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Miscellaneous

Managing (Custom) Configurations

PyMVPA provides a facility to handle arbitrary configuration settings. This facility can be used to control some aspects of the behavior of PyMVPA itself, as well as to store and query custom configuration items, e.g. to control one's own analysis scripts.

An instance of this configuration manager is loaded whenever the mvpa2 module is imported. It can be used from any script like this:

>>> from mvpa2 import cfg

By default the config manager reads settings from two config files (if any of them exists). The first is a file named .pymvpa2.cfg and located in the user's home directory. The second is pymvpa2.cfg in the current directory. Please note, that settings found in the second file override the ones in the first.

The syntax of both files is the one also known from the Windows INI files. Basically, Python's ConfigParser is used to read those file and the config supports whatever this parser can read. A minimal example config file might look like this:

[general]
verbose = 1

It consists of a section general containing a single setting verbose, which is set to 1. PyMVPA recognizes a number of such sections and configuration variables. A full list is shown at the end of this section and is also available in the source package (doc/examples/pymvpa2.cfg).

In addition to configuration files, the config manager also looks for special environment variables to read settings from. Names of such variables have to start with MVPA_ following by the an optional section name and the variable name itself (with _ as delimiter). If no section name is provided, the variables will be associated with section general. Some examples:

MVPA_VERBOSE=1

will become:

[general]
verbose = 1

However, :envvar:`MVPA_VERBOSE_OUTPUT` = stdout becomes:

[verbose]
output = stdout

Any lenght of variable name is allowed, e.g. MVPA_SEC1_LONG_VARIABLE_NAME=1 becomes:

[sec1]
long variable name = 1

Settings read from environment variables have the highest priority and override settings found in the config files. Therefore environment variables can be used to quickly adjust some setting without having to edit the config files.

The config manager can easily be queried from inside scripts. In addition to the interface of Python's ConfigParser it has a few convenience functions mostly to allow for a default value in case no setting was found. For example:

>>> cfg.getboolean('warnings', 'suppress', default=False)
True

queries the config manager whether warnings should be suppressed (i.e. if there is a variable suppress in section warnings). In case, there is no such setting, i.e. neither config files nor environment variables defined it, the default values is returned. Please see the documentation of ConfigManager for its full functionality.

The source tarballs includes an example configuration file (doc/examples/pymvpa2.cfg) with the comprehensive list of settings recognized by PyMVPA itself:

Progress Tracking

There are 3 types of messages PyMVPA can produce:

verbose
regular informative messages about generic actions being performed
debug
messages about the progress of computation, manipulation on data structures
warning
messages which are reported by mvpa if something goes a little unexpected but not critical

Redirecting Output

By default, all types of messages are printed by PyMVPA to the standard output. It is possible to redirect them to standard error, or a file, or a list of multiple such targets, by using environment variable MVPA_?_OUTPUT, where X is either VERBOSE, DEBUG, or WARNING correspondingly. E.g.:

export MVPA_VERBOSE_OUTPUT=stdout,/tmp/1 MVPA_WARNING_OUTPUT=/tmp/3 MVPA_DEBUG_OUTPUT=stderr,/tmp/2

would direct verbose messages to standard output as well as to /tmp/1 file, warnings will be stored only in /tmp/3, and debug output would appear on standard error output, as well as in the file /tmp/2.

PyMVPA output redirection though has no effect on external libraries debug output if corresponding debug target is enabled

shogun
debug output (if any of internal SG_ debug targets is enabled) appears on standard output
SMLR
debug output (if SMLR_ debug target is enabled) appears on standard output
LIBSVM
debug output (if LIBSVM debug target is enabled) appears on standard error

One of the possible redirections is Python's StringIO class. Instance of such class can be added to the handlers and queried later on for the information to be dumped to a file later on. It is useful if output path is specified at run time, thus it is impossible to redirect verbose or debug from the start of the program:

>>> import sys
>>> from mvpa2.base import verbose
>>> from StringIO import StringIO
>>> stringout = StringIO()
>>> verbose.handlers = [sys.stdout, stringout]
>>> verbose.level = 3
>>>
>>> verbose(1, 'msg1')
 msg1
>>> out_prefix='/tmp/'
>>>
>>> verbose(2, 'msg2')
  msg2
>>> # open('%sverbose.log' % out_prefix, 'w').write(stringout.getvalue())
>>> print stringout.getvalue(),
 msg1
  msg2
>>>

Verbose Messages

Primarily for a user of PyMVPA to provide information about the progress of their scripts. Such messages are printed out if their level specified as the first parameter to verbose function call is less than specified. There are two easy ways to specify verbosity level:

  • command line: you can use opt.verbose for precrafted command line option for to give facility to change it from your script (see examples)
  • environment variable :envvar:`MVPA_VERBOSE`
  • code: verbose.level property

The following verbosity levels are supported:

0:nothing besides errors
1:high level stuff -- top level operation or file operations
2:cmdline handling
3:n.a.
4:computation/algorithm relevant thing

Warning Messages

Reported by PyMVPA if something goes a little unexpected but not critical. By default they are printed just once per occasion, i.e. once per piece of code where it is called. Following environment variables control the behavior of warnings:

In python code, invocation of warning with argument bt = True enforces printout of traceback whenever warning tracebacks are disabled by default.

Debug Messages

Debug messages are used to track progress of any computation inside PyMVPA while the code run by python without optimization (i.e. without -O switch to python). They are specified not by the level but by some id usually specific for a particular PyMVPA routine. For example RFEC id causes debugging information about Recursive Feature Elimination call to be printed (See base module sources for the list of all ids, or print debug.registered property).

Analogous to verbosity level there are two easy ways to specify set of ids to be enabled (reported):

  • command line: you can use optDebug for precrafted command line option to provide it from your script (see examples). If in command line if optDebug is used, -d list is given, PyMVPA will print out list of known ids.
  • environment: variable :envvar:`MVPA_DEBUG` can contain comma-separated list of ids or python regular expressions to match multiple ids. Thus specifying :envvar:`MVPA_DEBUG` =CLF.* would enable all ids which start with CLF, and :envvar:`MVPA_DEBUG` =.* would enable all known ids.
  • code: debug.active property (e.g. debug.active = [ 'RFEC', 'CLF' ])

Besides printing debug messages, it is also possible to print some metric. You can define new metrics or select predefined ones:

vmem
(Linux specific): amount of virtual memory consumed by the task
pid
(Linux specific): PID of the process
reltime
How many seconds passed since previous debug printout
asctime
Time stamp
tb
Traceback (module1:line_number1[,line_number2...]>module2:line_number..) where this debug statement was requested
tbc
Concise traceback printout -- prefix common with the previous invocation is replaced with ...

To enable list of metrics you can use :envvar:`MVPA_DEBUG_METRICS` environment variable to list desired metric names comma-separated. If ALL is provided, it enables all the metrics.

As it was mentioned earlier, debug messages are printed only in non-optimized python invocation. That was done to eliminate any slowdown introduced by such 'debugging' output, which might appear at some computational bottleneck places in the code.

Some of the debug ids are defined to facilitate additional checking of the validity of the analysis. Their debug ids a prefixed by CHECK_. E.g. CHECK_RETRAIN id would cause additional checking of the data in retraining phase. Such additional testing might spot out some bugs in the internal logic, thus enabled when full test suite is ran.

PyMVPA Status Summary

While reporting found bugs, it is advised to provide information about the operating system/environment and availability of PyMVPA externals. Please use :func:`~mvpa2.base.info.wtf` to collect such useful information to be included with the bug reports.

Alternatively, same printout can be obtained upon not handled exception automagically, if environment variable :envvar:`MVPA_DEBUG_WTF` is set.

Additional Little Helpers

Random Number Generation

To facilitate reproducible troubleshooting, a seed value of random generator of NumPy can be provided in debug mode (python is called without -O) via environment variable :envvar:`MVPA_SEED` =<int>. Otherwise it gets seeded with random integer which can be displayed with debug id RANDOM e.g.:

> MVPA_SEED=123 MVPA_DEBUG=RANDOM python test_clf.py
[RANDOM] DBG: Seeding RNG with 123
...
> MVPA_DEBUG=RANDOM python test_clf.py
[RANDOM] DBG: Seeding RNG with 1447286079
...

Unittests at a Grasp

If it is needed to just quickly grasp through all unittests without making them to test multiple classifiers (implemented with sweeparg), define environmental variable :envvar:`MVPA_TESTS_QUICK` e.g.:

> MVPA_WARNINGS_SUPPRESS=no MVPA_TESTS_QUICK=yes python test_clf.py
...............
----------------------------------------------------------------------
Ran 15 tests in 0.845s

Some tests are not 100% deterministic as they operate on random data (e.g. the performance of a randomly initialized classifier). Therefore, in some cases, specific unit tests might fail when running the full test battery. To exclude these test cases (and only those where non-deterministic behavior immanent) one can use the :envvar:`MVPA_TESTS_LABILE` configuration and set it to 'off'.

FSL Bindings

PyMVPA contains a few little helpers to make interfacing with FSL easier. The purpose of these helpers is to increase the efficiency when doing an analysis by (re)using useful information that is already available from some FSL output. FSL usually stores most interesting information in the NIfTI format. Therefore it can be easily imported into PyMVPA using PyNIfTI. However, some information is stored in text files, e.g. estimated motion correction parameters and FEAT's three-column custom EV files. PyMVPA provides import and export helpers for both of them (among other stuff like a MELODIC results import helper).

Here is an example how the McFlirt parameter output can be used to perform motion-aware data detrending:

>>> from os import path
>>> import numpy as np
>>>
>>> # some dummy dataset
>>> from mvpa2.datasets import Dataset
>>> ds = Dataset(samples=np.random.normal(size=(19, 3)))
>>>
>>> # load motion correction output
>>> from mvpa2.misc.fsl.base import McFlirtParams
>>> mc = McFlirtParams(path.join('mvpa2', 'data', 'bold_mc.par'))
>>>
>>> # simple plot using pylab (use pylab.show() or pylab.savefig()
>>> # afterwards)
>>> mc.plot()
>>>
>>> # merge the correction parameters into the dataset itself
>>> for param in mc:
...     ds.sa['mc_' + param] = mc[param]
>>>
>>> # detrend some dataset with mc params as additonal regressors
>>> from mvpa2.mappers.detrend import poly_detrend
>>> res = poly_detrend(ds, opt_regs=['mc_x', 'mc_y', 'mc_z',
...                                  'mc_rot1', 'mc_rot2', 'mc_rot3'])
>>> # 'res' contains all regressors and their associated weights

All FSL bindings are located in the mvpa2.misc.fsl module.

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