Creating find() in JavaScript

find() in JavaScript | MengSreang Channel

👉find() គឺជា methods មួយដែលគេយកមកប្រើសម្រាប់ looping នៅក្នុង Array ។

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:

The built-in interactive_console.py tool (if you already have a consumer key & secret)
The Tumblr API console at https://api.tumblr.com/console
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

Arvilla Medhurst

1669188856

What Is R Programming Language? introduction & Basics

In this R article, we will learn about What Is R Programming Language? introduction & Basics. R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational tasks, C, C++, and Fortran codes are preferred.

Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicating the results

Program: R is a clear and accessible programming tool
Transform: R is made up of a collection of libraries designed specifically for data science
Discover: Investigate the data, refine your hypothesis and analyze them
Model: R provides a wide array of tools to capture the right model for your data
Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world.

What is R used for?

Statistical inference
Data analysis
Machine learning algorithm

As conclusion, R is the world’s most widely used statistics programming language. It’s the 1st choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission-critical business applications.

R-environment setup

Windows Installation – We can download the Windows installer version of R from R-3.2.2 for windows (32/64)

As it is a Windows installer (.exe) with the name “R-version-win.exe”. You can just double click and run the installer accepting the default settings. If your Windows is a 32-bit version, it installs the 32-bit version. But if your windows are 64-bit, then it installs both the 32-bit and 64-bit versions.

After installation, you can locate the icon to run the program in a directory structure “R\R3.2.2\bin\i386\Rgui.exe” under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.

R basic Syntax

R Programming is a very popular programming language that is broadly used in data analysis. The way in which we define its code is quite simple. The “Hello World!” is the basic program for all the languages, and now we will understand the syntax of R programming with the “Hello world” program. We can write our code either in the command prompt, or we can use an R script file.

R command prompt

Once you have R environment setup, then it’s easy to start your R command prompt by just typing the following command at your command prompt −
$R
This will launch R interpreter and you will get a prompt > where you can start typing your program as follows −

>myString <- "Hello, World"
>print (myString)
[1] "Hello, World!"

Here the first statement defines a string variable myString, where we assign a string “Hello, World!” and then the next statement print() is being used to print the value stored in myString variable.

R data-types

While doing programming in any programming language, you need to use various variables to store various information. Variables are nothing but reserved memory locations to store values. This means that when you create a variable you reserve some space in memory.

In contrast to other programming languages like C and java in R, the variables are not declared as some data type. The variables are assigned with R-Objects and the data type of the R-object becomes the data type of the variable. There are many types of R-objects. The frequently used ones are −

Vectors
Lists
Matrices
Arrays
Factors
Data Frames

Vectors

#create a vector and find the elements which are >5
v<-c(1,2,3,4,5,6,5,8)
v[v>5]

#subset
subset(v,v>5)

#position in the vector created in which square of the numbers of v is >10 holds good
which(v*v>10)

#to know the values 
v[v*v>10]

Output: [1] 6 8 Output: [1] 6 8 Output: [1] 4 5 6 7 8 Output: [1] 4 5 6 5 8

Matrices

A matrix is a two-dimensional rectangular data set. It can be created using a vector input to the matrix function.

#matrices: a vector with two dimensional attributes
mat<-matrix(c(1,2,3,4))
 
mat1<-matrix(c(1,2,3,4),nrow=2)
mat1

Output: [,1] [,2] [1,] 1 3 [2,] 2 4

mat2<-matrix(c(1,2,3,4),ncol=2,byrow=T)
mat2

Output: [,1] [,2] [1,] 1 2 [2,] 3 4

mat3<-matrix(c(1,2,3,4),byrow=T)
mat3

#transpose of matrix
mattrans<-t(mat)
mattrans

#create a character matrix called fruits with elements apple, orange, pear, grapes
fruits<-matrix(c("apple","orange","pear","grapes"),2)
#create 3×4 matrix of marks obtained in each quarterly exams for 4 different subjects 
X<-matrix(c(50,70,40,90,60, 80,50, 90,100, 50,30, 70),nrow=3)
X

#give row names and column names
rownames(X)<-paste(prefix="Test.",1:3)
subs<-c("Maths", "English", "Science", "History")
colnames(X)<-subs
X

Output: [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 Output: [,1] [,2] [,3] [,4] [1,] 1 2 3 4 Output: [,1] [,2] [,3] [,4] [1,] 50 90 50 50 [2,] 70 60 90 30 [3,] 40 80 100 70 Output: Maths English Science History Test. 1 50 90 50 50 Test. 2 70 60 90 30 Test. 3 40 80 100 70

Arrays

While matrices are confined to two dimensions, arrays can be of any number of dimensions. The array function takes a dim attribute which creates the required number of dimensions. In the below example we create an array with two elements which are 3×3 matrices each.

#Arrays
arr<-array(1:24,dim=c(3,4,2))
arr

#create an array using alphabets with dimensions 3 rows, 2 columns and 3 arrays
arr1<-array(letters[1:18],dim=c(3,2,3))

#select only 1st two matrix of an array
arr1[,,c(1:2)]

#LIST
X<-list(u=2, n='abc')
X
X$u

 [,1] [,2] [,3] [,4]

 [,1] [,2] [,3] [,4]

 [,1] [,2]

 [,1] [,2]

Dataframes

Data frames are tabular data objects. Unlike a matrix in a data frame, each column can contain different modes of data. The first column can be numeric while the second column can be character and the third column can be logical. It is a list of vectors of equal length.

#Dataframes
students<-c("J","L","M","K","I","F","R","S")
Subjects<-rep(c("science","maths"),each=2)
marks<-c(55,70,66,85,88,90,56,78)
data<-data.frame(students,Subjects,marks)
#Accessing dataframes
data[[1]]

data$Subjects
data[,1]

Output: [1] J L M K I F R S Levels: F I J K L M R S Output: data$Subjects [1] science science maths maths science science maths maths Levels: maths science

Factors

Factors are the r-objects which are created using a vector. It stores the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character or Boolean etc. in the input vector. They are useful in statistical modeling.

Factors are created using the factor() function. The nlevels function gives the count of levels.

#Factors
x<-c(1,2,3)
factor(x)

#apply function
data1<-data.frame(age=c(55,34,42,66,77),bmi=c(26,25,21,30,22))
d<-apply(data1,2,mean)
d

#create two vectors age and gender and find mean age with respect to gender
age<-c(33,34,55,54)
gender<-factor(c("m","f","m","f"))
tapply(age,gender,mean)

Output: [1] 1 2 3 Levels: 1 2 3 Output: age bmi 54.8 24.8 Output: f m 44 44

R Variables

A variable provides us with named storage that our programs can manipulate. A variable in R can store an atomic vector, a group of atomic vectors, or a combination of many R objects. A valid variable name consists of letters, numbers, and the dot or underlines characters.

Rules for writing Identifiers in R

Identifiers can be a combination of letters, digits, period (.), and underscore (_).
It must start with a letter or a period. If it starts with a period, it cannot be followed by a digit.
Reserved words in R cannot be used as identifiers.

Valid identifiers in R

total, sum, .fine.with.dot, this_is_acceptable, Number5

Invalid identifiers in R

tot@l, 5um, _fine, TRUE, .0ne

Best Practices

Earlier versions of R used underscore (_) as an assignment operator. So, the period (.) was used extensively in variable names having multiple words. Current versions of R support underscore as a valid identifier but it is good practice to use a period as word separators.
For example, a.variable.name is preferred over a_variable_name or alternatively we could use camel case as aVariableName.

Constants in R

Constants, as the name suggests, are entities whose value cannot be altered. Basic types of constant are numeric constants and character constants.

Numeric Constants

All numbers fall under this category. They can be of type integer, double or complex. It can be checked with the typeof() function.
Numeric Constants followed by L are regarded as integers and those followed by i are regarded as complex.

> typeof(5)
> typeof(5L)
> typeof(5L)

[1] “double” [1] “double” [[1] “double”

Character Constants

Character constants can be represented using either single quotes (‘) or double quotes (“) as delimiters.

> 'example'
> typeof("5")

[1] "example" [1] "character"

R Operators

Operators – Arithmetic, Relational, Logical, Assignment, and some of the Miscellaneous Operators that R programming language provides.

There are four main categories of Operators in the R programming language.

Arithmetic Operators
Relational Operators
Logical Operators
Assignment Operators
Mixed Operators

x <- 35
y<-10

x+y > x-y > x*y > x/y > x%/%y > x%%y > x^y [1] 45 [1] 25 [1] 350 [1] 3.5 [1] 3 [1] 5 [1]2.75e+15

Logical Operators

The below table shows the logical operators in R. Operators & and | perform element-wise operation producing result having a length of the longer operand. But && and || examines only the first element of the operands resulting in a single length logical vector.

a <- c(TRUE,TRUE,FALSE,0,6,7)
b <- c(FALSE,TRUE,FALSE,TRUE,TRUE,TRUE)
a&b 
[1] FALSE TRUE FALSE FALSE TRUE TRUE
a&&b
[1] FALSE
> a|b
[1] TRUE TRUE FALSE TRUE TRUE TRUE
> a||b
[1] TRUE
> !a
[1] FALSE FALSE TRUE TRUE FALSE FALSE
> !b
[1] TRUE FALSE TRUE FALSE FALSE FALSE

R functions

Functions are defined using the function() directive and are stored as R objects just like anything else. In particular, they are R objects of class “function”. Here’s a simple function that takes no arguments simply prints ‘Hi statistics’.

#define the function
f <- function() {
print("Hi statistics!!!")
}
#Call the function
f()

Output: [1] "Hi statistics!!!"

Now let’s define a function called standardize, and the function has a single argument x which is used in the body of a function.

#Define the function that will calculate standardized score.
standardize = function(x) {
m = mean(x)
sd = sd(x)
result = (x – m) / sd
result
}
input<- c(40:50) #Take input for what we want to calculate a standardized score.
standardize(input) #Call the function

Output: standardize(input) #Call the function [1] -1.5075567 -1.2060454 -0.9045340 -0.6030227 -0.3015113 0.0000000 0.3015113 0.6030227 0.9045340 1.2060454 1.5075567

Loop Functions

R has some very useful functions which implement looping in a compact form to make life easier. The very rich and powerful family of applied functions is made of intrinsically vectorized functions. These functions in R allow you to apply some function to a series of objects (eg. vectors, matrices, data frames, or files). They include:

lapply(): Loop over a list and evaluate a function on each element
sapply(): Same as lapply but try to simplify the result
apply(): Apply a function over the margins of an array
tapply(): Apply a function over subsets of a vector
mapply(): Multivariate version of lapply

There is another function called split() which is also useful, particularly in conjunction with lapply.

R Vectors

A vector is a sequence of data elements of the same basic type. Members in a vector are officially called components. Vectors are the most basic R data objects and there are six types of atomic vectors. They are logical, integer, double, complex, character, and raw.

The c() function can be used to create vectors of objects by concatenating things together. 
x <- c(1,2,3,4,5) #double
x #If you use only x auto-printing occurs
l <- c(TRUE, FALSE) #logical
l <- c(T, F) ## logical
c <- c("a", "b", "c", "d") ## character
i <- 1:20 ## integer
cm <- c(2+2i, 3+3i) ## complex
print(l)
print(c)
print(i)
print(cm)

You can see the type of each vector using typeof() function in R.
typeof(x)
typeof(l)
typeof(c)
typeof(i)
typeof(cm)

Output: print(l) [1] TRUE FALSE print(c) [1] "a" "b" "c" "d" print(i) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 print(cm) [1] 2+2i 3+3i Output: typeof(x) [1] "double" typeof(l) [1] "logical" typeof(c) [1] "character" typeof(i) [1] "integer" typeof(cm) [1] "complex"

Creating a vector using seq() function:

We can use the seq() function to create a vector within an interval by specifying step size or specifying the length of the vector.

seq(1:10) #By default it will be incremented by 1
seq(1, 20, length.out=5) # specify length of the vector
seq(1, 20, by=2) # specify step size

Output: > seq(1:10) #By default it will be incremented by 1 [1] 1 2 3 4 5 6 7 8 9 10 > seq(1, 20, length.out=5) # specify length of the vector [1] 1.00 5.75 10.50 15.25 20.00 > seq(1, 20, by=2) # specify step size [1] 1 3 5 7 9 11 13 15 17 19

Extract Elements from a Vector:

Elements of a vector can be accessed using indexing. The vector indexing can be logical, integer, or character. The [ ] brackets are used for indexing. Indexing starts with position 1, unlike most programming languages where indexing starts from 0.

Extract Using Integer as Index:

We can use integers as an index to access specific elements. We can also use negative integers to return all elements except that specific element.

x<- 101:110
x[1]   #access the first element
x[c(2,3,4,5)] #Extract 2nd, 3rd, 4th, and 5th elements
x[5:10]        #Extract all elements from 5th to 10th
x[c(-5,-10)] #Extract all elements except 5th and 10th
x[-c(5:10)] #Extract all elements except from 5th to 10th

Output: x[1] #Extract the first element [1] 101 x[c(2,3,4,5)] #Extract 2nd, 3rd, 4th, and 5th elements [1] 102 103 104 105 x[5:10] #Extract all elements from 5th to 10th [1] 105 106 107 108 109 110 x[c(-5,-10)] #Extract all elements except 5th and 10th [1] 101 102 103 104 106 107 108 109 x[-c(5:10)] #Extract all elements except from 5th to 10th [1] 101 102 103 104

Extract Using Logical Vector as Index:

If you use a logical vector for indexing, the position where the logical vector is TRUE will be returned.

x[x < 105]
x[x>=104]

Output: x[x < 105] [1] 101 102 103 104 x[x>=104] [1] 104 105 106 107 108 109 110

Modify a Vector in R:

We can modify a vector and assign a new value to it. You can truncate a vector by using reassignments. Check the below example.

x<- 10:12
x[1]<- 101 #Modify the first element
x
x[2]<-102 #Modify the 2nd element
x
x<- x[1:2] #Truncate the last element
x

Output: x [1] 101 11 12 x[2]<-102 #Modify the 2nd element x [1] 101 102 12 x<- x[1:2] #Truncate the last element x [1] 101 102

Arithmetic Operations on Vectors:

We can use arithmetic operations on two vectors of the same length. They can be added, subtracted, multiplied, or divided. Check the output of the below code.

# Create two vectors.
v1 <- c(1:10)
v2 <- c(101:110)

# Vector addition.
add.result <- v1+v2
print(add.result)
# Vector subtraction.
sub.result <- v2-v1
print(sub.result)
# Vector multiplication.
multi.result <- v1*v2
print(multi.result)
# Vector division.
divi.result <- v2/v1
print(divi.result)

Output: print(add.result) [1] 102 104 106 108 110 112 114 116 118 120 print(sub.result) [1] 100 100 100 100 100 100 100 100 100 100 print(multi.result) [1] 101 204 309 416 525 636 749 864 981 1100 print(divi.result) [1] 101.00000 51.00000 34.33333 26.00000 21.00000 17.66667 15.28571 13.50000 12.11111 11.00000

Find Minimum and Maximum in a Vector:

The minimum and the maximum of a vector can be found using the min() or the max() function. range() is also available which returns the minimum and maximum in a vector.

x<- 1001:1010
max(x) # Find the maximum
min(x) # Find the minimum
range(x) #Find the range

Output: max(x) # Find the maximum [1] 1010 min(x) # Find the minimum [1] 1001 range(x) #Find the range [1] 1001 1010

R Lists

The list is a data structure having elements of mixed data types. A vector having all elements of the same type is called an atomic vector but a vector having elements of a different type is called list.
We can check the type with typeof() or class() function and find the length using length()function.

x <- list("stat",5.1, TRUE, 1 + 4i)
x
class(x)
typeof(x)
length(x)

Output: x [[1]] [1] "stat" [[2]] [1] 5.1 [[3]] [1] TRUE [[4]] [1] 1+4i class(x) [1] “list” typeof(x) [1] “list” length(x) [1] 4

You can create an empty list of a prespecified length with the vector() function.

x <- vector("list", length = 10)
x

Output: x [[1]] NULL [[2]] NULL [[3]] NULL [[4]] NULL [[5]] NULL [[6]] NULL [[7]] NULL [[8]] NULL [[9]] NULL [[10]] NULL

How to extract elements from a list?

Lists can be subset using two syntaxes, the $ operator, and square brackets []. The $ operator returns a named element of a list. The [] syntax returns a list, while the [[]] returns an element of a list.

# subsetting
l$e
l["e"]
l[1:2]
l[c(1:2)] #index using integer vector
l[-c(3:length(l))] #negative index to exclude elements from 3rd up to last.
l[c(T,F,F,F,F)] # logical index to access elements

Output: > l$e [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 0 0 0 0 0 0 0 0 0 [2,] 0 1 0 0 0 0 0 0 0 0 [3,] 0 0 1 0 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 1 0 0 0 0 0 [6,] 0 0 0 0 0 1 0 0 0 0 [7,] 0 0 0 0 0 0 1 0 0 0 [8,] 0 0 0 0 0 0 0 1 0 0 [9,] 0 0 0 0 0 0 0 0 1 0 [10,] 0 0 0 0 0 0 0 0 0 1 > l["e"] $e [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 0 0 0 0 0 0 0 0 0 [2,] 0 1 0 0 0 0 0 0 0 0 [3,] 0 0 1 0 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 1 0 0 0 0 0 [6,] 0 0 0 0 0 1 0 0 0 0 [7,] 0 0 0 0 0 0 1 0 0 0 [8,] 0 0 0 0 0 0 0 1 0 0 [9,] 0 0 0 0 0 0 0 0 1 0 [10,] 0 0 0 0 0 0 0 0 0 1 > l[1:2] [[1]] [1] 1 2 3 4 [[2]] [1] FALSE > l[c(1:2)] #index using integer vector [[1]] [1] 1 2 3 4 [[2]] [1] FALSE > l[-c(3:length(l))] #negative index to exclude elements from 3rd up to last. [[1]] [1] 1 2 3 4 [[2]] [1] FALSE l[c(T,F,F,F,F)] [[1]] [1] 1 2 3 4

Modifying a List in R:

We can change components of a list through reassignment.

l[["name"]] <- "Kalyan Nandi"
l

Output: [[1]] [1] 1 2 3 4 [[2]] [1] FALSE [[3]] [1] “Hello Statistics!” $d function (arg = 42) { print(“Hello World!”) } $name [1] “Kalyan Nandi”

R Matrices

In R Programming Matrix is a two-dimensional data structure. They contain elements of the same atomic types. A Matrix can be created using the matrix() function. R can also be used for matrix calculations. Matrices have rows and columns containing a single data type. In a matrix, the order of rows and columns is important. Dimension can be checked directly with the dim() function and all attributes of an object can be checked with the attributes() function. Check the below example.

Creating a matrix in R

m <- matrix(nrow = 2, ncol = 3)
dim(m)
attributes(m)
m <- matrix(1:20, nrow = 4, ncol = 5)
m

Output: dim(m) [1] 2 3 attributes(m) $dim [1] 2 3 m <- matrix(1:20, nrow = 4, ncol = 5) m [,1] [,2] [,3] [,4] [,5] [1,] 1 5 9 13 17 [2,] 2 6 10 14 18 [3,] 3 7 11 15 19 [4,] 4 8 12 16 20

Matrices can be created by column-binding or row-binding with the cbind() and rbind() functions.

x<-1:3
y<-10:12
z<-30:32
cbind(x,y,z)
rbind(x,y,z)

Output: cbind(x,y,z) x y z [1,] 1 10 30 [2,] 2 11 31 [3,] 3 12 32 rbind(x,y,z) [,1] [,2] [,3] x 1 2 3 y 10 11 12 z 30 31 32

By default, the matrix function reorders a vector into columns, but we can also tell R to use rows instead.

x <-1:9
matrix(x, nrow = 3, ncol = 3)
matrix(x, nrow = 3, ncol = 3, byrow = TRUE)

Output cbind(x,y,z) x y z [1,] 1 10 30 [2,] 2 11 31 [3,] 3 12 32 rbind(x,y,z) [,1] [,2] [,3] x 1 2 3 y 10 11 12 z 30 31 32

R Arrays

In R, Arrays are the data types that can store data in more than two dimensions. An array can be created using the array() function. It takes vectors as input and uses the values in the dim parameter to create an array. If you create an array of dimensions (2, 3, 4) then it creates 4 rectangular matrices each with 2 rows and 3 columns. Arrays can store only data type.

Give a Name to Columns and Rows:

We can give names to the rows, columns, and matrices in the array by setting the dimnames parameter.

v1 <- c(1,2,3)
v2 <- 100:110
col.names <- c("Col1","Col2","Col3","Col4","Col5","Col6","Col7")
row.names <- c("Row1","Row2")
matrix.names <- c("Matrix1","Matrix2")
arr4 <- array(c(v1,v2), dim=c(2,7,2), dimnames = list(row.names,col.names, matrix.names))
arr4

Output: , , Matrix1 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110 , , Matrix2 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110

Accessing/Extracting Array Elements:

# Print the 2nd row of the 1st matrix of the array.
print(arr4[2,,1])
# Print the element in the 2nd row and 4th column of the 2nd matrix.
print(arr4[2,4,2])
# Print the 2nd Matrix.
print(arr4[,,2])

Output: > print(arr4[2,,1]) Col1 Col2 Col3 Col4 Col5 Col6 Col7 2 100 102 104 106 108 110 > > # Print the element in the 2nd row and 4th column of the 2nd matrix. > print(arr4[2,4,2]) [1] 104 > > # Print the 2nd Matrix. > print(arr4[,,2]) Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110

R Factors

Factors are used to represent categorical data and can be unordered or ordered. An example might be “Male” and “Female” if we consider gender. Factor objects can be created with the factor() function.

x <- factor(c("male", "female", "male", "male", "female"))
x
table(x)

Output: x [1] male female male male female Levels: female male table(x) x female male 2 3

By default, Levels are put in alphabetical order. If you print the above code you will get levels as female and male. But if you want to get your levels in a particular order then set levels parameter like this.

x <- factor(c("male", "female", "male", "male", "female"), levels=c("male", "female"))
x
table(x)

Output: x [1] male female male male female Levels: male female table(x) x male female 3 2

R Dataframes

Data frames are used to store tabular data in R. They are an important type of object in R and are used in a variety of statistical modeling applications. Data frames are represented as a special type of list where every element of the list has to have the same length. Each element of the list can be thought of as a column and the length of each element of the list is the number of rows. Unlike matrices, data frames can store different classes of objects in each column. Matrices must have every element be the same class (e.g. all integers or all numeric).

Creating a Data Frame:

Data frames can be created explicitly with the data.frame() function.

employee <- c('Ram','Sham','Jadu')
salary <- c(21000, 23400, 26800)
startdate <- as.Date(c('2016-11-1','2015-3-25','2017-3-14'))
employ_data <- data.frame(employee, salary, startdate)
employ_data
View(employ_data)

Output: employ_data employee salary startdate 1 Ram 21000 2016-11-01 2 Sham 23400 2015-03-25 3 Jadu 26800 2017-03-14 View(employ_data)

Get the Structure of the Data Frame:

If you look at the structure of the data frame now, you see that the variable employee is a character vector, as shown in the following output:

str(employ_data)

Output: > str(employ_data) 'data.frame': 3 obs. of 3 variables: $ employee : Factor w/ 3 levels "Jadu","Ram","Sham": 2 3 1 $ salary : num 21000 23400 26800 $ startdate: Date, format: "2016-11-01" "2015-03-25" "2017-03-14"

Note that the first column, employee, is of type factor, instead of a character vector. By default, data.frame() function converts character vector into factor. To suppress this behavior, we can pass the argument stringsAsFactors=FALSE.

employ_data <- data.frame(employee, salary, startdate, stringsAsFactors = FALSE)
str(employ_data)

Output: 'data.frame': 3 obs. of 3 variables: $ employee : chr "Ram" "Sham" "Jadu" $ salary : num 21000 23400 26800 $ startdate: Date, format: "2016-11-01" "2015-03-25" "2017-03-14"

R Packages

The primary location for obtaining R packages is CRAN.

You can obtain information about the available packages on CRAN with the available.packages() function.
a <- available.packages()

head(rownames(a), 30) # Show the names of the first 30 packages
Packages can be installed with the install.packages() function in R. To install a single package, pass the name of the lecture to the install.packages() function as the first argument.
The following code installs the ggplot2 package from CRAN.
install.packages(“ggplot2”)
You can install multiple R packages at once with a single call to install.packages(). Place the names of the R packages in a character vector.
install.packages(c(“caret”, “ggplot2”, “dplyr”))

Loading packages
Installing a package does not make it immediately available to you in R; you must load the package. The library() function is used to load packages into R. The following code is used to load the ggplot2 package into R. Do not put the package name in quotes.
library(ggplot2)
If you have Installed your packages without root access using the command install.packages(“ggplot2″, lib=”/data/Rpackages/”). Then to load use the below command.
library(ggplot2, lib.loc=”/data/Rpackages/”)
After loading a package, the functions exported by that package will be attached to the top of the search() list (after the workspace).
library(ggplot2)

search()

R – CSV() files

In R, we can read data from files stored outside the R environment. We can also write data into files that will be stored and accessed by the operating system. R can read and write into various file formats like CSV, Excel, XML, etc.

Getting and Setting the Working Directory

We can check which directory the R workspace is pointing to using the getwd() function. You can also set a new working directory using setwd()function.

# Get and print current working directory.
print(getwd())

# Set current working directory.
setwd("/web/com")

# Get and print current working directory.
print(getwd())

Output: [1] "/web/com/1441086124_2016" [1] "/web/com"

Input as CSV File

The CSV file is a text file in which the values in the columns are separated by a comma. Let’s consider the following data present in the file named input.csv.

You can create this file using windows notepad by copying and pasting this data. Save the file as input.csv using the save As All files(*.*) option in notepad.

Reading a CSV File

Following is a simple example of read.csv() function to read a CSV file available in your current working directory −

data <- read.csv("input.csv")
print(data)

  id,   name,    salary,   start_date,     dept

R- Charts and Graphs

R- Pie Charts

Pie charts are created with the function pie(x, labels=) where x is a non-negative numeric vector indicating the area of each slice and labels= notes a character vector of names for the slices.

Syntax

The basic syntax for creating a pie-chart using the R is −

pie(x, labels, radius, main, col, clockwise)

Following is the description of the parameters used −

x is a vector containing the numeric values used in the pie chart.
labels are used to give a description of the slices.
radius indicates the radius of the circle of the pie chart. (value between −1 and +1).
main indicates the title of the chart.
col indicates the color palette.
clockwise is a logical value indicating if the slices are drawn clockwise or anti-clockwise.

Simple Pie chart

# Simple Pie Chart
slices <- c(10, 12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls, main="Pie Chart of Countries")

3-D pie chart

The pie3D( ) function in the plotrix package provides 3D exploded pie charts.

# 3D Exploded Pie Chart
library(plotrix)
slices <- c(10, 12, 4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie3D(slices,labels=lbls,explode=0.1,
   main="Pie Chart of Countries ")

R -Bar Charts

A bar chart represents data in rectangular bars with a length of the bar proportional to the value of the variable. R uses the function barplot() to create bar charts. R can draw both vertical and Horizontal bars in the bar chart. In the bar chart, each of the bars can be given different colors.

Let us suppose, we have a vector of maximum temperatures (in degree Celsius) for seven days as follows.

max.temp <- c(22, 27, 26, 24, 23, 26, 28)
barplot(max.temp)

Some of the frequently used ones are, “main” to give the title, “xlab” and “ylab” to provide labels for the axes, names.arg for naming each bar, “col” to define color, etc.

We can also plot bars horizontally by providing the argument horiz=TRUE.

# barchart with added parameters
barplot(max.temp,
main = "Maximum Temperatures in a Week",
xlab = "Degree Celsius",
ylab = "Day",
names.arg = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),
col = "darkred",
horiz = TRUE)

Simply doing barplot(age) will not give us the required plot. It will plot 10 bars with height equal to the student’s age. But we want to know the number of students in each age category.

This count can be quickly found using the table() function, as shown below.

> table(age)
age
16 17 18 19 
1  2  6  1

Now plotting this data will give our required bar plot. Note below, that we define the argument “density” to shade the bars.

barplot(table(age),
main="Age Count of 10 Students",
xlab="Age",
ylab="Count",
border="red",
col="blue",
density=10
)

A histogram represents the frequencies of values of a variable bucketed into ranges. Histogram is similar to bar chat but the difference is it groups the values into continuous ranges. Each bar in histogram represents the height of the number of values present in that range.

R creates histogram using hist() function. This function takes a vector as an input and uses some more parameters to plot histograms.

Syntax

The basic syntax for creating a histogram using R is −

hist(v,main,xlab,xlim,ylim,breaks,col,border)

Following is the description of the parameters used −

v is a vector containing numeric values used in the histogram.
main indicates the title of the chart.
col is used to set the color of the bars.
border is used to set the border color of each bar.
xlab is used to give a description of the x-axis.
xlim is used to specify the range of values on the x-axis.
ylim is used to specify the range of values on the y-axis.
breaks are used to mention the width of each bar.

Example

A simple histogram is created using input vector, label, col, and border parameters.

The script given below will create and save the histogram in the current R working directory.

# Create data for the graph.
v <-  c(9,13,21,8,36,22,12,41,31,33,19)

# Give the chart file a name.
png(file = "histogram.png")

# Create the histogram.
hist(v,xlab = "Weight",col = "yellow",border = "blue")

# Save the file.
dev.off()

Range of X and Y values

To specify the range of values allowed in X axis and Y axis, we can use the xlim and ylim parameters.

The width of each bar can be decided by using breaks.

# Create data for the graph.
v <- c(9,13,21,8,36,22,12,41,31,33,19)

# Give the chart file a name.
png(file = "histogram_lim_breaks.png")

# Create the histogram.
hist(v,xlab = "Weight",col = "green",border = "red", xlim = c(0,40), ylim = c(0,5),
   breaks = 5)

# Save the file.
dev.off()

R vs SAS – Which Tool is Better?

The debate around data analytics tools has been going on forever. Each time a new one comes out, comparisons transpire. Although many aspects of the tool remain subjective, beginners want to know which tool is better to start with.
The most popular and widely used tools for data analytics are R and SAS. Both of them have been around for a long time and are often pitted against each other. So, let’s compare them based on the most relevant factors.

Availability and Cost: SAS is widely used in most private organizations as it is a commercial software. It is more expensive than any other data analytics tool available. It might thus be a bit difficult buying the software if you are an individual professional or a student starting out. On the other hand, R is an open source software and is completely free to use. Anyone can begin using it right away without having to spend a penny. So, regarding availability and cost, R is hands down the better tool.
Ease of learning: Since SAS is a commercial software, it has a whole lot of online resources available. Also, those who already know SQL might find it easier to adapt to SAS as it comes with PROC SQL option. The tool has a user-friendly GUI. It comes with an extensive documentation and tutorial base which can help early learners get started seamlessly. Whereas, the learning curve for R is quite steep. You need to learn to code at the root level and carrying out simple tasks demand a lot of time and effort with R. However, several forums and online communities post religiously about its usage.
Data Handling Capabilities: When it comes to data handling, both SAS and R perform well, but there are some caveats for the latter. While SAS can even churn through terabytes of data with ease, R might be constrained as it makes use of the available RAM in the machine. This can be a hassle for 32-bit systems with low RAM capacity. Due to this, R can at times become unresponsive or give an ‘out of memory’ error. Both of them can run parallel computations, support integrations for Hadoop, Spark, Cloudera and Apache Pig among others. Also, the availability of devices with better RAM capacity might negate the disadvantages of R.
Graphical Capabilities: Graphical capabilities or data visualization is the strongest forte of R. This is where SAS lacks behind in a major way. R has access to packages like GGPlot, RGIS, Lattice, and GGVIS among others which provide superior graphical competency. In comparison, Base SAS is struggling hard to catch up with the advancements in graphics and visualization in data analytics. Even the graphics packages available in SAS are poorly documented which makes them difficult to use.
Advancements in Tool: Advancements in the industry give way to advancements in tools, and both SAS and R hold up pretty well in this regard. SAS, being a corporate software, rolls out new features and technologies frequently with new versions of its software. However, the updates are not as fast as R since it is open source software and has many contributors throughout the world. Alternatively, the latest updates in SAS are pushed out after thorough testing, making them much more stable, and reliable than R. Both the tools come with a fair share of pros & cons.
Job Scenario: Currently, large corporations insist on using SAS, but SMEs and start-ups are increasingly opting for R, given that it’s free. The current job trend seems to show that while SAS is losing its momentum, R is gaining potential. The job scenario is on the cusp of change, and both the tools seem strong, but since R is on an uphill path, it can probably witness more jobs in the future, albeit not in huge corporates.
Deep Learning Support: While SAS has just begun work on adding deep learning support, R has added support for a few packages which enable deep learning capabilities in the tool. You can use KerasR and keras package in R which are mere interfaces for the original Keras package built on Python. Although none of the tools are excellent facilitators of deep learning, R has seen some recent active developments on this front.
Customer Service Support and Community: As one would expect from full-fledged commercial software, SAS offers excellent customer service support as well as the backing of a helpful community. Since R is free open-source software, expecting customer support will be hard to justify. However, it has a vast online community that can help you with almost everything. On the other hand, no matter what problem you face with SAS, you can immediately reach out to their customer support and get it solved without any hassles.

Final Verdict
As per estimations by the Economic Times, the analytics industry will grow to $16 billion till 2025 in India. If you wish to venture into this domain, there can’t be a better time. Just start learning the tool you think is better based on the comparison points above.

Original article source at: https://www.mygreatlearning.com

#r #programming

Tamale Moses

1669003576

Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python.

Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

Lists
Sets
Dictionaries
User-Defined Classes (It purely depends upon the user to define the characteristics)

Objects of built-in type that are immutable are:

Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
Strings
Tuples
Frozen Sets
User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

Identity – This refers to the address that the object refers to in the computer’s memory.
Type – This refers to the kind of object that is created. For example- integer, list, string etc.
Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

Check out this free python certificate course to get started with Python.

Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities

cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]

# Printing the elements from the list cities, separated by a comma & space

for city in cities:
		print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [2]: 0x1691d7de8c8

#Adding a new city to the list cities

cities.append(‘Chennai’)

#Printing the elements from the list cities, separated by a comma & space

for city in cities:
	print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [4]: 0x1691d7de8c8

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0.

#Creating a Tuple with variable name ‘foo’

foo = (1, 2)

#Changing the index[0] value from 1 to 3

foo[0] = 3
	
TypeError: 'tuple' object does not support item assignment

Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’

# Printing the elements of tuple weekdays

print(weekdays)

Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [2]: 0x1691cc35090

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

weekdays  +=  ‘Pythonday’,

#Printing the elements of tuple weekdays

print(weekdays)

Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [4]: 0x1691cc8ad68

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it. Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs: Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender

person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the tuple

print(person)

Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [2]: 0x1691ef47f88

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

person[0][1] = 4

#printing the updated tuple

print(person)

Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [4]: 0x1691ef47f88

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Also Read: Real-Time Object Detection Using TensorFlow

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

list1 = [(1, 2, 3), (4, 5, 6)]

#printing the list

print(list1)

Output [1]: [(1, 2, 3), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [2]: 0x1691d5b13c8

#changing object reference at index 0

list1[0] = (7, 8, 9)

#printing the list

Output [3]: [(7, 8, 9), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [4]: 0x1691d5b13c8

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’

x = 10

#printing the value of ‘x’

print(x)

Output [1]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(x)))

Output [2]: 0x538fb560

#creating an object of integer type with value 10 and reference variable name ‘y’

y = 10

#printing the value of ‘y’

print(y)

Output [3]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(y)))

Output [4]: 0x538fb560

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick check – Python Data Structures

Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.

FAQs

1. Difference between mutable vs immutable in Python?

Mutable Object	Immutable Object
State of the object can be modified after it is created.	State of the object can’t be modified once it is created.
They are not thread safe.	They are thread safe
Mutable classes are not final.	It is important to make the class final before creating an immutable object.

2. What are the mutable and immutable data types in Python?

Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

Some immutable data types are:

int, float, decimal, bool, string, tuple, range.

3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

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Original article source at: https://www.mygreatlearning.com

#python

Johathan McGlynn

1659283860

Activeinteraction: Manage Application Specific Business Logic Of Ruby

ActiveInteraction

ActiveInteraction manages application-specific business logic. It's an implementation of service objects designed to blend seamlessly into Rails.

ActiveInteraction gives you a place to put your business logic. It also helps you write safer code by validating that your inputs conform to your expectations. If ActiveModel deals with your nouns, then ActiveInteraction handles your verbs.

Installation
Basic usage
- Validations
Filters
- Basic Filters
- Advanced Filters
Rails
- Setup
- Controller
Advanced usage
Credits

API Documentation

Installation

Add it to your Gemfile:

gem 'active_interaction', '~> 5.1'

Or install it manually:

$ gem install active_interaction --version '~> 5.1'

This project uses Semantic Versioning. Check out GitHub releases for a detailed list of changes.

Basic usage

To define an interaction, create a subclass of ActiveInteraction::Base. Then you need to do two things:

Define your inputs. Use class filter methods to define what you expect your inputs to look like. For instance, if you need a boolean flag for pepperoni, use boolean :pepperoni. Check out the filters section for all the available options.

Define your business logic. Do this by implementing the #execute method. Each input you defined will be available as the type you specified. If any of the inputs are invalid, #execute won't be run. Filters are responsible for checking your inputs. Check out the validations section if you need more than that.

That covers the basics. Let's put it all together into a simple example that squares a number.

require 'active_interaction'

class Square < ActiveInteraction::Base
  float :x

  def execute
    x**2
  end
end

Call .run on your interaction to execute it. You must pass a single hash to .run. It will return an instance of your interaction. By convention, we call this an outcome. You can use the #valid? method to ask the outcome if it's valid. If it's invalid, take a look at its errors with #errors. In either case, the value returned from #execute will be stored in #result.

outcome = Square.run(x: 'two point one')
outcome.valid?
# => nil
outcome.errors.messages
# => {:x=>["is not a valid float"]}

outcome = Square.run(x: 2.1)
outcome.valid?
# => true
outcome.result
# => 4.41

You can also use .run! to execute interactions. It's like .run but more dangerous. It doesn't return an outcome. If the outcome would be invalid, it will instead raise an error. But if the outcome would be valid, it simply returns the result.

Square.run!(x: 'two point one')
# ActiveInteraction::InvalidInteractionError: X is not a valid float
Square.run!(x: 2.1)
# => 4.41

Validations

ActiveInteraction checks your inputs. Often you'll want more than that. For instance, you may want an input to be a string with at least one non-whitespace character. Instead of writing your own validation for that, you can use validations from ActiveModel.

These validations aren't provided by ActiveInteraction. They're from ActiveModel. You can also use any custom validations you wrote yourself in your interactions.

class SayHello < ActiveInteraction::Base
  string :name

  validates :name,
    presence: true

  def execute
    "Hello, #{name}!"
  end
end

When you run this interaction, two things will happen. First ActiveInteraction will check your inputs. Then ActiveModel will validate them. If both of those are happy, it will be executed.

SayHello.run!(name: nil)
# ActiveInteraction::InvalidInteractionError: Name is required

SayHello.run!(name: '')
# ActiveInteraction::InvalidInteractionError: Name can't be blank

SayHello.run!(name: 'Taylor')
# => "Hello, Taylor!"

Filters

You can define filters inside an interaction using the appropriate class method. Each method has the same signature:

Some symbolic names. These are the attributes to create.

An optional hash of options. Each filter supports at least these two options:

default is the fallback value to use if nil is given. To make a filter optional, set default: nil.

desc is a human-readable description of the input. This can be useful for generating documentation. For more information about this, read the descriptions section.

An optional block of sub-filters. Only array and hash filters support this. Other filters will ignore blocks when given to them.

Let's take a look at an example filter. It defines three inputs: x, y, and z. Those inputs are optional and they all share the same description ("an example filter").

array :x, :y, :z,
  default: nil,
  desc: 'an example filter' do
    # Some filters support sub-filters here.
  end

In general, filters accept values of the type they correspond to, plus a few alternatives that can be reasonably coerced. Typically the coercions come from Rails, so "1" can be interpreted as the boolean value true, the string "1", or the number 1.

Basic Filters

Array

In addition to accepting arrays, array inputs will convert ActiveRecord::Relations into arrays.

class ArrayInteraction < ActiveInteraction::Base
  array :toppings

  def execute
    toppings.size
  end
end

ArrayInteraction.run!(toppings: 'everything')
# ActiveInteraction::InvalidInteractionError: Toppings is not a valid array
ArrayInteraction.run!(toppings: [:cheese, 'pepperoni'])
# => 2

Use a block to constrain the types of elements an array can contain. Note that you can only have one filter inside an array block, and it must not have a name.

array :birthdays do
  date
end

For interface, object, and record filters, the name of the array filter will be singularized and used to determine the type of value passed. In the example below, the objects passed would need to be of type Cow.

array :cows do
  object
end

You can override this by passing the necessary information to the inner filter.

array :managers do
  object class: People
end

Errors that occur will be indexed based on the Rails configuration setting index_nested_attribute_errors. You can also manually override this setting with the :index_errors option. In this state is is possible to get multiple errors from a single filter.

class ArrayInteraction < ActiveInteraction::Base
  array :favorite_numbers, index_errors: true do
    integer
  end

  def execute
    favorite_numbers
  end
end

ArrayInteraction.run(favorite_numbers: [8, 'bazillion']).errors.details
=> {:"favorite_numbers[1]"=>[{:error=>:invalid_type, :type=>"array"}]}

With :index_errors set to false the error would have been:

{:favorite_numbers=>[{:error=>:invalid_type, :type=>"array"}]}

Boolean

Boolean filters convert the strings "1", "true", and "on" (case-insensitive) into true. They also convert "0", "false", and "off" into false. Blank strings will be treated as nil.

class BooleanInteraction < ActiveInteraction::Base
  boolean :kool_aid

  def execute
    'Oh yeah!' if kool_aid
  end
end

BooleanInteraction.run!(kool_aid: 1)
# ActiveInteraction::InvalidInteractionError: Kool aid is not a valid boolean
BooleanInteraction.run!(kool_aid: true)
# => "Oh yeah!"

File

File filters also accept TempFiles and anything that responds to #rewind. That means that you can pass the params from uploading files via forms in Rails.

class FileInteraction < ActiveInteraction::Base
  file :readme

  def execute
    readme.size
  end
end

FileInteraction.run!(readme: 'README.md')
# ActiveInteraction::InvalidInteractionError: Readme is not a valid file
FileInteraction.run!(readme: File.open('README.md'))
# => 21563

Hash

Hash filters accept hashes. The expected value types are given by passing a block and nesting other filters. You can have any number of filters inside a hash, including other hashes.

class HashInteraction < ActiveInteraction::Base
  hash :preferences do
    boolean :newsletter
    boolean :sweepstakes
  end

  def execute
    puts 'Thanks for joining the newsletter!' if preferences[:newsletter]
    puts 'Good luck in the sweepstakes!' if preferences[:sweepstakes]
  end
end

HashInteraction.run!(preferences: 'yes, no')
# ActiveInteraction::InvalidInteractionError: Preferences is not a valid hash
HashInteraction.run!(preferences: { newsletter: true, 'sweepstakes' => false })
# Thanks for joining the newsletter!
# => nil

Setting default hash values can be tricky. The default value has to be either nil or {}. Use nil to make the hash optional. Use {} if you want to set some defaults for values inside the hash.

hash :optional,
  default: nil
# => {:optional=>nil}

hash :with_defaults,
  default: {} do
    boolean :likes_cookies,
      default: true
  end
# => {:with_defaults=>{:likes_cookies=>true}}

By default, hashes remove any keys that aren't given as nested filters. To allow all hash keys, set strip: false. In general we don't recommend doing this, but it's sometimes necessary.

hash :stuff,
  strip: false

String

String filters define inputs that only accept strings.

class StringInteraction < ActiveInteraction::Base
  string :name

  def execute
    "Hello, #{name}!"
  end
end

StringInteraction.run!(name: 0xDEADBEEF)
# ActiveInteraction::InvalidInteractionError: Name is not a valid string
StringInteraction.run!(name: 'Taylor')
# => "Hello, Taylor!"

String filter strips leading and trailing whitespace by default. To disable it, set the strip option to false.

string :comment,
  strip: false

Symbol

Symbol filters define inputs that accept symbols. Strings will be converted into symbols.

class SymbolInteraction < ActiveInteraction::Base
  symbol :method

  def execute
    method.to_proc
  end
end

SymbolInteraction.run!(method: -> {})
# ActiveInteraction::InvalidInteractionError: Method is not a valid symbol
SymbolInteraction.run!(method: :object_id)
# => #<Proc:0x007fdc9ba94118>

Dates and times

Filters that work with dates and times behave similarly. By default, they all convert strings into their expected data types using .parse. Blank strings will be treated as nil. If you give the format option, they will instead convert strings using .strptime. Note that formats won't work with DateTime and Time filters if a time zone is set.

Date

class DateInteraction < ActiveInteraction::Base
  date :birthday

  def execute
    birthday + (18 * 365)
  end
end

DateInteraction.run!(birthday: 'yesterday')
# ActiveInteraction::InvalidInteractionError: Birthday is not a valid date
DateInteraction.run!(birthday: Date.new(1989, 9, 1))
# => #<Date: 2007-08-28 ((2454341j,0s,0n),+0s,2299161j)>

date :birthday,
  format: '%Y-%m-%d'

DateTime

class DateTimeInteraction < ActiveInteraction::Base
  date_time :now

  def execute
    now.iso8601
  end
end

DateTimeInteraction.run!(now: 'now')
# ActiveInteraction::InvalidInteractionError: Now is not a valid date time
DateTimeInteraction.run!(now: DateTime.now)
# => "2015-03-11T11:04:40-05:00"

date_time :start,
  format: '%Y-%m-%dT%H:%M:%S'

Time

In addition to converting strings with .parse (or .strptime), time filters convert numbers with .at.

class TimeInteraction < ActiveInteraction::Base
  time :epoch

  def execute
    Time.now - epoch
  end
end

TimeInteraction.run!(epoch: 'a long, long time ago')
# ActiveInteraction::InvalidInteractionError: Epoch is not a valid time
TimeInteraction.run!(epoch: Time.new(1970))
# => 1426068362.5136619

time :start,
  format: '%Y-%m-%dT%H:%M:%S'

Numbers

All numeric filters accept numeric input. They will also convert strings using the appropriate method from Kernel (like .Float). Blank strings will be treated as nil.

Decimal

class DecimalInteraction < ActiveInteraction::Base
  decimal :price

  def execute
    price * 1.0825
  end
end

DecimalInteraction.run!(price: 'one ninety-nine')
# ActiveInteraction::InvalidInteractionError: Price is not a valid decimal
DecimalInteraction.run!(price: BigDecimal(1.99, 2))
# => #<BigDecimal:7fe792a42028,'0.2165E1',18(45)>

To specify the number of significant digits, use the digits option.

decimal :dollars,
  digits: 2

Float

class FloatInteraction < ActiveInteraction::Base
  float :x

  def execute
    x**2
  end
end

FloatInteraction.run!(x: 'two point one')
# ActiveInteraction::InvalidInteractionError: X is not a valid float
FloatInteraction.run!(x: 2.1)
# => 4.41

Integer

class IntegerInteraction < ActiveInteraction::Base
  integer :limit

  def execute
    limit.downto(0).to_a
  end
end

IntegerInteraction.run!(limit: 'ten')
# ActiveInteraction::InvalidInteractionError: Limit is not a valid integer
IntegerInteraction.run!(limit: 10)
# => [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

When a String is passed into an integer input, the value will be coerced. A default base of 10 is used though it may be overridden with the base option. If a base of 0 is provided, the coercion will respect radix indicators present in the string.

class IntegerInteraction < ActiveInteraction::Base
  integer :limit1
  integer :limit2, base: 8
  integer :limit3, base: 0

  def execute
    [limit1, limit2, limit3]
  end
end

IntegerInteraction.run!(limit1: 71, limit2: 71, limit3: 71)
# => [71, 71, 71]
IntegerInteraction.run!(limit1: "071", limit2: "071", limit3: "0x71")
# => [71, 57, 113]
IntegerInteraction.run!(limit1: "08", limit2: "08", limit3: "08")
ActiveInteraction::InvalidInteractionError: Limit2 is not a valid integer, Limit3 is not a valid integer

Advanced Filters

Interface

Interface filters allow you to specify an interface that the passed value must meet in order to pass. The name of the interface is used to look for a constant inside the ancestor listing for the passed value. This allows for a variety of checks depending on what's passed. Class instances are checked for an included module or an inherited ancestor class. Classes are checked for an extended module or an inherited ancestor class. Modules are checked for an extended module.

class InterfaceInteraction < ActiveInteraction::Base
  interface :exception

  def execute
    exception
  end
end

InterfaceInteraction.run!(exception: Exception)
# ActiveInteraction::InvalidInteractionError: Exception is not a valid interface
InterfaceInteraction.run!(exception: NameError) # a subclass of Exception
# => NameError

You can use :from to specify a class or module. This would be the equivalent of what's above.

class InterfaceInteraction < ActiveInteraction::Base
  interface :error,
    from: Exception

  def execute
    error
  end
end

You can also create an anonymous interface on the fly by passing the methods option.

class InterfaceInteraction < ActiveInteraction::Base
  interface :serializer,
    methods: %i[dump load]

  def execute
    input = '{ "is_json" : true }'
    object = serializer.load(input)
    output = serializer.dump(object)

    output
  end
end

require 'json'

InterfaceInteraction.run!(serializer: Object.new)
# ActiveInteraction::InvalidInteractionError: Serializer is not a valid interface
InterfaceInteraction.run!(serializer: JSON)
# => "{\"is_json\":true}"

Object

Object filters allow you to require an instance of a particular class or one of its subclasses.

class Cow
  def moo
    'Moo!'
  end
end

class ObjectInteraction < ActiveInteraction::Base
  object :cow

  def execute
    cow.moo
  end
end

ObjectInteraction.run!(cow: Object.new)
# ActiveInteraction::InvalidInteractionError: Cow is not a valid object
ObjectInteraction.run!(cow: Cow.new)
# => "Moo!"

The class name is automatically determined by the filter name. If your filter name is different than your class name, use the class option. It can be either the class, a string, or a symbol.

object :dolly1,
  class: Sheep
object :dolly2,
  class: 'Sheep'
object :dolly3,
  class: :Sheep

If you have value objects or you would like to build one object from another, you can use the converter option. It is only called if the value provided is not an instance of the class or one of its subclasses. The converter option accepts a symbol that specifies a class method on the object class or a proc. Both will be passed the value and any errors thrown inside the converter will cause the value to be considered invalid. Any returned value that is not the correct class will also be treated as invalid. Any default that is not an instance of the class or subclass and is not nil will also be converted.

class ObjectInteraction < ActiveInteraction::Base
  object :ip_address,
    class: IPAddr,
    converter: :new

  def execute
    ip_address
  end
end

ObjectInteraction.run!(ip_address: '192.168.1.1')
# #<IPAddr: IPv4:192.168.1.1/255.255.255.255>

ObjectInteraction.run!(ip_address: 1)
# ActiveInteraction::InvalidInteractionError: Ip address is not a valid object

Record

Record filters allow you to require an instance of a particular class (or one of its subclasses) or a value that can be used to locate an instance of the object. If the value does not match, it will call find on the class of the record. This is particularly useful when working with ActiveRecord objects. Like an object filter, the class is derived from the name passed but can be specified with the class option. Any default that is not an instance of the class or subclass and is not nil will also be found. Blank strings passed in will be treated as nil.

class RecordInteraction < ActiveInteraction::Base
  record :encoding

  def execute
    encoding
  end
end

> RecordInteraction.run!(encoding: Encoding::US_ASCII)
=> #<Encoding:US-ASCII>

> RecordInteraction.run!(encoding: 'ascii')
=> #<Encoding:US-ASCII>

A different method can be specified by providing a symbol to the finder option.

Rails

ActiveInteraction plays nicely with Rails. You can use interactions to handle your business logic instead of models or controllers. To see how it all works, let's take a look at a complete example of a controller with the typical resourceful actions.

Setup

We recommend putting your interactions in app/interactions. It's also very helpful to group them by model. That way you can look in app/interactions/accounts for all the ways you can interact with accounts.

- app/
  - controllers/
    - accounts_controller.rb
  - interactions/
    - accounts/
      - create_account.rb
      - destroy_account.rb
      - find_account.rb
      - list_accounts.rb
      - update_account.rb
  - models/
    - account.rb
  - views/
    - account/
      - edit.html.erb
      - index.html.erb
      - new.html.erb
      - show.html.erb

Controller

Index

# GET /accounts
def index
  @accounts = ListAccounts.run!
end

Since we're not passing any inputs to ListAccounts, it makes sense to use .run! instead of .run. If it failed, that would mean we probably messed up writing the interaction.

class ListAccounts < ActiveInteraction::Base
  def execute
    Account.not_deleted.order(last_name: :asc, first_name: :asc)
  end
end

Show

Up next is the show action. For this one we'll define a helper method to handle raising the correct errors. We have to do this because calling .run! would raise an ActiveInteraction::InvalidInteractionError instead of an ActiveRecord::RecordNotFound. That means Rails would render a 500 instead of a 404.

# GET /accounts/:id
def show
  @account = find_account!
end

private

def find_account!
  outcome = FindAccount.run(params)

  if outcome.valid?
    outcome.result
  else
    fail ActiveRecord::RecordNotFound, outcome.errors.full_messages.to_sentence
  end
end

This probably looks a little different than you're used to. Rails commonly handles this with a before_filter that sets the @account instance variable. Why is all this interaction code better? Two reasons: One, you can reuse the FindAccount interaction in other places, like your API controller or a Resque task. And two, if you want to change how accounts are found, you only have to change one place.

Inside the interaction, we could use #find instead of #find_by_id. That way we wouldn't need the #find_account! helper method in the controller because the error would bubble all the way up. However, you should try to avoid raising errors from interactions. If you do, you'll have to deal with raised exceptions as well as the validity of the outcome.

class FindAccount < ActiveInteraction::Base
  integer :id

  def execute
    account = Account.not_deleted.find_by_id(id)

    if account
      account
    else
      errors.add(:id, 'does not exist')
    end
  end
end

Note that it's perfectly fine to add errors during execution. Not all errors have to come from checking or validation.

New

The new action will be a little different than the ones we've looked at so far. Instead of calling .run or .run!, it's going to initialize a new interaction. This is possible because interactions behave like ActiveModels.

# GET /accounts/new
def new
  @account = CreateAccount.new
end

Since interactions behave like ActiveModels, we can use ActiveModel validations with them. We'll use validations here to make sure that the first and last names are not blank. The validations section goes into more detail about this.

class CreateAccount < ActiveInteraction::Base
  string :first_name, :last_name

  validates :first_name, :last_name,
    presence: true

  def to_model
    Account.new
  end

  def execute
    account = Account.new(inputs)

    unless account.save
      errors.merge!(account.errors)
    end

    account
  end
end

We used a couple of advanced features here. The #to_model method helps determine the correct form to use in the view. Check out the section on forms for more about that. Inside #execute, we merge errors. This is a convenient way to move errors from one object to another. Read more about it in the errors section.

Create

The create action has a lot in common with the new action. Both of them use the CreateAccount interaction. And if creating the account fails, this action falls back to rendering the new action.

# POST /accounts
def create
  outcome = CreateAccount.run(params.fetch(:account, {}))

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:new)
  end
end

Note that we have to pass a hash to .run. Passing nil is an error.

Since we're using an interaction, we don't need strong parameters. The interaction will ignore any inputs that weren't defined by filters. So you can forget about params.require and params.permit because interactions handle that for you.

Destroy

The destroy action will reuse the #find_account! helper method we wrote earlier.

# DELETE /accounts/:id
def destroy
  DestroyAccount.run!(account: find_account!)
  redirect_to(accounts_url)
end

In this simple example, the destroy interaction doesn't do much. It's not clear that you gain anything by putting it in an interaction. But in the future, when you need to do more than account.destroy, you'll only have to update one spot.

class DestroyAccount < ActiveInteraction::Base
  object :account

  def execute
    account.destroy
  end
end

Edit

Just like the destroy action, editing uses the #find_account! helper. Then it creates a new interaction instance to use as a form object.

# GET /accounts/:id/edit
def edit
  account = find_account!
  @account = UpdateAccount.new(
    account: account,
    first_name: account.first_name,
    last_name: account.last_name)
end

The interaction that updates accounts is more complicated than the others. It requires an account to update, but the other inputs are optional. If they're missing, it'll ignore those attributes. If they're present, it'll update them.

class UpdateAccount < ActiveInteraction::Base
  object :account

  string :first_name, :last_name,
    default: nil

  validates :first_name,
    presence: true,
    unless: -> { first_name.nil? }
  validates :last_name,
    presence: true,
    unless: -> { last_name.nil? }

  def execute
    account.first_name = first_name if first_name.present?
    account.last_name = last_name if last_name.present?

    unless account.save
      errors.merge!(account.errors)
    end

    account
  end
end

Update

Hopefully you've gotten the hang of this by now. We'll use #find_account! to get the account. Then we'll build up the inputs for UpdateAccount. Then we'll run the interaction and either redirect to the updated account or back to the edit page.

# PUT /accounts/:id
def update
  inputs = { account: find_account! }.reverse_merge(params[:account])
  outcome = UpdateAccount.run(inputs)

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:edit)
  end
end

Advanced usage

Callbacks

ActiveSupport::Callbacks provides a powerful framework for defining callbacks. ActiveInteraction uses that framework to allow hooking into various parts of an interaction's lifecycle.

class Increment < ActiveInteraction::Base
  set_callback :filter, :before, -> { puts 'before filter' }

  integer :x

  set_callback :validate, :after, -> { puts 'after validate' }

  validates :x,
    numericality: { greater_than_or_equal_to: 0 }

  set_callback :execute, :around, lambda { |_interaction, block|
    puts '>>>'
    block.call
    puts '<<<'
  }

  def execute
    puts 'executing'
    x + 1
  end
end

Increment.run!(x: 1)
# before filter
# after validate
# >>>
# executing
# <<<
# => 2

In order, the available callbacks are filter, validate, and execute. You can set before, after, or around on any of them.

Composition

You can run interactions from within other interactions with #compose. If the interaction is successful, it'll return the result (just like if you had called it with .run!). If something went wrong, execution will halt immediately and the errors will be moved onto the caller.

class Add < ActiveInteraction::Base
  integer :x, :y

  def execute
    x + y
  end
end

class AddThree < ActiveInteraction::Base
  integer :x

  def execute
    compose(Add, x: x, y: 3)
  end
end

AddThree.run!(x: 5)
# => 8

To bring in filters from another interaction, use .import_filters. Combined with inputs, delegating to another interaction is a piece of cake.

class AddAndDouble < ActiveInteraction::Base
  import_filters Add

  def execute
    compose(Add, inputs) * 2
  end
end

Note that errors in composed interactions have a few tricky cases. See the errors section for more information about them.

Defaults

The default value for an input can take on many different forms. Setting the default to nil makes the input optional. Setting it to some value makes that the default value for that input. Setting it to a lambda will lazily set the default value for that input. That means the value will be computed when the interaction is run, as opposed to when it is defined.

Lambda defaults are evaluated in the context of the interaction, so you can use the values of other inputs in them.

# This input is optional.
time :a, default: nil
# This input defaults to `Time.at(123)`.
time :b, default: Time.at(123)
# This input lazily defaults to `Time.now`.
time :c, default: -> { Time.now }
# This input defaults to the value of `c` plus 10 seconds.
time :d, default: -> { c + 10 }

Descriptions

Use the desc option to provide human-readable descriptions of filters. You should prefer these to comments because they can be used to generate documentation. The interaction class has a .filters method that returns a hash of filters. Each filter has a #desc method that returns the description.

class Descriptive < ActiveInteraction::Base
  string :first_name,
    desc: 'your first name'
  string :last_name,
    desc: 'your last name'
end

Descriptive.filters.each do |name, filter|
  puts "#{name}: #{filter.desc}"
end
# first_name: your first name
# last_name: your last name

Errors

ActiveInteraction provides detailed errors for easier introspection and testing of errors. Detailed errors improve on regular errors by adding a symbol that represents the type of error that has occurred. Let's look at an example where an item is purchased using a credit card.

class BuyItem < ActiveInteraction::Base
  object :credit_card, :item
  hash :options do
    boolean :gift_wrapped
  end

  def execute
    order = credit_card.purchase(item)
    notify(credit_card.account)
    order
  end

  private def notify(account)
    # ...
  end
end

Having missing or invalid inputs causes the interaction to fail and return errors.

outcome = BuyItem.run(item: 'Thing', options: { gift_wrapped: 'yes' })
outcome.errors.messages
# => {:credit_card=>["is required"], :item=>["is not a valid object"], :"options.gift_wrapped"=>["is not a valid boolean"]}

Determining the type of error based on the string is difficult if not impossible. Calling #details instead of #messages on errors gives you the same list of errors with a testable label representing the error.

outcome.errors.details
# => {:credit_card=>[{:error=>:missing}], :item=>[{:error=>:invalid_type, :type=>"object"}], :"options.gift_wrapped"=>[{:error=>:invalid_type, :type=>"boolean"}]}

Detailed errors can also be manually added during the execute call by passing a symbol to #add instead of a string.

def execute
  errors.add(:monster, :no_passage)
end

ActiveInteraction also supports merging errors. This is useful if you want to delegate validation to some other object. For example, if you have an interaction that updates a record, you might want that record to validate itself. By using the #merge! helper on errors, you can do exactly that.

class UpdateThing < ActiveInteraction::Base
  object :thing

  def execute
    unless thing.save
      errors.merge!(thing.errors)
    end

    thing
  end
end

When a composed interaction fails, its errors are merged onto the caller. This generally produces good error messages, but there are a few cases to look out for.

class Inner < ActiveInteraction::Base
  boolean :x, :y
end

class Outer < ActiveInteraction::Base
  string :x
  boolean :z, default: nil

  def execute
    compose(Inner, x: x, y: z)
  end
end

outcome = Outer.run(x: 'yes')
outcome.errors.details
# => { :x    => [{ :error => :invalid_type, :type => "boolean" }],
#      :base => [{ :error => "Y is required" }] }
outcome.errors.full_messages.join(' and ')
# => "X is not a valid boolean and Y is required"

Since both interactions have an input called x, the inner error for that input is moved to the x error on the outer interaction. This results in a misleading error that claims the input x is not a valid boolean even though it's a string on the outer interaction.

Since only the inner interaction has an input called y, the inner error for that input is moved to the base error on the outer interaction. This results in a confusing error that claims the input y is required even though it's not present on the outer interaction.

Forms

The outcome returned by .run can be used in forms as though it were an ActiveModel object. You can also create a form object by calling .new on the interaction.

Given an application with an Account model we'll create a new Account using the CreateAccount interaction.

# GET /accounts/new
def new
  @account = CreateAccount.new
end

# POST /accounts
def create
  outcome = CreateAccount.run(params.fetch(:account, {}))

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:new)
  end
end

The form used to create a new Account has slightly more information on the form_for call than you might expect.

<%= form_for @account, as: :account, url: accounts_path do |f| %>
  <%= f.text_field :first_name %>
  <%= f.text_field :last_name %>
  <%= f.submit 'Create' %>
<% end %>

This is necessary because we want the form to act like it is creating a new Account. Defining to_model on the CreateAccount interaction tells the form to treat our interaction like an Account.

class CreateAccount < ActiveInteraction::Base
  # ...

  def to_model
    Account.new
  end
end

Now our form_for call knows how to generate the correct URL and param name (i.e. params[:account]).

# app/views/accounts/new.html.erb
<%= form_for @account do |f| %>
  <%# ... %>
<% end %>

If you have an interaction that updates an Account, you can define to_model to return the object you're updating.

class UpdateAccount < ActiveInteraction::Base
  # ...

  object :account

  def to_model
    account
  end
end

ActiveInteraction also supports formtastic and simple_form. The filters used to define the inputs on your interaction will relay type information to these gems. As a result, form fields will automatically use the appropriate input type.

Shared input options

It can be convenient to apply the same options to a bunch of inputs. One common use case is making many inputs optional. Instead of setting default: nil on each one of them, you can use with_options to reduce duplication.

with_options default: nil do
  date :birthday
  string :name
  boolean :wants_cake
end

Optional inputs

Optional inputs can be defined by using the :default option as described in the filters section. Within the interaction, provided and default values are merged to create inputs. There are times where it is useful to know whether a value was passed to run or the result of a filter default. In particular, it is useful when nil is an acceptable value. For example, you may optionally track your users' birthdays. You can use the inputs.given? predicate to see if an input was even passed to run. With inputs.given? you can also check the input of a hash or array filter by passing a series of keys or indexes to check.

class UpdateUser < ActiveInteraction::Base
  object :user
  date :birthday,
    default: nil

  def execute
    user.birthday = birthday if inputs.given?(:birthday)
    errors.merge!(user.errors) unless user.save
    user
  end
end

Now you have a few options. If you don't want to update their birthday, leave it out of the hash. If you want to remove their birthday, set birthday: nil. And if you want to update it, pass in the new value as usual.

user = User.find(...)

# Don't update their birthday.
UpdateUser.run!(user: user)

# Remove their birthday.
UpdateUser.run!(user: user, birthday: nil)

# Update their birthday.
UpdateUser.run!(user: user, birthday: Date.new(2000, 1, 2))

Translations

ActiveInteraction is i18n aware out of the box! All you have to do is add translations to your project. In Rails, these typically go into config/locales. For example, let's say that for some reason you want to print everything out backwards. Simply add translations for ActiveInteraction to your hsilgne locale.

# config/locales/hsilgne.yml
hsilgne:
  active_interaction:
    types:
      array: yarra
      boolean: naeloob
      date: etad
      date_time: emit etad
      decimal: lamiced
      file: elif
      float: taolf
      hash: hsah
      integer: regetni
      interface: ecafretni
      object: tcejbo
      string: gnirts
      symbol: lobmys
      time: emit
    errors:
      messages:
        invalid: dilavni si
        invalid_type: '%{type} dilav a ton si'
        missing: deriuqer si

Then set your locale and run interactions like normal.

class I18nInteraction < ActiveInteraction::Base
  string :name
end

I18nInteraction.run(name: false).errors.messages[:name]
# => ["is not a valid string"]

I18n.locale = :hsilgne
I18nInteraction.run(name: false).errors.messages[:name]
# => ["gnirts dilav a ton si"]

Everything else works like an activerecord entry. For example, to rename an attribute you can use attributes.

Here we'll rename the num attribute on an interaction named product:

en:
  active_interaction:
    attributes:
      product:
        num: 'Number'

Credits

ActiveInteraction is brought to you by Aaron Lasseigne. Along with Aaron, Taylor Fausak helped create and maintain ActiveInteraction but has since moved on.

If you want to contribute to ActiveInteraction, please read our contribution guidelines. A complete list of contributors is available on GitHub.

ActiveInteraction is licensed under the MIT License.

Author: AaronLasseigne
Source code: https://github.com/AaronLasseigne/active_interaction
License: MIT license

#ruby

Muhammad Price

1659511140

Roadie: Making HTML Emails Comfortable for The Ruby Rockstars

Roadie


:warning:	This gem is now in [passive maintenance mode][passive]. [(more)][passive]

Making HTML emails comfortable for the Ruby rockstars

Roadie tries to make sending HTML emails a little less painful by inlining stylesheets and rewriting relative URLs for you inside your emails.

How does it work?

Email clients have bad support for stylesheets, and some of them blocks stylesheets from downloading. The easiest way to handle this is to work with inline styles (style="..."), but that is error prone and hard to work with as you cannot use classes and/or reuse styling over your HTML.

This gem makes this easier by automatically inlining stylesheets into the document. You give Roadie your CSS, or let it find it by itself from the <link> and <style> tags in the markup, and it will go through all of the selectors assigning the styles to the matching elements. Careful attention has been put into selectors being applied in the correct order, so it should behave just like in the browser.

"Dynamic" selectors (:hover, :visited, :focus, etc.), or selectors not understood by Nokogiri will be inlined into a single <style> element for those email clients that support it. This changes specificity a great deal for these rules, so it might not work 100% out of the box. (See more about this below)

Roadie also rewrites all relative URLs in the email to an absolute counterpart, making images you insert and those referenced in your stylesheets work. No more headaches about how to write the stylesheets while still having them work with emails from your acceptance environments. You can disable this on specific elements using a data-roadie-ignore marker.

Features

Writes CSS styles inline.
- Respects !important styles.
- Does not overwrite styles already present in the style attribute of tags.
- Supports the same CSS selectors as Nokogiri; use CSS3 selectors in your emails!
- Keeps :hover, @media { ... } and friends around in a separate <style> element.
Makes image urls absolute.
- Hostname and port configurable on a per-environment basis.
- Can be disabled on individual elements.
Makes link hrefs and img srcs absolute.
Automatically adds proper HTML skeleton when missing; you don't have to create a layout for emails.
- Also supports HTML fragments / partial documents, where layout is not added.
Allows you to inject stylesheets in a number of ways, at runtime.
Removes data-roadie-ignore markers before finishing the HTML.

Install & Usage

Add this gem to your Gemfile as recommended by Rubygems and run bundle install.

gem 'roadie', '~> 4.0'

Your document instance can be configured with several options:

url_options - Dictates how absolute URLs should be built.
keep_uninlinable_css - Set to false to skip CSS that cannot be inlined.
merge_media_queries - Set to false to not group media queries. Some users might prefer to not group rules within media queries because it will result in rules getting reordered. e.g.

@media(max-width: 600px) { .col-6 { display: block; } }
@media(max-width: 400px) { .col-12 { display: inline-block; } }
@media(max-width: 600px) { .col-12 { display: block; } }

will become

@media(max-width: 600px) { .col-6 { display: block; } .col-12 { display: block; } }
@media(max-width: 400px) { .col-12 { display: inline-block; } }

asset_providers - A list of asset providers that are invoked when CSS files are referenced. See below.
external_asset_providers - A list of asset providers that are invoked when absolute CSS URLs are referenced. See below.
before_transformation - A callback run before transformation starts.
after_transformation - A callback run after transformation is completed.

Making URLs absolute

In order to make URLs absolute you need to first configure the URL options of the document.

html = '... <a href="/about-us">Read more!</a> ...'
document = Roadie::Document.new html
document.url_options = {host: "myapp.com", protocol: "https"}
document.transform
  # => "... <a href=\"https://myapp.com/about-us\">Read more!</a> ..."

The following URLs will be rewritten for you:

a[href] (HTML)
img[src] (HTML)
url() (CSS)

You can disable individual elements by adding an data-roadie-ignore marker on them. CSS will still be inlined on those elements, but URLs will not be rewritten.

<a href="|UNSUBSCRIBE_URL|" data-roadie-ignore>Unsubscribe</a>

Referenced stylesheets

By default, style and link elements in the email document's head are processed along with the stylesheets and removed from the head.

You can set a special data-roadie-ignore attribute on style and link tags that you want to ignore (the attribute will be removed, however). This is the place to put things like :hover selectors that you want to have for email clients allowing them.

Style and link elements with media="print" are also ignored.

<head>
  <link rel="stylesheet" type="text/css" href="/assets/emails/rock.css">         <!-- Will be inlined with normal providers -->
  <link rel="stylesheet" type="text/css" href="http://www.metal.org/metal.css">  <!-- Will be inlined with external providers, *IF* specified; otherwise ignored. -->
  <link rel="stylesheet" type="text/css" href="/assets/jazz.css" media="print">  <!-- Will NOT be inlined; print style -->
  <link rel="stylesheet" type="text/css" href="/ambient.css" data-roadie-ignore> <!-- Will NOT be inlined; ignored -->
  <style></style>                    <!-- Will be inlined -->
  <style data-roadie-ignore></style> <!-- Will NOT be inlined; ignored -->
</head>

Roadie will use the given asset providers to look for the actual CSS that is referenced. If you don't change the default, it will use the Roadie::FilesystemProvider which looks for stylesheets on the filesystem, relative to the current working directory.

Example:

# /home/user/foo/stylesheets/primary.css
body { color: green; }

# /home/user/foo/script.rb
html = <<-HTML
<html>
  <head>
  <link rel="stylesheet" type="text/css" href="/stylesheets/primary.css">
  </head>
  <body>
  </body>
</html>
HTML

Dir.pwd # => "/home/user/foo"
document = Roadie::Document.new html
document.transform # =>
                   # <!DOCTYPE html>
                   # <html>
                   #   <head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"></head>
                   #   <body style="color:green;"></body>
                   # </html>

If a referenced stylesheet cannot be found, the #transform method will raise an Roadie::CssNotFound error. If you instead want to ignore missing stylesheets, you can use the NullProvider.

Configuring providers

You can write your own providers if you need very specific behavior for your app, or you can use the built-in providers. Providers come in two groups: normal and external. Normal providers handle paths without host information (/style/foo.css) while external providers handle URLs with host information (//example.com/foo.css, localhost:3001/bar.css, and so on).

The default configuration is to not have any external providers configured, which will cause those referenced stylesheets to be ignored. Adding one or more providers for external assets causes all of them to be searched and inlined, so if you only want this to happen to specific stylesheets you need to add ignore markers to every other styleshheet (see above).

Included providers:

FilesystemProvider – Looks for files on the filesystem, relative to the given directory unless otherwise specified.
ProviderList – Wraps a list of other providers and searches them in order. The asset_providers setting is an instance of this. It behaves a lot like an array, so you can push, pop, shift and unshift to it.
NullProvider – Does not actually provide anything, it always finds empty stylesheets. Use this in tests or if you want to ignore stylesheets that cannot be found by your other providers (or if you want to force the other providers to never run).
NetHttpProvider – Downloads stylesheets using Net::HTTP. Can be given a whitelist of hosts to download from.
CachedProvider – Wraps another provider (or ProviderList) and caches responses inside the provided cache store.
PathRewriterProvider – Rewrites the passed path and then passes it on to another provider (or ProviderList).

If you want to search several locations on the filesystem, you can declare that:

document.asset_providers = [
  Roadie::FilesystemProvider.new(App.root.join("resources", "stylesheets")),
  Roadie::FilesystemProvider.new(App.root.join("system", "uploads", "stylesheets")),
]

`NullProvider`

If you want to ignore stylesheets that cannot be found instead of crashing, push the NullProvider to the end:

# Don't crash on missing assets
document.asset_providers << Roadie::NullProvider.new

# Don't download assets in tests
document.external_asset_providers.unshift Roadie::NullProvider.new

Note: This will cause the referenced stylesheet to be removed from the source code, so email client will never see it either.

`NetHttpProvider`

The NetHttpProvider will download the URLs that is is given using Ruby's standard Net::HTTP library.

You can give it a whitelist of hosts that downloads are allowed from:

document.external_asset_providers << Roadie::NetHttpProvider.new(
  whitelist: ["myapp.com", "assets.myapp.com", "cdn.cdnnetwork.co.jp"],
)
document.external_asset_providers << Roadie::NetHttpProvider.new # Allows every host

`CachedProvider`

You might want to cache providers from working several times. If you are sending several emails quickly from the same process, this might also save a lot of time on parsing the stylesheets if you use in-memory storage such as a hash.

You can wrap any other kind of providers with it, even a ProviderList:

document.external_asset_providers = Roadie::CachedProvider.new(document.external_asset_providers, my_cache)

If you don't pass a cache backend, it will use a normal Hash. The cache store must follow this protocol:

my_cache["key"] = some_stylesheet_instance # => #<Roadie::Stylesheet instance>
my_cache["key"]                            # => #<Roadie::Stylesheet instance>
my_cache["missing"]                        # => nil

Warning: The default Hash store will never be cleared, so make sure you don't allow the number of unique asset paths to grow too large in a single run. This is especially important if you run Roadie in a daemon that accepts arbritary documents, and/or if you use hash digests in your filenames. Making a new instance of CachedProvider will use a new Hash instance.

You can implement your own custom cache store by implementing the [] and []= methods.

class MyRoadieMemcacheStore
  def initialize(memcache)
    @memcache = memcache
  end

  def [](path)
    css = memcache.read("assets/#{path}/css")
    if css
      name = memcache.read("assets/#{path}/name") || "cached #{path}"
      Roadie::Stylesheet.new(name, css)
    end
  end

  def []=(path, stylesheet)
    memcache.write("assets/#{path}/css", stylesheet.to_s)
    memcache.write("assets/#{path}/name", stylesheet.name)
    stylesheet # You need to return the set Stylesheet
  end
end

document.external_asset_providers = Roadie::CachedProvider.new(
  document.external_asset_providers,
  MyRoadieMemcacheStore.new(MemcacheClient.instance)
)

If you are using Rspec, you can test your implementation by using the shared examples for the "roadie cache store" role:

require "roadie/rspec"

describe MyRoadieMemcacheStore do
  let(:memcache_client) { MemcacheClient.instance }
  subject { MyRoadieMemcacheStore.new(memcache_client) }

  it_behaves_like "roadie cache store" do
    before { memcache_client.clear }
  end
end

`PathRewriterProvider`

With this provider, you can rewrite the paths that are searched in order to more easily support another provider. Examples could include rewriting absolute URLs into something that can be found on the filesystem, or to access internal hosts instead of external ones.

filesystem = Roadie::FilesystemProvider.new("assets")
document.asset_providers << Roadie::PathRewriterProvider.new(filesystem) do |path|
  path.sub('stylesheets', 'css').downcase
end

document.external_asset_providers = Roadie::PathRewriterProvider.new(filesystem) do |url|
  if url =~ /myapp\.com/
    URI.parse(url).path.sub(%r{^/assets}, '')
  else
    url
  end
end

You can also wrap a list, for example to implement external_asset_providers by composing the normal asset_providers:

document.external_asset_providers =
  Roadie::PathRewriterProvider.new(document.asset_providers) do |url|
    URI.parse(url).path
  end

Writing your own provider

Writing your own provider is also easy. You need to provide:

#find_stylesheet(name), returning either a Roadie::Stylesheet or nil.
#find_stylesheet!(name), returning either a Roadie::Stylesheet or raising Roadie::CssNotFound.

class UserAssetsProvider
  def initialize(user_collection)
    @user_collection = user_collection
  end

  def find_stylesheet(name)
    if name =~ %r{^/users/(\d+)\.css$}
      user = @user_collection.find_user($1)
      Roadie::Stylesheet.new("user #{user.id} stylesheet", user.stylesheet)
    end
  end

  def find_stylesheet!(name)
    find_stylesheet(name) or
      raise Roadie::CssNotFound.new(
        css_name: name, message: "does not match a user stylesheet", provider: self
      )
  end

  # Instead of implementing #find_stylesheet!, you could also:
  #     include Roadie::AssetProvider
  # That will give you a default implementation without any error message. If
  # you have multiple error cases, it's recommended that you implement
  # #find_stylesheet! without #find_stylesheet and raise with an explanatory
  # error message.
end

# Try to look for a user stylesheet first, then fall back to normal filesystem lookup.
document.asset_providers = [
  UserAssetsProvider.new(app),
  Roadie::FilesystemProvider.new('./stylesheets'),
]

You can test for compliance by using the built-in RSpec examples:

require 'spec_helper'
require 'roadie/rspec'

describe MyOwnProvider do
  # Will use the default `subject` (MyOwnProvider.new)
  it_behaves_like "roadie asset provider", valid_name: "found.css", invalid_name: "does_not_exist.css"

  # Extra setup just for these tests:
  it_behaves_like "roadie asset provider", valid_name: "found.css", invalid_name: "does_not_exist.css" do
    subject { MyOwnProvider.new(...) }
    before { stub_dependencies }
  end
end

Keeping CSS that is impossible to inline

Some CSS is impossible to inline properly. :hover and ::after comes to mind. Roadie tries its best to keep these around by injecting them inside a new <style> element in the <head> (or at the beginning of the partial if transforming a partial document).

The problem here is that Roadie cannot possible adjust the specificity for you, so they will not apply the same way as they did before the styles were inlined.

Another caveat is that a lot of email clients does not support this (which is the entire point of inlining in the first place), so don't put anything important in here. Always handle the case of these selectors not being part of the email.

Specificity problems

Inlined styles will have much higher specificity than styles in a <style>. Here's an example:

<style>p:hover { color: blue; }</style>
<p style="color: green;">Hello world</p>

When hovering over this <p>, the color will not change as the color: green rule takes precedence. You can get it to work by adding !important to the :hover rule.

It would be foolish to try to automatically inject !important on every rule automatically, so this is a manual process.

Turning it off

If you'd rather skip this and have the styles not possible to inline disappear, you can turn off this feature by setting the keep_uninlinable_css option to false.

document.keep_uninlinable_css = false

Callbacks

Callbacks allow you to do custom work on documents before they are transformed. The Nokogiri document tree is passed to the callable along with the Roadie::Document instance:

class TrackNewsletterLinks
  def call(dom, document)
    dom.css("a").each { |link| fix_link(link) }
  end

  def fix_link(link)
    divider = (link['href'] =~ /?/ ? '&' : '?')
    link['href'] = link['href'] + divider + 'source=newsletter'
  end
end

document.before_transformation = ->(dom, document) {
  logger.debug "Inlining document with title #{dom.at_css('head > title').try(:text)}"
}
document.after_transformation = TrackNewsletterLinks.new

XHTML vs HTML

You can configure the underlying HTML/XML engine to output XHTML or HTML (which is the default). One usecase for this is that { tokens usually gets escaped to {, which would be a problem if you then pass the resulting HTML on to some other templating engine that uses those tokens (like Handlebars or Mustache).

document.mode = :xhtml

This will also affect the emitted <!DOCTYPE> if transforming a full document. Partial documents does not have a <!DOCTYPE>.

Build Status

Tested with Github CI using:

MRI 2.6
MRI 2.7
MRI 3.0
MRI 3.1

Let me know if you want any other runtime supported officially.

Versioning

This project follows Semantic Versioning and has been since version 1.0.0.

FAQ

Why is my markup changed in subtle ways?

Roadie uses Nokogiri to parse and regenerate the HTML of your email, which means that some unintentional changes might show up.

One example would be that Nokogiri might remove your  s in some cases.

Another example is Nokogiri's lack of HTML5 support, so certain new element might have spaces removed. I recommend you don't use HTML5 in emails anyway because of bad email client support (that includes web mail!).

I'm getting segmentation faults (or other C-like problems)! What should I do?

Roadie uses Nokogiri to parse the HTML of your email, so any C-like problems like segfaults are likely in that end. The best way to fix this is to first upgrade libxml2 on your system and then reinstall Nokogiri. Instructions on how to do this on most platforms, see Nokogiri's official install guide.

What happened to my `@keyframes`?

The CSS Parser used in Roadie does not handle keyframes. I don't think any email clients do either, but if you want to keep on trying you can add them manually to a <style> element (or a separate referenced stylesheet) and tell Roadie not to touch them.

My `@media` queries are reordered, how can I fix this?

Different @media query blocks with the same conditions are merged by default, which will change the order in some cases. You can disable this by setting merge_media_queries to false. (See Install & Usage section above).

How do I get rid of the `<body>` elements that are added?

It sounds like you want to transform a partial document. Maybe you are building partials or template fragments to later place in other documents. Use Document#transform_partial instead of Document#transform in order to treat the HTML as a partial document.

Can I skip URL rewriting on a specific element?

If you add the data-roadie-ignore attribute on an element, URL rewriting will not be performed on that element. This could be really useful for you if you intend to send the email through some other rendering pipeline that replaces some placeholders/variables.

<a href="/about-us">About us</a>
<a href="|UNSUBSCRIBE_URL|" data-roadie-ignore>Unsubscribe</a>

Note that this will not skip CSS inlining on the element; it will still get the correct styles applied.

What should I do about "Invalid URL" errors?

If the URL is invalid on purpose, see Can I skip URL rewriting on a specific element? above. Otherwise, you can try to parse it yourself using Ruby's URI class and see if you can figure it out.

require "uri"
URI.parse("https://example.com/best image.jpg") # raises
URI.parse("https://example.com/best%20image.jpg") # Works!

Documentation

Running specs

bundle install
rake

Security

Roadie is set up with the assumption that all CSS and HTML passing through it is under your control. It is not recommended to run arbritary HTML with the default settings.

Care has been given to try to secure all file system accesses, but it is never guaranteed that someone cannot access something they should not be able to access.

In order to secure Roadie against file system access, only use your own asset providers that you yourself can secure against your particular environment.

If you have found any security vulnerability, please email me at magnus.bergmark+security@gmail.com to disclose it. For very sensitive issues, please use my public GPG key. You can also encrypt your message with my public key and open an issue if you do not want to email me directly. Thank you.

History and contributors

This gem was previously tied to Rails. It is now framework-agnostic and supports any type of HTML documents. If you want to use it with Rails, check out roadie-rails.

Major contributors to Roadie:

Arttu Tervo (arttu) - Original Asset pipeline support
Ryunosuke SATO (tricknotes) - Initial Rails 4 support
Leung Ho Kuen (PikachuEXE) - A lot of bug reporting and triaging.

You can see all contributors on GitHub.

License

(The MIT License)

Magnus Bergmark magnus.bergmark@gmail.com

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the ‘Software’), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED ‘AS IS’, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Author: Mange
Source code: https://github.com/Mange/roadie
License: MIT license

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What happened to my `@keyframes`?

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