diff options
Diffstat (limited to 'doc/source/reference')
| -rw-r--r-- | doc/source/reference/arrays.classes.rst | 9 | ||||
| -rw-r--r-- | doc/source/reference/arrays.indexing.rst | 10 | ||||
| -rw-r--r-- | doc/source/reference/c-api.array.rst | 51 | ||||
| -rw-r--r-- | doc/source/reference/c-api.generalized-ufuncs.rst | 66 | ||||
| -rw-r--r-- | doc/source/reference/c-api.iterator.rst | 95 | ||||
| -rw-r--r-- | doc/source/reference/c-api.types-and-structures.rst | 12 | ||||
| -rw-r--r-- | doc/source/reference/c-api.ufunc.rst | 16 | ||||
| -rw-r--r-- | doc/source/reference/routines.array-creation.rst | 2 | ||||
| -rw-r--r-- | doc/source/reference/routines.array-manipulation.rst | 2 | ||||
| -rw-r--r-- | doc/source/reference/routines.io.rst | 7 | ||||
| -rw-r--r-- | doc/source/reference/routines.ma.rst | 5 | ||||
| -rw-r--r-- | doc/source/reference/routines.maskna.rst | 11 | ||||
| -rw-r--r-- | doc/source/reference/routines.polynomials.classes.rst | 6 | ||||
| -rw-r--r-- | doc/source/reference/routines.sort.rst | 1 | ||||
| -rw-r--r-- | doc/source/reference/ufuncs.rst | 17 |
15 files changed, 177 insertions, 133 deletions
diff --git a/doc/source/reference/arrays.classes.rst b/doc/source/reference/arrays.classes.rst index 036185782..e77dfc31e 100644 --- a/doc/source/reference/arrays.classes.rst +++ b/doc/source/reference/arrays.classes.rst @@ -41,7 +41,7 @@ Numpy provides several hooks that classes can customize: .. function:: class.__numpy_ufunc__(self, ufunc, method, i, inputs, **kwargs) - .. versionadded:: 1.9 + .. versionadded:: 1.10 Any class (ndarray subclass or not) can define this method to override behavior of Numpy's ufuncs. This works quite similarly to @@ -267,13 +267,6 @@ they inherit from the ndarray): :meth:`.flush() <memmap.flush>` which must be called manually by the user to ensure that any changes to the array actually get written to disk. -.. note:: - - Memory-mapped arrays use the the Python memory-map object which - (prior to Python 2.5) does not allow files to be larger than a - certain size depending on the platform. This size is always - < 2GB even on 64-bit systems. - .. autosummary:: :toctree: generated/ diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst index d04f89897..ef0180e0f 100644 --- a/doc/source/reference/arrays.indexing.rst +++ b/doc/source/reference/arrays.indexing.rst @@ -31,9 +31,9 @@ integer, or a tuple of slice objects and integers. :const:`Ellipsis` and :const:`newaxis` objects can be interspersed with these as well. In order to remain backward compatible with a common usage in Numeric, basic slicing is also initiated if the selection object is -any sequence (such as a :class:`list`) containing :class:`slice` +any non-ndarray sequence (such as a :class:`list`) containing :class:`slice` objects, the :const:`Ellipsis` object, or the :const:`newaxis` object, -but no integer arrays or other embedded sequences. +but not for integer arrays or other embedded sequences. .. index:: triple: ndarray; special methods; getslice @@ -46,8 +46,8 @@ scalar <arrays.scalars>` representing the corresponding item. As in Python, all indices are zero-based: for the *i*-th index :math:`n_i`, the valid range is :math:`0 \le n_i < d_i` where :math:`d_i` is the *i*-th element of the shape of the array. Negative indices are -interpreted as counting from the end of the array (*i.e.*, if *i < 0*, -it means :math:`n_i + i`). +interpreted as counting from the end of the array (*i.e.*, if +:math:`n_i < 0`, it means :math:`n_i + d_i`). All arrays generated by basic slicing are always :term:`views <view>` @@ -84,7 +84,7 @@ concepts to remember include: - Assume *n* is the number of elements in the dimension being sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and - *n* for *k < 0* . If *j* is not given it defaults to *n* for *k > 0* + *n - 1* for *k < 0* . If *j* is not given it defaults to *n* for *k > 0* and -1 for *k < 0* . If *k* is not given it defaults to 1. Note that ``::`` is the same as ``:`` and means select all indices along this axis. diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst index 23355bc91..08eba243e 100644 --- a/doc/source/reference/c-api.array.rst +++ b/doc/source/reference/c-api.array.rst @@ -108,10 +108,13 @@ sub-types). .. cfunction:: int PyArray_FLAGS(PyArrayObject* arr) -.. cfunction:: int PyArray_ITEMSIZE(PyArrayObject* arr) +.. cfunction:: npy_intp PyArray_ITEMSIZE(PyArrayObject* arr) Return the itemsize for the elements of this array. + Note that, in the old API that was deprecated in version 1.7, this function + had the return type ``int``. + .. cfunction:: int PyArray_TYPE(PyArrayObject* arr) Return the (builtin) typenumber for the elements of this array. @@ -460,7 +463,7 @@ From other objects .. cvar:: NPY_ARRAY_IN_ARRAY - :cdata:`NPY_ARRAY_CONTIGUOUS` \| :cdata:`NPY_ARRAY_ALIGNED` + :cdata:`NPY_ARRAY_C_CONTIGUOUS` \| :cdata:`NPY_ARRAY_ALIGNED` .. cvar:: NPY_ARRAY_IN_FARRAY @@ -1632,11 +1635,11 @@ Conversion Shape Manipulation ^^^^^^^^^^^^^^^^^^ -.. cfunction:: PyObject* PyArray_Newshape(PyArrayObject* self, PyArray_Dims* newshape) +.. cfunction:: PyObject* PyArray_Newshape(PyArrayObject* self, PyArray_Dims* newshape, NPY_ORDER order) Result will be a new array (pointing to the same memory location - as *self* if possible), but having a shape given by *newshape* - . If the new shape is not compatible with the strides of *self*, + as *self* if possible), but having a shape given by *newshape*. + If the new shape is not compatible with the strides of *self*, then a copy of the array with the new specified shape will be returned. @@ -1645,6 +1648,7 @@ Shape Manipulation Equivalent to :meth:`ndarray.reshape` (*self*, *shape*) where *shape* is a sequence. Converts *shape* to a :ctype:`PyArray_Dims` structure and calls :cfunc:`PyArray_Newshape` internally. + For back-ward compatability -- Not recommended .. cfunction:: PyObject* PyArray_Squeeze(PyArrayObject* self) @@ -1805,14 +1809,23 @@ Item selection and manipulation :cfunc:`PyArray_Sort` (...) can also be used to sort the array directly. -.. cfunction:: PyObject* PyArray_SearchSorted(PyArrayObject* self, PyObject* values) +.. cfunction:: PyObject* PyArray_SearchSorted(PyArrayObject* self, PyObject* values, NPY_SEARCHSIDE side, PyObject* perm) + + Equivalent to :meth:`ndarray.searchsorted` (*self*, *values*, *side*, + *perm*). Assuming *self* is a 1-d array in ascending order, then the + output is an array of indices the same shape as *values* such that, if + the elements in *values* were inserted before the indices, the order of + *self* would be preserved. No checking is done on whether or not self is + in ascending order. - Equivalent to :meth:`ndarray.searchsorted` (*self*, *values*). Assuming - *self* is a 1-d array in ascending order representing bin - boundaries then the output is an array the same shape as *values* - of bin numbers, giving the bin into which each item in *values* - would be placed. No checking is done on whether or not self is in - ascending order. + The *side* argument indicates whther the index returned should be that of + the first suitable location (if :cdata:`NPY_SEARCHLEFT`) or of the last + (if :cdata:`NPY_SEARCHRIGHT`). + + The *sorter* argument, if not ``NULL``, must be a 1D array of integer + indices the same length as *self*, that sorts it into ascending order. + This is typically the result of a call to :cfunc:`PyArray_ArgSort` (...) + Binary search is used to find the required insertion points. .. cfunction:: int PyArray_Partition(PyArrayObject *self, PyArrayObject * ktharray, int axis, NPY_SELECTKIND which) @@ -1886,10 +1899,10 @@ Calculation .. note:: - The out argument specifies where to place the result. If out is - NULL, then the output array is created, otherwise the output is - placed in out which must be the correct size and type. A new - reference to the ouput array is always returned even when out + The out argument specifies where to place the result. If out is + NULL, then the output array is created, otherwise the output is + placed in out which must be the correct size and type. A new + reference to the ouput array is always returned even when out is not NULL. The caller of the routine has the responsability to ``DECREF`` out if not NULL or a memory-leak will occur. @@ -3103,6 +3116,12 @@ Group 1 Useful to regain the GIL in situations where it was released using the BEGIN form of this macro. + .. cfunction:: NPY_BEGIN_THREADS_THRESHOLDED(int loop_size) + + Useful to release the GIL only if *loop_size* exceeds a + minimum threshold, currently set to 500. Should be matched + with a .. cmacro::`NPY_END_THREADS` to regain the GIL. + Group 2 """"""" diff --git a/doc/source/reference/c-api.generalized-ufuncs.rst b/doc/source/reference/c-api.generalized-ufuncs.rst index 14f33efcb..92dc8aec0 100644 --- a/doc/source/reference/c-api.generalized-ufuncs.rst +++ b/doc/source/reference/c-api.generalized-ufuncs.rst @@ -18,30 +18,52 @@ arguments is called the "signature" of a ufunc. For example, the ufunc numpy.add has signature ``(),()->()`` defining two scalar inputs and one scalar output. -Another example is the function ``inner1d(a,b)`` with a signature of -``(i),(i)->()``. This applies the inner product along the last axis of +Another example is the function ``inner1d(a, b)`` with a signature of +``(i),(i)->()``. This applies the inner product along the last axis of each input, but keeps the remaining indices intact. -For example, where ``a`` is of shape ``(3,5,N)`` -and ``b`` is of shape ``(5,N)``, this will return an output of shape ``(3,5)``. +For example, where ``a`` is of shape ``(3, 5, N)`` and ``b`` is of shape +``(5, N)``, this will return an output of shape ``(3,5)``. The underlying elementary function is called ``3 * 5`` times. In the signature, we specify one core dimension ``(i)`` for each input and zero core dimensions ``()`` for the output, since it takes two 1-d arrays and returns a scalar. By using the same name ``i``, we specify that the two -corresponding dimensions should be of the same size (or one of them is -of size 1 and will be broadcasted). +corresponding dimensions should be of the same size. The dimensions beyond the core dimensions are called "loop" dimensions. In -the above example, this corresponds to ``(3,5)``. - -The usual numpy "broadcasting" rules apply, where the signature -determines how the dimensions of each input/output object are split -into core and loop dimensions: - -#. While an input array has a smaller dimensionality than the corresponding - number of core dimensions, 1's are pre-pended to its shape. +the above example, this corresponds to ``(3, 5)``. + +The signature determines how the dimensions of each input/output array are +split into core and loop dimensions: + +#. Each dimension in the signature is matched to a dimension of the + corresponding passed-in array, starting from the end of the shape tuple. + These are the core dimensions, and they must be present in the arrays, or + an error will be raised. +#. Core dimensions assigned to the same label in the signature (e.g. the + ``i`` in ``inner1d``'s ``(i),(i)->()``) must have exactly matching sizes, + no broadcasting is performed. #. The core dimensions are removed from all inputs and the remaining - dimensions are broadcasted; defining the loop dimensions. -#. The output is given by the loop dimensions plus the output core dimensions. + dimensions are broadcast together, defining the loop dimensions. +#. The shape of each output is determined from the loop dimensions plus the + output's core dimensions + +Typically, the size of all core dimensions in an output will be determined by +the size of a core dimension with the same label in an input array. This is +not a requirement, and it is possible to define a signature where a label +comes up for the first time in an output, although some precautions must be +taken when calling such a function. An example would be the function +``euclidean_pdist(a)``, with signature ``(n,d)->(p)``, that given an array of +``n`` ``d``-dimensional vectors, computes all unique pairwise Euclidean +distances among them. The output dimension ``p`` must therefore be equal to +``n * (n - 1) / 2``, but it is the caller's responsibility to pass in an +output array of the right size. If the size of a core dimension of an output +cannot be determined from a passed in input or output array, an error will be +raised. + +Note: Prior to Numpy 1.10.0, less strict checks were in place: missing core +dimensions were created by prepending 1's to the shape as necessary, core +dimensions with the same label were broadcast together, and undetermined +dimensions were created with size 1. Definitions @@ -70,7 +92,7 @@ Core Dimension Dimension Name A dimension name represents a core dimension in the signature. Different dimensions may share a name, indicating that they are of - the same size (or are broadcastable). + the same size. Dimension Index A dimension index is an integer representing a dimension name. It @@ -93,8 +115,7 @@ following format: * Dimension lists for different arguments are separated by ``","``. Input/output arguments are separated by ``"->"``. * If one uses the same dimension name in multiple locations, this - enforces the same size (or broadcastable size) of the corresponding - dimensions. + enforces the same size of the corresponding dimensions. The formal syntax of signatures is as follows:: @@ -111,10 +132,9 @@ The formal syntax of signatures is as follows:: Notes: #. All quotes are for clarity. -#. Core dimensions that share the same name must be broadcastable, as - the two ``i`` in our example above. Each dimension name typically - corresponding to one level of looping in the elementary function's - implementation. +#. Core dimensions that share the same name must have the exact same size. + Each dimension name typically corresponds to one level of looping in the + elementary function's implementation. #. White spaces are ignored. Here are some examples of signatures: diff --git a/doc/source/reference/c-api.iterator.rst b/doc/source/reference/c-api.iterator.rst index 084fdcbce..1d90ce302 100644 --- a/doc/source/reference/c-api.iterator.rst +++ b/doc/source/reference/c-api.iterator.rst @@ -18,8 +18,6 @@ preservation of memory layouts, and buffering of data with the wrong alignment or type, without requiring difficult coding. This page documents the API for the iterator. -The C-API naming convention chosen is based on the one in the numpy-refactor -branch, so will integrate naturally into the refactored code base. The iterator is named ``NpyIter`` and functions are named ``NpyIter_*``. @@ -28,51 +26,6 @@ which may be of interest for those using this C API. In many instances, testing out ideas by creating the iterator in Python is a good idea before writing the C iteration code. -Converting from Previous NumPy Iterators ----------------------------------------- - -The existing iterator API includes functions like PyArrayIter_Check, -PyArray_Iter* and PyArray_ITER_*. The multi-iterator array includes -PyArray_MultiIter*, PyArray_Broadcast, and PyArray_RemoveSmallest. The -new iterator design replaces all of this functionality with a single object -and associated API. One goal of the new API is that all uses of the -existing iterator should be replaceable with the new iterator without -significant effort. In 1.6, the major exception to this is the neighborhood -iterator, which does not have corresponding features in this iterator. - -Here is a conversion table for which functions to use with the new iterator: - -===================================== ============================================= -*Iterator Functions* -:cfunc:`PyArray_IterNew` :cfunc:`NpyIter_New` -:cfunc:`PyArray_IterAllButAxis` :cfunc:`NpyIter_New` + ``axes`` parameter **or** - Iterator flag :cdata:`NPY_ITER_EXTERNAL_LOOP` -:cfunc:`PyArray_BroadcastToShape` **NOT SUPPORTED** (Use the support for - multiple operands instead.) -:cfunc:`PyArrayIter_Check` Will need to add this in Python exposure -:cfunc:`PyArray_ITER_RESET` :cfunc:`NpyIter_Reset` -:cfunc:`PyArray_ITER_NEXT` Function pointer from :cfunc:`NpyIter_GetIterNext` -:cfunc:`PyArray_ITER_DATA` :cfunc:`NpyIter_GetDataPtrArray` -:cfunc:`PyArray_ITER_GOTO` :cfunc:`NpyIter_GotoMultiIndex` -:cfunc:`PyArray_ITER_GOTO1D` :cfunc:`NpyIter_GotoIndex` or - :cfunc:`NpyIter_GotoIterIndex` -:cfunc:`PyArray_ITER_NOTDONE` Return value of ``iternext`` function pointer -*Multi-iterator Functions* -:cfunc:`PyArray_MultiIterNew` :cfunc:`NpyIter_MultiNew` -:cfunc:`PyArray_MultiIter_RESET` :cfunc:`NpyIter_Reset` -:cfunc:`PyArray_MultiIter_NEXT` Function pointer from :cfunc:`NpyIter_GetIterNext` -:cfunc:`PyArray_MultiIter_DATA` :cfunc:`NpyIter_GetDataPtrArray` -:cfunc:`PyArray_MultiIter_NEXTi` **NOT SUPPORTED** (always lock-step iteration) -:cfunc:`PyArray_MultiIter_GOTO` :cfunc:`NpyIter_GotoMultiIndex` -:cfunc:`PyArray_MultiIter_GOTO1D` :cfunc:`NpyIter_GotoIndex` or - :cfunc:`NpyIter_GotoIterIndex` -:cfunc:`PyArray_MultiIter_NOTDONE` Return value of ``iternext`` function pointer -:cfunc:`PyArray_Broadcast` Handled by :cfunc:`NpyIter_MultiNew` -:cfunc:`PyArray_RemoveSmallest` Iterator flag :cdata:`NPY_ITER_EXTERNAL_LOOP` -*Other Functions* -:cfunc:`PyArray_ConvertToCommonType` Iterator flag :cdata:`NPY_ITER_COMMON_DTYPE` -===================================== ============================================= - Simple Iteration Example ------------------------ @@ -91,6 +44,7 @@ number of non-zero elements in an array. NpyIter* iter; NpyIter_IterNextFunc *iternext; char** dataptr; + npy_intp nonzero_count; npy_intp* strideptr,* innersizeptr; /* Handle zero-sized arrays specially */ @@ -138,7 +92,7 @@ number of non-zero elements in an array. /* The location of the inner loop size which the iterator may update */ innersizeptr = NpyIter_GetInnerLoopSizePtr(iter); - /* The iteration loop */ + nonzero_count = 0; do { /* Get the inner loop data/stride/count values */ char* data = *dataptr; @@ -1296,3 +1250,48 @@ functions provide that information. .. index:: pair: iterator; C-API + +Converting from Previous NumPy Iterators +---------------------------------------- + +The old iterator API includes functions like PyArrayIter_Check, +PyArray_Iter* and PyArray_ITER_*. The multi-iterator array includes +PyArray_MultiIter*, PyArray_Broadcast, and PyArray_RemoveSmallest. The +new iterator design replaces all of this functionality with a single object +and associated API. One goal of the new API is that all uses of the +existing iterator should be replaceable with the new iterator without +significant effort. In 1.6, the major exception to this is the neighborhood +iterator, which does not have corresponding features in this iterator. + +Here is a conversion table for which functions to use with the new iterator: + +===================================== ============================================= +*Iterator Functions* +:cfunc:`PyArray_IterNew` :cfunc:`NpyIter_New` +:cfunc:`PyArray_IterAllButAxis` :cfunc:`NpyIter_New` + ``axes`` parameter **or** + Iterator flag :cdata:`NPY_ITER_EXTERNAL_LOOP` +:cfunc:`PyArray_BroadcastToShape` **NOT SUPPORTED** (Use the support for + multiple operands instead.) +:cfunc:`PyArrayIter_Check` Will need to add this in Python exposure +:cfunc:`PyArray_ITER_RESET` :cfunc:`NpyIter_Reset` +:cfunc:`PyArray_ITER_NEXT` Function pointer from :cfunc:`NpyIter_GetIterNext` +:cfunc:`PyArray_ITER_DATA` :cfunc:`NpyIter_GetDataPtrArray` +:cfunc:`PyArray_ITER_GOTO` :cfunc:`NpyIter_GotoMultiIndex` +:cfunc:`PyArray_ITER_GOTO1D` :cfunc:`NpyIter_GotoIndex` or + :cfunc:`NpyIter_GotoIterIndex` +:cfunc:`PyArray_ITER_NOTDONE` Return value of ``iternext`` function pointer +*Multi-iterator Functions* +:cfunc:`PyArray_MultiIterNew` :cfunc:`NpyIter_MultiNew` +:cfunc:`PyArray_MultiIter_RESET` :cfunc:`NpyIter_Reset` +:cfunc:`PyArray_MultiIter_NEXT` Function pointer from :cfunc:`NpyIter_GetIterNext` +:cfunc:`PyArray_MultiIter_DATA` :cfunc:`NpyIter_GetDataPtrArray` +:cfunc:`PyArray_MultiIter_NEXTi` **NOT SUPPORTED** (always lock-step iteration) +:cfunc:`PyArray_MultiIter_GOTO` :cfunc:`NpyIter_GotoMultiIndex` +:cfunc:`PyArray_MultiIter_GOTO1D` :cfunc:`NpyIter_GotoIndex` or + :cfunc:`NpyIter_GotoIterIndex` +:cfunc:`PyArray_MultiIter_NOTDONE` Return value of ``iternext`` function pointer +:cfunc:`PyArray_Broadcast` Handled by :cfunc:`NpyIter_MultiNew` +:cfunc:`PyArray_RemoveSmallest` Iterator flag :cdata:`NPY_ITER_EXTERNAL_LOOP` +*Other Functions* +:cfunc:`PyArray_ConvertToCommonType` Iterator flag :cdata:`NPY_ITER_COMMON_DTYPE` +===================================== ============================================= diff --git a/doc/source/reference/c-api.types-and-structures.rst b/doc/source/reference/c-api.types-and-structures.rst index f1e216a5c..473e25010 100644 --- a/doc/source/reference/c-api.types-and-structures.rst +++ b/doc/source/reference/c-api.types-and-structures.rst @@ -244,7 +244,7 @@ PyArrayDescr_Type Indicates that items of this data-type must be reference counted (using :cfunc:`Py_INCREF` and :cfunc:`Py_DECREF` ). - .. cvar:: NPY_ITEM_LISTPICKLE + .. cvar:: NPY_LIST_PICKLE Indicates arrays of this data-type must be converted to a list before pickling. @@ -646,9 +646,9 @@ PyUFunc_Type void **data; int ntypes; int check_return; - char *name; + const char *name; char *types; - char *doc; + const char *doc; void *ptr; PyObject *obj; PyObject *userloops; @@ -1031,9 +1031,9 @@ PyArray_Chunk This is equivalent to the buffer object structure in Python up to the ptr member. On 32-bit platforms (*i.e.* if :cdata:`NPY_SIZEOF_INT` - == :cdata:`NPY_SIZEOF_INTP` ) or in Python 2.5, the len member also - matches an equivalent member of the buffer object. It is useful to - represent a generic single- segment chunk of memory. + == :cdata:`NPY_SIZEOF_INTP`), the len member also matches an equivalent + member of the buffer object. It is useful to represent a generic + single-segment chunk of memory. .. code-block:: c diff --git a/doc/source/reference/c-api.ufunc.rst b/doc/source/reference/c-api.ufunc.rst index 71abffd04..3673958d9 100644 --- a/doc/source/reference/c-api.ufunc.rst +++ b/doc/source/reference/c-api.ufunc.rst @@ -114,7 +114,6 @@ Functions data type, it will be internally upcast to the int_ (or uint) data type. - :param doc: Allows passing in a documentation string to be stored with the ufunc. The documentation string should not contain the name @@ -128,6 +127,21 @@ Functions structure and it does get set with this value when the ufunc object is created. +.. cfunction:: PyObject* PyUFunc_FromFuncAndDataAndSignature(PyUFuncGenericFunction* func, + void** data, char* types, int ntypes, int nin, int nout, int identity, + char* name, char* doc, int check_return, char *signature) + + This function is very similar to PyUFunc_FromFuncAndData above, but has + an extra *signature* argument, to define generalized universal functions. + Similarly to how ufuncs are built around an element-by-element operation, + gufuncs are around subarray-by-subarray operations, the signature defining + the subarrays to operate on. + + :param signature: + The signature for the new gufunc. Setting it to NULL is equivalent + to calling PyUFunc_FromFuncAndData. A copy of the string is made, + so the passed in buffer can be freed. + .. cfunction:: int PyUFunc_RegisterLoopForType(PyUFuncObject* ufunc, int usertype, PyUFuncGenericFunction function, int* arg_types, void* data) diff --git a/doc/source/reference/routines.array-creation.rst b/doc/source/reference/routines.array-creation.rst index 23b35243b..c7c6ab815 100644 --- a/doc/source/reference/routines.array-creation.rst +++ b/doc/source/reference/routines.array-creation.rst @@ -20,6 +20,8 @@ Ones and zeros ones_like zeros zeros_like + full + full_like From existing data ------------------ diff --git a/doc/source/reference/routines.array-manipulation.rst b/doc/source/reference/routines.array-manipulation.rst index ca97bb479..81af0a315 100644 --- a/doc/source/reference/routines.array-manipulation.rst +++ b/doc/source/reference/routines.array-manipulation.rst @@ -54,6 +54,8 @@ Changing kind of array asmatrix asfarray asfortranarray + ascontiguousarray + asarray_chkfinite asscalar require diff --git a/doc/source/reference/routines.io.rst b/doc/source/reference/routines.io.rst index 26afbfb4f..b99754912 100644 --- a/doc/source/reference/routines.io.rst +++ b/doc/source/reference/routines.io.rst @@ -3,8 +3,8 @@ Input and output .. currentmodule:: numpy -NPZ files ---------- +Numpy binary files (NPY, NPZ) +----------------------------- .. autosummary:: :toctree: generated/ @@ -13,6 +13,9 @@ NPZ files savez savez_compressed +The format of these binary file types is documented in +http://docs.scipy.org/doc/numpy/neps/npy-format.html + Text files ---------- .. autosummary:: diff --git a/doc/source/reference/routines.ma.rst b/doc/source/reference/routines.ma.rst index 5cb38e83f..2408899b3 100644 --- a/doc/source/reference/routines.ma.rst +++ b/doc/source/reference/routines.ma.rst @@ -65,6 +65,8 @@ Inspecting the array ma.nonzero ma.shape ma.size + ma.is_masked + ma.is_mask ma.MaskedArray.data ma.MaskedArray.mask @@ -141,6 +143,7 @@ Joining arrays ma.column_stack ma.concatenate + ma.append ma.dstack ma.hstack ma.vstack @@ -181,6 +184,8 @@ Finding masked data ma.flatnotmasked_edges ma.notmasked_contiguous ma.notmasked_edges + ma.clump_masked + ma.clump_unmasked Modifying a mask diff --git a/doc/source/reference/routines.maskna.rst b/doc/source/reference/routines.maskna.rst deleted file mode 100644 index 2910acbac..000000000 --- a/doc/source/reference/routines.maskna.rst +++ /dev/null @@ -1,11 +0,0 @@ -NA-Masked Array Routines -======================== - -.. currentmodule:: numpy - -NA Values ---------- -.. autosummary:: - :toctree: generated/ - - isna diff --git a/doc/source/reference/routines.polynomials.classes.rst b/doc/source/reference/routines.polynomials.classes.rst index 14729f08b..c40795434 100644 --- a/doc/source/reference/routines.polynomials.classes.rst +++ b/doc/source/reference/routines.polynomials.classes.rst @@ -211,7 +211,7 @@ constant are 0, but both can be specified.:: In the first case the lower bound of the integration is set to -1 and the integration constant is 0. In the second the constant of integration is set to 1 as well. Differentiation is simpler since the only option is the -number times the polynomial is differentiated:: +number of times the polynomial is differentiated:: >>> p = P([1, 2, 3]) >>> p.deriv(1) @@ -270,7 +270,7 @@ polynomials up to degree 5 are plotted below. >>> import matplotlib.pyplot as plt >>> from numpy.polynomial import Chebyshev as T >>> x = np.linspace(-1, 1, 100) - >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="T_%d"%i) + >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="$T_%d$"%i) ... >>> plt.legend(loc="upper left") <matplotlib.legend.Legend object at 0x3b3ee10> @@ -284,7 +284,7 @@ The same plots over the range -2 <= `x` <= 2 look very different: >>> import matplotlib.pyplot as plt >>> from numpy.polynomial import Chebyshev as T >>> x = np.linspace(-2, 2, 100) - >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="T_%d"%i) + >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="$T_%d$"%i) ... >>> plt.legend(loc="lower right") <matplotlib.legend.Legend object at 0x3b3ee10> diff --git a/doc/source/reference/routines.sort.rst b/doc/source/reference/routines.sort.rst index 2b36aec75..c22fa0cd6 100644 --- a/doc/source/reference/routines.sort.rst +++ b/doc/source/reference/routines.sort.rst @@ -39,4 +39,3 @@ Counting :toctree: generated/ count_nonzero - count_reduce_items diff --git a/doc/source/reference/ufuncs.rst b/doc/source/reference/ufuncs.rst index 2ae794f59..3d6112058 100644 --- a/doc/source/reference/ufuncs.rst +++ b/doc/source/reference/ufuncs.rst @@ -313,16 +313,15 @@ advanced usage and will not typically be used. .. versionadded:: 1.6 + May be 'no', 'equiv', 'safe', 'same_kind', or 'unsafe'. + See :func:`can_cast` for explanations of the parameter values. + Provides a policy for what kind of casting is permitted. For compatibility - with previous versions of NumPy, this defaults to 'unsafe'. May be 'no', - 'equiv', 'safe', 'same_kind', or 'unsafe'. See :func:`can_cast` for - explanations of the parameter values. - - In a future version of numpy, this argument will default to - 'same_kind'. As part of this transition, starting in version 1.7, - ufuncs will produce a DeprecationWarning for calls which are - allowed under the 'unsafe' rules, but not under the 'same_kind' - rules. + with previous versions of NumPy, this defaults to 'unsafe' for numpy < 1.7. + In numpy 1.7 a transition to 'same_kind' was begun where ufuncs produce a + DeprecationWarning for calls which are allowed under the 'unsafe' + rules, but not under the 'same_kind' rules. In numpy 1.10 the default + will be 'same_kind'. *order* |