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When you own an e-commerce site, attracting visitors is just as important as achieving the best possible conversion rate. I have collected some useful tips to improve your e-commerce conversion rate.
Test to increase the conversion rate of a website
If you already have an e-commerce site, be sure to thoroughly test your web methods and solutions. First, you need to find what to test on your website. Everything can be tested, from the main title and layout to the navigation process. Testing your call-to-action buttons is also important, size alone doesn’t matter, choosing the right label is also essential.
Don’t ask for too many details
Online consumers appreciate quick and easy shopping processes, which is why you should try not to ask too much of them. For example, when asking about bank details, it is not mandatory to ask for the consumer’s name: it depends on your payment processor. If you can’t avoid the consumer name question, you can always avoid asking the family or workplace question.
Add social media and registration buttons
Most digital marketers admit that social media buttons can significantly increase the conversion rate. When visitors are allowed to use their social media accounts instead of signing up, they are more likely to reach the checkout process. In addition, some consumers like to share their latest purchases on their social network: this can attract more buyers which would lead to a higher conversion rate.
Call to action
As the owner of an e-commerce website, you should create relevant call-to-action buttons on your web page. Well-designed call-to-action buttons increase the conversion rate, however, it is essential to test them. For example, there can be a big difference between buying and ordering when it comes to conversion. Action words like “Order” instead of “Buy” or “Test” instead of “Get started” are better for your traffic and conversion.
Well-built e-commerce site to improve the conversion rate
Every detail counts on your e-commerce site. If you want to be successful, you should consider adding recommended products and the remaining stock of your items. Recommended items are useful in order to sell more stock and help consumers find related products more quickly. The number of items remaining in inventory is critical so that buyers can know their options. It would be annoying for an e-commerce site for a consumer to want to buy an item but that could not be done because it is no longer available.
Various payment methods for an e-commerce site
It might shock you, but not everyone uses Paypal. Consumers prefer to pay by check, credit cards, or bills. In addition, by offering your buyers bonuses during checkout, your conversion rate can benefit. Every now and then when consumers see a bonus field, they try to find a coupon code on Google. During this time, they are not on your website and can bounce back easily.
Increase trust and engagement on your merchant site
Including consumer reviews for items and a simple refund policy can increase consumer confidence. Buyers also like when a brand is humanized, this can be done via a photo or video on the landing page. This marketing technique can be done with your team or a photogenic person.
A free shipment for the B2C industry and/or a free trial for the B2B industry are effective in converting your website design. If you are giving away something for free, the likelihood that consumers will buy something from you later is high. Not all customers like to call customer service. That’s why any e-commerce site should have a live chat option.
In short, when you want to create a successful e-commerce site, every detail is important. You need to analyze and test the process in your sales funnel. This is not just your landing page, but all of the subpages that are essential in order to secure your victory.
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Install via pip:
$ pip install pytumblr
Install from source:
$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install
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:
interactive_console.py
tool (if you already have a consumer key & secret)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
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
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.
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
#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"])
# get posts with a given tag
client.tagged(tag, **params)
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.
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
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In this Python article, let's learn about 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 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 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.
Objects of built-in type that are mutable are:
Objects of built-in type that are immutable are:
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.
In Python, everything is treated as an object. Every object has these three attributes:
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.
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
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
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.
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.
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. |
list, dictionary, set, user-defined classes.
int, float, decimal, bool, string, tuple, range.
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.)
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.
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.
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
1653075360
HAML-Lint
haml-lint
is a tool to help keep your HAML files clean and readable. In addition to HAML-specific style and lint checks, it integrates with RuboCop to bring its powerful static analysis tools to your HAML documents.
You can run haml-lint
manually from the command line, or integrate it into your SCM hooks.
gem install haml_lint
If you'd rather install haml-lint
using bundler
, don't require
it in your Gemfile
:
gem 'haml_lint', require: false
Then you can still use haml-lint
from the command line, but its source code won't be auto-loaded inside your application.
Run haml-lint
from the command line by passing in a directory (or multiple directories) to recursively scan:
haml-lint app/views/
You can also specify a list of files explicitly:
haml-lint app/**/*.html.haml
haml-lint
will output any problems with your HAML, including the offending filename and line number.
haml-lint
assumes all files are encoded in UTF-8.
Command Line Flag | Description |
---|---|
--auto-gen-config | Generate a configuration file acting as a TODO list |
--auto-gen-exclude-limit | Number of failures to allow in the TODO list before the entire rule is excluded |
-c /--config | Specify which configuration file to use |
-e /--exclude | Exclude one or more files from being linted |
-i /--include-linter | Specify which linters you specifically want to run |
-x /--exclude-linter | Specify which linters you don't want to run |
-r /--reporter | Specify which reporter you want to use to generate the output |
-p /--parallel | Run linters in parallel using available CPUs |
--fail-fast | Specify whether to fail after the first file with lint |
--fail-level | Specify the minimum severity (warning or error) for which the lint should fail |
--[no-]color | Whether to output in color |
--[no-]summary | Whether to output a summary in the default reporter |
--show-linters | Show all registered linters |
--show-reporters | Display available reporters |
-h /--help | Show command line flag documentation |
-v /--version | Show haml-lint version |
-V /--verbose-version | Show haml-lint , haml , and ruby version information |
haml-lint
will automatically recognize and load any file with the name .haml-lint.yml
as a configuration file. It loads the configuration based on the directory haml-lint
is being run from, ascending until a configuration file is found. Any configuration loaded is automatically merged with the default configuration (see config/default.yml
).
Here's an example configuration file:
linters:
ImplicitDiv:
enabled: false
severity: error
LineLength:
max: 100
All linters have an enabled
option which can be true
or false
, which controls whether the linter is run, along with linter-specific options. The defaults are defined in config/default.yml
.
Option | Description |
---|---|
enabled | If false , this linter will never be run. This takes precedence over any other option. |
include | List of files or glob patterns to scope this linter to. This narrows down any files specified via the command line. |
exclude | List of files or glob patterns to exclude from this linter. This excludes any files specified via the command line or already filtered via the include option. |
severity | The severity of the linter. External tools consuming haml-lint output can use this to determine whether to warn or error based on the lints reported. |
The exclude
global configuration option allows you to specify a list of files or glob patterns to exclude from all linters. This is useful for ignoring third-party code that you don't maintain or care to lint. You can specify a single string or a list of strings for this option.
Some static blog generators such as Jekyll include leading frontmatter to the template for their own tracking purposes. haml-lint
allows you to ignore these headers by specifying the skip_frontmatter
option in your .haml-lint.yml
configuration:
skip_frontmatter: true
The inherits_from
global configuration option allows you to specify an inheritance chain for a configuration file. It accepts either a scalar value of a single file name or a vector of multiple files to inherit from. The inherited files are resolved in a first in, first out order and with "last one wins" precedence. For example:
inherits_from:
- .shared_haml-lint.yml
- .personal_haml-lint.yml
First, the default configuration is loaded. Then the .shared_haml-lint.yml
configuration is loaded, followed by .personal_haml-lint.yml
. Each of these overwrite each other in the event of a collision in configuration value. Once the inheritance chain is resolved, the base configuration is loaded and applies its rules to overwrite any in the intermediate configuration.
Lastly, in order to match your RuboCop configuration style, you can also use the inherit_from
directive, which is an alias for inherits_from
.
haml-lint
is an opinionated tool that helps you enforce a consistent style in your HAML files. As an opinionated tool, we've had to make calls about what we think are the "best" style conventions, even when there are often reasonable arguments for more than one possible style. While all of our choices have a rational basis, we think that the opinions themselves are less important than the fact that haml-lint
provides us with an automated and low-cost means of enforcing consistency.
Add the following to your configuration file:
require:
- './relative/path/to/my_first_linter.rb'
- 'absolute/path/to/my_second_linter.rb'
The files that are referenced by this config should have the following structure:
module HamlLint
# MyFirstLinter is the name of the linter in this example, but it can be anything
class Linter::MyFirstLinter < Linter
include LinterRegistry
def visit_tag
return unless node.tag_name == 'div'
record_lint(node, "You're not allowed divs!")
end
end
end
For more information on the different types on HAML node, please look through the HAML parser code: https://github.com/haml/haml/blob/master/lib/haml/parser.rb
Keep in mind that by default your linter will be disabled by default. So you will need to enable it in your configuration file to have it run.
One or more individual linters can be disabled locally in a file by adding a directive comment. These comments look like the following:
-# haml-lint:disable AltText, LineLength
[...]
-# haml-lint:enable AltText, LineLength
You can disable all linters for a section with the following:
-# haml-lint:disable all
A directive will disable the given linters for the scope of the block. This scope is inherited by child elements and sibling elements that come after the comment. For example:
-# haml-lint:disable AltText
#content
%img#will-not-show-lint-1{ src: "will-not-show-lint-1.png" }
-# haml-lint:enable AltText
%img#will-show-lint-1{ src: "will-show-lint-1.png" }
.sidebar
%img#will-show-lint-2{ src: "will-show-lint-2.png" }
%img#will-not-show-lint-2{ src: "will-not-show-lint-2.png" }
The #will-not-show-lint-1
image on line 2 will not raise an AltText
lint because of the directive on line 1. Since that directive is at the top level of the tree, it applies everywhere.
However, on line 4, the directive enables the AltText
linter for the remainder of the #content
element's content. This means that the #will-show-lint-1
image on line 5 will raise an AltText
lint because it is a sibling of the enabling directive that appears later in the #content
element. Likewise, the #will-show-lint-2
image on line 7 will raise an AltText
lint because it is a child of a sibling of the enabling directive.
Lastly, the #will-not-show-lint-2
image on line 8 will not raise an AltText
lint because the enabling directive on line 4 exists in a separate element and is not a sibling of the it.
If there are multiple directives for the same linter in an element, the last directive wins. For example:
-# haml-lint:enable AltText
%p Hello, world!
-# haml-lint:disable AltText
%img#will-not-show-lint{ src: "will-not-show-lint.png" }
There are two conflicting directives for the AltText
linter. The first one enables it, but the second one disables it. Since the disable directive came later, the #will-not-show-lint
element will not raise an AltText
lint.
You can use this functionality to selectively enable directives within a file by first using the haml-lint:disable all
directive to disable all linters in the file, then selectively using haml-lint:enable
to enable linters one at a time.
Adding a new linter into a project that wasn't previously using one can be a daunting task. To help ease the pain of starting to use Haml-Lint, you can generate a configuration file that will exclude all linters from reporting lint in files that currently have lint. This gives you something similar to a to-do list where the violations that you had when you started using Haml-Lint are listed for you to whittle away, but ensuring that any views you create going forward are properly linted.
To use this functionality, call Haml-Lint like:
haml-lint --auto-gen-config
This will generate a .haml-lint_todo.yml
file that contains all existing lint as exclusions. You can then add inherits_from: .haml-lint_todo.yml
to your .haml-lint.yml
configuration file to ensure these exclusions are used whenever you call haml-lint
.
By default, any rules with more than 15 violations will be disabled in the todo-file. You can increase this limit with the auto-gen-exclude-limit
option:
haml-lint --auto-gen-config --auto-gen-exclude-limit 100
If you use vim
, you can have haml-lint
automatically run against your HAML files after saving by using the Syntastic plugin. If you already have the plugin, just add let g:syntastic_haml_checkers = ['haml_lint']
to your .vimrc
.
If you use vim
8+ or Neovim
, you can have haml-lint
automatically run against your HAML files as you type by using the Asynchronous Lint Engine (ALE) plugin. ALE will automatically lint your HAML files if it detects haml-lint
in your PATH
.
If you use SublimeLinter 3
with Sublime Text 3
you can install the SublimeLinter-haml-lint plugin using Package Control.
If you use atom
, you can install the linter-haml plugin.
If you use TextMate 2
, you can install the Haml-Lint.tmbundle bundle.
If you use Visual Studio Code
, you can install the Haml Lint extension
If you'd like to integrate haml-lint
into your Git workflow, check out our Git hook manager, overcommit.
To execute haml-lint
via a Rake task, make sure you have rake
included in your gem path (e.g. via Gemfile
) add the following to your Rakefile
:
require 'haml_lint/rake_task'
HamlLint::RakeTask.new
By default, when you execute rake haml_lint
, the above configuration is equivalent to running haml-lint .
, which will lint all .haml
files in the current directory and its descendants.
You can customize your task by writing:
require 'haml_lint/rake_task'
HamlLint::RakeTask.new do |t|
t.config = 'custom/config.yml'
t.files = ['app/views', 'custom/*.haml']
t.quiet = true # Don't display output from haml-lint to STDOUT
end
You can also use this custom configuration with a set of files specified via the command line:
# Single quotes prevent shell glob expansion
rake 'haml_lint[app/views, custom/*.haml]'
Files specified in this manner take precedence over the task's files
attribute.
Code documentation is generated with YARD and hosted by RubyDoc.info.
We love getting feedback with or without pull requests. If you do add a new feature, please add tests so that we can avoid breaking it in the future.
Speaking of tests, we use Appraisal to test against both HAML 4 and 5. We use rspec
to write our tests. To run the test suite, execute the following from the root directory of the repository:
appraisal bundle install
appraisal bundle exec rspec
All major discussion surrounding HAML-Lint happens on the GitHub issues page.
If you're interested in seeing the changes and bug fixes between each version of haml-lint
, read the HAML-Lint Changelog.
Author: sds
Source Code: https://github.com/sds/haml-lint
License: MIT license
1619013192
Demo Click Here: https://cutt.ly/2vFKuxe
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