def __init__(self, *args, **kwargs):
super(RegressionTool, self).__init__(*args, **kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input_data') # Add input slot
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input_data',
{
# 'labels_n': (None,['Pathway']),
}))
self.config.set_defaults({
'variables': [],
})
self.addConfigPanel(RegressionConfigPanel, 'Settings')
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, *args, **kwargs):
super(TwoSampleT, self).__init__(*args, **kwargs)
self.config.set_defaults({
'experiment_control': None,
'experiment_test': None,
'related_or_independent': 'Related',
'assume_equal_variances': True,
'plot_distribution': True,
})
self.data.add_input('input_data') # Add input slot
# We need an input filter for this type; accepting *anything*
self.data.consumer_defs.append(
DataDefinition('input_data', {
})
)
self.addExperimentConfigPanel()
self.addConfigPanel(TwoSampleConfigPanel, 'Extra')
def __init__(self, **kwargs):
super(BarTool, self).__init__(**kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input') # Add input slot
self.data.add_output('output', is_public=False) # Hidden
self.views.addView(MplCategoryBarView(self), 'View')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition('input', {
'entities_t': (None, ['Compound']),
}))
t = self.addToolBar('Bar')
self.toolbars['bar'] = t
self.finalise()
def __init__(self, **kwargs):
super(AnnotateApp, self).__init__(**kwargs)
# Annotations is a list of dicts; each list a distinct annotation
# Targets is their mapping to the data; e.g. ('scales', 1) for scales[1]
self._annotations = defaultdict(list)
self._annotations_targets = dict()
# Source object for the data
self.addDataToolBar()
self._field_transforms = {'scales': float, 'labels': self.str_or_none, 'classes': self.str_or_none}
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)
t = self.getCreatedToolbar('Annotations', 'external-data')
import_dataAction = QAction(QIcon(os.path.join(utils.scriptdir, 'icons', 'disk--arrow.png')), 'Load annotations from file\\u2026', self.m)
import_dataAction.setStatusTip('Load annotations from .csv. file')
import_dataAction.triggered.connect(self.onLoadAnnotations)
t.addAction(import_dataAction)
self.addExternalDataToolbar() # Add standard source data options
annotations_dataAction = QAction(QIcon(os.path.join(utils.scriptdir, 'icons', 'pencil-field.png')), 'Edit annotation settings\\u2026', self.m)
annotations_dataAction.setStatusTip('Import additional annotations for a dataset including classes, labels, scales')
annotations_dataAction.triggered.connect(self.onEditAnnotationsSettings)
t.addAction(annotations_dataAction)
# We need an input filter for this type; accepting *anything*
self.data.consumer_defs.append(
DataDefinition('input', {
'labels_n': ('>0', '>0')
})
)
self.finalise()
def __init__(self, *args, **kwargs):
super(NMRLabMetabolabTool, self).__init__(*args, **kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input_data') # Add input slot
self.data.add_output('output_data')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input_data', {
'labels_n': ('>0', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
self.config.set_defaults({
'bin_size': 0.01,
'bin_offset': 0,
})
def __init__(self, *args, **kwargs):
super(PLSDATool, self).__init__(*args, **kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
self.addExperimentToolBar()
self.addFigureToolBar()
self.data.add_input('input_data') # 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_data', {})
)
def __init__(self, *args, **kwargs):
super(FilterApp, self).__init__(*args, **kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input_data') # Add input slot
self.data.add_output('output_data') # Add output slot
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input_data',
{ # Accept anything!
}))
self.config.set_defaults({
'target': 'Class',
'match': '.*',
})
self.addConfigPanel(FilterConfigPanel, 'Settings')
def __init__(self, *args, **kwargs):
super(SpectraNormApp, self).__init__(*args, **kwargs)
self.addDataToolBar()
self.addFigureToolBar()
self.data.add_input('input_data') # Add input slot
self.data.add_output('output_data')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input_data', {
'labels_n': ('>0', None),
'entities_t': (None, None),
'scales_t': (None, ['float']),
}))
self.config.set_defaults({
'algorithm': 'PQN',
})
self.addConfigPanel(SpectraNormConfigPanel, 'Settings')
def __init__(self, **kwargs):
super(FoldChangeApp, self).__init__(**kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.addDataToolBar()
self.addExperimentToolBar()
self.data.add_output('output')
self.table = QTableView()
self.table.setModel(self.data.o['output'].as_table)
self.views.addView(self.table, 'Table')
self.register_url_handler(self.url_handler)
self.data.add_input('input') # Add input slot
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input',
{
'classes_n': (">1", None), # At least one class
}))
self.config.set_defaults({
'use_baseline_minima': True,
})
t = self.addToolBar('Fold change')
t.cb_baseline_minima = QCheckBox('Auto minima')
self.config.add_handler('use_baseline_minima', t.cb_baseline_minima)
t.cb_baseline_minima.setStatusTip(
'Replace zero values with half of the smallest value')
t.addWidget(t.cb_baseline_minima)
self.toolbars['fold_change'] = t
self.finalise()
def __init__(self, *args, **kwargs):
super(PCATool, self).__init__(*args, **kwargs)
# Define automatic mapping (settings will determine the route; allow manual tweaks later)
self.data.add_input('input_data') # Add input slot
self.data.add_output('scores')
self.data.add_output('weights')
self.data.add_output('filtered_data')
# Setup data consumer options
self.data.consumer_defs.append(
DataDefinition(
'input_data',
{
# 'labels_n': (None,['Pathway']),
}))
self.config.set_defaults({
'number_of_components': 2,
'plot_sample_numbers': False,
})
self.addConfigPanel(PCAConfigPanel, 'PCA')
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()