row_id int64 0 48.4k | init_message stringlengths 1 342k | conversation_hash stringlengths 32 32 | scores dict |
|---|---|---|---|
17,381 | make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all vmake normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all the following: Sure! Here's an example of how you can incorporate NLP AI-generated riddles into a simple web-based game using HTML, CSS, and JavaScript:
HTML:
<!DOCTYPE html>
<html>
<head>
<title>Food Riddle Enigma</title>
<link rel="stylesheet" type="text/css" href="styles.css">
</head>
<body>
<div class="container">
<h1>Food Riddle Enigma</h1>
<p id="riddle"></p>
<input type="text" id="guess" placeholder="Enter your guess">
<button onclick="checkGuess()">Submit</button>
<p id="result"></p>
<button onclick="nextRiddle()">Next Riddle</button>
</div>
<script src="script.js"></script>
</body>
</html>
CSS (styles.css):
.container {
text-align: center;
max-width: 500px;
margin: 0 auto;
}
h1 {
color: #333;
}
button {
background-color: #333;
color: #fff;
padding: 10px 20px;
border: none;
cursor: pointer;
}
input {
padding: 10px;
width: 100%;
margin-bottom: 10px;
}
#result {
font-weight: bold;
}
JavaScript (script.js):
// Place your NLP AI-generated riddles in an array
var riddles = [
"I'm round and cheesy, baked to perfection...",
"I'm wrapped up tight, a bite-sized treat...",
"I'm a culinary creation, stacked high with care..."
];
// Corresponding food names
var answers = ["Pizza", "Sushi", "Burger"];
var currentIndex = 0;
function checkGuess() {
var guess = document.getElementById("guess").value;
var result = document.getElementById("result");
if (guess.toLowerCase() === answers[currentIndex].toLowerCase()) {
result.textContent = "Correct! It's " + answers[currentIndex] + "!";
} else {
result.textContent = "Wrong! Try again.";
}
}
function nextRiddle() {
currentIndex++;
if (currentIndex >= riddles.length) {
currentIndex = 0;
}
var riddle = document.getElementById("riddle");
riddle.textContent = riddles[currentIndex];
document.getElementById("guess").value = "";
document.getElementById("result").textContent = "";
}
// Initialize the first riddle on page load
window.onload = function() {
var riddle = document.getElementById("riddle");
riddle.textContent = riddles[currentIndex];
};
This code sets up a simple web-based game where users can guess the food name based on the given riddle. The script rotates through the riddles and checks the user's input against the corresponding answer. If the guess is correct, it displays a success message, and if it's incorrect, it prompts the user to try again. The "Next Riddle" button moves to the next riddle in the array.
Remember to replace the placeholder riddles with your NLP AI-generated riddles and adjust the styling to your preference. Additionally, you can expand the game's functionality by adding more riddles, a scoring system, and additional features based on your game concept.
I hope this example helps you get started with incorporating AI-generated riddles into your game! If you need any further assistance or have any specific requirements or questions, please let me know.
you output some (in dependence of hardness in terms used) food-related emojicons in a row that should auto-fit on the entire window size and resize, like from 3 to 5. all these emojicons should be shown instantly with only one riddle beneath them in some cute container that is related to particular emojicon in food terms used. if user guess it right, it get one score added (somewhere in the corner), if no then one score got removed. the entire concept is extremely easy in overall code to perform. you can try add your suggestions or super (not complex to perform) ideas if you like or wish. nonono, you cannot use submit!11111111111111111111111!!!!!!!!!!!!11111111111 you can try add your suggestions or super (not complex to perform) ideas if you like or wish?????????????????make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt | 140548f3b59f0b7ec98694552390f31c | {
"intermediate": 0.3798487186431885,
"beginner": 0.4149106442928314,
"expert": 0.2052406519651413
} |
17,382 | pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all vmake normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all the following: Sure! Here's an example of how you can incorporate NLP AI-generated riddles into a simple web-based game using HTML, CSS, and JavaScript:
HTML:
<!DOCTYPE html>
<html>
<head>
<title>Food Riddle Enigma</title>
high with care..."
];
// Corresponding food names
var answers = ["Pizza", "Sushi", "Burger"];
var currentIndex = 0;
function checkGuess() {
var guess = document.getElementById("guess").value;
var result = document.getElementById("result");
if (guess.toLowerCase() === answers[currentIndex].toLowerCase()) {
result.textContent = "Correct! It's " + answers[currentIndex] + "!";
} else {
result.textContent = "Wrong! Try again.";
}
}
function nextRiddle() {
currentIndex++;
if (currentIndex >= riddles.length) {
currentIndex = 0;
}
var riddle = document.getElementById("riddle");
riddle.textContent = riddles[currentIndex];
document.getElementById("guess").value = "";
document.getElementById("result").textContent = "";
}
// Initialize the first riddle on page load
window.onload = function() {
var riddle = document.getElementById("riddle");
riddle.textContent = riddles[currentIndex];
};
This code sets up a simple web-based game where users can guess the food name based on the given riddle. The script rotates through the riddles and checks the user's input against the corresponding answer. If the guess is correct, it displays a success message, and if it's incorrect, it prompts the user to try again. The "Next Riddle" button moves to the next riddle in the array.
Remember to replace the placeholder riddles with your NLP AI-generated riddles and adjust the styling to your preference. Additionally, you can expand the game's functionality by adding more riddles, a scoring system, and additional features based on your game concept.
I hope this example helps you get started with incorporating AI-generated riddles into your game! If you need any further assistance or have any specific requirements or questions, please let me know.
you output some (in dependence of hardness in terms used) food-related emojicons in a row that should auto-fit on the entire window size and resize, like from 3 to 5. all these emojicons should be shown instantly with only one riddle beneath them in some cute container that is related to particular emojicon in food terms used. if user guess it right, it get one score added (somewhere in the corner), if no then one score got removed. the entire concept is extremely easy in overall code to perform. you can try add your suggestions or super (not complex to perform) ideas if you like or wish. nonono, you cannot use submit!11111111111111111111111!!!!!!!!!!!!11111111111 you can try add your suggestions or super (not complex to perform) ideas if you like or wish?????????????????make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt | ddae82ec32cc69afc8b135a0123fc3f0 | {
"intermediate": 0.3083994686603546,
"beginner": 0.40324050188064575,
"expert": 0.28836002945899963
} |
17,383 | pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all vmake normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all the following: incorrect, it prompts the user to try again. The "Next Riddle" button moves to the next riddle in the array.
Remember to replace the placeholder riddles with your NLP AI-generated riddles and adjust the styling to your preference. Additionally, you can expand the game's functionality by adding more riddles, a scoring system, and additional features based on your game concept.
I hope this example helps you get started with incorporating AI-generated riddles into your game! If you need any further assistance or have any specific requirements or questions, please let me know.
you output some (in dependence of hardness in terms used) food-related emojicons in a row that should auto-fit on the entire window size and resize, like from 3 to 5. all these emojicons should be shown instantly with only one riddle beneath them in some cute container that is related to particular emojicon in food terms used. if user guess it right, it get one score added (somewhere in the corner), if no then one score got removed. the entire concept is extremely easy in overall code to perform. you can try add your suggestions or super (not complex to perform) ideas if you like or wish. nonono, you cannot use submit!11111111111111111111111!!!!!!!!!!!!11111111111 you can try add your suggestions or super (not complex to perform) ideas if you like or wish?????????????????make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all make normal pseudo-pre-prompt based on all pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pseudo-pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt pre-prompt | 82deaa5d5a5727c13957a783233c37c1 | {
"intermediate": 0.2598768472671509,
"beginner": 0.44669434428215027,
"expert": 0.2934287488460541
} |
17,384 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 2
1 # Compute image-hash
----> 2 model.compute_hash(method='phash', hash_size=hash_size)
4 # Find images with image-hash <= threshold
5 model.group(threshold=10)
Cell In[4], line 122, in Undouble.compute_hash(self, method, hash_size, return_dict)
118 self.results['img_hash_hex'] = self.bin2hex()
119 logger.info(
120 'Compute adjacency matrix [%gx%g] with absolute differences based on the image-hash of [%s].' % (
121 self.results['img_hash_bin'].shape[0], self.results['img_hash_bin'].shape[0], self.params['method']))
--> 122 self.results['adjmat'] = (self.results['img_hash_bin'][:, None, :] != self.results['img_hash_bin']).sum(2)
123 self.results.pop('labels', None)
124 self.results.pop('xycoord', None)
AttributeError: 'bool' object has no attribute 'sum' | f0b4a6ebd7422127af6fe877d4e534ca | {
"intermediate": 0.3447045683860779,
"beginner": 0.33692222833633423,
"expert": 0.3183731734752655
} |
17,385 | use java code a game | 17f0e9c6f0981567acae27cc809b9b68 | {
"intermediate": 0.331571489572525,
"beginner": 0.516693115234375,
"expert": 0.15173545479774475
} |
17,386 | the insturctions where: "Write a conclusion to your investigation (Include your hypothesis and your key results).
" and i said "In my hypothesis, I said the following: “The higher the ball is dropped from, the higher it will bounce back up. the harder the surface, the more it will bounce.” I didn’t say anything about bounce efficiency, I know that the bounce efficiency is more efficient when dropped from lower, despite not going higher than when dropped from a higher height. " can u rewrite my answer to be either longer, better grammar just better answer in general, put your answer in a codeblock | 09ecf6d6aad1990904993f0e3bf55745 | {
"intermediate": 0.3336185812950134,
"beginner": 0.37915685772895813,
"expert": 0.28722456097602844
} |
17,387 | c# netcore 部署到服务器报错An unhandled exception has occurred while executing the request.|System.IO.DirectoryNotFoundException: C:\Users\Administrator\AppData\Local\Temp\2\ | c880b1176988b7249be90296ea1b8534 | {
"intermediate": 0.4501943290233612,
"beginner": 0.29835277795791626,
"expert": 0.2514529526233673
} |
17,388 | write a VBA for get the cell value that before the value "m" and after "D" | 8b101c863cf845167b7c32eb47d3c7b1 | {
"intermediate": 0.29981622099876404,
"beginner": 0.20735661685466766,
"expert": 0.4928271174430847
} |
17,389 | java中如何可以在httppost使拼接上body | 6d8f86cfe0010e38fdc70a990a1cb4d8 | {
"intermediate": 0.38732993602752686,
"beginner": 0.320128470659256,
"expert": 0.29254159331321716
} |
17,390 | def insertion(arr):
for i in range(1,len(arr)):
key = arr[i]
j=i-1
while j>=0 and key<arr[j]:
arr[j]=arr[j]
j-=1
arr[j+1]=key
n=int(input("enter the no of elements")) i=0
arr=[]
for i in range(o,n):
p=int(intput("enter the element in the array"))
arr.append(p)
insertion(arr)
print("sorted array")
for i in range(len(arr))
print("%d",a[i]) correct hsi | 89accb626069de1917db5d4998a51c18 | {
"intermediate": 0.3601115047931671,
"beginner": 0.4264427125453949,
"expert": 0.2134457677602768
} |
17,391 | in asp .net web forms how logging with NLOG httpclient call to webapi | 51cff0bf0a5c8432557cd762502d70f8 | {
"intermediate": 0.6267080307006836,
"beginner": 0.187156543135643,
"expert": 0.18613535165786743
} |
17,392 | "<a name="manageAccessMatMenu" *ngIf="isFolderAdmin(item)" (click)="openManageAccessModal(item.uid)" href="javascript:void(0);">"
"isFolderAdmin(item: IEntry): boolean {
return this.sharedService.isFolderAdmin(item);
}"
Write angular test case for above to check if anchor tag with property manageAccessMatMenu is being displayed or not when "isFolderAdmin(item)" from sharedService returns true | 9394bd383669ec826fb086476ae1a829 | {
"intermediate": 0.47179242968559265,
"beginner": 0.2823217809200287,
"expert": 0.24588574469089508
} |
17,393 | in asp .net web forms how logging with NLOG httpclient call to webapi | 2db1b46f3ec911c01503e66082662102 | {
"intermediate": 0.6267080307006836,
"beginner": 0.187156543135643,
"expert": 0.18613535165786743
} |
17,394 | what does process.env do,does it access to the .env file? | 5288b4773b884c27df2fe5711a1bf1dc | {
"intermediate": 0.40066391229629517,
"beginner": 0.31436002254486084,
"expert": 0.2849760949611664
} |
17,395 | How to find the number of duplicated columns in pandas data frame based on specified columns? | a7f304af980a1e09edd7a59dbc86f3e1 | {
"intermediate": 0.41325294971466064,
"beginner": 0.14121945202350616,
"expert": 0.4455276131629944
} |
17,396 | s=input("enter the roamn number")
def roman(s):
d={'I':1,'V':5,'X':10'L':50,'C':100,'D':500,'M':1000,'IV':4,'IV':4,'IX'=9,'XL:90','CD':400,'CM'=900}
ans=0
for i in range(len(s))
if i+1!=len(s) and d[s[i]]<d[s[i+1]]:
ans=ans-d[s[i]]
else:
ans=ans+d[s[i]]
return ans
print("the decimal equivalent is ",roman(s)) correct this | b88a347bde0f47fc1d6642aab2585b1f | {
"intermediate": 0.3158387243747711,
"beginner": 0.46598589420318604,
"expert": 0.21817545592784882
} |
17,397 | hey there. | 56a5d8b58ea723804f73411ef3ab934d | {
"intermediate": 0.32833942770957947,
"beginner": 0.2722059190273285,
"expert": 0.39945462346076965
} |
17,398 | export interface Tag {
name: string;
color: string;
}
const [selectTags, setSelectTags] = useState<Tag[]>([]);
renderValue={(selected) => {
const selectedTruncate: string[] = truncate(selected.toString(), ofScreener ? 16 : 22)?.split(",");
нужно сделать selectedTruncate типа Tag, selected проверять с selectTags, сетать сетать у selectTags такой, какое имя равно selected | db841bc42733faac78e2ba81f5a8a830 | {
"intermediate": 0.4062747061252594,
"beginner": 0.329653263092041,
"expert": 0.2640719711780548
} |
17,399 | How to remove rows form pandas data frame those that have values starting with 81 in column XX? | 34ac88879e2209a3dfb32a43f89aca09 | {
"intermediate": 0.5387938022613525,
"beginner": 0.13245578110218048,
"expert": 0.3287504017353058
} |
17,400 | I have a access database named portfolio, storing the portfolio and benchmark information. the portfolio table contains 6 columns, “portfolio ID”, “tradedate”, “ticker”, “weight”, “price change” and “industry”. the benchmark table also has 6 columns: “bench ID”, “tradedate”, “ticker”, “weight”, “price change”, “industry”. please use python define a function brinson (portfolioID , bench ID, begindate, enddate), setup connection to the database, calculate and return the total daily excess return and each effect, and plot a chart in the end as well. Be careful, First, I need to run the brinson attribution grouping by industry. I recommand you using 4 additional serials, you can call it Q1 Q2 Q3 and Q4. Q1= sigma benchmark weight * benchmark return in each industry. Q2= sigma portfolio weight * benchmark return in each industry. Q3= sigma benchmark weight * portfolio return in each industry. Q4= sigma portfolio weight * portfolio return in each industry. As you can see, excess return=Q4 - Q1, allocation effect = Q2-Q1, Selection effect =Q3-Q1, and interaction effect= Q4 - Q3 - Q2 + Q1. Second, When running multi-period attribution. the excess rtn should be compounded correctly. For example when calculation the total effect of day1 to day2. you have the Q1 for day1 and Q1 for day2, then total Q1=Q1 day1 + (1+Q1 day1)*Q1 day2. then used the conpound Q values, you will get the right compund excess return and attribution result. | 5f06f2b2779ff82e72908012ae7b4438 | {
"intermediate": 0.5299606919288635,
"beginner": 0.19836333394050598,
"expert": 0.2716760039329529
} |
17,401 | Как мне стот изменить элемент с id decription , чтобы он помещался внтури и был ровным : <androidx.cardview.widget.CardView xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="200dp"
app:cardCornerRadius="4dp"
app:cardElevation="2dp">
<androidx.constraintlayout.widget.ConstraintLayout
android:layout_width="match_parent"
android:layout_height="match_parent">
<ImageView
android:id="@+id/cardBackgroundImage"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:adjustViewBounds="true"
android:scaleType="centerCrop" />
<View
android:id="@+id/view2"
android:layout_width="136dp"
android:layout_height="200dp"
android:background="@color/transparent70"
app:layout_constraintBottom_toBottomOf="parent"
app:layout_constraintStart_toStartOf="parent"
app:layout_constraintTop_toTopOf="parent"
app:layout_constraintVertical_bias="0.0" />
<TextView
android:id="@+id/Genre"
android:layout_width="103dp"
android:layout_height="19dp"
android:layout_alignParentStart="true"
android:layout_alignParentTop="true"
android:layout_marginBottom="4dp"
android:textColor="@color/white"
android:textSize="8dp"
android:textStyle="bold"
app:layout_constraintBottom_toTopOf="@+id/Rating"
app:layout_constraintEnd_toEndOf="@+id/view2"
app:layout_constraintHorizontal_bias="0.406"
app:layout_constraintStart_toStartOf="parent" />
<ImageView
android:layout_width="12dp"
android:layout_height="13dp"
android:layout_marginStart="8dp"
android:layout_marginBottom="16dp"
android:src="@drawable/baseline_star_24"
app:layout_constraintBottom_toBottomOf="@+id/view2"
app:layout_constraintStart_toStartOf="@+id/cardBackgroundImage" />
<TextView
android:id="@+id/Rating"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_below="@id/cardBackgroundImage"
android:layout_alignParentTop="true"
android:layout_alignParentEnd="true"
android:layout_marginBottom="15dp"
android:textColor="@color/white"
android:textStyle="bold"
app:layout_constraintBottom_toBottomOf="@+id/view2"
app:layout_constraintEnd_toStartOf="@+id/icon1"
app:layout_constraintHorizontal_bias="0.477"
app:layout_constraintStart_toStartOf="parent" />
<ImageView
android:id="@+id/icon1"
android:layout_width="37dp"
android:layout_height="22dp"
android:layout_marginBottom="16dp"
android:layout_weight="0.2"
android:src="@drawable/icon1"
app:layout_constraintBottom_toBottomOf="@+id/view2"
app:layout_constraintEnd_toEndOf="@+id/view2"
app:layout_constraintHorizontal_bias="0.779"
app:layout_constraintStart_toStartOf="@+id/cardBackgroundImage" />
<TextView
android:id="@+id/Title"
android:layout_width="136dp"
android:layout_height="25dp"
android:layout_weight="0.6"
android:textAlignment="center"
android:textColor="@color/white"
android:textStyle="bold"
app:layout_constraintBottom_toTopOf="@+id/description"
app:layout_constraintEnd_toEndOf="@+id/view2"
app:layout_constraintHorizontal_bias="0.0"
app:layout_constraintStart_toStartOf="@+id/cardBackgroundImage"
app:layout_constraintTop_toTopOf="@+id/view2"
app:layout_constraintVertical_bias="1.0" />
<TextView
android:id="@+id/description"
android:layout_width="136dp"
android:layout_height="102dp"
android:layout_marginTop="32dp"
android:layout_weight="0.6"
android:textColor="@color/silver"
android:textAlignment="center"
android:textSize="11sp"
app:layout_constraintEnd_toEndOf="@+id/view2"
app:layout_constraintHorizontal_bias="0.0"
app:layout_constraintStart_toStartOf="@+id/cardBackgroundImage"
app:layout_constraintTop_toTopOf="@+id/view2" />
</androidx.constraintlayout.widget.ConstraintLayout>
</androidx.cardview.widget.CardView> | 7ca323fd33fc6b16835dba70f3d3ea92 | {
"intermediate": 0.24626973271369934,
"beginner": 0.6097733378410339,
"expert": 0.14395691454410553
} |
17,402 | Show example C code of how the version directive is automatically added as the first line of a shader, in the right version number and optional "es", in programs using GLFW | 5701f56ebb55add14525281a31aa58ab | {
"intermediate": 0.5209349393844604,
"beginner": 0.19067558646202087,
"expert": 0.2883894443511963
} |
17,403 | How to read 2 sheets in one excel file from python as 2 dataframes? | aeb3e3649a5edb6b457db7f9957d5b7a | {
"intermediate": 0.5842281579971313,
"beginner": 0.11048857867717743,
"expert": 0.30528321862220764
} |
17,404 | how to capture traffic in ASA | 86ef1928102958cd10da37e50461b6a3 | {
"intermediate": 0.2844817340373993,
"beginner": 0.22618861496448517,
"expert": 0.4893296957015991
} |
17,405 | помоги с рефакторингом
[RequireComponent(typeof(UINavigator))]
public class UIContentController : MonoBehaviour
{
[SerializeField]
private MenuHeader menuHeader;
public PanelItemDatabase panelItemDatabase;
private Stack<PanelItem> previousScreens;
private PanelItem currentScreen;
private UINavigator navigator;
private void Start()
{
navigator = GetComponent<UINavigator>();
previousScreens = new Stack<PanelItem>();
GoToScreen(ItemName.StartScreen);
navigator.OnNextScreen += GoToScreen;
navigator.OnEarlyScreen += GoBack;
}
public void GoToScreen(ItemName screenName)
{
if (currentScreen!= null)
{
previousScreens.Push(currentScreen);
currentScreen.gameObject.SetActive(!currentScreen.FullScreen);
}
currentScreen = CreatePanelItem(GetPanelItem(screenName));
currentScreen.gameObject.SetActive(true);
navigator.SetCurrentPopup(currentScreen);
menuHeader.SetTextMenuHeader(screenName);
}
public void GoBack()
{
if (previousScreens.Count > 0)
{
Destroy(currentScreen.gameObject);
currentScreen = previousScreens.Pop();
currentScreen.gameObject.SetActive(true);
navigator.SetCurrentPopup(currentScreen);
menuHeader.SetTextMenuHeader(currentScreen.ItemName);
}
}
private PanelItem GetPanelItem(ItemName screenName)
{
panelItemDatabase.panelItemPrefabs.TryGetValue(screenName, out PanelItem panelItem);
return panelItem;
}
private PanelItem CreatePanelItem(PanelItem panelItem)
{
var panel = Instantiate(panelItem, transform);
return panel;
}
private void OnDestroy()
{
navigator.OnNextScreen -= GoToScreen;
navigator.OnEarlyScreen -= GoBack;
}
} | 0ab8734cf8a0254e877a9c6236146af1 | {
"intermediate": 0.4209795296192169,
"beginner": 0.3480132520198822,
"expert": 0.23100724816322327
} |
17,406 | Where Did I Click (on the .NET control)? | 28dc534f844343db5d992a2a94c4003a | {
"intermediate": 0.46826374530792236,
"beginner": 0.27253809571266174,
"expert": 0.2591981291770935
} |
17,407 | I want to learn DOM manipulating give me bullet points and a summary for top 40 used properties on it | 82f1f63621beadc08db6e9f59ecafad4 | {
"intermediate": 0.21952788531780243,
"beginner": 0.3670574128627777,
"expert": 0.41341471672058105
} |
17,408 | Please write me a VBA code to do the following;
When I double click on a cell in column D,
copy the value in column M to column G on the same row
then in the next empty cell on the same row enter todays date in the following format dd/mm | a5b3ff5c36beb41be30b0f6fcc33fb71 | {
"intermediate": 0.5151159763336182,
"beginner": 0.12154468148946762,
"expert": 0.363339364528656
} |
17,409 | HI | 04f706017889610f74d32b46abe4dbaf | {
"intermediate": 0.32988452911376953,
"beginner": 0.2611807882785797,
"expert": 0.40893468260765076
} |
17,410 | portfolio_query = f"SELECT * FROM Mockportfolio WHERE ID = {portfolioID} AND tradedate >= ‘2023/4/3’ AND tradedate <= ‘2023/4/4'”. please help me fix the code as the datatype of tradedate is datetime. thanks. | e7677183d8c0f28342aa435b0cae13d7 | {
"intermediate": 0.5555407404899597,
"beginner": 0.2169855386018753,
"expert": 0.2274736762046814
} |
17,411 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signals = []
# Retrieve depth data
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if buy_qty > sell_qty:
signals.append('bullish')
elif sell_qty > buy_qty:
signals.append('bearish')
if 'bullish' in signals and float(mark_price) < buy_price:
return 'buy'
elif 'bearish' in signals and float(mark_price) > sell_price:
return 'sell'
else:
return ''
But it giveing me wrong signals, can you remove my algorithm and set here my new algorithm: If (buy qty > sell qty) < mark price signals.append('sell')
elif (sell qty > buy qty) > mark price signals.append('buy') | d6f251a9c1eb8422baeca81f3d481632 | {
"intermediate": 0.3396407663822174,
"beginner": 0.16686280071735382,
"expert": 0.49349644780158997
} |
17,412 | How to close file ~$03.2023-04.2023.xlsx if this files is not open in Excel directly? | 1dd3e8ef199383db471575b0a017a0a2 | {
"intermediate": 0.4153943657875061,
"beginner": 0.3177517354488373,
"expert": 0.26685386896133423
} |
17,413 | In golang, how can I chek if a key exists in an array ? | 66d231bbd45fab1a8e5e5a4ca19f3937 | {
"intermediate": 0.6269635558128357,
"beginner": 0.10546164959669113,
"expert": 0.26757481694221497
} |
17,414 | I have a table1 with columns A, B, C, D, E, F and table2 with columns K, L, M, N, O, P, R. I would like to merge these two tables following the conditions A = K, B = L if L is not empty other wise C = M, and D = N. After that I need only columns A, B, C, D, E, F, O, P. These tables have different number of rows and columns. Write pandas Python script | 10fcf15d7b0e42a635388ffcedc69dbc | {
"intermediate": 0.37733009457588196,
"beginner": 0.20329712331295013,
"expert": 0.4193728268146515
} |
17,415 | create a react native functional component that receives an array of objects, based on this array it creates the same amount of react native switch components with a value of true. The component needs to keep track of all switch components changes | fdcef442e48c75e822d587f72a2a60a4 | {
"intermediate": 0.3516608774662018,
"beginner": 0.3110485374927521,
"expert": 0.33729055523872375
} |
17,416 | is there an entity collision by its bounding box in ogre3d 1.7 | ec85371b65b2cebc73b969c9dee6252a | {
"intermediate": 0.3977971374988556,
"beginner": 0.2746371924877167,
"expert": 0.32756567001342773
} |
17,417 | I am making a rnn. i have roughly 41000 samples. how much should be in validation set and how much in test set | 29ff9afc9a8f05a26fa361dbec628c6c | {
"intermediate": 0.29119873046875,
"beginner": 0.19931660592556,
"expert": 0.5094846487045288
} |
17,418 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signals = []
# Retrieve depth data
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if buy_qty > sell_qty and buy_qty > mark_price:
signals.append('sell')
elif sell_qty > buy_qty and sell_qty > mark_price:
signals.append('buy')
if 'buy' in signals:
return 'buy'
elif 'sell' in signals:
return 'sell'
else:
return ''
But it giveing me only signal to sell | 9de577e95423e90c0f4341e92a78f086 | {
"intermediate": 0.424064576625824,
"beginner": 0.29890313744544983,
"expert": 0.27703234553337097
} |
17,419 | i have this def. remove the need to define num_samples, use all files in the folder. def split_data(path, num_samples=None, validation_ratio=0.1, test_ratio=0.1):
image_paths = []
# Recursively traverse the directory structure
for root, _, filenames in os.walk(path):
for filename in filenames:
image_paths.append(os.path.join(root, filename))
# Use all available images if num_samples is not provided
if num_samples is None:
sampled_images = image_paths
else:
# Randomly select num_samples images
sampled_images = random.sample(image_paths, min(num_samples, len(image_paths)))
# Shuffle the sampled_images list
random.shuffle(sampled_images)
# Calculate the number of samples for validation and test sets
n_samples = len(sampled_images)
n_validation_samples = int(n_samples * validation_ratio)
n_test_samples = int(n_samples * test_ratio)
# Split into train, validation, and test sets
validation_filenames = sampled_images[:n_validation_samples]
test_filenames = sampled_images[n_validation_samples:n_validation_samples + n_test_samples]
train_filenames = sampled_images[n_validation_samples + n_test_samples:]
# Create new folders for validation and test sets
validation_folder = os.path.join(os.path.dirname(path), ‘Validation’)
test_folder = os.path.join(os.path.dirname(path), ‘Test’)
os.makedirs(validation_folder, exist_ok=True)
os.makedirs(test_folder, exist_ok=True)
# Move files to respective folders
for filename in validation_filenames:
shutil.move(filename, os.path.join(validation_folder, os.path.basename(filename)))
for filename in test_filenames:
shutil.move(filename, os.path.join(test_folder, os.path.basename(filename)))
return train_filenames, validation_filenames, test_filenames | f5daf08be7b4749eb2376f8d761a2d7b | {
"intermediate": 0.5079006552696228,
"beginner": 0.2434355467557907,
"expert": 0.2486637830734253
} |
17,420 | Replay: who won the marble game?
A group of N players finished the marble game. Find out which player was the winner.
You are given the sequence of dice rolled during the game.
The game rules
At the start, each player from 1 to N has a bag with 10 marbles.
On the table there are 5 marble parking holes - numbered from 1-5. A parking hole can hold max 1 marble.
Starting with player 1, players roll the dice (after each other in ascending order i.e. player 1, 2, ... player 1, 2 ...).
IF dice value is 6: the player removes 1 marble from the player's bag.
IF dice value is 1,2,3,4, or 5: the player transfers 1 marble from the player's bag into the corresponding parking hole IF that corresponding parking hole is empty. OTHERWISE, the player transfers the marble from the corresponding parking hole into the player's bag.
The player wins, if the player's bag contains no marbles after the turn.
Marbles in the parking holes belong to no player.
Input
Line 1: An integer N for how many players participated in the game
Line 2: Number of rolled dices during the game
Line 3: The sequence of dice values they rolled, separated by spaces
Output
Line 1 : the player who won the game
Constraints
2<=N<=9
1<=player<=N
1<=dice<=6
Example
Input
2
51
4 6 6 3 6 5 4 1 6 6 4 6 4 1 5 4 6 6 2 1 2 3 3 4 6 1 2 2 3 3 3 3 2 2 6 5 4 1 1 2 1 6 2 3 3 1 1 3 3 4 4
Output
1 Please solve this with C# code | c68c34c3f6d1d7468ebb7a3db9392174 | {
"intermediate": 0.45785459876060486,
"beginner": 0.2597016394138336,
"expert": 0.28244373202323914
} |
17,421 | The class Movie is started below. An instance of class Movie represents a film. This class has the following three class variables:
title, which is a string representing the title of the movie
studio, which is a string representing the studio that made the movie
rating, which is a string representing the rating of the movie (i.e. PG13, R, etc)
Write a constructor for the class Movie, which takes the title of the movie, studio, and rating as its arguments, and sets the respective class variables to these values.
Write a second constructor for the class Movie, which takes the title of the movie and studio as its arguments, and sets the respective class variables to these values, while the class variable rating is set to "PG".
Write a method GetPG, which takes an array of base type Movie as its argument, and returns a new array of only those movies in the input array with a rating of "PG". You may assume the input array is full of Movie instances. The returned array may be empty.
Write a piece of code that creates an instance of the class Movie:
with the title “Casino Royale”, the studio “Eon Productions” and the rating “PG13”;
with the title “Glass”, the studio “Buena Vista International” and the rating “PG13”;
with the title “Spider-Man: Into the Spider-Verse”, the studio “Columbia Pictures” and the rating “PG”. ... Please solve it with C# code | 2b0270fe38c40289d0063969649f5643 | {
"intermediate": 0.29120007157325745,
"beginner": 0.506940484046936,
"expert": 0.2018594592809677
} |
17,422 | mysql.connector.errors.IntegrityError: 1062 (23000): Duplicate entry 'v1.1.1' for key 'aimodels.Version' | 85db2e5350e6ca6adc6aaa25628b1cc0 | {
"intermediate": 0.374076247215271,
"beginner": 0.3298400342464447,
"expert": 0.2960837185382843
} |
17,423 | How to rename column in pandas data frame? | c4d42e1dd0374e276e244d4a6ef3da2f | {
"intermediate": 0.5489340424537659,
"beginner": 0.12098546326160431,
"expert": 0.33008044958114624
} |
17,424 | x=2323
print(f"my name is {format(x, ", .3f")}") | e1314d3cf7d4b5bb681ef51dfbf459bf | {
"intermediate": 0.24481789767742157,
"beginner": 0.5568853616714478,
"expert": 0.19829672574996948
} |
17,425 | RuntimeError: Error in void faiss::Clustering::train_encoded(faiss::Clustering::idx_t, const uint8_t*, const faiss::Index*, faiss::Index&, const float*) at /project/faiss/faiss/Clustering.cpp:283: Error: 'nx >= k' failed: Number of training points (2) should be at least as large as number of clusters (5) | 234a4ab75ddab3b7dad960d684e71981 | {
"intermediate": 0.42414405941963196,
"beginner": 0.23969219624996185,
"expert": 0.3361637592315674
} |
17,426 | Can you please write a vba code to do the following:
For each cell in column M3:M200 where the value is a three letter word,
if the Offset(0, -11) cell backgound is not RED
Then clear contents in range G:L of the same row | 2f3353cd660a7aac568be9c1bf0322b2 | {
"intermediate": 0.4535137414932251,
"beginner": 0.22163210809230804,
"expert": 0.3248541057109833
} |
17,427 | write a c program where user inputs only one letter and if he enters more than 1 letter an error message appears | 0ddebfa92e9f5da68a2a28318f91084c | {
"intermediate": 0.3166023790836334,
"beginner": 0.11231786757707596,
"expert": 0.5710797309875488
} |
17,428 | How to make slider in python | 9765651f6aacb478a90ec1c86f92330d | {
"intermediate": 0.3006437122821808,
"beginner": 0.15650080144405365,
"expert": 0.5428555011749268
} |
17,429 | hi | b5842570f613c52db20777619e6e1ca9 | {
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
} |
17,430 | write a matlab code for adding two numbers and show the result | 9f6aa06a41d823d61c2af01bb58490af | {
"intermediate": 0.33364349603652954,
"beginner": 0.2119864672422409,
"expert": 0.4543701112270355
} |
17,431 | how to “Navigate to the directory where your Python script (e.g., script.py) is saved using the cd command” | e4e138aa478532fb680bbfedbfac4ca8 | {
"intermediate": 0.31091082096099854,
"beginner": 0.29452618956565857,
"expert": 0.3945629596710205
} |
17,432 | write a matlab code for adding two numbers and show the result | 1416f662d01baed2400fe17f29a7c128 | {
"intermediate": 0.33364349603652954,
"beginner": 0.2119864672422409,
"expert": 0.4543701112270355
} |
17,433 | whats wrong with this code ">>> from openpyxl import Workbook
>>> import requests
>>>
>>> # URL of the web page
>>> url = "http://www.tsetmc.com/InstInfo/65883838195688438"
>>>
>>> # Send a GET request to the URL
>>> response = requests.get(url)
>>>
>>> # Check if the request was successful
>>> if response.status_code == 200:
... # Create a new Excel workbook
... workbook = Workbook()
... sheet = workbook.active
...
>>> # Write the content of the web page into the Excel sheet
>>> sheet['A1'] = response.text
File "<stdin>", line 1
sheet['A1'] = response.text
IndentationError: unexpected indent
>>>
>>> # Save the workbook
>>> workbook.save("web_content.xlsx")
File "<stdin>", line 1
workbook.save("web_content.xlsx")
IndentationError: unexpected indent
>>> print("Web content copied to Excel successfully.")
File "<stdin>", line 1
print("Web content copied to Excel successfully.")
IndentationError: unexpected indent
>>> else:
File "<stdin>", line 1
else:
^
SyntaxError: invalid syntax
>>> print("Failed to retrieve the web page.")
File "<stdin>", line 1
print("Failed to retrieve the web page.")
IndentationError: unexpected indent" | c2d4936c60424edaaa2a6c96507541a4 | {
"intermediate": 0.5370497107505798,
"beginner": 0.33520013093948364,
"expert": 0.12775014340877533
} |
17,434 | in c# i am making a table in a string, I have this code but it doesn't give me all the values in the same column, since the first column has a variable size:
for (int i = 1; i <= rooms; i++)
{
print += $@"
{i}{GetRooms(paidRooms, i),8}{GetRooms2(paidRooms2, i),8}";
}
the result is a string like this:
"
9 1 0
10 0 0
" | 0974ee50538696f2d750229e0682cccf | {
"intermediate": 0.2779485881328583,
"beginner": 0.5590065717697144,
"expert": 0.16304486989974976
} |
17,436 | How can I highlight a cell in range M3:M25 if the same value exists in range G3:G25 | c468a8471de1aef6dda22e2968673666 | {
"intermediate": 0.40655213594436646,
"beginner": 0.1963154375553131,
"expert": 0.3971324861049652
} |
17,437 | hi | 1f4fdd0eb8115a04c58eb95a14e8e582 | {
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
} |
17,438 | The following code is writing the date as mm/dd and not as dd/mm specified: If Not emptyCell Is Nothing Then
emptyCell.Value = Application.WorksheetFunction.text(Date, "dd/mm")
End If | 4e6b9a85733199f3c8b9bcb05858b06f | {
"intermediate": 0.3624289631843567,
"beginner": 0.404194712638855,
"expert": 0.23337633907794952
} |
17,439 | Let's consider the following manipulation. We start at n = 12. The square of 12 is 144. Reversing the digits of 144 we obtain 441. The square root of 441, m = 21, is, in this example, also an integer.
If the previous manipulation starting at an integer n yields another integer m, then we say that (n, m) is a perfect pair. For example, (12, 21) is a perfect pair.
You will be given a number n and you have to assess if it is the left element of a perfect pair. If it is, give the right member of the pair. If it isn't, output None.
Input
Line 1: A positive integer n.
Output
An integer m such that (n, m) is a perfect pair. If there isn't such m, the string None.
Constraints
1 <= n <= 10 ** 6
Example
Input
12
Output
21 ... Please solve with C# code. | a0783d2472eea09d35bb2bbca27ba8ac | {
"intermediate": 0.3974267840385437,
"beginner": 0.30579033493995667,
"expert": 0.29678288102149963
} |
17,440 | You are given two integers, N and X. N represents the price of an investment, and X represents the profit made from that investment. You can invest as many times as you want, and everytime the profit increment from its base value. Each round count as an investment.
Your task is to calculate the number of investments needed to reach full rentability, which means that the price of all previous investments have been exceeded. If it is impossible to reach rentability, you should return Bad Investment.
Example:
Input: N = 100, X = 60
Output: 3
Explanation:
At t1, total investment is 100 and total profit is 60.
At t2, total investment is 200 and total profit is 180 (60 + 120).
At t3, total investment is 300 and total profit is 360, so the total profit is higher than the total invested, thus the answer is 3.
Input
Line 1 : Integer N for the base investment
Line 2 : Integer X for the base of the incremental profit
Output
Line 1 : The number of investments needed to reach rentability, or Bad Investment
Constraints
N > 0
Example
Input
100
60
Output
3 ...Please solve it with C# Code. | c9315d22a108c55f496c64a1cef628aa | {
"intermediate": 0.4007737338542938,
"beginner": 0.3632766008377075,
"expert": 0.23594968020915985
} |
17,441 | char decryptedChar = Convert.ToChar(Convert.ToInt32(binaryChar, 2));
эта строка выдает ошибку "Could not find any recognizable digits"
как это исправить | 6f63d7c6e225aed519dbf49dcd66d34e | {
"intermediate": 0.3425159156322479,
"beginner": 0.359940767288208,
"expert": 0.29754334688186646
} |
17,442 | что такое StringBuilder в c# | eb09f8489075d3b5a84f54b827f28ebf | {
"intermediate": 0.5601842403411865,
"beginner": 0.2790585458278656,
"expert": 0.16075725853443146
} |
17,443 | Hi,i have a compile problem! | 75f5bc2b998d4a63eb5baa04c01041b6 | {
"intermediate": 0.18343769013881683,
"beginner": 0.43799519538879395,
"expert": 0.3785671591758728
} |
17,444 | Traceback (most recent call last):
File "C:\Users\Eisim\GeronologyBots\tests\tests_main.py", line 20, in <module>
find_tests(test_suite)
File "C:\Users\Eisim\GeronologyBots\tests\tests_main.py", line 10, in find_tests
add_test(test_suite, dir_path)
File "C:\Users\Eisim\GeronologyBots\tests\tests_main.py", line 14, in add_test
test_suite.addTest(test_loader.discover(start_dir=os.path.join(path_to_test_class, "")))
File "C:\python\lib\unittest\loader.py", line 346, in discover
raise ImportError('Start directory is not importable: %r' % start_dir)
ImportError: Start directory is not importable: 'C:\\Users\\Eisim\\GeronologyBots\\tests\\test_parser — копия'
Process finished with exit code 1 | df15dd5541dd435b4952519bf98d2911 | {
"intermediate": 0.3803460896015167,
"beginner": 0.2672511041164398,
"expert": 0.35240286588668823
} |
17,445 | Jane is stuck in a tree! Can you help Tarzan figure out how far he will fall before he reaches Jane?
Tarzan swings across n vines which he knows are xᵢ meters away from Jane.
If Tarzan swings from vines:
1 meter apart: He falls 1 meter
2 meters apart: He falls 4 meters
3 meters apart: He falls 9 meters
And so on...
Example:
n = 3
xᵢ = 5,3,0
1. Tarzan swings from x = 5 to x = 3 --> He will fall 4 (meters)
2. Tarzan swings from x = 3 to x = 0 --> He will fall another 9 (meters)
3. Tarzan will fall y = 13 (meters) before he reaches Jane
Input
Line 1: n, the number of vines Tarzan swings on
Line 2: n space separated xᵢ, the distances of the vines from Jane in descending order
Output
Line 1: y, the height Tarzan will fall before he reaches Jane.
Constraints
n < 15
0 ≤ x < 100
The last x will always be x = 0
Example
Input
3
5 3 0
Output
13 ...Please solve it with C# Code. | afff06de07d9457e083e6aaa9c13c5ca | {
"intermediate": 0.4184136390686035,
"beginner": 0.2612815499305725,
"expert": 0.32030484080314636
} |
17,446 | Is this line of code written correctly; If IsDate(Target.Offset(0, -7).Value) > TODAY()+14 Then | b871138522e9be84115e50abf58e716f | {
"intermediate": 0.39319032430648804,
"beginner": 0.3561156094074249,
"expert": 0.25069403648376465
} |
17,447 | Why is Bayesian statistics important? | 13cd1786c0802358463261803f6eb0cb | {
"intermediate": 0.4054624140262604,
"beginner": 0.33849918842315674,
"expert": 0.2560383975505829
} |
17,448 | I wanna make ny golang cli app run and display every time someone connects via ssh, how can I do that? | 93e00c11032602d0fd5b10f88504d50d | {
"intermediate": 0.6608537435531616,
"beginner": 0.08976785838603973,
"expert": 0.24937842786312103
} |
17,449 | hi | fee7fc0f64f2622dae39cd872cda632c | {
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
} |
17,450 | What is mean by dual link failover in networking? | dd37b1b608044df403bd55680f5c6b4e | {
"intermediate": 0.2183098942041397,
"beginner": 0.2398659884929657,
"expert": 0.5418241024017334
} |
17,451 | async function createWorkout(req, res) {
const { title, load, reps } = req.body
try {
//create a new document to db
const workout = await Workout.create({
title,
load,
reps
})
res.status(200).json(workout)
} catch (error) {
res.status(400).json({ error: error.message })
}
} | f67893e4e6ffe38ee89defe918b30580 | {
"intermediate": 0.41022807359695435,
"beginner": 0.37728071212768555,
"expert": 0.21249127388000488
} |
17,452 | --payerid, payment_date, payment_status
--1 2023-01-14 Open
--1 2023-02-07 InProgress
--1 2023-05-11 Closed
--2 2023-02-19 Open
--2 2023-04-17 InProgress
--2 2023-06-28 Closed
--3 2023-01-01 Open
--3 2023-02-22 InProgress
--3 2023-07-10 Closed
get me these results on mysql using case statement | c56f2fe3a6fe611fc0963e84894546bf | {
"intermediate": 0.38864707946777344,
"beginner": 0.43809136748313904,
"expert": 0.17326149344444275
} |
17,453 | asp.net web api authentication and authorization in jwt token | dc56a8534947fc7c9fc4efd530cdb236 | {
"intermediate": 0.4881567656993866,
"beginner": 0.2193419486284256,
"expert": 0.2925013303756714
} |
17,454 | I want to learn the DOM in JS give me steps to do it | 52dae4c06c1cdf95034e3125c932d964 | {
"intermediate": 0.4546132981777191,
"beginner": 0.2888915240764618,
"expert": 0.2564952075481415
} |
17,455 | (a) How much time does such a circuit take to compute its primary outputs? For the example above, the maximum delay for E is 2+4+3=9, and for F is 2+4+2=8. Write a program to compute the delay of every primary output in a combinational circuit (defined as the earliest time when the output is ready), given the circuit representation and delay information of the individual gates.
Input: Circuit file (see example file circuit.txt for diagram above)
Input: Gate Delay file (see example file gate_delays.txt for diagram above)
Output: Output Delay file (see example file output_delays.txt for diagram above) | d2285d8dec01dfb7a0ec1be6e7676147 | {
"intermediate": 0.370808869600296,
"beginner": 0.2833028733730316,
"expert": 0.34588828682899475
} |
17,456 | You are requested to write a python program that does the following:
Create Computation class with a default constructor (without parameters) allowing
to perform various calculations on integers numbers.
Create a method called Factorial() in the above class which allows to calculate the
factorial of an integer, n.
Create a method called Sum() in the above class allowing to calculate the sum of the
first n integers 1 + 2 + 3 + .. + n.
Instantiate the class, prompt the user to enter an integer, and write the necessary
statements to test the above methods. | fc46c9042070e33b2555681c355aca51 | {
"intermediate": 0.4431720972061157,
"beginner": 0.2499661147594452,
"expert": 0.3068618178367615
} |
17,457 | Please interpret the script below
bprice = 0.0
bprice := longCondition ? close + syminfo.mintick : nz(bprice[1])
if longCondition
strategy.order("long1", strategy.long, stop=bprice) | 919ac26e913c30ba0996c3a8b4478cfb | {
"intermediate": 0.27757149934768677,
"beginner": 0.4287799894809723,
"expert": 0.2936484217643738
} |
17,458 | create a c++ program that moves the cursor towards certain coloured pixels | 9de52002bbcf4b3da2504c4c0e08054a | {
"intermediate": 0.32685524225234985,
"beginner": 0.17377735674381256,
"expert": 0.49936744570732117
} |
17,459 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signals = []
# Retrieve depth data
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
# Check for bullish or bearish signals
if buy_qty > sell_qty:
signals.append('bullish')
elif sell_qty > buy_qty:
signals.append('bearish')
# Check for buy or sell based on signals and mark price
if 'bullish' in signals and float(mark_price) > buy_price:
return 'buy'
elif 'bearish' in signals and float(mark_price) < sell_price:
return 'sell'
else:
return ''
But it too giving me only signal to buy | 4eb786e2ab55d46897779bcdd2f19e52 | {
"intermediate": 0.400966078042984,
"beginner": 0.29136162996292114,
"expert": 0.30767229199409485
} |
17,460 | deleteOne vs findAndDelteOne in mongodb | d647f4c3ee9e57513e510ca8d49cc97c | {
"intermediate": 0.3928917944431305,
"beginner": 0.2570841908454895,
"expert": 0.3500240445137024
} |
17,461 | Как используя get_app_list разделить модели одного приложения в админке django на группы | 078acb5a6ad3af2c9444b5612c75fb4c | {
"intermediate": 0.5304890871047974,
"beginner": 0.19632823765277863,
"expert": 0.2731826901435852
} |
17,462 | export interface Tag {
id: string;
name: string;
}
export interface TagWithColor extends Tag {
color: string;
}
const colors = ["#FFE9B8", "#D0E9D7", "#D6E6FF"];
interface TagsSlice {
tags: TagWithColor[];
}
const initialState: TagsSlice = {
tags: [],
};
export const tagsSlice = createSlice({
name: "tagsSlice",
initialState,
reducers: {
setTags(state, action: PayloadAction<TagWithColor[]>) {
state.tags = action.payload;
},
в setTags сразу сделаем определение цвета и будем в редаксе хранить теги уже с их цветами. И для определения цвета использовать id % 3, 3 здесь это кол-во доступных цветов | 246921e6a9927c234727e0eff03d57a1 | {
"intermediate": 0.35789936780929565,
"beginner": 0.4314420223236084,
"expert": 0.21065863966941833
} |
17,463 | give me 10 easy problems in DOM in js | fbf052dcdcfd07d93006bd2bd117bc82 | {
"intermediate": 0.293139785528183,
"beginner": 0.4351799190044403,
"expert": 0.2716803252696991
} |
17,464 | from dotenv import load_dotenv
load_dotenv(verbose=True)
import os
import time
import signal
import shutil
import json
import traceback
from datetime import datetime, timedelta
print('✨ 正在初始化 INF-SJTU ...')
from modules.core.spider_manage import spmanage
from modules.core.news_manage import newsmanage
# CONFIG
from config import *
# MAIN
class Worker:
def __init__(self) -> None:
# 根目录
self.root_path = os.path.dirname(os.path.abspath(__file__))
# 检测缓存目录
os.makedirs(f'{self.root_path}/cache', exist_ok=True)
self.get_cache_path = lambda path: os.path.join(self.root_path, f'cache/{path}')
self.get_data_path = lambda path: os.path.join(self.root_path, f'data/{path}')
# # 当前日期
# self.current_date = datetime.now().strftime("%Y-%m-%d")
# 当前日期
current_date = datetime.now()
# 循环打印五年内的所有日期
for i in range(5*365):
new_date = current_date - timedelta(days=i)
self.current_date =new_date.strftime("%Y-%m-%d")
print(self.current_date)
# # 当前日期
# current_date = datetime.now()
# # 一年前的日期
# one_year_ago = current_date - timedelta(days=365)
# # 生成一年以内的所有日期
# dates_within_one_year = []
# while current_date >= one_year_ago:
# dates_within_one_year.append(current_date.strftime("%Y-%m-%d"))
# current_date -= timedelta(days=1)
# # 打印所有日期
# for date in dates_within_one_year:
# self.current_date =date.strftime("%Y-%m-%d")
# print(date)
# Modules
# 爬虫
self.spmanage = spmanage(self.root_path, self.get_cache_path, self.current_date)
# 新闻管理
self.newsmanage = newsmanage(self.root_path, self.get_cache_path, self.current_date)
pass
def run(self):
print('🧐 等待执行中！')
count = 0
while True:
try:
count += 1
print(f'✨ 第 {count} 次任务执行中...\n')
self.check_news_updated()
self.clear_old_news()
print('\n🧐 任务执行完毕！正在等待下一轮任务...')
time.sleep(EXECUTION_CYCLE) # 等待 1 小时
# 截获异常
except:
print('\n🚨 INF-SJTU 运行异常！\n')
traceback.print_exc()
time.sleep(1)
continue
""" MAIN | 定时触发 """
def check_news_updated(self):
""" 检测是否有新的新闻 """
print('🔖 正在检测是否有新的新闻...')
# 检测缓存目录中是否有对应日期的新闻
# 解放日报
jfdaily_news_path = self.get_cache_path(f'news/{self.current_date}/jfdaily/news.json')
if not os.path.exists(jfdaily_news_path):
# 触发爬取
raw_news = self.spmanage.jfdaily()
if raw_news:
# 预处理新闻
self.newsmanage.jfdaily_news_convert(raw_news)
# 新闻分类
self.newsmanage.news_classify('jfdaily')
# 索引新闻
# self.newsmanage.news_index('jfdaily')
# 生成问题
self.newsmanage.generate_qa('jfdaily')
# 阅读分级
self.newsmanage.generate_reading_level('jfdaily')
else:
# 检测补全缺少的信息
print('🔖 检测补全缺少的信息...')
with open(jfdaily_news_path, 'r', encoding='utf-8') as f:
raw_news = json.loads(f.read())
for news in raw_news:
# 检查 questions 是否存在
if 'questions' not in raw_news[news]:
print(f'🚧 侦测到「解放日报」的 {news} 新闻缺少问题！正在补全...')
# 生成问题
self.newsmanage.generate_qa('jfdaily', news)
# 检查 reading_level 是否存在
if 'reading_level' not in raw_news[news]:
print(f'🚧 侦测到「解放日报」的 {news} 新闻缺少阅读分级！正在补全...')
# 阅读分级
self.newsmanage.generate_reading_level('jfdaily')
# Nature Brief
naturebrief_news_path = self.get_cache_path(f'news/{self.current_date}/naturebrief/news.json')
if not os.path.exists(naturebrief_news_path):
# 触发爬取
raw_news = self.spmanage.naturebrief()
if raw_news:
# 预处理新闻
self.newsmanage.naturebrief_news_convert(raw_news)
# 新闻分类
self.newsmanage.news_classify('naturebrief')
# 索引新闻
# self.newsmanage.news_index('naturebrief')
# 生成问题
self.newsmanage.generate_qa('naturebrief')
# 阅读分级
self.newsmanage.generate_reading_level('naturebrief')
else:
# 检测补全缺少的信息
with open(naturebrief_news_path, 'r', encoding='utf-8') as f:
raw_news = json.loads(f.read())
for news in raw_news:
# 检查 questions 是否存在
if 'questions' not in raw_news[news]:
print(f'🚧 侦测到「Nature Brief」的 {news} 新闻缺少问题！正在补全...')
# 生成问题
self.newsmanage.generate_qa('naturebrief')
# 检查 reading_level 是否存在
if 'reading_level' not in raw_news[news]:
print(f'🚧 侦测到「Nature Brief」的 {news} 新闻缺少阅读分级！正在补全...')
# 阅读分级
self.newsmanage.generate_reading_level('naturebrief')
print('🔖 检测完毕！')
def clear_old_news(self):
""" 清理缓存目录下旧的新闻 | 三天前 """
print('🔖 正在清理缓存目录下旧的新闻...')
# 校验新闻目录
if not os.path.exists(self.get_cache_path('news')):
os.makedirs(self.get_cache_path(f'news/{self.current_date}'))
return
# 获取缓存目录下的第一级目录名列表
cache_dir_list = os.listdir(self.get_cache_path('news'))
# 获取近三天的日期列表
date_list = []
for i in range(3):
date_list.append((datetime.now() - timedelta(days=i)).strftime("%Y-%m-%d"))
# 清理
count = 0
for cache_dir in cache_dir_list:
if cache_dir not in date_list:
del_dir_path = self.get_cache_path(f'news/{cache_dir}')
# 解放日报向量数据库映射
jfdaily_db_mapping_path = self.get_cache_path(f'news/{cache_dir}/jfdaily/db_mapping.json')
# 删除对应的向量数据库
# 解析映射 json
with open(jfdaily_db_mapping_path, 'r', encoding='utf-8') as f:
db_mappings = json.loads(f.read())
# 删除对应的向量数据库
for db_mapping in db_mappings:
shutil.rmtree(self.get_data_path(f'vectordb/{db_mapping["dbName"]}'))
# # 递归删除缓存
shutil.rmtree(del_dir_path)
count += 1
if count == 0:
print('🔖 无旧新闻可清理！')
else:
print(f'🔖 清理完毕！共清理 {count} 天的旧新闻')
# 调试模式 | 直接执行
if __name__ == "__main__":
# 终止信号处理
def sigterm_handler(_signo, _stack_frame):
print('\n🚨 INF-SJTU 退出运行！')
os._exit(0)
signal.signal(signal.SIGTERM, sigterm_handler)
signal.signal(signal.SIGINT, sigterm_handler)
# 清空终端日志
os.system('cls||clear')
print('🐵 INF-SJTU RUNNING...\n')
# 启动 Worker
worker = Worker()
worker.run()
循环输出中self.current_date所有日期 | dcc45d031ed2c6dfce5777890ba279d4 | {
"intermediate": 0.34988486766815186,
"beginner": 0.4869290888309479,
"expert": 0.16318608820438385
} |
17,465 | import requests
import json
import re
from bs4 import BeautifulSoup
from datetime import datetime, timedelta
from typing import Union
class Main():
def __init__(self) -> None:
pass
def fetch(self, url) -> str:
headers = {
'accept':
'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',
'user-agent': 'application/json, text/javascript, */*; q=0.01',
}
r = requests.get(url, headers=headers)
r.raise_for_status()
r.encoding = r.apparent_encoding
return r.text
def getDailyNews(self) -> list[dict[str, Union[str, list[dict[str, str]]]]]:
# yyyy-mm-dd 格式化当天日期
# formatted_date = datetime.now().strftime("%Y-%m-%d")
current_date = datetime.now()
for i in range(365):
new_date = current_date - timedelta(days=i)
formatted_date =new_date.strftime("%Y-%m-%d")
print(formatted_date )
naviUrl = f'https://www.shobserver.com/staticsg/data/journal/{formatted_date}/navi.json'
try:
naviData = json.loads(self.fetch(naviUrl))
newsPages = naviData["pages"]
print(f'「解放日报」正在处理 {formatted_date} 的 {len(newsPages)} 版新闻...')
news = []
for newsPage in newsPages:
pageName = newsPage["pname"]
pageNo = newsPage["pnumber"]
articleList = newsPage["articleList"]
print(
f'「解放日报」{pageNo} 版 - {pageName} 共有 {len(articleList)} 条新闻')
for article in articleList:
title = article["title"]
subtitle = article["subtitle"]
aid = article["id"]
# 使用正则丢弃 title 含有广告的文章
if re.search(r'广告', title):
continue
articleContent, articlePictures = self.getArticle(
formatted_date, pageNo, aid)
news.append({
"id": f'{formatted_date}_{pageNo}-{aid}',
"title": title,
"subtitle": subtitle,
"content": articleContent,
"pictures": articlePictures
})
return news
except Exception as e:
print(f'「解放日报」新闻列表获取失败！\n{e}')
return []
def getArticle(self, date, pageNo, aid) -> tuple[str, list[object]]:
articleUrl = f'https://www.shobserver.com/staticsg/data/journal/{date}/{pageNo}/article/{aid}.json'
articleData = json.loads(self.fetch(articleUrl))["article"]
articleContent = BeautifulSoup(articleData["content"], 'html.parser')
# 转换 <br> 为 \n
for br in articleContent.find_all("br"):
br.replace_with("\n")
articlePictures = []
articlePictureJson = json.loads(articleData["pincurls"])
for articlePicture in articlePictureJson:
url = articlePicture["url"]
name = articlePicture["name"]
author = articlePicture["author"]
ttile = articlePicture["ttile"]
articlePictures.append({
"url": url,
"alt": ttile,
"title": ttile,
"source": name,
"author": author
})
print(
f'「解放日报」已解析 {pageNo} 版 - {articleData["title"]} | 字数 {len(articleContent)} | 图片 {len(articlePictures)} 张'
)
return articleContent.get_text(), articlePictures
jfdaily_spider = Main()
if __name__ == '__main__':
spider = Main()
news = spider.getDailyNews()
print(news)
如何获取每一天的数据并转换为json | e7b12c5113a771ab9b7c458f49da1dcb | {
"intermediate": 0.4021185338497162,
"beginner": 0.43804019689559937,
"expert": 0.15984134376049042
} |
17,466 | Traceback (most recent call last):
File "e:\桌面\spider\jfdaily.py", line 100, in <module>
artical = spider. getArticle()
TypeError: getArticle() missing 3 required positional arguments: 'date', 'pageNo', and 'aid' | a4f8bb9068f410f9fd36e3a8bf163dd1 | {
"intermediate": 0.45113739371299744,
"beginner": 0.35467997193336487,
"expert": 0.19418266415596008
} |
17,467 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signals = []
# Retrieve depth data
try:
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
except:
print("data ERROR")
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
try:
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
except:
print("data ERROR")
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if buy_qty > sell_qty:
signals.append('bullish')
elif sell_qty > buy_qty:
signals.append('bearish')
if 'bullish' in signals and mark_price > buy_price:
return 'buy'
elif 'bearish' in signals and mark_price < sell_price:
return 'sell'
else:
return ''
But it giving me only signal to buy I need code whic h will give me signal to buy or sell | b5914dafaea4c3a20fa470f53bbad064 | {
"intermediate": 0.3925378620624542,
"beginner": 0.3200069069862366,
"expert": 0.2874552309513092
} |
17,468 | Please can you help me correct this formula: =IF(A61<>"",(OR(C61="", C61<TODAY())),"REM","")) | 59e4fa29c51c32efa26b423480da8de2 | {
"intermediate": 0.23745715618133545,
"beginner": 0.35045990347862244,
"expert": 0.4120829999446869
} |
17,469 | Consider the following diagram representing a combinational digital circuit – each gate implements a Boolean function of its inputs and there are no cycles in the circuit. The numbers annotated on the gates represent the delay d through the gate, in nanoseconds. If the inputs of a gate are ready at time t, then the output is ready at time t+d. (a) How much time does such a circuit take to compute its primary outputs? For the example above, the maximum delay for E is 2+4+3=9, and for F is 2+4+2=8. Write a program to compute the delay of every primary output in a combinational circuit (defined as the earliest time when the output is ready), given the circuit representation and delay information of the individual gates.
Input: Circuit file (see example file circuit.txt for diagram above)
Input: Gate Delay file (see example file gate_delays.txt for diagram above)
Output: Output Delay file (see example file output_delays.txt for diagram above) in c++ | 9d1fe42555f1a02728b64cd6cc782e75 | {
"intermediate": 0.45533308386802673,
"beginner": 0.2719908356666565,
"expert": 0.27267611026763916
} |
17,470 | import pandas as pd
import matplotlib.pyplot as plt
import pyodbc
def brinson(portfolioID, benchID, beginDate, endDate):
# Setup connection to the database
conn_str = r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=C:\Users\LIQI\Documents\portfolio.accdb"
conn = pyodbc.connect(conn_str)
# Query the portfolio table
portfolio_query = f"SELECT * FROM Mockportfolio WHERE ID = {portfolioID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
portfolio_data = pd.read_sql(portfolio_query, conn)
portfolio_data['CONTRIBUTION'] = portfolio_data['WEIGHT']*portfolio_data['RETURN']
# Query the benchmark table
benchmark_query = f"SELECT * FROM benchmark WHERE BenchID = {benchID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
benchmark_data = pd.read_sql(benchmark_query, conn)
benchmark_data['CONTRIBUTION'] = benchmark_data['WEIGHT']*benchmark_data['RETURN']
conn.close()
# Group by industry and calculate the necessary values
benchmark_data_grouped = benchmark_data.groupby(['TRADEDATE', 'INDUSTRY']).sum()
portfolio_data_grouped = portfolio_data.groupby(['TRADEDATE', 'INDUSTRY']).sum()
benchmark_data_grouped['RETURN'] =benchmark_data_grouped['CONTRIBUTION'] / benchmark_data_grouped['WEIGHT']
portfolio_data_grouped['RETURN'] =portfolio_data_grouped['CONTRIBUTION'] / portfolio_data_grouped['WEIGHT']
portfolio_df = pd.merge(portfolio_data_grouped, benchmark_data_grouped, on=['TRADEDATE', 'INDUSTRY'], suffixes=('_P', '_B'))
portfolio_df['Q1_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_B']
portfolio_df['Q2_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_B']
portfolio_df['Q3_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_P']
portfolio_df['Q4_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_P']
portfolio_df['excess_return_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q1_daily']
portfolio_df['allocation_effect_daily'] = portfolio_df['Q2_daily'] - portfolio_df['Q1_daily']
portfolio_df['selection_effect_daily'] = portfolio_df['Q3_daily'] - portfolio_df['Q1_daily']
portfolio_df['interaction_effect_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q3_daily'] - portfolio_df['Q2_daily'] + portfolio_df['Q1_daily']
# Calculate the compounded excess return
portfolio_df['Q1_compound']=(1 + portfolio_df['Q1_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q2_compound']=(1 + portfolio_df['Q2_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q3_compound']=(1 + portfolio_df['Q3_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q4_compound']=(1 + portfolio_df['Q4_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['excess_return_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q1_compound']
portfolio_df['allocation_effect_compound'] = portfolio_df['Q2_compound'] - portfolio_df['Q1_compound']
portfolio_df['selection_effect_compound'] = portfolio_df['Q3_compound'] - portfolio_df['Q1_compound']
portfolio_df['interaction_effect_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q3_compound'] - portfolio_df['Q2_compound'] + portfolio_df['Q1_compound']
portfolio_df=portfolio_df.reset_index()
# Plot the chart
I finished the brinson attribution function for a portfolio. I want to further improve this. First, I try to imbed a dictionanry, in which each portfolioID links with an identical benchID, so that I can decrease the number of function above. Would you please help. thanks. | 091efc589668b8f3f98dd2a1c3cd1774 | {
"intermediate": 0.3560865521430969,
"beginner": 0.3737063705921173,
"expert": 0.27020707726478577
} |
17,471 | import pandas as pd
import matplotlib.pyplot as plt
import pyodbc
def brinson(portfolioID, beginDate, endDate, analyst=None, industry=None):
# Setup connection to the database
conn_str = r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=C:\Users\LIQI\Documents\portfolio.accdb"
conn = pyodbc.connect(conn_str)
# Define the portfolio-benchmark mapping dictionary
portfolio_benchmark_mapping = {
'300Active': 'CSI300',
'300Standard': 'CSI300',
'500Active': 'CSI500',
'500Standard':'CSI500'
}
# Get the corresponding benchID for the given portfolioID
if portfolioID not in portfolio_benchmark_mapping:
print(f"No benchmark found for portfolioID: {portfolioID}")
conn.close()
return
benchID = portfolio_benchmark_mapping[portfolioID]
# Query the portfolio table
portfolio_query = f"SELECT * FROM Mockportfolio WHERE ID = {portfolioID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
portfolio_data = pd.read_sql(portfolio_query, conn)
portfolio_data['CONTRIBUTION'] = portfolio_data['WEIGHT']*portfolio_data['RETURN']
# Query the benchmark table
benchmark_query = f"SELECT * FROM benchmark WHERE BenchID = {benchID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
benchmark_data = pd.read_sql(benchmark_query, conn)
benchmark_data['CONTRIBUTION'] = benchmark_data['WEIGHT']*benchmark_data['RETURN']
conn.close()
if analyst is not None:
# Filter portfolio data for the specified analyst
portfolio_data = portfolio_data[portfolio_data['Analyst'] == analyst]
benchmark_data = benchmark_data[benchmark_data['Analyst'] == analyst]
# Group by industry and calculate the necessary values
benchmark_data_grouped = benchmark_data.groupby(['TRADEDATE', 'INDUSTRY']).sum()
portfolio_data_grouped = portfolio_data.groupby(['TRADEDATE', 'INDUSTRY']).sum()
benchmark_data_grouped['RETURN'] =benchmark_data_grouped['CONTRIBUTION'] / benchmark_data_grouped['WEIGHT']
portfolio_data_grouped['RETURN'] =portfolio_data_grouped['CONTRIBUTION'] / portfolio_data_grouped['WEIGHT']
portfolio_df = pd.merge(portfolio_data_grouped, benchmark_data_grouped, on=['TRADEDATE', 'INDUSTRY'], suffixes=('_P', '_B'))
portfolio_df['Q1_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_B']
portfolio_df['Q2_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_B']
portfolio_df['Q3_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_P']
portfolio_df['Q4_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_P']
portfolio_df['excess_return_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q1_daily']
portfolio_df['allocation_effect_daily'] = portfolio_df['Q2_daily'] - portfolio_df['Q1_daily']
portfolio_df['selection_effect_daily'] = portfolio_df['Q3_daily'] - portfolio_df['Q1_daily']
portfolio_df['interaction_effect_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q3_daily'] - portfolio_df['Q2_daily'] + portfolio_df['Q1_daily']
# Calculate the compounded excess return of each industry
portfolio_df['Q1_compound']=(1 + portfolio_df['Q1_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q2_compound']=(1 + portfolio_df['Q2_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q3_compound']=(1 + portfolio_df['Q3_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q4_compound']=(1 + portfolio_df['Q4_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['excess_return_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q1_compound']
portfolio_df['allocation_effect_compound'] = portfolio_df['Q2_compound'] - portfolio_df['Q1_compound']
portfolio_df['selection_effect_compound'] = portfolio_df['Q3_compound'] - portfolio_df['Q1_compound']
portfolio_df['interaction_effect_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q3_compound'] - portfolio_df['Q2_compound'] + portfolio_df['Q1_compound']
portfolio_df=portfolio_df.reset_index()
# Calculate the compounded excess return of the entire portfolio
excess_return_compound = portfolio_df.groupby('TRADEDATE')['excess_return_compound'].sum().reset_index()
allocation_effect_compound = portfolio_df.groupby('TRADEDATE')['allocation_effect_compound'].sum().reset_index()
selection_effect_compound = portfolio_df.groupby('TRADEDATE')['selection_effect_compound'].sum().reset_index()
interaction_effect_compound = portfolio_df.groupby('TRADEDATE')['interaction_effect_compound'].sum().reset_index()
# Plot the chart for certain industry
if industry is not None:
# Filter portfolio data for the specified analyst
industry_data = portfolio_df[portfolio_df['INDUSTRY'] == industry]
plt.plot(industry_data['TRADEDATE'], industry_data['excess_return_compound'])
plt.plot(industry_data['TRADEDATE'], industry_data['allocation_effect_compound'])
plt.plot(industry_data['TRADEDATE'], industry_data['selection_effect_compound'])
plt.plot(industry_data['TRADEDATE'], industry_data['interaction_effect_compound'])
plt.xticks(rotation=45)
plt.title(f"Excess Return in {industry}")
plt.xlabel('TRADEDATE')
plt.ylabel('excess_return_compound')
plt.legend()
# Show the plot
plt.show()
# Plot the chart for portoflio
excess_return_compound = portfolio_df.groupby('TRADEDATE')['excess_return_compound'].sum().reset_index()
allocation_effect_compound = portfolio_df.groupby('TRADEDATE')['allocation_effect_compound'].sum().reset_index()
selection_effect_compound = portfolio_df.groupby('TRADEDATE')['selection_effect_compound'].sum().reset_index()
interaction_effect_compound = portfolio_df.groupby('TRADEDATE')['interaction_effect_compound'].sum().reset_index()
plt.plot(excess_return_compound['TRADEDATE'], excess_return_compound['excess_return_compound'],label='Excess Return')
plt.plot(allocation_effect_compound['TRADEDATE'], allocation_effect_compound['allocation_effect_compound'],label='Allocation Effect')
plt.plot(selection_effect_compound['TRADEDATE'], selection_effect_compound['selection_effect_compound'],label='Selection Effect')
plt.plot(interaction_effect_compound['TRADEDATE'], interaction_effect_compound['interaction_effect_compound'],label='Interaction Effect')
plt.xticks(rotation=45)
plt.title("Entire Portfolio Compound Excess Return")
plt.xlabel("Trade Date")
plt.ylabel("Excess Return")
plt.legend()
plt.show()
Please go throught this code and see if there exist any bug? thanks. | fba045a7d8fd7a0bca1abe89c38496b0 | {
"intermediate": 0.3138998746871948,
"beginner": 0.5111082792282104,
"expert": 0.17499186098575592
} |
17,472 | import pandas as pd
import matplotlib.pyplot as plt
import pyodbc
def brinson(portfolioID, beginDate, endDate, analyst=None, industry=None):
# Setup connection to the database
conn_str = r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=C:\Users\LIQI\Documents\portfolio.accdb"
try:
conn = pyodbc.connect(conn_str)
# Define the portfolio-benchmark mapping dictionary
portfolio_benchmark_mapping = {
'300Active': 'CSI300',
'300Standard': 'CSI300',
'500Active': 'CSI500',
'500Standard':'CSI500'
}
# Get the corresponding benchID for the given portfolioID
if portfolioID not in portfolio_benchmark_mapping:
print(f"No benchmark found for portfolioID: {portfolioID}")
conn.close()
return
benchID = portfolio_benchmark_mapping[portfolioID]
# Query the portfolio table
portfolio_query = f"SELECT * FROM Mockportfolio WHERE ID = {portfolioID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
portfolio_data = pd.read_sql(portfolio_query, conn)
portfolio_data['CONTRIBUTION'] = portfolio_data['WEIGHT']*portfolio_data['RETURN']
# Query the benchmark table
benchmark_query = f"SELECT * FROM benchmark WHERE BenchID = {benchID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
benchmark_data = pd.read_sql(benchmark_query, conn)
benchmark_data['CONTRIBUTION'] = benchmark_data['WEIGHT']*benchmark_data['RETURN']
if analyst is not None:
# Filter portfolio data for the specified analyst
portfolio_data = portfolio_data[portfolio_data['Analyst'] == analyst]
benchmark_data = benchmark_data[benchmark_data['Analyst'] == analyst]
# Group by industry and calculate the necessary values
benchmark_data_grouped = benchmark_data.groupby(['TRADEDATE', 'INDUSTRY']).sum()
portfolio_data_grouped = portfolio_data.groupby(['TRADEDATE', 'INDUSTRY']).sum()
benchmark_data_grouped['RETURN'] =benchmark_data_grouped['CONTRIBUTION'] / benchmark_data_grouped['WEIGHT']
portfolio_data_grouped['RETURN'] =portfolio_data_grouped['CONTRIBUTION'] / portfolio_data_grouped['WEIGHT']
portfolio_df = pd.merge(portfolio_data_grouped, benchmark_data_grouped, on=['TRADEDATE', 'INDUSTRY'], suffixes=('_P', '_B'))
portfolio_df['Q1_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_B']
portfolio_df['Q2_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_B']
portfolio_df['Q3_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_P']
portfolio_df['Q4_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_P']
portfolio_df['excess_return_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q1_daily']
portfolio_df['allocation_effect_daily'] = portfolio_df['Q2_daily'] - portfolio_df['Q1_daily']
portfolio_df['selection_effect_daily'] = portfolio_df['Q3_daily'] - portfolio_df['Q1_daily']
portfolio_df['interaction_effect_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q3_daily'] - portfolio_df['Q2_daily'] + portfolio_df['Q1_daily']
# Calculate the compounded excess return of each industry
portfolio_df['Q1_compound']=(1 + portfolio_df['Q1_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q2_compound']=(1 + portfolio_df['Q2_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q3_compound']=(1 + portfolio_df['Q3_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['Q4_compound']=(1 + portfolio_df['Q4_daily']).groupby(['INDUSTRY']).cumprod()-1
portfolio_df['excess_return_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q1_compound']
portfolio_df['allocation_effect_compound'] = portfolio_df['Q2_compound'] - portfolio_df['Q1_compound']
portfolio_df['selection_effect_compound'] = portfolio_df['Q3_compound'] - portfolio_df['Q1_compound']
portfolio_df['interaction_effect_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q3_compound'] - portfolio_df['Q2_compound'] + portfolio_df['Q1_compound']
portfolio_df=portfolio_df.reset_index()
# Calculate the compounded excess return of the entire portfolio
excess_return_compound = portfolio_df.groupby('TRADEDATE')['excess_return_compound'].sum().reset_index()
allocation_effect_compound = portfolio_df.groupby('TRADEDATE')['allocation_effect_compound'].sum().reset_index()
selection_effect_compound = portfolio_df.groupby('TRADEDATE')['selection_effect_compound'].sum().reset_index()
interaction_effect_compound = portfolio_df.groupby('TRADEDATE')['interaction_effect_compound'].sum().reset_index()
# Plot the chart for certain industry
if industry is not None:
# Filter portfolio data for the specified analyst
industry_data = portfolio_df[portfolio_df['INDUSTRY'] == industry]
plt.plot(industry_data['TRADEDATE'], industry_data['excess_return_compound'])
plt.plot(industry_data['TRADEDATE'], industry_data['allocation_effect_compound'])
plt.plot(industry_data['TRADEDATE'], industry_data['selection_effect_compound'])
plt.plot(industry_data['TRADEDATE'], industry_data['interaction_effect_compound'])
plt.xticks(rotation=45)
plt.title(f"Excess Return in {industry}")
plt.xlabel('TRADEDATE')
plt.ylabel('excess_return_compound')
plt.legend()
# Show the plot
plt.show()
# Plot the chart for portoflio
excess_return_compound = portfolio_df.groupby('TRADEDATE')['excess_return_compound'].sum().reset_index()
allocation_effect_compound = portfolio_df.groupby('TRADEDATE')['allocation_effect_compound'].sum().reset_index()
selection_effect_compound = portfolio_df.groupby('TRADEDATE')['selection_effect_compound'].sum().reset_index()
interaction_effect_compound = portfolio_df.groupby('TRADEDATE')['interaction_effect_compound'].sum().reset_index()
plt.plot(excess_return_compound['TRADEDATE'], excess_return_compound['excess_return_compound'],label='Excess Return')
plt.plot(allocation_effect_compound['TRADEDATE'], allocation_effect_compound['allocation_effect_compound'],label='Allocation Effect')
plt.plot(selection_effect_compound['TRADEDATE'], selection_effect_compound['selection_effect_compound'],label='Selection Effect')
plt.plot(interaction_effect_compound['TRADEDATE'], interaction_effect_compound['interaction_effect_compound'],label='Interaction Effect')
plt.xticks(rotation=45)
plt.title("Entire Portfolio Compound Excess Return")
plt.xlabel("Trade Date")
plt.ylabel("Excess Return")
plt.legend()
plt.show()
except Exception as e:
print(f"Error: {e}")
finally:
conn.close()
First please go through the code and help me degub. Then please improve the code based on my new requirements. I hope when industry=None , the function returns portfolio_df with these columns: “industry”, “excess_return_compound”, “allocation_effect_compound”, “selection_effect_compound”, “interaction_effect_compound” when the “TRDAEDATE”== enddate, and ranked by “selection_effect_compound”. | 0a29ce1cc74914efacc1424b1e363e06 | {
"intermediate": 0.35722479224205017,
"beginner": 0.4718254804611206,
"expert": 0.1709497570991516
} |
17,473 | from django.contrib import admin
from .models import Gkno
from .models import NumberCardForVote
from .models import TuFar
from .models import Zayavka
from .models import VidVbr
from .models import Vbr
from .models import VidRibol
from .models import Vodoem
from .models import TipVodoem
from .models import Okopof
from .models import Zagot
from .models import UchAuction
from .models import UchItogAuction
from .models import KvotaBassein
from .models import KvotaOsvoenie
from .models import SravnenieProshlProcent
from .models import SravnenieProshlTonn
from .models import AuctionLot
from .models import ItogAuctionLot
from .models import NumberAuctionLot
from import_export.admin import ImportExportModelAdmin
from .resources import GknoResource
from .resources import NumberCardForVoteResource
from .resources import TuFarResource
from .resources import ZayavkaResource
from .resources import VidVbrResource
from .resources import VbrResource
from .resources import VidRibolResource
from .resources import VodoemResource
from .resources import TipVodoemResource
from .resources import OkopofResource
from .resources import ZagotResource
from .resources import UchAuctionResource
from .resources import UchItogAuctionResource
from .resources import KvotaBasseinResource
from .resources import KvotaOsvoenieResource
from .resources import SravnenieProshlProcentResource
from .resources import SravnenieProshlTonnResource
from .resources import AuctionLotResource
from .resources import ItogAuctionLotResource
from .resources import NumberAuctionLotResource
# Следующий импорт для сортировки в админке
from django.contrib.admin import AdminSite
from django.contrib.auth.models import Group, User
from django.contrib.auth.admin import GroupAdmin, UserAdmin
# НАЧАЛО Данный блок для сортировки пунктов в админке в рамках одного приложения
class MyAdminSite(AdminSite):
def get_app_list(self, request):
"""
Return a sorted list of all the installed apps that have been
registered in this site.
"""
app_dict = self._build_app_dict(request)
# Sort the apps alphabetically.
##############################app_list = sorted(app_dict.values(), key=lambda x: x['name'].lower())
# Sort the models alphabetically within each app.
#for app in app_list:
# app['models'].sort(key=lambda x: x['name'])
###############################return app_list
# Разделение моделей на группы
grouped_app_dict = {'Group 1': {}, 'Group 2': {}}
for app_label, app_data in app_dict.items():
# Разделение моделей по вашему критерию
if app_label == 'far':
for model, model_dict in app_data['models'].items():
if model.startswith('Gk'):
grouped_app_dict['Group 1'][model] = model_dict
elif model.startswith('Num'):
grouped_app_dict['Group 2'][model] = model_dict
else:
# Добавление остальных моделей в группу "Other"
grouped_app_dict.setdefault('Other', {})['models'].update(app_data['models'])
return grouped_app_dict
# КОНЕЦ сртировки в админке
admin.site = MyAdminSite()
admin.site.register(Group, GroupAdmin)
admin.site.register(User, UserAdmin) почемувозникает ошибка Internal Server Error: /admin/
Traceback (most recent call last):
File "C:\WEB\far_django_on_blog\venv\Lib\site-packages\django\core\handlers\exception.py", line 55, in inner
response = get_response(request)
^^^^^^^^^^^^^^^^^^^^^
File "C:\WEB\far_django_on_blog\venv\Lib\site-packages\django\core\handlers\base.py", line 197, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\WEB\far_django_on_blog\venv\Lib\site-packages\django\contrib\admin\sites.py", line 261, in wrapper
return self.admin_view(view, cacheable)(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\WEB\far_django_on_blog\venv\Lib\site-packages\django\utils\decorators.py", line 134, in _wrapper_view
response = view_func(request, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\WEB\far_django_on_blog\venv\Lib\site-packages\django\views\decorators\cache.py", line 62, in _wrapper_view_func
response = view_func(request, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\WEB\far_django_on_blog\venv\Lib\site-packages\django\contrib\admin\sites.py", line 242, in inner
return view(request, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\WEB\far_django_on_blog\venv\Lib\site-packages\django\contrib\admin\sites.py", line 552, in index
app_list = self.get_app_list(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\WEB\far_django_on_blog\far\admin.py", line 83, in get_app_list
grouped_app_dict.setdefault('Other', {})['models'].update(app_data['models'])
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^
KeyError: 'models'
[12/Aug/2023 14:37:18] "GET /admin/ HTTP/1.1" 500 101325
как исправить | 12af0e4f0f138db7b0d25635a2745d42 | {
"intermediate": 0.3524172008037567,
"beginner": 0.4602501690387726,
"expert": 0.1873326450586319
} |
17,474 | //GET all workouts
router.get('/', getAllWorkouts) | caa43235aa2edf0bcc8f4f4d2a36f956 | {
"intermediate": 0.40162524580955505,
"beginner": 0.25898313522338867,
"expert": 0.3393915593624115
} |
17,475 | I have created a python file named brinson.ipynb, in which I defined a function with several paremeters. Now I want to create a xlsm file, using vba to arise the python file and run the specific function. The function would use the appointed excel range as values of paremeters (For example range(A1: E1)). then, the return dataframe and charts should be displayed on the sheet in a good format. Could you help me realize that? thanks. | acb92f8a484f448b2d5289d7d80dc468 | {
"intermediate": 0.46823039650917053,
"beginner": 0.23399081826210022,
"expert": 0.29777881503105164
} |
17,476 | I am a weather forecaster and I want to verification aerodrome warning , is therer a library in python for this? | f440df3f4523abb6d94767107d5062b7 | {
"intermediate": 0.8772415518760681,
"beginner": 0.05268532410264015,
"expert": 0.07007313519716263
} |
17,477 | Please Write a C# method named SwapPoints that accepts two Points as parameters and swaps their x/y values.
Consider the following example code that calls swapPoints:
Point p1 = new Point(5, 2);
Point p2 = new Point(-3, 6);
swapPoints(p1, p2);
Console.WriteLine("(" + p1.x + ", " + p1.y + ")");
Console.WriteLine("(" + p2.x + ", " + p2.y + ")");
The output produced from the above code should be:
(-3, 6)
(5, 2) | 01e5430ec7114510d74e1864e861d445 | {
"intermediate": 0.5492284297943115,
"beginner": 0.2538539469242096,
"expert": 0.19691766798496246
} |
17,478 | Write main class and Add the following method:
public string ShowUserNameAndBalance()
Your method should return a string that contains the account's name and balance separated by a comma and space. For example, if an account object named benben has the name "Benson" and a balance of 17.25, the call of benben.ShowUserNameAndBalance() should return:
Benson, $17.25
There are some special cases you should handle. If the balance is negative, put the - sign before the dollar sign. Also, always display the cents as a two-digit number. For example, if the same object had a balance of -17.5, your method should return:
Benson, -$17.50 Please solve with C# code. | 2f1a6de7bd9dc4c740689fa071932fd1 | {
"intermediate": 0.3019956350326538,
"beginner": 0.4305330812931061,
"expert": 0.26747122406959534
} |
17,479 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signals = []
# Retrieve depth data
try:
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
except:
print("data ERROR")
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
try:
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
except:
print("data ERROR")
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if buy_qty > sell_qty:
signals.append('bullish')
elif sell_qty > buy_qty:
signals.append('bearish')
if 'bearish' in signals and mark_price < sell_price:
return 'sell'
elif 'bullish' in signals and mark_price > buy_price:
return 'buy'
else:
return ''
Can you remove my trading strategy and set here new strategy based on this algorithm: If buy_qty > sell_qty signals = 'bullish' elif sell_qty > buy_qty signals = 'bearish'
If bullish in signals and buy_price < mark_price return buy
elif bearish in signals and sell_price > mark_price return sell else return '' | f04d9abd0ae6327c20e41b1fb1b6f334 | {
"intermediate": 0.36143815517425537,
"beginner": 0.24589107930660248,
"expert": 0.39267078042030334
} |
17,480 | string binary = Convert.ToString(r, 2);
binary[6] = "1";
в чем ошибка | 4b7283c3092ef158a31a1981c975f013 | {
"intermediate": 0.3958675265312195,
"beginner": 0.30087944865226746,
"expert": 0.30325305461883545
} |
17,481 | I would like a VBA code that can do the following;
In my active workbook 'Service Providers'
when I run module22, part of the code (shown below) checks if the sheet from where I have run the module, exists in an opened workbook 'Service Providers History'.
If the sheet does not exist in workbook 'Service Providers History' , a new sheet is created with the same name from where module22 was run in workbook 'Service Providers' .
Here is part of the code in Module22 that executes the above description:
Dim sourceWB As Workbook
Dim sourceWS As Worksheet
Dim historyWB As Workbook
Dim historyWS As Worksheet
Dim lastRow As Long
Dim pasteRow As Long
' Set source workbook and worksheet
Set sourceWB = ActiveWorkbook
Set sourceWS = sourceWB.ActiveSheet
' If the sheet doesn't exist, create a new sheet with the same name
If historyWS Is Nothing Then
sourceWS.Copy After:=historyWB.Sheets(historyWB.Sheets.Count)
Set historyWS = historyWB.ActiveSheet
historyWS.Name = sourceWS.Name
End If
What I would like to add to the code after the line 'historyWS.Name = sourceWS.Name' is described below.
Once the new sheet is created ( historyWS.Name = sourceWS.Name ), I would like to copy ONLY the values, formatting and column widths of the range B1:L5 from the sheet in workbook 'Service Providers' to B1:L5 in the new sheet in workbook 'Service Providers History'
THEN
Open a MsgBox "A new History Sheet was created. Check the sheet, and then close the History Workbook and run the History again".
Then EXIT THE SUB | b28a2b0deecb4343527277b211c1ad88 | {
"intermediate": 0.5051931142807007,
"beginner": 0.19639769196510315,
"expert": 0.29840919375419617
} |
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