Changes and enhancements in nitime interface:

1. Data reading not using csv2rec. 2. Analysis implemented using nitime objects. 3. Array outputs defined.

nipype/interfaces/nitime/analysis.py

@@ -10,7 +10,7 @@
 
 """
 
-
+import numpy as np
 from nipype.utils.misc import package_check
 package_check('nitime')
 package_check('matplotlib')
@@ -85,53 +85,78 @@ class CoherenceAnalyzer(BaseInterface):
 
  def _read_csv(self):
  """ Read from csv in_file and return a rec array """
+
+
+
  #Check that input conforms to expectations:
  first_row = open(self.inputs.in_file).readline()
  if not first_row[1].isalpha():
  raise ValueError("First row of in_file should contain ROI names as strings of characters")
 
- rec_array=csv2rec(self.inputs.in_file)
- return rec_array
 
+ roi_names = open(self.inputs.in_file).readline().replace('\"','').strip('\n').split(',')
+ data = np.loadtxt(self.inputs.in_file,skiprows=1,delimiter=',')
+
+ #rec_array=csv2rec(self.inputs.in_file)
+ return data,roi_names
+
  def _csv2ts(self):
  """ Read data from the in_file and generate a nitime TimeSeries object"""
- rec_array = self._read_csv()
- roi_names= data_rec.dtype.names
- n_samples = data_rec.shape[0]
- for n_idx in range(len(roi_names)):
- data[n_idx] = data_rec[roi_names[n_idx]]
-
- TS = TimeSeries(data=data,sampling_interval=TR,time_unit='s')
+ data,roi_names = self._read_csv()
+
+ TS = TimeSeries(data=data,
+  sampling_interval=self.inputs.TR,
+  time_unit='s')
+ 
+ TS.metadata = dict(ROIs=roi_names)
 
- return TimeSeries(data=data
+ return TS
 
 
-
  #Rewrite _run_interface, but not run
  def _run_interface(self,runtime):
  lb, ub = self.inputs.frequency_range
 
  if self.inputs.in_TS is Undefined:
  # get TS form csv and inputs.TR
- # calls _csv2ts
+ TS = self._csv2ts()
 
  else:
  # get TS from inputs.in_TS
-
-
- #Get the coherence matrix from the analyzer
- self.coherence = A.coherence
-
- if isdefined(self.inputs.output_csv_file):
- #write to a csv file and assign a value to self.coherence_file (a
- #file name + path)
-
+ TS = self.inputs.in_TS
+ # deal with creating or storing ROI names
+ if not TS.metadata.haskey('ROIs'):
+ TS.metadata['ROIs']=['roi_%d' % x for x,_ in enumerate(TS.data)]
+
+ A = CoherenceAnalyzer(TS,
+ method=dict(this_method='welch',
+ NFFT=self.inputs.NFFT,
+ n_overlap=self.inputs.n_overlap))
+
+ freq_idx = np.where((A.frequencies>self.inputs.frequency_range) *
+ (A.frequencies<self.inputs.frequency_range))[0]
+
+ #Get the coherence matrix from the analyzer, averaging on the last
+ #(frequency) dimension: (roi X roi array)
+ self.coherence = np.mean(A.coherence[:,:,freq_idx],-1)
+ # Get the time delay from analyzer, (roi X roi array)
+ self.delay = np.mean(A.delay[:,:,freq_idx],-1)
+ runtime.returncode = 0
+ return runtime
+
  #Rewrite _list_outputs (look at BET)
  def _list_outputs(self):
  outputs = self.output_spec().get()
 
- #Always defined:
- outputs['coherence']=self.coherence #The array 
+ #if isdefined(self.inputs.output_csv_file):
+
+ #write to a csv file and assign a value to self.coherence_file (a
+ #file name + path)
+
+ #Always defined (the arrays):
+ outputs['coherence']=self.coherence
+ outputs['delay']=self.delay
+
  #Conditional
  if isdefined(self.inputs.output_csv_file):
  outputs['coherence_csv']=self.coherence_file

nipype/interfaces/nitime/tests/test_nitime.py

@@ -21,6 +21,6 @@ def test_read_csv():
  yield assert_raises,ValueError,CA._read_csv 
 
  CA.inputs.in_file = example_data('fmri_timeseries.csv')
- rec_array = CA._read_csv()
- yield assert_equal, rec_array['wm'][0],10125.9
-
+ data,roi_names = CA._read_csv()
+ yield assert_equal, data[0][0],10125.9
+ yield assert_equal, roi_names[0],'WM'