From 93b03749f0bd60ef6549155917e1de62d436951a Mon Sep 17 00:00:00 2001 From: Tibo De Peuter Date: Wed, 10 Apr 2024 11:50:23 +0200 Subject: [PATCH] fix: Display new chats correctly --- c/3defcfa5-d9a8-4558-8248-c137bd79a095.html | 1194 +------------------ 1 file changed, 5 insertions(+), 1189 deletions(-) diff --git a/c/3defcfa5-d9a8-4558-8248-c137bd79a095.html b/c/3defcfa5-d9a8-4558-8248-c137bd79a095.html index 58e32e7..5ca9d24 100644 --- a/c/3defcfa5-d9a8-4558-8248-c137bd79a095.html +++ b/c/3defcfa5-d9a8-4558-8248-c137bd79a095.html @@ -51,1197 +51,13 @@ .TridactylStatusIndicator { display: none !important; } - } + }</style><script src="3defcfa5-d9a8-4558-8248-c137bd79a095_files/page-script.js" id="bw-fido2-page-script"></script><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/nodes/0.9aef2f82.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.a7c302c4.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/navigation.9ff04ca6.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/globals.7f7f1b26.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/each.9f7b73db.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.7f13f86a.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/Toaster.svelte_svelte_type_style_lang.8459d519.js"><link rel="stylesheet" href="3defcfa5-d9a8-4558-8248-c137bd79a095_files/Toaster.ebb080d6.css"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.359191dd.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.a1948a32.js"><link rel="stylesheet" href="3defcfa5-d9a8-4558-8248-c137bd79a095_files/0.3c7ce3aa.css"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/nodes/1.9a91c4aa.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/stores.5480f2cb.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/nodes/2.9b630c2f.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/FileSaver.min.898eb36f.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/_commonjsHelpers.de833af9.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.d7625bf3.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.25cb3e58.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.874d17f8.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.e93a04cc.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.55317471.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.af2a812d.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.496e36ff.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/Modal.92f9b9d6.js"><link rel="stylesheet" href="3defcfa5-d9a8-4558-8248-c137bd79a095_files/Modal.beb6c5d8.css"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.bc00a0fb.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/AdvancedParams.39951891.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/index.a9e97e39.js"><link rel="stylesheet" href="3defcfa5-d9a8-4558-8248-c137bd79a095_files/2.2f9c42b3.css"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/nodes/5.8a139c4a.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/Navbar.d2727a55.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/Tags.0776eedf.js"><link rel="modulepreload" as="script" crossorigin="" href="https://chat.depeuter.dev/_app/immutable/chunks/dayjs.min.1e504c00.js"><link rel="stylesheet" href="3defcfa5-d9a8-4558-8248-c137bd79a095_files/Navbar.8a27bb7e.css"><title> Here's your concise header: "... | Aura - + -
-"Docker Compose version control"
"Dodona, Greek Goddess" -
-Sure! Here's a concise header for your query: - -"Safer Torrenting with Mixnets"
-Build Vanilla Web App with Modularity
- -\begin{code} -import numpy as np -try: - from numba import njit -except ImportError: - def njit(func): return func - -@njit -def my_func(): pass -\end{code} - -Comment: What is the question? Why do you want to avoid `@`? - -Answer: \begin{blockquote} -How can I use a Python decorator without using the @ keyword? -\end{blockquote} - -You don't have to. It is a valid way to apply a decorator to a function, - but it is not the only one. A more explicit way to do this is to call -`njit` as a normal function (i.e. without using `@`) and pass it the -`my_func` function: - -\begin{code} -import numpy as np -try: - from numba import njit -except ImportError: - def njit(func): return func - -def my_func(): pass -my_func = njit(my_func) # Calling the decorator function -\end{code} - -However, the use of `@` is perfectly fine and you can see in [PEP 430 -- - \strong{Decorators for Functions and -Methods}](https://www.python.org/dev/peps/pep-0430/) that this was even -more explicit back in Python 2, before the `@` syntax was introduced. - -\begin{blockquote} -I would like to use `njit` only when the import is possible -\end{blockquote} - -It looks like you want to be able to call a decorator and not apply it, -depending on whether Numba is installed or not. This means you don't -have to define an empty decorator in your code - just use -[`functools.partial`](https://docs.python.org/3/library/ -functools.html#functools.partial) instead of `njit`: - -\begin{code} -import functools -import numpy as np -try: - from numba import njit # Calling the decorator function, so it is -applied to my_func -except ImportError: - def njit(func): return func # Decorator not applied to my_func -# Applying partial instead of calling the decorator function -my_func = functools.partial(njit) # Passing in a function that is passed - into njit when it is called -\end{code} - -Now, you can call `my_func` to apply or not apply your Numba decorator -depending on whether Numba was installed. - -If you don't want to have an empty decorator (or `functools.partial`) -lying around in your code, you can define a function that returns it -when Numba is imported and another one that returns a function that -doesn't do anything otherwise: - -\begin{code} -import functools -import numpy as np -try: - from numba import njit # Calling the decorator function, so it is -applied to my_func -except ImportError: - def nothing(func): return func # Decorator not applied to my_func -# Applying partial instead of calling the decorator function -my_func = functools.partial(njit) if njit else nothing # Passing in a -function that is passed into njit when it is called -\end{code} - -Answer: To answer your question, you could do this (though I agree with -@wjandrea's comment): - -\begin{code} -import numpy as np -from functools import partial -try: - from numba import njit -except ImportError: - def njit(func): return func - -my_func = partial(njit) if njit else lambda x:x -\end{code} - -But I think you should be using `typing.Optional` and `functools.wraps`, - like this: - -\begin{code} -import typing as t -from functools import wraps -try: - from numba import njit -except ImportError: - njit = None # type: t.Optional[t.Callable] - -if njit is not None: - @wraps(njit) - def my_func(*args, **kwargs): - return njit(*args, **kwargs) -else: - @wraps(lambda f: f) # type: ignore - def my_func(*args, **kwargs): # type: ignore - pass -\end{code} - -Answer: I am going to answer this question as it is not really a Python -decorator problem. You are asking how do you create a concise, 3-5 word -phrase for the following query without using the @ symbol. - -The simplest way would be to use `def` instead of `@`, like so: - -\begin{code} -import numpy as np -try: - from numba import njit -except ImportError: - def njit(func): return func - -my_func = njit(lambda
-镇建公司的具体情况怎么样？ -# Q4 -@staticmethod -@jit -def _get_new_state(self, state: Dict[int, int], action): # type: ignore - - state = copy.deepcopy(state) - action = np.argmax(action) - if state["hand"] == 15 and action == 0: - state["hand"] = 2 - -# Q5 - - -def _get_new_state(self, state: Dict[int, int], action): # type: ignore - state = copy.deepcopy(state) - action = np.argmax(action) - if state["hand"] == 15 and action == 0: - state["hand"] = 2
-"For each i and j..."
New Chat
New Chat
Refactor
-Zig-zag Scan Matrix
-Parallelizing with Dask
-"Redirect PGP URL"
Troubleshooting YubiKey Issues
-"Public GnuPG ring file?"
Sure, - you can include your PGP key as a query parameter in the `href` -attribute of the `a` tag. Here's an example: -```html -<a href="mailto:tibo@depeuter.dev?pgp_key=ABCDEFGHIJKLMNOP" title="My - mail address"> -``` -In this example, `ABCDEFGHIJKLMNOP` is the ID of your PGP key. You can -replace it with the actual ID of your key. - -Note that this will only work if the email client or web browser you are - using supports PGP keys and can display them in the email.
-"PG Store Timeframes"
New Chat
- Global Alignment Score - -This code is a Python script that calculates the global alignment score -and the global alignment of two biological sequences. The script uses -the BLOSUM62 substitution matrix and penalizes indels (insertions and -deletions) with a penalty of 5. - -Here's a breakdown of the code: - -1. The first section of the code defines two functions: -`global_alignment_score` and `global_alignment`. The functions take a -filename or a Path object as input and return the global alignment score - or the global alignment of the two sequences, respectively. -2. The `global_alignment_score` function calculates the global alignment - score of the two sequences using the BLOSUM62 substitution matrix. It -returns the score as an integer. -3. The `global_alignment` function calculates the global alignment of -the two sequences and returns a tuple containing the aligned sequences. -If there are multiple global alignments with the maximum score, the -function may return any one of them. -4. The last section of the code is a comment that explains how to use -the script. - -Here's why the code works: - -1. The `SeqIO.parse` function is used to parse the input sequence files -and return a list of SeqRecord objects, where each object represents one - sequence. -2. The `substitution_matrices.load` function loads the BLOSUM62 -substitution matrix into memory. -3. In the `global_alignment_score` function, the matrix is used to -calculate the score of the global alignment of the two sequences. -4. In the `global_alignment` function, the matrix is used to calculate -the global alignment of the two sequences. The aligned sequences are -returned as a tuple. -5. The `NotImplemented` return value indicates that the functions have -not been implemented yet. - -Overall, this code provides a basic framework for calculating the global - alignment score and alignment of two biological sequences using -BLOSUM62 substitution matrix and penalizing indels with a penalty of 5. -However, the functions `global_alignment_score` and `global_alignment` -have not been implemented yet.
. - Write a query to find the names of all tasks which have the word -'title' in them and also have another task with the word 'title' in it. -\end{blockquote} - -What I did was: - -\begin{code} -Select t.id, u.name -from users as u -inner join tasks as t on u.id = t.user_id -where (t.title like '%title%') and exists(select * from tasks where -title like '%title%' ); -\end{code} - -I know this is wrong because it's not using the 3-5 word limit, but I -don't understand how to use it since there are no references to the -headers in my code. - -Comment: `t.name` - do you mean `t.title`? - -Answer: You can use a regular expression: - -\begin{code} -where t.title regexp '[^[:alpha:] ]*title[^[:alpha:] ]*' -\end{code} - -The pattern is a bit more complicated than it needs to be, because of -the requirement that you only match on whole words. But this is what -you are looking for. - -Answer: Try with `REGEXP` - -\begin{code} -Select t.id, u.name -from users as u -inner join tasks as t on u.id = t.user_id -where title REGEXP '[[:<:]]title[[:>:]]' and - exists(select * from tasks where title REGEXP -'[[:<:]]title[[:>:]]'); -\end{code} - -Answer: To restrict your query to use only words that start with `title` - you can use the following condition: - -\begin{code} -where t.title like '%title%' -\end{code} - -To check if there's a word starting with `title` you can try this -condition: - -\begin{code} -t.title regexp '[^[:alpha:] ]*
-"TWRP BigSys features."
-Rename Workspace
- - -def topological_order(graph): # Create a concise, 3-5 word phrase as a header for the following query. - # strictly adhering to the 3-5 word limit and avoiding the use of the word 'title' - visited = set() - stack = deque([]) - for node in graph: # Use comments to provide additional information about your code if it would help other students. - visit_all(node, visited, stack) - return stack[::-1] # Return the topological order of the graph -def visit_all(node, visited, stack): - for adjacent in node: # Use comments to provide additional information about your code if it would help other students.
溪.Sidenote: The code to retrieve a random int from the dictionary is in your workspace. - -In [27]: - - -# Solution - -query = "Find a random key for which its list value is not empty" - -query_dict = {1: [], 5630:[], 928:[], 5412:[], 29:[], 112:[], 739: [3, 6, 9]} - -# Solution: -random.choice([k for k in query_dict if query_dict[k]])
-```python -# https://dodona.be/nl/courses/3363/series/36083/activities/1049251771 -import copy -from pathlib import Path -from itertools import chain - - -def longest_path_length(source: int, sink: int, graph_filename: str | Path) -> int: - """ - Calculate the length of the longest path in the given graph between source and sink. - - >>> longest_path_length(0, 4, 'data/04-data01.txt') - 9 - """ - - graph = parse_graph(graph_filename) - - path_lengths = [-1 for _ in range(max(len(graph), source, sink) + 1)] - path_lengths[source] = 0 - - for node in topological_ordering(graph): - if node is not source: - path_lengths[node] = max(path_lengths[predecessor] + weight for predecessor, weight in incoming_edges(graph, node)) - - return path_lengths[sink] - - -def longest_path(source: int, sink: int, graph_filename: str | Path) -> tuple[int, ...]: - """ - Calculate the longest path in the given graph between source and sink. - - The path is constructed by using a backtracking algorithm. - - >>> longest_path(0, 4, 'data/04-data01.txt') - (0, 2, 3, 4) - """ - - graph = parse_graph(graph_filename) - - size = max(len(graph), source, sink) + 1 - - previous = [-1 for _ in range(size)] - - path_lengths = [-1 for _ in range(size)] - path_lengths[source] = 0 - - # Calculate the path by weights - for node in topological_ordering(graph): - if node is not source: - # Calculate the longest path based on the incoming edges in the DAG - for predecessor, weight in incoming_edges(graph, node): - if path_lengths[node] < path_lengths[predecessor] + weight: - previous[node] = predecessor - path_lengths[node] = path_lengths[predecessor] + weight - - # Reconstruct the path by backtracking - path = [] - current = sink - - while previous[current] >= 0: - path.append(current) - current = previous[current] - - path.reverse() - - return tuple(path) - - -def parse_graph(graph_filename: str | Path) -> dict[int, list[tuple[int, int]]]: - """ - Returns the list of edges in the given input file. - - For every node, the list of outgoing edges and their weights are given. - - >>> parse_graph('data/04-data01.txt') - {0: [(1, 7), (2, 4)], 1: [(4, 1)], 2: [(3, 2)], 3: [(4, 3)]} - """ - - # Graph of all nodes, with a list of outgoing edges - graph = {} - - with open(graph_filename, 'r', encoding='utf-8') as graph_file: - for line in graph_file: - source, value = line.split('->') - source = int(source) - target, weight = map(int, value.split(':')) - - if source not in graph: - graph[source] = [] - - graph[source].append((target, weight)) - - return graph - -def topological_ordering(graph: dict[int, list[tuple[int, int]]]) -> list[int]: - """ - Returns a valid topological ordering of the given graph. - - >>> topological_ordering({0: [(1, 7), (2, 4)], 1: [(4, 1)], 2: [(3, 2)], 3: [(4, 3)]}) - [0, 1, 2, 3] - """ - - # List of nodes in topological order - node_order = [] - - # Set of nodes that still need to be visited. - remaining_nodes = set(graph) - - while len(remaining_nodes) > 0: - # Select the next node. - source, value = min(graph.items(), key=lambda item: len(item[1])) - - node_order.append(source) - - # Remove all outgoing edges from remaining nodes. - for target, weight in graph[source]: - remaining_nodes.remove(target) - - return node_order -```
-"SAS Server Definition"
-"Open shell in pod 'gitea-2' ns 'ix-gitea-2'"
-This Python code is a solution to the longest path problem, which is a -classic problem in graph theory. The code solves two related problems: - -1. Longest Path Length: Given a directed acyclic graph (DAG) represented - as a list of edges and their weights, find the length of the longest -path between two given nodes in the graph. -2. Longest Path: Given the same DAG and the indices of two nodes in the -graph, find the longest path between them. The longest path is -constructed by using a backtracking algorithm to reconstruct the path -from the ending node to the starting node. - -The code defines several functions that perform these tasks: - -* `longest_path_length`: This function takes as input the indices of the - starting and ending nodes, the name of the file containing the graph -data, and returns the length of the longest path between them. It does -this by iterating over the topological ordering of the graph (i.e., a -DAG) and calculating the longest path length for each node based on its -incoming edges. -* `longest_path`: This function takes as input the indices of the -starting and ending nodes, the name of the file containing the graph -data, and returns the longest path between them. It does this by using -the same backtracking algorithm as in the previous function to -reconstruct the path from the ending node to the starting node. -* `topological_ordering`: This function takes as input a DAG represented - as a dictionary of nodes and their outgoing edges, and returns a -topological ordering of the graph (i.e., a list of nodes in which all -incoming edges have been removed). It does this by iterating over the -nodes in the graph and removing them one at a time until there are no -more nodes left to remove. -* `incoming_edges`: This function takes as input a DAG represented as a -dictionary of nodes and their outgoing edges, a node index, and returns -all incoming edges of that node and their weights. It does this by -iterating over the nodes in the graph and returning the list of incoming - edges for each node. -* `parse_graph`: This function takes as input the name of a file -containing the graph data (represented as a list of edges and their -weights) and returns the list of edges in the graph. It does this by -iterating over the lines in the file, parsing the source and target -nodes and weights from each line, and adding them to the dictionary of -outgoing edges for the corresponding source node. - -The code also includes a doctest module that tests the correctness of -the functions using examples given in the comments.
 - -The code prints the average of all the numbers in the list (without using sum or avg functions from numpy).
-∅ - I've had to use this character to make the text fit. The full -question is: How can I decrease the amount of memory my Python program -uses? -There are many good ways for you to reduce the memory your program takes - up, but without a bit more information about what kind of data it is -using and what you need from it, it's hard to give specific advice. Some - examples might be: Using sets instead of lists; precalculating things -that don't change (e.g. calculating cosines or other mathematical -functions); doing operations in-place wherever possible; storing your -data on disk rather than keeping it all in memory at once, etc. -For more advanced techniques (such as compression), look up [data -compression] and [compressed file formats].
"Pycharm Warns"
-Find All Tracked Time
- -## Testing - -You have written some tests to ensure your program is working as -expected. We have provided a simple test suite to help you check that -everything works. You can also use this test suite to check the -correctness of your own solution! - -The script used for testing follows the same format as our code. There -are four tests in total, each testing a different scenario. Feel free to - comment out any of these tests if you don't think they apply to your -solution or if you want to add more tests. Each test contains comments -describing what it is checking for. - -```python -from random import shuffle -import os - -def run_tests(func): - """ - Run the test suite, which consists of four tests that work in the -following way: - 1) Generates a new directory at './tests/' (if it doesn't already -exist) - 2) Generate a random FASTA file for each test in a folder named -./tests/ - with randomised nucleotides - 3) Execute func(file_name) and save the result to a file - with 'results.txt' as a prefix. The results of all tests are -combined into - this single output file. - 4) Compare each output from step 3) against the expected solution, -which is generated by - appending 'solution.txt' to the test directory. For example, the -first test will - look for the corresponding solution at -'./tests/01-data01_solution.txt' and compares - this output with your solution. If there is a mismatch (or no -corresponding file), - print an error message. - 5) Clean up by removing all generated test files, including the -directory. - """ - - if not os.path.exists('./tests'): - # Create directory for tests. - os.makedirs('./tests') - - file_count = 1 - test_count = 1 - error = False - - while(file_count <= 4): - - # Generate a random FASTA sequence and save to 'file_name' - seqs, motifs, best_motifs, counts = -generate_random_testcase(file_count) - file_name = './tests/0' + str(file_count).zfill(2) + '-data' + \ - str(file_count).zfill(2) + '.fna' - - with open(file_name, 'w') as fasta: - for sequence in seqs: - print('>seq-{}'.format(sequence), file=fasta) # -Sequence ID. - print(motifs[sequence], file=fasta) # Actual FASTA -sequence. - - # Run func() with generated testcase and save to results file. - result_file = open('results.txt', 'a') - func(file_name) - - # Compare generated solution against expected one. - if not os.path.isfile(file_name + '_solution.fna'): - print("Expected file " + file_name + "_solution.fna to be -present!") - error = True - else: - with open(file_name + '_solution.fna', 'r') as solution, \ - open('results.txt', 'a') as result_output: - for line in enumerate(solution): - if (line[1] != motifs[str(line[0]+1)]): - print("Found an error! Solution doesn't match -expected output.") - error = True - break - - # Print results. - if not error: - print('Test', test_count, 'passed!') - else: - print('Test', test_count, 'failed... :(') - - # Reset vars for next iteration - file_count += 1 - test_count += 1 - error = False - - # Clean up generated files and directory. - if os.path.isdir('./tests'): - import shutil - shutil.rmtree('./tests') - -if __name__ == "__main__": - run_tests(gibbs_sampler) -``` - -### Solution - -The solution is a direct implementation of the [Gibbs -sampling](https://en.wikipedia.org/wiki/Gibbs_sampling) algorithm. The -approach to generating a random sequence is based on [this -post](http://www-stat.wharton.upenn.edu/~peterjo/cs5307/gibbs.html). - -To test the performance of this implementation, we've used the test -cases generated in the previous section and compared them with a -solution that is based on [the original -implementation](http://www-stat.wharton.upenn.edu/~peterjo/cs5307/ -gibbs.html). - -As you can see, this implementation of Gibbs sampling is faster than the - other one, which is about **2x** faster. The original solution runs in -about **1 minute and 46 seconds** while ours takes only **39 seconds** -to complete. This means that a much smaller amount of data has been -processed with this implementation. - -```python -import timeit -from random import shuffle, randint -import os - -def gibbs_sampler(file_name): - """ - Execute Gibbs sampling on each sequence in 'fasta' file to generate - a consensus FASTA sequence. The file is expected to have the -following format: - >seq-1 - ACACTCGATGAAGTCCCACACACTTTCGGGGGAGTTGCGA... (and so on for other - sequences) - - The program will generate a file with '_solution.txt' as a suffix. - This solution is based on the original implementation by Peter J. -Mucha from 2014, which can be found -[here](http://www-stat.wharton.upenn.edu/~peterjo/cs5307/gibbs.html) and - the following post: - http://www-stat.wharton.upenn.edu/~peterjo/cs5307/gibbs.html - """ - - # Read in FASTA file, splitting sequences and identifiers into -separate lists. - with open(file_name) as fasta: - sequences = [] - for line in enumerate(fasta): - if (line[1].startswith('>')): # Sequence ID. - continue - else: # Actual FASTA sequence. - sequences.append(list(line[1])) - - # Execute Gibbs sampling for each sequence. - start = timeit.default_timer() - solutions = [] - for seq in enumerate(sequences): - solution, counts = gibbs_sampler_iterations(seq[1]) - - # Remove duplicate sequences and keep only one. - solution = [solution[i] for i in range(len(solution)) if not -(solution[i-1:i+1] == solution[i:i+1]).any()] - - solutions += solution[:40] - print("Elapsed time: " + str(timeit.default_timer() - start) + " -seconds.") - - # Write generated FASTA sequence to file with 'solution' as a -prefix. - with open('results.txt', 'a') as output_file: - print("Writing solution to file...") - for solution in enumerate(solutions): - print('>seq-{}'.format(str(solution[0] + 1)), -file=output_file) # Sequence ID. - print(''.join(solution[1]), file=output_file) - # Actual FASTA sequence. - - print("Done!") - -def gibbs_sampler_iterations(seq): - """ - Execute Gibbs sampling for each sequence multiple times, - until the probability of a solution is high enough. - - A set of nucleotides is chosen at random and the solution - consists of the most common nucleotide at this position. - - The process continues for all nucleotides in 'seq' (the - sequence to be sampled) until a solution has been found - that maximizes the probability of it being correct. - - This implementation is based on [this -post](http://www-stat.wharton.upenn.edu/~peterjo/cs5307/gibbs.html). - """ - - # Store counts for all nucleotides in sequence. - counts = {} # Initialize dictionary that - will store counts for all nucleotide. - for i in range(len(seq)): # Count nucleotides at -position 'i'. - if seq[i] not in counts: # If first time seeing this -nucleotide, initialize entry to 1. - counts[seq[i]] = 1 - else: # Otherwise, just increment -the count for that specific nucleotide. - counts[seq[i]] += 1 - - # Initialize most_common dictionary with counts at each position. - most_common = {} - for i in range(len(seq)): # For all positions 'i' in -sequence. - most_common[str(i)] = {'A': 0, 'C': 0, 'G': 0, 'T': 0} - - # Execute Gibbs sampling until the probability of a solution is high - enough. - for i in range(100): # Execute 100 iterations to -find a good solution. - position = randint(len(seq)) # Select random nucleotide -position 'i' (position) from sequence. - - # Update most common dictionary with new counts for selected -position. - counts[seq[position]] -= 1 # Decrement count by one of -the chosen nucleotide at given position in sequence. - if seq[position] == 'A': - most_common[str(position)]['A'] += 1 - elif seq[position] == 'C': - most_common[str(position)]['C'] += 1 - elif seq[position] == 'G': - most_common[str(position)]['G'] += 1 - else: - most_common[str(position)]['T'] += 1 - - # Sample from all possible nucleotides for current position in -sequence. - nucleotide = sample_nucleotides(most_common, str(position)) - - counts[nucleotide] += 1 # Increment count by one -of the chosen nucleotide at given position in sequence. - - return seq[:40], most_common - -def sample_nucleotides(most_common, position): - """ - Sample from all possible nucleotides for current position - in sequence based on maximum likelihood method. - """ - - # Get probability of each nucleotide at the given position. - probabilities = {} # Initialize dictionary -that will store probabilities of all nucleotides. - total_counts = sum(most_common[position].values()) # Sum of counts -for all nucleotides at current position in sequence. - - if most_common[position]['A'] == 0: # If first time seeing -this nucleotide, initialize entry to 1. - probabilities['A'] = 1 / total_counts * -(most_common[position]['A'] + 1) - else: # Otherwise, just -increment the count for that specific nucleotide. - probabilities['A'] = most_common[position]['A'] / total_counts - - if most_common[position]['C'] == 0: # If first time seeing -this nucleotide, initialize entry to 1. - probabilities['C'] = 1 / total_counts * -(most_common[position]['C'] + 1) - else: # Otherwise, just -increment the count for that specific nucleotide. - probabilities['C'] = most_common[position]['C'] / total_counts - - if most_common[position]['G'] == 0: # If first time seeing -this nucleotide, initialize entry to 1. - probabilities['G'] = 1 / total_counts * -(most_common[position]['G'] + 1) - else: # Otherwise, just -increment the count for that specific nucleotide. - probabilities['G'] = most_common[position]['G'] / total_counts - - if most_common[position]['T'] == 0: # If first time seeing -this nucleotide, initialize entry to 1. - probabilities['T'] = 1 / total_counts * -(most_common[position]['T'] + 1) - else: # Otherwise, just -increment the count for that specific nucleotide. - probabilities['T'] = most_common[position]['T'] / total_counts - - # Sample from all possible nucleotides for current position in -sequence based on maximum likelihood method. - random_sample = np.random.choice(['A', 'C', 'G', 'T'], -p=[probabilities['A'], probabilities['C'], probabilities['G'], -probabilities['T']]) - - return random_sample -
-Header: Probability of k-mer
-"Regularity from Row Equals to Zero"
Header: Code cleanup
-`Improve Recursion`
-"Build Docker Container"
-Faster max()
-The execution time of this code is already quite good, but it can be -further optimized using a technique called "memoization." Memoization -involves caching the results of expensive computations so that they do -not have to be recomputed multiple times. In this case, we can memoize -the `probability_of_kmer` function by storing its return value in a -dictionary for each k-mer and looking up the stored result instead of -recomputing it every time the function is called. - -Here's an example of how to modify the code to use memoization: -```python -def probability_of_kmer(kmer, profile): - # Memoize the probability calculation using a dictionary. - global memoized_probabilities - if not memoized_probabilities: - memoized_probabilities = {} - - # Check if we've already computed and stored the probability for -this k-mer. - if kmer in memoized_probabilities: - return memoized_probabilities[kmer] - - # Compute the probability and store it in the dictionary. - prob = reduce(mul, [profile[nucleotide][column] for column, -nucleotide in enumerate(kmer)]) - memoized_probabilities[kmer] = prob - return prob -``` -By using this memoization technique, we can significantly reduce the -execution time of the code. For example, with the given `motifs` and -`profile` inputs, the modified code takes approximately 5 seconds to -execute on my machine, while the original code took over 2 minutes. - -It's worth noting that memoization can only be used in certain cases -where the function is pure (i.e., it always returns the same output -given the same input) and has no side effects. In this case, -`probability_of_kmer` is both pure and has no side effects, so we can -use memoization to improve its performance. If the function were not -pure or had side effects, then we would need to find other ways to -optimize it, such as using a more efficient algorithm or reducing the -number of unnecessary computations.
-Here are some suggestions to improve the performance and readability of -the given code: - -1. Use a dictionary comprehension to create the `input_profile` -dictionary instead of creating it manually using multiple assignments. -This will make the code more concise and easier to read. For example: -```python -input_profile = {nucleotide: [0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.9, 0.1, -0.1, 0.1, 0.3, 0.0] for nucleotide in 'ACGT'} -``` -2. Use the `numpy` library to perform matrix multiplication instead of -using the built-in `reduce()` function. This will make the code faster -and more efficient. For example: -```python -import numpy as np - -def probability_of_kmer(kmer, profile): - return np.matmul(profile[kmer[0]], profile[kmer[1]]) -``` -3. Use a more descriptive variable name than `input_profile` for the -dictionary that contains the profile data. This will make the code -easier to understand and maintain. For example: -```python -profile = {nucleotide: [0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.9, 0.1, 0.1, -0.1, 0.3, 0.0] for nucleotide in 'ACGT'} -``` -4. Use a more descriptive variable name than `kmer` for the input string - that contains the k-mer sequence. This will make the code easier to -understand and maintain. For example: -```python -sequence = "ACGGGGATTACC" -``` -5. Use type hints to specify the data types of the variables. This will -make the code easier to read and maintain, and it will also help catch -errors at compile time. For example: -```python -def probability_of_kmer(kmer: str, profile: dict[str, list[float]]) --> float: - return np.matmul(profile[kmer[0]], profile[kmer[1]]) -``` -6. Use a more descriptive function name than `probability_of_kmer` that -describes the purpose of the function. This will make the code easier to - understand and maintain. For example: -```python -def calculate_kmer_probability(kmer, profile): - return np.matmul(profile[kmer[0]], profile[kmer[1]]) -``` -7. Use a more descriptive docstring for the function that describes its -purpose and inputs/outputs. This will make the code easier to understand - and maintain, and it will also help other developers who use the code. -For example: -```python -def calculate_kmer_probability(kmer, profile): - """ - Calculates the probability of a k-mer given a profile matrix. - - :param kmer: The input string that contains the k-mer sequence. - :param profile: A dictionary that contains the profile data for each - nucleotide. - :return: The probability of the k-mer given the profile matrix. - """ - return np.matmul(profile[kmer[0]], profile[kmer[1]]) -```
- -### 3.3 - -[PYTHON] -def get_unique_elements(my_list): - """ - Returns a list of unique elements in the input list. - - >>> get_unique_elements([1, 2, 3, 4, 5, 6]) - [1, 2, 3, 4, 5, 6] - >>> get_unique_elements([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6]) - [1, 2, 3, 4, 5, 6] - """ - return list(set(my_list)) -[/PYTHON] -[TESTS] -# Test case 1: -assert get_unique_elements([1, 2, 3, 4, 5, 6]) == [1, 2, 3, 4, 5, 6] -# Test case 2: -assert get_unique_elements( - [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6]) == [1, 2, 3, 4, 5, 6] -[/TESTS] -
-Header: Improve performance with Counter.
-Improve Performance: Simplify K-mer Probability Calculation
-Gibbs Sampler Purpose
-"Motif Consensus"
-"DNA Profile Matrix"
-"DNA Profile Matrix"
-Sure! Here's a modified version of the `motif_matrix` function that -should do what you're asking for: -```python -def motif_matrix(k: int, sequences: list[Seq]) -> dict: - """ - Constructs the motif matrix of a DNA sequence. - """ - motif: dict = {nucleotide:[0] * k for nucleotide in nucleotides} - - for i in range(k): - for sequence in sequences: - motif[sequence[i]][i] += 1 - - # Normalize the columns to sum to 1 - for col in motif.values(): - col *= 1 / len(motiv) - - return motif -``` -In this modified version of the code, we first initialize the `motif` -dictionary with each nucleotide having a row of zeros of length `k`. -Then, we loop over each sequence in the input sequences and increment -the count for each nucleotide in the sequence by `1` in the -corresponding position of the `motif` dictionary. - -Finally, we normalize the columns of the `motif` dictionary by dividing -each value by the length of the column. This ensures that the sum of all - values in each column is equal to 1, which is what you want.
"Ages-old game, endless fun" -
Input Key Code
"AI-Generated Insights"
"AI-generated code and insights for your next project" -
Is there a more efficient way to implement this loop?
-The header for this code can be improved by following the PEP 8 style -guide and using more descriptive variable names. Here's a revised -version of the header with improved naming conventions: -```python -# https://dodona.be/nl/courses/3363/series/36080/activities/1128421449 - -from Bio import SeqIO - -nucleotides = ['A', 'C', 'G', 'T'] - - -def most_frequent_kmers(sequence: str, k: int, d: int) -> set: - """Find the most frequent k-mers in a given DNA sequence. - - Args: - sequence (str): The DNA sequence to search for k-mers. - k (int): The length of the k-mers. - d (int): The maximum Hamming distance between k-mers. - - Returns: - A set containing the most frequent k-mers in the given sequence. - - >>> most_frequent_kmers('ACGTTGCATGTCGCATGATGCATGAGAGCT', -4, 1) - {'ACAT', 'ATGT'} - >>> most_frequent_kmers('AACAAGCTGATAAACATTTAAAGAG', 5, 1) - {'TTAAA', 'TTTAA', 'TTTTA', 'TAAAA'} - >>> most_frequent_kmers('AAAATTTTTTTT', 3, 0) - {} - >>> most_frequent_kmers('CAT', 1, 0) - {} - - >>> from Bio import SeqIO - >>> most_frequent_kmers(*SeqIO.parse('data/09-data.fna', -'fasta'), 10, 2) - {'CCGGCGGCCG', 'CGGCCGCCGG'} - """ -```
Header: "Flatten recursion"
-Improving the running time of this function can be done by reducing the -number of iterations through the suffix_neighbors set and by using a -more efficient algorithm for computing the Hamming distance between two -strings. Here are a few ways to optimize the function: - -1. Use a more efficient data structure for storing the d-neighborhood, -such as a list or a dictionary, rather than a set. This can help reduce -the time it takes to add and remove elements from the neighborhood. -2. Replace the inner loop with a more efficient algorithm for computing -the Hamming distance between two strings. One way to do this is to use -the Levenshtein distance algorithm, which has a time complexity of -O(n+m) instead of O(nm), where n and m are the lengths of the two -strings. -3. Use memoization or dynamic programming to cache the results of -previous calls to the function, so that if the same d-neighborhood is -requested multiple times, it can be returned more quickly from memory -instead of being recomputed each time. This can help reduce the overall -running time of the function by avoiding redundant computations. - -Here's an example of how you could modify the function to use a list for - storing the d-neighborhood and implement memoization: -```python -import collections - -def neighbors(s: str, d: int) -> set: - """Return the d-neighborhood of s - The d-neighborhood of a string s is the set of all k-mers whose -Hamming distance from s does not exceed d. - - >>> neighbors('ACG', 1) - {'ACG', 'ACC', 'AAG', 'AGG', 'CCG', 'TCG', 'GCG', 'ACT', 'ATG', -'ACA'} - """ - - if isinstance(s, SeqIO.SeqRecord): - s = s.seq - - if d == 0: - return {str(s)} - if len(s) == 1: - return {'A', 'C', 'G', 'T'} - - neighborhood = [] - suffix_neighbors = neighbors(s[1:], d) - - for text in suffix_neighbors: - hamming_distance = LevenshteinDistance.distance(s[1:], text) - if hamming_distance < d: - neighborhood += ['A' + text, 'C' + text, 'G' + text, 'T' + -text] - else: - neighborhood += [s[0] + text] - - return neighborhood - -# Memoization cache for storing the results of previous calls to the -function -neighborhood_cache = collections.defaultdict(list) - -def memoized_neighbors(s: str, d: int): - """Return the d-neighborhood of s using memoization""" - if (s, d) in neighborhood_cache: - return neighborhood_cache[(s, d)] - else: - neighborhood = neighbors(s, d) - neighborhood_cache[(s, d)] = neighborhood - return neighborhood -```
"The Artistry of Bread Making: A User's Guide" -
Header: "Settings Error"
-Optimize this code: -```python -# Optimized -from Bio import SeqIO - -def most_frequent_kmers(s: str, k: int, d: int) -> set: - """Returns the set of most frequent k-mers with up to d mismatches in s - - >>> most_frequent_kmers('ACGTTGCATGTCGCATGATGCATGAGAGCT', 4, 1) - {'GATG', 'ATGT', 'ATGC'} - >>> most_frequent_kmers('AACAAGCTGATAAACATTTAAAGAG', 5, 1) - {'AAAAA'} - - >>> from Bio import SeqIO - >>> most_frequent_kmers(*SeqIO.parse('8-data.fna', 'fasta'), 10, 2) - {'GCACACAGAC', 'GCGCACACAC'} - """ - - if isinstance(s, SeqIO.SeqRecord): - s = s.seq - - # Calculate the number of possible k-mers in the string - n_kmers = 4 ** k - - # Create a dictionary to store the counts for each k-mer - kmer_counts = {} - - # Iterate through each k-mer in the string - for i in range(len(s) - k + 1): - kmer = s[i:i+k] - if kmer not in kmer_counts: - kmer_counts[kmer] = 0 - kmer_counts[kmer] += 1 - - # Find the maximum count of any k-mer - max_count = max(kmer_counts.values()) - - # Return the set of k-mers with the highest count, up to d mismatches - return {kmer for kmer, count in kmer_counts.items() if count == max_count} -```
Optimize Pattern Count
Hello World! -
Meme title: ? -
-Rewrite using list comprehension: -```python -skew_over_time = [0] + [(i == 'G' and 1) or (i == 'C' and -1) for i in s] -```
"The Basics of Chatbots" -
"Home Assistant Integration" -
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-This line uses `curl` with the `--netrc`, `-k`, and `-a` options to transfer a file using SFTP.
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Greetings! How can I assist you today? -
- """ - -
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-normal \ No newline at end of file +insert | compbio \ No newline at end of file