Re: relax on Mac with fink -- February 25, 2010 - 16:47

Hi,

This failure is because it takes 201 iterations to reach the minimum
compared to 204 with x86_64 hardware.  I have now removed this
iteration check as testing the values and chi-squared number is
sufficient to see that the minimum has been reached.  Could you update
the repository copy (svn up) and check again?

For the Fink specific changes to the scons/install.py file, is there a
way of signalling that this is a Fink install rather than a normal
install?  Maybe I could add an scons target called install_fink so
that you type:

$ scons install_fink

That way we can bring in these changes into the relax repository,
something which might be useful for installing relax on Macs in the
distant future.

Cheers,

Edward



On 25 February 2010 15:43, Jack Howarth <howarth@xxxxxxxxxxxxxxxx> wrote:
> Edward,
>    Thanks. I had to recreate a minfx-1.0.3.zip archive from
> the contents of the svn and reuse the missing setup.py file
> from the minfx-1.0.2.zip archive in order to build an updated
> minfx-py26 package. Building the relax 1.3 branch under fink
> (using the attached fink packaging files), I get the following
> results on powerpc-apple-darwin9.8.0 (which contain one new
> failure for this platform compared to the relax-1.2.0 release
> in the System/Functional tests). I'll check the results on
> i386 and x86_64 fink later tonight.
>                    Jack
> ps I noticed that minfx for both 1.0.2 and 1.0.3 doesn't report
> a version despite each installation having the version field
> set in setup.py. Is there a fix for this? Also, shouldn't the
> scons installation have a dependency check for the 1.0.3 release
> of minfx before it proceeds?
>
> relax --info
>
>
>
>                                     relax repository checkout
>
>                              Molecular dynamics by NMR data analysis
>
>                             Copyright (C) 2001-2006 Edward d'Auvergne
>                         Copyright (C) 2006-2010 the relax development team
>
> This is free software which you are welcome to modify and redistribute 
> under the conditions of the
> GNU General Public License (GPL).  This program, including all modules, is 
> licensed under the GPL
> and comes with absolutely no warranty.  For details type 'GPL' within the 
> relax prompt.
>
> Assistance in using the relax prompt and scripting interface can be 
> accessed by typing 'help' within
> the prompt.
>
> Hardware information:
>    Machine:                 Power Macintosh
>    Processor:               powerpc
>
> System information:
>    System:                  Darwin
>    Release:                 9.8.0
>    Version:                 Darwin Kernel Version 9.8.0: Wed Jul 15 
> 16:57:01 PDT 2009; root:xnu-1228.15.4~1/RELEASE_PPC
>    Mac version:             10.5.8 (, , ) PowerPC
>    Distribution:
>    Full platform string:    Darwin-9.8.0-Power_Macintosh-powerpc-32bit
>
> Software information:
>    Architecture:            32bit
>    Python version:          2.6.4
>    Python branch:           tags/r264
>    Python build:            r264:75706, Feb 24 2010 14:23:45
>    Python compiler:         GCC 4.0.1 (Apple Inc. build 5493)
>    Python implementation:   CPython
>    Python revision:         75706
>    Numpy version:           1.3.0
>    Libc version:
>
> Python packages (most are optional):
>
> Package              Installed       Version         Path
> minfx                True            Unknown         
> /sw/lib/python2.6/site-packages/minfx
> bmrblib              False
> numpy                True            1.3.0           
> /sw/lib/python2.6/site-packages/numpy
> ScientificPython     True            2.8             
> /sw/lib/python2.6/site-packages/Scientific
> wxPython             False
> mpi4py               False
> epydoc               False
> optparse             True            1.5.3           
> /sw/lib/python2.6/optparse.pyc
> Numeric              True            24.2            
> /sw/lib/python2.6/site-packages/Numeric/Numeric.pyc
> readline             True                            
> /sw/lib/python2.6/lib-dynload/readline.so
> profile              True                            
> /sw/lib/python2.6/profile.pyc
> bz2                  True                            
> /sw/lib/python2.6/lib-dynload/bz2.so
> gzip                 True                            
> /sw/lib/python2.6/gzip.pyc
> os.devnull           True                            
> /sw/lib/python2.6/os.pyc
>
> Compiled relax C modules:
>    Relaxation curve fitting: True
>
> relax --test-suite
> Echoing of user function calls has been enabled.
>
>
>
>
> #############################
> # System / functional tests #
> #############################
>
>
> ..............F....................................................................................................
> ======================================================================
> FAIL: Test the 'rigid' model for randomly rotated tensors with no motion.
> ----------------------------------------------------------------------
>
> relax> pipe.create(pipe_name='test', pipe_type='frame order')
>
>
>
>                                     relax repository checkout
>
>                              Molecular dynamics by NMR data analysis
>
>                             Copyright (C) 2001-2006 Edward d'Auvergne
>                         Copyright (C) 2006-2010 the relax development team
>
> This is free software which you are welcome to modify and redistribute 
> under the conditions of the
> GNU General Public License (GPL).  This program, including all modules, is 
> licensed under the GPL
> and comes with absolutely no warranty.  For details type 'GPL' within the 
> relax prompt.
>
> Assistance in using the relax prompt and scripting interface can be 
> accessed by typing 'help' within
> the prompt.
>
>
> relax> pipe.create(pipe_name='rigid', pipe_type='frame order')
>
> relax> 
> script(file='/sw/lib/relax-py26/test_suite/system_tests/scripts/frame_order/tensors_rigid_rand_rot.py',
>  quit=False)
> script = 
> '/sw/lib/relax-py26/test_suite/system_tests/scripts/frame_order/tensors_rigid_rand_rot.py'
> ----------------------------------------------------------------------------------------------------
> # Random rotation matrix:
> # [[ 0.33282568, -0.83581125,  0.43663098],
> #  [-0.92326661, -0.19462612,  0.33120905],
> #  [-0.19184846, -0.51336169, -0.83645319]]
> # Euler angles:
> # alpha: 5.0700283197712777
> # beta: 2.5615753919522359
> # gamma: 0.64895449611163691
>
>
> # The error value.
> error = 1.4741121114678945e-05
>
> # Load tensor 0.
> align_tensor.init(tensor='a 0', params=(0.00014221982216882766, 
> -0.00014454300156652134, -0.00070779621164871397, -0.00060161949408277324, 
> 0.00020200800707295083), param_types=0)
> align_tensor.init(tensor='b 0', params=(-1.3288330878574132e-05, 
> 0.00020354043164217626, -0.00046409902800134087, 0.0002493202418302213, 
> -0.00077964218698160488), param_types=0)
> align_tensor.init(tensor='a 0', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 0', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 0', domain='a')
> align_tensor.set_domain(tensor='b 0', domain='b')
>
> # Load tensor 1.
> align_tensor.init(tensor='a 1', params=(-0.00014307694949297205, 
> -0.00039671919293883539, -0.00024724524395487659, 0.00031948292975139144, 
> 0.00018868359624777637), param_types=0)
> align_tensor.init(tensor='b 1', params=(-9.738292410013338e-05, 
> -0.00038634774864149617, -0.00027912458757344276, -0.00038171766743202567, 
> -0.00011588335825493787), param_types=0)
> align_tensor.init(tensor='a 1', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 1', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 1', domain='a')
> align_tensor.set_domain(tensor='b 1', domain='b')
>
> # Load tensor 2.
> align_tensor.init(tensor='a 2', params=(-0.00022967898444150887, 
> -0.00027171643813494106, -0.00021961563147411279, 0.00010337393266477703, 
> 0.00029030226175831515), param_types=0)
> align_tensor.init(tensor='b 2', params=(-0.00017932499024246612, 
> -0.00033064833984871618, -0.00019167049464976276, -0.00018228662361670689, 
> -0.00024786515322241842), param_types=0)
> align_tensor.init(tensor='a 2', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 2', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 2', domain='a')
> align_tensor.set_domain(tensor='b 2', domain='b')
>
> # Load tensor 3.
> align_tensor.init(tensor='a 3', params=(0.00043690692358615301, 
> -0.00034379559287467062, -0.00019359695171683388, 0.00030194133983804048, 
> -6.314162250164486e-05), param_types=0)
> align_tensor.init(tensor='b 3', params=(3.2029991098699158e-05, 
> 0.0001030927713217096, -0.00040609134800855906, -0.00027871118513542376, 
> 0.00018429705265751148), param_types=0)
> align_tensor.init(tensor='a 3', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 3', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 3', domain='a')
> align_tensor.set_domain(tensor='b 3', domain='b')
>
> # Load tensor 4.
> align_tensor.init(tensor='a 4', params=(-0.00026249527958822807, 
> 0.00073561736796410628, 6.3975419225898133e-05, 6.2788017118057252e-05, 
> 0.00020119758245770023), param_types=0)
> align_tensor.init(tensor='b 4', params=(0.00023041655343338213, 
> -0.00028914097123516663, 8.5942868106736884e-05, 0.00057733961469646491, 
> 0.00023383246814246303), param_types=0)
> align_tensor.init(tensor='a 4', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 4', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 4', domain='a')
> align_tensor.set_domain(tensor='b 4', domain='b')
>
> # Load tensor 5.
> align_tensor.init(tensor='a 5', params=(0.00048180707211229368, 
> -0.00033930112217225942, 0.00011094068795736053, 0.00070350646902989675, 
> 0.00037537667271407197), param_types=0)
> align_tensor.init(tensor='b 5', params=(-0.00034205987160777676, 
> -5.6563966889313711e-05, -0.00048729767346789097, -0.00020195965056872761, 
> 0.00064352392049120096), param_types=0)
> align_tensor.init(tensor='a 5', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 5', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 5', domain='a')
> align_tensor.set_domain(tensor='b 5', domain='b')
>
> # Load tensor 6.
> align_tensor.init(tensor='a 6', params=(0.00035672066304092451, 
> -0.00026838578790208884, -0.00016936140664230585, 0.00017187371551506447, 
> -0.00030579015509609098), param_types=0)
> align_tensor.init(tensor='b 6', params=(0.00020255575866227554, 
> 0.00015766165657592193, -0.00022547338964377635, -0.00031137881231040781, 
> 9.8269840241030186e-05), param_types=0)
> align_tensor.init(tensor='a 6', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 6', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 6', domain='a')
> align_tensor.set_domain(tensor='b 6', domain='b')
>
> # Load tensor 7.
> align_tensor.init(tensor='a 7', params=(0.00017061308478202151, 
> -0.00076455273118810501, -0.00052048809712606505, 0.00049258369866413392, 
> -0.00013905141064073534), param_types=0)
> align_tensor.init(tensor='b 7', params=(0.00013226613079678079, 
> -0.00028875805425577231, -0.00055280116463899331, -0.00079483102252618661, 
> -0.00012673098706816532), param_types=0)
> align_tensor.init(tensor='a 7', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 7', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 7', domain='a')
> align_tensor.set_domain(tensor='b 7', domain='b')
>
> # Load tensor 8.
> align_tensor.init(tensor='a 8', params=(-0.00022193220790426714, 
> -0.00090073235703922686, 0.00050867766236886724, 0.00028215012727179065, 
> 0.0002562167583736733), param_types=0)
> align_tensor.init(tensor='b 8', params=(-0.00082779604132576475, 
> -0.0001229250183977039, 0.00026827297822125086, -0.00076816617763492308, 
> 1.787549543771558e-05), param_types=0)
> align_tensor.init(tensor='a 8', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 8', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 8', domain='a')
> align_tensor.set_domain(tensor='b 8', domain='b')
>
> # Load tensor 9.
> align_tensor.init(tensor='a 9', params=(0.00037091020965736575, 
> -0.00012230875848954012, -0.00016247713611487416, -0.00042725170061841107, 
> 9.0103851318397519e-05), param_types=0)
> align_tensor.init(tensor='b 9', params=(-0.00019129846420341554, 
> 0.00047556140822968502, -0.0001921404751338773, 0.00021386940177866865, 
> -0.00026418197641736997), param_types=0)
> align_tensor.init(tensor='a 9', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.init(tensor='b 9', params=(error, error, error, error, error), 
> param_types=0, errors=True)
> align_tensor.set_domain(tensor='a 9', domain='a')
> align_tensor.set_domain(tensor='b 9', domain='b')
> ----------------------------------------------------------------------------------------------------
>
> relax> align_tensor.init(tensor='a 0', params=(0.00014221982216882766, 
> -0.00014454300156652134, -0.00070779621164871397, -0.00060161949408277324, 
> 0.00020200800707295083), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 0', params=(-1.3288330878574132e-05, 
> 0.00020354043164217626, -0.00046409902800134087, 0.0002493202418302213, 
> -0.00077964218698160488), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 0', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 0', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 0', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 0', domain='b')
>
> relax> align_tensor.init(tensor='a 1', params=(-0.00014307694949297205, 
> -0.00039671919293883539, -0.00024724524395487659, 0.00031948292975139144, 
> 0.00018868359624777637), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 1', params=(-9.738292410013338e-05, 
> -0.00038634774864149617, -0.00027912458757344276, -0.00038171766743202567, 
> -0.00011588335825493787), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 1', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 1', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 1', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 1', domain='b')
>
> relax> align_tensor.init(tensor='a 2', params=(-0.00022967898444150887, 
> -0.00027171643813494106, -0.00021961563147411279, 0.00010337393266477703, 
> 0.00029030226175831515), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 2', params=(-0.00017932499024246612, 
> -0.00033064833984871618, -0.00019167049464976276, -0.00018228662361670689, 
> -0.00024786515322241842), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 2', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 2', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 2', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 2', domain='b')
>
> relax> align_tensor.init(tensor='a 3', params=(0.00043690692358615301, 
> -0.00034379559287467062, -0.00019359695171683388, 0.00030194133983804048, 
> -6.314162250164486e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 3', params=(3.2029991098699158e-05, 
> 0.0001030927713217096, -0.00040609134800855906, -0.00027871118513542376, 
> 0.00018429705265751148), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 3', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 3', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 3', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 3', domain='b')
>
> relax> align_tensor.init(tensor='a 4', params=(-0.00026249527958822807, 
> 0.00073561736796410628, 6.3975419225898133e-05, 6.2788017118057252e-05, 
> 0.00020119758245770023), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 4', params=(0.00023041655343338213, 
> -0.00028914097123516663, 8.5942868106736884e-05, 0.00057733961469646491, 
> 0.00023383246814246303), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 4', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 4', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 4', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 4', domain='b')
>
> relax> align_tensor.init(tensor='a 5', params=(0.00048180707211229368, 
> -0.00033930112217225942, 0.00011094068795736053, 0.00070350646902989675, 
> 0.00037537667271407197), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 5', params=(-0.00034205987160777676, 
> -5.6563966889313711e-05, -0.00048729767346789097, -0.00020195965056872761, 
> 0.00064352392049120096), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 5', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 5', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 5', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 5', domain='b')
>
> relax> align_tensor.init(tensor='a 6', params=(0.00035672066304092451, 
> -0.00026838578790208884, -0.00016936140664230585, 0.00017187371551506447, 
> -0.00030579015509609098), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 6', params=(0.00020255575866227554, 
> 0.00015766165657592193, -0.00022547338964377635, -0.00031137881231040781, 
> 9.8269840241030186e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 6', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 6', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 6', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 6', domain='b')
>
> relax> align_tensor.init(tensor='a 7', params=(0.00017061308478202151, 
> -0.00076455273118810501, -0.00052048809712606505, 0.00049258369866413392, 
> -0.00013905141064073534), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 7', params=(0.00013226613079678079, 
> -0.00028875805425577231, -0.00055280116463899331, -0.00079483102252618661, 
> -0.00012673098706816532), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 7', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 7', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 7', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 7', domain='b')
>
> relax> align_tensor.init(tensor='a 8', params=(-0.00022193220790426714, 
> -0.00090073235703922686, 0.00050867766236886724, 0.00028215012727179065, 
> 0.0002562167583736733), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 8', params=(-0.00082779604132576475, 
> -0.0001229250183977039, 0.00026827297822125086, -0.00076816617763492308, 
> 1.787549543771558e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 8', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 8', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 8', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 8', domain='b')
>
> relax> align_tensor.init(tensor='a 9', params=(0.00037091020965736575, 
> -0.00012230875848954012, -0.00016247713611487416, -0.00042725170061841107, 
> 9.0103851318397519e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='b 9', params=(-0.00019129846420341554, 
> 0.00047556140822968502, -0.0001921404751338773, 0.00021386940177866865, 
> -0.00026418197641736997), scale=1.0, angle_units='deg', param_types=0, 
> errors=False)
>
> relax> align_tensor.init(tensor='a 9', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.init(tensor='b 9', params=(1.4741121114678945e-05, 
> 1.4741121114678945e-05, 1.4741121114678945e-05, 1.4741121114678945e-05, 
> 1.4741121114678945e-05), scale=1.0, angle_units='deg', param_types=0, 
> errors=True)
>
> relax> align_tensor.set_domain(tensor='a 9', domain='a')
>
> relax> align_tensor.set_domain(tensor='b 9', domain='b')
>
>
> relax> align_tensor.reduction(full_tensor='a 0', red_tensor='b 0')
>
> relax> align_tensor.reduction(full_tensor='a 1', red_tensor='b 1')
>
> relax> align_tensor.reduction(full_tensor='a 2', red_tensor='b 2')
>
> relax> align_tensor.reduction(full_tensor='a 3', red_tensor='b 3')
>
> relax> align_tensor.reduction(full_tensor='a 4', red_tensor='b 4')
>
> relax> align_tensor.reduction(full_tensor='a 5', red_tensor='b 5')
>
> relax> align_tensor.reduction(full_tensor='a 6', red_tensor='b 6')
>
> relax> align_tensor.reduction(full_tensor='a 7', red_tensor='b 7')
>
> relax> align_tensor.reduction(full_tensor='a 8', red_tensor='b 8')
>
> relax> align_tensor.reduction(full_tensor='a 9', red_tensor='b 9')
>
> relax> frame_order.select_model(model='rigid')
>
> relax> frame_order.ref_domain(ref='a')
>
> relax> grid_search(lower=None, upper=None, inc=6, constraints=True, 
> verbosity=1)
> RelaxWarning: Constraints are as of yet not implemented - turning this 
> option off.
>
> Grid search
> ~~~~~~~~~~
>
> Searching through 108 grid nodes.
> k: 0        xk: [           0,           0,           0] fk: 60402.7971133
> k: 1        xk: [      1.0472,           0,           0] fk: 54077.0686203
> k: 2        xk: [      2.0944,           0,           0] fk: 39503.3552589
> k: 3        xk: [      3.1416,           0,           0] fk: 37578.7102679
> k: 35       xk: [       5.236,      1.9106,      1.0472] fk: 27129.433648
>
> relax> minimise(*args=('simplex',), func_tol=1e-25, 
> max_iterations=10000000, constraints=False, scaling=True, verbosity=1)
>
> Simplex minimisation
> ~~~~~~~~~~~~~~~~~~~
>
> k: 0        xk: [       5.236,      2.0062,      1.0472] fk: 22040.0462168
> k: 100      xk: [        5.07,      2.5616,     0.64895] fk: 
> 5.30640418091e-10
> k: 200      xk: [        5.07,      2.5616,     0.64895] fk: 
> 6.44793137198e-26
>
> Parameter values: [5.0700283197689195, 2.561575400736614, 
> 0.64895449611163547]
> Function value:   6.4479313719786626e-26
> Iterations:       201
> Function calls:   368
> Gradient calls:   0
> Hessian calls:    0
> Warning:          None
>
>
> relax> results.write(file='devnull', dir=None, compress_type=1, force=True)
> Opening the null device file for writing.
> Traceback (most recent call last):
>  File "/sw/lib/relax-py26/test_suite/system_tests/frame_order.py", line 
> 147, in test_opt_rigid_rand_rot
>    self.assertEqual(cdp.iter, 204, msg=self.mesg)
> AssertionError: Optimisation failure.
>
> System:           Darwin
> Release:          9.8.0
> Version:          Darwin Kernel Version 9.8.0: Wed Jul 15 16:57:01 PDT 
> 2009; root:xnu-1228.15.4~1/RELEASE_PPC
> Win32 version:
> Distribution:
> Architecture:     32bit
> Machine:          Power Macintosh
> Processor:        powerpc
> Python version:   2.6.4
> Numpy version:    1.3.0
> Libc version:
>
> alpha:                      5.0700283197689195
> beta:                        2.561575400736614
> gamma:                     0.64895449611163547
> chi2:                   6.4479313719786626e-26
> iter:                                      201
> f_count:                                   368
> g_count:                                     0
> h_count:                                     0
> warning:                                  None
>
>
> ----------------------------------------------------------------------
> Ran 115 tests in 315.229s
>
> FAILED (failures=1)
>
>
>
>
> ##############
> # Unit tests #
> ##############
>
>
> testing units...
> ----------------
>
> /sw/lib/relax-py26/test_suite/unit_tests
> ...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
> ----------------------------------------------------------------------
> Ran 1195 tests in 26.669s
>
> OK
>
>
>
>
> ###################################
> # Summary of the relax test suite #
> ###################################
>
>    System/functional tests 
> ............................................................. [ Failed ]
>    Unit tests 
> .......................................................................... 
> [ OK ]
>    Synopsis 
> ............................................................................
>  [ Failed ]
>
>
>
> On Thu, Feb 25, 2010 at 08:29:59AM +0100, Edward d'Auvergne wrote:
> > Hi,
> >
> > These errors correspond in changes in the minfx package.  In the
> > future I think I will try to package minfx,
> > http://gna.org/projects/minfx/ (and bmrblib,
> > http://gna.org/projects/bmrblib/) with relax.  I don't know how this
> > could be done with fink though.  To have a local install of minfx
> > within the relax source tree, just go to the base relax directory and
> > type:
> >
> > svn co http://svn.gna.org/svn/minfx/trunk/minfx
> >
> > Then if you run the test suite, most of the problems should be gone.
> >
> > Cheers,
> >
> > Edward