Reference

dataprep.py

class isoplot.main.dataprep.IsoplotData(datapath, verbose=False)

Bases: object

Class to prepare Isoplot Data for plotting

Parameters

datapath (str) – Path to .csv file containing Isocor output data

generate_template()

Generate .xlsx template that user must fill

get_data()

Read data from tsv file and store in object data attribute.

get_template(path)

Read user-filled template and catch any encoding errors

static load_isocor_data(path)

Function to read incoming data

static load_template(template_input, excel_sheet=0)

Function to read incoming template data

merge_data()

Merge template and data into pandas dataframe

prepare_data(export=True)

Final cleaning of data and export

plots.py

Module containing the plotting classes. The methods correspond to different types of plots to create

class isoplot.main.plots.InteractivePlot(stack, value, data, name, metabolite, condition, time, display, rtrn)

Bases: isoplot.main.plots.Plot

Class to generate the different interactive plots

mean_enrichment_meanplot()

Generate interactive mean_enrichment plots with meaned replicates

mean_enrichment_plot()

Generate interactive mean_enrichment plots

stacked_areaplot()

Generate interactive stacked areaplots

stacked_barplot()

Generate interactive stacked barplots

stacked_meanplot()

Generate interactive stacked barplots with meaned replicates

unstacked_barplot()

Generate interactive unstacked barplots

unstacked_meanplot()

Generate interactive unstacked barplots with meaned replicates

class isoplot.main.plots.Map(data, name, annot, fmt, display=False, rtrn=False)

Bases: object

Class to create maps from Isocor output (MS data from C13 labelling experiments)

Parameters

annot (Bool) – Should annotations be apparent on map or not

build_clustermap()

Create a clustermap of mean_enrichment data across all conditions & times & metabolites

build_heatmap()

Create a heatmap of mean_enrichment data across all conditions & times & metabolites

build_interactive_heatmap()

Create an interactive heatmap of mean_enrichment data across all conditions & times & metabolites

class isoplot.main.plots.Plot(stack, value, data, name, metabolite, condition, time, display, rtrn=False)

Bases: object

Plot objects Master Class from which the rest inherit

Parameters

  • stack (Bool) – Value to denote if barplots should stack

  • value (str) – Data to be plotted. Can be ‘isotopologue_fraction’, ‘corrected area’ or ‘mean_enrichment’

  • data (Pandas Dataframe) – IsoplotData object containing clean data

  • name (str) – Name for generated file directory where plots will go

  • metabolite (str) – metabolite to be plotted

  • condition (list) – List of conditions to be plotted

  • time (list) – List of times to be plotted

  • display (Bool) – Should plots be displayed when created

  • rtrn (Bool) – Should figure object be returned or not

HEIGHT = 640
WIDTH = 1080
static split_ids(ids)

Function to split IDs and get back lists of conditions, times and replicates in order

Parameters

ids (list) – IDs generated by IsoplotData Object

Returns

lists containing conditions, times and replicates

Return type

lists

class isoplot.main.plots.StaticPlot(stack, value, data, name, metabolite, condition, time, fmt, display, rtrn)

Bases: isoplot.main.plots.Plot

Class to generate the different static plots.

Parameters

  • fmt (str) – Output format of static plots (pdf, svg, png or jpeg)

  • display (Bool) – Should plots be displayed when created

barplot()

Creation of barplots

mean_barplot()

Creation of meaned barplots (on replicates)

mean_enrichment_meanplot()

Generate static mean_enrichment plots with meaned replicates

mean_enrichment_plot()

Generate static mean_enrichment plots

stacked_areaplot()

Creation of area stackplot (for cinetic data)

isoplotcli.py

Module containing the CLI class that will be used during the cli process to get arguments from user and generate the desired plots

class isoplot.ui.isoplotcli.IsoplotCli(home=None, run_home=None, static_plot=None, int_plot=None, maps=None, args=None)

Bases: object

dir_init(plot_type)

Initialize directory for plot

static get_cli_input(arg, param, data_object)

Function to get input from user and check for errors in spelling. If an error is detected input is asked once more. This function is used for galaxy implementation

Parameters

  • arg – list from which strings must be parsed

  • param (str) – name of what we are looking for

  • data_object (class: 'isoplot.dataprep.IsoplotData') – IsoplotData object containing final clean dataframe

Returns

Desired string after parsing

Return type

list

go_home()

Exit after work is done

initialize_cli()

Launch argument parsing and perform checks

plot_figs(metabolite_list, data_object, build_zip=False)

Function to control which plot methods are called depending on the arguments that were parsed

Parameters

  • metabolite_list (list of str) – metabolites to be plotted

  • data_object (class: 'isoplot.main.dataprep.IsoplotData') – object containing the prepared data

  • build_zip (bool) – should figures be returned and exported in zip

zip_export(figures, zip_file_name)

Function to save figures in figure list to zip file (taken from https://stackoverflow.com/questions/55616877/save-multiple-objects-to-zip-directly-from-memory-in-python)

Parameters

  • figures (list of tuples) – storage of figures and their respective file names in tuples: (name, fig)

  • zip_file_name (str) – name of the exported zip file

isoplot.ui.isoplotcli.parse_args()

Parse arguments from user input.

Returns

Argument Parser object

Return type

class: argparse.ArgumentParser

cli_process.py

Process that runs during Command-Line Interface usage

isoplot.main.cli_process.main()

isoplot_notebook.py

class isoplot.ui.isoplot_notebook.ValueHolder

Bases: object

x: int = None
isoplot.ui.isoplot_notebook.build_map(data, name, map_select, annot, fmt, display)
isoplot.ui.isoplot_notebook.check_version(name)
isoplot.ui.isoplot_notebook.dataprep_eventhandler(event)
isoplot.ui.isoplot_notebook.indibokplot(stack, value, data, name, metabolites, conditions, times, display, stackplot=False)
isoplot.ui.isoplot_notebook.indiplot(stack, value, data, name, metabolites, conditions, times, fmt, display, stackplot=False)
isoplot.ui.isoplot_notebook.make_mduploader()
isoplot.ui.isoplot_notebook.make_uploader()
isoplot.ui.isoplot_notebook.meanbokplot(stack, value, data, name, metabolites, conditions, times, display)
isoplot.ui.isoplot_notebook.meanplot(stack, value, data, name, metabolites, conditions, times, fmt, display)
isoplot.ui.isoplot_notebook.metadatabtn_eventhandler(event)