diff --git a/fsl/data/melodicresults.py b/fsl/data/melodicresults.py
index 89b45a913507d1640da16907158090f582fa753e..6de7dc48f146b196d4ec6f83a271fd37afd09b30 100644
--- a/fsl/data/melodicresults.py
+++ b/fsl/data/melodicresults.py
@@ -229,35 +229,8 @@ class MelodicClassification(props.HasProperties):
def load(self, filename):
- """Loads component labels from the specified file. The file is assuemd
- to be of the format generated by FIX or Melview; such a file should
- have a structure resembling the following::
-
- filtered_func_data.ica
- 1, Signal, False
- 2, Unclassified Noise, True
- 3, Unknown, False
- 4, Signal, False
- 5, Unclassified Noise, True
- 6, Unclassified Noise, True
- 7, Unclassified Noise, True
- 8, Signal, False
- [2, 5, 6, 7]
-
- The first line of the file contains the name of the melodic directory.
- Then, one line is present for each component, containing the following,
- separated by commas:
-
- - The component index (starting from 1).
- - One or more labels for the component (multiple labels must be
- comma-separated).
- - ``'True'`` if the component has been classified as *bad*,
- ``'False'`` otherwise.
-
- The last line of the file contains the index (starting from 1) of all
- *bad* components, i.e. those components which are not classified as
- signal or unknown.
-
+ """Loads component labels from the specified file. See the
+ :func:`loadMelodicLabelFile` function.
.. note:: This method adds to, but does not replace, any existing
component classifications stored by this
@@ -266,57 +239,27 @@ class MelodicClassification(props.HasProperties):
classifications.
"""
- with open(filename, 'rt') as f:
- lines = f.readlines()
-
- if len(lines) < 3:
- raise InvalidFixFileError('Invalid FIX classification '
- 'file - not enough lines')
-
- lines = [l.strip() for l in lines]
-
- # Ignore the first and last
- # lines - we're only interested
- # in the component labels
- compLines = lines[1:-1]
-
- if len(compLines) != self.__ncomps:
- raise InvalidFixFileError('Invalid FIX classification '
- 'file - number of components '
- 'do not match')
-
- # Parse the labels for every component
- # We dot not add the labels as we go
- # as, if something is wrong with the
- # file contents, we don't want this
- # MelodicClassification instance to
- # be modified. So we'll assign the
- # labels afterwards
- allLabels = []
- for i, compLine in enumerate(compLines):
-
- tokens = compLine.split(',')
- tokens = [t.strip() for t in tokens]
-
- if len(tokens) < 3:
- raise InvalidFixFileError('Invalid FIX classification '
- 'file - component line {} does '
- 'not have enough '
- 'tokens'.format(i + 1))
-
- compIdx = int(tokens[0])
- compLabels = tokens[1:-1]
-
- if compIdx != i + 1:
- raise InvalidFixFileError('Invalid FIX classification '
- 'file - component line {} has '
- 'wrong component number '
- '({})'.format(i, compIdx))
-
- allLabels.append(compLabels)
-
- # Now that all the labels are
- # read in, we can store them
+ # Read the labels in
+ _, allLabels = loadMelodicLabelFile(filename)
+
+ # More labels in the file than there are in
+ # melodic_IC - that doesn't make any sense.
+ if len(allLabels) > self.__ncomps:
+ raise InvalidFixFileError('The number of components in {} does '
+ 'not match the number of components in '
+ '{}!'.format(filename,
+ self.__melimage.dataSource))
+
+ # Less labels in the file than there are in
+ # the melodic_IC image - this is ok, as the
+ # file may have only contained a list of
+ # noisy components. We'll label the remaining
+ # components as 'Unknown'.
+ elif len(allLabels) < self.__ncomps:
+ for i in range(len(allLabels), self.__ncomps):
+ allLabels.append(['Unknown'])
+
+ # Add the labels to this melclass object
notifState = self.getNotificationState('labels')
self.disableNotification('labels')
@@ -330,44 +273,19 @@ class MelodicClassification(props.HasProperties):
def save(self, filename):
"""Saves the component classifications stored by this
- ``MeloidicClassification`` to the specified file. The classifications
- are saved in the format described in the :meth:`load` method.
-
- .. TODO:: Accept a dictionary of ``{label : display label}`` mappings,
- so we can output cased labels (e.g. ``'Signal'`` instead of
- ``'signal'``).
+ ``MeloidicClassification`` to the specified file. See the
+ :func:`saveMelodicLabelFile` function.
"""
- lines = []
- badComps = []
- image = self.__melimage
-
- # The first line - the melodic directory name
- lines.append(op.basename(image.getMelodicDir()))
-
- # A line for each component
- for comp in range(self.__ncomps):
-
- noise = not (self.hasLabel(comp, 'signal') or
- self.hasLabel(comp, 'unknown'))
-
- # Make sure there are no
- # commas in any label names
- labels = [self.getDisplayLabel(l) for l in self.getLabels(comp)]
- labels = [l.replace(',', '_') for l in labels]
-
- tokens = [str(comp + 1)] + labels + [str(noise)]
-
- lines.append(', '.join(tokens))
-
- if noise:
- badComps.append(comp)
+ allLabels = []
- # A line listing the bad components
- lines.append('[' + ', '.join([str(c + 1) for c in badComps]) + ']')
+ for c in range(self.__ncomps):
+ labels = [self.getDisplayLabel(l) for l in self.labels[c]]
+ allLabels.append(labels)
- with open(filename, 'wt') as f:
- f.write('\n'.join(lines) + '\n')
+ saveMelodicLabelFile(self.__melImage.getMelodicDir(),
+ allLabels,
+ filename)
def getLabels(self, component):
@@ -483,7 +401,162 @@ class MelodicClassification(props.HasProperties):
self.notify('labels')
-class InvalidFixFileError(Exception):
+def loadMelodicLabelFile(filename):
+ """Loads component labels from the specified file. The file is assuemd
+ to be of the format generated by FIX or Melview; such a file should
+ have a structure resembling the following::
+
+ filtered_func_data.ica
+ 1, Signal, False
+ 2, Unclassified Noise, True
+ 3, Unknown, False
+ 4, Signal, False
+ 5, Unclassified Noise, True
+ 6, Unclassified Noise, True
+ 7, Unclassified Noise, True
+ 8, Signal, False
+ [2, 5, 6, 7]
+
+ The first line of the file contains the name of the melodic directory.
+ Then, one line is present for each component, containing the following,
+ separated by commas:
+
+ - The component index (starting from 1).
+ - One or more labels for the component (multiple labels must be
+ comma-separated).
+ - ``'True'`` if the component has been classified as *bad*,
+ ``'False'`` otherwise.
+
+ The last line of the file contains the index (starting from 1) of all
+ *bad* components, i.e. those components which are not classified as
+ signal or unknown.
"""
+
+ with open(filename, 'rt') as f:
+ lines = f.readlines()
+
+ if len(lines) < 1:
+ raise InvalidFixFileError('Invalid FIX classification '
+ 'file - not enough lines')
+
+ lines = [l.strip() for l in lines]
+ lines = [l for l in lines if l != '']
+
+ # If the file contains a single
+ # line, we assume that it is just
+ # a list of noise components.
+ if len(lines) == 1:
+
+ melDir = None
+ noisyComps = map(int, lines[0][1:-1].split(', '))
+ allLabels = []
+
+ for i in range(max(noisyComps)):
+ if (i + 1) in noisyComps: allLabels.append(['Unclassified noise'])
+ else: allLabels.append(['Signal'])
+
+ # Otherwise, we assume that
+ # it is a full label file.
+ else:
+
+ melDir = lines[0]
+ noisyComps = map(int, lines[-1][1:-1].split(', '))
+
+ # Parse the labels for every component
+ # We dot not add the labels as we go
+ # as, if something is wrong with the
+ # file contents, we don't want this
+ # MelodicClassification instance to
+ # be modified. So we'll assign the
+ # labels afterwards
+ allLabels = []
+ for i, compLine in enumerate(lines[1:-1]):
+
+ tokens = compLine.split(',')
+ tokens = [t.strip() for t in tokens]
+
+ if len(tokens) < 3:
+ raise InvalidFixFileError('Invalid FIX classification '
+ 'file - component line {} does '
+ 'not have enough '
+ 'tokens'.format(i + 1))
+
+ compIdx = int(tokens[0])
+ compLabels = tokens[1:-1]
+
+ if compIdx != i + 1:
+ raise InvalidFixFileError('Invalid FIX classification '
+ 'file - component line {} has '
+ 'wrong component number '
+ '({})'.format(i, compIdx))
+
+ allLabels.append(compLabels)
+
+
+ # Validate the labels against
+ # the noisy list - all components
+ # in the noisy list should have
+ # the label 'unclassified noise'.
+ for i, labels in enumerate(allLabels):
+
+ for label in labels:
+ if label.lower() == 'unclassified noise' and \
+ (i + 1) not in noisyComps:
+
+ raise InvalidFixFileError('Noisy component {} has an invalid '
+ 'label: {}'.format(i + 1, label))
+
+ for comp in noisyComps:
+ labels = allLabels[comp - 1]
+ labels = [l.lower() for l in labels]
+
+ if 'unclassified noise' not in labels:
+ raise InvalidFixFileError('Noisy component {} is missing '
+ 'a noise label'.format(i))
+
+ return melDir, allLabels
+
+
+def saveMelodicLabelFile(melDir, allLabels, filename):
+ """Saves the component classifications stored by this
+ ``MeloidicClassification`` to the specified file. The classifications
+ are saved in the format described in the :meth:`load` method.
+ """
+
+ lines = []
+ noisyComps = []
+
+ # The first line - the melodic directory name
+ lines.append(op.abspath(melDir))
+
+ # A line for each component
+ for i, labels in enumerate(allLabels):
+
+ comp = i + 1
+ lowered = [l.lower() for l in labels]
+ noise = 'signal' not in lowered and 'unknown' not in lowered
+
+ # Make sure there are no
+ # commas in any label names
+ labels = [l.replace(',', '_') for l in labels]
+
+ tokens = [str(comp + 1)] + labels + [str(noise)]
+
+ lines.append(', '.join(tokens))
+
+ if noise:
+ noisyComps.append(comp)
+
+ # A line listing the bad components
+ lines.append('[' + ', '.join([str(c + 1) for c in noisyComps]) + ']')
+
+ with open(filename, 'wt') as f:
+ f.write('\n'.join(lines) + '\n')
+
+
+class InvalidFixFileError(Exception):
+ """Exception raised by the :meth:`MelodicClassification.load` method and
+ the :func:`loadMelodicLabelFile` function when an attempt is made to load
+ an invalid FIX label file.
"""
pass
diff --git a/fsl/fsleyes/luts/melodic-classes.lut b/fsl/fsleyes/luts/melodic-classes.lut
index aaf2c07fce35959dea4e16af2be7324c78148f4b..841c3cf0f6a01b8f52a58e4eeaa69f08907f4563 100644
--- a/fsl/fsleyes/luts/melodic-classes.lut
+++ b/fsl/fsleyes/luts/melodic-classes.lut
@@ -1,11 +1,11 @@
-1 0.419608 0.619608 0.992157 Signal
-2 0.490196 0.827451 0.000000 Unknown
-3 0.980392 0.478431 0.588235 Unclassified noise
-4 0.843137 0.776471 0.000000 Movement
-5 0.850980 0.435294 0.000000 Cardiac
-6 0.494118 0.964706 0.984314 White matter
-7 0.984313 0.564706 0.850980 Non-brain
-8 0.858824 0.854902 0.847059 MRI
-9 0.666667 0.972549 0.745098 Susceptibility-motion
-10 0.780392 0.470588 0.870588 Sagittal sinus
-11 0.427450 0.725490 0.603922 Respiratory
+1 0.39216 0.82745 0.00000 Signal
+2 0.91765 0.86275 0.00000 Unknown
+3 1.00000 0.37647 0.26275 Unclassified noise
+4 0.08235 0.82353 0.83922 Movement
+5 0.70588 0.27059 1.00000 Cardiac
+6 0.49412 0.96471 0.98431 White matter
+7 0.98431 0.56471 0.85098 Non-brain
+8 0.85882 0.85490 0.84706 MRI
+9 0.66667 0.97255 0.74510 Susceptibility-motion
+10 0.78039 0.47059 0.87059 Sagittal sinus
+11 0.42745 0.72549 0.60392 Respiratory
diff --git a/fsl/fsleyes/perspectives.py b/fsl/fsleyes/perspectives.py
index 03a4a6f537a434898b41b1fd73c44889c0cea801..20db6f42f6c8a209950a8c18e2cb079404b351e7 100644
--- a/fsl/fsleyes/perspectives.py
+++ b/fsl/fsleyes/perspectives.py
@@ -404,13 +404,13 @@ BUILT_IN_PERSPECTIVES = collections.OrderedDict((
('melodic',
textwrap.dedent("""
LightBoxPanel,TimeSeriesPanel,PowerSpectrumPanel,
- layout2|name=LightBoxPanel 1;caption=Lightbox View 1;state=67377088;dir=5;layer=0;row=0;pos=0;prop=100000;bestw=853;besth=-1;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=TimeSeriesPanel 2;caption=Time series 2;state=67377148;dir=3;layer=0;row=0;pos=0;prop=100000;bestw=-1;besth=472;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=PowerSpectrumPanel 3;caption=Power spectra 3;state=67377148;dir=3;layer=0;row=0;pos=1;prop=100000;bestw=-1;besth=472;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|dock_size(5,0,0)=22|dock_size(3,0,0)=493|
- OverlayListPanel,LightBoxToolBar,OverlayDisplayToolBar,LocationPanel,MelodicClassificationPanel,LookupTablePanel,
- layout2|name=Panel;caption=;state=768;dir=5;layer=0;row=0;pos=0;prop=100000;bestw=20;besth=20;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=OverlayListPanel;caption=Overlay list;state=67373052;dir=3;layer=0;row=0;pos=0;prop=100000;bestw=197;besth=80;minw=197;minh=80;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=197;floath=96;notebookid=-1;transparent=255|name=LightBoxToolBar;caption=Lightbox view toolbar;state=67382012;dir=1;layer=10;row=0;pos=0;prop=100000;bestw=757;besth=43;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=OverlayDisplayToolBar;caption=Display toolbar;state=67382012;dir=1;layer=11;row=0;pos=0;prop=100000;bestw=860;besth=49;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=LocationPanel;caption=Location;state=67373052;dir=3;layer=0;row=0;pos=1;prop=100000;bestw=440;besth=109;minw=440;minh=109;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=440;floath=125;notebookid=-1;transparent=255|name=MelodicClassificationPanel;caption=Melodic IC classification;state=67373052;dir=2;layer=0;row=0;pos=1;prop=100000;bestw=400;besth=100;minw=400;minh=100;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=400;floath=116;notebookid=-1;transparent=255|name=LookupTablePanel;caption=Lookup tables;state=67373052;dir=2;layer=0;row=0;pos=0;prop=100000;bestw=358;besth=140;minw=358;minh=140;maxw=-1;maxh=-1;floatx=3614;floaty=658;floatw=358;floath=156;notebookid=-1;transparent=255|dock_size(5,0,0)=22|dock_size(3,0,0)=130|dock_size(1,10,0)=45|dock_size(1,11,0)=10|dock_size(2,0,0)=402|
+ layout2|name=LightBoxPanel 1;caption=Lightbox View 1;state=67377088;dir=5;layer=0;row=0;pos=0;prop=100000;bestw=853;besth=-1;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=TimeSeriesPanel 2;caption=Time series 2;state=67377148;dir=3;layer=0;row=0;pos=0;prop=100000;bestw=-1;besth=472;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=PowerSpectrumPanel 3;caption=Power spectra 3;state=67377148;dir=3;layer=0;row=0;pos=1;prop=100000;bestw=-1;besth=472;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|dock_size(5,0,0)=22|dock_size(3,0,0)=195|
+ OverlayListPanel,OverlayDisplayToolBar,LocationPanel,LightBoxToolBar,MelodicClassificationPanel,
+ layout2|name=Panel;caption=;state=768;dir=5;layer=0;row=0;pos=0;prop=100000;bestw=20;besth=20;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=OverlayListPanel;caption=Overlay list;state=67373052;dir=3;layer=0;row=0;pos=0;prop=100000;bestw=204;besth=80;minw=197;minh=80;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=204;floath=96;notebookid=-1;transparent=255|name=OverlayDisplayToolBar;caption=Display toolbar;state=67382012;dir=1;layer=11;row=0;pos=0;prop=100000;bestw=810;besth=49;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=LocationPanel;caption=Location;state=67373052;dir=3;layer=0;row=0;pos=1;prop=100000;bestw=440;besth=111;minw=440;minh=109;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=440;floath=127;notebookid=-1;transparent=255|name=LightBoxToolBar;caption=Lightbox view toolbar;state=67382012;dir=1;layer=10;row=0;pos=0;prop=100000;bestw=753;besth=43;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|name=MelodicClassificationPanel;caption=Melodic IC classification;state=67373052;dir=2;layer=0;row=0;pos=0;prop=100000;bestw=400;besth=100;minw=400;minh=100;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=400;floath=116;notebookid=-1;transparent=255|dock_size(5,0,0)=22|dock_size(3,0,0)=130|dock_size(1,10,0)=45|dock_size(1,11,0)=51|dock_size(2,0,0)=402|
,
layout2|name=FigureCanvasWxAgg;caption=;state=768;dir=5;layer=0;row=0;pos=0;prop=100000;bestw=640;besth=480;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|dock_size(5,0,0)=642|
,
- layout2|name=FigureCanvasWxAgg;caption=;state=768;dir=5;layer=0;row=0;pos=0;prop=100000;bestw=640;besth=480;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|dock_size(5,0,0)=642|
+ layout2|name=FigureCanvasWxAgg;caption=;state=768;dir=5;layer=0;row=0;pos=0;prop=100000;bestw=640;besth=480;minw=-1;minh=-1;maxw=-1;maxh=-1;floatx=-1;floaty=-1;floatw=-1;floath=-1;notebookid=-1;transparent=255|dock_size(5,0,0)=642|'
""")),
('feat',