Merge pull request #66 from lynnzhao92/lynn

Lynn

code/extended_rms_outliers.py

@@ -1,8 +1,10 @@
 """
-Script to apply extended rms outlier detection on a run. 
-Results of the analysis are to understand the noise in 
-fMRI data and justify the need for additional preprocessing
-steps.
+EDA:
+
+This module detects the exteded RMS outliers and generates the plot for those outliers.
+Results of the analysis are to understand the noise in fMRI data and justify the need 
+for additional preprocessing steps.
+We detect the outliers on "sub011, task001_run_001"
 """
 
 import project_config

code/utils/correlation.py

@@ -1,9 +1,9 @@
 """
-Different methods for calculating correlations between
-the fMRI time courses and the neural prediction values.
-Both methods are needed so that we can compare the
-results from different baseline functions (square wave
-and gamma function) in the analysis.
+EDA: 
+
+Different methods for calculating correlations between the fMRI time courses 
+and the neural prediction values.Both methods are needed so that we can compare 
+the results from different baseline functions (square wave and gamma function) in the analysis.
 """
 
 import sys, os
@@ -18,9 +18,13 @@
 
 def correlation_map_linear(data, cond_filename):
   """
-  This is different from correlation_map in that it accepts a 2d data 
-  (n_samples, n_time_slices) so that it is suitable for working with
-  brain masks.
+  This function computes the correlation matrix based on the baseline method.
+
+  Input: 
+    data: brain image data
+    cond_filename: condition file which contains the info about time time_course
+  Output:
+    correlation matrix 
   """
   convolved = conv_main(data.shape[-1] + 5, cond_filename, TR)[5:]
   corrs = np.zeros((data.shape[:-1]))
@@ -36,8 +40,9 @@ def correlation_map_linear(data, cond_filename):
 def correlation_map_without_convoluation_linear(data, cond_filename):
   """
   This is different from correlation_map_without_convoluation in that it accepts a 2d data 
-  (n_samples, n_time_slices) so that it is suitable for working with
-  brain masks.
+  (n_samples, n_time_slices) and compute the correlations based on the square-wave time course 
+  using the given condition file. 
+
   """
   n_trs = data.shape[-1] + 5
   time_course = events2neural_rounded(cond_filename, TR, n_trs)

code/utils/tests/test_multiple_comparison.py

@@ -0,0 +1,17 @@
+"""
+Test multiple_comparison module
+
+Run with::
+	nosetests test_multiple_comparison.py
+"""
+
+import numpy as np
+from .. import multiple_comparison
+from numpy.testing import assert_array_equal, assert_almost_equal
+
+def test_multiple_comp():
+	res = np.linspace(0,1,100)
+	res.shape = (10,10)
+	actual = multiple_comparison.multiple_comp(res)
+	expected = [0, 0, 0, 0]
+	assert_array_equal(actual, expected)