Group items tagged

[IPython-user] ipython1 and farm tasking Brian Granger ellisonbg.net@gmail.... Wed Feb 27 16:29:03 CST 2008 Previous message: [IPython-user] ipython1 and farm tasking Next message: [IPython-user] yet another leopard/readline question Messages sorted by: [ date ] [ thread ] [ subject ] [ author ] Alex, First, I would suggest updating your ipython1 install from our svn repository. We are about to push out a major new version and the documentation is _much_ better. Also, there are many new features that will hopefully help you. Here is a simple example (using the latest svn of ipython1): In [1]: from ipython1.kernel import client In [2]: mec = client.MultiEngineClient(('127.0.0.1',10105)) In [3]: tc = client.TaskClient(('127.0.0.1',10113)) In [4]: def fold_package(x): ...: return 2.0*x ...: In [5]: mec.push_function(dict(fold_package=fold_package)) Out[5]: [None, None, None, None] In [6]: tasks = [client.Task("y=fold_package(x)",push={'x':x},pull=('y',)) for x in range(128)] In [7]: task_ids = [tc.run(t) for t in tasks] In [8]: tc.barrier(task_ids) In [9]: task_results = [tc.get_task_result(tid) for tid in task_ids] In [10]: results = [tr.ns.y for tr in task_results] In [11]: print results [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, 26.0, 28.0, 30.0, 32.0, 34.0, 36.0, 38.0, 40.0, 42.0, 44.0, 46.0, 48.0, 50.0, 52.0, 54.0, 56.0, 58.0, 60.0, 62.0, 64.0, 66.0, 68.0, 70.0, 72.0, 74.0, 76.0, 78.0, 80.0, 82.0, 84.0, 86.0, 88.0, 90.0, 92.0, 94.0, 96.0, 98.0, 100.0, 102.0, 104.0, 106.0, 108.0, 110.0, 112.0, 114.0, 116.0, 118.0, 120.0, 122.0, 124.0, 126.0, 128.0, 130.0, 132.0, 134.0, 136.0, 138.0, 140.0, 142.0, 144.0, 146.0, 148.0, 150.0, 152.0, 154.0, 156.0, 158.0, 160.0, 162.0, 164.0, 166.0, 168.0, 170.0, 172.0, 174.0, 176.0, 178.0, 180.0, 182.0, 184.0, 186.0, 188.0, 190.0, 192.0, 194.0, 196.0, 198.0, 200.0, 202.0, 204.0, 206.0, 208.0, 210.0, 212.0, 214.0, 216.0, 218.0, 220.0, 222.0, 224.0, 226.0, 228.0, 230.0, 232.0, 234.0, 236.0, 238.0, 240.0, 242.0, 244.0, 246.0, 248.0, 250.0, 252.0, 254.0] Or if you don't need load balancing: # This sends the fold_package function for you! results = mec.map(fold_package, range(128)) Let us know if you run into other problems. Cheers, Brian

Install using binary installer Download installer from http://ipython.scipy.org/dist/pyreadline-1.3.win32.exe make sure no other readline is installed run installer The configuration file pyreadlineconfig.ini can be copied from Python24/lib/site-packages/pyreadline/configuration to your HOME directory (usually c:/documents and settings/YOURNAME). When using ipython pyreadline is automatically activated. If you want to use pyreadline from the interactive prompt of the python interpreter then you have to include the code in Python24/lib/site-packages/pyreadline/configuration/startup.py in your pythonstartup file or set the PYTHONSTARTUP environment variable to point to that file. Manual can be found in Python24/share/doc/pyreadline

There are two applications that require using the NumPy array type in C extension modules: Access to numerical libraries: Extension modules can be used to make numerical libraries written in C (or languages linkable to C, such as Fortran) accessible to Python programs. The NumPy array type has the advantage of using the same data layout as arrays in C and Fortran. Mixed-language numerical code: In most numerical applications, only a small part of the total code is CPU time intensive. Only this part should thus be written in C, the rest can be written in Python. NumPy arrays are important for the interface between these two parts, because they provide equally simple access to their contents from Python and from C. This document is a tutorial for using NumPy arrays in C extensions.

Now that we have a dendrogram from which to inspect, let's find a suitable color threshold. One can see roughly three major clusters formed by using a distance threshold of 1.2. Now, let's plot another dendrogram using this as the color threshold. The legend displays the predictions of each flat cluster formed by cutting with this threshold

IPython on resizeable Win32 Console If you are on win32, you might want to use Console, an open source replacement for that inflexible old win32 terminal. Here's how. Install IPython using the .exe installer. Download stable version of Console (1.5 at this time). Create a shortcut to console.exe, right-click -> properties. Set target to e.g. C:\opt\Console\console.exe -c "/k python c:\python25\scripts\ipython.py -p sh" you can also create a tab dedicated to Ipython (and have other dedicated to other shells or command-line app). go to edit->settings->tabs : click on the add button and fill the following fields : * title : ipython ico : C:\Python24\py.ico shell : cmd.exe /k "ipython" start-up dir : whatever is good for you. note that python.exe need to be in the path Using IPython with Version 2 of Console use the -r option for creating the shortcut, e.g. C:\opt\Console2\console.exe -r "/k python c:\python25\scripts\ipython.py -p sh" Some advantages of Console2 over normal console (for IPython use) Distinctive taskbar icon Can reside in system tray There are many other benefits, so it's well worth setting up Example config for Console-2.00b120-Beta (you need to edit the xml file): http://vvtools.googlecode.com/svn/trunk/config/console.xml If you have problems with ctrl+C killing Console2 immediately, launch ipython from a normal Console2 session manually.

Mpmath is a pure-Python library for multiprecision floating-point arithmetic. It provides an extensive set of transcendental functions, unlimited exponent sizes, complex numbers, interval arithmetic, numerical integration and differentiation, root-finding, linear algebra, and much more. Almost any calculation can be performed just as well at 10-digit or 1000-digit precision, and in many cases mpmath implements asymptotically fast algorithms that scale well for extremely high precision work. If available, mpmath will (optionally) use gmpy to speed up high precision operations.

Your example works as you describe on recent matplotlib versions. I suspect you are using an old one. The preferred way of handling missing points in numpy, and therefore in matplotlib and pylab, however, is via masked arrays.import pylabimport numpy as npfrom numpy import maa = [1,2,3,4,5]b = np.array([6,2,np.nan,1,9])bm = ma.masked_where(np.isnan(b), b)pylab.plot(a,bm)pylab.show()There are many other examples of masked array use in the examples directory of the matplotlib distribution.EricFernando Abilleira wrote:> Dear sourceforge community,> > I come from a Matlab environment so I am used to plotting matrices that > contain NaN elements. This is very useful because in some cases one > doesn't have data for the entire matrix. If one tries plotting the data, > the NaN elements won't be plotted.

PyNGL (pronounced "pingle") is a Python language module used to visualize scientific data, with an emphasis on high quality 2D visualizations. A working knowledge of Python is assumed.

Robin, Give this a try: from IPython.Debugger import Tracer; debugger = Tracer() debugger() #add this line where you want to break barr