dipy

Diffusion Imaging in Python

For more information, please visit http://dipy.org

Subpackages

align         -- Registration, streamline alignment, volume resampling
boots         -- Bootstrapping algorithms
core          -- Spheres, gradient tables
core.geometry -- Spherical geometry, coordinate and vector manipulation
core.meshes   -- Point distributions on the sphere
data          -- Small testing datasets
external      -- Interfaces to external tools such as FSL
io            -- Loading/saving of dpy datasets
reconst       -- Signal reconstruction modules (tensor, spherical harmonics,
                 diffusion spectrum, etc.)
segment       -- Tractography segmentation
sims          -- MRI phantom signal simulation
tracking      -- Tractography, metrics for streamlines
viz           -- Visualization and GUIs

Utilities

test          -- Run unittests
__version__   -- Dipy version
bench Run benchmarks for module using nose.
get_info()
setup_test() Set numpy print options to “legacy” for new versions of numpy
test Run tests for module using nose.

bench

dipy.bench(self, label='fast', verbose=1, extra_argv=None)

Run benchmarks for module using nose.

Parameters:

label : {‘fast’, ‘full’, ‘’, attribute identifier}, optional

Identifies the benchmarks to run. This can be a string to pass to the nosetests executable with the ‘-A’ option, or one of several special values. Special values are: * ‘fast’ - the default - which corresponds to the nosetests -A

option of ‘not slow’.

  • ‘full’ - fast (as above) and slow benchmarks as in the ‘no -A’ option to nosetests - this is the same as ‘’.
  • None or ‘’ - run all tests.

attribute_identifier - string passed directly to nosetests as ‘-A’.

verbose : int, optional

Verbosity value for benchmark outputs, in the range 1-10. Default is 1.

extra_argv : list, optional

List with any extra arguments to pass to nosetests.

Returns:

success : bool

Returns True if running the benchmarks works, False if an error occurred.

Notes

Benchmarks are like tests, but have names starting with “bench” instead of “test”, and can be found under the “benchmarks” sub-directory of the module.

Each NumPy module exposes bench in its namespace to run all benchmarks for it.

Examples

>>> success = np.lib.bench() 
Running benchmarks for numpy.lib
...
using 562341 items:
unique:
0.11
unique1d:
0.11
ratio: 1.0
nUnique: 56230 == 56230
...
OK
>>> success 
True

get_info

dipy.get_info()

setup_test

dipy.setup_test()

Set numpy print options to “legacy” for new versions of numpy

If imported into a file, nosetest will run this before any doctests.

References

https://github.com/numpy/numpy/commit/710e0327687b9f7653e5ac02d222ba62c657a718 https://github.com/numpy/numpy/commit/734b907fc2f7af6e40ec989ca49ee6d87e21c495 https://github.com/nipy/nibabel/pull/556

test

dipy.test(self, label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None)

Run tests for module using nose.

Parameters:

label : {‘fast’, ‘full’, ‘’, attribute identifier}, optional

Identifies the tests to run. This can be a string to pass to the nosetests executable with the ‘-A’ option, or one of several special values. Special values are: * ‘fast’ - the default - which corresponds to the nosetests -A

option of ‘not slow’.

  • ‘full’ - fast (as above) and slow tests as in the ‘no -A’ option to nosetests - this is the same as ‘’.
  • None or ‘’ - run all tests.

attribute_identifier - string passed directly to nosetests as ‘-A’.

verbose : int, optional

Verbosity value for test outputs, in the range 1-10. Default is 1.

extra_argv : list, optional

List with any extra arguments to pass to nosetests.

doctests : bool, optional

If True, run doctests in module. Default is False.

coverage : bool, optional

If True, report coverage of NumPy code. Default is False. (This requires the `coverage module:

raise_warnings : None, str or sequence of warnings, optional

This specifies which warnings to configure as ‘raise’ instead of being shown once during the test execution. Valid strings are:

  • “develop” : equals (Warning,)
  • “release” : equals (), don’t raise on any warnings.

The default is to use the class initialization value.

Returns:

result : object

Returns the result of running the tests as a nose.result.TextTestResult object.

Notes

Each NumPy module exposes test in its namespace to run all tests for it. For example, to run all tests for numpy.lib:

>>> np.lib.test() 

Examples

>>> result = np.lib.test() 
Running unit tests for numpy.lib
...
Ran 976 tests in 3.933s

OK

>>> result.errors 
[]
>>> result.knownfail 
[]