c"># Convert ranges into floats in order to allow</span>
<span class="c"># doing operations with them without overflows</span>
<span class="n">soverlap</span> <span class="o">+=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="n">ranges</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="c"># Return the overlap as the ratio between overlaps and entire range</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="s">"string"</span><span class="p">:</span>
<span class="n">erange</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ranges</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="n">ranges</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="c"># Check that there is an effective range of values</span>
<span class="c"># Beware, erange can be negative in situations where</span>
<span class="c"># the values are suffering overflow. This can happen</span>
<span class="c"># specially on big signed integer values (on overflows,</span>
<span class="c"># the end value will become negative!).</span>
<span class="c"># Also, there is no way to compute overlap ratios for</span>
<span class="c"># non-numerical types. So, be careful and always check</span>
<span class="c"># that toverlap has a positive value (it must have been</span>
<span class="c"># initialized to -1. before) before using it.</span>
<span class="c"># F. Altet 2007-01-19</span>
<span class="k">if</span> <span class="n">erange</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">toverlap</span> <span class="o">=</span> <span class="n">soverlap</span> <span class="o">/</span> <span class="n">erange</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s">"overlaps (</span><span class="si">%s</span><span class="s">):"</span> <span class="o">%</span> <span class="n">message</span><span class="p">,</span> <span class="n">noverlaps</span><span class="p">,</span> <span class="n">toverlap</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">multiplicity</span><span class="p">)</span>
<span class="c"># For full indexes, set the 'is_csi' flag</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span> <span class="o">==</span> <span class="mi">8</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_file</span><span class="o">.</span><span class="n">_iswritable</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">is_csi</span> <span class="o">=</span> <span class="p">(</span><span class="n">noverlaps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="c"># Save the number of overlaps for future references</span>
<span class="bp">self</span><span class="o">.</span><span class="n">noverlaps</span> <span class="o">=</span> <span class="n">noverlaps</span>
<span class="k">return</span> <span class="p">(</span><span class="n">noverlaps</span><span class="p">,</span> <span class="n">multiplicity</span><span class="p">,</span> <span class="n">toverlap</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">read_sorted_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">what</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">):</span>
<span class="sd">"""Return the sorted or indices values in the specified range."""</span>
<span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span>
<span class="k">if</span> <span class="n">start</span> <span class="o">>=</span> <span class="n">stop</span><span class="p">:</span>
<span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="c"># Correction for negative values of step (reverse indices)</span>
<span class="k">if</span> <span class="n">step</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">start</span>
<span class="n">start</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">-</span> <span class="n">stop</span>
<span class="n">stop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span> <span class="o">-</span> <span class="n">tmp</span>
<span class="k">if</span> <span class="n">what</span> <span class="o">==</span> <span class="s">"sorted"</span><span class="p">:</span>
<span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">valuesLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">buffer_</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span>
<span class="n">valuesLR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indicesLR</span>
<span class="n">buffer_</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">"u</span><span class="si">%d</span><span class="s">"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span><span class="p">)</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">nrow_start</span> <span class="o">=</span> <span class="n">start</span> <span class="o">//</span> <span class="n">ss</span>
<span class="n">istart</span> <span class="o">=</span> <span class="n">start</span> <span class="o">%</span> <span class="n">ss</span>
<span class="n">nrow_stop</span> <span class="o">=</span> <span class="n">stop</span> <span class="o">//</span> <span class="n">ss</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="n">stop</span> <span class="o">-</span> <span class="n">start</span>
<span class="n">bstart</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">ilen</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">nrow</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">nrow_start</span><span class="p">,</span> <span class="n">nrow_stop</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">blen</span> <span class="o">=</span> <span class="n">ss</span> <span class="o">-</span> <span class="n">istart</span>
<span class="k">if</span> <span class="n">ilen</span> <span class="o">+</span> <span class="n">blen</span> <span class="o">></span> <span class="n">tlen</span><span class="p">:</span>
<span class="n">blen</span> <span class="o">=</span> <span class="n">tlen</span> <span class="o">-</span> <span class="n">ilen</span>
<span class="k">if</span> <span class="n">blen</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">break</span>
<span class="k">if</span> <span class="n">nrow</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice</span><span class="p">(</span>
<span class="n">values</span><span class="p">,</span> <span class="n">nrow</span><span class="p">,</span> <span class="n">buffer_</span><span class="p">[</span><span class="n">bstart</span><span class="p">:</span><span class="n">bstart</span> <span class="o">+</span> <span class="n">blen</span><span class="p">],</span> <span class="n">istart</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_slice_lr</span><span class="p">(</span>
<span class="n">valuesLR</span><span class="p">,</span> <span class="n">buffer_</span><span class="p">[</span><span class="n">bstart</span><span class="p">:</span><span class="n">bstart</span> <span class="o">+</span> <span class="n">blen</span><span class="p">],</span> <span class="n">istart</span><span class="p">)</span>
<span class="n">istart</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">bstart</span> <span class="o">+=</span> <span class="n">blen</span>
<span class="n">ilen</span> <span class="o">+=</span> <span class="n">blen</span>
<span class="k">return</span> <span class="n">buffer_</span><span class="p">[::</span><span class="n">step</span><span class="p">]</span>
<div class="viewcode-block" id="Index.read_sorted"><a class="viewcode-back" href="../../usersguide/libref/helper_classes.html#tables.index.Index.read_sorted">[docs]</a> <span class="k">def</span> <span class="nf">read_sorted</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Return the sorted values of index in the specified range.</span>
<span class="sd"> The meaning of the start, stop and step arguments is the same as in</span>
<span class="sd"> :meth:`Table.read_sorted`.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_sorted_indices</span><span class="p">(</span><span class="s">'sorted'</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span>
</div>
<span class="n">readSorted</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">read_sorted</span><span class="p">)</span>
<div class="viewcode-block" id="Index.read_indices"><a class="viewcode-back" href="../../usersguide/libref/helper_classes.html#tables.index.Index.read_indices">[docs]</a> <span class="k">def</span> <span class="nf">read_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Return the indices values of index in the specified range.</span>
<span class="sd"> The meaning of the start, stop and step arguments is the same as in</span>
<span class="sd"> :meth:`Table.read_sorted`.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_sorted_indices</span><span class="p">(</span><span class="s">'indices'</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span>
</div>
<span class="n">readIndices</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">read_indices</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_process_range</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">):</span>
<span class="sd">"""Get a range specifc for the index usage."""</span>
<span class="k">if</span> <span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span> <span class="ow">and</span> <span class="n">stop</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="c"># Special case for the behaviour of PyTables iterators</span>
<span class="n">stop</span> <span class="o">=</span> <span class="n">idx2long</span><span class="p">(</span><span class="n">start</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">start</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">start</span> <span class="o">=</span> <span class="il">0L</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">idx2long</span><span class="p">(</span><span class="n">start</span><span class="p">)</span>
<span class="k">if</span> <span class="n">stop</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">stop</span> <span class="o">=</span> <span class="n">idx2long</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nelements</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">stop</span> <span class="o">=</span> <span class="n">idx2long</span><span class="p">(</span><span class="n">stop</span><span class="p">)</span>
<span class="k">if</span> <span class="n">step</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">step</span> <span class="o">=</span> <span class="il">1L</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">step</span> <span class="o">=</span> <span class="n">idx2long</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span>
<span class="n">_processRange</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">_process_range</span><span class="p">)</span>
<div class="viewcode-block" id="Index.__getitem__"><a class="viewcode-back" href="../../usersguide/libref/helper_classes.html#tables.index.Index.__getitem__">[docs]</a> <span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
<span class="sd">"""Return the indices values of index in the specified range.</span>
<span class="sd"> If key argument is an integer, the corresponding index is returned. If</span>
<span class="sd"> key is a slice, the range of indices determined by it is returned. A</span>
<span class="sd"> negative value of step in slice is supported, meaning that the results</span>
<span class="sd"> will be returned in reverse order.</span>
<span class="sd"> This method is equivalent to :meth:`Index.read_indices`.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">is_idx</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
<span class="k">if</span> <span class="n">key</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="c"># To support negative values</span>
<span class="n">key</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_indices</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">key</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">slice</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_indices</span><span class="p">(</span><span class="n">key</span><span class="o">.</span><span class="n">start</span><span class="p">,</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span><span class="p">,</span> <span class="n">key</span><span class="o">.</span><span class="n">step</span><span class="p">)</span>
</div>
<span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span>
<span class="k">def</span> <span class="nf">restorecache</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="s">"Clean the limits cache and resize starts and lengths arrays"</span>
<span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_file</span><span class="o">.</span><span class="n">params</span>
<span class="c"># The sorted IndexArray is absolutely required to be in memory</span>
<span class="c"># at the same time than the Index instance, so create a strong</span>
<span class="c"># reference to it. We are not introducing leaks because the</span>
<span class="c"># strong reference will disappear when this Index instance is</span>
<span class="c"># to be closed.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sorted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sorted</span><span class="o">.</span><span class="n">boundscache</span> <span class="o">=</span> <span class="n">ObjectCache</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s">'BOUNDS_MAX_SLOTS'</span><span class="p">],</span>
<span class="n">params</span><span class="p">[</span><span class="s">'BOUNDS_MAX_SIZE'</span><span class="p">],</span>
<span class="s">'non-opt types bounds'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sorted</span><span class="o">.</span><span class="n">boundscache</span> <span class="o">=</span> <span class="n">ObjectCache</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s">'BOUNDS_MAX_SLOTS'</span><span class="p">],</span>
<span class="n">params</span><span class="p">[</span><span class="s">'BOUNDS_MAX_SIZE'</span><span class="p">],</span>
<span class="s">'non-opt types bounds'</span><span class="p">)</span>
<span class="sd">"""A cache for the bounds (2nd hash) data. Only used for</span>
<span class="sd"> non-optimized types searches."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">limboundscache</span> <span class="o">=</span> <span class="n">ObjectCache</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s">'LIMBOUNDS_MAX_SLOTS'</span><span class="p">],</span>
<span class="n">params</span><span class="p">[</span><span class="s">'LIMBOUNDS_MAX_SIZE'</span><span class="p">],</span>
<span class="s">'bounding limits'</span><span class="p">)</span>
<span class="sd">"""A cache for bounding limits."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sortedLRcache</span> <span class="o">=</span> <span class="n">ObjectCache</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s">'SORTEDLR_MAX_SLOTS'</span><span class="p">],</span>
<span class="n">params</span><span class="p">[</span><span class="s">'SORTEDLR_MAX_SIZE'</span><span class="p">],</span>
<span class="s">'last row chunks'</span><span class="p">)</span>
<span class="sd">"""A cache for the last row chunks. Only used for searches in</span>
<span class="sd"> the last row, and mainly useful for small indexes."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">starts</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nrows</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lengths</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nrows</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sorted</span><span class="o">.</span><span class="n">_init_sorted_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dirtycache</span> <span class="o">=</span> <span class="bp">False</span>
<span class="k">def</span> <span class="nf">search</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">):</span>
<span class="sd">"""Do a binary search in this index for an item."""</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">tref</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Entering search"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dirtycache</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">restorecache</span><span class="p">()</span>
<span class="c"># An empty item or if left limit is larger than the right one</span>
<span class="c"># means that the number of records is always going to be empty,</span>
<span class="c"># so we avoid further computation (including looking up the</span>
<span class="c"># limits cache).</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">item</span> <span class="ow">or</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">starts</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lengths</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c"># Check whether the item tuple is in the limits cache or not</span>
<span class="n">nslot</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">limboundscache</span><span class="o">.</span><span class="n">getslot</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nslot</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">startlengths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">limboundscache</span><span class="o">.</span><span class="n">getitem</span><span class="p">(</span><span class="n">nslot</span><span class="p">)</span>
<span class="c"># Reset the lengths array (not necessary for starts)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lengths</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c"># Now, set the interesting rows</span>
<span class="k">for</span> <span class="n">nrow</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">startlengths</span><span class="p">)):</span>
<span class="n">nrow2</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">length</span> <span class="o">=</span> <span class="n">startlengths</span><span class="p">[</span><span class="n">nrow</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">starts</span><span class="p">[</span><span class="n">nrow2</span><span class="p">]</span> <span class="o">=</span> <span class="n">start</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lengths</span><span class="p">[</span><span class="n">nrow2</span><span class="p">]</span> <span class="o">=</span> <span class="n">length</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="n">tlen</span> <span class="o">+</span> <span class="n">length</span>
<span class="k">return</span> <span class="n">tlen</span>
<span class="c"># The item is not in cache. Do the real lookup.</span>
<span class="nb">sorted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">opt_search_types</span><span class="p">:</span>
<span class="c"># The next are optimizations. However, they hide the</span>
<span class="c"># CPU functions consumptions from python profiles.</span>
<span class="c"># You may want to de-activate them during profiling.</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"int32"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_i</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"int64"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_ll</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"float16"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_e</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"float32"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_f</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"float64"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_d</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"float96"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_g</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"float128"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_g</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"uint32"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_ui</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"uint64"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_ull</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"int8"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_b</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"int16"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_s</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"uint8"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_ub</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s">"uint16"</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin_na_us</span><span class="p">(</span><span class="o">*</span><span class="n">item</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">False</span><span class="p">,</span> <span class="s">"This can't happen!"</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">search_scalar</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">)</span>
<span class="c"># Get possible remaining values in last row</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="c"># Look for more indexes in the last row</span>
<span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">search_last_row</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">starts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">start</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lengths</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">stop</span> <span class="o">-</span> <span class="n">start</span>
<span class="n">tlen</span> <span class="o">+=</span> <span class="n">stop</span> <span class="o">-</span> <span class="n">start</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">limboundscache</span><span class="o">.</span><span class="n">couldenablecache</span><span class="p">():</span>
<span class="c"># Get a startlengths tuple and save it in cache.</span>
<span class="c"># This is quite slow, but it is a good way to compress</span>
<span class="c"># the bounds info. Moreover, the .couldenablecache()</span>
<span class="c"># is doing a good work so as to avoid computing this</span>
<span class="c"># when it is not necessary to do it.</span>
<span class="n">startlengths</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">nrow</span><span class="p">,</span> <span class="n">length</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lengths</span><span class="p">):</span>
<span class="k">if</span> <span class="n">length</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">startlengths</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">nrow</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">starts</span><span class="p">[</span><span class="n">nrow</span><span class="p">],</span> <span class="n">length</span><span class="p">))</span>
<span class="c"># Compute the size of the recarray (aproximately)</span>
<span class="c"># The +1 at the end is important to avoid 0 lengths</span>
<span class="c"># (remember, the object headers take some space)</span>
<span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">startlengths</span><span class="p">)</span> <span class="o">*</span> <span class="mi">8</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span>
<span class="c"># Put this startlengths list in cache</span>
<span class="bp">self</span><span class="o">.</span><span class="n">limboundscache</span><span class="o">.</span><span class="n">setitem</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">startlengths</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Exiting search"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">return</span> <span class="n">tlen</span>
<span class="c"># This is an scalar version of search. It works with strings as well.</span>
<span class="k">def</span> <span class="nf">search_scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">,</span> <span class="nb">sorted</span><span class="p">):</span>
<span class="sd">"""Do a binary search in this index for an item."""</span>
<span class="n">tlen</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c"># Do the lookup for values fullfilling the conditions</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nslices</span><span class="p">):</span>
<span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span> <span class="o">=</span> <span class="nb">sorted</span><span class="o">.</span><span class="n">_search_bin</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">item</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">starts</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">start</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lengths</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">stop</span> <span class="o">-</span> <span class="n">start</span>
<span class="n">tlen</span> <span class="o">+=</span> <span class="n">stop</span> <span class="o">-</span> <span class="n">start</span>
<span class="k">return</span> <span class="n">tlen</span>
<span class="k">def</span> <span class="nf">search_last_row</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">):</span>
<span class="c"># Variable initialization</span>
<span class="n">item1</span><span class="p">,</span> <span class="n">item2</span> <span class="o">=</span> <span class="n">item</span>
<span class="n">bebounds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bebounds</span>
<span class="n">b0</span><span class="p">,</span> <span class="n">b1</span> <span class="o">=</span> <span class="n">bebounds</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">bebounds</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">bounds</span> <span class="o">=</span> <span class="n">bebounds</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">itemsize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">itemsize</span>
<span class="n">sortedLRcache</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sortedLRcache</span>
<span class="n">hi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelementsSLR</span> <span class="c"># maximum number of elements</span>
<span class="n">rchunksize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="n">nchunk</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="c"># Lookup for item1</span>
<span class="k">if</span> <span class="n">item1</span> <span class="o">></span> <span class="n">b0</span><span class="p">:</span>
<span class="k">if</span> <span class="n">item1</span> <span class="o"><=</span> <span class="n">b1</span><span class="p">:</span>
<span class="c"># Search the appropriate chunk in bounds cache</span>
<span class="n">nchunk</span> <span class="o">=</span> <span class="n">bisect_left</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">item1</span><span class="p">)</span>
<span class="c"># Lookup for this chunk in cache</span>
<span class="n">nslot</span> <span class="o">=</span> <span class="n">sortedLRcache</span><span class="o">.</span><span class="n">getslot</span><span class="p">(</span><span class="n">nchunk</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nslot</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">chunk</span> <span class="o">=</span> <span class="n">sortedLRcache</span><span class="o">.</span><span class="n">getitem</span><span class="p">(</span><span class="n">nslot</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">begin</span> <span class="o">=</span> <span class="n">rchunksize</span> <span class="o">*</span> <span class="n">nchunk</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">rchunksize</span> <span class="o">*</span> <span class="p">(</span><span class="n">nchunk</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">end</span> <span class="o">></span> <span class="n">hi</span><span class="p">:</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">hi</span>
<span class="c"># Read the chunk from disk</span>
<span class="n">chunk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span><span class="o">.</span><span class="n">_read_sorted_slice</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sorted</span><span class="p">,</span> <span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span>
<span class="c"># Put it in cache. It's important to *copy*</span>
<span class="c"># the buffer, as it is reused in future reads!</span>
<span class="n">sortedLRcache</span><span class="o">.</span><span class="n">setitem</span><span class="p">(</span><span class="n">nchunk</span><span class="p">,</span> <span class="n">chunk</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
<span class="p">(</span><span class="n">end</span> <span class="o">-</span> <span class="n">begin</span><span class="p">)</span> <span class="o">*</span> <span class="n">itemsize</span><span class="p">)</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">bisect_left</span><span class="p">(</span><span class="n">chunk</span><span class="p">,</span> <span class="n">item1</span><span class="p">)</span>
<span class="n">start</span> <span class="o">+=</span> <span class="n">rchunksize</span> <span class="o">*</span> <span class="n">nchunk</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">hi</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">start</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c"># Lookup for item2</span>
<span class="k">if</span> <span class="n">item2</span> <span class="o">>=</span> <span class="n">b0</span><span class="p">:</span>
<span class="k">if</span> <span class="n">item2</span> <span class="o"><</span> <span class="n">b1</span><span class="p">:</span>
<span class="c"># Search the appropriate chunk in bounds cache</span>
<span class="n">nchunk2</span> <span class="o">=</span> <span class="n">bisect_right</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">item2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nchunk2</span> <span class="o">!=</span> <span class="n">nchunk</span><span class="p">:</span>
<span class="c"># Lookup for this chunk in cache</span>
<span class="n">nslot</span> <span class="o">=</span> <span class="n">sortedLRcache</span><span class="o">.</span><span class="n">getslot</span><span class="p">(</span><span class="n">nchunk2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nslot</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">chunk</span> <span class="o">=</span> <span class="n">sortedLRcache</span><span class="o">.</span><span class="n">getitem</span><span class="p">(</span><span class="n">nslot</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">begin</span> <span class="o">=</span> <span class="n">rchunksize</span> <span class="o">*</span> <span class="n">nchunk2</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">rchunksize</span> <span class="o">*</span> <span class="p">(</span><span class="n">nchunk2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">end</span> <span class="o">></span> <span class="n">hi</span><span class="p">:</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">hi</span>
<span class="c"># Read the chunk from disk</span>
<span class="n">chunk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span><span class="o">.</span><span class="n">_read_sorted_slice</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sorted</span><span class="p">,</span> <span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span>
<span class="c"># Put it in cache. It's important to *copy*</span>
<span class="c"># the buffer, as it is reused in future reads!</span>
<span class="c"># See bug #60 in xot.carabos.com</span>
<span class="n">sortedLRcache</span><span class="o">.</span><span class="n">setitem</span><span class="p">(</span><span class="n">nchunk2</span><span class="p">,</span> <span class="n">chunk</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
<span class="p">(</span><span class="n">end</span> <span class="o">-</span> <span class="n">begin</span><span class="p">)</span> <span class="o">*</span> <span class="n">itemsize</span><span class="p">)</span>
<span class="n">stop</span> <span class="o">=</span> <span class="n">bisect_right</span><span class="p">(</span><span class="n">chunk</span><span class="p">,</span> <span class="n">item2</span><span class="p">)</span>
<span class="n">stop</span> <span class="o">+=</span> <span class="n">rchunksize</span> <span class="o">*</span> <span class="n">nchunk2</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">stop</span> <span class="o">=</span> <span class="n">hi</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">stop</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">return</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span>
<span class="n">searchLastRow</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">search_last_row</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">get_chunkmap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Compute a map with the interesting chunks in index."""</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">tref</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Entering get_chunkmap"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="n">ss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span>
<span class="n">nsb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslicesblock</span>
<span class="n">nslices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nslices</span>
<span class="n">lbucket</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lbucket</span>
<span class="n">indsize</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indsize</span>
<span class="n">bucketsinblock</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span><span class="p">)</span> <span class="o">/</span> <span class="n">lbucket</span>
<span class="n">nchunks</span> <span class="o">=</span> <span class="nb">long</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nelements</span><span class="p">)</span> <span class="o">/</span> <span class="n">lbucket</span><span class="p">))</span>
<span class="n">chunkmap</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">nchunks</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">"bool"</span><span class="p">)</span>
<span class="n">reduction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span>
<span class="n">starts</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">starts</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">reduction</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">stops</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">starts</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">lengths</span><span class="p">)</span> <span class="o">*</span> <span class="n">reduction</span>
<span class="n">starts</span><span class="p">[</span><span class="n">starts</span> <span class="o"><</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> <span class="c"># All negative values set to zero</span>
<span class="n">indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span>
<span class="k">for</span> <span class="n">nslice</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nrows</span><span class="p">):</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">starts</span><span class="p">[</span><span class="n">nslice</span><span class="p">]</span>
<span class="n">stop</span> <span class="o">=</span> <span class="n">stops</span><span class="p">[</span><span class="n">nslice</span><span class="p">]</span>
<span class="k">if</span> <span class="n">stop</span> <span class="o">></span> <span class="n">start</span><span class="p">:</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">'u</span><span class="si">%d</span><span class="s">'</span> <span class="o">%</span> <span class="n">indsize</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nslice</span> <span class="o"><</span> <span class="n">nslices</span><span class="p">:</span>
<span class="n">indices</span><span class="o">.</span><span class="n">_read_index_slice</span><span class="p">(</span><span class="n">nslice</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">idx</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indicesLR</span><span class="o">.</span><span class="n">_read_index_slice</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">idx</span><span class="p">)</span>
<span class="k">if</span> <span class="n">indsize</span> <span class="o">==</span> <span class="mi">8</span><span class="p">:</span>
<span class="n">idx</span> <span class="o">//=</span> <span class="n">lbucket</span>
<span class="k">elif</span> <span class="n">indsize</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="c"># The chunkmap size cannot be never larger than 'int_'</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">"int_"</span><span class="p">)</span>
<span class="n">offset</span> <span class="o">=</span> <span class="nb">long</span><span class="p">((</span><span class="n">nslice</span> <span class="o">//</span> <span class="n">nsb</span><span class="p">)</span> <span class="o">*</span> <span class="n">bucketsinblock</span><span class="p">)</span>
<span class="n">idx</span> <span class="o">+=</span> <span class="n">offset</span>
<span class="k">elif</span> <span class="n">indsize</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="c"># The chunkmap size cannot be never larger than 'int_'</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">"int_"</span><span class="p">)</span>
<span class="n">offset</span> <span class="o">=</span> <span class="p">(</span><span class="n">nslice</span> <span class="o">*</span> <span class="n">ss</span><span class="p">)</span> <span class="o">//</span> <span class="n">lbucket</span>
<span class="n">idx</span> <span class="o">+=</span> <span class="n">offset</span>
<span class="n">chunkmap</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="bp">True</span>
<span class="c"># The case lbucket < nrowsinchunk should only happen in tests</span>
<span class="n">nrowsinchunk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nrowsinchunk</span>
<span class="k">if</span> <span class="n">lbucket</span> <span class="o">!=</span> <span class="n">nrowsinchunk</span><span class="p">:</span>
<span class="c"># Map the 'coarse grain' chunkmap into the 'true' chunkmap</span>
<span class="n">nelements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nelements</span>
<span class="n">tnchunks</span> <span class="o">=</span> <span class="nb">long</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">nelements</span><span class="p">)</span> <span class="o">/</span> <span class="n">nrowsinchunk</span><span class="p">))</span>
<span class="n">tchunkmap</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">tnchunks</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s">"bool"</span><span class="p">)</span>
<span class="n">ratio</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">lbucket</span><span class="p">)</span> <span class="o">/</span> <span class="n">nrowsinchunk</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">chunkmap</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">starts</span> <span class="o">=</span> <span class="p">(</span><span class="n">idx</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">'int_'</span><span class="p">)</span>
<span class="n">stops</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s">'int_'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">)):</span>
<span class="n">tchunkmap</span><span class="p">[</span><span class="n">starts</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">stops</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="bp">True</span>
<span class="n">chunkmap</span> <span class="o">=</span> <span class="n">tchunkmap</span>
<span class="k">if</span> <span class="n">profile</span><span class="p">:</span>
<span class="n">show_stats</span><span class="p">(</span><span class="s">"Exiting get_chunkmap"</span><span class="p">,</span> <span class="n">tref</span><span class="p">)</span>
<span class="k">return</span> <span class="n">chunkmap</span>
<span class="k">def</span> <span class="nf">get_lookup_range</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ops</span><span class="p">,</span> <span class="n">limits</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">ops</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">limits</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">ops</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">limits</span><span class="p">)</span>
<span class="n">column</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">column</span>
<span class="n">coldtype</span> <span class="o">=</span> <span class="n">column</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">base</span>
<span class="n">itemsize</span> <span class="o">=</span> <span class="n">coldtype</span><span class="o">.</span><span class="n">itemsize</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">limits</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">ops</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s">'lt'</span><span class="p">,</span> <span class="s">'le'</span><span class="p">,</span> <span class="s">'eq'</span><span class="p">,</span> <span class="s">'ge'</span><span class="p">,</span> <span class="s">'gt'</span><span class="p">]</span>
<span class="n">limit</span> <span class="o">=</span> <span class="n">limits</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">op</span> <span class="o">=</span> <span class="n">ops</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">op</span> <span class="o">==</span> <span class="s">'lt'</span><span class="p">:</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">inftype</span><span class="p">(</span><span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">,</span> <span class="n">sign</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
<span class="n">nextafter</span><span class="p">(</span><span class="n">limit</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s">'le'</span><span class="p">:</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">inftype</span><span class="p">(</span><span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">,</span> <span class="n">sign</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
<span class="n">limit</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s">'gt'</span><span class="p">:</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">nextafter</span><span class="p">(</span><span class="n">limit</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">),</span>
<span class="n">inftype</span><span class="p">(</span><span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">,</span> <span class="n">sign</span><span class="o">=+</span><span class="mi">1</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s">'ge'</span><span class="p">:</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">limit</span><span class="p">,</span>
<span class="n">inftype</span><span class="p">(</span><span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">,</span> <span class="n">sign</span><span class="o">=+</span><span class="mi">1</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s">'eq'</span><span class="p">:</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">limit</span><span class="p">,</span> <span class="n">limit</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">limits</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">ops</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="p">(</span><span class="s">'gt'</span><span class="p">,</span> <span class="s">'ge'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">ops</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">in</span> <span class="p">(</span><span class="s">'lt'</span><span class="p">,</span> <span class="s">'le'</span><span class="p">)</span>
<span class="n">lower</span><span class="p">,</span> <span class="n">upper</span> <span class="o">=</span> <span class="n">limits</span>
<span class="k">if</span> <span class="n">lower</span> <span class="o">></span> <span class="n">upper</span><span class="p">:</span>
<span class="c"># ``a <[=] x <[=] b`` is always false if ``a > b``.</span>
<span class="k">return</span> <span class="p">()</span>
<span class="k">if</span> <span class="n">ops</span> <span class="o">==</span> <span class="p">(</span><span class="s">'gt'</span><span class="p">,</span> <span class="s">'lt'</span><span class="p">):</span> <span class="c"># lower < col < upper</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">nextafter</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">),</span>
<span class="n">nextafter</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">ops</span> <span class="o">==</span> <span class="p">(</span><span class="s">'ge'</span><span class="p">,</span> <span class="s">'lt'</span><span class="p">):</span> <span class="c"># lower <= col < upper</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">nextafter</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">ops</span> <span class="o">==</span> <span class="p">(</span><span class="s">'gt'</span><span class="p">,</span> <span class="s">'le'</span><span class="p">):</span> <span class="c"># lower < col <= upper</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">nextafter</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">coldtype</span><span class="p">,</span> <span class="n">itemsize</span><span class="p">),</span> <span class="n">upper</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">ops</span> <span class="o">==</span> <span class="p">(</span><span class="s">'ge'</span><span class="p">,</span> <span class="s">'le'</span><span class="p">):</span> <span class="c"># lower <= col <= upper</span>
<span class="n">range_</span> <span class="o">=</span> <span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
<span class="k">return</span> <span class="n">range_</span>
<span class="n">getLookupRange</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">get_lookup_range</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_f_remove</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">recursive</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Remove this Index object."""</span>
<span class="c"># Index removal is always recursive,</span>
<span class="c"># no matter what `recursive` says.</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Index</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_f_remove</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""This provides a more compact representation than __repr__"""</span>
<span class="c"># The filters</span>
<span class="n">filters</span> <span class="o">=</span> <span class="s">""</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filters</span><span class="o">.</span><span class="n">complevel</span><span class="p">:</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">filters</span><span class="o">.</span><span class="n">shuffle</span><span class="p">:</span>
<span class="n">filters</span> <span class="o">+=</span> <span class="s">", shuffle"</span>
<span class="n">filters</span> <span class="o">+=</span> <span class="s">", </span><span class="si">%s</span><span class="s">(</span><span class="si">%s</span><span class="s">)"</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filters</span><span class="o">.</span><span class="n">complib</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">filters</span><span class="o">.</span><span class="n">complevel</span><span class="p">)</span>
<span class="k">return</span> <span class="s">"Index(</span><span class="si">%s</span><span class="s">, </span><span class="si">%s%s</span><span class="s">).is_csi=</span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> \
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kind</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_csi</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""This provides more metainfo than standard __repr__"""</span>
<span class="n">cpathname</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="o">.</span><span class="n">_v_pathname</span> <span class="o">+</span> <span class="s">".cols."</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">column</span><span class="o">.</span><span class="n">pathname</span>
<span class="n">retstr</span> <span class="o">=</span> <span class="s">"""</span><span class="si">%s</span><span class="s"> (Index for column </span><span class="si">%s</span><span class="s">)</span>
<span class="s"> optlevel := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> kind := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> filters := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> is_csi := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> nelements := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> chunksize := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> slicesize := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> blocksize := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> superblocksize := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> filters := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> dirty := </span><span class="si">%s</span><span class="s"></span>
<span class="s"> byteorder := </span><span class="si">%r</span><span class="s">"""</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_v_pathname</span><span class="p">,</span> <span class="n">cpathname</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optlevel</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kind</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_csi</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nelements</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">chunksize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slicesize</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">blocksize</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">superblocksize</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dirty</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">byteorder</span><span class="p">)</span>
<span class="n">retstr</span> <span class="o">+=</span> <span class="s">"</span><span class="se">\n</span><span class="s"> sorted := </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">sorted</span>
<span class="n">retstr</span> <span class="o">+=</span> <span class="s">"</span><span class="se">\n</span><span class="s"> indices := </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span>
<span class="n">retstr</span> <span class="o">+=</span> <span class="s">"</span><span class="se">\n</span><span class="s"> ranges := </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span>
<span class="n">retstr</span> <span class="o">+=</span> <span class="s">"</span><span class="se">\n</span><span class="s"> bounds := </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounds</span>
<span class="n">retstr</span> <span class="o">+=</span> <span class="s">"</span><span class="se">\n</span><span class="s"> sortedLR := </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">sortedLR</span>
<span class="n">retstr</span> <span class="o">+=</span> <span class="s">"</span><span class="se">\n</span><span class="s"> indicesLR := </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">indicesLR</span>
<span class="k">return</span> <span class="n">retstr</span>
</div>
<span class="k">class</span> <span class="nc">IndexesDescG</span><span class="p">(</span><span class="n">NotLoggedMixin</span><span class="p">,</span> <span class="n">Group</span><span class="p">):</span>
<span class="n">_c_classid</span> <span class="o">=</span> <span class="s">'DINDEX'</span>
<span class="n">_c_classId</span> <span class="o">=</span> <span class="n">previous_api_property</span><span class="p">(</span><span class="s">'_c_classid'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_g_width_warning</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s">"the number of indexed columns on a single description group "</span>
<span class="s">"is exceeding the recommended maximum (</span><span class="si">%d</span><span class="s">); "</span>
<span class="s">"be ready to see PyTables asking for *lots* of memory "</span>
<span class="s">"and possibly slow I/O"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_max_group_width</span><span class="p">,</span>
<span class="n">PerformanceWarning</span><span class="p">)</span>
<span class="n">_g_widthWarning</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">_g_width_warning</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">IndexesTableG</span><span class="p">(</span><span class="n">NotLoggedMixin</span><span class="p">,</span> <span class="n">Group</span><span class="p">):</span>
<span class="n">_c_classid</span> <span class="o">=</span> <span class="s">'TINDEX'</span>
<span class="n">_c_classId</span> <span class="o">=</span> <span class="n">previous_api_property</span><span class="p">(</span><span class="s">'_c_classid'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_getauto</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="s">'AUTO_INDEX'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="p">:</span>
<span class="k">return</span> <span class="n">default_auto_index</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">AUTO_INDEX</span>
<span class="k">def</span> <span class="nf">_setauto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">auto</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">AUTO_INDEX</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">auto</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_delauto</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_attrs</span><span class="o">.</span><span class="n">AUTO_INDEX</span>
<span class="n">auto</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_getauto</span><span class="p">,</span> <span class="n">_setauto</span><span class="p">,</span> <span class="n">_delauto</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_g_width_warning</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s">"the number of indexed columns on a single table "</span>
<span class="s">"is exceeding the recommended maximum (</span><span class="si">%d</span><span class="s">); "</span>
<span class="s">"be ready to see PyTables asking for *lots* of memory "</span>
<span class="s">"and possibly slow I/O"</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_max_group_width</span><span class="p">,</span>
<span class="n">PerformanceWarning</span><span class="p">)</span>
<span class="n">_g_widthWarning</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">_g_width_warning</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_g_check_name</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s">'_i_'</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s">"names of index groups must start with ``_i_``: </span><span class="si">%s</span><span class="s">"</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>
<span class="n">_g_checkName</span> <span class="o">=</span> <span class="n">previous_api</span><span class="p">(</span><span class="n">_g_check_name</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_gettable</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">names</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_pathname</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">"/"</span><span class="p">)</span>
<span class="n">tablename</span> <span class="o">=</span> <span class="n">names</span><span class="o">.</span><span class="n">pop</span><span class="p">()[</span><span class="mi">3</span><span class="p">:]</span> <span class="c"># "_i_" is at the beginning</span>
<span class="n">parentpathname</span> <span class="o">=</span> <span class="s">"/"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">names</span><span class="p">)</span>
<span class="n">tablepathname</span> <span class="o">=</span> <span class="n">join_path</span><span class="p">(</span><span class="n">parentpathname</span><span class="p">,</span> <span class="n">tablename</span><span class="p">)</span>
<span class="n">table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_v_file</span><span class="o">.</span><span class="n">_get_node</span><span class="p">(</span><span class="n">tablepathname</span><span class="p">)</span>
<span class="k">return</span> <span class="n">table</span>
<span class="n">table</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span>
<span class="n">_gettable</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span>
<span class="s">"Accessor for the `Table` object of this `IndexesTableG` container."</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">OldIndex</span><span class="p">(</span><span class="n">NotLoggedMixin</span><span class="p">,</span> <span class="n">Group</span><span class="p">):</span>
<span class="sd">"""This is meant to hide indexes of PyTables 1.x files."""</span>
<span class="n">_c_classid</span> <span class="o">=</span> <span class="s">'CINDEX'</span>
<span class="n">_c_classId</span> <span class="o">=</span> <span class="n">previous_api_property</span><span class="p">(</span><span class="s">'_c_classid'</span><span class="p">)</span>
<span class="c">## Local Variables:</span>
<span class="c">## mode: python</span>
<span class="c">## py-indent-offset: 4</span>
<span class="c">## tab-width: 4</span>
<span class="c">## fill-column: 72</span>
<span class="c">## End:</span>
</pre></div>
</div>
</div>
</div>
<div class="sphinxsidebar">
<div class="sphinxsidebarwrapper">
<p class="logo"><a href="../../index.html">
<img class="logo" src="../../_static/logo-pytables-small.png" alt="Logo"/>
</a></p>
<div id="searchbox" style="display: none">
<h3>Quick search</h3>
<form class="search" action="../../search.html" method="get">
<input type="text" name="q" />
<input type="submit" value="Go" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
<p class="searchtip" style="font-size: 90%">
Enter search terms or a module, class or function name.
</p>
</div>
<script type="text/javascript">$('#searchbox').show(0);</script>
</div>
</div>
<div class="clearer"></div>
</div>
<div class="relbar-bottom">
<div class="related">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="../../genindex.html" title="General Index"
>index</a></li>
<li class="right" >
<a href="../../py-modindex.html" title="Python Module Index"
>modules</a> </li>
<li class="right" >
<a href="../../np-modindex.html" title="Python Module Index"
>modules</a> </li>
<li><a href="../../index.html">PyTables 3.1.1 documentation</a> »</li>
<li><a href="../index.html" >Module code</a> »</li>
<li><a href="../tables.html" >tables</a> »</li>
</ul>
</div>
</div>
<div class="footer">
© Copyright 2011-2014, PyTables maintainers.
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.2.2.
</div>
<!-- cloud_sptheme 1.3 -->
</body>
</html>
|