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  • tipfy for Google App Engine: Is it stable? Can auth/session components of tipfy be used with webapp?

    - by cv12
    I am building a web application on Google App Engine that requires users to register with the application and subsequently authenticate with it and maintain sessions. I don't want to force users to have Google accounts. Also, the target audience for the application is the average non-geek, so I'm not very keen on using OpenID or OAuth. I need something simple like: User registers with an e-mail and password, and then can log back in with those credentials. I understand that this approach does not provide the security benefits of Google or OpenID authentication, but I am prepared to trade foolproof security for end-user convenience and hassle-free experience. I explored Django, but decided that consecutive deprecations from appengine-helper to app-engine-patch to django-nonrel may signal that path may be a bit risky in the long-term. I'd like to use a code base that is likely to be maintained consistently. I also explored standalone session/auth packages like gaeutilities and suas. GAEUtilities looked a bit immature (e.g., the code wasn't pythonic in places, in my opinion) and SUAS did not give me a lot of comfort with the cookie-only sessions. I could be wrong with my assessment of these two, so I would appreciate input on those (or others that may serve my objective). Finally, I recently came across tipfy. It appears to be based on Werkzeug and Alex Martelli spoke highly of it here on stackoverflow. I have two primary questions related to tipfy: As a framework, is it as mature as webapp? Is it stable and likely to be maintained for some time? Since my primary interest is the auth/session components, can those components of the tipfy framework be used with webapp, independent of the broader tipfy framework? If yes, I would appreciate a few pointers to how I could go about doing that.

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  • Coding the Python way

    - by Aaron Moodie
    I've just spent the last half semester at Uni learning python. I've really enjoyed it, and was hoping for a few tips on how to write more 'pythonic' code. This is the __init__ class from a recent assignment I did. At the time I wrote it, I was trying to work out how I could re-write this using lambdas, or in a neater, more efficient way, but ran out of time. def __init__(self, dir): def _read_files(_, dir, files): for file in files: if file == "classes.txt": class_list = readtable(dir+"/"+file) for item in class_list: Enrol.class_info_dict[item[0]] = item[1:] if item[1] in Enrol.classes_dict: Enrol.classes_dict[item[1]].append(item[0]) else: Enrol.classes_dict[item[1]] = [item[0]] elif file == "subjects.txt": subject_list = readtable(dir+"/"+file) for item in subject_list: Enrol.subjects_dict[item[0]] = item[1] elif file == "venues.txt": venue_list = readtable(dir+"/"+file) for item in venue_list: Enrol.venues_dict[item[0]] = item[1:] elif file.endswith('.roll'): roll_list = readlines(dir+"/"+file) file = os.path.splitext(file)[0] Enrol.class_roll_dict[file] = roll_list for item in roll_list: if item in Enrol.enrolled_dict: Enrol.enrolled_dict[item].append(file) else: Enrol.enrolled_dict[item] = [file] try: os.path.walk(dir, _read_files, None) except: print "There was a problem reading the directory" As you can see, it's a little bulky. If anyone has the time or inclination, I'd really appreciate a few tips on some python best-practices. Thanks.

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  • small code redundancy within while-loops (doesn't feel clean)

    - by wallacoloo
    So, in Python (though I think it can be applied to many languages), I find myself with something like this quite often: the_input = raw_input("what to print?\n") while the_input != "quit": print the_input the_input = raw_input("what to print?\n") Maybe I'm being too picky, but I don't like how the line the_input = raw_input("what to print?\n") has to get repeated. It decreases maintainability and organization. But I don't see any workarounds for avoiding the duplicate code without further decreasing the problem. In some languages, I could write something like this: while ((the_input=raw_input("what to print?\n")) != "quit") { print the_input } This is definitely not Pythonic, and Python doesn't even allow for assignment within loop conditions AFAIK. This valid code fixes the redundancy, while 1: the_input = raw_input("what to print?\n") if the_input == "quit": break print the_input But doesn't feel quite right either. The while 1 implies that this loop will run forever; I'm using a loop, but giving it a fake condition and putting the real one inside it. Am I being too picky? Is there a better way to do this? Perhaps there's some language construct designed for this that I don't know of?

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  • Python - Checking for membership inside nested dict

    - by victorhooi
    heya, This is a followup questions to this one: http://stackoverflow.com/questions/2901422/python-dictreader-skipping-rows-with-missing-columns Turns out I was being silly, and using the wrong ID field. I'm using Python 3.x here. I have a dict of employees, indexed by a string, "directory_id". Each value is a nested dict with employee attributes (phone number, surname etc.). One of these values is a secondary ID, say "internal_id", and another is their manager, call it "manager_internal_id". The "internal_id" field is non-mandatory, and not every employee has one. (I've simplified the fields a little, both to make it easier to read, and also for privacy/compliance reasons). The issue here is that we index (key) each employee by their directory_id, but when we lookup their manager, we need to find managers by their "internal_id". Before, when employee.keys() was a list of internal_ids, I was using a membership check on this. Now, the last part of my if statement won't work, since the internal_ids is part of the dict values, instead of the key itself. def lookup_supervisor(manager_internal_id, employees): if manager_internal_idis not None and manager_internal_id!= "" and manager_internal_id in employees.keys(): return (employees[manager_internal_id]['mail'], employees[manager_internal_id]['givenName'], employees[manager_internal_id]['sn']) else: return ('Supervisor Not Found', 'Supervisor Not Found', 'Supervisor Not Found') So the first question is, how do I check whether the manager_internal_id is present in the dict's values. I've tried substituting employee.keys() with employee.values(), that didn't work. Also, I'm hoping for something a little more efficient, not sure if there's a way to get a subset of the values, specifically, all the entries for employees[directory_id]['internal_id']. Hopefully there's some Pythonic way of doing this, without using a massive heap of nested for/if loops. My second question is, how do I then cleanly return the required employee attributes (mail, givenname, surname etc.). My for loop is iterating over each employee, and calling lookup_supervisor. I'm feeling a bit stupid/stumped here. def tidy_data(employees): for directory_id, data in employees.items(): # We really shouldnt' be passing employees back and forth like this - hmm, classes? data['SupervisorEmail'], data['SupervisorFirstName'], data['SupervisorSurname'] = lookup_supervisor(data['manager_internal_id'], employees) Thanks in advance =), Victor

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  • Give a reference to a python instance attribute at class definition

    - by Guenther Jehle
    I have a class with attributes which have a reference to another attribute of this class. See class Device, value1 and value2 holding a reference to interface: class Interface(object): def __init__(self): self.port=None class Value(object): def __init__(self, interface, name): self.interface=interface self.name=name def get(self): return "Getting Value \"%s\" with interface \"%s\""%(self.name, self.interface.port) class Device(object): interface=Interface() value1=Value(interface, name="value1") value2=Value(interface, name="value2") def __init__(self, port): self.interface.port=port if __name__=="__main__": d1=Device("Foo") print d1.value1.get() # >>> Getting Value "value1" with interface "Foo" d2=Device("Bar") print d2.value1.get() # >>> Getting Value "value1" with interface "Bar" print d1.value1.get() # >>> Getting Value "value1" with interface "Bar" The last print is wrong, cause d1 should have the interface "Foo". I know whats going wrong: The line interface=Interface() line is executed, when the class definition is parsed (once). So every Device class has the same instance of interface. I could change the Device class to: class Device(object): interface=Interface() value1=Value(interface, name="value1") value2=Value(interface, name="value2") def __init__(self, port): self.interface=Interface() self.interface.port=port So this is also not working: The values still have the reference to the original interface instance and the self.interface is just another instance... The output now is: >>> Getting Value "value1" with interface "None" >>> Getting Value "value1" with interface "None" >>> Getting Value "value1" with interface "None" So how could I solve this the pythonic way? I could setup a function in the Device class to look for attributes with type Value and reassign them the new interface. Isn't this a common problem with a typical solution for it? Thanks!

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  • Counting entries in a list of dictionaries: for loop vs. list comprehension with map(itemgetter)

    - by Dennis Williamson
    In a Python program I'm writing I've compared using a for loop and increment variables versus list comprehension with map(itemgetter) and len() when counting entries in dictionaries which are in a list. It takes the same time using a each method. Am I doing something wrong or is there a better approach? Here is a greatly simplified and shortened data structure: list = [ {'key1': True, 'dontcare': False, 'ignoreme': False, 'key2': True, 'filenotfound': 'biscuits and gravy'}, {'key1': False, 'dontcare': False, 'ignoreme': False, 'key2': True, 'filenotfound': 'peaches and cream'}, {'key1': True, 'dontcare': False, 'ignoreme': False, 'key2': False, 'filenotfound': 'Abbott and Costello'}, {'key1': False, 'dontcare': False, 'ignoreme': True, 'key2': False, 'filenotfound': 'over and under'}, {'key1': True, 'dontcare': True, 'ignoreme': False, 'key2': True, 'filenotfound': 'Scotch and... well... neat, thanks'} ] Here is the for loop version: #!/usr/bin/env python # Python 2.6 # count the entries where key1 is True # keep a separate count for the subset that also have key2 True key1 = key2 = 0 for dictionary in list: if dictionary["key1"]: key1 += 1 if dictionary["key2"]: key2 += 1 print "Counts: key1: " + str(key1) + ", subset key2: " + str(key2) Output for the data above: Counts: key1: 3, subset key2: 2 Here is the other, perhaps more Pythonic, version: #!/usr/bin/env python # Python 2.6 # count the entries where key1 is True # keep a separate count for the subset that also have key2 True from operator import itemgetter KEY1 = 0 KEY2 = 1 getentries = itemgetter("key1", "key2") entries = map(getentries, list) key1 = len([x for x in entries if x[KEY1]]) key2 = len([x for x in entries if x[KEY1] and x[KEY2]]) print "Counts: key1: " + str(key1) + ", subset key2: " + str(key2) Output for the data above (same as before): Counts: key1: 3, subset key2: 2 I'm a tiny bit surprised these take the same amount of time. I wonder if there's something faster. I'm sure I'm overlooking something simple. One alternative I've considered is loading the data into a database and doing SQL queries, but the data doesn't need to persist and I'd have to profile the overhead of the data transfer, etc., and a database may not always be available. I have no control over the original form of the data. The code above is not going for style points.

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  • Python: Created nested dictionary from list of paths

    - by sberry2A
    I have a list of tuples the looks similar to this (simplified here, there are over 14,000 of these tuples with more complicated paths than Obj.part) [ (Obj1.part1, {<SPEC>}), (Obj1.partN, {<SPEC>}), (ObjK.partN, {<SPEC>}) ] Where Obj goes from 1 - 1000, part from 0 - 2000. These "keys" all have a dictionary of specs associated with them which act as a lookup reference for inspecting another binary file. The specs dict contains information such as the bit offset, bit size, and C type of the data pointed to by the path ObjK.partN. For example: Obj4.part500 might have this spec, {'size':32, 'offset':128, 'type':'int'} which would let me know that to access Obj4.part500 in the binary file I must unpack 32 bits from offset 128. So, now I want to take my list of strings and create a nested dictionary which in the simplified case will look like this data = { 'Obj1' : {'part1':{spec}, 'partN':{spec} }, 'ObjK' : {'part1':{spec}, 'partN':{spec} } } To do this I am currently doing two things, 1. I am using a dotdict class to be able to use dot notation for dictionary get / set. That class looks like this: class dotdict(dict): def __getattr__(self, attr): return self.get(attr, None) __setattr__ = dict.__setitem__ __delattr__ = dict.__delitem__ The method for creating the nested "dotdict"s looks like this: def addPath(self, spec, parts, base): if len(parts) > 1: item = base.setdefault(parts[0], dotdict()) self.addPath(spec, parts[1:], item) else: item = base.setdefault(parts[0], spec) return base Then I just do something like: for path, spec in paths: self.lookup = dotdict() self.addPath(spec, path.split("."), self.lookup) So, in the end self.lookup.Obj4.part500 points to the spec. Is there a better (more pythonic) way to do this?

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  • Parallel Tasking Concurrency with Dependencies on Python like GNU Make

    - by Brian Bruggeman
    I'm looking for a method or possibly a philosophical approach for how to do something like GNU Make within python. Currently, we utilize makefiles to execute processing because the makefiles are extremely good at parallel runs with changing single option: -j x. In addition, gnu make already has the dependency stacks built into it, so adding a secondary processor or the ability to process more threads just means updating that single option. I want that same power and flexibility in python, but I don't see it. As an example: all: dependency_a dependency_b dependency_c dependency_a: dependency_d stuff dependency_b: dependency_d stuff dependency_c: dependency_e stuff dependency_d: dependency_f stuff dependency_e: stuff dependency_f: stuff If we do a standard single thread operation (-j 1), the order of operation might be: dependency_f -> dependency_d -> dependency_a -> dependency_b -> dependency_e \ -> dependency_c For two threads (-j 2), we might see: 1: dependency_f -> dependency_d -> dependency_a -> dependency_b 2: dependency_e -> dependency_c Does anyone have any suggestions on either a package already built or an approach? I'm totally open, provided it's a pythonic solution/approach. Please and Thanks in advance!

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  • What's a good way to provide additional decoration/metadata for Python function parameters?

    - by Will Dean
    We're considering using Python (IronPython, but I don't think that's relevant) to provide a sort of 'macro' support for another application, which controls a piece of equipment. We'd like to write fairly simple functions in Python, which take a few arguments - these would be things like times and temperatures and positions. Different functions would take different arguments, and the main application would contain user interface (something like a property grid) which allows the users to provide values for the Python function arguments. So, for example function1 might take a time and a temperature, and function2 might take a position and a couple of times. We'd like to be able to dynamically build the user interface from the Python code. Things which are easy to do are to find a list of functions in a module, and (using inspect.getargspec) to get a list of arguments to each function. However, just a list of argument names is not really enough - ideally we'd like to be able to include some more information about each argument - for instance, it's 'type' (high-level type - time, temperature, etc, not language-level type), and perhaps a 'friendly name' or description. So, the question is, what are good 'pythonic' ways of adding this sort of information to a function. The two possibilities I have thought of are: Use a strict naming convention for arguments, and then infer stuff about them from their names (fetched using getargspec) Invent our own docstring meta-language (could be little more than CSV) and use the docstring for our metadata. Because Python seems pretty popular for building scripting into large apps, I imagine this is a solved problem with some common conventions, but I haven't been able to find them.

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  • Simplifying for-if messes with better structure?

    - by HH
    # Description: you are given a bitwise pattern and a string # you need to find the number of times the pattern matches in the string # any one liner or simple pythonic solution? import random def matchIt(yourString, yourPattern): """find the number of times yourPattern occurs in yourString""" count = 0 matchTimes = 0 # How can you simplify the for-if structures? for coin in yourString: #return to base if count == len(pattern): matchTimes = matchTimes + 1 count = 0 #special case to return to 2, there could be more this type of conditions #so this type of if-conditionals are screaming for a havoc if count == 2 and pattern[count] == 1: count = count - 1 #the work horse #it could be simpler by breaking the intial string of lenght 'l' #to blocks of pattern-length, the number of them is 'l - len(pattern)-1' if coin == pattern[count]: count=count+1 average = len(yourString)/matchTimes return [average, matchTimes] # Generates the list myString =[] for x in range(10000): myString= myString + [int(random.random()*2)] pattern = [1,0,0] result = matchIt(myString, pattern) print("The sample had "+str(result[1])+" matches and its size was "+str(len(myString))+".\n" + "So it took "+str(result[0])+" steps in average.\n" + "RESULT: "+str([a for a in "FAILURE" if result[0] != 8])) # Sample Output # # The sample had 1656 matches and its size was 10000. # So it took 6 steps in average. # RESULT: ['F', 'A', 'I', 'L', 'U', 'R', 'E']

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  • The use of getters and setters for different programming languages [closed]

    - by leonhart88
    So I know there are a lot of questions on getters and setters in general, but I couldn't find something exactly like my question. I was wondering if people change the use of get/set depending on different languages. I started learning with C++ and was taught to use getters and setters. This is what I understand: In C++ (and Java?), a variable can either be public or private, but we cannot have a mix. For example, I can't have a read-only variable that can still be changed inside the class. It's either all public (can read and change it), or all private (can't read and can only change inside the class). Because of this (and possibly other reasons), we use getters and setters. In MATLAB, I can control the "setaccess" and "getaccess" properties of variables, so that I can make things read-only (can directly access the property, but can't overwrite it). In this case, I don't feel like I need a getter because I can just do class.property. Also, in Python it is considered "Pythonic" to not use getters/setters and to only put things into properties if needed. I don't really understand why its OK to have all public variables in Python, because that's opposite of what I learned when I started with C++. I'm just curious what other people's thoughts are on this. Would you use getters and setters for all languages? Would you only use it for C++/Java and do direct access in MATLAB and Python (which is what I am currently doing)? Is the second option considered bad? For my purposes, I am only referring to simple getters and setters (just return/set the value and do not do anything else). Thanks!

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  • python list/dict property best practice

    - by jterrace
    I have a class object that stores some properties that are lists of other objects. Each of the items in the list has an identifier that can be accessed with the id property. I'd like to be able to read and write from these lists but also be able to access a dictionary keyed by their identifier. Let me illustrate with an example: class Child(object): def __init__(self, id, name): self.id = id self.name = name class Teacher(object): def __init__(self, id, name): self.id = id self.name = name class Classroom(object): def __init__(self, children, teachers): self.children = children self.teachers = teachers classroom = Classroom([Child('389','pete')], [Teacher('829','bob')]) This is a silly example, but it illustrates what I'm trying to do. I'd like to be able to interact with the classroom object like this: #access like a list print classroom.children[0] #append like it's a list classroom.children.append(Child('2344','joe')) #delete from like it's a list classroom.children.pop(0) But I'd also like to be able to access it like it's a dictionary, and the dictionary should be automatically updated when I modify the list: #access like a dict print classroom.childrenById['389'] I realize I could just make it a dict, but I want to avoid code like this: classroom.childrendict[child.id] = child I also might have several of these properties, so I don't want to add functions like addChild, which feels very un-pythonic anyway. Is there a way to somehow subclass dict and/or list and provide all of these functions easily with my class's properties? I'd also like to avoid as much code as possible.

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  • Dictionaries with more than one key per value in Python

    - by nickname
    I am attempting to create a nice interface to access a data set where each value has several possible keys. For example, suppose that I have both a number and a name for each value in the data set. I want to be able to access each value using either the number OR the name. I have considered several possible implementations: Using two separate dictionaries, one for the data values organized by number, and one for the data values organized by name. Simply assigning two keys to the same value in a dictionary. Creating dictionaries mapping each name to the corresponding number, and vice versa Attempting to create a hash function that maps each name to a number, etc. (related to the above) Creating an object to encapsulate all three pieces of data, then using one key to map dictionary keys to the objects and simply searching the dictionary to map the other key to the object. None of these seem ideal. The first seems ugly and unmaintainable. The second also seems fragile. The third/fourth seem plausible, but seem to require either much manual specification or an overly complex implementation. Finally, the fifth loses constant-time performance for one of the lookups. In C/C++, I believe that I would use pointers to reference the same piece of data from different keys. I know that the problem is rather similar to a database lookup problem by a non-key column, however, I would like (if possible), to maintain the approximate O(1) performance of Python dictionaries. What is the most Pythonic way to achieve this data structure?

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  • String comparison in Python: is vs. ==

    - by Coquelicot
    I noticed a Python script I was writing was acting squirrelly, and traced it to an infinite loop, where the loop condition was "while line is not ''". Running through it in the debugger, it turned out that line was in fact ''. When I changed it to != rather than 'is not', it worked fine. I did some searching, and found this question, the top answer to which seemed to be just what I needed. Except the answer it gave was counter to my experience. Specifically, the answerer wrote: For all built-in Python objects (like strings, lists, dicts, functions, etc.), if x is y, then x==y is also True. I double-checked the type of the variable, and it was in fact of type str (not unicode or something). Is his answer just wrong, or is there something else afoot? Also, is it generally considered better to just use '==' by default, even when comparing int or Boolean values? I've always liked to use 'is' because I find it more aesthetically pleasing and pythonic (which is how I fell into this trap...), but I wonder if it's intended to just be reserved for when you care about finding two objects with the same id.

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  • Extended slice that goes to beginning of sequence with negative stride

    - by recursive
    Bear with me while I explain my question. Skip down to the bold heading if you already understand extended slice list indexing. In python, you can index lists using slice notation. Here's an example: >>> A = list(range(10)) >>> A[0:5] [0, 1, 2, 3, 4] You can also include a stride, which acts like a "step": >>> A[0:5:2] [0, 2, 4] The stride is also allowed to be negative, meaning the elements are retrieved in reverse order: >>> A[5:0:-1] [5, 4, 3, 2, 1] But wait! I wanted to see [4, 3, 2, 1, 0]. Oh, I see, I need to decrement the start and end indices: >>> A[4:-1:-1] [] What happened? It's interpreting -1 as being at the end of the array, not the beginning. I know you can achieve this as follows: >>> A[4::-1] [4, 3, 2, 1, 0] But you can't use this in all cases. For example, in a method that's been passed indices. My question is: Is there any good pythonic way of using extended slices with negative strides and explicit start and end indices that include the first element of a sequence? This is what I've come up with so far, but it seems unsatisfying. >>> A[0:5][::-1] [4, 3, 2, 1, 0]

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  • How can I create a rules engine without using eval() or exec()?

    - by Angela
    I have a simple rules/conditions table in my database which is used to generate alerts for one of our systems. I want to create a rules engine or a domain specific language. A simple rule stored in this table would be..(omitting the relationships here) if temp > 40 send email Please note there would be many more such rules. A script runs once daily to evaluate these rules and perform the necessary actions. At the beginning, there was only one rule, so we had the script in place to only support that rule. However we now need to make it more scalable to support different conditions/rules. I have looked into rules engines , but I hope to achieve this in some simple pythonic way. At the moment, I have only come up with eval/exec and I know that is not the most recommended approach. So, what would be the best way to accomplish this?? ( The rules are stored as data in database so each object like "temperature", condition like "/=..etc" , value like "40,50..etc" and action like "email, sms, etc.." are stored in the database, i retrieve this to form the condition...if temp 50 send email, that was my idea to then use exec or eval on them to make it live code..but not sure if this is the right approach )

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  • Python/Biophysics- Trying to code a simple stochastic simulation!

    - by user359597
    Hey guys- I'm trying to figure out what to make of the following code- this is not the clear, intuitive python I've been learning. Was it written in C or something then wrapped in a python fxn? The code I wrote (not shown) is using the same math, but I couldn't figure out how to write a conditional loop. If anyone could explain/decipher/clean this up, I'd be really appreciative. I mean- is this 'good' python- or does it look funky? I'm brand new to this- but it's like the order of the fxns is messed up? I understand Gillespie's- I've successfully coded several simpler simulations. So in a nutshell- good code-(pythonic)? order? c? improvements? am i being an idiot? The code shown is the 'answer,' to the following question from a biophysics text (petri-net not shown and honestly not necessary to understand problem): "In a programming language of your choice, implement Gillespie’s First Reaction Algorithm to study the temporal behaviour of the reaction A---B in which the transition from A to B can only take place if another compound, C, is present, and where C dynamically interconverts with D, as modelled in the Petri-net below. Assume that there are 100 molecules of A, 1 of C, and no B or D present at the start of the reaction. Set kAB to 0.1 s-1 and both kCD and kDC to 1.0 s-1. Simulate the behaviour of the system over 100 s." def sim(): # Set the rate constants for all transitions kAB = 0.1 kCD = 1.0 kDC = 1.0 # Set up the initial state A = 100 B = 0 C = 1 D = 0 # Set the start and end times t = 0.0 tEnd = 100.0 print "Time\t", "Transition\t", "A\t", "B\t", "C\t", "D" # Compute the first interval transition, interval = transitionData(A, B, C, D, kAB, kCD, kDC) # Loop until the end time is exceded or no transition can fire any more while t <= tEnd and transition >= 0: print t, '\t', transition, '\t', A, '\t', B, '\t', C, '\t', D t += interval if transition == 0: A -= 1 B += 1 if transition == 1: C -= 1 D += 1 if transition == 2: C += 1 D -= 1 transition, interval = transitionData(A, B, C, D, kAB, kCD, kDC) def transitionData(A, B, C, D, kAB, kCD, kDC): """ Returns nTransition, the number of the firing transition (0: A->B, 1: C->D, 2: D->C), and interval, the interval between the time of the previous transition and that of the current one. """ RAB = kAB * A * C RCD = kCD * C RDC = kDC * D dt = [-1.0, -1.0, -1.0] if RAB > 0.0: dt[0] = -math.log(1.0 - random.random())/RAB if RCD > 0.0: dt[1] = -math.log(1.0 - random.random())/RCD if RDC > 0.0: dt[2] = -math.log(1.0 - random.random())/RDC interval = 1e36 transition = -1 for n in range(len(dt)): if dt[n] > 0.0 and dt[n] < interval: interval = dt[n] transition = n return transition, interval if __name__ == '__main__': sim()

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  • How to learn proper C++?

    - by Chris
    While reading a long series of really, really interesting threads, I've come to a realization: I don't think I really know C++. I know C, I know classes, I know inheritance, I know templates (& the STL) and I know exceptions. Not C++. To clarify, I've been writing "C++" for more than 5 years now. I know C, and I know that C and C++ share a common subset. What I've begun to realize, though, is that more times than not, I wind up treating C++ something vaguely like "C with classes," although I do practice RAII. I've never used Boost, and have only read up on TR1 and C++0x - I haven't used any of these features in practice. I don't use namespaces. I see a list of #defines, and I think - "Gracious, that's horrible! Very un-C++-like," only to go and mindlessly write class wrappers for the sake of it, and I wind up with large numbers (maybe a few per class) of static methods, and for some reason, that just doesn't seem right lately. The professional in me yells "just get the job done," the academic yells "you should write proper C++ when writing C++" and I feel like the point of balance is somewhere in between. I'd like to note that I don't want to program "pure" C++ just for the sake of it. I know several languages. I have a good feel for what "Pythonic" is. I know what clean and clear PHP is. Good C code I can read and write better than English. The issue is that I learned C by example, and picked up C++ as a "series of modifications" to C. And a lot of my early C++ work was creating class wrappers for C libraries. I feel like my own personal C-heavy background while learning C++ has sort of... clouded my acceptance of C++ in it's own right, as it's own language. Do the weathered C++ lags here have any advice for me? Good examples of clean, sharp C++ to learn from? What habits of C does my inner-C++ really need to break from? My goal here is not to go forth and trumpet "good" C++ paradigm from rooftops for the sake of it. C and C++ are two different languages, and I want to start treating them that way. How? Where to start? Thanks in advance! Cheers, -Chris

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  • Python/Biomolecular Physics- Trying to code a simple stochastic simulation of a system exhibiting co

    - by user359597
    *edited 6/17/10 I'm trying to understand how to improve my code (make it more pythonic). Also, I'm interested in writing more intuitive 'conditionals' that would describe scenarios that are commonplace in biochemistry. The conditional criteria in the below program is explained in Answer #2, but I am not satisfied with it- it is correct, but isn't obvious and isn't easy to implement for more complicated conditional scenarios. Ideas welcome. Comments/criticisms welcome. First posting experience @ stackoverflow- please comment on etiquette if needed. The code generates a list of values that are the solution to the following exercise: "In a programming language of your choice, implement Gillespie’s First Reaction Algorithm to study the temporal behaviour of the reaction A---B in which the transition from A to B can only take place if another compound, C, is present, and where C dynamically interconverts with D, as modelled in the Petri-net below. Assume that there are 100 molecules of A, 1 of C, and no B or D present at the start of the reaction. Set kAB to 0.1 s-1 and both kCD and kDC to 1.0 s-1. Simulate the behaviour of the system over 100 s." def sim(): # Set the rate constants for all transitions kAB = 0.1 kCD = 1.0 kDC = 1.0 # Set up the initial state A = 100 B = 0 C = 1 D = 0 # Set the start and end times t = 0.0 tEnd = 100.0 print "Time\t", "Transition\t", "A\t", "B\t", "C\t", "D" # Compute the first interval transition, interval = transitionData(A, B, C, D, kAB, kCD, kDC) # Loop until the end time is exceded or no transition can fire any more while t <= tEnd and transition >= 0: print t, '\t', transition, '\t', A, '\t', B, '\t', C, '\t', D t += interval if transition == 0: A -= 1 B += 1 if transition == 1: C -= 1 D += 1 if transition == 2: C += 1 D -= 1 transition, interval = transitionData(A, B, C, D, kAB, kCD, kDC) def transitionData(A, B, C, D, kAB, kCD, kDC): """ Returns nTransition, the number of the firing transition (0: A->B, 1: C->D, 2: D->C), and interval, the interval between the time of the previous transition and that of the current one. """ RAB = kAB * A * C RCD = kCD * C RDC = kDC * D dt = [-1.0, -1.0, -1.0] if RAB > 0.0: dt[0] = -math.log(1.0 - random.random())/RAB if RCD > 0.0: dt[1] = -math.log(1.0 - random.random())/RCD if RDC > 0.0: dt[2] = -math.log(1.0 - random.random())/RDC interval = 1e36 transition = -1 for n in range(len(dt)): if dt[n] > 0.0 and dt[n] < interval: interval = dt[n] transition = n return transition, interval if __name__ == '__main__': sim()

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  • Representing xml through a single class

    - by Charles
    I am trying to abstract away the difficulties of configuring an application that we use. This application takes a xml configuration file and it can be a bit bothersome to manually edit this file, especially when we are trying to setup some automatic testing scenarios. I am finding that reading xml is nice, pretty easy, you get a network of element nodes that you can just go through and build your structures quite nicely. However I am slowly finding that the reverse is not quite so nice. I want to be able to build a xml configuration file through a single easy to use interface and because xml is composed of a system of nodes I am having a lot of struggle trying to maintain the 'easy' part. Does anyone know of any examples or samples that easily and intuitively build xml files without declaring a bunch of element type classes and expect the user to build the network themselves? For example if my desired xml output is like so <cook version="1.1"> <recipe name="chocolate chip cookie"> <ingredients> <ingredient name="flour" amount="2" units="cups"/> <ingredient name="eggs" amount="2" units="" /> <ingredient name="cooking chocolate" amount="5" units="cups" /> </ingredients> <directions> <direction name="step 1">Preheat oven</direction> <direction name="step 2">Mix flour, egg, and chocolate</direction> <direction name="step 2">bake</direction> </directions> </recipe> <recipe name="hot dog"> ... How would I go about designing a class to build that network of elements and make one easy to use interface for creating recipes? Right now I have a recipe object, an ingredient object, and a direction object. The user must make each one, set the attributes in the class and attach them to the root object which assembles the xml elements and outputs the formatted xml. Its not very pretty and I just know there has to be a better way. I am using python so bonus points for pythonic solutions

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  • Dynamically loading modules in Python (+ threading question)

    - by morpheous
    I am writing a Python package which reads the list of modules (along with ancillary data) from a configuration file. I then want to iterate through each of the dynamically loaded modules and invoke a do_work() function in it which will spawn a new thread, so that the code runs in a separate thread. At the moment, I am importing the list of all known modules at the beginning of my main script - this is a nasty hack I feel, and is not very flexible, as well as being a maintenance pain. This is the function that spawns the threads. I will like to modify it to dynamically load the module when it is encountered. The key in the dictionary is the name of the module containing the code: def do_work(work_info): for (worker, dataset) in work_info.items(): #import the module defined by variable worker here... t = threading.Thread(target=worker.do_work, args=[dataset]) # I'll NOT dameonize since spawned children need to clean up on shutdown # Since the threads will be holding resources #t.daemon = True t.start() Question 1 When I call the function in my script (as written above), I get the following error: AttributeError: 'str' object has no attribute 'do_work' Which makes sense, since the dictionary key is a string (name of the module to be imported). When I add the statement: import worker before spawning the thread, I get the error: ImportError: No module named worker This is strange, since the variable name rather than the value it holds are being used - when I print the variable, I get the value (as I expect) whats going on? Question 2 As I mentioned in the comments section, I realize that the do_work() function written in the spawned children needs to cleanup after itself. My understanding is to write a clean_up function that is called when do_work() has completed successfully, or an unhandled exception is caught - is there anything more I need to do to ensure resources don't leak or leave the OS in an unstable state? Question 3 If I comment out the t.daemon flag statement, will the code stil run ASYNCHRONOUSLY?. The work carried out by the spawned children are pretty intensive, and I don't want to have to be waiting for one child to finish before spawning another child. BTW, I am aware that threading in Python is in reality, a kind of time sharing/slicing - thats ok Lastly is there a better (more Pythonic) way of doing what I'm trying to do?

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  • Python: Improving long cumulative sum

    - by Bo102010
    I have a program that operates on a large set of experimental data. The data is stored as a list of objects that are instances of a class with the following attributes: time_point - the time of the sample cluster - the name of the cluster of nodes from which the sample was taken code - the name of the node from which the sample was taken qty1 = the value of the sample for the first quantity qty2 = the value of the sample for the second quantity I need to derive some values from the data set, grouped in three ways - once for the sample as a whole, once for each cluster of nodes, and once for each node. The values I need to derive depend on the (time sorted) cumulative sums of qty1 and qty2: the maximum value of the element-wise sum of the cumulative sums of qty1 and qty2, the time point at which that maximum value occurred, and the values of qty1 and qty2 at that time point. I came up with the following solution: dataset.sort(key=operator.attrgetter('time_point')) # For the whole set sys_qty1 = 0 sys_qty2 = 0 sys_combo = 0 sys_max = 0 # For the cluster grouping cluster_qty1 = defaultdict(int) cluster_qty2 = defaultdict(int) cluster_combo = defaultdict(int) cluster_max = defaultdict(int) cluster_peak = defaultdict(int) # For the node grouping node_qty1 = defaultdict(int) node_qty2 = defaultdict(int) node_combo = defaultdict(int) node_max = defaultdict(int) node_peak = defaultdict(int) for t in dataset: # For the whole system ###################################################### sys_qty1 += t.qty1 sys_qty2 += t.qty2 sys_combo = sys_qty1 + sys_qty2 if sys_combo > sys_max: sys_max = sys_combo # The Peak class is to record the time point and the cumulative quantities system_peak = Peak(time_point=t.time_point, qty1=sys_qty1, qty2=sys_qty2) # For the cluster grouping ################################################## cluster_qty1[t.cluster] += t.qty1 cluster_qty2[t.cluster] += t.qty2 cluster_combo[t.cluster] = cluster_qty1[t.cluster] + cluster_qty2[t.cluster] if cluster_combo[t.cluster] > cluster_max[t.cluster]: cluster_max[t.cluster] = cluster_combo[t.cluster] cluster_peak[t.cluster] = Peak(time_point=t.time_point, qty1=cluster_qty1[t.cluster], qty2=cluster_qty2[t.cluster]) # For the node grouping ##################################################### node_qty1[t.node] += t.qty1 node_qty2[t.node] += t.qty2 node_combo[t.node] = node_qty1[t.node] + node_qty2[t.node] if node_combo[t.node] > node_max[t.node]: node_max[t.node] = node_combo[t.node] node_peak[t.node] = Peak(time_point=t.time_point, qty1=node_qty1[t.node], qty2=node_qty2[t.node]) This produces the correct output, but I'm wondering if it can be made more readable/Pythonic, and/or faster/more scalable. The above is attractive in that it only loops through the (large) dataset once, but unattractive in that I've essentially copied/pasted three copies of the same algorithm. To avoid the copy/paste issues of the above, I tried this also: def find_peaks(level, dataset): def grouping(object, attr_name): if attr_name == 'system': return attr_name else: return object.__dict__[attrname] cuml_qty1 = defaultdict(int) cuml_qty2 = defaultdict(int) cuml_combo = defaultdict(int) level_max = defaultdict(int) level_peak = defaultdict(int) for t in dataset: cuml_qty1[grouping(t, level)] += t.qty1 cuml_qty2[grouping(t, level)] += t.qty2 cuml_combo[grouping(t, level)] = (cuml_qty1[grouping(t, level)] + cuml_qty2[grouping(t, level)]) if cuml_combo[grouping(t, level)] > level_max[grouping(t, level)]: level_max[grouping(t, level)] = cuml_combo[grouping(t, level)] level_peak[grouping(t, level)] = Peak(time_point=t.time_point, qty1=node_qty1[grouping(t, level)], qty2=node_qty2[grouping(t, level)]) return level_peak system_peak = find_peaks('system', dataset) cluster_peak = find_peaks('cluster', dataset) node_peak = find_peaks('node', dataset) For the (non-grouped) system-level calculations, I also came up with this, which is pretty: dataset.sort(key=operator.attrgetter('time_point')) def cuml_sum(seq): rseq = [] t = 0 for i in seq: t += i rseq.append(t) return rseq time_get = operator.attrgetter('time_point') q1_get = operator.attrgetter('qty1') q2_get = operator.attrgetter('qty2') timeline = [time_get(t) for t in dataset] cuml_qty1 = cuml_sum([q1_get(t) for t in dataset]) cuml_qty2 = cuml_sum([q2_get(t) for t in dataset]) cuml_combo = [q1 + q2 for q1, q2 in zip(cuml_qty1, cuml_qty2)] combo_max = max(cuml_combo) time_max = timeline.index(combo_max) q1_at_max = cuml_qty1.index(time_max) q2_at_max = cuml_qty2.index(time_max) However, despite this version's cool use of list comprehensions and zip(), it loops through the dataset three times just for the system-level calculations, and I can't think of a good way to do the cluster-level and node-level calaculations without doing something slow like: timeline = defaultdict(int) cuml_qty1 = defaultdict(int) #...etc. for c in cluster_list: timeline[c] = [time_get(t) for t in dataset if t.cluster == c] cuml_qty1[c] = [q1_get(t) for t in dataset if t.cluster == c] #...etc. Does anyone here at Stack Overflow have suggestions for improvements? The first snippet above runs well for my initial dataset (on the order of a million records), but later datasets will have more records and clusters/nodes, so scalability is a concern. This is my first non-trivial use of Python, and I want to make sure I'm taking proper advantage of the language (this is replacing a very convoluted set of SQL queries, and earlier versions of the Python version were essentially very ineffecient straight transalations of what that did). I don't normally do much programming, so I may be missing something elementary. Many thanks!

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  • Writing more efficient xquery code (avoiding redundant iteration)

    - by Coquelicot
    Here's a simplified version of a problem I'm working on: I have a bunch of xml data that encodes information about people. Each person is uniquely identified by an 'id' attribute, but they may go by many names. For example, in one document, I might find <person id=1>Paul Mcartney</person> <person id=2>Ringo Starr</person> And in another I might find: <person id=1>Sir Paul McCartney</person> <person id=2>Richard Starkey</person> I want to use xquery to produce a new document that lists every name associated with a given id. i.e.: <person id=1> <name>Paul McCartney</name> <name>Sir Paul McCartney</name> <name>James Paul McCartney</name> </person> <person id=2> ... </person> The way I'm doing this now in xquery is something like this (pseudocode-esque): let $ids := distinct-terms( [all the id attributes on people] ) for $id in $ids return <person id={$id}> { for $unique-name in distinct-values ( for $name in ( [all names] ) where $name/@id=$id return $name ) return <name>{$unique-name}</name> } </person> The problem is that this is really slow. I imagine the bottleneck is the innermost loop, which executes once for every id (of which there are about 1200). I'm dealing with a fair bit of data (300 MB, spread over about 800 xml files), so even a single execution of the query in the inner loop takes about 12 seconds, which means that repeating it 1200 times will take about 4 hours (which might be optimistic - the process has been running for 3 hours so far). Not only is it slow, it's using a whole lot of virtual memory. I'm using Saxon, and I had to set java's maximum heap size to 10 GB (!) to avoid getting out of memory errors, and it's currently using 6 GB of physical memory. So here's how I'd really like to do this (in Pythonic pseudocode): persons = {} for id in ids: person[id] = set() for person in all_the_people_in_my_xml_document: persons[person.id].add(person.name) There, I just did it in linear time, with only one sweep of the xml document. Now, is there some way to do something similar in xquery? Surely if I can imagine it, a reasonable programming language should be able to do it (he said quixotically). The problem, I suppose, is that unlike Python, xquery doesn't (as far as I know) have anything like an associative array. Is there some clever way around this? Failing that, is there something better than xquery that I might use to accomplish my goal? Because really, the computational resources I'm throwing at this relatively simple problem are kind of ridiculous.

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  • Dynamically loading modules in Python (+ multi processing question)

    - by morpheous
    I am writing a Python package which reads the list of modules (along with ancillary data) from a configuration file. I then want to iterate through each of the dynamically loaded modules and invoke a do_work() function in it which will spawn a new process, so that the code runs ASYNCHRONOUSLY in a separate process. At the moment, I am importing the list of all known modules at the beginning of my main script - this is a nasty hack I feel, and is not very flexible, as well as being a maintenance pain. This is the function that spawns the processes. I will like to modify it to dynamically load the module when it is encountered. The key in the dictionary is the name of the module containing the code: def do_work(work_info): for (worker, dataset) in work_info.items(): #import the module defined by variable worker here... # [Edit] NOT using threads anymore, want to spawn processes asynchronously here... #t = threading.Thread(target=worker.do_work, args=[dataset]) # I'll NOT dameonize since spawned children need to clean up on shutdown # Since the threads will be holding resources #t.daemon = True #t.start() Question 1 When I call the function in my script (as written above), I get the following error: AttributeError: 'str' object has no attribute 'do_work' Which makes sense, since the dictionary key is a string (name of the module to be imported). When I add the statement: import worker before spawning the thread, I get the error: ImportError: No module named worker This is strange, since the variable name rather than the value it holds are being used - when I print the variable, I get the value (as I expect) whats going on? Question 2 As I mentioned in the comments section, I realize that the do_work() function written in the spawned children needs to cleanup after itself. My understanding is to write a clean_up function that is called when do_work() has completed successfully, or an unhandled exception is caught - is there anything more I need to do to ensure resources don't leak or leave the OS in an unstable state? Question 3 If I comment out the t.daemon flag statement, will the code stil run ASYNCHRONOUSLY?. The work carried out by the spawned children are pretty intensive, and I don't want to have to be waiting for one child to finish before spawning another child. BTW, I am aware that threading in Python is in reality, a kind of time sharing/slicing - thats ok Lastly is there a better (more Pythonic) way of doing what I'm trying to do? [Edit] After reading a little more about Pythons GIL and the threading (ahem - hack) in Python, I think its best to use separate processes instead (at least IIUC, the script can take advantage of multiple processes if they are available), so I will be spawning new processes instead of threads. I have some sample code for spawning processes, but it is a bit trivial (using lambad functions). I would like to know how to expand it, so that it can deal with running functions in a loaded module (like I am doing above). This is a snippet of what I have: def do_mp_bench(): q = mp.Queue() # Not only thread safe, but "process safe" p1 = mp.Process(target=lambda: q.put(sum(range(10000000)))) p2 = mp.Process(target=lambda: q.put(sum(range(10000000)))) p1.start() p2.start() r1 = q.get() r2 = q.get() return r1 + r2 How may I modify this to process a dictionary of modules and run a do_work() function in each loaded module in a new process?

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  • Python - Converting CSV to Objects - Code Design

    - by victorhooi
    Hi, I have a small script we're using to read in a CSV file containing employees, and perform some basic manipulations on that data. We read in the data (import_gd_dump), and create an Employees object, containing a list of Employee objects (maybe I should think of a better naming convention...lol). We then call clean_all_phone_numbers() on Employees, which calls clean_phone_number() on each Employee, as well as lookup_all_supervisors(), on Employees. import csv import re import sys #class CSVLoader: # """Virtual class to assist with loading in CSV files.""" # def import_gd_dump(self, input_file='Gp Directory 20100331 original.csv'): # gd_extract = csv.DictReader(open(input_file), dialect='excel') # employees = [] # for row in gd_extract: # curr_employee = Employee(row) # employees.append(curr_employee) # return employees # #self.employees = {row['dbdirid']:row for row in gd_extract} # Previously, this was inside a (virtual) class called "CSVLoader". # However, according to here (http://tomayko.com/writings/the-static-method-thing) - the idiomatic way of doing this in Python is not with a class-fucntion but with a module-level function def import_gd_dump(input_file='Gp Directory 20100331 original.csv'): """Return a list ('employee') of dict objects, taken from a Group Directory CSV file.""" gd_extract = csv.DictReader(open(input_file), dialect='excel') employees = [] for row in gd_extract: employees.append(row) return employees def write_gd_formatted(employees_dict, output_file="gd_formatted.csv"): """Read in an Employees() object, and write out each Employee() inside this to a CSV file""" gd_output_fieldnames = ('hrid', 'mail', 'givenName', 'sn', 'dbcostcenter', 'dbdirid', 'hrreportsto', 'PHFull', 'PHFull_message', 'SupervisorEmail', 'SupervisorFirstName', 'SupervisorSurname') try: gd_formatted = csv.DictWriter(open(output_file, 'w', newline=''), fieldnames=gd_output_fieldnames, extrasaction='ignore', dialect='excel') except IOError: print('Unable to open file, IO error (Is it locked?)') sys.exit(1) headers = {n:n for n in gd_output_fieldnames} gd_formatted.writerow(headers) for employee in employees_dict.employee_list: # We're using the employee object's inbuilt __dict__ attribute - hmm, is this good practice? gd_formatted.writerow(employee.__dict__) class Employee: """An Employee in the system, with employee attributes (name, email, cost-centre etc.)""" def __init__(self, employee_attributes): """We use the Employee constructor to convert a dictionary into instance attributes.""" for k, v in employee_attributes.items(): setattr(self, k, v) def clean_phone_number(self): """Perform some rudimentary checks and corrections, to make sure numbers are in the right format. Numbers should be in the form 0XYYYYYYYY, where X is the area code, and Y is the local number.""" if self.telephoneNumber is None or self.telephoneNumber == '': return '', 'Missing phone number.' else: standard_format = re.compile(r'^\+(?P<intl_prefix>\d{2})\((?P<area_code>\d)\)(?P<local_first_half>\d{4})-(?P<local_second_half>\d{4})') extra_zero = re.compile(r'^\+(?P<intl_prefix>\d{2})\(0(?P<area_code>\d)\)(?P<local_first_half>\d{4})-(?P<local_second_half>\d{4})') missing_hyphen = re.compile(r'^\+(?P<intl_prefix>\d{2})\(0(?P<area_code>\d)\)(?P<local_first_half>\d{4})(?P<local_second_half>\d{4})') if standard_format.search(self.telephoneNumber): result = standard_format.search(self.telephoneNumber) return '0' + result.group('area_code') + result.group('local_first_half') + result.group('local_second_half'), '' elif extra_zero.search(self.telephoneNumber): result = extra_zero.search(self.telephoneNumber) return '0' + result.group('area_code') + result.group('local_first_half') + result.group('local_second_half'), 'Extra zero in area code - ask user to remediate. ' elif missing_hyphen.search(self.telephoneNumber): result = missing_hyphen.search(self.telephoneNumber) return '0' + result.group('area_code') + result.group('local_first_half') + result.group('local_second_half'), 'Missing hyphen in local component - ask user to remediate. ' else: return '', "Number didn't match recognised format. Original text is: " + self.telephoneNumber class Employees: def __init__(self, import_list): self.employee_list = [] for employee in import_list: self.employee_list.append(Employee(employee)) def clean_all_phone_numbers(self): for employee in self.employee_list: #Should we just set this directly in Employee.clean_phone_number() instead? employee.PHFull, employee.PHFull_message = employee.clean_phone_number() # Hmm, the search is O(n^2) - there's probably a better way of doing this search? def lookup_all_supervisors(self): for employee in self.employee_list: if employee.hrreportsto is not None and employee.hrreportsto != '': for supervisor in self.employee_list: if supervisor.hrid == employee.hrreportsto: (employee.SupervisorEmail, employee.SupervisorFirstName, employee.SupervisorSurname) = supervisor.mail, supervisor.givenName, supervisor.sn break else: (employee.SupervisorEmail, employee.SupervisorFirstName, employee.SupervisorSurname) = ('Supervisor not found.', 'Supervisor not found.', 'Supervisor not found.') else: (employee.SupervisorEmail, employee.SupervisorFirstName, employee.SupervisorSurname) = ('Supervisor not set.', 'Supervisor not set.', 'Supervisor not set.') #Is thre a more pythonic way of doing this? def print_employees(self): for employee in self.employee_list: print(employee.__dict__) if __name__ == '__main__': db_employees = Employees(import_gd_dump()) db_employees.clean_all_phone_numbers() db_employees.lookup_all_supervisors() #db_employees.print_employees() write_gd_formatted(db_employees) Firstly, my preamble question is, can you see anything inherently wrong with the above, from either a class design or Python point-of-view? Is the logic/design sound? Anyhow, to the specifics: The Employees object has a method, clean_all_phone_numbers(), which calls clean_phone_number() on each Employee object inside it. Is this bad design? If so, why? Also, is the way I'm calling lookup_all_supervisors() bad? Originally, I wrapped the clean_phone_number() and lookup_supervisor() method in a single function, with a single for-loop inside it. clean_phone_number is O(n), I believe, lookup_supervisor is O(n^2) - is it ok splitting it into two loops like this? In clean_all_phone_numbers(), I'm looping on the Employee objects, and settings their values using return/assignment - should I be setting this inside clean_phone_number() itself? There's also a few things that I'm sorted of hacked out, not sure if they're bad practice - e.g. print_employee() and gd_formatted() both use __dict__, and the constructor for Employee uses setattr() to convert a dictionary into instance attributes. I'd value any thoughts at all. If you think the questions are too broad, let me know and I can repost as several split up (I just didn't want to pollute the boards with multiple similar questions, and the three questions are more or less fairly tightly related). Cheers, Victor

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