def __init__(self, **kwargs):
super(PLSDAApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
self.addExperimentToolBar()
self.addFigureToolBar()
self.views.addView(MplScatterView(self), 'Scores')
self.views.addView(MplSpectraView(self), 'LV1')
self.views.addView(MplSpectraView(self), 'LV2')
self.data.add_output('scores')
self.data.add_output('weights')
self.data.add_input('input') # Add input slot
self.config.set_defaults({
'number_of_components': 2,
'autoscale': False,
'algorithm': 'NIPALS',
})
self.addConfigPanel(PLSDAConfigPanel, 'PLSDA')
# Setup data consumer options
self.data.consumer_defs.append(DataDefinition('input', {}))
self.finalise()
def __init__(self, **kwargs):
super(PCAApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.views.addView(MplScatterView(self), 'Scores')
self.views.addView(MplSpectraView(self), 'PC1')
self.views.addView(MplSpectraView(self), 'PC2')
self.views.addView(MplSpectraView(self), 'PC3')
self.views.addView(MplSpectraView(self), 'PC4')
self.views.addView(MplSpectraView(self), 'PC5')
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('scores')
self.data.add_output('weights')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input',
{
# 'labels_n': (None,['Pathway']),
}))
self.config.set_defaults({
'number_of_components': 2,
})
self.addConfigPanel(PCAConfigPanel, 'PCA')
self.finalise()
def __init__(self, **kwargs):
super(IcoshiftApp, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(MplSpectraView(self), 'Spectra')
self.views.addView(MplDifferenceView(self), 'Shift')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input', {
'labels_n': ('>1', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
self.config.set_defaults({
'target': 'average',
'alignment_mode': 'whole',
'maximum_shift': 'f',
})
self.addConfigPanel(IcoshiftConfigPanel, 'Settings')
self.finalise()
def __init__(self, **kwargs):
super(NMRLabMetabolabTool, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table.setModel(self.data.o['output'].as_table)
self.views.addTab(MplSpectraView(self), 'View')
# Start matlab interface
self.matlab = mlabwrap.init()
#code = "addpath('%s')" % os.path.abspath( self.plugin.path )
#r = self.matlab.run_code(code)
#print r,"!!!!"
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input', {
'labels_n': ('>1', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
self.config.set_defaults({
'bin_size': 0.01,
'bin_offset': 0,
})
def __init__(self, **kwargs):
super(NMRPeakPickingApp, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(MplSpectraView(self), 'View')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input', {
'labels_n': ('>1', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
self.config.set_defaults({
'peak_threshold': 0.05,
'peak_separation': 0.5,
'peak_algorithm': 'Threshold',
})
self.addConfigPanel(PeakPickConfigPanel, 'Settings')
self.finalise()
def __init__(self, **kwargs):
super(MATLABTool, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table.setModel(self.data.o['output'].as_table)
self.views.addTab(MplSpectraView(self), 'View')
# Start matlab interface
self.matlab = mlabwrap.init()
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input', {
'labels_n': ('>1', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
self.config.set_defaults({
'bin_size': 0.01,
'bin_offset': 0,
})
def __init__(self, **kwargs):
super(MetaboHunterApp, self).__init__(**kwargs)
#Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar(default_pause_analysis=True)
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(MplSpectraView(self), 'View')
self.config.set_defaults({
'Metabotype': 'All',
'Database Source': 'HMDB',
'Sample pH': 'ph7',
'Solvent': 'water',
'Frequency': 'all',
'Method': 'HighestNumberNeighbourhood',
'Noise Threshold': 0.0,
'Confidence Threshold': 0.4,
'Tolerance': 0.1,
})
self.addConfigPanel(MetaboHunterConfigPanel, 'MetaboHunter')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition('input', {
'scales_t': (None, ['float']),
'entities_t': (None, None),
}))
self.finalise()
def __init__(self, **kwargs):
super(NMRPeakAdjApp, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.data.add_output('region')
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(MplSpectraView(self), 'View')
self.views.addView(MplSpectraView(self), 'Region')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition('input', {
'labels_n': ('>1', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
})
)
# Define default settings for pathway rendering
self.config.set_defaults({
# Peak target
'peak_target': 'TMSP',
'peak_target_ppm': 0.0,
'peak_target_ppm_tolerance': 0.5,
# Shifting
'shifting_enabled': True,
# Scaling
'scaling_enabled': True,
})
self.region_dso = None
self._automated_update_config = False
self.addConfigPanel( PeakAdjConfigPanel, 'Settings')
self.finalise()
def __init__(self, **kwargs):
super(TransformApp, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output') # Add output slot
self.table.setModel(self.data.o['output'].as_table)
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input',
{ # Accept anything!
}))
self.views.addView(MplSpectraView(self), 'View')
self.finalise()
def __init__(self, **kwargs):
super(SpectraTool, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.views.addTab(MplSpectraView(self), 'View')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input', {
'labels_n': ('>1', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
self.finalise()
def __init__(self, **kwargs):
super(BaselineCorrectionTool, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(MplSpectraView(self), 'View')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input', {
'labels_n': ('>1', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
# Define default settings for pathway rendering
self.config.set_defaults({
# Peak target
'algorithm': 'median',
# Baseline settings
'med_mw': 24,
'med_sf': 16,
'med_sigma': 5.0,
# cbf settings
'cbf_last_pc': 10,
# cbf_explicit settings
'cbf_explicit_start': 0,
'cbf_explicit_end': 100,
# base settings
'base_nl': [],
'base_nw': 0,
})
self.addConfigPanel(BaselineCorrectionConfigPanel, 'Settings')
self.finalise()
def __init__(self, **kwargs):
super(SpectraNormApp, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output')
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(MplSpectraView(self), 'View')
self.views.addView(MplDifferenceView(self), 'Difference')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input', {
'labels_n': ('>1', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
th = self.addToolBar('Spectra normalisation')
self.algorithms = {
'PQN': self.pqn,
'TSA': self.tsa,
}
self.config.set_defaults({
'algorithm': 'PQN',
})
self.addConfigPanel(SpectraNormConfigPanel, 'Settings')
self.finalise()