Plotting Functions¶
Throughout the toolbox, care has been taken to present results and project analyses into outputs of a form directly interpretable by the analyst, such as projecting the loadings of a multivariate model onto the spectrum in the case of NMR, and as an ionmap for LCMS.
See the Plot Gallery for a visual overview of the available plots.
The plotting
module contains function to generate several common visualisations.
Plots are built upon seaborn for aesthetics, or when interactivity is required, plotly.
Most plots support a set of common configuration parameters to allow customisation of various display options. Common parameters that may be specified as keyword arguments are:

plottingFunctions(*vars, **kwargs):
Parameters:  savePath (str) – If
None
plot interactively, otherwise save the figure to the path specified  figureFormat (str) – If saving the plot, use this format
 dpi (int) – Plot resolution
 figureSize (tuple(float, float)) – Dimensions of the figure
 savePath (str) – If
Interactive plots utilise the plotly framework to provide controls, when using plotly you should ensure that the environment is configured according to the instructions at Offline Plots in Plotly in Python

nPYc.plotting.
histogram
(values, inclusionVector=None, quantiles=None, histBins=100, color=None, logy=False, logx=False, **kwargs)¶ Plot a histogram of values, optionally segmented according to observed quantiles.
Quantiles can be calculated on a second inclusionVector when specified.
Parameters:  values (numpy.array or dict) – Values to plot
 inclusionVector (None or numpy.array) – Optional second vector with same size as values, used to select quantiles for plotting.
 quantiles (None or List) – List of quantile bounds to segment the histogram by
 title (str) – Title for the plot
 xlabel (str) – Label for the Xaxis
 histBins (int) – Number of bins to break the histgram into
 color (None or List) – List of specific colours to use for plotting
 logy (bool) – If
True
plot y on a log scale  logx (bool) – If
True
plot x on a log scale  xlim (tuple of int) – Specify upper and lower bounds of the X axis

nPYc.plotting.
plotBatchAndROCorrection
(msData, msDatacorrected, featureList, addViolin=True, sampleAnnotation=None, logy=False, title='', savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Visualise the runorder correction applied to features, by plotting the values before and after correction, along with the fit calculated.
Parameters:  msData (MSDataset) – Dataset prior to correction
 msDatacorrected (MSDataset) – Dataset postcorrection
 featureList (list[int,]) – List of ints specifying indices of features to plot
 addViolin (bool) – If
true
, plot distributions as violin plots in addition to the longitudinal trend  sampleAnnotation (dict) – Samples for annotation in plot, must include fields ‘rank’: index (int) and ‘id’: sample name (str, as in msData.sampleMetadata[‘Sample File Name’]). For example, item[‘AbundanceSamples’] in featureID.py.
 logy (bool) – If
True
plot intensities on a log10 scale  title (str) – Text to title each plot with
 savePath (None or str) – If
None
plot interactively, otherwise save the figures to the path specified

nPYc.plotting.
plotTIC
(msData, addViolin=True, addBatchShading=False, addLineAtGaps=False, colourByDetectorVoltage=False, logy=False, title='', withExclusions=True, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Visualise TIC for all or a subset of features coloured by either dilution value or detector voltage. With the option to shade by batch.
Note
addViolin and colourByDetectorVoltage are mutually exclusive.
Parameters:  msData (MSDataset) – Dataset object
 addViolin (bool) – If
True
adds violin plots of TIC distribution pre and post correction split by sample type  addBatchShading (bool) – If
True
shades plot according to sample batch  addLineAtGaps (bool) – If
True
adds line where acquisition time is greater than double the norm  colourByDetectorVoltage (bool) – If
True
colours points by detector voltage, else colours by dilution  logy (bool) – If
True
plot y on a log scale  title (str) – Title for the plot
 withExclusions (bool) – If
False
, discard masked features from the sum  savePath (None or str) – If
None
plot interactively, otherwise save the figure to the path specified  figureFormat (str) – If saving the plot, use this format
 dpi (int) – Plot resolution
 figureSize (tuple(float, float)) – Dimensions of the figure

nPYc.plotting.
plotTICinteractive
(msData, plottype='Sample Type', labelby='Run Order', withExclusions=True)¶ Interactively visualise TIC (coloured by batch and sample type) with plotly, provides tooltips to allow identification of samples.
Plots may be of two types: * ‘Sample Type’ to plot by sample type and coloured by batch * ‘Linearity Reference’ to plot LR samples coloured by dilution
Parameters:  msData (MSDataset) – Dataset object
 plottype (str) – Select plot type, may be either
Sample Type
orLinearity Reference
Returns: Data object to use with plotly

nPYc.plotting.
plotLRTIC
(msData, sampleMask=None, colourByDetectorVoltage=False, title='', label=False, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Visualise TIC for linearity reference (LR) samples (either all or a subset) coloured by either dilution value or detector voltage.
Parameters:  msData (MSDataset) – Dataset object
 sampleMask (None or array of bool) – Defines subset of samples to plot, if
None
use msData’s builtin sampleMask  colourByDetectorVoltage (bool) – If
True
colours points by detector voltage, else colours by dilution  title (str) – Title for the plot
 label (bool) – If
True
, labels points with run order values  savePath (None or str) – If
None
, plot interactively, otherwise attempt to save at this path.  format (str) – Format to save figure
 dpi (int) – Resolution to draw at
 figureSize (tuple(float, float)) – Specify size of figure

nPYc.plotting.
jointplotRSDvCorrelation
(rsd, correlation, histBins=100, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot a 2D histogram of feature RSDs vs correlations to dilution, with marginal histograms.
Parameters:  rsd (numpy.array) – Vector of feature relative standard deviations
 correlation (numpy.array) – Vector of correlation to dilution
 histBins (int) – Number of bins to break the histgram into
 savePath (None or str) – If
None
, plot interactively, otherwise attempt to save at this path  figureFormat (str) – If saving the plot, use this format
 dpi (int) – Plot resolution
 figureSize (tuple(float, float)) – Dimensions of the figure

nPYc.plotting.
plotCorrelationToLRbyFeature
(msData, featureMask=None, title='', maxNo=5, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Summary plots of correlation to dilution for a subset of features, separated by sample batch. Each figure includes a scatter plot of feature intensity vs dilution, TIC of LR and surrounding SP samples, and a heatmap of correlation to dilution for each LR batch subset, overall, and mean.
Parameters:  msData (MSDataset) – Dataset object
 featureMask (None or array of bool) – Limits plotting to a subset of features, if
None
use msData’s builtin sampleMask  title (str) – Title for the plot
 maxNo (int) – Optional number of features to plot (default=10, i.e., 10 randomly selected features in featureList will be plotted)
 savePath (None or str) – If
None
, plot interactively, otherwise attempt to save at this path.  figureFormat (str) – Format to save figure
 dpi (int) – Resolution to draw at
 figureSize (tuple(float, float)) – Specify size of figure

nPYc.plotting.
plotIonMap
(msData, useRetention=True, title=None, savePath=None, xlim=None, ylim=None, logx=False, logy=False, figureFormat='png', dpi=72, figureSize=(11, 7))¶ plotIonMap(msData, **kwargs):
Visualise features in a MSDataset, to visualise the features in terms of the raw data.
Plotting requires the presence of ‘m/z’ and ‘Retention Time’ columns in the
featureMetadata
table. If both ‘m/z’ and retention time are present, a 2D ion map is generated, otherwise a 1D massspectrum is plotted.Parameters:  msData (MSDataset) – Dataset object to visualise
 useRetention (bool) – If
False
ignore any Retention Time information and plot a 1D mass spectrum

nPYc.plotting.
plotRSDs
(dataset, ratio=False, savePath=None, color=None **kwargs)¶ Visualise analytical versus biological variance.
Plot RSDs calculated in studyreference samples (analytical variance), versus those calculated in study samples (biological variance). RSDs can be visualised either in absolute terms, or as a ratio to analytical variation (ratio=
True
).plotRSDs()
requires that the dataset have at least two samples with thePrecisionReference
assay role, if present, RSDs calculated on independent sets ofPrecisionReference
samples will also be plotted.Parameters:  dataset (Dataset) – Dataset object to plot, the object must have greater that one ‘Study Sample’ and ‘StudyReference Sample’ defined
 ratio (bool) – If
True
plot the ratio of analytical variance to biological variance instead of raw values  featureName (str) – featureMetadata column name by which to label features
 logx (bool) – If
True
plot RSDs on a log10 scaled axis  xlim (None or tuple(float, float)) – Tuple of (min, max) RSD values to plot
 hLines (None or list) – None or list of y positions at which to plot an horizontal line. Features are positioned from 1 to nFeat
 savePath (None or str) – If
None
plot interactively, otherwise save the figure to the path specified  color (None or seaborn.palettes._ColorPalette) – Allows the default colour pallet to be overridden
 featName (bool) – If
True
yaxis label is the feature Name, ifFalse
features are numbered.

nPYc.plotting.
plotRSDsInteractive
(dataset, featureName='Feature Name', ratio=False, logx=True)¶ Plotlybased interactive version of
plotRSDs()
Visualise analytical versus biological variance.
Plot RSDs calculated in studyreference samples (analytical variance), versus those calculated in study samples (biological variance). RSDs can be visualised either in absolute terms, or as a ratio to analytical variation (ratio=
True
).plotRSDsInteractive()
requires that the dataset have at least two samples with thePrecisionReference
assay role, if present, RSDs calculated on independent sets ofPrecisionReference
samples will also be plotted.Parameters:  dataset (Dataset) – Dataset object to plot, the object must have greater that one ‘Study Sample’ and ‘StudyReference Sample’ defined
 featureName (str) – featureMetadata column name by which to label features
 ratio (bool) – If
True
plot the ratio of analytical variance to biological variance instead of raw values  logx (bool) – If
True
plot RSDs on a log10 scaled axis

nPYc.plotting.
plotScree
(R2, Q2=None, title='', xlabel='', ylabel='', savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot a barchart of variance explained (R2) and predicted (Q2) (if available) for each PCA component.
Parameters:  R2 (numpy.array) – PCA R2 values
 Q2 (numpy.array) – PCA Q2 values
 title (str) – Title for the plot
 xlabel (str) – Label for the xaxis
 ylabel (str) – Label for the yaxis

nPYc.plotting.
plotOutliers
(values, runOrder, sampleType=None, addViolin=False, Fcrit=None, FcritAlpha=None, PcritPercentile=None, title='', xlabel='Run Order', ylabel='', savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot scatter plot of PCA outlier stats sumT (strong) or DmodX (moderate), with a line at [25, 50, 75, 95, 99] quantiles and at a critical value if specified
Parameters:  values (numpy.array) – dModX or sum of scores, measure of ‘fit’ for each sample
 runOrder (numpy.array) – Order of sample acquisition (samples are plotted in this order)
 sampleType (pandas.Series) – Sample type of each sample, must be from ‘Study Sample’, ‘Study Reference’, ‘LongTerm Reference’, or ‘Sample’ (see multivariateReport.py)
 addViolin (bool) – If True adds a violin plot of distribution of values
 Fcrit (float) – If not none, plots a line at Fcrit
 FcritAlpha (float) – Alpha value for Fcrit (for legend)
 PcritPercentile (float) – If not none, plots a line at this quantile
 title (str) – Title for the plot
 xlabel (str) – Label for the xaxis

nPYc.plotting.
plotSpectralVariance
(dataset, classes=None, quantiles=(25, 75), average='median', xlim=None, **kwargs)¶ Plot the average spectral profile of dataset, optionally with the bounds of variance calculated from quantiles shaded. By specifying a column from dataset.sampleMetadata in the classes argument, individual averages and ranges will be plotted for each unique label in dataset.sampleMetadata[classes].
Parameters:  dataset (Dataset) – Data to plot
 classes (None or column in dataset.sampleMetadata) – Plot by distinct classes specified
 quantiles (None or (min, max)) – Plot these quantile bounds
 average (str) – Method to calculate average spectrum, defaults to ‘median’, may also be ‘mean’
 xlim (None or (float, float)) – Tuple of (min, max) values to scale the xaxis to
 logy (bool) – If
True
plot intensities on a log10 scale  title (str) – Text to title each plot with

nPYc.plotting.
plotScores
(pcaModel, classes=None, classType=None, components=None, alpha=0.05, plotAssociation=None, title='', xlabel='', figures=None, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot PCA scores for each pair of components in PCAmodel, coloured by values defined in classes, and with Hotelling’s T2 ellipse (95%)
Parameters:  pcaModel (ChemometricsPCA) – PCA model object (scikitlearn based)
 classes (pandas.Series) – Measurement/groupings associated with each sample, e.g., BMI/treatment status
 classType (str) – Type of data in
classes
, either ‘Plot Sample Type’, ‘categorical’ or ‘continuous’, must be specified if classes is notNone
. IfclassType
is ‘Plot Sample Type’,classes
expects ‘Study Sample’, ‘Study Reference’, ‘LongTerm Reference’, ‘Serial Dilution’ or ‘Sample’.  components (tuple (int, int)) – If
None
plots all components in model, else plots those specified in components  alpha (float) – Significance value for plotting Hotellings ellipse
 plotAssociation (bool) – If
True
, plots the association between each set of PCA scores and the metadata values  significance (numpy.array) – Significance of association of scores from each component with values in classes from correlation or KruskalWallis test for example (see multivariateReport.py)
 title (str) – Title for the plot
 xlabel (str) – Label for the xaxis
 figures (dict) – If not
None
, saves location of each figure for output in html report (see multivariateReport.py)

nPYc.plotting.
plotScoresInteractive
(dataTrue, pcaModel, colourBy, components=[1, 2], alpha=0.05, withExclusions=False)¶ Interactively visualise PCA scores (coloured by a given sampleMetadata field, and for a given pair of components) with plotly, provides tooltips to allow identification of samples.
Parameters:  dataTrue (Dataset) – Dataset
 object pcaModel (PCA) – PCA model object (scikitlearn based)
 colourBy (str) – sampleMetadata field name to of which values to colour samples by
 components (list) – List of two integers, components to plot
 alpha (float) – Significance value for plotting Hotellings ellipse
 withExclusions (bool) – If
True
, only report on features and samples not masked by the sample and feature masks; must match between data and pcaModel

nPYc.plotting.
plotLoadings
(pcaModel, msData, title='', figures=None, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot PCA loadings for each component in PCAmodel. For NMR data plots the median spectrum coloured by the loading. For MS data plots an ion map (rt vs. mz) coloured by the loading.
Parameters:  pcaModel (ChemometricsPCA) – PCA model object (scikitlearn based)
 msData (Dataset) – Dataset object
 title (str) – Title for the plot
 figures (dict) – If not
None
, saves location of each figure for output in html report (see multivariateReport.py)

nPYc.plotting.
plotLoadingsInteractive
(dataTrue, pcaModel, component=1, withExclusions=False)¶ Interactively visualise PCA loadings (for a given pair of components) with plotly, provides tooltips to allow identification of features.
For MS data, plots RT vs. mz; for NMR plots ppm vs spectral intensity. Plots are coloured by the weight of the loadings.
Parameters:  dataTrue (Dataset) – Dataset
 pcaModel (ChemometricsPCA) – PCA model object (scikitlearn based)
 component (int) – Component(s) to plot (one component (int) or list of two integers)
 withExclusions (bool) – If
True
, only report on features and samples not masked by the sample and feature masks; must match between data and pcaModel

nPYc.plotting.
plotDiscreteLoadings
(pcaModel, nbComponentPerRow=3, firstComponent=1, sort=True, **kwargs)¶ Plot loadings for a linear model as a set of parallel vertical scatter plots.
Parameters:  pcaModel (ChemometricsPCA) – Model to plot
 nbComponentPerRow (int) – Number of sidebyside loading plots to place per row
 firstComponent (int) – Start plotting components from this component
 sort (bool) – Plot variable in order of their magnitude in component one

nPYc.plotting.
plotFeatureRanges
(dataset, compounds, logx=False, histBins=20, **kwargs)¶ Plot distributions plots of the values listed in compounds, on to a set of axes with a linked xaxis.
If reference ranges are specified in
featureMetadata
, a reference range will be drawn behind each plot. If reference ranges are available, distributions that for within the range will be shaded green, and those that fall outside red, where no reference range is available the distribution will be shaded blue.Parameters:  dataset (Dataset) – Dataset object to plot values from
 compounds (list) – List of features to plot
 logx (bool) – Calculate and plot histograms on a log10 scale, if the minumn values is below 1, the histogram is calculated by adding one to all values
 histBins (int) – Number of bins for histograms

nPYc.plotting.
plotMetadataDistribution
(sampleMetadata, valueType, figures=None, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot the distribution of a set of data, e.g., sampleMetadata fields. Plots a bar chart for categorical data, or a histogram for continuous data.
Parameters:  sampleMetadata (dataset.sampleMetadata) – Set of measurements/groupings associated with each sample, note can contain multiple columns, but they must be of one valueType
 valueType (str) – Type of data contained in sampleMetadata, one of
continuous
,categorical
ordate
 figures (dict) – If not
None
, saves location of each figure for output in html report (see multivariateReport.py)

nPYc.plotting.
plotLOQRunOrder
(targetedData, addCalibration=True, compareBatch=True, title='', savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Visualise ratio of LLOQ and ULOQ by run order, separated by batch. Option to add barchart that summarises across batch
Parameters:  targetedData (TargetedDataset) – TargetedDataset object
 addCalibration (bool) – If
True
add calibration samples  compareBatch (bool) – If
True
add barchart across batch, separated by SampleType  title (str) – Title for the plot
 savePath (None or str) – If
None
plot interactively, otherwise save the figure to the path specified  figureFormat (str) – If saving the plot, use this format
 dpi (int) – Plot resolution
 figureSize (tuple(float, float)) – Dimensions of the figure
Raises:  ValueError – if targetedData does not satisfy to the TargetedDataset definition for QC
 ValueError – if
calibration
does not match the number of batch

nPYc.plotting.
plotFeatureLOQ
(tData, splitByBatch=True, plotBatchLOQ=False, zoomLOQ=False, logY=False, tightYLim=True, nbPlotPerRow=3, metabolitesPerPlot=5, withExclusions=True, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Violin plot for each feature with line at LOQ concentrations. Option to split by batch, add each batch LOQs, split by SampleType.
Parameters:  tData (TargetedDataset) –
TargetedDataset
 splitByBatch (bool) – If
True
separate each violin plot by batch  plotBatchLOQ (bool) – If
True
add lines at LOQs (LLOQ/ULOQ) for each batch, and points for samples that will be out of LOQ  zoomLOQ (bool) – If
True
plots a zoomed ULOQ plot on top, all data in the centre and a zoomed LLOQ plot at the bottom  logY (bool) – If
True
logscale the yaxis  tightYLim (bool) – if
True
ylim are close to the points but can let LOQ lines outside, ifFalse
LOQ lines will be part of the plot.  nbPlotPerRow (int) – Number of plots to place on each row
 metabolitesPerPlot (int) – Maximum numper of metabolites to plot in on single figure
 savePath (None or str) – If
None
plot interactively, otherwise save the figure to the path specified  figureFormat (str) – If saving the plot, use this format
 dpi (int) – Plot resolution
 figureSize (tuple(float, float)) – Dimensions of the figure
Raises: ValueError – if targetedData does not satisfy to the TargetedDataset definition for QC
 tData (TargetedDataset) –

nPYc.plotting.
plotVariableScatter
(inputTable, logX=False, xLim=None, xLabel='', yLabel='', sampletypeColor=False, hLines=None, hLineStyle='', hBox=None, vLines=None, vLineStyle=':', vBox=None, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot values on xaxis, with ordering on the yaxis. Entries as rows are placed on the xaxis, values of all columns are plotted on yaxis with different colors. If sampletypeColor=True, only columns named as SampleTypes will be plotted and colored according to other reports, otherwise all columns are plotted. Ordering of the rows is conserved, the first item is placed at the top of the yaxis and the last row is at the bottom. If a column [‘yName’] is present, it is employed to label each yaxis entry.
Parameters:  inputTable (dataframe) – DataFrame or accuracy or precision values, with features as rows and sample types as columns ([‘Study Sample’, ‘Study Pool’, ‘External Reference’, ‘All Samples’, ‘nan’]). A ‘yName’ column can be present to display the feature name.
 logX (bool) – If
True
plot values on a log10 scaled x axis  xLim (None or tuple(float, float)) – Tuple of (min, max) values to plot
 xLabel (str) – Xaxis label
 yLabel (str) – Yaxis label
 sampletypeColor (bool) – If
True
only the sampleType columns are plotted with colors matching other reports  hLines (None or list) – None or list of y positions at which to plot an horizontal line. Features are positioned from 1 to nFeat
 hLineStyle (str) – One of the axhline linestyle (‘’, ‘–’, ‘.’, ‘:’)
 hBox (None or list) – None or list of tuple of y positions defining horizontal box. Features are positioned from 1 to nFeat
 vLines (None or list) – None or list of v positions at which to plot an vertical line. Unit is the same as the v axis.
 vLineStyle (str) – One of the axvline linestyle (‘’, ‘–’, ‘.’, ‘:’)
 vBox (None or list) – None or list of tuple of x positions defining horizontal box. Features are positioned from 1 to nFeat
 color (None or seaborn.palettes._ColorPalette) – Allows the default colour pallet to be overridden
 savePath (None or str) – If
None
plot interactively, otherwise save the figure to the path specified

nPYc.plotting.
plotAccuracyPrecision
(tData, accuracy=True, percentRange=None, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot Accuracy or Precision for a TargetedDataset.
Features at all present concentrations are shown on the yaxis, with accuracy or precision values on the xaxis. Accuracy are centered around 100%. If Precision values cover too wide a range, xaxis is log transformed.
Parameters:  tData (TargetedDataset) – TargetedDataset object to plot
 accuracy (bool) – If
True
plot the Accuracy of each measurements, ifFalse
plot the Precision of measurements.  percentRange (None or float) – If float [0, inf], add a rectangle covering the range of acceptable percentage; for Accuracy 100 +/ percentage, for Precision 0  percentage.
 savePath (None or str) – If
None
plot interactively, otherwise save the figure to the path specified  figureFormat (str) – If saving the plot, use this format
 dpi (int) – Plot resolution
 figureSize (tuple(float, float)) – Dimensions of the figure
Raises:  ValueError – if targetedData does not satisfy to the TargetedDataset definition for QC
 ValueError – if percentRange is not ‘None’ or float

nPYc.plotting.
plotCalibrationInteractive
(nmrData)¶ Build Plotly figure of calibration
Parameters: nmrData (NMRDataset) – Dataset to visualise Returns: Plotly figure object for displaly with iplot() Return type: plotly.graph_objs.Figure

nPYc.plotting.
plotLineWidth
(nmrData, **kwargs)¶ Visualise spectral line widths, plotting the median spectrum, the 95% variance, and any spectra where line width can not be calulated or exceeds the cutoff specified in
nmrData.Attributes['LWpeakRange']
.Parameters:  nmrData (NMRDataset) – Dataset object
 savePath (None or str) – If None, plot interactively, otherwise attempt to save at this path

nPYc.plotting.
plotLineWidthInteractive
(nmrData)¶ Interactive Plotly version of py:func:plotLineWidth
Visualise spectral line widths, plotting the median spectrum, the 95% variance, and any spectra where line width can not be calulated or exceeds the cutoff specified in
nmrData.Attributes['LWpeakRange']
.Parameters:  nmrData (NMRDataset) – Dataset object
 savePath (None or str) – If None, plot interactively, otherwise attempt to save at this path

nPYc.plotting.
plotBaseline
(nmrData, savePath=None, **kwargs)¶ Plot spectral baseline at the high and low end of the spectrum. Visualise the median, bounds of 95% variance, and outliers.
Parameters:  nmrData (NMRDataset) – Dataset object
 savePath (None or str) – If None, plot interactively, otherwise attempt to save at this path

nPYc.plotting.
plotBaselineInteractive
(nmrData)¶ Interactive Plotly version of py:func:plotBaseline.
Plot spectral baseline at the high and low end of the spectrum. Visualise the median, bounds of 95% variance, and outliers.
Parameters: nmrData (NMRDataset) – Dataset object

nPYc.plotting.
plotSolventResonance
(nmrData, **kwargs)¶ Plot the solvent region to be cut from the spectrum along with spectra failing solvent region checks.
Parameters:  nmrData (NMRDataset) – Dataset to plot
 savePath (None or str) – If
None
draw interactively, otherwise save to this path

nPYc.plotting.
plotSolventResonanceInteractive
(nmrData, title='Residual solvent resonance')¶ Ploty interactive version of
plotSolventResonance()
Plot the solvent region to be cut from the spectrum along with spectra failing solvent region checks.
Parameters: nmrData (NMRDataset) – Dataset to plot Returns: Plotly figure object to plot with iPlot

nPYc.plotting.
plotSpectraInteractive
(dataset, samples=None, xlim=None, featureNames=None, sampleLabels='Sample ID', nmrDataset=True)¶ Plot spectra from dataset.
#:param Dataset dataset: Dataset to plot from :param samples: Index of samples to plot, if
None
plot all spectra :type samples: None or list of int :param xlim: Tuple of (minimum value, maximum value) defining a feature range to plot :type xlim: (float, float)

nPYc.plotting.
plotIonMapInteractive
(dataset, title=None, xlim=None, ylim=None, logx=False, logy=False, featureName='Feature Name')¶ Visualise features in a MSDataset, as an ion map.
Plotting requires the presence of ‘m/z’ and ‘Retention Time’ columns in the
featureMetadata
table.Parameters: msData (MSDataset) – Dataset object to visualise

nPYc.plotting.
plotSpectralVarianceInteractive
(dataset, classes=None, quantiles=(25, 75), average='mean', xlim=None, title=None)¶ Plot the average spectral profile of dataset, optionally with the bounds of variance calculated from quantiles shaded. By specifying a column from dataset.sampleMetadata in the classes argument, individual averages and ranges will be plotted for each unique label in dataset.sampleMetadata[classes].
Parameters:  dataset (Dataset) – Data to plot
 classes (None or column in dataset.sampleMetadata) – Plot by distinct classes specified
 quantiles (None or (min, max)) – Plot these quantile bounds
 average (str) – Method to calculate average spectrum, defaults to ‘median’, may also be ‘mean’
 xlim (None or (float, float)) – Tuple of (min, max) values to scale the xaxis to

nPYc.plotting.
correlationSpectroscopyInteractive
(dataset, target, mode='SHY', correlationMethod='Pearson')¶ Conduct correlation spectroscopy analyses against the samples in dataset.
Mode may be one of:  SHY Correlate features in dataset to values in target
Parameters:  dataset (Dataset) – Correlations weill be projected into this dataset
 target (numpy.array) – Correlations are calculated to this
 mode (str) – Type of analysis to conduct
 correlationMethod (str) – Type of correlation to calculate, may be ‘Pearson’, or ‘Spearman’
Returns: Plotly figure
Return type:

nPYc.plotting.
plotTargetedFeatureDistribution
(datasetOriginal, featureName='Feature Name', featureMask=None, sampleTypes=['SS', 'SP', 'ER'], logx=False, figures=None, savePath=None, figureFormat='png', dpi=72, figureSize=(11, 7))¶ Plot the distribution (violin plots) of a set of features, e.g., peakPantheR outputs, coloured by sample type
Parameters:  dataset (MSDataset) –
MSDataset
 logx (bool) – If
True
logscale the xaxis  figures (dict) – If not
None
, saves location of each figure for output in html report (see _generateMSReport.py)
 dataset (MSDataset) –