Merge 8bcb00f into acecabb

code/convolving/behavdata.txt

@@ -0,0 +1,87 @@
+onset	gain	loss	PTval	respnum	respcat	RT
+0.00	20	15	5.15	0	-1	0.000
+4.00	18	12	6.12	2	1	1.793
+8.00	10	15	-4.85	3	0	1.637
+18.00	34	16	18.16	1	1	1.316
+24.00	18	5	13.05	1	1	1.670
+28.00	26	13	13.13	2	1	1.232
+38.00	32	17	15.17	2	1	1.502
+42.00	16	5	11.05	2	1	1.925
+46.00	22	7	15.07	1	1	1.061
+52.00	36	8	28.08	1	1	1.079
+56.00	16	10	6.10	2	1	1.477
+62.00	30	13	17.13	1	1	1.599
+74.00	14	17	-2.83	4	0	1.822
+78.00	28	19	9.19	2	1	1.405
+82.00	14	20	-5.80	4	0	1.206
+86.00	26	16	10.16	2	1	1.272
+90.00	40	5	35.05	1	1	0.900
+94.00	28	12	16.12	2	1	1.318
+98.00	20	16	4.16	2	1	1.367
+102.00	26	5	21.05	1	1	1.566
+106.00	32	20	12.20	2	1	1.360
+110.00	18	17	1.17	3	0	1.321
+116.00	20	6	14.06	1	1	1.433
+120.00	20	18	2.18	3	0	1.215
+126.00	40	7	33.07	1	1	0.824
+130.00	36	6	30.06	1	1	0.903
+134.00	16	20	-3.80	4	0	1.047
+140.00	40	15	25.15	1	1	1.169
+148.00	10	6	4.06	3	0	1.137
+156.00	12	15	-2.85	4	0	1.082
+160.00	34	17	17.17	2	1	1.096
+164.00	18	14	4.14	2	1	2.200
+170.00	40	13	27.13	1	1	0.776
+174.00	32	19	13.19	2	1	1.336
+178.00	14	12	2.12	3	0	1.585
+182.00	32	10	22.10	2	1	1.362
+192.00	36	17	19.17	2	1	1.786
+202.00	28	13	15.13	2	1	1.107
+208.00	34	13	21.13	1	1	1.243
+212.00	38	14	24.14	1	1	1.112
+220.00	32	11	21.11	1	1	1.235
+224.00	32	9	23.09	1	1	0.866
+234.00	30	16	14.16	2	1	1.627
+238.00	34	10	24.10	1	1	1.000
+246.00	18	8	10.08	2	1	1.526
+250.00	34	20	14.20	2	1	1.251
+256.00	32	16	16.16	2	1	2.280
+260.00	24	12	12.12	2	1	1.242
+266.00	38	12	26.12	1	1	1.011
+272.00	38	10	28.10	1	1	1.043
+280.00	24	7	17.07	1	1	1.172
+284.00	16	13	3.13	2	1	1.115
+296.00	36	19	17.19	2	1	1.750
+310.00	32	5	27.05	1	1	0.918
+316.00	34	9	25.09	1	1	0.734
+320.00	28	7	21.07	1	1	1.067
+324.00	24	13	11.13	2	1	1.600
+330.00	38	5	33.05	1	1	0.805
+336.00	22	9	13.09	1	1	1.390
+342.00	10	11	-0.89	4	0	1.806
+346.00	12	14	-1.86	4	0	1.600
+350.00	34	19	15.19	2	1	1.237
+360.00	38	7	31.07	1	1	0.957
+364.00	30	6	24.06	1	1	0.877
+368.00	26	18	8.18	2	1	1.454
+374.00	14	6	8.06	2	1	1.904
+378.00	16	12	4.12	2	1	1.486
+382.00	22	20	2.20	3	0	1.598
+386.00	12	17	-4.83	4	0	1.368
+398.00	10	7	3.07	2	1	1.320
+402.00	18	20	-1.80	4	0	2.302
+408.00	12	6	6.06	2	1	1.433
+412.00	32	6	26.06	1	1	0.999
+416.00	18	7	11.07	2	1	1.071
+420.00	40	10	30.10	1	1	0.783
+424.00	14	14	0.14	3	0	1.687
+428.00	30	19	11.19	2	1	1.167
+436.00	24	18	6.18	2	1	1.145
+440.00	10	12	-1.88	3	0	1.383
+444.00	40	19	21.19	1	1	0.862
+448.00	24	9	15.09	1	1	0.834
+458.00	16	18	-1.82	4	0	1.785
+462.00	36	12	24.12	2	1	1.280
+466.00	26	9	17.09	1	1	1.394
+470.00	20	12	8.12	2	1	1.249
+474.00	24	14	10.14	2	1	1.266

code/convolving/cond001.txt

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

code/convolving/convolve.py

@@ -0,0 +1,42 @@
+# Creating convolved
+
+import numpy as np  # the Python array package
+import matplotlib.pyplot as plt  # the Python plotting package
+from scipy.stats import gamma
+import nibabel as nib 
+img = nib.load('bold.nii')
+data = img.get_data()
+data = data[...,1:]
+
+
+def hrf(times):     
+    # Gamma pdf for the peak
+    peak_values = gamma.pdf(times, 6)
+    # Gamma pdf for the undershoot
+    undershoot_values = gamma.pdf(times, 12)
+    # Combine them
+    values = peak_values - 0.35 * undershoot_values
+    # Scale max to 0.6
+    return values / np.max(values) * 0.6
+
+def get_behave(filename):
+	# Read in behave.txt to get repetition times
+	df = [line.strip().split('\t') for line in open(filename)]
+	return df
+
+names = get_task('behavdata.txt')[0]
+# names = ['onset', 'gain', 'loss', 'PTval', 'respnum', 'respcat', 'RT']
+behave = get_behave('behavdata.txt')[1:]
+
+tr_times = [float(p[6]) for p in behave] # list of repetition times
+n_vol = data.shape[3] # number of volumes 
+
+from stimuli_v1 import events2neural # a new version of stimuli that deals with nonconstant TR
+neural_prediction = events2neural('cond001.txt', tr_times, n_vol) 
+
+hrf_at_trs = hrf(tr_times)
+convolved = np.convolve(neural_prediction, hrf_at_trs)
+n_to_remove = len(hrf_at_trs) - 1
+convolved = convolved[:-n_to_remove]
+
+

code/convolving/stimuli_v1.py

@@ -0,0 +1,42 @@
+""" Functions to work with standard OpenFMRI stimulus files
+
+The functions have docstrings according to the numpy docstring standard - see:
+
+    https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
+"""
+
+import numpy as np
+
+def events2neural(taskfname, tr, n_trs):
+    """ Return predicted neural time course from event file `task_fname`
+
+    Parameters
+    ----------
+    task_fname : str
+        Filename of event file
+    tr : float
+        TR in seconds
+    n_trs : int
+        Number of TRs in functional run
+
+    Returns
+    -------
+    time_course : array shape (n_trs,)
+        Predicted neural time course, one value per TR
+    """
+    task = [line.strip().split('\t') for line in open(taskfname)][1:]
+    # Check that the file is plausibly a task file
+    #if task.ndim != 2 or task.shape[1] != 3:
+    #    raise ValueError("Is {0} really a task file?", task_fname)
+    # Convert onset, duration seconds to TRs
+    for i in range(len(task)):
+        if tr[i] == 0:
+            tr[i] = 1
+        task[i][0] = int(float(task[i][0])/tr[i])
+        task[i][1] = int(float(task[i][1])/tr[i])
+
+    # Neural time course from onset, duration, amplitude for each event
+    time_course = np.zeros(n_trs)
+    for line in task:
+        time_course[line[0]:(line[0] + line[1])] = line[2]
+    return time_course

slides/progress.md

@@ -1,5 +1,5 @@
-% Project Aleph Progress Report
-% Ross Barnowski, Matthew Brett, Jarrod Millman
+% Project Eta Progress Report
+% Jon Jara, Will Sanderson, Juan Shishido, Paul Wu, Wendy Xu
 % November 12, 2015
 
 # Background