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| -rw-r--r-- | doc/source/glossary.rst | 117 |
1 files changed, 48 insertions, 69 deletions
diff --git a/doc/source/glossary.rst b/doc/source/glossary.rst index 90e8ccd91..ea2a9a6f2 100644 --- a/doc/source/glossary.rst +++ b/doc/source/glossary.rst @@ -19,34 +19,32 @@ Glossary ``...`` **When indexing an array**, shorthand that the missing axes, if they - exist, are full slices. + exist, are full slices. :: - .. code:: python - - >>> a.shape - (2, 3, 4) + >>> a.shape + (2, 3, 4) - >>> a[...].shape - (2, 3, 4) + >>> a[...].shape + (2, 3, 4) - >>> a[...,0].shape - (2, 3) + >>> a[...,0].shape + (2, 3) - >>> a[0,...].shape - (3, 4) + >>> a[0,...].shape + (3, 4) - >>> a[0,...,0].shape - (3,) + >>> a[0,...,0].shape + (3,) - It can be used at most once: :: + It can be used at most once: :: - >>> a[0,...,0,...].shape - --------------------------------------------------------------------------- - IndexError Traceback (most recent call last) - <ipython-input-45-e12b83e31ec3> in <module> - ----> 1 a[0,...,0,...].shape + >>> a[0,...,0,...].shape + --------------------------------------------------------------------------- + IndexError Traceback (most recent call last) + <ipython-input-45-e12b83e31ec3> in <module> + ----> 1 a[0,...,0,...].shape - IndexError: an index can only have a single ellipsis ('...') + IndexError: an index can only have a single ellipsis ('...') For details, see :doc:`Indexing. <reference/arrays.indexing>` @@ -60,9 +58,7 @@ Glossary The Python `slice <https://docs.python.org/3/glossary.html#term-slice>`_ operator. In ndarrays, slicing can be applied to every - axis: - - .. code:: python + axis: :: >>> a = np.arange(24).reshape(2,3,4) a @@ -78,9 +74,7 @@ Glossary array([[[16, 17, 18], [20, 21, 22]]]) - Trailing slices can be omitted: - - .. code:: python + Trailing slices can be omitted: :: >>> a[1] == a[1,:,:] array([[ True, True, True, True], @@ -127,7 +121,7 @@ Glossary The operation can be visualized this way: Imagine a slice of array ``a`` where axis X has a fixed index - and the other dimensions are left full (``:``). + and the other dimensions are left full (``:``). :: >>> a.shape (2,3,4) @@ -136,7 +130,7 @@ Glossary The slice has ``a``'s shape with the X dimension deleted. Saying an operation ``op`` is ``performed along X`` means that ``op`` takes as its - operands slices having every value of X: + operands slices having every value of X: :: >>> np.sum(a,axis=1) == a[:,0,:] + a[:,1,:] + a[:,2,:] array([[ True, True, True, True], @@ -157,9 +151,7 @@ Glossary and scalars this category includes lists (possibly nested and with different element types) and tuples. Any argument accepted by :doc:`numpy.array <reference/generated/numpy.array>` - is array_like. - - .. code:: + is array_like. :: >>> x = np.array([[1,2.0],[0,0],(1+1j,3.)]) @@ -172,7 +164,8 @@ Glossary array scalar For uniformity in handling operands, NumPy treats - a :doc:`scalar <reference/arrays.scalars>` as an array of zero dimension. + a :doc:`scalar <reference/arrays.scalars>` as an array of zero + dimension. `attribute <https://docs.python.org/3/glossary.html#term-attribute>`_ @@ -189,19 +182,17 @@ Glossary In higher dimensions the picture changes. NumPy prints higher-dimensional vectors as replications of row-by-column building - blocks, as in this three-dimensional vector: - - .. code:: python + blocks, as in this three-dimensional vector: :: - >>> a - array([[[ 0, 1, 2], - [ 3, 4, 5]], + >>> a + array([[[ 0, 1, 2], + [ 3, 4, 5]], - [[ 6, 7, 8], - [ 9, 10, 11]]]) + [[ 6, 7, 8], + [ 9, 10, 11]]]) - >>> a.shape - (2, 2, 3) + >>> a.shape + (2, 2, 3) ``a`` is depicted as a two-element array whose elements are 2x3 vectors. From this point of view, rows and columns are the final two axes, @@ -218,21 +209,19 @@ Glossary A convenient way to count dimensions in a printed vector is to count ``[`` symbols after the open-parenthesis. This is - useful in distinguishing, say, a (1,2,3) shape from a (2,3) shape: + useful in distinguishing, say, a (1,2,3) shape from a (2,3) shape: :: - .. code:: python - - >>> a.shape - (2, 3) - >>> a - array([[0, 1, 2], - [3, 4, 5]]) + >>> a.shape + (2, 3) + >>> a + array([[0, 1, 2], + [3, 4, 5]]) - >>> a.shape - (1, 2, 3) - >>> a - array([[[0, 1, 2], - [3, 4, 5]]]) + >>> a.shape + (1, 2, 3) + >>> a + array([[[0, 1, 2], + [3, 4, 5]]]) .base @@ -243,7 +232,7 @@ Glossary may may be borrowing the memory from still another object, so the owning object may be ``a.base.base.base...``. Despite advice to the contrary, testing ``base`` is not a surefire way to determine if two - arrays are `views. <#term-view>`_ + arrays are :term:`view`\ s. `big-endian <https://en.wikipedia.org/wiki/Endianness>`_ @@ -260,9 +249,7 @@ Glossary different sizes as if all were the same size. When NumPy operates on two arrays, it works element by - element -- for instance, ``c = a * b`` is - - .. code:: + element -- for instance, ``c = a * b`` is :: c[0,0,0] = a[0,0,0] * b[0,0,0] c[0,0,1] = a[0,0,1] * b[0,0,1] @@ -371,7 +358,7 @@ Glossary Bad or missing data can be cleanly ignored by putting it in a masked array, which has an internal boolean array indicating invalid - entries. Operations with masked arrays ignore these entries. + entries. Operations with masked arrays ignore these entries. :: >>> a = np.ma.masked_array([np.nan, 2, np.nan], [True, False, True]) >>> a @@ -482,9 +469,7 @@ Glossary subarray - An array nested in a :term:`structured data type`: - - .. code:: python + An array nested in a :term:`structured data type`: :: >>> dt = np.dtype([('a', np.int32), ('b', np.float32, (3,))]) >>> np.zeros(3, dtype=dt) @@ -497,18 +482,12 @@ Glossary An element of a strctured datatype that behaves like an ndarray. .. - The entry is in numpy.doc.structured_arrays:51 and - so can't be deleted. title An alias for a field name in a structured datatype. - .. - The entry is referenced in numpy.doc.structured_arrays:242 - and so can't be deleted. - `tuple <https://docs.python.org/3/glossary.html#term-tuple>`_ \ |