Question

我正在尝试使用我设置的频率创建一个带有刻度的x轴，以便我的所有数字看起来都一样。我有一个534个日期时间值的数组，称为summertime，我试图在那些日期时间之间制作x轴，频率为10.

 ax1.xaxis.set_ticks( np.arange(summertime.min(), summertime.max(), 10))

代码在崩溃之前运行了几天，而我正在更改变量等。没有错误出现，只是说它运行了一段时间然后导致内核崩溃。此外，看起来它正在耗尽我的计算机上的大部分内存来运行它。在尝试运行时，我看到它燃烧了20gb。

不完全确定发生了什么，但非常喜欢有关如何修复此错误的建议。

Ran the code. Been waiting 2 min. Memory jumped from 54% to 92%. Whole computer slowing down

The aftermath. Took several minutes and all computer functions slowed down. It quit Microsoft Word and Chrome suddenly (without asking) then restarted Kernel

Answer 1

语法没有错。问题可能是范围summertime.max（） - summertime.min（）太大了。

我刚刚执行了

    ax4.xaxis.set_ticks(np.arange(0.5,100,10))

在我的电脑上运行正常。但是如果我执行

   ax4.xaxis.set_ticks(np.arange(0.5,100000000,10))

它给了我内存错误。

您能否分享一下夏季数据框的信息。

Answer 2

显然，当你打电话

np.arange(summertime.min(), summertime.max(), 10)

该值10的单位是datetime对象的最小单位，微秒。鉴于您的开始和结束日期，对arange的调用正在尝试创建长度为198707200000的数组。这将需要超过1TB的内存。

你说“我试图在频率为10的那个日期时间之间制作x轴”，但是你没有说出那个10的值的单位是多少。 10分钟？小时？秒？

假设这是10个小时。您可以将该增量指定为微秒数，或者指定为datetime.timedelta对象。例如，

In [43]: mn = datetime.datetime(2015, 7, 24, 0, 10, 40) 

In [44]: mx = datetime.datetime(2015, 8, 16, 0, 8, 32)

In [45]: np.arange(mn, mx, 10*3600*1000000)  # Increment is 10 hours, expressed in microseconds
Out[45]: 
array(['2015-07-24T00:10:40.000000', '2015-07-24T10:10:40.000000',
       '2015-07-24T20:10:40.000000', '2015-07-25T06:10:40.000000',
       '2015-07-25T16:10:40.000000', '2015-07-26T02:10:40.000000',
       '2015-07-26T12:10:40.000000', '2015-07-26T22:10:40.000000',
       '2015-07-27T08:10:40.000000', '2015-07-27T18:10:40.000000',
       '2015-07-28T04:10:40.000000', '2015-07-28T14:10:40.000000',
       '2015-07-29T00:10:40.000000', '2015-07-29T10:10:40.000000',
       '2015-07-29T20:10:40.000000', '2015-07-30T06:10:40.000000',
       '2015-07-30T16:10:40.000000', '2015-07-31T02:10:40.000000',
       '2015-07-31T12:10:40.000000', '2015-07-31T22:10:40.000000',
       '2015-08-01T08:10:40.000000', '2015-08-01T18:10:40.000000',
       '2015-08-02T04:10:40.000000', '2015-08-02T14:10:40.000000',
       '2015-08-03T00:10:40.000000', '2015-08-03T10:10:40.000000',
       '2015-08-03T20:10:40.000000', '2015-08-04T06:10:40.000000',
       '2015-08-04T16:10:40.000000', '2015-08-05T02:10:40.000000',
       '2015-08-05T12:10:40.000000', '2015-08-05T22:10:40.000000',
       '2015-08-06T08:10:40.000000', '2015-08-06T18:10:40.000000',
       '2015-08-07T04:10:40.000000', '2015-08-07T14:10:40.000000',
       '2015-08-08T00:10:40.000000', '2015-08-08T10:10:40.000000',
       '2015-08-08T20:10:40.000000', '2015-08-09T06:10:40.000000',
       '2015-08-09T16:10:40.000000', '2015-08-10T02:10:40.000000',
       '2015-08-10T12:10:40.000000', '2015-08-10T22:10:40.000000',
       '2015-08-11T08:10:40.000000', '2015-08-11T18:10:40.000000',
       '2015-08-12T04:10:40.000000', '2015-08-12T14:10:40.000000',
       '2015-08-13T00:10:40.000000', '2015-08-13T10:10:40.000000',
       '2015-08-13T20:10:40.000000', '2015-08-14T06:10:40.000000',
       '2015-08-14T16:10:40.000000', '2015-08-15T02:10:40.000000',
       '2015-08-15T12:10:40.000000', '2015-08-15T22:10:40.000000'], dtype='datetime64[us]')

In [46]: np.arange(mn, mx, datetime.timedelta(hours=10))  # Increment is 10 hours, expressed using a datetime.timedelta
Out[46]: 
array(['2015-07-24T00:10:40.000000', '2015-07-24T10:10:40.000000',
       '2015-07-24T20:10:40.000000', '2015-07-25T06:10:40.000000',
       '2015-07-25T16:10:40.000000', '2015-07-26T02:10:40.000000',
       '2015-07-26T12:10:40.000000', '2015-07-26T22:10:40.000000',
       '2015-07-27T08:10:40.000000', '2015-07-27T18:10:40.000000',
       '2015-07-28T04:10:40.000000', '2015-07-28T14:10:40.000000',
       '2015-07-29T00:10:40.000000', '2015-07-29T10:10:40.000000',
       '2015-07-29T20:10:40.000000', '2015-07-30T06:10:40.000000',
       '2015-07-30T16:10:40.000000', '2015-07-31T02:10:40.000000',
       '2015-07-31T12:10:40.000000', '2015-07-31T22:10:40.000000',
       '2015-08-01T08:10:40.000000', '2015-08-01T18:10:40.000000',
       '2015-08-02T04:10:40.000000', '2015-08-02T14:10:40.000000',
       '2015-08-03T00:10:40.000000', '2015-08-03T10:10:40.000000',
       '2015-08-03T20:10:40.000000', '2015-08-04T06:10:40.000000',
       '2015-08-04T16:10:40.000000', '2015-08-05T02:10:40.000000',
       '2015-08-05T12:10:40.000000', '2015-08-05T22:10:40.000000',
       '2015-08-06T08:10:40.000000', '2015-08-06T18:10:40.000000',
       '2015-08-07T04:10:40.000000', '2015-08-07T14:10:40.000000',
       '2015-08-08T00:10:40.000000', '2015-08-08T10:10:40.000000',
       '2015-08-08T20:10:40.000000', '2015-08-09T06:10:40.000000',
       '2015-08-09T16:10:40.000000', '2015-08-10T02:10:40.000000',
       '2015-08-10T12:10:40.000000', '2015-08-10T22:10:40.000000',
       '2015-08-11T08:10:40.000000', '2015-08-11T18:10:40.000000',
       '2015-08-12T04:10:40.000000', '2015-08-12T14:10:40.000000',
       '2015-08-13T00:10:40.000000', '2015-08-13T10:10:40.000000',
       '2015-08-13T20:10:40.000000', '2015-08-14T06:10:40.000000',
       '2015-08-14T16:10:40.000000', '2015-08-15T02:10:40.000000',
       '2015-08-15T12:10:40.000000', '2015-08-15T22:10:40.000000'], dtype='datetime64[us]')

Numpy np.arange导致内核死亡并重新启动

2 个答案: