{ "__type": "IngestedDoc", "__tag": 4010, "_content": { "Notes": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "When called with only scalars, " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.block" }, { "__type": "Text", "__tag": 4046, "value": " is equivalent to an ndarray call. So " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.block([[1, 2], [3, 4]])" }, { "__type": "Text", "__tag": 4046, "value": " is equivalent to " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.array([[1, 2], [3, 4]])" }, { "__type": "Text", "__tag": 4046, "value": "." } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This function does not enforce that the blocks lie on a fixed grid. " }, { "__type": "InlineCode", "__tag": 4051, "value": "np.block([[a, b], [c, d]])" }, { "__type": "Text", "__tag": 4046, "value": " is not restricted to arrays of the form " } ] }, { "__type": "Code", "__tag": 4050, "value": "AAAbb\nAAAbb\ncccDD", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "But is also allowed to produce, for some " }, { "__type": "InlineCode", "__tag": 4051, "value": "a, b, c, d" }, { "__type": "Text", "__tag": 4046, "value": " " } ] }, { "__type": "Code", "__tag": 4050, "value": "AAAbb\nAAAbb\ncDDDD", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Since concatenation happens along the last axis first, " }, { "__type": "CrossRef", "__tag": 4002, "value": "block", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "numpy:block" }, "kind": "module" }, { "__type": "Text", "__tag": 4046, "value": " is " }, { "__type": "Emphasis", "__tag": 4047, "children": [ { "__type": "Text", "__tag": 4046, "value": "not" } ] }, { "__type": "Text", "__tag": 4046, "value": " capable of producing the following directly " } ] }, { "__type": "Code", "__tag": 4050, "value": "AAAbb\ncccbb\ncccDD", "execution_status": null }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Matlab's \"square bracket stacking\", " }, { "__type": "InlineCode", "__tag": 4051, "value": "[A, B, ...; 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Instead, leading axes of size 1 are inserted, to make " }, { "__type": "InlineCode", "__tag": 4051, "value": "block.ndim" }, { "__type": "Text", "__tag": 4046, "value": " the same for all blocks. 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hstack([1, 2, 3])\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "a = np.array([1, 2, 3])\nb = np.array([4, 5, 6])\nnp.block([a, b, 10]) # hstack([a, b, 10])\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "A = np.ones((2, 2), int)\nB = 2 * A\nnp.block([A, B]) # hstack([A, B])\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nWith a list of depth 2, `block` can be used in place of `vstack`:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "a = np.array([1, 2, 3])\nb = np.array([4, 5, 6])\nnp.block([[a], [b]]) # vstack([a, b])\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "A = np.ones((2, 2), int)\nB = 2 * A\nnp.block([[A], [B]]) # vstack([A, B])\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nIt can also be used in place of `atleast_1d` and `atleast_2d`:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "a = np.array(0)\nb = np.array([1])\nnp.block([a]) # 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