I have a field in my acore_characters table named 'rank' with a tinyint which ranges from 0 to 3 inclusive, based on player's progression. I need to read that value at both login and at certain specific circumstances.
I wrote the following PreparedStatement: "SELECT rank FROM acore_characters WHERE guid = ?" and then the code which is supposed to read that value:
uint16 GetCharactersRank(uint64 guid) {
PreparedStatement* stmt = CharacterDatabase.GetPreparedStatement(mystatement);
stmt->setUInt32(0, GetGUID());
PreparedQueryResult result = CharacterDatabase.Query(stmt);
if (result) {
[...truncated]
and then fetching the result and so on, but then I get a code C8361 when compiling because 'GetGUID':identifier not found in Player.cpp file...what goes wrong? The other GetGUID calls throughout the file dont give this result. I'm not very fond of c++, any help is very appreciated.
It's not recommended to directly patch the core to add customisations to it. Instead, use modules.
An example can be found here: Is it possible to turn a core patch into a module for AzerothCore?
You can have a look and copy the skeleton-module and start modifying it to create your own.
In your case, you probably want to use the OnLogin player hook.
I am attempting to mock-up a 'robot.properties' file to be utilized within my test cases with the Robot Framework. Inside my robot.properties file it contains things like for example:
project.username=stackoverflow
inside my test case file I have tried several times to 'import' the robot.properties file via adding within Settings: Resource ../path/to/properties and etc (see directory structure below), but when I attempt to pass 'project.username' as an argument to a test it passes it as the literal string value 'project.username' and not the value 'stack overflow'. I am new to Robot, I have implemented this in other languages like Java/C#, but I fully assume that the import is preventing me from accessing my value. Any help would be greatly appreciated, unfortunately this way of driving testing isn't really referenced much online that I can find.
Dir Structure:
Tests/Acceptance/MyTestCase.robot
Tests/robot.properties
If I try Library ../robot.properties I get:
"Import by filename is not supported"
If I try Resource ../robot.properties I get:
"Unsupported file format .properties"
Robot framework doesn't support a ".properties" file.
One solution is to use a variable file, which lets you define variables in python. Since you want to use dot notation, one way is to create a class and define your variables as properties of the class. The variable file can then make an instance of that class as a variable, and you can use extended variable syntax to access the variables.
The advantage to using a variable file over a plain text file is that you can create variables dynamically by calling other python functions. As a simple example, you could create a variable called "now" that contains the current date, or "host" that is the hostname of the machine running the test.
Example:
properties.py
import platform
class Properties(object):
username = "stackoverflow"
password = "SuperSecret!"
hostname = platform.uname()[1]
properties = Properties()
example.robot
*** Settings ***
Variables properties.py
Suite Setup log running on ${properties.hostname}
*** Test Cases ***
Example
should be equal ${properties.username} stackoverflow
I am trying to add a new premake5 field in my premake5 script but am having trouble understanding how to specify the field.kind .
I want to add a field that contains a list of (path, string ) pairs but can't work out how to specify the kind spec .
The documentation and examples are not particularly clear.
This is how I have registered my new field
premake.api.register( {
name = "mypathmappings",
scope = "config",
kind = "list:path:string", or "list:keyed:path:string"
}
)
and inside of a config scope I declare the field item like so
project myproject
mypathmappings { ["path/to/file1"] = "stringvalue1", ["path/to/file2"] = "stringvalue2"}
However when it comes to processing time I don't get what I'm expecting in the field:
function processpathmappings(cfg)
local pathmappings = cfg.mypathmappings
for path, value in pairs(pathmappings) do
--do something with the path and value but
--value is not a string as expected
end
end
Can someone explain how the complex kinds can be built up correctly from the field kinds registered in api.lua?
I get that "list:integer" specifies a list of integers but don't know how the "keyed" element works for example.
Right now, it is not possible to control the "kind" of the keys in a keyed value. The best you will be able to get with the current system is kind="keyed:string", which should give you the values (the strings) that you want, but the paths will not be processed by Premake (made absolute, etc.)
If it is feasible, you might want to flip it around to kind="keyed:path" and set the values like this:
mypathmappings { ["stringvalue1"] = "path/to/file1" }
But that relies on your string values being unique within a map.
In theory, Premake's field API could be extended to support kinds of keys; feel free to open a ticket or submit a pull request.
I am working on pdf document clustering over hadoop so I am learning mapreduce by reading some examples on internet.In wordcount examples have lines
job.get("map.input.file")
job.getboolean()
What is function of these functions?what is exactly map.input.file where is it to set? or is it just a name given to input folder?
Please post answer if anyone know.
For code see the following link
wordcount 2.0 example=http://hadoop.apache.org/docs/r1.0.4/mapred_tutorial.html
These are job configurations. i.e. set of configurations which are passed on to each mapper and reducer. Now, these configurations consist of well defined mapreduce/hadoop related configurations as well as user-defined configurations.
In your case, map.input.file is a pre-defined configuration and yes it is set to a comma separated list of all the paths you have set as input path.
While wordcount.skip.patterns is a custom configuration which is set as per user's input, and you may see this configuration to be set in run() as follows:
conf.setBoolean("wordcount.skip.patterns", true);
As for when to use get and when to use getBoolean, it should be self-explanatory, as whenever you want to set a value of type boolean you will use getBoolean and setBoolean to get and set the specific config value respectively. Similarly you have specific methods for other data types as well. If it is string then you may use get().
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In my endless quest in over-complicating simple stuff, I am researching the most 'Pythonic' way to provide global configuration variables inside the typical 'config.py' found in Python egg packages.
The traditional way (aah, good ol' #define!) is as follows:
MYSQL_PORT = 3306
MYSQL_DATABASE = 'mydb'
MYSQL_DATABASE_TABLES = ['tb_users', 'tb_groups']
Therefore global variables are imported in one of the following ways:
from config import *
dbname = MYSQL_DATABASE
for table in MYSQL_DATABASE_TABLES:
print table
or:
import config
dbname = config.MYSQL_DATABASE
assert(isinstance(config.MYSQL_PORT, int))
It makes sense, but sometimes can be a little messy, especially when you're trying to remember the names of certain variables. Besides, providing a 'configuration' object, with variables as attributes, might be more flexible. So, taking a lead from bpython config.py file, I came up with:
class Struct(object):
def __init__(self, *args):
self.__header__ = str(args[0]) if args else None
def __repr__(self):
if self.__header__ is None:
return super(Struct, self).__repr__()
return self.__header__
def next(self):
""" Fake iteration functionality.
"""
raise StopIteration
def __iter__(self):
""" Fake iteration functionality.
We skip magic attribues and Structs, and return the rest.
"""
ks = self.__dict__.keys()
for k in ks:
if not k.startswith('__') and not isinstance(k, Struct):
yield getattr(self, k)
def __len__(self):
""" Don't count magic attributes or Structs.
"""
ks = self.__dict__.keys()
return len([k for k in ks if not k.startswith('__')\
and not isinstance(k, Struct)])
and a 'config.py' that imports the class and reads as follows:
from _config import Struct as Section
mysql = Section("MySQL specific configuration")
mysql.user = 'root'
mysql.pass = 'secret'
mysql.host = 'localhost'
mysql.port = 3306
mysql.database = 'mydb'
mysql.tables = Section("Tables for 'mydb'")
mysql.tables.users = 'tb_users'
mysql.tables.groups = 'tb_groups'
and is used in this way:
from sqlalchemy import MetaData, Table
import config as CONFIG
assert(isinstance(CONFIG.mysql.port, int))
mdata = MetaData(
"mysql://%s:%s#%s:%d/%s" % (
CONFIG.mysql.user,
CONFIG.mysql.pass,
CONFIG.mysql.host,
CONFIG.mysql.port,
CONFIG.mysql.database,
)
)
tables = []
for name in CONFIG.mysql.tables:
tables.append(Table(name, mdata, autoload=True))
Which seems a more readable, expressive and flexible way of storing and fetching global variables inside a package.
Lamest idea ever? What is the best practice for coping with these situations? What is your way of storing and fetching global names and variables inside your package?
How about just using the built-in types like this:
config = {
"mysql": {
"user": "root",
"pass": "secret",
"tables": {
"users": "tb_users"
}
# etc
}
}
You'd access the values as follows:
config["mysql"]["tables"]["users"]
If you are willing to sacrifice the potential to compute expressions inside your config tree, you could use YAML and end up with a more readable config file like this:
mysql:
- user: root
- pass: secret
- tables:
- users: tb_users
and use a library like PyYAML to conventiently parse and access the config file
I like this solution for small applications:
class App:
__conf = {
"username": "",
"password": "",
"MYSQL_PORT": 3306,
"MYSQL_DATABASE": 'mydb',
"MYSQL_DATABASE_TABLES": ['tb_users', 'tb_groups']
}
__setters = ["username", "password"]
#staticmethod
def config(name):
return App.__conf[name]
#staticmethod
def set(name, value):
if name in App.__setters:
App.__conf[name] = value
else:
raise NameError("Name not accepted in set() method")
And then usage is:
if __name__ == "__main__":
# from config import App
App.config("MYSQL_PORT") # return 3306
App.set("username", "hi") # set new username value
App.config("username") # return "hi"
App.set("MYSQL_PORT", "abc") # this raises NameError
.. you should like it because:
uses class variables (no object to pass around/ no singleton required),
uses encapsulated built-in types and looks like (is) a method call on App,
has control over individual config immutability, mutable globals are the worst kind of globals.
promotes conventional and well named access / readability in your source code
is a simple class but enforces structured access, an alternative is to use #property, but that requires more variable handling code per item and is object-based.
requires minimal changes to add new config items and set its mutability.
--Edit--:
For large applications, storing values in a YAML (i.e. properties) file and reading that in as immutable data is a better approach (i.e. blubb/ohaal's answer).
For small applications, this solution above is simpler.
How about using classes?
# config.py
class MYSQL:
PORT = 3306
DATABASE = 'mydb'
DATABASE_TABLES = ['tb_users', 'tb_groups']
# main.py
from config import MYSQL
print(MYSQL.PORT) # 3306
Let's be honest, we should probably consider using a Python Software Foundation maintained library:
https://docs.python.org/3/library/configparser.html
Config example: (ini format, but JSON available)
[DEFAULT]
ServerAliveInterval = 45
Compression = yes
CompressionLevel = 9
ForwardX11 = yes
[bitbucket.org]
User = hg
[topsecret.server.com]
Port = 50022
ForwardX11 = no
Code example:
>>> import configparser
>>> config = configparser.ConfigParser()
>>> config.read('example.ini')
>>> config['DEFAULT']['Compression']
'yes'
>>> config['DEFAULT'].getboolean('MyCompression', fallback=True) # get_or_else
Making it globally-accessible:
import configpaser
class App:
__conf = None
#staticmethod
def config():
if App.__conf is None: # Read only once, lazy.
App.__conf = configparser.ConfigParser()
App.__conf.read('example.ini')
return App.__conf
if __name__ == '__main__':
App.config()['DEFAULT']['MYSQL_PORT']
# or, better:
App.config().get(section='DEFAULT', option='MYSQL_PORT', fallback=3306)
....
Downsides:
Uncontrolled global mutable state.
A small variation on Husky's idea that I use. Make a file called 'globals' (or whatever you like) and then define multiple classes in it, as such:
#globals.py
class dbinfo : # for database globals
username = 'abcd'
password = 'xyz'
class runtime :
debug = False
output = 'stdio'
Then, if you have two code files c1.py and c2.py, both can have at the top
import globals as gl
Now all code can access and set values, as such:
gl.runtime.debug = False
print(gl.dbinfo.username)
People forget classes exist, even if no object is ever instantiated that is a member of that class. And variables in a class that aren't preceded by 'self.' are shared across all instances of the class, even if there are none. Once 'debug' is changed by any code, all other code sees the change.
By importing it as gl, you can have multiple such files and variables that lets you access and set values across code files, functions, etc., but with no danger of namespace collision.
This lacks some of the clever error checking of other approaches, but is simple and easy to follow.
Similar to blubb's answer. I suggest building them with lambda functions to reduce code. Like this:
User = lambda passwd, hair, name: {'password':passwd, 'hair':hair, 'name':name}
#Col Username Password Hair Color Real Name
config = {'st3v3' : User('password', 'blonde', 'Steve Booker'),
'blubb' : User('12345678', 'black', 'Bubb Ohaal'),
'suprM' : User('kryptonite', 'black', 'Clark Kent'),
#...
}
#...
config['st3v3']['password'] #> password
config['blubb']['hair'] #> black
This does smell like you may want to make a class, though.
Or, as MarkM noted, you could use namedtuple
from collections import namedtuple
#...
User = namedtuple('User', ['password', 'hair', 'name']}
#Col Username Password Hair Color Real Name
config = {'st3v3' : User('password', 'blonde', 'Steve Booker'),
'blubb' : User('12345678', 'black', 'Bubb Ohaal'),
'suprM' : User('kryptonite', 'black', 'Clark Kent'),
#...
}
#...
config['st3v3'].password #> passwd
config['blubb'].hair #> black
I did that once. Ultimately I found my simplified basicconfig.py adequate for my needs. You can pass in a namespace with other objects for it to reference if you need to. You can also pass in additional defaults from your code. It also maps attribute and mapping style syntax to the same configuration object.
please check out the IPython configuration system, implemented via traitlets for the type enforcement you are doing manually.
Cut and pasted here to comply with SO guidelines for not just dropping links as the content of links changes over time.
traitlets documentation
Here are the main requirements we wanted our configuration system to have:
Support for hierarchical configuration information.
Full integration with command line option parsers. Often, you want to read a configuration file, but then override some of the values with command line options. Our configuration system automates this process and allows each command line option to be linked to a particular attribute in the configuration hierarchy that it will override.
Configuration files that are themselves valid Python code. This accomplishes many things. First, it becomes possible to put logic in your configuration files that sets attributes based on your operating system, network setup, Python version, etc. Second, Python has a super simple syntax for accessing hierarchical data structures, namely regular attribute access (Foo.Bar.Bam.name). Third, using Python makes it easy for users to import configuration attributes from one configuration file to another.
Fourth, even though Python is dynamically typed, it does have types that can be checked at runtime. Thus, a 1 in a config file is the integer ‘1’, while a '1' is a string.
A fully automated method for getting the configuration information to the classes that need it at runtime. Writing code that walks a configuration hierarchy to extract a particular attribute is painful. When you have complex configuration information with hundreds of attributes, this makes you want to cry.
Type checking and validation that doesn’t require the entire configuration hierarchy to be specified statically before runtime. Python is a very dynamic language and you don’t always know everything that needs to be configured when a program starts.
To acheive this they basically define 3 object classes and their relations to each other:
1) Configuration - basically a ChainMap / basic dict with some enhancements for merging.
2) Configurable - base class to subclass all things you'd wish to configure.
3) Application - object that is instantiated to perform a specific application function, or your main application for single purpose software.
In their words:
Application: Application
An application is a process that does a specific job. The most obvious application is the ipython command line program. Each application reads one or more configuration files and a single set of command line options and then produces a master configuration object for the application. This configuration object is then passed to the configurable objects that the application creates. These configurable objects implement the actual logic of the application and know how to configure themselves given the configuration object.
Applications always have a log attribute that is a configured Logger. This allows centralized logging configuration per-application.
Configurable: Configurable
A configurable is a regular Python class that serves as a base class for all main classes in an application. The Configurable base class is lightweight and only does one things.
This Configurable is a subclass of HasTraits that knows how to configure itself. Class level traits with the metadata config=True become values that can be configured from the command line and configuration files.
Developers create Configurable subclasses that implement all of the logic in the application. Each of these subclasses has its own configuration information that controls how instances are created.