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Appropriate indexed loops have been added to " }, { "__type": "InlineCode", "__tag": 4051, "value": "add" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "subtract" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "multiply" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "floor_divide" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "maximum" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "minimum" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "fmax" }, { "__type": "Text", "__tag": 4046, "value": ", and " }, { "__type": "InlineCode", "__tag": 4051, "value": "fmin" }, { "__type": "Text", "__tag": 4046, "value": "." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The internal logic is similar to the logic used for regular ufuncs, which also have fast paths." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Thanks to the " }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "D. E. Shaw group" } ], "url": "https://deshaw.com/", "title": "" }, { "__type": "Text", "__tag": 4046, "value": " for sponsoring this work." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-23136" } ], "url": "https://github.com/numpy/numpy/pull/23136", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "InlineCode", "__tag": 4051, "value": "ufunc.at" }, { "__type": "Text", "__tag": 4046, "value": " can be much faster" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Membership test on " }, { "__type": "InlineCode", "__tag": 4051, "value": "NpzFile" }, { "__type": "Text", "__tag": 4046, "value": " will no longer decompress the archive if it is successful." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-23661" } ], "url": "https://github.com/numpy/numpy/pull/23661", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Faster membership test on " }, { "__type": "InlineCode", "__tag": 4051, "value": "NpzFile" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [], "title": [ { "__type": "Text", "__tag": 4046, "value": "Changes" } ], "level": 1, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "In rare cases, using mainly " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.r_" }, { "__type": "Text", "__tag": 4046, "value": " with scalars can lead to different results. The main potential changes are highlighted by the following " } ] }, { "__type": "Code", "__tag": 4050, "value": ">>> np.r_[np.arange(5, dtype=np.uint8), -1].dtype\nint16 # rather than the default integer (int64 or int32)\n>>> np.r_[np.arange(5, dtype=np.int8), 255]\narray([ 0, 1, 2, 3, 4, 255], dtype=int16)", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Where the second example returned " } ] }, { "__type": "Code", "__tag": 4050, "value": "array([ 0, 1, 2, 3, 4, -1], dtype=int8)", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The first one is due to a signed integer scalar with an unsigned integer array, while the second is due to " }, { "__type": "InlineCode", "__tag": 4051, "value": "255" }, { "__type": "Text", "__tag": 4046, "value": " not fitting into " }, { "__type": "InlineCode", "__tag": 4051, "value": "int8" }, { "__type": "Text", "__tag": 4046, "value": " and NumPy currently inspecting values to make this work. (Note that the second example is expected to change in the future due to " }, { "__type": "InlineRole", "__tag": 4003, "value": "NEP 50 ", "domain": null, "role": "ref", "inventory": null }, { "__type": "Text", "__tag": 4046, "value": "; it will then raise an error.)" } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-22539" } ], "url": "https://github.com/numpy/numpy/pull/22539", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "InlineCode", "__tag": 4051, "value": "np.r_[]" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.c_[]" }, { "__type": "Text", "__tag": 4046, "value": " with certain scalar values" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "To speed up the " }, { "__type": "InlineCode", "__tag": 4051, "value": "__array_function__" }, { "__type": "Text", "__tag": 4046, "value": " dispatching, most NumPy functions are now wrapped into C-callables and are not proper Python functions or C methods. They still look and feel the same as before (like a Python function), and this should only improve performance and user experience (cleaner tracebacks). However, please inform the NumPy developers if this change confuses your program for some reason." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-23020" } ], "url": "https://github.com/numpy/numpy/pull/23020", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Most NumPy functions are wrapped into a C-callable" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "NumPy builds now depend on the C++ standard library, because the " }, { "__type": "InlineCode", "__tag": 4051, "value": "numpy.core._multiarray_umath" }, { "__type": "Text", "__tag": 4046, "value": " extension is linked with the C++ linker." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-23601" } ], "url": "https://github.com/numpy/numpy/pull/23601", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "C++ standard library usage" } ], "level": 2, "target": null } ], "local_refs": [] }