I am using the numpy.random.choice module to generate an 'array' of choices based on an array of functions:
def f(x):
return np.sin(x)
def g(x):
return np.cos(x)
base=[f, g]
funcs=np.random.choice(base,size=2)
This code will produce an 'array' of 2 items referencing a function from the base array.
The reason for this post is, I have printed the outcome of funcs and recieved:
[<function f at 0x00000225AC94F0D0> <function f at 0x00000225AC94F0D0>]
Clearly this returns a reference to the functions in some form, not that I understand what that form is or how to manipulate it, this is where the problem comes in. I want to change the choice of function, so that it is no longer random and instead depends on some conditions, so it might be:
for i in range(2):
if testvar=='true':
choice[i] = 0
if testvar== 'false':
choice[i] = 1
This would return an array of indicies to be put in later function
The problem is, the further operations of the code (I think) require this previous form of function reference: [ ] as an input, instead of a simple array of 0,1 Indicies and I don't know how I can get an array of form [ ] by using if statements.
I could be completely wrong about the rest of the code requiring this input, but I don't know how I can amend it, so am hence posting it here. The full code is as follows: (it is a slight variation of code provided by #Attack68 on Evolving functions in python) It aims to store a function that is multiplied by a random function on each iteration and integrates accordingly. (I have put a comment on the code above the function that is causing the problem)
import numpy as np
import scipy.integrate as int
def f(x):
return np.sin(x)
def g(x):
return np.cos(x)
base = [f, g]
funcs = np.random.choice(base, size=2)
print(funcs)
#The below function is where I believe the [<function...>] input to be required
def apply(x, funcs):
y = 1
for func in funcs:
y *= func(x)
return y
print('function value at 1.5 ', apply(1.5, funcs))
answer = int.quad(apply, 1, 2, args=(funcs,))
print('integration over [1,2]: ', answer)
Here is my attempt of implementing a non-random event:
import numpy as np
import scipy.integrate as int
import random
def f(x):
return np.sin(x)
def g(x):
return np.cos(x)
base = [f, g]
funcs = list()
for i in range(2):
testvar=random.randint(0,100) #In my actual code, this would not be random but dependent on some other situation I have not accounted for here
if testvar>50:
func_idx = 0 # choose a np.random operation: 0=f, 1=g
else:
func_idx= 1
funcs.append(func_idx)
#funcs = np.random.choice(base, size=10)
print(funcs)
def apply(x, funcs):
y = 1
for func in funcs:
y *= func(x)
return y
print('function value at 1.5 ', apply(1.5, funcs))
answer = int.quad(apply, 1, 2, args=(funcs,))
print('integration over [1,2]: ', answer)
This returns the following error:
TypeError: 'int' object is not callable
If: You are trying to refactor your original code that operates on a list of randomly chosen functions to a version that operates with random indices which correspond to items in a list of functions. Refactor apply.
def apply(x,indices,base=base):
y = 1
for i in indices:
f = base[i]
y *= f(x)
return y
...this returns a reference to the functions in some form, not that I understand what that form is or how to manipulate it...
Functions are objects, the list contains a reference to the objects themselves. They can be used by either assigning them to a name then calling them or indexing the list and calling the object:
>>> def f():
... return 'f'
>>> def g():
... return 'g'
>>> a = [f,g]
>>> q = a[0]
>>> q()
'f'
>>> a[1]()
'g'
>>> for thing in a:
print(thing())
f
g
Or you can pass them around:
>>> def h(thing):
... return thing()
>>> h(a[1])
'g'
>>>
If you still want to use your function apply as-is, you need to keep your input a list of functions. Instead of providing a list of indices, you can use those indices to create your list of functions.
Instead of apply(1.5, funcs), try:
apply(1.5, [base(n) for n in funcs])
Related
Updated Question
Following from my original post, with the use of #Attack68 's code, I have created a program that successfully evolved the function with a choice of multiplicative functions based on a random variable. However, now I am receiving an error saying the list indices must be integers (even though I'm fairly sure they are), I'm not sure what has happened, The code is as follows:
import numpy as np
import scipy.integrate as integrate
x=np.linspace(0.0,1.0,100)
n=10 #iterations
d=700.0
def f(x):
return np.sin(x)
def g(x,list_):
return np.cos(x)*apply(x,list_)
base = [f, g]
list_ = list()
for i in range(n):
testvar=np.random.randint(1, 100, 1)
if testvar> 50 and i!=0:
func_idx = 0 # choose a random operation: 0=ten, 1=inv
else:
func_idx= 1
list_.append(func_idx)
# now you have a list of indexes referencing your base functions so you can apply them:
def apply(x,list_):
y = 1
for i in range(len(list_)):
y *= base[list_[i]](x)
return y
print(list_)
#testint=integrate.quad(apply(x,list_),-d,d)[0]
#print(testint)
print(apply(list_, x))
I am now getting the error:
TypeError: list indices must be integers or slices, not numpy.float64
I am also attempting to get this to integrate the new function after each iteration but it seems that the form of this function is not callable by scipys quad integrator, any suggestions on how to integrate the evolving function on each iteration would also be appreciated.
Original:
I am creating a simulation in python where I consider a function that evolves over a loop. This function starts off defined as:
def f(x):
return 1.0
So simply a flat distribution. After each iteration of the loop, I want the function to be redefined depending on certain (random) conditions. It could be multiplied by cos(b*x) or it could be multiplied by some function A(x), the evolution will not be the same each time due to the randomness, so I cannot simply multiply by the same value each time.
The progression in one instance could be:
f(x)----> f(x)*A(x)----> f(x)*A(x)*A(x)...
but in another instance it could be:
f(x)----> f(x)*A(x)----> f(x)*A(x)*cos(x)...
or
f(x)----> f(x)*cos(x)----> f(x)*cos(x)*cos(x)...
etc.
after each, of n iterations of this evolution, I have to compute an integral that is related to the function, so I need to essentially update the function after each iteration to be called by scipys quad integrator.
I have tried to use arrays to manipulate the distribution instead and it works as far as the function evolution goes, but upon integration, it gives the incorrect result with numpy.trapz and I cannot work out why. Sci-pys quad integrator is more accurate anyway and I had managed to get this to work previously for the first iteration only, but it requires a function based input, so without this function evolution I cannot use it.
If someone could show me if/how this function evolution is possible that'd be great. If it is not possible, perhaps someone could try to help me understand what numpy.trapz actually does so I can workout how to fix it?
How about this:
class MyFunction:
def __init__(self):
def f1(x):
return 1.0
self.functions = [f1]
def append_function(self, fn):
self.functions.append(fn)
def __call__(self, x):
product = 1.0
for f in self.functions:
product *= f(x)
return product
This object starts off as simply returning 1.0. Later you add more functions and it returns the product of all of them.
Your description suggests your iterated values are combined through a product and are not in fact a composition of functions. A simple way of recording these is to have a set of base functions:
import numpy as np
import scipy.integrate as int
def two(x):
return x*2
def inv(x):
return 1/x
base = [two, inv]
funcs = np.random.choice(base, size=10)
def apply(x, funcs):
y = 1
for func in funcs:
y *= func(x)
return y
print('function value at 1.5 ', apply(1.5, funcs))
answer = int.quad(apply, 1, 2, args=(funcs,))
print('integration over [1,2]: ', answer)
I'm learning Python right now and I am just trying to get to grips with all of the syntax options.
Currently, the only thing that I can't seem to google up is what to do if I for some reason want to define a function which contains multiple other defines.
While I understand what to do if there's only 1 define inside the the larger define (val = f()(3,4) returns 7 if you exclude the second def below), I don't know how to correctly use the function below.
If it's possible, what is the syntax for a def function with an arbitrary amount of defined functions within it?
Code:
def f():
def x(a,b):
return a + b
return x
def y(c,d):
return c + d
return y
val = f()(3,4)(5,6)
print(val)
I expected the above to return either (7,11) or 11. However, it returns 'int object is not callable'
When you write val = f()(3,4)(5,6), you want f to return a function that also returns a function; compare with the simpler multi-line call:
t1 = f()
t2 = t1(3,4)
val = t2(5,6)
The function f defines and returns also has to define and return a function that can be called with 2 arguments. So, as #jonrsharpe said, you need more nesting:
def f():
def x(a, b):
def y(c, d):
return c + d
return y
return x
Now, f() produces the function named x, and f()(3,4) produces the function named y (ignoring its arguments 3 and 4 in the process), and f()(3,4)(5,6) evaluates (ultimately) to 5 + 6.
For example:
def func(a):
# how to get the name "x"
x = 1
func(x)
If I use inspect module I can get the stack frame object:
import inspect
def func(a):
print inspect.stack()
out:
[
(<frame object at 0x7fb973c7a988>, 'ts.py', 9, 'func', [' stack = inspect.stack()\n'], 0)
(<frame object at 0x7fb973d74c20>, 'ts.py', 18, '<module>', ['func(x)\n'], 0)
]
or use inspect.currentframe() I can get the current frame.
But I can only get the name "a" by inspect the function object.
Use inspect.stack I can get the call stack:"['func(x)\n']",
How can I get the name of actual parameters("x" here) when call the func by parsing the "['func(x)\n']"?
If I call func(x) then I get "x"
if I call func(y) then I get "y"
Edit:
A example:
def func(a):
# Add 1 to acttual parameter
...
x = 1
func(x)
print x # x expected to 2
y = 2
func(y)
print y # y expected to 3
Looking at your comment explanations, the reason you are trying to do this is:
Because I want to impelment Reference Parameters like in C++
You can't do that. In python, everything is passed by value, but that value is a reference to the object you pass. Now, if that object is mutable (like a list), you can modify it, but if it's immutable, a new object is created and set. In your code example, x is an int which is immutable, so if you want to change the value of x it, just return its new value from the function you are invoking:
def func(a):
b = 5 * a
# ... some math
return b
x = 1
x = func(x)
So what if you want to return multiple values? Use a tuple!
def func(a, b):
a, b = 5 * b, 6 * a
# ...
return b, a
a, b = 2, 3
a, b = func(a, b)
That is feasible, up to a point - but nonetheless,useless in any kind of "real code".
You can use it for backyard magic tricks:
Just retrieve the calling stack_frame like you are doing,
then loop linearly through the variables available in the calling frame for
one that references the same object you got as a parameter. The variables are in the
f_locals and f_globals dictionaries which are attributes of the frame object.
Of course, the parameter passed may not be in a variable name to start with:
it might be a constant in the caller, or it might be inside a dict or a list.
Either way, as #Nasser put it in the answer: it is not the way Python works. Objects exist in memory, and variable names, in each scope, just point to those objects. The names themselves are meaningless otherwise.
import inspect
from itertools import chain
def magic(x):
# note that in Python2, 'currentframe' could get a parameter
# to indicate which frame you want on the stack. Not so in Python3
fr = inspect.currentframe().f_back
for var_name, value in chain(fr.f_locals.items(), fr.f_globals.items()):
if value is x:
print ("The variable name in the caller context is {}".format(var_name))
def caler():
y = "My constant"
magic(y)
use lists as references
def func(a):
a[0] = a[0] + 1
x = [1]
func(x)
print x[0] # x expected to 2
y = [2]
func(y)
print y[0] # y expected to 3
This is my final solution. Use ast to parse the function statement and get the args.:
import inspect
import re
import ast
def func(a,b,c):
stack = inspect.stack()
stack_pre = stack[1]
function_call_string = ';'.join( stack_pre[4] ).strip('\n')
ret = re.search(r'(%s[ ]*\(.*\))' % func.func_name, function_call_string)
striped_function_call_string = ret.group()
parser = ast.parse(striped_function_call_string)
frame = inspect.currentframe()
for arg in parser.body[0].value.args:
iter_frame = frame.f_back
while iter_frame:
if arg.id in iter_frame.f_locals:
iter_frame.f_locals[arg.id] += 1
break
iter_frame = iter_frame.f_back
x=1;y=2;z=3;func(x,y,z)
print x,y,z
Out:
2 3 4
I am new to python. This might be a simple question, but if I have many functions that are dependent on each other how would I access lists from one function to use in another.
So...
def function_1():
list_1=[]
def function_2():
list_2= [2*x for x in list_1]
def function_3():
list_3= [x * y for x, y in zip(list_1, list_2)]
That is not the exact code but that is the idea of my problem. I would just put them all together in one function but I need them to be separate.
The correct way to do this would be to use a class. A class is an object that has internal variables (in your case, the three lists), and methods (functions that can access the internal methods). So, this would be:
class Foo(object):
def __init__(self, data=None):
self.list_1 = data if not data is None else []
def function_2():
self.list_2 = [2 * x for x in self.list_1]
And so on. For calling it:
foo = Foo() # list_1 is empty
foo2 = Foo([1,2,3]) # list_1 is not empty
foo2.function_2()
print foo2.list_2
# prints [2, 4, 6]
Make them arguments and return values:
def function_1():
return []
def function_2(list_1):
return [2*x for x in list_1]
def function_3(list_1, list_2):
return [x * y for x, y in zip(list_1, list_2)]
(this suggests that function_1 isn't much worth having...)
The exact way will depend on exactly how you want things to work, but here is a simple example:
def function_1():
return []
def function_2():
return [2*x for x in function_1()]
def function_3():
return [x * y for x, y in zip(function_1(), function_2())]
The key point is that functions do not generally just "do" things, they return things. If you have a value in one function that you want to use in another function, the first function should return that value. The second function should call the first function, and use its return value.
Functions are basically black boxes -- the outside world doesn't really know what goes on inside or what variables exist there. From the outside, other code only sees what goes in (the function's arguments) and what goes out (its return value).
So if your function computes some value that is to be used elsewhere, it should be returned as the result of the function.
E.g.,
def square(x):
return x * x
Takes a number, computes its square, and returns it.
Then you could do:
print(square(5))
and it will print 25.
So in your case you can return the lists and use them in the other functions, as the other answers showed:
def function_1():
return []
def function_2():
return [2*x for x in function_1()]
def function_3():
return [x * y for x, y in zip(function_1(), function_2())]
Imagine I've got a Python module with some function in it:
def sumvars(x, y, z):
s = x
s += y
s += z
return s
But sometimes I want to get results of some intermediate calculations (for example, I could have a function which reverses a matrix and would like to know the determinant which has been calculated as an intermediate step as well). Obviously, I wouldn't want to redo those calculations again if they were already done within that function.
My first idea is to return a dict:
def sumvars(x, y, z):
d = {}
s = x
d['first_step'] = s
s += y
d['second_step'] = s
s += z
d['final'] = s
return d
But I don't recall any functions in numpy or scipy which return dicts and so it seems like this might be not a good idea. (Why?) Also routinely I'll always have to type sumvars(x,y,z)['final'] for a default return value...
Another option I see is creating global variables but seems wrong having a bunch of them in my module, I would need to remember their names and in addition not being attached to the function itself looks like a bad design choice.
What would be the proper function design for such situation?
Generally when you have two different ways you want to return data, go ahead and make two different functions. "Flat is better than nested", after all. Just have one call the other so that you Don't Repeat Yourself.
For example, in the standard library, urllib.parse has parse_qs (which returns a dict) and parse_qsl (which returns a list). parse_qs just then calls the other:
def parse_qs(...):
parsed_result = {}
pairs = parse_qsl(qs, keep_blank_values, strict_parsing,
encoding=encoding, errors=errors)
for name, value in pairs:
if name in parsed_result:
parsed_result[name].append(value)
else:
parsed_result[name] = [value]
return parsed_result
Pretty straightforward. So in your example it seems fine to have
def sumvars(x, y, z):
return sumvars_with_intermediates(x, y, z).final
def sumvars_with_intermediates(x, y, z):
...
return my_namedtuple(final, first_step, second_step)
(I favor returning namedtuples instead of dicts from my APIs, it's just prettier)
Another obvious example is in re: re.findall is its own function, not some configuration flag to search.
Now, the standard library is a sprawling thing made by many authors, so you'll find counterexamples to every example. You'll far more often see the above pattern rather than one omnibus function that accepts some configuration flags, though, and I find it far more readable.
Put the common calculation into its own function as Jayanth Koushik recommended if that calculation can be named appropriately. If you want to return many values (an intermediate result and a final result) from a single function then a dict may be an overkill depending on what is your goal but in python it is much more natural to simply return a tuple if your function has many values to return:
def myfunc():
intermediate = 5
result = 6
return intermediate, result
# using the function:
intermediate, result = myfunc()
Not sure if function attributes is a good idea:
In [569]: def sumvars(x, y, z):
...: s = x
...: sumvars.first_step = s
...: s += y
...: sumvars.second_step = s
...: s += z
...: return s
In [570]: res=sumvars(1,2,3)
...: print res, sumvars.first_step, sumvars.second_step
...:
6 1 3
Note: as #BrenBarn mentioned, this idea is just like global variables, your previously calculated "intermediate results" could not be stored when you want to reuse them.
Just came up with this idea which could be a better solution:
def sumvars(x, y, z, mode = 'default'):
d = {}
s = x
d['first_step'] = s
s += y
d['second_step'] = s
s += z
d['final'] = s
if mode == 'default':
return s
else:
return d
I belive the proper solution is to use a class, to have a better grasp of what you are modeling. For example in the case of the Matrix, you could simply store the determinant in the "determinant" attribute.
Here is an example using your matrix example.
class Matrix:
determinant = 0
def calculate_determinant(self):
#calculations
return determinant
def some_method(self, args):
# some calculations here
self.determinant = self.calculate_determinant()
# other calculations
matrix = Matrix()
matrix.some_method(x, y, z)
print matrix.determinant
This also allows you to separate your method into simpler methods, like one for calculating the determinant of your matrix.
Another variation:
def sumvars(x, y, z, d=None):
s = x
if not d is None:
d['first_step'] = s
s += y
if not d is None:
d['second_step'] = s
s += z
return s
The function always returns the desired value without packing it into a tuple or dictionary. The intermediate results are still available, but only if requested. The call
sumvars(1, 2, 3)
just returns 6 without storing intermediate values. But the call
d = {}
sumvars(1, 2, 3, d)
returns the same answer 6 and inserts the intermediate calculations into the supplied dictionary.
Option 1. Make two separate functions.
Option 2. Use a generator:
>>> def my_func():
... yield 1
... yield 2
...
>>> result_gen = my_func()
>>> result_gen
<generator object my_func at 0x7f62a8449370>
>>> next(result_gen)
1
>>> next(result_gen)
2
>>> next(result_gen)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration
>>>
Inspired by #zhangxaochen solution, here's my take on your problem using class attributes:
class MyClass():
def __init__(self):
self.i = 4
def f(self):
s = self.i
MyClass.first_step = s
print(MyClass.first_step)
s += self.i
MyClass.second_step = s
print(MyClass.second_step)
s += self.i
return s
def main():
x = MyClass()
print(x.f()) # print final s
print(x.first_step)
print(x.second_step)
print(MyClass.second_step)
Note: I included several prints to make it more explicit how attribute values can be retrieved.
Result:
4
8
12
4
8
8