I have an external program, opentrack, that tracks head position using IR LED's. The position is updated in the program in real-time, but my problem is that I would like to get that data into my python code. Is it possible to do this?
I've been looking into subprocesses and things like that, but I still don't understand how to do it. Sorry for the very general question but I just need someone to point me into the right direction or to tell me that it isn't really possible.
The page you linked to clearly lists no fewer than 9 different ways in which opentrack can output the data.
So you need to figure out which one to use, and write Python code to act as a receiver for one of those methods. Perhaps UDP, since that sounds simple enough.
Hint: to decode any binary format, you're probably going to want to become familiar with Python's struct module.
Related
I have some experience in python and wanted to mess around with audio.
What I specifically wanted to do is change the specific frequencies of live audio and see what the output would be, but I can't seem to find a way to do it.
I was hoping there would be a library I could use since I am no expert, but it seems like my idea is so strange that such a library doesn't exist.
Does someone know how to that?
Greetings,
Neimod
PS: If it's for some reason easier to do in C++, that is an option for me
I'm creating a program in python (2.7) and I want to protect it from reverse engineering.
I compiled it using cx_freeze (supplies basic security- obfuscation and anti-debugging)
How can I add more protections such as obfuscation, packing, anti-debugging, encrypt the code recognize VM.
I thought maybe to encrypt to payload and decrypt it on run time, but I have no clue how to do it.
Generally speaking, it's almost impossible for you to make your program unbreakable as long as there's enough motive for the hackers.
But still you can make it harder to be reverse engineered, try to use cython to compile your core codes into pyd or so files.
There's no way to make anything digital safe nowadays.
What you CAN do is making it hard to a point where it's frustrating to do it, but I admit I don't know python specific ways to achieve that. The amount of security of your program is not actually a function of programsecurity, but of psychology.
Yes, psychology.
Given the fact that it's an arms race between crackers and anti-crackers, where both continuously attempt to top each other, the only thing one can do is trying to make it as frustrating as possible. How do we achieve that?
By being a pain in the rear!
Every additional step you take to make sure your code is hard to decipher is a good one.
For example could you turn your program into a single compiled block of bytecode, which you call from inside your program. Use an external library to encrypt it beforehand and decrypt it afterwards. Do the same with extra steps for codeblocks of functions. Or, have functions in precompiled blocks ready, but broken. At runtime, utilizing byteplay, repair the bytecode with bytes depending on other bytes of different functions, which would then stop your program from working when modified.
There are lots of ways of messing with people's heads and while I can't tell you any python specific ways, if you think in context of "How to be difficult", you'll find the weirdest ways of making it a mess to deal with your code.
Funnily enough this is much easier in assembly, than python, so maybe you should look into executing foreign code via ctypes or whatever.
Summon your inner Troll!
Story time: I was a Python programmer for a long time. Recently I joined in a company as a Python programmer. My manager was a Java programmer for a decade I guess. He gave me a project and at the initial review, he asked me that are we obfuscating the code? and I said, we don't do that kind of thing in Python. He said we do that kind of things in Java and we want the same thing to be implemented in python. Eventually I managed to obfuscate code just removing comments and spaces and renaming local variables) but entire python debugging process got messed up.
Then he asked me, Can we use ProGuard? I didn't know what the hell it was. After some googling I said it is for Java and cannot be used in Python. I also said whatever we are building we deploy in our own servers, so we don't need to actually protect the code. But he was reluctant and said, we have a set of procedures and they must be followed before deploying.
Eventually I quit my job after a year tired of fighting to convince them Python is not Java. I also had no interest in making them to think differently at that point of time.
TLDR; Because of the open source nature of the Python, there are no viable tools available to obfuscate or encrypt your code. I also don't think it is not a problem as long as you deploy the code in your own server (providing software as a service). But if you actually provide the product to the customer, there are some tools available to wrap up your code or byte code and give it like a executable file. But it is always possible to view your code if they want to. Or you choose some other language that provides better protection if it is absolutely necessary to protect your code. Again keep in mind that it is always possible to do reverse engineering on the code.
I am currently starting a kind of larger project in python and I am unsure about how to best structure it. Or to put it in different terms, how to build it in the most "pythonic" way. Let me try to explain the main functionality:
It is supposed to be a tool or toolset by which to extract data from different sources, at the moment mainly SQL-databases, in the future maybe also data from files stored on some network locations. It will probably consist of three main parts:
A data model which will hold all the data extracted from files / SQL. This will be some combination of classes / instances thereof. No big deal here
One or more scripts, which will control everything (Should the data be displayed? Outputted in another file? Which data exactly needs to be fetched? etc) Also pretty straightforward
And some module/class (or multiple modules) which will handle the data extraction of data. This is where I struggle mainly
So for the actual questions:
Should I place the classes of the data model and the "extractor" into one folder/package and access them from outside the package via my "control script"? Or should I place everything together?
How should I build the "extractor"? I already tried three different approaches for a SqlReader module/class: I tried making it just a simple module, not a class, but I didn't really find a clean way on how and where to initialize it. (Sql-connection needs to be set up) I tried making it a class and creating one instance, but then I need to pass around this instance into the different classes of the data model, because each needs to be able to extract data. And I tried making it a static class (defining
everything as a#classmethod) but again, I didn't like setting it up and it also kind of felt wrong.
Should the main script "know" about the extractor-module? Or should it just interact with the data model itself? If not, again the question, where, when and how to initialize the SqlReader
And last but not least, how do I make sure, I close the SQL-connection whenever my script ends? Meaning, even if it ends through an error. I am using cx_oracle by the way
I am happy about any hints / suggestions / answers etc. :)
For this project you will need the basic Data Science Toolkit: Pandas, Matplotlib, and maybe numpy. Also you will need SQLite3(built-in) or another SQL module to work with the databases.
Pandas: Used to extract, manipulate, analyze data.
Matplotlib: Visualize data, make human readable graphs for further data analyzation.
Numpy: Build fast, stable arrays of data that work much faster than python's lists.
Now, this is just a guideline, you will need to dig deeper in their documentation, then use what you need in your project.
Hope that this is what you were looking for!
Cheers
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I've written a few little things in python, and I am ramping up to build something a little more challenging.
The last project I made basically ingested some text files, did some regex over each file and structured the data in an useful way so I could investigate some data I have.
I found it quite tough near the end to remember what section operated on what part of the text, especially as the code grew as I 'fixed' things along the way.
In my head, I imagine my code to be a series of small interconnected modules - descrete .py files that I can leave to one side knowing what they do, and how they interoperate.
The colleague that showed me how to def functions basically meant that I ended up with one really long piece of code that I found really hard to navigate and troubleshoot.
(1) Is this the right way? or is there an easier way of making modules that pass variables between them, I think i would find this better, as I could visualise the flow better (mainly becuase its how I was used to working in MATLAB a few years ago I guess)
(2) Can you use this method to plan out the various layers of functions before hand to give you a 'map' to write towards?
(3) is there any easy to access tutorials for this kind of stuff? I often find the tutorials suddenly jump way over my head....
Thanks.
(1) It is possible to write a fine programme in a single .py file
(2) In any style of programming, it is always (apart from special, hardware-driven cases) best to break your code up into short functions (or methods) that accomplish a discrete task.
(3) Experienced programmers will frequent write their code one way, discover a problem, either write more code, or different code, and consider whether any of their existing code can be broken out into a separate function.
A sign that you need to do this is when you are sequentially assigning to variables to pass data down your function. Never copy-paste your code to another place, even with changes, unless it be to break it out as a function, and replace the original code with a call to that function.
(4) In many cases, it can be useful to organise your code into classes and objects, even when it is not technologically necessary to do so. It can help you see that you have defined a complete set of operations (or not) necessary on some collection of data.
(5) Programming is actually quite hard. Even among those who have a talent for it, it takes a while to be comfortable. As an illustration, when I was doing my master's degree, I and my (fairly talented) friends all felt only in our final year that we had begun to achieve a degree of facility and competence (and these are all people who had been programming since at least their teenage years).
The important thing is to keep learning and improving, rather than repeating the same one or two years of experience over and over.
(6) To that end, read books and articles. Try new things. Think.
Others have suggested studying other experienced programmers' code from open source projects, etc. and from tutorials and textbooks, which is sound advice. Sometimes a similar example is all you need to set you on the right path.
I also suggest to use your own frustration and experience as feedback to help yourself improve. Whenever you find yourself thinking any of the following:
It feels like I'm writing the same code over and over again with only small changes
I wrote this code myself, but I had to study it for a long time to re-learn how it works
Each time I go back and add something to this code it takes me longer to get it working again
There's a bug in this code somewhere, but I haven't a clue where
Surely somebody somewhere has solved this problem already
Why is this taking me so long to get done?
That means you have room for improvement in your technique. A lot of the difference between an expert and beginning programmer is the ability to do the following:
Don't Repeat Yourself (DRY): Instead of copy-pasting code, or writing the same code over and over with variations, write a single common routine with one or more parameters that can do all of those things. Then call that routine in multiple places.
Keep It Simple (KIS): Break up your code into simple well-defined behaviors/routines that make sense on their own, organized into classes/modules/packages, so that each part of the overall program is easy to understand and maintain. Write informative and concise comments, and document the calls even if you don't intend to publish them.
Divide & Conquer Testing: Thoroughly test each individual class, function, etc. by itself (preferably with a unit-testing framework) as you develop it, rather than only testing the entire application.
Don't Re-invent the Wheel: Use open source frameworks or other tools where possible to solve problems that are general and not specific to your application. In all but the most trivial cases, there is a risk that you do not fully understand the problem and your home-grown solution may be lacking in an important way.
Estimate Honestly: Study your own previous efforts to learn how long it takes you to do certain things. Try to work faster next time, but don't assume you will. Measure yourself and use your own experience to estimate future effort. Set expectations and bargain with scope.
It's hard to know even where to begin answering your question without a snippet of your code for reference. You might want to post your code to a free public site such as http://www.bitbucket.org/ or http://www.github.org/ and then include some specific questions about small snippets of code with links back to your repository. This allows respondents here to look at the code and comment on it. (Both of these options even include color syntax highlighting, and interested correspondent can even pull the code down, make changes and push up a patch or create their own branch of your code and send you a "pull" request so you can look at the differences and pull selected changesets back into your branch).
More generally there are a number of approaches to program design. You seem to be trying to re-invent a very old methodology which is referred to as "functional decomposition" --- look at the overall task at hand as a function (digest text files) and consider how that breaks down (decomposes) into smaller functions (ingest input files, parse them, prepare results, output those) and then breaking those down further until you have units which are small enough to be coded easily in your programming environment (Python).
Modern approaches (and tools) tend to use object oriented design methodologies. You might try reading: http://www.itmaybeahack.com/homepage/books/oodesign/build-python/html/index.html
I'm learning socket programming (in python) and I was wondering what the best/typical way of encapsulating data is? My packets will be used to issue run, stop, configure, etc. commands on the receiving side. Is it helpful to use JSON or just straight text?
I suggest you use a fixed, or mostly fixed format, as this make things easier.
By then using features such as the standard library's struct.Struct, with its pack() and umpack() methods, or possibly a slightly more featured pacakges such as Construct, you should have much of the parsing work done for you ;-)
I suggest plain text to begin with - it is easier to debug. The format that your text takes depends on what you're doing, how many commands, arguments, etc. Have you fleshed out how your commands will look? Once you figure out what that looks like it'll likely suggest a format all on its own.
Are you using TCP or UDP? TCP is easy since it is a stream, but if you're using UDP keep in mind the maximum size of UDP packets and thus how big your message can be.
If you're developing something as a learning exercise you might find it best to go with a structured text (ie. human readable and human writable) format.
An example would be to use a fixed number of fields per command, fixed width text fields and/or easily parsable field delimiters.
Generally text is less efficient in terms of packet size, but it does have the benefits that you can read it easily if you do a packet capture (eg. using wireshark) or if you want to use telnet to mimic a client.
And if this is only a learning exercise then ease of debugging is a significant issue.
Take a look at how scapy (an awesome Python packet manipulation library) implements it. Looks like that have a handful of fields.