{"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\/fr\/blog\/news\/vypusk-python-biblioteki-dlya-nauchnyh-vychislenij-numpy-1-19","title":{"rendered":"Publication de la biblioth\u00e8que Python pour les calculs scientifiques NumPy 1.19","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Disponible<\/a><\/noindex> sortie de la biblioth\u00e8que Python pour le calcul scientifique NumPy 1.19<\/a><\/noindex>, ax\u00e9e sur le travail avec des tableaux multidimensionnels et des matrices, tout en offrant une vaste collection de fonctions impl\u00e9mentant divers algorithmes li\u00e9s \u00e0 l'utilisation de matrices. NumPy est l'une des biblioth\u00e8ques les plus demand\u00e9es pour le calcul scientifique. Le code du projet est \u00e9crit en Python avec des optimisations en C et est distribu\u00e9<\/a><\/noindex> sous licence BSD.<\/p>\n

Dans NumPy 1.19, le support de Python 3.5 a \u00e9t\u00e9 interrompu et le code n\u00e9cessaire au fonctionnement avec Python 2 a \u00e9t\u00e9 supprim\u00e9 (la couche numpy.compat a \u00e9t\u00e9 conserv\u00e9e). Les versions prises en charge sont maintenant Python 3.6, 3.7 et 3.8. Le d\u00e9veloppement du module a \u00e9t\u00e9 poursuivi numpy.random<\/a><\/noindex> pour travailler avec des \u00e9chantillons al\u00e9atoires. Am\u00e9lioration du support des packages wheel NumPy sur l'architecture Aarch64 et lors de l'utilisation de l'impl\u00e9mentation Python PyPy<\/a><\/noindex>. \u00c9tendue<\/a><\/noindex> la fonctionnalit\u00e9 numpy.frompyfunc, np.str_, numpy.copy, numpy.linalg.multi_dot, numpy.count_nonzero et numpy.array_equal. Am\u00e9lioration de la d\u00e9tection des capacit\u00e9s CPU, telles que le support d'AVX. Ajout d'une impl\u00e9mentation qui fonctionne 5 \u00e0 7 fois plus vite np.exp<\/a><\/noindex> bas\u00e9e sur AVX512, appliqu\u00e9e aux donn\u00e9es d'entr\u00e9e de type np.float64.<\/p>\n

<\/a><\/noindex><\/p>\n

Source : opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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