romanbsd

def cache_disk(seconds = 900, cache_folder="/tmp"):
    def doCache(f):
        def inner_function(*args, **kwargs):

            # calculate a cache key based on the decorated method signature
            key = sha1(str(f.__module__) + str(f.__name__) + str(args) + str(kwargs)).hexdigest()
            filepath = os.path.join(cache_folder, key)

            # verify that the cached object exists and is less than $seconds old
            if os.path.exists(filepath):

romanbsd

import com.fasterxml.jackson.core.JsonParser;
import com.fasterxml.jackson.databind.DeserializationContext;
import com.fasterxml.jackson.databind.JsonDeserializer;
import com.fasterxml.jackson.databind.module.SimpleModule;
import java.io.IOException;

public class EmptyStringToNullDeserializer extends JsonDeserializer<String> {
    @Override
    public String deserialize(JsonParser jsonParser, DeserializationContext context) throws IOException {
        String value = jsonParser.getValueAsString();

romanbsd

import org.apache.commons.math3.stat.regression.SimpleRegression;
import java.util.List;

public class LinearRegressionCalculator {
    /**
     * Calculates the slope and intercept of the line of best fit using simple linear regression.
     *
     * @param data A List<Double> object that represents the 'y' values of the data points.
     * @return A double array of size 2 where the first element is the slope and the second element is the intercept.
     */

romanbsd

import java.util.AbstractList;
import java.util.Deque;
import java.util.LinkedList;

public class DequeList<T> extends AbstractList<T> {
    private final Deque<T> deque;

    public DequeList(Deque<T> deque) {
        this.deque = deque;
    }

romanbsd

import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;

public class PolynomialRegressionExample {
    public static void main(String[] args) {
        // Define your input data (x and y values)
        double[][] data = {
            {1.0, 2.3},
            {2.0, 4.5},
            {3.0, 6.7},
            // Add more data points as needed

romanbsd

export default function flattenArray(arr) {
  return arr.reduce(function (flatArray, element) {
    if (Array.isArray(element)) {
      return flatArray.concat(flattenArray(element));
    } else {
      return flatArray.concat(element);
    }
  }, []);
}

romanbsd

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
  <dict>
    <key>Label</key>
    <string>org.custom.keyboard-remap</string>
    <key>ProgramArguments</key>
    <array>
      <string>/usr/bin/hidutil</string>
      <string>property</string>

romanbsd

from typing import List, Union

from bs4 import BeautifulSoup
from bs4.element import Tag


class TableExtract:
    def __init__(self, headers: List[str], keep_html=False) -> None:
        self._headers = headers
        self._keep_html = keep_html

romanbsd

{
  "env": {
    "es6": true,
    "node": true,
    "browser": true
  },
  "parser": "@babel/eslint-parser",
  "extends": "eslint:recommended",
  "parserOptions": {
    "requireConfigFile": false,

romanbsd

from scipy.stats import norm
import numpy as np


N = norm.cdf


def d1_d2(S: float, K: float, T: float, r: float, sigma: float):
    d1 = (np.log(S / K) + (r + sigma ** 2 / 2) * T) / (sigma * np.sqrt(T))
    d2 = d1 - sigma * np.sqrt(T)