From d56402b43a5457137553216d88cd08e545c4bc8d Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauld.mccarthy@gmail.com>
Date: Thu, 3 Nov 2016 11:47:37 +0000
Subject: [PATCH] Tweaks to FEAT model fitting
---
fsl/data/featimage.py | 21 ++++++++++++++++++---
1 file changed, 18 insertions(+), 3 deletions(-)
diff --git a/fsl/data/featimage.py b/fsl/data/featimage.py
index 149b1c810..d9fa82cf4 100644
--- a/fsl/data/featimage.py
+++ b/fsl/data/featimage.py
@@ -309,6 +309,17 @@ class FEATImage(fslimage.Image):
if self.__design is None:
raise RuntimeError('No design')
+ # Here we are basically trying to
+ # replicate the behaviour of tsplot.
+ # There are some differences though -
+ # by default, tsplot weights the
+ # data by Z statistics. We're not
+ # doing that here.
+
+ # If we are not plotting a full model fit,
+ # normalise the contrast vector to unit
+ # length. Why? Because that's what tsplot
+ # does.
if not fullmodel:
contrast = np.array(contrast)
contrast = contrast / np.sqrt((contrast ** 2).sum())
@@ -318,17 +329,21 @@ class FEATImage(fslimage.Image):
if len(contrast) != numEVs:
raise ValueError('Contrast is wrong length')
-
+
X = self.__design.getDesign(xyz)
data = self[x, y, z, :]
modelfit = np.zeros(len(data))
for i in range(numEVs):
+ ev = X[:, i]
pe = self.getPE(i)[x, y, z]
- modelfit += X[:, i] * pe * contrast[i]
+ modelfit += ev * pe * contrast[i]
- return modelfit + data.mean()
+ # Make sure the full model fit
+ # has the same mean as the data
+ if fullmodel: return modelfit - modelfit.mean() + data.mean()
+ else: return modelfit
def partialFit(self, contrast, xyz, fullmodel=False):
--
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