Creating procedural walk movement | Prototype Series

In this episode of the Prototype Series, we’ve expanded the Procedural Boss project by creating a procedurally animated walk animation!

Timestamps:
00:00​ - Intro
00:54​ - Setting up the rig
03:55​ - Procedural animation
05:17​ - Download details

⭐ Project Download https://on.unity.com/3jX6PAY​
⭐ Training Session https://on.unity.com/3atx3rW

Subscribe: https://www.youtube.com/channel/UCG08EqOAXJk_YXPDsAvReSg

#unity #game-development

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Creating procedural walk movement | Prototype Series
Easter  Deckow

Easter Deckow

1655630160

PyTumblr: A Python Tumblr API v2 Client

PyTumblr

Installation

Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install

Usage

Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
    '<consumer_key>',
    '<consumer_secret>',
    '<oauth_token>',
    '<oauth_secret>',
)

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

  1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
  2. The Tumblr API console at https://api.tumblr.com/console
  3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

  • state - a string, the state of the post. Supported types are published, draft, queue, private
  • tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
  • tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
  • date - a string, the customized GMT that you want
  • format - a string, the format that your post is in. Support types are html or markdown
  • slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",
                    data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

  • title - a string, the title of post that you want. Supports HTML entities.
  • url - a string, the url that you want to create a link post for.
  • description - a string, the desciption of the link that you have
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

Shubham Ankit

Shubham Ankit

1657081614

How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation

What is OPENPYXL

Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl

CREATE A NEW WORKBOOK

We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook
wb.save(filename = "example.xlsx")

READING DATA FROM WORKBOOK

We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")

 

GETTING SHEETS FROM THE LOADED WORKBOOK

 

#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
sheet1.title
result = 'sheet2'

#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'

 

ACCESSING CELLS AND CELL VALUES

 

#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10

 

ITERATING THROUGH ROWS AND COLUMNS

 

#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)
    .value)

#getting the highest row number
ws.max_row
701

#getting the highest column number
ws.max_column
19

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.

Example:

#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


Example:

for row in ws.values:
  for value in row:
  print(value)

 

WRITING DATA TO AN EXCEL FILE

Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.

 

CREATING AND SAVING A NEW WORKBOOK

 

#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook
wb.save("new.xlsx")

 

ADDING AND REMOVING SHEETS

 

#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']

 

ADDING CELL VALUES

 

#checking the sheet value
ws['B2'].value
null

#adding value to cell
ws['B2'] = 367

#checking value
ws['B2'].value
367

 

ADDING FORMULAS

 

We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
 

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'

wb.save("new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.

image

 

MERGE/UNMERGE CELLS

Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.

image

UNMERGE CELLS

 

#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')

The above code will unmerge cells from B2 to C9.

INSERTING AN IMAGE

To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.

Example:

import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')

wb.save("new2.xlsx")

Result:

image

CREATING CHARTS

Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:

Example

import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")

Result
image


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/ 
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial 
Subscribe: https://www.youtube.com/c/TechWithTim/featured 

#python 

Harry Patel

Harry Patel

1614145832

A Complete Process to Create an App in 2021

It’s 2021, everything is getting replaced by a technologically emerged ecosystem, and mobile apps are one of the best examples to convey this message.

Though bypassing times, the development structure of mobile app has also been changed, but if you still follow the same process to create a mobile app for your business, then you are losing a ton of opportunities by not giving top-notch mobile experience to your users, which your competitors are doing.

You are about to lose potential existing customers you have, so what’s the ideal solution to build a successful mobile app in 2021?

This article will discuss how to build a mobile app in 2021 to help out many small businesses, startups & entrepreneurs by simplifying the mobile app development process for their business.

The first thing is to EVALUATE your mobile app IDEA means how your mobile app will change your target audience’s life and why your mobile app only can be the solution to their problem.

Now you have proposed a solution to a specific audience group, now start to think about the mobile app functionalities, the features would be in it, and simple to understand user interface with impressive UI designs.

From designing to development, everything is covered at this point; now, focus on a prelaunch marketing plan to create hype for your mobile app’s targeted audience, which will help you score initial downloads.

Boom, you are about to cross a particular download to generate a specific revenue through your mobile app.

#create an app in 2021 #process to create an app in 2021 #a complete process to create an app in 2021 #complete process to create an app in 2021 #process to create an app #complete process to create an app

Наиболее часто используемые структуры данных в Python

В любом языке программирования нам нужно иметь дело с данными. Теперь одной из самых фундаментальных вещей, которые нам нужны для работы с данными, является эффективное хранение, управление и доступ к ним организованным образом, чтобы их можно было использовать всякий раз, когда это необходимо для наших целей. Структуры данных используются для удовлетворения всех наших потребностей.

Что такое структуры данных?

Структуры данных являются фундаментальными строительными блоками языка программирования. Он направлен на обеспечение системного подхода для выполнения всех требований, упомянутых ранее в статье. Структуры данных в Python — это List, Tuple, Dictionary и Set . Они считаются неявными или встроенными структурами данных в Python . Мы можем использовать эти структуры данных и применять к ним многочисленные методы для управления, связывания, манипулирования и использования наших данных.

У нас также есть пользовательские структуры данных, определяемые пользователем, а именно Stack , Queue , Tree , Linked List и Graph . Они позволяют пользователям полностью контролировать их функциональность и использовать их для расширенных целей программирования. Однако в этой статье мы сосредоточимся на встроенных структурах данных.

Неявные структуры данных Python

Неявные структуры данных Python

СПИСОК

Списки помогают нам хранить наши данные последовательно с несколькими типами данных. Они сопоставимы с массивами за исключением того, что они могут одновременно хранить разные типы данных, такие как строки и числа. Каждый элемент или элемент в списке имеет назначенный индекс. Поскольку Python использует индексацию на основе 0, первый элемент имеет индекс 0, и подсчет продолжается. Последний элемент списка начинается с -1, что можно использовать для доступа к элементам от последнего к первому. Чтобы создать список, мы должны написать элементы внутри квадратных скобок .

Одна из самых важных вещей, которые нужно помнить о списках , это то, что они изменяемы . Это просто означает, что мы можем изменить элемент в списке, обратившись к нему напрямую как часть оператора присваивания с помощью оператора индексации. Мы также можем выполнять операции в нашем списке, чтобы получить желаемый результат. Давайте рассмотрим код, чтобы лучше понять список и операции со списками.

1. Создание списка

#creating the list
my_list = ['p', 'r', 'o', 'b', 'e']
print(my_list)

Выход

['p', 'r', 'o', 'b', 'e']

2. Доступ к элементам из списка

#accessing the list 
 
#accessing the first item of the list
my_list[0]

Выход

'p'
#accessing the third item of the list
my_list[2]
'o'

3. Добавление новых элементов в список

#adding item to the list
my_list + ['k']

Выход

['p', 'r', 'o', 'b', 'e', 'k']

4. Удаление элементов

#removing item from the list
#Method 1:
 
#Deleting list items
my_list = ['p', 'r', 'o', 'b', 'l', 'e', 'm']
 
# delete one item
del my_list[2]
 
print(my_list)
 
# delete multiple items
del my_list[1:5]
 
print(my_list)

Выход

['p', 'r', 'b', 'l', 'e', 'm']
['p', 'm']
#Method 2:
 
#with remove fucntion
my_list = ['p','r','o','k','l','y','m']
my_list.remove('p')
 
 
print(my_list)
 
#Method 3:
 
#with pop function
print(my_list.pop(1))
 
# Output: ['r', 'k', 'l', 'y', 'm']
print(my_list)

Выход

['r', 'o', 'k', 'l', 'y', 'm']
o
['r', 'k', 'l', 'y', 'm']

5. Список сортировки

#sorting of list in ascending order
 
my_list.sort()
print(my_list)

Выход

['k', 'l', 'm', 'r', 'y']
#sorting of list in descending order
 
my_list.sort(reverse=True)
print(my_list)

Выход

['y', 'r', 'm', 'l', 'k']

6. Нахождение длины списка

#finding the length of list
 
len(my_list)

Выход

5

КОРТЕЖ

Кортежи очень похожи на списки с той ключевой разницей, что кортеж является IMMUTABLE , в отличие от списка. Как только мы создаем кортеж или имеем кортеж, нам не разрешается изменять элементы внутри него. Однако если у нас есть элемент внутри кортежа, который сам является списком, только тогда мы можем получить доступ к этому списку или изменить его. Чтобы создать кортеж, мы должны написать элементы внутри круглых скобок . Как и со списками, у нас есть аналогичные методы, которые можно использовать с кортежами. Давайте рассмотрим некоторые фрагменты кода, чтобы понять, как использовать кортежи.

1. Создание кортежа

#creating of tuple
 
my_tuple = ("apple", "banana", "guava")
print(my_tuple)

Выход

('apple', 'banana', 'guava')

2. Доступ к элементам из кортежа

#accessing first element in tuple
 
my_tuple[1]

Выход

'banana'

3. Длина кортежа

#for finding the lenght of tuple
 
len(my_tuple)

Выход

3

4. Преобразование кортежа в список

#converting tuple into a list
 
my_tuple_list = list(my_tuple)
type(my_tuple_list)

Выход

list

5. Реверс кортежа

#Reversing a tuple
 
tuple(sorted(my_tuple, reverse=True)) 

Выход

('guava', 'banana', 'apple')

6. Сортировка кортежа

#sorting tuple in ascending order
 
tuple(sorted(my_tuple)) 

Выход

('apple', 'banana', 'guava')

7. Удаление элементов из кортежа

Для удаления элементов из кортежа мы сначала преобразовали кортеж в список, как мы сделали в одном из наших методов выше (пункт № 4), затем следовали тому же процессу списка и явно удалили весь кортеж, просто используя del заявление .

ТОЛКОВЫЙ СЛОВАРЬ

Словарь — это коллекция, которая просто означает, что она используется для хранения значения с некоторым ключом и извлечения значения по данному ключу. Мы можем думать об этом как о наборе пар ключ: значение, и каждый ключ в словаре должен быть уникальным , чтобы мы могли получить соответствующий доступ к соответствующим значениям .

Словарь обозначается фигурными скобками { } , содержащими пары ключ: значение. Каждая из пар в словаре разделена запятой. Элементы в словаре неупорядочены , последовательность не имеет значения, пока мы обращаемся к ним или сохраняем их.

Они ИЗМЕНЯЕМЫ , что означает, что мы можем добавлять, удалять или обновлять элементы в словаре. Вот несколько примеров кода, чтобы лучше понять словарь в Python.

Важно отметить, что мы не можем использовать изменяемый объект в качестве ключа в словаре. Таким образом, список не допускается в качестве ключа в словаре.

1. Создание словаря

#creating a dictionary
 
my_dict = {
    1:'Delhi',
    2:'Patna',
    3:'Bangalore'
}
print(my_dict)

Выход

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore'}

Здесь целые числа — это ключи словаря, а название города, связанное с целыми числами, — это значения словаря.

2. Доступ к элементам из словаря

#access an item
 
print(my_dict[1])

Выход

'Delhi'

3. Длина словаря

#length of the dictionary
 
len(my_dict)

Выход

3

4. Сортировка словаря

#sorting based on the key 
 
Print(sorted(my_dict.items()))
 
 
#sorting based on the values of dictionary
 
print(sorted(my_dict.values()))

Выход

[(1, 'Delhi'), (2, 'Bangalore'), (3, 'Patna')]
 
['Bangalore', 'Delhi', 'Patna']

5. Добавление элементов в Словарь

#adding a new item in dictionary 
 
my_dict[4] = 'Lucknow'
print(my_dict)

Выход

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore', 4: 'Lucknow'}

6. Удаление элементов из словаря

#for deleting an item from dict using the specific key
 
my_dict.pop(4)
print(my_dict)
 
#for deleting last item from the list
 
my_dict.popitem()
 
#for clearing the dictionary
 
my_dict.clear()
print(my_dict)

Выход

{1: 'Delhi', 2: 'Patna', 3: 'Bangalore'}
(3, 'Bangalore')
{}

УСТАНОВЛЕН

Set — это еще один тип данных в python, представляющий собой неупорядоченную коллекцию без повторяющихся элементов. Общие варианты использования набора — удаление повторяющихся значений и проверка принадлежности. Фигурные скобки или set()функция могут использоваться для создания наборов. Следует иметь в виду, что при создании пустого набора мы должны использовать set(), и . Последний создает пустой словарь. not { }

Вот несколько примеров кода, чтобы лучше понять наборы в python.

1. Создание набора

#creating set
 
my_set = {"apple", "mango", "strawberry", "apple"}
print(my_set)

Выход

{'apple', 'strawberry', 'mango'}

2. Доступ к элементам из набора

#to test for an element inside the set
 
"apple" in my_set

Выход

True

3. Длина набора

print(len(my_set))

Выход

3

4. Сортировка набора

print(sorted(my_set))

Выход

['apple', 'mango', 'strawberry']

5. Добавление элементов в Set

my_set.add("guava")
print(my_set)

Выход

{'apple', 'guava', 'mango', 'strawberry'}

6. Удаление элементов из Set

my_set.remove("mango")
print(my_set)

Выход

{'apple', 'guava', 'strawberry'}

Вывод

В этой статье мы рассмотрели наиболее часто используемые структуры данных в Python, а также рассмотрели различные связанные с ними методы.

Ссылка: https://www.askpython.com/python/data

#python #datastructures

Brook  Hudson

Brook Hudson

1659396000

Humidifier: A Ruby tool for Managing AWS CloudFormation Stacks

Humidifier 

Humidifier is a ruby tool for managing AWS CloudFormation stacks. You can use it to build and manage stacks programmatically or you can use it as a command line tool to manage stacks through configuration files.

Installation

Add this line to your application's Gemfile:

gem 'humidifier'

And then execute:

$ bundle

Or install it yourself as:

$ gem install humidifier

Getting started

Stacks are represented by the Humidifier::Stack class. You can set any of the top-level JSON attributes (such as name and description) through the initializer.

Resources are represented by an exact mapping from AWS resource names to Humidifier resources names (e.g. AWS::EC2::Instance becomes Humidifier::EC2::Instance). Resources have accessors for each JSON attribute. Each attribute can also be set through the initialize, update, and update_attribute methods.

Example usage

The below example will create a stack with two resources, a loader balancer and an auto scaling group. It then deploys the new stack and pauses execution until the stack is finished being created.

stack = Humidifier::Stack.new(name: 'Example-Stack')

stack.add(
  'LoaderBalancer',
  Humidifier::ElasticLoadBalancing::LoadBalancer.new(
    scheme: 'internal',
    listeners: [
      {
        load_balancer_port: 80,
        protocol: 'http',
        instance_port: 80,
        instance_protocol: 'http'
      }
    ]
  )
)

stack.add(
  'AutoScalingGroup',
  Humidifier::AutoScaling::AutoScalingGroup.new(
    min_size: '1',
    max_size: '20',
    availability_zones: ['us-east-1a'],
    load_balancer_names: [Humidifier.ref('LoadBalancer')]
  )
)

stack.deploy_and_wait

Interfacing with AWS

Once stacks have the appropriate resources, you can query AWS to handle all stack CRUD operations. The operations themselves are intuitively named (i.e. #create, #update, #delete). There are also convenience methods for validating a stack body (#valid?), checking the existence of a stack (#exists?), and creating or updating based on existence (#deploy).

There are additionally four functions on Humidifier::Stack that support waiting for execution in AWS to finish. They all have non-blocking corollaries, and are named after them. They are: #create_and_wait, #update_and_wait, #delete_and_wait, and #deploy_and_wait.

CloudFormation functions

You can use CFN intrinsic functions and references using Humidifier.fn.[name] and Humidifier.ref. They will build appropriate structures that know how to be dumped to CFN syntax.

Change Sets

Instead of immediately pushing your changes to CloudFormation, Humidifier also supports change sets. Change sets are a powerful feature that allow you to see the changes that will be made before you make them. To read more about change sets see the announcement article. To use them in Humidifier, Humidifier::Stack has the #create_change_set and #deploy_change_set methods. The #create_change_set method will create a change set on the stack. The #deploy_change_set method will create a change set if the stack currently exists, and otherwise will create the stack.

Introspection

To see the template body, you can check the #to_cf method on stacks, resources, fns, and refs. All of them will output a hash of what will be uploaded (except the stack, which will output a string representation).

Humidifier itself contains a registry of all possible resources that it supports. You can access it with Humidifier::registry which is a hash of AWS resource name pointing to the class.

Resources have an ::aws_name method to see how AWS references them. They also contain a ::props method that contains a hash of the name that Humidifier uses to reference the prop pointing to the appropriate prop object.

Large templates

When templates are especially large (larger than 51,200 bytes), they cannot be uploaded directly through the AWS SDK. You can configure Humidifier to seamlessly upload the templates to S3 and reference them using an S3 URL instead by:

Humidifier.configure do |config|
  config.s3_bucket = 'my.s3.bucket'
  config.s3_prefix = 'my-prefix/' # optional
end

Forcing uploading

You can force a stack to upload its template to S3 regardless of the size of the template. This is a useful option if you're going to be deploying multiple copies of a template or if you want a backup. You can set this option on a per-stack basis:

stack.deploy(force_upload: true)

or globally, by setting the configuration option:

Humidifier.configure do |config|
  config.force_upload = true
end

CLI

Humidifier can also be used as a CLI for managing resources through configuration files. For a step-by-step guide, read on, but if you'd like to see a working example, check out the example directory.

To get started, build a ruby script (for example humidifier) that executes the Humidifier::CLI class, like so:

#!/usr/bin/env ruby
require 'humidifier'

Humidifier.configure do |config|
  # optional, defaults to the current working directory, so that all of the
  # directories from the location that you run the CLI are assumed to contain
  # resource specifications
  config.stack_path = 'stacks'

  # optional, a default prefix to use before deploying to AWS
  config.stack_prefix = 'humidifier-'

  # specifies that `users.yml` files contain specifications for `AWS::IAM::User`
  # resources
  config.map :users, to: 'IAM::User'
end

Humidifier::CLI.start(ARGV)

Resource files

Inside of the stacks directory configured above, create a subdirectory for each CloudFormation stack that you want to deploy. With the above configuration, we can create YAML files in the form of users.yml for each stack, which will specify IAM users to create. The file format looks like the below:

EngUser:
  path: /humidifier/
  user_name: EngUser
  groups:
  - Engineering
  - Testing
  - Deployment

AdminUser:
  path: /humidifier/
  user_name: AdminUser
  groups:
  - Management
  - Administration

The top-level keys are the logical resource names that will be displayed in the CloudFormation screen. They point to a map of key/value pairs that will be passed on to humidifier. Any humidifier (and therefore any CloudFormation) attribute may be specified. For more information on CloudFormation templates and which attributes may be specified, see both the humidifier docs and the CloudFormation docs.

Mappers

Oftentimes, specifying these attributes can become repetitive, e.g., each user should automatically receive the same "path" attribute. Other times, you may want custom logic to execute depending on which AWS environment you're running in. Finally, you may want to reference resources in the same or other stacks.

Humidifier's solution for this is to allow customized "mapper" classes to take the user-provided attributes and transform them into the attributes that CloudFormation expects. Consider the following example for mapping a user:

class UserMapper < Humidifier::Config::Mapper
  GROUPS = {
    'eng' => %w[Engineering Testing Deployment],
    'admin' => %w[Management Administration]
  }

  defaults do |logical_name|
    { path: '/humidifier/', user_name: logical_name }
  end

  attribute :group do |group|
    groups = GROUPS[group]
    groups.any? ? { groups: GROUPS[group] } : {}
  end
end

Humidifier.configure do |config|
  config.map :users, to: 'IAM::User', using: UserMapper
end

This means that by default, all entries in the users.yml files will get a /humidifier/ path, the user_name attribute will be set based on the logical name that was provided for the resource, and you can additionally specify a group attribute, even though it is not native to CloudFormation. With this group attribute, it will actually map to the groups attribute that CloudFormation expects.

With this new mapper in place, we can simplify our YAML file to:

EngUser:
  group: eng

AdminUser:
  group: admin

Using the CLI

Now that you've configured your CLI, your resources, and your mappers, you can use the CLI to display, validate, and deploy your infrastructure to CloudFormation. Run your script without any arguments to get the help message and explanations for each command.

Each command has an --aws-profile (or -p) option for specifying which profile to authenticate against when querying AWS. You should ensure that this profile has the correct permissions for creating whatever resources are going to part of your stack. You can also rely on the AWS_* environment variables, or the EC2 instance profile if you're deploying from an instance. For more information, see the AWS docs under the "Configuration" section.

Below are the list of commands and some of their options.

change [?stack]

Creates a change set for either the specified stack or all stacks in the repo. The change set represents the changes between what is currently deployed versus the resources represented by the configuration.

deploy [?stack] [*parameters]

Creates or updates (depending on if the stack already exists) one or all stacks in the repo.

The deploy command also allows a --prefix command line argument that will override the default prefix (if one is configured) for the stack that is being deployed. This is especially useful when you're deploying multiple copies of the same stack (for instance, multiple autoscaling groups) that have different purposes or semantically mean newer versions of resources.

display [stack] [?pattern]

Displays the specified stack in JSON format on the command line. If you optionally pass a pattern argument, it will filter the resources down to just ones whose names match the given pattern.

stacks

Displays the names of all of the stacks that humidifier is managing.

upgrade

Downloads the latest CloudFormation resource specification. Periodically AWS will update the file that humidifier is based on, in which case the attributes of the resources that were changed could change. This gem usually stays relatively in sync, but if you need to use the latest specs and this gem has not yet released a new version containing them, then you can run this command to download the latest specs onto your system.

upload [?stack]

Upload one or all stacks in the repo to S3 for reference later. Note that this must be combined with the humidifier s3_bucket configuration option.

validate [?stack]

Validate that one or all stacks in the repo are properly configured and using values that CloudFormation understands.

version

Output the version of Humidifier as well as the version of the CloudFormation resource specification that you are using.

Parameters

CloudFormation template parameters can be specified by having a special parameters.yml file in your stack directory. This file should contain a YAML-encoded object whose keys are the names of the parameters and whose values are the parameter configuration (using the same underscore paradigm as humidifier resources for specifying configuration).

You can pass values to the CLI deploy command after the stack name on the command line as in:

humidifier deploy foobar Param1=Foo Param2=Bar

Those parameters will get passed in as values when the stack is deployed.

Shortcuts

A couple of convenient shortcuts are built into humidifier so that writing templates and mappers both can be more concise.

Automatic id properties

There are a lot of properties in the AWS CloudFormation resource specification that are simply pointers to other entities within the AWS ecosystem. For example, an AWS::EC2::VPCGatewayAttachment entity has a VpcId property that represents the ID of the associated AWS::EC2::VPC.

Because this pattern is so common, humidifier detects all properties ending in Id and allows you to specify them without the suffix. If you choose to use this format, humidifier will automatically turn that value into a CloudFormation resource reference.

Anonymous mappers

A lot of the time, mappers that you create will not be overly complicated, especially if you're using automatic id properties. So, the config.map method optionally takes a block, and allows you to specify the mapper inline. This is recommended for mappers that aren't too complicated as to warrant their own class (for instance, for testing purposes). An example of this using the UserMapper from above is below:

Humidifier.configure do |config|
  config.map :users, to: 'IAM::User' do
    GROUPS = {
      'eng' => %w[Engineering Testing Deployment],
      'admin' => %w[Management Administration]
    }

    defaults do |logical_name|
      { path: '/humidifier/', user_name: logical_name }
    end

    attribute :group do |group|
      groups = GROUPS[group]
      groups.any? ? { groups: GROUPS[group] } : {}
    end
  end
end

Cross-stack references

AWS allows cross-stack references through the intrinsic Fn::ImportValue function. You can take advantage of this with humidifier by using the export: true option on resources in your stacks. For instance, if in one stack you have a subnet that you need to reference in another, you could (stacks/vpc/subnets.yml):

ProductionPrivateSubnet2a:
  vpc: ProductionVPC
  cidr_block: 10.0.0.0/19
  availability_zone: us-west-2a
  export: true

ProductionPrivateSubnet2b:
  vpc: ProductionVPC
  cidr_block: 10.0.64.0/19
  availability_zone: us-west-2b
  export: true

ProductionPrivateSubnet2c:
  vpc: ProductionVPC
  cidr_block: 10.0.128.0/19
  availability_zone: us-west-2c
  export: true

And then in another stack, you could reference those values (stacks/rds/db_subnets_groups.yml):

ProductionDBSubnetGroup:
  db_subnet_group_description: Production DB private subnet group
  subnets:
  - ProductionPrivateSubnet2a
  - ProductionPrivateSubnet2b
  - ProductionPrivateSubnet2c

Within the configuration, you would specify to use the Fn::ImportValue function like so:

Humidifier.configure do |config|
  config.stack_path = 'stacks'

  config.map :subnets, to: 'EC2::Subnet'

  config.map :db_subnet_groups, to: 'RDS::DBSubnetGroup' do
    attribute :subnets do |subnet_names|
      subnet_ids =
        subnet_names.map do |subnet_name|
          Humidifier.fn.import_value(subnet_name)
        end

      { subnet_ids: subnet_ids }
    end
  end
end

If you specify export: true it will by default export a reference to the resource listed in the stack. You can also choose to export a different attribute by specifying the attribute as the value to export. For example, if we were creating instance profiles and wanted to export the Arn so that it could be referenced by an instance later, we could:

APIRoleInstanceProfile:
  depends_on: APIRole
  roles:
  - APIRole
  export: Arn

Development

To get started, ensure you have ruby installed, version 2.4 or later. From there, install the bundler gem: gem install bundler and then bundle install in the root of the repository.

Testing

The default rake task runs the tests. Styling is governed by rubocop. The docs are generated with yard. To run all three of these, run:

$ bundle exec rake
$ bundle exec rubocop
$ bundle exec rake yard

Specs

The specs pulled from the CFN docs is saved to CloudFormationResourceSpecification.json. You can update it by running bundle exec rake specs. This script will pull down the latest resource specification to be used with Humidifier.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/kddnewton/humidifier.

License

The gem is available as open source under the terms of the MIT License.


Author: kddnewton
Source code: https://github.com/kddnewton/humidifier
License: MIT license

#ruby  #ruby-on-rails