Implementing Webhook Handler in Python.

What is Webhook ?

Webhook is an asynchronous HTTP callback on an event occurrence. It is a simple server to server communication for reporting a specific event occurred on a server. The server on which event occurred will fire a HTTP POST request to another server on a URL which is provided by receiving server.

For example, whenever your colleague pushes code commits to github, an event has occurred on github’s server. Now if a webhook URL is provided in github settings, a webhook will be fired to that URL. This webhook will be a HTTP POST request with commit details inside the body in a specified format.  More details on github webhook can be found here.

In this post, I will share my experience of implementing webhook handler in python. For the readers, basic knowledge on implementing web application in python would be better.

Webhook Handler

A Webhook can be handled by simply providing a URL endpoint in a web application. Following is an example using Django. Add webhook url in urls.py

Now create view function in views.py which will parse the data and process it.  In most of the cases, webhook data is sent in JSON format. So lets load the webhook data and sent the data to process_webhook function.

Most of the web applications accept POST request after verifying CSRF token, but here we need to exempt it from this check. So put @csrf_token decorator above the view function. Also put an @require_post decorator to ensure the request is only POST.

The above implementation of URL endpoint will remain different for various other python web framework like Flask, tornado, twisted. But the below code  process_webhook function implementation will remain same irrespective of any framework.

Processing event

There may be different type events we need to handle. So, before proceeding to implement process_webhook function, lets create a python module named webhook_events.py, which will contain a single function for each type of event wherein will be the logic for that particular event. In other words, we are going to map event name with its function, which will handle the logic for that particular type of webhook event.

There are many ways to implement process_webhook function and how we map a webhook event with its function. We are going to discuss different implementation of process_webhook based on extendability. Most basic version of that is below.

A Better way

Now suppose, there are 10s of webhook to be served. We certainly don’t want to write repetitive code. So below is a better way of implementing process_webhook. Here we just replace dot in event name with underscore, so that we get the function name written in webhook_events.py for that event. If the function is not found that means event is not registered (not being served). In this way, no matter the number webhook to be served, just write the function to handle it, in webhook_events.py

Decorators

More robust and pythonic way of implementing process_webhook is by using decorators. Lets define a decorator in webhook_events.py which will map the event_name to its function. Here the EVENT_MAP is dictionary inside a setting module, which will contain event name as key and event function as its value.

In this case, the process_webhook will look like below:

This is the way which I prefer to implement webhook handler in python. How would you prefer ? Please feel free to comment below.

FCM – send push notifications using Python

What is FCM ?

FCM – Firebase Cloud Messaging is a cross-platform  ( Android, iOS and Chrome ) messaging solution that lets you reliably deliver messages at no cost. FCM is best suited if you want to send push notification to your app which you built to run on Android and iOS. The advantage you get is you don’t have to separately deal with GCM (Google Cloud Messaging deprecated now) and Apple’s APNS. You hand over your notification message to FCM and FCM takes care of communicating with apple’s APNS and Android messaging servers to reliably deliver those messages.

fcm-2

Using FCM we can send message to single device or multiple devices.  There are two different types of messages, notification and data. Notification messages include JSON keys that are understood and interpreted by phone’s operating system. If you want to include customized app specific JSON keys use data message. You can combine both notification and data JSON objects in single message. You can also send messages with different priority.

Note : – You need to set priority  to high  if you want phone to wake up and show notification on screen

Sending message with Python

We can use PyFCM to send messages via FCM. PyFCM is good for synchronous ( blocking ) python. We will discuss non-blocking option in next paragraph.

Install PyFCM using following command

The following code will send a push notification to

So, the PyFCM API is the pretty straight forward to use.

Sending FCM push notification using Twisted

PyFCM discussed in above paragraph is good enough if you want to send messages in blocking fashion. If you have to send high number of concurrent messages then using Twisted is a good option.

Twisted Matrix
Twisted Matrix

Network operations performed using twisted library don’t block. Thus it’s a good choice when network concurrency is required by program. We can use txFCM library to send FCM messages using twisted

Install txFCM using following command

Following code send FCM message using txFCM

txFCM is built on top of PyFCM so all the API call that are available in PyFCM are also available in txFCM.

Python metaclasses explained

Python is having obscure wonderful mechanism of classes and it’s implementation.   In python every thing is an object. If you define a class, that class it self is an object in memory and it is an instance of other class. You may call it as a class object. If you instantiate it you will get brand new object called instance( instance of class).

Probably, meta classes  are  sort of confusing concept. Many people are afraid of this concept, but believe me it is very nice and simple if you understand it.

In simple words, meta classes are classes which are responsible to create  a class object( in memory).

As mentioned above, when you define a class. A class object will be created in memory but behind the senses it is an instance of other class by default type. So, classes are created by meta classes. You can specify your custom meta class which is responsible to create a class.

Mostly, meta classes are used when you write APIs or frameworks. Like django uses metaclasses in models.

Meta Classes
Meta Classes

I would like to explain this concept by taking real world example, to get you good understanding. Let’s take a look at following picture.

Python MetaClasses Illustrated
Python MetaClasses Illustrated

As show in picture, A factory can be treated a metaclass. Where, it produces vending machine, can be considered as a class. Where, the class (vending machine) produces instances, which are cans, bottles etc.

An example representing normal class definition and instance creation

Python is having builtin function called isinstance. Using this function we can determine if any object is an instance of specified object.

In the above example we haven’t specified the metaclass. So, python would use default one. i.e type.

In python 2, we specify metaclass using magic method __metaclass__.
__metaclass__ can be specified either as a class attribute or global variable. When you specify it globally in module level, all classes will become instances of this metaclass. If you specify this as a class attribute, only that class will become instance of specified metaclass. In python3 this __meatclass__ attribute is removed in favour of using metaclass argument that you have to specify in class definition.

Implementation of simple class with metaclass in place

Here in this example, you can see class Foo is the instance of metaclass MetaClassThus, Foo gets created as an instance of MetaClass. And, i is an instance of class Foo. As you can see, we confirmed this relation using isinstance function.

Python metaclasses - instance, class and metaclass
Python metaclasses – instance, class and metaclass

There is no restriction over how many times one class can be used as a metaclass for other classes.

In the above example,  the meta class MetaCalss is used in both Foo and Bar classes. Like wise you can use it in as many classes you want. If you want to apply it for all module level classes. You better use global __metaclass__ attribute. As you can see in the following example

OutPut:

This module level __metaclass__ magic variable doesn’t work on new style classes as show in above code.

 

Python Metaclasses Key Notes

  • Metaclasses are callables
  • Subclasses inherit the metaclass
  • Restriction over multiple metaclasses in multiple inheritance
MetaClasses are callable

The MetaClass always need not be a class. You can use any callable as a metaclass. A Simple example demonstrates using callable (function) as a metaclass

If you are using callable as a metaclass class. It should have same signature(arguments) as type. That is,

Following example is useless but explanatory. You can hack the class creation using metaclass. You can see below example, Foo became 3 instead of class object as we did return 3 from metaclass.

Subclasses inherit the metaclass

Like all other attributes and methods subclasses inherit metaclass.

OutPut:

#TODO

Restriction over multiple metaclasses in inheritance

Classes can have multiple base classes. Those maetaclasses may have different metaclass. If so, every thing should be linear in the inheritance. That is, multi-level inheritance works fine. But, python is having restriction over having multiple metaclasses in multiple inheritance  from different bases. Following example shows that, raised exception because of having multiple metaclasses from two bases.

Where metaclasses used ?

The main use case for a metaclass is creating an API, best example of this is Django ORM.

If you are aware of django. We usually define a model as follows

But,

It won’t return an IntegerField object. Instead it will return int.

This hack is actually possible by defining  __metaclass__ on models.Model and it uses some magic that will turn the model Person you defined with simple statements into a complex hook to a database field.

Django makes complex look simple by exposing a simple API and using metaclasses inside.

Metaclasses in python3

In python3 we specify metaclass by passing keyword argument metaclass to class definition. This is one of the difference between python2 and python3.

Py3K translation tool can be used to convert old syntax to new syntax.

Summary

In python classes are actually instances of other classes (soft of)  called metaclasses. You can specify metaclass using magic method __metaclass__ either in class definition or gobally module level. These metaclasses are useful when you want customize class creation.

 

Conclusion

Metaclasses is the obscure feature in python. Use it, when you really need them.

Metaclasses are deeper magic that 99% of users should never worry about. If you wonder whether you need them, you don’t (the people who actually need them know with certainty that they need them, and don’t need an explanation about why).

Python Guru Tim Peters

Best real life example where metaclasses are being used is Django ORM. As like Django if you are writing framework or API, you can leverage metaclasses and you really need them.

Please share. Let me know your comments and queries 😉 😉

References:

[1] https://www.python.org/dev/peps/pep-3115/
[2]http://stackoverflow.com/questions/2149846/customary-to-inherit-metaclasses-from-type
[3]http://stackoverflow.com/questions/100003/what-is-a-metaclass-in-python

Writing shorthand statements in python

Python is having  shorthand statements and shorthand operators. These things will help you write more logic with less number of statements.

We will see those available shorthand statements.

lambda statement

Probably every body is aware of the lambda functions. The statement lambda is helpful to write single line functions with out naming a function. This will return the function reference where you can assign it to any arbitrary variable. It’s more like JavaScript anonymous functions.

Self called Lambda

You can write the lambda and you can make it call it self like self-invoking functions in javascript. Let’s see an example,

List Comprehension

List Comprehension is the great feature that python is having. Using this feature you can reduce the lot of code, you can reduces space complexity of the code. Simple for loops can be written using list comprehension.

Syntax:

L = [mapping-expression for element in source-list if filter-expression]

Where:

L       Variable, result gets assigned to

mapping-expression       Expression, which is executed on every loop if only filter-expression in if condition resolved as True

This list comprehension is equivalent to,

Example

Lets see list comprehension example. Get even number from the given range.

Usual code

List Comprehension

 

 

Dict Comprehension

Dict comprehension is available in python 2.7 and 3.x. This syntax will provide you the way to encapsulate several lines you use to create dictionaries into one line. It’s is similar to list comprehension but we use dict literals {} instead of []

Syntax:

{key:value for element in source-list if filter-expression }

Let’s how we use it by an example,

I have a list of fruits, I want to make it dictionary by changing their case

[‘APPLE’:, ‘MANGO’, ‘ORANGE’]

I want to convert all keys into lower case. This is we would do with out using comprehension

Using Simple list comprehension,

{i.upper(): 1 for i in l}

Set Comprehension

Set comprehension syntax is very much similar to dict comprehension with a small difference.

Let’s consider dict comprehension example. Using following statement you generate set

{i.upper() for i in l}

Where we haven’t specified value like we do in dict comprehension

Generator Expression

You might have already know about generators. Any function which contains yield statment is called generator. generator gives iterable where we can call next method to get the next item in the sequence.  Python got short notation for this generators like lambda. It is same as list comprehension but we enclose the expression with touple literals instead.

Generator Function

Generator Expression

Same generator function can written as follow,

😉

Shorthand If Else

Like C and javascript ternary operator (?:) you can write short hand if-else comparison. By taking readability into account we have following syntax in python

if-expression if (condition) else else-expression 

This is equivalent to,

if condiction:
    if-expression
else:
    else-expression

Tuple Unpacking

Python 3 even more powerful unpacking feature. Here it is,

Example:

a, rest = [1, 3, 4, 6]

In this case, a will get 1   and rest of the list will get assigned to variable rest. i.e  [3, 4, 6]

String Concatenation  with delimiter

If you want to concatenate list of strings with some random delimiter. You can do that by using string method join

 

How to create read only attributes and restrict setting attribute values on object in python

There are different way to prevent setting attributes and make attributes read only on object in python. We can use any one of the following way to  make attributes readonly

  1. Property Descriptor
  2. Using descriptor methods __get__ and __set__
  3. Using slots  (only restricts setting arbitary attributes)

Property Descriptor

Python ships with built in function called property. We can use this function to customize the way attributes be accessed and assigned.

First I will explain you about property before I get you idea about how it is useful to make attribute readonly.

Typical signature of the function property is

 property([fget[, fset[, fdel[, doc]]]]

As you can see here this function take four arguments, those are

fget is a function for getting an attribute value. fset is a function for setting an attribute value. fdel is a function for deleting an attribute value. And doc creates a docstring for the attribute.

All these function are for the sake of single attribute. That is fget function will be called when you access/get the attribute. fset function will be called when you are trying to set the attribute.

Simple example

Instantiate Foo and try to play the instance attribute x

I hope, you got what exactly the function property is and how we use it. In many cases we use this property to hide actual attributes and abstract them with another name.

You can use property as decorator also. Something like

Now let’s come to actual thing how we make attribute readonly.

It’s simple you just don’t define setter for the property attribute. Let’s see the following example

Here, as we didn’t define setter for the property attribute. So python won’t allow setting that specific attribute even you can’t delete if you don’t define fdel. Thus, attribute becomes read only. Still you can access b._money  and you can set that attribute there is no restriction over setting this internal attribute.

Descriptor methods __get__ and __set__

These magic methods define descriptor for the object attribute. To get complete understanding and usage about descriptor magic methods, please check other article .

Like fget and fset functions that property function takes, __get__ is used to define behavior when descriptor’s value is retrieved. __set__ method is used to define behavior  when descriptor value is getting set(assigned). Where __delete__ is used to define behavior when descriptor is getting deleted.

To restrict setting attribute and make it readonly. You have to use __set__ magic method of descriptor and raise exception in it.

Let’s see the simple example demonstrating descriptor object and readonly attributes using descriptors

Lets see the result and trying to set speed attribute

As you can see here, we can’t set the attribute speed on instance v of Vehicle. Because we are restricting it in descriptor method __set__ of class Speed

Python __slots__

The basic usage of __slots__ is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This will also gain us some performance due to lack of dynamic  attribute assignment. That is, it saves the overhead of one dict for every object that uses slots.

Think of you are creating lot of (hundreds, thousands) instances from the same class, this could be useful as memory and performance optimization tool.

If you are using __slots__ means you are defining static attributes on class. This is how we save memory and gain performance as there is not dynamic attribute assignment. Thus you can’t set new attributes on object.

You see, in the above example we are not able to set attribute c as it not given in __sots__. Any way it’s about restricting  assignment to new attributes and you can combine either above two methods to make existing attributes readonly.

 

References:

[1] __get__ and __set__ data descriptors don’t work on instance attributes http://stackoverflow.com/questions/23309698/why-is-the-descriptor-not-getting-called-when-defined-as-instance-attribute

[2] http://stackoverflow.com/questions/472000/usage-of-slots

Python: __new__ magic method explained

Python is Object oriented language, every thing is an object in python. Python is having special type of  methods called magic methods named with preceded and trailing double underscores.

When we talk about magic method __new__ we also need to talk about __init__

These methods will be called when you instantiate(The process of creating instance from class is called instantiation). That is when you create instance. The magic method __new__ will be called when instance is being created. Using this method you can customize the instance creation. This is only the method which will be called first then __init__ will be called to initialize instance when you are creating instance.

Method __new__ will take class reference as the first argument followed by arguments which are passed to constructor(Arguments passed to call of class to create instance). Method __new__ is responsible to create instance, so you can use this method to customize object creation. Typically method __new__ will return the created instance object reference. Method __init__ will be called once __new__ method completed execution.

You can create new instance of the class by invoking the superclass’s __new__ method using super. Something like super(currentclass, cls).__new__(cls, [,….])

Usual class declaration and instantiation

A class implementation with __new__ method overridden

OutPut:

Note:

You can create instance inside __new__  method either by using super function or by directly calling __new__ method over object  Where if parent class is object. That is,

instance = super(MyClass, cls).__new__(cls, *args, **kwargs)

or

instance = object.__new__(cls, *args, **kwargs)

Things to remember

If __new__ return instance of  it’s own class, then the __init__ method of newly created instance will be invoked with instance as first (like __init__(self, [, ….]) argument following by arguments passed to __new__ or call of class.  So, __init__ will called implicitly.

If __new__ method return something else other than instance of class,  then instances __init__ method will not be invoked. In this case you have to call __init__ method yourself.

Applications

Usually it’s uncommon to override __new__ method, but some times it is required if you are writing APIs or customizing class or instance creation or abstracting something using classes.

Singleton using __new__

You can implement the singleton design pattern using __new__ method. Where singleton class is a class that can only have one object. That is, instance of class.

Here is how you can restrict creating more than one instance by overriding __new__

It is not limited to singleton. You can also impose limit on total number created instances

 

Customize Instance Object

You can customize the instance created and make some operations over it before initializer __init__  being called.Also you can impose restriction on instance creation based on some constraints

 

Customize Returned Object

Usually when you instantiate class it will return the instance of that class.You can customize this behaviour and you can return some random object you want.

Following  one is simple example to demonstrate that returning random object other than class instance

Output:

Here you can see when we instantiate class it returns  3 instead of instance reference. Because we are returning 3 instead of created instance from __new__ method. We are calling __init__ explicitly.  As I mentioned above, we have to call __init__ explicitly if we are not returning instance object from __new__ method.

The __new__ method is also used in conjunction with meta classes to customize class creation

Conclusion

There are many possibilities on how you can use this feature.  Mostly it is not always required to override __new__ method unless you are doing something regarding instance creation.

Simplicity is better than complexity. Try to make life easier use this method only if it is necessary to use.

 

Python virtualenv – Virtual Environments

 

What is virtualenv

A Virtual Environment is a tool for python to keep the dependencies required by different projects isolated in separate places, by creating virtual Python environments.

It solves the problem “ProjectX depends on libraryA of version 1.0 but ProjectY needs library of version3.0” dilemma. Keeps the global site-packages directory clean and manageable.

Without virtualenv you would need to install/uninstall dependencies while you switch between projects that are having dependencies of same libraries but different versions.

For example, with virtualenv you can work on a project which requires Django 1.8 while also maintaining a project which requires Django 1.6.

Install virtualenv

The virtualenv is a tool to create isolated Python environments. virtualenv creates a folder which contains all the necessary executables and packages that a Python project would need.

Install virtualenv using pip:

 

virtualenv syntax

How to use virtualenv

  1. Create a virtual environment for a project:

Command virtuale venv will create a folder in the current directory with name venv and will put all necessary python libraries required to work virtual environment isolated. That is, which will contain the Python executable files, and also  the pip library where you can use to install other packages.

Here the name of the virtual environment is venv  as we have given that name to virtualenv command. If you don’t specify a name or directory, current directory will be used to create virtual environment.

You can also specify a Python interpreter of your choice.

This command will tell python virtualenv to use  the Python interpreter from location specified /usr/bin/python2.7 

  1. To Start using the virtual environment, it needs to be activated:

To activate virtual environment, we use the command source with argument as a file activate which will reside in bin directory of created virtual environment directory. The name of the current virtual environment will now appear on the left of the prompt (something like  (venv)User@HostName:/path/to/directory$ ) to let you know that it is activated. As long as this virtual environment activated, any package that you install using pip will be placed in the venv folder, isolated from the global Python installation.

You can Install packages as usual using pip:

  1. Once you are done working with virtual environment, you can deactivate it:

deactivate will be aviable as a bash command once you activate virtual environment. It can be used to deactivate(exit from) virtual environment.

So, this puts you back to the system’s default Python interpreter with all its installed libraries.

If you want to  delete a virtual environment, just delete its folder. (here in this case, it would be rm -rf venv.)

virtualenv Options

Creating virtual environment with the option --no-site-packages will not include the packages that are installed globally. This can be useful for keeping the package list clean in case it needs to be accessed later. [This is the default behavior for virtualenv 1.7 and later.]

Freeze Requirements

In order to keep your environment consistent or reinstall virutal environment or distriubute as package, it’s a good idea to have a list of dependencies/requirements. You can “freeze” the current state of the environment packages. To do so, run

This will create a requirements.txt file, which contains a simple list of all the packages in the current environment, and their respective versions. You can see the list of installed packages without the requirements format using “pip list”. Later it will be easier for you or other  developer to recreate or reuse environment and install dependencies.

This can help make sure consistency across installations, across deployments, and across developers.

Other environment virtualization tools for python

  • p – Dead simple interactive Python version management.
  • pyenv – Simple Python version management.
  • venv – (Python standard library in Python 3.3+) Creating lightweight virtual environments.
  • virtualenvwrapper – A set of extensions to virtualenv.

How to shuffle lines in the file in linux

We can shuffle lines in the file in linux using following commands

  • shuf
  • sed and sort
  • awk
  • python

As an example we will take a file shuffle_mylines.txt  having numbers till 10 each digit in a new line.

Create a file using following command

$ seq 10  > shuffle_mylines.txt

Command shuf

This command is light wight and straight forward. You just need to call this command with file name as an argument.

Shuffle lines using sed

You may have already know about command sed(Stream Editor). It is one of the command widely used for text processing in unix/linux. We can’t shuffle line using single sed command, but we will do by combining other commands. Let’s take a look at following command,

How does it work?

Breakdown of above command,

Commands we have used in the above example are,

  • cat
  • while loop
  • $RANDOM   environment variable
  • soft
  • tail
  • sed

 

Now, lets come to see how this command work. First command cat will read the file content and will pipe it to shell while loop

Where, while loop will read the piped input into variable x and will iterate over all lines to generate  output  <random_number>:<line> as you can see $RANDOM:$x. Where $RANDOM is the environment variable, each time you query this variable you will get random number. Which is useful for to shuffle lines.

Then, we will sort output of above while loop using sort command

Out put of this command will always be randomly shuffled lines. It’s because $RANDOM.

Output here would look like,

To remove preceded random values we will use sed.

That’s it. On every execution of this command you will get shuffled lines. You can redirect output to new file if you want to store using (>) or (>>).

 

Shuffle lines using awk

The awk is the programming language which is specially designed for text processing. We will use it to shuffle lines.

 

Another example using awk. It’s is similar to sed and sort example.

Shuffle lines in file using python

Python is popular scripting language widely used today from big projects to small scripts. We will see, how you can shuffle lines using python.

Python Example 1

In this example, we are passing file name as command line argument. Reading it and shuffling the lines of file and printing them on terminal.

The output can be redirected to  a file using redirect operator (> or >>)

Conclusion:

If you are looking for a quick shuffle command shuf is best choice or you can have a fun of using other ways to shuffle lines in the file.

 

 

How to write port-forwarding program using Twisted

Recently I was faced with an issue where a long running process is listening on loop back IP (127.0.0.1) on port 8080 on one of our servers and client programs on other machines are trying to access it on server’s local IP 10.91.20.66.  We ended up at this situation when we have updated server configuration and restarted the server program and forgot to change IP binding info in config file from loop back to local IP. Server got busy with it’s work, with lots of customer’s connections already, by the time we have discovered that some services of  server are not accessible to client programs on other machines. So, the dummy’s guide to fixing it by changing config and restarting the server program is not an option as we can’t risk to disconnect existing customers. So, hot patching is the only option until we can restart the program at next scheduled down time.

I could have fixed this in couple of ways either by adding few lines to iptables configuration or by writing simple socket program in python. The task is to forward data coming in on local IP port 8080 to loop back IP (127.0.0.1) port 8080 and send replies back to source address. Forwarding one socket data to other socket is pretty trivial using Python’s socket library and Twisted made it even more trivial, so I went with the following solution using Twisted.

That’s it. Now, all I needed to do is to run this program by the following command

This simple program is made possible by the heavy lifting done by twisted library. Interested folks can look under hood at twisted’s portforward.py module.

Sending emails asynchronously using Twisted – Part 2

In Part 1 of article, we saw how to send blocking emails using ‘smtplib’ module & non-blocking emails using Twisted framework. In this part, we will see how to send asynchronous emails to multiple recipients using Twisted

  • Sending multiple emails

    Refer following script.This script sends emails to given recipients asynchronously. Here we have used twisted.internet.defer.DeferredList API. This API is very useful in some scenarios. Suppose you have to finish multiple task asynchronously and then you have to finish one final task. For examples, your program is connected to 4 different clients & and before shutting it down, you have to make sure that all connections are closed properly. In such cases, DeferredList API is used. Create deferrands of each task & make their list. Pass this list to ‘DeferredList‘ API which will return you another deferrand. This final deferrand will be fired when all deferrands in list will be fired.


     
  • Sending multiple emails using coiterator

    Though above script runs fine, there is one problem. Here, recipients number is very small. But suppose you have to send emails to millions recipients then will this code work ?. Refer function ‘send_multiple_emails’.


    Here we have used ‘for’ loop which is blocking. So until this ‘for’ loop is iterated, program will not move to next line of code. For 3 recipients iteration will not take much time however for millions of recipients, it will not work.
    So lets modify our code to work like generators.

    Here, we have used twisted.internet.task.coiterate API. This API iterates over iterator by dividing reactor runtime between all iterators. Thus we can send millions of emails asynchronously.