arogulin

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arogulin

As Jenkins download center for plugins is down since November 12, you can modify your values for helm chart by adding the following initContainerEnv block:

master:
  initContainerEnv:
    - name: JENKINS_UC
      value: http://mirrors.jenkins-ci.org/updates/dynamic-stable-2.249.2/update-center.json
    - name: JENKINS_PLUGIN_INFO
      value: http://mirrors.jenkins-ci.org/updates/current/plugin-versions.json
 - name: JENKINS_UC_DOWNLOAD

arogulin

How to setup Vortex Race 3 for Mac with latest (on 2020-01-05) firmare V1.02.05.

For better understanding we will use the following naming convention:

  [ L1 ][ L2 ][ L3 ][ Space ][ R1 ][ R2 ][ R3 ]

1. Reset everything by pressing L3+R1 for 5 seconds. Left LED will blink white color while you're holding the keys. Release them after it stopeed blinking.
2. Get into one of the programmable layers (R2+RShift) – I like red, the super bright laser LED is the least super annoying in red.
3. Put the keyboard in Windows Mode (Pn+W), it's the least problematic one.

arogulin

1. Restart the app with the following JVM arguments:

-Dcom.sun.management.jmxremote.port=9010
-Dcom.sun.management.jmxremote.rmi.port=9010
-Dcom.sun.management.jmxremote=true
-Dcom.sun.management.jmxremote.ssl=false
-Dcom.sun.management.jmxremote.authenticate=false
-Dcom.sun.management.jmxremote.local.only=false

arogulin

On 64-bit JVM all objects will require a multiple of 8 bytes: 8, 16, 24, 32, 48, 56, 64, etc.

Empty String: 24 bytes
  String itself:
    24 bytes = 8 bytes "headers" + (4 bytes "size" + 4 bytes "hash" - not anymore since Java 8) + 4 bytes "value reference" + 4 bytes "padding to make 20 to become multiple of 8".
  char[0]:
    0 bytes = does not cost any memory for this string, because it's shared empty char array, used by all empty strings.
String with 3 chars: 48 bytes
  String itself:
    24 bytes = 8 bytes "headers" + 4 bytes "size" + 4 bytes "hash" + 4 bytes "value reference" + 4 bytes "padding to make 20 to become multiple of 8".

arogulin

Pandas methods:
- df.info() - quick description about data and amount of non-null values.
- df['category_field'].value_counts() - shows all categories and how many rows share each category.
- df.describe() - show summary of numerical attributes.
- df.hist(bins=50, figsize=(20,15)) - plots a histogram for each numerical attribute.
- df.where(df['age'] < 100, 100, inplace=True) - where the condition is false - replaces the value with the second argument. 
- df.mask(df['age'] > 100, 100, inplace=True) - the opposite of where().
- df.corr() - returns linear correlation matrix between every pair of attributes
  cm = df.corr()
  cm['some_attribte'].sort_values(ascending=False)