ccpn.AnalysisMetabolomics.ui.gui.modules package

Submodules

ccpn.AnalysisMetabolomics.ui.gui.modules.MetaboliteFinder module

class ccpn.AnalysisMetabolomics.ui.gui.modules.MetaboliteFinder.MetaboliteFinderModule(mainWindow, name='BMRB Metabolite Finder', **kwds)[source]

Bases: ccpn.ui.gui.modules.CcpnModule.CcpnModule

className = 'MetaboliteFinderModule'
includeSettingsWidget = False
maxSettingsState = 2
settingsPosition = 'top'

ccpn.AnalysisMetabolomics.ui.gui.modules.PcaModule module

Module Documentation here

Warning: this module can be overloaded with too many operations and plots hierarchies from PyQtGraph. Could be beneficial to split in more classes or custom subclasses.

class ccpn.AnalysisMetabolomics.ui.gui.modules.PcaModule.Decomposition(project)[source]

Bases: object

Base class for the Decomposition Module (the old “interactor”!)

buildSourceData(sources, includedRegion=None)[source]

Sets the __data with a dataframe: each row is a spectrum. Column 1 is the pid, all other columns are spectrum intensities.

Parameters

  • sources – list of pids

  • xRange – the region of interest in the spectrum

Returns

the sources back

buildSourceFromSpectra(spectra, xRange=None)[source]

Sets the __data with a dataframe: each row is a spectrum. Column 1 is the pid, all other columns are spectrum intensities. :param spectra: list of spectra :param xRange: the region of interest in the spectrum :return: the sources back

center(data)[source]
property centering

- None, mutable -

createSpectrumGroupFromScores(spectra, prefix='PCA_output')[source]
Parameters

outlinersDataFrame –

Returns

a spectrumGroup with the spectra which had outliners values

decompose(data=None)[source]

data: dataframe with index: obj, xs as columns, ys as rows get the data, init the pca model and then plot the results

property includedRegion

- None, mutable - Region of intestest for calculating the PCA

property loadings

- None, immutable - loadings as a pandas dataframe

property normalization

- None, mutable -

normalize(data)[source]
saveLoadingsToSpectra(prefix='PCA_output', descale=True, components=None)[source]
scale(data)[source]
property scaling

- None, mutable -

property scores

- None, immutable - scores as a pandas dataframe

property sources

- None, mutable - list of pids

static splitDataWithinRange(scores, xLabel, yLabel, minX, maxX, minY, maxY)[source]
Parameters

  • scores – dataframe with all scores

  • xLabel – label1 , eg PC1

  • yLabel – label1 , eg PC2

  • minX – min value for Y

  • maxX – Max value for X

  • minY – min value for Y

  • maxY – max value for Y

Returns

inners dataframe like scores but containing only the values within the ranges and outers (rest) not included in inners

property variance

- None, immutable - Variance as a pandas dataframe

class ccpn.AnalysisMetabolomics.ui.gui.modules.PcaModule.PcaModule(mainWindow, **kwargs)[source]

Bases: ccpn.ui.gui.modules.CcpnModule.CcpnModule

className = 'DecompositionModule'
getPcaResults()[source]

gets the results from the base class decomposition

getVarianceResults()[source]

gets the results from the base class decomposition

getVectorsResults()[source]

gets the results from the base class decomposition

includeSettingsWidget = True
maxSettingsState = 2
mouseMoved(event)[source]

use this if you need for example display the mouse coords on display :param event: :return:

plotPCAscatterResults(dataFrame, xAxisLabel='PC1', yAxisLabel='PC2', selectedObjs=None)[source]
Parameters

dataFrame – in the format from the PCA Class index: Pid –> obj Columns: PCx x= 1 to the end. Eg. PC1, PC2, etc values: floats

Returns

transform the dataFrame in the (pyqtGraph) plottable data format and plot it on the scatterPlot

plotVariance(varianceDataFrame)[source]
plotVectors(vectorsDataFrame, pcComponent)[source]
refreshPlots()[source]

Refreshes all module by resetting the sources

saveOutput()[source]
setScaling(scaling)[source]
settingsPosition = 'left'