More work on the nitime coherence interface

nipype/interfaces/nitime/analysis.py

@@ -10,26 +10,86 @@
 
 """
 
+from nipype.utils.misc import package_check
+
 from nipype.interfaces.base import (TraitedSpec, File, InputMultiPath,
  OutputMultiPath, Undefined, traits,
  BaseInterface)
 
 from nitime.analysis import CoherenceAnalyzer
-from nitime import TimeSeries
+from nitime.timeseries import TimeSeries
+
+package_check('nitime')
+package_check('matplotlib')
 
 class CoherenceAnalyzerInputSpec(TraitedSpec):
 
  #Input either csv file, or time-series object and use _xor_inputs to
  #discriminate
+ _xor_inputs('in_file','in_TS')
+ in_file = File(desc=('csv file with ROIs on the columns and ',
+ 'time-points on the rows. ROI names at the top row'),
+ exists=True,
+ requires=('sampling_rate'))
+
+ #If you gave just a file name, you need to specify the sampling_rate:
+ TR = traits.Float(desc=('The TR used to collect the data',
+ 'in your csv file <in_file>'))
+
+ in_TS = traits.Any(desc='a nitime TimeSeries object')
+
+ NFFT = traits.Range(low=32,value=64,
+ desc=('This is the size of the window used for ',
+ 'the spectral estimation. Use values between ',
+ '32 and the number of samples in your time-series.',
+ '(Defaults to 64.)'))
+ n_overlap = traits.Range(low=0,value=0,
+ desc=('The number of samples which overlap',
+ 'between subsequent windows.(Defaults to 0)'))
+
+ frequency_range = traits.ListFloat(value=[0.02, 0.15],
+ minlen=2,
+ maxlen=2,
+ desc=('The range of frequencies over',
+ 'which the analysis will average.',
+ '[low,high] (Default [0.02,0.15]'))
 
+ output_csv_file = traits.File(desc='File to write output')
+ output_figure_file = traits.File(desc='File to write output figures')
+ figure_type = traits.Enum('matrix','network',
+ desc=("The type of plot to generate, where ",
+ "'matrix' denotes a matrix image and",
+ "'network' denotes a graph representation"))
 
 class CoherenceAnalyzerOutputSpec(TraitedSpec):
+ coherence_array = traits.Array(desc=('The pairwise coherence values between',
+ 'the ROIs'))
+ timedelay_array = traits.Array(desc=('The pairwise time delays between the',
+ 'ROIs (in seconds)'))
+
+ coherence_csv = traits.File(desc = ('A csv file containing the pairwise ',
+ 'coherence values'))
+ timedelay_csv = traits.File(desc = ('A csv file containing the pairwise ',
+ 'time delay values'))
+
+ coherence_fig = traits.File(desc = ('Figure representing coherence values'))
+ timedelay_fig = traits.File(desc = ('Figure representing coherence values'))
+
+
 class CoherenceAnalyzer(BaseInterface):
 
- # Use the get_attr to pull out the input values from the InputSpec
+ input_spec = CoherenceAnalyzerInputSpec
+ output_spec = CoherenceAnayzerOutputSpec
 
+ def _read_csv(self):
 
+ return roi_labels,TS
+
  #Rewrite _run_interface, but not run
+ def _run_interface(self,runtime):
+ lb, ub = self.inputs.frequency_range
+ if
+
  #Rewrite _list_outputs (look at BET)
 
  def _list_outputs(self):

nipype/interfaces/nitime/tests/test_nitime.py

@@ -0,0 +1,19 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+import os
+import tempfile
+import shutil
+from matplotlib import mlab.csv2rec as csv2rec
+
+from nipype.testing import (assert_equal, assert_not_equal, assert_raises,
+ with_setup, TraitError, parametric, skipif)
+
+
+def test_read_csv():
+ """Test that reading the data from csv file gives you back a reasonable
+ time-series object """
+
+ #XXX need to finish this:
+ data_rec = csv2rec('data/fmri_timeseries.csv')
+
+