What is the logic of the variables within the loops? [pendente]

Can a variable be different from itself, depending on how it is inserted into a code?

For example, if I want to add 3 values ​​given by the user in Visualg, I use the following code:

algorithm "about 3 values"
counter, number, sum: whole
counter <- 1
While (counter <= 3) Knife
Type ("Value", counter, ":")
Read (number)
sum <- sum + number // My question is here
counter <- counter + 1 // And here too
Finish while
to write
TypeL ("The sum of the 3 values ​​entered is", S, ".")
final algorithm

In Python:

sum = 0
for c in rank (1, 4):
num = int (entry (f & # 39; Value {c}: & # 39;))
sum + = number # sum = sum + number
print (f & # 39;  nThe sum of the 3 values ​​entered is {sum}.)

The variable sum and counter in Visualg and sum in Python, in this case could be considered different from themselves when they are assigned as variable = variable + "something".

What I considered is that at the beginning it is the space that the variable occupies, after which, with the arithmetic operator of addition it would be its value, that is, there would be a difference in the variable when placing it assigning a value to itself.
Is it correct to say that? Is there any difference in this case?

I can not imagine that they are the same, no matter how hard I try to connect the dots, I get confused when I think about it.

Code Review: What techniques have you used to avoid loops in Mathematica?

It seems to me that the mathematics loop is very slow (compared to matlab). Because many people still use Mathematica to finish their work, there must be some techniques to avoid this problem.

Could you share your techniques of avoiding loops in math with beginners like me?

hd cg

BlackHatKings: General discussion of PPC
Posted by: MervinROX
Time of publication: June 16, 2019 at 18:15.

python – How to optimize a subset selection function that has nested loops, to choose the best combination of n choose k models?


Let den0 be the null model that does not contain predictors. This model simply predicts the sample mean of each observation.

For 𝑘 = 1,2, ...., 𝑛:
Set all models (𝑛  choose 𝑘) that contain exactly 𝑘 predictors.

Choose the best among these (𝑛 choose 𝑘) models, and call it 𝑀_𝑘.
Here the best is defined as having the smallest or smallest equivalent measure.

Select the best individual model among 𝑀0, 𝑀1, ..., 𝑀𝑛 using the cross validation prediction error, 𝐶𝑝, BIC, 𝑅2𝑎𝑑𝑗 or any other method.

I searched for a tutorial and created a function based on your script. It is basically used so that you can select dependent variables that are a subset of a data frame. It works but it is very very slow.

How would you flatten a nested loop like this?

# Loop over all possible combinations of k characteristics
for k in the range (1, len (X.columns) + 1):
# Reviewing all possible combinations: from 11 choose k
for combo in itertools.combinations (X.columns, k):
# Store temporary results
temp_results = fit_linear_reg (X[list(combo)], Y)

# Add RSS to RSS lists
RSS_list.append (temp_results[0])

I tried to implement an enumeration version but it did not work. I'm not sure, how can I flatten the nested loop, so that I can append the results of a function to a list.

# This function takes in a subset of a data frame that represents independent
# variables (X) and one column for the dependent variable (Y). This function adjusts
# separate models for each possible combination of the k predictors (which is
# based on the length of the X column) and then select the best subset. the
# The resulting output is a data frame.

Def BestSubsetSelection (X, Y):
# number of predictors
k = len (X.columns)
# Stores the RSS from a linear regression model.
RSS_list = []
    # Stores the square R of a linear regression model
R_squared_list = []
    # Stores the characteristics for a given iteration.
feature_list = []
    # Stores the number of functions used for a given iteration. This corresponds to the feature_list.
numb_features = []

    # Loop over all possible combinations of k characteristics
for k in the range (1, len (X.columns) + 1):
# Reviewing all possible combinations: from 11 choose k
for combo in itertools.combinations (X.columns, k):
# Store temporary results
temp_results = fit_linear_reg (X[list(combo)], Y)

# Add RSS to RSS lists
RSS_list.append (temp_results[0])

# Add R-Squared to the R-Squared list
R_squared_list.append (temp_results[1])

# Add functions / s to the list of functions
feature_list.append (combo)

# Add the number of functions to the list of function numbers
numb_features.append (len (combo))

df = pd.DataFrame ({
& # 39; No_of_Features & # 39 ;: numb_features,
& # 39; RSS & # 39 ;: RSS_list,
& # 39; R-Squared & # 39 ;: R_squared_list,
& # 39; Features & # 39 ;: feature_list

# Find the best subsets for each number of features

# The smallest RSS
df_min = df[df.groupby('No_of_Features')['RSS'].transform (min) == df['RSS']]# The highest value of R square
df_max = df[df.groupby('No_of_Features')['R-Squared'].transform (min) == df['R-Squared']]show (df_min)
show (df_max)

# Add columns to the data frame with the RSS and R-Squared values ​​of the best subset
df['min_RSS'] = df.groupby (& # 39; No_of_Features & # 39;)['RSS'].transform (min)
df['max_R_Squared'] = df.groupby (& # 39; No_of_Features & # 39;)['R-Squared'].transform (max)

`` `

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Python 3.x – How to make loops in scrapy?

I'm scraping a website from the Dmoz site. I want to make a loop in functions because the in loop that I am using in each function, I will have to put again and again in each function. Although its functionality is the same. The second thing I want to solve is to make a loop in performance response. follow because if I scraped more pages, I'll have to write this again and again. Is there any way of my two problems. I tried several times but I failed.

                                # save and call another page
performance response.follow (self.about_page, self.parse_about, meta = {& # 39; items & # 39 ;: items})
performance response.follow (self.editor, self.parse_editor, meta = {& # 39; items & # 39 ;: items})

def parse_about (auto, answer):
# do your stuff on the second page
items = response.meta['items']
        names = {& # 39; name1 & # 39 ;: & # 39; Headings & # 39 ;,
& # 39; name2 & # 39 ;: & # 39; paragraphs & # 39 ;,
& # 39; name3 & # 39 ;: & # 39; 3 projects & # 39 ;,
& # 39; name4 & # 39 ;: & # 39; About Dmoz & # 39 ;,
& # 39; name5 & # 39 ;: & # 39; languages ​​& # 39 ;,
& # 39; name6 & # 39 ;: & # 39; You can make a difference & # 39 ;,
& # 39; name7 & # 39 ;: & # 39; additional information & # 39;

finder = {& # 39; find1 & # 39 ;: & # 39; h2 :: text, #mainContent h1 :: text & # 39 ;,
& # 39; find2 & # 39 ;: & # 39; p :: text & # 39 ;,
& # 39; find3 & # 39 ;: & # 39; li ~ li + li b a :: text, li: nth-child (1) b a :: text & # 39 ;,
& # 39; find4 & # 39 ;: & # 39; .nav ul a :: text, li: nth-child (2) b a :: text & # 39 ;,
& # 39; find5 & # 39 ;: & # 39; .nav ~ .nav to :: text & # 39 ;,
& # 39; find6 & # 39 ;: & # 39; dd :: text, # about-submit :: text & # 39 ;,
& # 39; find7 & # 39 ;: & # 39; li :: text, # about-more-info to :: text & # 39;
for name, search in zip (names.values ​​(), finder.values ​​()):
articles[name] = response.css (find) .extract ()
performance items

PHP Tutorial Arrays & Loops | Promotion Forum

PHP, like many other languages, uses matrices to store information. Arrays can help keep data organized, easily retrievable and even translated into / from other languages ​​(such as JavaScript) to allow PHP to play with the user information that someone sent in a form. There are many different things we can do with the arrangements, but today we will simply discuss what they are, how to create one and how to "cycle" through them. I will show you an example that shows why the loop is an important skill that you should know in PHP or in any other language.

If you want to learn some basic PHP, you can check my PHP Tutorial for beginners that explains how to create / start a PHP file, declare variables, analyze variables (with if / else statements), display information on the page and combine strings of information together. Here we'll do a little bit of all that, so make sure you've read that tutorial first.

This tutorial will be repetitive to help you understand the matrices and loops. Enjoy!



"Ghost", "age" => 25);
// we have started a matrix with a name and age value

In this example we have two matrices. One of them is empty, and one of them is storing my current age and my username. As you can see, starting a matrix is ​​as easy as having its variable (like $ data) and then using the syntax training();
Within the matrix, you can do a variety of things. First we will take a look at our $ info matrix, which is a associative matrix.



"Ghost", "age" => 25); // associative matrix
echo $ info["name"]; // echo the value of the name of our matrix
$ myage = $ info["age"]; // set our age value to a variable

In this example we did two things. First, we echoed my name on the page. If we opened our PHP page, I would literally just say "Ghost" on the page. Age is not echoing, so it would not appear.

The reason why I made the variable $ myage was to show you that you can assign a value of matrix to a variable. This can be very useful for large arrays to prevent your script from having to read through a large array to get information. For example, if a matrix has 100 different data within it, it can be very beneficial to declare a variable for information like the one I made with $ myage if you plan to use the value much later.

Associative matrices essentially "associate" a name key with a value.
A key is what you see in the [] part of the recovery of the matrix. For example in $ info[“name”], the key is "name". In the following example, our key will be a number instead of a string.



"Phantom", "age" => 25, "role" => 2); // associative matrix

$ groups = array (0 => "User", 1 => "Moderator", 2 => "Administrator);
// This is a numerical matrix.

// Let's use the string concatenation of the PHP tutorial for beginners to show some sentences:
echo $ groups[0] . "It's the paper 0 
"; echo $ groups[1] . "is the role 1
"; echo $ groups[0] . "It's paper 2
"; ?>

Therefore, a number matrix increases by 1 each time a new value is added to the matrix. We can configure the matrix as I did, or we can add a value to the matrix quite simply. Watch this:



"Phantom", "age" => 25, "role" => 2); // associative matrix

$ groups = array ();
// This is an empty matrix.

$ groups[] = "User";
$ groups[] = "Moderator";
$ groups[] = "Administrator";

$ role = $ info["role"];
echo "{$ info["name"]} is a company {$ groups[$role]} ";

In this last example, we are using the information of role = 2 in the $ info matrix to find the name of the function in our matrix $ groups. We are also adding the roles to our $ groups array in a different way than before. When you use $ arrayName[] , is telling PHP that everything that comes after the equal sign must be added to that matrix.

So when we use $ groups[] = "User" first, then the groups look like this:


$ groups = array (0 => "User");

So obviously we add Moderator and Administrator too.
You may also have noticed that we use { Y } Around our variables / matrix references in our sentence. This is a way to make sure that quotes and syntax do not break our string value within the original "double quotes". It is similar to just using a variable within a string, but it is an additional syntax that does not change the value of our real variable, it only allows PHP to read it without any problem.

In our previous example, our tutorials page now shows:
"Ghost is an administrator of the company."

So, now that you know how to set up an associative array ($ info), a numeric array ($ groups) and an empty array to which we can add values ​​to ($ array[] = $ newvalue), it's time to learn about one more thing … multidimensional arrays.

Multidimensional matrices are matrices with matrices inside them.
We'll see:



"Ghost", "age" => 25, "role" => 2, "friends" => array ("Frank", "James", "Chris")); // associative matrix

$ groups = array ("User", "Moderator", "Administrator");
/ *
Yes, numeric arrays do not require that you include the syntax # => in front of each value
You can simply list the values ​​within the matrix, and PHP will automatically apply the keys 0, 1, 2 to our User, Moderator and Administrator values.
* /

$ role = $ info["role"];
echo "{$ info["name"]} is a company {$ groups[$role]} ";

This prints "Ghost is a company administrator". in the page.

As you can see, I made some new things. I did not use the numeric keys in the $ groups array because PHP solves them by default, and I also used the name of $ role in a sentence. I did this because $ role = 2, from the $ info array and $ groups[2]= the name of the group (Administrator). Remember, although we did not supply the numeric keys, PHP automatically made $ groups[0] = "User", 1 = Moderator and 2 = Administrator.

However, the most important thing I did was include a matrix within the matrix. Our "friends" key in the $ info matrix makes our $ info array not only associative, but also a multidimensional matrix. Also, you may have noticed that the $ info[“friends”] that is your own matrix is ​​a numerical matrix.

If we wanted to meet the first friend of this user, we could do …


echo $ info["friends"][0];

This will print "Frank" on the page because that is key 0 of the friends array: the first result.

Now let's go through our matrix.
We love the loops because they are perfect for repetitive functions. Instead of executing duplicate code for each element of the array, a loop will search all the different values ​​within the array and do things with it. We will go through our array of friends to do things with each other.



"Ghost", "age" => 25, "role" => 2, "friends" => array ("Frank", "James", "Chris")); // associative matrix

$ groups = array ("User", "Moderator", "Administrator");
/ *
Yes, numeric arrays do not require that you include the syntax # => in front of each value
You can simply list the values ​​within the matrix, and PHP will automatically apply the keys 0, 1, 2 to our User, Moderator and Administrator values.
* /

$ role = $ info["role"];
echo "{$ info["name"]} is a company {$ groups[$role]} 
"; // FOREACH LOOP foreach ($ info["friends"] like $ friend) { echo $ friend "is a friend of {$ info["name"]}
"; } ?>

In this example we see the following printed on the page.:
Ghost is an administrator of the company.
Frank is a friend of Ghost.
James is a friend of Ghost.
Chris is friends with Ghost.

This is because we use a foreach loop to do the same with each matrix value. We are saying "for each name in the array of friends … do something". Also, the syntax is quite simple, since foreach ($ array as $ variable) is easy to remember.

We could also change foreach to show some keys / matrix labels for values ​​like this … This adds an additional piece to our foreach statement by dividing the key KEY and VALUE. That way we can access the key # or name, and the value separately. It is useful if you need to do something like "if KEY == this, do something different". An example of this would be if you wanted to repeat everything except a matrix value, but in general, you will use most loops when you want to do the same with each value. In general, it will not pass through several matrix values ​​if all of them require a separate logic depending on the key, but there are times when it may be necessary.


foreach ($ info as $ key => $ value) {
echo "$ key";

This would be printed …
"name age role friends", because those are the key names in our matrix. Then, as we separate the key and the value in separate variables in our foreach, we can access both the name of the key and the value itself.

Consider the following …


"value1", "key2" => "value2");
foreach ($ arr as $ key => $ value) {
echo "The key $ is $ value 
"; } ?>

This shows:
The key1 is value1
The key2 is value2

We can also go through a matrix with a "for loop". It's my favorite type of loop, but it's a bit more complicated. I like the loops because they are very efficient and fast, but foreach can also be good. In fact, I first used foreach and I think it's a good loop to learn at the beginning because it's easy to remember.

We need to know the amount of values ​​within the matrix.



"Ghost", "age" => 25, "role" => 2, "friends" => array ("Frank", "James", "Chris")); // associative matrix

$ groups = array ("User", "Moderator", "Administrator");

$ role = $ info["role"];
echo "{$ info["name"]} is a company {$ groups[$role]}, and it's {$ info["age"]year old. 
"; $ num = count ($ info["friends"]); // count the number of values ​​in the matrix info => friends for ($ x = 0; $ x <$ num; $ x ++) { echo $ info["friends"][$x] . "is a friend of {$ info["name"]}
"; } ?>

In this example we see the following printed on the page.:
Ghost is an administrator of the company, and he is 25 years old.
Frank is a friend of Ghost.
James is a friend of Ghost.
Chris is friends with Ghost.

We are going to break the for loop.
We begin by obtaining the value of $ num. It's the number of friends, which is 3.
Then we start our for loop with a new variable that we call $ x.
$ x = 0
Then we say $ x <$ num. This means that our for loop will begin with $ x equal to 0, and will be repeated as long as $ x remains less than (<) $ num or in other words, while $ x is less than 3.

Then we use $ x ++;
This tells our for loop that each time the loop is executed, we want to increase $ x by 1. When you have a number stored in a variable, using $ variable ++ is the same as saying $ variable = $ variable + 1. It simply adds 1 to itself.

Therefore, in our for loop, $ x starts at 0. Each time the loop is executed, it increases by 1, and will continue to loop while $ x is less than $ num (3). So, in this case, the first loop $ x = 0, the second $ x = 1 and the third $ x = 2. After our first 3 loops, $ x would be equal to 3, and the loop stops because our rules they say that the loop should only be executed if $ x is LESS THAN 3, it is not equal to it.

In each of our loops, we access the name of friends with:
$ info[“friends”][$x]

So, our 3 loops are accessing the friend's name in this way:
$ info[“friends”][0]
$ info[“friends”][1]
$ info[“friends”][2]

Because 0, 1, 2 are the values ​​of our variable $ x for our 3 loops
0 refers to the first friend in our $ info[“friends”] array, 1 refers to the second friend and 2 refers to the third friend (because remember, the arrays start at 0 by default for the keys)

You may wonder why we use loops. Well, the loops are perfect for when the amount of information can vary in quantity. If all people have only one age and one name, we do not need to complete the information. However, for things like friends where people can have 0 friends, 3 friends or even hundreds of friends, the loops allow us to execute code for each value in the matrix, regardless of the amount. They can even duplicate each other: a loop inside a loop. This could be taken further to have dozens of nested loops, but there are very few cases where it would be necessary. The loops are good, but they can definitely get out of control.

For large amounts of data, it is important that you do not make your script / page load forever due to a large loop. An example of this would be a forum with 1 million publications in a category. Instead of looping through all 1 million content, we would get the first 25-100 results instead and we would do it. We would pass to the other content if the user went to page 2, for example. Loops are only as good as the programmer, so make sure you're not running unnecessary loops in Big Data unless you really have to.

So let's look at a final example of our for loop, with some matrices.



"Ghost", "age" => 25, "friends" => array (array ("name" => "Frank", "age" => 23), array ("name" => "Chris", "age" "=> 26), array (" name "=>" James "," age "=> 27));

$ num = count ($ info["friends"]); // count the number of values ​​in the matrix info => friends
for ($ x = 0; $ x < $num; $x++){ // loop through all the friends results
 // in this example, our friends array actually holds a bunch of separate arrays! 

 if($info["friends"][$x]["age"] > $ info["age"]) {// if our friend's age is greater than ours
$ data[] = $ info["friends"][$x]; // add this friend to the data matrix

} // end of for loop

print_r ($ data);

In this last example we are really putting everything together.
We start with an empty matrix, $ data.
We also have our $ info matrix that stores our name, age and our friends. The friends array contains several arrangements, which have information about the friend. When we go through the friends matrix, we can access the information of each friend using the appropriate matrix key (name or age). In this example, we are trying to find all the friends who are older than us. We discovered correctly that Chris and James are older than us, so they are added to the $ data matrix.

The result would be the same as creating the $ data array in this way:


"Chris", "age" => 26), array ("name" => "James", "age" => 27));

So, as you can see, you can access the data in the matrix with the loop, analyze them and then do things with that information, like building the $ data matrix separately so that only our older friends have it. Obviously, this is a very specific example, but you can do much more, including echo values ​​on the page, trigger another script to execute, or really anything you can imagine.

performance – Delete nested for loops in python

I have the following work code, but it is too slow because I have to execute the same code for several files inside a folder. Please, help me avoid the nested loop to make it more efficient.

def create_nodes (self):
for child in self.tree.getroot ():
columnNames = child.attrib
events = [grandchild.attrib for grandchild in child]
    return (columnNames, events)

def create_game_data (self):
columns = {}
for the column in self.create_nodes ()[0].llaves ():
columns[column] = []
        columnValues ​​= []
        for event in self.create_nodes ()[1]:
columnValues.append (self.create_nodes ()[0][column]) if column in self.create_nodes ()[0].keys () else columnValues.append ("NA")
columns[column] = columnValues
return columns

python: remove unnecessary loops when parsing the cable modem status table

I am writing a Python script that will be used to generate a line protocol to insert metrics into an InfluxDB. I am analyzing the status page of my cable modem. That part works. I receive the data and I can extract the table of information that I need.

The HTML for the table looks like this (yes, it has the commented lines)

The one underneath works, but feels inefficient. I feel that I have many loops and these feel unnecessary. Although I'm not sure how to be more efficient.

The only change between this and my real code is that I have moved the HTML to pastebin to reduce the length of this publication. That pastebin is here and the HTML is not written or editable by me. It is generated in the cable modem. As I can not see the results of my cable modem executing the code I use to extract it, I hope this is good enough.

from bs4 import BeautifulSoup
import requests

results_url = "https://pastebin.com/raw/bLZLFzy6"
content = requests.get (results_url) .text

measure = "modem"
hostname = "home-modem"

def strip_uom (data):
"" "Unit of measure of band

Some of our fields come with a unit of measurement. Tear that and keep it alone
The value of the field.
uom = ["dB", "Hz", "dBmV", "Ksym/sec"]
    data set = []
    for d in the data:
yes d.split ("")[-1] in uom:
dataset.append (d.split ("")[0])
dataset.append (d)
return data

soup = BeautifulSoup (content, & # 39; html.parser & # 39;)
dstable = soup.find (& # 39; table & # 39 ;, {& # 39; id & # 39 ;: & # 39; dsTable & # 39;})

# Pull the headings of the downstream table
# We want to make these labels friendly, so make them lowercase and eliminate spaces
dstable_tags = [td.get_text().lower().replace(" ", "_") for td in dstable.tr.find_all('td')]

# Extract the rest of the data for each row in the table; associate it with the correct label; Strip UoM
downstream_data = []
for the row in dstable.find_all (& # 39; tr & # 39;)[1:]:
column_values ​​= [col.get_text() for col in row.find_all('td')]
    downstream_data.append (dict (zip (dstable_tags, strip_uom (column_values))))

# Print line protocol lines for the inputs.exec plugin of telegraphy to manage
for the data in downstream_data:
line_protocol_line = f "{measurement}, hostname = {hostname}"
fields = []
    For key, value in data.items ():
# Check if our value is a number. If not, surround it with quotes.
# Do not use the float () value, since some numbers are returned as
# valid integers
_ = float (value)
fields.append (f & # 39; {key} = {value} & # 39;)
except ValueError:
fields.append (f & # 39; {key} = "{value}" & # 39;)
fieldset = ",". join (fields)
line_protocol_line = line_protocol_line + f ", channel = {data['channel']} {fieldset} "
print (line_protocol_line)

Finally, the output generated by this script is as follows:

modem, hostname = netgear-cm400, channel = 1 channel = 1, lock_status = "Blocked", modulation = "QAM 256", channel_id = 121, frequency = 585000000, power = 5.6, snr = 37.0, correctable = 19443, not correctable = 11263
modem, hostname = netgear-cm400, channel = 2 channel = 2, lock_status = "Blocked", modulation = "QAM 256", channel_id = 6, frequency = 591000000, power = 5.7, snr = 37.0, correctable = 19531, uncorrectables = 9512
modem, hostname = netgear-cm400, channel = 3 channel = 3, lock_status = "Blocked", modulation = "QAM 256", channel_id = 7, frequency = 597000000, power = 5.7, snr = 36.8, correctable = 17457, not correctable = 9736
modem, hostname = netgear-cm400, channel = 4 channel = 4, lock_status = "Blocked", modulation = "QAM 256", channel_id = 8, frequency = 603000000, power = 5.9, snr = 37.0, correctable = 12750, not correctable = 11156
modem, hostname = netgear-cm400, channel = 5 channel = 5, lock_status = "Blocked", modulation = "QAM 256", channel_id = 122, frequency = 609000000, power = 2.6, snr = 36.3, correctable = 1855538, not correctable = 18388
modem, hostname = netgear-cm400, channel = 6 channel = 6, lock_status = "Blocked", modulation = "QAM 256", channel_id = 10, frequency = 615000000, power = 2.6, snr = 37.0, correctable = 846194, not correctable = 14615
modem, hostname = netgear-cm400, channel = 7 channel = 7, lock_status = "Blocked", modulation = "QAM 256", channel_id = 11, frequency = 621000000, power = 2.6, snr = 37.6, correctable = 281431, not correctable = 13998
modem, hostname = netgear-cm400, channel = 8 channel = 8, lock_status = "Blocked", modulation = "QAM 256", channel_id = 12, frequency = 627000000, power = 2.4, snr = 36.1, correctable = 78059, not correctable = 13695

I have loops and comprehension lists to get the labels, associate data with labels for each row and generate the line protocol lines. I have two additional understandings in those and a loop to see if I need to delete an included unit of measure. Can I eliminate some of these to be more efficient?

I am running all this code in Python 3.6.7.

Python: update of personalized steps of Scipy basinhopping and restricted loops

I'm looking for the global minimum of a given function and trying to use its gradient (here just like Jacobin) to guide the step counter. However, my X It is the solution and so is my gradient. I am also trying to recover as quickly as possible the first X for which f (x) <1, therefore I am using a restriction.

  • How can I update the X Entrance and the Jacobin?
  • My f (x) <1 It is not very effective, so is there any alternative to fulfill my requirement?

This is my code (more or less):

MyBounds class (object):
def __init __ (self, xmax =[2*np.pi, 2*np.pi, 2*np.pi, 2*np.pi, 1.2, 1.2, 1.2, 1.2], xmin =[0, 0, 0, 0, 0, 0, 0, 0] ):
self.xmax = np.array (xmax)
self.xmin = np.array (xmin)

def __call __ (self, ** kwargs):
x = kwargs["x_new"]
        tmax = bool (np.all (x <= self.xmax))
        tmin = bool(np.all(x >= self.xmin))
returns tmax and tmin

MyTakeStep class (object):
def __init __ (self, stepsize = 1):
self.stepsize = stepize

def compute_step (self, jacobi_matrix, x, i):
yes jacobi_matrix[i] < 0: r = np.random.uniform(0,      2*np.pi-x[i])
        elif jacobi_matrix[i] >    0: r = np.random.uniform (0-x[i], 0)
else: r = 0
go back

def __call __ (self, x):
print ("THIS IS X:", x)
jacobi_matrix = jacobian (x)
print ("x:", x)
print ("jacobi:", jacobi_matrix)
X[0] + = self.compute_step (jacobi_matrix, x, 0)
X[1] + = self.compute_step (jacobi_matrix, x, 1)
X[2] + = self.compute_step (jacobi_matrix, x, 2)
X[3] + = self.compute_step (jacobi_matrix, x, 3)
X[4] + = self.compute_step (jacobi_matrix, x, 4)
X[5] + = self.compute_step (jacobi_matrix, x, 5)
X[6] + = self.compute_step (jacobi_matrix, x, 6)
X[7] + = self.compute_step (jacobi_matrix, x, 7)
print ("newx:", x)
return x

def f (x):
# components of the objective function
result = g1
result + = g2
result + = g3
return result

def jacobian (x):
print ("input_list in Jacobi:", x)

# define full derivatives
dG_dphi = dg1_dphi + dg2_dphi + dg3_dphi
dG_dr = dg1_dr + dg2_dr + dg3_dr
gradient = np.hstack ((dG_dphi, dG_dr))

print ("G:", gradient.shape, gradient, " n")
return gradient

def callback (x, f, accept):
print ("x:% 65s | f:% 5s | accept:% 5s"% (str ([round(e,3) for e in x]), str (round (f, 3)), accept))

def hopping_solver (min_f, min_x, input_excitation):
# define limits
mybounds = MyBounds ()
mytakestep = MyTakeStep ()
comb = [deg2rad(phi) for phi in  input_excitation[:4]]+ input_excitation[4:]
    print ("comb:", comb)
min_f = 10
tol = 0
Cons = {& # 39; type & # 39 ;: & # 39; ineq & # 39 ;, & # 39; fun & # 39 ;: lambda x: 1-f (x)}
k = {"method": & # 39; Nelder-Mead & # 39 ;, & # 39; restrictions & # 39 ;: cons, & # 39; jac & # 39 ;: jacobian, & tollet & # 39 ;: tol }
optimal_c = optimize.basinhopping (f,
x0 = comb,
Niter = 1000000,
T = 8,
step size = 1,
minimizer_kwargs = k,
take_step = mytakestep,
accept_test = mybounds,
callback = callback,
interval = 100000,
disp = true
niter_success = None)
print (optimum_c)
min_x, min_f = optimal_c['x'], optimum_c['fun']
    comb = min_x
sol = np.array (list ([nprad2deg(phi)paraphienlalista(optimum_c[nprad2deg(phi)forphiinlist(optimal_c[nprad2deg(phi)paraphienlalista(óptimo_c[nprad2deg(phi)forphiinlist(optimal_c['x'][:4])]) + list (optimum_c['x'][4:]))
min_x = sun
returns min_x, min_f

Any help is very appreciated, thanks in advance.