│ main.dart
│ theme.dart
│ util.dart
│
├───bloc
│ │ add_child_bloc.dart
│ │ bottom_nav_bloc.dart
│ │ theme_bloc.dart
│ │
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| # https://playwright.dev/ llms-full.txt | |
| ## Playwright Testing Framework | |
| [Skip to main content](https://playwright.dev/#__docusaurus_skipToContent_fallback) | |
|  | |
| ### Any browser • Any platform • One API | |
| **Cross-browser.** Playwright supports all modern rendering engines including Chromium, WebKit, and Firefox. |
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| # how to use | |
| # python analyze_tokens.py --dir /path/to/your/project --encoding cl100k_base --sort-by tokens --output tree | |
| import os | |
| import tiktoken | |
| from typing import List, Dict, Tuple | |
| import argparse | |
| import pathspec |
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| import requests | |
| import xml.etree.ElementTree as ET | |
| def get_bucket_contents(url, marker=None): | |
| """Make a request to the S3 bucket and return the response.""" | |
| params = {"marker": marker} if marker else {} | |
| response = requests.get(url, params=params) | |
| return response.text |
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| import 'dart:math'; | |
| List<double> normalize(List<int> array, int minRange, int maxRange) { | |
| int minValue = array.reduce(min); | |
| int maxValue = array.reduce(max); | |
| int valueRange = maxValue - minValue; |
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| import 'package:flutter/material.dart'; | |
| import 'dart:math' as math; | |
| void main() { | |
| runApp(MyApp()); | |
| } | |
| class MyApp extends StatelessWidget { | |
| @override |
| main.dart
|
+---models
| reports_detail.dart
| reports_list.dart
|
+---resources
| | api_provider.dart
| | graphql_service.dart
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| import 'package:flutter/material.dart'; | |
| final Color darkBlue = Color.fromARGB(255, 18, 32, 47); | |
| void main() { | |
| runApp(MyApp()); | |
| } | |
| class MyApp extends StatelessWidget { | |
| @override |
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| import requests | |
| from bs4 import BeautifulSoup | |
| base_url = "http://gen.lib.rus.ec" | |
| headers = {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', | |
| 'Accept-Encoding': 'gzip, deflate, sdch', | |
| 'Accept-Language': 'en-US,en;q=0.8', |
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| from collections import namedtuple | |
| import statistics as st | |
| from collections import Counter | |
| def kurtosis(a): | |
| N = len(a) | |
| mean = st.mean(a) | |
| sd = st.variance(a)**0.5 | |
| kurt = sum([(i-mean)**4 for i in a])/N/sd**4 | |
| return kurt |
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