{ "__type": "IngestedDoc", "__tag": 4010, "_content": {}, "_ordered_sections": [], "item_file": null, "item_line": null, "item_type": null, "aliases": [], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "see_also": [], "signature": null, "references": null, "qa": "release:1.20.0-notes", "arbitrary": [ { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This NumPy release is the largest so made to date, some 684 PRs contributed by 184 people have been merged. See the list of highlights below for more details. The Python versions supported for this release are 3.7-3.9, support for Python 3.6 has been dropped. 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For " }, { "__type": "InlineCode", "__tag": 4051, "value": "float" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "complex" }, { "__type": "Text", "__tag": 4046, "value": " you can use " }, { "__type": "InlineCode", "__tag": 4051, "value": "float64" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "complex128" }, { "__type": "Text", "__tag": 4046, "value": " if you wish to be more explicit about the precision." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "For " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.int" }, { "__type": "Text", "__tag": 4046, "value": " a direct replacement with " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.int_" }, { "__type": "Text", "__tag": 4046, "value": " or " }, { "__type": "InlineCode", "__tag": 4051, "value": "int" }, { "__type": "Text", "__tag": 4046, "value": " is also good and will not change behavior, but the precision will continue to depend on the computer and operating system. 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This can be the best type to use for indexing." } ] } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "When used with " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.dtype(...)" }, { "__type": "Text", "__tag": 4046, "value": " or " }, { "__type": "InlineCode", "__tag": 4051, "value": "dtype=..." }, { "__type": "Text", "__tag": 4046, "value": " changing it to the NumPy name as mentioned above will have no effect on the output. If used as a scalar with " } ] }, { "__type": "Code", "__tag": 4050, "value": "np.float(123)", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "changing it can subtly change the result. In this case, the Python version " }, { "__type": "InlineCode", "__tag": 4051, "value": "float(123)" }, { "__type": "Text", "__tag": 4046, "value": " or " }, { "__type": "InlineCode", "__tag": 4051, "value": "int(12.)" }, { "__type": "Text", "__tag": 4046, "value": " is normally preferable, although the NumPy version may be useful for consistency with NumPy arrays (for example, NumPy behaves differently for things like division by zero)." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-14882" } ], "url": "https://github.com/numpy/numpy/pull/14882", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Using the aliases of builtin types like " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.int" }, { "__type": "Text", "__tag": 4046, "value": " is deprecated" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Previously, this was an alias for passing " }, { "__type": "InlineCode", "__tag": 4051, "value": "shape=()" }, { "__type": "Text", "__tag": 4046, "value": ". This deprecation is emitted by " }, { "__type": "InlineRole", "__tag": 4003, "value": "PyArray_IntpConverter", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": " in the C API. If your API is intended to support passing " }, { "__type": "InlineCode", "__tag": 4051, "value": "None" }, { "__type": "Text", "__tag": 4046, "value": ", then you should check for " }, { "__type": "InlineCode", "__tag": 4051, "value": "None" }, { "__type": "Text", "__tag": 4046, "value": " prior to invoking the converter, so as to be able to distinguish " }, { "__type": "InlineCode", "__tag": 4051, "value": "None" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "()" }, { "__type": "Text", "__tag": 4046, "value": "." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-15886" } ], "url": "https://github.com/numpy/numpy/pull/15886", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Passing " }, { "__type": "InlineCode", "__tag": 4051, "value": "shape=None" }, { "__type": "Text", "__tag": 4046, "value": " to functions with a non-optional shape argument is deprecated" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "In the future, NumPy will raise an IndexError when an integer array index contains out of bound values even if a non-indexed dimension is of length 0. This will now emit a DeprecationWarning. This can happen when the array is previously empty, or an empty slice is involved " } ] }, { "__type": "Code", "__tag": 4050, "value": "arr1 = np.zeros((5, 0))\narr1[[20]]\narr2 = np.zeros((5, 5))\narr2[[20], :0]", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Previously the non-empty index " }, { "__type": "InlineCode", "__tag": 4051, "value": "[20]" }, { "__type": "Text", "__tag": 4046, "value": " was not checked for correctness. It will now be checked causing a deprecation warning which will be turned into an error. This also applies to assignments." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-15900" } ], "url": "https://github.com/numpy/numpy/pull/15900", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Indexing errors will be reported even when index result is empty" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Inexact and case insensitive matches for " }, { "__type": "InlineCode", "__tag": 4051, "value": "mode" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "searchside" }, { "__type": "Text", "__tag": 4046, "value": " were valid inputs earlier and will give a DeprecationWarning now. For example, below are some example usages which are now deprecated and will give a DeprecationWarning " } ] }, { "__type": "Code", "__tag": 4050, "value": "import numpy as np\narr = np.array([[3, 6, 6], [4, 5, 1]])\n# mode: inexact match\nnp.ravel_multi_index(arr, (7, 6), mode=\"clap\") # should be \"clip\"\n# searchside: inexact match\nnp.searchsorted(arr[0], 4, side='random') # should be \"right\"", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-16056" } ], "url": "https://github.com/numpy/numpy/pull/16056", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Inexact matches for " }, { "__type": "InlineCode", "__tag": 4051, "value": "mode" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "searchside" }, { "__type": "Text", "__tag": 4046, "value": " are deprecated" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The module " }, { "__type": "InlineRole", "__tag": 4003, "value": "numpy.dual", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": " is deprecated. 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In the long term this may be deprecated or the unsafe cast may be allowed generally to make assignment of arrays and scalars behave consistently." } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "NumPy Scalars are cast when assigned to arrays" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "When strings and other types are mixed, such as " } ] }, { "__type": "Code", "__tag": 4050, "value": "np.array([\"string\", np.float64(3.)], dtype=\"S\")", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The results will change, which may lead to string dtypes with longer strings in some cases. In particularly, if " }, { "__type": "InlineCode", "__tag": 4051, "value": "dtype=\"S\"" }, { "__type": "Text", "__tag": 4046, "value": " is not provided any numerical value will lead to a string results long enough to hold all possible numerical values. (e.g. \"S32\" for floats). Note that you should always provide " }, { "__type": "InlineCode", "__tag": 4051, "value": "dtype=\"S\"" }, { "__type": "Text", "__tag": 4046, "value": " when converting non-strings to strings." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "If " }, { "__type": "InlineCode", "__tag": 4051, "value": "dtype=\"S\"" }, { "__type": "Text", "__tag": 4046, "value": " is provided the results will be largely identical to before, but NumPy scalars (not a Python float like " }, { "__type": "InlineCode", "__tag": 4051, "value": "1.0" }, { "__type": "Text", "__tag": 4046, "value": "), will still enforce a uniform string length " } ] }, { "__type": "Code", "__tag": 4050, "value": "np.array([np.float64(3.)], dtype=\"S\") # gives \"S32\"\nnp.array([3.0], dtype=\"S\") # gives \"S3\"", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Previously the first version gave the same result as the second." } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Array coercion changes when Strings and other types are mixed" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Array coercion has been restructured. In general, this should not affect users. In extremely rare corner cases where array-likes are nested " } ] }, { "__type": "Code", "__tag": 4050, "value": "np.array([array_like1])", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Things will now be more consistent with " } ] }, { "__type": "Code", "__tag": 4050, "value": "np.array([np.array(array_like1)])", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This can subtly change output for some badly defined array-likes. One example for this are array-like objects which are not also sequences of matching shape. In NumPy 1.20, a warning will be given when an array-like is not also a sequence (but behaviour remains identical, see deprecations). 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This will result in differences when the (nested) sequence describes a different shape." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-16200" } ], "url": "https://github.com/numpy/numpy/pull/16200", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Array coercion restructure" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "In NumPy 1.17 " }, { "__type": "CrossRef", "__tag": 4002, "value": "numpy.broadcast_arrays", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "numpy", "version": "*", "kind": "api", "path": "numpy:broadcast_arrays" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " started warning when the resulting array was written to. This warning was skipped when the array was used through the buffer interface (e.g. " }, { "__type": "InlineCode", "__tag": 4051, "value": "memoryview(arr)" }, { "__type": "Text", "__tag": 4046, "value": "). The same thing will now occur for the two protocols " }, { "__type": "InlineCode", "__tag": 4051, "value": "__array_interface__" }, { "__type": "Text", "__tag": 4046, "value": ", and " }, { "__type": "InlineCode", "__tag": 4051, "value": "__array_struct__" }, { "__type": "Text", "__tag": 4046, "value": " returning read-only buffers instead of giving a warning." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-16350" } ], "url": "https://github.com/numpy/numpy/pull/16350", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Writing to the result of " }, { "__type": "InlineRole", "__tag": 4003, "value": "numpy.broadcast_arrays", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": " will export readonly buffers" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "To stay in sync with the deprecation for " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.dtype(\"Complex64\")" }, { "__type": "Text", "__tag": 4046, "value": " and other numeric-style (capital case) types. These were removed from " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.sctypeDict" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.typeDict" }, { "__type": "Text", "__tag": 4046, "value": ". You should use the lower case versions instead. Note that " }, { "__type": "InlineCode", "__tag": 4051, "value": "\"Complex64\"" }, { "__type": "Text", "__tag": 4046, "value": " corresponds to " }, { "__type": "InlineCode", "__tag": 4051, "value": "\"complex128\"" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "\"Complex32\"" }, { "__type": "Text", "__tag": 4046, "value": " corresponds to " }, { "__type": "InlineCode", "__tag": 4051, "value": "\"complex64\"" }, { "__type": "Text", "__tag": 4046, "value": ". The numpy style (new) versions, denote the full size and not the size of the real/imaginary part." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-16554" } ], "url": "https://github.com/numpy/numpy/pull/16554", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Numeric-style type names have been removed from type dictionaries" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The previous behavior was to fall back to addition and add the two arrays, which was thought to be unexpected behavior for a concatenation function." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-16570" } ], "url": "https://github.com/numpy/numpy/pull/16570", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "The " }, { "__type": "InlineCode", "__tag": 4051, "value": "operator.concat" }, { "__type": "Text", "__tag": 4046, "value": " function now raises TypeError for array arguments" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "An abstract property " }, { "__type": "InlineCode", "__tag": 4051, "value": "nickname" }, { "__type": "Text", "__tag": 4046, "value": " has been removed from " }, { "__type": "InlineCode", "__tag": 4051, "value": "ABCPolyBase" }, { "__type": "Text", "__tag": 4046, "value": " as it was no longer used in the derived convenience classes. This may affect users who have derived classes from " }, { "__type": "InlineCode", "__tag": 4051, "value": "ABCPolyBase" }, { "__type": "Text", "__tag": 4046, "value": " and overridden the methods for representation and display, e.g. " }, { "__type": "InlineCode", "__tag": 4051, "value": "__str__" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "__repr__" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "_repr_latex" }, { "__type": "Text", "__tag": 4046, "value": ", etc." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-16589" } ], "url": "https://github.com/numpy/numpy/pull/16589", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "InlineCode", "__tag": 4051, "value": "nickname" }, { "__type": "Text", "__tag": 4046, "value": " attribute removed from ABCPolyBase" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Float and timedelta promotion consistently raises a TypeError. 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" }, { "__type": "InlineCode", "__tag": 4051, "value": "np.promote_types(\"uint64\", \"m8\")" }, { "__type": "Text", "__tag": 4046, "value": " aligns with " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.promote_types(\"m8\", \"uint64\")" }, { "__type": "Text", "__tag": 4046, "value": " now and both raise a TypeError. Previously, " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.promote_types(\"uint64\", \"m8\")" }, { "__type": "Text", "__tag": 4046, "value": " returned " }, { "__type": "InlineCode", "__tag": 4051, "value": "\"m8\"" }, { "__type": "Text", "__tag": 4046, "value": " which was considered a bug." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-16592" } ], "url": "https://github.com/numpy/numpy/pull/16592", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "InlineCode", "__tag": 4051, "value": "float->timedelta" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "uint64->timedelta" }, { "__type": "Text", "__tag": 4046, "value": " promotion will raise a TypeError" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Previously, " }, { "__type": "CrossRef", "__tag": 4002, "value": "numpy.genfromtxt", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "numpy", "version": "*", "kind": "api", "path": "numpy:genfromtxt" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " failed to unpack if it was called with " }, { "__type": "InlineCode", "__tag": 4051, "value": "unpack=True" }, { "__type": "Text", "__tag": 4046, "value": " and a structured datatype was passed to the " }, { "__type": "InlineCode", "__tag": 4051, "value": "dtype" }, { "__type": "Text", "__tag": 4046, "value": " argument (or " }, { "__type": "InlineCode", "__tag": 4051, "value": "dtype=None" }, { "__type": "Text", "__tag": 4046, "value": " was passed and a structured datatype was inferred). 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This bug potentially affects " }, { "__type": "InlineRole", "__tag": 4003, "value": "~numpy.mgrid", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineRole", "__tag": 4003, "value": "~numpy.ogrid", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineRole", "__tag": 4003, "value": "~numpy.r_", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": ", and " }, { "__type": "InlineRole", "__tag": 4003, "value": "~numpy.c_", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": " when an input with dtype other than the default " }, { "__type": "InlineCode", "__tag": 4051, "value": "float64" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "complex128" }, { "__type": "Text", "__tag": 4046, "value": " and equivalent Python types were used. 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This will also be useful in the future for any typing codegen since it will ensure the project is built before type checking." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-17123" } ], "url": "https://github.com/numpy/numpy/pull/17123", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "InlineCode", "__tag": 4051, "value": "mypy" }, { "__type": "Text", "__tag": 4046, "value": " tests can be run via runtests.py" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineRole", "__tag": 4003, "value": "~numpy.distutils", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": " allows negation of libraries when determining BLAS/LAPACK libraries. This may be used to remove an item from the library resolution phase, i.e. to disallow NetLIB libraries one could do:" } ] }, { "__type": "Code", "__tag": 4050, "value": "NPY_BLAS_ORDER='^blas' NPY_LAPACK_ORDER='^lapack' python setup.py build", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "That will use any of the accelerated libraries instead." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-17219" } ], "url": "https://github.com/numpy/numpy/pull/17219", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Negation of user defined BLAS/LAPACK detection order" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "It is now possible to pass " }, { "__type": "InlineCode", "__tag": 4051, "value": "-j" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "--cpu-baseline" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "--cpu-dispatch" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "--disable-optimization" }, { "__type": "Text", "__tag": 4046, "value": " flags to ASV build when the " }, { "__type": "InlineCode", "__tag": 4051, "value": "--bench-compare" }, { "__type": "Text", "__tag": 4046, "value": " argument is used." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-17284" } ], "url": "https://github.com/numpy/numpy/pull/17284", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Allow passing optimizations arguments to asv build" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Support for the nvfortran compiler, a version of pgfortran, has been added." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-17344" } ], "url": "https://github.com/numpy/numpy/pull/17344", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "The NVIDIA HPC SDK nvfortran compiler is now supported" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The " }, { "__type": "InlineCode", "__tag": 4051, "value": "dtype" }, { "__type": "Text", "__tag": 4046, "value": " option is now available for " }, { "__type": "CrossRef", "__tag": 4002, "value": "numpy.cov", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "numpy", "version": "*", "kind": "api", "path": "numpy:cov" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "CrossRef", "__tag": 4002, "value": "numpy.corrcoef", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "numpy", "version": "*", "kind": "api", "path": "numpy:corrcoef" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": ". It specifies which data-type the returned result should have. By default the functions still return a " }, { "__type": "CrossRef", "__tag": 4002, "value": "numpy.float64", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "numpy", "version": "*", "kind": "api", "path": "numpy:float64" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " result." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-17456" } ], "url": "https://github.com/numpy/numpy/pull/17456", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "InlineCode", "__tag": 4051, "value": "dtype" }, { "__type": "Text", "__tag": 4046, "value": " option for " }, { "__type": "InlineCode", "__tag": 4051, "value": "cov" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "corrcoef" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [], "title": [ { "__type": "Text", "__tag": 4046, "value": "Improvements" } ], "level": 1, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The string representation (" }, { "__type": "InlineCode", "__tag": 4051, "value": "__str__" }, { "__type": "Text", "__tag": 4046, "value": ") of all six polynomial types in " }, { "__type": "CrossRef", "__tag": 4002, "value": "numpy.polynomial", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "numpy", "version": "*", "kind": "api", "path": "numpy.polynomial" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " has been updated to give the polynomial as a mathematical expression instead of an array of coefficients. 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Remove it." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "(" }, { "__type": "Link", "__tag": 4049, "children": [ { "__type": "Text", "__tag": 4046, "value": "gh-15759" } ], "url": "https://github.com/numpy/numpy/pull/15759", "title": "" }, { "__type": "Text", "__tag": 4046, "value": ")" } ] } ], "title": [ { "__type": "Text", "__tag": 4046, "value": "Remove the Accelerate library as a candidate LAPACK library" } ], "level": 2, "target": null }, { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "If elements of an object array have a " }, { "__type": "InlineCode", "__tag": 4051, "value": "repr" }, { "__type": "Text", "__tag": 4046, "value": " containing new lines, then the wrapped lines will be aligned by column. 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