Merge pull request nipy#223 from agramfort/depfix

making some dependencies optional for test suite (cfflib, nipy, nitime)

nipype/interfaces/cmtk/convert.py

@@ -7,14 +7,26 @@
 
 """
 
-from nipype.interfaces.base import (BaseInterface, BaseInterfaceInputSpec, traits,
-                                    File, TraitedSpec, InputMultiPath, isdefined)
-import networkx as nx
-import cfflib as cf
 import os
-from nipype.utils.filemanip import split_filename
 import datetime
 import string
+import warnings
+import networkx as nx
+
+from nipype.interfaces.base import (BaseInterface, BaseInterfaceInputSpec, traits,
+                                    File, TraitedSpec, InputMultiPath, isdefined)
+from nipype.utils.filemanip import split_filename
+from nipype.utils.misc import package_check
+
+have_cfflib = True
+try:
+    package_check('cfflib')
+except Exception, e:
+    have_cfflib = False
+    warnings.warn('cfflib not installed')
+else:
+    import cfflib as cf
+
 
 class CFFConverterInputSpec(BaseInterfaceInputSpec):
     graphml_networks = InputMultiPath(File(exists=True), desc='list of graphML networks')

nipype/interfaces/nipy/__init__.py

@@ -1,2 +1,2 @@
 from model import FitGLM, EstimateContrast
-from preprocess import ComputeMask
+from preprocess import ComputeMask

nipype/interfaces/nipy/model.py

@@ -1,13 +1,21 @@
+import warnings
 import os
 
 import nibabel as nb
 import numpy as np
 import pylab
 
 from nipype.utils.misc import package_check
-package_check('nipy')
-import nipy.modalities.fmri.design_matrix as dm
-import nipy.labs.glm as GLM
+
+have_nipy = True
+try:
+    package_check('nipy')
+except Exception, e:
+    have_nipy = False
+    warnings.warn('nipy not installed')
+else:
+    import nipy.modalities.fmri.design_matrix as dm
+    import nipy.labs.glm as GLM
 
 from nipype.interfaces.base import (BaseInterface, TraitedSpec, traits, File,
                                     OutputMultiPath, BaseInterfaceInputSpec,
@@ -16,10 +24,10 @@
 class FitGLMInputSpec(BaseInterfaceInputSpec):
     session_info = traits.List(minlen=1, maxlen=1, exists=True, desc='Session specific information generated by ``modelgen.SpecifyModel``, FitGLM\
     does not  support multiple runs uless they are concatenated (see SpecifyModel options)')
-    hrf_model = traits.Enum('Canonical', 'Canonical With Derivative', 'FIR', 
+    hrf_model = traits.Enum('Canonical', 'Canonical With Derivative', 'FIR',
 desc="that specifies the hemodynamic reponse function it can be 'Canonical', 'Canonical With Derivative' or 'FIR'",
 usedefault=True)
-    drift_model = traits.Enum("Cosine", "Polynomial", "Blank", 
+    drift_model = traits.Enum("Cosine", "Polynomial", "Blank",
 desc = "string that specifies the desired drift model, to be chosen among 'Polynomial', 'Cosine', 'Blank'",
 usedefault=True)
     TR = traits.Float(mandatory=True)
@@ -29,7 +37,7 @@ class FitGLMInputSpec(BaseInterfaceInputSpec):
     normalize_design_matrix = traits.Bool(False, desc="normalize (zscore) the regressors before fitting", usedefault=True)
     save_residuals = traits.Bool(False, usedefault=True)
     plot_design_matrix = traits.Bool(False, usedefault=True)
-    
+
 class FitGLMOutputSpec(TraitedSpec):
     beta = File(exists=True)
     nvbeta = traits.Any()
@@ -40,109 +48,109 @@ class FitGLMOutputSpec(TraitedSpec):
     reg_names = traits.List()
     residuals = traits.File()
     a = File(exists=True)
-    
+
 class FitGLM(BaseInterface):
     '''
     Fit GLM model based on the specified design. Supports only single or concatenated runs.
     '''
     input_spec = FitGLMInputSpec
     output_spec = FitGLMOutputSpec
-    
+
     def _run_interface(self, runtime):
-        
+
         session_info = self.inputs.session_info
-        
+
         functional_runs = self.inputs.session_info[0]['scans']
         if isinstance(functional_runs, str):
             functional_runs = [functional_runs]
-        nii = nb.load(functional_runs[0])              
+        nii = nb.load(functional_runs[0])
         data = nii.get_data()
 
-        
+
         if isdefined(self.inputs.mask):
             mask = nb.load(self.inputs.mask).get_data() > 0
         else:
             mask = np.ones(nii.shape[:3]) == 1
-            
+
         timeseries = data.copy()[mask,:]
         del data
-        
+
         for functional_run in functional_runs[1:]:
             nii = nb.load(functional_run)
             data = nii.get_data()
             npdata = data.copy()
-            del data          
+            del data
             timeseries = np.concatenate((timeseries,npdata[mask,:]), axis=1)
             del npdata
-            
+
         nscans = timeseries.shape[1]
-        
+
         if 'hpf' in session_info[0].keys():
             hpf = session_info[0]['hpf']
             drift_model=self.inputs.drift_model
         else:
             hpf=0
             drift_model = "Blank"
-        
+
         reg_names = []
         for reg in session_info[0]['regress']:
             reg_names.append(reg['name'])
-        
+
         reg_vals = np.zeros((nscans,len(reg_names)))
         for i in range(len(reg_names)):
             reg_vals[:,i] = np.array(session_info[0]['regress'][i]['val']).reshape(1,-1)
-        
-        
+
+
         frametimes= np.linspace(0, (nscans-1)*self.inputs.TR, nscans)
-        
+
         conditions = []
         onsets = []
         duration = []
-        
+
         for i,cond in enumerate(session_info[0]['cond']):
             onsets += cond['onset']
             conditions += [cond['name']]*len(cond['onset'])
             if len(cond['duration']) == 1:
                 duration += cond['duration']*len(cond['onset'])
-                
-        
+
+
         paradigm =  dm.BlockParadigm(con_id=conditions, onset=onsets, duration=duration)
         design_matrix, self._reg_names = dm.dmtx_light(frametimes, paradigm, drift_model=drift_model, hfcut=hpf,
-               hrf_model=self.inputs.hrf_model, 
+               hrf_model=self.inputs.hrf_model,
                add_regs=reg_vals,
                add_reg_names=reg_names
                )
         if self.inputs.normalize_design_matrix:
             for i in range(len(self._reg_names)-1):
                 design_matrix[:,i] = (design_matrix[:,i]-design_matrix[:,i].mean())/design_matrix[:,i].std()
-                
+
         if self.inputs.plot_design_matrix:
             pylab.pcolor(design_matrix)
             pylab.savefig("design_matrix.pdf")
             pylab.close()
             pylab.clf()
-        
+
         glm = GLM.glm()
         glm.fit(timeseries.T, design_matrix, method=self.inputs.method, model=self.inputs.model)
-        
-        
+
+
         self._beta_file = os.path.abspath("beta.nii")
         beta = np.zeros(mask.shape + (glm.beta.shape[0],))
         beta[mask,:] = glm.beta.T
         nb.save(nb.Nifti1Image(beta, nii.get_affine()), self._beta_file)
-        
+
         self._s2_file = os.path.abspath("s2.nii")
         s2 = np.zeros(mask.shape)
         s2[mask] = glm.s2
         nb.save(nb.Nifti1Image(s2, nii.get_affine()), self._s2_file)
-        
+
         if self.inputs.save_residuals:
             explained = np.dot(design_matrix,glm.beta)
             residuals = np.zeros(mask.shape + (nscans,))
             residuals[mask,:] = timeseries - explained.T
             self._residuals_file = os.path.abspath("residuals.nii")
             nb.save(nb.Nifti1Image(residuals, nii.get_affine()), self._residuals_file)
-        
+
         self._nvbeta = glm.nvbeta
         self._dof = glm.dof
         self._constants = glm._constants
@@ -154,9 +162,9 @@ def _run_interface(self, runtime):
             nb.save(nb.Nifti1Image(a, nii.get_affine()), self._a_file)
         self._model = glm.model
         self._method = glm.method
-        
+
         return runtime
-    
+
     def _list_outputs(self):
         outputs = self._outputs().get()
         outputs["beta"] = self._beta_file
@@ -171,7 +179,7 @@ def _list_outputs(self):
         if self.inputs.save_residuals:
             outputs["residuals"] = self._residuals_file
         return outputs
-    
+
 class EstimateContrastInputSpec(BaseInterfaceInputSpec):
     contrasts = traits.List(
         traits.Either(traits.Tuple(traits.Str,
@@ -207,28 +215,28 @@ class EstimateContrastInputSpec(BaseInterfaceInputSpec):
     axis = traits.Any(mandatory=True)
     reg_names = traits.List(mandatory=True)
     mask = traits.File(exists=True)
-    
+
 class EstimateContrastOutputSpec(TraitedSpec):
     stat_maps = OutputMultiPath(File(exists=True))
     z_maps = OutputMultiPath(File(exists=True))
     p_maps = OutputMultiPath(File(exists=True))
-    
+
 class EstimateContrast(BaseInterface):
     '''
     Estimate contrast of a fitted model.
     '''
     input_spec = EstimateContrastInputSpec
     output_spec = EstimateContrastOutputSpec
-    
+
     def _run_interface(self, runtime):
-                
+
         beta_nii = nb.load(self.inputs.beta)
         if isdefined(self.inputs.mask):
             mask = nb.load(self.inputs.mask).get_data() > 0
         else:
             mask = np.ones(beta_nii.shape[:3]) == 1
-        
-        
+
+
         glm = GLM.glm()
         nii = nb.load(self.inputs.beta)
         glm.beta = beta_nii.get_data().copy()[mask,:].T
@@ -237,44 +245,44 @@ def _run_interface(self, runtime):
         glm.dof = self.inputs.dof
         glm._axis = self.inputs.axis
         glm._constants = self.inputs.constants
-        
+
         reg_names = self.inputs.reg_names
-        
+
         self._stat_maps = []
         self._p_maps = []
         self._z_maps = []
         for contrast_def in self.inputs.contrasts:
             name = contrast_def[0]
             type = contrast_def[1]
             contrast = np.zeros(len(reg_names))
-            
+
             for i, reg_name in enumerate(reg_names):
                 if reg_name in contrast_def[2]:
                     idx = contrast_def[2].index(reg_name)
                     contrast[i] = contrast_def[3][idx]
-            
+
             est_contrast = glm.contrast(contrast)
-            
+
             stat_map = np.zeros(mask.shape)
             stat_map[mask] = est_contrast.stat().T
             stat_map_file = os.path.abspath(name + "_stat_map.nii")
             nb.save(nb.Nifti1Image(stat_map, nii.get_affine()), stat_map_file)
             self._stat_maps.append(stat_map_file)
-            
+
             p_map = np.zeros(mask.shape)
             p_map[mask] = est_contrast.pvalue().T
             p_map_file = os.path.abspath(name + "_p_map.nii")
             nb.save(nb.Nifti1Image(p_map, nii.get_affine()), p_map_file)
             self._p_maps.append(p_map_file)
-            
+
             z_map = np.zeros(mask.shape)
             z_map[mask] = est_contrast.zscore().T
             z_map_file = os.path.abspath(name + "_z_map.nii")
             nb.save(nb.Nifti1Image(z_map, nii.get_affine()), z_map_file)
             self._z_maps.append(z_map_file)
-        
+
         return runtime
-    
+
     def _list_outputs(self):
         outputs = self._outputs().get()
         outputs["stat_maps"] = self._stat_maps

nipype/interfaces/nipy/preprocess.py

@@ -1,15 +1,22 @@
 import os
+import warnings
 
 import nibabel as nb
 import numpy as np
 
 from nipype.utils.misc import package_check
-package_check('nipy')
-from nipy.labs.mask import compute_mask
+
+try:
+    package_check('nipy')
+except Exception, e:
+    warnings.warn('nipy not installed')
+else:
+    from nipy.labs.mask import compute_mask
 
 from nipype.interfaces.base import (TraitedSpec, BaseInterface, traits,
                                     BaseInterfaceInputSpec, isdefined, File)
 
+
 class ComputeMaskInputSpec(BaseInterfaceInputSpec):
     mean_volume = File(exists=True, mandatory=True, desc="mean EPI image, used to compute the threshold for the mask")
     reference_volume = File(exists=True, desc="reference volume used to compute the mask. If none is give, the \

nipype/interfaces/nitime/analysis.py

@@ -10,24 +10,31 @@
 
 """
 
+import warnings
 import numpy as np
 import tempfile
 from nipype.utils.misc import package_check
-package_check('nitime')
 package_check('matplotlib')
 
-
 from nipype.interfaces.base import (TraitedSpec, File, InputMultiPath,
                                     OutputMultiPath, Undefined, traits,
                                     BaseInterface, isdefined,
     BaseInterfaceInputSpec)
 
 from nipype.utils.filemanip import fname_presuffix
 
-import nitime.analysis as nta
-from nitime.timeseries import TimeSeries
-import nitime.viz as viz
-
+have_nitime = True
+try:
+    package_check('nitime')
+except Exception, e:
+    have_nitime = False
+    warnings.warn('nitime not installed')
+else:
+    import nitime.analysis as nta
+    from nitime.timeseries import TimeSeries
+    import nitime.viz as viz
+
+
 class CoherenceAnalyzerInputSpec(BaseInterfaceInputSpec):
 
     #Input either csv file, or time-series object and use _xor_inputs to