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  • Migrating from CPython to Jython

    - by itsadok
    I'm considering moving my code (around 30K LOC) from CPython to Jython, so that I could have better integration with my java code. Is there a checklist or a guide I should look at, to help my with the migration? Does anyone have experience with doing something similar? From reading the Jython site, most of the problems seem too obscure to bother me. I did notice that: thread safety is an issue Unicode support seems to be quite different, which may be a problem for me mysqldb doesn't work and needs to be replaced with zxJDBC Anything else? Related question: What are some strategies to write python code that works in CPython, Jython and IronPython

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  • cpython: when PyDict_GetItem is called and when dict_subscript?

    - by gruszczy
    I am reading cpython code for python 3k and I have noticed, that __missing__ is called only when dict_subscript is called, but not when PyDict_GetItem is used. What is the difference between those two methods and when each is called? If I pass an PyObject that is a subclass of dict and has __missing__ method, how can I force using it, since PyDict_GetItem doesn't do that.

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  • Why is the destructor called when the CPython garbage collector is disabled?

    - by Frederik
    I'm trying to understand the internals of the CPython garbage collector, specifically when the destructor is called. So far, the behavior is intuitive, but the following case trips me up: Disable the GC. Create an object, then remove a reference to it. The object is destroyed and the __del__ method is called. I thought this would only happen if the garbage collector was enabled. Can someone explain why this happens? Is there a way to defer calling the destructor? import gc import unittest _destroyed = False class MyClass(object): def __del__(self): global _destroyed _destroyed = True class GarbageCollectionTest(unittest.TestCase): def testExplicitGarbageCollection(self): gc.disable() ref = MyClass() ref = None # The next test fails. # The object is automatically destroyed even with the collector turned off. self.assertFalse(_destroyed) gc.collect() self.assertTrue(_destroyed) if __name__=='__main__': unittest.main() Disclaimer: this code is not meant for production -- I've already noted that this is very implementation-specific and does not work on Jython.

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  • PyPy -- How can it possible beat CPython?

    - by Vulcan Eager
    From the Google Open Source Blog: PyPy is a reimplementation of Python in Python, using advanced techniques to try to attain better performance than CPython. Many years of hard work have finally paid off. Our speed results often beat CPython, ranging from being slightly slower, to speedups of up to 2x on real application code, to speedups of up to 10x on small benchmarks. How is this possible? Which Python implementation was used to implement PyPy? CPython? And what are the chances of a PyPyPy or PyPyPyPy beating their score? (On a related note... why would anyone try something like this?)

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  • PyPy -- How can it possibly beat CPython?

    - by Vulcan Eager
    From the Google Open Source Blog: PyPy is a reimplementation of Python in Python, using advanced techniques to try to attain better performance than CPython. Many years of hard work have finally paid off. Our speed results often beat CPython, ranging from being slightly slower, to speedups of up to 2x on real application code, to speedups of up to 10x on small benchmarks. How is this possible? Which Python implementation was used to implement PyPy? CPython? And what are the chances of a PyPyPy or PyPyPyPy beating their score? (On a related note... why would anyone try something like this?)

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  • Using SQL Alchemy and pyodbc with IronPython 2.6.1

    - by beargle
    I'm using IronPython and the clr module to retrieve SQL Server information via SMO. I'd like to retrieve/store this data in a SQL Server database using SQL Alchemy, but am having some trouble loading the pyodbc module. Here's the setup: IronPython 2.6.1 (installed at D:\Program Files\IronPython) CPython 2.6.5 (installed at D:\Python26) SQL Alchemy 0.6.1 (installed at D:\Python26\Lib\site-packages\sqlalchemy) pyodbc 2.1.7 (installed at D:\Python26\Lib\site-packages) I have these entries in the IronPython site.py to import CPython standard and third-party libraries: # Add CPython standard libs and DLLs import sys sys.path.append(r"D:\Python26\Lib") sys.path.append(r"D:\Python26\DLLs") sys.path.append(r"D:\Python26\lib-tk") sys.path.append(r"D:\Python26") # Add CPython third-party libs sys.path.append(r"D:\Python26\Lib\site-packages") # sqlite3 sys.path.append(r"D:\Python26\Lib\sqlite3") # Add SQL Server SMO sys.path.append(r"D:\Program Files\Microsoft SQL Server\100\SDK\Assemblies") import clr clr.AddReferenceToFile('Microsoft.SqlServer.Smo.dll') clr.AddReferenceToFile('Microsoft.SqlServer.SqlEnum.dll') clr.AddReferenceToFile('Microsoft.SqlServer.ConnectionInfo.dll') SQL Alchemy imports OK in IronPython, put I receive this error message when trying to connect to SQL Server: IronPython 2.6.1 (2.6.10920.0) on .NET 2.0.50727.3607 Type "help", "copyright", "credits" or "license" for more information. >>> import sqlalchemy >>> e = sqlalchemy.MetaData("mssql://") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\Python26\Lib\site-packages\sqlalchemy\schema.py", line 1780, in __init__ File "D:\Python26\Lib\site-packages\sqlalchemy\schema.py", line 1828, in _bind_to File "D:\Python26\Lib\site-packages\sqlalchemy\engine\__init__.py", line 241, in create_engine File "D:\Python26\Lib\site-packages\sqlalchemy\engine\strategies.py", line 60, in create File "D:\Python26\Lib\site-packages\sqlalchemy\connectors\pyodbc.py", line 29, in dbapi ImportError: No module named pyodbc This code works just fine in CPython, but it looks like the pyodbc module isn't accessible from IronPython. Any suggestions? I realize that this may not be the best way to approach the problem, so I'm open to tackling this a different way. Just wanted to get some experience with using SQL Alchemy and pyodbc.

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  • Running CPython Applications With Visual Studio?

    - by user213060
    I would like to know how to use Visual Studio with CPython (the official python.org python interpreter, not IronPython). In particular, I am interested in getting "build" and "run" commands in Visual Studio working. Other features such as color highlighting and auto-complete, I am less concerned about. Also, can the "build" command be made to run py2exe or similar exe packagers?

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  • How to Develop CPython Applications With Visual Studio?

    - by user213060
    I would like to know how to use Visual Studio with CPython (the official python.org python interpreter, not IronPython). In particular, I am interested in getting "build" and "run" commands in Visual Studio working. Other features such as color highlighting and auto-complete, I am less concerned about. Also, can the "build" command be made to run py2exe or similar exe packagers?

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  • Integrating Jython Cpython

    - by eric.frederich
    I am about to begin a project where I will likely use PyQt or Pyside. I will need to interface with a buggy 3rd party piece of server software that provides C++ and Java APIs. The Java APIs are a lot easier to use because you get Exceptions where with the C++ libraries you get segfaults. Also, the Python bindings to the Java APIs are automatic with Jython whereas the Python bindings for the C++ APIs don't exist. So, how would a CPython PyQt client application be able to communicate with these Java APIs? How would you go about it? Would you have another separate Java process on the client that serializes / pickles objects and communicates with the PyQt process over a socket? I don't want to re-invent the wheel... is there some sort of standard interface for these types of things? Some technology I should look into? RPC, Corba, etc? Thanks, ~Eric

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  • Unexpected performance curve from CPython merge sort

    - by vkazanov
    I have implemented a naive merge sorting algorithm in Python. Algorithm and test code is below: import time import random import matplotlib.pyplot as plt import math from collections import deque def sort(unsorted): if len(unsorted) <= 1: return unsorted to_merge = deque(deque([elem]) for elem in unsorted) while len(to_merge) > 1: left = to_merge.popleft() right = to_merge.popleft() to_merge.append(merge(left, right)) return to_merge.pop() def merge(left, right): result = deque() while left or right: if left and right: elem = left.popleft() if left[0] > right[0] else right.popleft() elif not left and right: elem = right.popleft() elif not right and left: elem = left.popleft() result.append(elem) return result LOOP_COUNT = 100 START_N = 1 END_N = 1000 def test(fun, test_data): start = time.clock() for _ in xrange(LOOP_COUNT): fun(test_data) return time.clock() - start def run_test(): timings, elem_nums = [], [] test_data = random.sample(xrange(100000), END_N) for i in xrange(START_N, END_N): loop_test_data = test_data[:i] elapsed = test(sort, loop_test_data) timings.append(elapsed) elem_nums.append(len(loop_test_data)) print "%f s --- %d elems" % (elapsed, len(loop_test_data)) plt.plot(elem_nums, timings) plt.show() run_test() As much as I can see everything is OK and I should get a nice N*logN curve as a result. But the picture differs a bit: Things I've tried to investigate the issue: PyPy. The curve is ok. Disabled the GC using the gc module. Wrong guess. Debug output showed that it doesn't even run until the end of the test. Memory profiling using meliae - nothing special or suspicious. ` I had another implementation (a recursive one using the same merge function), it acts the similar way. The more full test cycles I create - the more "jumps" there are in the curve. So how can this behaviour be explained and - hopefully - fixed? UPD: changed lists to collections.deque UPD2: added the full test code UPD3: I use Python 2.7.1 on a Ubuntu 11.04 OS, using a quad-core 2Hz notebook. I tried to turn of most of all other processes: the number of spikes went down but at least one of them was still there.

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  • Can I treat IronPython as a Pythonic replacement to C#?

    - by Ewan Nathaniel
    Hey guys! I do understand that this topic has been covered in some way at StackOverflow but I'm still not able to figure out the exact answer: can I treat IronPython as a Pythonic replacement to C#? I use CPython every day, I love the Zen :) but my current task is a Windows-only application with a complex GUI and some other features which I would like to implement using .NET.

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  • Python + PyQt program freezes

    - by DSblizzard
    I wrote PyQt application. After it's start I close it (GUI), but timer don't stops and Python sometimes freezes. Only thing to unfreeze it - Ctrl-C, after which following message appears: Traceback (most recent call last): File "", line 262, in timerEvent KeyboardInterrupt timer don't stops again, and CPython works very slowly. How to avoid this problem?

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  • Nginx no longer servers uwsgi application behind HAProxy - Looks for static file instead

    - by Ralph
    We implemented our web application using web2py. It consists of several modules offering a REST API at various resources (e.g. /dids, /replicas, ...). The API is used by clients implementing requests.py. My problem is that our web app works fine if it's behind HAProxy and hosted by Apache using mod_wsgi. It also works fine if the clients interact with nginx directly. It doesn't work though when using HAProxy in front of nginx. My guess is that HAProxy somehow modifies the request and thus nginx behaves differently i.e. looking for a static file instead of calling the WSGI container. Unfortunately I can't figure out what's exactly going (wr)on(g). Here are the relevant config sections of these three component's config files. At least I guess they are interesting. If you miss anything, please let me know. 1) haproxy.conf frontend app-lb bind loadbalancer:443 ssl crt /etc/grid-security/hostcertkey.pem default_backend nginx-servers mode http backend nginx-servers balance leastconn option forwardfor server nginx-01 nginx-server-int-01.domain.com:80 check 2) nginx.conf: sendfile off; #tcp_nopush on; keepalive_timeout 65; include /etc/nginx/conf.d/*.conf; server { server_name nginx-server-int-01.domain.com; root /path/to/app/; location / { uwsgi_pass unix:///tmp/app.sock; include uwsgi_params; uwsgi_read_timeout 600; # Requests can run for a serious long time } 3) uwsgi.ini [uwsgi] chdir = /path/to/app/ chmod-socket = 777 no-default-app = True socket = /tmp/app.sock manage-script-name = True mount = /dids=did.py mount = /replicas=replica.py callable = application Now when I let my clients go against nginx-server-int-01.domain.com everything is fine. In the access.log of nginx lines like these are appearing: 128.142.XXX.XX0 - - [23/Aug/2014:01:29:20 +0200] "POST /dids/attachments HTTP/1.1" 201 17 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" 128.142.XXX.XX0 - - [23/Aug/2014:01:29:20 +0200] "POST /dids/attachments HTTP/1.1" 201 17 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" 128.142.XXX.XX0 - - [23/Aug/2014:01:29:20 +0200] "POST /dids/user.ogueta/cnt_mc12_8TeV.16304.stream_name_too_long.other.notype.004202218365415e990b9997ea859f20.user/dids HTTP/1.1" 201 17 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" 128.142.XXX.XX0 - - [23/Aug/2014:01:29:20 +0200] "POST /replicas/list HTTP/1.1" 200 5282 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" 128.142.XXX.XX0 - - [23/Aug/2014:01:29:20 +0200] "POST /replicas/list HTTP/1.1" 200 5094 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" 128.142.XXX.XX0 - - [23/Aug/2014:01:29:20 +0200] "POST /replicas/list HTTP/1.1" 200 528 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" 128.142.XXX.XX0 - - [23/Aug/2014:01:29:21 +0200] "GET /dids/mc13_14TeV/dids/search?project=mc13_14TeV&stream_name=%2Adummy&type=dataset&datatype=NTUP_SMDYMUMU HTTP/1.1" 401 73 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" 128.142.XXX.XX0 - - [23/Aug/2014:01:29:21 +0200] "POST /replicas/list HTTP/1.1" 200 713 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" 128.142.XXX.XX0 - - [23/Aug/2014:01:29:21 +0200] "POST /dids/attachments HTTP/1.1" 201 17 "-" "python-requests/2.3.0 CPython/2.6.6 Linux/2.6.32-358.23.2.el6.x86_64" "-" But when I switch the clients to go against HAProxy (loadbalancer.domain.com:443), the error.log of nginx shows lines like these: 2014/08/23 01:26:01 [error] 1705#0: *21231 open() "/usr/share/nginx/html/dids/attachments" failed (2: No such file or directory), client: 128.142.XXX.XX1, server: localhost, request: "POST /dids/attachments HTTP/1.1", host: "loadbalancer.domain.com" 2014/08/23 01:26:02 [error] 1705#0: *21232 open() "/usr/share/nginx/html/replicas/list" failed (2: No such file or directory), client: 128.142.XXX.XX1, server: localhost, request: "POST /replicas/list HTTP/1.1", host: "loadbalancer.domain.com" 2014/08/23 01:26:02 [error] 1705#0: *21233 open() "/usr/share/nginx/html/dids/attachments" failed (2: No such file or directory), client: 128.142.XXX.XX1, server: localhost, request: "POST /dids/attachments HTTP/1.1", host: "loadbalancer.domain.com" 2014/08/23 01:26:02 [error] 1705#0: *21234 open() "/usr/share/nginx/html/replicas/list" failed (2: No such file or directory), client: 128.142.XXX.XX1, server: localhost, request: "POST /replicas/list HTTP/1.1", host: "loadbalancer.domain.com" 2014/08/23 01:26:02 [error] 1705#0: *21235 open() "/usr/share/nginx/html/dids/attachments" failed (2: No such file or directory), client: 128.142.XXX.XXX, server: localhost, request: "POST /dids/attachments HTTP/1.1", host: "loadbalancer" 2014/08/23 01:26:02 [error] 1705#0: *21238 open() "/usr/share/nginx/html/replicas/list" failed (2: No such file or directory), client: 128.142.XXX.XXX, server: localhost, request: "POST /replicas/list HTTP/1.1", host: "loadbalancer.domain.com" 2014/08/23 01:26:02 [error] 1705#0: *21239 open() "/usr/share/nginx/html/dids/attachments" failed (2: No such file or directory), client: 128.142.XXX.XXX, server: localhost, request: "POST /dids/attachments HTTP/1.1", host: "loadbalancer.domain.com" 2014/08/23 01:26:02 [error] 1705#0: *21242 open() "/usr/share/nginx/html/replicas/list" failed (2: No such file or directory), client: 128.142.XXX.XXX, server: localhost, request: "POST /replicas/list HTTP/1.1", host: "loadbalancer.domain.com" 2014/08/23 01:26:02 [error] 1705#0: *21244 open() "/usr/share/nginx/html/dids/attachments" failed (2: No such file or directory), client: 128.142.XXX.XXX, server: localhost, request: "POST /dids/attachments HTTP/1.1", host: "loadbalancer.domain.com" As you can see, that request looks the same, only the client IP changed, from the client's host to the one from loadbalancer.domain.com. But due to what ever reasons ngxin seems to assume that it is a static file to be served which eventually results in the file not found message. I searched the web for multiple hours already, but without much luck so far. Any help is very much appreciated. Cheers, Ralph

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  • Best way to detect IronPython

    - by Adal
    I need to write a module which will be used from both CPython and IronPython. What's the best way to detect IronPython, since I need a slightly different behaviour in that case? I noticed that sys.platform is "win32" on CPython, but "cli" on IronPython. Is there another preferred/standard way of detecting it?

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  • Opinions on Unladen Swallow?

    - by vartec
    What are your opinions and expectations on Google's Unladen Swallow? From their project plan: We want to make Python faster, but we also want to make it easy for large, well-established applications to switch to Unladen Swallow. Produce a version of Python at least 5x faster than CPython. Python application performance should be stable. Maintain source-level compatibility with CPython applications. Maintain source-level compatibility with CPython extension modules. We do not want to maintain a Python implementation forever; we view our work as a branch, not a fork. And even sweeter: In addition, we intend to remove the GIL and fix the state of multithreading in Python. We believe this is possible through the implementation of a more sophisticated GC It almost looks too good to be true, like the best of PyPy and Stackless combined. More info: Jesse Noller: "Pycon: Unladen-Swallow" ArsTechnica: "Google searches for holy grail of Python performance" Update: as DNS pointed out, there was related question: http://stackoverflow.com/questions/695370/what-is-llvm-and-how-is-replacing-python-vm-with-llvm-increasing-speeds-5x

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  • What's the relationship between meta-circular interpreters, virtual machines and increased performance?

    - by Gomi
    I've read about meta-circular interpreters on the web (including SICP) and I've looked into the code of some implementations (such as PyPy and Narcissus). I've read quite a bit about two languages which made great use of metacircular evaluation, Lisp and Smalltalk. As far as I understood Lisp was the first self-hosting compiler and Smalltalk had the first "true" JIT implementation. One thing I've not fully understood is how can those interpreters/compilers achieve so good performance or, in other words, why is PyPy faster than CPython? Is it because of reflection? And also, my Smalltalk research led me to believe that there's a relationship between JIT, virtual machines and reflection. Virtual Machines such as the JVM and CLR allow a great deal of type introspection and I believe they make great use it in Just-in-Time (and AOT, I suppose?) compilation. But as far as I know, Virtual Machines are kind of like CPUs, in that they have a basic instruction set. Are Virtual Machines efficient because they include type and reference information, which would allow language-agnostic reflection? I ask this because many both interpreted and compiled languages are now using bytecode as a target (LLVM, Parrot, YARV, CPython) and traditional VMs like JVM and CLR have gained incredible boosts in performance. I've been told that it's about JIT, but as far as I know JIT is nothing new since Smalltalk and Sun's own Self have been doing it before Java. I don't remember VMs performing particularly well in the past, there weren't many non-academic ones outside of JVM and .NET and their performance was definitely not as good as it is now (I wish I could source this claim but I speak from personal experience). Then all of a sudden, in the late 2000s something changed and a lot of VMs started to pop up even for established languages, and with very good performance. Was something discovered about the JIT implementation that allowed pretty much every modern VM to skyrocket in performance? A paper or a book maybe?

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  • How to create Python module distribution to gracefully fall-back to pure Python code

    - by Craig McQueen
    I have written a Python module, and I have two versions: a pure Python implementation and a C extension. I've written the __init__.py file so that it tries to import the C extension, and if that fails, it imports the pure Python code (is that reasonable?). Now, I'd like to know what is the best way to distribute this module (e.g. write setup.py) so it can be easily used by people with or without the facility to build, or use, the C extension. My experience is limited but I see two possible cases: User does not have MS Visual Studio, or the GCC compiler suite, installed on their machine, to build the C extension User is running IronPython, Jython, or anything other than CPython. I only have used CPython. So I'm not sure how I could distribute this module so that it would work smoothly and be easy to install on those platforms, if they're unable to use the C extension.

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  • How to create Python module distribution to gracefully fall-back to pure Python code

    - by Craig McQueen
    I have written a Python module, and I have two versions: a pure Python implementation and a C extension. I've written the __init__.py file so that it tries to import the C extension, and if that fails, it imports the pure Python code (is that reasonable?). Now, I'd like to know what is the best way to distribute this module (e.g. write setup.py) so it can be easily used by people with or without the facility to build, or use, the C extension, just by running: python setup.py install My experience is limited, but I see two possible cases: User does not have MS Visual Studio, or the GCC compiler suite, installed on their machine, to build the C extension User is running IronPython, Jython, or anything other than CPython. I only have used CPython. So I'm not sure how I could distribute this module so that it would work smoothly and be easy to install on those platforms, if they're unable to use the C extension.

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  • PyPy 1.2 Released

    PyPy is a reimplementation of Python in Python, using advanced techniques to try to attain better performance than CPython . Many years of hard work have finally paid...

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  • Introducing the PyPy 1.2 release

    <b>PyPy Status Blog:</b> "We are pleased to announce PyPy's 1.2 release. This version 1.2 is a major milestone and it is the first release to ship a Just-in-Time compiler that is known to be faster than CPython"

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