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tkinter creating buttons in for loop passing command arguments
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Closed 2 years ago.
Problemset:
Context Menu should show filter variables dynamically and execute a function with parameters defined inside the callback.
Generic descriptions show properly, but function call is always executed with last set option.
What I have tried:
#!/usr/bin/env python
import Tkinter as tk
import ttk
from TkTreectrl import MultiListbox
class SomeClass(ttk.Frame):
def __init__(self, *args, **kwargs):
ttk.Frame.__init__(self, *args, **kwargs)
self.pack(expand=True, fill=tk.BOTH)
self.grid_rowconfigure(0, weight=1)
self.grid_columnconfigure(0, weight=1)
self.View=MultiListbox(self)
__columns=("Date","Time","Type","File","Line","-","Function","Message")
self.View.configure(columns=__columns, expandcolumns=(0,0,0,0,0,0,0,1))
self.View.bind("", self.cell_context)
self.View.grid(row=0, column=0, sticky=tk.NW+tk.SE)
self.__recordset = []
self.__recordset_filtered = False
#Some dummy values
self.__recordset.append(["Date", "Time", "INFO", "File", "12", "-", "Function", "Message Info"])
self.__recordset.append(["Date", "Time", "DEBUG", "File", "12", "-", "Function", "Message Info"])
self.__recordset.append(["Date", "Time", "WARNING", "File", "12", "-", "Function", "Message Info"])
self.__refresh()
def cleanView(self):
self.View.delete(0, tk.END)
def __refresh(self):
self.cleanView()
for row in self.__recordset:
self.View.insert(tk.END, *row)
def filter_records(self, column, value):
print("Filter Log Recordset by {column} and {value}".format(**locals()))
# Filter functionality works as expected
# [...]
def cell_context(self, event):
__cMenu=tk.Menu(self, tearoff=0)
if self.__recordset_filtered:
__cMenu.add_command(label="Show all", command=lambda: filter_records(0, ""))
else:
column=2
options=["INFO", "WARNING", "DEBUG"]
for i in range(len(options)):
option=options[i]
__cMenu.add_command(label="{}".format(option), command=lambda: self.filter_records(column, option))
# Also tried using for option in options here with same result as now
__cMenu.post(event.x_root, event.y_root)
if __name__=="__main__":
root=tk.Tk()
app=SomeClass(root)
root.mainloop()
The current output i get is:
Filter Log Recordset by 2 and DEBUG
No matter which of the three options i choose. I assume it has sth to do with the garbage collection that only the last option remains but i cannot figure out how to avoid this.
Any help is recommended.
Please read about minimal examples. Without reading your code, I believe you have run into a well known issue addressed in previous questions and answers that needs 2 lines to illustrate. Names in function bodies are evaluated when the function is executed.
funcs = [lambda: i for i in range(3)]
for f in funcs: print(f())
prints '2' 3 times because the 3 functions are identical and the 'i' in each is not evaluated until the call, when i == 2. However,
funcs = [lambda i=i:i for i in range(3)]
for f in funcs: print(f())
makes three different functions, each with a different captured value, so 0, 1, and 2 are printed. In your statement
__cMenu.add_command(label="{}".format(option),
command=lambda: self.filter_records(column, option))
add option=option before : to capture the different values of option. You might want to rewrite as
lambda opt=option: self.filter_records(column, opt)
to differentiate the loop variable from the function parameter. If column changed within the loop, it would need the same treatment.
Closures in Python capture variables, not values. For example consider:
def f():
x = 1
g = lambda : x
x = 2
return g()
What do you expect the result of calling f() to be? The correct answer is 2, because the lambda f captured the variable x, not its value 1 at the time of creation.
Now if for example we write:
L = [(lambda : i) for i in range(10)]
we created a list of 10 different lambdas, but all of them captured the same variable i, thus calling L[3]() the result will be 9 because the value of variable i at the end of the iteration was 9 (in Python a comprehension doesn't create a new binding for each iteration; it just keeps updating the same binding).
A "trick" that can be seen often in Python when capturing the value is the desired semantic is to use default arguments. In Python, differently from say C++, default value expressions are evaluated at function definition time (i.e. when the lambda is created) and not when the function is invoked. So in code like:
L = [(lambda j=i: j) for i in range(10)]
we're declaring a parameter j and setting as default the current value of i at the time the lambda was created. This means that when calling e.g. L[3]() the result will be 3 this time because of the default value of the "hidden" parameter (calling L[3](42) will return 42 of course).
More often you see the sightly more confusing form
lambda i=i: ...
where the "hidden" parameter has the same name as the variable of which we want to capture the value of.
I know I am late, but I found a messy workaround which gets the job done (tested in Python 3.7)
If you use a double lambda (like I said, very messy) you can preserve the value, like so:
Step 1: Create the nested lambda statement:
send_param = lambda val: lambda: print(val)
Step 2: Use the lambda statement:
send_param(i)
The send_param method returns the inner most lambda (lambda: print(val)) without executing the statement, until you call the result of send_param which takes no arguments, for example:
a = send_param(i)
a()
Only the second line will execute the print statement.
I'm creating a custom matrix library by extending the functionality present in the LinearAlgebra module. I have been doing this by creating custom structs in a custom MyLinearAlgebra module that directly imports the default Linear Algebra structs and overrides many of the common LA functionalities. My question is specifically about how to override the backslash function. Here is my "MyLinearAlgebra.jl":
module MyLinearAlgebra
import LinearAlgebra
import Base: getindex, setindex!, size
export
# Types
LocalMatrix,
SolutionVector,
# Functions
lp_error!,
lp_norm,
true_size
include("SolutionVector.jl")
include("LocalMatrix.jl")
end
Focusing solely on LocalMatrix.jl now, I have:
"""
struct LocalMatrix{T} <: AbstractMatrix{T}
Block diagonal structure for local matrix. `A[:,:,iS,iK]` is a block matrix for
state iS and element iK
"""
struct LocalMatrix{T} <: AbstractMatrix{T}
data::Array{T,4}
factorizations::Array{Any,2}
function LocalMatrix(data::Array{T,4}) where {T}
new{T}(data,Array{Any}(undef, size(data,3), size(data,4)))
end
end
# [... a lot of things that are already working including: ldiv!]
"""
ldiv!(A::LocalMatrix, x::SolutionVector)
In-place linear solve A\\x using block-diagonal LU factorizations. Compute this
block-diagonal factorization if not yet computed.
"""
function LinearAlgebra.ldiv!(A::LocalMatrix, x::SolutionVector)
println("my ldiv! works fine")
x
end
# [ ... and yet this does not work ]
"""
A::LocalMatrix \\ x::SolutionVector
Linear solve A\\x using block-diagonal LU factorizations. Compute this
block-diagonal factorization if not yet computed.
"""
function (\)(A::LocalMatrix, x::SolutionVector)
println("my \\ never prints out in any tests")
(m,n,ns,ne) = size(A.data)
(nx,nsx,nex) = size(x.data)
#assert n == nx && ne == nex && m == n
b = deepcopy(x)
LinearAlgebra.ldiv!(A, b)
end
In my tests, I can use the ldiv! function exactly as expected, but I cannot use the \ function - it simply uses some standard implementation written somewhere else. I believe this is presumably because my backslash function is not qualified as the LinearAlgebra backslash function, but I'm not sure. Trying to qualify the function as LinearAlgebra.(\)(A::LocalMatrix, x::SolutionVector) fails with a syntax error invalid function name. Is there another way to do this, or am I missing something more fundamental about modules here?
\ is defined in Base, so:
julia> "a" \ "b"
ERROR: MethodError: no method matching adjoint(::String)
julia> Base.:\(::String, ::String) = "hello"
julia> "a" \ "b"
"hello"
However, as it is imported to LinearAlgebra the following also works for me (I am using a fresh session):
julia> using LinearAlgebra
julia> "a" \ "b"
ERROR: MethodError: no method matching adjoint(::String)
julia> LinearAlgebra.:\(::String, ::String) = "hello"
julia> "a" \ "b"
"hello"
as Julia will add a method to the same function (defined in Base).
What does the # symbol do in Python?
An # symbol at the beginning of a line is used for class and function decorators:
PEP 318: Decorators
Python Decorators
The most common Python decorators are:
#property
#classmethod
#staticmethod
An # in the middle of a line is probably matrix multiplication:
# as a binary operator.
Example
class Pizza(object):
def __init__(self):
self.toppings = []
def __call__(self, topping):
# When using '#instance_of_pizza' before a function definition
# the function gets passed onto 'topping'.
self.toppings.append(topping())
def __repr__(self):
return str(self.toppings)
pizza = Pizza()
#pizza
def cheese():
return 'cheese'
#pizza
def sauce():
return 'sauce'
print pizza
# ['cheese', 'sauce']
This shows that the function/method/class you're defining after a decorator is just basically passed on as an argument to the function/method immediately after the # sign.
First sighting
The microframework Flask introduces decorators from the very beginning in the following format:
from flask import Flask
app = Flask(__name__)
#app.route("/")
def hello():
return "Hello World!"
This in turn translates to:
rule = "/"
view_func = hello
# They go as arguments here in 'flask/app.py'
def add_url_rule(self, rule, endpoint=None, view_func=None, **options):
pass
Realizing this finally allowed me to feel at peace with Flask.
In Python 3.5 you can overload # as an operator. It is named as __matmul__, because it is designed to do matrix multiplication, but it can be anything you want. See PEP465 for details.
This is a simple implementation of matrix multiplication.
class Mat(list):
def __matmul__(self, B):
A = self
return Mat([[sum(A[i][k]*B[k][j] for k in range(len(B)))
for j in range(len(B[0])) ] for i in range(len(A))])
A = Mat([[1,3],[7,5]])
B = Mat([[6,8],[4,2]])
print(A # B)
This code yields:
[[18, 14], [62, 66]]
This code snippet:
def decorator(func):
return func
#decorator
def some_func():
pass
Is equivalent to this code:
def decorator(func):
return func
def some_func():
pass
some_func = decorator(some_func)
In the definition of a decorator you can add some modified things that wouldn't be returned by a function normally.
What does the “at” (#) symbol do in Python?
In short, it is used in decorator syntax and for matrix multiplication.
In the context of decorators, this syntax:
#decorator
def decorated_function():
"""this function is decorated"""
is equivalent to this:
def decorated_function():
"""this function is decorated"""
decorated_function = decorator(decorated_function)
In the context of matrix multiplication, a # b invokes a.__matmul__(b) - making this syntax:
a # b
equivalent to
dot(a, b)
and
a #= b
equivalent to
a = dot(a, b)
where dot is, for example, the numpy matrix multiplication function and a and b are matrices.
How could you discover this on your own?
I also do not know what to search for as searching Python docs or Google does not return relevant results when the # symbol is included.
If you want to have a rather complete view of what a particular piece of python syntax does, look directly at the grammar file. For the Python 3 branch:
~$ grep -C 1 "#" cpython/Grammar/Grammar
decorator: '#' dotted_name [ '(' [arglist] ')' ] NEWLINE
decorators: decorator+
--
testlist_star_expr: (test|star_expr) (',' (test|star_expr))* [',']
augassign: ('+=' | '-=' | '*=' | '#=' | '/=' | '%=' | '&=' | '|=' | '^=' |
'<<=' | '>>=' | '**=' | '//=')
--
arith_expr: term (('+'|'-') term)*
term: factor (('*'|'#'|'/'|'%'|'//') factor)*
factor: ('+'|'-'|'~') factor | power
We can see here that # is used in three contexts:
decorators
an operator between factors
an augmented assignment operator
Decorator Syntax:
A google search for "decorator python docs" gives as one of the top results, the "Compound Statements" section of the "Python Language Reference." Scrolling down to the section on function definitions, which we can find by searching for the word, "decorator", we see that... there's a lot to read. But the word, "decorator" is a link to the glossary, which tells us:
decorator
A function returning another function, usually applied as a function transformation using the #wrapper syntax. Common
examples for decorators are classmethod() and staticmethod().
The decorator syntax is merely syntactic sugar, the following two
function definitions are semantically equivalent:
def f(...):
...
f = staticmethod(f)
#staticmethod
def f(...):
...
The same concept exists for classes, but is less commonly used there.
See the documentation for function definitions and class definitions
for more about decorators.
So, we see that
#foo
def bar():
pass
is semantically the same as:
def bar():
pass
bar = foo(bar)
They are not exactly the same because Python evaluates the foo expression (which could be a dotted lookup and a function call) before bar with the decorator (#) syntax, but evaluates the foo expression after bar in the other case.
(If this difference makes a difference in the meaning of your code, you should reconsider what you're doing with your life, because that would be pathological.)
Stacked Decorators
If we go back to the function definition syntax documentation, we see:
#f1(arg)
#f2
def func(): pass
is roughly equivalent to
def func(): pass
func = f1(arg)(f2(func))
This is a demonstration that we can call a function that's a decorator first, as well as stack decorators. Functions, in Python, are first class objects - which means you can pass a function as an argument to another function, and return functions. Decorators do both of these things.
If we stack decorators, the function, as defined, gets passed first to the decorator immediately above it, then the next, and so on.
That about sums up the usage for # in the context of decorators.
The Operator, #
In the lexical analysis section of the language reference, we have a section on operators, which includes #, which makes it also an operator:
The following tokens are operators:
+ - * ** / // % #
<< >> & | ^ ~
< > <= >= == !=
and in the next page, the Data Model, we have the section Emulating Numeric Types,
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
[...]
These methods are called to implement the binary arithmetic operations (+, -, *, #, /, //, [...]
And we see that __matmul__ corresponds to #. If we search the documentation for "matmul" we get a link to What's new in Python 3.5 with "matmul" under a heading "PEP 465 - A dedicated infix operator for matrix multiplication".
it can be implemented by defining __matmul__(), __rmatmul__(), and
__imatmul__() for regular, reflected, and in-place matrix multiplication.
(So now we learn that #= is the in-place version). It further explains:
Matrix multiplication is a notably common operation in many fields of
mathematics, science, engineering, and the addition of # allows
writing cleaner code:
S = (H # beta - r).T # inv(H # V # H.T) # (H # beta - r)
instead of:
S = dot((dot(H, beta) - r).T,
dot(inv(dot(dot(H, V), H.T)), dot(H, beta) - r))
While this operator can be overloaded to do almost anything, in numpy, for example, we would use this syntax to calculate the inner and outer product of arrays and matrices:
>>> from numpy import array, matrix
>>> array([[1,2,3]]).T # array([[1,2,3]])
array([[1, 2, 3],
[2, 4, 6],
[3, 6, 9]])
>>> array([[1,2,3]]) # array([[1,2,3]]).T
array([[14]])
>>> matrix([1,2,3]).T # matrix([1,2,3])
matrix([[1, 2, 3],
[2, 4, 6],
[3, 6, 9]])
>>> matrix([1,2,3]) # matrix([1,2,3]).T
matrix([[14]])
Inplace matrix multiplication: #=
While researching the prior usage, we learn that there is also the inplace matrix multiplication. If we attempt to use it, we may find it is not yet implemented for numpy:
>>> m = matrix([1,2,3])
>>> m #= m.T
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: In-place matrix multiplication is not (yet) supported. Use 'a = a # b' instead of 'a #= b'.
When it is implemented, I would expect the result to look like this:
>>> m = matrix([1,2,3])
>>> m #= m.T
>>> m
matrix([[14]])
What does the “at” (#) symbol do in Python?
# symbol is a syntactic sugar python provides to utilize decorator,
to paraphrase the question, It's exactly about what does decorator do in Python?
Put it simple decorator allow you to modify a given function's definition without touch its innermost (it's closure).
It's the most case when you import wonderful package from third party. You can visualize it, you can use it, but you cannot touch its innermost and its heart.
Here is a quick example,
suppose I define a read_a_book function on Ipython
In [9]: def read_a_book():
...: return "I am reading the book: "
...:
In [10]: read_a_book()
Out[10]: 'I am reading the book: '
You see, I forgot to add a name to it.
How to solve such a problem? Of course, I could re-define the function as:
def read_a_book():
return "I am reading the book: 'Python Cookbook'"
Nevertheless, what if I'm not allowed to manipulate the original function, or if there are thousands of such function to be handled.
Solve the problem by thinking different and define a new_function
def add_a_book(func):
def wrapper():
return func() + "Python Cookbook"
return wrapper
Then employ it.
In [14]: read_a_book = add_a_book(read_a_book)
In [15]: read_a_book()
Out[15]: 'I am reading the book: Python Cookbook'
Tada, you see, I amended read_a_book without touching it inner closure. Nothing stops me equipped with decorator.
What's about #
#add_a_book
def read_a_book():
return "I am reading the book: "
In [17]: read_a_book()
Out[17]: 'I am reading the book: Python Cookbook'
#add_a_book is a fancy and handy way to say read_a_book = add_a_book(read_a_book), it's a syntactic sugar, there's nothing more fancier about it.
If you are referring to some code in a python notebook which is using Numpy library, then # operator means Matrix Multiplication. For example:
import numpy as np
def forward(xi, W1, b1, W2, b2):
z1 = W1 # xi + b1
a1 = sigma(z1)
z2 = W2 # a1 + b2
return z2, a1
Decorators were added in Python to make function and method wrapping (a function that receives a function and returns an enhanced one) easier to read and understand. The original use case was to be able to define the methods as class methods or static methods on the head of their definition. Without the decorator syntax, it would require a rather sparse and repetitive definition:
class WithoutDecorators:
def some_static_method():
print("this is static method")
some_static_method = staticmethod(some_static_method)
def some_class_method(cls):
print("this is class method")
some_class_method = classmethod(some_class_method)
If the decorator syntax is used for the same purpose, the code is shorter and easier to understand:
class WithDecorators:
#staticmethod
def some_static_method():
print("this is static method")
#classmethod
def some_class_method(cls):
print("this is class method")
General syntax and possible implementations
The decorator is generally a named object ( lambda expressions are not allowed) that accepts a single argument when called (it will be the decorated function) and returns another callable object. "Callable" is used here instead of "function" with premeditation. While decorators are often discussed in the scope of methods and functions, they are not limited to them. In fact, anything that is callable (any object that implements the _call__ method is considered callable), can be used as a decorator and often objects returned by them are not simple functions but more instances of more complex classes implementing their own __call_ method.
The decorator syntax is simply only a syntactic sugar. Consider the following decorator usage:
#some_decorator
def decorated_function():
pass
This can always be replaced by an explicit decorator call and function reassignment:
def decorated_function():
pass
decorated_function = some_decorator(decorated_function)
However, the latter is less readable and also very hard to understand if multiple decorators are used on a single function.
Decorators can be used in multiple different ways as shown below:
As a function
There are many ways to write custom decorators, but the simplest way is to write a function that returns a subfunction that wraps the original function call.
The generic patterns is as follows:
def mydecorator(function):
def wrapped(*args, **kwargs):
# do some stuff before the original
# function gets called
result = function(*args, **kwargs)
# do some stuff after function call and
# return the result
return result
# return wrapper as a decorated function
return wrapped
As a class
While decorators almost always can be implemented using functions, there are some situations when using user-defined classes is a better option. This is often true when the decorator needs complex parametrization or it depends on a specific state.
The generic pattern for a nonparametrized decorator as a class is as follows:
class DecoratorAsClass:
def __init__(self, function):
self.function = function
def __call__(self, *args, **kwargs):
# do some stuff before the original
# function gets called
result = self.function(*args, **kwargs)
# do some stuff after function call and
# return the result
return result
Parametrizing decorators
In real code, there is often a need to use decorators that can be parametrized. When the function is used as a decorator, then the solution is simple—a second level of wrapping has to be used. Here is a simple example of the decorator that repeats the execution of a decorated function the specified number of times every time it is called:
def repeat(number=3):
"""Cause decorated function to be repeated a number of times.
Last value of original function call is returned as a result
:param number: number of repetitions, 3 if not specified
"""
def actual_decorator(function):
def wrapper(*args, **kwargs):
result = None
for _ in range(number):
result = function(*args, **kwargs)
return result
return wrapper
return actual_decorator
The decorator defined this way can accept parameters:
>>> #repeat(2)
... def foo():
... print("foo")
...
>>> foo()
foo
foo
Note that even if the parametrized decorator has default values for its arguments, the parentheses after its name is required. The correct way to use the preceding decorator with default arguments is as follows:
>>> #repeat()
... def bar():
... print("bar")
...
>>> bar()
bar
bar
bar
Finally lets see decorators with Properties.
Properties
The properties provide a built-in descriptor type that knows how to link an attribute to a set of methods. A property takes four optional arguments: fget , fset , fdel , and doc . The last one can be provided to define a docstring that is linked to the attribute as if it were a method. Here is an example of a Rectangle class that can be controlled either by direct access to attributes that store two corner points or by using the width , and height properties:
class Rectangle:
def __init__(self, x1, y1, x2, y2):
self.x1, self.y1 = x1, y1
self.x2, self.y2 = x2, y2
def _width_get(self):
return self.x2 - self.x1
def _width_set(self, value):
self.x2 = self.x1 + value
def _height_get(self):
return self.y2 - self.y1
def _height_set(self, value):
self.y2 = self.y1 + value
width = property(
_width_get, _width_set,
doc="rectangle width measured from left"
)
height = property(
_height_get, _height_set,
doc="rectangle height measured from top"
)
def __repr__(self):
return "{}({}, {}, {}, {})".format(
self.__class__.__name__,
self.x1, self.y1, self.x2, self.y2
)
The best syntax for creating properties is using property as a decorator. This will reduce the number of method signatures inside of the class
and make code more readable and maintainable. With decorators the above class becomes:
class Rectangle:
def __init__(self, x1, y1, x2, y2):
self.x1, self.y1 = x1, y1
self.x2, self.y2 = x2, y2
#property
def width(self):
"""rectangle height measured from top"""
return self.x2 - self.x1
#width.setter
def width(self, value):
self.x2 = self.x1 + value
#property
def height(self):
"""rectangle height measured from top"""
return self.y2 - self.y1
#height.setter
def height(self, value):
self.y2 = self.y1 + value
Starting with Python 3.5, the '#' is used as a dedicated infix symbol for MATRIX MULTIPLICATION (PEP 0465 -- see https://www.python.org/dev/peps/pep-0465/)
# can be a math operator or a DECORATOR but what you mean is a decorator.
This code:
def func(f):
return f
func(lambda :"HelloWorld")()
using decorators can be written like:
def func(f):
return f
#func
def name():
return "Hello World"
name()
Decorators can have arguments.
You can see this GeeksforGeeks post: https://www.geeksforgeeks.org/decorators-in-python/
It indicates that you are using a decorator. Here is Bruce Eckel's example from 2008.
Python decorator is like a wrapper of a function or a class. It’s still too conceptual.
def function_decorator(func):
def wrapped_func():
# Do something before the function is executed
func()
# Do something after the function has been executed
return wrapped_func
The above code is a definition of a decorator that decorates a function.
function_decorator is the name of the decorator.
wrapped_func is the name of the inner function, which is actually only used in this decorator definition. func is the function that is being decorated.
In the inner function wrapped_func, we can do whatever before and after the func is called. After the decorator is defined, we simply use it as follows.
#function_decorator
def func():
pass
Then, whenever we call the function func, the behaviours we’ve defined in the decorator will also be executed.
EXAMPLE :
from functools import wraps
def mydecorator(f):
#wraps(f)
def wrapped(*args, **kwargs):
print "Before decorated function"
r = f(*args, **kwargs)
print "After decorated function"
return r
return wrapped
#mydecorator
def myfunc(myarg):
print "my function", myarg
return "return value"
r = myfunc('asdf')
print r
Output :
Before decorated function
my function asdf
After decorated function
return value
To say what others have in a different way: yes, it is a decorator.
In Python, it's like:
Creating a function (follows under the # call)
Calling another function to operate on your created function. This returns a new function. The function that you call is the argument of the #.
Replacing the function defined with the new function returned.
This can be used for all kinds of useful things, made possible because functions are objects and just necessary just instructions.
# symbol is also used to access variables inside a plydata / pandas dataframe query, pandas.DataFrame.query.
Example:
df = pandas.DataFrame({'foo': [1,2,15,17]})
y = 10
df >> query('foo > #y') # plydata
df.query('foo > #y') # pandas
I'm attempting to write a Newton-Raphson solver in Julia. The Newton-Raphson method is shown in this image.
f(x) = x^2.5 - 3x^1.5 - 10
fprime(x) = 2.5x^1.5 - 4.5x^0.5
x = zeros(1000)
x[1] = 10
δ = 1 # a relatively large number compared to what we want the error to be
iter = 1
while δ > 1e-6
x[iter + 1] = x[iter] - f(x[iter])/fprime(x[iter])
iter += 1
δ = abs(x[iter] - x[iter + 1])
if iter == 100
break
end
end
println("The solution is ")
show(x[iter])
However, when I run the code, I get an error saying iter is not defined, even though I defined it just before the start of the loop. Is there some scoping problem I'm completely missing?
ERROR: LoadError: UndefVarError: iter not defined
Stacktrace:
[1] top-level scope at /Users/natemcintosh/Documents/Julia/Learning_julia.jl:11 [inlined]
[2] top-level scope at ./none:0
[3] include_string(::Module, ::String, ::String) at ./loading.jl:1002
[4] (::getfield(Atom, Symbol("##120#125")){String,String,Module})() at /Users/natemcintosh/.julia/packages/Atom/Pab0Z/src/eval.jl:120
[5] withpath(::getfield(Atom, Symbol("##120#125")){String,String,Module}, ::String) at /Users/natemcintosh/.julia/packages/CodeTools/8CjYJ/src/utils.jl:30
[6] withpath at /Users/natemcintosh/.julia/packages/Atom/Pab0Z/src/eval.jl:46 [inlined]
[7] #119 at /Users/natemcintosh/.julia/packages/Atom/Pab0Z/src/eval.jl:117 [inlined]
[8] hideprompt(::getfield(Atom, Symbol("##119#124")){String,String,Module}) at /Users/natemcintosh/.julia/packages/Atom/Pab0Z/src/repl.jl:76
[9] macro expansion at /Users/natemcintosh/.julia/packages/Atom/Pab0Z/src/eval.jl:116 [inlined]
[10] (::getfield(Atom, Symbol("##118#123")){Dict{String,Any}})() at ./task.jl:85
in expression starting at /Users/natemcintosh/Documents/Julia/Learning_julia.jl:10
I've tried printing x at the beginning of the while loop and it knows what x is, but thinks iter is undefined.
First let me give the solution:
There are three possible approaches
Approach 1. Prepend global before iter += 1 and change it to global iter += 1 and all will work (note however the comment below about δ - because it will not work correctly unless you also prepend global before δ = abs(x[iter] - x[iter + 1]), i.e. the code will run but will produce wrong results - approaches 2 and 3 do not have this problem).
Approach 2. Wrap your code inside a function like this:
f(x) = x^2.5 - 3x^1.5 - 10
fprime(x) = 2.5x^1.5 - 4.5x^0.5
function sol(f, fprime)
x = zeros(1000)
x[1] = 10
δ = 1 # a relatively large number compared to what we want the error to be
iter = 1
while δ > 1e-6
x[iter + 1] = x[iter] - f(x[iter])/fprime(x[iter])
iter += 1
δ = abs(x[iter] - x[iter + 1])
if iter == 100
break
end
end
println("The solution is ")
show(x[iter])
end
sol(f, fprime) # now we call it
Solution 3. Wrap your code in a let block by changing line function sol(f, fprime) in solution 2 to simply say let (you do not need to call sol then).
Now the reason why you have to do it.
In Julia 1.0 while introduces a new scope. The scoping rules in Julia 1.0 are that each variable that is assigned to inside a while loop is considered a local variable (this has changed, because Julia 0.6 distinguished hard and soft local scope, in Julia 1.0 this distinction is gone - all local scopes are the same).
In your code you assign values to two variables: iter and δ inside the loop. This means that they are treated by Julia as local so you are not allowed to access their value before they have a value assigned inside the loop.
You want to read iter in line x[iter + 1] = x[iter] - f(x[iter])/fprime(x[iter]) but assign a value to it only in the following line.
As for δ the thing is more tricky. You assign a value to it, but it is used in a loop condition while δ > 1e-6. However, this condition operates on variables defined in outer scope (global in the original case). So all will work, but the condition while δ > 1e-6 will always see that δ is equal to 1 as it looks at the value of the variable outside of the loop. So this condition will never trigger (and you will always run 100 iterations). In summary the code that does what you want is (although if you did not fix δ assignment you would not get a warning):
f(x) = x^2.5 - 3x^1.5 - 10
fprime(x) = 2.5x^1.5 - 4.5x^0.5
x = zeros(1000)
x[1] = 10
δ = 1 # a relatively large number compared to what we want the error to be
iter = 1
while δ > 1e-6
x[iter + 1] = x[iter] - f(x[iter])/fprime(x[iter])
global iter += 1
global δ = abs(x[iter] - x[iter + 1])
if iter == 100
break
end
end
println("The solution is ")
show(x[iter])
Finally notice that the line x[iter + 1] = x[iter] - f(x[iter])/fprime(x[iter]) works fine even if there is an assignment in it, because you do not rebind variable x in it, but only change one element of an array (so x points to the same address in memory and Julia treats it as a global variable all the time).
Also you might want to read this https://docs.julialang.org/en/latest/manual/variables-and-scoping/ in the Julia manual or the answer to this question Julia Variable scope is similar