{"id":86096,"date":"2020-06-21T13:42:25","date_gmt":"2020-06-21T11:42:25","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/vypusk-python-biblioteki-dlya-nauchnyh-vychislenij-numpy-1-19"},"modified":"2020-06-21T13:42:25","modified_gmt":"2020-06-21T11:42:25","slug":"vypusk-python-biblioteki-dlya-nauchnyh-vychislenij-numpy-1-19","status":"publish","type":"post","link":"https:\/\/prohoster.info\/no\/blog\/news\/vypusk-python-biblioteki-dlya-nauchnyh-vychislenij-numpy-1-19","title":{"rendered":"Utgivelsen av Python-biblioteket for vitenskapelige beregninger NumPy 1.19","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><noindex><a rel=\"nofollow\" href=\"https:\/\/www.mail-archive.com\/python-announce-list@python.org\/msg08971.html\">Tilgjengelig<\/a><\/noindex> utgivelse av Python-biblioteket for vitenskapelige beregninger <noindex><a rel=\"nofollow\" href=\"http:\/\/www.numpy.org\/\">NumPy 1.19<\/a><\/noindex>, som er fokusert p\u00e5 arbeid med fler-dimensjonale arrays og matriser, samt gir en omfattende samling funksjoner for implementering av ulike algoritmer relatert til bruk av matriser. NumPy er et av de mest etterspurte bibliotekene for vitenskapelige beregninger. Koden til prosjektet er skrevet i Python, med optimaliseringer i C, og <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/numpy\/numpy\/\">distribueres<\/a><\/noindex> under BSD-lisens.<\/p>\n<p>I NumPy 1.19 er Python 3.5 ikke lenger st\u00f8ttet, og koden for ytelse med Python 2 er fjernet (numpy.compat-laget er fortsatt igjen). De st\u00f8ttede versjonene er Python 3.6, 3.7 og 3.8. Utviklingen av modulen for arbeid med tilfeldige utvalg har fortsatt. <noindex><a rel=\"nofollow\" href=\"https:\/\/numpy.org\/devdocs\/reference\/random\/index.html\">numpy.random<\/a><\/noindex> Bedre st\u00f8tte for wheel-pakker av NumPy p\u00e5 Aarch64-arkitekturen og ved bruk av Python-implementeringen. <noindex><a rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=52089\">PyPy<\/a><\/noindex>. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/numpy\/numpy\/releases\/tag\/v1.19.0\">Utvidet<\/a><\/noindex> Funksjonaliteten til numpy.frompyfunc, np.str_, numpy.copy, numpy.linalg.multi_dot, numpy.count_nonzero og numpy.array_equal er forbedret. Det er forbedret CPU-funksjonsdeteksjon, som st\u00f8tte for AVX. En implementering som er 5-7 ganger raskere er lagt til.  <noindex><a rel=\"nofollow\" href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.exp.html\">np.exp<\/a><\/noindex> basert p\u00e5 AVX512, som brukes for inngangsdata med typen np.float64.<\/p>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Kilde: <a \ncontent=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=53203\">opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0414\u043e\u0441\u0442\u0443\u043f\u0435\u043d \u0440\u0435\u043b\u0438\u0437 Python-\u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0434\u043b\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0445 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0439 NumPy 1.19, \u043e\u0440\u0438\u0435\u043d\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0439 \u043d\u0430 \u0440\u0430\u0431\u043e\u0442\u0443 \u0441 \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u044b\u043c\u0438 \u043c\u0430\u0441\u0441\u0438\u0432\u0430\u043c\u0438 \u0438 \u043c\u0430\u0442\u0440\u0438\u0446\u0430\u043c\u0438, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0449\u0435\u0439 \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u043a\u043e\u043b\u043b\u0435\u043a\u0446\u0438\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0441 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0435\u0439 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0445 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u043c\u0430\u0442\u0440\u0438\u0446. NumPy \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043e\u0434\u043d\u043e\u0439 \u0438\u0437 \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0432\u043e\u0441\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a, \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u043c\u044b\u0445 \u0434\u043b\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0445 \u0440\u0430\u0441\u0447\u0451\u0442\u043e\u0432. \u041a\u043e\u0434 \u043f\u0440\u043e\u0435\u043a\u0442\u0430 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435\u043c \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0439 \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 \u0421\u0438 \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-86096","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.5 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0414\u043e\u0441\u0442\u0443\u043f\u0435\u043d \u0440\u0435\u043b\u0438\u0437 Python-\u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0434\u043b\u044f \u043d\u0430\u0443\u0447\u043d\u044b\u0445 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0439\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" 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