I'm just starting to learn Python and I have the following problem.
Using a package with method "bind", the following code works:
def callback(data):
print data
channel.bind(callback)
but when I try to wrap this inside a class:
class myclass:
def callback(data):
print data
def register_callback:
channel.bind(self.callback)
the call_back method is never called. I tried both "self.callback" and just "callback". Any ideas?
It is not clear to me how your code works, as (1) you did not post the implementation of channel.bind, and (2) your second example is incorrect in the definition of register_callback (it is using a self argument that is not part of the list of parameters of the method, and it lacks parentheses).
Nevertheless, remember that methods usually require a "self" parameter, which is implicitly passed every time you run self.function(), as this is converted internally to a function call with self as its first parameter: function(self, ...). Since your callback has just one argument data, this is probably the problem.
You cannot declare a method bind that is able to accept either a function or a class method (the same problem happens with every OOP language I know: C++, Pascal...).
There are many ways to do this, but, again, without a self-contained example that can be compiled, it is difficult to give suggestions.
You need to pass the self object as well:
def register_callback(self):
channel.bind(self.callback)
What you're doing is entirely possible, but I'm not sure exactly what your issue is, because your sample code as posted is not even syntactically valid. (The second method has no argument list whatsoever.)
Regardless, you might find the following sample code helpful:
def send_data(callback):
callback('my_data')
def callback(data):
print 'Free function callback called with data:', data
# The follwing prints "Free function callback called with data: my_data"
send_data(callback)
class ClassWithCallback(object):
def callback(self, data):
print 'Object method callback called with data:', data
def apply_callback(self):
send_data(self.callback)
# The following prints "Object method callback called with data: my_data"
ClassWithCallback().apply_callback()
# Indeed, the following does the same
send_data(ClassWithCallback().callback)
In Python it is possible to use free functions (callback in the example above) or bound methods (self.callback in the example above) in more or less the same situations, at least for simple tasks like the one you've outlined.
Related
I am writing a program in Python that communicates with a spectrometer from Avantes. There are some proprietary dlls available whose code I don't access to, but they have some decent documentation. I am having some trouble to find a good way to store the data received via callbacks.
The proprietary shared library
Basically, the dll contains a function that I have to call to start measuring and that receives a callback function that will be called whenever the spectrometer has finished a measurement. The function is the following:
int AVS_MeasureCallback(AvsHandle a_hDevice,void (*__Done)(AvsHandle*, int*),short a_Nmsr)
The first argument is a handle object that identifies the spectrometer, the second is the actual callback function and the third is the amount of measurements to be made.
The callback function will receive then receive another type of handle identifying the spetrometer and information about the amount of data available after a measurement.
Python library
I am using a library that has Python wrappers for many equipments, including my spectrometer.
def measure_callback(self, num_measurements, callback=None):
self.sdk.AVS_MeasureCallback(self._handle, callback, num_measurements)
And they also have defined the following decorator:
MeasureCallback = FUNCTYPE(None, POINTER(c_int32), POINTER(c_int32))
The idea is that when the callback function is finally called, this will trigger the get_data() function that will retrieve data from the equipment.
The recommended example is
#MeasureCallback
def callback_fcn(handle, info):
print('The DLL handle is:', handle.contents.value)
if info.contents.value == 0: # equals 0 if everything is okay (see manual)
print(' callback data:', ava.get_data())
ava.measure_callback(-1, callback_fcn)
My problem
I have to store the received data in a 2D numpy array that I have created somewhere else in my main code, but I can't figure out what is the best way to update this array with the new data available inside the callback function.
I wondered if I could pass this numpy array as an argument for the callback function, but even in this case I cannot find a good way to do this since it is expected that the callback function will have only those 2 arguments.
Edit 1
I found a possible solution here but I am not sure it is the best way to do it. I'd rather not create a new class just to hold a single numpy array inside.
Edit 2
I actually changed my mind about my approach, because inside my callback I'd like to do many operations with the received data and save the results in many different variables. So, I went back to the class approach mentioned here, where I would basically have a class with all the variables that will somehow be used in the callback function and that would also inherit or have an object of the class ava.
However, as shown in this other question, the self parameter is a problem in this case.
If you don't want to create a new class, you can use a function closure:
# Initialize it however you want
numpy_array = ...
def callback_fcn(handle, info):
# Do what you want with the value of the variable
store_data(numpy_array, ...)
# After the callback is called, you can access the changes made to the object
print(get_data(numpy_array))
How this works is that when the callback_fcn is defined, it keeps a reference to the value of the variable numpy_array, so when it's called, it can manipulate it, as if it were passed as an argument to the function. So you get the effect of passing it in, without the callback caller having to worry about it.
I finally managed to solve my problem with a solution envolving a new class and also a closure function to deal with the self parameter that is described here. Besides that, another problem would appear by garbage collection of the new created method.
My final solution is:
class spectrometer():
def measurement_callback(self,handle,info):
if info.contents.value >= 0:
timestamp,spectrum = self.ava.get_data()
self.spectral_data[self.spectrum_index,:] = np.ctypeslib.as_array(spectrum[0:pixel_amount])
self.timestamps[self.spectrum_index] = timestamp
self.spectrum_index += 1
def __init__(self,ava):
self.ava = ava
self.measurement_callback = MeasureCallback(self.measurement_callback)
def register_callback(self,scans,pattern_amount,pixel_amount):
self.spectrum_index = 0
self.timestamps = np.empty((pattern_amount),dtype=np.uint32)
self.spectral_data = np.empty((pattern_amount,pixel_amount),dtype=np.float64)
self.ava.measure_callback(scans, self.measurement_callback)
I'm currently writing code in Python 2.7, which involves creating an object, in which I have two class methods and other regular methods. I need to use this specific combination of methods because of the larger context of the code I am writing- it's not relevant to this question, so I won't go into depth.
Within my __init__ function, I am creating a Pool (a multiprocessing object). In the creation of that, I call a setup function. This setup function is a #classmethod. I define a few variables in this setup function by using the cls.variablename syntax. As I mentioned, I call this setup function within my init function (inside the Pool creation), so these variables should be getting created, based on what I understand.
Later in my code, I call a few other functions, which eventually leads to me calling another #classmethod within the same object I was talking about earlier (same object as the first #classmethod). Within this #classmethod, I try to access the cls.variables I created in the first #classmethod. However, Python is telling me that my object doesn't have an attribute "cls.variable" (using general names here, obviously my actual names are specific to my code).
ANYWAYS...I realize that's probably pretty confusing. Here's some (very) generalized code example to illustrate the same idea:
class General(object):
def __init__(self, A):
# this is correct syntax based on the resources I'm using,
# so the format of argument isn't the issue, in case anyone
# initially thinks that's the issue
self.pool = Pool(processes = 4, initializer=self._setup, initargs= (A, )
#classmethod
def _setup(cls, A):
cls.A = A
#leaving out other functions here that are NOT class methods, just regular methods
#classmethod
def get_results(cls):
print cls.A
The error I'm getting when I get to the equivalent of the print cls.A line is this:
AttributeError: type object 'General' has no attribute 'A'
edit to show usage of this code:
The way I'm calling this in my code is as such:
G = General(5)
G.get_results()
So, I'm creating an instance of the object (in which I create the Pool, which calls the setup function), and then calling get_results.
What am I doing wrong?
The reason General.A does not get defined in the main process is that multiprocessing.Pool only runs General._setup in the subprocesses. This means that it will not be called in the main process (where you call Pool).
You end up with 4 processes where in each of them there is General.A is defined, but not in the main process. You don't actually initialize a Pool like that (see this answer to the question How to use initializer to set up my multiprocess pool?)
You want an Object Pool which is not natively impemented in Python. There's a Python Implementation of the Object Pool Design Pattern question here on StackOverflow, but you can find a bunch by just searching online.
I am attempting to integrate a very old system and a newer system at work. The best I can do is to utilize an RSS firehouse type feed the system utilizes. The goal is to use this RSS feed to make the other system perform certain actions when certain people do things.
My idea is to wrap a decorator around certain functions to check if the user (a user ID provided in the RSS feed) has permissions in the new system.
My current solution has a lot of functions that look like this, which are called based on an action field in the feed:
actions_dict = {
...
'action1': function1
}
actions_dict[RSSFEED['action_taken']](RSSFEED['user_id'])
def function1(user_id):
if has_permissions(user_id):
# Do this function
I want to create a has_permissions decorator that takes the user_id so that I can remove this redundant has_permissions check in each of my functions.
#has_permissions(user_id)
def function1():
# Do this function
Unfortunately, I am not sure how to write such a decorator. All the tutorials I see have the #has_permissions() line with a hardcoded value, but in my case it needs to be passed at runtime and will be different each time the function is called.
How can I achieve this functionality?
In your question, you've named both, the check of the user_id, as well as the wanted decorator has_permissions, so I'm going with an example where names are more clear: Let's make a decorator that calls the underlying (decorated) function when the color (a string) is 'green'.
Python decorators are function factories
The decorator itself (if_green in my example below) is a function. It takes a function to be decorated as argument (named function in my example) and returns a function (run_function_if_green in the example). Usually, the returned function calls the passed function at some point, thereby "decorating" it with other actions it might run before or after it, or both.
Of course, it might only conditionally run it, as you seem to need:
def if_green(function):
def run_function_if_green(color, *args, **kwargs):
if color == 'green':
return function(*args, **kwargs)
return run_function_if_green
#if_green
def print_if_green():
print('what a nice color!')
print_if_green('red') # nothing happens
print_if_green('green') # => what a nice color!
What happens when you decorate a function with the decorator (as I did with print_if_green, here), is that the decorator (the function factory, if_green in my example) gets called with the original function (print_if_green as you see it in the code above). As is its nature, it returns a different function. Python then replaces the original function with the one returned by the decorator.
So in the subsequent calls, it's the returned function (run_function_if_green with the original print_if_green as function) that gets called as print_if_green and which conditionally calls further to that original print_if_green.
Functions factories can produce functions that take arguments
The call to the decorator (if_green) only happens once for each decorated function, not every time the decorated functions are called. But as the function returned by the decorator that one time permanently replaces the original function, it gets called instead of the original function every time that original function is invoked. And it can take arguments, if we allow it.
I've given it an argument color, which it uses itself to decide whether to call the decorated function. Further, I've given it the idiomatic vararg arguments, which it uses to call the wrapped function (if it calls it), so that I'm allowed to decorate functions taking an arbitrary number of positional and keyword arguments:
#if_green
def exclaim_if_green(exclamation):
print(exclamation, 'that IS a nice color!')
exclaim_if_green('red', 'Yay') # again, nothing
exclaim_if_green('green', 'Wow') # => Wow that IS a nice color!
The result of decorating a function with if_green is that a new first argument gets prepended to its signature, which will be invisible to the original function (as run_function_if_green doesn't forward it). As you are free in how you implement the function returned by the decorator, it could also call the original function with less, more or different arguments, do any required transformation on them before passing them to the original function or do other crazy stuff.
Concepts, concepts, concepts
Understanding decorators requires knowledge and understanding of various other concepts of the Python language. (Most of which aren't specific to Python, but one might still not be aware of them.)
For brevity's sake (this answer is long enough as it is), I've skipped or glossed over most of them. For a more comprehensive speedrun through (I think) all relevant ones, consult e.g. Understanding Python Decorators in 12 Easy Steps!.
The inputs to decorators (arguments, wrapped function) are rather static in python. There is no way to dynamically pass an argument like you're asking. If the user id can be extracted from somewhere at runtime inside the decorator function however, you can achieve what you want..
In Django for example, things like #login_required expect that the function they're wrapping has request as the first argument, and Request objects have a user attribute that they can utilize. Another, uglier option is to have some sort of global object you can get the current user from (see thread local storage).
The short answer is no: you cannot pass dynamic parameters to decorators.
But... you can certainly invoke them programmatically:
First let's create a decorator that can perform a permission check before executing a function:
import functools
def check_permissions(user_id):
def decorator(f):
#functools.wraps(f)
def wrapper(*args, **kw):
if has_permissions(user_id):
return f(*args, **kw)
else:
# what do you want to do if there aren't permissions?
...
return wrapper
return decorator
Now, when extracting an action from your dictionary, wrap it using the decorator to create a new callable that does an automatic permission check:
checked_action = check_permissions(RSSFEED['user_id'])(
actions_dict[RSSFEED['action_taken']])
Now, when you call checked_action it will first check the permissions corresponding to the user_id before executing the underlying action.
You may easily work around it, example:
from functools import wraps
def some_function():
print("some_function executed")
def some_decorator(decorator_arg1, decorator_arg2):
def decorate(func):
#wraps(func)
def wrapper(*args, **kwargs):
print(decorator_arg1)
ret = func(*args, **kwargs)
print(decorator_arg2)
return ret
return wrapper
return decorate
arg1 = "pre"
arg2 = "post"
decorated = some_decorator(arg1, arg2)(some_function)
In [4]: decorated()
pre
some_function executed
post
I want to envoke a method in my code in a supercass, to do some subclass- specific processing before continuing on. I come to python recently from C#... there, I'd probably use an interface. Here's the gist of it (as I picture it, but it's not working):
class superClass:
def do_specific_stuff(self): #To be implemented entirely by the subclass,
#but called from the superclass
pass
def do_general_stuff1(self):
#do misc
def do_general_stuff2(self):
#do more misc
def main_general_stuff(self):
do_general_stuff1()
do_specific_stuff()
do_general_stuff2()
I have a rather complicated implementation of this; this example is exactly what I need and far less painful to understand for a first- time viewer. Calling do_specific_stuff() at the moment gives me the error
'global name 'do_specific_stuff' is not defined.
When I add 'self' as in self.do_specific_stuff I get the error
'TypeError: do_specific_stuff() takes 0 positional arguments but 1 was given.' Any takers? Thanks in advance...
It needs to be
def main_general_stuff(self):
self.do_general_stuff1()
self.do_specific_stuff()
...
The problem is that you are missing the explicit reference to self: Python thinks you mean a global function without it. Note that there is no implicit this like in Java: You need to specify it.
I am using a block like this:
def served(fn) :
def wrapper(*args, **kwargs):
p = xmlrpclib.ServerProxy(SERVER, allow_none=True )
return (p.__getattr__(fn.__name__)(*args, **kwargs)) # do the function call
return functools.update_wrapper(wrapper,fn)
#served
def remote_function(a, b):
pass
to wrap a series of XML-RPC calls into a python module. The "served" decorator gets called on stub functions to expose operations on a remote server.
I'm creating stubs like this with the intention of being able to inspect them later for information about the function, specifically its arguments.
As listed, the code above does not transfer argument information from the original function to the wrapper. If I inspect with inspect.getargspec( remote_function ) then I get essentially an empty list, instead of args=['a','b'] that I was expecting.
I'm guessing I need to give additional direction to the functools.update_wrapper() call via the optional assigned parameter, but I'm not sure exactly what to add to that tuple to get the effect I want.
The name and the docstring are correctly transferred to the new function object, but can someone advise me on how to transfer argument definitions?
Thanks.
Previous questions here and here suggest that the decorator module can do this.