diff options
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 105 |
1 files changed, 91 insertions, 14 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 98a9cbaa4..88681b851 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -46,16 +46,16 @@ at the methods and attributes of an array. ndarray.__new__(subtype, shape=, dtype=float, buffer=None, offset=0, strides=None, order=None) - + There are two modes of creating an array using __new__: - 1) If buffer is None, then only shape, dtype, and order + 1) If buffer is None, then only shape, dtype, and order are used 2) If buffer is an object exporting the buffer interface, then all keywords are interpreted. - The dtype parameter can be any object that can be interpreted + The dtype parameter can be any object that can be interpreted as a numpy.dtype object. - - No __init__ method is needed because the array is fully + + No __init__ method is needed because the array is fully initialized after the __new__ method. """ ) @@ -82,7 +82,7 @@ add_newdoc('numpy.core', 'ndarray', ('__array_priority__', 'Array priority'), ('__array_finalize__', 'None') ] - ) + ) add_newdoc('numpy.core', 'flatiter', @@ -108,20 +108,20 @@ add_newdoc('numpy.core', 'broadcast', add_newdoc('numpy.core.multiarray','array', """array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0) - + Return an array from object with the specified date-type. Inputs: - object - an array, any object exposing the array interface, any - object whose __array__ method returns an array, or any + object - an array, any object exposing the array interface, any + object whose __array__ method returns an array, or any (nested) sequence. dtype - The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. This argument can only - be used to 'upcast' the array. For downcasting, use the + be used to 'upcast' the array. For downcasting, use the .astype(t) method. copy - If true, then force a copy. Otherwise a copy will only occur - if __array__ returns a copy, obj is a nested sequence, or + if __array__ returns a copy, obj is a nested sequence, or a copy is needed to satisfy any of the other requirements order - Specify the order of the array. If order is 'C', then the array will be in C-contiguous order (last-index varies the @@ -232,9 +232,9 @@ Join arrays together. The tuple of sequences (a1, a2, ...) are joined along the given axis (default is the first one) into a single numpy array. - + Example: - + >>> concatenate( ([0,1,2], [5,6,7]) ) array([0, 1, 2, 5, 6, 7]) @@ -313,7 +313,7 @@ then it is equivalent to condition.nonzero(). To group the indices by element, rather than dimension, use transpose(where(condition, | x, y)) - + instead. This always results in a 2d array, with a row of indices for each element that satisfies the condition. @@ -357,4 +357,81 @@ read-write buffer is attempted followed by a read-only buffer. """) +add_newdoc('numpy.core.multiarray', 'ndarray', ('sort', +"""a.sort(axis=-1, kind='quicksort') -> None. Sort a along the given axis. + +Keyword arguments: + +axis -- axis to be sorted (default -1) +kind -- sorting algorithm (default 'quicksort') + Possible values: 'quicksort', 'mergesort', or 'heapsort'. + +Returns: None. + +This method sorts a in place along the given axis using the algorithm +specified by the kind keyword. + +The various sorts may characterized by average speed, worst case +performance, need for work space, and whether they are stable. A stable +sort keeps items with the same key in the same relative order and is most +useful when used with argsort where the key might differ from the items +being sorted. The three available algorithms have the following properties: + +|------------------------------------------------------| +| kind | speed | worst case | work space | stable| +|------------------------------------------------------| +|'quicksort'| 1 | o(n) | 0 | no | +|'mergesort'| 2 | o(n*log(n)) | ~n/2 | yes | +|'heapsort' | 3 | o(n*log(n)) | 0 | no | +|------------------------------------------------------| + +All the sort algorithms make temporary copies of the data when the sort is +not along the last axis. Consequently, sorts along the last axis are faster +and use less space than sorts along other axis. + +""")) + +add_newdoc('numpy.core.multiarray', 'ndarray', ('argsort', +"""a.sort(axis=-1, kind='quicksort') -> indices that sort a along given axis. + +Keyword arguments: + +axis -- axis to be indirectly sorted (default -1) +kind -- sorting algorithm (default 'quicksort') + Possible values: 'quicksort', 'mergesort', or 'heapsort' + +Returns: array of indices that sort a along the specified axis. + +This method executes an indirect sort along the given axis using the +algorithm specified by the kind keyword. It returns an array of indices of +the same shape as a that index data along the given axis in sorted order. + +The various sorts are characterized by average speed, worst case +performance, need for work space, and whether they are stable. A stable +sort keeps items with the same key in the same relative order. The three +available algorithms have the following properties: + +|------------------------------------------------------| +| kind | speed | worst case | work space | stable| +|------------------------------------------------------| +|'quicksort'| 1 | o(n^2) | 0 | no | +|'mergesort'| 2 | o(n*log(n)) | ~n/2 | yes | +|'heapsort' | 3 | o(n*log(n)) | 0 | no | +|------------------------------------------------------| + +All the sort algorithms make temporary copies of the data when the sort is not +along the last axis. Consequently, sorts along the last axis are faster and use +less space than sorts along other axis. + +""")) + +add_newdoc('numpy.core.multiarray', 'ndarray', ('searchsorted', +"""a.searchsorted(v) + + Assuming that a is a 1-D array, in ascending order and represents + bin boundaries, then a.searchsorted(values) gives an array of bin + numbers, giving the bin into which each value would be placed. + This method is helpful for histograming. Note: No warning is + given if the boundaries, in a, are not in ascending order.; +""")) |