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class Solution:
    def merge(self, intervals: List[List[int]]) -> List[List[int]]:
      intervals.sort(key=lambda x: x[0])
      result = []
      for curr in intervals:
        if result and result[-1][1] >= curr[0]:
          result[-1][1] = max(result[-1][1], curr[1])
        else:
          result.append(curr)
      return result

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{
    "fields": {
       "project":
       {
          "key": "DT"
       },
       "summary": "JIRA REST via postman",
       "description": {
           "type": "doc",
           "version": 1,

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{
    "extends": [
        "prettier",
        "prettier/standard",
        "prettier/react",
        "plugin:react/recommended"
    ],
    "plugins": [
        "prettier"
    ],

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{
    "editor.codeActionsOnSave": {
        "source.fixAll.eslint": true
    },
    "editor.formatOnSave": true,
    "[javascript]": {
        "editor.formatOnSave": false
    },
    "[javascriptreact]": {
        "editor.formatOnSave": false

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// SPDX-License-Identifier: MIT
// ^ recommended, included machine readable in bytecode metadata
// Software Package Data Exchange is an open standard

pragma solidity ^0.8.7;
// ^ floating pragma, min 0.8.7 max excluding 0.9.0
// same as complex pragma: pragma solidity >=0.8.7 <0.9.0;
// major.breakingchanges.bugfixes
// only makes the compiler check for compatibility and throws error if not matching!
// should only be floating during development, fixed everywhere during testing & deployment

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Introduction

This gist started with a collection of resources I was maintaining on stream data processing — also known as distributed logs, data pipelines, event sourcing, CQRS, and other names.

Over time the set of resources grew quite large and I received some interest in a more guided, opinionated path for learning about stream data processing. So I added the reading list.

Please send me feedback!

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Array

• Two Sum
• Best Time to Buy and Sell Stock
• Contains Duplicate
• Product of Array Except Self
• Maximum Subarray
• Maximum Product Subarray
• Find Minimum in Rotated Sorted Array

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#=======================================================================#
# extract_data.py                                                       #
#=======================================================================#
# usage: extract_data.py [-h] [-i INPUT_DIR] [-o OUTPUT_DIR]
#
# This program extracts provision numbers from a set of documents.
#
# optional arguments:
#  -h, --help            show this help message and exit
#  -i INPUT_DIR, --input_dir INPUT_DIR

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#!/usr/bin/env python

import sys
import csv

def MeanAveragePrecision(valid_filename, attempt_filename, at=10):
    at = int(at)
    valid = dict()
    for line in csv.DictReader(open(valid_filename,'r')):
        valid.setdefault(line['source_node'],set()).update(line['destination_nodes'].split(" "))