138 lines
5.3 KiB
Python
138 lines
5.3 KiB
Python
"""
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===================
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Universal Functions
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===================
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Ufuncs are, generally speaking, mathematical functions or operations that are
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applied element-by-element to the contents of an array. That is, the result
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in each output array element only depends on the value in the corresponding
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input array (or arrays) and on no other array elements. NumPy comes with a
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large suite of ufuncs, and scipy extends that suite substantially. The simplest
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example is the addition operator: ::
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>>> np.array([0,2,3,4]) + np.array([1,1,-1,2])
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array([1, 3, 2, 6])
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The unfunc module lists all the available ufuncs in numpy. Documentation on
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the specific ufuncs may be found in those modules. This documentation is
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intended to address the more general aspects of unfuncs common to most of
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them. All of the ufuncs that make use of Python operators (e.g., +, -, etc.)
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have equivalent functions defined (e.g. add() for +)
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Type coercion
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=============
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What happens when a binary operator (e.g., +,-,\\*,/, etc) deals with arrays of
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two different types? What is the type of the result? Typically, the result is
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the higher of the two types. For example: ::
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float32 + float64 -> float64
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int8 + int32 -> int32
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int16 + float32 -> float32
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float32 + complex64 -> complex64
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There are some less obvious cases generally involving mixes of types
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(e.g. uints, ints and floats) where equal bit sizes for each are not
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capable of saving all the information in a different type of equivalent
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bit size. Some examples are int32 vs float32 or uint32 vs int32.
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Generally, the result is the higher type of larger size than both
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(if available). So: ::
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int32 + float32 -> float64
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uint32 + int32 -> int64
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Finally, the type coercion behavior when expressions involve Python
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scalars is different than that seen for arrays. Since Python has a
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limited number of types, combining a Python int with a dtype=np.int8
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array does not coerce to the higher type but instead, the type of the
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array prevails. So the rules for Python scalars combined with arrays is
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that the result will be that of the array equivalent the Python scalar
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if the Python scalar is of a higher 'kind' than the array (e.g., float
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vs. int), otherwise the resultant type will be that of the array.
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For example: ::
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Python int + int8 -> int8
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Python float + int8 -> float64
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ufunc methods
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=============
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Binary ufuncs support 4 methods.
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**.reduce(arr)** applies the binary operator to elements of the array in
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sequence. For example: ::
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>>> np.add.reduce(np.arange(10)) # adds all elements of array
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45
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For multidimensional arrays, the first dimension is reduced by default: ::
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>>> np.add.reduce(np.arange(10).reshape(2,5))
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array([ 5, 7, 9, 11, 13])
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The axis keyword can be used to specify different axes to reduce: ::
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>>> np.add.reduce(np.arange(10).reshape(2,5),axis=1)
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array([10, 35])
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**.accumulate(arr)** applies the binary operator and generates an an
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equivalently shaped array that includes the accumulated amount for each
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element of the array. A couple examples: ::
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>>> np.add.accumulate(np.arange(10))
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array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])
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>>> np.multiply.accumulate(np.arange(1,9))
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array([ 1, 2, 6, 24, 120, 720, 5040, 40320])
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The behavior for multidimensional arrays is the same as for .reduce(),
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as is the use of the axis keyword).
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**.reduceat(arr,indices)** allows one to apply reduce to selected parts
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of an array. It is a difficult method to understand. See the documentation
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at:
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**.outer(arr1,arr2)** generates an outer operation on the two arrays arr1 and
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arr2. It will work on multidimensional arrays (the shape of the result is
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the concatenation of the two input shapes.: ::
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>>> np.multiply.outer(np.arange(3),np.arange(4))
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array([[0, 0, 0, 0],
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[0, 1, 2, 3],
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[0, 2, 4, 6]])
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Output arguments
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================
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All ufuncs accept an optional output array. The array must be of the expected
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output shape. Beware that if the type of the output array is of a different
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(and lower) type than the output result, the results may be silently truncated
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or otherwise corrupted in the downcast to the lower type. This usage is useful
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when one wants to avoid creating large temporary arrays and instead allows one
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to reuse the same array memory repeatedly (at the expense of not being able to
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use more convenient operator notation in expressions). Note that when the
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output argument is used, the ufunc still returns a reference to the result.
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>>> x = np.arange(2)
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>>> np.add(np.arange(2),np.arange(2.),x)
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array([0, 2])
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>>> x
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array([0, 2])
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and & or as ufuncs
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==================
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Invariably people try to use the python 'and' and 'or' as logical operators
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(and quite understandably). But these operators do not behave as normal
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operators since Python treats these quite differently. They cannot be
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overloaded with array equivalents. Thus using 'and' or 'or' with an array
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results in an error. There are two alternatives:
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1) use the ufunc functions logical_and() and logical_or().
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2) use the bitwise operators & and \\|. The drawback of these is that if
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the arguments to these operators are not boolean arrays, the result is
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likely incorrect. On the other hand, most usages of logical_and and
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logical_or are with boolean arrays. As long as one is careful, this is
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a convenient way to apply these operators.
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"""
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from __future__ import division, absolute_import, print_function
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