I never realized just how poor a programmer I was until I came across this exercise below. I am to write a Python file that allows all of the tests below to pass without error.
I believe the file I write needs to be a class, but I have absolutely no idea what should be in my class. I know what the question is asking, but not how to make classes or to respond to the calls to the class with the appropriate object(s).
Please review the exercise code below, and then see my questions at the end.
File with tests:
import unittest
from allergies import Allergies
class AllergiesTests(unittest.TestCase):
def test_ignore_non_allergen_score_parts(self):
self.assertEqual(['eggs'], Allergies(257).list)
if __name__ == '__main__':
unittest.main()
1) I don't understand the "list" method at the end of this assertion. Is it the the Built-In Python function "list()," or is it an attribute that I need to define in my "Allergies" class?
2) What type of object is "Allergies(257).list"
self.assertEqual(['eggs'], Allergies(257).list)
3) Do I approach this by defining something like the following?
def list(self):
list_of_allergens = ['eggs','pollen','cat hair', 'shellfish']
return list_of_allergens[0]
1) I don't understand the "list" method at the end of this assertion. Is it the the Built-In Python function "list()," or is it an attribute that I need to define in my "Allergies" class?
From the ., you can tell that it's an attribute that you need to define on your Allergies class—or, rather, on each of its instances.*
2) What type of object is "Allergies(257).list"
Well, what is it supposed to compare equal to? ['eggs'] is a list of strings (well, of string). So, unless you're going to create a custom type that likes to compare equal to lists, you need a list.
3) Do I approach this by defining something like the following?
def list(self):
list_of_allergens = ['eggs','pollen','cat hair', 'shellfish']
return ist_of_allergens
No. You're on the wrong track right off the bat. This will make Allergies(257).list into a method. Even if that method returns a list when it's called, the test driver isn't calling it. It has to be a list. (Also, more obviously, ['eggs','pollen','cat hair', 'shellfish'] is not going to compare equal to ['eggs'], and ist_of_allergens isn't the same thing as list_of_allergens.)
So, where is that list going to come from? Well, your class is going to need to assign something to self.list somewhere. And, since the only code from your class that's getting called is your constructor (__new__) and initializer (__init__), that "somewhere" is pretty limited. And you probably haven't learned about __new__ yet, which means you have a choice of one place, which makes it pretty simple.
* Technically, you could use a class attribute here, but that seems less likely to be what they're looking for. For that matter, Allergies doesn't even have to be a class; it could be a function that just defines a new type on the fly, constructs it, and adds list to its dict. But both PEP 8 naming standards and "don't make things more complex for no good reason" both point to wanting a class here.
From how it's used, list is an attribute of the object returned by Allergies, which may be a function that returns an object or simply the call to construct an object of type Allergies. In this last case, the whole thing can be easily implemented as:
class Allergies:
def __init__(self, n):
# probably you should do something more
# interesting with n
if n==257:
self.list=['eggs']
This looks like one of the exercises from exercism.io.
I have completed the exercise, so I know what's involved.
'list' is supposed to be a class attribute of the class Allergies, and is itself an object of type list. At least that's one straight-forward way of dealing with it. I defined it in the __init__ method of the class. In my opinion, it's confusing that they called it 'list', as this clashes with Pythons list type.
snippet from my answer:
class Allergies(object):
allergens = ["eggs", "peanuts",
"shellfish", "strawberries",
"tomatoes", "chocolate",
"pollen","cats"]
def __init__(self, score):
# score_breakdown returns a list
self.list = self.score_breakdown(score) # let the name of this function be a little clue ;)
If I were you I would go and do some Python tutorials. I would start with basics, even if it feels like you are covering ground you already travelled. It's absolutely worth knowing your basics/fundamentals as solidly as possible. For this, I could recommend Udacity or codeacademy.
This is a question about a clean, pythonic way to juggle some different instance methods.
I have a class that operates a little differently depending on certain inputs. The differences didn't seem big enough to justify producing entirely new classes. I have to interface the class with one of several data "providers". I thought I was being smart when I introduced a dictionary:
self.interface_tools={'TYPE_A':{ ... various ..., 'data_supplier':self.current_data},
'TYPE_B':{ ... various ..., 'data_supplier':self.predicted_data} }
Then, as part of the class initialization, I have an input "source_name" and I do ...
# ... various ....
self.data_supplier = self.interface_tools[source_name]['data_supplier']
self.current_data and self.predicted_data need the same input parameter, so when it comes time to call the method, I don't have to distinguish them. I can just call
new_data = self.data_supplier(param1)
But now I need to interface with a new data source -- call it "TYPE_C" -- and it needs more input parameters. There are ways to do this, but nothing I can think of is very clean. For instance, I could just add the new parameters to the old data_suppliers and never use them, so then the call would look like
new_data = self.data_supplier(param1,param2,param3)
But I don't like that. I could add an if block
if self.data_source != 'TYPE_C':
new_data = self.data_supplie(param1)
else:
new_data = self.data_c_supplier(param1,param2,param3)
but avoiding if blocks like this was exactly what I was trying to do in the first place with that dictionary I came up with.
So the upshot is: I have a few "data_supplier" routines. Now that my project has expanded, they have different input lists. But I want my class to be able to treat them all the same to the extent possible. Any ideas? Thanks.
Sounds like your functions could be making use of variable length argument lists.
That said, you could also just make subclasses. They're fairly cheap to make, and would solve your problem here. This is pretty much the case they were designed for.
You could make all your data_suppliers accept a single argument and make it a dictionary or a list or even a NamedTuple.
I have a Python class that is initialized with a dictionary of settings, like this:
def __init__(self, settings):
self._settings = settings
Settings dictionary contains 50-100 different parameters that are used quite a lot in other methods:
def MakeTea(self):
tea = Tea()
if self._settings['use_sugar']:
tea.sugar_spoons = self._settings['spoons_of_sugar']
return tea
What I want to know is whether it makes sense to preload all the params into instance attributes like this:
def __init__(self, settings):
self._use_sugar = settings['use_sugar']
self._spoons_of_sugar = settings['spoons_of_sugar']
and use these attributes instead of looking up dictionary values every time I need them:
def MakeTea(self):
tea = Tea()
if self._use_sugar:
tea.sugar_spoons = _self._spoons_of_sugar
return tea
Now, I am fairly new to Python and I worked mostly with compiled languages where it really is a no-brainer: access to instance fields will be much faster than looking up values from any kind of hashtable-based structure. However, with Python being interpreted and all, I'm not sure that I'll have any significant performance gain because at the moment I have almost no knowledge of how Python interpreter works. For all I know, using attribute name in code may involve using some internal dictionaries of identifiers in interpreted environment, so I gain nothing.
So, the question: are there any significant performance benefits in extracting values from dictionary and putting them in instance attributes? Are there any other benefits or downsides of doing it? What's the good practice?
I strongly believe that this is an engineering decision rather than premature optimization. Also, I'm just curious and trying to write decent Python code, so the question seems valid to me whether I actually need those milliseconds or not.
You're comparing attribute access (self.setting) with attribute access (self.settings) plus a dictionary lookup (settings['setting']). Classes are actually implemented as dictionaries, so the problem reduces to two dictionary lookups vs. one. One lookup will be faster.
A simpler and faster way to copy an initialization dict than the one in the other answer is:
class Foobar(object):
def __init__(self, init_dict):
self.__dict__.update(init_dict)
However, I wouldn't do this for optimization purposes. It's both premature optimization (you don't know that you have a speed problem, or what your bottleneck is) and a micro-optimization (making an O(n2) algorithm O(n) will make more of a difference than removing an O(1) dictionary lookup from the original algorithm).
If somewhere, you're accessing one of these settings many, many times, just create a local reference to it, rather than polluting the namespace of Foobar instances with tons of settings.
These are two reasonable designs to consider, but you shouldn't choose one or the other for performance reasons. Instead of either one, I would probably create another object:
class Settings(object):
def __init__(self, init_dict):
self.__dict__.update(init_dict)
class Foobar(object):
def __init__(self, init_dict):
self.settings = Settings(init_dict)
just because I think self.settings.setting is nicer than self.settings['setting'] and it still keeps things organized.
This is a good use for a collections.namedtuple, if you know in advance what all the setting names are.
If you put them into the instance attributes then you'll be looking up your instance dictionary... so in the end you're just gonna be doing the same thing. So no real performance gain or loss.
Example:
>>> class Foobar(object):
def __init__(self, init_dict):
for arg in init_dict:
self.__setattr__(arg, init_dict[arg])
>>> foo = Foobar({'foobar': 'barfoo', 'shroobniz': 'foo'})
>>> print(foo.__dict__)
{'foobar': 'barfoo', 'shroobniz': 'foo'}
So if python looks up foo.__dict__ or foo._settings doesn't really make a difference.
I am working with models of neurons. One class I am designing is a cell class which is a topological description of a neuron (several compartments connected together). It has many parameters but they are all relevant, for example:
number of axon segments, apical bifibrications, somatic length, somatic diameter, apical length, branching randomness, branching length and so on and so on... there are about 15 parameters in total!
I can set all these to some default value but my class looks crazy with several lines for parameters. This kind of thing must happen occasionally to other people too, is there some obvious better way to design this or am I doing the right thing?
UPDATE:
As some of you have asked I have attached my code for the class, as you can see this class has a huge number of parameters (>15) but they are all used and are necessary to define the topology of a cell. The problem essentially is that the physical object they create is very complex. I have attached an image representation of objects produced by this class. How would experienced programmers do this differently to avoid so many parameters in the definition?
class LayerV(__Cell):
def __init__(self,somatic_dendrites=10,oblique_dendrites=10,
somatic_bifibs=3,apical_bifibs=10,oblique_bifibs=3,
L_sigma=0.0,apical_branch_prob=1.0,
somatic_branch_prob=1.0,oblique_branch_prob=1.0,
soma_L=30,soma_d=25,axon_segs=5,myelin_L=100,
apical_sec1_L=200,oblique_sec1_L=40,somadend_sec1_L=60,
ldecf=0.98):
import random
import math
#make main the regions:
axon=Axon(n_axon_seg=axon_segs)
soma=Soma(diam=soma_d,length=soma_L)
main_apical_dendrite=DendriticTree(bifibs=
apical_bifibs,first_sec_L=apical_sec1_L,
L_sigma=L_sigma,L_decrease_factor=ldecf,
first_sec_d=9,branch_prob=apical_branch_prob)
#make the somatic denrites
somatic_dends=self.dendrite_list(num_dends=somatic_dendrites,
bifibs=somatic_bifibs,first_sec_L=somadend_sec1_L,
first_sec_d=1.5,L_sigma=L_sigma,
branch_prob=somatic_branch_prob,L_decrease_factor=ldecf)
#make oblique dendrites:
oblique_dends=self.dendrite_list(num_dends=oblique_dendrites,
bifibs=oblique_bifibs,first_sec_L=oblique_sec1_L,
first_sec_d=1.5,L_sigma=L_sigma,
branch_prob=oblique_branch_prob,L_decrease_factor=ldecf)
#connect axon to soma:
axon_section=axon.get_connecting_section()
self.soma_body=soma.body
soma.connect(axon_section,region_end=1)
#connect apical dendrite to soma:
apical_dendrite_firstsec=main_apical_dendrite.get_connecting_section()
soma.connect(apical_dendrite_firstsec,region_end=0)
#connect oblique dendrites to apical first section:
for dendrite in oblique_dends:
apical_location=math.exp(-5*random.random()) #for now connecting randomly but need to do this on some linspace
apsec=dendrite.get_connecting_section()
apsec.connect(apical_dendrite_firstsec,apical_location,0)
#connect dendrites to soma:
for dend in somatic_dends:
dendsec=dend.get_connecting_section()
soma.connect(dendsec,region_end=random.random()) #for now connecting randomly but need to do this on some linspace
#assign public sections
self.axon_iseg=axon.iseg
self.axon_hill=axon.hill
self.axon_nodes=axon.nodes
self.axon_myelin=axon.myelin
self.axon_sections=[axon.hill]+[axon.iseg]+axon.nodes+axon.myelin
self.soma_sections=[soma.body]
self.apical_dendrites=main_apical_dendrite.all_sections+self.seclist(oblique_dends)
self.somatic_dendrites=self.seclist(somatic_dends)
self.dendrites=self.apical_dendrites+self.somatic_dendrites
self.all_sections=self.axon_sections+[self.soma_sections]+self.dendrites
UPDATE: This approach may be suited in your specific case, but it definitely has its downsides, see is kwargs an antipattern?
Try this approach:
class Neuron(object):
def __init__(self, **kwargs):
prop_defaults = {
"num_axon_segments": 0,
"apical_bifibrications": "fancy default",
...
}
for (prop, default) in prop_defaults.iteritems():
setattr(self, prop, kwargs.get(prop, default))
You can then create a Neuron like this:
n = Neuron(apical_bifibrications="special value")
I'd say there is nothing wrong with this approach - if you need 15 parameters to model something, you need 15 parameters. And if there's no suitable default value, you have to pass in all 15 parameters when creating an object. Otherwise, you could just set the default and change it later via a setter or directly.
Another approach is to create subclasses for certain common kinds of neurons (in your example) and provide good defaults for certain values, or derive the values from other parameters.
Or you could encapsulate parts of the neuron in separate classes and reuse these parts for the actual neurons you model. I.e., you could write separate classes for modeling a synapse, an axon, the soma, etc.
You could perhaps use a Python"dict" object ?
http://docs.python.org/tutorial/datastructures.html#dictionaries
Having so many parameters suggests that the class is probably doing too many things.
I suggest that you want to divide your class into several classes, each of which take some of your parameters. That way each class is simpler and won't take so many parameters.
Without knowing more about your code, I can't say exactly how you should split it up.
Looks like you could cut down the number of arguments by constructing objects such as Axon, Soma and DendriticTree outside of the LayerV constructor, and passing those objects instead.
Some of the parameters are only used in constructing e.g. DendriticTree, others are used in other places as well, so the problem it's not as clear cut, but I would definitely try that approach.
could you supply some example code of what you are working on? It would help to get an idea of what you are doing and get help to you sooner.
If it's just the arguments you are passing to the class that make it long, you don't have to put it all in __init__. You can set the parameters after you create the class, or pass a dictionary/class full of the parameters as an argument.
class MyClass(object):
def __init__(self, **kwargs):
arg1 = None
arg2 = None
arg3 = None
for (key, value) in kwargs.iteritems():
if hasattr(self, key):
setattr(self, key, value)
if __name__ == "__main__":
a_class = MyClass()
a_class.arg1 = "A string"
a_class.arg2 = 105
a_class.arg3 = ["List", 100, 50.4]
b_class = MyClass(arg1 = "Astring", arg2 = 105, arg3 = ["List", 100, 50.4])
After looking over your code and realizing I have no idea how any of those parameters relate to each other (soley because of my lack of knowledge on the subject of neuroscience) I would point you to a very good book on object oriented design. Building Skills in Object Oriented Design by Steven F. Lott is an excellent read and I think would help you, and anyone else in laying out object oriented programs.
It is released under the Creative Commons License, so is free for you to use, here is a link of it in PDF format http://homepage.mac.com/s_lott/books/oodesign/build-python/latex/BuildingSkillsinOODesign.pdf
I think your problem boils down to the overall design of your classes. Sometimes, though very rarely, you need a whole lot of arguments to initialize, and most of the responses here have detailed other ways of initialization, but in a lot of cases you can break the class up into more easier to handle and less cumbersome classes.
This is similar to the other solutions that iterate through a default dictionary, but it uses a more compact notation:
class MyClass(object):
def __init__(self, **kwargs):
self.__dict__.update(dict(
arg1=123,
arg2=345,
arg3=678,
), **kwargs)
Can you give a more detailed use case ? Maybe a prototype pattern will work:
If there are some similarities in groups of objects, a prototype pattern might help.
Do you have a lot of cases where one population of neurons is just like another except different in some way ? ( i.e. rather than having a small number of discrete classes,
you have a large number of classes that slightly differ from each other. )
Python is a classed based language, but just as you can simulate class based
programming in a prototype based language like Javascript, you can simulate
prototypes by giving your class a CLONE method, that creates a new object and
populates its ivars from the parent. Write the clone method so that keyword parameters
passed to it override the "inherited" parameters, so you can call it with something
like:
new_neuron = old_neuron.clone( branching_length=n1, branching_randomness=r2 )
I have never had to deal with this situation, or this topic. Your description implies to me that you may find, as you develop the design, that there are a number of additional classes that will become relevant - compartment is the most obvious. If these do emerge as classes in their own right, it is probable that some of your parameters become parameters of these additional classes.
You could create a class for your parameters.
Instead passing a bunch of parameters, you pass one class.
In my opinion, in your case the easy solution is to pass higher order objects as parameter.
For example, in your __init__ you have a DendriticTree that uses several arguments from your main class LayerV:
main_apical_dendrite = DendriticTree(
bifibs=apical_bifibs,
first_sec_L=apical_sec1_L,
L_sigma=L_sigma,
L_decrease_factor=ldecf,
first_sec_d=9,
branch_prob=apical_branch_prob
)
Instead of passing these 6 arguments to your LayerV you would pass the DendriticTree object directly (thus saving 5 arguments).
You probably want to have this values accessible everywhere, therefore you will have to save this DendriticTree:
class LayerV(__Cell):
def __init__(self, main_apical_dendrite, ...):
self.main_apical_dendrite = main_apical_dendrite
If you want to have a default value too, you can have:
class LayerV(__Cell):
def __init__(self, main_apical_dendrite=None, ...):
self.main_apical_dendrite = main_apical_dendrite or DendriticTree()
This way you delegate what the default DendriticTree should be to the class dedicated to that matter instead of having this logic in the higher order class that LayerV.
Finally, when you need to access the apical_bifibs you used to pass to LayerV you just access it via self.main_apical_dendrite.bifibs.
In general, even if the class you are creating is not a clear composition of several classes, your goal is to find a logical way to split your parameters. Not only to make your code cleaner, but mostly to help people understand what these parameter will be used for. In the extreme cases where you can't split them, I think it's totally ok to have a class with that many parameters. If there is no clear way to split arguments, then you'll probably end up with something even less clear than a list of 15 arguments.
If you feel like creating a class to group parameters together is overkill, then you can simply use collections.namedtuple which can have default values as shown here.
Want to reiterate what a number of people have said. Theres nothing wrong with that amount of parameters. Especially when it comes to scientific computing/programming
Take for example, sklearn's KMeans++ clustering implementation which has 11 parameters you can init with. Like that, there are numerous examples and nothing wrong with them
I would say there is nothing wrong if make sure you need those params. If you really wanna make it more readable I would recommend following style.
I wouldn't say that a best practice or what, it just make others easily know what is necessary for this Object and what is option.
class LayerV(__Cell):
# author: {name, url} who made this info
def __init__(self, no_default_params, some_necessary_params):
self.necessary_param = some_necessary_params
self.no_default_param = no_default_params
self.something_else = "default"
self.some_option = "default"
def b_option(self, value):
self.some_option = value
return self
def b_else(self, value):
self.something_else = value
return self
I think the benefit for this style is:
You can easily know the params which is necessary in __init__ method
Unlike setter, you don't need two lines to construct the object if you need set an option value.
The disadvantage is, you created more methods in your class than before.
sample:
la = LayerV("no_default", "necessary").b_else("sample_else")
After all, if you have a lot of "necessary" and "no_default" params, always think about is this class(method) do too many things.
If your answer is not, just go ahead.