from datetime import datetime

import numpy as np

# 纯Python写的程序

def pythonsum(n):

    a = []

    b = []

    c = []

    for i in range(n):

        a.append(i)

        b.append(i)

        c.append(a[i]**2 + b[i]**3)

    return c

# NumPy写的程序

def numpysum(n):

    a = np.arange(n,dtype=object) ** 2

    b = np.arange(n,dtype=object) ** 3

    c = a + b

    return c

# 进行比较测试

size = 30000

start = datetime.now()

c1 = pythonsum(size)

delta = datetime.now()-start

print("The last 2 elements of the sum",c1[-2:])

print("PythonSum elapsed time in microsecond",delta.microseconds)

start = datetime.now()

c2 = numpysum(size)

delta = datetime.now()-start

print("The last 2 elements of the sum",c2[-2:])

print("NumPySum elapsed time in microsecond",delta.microseconds)

# 10000的情况下：

#The last 2 elements of the sum [999500079996, 999800010000]

#PythonSum elapsed time in microsecond 15625

#The last 2 elements of the sum [999500079996 999800010000]

#NumPySum elapsed time in microsecond 15623

# 20000的情况下：

#The last 2 elements of the sum [7998000159996, 7999200020000]

#PythonSum elapsed time in microsecond 31247

#The last 2 elements of the sum [7998000159996 7999200020000]

#NumPySum elapsed time in microsecond 0

# 30000的情况下：

#The last 2 elements of the sum [26995500239996, 26998200030000]

#PythonSum elapsed time in microsecond 46871

#The last 2 elements of the sum [26995500239996 26998200030000]

#NumPySum elapsed time in microsecond 0

# -*- coding: utf-8 -*-

"""

Spyder Editor

This is a temporary script file.

"""

import numpy as np

help(np.arange)

#Help on built-in function arange in module numpy.core.multiarray:

#

#arange(...)

#    arange([start,] stop[, step,], dtype=None)

#

#    Return evenly spaced values within a given interval.

#

#    Values are generated within the half-open interval ``[start, stop)``

#    (in other words, the interval including `start` but excluding `stop`).

#    For integer arguments the function is equivalent to the Python built-in

#    `range <http://docs.python.org/lib/built-in-funcs.html>`_ function,

#    but returns an ndarray rather than a list.

#

#    When using a non-integer step, such as 0.1, the results will often not

#    be consistent.  It is better to use ``linspace`` for these cases.

#

#    Parameters

#    ----------

#    start : number, optional

#        Start of interval.  The interval includes this value.  The default

#        start value is 0.

#    stop : number

#        End of interval.  The interval does not include this value, except

#        in some cases where `step` is not an integer and floating point

#        round-off affects the length of `out`.

#    step : number, optional

#        Spacing between values.  For any output `out`, this is the distance

#        between two adjacent values, ``out[i+1] - out[i]``.  The default

#        step size is 1.  If `step` is specified as a position argument,

#        `start` must also be given.

#    dtype : dtype

#        The type of the output array.  If `dtype` is not given, infer the data

#        type from the other input arguments.

#

#    Returns

#    -------

#    arange : ndarray

#        Array of evenly spaced values.

#

#        For floating point arguments, the length of the result is

#        ``ceil((stop - start)/step)``.  Because of floating point overflow,

#        this rule may result in the last element of `out` being greater

#        than `stop`.

#

#    See Also

#    --------

#    linspace : Evenly spaced numbers with careful handling of endpoints.

#    ogrid: Arrays of evenly spaced numbers in N-dimensions.

#    mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.

#

#    Examples

#    --------

#np.arange(3)

#    array([0, 1, 2])

#np.arange(3.0)

#    array([ 0.,  1.,  2.])

#np.arange(3,7)

#    array([3, 4, 5, 6])

#np.arange(3,7,2)

#    array([3, 5])