updated test file for stimuli and cond_test1

code/utils/tests/test_stimuli.py

@@ -0,0 +1,79 @@
+from __future__ import print_function  # print('me') instead of print 'me'
+from __future__ import division  # 1/2 == 0.5, not 0
+from __future__ import absolute_import
+
+""" Test stimuli module
+Run tests with::
+    nosetests test_stimuli.py
+"""
+
+import numpy as np
+import numpy.testing as npt
+import scipy.stats
+from scipy.stats import gamma
+import numpy as np
+import matplotlib.pyplot as plt
+import nibabel as nib
+
+
+import os, sys
+sys.path.append(os.path.join(os.path.dirname(__file__), "../functions/"))
+
+import stimuli
+
+data_location=os.path.join(os.path.dirname(__file__), '../../../data/')
+
+def test_hrf():
+    time = np.arange(0,24, 1.0/100)
+    peak_values = gamma.pdf(time,6)
+    undershoot_values = gamma.pdf(time, 12)
+    values = peak_values - 0.35 * undershoot_values
+    hrf_totest = values/np.max(values) * 0.6 
+    npt.assert_array_equal(hrf_totest,stimuli.hrf(np.arange(0,24,1.0/100)))
+
+
+
+def test_events2neural():
+    # test events2neural function
+    cond_test1 = np.loadtxt(data_location+'cond_test1.txt')
+    neural = stimuli.events2neural(cond_test1, 2, 16)
+    # cond_test1.txt file is:
+    """
+    10    5.0    1
+    20    4.0    2
+    24    3.0    0.1
+    """
+    # Expected values for tr=2, n_trs=16
+    expected = np.zeros(16)
+    expected[5:7] = 1
+    expected[10:12] = 2
+    expected[12] = 0.1
+    npt.assert_array_equal(neural, expected)
+
+
+def test_events2neural_high():
+    """test with gain condition from subject 3's run 001
+
+    """
+    TR=2
+    n_trs=240
+    tr_div=100
+    condfile = np.loadtxt(data_location+'ds005/sub003/model/model001/onsets/task001_run001/cond002.txt')
+    onsets = condfile[:, 0]
+    durations_seconds = condfile[:, 1]
+    amplitudes = condfile[:, 2]
+    onsets_in_scans = onsets / TR 
+    high_res_times = np.arange(0, n_trs, 1.0/tr_div) * TR
+    high_res_neural = np.zeros(high_res_times.shape)
+    high_res_onset_indices = onsets_in_scans * tr_div
+    high_res_durations = durations_seconds / TR * tr_div
+    for hr_onset, hr_duration, amplitude in list(zip(high_res_onset_indices,high_res_durations,amplitudes)):
+        hr_onset = int(round(hr_onset))
+        hr_duration = int(round(hr_duration))
+        high_res_neural[hr_onset:hr_onset+hr_duration] = amplitude
+
+    cond = np.loadtxt(data_location+'ds005/sub003/model/model001/onsets/task001_run001/cond002.txt')
+    test_high_res_times, test_high_res_neural = stimuli.events2neural_high(cond)
+    npt.assert_array_equal(high_res_times, test_high_res_times)
+    npt.assert_array_equal(high_res_neural, test_high_res_neural)
+

data/cond_test1.txt

@@ -0,0 +1,3 @@
+10    5.0    1
+20    4.0    2
+24    3.0    0.1

data/ds005/sub003/model/model001/onsets/task001_run001/cond002.txt

@@ -0,0 +1,86 @@
+0.0000	3	-0.1390
+4.0000	3	-0.1890
+8.0000	3	-0.3890
+18.0000	3	0.2110
+24.0000	3	-0.1890
+28.0000	3	0.0110
+38.0000	3	0.1610
+42.0000	3	-0.2390
+46.0000	3	-0.0890
+52.0000	3	0.2610
+56.0000	3	-0.2390
+62.0000	3	0.1110
+74.0000	3	-0.2890
+78.0000	3	0.0610
+82.0000	3	-0.2890
+86.0000	3	0.0110
+90.0000	3	0.3610
+94.0000	3	0.0610
+98.0000	3	-0.1390
+102.0000	3	0.0110
+106.0000	3	0.1610
+110.0000	3	-0.1890
+116.0000	3	-0.1390
+120.0000	3	-0.1390
+126.0000	3	0.3610
+130.0000	3	0.2610
+134.0000	3	-0.2390
+140.0000	3	0.3610
+148.0000	3	-0.3890
+156.0000	3	-0.3390
+160.0000	3	0.2110
+164.0000	3	-0.1890
+170.0000	3	0.3610
+174.0000	3	0.1610
+178.0000	3	-0.2890
+182.0000	3	0.1610
+192.0000	3	0.2610
+202.0000	3	0.0610
+208.0000	3	0.2110
+212.0000	3	0.3110
+220.0000	3	0.1610
+224.0000	3	0.1610
+234.0000	3	0.1110
+238.0000	3	0.2110
+246.0000	3	-0.1890
+250.0000	3	0.2110
+256.0000	3	0.1610
+260.0000	3	-0.0390
+266.0000	3	0.3110
+272.0000	3	0.3110
+280.0000	3	-0.0390
+284.0000	3	-0.2390
+296.0000	3	0.2610
+310.0000	3	0.1610
+316.0000	3	0.2110
+320.0000	3	0.0610
+324.0000	3	-0.0390
+330.0000	3	0.3110
+336.0000	3	-0.0890
+342.0000	3	-0.3890
+346.0000	3	-0.3390
+350.0000	3	0.2110
+360.0000	3	0.3110
+364.0000	3	0.1110
+368.0000	3	0.0110
+374.0000	3	-0.2890
+378.0000	3	-0.2390
+382.0000	3	-0.0890
+386.0000	3	-0.3390
+398.0000	3	-0.3890
+402.0000	3	-0.1890
+408.0000	3	-0.3390
+412.0000	3	0.1610
+416.0000	3	-0.1890
+420.0000	3	0.3610
+424.0000	3	-0.2890
+428.0000	3	0.1110
+436.0000	3	-0.0390
+440.0000	3	-0.3890
+444.0000	3	0.3610
+448.0000	3	-0.0390
+458.0000	3	-0.2390
+462.0000	3	0.2610
+466.0000	3	0.0110
+470.0000	3	-0.1390
+474.0000	3	-0.0390