<div dir="ltr">Hi,<div><br></div><div>I am trying to run a whole-brain searchlight RSA (on a cluster with a lot of processing cores available) but every time I do it returns a memoryError. However, when I split it into hemispheres or run as an ROI, it successfully runs without MemoryError. I have tried following some other suggestions I found online but nothing has resolved the issue. Memory shouldn't be an issue as the cluster has plenty of RAM available. We've also tried to run this on another cluster at a separate institution using a high mem option and it still didn't work. I am pretty stumped and was wondering if anyone can help or has any potential solutions in mind?</div><div><br></div><div>Here is what the code looks like:</div><div><br></div><div><blockquote style="margin:0px 0px 0px 40px;border:none;padding:0px"><div><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    subject = subjectData(inputs.rootDir, inputs.ID)</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># load the trait similarity data</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    traitData = subject.importSimilarity()</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># load the neural data</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    neuralData = subject.importNeural()</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px">    </p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    neuralData = remove_invariant_features(neuralData)</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(196,26,22)"><span style="color:rgb(0,0,0)">    </span><span style="color:rgb(155,35,147)"><b>print</b></span><span style="color:rgb(0,0,0)"> (</span>"Complete loading neural data"<span style="color:rgb(0,0,0)">)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># create an object from the neuroCorrelation class</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    matrixCorr = neuroCorrelation(traitData)</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># set up searchlight parameters</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># set the radius based on the input</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    rad = inputs.rad</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># Fisher's z-transformation for correlation coefficient</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    FisherTransform = FxMapper(<span style="color:rgb(28,0,207)">'features'</span>, <span style="color:rgb(155,35,147)"><b>lambda</b></span> r: <span style="color:rgb(28,0,207)">0.5</span> * np.log((<span style="color:rgb(28,0,207)">1</span> + r) / (<span style="color:rgb(28,0,207)">1</span> - r)))</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># create the search light</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    sl = sphere_searchlight(matrixCorr.correlate, rad, results_backend=<span style="color:rgb(28,0,207)">'hdf5'</span>, postproc=FisherTransform)</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(196,26,22)"><span style="color:rgb(0,0,0)">    </span><span style="color:rgb(155,35,147)"><b>print</b></span><span style="color:rgb(0,0,0)"> (</span>"Complete search light set-up"<span style="color:rgb(0,0,0)">)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># apply the search light function to the neural data</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    sl_output = sl(neuralData)</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(196,26,22)"><span style="color:rgb(0,0,0)">    </span><span style="color:rgb(155,35,147)"><b>print</b></span><span style="color:rgb(0,0,0)"> (</span>"Search light completed"<span style="color:rgb(0,0,0)">)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># transform the search light output to an image data</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    sl_image = map2nifti(data=sl_output, dataset=neuralData)</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(83,101,121)"><span style="color:rgb(0,0,0)">    </span><i># save the output</i></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    outputDir = os.path.join(inputs.rootDir, <span style="color:rgb(196,26,22)">"RSA"</span>, <span style="color:rgb(196,26,22)">"searchLightResult"</span>, <span style="color:rgb(196,26,22)">"Subject{n}"</span>.format(n=inputs.ID),</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">                             <span style="color:rgb(196,26,22)">"sub{n}_sl_results_{v}{h}_P_R{r}.nii.gz"</span>.format(n=inputs.ID, v=inputs.valence, h=inputs.hemisphere, r=inputs.rad))</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    sl_image.to_filename(outputDir)</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0);min-height:14px"><br></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)">    <span style="color:rgb(155,35,147)"><b>print</b></span> (<span style="color:rgb(196,26,22)">"Complete with subject{n}"</span>.format(n=inputs.ID))</p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:12px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><br></p></div></blockquote>Here is the error:</div><div><br></div><div><blockquote style="margin:0px 0px 0px 40px;border:none;padding:0px"><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">/home/user/anaconda2/lib/python2.7/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  'a.item() instead', DeprecationWarning, stacklevel=1)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">Traceback (most recent call last):</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "./script/RSA_rad_FB_test.py", line 54, in <module></span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    sl_output = sl(neuralData)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/learner.py", line 258, in __call__</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    return super(Learner, self).__call__(ds)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/node.py", line 138, in __call__</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    result = self._postcall(ds, result)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/measures/base.py", line 128, in _postcall</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    result = super(Measure, self)._postcall(dataset, result)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/node.py", line 179, in _postcall</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    result = self._apply_postproc(ds, result)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/node.py", line 253, in _apply_postproc</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    result = self.__postproc(result)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/learner.py", line 258, in __call__</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    return super(Learner, self).__call__(ds)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/node.py", line 137, in __call__</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    result = self._call(ds, **(_call_kwargs or self._get_call_kwargs(ds)))</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/mappers/base.py", line 291, in _call</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    return self.forward(ds)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/mappers/base.py", line 215, in forward</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    return self._forward_dataset(data)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/mappers/fx.py", line 204, in _forward_dataset</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    col[attr] = a</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/collections.py", line 590, in __setitem__</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    value = ArrayCollectable(value)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/collections.py", line 185, in __init__</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    Collectable.__init__(self, value=value, name=name, doc=doc)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/collections.py", line 67, in __init__</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    self._set(value)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/mvpa2/base/collections.py", line 291, in _set</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    val = np.asanyarray(val)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">  File "/home/user/anaconda2/lib/python2.7/site-packages/numpy/core/numeric.py", line 591, in asanyarray</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">    return array(a, dtype, copy=False, order=order, subok=True)</span></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"></p><p style="margin:0px;font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:11px;line-height:normal;font-family:Menlo;color:rgb(0,0,0)"><span style="font-variant-ligatures:no-common-ligatures">MemoryError</span></p></blockquote><div><br></div><div>Any help would be greatly appreciated! Thank you.</div></div><div><br></div>-- <br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div style="color:rgb(0,0,0);font-size:12.8px">Jacob Elder</div><div style="color:rgb(0,0,0);font-size:12.8px">Ph.D. Student, Dept. of Psychology</div><div style="color:rgb(0,0,0);font-size:12.8px">University of California, Riverside</div><div style="color:rgb(0,0,0);font-size:12.8px"><a href="https://www.hugheslab.org/" target="_blank">The UCR Social Neuroscience Lab</a></div></div></div></div>