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import json
import requests
import pandas as pd

def openapi_to_excel(url, output_file):
    response = requests.get(url)
    spec = response.json()

    first_path = next(iter(spec['paths']))
    print(f"Example path structure for {first_path}:")

wlinds

import os, fnmatch, argparse

def clean_it(root_dir, file_extension):
    """
    Walks through all subdirs of the given directory and deletes files with the specified extension.
    Also removes any empty directories left after the file deletion.
    
    :param root_dir: The root directory to start the search from.
    :param file_extension: The file extension to look for and delete.
    """

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#!/bin/bash
# Silly workaround for keeping user preset folder on top in Vital library.
DEST_DIR="$HOME/Library/Mobile Documents/com~apple~CloudDocs/Audio/Vital/_User/Presets"
SOURCE_DIR="$HOME/Library/Mobile Documents/com~apple~CloudDocs/Audio/Vital/User/Presets"

fswatch -0 "$SOURCE_DIR" | while read -d "" event; do
    if [[ "$event" == *.vital ]]; then
        mv "$event" "$DEST_DIR/"
        echo "Moved $event to $DEST_DIR"

wlinds

import numpy as np

# https://www.svenskaspel.se/triss/spelguide/triss-30
prizes = np.array([
    (1, 2765000),
    (4, 300000),
    (6, 265000),
    (2, 100000),
    (3, 50000),
    (8, 20000),

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* {
    margin: 0;
    padding: 0;
    box-sizing: border-box;
}
  
body {
    font-family: 'Helvetica', sans-serif;
}
  

wlinds

import os, keyboard, time, json, threading

def format(elapsed):
    ms = int((elapsed * 1000) % 1000)
    seconds = int(elapsed % 60)
    minutes = int((elapsed // 60) % 60)
    hours = int((elapsed // 3600) % 24)
    return f"{hours:02}:{minutes:02}:{seconds:02}.{ms:03}"

def record():

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	1	2	3	4	5	6	7	8	9	10
C	16.35	32.70	65.40	130.80	261.60	523.20	1046.40	2092.80	4185.60	8371.20
C#	17.32	34.64	69.28	138.56	277.12	554.24	1108.48	2216.96	4433.92	8867.84
D	18.35	36.70	73.40	146.80	293.60	587.20	1174.40	2348.80	4697.60	9395.20
D#	19.45	38.90	77.80	155.60	311.20	622.40	1244.80	2489.60	4979.20	9958.40
E	20.60	41.20	82.40	164.80	329.60	659.20	1318.40	2636.80	5273.60	10547.20
F	21.83	43.66	87.32	174.64	349.28	698.56	1397.12	2794.24	5588.48	11176.96
F#	23.12	46.24	92.48	184.96	369.92	739.84	1479.68	2959.36	5918.72	11837.44
G	24.50	49.00	98.00	196.00	392.00	784.00	1568.00	3136.00	6272.00	12544.00

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import os
from pyftpdlib.authorizers import DummyAuthorizer
from pyftpdlib.handlers import FTPHandler
from pyftpdlib.servers import FTPServer

USERNAME = "admin"
PASSWORD = "foobar"

PERMISSION = 'elradfmwMT'

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import os
import soundfile as sf # https://github.com/bastibe/python-soundfile

def get_wav_sample_rate(file_path):
    try:
        with sf.SoundFile(file_path) as wav_file:
            return wav_file.samplerate
    except: print(f"Error processing file: {file_path}")

def count_sample_rates_in_dir(root_dir):

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import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

np.random.seed(42)
samples = 1000

X = 2 * np.random.rand(samples,1)                   # Features with uniform distribution
epsilon = np.random.normal(0,1, size=(samples,1))   # Random disturbance
y = 2 + 9 * X + epsilon                             # Simple linear regression model