vuecore.viz module#
To be broken down into modules.
- vuecore.viz.create_violinplot(df, x, y, color, color_map={})[source]#
This function creates traces for a simple violin plot.
- Parameters:
- Returns:
plotly figure.
Example:
result = create_violinplot(df, x='group', y='protein a', color='group', color_map={})
- vuecore.viz.generate_configuration_tree(report_pipeline, dataset_type)[source]#
This function retrieves the analysis pipeline from a dataset .yml file and creates a Cytoscape network, organized hierarchically.
- Parameters:
- Returns:
new Dash div with title and Cytoscape network, summarizing analysis pipeline.
- vuecore.viz.getMapperFigure(data, identifier, title)[source]#
This function uses the KeplerMapper python package to visualize high-dimensional data and generate a FigureWidget that can be shown or editted. This method is suitable for use in Jupyter notebooks. For more information visit https://kepler-mapper.scikit-tda.org/reference/stubs/kmapper.plotlyviz.plotlyviz.html.
- vuecore.viz.getPlotTraces(data, key='full', type='lines', div_factor=10010.0, horizontal=False)[source]#
This function returns traces for different kinds of plots.
- Parameters:
- Returns:
list of traces.
Example 1:
result = getPlotTraces(data, key='full', type = 'lines', horizontal=False)
Example 2:
result = getPlotTraces(data, key='full', type = 'scaled markers', div_factor=float(10^3000), horizontal=True)
- vuecore.viz.get_2_venn_diagram(data, identifier, cond1, cond2, args)[source]#
This function extracts the exlusive features in cond1 and cond2 and their common features, and build a two-circle venn diagram.
- Parameters:
- Arguments:
colors (dict) – dictionary with cond1 and cond2 as keys, and color codes as values.
title (str) – plot title.
- Returns:
two-circle venn diagram figure within <div id=”_dash-app-content”>.
Example:
result = get_2_venn_diagram(data, identifier='venn2', cond1='group1', cond2='group2', args={'color':{'group1':'blue', 'group2':'red'}, 'title':'Two-circle Venn diagram'} )
- vuecore.viz.get_WGCNAPlots(data, identifier)[source]#
Takes data from runWGCNA function and builds WGCNA plots.
- Parameters:
data – tuple with multiple pandas dataframes.
identifier (str) – is the id used to identify the div where the figure will be generated.
- Returns:
list of dcc.Graph.
- vuecore.viz.get_barplot(data, identifier, args)[source]#
This function plots a simple barplot.
- Parameters:
- Arguments:
title (str) – plot title.
x_title (str) – plot x axis title.
y_title (str) – plot y axis title.
height (str) – plot height.
width (str) – plot width.
- Returns:
barplot figure within the <div id=”_dash-app-content”>.
Example:
result = get_barplot(data, identifier='barplot', args={'title':'Figure with Barplot'})
- vuecore.viz.get_boxplot_grid(data, identifier, args)[source]#
This function plots a boxplot in a grid based on column values.
- Parameters:
- Arguments:
title (str) – plot title.
x (str) – name of column with x values.
y (str) – name of column with y values.
color (str) – name of column with colors
facet (str) – name of column specifying grouping
height (str) – plot height.
width (str) – plot width.
- Returns:
boxplot figure within the <div id=”_dash-app-content”>.
Example:
result = get_boxplot_grid(data, identifier='Boxplot', args:{"title":"Boxplot", 'x':'sample', 'y':'identifier', 'color':'group', 'facet':'qc_class', 'axis':'cols'} )
- vuecore.viz.get_clustergrammer_plot(data, identifier, args)[source]#
This function takes a pandas dataframe, calculates clustering, and generates the visualization json.
For more information visit MaayanLab/clustergrammer-py.
- Parameters:
- Arguments:
format (str) – defines if dataframe needs to be converted from ‘edgelist’ to matrix
title (str) – plot title
- Returns:
Dash Div with heatmap plot from Clustergrammer web-based tool
- vuecore.viz.get_cytoscape_network(net, identifier, args)[source]#
This function creates a Cytoscpae network in dash.
For more information visit https://dash.plot.ly/cytoscape.
- Parameters:
- Arguments:
title (str) – title of the figure.
stylesheet (list[dict]) – specifies the style for a group of elements, a class of elements, or a single element (accepts two keys ‘selector’ and ‘style’).
layout (dict) – specifies how the nodes should be positioned on the screen.
- Returns:
network figure within <div id=”_dash-app-content”>.
- vuecore.viz.get_density(x: ndarray, y: ndarray)[source]#
Get kernal density estimate for each (x, y) point.
- vuecore.viz.get_enrichment_plots(enrichment_results, identifier, args)[source]#
This function generates a scatter plot with enriched terms (y-axis) and their adjusted pvalues (x-axis)
- Parameters:
enrichment_results (pandas.DataFrame) – dataframe with the enrichment data to plot (see enrichment functions for format)
identifier (str) – identifier to be used in the app
args (dict) – dictionary containing the arguments needed to plot the figure (width, height, title)
- Return list:
list of scatter plots one for each enrichment table available (i.e pairwise comparisons)
Example:
figure = get_enrichment_plots(df, identifier='enrichment', args={'width':1500, 'height':800, 'title':'Enrichment'} )
- vuecore.viz.get_facet_grid_plot(data, identifier, args)[source]#
This function plots a scatterplot matrix where we can plot one variable against another to form a regular scatter plot, and we can pick a third faceting variable to form panels along the columns to segment the data even further, forming a bunch of vertical panels.
For more information visit https://plot.ly/python/facet-trellis/.
- Parameters:
- Arguments:
x (str) – name of the column containing values to plot in the x axis.
y (str) – name of the column containing values to plot in the y axis.
group (str) – name of the column containing the group.
class (str) – name of the column to be used as ‘facet’ column.
plot_type (str) – decides the type of plot to appear in the facet grid. The options are ‘scatter’, ‘scattergl’, ‘histogram’, ‘bar’, and ‘box’.
title (str) – plot title.
- Returns:
facet grid figure within the <div id=”_dash-app-content”>.
Example:
result = get_facet_grid_plot(data, identifier='facet_grid', args={'x':'a', 'y':'b', 'group':'group', 'class':'type', 'plot_type':'bar', 'title':'Facet Grid Plot'} )
- vuecore.viz.get_heatmapplot(data, identifier, args)[source]#
This function plots a simple Heatmap.
- Parameters:
- Arguments:
format (str) – defines the format of the input dataframe.
source (str) – name of the column containing the source.
target (str) – name of the column containing the target.
values (str) – name of the column containing the values to be plotted.
title (str) – title of the figure.
- Returns:
heatmap figure within the <div id=”_dash-app-content”>.
Example:
result = get_heatmapplot(data, identifier='heatmap', args={'format':'edgelist', 'source':'node1', 'target':'node2', 'values':'score', 'title':'Heatmap Plot'})
- vuecore.viz.get_histogram(data, identifier, args)[source]#
Basic histogram figure allows facets cols and rows
- Parameters:
- Arguments:
x (str) – name of the column containing values to plot in the x axis.
y (str) – name of the column containing values to plot in the y axis (if used).
color (str) – name of the column that defines how the histogram is colored (if used).
facet_row (str) – name of the column to be used as ‘facet’ row (if used).
facet_col (str) – name of the column to be used as ‘facet’ column (if used).
height (int) – height of the plot
width (int) – width of the plot
title (str) – plot title.
- Returns:
dash componenet with histogram figure
Example:
result = get_histogram(data, identifier='histogram', args={'x':'a', 'color':'group', 'facet_row':'sample', 'title':'Facet Grid Plot'} )
- vuecore.viz.get_markdown(text, args={})[source]#
Converts a given text into a Dash Markdown component. It includes a syntax for things like bold text and italics, links, inline code snippets, lists, quotes, and more.
For more information visit https://dash.plot.ly/dash-core-components/markdown.
- Parameters:
text (str) – markdown string (or array of strings) that adhreres to the CommonMark spec.
args (dict) – dictionary with items from https://dash.plot.ly/dash-core-components/markdown.
- Returns:
dash Markdown component.
- vuecore.viz.get_network(data, identifier, args)[source]#
This function filters an input dataframe based on a threshold score and builds a cytoscape network. For more information on ‘node_size’ parameter, visit https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.algorithms.centrality.betweenness_centrality.html and https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.algorithms.centrality.eigenvector_centrality_numpy.html.
- Parameters:
- Arguments:
source (str) – name of the column containing the source.
target (str) – name of the column containing the target.
cutoff (float) – value threshold for network building.
cutoff_abs (bool) – if True will take both positive and negative sides of the cutoff value.
values (str) – name of the column containing the values to be plotted.
node_size (str) – method used to determine node radius (‘betweenness’, ‘ev_centrality’, ‘degree’).
title (str) – plot title.
color_weight (bool) – if True, edges in network are colored red if score > 0 and blue if score < 0.
- Returns:
dictionary with the network in multiple formats: jupyetr-notebook compatible, web brower compatibles, data table, and json.
Example:
result = get_network(data, identifier='network', args={'source':'node1', 'target':'node2', 'cutoff':0.5, 'cutoff_abs':True, 'values':'weight', 'node_size':'degree', 'title':'Network Figure', 'color_weight': True})
- vuecore.viz.get_network_style(node_colors, color_edges)[source]#
This function uses a dictionary of nodes and colors and creates a stylesheet and layout for a network.
- Parameters:
- Returns:
stylesheet (list of dictionaries specifying the style for a group of elements, a class of elements, or a single element) and layout (dictionary specifying how the nodes should be positioned on the canvas).
- vuecore.viz.get_notebook_network_pyvis(graph, args={})[source]#
This function converts a Networkx graph into a PyVis graph supporting Jupyter notebook embedding.
- Parameters:
graph (graph) – networkX graph.
args (dict) – see below.
- Arguments:
height (int) – network canvas height.
width (int) – network canvas width.
- Returns:
PyVis graph.
Example:
result = get_notebook_network_pyvis(graph, args={'height':100, 'width':100})
- vuecore.viz.get_notebook_network_web(graph, args)[source]#
This function converts a networkX graph into a webweb interactive network in a browser.
- Parameters:
graph (graph) – networkX graph.
- Returns:
web network.
- vuecore.viz.get_parallel_plot(data, identifier, args)[source]#
This function creates a parallel coordinates plot, with sample groups as the different dimensions.
- Parameters:
- Arguments:
group (str) – name of the column containing the groups.
zscore (bool) – if True, calculates the z score of each values in the row, relative to the row mean and standard deviation.
color (str) – line color.
title (str) – plot title.
- Returns:
parallel plot figure within <div id=”_dash-app-content”> .
Example:
result = get_parallel_plot(data, identifier='parallel plot', args={'group':'group', 'zscore':True, 'color':'blue', 'title':'Parallel Plot'} )
- vuecore.viz.get_pca_plot(data, identifier, args)[source]#
This function creates a pca plot with scores and top “args[‘loadings’]” loadings.
- Parameters:
- Arguments:
loadings (int) – number of features with highest loading values to be displayed in the pca plot
title (str) – title of the figure
x_title (str) – plot x axis title
y_title (str) – plot y axis title
height (int) – plot height
width (int) – plot width
- Returns:
PCA figure within the <div id=”_dash-app-content”>.
Example:
result = get_pca_plot(data, identifier='pca', args={'loadings':15, 'title':'PCA Plot', 'x_title':'PC1', 'y_title':'PC2', 'height':100, 'width':100} )
- vuecore.viz.get_pieplot(data, identifier, args)[source]#
This function plots a simple Pie plot.
- Parameters:
- Arguments:
valueCol (str) – name of the column with the values to be plotted.
textCol (str) – name of the column containing information for the hoverinfo parameter.
height (str) – height of the plot.
width (str) – width of the plot.
- Returns:
Pieplot figure within the <div id=”_dash-app-content”>.
- vuecore.viz.get_polar_plot(df, identifier, args)[source]#
This function creates a Polar plot with data aggregated for a given group.
- Parameters:
df (pandas.DataFrame) – dataframe with the data to plot
identifier (str) – identifier to be used in the app
args (dict) – dictionary containing the arguments needed to plot the figure ( value_col (value to aggregate), group_col (group by), color_col (color by))
- Returns:
Dash Graph
Example:
figure = get_polar_plot(df, identifier='polar', args={'value_col':'intensity', 'group_col':'modifier', 'color_col':'group'} )
- vuecore.viz.get_ranking_plot(data, identifier, args)[source]#
Creates abundance multiplots (one per sample group).
- Parameters:
- Arguments:
group (str) – name of the column containing the group.
index (bool) – set to True when multi samples per group. Calculates the mean intensity for each protein in each group.
x_title (str) – title of plot x axis.
y_title (str) – title of plot y axis.
title (str) – plot title.
width (int) – plot width.
height (int) – plot height.
annotations (dict, optional) – dictionary where data points names are the keys and descriptions are the values.
- Returns:
multi abundance plot figure within the <div id=”_dash-app-content”>.
Example:
result = get_ranking_plot(data, identifier='ranking', args={'group':'group', 'index':'', 'x_title':'x_axis', 'y_title':'y_axis', 'title':'Ranking Plot', 'width':100, 'height':150, 'annotations':{ 'GPT~P24298': 'liver disease', 'CP~P00450': 'Wilson disease'} } )
- vuecore.viz.get_sankey_plot(data, identifier, args={'font': 12, 'height': 800, 'orientation': 'h', 'source': 'source', 'source_colors': 'source_colors', 'target': 'target', 'target_colors': 'target_colors', 'title': 'Sankey plot', 'valueformat': '.0f', 'weight': 'weight', 'width': 800})[source]#
This function generates a Sankey plot in Plotly.
- Parameters:
- Arguments:
source (str) – name of the column containing the source
target (str) – name of the column containing the target
weight (str) – name of the column containing the weight
source_colors (str) – name of the column in data that contains the colors of each source item
target_colors (str) – name of the column in data that contains the colors of each target item
title (str) – plot title
orientation (str) – whether to plot horizontal (‘h’) or vertical (‘v’)
valueformat (str) – how to show the value (‘.0f’)
width (int) – plot width
height (int) – plot height
font (int) – font size
- Returns:
dcc.Graph
Example:
result = get_sankey_plot(data, identifier='sankeyplot', args={'source':'source', 'target':'target', 'weight':'weight', 'source_colors':'source_colors', 'target_colors':'target_colors',' 'orientation': 'h', 'valueformat': '.0f', 'width':800, 'height':800, 'font':12, 'title':'Sankey plot'} )
- vuecore.viz.get_scatterplot(data, identifier, args)[source]#
This function plots a simple Scatterplot.
- Parameters:
- Arguments:
title (str) – title of the figure.
x (str) – column in dataframe with values for x
y (str) – column in dataframe with values for y
group (str) – column in dataframe with the groups - translates into colors (default None)
hovering_cols (list) – list of columns in dataframe that will be shown when hovering over a dot
size (str) – column in dataframe that contains the size of the dots (default None)
trendline (bool) – whether or not to draw a trendline
text (str) – column in dataframe that contains the values shown for each dot
x_title (str) – plot x axis title.
y_title (str) – plot y axis title.
height (int) – plot height.
width (int) – plot width.
colors (dict) – dictionary with colors to be used for each group
- Returns:
scatterplot figure within the <div id=”_dash-app-content”>.
Example:
result = get_scatterplot(data, identifier='scatter plot', args={'title':'Scatter Plot', 'x_title':'x_axis', 'y_title':'y_axis', 'height':100, 'width':100} )
- vuecore.viz.get_scatterplot_matrix(data, identifier, args)[source]#
This function pltos a multi scatterplot (one for each unique element in args[‘group’]).
- Parameters:
- Arguments:
group (str) – name of the column containing the group.
title (str) – plot title.
x_title (str) – plot x axis title.
y_title (str) – plot y axis title.
height (int) – plot height.
width (int) – plot width.
annotations (dict, optional) – dictionary where data points names are the keys and descriptions are the values.
- Returns:
multi scatterplot figure within the <div id=”_dash-app-content”>.
Example:
result = get_scatterplot_matrix(data, identifier='scatter matrix', args={'group':'group', 'title':'Scatter Plot Matrix', 'x_title':'x_axis', 'y_title':'y_axis', 'height':100, 'width':100, 'annotations':{ 'GPT~P24298': 'liver disease', 'CP~P00450': 'Wilson disease'} } )
- vuecore.viz.get_simple_scatterplot(data, identifier, args)[source]#
Plots a simple scatterplot with the possibility of including in-plot annotations of data points.
- Parameters:
data – long-format pandas dataframe with columns: ‘x’ (ranking position), ‘group’ (original dataframe position), ‘name’ (protein identifier), ‘y’ (LFQ intensity), ‘symbol’ (data point shape) and ‘size’ (data point size).
identifier (str) – id used to identify the div where the figure will be generated.
args (dict) – see below.
- Arguments:
annotations (dict) – dictionary where data points names are the keys and descriptions are the values.
title (str) – plot title.
x_title (str) – plot x axis title.
y_title (str) – plot y axis title.
height (int) – plot height.
width (int) – plot width.
- Returns:
annotated scatterplot figure within the <div id=”_dash-app-content”>.
Example:
result = get_simple_scatterplot(data, identifier='scatter plot', args={'annotations':{'GPT~P24298': 'liver disease', 'CP~P00450': 'Wilson disease'}', 'title':'Scatter Plot', 'x_title':'x_axis', 'y_title':'y_axis', 'height':100, 'width':100} )
- vuecore.viz.get_table(data, identifier, args)[source]#
This function converts a pandas dataframe into an interactive table for viewing, editing and exploring large datasets.
For more information visit https://dash.plot.ly/datatable.
- Parameters:
- Returns:
new Dash div containing title and interactive table.
Example:
result = get_table(data, identifier='table', title='Table Figure', subset = None)
- vuecore.viz.get_violinplot(data, identifier, args)[source]#
This function creates a violin plot for all columns in the input dataframe.
- Parameters:
- Arguments:
drop_cols (list) – column labels to be dropped from the dataframe.
group (str) – name of the column containing the group.
- Returns:
list of violion plots within the <div id=”_dash-app-content”>.
Example:
result = get_violinplot(data, identifier='violinplot, args={'drop_cols':['sample', 'subject'], 'group':'group'} )
- vuecore.viz.get_volcanoplot(results, args)[source]#
This function plots volcano plots for each internal dictionary in a nested dictionary.
- Parameters:
- Arguments:
fc (float) – fold change threshold.
range_x (list) – list with minimum and maximum values for x axis.
range_y (list) – list with minimum and maximum values for y axis.
x_title (str) – plot x axis title.
y_title (str) – plot y axis title.
colorscale (str) – string for predefined plotly colorscales or dict containing one or more of the keys listed in https://plot.ly/python/reference/#layout-colorscale.
showscale (bool) – determines whether or not a colorbar is displayed for a trace.
marker_size (int) – sets the marker size (in px).
- Returns:
list of volcano plot figures within the <div id=”_dash-app-content”>.
Example:
result = get_volcanoplot(results, args={'fc':2.0, 'range_x':[0, 1], 'range_y':[-1, 1], 'x_title':'x_axis', 'y_title':'y_title', 'colorscale':'Blues', 'showscale':True, 'marker_size':7} )
- vuecore.viz.get_wordcloud(data, identifier, args={'height': 700, 'margin': 1, 'max_font_size': 100, 'max_words': 400, 'stopwords': [], 'width': 700})[source]#
This function generates a Wordcloud based on the natural text in a pandas dataframe column.
- Parameters:
- Arguments:
text_col (str) – name of column containing the natural text used to generate the wordcloud.
stopwords (list) – list of words that will be eliminated.
max_words (int) – maximum number of words.
max_font_size (int) – maximum font size for the largest word.
margin (int) – plot margin size.
width (int) – width of the plot.
height (int) – height of the plot.
title (str) – plot title.
- Returns:
wordcloud figure within <div id=”_dash-app-content”>.
Example:
result = get_wordcloud(data, identifier='wordcloud', args={'stopwords':['BACKGROUND', 'CONCLUSION', 'RESULT', 'METHOD', 'CONCLUSIONS', 'RESULTS', 'METHODS'], 'max_words': 400, 'max_font_size': 100, 'width':700, 'height':700, 'margin': 1} )
- vuecore.viz.network_to_tables(graph, source, target)[source]#
Creates the graph edge list and node list and returns them as separate Pandas DataFrames.
- Parameters:
graph – networkX graph used to construct the Pandas DataFrame.
- Returns:
two Pandas DataFrames.
- vuecore.viz.plot_2_venn_diagram(cond1, cond2, unique1, unique2, intersection, identifier, args)[source]#
This function creates a simple non area-weighted two-circle venn diagram.
- Parameters:
cond1 (str) – label of the first circle.
cond2 (str) – label of the second circle.
unique1 (int) – number of features exclusive to cond1.
unique2 (int) – number of features exclusive to cond2.
identifier (str) – id used to identify the div where the figure will be generated.
args (dict) – see below.
- Parm int intersection:
number of features common to cond1 and cond2.
- Arguments:
colors (dict) – dictionary with cond1 and cond2 as keys, and color codes as values.
title (str) – plot title.
- Returns:
two-circle venn diagram figure within <div id=”_dash-app-content”>.
Example:
result = plot_2_venn_diagram(cond1='group1', cond2='group2', unique1=10, unique2=15, intersection=8, identifier='vennplot', args={'color':{'group1':'blue', 'group2':'red'}, 'title':'Two-circle Venn diagram'} )
- vuecore.viz.run_volcano(data, identifier, args={'alpha': 0.05, 'annotate_list': [], 'colorscale': 'Blues', 'fc': 2, 'marker_size': 8, 'num_annotations': 10, 'showscale': False, 'x_title': 'log2FC', 'y_title': '-log10(pvalue)'})[source]#
This function parsers the regulation data from statistical tests and creates volcano plots for all distinct group comparisons. Significant hits with lowest adjusted p-values are highlighed.
- Parameters:
data – pandas dataframe with format: ‘identifier’, ‘group1’, ‘group2’, ‘mean(group1’, ‘mean(group2)’, ‘log2FC’, ‘std_error’, ‘tail’, ‘t-statistics’, ‘padj_THSD’, ‘effsize’, ‘efftype’, ‘FC’, ‘rejected’, ‘F-statistics’, ‘pvalue’, ‘padj’, ‘correction’, ‘-log10 pvalue’ and ‘Method’.
identifier (str) – id used to identify the div where the figure will be generated.
args (dict) – see below.
- Arguments:
alpha (float) – adjusted p-value threshold for significant hits.
fc (float) – fold change threshold.
colorscale (str or dict) – name of predefined plotly colorscale or dictionary containing one or more of the keys listed in https://plot.ly/python/reference/#layout-colorscale.
showscale (bool) – determines whether or not a colorbar is displayed for a trace.
marker_size (int) – sets the marker size (in px).
x_title (str) – plot x axis title.
y_title (str) – plot y axis title.
num_annotations (int) – number of hits to be highlighted (if num_annotations = 10, highlights 10 hits with lowest significant adjusted p-value).
- Returns:
list of volcano plot figures within the <div id=”_dash-app-content”>.
Example:
result = run_volcano(data, identifier='volvano data', args={'alpha':0.05, 'fc':2.0, 'colorscale':'Blues', 'showscale':False, 'marker_size':6, 'x_title':'log2FC', 'y_title':'-log10(pvalue)', 'num_annotations':10} )
- vuecore.viz.save_DASH_plot(plot, name, plot_format='svg', directory='.', width=800, height=700)[source]#
This function saves a plotly figure to a specified directory, in a determined format.
- Parameters:
- Returns:
figure saved in directory
Example:
result = save_DASH_plot(plot, name='Plot example', plot_format='svg', directory='/data/plots')
- vuecore.viz.visualize_notebook_network(network, notebook_type='jupyter', layout={})[source]#
This function returns a Cytoscape network visualization for Jupyter notebooks
- Parameters:
network (tuple) – tuple with two dictionaries: network data and stylesheet (see get_network(data, identifier, args)).
notebook_type (str) – the type of notebook where the network will be visualized (currently only jupyter notebook is supported)
layout (dict) – specific layout properties (see https://dash.plot.ly/cytoscape/layout)
- Returns:
cyjupyter.cytoscape.Cytoscape object
Example:
net = get_network(clincorr.dropna(), identifier='corr', args={'source':'node1', 'target':'node2', 'cutoff':0, 'cutoff_abs':True, 'values':'weight','node_size':'degree', 'title':'Network Figure', 'color_weight': True}) visualize_notebook_network(net['notebook'], notebook_type='jupyter', layout={'width':'100%', 'height':'700px'})
- vuecore.viz.visualize_notebook_path(path, notebook_type='jupyter')[source]#
This function returns a Cytoscape network visualization for Jupyter notebooks
- Parameters:
object (pathlib.Path) – dash_html_components object with the cytoscape network (returned by get_cytoscape_network())
notebook_type (str) – the type of notebook where the network will be visualized (currently only jupyter notebook is supported)
layout (dict) – specific layout properties (see https://dash.plot.ly/cytoscape/layout)
- Returns:
cyjupyter.cytoscape.Cytoscape object
Example:
net = get_cytoscape_network(G, identifier='corr', args={'title':'Cytoscape path', 'stylesheet':stylesheet, 'layout': layout}) visualize_notebook_path(net, notebook_type='jupyter')