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Open in app Get started ITNEXT Published in ITNEXT You have 2 free member-only stories left this month. Sign up for Medium and get an extra one Kush Kush Follow Apr 15, 2021 · 7 min read · Listen Save How you can Control your Android Device with Python Photo by Caspar Camille Rubin on Unsplash Photo by Caspar Camille Rubin on Unsplash Introduction A while back I was thinking of ways in which I could annoy my friends by spamming them with messages for a few minutes, and while doing some research I came across the Android Debug Bridge. In this quick guide I will show you how you can interface with it using Python and how to create 2 quick scripts. The ADB (Android Debug Bridge) is a command line tool (CLI) which can be used to control and communicate with an Android device. You can do many things such as install apps, debug apps, find hidden features and use a shell to interface with the device directly. To enable the ADB, your device must firstly have Developer Options unlocked and USB debugging enabled. To unlock developer options, you can go to your devices settings and scroll down to the about section and find the build number of the current software which is on the device. Click the build number 7 times and Developer Options will be enabled. Then you can go to the Developer Options panel in the settings and enable USB debugging from there. Now the only other thing you need is a USB cable to connect your device to your computer. Here is what todays journey will look like: Installing the requirements Getting started The basics of writing scripts Creating a selfie timer Creating a definition searcher Installing the requirements The first of the 2 things we need to install, is the ADB tool on our computer. This comes automatically bundled with Android Studio, so if you already have that then do not worry. Otherwise, you can head over to the official docs and at the top of the page there should be instructions on how to install it. Once you have installed the ADB tool, you need to get the python library which we will use to interface with the ADB and our device. You can install the pure-python-adb library using pip install pure-python-adb. Optional: To make things easier for us while developing our scripts, we can install an open-source program called scrcpy which allows us to display and control our android device with our computer using a mouse and keyboard. To install it, you can head over to the Github repo and download the correct version for your operating system (Windows, macOS or Linux). If you are on Windows, then extract the zip file into a directory and add this directory to your path. This is so we can access the program from anywhere on our system just by typing in scrcpy into our terminal window. Getting started Now that all the dependencies are installed, we can start up our ADB and connect our device. Firstly, connect your device to your PC with the USB cable, if USB debugging is enabled then a message should pop up asking if it is okay for your PC to control the device, simply answer yes. Then on your PC, open up a terminal window and start the ADB server by typing in adb start-server. This should print out the following messages: * daemon not running; starting now at tcp:5037 * daemon started successfully If you also installed scrcpy, then you can start that by just typing scrcpy into the terminal. However, this will only work if you added it to your path, otherwise you can open the executable by changing your terminal directory to the directory of where you installed scrcpy and typing scrcpy.exe. Hopefully if everything works out, you should be able to see your device on your PC and be able to control it using your mouse and keyboard. Now we can create a new python file and check if we can find our connected device using the library: Here we import the AdbClient class and create a client object using it. Then we can get a list of devices connected. Lastly, we get the first device out of our list (it is generally the only one there if there is only one device connected). The basics of writing scripts The main way we are going to interface with our device is using the shell, through this we can send commands to simulate a touch at a specific location or to swipe from A to B. To simulate screen touches (taps) we first need to work out how the screen coordinates work. To help with these we can activate the pointer location setting in the developer options. Once activated, wherever you touch on the screen, you can see that the coordinates for that point appear at the top. The coordinate system works like this: A diagram to show how the coordinate system works A diagram to show how the coordinate system works The top left corner of the display has the x and y coordinates (0, 0) respectively, and the bottom right corners’ coordinates are the largest possible values of x and y. Now that we know how the coordinate system works, we need to check out the different commands we can run. I have made a list of commands and how to use them below for quick reference: Input tap x y Input text “hello world!” Input keyevent eventID Here is a list of some common eventID’s: 3: home button 4: back button 5: call 6: end call 24: volume up 25: volume down 26: turn device on or off 27: open camera 64: open browser 66: enter 67: backspace 207: contacts 220: brightness down 221: brightness up 277: cut 278: copy 279: paste If you wanted to find more, here is a long list of them here. Creating a selfie timer Now we know what we can do, let’s start doing it. In this first example I will show you how to create a quick selfie timer. To get started we need to import our libraries and create a connect function to connect to our device: You can see that the connect function is identical to the previous example of how to connect to your device, except here we return the device and client objects for later use. In our main code, we can call the connect function to retrieve the device and client objects. From there we can open up the camera app, wait 5 seconds and take a photo. It’s really that simple! As I said before, this is simply replicating what you would usually do, so thinking about how to do things is best if you do them yourself manually first and write down the steps. Creating a definition searcher We can do something a bit more complex now, and that is to ask the browser to find the definition of a particular word and take a screenshot to save it on our computer. The basic flow of this program will be as such: 1. Open the browser 2. Click the search bar 3. Enter the search query 4. Wait a few seconds 5. Take a screenshot and save it But, before we get started, you need to find the coordinates of your search bar in your default browser, you can use the method I suggested earlier to find them easily. For me they were (440, 200). To start, we will have to import the same libraries as before, and we will also have our same connect method. In our main function we can call the connect function, as well as assign a variable to the x and y coordinates of our search bar. Notice how this is a string and not a list or tuple, this is so we can easily incorporate the coordinates into our shell command. We can also take an input from the user to see what word they want to get the definition for: We will add that query to a full sentence which will then be searched, this is so that we can always get the definition. After that we can open the browser and input our search query into the search bar as such: Here we use the eventID 66 to simulate the press of the enter key to execute our search. If you wanted to, you could change the wait timings per your needs. Lastly, we will take a screenshot using the screencap method on our device object, and we can save that as a .png file: Here we must open the file in the write bytes mode because the screencap method returns bytes representing the image. If all went according to plan, you should have a quick script which searches for a specific word. Here it is working on my phone: A GIF to show how the definition searcher example works on my phone A GIF to show how the definition searcher example works on my phone Final thoughts Hopefully you have learned something new today, personally I never even knew this was a thing before I did some research into it. The cool thing is, that you can do anything you normal would be able to do, and more since it just simulates your own touches and actions! I hope you enjoyed the article and thank you for reading! 💖 468 9 468 9 More from ITNEXT Follow ITNEXT is a platform for IT developers & software engineers to share knowledge, connect, collaborate, learn and experience next-gen technologies. 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Stafford ·Apr 14, 2021 AWS IoT Core for LoRaWAN, AWS IoT Analytics, and Amazon QuickSight Lora 11 min read AWS IoT Core for LoRaWAN, Amazon IoT Analytics, and Amazon QuickSight Read more from ITNEXT Recommended from Medium Morpheus Morpheus Morpheus Swap — Resurrection Ashutosh Kumar Ashutosh Kumar GIT Branching strategies and GitFlow Balachandar Paulraj Balachandar Paulraj Delta Lake Clones: Systematic Approach for Testing, Sharing data Jason Porter Jason Porter Week 3 -Yieldly No-Loss Lottery Results Casino slot machines Mikolaj Szabó Mikolaj Szabó in HackerNoon.com Why functional programming matters Tt Tt Set Up LaTeX on Mac OS X Sierra Goutham Pratapa Goutham Pratapa Upgrade mongo to the latest build Julia Says Julia Says in Top Software Developers in the World How to Choose a Software Vendor AboutHelpTermsPrivacy Get the Medium app A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store

Mansoor1565 /

Pig-and-Python

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Introduction Pig and Python are very widespread systems for executing complex Hadoop map-reduce-based data-flows. It enhances a layer of abstraction on top of Hadoop’s map-reduce mechanisms. That is with the intention of permitting developers to take a high-level view of the data and operations on that data. Pig enables us to do things more openly. For instance, we may join two or more data sources. Writing a join as a map and reduce function is a bit of a drag and it’s commonly value avoiding. Therefore, Pig is great as it makes simpler multifaceted tasks. It offers a high-level scripting language that permits users to take more of a big-picture view of their data flow. Pig is particularly inordinate as it is extensible. This article will emphasize its extensibility. At the end of this article, we will be able to write PigLatin scripts that execute Python code as a part of a larger map-reduce workflow. Description Pig is composed of two main parts: A high-level data-flow language is called Pig Latin. An engine that analyses improves, and performs the Pig Latin scripts as a series of MapReduce jobs that are run on a Hadoop cluster. Pig is at ease to write, comprehend, and maintain as it is a data transformation language that enables the processing of data to be described as a sequence of transformations. It is similarly highly extensible through the use of the User Defined Functions (UDFs). User-Defined Functions (UDFs) A Pig UDF permits custom processing to be written in many languages, for example, Python. It is a function that is nearby to Pig. On the other hand, it is written in a language that isn’t PigLatin. Pig permits us to register UDFs for use within a PigLatin script. A UDF requires to fit a precise prototype An instance of a Pig application is the Extract, Transform, Load (ETL) process. That defines how an application extracts data from a data source, changes the data for querying and examination drives. It also loads the result onto a target data store. When Pig loads the data, it may execute projections, iterations, and other transformations. UDFs allow more multifaceted algorithms to be useful during the change phase. It may be stored back in HDFS after the data is done being processed by Pig. PigLatin scripts We can write the simplest Python UDF as; from pig_util import outputSchema @outputSchema('word:chararray') def hi_world(): return "hello world" The data output from a function has a particular form. Pig likes it if we require the schema of the data as then it distinguishes what it may do with that data. That’s what the output_schema decorator is for. There are a couple of diverse means to state a schema. If that were saved in a file named my_udfs.py we will be able to make use of it in a PigLatin script as; -- first register it to make it available REGISTER 'myudf.py' using jython as my_special_udfs users = LOAD 'user_data' AS (name: chararray); hello_users = FOREACH users GENERATE name, my_special_udfs.hi_world(); UDF arguments UDF has inputs and outputs as well. Look at the below some UDFs: def deal_with_a_string(s1): return s1 + " for the win!" def deal_with_two_strings(s1,s2): return s1 + " " + s2 def square_a_number(i): return i*i def now_for_a_bag(lBag): lOut = [] for i,l in enumerate(lBag): lNew = [i,] + l lOut.append(lNew) return lOut The following are UDFs in a PigLatin script: REGISTER 'myudf.py' using jython as myudfs users = LOAD 'user_data' AS (firstname: chararray, lastname:chararray,some_integer:int); winning_users = FOREACH users GENERATE myudfs.deal_with_a_string(firstname); full_names = FOREACH users GENERATE myudfs.deal_with_two_strings(firstname,lastname); squared_integers = FOREACH users GENERATE myudfs.square_a_number(some_integer); users_by_number = GROUP users by some_integer; indexed_users_by_number = FOREACH users_by_number GENERATE group,myudfs.now_for_a_bag(users); Outside Standard Python UDFs We can’t use NumPy from Jython. Moreover, Pig doesn’t actually permit Python Filter UDFs. We may only do stuff as: user_messages = LOAD 'user_twits' AS (name:chararray, message:chararray); --add a field that says whether it is naughty (1) or not (0) messages_with_rudeness = FOREACH user_messages GENERATE name,message,contains_naughty_words(message) as naughty; --then filter by the naughty field filtered_messages = FILTER messages_with_rudeness by (naughty==1); -- and finally strip away the naughty field rude_messages = FOREACH filtered_messages GENERATE name,message; Python Streaming UDFs Pig enables us to look into the Hadoop Streaming API. This allows us to get around the Jython issue when we require it to. Hadoop lets us write mappers and reducers in any language that provides us access to stdin and stdout. Therefore, that’s attractive much any language we want. Similar to Python 3 or even Cow. The following is a simple Python streaming script, let’s call it simple_stream.py: #! /usr/bin/env python import sys import string for line in sys.stdin: if len(line) == 0: continue l = line.split() #split the line by whitespace for i,s in enumerate(l): print "{key}\t{value}\n".format(key=i,value=s) # give out a key value pair for each word in the line The purpose is to develop Hadoop to run the script on each node. The hashbang line (#!) requires to be valid on every node. Each import statement must be valid on every node. Also, any system-level files or resources accessed inside the Python script must be accessible in the same way on every node. Use with simple_stream script DEFINE stream_alias 'simple_stream.py' SHIP('simple_stream.py'); user_messages = LOAD 'user_twits' AS (name:chararray, message:chararray); just_messages = FOREACH user_messages generate message; streamed = STREAM just_messages THROUGH stream_alias; DUMP streamed; The over-all format we are using is: DEFINE alias 'command' SHIP('files'); The alias is the name used to access the streaming function from inside the PigLatin script. The command is the system command Pig would call when it is essential to use the streaming function. Finally, SHIP tells Pig those files and dependencies Pig desires to distribute to the Hadoop nodes for the command to be able to work.

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