Drafts

global Field
global BaseModel
from pydantic import BaseModel
from pydantic import Field

Pydantic is a Python library for serializing data into models that can be validated with a deep set of built in valitators or your own custom validators, and deserialize back to JSON or dictionary.

Installation #

To install pydantic you will first need python and pip. Once you have pip installed you can install pydantic with pip.

pip install pydantic

Always install in a virtual environment

Creating a Pydantic model #

To get started with pydantic you will first need to create a Pydantic model. This is a python class that inherits from pydantic.BaseModel.

from pydantic import BaseModel
from pydantic import Field
from typing import Optional

class Person(BaseModel):
    name: str = Field(...)
    age: int

parsing an object #

person = Person(name="John Doe", age=30)
print(person)

name='John Doe' age=30

data serialization #

Pydantic has some very robust serialization methods that will automatically coherse your data into the type specified by the type-hint in the model if it can.

person = Person(name=12, age="30")
print(f'name: {person.name}, type: {type(person.name)}')
print(f'age: {person.age}, type: {type(person.age)}')

1 validation error for Person
name
  Input should be a valid string [type=string_type, input_value=12, input_type=int]
    For further information visit https://errors.pydantic.dev/2.3/v/string_type

person = Person(name="John Doe", age='thirty')
print(f'name: {person.name}, type: {type(person.name)}')
print(f'age: {person.age}, type: {type(person.age)}')

1 validation error for Person
age
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='thirty', input_type=str]
    For further information visit https://errors.pydantic.dev/2.3/v/int_parsing

loading from json #

serializing to json #

validation #

from kedro.pipeline import node

node(
    input="raw",
    output="int",
    func=my_func,
    tags=["one"],
)

• 11ty https://www.rockyourcode.com/how-to-deploy-eleventy-to-github-pages-with-github-actions/
• hugo puts it in the base url https://gohugo.io/getting-started/configuration/#baseurl
• mkdocs uses a special cli build command https://squidfunk.github.io/mkdocs-material/publishing-your-site/#github-pages

Upon first running an aws cli command using localstack you might end up with the following error.

Unable to locate credentials. You can configure credentials by running "aws configure".

Easy way #

The easy easiest way is to leverage a package called awscli-local.

pipx install awscli-local

Leveraging the awscli #

If you want to use the cli pro

pipx install awscli

aws config --profile localstack
# put what you want for the keys, but enter a valid region like us-east-1

alias aws='aws --endpoint-url http://localhost:4566 --profile localstack'

npx create-react-app todoreact

import React,{useState,useEffect} from 'react';
import './App.css';

function App() {
  const [data,setData]=useState([]);
  const [newName,setNewName]=useState([]);
  const getData=()=>{
    fetch('/api'
    ,{
      headers : {
        'Content-Type': 'application/json',
        'Accept': 'application/json'
       }
    }
    )
      .then(function(response){
        return response.json();
      })
      .then(function(myJson) {
        setData(myJson)
      });
  }
  useEffect(()=>{
    getData()
  },[])

  const addItem= async () => {
    const rawResponse = await fetch('/api/add/', {
      method: 'POST',
      headers: {
        'Accept': 'application/json',
        'Content-Type': 'application/json'
      },

    body: JSON.stringify({"name": newName})
    });
    const content = await rawResponse;

    console.log(content);
    getData()
  }




  return (
    <div className="App">
     {
       data && data.length>0 && data.map((item)=><p>{item.id}{item.priority}{item.name}<button>raise priority</button></p>)
     }
    <input type='text' value={newName} onChange={(e) => (setNewName(e.target.value))} />
    <button onClick={addItem} >add item</button>
    </div>
  );
}

export default App;

Hatch allows you to specify direct references for dependencies in your pyproject.toml file. This is useful when you want to depend on a package that is not available on PyPI or when you want to use a specific version from a Git repository. Often used for unreleased packages, or unreleased versions of packages.

docs

[project]
dependencies = ['markata', 'markata-todoui@git+https://github.com/waylonwalker/markata-todoui']

[tool.hatch.metadata]
allow-direct-references=true