move tsnr to own file

Author: Shoshana Berleant

nipype/algorithms/misc.py

@@ -39,6 +39,10 @@
                                BaseInterfaceInputSpec, isdefined,
                                DynamicTraitedSpec, Undefined)
 from nipype.utils.filemanip import fname_presuffix, split_filename
+
+# for backwards compatibility
+from nipype.algorithms.tsnr import TSNR
+
 iflogger = logging.getLogger('interface')
 
 
@@ -259,93 +263,6 @@ def _list_outputs(self):
         outputs['nifti_file'] = self._gen_output_file_name()
         return outputs
 
-
-class TSNRInputSpec(BaseInterfaceInputSpec):
-    in_file = InputMultiPath(File(exists=True), mandatory=True,
-                             desc='realigned 4D file or a list of 3D files')
-    regress_poly = traits.Range(low=1, desc='Remove polynomials')
-    tsnr_file = File('tsnr.nii.gz', usedefault=True, hash_files=False,
-                     desc='output tSNR file')
-    mean_file = File('mean.nii.gz', usedefault=True, hash_files=False,
-                     desc='output mean file')
-    stddev_file = File('stdev.nii.gz', usedefault=True, hash_files=False,
-                       desc='output tSNR file')
-    detrended_file = File('detrend.nii.gz', usedefault=True, hash_files=False,
-                          desc='input file after detrending')
-
-
-class TSNROutputSpec(TraitedSpec):
-    tsnr_file = File(exists=True, desc='tsnr image file')
-    mean_file = File(exists=True, desc='mean image file')
-    stddev_file = File(exists=True, desc='std dev image file')
-    detrended_file = File(desc='detrended input file')
-
-
-class TSNR(BaseInterface):
-    """Computes the time-course SNR for a time series
-
-    Typically you want to run this on a realigned time-series.
-
-    Example
-    -------
-
-    >>> tsnr = TSNR()
-    >>> tsnr.inputs.in_file = 'functional.nii'
-    >>> res = tsnr.run() # doctest: +SKIP
-
-    """
-    input_spec = TSNRInputSpec
-    output_spec = TSNROutputSpec
-
-    def _run_interface(self, runtime):
-        img = nb.load(self.inputs.in_file[0])
-        header = img.header.copy()
-        vollist = [nb.load(filename) for filename in self.inputs.in_file]
-        data = np.concatenate([vol.get_data().reshape(
-            vol.get_shape()[:3] + (-1,)) for vol in vollist], axis=3)
-        data = np.nan_to_num(data)
-
-        if data.dtype.kind == 'i':
-            header.set_data_dtype(np.float32)
-            data = data.astype(np.float32)
-
-        if isdefined(self.inputs.regress_poly):
-            timepoints = img.shape[-1]
-            X = np.ones((timepoints, 1))
-            for i in range(self.inputs.regress_poly):
-                X = np.hstack((X, legendre(
-                    i + 1)(np.linspace(-1, 1, timepoints))[:, None]))
-            betas = np.dot(np.linalg.pinv(X), np.rollaxis(data, 3, 2))
-            datahat = np.rollaxis(np.dot(X[:, 1:],
-                                         np.rollaxis(
-                                             betas[1:, :, :, :], 0, 3)),
-                                  0, 4)
-            data = data - datahat
-            img = nb.Nifti1Image(data, img.get_affine(), header)
-            nb.save(img, op.abspath(self.inputs.detrended_file))
-
-        meanimg = np.mean(data, axis=3)
-        stddevimg = np.std(data, axis=3)
-        tsnr = np.zeros_like(meanimg)
-        tsnr[stddevimg > 1.e-3] = meanimg[stddevimg > 1.e-3] / stddevimg[stddevimg > 1.e-3]
-        img = nb.Nifti1Image(tsnr, img.get_affine(), header)
-        nb.save(img, op.abspath(self.inputs.tsnr_file))
-        img = nb.Nifti1Image(meanimg, img.get_affine(), header)
-        nb.save(img, op.abspath(self.inputs.mean_file))
-        img = nb.Nifti1Image(stddevimg, img.get_affine(), header)
-        nb.save(img, op.abspath(self.inputs.stddev_file))
-        return runtime
-
-    def _list_outputs(self):
-        outputs = self._outputs().get()
-        for k in ['tsnr_file', 'mean_file', 'stddev_file']:
-            outputs[k] = op.abspath(getattr(self.inputs, k))
-
-        if isdefined(self.inputs.regress_poly):
-            outputs['detrended_file'] = op.abspath(self.inputs.detrended_file)
-        return outputs
-
-
 class GunzipInputSpec(BaseInterfaceInputSpec):
     in_file = File(exists=True, mandatory=True)
 

nipype/algorithms/tsnr.py

@@ -0,0 +1,105 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+'''
+Miscellaneous algorithms
+
+    Change directory to provide relative paths for doctests
+    >>> import os
+    >>> filepath = os.path.dirname(os.path.realpath(__file__))
+    >>> datadir = os.path.realpath(os.path.join(filepath, '../testing/data'))
+    >>> os.chdir(datadir)
+
+'''
+from nipype.interfaces.base import (BaseInterface, traits, TraitedSpec, File,
+                                    InputMultiPath, BaseInterfaceInputSpec,
+                                    isdefined)
+
+import nibabel as nb
+import numpy as np
+import os.path as op
+from scipy.special import legendre
+
+class TSNRInputSpec(BaseInterfaceInputSpec):
+    in_file = InputMultiPath(File(exists=True), mandatory=True,
+                             desc='realigned 4D file or a list of 3D files')
+    regress_poly = traits.Range(low=1, desc='Remove polynomials')
+    tsnr_file = File('tsnr.nii.gz', usedefault=True, hash_files=False,
+                     desc='output tSNR file')
+    mean_file = File('mean.nii.gz', usedefault=True, hash_files=False,
+                     desc='output mean file')
+    stddev_file = File('stdev.nii.gz', usedefault=True, hash_files=False,
+                       desc='output tSNR file')
+    detrended_file = File('detrend.nii.gz', usedefault=True, hash_files=False,
+                          desc='input file after detrending')
+
+
+class TSNROutputSpec(TraitedSpec):
+    tsnr_file = File(exists=True, desc='tsnr image file')
+    mean_file = File(exists=True, desc='mean image file')
+    stddev_file = File(exists=True, desc='std dev image file')
+    detrended_file = File(desc='detrended input file')
+
+
+class TSNR(BaseInterface):
+    """Computes the time-course SNR for a time series
+
+    Typically you want to run this on a realigned time-series.
+
+    Example
+    -------
+
+    >>> tsnr = TSNR()
+    >>> tsnr.inputs.in_file = 'functional.nii'
+    >>> res = tsnr.run() # doctest: +SKIP
+
+    """
+    input_spec = TSNRInputSpec
+    output_spec = TSNROutputSpec
+
+    def _run_interface(self, runtime):
+        img = nb.load(self.inputs.in_file[0])
+        header = img.header.copy()
+        vollist = [nb.load(filename) for filename in self.inputs.in_file]
+        data = np.concatenate([vol.get_data().reshape(
+            vol.get_shape()[:3] + (-1,)) for vol in vollist], axis=3)
+        data = np.nan_to_num(data)
+
+        if data.dtype.kind == 'i':
+            header.set_data_dtype(np.float32)
+            data = data.astype(np.float32)
+
+        if isdefined(self.inputs.regress_poly):
+            timepoints = img.shape[-1]
+            X = np.ones((timepoints, 1))
+            for i in range(self.inputs.regress_poly):
+                X = np.hstack((X, legendre(
+                    i + 1)(np.linspace(-1, 1, timepoints))[:, None]))
+            betas = np.dot(np.linalg.pinv(X), np.rollaxis(data, 3, 2))
+            datahat = np.rollaxis(np.dot(X[:, 1:],
+                                         np.rollaxis(
+                                             betas[1:, :, :, :], 0, 3)),
+                                  0, 4)
+            data = data - datahat
+            img = nb.Nifti1Image(data, img.get_affine(), header)
+            nb.save(img, op.abspath(self.inputs.detrended_file))
+
+        meanimg = np.mean(data, axis=3)
+        stddevimg = np.std(data, axis=3)
+        tsnr = np.zeros_like(meanimg)
+        tsnr[stddevimg > 1.e-3] = meanimg[stddevimg > 1.e-3] / stddevimg[stddevimg > 1.e-3]
+        img = nb.Nifti1Image(tsnr, img.get_affine(), header)
+        nb.save(img, op.abspath(self.inputs.tsnr_file))
+        img = nb.Nifti1Image(meanimg, img.get_affine(), header)
+        nb.save(img, op.abspath(self.inputs.mean_file))
+        img = nb.Nifti1Image(stddevimg, img.get_affine(), header)
+        nb.save(img, op.abspath(self.inputs.stddev_file))
+        return runtime
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        for k in ['tsnr_file', 'mean_file', 'stddev_file']:
+            outputs[k] = op.abspath(getattr(self.inputs, k))
+
+        if isdefined(self.inputs.regress_poly):
+            outputs['detrended_file'] = op.abspath(self.inputs.detrended_file)
+        return outputs