- cctbxbb - phenix-online.org

First svn commit
by James Stroud 18 Apr '13

18 Apr '13

Hello All, Since this is my first commit, I just want to make sure that I have done these things in the appropriate way. Here's what I did: 1. Added a wiki page: https://sourceforge.net/p/cctbx/wiki/Subversion%20set%20up/ 2. Added the method as_miller_arrays_map() to iotbx_mtz_ext.object, which is declared in iotbx/mtz/__init__.py. 3. Added a test for this functionality in the file iotbx/mtz/tst_miller_map.py Please let me know if the manner in which I did anything could use improvement. James

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Euro SciPy
by Graeme.Winter＠diamond.ac.uk 17 Apr '13

17 Apr '13

Hi Folks, Anyone going to this meeting? https://www.euroscipy.org/ Some of the scientific software team here at Diamond have a habit of going to this meeting and remarked that a contribution on CCTBX may be appropriate... (see below for meeting topics) Best wishes, Graeme Topics * Presentations of scientific tools and libraries using the Python language, including but not limited to: * Vector and array manipulation * Parallel computing * Scientific visualization * Scientific data flow and persistence * Algorithms implemented or exposed in Python * Web applications and portals for science and engineering- * Reports on the use of Python in scientific achievements or ongoing projects. * General-purpose Python tools that can be of special interest to the scientific community. -- This e-mail and any attachments may contain confidential, copyright and or privileged material, and are for the use of the intended addressee only. If you are not the intended addressee or an authorised recipient of the addressee please notify us of receipt by returning the e-mail and do not use, copy, retain, distribute or disclose the information in or attached to the e-mail. Any opinions expressed within this e-mail are those of the individual and not necessarily of Diamond Light Source Ltd. Diamond Light Source Ltd. cannot guarantee that this e-mail or any attachments are free from viruses and we cannot accept liability for any damage which you may sustain as a result of software viruses which may be transmitted in or with the message. Diamond Light Source Limited (company no. 4375679). Registered in England and Wales with its registered office at Diamond House, Harwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0DE, United Kingdom

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structure.replace_sites_... vs. structure.set_sites_...
by Jan Marten Simons 16 Apr '13

16 Apr '13

Hi, while working on a really fast way to calculate the structure factors of a few millions of different modifications of a structure with only the selected scatterers contributing to f_calc I came across this: Why are there two methods for changing the sites_(cart/frac) of the scatterers doing quite different things? set_sites_... seems to simply update the corresponding site information of the scatterer, while replace_sites_... seems to create a copy of the structure. Is this the only difference there is? Also If anybody got a nice idea for my original problem, I'm also very much interested. (I think building a larger structure and working with selections might be faster than creating lots of different structures containing only the desired scatterers, but I might be wrong on this.) Cheers, Jan

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pyyaml - iotbx.pdb incompatibility
by James Stroud 16 Apr '13

16 Apr '13

Hello All, There is a strange incompatibility between pyyaml and iotbx.pdb that depends on the order of import. If yaml is imported first, there is no problem. If iotbx.pdb is imported first, then importing yaml crashes. This is the yaml that comes with enthought python, but I don't think it's using libyaml, which is the C implementation. So the yaml, from what I can tell, is pure python. I'm wondering if anyone else can reproduce this error. James chernev 185% cctbx.python Enthought Python Distribution -- www.enthought.com Version: 7.3-2 (64-bit) Python 2.7.3 |EPD 7.3-2 (64-bit)| (default, Apr 12 2012, 11:14:05) [GCC 4.0.1 (Apple Inc. build 5493)] on darwin Type "credits", "demo" or "enthought" for more information. py> import yaml py> from iotbx import pdb py> raise SystemExit() chernev 186% cctbx.python Enthought Python Distribution -- www.enthought.com Version: 7.3-2 (64-bit) Python 2.7.3 |EPD 7.3-2 (64-bit)| (default, Apr 12 2012, 11:14:05) [GCC 4.0.1 (Apple Inc. build 5493)] on darwin Type "credits", "demo" or "enthought" for more information. py> from iotbx import pdb py> import yaml show_stack(1): /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/yaml/constructor.py(256) SafeConstructor show_stack(2): /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/yaml/constructor.py(161) <module> show_stack(3): /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/yaml/loader.py(8) <module> show_stack(4): /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/yaml/__init__.py(8) <module> show_stack(5): <stdin>(1) <module> libc backtrace (49 frames, most recent call last): 50 python2.7 0x0000000100000f54 start + 52 49 Python 0x0000000100108576 Py_Main + 3318 48 Python 0x00000001000f2c6e PyRun_AnyFileExFlags + 126 47 Python 0x00000001000f2b2e PyRun_InteractiveLoopFlags + 78 46 Python 0x00000001000f28eb PyRun_InteractiveOneFlags + 379 45 Python 0x00000001000cd2c6 PyEval_EvalCode + 54 44 Python 0x00000001000ccfc5 PyEval_EvalCodeEx + 1733 43 Python 0x00000001000c77fe PyEval_EvalFrameEx + 9502 42 Python 0x00000001000c4267 PyEval_CallObjectWithKeywords + 87 41 Python 0x000000010000d052 PyObject_Call + 98 40 Python 0x00000001000be823 builtin___import__ + 131 39 Python 0x00000001000e5cf8 PyImport_ImportModuleLevel + 344 38 Python 0x00000001000e536a load_next + 234 37 Python 0x00000001000e511a import_submodule + 314 36 Python 0x00000001000e4f59 load_package + 409 35 Python 0x00000001000e4632 load_source_module + 466 34 Python 0x00000001000e3981 PyImport_ExecCodeModuleEx + 209 33 Python 0x00000001000cd2c6 PyEval_EvalCode + 54 32 Python 0x00000001000ccfc5 PyEval_EvalCodeEx + 1733 31 Python 0x00000001000c77fe PyEval_EvalFrameEx + 9502 30 Python 0x00000001000c4267 PyEval_CallObjectWithKeywords + 87 29 Python 0x000000010000d052 PyObject_Call + 98 28 Python 0x00000001000be823 builtin___import__ + 131 27 Python 0x00000001000e5cf8 PyImport_ImportModuleLevel + 344 26 Python 0x00000001000e536a load_next + 234 25 Python 0x00000001000e511a import_submodule + 314 24 Python 0x00000001000e4632 load_source_module + 466 23 Python 0x00000001000e3981 PyImport_ExecCodeModuleEx + 209 22 Python 0x00000001000cd2c6 PyEval_EvalCode + 54 21 Python 0x00000001000ccfc5 PyEval_EvalCodeEx + 1733 20 Python 0x00000001000c77fe PyEval_EvalFrameEx + 9502 19 Python 0x00000001000c4267 PyEval_CallObjectWithKeywords + 87 18 Python 0x000000010000d052 PyObject_Call + 98 17 Python 0x00000001000be823 builtin___import__ + 131 16 Python 0x00000001000e5cf8 PyImport_ImportModuleLevel + 344 15 Python 0x00000001000e536a load_next + 234 14 Python 0x00000001000e511a import_submodule + 314 13 Python 0x00000001000e4632 load_source_module + 466 12 Python 0x00000001000e3981 PyImport_ExecCodeModuleEx + 209 11 Python 0x00000001000cd2c6 PyEval_EvalCode + 54 10 Python 0x00000001000ccfc5 PyEval_EvalCodeEx + 1733 9 Python 0x00000001000cb0e6 PyEval_EvalFrameEx + 24070 8 Python 0x00000001000ccfc5 PyEval_EvalCodeEx + 1733 7 Python 0x00000001000c7ea0 PyEval_EvalFrameEx + 11200 6 Python 0x0000000100010af8 PyNumber_Multiply + 40 5 Python 0x000000010000c569 binary_op1 + 137 4 ??? 0x0000000000000aa9 0x0 + 2729 3 libsystem_c.dylib 0x00007fff84b0ccfa _sigtramp + 26 2 boost_python_meta_ext.so 0x00000001007013c0 initboost_python_meta_ext + 0 Floating-point error (Python and libc call stacks above) This crash may be due to a problem in any imported Python module, including modules which are not part of the cctbx project. To disable the traps leading to this message, define these environment variables (e.g. assign the value 1): BOOST_ADAPTBX_FPE_DEFAULT BOOST_ADAPTBX_SIGNALS_DEFAULT This will NOT solve the problem, just mask it, but may allow you to proceed in case it is not critical. -- James Stroud Affiliate Howard Hughes Medical Institute UCLA-DOE Institute for Genomics and Proteomics

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Flex array slice assignment work-alike
by James Stroud 15 Apr '13

15 Apr '13

Hello All, It seems that flex arrays do not support slice assignment: py> from scitbx.array_family import flex py> ary = flex.float(range(5)) py> list(ary) [0.0, 1.0, 2.0, 3.0, 4.0] py> ary[:3] = flex.float([41, 42, 43]) --------------------------------------------------------------------------- ArgumentError Traceback (most recent call last) /usr/local/cctbx/cctbx_sources/mmtbx/dynamics/<ipython-input-13-5ce3d648bc6f> in <module>() ----> 1 ary[:3] = flex.float([41, 42, 43]) ArgumentError: Python argument types in float.__setitem__(float, slice, float) did not match C++ signature: __setitem__(scitbx::af::versa<float, scitbx::af::flex_grid<scitbx::af::small<long, 10ul> > > {lvalue}, scitbx::af::small<long, 10ul>, float) __setitem__(scitbx::af::versa<float, scitbx::af::flex_grid<scitbx::af::small<long, 10ul> > > {lvalue}, long, float) Slice assignment is common in python: py> alist = range(5) py> alist[:3] = [41, 42, 43] py> alist [41, 42, 43, 3, 4] Is there a flex array work-alike for this operation? I've found set_selected(): py> sel_ary = flex.bool([True, True, True, False, False]) py> ary.set_selected(sel_ary, flex.float([41, 42, 43])) <scitbx_array_family_flex_ext.float at 0x101df1158> py> list(ary) [41.0, 42.0, 43.0, 3.0, 4.0] But set_selected() is cumbersome for slice assignment because (1) you have to build a flex selection array somehow and (2) you have to put the new values into a flex array of the appropriate type. Is set_selected() the best way? If so, is there something in the cctbx libraries that makes building "slicing" selection arrays more efficient than say a list comprehension? sel_ary = flex.bool([True if i < 3 else False for i in xrange(5)]) James

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cif parser & ccp4 mon_lib incompatibility
by James Stroud 15 Apr '13

15 Apr '13

Hi All, It's a fresh install of the latest-greatest cctbx and of ccp4. Any ideas? % cctbx.python ./tst_sa.py CifParserError: /Applications/ccp4-6.3.0/lib/data/monomers/list/mon_lib_list.cif(line 1) : error 4 : Unexpected token, at offset -1 near global : unexpected input... expected one of : Actually dude, we didn't seem to be expecting anything here, or at least I could not work out what I was expecting, like so many of us these days! Here's the top of the cif file: global _lib_name mon_lib _lib_version 5.36 _lib_update 03/05/12 --------------------------------- # # --- LIST OF MONOMERS --- # data_comp_list loop_ _chem_comp.id _chem_comp.three_letter_code _chem_comp.name _chem_comp.group James

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Re: [cctbxbb] [ccp4bb] CCP4 Update victim of own success
by James Stroud 11 Apr '13

11 Apr '13

On Apr 11, 2013, at 6:17 AM, <eugene.krissinel(a)stfc.ac.uk> <eugene.krissinel(a)stfc.ac.uk> wrote: > Sorry that this was unclear. We assume that updater is used primarily from ccp4i, where nothing changed (and why it should be used from command line at all ?:)). Scriptability. On Apr 11, 2013, at 10:34 AM, Jim Pflugrath wrote: > I think James gets to 'fight' like in the old game of rogue by pressing the h, j, k, l keys on his keyboard (not a detachable one either). While Eugene gets to use a modern game controller or a Wii. > > Ooops, game is already over and James [Holton] has lost. But he wrote the game. James

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bin_centers() and d_star_sq
by James Stroud 09 Apr '13

09 Apr '13

Hello All, I'm genuinely perplexed about what factors should go into choosing an appropriate d_star_sq to calculate bin_centers() in cctbx.miller.binner. This decision may be a nuance because it doesn't seem change the output significantly (except for low resolution bins). But if it doesn't matter significantly, then why have the parameter in the first place? James py> binner = miller.setup_binner(n_bins=10) py> binner.show_summary() unused: - 81.7405 [ 0/0 ] bin 1: 81.7405 - 6.1396 [2341/2341] bin 2: 6.1396 - 4.8734 [2213/2213] bin 3: 4.8734 - 4.2574 [2185/2185] bin 4: 4.2574 - 3.8681 [2168/2168] bin 5: 3.8681 - 3.5909 [2177/2177] bin 6: 3.5909 - 3.3792 [2156/2156] bin 7: 3.3792 - 3.2099 [2137/2137] bin 8: 3.2099 - 3.0702 [2144/2144] bin 9: 3.0702 - 2.9520 [2133/2133] bin 10: 2.9520 - 2.8501 [2135/2135] unused: 2.8501 - [ 0/0 ] py> centers = binner.bin_centers(1) py> list(1 / centers) [11.421375552155686, 5.4336997182888345, 4.544594791289144, 4.053431451445772, 3.7243527058119903, 3.481806175995981, 3.292371389128289, 3.138504394416836, 3.0099330507138475, 2.9001646196929975] py> centers = binner.bin_centers(4) py> list(1 / centers**(1 / 4)) [7.301226942489155, 5.330776416864701, 4.514065417212759, 4.039592121777967, 3.716672320747147, 3.4770032890247498, 3.28912114749567, 3.13617782414047, 3.0081962210988116, 2.8988250671976883]

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match_indices.paired_miller_indices() ungraceful overshoot
by James Stroud 08 Apr '13

08 Apr '13

Hello all, I accidentally gave paired_miller_indices() a value > 1 for i_array, and got a segfault. The problem looks to be in match_indices.cpp, line 94: result.push_back(miller_indices_[i_array][pairs_[i][i_array]]); Maybe there should be a test that raises an exception on the python side, if possible. James

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flex and numpy caveats
by James Stroud 06 Apr '13

06 Apr '13

Hello All, I've noticed some incompatibilities between numpy and cctbx, which is understandable. However, the incompatibilities manifest either as nonsense errors or, worse, not at all. Following are a couple of examples I have found. 1. The first example should really throw an exception, because silent failures like this can be catastrophic: py> from numpy import random py> r = random.randint(2, size=10) py> r array([1, 0, 1, 1, 0, 1, 0, 1, 1, 1]) py> list(flex.bool(r)) [True, False, False, False, False, False, False, False, False, False] This example is clearly due to incorrect assumptions about the internal representations of numpy ndarrays. Much better behavior can be seen when using a sequence of python `int` objects: py> list(flex.bool(range(10))) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /Users/jstroud/Unison/Code/radialx/testdata/<ipython-input-754-dce96b02979e> in <module>() ----> 1 list(flex.bool(range(10))) TypeError: No registered converter was able to produce a C++ rvalue of type bool from this Python object of type int 2. Although not as potentially catastrophic as the first, the second example should (in a perfect world) either work or throw a more meaningful exception: py> flex.int(range(10))[r[0]] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /Users/jstroud/Unison/Code/radialx/testdata/<ipython-input-753-f14587f0ceeb> in <module>() ----> 1 flex.int(range(10))[r[0]] TypeError: 'numpy.int64' object is not iterable This example is obviously a result of type checking for a python `int`. The usual python approach of duck-type checking would avoid this problem: def __getitem__(self, i): try: i = int(i) except: pass return do_whatever_with_i(i) James

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