I have a class containing a list and some boolean methods.
class Cls:
data = [] // populated in __init__()
def flag1(self):
def flag2(self):
def flag3(self): # these all return booleans, based on the data
I want to create a higher level function, taking a parameter one of the flags, manipulating the data in a number of ways, applying the flag to the new data, and counting the number of results.
Something like:
def hof(self, fn):
count = 0
for i in range(1, 10):
new_obj = Cls(self.data+i)
if new_obj.fn():
count +=1
Is there any way to accomplish this without turning all the flags into static methods ?
===
Edit: Made it work, in a very hackish way:
class Cls:
data = []
def __init__(self):
self.data = value
class flag1(self):
return True
class flag2(self):
return False
# The hackish part
flag_dict = {
1: flag1,
2: flag2,
}
def hof(self, flag):
count = 0
for i in range(1,10):
new_obj = Cls(self.data + [i])
if self.flag_dict[flag](new_obj):
count +=1
return count
But it seems like a hack, and it's not quite understandable. Could someone point to a better way ?
Thanks.
You should be able to just pass the methods into the function like instance.hof(Cls.flag1), and internally, write it as if fn(new_obj):, with no need to make it a staticmethod.
Related
Given a class with class methods that contain only self input:
class ABC():
def __init__(self, input_dict)
self.variable_0 = input_dict['variable_0']
self.variable_1 = input_dict['variable_1']
self.variable_2 = input_dict['variable_2']
self.variable_3 = input_dict['variable_3']
def some_operation_0(self):
return self.variable_0 + self.variable_1
def some_operation_1(self):
return self.variable_2 + self.variable_3
First question: Is this very bad practice? Should I just refactor some_operation_0(self) to explicitly take the necessary inputs, some_operation_0(self, variable_0, variable_1)? If so, the testing is very straightforward.
Second question: What is the correct way to setup my unit test on the method some_operation_0(self)?
Should I setup a fixture in which I initialize input_dict, and then instantiate the class with a mock object?
#pytest.fixture
def generator_inputs():
f = open('inputs.txt', 'r')
input_dict = eval(f.read())
f.close()
mock_obj = ABC(input_dict)
def test_some_operation_0():
assert mock_obj.some_operation_0() == some_value
(I am new to both python and general unit testing...)
Those methods do take an argument: self. There is no need to mock anything. Instead, you can simply create an instance, and verify that the methods return the expected value when invoked.
For your example:
def test_abc():
a = ABC({'variable_0':0, 'variable_1':1, 'variable_2':2, 'variable_3':3))
assert a.some_operation_0() == 1
assert a.some_operation_1() == 5
If constructing an instance is very difficult, you might want to change your code so that the class can be instantiated from standard in-memory data structures (e.g. a dictionary). In that case, you could create a separate function that reads/parses data from a file and uses the "data-structure-based" __init__ method, e.g. make_abc() or a class method.
If this approach does not generalize to your real problem, you could imagine providing programmatic access to the key names or other metadata that ABC recognizes or cares about. Then, you could programmatically construct a "defaulted" instance, e.g. an instance where every value in the input dict is a default-constructed value (such as 0 for int):
class ABC():
PROPERTY_NAMES = ['variable_0', 'variable_1', 'variable_2', 'variable_3']
def __init__(self, input_dict):
# implementation omitted for brevity
pass
def some_operation_0(self):
return self.variable_0 + self.variable_1
def some_operation_1(self):
return self.variable_2 + self.variable_3
def test_abc():
a = ABC({name: 0 for name in ABC.PROPERTY_NAMES})
assert a.some_operation_0() == 0
assert a.some_operation_1() == 0
This is probably has a very solution but I am struggling with this a bit.
I have 3 almost similar functions I'd like to refactor into one.
Here's the function:
def delete_labels_replication_controller(provider, obj_type):
obj_label_list = []
obj_type_list = provider.mgmt.list_replication_controller()
def delete_labels_node(provider, obj_type):
obj_label_list = []
obj_type_list = provider.mgmt.list_node()
def delete_labels_image(provider, obj_type):
obj_label_list = []
obj_type_list = provider.mgmt.list_image()
Now, as you see, the only thing that changes is the provider.mgmt.xxx class and it changes according to the obj_type I want to pass into the function.
Question is how to use only 1 function for all of them and replace only the .xxx part of the object?
Thanks!
I am sure there is a better solution but maybe using getattr like this?
def delete_labels_by_type(obj_type):
obj_type_list = getattr(provider.mgmt, obj_type)()
return obj_type_list
Do like this
def myFunction(provider, obj_type, flag_type)
obj_label_list = []
if flag_type == 1:
obj_type_list = provider.mgmt.list_replication_controller()
elif flag_type == 2:
obj_type_list = provider.mgmt.list_node()
elif flag_type == 3:
obj_type_list = provider.mgmt.list_image()
You could just pass the obj_type_list as another parameter to the function:
def delete_labels(provider, obj_type, obj_type_list):
obj_label_list = []
...
and then call like this delete_labels(provider, obj_type, provider.mgmt.list_replication_controller()) (and if that's the only time you need the provider parameter, you can remove it entirely)
Or pass a reference to the getter method:
def delete_labels(provider, obj_type, getter):
obj_label_list = []
obj_type_list = getter(provider.mgmt)
...
and then call like this: delete_labels(provider, obj_type, MgmtClass.list_replication_controller)
In both cases, you can move the bulk of the three functions (the common part not shown in the question) into that new function and keep the original functions for easier usage:
def delete_labels_replication_controller(provider, obj_type):
return delete_labels(provider, obj_type, provider.mgmt.list_replication_controller())
I'm still new to python and this is probably going be one of those (stupid) boring questions. However, any help will be much appreciated. I'm programing something that involves many variables and I've decided to use a class to encapsulate all variables (hopefully making it easier to "read" for me as time passes), but it's not working as I thought it will. So, without further ado here is a part of the class that captures the gist.
import numpy as np
class variable:
def __init__(self, length):
self.length = length # time length`
def state_dynamic(self):
length = self.length
return np.zeros((2, np.size(length)))
def state_static(self):
length = self.length
return np.zeros((2, np.size(length)))
def control_dynamic(self):
length = self.length
return np.zeros((2, np.size(length)))
def control_static(self):
length = self.length
return np.zeros((2, np.size(length)))
def scheduling(self):
length = self.length
return np.zeros(np.size(length))
def disturbance(self):
length = self.length
dummy = np.random.normal(0., 0.1, np.size(length))
for i in range(20):
dummy[i+40] = np.random.normal(0., 0.01) + 1.
dummy[80:100] = 0.
return dummy
I've also tried this one:
import numpy as np
class variable:
def __init__(self, type_1, type_2, length):
self.type_1 = type_1 # belongs to set {state, control, scheduling, disturbance}
self.type_2 = type_2 # belongs to set {static, dynamic, none}
self.length = length # time length
def type_v(self):
type_1 = self.type_1
type_2 = self.type_2
length = self.length
if type_1 == 'state' and type_2 == 'dynamic':
return np.zeros((2, np.size(length)))
elif type_1 == 'state' and type_2 == 'static':
return np.zeros((2, np.size(length)))
elif type_1 == 'control' and type_2 == 'dynamic':
return np.zeros((2, np.size(length)))
elif type_1 == 'control' and type_2 == 'static':
return np.zeros((2, np.size(length)))
elif type_1 == 'scheduling' and type_2 == 'none':
return np.zeros(np.size(length))
elif type_1 == 'disturbance' and type_2 == 'none':
dummy = np.random.normal(0., 0.1, np.size(length))
for i in range(20):
dummy[i+40] = np.random.normal(0., 0.01) + 1.
dummy[80:100] = 0.
return dummy
Now, using the first one (the outcome is the same for the second class as well), when I write the following, say:
In [2]: time = np.linspace(0,10,100)
In [5]: v = variable(time)
In [6]: v1 = v.state_dynamic
In [7]: v1.size
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/<ipython-input-7-e6a5d17aeb75> in <module>()
----> 1 v1.size
AttributeError: 'function' object has no attribute 'size'
In [8]: v2 = variable(np.size(time)).state_dynamic
In [9]: v2
Out[9]: <bound method variable.state_dynamic of <__main__.variable instance at 0x3ad0a28>>
In [10]: v1[0,0]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/<ipython-input-10-092bc2b9f982> in <module>()
----> 1 v1[0,0]
TypeError: 'instancemethod' object has no attribute '__getitem__'
I was hoping that by writing
variable(length).state_dynamic
I'll access
np.zeros((2, np.size(length)))
Anyway, if I made something utterly stupid please let me know :) and feel free to give any kind of advice. Thank you in advance for your time and kind attention. Best regards.
EDIT #1:
#wheaties:
Thank you for a quick reply and help :)
What I'm currently trying to do is the following. I have to plot several "variables", e.g., state, control, dropouts, scheduling and disturbances. All the variables depend on three parameters, namely, dynamic, static and horizon. Further, state and control are np.zeros((2, np.size(length))), dropouts and scheduling are np.zeros(np.size(length)) and disturbance has specific form (see above). Initially, I declared them in the script and the list is very long and looks ugly. I use these variables to store responses of dynamical systems considered and to plot them. I don't know if this is a good way of doing this and if you have any suggestion please share.
Thanks again for your help.
Do you mean you want named access to a bunch of state information? The ordinary python idiom for class variables would look like this:
class Variable(object):
def __init__ (self, state_dynamic, state_static, control_static, control_dynamic, scheduling):
self.state_dynamic = state_dynamic
self.state_static = state_static
self.control_static = control_static
self.control_dynamic = control_dynamic
self.scheduling = control_dynamic
Which essentially creates a bucket with named fields that hold values you put in via the constructor. You can also create lightweight data classes using the namedtuple factory class, which avoids some of the boilerplate.
The other python idiom that might apply is to use the #property decorator as in #wheaties answer. This basically disguises a function call to make it look like a field. If what you're doing can be reduced to a functional basis this would make sense. This is an example of the idea (not based on your problem set, since I'm not sure I grok what you're doing in detail with all those identical variables) -- in this case I'm making a convenience wrapper for pulling individual flags out that are stored in a python number but really make a bit field:
class Bits(object):
def __init__(self, integer):
self.Integer = integer # pretend this is an integer between 0 and 8 representing 4 flags
#property
def locked(self):
# low bit = locked
return self.Integer & 1 == 1
#property
def available(self):
return self.Integer & 2 == 2
#property
def running_out_of_made_up_names(self):
return self.Integer & 4 == 4
#property
def really_desperate_now(self):
return self.Integer & 8 == 8
example = Bits(7)
print example.locked
# True
print example.really_desperate_now
# False
A method in Python is a function. If you want to get a value from a member function you have to end it with (). That said, some refactoring may help eliminate boilerplate and reduce the problem set size in your head. I'd suggest using a #property for some of these things, combined with a slight refactor
class variable:
def __init__(self, length):
self.length = length # time length`
#property
def state_dynamic(self):
return self.np_length
#property
def state_static(self):
return self.np_length
#property
def control_dynamic(self):
return self.np_length
#property
def control_static(self):
return self.np_length
#property
def scheduling(self):
return self.np_length
#property
def np_length(self):
return np.zeros(2, np.size(self.length))
That way you can use those functions as you would a member variable like you tried before:
var = variable(length).state_dynamic
What I can't tell from all this is what the difference is between all these variables? I don't see a single one. Are you assuming that you have to access them in order? If so, that's bad design and a problem. Never make that assumption.
I am maintaining a little library of useful functions for interacting with my company's APIs and I have come across (what I think is) a neat question that I can't find the answer to.
I frequently have to request large amounts of data from an API, so I do something like:
class Client(object):
def __init__(self):
self.data = []
def get_data(self, offset = 0):
done = False
while not done:
data = get_more_starting_at(offset)
self.data.extend(data)
offset += 1
if not data:
done = True
This works fine and allows me to restart the retrieval where I left off if something goes horribly wrong. However, since python functions are just regular objects, we can do stuff like:
def yo():
yo.hi = "yo!"
return None
and then we can interrogate yo about its properties later, like:
yo.hi => "yo!"
my question is: Can I rewrite my class-based example to pin the data to the function itself, without referring to the function by name. I know I can do this by:
def get_data(offset=0):
done = False
get_data.data = []
while not done:
data = get_more_starting_from(offset)
get_data.data.extend(data)
offset += 1
if not data:
done = True
return get_data.data
but I would like to do something like:
def get_data(offset=0):
done = False
self.data = [] # <===== this is the bit I can't figure out
while not done:
data = get_more_starting_from(offset)
self.data.extend(data) # <====== also this!
offset += 1
if not data:
done = True
return self.data # <======== want to refer to the "current" object
Is it possible to refer to the "current" object by anything other than its name?
Something like "this", "self", or "memememe!" is what I'm looking for.
I don't understand why you want to do this, but it's what a fixed point combinator allows you to do:
import functools
def Y(f):
#functools.wraps(f)
def Yf(*args):
return inner(*args)
inner = f(Yf)
return Yf
#Y
def get_data(f):
def inner_get_data(*args):
# This is your real get data function
# define it as normal
# but just refer to it as 'f' inside itself
print 'setting get_data.foo to', args
f.foo = args
return inner_get_data
get_data(1, 2, 3)
print get_data.foo
So you call get_data as normal, and it "magically" knows that f means itself.
You could do this, but (a) the data is not per-function-invocation, but per function (b) it's much easier to achieve this sort of thing with a class.
If you had to do it, you might do something like this:
def ybother(a,b,c,yrselflambda = lambda: ybother):
yrself = yrselflambda()
#other stuff
The lambda is necessary, because you need to delay evaluation of the term ybother until something has been bound to it.
Alternatively, and increasingly pointlessly:
from functools import partial
def ybother(a,b,c,yrself=None):
#whatever
yrself.data = [] # this will blow up if the default argument is used
#more stuff
bothered = partial(ybother, yrself=ybother)
Or:
def unbothered(a,b,c):
def inbothered(yrself):
#whatever
yrself.data = []
return inbothered, inbothered(inbothered)
This last version gives you a different function object each time, which you might like.
There are almost certainly introspective tricks to do this, but they are even less worthwhile.
Not sure what doing it like this gains you, but what about using a decorator.
import functools
def add_self(f):
#functools.wraps(f)
def wrapper(*args,**kwargs):
if not getattr(f, 'content', None):
f.content = []
return f(f, *args, **kwargs)
return wrapper
#add_self
def example(self, arg1):
self.content.append(arg1)
print self.content
example(1)
example(2)
example(3)
OUTPUT
[1]
[1, 2]
[1, 2, 3]
I've got a piece of code which contains a for loop to draw things from an XML file;
for evoNode in node.getElementsByTagName('evolution'):
evoName = getText(evoNode.getElementsByTagName( "type")[0].childNodes)
evoId = getText(evoNode.getElementsByTagName( "typeid")[0].childNodes)
evoLevel = getText(evoNode.getElementsByTagName( "level")[0].childNodes)
evoCost = getText(evoNode.getElementsByTagName("costperlevel")[0].childNodes)
evolutions.append("%s x %s" % (evoLevel, evoName))
Currently it outputs into a list called evolutions as it says in the last line of that code, for this and several other for functions with very similar functionality I need it to output into a class instead.
class evolutions:
def __init__(self, evoName, evoId, evoLevel, evoCost)
self.evoName = evoName
self.evoId = evoId
self.evoLevel = evoLevel
self.evoCost = evoCost
How to create a series of instances of this class, each of which is a response from that for function? Or what is a core practical solution? This one doesn't really need the class but one of the others really does.
A list comprehension might be a little cleaner. I'd also move the parsing logic to the constructor to clean up the implemenation:
class Evolution:
def __init__(self, node):
self.node = node
self.type = property("type")
self.typeid = property("typeid")
self.level = property("level")
self.costperlevel = property("costperlevel")
def property(self, prop):
return getText(self.node.getElementsByTagName(prop)[0].childNodes)
evolutionList = [Evolution(evoNode) for evoNode in node.getElementsByTagName('evolution')]
Alternatively, you could use map:
evolutionList = map(Evolution, node.getElementsByTagName('evolution'))
for evoNode in node.getElementsByTagName('evolution'):
evoName = getText(evoNode.getElementsByTagName("type")[0].childNodes)
evoId = getText(evoNode.getElementsByTagName("typeid")[0].childNodes)
evoLevel = getText(evoNode.getElementsByTagName("level")[0].childNodes)
evoCost = getText(evoNode.getElementsByTagName("costperlevel")[0].childNodes)
temporaryEvo = Evolutions(evoName, evoId, evoLevel, evoCost)
evolutionList.append(temporaryEvo)
# Or you can go with the 1 liner
evolutionList.append(Evolutions(evoName, evoId, evoLevel, evoCost))
I renamed your list because it shared the same name as your class and was confusing.