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Class objects and Multi threading in python

I am using multithreading for the first time and here is the problem in which I am stuck.
I have a class Workstation and want to use a separate thread for each instance of the class. Each instance has to draw some cell phone models defined in the global list, but multithreading does not work for all instances, it works well only for one object and the rest seem to hang.
Here is code for my class and in the main file I am using another thread(code for that also attached below the class code) that runs a while loop forever and inside that loop, I am calling "drawing_thread"
function for each object of workstation-class.
why threading only works for the first object, not for others
import requests, time, db_quries, threading
from datetime import datetime
import helper_functions as HF
LocIP = '192.168.100.100'
# Local port for server
LocPort = 4321
class Workstation:
def __init__(self,FCell):
"""
constructor for class workstation
:param FCell:
"""
self.FCell = FCell # cell number
#initial values of component's flag
self.frame_done = True
self.screen_done = False
self.kpad_done = False
#flags for order compeltion
self.make = 1
self.complete = 0
self.thread_flag=True
#mutators for object variables
def make_setter(self):
self.make = self.make+1
#print('FL1: Make ',self.make)
def set_m(self,m):
self.make=m
def complete_setter(self,complete):
self.complete =complete
#print('FL1: complete ',self.complete)
def set_frame(self, value):
self.frame_done = value
def set_screen(self, value):
self.screen_done = value
def set_kpad(self, value):
self.kpad_done = value
def set_thread_flag(self,value):
self.thread_flag = value
def drawing_thread(self,param):
self.thread_flag = False
drawing = threading.Thread(target=HF.production, args=param)
drawing.start()
function that called by thread in main file
def process_thread():
while True:
#print(len(OrderList))
if WS_obj_list[0].thread_flag and WS_obj_list[0].get_zone_status('Z1') != '-1':
WS_obj_list[0].drawing_thread((WS_obj_list[0], OrderList))
if WS_obj_list[1].thread_flag and WS_obj_list[1].get_zone_status('Z1') != '-1':#
WS_obj_list[1].drawing_thread((WS_obj_list[1], OrderList))
if WS_obj_list[2].thread_flag and WS_obj_list[2].get_zone_status('Z1') != '-1':
WS_obj_list[2].drawing_thread((WS_obj_list[2], OrderList))
time.sleep(1)

Manager / Container class, how to?

I am currently designing a software which needs to manage a certain hardware setup.
The hardware setup is as following :
System - The system contains two identical devices, and has certain functionality relative to the entire system.
Device - Each device contains two identical sub devices, and has certain functionality relative to both sub devices.
Sub device - Each sub device has 4 configurable entities (Controlled via the same hardware command - thus I don't count them as a sub-sub device).
What I want to achieve :
I want to control all configurable entities via the system manager (the entities are counted in a serial way), meaning I would be able to do the following :
system_instance = system_manager_class(some_params)
system_instance.some_func(0) # configure device_manager[0].sub_device_manager[0].entity[0]
system_instance.some_func(5) # configure device_manager[0].sub_device_manager[1].entity[1]
system_instance.some_func(8) # configure device_manager[1].sub_device_manager[1].entity[0]
What I have thought of doing :
I was thinking of creating an abstract class, which contains all sub device functions (with a call to a conversion function) and have the system_manager, device_manager and sub_device_manager inherit it. Thus all classes will have the same function name and I will be able to access them via the system manager.
Something around these lines :
class abs_sub_device():
#staticmethod
def convert_entity(self):
sub_manager = None
sub_entity_num = None
pass
def set_entity_to_2(entity_num):
sub_manager, sub_manager_entity_num = self.convert_entity(entity_num)
sub_manager.some_func(sub_manager_entity_num)
class system_manager(abs_sub_device):
def __init__(self):
self.device_manager_list = [] # Initiliaze device list
self.device_manager_list.append(device_manager())
self.device_manager_list.append(device_manager())
def convert_entity(self, entity_num):
relevant_device_manager = self.device_manager_list[entity_num // 4]
relevant_entity = entity_num % 4
return relevant_device_manage, relevant_entity
class device_manager(abs_sub_device):
def __init__(self):
self.sub_device_manager_list = [] # Initiliaze sub device list
self.sub_device_manager_list.append(sub_device_manager())
self.sub_device_manager_list.append(sub_device_manager())
def convert_entity(self, entity_num):
relevant_sub_device_manager = self.sub_device_manager_list[entity_num // 4]
relevant_entity = entity_num % 4
return relevant_sub_device_manager, relevant_entity
class sub_device_manager(abs_sub_device):
def __init__(self):
self.entity_list = [0] * 4
def set_entity_to_2(self, entity_num):
self.entity_list[entity_num] = 2
The code is for generic understanding of my design, not for actual functionality.
The problem :
It seems to me that the system I am trying to design is really generic and that there must be a built-in python way to do this, or that my entire object oriented look at it is wrong.
I would really like to know if some one has a better way of doing this.

After much thinking, I think I found a pretty generic way to solve the issue, using a combination of decorators, inheritance and dynamic function creation.
The main idea is as following :
1) Each layer dynamically creates all sub layer relevant functions for it self (Inside the init function, using a decorator on the init function)
2) Each function created dynamically converts the entity value according to a convert function (which is a static function of the abs_container_class), and calls the lowers layer function with the same name (see make_convert_function_method).
3) This basically causes all sub layer function to be implemented on the higher level with zero code duplication.
def get_relevant_class_method_list(class_instance):
method_list = [func for func in dir(class_instance) if callable(getattr(class_instance, func)) and not func.startswith("__") and not func.startswith("_")]
return method_list
def make_convert_function_method(name):
def _method(self, entity_num, *args):
sub_manager, sub_manager_entity_num = self._convert_entity(entity_num)
function_to_call = getattr(sub_manager, name)
function_to_call(sub_manager_entity_num, *args)
return _method
def container_class_init_decorator(function_object):
def new_init_function(self, *args):
# Call the init function :
function_object(self, *args)
# Get all relevant methods (Of one sub class is enough)
method_list = get_relevant_class_method_list(self.container_list[0])
# Dynamically create all sub layer functions :
for method_name in method_list:
_method = make_convert_function_method(method_name)
setattr(type(self), method_name, _method)
return new_init_function
class abs_container_class():
#staticmethod
def _convert_entity(self):
sub_manager = None
sub_entity_num = None
pass
class system_manager(abs_container_class):
#container_class_init_decorator
def __init__(self):
self.device_manager_list = [] # Initiliaze device list
self.device_manager_list.append(device_manager())
self.device_manager_list.append(device_manager())
self.container_list = self.device_manager_list
def _convert_entity(self, entity_num):
relevant_device_manager = self.device_manager_list[entity_num // 4]
relevant_entity = entity_num % 4
return relevant_device_manager, relevant_entity
class device_manager(abs_container_class):
#container_class_init_decorator
def __init__(self):
self.sub_device_manager_list = [] # Initiliaze sub device list
self.sub_device_manager_list.append(sub_device_manager())
self.sub_device_manager_list.append(sub_device_manager())
self.container_list = self.sub_device_manager_list
def _convert_entity(self, entity_num):
relevant_sub_device_manager = self.sub_device_manager_list[entity_num // 4]
relevant_entity = entity_num % 4
return relevant_sub_device_manager, relevant_entity
class sub_device_manager():
def __init__(self):
self.entity_list = [0] * 4
def set_entity_to_value(self, entity_num, required_value):
self.entity_list[entity_num] = required_value
print("I set the entity to : {}".format(required_value))
# This is used for auto completion purposes (Using pep convention)
class auto_complete_class(system_manager, device_manager, sub_device_manager):
pass
system_instance = system_manager() # type: auto_complete_class
system_instance.set_entity_to_value(0, 3)
There is still a little issue with this solution, auto-completion would not work since the highest level class has almost no static implemented function.
In order to solve this I cheated a bit, I created an empty class which inherited from all layers and stated to the IDE using pep convention that it is the type of the instance being created (# type: auto_complete_class).

Does this solve your Problem?
class EndDevice:
def __init__(self, entities_num):
self.entities = list(range(entities_num))
#property
def count_entities(self):
return len(self.entities)
def get_entity(self, i):
return str(i)
class Device:
def __init__(self, sub_devices):
self.sub_devices = sub_devices
#property
def count_entities(self):
return sum(sd.count_entities for sd in self.sub_devices)
def get_entity(self, i):
c = 0
for index, sd in enumerate(self.sub_devices):
if c <= i < sd.count_entities + c:
return str(index) + " " + sd.get_entity(i - c)
c += sd.count_entities
raise IndexError(i)
SystemManager = Device # Are the exact same. This also means you can stack that infinite
sub_devices1 = [EndDevice(4) for _ in range(2)]
sub_devices2 = [EndDevice(4) for _ in range(2)]
system_manager = SystemManager([Device(sub_devices1), Device(sub_devices2)])
print(system_manager.get_entity(0))
print(system_manager.get_entity(5))
print(system_manager.get_entity(15))

I can't think of a better way to do this than OOP, but inheritance will only give you one set of low-level functions for the system manager, so it wil be like having one device manager and one sub-device manager. A better thing to do will be, a bit like tkinter widgets, to have one system manager and initialise all the other managers like children in a tree, so:
system = SystemManager()
device1 = DeviceManager(system)
subDevice1 = SubDeviceManager(device1)
device2 = DeviceManager(system)
subDevice2 = SubDeviceManager(device2)
#to execute some_func on subDevice1
system.some_func(0, 0, *someParams)
We can do this by keeping a list of 'children' of the higher-level managers and having functions which reference the children.
class SystemManager:
def __init__(self):
self.children = []
def some_func(self, child, *params):
self.children[child].some_func(*params)
class DeviceManager:
def __init__(self, parent):
parent.children.append(self)
self.children = []
def some_func(self, child, *params):
self.children[child].some_func(*params)
class SubDeviceManager:
def __init__(self, parent):
parent.children.append(self)
#this may or may not have sub-objects, if it does we need to make it its own children list.
def some_func(self, *params):
#do some important stuff
Unfortunately, this does mean that if we want to call a function of a sub-device manager from the system manager without having lots of dots, we will have to define it again again in the system manager. What you can do instead is use the built-in exec() function, which will take in a string input and run it using the Python interpreter:
class SystemManager:
...
def execute(self, child, function, *args):
exec("self.children[child]."+function+"(*args)")
(and keep the device manager the same)
You would then write in the main program:
system.execute(0, "some_func", 0, *someArgs)
Which would call
device1.some_func(0, someArgs)

Here's what I'm thinking:
SystemManager().apply_to_entity(entity_num=7, lambda e: e.value = 2)
class EntitySuperManagerMixin():
"""Mixin to handle logic for managing entity managers."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs) # Supports any kind of __init__ call.
self._entity_manager_list = []
def apply_to_entity(self, entity_num, action):
relevant_entity_manager = self._entity_manager_list[index // 4]
relevant_entity_num = index % 4
return relevant_entity_manager.apply_to_entity(
relevant_entity_num, action)
class SystemManager(EntitySuperManagerMixin):
def __init__(self):
super().__init__()
# An alias for _entity_manager_list to improve readability.
self.device_manager_list = self._entity_manager_list
self.device_manager_list.extend(DeviceManager() for _ in range(4))
class DeviceManager(EntitySuperManagerMixin):
def __init__(self):
super().__init__()
# An alias for _entity_manager_list to improve readability.
self.sub_device_manager_list = self._entity_manager_list
self.sub_device_manager_list.extend(SubDeviceManager() for _ in range(4))
class SubDeviceManager():
"""Manages entities, not entity managers, thus doesn't inherit the mixin."""
def __init__(self):
# Entities need to be classes for this idea to work.
self._entity_list = [Entity() for _ in range(4)]
def apply_to_entity(self, entity_num, action):
return action(self._entity_list[entity_num])
class Entity():
def __init__(self, initial_value=0):
self.value = initial_value
With this structure:
Entity-specific functions can stay bound to the Entity class (where it belongs).
Manager-specific code needs to be updated in two places: EntitySuperManagerMixin and the lowest level manager (which would need custom behavior anyway since it deals with the actual entities, not other managers).

The way i see it if you want to dynamically configure different part of system you need some sort of addressing so if you input an ID or address with some parameter the system will know with address on which sub sistem you are talking about and then configure that system with parameter.
OOP is quite ok for that and then you can easily manipulate such data via bitwise operators.
So basic addressing is done via binary system , so to do that in python you need first to implement an address static attribute to your class with perhaps some basic further detailing if system grows.
Basic implementation of addres systems is as follows:
bin(71)
1010 1011
and if we divide it into nibbles
1010 - device manager 10
1011 - sub device manager 11
So in this example we have system of 15 device managers and 15 sub device menagers, and every device and sub device manager has its integer address.So let's say you want to access device manager no10 with sub device manager no11. You would need their address which is in binary 71 and you would go with:
system.config(address, parameter )
Where system.config funcion would look like this:
def config(self,address, parameter):
device_manager = (address&0xF0)>>4 #10
sub_device_manager = address&0xf # 11
if device_manager not in range(self.devices): raise LookupError("device manager not found")
if sub_device_manager not in range(self.devices[device_manager].device): raise LookupError("sub device manager not found")
self.devices[device_manager].device[sub_device_manager].implement(parameter)
In layman you would tell system that sub_device 11 from device 10 needs configuration with this parameter.
So how would this setup look in python inheritance class of some base class of system that could be then composited/inherited to different classes:
class systems(object):
parent = None #global parent element, defaults to None well for simplicity
def __init__(self):
self.addrMASK = 0xf # address mask for that nibble
self.addr = 0x1 # default address of that element
self.devices = [] # list of instances of device
self.data = { #some arbitrary data
"param1":"param_val",
"param2":"param_val",
"param3":"param_val",
}
def addSubSystem(self,sub_system): # connects elements to eachother
# checks for valiability
if not isinstance(sub_system,systems):
raise TypeError("defined input is not a system type") # to prevent passing an integer or something
# appends a device to system data
self.devices.append(sub_system)
# search parent variables from sub device manager to system
obj = self
while 1:
if obj.parent is not None:
obj.parent.addrMASK<<=4 #bitshifts 4 bits
obj.parent.addr <<=4 #bitshifts 4 bits
obj = obj.parent
else:break
#self management , i am lazy guy so i added this part so i wouldn't have to reset addresses manualy
self.addrMASK <<=4 #bitshifts 4 bits
self.addr <<=4 #bitshifts 4 bits
# this element is added so the obj address is coresponding to place in list, this could be done more eloquently but i didn't know what are your limitations
if not self.devices:
self.devices[ len(self.devices)-1 ].addr +=1
self.devices[ len(self.devices)-1 ].parent = self
# helpful for checking data ... gives the address of system
def __repr__(self):
return "system at {0:X}, {1:0X}".format(self.addr,self.addrMASK)
# extra helpful lists data as well
def __str__(self):
data = [ '{} : {}\n'.format(k,v) for k,v in self.data.items() ]
return " ".join([ repr(self),'\n',*data ])
#checking for data, skips looping over sub systems
def __contains__(self,system_index):
return system_index-1 in range(len(self.data))
# applying parameter change -- just an example
def apply(self,par_dict):
if not isinstance(par_dict,dict):
raise TypeError("parameter must be a dict type")
if any( key in self.data.keys() for key in par_dict.keys() ):
for k,v in par_dict.items():
if k in self.data.keys():
self.data[k]=v
else:pass
else:pass
# implementing parameters trough addresses
def implement(self,address,parameter_dictionary):
if address&self.addrMASK==self.addr:
if address-self.addr!=0:
item = (address-self.addr)>>4
self.devices[item-1].implement( address-self.addr,parameter_dictionary )
else:
self.apply(parameter_dictionary)
a = systems()
b = systems()
a.addSubSystem(b)
c = systems()
b.addSubSystem(c)
print('a')
print(a)
print('')
print('b')
print(b)
print('')
print('c')
print(c)
print('')
a.implement(0x100,{"param1":"a"})
a.implement(0x110,{"param1":"b"})
a.implement(0x111,{"param1":"c"})
print('a')
print(a)
print('')
print('b')
print(b)
print('')
print('c')
print(c)
print('')

Child calling parent's method without calling parent's __init__ in python

I have a program that have a gui in PyQt in the main thread. It communicates to a photo-detector and gets power readings in another thread, which sends a signal to the main thread to update the gui's power value.
Now I want to use a motor to automatically align my optical fiber, getting feedback from the photo-detector.
So I created a class that controls the motors, but I have to somehow pass the photo-detector readings to that class. First, I tried to access parent's power variable but it didn't work.
Then I created a method in my gui to return the variable's value and tried to access it from the motor class. I got a problem saying that I couldn't use parent's method without using its __init__ first. Is there a way to bypass it? I can't call the gui __init__ again, I just want to use one of its methods from within the child class.
If there is an alternative way to do this, I'd be happy as well.
PS: I guess I can't give the child class the photo-detector object because it is in another thread, right?
--Edit--
The gui code is:
class MyApp(QtGui.QMainWindow, Ui_MainWindow):
self.PDvalue = 0 #initial PD value
self.PDState = 0 #control the PD state (on-off)
self.PDport = self.dialog.pm100d.itemText(self.dialog.pm100d.currentIndex()) #gets pot info
def __init__(self):
... #a lot of other stuff
self.nano = AlgoNanoMax.NanoMax('COM12') #creates the motor object
self.nano_maxX.clicked.connect(self.NanoMaximizeX) #connect its fun to a buttom
self.actionConnect_PM100D.triggered.connect(self.ActionConnect_PM100D) #PD buttom
def NanoMaximizeX(self):
self.nano.maximize_nano_x() #uses motor object function
def ActionConnect_PM100D(self):
if self.PDState == 0: #check if PD is on
self.PD = PDThread(self.PDState, self.PDport) #creates thread
self.PD.valueupdate.connect(self.PDHandler) #signal connect
self.PD.dialogSignal.connect(self.PDdialog) #create error dialog
self.threads = []
self.threads.append(self.PD)
self.PD.start() #start thread
else:
self.PDState = 0
self.PD.state = 0 #stop thread
self.startpd.setText('Start PD') #change buttom name
def PDHandler(self, value):
self.PDvalue = value #slot to get pow from thread
def ReturnPow(self):
return self.PDvalue #return pow (I tried to use this to pass to the motor class)
def PDdialog(self):
self.dialog.set_instrument('PM100D') #I have a dialog that says error and asks you to type the right port
if self.dialog.exec_() == QtGui.QDialog.Accepted: #if Ok buttom try again
ret = self.dialog.pm100d.itemText(self.dialog.pm100d.currentIndex()) #new port
self.PD.port = str(ret)
self.PD.flagWhile = False #change PD stop loop condition to try again
else: #pressed cancel, so it gives up
self.PD.photodetector.__del__() #delete objects
self.PD.terminate() #stop thread
self.PD.quit()
Now the PD class, which is in another thread but in the same file as gui:
class PDThread(QtCore.QThread):
valueupdate = QtCore.pyqtSignal(float) #creating signals
dialogSignal = QtCore.pyqtSignal() #signal in case of error
state = 1 #used to stop thread
def __init__(self, state, port):
QtCore.QThread.__init__(self)
self.photodetector = PM100D() #creates the PD object
self.port = port
def run(self):
while True:
self.flagWhile = True #used to leave while
try:
self.photodetector.connect(self.port) #try to connect
except:
self.dialogSignal.emit() #emit error signal
while self.flagWhile == True:
time.sleep(0.5) #wait here until user press something in the dialog, which is in another thread
else:
break #leave loop when connected
window.PDState = 1 #change state of main gui buttom (change functionality to turn off if pressed again)
window.startpd.setText('Stop PD') #change buttom label
while self.state == 1:
time.sleep(0.016)
value = self.photodetector.get_pow() #get PD pow
self.valueupdate.emit(value) #emit it
The AlgoNanoMax file:
import gui
from NanoMax import Nano
class NanoMax(gui.MyApp): #inheriting parent
def __init__(self, mcontroller_port):
self.mcontroller = Nano(mcontroller_port) #mcontroller is the communication to the motor
def maximize_nano_x(self, step=0.001, spiral_number=3):
''' Alignment procedure with the nano motor X'''
print 'Optimizing X'
power = super(NanoMax, self).ReturnPow() #here I try to read from the photodetector
xpos = self.mcontroller.initial_position_x
position = []
position = [[power, xpos]]
xsign = 1
self.mcontroller.move_relative(self.mcontroller.xaxis, (-1) * spiral_number * step)
print 'X nano move: '+ str((-1) * spiral_number * step * 1000) + ' micrometers'
time.sleep(4)
power = super(NanoMax, self).ReturnPow()
xpos += (-1) * spiral_number * step
position.append([power, xpos])
for _ in xrange(2*spiral_number):
self.mcontroller.move_relative(self.mcontroller.xaxis, xsign * step)
print 'X nano move: '+ str(xsign * step * 1000) + ' micrometers'
time.sleep(5)
power = super(NanoMax, self).ReturnPow()
xpos += xsign * step
position.append([power, xpos])
pospower = [position[i][0] for i in xrange(len(position))]
optimalpoint = pospower.index(max(pospower))
x_shift = (-1) * (xpos - position[optimalpoint][1])
print 'Maximum power: ' + str(max(pospower)) + ' dBm'
print 'Current power: ' + str(super(NanoMax, self).ReturnPow()) + ' dBm'
self.mcontroller.move_relative(self.mcontroller.xaxis, x_shift)

The __init__ for NanoMax and MyApp should call super().__init__() to ensure initialization is done for all levels (if this is Python 2, you can't use no-arg super, so it would be super(NanoMax, self).__init__() and super(MyApp, self).__init__() respectively). This assumes the PyQT was properly written with new-style classes, and correct use of super itself; you're using super in other places, so presumably at least the former is true. Using super appropriately in all classes will ensure all levels are __init__-ed once, while manually listing super classes won't work in certain inheritance patterns, or might call some __init__s multiple times or not at all.
If there is a possibility that many levels might take arguments, you should also accept *args/**kwargs and forward them to the super().__init__ call so the arguments are forwarded where then need to go.
Combining the two, your code should look like:
class MyApp(QtGui.QMainWindow, Ui_MainWindow):
def __init__(self, *args, **kwargs):
super(MyApp, self).__init__(*args, **kwargs)
... rest of __init__ ...
class PDThread(QtCore.QThread):
def __init__(self, state, port, *args, **kwargs):
super(PDThread, self).__init__(*args, **kwargs)
...
class NanoMax(gui.MyApp): #inheriting parent
def __init__(self, mcontroller_port, *args, **kwargs):
super(NanoMax, self).__init__(*args, **kwargs)
self.mcontroller = Nano(mcontroller_port) #mcontroller is the communication to the motor
Note: If you've overloaded methods that the super class might call in its __init__ and your overloads depend on state set in your own __init__, you'll need to set up that state before, rather than after the super().__init__(...) call. Cooperative multiple inheritance can be a pain that way. Also note that using positional arguments for anything but the lowest level class can be ugly with multiple inheritance, so it may make sense to pass all arguments by keyword, and only accept and forward **kwargs, not *args, so people don't pass positional arguments in ways that break if the inheritance hierarchy changes slightly.

class MyApp(QtGui.QMainWindow, Ui_MainWindow):
self.PDvalue = 0 #initial PD value
self.PDState = 0 #control the PD state (on-off)
In the above code it is setting a variable outside of a function. To do this in a class don't put the self keyword in front of it. This way you can just have in the class definition
class MyApp(QtGui.QMainWindow, Ui_MainWindow):
PDvalue = 0 #initial PD value
PDState = 0 #control the PD state (on-off)
and in the super line
power = super(NanoMax, self).PDvalue
For example:
>>> class Hi:
H = 5
def __init__(self):
self.g = 6
>>> class Bye(Hi):
def H(self):
print(super(Bye, self).H)
>>> e = Bye()
>>> e.H()
5
>>>

Function offloaded to PyQt QThread is 2x slower

I've been trying to optimize my application, and although I made the function run on average 10.06 seconds when I profiled it by itself, when it is put on a QThread, it takes around 17-22 seconds.
It's 2x slower in the QThread. How do I fix that?
The function is actually initializing a class called DocxDocument, which is a document that I read from a Word file and parsed it for my needs.
I have a QThread that creates this class and uses Qt signals to send progress information back to the GUI. Here is the code from that class:
class DocxImporterThread(QThread):
'''
This thread is used to import a .docx document, report its progress, and
then return the document that it parsed.
'''
reportProgress = pyqtSignal(int)
reportError = pyqtSignal(Exception, str)
reportText = pyqtSignal(str)
def __init__(self, filePath):
QThread.__init__(self)
self.setPriority(QThread.HighestPriority)
self._filePath = filePath
self._docx = None
self._html = ''
self._bookmarks = None
self._pages = None
self._stop = False
def run(self):
def myProgressHook(percentage):
self.reportProgress.emit(percentage)
def myCancelHook():
return self._stop
try:
self._docx = DocxDocument(self._filePath, myProgressHook, myCancelHook)
if not self._stop:
self._html = self._docx.getMainPage()
self._bookmarks = self._docx.getHeadings()
self._pages = self._docx.getPages()
except Exception as ex2:
print 'ERROR: The .docx document didn\'t import.'
self.reportError.emit(ex2, traceback.format_exc())
The getMainPage(), getHeadings(), and getPages() are instantaneous because they just return a reference to something that the constructor already created. Here is the code I used to profile my DocxDocument class:
testFile = 'some_file.docx'
statSave = 'profile.out'
def progress(percent):
print ' --', percent, '--'
cProfile.run('DocxDocument(testFile)', filename=statSave)
myStats = pstats.Stats(statSave)
myStats.sort_stats('cumulative', 'name')
myStats.print_stats()
Thanks for your time in looking at this!

Python observer/observable library [duplicate]

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Are there any exemplary examples of the GoF Observer implemented in Python? I have a bit code which currently has bits of debugging code laced through the key class (currently generating messages to stderr if a magic env is set). Additionally, the class has an interface for incrementally return results as well as storing them (in memory) for post processing. (The class itself is a job manager for concurrently executing commands on remote machines over ssh).
Currently the usage of the class looks something like:
job = SSHJobMan(hostlist, cmd)
job.start()
while not job.done():
for each in job.poll():
incrementally_process(job.results[each])
time.sleep(0.2) # or other more useful work
post_process(job.results)
An alernative usage model is:
job = SSHJobMan(hostlist, cmd)
job.wait() # implicitly performs a start()
process(job.results)
This all works fine for the current utility. However it does lack flexibility. For example I currently support a brief output format or a progress bar as incremental results, I also support
brief, complete and "merged message" outputs for the post_process() function.
However, I'd like to support multiple results/output streams (progress bar to the terminal, debugging and warnings to a log file, outputs from successful jobs to one file/directory, error messages and other results from non-successful jobs to another, etc).
This sounds like a situation that calls for Observer ... have instances of my class accept registration from other objects and call them back with specific types of events as they occur.
I'm looking at PyPubSub since I saw several references to that in SO related questions. I'm not sure I'm ready to add the external dependency to my utility but I could see value in using their interface as a model for mine if that's going to make it easier for others to use. (The project is intended as both a standalone command line utility and a class for writing other scripts/utilities).
In short I know how to do what I want ... but there are numerous ways to accomplish it. I want suggestions on what's most likely to work for other users of the code in the long run.
The code itself is at: classh.

However it does lack flexibility.
Well... actually, this looks like a good design to me if an asynchronous API is what you want. It usually is. Maybe all you need is to switch from stderr to Python's logging module, which has a sort of publish/subscribe model of its own, what with Logger.addHandler() and so on.
If you do want to support observers, my advice is to keep it simple. You really only need a few lines of code.
class Event(object):
pass
class Observable(object):
def __init__(self):
self.callbacks = []
def subscribe(self, callback):
self.callbacks.append(callback)
def fire(self, **attrs):
e = Event()
e.source = self
for k, v in attrs.items():
setattr(e, k, v)
for fn in self.callbacks:
fn(e)
Your Job class can subclass Observable. When something of interest happens, call self.fire(type="progress", percent=50) or the like.

I think people in the other answers overdo it. You can easily achieve events in Python with less than 15 lines of code.
You simple have two classes: Event and Observer. Any class that wants to listen for an event, needs to inherit Observer and set to listen (observe) for a specific event. When an Event is instantiated and fired, all observers listening to that event will run the specified callback functions.
class Observer():
_observers = []
def __init__(self):
self._observers.append(self)
self._observables = {}
def observe(self, event_name, callback):
self._observables[event_name] = callback
class Event():
def __init__(self, name, data, autofire = True):
self.name = name
self.data = data
if autofire:
self.fire()
def fire(self):
for observer in Observer._observers:
if self.name in observer._observables:
observer._observables[self.name](self.data)
Example:
class Room(Observer):
def __init__(self):
print("Room is ready.")
Observer.__init__(self) # Observer's init needs to be called
def someone_arrived(self, who):
print(who + " has arrived!")
room = Room()
room.observe('someone arrived', room.someone_arrived)
Event('someone arrived', 'Lenard')
Output:
Room is ready.
Lenard has arrived!

A few more approaches...
Example: the logging module
Maybe all you need is to switch from stderr to Python's logging module, which has a powerful publish/subscribe model.
It's easy to get started producing log records.
# producer
import logging
log = logging.getLogger("myjobs") # that's all the setup you need
class MyJob(object):
def run(self):
log.info("starting job")
n = 10
for i in range(n):
log.info("%.1f%% done" % (100.0 * i / n))
log.info("work complete")
On the consumer side there's a bit more work. Unfortunately configuring logger output takes, like, 7 whole lines of code to do. ;)
# consumer
import myjobs, sys, logging
if user_wants_log_output:
ch = logging.StreamHandler(sys.stderr)
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s")
ch.setFormatter(formatter)
myjobs.log.addHandler(ch)
myjobs.log.setLevel(logging.INFO)
myjobs.MyJob().run()
On the other hand there's an amazing amount of stuff in the logging package. If you ever need to send log data to a rotating set of files, an email address, and the Windows Event Log, you're covered.
Example: simplest possible observer
But you don't need to use any library at all. An extremely simple way to support observers is to call a method that does nothing.
# producer
class MyJob(object):
def on_progress(self, pct):
"""Called when progress is made. pct is the percent complete.
By default this does nothing. The user may override this method
or even just assign to it."""
pass
def run(self):
n = 10
for i in range(n):
self.on_progress(100.0 * i / n)
self.on_progress(100.0)
# consumer
import sys, myjobs
job = myjobs.MyJob()
job.on_progress = lambda pct: sys.stdout.write("%.1f%% done\n" % pct)
job.run()
Sometimes instead of writing a lambda, you can just say job.on_progress = progressBar.update, which is nice.
This is about as simple as it gets. One drawback is that it doesn't naturally support multiple listeners subscribing to the same events.
Example: C#-like events
With a bit of support code, you can get C#-like events in Python. Here's the code:
# glue code
class event(object):
def __init__(self, func):
self.__doc__ = func.__doc__
self._key = ' ' + func.__name__
def __get__(self, obj, cls):
try:
return obj.__dict__[self._key]
except KeyError, exc:
be = obj.__dict__[self._key] = boundevent()
return be
class boundevent(object):
def __init__(self):
self._fns = []
def __iadd__(self, fn):
self._fns.append(fn)
return self
def __isub__(self, fn):
self._fns.remove(fn)
return self
def __call__(self, *args, **kwargs):
for f in self._fns[:]:
f(*args, **kwargs)
The producer declares the event using a decorator:
# producer
class MyJob(object):
#event
def progress(pct):
"""Called when progress is made. pct is the percent complete."""
def run(self):
n = 10
for i in range(n+1):
self.progress(100.0 * i / n)
#consumer
import sys, myjobs
job = myjobs.MyJob()
job.progress += lambda pct: sys.stdout.write("%.1f%% done\n" % pct)
job.run()
This works exactly like the "simple observer" code above, but you can add as many listeners as you like using +=. (Unlike C#, there are no event handler types, you don't have to new EventHandler(foo.bar) when subscribing to an event, and you don't have to check for null before firing the event. Like C#, events do not squelch exceptions.)
How to choose
If logging does everything you need, use that. Otherwise do the simplest thing that works for you. The key thing to note is that you don't need to take on a big external dependency.

How about an implementation where objects aren't kept alive just because they're observing something? Below please find an implementation of the observer pattern with the following features:
Usage is pythonic. To add an observer to a bound method .bar of instance foo, just do foo.bar.addObserver(observer).
Observers are not kept alive by virtue of being observers. In other words, the observer code uses no strong references.
No sub-classing necessary (descriptors ftw).
Can be used with unhashable types.
Can be used as many times you want in a single class.
(bonus) As of today the code exists in a proper downloadable, installable package on github.
Here's the code (the github package or PyPI package have the most up to date implementation):
import weakref
import functools
class ObservableMethod(object):
"""
A proxy for a bound method which can be observed.
I behave like a bound method, but other bound methods can subscribe to be
called whenever I am called.
"""
def __init__(self, obj, func):
self.func = func
functools.update_wrapper(self, func)
self.objectWeakRef = weakref.ref(obj)
self.callbacks = {} #observing object ID -> weak ref, methodNames
def addObserver(self, boundMethod):
"""
Register a bound method to observe this ObservableMethod.
The observing method will be called whenever this ObservableMethod is
called, and with the same arguments and keyword arguments. If a
boundMethod has already been registered to as a callback, trying to add
it again does nothing. In other words, there is no way to sign up an
observer to be called back multiple times.
"""
obj = boundMethod.__self__
ID = id(obj)
if ID in self.callbacks:
s = self.callbacks[ID][1]
else:
wr = weakref.ref(obj, Cleanup(ID, self.callbacks))
s = set()
self.callbacks[ID] = (wr, s)
s.add(boundMethod.__name__)
def discardObserver(self, boundMethod):
"""
Un-register a bound method.
"""
obj = boundMethod.__self__
if id(obj) in self.callbacks:
self.callbacks[id(obj)][1].discard(boundMethod.__name__)
def __call__(self, *arg, **kw):
"""
Invoke the method which I proxy, and all of it's callbacks.
The callbacks are called with the same *args and **kw as the main
method.
"""
result = self.func(self.objectWeakRef(), *arg, **kw)
for ID in self.callbacks:
wr, methodNames = self.callbacks[ID]
obj = wr()
for methodName in methodNames:
getattr(obj, methodName)(*arg, **kw)
return result
#property
def __self__(self):
"""
Get a strong reference to the object owning this ObservableMethod
This is needed so that ObservableMethod instances can observe other
ObservableMethod instances.
"""
return self.objectWeakRef()
class ObservableMethodDescriptor(object):
def __init__(self, func):
"""
To each instance of the class using this descriptor, I associate an
ObservableMethod.
"""
self.instances = {} # Instance id -> (weak ref, Observablemethod)
self._func = func
def __get__(self, inst, cls):
if inst is None:
return self
ID = id(inst)
if ID in self.instances:
wr, om = self.instances[ID]
if not wr():
msg = "Object id %d should have been cleaned up"%(ID,)
raise RuntimeError(msg)
else:
wr = weakref.ref(inst, Cleanup(ID, self.instances))
om = ObservableMethod(inst, self._func)
self.instances[ID] = (wr, om)
return om
def __set__(self, inst, val):
raise RuntimeError("Assigning to ObservableMethod not supported")
def event(func):
return ObservableMethodDescriptor(func)
class Cleanup(object):
"""
I manage remove elements from a dict whenever I'm called.
Use me as a weakref.ref callback to remove an object's id from a dict
when that object is garbage collected.
"""
def __init__(self, key, d):
self.key = key
self.d = d
def __call__(self, wr):
del self.d[self.key]
To use this we just decorate methods we want to make observable with #event. Here's an example
class Foo(object):
def __init__(self, name):
self.name = name
#event
def bar(self):
print("%s called bar"%(self.name,))
def baz(self):
print("%s called baz"%(self.name,))
a = Foo('a')
b = Foo('b')
a.bar.addObserver(b.bar)
a.bar()

From wikipedia:
from collections import defaultdict
class Observable (defaultdict):
def __init__ (self):
defaultdict.__init__(self, object)
def emit (self, *args):
'''Pass parameters to all observers and update states.'''
for subscriber in self:
response = subscriber(*args)
self[subscriber] = response
def subscribe (self, subscriber):
'''Add a new subscriber to self.'''
self[subscriber]
def stat (self):
'''Return a tuple containing the state of each observer.'''
return tuple(self.values())
The Observable is used like this.
myObservable = Observable ()
# subscribe some inlined functions.
# myObservable[lambda x, y: x * y] would also work here.
myObservable.subscribe(lambda x, y: x * y)
myObservable.subscribe(lambda x, y: float(x) / y)
myObservable.subscribe(lambda x, y: x + y)
myObservable.subscribe(lambda x, y: x - y)
# emit parameters to each observer
myObservable.emit(6, 2)
# get updated values
myObservable.stat() # returns: (8, 3.0, 4, 12)

Based on Jason's answer, I implemented the C#-like events example as a fully-fledged python module including documentation and tests. I love fancy pythonic stuff :)
So, if you want some ready-to-use solution, you can just use the code on github.

Example: twisted log observers
To register an observer yourCallable() (a callable that accepts a dictionary) to receive all log events (in addition to any other observers):
twisted.python.log.addObserver(yourCallable)
Example: complete producer/consumer example
From Twisted-Python mailing list:
#!/usr/bin/env python
"""Serve as a sample implementation of a twisted producer/consumer
system, with a simple TCP server which asks the user how many random
integers they want, and it sends the result set back to the user, one
result per line."""
import random
from zope.interface import implements
from twisted.internet import interfaces, reactor
from twisted.internet.protocol import Factory
from twisted.protocols.basic import LineReceiver
class Producer:
"""Send back the requested number of random integers to the client."""
implements(interfaces.IPushProducer)
def __init__(self, proto, cnt):
self._proto = proto
self._goal = cnt
self._produced = 0
self._paused = False
def pauseProducing(self):
"""When we've produced data too fast, pauseProducing() will be
called (reentrantly from within resumeProducing's transport.write
method, most likely), so set a flag that causes production to pause
temporarily."""
self._paused = True
print('pausing connection from %s' % (self._proto.transport.getPeer()))
def resumeProducing(self):
self._paused = False
while not self._paused and self._produced < self._goal:
next_int = random.randint(0, 10000)
self._proto.transport.write('%d\r\n' % (next_int))
self._produced += 1
if self._produced == self._goal:
self._proto.transport.unregisterProducer()
self._proto.transport.loseConnection()
def stopProducing(self):
pass
class ServeRandom(LineReceiver):
"""Serve up random data."""
def connectionMade(self):
print('connection made from %s' % (self.transport.getPeer()))
self.transport.write('how many random integers do you want?\r\n')
def lineReceived(self, line):
cnt = int(line.strip())
producer = Producer(self, cnt)
self.transport.registerProducer(producer, True)
producer.resumeProducing()
def connectionLost(self, reason):
print('connection lost from %s' % (self.transport.getPeer()))
factory = Factory()
factory.protocol = ServeRandom
reactor.listenTCP(1234, factory)
print('listening on 1234...')
reactor.run()

OP asks "Are there any exemplary examples of the GoF Observer implemented in Python?"
This is an example in Python 3.7. This Observable class meets the requirement of creating a relationship between one observable and many observers while remaining independent of their structure.
from functools import partial
from dataclasses import dataclass, field
import sys
from typing import List, Callable
#dataclass
class Observable:
observers: List[Callable] = field(default_factory=list)
def register(self, observer: Callable):
self.observers.append(observer)
def deregister(self, observer: Callable):
self.observers.remove(observer)
def notify(self, *args, **kwargs):
for observer in self.observers:
observer(*args, **kwargs)
def usage_demo():
observable = Observable()
# Register two anonymous observers using lambda.
observable.register(
lambda *args, **kwargs: print(f'Observer 1 called with args={args}, kwargs={kwargs}'))
observable.register(
lambda *args, **kwargs: print(f'Observer 2 called with args={args}, kwargs={kwargs}'))
# Create an observer function, register it, then deregister it.
def callable_3():
print('Observer 3 NOT called.')
observable.register(callable_3)
observable.deregister(callable_3)
# Create a general purpose observer function and register four observers.
def callable_x(*args, **kwargs):
print(f'{args[0]} observer called with args={args}, kwargs={kwargs}')
for gui_field in ['Form field 4', 'Form field 5', 'Form field 6', 'Form field 7']:
observable.register(partial(callable_x, gui_field))
observable.notify('test')
if __name__ == '__main__':
sys.exit(usage_demo())

A functional approach to observer design:
def add_listener(obj, method_name, listener):
# Get any existing listeners
listener_attr = method_name + '_listeners'
listeners = getattr(obj, listener_attr, None)
# If this is the first listener, then set up the method wrapper
if not listeners:
listeners = [listener]
setattr(obj, listener_attr, listeners)
# Get the object's method
method = getattr(obj, method_name)
#wraps(method)
def method_wrapper(*args, **kwags):
method(*args, **kwags)
for l in listeners:
l(obj, *args, **kwags) # Listener also has object argument
# Replace the original method with the wrapper
setattr(obj, method_name, method_wrapper)
else:
# Event is already set up, so just add another listener
listeners.append(listener)
def remove_listener(obj, method_name, listener):
# Get any existing listeners
listener_attr = method_name + '_listeners'
listeners = getattr(obj, listener_attr, None)
if listeners:
# Remove the listener
next((listeners.pop(i)
for i, l in enumerate(listeners)
if l == listener),
None)
# If this was the last listener, then remove the method wrapper
if not listeners:
method = getattr(obj, method_name)
delattr(obj, listener_attr)
setattr(obj, method_name, method.__wrapped__)
These methods can then be used to add a listener to any class method. For example:
class MyClass(object):
def __init__(self, prop):
self.prop = prop
def some_method(self, num, string):
print('method:', num, string)
def listener_method(obj, num, string):
print('listener:', num, string, obj.prop)
my = MyClass('my_prop')
add_listener(my, 'some_method', listener_method)
my.some_method(42, 'with listener')
remove_listener(my, 'some_method', listener_method)
my.some_method(42, 'without listener')
And the output is:
method: 42 with listener
listener: 42 with listener my_prop
method: 42 without listener