flutter 기본 개념 1

 

Scaffold 

- 화면 뼈대 역할 

- 기본적으로 AppBar body floatingActionButton 같은걸 배치해줌 

return Scaffold(

      appBar: AppBar(title: const Text('일기')),

      body: Column(

        children: [

          TableCalendar(

            firstDay: DateTime(2000),

            lastDay: DateTime(2100),

            focusedDay: _focusedDay,

            selectedDayPredicate: (day) => isSameDay(day, _selectedDay),

            onDaySelected: (selectedDay, focusedDay) {

Navigator

- 페이지 전환 담당

- push 새 페이지로 이동

- pop 이전 페이지로 이동

 onDaySelected: (selectedDay, focusedDay) {

      Navigator.push(

                  context,

                  MaterialPageRoute(

                    builder: (context) => DiaryWritePage(selectedDate: selectedDay),

                  ),

                );

              },

child - 하나의 위젯만 자식으로 가짐

children - 여러개의 위젯을 자식으로 가짐

padding

- 상하좌우 x 픽셀의 여백

padding: const EdgeInsets.all(16.0),

expanded 

- 공간을 꽉 채우게할떄 사용

sizeBox

- 딱 정해진 크기의 공간 만들기

- 위젯 사이 간격 띄우기 

TextEditingController 

- Textfield에 입력된 값 가져오기

ListView

- 여러개의 위젯을 스크롤 가능하게 정렬 

ListView(

  children: [

    Text("첫 번째"),

    Text("두 번째"),

    Text("세 번째"),

  ],

)

콜백 함수가 필요한 이유

- 이벤트는 언제 일어날지 모름 -> 미리 함수를 등록만 해둠

MaterialPageRoute

Material 디자인 스타일로 페이지 전환하는 라우트 객체

7 Pivotal Web Development Trends Shaping 2023

 The landscape of web development is in constant flux, with emerging trends reshaping how we build and experience websites. In 2023, seven key trends stand out, representing the cutting edge of web development.

Mobile-First Approach: With over 59.5% of web traffic originating from mobile devices, designing for mobile-first is no longer an option but a necessity. Responsive layouts, meta viewport tags, and layout simplifiers like Flexbox are pivotal to success in web development.

Page Speed Optimization: In today's fast-paced digital world, users expect websites to load instantly. Google's ranking algorithms prioritize fast-loading websites. Techniques like image compression, code minification, and content delivery networks (CDNs) are vital for optimal page loading times.

Voice Search Integration: Voice assistants are becoming integral to accessing information. Developers are optimizing websites for voice search, ensuring accessibility and adaptability to changing user preferences.

Gradient Colors and Glassmorphism: Gradient colors and glassmorphism design elements are enhancing visual appeal. Gradient designs bring creativity and personality to web interfaces, while glassmorphism adds depth and texture.

Progressive Web Applications (PWAs): PWAs combine web and mobile app features, offering seamless offline functionality and app-like experiences. They are gaining traction as versatile web solutions.

AI Chatbots: AI chatbots are improving customer service with round-the-clock support and streamlined interactions. They are employed for user engagement, answering queries, and initiating sales conversations.

Interconnected Web Applications: API-first development is fostering interconnected web applications. This trend enables seamless data sharing between apps, creating a more cohesive web experience.

As we enter 2023, web development's evolution is marked by these seven trends. Adapting to this dynamic landscape will be pivotal for web developers and businesses aiming to succeed in the digital realm. The web's future is exciting, and those who embrace these innovations will lead the way.

Revolutionizing App Development: React Native Unveiled!

 In the realm of mobile app development, React Native emerges as a prominent open-source framework created by Facebook. This powerful tool allows developers to craft mobile applications for iOS, Android, Web, and UWP simultaneously, all while writing code in JavaScript.

Understanding Cross-Platform Development

To fully grasp React Native's significance, it's crucial to understand the concept of cross-platform development. This approach enables developers to create applications that can run on multiple platforms, saving time and resources. React Native, alongside frameworks like Flutter, serves as a prime example of a cross-platform solution.

1. React Native Development Methods

React Native development can be approached in two primary ways: Expo CLI and React Native CLI.

A. Expo CLI

Pros:

Ease of Initial Setup: Expo CLI simplifies the setup process, making it incredibly straightforward for beginners.

Abundance of Libraries and Plugins: The Expo ecosystem offers a rich selection of libraries and plugins, enhancing development efficiency.

Cons:

App Size: Apps developed with Expo tend to have larger file sizes.

Build Speed: The build process might be slower compared to React Native CLI.

Limited Native Module Support: Expo restricts the usage of native modules, limiting certain functionalities.

B. React Native CLI

Pros:

Fast Build Speed: React Native CLI offers a quicker build speed compared to Expo.

High Freedom with Native Modules: Developers have greater freedom to use native modules.

Interaction with OS Layer: It enables direct interaction with the operating system layer.

Cons:

Complex Setup: Setting up a project with React Native CLI can be complex and time-consuming.

Platform-Specific Setup: You need to install Android Studio and Xcode for building and deploying on Android and iOS.

Mac OS Dependency: Building iOS apps requires a Mac OS environment.

2. Expo CLI Quickstart

To start with Expo CLI, follow these steps:

Create a React Native project using Expo: $ expo init [project name]

Install Expo globally: $ npm install -g expo-cli or $ yarn global add expo-cli

3. React Native CLI Quickstart

For React Native CLI, follow these steps:

Create a React Native project: $ npx react-native init [project name]

Optionally specify a version: $ npx react-native init [project name] --version X.XX.X

For TypeScript-based projects: $ npx react-native init [project name] --template react-native-template-typescript

Install types for React and React Native: $ yarn add @types/react @types/react-native --dev

In Conclusion

React Native is a game-changing framework that facilitates cross-platform mobile app development. Whether you opt for Expo CLI for its simplicity or React Native CLI for more advanced, platform-specific development, React Native provides a versatile toolkit for crafting efficient, responsive, and visually appealing mobile applications. Understanding your project's requirements and your own development expertise will help you choose the right approach. Happy coding!

Dynamic Programming (DP) Algorithms with Python

Introduction

Dynamic Programming (DP) is a fundamental concept in computer science and algorithmic problem-solving. It's a technique that allows us to tackle complex problems by breaking them down into simpler subproblems and reusing solutions to those subproblems to solve the larger problem efficiently. In this comprehensive guide, we will explore the essence of DP, its principles, and its application through Python code examples.

What is Dynamic Programming (DP)?

At its core, DP is an optimization technique used for solving problems with overlapping subproblems. It relies on two key principles: Optimal Substructure: The optimal solution to a larger problem can be constructed from optimal solutions of its subproblems.

Overlapping Subproblems: The same subproblems are encountered multiple times during the computation.

The DP Approach

DP problems can be categorized into two main types: Top-Down (Memoization) and Bottom-Up (Tabulation).

Top-Down (Memoization): In this approach, we solve the problem recursively, but we store the results of each subproblem in a data structure (typically a dictionary) to avoid redundant computations. It's like breaking down the problem into smaller pieces and memoizing their solutions.

Bottom-Up (Tabulation): In this approach, we start from the smallest subproblems and build up to the larger problem. We use an array or table to store solutions to subproblems and progressively solve larger subproblems based on the results of smaller ones.

Example Problem: Computing the Fibonacci Sequence Let's take the classic example of computing the Fibonacci sequence.

The Fibonacci sequence is defined as follows: 

F(0) = 0

F(1) = 1

F(n) = F(n-1) + F(n-2), for n > 1

Here's a Python code example for calculating the nth Fibonacci number using the bottom-up (tabulation) DP approach: 

def fibonacci(n):

    fib = [0] * (n + 1)

    fib[0] = 0

    fib[1] = 1

    for i in range(2, n + 1):

        fib[i] = fib[i - 1] + fib[i - 2]

        return fib[n]

n = 10

result = fibonacci(n)

print(f"The {n}-th Fibonacci number is {result}") 

Conclusion

Dynamic Programming is a powerful technique that can be applied to a wide range of algorithmic problems, from finding optimal paths in graphs to optimizing recursive algorithms. By understanding its principles and mastering its implementation in Python, you'll be better equipped to tackle complex problems efficiently.

In future articles, we will explore more advanced DP topics, such as DP on graphs and string manipulation. Stay tuned for more insights and practical examples!

파이썬으로 다이나믹 프로그래밍(DP) 알고리즘 이해하기

다이나믹 프로그래밍(DP)은 많은 알고리즘 문제를 해결하는 데 사용되는 강력한 기술 중 하나입니다. 이번 글에서는 파이썬을 사용하여 DP 알고리즘을 어떻게 구현하고 활용하는지에 대해 알아보겠습니다.

다이나믹 프로그래밍(DP)이란?

DP는 큰 문제를 작은 하위 문제로 분할하여 풀고, 이 하위 문제들의 결과를 저장해 놓고 중복 계산을 피하는 방식으로 동작합니다. 이를 통해 실행 시간을 크게 단축할 수 있습니다.

예시 문제: 피보나치 수열 계산

피보나치 수열은 대표적인 DP 예제 중 하나입니다. 다음은 파이썬으로 피보나치 수열을 계산하는 DP 코드의 예시입니다.

def fibonacci(n):

    fib = [0] * (n + 1)

    fib[0] = 0

    fib[1] = 1

    for i in range(2, n + 1):

        fib[i] = fib[i - 1] + fib[i - 2]

    return fib[n]

n = 10

result = fibonacci(n)

print(f"The {n}-th Fibonacci number is {result}")

해설:

fib 리스트를 초기화하고 초기 값(0과 1)을 설정합니다.

반복문을 사용하여 피보나치 수열을 계산하고 fib 리스트에 저장합니다.

결과값을 반환합니다.

이 코드를 실행하면 10번째 피보나치 수를 계산하고 출력합니다.

이런 식으로 파이썬을 사용하여 DP 알고리즘을 구현하고 문제를 해결할 수 있습니다. DP는 다양한 알고리즘 문제에서 사용되므로, 이 개념을 익히고 응용하는 것은 프로그래밍 실력 향상에 도움이 될 것입니다.

다음 포스트에서는 DP 알고리즘의 고급 주제와 더 복잡한 예제를 살펴보겠습니다. 따라서 계속해서 이 블로그를 방문해주세요!

이상으로 DP 알고리즘에 대한 블로그 글 예시를 제공해 드렸습니다. 더 자세한 내용이나 다른 알고리즘에 대한 설명을 추가하고 싶은 내용이 있다면 알려주세요!

파이썬 pyinstall 안될때 (autoit)

1. pyinstall.exe -F main.py 실행해서 spec파일 생성 

2. spec 파일에 아래와 같이 추가 

datas=[

    [

    "C:\\Python311\\Lib\\site-packages\\autoit\\lib\\AutoItX3_x64.dll",

    "autoit\\lib"

    ]

    ],

    hiddenimports=["autoit.init", "autoit.autoit", "autoit.control",

"autoit.process", "autoit.win"],

3. pyinstall.exe main.spec 실행

참조 

https://stackoverflow.com/questions/59190771/pyinstaller-oserror-cannot-load-autoitx-from-path

파이썬 정리 1

 1. 클로저 

x = 10  # 전역변수

def foo():

    global x 

    x = 11

    print(x)

foo()

print(x) # 출력

파이썬은 그냥 바로 선언하면 전역변수임

그 전역변수를 함수에서 사용하려면 global 로 지정해야됨

2. 언더바 사용

값을 무시하고 싶을때

x,_,y = 1,2,3 

이렇게 선언하면 중간의 2 값은 무시하고 

x는 1, y는 3으로 선언됨

x,*_,y = 1,2,3,4,5

이렇게하면 중간 값들은 다 생략하고 1, 5 만 선언됨

for을 사용할때 index가 필요 없을떄

for _ in range(10):

-> 인덱스가 필요없고 그냥 10번 반복하고 싶을때 

메소드에 _가 붙을때

-> 외부 사용자가 사용하지 말라는 권유의 문법 / 만약 import * 를 하면 _가 앞에붙어있는 변수나 메소드는 호출이 되지 않음

-> 매직 메소드 / 클래스를 작성할때 __init__ , __str__ 등의 사용

3. dfs , bfs 파이썬으로 구현 

def bfs(v):

    q = deque()

    q.append(v)

    visit_list[v] = 1

    while q:

        v = q.popleft()

        print(v, end = " ")

        for i in range(1, n + 1):

            if visit_list[i] == 0 and graph[v][i] == 1:

                q.append(i)

                visit_list[i] = 1

#스택

def dfs(v):

    q = deque()

    q.append(v)

    while q:

        v = q.pop()

        if visit_list2[v] == 0:

            visit_list2[v] = 1

            print(v, end = " ")

            temp = deque()

            for i in range(1, n + 1):

                if visit_list2[i] == 0 and graph[v][i] == 1:

                    temp.append(i)

            temp.reverse()

            q = q + temp

# 재귀

def dfs(v):

    visit_list2[v] = 1

    print(v, end = " ")

    for i in range(1, n + 1):

        if visit_list2[i] == 0 and graph[v][i] == 1:

            dfs(i)

4. dict 

초기화

data = {}

데이터 접근

data["aa"] = "zxc"

data["bb"] = 1

print(data["aa"])

데이터 확인

data.get("aa") -->> "zxc"

data.get("cc") -->> None 반환

"aa" in data --> true

"cc" in data --> false

데이터 삭제

del data["aa"]

순회

for keys in data:

~~~

for로 돌면 key값들을 반환함 

for key, value in data.items():

~~

items를 사용하면 value도 반환

사전 병합

{ **data1, **data2 }

5. DP 구현

import sys

read = sys.stdin.readline

data = {}

def dp(x):

    if x == 1:

        return 1

    if x == 2:

        return 1

    if data.get(str(x)):

        return data[str(x)]

    data[str(x)] = dp(x-1) + dp(x-2)

    return data[str(x)]

print(str(dp(50)))