Tutorial¶

Network Configuration¶

Network object supports following properties for configuring the network diagram.

scale: scaling factor for layout algorithm
fontsize: fontsize for labels
edgelw: linewidth of edges
nodeColor: node color
reactionNodeColor: reaction node color
labelColor: label color
labelReactionIds: boolean flag for labeling reaction ids
reactionColor: edge color
modifierColor: modifier edge color
boundaryColor: boundary node color
nodeEdgeColor: node edge color
nodeEdgelw: linewidth of node edges
highlight: list of species ids or reaction ids to highlight
hlNodeColor: node color of highlighted nodes
hlNodeEdgeColor: node edge color of highlighted nodes
drawReactionNode: boolean flag for drawing reaction nodes
breakBoundary: boolean flag for breaking all boundary species into separate nodes
tightLayout: boolean flag for tighter node layout
analyzeFlux: boolean flag for visualizing flux
analyzeRates: boolean flag for visualizing species rate of changes
analyzeColorHigh: color to use for higher values during analysis
analyzeColorLow: color to use for lower values during analysis
analyzeColorMap: colormap to use for analysis.
analyzeColorScale: boolean flag for using colormaps. Setting this true ignore analyzeColorHigh and analyzeColorLow
drawInlineTimeCourse: boolean flag for plotting inline time-cource simulation
simTime: value for simulation duration
forceAnalysisAtSimTime: boolean flag for running analysis at the end of simTime
plotColorbar: boolean flag for visualizing color bar
inlineTimeCourseSelections: list of species to plot for time-course simulation
customAxis: use custom matplotlib.pyplot.axis object for the diagram
layoutAlgorithm: specify layout algorithm

You can define these properties and run draw() to generate customized network diagrams.

Detached Boundary¶

Detach common boundary species by setting breakBoundary=True.

net = npl.Network(npl.testmodels.REPRESSILATOR)
net.draw()
net.breakBoundary = True
net.draw()

Flux Visualization¶

Visualize flux using colormap via setting analyzeFlux=True. Set analyzeColorScale=True to scale the colormap to minimum and maximum values. You can supply your own colormaps. To plot colorbar, set plotColorbar=True.

net.analyzeColorScale = True
net.analyzeFlux = True
net.analyzeColorMap = 'viridis'
net.plotColorbar = True

Rate of Changes Visualization¶

Visualize species rates of change using colormap via setting analyzeRates=True. Set analyzeColorScale=True to scale the colormap to minimum and maximum values. The resulting plot will show species rate of change at t=simTime. You can supply your own colormaps. To plot colorbar, set plotColorbar=True.

net.analyzeColorScale = True
net.analyzeRates = True
net.analyzeColorMap = 'viridis'
net.simTime = 3000
net.plotColorbar = True

Inline Time-course Plot¶

Plot inline time-course plot along with network diagrams with matching colors by setting drwaInlineTimeCourse=True. The simulation runs for t=simTime. You can selection specific species to plot by passing a list to inlineTimeCourseSelections property.

net.drawInlineTimeCourse = True
net.inlineTimeCourseSelections = ['MKKK_P', 'MKKK', 'MAPK_PP']
net.simTime = 4000

Highlighting¶

Highlight specific nodes by passing a list to highlight property. Choose the highlight colors using hlNodeColor and hlNodeEdgeColor properties.

net.drawReactionNode = False
net.nodeEdgelw = 3
net.highlight = ['S1']
net.draw()

Selecting Layout Algorithm¶

Currently, netplotlib supports following layout algorithms:

‘kamada-kawai’

‘spring’

‘dot’

‘neato’

‘twopi’

By default, netplotlib uses Kamada-Kawai layout algorithm. Certain layout algorithms require external graphviz to be configured and pygraphviz package installed.

NetworkEnsemble Configuration¶

NetworkEnsemble object supports following properties for configuring the network diagram.

scale: scaling factor for layout algorithm
fontsize: fontsize for labels
edgelw: linewidth of edges
nodeColor: node color
reactionNodeColor: reaction node color
labelColor: label color
labelReactionIds: boolean flag for labeling reaction ids
reactionColor: edge color
modifierColor: modifier edge color
boundaryColor: boundary node color
nodeEdgeColor: node edge color
nodeEdgelw: linewidth of node edges
highlight: list of species ids or reaction ids to highlight
hlNodeColor: node color of highlighted nodes
hlNodeEdgeColor: node edge color of highlighted nodes
edgeLabel: boolean flag for displaying edge weights
edgeLabelFontSize: fontsize of edge weight labels
drawReactionNode: flag for drawing reaction nodes
breakBoundary: boolean flag for breaking all boundary species into separate nodes
weights: list of custom weights to override
edgeTransparency: boolean flag for changing the transparency of the edges accordin to edge weights
plottingThreshold: value of threshold to prevent from displaying weighted edges
removeBelowThreshold: boolean flag for preventing weighted edges below plottingThreshold from displaying
analyzeFlux: boolean flag for visualizing flux
customAxis: use custom matplotlib.pyplot.axis object for the diagram
layoutAlgorithm: specify layout algorithm

Grid Plot¶

Plot a grid plot of network diagrams of individual models in the list by running drawNetworkGrid() function.

net.drawNetworkGrid(3, 3)

Weighted Network Diagram¶

Combine models in the list and generate a one network diagram where the edges are weighted according to the frequency. To generate a weighted network diagram, run drawWeightedDiagram() function. Below are images of an example where an ensemble of three models are combined into a weighted network diagram.

net.drawWeightedDiagram()

It is possible to set a threshold where edges below the threshold are removed from the resulting network diagram. To set a threshold, use plottingThreshold and removeBelowThreshold properties. For example, if plottingThreshold=0.5 and removeBelowThreshold=True, any edges that appear in less than half of the model ensemble will be ignored. However, sometimes you might want to put the ignored reactions back while keeping the layout. To do so, set removeBelowThreshold=False while keeping the plottingThreshold.

net.removeBelowThreshold = True
net.plottingThreshold = 0.5
net.drawWeightedDiagram()
net.removeBelowThreshold = False
net.drawWeightedDiagram()

Test Cases¶

Netplotlib comes with set of test cases. All test cases are available under netplotlib.testmodels submodule.