[pymvpa] design matrix identical across sessions
Wolfgang Pauli
wolfgang.m.pauli at gmail.com
Tue May 17 23:41:38 UTC 2016
Quick update. Yaroslav and I were discussing this offline. It turned out
that it was necessary to adjust the time_coords of all events in all
sessions (chunks) after the first session. Basically, the time_coords have
to be relative to the beginning of the first session, rather than relative
to the beginning of the current session.
Thanks,
Wolfgang
On Mon, May 9, 2016 at 6:27 PM, Yaroslav Halchenko <debian at onerussian.com>
wrote:
> sorry Wolfgang, I have no mental capacity left to parse those outputs...
> it kinda looks ok, but you better just h5save sample dataset
> (ds[:,0] should be enough) and those events and then accompany with the
> code operating on those which would demonstrate the problem. you can
> send directly to my email (ml will not allow for large attachments)
> That would be the best way to reproduce and understand (tomorrow morning
> I guess)
>
>
> On Mon, 09 May 2016, Wolfgang Pauli wrote:
>
> > Hi,A
> > Thank you for the quick response. I did infact have
> > condition_attr=['targets', 'chunks'], sorry about the typo. Have been
> > trying to debug this for too long ...
> > Regarding the even definitions. Please see below the event definitions
> > according to.A
> > onsets = []
> > targets = []
> > chunks = []
> > for ev in events:
> > A A onsets.append(ev['onset'])
> > A A targets.append(ev['targets'])
> > A A chunks.append(ev['chunks'])
> > >>> targets
> > ['csA', 'csX_j_1', 'csB', 'csY_n_1', 'csA', 'csX_j_1', 'csB',
> 'csY_n_1',
> > 'csA', 'csX_j_1', 'csB', 'csY_n_1', 'csA', 'csX_j_1', 'csB',
> 'csY_n_1',
> > 'csB', 'csA', 'csX_j_1', 'csB', 'csY_n_1', 'csA', 'csX_j_1', 'csB',\
> > A 'csY_n_1', 'csA', 'csX_j_1', 'csB', 'csY_n_1', 'csA', 'csX_j_1',
> 'csB',
> > 'csY_n_1', 'csA', 'csX_j_1', 'csB', 'csB', 'csY_n_1', 'csA', 'csA',
> > 'csX_j_1', 'csB', 'csY_n_1', 'csB', 'csA', 'csX_j_1', 'csA',
> 'csX_j_1',\
> > A 'csB', 'csY_n_1', 'csA', 'csB', 'csY_n_1', 'csA', 'csC', 'csX_n_1',
> > 'csD', 'csY_j_1', 'csD', 'csY_j_1', 'csC', 'csX_n_1', 'csD',
> 'csY_j_1',
> > 'csC', 'csX_n_1', 'csD', 'csY_j_1', 'csC', 'csX_n_1', 'csD',
> 'csY_j_1',\
> > A 'csC', 'csC', 'csX_n_1', 'csC', 'csX_n_1', 'csD', 'csY_j_1', 'csC',
> > 'csX_n_1', 'csC', 'csC', 'csX_n_1', 'csD', 'csD', 'csY_j_1', 'csC',
> > 'csX_n_1', 'csD', 'csY_j_1', 'csC', 'csX_n_1', 'csD', 'csY_j_1',
> 'csD',
> > 'cs\
> > D', 'csY_j_1', 'csC', 'csX_n_1', 'csD', 'csY_j_1', 'csC', 'csD',
> > 'csY_j_1', 'csD', 'csC', 'csX_n_1', 'csE', 'csX_j_2', 'csE',
> 'csX_j_2',
> > 'csE', 'csX_j_2', 'csF', 'csY_n_2', 'csE', 'csX_j_2', 'csF',
> 'csY_n_2',
> > 'cs\
> > E', 'csX_j_2', 'csF', 'csY_n_2', 'csE', 'csX_j_2', 'csF', 'csY_n_2',
> > 'csF', 'csF', 'csY_n_2', 'csE', 'csX_j_2', 'csF', 'csY_n_2', 'csE',
> 'csF',
> > 'csY_n_2', 'csE', 'csX_j_2', 'csF', 'csY_n_2', 'csE', 'csX_j_2', 'cs\
> > F', 'csY_n_2', 'csE', 'csX_j_2', 'csF', 'csY_n_2', 'csE', 'csX_j_2',
> > 'csF', 'csY_n_2', 'csE', 'csF', 'csY_n_2', 'csF', 'csE', 'csF', 'csE',
> > 'csX_j_2', 'csH', 'csY_j_2', 'csH', 'csY_j_2', 'csH', 'csY_j_2',
> 'csG', \
> > 'csX_n_2', 'csH', 'csY_j_2', 'csG', 'csX_n_2', 'csH', 'csY_j_2',
> 'csG',
> > 'csX_n_2', 'csH', 'csY_j_2', 'csG', 'csX_n_2', 'csH', 'csY_j_2',
> 'csH',
> > 'csH', 'csY_j_2', 'csG', 'csX_n_2', 'csH', 'csY_j_2', 'csG', 'csX_n_\
> > 2', 'csG', 'csG', 'csX_n_2', 'csH', 'csG', 'csX_n_2', 'csH',
> 'csY_j_2',
> > 'csG', 'csX_n_2', 'csG', 'csG', 'csX_n_2', 'csH', 'csY_j_2', 'csG',
> > 'csX_n_2', 'csH', 'csY_j_2', 'csH', 'csG', 'csX_n_2', 'csG']
> > >>> onsets
> > [4.2000001668930054, 7.2000002861022949, 20.400000810623169,
> > 25.200001001358032, 39.600001573562622, 45.600001811981201,
> > 61.800002455711365, 66.600002646446228, 80.40000319480896,
> > 84.000003337860107, 94.800003767\
> > 01355, 98.400003910064697, 114.000004529953, 117.00000464916229,
> > 127.20000505447388, 132.00000524520874, 144.0000057220459,
> > 162.00000643730164, 166.20000660419464, 178.80000710487366,
> > 184.20000731945038, 199.8000\
> > 0793933868, 203.40000808238983, 216.00000858306885, 219.60000872612,
> > 230.40000915527344, 235.80000936985016, 249.60000991821289,
> > 254.40001010894775, 270.00001072883606, 275.40001094341278,
> > 291.00001156330109, 295\
> > .20001173019409, 306.00001215934753, 310.8000123500824,
> > 327.00001299381256, 346.20001375675201, 351.60001397132874,
> > 367.8000146150589, 386.40001535415649, 392.40001559257507,
> > 409.80001628398895, 415.2000164985656\
> > 7, 432.60001718997955, 451.80001795291901, 456.00001811981201,
> > 469.20001864433289, 473.40001881122589, 487.80001938343048,
> > 492.60001957416534, 507.00002014636993, 528.00002098083496,
> > 532.80002117156982, 548.40002\
> > 179145813, 606.60002410411835, 610.80002427101135, 624.00002479553223,
> > 628.80002498626709, 640.20002543926239, 644.4000256061554,
> > 658.20002615451813, 661.80002629756927, 673.80002677440643,
> > 676.80002689361572, 68\
> > 8.20002734661102, 693.00002753734589, 703.20002794265747,
> > 707.40002810955048, 720.00002861022949, 725.40002882480621,
> > 737.40002930164337, 740.40002942085266, 755.40003001689911,
> > 777.00003087520599, 781.2000310420\
> > 99, 793.20003151893616, 798.60003173351288, 811.20003223419189,
> > 815.4000324010849, 828.60003292560577, 834.0000331401825,
> > 846.00003361701965, 862.80003428459167, 867.00003445148468,
> > 878.40003490447998, 897.000035\
> > 64357758, 901.20003581047058, 917.40003645420074, 922.20003664493561,
> > 939.60003733634949, 943.80003750324249, 955.20003795623779,
> > 958.80003809928894, 972.60003864765167, 976.20003879070282,
> > 989.40003931522369, 10\
> > 08.0000400543213, 1012.2000402212143, 1025.4000407457352,
> > 1030.8000409603119, 1043.4000414609909, 1047.0000416040421,
> > 1062.0000422000885, 1083.0000430345535, 1087.8000432252884,
> > 1100.4000437259674, 1119.000044465\
> > 065, 1122.0000445842743, 1206.6000479459763, 1210.8000481128693,
> > 1225.8000487089157, 1230.6000488996506, 1243.2000494003296,
> > 1247.4000495672226, 1260.0000500679016, 1266.0000503063202,
> > 1279.8000508546829, 1284.00\
> > 00510215759, 1299.0000516176224, 1304.4000518321991,
> 1318.2000523805618,
> > 1323.6000525951385, 1335.6000530719757, 1339.2000532150269,
> > 1354.2000538110733, 1358.4000539779663, 1369.2000544071198,
> > 1374.0000545978546,\
> > A 1390.2000552415848, 1407.6000559329987, 1412.4000561237335,
> > 1428.0000567436218, 1434.0000569820404, 1448.400057554245,
> > 1453.8000577688217, 1470.0000584125519, 1485.0000590085983,
> > 1488.0000591278076, 1504.8000597\
> > 953796, 1510.8000600337982, 1523.4000605344772, 1527.6000607013702,
> > 1543.2000613212585, 1547.4000614881516, 1559.4000619649887,
> > 1564.8000621795654, 1579.20006275177, 1584.6000629663467,
> > 1600.200063586235, 1603.20\
> > 00637054443, 1615.2000641822815, 1618.2000643014908,
> 1630.8000648021698,
> > 1634.4000649452209, 1647.6000654697418, 1662.0000660419464,
> > 1666.2000662088394, 1679.4000667333603, 1696.2000674009323,
> > 1714.200068116188, \
> > 1735.2000689506531, 1741.2000691890717, 1806.6000717878342,
> > 1809.6000719070435, 1824.000072479248, 1830.0000727176666,
> > 1842.0000731945038, 1848.0000734329224, 1860.0000739097595,
> > 1865.4000741243362, 1879.80007469\
> > 65408, 1884.6000748872757, 1900.200075507164, 1904.400075674057,
> > 1919.4000762701035, 1923.0000764131546, 1937.4000769853592,
> > 1943.4000772237778, 1956.0000777244568, 1960.8000779151917,
> > 1974.6000784635544, 1977.60\
> > 00785827637, 1993.200079202652, 1998.6000794172287,
> 2011.2000799179077,
> > 2026.2000805139542, 2030.4000806808472, 2042.4000811576843,
> > 2047.800081372261, 2059.8000818490982, 2065.2000820636749,
> > 2077.2000825405121, 2\
> > 080.8000826835632, 2092.8000831604004, 2107.8000837564468,
> > 2112.6000839471817, 2125.8000844717026, 2140.800085067749,
> > 2145.000085234642, 2158.8000857830048, 2164.2000859975815,
> > 2176.8000864982605, 2179.8000866174\
> > 698, 2195.4000872373581, 2212.2000879049301, 2217.000088095665,
> > 2232.6000887155533, 2235.6000888347626, 2250.0000894069672,
> > 2254.2000895738602, 2269.8000901937485, 2274.6000903844833,
> > 2289.0000909566879, 2306.400\
> > 0916481018, 2311.8000918626785, 2322.0000922679901]
> > >>> chunksA
> > [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> 0,
> > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
> 0, 0,
> > 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1\
> > , 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> 1, 1,
> > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> 2, 2,
> > 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, \
> > 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
> 3, 3,
> > 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
> 3, 3,
> > 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\
> > A 3, 3, 3, 3]
> > This looks correct, no? I am trying to attach the image plot of the
> design
> > matrix, split into the 4 sessions.A
> > Best,
> > Wolfganga**
> > [IMG]A figure_1.png
> > a**
> > On Mon, May 9, 2016 at 5:50 PM, Yaroslav Halchenko <
> debian at onerussian.com>
> > wrote:
>
> > On Mon, 09 May 2016, Wolfgang Pauli wrote:
>
> > > Hi,
>
> > > I am trying to perform an mvpa analysis of an experiment in which
> I
> > have 16
> > > different trial types and 4 sessions, 4 trial types in each
> session
> > (run).
> > > Based on the tutorial, I was getting started by using
> > fit_event_hrf_model,
> > > like so:
>
> > > evds = fit_event_hrf_model(ds, events, time_attr='time_coords',
> > > condition_attr=('onset'), return_model=True)
>
> > > where ds is an openfmri dataset (get_model_bold_dataset).
>
> > > I was trying to figure out why I would always get the warning
> that the
> > > design matrix was singular, and eventually ended up investigating
> the
> > > design matrix of the model.
>
> > > I used matplotlib to plot the design matrix, split it up into the
> four
> > > session, and found that the four parts were IDENTICAL. What could
> I be
> > > doing wrong? Obviously, it shouldn't be the same, because there
> are
> > > different trial types in the four sessions, and the trial order is
> > also
> > > randomized.
>
> > > Furthermore, the design matrix has 26 regressors. I don't quite
> > understand
> > > where that number is coming from, as I have 16 unique event
> types, and
> > 4
> > > sessions.
>
> > most probably that events definition was was the same in each of the
> > chunks... but also note that if you do it in a dataset which has
> > multiple chunks, you want to have condition_attr=['onset',
> 'chunks'] if
> > you want to make a model per each onset x chunk pair as was demoed
> e.g.
> > atA
> http://www.pymvpa.org/tutorial_eventrelated.html#response-modeling
>
> > if you share your data, and ideally your investigation script then
> we
> > could may be look deeper
> --
> Yaroslav O. Halchenko
> Center for Open Neuroscience http://centerforopenneuroscience.org
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
> WWW: http://www.linkedin.com/in/yarik
>
> _______________________________________________
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