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Using a Python Script in My Hype 3 Build

I have a Python script that I would like to run in my Hype 3 build. The script takes an input (several) and outputs an answer based on on the input (but that’s handled in the script) how I could do that?

Tumult Hype’s Export Scripts infrastructure allows code to be run upon export and/or preview. This can be python code, in fact the sample scripts we have are all in Python.
General info: https://tumult.com/hype/export-scripts/
Developer docs & code: https://github.com/tumult/hype-export-scripts/
With this, you can arbitrarily modify Hype’s output however you see fit.
You’ll get a new File > Export as HTML5 > … menu item; this allows choosing a location to save. I don’t think there’s any way to bypass the save dialog at this point. You probably just ignore it. (Though I guess you could also be clever and have a Preview stick your document wherever you want, since previewing doesn’t have a prompt).

"writing a python binding" vs "using command-line directly"

I have a question regarding python bindings.
I have a command-line which exposes some functionality and code is re-factored to provide the functionality through a shared library. I wanted to know what the real advantage that I get from "writing a python binding for the shared library" vs "calling the command line directly".
One obvious advantage I think will be performance, the shared library will link to the same process and the functionality can called within the same process. It will avoid spawning a new process through the command line.
Any other advantages I can get from writing a python binding for such a case ?
Thanks.

I can hardly imagine a case where one would prefer wrapping a library's command line interface over wrapping the library itself. (Unless there is a library that comes with a neat command line interface while being a total mess internally; but the OP indicates that the same functionality available via the command line is easily accessible in terms of library function calls).
The biggest advantage of writing a Python binding is a clearly defined data interface between the library and Python. Ideally, the library can operate directly on memory managed by Python, without any data copying involved.
To illustrate this, let's assume a library function does something more complicated than printing the current time, i.e., it obtains a significant amount of data as an input, performs some operation, and returns a significant amount of data as an output. If the input data is expected as an input file, Python would need to generate this file first. It must make sure that the OS has finished writing the file before calling the library via the command line (I have seen several C libraries where sleep(1) calls were used as a band-aid for this issue...). And Python must get the output back in some way.
If the command line interface does not rely on files but obtains all arguments on the command line and prints the output on stdout, Python probably needs to convert between binary data and string format, not always with the expected results. It also needs to pipe stdout back and parse it. Not a problem, but getting all this right is a lot of work.
What about error handling? Well, the command line interface will probably handle errors by printing error messages on stderr. So Python needs to capture, parse and process these as well. OTOH, the corresponding library function will almost certainly make a success flag accessible to the calling program. This is much more directly usable for Python.
All of this is obviously affecting performance, which you already mentioned.
As another point, if you are developing the library yourself, you will probably find after some time that the Python workflow has made the whole command line interface obsolete, so you can drop supporting it altogether and save yourself a lot of time.
So I think there is a clear case to be made for the Python bindings. To me, one of the biggest strengths of Python is the ease with which such wrappers can be created and maintained. Unfortunately, there are about 7 or 8 equally easy ways to do this. To get started, I recommend ctypes, since it does not require a compiler and will work with PyPy. For best performance use the native C-Python API, which I also found very easy to learn.

Using Python 3 with Python 2

I have a Python 3 file. I want to use an open-source tool on the internet (nltk), but unfortunately it only supports Python 2. There is no way for me to convert it to Python 3, nor can I convert my Python 3 file to Python 2.
If the user does not give a certain argument (on argparse) then I do something in my file. If the user does give a certain argument, however, I need to use nltk.
Writing a Python 2 script that uses nltk and then executing script that in my Python 3 script
My current idea is to write a script in Python 2 that does what I want with nltk and then run that from my current Python 3 script. However, I don't actually know how to do this.
I found this code: os.system(command) and so I will modify it to be os.system("python py2.py") (where py2.py is my newly written Python 2 file).
I'm not sure if that will work.
I also don't know if that is the most efficient way to solve my problem. I cannot find any information about it on the internet.
The data transferred will probably be quite large. Currently, my test data is about 6600 lines, utf-8. Functionality is more important than how long it takes (to a certain extent) in my case.
Also, how would I pass values from my Python 2 script to my Python 3 script?
Thanks

Is there any other way to do this?
Well, if you're sure you can't convert your script to Python 2, then having one script call the other by running the Python interpreter probably is the best way. (And, this being Python, the best way is, or at least should be, the only way.)
But are you sure? Between the six module, the 3to2 tool, and __future__ statements, it may not be as hard as you think.
Anyway, if you do need to have one script call the other, you should almost never use os.system. As the docs for that function say:
The subprocess module provides more powerful facilities for spawning new processes and retrieving their results; using that module is preferable to using this function. See the Replacing Older Functions with the subprocess Module section in the subprocess documentation for some helpful recipes.
The simplest version is this:
subprocess.check_call(["python", "py2.py"])
This runs your script, waits for it to finish, and raises an exception if the script returns failure—basically, what you wanted to do with os.system, but better. (For example, it doesn't spawn an unnecessary extra shell, it takes care of error handling, etc.)
That assumes whatever other data you need to share is being shared in some implicit, external way (e.g., by accessing files with the same name). You might be better off passing data to py2.py as command-line arguments and/or stdin, passing data back as via stdout, or even opening an explicit pipe or socket to pass things over. Without knowing more about exactly what you need to do, it's hard to suggest anything, but the docs, especially the section Replacing Older Functions with the subprocess Module have lots of discussion on the options.
To give you an idea, here's a simple example: to pass one of your filename arguments to py2.py, and then get data back from py2.py to py3.py, just have py3.py do this:
py2output = subprocess.check_output(["python", "py2.py", my_args[0]])
And then in py2.py, just print whatever you want to send back.

The Anyone hear when NLTK 3.0 will be out? here in SO points out that...
There's a Python 3 branch:
https://github.com/nltk/nltk/tree/nltk-py3k
The answer is from July 2011. It could be improved since then.
I have just looked at https://github.com/nltk/nltk. There is at least the document that talks about Python 3 port related things https://github.com/nltk/nltk/blob/2and3/web/dev/python3porting.rst.
Here is a longer discussion on NLTK and Python 3 that you may be interested in.
And the Grants to Assist Kivy, NLTK in Porting to Python 3 (published 3 days ago) is directly related to the problem.

How to dynamically interpose C functions from Python on Linux (without LD_PRELOAD)?

How do I, at run-time (no LD_PRELOAD), intercept/hook a C function like fopen() on Linux, a la Detours for Windows? I'd like to do this from Python (hence, I'm assuming that the program is already running a CPython VM) and also reroute to Python code. I'm fine with just hooking shared library functions. I'd also like to do this without having to change the way the program is run.
One idea is to roll my own tool based on ptrace(), or on rewriting code found with dlsym() or in the PLT, and targeting ctypes-generated C-callable functions, but I thought I'd ask here first. Thanks.

You'll find from one of ltrace developer a way to do this. See this post, which includes a full patch in order to catch dynamically loaded library. In order to call it from python, you'll probably need to make a C module.

google-perftools has their own implementation of Detour under src/windows/preamble_patcher* . This is windows-only at the moment, but I don't see any reason it wouldn't work on any x86 machine except for the fact that it uses win32 functions to look up symbol addresses.
A quick scan of the code and I see these win32 functions used, all of which have linux versions:
GetModuleHandle/GetProcAddress : get the function address. dlsym can do this.
VirtualProtect : to allow modification of the assembly. mprotect.
GetCurrentProcess: getpid
FlushInstructionCache (apparently a nop according to the comments)
It doesn't seem too hard to get this compiled and linked into python, but I'd send a message to the perftools devs and see what they think.

How can I sandbox Python in pure Python?

I'm developing a web game in pure Python, and want some simple scripting available to allow for more dynamic game content. Game content can be added live by privileged users.
It would be nice if the scripting language could be Python. However, it can't run with access to the environment the game runs on since a malicious user could wreak havoc which would be bad. Is it possible to run sandboxed Python in pure Python?
Update: In fact, since true Python support would be way overkill, a simple scripting language with Pythonic syntax would be perfect.
If there aren't any Pythonic script interpreters, are there any other open source script interpreters written in pure Python that I could use? The requirements are support for variables, basic conditionals and function calls (not definitions).

This is really non-trivial.
There are two ways to sandbox Python. One is to create a restricted environment (i.e., very few globals etc.) and exec your code inside this environment. This is what Messa is suggesting. It's nice but there are lots of ways to break out of the sandbox and create trouble. There was a thread about this on Python-dev a year ago or so in which people did things from catching exceptions and poking at internal state to break out to byte code manipulation. This is the way to go if you want a complete language.
The other way is to parse the code and then use the ast module to kick out constructs you don't want (e.g. import statements, function calls etc.) and then to compile the rest. This is the way to go if you want to use Python as a config language etc.
Another way (which might not work for you since you're using GAE), is the PyPy sandbox. While I haven't used it myself, word on the intertubes is that it's the only real sandboxed Python out there.
Based on your description of the requirements (The requirements are support for variables, basic conditionals and function calls (not definitions)) , you might want to evaluate approach 2 and kick out everything else from the code. It's a little tricky but doable.

Roughly ten years after the original question, Python 3.8.0 comes with auditing. Can it help? Let's limit the discussion to hard-drive writing for simplicity - and see:
from sys import addaudithook
def block_mischief(event,arg):
if 'WRITE_LOCK' in globals() and ((event=='open' and arg[1]!='r')
or event.split('.')[0] in ['subprocess', 'os', 'shutil', 'winreg']): raise IOError('file write forbidden')
addaudithook(block_mischief)
So far exec could easily write to disk:
exec("open('/tmp/FILE','w').write('pwned by l33t h4xx0rz')", dict(locals()))
But we can forbid it at will, so that no wicked user can access the disk from the code supplied to exec(). Pythonic modules like numpy or pickle eventually use the Python's file access, so they are banned from disk write, too. External program calls have been explicitly disabled, too.
WRITE_LOCK = True
exec("open('/tmp/FILE','w').write('pwned by l33t h4xx0rz')", dict(locals()))
exec("open('/tmp/FILE','a').write('pwned by l33t h4xx0rz')", dict(locals()))
exec("numpy.savetxt('/tmp/FILE', numpy.eye(3))", dict(locals()))
exec("import subprocess; subprocess.call('echo PWNED >> /tmp/FILE', shell=True)", dict(locals()))
An attempt of removing the lock from within exec() seems to be futile, since the auditing hook uses a different copy of locals that is not accessible for the code ran by exec. Please prove me wrong.
exec("print('muhehehe'); del WRITE_LOCK; open('/tmp/FILE','w')", dict(locals()))
...
OSError: file write forbidden
Of course, the top-level code can enable file I/O again.
del WRITE_LOCK
exec("open('/tmp/FILE','w')", dict(locals()))
Sandboxing within Cpython has proven extremely hard and many previous attempts have failed. This approach is also not entirely secure e.g. for public web access:
perhaps hypothetical compiled modules that use direct OS calls cannot be audited by Cpython - whitelisting the safe pure pythonic modules is recommended.
Definitely there is still the possibility of crashing or overloading the Cpython interpreter.
Maybe there remain even some loopholes to write the files on the harddrive, too. But I could not use any of the usual sandbox-evasion tricks to write a single byte. We can say the "attack surface" of Python ecosystem reduces to rather a narrow list of events to be (dis)allowed: https://docs.python.org/3/library/audit_events.html
I would be thankful to anybody pointing me to the flaws of this approach.
EDIT: So this is not safe either! I am very thankful to #Emu for his clever hack using exception catching and introspection:
#!/usr/bin/python3.8
from sys import addaudithook
def block_mischief(event,arg):
if 'WRITE_LOCK' in globals() and ((event=='open' and arg[1]!='r') or event.split('.')[0] in ['subprocess', 'os', 'shutil', 'winreg']):
raise IOError('file write forbidden')
addaudithook(block_mischief)
WRITE_LOCK = True
exec("""
import sys
def r(a, b):
try:
raise Exception()
except:
del sys.exc_info()[2].tb_frame.f_back.f_globals['WRITE_LOCK']
import sys
w = type('evil',(object,),{'__ne__':r})()
sys.audit('open', None, w)
open('/tmp/FILE','w').write('pwned by l33t h4xx0rz')""", dict(locals()))
I guess that auditing+subprocessing is the way to go, but do not use it on production machines:
https://bitbucket.org/fdominec/experimental_sandbox_in_cpython38/src/master/sandbox_experiment.py

AFAIK it is possible to run a code in a completely isolated environment:
exec somePythonCode in {'__builtins__': {}}, {}
But in such environment you can do almost nothing :) (you can not even import a module; but still a malicious user can run an infinite recursion or cause running out of memory.) Probably you would want to add some modules that will be the interface to you game engine.

I'm not sure why nobody mentions this, but Zope 2 has a thing called Python Script, which is exactly that - restricted Python executed in a sandbox, without any access to filesystem, with access to other Zope objects controlled by Zope security machinery, with imports limited to a safe subset.
Zope in general is pretty safe, so I would imagine there are no known or obvious ways to break out of the sandbox.
I'm not sure how exactly Python Scripts are implemented, but the feature was around since like year 2000.
And here's the magic behind PythonScripts, with detailed documentation: http://pypi.python.org/pypi/RestrictedPython - it even looks like it doesn't have any dependencies on Zope, so can be used standalone.
Note that this is not for safely running arbitrary python code (most of the random scripts will fail on first import or file access), but rather for using Python for limited scripting within a Python application.
This answer is from my comment to a question closed as a duplicate of this one: Python from Python: restricting functionality?

I would look into a two server approach. The first server is the privileged web server where your code lives. The second server is a very tightly controlled server that only provides a web service or RPC service and runs the untrusted code. You provide your content creator with your custom interface. For example you if you allowed the end user to create items, you would have a look up that called the server with the code to execute and the set of parameters.
Here's and abstract example for a healing potion.
{function_id='healing potion', action='use', target='self', inventory_id='1234'}
The response might be something like
{hp='+5' action={destroy_inventory_item, inventory_id='1234'}}

Hmm. This is a thought experiment, I don't know of it being done:
You could use the compiler package to parse the script. You can then walk this tree, prefixing all identifiers - variables, method names e.t.c. (also has|get|setattr invocations and so on) - with a unique preamble so that they cannot possibly refer to your variables. You could also ensure that the compiler package itself was not invoked, and perhaps other blacklisted things such as opening files. You then emit the python code for this, and compiler.compile it.
The docs note that the compiler package is not in Python 3.0, but does not mention what the 3.0 alternative is.
In general, this is parallel to how forum software and such try to whitelist 'safe' Javascript or HTML e.t.c. And they historically have a bad record of stomping all the escapes. But you might have more luck with Python :)

I think your best bet is going to be a combination of the replies thus far.
You'll want to parse and sanitise the input - removing any import statements for example.
You can then use Messa's exec sample (or something similar) to allow the code execution against only the builtin variables of your choosing - most likely some sort of API defined by yourself that provides the programmer access to the functionality you deem relevant.