17%
09.01.2013
095 095 000 Pre-fail Always - 0
4 Start_Stop_Count 0x0032 100 100 020 Old_age Always - 11
5 Reallocated_Sector_Ct 0x0033 100 100 010 Pre-fail Always - 0
[...]
199 UDMA_CRC_Error_Count 0x003e 200 200 000
17%
10.04.2015
-WMIObject Win32_OperatingSystem | fl Name, BuildNumber, Version
Name: Microsoft Windows 8.1 Enterprise|C:\Windows|\Device\Harddisk0\Partition4
BuildNumber: 9600
Version: 6.3.9600
The command
17%
17.02.2015
vectors with 100,000 equally distributed random numbers from the interval 0.5 to 65 each, which round() then rounds to 1 and 6.
Each vector component simulates a throw of the dice. The call to hist
17%
10.06.2014
"ram": 2048,
07 "resolvers": ["192.168.111.254"],
08 "disks": [
09 {
10 "image_uuid": "1fc068b0-13b0-11e2-9f4e-2f3f6a96d9bc",
11 "boot": true,
12 "model": "virtio"
13 }
14
17%
10.06.2015
://analyticsacademy.withgoogle.com/course02/assets/html/GoogleAnalyticsAcademy-PlatformPrinciples-Lesson1.2-TextLesson.html
NetFlow Export Datagram Formats: http://www.cisco.com/c/en/us/td/docs/net_mgmt/netflow_collection_engine/5-0-3/user
17%
27.09.2021
counter = 1,counter_limit
25 my_record%x = counter
26 my_record%y = counter + 1
27 my_record%z = counter + 2
28 my_record%value = counter * 10.0
29
17%
17.05.2017
_ACC_RDWR_F, FILE_ID, IERR, PLIST_ID)
96
97 ! Close the property list
98 CALL h5pclose_f(PLIST_ID, IERR)
99
100 ! Create the dataset names based on MPI rank
101 WRITE(C,"(i0)") MPI_RANK + 1
102 DATASET
17%
01.02.2013
at the output of uptime
[1] on OS X:
13:03 up 2 days, 12:01, 2 users, load averages: 0.52 0.59 0.63
The uptime
command displays the load average in its common form, averaging the last one, five, and 15
17%
31.10.2025
at the output of uptime [1] on OS X:
13:03 up 2 days, 12:01, 2 users, load averages: 0.52 0.59 0.63
The uptime command displays the load average in its common form, averaging the last one, five, and 15 minutes
17%
21.03.2017
# ===================
09 #
10 if __name__ == '__main__':
11
12 f = h5py.File("mytestfile.hdf5", "w")
13
14 dset = f.create_dataset("mydataset", (100,), dtype='i')
15
16 dset[...] = np.arange(100)
17
